365 lines
12 KiB
Python
365 lines
12 KiB
Python
import os
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import tempfile
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import numpy as np
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from bokeh.layouts import column, row
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from bokeh.models import (
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Button,
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CellEditor,
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CheckboxEditor,
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ColumnDataSource,
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DataTable,
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Div,
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Panel,
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Select,
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Spacer,
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Span,
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TableColumn,
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TextAreaInput,
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Whisker,
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)
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from bokeh.plotting import figure
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import pyzebra
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from pyzebra import EXPORT_TARGETS, app
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def create():
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dataset = []
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app_dlfiles = app.DownloadFiles(n_files=2)
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def _init_datatable():
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scan_list = [s["idx"] for s in dataset]
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hkl = [f'{s["h"]} {s["k"]} {s["l"]}' for s in dataset]
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export = [s["export"] for s in dataset]
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twotheta = [np.median(s["twotheta"]) if "twotheta" in s else None for s in dataset]
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gamma = [np.median(s["gamma"]) if "gamma" in s else None for s in dataset]
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omega = [np.median(s["omega"]) if "omega" in s else None for s in dataset]
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chi = [np.median(s["chi"]) if "chi" in s else None for s in dataset]
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phi = [np.median(s["phi"]) if "phi" in s else None for s in dataset]
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nu = [np.median(s["nu"]) if "nu" in s else None for s in dataset]
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scan_table_source.data.update(
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scan=scan_list,
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hkl=hkl,
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fit=[0] * len(scan_list),
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export=export,
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twotheta=twotheta,
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gamma=gamma,
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omega=omega,
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chi=chi,
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phi=phi,
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nu=nu,
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)
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scan_table_source.selected.indices = []
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scan_table_source.selected.indices = [0]
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merge_options = [(str(i), f"{i} ({idx})") for i, idx in enumerate(scan_list)]
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merge_from_select.options = merge_options
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merge_from_select.value = merge_options[0][0]
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def _update_table():
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fit_ok = [(1 if "fit" in scan else 0) for scan in dataset]
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export = [scan["export"] for scan in dataset]
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scan_table_source.data.update(fit=fit_ok, export=export)
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def _update_plot():
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scan = _get_selected_scan()
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scan_motor = scan["scan_motor"]
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y = scan["counts"]
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y_err = scan["counts_err"]
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x = scan[scan_motor]
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plot.axis[0].axis_label = scan_motor
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scatter_source.data.update(x=x, y=y, y_upper=y + y_err, y_lower=y - y_err)
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fit = scan.get("fit")
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if fit is not None:
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x_fit = np.linspace(x[0], x[-1], 100)
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fit_source.data.update(x=x_fit, y=fit.eval(x=x_fit))
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x_bkg = []
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y_bkg = []
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xs_peak = []
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ys_peak = []
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comps = fit.eval_components(x=x_fit)
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for i, model in enumerate(app_fitctrl.params):
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if "linear" in model:
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x_bkg = x_fit
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y_bkg = comps[f"f{i}_"]
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elif any(val in model for val in ("gaussian", "voigt", "pvoigt")):
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xs_peak.append(x_fit)
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ys_peak.append(comps[f"f{i}_"])
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bkg_source.data.update(x=x_bkg, y=y_bkg)
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peak_source.data.update(xs=xs_peak, ys=ys_peak)
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else:
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fit_source.data.update(x=[], y=[])
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bkg_source.data.update(x=[], y=[])
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peak_source.data.update(xs=[], ys=[])
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app_fitctrl.update_result_textarea(scan)
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app_inputctrl = app.InputControls(
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dataset, app_dlfiles, on_file_open=_init_datatable, on_monitor_change=_update_plot
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)
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# Main plot
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plot = figure(
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x_axis_label="Scan motor",
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y_axis_label="Counts",
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plot_height=470,
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plot_width=700,
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tools="pan,wheel_zoom,reset",
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)
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scatter_source = ColumnDataSource(dict(x=[0], y=[0], y_upper=[0], y_lower=[0]))
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plot.circle(
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source=scatter_source, line_color="steelblue", fill_color="steelblue", legend_label="data"
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)
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plot.add_layout(Whisker(source=scatter_source, base="x", upper="y_upper", lower="y_lower"))
