rasmus/nafuma
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions

Merge pull request #1 from rasmusthog/halvor_xanes

Browse source 
Halvor xanes
This commit is contained in:
Rasmus Vester Thøgersen 2022-04-08 13:36:30 +02:00 • committed by GitHub
commit 092ecfa380
No known key found for this signature in database
GPG key ID: 4AEE18F83AFDEB23
3 changed files with 281 additions and 71 deletions
Download patch file Download diff file Expand all files Collapse all files

201
nafuma/xanes/calib.py
View file

@ -1,7 +1,10 @@
import pandas as pd
import numpy as np
import os
import matplotlib.pyplot as plt
import beamtime.auxillary as aux
import beamtime.xanes as xas
import beamtime.xanes.io as io
def rbkerbest():
print("ROSENBORG!<3")
@ -12,83 +15,143 @@ def rbkerbest():
##Better to make a new function that loops through the files, and performing the split_xanes_scan on
#Tryiung to make a function that can decide which edge it is based on the first ZapEnergy-value
def finding_edge(df):
if 5.9 < df["ZapEnergy"][0] < 6.5:
edge='Mn'
return(edge)
if 8.0 < df["ZapEnergy"][0] < 8.6:
edge='Ni'
return(edge)
<<<<<<< HEAD:beamtime/xanes/calib.py
#def pre_edge_subtraction(df,filenames, options={}):
def test(innmat):
df_test= xas.io.put_in_dataframe(innmat)
print(df_test)
def pre_edge_subtraction(path, options={}):
required_options = ['print','troubleshoot']
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)
df= xas.io.put_in_dataframe(path)
edge=finding_edge(df)
=======
def split_xanes_scan(filename, destination=None, replace=False):
#root is the path to the beamtime-folder
#destination should be the path to the processed data
>>>>>>> master:nafuma/xanes/calib.py
#insert a for-loop to go through all the folders.dat-files in the folder root\xanes\raw
#Defining the end of the region used to define the background, thus start of the edge
#implement widget
if edge == 'Mn':
edge_start = 6.42
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)
with open(filename, 'r') as f:
lines = f.readlines()
datas = []
data = []
headers = []
header = ''
start = False
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_normalization(path, options={}):
required_options = ['print']
default_options = {
'print': False
}
options = aux.update_options(options=options, required_options=required_options, default_options=default_options)
for line in lines:
if line[0:2] == "#L":
start = True
header = line[2:].split()
continue
elif line[0:2] == "#C":
start = False
if data:
datas.append(data)
data = []
if header:
headers.append(header)
header = ''
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':
edge_stop = 6.565
if edge == 'Ni':
edge_stop = 8.361
if start == False:
continue
else:
data.append(line.split())
edges = {'Mn': [6.0, 6.1, 6.2, 6.3, 6.4, 6.5], 'Fe': [6.8, 6.9, 7.0, 7.1, 7.2], 'Co': [7.6, 7.7, 7.8, 7.9], 'Ni': [8.1, 8.2, 8.3, 8.4, 8.5]}
edge_count = {'Mn': 0, 'Fe': 0, 'Co': 0, 'Ni': 0}
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
for ind, data in enumerate(datas):
df = pd.DataFrame(data)
df.columns = headers[ind]
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
edge_start = np.round((float(df["ZapEnergy"].min())), 1)
#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"]))
for edge, energies in edges.items():
if edge_start in energies:
edge_actual = edge
edge_count[edge] += 1
filename = filename.split('/')[-1]
count = str(edge_count[edge_actual]).zfill(4)
# Save
if destination:
cwd = os.getcwd()
if not os.path.isdir(destination):
os.mkdir(destination)
os.chdir(destination)
df.to_csv('{}_{}_{}.dat'.format(filename.split('.')[0], edge_actual, count))
os.chdir(cwd)
else:
df.to_csv('{}_{}_{}.dat'.format(filename.split('.')[0], edge_actual, count))
return df_bkgd_sub, df_postedge

