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This is an outdated way of pyzebra deployment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

.travis.yml

@@ -1,33 +0,0 @@
-language: python
-python:
-  - 3.6
-  - 3.7
-  - 3.8
-
-# Build only tagged commits
-if: tag IS present
-
-before_install:
-  - wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh
-  - bash miniconda.sh -b -p $HOME/miniconda
-  - export PATH="$HOME/miniconda/bin:$PATH"
-  - conda config --append channels conda-forge
-  - conda config --set always_yes yes
-  - conda config --set anaconda_upload no
-
-install:
-  - conda update -q conda
-  - conda install -q python=$TRAVIS_PYTHON_VERSION conda-build anaconda-client
-
-script:
-  - conda build conda-recipe
-
-deploy:
-  provider: script
-  script: anaconda -t $ANACONDA_TOKEN upload $HOME/miniconda/conda-bld/**/pyzebra-*.tar.bz2
-  on:
-    branch: master
-    tags: true
-
-notifications:
-  email: false

.vscode/launch.json

@@ -5,9 +5,10 @@
             "name": "pyzebra",
             "type": "python",
             "request": "launch",
-            "program": "${workspaceFolder}/pyzebra/cli.py",
+            "program": "${workspaceFolder}/pyzebra/app/cli.py",
             "console": "internalConsole",
             "env": {},
+            "justMyCode": false,
         },
     ]
 }

conda-recipe/bld.bat

@@ -0,0 +1,2 @@
+"%PYTHON%" setup.py install --single-version-externally-managed --record=record.txt
+if errorlevel 1 exit 1

conda-recipe/meta.yaml

@@ -8,27 +8,27 @@ source:
   path: ..
 
 build:
+  noarch: python
   number: 0
   entry_points:
-    - pyzebra = pyzebra.cli:main
+    - pyzebra = pyzebra.app.cli:main
 
 requirements:
   build:
-    - python
+    - python >=3.8
     - setuptools
   run:
-    - python
+    - python >=3.8
     - numpy
     - scipy
     - h5py
-    - bokeh
+    - bokeh =2.4
     - numba
-    - lmfit
-    - uncertainties
+    - 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

@@ -3,17 +3,23 @@
 import argparse
 import os
 import re
+import subprocess
 
 
 def main():
-    filepath = "pyzebra/__init__.py"
+    default_branch = "main"
+    branch = subprocess.check_output("git rev-parse --abbrev-ref HEAD", shell=True).decode().strip()
+    if branch != default_branch:
+        print(f"Aborting, not on '{default_branch}' branch.")
+        return
+
+    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"])
-    parser.add_argument("tag_msg", type=str, help="tag message")
     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)
@@ -31,11 +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 tag -a {new_version} -m '{args.tag_msg}'")
+    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

@@ -1,10 +1,9 @@
-import pyzebra.ccl_dict_operation
 from pyzebra.anatric import *
-from pyzebra.ccl_findpeaks import ccl_findpeaks
-from pyzebra.comm_export import export_comm
-from pyzebra.fit2 import fitccl
+from pyzebra.ccl_io import *
+from pyzebra.ccl_process import *
 from pyzebra.h5 import *
-from pyzebra.load_1D import load_1D, parse_1D
+from pyzebra.sxtal_refgen import *
+from pyzebra.utils import *
 from pyzebra.xtal import *
 
-__version__ = "0.1.1"
+__version__ = "0.7.11"

pyzebra/anatric.py

@@ -1,13 +1,11 @@
+import logging
 import subprocess
 import xml.etree.ElementTree as ET
 
+logger = logging.getLogger(__name__)
+
+DATA_FACTORY_IMPLEMENTATION = ["trics", "morph", "d10"]
 
-ANATRIC_PATH = "/afs/psi.ch/project/sinq/rhel7/bin/anatric"
-DATA_FACTORY_IMPLEMENTATION = [
-    "trics",
-    "morph",
-    "d10",
-]
 REFLECTION_PRINTER_FORMATS = [
     "rafin",
     "rafinf",
@@ -21,11 +19,21 @@ REFLECTION_PRINTER_FORMATS = [
     "oksana",
 ]
 
+ANATRIC_PATH = "/afs/psi.ch/project/sinq/rhel8/bin/anatric"
 ALGORITHMS = ["adaptivemaxcog", "adaptivedynamic"]
 
 
-def anatric(config_file):
-    subprocess.run([ANATRIC_PATH, config_file], check=True)
+def anatric(config_file, anatric_path=ANATRIC_PATH, cwd=None, log=logger):
+    comp_proc = subprocess.run(
+        [anatric_path, config_file],
+        stdout=subprocess.PIPE,
+        stderr=subprocess.STDOUT,
+        cwd=cwd,
+        check=True,
+        text=True,
+    )
+    log.info(" ".join(comp_proc.args))
+    log.info(comp_proc.stdout)
 
 
 class AnatricConfig:
@@ -52,10 +60,13 @@ class AnatricConfig:
     def save_as(self, filename):
         self._tree.write(filename)
 
+    def tostring(self):
+        return ET.tostring(self._tree.getroot(), encoding="unicode")
+
     def _get_attr(self, name, tag, attr):
         elem = self._tree.find(name).find(tag)
         if elem is None:
-            return None
+            return ""
         return elem.attrib[attr]
 
     def _set_attr(self, name, tag, attr, value):
@@ -218,7 +229,7 @@ class AnatricConfig:
         elem = self._tree.find("crystal").find("UB")
         if elem is not None:
             return elem.text
-        return None
+        return ""
 
     @crystal_UB.setter
     def crystal_UB(self, value):
@@ -237,12 +248,37 @@ class AnatricConfig:
 
     @property
     def dataFactory_dist1(self):
-        return self._tree.find("DataFactory").find("dist1").attrib["value"]
+        elem = self._tree.find("DataFactory").find("dist1")
+        if elem is not None:
+            return elem.attrib["value"]
+        return ""
 
     @dataFactory_dist1.setter
     def dataFactory_dist1(self, value):
         self._tree.find("DataFactory").find("dist1").attrib["value"] = value
 
+    @property
+    def dataFactory_dist2(self):
+        elem = self._tree.find("DataFactory").find("dist2")
+        if elem is not None:
+            return elem.attrib["value"]
+        return ""
+
+    @dataFactory_dist2.setter
+    def dataFactory_dist2(self, value):
+        self._tree.find("DataFactory").find("dist2").attrib["value"] = value
+
+    @property
+    def dataFactory_dist3(self):
+        elem = self._tree.find("DataFactory").find("dist3")
+        if elem is not None:
+            return elem.attrib["value"]
+        return ""
+
+    @dataFactory_dist3.setter
+    def dataFactory_dist3(self, value):
+        self._tree.find("DataFactory").find("dist3").attrib["value"] = value
+
     @property
     def reflectionPrinter_format(self):
         return self._tree.find("ReflectionPrinter").attrib["format"]
@@ -254,6 +290,14 @@ class AnatricConfig:
 
         self._tree.find("ReflectionPrinter").attrib["format"] = value
 
+    @property
+    def reflectionPrinter_file(self):
+        return self._tree.find("ReflectionPrinter").attrib["file"]
+
+    @reflectionPrinter_file.setter
+    def reflectionPrinter_file(self, value):
+        self._tree.find("ReflectionPrinter").attrib["file"] = value
+
     @property
     def algorithm(self):
         return self._tree.find("Algorithm").attrib["implementation"]
@@ -270,7 +314,7 @@ class AnatricConfig:
     def _get_alg_attr(self, alg, tag, attr):
         param_elem = self._alg_elems[alg].find(tag)
         if param_elem is None:
-            return None
+            return ""
         return param_elem.attrib[attr]
 
     def _set_alg_attr(self, alg, tag, attr, value):

pyzebra/app/__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

@@ -1,51 +0,0 @@
-import argparse
-import logging
-import sys
-from io import StringIO
-
-from bokeh.io import curdoc
-from bokeh.layouts import column, row
-from bokeh.models import Tabs, TextAreaInput
-
-import panel_ccl_integrate
-import panel_hdf_anatric
-import panel_hdf_viewer
-
-parser = argparse.ArgumentParser(
-    prog="pyzebra", formatter_class=argparse.ArgumentDefaultsHelpFormatter
-)
-
-args = parser.parse_args()
-
-doc = curdoc()
-doc.title = "pyzebra"
-
-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)
-
-# Final layout
-tab_hdf_viewer = panel_hdf_viewer.create()
-tab_hdf_anatric = panel_hdf_anatric.create()
-tab_ccl_integrate = panel_ccl_integrate.create()
-
-doc.add_root(
-    column(
-        Tabs(tabs=[tab_hdf_viewer, tab_hdf_anatric, tab_ccl_integrate]),
-        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

@@ -0,0 +1,12 @@
+import os
+import subprocess
+import sys
+
+
+def main():
+    app_path = os.path.dirname(os.path.abspath(__file__))
+    subprocess.run(["bokeh", "serve", app_path, *sys.argv[1:]], check=True)
+
+
+if __name__ == "__main__":
+    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/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

@@ -1,548 +1,367 @@
-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 (
-    Asterisk,
-    BasicTicker,
     Button,
+    CellEditor,
+    CheckboxEditor,
     ColumnDataSource,
-    CustomJS,
-    DataRange1d,
     DataTable,
     Div,
-    FileInput,
-    Grid,
-    Line,
-    LinearAxis,
     Panel,
-    Plot,
-    RadioButtonGroup,
-    Scatter,
     Select,
     Spacer,
     Span,
-    Spinner,
     TableColumn,
     TextAreaInput,
-    TextInput,
-    Toggle,
     Whisker,
 )
+from bokeh.plotting import figure
 
 import pyzebra
-
-
-javaScript = """
-setTimeout(function() {
-    const filename = 'output' + js_data.data['ext']
-    const blob = new Blob([js_data.data['cont']], {type: 'text/plain'})
-    const link = document.createElement('a');
-    document.body.appendChild(link);
-    const url = window.URL.createObjectURL(blob);
-    link.href = url;
-    link.download = filename;
-    link.click();
-    window.URL.revokeObjectURL(url);
-    document.body.removeChild(link);
-}, 500);
-"""
-
-PROPOSAL_PATH = "/afs/psi.ch/project/sinqdata/2020/zebra/"
+from pyzebra import EXPORT_TARGETS, app
 
 
 def create():
-    det_data = {}
-    peak_pos_textinput_lock = False
-    js_data = ColumnDataSource(data=dict(cont=[], ext=[]))
+    doc = curdoc()
+    log = doc.logger
+    dataset = []
+    app_dlfiles = app.DownloadFiles(n_files=2)
 
-    def proposal_textinput_callback(_attr, _old, new):
-        ccl_path = os.path.join(PROPOSAL_PATH, new)
-        ccl_file_list = []
-        for file in os.listdir(ccl_path):
-            if file.endswith(".ccl"):
-                ccl_file_list.append((os.path.join(ccl_path, file), file))
-        ccl_file_select.options = ccl_file_list
-        ccl_file_select.value = ccl_file_list[0][0]
+    def _init_datatable():
+        scan_list = [s["idx"] for s in dataset]
+        hkl = [f'{s["h"]} {s["k"]} {s["l"]}' for s in dataset]
+        export = [s["export"] for s in dataset]
 
-    proposal_textinput = TextInput(title="Enter proposal number:", default_size=145)
-    proposal_textinput.on_change("value", proposal_textinput_callback)
+        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]
 
-    def ccl_file_select_callback(_attr, _old, new):
-        nonlocal det_data
-        with open(new) as file:
-            _, ext = os.path.splitext(new)
-            det_data = pyzebra.parse_1D(file, ext)
-
-        scan_list = list(det_data["scan"].keys())
-        hkl = [
-            f'{int(m["h_index"])} {int(m["k_index"])} {int(m["l_index"])}'
-            for m in det_data["scan"].values()
-        ]
         scan_table_source.data.update(
-            scan=scan_list, hkl=hkl, peaks=[0] * len(scan_list), fit=[0] * len(scan_list)
+            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]
 
-    ccl_file_select = Select(title="Available .ccl files")
-    ccl_file_select.on_change("value", ccl_file_select_callback)
-
-    def upload_button_callback(_attr, _old, new):
-        nonlocal det_data
-        with io.StringIO(base64.b64decode(new).decode()) as file:
-            _, ext = os.path.splitext(upload_button.filename)
-            det_data = pyzebra.parse_1D(file, ext)
-
-        scan_list = list(det_data["scan"].keys())
-        hkl = [
-            f'{int(m["h_index"])} {int(m["k_index"])} {int(m["l_index"])}'
-            for m in det_data["scan"].values()
-        ]
-        scan_table_source.data.update(
-            scan=scan_list, hkl=hkl, peaks=[0] * len(scan_list), fit=[0] * len(scan_list)
-        )
-        scan_table_source.selected.indices = []
-        scan_table_source.selected.indices = [0]
-
-    upload_button = FileInput(accept=".ccl")
-    upload_button.on_change("value", upload_button_callback)
+        merge_options = [(str(i), f"{i} ({idx})") for i, idx in enumerate(scan_list)]
+        merge_from_select.options = merge_options
+        merge_from_select.value = merge_options[0][0]
 
     def _update_table():
-        num_of_peaks = [scan.get("num_of_peaks", 0) for scan in det_data["scan"].values()]
-        fit_ok = [(1 if "fit" in scan else 0) for scan in det_data["scan"].values()]
-        scan_table_source.data.update(peaks=num_of_peaks, fit=fit_ok)
+        fit_ok = [(1 if "fit" in scan else 0) for scan in dataset]
+        export = [scan["export"] for scan in dataset]
+        scan_table_source.data.update(fit=fit_ok, export=export)
 
-    def _update_plot(ind):
-        nonlocal peak_pos_textinput_lock
-        peak_pos_textinput_lock = True
+    def _update_plot():
+        scan = _get_selected_scan()
+        scan_motor = scan["scan_motor"]
 
-        scan = det_data["scan"][ind]
-        y = scan["Counts"]
-        x = scan["om"]
+        y = scan["counts"]
+        y_err = scan["counts_err"]
+        x = scan[scan_motor]
 
-        plot_scatter_source.data.update(x=x, y=y, y_upper=y + np.sqrt(y), y_lower=y - np.sqrt(y))
-
-        num_of_peaks = scan.get("num_of_peaks")
-        if num_of_peaks is not None and num_of_peaks > 0:
-            peak_indexes = scan["peak_indexes"]
-            if len(peak_indexes) == 1:
-                peak_pos_textinput.value = str(scan["om"][peak_indexes[0]])
-            else:
-                peak_pos_textinput.value = str([scan["om"][ind] for ind in peak_indexes])
-
-            plot_peak_source.data.update(x=scan["om"][peak_indexes], y=scan["peak_heights"])
-            plot_line_smooth_source.data.update(x=x, y=scan["smooth_peaks"])
-        else:
-            peak_pos_textinput.value = None
-            plot_peak_source.data.update(x=[], y=[])
-            plot_line_smooth_source.data.update(x=[], y=[])
-
-        peak_pos_textinput_lock = False
+        plot.axis[0].axis_label = scan_motor
+        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:
-            plot_gauss_source.data.update(x=x, y=scan["fit"]["comps"]["gaussian"])
-            plot_bkg_source.data.update(x=x, y=scan["fit"]["comps"]["background"])
-            params = fit["result"].params
-            fit_output_textinput.value = (
-                "%s \n"
-                "Gaussian: centre = %9.4f, sigma = %9.4f, area = %9.4f \n"
-                "background: slope = %9.4f, intercept = %9.4f \n"
-                "Int. area = %9.4f +/- %9.4f \n"
-                "fit area = %9.4f +/- %9.4f \n"
-                "ratio((fit-int)/fit) = %9.4f"
-                % (
-                    ind,
-                    params["g_cen"].value,
-                    params["g_width"].value,
-                    params["g_amp"].value,
-                    params["slope"].value,
-                    params["intercept"].value,
-                    fit["int_area"].n,
-                    fit["int_area"].s,
-                    params["g_amp"].value,
-                    params["g_amp"].stderr,
-                    (params["g_amp"].value - fit["int_area"].n) / params["g_amp"].value,
-                )
-            )
-            numfit_min, numfit_max = fit["numfit"]
-            if numfit_min is None:
-                numfit_min_span.location = None
-            else:
-                numfit_min_span.location = x[numfit_min]
+            x_fit = np.linspace(x[0], x[-1], 100)
+            fit_source.data.update(x=x_fit, y=fit.eval(x=x_fit))
 
-            if numfit_max is None:
-                numfit_max_span.location = None
-            else:
-                numfit_max_span.location = x[numfit_max]
+            x_bkg = []
+            y_bkg = []
+            xs_peak = []
+            ys_peak = []
+            comps = fit.eval_components(x=x_fit)
+            for i, model in enumerate(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)
 
         else:
-            plot_gauss_source.data.update(x=[], y=[])
-            plot_bkg_source.data.update(x=[], y=[])
-            fit_output_textinput.value = ""
-            numfit_min_span.location = None
-            numfit_max_span.location = None
+            fit_source.data.update(x=[], y=[])
+            bkg_source.data.update(x=[], y=[])
+            peak_source.data.update(xs=[], ys=[])
+
+        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(
-        x_range=DataRange1d(),
-        y_range=DataRange1d(),
-        plot_height=400,
-        plot_width=700,
-        toolbar_location=None,
+    plot = figure(
+        x_axis_label="Scan motor",
+        y_axis_label="Counts",
+        height=470,
+        width=700,
+        tools="pan,wheel_zoom,reset",
     )
 
-    plot.add_layout(LinearAxis(axis_label="Counts"), place="left")
-    plot.add_layout(LinearAxis(axis_label="Omega"), place="below")
-
-    plot.add_layout(Grid(dimension=0, ticker=BasicTicker()))
-    plot.add_layout(Grid(dimension=1, ticker=BasicTicker()))
-
-    plot_scatter_source = ColumnDataSource(dict(x=[0], y=[0], y_upper=[0], y_lower=[0]))
-    plot.add_glyph(plot_scatter_source, Scatter(x="x", y="y", line_color="steelblue"))
-    plot.add_layout(Whisker(source=plot_scatter_source, base="x", upper="y_upper", lower="y_lower"))
-
-    plot_line_smooth_source = ColumnDataSource(dict(x=[0], y=[0]))
-    plot.add_glyph(
-        plot_line_smooth_source, Line(x="x", y="y", line_color="steelblue", line_dash="dashed")
+    scatter_source = ColumnDataSource(dict(x=[0], y=[0], y_upper=[0], y_lower=[0]))
+    plot.circle(
+        source=scatter_source, line_color="steelblue", fill_color="steelblue", legend_label="data"
     )
+    plot.add_layout(Whisker(source=scatter_source, base="x", upper="y_upper", lower="y_lower"))
 
-    plot_gauss_source = ColumnDataSource(dict(x=[0], y=[0]))
-    plot.add_glyph(plot_gauss_source, Line(x="x", y="y", line_color="red", line_dash="dashed"))
+    fit_source = ColumnDataSource(dict(x=[0], y=[0]))
+    plot.line(source=fit_source, legend_label="best fit")
 
-    plot_bkg_source = ColumnDataSource(dict(x=[0], y=[0]))
-    plot.add_glyph(plot_bkg_source, Line(x="x", y="y", line_color="green", line_dash="dashed"))
+    bkg_source = ColumnDataSource(dict(x=[0], y=[0]))
+    plot.line(source=bkg_source, line_color="green", line_dash="dashed", legend_label="linear")
 
-    plot_peak_source = ColumnDataSource(dict(x=[], y=[]))
-    plot.add_glyph(plot_peak_source, Asterisk(x="x", y="y", size=10, line_color="red"))
+    peak_source = ColumnDataSource(dict(xs=[[0]], ys=[[0]]))
+    plot.multi_line(source=peak_source, line_color="red", line_dash="dashed", legend_label="peak")
 
-    numfit_min_span = Span(location=None, dimension="height", line_dash="dashed")
-    plot.add_layout(numfit_min_span)
+    fit_from_span = Span(location=None, dimension="height", line_dash="dashed")
+    plot.add_layout(fit_from_span)
 
-    numfit_max_span = Span(location=None, dimension="height", line_dash="dashed")
-    plot.add_layout(numfit_max_span)
+    fit_to_span = Span(location=None, dimension="height", line_dash="dashed")
+    plot.add_layout(fit_to_span)
+
+    plot.y_range.only_visible = True
+    plot.toolbar.logo = None
+    plot.legend.click_policy = "hide"
 
     # Scan select
-    def scan_table_callback(_attr, _old, new):
-        if new:
-            _update_plot(scan_table_source.data["scan"][new[-1]])
+    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 scan, export in zip(dataset, new["export"]):
+            scan["export"] = export
+        _update_preview()
+
+    scan_table_source = ColumnDataSource(
+        dict(
+            scan=[],
+            hkl=[],
+            fit=[],
+            export=[],
+            twotheta=[],
+            gamma=[],
+            omega=[],
+            chi=[],
+            phi=[],
+            nu=[],
+        )
+    )
+    scan_table_source.on_change("data", scan_table_source_callback)
+    scan_table_source.selected.on_change("indices", scan_table_select_callback)
 
-    scan_table_source = ColumnDataSource(dict(scan=[], hkl=[], peaks=[], fit=[]))
     scan_table = DataTable(
         source=scan_table_source,
         columns=[
-            TableColumn(field="scan", title="scan"),
-            TableColumn(field="hkl", title="hkl"),
-            TableColumn(field="peaks", title="Peaks"),
-            TableColumn(field="fit", title="Fit"),
+            TableColumn(field="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=200,
-        index_position=None,
+        width=310,  # +60 because of the index column, but excluding twotheta onwards
+        height=350,
+        autosize_mode="none",
+        editable=True,
     )
 
