Updating for version 0.6.5

Fix #47

.vscode/launch.json

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

conda-recipe/meta.yaml

@@ -15,19 +15,16 @@ build:
 
 requirements:
   build:
-    - python >=3.6
+    - python >=3.7
     - setuptools
   run:
-    - python >=3.6
+    - python >=3.7
     - numpy
     - scipy
-    - pandas
     - h5py
-    - bokeh =2.2
-    - matplotlib
+    - bokeh =2.4
     - numba
-    - lmfit
-    - uncertainties
+    - lmfit >=1.0.2
 
 
 about:

pyzebra/__init__.py

@@ -1,9 +1,8 @@
 from pyzebra.anatric import *
-from pyzebra.ccl_findpeaks import ccl_findpeaks
-from pyzebra.ccl_io import export_comm, load_1D, parse_1D
-from pyzebra.fit2 import fitccl
+from pyzebra.ccl_io import *
+from pyzebra.ccl_process import *
 from pyzebra.h5 import *
-from pyzebra.merge_function import add_dict, unified_merge
+from pyzebra.utils import *
 from pyzebra.xtal import *
 
-__version__ = "0.2.2"
+__version__ = "0.6.5"

pyzebra/anatric.py

@@ -7,6 +7,7 @@ DATA_FACTORY_IMPLEMENTATION = [
     "morph",
     "d10",
 ]
+
 REFLECTION_PRINTER_FORMATS = [
     "rafin",
     "rafinf",
@@ -20,11 +21,21 @@ REFLECTION_PRINTER_FORMATS = [
     "oksana",
 ]
 
+ANATRIC_PATH = "/afs/psi.ch/project/sinq/rhel7/bin/anatric"
 ALGORITHMS = ["adaptivemaxcog", "adaptivedynamic"]
 
 
-def anatric(config_file, anatric_path="/afs/psi.ch/project/sinq/rhel7/bin/anatric"):
-    subprocess.run([anatric_path, config_file], check=True)
+def anatric(config_file, anatric_path=ANATRIC_PATH, cwd=None):
+    comp_proc = subprocess.run(
+        [anatric_path, config_file],
+        stdout=subprocess.PIPE,
+        stderr=subprocess.STDOUT,
+        cwd=cwd,
+        check=True,
+        text=True,
+    )
+    print(" ".join(comp_proc.args))
+    print(comp_proc.stdout)
 
 
 class AnatricConfig:
@@ -51,10 +62,13 @@ class AnatricConfig:
     def save_as(self, filename):
         self._tree.write(filename)
 
+    def tostring(self):
+        return ET.tostring(self._tree.getroot(), encoding="unicode")
+
     def _get_attr(self, name, tag, attr):
         elem = self._tree.find(name).find(tag)
         if elem is None:
-            return None
+            return ""
         return elem.attrib[attr]
 
     def _set_attr(self, name, tag, attr, value):
@@ -217,7 +231,7 @@ class AnatricConfig:
         elem = self._tree.find("crystal").find("UB")
         if elem is not None:
             return elem.text
-        return None
+        return ""
 
     @crystal_UB.setter
     def crystal_UB(self, value):
@@ -236,12 +250,37 @@ class AnatricConfig:
 
     @property
     def dataFactory_dist1(self):
-        return self._tree.find("DataFactory").find("dist1").attrib["value"]
+        elem = self._tree.find("DataFactory").find("dist1")
+        if elem is not None:
+            return elem.attrib["value"]
+        return ""
 
     @dataFactory_dist1.setter
     def dataFactory_dist1(self, value):
         self._tree.find("DataFactory").find("dist1").attrib["value"] = value
 
+    @property
+    def dataFactory_dist2(self):
+        elem = self._tree.find("DataFactory").find("dist2")
+        if elem is not None:
+            return elem.attrib["value"]
+        return ""
+
+    @dataFactory_dist2.setter
+    def dataFactory_dist2(self, value):
+        self._tree.find("DataFactory").find("dist2").attrib["value"] = value
+
+    @property
+    def dataFactory_dist3(self):
+        elem = self._tree.find("DataFactory").find("dist3")
+        if elem is not None:
+            return elem.attrib["value"]
+        return ""
+
+    @dataFactory_dist3.setter
+    def dataFactory_dist3(self, value):
+        self._tree.find("DataFactory").find("dist3").attrib["value"] = value
+
     @property
     def reflectionPrinter_format(self):
         return self._tree.find("ReflectionPrinter").attrib["format"]
@@ -253,6 +292,14 @@ class AnatricConfig:
 
         self._tree.find("ReflectionPrinter").attrib["format"] = value
 
+    @property
+    def reflectionPrinter_file(self):
+        return self._tree.find("ReflectionPrinter").attrib["file"]
+
+    @reflectionPrinter_file.setter
+    def reflectionPrinter_file(self, value):
+        self._tree.find("ReflectionPrinter").attrib["file"] = value
+
     @property
     def algorithm(self):
         return self._tree.find("Algorithm").attrib["implementation"]
@@ -269,7 +316,7 @@ class AnatricConfig:
     def _get_alg_attr(self, alg, tag, attr):
         param_elem = self._alg_elems[alg].find(tag)
         if param_elem is None:
-            return None
+            return ""
         return param_elem.attrib[attr]
 
     def _set_alg_attr(self, alg, tag, attr, value):

pyzebra/app/app.py

@@ -2,15 +2,18 @@ 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()
 
@@ -24,15 +27,41 @@ bokeh_logger = logging.getLogger("bokeh")
 bokeh_logger.addHandler(bokeh_handler)
 bokeh_log_textareainput = TextAreaInput(title="server output:", height=150)
 
-# Final layout
-tab_hdf_viewer = panel_hdf_viewer.create()
-tab_hdf_anatric = panel_hdf_anatric.create()
-tab_ccl_integrate = panel_ccl_integrate.create()
-tab_param_study = panel_param_study.create()
+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=[tab_hdf_viewer, tab_hdf_anatric, tab_ccl_integrate, tab_param_study]),
+        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(),
+            ]
+        ),
         row(stdout_textareainput, bokeh_log_textareainput, sizing_mode="scale_both"),
     )
 )

pyzebra/app/cli.py

@@ -6,6 +6,7 @@ from bokeh.application.application import Application
 from bokeh.application.handlers import ScriptHandler
 from bokeh.server.server import Server
 
+from pyzebra.anatric import ANATRIC_PATH
 from pyzebra.app.handler import PyzebraHandler
 
 logging.basicConfig(format="%(asctime)s %(message)s", level=logging.INFO)
@@ -38,10 +39,11 @@ def main():
     )
 
     parser.add_argument(
-        "--anatric-path",
-        type=str,
-        default="/afs/psi.ch/project/sinq/rhel7/bin/anatric",
-        help="path to anatric executable",
+        "--anatric-path", type=str, default=ANATRIC_PATH, help="path to anatric executable",
+    )
+
+    parser.add_argument(
+        "--spind-path", type=str, default=None, help="path to spind scripts folder",
     )
 
     parser.add_argument(
@@ -55,7 +57,7 @@ def main():
 
     logger.info(app_path)
 
-    pyzebra_handler = PyzebraHandler(args.anatric_path)
+    pyzebra_handler = PyzebraHandler(args.anatric_path, args.spind_path)
     handler = ScriptHandler(filename=app_path, argv=args.args)
     server = Server(
         {"/": Application(pyzebra_handler, handler)},

pyzebra/app/handler.py

@@ -5,7 +5,7 @@ class PyzebraHandler(Handler):
     """Provides a mechanism for generic bokeh applications to build up new streamvis documents.
     """
 
-    def __init__(self, anatric_path):
+    def __init__(self, anatric_path, spind_path):
         """Initialize a pyzebra handler for bokeh applications.
 
         Args:
@@ -14,6 +14,7 @@ class PyzebraHandler(Handler):
         super().__init__()  # no-op
 
         self.anatric_path = anatric_path
+        self.spind_path = spind_path
 
     def modify_document(self, doc):
         """Modify an application document with pyzebra specific features.
@@ -26,5 +27,6 @@ class PyzebraHandler(Handler):
         """
         doc.title = "pyzebra"
         doc.anatric_path = self.anatric_path
+        doc.spind_path = self.spind_path
 
         return doc

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

@@ -3,15 +3,16 @@ import io
 import os
 import tempfile
 import types
-from copy import deepcopy
 
 import numpy as np
+from bokeh.io import curdoc
 from bokeh.layouts import column, row
 from bokeh.models import (
-    Asterisk,
     BasicTicker,
     Button,
+    CellEditor,
     CheckboxEditor,
+    CheckboxGroup,
     ColumnDataSource,
     CustomJS,
     DataRange1d,
@@ -20,14 +21,16 @@ from bokeh.models import (
     Dropdown,
     FileInput,
     Grid,
+    Legend,
     Line,
     LinearAxis,
+    MultiLine,
     MultiSelect,
     NumberEditor,
     Panel,
     PanTool,
     Plot,
-    RadioButtonGroup,
+    RadioGroup,
     ResetTool,
     Scatter,
     Select,
@@ -36,211 +39,310 @@ from bokeh.models import (
     Spinner,
     TableColumn,
     TextAreaInput,
-    TextInput,
-    Toggle,
     WheelZoomTool,
     Whisker,
 )
 
 import pyzebra
-from pyzebra.ccl_io import AREA_METHODS
+from pyzebra.ccl_io import EXPORT_TARGETS
+from pyzebra.ccl_process import AREA_METHODS
 
 
 javaScript = """
-setTimeout(function() {
-    const filename = 'output' + js_data.data['ext']
-    const blob = new Blob([js_data.data['cont']], {type: 'text/plain'})
-    const link = document.createElement('a');
-    document.body.appendChild(link);
-    const url = window.URL.createObjectURL(blob);
-    link.href = url;
-    link.download = filename;
-    link.click();
-    window.URL.revokeObjectURL(url);
-    document.body.removeChild(link);
-}, 500);
-"""
+let j = 0;
+for (let i = 0; i < js_data.data['fname'].length; i++) {
+    if (js_data.data['content'][i] === "") continue;
 
-PROPOSAL_PATH = "/afs/psi.ch/project/sinqdata/2020/zebra/"
+    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():
-    det_data = {}
+    doc = curdoc()
+    det_data = []
     fit_params = {}
-    peak_pos_textinput_lock = False
-    js_data = ColumnDataSource(data=dict(cont=[], ext=[]))
+    js_data = ColumnDataSource(data=dict(content=["", ""], fname=["", ""], ext=["", ""]))
 
-    def proposal_textinput_callback(_attr, _old, new):
-        ccl_path = os.path.join(PROPOSAL_PATH, new.strip())
-        ccl_file_list = []
-        for file in os.listdir(ccl_path):
-            if file.endswith(".ccl"):
-                ccl_file_list.append((os.path.join(ccl_path, file), file))
-        ccl_file_select.options = ccl_file_list
-        ccl_file_select.value = ccl_file_list[0][0]
+    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", ".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
 
-    proposal_textinput = TextInput(title="Enter proposal number:", default_size=145)
-    proposal_textinput.on_change("value", proposal_textinput_callback)
+    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():
-        scan_list = list(det_data["scan"].keys())
-        hkl = [
-            f'{int(m["h_index"])} {int(m["k_index"])} {int(m["l_index"])}'
-            if det_data["meta"]["indices"] == "hkl"
-            else f'{m["h_index"]} {m["k_index"]} {m["l_index"]}'
-            for m in det_data["scan"].values()
-        ]
+        scan_list = [s["idx"] for s in det_data]
+        hkl = [f'{s["h"]} {s["k"]} {s["l"]}' 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,
-            peaks=[0] * len(scan_list),
             fit=[0] * len(scan_list),
-            export=[True] * 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]
 
-    def ccl_file_select_callback(_attr, _old, new):
+        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
-        with open(new) as file:
-            _, ext = os.path.splitext(new)
-            det_data = pyzebra.parse_1D(file, ext)
+        new_data = []
+        for f_path in file_select.value:
+            with open(f_path) as file:
+                f_name = os.path.basename(f_path)
+                base, ext = os.path.splitext(f_name)
+                try:
+                    file_data = pyzebra.parse_1D(file, ext)
+                except:
+                    print(f"Error loading {f_name}")
+                    continue
 
-        _init_datatable()
+            pyzebra.normalize_dataset(file_data, monitor_spinner.value)
 
-    ccl_file_select = Select(title="Available .ccl files")
-    ccl_file_select.on_change("value", ccl_file_select_callback)
+            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)
 
-    def upload_button_callback(_attr, _old, new):
+        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:
+                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(file_data, monitor_spinner.value)
+            pyzebra.merge_datasets(det_data, file_data)
+
+        if file_data:
+            _init_datatable()
+
+    file_append_button = Button(label="Append", width=100, disabled=True)
+    file_append_button.on_click(file_append_button_callback)
+
+    def upload_button_callback(_attr, _old, _new):
         nonlocal det_data
-        with io.StringIO(base64.b64decode(new).decode()) as file:
-            _, ext = os.path.splitext(upload_button.filename)
-            det_data = pyzebra.parse_1D(file, ext)
+        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)
+                try:
+                    file_data = pyzebra.parse_1D(file, ext)
+                except:
+                    print(f"Error loading {f_name}")
+                    continue
 
-        _init_datatable()
+            pyzebra.normalize_dataset(file_data, monitor_spinner.value)
 
-    upload_button = FileInput(accept=".ccl")
-    upload_button.on_change("value", upload_button_callback)
+            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)
 
-    def append_upload_button_callback(_attr, _old, new):
-        nonlocal det_data
-        with io.StringIO(base64.b64decode(new).decode()) as file:
-            _, ext = os.path.splitext(append_upload_button.filename)
-            append_data = pyzebra.parse_1D(file, ext)
+        if new_data:
+            det_data = new_data
+            _init_datatable()
+            append_upload_button.disabled = False
 
-        pyzebra.unified_merge(det_data, append_data)
+    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)
+    # for on_change("value", ...) or on_change("filename", ...),
+    # see https://github.com/bokeh/bokeh/issues/11461
+    upload_button.on_change("filename", upload_button_callback)
 
-        _init_datatable()
+    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)
+                try:
+                    file_data = pyzebra.parse_1D(file, ext)
+                except:
+                    print(f"Error loading {f_name}")
+                    continue
 
-    append_upload_button = FileInput(accept=".ccl,.dat")
-    append_upload_button.on_change("value", append_upload_button_callback)
+            pyzebra.normalize_dataset(file_data, monitor_spinner.value)
+            pyzebra.merge_datasets(det_data, file_data)
+
+        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)
+    # 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()
+
+    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():
-        num_of_peaks = [len(scan.get("peak_indexes", [])) for scan in det_data["scan"].values()]
-        fit_ok = [(1 if "fit" in scan else 0) for scan in det_data["scan"].values()]
-        scan_table_source.data.update(peaks=num_of_peaks, fit=fit_ok)
+        fit_ok = [(1 if "fit" in scan else 0) for scan in det_data]
+        export = [scan["export"] for scan in det_data]
+        scan_table_source.data.update(fit=fit_ok, export=export)
 
-    def _update_plot(scan):
-        nonlocal peak_pos_textinput_lock
-        peak_pos_textinput_lock = True
+    def _update_plot():
+        scan = _get_selected_scan()
+        scan_motor = scan["scan_motor"]
 
-        y = scan["Counts"]
-        x = scan["om"]
+        y = scan["counts"]
+        y_err = scan["counts_err"]
+        x = scan[scan_motor]
 
-        plot_scatter_source.data.update(x=x, y=y, y_upper=y + np.sqrt(y), y_lower=y - np.sqrt(y))
-
-        num_of_peaks = len(scan.get("peak_indexes", []))
-        if num_of_peaks is not None and num_of_peaks > 0:
-            peak_indexes = scan["peak_indexes"]
-            if len(peak_indexes) == 1:
-                peak_pos_textinput.value = str(scan["om"][peak_indexes[0]])
-            else:
-                peak_pos_textinput.value = str([scan["om"][ind] for ind in peak_indexes])
-
-            plot_peak_source.data.update(x=scan["om"][peak_indexes], y=scan["peak_heights"])
-            plot_line_smooth_source.data.update(x=x, y=scan["smooth_peaks"])
-        else:
-            peak_pos_textinput.value = None
-            plot_peak_source.data.update(x=[], y=[])
-            plot_line_smooth_source.data.update(x=[], y=[])
-
-        peak_pos_textinput_lock = False
+        plot.axis[0].axis_label = scan_motor
+        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:
-            x = scan["fit"]["x_fit"]
-            plot_gauss_source.data.update(x=x, y=scan["fit"]["comps"]["gaussian"])
-            plot_bkg_source.data.update(x=x, y=scan["fit"]["comps"]["background"])
-            params = fit["result"].params
-            fit_output_textinput.value = (
-                "Gaussian: centre = %9.4f, sigma = %9.4f, area = %9.4f \n"
-                "background: slope = %9.4f, intercept = %9.4f \n"
-                "Int. area = %9.4f +/- %9.4f \n"
-                "fit area = %9.4f +/- %9.4f \n"
-                "ratio((fit-int)/fit) = %9.4f"
-                % (
-                    params["g_cen"].value,
-                    params["g_width"].value,
-                    params["g_amp"].value,
-                    params["slope"].value,
-                    params["intercept"].value,
-                    fit["int_area"].n,
-                    fit["int_area"].s,
-                    params["g_amp"].value,
-                    params["g_amp"].stderr,
-                    (params["g_amp"].value - fit["int_area"].n) / params["g_amp"].value,
-                )
-            )
-            numfit_min, numfit_max = fit["numfit"]
-            if numfit_min is None:
-                numfit_min_span.location = None
-            else:
-                numfit_min_span.location = x[numfit_min]
+            x_fit = np.linspace(x[0], x[-1], 100)
+            plot_fit_source.data.update(x=x_fit, y=fit.eval(x=x_fit))
 
-            if numfit_max is None:
-                numfit_max_span.location = None
-            else:
-                numfit_max_span.location = x[numfit_max]
+            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}_"])
+
+            plot_bkg_source.data.update(x=x_bkg, y=y_bkg)
+            plot_peak_source.data.update(xs=xs_peak, ys=ys_peak)
+
+            fit_output_textinput.value = fit.fit_report()
 
         else:
-            plot_gauss_source.data.update(x=[], y=[])
+            plot_fit_source.data.update(x=[], y=[])
             plot_bkg_source.data.update(x=[], y=[])
+            plot_peak_source.data.update(xs=[], ys=[])
             fit_output_textinput.value = ""
-            numfit_min_span.location = None
-            numfit_max_span.location = None
 
     # Main plot
-    plot = Plot(x_range=DataRange1d(), y_range=DataRange1d(), plot_height=400, plot_width=700)
+    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="Omega"), place="below")
+    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_scatter_source = ColumnDataSource(dict(x=[0], y=[0], y_upper=[0], y_lower=[0]))
-    plot.add_glyph(plot_scatter_source, Scatter(x="x", y="y", line_color="steelblue"))
+    plot_scatter = plot.add_glyph(
+        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"))
 
-    plot_line_smooth_source = ColumnDataSource(dict(x=[0], y=[0]))
-    plot.add_glyph(
-        plot_line_smooth_source, Line(x="x", y="y", line_color="steelblue", line_dash="dashed")
-    )
-
-    plot_gauss_source = ColumnDataSource(dict(x=[0], y=[0]))
-    plot.add_glyph(plot_gauss_source, Line(x="x", y="y", line_color="red", line_dash="dashed"))
+    plot_fit_source = ColumnDataSource(dict(x=[0], y=[0]))
+    plot_fit = plot.add_glyph(plot_fit_source, Line(x="x", y="y"))
 
     plot_bkg_source = ColumnDataSource(dict(x=[0], y=[0]))
-    plot.add_glyph(plot_bkg_source, Line(x="x", y="y", line_color="green", line_dash="dashed"))
+    plot_bkg = plot.add_glyph(
+        plot_bkg_source, Line(x="x", y="y", line_color="green", line_dash="dashed")
+    )
 
-    plot_peak_source = ColumnDataSource(dict(x=[], y=[]))
-    plot.add_glyph(plot_peak_source, Asterisk(x="x", y="y", size=10, line_color="red"))
+    plot_peak_source = ColumnDataSource(dict(xs=[[0]], ys=[[0]]))
+    plot_peak = plot.add_glyph(
+        plot_peak_source, MultiLine(xs="xs", ys="ys", line_color="red", line_dash="dashed")
+    )
 
-    numfit_min_span = Span(location=None, dimension="height", line_dash="dashed")
-    plot.add_layout(numfit_min_span)
+    fit_from_span = Span(location=None, dimension="height", line_dash="dashed")
+    plot.add_layout(fit_from_span)
 
-    numfit_max_span = Span(location=None, dimension="height", line_dash="dashed")
-    plot.add_layout(numfit_max_span)
+    fit_to_span = Span(location=None, dimension="height", line_dash="dashed")
+    plot.add_layout(fit_to_span)
+
+    plot.add_layout(
+        Legend(
+            items=[
+                ("data", [plot_scatter]),
+                ("best fit", [plot_fit]),
+                ("peak", [plot_peak]),
+                ("linear", [plot_bkg]),
+            ],
+            location="top_left",
+            click_policy="hide",
+        )
+    )
 
     plot.add_tools(PanTool(), WheelZoomTool(), ResetTool())
     plot.toolbar.logo = None
@@ -261,62 +363,95 @@ def create():
             # skip unnecessary update caused by selection drop
             return
 
-        _update_plot(det_data["scan"][scan_table_source.data["scan"][new[0]]])
+        _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 scan, export in zip(det_data, new["export"]):
+            scan["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_source = ColumnDataSource(dict(scan=[], hkl=[], peaks=[], fit=[], export=[]))
     scan_table = DataTable(
         source=scan_table_source,
         columns=[
-            TableColumn(field="scan", title="scan"),
-            TableColumn(field="hkl", title="hkl"),
-            TableColumn(field="peaks", title="Peaks"),
-            TableColumn(field="fit", title="Fit"),
-            TableColumn(field="export", title="Export", editor=CheckboxEditor()),
+            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=250,
-        index_position=None,
+        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():
-        selected_index = scan_table_source.selected.indices[0]
-        selected_scan_id = scan_table_source.data["scan"][selected_index]
-        return det_data["scan"][selected_scan_id]
+        return det_data[scan_table_source.selected.indices[0]]
 
-    def peak_pos_textinput_callback(_attr, _old, new):
-        if new is not None and not peak_pos_textinput_lock:
-            scan = _get_selected_scan()
+    merge_from_select = Select(title="scan:", width=145)
 
-            peak_ind = (np.abs(scan["om"] - float(new))).argmin()
-            scan["peak_indexes"] = np.array([peak_ind], dtype=np.int64)
-            scan["peak_heights"] = np.array([scan["smooth_peaks"][peak_ind]])
-            _update_table()
-            _update_plot(scan)
+    def merge_button_callback():
+        scan_into = _get_selected_scan()
+        scan_from = det_data[int(merge_from_select.value)]
 
-    peak_pos_textinput = TextInput(title="Peak position:", default_size=145)
-    peak_pos_textinput.on_change("value", peak_pos_textinput_callback)
+        if scan_into is scan_from:
+            print("WARNING: Selected scans for merging are identical")
+            return
 
-    peak_int_ratio_spinner = Spinner(
-        title="Peak intensity ratio:", value=0.8, step=0.01, low=0, high=1, default_size=145
-    )
-    peak_prominence_spinner = Spinner(title="Peak prominence:", value=50, low=0, default_size=145)
-    smooth_toggle = Toggle(label="Smooth curve", default_size=145)
-    window_size_spinner = Spinner(title="Window size:", value=7, step=2, low=1, default_size=145)
-    poly_order_spinner = Spinner(title="Poly order:", value=3, low=0, default_size=145)
+        pyzebra.merge_scans(scan_into, scan_from)
+        _update_table()
+        _update_plot()
 