150
nafuma/xanes/io.py
View file

@ -1,2 +1,148 @@
#hello
#yeah
import pandas as pd
import matplotlib.pyplot as plt
import os
import numpy as np
def split_xanes_scan(root, destination=None, replace=False):
#root is the path to the beamtime-folder
#destination should be the path to the processed data
#insert a for-loop to go through all the folders.dat-files in the folder root\xanes\raw
with open(filename, 'r') as f:
lines = f.readlines()
datas = []
data = []
headers = []
header = ''
start = False
for line in lines:
if line[0:2] == "#L":
start = True
header = line[2:].split()
continue
elif line[0:2] == "#C":
start = False
if data:
datas.append(data)
data = []
if header:
headers.append(header)
header = ''
if start == False:
continue
else:
data.append(line.split())
edges = {'Mn': [6.0, 6.1, 6.2, 6.3, 6.4, 6.5], 'Fe': [6.8, 6.9, 7.0, 7.1, 7.2], 'Co': [7.6, 7.7, 7.8, 7.9], 'Ni': [8.1, 8.2, 8.3, 8.4, 8.5]}
edge_count = {'Mn': 0, 'Fe': 0, 'Co': 0, 'Ni': 0}
for ind, data in enumerate(datas):
df = pd.DataFrame(data)
df.columns = headers[ind]
edge_start = np.round((float(df["ZapEnergy"].min())), 1)
for edge, energies in edges.items():
if edge_start in energies:
edge_actual = edge
edge_count[edge] += 1
filename = filename.split('/')[-1]
count = str(edge_count[edge_actual]).zfill(4)
# Save
if destination:
cwd = os.getcwd()
if not os.path.isdir(destination):
os.mkdir(destination)
os.chdir(destination)
df.to_csv('{}_{}_{}.dat'.format(filename.split('.')[0], edge_actual, count))
os.chdir(cwd)
else:
df.to_csv('{}_{}_{}.dat'.format(filename.split('.')[0], edge_actual, count))
#Function that "collects" all the files in a folder, only accepting .dat-files from xanes-measurements
def get_filenames(path):
cwd = os.getcwd()
# Change into path provided
os.chdir(path)
filenames = [os.path.join(path, filename) for filename in os.listdir() if os.path.isfile(filename) and filename[-4:] == '.dat'] #changed
# Change directory back to where you ran the script from
os.chdir(cwd)
return filenames
def put_in_dataframe(path):
filenames = get_filenames(path)
#making the column names to be used in the dataframe, making sure the first column is the ZapEnergy
column_names = ["ZapEnergy"]
for i in range(len(filenames)):
column_names.append(filenames[i])
#Taking the first file in the folder and extracting ZapEnergies and intensity from that (only need the intensity from the rest)
first = pd.read_csv(filenames[0], skiprows=0)
#Making a data frame with the correct columns, and will fill inn data afterwards
df = pd.DataFrame(columns = column_names)
#First putting in the 2theta-values
df["ZapEnergy"]=first["ZapEnergy"]
#filling in the intensities from all files into the corresponding column in the dataframe
for i in range(len(filenames)):
df2 = pd.read_csv(filenames[i])
df2 = df2.drop(['Mon','Det1','Det2','Det3','Det4','Det5', 'Det6','Ion1'], axis=1) #, axis=1)
df2 = df2.drop(['MonEx','Ion2','Htime','MusstEnc1','MusstEnc3','MusstEnc4', 'TwoTheta', 'ZCryo'], axis=1)
df2 = df2.drop(['ZBlower1', 'ZBlower2', 'ZSrcur'], axis=1)#, axis=19) #removing the sigma at this point
############## THIS PART PICKS OUT WHICH ROI IS OF INTEREST, BUT MUST BE FIXED IF LOOKING AT THREE EDGES (roi00,roi01,roi02) #####################
if 'xmap_roi01' in df2.columns:
#Trying to pick the roi with the highest difference between maximum and minimum intensity --> biggest edge shift
if max(df2["xmap_roi00"])-min(df2["xmap_roi00"])>max(df2["xmap_roi01"])-min(df2["xmap_roi01"]):
df[filenames[i]]=df2["xmap_roi00"] #forMn
else:
df[filenames[i]]=df2["xmap_roi01"] #forNi
else:
df[filenames[i]]=df2["xmap_roi00"]
###############################################################################################
i=i+1
#print(df)
#If I want to make a csv-file of the raw data. Decided that was not necessary:
#df.to_csv('static-Mn-edge.csv') #writing it to a csv, first row is datapoint (index), second column is 2theta, and from there the scans starts
return df

1
test.txt Normal file
View file

@ -0,0 +1 @@
hei på dej