Updating for version 0.7.0

Datasets has changed from a single number to arrays

.github/workflows/deployment.yaml

@@ -16,7 +16,6 @@ jobs:
       run: |
         $CONDA/bin/conda install --quiet --yes conda-build anaconda-client
         $CONDA/bin/conda config --append channels conda-forge
-        $CONDA/bin/conda config --set channel_priority strict
         $CONDA/bin/conda config --set anaconda_upload yes
 
     - name: Build and upload

.vscode/launch.json

@@ -8,6 +8,7 @@
             "program": "${workspaceFolder}/pyzebra/app/cli.py",
             "console": "internalConsole",
             "env": {},
+            "justMyCode": false,
         },
     ]
 }

conda-recipe/meta.yaml

@@ -22,9 +22,9 @@ requirements:
     - numpy
     - scipy
     - h5py
-    - bokeh =2.3
+    - bokeh =2.4
     - numba
-    - lmfit
+    - lmfit >=1.0.2
 
 
 about:

pyzebra/__init__.py

@@ -1,11 +1,9 @@
 from pyzebra.anatric import *
 from pyzebra.ccl_io import *
-from pyzebra.h5 import *
-from pyzebra.xtal import *
 from pyzebra.ccl_process import *
+from pyzebra.h5 import *
+from pyzebra.utils import *
+from pyzebra.xtal import *
+from pyzebra.sxtal_refgen import *
 
-ZEBRA_PROPOSALS_PATHS = [
-    f"/afs/psi.ch/project/sinqdata/{year}/zebra/" for year in (2016, 2017, 2018, 2020, 2021)
-]
-
-__version__ = "0.4.0"
+__version__ = "0.7.0"

pyzebra/app/app.py

@@ -2,16 +2,19 @@ import logging
 import sys
 from io import StringIO
 
+import pyzebra
 from bokeh.io import curdoc
 from bokeh.layouts import column, row
-from bokeh.models import Tabs, TextAreaInput
+from bokeh.models import Button, Panel, Tabs, TextAreaInput, TextInput
 
 import panel_ccl_integrate
+import panel_ccl_compare
 import panel_hdf_anatric
+import panel_hdf_param_study
 import panel_hdf_viewer
 import panel_param_study
 import panel_spind
-
+import panel_ccl_prepare
 
 doc = curdoc()
 
@@ -25,16 +28,46 @@ bokeh_logger = logging.getLogger("bokeh")
 bokeh_logger.addHandler(bokeh_handler)
 bokeh_log_textareainput = TextAreaInput(title="server output:", height=150)
 
-# Final layout
-tab_hdf_viewer = panel_hdf_viewer.create()
-tab_hdf_anatric = panel_hdf_anatric.create()
-tab_ccl_integrate = panel_ccl_integrate.create()
-tab_param_study = panel_param_study.create()
-tab_spind = panel_spind.create()
+def proposal_textinput_callback(_attr, _old, _new):
+    apply_button.disabled = False
 
+proposal_textinput = TextInput(title="Proposal number:", name="")
+proposal_textinput.on_change("value_input", proposal_textinput_callback)
+doc.proposal_textinput = proposal_textinput
+
+def apply_button_callback():
+    proposal = proposal_textinput.value.strip()
+    if proposal:
+        try:
+            proposal_path = pyzebra.find_proposal_path(proposal)
+        except ValueError as e:
+            print(e)
+            return
+        apply_button.disabled = True
+    else:
+        proposal_path = ""
+
+    proposal_textinput.name = proposal_path
+
+apply_button = Button(label="Apply", button_type="primary")
+apply_button.on_click(apply_button_callback)
+
+# Final layout
 doc.add_root(
     column(
-        Tabs(tabs=[tab_hdf_viewer, tab_hdf_anatric, tab_ccl_integrate, tab_param_study, tab_spind]),
+        Tabs(
+            tabs=[
+                Panel(child=column(proposal_textinput, apply_button), title="user config"),
+                panel_hdf_viewer.create(),
+                panel_hdf_anatric.create(),
+                panel_ccl_prepare.create(),
+                panel_ccl_integrate.create(),
+                panel_ccl_compare.create(),
+                panel_param_study.create(),
+                panel_hdf_param_study.create(),
+                panel_spind.create(),
+            ]
+        ),
         row(stdout_textareainput, bokeh_log_textareainput, sizing_mode="scale_both"),
     )
 )

pyzebra/app/cli.py

@@ -6,7 +6,7 @@ from bokeh.application.application import Application
 from bokeh.application.handlers import ScriptHandler
 from bokeh.server.server import Server
 
-from pyzebra.anatric import ANATRIC_PATH
+from pyzebra import ANATRIC_PATH, SXTAL_REFGEN_PATH
 from pyzebra.app.handler import PyzebraHandler
 
 logging.basicConfig(format="%(asctime)s %(message)s", level=logging.INFO)
@@ -42,6 +42,13 @@ def main():
         "--anatric-path", type=str, default=ANATRIC_PATH, help="path to anatric executable",
     )
 
+    parser.add_argument(
+        "--sxtal-refgen-path",
+        type=str,
+        default=SXTAL_REFGEN_PATH,
+        help="path to Sxtal_Refgen executable",
+    )
+
     parser.add_argument(
         "--spind-path", type=str, default=None, help="path to spind scripts folder",
     )

pyzebra/app/panel_ccl_compare.py

@@ -0,0 +1,718 @@
+import base64
+import io
+import os
+import tempfile
+import types
+
+import numpy as np
+from bokeh.io import curdoc
+from bokeh.layouts import column, row
+from bokeh.models import (
+    BasicTicker,
+    Button,
+    CellEditor,
+    CheckboxEditor,
+    CheckboxGroup,
+    ColumnDataSource,
+    CustomJS,
+    DataRange1d,
+    DataTable,
+    Div,
+    Dropdown,
+    FileInput,
+    Grid,
+    Legend,
+    Line,
+    LinearAxis,
+    MultiLine,
+    MultiSelect,
+    NumberEditor,
+    Panel,
+    PanTool,
+    Plot,
+    RadioGroup,
+    ResetTool,
+    Scatter,
+    Select,
+    Spacer,
+    Span,
+    Spinner,
+    TableColumn,
+    TextAreaInput,
+    WheelZoomTool,
+    Whisker,
+)
+
+import pyzebra
+from pyzebra.ccl_io import EXPORT_TARGETS
+from pyzebra.ccl_process import AREA_METHODS
+
+
+javaScript = """
+let j = 0;
+for (let i = 0; i < js_data.data['fname'].length; i++) {
+    if (js_data.data['content'][i] === "") continue;
+
+    setTimeout(function() {
+        const blob = new Blob([js_data.data['content'][i]], {type: 'text/plain'})
+        const link = document.createElement('a');
+        document.body.appendChild(link);
+        const url = window.URL.createObjectURL(blob);
+        link.href = url;
+        link.download = js_data.data['fname'][i] + js_data.data['ext'][i];
+        link.click();
+        window.URL.revokeObjectURL(url);
+        document.body.removeChild(link);
+    }, 100 * j)
+
+    j++;
+}
+"""
+
+
+def create():
+    doc = curdoc()
+    dataset1 = []
+    dataset2 = []
+    fit_params = {}
+    js_data = ColumnDataSource(data=dict(content=["", ""], fname=["", ""], ext=["", ""]))
+
+    def file_select_update_for_proposal():
+        proposal_path = proposal_textinput.name
+        if proposal_path:
+            file_list = []
+            for file in os.listdir(proposal_path):
+                if file.endswith((".ccl")):
+                    file_list.append((os.path.join(proposal_path, file), file))
+            file_select.options = file_list
+            file_open_button.disabled = False
+        else:
+            file_select.options = []
+            file_open_button.disabled = True
+
+    doc.add_periodic_callback(file_select_update_for_proposal, 5000)
+
+    def proposal_textinput_callback(_attr, _old, _new):
+        file_select_update_for_proposal()
+
+    proposal_textinput = doc.proposal_textinput
+    proposal_textinput.on_change("name", proposal_textinput_callback)
+
+    def _init_datatable():
+        # dataset2 should have the same metadata as dataset1
+        scan_list = [s["idx"] for s in dataset1]
+        hkl = [f'{s["h"]} {s["k"]} {s["l"]}' for s in dataset1]
+        export = [s["export"] for s in dataset1]
+
+        twotheta = [np.median(s["twotheta"]) if "twotheta" in s else None for s in dataset1]
+        gamma = [np.median(s["gamma"]) if "gamma" in s else None for s in dataset1]
+        omega = [np.median(s["omega"]) if "omega" in s else None for s in dataset1]
+        chi = [np.median(s["chi"]) if "chi" in s else None for s in dataset1]
+        phi = [np.median(s["phi"]) if "phi" in s else None for s in dataset1]
+        nu = [np.median(s["nu"]) if "nu" in s else None for s in dataset1]
+
+        scan_table_source.data.update(
+            scan=scan_list,
+            hkl=hkl,
+            fit=[0] * len(scan_list),
+            export=export,
+            twotheta=twotheta,
+            gamma=gamma,
+            omega=omega,
+            chi=chi,
+            phi=phi,
+            nu=nu,
+        )
+        scan_table_source.selected.indices = []
+        scan_table_source.selected.indices = [0]
+
+        merge_options = [(str(i), f"{i} ({idx})") for i, idx in enumerate(scan_list)]
+        merge_from_select.options = merge_options
+        merge_from_select.value = merge_options[0][0]
+
+    file_select = MultiSelect(title="Select 2 .ccl files:", width=210, height=250)
+
+    def file_open_button_callback():
+        if len(file_select.value) != 2:
+            print("WARNING: Select exactly 2 .ccl files.")
+            return
+
+        new_data1 = []
+        new_data2 = []
+        for ind, f_path in enumerate(file_select.value):
+            with open(f_path) as file:
+                f_name = os.path.basename(f_path)
+                base, ext = os.path.splitext(f_name)
+                try:
+                    file_data = pyzebra.parse_1D(file, ext)
+                except:
+                    print(f"Error loading {f_name}")
+                    return
+
+            pyzebra.normalize_dataset(file_data, monitor_spinner.value)
+            pyzebra.merge_duplicates(file_data)
+
+            if ind == 0:
+                js_data.data.update(fname=[base, base])
+                new_data1 = file_data
+            else:  # ind = 1
+                new_data2 = file_data
+
+        # ignore extra scans at the end of the longest of the two files
+        min_len = min(len(new_data1), len(new_data2))
+        new_data1 = new_data1[:min_len]
+        new_data2 = new_data2[:min_len]
+
+        nonlocal dataset1, dataset2
+        dataset1 = new_data1
+        dataset2 = new_data2
+        _init_datatable()
+
+    file_open_button = Button(label="Open New", width=100, disabled=True)
+    file_open_button.on_click(file_open_button_callback)
+
+    def upload_button_callback(_attr, _old, _new):
+        if len(upload_button.filename) != 2:
+            print("WARNING: Upload exactly 2 .ccl files.")
+            return
+
+        new_data1 = []
+        new_data2 = []
+        for ind, (f_str, f_name) in enumerate(zip(upload_button.value, upload_button.filename)):
+            with io.StringIO(base64.b64decode(f_str).decode()) as file:
+                base, ext = os.path.splitext(f_name)
+                try:
+                    file_data = pyzebra.parse_1D(file, ext)
+                except:
+                    print(f"Error loading {f_name}")
+                    return
+
+            pyzebra.normalize_dataset(file_data, monitor_spinner.value)
+            pyzebra.merge_duplicates(file_data)
+
+            if ind == 0:
+                js_data.data.update(fname=[base, base])
+                new_data1 = file_data
+            else:  # ind = 1
+                new_data2 = file_data
+
+        # ignore extra scans at the end of the longest of the two files
+        min_len = min(len(new_data1), len(new_data2))
+        new_data1 = new_data1[:min_len]
+        new_data2 = new_data2[:min_len]
+
+        nonlocal dataset1, dataset2
+        dataset1 = new_data1
+        dataset2 = new_data2
+        _init_datatable()
+
+    upload_div = Div(text="or upload 2 .ccl files:", margin=(5, 5, 0, 5))
+    upload_button = FileInput(accept=".ccl", multiple=True, width=200)
+    # for on_change("value", ...) or on_change("filename", ...),
+    # see https://github.com/bokeh/bokeh/issues/11461
+    upload_button.on_change("filename", upload_button_callback)
+
+    def monitor_spinner_callback(_attr, old, new):
+        if dataset1 and dataset2:
+            pyzebra.normalize_dataset(dataset1, new)
+            pyzebra.normalize_dataset(dataset2, new)
+            _update_plot()
+
+    monitor_spinner = Spinner(title="Monitor:", mode="int", value=100_000, low=1, width=145)
+    monitor_spinner.on_change("value", monitor_spinner_callback)
+
+    def _update_table():
+        fit_ok = [(1 if "fit" in scan else 0) for scan in dataset1]
+        export = [scan["export"] for scan in dataset1]
+        scan_table_source.data.update(fit=fit_ok, export=export)
+
+    def _update_plot():
+        plot_scatter_source = [plot_scatter1_source, plot_scatter2_source]
+        plot_fit_source = [plot_fit1_source, plot_fit2_source]
+        plot_bkg_source = [plot_bkg1_source, plot_bkg2_source]
+        plot_peak_source = [plot_peak1_source, plot_peak2_source]
+        fit_output = ""
+
+        for ind, scan in enumerate(_get_selected_scan()):
+            scatter_source = plot_scatter_source[ind]
+            fit_source = plot_fit_source[ind]
+            bkg_source = plot_bkg_source[ind]
+            peak_source = plot_peak_source[ind]
+            scan_motor = scan["scan_motor"]
+
+            y = scan["counts"]
+            y_err = scan["counts_err"]
+            x = scan[scan_motor]
+
+            plot.axis[0].axis_label = scan_motor
+            scatter_source.data.update(x=x, y=y, y_upper=y + y_err, y_lower=y - y_err)
+
+            fit = scan.get("fit")
+            if fit is not None:
+                x_fit = np.linspace(x[0], x[-1], 100)
+                fit_source.data.update(x=x_fit, y=fit.eval(x=x_fit))
+
+                x_bkg = []
+                y_bkg = []
+                xs_peak = []
+                ys_peak = []
+                comps = fit.eval_components(x=x_fit)
+                for i, model in enumerate(fit_params):
+                    if "linear" in model:
+                        x_bkg = x_fit
+                        y_bkg = comps[f"f{i}_"]
+
+                    elif any(val in model for val in ("gaussian", "voigt", "pvoigt")):
+                        xs_peak.append(x_fit)
+                        ys_peak.append(comps[f"f{i}_"])
+
+                bkg_source.data.update(x=x_bkg, y=y_bkg)
+                peak_source.data.update(xs=xs_peak, ys=ys_peak)
+                if fit_output:
+                    fit_output = fit_output + "\n\n"
+                fit_output = fit_output + fit.fit_report()
+
+            else:
+                fit_source.data.update(x=[], y=[])
+                bkg_source.data.update(x=[], y=[])
+                peak_source.data.update(xs=[], ys=[])
+
+        fit_output_textinput.value = fit_output
+
+    # Main plot
+    plot = Plot(
+        x_range=DataRange1d(),
+        y_range=DataRange1d(only_visible=True),
+        plot_height=470,
+        plot_width=700,
+    )
+
+    plot.add_layout(LinearAxis(axis_label="Counts"), place="left")
+    plot.add_layout(LinearAxis(axis_label="Scan motor"), place="below")
+
+    plot.add_layout(Grid(dimension=0, ticker=BasicTicker()))
+    plot.add_layout(Grid(dimension=1, ticker=BasicTicker()))
+
+    plot_scatter1_source = ColumnDataSource(dict(x=[0], y=[0], y_upper=[0], y_lower=[0]))
+    plot_scatter1 = plot.add_glyph(
+        plot_scatter1_source, Scatter(x="x", y="y", line_color="steelblue", fill_color="steelblue")
+    )
+    plot.add_layout(
+        Whisker(source=plot_scatter1_source, base="x", upper="y_upper", lower="y_lower")
+    )
+
+    plot_scatter2_source = ColumnDataSource(dict(x=[0], y=[0], y_upper=[0], y_lower=[0]))
+    plot_scatter2 = plot.add_glyph(
+        plot_scatter2_source, Scatter(x="x", y="y", line_color="firebrick", fill_color="firebrick")
+    )
+    plot.add_layout(
+        Whisker(source=plot_scatter2_source, base="x", upper="y_upper", lower="y_lower")
+    )
+
+    plot_fit1_source = ColumnDataSource(dict(x=[0], y=[0]))
+    plot_fit1 = plot.add_glyph(plot_fit1_source, Line(x="x", y="y"))
+
+    plot_fit2_source = ColumnDataSource(dict(x=[0], y=[0]))
+    plot_fit2 = plot.add_glyph(plot_fit2_source, Line(x="x", y="y"))
+
+    plot_bkg1_source = ColumnDataSource(dict(x=[0], y=[0]))
+    plot_bkg1 = plot.add_glyph(
+        plot_bkg1_source, Line(x="x", y="y", line_color="steelblue", line_dash="dashed")
+    )
+
+    plot_bkg2_source = ColumnDataSource(dict(x=[0], y=[0]))
+    plot_bkg2 = plot.add_glyph(
+        plot_bkg2_source, Line(x="x", y="y", line_color="firebrick", line_dash="dashed")
+    )
+
+    plot_peak1_source = ColumnDataSource(dict(xs=[[0]], ys=[[0]]))
+    plot_peak1 = plot.add_glyph(
+        plot_peak1_source, MultiLine(xs="xs", ys="ys", line_color="steelblue", line_dash="dashed")
+    )
+
+    plot_peak2_source = ColumnDataSource(dict(xs=[[0]], ys=[[0]]))
+    plot_peak2 = plot.add_glyph(
+        plot_peak2_source, MultiLine(xs="xs", ys="ys", line_color="firebrick", line_dash="dashed")
+    )
+
+    fit_from_span = Span(location=None, dimension="height", line_dash="dashed")
+    plot.add_layout(fit_from_span)
+
+    fit_to_span = Span(location=None, dimension="height", line_dash="dashed")
+    plot.add_layout(fit_to_span)
+
+    plot.add_layout(
+        Legend(
+            items=[
+                ("data 1", [plot_scatter1]),
+                ("data 2", [plot_scatter2]),
+                ("best fit 1", [plot_fit1]),
+                ("best fit 2", [plot_fit2]),
+                ("peak 1", [plot_peak1]),
+                ("peak 2", [plot_peak2]),
+                ("linear 1", [plot_bkg1]),
+                ("linear 2", [plot_bkg2]),
+            ],
+            location="top_left",
+            click_policy="hide",
+        )
+    )
+
+    plot.add_tools(PanTool(), WheelZoomTool(), ResetTool())
+    plot.toolbar.logo = None
+
+    # Scan select
+    def scan_table_select_callback(_attr, old, new):
+        if not new:
+            # skip empty selections
+            return
+
+        # Avoid selection of multiple indicies (via Shift+Click or Ctrl+Click)
+        if len(new) > 1:
+            # drop selection to the previous one
+            scan_table_source.selected.indices = old
+            return
+
+        if len(old) > 1:
+            # skip unnecessary update caused by selection drop
+            return
+
+        _update_plot()
+
+    def scan_table_source_callback(_attr, _old, new):
+        # unfortunately, we don't know if the change comes from data update or user input
+        # also `old` and `new` are the same for non-scalars
+        for scan1, scan2, export in zip(dataset1, dataset2, new["export"]):
+            scan1["export"] = export
+            scan2["export"] = export
+        _update_preview()
+
+    scan_table_source = ColumnDataSource(
+        dict(
+            scan=[],
+            hkl=[],
+            fit=[],
+            export=[],
+            twotheta=[],
+            gamma=[],
+            omega=[],
+            chi=[],
+            phi=[],
+            nu=[],
+        )
+    )
+    scan_table_source.on_change("data", scan_table_source_callback)
+    scan_table_source.selected.on_change("indices", scan_table_select_callback)
+
+    scan_table = DataTable(
+        source=scan_table_source,
+        columns=[
+            TableColumn(field="scan", title="Scan", editor=CellEditor(), width=50),
+            TableColumn(field="hkl", title="hkl", editor=CellEditor(), width=100),
+            TableColumn(field="fit", title="Fit", editor=CellEditor(), width=50),
+            TableColumn(field="export", title="Export", editor=CheckboxEditor(), width=50),
+            TableColumn(field="twotheta", title="2theta", editor=CellEditor(), width=50),
+            TableColumn(field="gamma", title="gamma", editor=CellEditor(), width=50),
+            TableColumn(field="omega", title="omega", editor=CellEditor(), width=50),
+            TableColumn(field="chi", title="chi", editor=CellEditor(), width=50),
+            TableColumn(field="phi", title="phi", editor=CellEditor(), width=50),
+            TableColumn(field="nu", title="nu", editor=CellEditor(), width=50),
+        ],
+        width=310,  # +60 because of the index column, but excluding twotheta onwards
+        height=350,
+        autosize_mode="none",
+        editable=True,
+    )
+
+    def _get_selected_scan():
+        ind = scan_table_source.selected.indices[0]
+        return dataset1[ind], dataset2[ind]
+
+    merge_from_select = Select(title="scan:", width=145)
+
+    def merge_button_callback():
+        scan_into1, scan_into2 = _get_selected_scan()
+        scan_from1 = dataset1[int(merge_from_select.value)]
+        scan_from2 = dataset2[int(merge_from_select.value)]
+
+        if scan_into1 is scan_from1:
+            print("WARNING: Selected scans for merging are identical")
+            return
+
+        pyzebra.merge_scans(scan_into1, scan_from1)
+        pyzebra.merge_scans(scan_into2, scan_from2)
+        _update_table()
+        _update_plot()
+
+    merge_button = Button(label="Merge into current", width=145)
+    merge_button.on_click(merge_button_callback)
+
+    def restore_button_callback():
+        scan1, scan2 = _get_selected_scan()
+        pyzebra.restore_scan(scan1)
+        pyzebra.restore_scan(scan2)
+        _update_table()
+        _update_plot()
+
+    restore_button = Button(label="Restore scan", width=145)
+    restore_button.on_click(restore_button_callback)
+
+    def fit_from_spinner_callback(_attr, _old, new):
+        fit_from_span.location = new
+
+    fit_from_spinner = Spinner(title="Fit from:", width=145)
+    fit_from_spinner.on_change("value", fit_from_spinner_callback)
+
+    def fit_to_spinner_callback(_attr, _old, new):
+        fit_to_span.location = new
+
+    fit_to_spinner = Spinner(title="to:", width=145)
+    fit_to_spinner.on_change("value", fit_to_spinner_callback)
+
+    def fitparams_add_dropdown_callback(click):
+        # bokeh requires (str, str) for MultiSelect options
+        new_tag = f"{click.item}-{fitparams_select.tags[0]}"
+        fitparams_select.options.append((new_tag, click.item))
+        fit_params[new_tag] = fitparams_factory(click.item)
+        fitparams_select.tags[0] += 1
+
+    fitparams_add_dropdown = Dropdown(
+        label="Add fit function",
+        menu=[
+            ("Linear", "linear"),
+            ("Gaussian", "gaussian"),
+            ("Voigt", "voigt"),
+            ("Pseudo Voigt", "pvoigt"),
+            # ("Pseudo Voigt1", "pseudovoigt1"),
+        ],
+        width=145,
+    )
+    fitparams_add_dropdown.on_click(fitparams_add_dropdown_callback)
+
+    def fitparams_select_callback(_attr, old, new):
+        # Avoid selection of multiple indicies (via Shift+Click or Ctrl+Click)
+        if len(new) > 1:
+            # drop selection to the previous one
+            fitparams_select.value = old
+            return
+
+        if len(old) > 1:
+            # skip unnecessary update caused by selection drop
+            return
+
+        if new:
+            fitparams_table_source.data.update(fit_params[new[0]])
+        else:
+            fitparams_table_source.data.update(dict(param=[], value=[], vary=[], min=[], max=[]))
+
+    fitparams_select = MultiSelect(options=[], height=120, width=145)
+    fitparams_select.tags = [0]
+    fitparams_select.on_change("value", fitparams_select_callback)
+
+    def fitparams_remove_button_callback():
+        if fitparams_select.value:
+            sel_tag = fitparams_select.value[0]
+            del fit_params[sel_tag]
+            for elem in fitparams_select.options:
+                if elem[0] == sel_tag:
+                    fitparams_select.options.remove(elem)
+                    break
+
+            fitparams_select.value = []
+
+    fitparams_remove_button = Button(label="Remove fit function", width=145)
+    fitparams_remove_button.on_click(fitparams_remove_button_callback)
+
+    def fitparams_factory(function):
+        if function == "linear":
+            params = ["slope", "intercept"]
+        elif function == "gaussian":
+            params = ["amplitude", "center", "sigma"]
+        elif function == "voigt":
+            params = ["amplitude", "center", "sigma", "gamma"]
+        elif function == "pvoigt":
+            params = ["amplitude", "center", "sigma", "fraction"]
+        elif function == "pseudovoigt1":
+            params = ["amplitude", "center", "g_sigma", "l_sigma", "fraction"]
+        else:
+            raise ValueError("Unknown fit function")
+
+        n = len(params)
+        fitparams = dict(
+            param=params, value=[None] * n, vary=[True] * n, min=[None] * n, max=[None] * n,
+        )
+
+        if function == "linear":
+            fitparams["value"] = [0, 1]
+            fitparams["vary"] = [False, True]
+            fitparams["min"] = [None, 0]
+
+        elif function == "gaussian":
+            fitparams["min"] = [0, None, None]
+
+        return fitparams
+
+    fitparams_table_source = ColumnDataSource(dict(param=[], value=[], vary=[], min=[], max=[]))
+    fitparams_table = DataTable(
+        source=fitparams_table_source,
+        columns=[
+            TableColumn(field="param", title="Parameter", editor=CellEditor()),
+            TableColumn(field="value", title="Value", editor=NumberEditor()),
+            TableColumn(field="vary", title="Vary", editor=CheckboxEditor()),
+            TableColumn(field="min", title="Min", editor=NumberEditor()),
+            TableColumn(field="max", title="Max", editor=NumberEditor()),
+        ],
+        height=200,
+        width=350,
+        index_position=None,
+        editable=True,
+        auto_edit=True,
+    )
+
+    # start with `background` and `gauss` fit functions added
+    fitparams_add_dropdown_callback(types.SimpleNamespace(item="linear"))
+    fitparams_add_dropdown_callback(types.SimpleNamespace(item="gaussian"))
+    fitparams_select.value = ["gaussian-1"]  # add selection to gauss
+
+    fit_output_textinput = TextAreaInput(title="Fit results:", width=750, height=200)
+
+    def proc_all_button_callback():
+        for scan in [*dataset1, *dataset2]:
+            if scan["export"]:
+                pyzebra.fit_scan(
+                    scan, fit_params, fit_from=fit_from_spinner.value, fit_to=fit_to_spinner.value
+                )
+                pyzebra.get_area(
+                    scan,
+                    area_method=AREA_METHODS[area_method_radiobutton.active],
+                    lorentz=lorentz_checkbox.active,
+                )
+
+        _update_plot()
+        _update_table()
+
+    proc_all_button = Button(label="Process All", button_type="primary", width=145)
+    proc_all_button.on_click(proc_all_button_callback)
+
+    def proc_button_callback():
+        for scan in _get_selected_scan():
+            pyzebra.fit_scan(
+                scan, fit_params, fit_from=fit_from_spinner.value, fit_to=fit_to_spinner.value
+            )
+            pyzebra.get_area(
+                scan,
+                area_method=AREA_METHODS[area_method_radiobutton.active],
+                lorentz=lorentz_checkbox.active,
+            )
+
+        _update_plot()
+        _update_table()
+
+    proc_button = Button(label="Process Current", width=145)
+    proc_button.on_click(proc_button_callback)
+
+    area_method_div = Div(text="Intensity:", margin=(5, 5, 0, 5))
+    area_method_radiobutton = RadioGroup(labels=["Function", "Area"], active=0, width=145)
+
+    intensity_diff_div = Div(text="Intensity difference:", margin=(5, 5, 0, 5))
+    intensity_diff_radiobutton = RadioGroup(
+        labels=["file1 - file2", "file2 - file1"], active=0, width=145
+    )
+
+    lorentz_checkbox = CheckboxGroup(labels=["Lorentz Correction"], width=145, margin=(13, 5, 5, 5))
+
+    export_preview_textinput = TextAreaInput(title="Export file(s) preview:", width=500, height=400)
+
+    def _update_preview():
+        with tempfile.TemporaryDirectory() as temp_dir:
+            temp_file = temp_dir + "/temp"
+            export_data1 = []
+            export_data2 = []
+            for scan1, scan2 in zip(dataset1, dataset2):
+                if scan1["export"]:
+                    export_data1.append(scan1)
+                    export_data2.append(scan2)
+
+            if intensity_diff_radiobutton.active:
+                export_data1, export_data2 = export_data2, export_data1
+
+            pyzebra.export_ccl_compare(
+                export_data1,
+                export_data2,
+                temp_file,
+                export_target_select.value,
+                hkl_precision=int(hkl_precision_select.value),
+            )
+
+            exported_content = ""
+            file_content = []
+            for ext in EXPORT_TARGETS[export_target_select.value]:
+                fname = temp_file + ext
+                if os.path.isfile(fname):
+                    with open(fname) as f:
+                        content = f.read()
+                        exported_content += f"{ext} file:\n" + content
+                else:
+                    content = ""
+                file_content.append(content)
+
+            js_data.data.update(content=file_content)
+            export_preview_textinput.value = exported_content
+
+    def export_target_select_callback(_attr, _old, new):
+        js_data.data.update(ext=EXPORT_TARGETS[new])
+        _update_preview()
+
+    export_target_select = Select(
+        title="Export target:", options=list(EXPORT_TARGETS.keys()), value="fullprof", width=80
+    )
+    export_target_select.on_change("value", export_target_select_callback)
+    js_data.data.update(ext=EXPORT_TARGETS[export_target_select.value])
+
+    def hkl_precision_select_callback(_attr, _old, _new):
+        _update_preview()
+
+    hkl_precision_select = Select(
+        title="hkl precision:", options=["2", "3", "4"], value="2", width=80
+    )
+    hkl_precision_select.on_change("value", hkl_precision_select_callback)
+
+    save_button = Button(label="Download File(s)", button_type="success", width=200)
+    save_button.js_on_click(CustomJS(args={"js_data": js_data}, code=javaScript))
+
+    fitpeak_controls = row(
+        column(fitparams_add_dropdown, fitparams_select, fitparams_remove_button),
+        fitparams_table,
+        Spacer(width=20),
+        column(
+            fit_from_spinner,
+            lorentz_checkbox,
+            area_method_div,
+            area_method_radiobutton,
+            intensity_diff_div,
+            intensity_diff_radiobutton,
+        ),
+        column(fit_to_spinner, proc_button, proc_all_button),
+    )
+
+    scan_layout = column(
+        scan_table,
+        row(monitor_spinner, column(Spacer(height=19), restore_button)),
+        row(column(Spacer(height=19), merge_button), merge_from_select),
+    )
+
+    import_layout = column(file_select, file_open_button, upload_div, upload_button)
+
+    export_layout = column(
+        export_preview_textinput,
+        row(
+            export_target_select, hkl_precision_select, column(Spacer(height=19), row(save_button))
+        ),
+    )
+
+    tab_layout = column(
+        row(import_layout, scan_layout, plot, Spacer(width=30), export_layout),
+        row(fitpeak_controls, fit_output_textinput),
+    )
+
+    return Panel(child=tab_layout, title="ccl compare")

