686 lines
30 KiB
Python
686 lines
30 KiB
Python
import seaborn as sns
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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 ipywidgets as widgets
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from IPython.display import display
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import nafuma.xrd as xrd
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import nafuma.auxillary as aux
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import nafuma.plotting as btp
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def plot_diffractogram(data, options={}):
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''' Plots a diffractogram.
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Input:
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data (dict): Must include path = string to diffractogram data, and plot_kind = (recx, beamline, image)'''
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# Update options
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required_options = ['x_vals', 'y_vals', 'ylabel', 'xlabel', 'xunit', 'yunit', 'line', 'scatter', 'xlim', 'ylim', 'normalise', 'offset', 'offset_x', 'offset_y', 'offset_change',
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'reflections_plot', 'reflections_indices', 'reflections_data', 'heatmap', 'cmap', 'plot_kind', 'palettes', 'interactive', 'rc_params', 'format_params', 'interactive_session_active']
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default_options = {
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'x_vals': '2th',
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'y_vals': 'I',
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'ylabel': 'Intensity', 'xlabel': '2theta',
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'xunit': 'deg', 'yunit': 'a.u.',
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'xlim': None, 'ylim': None,
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'normalise': True,
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'offset': True,
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'offset_x': 0,
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'offset_y': 1,
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'offset_change': False,
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'line': True, # whether or not to plot diffractogram as a line plot
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'scatter': False, # whether or not to plot individual data points
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'reflections_plot': False, # whether to plot reflections as a plot
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'reflections_indices': False, # whether to plot the reflection indices
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'reflections_data': None, # Should be passed as a list of dictionaries on the form {path: rel_path, reflection_indices: number of indices, colour: [r,g,b], min_alpha: 0-1]
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'heatmap': False,
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'cmap': 'viridis',
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'plot_kind': None,
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'palettes': [('qualitative', 'Dark2_8')],
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'interactive': False,
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'interactive_session_active': False,
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'rc_params': {},
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'format_params': {},
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}
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if 'offset_y' not in options.keys():
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if len(data['path']) > 10:
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default_options['offset_y'] = 0.05
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options = aux.update_options(options=options, required_options=required_options, default_options=default_options)
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#options['current_offset_y'] = options['offset_y']
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# Convert data['path'] to list to allow iteration over this to accommodate both single and multiple diffractograms
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if not isinstance(data['path'], list):
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data['path'] = [data['path']]
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# Check if there is some data stored already, load in data if not. This speeds up replotting in interactive mode.
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if not 'diffractogram' in data.keys():
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# Initialise empty list for diffractograms and wavelengths
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data['diffractogram'] = [None for _ in range(len(data['path']))]
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# If wavelength is not manually passed it should be automatically gathered from the .xy-file
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if 'wavelength' not in data.keys():
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data['wavelength'] = [None for _ in range(len(data['path']))]
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else:
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# If only a single value is passed it should be set to be the same for all diffractograms passed
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if not isinstance(data['wavelength'], list):
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data['wavelength'] = [data['wavelength'] for _ in range(len(data['path']))]
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for index in range(len(data['path'])):
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diffractogram, wavelength = xrd.io.read_data(data=data, options=options, index=index)
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data['diffractogram'][index] = diffractogram
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data['wavelength'][index] = wavelength
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# FIXME This is a quick fix as the image is not reloaded when passing multiple beamline datasets
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data['image'] = None
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# Sets the xlim if this has not bee specified
