Updating for version 0.3.0

fit_columns was deprecated in bokeh/2.2

.github/workflows/deployment.yaml

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

.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

@@ -5,7 +5,7 @@
             "name": "pyzebra",
             "type": "python",
             "request": "launch",
-            "program": "${workspaceFolder}/pyzebra/cli.py",
+            "program": "${workspaceFolder}/pyzebra/app/cli.py",
             "console": "internalConsole",
             "env": {},
         },

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

@@ -8,20 +8,22 @@ source:
   path: ..
 
 build:
+  noarch: python
   number: 0
   entry_points:
-    - pyzebra = pyzebra.cli:main
+    - pyzebra = pyzebra.app.cli:main
 
 requirements:
   build:
-    - python
+    - python >=3.7
     - setuptools
   run:
-    - python
+    - python >=3.7
     - numpy
     - scipy
     - h5py
-    - bokeh
+    - bokeh =2.3
+    - matplotlib
     - numba
     - lmfit
     - uncertainties

make_release.py

@@ -3,14 +3,19 @@
 import argparse
 import os
 import re
+import subprocess
 
 
 def main():
+    branch = subprocess.check_output("git rev-parse --abbrev-ref HEAD", shell=True).decode().strip()
+    if branch != "master":
+        print("Aborting, not on 'master' branch.")
+        return
+
     filepath = "pyzebra/__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:
@@ -35,7 +40,7 @@ def main():
         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 tag -a {new_version} -m 'Release {new_version}'")
 
 
 if __name__ == "__main__":

pyzebra/__init__.py

@@ -1,10 +1,7 @@
-import pyzebra.ccl_dict_operation
 from pyzebra.anatric import *
-from pyzebra.ccl_findpeaks import ccl_findpeaks
+from pyzebra.ccl_io import *
-from pyzebra.comm_export import export_comm
-from pyzebra.fit2 import fitccl
 from pyzebra.h5 import *
-from pyzebra.load_1D import load_1D, parse_1D
 from pyzebra.xtal import *
+from pyzebra.ccl_process import *
 
-__version__ = "0.1.0"
+__version__ = "0.3.0"

pyzebra/anatric.py

@@ -2,7 +2,6 @@ import subprocess
 import xml.etree.ElementTree as ET
 
 
-ANATRIC_PATH = "/afs/psi.ch/project/sinq/rhel7/bin/anatric"
 DATA_FACTORY_IMPLEMENTATION = [
     "trics",
     "morph",
@@ -24,8 +23,8 @@ REFLECTION_PRINTER_FORMATS = [
 ALGORITHMS = ["adaptivemaxcog", "adaptivedynamic"]
 
 
-def anatric(config_file):
+def anatric(config_file, anatric_path="/afs/psi.ch/project/sinq/rhel7/bin/anatric"):
-    subprocess.run([ANATRIC_PATH, config_file], check=True)
+    subprocess.run([anatric_path, config_file], check=True)
 
 
 class AnatricConfig:

pyzebra/app/app.py

@@ -1,4 +1,3 @@
-import argparse
 import logging
 import sys
 from io import StringIO
@@ -10,15 +9,11 @@ from bokeh.models import Tabs, TextAreaInput
 import panel_ccl_integrate
 import panel_hdf_anatric
 import panel_hdf_viewer
+import panel_param_study
+import panel_spind
 
-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)
@@ -26,7 +21,7 @@ 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 = logging.getLogger("bokeh")
 bokeh_logger.addHandler(bokeh_handler)
 bokeh_log_textareainput = TextAreaInput(title="server output:", height=150)
 
@@ -34,10 +29,12 @@ bokeh_log_textareainput = TextAreaInput(title="server output:", height=150)
 tab_hdf_viewer = panel_hdf_viewer.create()
 tab_hdf_anatric = panel_hdf_anatric.create()
 tab_ccl_integrate = panel_ccl_integrate.create()
+tab_param_study = panel_param_study.create()
+tab_spind = panel_spind.create()
 
 doc.add_root(
     column(
-        Tabs(tabs=[tab_hdf_viewer, tab_hdf_anatric, tab_ccl_integrate]),
+        Tabs(tabs=[tab_hdf_viewer, tab_hdf_anatric, tab_ccl_integrate, tab_param_study, tab_spind]),
         row(stdout_textareainput, bokeh_log_textareainput, sizing_mode="scale_both"),
     )
 )

pyzebra/app/cli.py

@@ -6,6 +6,8 @@ from bokeh.application.application import Application
 from bokeh.application.handlers import ScriptHandler
 from bokeh.server.server import Server
 
