Updating for version 0.6.0

Fix #41

.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 +1,8 @@
 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 *
 
-ZEBRA_PROPOSALS_PATHS = [
-    f"/afs/psi.ch/project/sinqdata/{year}/zebra/" for year in (2016, 2017, 2018, 2020, 2021)
-]
-
-__version__ = "0.5.0"
+__version__ = "0.6.0"

pyzebra/app/app.py

@@ -2,18 +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
 
-
 doc = curdoc()
 
 sys.stdout = StringIO()
@@ -26,14 +27,36 @@ bokeh_logger = logging.getLogger("bokeh")
 bokeh_logger.addHandler(bokeh_handler)
 bokeh_log_textareainput = TextAreaInput(title="server output:", height=150)
 
+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():
+    try:
+        proposal_path = pyzebra.find_proposal_path(proposal_textinput.value)
+    except ValueError as e:
+        print(e)
+        return
+
+    proposal_textinput.name = proposal_path
+    apply_button.disabled = True
+
+apply_button = Button(label="Apply", button_type="primary")
+apply_button.on_click(apply_button_callback)
+
 # Final layout
 doc.add_root(
     column(
         Tabs(
             tabs=[
+                Panel(child=column(proposal_textinput, apply_button), title="user config"),
                 panel_hdf_viewer.create(),
                 panel_hdf_anatric.create(),
                 panel_ccl_integrate.create(),
+                panel_ccl_compare.create(),
                 panel_param_study.create(),
                 panel_hdf_param_study.create(),
                 panel_spind.create(),

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()
+    det_data1 = []
+    det_data2 = []
+    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():
+        # det_data2 should have the same metadata to det_data1
+        scan_list = [s["idx"] for s in det_data1]
+        hkl = [f'{s["h"]} {s["k"]} {s["l"]}' for s in det_data1]
+        export = [s["export"] for s in det_data1]
+
+        twotheta = [np.median(s["twotheta"]) if "twotheta" in s else None for s in det_data1]
+        gamma = [np.median(s["gamma"]) if "gamma" in s else None for s in det_data1]
+        omega = [np.median(s["omega"]) if "omega" in s else None for s in det_data1]
+        chi = [np.median(s["chi"]) if "chi" in s else None for s in det_data1]
+        phi = [np.median(s["phi"]) if "phi" in s else None for s in det_data1]
+        nu = [np.median(s["nu"]) if "nu" in s else None for s in det_data1]
+
+        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 det_data1, det_data2
+        det_data1 = new_data1
+        det_data2 = 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 det_data1, det_data2
+        det_data1 = new_data1
+        det_data2 = 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 det_data1 and det_data2:
+            pyzebra.normalize_dataset(det_data1, new)
+            pyzebra.normalize_dataset(det_data2, 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_data1]
+        export = [scan["export"] for scan in det_data1]
+        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(det_data1, det_data2, 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 det_data1[ind], det_data2[ind]
+
+    merge_from_select = Select(title="scan:", width=145)
+
+    def merge_button_callback():
+        scan_into1, scan_into2 = _get_selected_scan()
+        scan_from1 = det_data1[int(merge_from_select.value)]
+        scan_from2 = det_data2[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 [*det_data1, *det_data2]:
+            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(det_data1, det_data2):
+                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 = {}
+    det_data = []
     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]
+        export = [s["export"] for s in det_data]
+
+        twotheta = [np.median(s["twotheta"]) if "twotheta" in s else None for s in det_data]
+        gamma = [np.median(s["gamma"]) if "gamma" in s else None for s in det_data]
+        omega = [np.median(s["omega"]) if "omega" in s else None for s in det_data]
+        chi = [np.median(s["chi"]) if "chi" in s else None for s in det_data]
+        phi = [np.median(s["phi"]) if "phi" in s else None for s in det_data]
+        nu = [np.median(s["nu"]) if "nu" in s else None for s in det_data]
+
         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]
@@ -126,99 +134,133 @@ def create():
 
     def file_open_button_callback():
         nonlocal det_data
-        det_data = []
+        new_data = []
         for f_path in file_select.value:
             with open(f_path) as file:
-                base, ext = os.path.splitext(os.path.basename(f_path))
-                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])
+                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}")
+                    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:
+            det_data = 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():
+        file_data = []
         for f_path in file_select.value:
             with open(f_path) as file:
-                _, ext = os.path.splitext(f_path)
-                append_data = pyzebra.parse_1D(file, ext)
+                f_name = os.path.basename(f_path)
+                _, ext = os.path.splitext(f_name)
+                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(det_data, 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):
+    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):
+        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:
+            det_data = 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(det_data, 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())
+            _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)
+        export = [scan["export"] for scan in det_data]
+        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"))
 