-    integ_from = Spinner(title="Integrate from:", default_size=145)
-    integ_to = Spinner(title="to:", default_size=145)
+    merge_button = Button(label="Merge into current", width=145)
+    merge_button.on_click(merge_button_callback)
 
-    def fitparam_reset_button_callback():
-        ...
+    def restore_button_callback():
+        pyzebra.restore_scan(_get_selected_scan())
+        _update_table()
+        _update_plot()
 
-    fitparam_reset_button = Button(label="Reset to defaults", default_size=145, disabled=True)
-    fitparam_reset_button.on_click(fitparam_reset_button_callback)
+    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):
-        new_tag = str(fitparams_select.tags[0])  # bokeh requires (str, str) for MultiSelect options
+        # 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
@@ -324,14 +459,13 @@ def create():
     fitparams_add_dropdown = Dropdown(
         label="Add fit function",
         menu=[
-            ("Background", "background"),
-            ("Gauss", "gauss"),
+            ("Linear", "linear"),
+            ("Gaussian", "gaussian"),
             ("Voigt", "voigt"),
-            ("Pseudo Voigt", "pseudovoigt"),
-            ("Pseudo Voigt1", "pseudovoigt1"),
+            ("Pseudo Voigt", "pvoigt"),
+            # ("Pseudo Voigt1", "pseudovoigt1"),
         ],
-        default_size=145,
-        disabled=True,
+        width=145,
     )
     fitparams_add_dropdown.on_click(fitparams_add_dropdown_callback)
 
@@ -349,9 +483,9 @@ def create():
         if new:
             fitparams_table_source.data.update(fit_params[new[0]])
         else:
-            fitparams_table_source.data.update(dict(param=[], guess=[], vary=[], min=[], max=[]))
+            fitparams_table_source.data.update(dict(param=[], value=[], vary=[], min=[], max=[]))
 
-    fitparams_select = MultiSelect(options=[], height=120, default_size=145)
+    fitparams_select = MultiSelect(options=[], height=120, width=145)
     fitparams_select.tags = [0]
     fitparams_select.on_change("value", fitparams_select_callback)
 
@@ -366,36 +500,44 @@ def create():
 
             fitparams_select.value = []
 
-    fitparams_remove_button = Button(label="Remove fit function", default_size=145, disabled=True)
+    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 == "background":
-            params = ["slope", "offset"]
-        elif function == "gauss":
-            params = ["center", "sigma", "amplitude"]
+        if function == "linear":
+            params = ["slope", "intercept"]
+        elif function == "gaussian":
+            params = ["amplitude", "center", "sigma"]
         elif function == "voigt":
-            params = ["center", "sigma", "amplitude", "gamma"]
-        elif function == "pseudovoigt":
-            params = ["center", "sigma", "amplitude", "fraction"]
+            params = ["amplitude", "center", "sigma", "gamma"]
+        elif function == "pvoigt":
+            params = ["amplitude", "center", "sigma", "fraction"]
         elif function == "pseudovoigt1":
-            params = ["center", "g_sigma", "l_sigma", "amplitude", "fraction"]
+            params = ["amplitude", "center", "g_sigma", "l_sigma", "fraction"]
         else:
             raise ValueError("Unknown fit function")
 
         n = len(params)
         fitparams = dict(
-            param=params, guess=[None] * n, vary=[True] * n, min=[None] * n, max=[None] * n,
+            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=[], guess=[], vary=[], min=[], max=[]))
+    fitparams_table_source = ColumnDataSource(dict(param=[], value=[], vary=[], min=[], max=[]))
     fitparams_table = DataTable(
         source=fitparams_table_source,
         columns=[
-            TableColumn(field="param", title="Parameter"),
-            TableColumn(field="guess", title="Guess", editor=NumberEditor()),
+            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()),
@@ -408,183 +550,138 @@ def create():
     )
 
     # start with `background` and `gauss` fit functions added
-    fitparams_add_dropdown_callback(types.SimpleNamespace(item="background"))
-    fitparams_add_dropdown_callback(types.SimpleNamespace(item="gauss"))
+    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=450, height=400)
+    fit_output_textinput = TextAreaInput(title="Fit results:", width=750, height=200)
 
-    def _get_peakfind_params():
-        return dict(
-            int_threshold=peak_int_ratio_spinner.value,
-            prominence=peak_prominence_spinner.value,
-            smooth=smooth_toggle.active,
-            window_size=window_size_spinner.value,
-            poly_order=poly_order_spinner.value,
+    def proc_all_button_callback():
+        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
+                )
+                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():
+        scan = _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,
         )
 
-    def peakfind_all_button_callback():
-        peakfind_params = _get_peakfind_params()
-        for scan in det_data["scan"].values():
-            pyzebra.ccl_findpeaks(scan, **peakfind_params)
-
-        _update_table()
-        _update_plot(_get_selected_scan())
-
-    peakfind_all_button = Button(label="Peak Find All", button_type="primary", default_size=145)
-    peakfind_all_button.on_click(peakfind_all_button_callback)
-
-    def peakfind_button_callback():
-        scan = _get_selected_scan()
-        pyzebra.ccl_findpeaks(scan, **_get_peakfind_params())
-
-        _update_table()
-        _update_plot(scan)
-
-    peakfind_button = Button(label="Peak Find Current", default_size=145)
-    peakfind_button.on_click(peakfind_button_callback)
-
-    def _get_fit_params():
-        return dict(
-            guess=fit_params["1"]["guess"] + fit_params["0"]["guess"],
-            vary=fit_params["1"]["vary"] + fit_params["0"]["vary"],
-            constraints_min=fit_params["1"]["min"] + fit_params["0"]["min"],
-            constraints_max=fit_params["1"]["max"] + fit_params["0"]["max"],
-            numfit_min=integ_from.value,
-            numfit_max=integ_to.value,
-            binning=bin_size_spinner.value,
-        )
-
-    def fit_all_button_callback():
-        fit_params = _get_fit_params()
-        for scan in det_data["scan"].values():
-            # fit_params are updated inplace within `fitccl`
-            pyzebra.fitccl(scan, **deepcopy(fit_params))
-
-        _update_plot(_get_selected_scan())
+        _update_plot()
         _update_table()
 
-    fit_all_button = Button(label="Fit All", button_type="primary", default_size=145)
-    fit_all_button.on_click(fit_all_button_callback)
+    proc_button = Button(label="Process Current", width=145)
+    proc_button.on_click(proc_button_callback)
 
-    def fit_button_callback():
-        scan = _get_selected_scan()
-        pyzebra.fitccl(scan, **_get_fit_params())
+    area_method_div = Div(text="Intensity:", margin=(5, 5, 0, 5))
+    area_method_radiobutton = RadioGroup(labels=["Function", "Area"], active=0, width=145)
 
-        _update_plot(scan)
-        _update_table()
+    lorentz_checkbox = CheckboxGroup(labels=["Lorentz Correction"], width=145, margin=(13, 5, 5, 5))
 
-    fit_button = Button(label="Fit Current", default_size=145)
-    fit_button.on_click(fit_button_callback)
+    export_preview_textinput = TextAreaInput(title="Export file(s) preview:", width=500, height=400)
 
-    def area_method_radiobutton_callback(_attr, _old, new):
-        det_data["meta"]["area_method"] = AREA_METHODS[new]
+    def _update_preview():
+        with tempfile.TemporaryDirectory() as temp_dir:
+            temp_file = temp_dir + "/temp"
+            export_data = []
+            for scan in det_data:
+                if scan["export"]:
+                    export_data.append(scan)
 
-    area_method_radiobutton = RadioButtonGroup(
-        labels=["Fit area", "Int area"], active=0, default_size=145
+            pyzebra.export_1D(
+                export_data,
+                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
     )
-    area_method_radiobutton.on_change("active", area_method_radiobutton_callback)
+    export_target_select.on_change("value", export_target_select_callback)
+    js_data.data.update(ext=EXPORT_TARGETS[export_target_select.value])
 
-    bin_size_spinner = Spinner(title="Bin size:", value=1, low=1, step=1, default_size=145)
+    def hkl_precision_select_callback(_attr, _old, _new):
+        _update_preview()
 
-    lorentz_toggle = Toggle(label="Lorentz Correction", default_size=145)
+    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)
 
-    preview_output_textinput = TextAreaInput(title="Export file preview:", width=450, height=400)
-
-    def preview_output_button_callback():
-        if det_data["meta"]["indices"] == "hkl":
-            ext = ".comm"
-        elif det_data["meta"]["indices"] == "real":
-            ext = ".incomm"
-
-        with tempfile.TemporaryDirectory() as temp_dir:
-            temp_file = temp_dir + "/temp"
-            export_data = deepcopy(det_data)
-            for s, export in zip(scan_table_source.data["scan"], scan_table_source.data["export"]):
-                if not export:
-                    del export_data["scan"][s]
-            pyzebra.export_comm(
-                export_data,
-                temp_file,
-                lorentz=lorentz_toggle.active,
-                hkl_precision=int(hkl_precision_select.value),
-            )
-
-            with open(f"{temp_file}{ext}") as f:
-                preview_output_textinput.value = f.read()
-
-    preview_output_button = Button(label="Preview file", default_size=220)
-    preview_output_button.on_click(preview_output_button_callback)
-
-    hkl_precision_select = Select(options=["2", "3", "4"], value="2", default_size=220)
-
-    def export_results(det_data):
-        if det_data["meta"]["indices"] == "hkl":
-            ext = ".comm"
-        elif det_data["meta"]["indices"] == "real":
-            ext = ".incomm"
-
-        with tempfile.TemporaryDirectory() as temp_dir:
-            temp_file = temp_dir + "/temp"
-            export_data = deepcopy(det_data)
-            for s, export in zip(scan_table_source.data["scan"], scan_table_source.data["export"]):
-                if not export:
-                    del export_data["scan"][s]
-            pyzebra.export_comm(
-                export_data,
-                temp_file,
-                lorentz=lorentz_toggle.active,
-                hkl_precision=int(hkl_precision_select.value),
-            )
-
-            with open(f"{temp_file}{ext}") as f:
-                output_content = f.read()
-
-        return output_content, ext
-
-    def save_button_callback():
-        cont, ext = export_results(det_data)
-        js_data.data.update(cont=[cont], ext=[ext])
-
-    save_button = Button(label="Download file", button_type="success", default_size=220)
-    save_button.on_click(save_button_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))
 
-    findpeak_controls = column(
-        row(peak_pos_textinput, column(Spacer(height=19), smooth_toggle)),
-        row(peak_int_ratio_spinner, peak_prominence_spinner),
-        row(window_size_spinner, poly_order_spinner),
-        row(peakfind_button, peakfind_all_button),
-    )
-
     fitpeak_controls = row(
         column(fitparams_add_dropdown, fitparams_select, fitparams_remove_button),
         fitparams_table,
         Spacer(width=20),
-        column(
-            row(integ_from, integ_to),
-            row(bin_size_spinner, column(Spacer(height=19), lorentz_toggle)),
-            row(fitparam_reset_button, area_method_radiobutton),
-            row(fit_button, fit_all_button),
-        ),
+        column(fit_from_spinner, lorentz_checkbox, area_method_div, area_method_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,
+        row(file_open_button, file_append_button),
+        upload_div,
+        upload_button,
+        append_upload_div,
+        append_upload_button,
     )
 
     export_layout = column(
-        preview_output_textinput,
-        row(column(preview_output_button, hkl_precision_select), save_button),
+        export_preview_textinput,
+        row(
+            export_target_select, hkl_precision_select, column(Spacer(height=19), row(save_button))
+        ),
     )
 
-    upload_div = Div(text="Or upload .ccl file:")
-    append_upload_div = Div(text="append extra .ccl/.dat files:")
     tab_layout = column(
-        row(proposal_textinput, ccl_file_select),
-        row(
-            column(Spacer(height=5), upload_div),
-            upload_button,
-            column(Spacer(height=5), append_upload_div),
-            append_upload_button,
-        ),
-        row(scan_table, plot, Spacer(width=30), fit_output_textinput, export_layout),
-        row(findpeak_controls, Spacer(width=30), fitpeak_controls),
+        row(import_layout, scan_layout, plot, Spacer(width=30), export_layout),
+        row(fitpeak_controls, fit_output_textinput),
     )
 
     return Panel(child=tab_layout, title="ccl integrate")

pyzebra/app/panel_hdf_anatric.py

@@ -1,5 +1,6 @@
 import base64
 import io
+import os
 import re
 import tempfile
 
@@ -10,8 +11,9 @@ from bokeh.models import (
     Div,
     FileInput,
     Panel,
-    RadioButtonGroup,
     Select,
+    Spacer,
+    Tabs,
     TextAreaInput,
     TextInput,
 )
@@ -28,7 +30,7 @@ def create():
         config.load_from_file(file)
 
         logfile_textinput.value = config.logfile
-        logfile_verbosity_select.value = config.logfile_verbosity
+        logfile_verbosity.value = config.logfile_verbosity
 
         filelist_type.value = config.filelist_type
         filelist_format_textinput.value = config.filelist_format
@@ -43,11 +45,16 @@ def create():
         ub_textareainput.value = config.crystal_UB
 
         dataFactory_implementation_select.value = config.dataFactory_implementation
-        dataFactory_dist1_textinput.value = config.dataFactory_dist1
+        if config.dataFactory_dist1 is not None:
+            dataFactory_dist1_textinput.value = config.dataFactory_dist1
+        if config.dataFactory_dist2 is not None:
+            dataFactory_dist2_textinput.value = config.dataFactory_dist2
+        if config.dataFactory_dist3 is not None:
+            dataFactory_dist3_textinput.value = config.dataFactory_dist3
         reflectionPrinter_format_select.value = config.reflectionPrinter_format
 
-        set_active_widgets(config.algorithm)
         if config.algorithm == "adaptivemaxcog":
+            algorithm_params.active = 0
             threshold_textinput.value = config.threshold
             shell_textinput.value = config.shell
             steepness_textinput.value = config.steepness
@@ -56,6 +63,7 @@ def create():
             aps_window_textinput.value = str(tuple(map(int, config.aps_window.values())))
 
         elif config.algorithm == "adaptivedynamic":
+            algorithm_params.active = 1
             adm_window_textinput.value = str(tuple(map(int, config.adm_window.values())))
             border_textinput.value = str(tuple(map(int, config.border.values())))
             minWindow_textinput.value = str(tuple(map(int, config.minWindow.values())))
@@ -65,46 +73,16 @@ def create():
             loop_textinput.value = config.loop
             minPeakCount_textinput.value = config.minPeakCount
             displacementCurve_textinput.value = "\n".join(map(str, config.displacementCurve))
+
         else:
             raise ValueError("Unknown processing mode.")
 
-    def set_active_widgets(implementation):
-        if implementation == "adaptivemaxcog":
-            mode_radio_button_group.active = 0
-            disable_adaptivemaxcog = False
-            disable_adaptivedynamic = True
-
-        elif implementation == "adaptivedynamic":
-            mode_radio_button_group.active = 1
-            disable_adaptivemaxcog = True
-            disable_adaptivedynamic = False
-        else:
-            raise ValueError("Implementation can be either 'adaptivemaxcog' or 'adaptivedynamic'")
-
-        threshold_textinput.disabled = disable_adaptivemaxcog
-        shell_textinput.disabled = disable_adaptivemaxcog
-        steepness_textinput.disabled = disable_adaptivemaxcog
-        duplicateDistance_textinput.disabled = disable_adaptivemaxcog
-        maxequal_textinput.disabled = disable_adaptivemaxcog
-        aps_window_textinput.disabled = disable_adaptivemaxcog
-
-        adm_window_textinput.disabled = disable_adaptivedynamic
-        border_textinput.disabled = disable_adaptivedynamic
-        minWindow_textinput.disabled = disable_adaptivedynamic
-        reflectionFile_textinput.disabled = disable_adaptivedynamic
-        targetMonitor_textinput.disabled = disable_adaptivedynamic
-        smoothSize_textinput.disabled = disable_adaptivedynamic
-        loop_textinput.disabled = disable_adaptivedynamic
-        minPeakCount_textinput.disabled = disable_adaptivedynamic
-        displacementCurve_textinput.disabled = disable_adaptivedynamic
-
-    upload_div = Div(text="Open XML configuration file:")
-
     def upload_button_callback(_attr, _old, new):
         with io.BytesIO(base64.b64decode(new)) as file:
             _load_config_file(file)
 
-    upload_button = FileInput(accept=".xml")
+    upload_div = Div(text="Open .xml config:")
+    upload_button = FileInput(accept=".xml", width=200)
     upload_button.on_change("value", upload_button_callback)
 
     # General parameters
@@ -112,16 +90,14 @@ def create():
     def logfile_textinput_callback(_attr, _old, new):
         config.logfile = new
 
-    logfile_textinput = TextInput(title="Logfile:", value="logfile.log", width=520)
+    logfile_textinput = TextInput(title="Logfile:", value="logfile.log")
     logfile_textinput.on_change("value", logfile_textinput_callback)
 
-    def logfile_verbosity_select_callback(_attr, _old, new):
+    def logfile_verbosity_callback(_attr, _old, new):
         config.logfile_verbosity = new
 
-    logfile_verbosity_select = Select(
-        title="verbosity:", options=["0", "5", "10", "15", "30"], width=70
-    )
-    logfile_verbosity_select.on_change("value", logfile_verbosity_select_callback)
+    logfile_verbosity = TextInput(title="verbosity:", width=70)
+    logfile_verbosity.on_change("value", logfile_verbosity_callback)
 
     # ---- FileList
     def filelist_type_callback(_attr, _old, new):
@@ -133,7 +109,7 @@ def create():
     def filelist_format_textinput_callback(_attr, _old, new):
         config.filelist_format = new
 
-    filelist_format_textinput = TextInput(title="format:", width=490)
+    filelist_format_textinput = TextInput(title="format:", width=290)
     filelist_format_textinput.on_change("value", filelist_format_textinput_callback)
 
     def filelist_datapath_textinput_callback(_attr, _old, new):
@@ -148,20 +124,20 @@ def create():
             ranges.append(re.findall(r"\b\d+\b", line))
         config.filelist_ranges = ranges
 
-    filelist_ranges_textareainput = TextAreaInput(title="ranges:", height=100)
+    filelist_ranges_textareainput = TextAreaInput(title="ranges:", rows=1)
     filelist_ranges_textareainput.on_change("value", filelist_ranges_textareainput_callback)
 
     # ---- crystal
     def crystal_sample_textinput_callback(_attr, _old, new):
         config.crystal_sample = new
 
-    crystal_sample_textinput = TextInput(title="Sample Name:")
+    crystal_sample_textinput = TextInput(title="Sample Name:", width=290)
     crystal_sample_textinput.on_change("value", crystal_sample_textinput_callback)
 
     def lambda_textinput_callback(_attr, _old, new):
         config.crystal_lambda = new
 
-    lambda_textinput = TextInput(title="lambda:", width=140)
+    lambda_textinput = TextInput(title="lambda:", width=100)
     lambda_textinput.on_change("value", lambda_textinput_callback)
 
     def ub_textareainput_callback(_attr, _old, new):
@@ -173,19 +149,19 @@ def create():
     def zeroOM_textinput_callback(_attr, _old, new):
         config.crystal_zeroOM = new
 
-    zeroOM_textinput = TextInput(title="zeroOM:", width=140)
+    zeroOM_textinput = TextInput(title="zeroOM:", width=100)
     zeroOM_textinput.on_change("value", zeroOM_textinput_callback)
 
     def zeroSTT_textinput_callback(_attr, _old, new):
         config.crystal_zeroSTT = new
 
-    zeroSTT_textinput = TextInput(title="zeroSTT:", width=140)
+    zeroSTT_textinput = TextInput(title="zeroSTT:", width=100)
     zeroSTT_textinput.on_change("value", zeroSTT_textinput_callback)
 
     def zeroCHI_textinput_callback(_attr, _old, new):
         config.crystal_zeroCHI = new
 
-    zeroCHI_textinput = TextInput(title="zeroCHI:", width=140)
+    zeroCHI_textinput = TextInput(title="zeroCHI:", width=100)
     zeroCHI_textinput.on_change("value", zeroCHI_textinput_callback)
 
     # ---- DataFactory
@@ -193,16 +169,28 @@ def create():
         config.dataFactory_implementation = new
 
     dataFactory_implementation_select = Select(
-        title="DataFactory implementation:", options=DATA_FACTORY_IMPLEMENTATION, width=300,
+        title="DataFactory implement.:", options=DATA_FACTORY_IMPLEMENTATION, width=145,
     )
     dataFactory_implementation_select.on_change("value", dataFactory_implementation_select_callback)
 
     def dataFactory_dist1_textinput_callback(_attr, _old, new):
         config.dataFactory_dist1 = new
 
-    dataFactory_dist1_textinput = TextInput(title="dist1:", width=290)
+    dataFactory_dist1_textinput = TextInput(title="dist1:", width=75)
     dataFactory_dist1_textinput.on_change("value", dataFactory_dist1_textinput_callback)
 
+    def dataFactory_dist2_textinput_callback(_attr, _old, new):
+        config.dataFactory_dist2 = new
+
+    dataFactory_dist2_textinput = TextInput(title="dist2:", width=75)
+    dataFactory_dist2_textinput.on_change("value", dataFactory_dist2_textinput_callback)
+
+    def dataFactory_dist3_textinput_callback(_attr, _old, new):
+        config.dataFactory_dist3 = new
+
+    dataFactory_dist3_textinput = TextInput(title="dist3:", width=75)
+    dataFactory_dist3_textinput.on_change("value", dataFactory_dist3_textinput_callback)
+
     # ---- BackgroundProcessor
 
     # ---- DetectorEfficency
@@ -212,7 +200,7 @@ def create():
         config.reflectionPrinter_format = new
 
     reflectionPrinter_format_select = Select(
-        title="ReflectionPrinter format:", options=REFLECTION_PRINTER_FORMATS, width=300,
+        title="ReflectionPrinter format:", options=REFLECTION_PRINTER_FORMATS, width=145,
     )
     reflectionPrinter_format_select.on_change("value", reflectionPrinter_format_select_callback)
 
@@ -221,42 +209,42 @@ def create():
     def threshold_textinput_callback(_attr, _old, new):
         config.threshold = new
 
-    threshold_textinput = TextInput(title="Threshold:")
+    threshold_textinput = TextInput(title="Threshold:", width=145)
     threshold_textinput.on_change("value", threshold_textinput_callback)
 
     # ---- shell
     def shell_textinput_callback(_attr, _old, new):
         config.shell = new
 
-    shell_textinput = TextInput(title="Shell:")
+    shell_textinput = TextInput(title="Shell:", width=145)
     shell_textinput.on_change("value", shell_textinput_callback)
 
     # ---- steepness
     def steepness_textinput_callback(_attr, _old, new):
         config.steepness = new
 
-    steepness_textinput = TextInput(title="Steepness:")
+    steepness_textinput = TextInput(title="Steepness:", width=145)
     steepness_textinput.on_change("value", steepness_textinput_callback)
 
     # ---- duplicateDistance
     def duplicateDistance_textinput_callback(_attr, _old, new):
         config.duplicateDistance = new
 
-    duplicateDistance_textinput = TextInput(title="Duplicate Distance:")
+    duplicateDistance_textinput = TextInput(title="Duplicate Distance:", width=145)
     duplicateDistance_textinput.on_change("value", duplicateDistance_textinput_callback)
 
     # ---- maxequal
     def maxequal_textinput_callback(_attr, _old, new):
         config.maxequal = new
 
-    maxequal_textinput = TextInput(title="Max Equal:")
+    maxequal_textinput = TextInput(title="Max Equal:", width=145)
     maxequal_textinput.on_change("value", maxequal_textinput_callback)
 