-    scan_table_source.selected.on_change("indices", scan_table_callback)
+    def _get_selected_scan():
+        return dataset[scan_table_source.selected.indices[0]]
 
-    def peak_pos_textinput_callback(_attr, _old, new):
-        if new is not None and not peak_pos_textinput_lock:
-            sel_ind = scan_table_source.selected.indices[-1]
-            scan_name = scan_table_source.data["scan"][sel_ind]
-            scan = det_data["scan"][scan_name]
+    merge_from_select = Select(title="scan:", width=145)
 
-            scan["num_of_peaks"] = 1
-            peak_ind = (np.abs(scan["om"] - float(new))).argmin()
-            scan["peak_indexes"] = np.array([peak_ind], dtype=np.int64)
-            scan["peak_heights"] = np.array([scan["smooth_peaks"][peak_ind]])
-            _update_table()
-            _update_plot(scan_name)
+    def merge_button_callback():
+        scan_into = _get_selected_scan()
+        scan_from = dataset[int(merge_from_select.value)]
 
-    peak_pos_textinput = TextInput(title="Peak position:", default_size=145)
-    peak_pos_textinput.on_change("value", peak_pos_textinput_callback)
+        if scan_into is scan_from:
+            log.warning("Selected scans for merging are identical")
+            return
 
-    peak_int_ratio_spinner = Spinner(
-        title="Peak intensity ratio:", value=0.8, step=0.01, low=0, high=1, default_size=145
-    )
-    peak_prominence_spinner = Spinner(title="Peak prominence:", value=50, low=0, default_size=145)
-    smooth_toggle = Toggle(label="Smooth curve", default_size=145)
-    window_size_spinner = Spinner(title="Window size:", value=7, step=2, low=1, default_size=145)
-    poly_order_spinner = Spinner(title="Poly order:", value=3, low=0, default_size=145)
+        pyzebra.merge_scans(scan_into, scan_from, log=log)
+        _update_table()
+        _update_plot()
 
-    centre_guess = Spinner(default_size=100)
-    centre_vary = Toggle(default_size=100, active=True)
-    centre_min = Spinner(default_size=100)
-    centre_max = Spinner(default_size=100)
-    sigma_guess = Spinner(default_size=100)
-    sigma_vary = Toggle(default_size=100, active=True)
-    sigma_min = Spinner(default_size=100)
-    sigma_max = Spinner(default_size=100)
-    ampl_guess = Spinner(default_size=100)
-    ampl_vary = Toggle(default_size=100, active=True)
-    ampl_min = Spinner(default_size=100)
-    ampl_max = Spinner(default_size=100)
-    slope_guess = Spinner(default_size=100)
-    slope_vary = Toggle(default_size=100, active=True)
-    slope_min = Spinner(default_size=100)
-    slope_max = Spinner(default_size=100)
-    offset_guess = Spinner(default_size=100)
-    offset_vary = Toggle(default_size=100, active=True)
-    offset_min = Spinner(default_size=100)
-    offset_max = Spinner(default_size=100)
-    integ_from = Spinner(title="Integrate from:", default_size=145)
-    integ_to = Spinner(title="to:", default_size=145)
+    merge_button = Button(label="Merge into current", width=145)
+    merge_button.on_click(merge_button_callback)
 
-    def fitparam_reset_button_callback():
-        centre_guess.value = None
-        centre_vary.active = True
-        centre_min.value = None
-        centre_max.value = None
-        sigma_guess.value = None
-        sigma_vary.active = True
-        sigma_min.value = None
-        sigma_max.value = None
-        ampl_guess.value = None
-        ampl_vary.active = True
-        ampl_min.value = None
-        ampl_max.value = None
-        slope_guess.value = None
-        slope_vary.active = True
-        slope_min.value = None
-        slope_max.value = None
-        offset_guess.value = None
-        offset_vary.active = True
-        offset_min.value = None
-        offset_max.value = None
-        integ_from.value = None
-        integ_to.value = None
+    def restore_button_callback():
+        pyzebra.restore_scan(_get_selected_scan())
+        _update_table()
+        _update_plot()
 
-    fitparam_reset_button = Button(label="Reset to defaults", default_size=145)
-    fitparam_reset_button.on_click(fitparam_reset_button_callback)
+    restore_button = Button(label="Restore scan", width=145)
+    restore_button.on_click(restore_button_callback)
 
-    fit_output_textinput = TextAreaInput(title="Fit results:", width=450, height=400)
+    app_fitctrl = app.FitControls()
 
-    def peakfind_all_button_callback():
-        for scan in det_data["scan"].values():
-            pyzebra.ccl_findpeaks(
-                scan,
-                int_threshold=peak_int_ratio_spinner.value,
-                prominence=peak_prominence_spinner.value,
-                smooth=smooth_toggle.active,
-                window_size=window_size_spinner.value,
-                poly_order=poly_order_spinner.value,
-            )
+    def 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(dataset)
+
+        _update_plot()
         _update_table()
 
-        sel_ind = scan_table_source.selected.indices[-1]
-        _update_plot(scan_table_source.data["scan"][sel_ind])
+    proc_all_button = Button(label="Process All", button_type="primary", width=145)
+    proc_all_button.on_click(proc_all_button_callback)
 
-    peakfind_all_button = Button(label="Peak Find All", button_type="primary", default_size=145)
-    peakfind_all_button.on_click(peakfind_all_button_callback)
+    def proc_button_callback():
+        app_fitctrl.fit_scan(_get_selected_scan())
 
-    def peakfind_button_callback():
-        sel_ind = scan_table_source.selected.indices[-1]
-        scan = scan_table_source.data["scan"][sel_ind]
-        pyzebra.ccl_findpeaks(
-            det_data["scan"][scan],
-            int_threshold=peak_int_ratio_spinner.value,
-            prominence=peak_prominence_spinner.value,
-            smooth=smooth_toggle.active,
-            window_size=window_size_spinner.value,
-            poly_order=poly_order_spinner.value,
-        )
-
-        _update_table()
-        _update_plot(scan)
-
-    peakfind_button = Button(label="Peak Find Current", default_size=145)
-    peakfind_button.on_click(peakfind_button_callback)
-
-    def fit_all_button_callback():
-        for scan in det_data["scan"].values():
-            pyzebra.fitccl(
-                scan,
-                guess=[
-                    centre_guess.value,
-                    sigma_guess.value,
-                    ampl_guess.value,
-                    slope_guess.value,
-                    offset_guess.value,
-                ],
-                vary=[
-                    centre_vary.active,
-                    sigma_vary.active,
-                    ampl_vary.active,
-                    slope_vary.active,
-                    offset_vary.active,
-                ],
-                constraints_min=[
-                    centre_min.value,
-                    sigma_min.value,
-                    ampl_min.value,
-                    slope_min.value,
-                    offset_min.value,
-                ],
-                constraints_max=[
-                    centre_max.value,
-                    sigma_max.value,
-                    ampl_max.value,
-                    slope_max.value,
-                    offset_max.value,
-                ],
-                numfit_min=integ_from.value,
-                numfit_max=integ_to.value,
-            )
-
-        sel_ind = scan_table_source.selected.indices[-1]
-        _update_plot(scan_table_source.data["scan"][sel_ind])
+        _update_plot()
         _update_table()
 
-    fit_all_button = Button(label="Fit All", button_type="primary", default_size=145)
-    fit_all_button.on_click(fit_all_button_callback)
+    proc_button = Button(label="Process Current", width=145)
+    proc_button.on_click(proc_button_callback)
 
-    def fit_button_callback():
-        sel_ind = scan_table_source.selected.indices[-1]
-        scan = scan_table_source.data["scan"][sel_ind]
-
-        pyzebra.fitccl(
-            det_data["scan"][scan],
-            guess=[
-                centre_guess.value,
-                sigma_guess.value,
-                ampl_guess.value,
-                slope_guess.value,
-                offset_guess.value,
-            ],
-            vary=[
-                centre_vary.active,
-                sigma_vary.active,
-                ampl_vary.active,
-                slope_vary.active,
-                offset_vary.active,
-            ],
-            constraints_min=[
-                centre_min.value,
-                sigma_min.value,
-                ampl_min.value,
-                slope_min.value,
-                offset_min.value,
-            ],
-            constraints_max=[
-                centre_max.value,
-                sigma_max.value,
-                ampl_max.value,
-                slope_max.value,
-                offset_max.value,
-            ],
-            numfit_min=integ_from.value,
-            numfit_max=integ_to.value,
-        )
-
-        _update_plot(scan)
-        _update_table()
-
-    fit_button = Button(label="Fit Current", default_size=145)
-    fit_button.on_click(fit_button_callback)
-
-    def area_method_radiobutton_callback(_attr, _old, new):
-        det_data["meta"]["area_method"] = ("fit", "integ")[new]
-
-    area_method_radiobutton = RadioButtonGroup(
-        labels=["Fit", "Integral"], active=0, default_size=145
-    )
-    area_method_radiobutton.on_change("active", area_method_radiobutton_callback)
-
-    preview_output_textinput = TextAreaInput(title="Export file preview:", width=450, height=400)
-
-    def preview_output_button_callback():
-        if det_data["meta"]["indices"] == "hkl":
-            ext = ".comm"
-        elif det_data["meta"]["indices"] == "real":
-            ext = ".incomm"
+    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"
-            pyzebra.export_comm(det_data, temp_file)
+            export_data = []
+            for scan in dataset:
+                if scan["export"]:
+                    export_data.append(scan)
 
-            with open(f"{temp_file}{ext}") as f:
-                preview_output_textinput.value = f.read()
+            pyzebra.export_1D(
+                export_data,
+                temp_file,
+                export_target_select.value,
+                hkl_precision=int(hkl_precision_select.value),
+            )
 
-    preview_output_button = Button(label="Preview file", default_size=220)
-    preview_output_button.on_click(preview_output_button_callback)
+            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)
 
-    def export_results(det_data):
-        if det_data["meta"]["indices"] == "hkl":
-            ext = ".comm"
-        elif det_data["meta"]["indices"] == "real":
-            ext = ".incomm"
+            app_dlfiles.set_contents(file_content)
+            export_preview_textinput.value = exported_content
 
-        with tempfile.TemporaryDirectory() as temp_dir:
-            temp_file = temp_dir + "/temp"
-            pyzebra.export_comm(det_data, temp_file)
+    def export_target_select_callback(_attr, _old, new):
+        app_dlfiles.set_extensions(EXPORT_TARGETS[new])
+        _update_preview()
 
-            with open(f"{temp_file}{ext}") as f:
-                output_content = f.read()
-
-        return output_content, ext
-
-    def save_button_callback():
-        cont, ext = export_results(det_data)
-        js_data.data.update(cont=[cont], ext=[ext])
-
-    save_button = Button(label="Download file", button_type="success", default_size=220)
-    save_button.on_click(save_button_callback)
-    save_button.js_on_click(CustomJS(args={"js_data": js_data}, code=javaScript))
-
-    findpeak_controls = column(
-        row(peak_pos_textinput, column(Spacer(height=19), smooth_toggle)),
-        row(peak_int_ratio_spinner, peak_prominence_spinner),
-        row(window_size_spinner, poly_order_spinner),
-        row(peakfind_button, peakfind_all_button),
+    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])
 
-    div_1 = Div(text="Guess:")
-    div_2 = Div(text="Vary:")
-    div_3 = Div(text="Min:")
-    div_4 = Div(text="Max:")
-    div_5 = Div(text="Gauss Centre:", margin=[5, 5, -5, 5])
-    div_6 = Div(text="Gauss Sigma:", margin=[5, 5, -5, 5])
-    div_7 = Div(text="Gauss Ampl.:", margin=[5, 5, -5, 5])
-    div_8 = Div(text="Slope:", margin=[5, 5, -5, 5])
-    div_9 = Div(text="Offset:", margin=[5, 5, -5, 5])
+    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(
-            Spacer(height=36),
-            div_1,
-            Spacer(height=12),
-            div_2,
-            Spacer(height=12),
-            div_3,
-            Spacer(height=12),
-            div_4,
+            app_fitctrl.add_function_button,
+            app_fitctrl.function_select,
+            app_fitctrl.remove_function_button,
         ),
-        column(div_5, centre_guess, centre_vary, centre_min, centre_max),
-        column(div_6, sigma_guess, sigma_vary, sigma_min, sigma_max),
-        column(div_7, ampl_guess, ampl_vary, ampl_min, ampl_max),
-        column(div_8, slope_guess, slope_vary, slope_min, slope_max),
-        column(div_9, offset_guess, offset_vary, offset_min, offset_max),
+        app_fitctrl.params_table,
         Spacer(width=20),
         column(
-            row(integ_from, integ_to),
-            row(fitparam_reset_button, area_method_radiobutton),
-            row(fit_button, fit_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(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(
+        app_inputctrl.filelist_select,
+        row(app_inputctrl.open_button, app_inputctrl.append_button),
+        upload_div,
+        app_inputctrl.upload_button,
+        append_upload_div,
+        app_inputctrl.append_upload_button,
+    )
+
+    export_layout = column(
+        export_preview_textinput,
+        row(
+            export_target_select,
+            hkl_precision_select,
+            column(Spacer(height=19), row(app_dlfiles.button)),
         ),
     )
 
-    export_layout = column(preview_output_textinput, row(preview_output_button, save_button))
-
-    upload_div = Div(text="Or upload .ccl file:")
     tab_layout = column(
-        row(proposal_textinput, ccl_file_select),
-        row(column(Spacer(height=5), upload_div), upload_button),
-        row(scan_table, plot, Spacer(width=30), fit_output_textinput, export_layout),
-        row(findpeak_controls, Spacer(width=30), fitpeak_controls),
+        row(import_layout, scan_layout, plot, Spacer(width=30), export_layout),
+        row(fitpeak_controls, 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

@@ -1,5 +1,6 @@
 import base64
 import io
+import os
 import re
 import tempfile
 
@@ -10,24 +11,27 @@ from bokeh.models import (
     Div,
     FileInput,
     Panel,
-    RadioButtonGroup,
     Select,
+    Spacer,
+    Tabs,
     TextAreaInput,
     TextInput,
 )
 
 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):
         config.load_from_file(file)
 
         logfile_textinput.value = config.logfile
-        logfile_verbosity_select.value = config.logfile_verbosity
+        logfile_verbosity.value = config.logfile_verbosity
 
         filelist_type.value = config.filelist_type
         filelist_format_textinput.value = config.filelist_format
@@ -42,11 +46,16 @@ def create():
         ub_textareainput.value = config.crystal_UB
 
         dataFactory_implementation_select.value = config.dataFactory_implementation
-        dataFactory_dist1_textinput.value = config.dataFactory_dist1
+        if config.dataFactory_dist1 is not None:
+            dataFactory_dist1_textinput.value = config.dataFactory_dist1
+        if config.dataFactory_dist2 is not None:
+            dataFactory_dist2_textinput.value = config.dataFactory_dist2
+        if config.dataFactory_dist3 is not None:
+            dataFactory_dist3_textinput.value = config.dataFactory_dist3
         reflectionPrinter_format_select.value = config.reflectionPrinter_format
 
-        set_active_widgets(config.algorithm)
         if config.algorithm == "adaptivemaxcog":
+            algorithm_params.active = 0
             threshold_textinput.value = config.threshold
             shell_textinput.value = config.shell
             steepness_textinput.value = config.steepness
@@ -55,6 +64,7 @@ def create():
             aps_window_textinput.value = str(tuple(map(int, config.aps_window.values())))
 
         elif config.algorithm == "adaptivedynamic":
+            algorithm_params.active = 1
             adm_window_textinput.value = str(tuple(map(int, config.adm_window.values())))
             border_textinput.value = str(tuple(map(int, config.border.values())))
             minWindow_textinput.value = str(tuple(map(int, config.minWindow.values())))
@@ -64,46 +74,16 @@ def create():
             loop_textinput.value = config.loop
             minPeakCount_textinput.value = config.minPeakCount
             displacementCurve_textinput.value = "\n".join(map(str, config.displacementCurve))
+
         else:
             raise ValueError("Unknown processing mode.")
 
-    def set_active_widgets(implementation):
-        if implementation == "adaptivemaxcog":
-            mode_radio_button_group.active = 0
-            disable_adaptivemaxcog = False
-            disable_adaptivedynamic = True
-
-        elif implementation == "adaptivedynamic":
-            mode_radio_button_group.active = 1
-            disable_adaptivemaxcog = True
-            disable_adaptivedynamic = False
-        else:
-            raise ValueError("Implementation can be either 'adaptivemaxcog' or 'adaptivedynamic'")
-
-        threshold_textinput.disabled = disable_adaptivemaxcog
-        shell_textinput.disabled = disable_adaptivemaxcog
-        steepness_textinput.disabled = disable_adaptivemaxcog
-        duplicateDistance_textinput.disabled = disable_adaptivemaxcog
-        maxequal_textinput.disabled = disable_adaptivemaxcog
-        aps_window_textinput.disabled = disable_adaptivemaxcog
-
-        adm_window_textinput.disabled = disable_adaptivedynamic
-        border_textinput.disabled = disable_adaptivedynamic
-        minWindow_textinput.disabled = disable_adaptivedynamic
-        reflectionFile_textinput.disabled = disable_adaptivedynamic
-        targetMonitor_textinput.disabled = disable_adaptivedynamic
-        smoothSize_textinput.disabled = disable_adaptivedynamic
-        loop_textinput.disabled = disable_adaptivedynamic
-        minPeakCount_textinput.disabled = disable_adaptivedynamic
-        displacementCurve_textinput.disabled = disable_adaptivedynamic
-
-    upload_div = Div(text="Open XML configuration file:")
-
     def upload_button_callback(_attr, _old, new):
         with io.BytesIO(base64.b64decode(new)) as file:
             _load_config_file(file)
 
-    upload_button = FileInput(accept=".xml")
+    upload_div = Div(text="Open .xml config:")
+    upload_button = FileInput(accept=".xml", width=200)
     upload_button.on_change("value", upload_button_callback)
 
     # General parameters
@@ -111,16 +91,14 @@ def create():
     def logfile_textinput_callback(_attr, _old, new):
         config.logfile = new
 
-    logfile_textinput = TextInput(title="Logfile:", value="logfile.log", width=520)
+    logfile_textinput = TextInput(title="Logfile:", value="logfile.log")
     logfile_textinput.on_change("value", logfile_textinput_callback)
 
-    def logfile_verbosity_select_callback(_attr, _old, new):
+    def logfile_verbosity_callback(_attr, _old, new):
         config.logfile_verbosity = new
 
-    logfile_verbosity_select = Select(
-        title="verbosity:", options=["0", "5", "10", "15", "30"], width=70
-    )
-    logfile_verbosity_select.on_change("value", logfile_verbosity_select_callback)
+    logfile_verbosity = TextInput(title="verbosity:", width=70)
+    logfile_verbosity.on_change("value", logfile_verbosity_callback)
 
     # ---- FileList
     def filelist_type_callback(_attr, _old, new):
@@ -132,7 +110,7 @@ def create():
     def filelist_format_textinput_callback(_attr, _old, new):
         config.filelist_format = new
 
-    filelist_format_textinput = TextInput(title="format:", width=490)
+    filelist_format_textinput = TextInput(title="format:", width=290)
     filelist_format_textinput.on_change("value", filelist_format_textinput_callback)
 
     def filelist_datapath_textinput_callback(_attr, _old, new):
@@ -147,20 +125,20 @@ def create():
             ranges.append(re.findall(r"\b\d+\b", line))
         config.filelist_ranges = ranges
 
-    filelist_ranges_textareainput = TextAreaInput(title="ranges:", height=100)
+    filelist_ranges_textareainput = TextAreaInput(title="ranges:", rows=1)
     filelist_ranges_textareainput.on_change("value", filelist_ranges_textareainput_callback)
 
     # ---- crystal
     def crystal_sample_textinput_callback(_attr, _old, new):
         config.crystal_sample = new
 
-    crystal_sample_textinput = TextInput(title="Sample Name:")
+    crystal_sample_textinput = TextInput(title="Sample Name:", width=290)
     crystal_sample_textinput.on_change("value", crystal_sample_textinput_callback)
 
     def lambda_textinput_callback(_attr, _old, new):
         config.crystal_lambda = new
 
-    lambda_textinput = TextInput(title="lambda:", width=140)
+    lambda_textinput = TextInput(title="lambda:", width=100)
     lambda_textinput.on_change("value", lambda_textinput_callback)
 
     def ub_textareainput_callback(_attr, _old, new):
@@ -172,19 +150,19 @@ def create():
     def zeroOM_textinput_callback(_attr, _old, new):
         config.crystal_zeroOM = new
 
-    zeroOM_textinput = TextInput(title="zeroOM:", width=140)
+    zeroOM_textinput = TextInput(title="zeroOM:", width=100)
     zeroOM_textinput.on_change("value", zeroOM_textinput_callback)
 
     def zeroSTT_textinput_callback(_attr, _old, new):
         config.crystal_zeroSTT = new
 
-    zeroSTT_textinput = TextInput(title="zeroSTT:", width=140)
+    zeroSTT_textinput = TextInput(title="zeroSTT:", width=100)
     zeroSTT_textinput.on_change("value", zeroSTT_textinput_callback)
 
     def zeroCHI_textinput_callback(_attr, _old, new):
         config.crystal_zeroCHI = new
 
-    zeroCHI_textinput = TextInput(title="zeroCHI:", width=140)
+    zeroCHI_textinput = TextInput(title="zeroCHI:", width=100)
     zeroCHI_textinput.on_change("value", zeroCHI_textinput_callback)
 