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fit_source = ColumnDataSource(dict(x=[0], y=[0]))
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plot.line(source=fit_source, legend_label="best fit")
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bkg_source = ColumnDataSource(dict(x=[0], y=[0]))
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plot.line(source=bkg_source, line_color="green", line_dash="dashed", legend_label="linear")
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peak_source = ColumnDataSource(dict(xs=[[0]], ys=[[0]]))
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plot.multi_line(source=peak_source, line_color="red", line_dash="dashed", legend_label="peak")
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fit_from_span = Span(location=None, dimension="height", line_dash="dashed")
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plot.add_layout(fit_from_span)
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fit_to_span = Span(location=None, dimension="height", line_dash="dashed")
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plot.add_layout(fit_to_span)
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plot.y_range.only_visible = True
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plot.toolbar.logo = None
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plot.legend.click_policy = "hide"
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# Scan select
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def scan_table_select_callback(_attr, old, new):
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if not new:
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# skip empty selections
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return
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# Avoid selection of multiple indicies (via Shift+Click or Ctrl+Click)
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if len(new) > 1:
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# drop selection to the previous one
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scan_table_source.selected.indices = old
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return
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if len(old) > 1:
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# skip unnecessary update caused by selection drop
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return
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_update_plot()
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def scan_table_source_callback(_attr, _old, new):
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# unfortunately, we don't know if the change comes from data update or user input
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# also `old` and `new` are the same for non-scalars
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for scan, export in zip(dataset, new["export"]):
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scan["export"] = export
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_update_preview()
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scan_table_source = ColumnDataSource(
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dict(
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scan=[],
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hkl=[],
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fit=[],
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export=[],
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twotheta=[],
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gamma=[],
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omega=[],
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chi=[],
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phi=[],
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nu=[],
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)
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)
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scan_table_source.on_change("data", scan_table_source_callback)
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scan_table_source.selected.on_change("indices", scan_table_select_callback)
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scan_table = DataTable(
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source=scan_table_source,
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columns=[
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TableColumn(field="scan", title="Scan", editor=CellEditor(), width=50),
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TableColumn(field="hkl", title="hkl", editor=CellEditor(), width=100),
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TableColumn(field="fit", title="Fit", editor=CellEditor(), width=50),
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TableColumn(field="export", title="Export", editor=CheckboxEditor(), width=50),
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TableColumn(field="twotheta", title="2theta", editor=CellEditor(), width=50),
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TableColumn(field="gamma", title="gamma", editor=CellEditor(), width=50),
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TableColumn(field="omega", title="omega", editor=CellEditor(), width=50),
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TableColumn(field="chi", title="chi", editor=CellEditor(), width=50),
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TableColumn(field="phi", title="phi", editor=CellEditor(), width=50),
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TableColumn(field="nu", title="nu", editor=CellEditor(), width=50),
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],
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width=310, # +60 because of the index column, but excluding twotheta onwards
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height=350,
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autosize_mode="none",
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editable=True,
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)
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def _get_selected_scan():
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return dataset[scan_table_source.selected.indices[0]]
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merge_from_select = Select(title="scan:", width=145)
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def merge_button_callback():
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scan_into = _get_selected_scan()
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scan_from = dataset[int(merge_from_select.value)]
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if scan_into is scan_from:
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print("WARNING: Selected scans for merging are identical")
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return
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pyzebra.merge_scans(scan_into, scan_from)
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_update_table()
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_update_plot()
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merge_button = Button(label="Merge into current", width=145)
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merge_button.on_click(merge_button_callback)
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def restore_button_callback():
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pyzebra.restore_scan(_get_selected_scan())
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_update_table()
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_update_plot()
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restore_button = Button(label="Restore scan", width=145)
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restore_button.on_click(restore_button_callback)
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app_fitctrl = app.FitControls()
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def fit_from_spinner_callback(_attr, _old, new):
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fit_from_span.location = new
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app_fitctrl.from_spinner.on_change("value", fit_from_spinner_callback)
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def fit_to_spinner_callback(_attr, _old, new):
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fit_to_span.location = new
