Refactor split_scans
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rasmusvt
parent
9c6a7d5991
commit
7214746af1
1 changed files with 93 additions and 66 deletions
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@ -3,93 +3,119 @@ 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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def split_xanes_scan(root, destination=None, replace=False):
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def split_scan_data(data: dict, options={}):
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required_options = ['save', 'save_folder', 'replace', 'add_rois']
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default_options = {
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'save': False,
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'save_folder': '.',
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'replace': False,
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'add_rois': False
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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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#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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#insert a for-loop to go through all the folders.dat-files in the folder root\xanes\raw
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# FIXME Only adding this variable to pass the Linting-tests - will refactor this later
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filename = 'dummy'
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with open(filename, 'r') as f:
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lines = f.readlines()
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if not isinstance(data['path'], list):
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data['path'] = [data['path']]
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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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all_scans = []
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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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continue
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for filename in data['path']:
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elif line[0:2] == "#C":
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start = False
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with open(filename, 'r') as f:
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lines = f.readlines()
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if data:
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datas.append(data)
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data = []
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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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if header:
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headers.append(header)
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header = ''
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for line in lines:
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# Header line starts with #L - reads headers, and toggles data read-in on
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if line[0:2] == "#L":
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header, read_data = line[2:].split(), True
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continue
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# First line after data started with #C - stops data read-in
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elif line[0:2] == "#C":
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read_data = False
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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); header = ''
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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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scan_data.append(line.split())
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if start == False:
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continue
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else:
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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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if not ('xmap_roi00' in headers[i]) and (not 'xmap_roi01' in headers[i]):
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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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edge = find_element({'xanes_data_original': xanes_df})
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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']:
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edge_start = np.round((float(df["ZapEnergy"].min())), 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 scans:
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xanes_df = scans[0]
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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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for i, scan in enumerate(scans):
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if i > 0:
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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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if not os.path.isdir(options['save_folder']):
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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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# 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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else:
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df.to_csv('{}_{}_{}.dat'.format(filename.split('.')[0], edge_actual, count))
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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'] 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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scan.to_csv(path)
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all_scans.append(edges)
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return all_scans
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@ -117,6 +143,7 @@ def read_data(data: dict, options={}) -> pd.DataFrame:
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columns.append(filename)
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scan_data = pd.read_csv(filename)
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scan_data = scan_data[[determine_active_roi(scan_data)]]
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xanes_data = pd.concat([xanes_data, scan_data], axis=1)
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