     # ---- DataFactory
@@ -192,16 +170,28 @@ def create():
         config.dataFactory_implementation = new
 
     dataFactory_implementation_select = Select(
-        title="DataFactory implementation:", options=DATA_FACTORY_IMPLEMENTATION, width=300,
+        title="DataFactory implement.:", options=DATA_FACTORY_IMPLEMENTATION, width=145
     )
     dataFactory_implementation_select.on_change("value", dataFactory_implementation_select_callback)
 
     def dataFactory_dist1_textinput_callback(_attr, _old, new):
         config.dataFactory_dist1 = new
 
-    dataFactory_dist1_textinput = TextInput(title="dist1:", width=290)
+    dataFactory_dist1_textinput = TextInput(title="dist1:", width=75)
     dataFactory_dist1_textinput.on_change("value", dataFactory_dist1_textinput_callback)
 
+    def dataFactory_dist2_textinput_callback(_attr, _old, new):
+        config.dataFactory_dist2 = new
+
+    dataFactory_dist2_textinput = TextInput(title="dist2:", width=75)
+    dataFactory_dist2_textinput.on_change("value", dataFactory_dist2_textinput_callback)
+
+    def dataFactory_dist3_textinput_callback(_attr, _old, new):
+        config.dataFactory_dist3 = new
+
+    dataFactory_dist3_textinput = TextInput(title="dist3:", width=75)
+    dataFactory_dist3_textinput.on_change("value", dataFactory_dist3_textinput_callback)
+
     # ---- BackgroundProcessor
 
     # ---- DetectorEfficency
@@ -211,7 +201,7 @@ def create():
         config.reflectionPrinter_format = new
 
     reflectionPrinter_format_select = Select(
-        title="ReflectionPrinter format:", options=REFLECTION_PRINTER_FORMATS, width=300,
+        title="ReflectionPrinter format:", options=REFLECTION_PRINTER_FORMATS, width=145
     )
     reflectionPrinter_format_select.on_change("value", reflectionPrinter_format_select_callback)
 
@@ -220,42 +210,42 @@ def create():
     def threshold_textinput_callback(_attr, _old, new):
         config.threshold = new
 
-    threshold_textinput = TextInput(title="Threshold:")
+    threshold_textinput = TextInput(title="Threshold:", width=145)
     threshold_textinput.on_change("value", threshold_textinput_callback)
 
     # ---- shell
     def shell_textinput_callback(_attr, _old, new):
         config.shell = new
 
-    shell_textinput = TextInput(title="Shell:")
+    shell_textinput = TextInput(title="Shell:", width=145)
     shell_textinput.on_change("value", shell_textinput_callback)
 
     # ---- steepness
     def steepness_textinput_callback(_attr, _old, new):
         config.steepness = new
 
-    steepness_textinput = TextInput(title="Steepness:")
+    steepness_textinput = TextInput(title="Steepness:", width=145)
     steepness_textinput.on_change("value", steepness_textinput_callback)
 
     # ---- duplicateDistance
     def duplicateDistance_textinput_callback(_attr, _old, new):
         config.duplicateDistance = new
 
-    duplicateDistance_textinput = TextInput(title="Duplicate Distance:")
+    duplicateDistance_textinput = TextInput(title="Duplicate Distance:", width=145)
     duplicateDistance_textinput.on_change("value", duplicateDistance_textinput_callback)
 
     # ---- maxequal
     def maxequal_textinput_callback(_attr, _old, new):
         config.maxequal = new
 
-    maxequal_textinput = TextInput(title="Max Equal:")
+    maxequal_textinput = TextInput(title="Max Equal:", width=145)
     maxequal_textinput.on_change("value", maxequal_textinput_callback)
 
     # ---- window
     def aps_window_textinput_callback(_attr, _old, new):
         config.aps_window = dict(zip(("x", "y", "z"), re.findall(r"\b\d+\b", new)))
 
-    aps_window_textinput = TextInput(title="Window (x, y, z):")
+    aps_window_textinput = TextInput(title="Window (x, y, z):", width=145)
     aps_window_textinput.on_change("value", aps_window_textinput_callback)
 
     # Adaptive Dynamic Mask Integration (adaptivedynamic)
@@ -263,56 +253,56 @@ def create():
     def adm_window_textinput_callback(_attr, _old, new):
         config.adm_window = dict(zip(("x", "y", "z"), re.findall(r"\b\d+\b", new)))
 
-    adm_window_textinput = TextInput(title="Window (x, y, z):")
+    adm_window_textinput = TextInput(title="Window (x, y, z):", width=145)
     adm_window_textinput.on_change("value", adm_window_textinput_callback)
 
     # ---- border
     def border_textinput_callback(_attr, _old, new):
         config.border = dict(zip(("x", "y", "z"), re.findall(r"\b\d+\b", new)))
 
-    border_textinput = TextInput(title="Border (x, y, z):")
+    border_textinput = TextInput(title="Border (x, y, z):", width=145)
     border_textinput.on_change("value", border_textinput_callback)
 
     # ---- minWindow
     def minWindow_textinput_callback(_attr, _old, new):
         config.minWindow = dict(zip(("x", "y", "z"), re.findall(r"\b\d+\b", new)))
 
-    minWindow_textinput = TextInput(title="Min Window (x, y, z):")
+    minWindow_textinput = TextInput(title="Min Window (x, y, z):", width=145)
     minWindow_textinput.on_change("value", minWindow_textinput_callback)
 
     # ---- reflectionFile
     def reflectionFile_textinput_callback(_attr, _old, new):
         config.reflectionFile = new
 
-    reflectionFile_textinput = TextInput(title="Reflection File:")
+    reflectionFile_textinput = TextInput(title="Reflection File:", width=145)
     reflectionFile_textinput.on_change("value", reflectionFile_textinput_callback)
 
     # ---- targetMonitor
     def targetMonitor_textinput_callback(_attr, _old, new):
         config.targetMonitor = new
 
-    targetMonitor_textinput = TextInput(title="Target Monitor:")
+    targetMonitor_textinput = TextInput(title="Target Monitor:", width=145)
     targetMonitor_textinput.on_change("value", targetMonitor_textinput_callback)
 
     # ---- smoothSize
     def smoothSize_textinput_callback(_attr, _old, new):
         config.smoothSize = new
 
-    smoothSize_textinput = TextInput(title="Smooth Size:")
+    smoothSize_textinput = TextInput(title="Smooth Size:", width=145)
     smoothSize_textinput.on_change("value", smoothSize_textinput_callback)
 
     # ---- loop
     def loop_textinput_callback(_attr, _old, new):
         config.loop = new
 
-    loop_textinput = TextInput(title="Loop:")
+    loop_textinput = TextInput(title="Loop:", width=145)
     loop_textinput.on_change("value", loop_textinput_callback)
 
     # ---- minPeakCount
     def minPeakCount_textinput_callback(_attr, _old, new):
         config.minPeakCount = new
 
-    minPeakCount_textinput = TextInput(title="Min Peak Count:")
+    minPeakCount_textinput = TextInput(title="Min Peak Count:", width=145)
     minPeakCount_textinput.on_change("value", minPeakCount_textinput_callback)
 
     # ---- displacementCurve
@@ -323,87 +313,87 @@ def create():
         config.displacementCurve = maps
 
     displacementCurve_textinput = TextAreaInput(
-        title="Displacement Curve (twotheta, x, y):", height=100
+        title="Displ. Curve (2θ, x, y):", width=145, height=100
     )
     displacementCurve_textinput.on_change("value", displacementCurve_textinput_callback)
 
-    def mode_radio_button_group_callback(active):
-        if active == 0:
+    def algorithm_tabs_callback(_attr, _old, new):
+        if new == 0:
             config.algorithm = "adaptivemaxcog"
-            set_active_widgets("adaptivemaxcog")
         else:
             config.algorithm = "adaptivedynamic"
-            set_active_widgets("adaptivedynamic")
 
-    mode_radio_button_group = RadioButtonGroup(
-        labels=["Adaptive Peak Detection", "Adaptive Dynamic Integration"], active=0
+    algorithm_params = Tabs(
+        tabs=[
+            Panel(
+                child=column(
+                    row(threshold_textinput, shell_textinput, steepness_textinput),
+                    row(duplicateDistance_textinput, maxequal_textinput, aps_window_textinput),
+                ),
+                title="Peak Search",
+            ),
+            Panel(
+                child=column(
+                    row(adm_window_textinput, border_textinput, minWindow_textinput),
+                    row(reflectionFile_textinput, targetMonitor_textinput, smoothSize_textinput),
+                    row(loop_textinput, minPeakCount_textinput, displacementCurve_textinput),
+                ),
+                title="Dynamic Integration",
+            ),
+        ]
     )
-    mode_radio_button_group.on_click(mode_radio_button_group_callback)
-    set_active_widgets("adaptivemaxcog")
+    algorithm_params.on_change("active", algorithm_tabs_callback)
 
     def process_button_callback():
         with tempfile.TemporaryDirectory() as temp_dir:
-            temp_file = temp_dir + "/temp.xml"
+            temp_file = temp_dir + "/config.xml"
             config.save_as(temp_file)
-            pyzebra.anatric(temp_file)
+            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(config.logfile) as f_log:
+            with open(os.path.join(temp_dir, config.logfile)) as f_log:
                 output_log.value = f_log.read()
 
+            with open(os.path.join(temp_dir, config.reflectionPrinter_file)) as f_res:
+                output_res.value = f_res.read()
+
     process_button = Button(label="Process", button_type="primary")
     process_button.on_click(process_button_callback)
 
-    output_log = TextAreaInput(title="Logfile output:", height=700, disabled=True)
-    output_config = TextAreaInput(title="Current config:", height=700, width=400, disabled=True)
+    output_log = TextAreaInput(title="Logfile output:", height=320, width=465, disabled=True)
+    output_res = TextAreaInput(title="Result output:", height=320, width=465, disabled=True)
+    output_config = TextAreaInput(title="Current config:", height=320, width=465, disabled=True)
+
+    general_params_layout = column(
+        row(column(Spacer(height=2), upload_div), upload_button),
+        row(logfile_textinput, logfile_verbosity),
+        row(filelist_type, filelist_format_textinput),
+        filelist_datapath_textinput,
+        filelist_ranges_textareainput,
+        row(crystal_sample_textinput, lambda_textinput),
+        ub_textareainput,
+        row(zeroOM_textinput, zeroSTT_textinput, zeroCHI_textinput),
+        row(
+            dataFactory_implementation_select,
+            dataFactory_dist1_textinput,
+            dataFactory_dist2_textinput,
+            dataFactory_dist3_textinput,
+        ),
+        row(reflectionPrinter_format_select),
+    )
 
     tab_layout = row(
-        column(
-            upload_div,
-            upload_button,
-            row(logfile_textinput, logfile_verbosity_select),
-            row(filelist_type, filelist_format_textinput),
-            filelist_datapath_textinput,
-            filelist_ranges_textareainput,
-            crystal_sample_textinput,
-            row(lambda_textinput, zeroOM_textinput, zeroSTT_textinput, zeroCHI_textinput),
-            ub_textareainput,
-            row(dataFactory_implementation_select, dataFactory_dist1_textinput),
-            reflectionPrinter_format_select,
-            process_button,
-        ),
-        column(
-            mode_radio_button_group,
-            row(
-                column(
-                    threshold_textinput,
-                    shell_textinput,
-                    steepness_textinput,
-                    duplicateDistance_textinput,
-                    maxequal_textinput,
-                    aps_window_textinput,
-                ),
-                column(
-                    adm_window_textinput,
-                    border_textinput,
-                    minWindow_textinput,
-                    reflectionFile_textinput,
-                    targetMonitor_textinput,
-                    smoothSize_textinput,
-                    loop_textinput,
-                    minPeakCount_textinput,
-                    displacementCurve_textinput,
-                ),
-            ),
-        ),
-        output_config,
-        output_log,
+        general_params_layout,
+        column(output_config, algorithm_params, row(process_button)),
+        column(output_log, output_res),
     )
 
     async def update_config():
-        config.save_as("debug.xml")
-        with open("debug.xml") as f_config:
-            output_config.value = f_config.read()
+        output_config.value = config.tostring()
 
-    curdoc().add_periodic_callback(update_config, 1000)
+    doc.add_periodic_callback(update_config, 1000)
 
     return Panel(child=tab_layout, title="hdf anatric")

pyzebra/app/panel_hdf_param_study.py

@@ -0,0 +1,518 @@
+import base64
+import io
+import os
+
+import numpy as np
+from bokeh.io import curdoc
+from bokeh.layouts import column, gridplot, row
+from bokeh.models import (
+    Button,
+    CellEditor,
+    CheckboxGroup,
+    ColumnDataSource,
+    DataTable,
+    Div,
+    FileInput,
+    LinearColorMapper,
+    MultiSelect,
+    NumberEditor,
+    NumberFormatter,
+    Panel,
+    Range1d,
+    Select,
+    Spinner,
+    TableColumn,
+    Tabs,
+)
+from bokeh.plotting import figure
+
+import pyzebra
+
+IMAGE_W = 256
+IMAGE_H = 128
+IMAGE_PLOT_W = int(IMAGE_W * 2.4) + 52
+IMAGE_PLOT_H = int(IMAGE_H * 2.4) + 27
+
+
+def create():
+    doc = curdoc()
+    log = doc.logger
+    dataset = []
+    cami_meta = {}
+
+    num_formatter = NumberFormatter(format="0.00", nan_format="")
+
+    def file_select_update():
+        if data_source.value == "proposal number":
+            proposal_path = proposal_textinput.name
+            if proposal_path:
+                file_list = []
+                for file in os.listdir(proposal_path):
+                    if file.endswith(".hdf"):
+                        file_list.append((os.path.join(proposal_path, file), file))
+                file_select.options = file_list
+            else:
+                file_select.options = []
+
+        else:  # "cami file"
+            if not cami_meta:
+                file_select.options = []
+                return
+
+            file_list = cami_meta["filelist"]
+            file_select.options = [(entry, os.path.basename(entry)) for entry in file_list]
+
+    def data_source_callback(_attr, _old, _new):
+        file_select_update()
+
+    data_source = Select(
+        title="Data Source:",
+        value="proposal number",
+        options=["proposal number", "cami file"],
+        width=210,
+    )
+    data_source.on_change("value", data_source_callback)
+
+    doc.add_periodic_callback(file_select_update, 5000)
+
+    def proposal_textinput_callback(_attr, _old, _new):
+        file_select_update()
+
+    proposal_textinput = doc.proposal_textinput
+    proposal_textinput.on_change("name", proposal_textinput_callback)
+
+    def upload_button_callback(_attr, _old, new):
+        nonlocal cami_meta
+        with io.StringIO(base64.b64decode(new).decode()) as file:
+            cami_meta = pyzebra.parse_h5meta(file)
+        data_source.value = "cami file"
+        file_select_update()
+
+    upload_div = Div(text="or upload .cami file:", margin=(5, 5, 0, 5))
+    upload_button = FileInput(accept=".cami", width=200)
+    upload_button.on_change("value", upload_button_callback)
+
+    file_select = MultiSelect(title="Available .hdf files:", width=210, height=320)
+
+    def _init_datatable():
+        file_list = []
+        for scan in dataset:
+            file_list.append(os.path.basename(scan["original_filename"]))
+
+        scan_table_source.data.update(
+            file=file_list,
+            param=[None] * len(dataset),
+            frame=[None] * len(dataset),
+            x_pos=[None] * len(dataset),
+            y_pos=[None] * len(dataset),
+        )
+        scan_table_source.selected.indices = []
+        scan_table_source.selected.indices = [0]
+
+        param_select.value = "user defined"
+
+    def _update_table():
+        frame = []
+        x_pos = []
+        y_pos = []
+        for scan in dataset:
+            if "fit" in scan:
+                framei = scan["fit"]["frame"]
+                x_posi = scan["fit"]["x_pos"]
+                y_posi = scan["fit"]["y_pos"]
+            else:
+                framei = x_posi = y_posi = None
+
+            frame.append(framei)
+            x_pos.append(x_posi)
+            y_pos.append(y_posi)
+
+        scan_table_source.data.update(frame=frame, x_pos=x_pos, y_pos=y_pos)
+
+    def _file_open():
+        new_data = []
+        for f_name in file_select.value:
+            try:
+                new_data.append(pyzebra.read_detector_data(f_name))
+            except KeyError 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():
+        _file_open()
+
+    file_append_button = Button(label="Append", width=100)
+    file_append_button.on_click(file_append_button_callback)
+
+    # Scan select
+    def scan_table_select_callback(_attr, old, new):
+        if not new:
+            # skip empty selections
+            return
+
+        # Avoid selection of multiple indicies (via Shift+Click or Ctrl+Click)
+        if len(new) > 1:
+            # drop selection to the previous one
+            scan_table_source.selected.indices = old
+            return
+
+        if len(old) > 1:
+            # skip unnecessary update caused by selection drop
+            return
+
+        scan = dataset[new[0]]
+
+        zebra_mode = scan["zebra_mode"]
+        if zebra_mode == "nb":
+            metadata_table_source.data.update(geom=["normal beam"])
+        else:  # zebra_mode == "bi"
+            metadata_table_source.data.update(geom=["bisecting"])
+
+        if "mf" in scan:
+            metadata_table_source.data.update(mf=[scan["mf"][0]])
+        else:
+            metadata_table_source.data.update(mf=[None])
+
+        if "temp" in scan:
+            metadata_table_source.data.update(temp=[scan["temp"][0]])
+        else:
+            metadata_table_source.data.update(temp=[None])
+
+        _update_proj_plots()
+
+    def scan_table_source_callback(_attr, _old, _new):
+        pass
+
+    scan_table_source = ColumnDataSource(dict(file=[], param=[], frame=[], x_pos=[], y_pos=[]))
+    scan_table_source.selected.on_change("indices", scan_table_select_callback)
+    scan_table_source.on_change("data", scan_table_source_callback)
+
+    scan_table = DataTable(
+        source=scan_table_source,
+        columns=[
+            TableColumn(field="file", title="file", editor=CellEditor(), width=150),
+            TableColumn(
+                field="param",
+                title="param",
+                formatter=num_formatter,
+                editor=NumberEditor(),
+                width=50,
+            ),
+            TableColumn(
+                field="frame", title="Frame", formatter=num_formatter, editor=CellEditor(), width=70
+            ),
+            TableColumn(
+                field="x_pos", title="X", formatter=num_formatter, editor=CellEditor(), width=70
+            ),
+            TableColumn(
+                field="y_pos", title="Y", formatter=num_formatter, editor=CellEditor(), width=70
+            ),
+        ],
+        width=470,  # +60 because of the index column
+        height=420,
+        editable=True,
+        autosize_mode="none",
+    )
+
+    def _get_selected_scan():
+        return dataset[scan_table_source.selected.indices[0]]
+
+    def param_select_callback(_attr, _old, new):
+        if new == "user defined":
+            param = [None] * len(dataset)
+        else:
+            # TODO: which value to take?
+            param = [scan[new][0] for scan in dataset]
+
+        scan_table_source.data["param"] = param
+        _update_param_plot()
+
+    param_select = Select(
+        title="Parameter:",
+        options=["user defined", "temp", "mf", "h", "k", "l"],
+        value="user defined",
+        width=145,
+    )
+    param_select.on_change("value", param_select_callback)
+
+    def _update_proj_plots():
+        scan = _get_selected_scan()
+        counts = scan["counts"]
+        n_im, n_y, n_x = counts.shape
+        im_proj_x = np.mean(counts, axis=1)
+        im_proj_y = np.mean(counts, axis=2)
+
+        # normalize for simpler colormapping
+        im_proj_max_val = max(np.max(im_proj_x), np.max(im_proj_y))
+        im_proj_x = 1000 * im_proj_x / im_proj_max_val
+        im_proj_y = 1000 * im_proj_y / im_proj_max_val
+
+        proj_x_image_source.data.update(image=[im_proj_x], dw=[n_x], 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(im_proj_x), np.min(im_proj_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
+
+        frame_range.start = 0
+        frame_range.end = n_im
+        frame_range.reset_start = 0
+        frame_range.reset_end = n_im
+        frame_range.bounds = (0, n_im)
+
+        scan_motor = scan["scan_motor"]
+        proj_y_plot.yaxis.axis_label = f"Scanning motor, {scan_motor}"
+
+        var = scan[scan_motor]
+        var_start = var[0]
+        var_end = var[-1] + (var[-1] - var[0]) / (n_im - 1)
+
+        scanning_motor_range.start = var_start
+        scanning_motor_range.end = var_end
+        scanning_motor_range.reset_start = var_start
+        scanning_motor_range.reset_end = var_end
+        # handle both, ascending and descending sequences
+        scanning_motor_range.bounds = (min(var_start, var_end), max(var_start, var_end))
+
+    # shared frame ranges
+    frame_range = Range1d(0, 1, bounds=(0, 1))
+    scanning_motor_range = Range1d(0, 1, bounds=(0, 1))
+    color_mapper_proj = LinearColorMapper()
+
+    det_x_range = Range1d(0, IMAGE_W, bounds=(0, IMAGE_W))
+    proj_x_plot = figure(
+        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},
+        height=540,
+        width=IMAGE_PLOT_W - 3,
+        tools="pan,box_zoom,wheel_zoom,reset",
+        active_scroll="wheel_zoom",
+    )
+
+    proj_x_plot.yaxis.major_label_orientation = "vertical"
+    proj_x_plot.toolbar.tools[2].maintain_focus = False
+
+    proj_x_image_source = ColumnDataSource(
+        dict(image=[np.zeros((1, 1), dtype="float32")], x=[0], y=[0], dw=[IMAGE_W], dh=[1])
+    )
+
+    proj_x_plot.image(source=proj_x_image_source, color_mapper=color_mapper_proj)
+
+    det_y_range = Range1d(0, IMAGE_H, bounds=(0, IMAGE_H))
+    proj_y_plot = figure(
+        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},
+        height=540,
+        width=IMAGE_PLOT_H + 22,
+        tools="pan,box_zoom,wheel_zoom,reset",
+        active_scroll="wheel_zoom",
+    )
+
+    proj_y_plot.yaxis.y_range_name = "scanning_motor"
+    proj_y_plot.yaxis.major_label_orientation = "vertical"
+    proj_y_plot.toolbar.tools[2].maintain_focus = False
+
+    proj_y_image_source = ColumnDataSource(
+        dict(image=[np.zeros((1, 1), dtype="float32")], x=[0], y=[0], dw=[IMAGE_H], dh=[1])
+    )
+
+    proj_y_plot.image(source=proj_y_image_source, color_mapper=color_mapper_proj)
+
+    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"
+
+    def proj_auto_checkbox_callback(_attr, _old, new):
+        if 0 in new:
+            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_proj_plots()
+
+    proj_auto_checkbox = CheckboxGroup(
+        labels=["Projections Intensity Range"], active=[0], width=145, margin=[10, 5, 0, 5]
+    )
+    proj_auto_checkbox.on_change("active", proj_auto_checkbox_callback)
+
+    def proj_display_max_spinner_callback(_attr, _old, new):
+        color_mapper_proj.high = new
+
+    proj_display_max_spinner = Spinner(
+        value=1, disabled=bool(proj_auto_checkbox.active), mode="int", width=100
+    )
+    proj_display_max_spinner.on_change("value", proj_display_max_spinner_callback)
+
+    def proj_display_min_spinner_callback(_attr, _old, new):
+        color_mapper_proj.low = new
+
+    proj_display_min_spinner = Spinner(
+        value=0, disabled=bool(proj_auto_checkbox.active), mode="int", width=100
+    )
+    proj_display_min_spinner.on_change("value", proj_display_min_spinner_callback)
+
+    metadata_table_source = ColumnDataSource(dict(geom=[""], temp=[None], mf=[None]))
+    metadata_table = DataTable(
+        source=metadata_table_source,
+        columns=[
+            TableColumn(field="geom", title="Geometry", width=100),
+            TableColumn(field="temp", title="Temperature", formatter=num_formatter, width=100),
+            TableColumn(field="mf", title="Magnetic Field", formatter=num_formatter, width=100),
+        ],
+        width=300,
+        height=50,
+        autosize_mode="none",
+        index_position=None,
+    )
+
+    def _update_param_plot():
+        x = []
+        y = []
+        fit_param = fit_param_select.value
+        for s, p in zip(dataset, scan_table_source.data["param"]):
+            if "fit" in s and fit_param:
+                x.append(p)
+                y.append(s["fit"][fit_param])
+        param_scatter_source.data.update(x=x, y=y)
+
+    # Parameter plot
+    param_plot = figure(
+        x_axis_label="Parameter",
+        y_axis_label="Fit parameter",
+        height=400,
+        width=700,
+        tools="pan,wheel_zoom,reset",
+    )
+
+    param_scatter_source = ColumnDataSource(dict(x=[], y=[]))
+    param_plot.circle(source=param_scatter_source)
+
+    param_plot.toolbar.logo = None
+
+    def fit_param_select_callback(_attr, _old, _new):
+        _update_param_plot()
+
+    fit_param_select = Select(title="Fit parameter", options=[], width=145)
+    fit_param_select.on_change("value", fit_param_select_callback)
+
+    def proc_all_button_callback():
+        for scan in dataset:
+            pyzebra.fit_event(
+                scan,
+                int(np.floor(frame_range.start)),
+                int(np.ceil(frame_range.end)),
+                int(np.floor(det_y_range.start)),
+                int(np.ceil(det_y_range.end)),
+                int(np.floor(det_x_range.start)),
+                int(np.ceil(det_x_range.end)),
+            )
+
+        _update_table()
+
+        for scan in dataset:
+            if "fit" in scan:
+                options = list(scan["fit"].keys())
+                fit_param_select.options = options
+                fit_param_select.value = options[0]
+                break
+
+        _update_param_plot()
+
+    proc_all_button = Button(label="Process All", button_type="primary", width=145)
+    proc_all_button.on_click(proc_all_button_callback)
+
+    def proc_button_callback():
+        scan = _get_selected_scan()
+        pyzebra.fit_event(
+            scan,
+            int(np.floor(frame_range.start)),
+            int(np.ceil(frame_range.end)),
+            int(np.floor(det_y_range.start)),
+            int(np.ceil(det_y_range.end)),
+            int(np.floor(det_x_range.start)),
+            int(np.ceil(det_x_range.end)),
+        )
+
+        _update_table()
+
+        for scan in dataset:
+            if "fit" in scan:
+                options = list(scan["fit"].keys())
+                fit_param_select.options = options
+                fit_param_select.value = options[0]
+                break
+
+        _update_param_plot()
+
+    proc_button = Button(label="Process Current", width=145)
+    proc_button.on_click(proc_button_callback)
+
+    layout_controls = row(
+        colormap_select,
+        column(proj_auto_checkbox, row(proj_display_min_spinner, proj_display_max_spinner)),
+        proc_button,
+        proc_all_button,
+    )
+
+    layout_proj = column(
+        gridplot(
+            [[proj_x_plot, proj_y_plot]], toolbar_options={"logo": None}, toolbar_location="right"
+        ),
+        layout_controls,
+    )
+
+    # Plot tabs
+    plots = Tabs(
+        tabs=[
+            Panel(child=layout_proj, title="single scan"),
+            Panel(child=column(param_plot, row(fit_param_select)), title="parameter plot"),
+        ]
+    )
+
+    # Final layout
+    import_layout = column(
+        data_source,
+        upload_div,
+        upload_button,
+        file_select,
+        row(file_open_button, file_append_button),
+    )
+
+    scan_layout = column(scan_table, row(param_select, metadata_table))
+
+    tab_layout = column(row(import_layout, scan_layout, plots))
+
+    return Panel(child=tab_layout, title="hdf param study")

