import seaborn as sns import matplotlib.pyplot as plt from matplotlib.ticker import (MultipleLocator, FormatStrFormatter,AutoMinorLocator) import pandas as pd import numpy as np import math import ipywidgets as widgets import beamtime.xrd as xrd import beamtime.auxillary as aux import beamtime.plotting as btp def plot_diffractogram(data, options={}): ''' Plots a diffractogram. Input: data (dict): Must include path = string to diffractogram data, and plot_kind = (recx, beamline, image)''' # Update options required_options = ['x_vals', 'y_vals', 'ylabel', 'xlabel', 'xunit', 'yunit', 'line', 'scatter', 'xlim', 'ylim', 'normalise', 'offset', 'offset_x', 'offset_y', 'reflections_plot', 'reflections_indices', 'reflections_data', 'heatmap', 'cmap', 'plot_kind', 'palettes', 'interactive', 'rc_params', 'format_params', 'interactive_session_active'] default_options = { 'x_vals': '2th', 'y_vals': 'I', 'ylabel': 'Intensity', 'xlabel': '2theta', 'xunit': 'deg', 'yunit': 'a.u.', 'xlim': None, 'ylim': None, 'normalise': True, 'offset': True, 'offset_x': 0, 'offset_y': 1, 'line': True, # whether or not to plot diffractogram as a line plot 'scatter': False, # whether or not to plot individual data points 'reflections_plot': False, # whether to plot reflections as a plot 'reflections_indices': False, # whether to plot the reflection indices '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] 'heatmap': False, 'cmap': 'viridis', 'plot_kind': None, 'palettes': [('qualitative', 'Dark2_8')], 'interactive': False, 'interactive_session_active': False, 'rc_params': {}, 'format_params': {}, } if 'offset_y' not in options.keys(): if len(data['path']) > 10: default_options['offset_y'] = 0.05 options = aux.update_options(options=options, required_options=required_options, default_options=default_options) # Convert data['path'] to list to allow iteration over this to accommodate both single and multiple diffractograms if not isinstance(data['path'], list): data['path'] = [data['path']] # Check if there is some data stored already, load in data if not. This speeds up replotting in interactive mode. if not 'diffractogram' in data.keys(): # Initialise empty list for diffractograms and wavelengths data['diffractogram'] = [None for _ in range(len(data['path']))] data['wavelength'] = [None for _ in range(len(data['path']))] for index in range(len(data['path'])): diffractogram, wavelength = xrd.io.read_data(data=data, options=options, index=index) data['diffractogram'][index] = diffractogram data['wavelength'][index] = wavelength # Sets the xlim if this has not bee specified if not options['xlim']: options['xlim'] = [data['diffractogram'][0][options['x_vals']].min(), data['diffractogram'][0][options['x_vals']].max()] if options['heatmap']: data['heatmap'], data['heatmap_xticks'], data['heatmap_xticklabels'] = generate_heatmap(data=data, options=options) options['xlim'] = options['heatmap_xlim'] else: if not isinstance(data['diffractogram'], list): data['diffractogram'] = [data['diffractogram']] data['wavelength'] = [data['wavelength']] if options['interactive_session_active']: if options['offset']: if (options['offset_x'] != options['current_offset_x']) or (options['offset_y'] != options['current_offset_y']): for i, (diff, wl) in enumerate(zip(data['diffractogram'], data['wavelength'])): xrd.io.apply_offset(diff, wl, i, options) # Start inteactive session with ipywidgets. Disables options['interactive'] in order for the interactive loop to not start another interactive session if options['interactive']: options['interactive'] = False options['interactive_session_active'] = True plot_diffractogram_interactive(data=data, options=options) return # Makes a list out of reflections_data if it only passed as a dict, as it will be looped through later if options['reflections_data']: if not isinstance(options['reflections_data'], list): options['reflections_data'] = [options['reflections_data']] # Determine number of subplots and height ratios between them if len(options['reflections_data']) >= 1: options = determine_grid_layout(options=options) # Prepare plot, and read and process data fig, ax = btp.prepare_plot(options=options) # Assign the correct axes if options['reflections_plot'] or options['reflections_indices']: if options['reflections_indices']: indices_ax = ax[0] if options['reflections_plot']: ref_axes = [axx for axx in ax[range(1,len(options['reflections_data'])+1)]] else: ref_axes = [axx for axx in ax[range(0,len(options['reflections_data']))]] ax = ax[-1] if len(data['path']) < 10: colours = btp.generate_colours(options['palettes']) else: colours = btp.generate_colours(['black'], kind='single') # FIXME Must be changed to map the x-value to the 2th-value somehow if options['heatmap']: sns.heatmap(data['heatmap'], cmap=options['cmap'], cbar=False, ax=ax) ax.set_xticks(data['heatmap_xticks']) ax.set_xticklabels(data['heatmap_xticklabels']) ax.tick_params(axis='x', which='minor', bottom=False, top=False) else: for diffractogram in data['diffractogram']: if options['line']: diffractogram.plot(x=options['x_vals'], y=options['y_vals'], ax=ax, c=next(colours), zorder=1) if options['scatter']: 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)])) fig, ax = btp.adjust_plot(fig=fig, ax=ax, options=options) # Make the reflection plots. By default, the wavelength of the first diffractogram will be used for these. if options['reflections_plot'] and options['reflections_data']: options['xlim'] = ax.get_xlim() options['to_wavelength'] = data['wavelength'][0] for reference, axis in zip(options['reflections_data'], ref_axes): plot_reflection_table(data=reference, ax=axis, options=options) # Print the reflection indices. By default, the wavelength of the first diffractogram will be used for this. if options['reflections_indices'] and options['reflections_data']: options['xlim'] = ax.get_xlim() options['to_wavelength'] = data['wavelength'][0] for reference in options['reflections_data']: plot_reflection_indices(data=reference, ax=indices_ax, options=options) if options['interactive_session_active']: btp.update_widgets(options=options) return data['diffractogram'], fig, ax def generate_heatmap(data, options={}): required_options = ['x_tick_locators'] default_options = { 'x_tick_locators': [0.5, 0.1] } options = aux.update_options(options=options, required_options=required_options, default_options=default_options) twotheta = [] intensities = [] scans = [] for i, d in enumerate(data['diffractogram']): twotheta = np.append(twotheta, d['2th'].to_numpy()) intensities = np.append(intensities, d['I'].to_numpy()) scans = np.append(scans, np.full(len(d['2th'].to_numpy()), int(i))) # Generate ticks and xtick-labels twotheta_max = data['diffractogram'][0]['2th'].max() twotheta_min = data['diffractogram'][0]['2th'].min() twotheta_span = twotheta_max - twotheta_min ndatapoints = len(data['diffractogram'][0]['2th']) steps = twotheta_span / ndatapoints twotheta_label_max = aux.floor(twotheta_max, roundto=options['x_tick_locators'][0]) twotheta_label_min = aux.ceil(twotheta_min, roundto=options['x_tick_locators'][0]) label_steps = (twotheta_label_max - twotheta_label_min)/options['x_tick_locators'][0] xtick_labels = np.linspace(twotheta_label_min, twotheta_label_max, num=int(label_steps)+1) options['x_tick_locators'] = None xticks = [] for tick in xtick_labels: xticks.append((tick - twotheta_min)/steps) heatmap = pd.DataFrame({'2th': twotheta, 'scan': scans, 'I': intensities}) heatmap = heatmap.reset_index().pivot_table(index='scan', columns='2th', values='I') options['heatmap_xlim'] = [(options['xlim'][0] - twotheta_min)/steps, (options['xlim'][1] - twotheta_min)/steps] print(xticks, xtick_labels) return heatmap, xticks, xtick_labels # #results = np.transpose(np.vstack([twotheta, scans, intensities])) def determine_grid_layout(options): nrows = 1 if not options['reflections_indices'] else 2 if options['reflections_plot']: for reference in options['reflections_data']: nrows += 1 options['format_params']['nrows'] = nrows options['format_params']['grid_ratio_height'] = [1 for i in range(nrows-1)]+[10] return