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if not options['xlim']:
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options['xlim'] = [data['diffractogram'][0][options['x_vals']].min(), data['diffractogram'][0][options['x_vals']].max()]
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# Generate heatmap data
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data['heatmap'], data['heatmap_xticks'], data['heatmap_xticklabels'] = generate_heatmap(data=data, options=options)
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options['heatmap_loaded'] = True
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if options['heatmap']:
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options['xlim'] = options['heatmap_xlim']
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else:
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if not isinstance(data['diffractogram'], list):
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data['diffractogram'] = [data['diffractogram']]
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data['wavelength'] = [data['wavelength']]
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if options['interactive_session_active']:
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if options['offset']:
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if (options['offset_x'] != options['current_offset_x']) or (options['offset_y'] != options['current_offset_y']):
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for i, (diff, wl) in enumerate(zip(data['diffractogram'], data['wavelength'])):
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xrd.io.apply_offset(diff, wl, i, options)
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# Start inteactive session with ipywidgets. Disables options['interactive'] in order for the interactive loop to not start another interactive session
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if options['interactive']:
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options['interactive'] = False
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options['interactive_session_active'] = True
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plot_diffractogram_interactive(data=data, options=options)
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return
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# Makes a list out of reflections_data if it only passed as a dict, as it will be looped through later
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if options['reflections_data']:
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if not isinstance(options['reflections_data'], list):
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options['reflections_data'] = [options['reflections_data']]
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# Determine number of subplots and height ratios between them
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if options['reflections_data'] and len(options['reflections_data']) >= 1:
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options = determine_grid_layout(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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# Assign the correct axes
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if options['reflections_plot'] or options['reflections_indices']:
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if options['reflections_indices']:
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indices_ax = ax[0]
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if options['reflections_plot']:
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ref_axes = [axx for axx in ax[range(1,len(options['reflections_data'])+1)]]
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else:
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ref_axes = [axx for axx in ax[range(0,len(options['reflections_data']))]]
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ax = ax[-1]
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if len(data['path']) < 10:
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colours = btp.generate_colours(options['palettes'])
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else:
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colours = btp.generate_colours(['black'], kind='single')
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if options['heatmap']:
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sns.heatmap(data['heatmap'], cmap=options['cmap'], cbar=False, ax=ax)
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ax.set_xticks(data['heatmap_xticks'][options['x_vals']])
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ax.set_xticklabels(data['heatmap_xticklabels'][options['x_vals']])
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ax.tick_params(axis='x', which='minor', bottom=False, top=False)
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else:
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for diffractogram in data['diffractogram']:
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if options['line']:
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diffractogram.plot(x=options['x_vals'], y=options['y_vals'], ax=ax, c=next(colours), zorder=1)
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if options['scatter']:
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ax.scatter(x=diffractogram[options['x_vals']], y = diffractogram[options['y_vals']], c=[(1,1,1,0)], edgecolors=[next(colours)], linewidths=plt.rcParams['lines.markeredgewidth'], zorder=2) #, edgecolors=np.array([next(colours)]))
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fig, ax = btp.adjust_plot(fig=fig, ax=ax, options=options)
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# Make the reflection plots. By default, the wavelength of the first diffractogram will be used for these.
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if options['reflections_plot'] and options['reflections_data']:
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options['xlim'] = ax.get_xlim()
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options['to_wavelength'] = data['wavelength'][0]
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for reflections_params, axis in zip(options['reflections_data'], ref_axes):
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plot_reflection_table(data=data, reflections_params=reflections_params, ax=axis, options=options)
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# Print the reflection indices. By default, the wavelength of the first diffractogram will be used for this.