pyzebra/app/panel_param_study.py

@@ -0,0 +1,518 @@
+import itertools
+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,
+    HoverTool,
+    LinearColorMapper,
+    NumberEditor,
+    Panel,
+    Range1d,
+    Select,
+    Spacer,
+    Span,
+    TableColumn,
+    Tabs,
+    TextAreaInput,
+    Whisker,
+)
+from bokeh.palettes import Category10, Plasma256
+from bokeh.plotting import figure
+from scipy import interpolate
+
+import pyzebra
+from pyzebra import app
+
+
+def color_palette(n_colors):
+    palette = itertools.cycle(Category10[10])
+    return list(itertools.islice(palette, n_colors))
+
+
+def create():
+    doc = curdoc()
+    log = doc.logger
+    dataset = []
+    app_dlfiles = app.DownloadFiles(n_files=1)
+
+    def _init_datatable():
+        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 dataset:
+            file_list.append(os.path.basename(scan["original_filename"]))
+
+        scan_table_source.data.update(
+            file=file_list, scan=scan_list, param=param, fit=[0] * len(scan_list), export=export
+        )
+        scan_table_source.selected.indices = []
+        scan_table_source.selected.indices = [0]
+
+        scan_motor_select.options = dataset[0]["scan_motors"]
+        scan_motor_select.value = dataset[0]["scan_motor"]
+
+        merge_options = [(str(i), f"{i} ({idx})") for i, idx in enumerate(scan_list)]
+        merge_from_select.options = merge_options
+        merge_from_select.value = merge_options[0][0]
+
+    def scan_motor_select_callback(_attr, _old, new):
+        if dataset:
+            for scan in dataset:
+                scan["scan_motor"] = new
+            _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 dataset]
+        export = [scan["export"] for scan in dataset]
+        if param_select.value == "user defined":
+            param = [None] * len(dataset)
+        else:
+            param = [scan[param_select.value] for scan in dataset]
+
+        scan_table_source.data.update(fit=fit_ok, export=export, param=param)
+
+    def _update_single_scan_plot():
+        scan = _get_selected_scan()
+        scan_motor = scan["scan_motor"]
+
+        y = scan["counts"]
+        y_err = scan["counts_err"]
+        x = scan[scan_motor]
+
+        plot.axis[0].axis_label = scan_motor
+        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)
+
+        else:
+            fit_source.data.update(x=[], y=[])
+            bkg_source.data.update(x=[], y=[])
+            peak_source.data.update(xs=[], ys=[])
+
+        app_fitctrl.update_result_textarea(scan)
+
+    def _update_overview():
+        xs = []
+        ys = []
+        param = []
+        x = []
+        y = []
+        par = []
+        for s, p in enumerate(scan_table_source.data["param"]):
+            if p is not None:
+                scan = dataset[s]
+                scan_motor = scan["scan_motor"]
+                xs.append(scan[scan_motor])
+                x.extend(scan[scan_motor])
+                ys.append(scan["counts"])
+                y.extend([float(p)] * len(scan[scan_motor]))
+                param.append(float(p))
+                par.extend(scan["counts"])
+
+        if dataset:
+            scan_motor = dataset[0]["scan_motor"]
+            ov_plot.axis[0].axis_label = scan_motor
+            ov_param_plot.axis[0].axis_label = scan_motor
+
+        ov_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:
+            x1, x2 = min(x), max(x)
+            y1, y2 = min(y), max(y)
+            grid_x, grid_y = np.meshgrid(
+                np.linspace(x1, x2, ov_param_plot.inner_width),
+                np.linspace(y1, y2, ov_param_plot.inner_height),
+            )
+            image = interpolate.griddata((x, y), par, (grid_x, grid_y))
+            ov_param_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_image_source.data.update(image=[], x=[], y=[], dw=[], dh=[])
+
+    def _update_param_plot():
+        x = []
+        y = []
+        y_lower = []
+        y_upper = []
+        fit_param = fit_param_select.value
+        for s, p in zip(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_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 = figure(
+        x_axis_label="Scan motor",
+        y_axis_label="Counts",
+        height=450,
+        width=700,
+        tools="pan,wheel_zoom,reset",
+    )
+
+    scatter_source = ColumnDataSource(dict(x=[0], y=[0], y_upper=[0], y_lower=[0]))
+    plot.circle(
+        source=scatter_source, line_color="steelblue", fill_color="steelblue", legend_label="data"
+    )
+    plot.add_layout(Whisker(source=scatter_source, base="x", upper="y_upper", lower="y_lower"))
+
+    fit_source = ColumnDataSource(dict(x=[0], y=[0]))
+    plot.line(source=fit_source, legend_label="best fit")
+
+    bkg_source = ColumnDataSource(dict(x=[0], y=[0]))
+    plot.line(source=bkg_source, line_color="green", line_dash="dashed", legend_label="linear")
+
+    peak_source = ColumnDataSource(dict(xs=[[0]], ys=[[0]]))
+    plot.multi_line(source=peak_source, line_color="red", line_dash="dashed", legend_label="peak")
+
+    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"
+
+    # Overview multilines plot
+    ov_plot = figure(
+        x_axis_label="Scan motor",
+        y_axis_label="Counts",
+        height=450,
+        width=700,
+        tools="pan,wheel_zoom,reset",
+    )
+
+    ov_mline_source = ColumnDataSource(dict(xs=[], ys=[], param=[], color=[]))
+    ov_plot.multi_line(source=ov_mline_source, line_color="color")
+
+    ov_plot.add_tools(HoverTool(tooltips=[("param", "@param")]))
+
+    ov_plot.toolbar.logo = None
+
+    # Overview params plot
+    ov_param_plot = figure(
+        x_axis_label="Scan motor",
+        y_axis_label="Param",
+        x_range=Range1d(),
+        y_range=Range1d(),
+        height=450,
+        width=700,
+        tools="pan,wheel_zoom,reset",
+    )
+
+    color_mapper = LinearColorMapper(palette=Plasma256)
+    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_scatter_source = ColumnDataSource(dict(x=[], y=[]))
+    ov_param_plot.dot(source=ov_param_scatter_source, size=15, color="black")
+
+    ov_param_plot.toolbar.logo = None
+
+    # Parameter plot
+    param_plot = figure(
+        x_axis_label="Parameter",
+        y_axis_label="Fit parameter",
+        height=400,
+        width=700,
+        tools="pan,wheel_zoom,reset",
+    )
+
+    param_scatter_source = ColumnDataSource(dict(x=[], y=[], y_upper=[], y_lower=[]))
+    param_plot.circle(source=param_scatter_source)
+    param_plot.add_layout(
+        Whisker(source=param_scatter_source, base="x", upper="y_upper", lower="y_lower")
+    )
+
+    param_plot.toolbar.logo = None
+
+    def fit_param_select_callback(_attr, _old, _new):
+        _update_param_plot()
+
+    fit_param_select = Select(title="Fit parameter", options=[], width=145)
+    fit_param_select.on_change("value", fit_param_select_callback)
+
+    # Plot tabs
+    plots = Tabs(
+        tabs=[
+            Panel(child=plot, title="single scan"),
+            Panel(child=ov_plot, title="overview"),
+            Panel(child=ov_param_plot, title="overview map"),
+            Panel(child=column(param_plot, row(fit_param_select)), title="parameter plot"),
+        ]
+    )
+
+    # 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_single_scan_plot()
+
+    def scan_table_source_callback(_attr, _old, new):
+        # unfortunately, we don't know if the change comes from data update or user input
+        # also `old` and `new` are the same for non-scalars
+        for scan, export in zip(dataset, new["export"]):
+            scan["export"] = export
+        _update_overview()
+        _update_param_plot()
+        _update_preview()
+
+    scan_table_source = ColumnDataSource(dict(file=[], scan=[], param=[], fit=[], export=[]))
+    scan_table_source.on_change("data", scan_table_source_callback)
+    scan_table_source.selected.on_change("indices", scan_table_select_callback)
+
+    scan_table = DataTable(
+        source=scan_table_source,
+        columns=[
+            TableColumn(field="file", title="file", editor=CellEditor(), width=150),
+            TableColumn(field="scan", title="scan", editor=CellEditor(), width=50),
+            TableColumn(field="param", title="param", editor=NumberEditor(), width=50),
+            TableColumn(field="fit", title="Fit", editor=CellEditor(), width=50),
+            TableColumn(field="export", title="Export", editor=CheckboxEditor(), width=50),
+        ],
+        width=410,  # +60 because of the index column
+        height=350,
+        editable=True,
+        autosize_mode="none",
+    )
+
+    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 dataset[scan_table_source.selected.indices[0]]
+
+    def param_select_callback(_attr, _old, _new):
+        _update_table()
+
+    param_select = Select(
+        title="Parameter:",
+        options=["user defined", "temp", "mf", "h", "k", "l"],
+        value="user defined",
+        width=145,
+    )
+    param_select.on_change("value", param_select_callback)
+
+    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(dataset)
+
+        _update_single_scan_plot()
+        _update_overview()
+        _update_table()
+
+        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
+
+    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():
+        app_fitctrl.fit_scan(_get_selected_scan())
+
+        _update_single_scan_plot()
+        _update_overview()
+        _update_table()
+
+        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
+
+    proc_button = Button(label="Process Current", width=145)
+    proc_button.on_click(proc_button_callback)
+
+    export_preview_textinput = TextAreaInput(title="Export file preview:", width=450, height=400)
+
+    def _update_preview():
+        with tempfile.TemporaryDirectory() as temp_dir:
+            temp_file = temp_dir + "/temp"
+            export_data = []
+            param_data = []
+            for scan, param in zip(dataset, scan_table_source.data["param"]):
+                if scan["export"] and param:
+                    export_data.append(scan)
+                    param_data.append(param)
+
+            pyzebra.export_param_study(export_data, param_data, temp_file)
+
+            exported_content = ""
+            file_content = []
+
+            fname = temp_file
+            if os.path.isfile(fname):
+                with open(fname) as f:
+                    content = f.read()
+                    exported_content += content
+            else:
+                content = ""
+            file_content.append(content)
+
+            app_dlfiles.set_contents(file_content)
+            export_preview_textinput.value = exported_content
+
+    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,
+        ),
+        column(app_fitctrl.to_spinner, proc_button, proc_all_button),
+    )
+
+    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(
+        app_inputctrl.filelist_select,
+        row(app_inputctrl.open_button, app_inputctrl.append_button),
+        upload_div,
+        app_inputctrl.upload_button,
+        append_upload_div,
+        app_inputctrl.append_upload_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, 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

