413 lines
12 KiB
Python
413 lines
12 KiB
Python
from sympy import re
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import fabio, pyFAI
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import pandas as pd
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import numpy as np
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import os
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import shutil
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import zipfile
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import xml.etree.ElementTree as ET
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import beamtime.auxillary as aux
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def get_image_array(path):
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image = fabio.open(path)
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image_array = image.data
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return image_array
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def get_image_headers(path):
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image = fabio.open(path)
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return image.header
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def integrate_1d(data, options={}):
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''' Integrates an image file to a 1D diffractogram.
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Required content of data:
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calibrant (str): path to .poni-file
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nbins (int): Number of bins to divide image into
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path (str) (optional, dependent on image): path to image file - either this or image must be specified. If both is passed, image is prioritsed
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image (NumPy 2D Array) (optional, dependent on path): image array as extracted from get_image_array
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Output:
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df: DataFrame contianing 1D diffractogram if option 'return' is True
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'''
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required_options = ['unit', 'save', 'save_filename', 'save_extension', 'save_folder', 'overwrite']
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default_options = {
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'unit': '2th_deg',
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'save': False,
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'save_filename': None,
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'save_extension': '_integrated.xy',
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'save_folder': '.',
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'overwrite': False}
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options = aux.update_options(options=options, required_options=required_options, default_options=default_options)
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# Get image array from filename if not passed
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if 'image' not in data.keys():
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data['image'] = get_image_array(data['path'])
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# Instanciate the azimuthal integrator from pyFAI from the calibrant (.poni-file)
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ai = pyFAI.load(data['calibrant'])
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# Determine filename
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filename = make_filename(data=data, options=options)
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# Make save_folder if this does not exist already
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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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res = ai.integrate1d(data['image'], data['nbins'], unit=options['unit'], filename=filename)
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data['path'] = filename
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diffractogram = read_xy(data=data, options=options)
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if not options['save']:
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os.remove(filename)
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shutil.rmtree('tmp')
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return diffractogram
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def make_filename(data, options):
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# Define save location for integrated diffractogram data
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if not options['save']:
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options['save_folder'] = 'tmp'
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filename = os.path.join(options['save_folder'], 'tmp_diff.dat')
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elif options['save']:
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# Case 1: No filename is given.
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if not options['save_filename']:
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# If a path is given instead of an image array, the path is taken as the trunk of the savename
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if data['path']:
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# Make filename by joining the save_folder, the filename (with extension deleted) and adding the save_extension
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filename = os.path.join(options['save_folder'], os.path.split(data['path'])[-1].split('.')[0] + options['save_extension'])
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else:
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# Make filename just "integrated.dat" in the save_folder
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filename = os.path.join(options['save_folder'], 'integrated.xy')
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else:
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filename = os.path.join(options['save_folder'], options['save_filename'])
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if not options['overwrite']:
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trunk = filename.split('.')[0]
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extension = filename.split('.')[-1]
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counter = 0
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while os.path.isfile(filename):
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# Rename first file to match naming scheme if already exists
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if counter == 0:
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os.rename(filename, trunk + '_' + str(counter).zfill(4) + '.' + extension)
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# Increment counter and make new filename
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counter += 1
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counter_string = str(counter)
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filename = trunk + '_' + counter_string.zfill(4) + '.' + extension
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return filename
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def generate_image_list(path, options=None):
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''' Generates a list of paths to pass to the average_images() function'''
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required_options = ['scans_per_image']
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default_options = {
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'scans_per_image': 5
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}
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def average_images(images):
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''' Takes a list of path to image files, reads them and averages them before returning the average image'''
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image_arrays = []
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for image in images:
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image_array = xrd.io.get_image_array(image)
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image_arrays.append(image_array)
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image_arrays = np.array(image_arrays)
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image_average = image_arrays.mean(axis=0)
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return image_average
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def subtract_dark(image, dark):
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return image - dark
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def view_integrator(calibrant):
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''' Prints out information about the azimuthal integrator
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Input:
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calibrant: Path to the azimuthal integrator file (.PONI)
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Output:
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None'''
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ai = pyFAI.load(calibrant)
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print("pyFAI version:", pyFAI.version)
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print("\nIntegrator: \n", ai)
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def read_brml(data, options={}):
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required_options = ['extract_folder', 'save_folder']
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default_options = {
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'extract_folder': 'tmp',
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'save_folder': None
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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 os.path.isdir(options['extract_folder']):
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os.mkdir(options['extract_folder'])
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# Extract the RawData0.xml file from the brml-file
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with zipfile.ZipFile(data['path'], 'r') as brml:
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for info in brml.infolist():
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if "RawData" in info.filename:
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brml.extract(info.filename, options['extract_folder'])
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# Parse the RawData0.xml file
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path = os.path.join(options['extract_folder'], 'Experiment0/RawData0.xml')
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tree = ET.parse(path)