+from pyzebra.app.handler import PyzebraHandler
+
 logging.basicConfig(format="%(asctime)s %(message)s", level=logging.INFO)
 logger = logging.getLogger(__name__)
 
@@ -16,7 +18,7 @@ def main():
     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")
+    app_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "app.py")
 
     parser = argparse.ArgumentParser(
         prog="pyzebra", formatter_class=argparse.ArgumentDefaultsHelpFormatter
@@ -35,6 +37,13 @@ def main():
         help="hostname that can connect to the server websocket",
     )
 
+    parser.add_argument(
+        "--anatric-path",
+        type=str,
+        default=None,
+        help="path to anatric executable",
+    )
+
     parser.add_argument(
         "--args",
         nargs=argparse.REMAINDER,
@@ -46,9 +55,10 @@ def main():
 
     logger.info(app_path)
 
+    pyzebra_handler = PyzebraHandler(args.anatric_path)
     handler = ScriptHandler(filename=app_path, argv=args.args)
     server = Server(
-        {"/": Application(handler)},
+        {"/": Application(pyzebra_handler, handler)},
         port=args.port,
         allow_websocket_origin=args.allow_websocket_origin,
     )

pyzebra/app/handler.py

@@ -0,0 +1,30 @@
+from bokeh.application.handlers import Handler
+
+
+class PyzebraHandler(Handler):
+    """Provides a mechanism for generic bokeh applications to build up new streamvis documents.
+    """
+
+    def __init__(self, anatric_path):
+        """Initialize a pyzebra handler for bokeh applications.
+
+        Args:
+            args (Namespace): Command line parsed arguments.
+        """
+        super().__init__()  # no-op
+
+        self.anatric_path = anatric_path
+
+    def modify_document(self, doc):
+        """Modify an application document with pyzebra specific features.
+
+        Args:
+            doc (Document) : A bokeh Document to update in-place
+
+        Returns:
+            Document
+        """
+        doc.title = "pyzebra"
+        doc.anatric_path = self.anatric_path
+
+        return doc

pyzebra/app/panel_ccl_integrate.py

@@ -2,25 +2,33 @@ import base64
 import io
 import os
 import tempfile
+import types
 
 import numpy as np
 from bokeh.layouts import column, row
 from bokeh.models import (
-    Asterisk,
     BasicTicker,
     Button,
+    CheckboxEditor,
     ColumnDataSource,
     CustomJS,
     DataRange1d,
     DataTable,
     Div,
+    Dropdown,
     FileInput,
     Grid,
+    Legend,
     Line,
     LinearAxis,
+    MultiLine,
+    MultiSelect,
+    NumberEditor,
     Panel,
+    PanTool,
     Plot,
     RadioButtonGroup,
+    ResetTool,
     Scatter,
     Select,
     Spacer,
@@ -30,25 +38,28 @@ from bokeh.models import (
     TextAreaInput,
     TextInput,
     Toggle,
+    WheelZoomTool,
     Whisker,
 )
 
 import pyzebra
+from pyzebra.ccl_io import AREA_METHODS
 
 
 javaScript = """
-setTimeout(function() {
+for (let i = 0; i < js_data.data['fname'].length; i++) {
-    const filename = 'output' + js_data.data['ext']
+    if (js_data.data['content'][i] === "") continue;
-    const blob = new Blob([js_data.data['cont']], {type: 'text/plain'})
+
+    const blob = new Blob([js_data.data['content'][i]], {type: 'text/plain'})
     const link = document.createElement('a');
     document.body.appendChild(link);
     const url = window.URL.createObjectURL(blob);
     link.href = url;
-    link.download = filename;
+    link.download = js_data.data['fname'][i];
     link.click();
     window.URL.revokeObjectURL(url);
     document.body.removeChild(link);
-}, 500);
+}
 """
 
 PROPOSAL_PATH = "/afs/psi.ch/project/sinqdata/2020/zebra/"
@@ -56,388 +67,404 @@ PROPOSAL_PATH = "/afs/psi.ch/project/sinqdata/2020/zebra/"
 
 def create():
     det_data = {}
-    peak_pos_textinput_lock = False
+    fit_params = {}
-    js_data = ColumnDataSource(data=dict(cont=[], ext=[]))
+    js_data = ColumnDataSource(data=dict(content=["", ""], fname=["", ""]))
 
     def proposal_textinput_callback(_attr, _old, new):
-        ccl_path = os.path.join(PROPOSAL_PATH, new)
+        ccl_path = os.path.join(PROPOSAL_PATH, new.strip())
         ccl_file_list = []
         for file in os.listdir(ccl_path):
-            if file.endswith(".ccl"):
+            if file.endswith((".ccl", ".dat")):
                 ccl_file_list.append((os.path.join(ccl_path, file), file))
-        ccl_file_select.options = ccl_file_list
+        file_select.options = ccl_file_list
-        ccl_file_select.value = ccl_file_list[0][0]
 