pyzebra/app/panel_ccl_integrate.py

@@ -10,6 +10,7 @@ from bokeh.layouts import column, row
 from bokeh.models import (
     BasicTicker,
     Button,
+    CellEditor,
     CheckboxEditor,
     CheckboxGroup,
     ColumnDataSource,
@@ -38,7 +39,6 @@ from bokeh.models import (
     Spinner,
     TableColumn,
     TextAreaInput,
-    TextInput,
     WheelZoomTool,
     Whisker,
 )
@@ -72,48 +72,56 @@ for (let i = 0; i < js_data.data['fname'].length; i++) {
 
 def create():
     doc = curdoc()
-    det_data = {}
+    dataset = []
     fit_params = {}
     js_data = ColumnDataSource(data=dict(content=["", ""], fname=["", ""], ext=["", ""]))
 
     def file_select_update_for_proposal():
-        proposal = proposal_textinput.value.strip()
-        if not proposal:
+        proposal_path = proposal_textinput.name
+        if proposal_path:
+            file_list = []
+            for file in os.listdir(proposal_path):
+                if file.endswith((".ccl", ".dat")):
+                    file_list.append((os.path.join(proposal_path, file), file))
+            file_select.options = file_list
+            file_open_button.disabled = False
+            file_append_button.disabled = False
+        else:
             file_select.options = []
             file_open_button.disabled = True
             file_append_button.disabled = True
-            return
-
-        for zebra_proposals_path in pyzebra.ZEBRA_PROPOSALS_PATHS:
-            proposal_path = os.path.join(zebra_proposals_path, proposal)
-            if os.path.isdir(proposal_path):
-                # found it
-                break
-        else:
-            raise ValueError(f"Can not find data for proposal '{proposal}'.")
-
-        file_list = []
-        for file in os.listdir(proposal_path):
-            if file.endswith((".ccl", ".dat")):
-                file_list.append((os.path.join(proposal_path, file), file))
-        file_select.options = file_list
-        file_open_button.disabled = False
-        file_append_button.disabled = False
 
     doc.add_periodic_callback(file_select_update_for_proposal, 5000)
 
     def proposal_textinput_callback(_attr, _old, _new):
         file_select_update_for_proposal()
 
-    proposal_textinput = TextInput(title="Proposal number:", width=210)
-    proposal_textinput.on_change("value", proposal_textinput_callback)
+    proposal_textinput = doc.proposal_textinput
+    proposal_textinput.on_change("name", proposal_textinput_callback)
 
     def _init_datatable():
-        scan_list = [s["idx"] for s in det_data]
-        hkl = [f'{s["h"]} {s["k"]} {s["l"]}' for s in det_data]
-        export = [s.get("active", True) for s in det_data]
+        scan_list = [s["idx"] for s in dataset]
+        hkl = [f'{s["h"]} {s["k"]} {s["l"]}' for s in dataset]
+        export = [s["export"] for s in dataset]
+
+        twotheta = [np.median(s["twotheta"]) if "twotheta" in s else None for s in dataset]
+        gamma = [np.median(s["gamma"]) if "gamma" in s else None for s in dataset]
+        omega = [np.median(s["omega"]) if "omega" in s else None for s in dataset]
+        chi = [np.median(s["chi"]) if "chi" in s else None for s in dataset]
+        phi = [np.median(s["phi"]) if "phi" in s else None for s in dataset]
+        nu = [np.median(s["nu"]) if "nu" in s else None for s in dataset]
+
         scan_table_source.data.update(
-            scan=scan_list, hkl=hkl, fit=[0] * len(scan_list), export=export,
+            scan=scan_list,
+            hkl=hkl,
+            fit=[0] * len(scan_list),
+            export=export,
+            twotheta=twotheta,
+            gamma=gamma,
+            omega=omega,
+            chi=chi,
+            phi=phi,
+            nu=nu,
         )
         scan_table_source.selected.indices = []
         scan_table_source.selected.indices = [0]
@@ -125,100 +133,134 @@ def create():
     file_select = MultiSelect(title="Available .ccl/.dat files:", width=210, height=250)
 
     def file_open_button_callback():
-        nonlocal det_data
-        det_data = []
-        for f_name in file_select.value:
-            with open(f_name) as file:
+        nonlocal dataset
+        new_data = []
+        for f_path in file_select.value:
+            with open(f_path) as file:
+                f_name = os.path.basename(f_path)
                 base, ext = os.path.splitext(f_name)
-                if det_data:
-                    append_data = pyzebra.parse_1D(file, ext)
-                    pyzebra.normalize_dataset(append_data, monitor_spinner.value)
-                    pyzebra.merge_datasets(det_data, append_data)
-                else:
-                    det_data = pyzebra.parse_1D(file, ext)
-                    pyzebra.normalize_dataset(det_data, monitor_spinner.value)
-                    pyzebra.merge_duplicates(det_data)
-                    js_data.data.update(fname=[base, base])
+                try:
+                    file_data = pyzebra.parse_1D(file, ext)
+                except:
+                    print(f"Error loading {f_name}")
+                    continue
 
-        _init_datatable()
-        append_upload_button.disabled = False
+            pyzebra.normalize_dataset(file_data, monitor_spinner.value)
+
+            if not new_data:  # first file
+                new_data = file_data
+                pyzebra.merge_duplicates(new_data)
+                js_data.data.update(fname=[base, base])
+            else:
+                pyzebra.merge_datasets(new_data, file_data)
+
+        if new_data:
+            dataset = new_data
+            _init_datatable()
+            append_upload_button.disabled = False
 
     file_open_button = Button(label="Open New", width=100, disabled=True)
     file_open_button.on_click(file_open_button_callback)
 
     def file_append_button_callback():
-        for f_name in file_select.value:
-            with open(f_name) as file:
+        file_data = []
+        for f_path in file_select.value:
+            with open(f_path) as file:
+                f_name = os.path.basename(f_path)
                 _, ext = os.path.splitext(f_name)
-                append_data = pyzebra.parse_1D(file, ext)
+                try:
+                    file_data = pyzebra.parse_1D(file, ext)
+                except:
+                    print(f"Error loading {f_name}")
+                    continue
 
-            pyzebra.normalize_dataset(append_data, monitor_spinner.value)
-            pyzebra.merge_datasets(det_data, append_data)
+            pyzebra.normalize_dataset(file_data, monitor_spinner.value)
+            pyzebra.merge_datasets(dataset, file_data)
 
-        _init_datatable()
+        if file_data:
+            _init_datatable()
 
     file_append_button = Button(label="Append", width=100, disabled=True)
     file_append_button.on_click(file_append_button_callback)
 
-    def upload_button_callback(_attr, _old, new):
-        nonlocal det_data
-        det_data = []
-        proposal_textinput.value = ""
-        for f_str, f_name in zip(new, upload_button.filename):
+    def upload_button_callback(_attr, _old, _new):
+        nonlocal dataset
+        new_data = []
+        for f_str, f_name in zip(upload_button.value, upload_button.filename):
             with io.StringIO(base64.b64decode(f_str).decode()) as file:
                 base, ext = os.path.splitext(f_name)
-                if det_data:
-                    append_data = pyzebra.parse_1D(file, ext)
-                    pyzebra.normalize_dataset(append_data, monitor_spinner.value)
-                    pyzebra.merge_datasets(det_data, append_data)
-                else:
-                    det_data = pyzebra.parse_1D(file, ext)
-                    pyzebra.normalize_dataset(det_data, monitor_spinner.value)
-                    pyzebra.merge_duplicates(det_data)
-                    js_data.data.update(fname=[base, base])
+                try:
+                    file_data = pyzebra.parse_1D(file, ext)
+                except:
+                    print(f"Error loading {f_name}")
+                    continue
 
-        _init_datatable()
-        append_upload_button.disabled = False
+            pyzebra.normalize_dataset(file_data, monitor_spinner.value)
+
+            if not new_data:  # first file
+                new_data = file_data
+                pyzebra.merge_duplicates(new_data)
+                js_data.data.update(fname=[base, base])
+            else:
+                pyzebra.merge_datasets(new_data, file_data)
+
+        if new_data:
+            dataset = new_data
+            _init_datatable()
+            append_upload_button.disabled = False
 
     upload_div = Div(text="or upload new .ccl/.dat files:", margin=(5, 5, 0, 5))
     upload_button = FileInput(accept=".ccl,.dat", multiple=True, width=200)
-    upload_button.on_change("value", upload_button_callback)
+    # for on_change("value", ...) or on_change("filename", ...),
+    # see https://github.com/bokeh/bokeh/issues/11461
+    upload_button.on_change("filename", upload_button_callback)
 
-    def append_upload_button_callback(_attr, _old, new):
-        for f_str, f_name in zip(new, append_upload_button.filename):
+    def append_upload_button_callback(_attr, _old, _new):
+        file_data = []
+        for f_str, f_name in zip(append_upload_button.value, append_upload_button.filename):
             with io.StringIO(base64.b64decode(f_str).decode()) as file:
                 _, ext = os.path.splitext(f_name)
-                append_data = pyzebra.parse_1D(file, ext)
+                try:
+                    file_data = pyzebra.parse_1D(file, ext)
+                except:
+                    print(f"Error loading {f_name}")
+                    continue
 
-            pyzebra.normalize_dataset(append_data, monitor_spinner.value)
-            pyzebra.merge_datasets(det_data, append_data)
+            pyzebra.normalize_dataset(file_data, monitor_spinner.value)
+            pyzebra.merge_datasets(dataset, file_data)
 
-        _init_datatable()
+        if file_data:
+            _init_datatable()
 
     append_upload_div = Div(text="append extra files:", margin=(5, 5, 0, 5))
     append_upload_button = FileInput(accept=".ccl,.dat", multiple=True, width=200, disabled=True)
-    append_upload_button.on_change("value", append_upload_button_callback)
+    # for on_change("value", ...) or on_change("filename", ...),
+    # see https://github.com/bokeh/bokeh/issues/11461
+    append_upload_button.on_change("filename", append_upload_button_callback)
 
     def monitor_spinner_callback(_attr, old, new):
-        if det_data:
-            pyzebra.normalize_dataset(det_data, new)
-            _update_plot(_get_selected_scan())
+        if dataset:
+            pyzebra.normalize_dataset(dataset, new)
+            _update_plot()
 
     monitor_spinner = Spinner(title="Monitor:", mode="int", value=100_000, low=1, width=145)
     monitor_spinner.on_change("value", monitor_spinner_callback)
 
     def _update_table():
-        fit_ok = [(1 if "fit" in scan else 0) for scan in det_data]
-        scan_table_source.data.update(fit=fit_ok)
+        fit_ok = [(1 if "fit" in scan else 0) for scan in dataset]
+        export = [scan["export"] for scan in dataset]
+        scan_table_source.data.update(fit=fit_ok, export=export)
 
-    def _update_plot(scan):
+    def _update_plot():
+        scan = _get_selected_scan()
         scan_motor = scan["scan_motor"]
 
         y = scan["counts"]
+        y_err = scan["counts_err"]
         x = scan[scan_motor]
 
         plot.axis[0].axis_label = scan_motor
-        plot_scatter_source.data.update(x=x, y=y, y_upper=y + np.sqrt(y), y_lower=y - np.sqrt(y))
+        plot_scatter_source.data.update(x=x, y=y, y_upper=y + y_err, y_lower=y - y_err)
 
         fit = scan.get("fit")
         if fit is not None:
@@ -266,7 +308,7 @@ def create():
 
     plot_scatter_source = ColumnDataSource(dict(x=[0], y=[0], y_upper=[0], y_lower=[0]))
     plot_scatter = plot.add_glyph(
-        plot_scatter_source, Scatter(x="x", y="y", line_color="steelblue")
+        plot_scatter_source, Scatter(x="x", y="y", line_color="steelblue", fill_color="steelblue")
     )
     plot.add_layout(Whisker(source=plot_scatter_source, base="x", upper="y_upper", lower="y_lower"))
 
@@ -321,52 +363,76 @@ def create():
             # skip unnecessary update caused by selection drop
             return
 
-        _update_plot(det_data[new[0]])
+        _update_plot()
 
-    def scan_table_source_callback(_attr, _old, _new):
+    def scan_table_source_callback(_attr, _old, new):
+        # unfortunately, we don't know if the change comes from data update or user input
+        # also `old` and `new` are the same for non-scalars
+        for scan, export in zip(dataset, new["export"]):
+            scan["export"] = export
         _update_preview()
 
-    scan_table_source = ColumnDataSource(dict(scan=[], hkl=[], fit=[], export=[]))
+    scan_table_source = ColumnDataSource(
+        dict(
+            scan=[],
+            hkl=[],
+            fit=[],
+            export=[],
+            twotheta=[],
+            gamma=[],
+            omega=[],
+            chi=[],
+            phi=[],
+            nu=[],
+        )
+    )
     scan_table_source.on_change("data", scan_table_source_callback)
+    scan_table_source.selected.on_change("indices", scan_table_select_callback)
 
     scan_table = DataTable(
         source=scan_table_source,
         columns=[
-            TableColumn(field="scan", title="Scan", width=50),
-            TableColumn(field="hkl", title="hkl", width=100),
-            TableColumn(field="fit", title="Fit", width=50),
+            TableColumn(field="scan", title="Scan", editor=CellEditor(), width=50),
+            TableColumn(field="hkl", title="hkl", editor=CellEditor(), width=100),
+            TableColumn(field="fit", title="Fit", editor=CellEditor(), width=50),
             TableColumn(field="export", title="Export", editor=CheckboxEditor(), width=50),
+            TableColumn(field="twotheta", title="2theta", editor=CellEditor(), width=50),
+            TableColumn(field="gamma", title="gamma", editor=CellEditor(), width=50),
+            TableColumn(field="omega", title="omega", editor=CellEditor(), width=50),
+            TableColumn(field="chi", title="chi", editor=CellEditor(), width=50),
+            TableColumn(field="phi", title="phi", editor=CellEditor(), width=50),
+            TableColumn(field="nu", title="nu", editor=CellEditor(), width=50),
         ],
-        width=310,  # +60 because of the index column
+        width=310,  # +60 because of the index column, but excluding twotheta onwards
         height=350,
         autosize_mode="none",
         editable=True,
     )
 
-    scan_table_source.selected.on_change("indices", scan_table_select_callback)
-
     def _get_selected_scan():
-        return det_data[scan_table_source.selected.indices[0]]
+        return dataset[scan_table_source.selected.indices[0]]
 
     merge_from_select = Select(title="scan:", width=145)
 
     def merge_button_callback():
         scan_into = _get_selected_scan()
-        scan_from = det_data[int(merge_from_select.value)]
+        scan_from = dataset[int(merge_from_select.value)]
 
         if scan_into is scan_from:
             print("WARNING: Selected scans for merging are identical")
             return
 
         pyzebra.merge_scans(scan_into, scan_from)
-        _update_plot(_get_selected_scan())
+        _update_table()
+        _update_plot()
 
     merge_button = Button(label="Merge into current", width=145)
     merge_button.on_click(merge_button_callback)
 
     def restore_button_callback():
         pyzebra.restore_scan(_get_selected_scan())
-        _update_plot(_get_selected_scan())
+        _update_table()
+        _update_plot()
 
     restore_button = Button(label="Restore scan", width=145)
     restore_button.on_click(restore_button_callback)
@@ -470,7 +536,7 @@ def create():
     fitparams_table = DataTable(
         source=fitparams_table_source,
         columns=[
-            TableColumn(field="param", title="Parameter"),
+            TableColumn(field="param", title="Parameter", editor=CellEditor()),
             TableColumn(field="value", title="Value", editor=NumberEditor()),
             TableColumn(field="vary", title="Vary", editor=CheckboxEditor()),
             TableColumn(field="min", title="Min", editor=NumberEditor()),
@@ -491,8 +557,8 @@ def create():
     fit_output_textinput = TextAreaInput(title="Fit results:", width=750, height=200)
 
     def proc_all_button_callback():
-        for scan, export in zip(det_data, scan_table_source.data["export"]):
-            if export:
+        for scan in dataset:
+            if scan["export"]:
                 pyzebra.fit_scan(
                     scan, fit_params, fit_from=fit_from_spinner.value, fit_to=fit_to_spinner.value
                 )
@@ -502,7 +568,7 @@ def create():
                     lorentz=lorentz_checkbox.active,
                 )
 
-        _update_plot(_get_selected_scan())
+        _update_plot()
         _update_table()
 
     proc_all_button = Button(label="Process All", button_type="primary", width=145)
@@ -519,7 +585,7 @@ def create():
             lorentz=lorentz_checkbox.active,
         )
 
-        _update_plot(scan)
+        _update_plot()
         _update_table()
 
     proc_button = Button(label="Process Current", width=145)
@@ -536,9 +602,9 @@ def create():
         with tempfile.TemporaryDirectory() as temp_dir:
             temp_file = temp_dir + "/temp"
             export_data = []
-            for s, export in zip(det_data, scan_table_source.data["export"]):
-                if export:
-                    export_data.append(s)
+            for scan in dataset:
+                if scan["export"]:
+                    export_data.append(scan)
 
             pyzebra.export_1D(
                 export_data,
@@ -598,7 +664,6 @@ def create():
     )
 
     import_layout = column(
-        proposal_textinput,
         file_select,
         row(file_open_button, file_append_button),
         upload_div,

