Merge pull request #13 from rasmusthog/rasmus_xanes
Merge new XANES-functionality into main
This commit is contained in:
Rasmus Vester Thøgersen
GitHub
commit
fb63451fdd
4 changed files with 284 additions and 13 deletions
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@ -1 +1 @@
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from . import io, calib, edges
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from . import io, calib, plot, edges
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@ -462,8 +462,6 @@ def smoothing(data: dict, options={}):
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# Make plots ...
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if options['save_plots'] or options['show_plots']:
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edge_pos = estimate_edge_position(data=data, options=options)
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step_length = data['xanes_data']['ZapEnergy'].iloc[1] - data['xanes_data']['ZapEnergy'].iloc[0]
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@ -563,8 +561,8 @@ def estimate_edge_position(data: dict, options={}, index=0):
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options = aux.update_options(options=options, required_options=required_options, default_options=default_options)
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#making new dataframe to keep the differentiated data
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df_diff = pd.DataFrame(data['xanes_data_original']["ZapEnergy"])
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df_diff[data['path'][index]]=data['xanes_data_original'][data['path'][index]].diff(periods=options['periods'])
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df_diff = pd.DataFrame(data['xanes_data']["ZapEnergy"])
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df_diff[data['path'][index]]=data['xanes_data'][data['path'][index]].diff(periods=options['periods'])
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#shifting column values up so that average differential fits right between the points used in the calculation
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df_diff[data['path'][index]]=df_diff[data['path'][index]].shift(-int(options['periods']/2))
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@ -81,10 +81,19 @@ def split_scan_data(data: dict, options={}) -> list:
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for i, scan_data in enumerate(scan_datas):
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if 'ZapEnergy' not in headers[i]:
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if options['log']:
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aux.write_log(message=f'... No valid scan data found... ({i+1}/{len(scan_datas)})', options=options)
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continue
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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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@ -183,15 +192,111 @@ def split_scan_data(data: dict, options={}) -> list:
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def save_data(data: dict, options={}) -> None:
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required_options = ['save_folder', 'overwrite', 'log', 'logfile', 'filename']
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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")}_save_files.log',
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'save_folder': 'saved_scans',
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'overwrite': False,
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'filename': f'{datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S")}_exported_data.dat',
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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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# Check if there is any data to be saved
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if not 'xanes_data' in data.keys():
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if options['log']:
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aux.write_log(message=f'There is not saved scan data in data. Exiting without saving...', options=options)
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return None
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if not isinstance(data['xanes_data'], pd.DataFrame):
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if options['log']:
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aux.write_log(message=f'data["xanes_data"] has an invalid format. Exiting without saving...', options=options)
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return None
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# Make folder(s) if it/they do(es)n't exist
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if not os.path.exists(options['save_folder']):
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if options['log']:
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aux.write_log(message=f'Destination folder does not exist. Creating folder...', options=options)
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os.makedirs(options['save_folder'])
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if os.path.exists(os.path.join('save_folder', options['filename'])):
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if not options['overwrite']:
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if options['log']:
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aux.write_log(message=f'File already exists and overwrite disabled. Exiting without saving...', options=options)
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return None
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with open(os.path.join(options['save_folder'], options['filename']), 'w') as f:
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if 'e0_diff' in data.keys():
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f.write(f'# Number of header lines: {len(data["path"])+1} \n')
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for i, (path, e0) in enumerate(data['e0_diff'].items()):
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f.write(f'# Scan_{i} \t {e0} \n')
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else:
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f.write(f'# Number of header lines: {1}')
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data['xanes_data'].to_csv(f, sep='\t', index=False)
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#data['xanes_data'].to_csv(os.path.join(options['save_folder'], options['filename']), sep='\t', index=False)
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def load_data(path: str) -> dict:
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# FIXME Let this function be called by read_data() if some criterium is passed
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data = {}
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with open(path, 'r') as f:
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line = f.readline()
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header_lines = int(line.split()[-1])
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if header_lines > 1:
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edge_positions = []
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line = f.readline()
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while line[0] == '#':
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edge_positions.append(line.split()[-1])
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line = f.readline()
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data['xanes_data'] = pd.read_csv(path, sep='\t', skiprows=header_lines)
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data['path'] = data['xanes_data'].columns.to_list()
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data['path'].remove('ZapEnergy')
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if header_lines > 1:
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data['e0_diff'] = {}
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for path, edge_position in zip(data['path'], edge_positions):
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data['e0_diff'][path] = float(edge_position)
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return data
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def read_data(data: dict, options={}) -> pd.DataFrame:
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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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required_options = ['adjust', 'mode']
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default_options = {
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'adjust': 0
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'adjust': 0,
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'mode': 'fluoresence'
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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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@ -211,11 +316,15 @@ def read_data(data: dict, options={}) -> pd.DataFrame:
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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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if options['mode'] == 'fluoresence':
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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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elif options['mode'] == 'transmission':
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scan_data = scan_data['MonEx'] / scan_data['Ion2']