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if options['reflections_indices'] and options['reflections_data']:
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options['xlim'] = ax.get_xlim()
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options['to_wavelength'] = data['wavelength'][0]
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for reflections_params in options['reflections_data']:
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plot_reflection_indices(data=data, reflections_params=reflections_params, ax=indices_ax, options=options)
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if options['interactive_session_active']:
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options['current_y_offset'] = options['widget'].kwargs['offset_y']
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update_widgets(data=data, options=options)
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return data['diffractogram'], fig, ax
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def generate_heatmap(data, options={}):
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required_options = ['x_tick_locators']
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default_options = {
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'x_tick_locators': [0.5, 0.1]
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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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twotheta = []
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intensities = []
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scans = []
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for i, d in enumerate(data['diffractogram']):
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twotheta = np.append(twotheta, d['2th'].to_numpy())
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intensities = np.append(intensities, d['I'].to_numpy())
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scans = np.append(scans, np.full(len(d['2th'].to_numpy()), int(i)))
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heatmap = pd.DataFrame({'2th': twotheta, 'scan': scans, 'I': intensities})
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xrd.io.translate_wavelengths(data=heatmap, wavelength=data['wavelength'][0])
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min_dict = {'2th': heatmap['2th'].min(), '2th_cuka': heatmap['2th_cuka'].min(), '2th_moka': heatmap['2th_moka'].min(),
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'q': heatmap['q'].min(), 'q2': heatmap['q2'].min(), 'q4': heatmap['q4'].min(), '1/d': heatmap['1/d'].min()}
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max_dict = {'2th': heatmap['2th'].max(), '2th_cuka': heatmap['2th_cuka'].max(), '2th_moka': heatmap['2th_moka'].max(),
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'q': heatmap['q'].max(), 'q2': heatmap['q2'].max(), 'q4': heatmap['q4'].max(), '1/d': heatmap['1/d'].max()}
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ndatapoints = len(data['diffractogram'][0]['2th'])
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xlims = [0, ndatapoints, 0, ndatapoints] # 0: xmin, 1: xmax, 2: xmin_start, 3: xmax_start
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xticks = {}
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xticklabels = {}
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for xval in min_dict.keys():
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# Add xticks labels
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label_max = aux.floor(max_dict[xval], roundto=options['x_tick_locators'][0])
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label_min = aux.ceil(min_dict[xval], roundto=options['x_tick_locators'][0])
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label_steps = (label_max - label_min)/options['x_tick_locators'][0]
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xticklabels[xval] = np.linspace(label_min, label_max, num=int(label_steps)+1)
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# Add xticks
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xval_span = max_dict[xval] - min_dict[xval]
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steps = xval_span / ndatapoints
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xticks_xval = []
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for tick in xticklabels[xval]:
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xticks_xval.append((tick-min_dict[xval])/steps)
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xticks[xval] = xticks_xval
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options['x_tick_locators'] = None
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heatmap = heatmap.reset_index().pivot_table(index='scan', columns='2th', values='I')
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options['heatmap_xlim'] = xlims
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return heatmap, xticks, xticklabels
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# #results = np.transpose(np.vstack([twotheta, scans, intensities]))
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def determine_grid_layout(options):
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nrows = 1 if not options['reflections_indices'] else 2
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if options['reflections_plot']:
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for reference in options['reflections_data']:
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nrows += 1
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options['format_params']['nrows'] = nrows
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options['format_params']['grid_ratio_height'] = [1 for i in range(nrows-1)]+[10]
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return options
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def plot_diffractogram_interactive(data, options):
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# Format here is xminmax[0]: xmin, xminmax[1]: xmax, xminmax[2]: xmin_start, xminmax[3]: xmax_start, where "_start" denotes starting value of the slider
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xminmax = { '2th': [None, None, None, None], '2th_cuka': [None, None, None, None], '2th_moka': [None, None, None, None],
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'd': [None, None, None, None], '1/d': [None, None, None, None],
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'q': [None, None, None, None], 'q2': [None, None, None, None], 'q4': [None, None, None, None],
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'heatmap': [None, None, None, None], 'start': [None, None, None, None]}
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yminmax = { 'diff': [None, None, None, None], 'heatmap': [None, None, None, None], 'start': [None, None, None, None]}
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update_xminmax(xminmax=xminmax, data=data, options=options)
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update_yminmax(yminmax=yminmax, data=data, options=options)
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options['xminmax'], options['yminmax'] = xminmax, yminmax
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# Get start values for ylim slider based on choice (FIXME This can be impleneted into update_yminmax). Can also make a 'start' item that stores the start values, instead of having 4 items in 'diff' as it is now.
