Updating for version 0.7.11

This is an outdated way of pyzebra deployment

.gitea/workflows/deploy.yaml

@@ -0,0 +1,53 @@
+name: pyzebra CI/CD pipeline
+
+on:
+  push:
+    branches:
+      - main
+    tags:
+      - '*'
+
+env:
+  CONDA: /opt/miniforge3
+
+jobs:
+  prepare:
+    runs-on: pyzebra
+    steps:
+      - run: $CONDA/bin/conda config --add channels conda-forge
+      - run: $CONDA/bin/conda config --set solver libmamba
+
+  test-env:
+    runs-on: pyzebra
+    needs: prepare
+    if: github.ref == 'refs/heads/main'
+    env:
+      BUILD_DIR: ${{ runner.temp }}/conda_build
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+      - run: $CONDA/bin/conda build --no-anaconda-upload --output-folder $BUILD_DIR ./conda-recipe
+      - run: $CONDA/bin/conda remove --name test --all --keep-env -y
+      - run: $CONDA/bin/conda install --name test --channel $BUILD_DIR python=3.8 pyzebra -y
+      - run: sudo systemctl restart pyzebra-test.service
+
+  prod-env:
+    runs-on: pyzebra
+    needs: prepare
+    if: startsWith(github.ref, 'refs/tags/')
+    env:
+      BUILD_DIR: ${{ runner.temp }}/conda_build
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+      - run: $CONDA/bin/conda build --token ${{ secrets.ANACONDA_TOKEN }} --output-folder $BUILD_DIR ./conda-recipe
+      - run: $CONDA/bin/conda remove --name prod --all --keep-env -y
+      - run: $CONDA/bin/conda install --name prod --channel $BUILD_DIR python=3.8 pyzebra -y
+      - run: sudo systemctl restart pyzebra-prod.service
+
+  cleanup:
+    runs-on: pyzebra
+    needs: [test-env, prod-env]
+    if: always()
+    steps:
+      - run: $CONDA/bin/conda build purge-all

.github/workflows/deployment.yaml

@@ -1,25 +0,0 @@
-name: Deployment
-
-on:
-  push:
-    tags:
-      - '*'
-
-jobs:
-  publish-conda-package:
-    runs-on: ubuntu-latest
-
-    steps:
-    - uses: actions/checkout@v2
-
-    - name: Prepare
-      run: |
-        $CONDA/bin/conda install --quiet --yes conda-build anaconda-client
-        $CONDA/bin/conda config --append channels conda-forge
-        $CONDA/bin/conda config --set anaconda_upload yes
-
-    - name: Build and upload
-      env:
-        ANACONDA_TOKEN: ${{ secrets.ANACONDA_TOKEN }}
-      run: |
-        $CONDA/bin/conda build --token $ANACONDA_TOKEN conda-recipe

.vscode/launch.json

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

conda-recipe/meta.yaml

@@ -15,20 +15,20 @@ build:
 
 requirements:
   build:
-    - python >=3.7
+    - python >=3.8
     - setuptools
   run:
-    - python >=3.7
+    - python >=3.8
     - numpy
     - scipy
     - h5py
     - bokeh =2.4
     - numba
-    - lmfit
+    - lmfit >=1.0.2
 
 
 about:
-  home: https://github.com/paulscherrerinstitute/pyzebra
+  home: https://gitlab.psi.ch/zebra/pyzebra
   summary: {{ data['description'] }}
   license: GNU GPLv3
   license_file: LICENSE

make_release.py

@@ -7,18 +7,19 @@ import subprocess
 
 
 def main():
+    default_branch = "main"
     branch = subprocess.check_output("git rev-parse --abbrev-ref HEAD", shell=True).decode().strip()
-    if branch != "master":
+    if branch != default_branch:
-        print("Aborting, not on 'master' branch.")
+        print(f"Aborting, not on '{default_branch}' branch.")
         return
 
-    filepath = "pyzebra/__init__.py"
+    version_filepath = os.path.join(os.path.basename(os.path.dirname(__file__)), "__init__.py")
 
     parser = argparse.ArgumentParser()
     parser.add_argument("level", type=str, choices=["patch", "minor", "major"])
     args = parser.parse_args()
 
-    with open(filepath) as f:
+    with open(version_filepath) as f:
         file_content = f.read()
 
     version = re.search(r'__version__ = "(.*?)"', file_content).group(1)
@@ -36,11 +37,12 @@ def main():
 
     new_version = f"{major}.{minor}.{patch}"
 
-    with open(filepath, "w") as f:
+    with open(version_filepath, "w") as f:
         f.write(re.sub(r'__version__ = "(.*?)"', f'__version__ = "{new_version}"', file_content))
 
-    os.system(f"git commit {filepath} -m 'Updating for version {new_version}'")
+    os.system(f"git commit {version_filepath} -m 'Updating for version {new_version}'")
     os.system(f"git tag -a {new_version} -m 'Release {new_version}'")
+    os.system("git push --follow-tags")
 
 
 if __name__ == "__main__":

pyzebra/__init__.py

@@ -2,7 +2,8 @@ from pyzebra.anatric import *
 from pyzebra.ccl_io import *
 from pyzebra.ccl_process import *
 from pyzebra.h5 import *
+from pyzebra.sxtal_refgen import *
 from pyzebra.utils import *
 from pyzebra.xtal import *
 
-__version__ = "0.5.2"
+__version__ = "0.7.11"

pyzebra/anatric.py

@@ -1,12 +1,10 @@
+import logging
 import subprocess
 import xml.etree.ElementTree as ET
 
+logger = logging.getLogger(__name__)
 
-DATA_FACTORY_IMPLEMENTATION = [
+DATA_FACTORY_IMPLEMENTATION = ["trics", "morph", "d10"]
-    "trics",
-    "morph",
-    "d10",
-]
 
 REFLECTION_PRINTER_FORMATS = [
     "rafin",
@@ -21,11 +19,11 @@ REFLECTION_PRINTER_FORMATS = [
     "oksana",
 ]
 
-ANATRIC_PATH = "/afs/psi.ch/project/sinq/rhel7/bin/anatric"
+ANATRIC_PATH = "/afs/psi.ch/project/sinq/rhel8/bin/anatric"
 ALGORITHMS = ["adaptivemaxcog", "adaptivedynamic"]
 
 
-def anatric(config_file, anatric_path=ANATRIC_PATH, cwd=None):
+def anatric(config_file, anatric_path=ANATRIC_PATH, cwd=None, log=logger):
     comp_proc = subprocess.run(
         [anatric_path, config_file],
         stdout=subprocess.PIPE,
@@ -34,8 +32,8 @@ def anatric(config_file, anatric_path=ANATRIC_PATH, cwd=None):
         check=True,
         text=True,
     )
-    print(" ".join(comp_proc.args))
+    log.info(" ".join(comp_proc.args))
-    print(comp_proc.stdout)
+    log.info(comp_proc.stdout)
 
 
 class AnatricConfig:

pyzebra/app/__init__.py

@@ -0,0 +1,4 @@
+from pyzebra.app.download_files import DownloadFiles
+from pyzebra.app.fit_controls import FitControls
+from pyzebra.app.input_controls import InputControls
+from pyzebra.app.plot_hkl import PlotHKL

pyzebra/app/app.py

@@ -1,73 +0,0 @@
-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 Button, Panel, Tabs, TextAreaInput, TextInput
-
-import panel_ccl_integrate
-import panel_hdf_anatric
-import panel_hdf_param_study
-import panel_hdf_viewer
-import panel_param_study
-import panel_spind
-
-doc = curdoc()
-
-sys.stdout = StringIO()
-stdout_textareainput = TextAreaInput(title="print output:", height=150)
-
-bokeh_stream = StringIO()
-bokeh_handler = logging.StreamHandler(bokeh_stream)
-bokeh_handler.setFormatter(logging.Formatter(logging.BASIC_FORMAT))
-bokeh_logger = logging.getLogger("bokeh")
-bokeh_logger.addHandler(bokeh_handler)
-bokeh_log_textareainput = TextAreaInput(title="server output:", height=150)
-
-def proposal_textinput_callback(_attr, _old, _new):
-    apply_button.disabled = False
-
-proposal_textinput = TextInput(title="Proposal number:", name="")
-proposal_textinput.on_change("value_input", proposal_textinput_callback)
-doc.proposal_textinput = proposal_textinput
-
-def apply_button_callback():
-    try:
-        proposal_path = pyzebra.find_proposal_path(proposal_textinput.value)
-    except ValueError as e:
-        print(e)
-        return
-
-    proposal_textinput.name = proposal_path
-    apply_button.disabled = True
-
-apply_button = Button(label="Apply", button_type="primary")
-apply_button.on_click(apply_button_callback)
-
-# Final layout
-doc.add_root(
-    column(
-        Tabs(
-            tabs=[
-                Panel(child=column(proposal_textinput, apply_button), title="user config"),
-                panel_hdf_viewer.create(),
-                panel_hdf_anatric.create(),
-                panel_ccl_integrate.create(),
-                panel_param_study.create(),
-                panel_hdf_param_study.create(),
-                panel_spind.create(),
-            ]
-        ),
-        row(stdout_textareainput, bokeh_log_textareainput, sizing_mode="scale_both"),
-    )
-)
-
-
-def update_stdout():
-    stdout_textareainput.value = sys.stdout.getvalue()
-    bokeh_log_textareainput.value = bokeh_stream.getvalue()
-
-
-doc.add_periodic_callback(update_stdout, 1000)

pyzebra/app/cli.py

@@ -1,72 +1,11 @@
-import argparse
-import logging
 import os
-
+import subprocess
-from bokeh.application.application import Application
+import sys
-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)
-logger = logging.getLogger(__name__)
 
 
 def main():
-    """The pyzebra command line interface.
+    app_path = os.path.dirname(os.path.abspath(__file__))
-
+    subprocess.run(["bokeh", "serve", app_path, *sys.argv[1:]], check=True)
-    This is a wrapper around a bokeh server that provides an interface to launch the application,
-    bundled with the pyzebra package.
-    """
-    app_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "app.py")
-
-    parser = argparse.ArgumentParser(
-        prog="pyzebra", formatter_class=argparse.ArgumentDefaultsHelpFormatter
-    )
-
-    parser.add_argument(
-        "--port", type=int, default=5006, help="port to listen on for HTTP requests"
-    )
-
-    parser.add_argument(
-        "--allow-websocket-origin",
-        metavar="HOST[:PORT]",
-        type=str,
-        action="append",
-        default=None,
-        help="hostname that can connect to the server websocket",
-    )
-
-    parser.add_argument(
-        "--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(
-        "--args",
-        nargs=argparse.REMAINDER,
-        default=[],
-        help="command line arguments for the pyzebra application",
-    )
-
-    args = parser.parse_args()
-
-    logger.info(app_path)
-
-    pyzebra_handler = PyzebraHandler(args.anatric_path, args.spind_path)
-    handler = ScriptHandler(filename=app_path, argv=args.args)
-    server = Server(
-        {"/": Application(pyzebra_handler, handler)},
-        port=args.port,
-        allow_websocket_origin=args.allow_websocket_origin,
-    )
-
-    server.start()
-    server.io_loop.start()
 
 
 if __name__ == "__main__":

pyzebra/app/download_files.py

@@ -0,0 +1,45 @@
+from bokeh.models import Button, ColumnDataSource, CustomJS
+
+js_code = """
+let j = 0;
+for (let i = 0; i < source.data['name'].length; i++) {
+    if (source.data['content'][i] === "") continue;
+
+    setTimeout(function() {
+        const blob = new Blob([source.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 = source.data['name'][i] + source.data['ext'][i];
+        link.click();
+        window.URL.revokeObjectURL(url);
+        document.body.removeChild(link);
+    }, 100 * j)
+
+    j++;
+}
+"""
+
+
+class DownloadFiles:
+    def __init__(self, n_files):
+        self.n_files = n_files
+        source = ColumnDataSource(
+            data=dict(content=[""] * n_files, name=[""] * n_files, ext=[""] * n_files)
+        )
+        self._source = source
+
+        label = "Download File" if n_files == 1 else "Download Files"
+        button = Button(label=label, button_type="success", width=200)
+        button.js_on_click(CustomJS(args={"source": source}, code=js_code))
+        self.button = button
+
+    def set_contents(self, contents):
+        self._source.data.update(content=contents)
+
+    def set_names(self, names):
+        self._source.data.update(name=names)
+
+    def set_extensions(self, extensions):
+        self._source.data.update(ext=extensions)

pyzebra/app/fit_controls.py

@@ -0,0 +1,175 @@
+import types
+
+from bokeh.io import curdoc
+from bokeh.models import (
+    Button,
+    CellEditor,
+    CheckboxEditor,
+    CheckboxGroup,
+    ColumnDataSource,
+    DataTable,
+    Dropdown,
+    MultiSelect,
+    NumberEditor,
+    RadioGroup,
+    Spinner,
+    TableColumn,
+    TextAreaInput,
+)
+
+import pyzebra
+
+
+def _params_factory(function):
+    if function == "linear":
+        param_names = ["slope", "intercept"]
+    elif function == "gaussian":
+        param_names = ["amplitude", "center", "sigma"]
+    elif function == "voigt":
+        param_names = ["amplitude", "center", "sigma", "gamma"]
+    elif function == "pvoigt":
+        param_names = ["amplitude", "center", "sigma", "fraction"]
+    elif function == "pseudovoigt1":
+        param_names = ["amplitude", "center", "g_sigma", "l_sigma", "fraction"]
+    else:
+        raise ValueError("Unknown fit function")
+
+    n = len(param_names)
+    params = dict(
+        param=param_names, value=[None] * n, vary=[True] * n, min=[None] * n, max=[None] * n
+    )
+
+    if function == "linear":
+        params["value"] = [0, 1]
+        params["vary"] = [False, True]
+        params["min"] = [None, 0]
+
+    elif function == "gaussian":
+        params["min"] = [0, None, None]
+
+    return params
+
+
+class FitControls:
+    def __init__(self):
+        self.log = curdoc().logger
+        self.params = {}
+
+        def add_function_button_callback(click):
+            # bokeh requires (str, str) for MultiSelect options
+            new_tag = f"{click.item}-{function_select.tags[0]}"
+            function_select.options.append((new_tag, click.item))
+            self.params[new_tag] = _params_factory(click.item)
+            function_select.tags[0] += 1
+
+        add_function_button = Dropdown(
+            label="Add fit function",
+            menu=[
+                ("Linear", "linear"),
+                ("Gaussian", "gaussian"),
+                ("Voigt", "voigt"),
+                ("Pseudo Voigt", "pvoigt"),
+                # ("Pseudo Voigt1", "pseudovoigt1"),
+            ],
+            width=145,
+        )
+        add_function_button.on_click(add_function_button_callback)
+        self.add_function_button = add_function_button
+
+        def function_list_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
+                function_select.value = old
+                return
+
+            if len(old) > 1:
+                # skip unnecessary update caused by selection drop
+                return
+
+            if new:
+                params_table_source.data.update(self.params[new[0]])
+            else:
+                params_table_source.data.update(dict(param=[], value=[], vary=[], min=[], max=[]))
+
+        function_select = MultiSelect(options=[], height=120, width=145)
+        function_select.tags = [0]
+        function_select.on_change("value", function_list_callback)
+        self.function_select = function_select
+
+        def remove_function_button_callback():
+            if function_select.value:
+                sel_tag = function_select.value[0]
+                del self.params[sel_tag]
+                for elem in function_select.options:
+                    if elem[0] == sel_tag:
+                        function_select.options.remove(elem)
+                        break
+
+                function_select.value = []
+
+        remove_function_button = Button(label="Remove fit function", width=145)
+        remove_function_button.on_click(remove_function_button_callback)
+        self.remove_function_button = remove_function_button
+
+        params_table_source = ColumnDataSource(dict(param=[], value=[], vary=[], min=[], max=[]))
+        self.params_table = DataTable(
+            source=params_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
+        add_function_button_callback(types.SimpleNamespace(item="linear"))
+        add_function_button_callback(types.SimpleNamespace(item="gaussian"))
+        function_select.value = ["gaussian-1"]  # put selection on gauss
+
+        self.from_spinner = Spinner(title="Fit from:", width=145)
+        self.to_spinner = Spinner(title="to:", width=145)
+
+        self.area_method_radiogroup = RadioGroup(labels=["Function", "Area"], active=0, width=145)
+
+        self.lorentz_checkbox = CheckboxGroup(
+            labels=["Lorentz Correction"], width=145, margin=(13, 5, 5, 5)
+        )
+
+        self.result_textarea = TextAreaInput(title="Fit results:", width=750, height=200)
+
+    def _process_scan(self, scan):
+        pyzebra.fit_scan(
+            scan,
+            self.params,
+            fit_from=self.from_spinner.value,
+            fit_to=self.to_spinner.value,
+            log=self.log,
+        )
+        pyzebra.get_area(
+            scan,
+            area_method=pyzebra.AREA_METHODS[self.area_method_radiogroup.active],
+            lorentz=self.lorentz_checkbox.active,
+        )
+
+    def fit_scan(self, scan):
+        self._process_scan(scan)
+
+    def fit_dataset(self, dataset):
+        for scan in dataset:
+            if scan["export"]:
+                self._process_scan(scan)
+
+    def update_result_textarea(self, scan):
+        fit = scan.get("fit")
+        if fit is None:
+            self.result_textarea.value = ""
+        else:
+            self.result_textarea.value = fit.fit_report()

pyzebra/app/handler.py

@@ -1,32 +0,0 @@
-from bokeh.application.handlers import Handler
-
-
-class PyzebraHandler(Handler):
-    """Provides a mechanism for generic bokeh applications to build up new streamvis documents.
-    """
-
-    def __init__(self, anatric_path, spind_path):
-        """Initialize a pyzebra handler for bokeh applications.
-
-        Args:
-            args (Namespace): Command line parsed arguments.
-        """
-        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.
-
-        Args:
-            doc (Document) : A bokeh Document to update in-place
-
-        Returns:
-            Document
-        """
-        doc.title = "pyzebra"
-        doc.anatric_path = self.anatric_path
-        doc.spind_path = self.spind_path
-
-        return doc

pyzebra/app/input_controls.py

@@ -0,0 +1,159 @@
+import base64
+import io
+import os
+
+from bokeh.io import curdoc
+from bokeh.models import Button, FileInput, MultiSelect, Spinner
+
+import pyzebra
+
+
+class InputControls:
+    def __init__(self, dataset, dlfiles, on_file_open=lambda: None, on_monitor_change=lambda: None):
+        doc = curdoc()
+        log = doc.logger
+
+        def filelist_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))
+                filelist_select.options = file_list
+                open_button.disabled = False
+                append_button.disabled = False
+            else:
+                filelist_select.options = []
+                open_button.disabled = True
+                append_button.disabled = True
+
+        doc.add_periodic_callback(filelist_select_update_for_proposal, 5000)
+
+        def proposal_textinput_callback(_attr, _old, _new):
+            filelist_select_update_for_proposal()
+
+        proposal_textinput = doc.proposal_textinput
+        proposal_textinput.on_change("name", proposal_textinput_callback)
+
+        filelist_select = MultiSelect(title="Available .ccl/.dat files:", width=210, height=250)
+        self.filelist_select = filelist_select
+
+        def open_button_callback():
+            new_data = []
+            for f_path in self.filelist_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, log=log)
+                    except Exception as e:
+                        log.exception(e)
+                        continue
+
+                pyzebra.normalize_dataset(file_data, monitor_spinner.value)
+
+                if not new_data:  # first file
+                    new_data = file_data
+                    pyzebra.merge_duplicates(new_data, log=log)
+                    dlfiles.set_names([base] * dlfiles.n_files)
+                else:
+                    pyzebra.merge_datasets(new_data, file_data, log=log)
+
+            if new_data:
+                dataset.clear()
+                dataset.extend(new_data)
+                on_file_open()
+                append_upload_button.disabled = False
+
+        open_button = Button(label="Open New", width=100, disabled=True)
+        open_button.on_click(open_button_callback)
+        self.open_button = open_button
+
+        def append_button_callback():
+            file_data = []
+            for f_path in self.filelist_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, log=log)
+                    except Exception as e:
+                        log.exception(e)
+                        continue
+
+                pyzebra.normalize_dataset(file_data, monitor_spinner.value)
+                pyzebra.merge_datasets(dataset, file_data, log=log)
+
+            if file_data:
+                on_file_open()
+
+        append_button = Button(label="Append", width=100, disabled=True)
+        append_button.on_click(append_button_callback)
+        self.append_button = append_button
+
+        def upload_button_callback(_attr, _old, _new):
+            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, log=log)
+                    except Exception as e:
+                        log.exception(e)
+                        continue
+
+                pyzebra.normalize_dataset(file_data, monitor_spinner.value)
+
+                if not new_data:  # first file
+                    new_data = file_data
+                    pyzebra.merge_duplicates(new_data, log=log)
+                    dlfiles.set_names([base] * dlfiles.n_files)
+                else:
+                    pyzebra.merge_datasets(new_data, file_data, log=log)
+
+            if new_data:
+                dataset.clear()
+                dataset.extend(new_data)
+                on_file_open()
+                append_upload_button.disabled = False
+
+        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)
+        self.upload_button = upload_button
+
+        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, log=log)
+                    except Exception as e:
+                        log.exception(e)
+                        continue
+
+                pyzebra.normalize_dataset(file_data, monitor_spinner.value)
+                pyzebra.merge_datasets(dataset, file_data, log=log)
+
+            if file_data:
+                on_file_open()
+
+        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)
+        self.append_upload_button = append_upload_button
+
+        def monitor_spinner_callback(_attr, _old, new):
+            if dataset:
+                pyzebra.normalize_dataset(dataset, new)
+                on_monitor_change()
+
+        monitor_spinner = Spinner(title="Monitor:", mode="int", value=100_000, low=1, width=145)
+        monitor_spinner.on_change("value", monitor_spinner_callback)
+        self.monitor_spinner = monitor_spinner

pyzebra/app/main.py

@@ -0,0 +1,112 @@
+import argparse
+import logging
+from io import StringIO
+
+from bokeh.io import curdoc
+from bokeh.layouts import column, row
+from bokeh.models import Button, Panel, Tabs, TextAreaInput, TextInput
+
+import pyzebra
+from pyzebra.app import (
+    panel_ccl_compare,
+    panel_ccl_integrate,
+    panel_ccl_prepare,
+    panel_hdf_anatric,
+    panel_hdf_param_study,
+    panel_hdf_viewer,
+    panel_param_study,
+    panel_plot_data,
+    panel_spind,
+)
+
+doc = curdoc()
+doc.title = "pyzebra"
+
+parser = argparse.ArgumentParser()
+
+parser.add_argument(
+    "--anatric-path", type=str, default=pyzebra.ANATRIC_PATH, help="path to anatric executable"
+)
+
+parser.add_argument(
+    "--sxtal-refgen-path",
+    type=str,
+    default=pyzebra.SXTAL_REFGEN_PATH,
+    help="path to Sxtal_Refgen executable",
+)
+
+parser.add_argument("--spind-path", type=str, default=None, help="path to spind scripts folder")
+
+args = parser.parse_args()
+
+doc.anatric_path = args.anatric_path
+doc.spind_path = args.spind_path
+doc.sxtal_refgen_path = args.sxtal_refgen_path
+
+stream = StringIO()
+handler = logging.StreamHandler(stream)
+handler.setFormatter(
+    logging.Formatter(fmt="%(asctime)s %(levelname)s: %(message)s", datefmt="%Y-%m-%d %H:%M:%S")
+)
+logger = logging.getLogger(str(id(doc)))
+logger.setLevel(logging.INFO)
+logger.addHandler(handler)
+doc.logger = logger
+
+log_textareainput = TextAreaInput(title="Logging output:")
+
+
+def proposal_textinput_callback(_attr, _old, _new):
+    apply_button.disabled = False
+
+
+proposal_textinput = TextInput(title="Proposal number:", name="")
+proposal_textinput.on_change("value_input", proposal_textinput_callback)
+doc.proposal_textinput = proposal_textinput
+
+
+def apply_button_callback():
+    proposal = proposal_textinput.value.strip()
+    if proposal:
+        try:
+            proposal_path = pyzebra.find_proposal_path(proposal)
+        except ValueError as e:
+            logger.exception(e)
+            return
+        apply_button.disabled = True
+    else:
+        proposal_path = ""
+
+    proposal_textinput.name = proposal_path
+
+
+apply_button = Button(label="Apply", button_type="primary")
+apply_button.on_click(apply_button_callback)
+
+# Final layout
+doc.add_root(
+    column(
+        Tabs(
+            tabs=[
+                Panel(child=column(proposal_textinput, apply_button), title="user config"),
+                panel_hdf_viewer.create(),
+                panel_hdf_anatric.create(),
+                panel_ccl_prepare.create(),
+                panel_plot_data.create(),
+                panel_ccl_integrate.create(),
+                panel_ccl_compare.create(),
+                panel_param_study.create(),
+                panel_hdf_param_study.create(),
+                panel_spind.create(),
+            ]
+        ),
+        row(log_textareainput, sizing_mode="scale_both"),
+    )
+)
+
+
+def update_stdout():
+    log_textareainput.value = stream.getvalue()
+
+
+doc.add_periodic_callback(update_stdout, 1000)