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app_fitctrl.to_spinner.on_change("value", fit_to_spinner_callback)
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def proc_all_button_callback():
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app_fitctrl.fit_dataset(dataset)
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_update_plot()
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_update_table()
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proc_all_button = Button(label="Process All", button_type="primary", width=145)
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proc_all_button.on_click(proc_all_button_callback)
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def proc_button_callback():
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app_fitctrl.fit_scan(_get_selected_scan())
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_update_plot()
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_update_table()
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proc_button = Button(label="Process Current", width=145)
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proc_button.on_click(proc_button_callback)
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export_preview_textinput = TextAreaInput(title="Export file(s) preview:", width=500, height=400)
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def _update_preview():
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with tempfile.TemporaryDirectory() as temp_dir:
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temp_file = temp_dir + "/temp"
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export_data = []
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for scan in dataset:
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if scan["export"]:
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export_data.append(scan)
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pyzebra.export_1D(
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export_data,
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temp_file,
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export_target_select.value,
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hkl_precision=int(hkl_precision_select.value),
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)
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exported_content = ""
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file_content = []
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for ext in EXPORT_TARGETS[export_target_select.value]:
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fname = temp_file + ext
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if os.path.isfile(fname):
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with open(fname) as f:
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content = f.read()
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exported_content += f"{ext} file:\n" + content
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else:
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content = ""
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file_content.append(content)
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app_dlfiles.set_contents(file_content)
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export_preview_textinput.value = exported_content
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def export_target_select_callback(_attr, _old, new):
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app_dlfiles.set_extensions(EXPORT_TARGETS[new])
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_update_preview()
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export_target_select = Select(
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title="Export target:", options=list(EXPORT_TARGETS.keys()), value="fullprof", width=80
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)
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export_target_select.on_change("value", export_target_select_callback)
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app_dlfiles.set_extensions(EXPORT_TARGETS[export_target_select.value])
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def hkl_precision_select_callback(_attr, _old, _new):
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_update_preview()
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hkl_precision_select = Select(
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title="hkl precision:", options=["2", "3", "4"], value="2", width=80
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)
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hkl_precision_select.on_change("value", hkl_precision_select_callback)
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area_method_div = Div(text="Intensity:", margin=(5, 5, 0, 5))
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fitpeak_controls = row(
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column(
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app_fitctrl.add_function_button,
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app_fitctrl.function_select,
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app_fitctrl.remove_function_button,
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),
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app_fitctrl.params_table,
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Spacer(width=20),
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column(
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app_fitctrl.from_spinner,
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app_fitctrl.lorentz_checkbox,
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area_method_div,
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app_fitctrl.area_method_radiogroup,
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),
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column(app_fitctrl.to_spinner, proc_button, proc_all_button),
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)
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scan_layout = column(
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scan_table,
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row(app_inputctrl.monitor_spinner, column(Spacer(height=19), restore_button)),
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row(column(Spacer(height=19), merge_button), merge_from_select),
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)
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upload_div = Div(text="or upload new .ccl/.dat files:", margin=(5, 5, 0, 5))
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append_upload_div = Div(text="append extra files:", margin=(5, 5, 0, 5))
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import_layout = column(
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app_inputctrl.filelist_select,
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row(app_inputctrl.open_button, app_inputctrl.append_button),
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upload_div,
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app_inputctrl.upload_button,
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append_upload_div,
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app_inputctrl.append_upload_button,
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)
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export_layout = column(
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export_preview_textinput,
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row(
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export_target_select,
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hkl_precision_select,
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column(Spacer(height=19), row(app_dlfiles.button)),
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),
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)
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tab_layout = column(
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row(import_layout, scan_layout, plot, Spacer(width=30), export_layout),
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row(fitpeak_controls, app_fitctrl.result_textarea),
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)
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return Panel(child=tab_layout, title="ccl integrate")
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