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if options['heatmap']:
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ymin = yminmax['heatmap'][0]
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ymax = yminmax['heatmap'][1]
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ymin_start = yminmax['heatmap'][0]
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ymax_start = yminmax['heatmap'][1]
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elif not options['heatmap']:
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ymin = yminmax['diff'][0]
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ymax = yminmax['diff'][1]
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ymin_start = yminmax['diff'][2]
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ymax_start = yminmax['diff'][3]
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# FIXME The start values for xlim should probably also be decided by initial value of x_vals, and can likewise be implemented in update_xminmax()
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options['widgets'] = {
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'xlim': {
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'w': widgets.FloatRangeSlider(value=[xminmax['start'][2], xminmax['start'][3]], min=xminmax['start'][0], max=xminmax['start'][1], step=0.5, layout=widgets.Layout(width='95%')),
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'state': options['x_vals'],
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'2th_default': {'min': xminmax['2th'][0], 'max': xminmax['2th'][1], 'value': [xminmax['2th'][0], xminmax['2th'][1]], 'step': 0.5},
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'2th_cuka_default': {'min': xminmax['2th_cuka'][0], 'max': xminmax['2th_cuka'][1], 'value': [xminmax['2th_cuka'][0], xminmax['2th_cuka'][1]], 'step': 0.5},
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'2th_moka_default': {'min': xminmax['2th_moka'][0], 'max': xminmax['2th_moka'][1], 'value': [xminmax['2th_moka'][0], xminmax['2th_moka'][1]], 'step': 0.5},
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'd_default': {'min': xminmax['d'][0], 'max': xminmax['d'][1], 'value': [xminmax['d'][0], xminmax['d'][1]], 'step': 0.5},
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'1/d_default': {'min': xminmax['1/d'][0], 'max': xminmax['1/d'][1], 'value': [xminmax['1/d'][0], xminmax['1/d'][1]], 'step': 0.5},
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'q_default': {'min': xminmax['q'][0], 'max': xminmax['q'][1], 'value': [xminmax['q'][0], xminmax['q'][1]], 'step': 0.5},
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'q2_default': {'min': xminmax['q2'][0], 'max': xminmax['q2'][1], 'value': [xminmax['q2'][0], xminmax['q2'][1]], 'step': 0.5},
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'q4_default': {'min': xminmax['q4'][0], 'max': xminmax['q4'][1], 'value': [xminmax['q4'][0], xminmax['q4'][1]], 'step': 0.5},
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'heatmap_default': {'min': xminmax['heatmap'][0], 'max': xminmax['heatmap'][1], 'value': [xminmax['heatmap'][0], xminmax['heatmap'][1]], 'step': 10}
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},
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'ylim': {
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'w': widgets.FloatRangeSlider(value=[yminmax['start'][2], yminmax['start'][3]], min=yminmax['start'][0], max=yminmax['start'][1], step=0.01, layout=widgets.Layout(width='95%')),
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'state': 'heatmap' if options['heatmap'] else 'diff',
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'diff_default': {'min': yminmax['diff'][0], 'max': yminmax['diff'][1], 'value': [yminmax['diff'][2], yminmax['diff'][3]], 'step': 0.01},
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'heatmap_default': {'min': yminmax['heatmap'][0], 'max': yminmax['heatmap'][1], 'value': [yminmax['heatmap'][0], yminmax['heatmap'][1]], 'step': 0.01}
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}
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}
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if options['reflections_data']:
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w = widgets.interactive(btp.ipywidgets_update, func=widgets.fixed(plot_diffractogram), data=widgets.fixed(data), options=widgets.fixed(options),
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scatter=widgets.ToggleButton(value=False),
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line=widgets.ToggleButton(value=True),
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reflections_plot=widgets.ToggleButton(value=True),
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reflections_indices=widgets.ToggleButton(value=False),
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heatmap=widgets.ToggleButton(value=options['heatmap']),
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x_vals=widgets.Dropdown(options=['2th', 'd', '1/d', 'q', 'q2', 'q4', '2th_cuka', '2th_moka'], value='2th', description='X-values'),
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xlim=options['widgets']['xlim']['w'],
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ylim=options['widgets']['ylim']['w'],
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offset_y=widgets.BoundedFloatText(value=options['offset_y'], min=-5, max=5, step=0.01, description='offset_y'),
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offset_x=widgets.BoundedFloatText(value=options['offset_x'], min=-1, max=1, step=0.01, description='offset_x')
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)
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else:
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w = widgets.interactive(btp.ipywidgets_update, func=widgets.fixed(plot_diffractogram), data=widgets.fixed(data), options=widgets.fixed(options),
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scatter=widgets.ToggleButton(value=False),
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line=widgets.ToggleButton(value=True),
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heatmap=widgets.ToggleButton(value=options['heatmap']),
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x_vals=widgets.Dropdown(options=['2th', 'd', '1/d', 'q', 'q2', 'q4', '2th_cuka', '2th_moka'], value='2th', description='X-values'),
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xlim=options['widgets']['xlim']['w'],
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ylim=options['widgets']['ylim']['w'],
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offset_y=widgets.BoundedFloatText(value=options['offset_y'], min=-5, max=5, step=0.01, description='offset_y'),
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offset_x=widgets.BoundedFloatText(value=options['offset_x'], min=-1, max=1, step=0.01, description='offset_x'))
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options['widget'] = w
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display(w)
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def update_xminmax(xminmax, data, options={}):
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''' Finds minimum and maximum values of each column and updates the minmax dictionary to contain the correct values.