-    proposal_textinput = TextInput(title="Enter proposal number:", default_size=145)
+    proposal_textinput = TextInput(title="Proposal number:", default_size=145)
     proposal_textinput.on_change("value", proposal_textinput_callback)
 
-    def ccl_file_select_callback(_attr, _old, new):
+    def _init_datatable():
-        nonlocal det_data
+        scan_list = [s["idx"] for s in det_data]
-        with open(new) as file:
+        hkl = [f'{s["h"]} {s["k"]} {s["l"]}' for s in det_data]
-            _, ext = os.path.splitext(new)
+        export = [s.get("active", True) for s in det_data]
-            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,
         )
         scan_table_source.selected.indices = []
         scan_table_source.selected.indices = [0]
 
-    ccl_file_select = Select(title="Available .ccl files")
+        merge_options = [(str(i), f"{i} ({idx})") for i, idx in enumerate(scan_list)]
-    ccl_file_select.on_change("value", ccl_file_select_callback)
+        merge_source_select.options = merge_options
+        merge_source_select.value = merge_options[0][0]
+        merge_dest_select.options = merge_options
+        merge_dest_select.value = merge_options[0][0]
+
+    def ccl_file_select_callback(_attr, _old, _new):
+        pass
+
+    file_select = MultiSelect(title="Available .ccl/.dat files:", default_size=200, height=250)
+    file_select.on_change("value", ccl_file_select_callback)
+
+    def file_open_button_callback():
+        nonlocal det_data
+        det_data = []
+        for f_name in file_select.value:
+            with open(f_name) as file:
+                base, ext = os.path.splitext(f_name)
+                if det_data:
+                    append_data = pyzebra.parse_1D(file, ext)
+                    pyzebra.normalize_dataset(append_data, monitor_spinner.value)
+                    pyzebra.merge_datasets(det_data, append_data)
+                else:
+                    det_data = pyzebra.parse_1D(file, ext)
+                    pyzebra.normalize_dataset(det_data, monitor_spinner.value)
+                    pyzebra.merge_duplicates(det_data)
+                    js_data.data.update(fname=[base + ".comm", base + ".incomm"])
+
+        _init_datatable()
+
+    file_open_button = Button(label="Open New", default_size=100)
+    file_open_button.on_click(file_open_button_callback)
+
+    def file_append_button_callback():
+        for f_name in file_select.value:
+            with open(f_name) as file:
+                _, ext = os.path.splitext(f_name)
+                append_data = pyzebra.parse_1D(file, ext)
+
+            pyzebra.normalize_dataset(append_data, monitor_spinner.value)
+            pyzebra.merge_datasets(det_data, append_data)
+
+        _init_datatable()
+
+    file_append_button = Button(label="Append", default_size=100)
+    file_append_button.on_click(file_append_button_callback)
 
     def upload_button_callback(_attr, _old, new):
         nonlocal det_data
-        with io.StringIO(base64.b64decode(new).decode()) as file:
+        det_data = []
-            _, ext = os.path.splitext(upload_button.filename)
+        for f_str, f_name in zip(new, upload_button.filename):
-            det_data = pyzebra.parse_1D(file, ext)
+            with io.StringIO(base64.b64decode(f_str).decode()) as file:
+                base, ext = os.path.splitext(f_name)
+                if det_data:
+                    append_data = pyzebra.parse_1D(file, ext)
+                    pyzebra.normalize_dataset(append_data, monitor_spinner.value)
+                    pyzebra.merge_datasets(det_data, append_data)
+                else:
+                    det_data = pyzebra.parse_1D(file, ext)
+                    pyzebra.normalize_dataset(det_data, monitor_spinner.value)
+                    pyzebra.merge_duplicates(det_data)
+                    js_data.data.update(fname=[base + ".comm", base + ".incomm"])
 
-        scan_list = list(det_data["scan"].keys())
+        _init_datatable()
-        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_div = Div(text="or upload new .ccl/.dat files:", margin=(5, 5, 0, 5))
+    upload_button = FileInput(accept=".ccl,.dat", multiple=True, default_size=200)
     upload_button.on_change("value", upload_button_callback)
 