pyzebra/app/panel_ccl_compare.py

@@ -0,0 +1,538 @@
+import base64
+import io
+import os
+import tempfile
+
+import numpy as np
+from bokeh.io import curdoc
+from bokeh.layouts import column, row
+from bokeh.models import (
+    Button,
+    CellEditor,
+    CheckboxEditor,
+    ColumnDataSource,
+    DataTable,
+    Div,
+    FileInput,
+    MultiSelect,
+    Panel,
+    RadioGroup,
+    Select,
+    Spacer,
+    Span,
+    Spinner,
+    TableColumn,
+    TextAreaInput,
+    Whisker,
+)
+from bokeh.plotting import figure
+
+import pyzebra
+from pyzebra import EXPORT_TARGETS, app
+
+
+def create():
+    doc = curdoc()
+    log = doc.logger
+    dataset1 = []
+    dataset2 = []
+    app_dlfiles = app.DownloadFiles(n_files=2)
+
+    def file_select_update_for_proposal():
+        proposal_path = proposal_textinput.name
+        if proposal_path:
+            file_list = []
+            for file in os.listdir(proposal_path):
+                if file.endswith((".ccl")):
+                    file_list.append((os.path.join(proposal_path, file), file))
+            file_select.options = file_list
+            file_open_button.disabled = False
+        else:
+            file_select.options = []
+            file_open_button.disabled = True
+
+    doc.add_periodic_callback(file_select_update_for_proposal, 5000)
+
+    def proposal_textinput_callback(_attr, _old, _new):
+        file_select_update_for_proposal()
+
+    proposal_textinput = doc.proposal_textinput
+    proposal_textinput.on_change("name", proposal_textinput_callback)
+
+    def _init_datatable():
+        # dataset2 should have the same metadata as dataset1
+        scan_list = [s["idx"] for s in dataset1]
+        hkl = [f'{s["h"]} {s["k"]} {s["l"]}' for s in dataset1]
+        export = [s["export"] for s in dataset1]
+
+        twotheta = [np.median(s["twotheta"]) if "twotheta" in s else None for s in dataset1]
+        gamma = [np.median(s["gamma"]) if "gamma" in s else None for s in dataset1]
+        omega = [np.median(s["omega"]) if "omega" in s else None for s in dataset1]
+        chi = [np.median(s["chi"]) if "chi" in s else None for s in dataset1]
+        phi = [np.median(s["phi"]) if "phi" in s else None for s in dataset1]
+        nu = [np.median(s["nu"]) if "nu" in s else None for s in dataset1]
+
+        scan_table_source.data.update(
+            scan=scan_list,
+            hkl=hkl,
+            fit=[0] * len(scan_list),
+            export=export,
+            twotheta=twotheta,
+            gamma=gamma,
+            omega=omega,
+            chi=chi,
+            phi=phi,
+            nu=nu,
+        )
+        scan_table_source.selected.indices = []
+        scan_table_source.selected.indices = [0]
+
+        merge_options = [(str(i), f"{i} ({idx})") for i, idx in enumerate(scan_list)]
+        merge_from_select.options = merge_options
+        merge_from_select.value = merge_options[0][0]
+
+    file_select = MultiSelect(title="Select 2 .ccl files:", width=210, height=250)
+
+    def file_open_button_callback():
+        if len(file_select.value) != 2:
+            log.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, log=log)
+                except Exception as e:
+                    log.exception(e)
+                    return
+
+            pyzebra.normalize_dataset(file_data, monitor_spinner.value)
+            pyzebra.merge_duplicates(file_data, log=log)
+
+            if ind == 0:
+                app_dlfiles.set_names([base, base])
+                new_data1 = file_data
+            else:  # ind = 1
+                new_data2 = file_data
+
+        # ignore extra scans at the end of the longest of the two files
+        min_len = min(len(new_data1), len(new_data2))
+        new_data1 = new_data1[:min_len]
+        new_data2 = new_data2[:min_len]
+
+        nonlocal dataset1, dataset2
+        dataset1 = new_data1
+        dataset2 = new_data2
+        _init_datatable()
+
+    file_open_button = Button(label="Open New", width=100, disabled=True)
+    file_open_button.on_click(file_open_button_callback)
+
+    def upload_button_callback(_attr, _old, _new):
+        if len(upload_button.filename) != 2:
+            log.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, log=log)
+                except Exception as e:
+                    log.exception(e)
+                    return
+
+            pyzebra.normalize_dataset(file_data, monitor_spinner.value)
+            pyzebra.merge_duplicates(file_data, log=log)
+
+            if ind == 0:
+                app_dlfiles.set_names([base, base])
+                new_data1 = file_data
+            else:  # ind = 1
+                new_data2 = file_data
+
+        # ignore extra scans at the end of the longest of the two files
+        min_len = min(len(new_data1), len(new_data2))
+        new_data1 = new_data1[:min_len]
+        new_data2 = new_data2[:min_len]
+
+        nonlocal dataset1, dataset2
+        dataset1 = new_data1
+        dataset2 = new_data2
+        _init_datatable()
+
+    upload_div = Div(text="or upload 2 .ccl files:", margin=(5, 5, 0, 5))
+    upload_button = FileInput(accept=".ccl", multiple=True, width=200)
+    # for on_change("value", ...) or on_change("filename", ...),
+    # see https://github.com/bokeh/bokeh/issues/11461
+    upload_button.on_change("filename", upload_button_callback)
+
+    def monitor_spinner_callback(_attr, old, new):
+        if dataset1 and dataset2:
+            pyzebra.normalize_dataset(dataset1, new)
+            pyzebra.normalize_dataset(dataset2, new)
+            _update_plot()
+
+    monitor_spinner = Spinner(title="Monitor:", mode="int", value=100_000, low=1, width=145)
+    monitor_spinner.on_change("value", monitor_spinner_callback)
+
+    def _update_table():
+        fit_ok = [(1 if "fit" in scan else 0) for scan in dataset1]
+        export = [scan["export"] for scan in dataset1]
+        scan_table_source.data.update(fit=fit_ok, export=export)
+
+    def _update_plot():
+        scatter_sources = [scatter1_source, scatter2_source]
+        fit_sources = [fit1_source, fit2_source]
+        bkg_sources = [bkg1_source, bkg2_source]
+        peak_sources = [peak1_source, peak2_source]
+        fit_output = ""
+
+        for ind, scan in enumerate(_get_selected_scan()):
+            scatter_source = scatter_sources[ind]
+            fit_source = fit_sources[ind]
+            bkg_source = bkg_sources[ind]
+            peak_source = peak_sources[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(app_fitctrl.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=[])
+
+        app_fitctrl.result_textarea.value = fit_output
+
+    # Main plot
+    plot = figure(
+        x_axis_label="Scan motor",
+        y_axis_label="Counts",
+        height=470,
+        width=700,
+        tools="pan,wheel_zoom,reset",
+    )
+
+    scatter1_source = ColumnDataSource(dict(x=[0], y=[0], y_upper=[0], y_lower=[0]))
+    plot.circle(
+        source=scatter1_source,
+        line_color="steelblue",
+        fill_color="steelblue",
+        legend_label="data 1",
+    )
+    plot.add_layout(Whisker(source=scatter1_source, base="x", upper="y_upper", lower="y_lower"))
+
+    scatter2_source = ColumnDataSource(dict(x=[0], y=[0], y_upper=[0], y_lower=[0]))
+    plot.circle(
+        source=scatter2_source,
+        line_color="firebrick",
+        fill_color="firebrick",
+        legend_label="data 2",
+    )
+    plot.add_layout(Whisker(source=scatter2_source, base="x", upper="y_upper", lower="y_lower"))
+
+    fit1_source = ColumnDataSource(dict(x=[0], y=[0]))
+    plot.line(source=fit1_source, legend_label="best fit 1")
+
+    fit2_source = ColumnDataSource(dict(x=[0], y=[0]))
+    plot.line(source=fit2_source, line_color="firebrick", legend_label="best fit 2")
+
+    bkg1_source = ColumnDataSource(dict(x=[0], y=[0]))
+    plot.line(
+        source=bkg1_source, line_color="steelblue", line_dash="dashed", legend_label="linear 1"
+    )
+
+    bkg2_source = ColumnDataSource(dict(x=[0], y=[0]))
+    plot.line(
+        source=bkg2_source, line_color="firebrick", line_dash="dashed", legend_label="linear 2"
+    )
+
+    peak1_source = ColumnDataSource(dict(xs=[[0]], ys=[[0]]))
+    plot.multi_line(
+        source=peak1_source, line_color="steelblue", line_dash="dashed", legend_label="peak 1"
+    )
+
+    peak2_source = ColumnDataSource(dict(xs=[[0]], ys=[[0]]))
+    plot.multi_line(
+        source=peak2_source, line_color="firebrick", line_dash="dashed", legend_label="peak 2"
+    )
+
+    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.y_range.only_visible = True
+    plot.toolbar.logo = None
+    plot.legend.click_policy = "hide"
+
+    # Scan select
+    def scan_table_select_callback(_attr, old, new):
+        if not new:
+            # skip empty selections
+            return
+
+        # Avoid selection of multiple indicies (via Shift+Click or Ctrl+Click)
+        if len(new) > 1:
+            # drop selection to the previous one
+            scan_table_source.selected.indices = old
+            return
+
+        if len(old) > 1:
+            # skip unnecessary update caused by selection drop
+            return
+
+        _update_plot()
+
+    def scan_table_source_callback(_attr, _old, new):
+        # unfortunately, we don't know if the change comes from data update or user input
+        # also `old` and `new` are the same for non-scalars
+        for scan1, scan2, export in zip(dataset1, dataset2, new["export"]):
+            scan1["export"] = export
+            scan2["export"] = export
+        _update_preview()
+
+    scan_table_source = ColumnDataSource(
+        dict(
+            scan=[],
+            hkl=[],
+            fit=[],
+            export=[],
+            twotheta=[],
+            gamma=[],
+            omega=[],
+            chi=[],
+            phi=[],
+            nu=[],
+        )
+    )
+    scan_table_source.on_change("data", scan_table_source_callback)
+    scan_table_source.selected.on_change("indices", scan_table_select_callback)
+
+    scan_table = DataTable(
+        source=scan_table_source,
+        columns=[
+            TableColumn(field="scan", title="Scan", editor=CellEditor(), width=50),
+            TableColumn(field="hkl", title="hkl", editor=CellEditor(), width=100),
+            TableColumn(field="fit", title="Fit", editor=CellEditor(), width=50),
+            TableColumn(field="export", title="Export", editor=CheckboxEditor(), width=50),
+            TableColumn(field="twotheta", title="2theta", editor=CellEditor(), width=50),
+            TableColumn(field="gamma", title="gamma", editor=CellEditor(), width=50),
+            TableColumn(field="omega", title="omega", editor=CellEditor(), width=50),
+            TableColumn(field="chi", title="chi", editor=CellEditor(), width=50),
+            TableColumn(field="phi", title="phi", editor=CellEditor(), width=50),
+            TableColumn(field="nu", title="nu", editor=CellEditor(), width=50),
+        ],
+        width=310,  # +60 because of the index column, but excluding twotheta onwards
+        height=350,
+        autosize_mode="none",
+        editable=True,
+    )
+
+    def _get_selected_scan():
+        ind = scan_table_source.selected.indices[0]
+        return dataset1[ind], dataset2[ind]
+
+    merge_from_select = Select(title="scan:", width=145)
+
+    def merge_button_callback():
+        scan_into1, scan_into2 = _get_selected_scan()
+        scan_from1 = dataset1[int(merge_from_select.value)]
+        scan_from2 = dataset2[int(merge_from_select.value)]
+
+        if scan_into1 is scan_from1:
+            log.warning("Selected scans for merging are identical")
+            return
+
+        pyzebra.merge_scans(scan_into1, scan_from1, log=log)
+        pyzebra.merge_scans(scan_into2, scan_from2, log=log)
+        _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)
+
+    app_fitctrl = app.FitControls()
+
+    def fit_from_spinner_callback(_attr, _old, new):
+        fit_from_span.location = new
+
+    app_fitctrl.from_spinner.on_change("value", fit_from_spinner_callback)
+
+    def fit_to_spinner_callback(_attr, _old, new):
+        fit_to_span.location = new
+
+    app_fitctrl.to_spinner.on_change("value", fit_to_spinner_callback)
+
+    def proc_all_button_callback():
+        app_fitctrl.fit_dataset(dataset1)
+        app_fitctrl.fit_dataset(dataset2)
+
+        _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():
+        scan1, scan2 = _get_selected_scan()
+        app_fitctrl.fit_scan(scan1)
+        app_fitctrl.fit_scan(scan2)
+
+        _update_plot()
+        _update_table()
+
+    proc_button = Button(label="Process Current", width=145)
+    proc_button.on_click(proc_button_callback)
+
+    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
+    )
+
+    export_preview_textinput = TextAreaInput(title="Export file(s) preview:", width=500, height=400)
+
+    def _update_preview():
+        with tempfile.TemporaryDirectory() as temp_dir:
+            temp_file = temp_dir + "/temp"
+            export_data1 = []
+            export_data2 = []
+            for scan1, scan2 in zip(dataset1, dataset2):
+                if scan1["export"]:
+                    export_data1.append(scan1)
+                    export_data2.append(scan2)
+
+            if intensity_diff_radiobutton.active:
+                export_data1, export_data2 = export_data2, export_data1
+
+            pyzebra.export_ccl_compare(
+                export_data1,
+                export_data2,
+                temp_file,
+                export_target_select.value,
+                hkl_precision=int(hkl_precision_select.value),
+            )
+
+            exported_content = ""
+            file_content = []
+            for ext in EXPORT_TARGETS[export_target_select.value]:
+                fname = temp_file + ext
+                if os.path.isfile(fname):
+                    with open(fname) as f:
+                        content = f.read()
+                        exported_content += f"{ext} file:\n" + content
+                else:
+                    content = ""
+                file_content.append(content)
+
+            app_dlfiles.set_contents(file_content)
+            export_preview_textinput.value = exported_content
+
+    def export_target_select_callback(_attr, _old, new):
+        app_dlfiles.set_extensions(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)
+    app_dlfiles.set_extensions(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)
+
+    area_method_div = Div(text="Intensity:", margin=(5, 5, 0, 5))
+    fitpeak_controls = row(
+        column(
+            app_fitctrl.add_function_button,
+            app_fitctrl.function_select,
+            app_fitctrl.remove_function_button,
+        ),
+        app_fitctrl.params_table,
+        Spacer(width=20),
+        column(
+            app_fitctrl.from_spinner,
+            app_fitctrl.lorentz_checkbox,
+            area_method_div,
+            app_fitctrl.area_method_radiogroup,
+            intensity_diff_div,
+            intensity_diff_radiobutton,
+        ),
+        column(app_fitctrl.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(app_dlfiles.button)),
+        ),
+    )
+
+    tab_layout = column(
+        row(import_layout, scan_layout, plot, Spacer(width=30), export_layout),
+        row(fitpeak_controls, app_fitctrl.result_textarea),
+    )
+
+    return Panel(child=tab_layout, title="ccl compare")

pyzebra/app/panel_ccl_integrate.py

@@ -1,110 +1,59 @@
-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,
 )
+from bokeh.plotting import figure
 
 import pyzebra
-from pyzebra.ccl_io import EXPORT_TARGETS
+from pyzebra import EXPORT_TARGETS, app
-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_data = {}
+    log = doc.logger
-    fit_params = {}
+    dataset = []
-    js_data = ColumnDataSource(data=dict(content=["", ""], fname=["", ""], ext=["", ""]))
+    app_dlfiles = app.DownloadFiles(n_files=2)
-
-    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
-
-    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 = [s["idx"] for s in det_data]
+        scan_list = [s["idx"] for s in dataset]
-        hkl = [f'{s["h"]} {s["k"]} {s["l"]}' for s in det_data]
+        hkl = [f'{s["h"]} {s["k"]} {s["l"]}' for s in dataset]
-        export = [s.get("active", True) for s in det_data]
+        export = [s["export"] for s in dataset]
+
+        twotheta = [np.median(s["twotheta"]) if "twotheta" in s else None for s in dataset]
+        gamma = [np.median(s["gamma"]) if "gamma" in s else None for s in dataset]
+        omega = [np.median(s["omega"]) if "omega" in s else None for s in dataset]
+        chi = [np.median(s["chi"]) if "chi" in s else None for s in dataset]
+        phi = [np.median(s["phi"]) if "phi" in s else None for s in dataset]
+        nu = [np.median(s["nu"]) if "nu" in s else None for s in dataset]
+
         scan_table_source.data.update(
-            scan=scan_list, hkl=hkl, fit=[0] * len(scan_list), export=export,
+            scan=scan_list,
+            hkl=hkl,
+            fit=[0] * len(scan_list),
+            export=export,
+            twotheta=twotheta,
+            gamma=gamma,
+            omega=omega,
+            chi=chi,
+            phi=phi,
+            nu=nu,
         )
         scan_table_source.selected.indices = []
         scan_table_source.selected.indices = [0]
@@ -113,94 +62,9 @@ def create():
         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 _update_table():
-
+        fit_ok = [(1 if "fit" in scan else 0) for scan in dataset]
-    def file_open_button_callback():
+        export = [scan["export"] for scan in dataset]
-        nonlocal det_data
-        for f_ind, f_path in enumerate(file_select.value):
-            with open(f_path) as file:
-                base, ext = os.path.splitext(os.path.basename(f_path))
-                file_data = pyzebra.parse_1D(file, ext)
-
-            pyzebra.normalize_dataset(file_data, monitor_spinner.value)
-
-            if f_ind == 0:  # first file
-                det_data = file_data
-                pyzebra.merge_duplicates(det_data)
-                js_data.data.update(fname=[base, base])
-            else:
-                pyzebra.merge_datasets(det_data, file_data)
-
-        _init_datatable()
-        append_upload_button.disabled = False
-
-    file_open_button = Button(label="Open New", width=100, disabled=True)
-    file_open_button.on_click(file_open_button_callback)
-
-    def file_append_button_callback():
-        for f_path in file_select.value:
-            with open(f_path) as file:
-                _, ext = os.path.splitext(f_path)
-                file_data = pyzebra.parse_1D(file, ext)
-
-            pyzebra.normalize_dataset(file_data, monitor_spinner.value)
-            pyzebra.merge_datasets(det_data, file_data)
-
-        _init_datatable()
-
-    file_append_button = Button(label="Append", width=100, disabled=True)
-    file_append_button.on_click(file_append_button_callback)
-
-    def upload_button_callback(_attr, _old, new):
-        nonlocal det_data
-        det_data = []
-        for f_str, f_name in zip(new, upload_button.filename):
-            with io.StringIO(base64.b64decode(f_str).decode()) as file:
-                base, ext = os.path.splitext(f_name)
-                file_data = pyzebra.parse_1D(file, ext)
-
-            pyzebra.normalize_dataset(file_data, monitor_spinner.value)
-
-            if not det_data:  # first file
-                det_data = file_data
-                pyzebra.merge_duplicates(det_data)
-                js_data.data.update(fname=[base, base])
-            else:
-                pyzebra.merge_datasets(det_data, file_data)
-
-        _init_datatable()
-        append_upload_button.disabled = False
-
-    upload_div = Div(text="or upload new .ccl/.dat files:", margin=(5, 5, 0, 5))
-    upload_button = FileInput(accept=".ccl,.dat", multiple=True, width=200)
-    upload_button.on_change("value", upload_button_callback)
-
-    def append_upload_button_callback(_attr, _old, new):
-        for f_str, f_name in zip(new, append_upload_button.filename):
-            with io.StringIO(base64.b64decode(f_str).decode()) as file:
-                _, ext = os.path.splitext(f_name)
-                file_data = pyzebra.parse_1D(file, ext)
-
-            pyzebra.normalize_dataset(file_data, monitor_spinner.value)
-            pyzebra.merge_datasets(det_data, file_data)
-
-        _init_datatable()
-
-    append_upload_div = Div(text="append extra files:", margin=(5, 5, 0, 5))
-    append_upload_button = FileInput(accept=".ccl,.dat", multiple=True, width=200, disabled=True)
-    append_upload_button.on_change("value", append_upload_button_callback)
-
-    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_datatable():
-        fit_ok = [(1 if "fit" in scan else 0) for scan in det_data]
-        export = [scan.get("active", True) for scan in det_data]
         scan_table_source.data.update(fit=fit_ok, export=export)
 
     def _update_plot():
@@ -212,19 +76,19 @@ def create():
         x = scan[scan_motor]
 
         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)
+        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)
-            plot_fit_source.data.update(x=x_fit, y=fit.eval(x=x_fit))
+            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):
+            for i, model in enumerate(app_fitctrl.params):
                 if "linear" in model:
                     x_bkg = x_fit
                     y_bkg = comps[f"f{i}_"]
@@ -233,49 +97,43 @@ def create():
                     xs_peak.append(x_fit)
                     ys_peak.append(comps[f"f{i}_"])
 
-            plot_bkg_source.data.update(x=x_bkg, y=y_bkg)
+            bkg_source.data.update(x=x_bkg, y=y_bkg)
-            plot_peak_source.data.update(xs=xs_peak, ys=ys_peak)
+            peak_source.data.update(xs=xs_peak, ys=ys_peak)
-
-            fit_output_textinput.value = fit.fit_report()
 
         else:
-            plot_fit_source.data.update(x=[], y=[])
+            fit_source.data.update(x=[], y=[])
-            plot_bkg_source.data.update(x=[], y=[])
+            bkg_source.data.update(x=[], y=[])
-            plot_peak_source.data.update(xs=[], ys=[])
+            peak_source.data.update(xs=[], ys=[])
-            fit_output_textinput.value = ""
+
+        app_fitctrl.update_result_textarea(scan)
+
+    app_inputctrl = app.InputControls(
+        dataset, app_dlfiles, on_file_open=_init_datatable, on_monitor_change=_update_plot
+    )
 
     # Main plot
-    plot = Plot(
+    plot = figure(
-        x_range=DataRange1d(),
+        x_axis_label="Scan motor",
-        y_range=DataRange1d(only_visible=True),
+        y_axis_label="Counts",
-        plot_height=470,
+        height=470,
-        plot_width=700,
+        width=700,
+        tools="pan,wheel_zoom,reset",
     )
 
-    plot.add_layout(LinearAxis(axis_label="Counts"), place="left")
+    scatter_source = ColumnDataSource(dict(x=[0], y=[0], y_upper=[0], y_lower=[0]))
-    plot.add_layout(LinearAxis(axis_label="Scan motor"), place="below")
+    plot.circle(
-
+        source=scatter_source, line_color="steelblue", fill_color="steelblue", legend_label="data"
-    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_scatter = plot.add_glyph(
-        plot_scatter_source, Scatter(x="x", y="y", line_color="steelblue")
     )
-    plot.add_layout(Whisker(source=plot_scatter_source, base="x", upper="y_upper", lower="y_lower"))
+    plot.add_layout(Whisker(source=scatter_source, base="x", upper="y_upper", lower="y_lower"))
 
-    plot_fit_source = ColumnDataSource(dict(x=[0], y=[0]))
+    fit_source = ColumnDataSource(dict(x=[0], y=[0]))
-    plot_fit = plot.add_glyph(plot_fit_source, Line(x="x", y="y"))
+    plot.line(source=fit_source, legend_label="best fit")
 
-    plot_bkg_source = ColumnDataSource(dict(x=[0], y=[0]))
+    bkg_source = ColumnDataSource(dict(x=[0], y=[0]))
-    plot_bkg = plot.add_glyph(
+    plot.line(source=bkg_source, line_color="green", line_dash="dashed", legend_label="linear")
-        plot_bkg_source, Line(x="x", y="y", line_color="green", line_dash="dashed")
-    )
 
-    plot_peak_source = ColumnDataSource(dict(xs=[[0]], ys=[[0]]))
+    peak_source = ColumnDataSource(dict(xs=[[0]], ys=[[0]]))
-    plot_peak = plot.add_glyph(
+    plot.multi_line(source=peak_source, line_color="red", line_dash="dashed", legend_label="peak")
-        plot_peak_source, MultiLine(xs="xs", ys="ys", line_color="red", line_dash="dashed")
-    )
 
     fit_from_span = Span(location=None, dimension="height", line_dash="dashed")
     plot.add_layout(fit_from_span)
@@ -283,21 +141,9 @@ def create():
     fit_to_span = Span(location=None, dimension="height", line_dash="dashed")
     plot.add_layout(fit_to_span)
 
-    plot.add_layout(
+    plot.y_range.only_visible = True
-        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
+    plot.legend.click_policy = "hide"
 
     # Scan select
     def scan_table_select_callback(_attr, old, new):
@@ -317,10 +163,27 @@ def create():
 
         _update_plot()
 
-    def scan_table_source_callback(_attr, _old, _new):
+    def scan_table_source_callback(_attr, _old, new):
+        # unfortunately, we don't know if the change comes from data update or user input
+        # also `old` and `new` are the same for non-scalars
+        for scan, export in zip(dataset, new["export"]):
+            scan["export"] = export
         _update_preview()
 
-    scan_table_source = ColumnDataSource(dict(scan=[], hkl=[], fit=[], export=[]))
+    scan_table_source = ColumnDataSource(
+        dict(
+            scan=[],
+            hkl=[],
+            fit=[],
+            export=[],
+            twotheta=[],
+            gamma=[],
+            omega=[],
+            chi=[],
+            phi=[],
+            nu=[],
+        )
+    )
     scan_table_source.on_change("data", scan_table_source_callback)
     scan_table_source.selected.on_change("indices", scan_table_select_callback)
 
@@ -331,28 +194,34 @@ def create():
             TableColumn(field="hkl", title="hkl", editor=CellEditor(), width=100),
             TableColumn(field="fit", title="Fit", editor=CellEditor(), width=50),
             TableColumn(field="export", title="Export", editor=CheckboxEditor(), width=50),
+            TableColumn(field="twotheta", title="2theta", editor=CellEditor(), width=50),
+            TableColumn(field="gamma", title="gamma", editor=CellEditor(), width=50),
+            TableColumn(field="omega", title="omega", editor=CellEditor(), width=50),
+            TableColumn(field="chi", title="chi", editor=CellEditor(), width=50),
+            TableColumn(field="phi", title="phi", editor=CellEditor(), width=50),
+            TableColumn(field="nu", title="nu", editor=CellEditor(), width=50),
         ],
-        width=310,  # +60 because of the index column
+        width=310,  # +60 because of the index column, but excluding twotheta onwards
         height=350,
         autosize_mode="none",
         editable=True,
     )
 
     def _get_selected_scan():
-        return det_data[scan_table_source.selected.indices[0]]
+        return dataset[scan_table_source.selected.indices[0]]
 
     merge_from_select = Select(title="scan:", width=145)
 
     def merge_button_callback():
         scan_into = _get_selected_scan()
-        scan_from = det_data[int(merge_from_select.value)]
+        scan_from = dataset[int(merge_from_select.value)]
 
         if scan_into is scan_from:
-            print("WARNING: Selected scans for merging are identical")
+            log.warning("Selected scans for merging are identical")
             return
 
-        pyzebra.merge_scans(scan_into, scan_from)
+        pyzebra.merge_scans(scan_into, scan_from, log=log)
-        _update_datatable()
+        _update_table()
         _update_plot()
 
     merge_button = Button(label="Merge into current", width=145)
@@ -360,180 +229,51 @@ def create():
 
     def restore_button_callback():
         pyzebra.restore_scan(_get_selected_scan())
-        _update_datatable()
+        _update_table()
         _update_plot()
 
     restore_button = Button(label="Restore scan", width=145)
     restore_button.on_click(restore_button_callback)
 
+    app_fitctrl = app.FitControls()
+
     def fit_from_spinner_callback(_attr, _old, new):
         fit_from_span.location = new
 
-    fit_from_spinner = Spinner(title="Fit from:", width=145)
+    app_fitctrl.from_spinner.on_change("value", fit_from_spinner_callback)
-    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)
+    app_fitctrl.to_spinner.on_change("value", fit_to_spinner_callback)
-    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, export in zip(det_data, scan_table_source.data["export"]):
+        app_fitctrl.fit_dataset(dataset)
-            if 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_datatable()
+        _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()
+        app_fitctrl.fit_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,
-        )
 
         _update_plot()
-        _update_datatable()
+        _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)
-
-    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_data = []
-            for s, export in zip(det_data, scan_table_source.data["export"]):
+            for scan in dataset:
-                if export:
+                if scan["export"]:
-                    export_data.append(s)
+                    export_data.append(scan)
 
             pyzebra.export_1D(
                 export_data,
@@ -554,18 +294,18 @@ def create():
                     content = ""
                 file_content.append(content)
 
-            js_data.data.update(content=file_content)
+            app_dlfiles.set_contents(file_content)
             export_preview_textinput.value = exported_content
 
     def export_target_select_callback(_attr, _old, new):
-        js_data.data.update(ext=EXPORT_TARGETS[new])
+        app_dlfiles.set_extensions(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])
+    app_dlfiles.set_extensions(EXPORT_TARGETS[export_target_select.value])
 
     def hkl_precision_select_callback(_attr, _old, _new):
         _update_preview()
@@ -575,42 +315,53 @@ def create():
     )
     hkl_precision_select.on_change("value", hkl_precision_select_callback)
 
-    save_button = Button(label="Download File(s)", button_type="success", width=200)
+    area_method_div = Div(text="Intensity:", margin=(5, 5, 0, 5))
-    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),
+        column(
-        fitparams_table,
+            app_fitctrl.add_function_button,
+            app_fitctrl.function_select,
+            app_fitctrl.remove_function_button,
+        ),
+        app_fitctrl.params_table,
         Spacer(width=20),
-        column(fit_from_spinner, lorentz_checkbox, area_method_div, area_method_radiobutton),
+        column(
-        column(fit_to_spinner, proc_button, proc_all_button),
+            app_fitctrl.from_spinner,
+            app_fitctrl.lorentz_checkbox,
+            area_method_div,
+            app_fitctrl.area_method_radiogroup,
+        ),
+        column(app_fitctrl.to_spinner, proc_button, proc_all_button),
     )
 
     scan_layout = column(
         scan_table,
-        row(monitor_spinner, column(Spacer(height=19), restore_button)),
+        row(app_inputctrl.monitor_spinner, column(Spacer(height=19), restore_button)),
         row(column(Spacer(height=19), merge_button), merge_from_select),
     )
 
+    upload_div = Div(text="or upload new .ccl/.dat files:", margin=(5, 5, 0, 5))
+    append_upload_div = Div(text="append extra files:", margin=(5, 5, 0, 5))
     import_layout = column(
-        file_select,
+        app_inputctrl.filelist_select,
-        row(file_open_button, file_append_button),
+        row(app_inputctrl.open_button, app_inputctrl.append_button),
         upload_div,
-        upload_button,
+        app_inputctrl.upload_button,
         append_upload_div,
-        append_upload_button,
+        app_inputctrl.append_upload_button,
     )
 
     export_layout = column(
         export_preview_textinput,
         row(
-            export_target_select, hkl_precision_select, column(Spacer(height=19), row(save_button))
+            export_target_select,
+            hkl_precision_select,
+            column(Spacer(height=19), row(app_dlfiles.button)),
         ),
     )
 
     tab_layout = column(
         row(import_layout, scan_layout, plot, Spacer(width=30), export_layout),
-        row(fitpeak_controls, fit_output_textinput),
+        row(fitpeak_controls, app_fitctrl.result_textarea),
     )
 
     return Panel(child=tab_layout, title="ccl integrate")