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

pyzebra/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_dict_operation.py

@@ -1,513 +0,0 @@
-import numpy as np
-import uncertainties as u
-
-from .fit2 import create_uncertanities
-
-
-def add_dict(dict1, dict2):
-    """adds two dictionaries, meta of the new is saved as meata+original_filename and
-    measurements are shifted to continue with numbering of first dict
-    :arg dict1 : dictionarry to add to
-    :arg dict2 : dictionarry from which to take the measurements
-    :return dict1 : combined dictionary
-    Note: dict1 must be made from ccl, otherwise we would have to change the structure of loaded
-    dat file"""
-    max_measurement_dict1 = max([int(str(keys)[1:]) for keys in dict1["scan"]])
-    if dict2["meta"]["data_type"] == ".ccl":
-        new_filenames = [
-            "M" + str(x + max_measurement_dict1)
-            for x in [int(str(keys)[1:]) for keys in dict2["scan"]]
-        ]
-        new_meta_name = "meta" + str(dict2["meta"]["original_filename"])
-        if new_meta_name not in dict1:
-            for keys, name in zip(dict2["scan"], new_filenames):
-                dict2["scan"][keys]["file_of_origin"] = str(dict2["meta"]["original_filename"])
-                dict1["scan"][name] = dict2["scan"][keys]
-
-            dict1[new_meta_name] = dict2["meta"]
-
-        else:
-            raise KeyError(
-                str(
-                    "The file %s has alredy been added to %s"
-                    % (dict2["meta"]["original_filename"], dict1["meta"]["original_filename"])
-                )
-            )
-    elif dict2["meta"]["data_type"] == ".dat":
-        d = {}
-        new_name = "M" + str(max_measurement_dict1 + 1)
-        hkl = dict2["meta"]["title"]
-        d["h_index"] = float(hkl.split()[-3])
-        d["k_index"] = float(hkl.split()[-2])
-        d["l_index"] = float(hkl.split()[-1])
-        d["number_of_measurements"] = len(dict2["scan"]["NP"])
-        d["om"] = dict2["scan"]["om"]
-        d["Counts"] = dict2["scan"]["Counts"]
-        d["monitor"] = dict2["scan"]["Monitor1"][0]
-        d["temperature"] = dict2["meta"]["temp"]
-        d["mag_field"] = dict2["meta"]["mf"]
-        d["omega_angle"] = dict2["meta"]["omega"]
-        dict1["scan"][new_name] = d
-        print(hkl.split())
-        for keys in d:
-            print(keys)
-
-        print("s")
-
-    return dict1
-
-
-def auto(dict):
-    """takes just unique tuples from all tuples in dictionary returend by scan_dict
-    intendet for automatic merge if you doesent want to specify what scans to merge together
-    args: dict - dictionary from scan_dict function
-    :return dict - dict without repetitions"""
-    for keys in dict:
-        tuple_list = dict[keys]
-        new = list()
-        for i in range(len(tuple_list)):
-            if tuple_list[0][0] == tuple_list[i][0]:
-                new.append(tuple_list[i])
-        dict[keys] = new
-    return dict
-
-
-def scan_dict(dict):
-    """scans dictionary for duplicate hkl indexes
-    :arg dict : dictionary to scan
-    :return  dictionary with matching scans, if there are none, the dict is empty
-    note: can be checked by "not d", true if empty
-    """
-
-    d = {}
-    for i in dict["scan"]:
-        for j in dict["scan"]:
-            if dict["scan"][str(i)] != dict["scan"][str(j)]:
-                itup = (
-                    dict["scan"][str(i)]["h_index"],
-                    dict["scan"][str(i)]["k_index"],
-                    dict["scan"][str(i)]["l_index"],
-                )
-                jtup = (
-                    dict["scan"][str(j)]["h_index"],
-                    dict["scan"][str(j)]["k_index"],
-                    dict["scan"][str(j)]["l_index"],
-                )
-                if itup != jtup:
-                    pass
-                else:
-
-                    if str(itup) not in d:
-                        d[str(itup)] = list()
-                        d[str(itup)].append((i, j))
-                    else:
-                        d[str(itup)].append((i, j))
-            else:
-                continue
-    return d
-
-
-def compare_hkl(dict1, dict2):
-    """Compares two dictionaries based on hkl indexes and return dictionary with str(h k l) as
-    key and tuple with keys to same scan in dict1 and dict2
-    :arg dict1 : first dictionary
-    :arg dict2 : second dictionary
-    :return d : dict with matches
-    example of one key: '0.0 0.0 -1.0 : ('M1', 'M9')' meaning that 001 hkl scan is M1 in
-    first dict and M9 in second"""
-    d = {}
-    dupl = 0
-    for keys in dict1["scan"]:
-        for key in dict2["scan"]:
-            if (
-                dict1["scan"][str(keys)]["h_index"] == dict2["scan"][str(key)]["h_index"]
-                and dict1["scan"][str(keys)]["k_index"] == dict2["scan"][str(key)]["k_index"]
-                and dict1["scan"][str(keys)]["l_index"] == dict2["scan"][str(key)]["l_index"]
-            ):
-
-                if (
-                    str(
-                        (
-                            str(dict1["scan"][str(keys)]["h_index"])
-                            + " "
-                            + str(dict1["scan"][str(keys)]["k_index"])
-                            + " "
-                            + str(dict1["scan"][str(keys)]["l_index"])
-                        )
-                    )
-                    not in d
-                ):
-                    d[
-                        str(
-                            str(dict1["scan"][str(keys)]["h_index"])
-                            + " "
-                            + str(dict1["scan"][str(keys)]["k_index"])
-                            + " "
-                            + str(dict1["scan"][str(keys)]["l_index"])
-                        )
-                    ] = (str(keys), str(key))
-                else:
-                    dupl = dupl + 1
-                    d[
-                        str(
-                            str(dict1["scan"][str(keys)]["h_index"])
-                            + " "
-                            + str(dict1["scan"][str(keys)]["k_index"])
-                            + " "
-                            + str(dict1["scan"][str(keys)]["l_index"])
-                            + "_dupl"
-                            + str(dupl)
-                        )
-                    ] = (str(keys), str(key))
-            else:
-                continue
-
-    return d
-
-
-def create_tuples(x, y, y_err):
-    """creates tuples for sorting and merginng of the data
-    Counts need to be normalized to monitor before"""
-    t = list()
-    for i in range(len(x)):
-        tup = (x[i], y[i], y_err[i])
-        t.append(tup)
-    return t
-
-
-def normalize(dict, key, monitor):
-    """Normalizes the scan to monitor, checks if sigma exists, otherwise creates it
-    :arg dict : dictionary to from which to tkae the scan
-    :arg key : which scan to normalize from dict1
-    :arg monitor : final monitor
-    :return counts - normalized counts
-    :return sigma - normalized sigma"""
-
-    counts = np.array(dict["scan"][key]["Counts"])
-    sigma = np.sqrt(counts) if "sigma" not in dict["scan"][key] else dict["scan"][key]["sigma"]
-    monitor_ratio = monitor / dict["scan"][key]["monitor"]
-    scaled_counts = counts * monitor_ratio
-    scaled_sigma = np.array(sigma) * monitor_ratio
-
-    return scaled_counts, scaled_sigma
-
-
-def merge(dict1, dict2, keys, auto=True, monitor=100000):
-    """merges the two tuples and sorts them, if om value is same, Counts value is average
-    averaging is propagated into sigma if dict1 == dict2, key[1] is deleted after merging
-    :arg dict1 : dictionary to which scan will be merged
-    :arg dict2 : dictionary from which scan will be merged
-    :arg keys : tuple with key to dict1 and dict2
-    :arg auto : if true, when monitors are same, does not change it, if flase, takes monitor always
-    :arg monitor : final monitor after merging
-    note: dict1 and dict2 can be same dict
-    :return dict1 with merged scan"""
-    if auto:
-        if dict1["scan"][keys[0]]["monitor"] == dict2["scan"][keys[1]]["monitor"]:
-            monitor = dict1["scan"][keys[0]]["monitor"]
-
-    # load om and Counts
-    x1, x2 = dict1["scan"][keys[0]]["om"], dict2["scan"][keys[1]]["om"]
-    cor_y1, y_err1 = normalize(dict1, keys[0], monitor=monitor)
-    cor_y2, y_err2 = normalize(dict2, keys[1], monitor=monitor)
-    # creates touples (om, Counts, sigma) for sorting and further processing
-    tuple_list = create_tuples(x1, cor_y1, y_err1) + create_tuples(x2, cor_y2, y_err2)
-    # Sort the list on om and add 0 0 0 tuple to the last position
-    sorted_t = sorted(tuple_list, key=lambda tup: tup[0])
-    sorted_t.append((0, 0, 0))
-    om, Counts, sigma = [], [], []
-    seen = list()
-    for i in range(len(sorted_t) - 1):
-        if sorted_t[i][0] not in seen:
-            if sorted_t[i][0] != sorted_t[i + 1][0]:
-                om = np.append(om, sorted_t[i][0])
-                Counts = np.append(Counts, sorted_t[i][1])
-                sigma = np.append(sigma, sorted_t[i][2])
-            else:
-                om = np.append(om, sorted_t[i][0])
-                counts1, counts2 = sorted_t[i][1], sorted_t[i + 1][1]
-                sigma1, sigma2 = sorted_t[i][2], sorted_t[i + 1][2]
-                count_err1 = u.ufloat(counts1, sigma1)
-                count_err2 = u.ufloat(counts2, sigma2)
-                avg = (count_err1 + count_err2) / 2
-                Counts = np.append(Counts, avg.n)
-                sigma = np.append(sigma, avg.s)
-                seen.append(sorted_t[i][0])
-        else:
-            continue
-
-    if dict1 == dict2:
-        del dict1["scan"][keys[1]]
-
-    note = (
-        f"This scan was merged with scan {keys[1]} from "
-        f'file {dict2["meta"]["original_filename"]} \n'
-    )
-    if "notes" not in dict1["scan"][str(keys[0])]:
-        dict1["scan"][str(keys[0])]["notes"] = note
-    else:
-        dict1["scan"][str(keys[0])]["notes"] += note
-
-    dict1["scan"][keys[0]]["om"] = om
-    dict1["scan"][keys[0]]["Counts"] = Counts
-    dict1["scan"][keys[0]]["sigma"] = sigma
-    dict1["scan"][keys[0]]["monitor"] = monitor
-    print("merging done")
-    return dict1
-
-
-def substract_measurement(dict1, dict2, keys, auto=True, monitor=100000):
-    """Substracts two scan (scan key2 from dict2 from measurent key1 in dict1), expects om to be same
-    :arg dict1 : dictionary to which scan will be merged
-    :arg dict2 : dictionary from which scan will be merged
-    :arg keys : tuple with key to dict1 and dict2
-    :arg auto : if true, when monitors are same, does not change it, if flase, takes monitor always
-    :arg monitor : final monitor after merging
-    :returns d : dict1 with substracted Counts from  dict2 and sigma that comes from the substraction"""
-
-    if len(dict1["scan"][keys[0]]["om"]) != len(dict2["scan"][keys[1]]["om"]):
-        raise ValueError("Omegas have different lengths, cannot be substracted")
-
-    if auto:
-        if dict1["scan"][keys[0]]["monitor"] == dict2["scan"][keys[1]]["monitor"]:
-            monitor = dict1["scan"][keys[0]]["monitor"]
-
-    cor_y1, y_err1 = normalize(dict1, keys[0], monitor=monitor)
-    cor_y2, y_err2 = normalize(dict2, keys[1], monitor=monitor)
-
-    dict1_count_err = create_uncertanities(cor_y1, y_err1)
-    dict2_count_err = create_uncertanities(cor_y2, y_err2)
-
-    res = np.subtract(dict1_count_err, dict2_count_err)
-
-    res_nom = []
-    res_err = []
-    for k in range(len(res)):
-        res_nom = np.append(res_nom, res[k].n)
-        res_err = np.append(res_err, res[k].s)
-
-    if len([num for num in res_nom if num < 0]) >= 0.3 * len(res_nom):
-        print(
-            f"Warning! percentage of negative numbers in scan subsracted {keys[0]} is "
-            f"{len([num for num in res_nom if num < 0]) / len(res_nom)}"
-        )
-
-    dict1["scan"][str(keys[0])]["Counts"] = res_nom
-    dict1["scan"][str(keys[0])]["sigma"] = res_err
-    dict1["scan"][str(keys[0])]["monitor"] = monitor
-    note = (
-        f'Scan {keys[1]} from file {dict2["meta"]["original_filename"]} '
-        f"was substracted from this scan \n"
-    )
-    if "notes" not in dict1["scan"][str(keys[0])]:
-        dict1["scan"][str(keys[0])]["notes"] = note
-    else:
-        dict1["scan"][str(keys[0])]["notes"] += note
-    return dict1
-
-
-def compare_dict(dict1, dict2):
-    """takes two ccl dictionaries and compare different values for each key
-    :arg dict1 : dictionary 1 (ccl)
-    :arg dict2 : dictionary 2 (ccl)
-    :returns warning : dictionary with keys from primary files (if they differ) with
-    information of how many scan differ and which ones differ
-    :returns report_string string comparing all different values respecively of measurements"""
-
-    if dict1["meta"]["data_type"] != dict2["meta"]["data_type"]:
-        print("select two dicts")
-        return
-    S = []
-    conflicts = {}
-    warnings = {}
-
-    comp = compare_hkl(dict1, dict2)
-    d1 = scan_dict(dict1)
-    d2 = scan_dict(dict2)
-    if not d1:
-        S.append("There are no duplicates in %s (dict1) \n" % dict1["meta"]["original_filename"])
-    else:
-        S.append(
-            "There are %d duplicates in %s (dict1) \n"
-            % (len(d1), dict1["meta"]["original_filename"])
-        )
-        warnings["Duplicates in dict1"] = list()
-        for keys in d1:
-            S.append("Measurements %s with hkl %s \n" % (d1[keys], keys))
-            warnings["Duplicates in dict1"].append(d1[keys])
-    if not d2:
-        S.append("There are no duplicates in %s (dict2) \n" % dict2["meta"]["original_filename"])
-    else:
-        S.append(
-            "There are %d duplicates in %s (dict2) \n"
-            % (len(d2), dict2["meta"]["original_filename"])
-        )
-        warnings["Duplicates in dict2"] = list()
-        for keys in d2:
-            S.append("Measurements %s with hkl %s \n" % (d2[keys], keys))
-            warnings["Duplicates in dict2"].append(d2[keys])
-
-    # compare meta
-    S.append("Different values in meta: \n")
-    different_meta = {
-        k: dict1["meta"][k]
-        for k in dict1["meta"]
-        if k in dict2["meta"] and dict1["meta"][k] != dict2["meta"][k]
-    }
-    exlude_meta_set = ["original_filename", "date", "title"]
-    for keys in different_meta:
-        if keys in exlude_meta_set:
-            continue
-        else:
-            if keys not in conflicts:
-                conflicts[keys] = 1
-            else:
-                conflicts[keys] = conflicts[keys] + 1
-
-            S.append("   Different values in %s \n" % str(keys))
-            S.append("           dict1: %s \n" % str(dict1["meta"][str(keys)]))
-            S.append("           dict2: %s \n" % str(dict2["meta"][str(keys)]))
-
-    # compare Measurements
-    S.append(
-        "Number of measurements in %s = %s \n"
-        % (dict1["meta"]["original_filename"], len(dict1["scan"]))
-    )
-    S.append(
-        "Number of measurements in %s = %s \n"
-        % (dict2["meta"]["original_filename"], len(dict2["scan"]))
-    )
-    S.append("Different values in Measurements:\n")
-    select_set = ["om", "Counts", "sigma"]
-    exlude_set = ["time", "Counts", "date", "notes"]
-    for keys1 in comp:
-        for key2 in dict1["scan"][str(comp[str(keys1)][0])]:
-            if key2 in exlude_set:
-                continue
-            if key2 not in select_set:
-                try:
-                    if (
-                        dict1["scan"][comp[str(keys1)][0]][str(key2)]
-                        != dict2["scan"][str(comp[str(keys1)][1])][str(key2)]
-                    ):
-                        S.append(
-                            "Scan value "
-                            "%s"
-                            ", with hkl %s differs in meausrements %s and %s \n"
-                            % (key2, keys1, comp[str(keys1)][0], comp[str(keys1)][1])
-                        )
-                        S.append(
-                            "     dict1:   %s \n"
-                            % str(dict1["scan"][comp[str(keys1)][0]][str(key2)])
-                        )
-                        S.append(
-                            "     dict2:   %s \n"
-                            % str(dict2["scan"][comp[str(keys1)][1]][str(key2)])
-                        )
-                        if key2 not in conflicts:
-                            conflicts[key2] = {}
-                            conflicts[key2]["amount"] = 1
-                            conflicts[key2]["scan"] = str(comp[str(keys1)])
-                        else:
-
-                            conflicts[key2]["amount"] = conflicts[key2]["amount"] + 1
-                            conflicts[key2]["scan"] = (
-                                conflicts[key2]["scan"] + " " + (str(comp[str(keys1)]))
-                            )
-                except KeyError as e:
-                    print("Missing keys, some files were probably merged or substracted")
-                    print(e.args)
-
-            else:
-                try:
-                    comparison = list(dict1["scan"][comp[str(keys1)][0]][str(key2)]) == list(
-                        dict2["scan"][comp[str(keys1)][1]][str(key2)]
-                    )
-                    if len(list(dict1["scan"][comp[str(keys1)][0]][str(key2)])) != len(
-                        list(dict2["scan"][comp[str(keys1)][1]][str(key2)])
-                    ):
-                        if str("different length of %s" % key2) not in warnings:
-                            warnings[str("different length of %s" % key2)] = list()
-                            warnings[str("different length of %s" % key2)].append(
-                                (str(comp[keys1][0]), str(comp[keys1][1]))
-                            )
-                        else:
-                            warnings[str("different length of %s" % key2)].append(
-                                (str(comp[keys1][0]), str(comp[keys1][1]))
-                            )
-                    if not comparison:
-                        S.append(
-                            "Scan value "
-                            "%s"
-                            " differs in scan %s and %s \n"
-                            % (key2, comp[str(keys1)][0], comp[str(keys1)][1])
-                        )
-                        S.append(
-                            "       dict1:   %s \n"
-                            % str(list(dict1["scan"][comp[str(keys1)][0]][str(key2)]))
-                        )
-                        S.append(
-                            "       dict2:   %s \n"
-                            % str(list(dict2["scan"][comp[str(keys1)][1]][str(key2)]))
-                        )
-                        if key2 not in conflicts:
-                            conflicts[key2] = {}
-                            conflicts[key2]["amount"] = 1
-                            conflicts[key2]["scan"] = str(comp[str(keys1)])
-                        else:
-                            conflicts[key2]["amount"] = conflicts[key2]["amount"] + 1
-                            conflicts[key2]["scan"] = (
-                                conflicts[key2]["scan"] + " " + (str(comp[str(keys1)]))
-                            )
-                except KeyError as e:
-                    print("Missing keys, some files were probably merged or substracted")
-                    print(e.args)
-
-    for keys in conflicts:
-        try:
-            conflicts[str(keys)]["scan"] = conflicts[str(keys)]["scan"].split(" ")
-        except:
-            continue
-    report_string = "".join(S)
-    return warnings, conflicts, report_string
-
-
-def guess_next(dict1, dict2, comp):
-    """iterates thorough the scans and tries to decide if the scans should be
-    substracted or merged"""
-    threshold = 0.05
-    for keys in comp:
-        if (
-            abs(
-                (
-                    dict1["scan"][str(comp[keys][0])]["temperature"]
-                    - dict2["scan"][str(comp[keys][1])]["temperature"]
-                )
-                / dict2["scan"][str(comp[keys][1])]["temperature"]
-            )
-            < threshold
-            and abs(
-                (
-                    dict1["scan"][str(comp[keys][0])]["mag_field"]
-                    - dict2["scan"][str(comp[keys][1])]["mag_field"]
-                )
-                / dict2["scan"][str(comp[keys][1])]["mag_field"]
-            )
-            < threshold
-        ):
-            comp[keys] = comp[keys] + tuple("m")
-        else:
-            comp[keys] = comp[keys] + tuple("s")
-
-    return comp
-
-
-def process_dict(dict1, dict2, comp):
-    """substracts or merges scans, guess_next function must run first """
-    for keys in comp:
-        if comp[keys][2] == "s":
-            substract_measurement(dict1, dict2, comp[keys])
-        elif comp[keys][2] == "m":
-            merge(dict1, dict2, comp[keys])
-
-    return dict1

pyzebra/ccl_findpeaks.py

@@ -1,75 +0,0 @@
-import numpy as np
-import scipy as sc
-from scipy.interpolate import interp1d
-from scipy.signal import savgol_filter
-
-
-def ccl_findpeaks(
-    scan, int_threshold=0.8, prominence=50, smooth=False, window_size=7, poly_order=3
-):
-
-    """function iterates through the dictionary created by load_cclv2 and locates peaks for each scan
-    args:   scan - a single scan,
-
-            int_threshold - fraction of threshold_intensity/max_intensity, must be positive num between 0 and 1
-                        i.e. will only detect peaks above 75% of max intensity
-
-            prominence - defines a drop of values that must be between two peaks, must be positive number
-                        i.e. if promimence is 20, it will detect two neigbouring peaks of 300 and 310 intesities,
-                        if none of the itermediate values are lower that 290
-
-            smooth  - if true, smooths data by savitzky golay filter, if false - no smoothing
-
-            window_size - window size for savgol filter, must be odd positive integer
-
-            poly_order =  order of the polynomial used in savgol filter, must be positive integer smaller than
-            window_size returns: dictionary with following structure:
-                                D{M34{  'num_of_peaks': 1,              #num of peaks
-                                        'peak_indexes': [20],           # index of peaks in omega array
-                                        'peak_heights': [90.],          # height of the peaks (if data vere smoothed
-                                                                        its the heigh of the peaks in smoothed data)
-    """
-    if not 0 <= int_threshold <= 1:
-        int_threshold = 0.8
-        print(
-            "Invalid value for int_threshold, select value between 0 and 1, new value set to:",
-            int_threshold,
-        )
-
-    if not isinstance(window_size, int) or (window_size % 2) == 0 or window_size <= 1:
-        window_size = 7
-        print(
-            "Invalid value for window_size, select positive odd integer, new value set to!:",
-            window_size,
-        )
-
-    if not isinstance(poly_order, int) or window_size < poly_order:
-        poly_order = 3
-        print(
-            "Invalid value for poly_order, select positive integer smaller than window_size, new value set to:",
-            poly_order,
-        )
-
-    if not isinstance(prominence, (int, float)) and prominence < 0:
-        prominence = 50
-        print("Invalid value for prominence, select positive number, new value set to:", prominence)
-
-    omega = scan["om"]
-    counts = np.array(scan["Counts"])
-    if smooth:
-        itp = interp1d(omega, counts, kind="linear")
-        absintensity = [abs(number) for number in counts]
-        lowest_intensity = min(absintensity)
-        counts[counts < 0] = lowest_intensity
-        smooth_peaks = savgol_filter(itp(omega), window_size, poly_order)
-
-    else:
-        smooth_peaks = counts
-
-    peaks, properties = sc.signal.find_peaks(
-        smooth_peaks, height=int_threshold * max(smooth_peaks), prominence=prominence
-    )
-    scan["num_of_peaks"] = len(peaks)
-    scan["peak_indexes"] = peaks
-    scan["peak_heights"] = properties["peak_heights"]
-    scan["smooth_peaks"] = smooth_peaks  # smoothed curve