     # ---- window
     def aps_window_textinput_callback(_attr, _old, new):
         config.aps_window = dict(zip(("x", "y", "z"), re.findall(r"\b\d+\b", new)))
 
-    aps_window_textinput = TextInput(title="Window (x, y, z):")
+    aps_window_textinput = TextInput(title="Window (x, y, z):", width=145)
     aps_window_textinput.on_change("value", aps_window_textinput_callback)
 
     # Adaptive Dynamic Mask Integration (adaptivedynamic)
@@ -264,56 +252,56 @@ def create():
     def adm_window_textinput_callback(_attr, _old, new):
         config.adm_window = dict(zip(("x", "y", "z"), re.findall(r"\b\d+\b", new)))
 
-    adm_window_textinput = TextInput(title="Window (x, y, z):")
+    adm_window_textinput = TextInput(title="Window (x, y, z):", width=145)
     adm_window_textinput.on_change("value", adm_window_textinput_callback)
 
     # ---- border
     def border_textinput_callback(_attr, _old, new):
         config.border = dict(zip(("x", "y", "z"), re.findall(r"\b\d+\b", new)))
 
-    border_textinput = TextInput(title="Border (x, y, z):")
+    border_textinput = TextInput(title="Border (x, y, z):", width=145)
     border_textinput.on_change("value", border_textinput_callback)
 
     # ---- minWindow
     def minWindow_textinput_callback(_attr, _old, new):
         config.minWindow = dict(zip(("x", "y", "z"), re.findall(r"\b\d+\b", new)))
 
-    minWindow_textinput = TextInput(title="Min Window (x, y, z):")
+    minWindow_textinput = TextInput(title="Min Window (x, y, z):", width=145)
     minWindow_textinput.on_change("value", minWindow_textinput_callback)
 
     # ---- reflectionFile
     def reflectionFile_textinput_callback(_attr, _old, new):
         config.reflectionFile = new
 
-    reflectionFile_textinput = TextInput(title="Reflection File:")
+    reflectionFile_textinput = TextInput(title="Reflection File:", width=145)
     reflectionFile_textinput.on_change("value", reflectionFile_textinput_callback)
 
     # ---- targetMonitor
     def targetMonitor_textinput_callback(_attr, _old, new):
         config.targetMonitor = new
 
-    targetMonitor_textinput = TextInput(title="Target Monitor:")
+    targetMonitor_textinput = TextInput(title="Target Monitor:", width=145)
     targetMonitor_textinput.on_change("value", targetMonitor_textinput_callback)
 
     # ---- smoothSize
     def smoothSize_textinput_callback(_attr, _old, new):
         config.smoothSize = new
 
-    smoothSize_textinput = TextInput(title="Smooth Size:")
+    smoothSize_textinput = TextInput(title="Smooth Size:", width=145)
     smoothSize_textinput.on_change("value", smoothSize_textinput_callback)
 
     # ---- loop
     def loop_textinput_callback(_attr, _old, new):
         config.loop = new
 
-    loop_textinput = TextInput(title="Loop:")
+    loop_textinput = TextInput(title="Loop:", width=145)
     loop_textinput.on_change("value", loop_textinput_callback)
 
     # ---- minPeakCount
     def minPeakCount_textinput_callback(_attr, _old, new):
         config.minPeakCount = new
 
-    minPeakCount_textinput = TextInput(title="Min Peak Count:")
+    minPeakCount_textinput = TextInput(title="Min Peak Count:", width=145)
     minPeakCount_textinput.on_change("value", minPeakCount_textinput_callback)
 
     # ---- displacementCurve
@@ -324,88 +312,82 @@ def create():
         config.displacementCurve = maps
 
     displacementCurve_textinput = TextAreaInput(
-        title="Displacement Curve (twotheta, x, y):", height=100
+        title="Displ. Curve (2θ, x, y):", width=145, height=100
     )
     displacementCurve_textinput.on_change("value", displacementCurve_textinput_callback)
 
-    def mode_radio_button_group_callback(active):
-        if active == 0:
+    def algorithm_tabs_callback(_attr, _old, new):
+        if new == 0:
             config.algorithm = "adaptivemaxcog"
-            set_active_widgets("adaptivemaxcog")
         else:
             config.algorithm = "adaptivedynamic"
-            set_active_widgets("adaptivedynamic")
 
-    mode_radio_button_group = RadioButtonGroup(
-        labels=["Adaptive Peak Detection", "Adaptive Dynamic Integration"], active=0
+    algorithm_params = Tabs(
+        tabs=[
+            Panel(
+                child=column(
+                    row(threshold_textinput, shell_textinput, steepness_textinput),
+                    row(duplicateDistance_textinput, maxequal_textinput, aps_window_textinput),
+                ),
+                title="Peak Search",
+            ),
+            Panel(
+                child=column(
+                    row(adm_window_textinput, border_textinput, minWindow_textinput),
+                    row(reflectionFile_textinput, targetMonitor_textinput, smoothSize_textinput),
+                    row(loop_textinput, minPeakCount_textinput, displacementCurve_textinput),
+                ),
+                title="Dynamic Integration",
+            ),
+        ]
     )
-    mode_radio_button_group.on_click(mode_radio_button_group_callback)
-    set_active_widgets("adaptivemaxcog")
+    algorithm_params.on_change("active", algorithm_tabs_callback)
 
     def process_button_callback():
         with tempfile.TemporaryDirectory() as temp_dir:
-            temp_file = temp_dir + "/temp.xml"
+            temp_file = temp_dir + "/config.xml"
             config.save_as(temp_file)
-            pyzebra.anatric(temp_file, anatric_path=doc.anatric_path)
+            pyzebra.anatric(temp_file, anatric_path=doc.anatric_path, cwd=temp_dir)
 
-            with open(config.logfile) as f_log:
+            with open(os.path.join(temp_dir, config.logfile)) as f_log:
                 output_log.value = f_log.read()
 
+            with open(os.path.join(temp_dir, config.reflectionPrinter_file)) as f_res:
+                output_res.value = f_res.read()
+
     process_button = Button(label="Process", button_type="primary")
     process_button.on_click(process_button_callback)
 
-    output_log = TextAreaInput(title="Logfile output:", height=700, disabled=True)
-    output_config = TextAreaInput(title="Current config:", height=700, width=400, disabled=True)
+    output_log = TextAreaInput(title="Logfile output:", height=320, width=465, disabled=True)
+    output_res = TextAreaInput(title="Result output:", height=320, width=465, disabled=True)
+    output_config = TextAreaInput(title="Current config:", height=320, width=465, disabled=True)
+
+    general_params_layout = column(
+        row(column(Spacer(height=2), upload_div), upload_button),
+        row(logfile_textinput, logfile_verbosity),
+        row(filelist_type, filelist_format_textinput),
+        filelist_datapath_textinput,
+        filelist_ranges_textareainput,
+        row(crystal_sample_textinput, lambda_textinput),
+        ub_textareainput,
+        row(zeroOM_textinput, zeroSTT_textinput, zeroCHI_textinput),
+        row(
+            dataFactory_implementation_select,
+            dataFactory_dist1_textinput,
+            dataFactory_dist2_textinput,
+            dataFactory_dist3_textinput,
+        ),
+        row(reflectionPrinter_format_select),
+    )
 
     tab_layout = row(
-        column(
-            upload_div,
-            upload_button,
-            row(logfile_textinput, logfile_verbosity_select),
-            row(filelist_type, filelist_format_textinput),
-            filelist_datapath_textinput,
-            filelist_ranges_textareainput,
-            crystal_sample_textinput,
-            row(lambda_textinput, zeroOM_textinput, zeroSTT_textinput, zeroCHI_textinput),
-            ub_textareainput,
-            row(dataFactory_implementation_select, dataFactory_dist1_textinput),
-            reflectionPrinter_format_select,
-            process_button,
-        ),
-        column(
-            mode_radio_button_group,
-            row(
-                column(
-                    threshold_textinput,
-                    shell_textinput,
-                    steepness_textinput,
-                    duplicateDistance_textinput,
-                    maxequal_textinput,
-                    aps_window_textinput,
-                ),
-                column(
-                    adm_window_textinput,
-                    border_textinput,
-                    minWindow_textinput,
-                    reflectionFile_textinput,
-                    targetMonitor_textinput,
-                    smoothSize_textinput,
-                    loop_textinput,
-                    minPeakCount_textinput,
-                    displacementCurve_textinput,
-                ),
-            ),
-        ),
-        output_config,
-        output_log,
+        general_params_layout,
+        column(output_config, algorithm_params, row(process_button)),
+        column(output_log, output_res),
     )
 
     async def update_config():
-        with tempfile.TemporaryDirectory() as temp_dir:
-            temp_file = temp_dir + "/debug.xml"
-            config.save_as(temp_file)
-            with open(temp_file) as f_config:
-                output_config.value = f_config.read()
+        output_config.value = config.tostring()
 
     doc.add_periodic_callback(update_config, 1000)
 

pyzebra/app/panel_hdf_param_study.py

@@ -0,0 +1,589 @@
+import base64
+import io
+import os
+
+import numpy as np
+from bokeh.io import curdoc
+from bokeh.layouts import column, gridplot, row
+from bokeh.models import (
+    BasicTicker,
+    BoxZoomTool,
+    Button,
+    CellEditor,
+    CheckboxGroup,
+    ColumnDataSource,
+    DataRange1d,
+    DataTable,
+    Div,
+    FileInput,
+    Grid,
+    MultiSelect,
+    NumberEditor,
+    NumberFormatter,
+    Image,
+    LinearAxis,
+    LinearColorMapper,
+    Panel,
+    PanTool,
+    Plot,
+    Range1d,
+    ResetTool,
+    Scatter,
+    Select,
+    Spinner,
+    TableColumn,
+    Tabs,
+    Title,
+    WheelZoomTool,
+)
+from bokeh.palettes import Cividis256, Greys256, Plasma256  # pylint: disable=E0611
+
+import pyzebra
+
+IMAGE_W = 256
+IMAGE_H = 128
+IMAGE_PLOT_W = int(IMAGE_W * 2) + 52
+IMAGE_PLOT_H = int(IMAGE_H * 2) + 27
+
+
+def create():
+    doc = curdoc()
+    zebra_data = []
+    det_data = {}
+    cami_meta = {}
+
+    num_formatter = NumberFormatter(format="0.00", nan_format="")
+
+    def file_select_update():
+        if data_source.value == "proposal number":
+            proposal_path = proposal_textinput.name
+            if proposal_path:
+                file_list = []
+                for file in os.listdir(proposal_path):
+                    if file.endswith(".hdf"):
+                        file_list.append((os.path.join(proposal_path, file), file))
+                file_select.options = file_list
+            else:
+                file_select.options = []
+
+        else:  # "cami file"
+            if not cami_meta:
+                file_select.options = []
+                return
+
+            file_list = cami_meta["filelist"]
+            file_select.options = [(entry, os.path.basename(entry)) for entry in file_list]
+
+    def data_source_callback(_attr, _old, _new):
+        file_select_update()
+
+    data_source = Select(
+        title="Data Source:",
+        value="proposal number",
+        options=["proposal number", "cami file"],
+        width=210,
+    )
+    data_source.on_change("value", data_source_callback)
+
+    doc.add_periodic_callback(file_select_update, 5000)
+
+    def proposal_textinput_callback(_attr, _old, _new):
+        file_select_update()
+
+    proposal_textinput = doc.proposal_textinput
+    proposal_textinput.on_change("name", proposal_textinput_callback)
+
+    def upload_button_callback(_attr, _old, new):
+        nonlocal cami_meta
+        with io.StringIO(base64.b64decode(new).decode()) as file:
+            cami_meta = pyzebra.parse_h5meta(file)
+        data_source.value = "cami file"
+        file_select_update()
+
+    upload_div = Div(text="or upload .cami file:", margin=(5, 5, 0, 5))
+    upload_button = FileInput(accept=".cami", width=200)
+    upload_button.on_change("value", upload_button_callback)
+
+    file_select = MultiSelect(title="Available .hdf files:", width=210, height=320)
+
+    def _init_datatable():
+        file_list = []
+        for scan in zebra_data:
+            file_list.append(os.path.basename(scan["original_filename"]))
+
+        scan_table_source.data.update(
+            file=file_list,
+            param=[None] * len(zebra_data),
+            frame=[None] * len(zebra_data),
+            x_pos=[None] * len(zebra_data),
+            y_pos=[None] * len(zebra_data),
+        )
+        scan_table_source.selected.indices = []
+        scan_table_source.selected.indices = [0]
+
+        param_select.value = "user defined"
+
+    def _update_table():
+        frame = []
+        x_pos = []
+        y_pos = []
+        for scan in zebra_data:
+            if "fit" in scan:
+                framei = scan["fit"]["frame"]
+                x_posi = scan["fit"]["x_pos"]
+                y_posi = scan["fit"]["y_pos"]
+            else:
+                framei = x_posi = y_posi = None
+
+            frame.append(framei)
+            x_pos.append(x_posi)
+            y_pos.append(y_posi)
+
+        scan_table_source.data.update(frame=frame, x_pos=x_pos, y_pos=y_pos)
+
+    def _file_open():
+        new_data = []
+        for f_name in file_select.value:
+            try:
+                new_data.append(pyzebra.read_detector_data(f_name))
+            except KeyError:
+                print("Could not read data from the file.")
+                return
+
+        zebra_data.extend(new_data)
+
+        _init_datatable()
+
+    def file_open_button_callback():
+        nonlocal zebra_data
+        zebra_data = []
+        _file_open()
+
+    file_open_button = Button(label="Open New", width=100)
+    file_open_button.on_click(file_open_button_callback)
+
+    def file_append_button_callback():
+        _file_open()
+
+    file_append_button = Button(label="Append", width=100)
+    file_append_button.on_click(file_append_button_callback)
+
+    # Scan select
+    def scan_table_select_callback(_attr, old, new):
+        nonlocal det_data
+
+        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
+
+        det_data = zebra_data[new[0]]
+
+        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"])
+
+        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):
+        pass
+
+    scan_table_source = ColumnDataSource(dict(file=[], param=[], frame=[], x_pos=[], y_pos=[]))
+    scan_table_source.selected.on_change("indices", scan_table_select_callback)
+    scan_table_source.on_change("data", scan_table_source_callback)
+
+    scan_table = DataTable(
+        source=scan_table_source,
+        columns=[
+            TableColumn(field="file", title="file", editor=CellEditor(), width=150),
+            TableColumn(
+                field="param",
+                title="param",
+                formatter=num_formatter,
+                editor=NumberEditor(),
+                width=50,
+            ),
+            TableColumn(
+                field="frame", title="Frame", formatter=num_formatter, editor=CellEditor(), width=70
+            ),
+            TableColumn(
+                field="x_pos", title="X", formatter=num_formatter, editor=CellEditor(), width=70
+            ),
+            TableColumn(
+                field="y_pos", title="Y", formatter=num_formatter, editor=CellEditor(), width=70
+            ),
+        ],
+        width=470,  # +60 because of the index column
+        height=420,
+        editable=True,
+        autosize_mode="none",
+    )
+
+    def param_select_callback(_attr, _old, new):
+        if new == "user defined":
+            param = [None] * len(zebra_data)
+        else:
+            # TODO: which value to take?
+            param = [scan[new][0] for scan in zebra_data]
+
+        scan_table_source.data["param"] = param
+        _update_param_plot()
+
+    param_select = Select(
+        title="Parameter:",
+        options=["user defined", "temp", "mf", "h", "k", "l"],
+        value="user defined",
+        width=145,
+    )
+    param_select.on_change("value", param_select_callback)
+
+    def update_overview_plot():
+        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])
+
+        if proj_auto_checkbox.active:
+            im_min = min(np.min(overview_x), np.min(overview_y))
+            im_max = max(np.max(overview_x), np.max(overview_y))
+
+            proj_display_min_spinner.value = im_min
+            proj_display_max_spinner.value = im_max
+
+            overview_plot_x_image_glyph.color_mapper.low = im_min
+            overview_plot_y_image_glyph.color_mapper.low = im_min
+            overview_plot_x_image_glyph.color_mapper.high = im_max
+            overview_plot_y_image_glyph.color_mapper.high = im_max
+
+        frame_range.start = 0
+        frame_range.end = n_im
+        frame_range.reset_start = 0
+        frame_range.reset_end = n_im
+        frame_range.bounds = (0, n_im)
+
+        scan_motor = det_data["scan_motor"]
+        overview_plot_y.axis[1].axis_label = f"Scanning motor, {scan_motor}"
+
+        var = det_data[scan_motor]
+        var_start = var[0]
+        var_end = var[-1] + (var[-1] - var[0]) / (n_im - 1)
+
+        scanning_motor_range.start = var_start
+        scanning_motor_range.end = var_end
+        scanning_motor_range.reset_start = var_start
+        scanning_motor_range.reset_end = var_end
+        # handle both, ascending and descending sequences
+        scanning_motor_range.bounds = (min(var_start, var_end), max(var_start, var_end))
+
+    # shared frame ranges
+    frame_range = Range1d(0, 1, bounds=(0, 1))
+    scanning_motor_range = Range1d(0, 1, bounds=(0, 1))
+
+    det_x_range = Range1d(0, IMAGE_W, bounds=(0, IMAGE_W))
+    overview_plot_x = Plot(
+        title=Title(text="Projections on X-axis"),
+        x_range=det_x_range,
+        y_range=frame_range,
+        extra_y_ranges={"scanning_motor": scanning_motor_range},
+        plot_height=400,
+        plot_width=IMAGE_PLOT_W - 3,
+    )
+
+    # ---- tools
+    wheelzoomtool = WheelZoomTool(maintain_focus=False)
+    overview_plot_x.toolbar.logo = None
+    overview_plot_x.add_tools(
+        PanTool(), BoxZoomTool(), wheelzoomtool, ResetTool(),
+    )
+    overview_plot_x.toolbar.active_scroll = wheelzoomtool
+
+    # ---- axes
+    overview_plot_x.add_layout(LinearAxis(axis_label="Coordinate X, pix"), place="below")
+    overview_plot_x.add_layout(
+        LinearAxis(axis_label="Frame", major_label_orientation="vertical"), place="left"
+    )
+
+    # ---- grid lines
+    overview_plot_x.add_layout(Grid(dimension=0, ticker=BasicTicker()))
+    overview_plot_x.add_layout(Grid(dimension=1, ticker=BasicTicker()))
+
+    # ---- rgba image glyph
+    overview_plot_x_image_source = ColumnDataSource(
+        dict(image=[np.zeros((1, 1), dtype="float32")], x=[0], y=[0], dw=[IMAGE_W], dh=[1])
+    )
+
+    overview_plot_x_image_glyph = Image(image="image", x="x", y="y", dw="dw", dh="dh")
+    overview_plot_x.add_glyph(
+        overview_plot_x_image_source, overview_plot_x_image_glyph, name="image_glyph"
+    )
+
+    det_y_range = Range1d(0, IMAGE_H, bounds=(0, IMAGE_H))
+    overview_plot_y = Plot(
+        title=Title(text="Projections on Y-axis"),
+        x_range=det_y_range,
+        y_range=frame_range,
+        extra_y_ranges={"scanning_motor": scanning_motor_range},
+        plot_height=400,
+        plot_width=IMAGE_PLOT_H + 22,
+    )
+
+    # ---- tools
+    wheelzoomtool = WheelZoomTool(maintain_focus=False)
+    overview_plot_y.toolbar.logo = None
+    overview_plot_y.add_tools(
+        PanTool(), BoxZoomTool(), wheelzoomtool, ResetTool(),
+    )
+    overview_plot_y.toolbar.active_scroll = wheelzoomtool
+
+    # ---- axes
+    overview_plot_y.add_layout(LinearAxis(axis_label="Coordinate Y, pix"), place="below")
+    overview_plot_y.add_layout(
+        LinearAxis(
+            y_range_name="scanning_motor",
+            axis_label="Scanning motor",
+            major_label_orientation="vertical",
+        ),
+        place="right",
+    )
+
+    # ---- grid lines
+    overview_plot_y.add_layout(Grid(dimension=0, ticker=BasicTicker()))
+    overview_plot_y.add_layout(Grid(dimension=1, ticker=BasicTicker()))
+
+    # ---- rgba image glyph
+    overview_plot_y_image_source = ColumnDataSource(
+        dict(image=[np.zeros((1, 1), dtype="float32")], x=[0], y=[0], dw=[IMAGE_H], dh=[1])
+    )
+
+    overview_plot_y_image_glyph = Image(image="image", x="x", y="y", dw="dw", dh="dh")
+    overview_plot_y.add_glyph(
+        overview_plot_y_image_source, overview_plot_y_image_glyph, name="image_glyph"
+    )
+
+    cmap_dict = {
+        "gray": Greys256,
+        "gray_reversed": Greys256[::-1],
+        "plasma": Plasma256,
+        "cividis": Cividis256,
+    }
+
+    def colormap_callback(_attr, _old, new):
+        overview_plot_x_image_glyph.color_mapper = LinearColorMapper(palette=cmap_dict[new])
+        overview_plot_y_image_glyph.color_mapper = LinearColorMapper(palette=cmap_dict[new])
+
+    colormap = Select(title="Colormap:", options=list(cmap_dict.keys()), width=210)
+    colormap.on_change("value", colormap_callback)
+    colormap.value = "plasma"
+
+    PROJ_STEP = 1
+
+    def proj_auto_checkbox_callback(state):
+        if state:
+            proj_display_min_spinner.disabled = True
+            proj_display_max_spinner.disabled = True
+        else:
+            proj_display_min_spinner.disabled = False
+            proj_display_max_spinner.disabled = False
+
+        update_overview_plot()
+
+    proj_auto_checkbox = CheckboxGroup(
+        labels=["Projections Intensity Range"], active=[0], width=145, margin=[10, 5, 0, 5]
+    )
+    proj_auto_checkbox.on_click(proj_auto_checkbox_callback)
+
+    def proj_display_max_spinner_callback(_attr, _old_value, new_value):
+        proj_display_min_spinner.high = new_value - PROJ_STEP
+        overview_plot_x_image_glyph.color_mapper.high = new_value
+        overview_plot_y_image_glyph.color_mapper.high = new_value
+
+    proj_display_max_spinner = Spinner(
+        low=0 + PROJ_STEP,
+        value=1,
+        step=PROJ_STEP,
+        disabled=bool(proj_auto_checkbox.active),
+        width=100,
+        height=31,
+    )
+    proj_display_max_spinner.on_change("value", proj_display_max_spinner_callback)
+
+    def proj_display_min_spinner_callback(_attr, _old_value, new_value):
+        proj_display_max_spinner.low = new_value + PROJ_STEP
+        overview_plot_x_image_glyph.color_mapper.low = new_value
+        overview_plot_y_image_glyph.color_mapper.low = new_value
+
+    proj_display_min_spinner = Spinner(
+        low=0,
+        high=1 - PROJ_STEP,
+        value=0,
+        step=PROJ_STEP,
+        disabled=bool(proj_auto_checkbox.active),
+        width=100,
+        height=31,
+    )
+    proj_display_min_spinner.on_change("value", proj_display_min_spinner_callback)
+
+    metadata_table_source = ColumnDataSource(dict(geom=[""], temp=[None], mf=[None]))
+    metadata_table = DataTable(
+        source=metadata_table_source,
+        columns=[
+            TableColumn(field="geom", title="Geometry", width=100),
+            TableColumn(field="temp", title="Temperature", formatter=num_formatter, width=100),
+            TableColumn(field="mf", title="Magnetic Field", formatter=num_formatter, width=100),
+        ],
+        width=300,
+        height=50,
+        autosize_mode="none",
+        index_position=None,
+    )
+
+    def _update_param_plot():
+        x = []
+        y = []
+        fit_param = fit_param_select.value
+        for s, p in zip(zebra_data, scan_table_source.data["param"]):
+            if "fit" in s and fit_param:
+                x.append(p)
+                y.append(s["fit"][fit_param])
+        param_plot_scatter_source.data.update(x=x, y=y)
+
+    # Parameter plot
+    param_plot = Plot(x_range=DataRange1d(), y_range=DataRange1d(), plot_height=400, plot_width=700)
+
+    param_plot.add_layout(LinearAxis(axis_label="Fit parameter"), place="left")
+    param_plot.add_layout(LinearAxis(axis_label="Parameter"), place="below")
+
+    param_plot.add_layout(Grid(dimension=0, ticker=BasicTicker()))
+    param_plot.add_layout(Grid(dimension=1, ticker=BasicTicker()))
+
+    param_plot_scatter_source = ColumnDataSource(dict(x=[], y=[]))
+    param_plot.add_glyph(param_plot_scatter_source, Scatter(x="x", y="y"))
+
+    param_plot.add_tools(PanTool(), WheelZoomTool(), ResetTool())
+    param_plot.toolbar.logo = None
+
+    def fit_param_select_callback(_attr, _old, _new):
+        _update_param_plot()
+
+    fit_param_select = Select(title="Fit parameter", options=[], width=145)
+    fit_param_select.on_change("value", fit_param_select_callback)
+
+    def proc_all_button_callback():
+        for scan in zebra_data:
+            pyzebra.fit_event(
+                scan,
+                int(np.floor(frame_range.start)),
+                int(np.ceil(frame_range.end)),
+                int(np.floor(det_y_range.start)),
+                int(np.ceil(det_y_range.end)),
+                int(np.floor(det_x_range.start)),
+                int(np.ceil(det_x_range.end)),
+            )
+
+        _update_table()
+
+        for scan in zebra_data:
+            if "fit" in scan:
+                options = list(scan["fit"].keys())
+                fit_param_select.options = options
+                fit_param_select.value = options[0]
+                break
+
+        _update_param_plot()
+
+    proc_all_button = Button(label="Process All", button_type="primary", width=145)
+    proc_all_button.on_click(proc_all_button_callback)
+
+    def proc_button_callback():
+        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()
+
+        for scan in zebra_data:
+            if "fit" in scan:
+                options = list(scan["fit"].keys())
+                fit_param_select.options = options
+                fit_param_select.value = options[0]
+                break
+
+        _update_param_plot()
+
+    proc_button = Button(label="Process Current", width=145)
+    proc_button.on_click(proc_button_callback)
+
+    layout_controls = row(
+        colormap,
+        column(proj_auto_checkbox, row(proj_display_min_spinner, proj_display_max_spinner)),
+        proc_button,
+        proc_all_button,
+    )
+
+    layout_overview = column(
+        gridplot(
+            [[overview_plot_x, overview_plot_y]],
+            toolbar_options=dict(logo=None),
+            merge_tools=True,
+            toolbar_location="left",
+        ),
+        layout_controls,
+    )
+
+    # Plot tabs
+    plots = Tabs(
+        tabs=[
+            Panel(child=layout_overview, title="single scan"),
+            Panel(child=column(param_plot, row(fit_param_select)), title="parameter plot"),
+        ]
+    )
+
+    # Final layout
+    import_layout = column(
+        data_source,
+        upload_div,
+        upload_button,
+        file_select,
+        row(file_open_button, file_append_button),
+    )
+
+    scan_layout = column(scan_table, row(param_select, metadata_table))
+
+    tab_layout = column(row(import_layout, scan_layout, plots))
+
+    return Panel(child=tab_layout, title="hdf param study")