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Input:
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minmax (dict): contains '''
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xminmax['2th'] = [None, None, None, None]
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for index, diffractogram in enumerate(data['diffractogram']):
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if not xminmax['2th'][0] or diffractogram['2th'].min() < xminmax['2th'][0]:
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xminmax['2th'][0] = diffractogram['2th'].min()
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min_index = index
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if not xminmax['2th'][1] or diffractogram['2th'].max() > xminmax['2th'][1]:
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xminmax['2th'][1] = diffractogram['2th'].max()
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max_index = index
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xminmax['2th'][2], xminmax['2th'][3] = xminmax['2th'][0], xminmax['2th'][1]
|
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xminmax['2th_cuka'][0], xminmax['2th_cuka'][1] = data['diffractogram'][min_index]['2th_cuka'].min(), data['diffractogram'][max_index]['2th_cuka'].max()
|
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xminmax['2th_cuka'][2], xminmax['2th_cuka'][3] = xminmax['2th_cuka'][0], xminmax['2th_cuka'][1]
|
|
|
|
xminmax['2th_moka'][0], xminmax['2th_moka'][1] = data['diffractogram'][min_index]['2th_moka'].min(), data['diffractogram'][max_index]['2th_moka'].max()
|
|
xminmax['2th_moka'][2], xminmax['2th_moka'][3] = xminmax['2th_moka'][0], xminmax['2th_moka'][1]
|
|
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|
xminmax['d'][0], xminmax['d'][1] = data['diffractogram'][max_index]['d'].min(), data['diffractogram'][min_index]['d'].max() # swapped, intended
|
|
xminmax['d'][2], xminmax['d'][3] = xminmax['d'][0], xminmax['d'][1]
|
|
|
|
xminmax['1/d'][0], xminmax['1/d'][1] = data['diffractogram'][min_index]['1/d'].min(), data['diffractogram'][max_index]['1/d'].max()
|
|
xminmax['1/d'][2], xminmax['1/d'][3] = xminmax['1/d'][0], xminmax['1/d'][1]
|
|
|
|
xminmax['q'][0], xminmax['q'][1] = data['diffractogram'][min_index]['q'].min(), data['diffractogram'][max_index]['q'].max()
|
|
xminmax['q'][2], xminmax['q'][3] = xminmax['q'][0], xminmax['q'][1]
|
|
|
|
xminmax['q2'][0], xminmax['q2'][1] = data['diffractogram'][min_index]['q2'].min(), data['diffractogram'][max_index]['q2'].max()
|
|
xminmax['q2'][2], xminmax['q2'][3] = xminmax['q2'][0], xminmax['q2'][1]
|
|
|
|
xminmax['q4'][0], xminmax['q4'][1] = data['diffractogram'][min_index]['q4'].min(), data['diffractogram'][max_index]['q4'].max()
|
|
xminmax['q4'][2], xminmax['q4'][3] = xminmax['q4'][0], xminmax['q4'][1]
|
|
|
|
|
|
xminmax['heatmap'] = options['heatmap_xlim'] # This value is set in the generate_heatmap()-function
|
|
|
|
|
|
xminmax['start'][0], xminmax['start'][1] = xminmax[options['x_vals']][0], xminmax[options['x_vals']][1]
|
|
xminmax['start'][2], xminmax['start'][3] = xminmax[options['x_vals']][2], xminmax[options['x_vals']][3]
|
|
|
|
|
|
def update_yminmax(yminmax: dict, data: dict, options={}) -> None:
|
|
|
|
yminmax['diff'] = [None, None, None, None]
|
|
# Go through diffractograms and find the minimum and maximum intensity values
|