pyzebra/app/panel_ccl_prepare.py

@@ -0,0 +1,745 @@
+import base64
+import io
+import os
+import subprocess
+import tempfile
+
+import numpy as np
+from bokeh.layouts import column, row
+from bokeh.models import (
+    Arrow,
+    BoxZoomTool,
+    Button,
+    CheckboxGroup,
+    ColumnDataSource,
+    CustomJS,
+    Div,
+    Ellipse,
+    FileInput,
+    Legend,
+    LegendItem,
+    LinearAxis,
+    MultiLine,
+    MultiSelect,
+    NormalHead,
+    NumericInput,
+    Panel,
+    PanTool,
+    Plot,
+    RadioGroup,
+    Range1d,
+    ResetTool,
+    Scatter,
+    Select,
+    Spacer,
+    Spinner,
+    Text,
+    TextAreaInput,
+    TextInput,
+    WheelZoomTool,
+)
+from bokeh.palettes import Dark2
+
+import pyzebra
+
+
+javaScript = """
+let j = 0;
+for (let i = 0; i < js_data.data['fname'].length; i++) {
+    if (js_data.data['content'][i] === "") continue;
+
+    setTimeout(function() {
+        const blob = new Blob([js_data.data['content'][i]], {type: 'text/plain'})
+        const link = document.createElement('a');
+        document.body.appendChild(link);
+        const url = window.URL.createObjectURL(blob);
+        link.href = url;
+        link.download = js_data.data['fname'][i];
+        link.click();
+        window.URL.revokeObjectURL(url);
+        document.body.removeChild(link);
+    }, 100 * j)
+
+    j++;
+}
+"""
+
+ANG_CHUNK_DEFAULTS = {"2theta": 30, "gamma": 30, "omega": 30, "chi": 35, "phi": 35, "nu": 10}
+SORT_OPT_BI = ["2theta", "chi", "phi", "omega"]
+SORT_OPT_NB = ["gamma", "nu", "omega"]
+
+
+def create():
+    ang_lims = None
+    cif_data = None
+    params = None
+    res_files = {}
+    js_data = ColumnDataSource(data=dict(content=[""], fname=[""]))
+
+    anglim_div = Div(text="Angular min/max limits:", margin=(5, 5, 0, 5))
+    sttgamma_ti = TextInput(title="stt/gamma", width=100)
+    omega_ti = TextInput(title="omega", width=100)
+    chinu_ti = TextInput(title="chi/nu", width=100)
+    phi_ti = TextInput(title="phi", width=100)
+
+    def _update_ang_lims(ang_lims):
+        sttgamma_ti.value = " ".join(ang_lims["gamma"][:2])
+        omega_ti.value = " ".join(ang_lims["omega"][:2])
+        if ang_lims["geom"] == "nb":
+            chinu_ti.value = " ".join(ang_lims["nu"][:2])
+            phi_ti.value = ""
+        else:  # ang_lims["geom"] == "bi"
+            chinu_ti.value = " ".join(ang_lims["chi"][:2])
+            phi_ti.value = " ".join(ang_lims["phi"][:2])
+
+    def _update_params(params):
+        if "WAVE" in params:
+            wavelen_input.value = params["WAVE"]
+        if "SPGR" in params:
+            cryst_space_group.value = params["SPGR"]
+        if "CELL" in params:
+            cryst_cell.value = params["CELL"]
+        if "UBMAT" in params:
+            ub_matrix.value = " ".join(params["UBMAT"])
+        if "HLIM" in params:
+            ranges_hkl.value = params["HLIM"]
+        if "SRANG" in params:
+            ranges_srang.value = params["SRANG"]
+        if "lattiCE" in params:
+            magstruct_lattice.value = params["lattiCE"]
+        if "kvect" in params:
+            magstruct_kvec.value = params["kvect"]
+
+    def open_geom_callback(_attr, _old, new):
+        nonlocal ang_lims
+        with io.StringIO(base64.b64decode(new).decode()) as fileobj:
+            ang_lims = pyzebra.read_geom_file(fileobj)
+        _update_ang_lims(ang_lims)
+
+    open_geom_div = Div(text="Open GEOM:")
+    open_geom = FileInput(accept=".geom", width=200)
+    open_geom.on_change("value", open_geom_callback)
+
+    def open_cfl_callback(_attr, _old, new):
+        nonlocal params
+        with io.StringIO(base64.b64decode(new).decode()) as fileobj:
+            params = pyzebra.read_cfl_file(fileobj)
+            _update_params(params)
+
+    open_cfl_div = Div(text="Open CFL:")
+    open_cfl = FileInput(accept=".cfl", width=200)
+    open_cfl.on_change("value", open_cfl_callback)
+
+    def open_cif_callback(_attr, _old, new):
+        nonlocal cif_data
+        with io.StringIO(base64.b64decode(new).decode()) as fileobj:
+            cif_data = pyzebra.read_cif_file(fileobj)
+            _update_params(cif_data)
+
+    open_cif_div = Div(text="Open CIF:")
+    open_cif = FileInput(accept=".cif", width=200)
+    open_cif.on_change("value", open_cif_callback)
+
+    wavelen_div = Div(text="Wavelength:", margin=(5, 5, 0, 5))
+    wavelen_input = TextInput(title="value", width=70)
+
+    def wavelen_select_callback(_attr, _old, new):
+        if new:
+            wavelen_input.value = new
+        else:
+            wavelen_input.value = ""
+
+    wavelen_select = Select(
+        title="preset", options=["", "0.788", "1.178", "1.383", "2.305"], width=70
+    )
+    wavelen_select.on_change("value", wavelen_select_callback)
+
+    cryst_div = Div(text="Crystal structure:", margin=(5, 5, 0, 5))
+    cryst_space_group = TextInput(title="space group", width=100)
+    cryst_cell = TextInput(title="cell", width=250)
+
+    def ub_matrix_calc_callback():
+        params = dict()
+        params["SPGR"] = cryst_space_group.value
+        params["CELL"] = cryst_cell.value
+        ub = pyzebra.calc_ub_matrix(params)
+        ub_matrix.value = " ".join(ub)
+
+    ub_matrix_calc = Button(label="UB matrix:", button_type="primary", width=100)
+    ub_matrix_calc.on_click(ub_matrix_calc_callback)
+
+    ub_matrix = TextInput(title="\u200B", width=600)
+
+    ranges_div = Div(text="Ranges:", margin=(5, 5, 0, 5))
+    ranges_hkl = TextInput(title="HKL", value="-25 25 -25 25 -25 25", width=250)
+    ranges_srang = TextInput(title="sin(​θ​)/λ", value="0.0 0.7", width=100)
+
+    magstruct_div = Div(text="Magnetic structure:", margin=(5, 5, 0, 5))
+    magstruct_lattice = TextInput(title="lattice", width=100)
+    magstruct_kvec = TextAreaInput(title="k vector", width=150)
+
+    def sorting0_callback(_attr, _old, new):
+        sorting_0_dt.value = ANG_CHUNK_DEFAULTS[new]
+
+    def sorting1_callback(_attr, _old, new):
+        sorting_1_dt.value = ANG_CHUNK_DEFAULTS[new]
+
+    def sorting2_callback(_attr, _old, new):
+        sorting_2_dt.value = ANG_CHUNK_DEFAULTS[new]
+
+    sorting_0 = Select(title="1st", width=100)
+    sorting_0.on_change("value", sorting0_callback)
+    sorting_0_dt = NumericInput(title="Δ", width=70)
+    sorting_1 = Select(title="2nd", width=100)
+    sorting_1.on_change("value", sorting1_callback)
+    sorting_1_dt = NumericInput(title="Δ", width=70)
+    sorting_2 = Select(title="3rd", width=100)
+    sorting_2.on_change("value", sorting2_callback)
+    sorting_2_dt = NumericInput(title="Δ", width=70)
+
+    def geom_radiogroup_callback(_attr, _old, new):
+        nonlocal ang_lims, params
+        if new == 0:
+            geom_file = pyzebra.get_zebraBI_default_geom_file()
+            sort_opt = SORT_OPT_BI
+        else:
+            geom_file = pyzebra.get_zebraNB_default_geom_file()
+            sort_opt = SORT_OPT_NB
+        cfl_file = pyzebra.get_zebra_default_cfl_file()
+
+        ang_lims = pyzebra.read_geom_file(geom_file)
+        _update_ang_lims(ang_lims)
+        params = pyzebra.read_cfl_file(cfl_file)
+        _update_params(params)
+
+        sorting_0.options = sorting_1.options = sorting_2.options = sort_opt
+        sorting_0.value = sort_opt[0]
+        sorting_1.value = sort_opt[1]
+        sorting_2.value = sort_opt[2]
+
+    geom_radiogroup_div = Div(text="Geometry:", margin=(5, 5, 0, 5))
+    geom_radiogroup = RadioGroup(labels=["bisecting", "normal beam"], width=150)
+    geom_radiogroup.on_change("active", geom_radiogroup_callback)
+    geom_radiogroup.active = 0
+
+    def go_button_callback():
+        ang_lims["gamma"][0], ang_lims["gamma"][1] = sttgamma_ti.value.strip().split()
+        ang_lims["omega"][0], ang_lims["omega"][1] = omega_ti.value.strip().split()
+        if ang_lims["geom"] == "nb":
+            ang_lims["nu"][0], ang_lims["nu"][1] = chinu_ti.value.strip().split()
+        else:  # ang_lims["geom"] == "bi"
+            ang_lims["chi"][0], ang_lims["chi"][1] = chinu_ti.value.strip().split()
+            ang_lims["phi"][0], ang_lims["phi"][1] = phi_ti.value.strip().split()
+
+        if cif_data:
+            params.update(cif_data)
+
+        params["WAVE"] = wavelen_input.value
+        params["SPGR"] = cryst_space_group.value
+        params["CELL"] = cryst_cell.value
+        params["UBMAT"] = ub_matrix.value.split()
+        params["HLIM"] = ranges_hkl.value
+        params["SRANG"] = ranges_srang.value
+        params["lattiCE"] = magstruct_lattice.value
+        kvects = magstruct_kvec.value.split("\n")
+
+        with tempfile.TemporaryDirectory() as temp_dir:
+            geom_path = os.path.join(temp_dir, "zebra.geom")
+            if open_geom.value:
+                geom_template = io.StringIO(base64.b64decode(open_geom.value).decode())
+            else:
+                geom_template = None
+            pyzebra.export_geom_file(geom_path, ang_lims, geom_template)
+
+            print(f"Content of {geom_path}:")
+            with open(geom_path) as f:
+                print(f.read())
+
+            priority = [sorting_0.value, sorting_1.value, sorting_2.value]
+            chunks = [sorting_0_dt.value, sorting_1_dt.value, sorting_2_dt.value]
+            if geom_radiogroup.active == 0:
+                sort_hkl_file = pyzebra.sort_hkl_file_bi
+                priority.extend(set(SORT_OPT_BI) - set(priority))
+            else:
+                sort_hkl_file = pyzebra.sort_hkl_file_nb
+
+            # run sxtal_refgen for each kvect provided
+            for i, kvect in enumerate(kvects, start=1):
+                params["kvect"] = kvect
+                if open_cfl.filename:
+                    base_fname = f"{os.path.splitext(open_cfl.filename)[0]}_{i}"
+                else:
+                    base_fname = f"zebra_{i}"
+
+                cfl_path = os.path.join(temp_dir, base_fname + ".cfl")
+                if open_cfl.value:
+                    cfl_template = io.StringIO(base64.b64decode(open_cfl.value).decode())
+                else:
+                    cfl_template = None
+                pyzebra.export_cfl_file(cfl_path, params, cfl_template)
+
+                print(f"Content of {cfl_path}:")
+                with open(cfl_path) as f:
+                    print(f.read())
+
+                comp_proc = subprocess.run(
+                    [pyzebra.SXTAL_REFGEN_PATH, cfl_path],
+                    cwd=temp_dir,
+                    check=True,
+                    stdout=subprocess.PIPE,
+                    stderr=subprocess.STDOUT,
+                    text=True,
+                )
+                print(" ".join(comp_proc.args))
+                print(comp_proc.stdout)
+
+                if i == 1:  # all hkl files are identical, so keep only one
+                    hkl_fname = base_fname + ".hkl"
+                    hkl_fpath = os.path.join(temp_dir, hkl_fname)
+                    with open(hkl_fpath) as f:
+                        res_files[hkl_fname] = f.read()
+
+                    hkl_fname_sorted = base_fname + "_sorted.hkl"
+                    hkl_fpath_sorted = os.path.join(temp_dir, hkl_fname_sorted)
+                    sort_hkl_file(hkl_fpath, hkl_fpath_sorted, priority, chunks)
+                    with open(hkl_fpath_sorted) as f:
+                        res_files[hkl_fname_sorted] = f.read()
+
+                mhkl_fname = base_fname + ".mhkl"
+                mhkl_fpath = os.path.join(temp_dir, mhkl_fname)
+                with open(mhkl_fpath) as f:
+                    res_files[mhkl_fname] = f.read()
+
+                mhkl_fname_sorted = base_fname + "_sorted.mhkl"
+                mhkl_fpath_sorted = os.path.join(temp_dir, hkl_fname_sorted)
+                sort_hkl_file(mhkl_fpath, mhkl_fpath_sorted, priority, chunks)
+                with open(mhkl_fpath_sorted) as f:
+                    res_files[mhkl_fname_sorted] = f.read()
+
+            created_lists.options = list(res_files)
+
+    go_button = Button(label="GO", button_type="primary", width=50)
+    go_button.on_click(go_button_callback)
+
+    def created_lists_callback(_attr, _old, new):
+        sel_file = new[0]
+        file_text = res_files[sel_file]
+        preview_lists.value = file_text
+        js_data.data.update(content=[file_text], fname=[sel_file])
+
+    created_lists = MultiSelect(title="Created lists:", width=200, height=150)
+    created_lists.on_change("value", created_lists_callback)
+    preview_lists = TextAreaInput(title="Preview selected list:", width=600, height=150)
+
+    download_file = Button(label="Download file", button_type="success", width=200)
+    download_file.js_on_click(CustomJS(args={"js_data": js_data}, code=javaScript))
+    plot_list = Button(label="Plot selected list", button_type="primary", width=200, disabled=True)
+
+    measured_data_div = Div(text="Measured data:")
+    measured_data = FileInput(accept=".ccl", multiple=True, width=200)
+
+    min_grid_x = -10
+    max_grid_x = 10
+    min_grid_y = -5
+    max_grid_y = 5
+    cmap = Dark2[8]
+    syms = ["circle", "inverted_triangle", "square", "diamond", "star", "triangle"]
+
+    # Define resolution function
+    def _res_fun(stt, wave, res_mult):
+        expr = np.tan(stt / 2 * np.pi / 180)
+        fwhm = np.sqrt(0.4639 * expr ** 2 - 0.4452 * expr + 0.1506) * res_mult  # res in deg
+        return fwhm
+
+    def plot_file_callback():
+        orth_dir = list(map(float, hkl_normal.value.split()))
+        cut_tol = hkl_delta.value
+        cut_or = hkl_cut.value
+        x_dir = list(map(float, hkl_in_plane_x.value.split()))
+        y_dir = list(map(float, hkl_in_plane_y.value.split()))
+
+        k = np.array(k_vectors.value.split()).astype(float).reshape(3, 3)
+        tol_k = 0.1
+
+        # Plotting options
+        grid_flag = 1
+        grid_minor_flag = 1
+        grid_div = 2  # Number of minor division lines per unit
+
+        # different symbols based on file number
+        file_flag = 0 in disting_opt_cb.active
+        # scale marker size according to intensity
+        intensity_flag = 1 in disting_opt_cb.active
+        # use color to mark different propagation vectors
+        prop_legend_flag = 2 in disting_opt_cb.active
+        # use resolution ellipsis
+        res_flag = disting_opt_rb.active
+        # multiplier for resolution function (in case of samples with large mosaicity)
+        res_mult = res_mult_ni.value
+
+        md_fnames = measured_data.filename
+        md_fdata = measured_data.value
+
+        # Load first data cile, read angles and define matrices to perform conversion to cartesian coordinates and back
+        with io.StringIO(base64.b64decode(md_fdata[0]).decode()) as file:
+            _, ext = os.path.splitext(md_fnames[0])
+            try:
+                file_data = pyzebra.parse_1D(file, ext)
+            except:
+                print(f"Error loading {md_fnames[0]}")
+                return
+
+        alpha = file_data[0]["alpha_cell"] * np.pi / 180.0
+        beta = file_data[0]["beta_cell"] * np.pi / 180.0
+        gamma = file_data[0]["gamma_cell"] * np.pi / 180.0
+
+        # reciprocal angle parameters
+        alpha_star = np.arccos(
+            (np.cos(beta) * np.cos(gamma) - np.cos(alpha)) / (np.sin(beta) * np.sin(gamma))
+        )
+        beta_star = np.arccos(
+            (np.cos(alpha) * np.cos(gamma) - np.cos(beta)) / (np.sin(alpha) * np.sin(gamma))
+        )
+        gamma_star = np.arccos(
+            (np.cos(alpha) * np.cos(beta) - np.cos(gamma)) / (np.sin(alpha) * np.sin(beta))
+        )
+
+        # conversion matrix:
+        M = np.array(
+            [
+                [1, np.cos(gamma_star), np.cos(beta_star)],
+                [0, np.sin(gamma_star), -np.sin(beta_star) * np.cos(alpha)],
+                [0, 0, np.sin(beta_star) * np.sin(alpha)],
+            ]
+        )
+        M_inv = np.linalg.inv(M)
+
+        # Calculate in-plane y-direction
+        x_c = M @ x_dir
+        y_c = M @ y_dir
+        o_c = M @ orth_dir
+
+        # Normalize all directions
+        y_c = y_c / np.linalg.norm(y_c)
+        x_c = x_c / np.linalg.norm(x_c)
+        o_c = o_c / np.linalg.norm(o_c)
+
+        # Read all data
+        hkl_coord = []
+        intensity_vec = []
+        k_flag_vec = []
+        file_flag_vec = []
+        res_vec_x = []
+        res_vec_y = []
+        res_N = 10
+
+        for j in range(len(md_fnames)):
+            with io.StringIO(base64.b64decode(md_fdata[j]).decode()) as file:
+                _, ext = os.path.splitext(md_fnames[j])
+                try:
+                    file_data = pyzebra.parse_1D(file, ext)
+                except:
+                    print(f"Error loading {md_fnames[j]}")
+                    return
+
+            # Loop throguh all data
+            for scan in file_data:
+                om = scan["omega"]
+                gammad = scan["twotheta"]
+                chi = scan["chi"]
+                phi = scan["phi"]
+                nud = 0  # 1d detector
+                ub = scan["ub"]
+                ddist = float(scan["detectorDistance"])
+                counts = scan["counts"]
+                mon = scan["monitor"]
+
+                # Determine wavelength from mcvl value (is wavelength stored anywhere???)
+                mcvl = scan["mcvl"]
+                if mcvl == 2.2:
+                    wave = 1.178
+                elif mcvl == 7.0:
+                    wave = 1.383
+                else:
+                    wave = 2.3
+
+                # Calculate resolution in degrees
+                res = _res_fun(gammad, wave, res_mult)
+
+                # convert to resolution in hkl along scan line
+                ang2hkl_1d = pyzebra.ang2hkl_1d
+                res_x = []
+                res_y = []
+                for _om in np.linspace(om[0], om[-1], num=res_N):
+                    expr1 = ang2hkl_1d(wave, ddist, gammad, _om + res / 2, chi, phi, nud, ub)
+                    expr2 = ang2hkl_1d(wave, ddist, gammad, _om - res / 2, chi, phi, nud, ub)
+                    hkl_temp = M @ (np.abs(expr1 - expr2) / 2)
+                    res_x.append(hkl_temp[0])
+                    res_y.append(hkl_temp[1])
+
+                # Get first and final hkl
+                hkl1 = ang2hkl_1d(wave, ddist, gammad, om[0], chi, phi, nud, ub)
+                hkl2 = ang2hkl_1d(wave, ddist, gammad, om[-1], chi, phi, nud, ub)
+
+                # Get hkl at best intensity
+                hkl_m = ang2hkl_1d(wave, ddist, gammad, om[np.argmax(counts)], chi, phi, nud, ub)
+
+                # Estimate intensity for marker size scaling
+                y1 = counts[0]
+                y2 = counts[-1]
+                x1 = om[0]
+                x2 = om[-1]
+                a = (y1 - y2) / (x1 - x2)
+                b = y1 - a * x1
+                intensity_exp = np.sum(counts - (a * om + b))
+                c = int(intensity_exp / mon * 10000)
+
+                # Recognize k_flag_vec
+                min_hkl_m = np.minimum(1 - hkl_m % 1, hkl_m % 1)
+                for j2, _k in enumerate(k):
+                    if all(np.abs(min_hkl_m - _k) < tol_k):
+                        k_flag_vec.append(j2)
+                        break
+                else:
+                    k_flag_vec.append(len(k))
+
+                # Save data
+                hkl_coord.append([hkl1, hkl2, hkl_m])
+                intensity_vec.append(c)
+                file_flag_vec.append(j)
+                res_vec_x.append(res_x)
+                res_vec_y.append(res_y)
+
+        plot.x_range.start = plot.x_range.reset_start = -2
+        plot.x_range.end = plot.x_range.reset_end = 5
+        plot.y_range.start = plot.y_range.reset_start = -4
+        plot.y_range.end = plot.y_range.reset_end = 3.5
+
+        xs, ys = [], []
+        xs_minor, ys_minor = [], []
+        if grid_flag:
+            for yy in np.arange(min_grid_y, max_grid_y, 1):
+                hkl1 = M @ [0, yy, 0]
+                xs.append([min_grid_y, max_grid_y])
+                ys.append([hkl1[1], hkl1[1]])
+
+            for xx in np.arange(min_grid_x, max_grid_x, 1):
+                hkl1 = M @ [xx, min_grid_x, 0]
+                hkl2 = M @ [xx, max_grid_x, 0]
+                xs.append([hkl1[0], hkl2[0]])
+                ys.append([hkl1[1], hkl2[1]])
+
+            if grid_minor_flag:
+                for yy in np.arange(min_grid_y, max_grid_y, 1 / grid_div):
+                    hkl1 = M @ [0, yy, 0]
+                    xs_minor.append([min_grid_y, max_grid_y])
+                    ys_minor.append([hkl1[1], hkl1[1]])
+
+                for xx in np.arange(min_grid_x, max_grid_x, 1 / grid_div):
+                    hkl1 = M @ [xx, min_grid_x, 0]
+                    hkl2 = M @ [xx, max_grid_x, 0]
+                    xs_minor.append([hkl1[0], hkl2[0]])
+                    ys_minor.append([hkl1[1], hkl2[1]])
+
+        grid_source.data.update(xs=xs, ys=ys)
+        minor_grid_source.data.update(xs=xs_minor, ys=ys_minor)
+
+        el_x, el_y, el_w, el_h, el_c = [], [], [], [], []
+        scan_xs, scan_ys, scan_x, scan_y = [], [], [], []
+        scan_m, scan_s, scan_c, scan_l = [], [], [], []
+        for j in range(len(hkl_coord)):
+            # Get middle hkl from list
+            hklm = M @ hkl_coord[j][2]
+
+            # Decide if point is in the cut
+            proj = np.dot(hklm, o_c)
+            if abs(proj - cut_or) >= cut_tol:
+                continue
+
+            hkl1 = M @ hkl_coord[j][0]
+            hkl2 = M @ hkl_coord[j][1]
+
+            if intensity_flag:
+                markersize = max(1, int(intensity_vec[j] / max(intensity_vec) * 20))
+            else:
+                markersize = 4
+
+            if file_flag:
+                plot_symbol = syms[file_flag_vec[j]]
+            else:
+                plot_symbol = "circle"
+
+            if prop_legend_flag:
+                col_value = cmap[k_flag_vec[j]]
+            else:
+                col_value = "black"
+
+            if res_flag:
+                # Generate series of ellipses along scan line
+                el_x.extend(np.linspace(hkl1[0], hkl2[0], num=res_N))
+                el_y.extend(np.linspace(hkl1[1], hkl2[1], num=res_N))
+                el_w.extend(np.array(res_vec_x[j]) * 2)
+                el_h.extend(np.array(res_vec_y[j]) * 2)
+                el_c.extend([col_value] * res_N)
+            else:
+                # Plot scan line
+                scan_xs.append([hkl1[0], hkl2[0]])
+                scan_ys.append([hkl1[1], hkl2[1]])
+
+                # Plot middle point of scan
+                scan_x.append(hklm[0])
+                scan_y.append(hklm[1])
+                scan_m.append(plot_symbol)
+                scan_s.append(markersize)
+
+                # Color and legend label
+                scan_c.append(col_value)
+                scan_l.append(md_fnames[file_flag_vec[j]])
+
+        ellipse_source.data.update(x=el_x, y=el_y, w=el_w, h=el_h, c=el_c)
+        scan_source.data.update(
+            xs=scan_xs, ys=scan_ys, x=scan_x, y=scan_y, m=scan_m, s=scan_s, c=scan_c, l=scan_l,
+        )
+
+        arrow1.visible = True
+        arrow1.x_end = x_c[0]
+        arrow1.y_end = x_c[1]
+        arrow2.visible = True
+        arrow2.x_end = y_c[0]
+        arrow2.y_end = y_c[1]
+
+        kvect_source.data.update(
+            text_x=[x_c[0] / 2, y_c[0] / 2 - 0.1],
+            text_y=[x_c[1] - 0.1, y_c[1] / 2],
+            text=["h", "k"],
+        )
+
+        # Legend items for different file entries (symbol)
+        legend_items = []
+        if not res_flag and file_flag:
+            labels, inds = np.unique(scan_source.data["l"], return_index=True)
+            for label, ind in zip(labels, inds):
+                legend_items.append(LegendItem(label=label, renderers=[scatter], index=ind))
+
+        # Legend items for propagation vector (color)
+        if prop_legend_flag:
+            if res_flag:
+                source, render = ellipse_source, ellipse
+            else:
+                source, render = scan_source, mline
+
+            labels, inds = np.unique(source.data["c"], return_index=True)
+            for label, ind in zip(labels, inds):
+                label = f"k={k[cmap.index(label)]}"
+                legend_items.append(LegendItem(label=label, renderers=[render], index=ind))
+
+        plot.legend.items = legend_items
+
+    plot_file = Button(label="Plot selected file(s)", button_type="primary", width=200)
+    plot_file.on_click(plot_file_callback)
+
+    plot = Plot(x_range=Range1d(), y_range=Range1d(), plot_height=450, plot_width=600)
+    plot.add_tools(PanTool(), WheelZoomTool(), BoxZoomTool(), ResetTool())
+    plot.toolbar.logo = None
+
+    plot.add_layout(LinearAxis(), place="left")
+    plot.add_layout(LinearAxis(), place="below")
+
+    arrow1 = Arrow(x_start=0, y_start=0, x_end=0, y_end=0, end=NormalHead(size=10), visible=False)
+    plot.add_layout(arrow1)
+    arrow2 = Arrow(x_start=0, y_start=0, x_end=0, y_end=0, end=NormalHead(size=10), visible=False)
+    plot.add_layout(arrow2)
+
+    kvect_source = ColumnDataSource(dict(text_x=[], text_y=[], text=[]))
+    plot.add_glyph(kvect_source, Text(x="text_x", y="text_y", text="text"))
+
+    grid_source = ColumnDataSource(dict(xs=[], ys=[]))
+    minor_grid_source = ColumnDataSource(dict(xs=[], ys=[]))
+    plot.add_glyph(grid_source, MultiLine(xs="xs", ys="ys", line_color="gray"))
+    plot.add_glyph(
+        minor_grid_source, MultiLine(xs="xs", ys="ys", line_color="gray", line_dash="dotted")
+    )
+
+    ellipse_source = ColumnDataSource(dict(x=[], y=[], w=[], h=[], c=[]))
+    ellipse = plot.add_glyph(
+        ellipse_source, Ellipse(x="x", y="y", width="w", height="h", fill_color="c", line_color="c")
+    )
+
+    scan_source = ColumnDataSource(dict(xs=[], ys=[], x=[], y=[], m=[], s=[], c=[], l=[]))
+    mline = plot.add_glyph(scan_source, MultiLine(xs="xs", ys="ys", line_color="c"))
+    scatter = plot.add_glyph(
+        scan_source, Scatter(x="x", y="y", marker="m", size="s", fill_color="c", line_color="c")
+    )
+
+    plot.add_layout(Legend(items=[], location="top_left", click_policy="hide"))
+
+    hkl_div = Div(text="HKL:", margin=(5, 5, 0, 5))
+    hkl_normal = TextInput(title="normal", value="0 0 1", width=70)
+    hkl_cut = Spinner(title="cut", value=0, step=0.1, width=70)
+    hkl_delta = NumericInput(title="delta", value=0.1, mode="float", width=70)
+    hkl_in_plane_x = TextInput(title="in-plane X", value="1 0 0", width=70)
+    hkl_in_plane_y = TextInput(title="in-plane Y", value="0 1 0", width=70)
+
+    disting_opt_div = Div(text="Distinguish options:", margin=(5, 5, 0, 5))
+    disting_opt_cb = CheckboxGroup(
+        labels=["files (symbols)", "intensities (size)", "k vectors nucl/magn (colors)"],
+        active=[0, 1, 2],
+        width=200,
+    )
+    disting_opt_rb = RadioGroup(
+        labels=["scan direction", "resolution ellipsoid"], active=0, width=200
+    )
+
+    k_vectors = TextAreaInput(
+        title="k vectors:", value="0.0 0.0 0.0\n0.5 0.0 0.0\n0.5 0.5 0.0", width=150,
+    )
+    res_mult_ni = NumericInput(title="Resolution mult:", value=10, mode="int", width=100)
+
+    fileinput_layout = row(open_cfl_div, open_cfl, open_cif_div, open_cif, open_geom_div, open_geom)
+
+    geom_layout = column(geom_radiogroup_div, geom_radiogroup)
+    wavelen_layout = column(wavelen_div, row(wavelen_select, wavelen_input))
+    anglim_layout = column(anglim_div, row(sttgamma_ti, omega_ti, chinu_ti, phi_ti))
+    cryst_layout = column(cryst_div, row(cryst_space_group, cryst_cell))
+    ubmat_layout = row(column(Spacer(height=18), ub_matrix_calc), ub_matrix)
+    ranges_layout = column(ranges_div, row(ranges_hkl, ranges_srang))
+    magstruct_layout = column(magstruct_div, row(magstruct_lattice, magstruct_kvec))
+    sorting_layout = row(
+        sorting_0,
+        sorting_0_dt,
+        Spacer(width=30),
+        sorting_1,
+        sorting_1_dt,
+        Spacer(width=30),
+        sorting_2,
+        sorting_2_dt,
+    )
+
+    column1_layout = column(
+        fileinput_layout,
+        Spacer(height=10),
+        row(geom_layout, wavelen_layout, Spacer(width=50), anglim_layout),
+        cryst_layout,
+        ubmat_layout,
+        row(ranges_layout, Spacer(width=50), magstruct_layout),
+        row(sorting_layout, Spacer(width=30), column(Spacer(height=18), go_button)),
+        row(created_lists, preview_lists),
+        row(download_file, plot_list),
+    )
+
+    hkl_layout = column(
+        hkl_div,
+        row(hkl_normal, hkl_cut, hkl_delta, Spacer(width=10), hkl_in_plane_x, hkl_in_plane_y),
+    )
+    disting_layout = column(disting_opt_div, row(disting_opt_cb, disting_opt_rb))
+
+    column2_layout = column(
+        row(measured_data_div, measured_data, plot_file),
+        plot,
+        row(hkl_layout, k_vectors),
+        row(disting_layout, res_mult_ni),
+    )
+
+    tab_layout = row(column1_layout, column2_layout)
+
+    return Panel(child=tab_layout, title="ccl prepare")

