Rasmus Vester Thøgersen
GitHub
commit
c479575c85
5 changed files with 1361 additions and 245 deletions
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@ -1,5 +1,6 @@
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import json
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import numpy as np
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import os
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def update_options(options, required_options, default_options):
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''' Takes a dictionary of options along with a list of required options and dictionary of default options, and sets all keyval-pairs of options that is not already defined to the default values'''
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@ -11,11 +12,26 @@ def update_options(options, required_options, default_options):
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return options
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def save_options(options, path):
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def save_options(options, path, ignore=None):
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''' Saves any options dictionary to a JSON-file in the specified path'''
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options_copy = options.copy()
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if ignore:
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if not isinstance(ignore, list):
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ignore = [ignore]
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for i in ignore:
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options_copy[i] = 'Removed'
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if not os.path.isdir(os.path.dirname(path)):
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if os.path.dirname(path):
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os.makedirs(os.path.dirname(path))
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with open(path, 'w') as f:
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json.dump(options,f)
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json.dump(options_copy, f, skipkeys=True, indent=4)
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def load_options(path):
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@ -53,3 +69,46 @@ def floor(a, roundto=1):
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a = np.floor(a*fac) / fac
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return a
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def write_log(message, options={}):
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from datetime import datetime
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required_options = ['logfile']
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default_options = {
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'logfile': f'{datetime.now().strftime("%Y-%m-%d-%H-%M-%S.log")}'
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}
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options = update_options(options=options, required_options=required_options, default_options=default_options)
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if not os.path.isdir(os.path.dirname(options['logfile'])):
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os.makedirs(os.path.dirname(options['logfile']))
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now = datetime.now().strftime('%Y/%m/%d %H:%M:%S')
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message = f'[{now}] {message} \n'
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with open(options['logfile'], 'a') as f:
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f.write(message)
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#Function that "collects" all the files in a folder, only accepting .dat-files from xanes-measurements
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def get_filenames(path, ext, filter=''):
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''' Collects all filenames from specified path with a specificed extension
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Input:
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path: path to find all filenames (relative or absolute)
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ext: extension (including ".")'''
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filenames = [os.path.join(path, filename) for filename in os.listdir(path) if os.path.isfile(os.path.join(path, filename)) and filename.endswith(ext) and filter in filename]
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return filenames
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def move_list_element_last(filenames,string):
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for i,file in enumerate(filenames):
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if string in file:
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del filenames[i]
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filenames.append(file)
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return filenames
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@ -1 +1 @@
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from . import io, calib
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from . import io, calib, edges
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File diff suppressed because it is too large
Load diff
30
nafuma/xanes/edges.py
Normal file
30
nafuma/xanes/edges.py
Normal file
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@ -0,0 +1,30 @@
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import pandas as pd
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import numpy as np
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from scipy.constants import c, h
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# From 2019 redefinition of SI base units: https://en.wikipedia.org/wiki/2019_redefinition_of_the_SI_base_units
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keV_per_J = (1 / 1.602176634e-19) / 1000
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# kXu values taken from International Tables for Crystallography Volume , Kulwer Academic Publishers - Dordrect / Boston / London (1992)
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K = { 'Z': [ 1, 2,
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3, 4, 5, 6, 7, 8, 9, 10,
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11, 12, 13, 14, 15, 16, 17, 18,
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19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36,
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37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48],
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'Atom': [ 'H', 'He',
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'Li', 'Be', 'B', 'C', 'N', 'O', 'F', 'Ne',
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'Na', 'Mg', 'Al', 'Si', 'P', 'S', 'Cl', 'Ar',
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'K', 'Ca', 'Sc', 'Ti', 'V', 'Cr', 'Mn', 'Fe', 'Co', 'Ni', 'Cu', 'Zn', 'Ga', 'Ge', 'As', 'Se', 'Br', 'Kr',
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'Rb', 'Sr', 'Y', 'Zr', 'Nb', 'Mo', 'Tc', 'Ru', 'Rh', 'Pd', 'Ag', 'Cd'],
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'kXu': [ np.nan, np.nan,
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226.5, np.nan, np.nan, 43.68, 30.99, 23.32, np.nan, np.nan,
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np.nan, 9.5117, 7.9511, 6.7446, 5.7866, 5.0182, 4.3969, 3.8707,
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3.43645, 3.07016, 2.7573, 2.49730, 2.26902, 2.07012, 1.89636, 1.74334, 1.60811, 1.48802, 1.38043, 1.2833, 1.19567, 1.11652, 1.04497, 0.97978, 0.91995, 0.86547,
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0.81549, 0.76969, 0.72762, 0.68877, 0.65291, 0.61977, 0.5891, 0.56047, 0.53378, 0.50915, 0.48582, 0.46409]}
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K = pd.DataFrame(K)
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K['keV'] = np.round(h*c/(K['kXu']*10**-10) * keV_per_J, 3)
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# FIXME If needed, add energies for L-edges as well.