pyzebra/app/panel_ccl_prepare.py

@@ -0,0 +1,724 @@
+import base64
+import io
+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 (
+    Arrow,
+    Button,
+    CheckboxGroup,
+    ColumnDataSource,
+    Div,
+    FileInput,
+    HoverTool,
+    Legend,
+    LegendItem,
+    MultiSelect,
+    NormalHead,
+    NumericInput,
+    Panel,
+    RadioGroup,
+    Select,
+    Spacer,
+    Spinner,
+    TextAreaInput,
+    TextInput,
+)
+from bokeh.palettes import Dark2
+from bokeh.plotting import figure
+
+import pyzebra
+from pyzebra import app
+
+ANG_CHUNK_DEFAULTS = {"2theta": 30, "gamma": 30, "omega": 30, "chi": 35, "phi": 35, "nu": 10}
+SORT_OPT_BI = ["2theta", "chi", "phi", "omega"]
+SORT_OPT_NB = ["gamma", "nu", "omega"]
+
+
+def create():
+    doc = curdoc()
+    log = doc.logger
+    ang_lims = {}
+    cif_data = {}
+    params = {}
+    res_files = {}
+    _update_slice = None
+    app_dlfiles = app.DownloadFiles(n_files=1)
+
+    anglim_div = Div(text="Angular min/max limits:", margin=(5, 5, 0, 5))
+    sttgamma_ti = TextInput(title="stt/gamma", width=100)
+    omega_ti = TextInput(title="omega", width=100)
+    chinu_ti = TextInput(title="chi/nu", width=100)
+    phi_ti = TextInput(title="phi", width=100)
+
+    def _update_ang_lims(ang_lims):
+        sttgamma_ti.value = " ".join(ang_lims["gamma"][:2])
+        omega_ti.value = " ".join(ang_lims["omega"][:2])
+        if ang_lims["geom"] == "nb":
+            chinu_ti.value = " ".join(ang_lims["nu"][:2])
+            phi_ti.value = ""
+        else:  # ang_lims["geom"] == "bi"
+            chinu_ti.value = " ".join(ang_lims["chi"][:2])
+            phi_ti.value = " ".join(ang_lims["phi"][:2])
+
+    def _update_params(params):
+        if "WAVE" in params:
+            wavelen_input.value = params["WAVE"]
+        if "SPGR" in params:
+            cryst_space_group.value = params["SPGR"]
+        if "CELL" in params:
+            cryst_cell.value = params["CELL"]
+        if "UBMAT" in params:
+            ub_matrix.value = " ".join(params["UBMAT"])
+        if "HLIM" in params:
+            ranges_hkl.value = params["HLIM"]
+        if "SRANG" in params:
+            ranges_srang.value = params["SRANG"]
+        if "lattiCE" in params:
+            magstruct_lattice.value = params["lattiCE"]
+        if "kvect" in params:
+            magstruct_kvec.value = params["kvect"]
+
+    def open_geom_callback(_attr, _old, new):
+        nonlocal ang_lims
+        with io.StringIO(base64.b64decode(new).decode()) as fileobj:
+            ang_lims = pyzebra.read_geom_file(fileobj)
+        _update_ang_lims(ang_lims)
+
+    open_geom_div = Div(text="Open GEOM:")
+    open_geom = FileInput(accept=".geom", width=200)
+    open_geom.on_change("value", open_geom_callback)
+
+    def open_cfl_callback(_attr, _old, new):
+        nonlocal params
+        with io.StringIO(base64.b64decode(new).decode()) as fileobj:
+            params = pyzebra.read_cfl_file(fileobj)
+            _update_params(params)
+
+    open_cfl_div = Div(text="Open CFL:")
+    open_cfl = FileInput(accept=".cfl", width=200)
+    open_cfl.on_change("value", open_cfl_callback)
+
+    def open_cif_callback(_attr, _old, new):
+        nonlocal cif_data
+        with io.StringIO(base64.b64decode(new).decode()) as fileobj:
+            cif_data = pyzebra.read_cif_file(fileobj)
+            _update_params(cif_data)
+
+    open_cif_div = Div(text="Open CIF:")
+    open_cif = FileInput(accept=".cif", width=200)
+    open_cif.on_change("value", open_cif_callback)
+
+    wavelen_div = Div(text="Wavelength:", margin=(5, 5, 0, 5))
+    wavelen_input = TextInput(title="value", width=70)
+
+    def wavelen_select_callback(_attr, _old, new):
+        if new:
+            wavelen_input.value = new
+        else:
+            wavelen_input.value = ""
+
+    wavelen_select = Select(
+        title="preset", options=["", "0.788", "1.178", "1.383", "2.305"], width=70
+    )
+    wavelen_select.on_change("value", wavelen_select_callback)
+
+    cryst_div = Div(text="Crystal structure:", margin=(5, 5, 0, 5))
+    cryst_space_group = TextInput(title="space group", width=100)
+    cryst_cell = TextInput(title="cell", width=250)
+
+    def ub_matrix_calc_callback():
+        params = dict()
+        params["SPGR"] = cryst_space_group.value
+        params["CELL"] = cryst_cell.value
+        try:
+            ub = pyzebra.calc_ub_matrix(params, log=log)
+        except Exception as e:
+            log.exception(e)
+            return
+        ub_matrix.value = " ".join(ub)
+
+    ub_matrix_calc = Button(label="UB matrix:", button_type="primary", width=100)
+    ub_matrix_calc.on_click(ub_matrix_calc_callback)
+
+    ub_matrix = TextInput(title="\u200B", width=600)
+
+    ranges_div = Div(text="Ranges:", margin=(5, 5, 0, 5))
+    ranges_hkl = TextInput(title="HKL", value="-25 25 -25 25 -25 25", width=250)
+    ranges_srang = TextInput(title="sin(​θ​)/λ", value="0.0 0.7", width=100)
+
+    magstruct_div = Div(text="Magnetic structure:", margin=(5, 5, 0, 5))
+    magstruct_lattice = TextInput(title="lattice", width=100)
+    magstruct_kvec = TextAreaInput(title="k vector", width=150)
+
+    def sorting0_callback(_attr, _old, new):
+        sorting_0_dt.value = ANG_CHUNK_DEFAULTS[new]
+
+    def sorting1_callback(_attr, _old, new):
+        sorting_1_dt.value = ANG_CHUNK_DEFAULTS[new]
+
+    def sorting2_callback(_attr, _old, new):
+        sorting_2_dt.value = ANG_CHUNK_DEFAULTS[new]
+
+    sorting_0 = Select(title="1st", width=100)
+    sorting_0.on_change("value", sorting0_callback)
+    sorting_0_dt = NumericInput(title="Δ", width=70)
+    sorting_1 = Select(title="2nd", width=100)
+    sorting_1.on_change("value", sorting1_callback)
+    sorting_1_dt = NumericInput(title="Δ", width=70)
+    sorting_2 = Select(title="3rd", width=100)
+    sorting_2.on_change("value", sorting2_callback)
+    sorting_2_dt = NumericInput(title="Δ", width=70)
+
+    def geom_radiogroup_callback(_attr, _old, new):
+        nonlocal ang_lims, params
+        if new == 0:
+            geom_file = pyzebra.get_zebraBI_default_geom_file()
+            sort_opt = SORT_OPT_BI
+        else:
+            geom_file = pyzebra.get_zebraNB_default_geom_file()
+            sort_opt = SORT_OPT_NB
+        cfl_file = pyzebra.get_zebra_default_cfl_file()
+
+        ang_lims = pyzebra.read_geom_file(geom_file)
+        _update_ang_lims(ang_lims)
+        params = pyzebra.read_cfl_file(cfl_file)
+        _update_params(params)
+
+        sorting_0.options = sorting_1.options = sorting_2.options = sort_opt
+        sorting_0.value = sort_opt[0]
+        sorting_1.value = sort_opt[1]
+        sorting_2.value = sort_opt[2]
+
+    geom_radiogroup_div = Div(text="Geometry:", margin=(5, 5, 0, 5))
+    geom_radiogroup = RadioGroup(labels=["bisecting", "normal beam"], width=150)
+    geom_radiogroup.on_change("active", geom_radiogroup_callback)
+    geom_radiogroup.active = 0
+
+    def go_button_callback():
+        ang_lims["gamma"][0], ang_lims["gamma"][1] = sttgamma_ti.value.strip().split()
+        ang_lims["omega"][0], ang_lims["omega"][1] = omega_ti.value.strip().split()
+        if ang_lims["geom"] == "nb":
+            ang_lims["nu"][0], ang_lims["nu"][1] = chinu_ti.value.strip().split()
+        else:  # ang_lims["geom"] == "bi"
+            ang_lims["chi"][0], ang_lims["chi"][1] = chinu_ti.value.strip().split()
+            ang_lims["phi"][0], ang_lims["phi"][1] = phi_ti.value.strip().split()
+
+        if cif_data:
+            params.update(cif_data)
+
+        params["WAVE"] = wavelen_input.value
+        params["SPGR"] = cryst_space_group.value
+        params["CELL"] = cryst_cell.value
+        params["UBMAT"] = ub_matrix.value.split()
+        params["HLIM"] = ranges_hkl.value
+        params["SRANG"] = ranges_srang.value
+        params["lattiCE"] = magstruct_lattice.value
+        kvects = magstruct_kvec.value.split("\n")
+
+        with tempfile.TemporaryDirectory() as temp_dir:
+            geom_path = os.path.join(temp_dir, "zebra.geom")
+            if open_geom.value:
+                geom_template = io.StringIO(base64.b64decode(open_geom.value).decode())
+            else:
+                geom_template = None
+            pyzebra.export_geom_file(geom_path, ang_lims, geom_template)
+
+            log.info(f"Content of {geom_path}:")
+            with open(geom_path) as f:
+                log.info(f.read())
+
+            priority = [sorting_0.value, sorting_1.value, sorting_2.value]
+            chunks = [sorting_0_dt.value, sorting_1_dt.value, sorting_2_dt.value]
+            if geom_radiogroup.active == 0:
+                sort_hkl_file = pyzebra.sort_hkl_file_bi
+                priority.extend(set(SORT_OPT_BI) - set(priority))
+            else:
+                sort_hkl_file = pyzebra.sort_hkl_file_nb
+
+            # run sxtal_refgen for each kvect provided
+            for i, kvect in enumerate(kvects, start=1):
+                params["kvect"] = kvect
+                if open_cfl.filename:
+                    base_fname = f"{os.path.splitext(open_cfl.filename)[0]}_{i}"
+                else:
+                    base_fname = f"zebra_{i}"
+
+                cfl_path = os.path.join(temp_dir, base_fname + ".cfl")
+                if open_cfl.value:
+                    cfl_template = io.StringIO(base64.b64decode(open_cfl.value).decode())
+                else:
+                    cfl_template = None
+                pyzebra.export_cfl_file(cfl_path, params, cfl_template)
+
+                log.info(f"Content of {cfl_path}:")
+                with open(cfl_path) as f:
+                    log.info(f.read())
+
+                comp_proc = subprocess.run(
+                    [pyzebra.SXTAL_REFGEN_PATH, cfl_path],
+                    cwd=temp_dir,
+                    check=True,
+                    stdout=subprocess.PIPE,
+                    stderr=subprocess.STDOUT,
+                    text=True,
+                )
+                log.info(" ".join(comp_proc.args))
+                log.info(comp_proc.stdout)
+
+                if i == 1:  # all hkl files are identical, so keep only one
+                    hkl_fname = base_fname + ".hkl"
+                    hkl_fpath = os.path.join(temp_dir, hkl_fname)
+                    with open(hkl_fpath) as f:
+                        res_files[hkl_fname] = f.read()
+
+                    hkl_fname_sorted = base_fname + "_sorted.hkl"
+                    hkl_fpath_sorted = os.path.join(temp_dir, hkl_fname_sorted)
+                    sort_hkl_file(hkl_fpath, hkl_fpath_sorted, priority, chunks)
+                    with open(hkl_fpath_sorted) as f:
+                        res_files[hkl_fname_sorted] = f.read()
+
+                mhkl_fname = base_fname + ".mhkl"
+                mhkl_fpath = os.path.join(temp_dir, mhkl_fname)
+                with open(mhkl_fpath) as f:
+                    res_files[mhkl_fname] = f.read()
+
+                mhkl_fname_sorted = base_fname + "_sorted.mhkl"
+                mhkl_fpath_sorted = os.path.join(temp_dir, hkl_fname_sorted)
+                sort_hkl_file(mhkl_fpath, mhkl_fpath_sorted, priority, chunks)
+                with open(mhkl_fpath_sorted) as f:
+                    res_files[mhkl_fname_sorted] = f.read()
+
+            created_lists.options = list(res_files)
+
+    go_button = Button(label="GO", button_type="primary", width=50)
+    go_button.on_click(go_button_callback)
+
+    def created_lists_callback(_attr, _old, new):
+        sel_file = new[0]
+        file_text = res_files[sel_file]
+        preview_lists.value = file_text
+        app_dlfiles.set_contents([file_text])
+        app_dlfiles.set_names([sel_file])
+
+    created_lists = MultiSelect(title="Created lists:", width=200, height=150)
+    created_lists.on_change("value", created_lists_callback)
+    preview_lists = TextAreaInput(title="Preview selected list:", width=600, height=150)
+
+    def plot_list_callback():
+        nonlocal _update_slice
+        fname = created_lists.value
+        with io.StringIO(preview_lists.value) as fileobj:
+            fdata = pyzebra.parse_hkl(fileobj, fname)
+        _update_slice = _prepare_plotting(fname, [fdata])
+        _update_slice()
+
+    plot_list = Button(label="Plot selected list", button_type="primary", width=200)
+    plot_list.on_click(plot_list_callback)
+
+    # Plot
+    upload_data_div = Div(text="Open hkl/mhkl data:")
+    upload_data = FileInput(accept=".hkl,.mhkl", multiple=True, width=200)
+
+    min_grid_x = -10
+    max_grid_x = 10
+    min_grid_y = -10
+    max_grid_y = 10
+    cmap = Dark2[8]
+    syms = ["circle", "inverted_triangle", "square", "diamond", "star", "triangle"]
+
+    def _prepare_plotting(filenames, filedata):
+        orth_dir = list(map(float, hkl_normal.value.split()))
+        x_dir = list(map(float, hkl_in_plane_x.value.split()))
+
+        k = np.array(k_vectors.value.split()).astype(float).reshape(-1, 3)
+        tol_k = tol_k_ni.value
+
+        lattice = list(map(float, cryst_cell.value.strip().split()))
+        alpha = lattice[3] * np.pi / 180.0
+        beta = lattice[4] * np.pi / 180.0
+        gamma = lattice[5] * np.pi / 180.0
+
+        # reciprocal angle parameters
+        beta_star = np.arccos(
+            (np.cos(alpha) * np.cos(gamma) - np.cos(beta)) / (np.sin(alpha) * np.sin(gamma))
+        )
+        gamma_star = np.arccos(
+            (np.cos(alpha) * np.cos(beta) - np.cos(gamma)) / (np.sin(alpha) * np.sin(beta))
+        )
+
+        # conversion matrix
+        M = np.array(
+            [
+                [1, np.cos(gamma_star), np.cos(beta_star)],
+                [0, np.sin(gamma_star), -np.sin(beta_star) * np.cos(alpha)],
+                [0, 0, np.sin(beta_star) * np.sin(alpha)],
+            ]
+        )
+
+        # Get last lattice vector
+        y_dir = np.cross(x_dir, orth_dir)  # Second axes of plotting plane
+
+        # Rescale such that smallest element of y-dir vector is 1
+        y_dir2 = y_dir[y_dir != 0]
+        min_val = np.min(np.abs(y_dir2))
+        y_dir = y_dir / min_val
+
+        # Possibly flip direction of ydir:
+        if y_dir[np.argmax(abs(y_dir))] < 0:
+            y_dir = -y_dir
+
+        # Display the resulting y_dir
+        hkl_in_plane_y.value = " ".join([f"{val:.1f}" for val in y_dir])
+
+        # Save length of lattice vectors
+        x_length = np.linalg.norm(x_dir)
+        y_length = np.linalg.norm(y_dir)
+
+        # Save str for labels
+        xlabel_str = " ".join(map(str, x_dir))
+        ylabel_str = " ".join(map(str, y_dir))
+
+        # Normalize lattice vectors
+        y_dir = y_dir / np.linalg.norm(y_dir)
+        x_dir = x_dir / np.linalg.norm(x_dir)
+        orth_dir = orth_dir / np.linalg.norm(orth_dir)
+
+        # Calculate cartesian equivalents of lattice vectors
+        x_c = np.matmul(M, x_dir)
+        y_c = np.matmul(M, y_dir)
+        o_c = np.matmul(M, orth_dir)
+
+        # Calulcate vertical direction in plotting plame
+        y_vert = np.cross(x_c, o_c)  # verical direction in plotting plane
+        if y_vert[np.argmax(abs(y_vert))] < 0:
+            y_vert = -y_vert
+        y_vert = y_vert / np.linalg.norm(y_vert)
+
+        # Normalize all directions
+        y_c = y_c / np.linalg.norm(y_c)
+        x_c = x_c / np.linalg.norm(x_c)
+        o_c = o_c / np.linalg.norm(o_c)
+
+        # Read all data
+        hkl_coord = []
+        intensity_vec = []
+        k_flag_vec = []
+        file_flag_vec = []
+
+        for j, fdata in enumerate(filedata):
+            for ind in range(len(fdata["counts"])):
+                # Recognize k_flag_vec
+                hkl = np.array([fdata["h"][ind], fdata["k"][ind], fdata["l"][ind]])
+                reduced_hkl_m = np.minimum(1 - hkl % 1, hkl % 1)
+                for k_ind, _k in enumerate(k):
+                    if all(np.abs(reduced_hkl_m - _k) < tol_k):
+                        k_flag_vec.append(k_ind)
+                        break
+                else:
+                    # not required
+                    continue
+
+                # Save data
+                hkl_coord.append(hkl)
+                intensity_vec.append(fdata["counts"][ind])
+                file_flag_vec.append(j)
+
+        x_spacing = np.dot(M @ x_dir, x_c) * x_length
+        y_spacing = np.dot(M @ y_dir, y_vert) * y_length
+        y_spacingx = np.dot(M @ y_dir, x_c) * y_length
+
+        # Plot coordinate system
+        arrow1.x_end = x_spacing
+        arrow1.y_end = 0
+        arrow2.x_end = y_spacingx
+        arrow2.y_end = y_spacing
+
+        # Add labels
+        kvect_source.data.update(
+            x=[x_spacing / 4, -0.1],
+            y=[x_spacing / 4 - 0.5, y_spacing / 2],
+            text=[xlabel_str, ylabel_str],
+        )
+
+        # Plot grid lines
+        xs, ys = [], []
+        xs_minor, ys_minor = [], []
+        for yy in np.arange(min_grid_y, max_grid_y, 1):
+            # Calculate end and start point
+            hkl1 = min_grid_x * x_dir + yy * y_dir
+            hkl2 = max_grid_x * x_dir + yy * y_dir
+            hkl1 = M @ hkl1
+            hkl2 = M @ hkl2
+
+            # Project points onto axes
+            x1 = np.dot(x_c, hkl1) * x_length
+            y1 = np.dot(y_vert, hkl1) * y_length
+            x2 = np.dot(x_c, hkl2) * x_length
+            y2 = np.dot(y_vert, hkl2) * y_length
+
+            xs.append([x1, x2])
+            ys.append([y1, y2])
+
+        for xx in np.arange(min_grid_x, max_grid_x, 1):
+            # Calculate end and start point
+            hkl1 = xx * x_dir + min_grid_y * y_dir
+            hkl2 = xx * x_dir + max_grid_y * y_dir
+            hkl1 = M @ hkl1
+            hkl2 = M @ hkl2
+
+            # Project points onto axes
+            x1 = np.dot(x_c, hkl1) * x_length
+            y1 = np.dot(y_vert, hkl1) * y_length
+            x2 = np.dot(x_c, hkl2) * x_length
+            y2 = np.dot(y_vert, hkl2) * y_length
+
+            xs.append([x1, x2])
+            ys.append([y1, y2])
+
+        for yy in np.arange(min_grid_y, max_grid_y, 0.5):
+            # Calculate end and start point
+            hkl1 = min_grid_x * x_dir + yy * y_dir
+            hkl2 = max_grid_x * x_dir + yy * y_dir
+            hkl1 = M @ hkl1
+            hkl2 = M @ hkl2
+
+            # Project points onto axes
+            x1 = np.dot(x_c, hkl1) * x_length
+            y1 = np.dot(y_vert, hkl1) * y_length
+            x2 = np.dot(x_c, hkl2) * x_length
+            y2 = np.dot(y_vert, hkl2) * y_length
+
+            xs_minor.append([x1, x2])
+            ys_minor.append([y1, y2])
+
+        for xx in np.arange(min_grid_x, max_grid_x, 0.5):
+            # Calculate end and start point
+            hkl1 = xx * x_dir + min_grid_y * y_dir
+            hkl2 = xx * x_dir + max_grid_y * y_dir
+            hkl1 = M @ hkl1
+            hkl2 = M @ hkl2
+
+            # Project points onto axes
+            x1 = np.dot(x_c, hkl1) * x_length
+            y1 = np.dot(y_vert, hkl1) * y_length
+            x2 = np.dot(x_c, hkl2) * x_length
+            y2 = np.dot(y_vert, hkl2) * y_length
+
+            xs_minor.append([x1, x2])
+            ys_minor.append([y1, y2])
+
+        grid_source.data.update(xs=xs, ys=ys)
+        minor_grid_source.data.update(xs=xs_minor, ys=ys_minor)
+
+        def _update_slice():
+            cut_tol = hkl_delta.value
+            cut_or = hkl_cut.value
+
+            # different symbols based on file number
+            file_flag = 0 in disting_opt_cb.active
+            # scale marker size according to intensity
+            intensity_flag = 1 in disting_opt_cb.active
+            # use color to mark different propagation vectors
+            prop_legend_flag = 2 in disting_opt_cb.active
+
+            scan_x, scan_y = [], []
+            scan_m, scan_s, scan_c, scan_l, scan_hkl = [], [], [], [], []
+            for j in range(len(hkl_coord)):
+                # Get middle hkl from list
+                hklm = M @ hkl_coord[j]
+
+                # Decide if point is in the cut
+                proj = np.dot(hklm, o_c)
+                if abs(proj - cut_or) >= cut_tol:
+                    continue
+
+                # Project onto axes
+                hklmx = np.dot(hklm, x_c)
+                hklmy = np.dot(hklm, y_vert)
+
+                if intensity_flag and max(intensity_vec) != 0:
+                    markersize = max(6, int(intensity_vec[j] / max(intensity_vec) * 30))
+                else:
+                    markersize = 6
+
+                if file_flag:
+                    plot_symbol = syms[file_flag_vec[j]]
+                else:
+                    plot_symbol = "circle"
+
+                if prop_legend_flag:
+                    col_value = cmap[k_flag_vec[j]]
+                else:
+                    col_value = "black"
+
+                # Plot middle point of scan
+                scan_x.append(hklmx)
+                scan_y.append(hklmy)
+                scan_m.append(plot_symbol)
+                scan_s.append(markersize)
+
+                # Color and legend label
+                scan_c.append(col_value)
+                scan_l.append(filenames[file_flag_vec[j]])
+                scan_hkl.append(hkl_coord[j])
+
+            scatter_source.data.update(
+                x=scan_x, y=scan_y, m=scan_m, s=scan_s, c=scan_c, l=scan_l, hkl=scan_hkl
+            )
+
+            # Legend items for different file entries (symbol)
+            legend_items = []
+            if file_flag:
+                labels, inds = np.unique(scatter_source.data["l"], return_index=True)
+                for label, ind in zip(labels, inds):
+                    legend_items.append(LegendItem(label=label, renderers=[scatter], index=ind))
+
+            # Legend items for propagation vector (color)
+            if prop_legend_flag:
+                labels, inds = np.unique(scatter_source.data["c"], return_index=True)
+                for label, ind in zip(labels, inds):
+                    label = f"k={k[cmap.index(label)]}"
+                    legend_items.append(LegendItem(label=label, renderers=[scatter], index=ind))
+
+            plot.legend.items = legend_items
+
+        return _update_slice
+
+    def plot_file_callback():
+        nonlocal _update_slice
+        fnames = []
+        fdata = []
+        for j, fname in enumerate(upload_data.filename):
+            with io.StringIO(base64.b64decode(upload_data.value[j]).decode()) as file:
+                _, ext = os.path.splitext(fname)
+                try:
+                    file_data = pyzebra.parse_hkl(file, ext)
+                except Exception as e:
+                    log.exception(e)
+                    return
+
+            fnames.append(fname)
+            fdata.append(file_data)
+
+        _update_slice = _prepare_plotting(fnames, fdata)
+        _update_slice()
+
+    plot_file = Button(label="Plot selected file(s)", button_type="primary", width=200)
+    plot_file.on_click(plot_file_callback)
+
+    plot = figure(height=550, width=550 + 32, tools="pan,wheel_zoom,reset")
+    plot.toolbar.logo = None
+
+    plot.xaxis.visible = False
+    plot.xgrid.visible = False
+    plot.yaxis.visible = False
+    plot.ygrid.visible = False
+
+    arrow1 = Arrow(x_start=0, y_start=0, x_end=0, y_end=0, end=NormalHead(size=10))
+    plot.add_layout(arrow1)
+    arrow2 = Arrow(x_start=0, y_start=0, x_end=0, y_end=0, end=NormalHead(size=10))
+    plot.add_layout(arrow2)
+
+    kvect_source = ColumnDataSource(dict(x=[], y=[], text=[]))
+    plot.text(source=kvect_source)
+
+    grid_source = ColumnDataSource(dict(xs=[], ys=[]))
+    plot.multi_line(source=grid_source, line_color="gray")
+
+    minor_grid_source = ColumnDataSource(dict(xs=[], ys=[]))
+    plot.multi_line(source=minor_grid_source, line_color="gray", line_dash="dotted")
+
+    scatter_source = ColumnDataSource(dict(x=[], y=[], m=[], s=[], c=[], l=[], hkl=[]))
+    scatter = plot.scatter(
+        source=scatter_source, marker="m", size="s", fill_color="c", line_color="c"
+    )
+
+    plot.x_range.renderers = [scatter]
+    plot.y_range.renderers = [scatter]
+
+    plot.add_layout(Legend(items=[], location="top_left", click_policy="hide"))
+
+    plot.add_tools(HoverTool(renderers=[scatter], tooltips=[("hkl", "@hkl")]))
+
+    hkl_div = Div(text="HKL:", margin=(5, 5, 0, 5))
+    hkl_normal = TextInput(title="normal", value="0 0 1", width=70)
+
+    def hkl_cut_callback(_attr, _old, _new):
+        if _update_slice is not None:
+            _update_slice()
+
+    hkl_cut = Spinner(title="cut", value=0, step=0.1, width=70)
+    hkl_cut.on_change("value_throttled", hkl_cut_callback)
+
+    hkl_delta = NumericInput(title="delta", value=0.1, mode="float", width=70)
+    hkl_in_plane_x = TextInput(title="in-plane X", value="1 0 0", width=70)
+    hkl_in_plane_y = TextInput(title="in-plane Y", value="", width=100, disabled=True)
+
+    disting_opt_div = Div(text="Distinguish options:", margin=(5, 5, 0, 5))
+    disting_opt_cb = CheckboxGroup(
+        labels=["files (symbols)", "intensities (size)", "k vectors nucl/magn (colors)"],
+        active=[0, 1, 2],
+        width=200,
+    )
+
+    k_vectors = TextAreaInput(
+        title="k vectors:", value="0.0 0.0 0.0\n0.5 0.0 0.0\n0.5 0.5 0.0", width=150
+    )
+    tol_k_ni = NumericInput(title="k tolerance:", value=0.01, mode="float", width=100)
+
+    fileinput_layout = row(open_cfl_div, open_cfl, open_cif_div, open_cif, open_geom_div, open_geom)
+
+    geom_layout = column(geom_radiogroup_div, geom_radiogroup)
+    wavelen_layout = column(wavelen_div, row(wavelen_select, wavelen_input))
+    anglim_layout = column(anglim_div, row(sttgamma_ti, omega_ti, chinu_ti, phi_ti))
+    cryst_layout = column(cryst_div, row(cryst_space_group, cryst_cell))
+    ubmat_layout = row(column(Spacer(height=19), ub_matrix_calc), ub_matrix)
+    ranges_layout = column(ranges_div, row(ranges_hkl, ranges_srang))
+    magstruct_layout = column(magstruct_div, row(magstruct_lattice, magstruct_kvec))
+    sorting_layout = row(
+        sorting_0,
+        sorting_0_dt,
+        Spacer(width=30),
+        sorting_1,
+        sorting_1_dt,
+        Spacer(width=30),
+        sorting_2,
+        sorting_2_dt,
+    )
+
+    column1_layout = column(
+        fileinput_layout,
+        Spacer(height=10),
+        row(geom_layout, wavelen_layout, Spacer(width=50), anglim_layout),
+        cryst_layout,
+        ubmat_layout,
+        row(ranges_layout, Spacer(width=50), magstruct_layout),
+        row(sorting_layout, Spacer(width=30), column(Spacer(height=19), go_button)),
+        row(created_lists, preview_lists),
+        row(app_dlfiles.button, plot_list),
+    )
+
+    column2_layout = column(
+        row(upload_data_div, upload_data, plot_file),
+        row(
+            plot,
+            column(
+                hkl_div,
+                row(hkl_normal, hkl_cut, hkl_delta),
+                row(hkl_in_plane_x, hkl_in_plane_y),
+                k_vectors,
+                tol_k_ni,
+                disting_opt_div,
+                disting_opt_cb,
+            ),
+        ),
+    )
+
+    tab_layout = row(column1_layout, column2_layout)
+
+    return Panel(child=tab_layout, title="ccl prepare")

pyzebra/app/panel_hdf_anatric.py

@@ -19,11 +19,12 @@ from bokeh.models import (
 )
 
 import pyzebra
-from pyzebra.anatric import DATA_FACTORY_IMPLEMENTATION, REFLECTION_PRINTER_FORMATS
+from pyzebra import DATA_FACTORY_IMPLEMENTATION, REFLECTION_PRINTER_FORMATS
 
 
 def create():
     doc = curdoc()
+    log = doc.logger
     config = pyzebra.AnatricConfig()
 
     def _load_config_file(file):
@@ -169,7 +170,7 @@ def create():
         config.dataFactory_implementation = new
 
     dataFactory_implementation_select = Select(
-        title="DataFactory implement.:", options=DATA_FACTORY_IMPLEMENTATION, width=145,
+        title="DataFactory implement.:", options=DATA_FACTORY_IMPLEMENTATION, width=145
     )
     dataFactory_implementation_select.on_change("value", dataFactory_implementation_select_callback)
 
@@ -200,7 +201,7 @@ def create():
         config.reflectionPrinter_format = new
 
     reflectionPrinter_format_select = Select(
-        title="ReflectionPrinter format:", options=REFLECTION_PRINTER_FORMATS, width=145,
+        title="ReflectionPrinter format:", options=REFLECTION_PRINTER_FORMATS, width=145
     )
     reflectionPrinter_format_select.on_change("value", reflectionPrinter_format_select_callback)
 
@@ -347,7 +348,11 @@ def create():
         with tempfile.TemporaryDirectory() as temp_dir:
             temp_file = temp_dir + "/config.xml"
             config.save_as(temp_file)
-            pyzebra.anatric(temp_file, anatric_path=doc.anatric_path, cwd=temp_dir)
+            try:
+                pyzebra.anatric(temp_file, anatric_path=doc.anatric_path, cwd=temp_dir, log=log)
+            except Exception as e:
+                log.exception(e)
+                return
 
             with open(os.path.join(temp_dir, config.logfile)) as f_log:
                 output_log.value = f_log.read()

pyzebra/app/panel_hdf_param_study.py

@@ -1,57 +1,43 @@
 import base64
 import io
-import math
 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,
+    LinearColorMapper,
     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
+from bokeh.plotting import figure
-from scipy.optimize import curve_fit
 
 import pyzebra
 
 IMAGE_W = 256
 IMAGE_H = 128
-IMAGE_PLOT_W = int(IMAGE_W * 2) + 52
+IMAGE_PLOT_W = int(IMAGE_W * 2.4) + 52
-IMAGE_PLOT_H = int(IMAGE_H * 2) + 27
+IMAGE_PLOT_H = int(IMAGE_H * 2.4) + 27
 
 
 def create():
     doc = curdoc()
-    zebra_data = []
+    log = doc.logger
-    det_data = {}
+    dataset = []
     cami_meta = {}
 
     num_formatter = NumberFormatter(format="0.00", nan_format="")
@@ -110,15 +96,15 @@ def create():
 
     def _init_datatable():
         file_list = []
-        for scan in zebra_data:
+        for scan in dataset:
             file_list.append(os.path.basename(scan["original_filename"]))
 
         scan_table_source.data.update(
             file=file_list,
-            param=[None] * len(zebra_data),
+            param=[None] * len(dataset),
-            frame=[None] * len(zebra_data),
+            frame=[None] * len(dataset),
-            x_pos=[None] * len(zebra_data),
+            x_pos=[None] * len(dataset),
-            y_pos=[None] * len(zebra_data),
+            y_pos=[None] * len(dataset),
         )
         scan_table_source.selected.indices = []
         scan_table_source.selected.indices = [0]
@@ -129,7 +115,7 @@ def create():
         frame = []
         x_pos = []
         y_pos = []
-        for scan in zebra_data:
+        for scan in dataset:
             if "fit" in scan:
                 framei = scan["fit"]["frame"]
                 x_posi = scan["fit"]["x_pos"]
@@ -143,30 +129,35 @@ def create():
 
         scan_table_source.data.update(frame=frame, x_pos=x_pos, y_pos=y_pos)
 