pyzebra/app/panel_hdf_param_study.py

@@ -0,0 +1,591 @@
+import base64
+import io
+import os
+
+import numpy as np
+from bokeh.io import curdoc
+from bokeh.layouts import column, gridplot, row
+from bokeh.models import (
+    BasicTicker,
+    BoxZoomTool,
+    Button,
+    CellEditor,
+    CheckboxGroup,
+    ColumnDataSource,
+    DataRange1d,
+    DataTable,
+    Div,
+    FileInput,
+    Grid,
+    MultiSelect,
+    NumberEditor,
+    NumberFormatter,
+    Image,
+    LinearAxis,
+    LinearColorMapper,
+    Panel,
+    PanTool,
+    Plot,
+    Range1d,
+    ResetTool,
+    Scatter,
+    Select,
+    Spinner,
+    TableColumn,
+    Tabs,
+    Title,
+    WheelZoomTool,
+)
+from bokeh.palettes import Cividis256, Greys256, Plasma256  # pylint: disable=E0611
+
+import pyzebra
+
+IMAGE_W = 256
+IMAGE_H = 128
+IMAGE_PLOT_W = int(IMAGE_W * 2) + 52
+IMAGE_PLOT_H = int(IMAGE_H * 2) + 27
+
+
+def create():
+    doc = curdoc()
+    dataset = []
+    cami_meta = {}
+
+    num_formatter = NumberFormatter(format="0.00", nan_format="")
+
+    def file_select_update():
+        if data_source.value == "proposal number":
+            proposal_path = proposal_textinput.name
+            if proposal_path:
+                file_list = []
+                for file in os.listdir(proposal_path):
+                    if file.endswith(".hdf"):
+                        file_list.append((os.path.join(proposal_path, file), file))
+                file_select.options = file_list
+            else:
+                file_select.options = []
+
+        else:  # "cami file"
+            if not cami_meta:
+                file_select.options = []
+                return
+
+            file_list = cami_meta["filelist"]
+            file_select.options = [(entry, os.path.basename(entry)) for entry in file_list]
+
+    def data_source_callback(_attr, _old, _new):
+        file_select_update()
+
+    data_source = Select(
+        title="Data Source:",
+        value="proposal number",
+        options=["proposal number", "cami file"],
+        width=210,
+    )
+    data_source.on_change("value", data_source_callback)
+
+    doc.add_periodic_callback(file_select_update, 5000)
+
+    def proposal_textinput_callback(_attr, _old, _new):
+        file_select_update()
+
+    proposal_textinput = doc.proposal_textinput
+    proposal_textinput.on_change("name", proposal_textinput_callback)
+
+    def upload_button_callback(_attr, _old, new):
+        nonlocal cami_meta
+        with io.StringIO(base64.b64decode(new).decode()) as file:
+            cami_meta = pyzebra.parse_h5meta(file)
+        data_source.value = "cami file"
+        file_select_update()
+
+    upload_div = Div(text="or upload .cami file:", margin=(5, 5, 0, 5))
+    upload_button = FileInput(accept=".cami", width=200)
+    upload_button.on_change("value", upload_button_callback)
+
+    file_select = MultiSelect(title="Available .hdf files:", width=210, height=320)
+
+    def _init_datatable():
+        file_list = []
+        for scan in dataset:
+            file_list.append(os.path.basename(scan["original_filename"]))
+
+        scan_table_source.data.update(
+            file=file_list,
+            param=[None] * len(dataset),
+            frame=[None] * len(dataset),
+            x_pos=[None] * len(dataset),
+            y_pos=[None] * len(dataset),
+        )
+        scan_table_source.selected.indices = []
+        scan_table_source.selected.indices = [0]
+
+        param_select.value = "user defined"
+
+    def _update_table():
+        frame = []
+        x_pos = []
+        y_pos = []
+        for scan in dataset:
+            if "fit" in scan:
+                framei = scan["fit"]["frame"]
+                x_posi = scan["fit"]["x_pos"]
+                y_posi = scan["fit"]["y_pos"]
+            else:
+                framei = x_posi = y_posi = None
+
+            frame.append(framei)
+            x_pos.append(x_posi)
+            y_pos.append(y_posi)
+
+        scan_table_source.data.update(frame=frame, x_pos=x_pos, y_pos=y_pos)
+
+    def _file_open():
+        new_data = []
+        for f_name in file_select.value:
+            try:
+                new_data.append(pyzebra.read_detector_data(f_name))
+            except KeyError:
+                print("Could not read data from the file.")
+                return
+
+        dataset.extend(new_data)
+
+        _init_datatable()
+
+    def file_open_button_callback():
+        nonlocal dataset
+        dataset = []
+        _file_open()
+
+    file_open_button = Button(label="Open New", width=100)
+    file_open_button.on_click(file_open_button_callback)
+
+    def file_append_button_callback():
+        _file_open()
+
+    file_append_button = Button(label="Append", width=100)
+    file_append_button.on_click(file_append_button_callback)
+
+    # Scan select
+    def scan_table_select_callback(_attr, old, new):
+        if not new:
+            # skip empty selections
+            return
+
+        # Avoid selection of multiple indicies (via Shift+Click or Ctrl+Click)
+        if len(new) > 1:
+            # drop selection to the previous one
+            scan_table_source.selected.indices = old
+            return
+
+        if len(old) > 1:
+            # skip unnecessary update caused by selection drop
+            return
+
+        scan = dataset[new[0]]
+
+        zebra_mode = scan["zebra_mode"]
+        if zebra_mode == "nb":
+            metadata_table_source.data.update(geom=["normal beam"])
+        else:  # zebra_mode == "bi"
+            metadata_table_source.data.update(geom=["bisecting"])
+
+        if "mf" in scan:
+            metadata_table_source.data.update(mf=[scan["mf"][0]])
+        else:
+            metadata_table_source.data.update(mf=[None])
+
+        if "temp" in scan:
+            metadata_table_source.data.update(temp=[scan["temp"][0]])
+        else:
+            metadata_table_source.data.update(temp=[None])
+
+        update_overview_plot()
+
+    def scan_table_source_callback(_attr, _old, _new):
+        pass
+
+    scan_table_source = ColumnDataSource(dict(file=[], param=[], frame=[], x_pos=[], y_pos=[]))
+    scan_table_source.selected.on_change("indices", scan_table_select_callback)
+    scan_table_source.on_change("data", scan_table_source_callback)
+
+    scan_table = DataTable(
+        source=scan_table_source,
+        columns=[
+            TableColumn(field="file", title="file", editor=CellEditor(), width=150),
+            TableColumn(
+                field="param",
+                title="param",
+                formatter=num_formatter,
+                editor=NumberEditor(),
+                width=50,
+            ),
+            TableColumn(
+                field="frame", title="Frame", formatter=num_formatter, editor=CellEditor(), width=70
+            ),
+            TableColumn(
+                field="x_pos", title="X", formatter=num_formatter, editor=CellEditor(), width=70
+            ),
+            TableColumn(
+                field="y_pos", title="Y", formatter=num_formatter, editor=CellEditor(), width=70
+            ),
+        ],
+        width=470,  # +60 because of the index column
+        height=420,
+        editable=True,
+        autosize_mode="none",
+    )
+
+    def _get_selected_scan():
+        return dataset[scan_table_source.selected.indices[0]]
+
+    def param_select_callback(_attr, _old, new):
+        if new == "user defined":
+            param = [None] * len(dataset)
+        else:
+            # TODO: which value to take?
+            param = [scan[new][0] for scan in dataset]
+
+        scan_table_source.data["param"] = param
+        _update_param_plot()
+
+    param_select = Select(
+        title="Parameter:",
+        options=["user defined", "temp", "mf", "h", "k", "l"],
+        value="user defined",
+        width=145,
+    )
+    param_select.on_change("value", param_select_callback)
+
+    def update_overview_plot():
+        scan = _get_selected_scan()
+        counts = scan["counts"]
+        n_im, n_y, n_x = counts.shape
+        overview_x = np.mean(counts, axis=1)
+        overview_y = np.mean(counts, axis=2)
+
+        # normalize for simpler colormapping
+        overview_max_val = max(np.max(overview_x), np.max(overview_y))
+        overview_x = 1000 * overview_x / overview_max_val
+        overview_y = 1000 * overview_y / overview_max_val
+
+        overview_plot_x_image_source.data.update(image=[overview_x], dw=[n_x], dh=[n_im])
+        overview_plot_y_image_source.data.update(image=[overview_y], dw=[n_y], dh=[n_im])
+
+        if proj_auto_checkbox.active:
+            im_min = min(np.min(overview_x), np.min(overview_y))
+            im_max = max(np.max(overview_x), np.max(overview_y))
+
+            proj_display_min_spinner.value = im_min
+            proj_display_max_spinner.value = im_max
+
+            overview_plot_x_image_glyph.color_mapper.low = im_min
+            overview_plot_y_image_glyph.color_mapper.low = im_min
+            overview_plot_x_image_glyph.color_mapper.high = im_max
+            overview_plot_y_image_glyph.color_mapper.high = im_max
+
+        frame_range.start = 0
+        frame_range.end = n_im
+        frame_range.reset_start = 0
+        frame_range.reset_end = n_im
+        frame_range.bounds = (0, n_im)
+
+        scan_motor = scan["scan_motor"]
+        overview_plot_y.axis[1].axis_label = f"Scanning motor, {scan_motor}"
+
+        var = scan[scan_motor]
+        var_start = var[0]
+        var_end = var[-1] + (var[-1] - var[0]) / (n_im - 1)
+
+        scanning_motor_range.start = var_start
+        scanning_motor_range.end = var_end
+        scanning_motor_range.reset_start = var_start
+        scanning_motor_range.reset_end = var_end
+        # handle both, ascending and descending sequences
+        scanning_motor_range.bounds = (min(var_start, var_end), max(var_start, var_end))
+
+    # shared frame ranges
+    frame_range = Range1d(0, 1, bounds=(0, 1))
+    scanning_motor_range = Range1d(0, 1, bounds=(0, 1))
+
+    det_x_range = Range1d(0, IMAGE_W, bounds=(0, IMAGE_W))
+    overview_plot_x = Plot(
+        title=Title(text="Projections on X-axis"),
+        x_range=det_x_range,
+        y_range=frame_range,
+        extra_y_ranges={"scanning_motor": scanning_motor_range},
+        plot_height=400,
+        plot_width=IMAGE_PLOT_W - 3,
+    )
+
+    # ---- tools
+    wheelzoomtool = WheelZoomTool(maintain_focus=False)
+    overview_plot_x.toolbar.logo = None
+    overview_plot_x.add_tools(
+        PanTool(), BoxZoomTool(), wheelzoomtool, ResetTool(),
+    )
+    overview_plot_x.toolbar.active_scroll = wheelzoomtool
+
+    # ---- axes
+    overview_plot_x.add_layout(LinearAxis(axis_label="Coordinate X, pix"), place="below")
+    overview_plot_x.add_layout(
+        LinearAxis(axis_label="Frame", major_label_orientation="vertical"), place="left"
+    )
+
+    # ---- grid lines
+    overview_plot_x.add_layout(Grid(dimension=0, ticker=BasicTicker()))
+    overview_plot_x.add_layout(Grid(dimension=1, ticker=BasicTicker()))
+
+    # ---- rgba image glyph
+    overview_plot_x_image_source = ColumnDataSource(
+        dict(image=[np.zeros((1, 1), dtype="float32")], x=[0], y=[0], dw=[IMAGE_W], dh=[1])
+    )
+
+    overview_plot_x_image_glyph = Image(image="image", x="x", y="y", dw="dw", dh="dh")
+    overview_plot_x.add_glyph(
+        overview_plot_x_image_source, overview_plot_x_image_glyph, name="image_glyph"
+    )
+
+    det_y_range = Range1d(0, IMAGE_H, bounds=(0, IMAGE_H))
+    overview_plot_y = Plot(
+        title=Title(text="Projections on Y-axis"),
+        x_range=det_y_range,
+        y_range=frame_range,
+        extra_y_ranges={"scanning_motor": scanning_motor_range},
+        plot_height=400,
+        plot_width=IMAGE_PLOT_H + 22,
+    )
+
+    # ---- tools
+    wheelzoomtool = WheelZoomTool(maintain_focus=False)
+    overview_plot_y.toolbar.logo = None
+    overview_plot_y.add_tools(
+        PanTool(), BoxZoomTool(), wheelzoomtool, ResetTool(),
+    )
+    overview_plot_y.toolbar.active_scroll = wheelzoomtool
+
+    # ---- axes
+    overview_plot_y.add_layout(LinearAxis(axis_label="Coordinate Y, pix"), place="below")
+    overview_plot_y.add_layout(
+        LinearAxis(
+            y_range_name="scanning_motor",
+            axis_label="Scanning motor",
+            major_label_orientation="vertical",
+        ),
+        place="right",
+    )
+
+    # ---- grid lines
+    overview_plot_y.add_layout(Grid(dimension=0, ticker=BasicTicker()))
+    overview_plot_y.add_layout(Grid(dimension=1, ticker=BasicTicker()))
+
+    # ---- rgba image glyph
+    overview_plot_y_image_source = ColumnDataSource(
+        dict(image=[np.zeros((1, 1), dtype="float32")], x=[0], y=[0], dw=[IMAGE_H], dh=[1])
+    )
+
+    overview_plot_y_image_glyph = Image(image="image", x="x", y="y", dw="dw", dh="dh")
+    overview_plot_y.add_glyph(
+        overview_plot_y_image_source, overview_plot_y_image_glyph, name="image_glyph"
+    )
+
+    cmap_dict = {
+        "gray": Greys256,
+        "gray_reversed": Greys256[::-1],
+        "plasma": Plasma256,
+        "cividis": Cividis256,
+    }
+
+    def colormap_callback(_attr, _old, new):
+        overview_plot_x_image_glyph.color_mapper = LinearColorMapper(palette=cmap_dict[new])
+        overview_plot_y_image_glyph.color_mapper = LinearColorMapper(palette=cmap_dict[new])
+
+    colormap = Select(title="Colormap:", options=list(cmap_dict.keys()), width=210)
+    colormap.on_change("value", colormap_callback)
+    colormap.value = "plasma"
+
+    PROJ_STEP = 1
+
+    def proj_auto_checkbox_callback(state):
+        if state:
+            proj_display_min_spinner.disabled = True
+            proj_display_max_spinner.disabled = True
+        else:
+            proj_display_min_spinner.disabled = False
+            proj_display_max_spinner.disabled = False
+
+        update_overview_plot()
+
+    proj_auto_checkbox = CheckboxGroup(
+        labels=["Projections Intensity Range"], active=[0], width=145, margin=[10, 5, 0, 5]
+    )
+    proj_auto_checkbox.on_click(proj_auto_checkbox_callback)
+
+    def proj_display_max_spinner_callback(_attr, _old_value, new_value):
+        proj_display_min_spinner.high = new_value - PROJ_STEP
+        overview_plot_x_image_glyph.color_mapper.high = new_value
+        overview_plot_y_image_glyph.color_mapper.high = new_value
+
+    proj_display_max_spinner = Spinner(
+        low=0 + PROJ_STEP,
+        value=1,
+        step=PROJ_STEP,
+        disabled=bool(proj_auto_checkbox.active),
+        width=100,
+        height=31,
+    )
+    proj_display_max_spinner.on_change("value", proj_display_max_spinner_callback)
+
+    def proj_display_min_spinner_callback(_attr, _old_value, new_value):
+        proj_display_max_spinner.low = new_value + PROJ_STEP
+        overview_plot_x_image_glyph.color_mapper.low = new_value
+        overview_plot_y_image_glyph.color_mapper.low = new_value
+
+    proj_display_min_spinner = Spinner(
+        low=0,
+        high=1 - PROJ_STEP,
+        value=0,
+        step=PROJ_STEP,
+        disabled=bool(proj_auto_checkbox.active),
+        width=100,
+        height=31,
+    )
+    proj_display_min_spinner.on_change("value", proj_display_min_spinner_callback)
+
+    metadata_table_source = ColumnDataSource(dict(geom=[""], temp=[None], mf=[None]))
+    metadata_table = DataTable(
+        source=metadata_table_source,
+        columns=[
+            TableColumn(field="geom", title="Geometry", width=100),
+            TableColumn(field="temp", title="Temperature", formatter=num_formatter, width=100),
+            TableColumn(field="mf", title="Magnetic Field", formatter=num_formatter, width=100),
+        ],
+        width=300,
+        height=50,
+        autosize_mode="none",
+        index_position=None,
+    )
+
+    def _update_param_plot():
+        x = []
+        y = []
+        fit_param = fit_param_select.value
+        for s, p in zip(dataset, scan_table_source.data["param"]):
+            if "fit" in s and fit_param:
+                x.append(p)
+                y.append(s["fit"][fit_param])
+        param_plot_scatter_source.data.update(x=x, y=y)
+
+    # Parameter plot
+    param_plot = Plot(x_range=DataRange1d(), y_range=DataRange1d(), plot_height=400, plot_width=700)
+
+    param_plot.add_layout(LinearAxis(axis_label="Fit parameter"), place="left")
+    param_plot.add_layout(LinearAxis(axis_label="Parameter"), place="below")
+
+    param_plot.add_layout(Grid(dimension=0, ticker=BasicTicker()))
+    param_plot.add_layout(Grid(dimension=1, ticker=BasicTicker()))
+
+    param_plot_scatter_source = ColumnDataSource(dict(x=[], y=[]))
+    param_plot.add_glyph(param_plot_scatter_source, Scatter(x="x", y="y"))
+
+    param_plot.add_tools(PanTool(), WheelZoomTool(), ResetTool())
+    param_plot.toolbar.logo = None
+
+    def fit_param_select_callback(_attr, _old, _new):
+        _update_param_plot()
+
+    fit_param_select = Select(title="Fit parameter", options=[], width=145)
+    fit_param_select.on_change("value", fit_param_select_callback)
+
+    def proc_all_button_callback():
+        for scan in dataset:
+            pyzebra.fit_event(
+                scan,
+                int(np.floor(frame_range.start)),
+                int(np.ceil(frame_range.end)),
+                int(np.floor(det_y_range.start)),
+                int(np.ceil(det_y_range.end)),
+                int(np.floor(det_x_range.start)),
+                int(np.ceil(det_x_range.end)),
+            )
+
+        _update_table()
+
+        for scan in dataset:
+            if "fit" in scan:
+                options = list(scan["fit"].keys())
+                fit_param_select.options = options
+                fit_param_select.value = options[0]
+                break
+
+        _update_param_plot()
+
+    proc_all_button = Button(label="Process All", button_type="primary", width=145)
+    proc_all_button.on_click(proc_all_button_callback)
+
+    def proc_button_callback():
+        scan = _get_selected_scan()
+        pyzebra.fit_event(
+            scan,
+            int(np.floor(frame_range.start)),
+            int(np.ceil(frame_range.end)),
+            int(np.floor(det_y_range.start)),
+            int(np.ceil(det_y_range.end)),
+            int(np.floor(det_x_range.start)),
+            int(np.ceil(det_x_range.end)),
+        )
+
+        _update_table()
+
+        for scan in dataset:
+            if "fit" in scan:
+                options = list(scan["fit"].keys())
+                fit_param_select.options = options
+                fit_param_select.value = options[0]
+                break
+
+        _update_param_plot()
+
+    proc_button = Button(label="Process Current", width=145)
+    proc_button.on_click(proc_button_callback)
+
+    layout_controls = row(
+        colormap,
+        column(proj_auto_checkbox, row(proj_display_min_spinner, proj_display_max_spinner)),
+        proc_button,
+        proc_all_button,
+    )
+
+    layout_overview = column(
+        gridplot(
+            [[overview_plot_x, overview_plot_y]],
+            toolbar_options=dict(logo=None),
+            merge_tools=True,
+            toolbar_location="left",
+        ),
+        layout_controls,
+    )
+
+    # Plot tabs
+    plots = Tabs(
+        tabs=[
+            Panel(child=layout_overview, title="single scan"),
+            Panel(child=column(param_plot, row(fit_param_select)), title="parameter plot"),
+        ]
+    )
+
+    # Final layout
+    import_layout = column(
+        data_source,
+        upload_div,
+        upload_button,
+        file_select,
+        row(file_open_button, file_append_button),
+    )
+
+    scan_layout = column(scan_table, row(param_select, metadata_table))
+
+    tab_layout = column(row(import_layout, scan_layout, plots))
+
+    return Panel(child=tab_layout, title="hdf param study")

pyzebra/app/panel_hdf_viewer.py

@@ -1,6 +1,5 @@
 import base64
 import io
-import math
 import os
 
 import numpy as np
@@ -12,6 +11,7 @@ from bokeh.models import (
     BoxEditTool,
     BoxZoomTool,
     Button,
+    CellEditor,
     CheckboxGroup,
     ColumnDataSource,
     DataRange1d,
@@ -37,12 +37,11 @@ from bokeh.models import (
     Spacer,
     Spinner,
     TableColumn,
-    TextInput,
+    Tabs,
     Title,
     WheelZoomTool,
 )
 from bokeh.palettes import Cividis256, Greys256, Plasma256  # pylint: disable=E0611
-from scipy.optimize import curve_fit
 
 import pyzebra
 
@@ -54,57 +53,281 @@ IMAGE_PLOT_H = int(IMAGE_H * 2) + 27
 
 def create():
     doc = curdoc()
-    det_data = {}
+    dataset = []
     cami_meta = {}
 
     num_formatter = NumberFormatter(format="0.00", nan_format="")
 
-    def file_select_update_for_proposal():
-        proposal = proposal_textinput.value.strip()
-        if not proposal:
-            return
+    def file_select_update():
+        if data_source.value == "proposal number":
+            proposal_path = proposal_textinput.name
+            if proposal_path:
+                file_list = []
+                for file in os.listdir(proposal_path):
+                    if file.endswith(".hdf"):
+                        file_list.append((os.path.join(proposal_path, file), file))
+                file_select.options = file_list
+            else:
+                file_select.options = []
 
-        for zebra_proposals_path in pyzebra.ZEBRA_PROPOSALS_PATHS:
-            proposal_path = os.path.join(zebra_proposals_path, proposal)
-            if os.path.isdir(proposal_path):
-                # found it
-                break
-        else:
-            raise ValueError(f"Can not find data for proposal '{proposal}'.")
+        else:  # "cami file"
+            if not cami_meta:
+                file_select.options = []
+                return
 
-        file_list = []
-        for file in os.listdir(proposal_path):
-            if file.endswith(".hdf"):
-                file_list.append((os.path.join(proposal_path, file), file))
-        file_select.options = file_list
-
-    doc.add_periodic_callback(file_select_update_for_proposal, 5000)
-
-    def proposal_textinput_callback(_attr, _old, _new):
-        nonlocal cami_meta
-        cami_meta = {}
-        file_select_update_for_proposal()
-
-    proposal_textinput = TextInput(title="Proposal number:", width=210)
-    proposal_textinput.on_change("value", proposal_textinput_callback)
-
-    def upload_button_callback(_attr, _old, new):
-        nonlocal cami_meta
-        proposal_textinput.value = ""
-        with io.StringIO(base64.b64decode(new).decode()) as file:
-            cami_meta = pyzebra.parse_h5meta(file)
             file_list = cami_meta["filelist"]
             file_select.options = [(entry, os.path.basename(entry)) for entry in file_list]
 
-    upload_div = Div(text="or upload .cami file:", margin=(5, 5, 0, 5))
-    upload_button = FileInput(accept=".cami", width=200)
-    upload_button.on_change("value", upload_button_callback)
+    def data_source_callback(_attr, _old, _new):
+        file_select_update()
 