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xanes_data = pd.concat([xanes_data, scan_data], axis=1)
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164
nafuma/xanes/plot.py
Normal file
164
nafuma/xanes/plot.py
Normal file
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@ -0,0 +1,164 @@
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import matplotlib.pyplot as plt
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from matplotlib.ticker import (MultipleLocator, FormatStrFormatter,AutoMinorLocator)
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import pandas as pd
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import numpy as np
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import math
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import datetime
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#import ipywidgets as widgets
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#from IPython.display import display
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import nafuma.xanes as xas
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import nafuma.plotting as btp
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import nafuma.auxillary as aux
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def plot_xanes(data, options={}):
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# Update options
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required_options = ['which_scans', 'xlabel', 'ylabel', 'xunit', 'yunit', 'exclude_scans', 'colours', 'gradient', 'rc_params', 'format_params']
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default_options = {
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'which_scans': 'all',
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'xlabel': 'Energy', 'ylabel': 'Intensity',
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'xunit': 'keV', 'yunit': 'arb. u.',
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'exclude_scans': [],
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'colours': None,
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'gradient': False,
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'rc_params': {},
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'format_params': {}}
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options = aux.update_options(options=options, required_options=required_options, default_options=default_options)
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if not 'xanes_data' in data.keys():
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data['xanes_data'] = xas.io.load_data(data=data, options=options)
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# Update list of cycles to correct indices
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update_scans_list(data=data, options=options)
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colours = generate_colours(scans=options['which_scans'], options=options)
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# Prepare plot, and read and process data
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fig, ax = btp.prepare_plot(options=options)
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# Add counter to pick out correct colour
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counter = 0
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for i, path in enumerate(data['path']):
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if i in options['which_scans']:
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data['xanes_data'].plot(x='ZapEnergy', y=path, ax=ax, c=colours[counter])
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counter += 1
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fig, ax = btp.adjust_plot(fig=fig, ax=ax, options=options)
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#if options['interactive_session_active']:
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return fig, ax
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def pick_out_scans(metadata: dict, timestamp: list):
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# If either start or end are None, set to way back when or way into the future
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if not timestamp[0]:
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timestamp[0] = datetime.datetime.strptime('1970 01 01 00:00:00', '%Y %m %d %H:%M:%S')
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else:
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timestamp[0] = datetime.datetime.strptime(timestamp[0], "%d.%b %y %H.%M.%S")
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if not timestamp[1]:
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timestamp[1] = datetime.datetime.strptime('3000 01 01 00:00:00', '%Y %m %d %H:%M:%S')
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else:
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timestamp[1] = datetime.datetime.strptime(timestamp[1], "%d.%b %y %H.%M.%S")
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scans = []
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for i, time in enumerate(metadata['time']):
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if time >= timestamp[0] and time <= timestamp[1]:
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scans.append(i)
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return scans
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def update_scans_list(data, options: dict) -> None:
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if options['which_scans'] == 'all':
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options['which_scans'] = [i for i in range(len(data['path']))]
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elif isinstance(options['which_scans'], list):
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scans =[]
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for scan in options['which_scans']:
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if isinstance(scan, int):
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scans.append(scan-1)
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elif isinstance(scan, tuple):
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interval = [i-1 for i in range(scan[0], scan[1]+1)]
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scans.extend(interval)
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options['which_scans'] = scans
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# Tuple is used to define an interval - as elements tuples can't be assigned, I convert it to a list here.
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elif isinstance(options['which_scans'], tuple):
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which_scans = list(options['which_scans'])
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if which_scans[0] <= 0:
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which_scans[0] = 1
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elif which_scans[1] < 0:
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which_scans[1] = len(options['which_scans'])
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options['which_scans'] = [i-1 for i in range(which_scans[0], which_scans[1]+1)]
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for i, scan in enumerate(options['which_scans']):
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if scan in options['exclude_scans']:
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del options['which_scans'][i]
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def generate_colours(scans, options):
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# FIXME Make this a generalised function and use this instead of this and in the electrochemsitry submodule
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# Assign colours from the options dictionary if it is defined, otherwise use standard colours.
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if options['colours']:
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colour = options['colours']
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else:
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#colour = (214/255, 143/255, 214/255) # Plum Web (#D68FD6), coolors.co
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colour = (90/255, 42/255, 39/255) # Caput Mortuum(#5A2A27), coolors.co
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# If gradient is enabled, find start and end points for each colour
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if options['gradient']:
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add = min([(1-x)*0.75 for x in colour])
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colour_start = colour
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colour_end = [x+add for x in colour]
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# Generate lists of colours
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colours = []
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for scan_number in range(0, len(scans)):
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if options['gradient']:
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weight_start = (len(scans) - scan_number)/len(scans)
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weight_end = scan_number/len(scans)
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colour = [weight_start*start_colour + weight_end*end_colour for start_colour, end_colour in zip(colour_start, colour_end)]
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colours.append(colour)
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return colours
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