pyzebra/app/panel_hdf_viewer.py

@@ -3,17 +3,23 @@ import io
 import os
 
 import numpy as np
+from bokeh.events import MouseEnter
+from bokeh.io import curdoc
 from bokeh.layouts import column, gridplot, row
 from bokeh.models import (
     BasicTicker,
     BoxEditTool,
     BoxZoomTool,
     Button,
+    CheckboxGroup,
     ColumnDataSource,
     DataRange1d,
+    DataTable,
     Div,
     FileInput,
     Grid,
+    MultiSelect,
+    NumberFormatter,
     HoverTool,
     Image,
     Line,
@@ -22,16 +28,16 @@ from bokeh.models import (
     Panel,
     PanTool,
     Plot,
-    RadioButtonGroup,
     Range1d,
     Rect,
     ResetTool,
     Select,
+    Slider,
     Spacer,
     Spinner,
-    TextAreaInput,
+    TableColumn,
+    Tabs,
     Title,
-    Toggle,
     WheelZoomTool,
 )
 from bokeh.palettes import Cividis256, Greys256, Plasma256  # pylint: disable=E0611
@@ -40,21 +46,112 @@ import pyzebra
 
 IMAGE_W = 256
 IMAGE_H = 128
+IMAGE_PLOT_W = int(IMAGE_W * 2) + 52
+IMAGE_PLOT_H = int(IMAGE_H * 2) + 27
 
 
 def create():
+    doc = curdoc()
     det_data = {}
-    roi_selection = {}
+    cami_meta = {}
 
-    def upload_button_callback(_attr, _old, new):
+    num_formatter = NumberFormatter(format="0.00", nan_format="")
+
+    def file_select_update():
+        if data_source.value == "proposal number":
+            proposal_path = proposal_textinput.name
+            if proposal_path:
+                file_list = []
+                for file in os.listdir(proposal_path):
+                    if file.endswith(".hdf"):
+                        file_list.append((os.path.join(proposal_path, file), file))
+                file_select.options = file_list
+            else:
+                file_select.options = []
+
+        else:  # "cami file"
+            if not cami_meta:
+                file_select.options = []
+                return
+
+            file_list = cami_meta["filelist"]
+            file_select.options = [(entry, os.path.basename(entry)) for entry in file_list]
+
+    def data_source_callback(_attr, _old, _new):
+        file_select_update()
+
+    data_source = Select(
+        title="Data Source:",
+        value="proposal number",
+        options=["proposal number", "cami file"],
+        width=210,
+    )
+    data_source.on_change("value", data_source_callback)
+
+    doc.add_periodic_callback(file_select_update, 5000)
+
+    def proposal_textinput_callback(_attr, _old, _new):
+        file_select_update()
+
+    proposal_textinput = doc.proposal_textinput
+    proposal_textinput.on_change("name", proposal_textinput_callback)
+
+    def upload_cami_button_callback(_attr, _old, new):
+        nonlocal cami_meta
         with io.StringIO(base64.b64decode(new).decode()) as file:
-            h5meta_list = pyzebra.parse_h5meta(file)
-            file_list = h5meta_list["filelist"]
-            filelist.options = [(entry, os.path.basename(entry)) for entry in file_list]
-            filelist.value = file_list[0]
+            cami_meta = pyzebra.parse_h5meta(file)
+        data_source.value = "cami file"
+        file_select_update()
 
-    upload_button = FileInput(accept=".cami")
-    upload_button.on_change("value", upload_button_callback)
+    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 _file_open(file, cami_meta):
+        nonlocal det_data
+        try:
+            det_data = pyzebra.read_detector_data(file, cami_meta)
+        except KeyError:
+            print("Could not read data from the file.")
+            return
+
+        last_im_index = det_data["data"].shape[0] - 1
+
+        index_spinner.value = 0
+        index_spinner.high = last_im_index
+        if last_im_index == 0:
+            index_slider.disabled = True
+        else:
+            index_slider.disabled = False
+            index_slider.end = last_im_index
+
+        zebra_mode = 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):
+        _file_open(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":
+            _file_open(file_select.value[0], None)
+        else:
+            _file_open(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:
@@ -73,7 +170,7 @@ def create():
         )
         image_source.data.update(image=[current_image])
 
-        if auto_toggle.active:
+        if main_auto_checkbox.active:
             im_min = np.min(current_image)
             im_max = np.max(current_image)
 
@@ -83,30 +180,63 @@ def create():
             image_glyph.color_mapper.low = im_min
             image_glyph.color_mapper.high = im_max
 
-        if "magnetic_field" in det_data:
-            magnetic_field_spinner.value = det_data["magnetic_field"][index]
+        if "mf" in det_data:
+            metadata_table_source.data.update(mf=[det_data["mf"][index]])
         else:
-            magnetic_field_spinner.value = None
+            metadata_table_source.data.update(mf=[None])
 
-        if "temperature" in det_data:
-            temperature_spinner.value = det_data["temperature"][index]
+        if "temp" in det_data:
+            metadata_table_source.data.update(temp=[det_data["temp"][index]])
         else:
-            temperature_spinner.value = None
+            metadata_table_source.data.update(temp=[None])
 
         gamma, nu = calculate_pol(det_data, index)
-        omega = np.ones((IMAGE_H, IMAGE_W)) * det_data["rot_angle"][index]
+        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)
 
-        overview_plot_x_image_source.data.update(image=[overview_x], dw=[n_x])
-        overview_plot_y_image_source.data.update(image=[overview_y], dw=[n_y])
+        # 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
 
-        if proj_auto_toggle.active:
+        overview_plot_x_image_source.data.update(image=[overview_x], dw=[n_x], dh=[n_im])
+        overview_plot_y_image_source.data.update(image=[overview_y], dw=[n_y], dh=[n_im])
+
+        if proj_auto_checkbox.active:
             im_min = min(np.min(overview_x), np.min(overview_y))
             im_max = max(np.max(overview_x), np.max(overview_y))
 
@@ -118,46 +248,82 @@ def create():
             overview_plot_x_image_glyph.color_mapper.high = im_max
             overview_plot_y_image_glyph.color_mapper.high = im_max
 
-        if frame_button_group.active == 0:  # Frame
-            overview_plot_x.axis[1].axis_label = "Frame"
-            overview_plot_y.axis[1].axis_label = "Frame"
+        frame_range.start = 0
+        frame_range.end = n_im
+        frame_range.reset_start = 0
+        frame_range.reset_end = n_im
+        frame_range.bounds = (0, n_im)
 
-            overview_plot_x_image_source.data.update(y=[0], dh=[n_im])
-            overview_plot_y_image_source.data.update(y=[0], dh=[n_im])
+        scan_motor = det_data["scan_motor"]
+        overview_plot_y.axis[1].axis_label = f"Scanning motor, {scan_motor}"
 
-        elif frame_button_group.active == 1:  # Omega
-            overview_plot_x.axis[1].axis_label = "Omega"
-            overview_plot_y.axis[1].axis_label = "Omega"
+        var = det_data[scan_motor]
+        var_start = var[0]
+        var_end = var[-1] + (var[-1] - var[0]) / (n_im - 1) if n_im != 1 else var_start + 1
 
-            om = det_data["rot_angle"]
-            om_start = om[0]
-            om_end = (om[-1] - om[0]) * n_im / (n_im - 1)
-            overview_plot_x_image_source.data.update(y=[om_start], dh=[om_end])
-            overview_plot_y_image_source.data.update(y=[om_start], dh=[om_end])
+        scanning_motor_range.start = var_start
+        scanning_motor_range.end = var_end
+        scanning_motor_range.reset_start = var_start
+        scanning_motor_range.reset_end = var_end
+        # handle both, ascending and descending sequences
+        scanning_motor_range.bounds = (min(var_start, var_end), max(var_start, var_end))
 
-    def filelist_callback(_attr, _old, new):
-        nonlocal det_data
-        det_data = pyzebra.read_detector_data(new)
+        gamma = image_source.data["gamma"][0]
+        gamma_start = gamma[0, 0]
+        gamma_end = gamma[0, -1]
 
-        index_spinner.value = 0
-        index_spinner.high = det_data["data"].shape[0] - 1
-        update_image(0)
-        update_overview_plot()
+        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))
 
-    filelist = Select()
-    filelist.on_change("value", filelist_callback)
+        nu = image_source.data["nu"][0]
+        nu_start = nu[0, 0]
+        nu_end = nu[-1, 0]
 
-    def index_spinner_callback(_attr, _old, new):
+        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):
+        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
+            file_select.value = old
+            return
+
+        if len(old) > 1:
+            # skip unnecessary update caused by selection drop
+            return
+
+        file_open_button_callback()
+
+    file_select = MultiSelect(title="Available .hdf files:", width=210, height=250)
+    file_select.on_change("value", file_select_callback)
+
+    def index_callback(_attr, _old, new):
         update_image(new)
 
-    index_spinner = Spinner(title="Image index:", value=0, low=0)
-    index_spinner.on_change("value", index_spinner_callback)
+    index_slider = Slider(value=0, start=0, end=1, show_value=False, width=400)
+
+    index_spinner = Spinner(title="Image index:", value=0, low=0, width=100)
+    index_spinner.on_change("value", index_callback)
+
+    index_slider.js_link("value_throttled", index_spinner, "value")
+    index_spinner.js_link("value", index_slider, "value")
 
     plot = Plot(
         x_range=Range1d(0, IMAGE_W, bounds=(0, IMAGE_W)),
         y_range=Range1d(0, IMAGE_H, bounds=(0, IMAGE_H)),
-        plot_height=IMAGE_H * 3,
-        plot_width=IMAGE_W * 3,
+        plot_height=IMAGE_PLOT_H,
+        plot_width=IMAGE_PLOT_W,
         toolbar_location="left",
     )
 
@@ -206,12 +372,21 @@ def create():
     image_glyph = Image(image="image", x="x", y="y", dw="dw", dh="dh")
     plot.add_glyph(image_source, image_glyph, name="image_glyph")
 
+    # calculate hkl-indices of first mouse entry
+    def mouse_enter_callback(_event):
+        if det_data and np.array_equal(image_source.data["h"][0], np.zeros((1, 1))):
+            index = index_spinner.value
+            h, k, l = calculate_hkl(det_data, index)
+            image_source.data.update(h=[h], k=[k], l=[l])
+
+    plot.on_event(MouseEnter, mouse_enter_callback)
+
     # ---- projections
     proj_v = Plot(
         x_range=plot.x_range,
         y_range=DataRange1d(),
-        plot_height=200,
-        plot_width=IMAGE_W * 3,
+        plot_height=150,
+        plot_width=IMAGE_PLOT_W,
         toolbar_location=None,
     )
 
@@ -227,8 +402,8 @@ def create():
     proj_h = Plot(
         x_range=DataRange1d(),
         y_range=plot.y_range,
-        plot_height=IMAGE_H * 3,
-        plot_width=200,
+        plot_height=IMAGE_PLOT_H,
+        plot_width=150,
         toolbar_location=None,
     )
 
@@ -284,15 +459,20 @@ def create():
     )
     plot.toolbar.active_scroll = wheelzoomtool
 
-    # shared frame range
-    frame_range = DataRange1d()
+    # shared frame ranges
+    frame_range = Range1d(0, 1, bounds=(0, 1))
+    scanning_motor_range = Range1d(0, 1, bounds=(0, 1))
+
     det_x_range = Range1d(0, IMAGE_W, bounds=(0, IMAGE_W))
+    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,
-        plot_height=500,
-        plot_width=IMAGE_W * 3,
+        extra_x_ranges={"gamma": gamma_range},
+        extra_y_ranges={"scanning_motor": scanning_motor_range},
+        plot_height=450,
+        plot_width=IMAGE_PLOT_W - 3,
     )
 
     # ---- tools
@@ -305,6 +485,9 @@ 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"
     )
@@ -324,12 +507,15 @@ 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,
-        plot_height=500,
-        plot_width=IMAGE_H * 3,
+        extra_x_ranges={"nu": nu_range},
+        extra_y_ranges={"scanning_motor": scanning_motor_range},
+        plot_height=450,
+        plot_width=IMAGE_PLOT_H + 22,
     )
 
     # ---- tools
@@ -342,8 +528,14 @@ 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(axis_label="Frame", major_label_orientation="vertical"), place="left"
+        LinearAxis(
+            y_range_name="scanning_motor",
+            axis_label="Scanning motor",
+            major_label_orientation="vertical",
+        ),
+        place="right",
     )
 
     # ---- grid lines
@@ -360,17 +552,11 @@ def create():
         overview_plot_y_image_source, overview_plot_y_image_glyph, name="image_glyph"
     )
 
-    def frame_button_group_callback(_active):
-        update_overview_plot()
-
-    frame_button_group = RadioButtonGroup(labels=["Frames", "Omega"], active=0)
-    frame_button_group.on_click(frame_button_group_callback)
-
     roi_avg_plot = Plot(
         x_range=DataRange1d(),
         y_range=DataRange1d(),
-        plot_height=200,
-        plot_width=IMAGE_W * 3,
+        plot_height=150,
+        plot_width=IMAGE_PLOT_W,
         toolbar_location="left",
     )
 
@@ -400,15 +586,13 @@ def create():
         overview_plot_x_image_glyph.color_mapper = LinearColorMapper(palette=cmap_dict[new])
         overview_plot_y_image_glyph.color_mapper = LinearColorMapper(palette=cmap_dict[new])
 
-    colormap = Select(title="Colormap:", options=list(cmap_dict.keys()), default_size=145)
+    colormap = Select(title="Colormap:", options=list(cmap_dict.keys()), width=210)
     colormap.on_change("value", colormap_callback)
     colormap.value = "plasma"
 
-    radio_button_group = RadioButtonGroup(labels=["normal beam", "bisecting"], active=0)
-
     STEP = 1
-    # ---- colormap auto toggle button
-    def auto_toggle_callback(state):
+
+    def main_auto_checkbox_callback(state):
         if state:
             display_min_spinner.disabled = True
             display_max_spinner.disabled = True
@@ -418,45 +602,43 @@ def create():
 
         update_image()
 
-    auto_toggle = Toggle(
-        label="Main Auto Range", active=True, button_type="default", default_size=125
+    main_auto_checkbox = CheckboxGroup(
+        labels=["Frame Intensity Range"], active=[0], width=145, margin=[10, 5, 0, 5]
     )
-    auto_toggle.on_click(auto_toggle_callback)
+    main_auto_checkbox.on_click(main_auto_checkbox_callback)
 
-    # ---- colormap display max value
     def display_max_spinner_callback(_attr, _old_value, new_value):
         display_min_spinner.high = new_value - STEP
         image_glyph.color_mapper.high = new_value
 
     display_max_spinner = Spinner(
-        title="Max Value:",
         low=0 + STEP,
         value=1,
         step=STEP,
-        disabled=auto_toggle.active,
-        default_size=80,
+        disabled=bool(main_auto_checkbox.active),
+        width=100,
+        height=31,
     )
     display_max_spinner.on_change("value", display_max_spinner_callback)
 
-    # ---- colormap display min value
     def display_min_spinner_callback(_attr, _old_value, new_value):
         display_max_spinner.low = new_value + STEP
         image_glyph.color_mapper.low = new_value
 
     display_min_spinner = Spinner(
-        title="Min Value:",
         low=0,
         high=1 - STEP,
         value=0,
         step=STEP,
-        disabled=auto_toggle.active,
-        default_size=80,
+        disabled=bool(main_auto_checkbox.active),
+        width=100,
+        height=31,
     )
     display_min_spinner.on_change("value", display_min_spinner_callback)
 
-    PROJ_STEP = 0.1
-    # ---- proj colormap auto toggle button
-    def proj_auto_toggle_callback(state):
+    PROJ_STEP = 1
+
+    def proj_auto_checkbox_callback(state):
         if state:
             proj_display_min_spinner.disabled = True
             proj_display_max_spinner.disabled = True
@@ -466,103 +648,221 @@ def create():
 
         update_overview_plot()
 
-    proj_auto_toggle = Toggle(
-        label="Proj Auto Range", active=True, button_type="default", default_size=125
+    proj_auto_checkbox = CheckboxGroup(
+        labels=["Projections Intensity Range"], active=[0], width=145, margin=[10, 5, 0, 5]
     )
-    proj_auto_toggle.on_click(proj_auto_toggle_callback)
+    proj_auto_checkbox.on_click(proj_auto_checkbox_callback)
 
-    # ---- proj colormap display max value
     def proj_display_max_spinner_callback(_attr, _old_value, new_value):
         proj_display_min_spinner.high = new_value - PROJ_STEP
         overview_plot_x_image_glyph.color_mapper.high = new_value
         overview_plot_y_image_glyph.color_mapper.high = new_value
 
     proj_display_max_spinner = Spinner(
-        title="Max Value:",
         low=0 + PROJ_STEP,
         value=1,
         step=PROJ_STEP,
-        disabled=proj_auto_toggle.active,
-        default_size=80,
+        disabled=bool(proj_auto_checkbox.active),
+        width=100,
+        height=31,
     )
     proj_display_max_spinner.on_change("value", proj_display_max_spinner_callback)
 
-    # ---- proj colormap display min value
     def proj_display_min_spinner_callback(_attr, _old_value, new_value):
         proj_display_max_spinner.low = new_value + PROJ_STEP
         overview_plot_x_image_glyph.color_mapper.low = new_value
         overview_plot_y_image_glyph.color_mapper.low = new_value
 
     proj_display_min_spinner = Spinner(
-        title="Min Value:",
         low=0,
         high=1 - PROJ_STEP,
         value=0,
         step=PROJ_STEP,
-        disabled=proj_auto_toggle.active,
-        default_size=80,
+        disabled=bool(proj_auto_checkbox.active),
+        width=100,
+        height=31,
     )
     proj_display_min_spinner.on_change("value", proj_display_min_spinner_callback)
 
-    def hkl_button_callback():
-        index = index_spinner.value
-        geometry = "bi" if radio_button_group.active else "nb"
-        h, k, l = calculate_hkl(det_data, index, geometry)
-        image_source.data.update(h=[h], k=[k], l=[l])
+    events_data = dict(
+        wave=[],
+        ddist=[],
+        cell=[],
+        frame=[],
+        x_pos=[],
+        y_pos=[],
+        intensity=[],
+        snr_cnts=[],
+        gamma=[],
+        omega=[],
+        chi=[],
+        phi=[],
+        nu=[],
+    )
+    doc.events_data = events_data
 
-    hkl_button = Button(label="Calculate hkl (slow)")
-    hkl_button.on_click(hkl_button_callback)
+    events_table_source = ColumnDataSource(events_data)
+    events_table = DataTable(
+        source=events_table_source,
+        columns=[
+            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="intensity", title="Intensity", formatter=num_formatter, width=70),
+            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=630,
+        autosize_mode="none",
+        index_position=None,
+    )
 
-    selection_list = TextAreaInput(rows=7)
+    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 selection_button_callback():
-        nonlocal roi_selection
-        selection = [
-            int(np.floor(det_x_range.start)),
-            int(np.ceil(det_x_range.end)),
-            int(np.floor(det_y_range.start)),
-            int(np.ceil(det_y_range.end)),
+    def add_event_button_callback():
+        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)),
+        )
 
-        filename_id = filelist.value[-8:-4]
-        if filename_id in roi_selection:
-            roi_selection[f"{filename_id}"].append(selection)
-        else:
-            roi_selection[f"{filename_id}"] = [selection]
+        wave = det_data["wave"]
+        ddist = det_data["ddist"]
+        cell = det_data["cell"]
 
-        selection_list.value = str(roi_selection)
+        gamma = det_data["gamma"][0]
+        omega = det_data["omega"][0]
+        nu = det_data["nu"][0]
+        chi = det_data["chi"][0]
+        phi = det_data["phi"][0]
 
-    selection_button = Button(label="Add selection")
-    selection_button.on_click(selection_button_callback)
+        scan_motor = det_data["scan_motor"]
+        var_angle = det_data[scan_motor]
 