@@ -321,30 +363,52 @@ 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(det_data, 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]]
 
@@ -359,14 +423,16 @@ def create():
             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 det_data:
+            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 det_data:
+                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_hdf_param_study.py

@@ -1,6 +1,5 @@
 import base64
 import io
-import math
 import os
 
 import numpy as np
@@ -10,6 +9,7 @@ from bokeh.models import (
     BasicTicker,
     BoxZoomTool,
     Button,
+    CellEditor,
     CheckboxGroup,
     ColumnDataSource,
     DataRange1d,
@@ -33,12 +33,10 @@ from bokeh.models import (
     Spinner,
     TableColumn,
     Tabs,
-    TextInput,
     Title,
     WheelZoomTool,
 )
 from bokeh.palettes import Cividis256, Greys256, Plasma256  # pylint: disable=E0611
-from scipy.optimize import curve_fit
 
 import pyzebra
 
@@ -56,42 +54,51 @@ def create():
 
     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
+            file_list = cami_meta["filelist"]
+            file_select.options = [(entry, os.path.basename(entry)) for entry in file_list]
 
-    doc.add_periodic_callback(file_select_update_for_proposal, 5000)
+    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):
-        nonlocal cami_meta
-        cami_meta = {}
-        file_select_update_for_proposal()
+        file_select_update()
 
-    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 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]
+        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)
@@ -180,7 +187,16 @@ def create():
         else:  # zebra_mode == "bi"
             metadata_table_source.data.update(geom=["bisecting"])
 
-        update_image(0)
+        if "mf" in det_data:
+            metadata_table_source.data.update(mf=[det_data["mf"][0]])
+        else:
+            metadata_table_source.data.update(mf=[None])
+
+        if "temp" in det_data:
+            metadata_table_source.data.update(temp=[det_data["temp"][0]])
+        else:
+            metadata_table_source.data.update(temp=[None])
+
         update_overview_plot()
 
     def scan_table_source_callback(_attr, _old, _new):
@@ -193,7 +209,7 @@ def create():
     scan_table = DataTable(
         source=scan_table_source,
         columns=[
-            TableColumn(field="file", title="file", width=150),
+            TableColumn(field="file", title="file", editor=CellEditor(), width=150),
             TableColumn(
                 field="param",
                 title="param",
@@ -201,9 +217,15 @@ def create():
                 editor=NumberEditor(),
                 width=50,
             ),
-            TableColumn(field="frame", title="Frame", formatter=num_formatter, width=70),
-            TableColumn(field="x_pos", title="X", formatter=num_formatter, width=70),
-            TableColumn(field="y_pos", title="Y", formatter=num_formatter, width=70),
+            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,
@@ -229,23 +251,17 @@ def create():
     )
     param_select.on_change("value", param_select_callback)
 
-    def update_image(index=None):
-        if "mf" in det_data:
-            metadata_table_source.data.update(mf=[det_data["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]])
-        else:
-            metadata_table_source.data.update(temp=[None])
-
     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)
 
+        # 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])
 
@@ -381,7 +397,7 @@ def create():
     colormap.on_change("value", colormap_callback)
     colormap.value = "plasma"
 
-    PROJ_STEP = 0.1
+    PROJ_STEP = 1
 
     def proj_auto_checkbox_callback(state):
         if state:
@@ -429,68 +445,6 @@ def create():
     )
     proj_display_min_spinner.on_change("value", proj_display_min_spinner_callback)
 
-    def fit_event(scan):
-        p0 = [1.0, 0.0, 1.0]
-        maxfev = 100000
-
-        # wave = scan["wave"]
-        # ddist = scan["ddist"]
-        # cell = scan["cell"]
-
-        # gamma = scan["gamma"][0]
-        # omega = scan["omega"][0]
-        # nu = scan["nu"][0]
-        # chi = scan["chi"][0]
-        # phi = scan["phi"][0]
-
-        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 = scan["data"][fr0:frN, y0:yN, x0:xN]
-
-        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_step = var_C - var_F
-        # var_p = var_F + var_step * frStep
-
-        # if scan_motor == "gamma":
-        #     gamma = var_p
-        # elif scan_motor == "omega":
-        #     omega = var_p
-        # elif scan_motor == "nu":
-        #     nu = var_p
-        # elif scan_motor == "chi":
-        #     chi = var_p
-        # elif scan_motor == "phi":
-        #     phi = var_p
-
-        intensity = coeff[1] * abs(coeff[2] * var_step) * math.sqrt(2) * math.sqrt(np.pi)
-
-        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]
-
-        scan["fit"] = {"frame": frC, "x_pos": x_pos, "y_pos": y_pos, "intensity": intensity}
-
     metadata_table_source = ColumnDataSource(dict(geom=[""], temp=[None], mf=[None]))
     metadata_table = DataTable(
         source=metadata_table_source,
@@ -538,7 +492,15 @@ def create():
 
     def proc_all_button_callback():
         for scan in zebra_data:
-            fit_event(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()
 