-    def file_open_button_callback():
+    def _file_open():
-        nonlocal zebra_data
+        new_data = []
-        zebra_data = []
         for f_name in file_select.value:
-            zebra_data.append(pyzebra.read_detector_data(f_name))
+            try:
+                new_data.append(pyzebra.read_detector_data(f_name))
+            except KeyError as e:
+                log.exception(e)
+                return
+
+        dataset.extend(new_data)
 
         _init_datatable()
 
+    def file_open_button_callback():
+        nonlocal dataset
+        dataset = []
+        _file_open()
+
     file_open_button = Button(label="Open New", width=100)
     file_open_button.on_click(file_open_button_callback)
 
     def file_append_button_callback():
-        for f_name in file_select.value:
+        _file_open()
-            zebra_data.append(pyzebra.read_detector_data(f_name))
-
-        _init_datatable()
 
     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
@@ -181,25 +172,25 @@ def create():
             # skip unnecessary update caused by selection drop
             return
 
-        det_data = zebra_data[new[0]]
+        scan = dataset[new[0]]
 
-        zebra_mode = det_data["zebra_mode"]
+        zebra_mode = scan["zebra_mode"]
         if zebra_mode == "nb":
             metadata_table_source.data.update(geom=["normal beam"])
         else:  # zebra_mode == "bi"
             metadata_table_source.data.update(geom=["bisecting"])
 
-        if "mf" in det_data:
+        if "mf" in scan:
-            metadata_table_source.data.update(mf=[det_data["mf"][0]])
+            metadata_table_source.data.update(mf=[scan["mf"][0]])
         else:
             metadata_table_source.data.update(mf=[None])
 
-        if "temp" in det_data:
+        if "temp" in scan:
-            metadata_table_source.data.update(temp=[det_data["temp"][0]])
+            metadata_table_source.data.update(temp=[scan["temp"][0]])
         else:
             metadata_table_source.data.update(temp=[None])
 
-        update_overview_plot()
+        _update_proj_plots()
 
     def scan_table_source_callback(_attr, _old, _new):
         pass
@@ -235,12 +226,15 @@ def create():
         autosize_mode="none",
     )
 
+    def _get_selected_scan():
+        return dataset[scan_table_source.selected.indices[0]]
+
     def param_select_callback(_attr, _old, new):
         if new == "user defined":
-            param = [None] * len(zebra_data)
+            param = [None] * len(dataset)
         else:
             # TODO: which value to take?
-            param = [scan[new][0] for scan in zebra_data]
+            param = [scan[new][0] for scan in dataset]
 
         scan_table_source.data["param"] = param
         _update_param_plot()
@@ -253,42 +247,38 @@ def create():
     )
     param_select.on_change("value", param_select_callback)
 
-    def update_overview_plot():
+    def _update_proj_plots():
-        h5_data = det_data["data"]
+        scan = _get_selected_scan()
-        n_im, n_y, n_x = h5_data.shape
+        counts = scan["counts"]
-        overview_x = np.mean(h5_data, axis=1)
+        n_im, n_y, n_x = counts.shape
-        overview_y = np.mean(h5_data, axis=2)
+        im_proj_x = np.mean(counts, axis=1)
+        im_proj_y = np.mean(counts, axis=2)
 
         # normalize for simpler colormapping
-        overview_max_val = max(np.max(overview_x), np.max(overview_y))
+        im_proj_max_val = max(np.max(im_proj_x), np.max(im_proj_y))
-        overview_x = 1000 * overview_x / overview_max_val
+        im_proj_x = 1000 * im_proj_x / im_proj_max_val
-        overview_y = 1000 * overview_y / overview_max_val
+        im_proj_y = 1000 * im_proj_y / im_proj_max_val
 
-        overview_plot_x_image_source.data.update(image=[overview_x], dw=[n_x], dh=[n_im])
+        proj_x_image_source.data.update(image=[im_proj_x], dw=[n_x], dh=[n_im])
-        overview_plot_y_image_source.data.update(image=[overview_y], dw=[n_y], dh=[n_im])
+        proj_y_image_source.data.update(image=[im_proj_y], dw=[n_y], dh=[n_im])
 
         if proj_auto_checkbox.active:
-            im_min = min(np.min(overview_x), np.min(overview_y))
+            im_min = min(np.min(im_proj_x), np.min(im_proj_y))
-            im_max = max(np.max(overview_x), np.max(overview_y))
+            im_max = max(np.max(im_proj_x), np.max(im_proj_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"]
+        scan_motor = scan["scan_motor"]
-        overview_plot_y.axis[1].axis_label = f"Scanning motor, {scan_motor}"
+        proj_y_plot.yaxis.axis_label = f"Scanning motor, {scan_motor}"
 
-        var = det_data[scan_motor]
+        var = scan[scan_motor]
         var_start = var[0]
         var_end = var[-1] + (var[-1] - var[0]) / (n_im - 1)
 
@@ -302,213 +292,98 @@ def create():
     # shared frame ranges
     frame_range = Range1d(0, 1, bounds=(0, 1))
     scanning_motor_range = Range1d(0, 1, bounds=(0, 1))
+    color_mapper_proj = LinearColorMapper()
 
     det_x_range = Range1d(0, IMAGE_W, bounds=(0, IMAGE_W))
-    overview_plot_x = Plot(
+    proj_x_plot = figure(
-        title=Title(text="Projections on X-axis"),
+        title="Projections on X-axis",
+        x_axis_label="Coordinate X, pix",
+        y_axis_label="Frame",
         x_range=det_x_range,
         y_range=frame_range,
         extra_y_ranges={"scanning_motor": scanning_motor_range},
-        plot_height=400,
+        height=540,
-        plot_width=IMAGE_PLOT_W - 3,
+        width=IMAGE_PLOT_W - 3,
+        tools="pan,box_zoom,wheel_zoom,reset",
+        active_scroll="wheel_zoom",
     )
 
-    # ---- tools
+    proj_x_plot.yaxis.major_label_orientation = "vertical"
-    wheelzoomtool = WheelZoomTool(maintain_focus=False)
+    proj_x_plot.toolbar.tools[2].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
+    proj_x_image_source = ColumnDataSource(
-    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")
+    proj_x_plot.image(source=proj_x_image_source, color_mapper=color_mapper_proj)
-    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(
+    proj_y_plot = figure(
-        title=Title(text="Projections on Y-axis"),
+        title="Projections on Y-axis",
+        x_axis_label="Coordinate Y, pix",
+        y_axis_label="Scanning motor",
+        y_axis_location="right",
         x_range=det_y_range,
         y_range=frame_range,
         extra_y_ranges={"scanning_motor": scanning_motor_range},
-        plot_height=400,
+        height=540,
-        plot_width=IMAGE_PLOT_H + 22,
+        width=IMAGE_PLOT_H + 22,
+        tools="pan,box_zoom,wheel_zoom,reset",
+        active_scroll="wheel_zoom",
     )
 
-    # ---- tools
+    proj_y_plot.yaxis.y_range_name = "scanning_motor"
-    wheelzoomtool = WheelZoomTool(maintain_focus=False)
+    proj_y_plot.yaxis.major_label_orientation = "vertical"
-    overview_plot_y.toolbar.logo = None
+    proj_y_plot.toolbar.tools[2].maintain_focus = False
-    overview_plot_y.add_tools(
-        PanTool(), BoxZoomTool(), wheelzoomtool, ResetTool(),
-    )
-    overview_plot_y.toolbar.active_scroll = wheelzoomtool
 
-    # ---- axes
+    proj_y_image_source = ColumnDataSource(
-    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")
+    proj_y_plot.image(source=proj_y_image_source, color_mapper=color_mapper_proj)
-    overview_plot_y.add_glyph(
+
-        overview_plot_y_image_source, overview_plot_y_image_glyph, name="image_glyph"
+    def colormap_select_callback(_attr, _old, new):
+        color_mapper_proj.palette = new
+
+    colormap_select = Select(
+        title="Colormap:",
+        options=[("Greys256", "greys"), ("Plasma256", "plasma"), ("Cividis256", "cividis")],
+        width=210,
     )
+    colormap_select.on_change("value", colormap_select_callback)
+    colormap_select.value = "Plasma256"
 
-    cmap_dict = {
+    def proj_auto_checkbox_callback(_attr, _old, new):
-        "gray": Greys256,
+        if 0 in new:
-        "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()
+        _update_proj_plots()
 
     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)
+    proj_auto_checkbox.on_change("active", proj_auto_checkbox_callback)
 
-    def proj_display_max_spinner_callback(_attr, _old_value, new_value):
+    def proj_display_max_spinner_callback(_attr, _old, new):
-        proj_display_min_spinner.high = new_value - PROJ_STEP
+        color_mapper_proj.high = new
-        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, disabled=bool(proj_auto_checkbox.active), mode="int", width=100
-        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):
+    def proj_display_min_spinner_callback(_attr, _old, new):
-        proj_display_max_spinner.low = new_value + PROJ_STEP
+        color_mapper_proj.low = new
-        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,
+        value=0, disabled=bool(proj_auto_checkbox.active), mode="int", width=100
-        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)
 
-    def fit_event(scan):
-        p0 = [1.0, 0.0, 1.0]
-        maxfev = 100000
-
-        # wave = scan["wave"]
-        # ddist = scan["ddist"]
-        # cell = scan["cell"]
-
-        # gamma = scan["gamma"][0]
-        # omega = scan["omega"][0]
-        # nu = scan["nu"][0]
-        # chi = scan["chi"][0]
-        # phi = scan["phi"][0]
-
-        scan_motor = scan["scan_motor"]
-        var_angle = scan[scan_motor]
-
-        x0 = int(np.floor(det_x_range.start))
-        xN = int(np.ceil(det_x_range.end))
-        y0 = int(np.floor(det_y_range.start))
-        yN = int(np.ceil(det_y_range.end))
-        fr0 = int(np.floor(frame_range.start))
-        frN = int(np.ceil(frame_range.end))
-        data_roi = scan["data"][fr0:frN, y0:yN, x0:xN]
-
-        cnts = np.sum(data_roi, axis=(1, 2))
-        coeff, _ = curve_fit(gauss, range(len(cnts)), cnts, p0=p0, maxfev=maxfev)
-
-        # m = cnts.mean()
-        # sd = cnts.std()
-        # snr_cnts = np.where(sd == 0, 0, m / sd)
-
-        frC = fr0 + coeff[1]
-        var_F = var_angle[math.floor(frC)]
-        var_C = var_angle[math.ceil(frC)]
-        # frStep = frC - math.floor(frC)
-        var_step = var_C - var_F
-        # var_p = var_F + var_step * frStep
-
-        # if scan_motor == "gamma":
-        #     gamma = var_p
-        # elif scan_motor == "omega":
-        #     omega = var_p
-        # elif scan_motor == "nu":
-        #     nu = var_p
-        # elif scan_motor == "chi":
-        #     chi = var_p
-        # elif scan_motor == "phi":
-        #     phi = var_p
-
-        intensity = coeff[1] * abs(coeff[2] * var_step) * math.sqrt(2) * math.sqrt(np.pi)
-
-        projX = np.sum(data_roi, axis=(0, 1))
-        coeff, _ = curve_fit(gauss, range(len(projX)), projX, p0=p0, maxfev=maxfev)
-        x_pos = x0 + coeff[1]
-
-        projY = np.sum(data_roi, axis=(0, 2))
-        coeff, _ = curve_fit(gauss, range(len(projY)), projY, p0=p0, maxfev=maxfev)
-        y_pos = y0 + coeff[1]
-
-        scan["fit"] = {"frame": frC, "x_pos": x_pos, "y_pos": y_pos, "intensity": intensity}
-
     metadata_table_source = ColumnDataSource(dict(geom=[""], temp=[None], mf=[None]))
     metadata_table = DataTable(
         source=metadata_table_source,
@@ -527,25 +402,24 @@ def create():
         x = []
         y = []
         fit_param = fit_param_select.value
-        for s, p in zip(zebra_data, scan_table_source.data["param"]):
+        for s, p in zip(dataset, scan_table_source.data["param"]):
             if "fit" in s and fit_param:
                 x.append(p)
                 y.append(s["fit"][fit_param])
-        param_plot_scatter_source.data.update(x=x, y=y)
+        param_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 = figure(
+        x_axis_label="Parameter",
+        y_axis_label="Fit parameter",
+        height=400,
+        width=700,
+        tools="pan,wheel_zoom,reset",
+    )
 
-    param_plot.add_layout(LinearAxis(axis_label="Fit parameter"), place="left")
+    param_scatter_source = ColumnDataSource(dict(x=[], y=[]))
-    param_plot.add_layout(LinearAxis(axis_label="Parameter"), place="below")
+    param_plot.circle(source=param_scatter_source)
 
-    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):
@@ -555,12 +429,20 @@ def create():
     fit_param_select.on_change("value", fit_param_select_callback)
 
     def proc_all_button_callback():
-        for scan in zebra_data:
+        for scan in dataset:
-            fit_event(scan)
+            pyzebra.fit_event(
+                scan,
+                int(np.floor(frame_range.start)),
+                int(np.ceil(frame_range.end)),
+                int(np.floor(det_y_range.start)),
+                int(np.ceil(det_y_range.end)),
+                int(np.floor(det_x_range.start)),
+                int(np.ceil(det_x_range.end)),
+            )
 
         _update_table()
 
-        for scan in zebra_data:
+        for scan in dataset:
             if "fit" in scan:
                 options = list(scan["fit"].keys())
                 fit_param_select.options = options
@@ -573,11 +455,20 @@ def create():
     proc_all_button.on_click(proc_all_button_callback)
 
     def proc_button_callback():
-        fit_event(det_data)
+        scan = _get_selected_scan()
+        pyzebra.fit_event(
+            scan,
+            int(np.floor(frame_range.start)),
+            int(np.ceil(frame_range.end)),
+            int(np.floor(det_y_range.start)),
+            int(np.ceil(det_y_range.end)),
+            int(np.floor(det_x_range.start)),
+            int(np.ceil(det_x_range.end)),
+        )
 
         _update_table()
 
-        for scan in zebra_data:
+        for scan in dataset:
             if "fit" in scan:
                 options = list(scan["fit"].keys())
                 fit_param_select.options = options
@@ -590,18 +481,15 @@ def create():
     proc_button.on_click(proc_button_callback)
 
     layout_controls = row(
-        colormap,
+        colormap_select,
         column(proj_auto_checkbox, row(proj_display_min_spinner, proj_display_max_spinner)),
         proc_button,
         proc_all_button,
     )
 
-    layout_overview = column(
+    layout_proj = column(
         gridplot(
-            [[overview_plot_x, overview_plot_y]],
+            [[proj_x_plot, proj_y_plot]], toolbar_options={"logo": None}, toolbar_location="right"
-            toolbar_options=dict(logo=None),
-            merge_tools=True,
-            toolbar_location="left",
         ),
         layout_controls,
     )
@@ -609,7 +497,7 @@ def create():
     # Plot tabs
     plots = Tabs(
         tabs=[
-            Panel(child=layout_overview, title="single scan"),
+            Panel(child=layout_proj, title="single scan"),
             Panel(child=column(param_plot, row(fit_param_select)), title="parameter plot"),
         ]
     )
@@ -628,14 +516,3 @@ def create():
     tab_layout = column(row(import_layout, scan_layout, plots))
 
     return Panel(child=tab_layout, title="hdf param study")
-
-
-def gauss(x, *p):
-    """Defines Gaussian function
-    Args:
-        A - amplitude, mu - position of the center, sigma - width
-    Returns:
-        Gaussian function
-    """
-    A, mu, sigma = p
-    return A * np.exp(-((x - mu) ** 2) / (2.0 * sigma ** 2))

pyzebra/app/panel_param_study.py

@@ -1,78 +1,36 @@
-import base64
-import io
 import itertools
 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,
     HoverTool,
-    Image,
+    LinearColorMapper,
-    Legend,
-    Line,
-    LinearAxis,
-    MultiLine,
-    MultiSelect,
     NumberEditor,
     Panel,
-    PanTool,
+    Range1d,
-    Plot,
-    RadioGroup,
-    ResetTool,
-    Scatter,
     Select,
     Spacer,
     Span,
-    Spinner,
     TableColumn,
     Tabs,
     TextAreaInput,
-    WheelZoomTool,
     Whisker,
 )
-from bokeh.palettes import Category10, Turbo256
+from bokeh.palettes import Category10, Plasma256
-from bokeh.transform import linear_cmap
+from bokeh.plotting import figure
 from scipy import interpolate
 
 import pyzebra
-from pyzebra.ccl_process import AREA_METHODS
+from pyzebra import app
-
-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 color_palette(n_colors):
@@ -82,154 +40,54 @@ def color_palette(n_colors):
 
 def create():
     doc = curdoc()
-    det_data = []
+    log = doc.logger
-    fit_params = {}
+    dataset = []
-    js_data = ColumnDataSource(data=dict(content=[""], fname=[""], ext=[""]))
+    app_dlfiles = app.DownloadFiles(n_files=1)
-
-    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
-
-    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 = [s["idx"] for s in det_data]
+        scan_list = [s["idx"] for s in dataset]
+        export = [s["export"] for s in dataset]
+        if param_select.value == "user defined":
+            param = [None] * len(dataset)
+        else:
+            param = [scan[param_select.value] for scan in dataset]
+
         file_list = []
-        for scan in det_data:
+        for scan in dataset:
             file_list.append(os.path.basename(scan["original_filename"]))
 
         scan_table_source.data.update(
-            file=file_list,
+            file=file_list, scan=scan_list, param=param, fit=[0] * len(scan_list), export=export
-            scan=scan_list,
-            param=[None] * len(scan_list),
-            fit=[0] * len(scan_list),
-            export=[True] * len(scan_list),
         )
         scan_table_source.selected.indices = []
         scan_table_source.selected.indices = [0]
 
-        scan_motor_select.options = det_data[0]["scan_motors"]
+        scan_motor_select.options = dataset[0]["scan_motors"]
-        scan_motor_select.value = det_data[0]["scan_motor"]
+        scan_motor_select.value = dataset[0]["scan_motor"]
-        param_select.value = "user defined"
 
-    file_select = MultiSelect(title="Available .ccl/.dat files:", width=210, height=250)
+        merge_options = [(str(i), f"{i} ({idx})") for i, idx in enumerate(scan_list)]
-
+        merge_from_select.options = merge_options
-    def file_open_button_callback():
+        merge_from_select.value = merge_options[0][0]
-        nonlocal det_data
-        for f_ind, f_path in enumerate(file_select.value):
-            with open(f_path) as file:
-                base, ext = os.path.splitext(os.path.basename(f_path))
-                file_data = pyzebra.parse_1D(file, ext)
-
-            pyzebra.normalize_dataset(file_data, monitor_spinner.value)
-
-            if f_ind == 0:  # first file
-                det_data = file_data
-                pyzebra.merge_duplicates(det_data)
-                js_data.data.update(fname=[base])
-            else:
-                pyzebra.merge_datasets(det_data, file_data)
-
-        _init_datatable()
-        append_upload_button.disabled = False
-
-    file_open_button = Button(label="Open New", width=100, disabled=True)
-    file_open_button.on_click(file_open_button_callback)
-
-    def file_append_button_callback():
-        for f_path in file_select.value:
-            with open(f_path) as file:
-                _, ext = os.path.splitext(f_path)
-                file_data = pyzebra.parse_1D(file, ext)
-
-            pyzebra.normalize_dataset(file_data, monitor_spinner.value)
-            pyzebra.merge_datasets(det_data, file_data)
-
-        _init_datatable()
-
-    file_append_button = Button(label="Append", width=100, disabled=True)
-    file_append_button.on_click(file_append_button_callback)
-
-    def upload_button_callback(_attr, _old, new):
-        nonlocal det_data
-        det_data = []
-        for f_str, f_name in zip(new, upload_button.filename):
-            with io.StringIO(base64.b64decode(f_str).decode()) as file:
-                base, ext = os.path.splitext(f_name)
-                file_data = pyzebra.parse_1D(file, ext)
-
-            pyzebra.normalize_dataset(file_data, monitor_spinner.value)
-
-            if not det_data:  # first file
-                det_data = file_data
-                pyzebra.merge_duplicates(det_data)
-                js_data.data.update(fname=[base])
-            else:
-                pyzebra.merge_datasets(det_data, file_data)
-
-        _init_datatable()
-        append_upload_button.disabled = False
-
-    upload_div = Div(text="or upload new .ccl/.dat files:", margin=(5, 5, 0, 5))
-    upload_button = FileInput(accept=".ccl,.dat", multiple=True, width=200)
-    upload_button.on_change("value", upload_button_callback)
-
-    def append_upload_button_callback(_attr, _old, new):
-        for f_str, f_name in zip(new, append_upload_button.filename):
-            with io.StringIO(base64.b64decode(f_str).decode()) as file:
-                _, ext = os.path.splitext(f_name)
-                file_data = pyzebra.parse_1D(file, ext)
-
-            pyzebra.normalize_dataset(file_data, monitor_spinner.value)
-            pyzebra.merge_datasets(det_data, file_data)
-
-        _init_datatable()
-
-    append_upload_div = Div(text="append extra files:", margin=(5, 5, 0, 5))
-    append_upload_button = FileInput(accept=".ccl,.dat", multiple=True, width=200, disabled=True)
-    append_upload_button.on_change("value", append_upload_button_callback)
-
-    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 scan_motor_select_callback(_attr, _old, new):
-        if det_data:
+        if dataset:
-            for scan in det_data:
+            for scan in dataset:
                 scan["scan_motor"] = new
-            _update_plot()
+            _update_single_scan_plot()
+            _update_overview()
 
     scan_motor_select = Select(title="Scan motor:", options=[], width=145)
     scan_motor_select.on_change("value", scan_motor_select_callback)
 
     def _update_table():
-        fit_ok = [(1 if "fit" in scan else 0) for scan in det_data]
+        fit_ok = [(1 if "fit" in scan else 0) for scan in dataset]
-        scan_table_source.data.update(fit=fit_ok)
+        export = [scan["export"] for scan in dataset]
+        if param_select.value == "user defined":
+            param = [None] * len(dataset)
+        else:
+            param = [scan[param_select.value] for scan in dataset]
 
-    def _update_plot():
+        scan_table_source.data.update(fit=fit_ok, export=export, param=param)
-        _update_single_scan_plot()
-        _update_overview()
 
     def _update_single_scan_plot():
         scan = _get_selected_scan()
@@ -240,19 +98,19 @@ def create():
         x = scan[scan_motor]
 
         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)
+        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)
-            plot_fit_source.data.update(x=x_fit, y=fit.eval(x=x_fit))
+            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):
+            for i, model in enumerate(app_fitctrl.params):
                 if "linear" in model:
                     x_bkg = x_fit
                     y_bkg = comps[f"f{i}_"]
@@ -261,16 +119,15 @@ def create():
                     xs_peak.append(x_fit)
                     ys_peak.append(comps[f"f{i}_"])
 
-            plot_bkg_source.data.update(x=x_bkg, y=y_bkg)
+            bkg_source.data.update(x=x_bkg, y=y_bkg)
-            plot_peak_source.data.update(xs=xs_peak, ys=ys_peak)
+            peak_source.data.update(xs=xs_peak, ys=ys_peak)
-
-            fit_output_textinput.value = fit.fit_report()
 
         else:
-            plot_fit_source.data.update(x=[], y=[])
+            fit_source.data.update(x=[], y=[])
-            plot_bkg_source.data.update(x=[], y=[])
+            bkg_source.data.update(x=[], y=[])
-            plot_peak_source.data.update(xs=[], ys=[])
+            peak_source.data.update(xs=[], ys=[])
-            fit_output_textinput.value = ""
+
+        app_fitctrl.update_result_textarea(scan)
 
     def _update_overview():
         xs = []
@@ -281,7 +138,7 @@ def create():
         par = []
         for s, p in enumerate(scan_table_source.data["param"]):
             if p is not None:
-                scan = det_data[s]
+                scan = dataset[s]
                 scan_motor = scan["scan_motor"]
                 xs.append(scan[scan_motor])
                 x.extend(scan[scan_motor])
@@ -290,32 +147,39 @@ def create():
                 param.append(float(p))
                 par.extend(scan["counts"])
 
-        if det_data:
+        if dataset:
-            scan_motor = det_data[0]["scan_motor"]
+            scan_motor = dataset[0]["scan_motor"]
             ov_plot.axis[0].axis_label = scan_motor
             ov_param_plot.axis[0].axis_label = scan_motor
 
-        ov_plot_mline_source.data.update(xs=xs, ys=ys, param=param, color=color_palette(len(xs)))
+        ov_mline_source.data.update(xs=xs, ys=ys, param=param, color=color_palette(len(xs)))
+
+        ov_param_scatter_source.data.update(x=x, y=y)
 
         if y:
-            mapper["transform"].low = np.min([np.min(y) for y in ys])
-            mapper["transform"].high = np.max([np.max(y) for y in ys])
-        ov_param_plot_scatter_source.data.update(x=x, y=y, param=par)
-
-        if y:
-            interp_f = interpolate.interp2d(x, y, par)
             x1, x2 = min(x), max(x)
             y1, y2 = min(y), max(y)
-            image = interp_f(
+            grid_x, grid_y = np.meshgrid(
-                np.linspace(x1, x2, ov_param_plot.inner_width // 10),
+                np.linspace(x1, x2, ov_param_plot.inner_width),
-                np.linspace(y1, y2, ov_param_plot.inner_height // 10),
+                np.linspace(y1, y2, ov_param_plot.inner_height),
-                assume_sorted=True,
             )
-            ov_param_plot_image_source.data.update(
+            image = interpolate.griddata((x, y), par, (grid_x, grid_y))
+            ov_param_image_source.data.update(
                 image=[image], x=[x1], y=[y1], dw=[x2 - x1], dh=[y2 - y1]
             )
+
+            x_range = ov_param_plot.x_range
+            x_range.start, x_range.end = x1, x2
+            x_range.reset_start, x_range.reset_end = x1, x2
+            x_range.bounds = (x1, x2)
+
+            y_range = ov_param_plot.y_range
+            y_range.start, y_range.end = y1, y2
+            y_range.reset_start, y_range.reset_end = y1, y2
+            y_range.bounds = (y1, y2)
+
         else:
-            ov_param_plot_image_source.data.update(image=[], x=[], y=[], dw=[], dh=[])
+            ov_param_image_source.data.update(image=[], x=[], y=[], dw=[], dh=[])
 
     def _update_param_plot():
         x = []
@@ -323,49 +187,50 @@ def create():
         y_lower = []
         y_upper = []
         fit_param = fit_param_select.value
-        for s, p in zip(det_data, scan_table_source.data["param"]):
+        for s, p in zip(dataset, scan_table_source.data["param"]):
             if "fit" in s and fit_param:
                 x.append(p)
                 param_fit_val = s["fit"].params[fit_param].value
                 param_fit_std = s["fit"].params[fit_param].stderr
+                if param_fit_std is None:
+                    param_fit_std = 0
                 y.append(param_fit_val)
                 y_lower.append(param_fit_val - param_fit_std)
                 y_upper.append(param_fit_val + param_fit_std)
 
-        param_plot_scatter_source.data.update(x=x, y=y, y_lower=y_lower, y_upper=y_upper)
+        param_scatter_source.data.update(x=x, y=y, y_lower=y_lower, y_upper=y_upper)
+
+    def _monitor_change():
+        _update_single_scan_plot()
+        _update_overview()
+
+    app_inputctrl = app.InputControls(
+        dataset, app_dlfiles, on_file_open=_init_datatable, on_monitor_change=_monitor_change
+    )
 
     # Main plot
-    plot = Plot(
+    plot = figure(
-        x_range=DataRange1d(),
+        x_axis_label="Scan motor",
-        y_range=DataRange1d(only_visible=True),
+        y_axis_label="Counts",
-        plot_height=450,
+        height=450,
-        plot_width=700,
+        width=700,
+        tools="pan,wheel_zoom,reset",
     )
 
-    plot.add_layout(LinearAxis(axis_label="Counts"), place="left")
+    scatter_source = ColumnDataSource(dict(x=[0], y=[0], y_upper=[0], y_lower=[0]))
-    plot.add_layout(LinearAxis(axis_label="Scan motor"), place="below")
+    plot.circle(
-
+        source=scatter_source, line_color="steelblue", fill_color="steelblue", legend_label="data"
-    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_scatter = plot.add_glyph(
-        plot_scatter_source, Scatter(x="x", y="y", line_color="steelblue")
     )
-    plot.add_layout(Whisker(source=plot_scatter_source, base="x", upper="y_upper", lower="y_lower"))
+    plot.add_layout(Whisker(source=scatter_source, base="x", upper="y_upper", lower="y_lower"))
 
-    plot_fit_source = ColumnDataSource(dict(x=[0], y=[0]))
+    fit_source = ColumnDataSource(dict(x=[0], y=[0]))
-    plot_fit = plot.add_glyph(plot_fit_source, Line(x="x", y="y"))
+    plot.line(source=fit_source, legend_label="best fit")
 
-    plot_bkg_source = ColumnDataSource(dict(x=[0], y=[0]))
+    bkg_source = ColumnDataSource(dict(x=[0], y=[0]))
-    plot_bkg = plot.add_glyph(
+    plot.line(source=bkg_source, line_color="green", line_dash="dashed", legend_label="linear")
-        plot_bkg_source, Line(x="x", y="y", line_color="green", line_dash="dashed")
-    )
 
-    plot_peak_source = ColumnDataSource(dict(xs=[[0]], ys=[[0]]))
+    peak_source = ColumnDataSource(dict(xs=[[0]], ys=[[0]]))
-    plot_peak = plot.add_glyph(
+    plot.multi_line(source=peak_source, line_color="red", line_dash="dashed", legend_label="peak")
-        plot_peak_source, MultiLine(xs="xs", ys="ys", line_color="red", line_dash="dashed")
-    )
 
     fit_from_span = Span(location=None, dimension="height", line_dash="dashed")
     plot.add_layout(fit_from_span)
@@ -373,82 +238,61 @@ def create():
     fit_to_span = Span(location=None, dimension="height", line_dash="dashed")
     plot.add_layout(fit_to_span)
 
-    plot.add_layout(
+    plot.y_range.only_visible = True
-        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
+    plot.legend.click_policy = "hide"
 