-    magnetic_field_spinner = Spinner(
-        title="Magnetic field:", format="0.00", width=145, disabled=True
+        snr_cnts = det_data["fit"]["snr"]
+        frC = det_data["fit"]["frame"]
+
+        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
+
+        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 = det_data["fit"]["intensity"]
+        x_pos = det_data["fit"]["x_pos"]
+        y_pos = det_data["fit"]["y_pos"]
+
+        if det_data["zebra_mode"] == "nb":
+            chi = None
+            phi = None
+
+        events_data["wave"].append(wave)
+        events_data["ddist"].append(ddist)
+        events_data["cell"].append(cell)
+        events_data["frame"].append(frC)
+        events_data["x_pos"].append(x_pos)
+        events_data["y_pos"].append(y_pos)
+        events_data["intensity"].append(intensity)
+        events_data["snr_cnts"].append(snr_cnts)
+        events_data["gamma"].append(gamma)
+        events_data["omega"].append(omega)
+        events_data["chi"].append(chi)
+        events_data["phi"].append(phi)
+        events_data["nu"].append(nu)
+
+        events_table_source.data = events_data
+
+    add_event_button = Button(label="Add peak center", width=145)
+    add_event_button.on_click(add_event_button_callback)
+
+    def remove_event_button_callback():
+        ind2remove = events_table_source.selected.indices
+        for value in events_data.values():
+            for ind in reversed(ind2remove):
+                del value[ind]
+
+        events_table_source.data = events_data
+
+    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]))
+    metadata_table = DataTable(
+        source=metadata_table_source,
+        columns=[
+            TableColumn(field="geom", title="Geometry", width=100),
+            TableColumn(field="temp", title="Temperature", formatter=num_formatter, width=100),
+            TableColumn(field="mf", title="Magnetic Field", formatter=num_formatter, width=100),
+        ],
+        width=300,
+        height=50,
+        autosize_mode="none",
+        index_position=None,
     )
-    temperature_spinner = Spinner(title="Temperature:", format="0.00", width=145, disabled=True)
 
     # Final layout
+    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(
-        row(colormap),
-        row(column(Spacer(height=19), auto_toggle), display_max_spinner, display_min_spinner),
-        row(
-            column(Spacer(height=19), proj_auto_toggle),
-            proj_display_max_spinner,
-            proj_display_min_spinner,
-        ),
+        colormap,
+        main_auto_checkbox,
+        row(display_min_spinner, display_max_spinner),
+        proj_auto_checkbox,
+        row(proj_display_min_spinner, proj_display_max_spinner),
     )
-    geometry_div = Div(text="Geometry:", margin=[5, 5, -5, 5])
-    hkl_layout = column(column(geometry_div, radio_button_group), hkl_button)
-    params_layout = row(magnetic_field_spinner, temperature_spinner)
 
-    layout_controls = row(
-        column(selection_button, selection_list),
-        Spacer(width=20),
-        column(frame_button_group, colormap_layout),
-        Spacer(width=20),
-        column(index_spinner, params_layout, hkl_layout),
+    layout_controls = column(
+        row(metadata_table, index_spinner, column(Spacer(height=25), index_slider)),
+        row(column(add_event_button, remove_event_button), peak_tables),
     )
 
     layout_overview = column(
@@ -574,59 +874,51 @@ def create():
         ),
     )
 
-    upload_div = Div(text="Upload .cami file:")
     tab_layout = row(
-        column(
-            row(column(Spacer(height=5), upload_div), upload_button, filelist),
-            layout_overview,
-            layout_controls,
-        ),
+        column(import_layout, colormap_layout),
+        column(layout_overview, layout_controls),
         column(roi_avg_plot, layout_image),
     )
 
     return Panel(child=tab_layout, title="hdf viewer")
 
 
-def calculate_hkl(det_data, index, geometry):
+def calculate_hkl(det_data, index):
     h = np.empty(shape=(IMAGE_H, IMAGE_W))
     k = np.empty(shape=(IMAGE_H, IMAGE_W))
     l = np.empty(shape=(IMAGE_H, IMAGE_W))
 
     wave = det_data["wave"]
     ddist = det_data["ddist"]
-    gammad = det_data["pol_angle"][index]
-    om = det_data["rot_angle"][index]
-    nud = det_data["tlt_angle"]
-    ub = det_data["UB"]
+    gammad = det_data["gamma"][index]
+    om = det_data["omega"][index]
+    nud = det_data["nu"]
+    ub = det_data["ub"]
+    geometry = det_data["zebra_mode"]
 
     if geometry == "bi":
-        ch = det_data["chi_angle"][index]
-        ph = det_data["phi_angle"][index]
+        chi = det_data["chi"][index]
+        phi = det_data["phi"][index]
     elif geometry == "nb":
-        ch = 0
-        ph = 0
+        chi = 0
+        phi = 0
     else:
         raise ValueError(f"Unknown geometry type '{geometry}'")
 
     for xi in np.arange(IMAGE_W):
         for yi in np.arange(IMAGE_H):
             h[yi, xi], k[yi, xi], l[yi, xi] = pyzebra.ang2hkl(
-                wave, ddist, gammad, om, ch, ph, nud, ub, xi, yi
+                wave, ddist, gammad, om, chi, phi, nud, ub, xi, yi
             )
 
     return h, k, l
 
 
 def calculate_pol(det_data, index):
-    gamma = np.empty(shape=(IMAGE_H, IMAGE_W))
-    nu = np.empty(shape=(IMAGE_H, IMAGE_W))
-
     ddist = det_data["ddist"]
-    gammad = det_data["pol_angle"][index]
-    nud = det_data["tlt_angle"]
-
-    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)
+    gammad = det_data["gamma"][index]
+    nud = det_data["nu"]
+    yi, xi = np.ogrid[:IMAGE_H, :IMAGE_W]
+    gamma, nu = pyzebra.det2pol(ddist, gammad, nud, xi, yi)
 
     return gamma, nu

pyzebra/app/panel_spind.py

@@ -0,0 +1,223 @@
+import os
+import subprocess
+import tempfile
+
+import numpy as np
+from bokeh.io import curdoc
+from bokeh.layouts import column, row
+from bokeh.models import (
+    Button,
+    ColumnDataSource,
+    DataTable,
+    Panel,
+    Spinner,
+    TableColumn,
+    TextAreaInput,
+    TextInput,
+)
+
+import pyzebra
+
+
+def create():
+    doc = curdoc()
+    events_data = doc.events_data
+
+    npeaks_spinner = Spinner(title="Number of peaks from hdf_view panel:", disabled=True)
+    lattice_const_textinput = TextInput(title="Lattice constants:")
+    max_res_spinner = Spinner(title="max-res:", value=2, step=0.01, width=145)
+    seed_pool_size_spinner = Spinner(title="seed-pool-size:", value=5, step=0.01, width=145)
+    seed_len_tol_spinner = Spinner(title="seed-len-tol:", value=0.02, step=0.01, width=145)
+    seed_angle_tol_spinner = Spinner(title="seed-angle-tol:", value=1, step=0.01, width=145)
+    eval_hkl_tol_spinner = Spinner(title="eval-hkl-tol:", value=0.15, step=0.01, width=145)
+
+    diff_vec = []
+    ub_matrices = []
+
+    def process_button_callback():
+        # drop table selection to clear result fields
+        results_table_source.selected.indices = []
+
+        nonlocal diff_vec
+        with tempfile.TemporaryDirectory() as temp_dir:
+            temp_peak_list_dir = os.path.join(temp_dir, "peak_list")
+            os.mkdir(temp_peak_list_dir)
+            temp_event_file = os.path.join(temp_peak_list_dir, "event-0.txt")
+            temp_hkl_file = os.path.join(temp_dir, "hkl.h5")
+
+            comp_proc = subprocess.run(
+                [
+                    "mpiexec",
+                    "-n",
+                    "2",
+                    "python",
+                    os.path.join(doc.spind_path, "gen_hkl_table.py"),
+                    lattice_const_textinput.value,
+                    "--max-res",
+                    str(max_res_spinner.value),
+                    "-o",
+                    temp_hkl_file,
+                ],
+                check=True,
+                stdout=subprocess.PIPE,
+                stderr=subprocess.STDOUT,
+                text=True,
+            )
+            print(" ".join(comp_proc.args))
+            print(comp_proc.stdout)
+
+            # prepare an event file
+            diff_vec = []
+            with open(temp_event_file, "w") as f:
+                npeaks = len(next(iter(doc.events_data.values())))
+                for ind in range(npeaks):
+                    wave = events_data["wave"][ind]
+                    ddist = events_data["ddist"][ind]
+                    x_pos = events_data["x_pos"][ind]
+                    y_pos = events_data["y_pos"][ind]
+                    intensity = events_data["intensity"][ind]
+                    snr_cnts = events_data["snr_cnts"][ind]
+                    gamma = events_data["gamma"][ind]
+                    omega = events_data["omega"][ind]
+                    chi = events_data["chi"][ind]
+                    phi = events_data["phi"][ind]
+                    nu = events_data["nu"][ind]
+
+                    ga, nu = pyzebra.det2pol(ddist, gamma, nu, x_pos, y_pos)
+                    diff_vector = pyzebra.z1frmd(wave, ga, omega, chi, phi, nu)
+                    d_spacing = float(pyzebra.dandth(wave, diff_vector)[0])
+                    diff_vector = diff_vector.flatten() * 1e10
+                    dv1, dv2, dv3 = diff_vector
+
+                    diff_vec.append(diff_vector)
+                    f.write(
+                        f"{x_pos} {y_pos} {intensity} {snr_cnts} {dv1} {dv2} {dv3} {d_spacing}\n"
+                    )
+
+            print(f"Content of {temp_event_file}:")
+            with open(temp_event_file) as f:
+                print(f.read())
+
+            comp_proc = subprocess.run(
+                [
+                    "mpiexec",
+                    "-n",
+                    "2",
+                    "python",
+                    os.path.join(doc.spind_path, "SPIND.py"),
+                    temp_peak_list_dir,
+                    temp_hkl_file,
+                    "-o",
+                    temp_dir,
+                    "--seed-pool-size",
+                    str(seed_pool_size_spinner.value),
+                    "--seed-len-tol",
+                    str(seed_len_tol_spinner.value),
+                    "--seed-angle-tol",
+                    str(seed_angle_tol_spinner.value),
+                    "--eval-hkl-tol",
+                    str(eval_hkl_tol_spinner.value),
+                ],
+                check=True,
+                stdout=subprocess.PIPE,
+                stderr=subprocess.STDOUT,
+                text=True,
+            )
+            print(" ".join(comp_proc.args))
+            print(comp_proc.stdout)
+
+            spind_out_file = os.path.join(temp_dir, "spind.txt")
+            spind_res = dict(
+                label=[], crystal_id=[], match_rate=[], matched_peaks=[], column_5=[], ub_matrix=[],
+            )
+            try:
+                with open(spind_out_file) as f_out:
+                    for line in f_out:
+                        c1, c2, c3, c4, c5, *c_rest = line.split()
+                        spind_res["label"].append(c1)
+                        spind_res["crystal_id"].append(c2)
+                        spind_res["match_rate"].append(c3)
+                        spind_res["matched_peaks"].append(c4)
+                        spind_res["column_5"].append(c5)
+
+                        # last digits are spind UB matrix
+                        vals = list(map(float, c_rest))
+                        ub_matrix_spind = np.transpose(np.array(vals).reshape(3, 3))
+                        ub_matrix = np.linalg.inv(ub_matrix_spind)
+                        ub_matrices.append(ub_matrix)
+                        spind_res["ub_matrix"].append(str(ub_matrix_spind * 1e-10))
+
+                print(f"Content of {spind_out_file}:")
+                with open(spind_out_file) as f:
+                    print(f.read())
+
+            except FileNotFoundError:
+                print("No results from spind")
+
+            results_table_source.data.update(spind_res)
+
+    process_button = Button(label="Process", button_type="primary")
+    process_button.on_click(process_button_callback)
+
+    if doc.spind_path is None:
+        process_button.disabled = True
+
+    ub_matrix_textareainput = TextAreaInput(title="UB matrix:", rows=7, width=400)
+    hkl_textareainput = TextAreaInput(title="hkl values:", rows=7, width=400)
+
+    def results_table_select_callback(_attr, old, new):
+        if new:
+            ind = new[0]
+            ub_matrix = ub_matrices[ind]
+            res = ""
+            for vec in diff_vec:
+                res += f"{ub_matrix @ vec}\n"
+            ub_matrix_textareainput.value = str(ub_matrix * 1e10)
+            hkl_textareainput.value = res
+        else:
+            ub_matrix_textareainput.value = ""
+            hkl_textareainput.value = ""
+
+    results_table_source = ColumnDataSource(
+        dict(label=[], crystal_id=[], match_rate=[], matched_peaks=[], column_5=[], ub_matrix=[])
+    )
+    results_table = DataTable(
+        source=results_table_source,
+        columns=[
+            TableColumn(field="label", title="Label", width=50),
+            TableColumn(field="crystal_id", title="Crystal ID", width=100),
+            TableColumn(field="match_rate", title="Match Rate", width=100),
+            TableColumn(field="matched_peaks", title="Matched Peaks", width=100),
+            TableColumn(field="column_5", title="", width=100),
+            TableColumn(field="ub_matrix", title="UB Matrix", width=700),
+        ],
+        height=300,
+        width=1200,
+        autosize_mode="none",
+        index_position=None,
+    )
+
+    results_table_source.selected.on_change("indices", results_table_select_callback)
+
+    tab_layout = row(
+        column(
+            npeaks_spinner,
+            lattice_const_textinput,
+            row(max_res_spinner, seed_pool_size_spinner),
+            row(seed_len_tol_spinner, seed_angle_tol_spinner),
+            row(eval_hkl_tol_spinner),
+            process_button,
+        ),
+        column(results_table, row(ub_matrix_textareainput, hkl_textareainput)),
+    )
+
+    async def update_npeaks_spinner():
+        npeaks = len(next(iter(doc.events_data.values())))
+        npeaks_spinner.value = npeaks
+        # TODO: check cell parameter for consistency?
+        if npeaks:
+            lattice_const_textinput.value = ",".join(map(str, doc.events_data["cell"][0]))
+
+    doc.add_periodic_callback(update_npeaks_spinner, 1000)
+
+    return Panel(child=tab_layout, title="spind")

pyzebra/ccl_findpeaks.py

@@ -1,75 +0,0 @@
-import numpy as np
-import scipy as sc
-from scipy.interpolate import interp1d
-from scipy.signal import savgol_filter
-
-
-def ccl_findpeaks(
-    scan,
-    int_threshold=0.8,
-    prominence=50,
-    smooth=False,
-    window_size=7,
-    poly_order=3,
-    variable="om",
-):
-
-    """function iterates through the dictionary created by load_cclv2 and locates peaks for each scan
-    args:   scan - a single scan,
-
-            int_threshold - fraction of threshold_intensity/max_intensity, must be positive num between 0 and 1
-                        i.e. will only detect peaks above 75% of max intensity
-
-            prominence - defines a drop of values that must be between two peaks, must be positive number
-                        i.e. if promimence is 20, it will detect two neigbouring peaks of 300 and 310 intesities,
-                        if none of the itermediate values are lower that 290
-
-            smooth  - if true, smooths data by savitzky golay filter, if false - no smoothing
-
-            window_size - window size for savgol filter, must be odd positive integer
-
-            poly_order =  order of the polynomial used in savgol filter, must be positive integer smaller than
-    """
-    if not 0 <= int_threshold <= 1:
-        int_threshold = 0.8
-        print(
-            "Invalid value for int_threshold, select value between 0 and 1, new value set to:",
-            int_threshold,
-        )
-
-    if not isinstance(window_size, int) or (window_size % 2) == 0 or window_size <= 1:
-        window_size = 7
-        print(
-            "Invalid value for window_size, select positive odd integer, new value set to!:",
-            window_size,
-        )
-
-    if not isinstance(poly_order, int) or window_size < poly_order:
-        poly_order = 3
-        print(
-            "Invalid value for poly_order, select positive integer smaller than window_size, new value set to:",
-            poly_order,
-        )
-
-    if not isinstance(prominence, (int, float)) and prominence < 0:
-        prominence = 50
-        print("Invalid value for prominence, select positive number, new value set to:", prominence)
-
-    omega = scan[variable]
-    counts = np.array(scan["Counts"])
-    if smooth:
-        itp = interp1d(omega, counts, kind="linear")
-        absintensity = [abs(number) for number in counts]
-        lowest_intensity = min(absintensity)
-        counts[counts < 0] = lowest_intensity
-        smooth_peaks = savgol_filter(itp(omega), window_size, poly_order)
-
-    else:
-        smooth_peaks = counts
-
-    peaks, properties = sc.signal.find_peaks(
-        smooth_peaks, height=int_threshold * max(smooth_peaks), prominence=prominence
-    )
-    scan["peak_indexes"] = peaks
-    scan["peak_heights"] = properties["peak_heights"]
-    scan["smooth_peaks"] = smooth_peaks  # smoothed curve

pyzebra/ccl_io.py

@@ -58,40 +58,25 @@ META_VARS_FLOAT = (
 
 META_UB_MATRIX = ("ub1j", "ub2j", "ub3j")
 
-CCL_FIRST_LINE = (
-    ("scan_number", int),
-    ("h_index", float),
-    ("k_index", float),
-    ("l_index", float),
-)
+CCL_FIRST_LINE = (("idx", int), ("h", float), ("k", float), ("l", float))
 
 CCL_ANGLES = {
-    "bi": (
-        ("twotheta_angle", float),
-        ("omega_angle", float),
-        ("chi_angle", float),
-        ("phi_angle", float),
-    ),
-    "nb": (
-        ("gamma_angle", float),
-        ("omega_angle", float),
-        ("nu_angle", float),
-        ("unkwn_angle", float),
-    ),
+    "bi": (("twotheta", float), ("omega", float), ("chi", float), ("phi", float)),
+    "nb": (("gamma", float), ("omega", float), ("nu", float), ("skip_angle", float)),
 }
 
 CCL_SECOND_LINE = (
     ("n_points", int),
     ("angle_step", float),
     ("monitor", float),
-    ("temperature", float),
-    ("mag_field", float),
+    ("temp", float),
+    ("mf", float),
     ("date", str),
     ("time", str),
-    ("scan_type", str),
+    ("scan_motor", str),
 )
 
-AREA_METHODS = ("fit_area", "int_area")
+EXPORT_TARGETS = {"fullprof": (".comm", ".incomm"), "jana": (".col", ".incol")}
 
 
 def load_1D(filepath):
@@ -114,61 +99,103 @@ def load_1D(filepath):
 
 
 def parse_1D(fileobj, data_type):
+    metadata = {"data_type": data_type}
+
     # read metadata
-    metadata = {}
     for line in fileobj:
         if "=" in line:
-            variable, value = line.split("=")
+            variable, value = line.split("=", 1)
             variable = variable.strip()
-            if variable in META_VARS_FLOAT:
+            value = value.strip()
+
+            if variable in META_VARS_STR:
+                metadata[variable] = value
+
+            elif variable in META_VARS_FLOAT:
+                if variable == "2-theta":  # fix that angle name not to be an expression
+                    variable = "twotheta"
+                if variable in ("a", "b", "c", "alpha", "beta", "gamma"):
+                    variable += "_cell"
                 metadata[variable] = float(value)
-            elif variable in META_VARS_STR:
-                metadata[variable] = str(value)[:-1].strip()
+
             elif variable in META_UB_MATRIX:
-                metadata[variable] = re.findall(r"[-+]?\d*\.\d+|\d+", str(value))
+                if "ub" not in metadata:
+                    metadata["ub"] = np.zeros((3, 3))
+                row = int(variable[-2]) - 1
+                metadata["ub"][row, :] = list(map(float, value.split()))
 
         if "#data" in line:
             # this is the end of metadata and the start of data section
             break
 
+    # handle older files that don't contain "zebra_mode" metadata
+    if "zebra_mode" not in metadata:
+        metadata["zebra_mode"] = "nb"
+
     # read data
-    scan = {}
+    scan = []
     if data_type == ".ccl":
-        ccl_first_line = (*CCL_FIRST_LINE, *CCL_ANGLES[metadata["zebra_mode"]])
+        ccl_first_line = CCL_FIRST_LINE + CCL_ANGLES[metadata["zebra_mode"]]
         ccl_second_line = CCL_SECOND_LINE
 
         for line in fileobj:
-            d = {}
+            # skip empty/whitespace lines before start of any scan
+            if not line or line.isspace():
+                continue
+
+            s = {}
+            s["export"] = True
 
             # first line
             for param, (param_name, param_type) in zip(line.split(), ccl_first_line):
-                d[param_name] = param_type(param)
+                s[param_name] = param_type(param)
 
             # second line
             next_line = next(fileobj)
             for param, (param_name, param_type) in zip(next_line.split(), ccl_second_line):
-                d[param_name] = param_type(param)
+                s[param_name] = param_type(param)
 
-            d["om"] = np.linspace(
-                d["omega_angle"] - (d["n_points"] / 2) * d["angle_step"],
-                d["omega_angle"] + (d["n_points"] / 2) * d["angle_step"],
-                d["n_points"],
-            )
+            if s["scan_motor"] != "om":
+                raise Exception("Unsupported variable name in ccl file.")
+
+            # "om" -> "omega"
+            s["scan_motor"] = "omega"
+            s["scan_motors"] = ["omega", ]
+            # overwrite metadata, because it only refers to the scan center
+            half_dist = (s["n_points"] - 1) / 2 * s["angle_step"]
+            s["omega"] = np.linspace(s["omega"] - half_dist, s["omega"] + half_dist, s["n_points"])
 
             # subsequent lines with counts
             counts = []
-            while len(counts) < d["n_points"]:
-                counts.extend(map(int, next(fileobj).split()))
-            d["Counts"] = counts
+            while len(counts) < s["n_points"]:
+                counts.extend(map(float, next(fileobj).split()))
+            s["counts"] = np.array(counts)
+            s["counts_err"] = np.sqrt(np.maximum(s["counts"], 1))
 
-            scan[d["scan_number"]] = d
+            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"]))
+
+            scan.append({**metadata, **s})
 
     elif data_type == ".dat":
-        # skip the first 2 rows, the third row contans the column names
-        next(fileobj)
-        next(fileobj)
-        col_names = next(fileobj).split()
-        data_cols = defaultdict(list)
+        # TODO: this might need to be adapted in the future, when "gamma" will be added to dat files
+        if metadata["zebra_mode"] == "nb":
+            metadata["gamma"] = metadata["twotheta"]
+
+        s = defaultdict(list)
+        s["export"] = True
+
+        match = re.search("Scanning Variables: (.*), Steps: (.*)", next(fileobj))
+        motors = [motor.lower() for motor in match.group(1).split(", ")]
+        steps = [float(step) for step in match.group(2).split()]
+
+        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()))
 
         for line in fileobj:
             if "END-OF-DATA" in line:
@@ -176,100 +203,189 @@ def parse_1D(fileobj, data_type):
                 break
 
             for name, val in zip(col_names, line.split()):
-                data_cols[name].append(float(val))
+                s[name].append(float(val))
 
-        try:
-            data_cols["h_index"] = float(metadata["title"].split()[-3])
-            data_cols["k_index"] = float(metadata["title"].split()[-2])
-            data_cols["l_index"] = float(metadata["title"].split()[-1])
-        except (ValueError, IndexError):
-            print("seems hkl is not in title")
+        for name in col_names:
+            s[name] = np.array(s[name])
 
-        data_cols["om"] = np.array(data_cols["om"])
+        s["counts_err"] = np.sqrt(np.maximum(s["counts"], 1))
 
-        data_cols["temperature"] = metadata["temp"]
-        try:
-            data_cols["mag_field"] = metadata["mf"]
-        except KeyError:
-            print("Mag_field not present in dat file")
+        s["scan_motors"] = []
+        for motor, step in zip(motors, steps):
+            if step == 0:
+                # it's not a scan motor, so keep only the median value
+                s[motor] = np.median(s[motor])
+            else:
+                s["scan_motors"].append(motor)
 
-        data_cols["omega_angle"] = metadata["omega"]
-        data_cols["n_points"] = len(data_cols["om"])
-        data_cols["monitor"] = data_cols["Monitor1"][0]
-        data_cols["twotheta_angle"] = metadata["2-theta"]
-        data_cols["chi_angle"] = metadata["chi"]
-        data_cols["phi_angle"] = metadata["phi"]
-        data_cols["nu_angle"] = metadata["nu"]
+        # "om" -> "omega"
+        if "om" in s["scan_motors"]:
+            s["scan_motors"][s["scan_motors"].index("om")] = "omega"
+            s["omega"] = s["om"]
+            del s["om"]
 
-        data_cols["scan_number"] = 1
-        scan[data_cols["scan_number"]] = dict(data_cols)
+        # "tt" -> "temp"
+        if "tt" in s["scan_motors"]:
+            s["scan_motors"][s["scan_motors"].index("tt")] = "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")
+
+        for param in ("mf", "temp"):
+            if param not in metadata:
+                s[param] = 0
+
+        s["idx"] = 1
+
+        scan.append({**metadata, **s})
 
     else:
         print("Unknown file extention")
 
-    # utility information
-    if all(
-        s["h_index"].is_integer() and s["k_index"].is_integer() and s["l_index"].is_integer()
-        for s in scan.values()
-    ):
-        metadata["indices"] = "hkl"
-    else:
-        metadata["indices"] = "real"
-
-    metadata["data_type"] = data_type
-    metadata["area_method"] = AREA_METHODS[0]
-
-    return {"meta": metadata, "scan": scan}
+    return scan
 
 
-def export_comm(data, path, lorentz=False, hkl_precision=2):
-    """exports data in the *.comm format
-    :param lorentz: perform Lorentz correction
-    :param path: path to file + name
-    :arg data - data to export, is dict after peak fitting
+def export_1D(data, path, export_target, hkl_precision=2):
+    """Exports data in the .comm/.incomm format for fullprof or .col/.incol format for jana.
 