@@ -555,7 +517,15 @@ def create():
     proc_all_button.on_click(proc_all_button_callback)
 
     def proc_button_callback():
-        fit_event(det_data)
+        pyzebra.fit_event(
+            det_data,
+            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()
 
@@ -598,7 +568,7 @@ def create():
 
     # Final layout
     import_layout = column(
-        proposal_textinput,
+        data_source,
         upload_div,
         upload_button,
         file_select,
@@ -610,14 +580,3 @@ def create():
     tab_layout = column(row(import_layout, scan_layout, plots))
 
     return Panel(child=tab_layout, title="hdf param study")
-
-
-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))

pyzebra/app/panel_hdf_viewer.py

@@ -1,6 +1,5 @@
 import base64
 import io
-import math
 import os
 
 import numpy as np
@@ -37,12 +36,11 @@ 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
 
@@ -59,46 +57,91 @@ def create():
 
     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()
+
+    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 _open_file(file, cami_meta):
+        nonlocal det_data
+        det_data = pyzebra.read_detector_data(file, 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()
+
+    def upload_hdf_button_callback(_attr, _old, new):
+        _open_file(io.BytesIO(base64.b64decode(new)), None)
+
+    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():
+        if not file_select.value:
+            return
+
+        if data_source.value == "proposal number":
+            _open_file(file_select.value[0], None)
+        else:
+            _open_file(file_select.value[0], cami_meta)
+
+    file_open_button = Button(label="Open New", width=100)
+    file_open_button.on_click(file_open_button_callback)
 
     def update_image(index=None):
         if index is None:
@@ -141,12 +184,45 @@ def create():
         omega = np.ones((IMAGE_H, IMAGE_W)) * det_data["omega"][index]
         image_source.data.update(gamma=[gamma], nu=[nu], omega=[omega])
 
+        # update detector center angles
+        det_c_x = int(IMAGE_W / 2)
+        det_c_y = int(IMAGE_H / 2)
+        if det_data["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 = det_data["wave"]
+            ddist = det_data["ddist"]
+            gammad = det_data["gamma"][index]
+            om = det_data["omega"][index]
+            ch = det_data["chi"][index]
+            ph = det_data["phi"][index]
+            nud = det_data["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():
         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)
 
+        # 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])
 
@@ -182,8 +258,27 @@ def create():
         # handle both, ascending and descending sequences
         scanning_motor_range.bounds = (min(var_start, var_end), max(var_start, var_end))
 
+        gamma = image_source.data["gamma"][0]
+        gamma_start = gamma[0, 0]
+        gamma_end = gamma[0, -1]
+
+        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))
+
+        nu = image_source.data["nu"][0]
+        nu_start = nu[0, 0]
+        nu_end = nu[-1, 0]
+
+        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))
+
     def file_select_callback(_attr, old, new):
-        nonlocal det_data
         if not new:
             # skip empty selections
             return
@@ -198,20 +293,7 @@ def create():
             # skip unnecessary update caused by selection drop
             return
 
-        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()
+        file_open_button_callback()
 
     file_select = MultiSelect(title="Available .hdf files:", width=210, height=250)
     file_select.on_change("value", file_select_callback)
@@ -372,12 +454,14 @@ 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 +475,9 @@ 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 +497,14 @@ 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 +518,7 @@ 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",
@@ -536,7 +626,7 @@ 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:
@@ -621,9 +711,32 @@ 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
+        pyzebra.fit_event(
+            det_data,
+            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"]
@@ -638,25 +751,12 @@ def create():
         scan_motor = det_data["scan_motor"]
         var_angle = det_data[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 = det_data["fit"]["snr"]
+        frC = det_data["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 +771,13 @@ 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 = det_data["fit"]["intensity"]
+        x_pos = det_data["fit"]["x_pos"]
+        y_pos = det_data["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 det_data["zebra_mode"] == "nb":
+            chi = None
+            phi = None
 