|
for diffractogram in data['diffractogram']:
|
|
if not yminmax['diff'][0] or (yminmax['diff'][0] > (diffractogram['I'].min())):
|
|
yminmax['diff'][0] = diffractogram['I'].min()
|
|
|
|
if not yminmax['diff'][1] or (yminmax['diff'][1] < (diffractogram['I'].max())):
|
|
yminmax['diff'][1] = diffractogram['I'].max()
|
|
|
|
|
|
# Set start values of ymin and ymax to be slightly below lowest data points and slightly above highest data points to give some whitespace around the plot
|
|
yminmax['diff'][2] = yminmax['diff'][0] - 0.1*yminmax['diff'][1]
|
|
yminmax['diff'][3] = yminmax['diff'][1] + 0.2*yminmax['diff'][1]
|
|
|
|
# Allow for adjustment up to five times ymax above and below data
|
|
yminmax['diff'][0] = yminmax['diff'][0] - 5*yminmax['diff'][1]
|
|
yminmax['diff'][1] = yminmax['diff'][1]*5
|
|
|
|
|
|
# Set start values to the edges of the dataset
|
|
yminmax['heatmap'][0], yminmax['heatmap'][1] = 0, data['heatmap'].shape[0]
|
|
yminmax['heatmap'][2], yminmax['heatmap'][3] = yminmax['heatmap'][0], yminmax['heatmap'][1]
|
|
|
|
|
|
if options['heatmap']:
|
|
yminmax['start'][0], yminmax['start'][1] = yminmax['heatmap'][0], yminmax['heatmap'][1]
|
|
yminmax['start'][2], yminmax['start'][3] = yminmax['heatmap'][0], yminmax['heatmap'][1]
|
|
|
|
else:
|
|
# The third and fourth index are different here to not be zoomed completely out to begin with.
|
|
yminmax['start'][0], yminmax['start'][1] = yminmax['diff'][0], yminmax['diff'][1]
|
|
yminmax['start'][2], yminmax['start'][3] = yminmax['diff'][2], yminmax['diff'][3]
|
|
|
|
|
|
def update_defaults(widget: dict, minmax: dict) -> None:
|
|
''' Updates the default x- or y-limits of a given widget. Refer to plot_diffractogram_interactive() to see the form of the widget that is passed in. An update of the min/max-values is done just prior to calling this function.
|
|
Changes dictionaries in place.
|
|
|
|
Input:
|
|
widget (dict): A dictionary containing the widget itself (widget['w']) and all its default-values (e.g. widget['2th_default'])
|
|
minmax (dict): A dictionary containing min and max values, as well as min_start and max_start values. (e.g. minmax['2th'] is a list with four elements: [xmin, xmax, xmin_start, xmax_start])
|
|
|
|
Output:
|
|
None.'''
|
|
|
|
for name, attr in widget.items():
|
|
if name.endswith('default'):
|
|
attr['min'] = minmax[name.replace('_default', '')][0]
|
|
attr['max'] = minmax[name.replace('_default', '')][1]
|
|
attr['value'] = [minmax[name.replace('_default', '')][2], minmax[name.replace('_default', '')][3]]
|
|
|
|
|
|
def update_widgets(data, options):
|
|
|
|
|
|
for widget_name, widget in options['widgets'].items():
|
|
|
|
# Make changes to xlim-widget
|
|
if widget_name == 'xlim':
|
|
# First update the min and max values
|
|
update_xminmax(xminmax=options['xminmax'], data=data, options=options)
|
|
update_defaults(widget=widget, minmax=options['xminmax'])
|
|
|
|
|
|
if options['heatmap'] and (widget['state'] != 'heatmap'):
|
|
|
|
|
|
setattr(widget['w'], 'min', widget['heatmap_default']['min'])
|
|
setattr(widget['w'], 'max', widget['heatmap_default']['max'])
|
|
setattr(widget['w'], 'value', widget['heatmap_default']['value'])
|
|
setattr(widget['w'], 'step', widget['heatmap_default']['step'])
|
|
|
|
widget['state'] = 'heatmap'
|
|
|
|
elif not options['heatmap'] and (widget['state'] != options['x_vals']):