+    Scans with integer/real hkl values are saved in .comm/.incomm or .col/.incol files
+    correspondingly. If no scans are present for a particular output format, that file won't be
+    created.
     """
-    zebra_mode = data["meta"]["zebra_mode"]
-    if data["meta"]["indices"] == "hkl":
-        extension = ".comm"
-    else:  # data["meta"]["indices"] == "real":
-        extension = ".incomm"
+    if export_target not in EXPORT_TARGETS:
+        raise ValueError(f"Unknown export target: {export_target}.")
 
-    with open(str(path + extension), "w") as out_file:
-        for key, scan in data["scan"].items():
-            if "fit" not in scan:
-                print("Scan skipped - no fit value for:", key)
-                continue
+    zebra_mode = data[0]["zebra_mode"]
+    exts = EXPORT_TARGETS[export_target]
+    file_content = {ext: [] for ext in exts}
 
-            scan_str = f"{key:6}"
+    for scan in data:
+        if "fit" not in scan:
+            continue
 
-            h, k, l = scan["h_index"], scan["k_index"], scan["l_index"]
-            if data["meta"]["indices"] == "hkl":
-                hkl_str = f"{int(h):6}{int(k):6}{int(l):6}"
-            else:  # data["meta"]["indices"] == "real"
-                hkl_str = f"{h:8.{hkl_precision}f}{k:8.{hkl_precision}f}{l:8.{hkl_precision}f}"
+        idx_str = f"{scan['idx']:6}"
 
-            area_method = data["meta"]["area_method"]
-            area_n = scan["fit"][area_method].n
-            area_s = scan["fit"][area_method].s
+        h, k, l = scan["h"], scan["k"], scan["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}"
 
-            # apply lorentz correction to area
-            if lorentz:
-                if zebra_mode == "bi":
-                    twotheta_angle = np.deg2rad(scan["twotheta_angle"])
-                    corr_factor = np.sin(twotheta_angle)
-                else:  # zebra_mode == "nb":
-                    gamma_angle = np.deg2rad(scan["gamma_angle"])
-                    nu_angle = np.deg2rad(scan["nu_angle"])
-                    corr_factor = np.sin(gamma_angle) * np.cos(nu_angle)
+        area_n, area_s = scan["area"]
+        area_str = f"{area_n:10.2f}{area_s:10.2f}"
 
-                area_n = np.abs(area_n * corr_factor)
-                area_s = np.abs(area_s * corr_factor)
+        ang_str = ""
+        for angle, _ in CCL_ANGLES[zebra_mode]:
+            if angle == scan["scan_motor"]:
+                angle_center = (np.min(scan[angle]) + np.max(scan[angle])) / 2
+            else:
+                angle_center = scan[angle]
 
-            area_str = f"{area_n:10.2f}{area_s:10.2f}"
+            if angle == "twotheta" and export_target == "jana":
+                angle_center /= 2
 
-            ang_str = ""
-            for angle, _ in CCL_ANGLES[zebra_mode]:
-                ang_str = ang_str + f"{scan[angle]:8}"
+            ang_str = ang_str + f"{angle_center:8g}"
 
-            out_file.write(scan_str + hkl_str + area_str + ang_str + "\n")
+        if export_target == "jana":
+            ang_str = ang_str + f"{scan['temp']:8}" + f"{scan['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_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):
+        if "fit" not in scan:
+            continue
+
+        if not file_content:
+            title_str = f"{'param':12}"
+            for fit_param_name in scan["fit"].params:
+                title_str = title_str + f"{fit_param_name:20}" + f"{'std_' + fit_param_name:20}"
+            title_str = title_str + "file"
+            file_content.append(title_str + "\n")
+
+        param_str = f"{param:<12.2f}"
+
+        fit_str = ""
+        for fit_param in scan["fit"].params.values():
+            fit_str = fit_str + f"{fit_param.value:<20.2f}" + f"{fit_param.stderr:<20.2f}"
+
+        _, fname_str = os.path.split(scan["original_filename"])
+
+        file_content.append(param_str + fit_str + fname_str + "\n")
+
+    if file_content:
+        with open(path, "w") as out_file:
+            out_file.writelines(file_content)

pyzebra/ccl_process.py

@@ -0,0 +1,289 @@
+import os
+
+import numpy as np
+from lmfit.models import Gaussian2dModel, GaussianModel, LinearModel, PseudoVoigtModel, VoigtModel
+from scipy.integrate import simpson, trapezoid
+
+from .ccl_io import CCL_ANGLES
+
+PARAM_PRECISIONS = {
+    "twotheta": 0.1,
+    "chi": 0.1,
+    "nu": 0.1,
+    "phi": 0.05,
+    "omega": 0.05,
+    "gamma": 0.05,
+    "temp": 1,
+    "mf": 0.001,
+    "ub": 0.01,
+}
+
+MAX_RANGE_GAP = {
+    "omega": 0.5,
+}
+
+AREA_METHODS = ("fit_area", "int_area")
+
+
+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
+
+
+def merge_duplicates(dataset):
+    merged = np.zeros(len(dataset), dtype=np.bool)
+    for ind_into, scan_into in enumerate(dataset):
+        for ind_from, scan_from in enumerate(dataset[ind_into + 1 :], start=ind_into + 1):
+            if _parameters_match(scan_into, scan_from) and not merged[ind_from]:
+                merge_scans(scan_into, scan_from)
+                merged[ind_from] = True
+
+
+def _parameters_match(scan1, scan2):
+    zebra_mode = scan1["zebra_mode"]
+    if zebra_mode != scan2["zebra_mode"]:
+        return False
+
+    for param in ("ub", "temp", "mf", *(vars[0] for vars in CCL_ANGLES[zebra_mode])):
+        if param.startswith("skip"):
+            # ignore skip parameters, like the last angle in 'nb' zebra mode
+            continue
+
+        if param == scan1["scan_motor"] == scan2["scan_motor"]:
+            # check if ranges of variable parameter overlap
+            range1 = scan1[param]
+            range2 = scan2[param]
+            # maximum gap between ranges of the scanning parameter (default 0)
+            max_range_gap = MAX_RANGE_GAP.get(param, 0)
+            if max(range1[0] - range2[-1], range2[0] - range1[-1]) > max_range_gap:
+                return False
+
+        elif np.max(np.abs(scan1[param] - scan2[param])) > PARAM_PRECISIONS[param]:
+            return False
+
+    return True
+
+
+def merge_datasets(dataset_into, dataset_from):
+    scan_motors_into = dataset_into[0]["scan_motors"]
+    scan_motors_from = dataset_from[0]["scan_motors"]
+    if scan_motors_into != scan_motors_from:
+        print(f"Scan motors mismatch between datasets: {scan_motors_into} vs {scan_motors_from}")
+        return
+
+    merged = np.zeros(len(dataset_from), dtype=np.bool)
+    for scan_into in dataset_into:
+        for ind, scan_from in enumerate(dataset_from):
+            if _parameters_match(scan_into, scan_from) and not merged[ind]:
+                merge_scans(scan_into, scan_from)
+                merged[ind] = True
+
+    for scan_from in dataset_from:
+        dataset_into.append(scan_from)
+
+
+def merge_scans(scan_into, scan_from):
+    if "init_scan" not in scan_into:
+        scan_into["init_scan"] = scan_into.copy()
+
+    if "merged_scans" not in scan_into:
+        scan_into["merged_scans"] = []
+
+    if scan_from in scan_into["merged_scans"]:
+        return
+
+    scan_into["merged_scans"].append(scan_from)
+
+    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) / num_tmp
+
+    scan_from["export"] = False
+
+    fname1 = os.path.basename(scan_into["original_filename"])
+    fname2 = os.path.basename(scan_from["original_filename"])
+    print(f'Merging scans: {scan_into["idx"]} ({fname1}) <-- {scan_from["idx"]} ({fname2})')
+
+
+def restore_scan(scan):
+    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):
+    if fit_from is None:
+        fit_from = -np.inf
+    if fit_to is 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
+    for model_index, (model_name, model_param) in enumerate(model_dict.items()):
+        model_name, _ = model_name.split("-")
+        prefix = f"f{model_index}_"
+
+        if model_name == "linear":
+            _model = LinearModel(prefix=prefix)
+        elif model_name == "gaussian":
+            _model = GaussianModel(prefix=prefix)
+        elif model_name == "voigt":
+            _model = VoigtModel(prefix=prefix)
+        elif model_name == "pvoigt":
+            _model = PseudoVoigtModel(prefix=prefix)
+        else:
+            raise ValueError(f"Unknown model name: '{model_name}'")
+
+        _init_guess = _model.guess(y_fit, x=x_fit)
+
+        for param_index, param_name in enumerate(model_param["param"]):
+            param_hints = {}
+            for hint_name in ("value", "vary", "min", "max"):
+                tmp = model_param[hint_name][param_index]
+                if tmp is None:
+                    param_hints[hint_name] = getattr(_init_guess[prefix + param_name], hint_name)
+                else:
+                    param_hints[hint_name] = tmp
+
+            if "center" in param_name:
+                if np.isneginf(param_hints["min"]):
+                    param_hints["min"] = np.min(x_fit)
+
+                if np.isposinf(param_hints["max"]):
+                    param_hints["max"] = np.max(x_fit)
+
+            if "sigma" in param_name:
+                if np.isposinf(param_hints["max"]):
+                    param_hints["max"] = np.max(x_fit) - np.min(x_fit)
+
+            _model.set_param_hint(param_name, **param_hints)
+
+        if model is None:
+            model = _model
+        else:
+            model += _model
+
+    scan["fit"] = model.fit(y_fit, x=x_fit, weights=1 / y_err)
+
+
+def get_area(scan, area_method, lorentz):
+    if "fit" not in scan:
+        return
+
+    if area_method not in AREA_METHODS:
+        raise ValueError(f"Unknown area method: {area_method}.")
+
+    if area_method == "fit_area":
+        area_v = 0
+        area_s = 0
+        for name, param in scan["fit"].params.items():
+            if "amplitude" in name:
+                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"]
+        x_val = scan[scan["scan_motor"]]
+        y_bkg = scan["fit"].eval_components(x=x_val)["f0_"]
+        area_v = simpson(y_val, x=x_val) - trapezoid(y_bkg, x=x_val)
+        area_s = np.sqrt(area_v)
+
+    if lorentz:
+        # lorentz correction to area
+        if scan["zebra_mode"] == "bi":
+            twotheta = np.deg2rad(scan["twotheta"])
+            corr_factor = np.sin(twotheta)
+        else:  # zebra_mode == "nb":
+            gamma = np.deg2rad(scan["gamma"])
+            nu = np.deg2rad(scan["nu"])
+            corr_factor = np.sin(gamma) * np.cos(nu)
+
+        area_v = np.abs(area_v * corr_factor)
+        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/fit2.py

@@ -1,228 +0,0 @@
-import numpy as np
-import uncertainties as u
-from lmfit import Model, Parameters
-from scipy.integrate import simps
-
-
-def bin_data(array, binsize):
-    if isinstance(binsize, int) and 0 < binsize < len(array):
-        return [
-            np.mean(array[binsize * i : binsize * i + binsize])
-            for i in range(int(np.ceil(len(array) / binsize)))
-        ]
-    else:
-        print("Binsize need to be positive integer smaller than lenght of array")
-        return array
-
-
-def find_nearest(array, value):
-    # find nearest value and return index
-    array = np.asarray(array)
-    idx = (np.abs(array - value)).argmin()
-    return idx
-
-
-def create_uncertanities(y, y_err):
-    # create array with uncertanities for error propagation
-    combined = np.array([])
-    for i in range(len(y)):
-        part = u.ufloat(y[i], y_err[i])
-        combined = np.append(combined, part)
-    return combined
-
-
-def fitccl(
-    scan,
-    guess,
-    vary,
-    constraints_min,
-    constraints_max,
-    numfit_min=None,
-    numfit_max=None,
-    binning=None,
-):
-    """Made for fitting of ccl date where 1 peak is expected. Allows for combination of gaussian and linear model combination
-    :param scan: scan in the data dict (i.e. M123)
-    :param guess: initial guess for the fitting, if none, some values are added automatically in order (see below)
-    :param vary: True if parameter can vary during fitting, False if it to be fixed
-    :param numfit_min: minimal value on x axis for numerical integration - if none is centre of gaussian minus 3 sigma
-    :param numfit_max: maximal value on x axis for numerical integration - if none is centre of gaussian plus 3 sigma
-    :param constraints_min: min constranits value for fit
-    :param constraints_max: max constranits value for fit
-    :param binning : binning of the data
-    :return data dict with additional values
-    order for guess, vary, constraints_min, constraints_max:
-    [Gaussian centre, Gaussian sigma, Gaussian amplitude, background slope, background intercept]
-    examples:
-    guess = [None, None, 100,  0, None]
-    vary = [True, True, True, True,  True]
-    constraints_min = [23, None, 50, 0, 0]
-    constraints_min = [80, None, 1000, 0, 100]
-    """
-    if "peak_indexes" not in scan:
-        scan["peak_indexes"] = []
-
-    if len(scan["peak_indexes"]) > 1:
-        # return in case of more than 1 peaks
-        return
-
-    if binning is None or binning == 0 or binning == 1:
-        x = list(scan["om"])
-        y = list(scan["Counts"])
-        y_err = list(np.sqrt(y)) if scan.get("sigma", None) is None else list(scan["sigma"])
-        if not scan["peak_indexes"]:
-            centre = np.mean(x)
-        else:
-            centre = x[int(scan["peak_indexes"])]
-    else:
-        x = list(scan["om"])
-        if not scan["peak_indexes"]:
-            centre = np.mean(x)
-        else:
-            centre = x[int(scan["peak_indexes"])]
-        x = bin_data(x, binning)
-        y = list(scan["Counts"])
-        y_err = list(np.sqrt(y)) if scan.get("sigma", None) is None else list(scan["sigma"])
-        combined = bin_data(create_uncertanities(y, y_err), binning)
-        y = [combined[i].n for i in range(len(combined))]
-        y_err = [combined[i].s for i in range(len(combined))]
-
-    if len(scan["peak_indexes"]) == 0:
-        # Case for no peak, gaussian in centre, sigma as 20% of range
-        peak_index = find_nearest(x, np.mean(x))
-        guess[0] = centre if guess[0] is None else guess[0]
-        guess[1] = (x[-1] - x[0]) / 5 if guess[1] is None else guess[1]
-        guess[2] = 50 if guess[2] is None else guess[2]
-        guess[3] = 0 if guess[3] is None else guess[3]
-        guess[4] = np.mean(y) if guess[4] is None else guess[4]
-        constraints_min[2] = 0
-
-    elif len(scan["peak_indexes"]) == 1:
-        # case for one peak, takse into account users guesses
-        peak_height = scan["peak_heights"]
-        guess[0] = centre if guess[0] is None else guess[0]
-        guess[1] = 0.1 if guess[1] is None else guess[1]
-        guess[2] = float(peak_height / 10) if guess[2] is None else float(guess[2])
-        guess[3] = 0 if guess[3] is None else guess[3]
-        guess[4] = np.median(x) if guess[4] is None else guess[4]
-        constraints_min[0] = np.min(x) if constraints_min[0] is None else constraints_min[0]
-        constraints_max[0] = np.max(x) if constraints_max[0] is None else constraints_max[0]
-
-    def gaussian(x, g_cen, g_width, g_amp):
-        """1-d gaussian: gaussian(x, amp, cen, wid)"""
-        return (g_amp / (np.sqrt(2 * np.pi) * g_width)) * np.exp(
-            -((x - g_cen) ** 2) / (2 * g_width ** 2)
-        )
-
-    def background(x, slope, intercept):
-        """background"""
-        return slope * (x - centre) + intercept
-
-    mod = Model(gaussian) + Model(background)
-    params = Parameters()
-    params.add_many(
-        ("g_cen", guess[0], bool(vary[0]), np.min(x), np.max(x), None, None),
-        ("g_width", guess[1], bool(vary[1]), constraints_min[1], constraints_max[1], None, None),
-        ("g_amp", guess[2], bool(vary[2]), constraints_min[2], constraints_max[2], None, None),
-        ("slope", guess[3], bool(vary[3]), constraints_min[3], constraints_max[3], None, None),
-        ("intercept", guess[4], bool(vary[4]), constraints_min[4], constraints_max[4], None, None),
-    )
-    # the weighted fit
-    weights = [np.abs(1 / val) if val != 0 else 1 for val in y_err]
-    try:
-        result = mod.fit(y, params, weights=weights, x=x, calc_covar=True)
-    except ValueError:
-        print(f"Couldn't fit scan {scan['scan_number']}")
-        return
-
-    if result.params["g_amp"].stderr is None:
-        result.params["g_amp"].stderr = result.params["g_amp"].value
-    elif result.params["g_amp"].stderr > result.params["g_amp"].value:
-        result.params["g_amp"].stderr = result.params["g_amp"].value
-
-    # u.ufloat to work with uncertanities
-    fit_area = u.ufloat(result.params["g_amp"].value, result.params["g_amp"].stderr)
-    comps = result.eval_components()
-
-    if len(scan["peak_indexes"]) == 0:
-        # for case of no peak, there is no reason to integrate, therefore fit and int are equal
-        int_area = fit_area
-
-    elif len(scan["peak_indexes"]) == 1:
-        gauss_3sigmamin = find_nearest(
-            x, result.params["g_cen"].value - 3 * result.params["g_width"].value
-        )
-        gauss_3sigmamax = find_nearest(
-            x, result.params["g_cen"].value + 3 * result.params["g_width"].value
-        )
-        numfit_min = gauss_3sigmamin if numfit_min is None else find_nearest(x, numfit_min)
-        numfit_max = gauss_3sigmamax if numfit_max is None else find_nearest(x, numfit_max)
-
-        it = -1
-        while abs(numfit_max - numfit_min) < 3:
-            # in the case the peak is very thin and numerical integration would be on zero omega
-            # difference, finds closes values
-            it = it + 1
-            numfit_min = find_nearest(
-                x,
-                result.params["g_cen"].value - 3 * (1 + it / 10) * result.params["g_width"].value,
-            )
-            numfit_max = find_nearest(
-                x,
-                result.params["g_cen"].value + 3 * (1 + it / 10) * result.params["g_width"].value,
-            )
-
-        if x[numfit_min] < np.min(x):
-            # makes sure that the values supplied by user lay in the omega range
-            # can be ommited for users who know what they're doing
-            numfit_min = gauss_3sigmamin
-            print("Minimal integration value outside of x range")
-        elif x[numfit_min] >= x[numfit_max]:
-            numfit_min = gauss_3sigmamin
-            print("Minimal integration value higher than maximal")
-        else:
-            pass
-        if x[numfit_max] > np.max(x):
-            numfit_max = gauss_3sigmamax
-            print("Maximal integration value outside of x range")
-        elif x[numfit_max] <= x[numfit_min]:
-            numfit_max = gauss_3sigmamax
-            print("Maximal integration value lower than minimal")
-        else:
-            pass
-
-        count_errors = create_uncertanities(y, y_err)
-        # create error vector for numerical integration propagation
-        num_int_area = simps(count_errors[numfit_min:numfit_max], x[numfit_min:numfit_max])
-        slope_err = u.ufloat(result.params["slope"].value, result.params["slope"].stderr)
-        # pulls the nominal and error values from fit (slope)
-        intercept_err = u.ufloat(
-            result.params["intercept"].value, result.params["intercept"].stderr
-        )
-        # pulls the nominal and error values from fit (intercept)
-
-        background_errors = np.array([])
-        for j in range(len(x[numfit_min:numfit_max])):
-            # creates nominal and error vector for numerical integration of background
-            bg = slope_err * (x[j] - centre) + intercept_err
-            background_errors = np.append(background_errors, bg)
-
-        num_int_background = simps(background_errors, x[numfit_min:numfit_max])
-        int_area = num_int_area - num_int_background
-
-    d = {}
-    for pars in result.params:
-        d[str(pars)] = (result.params[str(pars)].value, result.params[str(pars)].vary)
-
-    print("Scan", scan["scan_number"])
-    print(result.fit_report())
-
-    d["ratio"] = (result.params["g_amp"].value - int_area.n) / result.params["g_amp"].value
-    d["int_area"] = int_area
-    d["fit_area"] = u.ufloat(result.params["g_amp"].value, result.params["g_amp"].stderr)
-    d["full_report"] = result.fit_report()
-    d["result"] = result
-    d["comps"] = comps
-    d["numfit"] = [numfit_min, numfit_max]
-    d["x_fit"] = x
-    scan["fit"] = d