options def plot_diffractogram_interactive(data, options): minmax = {'2th': [None, None], '2th_cuka': [None, None], '2th_moka': [None, None], 'd': [None, None], '1/d': [None, None], 'q': [None, None], 'q2': [None, None], 'q4': [None, None]} update_minmax(minmax, data) ymin, ymax = None, None for index, diffractogram in enumerate(data['diffractogram']): if not ymin or (ymin > (diffractogram['I'].min())): #+index*options['offset_y'])): ymin = diffractogram['I'].min()#+index*options['offset_y'] if not ymax or (ymax < (diffractogram['I'].max())):#+index*options['offset_y'])): ymax = diffractogram['I'].max()#+index*options['offset_y'] ymin_start = ymin - 0.1*ymax ymax_start = ymax+0.2*ymax ymin = ymin - 5*ymax ymax = ymax*5 options['widgets'] = { 'xlim': { 'w': widgets.FloatRangeSlider(value=[minmax['2th'][0], minmax['2th'][1]], min=minmax['2th'][0], max=minmax['2th'][1], step=0.5, layout=widgets.Layout(width='95%')), 'state': '2th', '2th_default': {'min': minmax['2th'][0], 'max': minmax['2th'][1], 'value': [minmax['2th'][0], minmax['2th'][1]], 'step': 0.5}, '2th_cuka_default': {'min': minmax['2th_cuka'][0], 'max': minmax['2th_cuka'][1], 'value': [minmax['2th_cuka'][0], minmax['2th_cuka'][1]], 'step': 0.5}, '2th_moka_default': {'min': minmax['2th_moka'][0], 'max': minmax['2th_moka'][1], 'value': [minmax['2th_moka'][0], minmax['2th_moka'][1]], 'step': 0.5}, 'd_default': {'min': minmax['d'][0], 'max': minmax['d'][1], 'value': [minmax['d'][0], minmax['d'][1]], 'step': 0.5}, '1/d_default': {'min': minmax['1/d'][0], 'max': minmax['1/d'][1], 'value': [minmax['1/d'][0], minmax['1/d'][1]], 'step': 0.5}, 'q_default': {'min': minmax['q'][0], 'max': minmax['q'][1], 'value': [minmax['q'][0], minmax['q'][1]], 'step': 0.5}, 'q2_default': {'min': minmax['q2'][0], 'max': minmax['q2'][1], 'value': [minmax['q2'][0], minmax['q2'][1]], 'step': 0.5}, 'q4_default': {'min': minmax['q4'][0], 'max': minmax['q4'][1], 'value': [minmax['q4'][0], minmax['q4'][1]], 'step': 0.5} } } if options['reflections_data']: w = widgets.interactive(btp.ipywidgets_update, func=widgets.fixed(plot_diffractogram), data=widgets.fixed(data), options=widgets.fixed(options), scatter=widgets.ToggleButton(value=False), line=widgets.ToggleButton(value=True), reflections_plot=widgets.ToggleButton(value=True), reflections_indices=widgets.ToggleButton(value=False), x_vals=widgets.Dropdown(options=['2th', 'd', '1/d', 'q', 'q2', 'q4', '2th_cuka', '2th_moka'], value='2th', description='X-values'), xlim=options['widgets']['xlim']['w'], ylim=widgets.FloatRangeSlider(value=[ymin_start, ymax_start], min=ymin, max=ymax, step=0.5, layout=widgets.Layout(width='95%')), offset_y=widgets.BoundedFloatText(value=options['offset_y'], min=-5, max=5, step=0.01), offset_x=widgets.BoundedFloatText(value=options['offset_x'], min=-1, max=1, step=0.01) ) else: w = widgets.interactive(btp.ipywidgets_update, func=widgets.fixed(plot_diffractogram), data=widgets.fixed(data), options=widgets.fixed(options), scatter=widgets.ToggleButton(value=False), line=widgets.ToggleButton(value=True), xlim=options['widgets']['xlim']['w']) display(w) def update_minmax(minmax, data): ''' Finds minimum and maximum values of each column and updates the minmax dictionary to contain the correct values. Input: minmax (dict): contains ''' for index, diffractogram in enumerate(data['diffractogram']): if not minmax['2th'][0] or diffractogram['2th'].min() < minmax['2th'][0]: minmax['2th'][0] = diffractogram['2th'].min() min_index = index if not minmax['2th'][1] or diffractogram['2th'].max() > minmax['2th'][1]: minmax['2th'][1] = diffractogram['2th'].max() max_index = index minmax['2th_cuka'][0], minmax['2th_cuka'][1] = data['diffractogram'][min_index]['2th_cuka'].min(), data['diffractogram'][max_index]['2th_cuka'].max() minmax['2th_moka'][0], minmax['2th_moka'][1] = data['diffractogram'][min_index]['2th_moka'].min(), data['diffractogram'][max_index]['2th_moka'].max() minmax['d'][0], minmax['d'][1] = data['diffractogram'][max_index]['d'].min(), data['diffractogram'][min_index]['d'].max() # swapped, intended