+    def append_upload_button_callback(_attr, _old, new):
+        for f_str, f_name in zip(new, append_upload_button.filename):
+            with io.StringIO(base64.b64decode(f_str).decode()) as file:
+                _, ext = os.path.splitext(f_name)
+                append_data = pyzebra.parse_1D(file, ext)
+
+            pyzebra.normalize_dataset(append_data, monitor_spinner.value)
+            pyzebra.merge_datasets(det_data, append_data)
+
+        _init_datatable()
+
+    append_upload_div = Div(text="append extra files:", margin=(5, 5, 0, 5))
+    append_upload_button = FileInput(accept=".ccl,.dat", multiple=True, default_size=200)
+    append_upload_button.on_change("value", append_upload_button_callback)
+
+    def monitor_spinner_callback(_attr, old, new):
+        if det_data:
+            pyzebra.normalize_dataset(det_data, new)
+            _update_plot(_get_selected_scan())
+
+    monitor_spinner = Spinner(title="Monitor:", mode="int", value=100_000, low=1, width=145)
+    monitor_spinner.on_change("value", monitor_spinner_callback)
+
     def _update_table():
-        num_of_peaks = [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]
-        fit_ok = [(1 if "fit" in scan else 0) for scan in det_data["scan"].values()]
+        scan_table_source.data.update(fit=fit_ok)
-        scan_table_source.data.update(peaks=num_of_peaks, fit=fit_ok)
 
-    def _update_plot(ind):
+    def _update_plot(scan):
-        nonlocal peak_pos_textinput_lock
+        scan_motor = scan["scan_motor"]
-        peak_pos_textinput_lock = True
 
-        scan = det_data["scan"][ind]
         y = scan["Counts"]
-        x = scan["om"]
+        x = scan[scan_motor]
 
+        plot.axis[0].axis_label = scan_motor
         plot_scatter_source.data.update(x=x, y=y, y_upper=y + np.sqrt(y), y_lower=y - np.sqrt(y))
 
-        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
-
         fit = scan.get("fit")
         if fit is not None:
-            plot_gauss_source.data.update(x=x, y=scan["fit"]["comps"]["gaussian"])
+            x_fit = np.linspace(x[0], x[-1], 100)
-            plot_bkg_source.data.update(x=x, y=scan["fit"]["comps"]["background"])
+            plot_fit_source.data.update(x=x_fit, y=fit.eval(x=x_fit))
-            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]
 
-            if numfit_max is None:
+            x_bkg = []
-                numfit_max_span.location = None
+            y_bkg = []
-            else:
+            xs_peak = []
-                numfit_max_span.location = x[numfit_max]
+            ys_peak = []
+            comps = fit.eval_components(x=x_fit)
+            for i, model in enumerate(fit_params):
+                if "linear" in model:
+                    x_bkg = x_fit
+                    y_bkg = comps[f"f{i}_"]
+
+                elif any(val in model for val in ("gaussian", "voigt", "pvoigt")):
+                    xs_peak.append(x_fit)
+                    ys_peak.append(comps[f"f{i}_"])
+
+            plot_bkg_source.data.update(x=x_bkg, y=y_bkg)
+            plot_peak_source.data.update(xs=xs_peak, ys=ys_peak)
+
+            fit_output_textinput.value = fit.fit_report()
 
         else:
-            plot_gauss_source.data.update(x=[], y=[])
+            plot_fit_source.data.update(x=[], y=[])
             plot_bkg_source.data.update(x=[], y=[])
+            plot_peak_source.data.update(xs=[], ys=[])
             fit_output_textinput.value = ""
-            numfit_min_span.location = None
-            numfit_max_span.location = None
 
     # Main plot
     plot = Plot(
         x_range=DataRange1d(),
-        y_range=DataRange1d(),
+        y_range=DataRange1d(only_visible=True),
-        plot_height=400,
+        plot_height=470,
         plot_width=700,
-        toolbar_location=None,
     )
 
     plot.add_layout(LinearAxis(axis_label="Counts"), place="left")
-    plot.add_layout(LinearAxis(axis_label="Omega"), place="below")
+    plot.add_layout(LinearAxis(axis_label="Scan motor"), place="below")
 
     plot.add_layout(Grid(dimension=0, ticker=BasicTicker()))
     plot.add_layout(Grid(dimension=1, ticker=BasicTicker()))
 
     plot_scatter_source = ColumnDataSource(dict(x=[0], y=[0], y_upper=[0], y_lower=[0]))
-    plot.add_glyph(plot_scatter_source, Scatter(x="x", y="y", line_color="steelblue"))
+    plot_scatter = plot.add_glyph(
+        plot_scatter_source, Scatter(x="x", y="y", line_color="steelblue")
+    )
     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_fit_source = ColumnDataSource(dict(x=[0], y=[0]))
-    plot.add_glyph(
+    plot_fit = plot.add_glyph(plot_fit_source, Line(x="x", y="y"))
-        plot_line_smooth_source, Line(x="x", y="y", line_color="steelblue", line_dash="dashed")
-    )
-
-    plot_gauss_source = ColumnDataSource(dict(x=[0], y=[0]))
-    plot.add_glyph(plot_gauss_source, Line(x="x", y="y", line_color="red", line_dash="dashed"))
 
     plot_bkg_source = ColumnDataSource(dict(x=[0], y=[0]))
-    plot.add_glyph(plot_bkg_source, Line(x="x", y="y", line_color="green", line_dash="dashed"))
+    plot_bkg = plot.add_glyph(
+        plot_bkg_source, Line(x="x", y="y", line_color="green", line_dash="dashed")
+    )
 