pyzebra/ccl_io.py

@@ -0,0 +1,448 @@
+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",
+    "comment",
+    "user",
+    "proposal_id",
+    "original_filename",
+    "date",
+    "zebra_mode",
+    "zebramode",
+    "sample_name",
+)
+
+META_VARS_FLOAT = (
+    "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",
+    "snhm",
+    "s1vt",
+    "s1vb",
+    "s1hr",
+    "s1hl",
+    "s2vt",
+    "s2vb",
+    "s2hr",
+    "s2hl",
+    "a5",
+    "a6",
+    "a4t",
+    "s2ant",
+    "s2anb",
+    "s2anl",
+    "s2anr",
+)
+
+META_UB_MATRIX = ("ub1j", "ub2j", "ub3j", "UB")
+
+CCL_FIRST_LINE = (("idx", int), ("h", float), ("k", float), ("l", float))
+
+CCL_ANGLES = {
+    "bi": (("twotheta", float), ("omega", float), ("chi", float), ("phi", float)),
+    "nb": (("gamma", float), ("omega", float), ("nu", float), ("skip_angle", float)),
+}
+
+CCL_SECOND_LINE = (
+    ("n_points", int),
+    ("angle_step", float),
+    ("monitor", float),
+    ("temp", float),
+    ("mf", float),
+    ("date", str),
+    ("time", str),
+    ("scan_motor", str),
+)
+
+EXPORT_TARGETS = {"fullprof": (".comm", ".incomm"), "jana": (".col", ".incol")}
+
+
+def load_1D(filepath):
+    """
+    Loads *.ccl or *.dat file (Distinguishes them based on last 3 chars in string of filepath
+    to add more variables to read, extend the elif list
+    the file must include '#data' and number of points in right place to work properly
+
+    :arg filepath
+    :returns det_variables
+    - dictionary of all detector/scan variables and dictinionary for every scan.
+    Names of these dictionaries are M + scan number. They include HKL indeces, angles,
+    monitors, stepsize and array of counts
+    """
+    with open(filepath, "r") as infile:
+        _, ext = os.path.splitext(filepath)
+        dataset = parse_1D(infile, data_type=ext)
+
+    return dataset
+
+
+def parse_1D(fileobj, data_type, log=logger):
+    metadata = {"data_type": data_type}
+
+    # read metadata
+    for line in fileobj:
+        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
+    dataset = []
+    if data_type == ".ccl":
+        ccl_first_line = CCL_FIRST_LINE + CCL_ANGLES[metadata["zebra_mode"]]
+        ccl_second_line = CCL_SECOND_LINE
+
+        for line in fileobj:
+            # skip empty/whitespace lines before start of any scan
+            if not line or line.isspace():
+                continue
+
+            scan = {}
+            scan["export"] = True
+
+            # first line
+            for param, (param_name, param_type) in zip(line.split(), ccl_first_line):
+                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):
+                scan[param_name] = param_type(param)
+
+            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"
+            scan["scan_motor"] = "omega"
+            scan["scan_motors"] = ["omega"]
+            # overwrite metadata, because it only refers to the scan center
+            half_dist = (scan["n_points"] - 1) / 2 * scan["angle_step"]
+            scan["omega"] = np.linspace(
+                scan["omega"] - half_dist, scan["omega"] + half_dist, scan["n_points"]
+            )
+
+            # subsequent lines with counts
+            counts = []
+            while len(counts) < scan["n_points"]:
+                counts.extend(map(float, next(fileobj).split()))
+            scan["counts"] = np.array(counts)
+            scan["counts_err"] = np.sqrt(np.maximum(scan["counts"], 1))
+
+            if scan["h"].is_integer() and scan["k"].is_integer() and scan["l"].is_integer():
+                scan["h"], scan["k"], scan["l"] = map(int, (scan["h"], scan["k"], scan["l"]))
+
+            dataset.append({**metadata, **scan})
+
+    elif data_type == ".dat":
+        if metadata["zebra_mode"] == "nb":
+            if "gamma_angle" in metadata:
+                # support for the new format
+                metadata["gamma"] = metadata["gamma_angle"]
+            else:
+                metadata["gamma"] = metadata["twotheta"]
+
+        scan = defaultdict(list)
+        scan["export"] = True
+
+        match = re.search("Scanning Variables: (.*), Steps: (.*)", next(fileobj))
+        motors = [motor.strip().lower() for motor in match.group(1).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.")
+        scan["n_points"] = int(match.group(1))
+        scan["monitor"] = float(match.group(3))
+
+        col_names = list(map(str.lower, next(fileobj).split()))
+
+        for line in fileobj:
+            if "END-OF-DATA" in line:
+                # this is the end of data
+                break
+
+            for name, val in zip(col_names, line.split()):
+                scan[name].append(float(val))
+
+        for name in col_names:
+            scan[name] = np.array(scan[name])
+
+        scan["counts_err"] = np.sqrt(np.maximum(scan["counts"], 1))
+
+        scan["scan_motors"] = []
+        for motor, step in zip(motors, steps):
+            if step == 0:
+                # it's not a scan motor, so keep only the median value
+                scan[motor] = np.median(scan[motor])
+            else:
+                scan["scan_motors"].append(motor)
+
+        # "om" -> "omega"
+        if "om" in scan["scan_motors"]:
+            scan["scan_motors"][scan["scan_motors"].index("om")] = "omega"
+            scan["omega"] = scan["om"]
+            del scan["om"]
+
+        # "tt" -> "temp"
+        if "tt" in scan["scan_motors"]:
+            scan["scan_motors"][scan["scan_motors"].index("tt")] = "temp"
+            scan["temp"] = scan["tt"]
+            del scan["tt"]
+
+        # "mf" stays "mf"
+        # "phi" stays "phi"
+
+        scan["scan_motor"] = scan["scan_motors"][0]
+
+        if "h" not in scan:
+            scan["h"] = scan["k"] = scan["l"] = float("nan")
+
+        for param in ("mf", "temp"):
+            if param not in metadata:
+                scan[param] = 0
+
+        scan["idx"] = 1
+
+        dataset.append({**metadata, **scan})
+
+    else:
+        log.error("Unknown file extention")
+
+    return dataset
+
+
+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
+    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 = dataset[0]["zebra_mode"]
+    exts = EXPORT_TARGETS[export_target]
+    file_content = {ext: [] for ext in exts}
+
+    for scan in dataset:
+        if "fit" not in scan:
+            continue
+
+        idx_str = f"{scan['idx']:6}"
+
+        h, k, l = scan["h"], scan["k"], scan["l"]
+        hkl_are_integers = isinstance(h, int)  # if True, other indices are of type 'int' too
+        if hkl_are_integers:
+            hkl_str = f"{h:4}{k:4}{l:4}"
+        else:
+            hkl_str = f"{h:8.{hkl_precision}f}{k:8.{hkl_precision}f}{l:8.{hkl_precision}f}"
+
+        area_n, area_s = scan["area"]
+        area_str = f"{area_n:10.2f}{area_s:10.2f}"
+
+        ang_str = ""
+        for angle, _ in CCL_ANGLES[zebra_mode]:
+            if angle == scan["scan_motor"]:
+                angle_center = (np.min(scan[angle]) + np.max(scan[angle])) / 2
+            else:
+                angle_center = scan[angle]
+
+            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"{scan['temp']:8}" + f"{scan['monitor']:8}"
+
+        ref = file_content[exts[0]] if hkl_are_integers else file_content[exts[1]]
+        ref.append(idx_str + hkl_str + area_str + ang_str + "\n")
+
+    for ext, content in file_content.items():
+        if content:
+            with open(path + ext, "w") as out_file:
+                out_file.writelines(content)
+
+
+def export_ccl_compare(dataset1, dataset2, path, export_target, hkl_precision=2):
+    """Exports compare data in the .comm/.incomm format for fullprof or .col/.incol format for jana.
+
+    Scans with integer/real hkl values are saved in .comm/.incomm or .col/.incol files
+    correspondingly. If no scans are present for a particular output format, that file won't be
+    created.
+    """
+    if export_target not in EXPORT_TARGETS:
+        raise ValueError(f"Unknown export target: {export_target}.")
+
+    zebra_mode = dataset1[0]["zebra_mode"]
+    exts = EXPORT_TARGETS[export_target]
+    file_content = {ext: [] for ext in exts}
+
+    for scan1, scan2 in zip(dataset1, dataset2):
+        if "fit" not in scan1:
+            continue
+
+        idx_str = f"{scan1['idx']:6}"
+
+        h, k, l = scan1["h"], scan1["k"], scan1["l"]
+        hkl_are_integers = isinstance(h, int)  # if True, other indices are of type 'int' too
+        if hkl_are_integers:
+            hkl_str = f"{h:4}{k:4}{l:4}"
+        else:
+            hkl_str = f"{h:8.{hkl_precision}f}{k:8.{hkl_precision}f}{l:8.{hkl_precision}f}"
+
+        area_n1, area_s1 = scan1["area"]
+        area_n2, area_s2 = scan2["area"]
+        area_n = area_n1 - area_n2
+        area_s = np.sqrt(area_s1**2 + area_s2**2)
+        area_str = f"{area_n:10.2f}{area_s:10.2f}"
+
+        ang_str = ""
+        for angle, _ in CCL_ANGLES[zebra_mode]:
+            if angle == scan1["scan_motor"]:
+                angle_center = (np.min(scan1[angle]) + np.max(scan1[angle])) / 2
+            else:
+                angle_center = scan1[angle]
+
+            if angle == "twotheta" and export_target == "jana":
+                angle_center /= 2
+
+            ang_str = ang_str + f"{angle_center:8g}"
+
+        if export_target == "jana":
+            ang_str = ang_str + f"{scan1['temp']:8}" + f"{scan1['monitor']:8}"
+
+        ref = file_content[exts[0]] if hkl_are_integers else file_content[exts[1]]
+        ref.append(idx_str + hkl_str + area_str + ang_str + "\n")
+
+    for ext, content in file_content.items():
+        if content:
+            with open(path + ext, "w") as out_file:
+                out_file.writelines(content)
+
+
+def export_param_study(dataset, param_data, path):
+    file_content = []
+    for scan, param in zip(dataset, param_data):
+        if "fit" not in scan:
+            continue
+
+        if not file_content:
+            title_str = f"{'param':12}"
+            for fit_param_name in scan["fit"].params:
+                title_str = title_str + f"{fit_param_name:20}" + f"{'std_' + fit_param_name:20}"
+            title_str = title_str + "file"
+            file_content.append(title_str + "\n")
+
+        param_str = f"{param:<12.2f}"
+
+        fit_str = ""
+        for fit_param in scan["fit"].params.values():
+            fit_param_val = fit_param.value
+            fit_param_std = fit_param.stderr
+            if fit_param_std is None:
+                fit_param_std = 0
+            fit_str = fit_str + f"{fit_param_val:<20.2f}" + f"{fit_param_std:<20.2f}"
+
+        _, fname_str = os.path.split(scan["original_filename"])
+
+        file_content.append(param_str + fit_str + fname_str + "\n")
+
+    if file_content:
+        with open(path, "w") as out_file:
+            out_file.writelines(file_content)

pyzebra/ccl_process.py

@@ -0,0 +1,341 @@
+import logging
+import os
+
+import numpy as np
+from lmfit.models import GaussianModel, LinearModel, PseudoVoigtModel, VoigtModel
+from scipy.integrate import simpson, trapezoid
+
+from pyzebra import CCL_ANGLES
+
+logger = logging.getLogger(__name__)
+
+PARAM_PRECISIONS = {
+    "twotheta": 0.1,
+    "chi": 0.1,
+    "nu": 0.1,
+    "phi": 0.05,
+    "omega": 0.05,
+    "gamma": 0.05,
+    "temp": 1,
+    "mf": 0.001,
+    "ub": 0.01,
+}
+
+MAX_RANGE_GAP = {"omega": 0.5}
+
+MOTOR_POS_PRECISION = 0.01
+
+AREA_METHODS = ("fit_area", "int_area")
+
+
+def normalize_dataset(dataset, monitor=100_000):
+    for scan in dataset:
+        monitor_ratio = monitor / scan["monitor"]
+        scan["counts"] *= monitor_ratio
+        scan["counts_err"] *= monitor_ratio
+        scan["monitor"] = monitor
+
+
+def merge_duplicates(dataset, log=logger):
+    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, log=log)
+                merged[ind_from] = True
+
+
+def _parameters_match(scan1, scan2):
+    zebra_mode = scan1["zebra_mode"]
+    if zebra_mode != scan2["zebra_mode"]:
+        return False
+
+    for param in ("ub", *(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
+            r1_start, r1_end = scan1[param][0], scan1[param][-1]
+            r2_start, r2_end = scan2[param][0], scan2[param][-1]
+            # support reversed ranges
+            if r1_start > r1_end:
+                r1_start, r1_end = r1_end, r1_start
+            if r2_start > r2_end:
+                r2_start, r2_end = r2_end, r2_start
+            # maximum gap between ranges of the scanning parameter (default 0)
+            max_range_gap = MAX_RANGE_GAP.get(param, 0)
+            if max(r1_start - r2_end, r2_start - r1_end) > max_range_gap:
+                return False
+
+        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, log=logger):
+    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]:
+                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, 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"] = []
+
+    if scan_from in scan_into["merged_scans"]:
+        return
+
+    scan_into["merged_scans"].append(scan_from)
+
+    scan_motor = scan_into["scan_motor"]  # the same as scan_from["scan_motor"]
+
+    pos_all = np.array([])
+    val_all = np.array([])
+    err_all = np.array([])
+    for scan in [scan_into["init_scan"], *scan_into["merged_scans"]]:
+        pos_all = np.append(pos_all, scan[scan_motor])
+        val_all = np.append(val_all, scan["counts"])
+        err_all = np.append(err_all, scan["counts_err"] ** 2)
+
+    sort_index = np.argsort(pos_all)
+    pos_all = pos_all[sort_index]
+    val_all = val_all[sort_index]
+    err_all = err_all[sort_index]
+
+    pos_tmp = pos_all[:1]
+    val_tmp = val_all[:1]
+    err_tmp = err_all[:1]
+    num_tmp = np.array([1])
+    for pos, val, err in zip(pos_all[1:], val_all[1:], err_all[1:]):
+        if pos - pos_tmp[-1] < MOTOR_POS_PRECISION:
+            # the repeated motor position
+            val_tmp[-1] += val
+            err_tmp[-1] += err
+            num_tmp[-1] += 1
+        else:
+            # a new motor position
+            pos_tmp = np.append(pos_tmp, pos)
+            val_tmp = np.append(val_tmp, val)
+            err_tmp = np.append(err_tmp, err)
+            num_tmp = np.append(num_tmp, 1)
+
+    scan_into[scan_motor] = pos_tmp
+    scan_into["counts"] = val_tmp / num_tmp
+    scan_into["counts_err"] = np.sqrt(err_tmp) / num_tmp
+
+    scan_from["export"] = False
+
+    fname1 = os.path.basename(scan_into["original_filename"])
+    fname2 = os.path.basename(scan_from["original_filename"])
+    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["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, log=logger):
+    if fit_from is None:
+        fit_from = -np.inf
+    if fit_to is None:
+        fit_to = np.inf
+
+    y_fit = scan["counts"]
+    y_err = scan["counts_err"]
+    x_fit = scan[scan["scan_motor"]]
+
+    # apply fitting range
+    fit_ind = (fit_from <= x_fit) & (x_fit <= fit_to)
+    if not np.any(fit_ind):
+        log.warning(f"No data in fit range for scan {scan['idx']}")
+        return
+
+    y_fit = y_fit[fit_ind]
+    y_err = y_err[fit_ind]
+    x_fit = x_fit[fit_ind]
+
+    model = None
+    for model_index, (model_name, model_param) in enumerate(model_dict.items()):
+        model_name, _ = model_name.split("-")
+        prefix = f"f{model_index}_"
+
+        if model_name == "linear":
+            _model = LinearModel(prefix=prefix)
+        elif model_name == "gaussian":
+            _model = GaussianModel(prefix=prefix)
+        elif model_name == "voigt":
+            _model = VoigtModel(prefix=prefix)
+        elif model_name == "pvoigt":
+            _model = PseudoVoigtModel(prefix=prefix)
+        else:
+            raise ValueError(f"Unknown model name: '{model_name}'")
+
+        _init_guess = _model.guess(y_fit, x=x_fit)
+
+        for param_index, param_name in enumerate(model_param["param"]):
+            param_hints = {}
+            for hint_name in ("value", "vary", "min", "max"):
+                tmp = model_param[hint_name][param_index]
+                if tmp is None:
+                    param_hints[hint_name] = getattr(_init_guess[prefix + param_name], hint_name)
+                else:
+                    param_hints[hint_name] = tmp
+
+            if "center" in param_name:
+                if np.isneginf(param_hints["min"]):
+                    param_hints["min"] = np.min(x_fit)
+
+                if np.isposinf(param_hints["max"]):
+                    param_hints["max"] = np.max(x_fit)
+
+            if "sigma" in param_name:
+                if np.isposinf(param_hints["max"]):
+                    param_hints["max"] = np.max(x_fit) - np.min(x_fit)
+
+            _model.set_param_hint(param_name, **param_hints)
+
+        if model is None:
+            model = _model
+        else:
+            model += _model
+
+    scan["fit"] = model.fit(y_fit, x=x_fit, weights=1 / y_err)
+
+
+def get_area(scan, area_method, lorentz):
+    if "fit" not in scan:
+        return
+
+    if area_method not in AREA_METHODS:
+        raise ValueError(f"Unknown area method: {area_method}.")
+
+    if area_method == "fit_area":
+        area_v = 0
+        area_s = 0
+        for name, param in scan["fit"].params.items():
+            if "amplitude" in name:
+                area_v += np.nan if param.value is None else param.value
+                area_s += np.nan if param.stderr is None else param.stderr
+
+    else:  # area_method == "int_area"
+        y_val = scan["counts"]
+        x_val = scan[scan["scan_motor"]]
+        y_bkg = scan["fit"].eval_components(x=x_val)["f0_"]
+        area_v = simpson(y_val, x=x_val) - trapezoid(y_bkg, x=x_val)
+        area_s = np.sqrt(area_v)
+
+    if lorentz:
+        # lorentz correction to area
+        if scan["zebra_mode"] == "bi":
+            twotheta = np.deg2rad(scan["twotheta"])
+            corr_factor = np.sin(twotheta)
+        else:  # zebra_mode == "nb":
+            gamma = np.deg2rad(scan["gamma"])
+            nu = np.deg2rad(scan["nu"])
+            corr_factor = np.sin(gamma) * np.cos(nu)
+
+        area_v = np.abs(area_v * corr_factor)
+        area_s = np.abs(area_s * corr_factor)
+
+    scan["area"] = (area_v, area_s)

pyzebra/cli.py

@@ -1,61 +0,0 @@
-import argparse
-import logging
-import os
-
-from bokeh.application.application import Application
-from bokeh.application.handlers import ScriptHandler
-from bokeh.server.server import Server
-
-logging.basicConfig(format="%(asctime)s %(message)s", level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-
-def main():
-    """The pyzebra command line interface.
-
-    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", "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(
-        "--args",
-        nargs=argparse.REMAINDER,
-        default=[],
-        help="command line arguments for the pyzebra application",
-    )
-
-    args = parser.parse_args()
-
-    logger.info(app_path)
-
-    handler = ScriptHandler(filename=app_path, argv=args.args)
-    server = Server(
-        {"/": Application(handler)},
-        port=args.port,
-        allow_websocket_origin=args.allow_websocket_origin,
-    )
-
-    server.start()
-    server.io_loop.start()
-
-
-if __name__ == "__main__":
-    main()

pyzebra/comm_export.py

@@ -1,80 +0,0 @@
-import numpy as np
-
-
-def correction(value, lorentz=True, zebra_mode="--", ang1=0, ang2=0):
-    if lorentz is False:
-        return value
-    else:
-        if zebra_mode == "bi":
-            corr_value = np.abs(value * np.sin(ang1))
-            return corr_value
-        elif zebra_mode == "nb":
-            corr_value = np.abs(value * np.sin(ang1) * np.cos(ang2))
-            return corr_value
-
-
-def export_comm(data, path, lorentz=False):
-    """exports data in the *.comm format
-    :param lorentz: perform Lorentz correction
-    :param path: path to file + name
-    :arg data - data to export, is dict after peak fitting
-
-    """
-    zebra_mode = data["meta"]["zebra_mode"]
-    align = ">"
-    if data["meta"]["indices"] == "hkl":
-        extension = ".comm"
-        padding = [6, 4, 10, 8]
-    elif data["meta"]["indices"] == "real":
-        extension = ".incomm"
-        padding = [4, 6, 10, 8]
-
-    with open(str(path + extension), "w") as out_file:
-        for key, scan in data["scan"].items():
-            if "fit" not in scan:
-                print("Scan skipped - no fit value for:", key)
-                continue
-            scan_number_str = f"{key:{align}{padding[0]}}"
-            h_str = f'{int(scan["h_index"]):{padding[1]}}'
-            k_str = f'{int(scan["k_index"]):{padding[1]}}'
-            l_str = f'{int(scan["l_index"]):{padding[1]}}'
-            if data["meta"]["area_method"] == "fit":
-                area = float(scan["fit"]["fit_area"].n)
-                sigma_str = (
-                    f'{"{:8.2f}".format(float(scan["fit"]["fit_area"].s)):{align}{padding[2]}}'
-                )
-            elif data["meta"]["area_method"] == "integ":
-                area = float(scan["fit"]["int_area"].n)
-                sigma_str = (
-                    f'{"{:8.2f}".format(float(scan["fit"]["int_area"].s)):{align}{padding[2]}}'
-                )
-
-            if zebra_mode == "bi":
-                area = correction(area, lorentz, zebra_mode, scan["twotheta_angle"])
-                int_str = f'{"{:8.2f}".format(area):{align}{padding[2]}}'
-                angle_str1 = f'{scan["twotheta_angle"]:{padding[3]}}'
-                angle_str2 = f'{scan["omega_angle"]:{padding[3]}}'
-                angle_str3 = f'{scan["chi_angle"]:{padding[3]}}'
-                angle_str4 = f'{scan["phi_angle"]:{padding[3]}}'
-            elif zebra_mode == "nb":
-                area = correction(area, lorentz, zebra_mode, scan["gamma_angle"], scan["nu_angle"])
-                int_str = f'{"{:8.2f}".format(area):{align}{padding[2]}}'
-                angle_str1 = f'{scan["gamma_angle"]:{padding[3]}}'
-                angle_str2 = f'{scan["omega_angle"]:{padding[3]}}'
-                angle_str3 = f'{scan["nu_angle"]:{padding[3]}}'
-                angle_str4 = f'{scan["unkwn_angle"]:{padding[3]}}'
-
-            line = (
-                scan_number_str
-                + h_str
-                + k_str
-                + l_str
-                + int_str
-                + sigma_str
-                + angle_str1
-                + angle_str2
-                + angle_str3
-                + angle_str4
-                + "\n"
-            )
-            out_file.write(line)