     # Overview multilines plot
-    ov_plot = Plot(x_range=DataRange1d(), y_range=DataRange1d(), plot_height=450, plot_width=700)
+    ov_plot = figure(
+        x_axis_label="Scan motor",
+        y_axis_label="Counts",
+        height=450,
+        width=700,
+        tools="pan,wheel_zoom,reset",
+    )
 
-    ov_plot.add_layout(LinearAxis(axis_label="Counts"), place="left")
+    ov_mline_source = ColumnDataSource(dict(xs=[], ys=[], param=[], color=[]))
-    ov_plot.add_layout(LinearAxis(axis_label="Scan motor"), place="below")
+    ov_plot.multi_line(source=ov_mline_source, line_color="color")
 
-    ov_plot.add_layout(Grid(dimension=0, ticker=BasicTicker()))
+    ov_plot.add_tools(HoverTool(tooltips=[("param", "@param")]))
-    ov_plot.add_layout(Grid(dimension=1, ticker=BasicTicker()))
 
-    ov_plot_mline_source = ColumnDataSource(dict(xs=[], ys=[], param=[], color=[]))
-    ov_plot.add_glyph(ov_plot_mline_source, MultiLine(xs="xs", ys="ys", line_color="color"))
-
-    hover_tool = HoverTool(tooltips=[("param", "@param")])
-    ov_plot.add_tools(PanTool(), WheelZoomTool(), hover_tool, ResetTool())
-
-    ov_plot.add_tools(PanTool(), WheelZoomTool(), ResetTool())
     ov_plot.toolbar.logo = None
 
-    # Overview perams plot
+    # Overview params plot
-    ov_param_plot = Plot(
+    ov_param_plot = figure(
-        x_range=DataRange1d(), y_range=DataRange1d(), plot_height=450, plot_width=700
+        x_axis_label="Scan motor",
+        y_axis_label="Param",
+        x_range=Range1d(),
+        y_range=Range1d(),
+        height=450,
+        width=700,
+        tools="pan,wheel_zoom,reset",
     )
 
-    ov_param_plot.add_layout(LinearAxis(axis_label="Param"), place="left")
+    color_mapper = LinearColorMapper(palette=Plasma256)
-    ov_param_plot.add_layout(LinearAxis(axis_label="Scan motor"), place="below")
+    ov_param_image_source = ColumnDataSource(dict(image=[], x=[], y=[], dw=[], dh=[]))
+    ov_param_plot.image(source=ov_param_image_source, color_mapper=color_mapper)
 
-    ov_param_plot.add_layout(Grid(dimension=0, ticker=BasicTicker()))
+    ov_param_scatter_source = ColumnDataSource(dict(x=[], y=[]))
-    ov_param_plot.add_layout(Grid(dimension=1, ticker=BasicTicker()))
+    ov_param_plot.dot(source=ov_param_scatter_source, size=15, color="black")
 
-    ov_param_plot_image_source = ColumnDataSource(dict(image=[], x=[], y=[], dw=[], dh=[]))
-    ov_param_plot.add_glyph(
-        ov_param_plot_image_source, Image(image="image", x="x", y="y", dw="dw", dh="dh")
-    )
-
-    ov_param_plot_scatter_source = ColumnDataSource(dict(x=[], y=[], param=[]))
-    mapper = linear_cmap(field_name="param", palette=Turbo256, low=0, high=50)
-    ov_param_plot.add_glyph(
-        ov_param_plot_scatter_source,
-        Scatter(x="x", y="y", line_color=mapper, fill_color=mapper, size=10),
-    )
-
-    ov_param_plot.add_tools(PanTool(), WheelZoomTool(), ResetTool())
     ov_param_plot.toolbar.logo = None
 
     # Parameter plot
-    param_plot = Plot(x_range=DataRange1d(), y_range=DataRange1d(), plot_height=400, plot_width=700)
+    param_plot = figure(
-
+        x_axis_label="Parameter",
-    param_plot.add_layout(LinearAxis(axis_label="Fit parameter"), place="left")
+        y_axis_label="Fit parameter",
-    param_plot.add_layout(LinearAxis(axis_label="Parameter"), place="below")
+        height=400,
-
+        width=700,
-    param_plot.add_layout(Grid(dimension=0, ticker=BasicTicker()))
+        tools="pan,wheel_zoom,reset",
-    param_plot.add_layout(Grid(dimension=1, ticker=BasicTicker()))
+    )
-
+
-    param_plot_scatter_source = ColumnDataSource(dict(x=[], y=[], y_upper=[], y_lower=[]))
+    param_scatter_source = ColumnDataSource(dict(x=[], y=[], y_upper=[], y_lower=[]))
-    param_plot.add_glyph(param_plot_scatter_source, Scatter(x="x", y="y"))
+    param_plot.circle(source=param_scatter_source)
-    param_plot.add_layout(
+    param_plot.add_layout(
-        Whisker(source=param_plot_scatter_source, base="x", upper="y_upper", lower="y_lower")
+        Whisker(source=param_scatter_source, base="x", upper="y_upper", lower="y_lower")
     )
 
-    param_plot.add_tools(PanTool(), WheelZoomTool(), ResetTool())
     param_plot.toolbar.logo = None
 
     def fit_param_select_callback(_attr, _old, _new):
@@ -483,9 +327,15 @@ def create():
             # skip unnecessary update caused by selection drop
             return
 
-        _update_plot()
+        _update_single_scan_plot()
 
-    def scan_table_source_callback(_attr, _old, _new):
+    def scan_table_source_callback(_attr, _old, new):
+        # unfortunately, we don't know if the change comes from data update or user input
+        # also `old` and `new` are the same for non-scalars
+        for scan, export in zip(dataset, new["export"]):
+            scan["export"] = export
+        _update_overview()
+        _update_param_plot()
         _update_preview()
 
     scan_table_source = ColumnDataSource(dict(file=[], scan=[], param=[], fit=[], export=[]))
@@ -502,21 +352,43 @@ def create():
             TableColumn(field="export", title="Export", editor=CheckboxEditor(), width=50),
         ],
         width=410,  # +60 because of the index column
+        height=350,
         editable=True,
         autosize_mode="none",
     )
 
+    merge_from_select = Select(title="scan:", width=145)
+
+    def merge_button_callback():
+        scan_into = _get_selected_scan()
+        scan_from = dataset[int(merge_from_select.value)]
+
+        if scan_into is scan_from:
+            log.warning("Selected scans for merging are identical")
+            return
+
+        pyzebra.merge_scans(scan_into, scan_from, log=log)
+        _update_table()
+        _update_single_scan_plot()
+        _update_overview()
+
+    merge_button = Button(label="Merge into current", width=145)
+    merge_button.on_click(merge_button_callback)
+
+    def restore_button_callback():
+        pyzebra.restore_scan(_get_selected_scan())
+        _update_table()
+        _update_single_scan_plot()
+        _update_overview()
+
+    restore_button = Button(label="Restore scan", width=145)
+    restore_button.on_click(restore_button_callback)
+
     def _get_selected_scan():
-        return det_data[scan_table_source.selected.indices[0]]
+        return dataset[scan_table_source.selected.indices[0]]
 
-    def param_select_callback(_attr, _old, new):
+    def param_select_callback(_attr, _old, _new):
-        if new == "user defined":
+        _update_table()
-            param = [None] * len(det_data)
-        else:
-            param = [scan[new] for scan in det_data]
-
-        scan_table_source.data["param"] = param
-        _update_param_plot()
 
     param_select = Select(
         title="Parameter:",
@@ -526,181 +398,52 @@ def create():
     )
     param_select.on_change("value", param_select_callback)
 
+    app_fitctrl = app.FitControls()
+
     def fit_from_spinner_callback(_attr, _old, new):
         fit_from_span.location = new
 
-    fit_from_spinner = Spinner(title="Fit from:", width=145)
+    app_fitctrl.from_spinner.on_change("value", fit_from_spinner_callback)
-    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)
+    app_fitctrl.to_spinner.on_change("value", fit_to_spinner_callback)
-    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, export in zip(det_data, scan_table_source.data["export"]):
+        app_fitctrl.fit_dataset(dataset)
-            if 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_single_scan_plot()
+        _update_overview()
         _update_table()
 
-        for scan in det_data:
+        for scan in dataset:
             if "fit" in scan:
                 options = list(scan["fit"].params.keys())
                 fit_param_select.options = options
                 fit_param_select.value = options[0]
                 break
-        _update_param_plot()
 
     proc_all_button = Button(label="Process All", button_type="primary", width=145)
     proc_all_button.on_click(proc_all_button_callback)
 
     def proc_button_callback():
-        scan = _get_selected_scan()
+        app_fitctrl.fit_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,
-        )
 
-        _update_plot()
+        _update_single_scan_plot()
+        _update_overview()
         _update_table()
 
-        for scan in det_data:
+        for scan in dataset:
             if "fit" in scan:
                 options = list(scan["fit"].params.keys())
                 fit_param_select.options = options
                 fit_param_select.value = options[0]
                 break
-        _update_param_plot()
 
     proc_button = Button(label="Process Current", width=145)
     proc_button.on_click(proc_button_callback)
 
-    area_method_div = Div(text="Intensity:", margin=(5, 5, 0, 5))
-    area_method_radiobutton = RadioGroup(labels=["Function", "Area"], active=0, width=145)
-
-    lorentz_checkbox = CheckboxGroup(labels=["Lorentz Correction"], width=145, margin=(13, 5, 5, 5))
-
     export_preview_textinput = TextAreaInput(title="Export file preview:", width=450, height=400)
 
     def _update_preview():
@@ -708,12 +451,10 @@ def create():
             temp_file = temp_dir + "/temp"
             export_data = []
             param_data = []
-            for s, p, export in zip(
+            for scan, param in zip(dataset, scan_table_source.data["param"]):
-                det_data, scan_table_source.data["param"], scan_table_source.data["export"]
+                if scan["export"] and param:
-            ):
+                    export_data.append(scan)
-                if export:
+                    param_data.append(param)
-                    export_data.append(s)
-                    param_data.append(p)
 
             pyzebra.export_param_study(export_data, param_data, temp_file)
 
@@ -729,36 +470,49 @@ def create():
                 content = ""
             file_content.append(content)
 
-            js_data.data.update(content=file_content)
+            app_dlfiles.set_contents(file_content)
             export_preview_textinput.value = exported_content
 
-    save_button = Button(label="Download File", button_type="success", width=220)
+    area_method_div = Div(text="Intensity:", margin=(5, 5, 0, 5))
-    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),
+        column(
-        fitparams_table,
+            app_fitctrl.add_function_button,
+            app_fitctrl.function_select,
+            app_fitctrl.remove_function_button,
+        ),
+        app_fitctrl.params_table,
         Spacer(width=20),
-        column(fit_from_spinner, lorentz_checkbox, area_method_div, area_method_radiobutton),
+        column(
-        column(fit_to_spinner, proc_button, proc_all_button),
+            app_fitctrl.from_spinner,
+            app_fitctrl.lorentz_checkbox,
+            area_method_div,
+            app_fitctrl.area_method_radiogroup,
+        ),
+        column(app_fitctrl.to_spinner, proc_button, proc_all_button),
     )
 
-    scan_layout = column(scan_table, row(monitor_spinner, scan_motor_select, param_select))
+    scan_layout = column(
+        scan_table,
+        row(app_inputctrl.monitor_spinner, scan_motor_select, param_select),
+        row(column(Spacer(height=19), row(restore_button, merge_button)), merge_from_select),
+    )
 
+    upload_div = Div(text="or upload new .ccl/.dat files:", margin=(5, 5, 0, 5))
+    append_upload_div = Div(text="append extra files:", margin=(5, 5, 0, 5))
     import_layout = column(
-        file_select,
+        app_inputctrl.filelist_select,
-        row(file_open_button, file_append_button),
+        row(app_inputctrl.open_button, app_inputctrl.append_button),
         upload_div,
-        upload_button,
+        app_inputctrl.upload_button,
         append_upload_div,
-        append_upload_button,
+        app_inputctrl.append_upload_button,
     )
 
-    export_layout = column(export_preview_textinput, row(save_button))
+    export_layout = column(export_preview_textinput, row(app_dlfiles.button))
 
     tab_layout = column(
         row(import_layout, scan_layout, plots, Spacer(width=30), export_layout),
-        row(fitpeak_controls, fit_output_textinput),
+        row(fitpeak_controls, app_fitctrl.result_textarea),
     )
 
     return Panel(child=tab_layout, title="param study")

pyzebra/app/panel_plot_data.py

@@ -0,0 +1,442 @@
+import base64
+import io
+import os
+
+import numpy as np
+from bokeh.io import curdoc
+from bokeh.layouts import column, row
+from bokeh.models import (
+    Button,
+    CheckboxGroup,
+    ColorBar,
+    ColumnDataSource,
+    DataRange1d,
+    Div,
+    FileInput,
+    LinearColorMapper,
+    LogColorMapper,
+    NumericInput,
+    Panel,
+    RadioGroup,
+    Select,
+    Spacer,
+    Spinner,
+    TextInput,
+)
+from bokeh.plotting import figure
+from scipy import interpolate
+
+import pyzebra
+from pyzebra import app
+from pyzebra.app.panel_hdf_viewer import calculate_hkl
+
+
+def create():
+    doc = curdoc()
+    log = doc.logger
+    _update_slice = None
+    measured_data_div = Div(text="Measured <b>HDF</b> data:")
+    measured_data = FileInput(accept=".hdf", multiple=True, width=200)
+
+    upload_hkl_div = Div(text="Open hkl/mhkl data:")
+    upload_hkl_fi = FileInput(accept=".hkl,.mhkl", multiple=True, width=200)
+
+    def _prepare_plotting():
+        flag_ub = bool(redef_ub_cb.active)
+        flag_lattice = bool(redef_lattice_cb.active)
+
+        # Define horizontal direction of plotting plane, vertical direction will be calculated
+        # automatically
+        x_dir = list(map(float, hkl_in_plane_x.value.split()))
+
+        # Define direction orthogonal to plotting plane. Together with orth_cut, this parameter also
+        # defines the position of the cut, ie cut will be taken at orth_dir = [x,y,z]*orth_cut +- delta,
+        # where delta is max distance a data point can have from cut in rlu units
+        orth_dir = list(map(float, hkl_normal.value.split()))
+
+        # Load data files
+        md_fnames = measured_data.filename
+        md_fdata = measured_data.value
+
+        for ind, (fname, fdata) in enumerate(zip(md_fnames, md_fdata)):
+            # Read data
+            try:
+                det_data = pyzebra.read_detector_data(io.BytesIO(base64.b64decode(fdata)))
+            except Exception as e:
+                log.exception(e)
+                return None
+
+            if ind == 0:
+                if not flag_ub:
+                    redef_ub_ti.value = " ".join(map(str, det_data["ub"].ravel()))
+                if not flag_lattice:
+                    redef_lattice_ti.value = " ".join(map(str, det_data["cell"]))
+
+            num_slices = np.shape(det_data["counts"])[0]
+
+            # Change parameter
+            if flag_ub:
+                ub = list(map(float, redef_ub_ti.value.strip().split()))
+                det_data["ub"] = np.array(ub).reshape(3, 3)
+
+            # Convert h k l for all images in file
+            h_temp = np.empty(np.shape(det_data["counts"]))
+            k_temp = np.empty(np.shape(det_data["counts"]))
+            l_temp = np.empty(np.shape(det_data["counts"]))
+            for i in range(num_slices):
+                h_temp[i], k_temp[i], l_temp[i] = calculate_hkl(det_data, i)
+
+            # Append to matrix
+            if ind == 0:
+                h = h_temp
+                k = k_temp
+                l = l_temp
+                I_matrix = det_data["counts"]
+            else:
+                h = np.append(h, h_temp, axis=0)
+                k = np.append(k, k_temp, axis=0)
+                l = np.append(l, l_temp, axis=0)
+                I_matrix = np.append(I_matrix, det_data["counts"], axis=0)
+
+        if flag_lattice:
+            vals = list(map(float, redef_lattice_ti.value.strip().split()))
+            lattice = np.array(vals)
+        else:
+            lattice = det_data["cell"]
+
+        # Define matrix for converting to cartesian coordinates and back
+        alpha = lattice[3] * np.pi / 180.0
+        beta = lattice[4] * np.pi / 180.0
+        gamma = lattice[5] * np.pi / 180.0
+
+        # reciprocal angle parameters
+        beta_star = np.arccos(
+            (np.cos(alpha) * np.cos(gamma) - np.cos(beta)) / (np.sin(alpha) * np.sin(gamma))
+        )
+        gamma_star = np.arccos(
+            (np.cos(alpha) * np.cos(beta) - np.cos(gamma)) / (np.sin(alpha) * np.sin(beta))
+        )
+
+        # conversion matrix:
+        M = np.array(
+            [
+                [1, 1 * np.cos(gamma_star), 1 * np.cos(beta_star)],
+                [0, 1 * np.sin(gamma_star), -np.sin(beta_star) * np.cos(alpha)],
+                [0, 0, 1 * np.sin(beta_star) * np.sin(alpha)],
+            ]
+        )
+
+        # Get last lattice vector
+        y_dir = np.cross(x_dir, orth_dir)  # Second axes of plotting plane
+
+        # Rescale such that smallest element of y-dir vector is 1
+        y_dir2 = y_dir[y_dir != 0]
+        min_val = np.min(np.abs(y_dir2))
+        y_dir = y_dir / min_val
+
+        # Possibly flip direction of ydir:
+        if y_dir[np.argmax(abs(y_dir))] < 0:
+            y_dir = -y_dir
+
+        # Display the resulting y_dir
+        hkl_in_plane_y.value = " ".join([f"{val:.1f}" for val in y_dir])
+
+        # # Save length of lattice vectors
+        # x_length = np.linalg.norm(x_dir)
+        # y_length = np.linalg.norm(y_dir)
+
+        # # Save str for labels
+        # xlabel_str = " ".join(map(str, x_dir))
+        # ylabel_str = " ".join(map(str, y_dir))
+
+        # Normalize lattice vectors
+        y_dir = y_dir / np.linalg.norm(y_dir)
+        x_dir = x_dir / np.linalg.norm(x_dir)
+        orth_dir = orth_dir / np.linalg.norm(orth_dir)
+
+        # Calculate cartesian equivalents of lattice vectors
+        x_c = np.matmul(M, x_dir)
+        y_c = np.matmul(M, y_dir)
+        o_c = np.matmul(M, orth_dir)
+
+        # Calulcate vertical direction in plotting plame
+        y_vert = np.cross(x_c, o_c)  # verical direction in plotting plane
+        if y_vert[np.argmax(abs(y_vert))] < 0:
+            y_vert = -y_vert
+        y_vert = y_vert / np.linalg.norm(y_vert)
+
+        # Normalize all directions
+        y_c = y_c / np.linalg.norm(y_c)
+        x_c = x_c / np.linalg.norm(x_c)
+        o_c = o_c / np.linalg.norm(o_c)
+
+        # Convert all hkls to cartesian
+        hkl = [[h, k, l]]
+        hkl = np.transpose(hkl)
+        hkl_c = np.matmul(M, hkl)
+
+        # Prepare hkl/mhkl data
+        hkl_coord = []
+        for j, fname in enumerate(upload_hkl_fi.filename):
+            with io.StringIO(base64.b64decode(upload_hkl_fi.value[j]).decode()) as file:
+                _, ext = os.path.splitext(fname)
+                try:
+                    fdata = pyzebra.parse_hkl(file, ext)
+                except Exception as e:
+                    log.exception(e)
+                    return
+
+            for ind in range(len(fdata["counts"])):
+                # Recognize k_flag_vec
+                hkl = np.array([fdata["h"][ind], fdata["k"][ind], fdata["l"][ind]])
+
+                # Save data
+                hkl_coord.append(hkl)
+
+        def _update_slice():
+            # Where should cut be along orthogonal direction (Mutliplication factor onto orth_dir)
+            orth_cut = hkl_cut.value
+
+            # Width of cut
+            delta = hkl_delta.value
+
+            # Calculate distance of all points to plane
+            Q = np.array(o_c) * orth_cut
+            N = o_c / np.sqrt(np.sum(o_c**2))
+            v = np.empty(np.shape(hkl_c))
+            v[:, :, :, :, 0] = hkl_c[:, :, :, :, 0] - Q
+            dist = np.abs(np.dot(N, v))
+            dist = np.squeeze(dist)
+            dist = np.transpose(dist)
+
+            # Find points within acceptable distance of plane defined by o_c
+            ind = np.where(abs(dist) < delta)
+            if ind[0].size == 0:
+                image_source.data.update(image=[np.zeros((1, 1))])
+                return
+
+            # Project points onto axes
+            x = np.dot(x_c / np.sqrt(np.sum(x_c**2)), hkl_c)
+            y = np.dot(y_c / np.sqrt(np.sum(y_c**2)), hkl_c)
+
+            # take care of dimensions
+            x = np.squeeze(x)
+            x = np.transpose(x)
+            y = np.squeeze(y)
+            y = np.transpose(y)
+
+            # Get slices:
+            x_slice = x[ind]
+            y_slice = y[ind]
+            I_slice = I_matrix[ind]
+
+            # Meshgrid limits for plotting
+            if auto_range_cb.active:
+                min_x = np.min(x_slice)
+                max_x = np.max(x_slice)
+                min_y = np.min(y_slice)
+                max_y = np.max(y_slice)
+                xrange_min_ni.value = min_x
+                xrange_max_ni.value = max_x
+                yrange_min_ni.value = min_y
+                yrange_max_ni.value = max_y
+            else:
+                min_x = xrange_min_ni.value
+                max_x = xrange_max_ni.value
+                min_y = yrange_min_ni.value
+                max_y = yrange_max_ni.value
+
+            delta_x = xrange_step_ni.value
+            delta_y = yrange_step_ni.value
+
+            # Create interpolated mesh grid for plotting
+            grid_x, grid_y = np.mgrid[min_x:max_x:delta_x, min_y:max_y:delta_y]
+            I = interpolate.griddata((x_slice, y_slice), I_slice, (grid_x, grid_y))
+
+            # Update plot
+            display_min_ni.value = 0
+            display_max_ni.value = np.max(I_slice) * 0.25
+            image_source.data.update(
+                image=[I.T], x=[min_x], dw=[max_x - min_x], y=[min_y], dh=[max_y - min_y]
+            )
+
+            scan_x, scan_y = [], []
+            for j in range(len(hkl_coord)):
+                # Get middle hkl from list
+                hklm = M @ hkl_coord[j]
+
+                # Decide if point is in the cut
+                proj = np.dot(hklm, o_c)
+                if abs(proj - orth_cut) >= delta:
+                    continue
+
+                # Project onto axes
+                hklmx = np.dot(hklm, x_c)
+                hklmy = np.dot(hklm, y_vert)
+
+                # Plot middle point of scan
+                scan_x.append(hklmx)
+                scan_y.append(hklmy)
+
+            scatter_source.data.update(x=scan_x, y=scan_y)
+
+        return _update_slice
+
+    def plot_file_callback():
+        nonlocal _update_slice
+        _update_slice = _prepare_plotting()
+        _update_slice()
+
+    plot_file = Button(label="Plot selected file(s)", button_type="primary", width=200)
+    plot_file.on_click(plot_file_callback)
+
+    plot = figure(
+        x_range=DataRange1d(),
+        y_range=DataRange1d(),
+        height=550 + 27,
+        width=550 + 117,
+        tools="pan,wheel_zoom,reset",
+    )
+    plot.toolbar.logo = None
+
+    lin_color_mapper = LinearColorMapper(nan_color=(0, 0, 0, 0), low=0, high=1)
+    log_color_mapper = LogColorMapper(nan_color=(0, 0, 0, 0), low=0, high=1)
+    image_source = ColumnDataSource(dict(image=[np.zeros((1, 1))], x=[0], y=[0], dw=[1], dh=[1]))
+    plot_image = plot.image(source=image_source, color_mapper=lin_color_mapper)
+
+    lin_color_bar = ColorBar(color_mapper=lin_color_mapper, width=15)
+    log_color_bar = ColorBar(color_mapper=log_color_mapper, width=15, visible=False)
+    plot.add_layout(lin_color_bar, "right")
+    plot.add_layout(log_color_bar, "right")
+
+    scatter_source = ColumnDataSource(dict(x=[], y=[]))
+    plot.scatter(source=scatter_source, size=4, fill_color="green", line_color="green")
+
+    hkl_div = Div(text="HKL:", margin=(5, 5, 0, 5))
+    hkl_normal = TextInput(title="normal", value="0 0 1", width=70)
+
+    def hkl_cut_callback(_attr, _old, _new):
+        if _update_slice is not None:
+            _update_slice()
+
+    hkl_cut = Spinner(title="cut", value=0, step=0.1, width=70)
+    hkl_cut.on_change("value_throttled", hkl_cut_callback)
+
+    hkl_delta = NumericInput(title="delta", value=0.1, mode="float", width=70)
+    hkl_in_plane_x = TextInput(title="in-plane X", value="1 0 0", width=70)
+    hkl_in_plane_y = TextInput(title="in-plane Y", value="", width=100, disabled=True)
+
+    def redef_lattice_cb_callback(_attr, _old, new):
+        if 0 in new:
+            redef_lattice_ti.disabled = False
+        else:
+            redef_lattice_ti.disabled = True
+
+    redef_lattice_cb = CheckboxGroup(labels=["Redefine lattice:"], width=110)
+    redef_lattice_cb.on_change("active", redef_lattice_cb_callback)
+    redef_lattice_ti = TextInput(width=490, disabled=True)
+
+    def redef_ub_cb_callback(_attr, _old, new):
+        if 0 in new:
+            redef_ub_ti.disabled = False
+        else:
+            redef_ub_ti.disabled = True
+
+    redef_ub_cb = CheckboxGroup(labels=["Redefine UB:"], width=110)
+    redef_ub_cb.on_change("active", redef_ub_cb_callback)
+    redef_ub_ti = TextInput(width=490, disabled=True)
+
+    def colormap_select_callback(_attr, _old, new):
+        lin_color_mapper.palette = new
+        log_color_mapper.palette = new
+
+    colormap_select = Select(
+        title="Colormap:",
+        options=[("Greys256", "greys"), ("Plasma256", "plasma"), ("Cividis256", "cividis")],
+        width=100,
+    )
+    colormap_select.on_change("value", colormap_select_callback)
+    colormap_select.value = "Plasma256"
+
+    def display_min_ni_callback(_attr, _old, new):
+        lin_color_mapper.low = new
+        log_color_mapper.low = new
+
+    display_min_ni = NumericInput(title="Intensity min:", value=0, mode="float", width=70)
+    display_min_ni.on_change("value", display_min_ni_callback)
+
+    def display_max_ni_callback(_attr, _old, new):
+        lin_color_mapper.high = new
+        log_color_mapper.high = new
+
+    display_max_ni = NumericInput(title="max:", value=1, mode="float", width=70)
+    display_max_ni.on_change("value", display_max_ni_callback)
+
+    def colormap_scale_rg_callback(_attr, _old, new):
+        if new == 0:  # Linear
+            plot_image.glyph.color_mapper = lin_color_mapper
+            lin_color_bar.visible = True
+            log_color_bar.visible = False
+
+        else:  # Logarithmic
+            if display_min_ni.value > 0 and display_max_ni.value > 0:
+                plot_image.glyph.color_mapper = log_color_mapper
+                lin_color_bar.visible = False
+                log_color_bar.visible = True
+            else:
+                colormap_scale_rg.active = 0
+
+    colormap_scale_rg = RadioGroup(labels=["Linear", "Logarithmic"], active=0, width=100)
+    colormap_scale_rg.on_change("active", colormap_scale_rg_callback)
+
+    xrange_min_ni = NumericInput(title="x range min:", value=0, mode="float", width=70)
+    xrange_max_ni = NumericInput(title="max:", value=1, mode="float", width=70)
+    xrange_step_ni = NumericInput(title="x mesh:", value=0.01, mode="float", width=70)
+
+    yrange_min_ni = NumericInput(title="y range min:", value=0, mode="float", width=70)
+    yrange_max_ni = NumericInput(title="max:", value=1, mode="float", width=70)
+    yrange_step_ni = NumericInput(title="y mesh:", value=0.01, mode="float", width=70)
+
+    def auto_range_cb_callback(_attr, _old, new):
+        if 0 in new:
+            xrange_min_ni.disabled = True
+            xrange_max_ni.disabled = True
+            yrange_min_ni.disabled = True
+            yrange_max_ni.disabled = True
+        else:
+            xrange_min_ni.disabled = False
+            xrange_max_ni.disabled = False
+            yrange_min_ni.disabled = False
+            yrange_max_ni.disabled = False
+
+    auto_range_cb = CheckboxGroup(labels=["Auto range:"], width=110)
+    auto_range_cb.on_change("active", auto_range_cb_callback)
+    auto_range_cb.active = [0]
+
+    column1_layout = column(
+        row(
+            column(row(measured_data_div, measured_data), row(upload_hkl_div, upload_hkl_fi)),
+            plot_file,
+        ),
+        row(
+            plot,
+            column(
+                hkl_div,
+                row(hkl_normal, hkl_cut, hkl_delta),
+                row(hkl_in_plane_x, hkl_in_plane_y),
+                row(colormap_select, column(Spacer(height=15), colormap_scale_rg)),
+                row(display_min_ni, display_max_ni),
+                row(column(Spacer(height=19), auto_range_cb)),
+                row(xrange_min_ni, xrange_max_ni),
+                row(yrange_min_ni, yrange_max_ni),
+                row(xrange_step_ni, yrange_step_ni),
+            ),
+        ),
+        row(column(Spacer(height=7), redef_lattice_cb), redef_lattice_ti),
+        row(column(Spacer(height=7), redef_ub_cb), redef_ub_ti),
+    )
+    column2_layout = app.PlotHKL().layout
+
+    tab_layout = row(column1_layout, Spacer(width=50), column2_layout)
+
+    return Panel(child=tab_layout, title="plot data")

pyzebra/app/panel_spind.py

@@ -21,6 +21,7 @@ import pyzebra
 
 def create():
     doc = curdoc()
+    log = doc.logger
     events_data = doc.events_data
 
     npeaks_spinner = Spinner(title="Number of peaks from hdf_view panel:", disabled=True)
@@ -63,8 +64,8 @@ def create():
                 stderr=subprocess.STDOUT,
                 text=True,
             )
-            print(" ".join(comp_proc.args))
+            log.info(" ".join(comp_proc.args))
-            print(comp_proc.stdout)
+            log.info(comp_proc.stdout)
 
