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Refactor read_data and move get_filenames

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rasmusvt 2022-06-16 17:56:08 +02:00
parent 0b89524ef1
commit e7a95d65ed
1 changed files with 6 additions and 65 deletions
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71
nafuma/xanes/io.py
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@ -89,28 +89,15 @@ def split_xanes_scan(root, destination=None, replace=False):
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 read_data(data: dict, options={}) -> pd.DataFrame:
# FIXME Handle the case when dataseries are not the same size
required_options = []
default_options = {
@ -138,6 +125,8 @@ def read_data(data: dict, options={}) -> pd.DataFrame:
def determine_active_roi(scan_data):
# FIXME For Co-edge, this gave a wrong scan
#Trying to pick the roi with the highest difference between maximum and minimum intensity --> biggest edge shift
if max(scan_data["xmap_roi00"])-min(scan_data["xmap_roi00"])>max(scan_data["xmap_roi01"])-min(scan_data["xmap_roi01"]):
@ -146,51 +135,3 @@ def determine_active_roi(scan_data):
active_roi = 'xmap_roi01'
return active_roi
def put_into_dataframe(data: dict, options={}) -> pd.DataFrame:
filenames = get_filenames(data)
#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