-    def update_image(index=None):
+    data_source = Select(
+        title="Data Source:",
+        value="proposal number",
+        options=["proposal number", "cami file"],
+        width=210,
+    )
+    data_source.on_change("value", data_source_callback)
+
+    doc.add_periodic_callback(file_select_update, 5000)
+
+    def proposal_textinput_callback(_attr, _old, _new):
+        file_select_update()
+
+    proposal_textinput = doc.proposal_textinput
+    proposal_textinput.on_change("name", proposal_textinput_callback)
+
+    def upload_cami_button_callback(_attr, _old, new):
+        nonlocal cami_meta
+        with io.StringIO(base64.b64decode(new).decode()) as file:
+            cami_meta = pyzebra.parse_h5meta(file)
+        data_source.value = "cami file"
+        file_select_update()
+
+    upload_cami_div = Div(text="or upload .cami file:", margin=(5, 5, 0, 5))
+    upload_cami_button = FileInput(accept=".cami", width=200)
+    upload_cami_button.on_change("value", upload_cami_button_callback)
+
+    def upload_hdf_button_callback(_attr, _old, new):
+        nonlocal dataset
+        try:
+            scan = pyzebra.read_detector_data(io.BytesIO(base64.b64decode(new)), None)
+        except KeyError:
+            print("Could not read data from the file.")
+            return
+
+        dataset = [scan]
+        last_im_index = scan["counts"].shape[0] - 1
+
+        index_spinner.value = 0
+        index_spinner.high = last_im_index
+        if last_im_index == 0:
+            index_slider.disabled = True
+        else:
+            index_slider.disabled = False
+            index_slider.end = last_im_index
+
+        zebra_mode = scan["zebra_mode"]
+        if zebra_mode == "nb":
+            metadata_table_source.data.update(geom=["normal beam"])
+        else:  # zebra_mode == "bi"
+            metadata_table_source.data.update(geom=["bisecting"])
+
+        _init_datatable()
+
+    upload_hdf_div = Div(text="or upload .hdf file:", margin=(5, 5, 0, 5))
+    upload_hdf_button = FileInput(accept=".hdf", width=200)
+    upload_hdf_button.on_change("value", upload_hdf_button_callback)
+
+    def file_open_button_callback():
+        nonlocal dataset
+        new_data = []
+        cm = cami_meta if data_source.value == "cami file" else None
+        for f_path in file_select.value:
+            f_name = os.path.basename(f_path)
+            try:
+                file_data = [pyzebra.read_detector_data(f_path, cm)]
+            except:
+                print(f"Error loading {f_name}")
+                continue
+
+            pyzebra.normalize_dataset(file_data, monitor_spinner.value)
+
+            if not new_data:  # first file
+                new_data = file_data
+            else:
+                pyzebra.merge_datasets(new_data, file_data)
+
+        if new_data:
+            dataset = new_data
+            _init_datatable()
+
+    file_open_button = Button(label="Open New", width=100)
+    file_open_button.on_click(file_open_button_callback)
+
+    def file_append_button_callback():
+        file_data = []
+        for f_path in file_select.value:
+            f_name = os.path.basename(f_path)
+            try:
+                file_data = [pyzebra.read_detector_data(f_path, None)]
+            except:
+                print(f"Error loading {f_name}")
+                continue
+
+            pyzebra.normalize_dataset(file_data, monitor_spinner.value)
+            pyzebra.merge_datasets(dataset, file_data)
+
+        if file_data:
+            _init_datatable()
+
+    file_append_button = Button(label="Append", width=100)
+    file_append_button.on_click(file_append_button_callback)
+
+    def _init_datatable():
+        scan_list = [s["idx"] for s in dataset]
+        export = [s["export"] for s in dataset]
+
+        twotheta = [np.median(s["twotheta"]) if "twotheta" in s else None for s in dataset]
+        gamma = [np.median(s["gamma"]) if "gamma" in s else None for s in dataset]
+        omega = [np.median(s["omega"]) if "omega" in s else None for s in dataset]
+        chi = [np.median(s["chi"]) if "chi" in s else None for s in dataset]
+        phi = [np.median(s["phi"]) if "phi" in s else None for s in dataset]
+        nu = [np.median(s["nu"]) if "nu" in s else None for s in dataset]
+
+        scan_table_source.data.update(
+            scan=scan_list,
+            fit=[0] * len(scan_list),
+            export=export,
+            twotheta=twotheta,
+            gamma=gamma,
+            omega=omega,
+            chi=chi,
+            phi=phi,
+            nu=nu,
+        )
+        scan_table_source.selected.indices = []
+        scan_table_source.selected.indices = [0]
+
+        merge_options = [(str(i), f"{i} ({idx})") for i, idx in enumerate(scan_list)]
+        merge_from_select.options = merge_options
+        merge_from_select.value = merge_options[0][0]
+
+    def scan_table_select_callback(_attr, old, new):
+        if not new:
+            # skip empty selections
+            return
+
+        # Avoid selection of multiple indicies (via Shift+Click or Ctrl+Click)
+        if len(new) > 1:
+            # drop selection to the previous one
+            scan_table_source.selected.indices = old
+            return
+
+        if len(old) > 1:
+            # skip unnecessary update caused by selection drop
+            return
+
+        scan = _get_selected_scan()
+        last_im_index = scan["counts"].shape[0] - 1
+
+        index_spinner.value = 0
+        index_spinner.high = last_im_index
+        if last_im_index == 0:
+            index_slider.disabled = True
+        else:
+            index_slider.disabled = False
+            index_slider.end = last_im_index
+
+        zebra_mode = scan["zebra_mode"]
+        if zebra_mode == "nb":
+            metadata_table_source.data.update(geom=["normal beam"])
+        else:  # zebra_mode == "bi"
+            metadata_table_source.data.update(geom=["bisecting"])
+
+        _update_image()
+        _update_overview_plot()
+
+    def scan_table_source_callback(_attr, _old, new):
+        # unfortunately, we don't know if the change comes from data update or user input
+        # also `old` and `new` are the same for non-scalars
+        for scan, export in zip(dataset, new["export"]):
+            scan["export"] = export
+
+    scan_table_source = ColumnDataSource(
+        dict(scan=[], fit=[], export=[], twotheta=[], gamma=[], omega=[], chi=[], phi=[], nu=[],)
+    )
+    scan_table_source.on_change("data", scan_table_source_callback)
+    scan_table_source.selected.on_change("indices", scan_table_select_callback)
+
+    scan_table = DataTable(
+        source=scan_table_source,
+        columns=[
+            TableColumn(field="scan", title="Scan", editor=CellEditor(), width=50),
+            TableColumn(field="fit", title="Fit", editor=CellEditor(), width=50),
+            TableColumn(field="export", title="Export", editor=CellEditor(), width=50),
+            TableColumn(field="twotheta", title="2theta", editor=CellEditor(), width=50),
+            TableColumn(field="gamma", title="gamma", editor=CellEditor(), width=50),
+            TableColumn(field="omega", title="omega", editor=CellEditor(), width=50),
+            TableColumn(field="chi", title="chi", editor=CellEditor(), width=50),
+            TableColumn(field="phi", title="phi", editor=CellEditor(), width=50),
+            TableColumn(field="nu", title="nu", editor=CellEditor(), width=50),
+        ],
+        width=310,  # +60 because of the index column, but excluding twotheta onwards
+        height=350,
+        autosize_mode="none",
+        editable=True,
+    )
+
+    def _get_selected_scan():
+        return dataset[scan_table_source.selected.indices[0]]
+
+    def _update_table():
+        export = [scan["export"] for scan in dataset]
+        scan_table_source.data.update(export=export)
+
+    def monitor_spinner_callback(_attr, old, new):
+        if dataset:
+            pyzebra.normalize_dataset(dataset, new)
+            _update_image()
+            _update_overview_plot()
+
+    monitor_spinner = Spinner(title="Monitor:", mode="int", value=100_000, low=1, width=145)
+    monitor_spinner.on_change("value", monitor_spinner_callback)
+
+    merge_from_select = Select(title="scan:", width=145)
+
+    def merge_button_callback():
+        scan_into = _get_selected_scan()
+        scan_from = dataset[int(merge_from_select.value)]
+
+        if scan_into is scan_from:
+            print("WARNING: Selected scans for merging are identical")
+            return
+
+        pyzebra.merge_h5_scans(scan_into, scan_from)
+        _update_table()
+        _update_image()
+        _update_overview_plot()
+
+    merge_button = Button(label="Merge into current", width=145)
+    merge_button.on_click(merge_button_callback)
+
+    def restore_button_callback():
+        pyzebra.restore_scan(_get_selected_scan())
+        _update_table()
+        _update_image()
+        _update_overview_plot()
+
+    restore_button = Button(label="Restore scan", width=145)
+    restore_button.on_click(restore_button_callback)
+
+    def _update_image(index=None):
         if index is None:
             index = index_spinner.value
 
-        current_image = det_data["data"][index]
+        scan = _get_selected_scan()
+        current_image = scan["counts"][index]
         proj_v_line_source.data.update(
             x=np.arange(0, IMAGE_W) + 0.5, y=np.mean(current_image, axis=0)
         )
@@ -127,25 +350,59 @@ def create():
             image_glyph.color_mapper.low = im_min
             image_glyph.color_mapper.high = im_max
 
-        if "mf" in det_data:
-            metadata_table_source.data.update(mf=[det_data["mf"][index]])
+        if "mf" in scan:
+            metadata_table_source.data.update(mf=[scan["mf"][index]])
         else:
             metadata_table_source.data.update(mf=[None])
 
-        if "temp" in det_data:
-            metadata_table_source.data.update(temp=[det_data["temp"][index]])
+        if "temp" in scan:
+            metadata_table_source.data.update(temp=[scan["temp"][index]])
         else:
             metadata_table_source.data.update(temp=[None])
 
-        gamma, nu = calculate_pol(det_data, index)
-        omega = np.ones((IMAGE_H, IMAGE_W)) * det_data["omega"][index]
+        gamma, nu = calculate_pol(scan, index)
+        omega = np.ones((IMAGE_H, IMAGE_W)) * scan["omega"][index]
         image_source.data.update(gamma=[gamma], nu=[nu], omega=[omega])
 
-    def update_overview_plot():
-        h5_data = det_data["data"]
-        n_im, n_y, n_x = h5_data.shape
-        overview_x = np.mean(h5_data, axis=1)
-        overview_y = np.mean(h5_data, axis=2)
+        # update detector center angles
+        det_c_x = int(IMAGE_W / 2)
+        det_c_y = int(IMAGE_H / 2)
+        if scan["zebra_mode"] == "nb":
+            gamma_c = gamma[det_c_y, det_c_x]
+            nu_c = nu[det_c_y, det_c_x]
+            omega_c = omega[det_c_y, det_c_x]
+            chi_c = None
+            phi_c = None
+
+        else:  # zebra_mode == "bi"
+            wave = scan["wave"]
+            ddist = scan["ddist"]
+            gammad = scan["gamma"][index]
+            om = scan["omega"][index]
+            ch = scan["chi"][index]
+            ph = scan["phi"][index]
+            nud = scan["nu"]
+
+            nu_c = 0
+            chi_c, phi_c, gamma_c, omega_c = pyzebra.ang_proc(
+                wave, ddist, gammad, om, ch, ph, nud, det_c_x, det_c_y
+            )
+
+        detcenter_table_source.data.update(
+            gamma=[gamma_c], nu=[nu_c], omega=[omega_c], chi=[chi_c], phi=[phi_c],
+        )
+
+    def _update_overview_plot():
+        scan = _get_selected_scan()
+        counts = scan["counts"]
+        n_im, n_y, n_x = counts.shape
+        overview_x = np.mean(counts, axis=1)
+        overview_y = np.mean(counts, axis=2)
+
+        # normalize for simpler colormapping
+        overview_max_val = max(np.max(overview_x), np.max(overview_y))
+        overview_x = 1000 * overview_x / overview_max_val
+        overview_y = 1000 * overview_y / overview_max_val
 
         overview_plot_x_image_source.data.update(image=[overview_x], dw=[n_x], dh=[n_im])
         overview_plot_y_image_source.data.update(image=[overview_y], dw=[n_y], dh=[n_im])
@@ -168,12 +425,12 @@ def create():
         frame_range.reset_end = n_im
         frame_range.bounds = (0, n_im)
 
-        scan_motor = det_data["scan_motor"]
+        scan_motor = scan["scan_motor"]
         overview_plot_y.axis[1].axis_label = f"Scanning motor, {scan_motor}"
 
-        var = det_data[scan_motor]
+        var = scan[scan_motor]
         var_start = var[0]
-        var_end = var[-1] + (var[-1] - var[0]) / (n_im - 1)
+        var_end = var[-1] + (var[-1] - var[0]) / (n_im - 1) if n_im != 1 else var_start + 1
 
         scanning_motor_range.start = var_start
         scanning_motor_range.end = var_end
@@ -182,42 +439,30 @@ def create():
         # handle both, ascending and descending sequences
         scanning_motor_range.bounds = (min(var_start, var_end), max(var_start, var_end))
 
-    def file_select_callback(_attr, old, new):
-        nonlocal det_data
-        if not new:
-            # skip empty selections
-            return
+        gamma = image_source.data["gamma"][0]
+        gamma_start = gamma[0, 0]
+        gamma_end = gamma[0, -1]
 
-        # Avoid selection of multiple indicies (via Shift+Click or Ctrl+Click)
-        if len(new) > 1:
-            # drop selection to the previous one
-            file_select.value = old
-            return
+        gamma_range.start = gamma_start
+        gamma_range.end = gamma_end
+        gamma_range.reset_start = gamma_start
+        gamma_range.reset_end = gamma_end
+        gamma_range.bounds = (min(gamma_start, gamma_end), max(gamma_start, gamma_end))
 
-        if len(old) > 1:
-            # skip unnecessary update caused by selection drop
-            return
+        nu = image_source.data["nu"][0]
+        nu_start = nu[0, 0]
+        nu_end = nu[-1, 0]
 
-        det_data = pyzebra.read_detector_data(new[0], cami_meta)
-
-        index_spinner.value = 0
-        index_spinner.high = det_data["data"].shape[0] - 1
-        index_slider.end = det_data["data"].shape[0] - 1
-
-        zebra_mode = det_data["zebra_mode"]
-        if zebra_mode == "nb":
-            metadata_table_source.data.update(geom=["normal beam"])
-        else:  # zebra_mode == "bi"
-            metadata_table_source.data.update(geom=["bisecting"])
-
-        update_image(0)
-        update_overview_plot()
+        nu_range.start = nu_start
+        nu_range.end = nu_end
+        nu_range.reset_start = nu_start
+        nu_range.reset_end = nu_end
+        nu_range.bounds = (min(nu_start, nu_end), max(nu_start, nu_end))
 
     file_select = MultiSelect(title="Available .hdf files:", width=210, height=250)
-    file_select.on_change("value", file_select_callback)
 
     def index_callback(_attr, _old, new):
-        update_image(new)
+        _update_image(new)
 
     index_slider = Slider(value=0, start=0, end=1, show_value=False, width=400)
 
@@ -282,9 +527,10 @@ def create():
 
     # calculate hkl-indices of first mouse entry
     def mouse_enter_callback(_event):
-        if det_data and np.array_equal(image_source.data["h"][0], np.zeros((1, 1))):
+        if dataset and np.array_equal(image_source.data["h"][0], np.zeros((1, 1))):
+            scan = _get_selected_scan()
             index = index_spinner.value
-            h, k, l = calculate_hkl(det_data, index)
+            h, k, l = calculate_hkl(scan, index)
             image_source.data.update(h=[h], k=[k], l=[l])
 
     plot.on_event(MouseEnter, mouse_enter_callback)
@@ -346,13 +592,14 @@ def create():
 
     def box_edit_callback(_attr, _old, new):
         if new["x"]:
-            h5_data = det_data["data"]
-            x_val = np.arange(h5_data.shape[0])
+            scan = _get_selected_scan()
+            counts = scan["counts"]
+            x_val = np.arange(counts.shape[0])
             left = int(np.floor(new["x"][0]))
             right = int(np.ceil(new["x"][0] + new["width"][0]))
             bottom = int(np.floor(new["y"][0]))
             top = int(np.ceil(new["y"][0] + new["height"][0]))
-            y_val = np.sum(h5_data[:, bottom:top, left:right], axis=(1, 2))
+            y_val = np.sum(counts[:, bottom:top, left:right], axis=(1, 2))
         else:
             x_val = []
             y_val = []
@@ -372,12 +619,14 @@ def create():
     scanning_motor_range = Range1d(0, 1, bounds=(0, 1))
 
     det_x_range = Range1d(0, IMAGE_W, bounds=(0, IMAGE_W))
+    gamma_range = Range1d(0, 1, bounds=(0, 1))
     overview_plot_x = Plot(
         title=Title(text="Projections on X-axis"),
         x_range=det_x_range,
         y_range=frame_range,
+        extra_x_ranges={"gamma": gamma_range},
         extra_y_ranges={"scanning_motor": scanning_motor_range},
-        plot_height=400,
+        plot_height=450,
         plot_width=IMAGE_PLOT_W - 3,
     )
 
@@ -391,6 +640,9 @@ def create():
 
     # ---- axes
     overview_plot_x.add_layout(LinearAxis(axis_label="Coordinate X, pix"), place="below")
+    overview_plot_x.add_layout(
+        LinearAxis(x_range_name="gamma", axis_label="Gamma, deg"), place="above"
+    )
     overview_plot_x.add_layout(
         LinearAxis(axis_label="Frame", major_label_orientation="vertical"), place="left"
     )
@@ -410,12 +662,14 @@ def create():
     )
 
     det_y_range = Range1d(0, IMAGE_H, bounds=(0, IMAGE_H))
+    nu_range = Range1d(0, 1, bounds=(0, 1))
     overview_plot_y = Plot(
         title=Title(text="Projections on Y-axis"),
         x_range=det_y_range,
         y_range=frame_range,
+        extra_x_ranges={"nu": nu_range},
         extra_y_ranges={"scanning_motor": scanning_motor_range},
-        plot_height=400,
+        plot_height=450,
         plot_width=IMAGE_PLOT_H + 22,
     )
 
@@ -429,6 +683,7 @@ def create():
 
     # ---- axes
     overview_plot_y.add_layout(LinearAxis(axis_label="Coordinate Y, pix"), place="below")
+    overview_plot_y.add_layout(LinearAxis(x_range_name="nu", axis_label="Nu, deg"), place="above")
     overview_plot_y.add_layout(
         LinearAxis(
             y_range_name="scanning_motor",
@@ -500,7 +755,7 @@ def create():
             display_min_spinner.disabled = False
             display_max_spinner.disabled = False
 
-        update_image()
+        _update_image()
 
     main_auto_checkbox = CheckboxGroup(
         labels=["Frame Intensity Range"], active=[0], width=145, margin=[10, 5, 0, 5]
@@ -536,7 +791,7 @@ def create():
     )
     display_min_spinner.on_change("value", display_min_spinner_callback)
 
-    PROJ_STEP = 0.1
+    PROJ_STEP = 1
 
     def proj_auto_checkbox_callback(state):
         if state:
@@ -546,7 +801,7 @@ def create():
             proj_display_min_spinner.disabled = False
             proj_display_max_spinner.disabled = False
 
-        update_overview_plot()
+        _update_overview_plot()
 
     proj_auto_checkbox = CheckboxGroup(
         labels=["Projections Intensity Range"], active=[0], width=145, margin=[10, 5, 0, 5]
@@ -621,42 +876,53 @@ def create():
         index_position=None,
     )
 
+    detcenter_table_source = ColumnDataSource(dict(gamma=[], omega=[], chi=[], phi=[], nu=[]))
+    detcenter_table = DataTable(
+        source=detcenter_table_source,
+        columns=[
+            TableColumn(field="gamma", title="Gamma", formatter=num_formatter, width=70),
+            TableColumn(field="omega", title="Omega", formatter=num_formatter, width=70),
+            TableColumn(field="chi", title="Chi", formatter=num_formatter, width=70),
+            TableColumn(field="phi", title="Phi", formatter=num_formatter, width=70),
+            TableColumn(field="nu", title="Nu", formatter=num_formatter, width=70),
+        ],
+        height=150,
+        width=350,
+        autosize_mode="none",
+        index_position=None,
+    )
+
     def add_event_button_callback():
-        p0 = [1.0, 0.0, 1.0]
-        maxfev = 100000
+        scan = _get_selected_scan()
+        pyzebra.fit_event(
+            scan,
+            int(np.floor(frame_range.start)),
+            int(np.ceil(frame_range.end)),
+            int(np.floor(det_y_range.start)),
+            int(np.ceil(det_y_range.end)),
+            int(np.floor(det_x_range.start)),
+            int(np.ceil(det_x_range.end)),
+        )
 
-        wave = det_data["wave"]
-        ddist = det_data["ddist"]
-        cell = det_data["cell"]
+        wave = scan["wave"]
+        ddist = scan["ddist"]
+        cell = scan["cell"]
 
-        gamma = det_data["gamma"][0]
-        omega = det_data["omega"][0]
-        nu = det_data["nu"][0]
-        chi = det_data["chi"][0]
-        phi = det_data["phi"][0]
+        gamma = scan["gamma"][0]
+        omega = scan["omega"][0]
+        nu = scan["nu"][0]
+        chi = scan["chi"][0]
+        phi = scan["phi"][0]
 
-        scan_motor = det_data["scan_motor"]
-        var_angle = det_data[scan_motor]
+        scan_motor = scan["scan_motor"]
+        var_angle = scan[scan_motor]
 
-        x0 = int(np.floor(det_x_range.start))
-        xN = int(np.ceil(det_x_range.end))
-        y0 = int(np.floor(det_y_range.start))
-        yN = int(np.ceil(det_y_range.end))
-        fr0 = int(np.floor(frame_range.start))
-        frN = int(np.ceil(frame_range.end))
-        data_roi = det_data["data"][fr0:frN, y0:yN, x0:xN]
+        snr_cnts = scan["fit"]["snr"]
+        frC = scan["fit"]["frame"]
 
-        cnts = np.sum(data_roi, axis=(1, 2))
-        coeff, _ = curve_fit(gauss, range(len(cnts)), cnts, p0=p0, maxfev=maxfev)
-
-        m = cnts.mean()
-        sd = cnts.std()
-        snr_cnts = np.where(sd == 0, 0, m / sd)
-
-        frC = fr0 + coeff[1]
-        var_F = var_angle[math.floor(frC)]
-        var_C = var_angle[math.ceil(frC)]
-        frStep = frC - math.floor(frC)
+        var_F = var_angle[int(np.floor(frC))]
+        var_C = var_angle[int(np.ceil(frC))]
+        frStep = frC - np.floor(frC)
         var_step = var_C - var_F
         var_p = var_F + var_step * frStep
 
@@ -671,15 +937,13 @@ def create():
         elif scan_motor == "phi":
             phi = var_p
 
-        intensity = coeff[1] * abs(coeff[2] * var_step) * math.sqrt(2) * math.sqrt(np.pi)
+        intensity = scan["fit"]["intensity"]
+        x_pos = scan["fit"]["x_pos"]
+        y_pos = scan["fit"]["y_pos"]
 
-        projX = np.sum(data_roi, axis=(0, 1))
-        coeff, _ = curve_fit(gauss, range(len(projX)), projX, p0=p0, maxfev=maxfev)
-        x_pos = x0 + coeff[1]
-
-        projY = np.sum(data_roi, axis=(0, 2))
-        coeff, _ = curve_fit(gauss, range(len(projY)), projY, p0=p0, maxfev=maxfev)
-        y_pos = y0 + coeff[1]
+        if scan["zebra_mode"] == "nb":
+            chi = None
+            phi = None
 
         events_data["wave"].append(wave)
         events_data["ddist"].append(ddist)
@@ -697,7 +961,7 @@ def create():
 
         events_table_source.data = events_data
 
-    add_event_button = Button(label="Add spind event", width=145)
+    add_event_button = Button(label="Add peak center", width=145)
     add_event_button.on_click(add_event_button_callback)
 
     def remove_event_button_callback():
@@ -708,7 +972,7 @@ def create():
 
         events_table_source.data = events_data
 
-    remove_event_button = Button(label="Remove spind event", width=145)
+    remove_event_button = Button(label="Remove peak center", width=145)
     remove_event_button.on_click(remove_event_button_callback)
 
     metadata_table_source = ColumnDataSource(dict(geom=[""], temp=[None], mf=[None]))
@@ -726,7 +990,23 @@ def create():
     )
 
     # Final layout
-    import_layout = column(proposal_textinput, upload_div, upload_button, file_select)
+    peak_tables = Tabs(
+        tabs=[
+            Panel(child=events_table, title="Actual peak center"),
+            Panel(child=detcenter_table, title="Peak in the detector center"),
+        ]
+    )
+
+    import_layout = column(
+        data_source,
+        upload_cami_div,
+        upload_cami_button,
+        upload_hdf_div,
+        upload_hdf_button,
+        file_select,
+        row(file_open_button, file_append_button),
+    )
+
     layout_image = column(gridplot([[proj_v, None], [plot, proj_h]], merge_tools=False))
     colormap_layout = column(
         colormap,
@@ -738,7 +1018,7 @@ def create():
 
     layout_controls = column(
         row(metadata_table, index_spinner, column(Spacer(height=25), index_slider)),
-        row(column(add_event_button, remove_event_button), events_table),
+        row(column(add_event_button, remove_event_button), peak_tables),
     )
 
     layout_overview = column(
@@ -750,42 +1030,37 @@ def create():
         ),
     )
 
+    scan_layout = column(
+        scan_table,
+        row(monitor_spinner, column(Spacer(height=19), restore_button)),
+        row(column(Spacer(height=19), merge_button), merge_from_select),
+    )
+
     tab_layout = row(
         column(import_layout, colormap_layout),
-        column(layout_overview, layout_controls),
+        column(row(scan_layout, layout_overview), layout_controls),
         column(roi_avg_plot, layout_image),
     )
 
     return Panel(child=tab_layout, title="hdf viewer")
 
 
-def gauss(x, *p):
-    """Defines Gaussian function
-    Args:
-        A - amplitude, mu - position of the center, sigma - width
-    Returns:
-        Gaussian function
-    """
-    A, mu, sigma = p
-    return A * np.exp(-((x - mu) ** 2) / (2.0 * sigma ** 2))
-
-
-def calculate_hkl(det_data, index):
+def calculate_hkl(scan, index):
     h = np.empty(shape=(IMAGE_H, IMAGE_W))
     k = np.empty(shape=(IMAGE_H, IMAGE_W))
     l = np.empty(shape=(IMAGE_H, IMAGE_W))
 
-    wave = det_data["wave"]
-    ddist = det_data["ddist"]
-    gammad = det_data["gamma"][index]
-    om = det_data["omega"][index]
-    nud = det_data["nu"]
-    ub = det_data["ub"]
-    geometry = det_data["zebra_mode"]
+    wave = scan["wave"]
+    ddist = scan["ddist"]
+    gammad = scan["gamma"][index]
+    om = scan["omega"][index]
+    nud = scan["nu"]
+    ub = scan["ub"]
+    geometry = scan["zebra_mode"]
 
     if geometry == "bi":
-        chi = det_data["chi"][index]
-        phi = det_data["phi"][index]
+        chi = scan["chi"][index]
+        phi = scan["phi"][index]
     elif geometry == "nb":
         chi = 0
         phi = 0
@@ -801,16 +1076,11 @@ def calculate_hkl(det_data, index):
     return h, k, l
 
 
-def calculate_pol(det_data, index):
-    gamma = np.empty(shape=(IMAGE_H, IMAGE_W))
-    nu = np.empty(shape=(IMAGE_H, IMAGE_W))
-
-    ddist = det_data["ddist"]
-    gammad = det_data["gamma"][index]
-    nud = det_data["nu"]
-
-    for xi in np.arange(IMAGE_W):
-        for yi in np.arange(IMAGE_H):
-            gamma[yi, xi], nu[yi, xi] = pyzebra.det2pol(ddist, gammad, nud, xi, yi)
+def calculate_pol(scan, index):
+    ddist = scan["ddist"]
+    gammad = scan["gamma"][index]
+    nud = scan["nu"]
+    yi, xi = np.ogrid[:IMAGE_H, :IMAGE_W]
+    gamma, nu = pyzebra.det2pol(ddist, gammad, nud, xi, yi)
 
     return gamma, nu

pyzebra/app/panel_param_study.py

@@ -11,6 +11,7 @@ from bokeh.layouts import column, row
 from bokeh.models import (
     BasicTicker,
     Button,
+    CellEditor,
     CheckboxEditor,
     CheckboxGroup,
     ColumnDataSource,
@@ -26,6 +27,7 @@ from bokeh.models import (
     Legend,
     Line,
     LinearAxis,
+    LinearColorMapper,
     MultiLine,
     MultiSelect,
     NumberEditor,
@@ -33,6 +35,7 @@ from bokeh.models import (
     PanTool,
     Plot,
     RadioGroup,
+    Range1d,
     ResetTool,
     Scatter,
     Select,
@@ -42,30 +45,33 @@ from bokeh.models import (
     TableColumn,
     Tabs,
     TextAreaInput,
-    TextInput,
     WheelZoomTool,
     Whisker,
 )
-from bokeh.palettes import Category10, Turbo256
-from bokeh.transform import linear_cmap
+from bokeh.palettes import Category10, Plasma256
 from scipy import interpolate
 
 import pyzebra
 from pyzebra.ccl_process import AREA_METHODS
 
 javaScript = """
+let j = 0;
 for (let i = 0; i < js_data.data['fname'].length; i++) {
     if (js_data.data['content'][i] === "") continue;
 