         events_data["wave"].append(wave)
         events_data["ddist"].append(ddist)
@@ -697,7 +795,7 @@ 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 +806,7 @@ 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 +824,23 @@ 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,
+        file_open_button,
+    )
+
     layout_image = column(gridplot([[proj_v, None], [plot, proj_h]], merge_tools=False))
     colormap_layout = column(
         colormap,
@@ -738,7 +852,7 @@ 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(
@@ -759,17 +873,6 @@ def create():
     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):
     h = np.empty(shape=(IMAGE_H, IMAGE_W))
     k = np.empty(shape=(IMAGE_H, IMAGE_W))
@@ -802,15 +905,10 @@ def calculate_hkl(det_data, index):
 
 
 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)
+    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,
@@ -42,7 +43,6 @@ from bokeh.models import (
     TableColumn,
     Tabs,
     TextAreaInput,
-    TextInput,
     WheelZoomTool,
     Whisker,
 )
@@ -87,39 +87,36 @@ def create():
     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]
+        export = [s["export"] for s in det_data]
+        if param_select.value == "user defined":
+            param = [None] * len(det_data)
+        else:
+            param = [scan[param_select.value] for scan in det_data]
+
         file_list = []
         for scan in det_data:
             file_list.append(os.path.basename(scan["original_filename"]))
@@ -127,96 +124,133 @@ def create():
         scan_table_source.data.update(
             file=file_list,
             scan=scan_list,
-            param=[None] * len(scan_list),
+            param=param,
             fit=[0] * len(scan_list),
-            export=[True] * 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"
+
+        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 = []
+        new_data = []
         for f_path in file_select.value:
             with open(f_path) as file:
-                base, ext = os.path.splitext(os.path.basename(f_path))
-                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])
+                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}")
+                    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:
+            det_data = 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():
+        file_data = []
         for f_path in file_select.value:
             with open(f_path) as file:
-                _, ext = os.path.splitext(f_path)
-                append_data = pyzebra.parse_1D(file, ext)
+                f_name = os.path.basename(f_path)
+                _, ext = os.path.splitext(f_name)
+                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(det_data, 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):
+    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):
+        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])
+                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:
+            det_data = 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(det_data, 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()
+            _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)
@@ -225,27 +259,32 @@ def create():
         if det_data:
             for scan in det_data:
                 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)
+        export = [scan["export"] for scan in det_data]
+        if param_select.value == "user defined":
+            param = [None] * len(det_data)
+        else:
+            param = [scan[param_select.value] for scan in det_data]
 
-    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:
@@ -325,13 +364,19 @@ def create():
     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"]):
             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
+                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(
@@ -349,7 +394,7 @@ 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"))
 
@@ -441,8 +486,11 @@ 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
@@ -479,46 +527,68 @@ 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(det_data, 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 = det_data[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]]
 
-    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:",
@@ -627,7 +697,7 @@ 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()),
@@ -648,8 +718,8 @@ 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 det_data:
+            if scan["export"]:
                 pyzebra.fit_scan(
                     scan, fit_params, fit_from=fit_from_spinner.value, fit_to=fit_to_spinner.value
                 )
@@ -659,7 +729,8 @@ def create():
                     lorentz=lorentz_checkbox.active,
                 )
 
-        _update_plot()
+        _update_single_scan_plot()
+        _update_overview()
         _update_table()
 
         for scan in det_data:
@@ -668,7 +739,6 @@ def create():
                 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)
@@ -684,7 +754,8 @@ def create():
             lorentz=lorentz_checkbox.active,
         )
 
-        _update_plot()
+        _update_single_scan_plot()
+        _update_overview()
         _update_table()
 
         for scan in det_data:
@@ -693,7 +764,6 @@ def create():
                 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)
@@ -710,12 +780,10 @@ def create():
             temp_file = temp_dir + "/temp"
             export_data = []
             param_data = []
-            for s, p, export in zip(
-                det_data, scan_table_source.data["param"], scan_table_source.data["export"]
-            ):
-                if export:
-                    export_data.append(s)
-                    param_data.append(p)
+            for scan, param in zip(det_data, scan_table_source.data["param"]):
+                if scan["export"]:
+                    export_data.append(scan)
+                    param_data.append(param)
 
             pyzebra.export_param_study(export_data, param_data, temp_file)
 
@@ -745,10 +813,13 @@ 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/ccl_io.py