-    plot_peak_source = ColumnDataSource(dict(x=[], y=[]))
+    plot_peak_source = ColumnDataSource(dict(xs=[0], ys=[0]))
-    plot.add_glyph(plot_peak_source, Asterisk(x="x", y="y", size=10, line_color="red"))
+    plot_peak = plot.add_glyph(
+        plot_peak_source, MultiLine(xs="xs", ys="ys", line_color="red", line_dash="dashed")
+    )
 
-    numfit_min_span = Span(location=None, dimension="height", line_dash="dashed")
+    fit_from_span = Span(location=None, dimension="height", line_dash="dashed")
-    plot.add_layout(numfit_min_span)
+    plot.add_layout(fit_from_span)
 
-    numfit_max_span = Span(location=None, dimension="height", line_dash="dashed")
+    fit_to_span = Span(location=None, dimension="height", line_dash="dashed")
-    plot.add_layout(numfit_max_span)
+    plot.add_layout(fit_to_span)
+
+    plot.add_layout(
+        Legend(
+            items=[
+                ("data", [plot_scatter]),
+                ("best fit", [plot_fit]),
+                ("peak", [plot_peak]),
+                ("linear", [plot_bkg]),
+            ],
+            location="top_left",
+            click_policy="hide",
+        )
+    )
+
+    plot.add_tools(PanTool(), WheelZoomTool(), ResetTool())
+    plot.toolbar.logo = None
 
     # Scan select
-    def scan_table_callback(_attr, _old, new):
+    def scan_table_select_callback(_attr, old, new):
-        if new:
+        if not new:
-            _update_plot(scan_table_source.data["scan"][new[-1]])
+            # skip empty selections
+            return
 
-    scan_table_source = ColumnDataSource(dict(scan=[], hkl=[], peaks=[], fit=[]))
+        # 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(det_data[new[0]])
+
+    scan_table_source = ColumnDataSource(dict(scan=[], hkl=[], fit=[], export=[]))
     scan_table = DataTable(
         source=scan_table_source,
         columns=[
-            TableColumn(field="scan", title="scan"),
+            TableColumn(field="scan", title="Scan", width=50),
-            TableColumn(field="hkl", title="hkl"),
+            TableColumn(field="hkl", title="hkl", width=100),
-            TableColumn(field="peaks", title="Peaks"),
+            TableColumn(field="fit", title="Fit", width=50),
-            TableColumn(field="fit", title="Fit"),
+            TableColumn(field="export", title="Export", editor=CheckboxEditor(), width=50),
         ],
-        width=200,
+        width=310,  # +60 because of the index column
-        index_position=None,
+        height=350,
+        autosize_mode="none",
+        editable=True,
     )
 
-    scan_table_source.selected.on_change("indices", scan_table_callback)
+    scan_table_source.selected.on_change("indices", scan_table_select_callback)
 
-    def peak_pos_textinput_callback(_attr, _old, new):
+    def _get_selected_scan():
-        if new is not None and not peak_pos_textinput_lock:
+        return det_data[scan_table_source.selected.indices[0]]
-            sel_ind = scan_table_source.selected.indices[-1]
-            scan_name = scan_table_source.data["scan"][sel_ind]
-            scan = det_data["scan"][scan_name]
 
-            scan["num_of_peaks"] = 1
+    merge_dest_select = Select(title="destination:", width=100)
-            peak_ind = (np.abs(scan["om"] - float(new))).argmin()
+    merge_source_select = Select(title="source:", width=100)
-            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)
 
-    peak_pos_textinput = TextInput(title="Peak position:", default_size=145)
+    def merge_button_callback():
-    peak_pos_textinput.on_change("value", peak_pos_textinput_callback)
+        scan_dest_ind = int(merge_dest_select.value)
+        scan_source_ind = int(merge_source_select.value)
 