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@ -2,147 +2,307 @@ import pandas as pd
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import matplotlib.pyplot as plt
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import os
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import numpy as np
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import nafuma.auxillary as aux
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from nafuma.xanes.calib import find_element
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import datetime
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def split_xanes_scan(filename, destination=None, replace=False):
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#root is the path to the beamtime-folder
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#destination should be the path to the processed data
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def split_scan_data(data: dict, options={}) -> list:
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''' Splits a XANES-file from BM31 into different files depending on the edge. Has the option to add intensities of all scans of same edge into the same file.
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As of now only picks out xmap_rois (fluoresence mode) and for Mn, Fe, Co and Ni K-edges.'''
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#insert a for-loop to go through all the folders.dat-files in the folder root\xanes\raw
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required_options = ['log', 'logfile', 'save', 'save_folder', 'replace', 'active_roi', 'add_rois', 'return']
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default_options = {
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'log': False,
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'logfile': f'{datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S")}_split_edges.log',
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'save': False, # whether to save the files or not
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'save_folder': '.', # root folder of where to save the files
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'replace': False, # whether to replace the files if they already exist
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'active_roi': None,
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'add_rois': False, # Whether to add the rois of individual scans of the same edge together
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'return': True
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}
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options = aux.update_options(options=options, required_options=required_options, default_options=default_options)
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if not isinstance(data['path'], list):
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data['path'] = [data['path']]
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all_scans = []
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if options['log']:
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aux.write_log(message='Starting file splitting...', options=options)
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for filename in data['path']:
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if options['log']:
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aux.write_log(message=f'Reading {filename}...', options=options)
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with open(filename, 'r') as f:
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lines = f.readlines()
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datas = []
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data = []
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headers = []
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header = ''
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start = False
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timestamps = []
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scan_datas, scan_data = [], []
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headers, header = [], ''
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read_data = False
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for line in lines:
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if line[0:2] == "#L":
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start = True
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header = line[2:].split()
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for i, line in enumerate(lines):
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# Header line starts with #L - reads headers, and toggles data read-in on
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if 'zapline mono' in line:
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timestamps.append(lines[i+1].strip('#D'))
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elif line[0:2] == "#L":
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header, read_data = line[2:].split(), True
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if options['log']:
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aux.write_log(message='... Found scan data. Starting read-in...', options=options)
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continue
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elif line[0:2] == "#C":
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start = False
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# First line after data started with #C - stops data read-in
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elif line[0:2] == "#C" or line[0:2] == '#S':
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read_data = False
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if data:
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datas.append(data)
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data = []
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if scan_data:
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scan_datas.append(scan_data); scan_data = []
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if header:
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headers.append(header)
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header = ''
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headers.append(header); header = ''
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if start == False:
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# Ignore line if read-in not toggled
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if read_data == False:
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continue
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# Read in data if it is
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else:
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data.append(line.split())
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scan_data.append(line.split())
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edges = {'Mn': [], 'Fe': [], 'Co': [], 'Ni': []}
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for i, scan_data in enumerate(scan_datas):
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xanes_df = pd.DataFrame(scan_data).apply(pd.to_numeric)
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xanes_df.columns = headers[i]
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edge = find_element({'xanes_data_original': xanes_df})
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if options['log']:
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aux.write_log(message=f'... Starting data clean-up ({edge}-edge)... ({i+1}/{len(scan_datas)})', options=options)
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if not ('xmap_roi00' in headers[i]) and (not 'xmap_roi01' in headers[i]):
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if options['log']:
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aux.write_log(message='... ... Did not find fluoresence data. Skipping...', options=options)
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continue
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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]}
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edge_count = {'Mn': 0, 'Fe': 0, 'Co': 0, 'Ni': 0}
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edges[edge].append(xanes_df)
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for ind, data in enumerate(datas):
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df = pd.DataFrame(data)
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df.columns = headers[ind]
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if options['add_rois']:
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edge_start = np.round((float(df["ZapEnergy"].min())), 1)
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if options['log']:
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aux.write_log(message=f'... Addition of rois enabled. Starting addition...', options=options)
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for edge, energies in edges.items():
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if edge_start in energies:
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edge_actual = edge
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edge_count[edge] += 1
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added_edges = {'Mn': [], 'Fe': [], 'Co': [], 'Ni': []}
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for edge, scans in edges.items():
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if options['log']:
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aux.write_log(message=f'... ... Adding rois of the {edge}-edge...', options=options)
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if scans:
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xanes_df = scans[0]
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for i, scan in enumerate(scans):
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if i > 0:
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if options['log']:
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aux.write_log(message=f'... ... ... Adding {i+1}/{len(scans)}', options=options)
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if 'xmap_roi00' in xanes_df.columns:
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xanes_df['xmap_roi00'] += scan['xmap_roi00']
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if 'xmap_roi01' in xanes_df.columns:
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xanes_df['xmap_roi01'] += scan['xmap_roi01']
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added_edges[edge].append(xanes_df)
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edges = added_edges
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if options['save']:
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#FIXME If there is something wrong with the input file, the file will not be saved but log-file still sais it is saved. Goes from "Saving data to ..." to "All done!" no matter if it fals or not.