|
|
# Then loop through all attributes in the widget and change to current mode.
|
|
for arg in widget[f'{options["x_vals"]}_default']:
|
|
|
|
# If new min value is larger than previous max, or new max value is smaller than previous min, set the opposite first
|
|
if arg == 'min':
|
|
if widget[f'{options["x_vals"]}_default']['min'] > getattr(widget['w'], 'max'):
|
|
setattr(widget['w'], 'max', widget[f'{options["x_vals"]}_default']['max'])
|
|
|
|
elif arg == 'max':
|
|
if widget[f'{options["x_vals"]}_default']['max'] < getattr(widget['w'], 'min'):
|
|
setattr(widget['w'], 'min', widget[f'{options["x_vals"]}_default']['min'])
|
|
|
|
|
|
setattr(widget['w'], arg, widget[f'{options["x_vals"]}_default'][arg])
|
|
|
|
|
|
widget['state'] = options['x_vals']
|
|
|
|
# Make changes to ylim-widget
|
|
elif widget_name == 'ylim':
|
|
update_yminmax(yminmax=options['yminmax'], data=data, options=options)
|
|
update_defaults(widget=widget, minmax=options['yminmax'])
|
|
|
|
state = 'heatmap' if options['heatmap'] else 'diff'
|
|
|
|
if widget['state'] != state or options['offset_change']:
|
|
|
|
for arg in widget[f'{state}_default']:
|
|
# If new min value is larger than previous max, or new max value is smaller than previous min, set the opposite first
|
|
if arg == 'min':
|
|
if widget[f'{state}_default']['min'] > getattr(widget['w'], 'max'):
|
|
setattr(widget['w'], 'max', widget[f'{state}_default']['max'])
|
|
|
|
elif arg == 'max':
|
|
if widget[f'{state}_default']['max'] < getattr(widget['w'], 'min'):
|
|
setattr(widget['w'], 'min', widget[f'{state}_default']['min'])
|
|
|
|
|
|
setattr(widget['w'], arg, widget[f'{state}_default'][arg])
|
|
|
|
options['offset_change'] = False
|
|
widget['state'] = state
|
|
|
|
|
|
|
|
|
|
def plot_reflection_indices(data, reflections_params, ax, options={}):
|
|
''' Print reflection indices from output generated by VESTA.
|
|
|
|
Required contents of data:
|
|
path (str): relative path to reflection table file'''
|
|
|
|
required_options = ['reflection_indices', 'text_colour', 'hide_indices']
|
|
|
|
default_options = {
|
|
'reflection_indices': 3, # Number of reflection indices to plot, from highest intensity and working its way down
|
|
'text_colour': 'black',
|
|
'hide_indices': False
|
|
}
|
|
|
|
reflections_params = aux.update_options(options=reflections_params, required_options=required_options, default_options=default_options)
|
|
|
|
if not reflections_params['hide_indices']:
|
|
reflection_table = xrd.io.load_reflection_table(data=data, reflections_params=reflections_params, options=options)
|
|
|
|
if reflections_params['reflection_indices'] > 0:
|
|
|
|
# Get the data['reflection_indices'] number of highest reflections within the subrange options['xlim']
|
|
x_vals = 'heatmap' if options['heatmap'] else options['x_vals']
|
|
reflection_indices = reflection_table.loc[(reflection_table[x_vals] > options['xlim'][0]) & (reflection_table[x_vals] < options['xlim'][1])].nlargest(options['reflection_indices'], 'I')
|
|
|
|
# Plot the indices
|
|
for i in range(reflections_params['reflection_indices']):
|
|
if reflection_indices.shape[0] > i:
|
|
ax.text(s=f'({reflection_indices["h"].iloc[i]} {reflection_indices["k"].iloc[i]} {reflection_indices["l"].iloc[i]})', x=reflection_indices[x_vals].iloc[i], y=0, fontsize=2.5, rotation=90, va='bottom', ha='center', c=reflections_params['text_colour'])
|
|
|
|
|
|
if options['xlim']:
|
|
ax.set_xlim(options['xlim'])
|
|
|
|
ax.axis('off')
|
|
|
|
|
|
return
|
|
|
|
def plot_reflection_table(data, reflections_params, ax=None, options={}):
|
|
''' Plots a reflection table from output generated by VESTA.