pyzebra/fit2.py

@@ -1,227 +0,0 @@
-import numpy as np
-import uncertainties as u
-from lmfit import Model, Parameters
-from scipy.integrate import simps
-
-
-def bin_data(array, binsize):
-    if isinstance(binsize, int) and 0 < binsize < len(array):
-        return [
-            np.mean(array[binsize * i : binsize * i + binsize])
-            for i in range(int(np.ceil(len(array) / binsize)))
-        ]
-    else:
-        print("Binsize need to be positive integer smaller than lenght of array")
-        return array
-
-
-def find_nearest(array, value):
-    # find nearest value and return index
-    array = np.asarray(array)
-    idx = (np.abs(array - value)).argmin()
-    return idx
-
-
-def create_uncertanities(y, y_err):
-    # create array with uncertanities for error propagation
-    combined = np.array([])
-    for i in range(len(y)):
-        part = u.ufloat(y[i], y_err[i])
-        combined = np.append(combined, part)
-    return combined
-
-
-def fitccl(
-    scan,
-    guess,
-    vary,
-    constraints_min,
-    constraints_max,
-    numfit_min=None,
-    numfit_max=None,
-    binning=None,
-):
-    """Made for fitting of ccl date where 1 peak is expected. Allows for combination of gaussian and linear model combination
-    :param scan: scan in the data dict (i.e. M123)
-    :param guess: initial guess for the fitting, if none, some values are added automatically in order (see below)
-    :param vary: True if parameter can vary during fitting, False if it to be fixed
-    :param numfit_min: minimal value on x axis for numerical integration - if none is centre of gaussian minus 3 sigma
-    :param numfit_max: maximal value on x axis for numerical integration - if none is centre of gaussian plus 3 sigma
-    :param constraints_min: min constranits value for fit
-    :param constraints_max: max constranits value for fit
-    :param binning : binning of the data
-    :return data dict with additional values
-    order for guess, vary, constraints_min, constraints_max:
-    [Gaussian centre, Gaussian sigma, Gaussian amplitude, background slope, background intercept]
-    examples:
-    guess = [None, None, 100,  0, None]
-    vary = [True, True, True, True,  True]
-    constraints_min = [23, None, 50, 0, 0]
-    constraints_min = [80, None, 1000, 0, 100]
-    """
-    if len(scan["peak_indexes"]) > 1:
-        # return in case of more than 1 peaks
-        print("More than 1 peak, scan skipped")
-        return
-    if binning is None or binning == 0 or binning == 1:
-        x = list(scan["om"])
-        y = list(scan["Counts"])
-        y_err = list(np.sqrt(y)) if scan.get("sigma", None) is None else list(scan["sigma"])
-        print(scan["peak_indexes"])
-        if not scan["peak_indexes"]:
-            centre = np.mean(x)
-        else:
-            centre = x[int(scan["peak_indexes"])]
-    else:
-        x = list(scan["om"])
-        if not scan["peak_indexes"]:
-            centre = np.mean(x)
-        else:
-            centre = x[int(scan["peak_indexes"])]
-        x = bin_data(x, binning)
-        y = list(scan["Counts"])
-        y_err = list(np.sqrt(y)) if scan.get("sigma", None) is None else list(scan["sigma"])
-        combined = bin_data(create_uncertanities(y, y_err), binning)
-        y = [combined[i].n for i in range(len(combined))]
-        y_err = [combined[i].s for i in range(len(combined))]
-
-    if len(scan["peak_indexes"]) == 0:
-        # Case for no peak, gaussian in centre, sigma as 20% of range
-        print("No peak")
-        peak_index = find_nearest(x, np.mean(x))
-        guess[0] = centre if guess[0] is None else guess[0]
-        guess[1] = (x[-1] - x[0]) / 5 if guess[1] is None else guess[1]
-        guess[2] = 50 if guess[2] is None else guess[2]
-        guess[3] = 0 if guess[3] is None else guess[3]
-        guess[4] = np.mean(y) if guess[4] is None else guess[4]
-        constraints_min[2] = 0
-
-    elif len(scan["peak_indexes"]) == 1:
-        # case for one peak, takse into account users guesses
-        print("one peak")
-        peak_height = scan["peak_heights"]
-        guess[0] = centre if guess[0] is None else guess[0]
-        guess[1] = 0.1 if guess[1] is None else guess[1]
-        guess[2] = float(peak_height / 10) if guess[2] is None else float(guess[2])
-        guess[3] = 0 if guess[3] is None else guess[3]
-        guess[4] = np.median(x) if guess[4] is None else guess[4]
-        constraints_min[0] = np.min(x) if constraints_min[0] is None else constraints_min[0]
-        constraints_max[0] = np.max(x) if constraints_max[0] is None else constraints_max[0]
-
-    def gaussian(x, g_cen, g_width, g_amp):
-        """1-d gaussian: gaussian(x, amp, cen, wid)"""
-        return (g_amp / (np.sqrt(2 * np.pi) * g_width)) * np.exp(
-            -((x - g_cen) ** 2) / (2 * g_width ** 2)
-        )
-
-    def background(x, slope, intercept):
-        """background"""
-        return slope * (x - centre) + intercept
-
-    mod = Model(gaussian) + Model(background)
-    params = Parameters()
-    params.add_many(
-        ("g_cen", guess[0], bool(vary[0]), np.min(x), np.max(x), None, None),
-        ("g_width", guess[1], bool(vary[1]), constraints_min[1], constraints_max[1], None, None),
-        ("g_amp", guess[2], bool(vary[2]), constraints_min[2], constraints_max[2], None, None),
-        ("slope", guess[3], bool(vary[3]), constraints_min[3], constraints_max[3], None, None),
-        ("intercept", guess[4], bool(vary[4]), constraints_min[4], constraints_max[4], None, None),
-    )
-    # the weighted fit
-    try:
-        result = mod.fit(
-            y, params, weights=[np.abs(1 / val) for val in y_err], x=x, calc_covar=True,
-        )
-    except ValueError:
-        return
-
-    if result.params["g_amp"].stderr is None:
-        result.params["g_amp"].stderr = result.params["g_amp"].value
-    elif result.params["g_amp"].stderr > result.params["g_amp"].value:
-        result.params["g_amp"].stderr = result.params["g_amp"].value
-
-    # u.ufloat to work with uncertanities
-    fit_area = u.ufloat(result.params["g_amp"].value, result.params["g_amp"].stderr)
-    comps = result.eval_components()
-
-    if len(scan["peak_indexes"]) == 0:
-        # for case of no peak, there is no reason to integrate, therefore fit and int are equal
-        int_area = fit_area
-
-    elif len(scan["peak_indexes"]) == 1:
-        gauss_3sigmamin = find_nearest(
-            x, result.params["g_cen"].value - 3 * result.params["g_width"].value
-        )
-        gauss_3sigmamax = find_nearest(
-            x, result.params["g_cen"].value + 3 * result.params["g_width"].value
-        )
-        numfit_min = gauss_3sigmamin if numfit_min is None else find_nearest(x, numfit_min)
-        numfit_max = gauss_3sigmamax if numfit_max is None else find_nearest(x, numfit_max)
-
-        it = -1
-        while abs(numfit_max - numfit_min) < 3:
-            # in the case the peak is very thin and numerical integration would be on zero omega
-            # difference, finds closes values
-            it = it + 1
-            numfit_min = find_nearest(
-                x,
-                result.params["g_cen"].value - 3 * (1 + it / 10) * result.params["g_width"].value,
-            )
-            numfit_max = find_nearest(
-                x,
-                result.params["g_cen"].value + 3 * (1 + it / 10) * result.params["g_width"].value,
-            )
-
-        if x[numfit_min] < np.min(x):
-            # makes sure that the values supplied by user lay in the omega range
-            # can be ommited for users who know what they're doing
-            numfit_min = gauss_3sigmamin
-            print("Minimal integration value outside of x range")
-        elif x[numfit_min] >= x[numfit_max]:
-            numfit_min = gauss_3sigmamin
-            print("Minimal integration value higher than maximal")
-        else:
-            pass
-        if x[numfit_max] > np.max(x):
-            numfit_max = gauss_3sigmamax
-            print("Maximal integration value outside of x range")
-        elif x[numfit_max] <= x[numfit_min]:
-            numfit_max = gauss_3sigmamax
-            print("Maximal integration value lower than minimal")
-        else:
-            pass
-
-        count_errors = create_uncertanities(y, y_err)
-        # create error vector for numerical integration propagation
-        num_int_area = simps(count_errors[numfit_min:numfit_max], x[numfit_min:numfit_max])
-        slope_err = u.ufloat(result.params["slope"].value, result.params["slope"].stderr)
-        # pulls the nominal and error values from fit (slope)
-        intercept_err = u.ufloat(
-            result.params["intercept"].value, result.params["intercept"].stderr
-        )
-        # pulls the nominal and error values from fit (intercept)
-
-        background_errors = np.array([])
-        for j in range(len(x[numfit_min:numfit_max])):
-            # creates nominal and error vector for numerical integration of background
-            bg = slope_err * (x[j] - centre) + intercept_err
-            background_errors = np.append(background_errors, bg)
-
-        num_int_background = simps(background_errors, x[numfit_min:numfit_max])
-        int_area = num_int_area - num_int_background
-
-    d = {}
-    for pars in result.params:
-        d[str(pars)] = (result.params[str(pars)].value, result.params[str(pars)].vary)
-    print(result.fit_report())
-
-    print((result.params["g_amp"].value - int_area.n) / result.params["g_amp"].value)
-
-    d["ratio"] = (result.params["g_amp"].value - int_area.n) / result.params["g_amp"].value
-    d["int_area"] = int_area
-    d["fit_area"] = u.ufloat(result.params["g_amp"].value, result.params["g_amp"].stderr)
-    d["full_report"] = result.fit_report()
-    d["result"] = result
-    d["comps"] = comps
-    d["numfit"] = [numfit_min, numfit_max]
-    scan["fit"] = d

pyzebra/fitvol3.py

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

pyzebra/h5.py

@@ -1,4 +1,10 @@
 import h5py
+import numpy as np
+from lmfit.models import Gaussian2dModel, GaussianModel
+
+META_MATRIX = ("UB",)
+META_CELL = ("cell",)
+META_STR = ("name",)
 
 
 def read_h5meta(filepath):
@@ -23,49 +29,184 @@ def parse_h5meta(file):
         line = line.strip()
         if line.startswith("#begin "):
             section = line[len("#begin ") :]
-            content[section] = []
+            if section in ("detector parameters", "crystal"):
+                content[section] = {}
+            else:
+                content[section] = []
 
         elif line.startswith("#end"):
             section = None
 
         elif section:
-            content[section].append(line)
+            if section in ("detector parameters", "crystal"):
+                if "=" in line:
+                    variable, value = line.split("=", 1)
+                    variable = variable.strip()
+                    value = value.strip()
+
+                    if variable in META_STR:
+                        pass
+                    elif variable in META_CELL:
+                        value = np.array(value.split(",")[:6], dtype=float)
+                    elif variable in META_MATRIX:
+                        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
+            else:
+                content[section].append(line)
 
     return content
 
 
-def read_detector_data(filepath):
+def read_detector_data(filepath, cami_meta=None):
     """Read detector data and angles from an h5 file.
 
     Args:
         filepath (str): File path of an h5 file.
 
     Returns:
-        ndarray: A 3D array of data, rot_angle, pol_angle, tilt_angle.
+        ndarray: A 3D array of data, omega, gamma, nu.
     """
     with h5py.File(filepath, "r") as h5f:
-        data = h5f["/entry1/area_detector2/data"][:]
+        counts = h5f["/entry1/area_detector2/data"][:].astype(float)
 
-        # reshape data to a correct shape (2006 issue)
-        n, cols, rows = data.shape
-        data = data.reshape(n, rows, cols)
+        n, cols, rows = counts.shape
+        if "/entry1/experiment_identifier" in h5f:  # old format
+            # reshape images (counts) to a correct shape (2006 issue)
+            counts = counts.reshape(n, rows, cols)
+        else:
+            counts = counts.swapaxes(1, 2)
 
-        det_data = {"data": data}
+        scan = {"counts": counts, "counts_err": np.sqrt(np.maximum(counts, 1))}
+        scan["original_filename"] = filepath
+        scan["export"] = True
 
-        det_data["rot_angle"] = h5f["/entry1/area_detector2/rotation_angle"][:]  # om, sometimes ph
-        det_data["pol_angle"] = h5f["/entry1/ZEBRA/area_detector2/polar_angle"][:]  # gammad
-        det_data["tlt_angle"] = h5f["/entry1/ZEBRA/area_detector2/tilt_angle"][:]  # nud
-        det_data["ddist"] = h5f["/entry1/ZEBRA/area_detector2/distance"][:]
-        det_data["wave"] = h5f["/entry1/ZEBRA/monochromator/wavelength"][:]
-        det_data["chi_angle"] = h5f["/entry1/sample/chi"][:]  # ch
-        det_data["phi_angle"] = h5f["/entry1/sample/phi"][:]  # ph
-        det_data["UB"] = h5f["/entry1/sample/UB"][:].reshape(3, 3)
+        if "/entry1/zebra_mode" in h5f:
+            scan["zebra_mode"] = h5f["/entry1/zebra_mode"][0].decode()
+        else:
+            scan["zebra_mode"] = "nb"
+
+        # overwrite zebra_mode from cami
+        if cami_meta is not None:
+            if "zebra_mode" in cami_meta:
+                scan["zebra_mode"] = cami_meta["zebra_mode"][0]
+
+        if "/entry1/control/Monitor" in h5f:
+            scan["monitor"] = h5f["/entry1/control/Monitor"][0]
+        else:  # old path
+            scan["monitor"] = h5f["/entry1/control/data"][0]
+
+        scan["idx"] = 1
+
+        if "/entry1/sample/rotation_angle" in h5f:
+            scan["omega"] = h5f["/entry1/sample/rotation_angle"][:]
+        else:
+            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["magnetic_field"] = 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["temperature"] = h5f["/entry1/sample/temperature"][:]
+            scan["temp"] = h5f["/entry1/sample/temperature"][:]
+        elif "/entry1/sample/Ts/value" in h5f:
+            scan["temp"] = h5f["/entry1/sample/Ts/value"][:]
 
-    return det_data
+        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:
+                    scan["name"] = cami_meta_crystal["name"]
+                if "UB" in cami_meta_crystal:
+                    scan["ub"] = cami_meta_crystal["UB"]
+                if "cell" in cami_meta_crystal:
+                    scan["cell"] = cami_meta_crystal["cell"]
+                if "lambda" in cami_meta_crystal:
+                    scan["wave"] = cami_meta_crystal["lambda"]
+
+            if "detector parameters" in cami_meta:
+                cami_meta_detparam = cami_meta["detector parameters"]
+                if "dist2" in cami_meta_detparam:
+                    scan["ddist"] = cami_meta_detparam["dist2"]
+
+    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/load_1D.py

@@ -1,221 +0,0 @@
-import os
-import re
-from collections import defaultdict
-from decimal import Decimal
-
-import numpy as np
-
-META_VARS_STR = (
-    "instrument",
-    "title",
-    "sample",
-    "user",
-    "ProposalID",
-    "original_filename",
-    "date",
-    "zebra_mode",
-    "proposal",
-    "proposal_user",
-    "proposal_title",
-    "proposal_email",
-    "detectorDistance",
-)
-META_VARS_FLOAT = (
-    "omega",
-    "mf",
-    "2-theta",
-    "chi",
-    "phi",
-    "nu",
-    "temp",
-    "wavelenght",
-    "a",
-    "b",
-    "c",
-    "alpha",
-    "beta",
-    "gamma",
-    "cex1",
-    "cex2",
-    "mexz",
-    "moml",
-    "mcvl",
-    "momu",
-    "mcvu",
-    "snv",
-    "snh",
-    "snvm",
-    "snhm",
-    "s1vt",
-    "s1vb",
-    "s1hr",
-    "s1hl",
-    "s2vt",
-    "s2vb",
-    "s2hr",
-    "s2hl",
-)
-META_UB_MATRIX = ("ub1j", "ub2j", "ub3j")
-
-CCL_FIRST_LINE = (
-    # the first element is `scan_number`, which we don't save to metadata
-    ("h_index", float),
-    ("k_index", float),
-    ("l_index", float),
-)
-
-CCL_FIRST_LINE_BI = (
-    *CCL_FIRST_LINE,
-    ("twotheta_angle", float),
-    ("omega_angle", float),
-    ("chi_angle", float),
-    ("phi_angle", float),
-)
-
-CCL_FIRST_LINE_NB = (
-    *CCL_FIRST_LINE,
-    ("gamma_angle", float),
-    ("omega_angle", float),
-    ("nu_angle", float),
-    ("unkwn_angle", float),
-)
-
-CCL_SECOND_LINE = (
-    ("number_of_measurements", int),
-    ("angle_step", float),
-    ("monitor", float),
-    ("temperature", float),
-    ("mag_field", float),
-    ("date", str),
-    ("time", str),
-    ("scan_type", str),
-)
-
-
-def load_1D(filepath):
-    """
-    Loads *.ccl or *.dat file (Distinguishes them based on last 3 chars in string of filepath
-    to add more variables to read, extend the elif list
-    the file must include '#data' and number of points in right place to work properly
-
-    :arg filepath
-    :returns det_variables
-    - dictionary of all detector/scan variables and dictinionary for every scan.
-    Names of these dictionaries are M + scan number. They include HKL indeces, angles,
-    monitors, stepsize and array of counts
-    """
-    with open(filepath, "r") as infile:
-        _, ext = os.path.splitext(filepath)
-        det_variables = parse_1D(infile, data_type=ext)
-
-    return det_variables
-
-
-def parse_1D(fileobj, data_type):
-    # read metadata
-    metadata = {}
-    for line in fileobj:
-        if "=" in line:
-            variable, value = line.split("=")
-            variable = variable.strip()
-            if variable in META_VARS_FLOAT:
-                metadata[variable] = float(value)
-            elif variable in META_VARS_STR:
-                metadata[variable] = str(value)[:-1].strip()
-            elif variable in META_UB_MATRIX:
-                metadata[variable] = re.findall(r"[-+]?\d*\.\d+|\d+", str(value))
-
-        if "#data" in line:
-            # this is the end of metadata and the start of data section
-            break
-
-    # read data
-    scan = {}
-    if data_type == ".ccl":
-        decimal = list()
-
-        if metadata["zebra_mode"] == "bi":
-            ccl_first_line = CCL_FIRST_LINE_BI
-        elif metadata["zebra_mode"] == "nb":
-            ccl_first_line = CCL_FIRST_LINE_NB
-        ccl_second_line = CCL_SECOND_LINE
-
-        for line in fileobj:
-            d = {}
-
-            # first line
-            scan_number, *params = line.split()
-            for param, (param_name, param_type) in zip(params, ccl_first_line):
-                d[param_name] = param_type(param)
-
-            decimal.append(bool(Decimal(d["h_index"]) % 1 == 0))
-            decimal.append(bool(Decimal(d["k_index"]) % 1 == 0))
-            decimal.append(bool(Decimal(d["l_index"]) % 1 == 0))
-
-            # second line
-            next_line = next(fileobj)
-            params = next_line.split()
-            for param, (param_name, param_type) in zip(params, ccl_second_line):
-                d[param_name] = param_type(param)
-
-            d["om"] = np.linspace(
-                d["omega_angle"] - (d["number_of_measurements"] / 2) * d["angle_step"],
-                d["omega_angle"] + (d["number_of_measurements"] / 2) * d["angle_step"],
-                d["number_of_measurements"],
-            )
-
-            # subsequent lines with counts
-            counts = []
-            while len(counts) < d["number_of_measurements"]:
-                counts.extend(map(int, next(fileobj).split()))
-            d["Counts"] = counts
-
-            scan[int(scan_number)] = d
-
-            if all(decimal):
-                metadata["indices"] = "hkl"
-            else:
-                metadata["indices"] = "real"
-
-    elif data_type == ".dat":
-        # skip the first 2 rows, the third row contans the column names
-        next(fileobj)
-        next(fileobj)
-        col_names = next(fileobj).split()
-        data_cols = defaultdict(list)
-
-        for line in fileobj:
-            if "END-OF-DATA" in line:
-                # this is the end of data
-                break
-
-            for name, val in zip(col_names, line.split()):
-                data_cols[name].append(float(val))
-
-        try:
-            data_cols["h_index"] = float(metadata["title"].split()[-3])
-            data_cols["k_index"] = float(metadata["title"].split()[-2])
-            data_cols["l_index"] = float(metadata["title"].split()[-1])
-        except (ValueError, IndexError):
-            print("seems hkl is not in title")
-
-        data_cols["temperature"] = metadata["temp"]
-        data_cols["mag_field"] = metadata["mf"]
-        data_cols["omega_angle"] = metadata["omega"]
-        data_cols["number_of_measurements"] = len(data_cols["om"])
-        data_cols["monitor"] = data_cols["Monitor1"][0]
-        data_cols["twotheta_angle"] = metadata["2-theta"]
-        data_cols["chi_angle"] = metadata["chi"]
-        data_cols["phi_angle"] = metadata["phi"]
-        data_cols["nu_angle"] = metadata["nu"]
-
-        scan[1] = dict(data_cols)
-
-    else:
-        print("Unknown file extention")
-
-    # utility information
-    metadata["data_type"] = data_type
-    metadata["area_method"] = "fit"
-
-    return {"meta": metadata, "scan": scan}