-    peak_int_ratio_spinner = Spinner(
+        if scan_dest_ind == scan_source_ind:
-        title="Peak intensity ratio:", value=0.8, step=0.01, low=0, high=1, default_size=145
+            print("WARNING: Selected scans for merging are identical")
+            return
+
+        pyzebra.merge_scans(det_data[scan_dest_ind], det_data[scan_source_ind])
+        _update_plot(_get_selected_scan())
+
+    merge_button = Button(label="Merge scans", width=145)
+    merge_button.on_click(merge_button_callback)
+
+    def fit_from_spinner_callback(_attr, _old, new):
+        fit_from_span.location = new
+
+    fit_from_spinner = Spinner(title="Fit from:", default_size=145)
+    fit_from_spinner.on_change("value", fit_from_spinner_callback)
+
+    def fit_to_spinner_callback(_attr, _old, new):
+        fit_to_span.location = new
+
+    fit_to_spinner = Spinner(title="to:", default_size=145)
+    fit_to_spinner.on_change("value", fit_to_spinner_callback)
+
+    def fitparams_add_dropdown_callback(click):
+        # bokeh requires (str, str) for MultiSelect options
+        new_tag = f"{click.item}-{fitparams_select.tags[0]}"
+        fitparams_select.options.append((new_tag, click.item))
+        fit_params[new_tag] = fitparams_factory(click.item)
+        fitparams_select.tags[0] += 1
+
+    fitparams_add_dropdown = Dropdown(
+        label="Add fit function",
+        menu=[
+            ("Linear", "linear"),
+            ("Gaussian", "gaussian"),
+            ("Voigt", "voigt"),
+            ("Pseudo Voigt", "pvoigt"),
+            # ("Pseudo Voigt1", "pseudovoigt1"),
+        ],
+        default_size=145,
+        disabled=True,
     )
-    peak_prominence_spinner = Spinner(title="Peak prominence:", value=50, low=0, default_size=145)
+    fitparams_add_dropdown.on_click(fitparams_add_dropdown_callback)
-    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)
 
-    centre_guess = Spinner(default_size=100)
+    def fitparams_select_callback(_attr, old, new):
-    centre_vary = Toggle(default_size=100, active=True)
+        # Avoid selection of multiple indicies (via Shift+Click or Ctrl+Click)
-    centre_min = Spinner(default_size=100)
+        if len(new) > 1:
-    centre_max = Spinner(default_size=100)
+            # drop selection to the previous one
-    sigma_guess = Spinner(default_size=100)
+            fitparams_select.value = old
-    sigma_vary = Toggle(default_size=100, active=True)
+            return
-    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)
 
-    def fitparam_reset_button_callback():
+        if len(old) > 1:
-        centre_guess.value = None
+            # skip unnecessary update caused by selection drop
-        centre_vary.active = True
+            return
-        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
 
-    fitparam_reset_button = Button(label="Reset to defaults", default_size=145)
+        if new:
-    fitparam_reset_button.on_click(fitparam_reset_button_callback)
+            fitparams_table_source.data.update(fit_params[new[0]])
+        else:
+            fitparams_table_source.data.update(dict(param=[], value=[], vary=[], min=[], max=[]))
 
-    fit_output_textinput = TextAreaInput(title="Fit results:", width=450, height=400)
+    fitparams_select = MultiSelect(options=[], height=120, default_size=145)
+    fitparams_select.tags = [0]
+    fitparams_select.on_change("value", fitparams_select_callback)
 
-    def peakfind_all_button_callback():
+    def fitparams_remove_button_callback():
-        for scan in det_data["scan"].values():
+        if fitparams_select.value:
-            pyzebra.ccl_findpeaks(
+            sel_tag = fitparams_select.value[0]
-                scan,
+            del fit_params[sel_tag]
-                int_threshold=peak_int_ratio_spinner.value,
+            for elem in fitparams_select.options:
-                prominence=peak_prominence_spinner.value,
+                if elem[0] == sel_tag:
-                smooth=smooth_toggle.active,
+                    fitparams_select.options.remove(elem)
-                window_size=window_size_spinner.value,
+                    break
-                poly_order=poly_order_spinner.value,
-            )
 
-        _update_table()
+            fitparams_select.value = []
 
-        sel_ind = scan_table_source.selected.indices[-1]
+    fitparams_remove_button = Button(label="Remove fit function", default_size=145, disabled=True)
-        _update_plot(scan_table_source.data["scan"][sel_ind])
+    fitparams_remove_button.on_click(fitparams_remove_button_callback)
 