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if options['log']:
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aux.write_log(message=f'... Saving data to {options["save_folder"]}', options=options)
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if not os.path.isdir(options['save_folder']):
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if options['log']:
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aux.write_log(message=f'... ... {options["save_folder"]} does not exist. Creating folder.', options=options)
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os.makedirs(options['save_folder'])
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filename = os.path.basename(filename).split('.')[0]
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filename = filename.split('/')[-1]
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count = str(edge_count[edge_actual]).zfill(4)
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for edge, scans in edges.items():
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for i, scan in enumerate(scans):
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count = '' if options['add_rois'] else '_'+str(i).zfill(4)
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path = os.path.join(options['save_folder'], f'{filename}_{edge}{count}.dat')
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if not os.path.isfile(path):
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with open(path, 'w', newline = '\n') as f:
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f.write(f'# Time: {timestamps[i]}')
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scan.to_csv(f)
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if options['log']:
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aux.write_log(message=f'... ... Scan saved to {path}', options=options)
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elif options['replace'] and os.path.isfile(path):
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with open(path, 'w', newline = '\n') as f:
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scan.to_csv(f)
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if options['log']:
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aux.write_log(message=f'... ... File already exists. Overwriting to {path}', options=options)
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elif not options['replace'] and os.path.isfile(path):
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if options['log']:
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aux.write_log(message=f'... ... File already exists. Skipping...', options=options)
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all_scans.append(edges)
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if options['log']:
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aux.write_log(message=f'All done!', options=options)
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# Save
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if destination:
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cwd = os.getcwd()
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if not os.path.isdir(destination):
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os.mkdir(destination)
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os.chdir(destination)
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df.to_csv('{}_{}_{}.dat'.format(filename.split('.')[0], edge_actual, count))
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os.chdir(cwd)
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if options['return']:
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return all_scans
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else:
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df.to_csv('{}_{}_{}.dat'.format(filename.split('.')[0], edge_actual, count))
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#Function that "collects" all the files in a folder, only accepting .dat-files from xanes-measurements
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def get_filenames(path):
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cwd = os.getcwd()
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# Change into path provided
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os.chdir(path)
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filenames = [os.path.join(path, filename) for filename in os.listdir() if os.path.isfile(filename) and filename[-4:] == '.dat'] #changed
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return
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# Change directory back to where you ran the script from
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os.chdir(cwd)
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def read_data(data: dict, options={}) -> pd.DataFrame:
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return filenames
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def put_in_dataframe(path):
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filenames = get_filenames(path)
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# FIXME Handle the case when dataseries are not the same size
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# FIXME Add possibility to extract TIME (for operando runs) and Blower Temp (for variable temperature runs)
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# FIXME Add possibility to iport transmission data
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required_options = ['adjust']
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default_options = {
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'adjust': 0
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}
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#making the column names to be used in the dataframe, making sure the first column is the ZapEnergy
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column_names = ["ZapEnergy"]
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options = aux.update_options(options=options, required_options=required_options, default_options=default_options)
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for i in range(len(filenames)):
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column_names.append(filenames[i])
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columns = ['ZapEnergy']
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#Taking the first file in the folder and extracting ZapEnergies and intensity from that (only need the intensity from the rest)