|
|
|
|
Required contents of data:
|
|
path (str): relative path to reflection table file'''
|
|
|
|
required_options = ['reflection_indices', 'reflections_colour', 'min_alpha', 'wavelength', 'format_params', 'rc_params', 'label']
|
|
|
|
default_options = {
|
|
'reflection_indices': 0, # Number of indices to print
|
|
'reflections_colour': [0,0,0],
|
|
'min_alpha': 0,
|
|
'wavelength': 1.54059, # CuKalpha, [Å]
|
|
'format_params': {},
|
|
'rc_params': {},
|
|
'label': None
|
|
}
|
|
|
|
if 'colour' in data.keys():
|
|
options['reflections_colour'] = reflections_params['colour']
|
|
if 'min_alpha' in reflections_params.keys():
|
|
options['min_alpha'] = reflections_params['min_alpha']
|
|
if 'reflection_indices' in reflections_params.keys():
|
|
options['reflection_indices'] = reflections_params['reflection_indices']
|
|
if 'label' in reflections_params.keys():
|
|
options['label'] = reflections_params['label']
|
|
if 'wavelength' in reflections_params.keys():
|
|
options['wavelength'] = reflections_params['wavelength']
|
|
|
|
options = aux.update_options(options=options, required_options=required_options, default_options=default_options)
|
|
|
|
|
|
if not ax:
|
|
_, ax = btp.prepare_plot(options)
|
|
|
|
x_vals = 'heatmap' if options['heatmap'] else options['x_vals']
|
|
|
|
reflection_table = xrd.io.load_reflection_table(data=data, reflections_params=reflections_params, options=options)
|
|
reflections, intensities = reflection_table[x_vals], reflection_table['I']
|
|
|
|
|
|
|
|
colours = []
|
|
|
|
for ref, intensity in zip(reflections, intensities):
|
|
|
|
colour = list(options['reflections_colour'])
|
|
rel_intensity = (intensity / intensities.max())*(1-options['min_alpha']) + options['min_alpha']
|
|
colour.append(rel_intensity)
|
|
colours.append(colour)
|
|
|
|
|
|
|
|
|
|
ax.vlines(x=reflections, ymin=-1, ymax=1, colors=colours, lw=0.5)
|
|
ax.set_ylim([-0.5,0.5])
|
|
|
|
|
|
ax.tick_params(which='both', bottom=False, labelbottom=False, right=False, labelright=False, left=False, labelleft=False, top=False, labeltop=False)
|
|
|
|
if options['xlim']:
|
|
ax.set_xlim(options['xlim'])
|
|
|
|
|
|
if options['label']:
|
|
xlim_range = ax.get_xlim()[1] - ax.get_xlim()[0]
|
|
ylim_avg = (ax.get_ylim()[0]+ax.get_ylim()[1])/2
|
|
|
|
ax.text(s=reflections_params['label'], x=(ax.get_xlim()[0]-0.01*xlim_range), y=ylim_avg, ha = 'right', va = 'center')
|
|
|
|
|
|
|
|
|
|
|
|
def prettify_labels(label):
|
|
|
|
labels_dict = {
|
|
'2th': '2$\\theta$',
|
|
'I': 'Intensity'
|
|
}
|
|
|
|
return labels_dict[label]
|
|
|
|
|
|
|
|
def reverse_diffractograms(diffractograms):
|
|
|
|
rev_diffractograms = []
|
|
|
|
for i in len(diffractograms):
|
|
rev_diffractograms.append(diffractograms.pop())
|
|
|
|
return rev_diffractograms
|
|
|
|
#def plot_heatmap():
|