-    peakfind_all_button = Button(label="Peak Find All", button_type="primary", default_size=145)
+    def fitparams_factory(function):
-    peakfind_all_button.on_click(peakfind_all_button_callback)
+        if function == "linear":
+            params = ["slope", "intercept"]
+        elif function == "gaussian":
+            params = ["amplitude", "center", "sigma"]
+        elif function == "voigt":
+            params = ["amplitude", "center", "sigma", "gamma"]
+        elif function == "pvoigt":
+            params = ["amplitude", "center", "sigma", "fraction"]
+        elif function == "pseudovoigt1":
+            params = ["amplitude", "center", "g_sigma", "l_sigma", "fraction"]
+        else:
+            raise ValueError("Unknown fit function")
 
-    def peakfind_button_callback():
+        n = len(params)
-        sel_ind = scan_table_source.selected.indices[-1]
+        fitparams = dict(
-        scan = scan_table_source.data["scan"][sel_ind]
+            param=params, value=[None] * n, vary=[True] * n, min=[None] * n, max=[None] * n,
-        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()
+        return fitparams
-        _update_plot(scan)
 
-    peakfind_button = Button(label="Peak Find Current", default_size=145)
+    fitparams_table_source = ColumnDataSource(dict(param=[], value=[], vary=[], min=[], max=[]))
-    peakfind_button.on_click(peakfind_button_callback)
+    fitparams_table = DataTable(
+        source=fitparams_table_source,
+        columns=[
+            TableColumn(field="param", title="Parameter"),
+            TableColumn(field="value", title="Value", editor=NumberEditor()),
+            TableColumn(field="vary", title="Vary", editor=CheckboxEditor()),
+            TableColumn(field="min", title="Min", editor=NumberEditor()),
+            TableColumn(field="max", title="Max", editor=NumberEditor()),
+        ],
+        height=200,
+        width=350,
+        index_position=None,
+        editable=True,
+        auto_edit=True,
+    )
+
+    # start with `background` and `gauss` fit functions added
+    fitparams_add_dropdown_callback(types.SimpleNamespace(item="linear"))
+    fitparams_add_dropdown_callback(types.SimpleNamespace(item="gaussian"))
+    fitparams_select.value = ["gaussian-1"]  # add selection to gauss
+
+    fit_output_textinput = TextAreaInput(title="Fit results:", width=750, height=200)
 
     def fit_all_button_callback():
-        for scan in det_data["scan"].values():
+        for scan, export in zip(det_data, scan_table_source.data["export"]):
-            pyzebra.fitccl(
+            if export:
-                scan,
+                pyzebra.fit_scan(
-                guess=[
+                    scan, fit_params, fit_from=fit_from_spinner.value, fit_to=fit_to_spinner.value
-                    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(_get_selected_scan())
-        _update_plot(scan_table_source.data["scan"][sel_ind])
         _update_table()
 
     fit_all_button = Button(label="Fit All", button_type="primary", default_size=145)
     fit_all_button.on_click(fit_all_button_callback)
 
     def fit_button_callback():
-        sel_ind = scan_table_source.selected.indices[-1]
+        scan = _get_selected_scan()
-        scan = scan_table_source.data["scan"][sel_ind]
+        pyzebra.fit_scan(
-
+            scan, fit_params, fit_from=fit_from_spinner.value, fit_to=fit_to_spinner.value
-        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)
@@ -446,103 +473,95 @@ def create():
     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
+        labels=["Fit area", "Int area"], active=0, default_size=145, disabled=True
     )
-    area_method_radiobutton.on_change("active", area_method_radiobutton_callback)
 
-    preview_output_textinput = TextAreaInput(title="Export file preview:", width=450, height=400)
+    bin_size_spinner = Spinner(
+        title="Bin size:", value=1, low=1, step=1, default_size=145, disabled=True
+    )
 
-    def preview_output_button_callback():
+    lorentz_toggle = Toggle(label="Lorentz Correction", default_size=145)
-        if det_data["meta"]["indices"] == "hkl":
-            ext = ".comm"
-        elif det_data["meta"]["indices"] == "real":
-            ext = ".incomm"
 
+    export_preview_textinput = TextAreaInput(title="Export preview:", width=500, height=400)
+
+    def preview_button_callback():
         with tempfile.TemporaryDirectory() as temp_dir:
             temp_file = temp_dir + "/temp"
-            pyzebra.export_comm(det_data, temp_file)
+            export_data = []
+            for s, export in zip(det_data, scan_table_source.data["export"]):
+                if export:
+                    export_data.append(s)
 
-            with open(f"{temp_file}{ext}") as f:
+            pyzebra.export_1D(
-                preview_output_textinput.value = f.read()
+                export_data,
+                temp_file,
+                area_method=AREA_METHODS[int(area_method_radiobutton.active)],
+                lorentz=lorentz_toggle.active,
+                hkl_precision=int(hkl_precision_select.value),
+            )
 