             # prepare an event file
             diff_vec = []
@@ -94,9 +95,9 @@ def create():
                         f"{x_pos} {y_pos} {intensity} {snr_cnts} {dv1} {dv2} {dv3} {d_spacing}\n"
                     )
 
-            print(f"Content of {temp_event_file}:")
+            log.info(f"Content of {temp_event_file}:")
             with open(temp_event_file) as f:
-                print(f.read())
+                log.info(f.read())
 
             comp_proc = subprocess.run(
                 [
@@ -123,12 +124,12 @@ def create():
                 stderr=subprocess.STDOUT,
                 text=True,
             )
-            print(" ".join(comp_proc.args))
+            log.info(" ".join(comp_proc.args))
-            print(comp_proc.stdout)
+            log.info(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=[],
+                label=[], crystal_id=[], match_rate=[], matched_peaks=[], column_5=[], ub_matrix=[]
             )
             try:
                 with open(spind_out_file) as f_out:
@@ -143,16 +144,15 @@ def create():
                         # 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)
-                        ub_matrices.append(ub_matrix)
                         spind_res["ub_matrix"].append(str(ub_matrix_spind * 1e-10))
 
-                print(f"Content of {spind_out_file}:")
+                log.info(f"Content of {spind_out_file}:")
                 with open(spind_out_file) as f:
-                    print(f.read())
+                    log.info(f.read())
 
             except FileNotFoundError:
-                print("No results from spind")
+                log.warning("No results from spind")
 
             results_table_source.data.update(spind_res)
 
@@ -168,11 +168,11 @@ def create():
     def results_table_select_callback(_attr, old, new):
         if new:
             ind = new[0]
-            ub_matrix = ub_matrices[ind]
+            ub_matrix_spind = ub_matrices[ind]
             res = ""
             for vec in diff_vec:
-                res += f"{ub_matrix @ vec}\n"
+                res += f"{np.linalg.inv(ub_matrix_spind) @ vec}\n"
-            ub_matrix_textareainput.value = str(ub_matrix * 1e10)
+            ub_matrix_textareainput.value = str(ub_matrix_spind * 1e-10)
             hkl_textareainput.value = res
         else:
             ub_matrix_textareainput.value = ""

pyzebra/app/plot_hkl.py

@@ -0,0 +1,549 @@
+import base64
+import io
+import os
+
+import numpy as np
+from bokeh.io import curdoc
+from bokeh.layouts import column, row
+from bokeh.models import (
+    Arrow,
+    Button,
+    CheckboxGroup,
+    ColumnDataSource,
+    Div,
+    FileInput,
+    HoverTool,
+    Legend,
+    LegendItem,
+    NormalHead,
+    NumericInput,
+    RadioGroup,
+    Spinner,
+    TextAreaInput,
+    TextInput,
+)
+from bokeh.palettes import Dark2
+from bokeh.plotting import figure
+from scipy.integrate import simpson, trapezoid
+
+import pyzebra
+
+
+class PlotHKL:
+    def __init__(self):
+        doc = curdoc()
+        log = doc.logger
+
+        _update_slice = None
+        measured_data_div = Div(text="Measured <b>CCL</b> data:")
+        measured_data = FileInput(accept=".ccl", multiple=True, width=200)
+
+        upload_hkl_div = Div(text="Open hkl/mhkl data:")
+        upload_hkl_fi = FileInput(accept=".hkl,.mhkl", multiple=True, width=200)
+
+        min_grid_x = -10
+        max_grid_x = 10
+        min_grid_y = -10
+        max_grid_y = 10
+        cmap = Dark2[8]
+        syms = ["circle", "inverted_triangle", "square", "diamond", "star", "triangle"]
+
+        def _prepare_plotting():
+            orth_dir = list(map(float, hkl_normal.value.split()))
+            x_dir = list(map(float, hkl_in_plane_x.value.split()))
+
+            k = np.array(k_vectors.value.split()).astype(float).reshape(-1, 3)
+            tol_k = tol_k_ni.value
+
+            # multiplier for resolution function (in case of samples with large mosaicity)
+            res_mult = res_mult_ni.value
+
+            md_fnames = measured_data.filename
+            md_fdata = measured_data.value
+
+            # Load first data file, read angles and define matrices to perform conversion to cartesian
+            # coordinates and back
+            with io.StringIO(base64.b64decode(md_fdata[0]).decode()) as file:
+                _, ext = os.path.splitext(md_fnames[0])
+                try:
+                    file_data = pyzebra.parse_1D(file, ext, log=log)
+                except Exception as e:
+                    log.exception(e)
+                    return None
+
+            alpha = file_data[0]["alpha_cell"] * np.pi / 180.0
+            beta = file_data[0]["beta_cell"] * np.pi / 180.0
+            gamma = file_data[0]["gamma_cell"] * np.pi / 180.0
+
+            # reciprocal angle parameters
+            beta_star = np.arccos(
+                (np.cos(alpha) * np.cos(gamma) - np.cos(beta)) / (np.sin(alpha) * np.sin(gamma))
+            )
+            gamma_star = np.arccos(
+                (np.cos(alpha) * np.cos(beta) - np.cos(gamma)) / (np.sin(alpha) * np.sin(beta))
+            )
+
+            # conversion matrix
+            M = np.array(
+                [
+                    [1, np.cos(gamma_star), np.cos(beta_star)],
+                    [0, np.sin(gamma_star), -np.sin(beta_star) * np.cos(alpha)],
+                    [0, 0, np.sin(beta_star) * np.sin(alpha)],
+                ]
+            )
+
+            # Get last lattice vector
+            y_dir = np.cross(x_dir, orth_dir)  # Second axes of plotting plane
+
+            # Rescale such that smallest element of y-dir vector is 1
+            y_dir2 = y_dir[y_dir != 0]
+            min_val = np.min(np.abs(y_dir2))
+            y_dir = y_dir / min_val
+
+            # Possibly flip direction of ydir:
+            if y_dir[np.argmax(abs(y_dir))] < 0:
+                y_dir = -y_dir
+
+            # Display the resulting y_dir
+            hkl_in_plane_y.value = " ".join([f"{val:.1f}" for val in y_dir])
+
+            # Save length of lattice vectors
+            x_length = np.linalg.norm(x_dir)
+            y_length = np.linalg.norm(y_dir)
+
+            # Save str for labels
+            xlabel_str = " ".join(map(str, x_dir))
+            ylabel_str = " ".join(map(str, y_dir))
+
+            # Normalize lattice vectors
+            y_dir = y_dir / np.linalg.norm(y_dir)
+            x_dir = x_dir / np.linalg.norm(x_dir)
+            orth_dir = orth_dir / np.linalg.norm(orth_dir)
+
+            # Calculate cartesian equivalents of lattice vectors
+            x_c = np.matmul(M, x_dir)
+            y_c = np.matmul(M, y_dir)
+            o_c = np.matmul(M, orth_dir)
+
+            # Calulcate vertical direction in plotting plame
+            y_vert = np.cross(x_c, o_c)  # verical direction in plotting plane
+            if y_vert[np.argmax(abs(y_vert))] < 0:
+                y_vert = -y_vert
+            y_vert = y_vert / np.linalg.norm(y_vert)
+
+            # Normalize all directions
+            y_c = y_c / np.linalg.norm(y_c)
+            x_c = x_c / np.linalg.norm(x_c)
+            o_c = o_c / np.linalg.norm(o_c)
+
+            # Read all data
+            hkl_coord = []
+            intensity_vec = []
+            k_flag_vec = []
+            file_flag_vec = []
+            res_vec = []
+            res_N = 10
+
+            for j, md_fname in enumerate(md_fnames):
+                with io.StringIO(base64.b64decode(md_fdata[j]).decode()) as file:
+                    _, ext = os.path.splitext(md_fname)
+                    try:
+                        file_data = pyzebra.parse_1D(file, ext, log=log)
+                    except Exception as e:
+                        log.exception(e)
+                        return None
+
+                pyzebra.normalize_dataset(file_data)
+
+                # Loop throguh all data
+                for scan in file_data:
+                    om = scan["omega"]
+                    gammad = scan["twotheta"]
+                    chi = scan["chi"]
+                    phi = scan["phi"]
+                    nud = 0  # 1d detector
+                    ub_inv = np.linalg.inv(scan["ub"])
+                    counts = scan["counts"]
+                    wave = scan["wavelength"]
+
+                    # Calculate resolution in degrees
+                    expr = np.tan(gammad / 2 * np.pi / 180)
+                    fwhm = np.sqrt(0.4639 * expr**2 - 0.4452 * expr + 0.1506) * res_mult
+                    res = 4 * np.pi / wave * np.sin(fwhm * np.pi / 180)
+
+                    # Get first and final hkl
+                    hkl1 = pyzebra.ang2hkl_1d(wave, gammad, om[0], chi, phi, nud, ub_inv)
+                    hkl2 = pyzebra.ang2hkl_1d(wave, gammad, om[-1], chi, phi, nud, ub_inv)
+
+                    # Get hkl at best intensity
+                    hkl_m = pyzebra.ang2hkl_1d(
+                        wave, gammad, om[np.argmax(counts)], chi, phi, nud, ub_inv
+                    )
+
+                    # Estimate intensity for marker size scaling
+                    y_bkg = [counts[0], counts[-1]]
+                    x_bkg = [om[0], om[-1]]
+                    c = int(simpson(counts, x=om) - trapezoid(y_bkg, x=x_bkg))
+
+                    # Recognize k_flag_vec
+                    reduced_hkl_m = np.minimum(1 - hkl_m % 1, hkl_m % 1)
+                    for ind, _k in enumerate(k):
+                        if all(np.abs(reduced_hkl_m - _k) < tol_k):
+                            k_flag_vec.append(ind)
+                            break
+                    else:
+                        # not required
+                        continue
+
+                    # Save data
+                    hkl_coord.append([hkl1, hkl2, hkl_m])
+                    intensity_vec.append(c)
+                    file_flag_vec.append(j)
+                    res_vec.append(res)
+
+            x_spacing = np.dot(M @ x_dir, x_c) * x_length
+            y_spacing = np.dot(M @ y_dir, y_vert) * y_length
+            y_spacingx = np.dot(M @ y_dir, x_c) * y_length
+
+            # Plot coordinate system
+            arrow1.x_end = x_spacing
+            arrow1.y_end = 0
+            arrow2.x_end = y_spacingx
+            arrow2.y_end = y_spacing
+
+            # Add labels
+            kvect_source.data.update(
+                x=[x_spacing / 4, -0.1],
+                y=[x_spacing / 4 - 0.5, y_spacing / 2],
+                text=[xlabel_str, ylabel_str],
+            )
+
+            # Plot grid lines
+            xs, ys = [], []
+            xs_minor, ys_minor = [], []
+            for yy in np.arange(min_grid_y, max_grid_y, 1):
+                # Calculate end and start point
+                hkl1 = min_grid_x * x_dir + yy * y_dir
+                hkl2 = max_grid_x * x_dir + yy * y_dir
+                hkl1 = M @ hkl1
+                hkl2 = M @ hkl2
+
+                # Project points onto axes
+                x1 = np.dot(x_c, hkl1) * x_length
+                y1 = np.dot(y_vert, hkl1) * y_length
+                x2 = np.dot(x_c, hkl2) * x_length
+                y2 = np.dot(y_vert, hkl2) * y_length
+
+                xs.append([x1, x2])
+                ys.append([y1, y2])
+
+            for xx in np.arange(min_grid_x, max_grid_x, 1):
+                # Calculate end and start point
+                hkl1 = xx * x_dir + min_grid_y * y_dir
+                hkl2 = xx * x_dir + max_grid_y * y_dir
+                hkl1 = M @ hkl1
+                hkl2 = M @ hkl2
+
+                # Project points onto axes
+                x1 = np.dot(x_c, hkl1) * x_length
+                y1 = np.dot(y_vert, hkl1) * y_length
+                x2 = np.dot(x_c, hkl2) * x_length
+                y2 = np.dot(y_vert, hkl2) * y_length
+
+                xs.append([x1, x2])
+                ys.append([y1, y2])
+
+            for yy in np.arange(min_grid_y, max_grid_y, 0.5):
+                # Calculate end and start point
+                hkl1 = min_grid_x * x_dir + yy * y_dir
+                hkl2 = max_grid_x * x_dir + yy * y_dir
+                hkl1 = M @ hkl1
+                hkl2 = M @ hkl2
+
+                # Project points onto axes
+                x1 = np.dot(x_c, hkl1) * x_length
+                y1 = np.dot(y_vert, hkl1) * y_length
+                x2 = np.dot(x_c, hkl2) * x_length
+                y2 = np.dot(y_vert, hkl2) * y_length
+
+                xs_minor.append([x1, x2])
+                ys_minor.append([y1, y2])
+
+            for xx in np.arange(min_grid_x, max_grid_x, 0.5):
+                # Calculate end and start point
+                hkl1 = xx * x_dir + min_grid_y * y_dir
+                hkl2 = xx * x_dir + max_grid_y * y_dir
+                hkl1 = M @ hkl1
+                hkl2 = M @ hkl2
+
+                # Project points onto axes
+                x1 = np.dot(x_c, hkl1) * x_length
+                y1 = np.dot(y_vert, hkl1) * y_length
+                x2 = np.dot(x_c, hkl2) * x_length
+                y2 = np.dot(y_vert, hkl2) * y_length
+
+                xs_minor.append([x1, x2])
+                ys_minor.append([y1, y2])
+
+            grid_source.data.update(xs=xs, ys=ys)
+            minor_grid_source.data.update(xs=xs_minor, ys=ys_minor)
+
+            # Prepare hkl/mhkl data
+            hkl_coord2 = []
+            for j, fname in enumerate(upload_hkl_fi.filename):
+                with io.StringIO(base64.b64decode(upload_hkl_fi.value[j]).decode()) as file:
+                    _, ext = os.path.splitext(fname)
+                    try:
+                        fdata = pyzebra.parse_hkl(file, ext)
+                    except Exception as e:
+                        log.exception(e)
+                        return
+
+                for ind in range(len(fdata["counts"])):
+                    # Recognize k_flag_vec
+                    hkl = np.array([fdata["h"][ind], fdata["k"][ind], fdata["l"][ind]])
+
+                    # Save data
+                    hkl_coord2.append(hkl)
+
+            def _update_slice():
+                cut_tol = hkl_delta.value
+                cut_or = hkl_cut.value
+
+                # different symbols based on file number
+                file_flag = 0 in disting_opt_cb.active
+                # scale marker size according to intensity
+                intensity_flag = 1 in disting_opt_cb.active
+                # use color to mark different propagation vectors
+                prop_legend_flag = 2 in disting_opt_cb.active
+                # use resolution ellipsis
+                res_flag = disting_opt_rb.active
+
+                el_x, el_y, el_w, el_h, el_c = [], [], [], [], []
+                scan_xs, scan_ys, scan_x, scan_y = [], [], [], []
+                scan_m, scan_s, scan_c, scan_l, scan_hkl = [], [], [], [], []
+                for j in range(len(hkl_coord)):
+                    # Get middle hkl from list
+                    hklm = M @ hkl_coord[j][2]
+
+                    # Decide if point is in the cut
+                    proj = np.dot(hklm, o_c)
+                    if abs(proj - cut_or) >= cut_tol:
+                        continue
+
+                    hkl1 = M @ hkl_coord[j][0]
+                    hkl2 = M @ hkl_coord[j][1]
+
+                    # Project onto axes
+                    hkl1x = np.dot(hkl1, x_c)
+                    hkl1y = np.dot(hkl1, y_vert)
+                    hkl2x = np.dot(hkl2, x_c)
+                    hkl2y = np.dot(hkl2, y_vert)
+                    hklmx = np.dot(hklm, x_c)
+                    hklmy = np.dot(hklm, y_vert)
+
+                    if intensity_flag:
+                        markersize = max(6, int(intensity_vec[j] / max(intensity_vec) * 30))
+                    else:
+                        markersize = 6
+
+                    if file_flag:
+                        plot_symbol = syms[file_flag_vec[j]]
+                    else:
+                        plot_symbol = "circle"
+
+                    if prop_legend_flag:
+                        col_value = cmap[k_flag_vec[j]]
+                    else:
+                        col_value = "black"
+
+                    if res_flag:
+                        # Generate series of circles along scan line
+                        res = res_vec[j]
+                        el_x.extend(np.linspace(hkl1x, hkl2x, num=res_N))
+                        el_y.extend(np.linspace(hkl1y, hkl2y, num=res_N))
+                        el_w.extend([res / 2] * res_N)
+                        el_h.extend([res / 2] * res_N)
+                        el_c.extend([col_value] * res_N)
+                    else:
+                        # Plot scan line
+                        scan_xs.append([hkl1x, hkl2x])
+                        scan_ys.append([hkl1y, hkl2y])
+
+                        # Plot middle point of scan
+                        scan_x.append(hklmx)
+                        scan_y.append(hklmy)
+                        scan_m.append(plot_symbol)
+                        scan_s.append(markersize)
+
+                        # Color and legend label
+                        scan_c.append(col_value)
+                        scan_l.append(md_fnames[file_flag_vec[j]])
+                        scan_hkl.append(hkl_coord[j][2])
+
+                ellipse_source.data.update(x=el_x, y=el_y, width=el_w, height=el_h, c=el_c)
+                scan_source.data.update(
+                    xs=scan_xs,
+                    ys=scan_ys,
+                    x=scan_x,
+                    y=scan_y,
+                    m=scan_m,
+                    s=scan_s,
+                    c=scan_c,
+                    l=scan_l,
+                    hkl=scan_hkl,
+                )
+
+                # Legend items for different file entries (symbol)
+                legend_items = []
+                if not res_flag and file_flag:
+                    labels, inds = np.unique(scan_source.data["l"], return_index=True)
+                    for label, ind in zip(labels, inds):
+                        legend_items.append(LegendItem(label=label, renderers=[scatter], index=ind))
+
+                # Legend items for propagation vector (color)
+                if prop_legend_flag:
+                    if res_flag:
+                        source, render = ellipse_source, ellipse
+                    else:
+                        source, render = scan_source, mline
+
+                    labels, inds = np.unique(source.data["c"], return_index=True)
+                    for label, ind in zip(labels, inds):
+                        label = f"k={k[cmap.index(label)]}"
+                        legend_items.append(LegendItem(label=label, renderers=[render], index=ind))
+
+                plot.legend.items = legend_items
+
+                scan_x2, scan_y2, scan_hkl2 = [], [], []
+                for j in range(len(hkl_coord2)):
+                    # Get middle hkl from list
+                    hklm = M @ hkl_coord2[j]
+
+                    # Decide if point is in the cut
+                    proj = np.dot(hklm, o_c)
+                    if abs(proj - cut_or) >= cut_tol:
+                        continue
+
+                    # Project onto axes
+                    hklmx = np.dot(hklm, x_c)
+                    hklmy = np.dot(hklm, y_vert)
+
+                    scan_x2.append(hklmx)
+                    scan_y2.append(hklmy)
+                    scan_hkl2.append(hkl_coord2[j])
+
+                scatter_source2.data.update(x=scan_x2, y=scan_y2, hkl=scan_hkl2)
+
+            return _update_slice
+
+        def plot_file_callback():
+            nonlocal _update_slice
+            _update_slice = _prepare_plotting()
+            _update_slice()
+
+        plot_file = Button(label="Plot selected file(s)", button_type="primary", width=200)
+        plot_file.on_click(plot_file_callback)
+
+        plot = figure(height=550, width=550 + 32, tools="pan,wheel_zoom,reset")
+        plot.toolbar.logo = None
+
+        plot.xaxis.visible = False
+        plot.xgrid.visible = False
+        plot.yaxis.visible = False
+        plot.ygrid.visible = False
+
+        arrow1 = Arrow(x_start=0, y_start=0, x_end=0, y_end=0, end=NormalHead(size=10))
+        plot.add_layout(arrow1)
+        arrow2 = Arrow(x_start=0, y_start=0, x_end=0, y_end=0, end=NormalHead(size=10))
+        plot.add_layout(arrow2)
+
+        kvect_source = ColumnDataSource(dict(x=[], y=[], text=[]))
+        plot.text(source=kvect_source)
+
+        grid_source = ColumnDataSource(dict(xs=[], ys=[]))
+        plot.multi_line(source=grid_source, line_color="gray")
+
+        minor_grid_source = ColumnDataSource(dict(xs=[], ys=[]))
+        plot.multi_line(source=minor_grid_source, line_color="gray", line_dash="dotted")
+
+        ellipse_source = ColumnDataSource(dict(x=[], y=[], width=[], height=[], c=[]))
+        ellipse = plot.ellipse(source=ellipse_source, fill_color="c", line_color="c")
+
+        scan_source = ColumnDataSource(
+            dict(xs=[], ys=[], x=[], y=[], m=[], s=[], c=[], l=[], hkl=[])
+        )
+        mline = plot.multi_line(source=scan_source, line_color="c")
+        scatter = plot.scatter(
+            source=scan_source, marker="m", size="s", fill_color="c", line_color="c"
+        )
+
+        scatter_source2 = ColumnDataSource(dict(x=[], y=[], hkl=[]))
+        scatter2 = plot.scatter(
+            source=scatter_source2, size=4, fill_color="green", line_color="green"
+        )
+
+        plot.x_range.renderers = [ellipse, mline, scatter, scatter2]
+        plot.y_range.renderers = [ellipse, mline, scatter, scatter2]
+
+        plot.add_layout(Legend(items=[], location="top_left", click_policy="hide"))
+
+        plot.add_tools(HoverTool(renderers=[scatter, scatter2], tooltips=[("hkl", "@hkl")]))
+
+        hkl_div = Div(text="HKL:", margin=(5, 5, 0, 5))
+        hkl_normal = TextInput(title="normal", value="0 0 1", width=70)
+
+        def hkl_cut_callback(_attr, _old, _new):
+            if _update_slice is not None:
+                _update_slice()
+
+        hkl_cut = Spinner(title="cut", value=0, step=0.1, width=70)
+        hkl_cut.on_change("value_throttled", hkl_cut_callback)
+
+        hkl_delta = NumericInput(title="delta", value=0.1, mode="float", width=70)
+        hkl_in_plane_x = TextInput(title="in-plane X", value="1 0 0", width=70)
+        hkl_in_plane_y = TextInput(title="in-plane Y", value="", width=100, disabled=True)
+
+        disting_opt_div = Div(text="Distinguish options:", margin=(5, 5, 0, 5))
+        disting_opt_cb = CheckboxGroup(
+            labels=["files (symbols)", "intensities (size)", "k vectors nucl/magn (colors)"],
+            active=[0, 1, 2],
+            width=200,
+        )
+        disting_opt_rb = RadioGroup(
+            labels=["scan direction", "resolution ellipsoid"], active=0, width=200
+        )
+
+        k_vectors = TextAreaInput(
+            title="k vectors:", value="0.0 0.0 0.0\n0.5 0.0 0.0\n0.5 0.5 0.0", width=150
+        )
+        res_mult_ni = NumericInput(title="Resolution mult:", value=10, mode="int", width=100)
+        tol_k_ni = NumericInput(title="k tolerance:", value=0.01, mode="float", width=100)
+
+        def show_legend_cb_callback(_attr, _old, new):
+            plot.legend.visible = 0 in new
+
+        show_legend_cb = CheckboxGroup(labels=["Show legend"], active=[0])
+        show_legend_cb.on_change("active", show_legend_cb_callback)
+
+        layout = column(
+            row(
+                column(row(measured_data_div, measured_data), row(upload_hkl_div, upload_hkl_fi)),
+                plot_file,
+            ),
+            row(
+                plot,
+                column(
+                    hkl_div,
+                    row(hkl_normal, hkl_cut, hkl_delta),
+                    row(hkl_in_plane_x, hkl_in_plane_y),
+                    k_vectors,
+                    row(tol_k_ni, res_mult_ni),
+                    disting_opt_div,
+                    disting_opt_cb,
+                    disting_opt_rb,
+                    show_legend_cb,
+                ),
+            ),
+        )
+        self.layout = layout

pyzebra/ccl_io.py

@@ -1,47 +1,56 @@
+import logging
 import os
 import re
+from ast import literal_eval
 from collections import defaultdict
 
 import numpy as np
 
+logger = logging.getLogger(__name__)
+
 META_VARS_STR = (
     "instrument",
     "title",
-    "sample",
+    "comment",
     "user",
-    "ProposalID",
+    "proposal_id",
     "original_filename",
     "date",
     "zebra_mode",
-    "proposal",
+    "zebramode",
-    "proposal_user",
+    "sample_name",
-    "proposal_title",
-    "proposal_email",
-    "detectorDistance",
 )
 
 META_VARS_FLOAT = (
-    "omega",
-    "mf",
-    "2-theta",
-    "chi",
-    "phi",
-    "nu",
-    "temp",
-    "wavelenght",
     "a",
     "b",
     "c",
     "alpha",
     "beta",
     "gamma",
+    "omega",
+    "chi",
+    "phi",
+    "temp",
+    "mf",
+    "temperature",
+    "magnetic_field",
     "cex1",
     "cex2",
+    "wavelength",
     "mexz",
     "moml",
     "mcvl",
     "momu",
     "mcvu",
+    "2-theta",
+    "twotheta",
+    "nu",
+    "gamma_angle",
+    "polar_angle",
+    "tilt_angle",
+    "distance",
+    "distance_an",
     "snv",
     "snh",
     "snvm",
@@ -54,9 +63,16 @@ META_VARS_FLOAT = (
     "s2vb",
     "s2hr",
     "s2hl",
+    "a5",
+    "a6",
+    "a4t",
+    "s2ant",
+    "s2anb",
+    "s2anl",
+    "s2anr",
 )
 
-META_UB_MATRIX = ("ub1j", "ub2j", "ub3j")
+META_UB_MATRIX = ("ub1j", "ub2j", "ub3j", "UB")
 
 CCL_FIRST_LINE = (("idx", int), ("h", float), ("k", float), ("l", float))
 
@@ -93,47 +109,68 @@ def load_1D(filepath):
     """
     with open(filepath, "r") as infile:
         _, ext = os.path.splitext(filepath)
-        det_variables = parse_1D(infile, data_type=ext)
+        dataset = parse_1D(infile, data_type=ext)
 
-    return det_variables
+    return dataset
 
 
-def parse_1D(fileobj, data_type):
+def parse_1D(fileobj, data_type, log=logger):
     metadata = {"data_type": data_type}
 
     # read metadata
     for line in fileobj:
-        if "=" in line:
-            variable, value = line.split("=", 1)
-            variable = variable.strip()
-            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_UB_MATRIX:
-                if "ub" not in metadata:
-                    metadata["ub"] = np.zeros((3, 3))
-                row = int(variable[-2]) - 1
-                metadata["ub"][row, :] = list(map(float, value.split()))
-
         if "#data" in line:
             # this is the end of metadata and the start of data section
             break
 
+        if "=" not in line:
+            # skip comments / empty lines
+            continue
+
+        var_name, value = line.split("=", 1)
+        var_name = var_name.strip()
+        value = value.strip()
+
+        if value == "UNKNOWN":
+            metadata[var_name] = None
+            continue
+
+        try:
+            if var_name in META_VARS_STR:
+                if var_name == "zebramode":
+                    var_name = "zebra_mode"
+                metadata[var_name] = value
+
+            elif var_name in META_VARS_FLOAT:
+                if var_name == "2-theta":  # fix that angle name not to be an expression
+                    var_name = "twotheta"
+                if var_name == "temperature":
+                    var_name = "temp"
+                if var_name == "magnetic_field":
+                    var_name = "mf"
+                if var_name in ("a", "b", "c", "alpha", "beta", "gamma"):
+                    var_name += "_cell"
+                metadata[var_name] = float(value)
+
+            elif var_name in META_UB_MATRIX:
+                if var_name == "UB":
+                    metadata["ub"] = np.array(literal_eval(value)).reshape(3, 3)
+                else:
+                    if "ub" not in metadata:
+                        metadata["ub"] = np.zeros((3, 3))
+                    row = int(var_name[-2]) - 1
+                    metadata["ub"][row, :] = list(map(float, value.split()))
+
+        except Exception:
+            log.error(f"Error reading {var_name} with value '{value}'")
+            metadata[var_name] = 0
+
     # handle older files that don't contain "zebra_mode" metadata
     if "zebra_mode" not in metadata:
         metadata["zebra_mode"] = "nb"
 
     # read data
-    scan = []
+    dataset = []
     if data_type == ".ccl":
         ccl_first_line = CCL_FIRST_LINE + CCL_ANGLES[metadata["zebra_mode"]]
         ccl_second_line = CCL_SECOND_LINE
@@ -143,54 +180,73 @@ def parse_1D(fileobj, data_type):
             if not line or line.isspace():
                 continue
 
-            s = {}
+            scan = {}
+            scan["export"] = True
 
             # first line
             for param, (param_name, param_type) in zip(line.split(), ccl_first_line):
-                s[param_name] = param_type(param)
+                scan[param_name] = param_type(param)
+
+            # rename 0 index scan to 1
+            if scan["idx"] == 0:
+                scan["idx"] = 1
 
             # second line
             next_line = next(fileobj)
             for param, (param_name, param_type) in zip(next_line.split(), ccl_second_line):
-                s[param_name] = param_type(param)
+                scan[param_name] = param_type(param)
 
-            if s["scan_motor"] != "om":
+            if "scan_motor" not in scan:
+                scan["scan_motor"] = "om"
+
+            if scan["scan_motor"] == "o2t":
+                scan["scan_motor"] = "om"
+
+            if scan["scan_motor"] != "om":
                 raise Exception("Unsupported variable name in ccl file.")
 