-    const blob = new Blob([js_data.data['content'][i]], {type: 'text/plain'})
-    const link = document.createElement('a');
-    document.body.appendChild(link);
-    const url = window.URL.createObjectURL(blob);
-    link.href = url;
-    link.download = js_data.data['fname'][i];
-    link.click();
-    window.URL.revokeObjectURL(url);
-    document.body.removeChild(link);
+    setTimeout(function() {
+        const blob = new Blob([js_data.data['content'][i]], {type: 'text/plain'})
+        const link = document.createElement('a');
+        document.body.appendChild(link);
+        const url = window.URL.createObjectURL(blob);
+        link.href = url;
+        link.download = js_data.data['fname'][i] + js_data.data['ext'][i];
+        link.click();
+        window.URL.revokeObjectURL(url);
+        document.body.removeChild(link);
+    }, 100 * j)
+
+    j++;
 }
 """
 
@@ -77,170 +83,205 @@ def color_palette(n_colors):
 
 def create():
     doc = curdoc()
-    det_data = []
+    dataset = []
     fit_params = {}
-    js_data = ColumnDataSource(data=dict(content=["", ""], fname=["", ""]))
+    js_data = ColumnDataSource(data=dict(content=[""], fname=[""], ext=[""]))
 
     def file_select_update_for_proposal():
-        proposal = proposal_textinput.value.strip()
-        if not proposal:
+        proposal_path = proposal_textinput.name
+        if proposal_path:
+            file_list = []
+            for file in os.listdir(proposal_path):
+                if file.endswith((".ccl", ".dat")):
+                    file_list.append((os.path.join(proposal_path, file), file))
+            file_select.options = file_list
+            file_open_button.disabled = False
+            file_append_button.disabled = False
+        else:
             file_select.options = []
             file_open_button.disabled = True
             file_append_button.disabled = True
-            return
-
-        for zebra_proposals_path in pyzebra.ZEBRA_PROPOSALS_PATHS:
-            proposal_path = os.path.join(zebra_proposals_path, proposal)
-            if os.path.isdir(proposal_path):
-                # found it
-                break
-        else:
-            raise ValueError(f"Can not find data for proposal '{proposal}'.")
-
-        file_list = []
-        for file in os.listdir(proposal_path):
-            if file.endswith((".ccl", ".dat")):
-                file_list.append((os.path.join(proposal_path, file), file))
-        file_select.options = file_list
-        file_open_button.disabled = False
-        file_append_button.disabled = False
 
     doc.add_periodic_callback(file_select_update_for_proposal, 5000)
 
     def proposal_textinput_callback(_attr, _old, _new):
         file_select_update_for_proposal()
 
-    proposal_textinput = TextInput(title="Proposal number:", width=210)
-    proposal_textinput.on_change("value", proposal_textinput_callback)
+    proposal_textinput = doc.proposal_textinput
+    proposal_textinput.on_change("name", proposal_textinput_callback)
 
     def _init_datatable():
-        scan_list = [s["idx"] for s in det_data]
+        scan_list = [s["idx"] for s in dataset]
+        export = [s["export"] for s in dataset]
+        if param_select.value == "user defined":
+            param = [None] * len(dataset)
+        else:
+            param = [scan[param_select.value] for scan in dataset]
+
         file_list = []
-        for scan in det_data:
+        for scan in dataset:
             file_list.append(os.path.basename(scan["original_filename"]))
 
         scan_table_source.data.update(
-            file=file_list,
-            scan=scan_list,
-            param=[None] * len(scan_list),
-            fit=[0] * len(scan_list),
-            export=[True] * len(scan_list),
+            file=file_list, scan=scan_list, param=param, fit=[0] * len(scan_list), export=export,
         )
         scan_table_source.selected.indices = []
         scan_table_source.selected.indices = [0]
 
-        scan_motor_select.options = det_data[0]["scan_motors"]
-        scan_motor_select.value = det_data[0]["scan_motor"]
-        param_select.value = "user defined"
+        scan_motor_select.options = dataset[0]["scan_motors"]
+        scan_motor_select.value = dataset[0]["scan_motor"]
+
+        merge_options = [(str(i), f"{i} ({idx})") for i, idx in enumerate(scan_list)]
+        merge_from_select.options = merge_options
+        merge_from_select.value = merge_options[0][0]
 
     file_select = MultiSelect(title="Available .ccl/.dat files:", width=210, height=250)
 
     def file_open_button_callback():
-        nonlocal det_data
-        det_data = []
-        for f_name in file_select.value:
-            with open(f_name) as file:
+        nonlocal dataset
+        new_data = []
+        for f_path in file_select.value:
+            with open(f_path) as file:
+                f_name = os.path.basename(f_path)
                 base, ext = os.path.splitext(f_name)
-                if det_data:
-                    append_data = pyzebra.parse_1D(file, ext)
-                    pyzebra.normalize_dataset(append_data, monitor_spinner.value)
-                    det_data.extend(append_data)
-                else:
-                    det_data = pyzebra.parse_1D(file, ext)
-                    pyzebra.normalize_dataset(det_data, monitor_spinner.value)
-                    js_data.data.update(fname=[base + ".comm", base + ".incomm"])
+                try:
+                    file_data = pyzebra.parse_1D(file, ext)
+                except:
+                    print(f"Error loading {f_name}")
+                    continue
 
-        _init_datatable()
-        append_upload_button.disabled = False
+            pyzebra.normalize_dataset(file_data, monitor_spinner.value)
+
+            if not new_data:  # first file
+                new_data = file_data
+                pyzebra.merge_duplicates(new_data)
+                js_data.data.update(fname=[base])
+            else:
+                pyzebra.merge_datasets(new_data, file_data)
+
+        if new_data:
+            dataset = new_data
+            _init_datatable()
+            append_upload_button.disabled = False
 
     file_open_button = Button(label="Open New", width=100, disabled=True)
     file_open_button.on_click(file_open_button_callback)
 
     def file_append_button_callback():
-        for f_name in file_select.value:
-            with open(f_name) as file:
+        file_data = []
+        for f_path in file_select.value:
+            with open(f_path) as file:
+                f_name = os.path.basename(f_path)
                 _, ext = os.path.splitext(f_name)
-                append_data = pyzebra.parse_1D(file, ext)
+                try:
+                    file_data = pyzebra.parse_1D(file, ext)
+                except:
+                    print(f"Error loading {f_name}")
+                    continue
 
-            pyzebra.normalize_dataset(append_data, monitor_spinner.value)
-            det_data.extend(append_data)
+            pyzebra.normalize_dataset(file_data, monitor_spinner.value)
+            pyzebra.merge_datasets(dataset, file_data)
 
-        _init_datatable()
+        if file_data:
+            _init_datatable()
 
     file_append_button = Button(label="Append", width=100, disabled=True)
     file_append_button.on_click(file_append_button_callback)
 
-    def upload_button_callback(_attr, _old, new):
-        nonlocal det_data
-        det_data = []
-        proposal_textinput.value = ""
-        for f_str, f_name in zip(new, upload_button.filename):
+    def upload_button_callback(_attr, _old, _new):
+        nonlocal dataset
+        new_data = []
+        for f_str, f_name in zip(upload_button.value, upload_button.filename):
             with io.StringIO(base64.b64decode(f_str).decode()) as file:
                 base, ext = os.path.splitext(f_name)
-                if det_data:
-                    append_data = pyzebra.parse_1D(file, ext)
-                    pyzebra.normalize_dataset(append_data, monitor_spinner.value)
-                    det_data.extend(append_data)
-                else:
-                    det_data = pyzebra.parse_1D(file, ext)
-                    pyzebra.normalize_dataset(det_data, monitor_spinner.value)
-                    js_data.data.update(fname=[base + ".comm", base + ".incomm"])
+                try:
+                    file_data = pyzebra.parse_1D(file, ext)
+                except:
+                    print(f"Error loading {f_name}")
+                    continue
 
-        _init_datatable()
-        append_upload_button.disabled = False
+            pyzebra.normalize_dataset(file_data, monitor_spinner.value)
+
+            if not new_data:  # first file
+                new_data = file_data
+                pyzebra.merge_duplicates(new_data)
+                js_data.data.update(fname=[base])
+            else:
+                pyzebra.merge_datasets(new_data, file_data)
+
+        if new_data:
+            dataset = new_data
+            _init_datatable()
+            append_upload_button.disabled = False
 
     upload_div = Div(text="or upload new .ccl/.dat files:", margin=(5, 5, 0, 5))
     upload_button = FileInput(accept=".ccl,.dat", multiple=True, width=200)
-    upload_button.on_change("value", upload_button_callback)
+    # for on_change("value", ...) or on_change("filename", ...),
+    # see https://github.com/bokeh/bokeh/issues/11461
+    upload_button.on_change("filename", upload_button_callback)
 
-    def append_upload_button_callback(_attr, _old, new):
-        for f_str, f_name in zip(new, append_upload_button.filename):
+    def append_upload_button_callback(_attr, _old, _new):
+        file_data = []
+        for f_str, f_name in zip(append_upload_button.value, append_upload_button.filename):
             with io.StringIO(base64.b64decode(f_str).decode()) as file:
                 _, ext = os.path.splitext(f_name)
-                append_data = pyzebra.parse_1D(file, ext)
+                try:
+                    file_data = pyzebra.parse_1D(file, ext)
+                except:
+                    print(f"Error loading {f_name}")
+                    continue
 
-            pyzebra.normalize_dataset(append_data, monitor_spinner.value)
-            det_data.extend(append_data)
+            pyzebra.normalize_dataset(file_data, monitor_spinner.value)
+            pyzebra.merge_datasets(dataset, file_data)
 
-        _init_datatable()
+        if file_data:
+            _init_datatable()
 
     append_upload_div = Div(text="append extra files:", margin=(5, 5, 0, 5))
     append_upload_button = FileInput(accept=".ccl,.dat", multiple=True, width=200, disabled=True)
-    append_upload_button.on_change("value", append_upload_button_callback)
+    # for on_change("value", ...) or on_change("filename", ...),
+    # see https://github.com/bokeh/bokeh/issues/11461
+    append_upload_button.on_change("filename", append_upload_button_callback)
 
     def monitor_spinner_callback(_attr, _old, new):
-        if det_data:
-            pyzebra.normalize_dataset(det_data, new)
-            _update_plot()
+        if dataset:
+            pyzebra.normalize_dataset(dataset, new)
+            _update_single_scan_plot()
+            _update_overview()
 
     monitor_spinner = Spinner(title="Monitor:", mode="int", value=100_000, low=1, width=145)
     monitor_spinner.on_change("value", monitor_spinner_callback)
 
     def scan_motor_select_callback(_attr, _old, new):
-        if det_data:
-            for scan in det_data:
+        if dataset:
+            for scan in dataset:
                 scan["scan_motor"] = new
-            _update_plot()
+            _update_single_scan_plot()
+            _update_overview()
 
     scan_motor_select = Select(title="Scan motor:", options=[], width=145)
     scan_motor_select.on_change("value", scan_motor_select_callback)
 
     def _update_table():
-        fit_ok = [(1 if "fit" in scan else 0) for scan in det_data]
-        scan_table_source.data.update(fit=fit_ok)
+        fit_ok = [(1 if "fit" in scan else 0) for scan in dataset]
+        export = [scan["export"] for scan in dataset]
+        if param_select.value == "user defined":
+            param = [None] * len(dataset)
+        else:
+            param = [scan[param_select.value] for scan in dataset]
 
-    def _update_plot():
-        _update_single_scan_plot(_get_selected_scan())
-        _update_overview()
+        scan_table_source.data.update(fit=fit_ok, export=export, param=param)
 
-    def _update_single_scan_plot(scan):
+    def _update_single_scan_plot():
+        scan = _get_selected_scan()
         scan_motor = scan["scan_motor"]
 
         y = scan["counts"]
+        y_err = scan["counts_err"]
         x = scan[scan_motor]
 
         plot.axis[0].axis_label = scan_motor
-        plot_scatter_source.data.update(x=x, y=y, y_upper=y + np.sqrt(y), y_lower=y - np.sqrt(y))
+        plot_scatter_source.data.update(x=x, y=y, y_upper=y + y_err, y_lower=y - y_err)
 
         fit = scan.get("fit")
         if fit is not None:
@@ -281,7 +322,7 @@ def create():
         par = []
         for s, p in enumerate(scan_table_source.data["param"]):
             if p is not None:
-                scan = det_data[s]
+                scan = dataset[s]
                 scan_motor = scan["scan_motor"]
                 xs.append(scan[scan_motor])
                 x.extend(scan[scan_motor])
@@ -290,43 +331,58 @@ def create():
                 param.append(float(p))
                 par.extend(scan["counts"])
 
-        if det_data:
-            scan_motor = det_data[0]["scan_motor"]
+        if dataset:
+            scan_motor = dataset[0]["scan_motor"]
             ov_plot.axis[0].axis_label = scan_motor
             ov_param_plot.axis[0].axis_label = scan_motor
 
         ov_plot_mline_source.data.update(xs=xs, ys=ys, param=param, color=color_palette(len(xs)))
 
-        if y:
-            mapper["transform"].low = np.min([np.min(y) for y in ys])
-            mapper["transform"].high = np.max([np.max(y) for y in ys])
-        ov_param_plot_scatter_source.data.update(x=x, y=y, param=par)
+        ov_param_plot_scatter_source.data.update(x=x, y=y)
 
         if y:
-            interp_f = interpolate.interp2d(x, y, par)
             x1, x2 = min(x), max(x)
             y1, y2 = min(y), max(y)
-            image = interp_f(
-                np.linspace(x1, x2, ov_param_plot.inner_width // 10),
-                np.linspace(y1, y2, ov_param_plot.inner_height // 10),
-                assume_sorted=True,
+            grid_x, grid_y = np.meshgrid(
+                np.linspace(x1, x2, ov_param_plot.inner_width),
+                np.linspace(y1, y2, ov_param_plot.inner_height),
             )
+            image = interpolate.griddata((x, y), par, (grid_x, grid_y))
             ov_param_plot_image_source.data.update(
                 image=[image], x=[x1], y=[y1], dw=[x2 - x1], dh=[y2 - y1]
             )
+
+            x_range = ov_param_plot.x_range
+            x_range.start, x_range.end = x1, x2
+            x_range.reset_start, x_range.reset_end = x1, x2
+            x_range.bounds = (x1, x2)
+
+            y_range = ov_param_plot.y_range
+            y_range.start, y_range.end = y1, y2
+            y_range.reset_start, y_range.reset_end = y1, y2
+            y_range.bounds = (y1, y2)
+
         else:
             ov_param_plot_image_source.data.update(image=[], x=[], y=[], dw=[], dh=[])
 
     def _update_param_plot():
         x = []
         y = []
+        y_lower = []
+        y_upper = []
         fit_param = fit_param_select.value
-        for s, p in zip(det_data, scan_table_source.data["param"]):
+        for s, p in zip(dataset, scan_table_source.data["param"]):
             if "fit" in s and fit_param:
                 x.append(p)
-                y.append(s["fit"].values[fit_param])
+                param_fit_val = s["fit"].params[fit_param].value
+                param_fit_std = s["fit"].params[fit_param].stderr
+                if param_fit_std is None:
+                    param_fit_std = 0
+                y.append(param_fit_val)
+                y_lower.append(param_fit_val - param_fit_std)
+                y_upper.append(param_fit_val + param_fit_std)
 
-        param_plot_scatter_source.data.update(x=x, y=y)
+        param_plot_scatter_source.data.update(x=x, y=y, y_lower=y_lower, y_upper=y_upper)
 
     # Main plot
     plot = Plot(
@@ -344,7 +400,7 @@ def create():
 
     plot_scatter_source = ColumnDataSource(dict(x=[0], y=[0], y_upper=[0], y_lower=[0]))
     plot_scatter = plot.add_glyph(
-        plot_scatter_source, Scatter(x="x", y="y", line_color="steelblue")
+        plot_scatter_source, Scatter(x="x", y="y", line_color="steelblue", fill_color="steelblue")
     )
     plot.add_layout(Whisker(source=plot_scatter_source, base="x", upper="y_upper", lower="y_lower"))
 
@@ -402,9 +458,7 @@ def create():
     ov_plot.toolbar.logo = None
 
     # Overview perams plot
-    ov_param_plot = Plot(
-        x_range=DataRange1d(), y_range=DataRange1d(), plot_height=450, plot_width=700
-    )
+    ov_param_plot = Plot(x_range=Range1d(), y_range=Range1d(), plot_height=450, plot_width=700)
 
     ov_param_plot.add_layout(LinearAxis(axis_label="Param"), place="left")
     ov_param_plot.add_layout(LinearAxis(axis_label="Scan motor"), place="below")
@@ -412,16 +466,16 @@ def create():
     ov_param_plot.add_layout(Grid(dimension=0, ticker=BasicTicker()))
     ov_param_plot.add_layout(Grid(dimension=1, ticker=BasicTicker()))
 
+    color_mapper = LinearColorMapper(palette=Plasma256)
     ov_param_plot_image_source = ColumnDataSource(dict(image=[], x=[], y=[], dw=[], dh=[]))
     ov_param_plot.add_glyph(
-        ov_param_plot_image_source, Image(image="image", x="x", y="y", dw="dw", dh="dh")
+        ov_param_plot_image_source,
+        Image(image="image", x="x", y="y", dw="dw", dh="dh", color_mapper=color_mapper),
     )
 
-    ov_param_plot_scatter_source = ColumnDataSource(dict(x=[], y=[], param=[]))
-    mapper = linear_cmap(field_name="param", palette=Turbo256, low=0, high=50)
+    ov_param_plot_scatter_source = ColumnDataSource(dict(x=[], y=[]))
     ov_param_plot.add_glyph(
-        ov_param_plot_scatter_source,
-        Scatter(x="x", y="y", line_color=mapper, fill_color=mapper, size=10),
+        ov_param_plot_scatter_source, Scatter(x="x", y="y", marker="dot", size=15),
     )
 
     ov_param_plot.add_tools(PanTool(), WheelZoomTool(), ResetTool())
@@ -436,8 +490,11 @@ def create():
     param_plot.add_layout(Grid(dimension=0, ticker=BasicTicker()))
     param_plot.add_layout(Grid(dimension=1, ticker=BasicTicker()))
 
-    param_plot_scatter_source = ColumnDataSource(dict(x=[], y=[]))
+    param_plot_scatter_source = ColumnDataSource(dict(x=[], y=[], y_upper=[], y_lower=[]))
     param_plot.add_glyph(param_plot_scatter_source, Scatter(x="x", y="y"))
+    param_plot.add_layout(
+        Whisker(source=param_plot_scatter_source, base="x", upper="y_upper", lower="y_lower")
+    )
 
     param_plot.add_tools(PanTool(), WheelZoomTool(), ResetTool())
     param_plot.toolbar.logo = None
@@ -474,46 +531,68 @@ def create():
             # skip unnecessary update caused by selection drop
             return
 
-        _update_plot()
+        _update_single_scan_plot()
 
-    def scan_table_source_callback(_attr, _old, _new):
+    def scan_table_source_callback(_attr, _old, new):
+        # unfortunately, we don't know if the change comes from data update or user input
+        # also `old` and `new` are the same for non-scalars
+        for scan, export in zip(dataset, new["export"]):
+            scan["export"] = export
+        _update_overview()
+        _update_param_plot()
         _update_preview()
 
     scan_table_source = ColumnDataSource(dict(file=[], scan=[], param=[], fit=[], export=[]))
     scan_table_source.on_change("data", scan_table_source_callback)
+    scan_table_source.selected.on_change("indices", scan_table_select_callback)
 
     scan_table = DataTable(
         source=scan_table_source,
         columns=[
-            TableColumn(field="file", title="file", width=150),
-            TableColumn(field="scan", title="scan", width=50),
+            TableColumn(field="file", title="file", editor=CellEditor(), width=150),
+            TableColumn(field="scan", title="scan", editor=CellEditor(), width=50),
             TableColumn(field="param", title="param", editor=NumberEditor(), width=50),
-            TableColumn(field="fit", title="Fit", width=50),
+            TableColumn(field="fit", title="Fit", editor=CellEditor(), width=50),
             TableColumn(field="export", title="Export", editor=CheckboxEditor(), width=50),
         ],
         width=410,  # +60 because of the index column
+        height=350,
         editable=True,
         autosize_mode="none",
     )
 
-    def scan_table_source_callback(_attr, _old, _new):
-        if scan_table_source.selected.indices:
-            _update_plot()
+    merge_from_select = Select(title="scan:", width=145)
 
-    scan_table_source.selected.on_change("indices", scan_table_select_callback)
-    scan_table_source.on_change("data", scan_table_source_callback)
+    def merge_button_callback():
+        scan_into = _get_selected_scan()
+        scan_from = dataset[int(merge_from_select.value)]
+
+        if scan_into is scan_from:
+            print("WARNING: Selected scans for merging are identical")
+            return
+
+        pyzebra.merge_scans(scan_into, scan_from)
+        _update_table()
+        _update_single_scan_plot()
+        _update_overview()
+
+    merge_button = Button(label="Merge into current", width=145)
+    merge_button.on_click(merge_button_callback)
+
+    def restore_button_callback():
+        pyzebra.restore_scan(_get_selected_scan())
+        _update_table()
+        _update_single_scan_plot()
+        _update_overview()
+
+    restore_button = Button(label="Restore scan", width=145)
+    restore_button.on_click(restore_button_callback)
 
     def _get_selected_scan():
-        return det_data[scan_table_source.selected.indices[0]]
+        return dataset[scan_table_source.selected.indices[0]]
 
-    def param_select_callback(_attr, _old, new):
-        if new == "user defined":
-            param = [None] * len(det_data)
-        else:
-            param = [scan[new] for scan in det_data]
-
-        scan_table_source.data["param"] = param
-        _update_param_plot()
+    def param_select_callback(_attr, _old, _new):
+        _update_table()
 
     param_select = Select(
         title="Parameter:",
@@ -622,7 +701,7 @@ def create():
     fitparams_table = DataTable(
         source=fitparams_table_source,
         columns=[
-            TableColumn(field="param", title="Parameter"),
+            TableColumn(field="param", title="Parameter", editor=CellEditor()),
             TableColumn(field="value", title="Value", editor=NumberEditor()),
             TableColumn(field="vary", title="Vary", editor=CheckboxEditor()),
             TableColumn(field="min", title="Min", editor=NumberEditor()),
@@ -643,8 +722,8 @@ def create():
     fit_output_textinput = TextAreaInput(title="Fit results:", width=750, height=200)
 
     def proc_all_button_callback():
-        for scan, export in zip(det_data, scan_table_source.data["export"]):
-            if export:
+        for scan in dataset:
+            if scan["export"]:
                 pyzebra.fit_scan(
                     scan, fit_params, fit_from=fit_from_spinner.value, fit_to=fit_to_spinner.value
                 )
@@ -654,16 +733,16 @@ def create():
                     lorentz=lorentz_checkbox.active,
                 )
 
-        _update_plot()
+        _update_single_scan_plot()
+        _update_overview()
         _update_table()
 
-        for scan in det_data:
+        for scan in dataset:
             if "fit" in scan:
                 options = list(scan["fit"].params.keys())
                 fit_param_select.options = options
                 fit_param_select.value = options[0]
                 break
-        _update_param_plot()
 
     proc_all_button = Button(label="Process All", button_type="primary", width=145)
     proc_all_button.on_click(proc_all_button_callback)
@@ -679,16 +758,16 @@ def create():
             lorentz=lorentz_checkbox.active,
         )
 
-        _update_plot()
+        _update_single_scan_plot()
+        _update_overview()
         _update_table()
 
-        for scan in det_data:
+        for scan in dataset:
             if "fit" in scan:
                 options = list(scan["fit"].params.keys())
                 fit_param_select.options = options
                 fit_param_select.value = options[0]
                 break
-        _update_param_plot()
 
     proc_button = Button(label="Process Current", width=145)
     proc_button.on_click(proc_button_callback)
@@ -698,34 +777,36 @@ def create():
 
     lorentz_checkbox = CheckboxGroup(labels=["Lorentz Correction"], width=145, margin=(13, 5, 5, 5))
 
-    export_preview_textinput = TextAreaInput(title="Export file(s) preview:", width=450, height=400)
+    export_preview_textinput = TextAreaInput(title="Export file preview:", width=450, height=400)
 
     def _update_preview():
         with tempfile.TemporaryDirectory() as temp_dir:
             temp_file = temp_dir + "/temp"
             export_data = []
-            for s, export in zip(det_data, scan_table_source.data["export"]):
-                if export:
-                    export_data.append(s)
+            param_data = []
+            for scan, param in zip(dataset, scan_table_source.data["param"]):
+                if scan["export"] and param:
+                    export_data.append(scan)
+                    param_data.append(param)
 
-            # pyzebra.export_1D(export_data, temp_file, "fullprof")
+            pyzebra.export_param_study(export_data, param_data, temp_file)
 
             exported_content = ""
             file_content = []
-            for ext in (".comm", ".incomm"):
-                fname = temp_file + ext
-                if os.path.isfile(fname):
-                    with open(fname) as f:
-                        content = f.read()
-                        exported_content += f"{ext} file:\n" + content
-                else:
-                    content = ""
-                file_content.append(content)
+
+            fname = temp_file
+            if os.path.isfile(fname):
+                with open(fname) as f:
+                    content = f.read()
+                    exported_content += content
+            else:
+                content = ""
+            file_content.append(content)
 
             js_data.data.update(content=file_content)
             export_preview_textinput.value = exported_content
 
-    save_button = Button(label="Download File(s)", button_type="success", width=220)
+    save_button = Button(label="Download File", button_type="success", width=220)
     save_button.js_on_click(CustomJS(args={"js_data": js_data}, code=javaScript))
 
     fitpeak_controls = row(
@@ -736,10 +817,13 @@ def create():
         column(fit_to_spinner, proc_button, proc_all_button),
     )
 