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first = pd.read_csv(filenames[0], skiprows=0)
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if not isinstance(data['path'], list):
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data['path'] = [data['path']]
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#Making a data frame with the correct columns, and will fill inn data afterwards
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df = pd.DataFrame(columns = column_names)
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#First putting in the 2theta-values
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df["ZapEnergy"]=first["ZapEnergy"]
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# Initialise DataFrame with only ZapEnergy-column
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xanes_data = pd.read_csv(data['path'][0], skiprows=1)[['ZapEnergy']]
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xanes_data['ZapEnergy'] += options['adjust']
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#filling in the intensities from all files into the corresponding column in the dataframe
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for i in range(len(filenames)):
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df2 = pd.read_csv(filenames[i])
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df2 = df2.drop(['Mon','Det1','Det2','Det3','Det4','Det5', 'Det6','Ion1'], axis=1) #, axis=1)
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df2 = df2.drop(['MonEx','Ion2','Htime','MusstEnc1','MusstEnc3','MusstEnc4', 'TwoTheta', 'ZCryo'], axis=1)
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df2 = df2.drop(['ZBlower1', 'ZBlower2', 'ZSrcur'], axis=1)#, axis=19) #removing the sigma at this point
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############## THIS PART PICKS OUT WHICH ROI IS OF INTEREST, BUT MUST BE FIXED IF LOOKING AT THREE EDGES (roi00,roi01,roi02) #####################
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if 'xmap_roi01' in df2.columns:
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for filename in data['path']:
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columns.append(filename)
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scan_data = pd.read_csv(filename, skiprows=1)
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if not options['active_roi']:
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scan_data = scan_data[[determine_active_roi(scan_data)]]
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else:
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scan_data = scan_data[options['active_roi']]
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xanes_data = pd.concat([xanes_data, scan_data], axis=1)
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xanes_data.columns = columns
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return xanes_data
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def read_metadata(data: dict, options={}) -> dict:
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required_options = ['get_temperature', 'get_timestamp', 'adjust_time']
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default_options = {
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'get_temperature': True,
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'get_timestamp': True,
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'adjust_time': False
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}
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|
||||
options = aux.update_options(options=options, required_options=required_options, default_options=default_options)
|
||||
|
||||
|
||||
temperatures = []
|
||||
timestamps = []
|
||||
|
||||
for filename in data['path']:
|
||||
scan_data = pd.read_csv(filename, skiprows=1)
|
||||
|
||||
if options['get_temperature']:
|
||||
temperatures.append(scan_data['ZBlower2'].mean())
|
||||
|
||||
if options['get_timestamp']:
|
||||
|
||||
with open(filename, 'r') as f:
|
||||
time = f.readline().strip('# Time: ')
|
||||
time = datetime.datetime.strptime(time, "%a %b %d %H:%M:%S %Y ")
|
||||
|
||||
if options['adjust_time']:
|
||||
time_elapsed = scan_data['Htime'].iloc[-1] - scan_data['Htime'].iloc[0]
|
||||
|
||||
time += datetime.timedelta(microseconds=time_elapsed)/2
|
||||
|
||||
|
||||
timestamps.append(time)
|
||||
|
||||
|
||||
metadata = {'time': timestamps, 'temperature': temperatures}
|
||||
|
||||
return metadata
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
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(df2["xmap_roi00"])-min(df2["xmap_roi00"])>max(df2["xmap_roi01"])-min(df2["xmap_roi01"]):
|
||||
df[filenames[i]]=df2["xmap_roi00"] #forMn
|
||||
# if max(scan_data["xmap_roi00"])-min(scan_data["xmap_roi00"])>max(scan_data["xmap_roi01"])-min(scan_data["xmap_roi01"]):
|
||||
# active_roi = 'xmap_roi00'
|
||||
# else:
|
||||
# active_roi = 'xmap_roi01'
|
||||
|
||||
|
||||
if not ('xmap_roi00' in scan_data.columns) or not ('xmap_roi01' in scan_data.columns):
|
||||
if 'xmap_roi00' in scan_data.columns:
|
||||
active_roi = 'xmap_roi00'
|
||||
elif 'xmap_roi01' in scan_data.columns:
|
||||
active_roi = 'xmap_roi01'
|
||||
|
||||
elif (scan_data['xmap_roi00'].iloc[0:100].mean() < scan_data['xmap_roi00'].iloc[-100:].mean()) and (scan_data['xmap_roi01'].iloc[0:100].mean() < scan_data['xmap_roi01'].iloc[-100:].mean()):
|
||||
if (scan_data['xmap_roi00'].iloc[:int(scan_data.shape[0]/2)].max() - scan_data['xmap_roi00'].iloc[0])/scan_data['xmap_roi00'].max() > (scan_data['xmap_roi01'].iloc[:int(scan_data.shape[0]/2)].max() - scan_data['xmap_roi01'].iloc[0])/scan_data['xmap_roi01'].max():
|
||||
active_roi = 'xmap_roi00'
|
||||
else:
|
||||
df[filenames[i]]=df2["xmap_roi01"] #forNi
|
||||
active_roi = 'xmap_roi01'
|
||||
|
||||
elif scan_data['xmap_roi00'].iloc[0:100].mean() < scan_data['xmap_roi00'].iloc[-100:].mean():
|
||||
active_roi = 'xmap_roi00'
|
||||
|
||||
elif scan_data['xmap_roi01'].iloc[0:100].mean() < scan_data['xmap_roi01'].iloc[-100:].mean():
|
||||
active_roi = 'xmap_roi01'
|
||||
|
||||
else:
|
||||
df[filenames[i]]=df2["xmap_roi00"]
|
||||
###############################################################################################
|
||||
active_roi = None
|
||||
|
||||
i=i+1
|
||||
return active_roi
|
||||
|
||||
|
||||
#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
|
||||
Loading…
Add table
Add a link
Reference in a new issue