minmax['1/d'][0], minmax['1/d'][1] = data['diffractogram'][min_index]['1/d'].min(), data['diffractogram'][max_index]['1/d'].max() minmax['q'][0], minmax['q'][1] = data['diffractogram'][min_index]['q'].min(), data['diffractogram'][max_index]['q'].max() minmax['q2'][0], minmax['q2'][1] = data['diffractogram'][min_index]['q2'].min(), data['diffractogram'][max_index]['q2'].max() minmax['q4'][0], minmax['q4'][1] = data['diffractogram'][min_index]['q4'].min(), data['diffractogram'][max_index]['q4'].max() def update_widgets(options): for widget in options['widgets'].values(): if widget['state'] != options['x_vals']: for arg in widget[f'{options["x_vals"]}_default']: setattr(widget['w'], arg, widget[f'{options["x_vals"]}_default'][arg]) widget['state'] = options['x_vals'] def plot_reflection_indices(data, 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 } data = aux.update_options(options=data, required_options=required_options, default_options=default_options) if not data['hide_indices']: reflection_table = xrd.io.load_reflection_table(data=data, options=options) if data['reflection_indices'] > 0: # Get the data['reflection_indices'] number of highest reflections within the subrange options['xlim'] reflection_indices = reflection_table.loc[(reflection_table[options['x_vals']] > options['xlim'][0]) & (reflection_table[options['x_vals']] < options['xlim'][1])].nlargest(options['reflection_indices'], 'I') # Plot the indices for i in range(data['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[options['x_vals']].iloc[i], y=0, fontsize=2.5, rotation=90, va='bottom', ha='center', c=data['text_colour']) if options['xlim']: ax.set_xlim(options['xlim']) ax.axis('off') return def plot_reflection_table(data, 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'] = data['colour'] if 'min_alpha' in data.keys(): options['min_alpha'] = data['min_alpha'] if 'reflection_indices' in data.keys(): options['reflection_indices'] = data['reflection_indices'] if 'label' in data.keys(): options['label'] = data['label'] if 'wavelength' in data.keys(): options['wavelength'] = data['wavelength'] options = aux.update_options(options=options, required_options=required_options, default_options=default_options) if not ax: _, ax = btp.prepare_plot(options) reflection_table = xrd.io.load_reflection_table(data=data, options=options) reflections, intensities = reflection_table[options['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=data['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 plot_diffractograms(paths, kind, options=None): fig, ax = prepare_diffractogram_plot(options=options) diffractograms = [] for path in paths: diffractogram = xrd.io.read_data(path=path, kind=kind, options=options) diffractograms.append(diffractogram) required_options = ['type', 'xvals', 'yvals', 'x_offset', 'y_offset', 'normalise', 'normalise_around', 'reverse_order'] default_options = { 'type': 'stacked', 'xvals': '2th', 'yvals': 'I', 'x_offset': 0, 'y_offset': 0.2, 'normalise': True, 'normalise_around': None, 'reverse_order': False } # If reverse_order is enabled, reverse the order if options['reverse_order']: diffractograms = reverse_diffractograms(diffractograms) # If normalise is enbaled, normalise all the diffractograms if options['normalise']: if not options['normalise_around']: for diffractogram in diffractograms: diffractogram["I"] = diffractogram["I"]/diffractogram["I"].max() else: diffractogram["I"] = diffractogram["I"]/diffractogram["I"].loc[(diffractogram['2th'] > options['normalise_around'][0]) & (diffractogram['2th'] < options['normalise_around'][1])].max() if options['type'] == 'stacked': for diffractogram in diffractograms: diffractogram.plot(x=options['xvals'], y=options['yvals'], ax=ax) fig, ax = prettify_diffractogram_plot(fig=fig, ax=ax, options=options) return diffractogram, fig, ax def reverse_diffractograms(diffractograms): rev_diffractograms = [] for i in len(diffractograms): rev_diffractograms.append(diffractograms.pop()) return rev_diffractograms #def plot_heatmap():