-    scan_layout = column(scan_table, row(monitor_spinner, scan_motor_select, param_select))
+    scan_layout = column(
+        scan_table,
+        row(monitor_spinner, scan_motor_select, param_select),
+        row(column(Spacer(height=19), row(restore_button, merge_button)), merge_from_select),
+    )
 
     import_layout = column(
-        proposal_textinput,
         file_select,
         row(file_open_button, file_append_button),
         upload_div,

pyzebra/app/panel_spind.py

@@ -24,7 +24,7 @@ def create():
     events_data = doc.events_data
 
     npeaks_spinner = Spinner(title="Number of peaks from hdf_view panel:", disabled=True)
-    lattice_const_textinput = TextInput(title="Lattice constants:", disabled=True)
+    lattice_const_textinput = TextInput(title="Lattice constants:")
     max_res_spinner = Spinner(title="max-res:", value=2, step=0.01, width=145)
     seed_pool_size_spinner = Spinner(title="seed-pool-size:", value=5, step=0.01, width=145)
     seed_len_tol_spinner = Spinner(title="seed-len-tol:", value=0.02, step=0.01, width=145)

pyzebra/ccl_io.py

@@ -1,5 +1,6 @@
 import os
 import re
+from ast import literal_eval
 from collections import defaultdict
 
 import numpy as np
@@ -56,7 +57,7 @@ META_VARS_FLOAT = (
     "s2hl",
 )
 
-META_UB_MATRIX = ("ub1j", "ub2j", "ub3j")
+META_UB_MATRIX = ("ub1j", "ub2j", "ub3j", "UB")
 
 CCL_FIRST_LINE = (("idx", int), ("h", float), ("k", float), ("l", float))
 
@@ -93,9 +94,9 @@ def load_1D(filepath):
     """
     with open(filepath, "r") as infile:
         _, ext = os.path.splitext(filepath)
-        det_variables = parse_1D(infile, data_type=ext)
+        dataset = parse_1D(infile, data_type=ext)
 
-    return det_variables
+    return dataset
 
 
 def parse_1D(fileobj, data_type):
@@ -108,21 +109,29 @@ def parse_1D(fileobj, data_type):
             variable = variable.strip()
             value = value.strip()
 
-            if variable in META_VARS_STR:
-                metadata[variable] = value
+            try:
+                if variable in META_VARS_STR:
+                    metadata[variable] = value
 
-            elif variable in META_VARS_FLOAT:
-                if variable == "2-theta":  # fix that angle name not to be an expression
-                    variable = "twotheta"
-                if variable in ("a", "b", "c", "alpha", "beta", "gamma"):
-                    variable += "_cell"
-                metadata[variable] = float(value)
+                elif variable in META_VARS_FLOAT:
+                    if variable == "2-theta":  # fix that angle name not to be an expression
+                        variable = "twotheta"
+                    if variable in ("a", "b", "c", "alpha", "beta", "gamma"):
+                        variable += "_cell"
+                    metadata[variable] = float(value)
 
-            elif variable in META_UB_MATRIX:
-                if "ub" not in metadata:
-                    metadata["ub"] = np.zeros((3, 3))
-                row = int(variable[-2]) - 1
-                metadata["ub"][row, :] = list(map(float, value.split()))
+                elif variable in META_UB_MATRIX:
+                    if variable == "UB":
+                        metadata["ub"] = np.array(literal_eval(value)).reshape(3, 3)
+                    else:
+                        if "ub" not in metadata:
+                            metadata["ub"] = np.zeros((3, 3))
+                        row = int(variable[-2]) - 1
+                        metadata["ub"][row, :] = list(map(float, value.split()))
+
+            except Exception:
+                print(f"Error reading {variable} with value '{value}'")
+                metadata[variable] = 0
 
         if "#data" in line:
             # this is the end of metadata and the start of data section
@@ -133,7 +142,7 @@ def parse_1D(fileobj, data_type):
         metadata["zebra_mode"] = "nb"
 
     # read data
-    scan = []
+    dataset = []
     if data_type == ".ccl":
         ccl_first_line = CCL_FIRST_LINE + CCL_ANGLES[metadata["zebra_mode"]]
         ccl_second_line = CCL_SECOND_LINE
@@ -143,44 +152,52 @@ def parse_1D(fileobj, data_type):
             if not line or line.isspace():
                 continue
 
-            s = {}
+            scan = {}
+            scan["export"] = True
 
             # first line
             for param, (param_name, param_type) in zip(line.split(), ccl_first_line):
-                s[param_name] = param_type(param)
+                scan[param_name] = param_type(param)
 
             # second line
             next_line = next(fileobj)
             for param, (param_name, param_type) in zip(next_line.split(), ccl_second_line):
-                s[param_name] = param_type(param)
+                scan[param_name] = param_type(param)
 
-            if s["scan_motor"] != "om":
+            if "scan_motor" not in scan:
+                scan["scan_motor"] = "om"
+
+            if scan["scan_motor"] != "om":
                 raise Exception("Unsupported variable name in ccl file.")
 
             # "om" -> "omega"
-            s["scan_motor"] = "omega"
-            s["scan_motors"] = ["omega", ]
+            scan["scan_motor"] = "omega"
+            scan["scan_motors"] = ["omega", ]
             # overwrite metadata, because it only refers to the scan center
-            half_dist = (s["n_points"] - 1) / 2 * s["angle_step"]
-            s["omega"] = np.linspace(s["omega"] - half_dist, s["omega"] + half_dist, s["n_points"])
+            half_dist = (scan["n_points"] - 1) / 2 * scan["angle_step"]
+            scan["omega"] = np.linspace(
+                scan["omega"] - half_dist, scan["omega"] + half_dist, scan["n_points"]
+            )
 
             # subsequent lines with counts
             counts = []
-            while len(counts) < s["n_points"]:
+            while len(counts) < scan["n_points"]:
                 counts.extend(map(float, next(fileobj).split()))
-            s["counts"] = np.array(counts)
+            scan["counts"] = np.array(counts)
+            scan["counts_err"] = np.sqrt(np.maximum(scan["counts"], 1))
 
-            if s["h"].is_integer() and s["k"].is_integer() and s["l"].is_integer():
-                s["h"], s["k"], s["l"] = map(int, (s["h"], s["k"], s["l"]))
+            if scan["h"].is_integer() and scan["k"].is_integer() and scan["l"].is_integer():
+                scan["h"], scan["k"], scan["l"] = map(int, (scan["h"], scan["k"], scan["l"]))
 
-            scan.append({**metadata, **s})
+            dataset.append({**metadata, **scan})
 
     elif data_type == ".dat":
         # TODO: this might need to be adapted in the future, when "gamma" will be added to dat files
         if metadata["zebra_mode"] == "nb":
             metadata["gamma"] = metadata["twotheta"]
 
-        s = defaultdict(list)
+        scan = defaultdict(list)
+        scan["export"] = True
 
         match = re.search("Scanning Variables: (.*), Steps: (.*)", next(fileobj))
         motors = [motor.lower() for motor in match.group(1).split(", ")]
@@ -189,7 +206,8 @@ def parse_1D(fileobj, data_type):
         match = re.search("(.*) Points, Mode: (.*), Preset (.*)", next(fileobj))
         if match.group(2) != "Monitor":
             raise Exception("Unknown mode in dat file.")
-        s["monitor"] = float(match.group(3))
+        scan["n_points"] = int(match.group(1))
+        scan["monitor"] = float(match.group(3))
 
         col_names = list(map(str.lower, next(fileobj).split()))
 
@@ -199,57 +217,56 @@ def parse_1D(fileobj, data_type):
                 break
 
             for name, val in zip(col_names, line.split()):
-                s[name].append(float(val))
+                scan[name].append(float(val))
 
         for name in col_names:
-            s[name] = np.array(s[name])
+            scan[name] = np.array(scan[name])
 
-        s["scan_motors"] = []
+        scan["counts_err"] = np.sqrt(np.maximum(scan["counts"], 1))
+
+        scan["scan_motors"] = []
         for motor, step in zip(motors, steps):
             if step == 0:
                 # it's not a scan motor, so keep only the median value
-                s[motor] = np.median(s[motor])
+                scan[motor] = np.median(scan[motor])
             else:
-                s["scan_motors"].append(motor)
-        s["scan_motor"] = s["scan_motors"][0]
+                scan["scan_motors"].append(motor)
 
         # "om" -> "omega"
-        if "om" in s["scan_motors"]:
-            s["scan_motors"][s["scan_motors"].index("om")] = "omega"
-            if s["scan_motor"] == "om":
-                s["scan_motor"] = "omega"
-            s["omega"] = s["om"]
-            del s["om"]
+        if "om" in scan["scan_motors"]:
+            scan["scan_motors"][scan["scan_motors"].index("om")] = "omega"
+            scan["omega"] = scan["om"]
+            del scan["om"]
 
         # "tt" -> "temp"
-        elif "tt" in s["scan_motors"]:
-            s["scan_motors"][s["scan_motors"].index("tt")] = "temp"
-            if s["scan_motor"] == "tt":
-                s["scan_motor"] = "temp"
-            s["temp"] = s["tt"]
-            del s["tt"]
+        if "tt" in scan["scan_motors"]:
+            scan["scan_motors"][scan["scan_motors"].index("tt")] = "temp"
+            scan["temp"] = scan["tt"]
+            del scan["tt"]
 
         # "mf" stays "mf"
         # "phi" stays "phi"
 
-        if "h" not in s:
-            s["h"] = s["k"] = s["l"] = float("nan")
+        scan["scan_motor"] = scan["scan_motors"][0]
+
+        if "h" not in scan:
+            scan["h"] = scan["k"] = scan["l"] = float("nan")
 
         for param in ("mf", "temp"):
             if param not in metadata:
-                s[param] = 0
+                scan[param] = 0
 
-        s["idx"] = 1
+        scan["idx"] = 1
 
-        scan.append({**metadata, **s})
+        dataset.append({**metadata, **scan})
 
     else:
         print("Unknown file extention")
 
-    return scan
+    return dataset
 
 
-def export_1D(data, path, export_target, hkl_precision=2):
+def export_1D(dataset, path, export_target, hkl_precision=2):
     """Exports data in the .comm/.incomm format for fullprof or .col/.incol format for jana.
 
     Scans with integer/real hkl values are saved in .comm/.incomm or .col/.incol files
@@ -259,11 +276,11 @@ def export_1D(data, path, export_target, hkl_precision=2):
     if export_target not in EXPORT_TARGETS:
         raise ValueError(f"Unknown export target: {export_target}.")
 
-    zebra_mode = data[0]["zebra_mode"]
+    zebra_mode = dataset[0]["zebra_mode"]
     exts = EXPORT_TARGETS[export_target]
     file_content = {ext: [] for ext in exts}
 
-    for scan in data:
+    for scan in dataset:
         if "fit" not in scan:
             continue
 
@@ -301,3 +318,92 @@ def export_1D(data, path, export_target, hkl_precision=2):
         if content:
             with open(path + ext, "w") as out_file:
                 out_file.writelines(content)
+
+
+def export_ccl_compare(dataset1, dataset2, path, export_target, hkl_precision=2):
+    """Exports compare data in the .comm/.incomm format for fullprof or .col/.incol format for jana.
+
+    Scans with integer/real hkl values are saved in .comm/.incomm or .col/.incol files
+    correspondingly. If no scans are present for a particular output format, that file won't be
+    created.
+    """
+    if export_target not in EXPORT_TARGETS:
+        raise ValueError(f"Unknown export target: {export_target}.")
+
+    zebra_mode = dataset1[0]["zebra_mode"]
+    exts = EXPORT_TARGETS[export_target]
+    file_content = {ext: [] for ext in exts}
+
+    for scan1, scan2 in zip(dataset1, dataset2):
+        if "fit" not in scan1:
+            continue
+
+        idx_str = f"{scan1['idx']:6}"
+
+        h, k, l = scan1["h"], scan1["k"], scan1["l"]
+        hkl_are_integers = isinstance(h, int)  # if True, other indices are of type 'int' too
+        if hkl_are_integers:
+            hkl_str = f"{h:4}{k:4}{l:4}"
+        else:
+            hkl_str = f"{h:8.{hkl_precision}f}{k:8.{hkl_precision}f}{l:8.{hkl_precision}f}"
+
+        area_n1, area_s1 = scan1["area"]
+        area_n2, area_s2 = scan2["area"]
+        area_n = area_n1 - area_n2
+        area_s = np.sqrt(area_s1 ** 2 + area_s2 ** 2)
+        area_str = f"{area_n:10.2f}{area_s:10.2f}"
+
+        ang_str = ""
+        for angle, _ in CCL_ANGLES[zebra_mode]:
+            if angle == scan1["scan_motor"]:
+                angle_center = (np.min(scan1[angle]) + np.max(scan1[angle])) / 2
+            else:
+                angle_center = scan1[angle]
+
+            if angle == "twotheta" and export_target == "jana":
+                angle_center /= 2
+
+            ang_str = ang_str + f"{angle_center:8g}"
+
+        if export_target == "jana":
+            ang_str = ang_str + f"{scan1['temp']:8}" + f"{scan1['monitor']:8}"
+
+        ref = file_content[exts[0]] if hkl_are_integers else file_content[exts[1]]
+        ref.append(idx_str + hkl_str + area_str + ang_str + "\n")
+
+    for ext, content in file_content.items():
+        if content:
+            with open(path + ext, "w") as out_file:
+                out_file.writelines(content)
+
+
+def export_param_study(dataset, param_data, path):
+    file_content = []
+    for scan, param in zip(dataset, param_data):
+        if "fit" not in scan:
+            continue
+
+        if not file_content:
+            title_str = f"{'param':12}"
+            for fit_param_name in scan["fit"].params:
+                title_str = title_str + f"{fit_param_name:20}" + f"{'std_' + fit_param_name:20}"
+            title_str = title_str + "file"
+            file_content.append(title_str + "\n")
+
+        param_str = f"{param:<12.2f}"
+
+        fit_str = ""
+        for fit_param in scan["fit"].params.values():
+            fit_param_val = fit_param.value
+            fit_param_std = fit_param.stderr
+            if fit_param_std is None:
+                fit_param_std = 0
+            fit_str = fit_str + f"{fit_param_val:<20.2f}" + f"{fit_param_std:<20.2f}"
+
+        _, fname_str = os.path.split(scan["original_filename"])
+
+        file_content.append(param_str + fit_str + fname_str + "\n")
+
+    if file_content:
+        with open(path, "w") as out_file:
+            out_file.writelines(file_content)

pyzebra/ccl_process.py

@@ -1,4 +1,3 @@
-import itertools
 import os
 
 import numpy as np
@@ -23,6 +22,8 @@ MAX_RANGE_GAP = {
     "omega": 0.5,
 }
 
+MOTOR_POS_PRECISION = 0.01
+
 AREA_METHODS = ("fit_area", "int_area")
 
 
@@ -30,6 +31,7 @@ def normalize_dataset(dataset, monitor=100_000):
     for scan in dataset:
         monitor_ratio = monitor / scan["monitor"]
         scan["counts"] *= monitor_ratio
+        scan["counts_err"] *= monitor_ratio
         scan["monitor"] = monitor
 
 
@@ -47,32 +49,49 @@ def _parameters_match(scan1, scan2):
     if zebra_mode != scan2["zebra_mode"]:
         return False
 
-    for param in ("ub", "temp", "mf", *(vars[0] for vars in CCL_ANGLES[zebra_mode])):
+    for param in ("ub", *(vars[0] for vars in CCL_ANGLES[zebra_mode])):
         if param.startswith("skip"):
             # ignore skip parameters, like the last angle in 'nb' zebra mode
             continue
 
         if param == scan1["scan_motor"] == scan2["scan_motor"]:
             # check if ranges of variable parameter overlap
-            range1 = scan1[param]
-            range2 = scan2[param]
+            r1_start, r1_end = scan1[param][0], scan1[param][-1]
+            r2_start, r2_end = scan2[param][0], scan2[param][-1]
+            # support reversed ranges
+            if r1_start > r1_end:
+                r1_start, r1_end = r1_end, r1_start
+            if r2_start > r2_end:
+                r2_start, r2_end = r2_end, r2_start
             # maximum gap between ranges of the scanning parameter (default 0)
             max_range_gap = MAX_RANGE_GAP.get(param, 0)
-            if max(range1[0] - range2[-1], range2[0] - range1[-1]) > max_range_gap:
+            if max(r1_start - r2_end, r2_start - r1_end) > max_range_gap:
                 return False
 
-        elif np.max(np.abs(scan1[param] - scan2[param])) > PARAM_PRECISIONS[param]:
+        elif (
+            np.max(np.abs(np.median(scan1[param]) - np.median(scan2[param])))
+            > PARAM_PRECISIONS[param]
+        ):
             return False
 
     return True
 
 
 def merge_datasets(dataset_into, dataset_from):
+    scan_motors_into = dataset_into[0]["scan_motors"]
+    scan_motors_from = dataset_from[0]["scan_motors"]
+    if scan_motors_into != scan_motors_from:
+        print(f"Scan motors mismatch between datasets: {scan_motors_into} vs {scan_motors_from}")
+        return
+
     merged = np.zeros(len(dataset_from), dtype=np.bool)
     for scan_into in dataset_into:
         for ind, scan_from in enumerate(dataset_from):
             if _parameters_match(scan_into, scan_from) and not merged[ind]:
-                merge_scans(scan_into, scan_from)
+                if scan_into["counts"].ndim == 3:
+                    merge_h5_scans(scan_into, scan_from)
+                else:  # scan_into["counts"].ndim == 1
+                    merge_scans(scan_into, scan_from)
                 merged[ind] = True
 
     for scan_from in dataset_from:
@@ -80,20 +99,118 @@ def merge_datasets(dataset_into, dataset_from):
 
 
 def merge_scans(scan_into, scan_from):
-    # TODO: does it need to be "scan_motor" instead of omega for a generalized solution?
-    if "init_omega" not in scan_into:
-        scan_into["init_omega"] = scan_into["omega"]
-        scan_into["init_counts"] = scan_into["counts"]
+    if "init_scan" not in scan_into:
+        scan_into["init_scan"] = scan_into.copy()
 
-    omega = np.concatenate((scan_into["omega"], scan_from["omega"]))
-    counts = np.concatenate((scan_into["counts"], scan_from["counts"]))
+    if "merged_scans" not in scan_into:
+        scan_into["merged_scans"] = []
 
-    index = np.argsort(omega)
+    if scan_from in scan_into["merged_scans"]:
+        return
 
-    scan_into["omega"] = omega[index]
-    scan_into["counts"] = counts[index]
+    scan_into["merged_scans"].append(scan_from)
 
-    scan_from["active"] = False
+    scan_motor = scan_into["scan_motor"]  # the same as scan_from["scan_motor"]
+
+    pos_all = np.array([])
+    val_all = np.array([])
+    err_all = np.array([])
+    for scan in [scan_into["init_scan"], *scan_into["merged_scans"]]:
+        pos_all = np.append(pos_all, scan[scan_motor])
+        val_all = np.append(val_all, scan["counts"])
+        err_all = np.append(err_all, scan["counts_err"] ** 2)
+
+    sort_index = np.argsort(pos_all)
+    pos_all = pos_all[sort_index]
+    val_all = val_all[sort_index]
+    err_all = err_all[sort_index]
+
+    pos_tmp = pos_all[:1]
+    val_tmp = val_all[:1]
+    err_tmp = err_all[:1]
+    num_tmp = np.array([1])
+    for pos, val, err in zip(pos_all[1:], val_all[1:], err_all[1:]):
+        if pos - pos_tmp[-1] < MOTOR_POS_PRECISION:
+            # the repeated motor position
+            val_tmp[-1] += val
+            err_tmp[-1] += err
+            num_tmp[-1] += 1
+        else:
+            # a new motor position
+            pos_tmp = np.append(pos_tmp, pos)
+            val_tmp = np.append(val_tmp, val)
+            err_tmp = np.append(err_tmp, err)
+            num_tmp = np.append(num_tmp, 1)
+
+    scan_into[scan_motor] = pos_tmp
+    scan_into["counts"] = val_tmp / num_tmp
+    scan_into["counts_err"] = np.sqrt(err_tmp) / num_tmp
+
+    scan_from["export"] = False
+
+    fname1 = os.path.basename(scan_into["original_filename"])
+    fname2 = os.path.basename(scan_from["original_filename"])
+    print(f'Merging scans: {scan_into["idx"]} ({fname1}) <-- {scan_from["idx"]} ({fname2})')
+
+
+def merge_h5_scans(scan_into, scan_from):
+    if "init_scan" not in scan_into:
+        scan_into["init_scan"] = scan_into.copy()
+
+    if "merged_scans" not in scan_into:
+        scan_into["merged_scans"] = []
+
+    for scan in scan_into["merged_scans"]:
+        if scan_from is scan:
+            print("Already merged scan")
+            return
+
+    scan_into["merged_scans"].append(scan_from)
+
+    scan_motor = scan_into["scan_motor"]  # the same as scan_from["scan_motor"]
+
+    pos_all = [scan_into["init_scan"][scan_motor]]
+    val_all = [scan_into["init_scan"]["counts"]]
+    err_all = [scan_into["init_scan"]["counts_err"] ** 2]
+    for scan in scan_into["merged_scans"]:
+        pos_all.append(scan[scan_motor])
+        val_all.append(scan["counts"])
+        err_all.append(scan["counts_err"] ** 2)
+    pos_all = np.concatenate(pos_all)
+    val_all = np.concatenate(val_all)
+    err_all = np.concatenate(err_all)
+
+    sort_index = np.argsort(pos_all)
+    pos_all = pos_all[sort_index]
+    val_all = val_all[sort_index]
+    err_all = err_all[sort_index]
+
+    pos_tmp = [pos_all[0]]
+    val_tmp = [val_all[:1]]
+    err_tmp = [err_all[:1]]
+    num_tmp = [1]
+    for pos, val, err in zip(pos_all[1:], val_all[1:], err_all[1:]):
+        if pos - pos_tmp[-1] < MOTOR_POS_PRECISION:
+            # the repeated motor position
+            val_tmp[-1] += val
+            err_tmp[-1] += err
+            num_tmp[-1] += 1
+        else:
+            # a new motor position
+            pos_tmp.append(pos)
+            val_tmp.append(val[None, :])
+            err_tmp.append(err[None, :])
+            num_tmp.append(1)
+    pos_tmp = np.array(pos_tmp)
+    val_tmp = np.concatenate(val_tmp)
+    err_tmp = np.concatenate(err_tmp)
+    num_tmp = np.array(num_tmp)
+
+    scan_into[scan_motor] = pos_tmp
+    scan_into["counts"] = val_tmp / num_tmp[:, None, None]
+    scan_into["counts_err"] = np.sqrt(err_tmp) / num_tmp[:, None, None]
+
+    scan_from["export"] = False
 
     fname1 = os.path.basename(scan_into["original_filename"])
     fname2 = os.path.basename(scan_from["original_filename"])
@@ -101,11 +218,18 @@ def merge_scans(scan_into, scan_from):
 
 
 def restore_scan(scan):
-    if "init_omega" in scan:
-        scan["omega"] = scan["init_omega"]
-        scan["counts"] = scan["init_counts"]
-        del scan["init_omega"]
-        del scan["init_counts"]
+    if "merged_scans" in scan:
+        for merged_scan in scan["merged_scans"]:
+            merged_scan["export"] = True
+
+    if "init_scan" in scan:
+        tmp = scan["init_scan"]
+        scan.clear()
+        scan.update(tmp)
+        # force scan export to True, otherwise in the sequence of incorrectly merged scans
+        # a <- b <- c the scan b will be restored with scan["export"] = False if restoring executed
+        # in the same order, i.e. restore a -> restore b
+        scan["export"] = True
 
 
 def fit_scan(scan, model_dict, fit_from=None, fit_to=None):
@@ -115,11 +239,17 @@ def fit_scan(scan, model_dict, fit_from=None, fit_to=None):
         fit_to = np.inf
 
     y_fit = scan["counts"]
+    y_err = scan["counts_err"]
     x_fit = scan[scan["scan_motor"]]
 
     # apply fitting range
     fit_ind = (fit_from <= x_fit) & (x_fit <= fit_to)
+    if not np.any(fit_ind):
+        print(f"No data in fit range for scan {scan['idx']}")
+        return
+
     y_fit = y_fit[fit_ind]
+    y_err = y_err[fit_ind]
     x_fit = x_fit[fit_ind]
 
     model = None
@@ -167,11 +297,13 @@ def fit_scan(scan, model_dict, fit_from=None, fit_to=None):
         else:
             model += _model
 
-    weights = [1 / np.sqrt(val) if val != 0 else 1 for val in y_fit]
-    scan["fit"] = model.fit(y_fit, x=x_fit, weights=weights)
+    scan["fit"] = model.fit(y_fit, x=x_fit, weights=1 / y_err)
 
 
 def get_area(scan, area_method, lorentz):
+    if "fit" not in scan:
+        return
+
     if area_method not in AREA_METHODS:
         raise ValueError(f"Unknown area method: {area_method}.")
 