pyzebra/param_study_moduls.py

@@ -1,383 +0,0 @@
-from load_1D import load_1D
-import pandas as pd
-from mpl_toolkits.mplot3d import Axes3D  # dont delete, otherwise waterfall wont work
-import matplotlib.pyplot as plt
-import matplotlib as mpl
-import numpy as np
-import pickle
-import scipy.io as sio
-import uncertainties as u
-
-
-def create_tuples(x, y, y_err):
-    """creates tuples for sorting and merginng of the data
-    Counts need to be normalized to monitor before"""
-    t = list()
-    for i in range(len(x)):
-        tup = (x[i], y[i], y_err[i])
-        t.append(tup)
-    return t
-
-
-def load_dats(filepath):
-    """reads the txt file, get headers and data
-    :arg filepath to txt file or list of filepaths to the files
-    :return ccl like dictionary"""
-    if isinstance(filepath, str):
-        data_type = "txt"
-        file_list = list()
-        with open(filepath, "r") as infile:
-            col_names = next(infile).split(",")
-            col_names = [col_names[i].rstrip() for i in range(len(col_names))]
-            for line in infile:
-                if "END" in line:
-                    break
-                file_list.append(tuple(line.split(",")))
-    elif isinstance(filepath, list):
-        data_type = "list"
-        file_list = filepath
-    dict1 = {}
-    for i in range(len(file_list)):
-        if not dict1:
-            if data_type == "txt":
-                dict1 = load_1D(file_list[0][0])
-            else:
-                dict1 = load_1D(file_list[0])
-        else:
-            if data_type == "txt":
-                dict1 = add_dict(dict1, load_1D(file_list[i][0]))
-            else:
-                dict1 = add_dict(dict1, load_1D(file_list[i]))
-        dict1["scan"][i + 1]["params"] = {}
-        if data_type == "txt":
-            for x in range(len(col_names) - 1):
-                dict1["scan"][i + 1]["params"][col_names[x + 1]] = file_list[i][x + 1]
-
-    return dict1
-
-
-def create_dataframe(dict1):
-    """Creates pandas dataframe from the dictionary
-    :arg ccl like dictionary
-    :return pandas dataframe"""
-    # create dictionary to which we pull only wanted items before transforming it to pd.dataframe
-    pull_dict = {}
-    pull_dict["filenames"] = list()
-    for key in dict1["scan"][1]["params"]:
-        pull_dict[key] = list()
-    pull_dict["temperature"] = list()
-    pull_dict["mag_field"] = list()
-    pull_dict["fit_area"] = list()
-    pull_dict["int_area"] = list()
-    pull_dict["om"] = list()
-    pull_dict["Counts"] = list()
-
-    # populate the dict
-    for keys in dict1["scan"]:
-        if "file_of_origin" in dict1["scan"][keys]:
-            pull_dict["filenames"].append(dict1["scan"][keys]["file_of_origin"].split("/")[-1])
-        else:
-            pull_dict["filenames"].append(dict1["meta"]["original_filename"].split("/")[-1])
-        for key in dict1["scan"][keys]["params"]:
-            pull_dict[str(key)].append(float(dict1["scan"][keys]["params"][key]))
-        pull_dict["temperature"].append(dict1["scan"][keys]["temperature"])
-        pull_dict["mag_field"].append(dict1["scan"][keys]["mag_field"])
-        pull_dict["fit_area"].append(dict1["scan"][keys]["fit"]["fit_area"])
-        pull_dict["int_area"].append(dict1["scan"][keys]["fit"]["int_area"])
-        pull_dict["om"].append(dict1["scan"][keys]["om"])
-        pull_dict["Counts"].append(dict1["scan"][keys]["Counts"])
-
-    return pd.DataFrame(data=pull_dict)
-
-
-def sort_dataframe(dataframe, sorting_parameter):
-    """sorts the data frame and resets index"""
-    data = dataframe.sort_values(by=sorting_parameter)
-    data = data.reset_index(drop=True)
-    return data
-
-
-def make_graph(data, sorting_parameter, style):
-    """Makes the graph from the data based on style and sorting parameter
-    :arg data : pandas dataframe with data after sorting
-    :arg sorting_parameter to pull the correct variable and name
-    :arg style of the graph - waterfall, scatter, heatmap
-    :return matplotlib figure"""
-    if style == "waterfall":
-        mpl.rcParams["legend.fontsize"] = 10
-        fig = plt.figure()
-        ax = fig.gca(projection="3d")
-        for i in range(len(data)):
-            x = data["om"][i]
-            z = data["Counts"][i]
-            yy = [data[sorting_parameter][i]] * len(x)
-            ax.plot(x, yy, z, label=str("%s = %f" % (sorting_parameter, yy[i])))
-
-        ax.legend()
-        ax.set_xlabel("Omega")
-        ax.set_ylabel(sorting_parameter)
-        ax.set_zlabel("counts")
-
-    elif style == "scatter":
-        fig = plt.figure()
-        plt.errorbar(
-            data[sorting_parameter],
-            [data["fit_area"][i].n for i in range(len(data["fit_area"]))],
-            [data["fit_area"][i].s for i in range(len(data["fit_area"]))],
-            capsize=5,
-            ecolor="green",
-        )
-        plt.xlabel(str(sorting_parameter))
-        plt.ylabel("Intesity")
-
-    elif style == "heat":
-        new_om = list()
-        for i in range(len(data)):
-            new_om = np.append(new_om, np.around(data["om"][i], 2), axis=0)
-        unique_om = np.unique(new_om)
-        color_matrix = np.zeros(shape=(len(data), len(unique_om)))
-        for i in range(len(data)):
-            for j in range(len(data["om"][i])):
-                if np.around(data["om"][i][j], 2) in np.unique(new_om):
-                    color_matrix[i, j] = data["Counts"][i][j]
-                else:
-                    continue
-
-        fig = plt.figure()
-        plt.pcolormesh(unique_om, data[sorting_parameter], color_matrix, shading="gouraud")
-        plt.xlabel("omega")
-        plt.ylabel(sorting_parameter)
-        plt.colorbar()
-        plt.clim(color_matrix.mean(), color_matrix.max())
-
-    return fig
-
-
-def save_dict(obj, name):
-    """saves dictionary as pickle file in binary format
-    :arg obj - object to save
-    :arg name - name of the file
-    NOTE: path should be added later"""
-    with open(name + ".pkl", "wb") as f:
-        pickle.dump(obj, f, pickle.HIGHEST_PROTOCOL)
-
-
-def load_dict(name):
-    """load dictionary from picle file
-    :arg name - name of the file to load
-    NOTE: expect the file in the same folder, path should be added later
-    :return dictionary"""
-    with open(name + ".pkl", "rb") as f:
-        return pickle.load(f)
-
-
-# pickle, mat, h5, txt, csv, json
-def save_table(data, filetype, name, path=None):
-    print("Saving: ", filetype)
-    path = "" if path is None else path
-    if filetype == "pickle":
-        # to work with uncertanities, see uncertanity module
-        with open(path + name + ".pkl", "wb") as f:
-            pickle.dump(data, f, pickle.HIGHEST_PROTOCOL)
-    if filetype == "mat":
-        # matlab doesent allow some special character to be in var names, also cant start with
-        # numbers, in need, add some to the romove_character list
-        data["fit_area_nom"] = [data["fit_area"][i].n for i in range(len(data["fit_area"]))]
-        data["fit_area_err"] = [data["fit_area"][i].s for i in range(len(data["fit_area"]))]
-        data["int_area_nom"] = [data["int_area"][i].n for i in range(len(data["int_area"]))]
-        data["int_area_err"] = [data["int_area"][i].s for i in range(len(data["int_area"]))]
-        data = data.drop(columns=["fit_area", "int_area"])
-        remove_characters = [" ", "[", "]", "{", "}", "(", ")"]
-        for character in remove_characters:
-            data.columns = [
-                data.columns[i].replace(character, "") for i in range(len(data.columns))
-            ]
-        sio.savemat((path + name + ".mat"), {name: col.values for name, col in data.items()})
-    if filetype == "csv" or "txt":
-        data["fit_area_nom"] = [data["fit_area"][i].n for i in range(len(data["fit_area"]))]
-        data["fit_area_err"] = [data["fit_area"][i].s for i in range(len(data["fit_area"]))]
-        data["int_area_nom"] = [data["int_area"][i].n for i in range(len(data["int_area"]))]
-        data["int_area_err"] = [data["int_area"][i].s for i in range(len(data["int_area"]))]
-        data = data.drop(columns=["fit_area", "int_area", "om", "Counts"])
-        if filetype == "csv":
-            data.to_csv(path + name + ".csv")
-        if filetype == "txt":
-            with open((path + name + ".txt"), "w") as outfile:
-                data.to_string(outfile)
-    if filetype == "h5":
-        hdf = pd.HDFStore((path + name + ".h5"))
-        hdf.put("data", data)
-        hdf.close()
-    if filetype == "json":
-        data.to_json((path + name + ".json"))
-
-    def normalize(dict, key, monitor):
-        """Normalizes the measurement to monitor, checks if sigma exists, otherwise creates it
-        :arg dict : dictionary to from which to tkae the scan
-        :arg key : which scan to normalize from dict1
-        :arg monitor : final monitor
-        :return counts - normalized counts
-        :return sigma - normalized sigma"""
-
-        counts = np.array(dict["scan"][key]["Counts"])
-        sigma = np.sqrt(counts) if "sigma" not in dict["scan"][key] else dict["scan"][key]["sigma"]
-        monitor_ratio = monitor / dict["scan"][key]["monitor"]
-        scaled_counts = counts * monitor_ratio
-        scaled_sigma = np.array(sigma) * monitor_ratio
-
-        return scaled_counts, scaled_sigma
-
-    def merge(dict1, dict2, scand_dict_result, keep=True, monitor=100000):
-        """merges the two tuples and sorts them, if om value is same, Counts value is average
-        averaging is propagated into sigma if dict1 == dict2, key[1] is deleted after merging
-        :arg dict1 : dictionary to which measurement will be merged
-        :arg dict2 : dictionary from which measurement will be merged
-        :arg scand_dict_result : result of scan_dict after auto function
-        :arg keep : if true, when monitors are same, does not change it, if flase, takes monitor
-        always
-        :arg monitor : final monitor after merging
-        note: dict1 and dict2 can be same dict
-        :return dict1 with merged scan"""
-        for keys in scand_dict_result:
-            for j in range(len(scand_dict_result[keys])):
-                first, second = scand_dict_result[keys][j][0], scand_dict_result[keys][j][1]
-                print(first, second)
-                if keep:
-                    if dict1["scan"][first]["monitor"] == dict2["scan"][second]["monitor"]:
-                        monitor = dict1["scan"][first]["monitor"]
-
-                # load om and Counts
-                x1, x2 = dict1["scan"][first]["om"], dict2["scan"][second]["om"]
-                cor_y1, y_err1 = normalize(dict1, first, monitor=monitor)
-                cor_y2, y_err2 = normalize(dict2, second, monitor=monitor)
-                # creates touples (om, Counts, sigma) for sorting and further processing
-                tuple_list = create_tuples(x1, cor_y1, y_err1) + create_tuples(x2, cor_y2, y_err2)
-                # Sort the list on om and add 0 0 0 tuple to the last position
-                sorted_t = sorted(tuple_list, key=lambda tup: tup[0])
-                sorted_t.append((0, 0, 0))
-                om, Counts, sigma = [], [], []
-                seen = list()
-                for i in range(len(sorted_t) - 1):
-                    if sorted_t[i][0] not in seen:
-                        if sorted_t[i][0] != sorted_t[i + 1][0]:
-                            om = np.append(om, sorted_t[i][0])
-                            Counts = np.append(Counts, sorted_t[i][1])
-                            sigma = np.append(sigma, sorted_t[i][2])
-                        else:
-                            om = np.append(om, sorted_t[i][0])
-                            counts1, counts2 = sorted_t[i][1], sorted_t[i + 1][1]
-                            sigma1, sigma2 = sorted_t[i][2], sorted_t[i + 1][2]
-                            count_err1 = u.ufloat(counts1, sigma1)
-                            count_err2 = u.ufloat(counts2, sigma2)
-                            avg = (count_err1 + count_err2) / 2
-                            Counts = np.append(Counts, avg.n)
-                            sigma = np.append(sigma, avg.s)
-                            seen.append(sorted_t[i][0])
-                    else:
-                        continue
-
-                if dict1 == dict2:
-                    del dict1["scan"][second]
-
-                note = (
-                    f"This measurement was merged with measurement {second} from "
-                    f'file {dict2["meta"]["original_filename"]} \n'
-                )
-                if "notes" not in dict1["scan"][first]:
-                    dict1["scan"][first]["notes"] = note
-                else:
-                    dict1["scan"][first]["notes"] += note
-
-                dict1["scan"][first]["om"] = om
-                dict1["scan"][first]["Counts"] = Counts
-                dict1["scan"][first]["sigma"] = sigma
-                dict1["scan"][first]["monitor"] = monitor
-                print("merging done")
-        return dict1
-
-
-def add_dict(dict1, dict2):
-    """adds two dictionaries, meta of the new is saved as meata+original_filename and
-    measurements are shifted to continue with numbering of first dict
-    :arg dict1 : dictionarry to add to
-    :arg dict2 : dictionarry from which to take the measurements
-    :return dict1 : combined dictionary
-    Note: dict1 must be made from ccl, otherwise we would have to change the structure of loaded
-    dat file"""
-    if dict1["meta"]["zebra_mode"] != dict2["meta"]["zebra_mode"]:
-        print("You are trying to add scans measured with different zebra modes")
-        return
-    max_measurement_dict1 = max([keys for keys in dict1["scan"]])
-    new_filenames = np.arange(
-        max_measurement_dict1 + 1, max_measurement_dict1 + 1 + len(dict2["scan"])
-    )
-    new_meta_name = "meta" + str(dict2["meta"]["original_filename"])
-    if new_meta_name not in dict1:
-        for keys, name in zip(dict2["scan"], new_filenames):
-            dict2["scan"][keys]["file_of_origin"] = str(dict2["meta"]["original_filename"])
-            dict1["scan"][name] = dict2["scan"][keys]
-
-        dict1[new_meta_name] = dict2["meta"]
-    else:
-        raise KeyError(
-            str(
-                "The file %s has alredy been added to %s"
-                % (dict2["meta"]["original_filename"], dict1["meta"]["original_filename"])
-            )
-        )
-    return dict1
-
-
-def auto(dict):
-    """takes just unique tuples from all tuples in dictionary returend by scan_dict
-    intendet for automatic merge if you doesent want to specify what scans to merge together
-    args: dict - dictionary from scan_dict function
-    :return dict - dict without repetitions"""
-    for keys in dict:
-        tuple_list = dict[keys]
-        new = list()
-        for i in range(len(tuple_list)):
-            if tuple_list[0][0] == tuple_list[i][0]:
-                new.append(tuple_list[i])
-        dict[keys] = new
-    return dict
-
-
-def scan_dict(dict, precision=0.5):
-    """scans dictionary for duplicate angles indexes
-    :arg dict : dictionary to scan
-    :arg precision : in deg, sometimes angles are zero so its easier this way, instead of
-    checking zero division
-    :return  dictionary with matching scans, if there are none, the dict is empty
-    note: can be checked by "not d", true if empty
-    """
-
-    if dict["meta"]["zebra_mode"] == "bi":
-        angles = ["twotheta_angle", "omega_angle", "chi_angle", "phi_angle"]
-    elif dict["meta"]["zebra_mode"] == "nb":
-        angles = ["gamma_angle", "omega_angle", "nu_angle"]
-    else:
-        print("Unknown zebra mode")
-        return
-
-    d = {}
-    for i in dict["scan"]:
-        for j in dict["scan"]:
-            if dict["scan"][i] != dict["scan"][j]:
-                itup = list()
-                for k in angles:
-                    itup.append(abs(abs(dict["scan"][i][k]) - abs(dict["scan"][j][k])))
-
-                if all(i <= precision for i in itup):
-                    if str([np.around(dict["scan"][i][k], 1) for k in angles]) not in d:
-                        d[str([np.around(dict["scan"][i][k], 1) for k in angles])] = list()
-                        d[str([np.around(dict["scan"][i][k], 1) for k in angles])].append((i, j))
-                    else:
-                        d[str([np.around(dict["scan"][i][k], 1) for k in angles])].append((i, j))
-
-                else:
-                    pass
-
-            else:
-                continue
-    return d

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

@@ -0,0 +1,36 @@
+import os
+
+import numpy as np
+
+SINQ_PATH = "/afs/psi.ch/project/sinqdata"
+ZEBRA_PROPOSALS_PATH = os.path.join(SINQ_PATH, "{year}/zebra/{proposal}")
+
+
+def find_proposal_path(proposal):
+    for entry in os.scandir(SINQ_PATH):
+        if entry.is_dir() and len(entry.name) == 4 and entry.name.isdigit():
+            proposal_path = ZEBRA_PROPOSALS_PATH.format(year=entry.name, proposal=proposal)
+            if os.path.isdir(proposal_path):
+                # found it
+                break
+    else:
+        raise ValueError(f"Can not find data for proposal '{proposal}'")
+
+    return proposal_path
+
+
+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

@@ -1,19 +1,17 @@
-import math
-
 import numpy as np
 from numba import njit
-from scipy.optimize import curve_fit
-
-import pyzebra
-
-try:
-    from matplotlib import pyplot as plt
-except ImportError:
-    print("matplotlib is not available")
 
 pi_r = 180 / np.pi
+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
 
@@ -25,36 +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[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
+    z4 = [np.sin(ga_r) * np.cos(nu_r), np.cos(ga_r) * np.cos(nu_r) - 1.0, np.sin(nu_r)]
 
-    return z4
+    return np.array(z4) / wave
 
 
 @njit(cache=True)
-def phimat(phi):
-    """BUSING AND LEVY CONVENTION ROTATION MATRIX FOR PHI OR OMEGA
+def phimat_T(phi):
+    """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] = -dum[0, 1]
+    dum[1, 0] = np.sin(ph_r)
+    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
 
@@ -65,30 +61,28 @@ def z1frnb(wave, ga, nu, om):
         Z1
     """
     z4 = z4frgn(wave, ga, nu)
-    dum = phimat(phi=om)
-    dumt = np.transpose(dum)
-    z3 = dumt.dot(z4)
+    z3 = phimat_T(phi=om).dot(z4)
 
     return z3
 
 
 @njit(cache=True)
-def chimat(chi):
-    """BUSING AND LEVY CONVENTION ROTATION MATRIX FOR CHI
+def chimat_T(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
@@ -104,13 +98,8 @@ def z1frz3(z3, chi, phi):
     Returns:
         Z1
     """
-    dum1 = chimat(chi)
-    dum2 = np.transpose(dum1)
-    z2 = dum2.dot(z3)
-
-    dum1 = phimat(phi)
-    dum2 = np.transpose(dum1)
-    z1 = dum2.dot(z2)
+    z2 = chimat_T(chi).dot(z3)
+    z1 = phimat_T(phi).dot(z2)
 
     return z1
 
@@ -292,185 +281,81 @@ def fixdnu(wave, z1, ch2, ph2, nu):
     return ch, ph, ga, om
 
 
-# for test run:
-#   angtohkl(wave=1.18,ddist=616,gammad=48.66,om=-22.80,ch=0,ph=0,nud=0,x=128,y=64)
-
-
-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
-    """
+def ang2hkl(wave, ddist, gammad, om, ch, ph, nud, ub_inv, 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 = ubinv @ z1
+    hkl = ub_inv @ z1
 
     return hkl
 
 
-def gauss(x, *p):
-    """Defines Gaussian function
+def ang2hkl_det(wave, ddist, gammad, om, chi, phi, nud, ub_inv):
+    """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 - amplitude, mu - position of the center, sigma - width
+    a = xobs
+    b = ddist * np.cos(yobs / ddist)
+    z = ddist * np.sin(yobs / ddist)
+    d = np.sqrt(a * a + b * b)
 
-    Returns:
-        Gaussian function
-    """
-    A, mu, sigma = p
-    return A * np.exp(-((x - mu) ** 2) / (2.0 * sigma ** 2))
+    gamma = gammad + np.arctan2(a, b) * pi_r
+    nu = nud + np.arctan2(z, d) * pi_r
+
+    gamma_r = gamma / pi_r
+    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 box_int(file, box):
-    """Calculates center of the peak in the NB-geometry angles and Intensity of the peak
+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
 
-    Args:
-        file name, box size [x0:xN, y0:yN, fr0:frN]
+    return hkl
 
-    Returns:
-        gamma, omPeak, nu polar angles, Int and data for 3 fit plots
-    """
 
-    dat = pyzebra.read_detector_data(file)
+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)
 
-    sttC = dat["pol_angle"][0]
-    om = dat["rot_angle"]
-    nuC = dat["tlt_angle"][0]
-    ddist = dat["ddist"]
-
-    # defining indices
-    x0, xN, y0, yN, fr0, frN = box
-
-    # omega fit
-    om = dat["rot_angle"][fr0:frN]
-    cnts = np.sum(dat["data"][fr0:frN, y0:yN, x0:xN], axis=(1, 2))
-
-    p0 = [1.0, 0.0, 1.0]
-    coeff, var_matrix = curve_fit(gauss, range(len(cnts)), cnts, p0=p0)
-
-    frC = fr0 + coeff[1]
-    omF = dat["rot_angle"][math.floor(frC)]
-    omC = dat["rot_angle"][math.ceil(frC)]
-    frStep = frC - math.floor(frC)
-    omStep = omC - omF
-    omP = omF + omStep * frStep
-    Int = coeff[1] * abs(coeff[2] * omStep) * math.sqrt(2) * math.sqrt(np.pi)
-    # omega plot
-    x_fit = np.linspace(0, len(cnts), 100)
-    y_fit = gauss(x_fit, *coeff)
-    plt.figure()
-    plt.subplot(131)
-    plt.plot(range(len(cnts)), cnts)
-    plt.plot(x_fit, y_fit)
-    plt.ylabel("Intensity in the box")
-    plt.xlabel("Frame N of the box")
-    label = "om"
-    # gamma fit
-    sliceXY = dat["data"][fr0:frN, y0:yN, x0:xN]
-    sliceXZ = np.sum(sliceXY, axis=1)
-    sliceYZ = np.sum(sliceXY, axis=2)
-
-    projX = np.sum(sliceXZ, axis=0)
-    p0 = [1.0, 0.0, 1.0]
-    coeff, var_matrix = curve_fit(gauss, range(len(projX)), projX, p0=p0)
-    x = x0 + coeff[1]
-    # gamma plot
-    x_fit = np.linspace(0, len(projX), 100)
-    y_fit = gauss(x_fit, *coeff)
-    plt.subplot(132)
-    plt.plot(range(len(projX)), projX)
-    plt.plot(x_fit, y_fit)
-    plt.ylabel("Intensity in the box")
-    plt.xlabel("X-pixel of the box")
-
-    # nu fit
-    projY = np.sum(sliceYZ, axis=0)
-    p0 = [1.0, 0.0, 1.0]
-    coeff, var_matrix = curve_fit(gauss, range(len(projY)), projY, p0=p0)
-    y = y0 + coeff[1]
-    # nu plot
-    x_fit = np.linspace(0, len(projY), 100)
-    y_fit = gauss(x_fit, *coeff)
-    plt.subplot(133)
-    plt.plot(range(len(projY)), projY)
-    plt.plot(x_fit, y_fit)
-    plt.ylabel("Intensity in the box")
-    plt.xlabel("Y-pixel of the box")
-
-    ga, nu = pyzebra.det2pol(ddist, sttC, nuC, x, y)
-
-    return ga[0], omP, nu[0], Int
+    return ch, ph, ga, om