@@ -144,6 +144,7 @@ def parse_1D(fileobj, data_type):
                 continue
 
             s = {}
+            s["export"] = True
 
             # first line
             for param, (param_name, param_type) in zip(line.split(), ccl_first_line):
@@ -169,6 +170,7 @@ def parse_1D(fileobj, data_type):
             while len(counts) < s["n_points"]:
                 counts.extend(map(float, next(fileobj).split()))
             s["counts"] = np.array(counts)
+            s["counts_err"] = np.sqrt(s["counts"])
 
             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"]))
@@ -181,6 +183,7 @@ def parse_1D(fileobj, data_type):
             metadata["gamma"] = metadata["twotheta"]
 
         s = defaultdict(list)
+        s["export"] = True
 
         match = re.search("Scanning Variables: (.*), Steps: (.*)", next(fileobj))
         motors = [motor.lower() for motor in match.group(1).split(", ")]
@@ -189,6 +192,7 @@ 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["n_points"] = int(match.group(1))
         s["monitor"] = float(match.group(3))
 
         col_names = list(map(str.lower, next(fileobj).split()))
@@ -204,6 +208,8 @@ def parse_1D(fileobj, data_type):
         for name in col_names:
             s[name] = np.array(s[name])
 
+        s["counts_err"] = np.sqrt(s["counts"])
+
         s["scan_motors"] = []
         for motor, step in zip(motors, steps):
             if step == 0:
@@ -211,27 +217,24 @@ def parse_1D(fileobj, data_type):
                 s[motor] = np.median(s[motor])
             else:
                 s["scan_motors"].append(motor)
-        s["scan_motor"] = s["scan_motors"][0]
 
         # "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"]
 
         # "tt" -> "temp"
-        elif "tt" in s["scan_motors"]:
+        if "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"]
 
         # "mf" stays "mf"
         # "phi" stays "phi"
 
+        s["scan_motor"] = s["scan_motors"][0]
+
         if "h" not in s:
             s["h"] = s["k"] = s["l"] = float("nan")
 
@@ -303,6 +306,63 @@ def export_1D(data, path, export_target, hkl_precision=2):
                 out_file.writelines(content)
 
 
+def export_ccl_compare(data1, data2, 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 = data1[0]["zebra_mode"]
+    exts = EXPORT_TARGETS[export_target]
+    file_content = {ext: [] for ext in exts}
+
+    for scan1, scan2 in zip(data1, data2):
+        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(data, param_data, path):
     file_content = []
     for scan, param in zip(data, param_data):

pyzebra/ccl_process.py

@@ -1,8 +1,7 @@
-import itertools
 import os
 
 import numpy as np
-from lmfit.models import GaussianModel, LinearModel, PseudoVoigtModel, VoigtModel
+from lmfit.models import Gaussian2dModel, GaussianModel, LinearModel, PseudoVoigtModel, VoigtModel
 from scipy.integrate import simpson, trapezoid
 
 from .ccl_io import CCL_ANGLES
@@ -30,6 +29,7 @@ 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
 
 
@@ -68,6 +68,12 @@ def _parameters_match(scan1, scan2):
 
 
 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):
@@ -80,20 +86,54 @@ 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] < 0.0005:
+            # 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)
+
+    scan_from["export"] = False
 
     fname1 = os.path.basename(scan_into["original_filename"])
     fname2 = os.path.basename(scan_from["original_filename"])
@@ -101,11 +141,18 @@ 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 +162,17 @@ 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 +220,14 @@ 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]
+    weights = [1 / y_err if y_err != 0 else 1 for y_err in y_err]
     scan["fit"] = model.fit(y_fit, x=x_fit, weights=weights)
 
 
 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 +236,8 @@ 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"]
@@ -208,3 +260,31 @@ def get_area(scan, area_method, lorentz):
         area_s = np.abs(area_s * corr_factor)
 
     scan["area"] = (area_v, area_s)
+
+
+def fit_event(scan, fr_from, fr_to, y_from, y_to, x_from, x_to):
+    data_roi = scan["data"][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/utils.py

@@ -0,0 +1,20 @@
+import os
+
+ZEBRA_PROPOSALS_PATHS = [
+    f"/afs/psi.ch/project/sinqdata/{year}/zebra/" for year in (2016, 2017, 2018, 2020, 2021)
+]
+
+def find_proposal_path(proposal):
+    proposal = proposal.strip()
+    if proposal:
+        for zebra_proposals_path in 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:
+        proposal_path = ""
+
+    return proposal_path

pyzebra/xtal.py

@@ -372,6 +372,17 @@ def ang2hkl(wave, ddist, gammad, om, ch, ph, nud, ub, x, y):
     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