pyzebra/fitvol3.py

@@ -1,167 +0,0 @@
-import numpy as np
-from lmfit import Model, Parameters
-from scipy.integrate import simps
-import matplotlib.pyplot as plt
-import uncertainties as u
-from lmfit.models import GaussianModel
-from lmfit.models import VoigtModel
-from lmfit.models import PseudoVoigtModel
-
-
-def bin_data(array, binsize):
-    if isinstance(binsize, int) and 0 < binsize < len(array):
-        return [
-            np.mean(array[binsize * i : binsize * i + binsize])
-            for i in range(int(np.ceil(len(array) / binsize)))
-        ]
-    else:
-        print("Binsize need to be positive integer smaller than lenght of array")
-        return array
-
-
-def create_uncertanities(y, y_err):
-    # create array with uncertanities for error propagation
-    combined = np.array([])
-    for i in range(len(y)):
-        part = u.ufloat(y[i], y_err[i])
-        combined = np.append(combined, part)
-    return combined
-
-
-def find_nearest(array, value):
-    # find nearest value and return index
-    array = np.asarray(array)
-    idx = (np.abs(array - value)).argmin()
-    return idx
-
-
-# predefined peak positions
-# peaks = [6.2,  8.1, 9.9, 11.5]
-peaks = [23.5, 24.5]
-# peaks = [24]
-def fitccl(scan, variable="om", peak_type="gauss", binning=None):
-
-    x = list(scan[variable])
-    y = list(scan["Counts"])
-    peak_centre = np.mean(x)
-    if binning is None or binning == 0 or binning == 1:
-        x = list(scan["om"])
-        y = list(scan["Counts"])
-        y_err = list(np.sqrt(y)) if scan.get("sigma", None) is None else list(scan["sigma"])
-        print(scan["peak_indexes"])
-        if not scan["peak_indexes"]:
-            peak_centre = np.mean(x)
-        else:
-            centre = x[int(scan["peak_indexes"])]
-    else:
-        x = list(scan["om"])
-        if not scan["peak_indexes"]:
-            peak_centre = np.mean(x)
-        else:
-            peak_centre = x[int(scan["peak_indexes"])]
-        x = bin_data(x, binning)
-        y = list(scan["Counts"])
-        y_err = list(np.sqrt(y)) if scan.get("sigma", None) is None else list(scan["sigma"])
-        combined = bin_data(create_uncertanities(y, y_err), binning)
-        y = [combined[i].n for i in range(len(combined))]
-        y_err = [combined[i].s for i in range(len(combined))]
-
-    def background(x, slope, intercept):
-        """background"""
-        return slope * (x - peak_centre) + intercept
-
-    def gaussian(x, center, g_sigma, amplitude):
-        """1-d gaussian: gaussian(x, amp, cen, wid)"""
-        return (amplitude / (np.sqrt(2.0 * np.pi) * g_sigma)) * np.exp(
-            -((x - center) ** 2) / (2 * g_sigma ** 2)
-        )
-
-    def lorentzian(x, center, l_sigma, amplitude):
-        """1d lorentzian"""
-        return (amplitude / (1 + ((1 * x - center) / l_sigma) ** 2)) / (np.pi * l_sigma)
-
-    def pseudoVoigt1(x, center, g_sigma, amplitude, l_sigma, fraction):
-        """PseudoVoight peak with different widths of lorenzian and gaussian"""
-        return (1 - fraction) * gaussian(x, center, g_sigma, amplitude) + fraction * (
-            lorentzian(x, center, l_sigma, amplitude)
-        )
-
-    mod = Model(background)
-    params = Parameters()
-    params.add_many(
-        ("slope", 0, True, None, None, None, None), ("intercept", 0, False, None, None, None, None)
-    )
-    for i in range(len(peaks)):
-        if peak_type == "gauss":
-            mod = mod + GaussianModel(prefix="p%d_" % (i + 1))
-            params.add(str("p%d_" % (i + 1) + "amplitude"), 20, True, 0, None, None)
-            params.add(str("p%d_" % (i + 1) + "center"), peaks[i], True, None, None, None)
-            params.add(str("p%d_" % (i + 1) + "sigma"), 0.2, True, 0, 5, None)
-        elif peak_type == "voigt":
-            mod = mod + VoigtModel(prefix="p%d_" % (i + 1))
-            params.add(str("p%d_" % (i + 1) + "amplitude"), 20, True, 0, None, None)
-            params.add(str("p%d_" % (i + 1) + "center"), peaks[i], True, None, None, None)
-            params.add(str("p%d_" % (i + 1) + "sigma"), 0.2, True, 0, 3, None)
-            params.add(str("p%d_" % (i + 1) + "gamma"), 0.2, True, 0, 5, None)
-        elif peak_type == "pseudovoigt":
-            mod = mod + PseudoVoigtModel(prefix="p%d_" % (i + 1))
-            params.add(str("p%d_" % (i + 1) + "amplitude"), 20, True, 0, None, None)
-            params.add(str("p%d_" % (i + 1) + "center"), peaks[i], True, None, None, None)
-            params.add(str("p%d_" % (i + 1) + "sigma"), 0.2, True, 0, 5, None)
-            params.add(str("p%d_" % (i + 1) + "fraction"), 0.5, True, -5, 5, None)
-        elif peak_type == "pseudovoigt1":
-            mod = mod + Model(pseudoVoigt1, prefix="p%d_" % (i + 1))
-            params.add(str("p%d_" % (i + 1) + "amplitude"), 20, True, 0, None, None)
-            params.add(str("p%d_" % (i + 1) + "center"), peaks[i], True, None, None, None)
-            params.add(str("p%d_" % (i + 1) + "g_sigma"), 0.2, True, 0, 5, None)
-            params.add(str("p%d_" % (i + 1) + "l_sigma"), 0.2, True, 0, 5, None)
-            params.add(str("p%d_" % (i + 1) + "fraction"), 0.5, True, 0, 1, None)
-    # add parameters
-
-    result = mod.fit(
-        y, params, weights=[np.abs(1 / y_err[i]) for i in range(len(y_err))], x=x, calc_covar=True
-    )
-
-    comps = result.eval_components()
-
-    reportstring = list()
-    for keys in result.params:
-        if result.params[keys].value is not None:
-            str2 = np.around(result.params[keys].value, 3)
-        else:
-            str2 = 0
-        if result.params[keys].stderr is not None:
-            str3 = np.around(result.params[keys].stderr, 3)
-        else:
-            str3 = 0
-        reportstring.append("%s = %2.3f +/- %2.3f" % (keys, str2, str3))
-
-    reportstring = "\n".join(reportstring)
-
-    plt.figure(figsize=(20, 10))
-    plt.plot(x, result.best_fit, "k-", label="Best fit")
-
-    plt.plot(x, y, "b-", label="Original data")
-    plt.plot(x, comps["background"], "g--", label="Line component")
-    for i in range(len(peaks)):
-        plt.plot(
-            x,
-            comps[str("p%d_" % (i + 1))],
-            "r--",
-        )
-        plt.fill_between(x, comps[str("p%d_" % (i + 1))], alpha=0.4, label=str("p%d_" % (i + 1)))
-    plt.legend()
-    plt.text(
-        np.min(x),
-        np.max(y),
-        reportstring,
-        fontsize=9,
-        verticalalignment="top",
-    )
-    plt.title(str(peak_type))
-
-    plt.xlabel("Omega [deg]")
-    plt.ylabel("Counts [a.u.]")
-    plt.show()
-
-    print(result.fit_report())

pyzebra/h5.py

@@ -1,6 +1,11 @@
 import h5py
+import numpy as np
 
 
+META_MATRIX = ("UB")
+META_CELL = ("cell")
+META_STR = ("name")
+
 def read_h5meta(filepath):
     """Open and parse content of a h5meta file.
 
@@ -23,25 +28,44 @@ def parse_h5meta(file):
         line = line.strip()
         if line.startswith("#begin "):
             section = line[len("#begin ") :]
-            content[section] = []
+            if section in ("detector parameters", "crystal"):
+                content[section] = {}
+            else:
+                content[section] = []
 
         elif line.startswith("#end"):
             section = None
 
         elif section:
-            content[section].append(line)
+            if section in ("detector parameters", "crystal"):
+                if "=" in line:
+                    variable, value = line.split("=", 1)
+                    variable = variable.strip()
+                    value = value.strip()
+
+                    if variable in META_STR:
+                        pass
+                    elif variable in META_CELL:
+                        value = np.array(value.split(",")[:6], dtype=np.float)
+                    elif variable in META_MATRIX:
+                        value = np.array(value.split(",")[:9], dtype=np.float).reshape(3, 3)
+                    else:  # default is a single float number
+                        value = float(value)
+                    content[section][variable] = value
+            else:
+                content[section].append(line)
 
     return content
 
 
-def read_detector_data(filepath):
+def read_detector_data(filepath, cami_meta=None):
     """Read detector data and angles from an h5 file.
 
     Args:
         filepath (str): File path of an h5 file.
 
     Returns:
-        ndarray: A 3D array of data, rot_angle, pol_angle, tilt_angle.
+        ndarray: A 3D array of data, omega, gamma, nu.
     """
     with h5py.File(filepath, "r") as h5f:
         data = h5f["/entry1/area_detector2/data"][:]
@@ -51,21 +75,68 @@ def read_detector_data(filepath):
         data = data.reshape(n, rows, cols)
 
         det_data = {"data": data}
+        det_data["original_filename"] = filepath
 
-        det_data["rot_angle"] = h5f["/entry1/area_detector2/rotation_angle"][:]  # om, sometimes ph
-        det_data["pol_angle"] = h5f["/entry1/ZEBRA/area_detector2/polar_angle"][:]  # gammad
-        det_data["tlt_angle"] = h5f["/entry1/ZEBRA/area_detector2/tilt_angle"][:]  # nud
+        if "/entry1/zebra_mode" in h5f:
+            det_data["zebra_mode"] = h5f["/entry1/zebra_mode"][0].decode()
+        else:
+            det_data["zebra_mode"] = "nb"
+
+        # overwrite zebra_mode from cami
+        if cami_meta is not None:
+            if "zebra_mode" in cami_meta:
+                det_data["zebra_mode"] = cami_meta["zebra_mode"][0]
+
+        # om, sometimes ph
+        if det_data["zebra_mode"] == "nb":
+            det_data["omega"] = h5f["/entry1/area_detector2/rotation_angle"][:]
+        else:  # bi
+            det_data["omega"] = h5f["/entry1/sample/rotation_angle"][:]
+
+        det_data["gamma"] = h5f["/entry1/ZEBRA/area_detector2/polar_angle"][:]  # gammad
+        det_data["nu"] = h5f["/entry1/ZEBRA/area_detector2/tilt_angle"][:]  # nud
         det_data["ddist"] = h5f["/entry1/ZEBRA/area_detector2/distance"][:]
         det_data["wave"] = h5f["/entry1/ZEBRA/monochromator/wavelength"][:]
-        det_data["chi_angle"] = h5f["/entry1/sample/chi"][:]  # ch
-        det_data["phi_angle"] = h5f["/entry1/sample/phi"][:]  # ph
-        det_data["UB"] = h5f["/entry1/sample/UB"][:].reshape(3, 3)
+        det_data["chi"] = h5f["/entry1/sample/chi"][:]  # ch
+        det_data["phi"] = h5f["/entry1/sample/phi"][:]  # ph
+        det_data["ub"] = h5f["/entry1/sample/UB"][:].reshape(3, 3)
+        det_data["name"] = h5f["/entry1/sample/name"][0].decode()
+        det_data["cell"] = h5f["/entry1/sample/cell"][:]
+
+        if n == 1:
+            # a default motor for a single frame file
+            det_data["scan_motor"] = "omega"
+        else:
+            for var in ("omega", "gamma", "nu", "chi", "phi"):
+                if abs(det_data[var][0] - det_data[var][-1]) > 0.1:
+                    det_data["scan_motor"] = var
+                    break
+            else:
+                raise ValueError("No angles that vary")
 
         # optional parameters
         if "/entry1/sample/magnetic_field" in h5f:
-            det_data["magnetic_field"] = h5f["/entry1/sample/magnetic_field"][:]
+            det_data["mf"] = h5f["/entry1/sample/magnetic_field"][:]
 
         if "/entry1/sample/temperature" in h5f:
-            det_data["temperature"] = h5f["/entry1/sample/temperature"][:]
+            det_data["temp"] = h5f["/entry1/sample/temperature"][:]
+
+        # overwrite metadata from .cami
+        if cami_meta is not None:
+            if "crystal" in cami_meta:
+                cami_meta_crystal = cami_meta["crystal"]
+                if "name" in cami_meta_crystal:
+                    det_data["name"] = cami_meta_crystal["name"]
+                if "UB" in cami_meta_crystal:
+                    det_data["ub"] = cami_meta_crystal["UB"]
+                if "cell" in cami_meta_crystal:
+                    det_data["cell"] = cami_meta_crystal["cell"]
+                if "lambda" in cami_meta_crystal:
+                    det_data["wave"] = cami_meta_crystal["lambda"]
+
+            if "detector parameters" in cami_meta:
+                cami_meta_detparam = cami_meta["detector parameters"]
+                if "dist2" in cami_meta_detparam:
+                    det_data["ddist"] = cami_meta_detparam["dist2"]
 
     return det_data

pyzebra/merge_function.py

@@ -1,302 +0,0 @@
-import numpy as np
-import uncertainties as u
-
-
-def create_tuples(x, y, y_err):
-    """creates tuples for sorting and merginng of the data
-    Counts need to be normalized to monitor before"""
-    t = list()
-    for i in range(len(x)):
-        tup = (x[i], y[i], y_err[i])
-        t.append(tup)
-    return t
-
-
-def normalize(scan, monitor):
-    """Normalizes the measurement to monitor, checks if sigma exists, otherwise creates it
-    :arg dict : dictionary to from which to tkae the scan
-    :arg key : which scan to normalize from dict1
-    :arg monitor : final monitor
-    :return counts - normalized counts
-    :return sigma - normalized sigma"""
-
-    counts = np.array(scan["Counts"])
-    sigma = np.sqrt(counts) if "sigma" not in scan else scan["sigma"]
-    monitor_ratio = monitor / scan["monitor"]
-    scaled_counts = counts * monitor_ratio
-    scaled_sigma = np.array(sigma) * monitor_ratio
-
-    return scaled_counts, scaled_sigma
-
-
-def merge(scan1, scan2, keep=True, monitor=100000):
-    """merges the two tuples and sorts them, if om value is same, Counts value is average
-    averaging is propagated into sigma if dict1 == dict2, key[1] is deleted after merging
-    :arg dict1 : dictionary to which measurement will be merged
-    :arg dict2 : dictionary from which measurement will be merged
-    :arg scand_dict_result : result of scan_dict after auto function
-    :arg keep : if true, when monitors are same, does not change it, if flase, takes monitor
-    always
-    :arg monitor : final monitor after merging
-    note: dict1 and dict2 can be same dict
-    :return dict1 with merged scan"""
-
-    if keep:
-        if scan1["monitor"] == scan2["monitor"]:
-            monitor = scan1["monitor"]
-
-    # load om and Counts
-    x1, x2 = scan1["om"], scan2["om"]
-    cor_y1, y_err1 = normalize(scan1, monitor=monitor)
-    cor_y2, y_err2 = normalize(scan2, monitor=monitor)
-    # creates touples (om, Counts, sigma) for sorting and further processing
-    tuple_list = create_tuples(x1, cor_y1, y_err1) + create_tuples(x2, cor_y2, y_err2)
-    # Sort the list on om and add 0 0 0 tuple to the last position
-    sorted_t = sorted(tuple_list, key=lambda tup: tup[0])
-    sorted_t.append((0, 0, 0))
-    om, Counts, sigma = [], [], []
-    seen = list()
-    for i in range(len(sorted_t) - 1):
-        if sorted_t[i][0] not in seen:
-            if sorted_t[i][0] != sorted_t[i + 1][0]:
-                om = np.append(om, sorted_t[i][0])
-                Counts = np.append(Counts, sorted_t[i][1])
-                sigma = np.append(sigma, sorted_t[i][2])
-            else:
-                om = np.append(om, sorted_t[i][0])
-                counts1, counts2 = sorted_t[i][1], sorted_t[i + 1][1]
-                sigma1, sigma2 = sorted_t[i][2], sorted_t[i + 1][2]
-                count_err1 = u.ufloat(counts1, sigma1)
-                count_err2 = u.ufloat(counts2, sigma2)
-                avg = (count_err1 + count_err2) / 2
-                Counts = np.append(Counts, avg.n)
-                sigma = np.append(sigma, avg.s)
-                seen.append(sorted_t[i][0])
-        else:
-            continue
-    scan1["om"] = om
-    scan1["Counts"] = Counts
-    scan1["sigma"] = sigma
-    scan1["monitor"] = monitor
-    print("merging done")
-
-
-def check_UB(dict1, dict2, precision=0.01):
-    truth_list = list()
-    for i in ["ub1j", "ub2j", "ub3j"]:
-        for j in range(3):
-            if abs(abs(float(dict1["meta"][i][j])) - abs(float(dict2["meta"][i][j]))) < precision:
-
-                truth_list.append(True)
-            else:
-                truth_list.append(False)
-
-    # print(truth_list)
-    if all(truth_list):
-        return True
-    else:
-        return False
-
-
-def check_zebramode(dict1, dict2):
-    if dict1["meta"]["zebra_mode"] == dict2["meta"]["zebra_mode"]:
-        return True
-    else:
-        return False
-
-
-def check_angles(scan1, scan2, angles, precision):
-    truth_list = list()
-    for item in angles:
-        if abs(abs(scan1[item]) - abs(scan2[item])) <= precision[item]:
-            truth_list.append(True)
-        else:
-            truth_list.append(False)
-    if all(truth_list):
-        return True
-    else:
-        return False
-
-
-def check_temp_mag(scan1, scan2):
-    temp_diff = 1
-    mag_diff = 0.001
-    truth_list = list()
-    try:
-        if abs(abs(scan1["mag_field"]) - abs(scan2["mag_field"])) <= mag_diff:
-            truth_list.append(True)
-        else:
-            truth_list.append(False)
-    except KeyError:
-        print("mag_field missing")
-
-    try:
-        if abs(abs(scan1["temperature"]) - abs(scan2["temperature"])) <= temp_diff:
-            truth_list.append(True)
-        else:
-            truth_list.append(False)
-    except KeyError:
-        print("temperature missing")
-
-    if all(truth_list):
-        return True
-    else:
-        return False
-
-
-def merge_dups(dictionary, angles):
-    precision = {
-        "twotheta_angle": 0.1,
-        "chi_angle": 0.1,
-        "nu_angle": 0.1,
-        "phi_angle": 0.05,
-        "omega_angle": 0.05,
-        "gamma_angle": 0.05,
-    }
-
-    for i in list(dictionary["scan"]):
-        for j in list(dictionary["scan"]):
-            if i == j:
-                continue
-            else:
-                # print(i, j)
-                if check_angles(dictionary["scan"][i], dictionary["scan"][j], angles, precision):
-                    merge(dictionary["scan"][i], dictionary["scan"][j])
-                    print("merged %d with %d" % (i, j))
-
-                    del dictionary["scan"][j]
-                    merge_dups(dictionary, angles)
-                    break
-        else:
-            continue
-        break
-
-
-def add_scan(dict1, dict2, scan_to_add):
-    max_scan = np.max(list(dict1["scan"]))
-    dict1["scan"][max_scan + 1] = dict2["scan"][scan_to_add]
-    if dict1.get("extra_meta") is None:
-        dict1["extra_meta"] = {}
-    dict1["extra_meta"][max_scan + 1] = dict2["meta"]
-    del dict2["scan"][scan_to_add]
-
-
-def process(dict1, dict2, angles, precision):
-    # stop when the second dict is empty
-    # print(dict2["scan"])
-    if dict2["scan"]:
-        print("doing something")
-        # check UB matrixes
-        if check_UB(dict1, dict2):
-            # iterate over second dict and check for matches
-            for i in list(dict2["scan"]):
-                for j in list(dict1["scan"]):
-                    if check_angles(dict1["scan"][j], dict2["scan"][i], angles, precision):
-                        # angles good, see the mag and temp
-                        if check_temp_mag(dict1["scan"][j], dict2["scan"][i]):
-                            merge(dict1["scan"][j], dict2["scan"][i])
-                            print("merged")
-                            del dict2["scan"][i]
-                            process(dict1, dict2, angles, precision)
-                            break
-                        else:
-                            add_scan(dict1, dict2, i)
-                            print("scan added r")
-                            process(dict1, dict2, angles, precision)
-                            break
-                    else:
-                        add_scan(dict1, dict2, i)
-                        print("scan added l")
-                        process(dict1, dict2, angles, precision)
-                        break
-                else:
-                    continue
-                break
-
-        else:
-            # ask user if he really wants to add
-            print("UBs are different, do you really wish to add  datasets? Y/N")
-            dict1 = add_dict(dict1, dict2)
-    return
-
-
-"""
-    1. check for bisecting or normal beam geometry in data files; select stt, om, chi, phi for bisecting; select stt, om, nu for normal beam
-    2. in the ccl files, check for identical stt, chi and nu within 0.1 degree, and, at the same time, for identical om and phi within 0.05 degree;
-    3. in the dat files, check for identical stt, chi and nu within 0.1 degree, and, at the same time,
-    for identical phi within 0.05 degree, and, at the same time, for identical om within 5 degree."""
-
-
-def unified_merge(dict1, dict2):
-    if not check_zebramode(dict1, dict2):
-        print("You are trying to add two files with different zebra mdoe")
-        return
-
-    # decide angles
-    if dict1["meta"]["zebra_mode"] == "bi":
-        angles = ["twotheta_angle", "omega_angle", "chi_angle", "phi_angle"]
-    elif dict1["meta"]["zebra_mode"] == "nb":
-        angles = ["gamma_angle", "omega_angle", "nu_angle"]
-
-    # precision of angles to check
-    precision = {
-        "twotheta_angle": 0.1,
-        "chi_angle": 0.1,
-        "nu_angle": 0.1,
-        "phi_angle": 0.05,
-        "omega_angle": 5,
-        "gamma_angle": 0.05,
-    }
-    if (dict1["meta"]["data_type"] == "ccl") and (dict2["meta"]["data_type"] == "ccl"):
-        precision["omega_angle"] = 0.05
-
-    # check for duplicates in original files
-    for d in dict1, dict2:
-        # no duplicates in dats
-        if d["meta"]["data_type"] == "dat":
-            continue
-        else:
-            merge_dups(d, angles)
-
-    process(dict1, dict2, angles, precision)
-
-
-def add_dict(dict1, dict2):
-    """adds two dictionaries, meta of the new is saved as meata+original_filename and
-    measurements are shifted to continue with numbering of first dict
-    :arg dict1 : dictionarry to add to
-    :arg dict2 : dictionarry from which to take the measurements
-    :return dict1 : combined dictionary
-    Note: dict1 must be made from ccl, otherwise we would have to change the structure of loaded
-    dat file"""
-    try:
-        if dict1["meta"]["zebra_mode"] != dict2["meta"]["zebra_mode"]:
-            print("You are trying to add scans measured with different zebra modes")
-            return
-    # this is for the qscan case
-    except KeyError:
-        print("Zebra mode not specified")
-    max_measurement_dict1 = max([keys for keys in dict1["scan"]])
-    new_filenames = np.arange(
-        max_measurement_dict1 + 1, max_measurement_dict1 + 1 + len(dict2["scan"])
-    )
-
-    if dict1.get("extra_meta") is None:
-        dict1["extra_meta"] = {}
-
-    new_meta_name = "meta" + str(dict2["meta"]["original_filename"])
-    if new_meta_name not in dict1:
-        for keys, name in zip(dict2["scan"], new_filenames):
-            dict2["scan"][keys]["file_of_origin"] = str(dict2["meta"]["original_filename"])
-            dict1["scan"][name] = dict2["scan"][keys]
-            dict1["extra_meta"][name] = dict2["meta"]
-
-        dict1[new_meta_name] = dict2["meta"]
-    else:
-        raise KeyError(
-            str(
-                "The file %s has alredy been added to %s"
-                % (dict2["meta"]["original_filename"], dict1["meta"]["original_filename"])
-            )
-        )
-    return dict1