             # "om" -> "omega"
-            s["scan_motor"] = "omega"
+            scan["scan_motor"] = "omega"
-            s["scan_motors"] = ["omega", ]
+            scan["scan_motors"] = ["omega"]
             # overwrite metadata, because it only refers to the scan center
-            half_dist = (s["n_points"] - 1) / 2 * s["angle_step"]
+            half_dist = (scan["n_points"] - 1) / 2 * scan["angle_step"]
-            s["omega"] = np.linspace(s["omega"] - half_dist, s["omega"] + half_dist, s["n_points"])
+            scan["omega"] = np.linspace(
+                scan["omega"] - half_dist, scan["omega"] + half_dist, scan["n_points"]
+            )
 
             # subsequent lines with counts
             counts = []
-            while len(counts) < s["n_points"]:
+            while len(counts) < scan["n_points"]:
                 counts.extend(map(float, next(fileobj).split()))
-            s["counts"] = np.array(counts)
+            scan["counts"] = np.array(counts)
-            s["counts_err"] = np.sqrt(s["counts"])
+            scan["counts_err"] = np.sqrt(np.maximum(scan["counts"], 1))
 
-            if s["h"].is_integer() and s["k"].is_integer() and s["l"].is_integer():
+            if scan["h"].is_integer() and scan["k"].is_integer() and scan["l"].is_integer():
-                s["h"], s["k"], s["l"] = map(int, (s["h"], s["k"], s["l"]))
+                scan["h"], scan["k"], scan["l"] = map(int, (scan["h"], scan["k"], scan["l"]))
 
-            scan.append({**metadata, **s})
+            dataset.append({**metadata, **scan})
 
     elif data_type == ".dat":
-        # TODO: this might need to be adapted in the future, when "gamma" will be added to dat files
         if metadata["zebra_mode"] == "nb":
-            metadata["gamma"] = metadata["twotheta"]
+            if "gamma_angle" in metadata:
+                # support for the new format
+                metadata["gamma"] = metadata["gamma_angle"]
+            else:
+                metadata["gamma"] = metadata["twotheta"]
 
-        s = defaultdict(list)
+        scan = defaultdict(list)
+        scan["export"] = True
 
         match = re.search("Scanning Variables: (.*), Steps: (.*)", next(fileobj))
-        motors = [motor.lower() for motor in match.group(1).split(", ")]
+        motors = [motor.strip().lower() for motor in match.group(1).split(",")]
-        steps = [float(step) for step in match.group(2).split()]
+        # Steps can be separated by " " or ", "
+        steps = [float(step.strip(",")) 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["monitor"] = float(match.group(3))
+        scan["n_points"] = int(match.group(1))
+        scan["monitor"] = float(match.group(3))
 
         col_names = list(map(str.lower, next(fileobj).split()))
 
@@ -200,56 +256,56 @@ def parse_1D(fileobj, data_type):
                 break
 
             for name, val in zip(col_names, line.split()):
-                s[name].append(float(val))
+                scan[name].append(float(val))
 
         for name in col_names:
-            s[name] = np.array(s[name])
+            scan[name] = np.array(scan[name])
 
-        s["counts_err"] = np.sqrt(s["counts"])
+        scan["counts_err"] = np.sqrt(np.maximum(scan["counts"], 1))
 
-        s["scan_motors"] = []
+        scan["scan_motors"] = []
         for motor, step in zip(motors, steps):
             if step == 0:
                 # it's not a scan motor, so keep only the median value
-                s[motor] = np.median(s[motor])
+                scan[motor] = np.median(scan[motor])
             else:
-                s["scan_motors"].append(motor)
+                scan["scan_motors"].append(motor)
 
         # "om" -> "omega"
-        if "om" in s["scan_motors"]:
+        if "om" in scan["scan_motors"]:
-            s["scan_motors"][s["scan_motors"].index("om")] = "omega"
+            scan["scan_motors"][scan["scan_motors"].index("om")] = "omega"
-            s["omega"] = s["om"]
+            scan["omega"] = scan["om"]
-            del s["om"]
+            del scan["om"]
 
         # "tt" -> "temp"
-        if "tt" in s["scan_motors"]:
+        if "tt" in scan["scan_motors"]:
-            s["scan_motors"][s["scan_motors"].index("tt")] = "temp"
+            scan["scan_motors"][scan["scan_motors"].index("tt")] = "temp"
-            s["temp"] = s["tt"]
+            scan["temp"] = scan["tt"]
-            del s["tt"]
+            del scan["tt"]
 
         # "mf" stays "mf"
         # "phi" stays "phi"
 
-        s["scan_motor"] = s["scan_motors"][0]
+        scan["scan_motor"] = scan["scan_motors"][0]
 
-        if "h" not in s:
+        if "h" not in scan:
-            s["h"] = s["k"] = s["l"] = float("nan")
+            scan["h"] = scan["k"] = scan["l"] = float("nan")
 
         for param in ("mf", "temp"):
             if param not in metadata:
-                s[param] = 0
+                scan[param] = 0
 
-        s["idx"] = 1
+        scan["idx"] = 1
 
-        scan.append({**metadata, **s})
+        dataset.append({**metadata, **scan})
 
     else:
-        print("Unknown file extention")
+        log.error("Unknown file extention")
 
-    return scan
+    return dataset
 
 
-def export_1D(data, path, export_target, hkl_precision=2):
+def export_1D(dataset, path, export_target, hkl_precision=2):
     """Exports data in the .comm/.incomm format for fullprof or .col/.incol format for jana.
 
     Scans with integer/real hkl values are saved in .comm/.incomm or .col/.incol files
@@ -259,11 +315,11 @@ def export_1D(data, path, export_target, hkl_precision=2):
     if export_target not in EXPORT_TARGETS:
         raise ValueError(f"Unknown export target: {export_target}.")
 
-    zebra_mode = data[0]["zebra_mode"]
+    zebra_mode = dataset[0]["zebra_mode"]
     exts = EXPORT_TARGETS[export_target]
     file_content = {ext: [] for ext in exts}
 
-    for scan in data:
+    for scan in dataset:
         if "fit" not in scan:
             continue
 
@@ -303,9 +359,66 @@ def export_1D(data, path, export_target, hkl_precision=2):
                 out_file.writelines(content)
 
 
-def export_param_study(data, param_data, path):
+def export_ccl_compare(dataset1, dataset2, path, export_target, hkl_precision=2):
+    """Exports compare data in the .comm/.incomm format for fullprof or .col/.incol format for jana.
+
+    Scans with integer/real hkl values are saved in .comm/.incomm or .col/.incol files
+    correspondingly. If no scans are present for a particular output format, that file won't be
+    created.
+    """
+    if export_target not in EXPORT_TARGETS:
+        raise ValueError(f"Unknown export target: {export_target}.")
+
+    zebra_mode = dataset1[0]["zebra_mode"]
+    exts = EXPORT_TARGETS[export_target]
+    file_content = {ext: [] for ext in exts}
+
+    for scan1, scan2 in zip(dataset1, dataset2):
+        if "fit" not in scan1:
+            continue
+
+        idx_str = f"{scan1['idx']:6}"
+
+        h, k, l = scan1["h"], scan1["k"], scan1["l"]
+        hkl_are_integers = isinstance(h, int)  # if True, other indices are of type 'int' too
+        if hkl_are_integers:
+            hkl_str = f"{h:4}{k:4}{l:4}"
+        else:
+            hkl_str = f"{h:8.{hkl_precision}f}{k:8.{hkl_precision}f}{l:8.{hkl_precision}f}"
+
+        area_n1, area_s1 = scan1["area"]
+        area_n2, area_s2 = scan2["area"]
+        area_n = area_n1 - area_n2
+        area_s = np.sqrt(area_s1**2 + area_s2**2)
+        area_str = f"{area_n:10.2f}{area_s:10.2f}"
+
+        ang_str = ""
+        for angle, _ in CCL_ANGLES[zebra_mode]:
+            if angle == scan1["scan_motor"]:
+                angle_center = (np.min(scan1[angle]) + np.max(scan1[angle])) / 2
+            else:
+                angle_center = scan1[angle]
+
+            if angle == "twotheta" and export_target == "jana":
+                angle_center /= 2
+
+            ang_str = ang_str + f"{angle_center:8g}"
+
+        if export_target == "jana":
+            ang_str = ang_str + f"{scan1['temp']:8}" + f"{scan1['monitor']:8}"
+
+        ref = file_content[exts[0]] if hkl_are_integers else file_content[exts[1]]
+        ref.append(idx_str + hkl_str + area_str + ang_str + "\n")
+
+    for ext, content in file_content.items():
+        if content:
+            with open(path + ext, "w") as out_file:
+                out_file.writelines(content)
+
+
+def export_param_study(dataset, param_data, path):
     file_content = []
-    for scan, param in zip(data, param_data):
+    for scan, param in zip(dataset, param_data):
         if "fit" not in scan:
             continue
 
@@ -320,7 +433,11 @@ def export_param_study(data, param_data, path):
 
         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}"
+            fit_param_val = fit_param.value
+            fit_param_std = fit_param.stderr
+            if fit_param_std is None:
+                fit_param_std = 0
+            fit_str = fit_str + f"{fit_param_val:<20.2f}" + f"{fit_param_std:<20.2f}"
 
         _, fname_str = os.path.split(scan["original_filename"])
 

pyzebra/ccl_process.py

@@ -1,10 +1,13 @@
+import logging
 import os
 
 import numpy as np
 from lmfit.models import GaussianModel, LinearModel, PseudoVoigtModel, VoigtModel
 from scipy.integrate import simpson, trapezoid
 
-from .ccl_io import CCL_ANGLES
+from pyzebra import CCL_ANGLES
+
+logger = logging.getLogger(__name__)
 
 PARAM_PRECISIONS = {
     "twotheta": 0.1,
@@ -18,9 +21,9 @@ PARAM_PRECISIONS = {
     "ub": 0.01,
 }
 
-MAX_RANGE_GAP = {
+MAX_RANGE_GAP = {"omega": 0.5}
-    "omega": 0.5,
+
-}
+MOTOR_POS_PRECISION = 0.01
 
 AREA_METHODS = ("fit_area", "int_area")
 
@@ -33,12 +36,12 @@ def normalize_dataset(dataset, monitor=100_000):
         scan["monitor"] = monitor
 
 
-def merge_duplicates(dataset):
+def merge_duplicates(dataset, log=logger):
-    merged = np.zeros(len(dataset), dtype=np.bool)
+    merged = np.zeros(len(dataset), dtype=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)
+                merge_scans(scan_into, scan_from, log=log)
                 merged[ind_from] = True
 
 
@@ -47,40 +50,58 @@ def _parameters_match(scan1, scan2):
     if zebra_mode != scan2["zebra_mode"]:
         return False
 
-    for param in ("ub", "temp", "mf", *(vars[0] for vars in CCL_ANGLES[zebra_mode])):
+    for param in ("ub", *(vars[0] for vars in CCL_ANGLES[zebra_mode])):
         if param.startswith("skip"):
             # ignore skip parameters, like the last angle in 'nb' zebra mode
             continue
 
         if param == scan1["scan_motor"] == scan2["scan_motor"]:
             # check if ranges of variable parameter overlap
-            range1 = scan1[param]
+            r1_start, r1_end = scan1[param][0], scan1[param][-1]
-            range2 = scan2[param]
+            r2_start, r2_end = scan2[param][0], scan2[param][-1]
+            # support reversed ranges
+            if r1_start > r1_end:
+                r1_start, r1_end = r1_end, r1_start
+            if r2_start > r2_end:
+                r2_start, r2_end = r2_end, r2_start
             # maximum gap between ranges of the scanning parameter (default 0)
             max_range_gap = MAX_RANGE_GAP.get(param, 0)
-            if max(range1[0] - range2[-1], range2[0] - range1[-1]) > max_range_gap:
+            if max(r1_start - r2_end, r2_start - r1_end) > max_range_gap:
                 return False
 
-        elif np.max(np.abs(scan1[param] - scan2[param])) > PARAM_PRECISIONS[param]:
+        elif (
+            np.max(np.abs(np.median(scan1[param]) - np.median(scan2[param])))
+            > PARAM_PRECISIONS[param]
+        ):
             return False
 
     return True
 
 
-def merge_datasets(dataset_into, dataset_from):
+def merge_datasets(dataset_into, dataset_from, log=logger):
-    merged = np.zeros(len(dataset_from), dtype=np.bool)
+    scan_motors_into = dataset_into[0]["scan_motors"]
+    scan_motors_from = dataset_from[0]["scan_motors"]
+    if scan_motors_into != scan_motors_from:
+        log.warning(
+            f"Scan motors mismatch between datasets: {scan_motors_into} vs {scan_motors_from}"
+        )
+        return
+
+    merged = np.zeros(len(dataset_from), dtype=bool)
     for scan_into in dataset_into:
         for ind, scan_from in enumerate(dataset_from):
             if _parameters_match(scan_into, scan_from) and not merged[ind]:
-                merge_scans(scan_into, scan_from)
+                if scan_into["counts"].ndim == 3:
+                    merge_h5_scans(scan_into, scan_from, log=log)
+                else:  # scan_into["counts"].ndim == 1
+                    merge_scans(scan_into, scan_from, log=log)
                 merged[ind] = True
 
     for scan_from in dataset_from:
         dataset_into.append(scan_from)
 
 
-def merge_scans(scan_into, scan_from):
+def merge_scans(scan_into, scan_from, log=logger):
-    # TODO: does it need to be "scan_motor" instead of omega for a generalized solution?
     if "init_scan" not in scan_into:
         scan_into["init_scan"] = scan_into.copy()
 
@@ -92,50 +113,129 @@ def merge_scans(scan_into, scan_from):
 
     scan_into["merged_scans"].append(scan_from)
 
-    if (
+    scan_motor = scan_into["scan_motor"]  # the same as scan_from["scan_motor"]
-        scan_into["omega"].shape == scan_from["omega"].shape
-        and np.max(np.abs(scan_into["omega"] - scan_from["omega"])) < 0.0005
-    ):
-        counts_tmp = 0
-        counts_err_tmp = 0
 
-        for scan in [scan_into["init_scan"], *scan_into["merged_scans"]]:
+    pos_all = np.array([])
-            counts_tmp += scan["counts"]
+    val_all = np.array([])
-            counts_err_tmp += scan["counts_err"] ** 2
+    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)
 
-        scan_into["counts"] = counts_tmp / (1 + len(scan_into["merged_scans"]))
+    sort_index = np.argsort(pos_all)
-        scan_into["counts_err"] = np.sqrt(counts_err_tmp)
+    pos_all = pos_all[sort_index]
+    val_all = val_all[sort_index]
+    err_all = err_all[sort_index]
 
-    else:
+    pos_tmp = pos_all[:1]
-        omega = np.concatenate((scan_into["omega"], scan_from["omega"]))
+    val_tmp = val_all[:1]
-        counts = np.concatenate((scan_into["counts"], scan_from["counts"]))
+    err_tmp = err_all[:1]
-        counts_err = np.concatenate((scan_into["counts_err"], scan_from["counts_err"]))
+    num_tmp = np.array([1])
+    for pos, val, err in zip(pos_all[1:], val_all[1:], err_all[1:]):
+        if pos - pos_tmp[-1] < MOTOR_POS_PRECISION:
+            # the repeated motor position
+            val_tmp[-1] += val
+            err_tmp[-1] += err
+            num_tmp[-1] += 1
+        else:
+            # a new motor position
+            pos_tmp = np.append(pos_tmp, pos)
+            val_tmp = np.append(val_tmp, val)
+            err_tmp = np.append(err_tmp, err)
+            num_tmp = np.append(num_tmp, 1)
 
-        index = np.argsort(omega)
+    scan_into[scan_motor] = pos_tmp
+    scan_into["counts"] = val_tmp / num_tmp
+    scan_into["counts_err"] = np.sqrt(err_tmp) / num_tmp
 
-        scan_into["omega"] = omega[index]
+    scan_from["export"] = False
-        scan_into["counts"] = counts[index]
-        scan_into["counts_err"] = counts_err[index]
-
-    scan_from["active"] = 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})')
+    log.info(f'Merging scans: {scan_into["idx"]} ({fname1}) <-- {scan_from["idx"]} ({fname2})')
+
+
+def merge_h5_scans(scan_into, scan_from, log=logger):
+    if "init_scan" not in scan_into:
+        scan_into["init_scan"] = scan_into.copy()
+
+    if "merged_scans" not in scan_into:
+        scan_into["merged_scans"] = []
+
+    for scan in scan_into["merged_scans"]:
+        if scan_from is scan:
+            log.warning("Already merged scan")
+            return
+
+    scan_into["merged_scans"].append(scan_from)
+
+    scan_motor = scan_into["scan_motor"]  # the same as scan_from["scan_motor"]
+
+    pos_all = [scan_into["init_scan"][scan_motor]]
+    val_all = [scan_into["init_scan"]["counts"]]
+    err_all = [scan_into["init_scan"]["counts_err"] ** 2]
+    for scan in scan_into["merged_scans"]:
+        pos_all.append(scan[scan_motor])
+        val_all.append(scan["counts"])
+        err_all.append(scan["counts_err"] ** 2)
+    pos_all = np.concatenate(pos_all)
+    val_all = np.concatenate(val_all)
+    err_all = np.concatenate(err_all)
+
+    sort_index = np.argsort(pos_all)
+    pos_all = pos_all[sort_index]
+    val_all = val_all[sort_index]
+    err_all = err_all[sort_index]
+
+    pos_tmp = [pos_all[0]]
+    val_tmp = [val_all[:1]]
+    err_tmp = [err_all[:1]]
+    num_tmp = [1]
+    for pos, val, err in zip(pos_all[1:], val_all[1:], err_all[1:]):
+        if pos - pos_tmp[-1] < MOTOR_POS_PRECISION:
+            # the repeated motor position
+            val_tmp[-1] += val
+            err_tmp[-1] += err
+            num_tmp[-1] += 1
+        else:
+            # a new motor position
+            pos_tmp.append(pos)
+            val_tmp.append(val[None, :])
+            err_tmp.append(err[None, :])
+            num_tmp.append(1)
+    pos_tmp = np.array(pos_tmp)
+    val_tmp = np.concatenate(val_tmp)
+    err_tmp = np.concatenate(err_tmp)
+    num_tmp = np.array(num_tmp)
+
+    scan_into[scan_motor] = pos_tmp
+    scan_into["counts"] = val_tmp / num_tmp[:, None, None]
+    scan_into["counts_err"] = np.sqrt(err_tmp) / num_tmp[:, None, None]
+
+    scan_from["export"] = False
+
+    fname1 = os.path.basename(scan_into["original_filename"])
+    fname2 = os.path.basename(scan_from["original_filename"])
+    log.info(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["active"] = True
+            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):
+def fit_scan(scan, model_dict, fit_from=None, fit_to=None, log=logger):
     if fit_from is None:
         fit_from = -np.inf
     if fit_to is None:
@@ -148,7 +248,7 @@ def fit_scan(scan, model_dict, fit_from=None, fit_to=None):
     # 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']}")
+        log.warning(f"No data in fit range for scan {scan['idx']}")
         return
 
     y_fit = y_fit[fit_ind]
@@ -200,8 +300,7 @@ def fit_scan(scan, model_dict, fit_from=None, fit_to=None):
         else:
             model += _model
 
-    weights = [1 / y_err if y_err != 0 else 1 for y_err in y_err]
+    scan["fit"] = model.fit(y_fit, x=x_fit, weights=1 / y_err)
-    scan["fit"] = model.fit(y_fit, x=x_fit, weights=weights)
 
 
 def get_area(scan, area_method, lorentz):
@@ -216,12 +315,8 @@ def get_area(scan, area_method, lorentz):
         area_s = 0
         for name, param in scan["fit"].params.items():
             if "amplitude" in name:
-                if param.stderr is None:
+                area_v += np.nan if param.value is None else param.value
-                    area_v = np.nan
+                area_s += np.nan if param.stderr is None else param.stderr
-                    area_s = np.nan
-                else:
-                    area_v += param.value
-                    area_s += param.stderr
 
     else:  # area_method == "int_area"
         y_val = scan["counts"]

pyzebra/h5.py

@@ -1,10 +1,11 @@
 import h5py
 import numpy as np
+from lmfit.models import Gaussian2dModel, GaussianModel
 
+META_MATRIX = ("UB",)
+META_CELL = ("cell",)
+META_STR = ("name",)
 
-META_MATRIX = ("UB")
-META_CELL = ("cell")
-META_STR = ("name")
 
 def read_h5meta(filepath):
     """Open and parse content of a h5meta file.
@@ -46,9 +47,9 @@ def parse_h5meta(file):
                     if variable in META_STR:
                         pass
                     elif variable in META_CELL:
-                        value = np.array(value.split(",")[:6], dtype=np.float)
+                        value = np.array(value.split(",")[:6], dtype=float)
                     elif variable in META_MATRIX:
-                        value = np.array(value.split(",")[:9], dtype=np.float).reshape(3, 3)
+                        value = np.array(value.split(",")[:9], dtype=float).reshape(3, 3)
                     else:  # default is a single float number
                         value = float(value)
                     content[section][variable] = value
@@ -68,71 +69,144 @@ def read_detector_data(filepath, cami_meta=None):
         ndarray: A 3D array of data, omega, gamma, nu.
     """
     with h5py.File(filepath, "r") as h5f:
-        data = h5f["/entry1/area_detector2/data"][:]
+        counts = h5f["/entry1/area_detector2/data"][:].astype(float)
 
-        # reshape data to a correct shape (2006 issue)
+        n, cols, rows = counts.shape
-        n, cols, rows = data.shape
+        if "/entry1/experiment_identifier" in h5f:  # old format
-        data = data.reshape(n, rows, cols)
+            # reshape images (counts) to a correct shape (2006 issue)
+            counts = counts.reshape(n, rows, cols)
+        else:
+            counts = counts.swapaxes(1, 2)
 
-        det_data = {"data": data}
+        scan = {"counts": counts, "counts_err": np.sqrt(np.maximum(counts, 1))}
-        det_data["original_filename"] = filepath
+        scan["original_filename"] = filepath
+        scan["export"] = True
 
         if "/entry1/zebra_mode" in h5f:
-            det_data["zebra_mode"] = h5f["/entry1/zebra_mode"][0].decode()
+            scan["zebra_mode"] = h5f["/entry1/zebra_mode"][0].decode()
         else:
-            det_data["zebra_mode"] = "nb"
+            scan["zebra_mode"] = "nb"
 
         # overwrite zebra_mode from cami
         if cami_meta is not None:
             if "zebra_mode" in cami_meta:
-                det_data["zebra_mode"] = cami_meta["zebra_mode"][0]
+                scan["zebra_mode"] = cami_meta["zebra_mode"][0]
 
-        # om, sometimes ph
+        if "/entry1/control/Monitor" in h5f:
-        if det_data["zebra_mode"] == "nb":
+            scan["monitor"] = h5f["/entry1/control/Monitor"][0]
-            det_data["omega"] = h5f["/entry1/area_detector2/rotation_angle"][:]
+        else:  # old path
-        else:  # bi
+            scan["monitor"] = h5f["/entry1/control/data"][0]
-            det_data["omega"] = h5f["/entry1/sample/rotation_angle"][:]
 
-        det_data["gamma"] = h5f["/entry1/ZEBRA/area_detector2/polar_angle"][:]  # gammad
+        scan["idx"] = 1
-        det_data["nu"] = h5f["/entry1/ZEBRA/area_detector2/tilt_angle"][:]  # nud
-        det_data["ddist"] = h5f["/entry1/ZEBRA/area_detector2/distance"][:]
-        det_data["wave"] = h5f["/entry1/ZEBRA/monochromator/wavelength"][:]
-        det_data["chi"] = h5f["/entry1/sample/chi"][:]  # ch
-        det_data["phi"] = h5f["/entry1/sample/phi"][:]  # ph
-        det_data["ub"] = h5f["/entry1/sample/UB"][:].reshape(3, 3)
-        det_data["name"] = h5f["/entry1/sample/name"][0].decode()
-        det_data["cell"] = h5f["/entry1/sample/cell"][:]
 
-        for var in ("omega", "gamma", "nu", "chi", "phi"):
+        if "/entry1/sample/rotation_angle" in h5f:
-            if abs(det_data[var][0] - det_data[var][-1]) > 0.1:
+            scan["omega"] = h5f["/entry1/sample/rotation_angle"][:]
-                det_data["scan_motor"] = var
-                break
         else:
-            raise ValueError("No angles that vary")
+            scan["omega"] = h5f["/entry1/area_detector2/rotation_angle"][:]
+        if len(scan["omega"]) == 1:
+            scan["omega"] = np.ones(n) * scan["omega"]
+
+        scan["gamma"] = h5f["/entry1/ZEBRA/area_detector2/polar_angle"][:]
+        scan["twotheta"] = h5f["/entry1/ZEBRA/area_detector2/polar_angle"][:]
+        if len(scan["gamma"]) == 1:
+            scan["gamma"] = np.ones(n) * scan["gamma"]
+            scan["twotheta"] = np.ones(n) * scan["twotheta"]
+        scan["nu"] = h5f["/entry1/ZEBRA/area_detector2/tilt_angle"][0]
+        scan["ddist"] = h5f["/entry1/ZEBRA/area_detector2/distance"][0]
+        scan["wave"] = h5f["/entry1/ZEBRA/monochromator/wavelength"][0]
+        if scan["zebra_mode"] == "nb":
+            scan["chi"] = np.array([180])
+            scan["phi"] = np.array([0])
+        elif scan["zebra_mode"] == "bi":
+            scan["chi"] = h5f["/entry1/sample/chi"][:]
+            scan["phi"] = h5f["/entry1/sample/phi"][:]
+        if len(scan["chi"]) == 1:
+            scan["chi"] = np.ones(n) * scan["chi"]
+        if len(scan["phi"]) == 1:
+            scan["phi"] = np.ones(n) * scan["phi"]
+        if h5f["/entry1/sample/UB"].size == 0:
+            scan["ub"] = np.eye(3) * 0.177
+        else:
+            scan["ub"] = h5f["/entry1/sample/UB"][:].reshape(3, 3)
+        scan["name"] = h5f["/entry1/sample/name"][0].decode()
+        scan["cell"] = h5f["/entry1/sample/cell"][:]
+
+        if n == 1:
+            # a default motor for a single frame file
+            scan["scan_motor"] = "omega"
+        else:
+            for var in ("omega", "gamma", "chi", "phi"):  # TODO: also nu?
+                if abs(scan[var][0] - scan[var][-1]) > 0.1:
+                    scan["scan_motor"] = var
+                    break
+            else:
+                raise ValueError("No angles that vary")
+
+        scan["scan_motors"] = [scan["scan_motor"]]
 
         # optional parameters
         if "/entry1/sample/magnetic_field" in h5f:
-            det_data["mf"] = h5f["/entry1/sample/magnetic_field"][:]
+            scan["mf"] = h5f["/entry1/sample/magnetic_field"][:]
+
+        if "mf" in scan:
+            # TODO: NaNs are not JSON compliant, so replace them with None
+            # this is not a great solution, but makes it safe to use the array in bokeh
+            scan["mf"] = np.where(np.isnan(scan["mf"]), None, scan["mf"])
 
         if "/entry1/sample/temperature" in h5f:
-            det_data["temp"] = h5f["/entry1/sample/temperature"][:]
+            scan["temp"] = h5f["/entry1/sample/temperature"][:]
+        elif "/entry1/sample/Ts/value" in h5f:
+            scan["temp"] = h5f["/entry1/sample/Ts/value"][:]
+
+        if "temp" in scan:
+            # TODO: NaNs are not JSON compliant, so replace them with None
+            # this is not a great solution, but makes it safe to use the array in bokeh
+            scan["temp"] = np.where(np.isnan(scan["temp"]), None, scan["temp"])
 
         # overwrite metadata from .cami
         if cami_meta is not None:
             if "crystal" in cami_meta:
                 cami_meta_crystal = cami_meta["crystal"]
                 if "name" in cami_meta_crystal:
-                    det_data["name"] = cami_meta_crystal["name"]
+                    scan["name"] = cami_meta_crystal["name"]
                 if "UB" in cami_meta_crystal:
-                    det_data["ub"] = cami_meta_crystal["UB"]
+                    scan["ub"] = cami_meta_crystal["UB"]
                 if "cell" in cami_meta_crystal:
-                    det_data["cell"] = cami_meta_crystal["cell"]
+                    scan["cell"] = cami_meta_crystal["cell"]
                 if "lambda" in cami_meta_crystal:
-                    det_data["wave"] = cami_meta_crystal["lambda"]
+                    scan["wave"] = cami_meta_crystal["lambda"]
 
             if "detector parameters" in cami_meta:
                 cami_meta_detparam = cami_meta["detector parameters"]
-                if "dist1" in cami_meta_detparam:
+                if "dist2" in cami_meta_detparam:
-                    det_data["ddist"] = cami_meta_detparam["dist1"]
+                    scan["ddist"] = cami_meta_detparam["dist2"]
 