@@ -180,12 +312,8 @@ def get_area(scan, area_method, lorentz):
         area_s = 0
         for name, param in scan["fit"].params.items():
             if "amplitude" in name:
-                if param.stderr is None:
-                    area_v = np.nan
-                    area_s = np.nan
-                else:
-                    area_v += param.value
-                    area_s += param.stderr
+                area_v += np.nan if param.value is None else param.value
+                area_s += np.nan if param.stderr is None else param.stderr
 
     else:  # area_method == "int_area"
         y_val = scan["counts"]

pyzebra/h5.py

@@ -1,10 +1,10 @@
 import h5py
 import numpy as np
+from lmfit.models import Gaussian2dModel, GaussianModel
 
-
-META_MATRIX = ("UB")
-META_CELL = ("cell")
-META_STR = ("name")
+META_MATRIX = ("UB", )
+META_CELL = ("cell", )
+META_STR = ("name", )
 
 def read_h5meta(filepath):
     """Open and parse content of a h5meta file.
@@ -68,70 +68,127 @@ def read_detector_data(filepath, cami_meta=None):
         ndarray: A 3D array of data, omega, gamma, nu.
     """
     with h5py.File(filepath, "r") as h5f:
-        data = h5f["/entry1/area_detector2/data"][:]
+        counts = h5f["/entry1/area_detector2/data"][:].astype(np.float64)
 
-        # reshape data to a correct shape (2006 issue)
-        n, cols, rows = data.shape
-        data = data.reshape(n, rows, cols)
+        n, cols, rows = counts.shape
+        if "/entry1/experiment_identifier" in h5f:  # old format
+            # reshape images (counts) to a correct shape (2006 issue)
+            counts = counts.reshape(n, rows, cols)
+        else:
+            counts = counts.swapaxes(1, 2)
 
-        det_data = {"data": data}
+        scan = {"counts": counts, "counts_err": np.sqrt(np.maximum(counts, 1))}
+        scan["original_filename"] = filepath
+        scan["export"] = True
 
         if "/entry1/zebra_mode" in h5f:
-            det_data["zebra_mode"] = h5f["/entry1/zebra_mode"][0].decode()
+            scan["zebra_mode"] = h5f["/entry1/zebra_mode"][0].decode()
         else:
-            det_data["zebra_mode"] = "nb"
+            scan["zebra_mode"] = "nb"
 
         # overwrite zebra_mode from cami
         if cami_meta is not None:
             if "zebra_mode" in cami_meta:
-                det_data["zebra_mode"] = cami_meta["zebra_mode"][0]
+                scan["zebra_mode"] = cami_meta["zebra_mode"][0]
 
-        # om, sometimes ph
-        if det_data["zebra_mode"] == "nb":
-            det_data["omega"] = h5f["/entry1/area_detector2/rotation_angle"][:]
-        else:  # bi
-            det_data["omega"] = h5f["/entry1/sample/rotation_angle"][:]
+        if "/entry1/control/Monitor" in h5f:
+            scan["monitor"] = h5f["/entry1/control/Monitor"][0]
+        else:  # old path
+            scan["monitor"] = h5f["/entry1/control/data"][0]
 
-        det_data["gamma"] = h5f["/entry1/ZEBRA/area_detector2/polar_angle"][:]  # gammad
-        det_data["nu"] = h5f["/entry1/ZEBRA/area_detector2/tilt_angle"][:]  # nud
-        det_data["ddist"] = h5f["/entry1/ZEBRA/area_detector2/distance"][:]
-        det_data["wave"] = h5f["/entry1/ZEBRA/monochromator/wavelength"][:]
-        det_data["chi"] = h5f["/entry1/sample/chi"][:]  # ch
-        det_data["phi"] = h5f["/entry1/sample/phi"][:]  # ph
-        det_data["ub"] = h5f["/entry1/sample/UB"][:].reshape(3, 3)
-        det_data["name"] = h5f["/entry1/sample/name"][0].decode()
-        det_data["cell"] = h5f["/entry1/sample/cell"][:]
+        scan["idx"] = 1
 
-        for var in ("omega", "gamma", "nu", "chi", "phi"):
-            if abs(det_data[var][0] - det_data[var][-1]) > 0.1:
-                det_data["scan_motor"] = var
-                break
+        if "/entry1/sample/rotation_angle" in h5f:
+            scan["omega"] = h5f["/entry1/sample/rotation_angle"][:]
         else:
-            raise ValueError("No angles that vary")
+            scan["omega"] = h5f["/entry1/area_detector2/rotation_angle"][:]
+        if len(scan["omega"]) == 1:
+            scan["omega"] = np.ones(n) * scan["omega"]
+
+        scan["gamma"] = h5f["/entry1/ZEBRA/area_detector2/polar_angle"][:]
+        scan["twotheta"] = h5f["/entry1/ZEBRA/area_detector2/polar_angle"][:]
+        if len(scan["gamma"]) == 1:
+            scan["gamma"] = np.ones(n) * scan["gamma"]
+            scan["twotheta"] = np.ones(n) * scan["twotheta"]
+        scan["nu"] = h5f["/entry1/ZEBRA/area_detector2/tilt_angle"][:1]
+        scan["ddist"] = h5f["/entry1/ZEBRA/area_detector2/distance"][:1]
+        scan["wave"] = h5f["/entry1/ZEBRA/monochromator/wavelength"][:1]
+        scan["chi"] = h5f["/entry1/sample/chi"][:]
+        if len(scan["chi"]) == 1:
+            scan["chi"] = np.ones(n) * scan["chi"]
+        scan["phi"] = h5f["/entry1/sample/phi"][:]
+        if len(scan["phi"]) == 1:
+            scan["phi"] = np.ones(n) * scan["phi"]
+        scan["ub"] = h5f["/entry1/sample/UB"][:].reshape(3, 3)
+        scan["name"] = h5f["/entry1/sample/name"][0].decode()
+        scan["cell"] = h5f["/entry1/sample/cell"][:]
+
+        if n == 1:
+            # a default motor for a single frame file
+            scan["scan_motor"] = "omega"
+        else:
+            for var in ("omega", "gamma", "nu", "chi", "phi"):
+                if abs(scan[var][0] - scan[var][-1]) > 0.1:
+                    scan["scan_motor"] = var
+                    break
+            else:
+                raise ValueError("No angles that vary")
+
+        scan["scan_motors"] = [scan["scan_motor"], ]
 
         # optional parameters
         if "/entry1/sample/magnetic_field" in h5f:
-            det_data["mf"] = h5f["/entry1/sample/magnetic_field"][:]
+            scan["mf"] = h5f["/entry1/sample/magnetic_field"][:]
 
         if "/entry1/sample/temperature" in h5f:
-            det_data["temp"] = h5f["/entry1/sample/temperature"][:]
+            scan["temp"] = h5f["/entry1/sample/temperature"][:]
+        elif "/entry1/sample/Ts/value" in h5f:
+            scan["temp"] = h5f["/entry1/sample/Ts/value"][:]
 
         # overwrite metadata from .cami
         if cami_meta is not None:
             if "crystal" in cami_meta:
                 cami_meta_crystal = cami_meta["crystal"]
                 if "name" in cami_meta_crystal:
-                    det_data["name"] = cami_meta_crystal["name"]
+                    scan["name"] = cami_meta_crystal["name"]
                 if "UB" in cami_meta_crystal:
-                    det_data["ub"] = cami_meta_crystal["UB"]
+                    scan["ub"] = cami_meta_crystal["UB"]
                 if "cell" in cami_meta_crystal:
-                    det_data["cell"] = cami_meta_crystal["cell"]
+                    scan["cell"] = cami_meta_crystal["cell"]
                 if "lambda" in cami_meta_crystal:
-                    det_data["wave"] = cami_meta_crystal["lambda"]
+                    scan["wave"] = cami_meta_crystal["lambda"]
 
             if "detector parameters" in cami_meta:
                 cami_meta_detparam = cami_meta["detector parameters"]
-                if "dist1" in cami_meta_detparam:
-                    det_data["ddist"] = cami_meta_detparam["dist1"]
+                if "dist2" in cami_meta_detparam:
+                    scan["ddist"] = cami_meta_detparam["dist2"]
 
-    return det_data
+    return scan
+
+
+def fit_event(scan, fr_from, fr_to, y_from, y_to, x_from, x_to):
+    data_roi = scan["counts"][fr_from:fr_to, y_from:y_to, x_from:x_to]
+
+    model = GaussianModel()
+    fr = np.arange(fr_from, fr_to)
+    counts_per_fr = np.sum(data_roi, axis=(1, 2))
+    params = model.guess(counts_per_fr, fr)
+    result = model.fit(counts_per_fr, x=fr, params=params)
+    frC = result.params["center"].value
+    intensity = result.params["height"].value
+
+    counts_std = counts_per_fr.std()
+    counts_mean = counts_per_fr.mean()
+    snr = 0 if counts_std == 0 else counts_mean / counts_std
+
+    model = Gaussian2dModel()
+    xs, ys = np.meshgrid(np.arange(x_from, x_to), np.arange(y_from, y_to))
+    xs = xs.flatten()
+    ys = ys.flatten()
+    counts = np.sum(data_roi, axis=0).flatten()
+    params = model.guess(counts, xs, ys)
+    result = model.fit(counts, x=xs, y=ys, params=params)
+    xC = result.params["centerx"].value
+    yC = result.params["centery"].value
+
+    scan["fit"] = {"frame": frC, "x_pos": xC, "y_pos": yC, "intensity": intensity, "snr": snr}

pyzebra/sxtal_refgen.py

@@ -0,0 +1,483 @@
+import io
+import os
+import subprocess
+import tempfile
+from math import ceil, floor
+
+import numpy as np
+
+SXTAL_REFGEN_PATH = "/afs/psi.ch/project/sinq/rhel7/bin/Sxtal_Refgen"
+
+_zebraBI_default_geom = """GEOM          2 Bissecting - HiCHI
+BLFR          z-up
+DIST_UNITS    mm
+ANGL_UNITS    deg
+DET_TYPE      Point  ipsd 1
+DIST_DET      488
+DIM_XY         1.0   1.0    1    1
+GAPS_DET       0  0
+
+SETTING       1 0 0   0 1 0   0 0 1
+NUM_ANG  4
+ANG_LIMITS         Min      Max    Offset
+        Gamma     0.0    128.0     0.00
+        Omega     0.0     64.0     0.00
+        Chi      80.0    211.0     0.00
+        Phi       0.0    360.0     0.00
+
+DET_OFF      0       0      0
+"""
+
+_zebraNB_default_geom = """GEOM          3 Normal Beam
+BLFR          z-up
+DIST_UNITS    mm
+ANGL_UNITS    deg
+DET_TYPE      Point  ipsd 1
+DIST_DET      448
+DIM_XY         1.0   1.0    1    1
+GAPS_DET       0  0
+
+SETTING       1 0 0   0 1 0   0 0 1
+NUM_ANG  3
+ANG_LIMITS         Min      Max    Offset
+        Gamma      0.0     128.0     0.00
+        Omega   -180.0     180.0     0.00
+        Nu       -15.0      15.0     0.00
+
+DET_OFF      0       0      0
+"""
+
+_zebra_default_cfl = """TITLE mymaterial
+SPGR  P 63 2 2
+CELL  5.73 5.73 11.89 90 90 120
+
+WAVE 1.383
+
+UBMAT
+0.000000    0.000000    0.084104
+0.000000    0.174520   -0.000000
+0.201518    0.100759    0.000000
+
+INSTR  zebra.geom
+
+ORDER   1 2 3
+
+ANGOR   gamma
+
+HLIM    -25 25 -25 25 -25 25
+SRANG   0.0  0.7
+
+Mag_Structure
+lattiCE P 1
+kvect        0.0 0.0 0.0
+magcent
+symm  x,y,z
+msym  u,v,w, 0.0
+End_Mag_Structure
+"""
+
+
+def get_zebraBI_default_geom_file():
+    return io.StringIO(_zebraBI_default_geom)
+
+
+def get_zebraNB_default_geom_file():
+    return io.StringIO(_zebraNB_default_geom)
+
+
+def get_zebra_default_cfl_file():
+    return io.StringIO(_zebra_default_cfl)
+
+
+def read_geom_file(fileobj):
+    ang_lims = dict()
+    for line in fileobj:
+        if "!" in line:  # remove comments that start with ! sign
+            line, _ = line.split(sep="!", maxsplit=1)
+
+        if line.startswith("GEOM"):
+            _, val = line.split(maxsplit=1)
+            if val.startswith("2"):
+                ang_lims["geom"] = "bi"
+            else:  # val.startswith("3")
+                ang_lims["geom"] = "nb"
+
+        elif line.startswith("ANG_LIMITS"):
+            # read angular limits
+            for line in fileobj:
+                if not line or line.isspace():
+                    break
+
+                ang, ang_min, ang_max, ang_offset = line.split()
+                ang_lims[ang.lower()] = [ang_min, ang_max, ang_offset]
+
+            if "2theta" in ang_lims:  # treat 2theta as gamma
+                ang_lims["gamma"] = ang_lims.pop("2theta")
+
+    return ang_lims
+
+
+def export_geom_file(path, ang_lims, template=None):
+    if ang_lims["geom"] == "bi":
+        template_file = get_zebraBI_default_geom_file()
+        n_ang = 4
+    else:  # ang_lims["geom"] == "nb"
+        template_file = get_zebraNB_default_geom_file()
+        n_ang = 3
+
+    if template is not None:
+        template_file = template
+
+    with open(path, "w") as out_file:
+        for line in template_file:
+            out_file.write(line)
+
+            if line.startswith("ANG_LIMITS"):
+                for _ in range(n_ang):
+                    next_line = next(template_file)
+                    ang, _, _, _ = next_line.split()
+
+                    if ang == "2theta":  # treat 2theta as gamma
+                        ang = "Gamma"
+                    vals = ang_lims[ang.lower()]
+
+                    out_file.write(f"{'':<8}{ang:<10}{vals[0]:<10}{vals[1]:<10}{vals[2]:<10}\n")
+
+
+def calc_ub_matrix(params):
+    with tempfile.TemporaryDirectory() as temp_dir:
+        cfl_file = os.path.join(temp_dir, "ub_matrix.cfl")
+
+        with open(cfl_file, "w") as fileobj:
+            for key, value in params.items():
+                fileobj.write(f"{key} {value}\n")
+
+        comp_proc = subprocess.run(
+            [SXTAL_REFGEN_PATH, cfl_file],
+            cwd=temp_dir,
+            check=True,
+            stdout=subprocess.PIPE,
+            stderr=subprocess.STDOUT,
+            text=True,
+        )
+        print(" ".join(comp_proc.args))
+        print(comp_proc.stdout)
+
+        sfa_file = os.path.join(temp_dir, "ub_matrix.sfa")
+        ub_matrix = []
+        with open(sfa_file, "r") as fileobj:
+            for line in fileobj:
+                if "BL_M" in line:  # next 3 lines contain the matrix
+                    for _ in range(3):
+                        next_line = next(fileobj)
+                        *vals, _ = next_line.split(maxsplit=3)
+                        ub_matrix.extend(vals)
+
+    return ub_matrix
+
+
+def read_cfl_file(fileobj):
+    params = {
+        "SPGR": None,
+        "CELL": None,
+        "WAVE": None,
+        "UBMAT": None,
+        "HLIM": None,
+        "SRANG": None,
+        "lattiCE": None,
+        "kvect": None,
+    }
+    param_names = tuple(params)
+
+    for line in fileobj:
+        line = line.strip()
+        if "!" in line:  # remove comments that start with ! sign
+            line, _ = line.split(sep="!", maxsplit=1)
+
+        if line.startswith(param_names):
+            if line.startswith("UBMAT"):  # next 3 lines contain the matrix
+                param, val = "UBMAT", []
+                for _ in range(3):
+                    next_line = next(fileobj).strip()
+                    val.extend(next_line.split(maxsplit=2))
+            else:
+                param, val = line.split(maxsplit=1)
+
+            params[param] = val
+
+    return params
+
+
+def read_cif_file(fileobj):
+    params = {"SPGR": None, "CELL": None, "ATOM": []}
+
+    cell_params = {
+        "_cell_length_a": None,
+        "_cell_length_b": None,
+        "_cell_length_c": None,
+        "_cell_angle_alpha": None,
+        "_cell_angle_beta": None,
+        "_cell_angle_gamma": None,
+    }
+    cell_param_names = tuple(cell_params)
+
+    atom_param_pos = {
+        "_atom_site_label": 0,
+        "_atom_site_type_symbol": None,
+        "_atom_site_fract_x": None,
+        "_atom_site_fract_y": None,
+        "_atom_site_fract_z": None,
+        "_atom_site_U_iso_or_equiv": None,
+        "_atom_site_occupancy": None,
+    }
+    atom_param_names = tuple(atom_param_pos)
+
+    for line in fileobj:
+        line = line.strip()
+        if line.startswith("_space_group_name_H-M_alt"):
+            _, val = line.split(maxsplit=1)
+            params["SPGR"] = val.strip("'")
+
+        elif line.startswith(cell_param_names):
+            param, val = line.split(maxsplit=1)
+            cell_params[param] = val
+
+        elif line.startswith("_atom_site_label"):  # assume this is the start of atom data
+            for ind, line in enumerate(fileobj, start=1):
+                line = line.strip()
+
+                # read fields
+                if line.startswith("_atom_site"):
+                    if line.startswith(atom_param_names):
+                        atom_param_pos[line] = ind
+                    continue
+
+                # read data till an empty line
+                if not line:
+                    break
+                vals = line.split()
+                params["ATOM"].append(" ".join([vals[ind] for ind in atom_param_pos.values()]))
+
+    if None not in cell_params.values():
+        params["CELL"] = " ".join(cell_params.values())
+
+    return params
+
+
+def export_cfl_file(path, params, template=None):
+    param_names = tuple(params)
+    if template is None:
+        template_file = get_zebra_default_cfl_file()
+    else:
+        template_file = template
+
+    atom_done = False
+    with open(path, "w") as out_file:
+        for line in template_file:
+            if line.startswith(param_names):
+                if line.startswith("UBMAT"):  # only UBMAT values are not on the same line
+                    out_file.write(line)
+                    for i in range(3):
+                        next(template_file)
+                        out_file.write(" ".join(params["UBMAT"][3 * i : 3 * (i + 1)]) + "\n")
+
+                elif line.startswith("ATOM"):
+                    if "ATOM" in params:
+                        # replace all ATOM with values in params
+                        while line.startswith("ATOM"):
+                            line = next(template_file)
+                        for atom_line in params["ATOM"]:
+                            out_file.write(f"ATOM {atom_line}\n")
+                        atom_done = True
+
+                else:
+                    param, _ = line.split(maxsplit=1)
+                    out_file.write(f"{param} {params[param]}\n")
+
+            elif line.startswith("INSTR"):
+                # replace it with a default name
+                out_file.write("INSTR  zebra.geom\n")
+
+            else:
+                out_file.write(line)
+
+        # append ATOM data if it's present and a template did not contain it
+        if "ATOM" in params and not atom_done:
+            out_file.write("\n")
+            for atom_line in params["ATOM"]:
+                out_file.write(f"ATOM {atom_line}\n")
+
+
+def sort_hkl_file_bi(file_in, file_out, priority, chunks):
+    with open(file_in) as fileobj:
+        file_in_data = fileobj.readlines()
+
+    data = np.genfromtxt(file_in, skip_header=3)
+    stt = data[:, 4]
+    omega = data[:, 5]
+    chi = data[:, 6]
+    phi = data[:, 7]
+
+    lines = file_in_data[3:]
+    lines_update = []
+
+    angles = {"2theta": stt, "omega": omega, "chi": chi, "phi": phi}
+
+    # Reverse flag
+    to_reverse = False
+    to_reverse_p2 = False
+    to_reverse_p3 = False
+
+    # Get indices within first priority
+    ang_p1 = angles[priority[0]]
+    begin_p1 = floor(min(ang_p1))
+    end_p1 = ceil(max(ang_p1))
+    delta_p1 = chunks[0]
+    for p1 in range(begin_p1, end_p1, delta_p1):
+        ind_p1 = [j for j, x in enumerate(ang_p1) if p1 <= x and x < p1 + delta_p1]
+
+        stt_new = [stt[x] for x in ind_p1]
+        omega_new = [omega[x] for x in ind_p1]
+        chi_new = [chi[x] for x in ind_p1]
+        phi_new = [phi[x] for x in ind_p1]
+        lines_new = [lines[x] for x in ind_p1]
+
+        angles_p2 = {"stt": stt_new, "omega": omega_new, "chi": chi_new, "phi": phi_new}
+
+        # Get indices for second priority
+        ang_p2 = angles_p2[priority[1]]
+        if len(ang_p2) > 0 and to_reverse_p2:
+            begin_p2 = ceil(max(ang_p2))
+            end_p2 = floor(min(ang_p2))
+            delta_p2 = -chunks[1]
+        elif len(ang_p2) > 0 and not to_reverse_p2:
+            end_p2 = ceil(max(ang_p2))
+            begin_p2 = floor(min(ang_p2))
+            delta_p2 = chunks[1]
+        else:
+            end_p2 = 0
+            begin_p2 = 0
+            delta_p2 = 1
+
+        to_reverse_p2 = not to_reverse_p2
+
+        for p2 in range(begin_p2, end_p2, delta_p2):
+            min_p2 = min([p2, p2 + delta_p2])
+            max_p2 = max([p2, p2 + delta_p2])
+            ind_p2 = [j for j, x in enumerate(ang_p2) if min_p2 <= x and x < max_p2]
+
+            stt_new2 = [stt_new[x] for x in ind_p2]
+            omega_new2 = [omega_new[x] for x in ind_p2]
+            chi_new2 = [chi_new[x] for x in ind_p2]
+            phi_new2 = [phi_new[x] for x in ind_p2]
+            lines_new2 = [lines_new[x] for x in ind_p2]
+
+            angles_p3 = {"stt": stt_new2, "omega": omega_new2, "chi": chi_new2, "phi": phi_new2}
+
+            # Get indices for third priority
+            ang_p3 = angles_p3[priority[2]]
+            if len(ang_p3) > 0 and to_reverse_p3:
+                begin_p3 = ceil(max(ang_p3)) + chunks[2]
+                end_p3 = floor(min(ang_p3)) - chunks[2]
+                delta_p3 = -chunks[2]
+            elif len(ang_p3) > 0 and not to_reverse_p3:
+                end_p3 = ceil(max(ang_p3)) + chunks[2]
+                begin_p3 = floor(min(ang_p3)) - chunks[2]
+                delta_p3 = chunks[2]
+            else:
+                end_p3 = 0
+                begin_p3 = 0
+                delta_p3 = 1
+
+            to_reverse_p3 = not to_reverse_p3
+
+            for p3 in range(begin_p3, end_p3, delta_p3):
+                min_p3 = min([p3, p3 + delta_p3])
+                max_p3 = max([p3, p3 + delta_p3])
+                ind_p3 = [j for j, x in enumerate(ang_p3) if min_p3 <= x and x < max_p3]
+
+                angle_new3 = [angles_p3[priority[3]][x] for x in ind_p3]
+
+                ind_final = [x for _, x in sorted(zip(angle_new3, ind_p3), reverse=to_reverse)]
+
+                to_reverse = not to_reverse
+
+                for i in ind_final:
+                    lines_update.append(lines_new2[i])
+
+    with open(file_out, "w") as fileobj:
+        for _ in range(3):
+            fileobj.write(file_in_data.pop(0))
+
+        fileobj.writelines(lines_update)
+
+
+def sort_hkl_file_nb(file_in, file_out, priority, chunks):
+    with open(file_in) as fileobj:
+        file_in_data = fileobj.readlines()
+
+    data = np.genfromtxt(file_in, skip_header=3)
+    gamma = data[:, 4]
+    omega = data[:, 5]
+    nu = data[:, 6]
+
+    lines = file_in_data[3:]
+    lines_update = []
+
+    angles = {"gamma": gamma, "omega": omega, "nu": nu}
+
+    to_reverse = False
+    to_reverse_p2 = False
+
+    # Get indices within first priority
+    ang_p1 = angles[priority[0]]
+    begin_p1 = floor(min(ang_p1))
+    end_p1 = ceil(max(ang_p1))
+    delta_p1 = chunks[0]
+    for p1 in range(begin_p1, end_p1, delta_p1):
+        ind_p1 = [j for j, x in enumerate(ang_p1) if p1 <= x and x < p1 + delta_p1]
+
+        # Get angles from within nu range
+        lines_new = [lines[x] for x in ind_p1]
+        gamma_new = [gamma[x] for x in ind_p1]
+        omega_new = [omega[x] for x in ind_p1]
+        nu_new = [nu[x] for x in ind_p1]
+
+        angles_p2 = {"gamma": gamma_new, "omega": omega_new, "nu": nu_new}
+
+        # Get indices for second priority
+        ang_p2 = angles_p2[priority[1]]
+        if len(gamma_new) > 0 and to_reverse_p2:
+            begin_p2 = ceil(max(ang_p2))
+            end_p2 = floor(min(ang_p2))
+            delta_p2 = -chunks[1]
+        elif len(gamma_new) > 0 and not to_reverse_p2:
+            end_p2 = ceil(max(ang_p2))
+            begin_p2 = floor(min(ang_p2))
+            delta_p2 = chunks[1]
+        else:
+            end_p2 = 0
+            begin_p2 = 0
+            delta_p2 = 1
+
+        to_reverse_p2 = not to_reverse_p2
+
+        for p2 in range(begin_p2, end_p2, delta_p2):
+            min_p2 = min([p2, p2 + delta_p2])
+            max_p2 = max([p2, p2 + delta_p2])
+            ind_p2 = [j for j, x in enumerate(ang_p2) if min_p2 <= x and x < max_p2]
+
+            angle_new2 = [angles_p2[priority[2]][x] for x in ind_p2]
+
+            ind_final = [x for _, x in sorted(zip(angle_new2, ind_p2), reverse=to_reverse)]
+
+            to_reverse = not to_reverse
+
+            for i in ind_final:
+                lines_update.append(lines_new[i])
+
+    with open(file_out, "w") as fileobj:
+        for _ in range(3):
+            fileobj.write(file_in_data.pop(0))
+
+        fileobj.writelines(lines_update)

pyzebra/utils.py

@@ -0,0 +1,17 @@
+import os
+
+SINQ_PATH = "/afs/psi.ch/project/sinqdata"
+ZEBRA_PROPOSALS_PATH = os.path.join(SINQ_PATH, "{year}/zebra/{proposal}")
+
+
+def find_proposal_path(proposal):
+    for entry in os.scandir(SINQ_PATH):
+        if entry.is_dir() and len(entry.name) == 4 and entry.name.isdigit():
+            proposal_path = ZEBRA_PROPOSALS_PATH.format(year=entry.name, proposal=proposal)
+            if os.path.isdir(proposal_path):
+                # found it
+                break
+    else:
+        raise ValueError(f"Can not find data for proposal '{proposal}'.")
+
+    return proposal_path

pyzebra/xtal.py

@@ -372,6 +372,27 @@ def ang2hkl(wave, ddist, gammad, om, ch, ph, nud, ub, x, y):
     return hkl
 
 
+def ang2hkl_1d(wave, ddist, ga, om, ch, ph, nu, ub):
+    """Calculate hkl-indices of a reflection from its position (angles) at the 1d-detector
+    """
+    z1 = z1frmd(wave, ga, om, ch, ph, nu)
+    ubinv = np.linalg.inv(ub)
+    hkl = ubinv @ z1
+
+    return hkl
+
+
+def ang_proc(wave, ddist, gammad, om, ch, ph, nud, x, y):
+    """Utility function to calculate ch, ph, ga, om
+    """
+    ga, nu = det2pol(ddist, gammad, nud, x, y)
+    z1 = z1frmd(wave, ga, om, ch, ph, nu)
+    ch2, ph2 = eqchph(z1)
+    ch, ph, ga, om = fixdnu(wave, z1, ch2, ph2, nu)
+
+    return ch, ph, ga, om
+
+
 def gauss(x, *p):
     """Defines Gaussian function