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root = tree.getroot()
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shutil.rmtree(options['extract_folder'])
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diffractogram = []
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for chain in root.findall('./DataRoutes/DataRoute'):
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for scantype in chain.findall('ScanInformation/ScanMode'):
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if scantype.text == 'StillScan':
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if chain.get('Description') == 'Originally measured data.':
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for scandata in chain.findall('Datum'):
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scandata = scandata.text.split(',')
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scandata = [float(i) for i in scandata]
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twotheta, intensity = float(scandata[2]), float(scandata[3])
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else:
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if chain.get('Description') == 'Originally measured data.':
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for scandata in chain.findall('Datum'):
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scandata = scandata.text.split(',')
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twotheta, intensity = float(scandata[2]), float(scandata[3])
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if twotheta > 0:
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diffractogram.append({'2th': twotheta, 'I': intensity})
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if 'wavelength' not in data.keys():
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for chain in root.findall('./FixedInformation/Instrument/PrimaryTracks/TrackInfoData/MountedOptics/InfoData/Tube/WaveLengthAlpha1'):
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data['wavelength'] = float(chain.attrib['Value'])
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diffractogram = pd.DataFrame(diffractogram)
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if options['save_folder']:
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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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diffractogram.to_csv(options['save_folder'])
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return diffractogram
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def read_xy(data, options={}):
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if 'wavelength' not in data.keys():
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find_wavelength_from_xy(data=data)
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with open(data['path'], 'r') as f:
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position = 0
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current_line = f.readline()
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while current_line[0] == '#' or current_line[0] == '\'':
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position = f.tell()
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current_line = f.readline()
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f.seek(position)
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diffractogram = pd.read_csv(f, header=None, delim_whitespace=True)
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if diffractogram.shape[1] == 2:
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diffractogram.columns = ['2th', 'I']
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elif diffractogram.shape[1] == 3:
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diffractogram.columns = ['2th', 'I', 'sigma']
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return diffractogram
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def read_data(data, options={}):
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beamline_extensions = ['mar3450', 'edf', 'cbf']
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file_extension = data['path'].split('.')[-1]
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if file_extension in beamline_extensions:
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diffractogram = integrate_1d(data=data, options=options)
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elif file_extension == 'brml':
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diffractogram = read_brml(data=data, options=options)
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elif file_extension in['xy', 'xye']:
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diffractogram = read_xy(data=data, options=options)
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diffractogram = translate_wavelengths(data=diffractogram, wavelength=data['wavelength'])
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return diffractogram
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def load_reflection_table(data, options={}):
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required_options = ['wavelength', 'to_wavelength']
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default_options = {
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'wavelength': 1.54059,
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'to_wavelength': None
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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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# VESTA outputs the file with a header that has a space between the parameter and units - so there is some extra code to rectify the issue
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# that ensues from this formatting
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reflections = pd.read_csv(data['path'], delim_whitespace=True)
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# Remove the extra column that appears from the headers issue
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reflections.drop(reflections.columns[-1], axis=1, inplace=True)
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with open(data['path'], 'r') as f:
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line = f.readline()
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headers = line.split()
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# Delete the fourth element which is '(Å)'
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del headers[4]
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# Change name of column to avoid using greek letters
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headers[7] = '2th'
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# Set the new modified headers as the headers of
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reflections.columns = headers
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reflections = translate_wavelengths(data=reflections, wavelength=options['wavelength'], to_wavelength=options['to_wavelength'])
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#print(reflections)
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return reflections
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def translate_wavelengths(data, wavelength, to_wavelength=None):
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pd.options.mode.chained_assignment = None
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# Translate to CuKalpha
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cuka = 1.54059 # Å
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if cuka > wavelength:
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max_2th_cuka = 2*np.arcsin(wavelength/cuka) * 180/np.pi
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else:
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max_2th_cuka = data['2th'].max()
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data['2th_cuka'] = np.NAN
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data['2th_cuka'].loc[data['2th'] <= max_2th_cuka] = 2*np.arcsin(cuka/wavelength * np.sin((data['2th']/2) * np.pi/180)) * 180/np.pi
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# Translate to MoKalpha
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moka = 0.71073 # Å
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if moka > wavelength:
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max_2th_moka = 2*np.arcsin(wavelength/moka) * 180/np.pi
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else:
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max_2th_moka = data['2th'].max()
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data['2th_moka'] = np.NAN
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data['2th_moka'].loc[data['2th'] <= max_2th_moka] = 2*np.arcsin(moka/wavelength * np.sin((data['2th']/2) * np.pi/180)) * 180/np.pi
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# Convert to other parameters
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data['d'] = wavelength / (2*np.sin((2*data['2th']*np.pi/180)/2))
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data['1/d'] = 1/data['d']
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data['q'] = np.abs((4*np.pi/wavelength)*np.sin(data['2th']/2 * np.pi/180))
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data['q2'] = data['q']**2
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data['q4'] = data['q']**4
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if to_wavelength:
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if to_wavelength > cuka:
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max_2th = 2*np.arcsin(cuka/to_wavelength) * 180/np.pi
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else:
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max_2th = data['2th_cuka'].max()
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data['2th'] = np.NAN
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data['2th'].loc[data['2th_cuka'] <= max_2th] = 2*np.arcsin(to_wavelength/cuka * np.sin((data['2th_cuka']/2) * np.pi/180)) * 180/np.pi
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return data
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def find_wavelength_from_xy(data):
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wavelength_dict = {'Cu': 1.54059, 'Mo': 0.71073}
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with open(data['path'], 'r') as f:
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lines = f.readlines()
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for line in lines:
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if 'Anode' in line:
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anode = line.split()[8].strip('"')
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data['wavelength'] = wavelength_dict[anode]
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elif 'Wavelength' in line:
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data['wavelength'] = float(line.split()[2])*10**10
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