-    return det_data
+    return scan
+
+
+def fit_event(scan, fr_from, fr_to, y_from, y_to, x_from, x_to):
+    data_roi = scan["counts"][fr_from:fr_to, y_from:y_to, x_from:x_to]
+
+    model = GaussianModel()
+    fr = np.arange(fr_from, fr_to)
+    counts_per_fr = np.sum(data_roi, axis=(1, 2))
+    params = model.guess(counts_per_fr, fr)
+    result = model.fit(counts_per_fr, x=fr, params=params)
+    frC = result.params["center"].value
+    intensity = result.params["height"].value
+
+    counts_std = counts_per_fr.std()
+    counts_mean = counts_per_fr.mean()
+    snr = 0 if counts_std == 0 else counts_mean / counts_std
+
+    model = Gaussian2dModel()
+    xs, ys = np.meshgrid(np.arange(x_from, x_to), np.arange(y_from, y_to))
+    xs = xs.flatten()
+    ys = ys.flatten()
+    counts = np.sum(data_roi, axis=0).flatten()
+    params = model.guess(counts, xs, ys)
+    result = model.fit(counts, x=xs, y=ys, params=params)
+    xC = result.params["centerx"].value
+    yC = result.params["centery"].value
+
+    scan["fit"] = {"frame": frC, "x_pos": xC, "y_pos": yC, "intensity": intensity, "snr": snr}

pyzebra/sxtal_refgen.py

@@ -0,0 +1,486 @@
+import io
+import logging
+import os
+import subprocess
+import tempfile
+from math import ceil, floor
+
+import numpy as np
+
+logger = logging.getLogger(__name__)
+
+SXTAL_REFGEN_PATH = "/afs/psi.ch/project/sinq/rhel8/bin/Sxtal_Refgen"
+
+_zebraBI_default_geom = """GEOM          2 Bissecting - HiCHI
+BLFR          z-up
+DIST_UNITS    mm
+ANGL_UNITS    deg
+DET_TYPE      Point  ipsd 1
+DIST_DET      488
+DIM_XY         1.0   1.0    1    1
+GAPS_DET       0  0
+
+SETTING       1 0 0   0 1 0   0 0 1
+NUM_ANG  4
+ANG_LIMITS         Min      Max    Offset
+        Gamma     0.0    128.0     0.00
+        Omega     0.0     64.0     0.00
+        Chi      80.0    211.0     0.00
+        Phi       0.0    360.0     0.00
+
+DET_OFF      0       0      0
+"""
+
+_zebraNB_default_geom = """GEOM          3 Normal Beam
+BLFR          z-up
+DIST_UNITS    mm
+ANGL_UNITS    deg
+DET_TYPE      Point  ipsd 1
+DIST_DET      448
+DIM_XY         1.0   1.0    1    1
+GAPS_DET       0  0
+
+SETTING       1 0 0   0 1 0   0 0 1
+NUM_ANG  3
+ANG_LIMITS         Min      Max    Offset
+        Gamma      0.0     128.0     0.00
+        Omega   -180.0     180.0     0.00
+        Nu       -15.0      15.0     0.00
+
+DET_OFF      0       0      0
+"""
+
+_zebra_default_cfl = """TITLE mymaterial
+SPGR  P 63 2 2
+CELL  5.73 5.73 11.89 90 90 120
+
+WAVE 1.383
+
+UBMAT
+0.000000    0.000000    0.084104
+0.000000    0.174520   -0.000000
+0.201518    0.100759    0.000000
+
+INSTR  zebra.geom
+
+ORDER   1 2 3
+
+ANGOR   gamma
+
+HLIM    -25 25 -25 25 -25 25
+SRANG   0.0  0.7
+
+Mag_Structure
+lattiCE P 1
+kvect        0.0 0.0 0.0
+magcent
+symm  x,y,z
+msym  u,v,w, 0.0
+End_Mag_Structure
+"""
+
+
+def get_zebraBI_default_geom_file():
+    return io.StringIO(_zebraBI_default_geom)
+
+
+def get_zebraNB_default_geom_file():
+    return io.StringIO(_zebraNB_default_geom)
+
+
+def get_zebra_default_cfl_file():
+    return io.StringIO(_zebra_default_cfl)
+
+
+def read_geom_file(fileobj):
+    ang_lims = dict()
+    for line in fileobj:
+        if "!" in line:  # remove comments that start with ! sign
+            line, _ = line.split(sep="!", maxsplit=1)
+
+        if line.startswith("GEOM"):
+            _, val = line.split(maxsplit=1)
+            if val.startswith("2"):
+                ang_lims["geom"] = "bi"
+            else:  # val.startswith("3")
+                ang_lims["geom"] = "nb"
+
+        elif line.startswith("ANG_LIMITS"):
+            # read angular limits
+            for line in fileobj:
+                if not line or line.isspace():
+                    break
+
+                ang, ang_min, ang_max, ang_offset = line.split()
+                ang_lims[ang.lower()] = [ang_min, ang_max, ang_offset]
+
+            if "2theta" in ang_lims:  # treat 2theta as gamma
+                ang_lims["gamma"] = ang_lims.pop("2theta")
+
+    return ang_lims
+
+
+def export_geom_file(path, ang_lims, template=None):
+    if ang_lims["geom"] == "bi":
+        template_file = get_zebraBI_default_geom_file()
+        n_ang = 4
+    else:  # ang_lims["geom"] == "nb"
+        template_file = get_zebraNB_default_geom_file()
+        n_ang = 3
+
+    if template is not None:
+        template_file = template
+
+    with open(path, "w") as out_file:
+        for line in template_file:
+            out_file.write(line)
+
+            if line.startswith("ANG_LIMITS"):
+                for _ in range(n_ang):
+                    next_line = next(template_file)
+                    ang, _, _, _ = next_line.split()
+
+                    if ang == "2theta":  # treat 2theta as gamma
+                        ang = "Gamma"
+                    vals = ang_lims[ang.lower()]
+
+                    out_file.write(f"{'':<8}{ang:<10}{vals[0]:<10}{vals[1]:<10}{vals[2]:<10}\n")
+
+
+def calc_ub_matrix(params, log=logger):
+    with tempfile.TemporaryDirectory() as temp_dir:
+        cfl_file = os.path.join(temp_dir, "ub_matrix.cfl")
+
+        with open(cfl_file, "w") as fileobj:
+            for key, value in params.items():
+                fileobj.write(f"{key} {value}\n")
+
+        comp_proc = subprocess.run(
+            [SXTAL_REFGEN_PATH, cfl_file],
+            cwd=temp_dir,
+            check=True,
+            stdout=subprocess.PIPE,
+            stderr=subprocess.STDOUT,
+            text=True,
+        )
+        log.info(" ".join(comp_proc.args))
+        log.info(comp_proc.stdout)
+
+        sfa_file = os.path.join(temp_dir, "ub_matrix.sfa")
+        ub_matrix = []
+        with open(sfa_file, "r") as fileobj:
+            for line in fileobj:
+                if "BL_M" in line:  # next 3 lines contain the matrix
+                    for _ in range(3):
+                        next_line = next(fileobj)
+                        *vals, _ = next_line.split(maxsplit=3)
+                        ub_matrix.extend(vals)
+
+    return ub_matrix
+
+
+def read_cfl_file(fileobj):
+    params = {
+        "SPGR": None,
+        "CELL": None,
+        "WAVE": None,
+        "UBMAT": None,
+        "HLIM": None,
+        "SRANG": None,
+        "lattiCE": None,
+        "kvect": None,
+    }
+    param_names = tuple(params)
+
+    for line in fileobj:
+        line = line.strip()
+        if "!" in line:  # remove comments that start with ! sign
+            line, _ = line.split(sep="!", maxsplit=1)
+
+        if line.startswith(param_names):
+            if line.startswith("UBMAT"):  # next 3 lines contain the matrix
+                param, val = "UBMAT", []
+                for _ in range(3):
+                    next_line = next(fileobj).strip()
+                    val.extend(next_line.split(maxsplit=2))
+            else:
+                param, val = line.split(maxsplit=1)
+
+            params[param] = val
+
+    return params
+
+
+def read_cif_file(fileobj):
+    params = {"SPGR": None, "CELL": None, "ATOM": []}
+
+    cell_params = {
+        "_cell_length_a": None,
+        "_cell_length_b": None,
+        "_cell_length_c": None,
+        "_cell_angle_alpha": None,
+        "_cell_angle_beta": None,
+        "_cell_angle_gamma": None,
+    }
+    cell_param_names = tuple(cell_params)
+
+    atom_param_pos = {
+        "_atom_site_label": 0,
+        "_atom_site_type_symbol": None,
+        "_atom_site_fract_x": None,
+        "_atom_site_fract_y": None,
+        "_atom_site_fract_z": None,
+        "_atom_site_U_iso_or_equiv": None,
+        "_atom_site_occupancy": None,
+    }
+    atom_param_names = tuple(atom_param_pos)
+
+    for line in fileobj:
+        line = line.strip()
+        if line.startswith("_space_group_name_H-M_alt"):
+            _, val = line.split(maxsplit=1)
+            params["SPGR"] = val.strip("'")
+
+        elif line.startswith(cell_param_names):
+            param, val = line.split(maxsplit=1)
+            cell_params[param] = val
+
+        elif line.startswith("_atom_site_label"):  # assume this is the start of atom data
+            for ind, line in enumerate(fileobj, start=1):
+                line = line.strip()
+
+                # read fields
+                if line.startswith("_atom_site"):
+                    if line.startswith(atom_param_names):
+                        atom_param_pos[line] = ind
+                    continue
+
+                # read data till an empty line
+                if not line:
+                    break
+                vals = line.split()
+                params["ATOM"].append(" ".join([vals[ind] for ind in atom_param_pos.values()]))
+
+    if None not in cell_params.values():
+        params["CELL"] = " ".join(cell_params.values())
+
+    return params
+
+
+def export_cfl_file(path, params, template=None):
+    param_names = tuple(params)
+    if template is None:
+        template_file = get_zebra_default_cfl_file()
+    else:
+        template_file = template
+
+    atom_done = False
+    with open(path, "w") as out_file:
+        for line in template_file:
+            if line.startswith(param_names):
+                if line.startswith("UBMAT"):  # only UBMAT values are not on the same line
+                    out_file.write(line)
+                    for i in range(3):
+                        next(template_file)
+                        out_file.write(" ".join(params["UBMAT"][3 * i : 3 * (i + 1)]) + "\n")
+
+                elif line.startswith("ATOM"):
+                    if "ATOM" in params:
+                        # replace all ATOM with values in params
+                        while line.startswith("ATOM"):
+                            line = next(template_file)
+                        for atom_line in params["ATOM"]:
+                            out_file.write(f"ATOM {atom_line}\n")
+                        atom_done = True
+
+                else:
+                    param, _ = line.split(maxsplit=1)
+                    out_file.write(f"{param} {params[param]}\n")
+
+            elif line.startswith("INSTR"):
+                # replace it with a default name
+                out_file.write("INSTR  zebra.geom\n")
+
+            else:
+                out_file.write(line)
+
+        # append ATOM data if it's present and a template did not contain it
+        if "ATOM" in params and not atom_done:
+            out_file.write("\n")
+            for atom_line in params["ATOM"]:
+                out_file.write(f"ATOM {atom_line}\n")
+
+
+def sort_hkl_file_bi(file_in, file_out, priority, chunks):
+    with open(file_in) as fileobj:
+        file_in_data = fileobj.readlines()
+
+    data = np.genfromtxt(file_in, skip_header=3)
+    stt = data[:, 4]
+    omega = data[:, 5]
+    chi = data[:, 6]
+    phi = data[:, 7]
+
+    lines = file_in_data[3:]
+    lines_update = []
+
+    angles = {"2theta": stt, "omega": omega, "chi": chi, "phi": phi}
+
+    # Reverse flag
+    to_reverse = False
+    to_reverse_p2 = False
+    to_reverse_p3 = False
+
+    # Get indices within first priority
+    ang_p1 = angles[priority[0]]
+    begin_p1 = floor(min(ang_p1))
+    end_p1 = ceil(max(ang_p1))
+    delta_p1 = chunks[0]
+    for p1 in range(begin_p1, end_p1, delta_p1):
+        ind_p1 = [j for j, x in enumerate(ang_p1) if p1 <= x and x < p1 + delta_p1]
+
+        stt_new = [stt[x] for x in ind_p1]
+        omega_new = [omega[x] for x in ind_p1]
+        chi_new = [chi[x] for x in ind_p1]
+        phi_new = [phi[x] for x in ind_p1]
+        lines_new = [lines[x] for x in ind_p1]
+
+        angles_p2 = {"stt": stt_new, "omega": omega_new, "chi": chi_new, "phi": phi_new}
+
+        # Get indices for second priority
+        ang_p2 = angles_p2[priority[1]]
+        if len(ang_p2) > 0 and to_reverse_p2:
+            begin_p2 = ceil(max(ang_p2))
+            end_p2 = floor(min(ang_p2))
+            delta_p2 = -chunks[1]
+        elif len(ang_p2) > 0 and not to_reverse_p2:
+            end_p2 = ceil(max(ang_p2))
+            begin_p2 = floor(min(ang_p2))
+            delta_p2 = chunks[1]
+        else:
+            end_p2 = 0
+            begin_p2 = 0
+            delta_p2 = 1
+
+        to_reverse_p2 = not to_reverse_p2
+
+        for p2 in range(begin_p2, end_p2, delta_p2):
+            min_p2 = min([p2, p2 + delta_p2])
+            max_p2 = max([p2, p2 + delta_p2])
+            ind_p2 = [j for j, x in enumerate(ang_p2) if min_p2 <= x and x < max_p2]
+
+            stt_new2 = [stt_new[x] for x in ind_p2]
+            omega_new2 = [omega_new[x] for x in ind_p2]
+            chi_new2 = [chi_new[x] for x in ind_p2]
+            phi_new2 = [phi_new[x] for x in ind_p2]
+            lines_new2 = [lines_new[x] for x in ind_p2]
+
+            angles_p3 = {"stt": stt_new2, "omega": omega_new2, "chi": chi_new2, "phi": phi_new2}
+
+            # Get indices for third priority
+            ang_p3 = angles_p3[priority[2]]
+            if len(ang_p3) > 0 and to_reverse_p3:
+                begin_p3 = ceil(max(ang_p3)) + chunks[2]
+                end_p3 = floor(min(ang_p3)) - chunks[2]
+                delta_p3 = -chunks[2]
+            elif len(ang_p3) > 0 and not to_reverse_p3:
+                end_p3 = ceil(max(ang_p3)) + chunks[2]
+                begin_p3 = floor(min(ang_p3)) - chunks[2]
+                delta_p3 = chunks[2]
+            else:
+                end_p3 = 0
+                begin_p3 = 0
+                delta_p3 = 1
+
+            to_reverse_p3 = not to_reverse_p3
+
+            for p3 in range(begin_p3, end_p3, delta_p3):
+                min_p3 = min([p3, p3 + delta_p3])
+                max_p3 = max([p3, p3 + delta_p3])
+                ind_p3 = [j for j, x in enumerate(ang_p3) if min_p3 <= x and x < max_p3]
+
+                angle_new3 = [angles_p3[priority[3]][x] for x in ind_p3]
+
+                ind_final = [x for _, x in sorted(zip(angle_new3, ind_p3), reverse=to_reverse)]
+
+                to_reverse = not to_reverse
+
+                for i in ind_final:
+                    lines_update.append(lines_new2[i])
+
+    with open(file_out, "w") as fileobj:
+        for _ in range(3):
+            fileobj.write(file_in_data.pop(0))
+
+        fileobj.writelines(lines_update)
+
+
+def sort_hkl_file_nb(file_in, file_out, priority, chunks):
+    with open(file_in) as fileobj:
+        file_in_data = fileobj.readlines()
+
+    data = np.genfromtxt(file_in, skip_header=3)
+    gamma = data[:, 4]
+    omega = data[:, 5]
+    nu = data[:, 6]
+
+    lines = file_in_data[3:]
+    lines_update = []
+
+    angles = {"gamma": gamma, "omega": omega, "nu": nu}
+
+    to_reverse = False
+    to_reverse_p2 = False
+
+    # Get indices within first priority
+    ang_p1 = angles[priority[0]]
+    begin_p1 = floor(min(ang_p1))
+    end_p1 = ceil(max(ang_p1))
+    delta_p1 = chunks[0]
+    for p1 in range(begin_p1, end_p1, delta_p1):
+        ind_p1 = [j for j, x in enumerate(ang_p1) if p1 <= x and x < p1 + delta_p1]
+
+        # Get angles from within nu range
+        lines_new = [lines[x] for x in ind_p1]
+        gamma_new = [gamma[x] for x in ind_p1]
+        omega_new = [omega[x] for x in ind_p1]
+        nu_new = [nu[x] for x in ind_p1]
+
+        angles_p2 = {"gamma": gamma_new, "omega": omega_new, "nu": nu_new}
+
+        # Get indices for second priority
+        ang_p2 = angles_p2[priority[1]]
+        if len(gamma_new) > 0 and to_reverse_p2:
+            begin_p2 = ceil(max(ang_p2))
+            end_p2 = floor(min(ang_p2))
+            delta_p2 = -chunks[1]
+        elif len(gamma_new) > 0 and not to_reverse_p2:
+            end_p2 = ceil(max(ang_p2))
+            begin_p2 = floor(min(ang_p2))
+            delta_p2 = chunks[1]
+        else:
+            end_p2 = 0
+            begin_p2 = 0
+            delta_p2 = 1
+
+        to_reverse_p2 = not to_reverse_p2
+
+        for p2 in range(begin_p2, end_p2, delta_p2):
+            min_p2 = min([p2, p2 + delta_p2])
+            max_p2 = max([p2, p2 + delta_p2])
+            ind_p2 = [j for j, x in enumerate(ang_p2) if min_p2 <= x and x < max_p2]
+
+            angle_new2 = [angles_p2[priority[2]][x] for x in ind_p2]
+
+            ind_final = [x for _, x in sorted(zip(angle_new2, ind_p2), reverse=to_reverse)]
+
+            to_reverse = not to_reverse
+
+            for i in ind_final:
+                lines_update.append(lines_new[i])
+
+    with open(file_out, "w") as fileobj:
+        for _ in range(3):
+            fileobj.write(file_in_data.pop(0))
+
+        fileobj.writelines(lines_update)

pyzebra/utils.py

@@ -1,20 +1,36 @@
 import os
 
-ZEBRA_PROPOSALS_PATHS = [
+import numpy as np
-    f"/afs/psi.ch/project/sinqdata/{year}/zebra/" for year in (2016, 2017, 2018, 2020, 2021)
+
-]
+SINQ_PATH = "/afs/psi.ch/project/sinqdata"
+ZEBRA_PROPOSALS_PATH = os.path.join(SINQ_PATH, "{year}/zebra/{proposal}")
+
 
 def find_proposal_path(proposal):
-    proposal = proposal.strip()
+    for entry in os.scandir(SINQ_PATH):
-    if proposal:
+        if entry.is_dir() and len(entry.name) == 4 and entry.name.isdigit():
-        for zebra_proposals_path in ZEBRA_PROPOSALS_PATHS:
+            proposal_path = ZEBRA_PROPOSALS_PATH.format(year=entry.name, proposal=proposal)
-            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 = ""
+        raise ValueError(f"Can not find data for proposal '{proposal}'")
 
     return proposal_path
+
+
+def parse_hkl(fileobj, data_type):
+    next(fileobj)
+    fields = map(str.lower, next(fileobj).strip("!").strip().split())
+    next(fileobj)
+    data = np.loadtxt(fileobj, unpack=True)
+    res = dict(zip(fields, data))
+
+    # adapt to .ccl/.dat files naming convention
+    res["counts"] = res.pop("f2")
+
+    if data_type == ".hkl":
+        for ind in ("h", "k", "l"):
+            res[ind] = res[ind].astype(int)
+
+    return res

pyzebra/xtal.py

@@ -2,8 +2,16 @@ import numpy as np
 from numba import njit
 
 pi_r = 180 / np.pi
+IMAGE_W = 256
+IMAGE_H = 128
+
+XNORM = 128
+YNORM = 64
+XPIX = 0.734
+YPIX = 1.4809
 
 
+@njit(cache=True)
 def z4frgn(wave, ga, nu):
     """CALCULATES DIFFRACTION VECTOR IN LAB SYSTEM FROM GA AND NU
 
@@ -15,36 +23,34 @@ def z4frgn(wave, ga, nu):
     """
     ga_r = ga / pi_r
     nu_r = nu / pi_r
-    z4 = [0.0, 0.0, 0.0]
+    z4 = [np.sin(ga_r) * np.cos(nu_r), np.cos(ga_r) * np.cos(nu_r) - 1.0, np.sin(nu_r)]
-    z4[0] = (np.sin(ga_r) * np.cos(nu_r)) / wave
-    z4[1] = (np.cos(ga_r) * np.cos(nu_r) - 1.0) / wave
-    z4[2] = (np.sin(nu_r)) / wave
 
-    return z4
+    return np.array(z4) / wave
 
 
 @njit(cache=True)
-def phimat(phi):
+def phimat_T(phi):
-    """BUSING AND LEVY CONVENTION ROTATION MATRIX FOR PHI OR OMEGA
+    """TRANSPOSED BUSING AND LEVY CONVENTION ROTATION MATRIX FOR PHI OR OMEGA
 
     Args:
         PHI
 
     Returns:
-        DUM
+        DUM_T
     """
     ph_r = phi / pi_r
 
-    dum = np.zeros(9).reshape(3, 3)
+    dum = np.zeros((3, 3))
     dum[0, 0] = np.cos(ph_r)
-    dum[0, 1] = np.sin(ph_r)
+    dum[1, 0] = np.sin(ph_r)
-    dum[1, 0] = -dum[0, 1]
+    dum[0, 1] = -dum[1, 0]
     dum[1, 1] = dum[0, 0]
     dum[2, 2] = 1
 
     return dum
 
 
+@njit(cache=True)
 def z1frnb(wave, ga, nu, om):
     """CALCULATE DIFFRACTION VECTOR Z1 FROM GA, OM, NU, ASSUMING CH=PH=0
 
@@ -55,30 +61,28 @@ def z1frnb(wave, ga, nu, om):
         Z1
     """
     z4 = z4frgn(wave, ga, nu)
-    dum = phimat(phi=om)
+    z3 = phimat_T(phi=om).dot(z4)
-    dumt = np.transpose(dum)
-    z3 = dumt.dot(z4)
 
     return z3
 
 
 @njit(cache=True)
-def chimat(chi):
+def chimat_T(chi):
-    """BUSING AND LEVY CONVENTION ROTATION MATRIX FOR CHI
+    """TRANSPOSED BUSING AND LEVY CONVENTION ROTATION MATRIX FOR CHI
 
     Args:
         CHI
 
     Returns:
-        DUM
+        DUM_T
     """
     ch_r = chi / pi_r
 
-    dum = np.zeros(9).reshape(3, 3)
+    dum = np.zeros((3, 3))
     dum[0, 0] = np.cos(ch_r)
-    dum[0, 2] = np.sin(ch_r)
+    dum[2, 0] = np.sin(ch_r)
     dum[1, 1] = 1
-    dum[2, 0] = -dum[0, 2]
+    dum[0, 2] = -dum[2, 0]
     dum[2, 2] = dum[0, 0]
 
     return dum
@@ -94,13 +98,8 @@ def z1frz3(z3, chi, phi):
     Returns:
         Z1
     """
-    dum1 = chimat(chi)
+    z2 = chimat_T(chi).dot(z3)
-    dum2 = np.transpose(dum1)
+    z1 = phimat_T(phi).dot(z2)
-    z2 = dum2.dot(z3)
-
-    dum1 = phimat(phi)
-    dum2 = np.transpose(dum1)
-    z1 = dum2.dot(z2)
 
     return z1
 
@@ -282,104 +281,81 @@ def fixdnu(wave, z1, ch2, ph2, nu):
     return ch, ph, ga, om
 
 
-# for test run:
+def ang2hkl(wave, ddist, gammad, om, ch, ph, nud, ub_inv, x, y):
-#   angtohkl(wave=1.18,ddist=616,gammad=48.66,om=-22.80,ch=0,ph=0,nud=0,x=128,y=64)
+    """Calculate hkl-indices of a reflection from its position (x,y,angles) at the 2d-detector"""
-
-
-def angtohkl(wave, ddist, gammad, om, ch, ph, nud, x, y):
-    """finds hkl-indices of a reflection from its position (x,y,angles) at the 2d-detector
-
-    Args:
-        gammad, om, ch, ph, nud, xobs, yobs
-
-    Returns:
-
-    """
-    # define ub matrix if testing angtohkl(wave=1.18,ddist=616,gammad=48.66,om=-22.80,ch=0,ph=0,nud=0,x=128,y=64) against f90:
-    #    ub = np.array([-0.0178803,-0.0749231,0.0282804,-0.0070082,-0.0368001,-0.0577467,0.1609116,-0.0099281,0.0006274]).reshape(3,3)
-    ub = np.array(
-        [0.04489, 0.02045, -0.2334, -0.06447, 0.00129, -0.16356, -0.00328, 0.2542, 0.0196]
-    ).reshape(3, 3)
-    print(
-        "The input values are: ga=",
-        gammad,
-        ", om=",
-        om,
-        ", ch=",
-        ch,
-        ", ph=",
-        ph,
-        ", nu=",
-        nud,
-        ", x=",
-        x,
-        ", y=",
-        y,
-    )
-
-    ga, nu = det2pol(ddist, gammad, nud, x, y)
-
-    print(
-        "The calculated actual angles are: ga=",
-        ga,
-        ", om=",
-        om,
-        ", ch=",
-        ch,
-        ", ph=",
-        ph,
-        ", nu=",
-        nu,
-    )
-
-    z1 = z1frmd(wave, ga, om, ch, ph, nu)
-
-    print("The diffraction vector is:", z1[0], z1[1], z1[2])
-
-    ubinv = np.linalg.inv(ub)
-
-    h = ubinv[0, 0] * z1[0] + ubinv[0, 1] * z1[1] + ubinv[0, 2] * z1[2]
-    k = ubinv[1, 0] * z1[0] + ubinv[1, 1] * z1[1] + ubinv[1, 2] * z1[2]
-    l = ubinv[2, 0] * z1[0] + ubinv[2, 1] * z1[1] + ubinv[2, 2] * z1[2]
-
-    print("The Miller indexes are:", h, k, l)
-
-    ch2, ph2 = eqchph(z1)
-    ch, ph, ga, om = fixdnu(wave, z1, ch2, ph2, nu)
-
-    print(
-        "Bisecting angles to put reflection into the detector center: ga=",
-        ga,
-        ", om=",
-        om,
-        ", ch=",
-        ch,
-        ", ph=",
-        ph,
-        ", nu=",
-        nu,
-    )
-
-
-def ang2hkl(wave, ddist, gammad, om, ch, ph, nud, ub, x, y):
-    """Calculate hkl-indices of a reflection from its position (x,y,angles) at the 2d-detector
-    """
     ga, nu = det2pol(ddist, gammad, nud, x, y)
     z1 = z1frmd(wave, ga, om, ch, ph, nu)
-    ubinv = np.linalg.inv(ub)
+    hkl = ub_inv @ z1
-    hkl = ubinv @ z1
 
     return hkl
 
 
-def gauss(x, *p):
+def ang2hkl_det(wave, ddist, gammad, om, chi, phi, nud, ub_inv):
-    """Defines Gaussian function
+    """Calculate hkl-indices of a reflection from its position (x,y,angles) at the 2d-detector"""
+    xv, yv = np.meshgrid(range(IMAGE_W), range(IMAGE_H))
+    xobs = (xv.ravel() - XNORM) * XPIX
+    yobs = (yv.ravel() - YNORM) * YPIX
 
-    Args:
+    a = xobs
-        A - amplitude, mu - position of the center, sigma - width
+    b = ddist * np.cos(yobs / ddist)
+    z = ddist * np.sin(yobs / ddist)
+    d = np.sqrt(a * a + b * b)
 
-    Returns:
+    gamma = gammad + np.arctan2(a, b) * pi_r
-        Gaussian function
+    nu = nud + np.arctan2(z, d) * pi_r
-    """
+
-    A, mu, sigma = p
+    gamma_r = gamma / pi_r
-    return A * np.exp(-((x - mu) ** 2) / (2.0 * sigma ** 2))
+    nu_r = nu / pi_r
+    z4 = np.vstack(
+        (
+            np.sin(gamma_r) * np.cos(nu_r) / wave,
+            (np.cos(gamma_r) * np.cos(nu_r) - 1) / wave,
+            np.sin(nu_r) / wave,
+        )
+    )
+
+    om_r = om / pi_r
+    dum3 = np.zeros((3, 3))
+    dum3[0, 0] = np.cos(om_r)
+    dum3[1, 0] = np.sin(om_r)
+    dum3[0, 1] = -dum3[1, 0]
+    dum3[1, 1] = dum3[0, 0]
+    dum3[2, 2] = 1
+
+    chi_r = chi / pi_r
+    dum2 = np.zeros((3, 3))
+    dum2[0, 0] = np.cos(chi_r)
+    dum2[2, 0] = np.sin(chi_r)
+    dum2[1, 1] = 1
+    dum2[0, 2] = -dum2[2, 0]
+    dum2[2, 2] = dum2[0, 0]
+
+    phi_r = phi / pi_r
+    dum1 = np.zeros((3, 3))
+    dum1[0, 0] = np.cos(phi_r)
+    dum1[1, 0] = np.sin(phi_r)
+    dum1[0, 1] = -dum1[1, 0]
+    dum1[1, 1] = dum1[0, 0]
+    dum1[2, 2] = 1
+
+    hkl = (ub_inv @ dum1 @ dum2 @ dum3 @ z4).reshape(3, IMAGE_H, IMAGE_W)
+
+    return hkl
+
+
+def ang2hkl_1d(wave, ga, om, ch, ph, nu, ub_inv):
+    """Calculate hkl-indices of a reflection from its position (angles) at the 1d-detector"""
+    z1 = z1frmd(wave, ga, om, ch, ph, nu)
+    hkl = ub_inv @ z1
+
+    return hkl
+
+
+def ang_proc(wave, ddist, gammad, om, ch, ph, nud, x, y):
+    """Utility function to calculate ch, ph, ga, om"""
+    ga, nu = det2pol(ddist, gammad, nud, x, y)
+    z1 = z1frmd(wave, ga, om, ch, ph, nu)
+    ch2, ph2 = eqchph(z1)
+    ch, ph, ga, om = fixdnu(wave, z1, ch2, ph2, nu)
+
+    return ch, ph, ga, om

scripts/pyzebra-start.sh

@@ -1,4 +0,0 @@
-source /home/pyzebra/miniconda3/etc/profile.d/conda.sh
-
-conda activate prod
-pyzebra --port=80 --allow-websocket-origin=pyzebra.psi.ch:80 --spind-path=/home/pyzebra/spind

scripts/pyzebra-test-start.sh

@@ -1,4 +0,0 @@
-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 --spind-path=/home/pyzebra/spind

scripts/pyzebra-test.service

@@ -1,11 +0,0 @@
-[Unit]
-Description=pyzebra-test web server (runs on port 5006)
-
-[Service]
-Type=simple
-User=pyzebra
-ExecStart=/bin/bash /usr/local/sbin/pyzebra-test-start.sh
-Restart=always
-
-[Install]
-WantedBy=multi-user.target

scripts/pyzebra.service

@@ -1,10 +0,0 @@
-[Unit]
-Description=pyzebra web server
-
-[Service]
-Type=simple
-ExecStart=/bin/bash /usr/local/sbin/pyzebra-start.sh
-Restart=always
-
-[Install]
-WantedBy=multi-user.target