pyzebra/param_study_moduls.py

@@ -1,488 +0,0 @@
-import pickle
-
-import matplotlib as mpl
-import matplotlib.pyplot as plt
-import numpy as np
-import pandas as pd
-import scipy.io as sio
-import uncertainties as u
-from mpl_toolkits.mplot3d import Axes3D  # dont delete, otherwise waterfall wont work
-import collections
-
-from .ccl_io import load_1D
-
-def create_tuples(x, y, y_err):
-    """creates tuples for sorting and merginng of the data
-    Counts need to be normalized to monitor before"""
-    t = list()
-    for i in range(len(x)):
-        tup = (x[i], y[i], y_err[i])
-        t.append(tup)
-    return t
-
-
-def load_dats(filepath):
-    """reads the txt file, get headers and data
-    :arg filepath to txt file or list of filepaths to the files
-    :return ccl like dictionary"""
-    if isinstance(filepath, str):
-        data_type = "txt"
-        file_list = list()
-        with open(filepath, "r") as infile:
-            col_names = next(infile).split(",")
-            col_names = [col_names[i].rstrip() for i in range(len(col_names))]
-            for line in infile:
-                if "END" in line:
-                    break
-                file_list.append(tuple(line.split(",")))
-    elif isinstance(filepath, list):
-        data_type = "list"
-        file_list = filepath
-    dict1 = {}
-    for i in range(len(file_list)):
-        if not dict1:
-            if data_type == "txt":
-                dict1 = load_1D(file_list[0][0])
-            else:
-                dict1 = load_1D(file_list[0])
-        else:
-            if data_type == "txt":
-                dict1 = add_dict(dict1, load_1D(file_list[i][0]))
-            else:
-
-                dict1 = add_dict(dict1, load_1D(file_list[i]))
-        dict1["scan"][i + 1]["params"] = {}
-        if data_type == "txt":
-            for x in range(len(col_names) - 1):
-                dict1["scan"][i + 1]["params"][col_names[x + 1]] = float(file_list[i][x + 1])
-    return dict1
-
-
-def create_dataframe(dict1, variables):
-    """Creates pandas dataframe from the dictionary
-    :arg ccl like dictionary
-    :return pandas dataframe"""
-    # create dictionary to which we pull only wanted items before transforming it to pd.dataframe
-    pull_dict = {}
-    pull_dict["filenames"] = list()
-    for keys in variables:
-        for item in variables[keys]:
-            pull_dict[item] = list()
-    pull_dict["fit_area"] = list()
-    pull_dict["int_area"] = list()
-    pull_dict["Counts"] = list()
-
-    for keys in pull_dict:
-        print(keys)
-
-    # populate the dict
-    for keys in dict1["scan"]:
-        if "file_of_origin" in dict1["scan"][keys]:
-            pull_dict["filenames"].append(dict1["scan"][keys]["file_of_origin"].split("/")[-1])
-        else:
-            pull_dict["filenames"].append(dict1["meta"]["original_filename"].split("/")[-1])
-
-        pull_dict["fit_area"].append(dict1["scan"][keys]["fit"]["fit_area"])
-        pull_dict["int_area"].append(dict1["scan"][keys]["fit"]["int_area"])
-        pull_dict["Counts"].append(dict1["scan"][keys]["Counts"])
-        for key in variables:
-            for i in variables[key]:
-                pull_dict[i].append(_finditem(dict1["scan"][keys], i))
-
-    return pd.DataFrame(data=pull_dict)
-
-
-def sort_dataframe(dataframe, sorting_parameter):
-    """sorts the data frame and resets index"""
-    data = dataframe.sort_values(by=sorting_parameter)
-    data = data.reset_index(drop=True)
-    return data
-
-
-def make_graph(data, sorting_parameter, style):
-    """Makes the graph from the data based on style and sorting parameter
-    :arg data : pandas dataframe with data after sorting
-    :arg sorting_parameter to pull the correct variable and name
-    :arg style of the graph - waterfall, scatter, heatmap
-    :return matplotlib figure"""
-    if style == "waterfall":
-        mpl.rcParams["legend.fontsize"] = 10
-        fig = plt.figure()
-        ax = fig.gca(projection="3d")
-        for i in range(len(data)):
-            x = data["om"][i]
-            z = data["Counts"][i]
-            yy = [data[sorting_parameter][i]] * len(x)
-            ax.plot(x, yy, z, label=str("%s = %f" % (sorting_parameter, yy[i])))
-
-        ax.legend()
-        ax.set_xlabel("Omega")
-        ax.set_ylabel(sorting_parameter)
-        ax.set_zlabel("counts")
-
-    elif style == "scatter":
-        fig = plt.figure()
-        plt.errorbar(
-            data[sorting_parameter],
-            [data["fit_area"][i].n for i in range(len(data["fit_area"]))],
-            [data["fit_area"][i].s for i in range(len(data["fit_area"]))],
-            capsize=5,
-            ecolor="green",
-        )
-        plt.xlabel(str(sorting_parameter))
-        plt.ylabel("Intesity")
-
-    elif style == "heat":
-        new_om = list()
-        for i in range(len(data)):
-            new_om = np.append(new_om, np.around(data["om"][i], 2), axis=0)
-        unique_om = np.unique(new_om)
-        color_matrix = np.zeros(shape=(len(data), len(unique_om)))
-        for i in range(len(data)):
-            for j in range(len(data["om"][i])):
-                if np.around(data["om"][i][j], 2) in np.unique(new_om):
-                    color_matrix[i, j] = data["Counts"][i][j]
-                else:
-                    continue
-
-        fig = plt.figure()
-        plt.pcolormesh(unique_om, data[sorting_parameter], color_matrix, shading="gouraud")
-        plt.xlabel("omega")
-        plt.ylabel(sorting_parameter)
-        plt.colorbar()
-        plt.clim(color_matrix.mean(), color_matrix.max())
-
-    return fig
-
-
-def save_dict(obj, name):
-    """saves dictionary as pickle file in binary format
-    :arg obj - object to save
-    :arg name - name of the file
-    NOTE: path should be added later"""
-    with open(name + ".pkl", "wb") as f:
-        pickle.dump(obj, f, pickle.HIGHEST_PROTOCOL)
-
-
-def load_dict(name):
-    """load dictionary from picle file
-    :arg name - name of the file to load
-    NOTE: expect the file in the same folder, path should be added later
-    :return dictionary"""
-    with open(name + ".pkl", "rb") as f:
-        return pickle.load(f)
-
-
-# pickle, mat, h5, txt, csv, json
-def save_table(data, filetype, name, path=None):
-    print("Saving: ", filetype)
-    path = "" if path is None else path
-    if filetype == "pickle":
-        # to work with uncertanities, see uncertanity module
-        with open(path + name + ".pkl", "wb") as f:
-            pickle.dump(data, f, pickle.HIGHEST_PROTOCOL)
-    if filetype == "mat":
-        # matlab doesent allow some special character to be in var names, also cant start with
-        # numbers, in need, add some to the romove_character list
-        data["fit_area_nom"] = [data["fit_area"][i].n for i in range(len(data["fit_area"]))]
-        data["fit_area_err"] = [data["fit_area"][i].s for i in range(len(data["fit_area"]))]
-        data["int_area_nom"] = [data["int_area"][i].n for i in range(len(data["int_area"]))]
-        data["int_area_err"] = [data["int_area"][i].s for i in range(len(data["int_area"]))]
-        data = data.drop(columns=["fit_area", "int_area"])
-        remove_characters = [" ", "[", "]", "{", "}", "(", ")"]
-        for character in remove_characters:
-            data.columns = [
-                data.columns[i].replace(character, "") for i in range(len(data.columns))
-            ]
-        sio.savemat((path + name + ".mat"), {name: col.values for name, col in data.items()})
-    if filetype == "csv" or "txt":
-        data["fit_area_nom"] = [data["fit_area"][i].n for i in range(len(data["fit_area"]))]
-        data["fit_area_err"] = [data["fit_area"][i].s for i in range(len(data["fit_area"]))]
-        data["int_area_nom"] = [data["int_area"][i].n for i in range(len(data["int_area"]))]
-        data["int_area_err"] = [data["int_area"][i].s for i in range(len(data["int_area"]))]
-        data = data.drop(columns=["fit_area", "int_area", "om", "Counts"])
-        if filetype == "csv":
-            data.to_csv(path + name + ".csv")
-        if filetype == "txt":
-            with open((path + name + ".txt"), "w") as outfile:
-                data.to_string(outfile)
-    if filetype == "h5":
-        hdf = pd.HDFStore((path + name + ".h5"))
-        hdf.put("data", data)
-        hdf.close()
-    if filetype == "json":
-        data.to_json((path + name + ".json"))
-
-
-def normalize(scan, monitor):
-    """Normalizes the measurement to monitor, checks if sigma exists, otherwise creates it
-    :arg dict : dictionary to from which to tkae the scan
-    :arg key : which scan to normalize from dict1
-    :arg monitor : final monitor
-    :return counts - normalized counts
-    :return sigma - normalized sigma"""
-
-    counts = np.array(scan["Counts"])
-    sigma = np.sqrt(counts) if "sigma" not in scan else scan["sigma"]
-    monitor_ratio = monitor / scan["monitor"]
-    scaled_counts = counts * monitor_ratio
-    scaled_sigma = np.array(sigma) * monitor_ratio
-
-    return scaled_counts, scaled_sigma
-
-
-def merge(scan1, scan2, keep=True, monitor=100000):
-    """merges the two tuples and sorts them, if om value is same, Counts value is average
-    averaging is propagated into sigma if dict1 == dict2, key[1] is deleted after merging
-    :arg dict1 : dictionary to which measurement will be merged
-    :arg dict2 : dictionary from which measurement will be merged
-    :arg scand_dict_result : result of scan_dict after auto function
-    :arg keep : if true, when monitors are same, does not change it, if flase, takes monitor
-    always
-    :arg monitor : final monitor after merging
-    note: dict1 and dict2 can be same dict
-    :return dict1 with merged scan"""
-
-    if keep:
-        if scan1["monitor"] == scan2["monitor"]:
-            monitor = scan1["monitor"]
-
-    # load om and Counts
-    x1, x2 = scan1["om"], scan2["om"]
-    cor_y1, y_err1 = normalize(scan1, monitor=monitor)
-    cor_y2, y_err2 = normalize(scan2, monitor=monitor)
-    # creates touples (om, Counts, sigma) for sorting and further processing
-    tuple_list = create_tuples(x1, cor_y1, y_err1) + create_tuples(x2, cor_y2, y_err2)
-    # Sort the list on om and add 0 0 0 tuple to the last position
-    sorted_t = sorted(tuple_list, key=lambda tup: tup[0])
-    sorted_t.append((0, 0, 0))
-    om, Counts, sigma = [], [], []
-    seen = list()
-    for i in range(len(sorted_t) - 1):
-        if sorted_t[i][0] not in seen:
-            if sorted_t[i][0] != sorted_t[i + 1][0]:
-                om = np.append(om, sorted_t[i][0])
-                Counts = np.append(Counts, sorted_t[i][1])
-                sigma = np.append(sigma, sorted_t[i][2])
-            else:
-                om = np.append(om, sorted_t[i][0])
-                counts1, counts2 = sorted_t[i][1], sorted_t[i + 1][1]
-                sigma1, sigma2 = sorted_t[i][2], sorted_t[i + 1][2]
-                count_err1 = u.ufloat(counts1, sigma1)
-                count_err2 = u.ufloat(counts2, sigma2)
-                avg = (count_err1 + count_err2) / 2
-                Counts = np.append(Counts, avg.n)
-                sigma = np.append(sigma, avg.s)
-                seen.append(sorted_t[i][0])
-        else:
-            continue
-    scan1["om"] = om
-    scan1["Counts"] = Counts
-    scan1["sigma"] = sigma
-    scan1["monitor"] = monitor
-    print("merging done")
-
-
-def add_dict(dict1, dict2):
-    """adds two dictionaries, meta of the new is saved as meata+original_filename and
-    measurements are shifted to continue with numbering of first dict
-    :arg dict1 : dictionarry to add to
-    :arg dict2 : dictionarry from which to take the measurements
-    :return dict1 : combined dictionary
-    Note: dict1 must be made from ccl, otherwise we would have to change the structure of loaded
-    dat file"""
-    try:
-        if dict1["meta"]["zebra_mode"] != dict2["meta"]["zebra_mode"]:
-            print("You are trying to add scans measured with different zebra modes")
-            return
-    # this is for the qscan case
-    except KeyError:
-        print("Zebra mode not specified")
-    max_measurement_dict1 = max([keys for keys in dict1["scan"]])
-    new_filenames = np.arange(
-        max_measurement_dict1 + 1, max_measurement_dict1 + 1 + len(dict2["scan"])
-    )
-    new_meta_name = "meta" + str(dict2["meta"]["original_filename"])
-    if new_meta_name not in dict1:
-        for keys, name in zip(dict2["scan"], new_filenames):
-            dict2["scan"][keys]["file_of_origin"] = str(dict2["meta"]["original_filename"])
-            dict1["scan"][name] = dict2["scan"][keys]
-
-        dict1[new_meta_name] = dict2["meta"]
-    else:
-        raise KeyError(
-            str(
-                "The file %s has alredy been added to %s"
-                % (dict2["meta"]["original_filename"], dict1["meta"]["original_filename"])
-            )
-        )
-    return dict1
-
-
-def auto(dict):
-    """takes just unique tuples from all tuples in dictionary returend by scan_dict
-    intendet for automatic merge if you doesent want to specify what scans to merge together
-    args: dict - dictionary from scan_dict function
-    :return dict - dict without repetitions"""
-    for keys in dict:
-        tuple_list = dict[keys]
-        new = list()
-        for i in range(len(tuple_list)):
-            if tuple_list[0][0] == tuple_list[i][0]:
-                new.append(tuple_list[i])
-        dict[keys] = new
-    return dict
-
-
-def scan_dict(dict, precision=0.5):
-    """scans dictionary for duplicate angles indexes
-    :arg dict : dictionary to scan
-    :arg precision : in deg, sometimes angles are zero so its easier this way, instead of
-    checking zero division
-    :return  dictionary with matching scans, if there are none, the dict is empty
-    note: can be checked by "not d", true if empty
-    """
-
-    if dict["meta"]["zebra_mode"] == "bi":
-        angles = ["twotheta_angle", "omega_angle", "chi_angle", "phi_angle"]
-    elif dict["meta"]["zebra_mode"] == "nb":
-        angles = ["gamma_angle", "omega_angle", "nu_angle"]
-    else:
-        print("Unknown zebra mode")
-        return
-
-    d = {}
-    for i in dict["scan"]:
-        for j in dict["scan"]:
-            if dict["scan"][i] != dict["scan"][j]:
-                itup = list()
-                for k in angles:
-                    itup.append(abs(abs(dict["scan"][i][k]) - abs(dict["scan"][j][k])))
-
-                if all(i <= precision for i in itup):
-                    print(itup)
-                    print([dict["scan"][i][k] for k in angles])
-                    print([dict["scan"][j][k] for k in angles])
-                    if str([np.around(dict["scan"][i][k], 0) for k in angles]) not in d:
-                        d[str([np.around(dict["scan"][i][k], 0) for k in angles])] = list()
-                        d[str([np.around(dict["scan"][i][k], 0) for k in angles])].append((i, j))
-                    else:
-                        d[str([np.around(dict["scan"][i][k], 0) for k in angles])].append((i, j))
-
-                else:
-                    pass
-
-            else:
-                continue
-
-    return d
-
-
-def _finditem(obj, key):
-    if key in obj:
-        return obj[key]
-    for k, v in obj.items():
-        if isinstance(v, dict):
-            item = _finditem(v, key)
-            if item is not None:
-                return item
-
-
-def most_common(lst):
-    return max(set(lst), key=lst.count)
-
-
-def variables(dictionary):
-    """Funcrion to guess what variables will be used in the param study
-    i call pripary variable the one the array like variable, usually omega
-    and secondary the slicing variable, different for each scan,for example temperature"""
-    # find all variables that are in all scans
-    stdev_precision = 0.05
-    all_vars = list()
-    for keys in dictionary["scan"]:
-        all_vars.append([key for key in dictionary["scan"][keys] if key != "params"])
-        if dictionary["scan"][keys]["params"]:
-            all_vars.append(key for key in dictionary["scan"][keys]["params"])
-
-    all_vars = [i for sublist in all_vars for i in sublist]
-    # get the ones that are in all scans
-    b = collections.Counter(all_vars)
-    inall = [key for key in b if b[key] == len(dictionary["scan"])]
-    # delete those that are obviously wrong
-    wrong = [
-        "NP",
-        "Counts",
-        "Monitor1",
-        "Monitor2",
-        "Monitor3",
-        "h_index",
-        "l_index",
-        "k_index",
-        "n_points",
-        "monitor",
-        "Time",
-        "omega_angle",
-        "twotheta_angle",
-        "chi_angle",
-        "phi_angle",
-        "nu_angle",
-    ]
-    inall_red = [i for i in inall if i not in wrong]
-
-    # check for primary variable, needs to be list, we dont suspect the
-    # primary variable be as a parameter (be in scan[params])
-    primary_candidates = list()
-    for key in dictionary["scan"]:
-        for i in inall_red:
-            if isinstance(_finditem(dictionary["scan"][key], i), list):
-                if np.std(_finditem(dictionary["scan"][key], i)) > stdev_precision:
-                    primary_candidates.append(i)
-    # check which of the primary are in every scan
-    primary_candidates = collections.Counter(primary_candidates)
-    second_round_primary_candidates = [
-        key for key in primary_candidates if primary_candidates[key] == len(dictionary["scan"])
-    ]
-
-    if len(second_round_primary_candidates) == 1:
-        print("We've got a primary winner!", second_round_primary_candidates)
-    else:
-        print("Still not sure with primary:(", second_round_primary_candidates)
-
-    # check for secondary variable, we suspect a float\int or not changing array
-    # we dont need to check for primary ones
-    secondary_candidates = [i for i in inall_red if i not in second_round_primary_candidates]
-    # print("secondary candidates", secondary_candidates)
-    # select arrays and floats and ints
-    second_round_secondary_candidates = list()
-    for key in dictionary["scan"]:
-        for i in secondary_candidates:
-            if isinstance(_finditem(dictionary["scan"][key], i), float):
-                second_round_secondary_candidates.append(i)
-            elif isinstance(_finditem(dictionary["scan"][key], i), int):
-                second_round_secondary_candidates.append(i)
-            elif isinstance(_finditem(dictionary["scan"][key], i), list):
-                if np.std(_finditem(dictionary["scan"][key], i)) < stdev_precision:
-                    second_round_secondary_candidates.append(i)
-
-    second_round_secondary_candidates = collections.Counter(second_round_secondary_candidates)
-    second_round_secondary_candidates = [
-        key
-        for key in second_round_secondary_candidates
-        if second_round_secondary_candidates[key] == len(dictionary["scan"])
-    ]
-    # print("secondary candidates after second round", second_round_secondary_candidates)
-    # now we check if they vary between the scans
-    third_round_sec_candidates = list()
-    for i in second_round_secondary_candidates:
-        check_array = list()
-        for keys in dictionary["scan"]:
-            check_array.append(np.average(_finditem(dictionary["scan"][keys], i)))
-        # print(i, check_array, np.std(check_array))
-        if np.std(check_array) > stdev_precision:
-            third_round_sec_candidates.append(i)
-    if len(third_round_sec_candidates) == 1:
-        print("We've got a secondary winner!", third_round_sec_candidates)
-    else:
-        print("Still not sure with secondary :(", third_round_sec_candidates)
-
-    return {"primary": second_round_primary_candidates, "secondary": third_round_sec_candidates}

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

@@ -1,15 +1,5 @@
-import math
-
 import numpy as np
 from numba import njit
-from scipy.optimize import curve_fit
-
-import pyzebra
-
-try:
-    from matplotlib import pyplot as plt
-except ImportError:
-    print("matplotlib is not available")
 
 pi_r = 180 / np.pi
 
@@ -382,6 +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
 
@@ -393,84 +394,3 @@ def gauss(x, *p):
     """
     A, mu, sigma = p
     return A * np.exp(-((x - mu) ** 2) / (2.0 * sigma ** 2))
-
-
-def box_int(file, box):
-    """Calculates center of the peak in the NB-geometry angles and Intensity of the peak
-
-    Args:
-        file name, box size [x0:xN, y0:yN, fr0:frN]
-
-    Returns:
-        gamma, omPeak, nu polar angles, Int and data for 3 fit plots
-    """
-
-    dat = pyzebra.read_detector_data(file)
-
-    sttC = dat["pol_angle"][0]
-    om = dat["rot_angle"]
-    nuC = dat["tlt_angle"][0]
-    ddist = dat["ddist"]
-
-    # defining indices
-    x0, xN, y0, yN, fr0, frN = box
-
-    # omega fit
-    om = dat["rot_angle"][fr0:frN]
-    cnts = np.sum(dat["data"][fr0:frN, y0:yN, x0:xN], axis=(1, 2))
-
-    p0 = [1.0, 0.0, 1.0]
-    coeff, var_matrix = curve_fit(gauss, range(len(cnts)), cnts, p0=p0)
-
-    frC = fr0 + coeff[1]
-    omF = dat["rot_angle"][math.floor(frC)]
-    omC = dat["rot_angle"][math.ceil(frC)]
-    frStep = frC - math.floor(frC)
-    omStep = omC - omF
-    omP = omF + omStep * frStep
-    Int = coeff[1] * abs(coeff[2] * omStep) * math.sqrt(2) * math.sqrt(np.pi)
-    # omega plot
-    x_fit = np.linspace(0, len(cnts), 100)
-    y_fit = gauss(x_fit, *coeff)
-    plt.figure()
-    plt.subplot(131)
-    plt.plot(range(len(cnts)), cnts)
-    plt.plot(x_fit, y_fit)
-    plt.ylabel("Intensity in the box")
-    plt.xlabel("Frame N of the box")
-    label = "om"
-    # gamma fit
-    sliceXY = dat["data"][fr0:frN, y0:yN, x0:xN]
-    sliceXZ = np.sum(sliceXY, axis=1)
-    sliceYZ = np.sum(sliceXY, axis=2)
-
-    projX = np.sum(sliceXZ, axis=0)
-    p0 = [1.0, 0.0, 1.0]
-    coeff, var_matrix = curve_fit(gauss, range(len(projX)), projX, p0=p0)
-    x = x0 + coeff[1]
-    # gamma plot
-    x_fit = np.linspace(0, len(projX), 100)
-    y_fit = gauss(x_fit, *coeff)
-    plt.subplot(132)
-    plt.plot(range(len(projX)), projX)
-    plt.plot(x_fit, y_fit)
-    plt.ylabel("Intensity in the box")
-    plt.xlabel("X-pixel of the box")
-
-    # nu fit
-    projY = np.sum(sliceYZ, axis=0)
-    p0 = [1.0, 0.0, 1.0]
-    coeff, var_matrix = curve_fit(gauss, range(len(projY)), projY, p0=p0)
-    y = y0 + coeff[1]
-    # nu plot
-    x_fit = np.linspace(0, len(projY), 100)
-    y_fit = gauss(x_fit, *coeff)
-    plt.subplot(133)
-    plt.plot(range(len(projY)), projY)
-    plt.plot(x_fit, y_fit)
-    plt.ylabel("Intensity in the box")
-    plt.xlabel("Y-pixel of the box")
-
-    ga, nu = pyzebra.det2pol(ddist, sttC, nuC, x, y)
-
-    return ga[0], omP, nu[0], Int

scripts/pyzebra-start.sh

@@ -1,4 +1,4 @@
 source /home/pyzebra/miniconda3/etc/profile.d/conda.sh
 
 conda activate prod
-pyzebra --port=80 --allow-websocket-origin=pyzebra.psi.ch:80
+pyzebra --port=80 --allow-websocket-origin=pyzebra.psi.ch:80 --spind-path=/home/pyzebra/spind

scripts/pyzebra-test-start.sh

@@ -1,4 +1,4 @@
 source /home/pyzebra/miniconda3/etc/profile.d/conda.sh
 
 conda activate test
-python ~/pyzebra/pyzebra/app/cli.py --allow-websocket-origin=pyzebra.psi.ch:5006
+python ~/pyzebra/pyzebra/app/cli.py --allow-websocket-origin=pyzebra.psi.ch:5006 --spind-path=/home/pyzebra/spind