-    preview_output_button = Button(label="Preview file", default_size=220)
+            exported_content = ""
-    preview_output_button.on_click(preview_output_button_callback)
+            file_content = []
+            for ext in (".comm", ".incomm"):
+                fname = temp_file + ext
+                if os.path.isfile(fname):
+                    with open(fname) as f:
+                        content = f.read()
+                        exported_content += f"{ext} file:\n" + content
+                else:
+                    content = ""
+                file_content.append(content)
 
-    def export_results(det_data):
+            js_data.data.update(content=file_content)
-        if det_data["meta"]["indices"] == "hkl":
+            export_preview_textinput.value = exported_content
-            ext = ".comm"
-        elif det_data["meta"]["indices"] == "real":
-            ext = ".incomm"
 
-        with tempfile.TemporaryDirectory() as temp_dir:
+    preview_button = Button(label="Preview", default_size=200)
-            temp_file = temp_dir + "/temp"
+    preview_button.on_click(preview_button_callback)
-            pyzebra.export_comm(det_data, temp_file)
 
-            with open(f"{temp_file}{ext}") as f:
+    hkl_precision_select = Select(
-                output_content = f.read()
+        title="hkl precision:", options=["2", "3", "4"], value="2", default_size=80
+    )
 
-        return output_content, ext
+    save_button = Button(label="Download preview", button_type="success", default_size=200)
-
-    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),
-    )
-
-    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])
     fitpeak_controls = row(
-        column(
+        column(fitparams_add_dropdown, fitparams_select, fitparams_remove_button),
-            Spacer(height=36),
+        fitparams_table,
-            div_1,
-            Spacer(height=12),
-            div_2,
-            Spacer(height=12),
-            div_3,
-            Spacer(height=12),
-            div_4,
-        ),
-        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),
         Spacer(width=20),
         column(
-            row(integ_from, integ_to),
+            row(fit_from_spinner, fit_to_spinner),
-            row(fitparam_reset_button, area_method_radiobutton),
+            row(bin_size_spinner, column(Spacer(height=19), lorentz_toggle)),
+            row(area_method_radiobutton),
             row(fit_button, fit_all_button),
         ),
     )
 
-    export_layout = column(preview_output_textinput, row(preview_output_button, save_button))
+    scan_layout = column(
+        scan_table,
+        monitor_spinner,
+        row(column(Spacer(height=19), merge_button), merge_dest_select, merge_source_select),
+    )
+
+    import_layout = column(
+        proposal_textinput,
+        file_select,
+        row(file_open_button, file_append_button),
+        upload_div,
+        upload_button,
+        append_upload_div,
+        append_upload_button,
+    )
+
+    export_layout = column(
+        export_preview_textinput,
+        row(hkl_precision_select, column(Spacer(height=19), row(preview_button, save_button))),
+    )
 
-    upload_div = Div(text="Or upload .ccl file:")
     tab_layout = column(
-        row(proposal_textinput, ccl_file_select),
+        row(import_layout, scan_layout, plot, Spacer(width=30), export_layout),
-        row(column(Spacer(height=5), upload_div), upload_button),
+        row(fitpeak_controls, fit_output_textinput),
-        row(scan_table, plot, Spacer(width=30), fit_output_textinput, export_layout),
-        row(findpeak_controls, Spacer(width=30), fitpeak_controls),
     )
 
     return Panel(child=tab_layout, title="ccl integrate")

pyzebra/app/panel_hdf_anatric.py

@@ -12,6 +12,7 @@ from bokeh.models import (
     Panel,
     RadioButtonGroup,
     Select,
+    Spacer,
     TextAreaInput,
     TextInput,
 )
@@ -21,6 +22,7 @@ from pyzebra.anatric import DATA_FACTORY_IMPLEMENTATION, REFLECTION_PRINTER_FORM
 
 
 def create():
+    doc = curdoc()
     config = pyzebra.AnatricConfig()
 
     def _load_config_file(file):
@@ -97,12 +99,11 @@ def create():
         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_div = Div(text="Open XML configuration file:")
     upload_button = FileInput(accept=".xml")
     upload_button.on_change("value", upload_button_callback)
 
@@ -111,7 +112,7 @@ 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", width=320)
     logfile_textinput.on_change("value", logfile_textinput_callback)
 
     def logfile_verbosity_select_callback(_attr, _old, new):
@@ -132,7 +133,7 @@ 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):
@@ -160,7 +161,7 @@ def create():
     def lambda_textinput_callback(_attr, _old, new):
         config.crystal_lambda = new
 
-    lambda_textinput = TextInput(title="lambda:", width=140)
+    lambda_textinput = TextInput(title="lambda:", width=145)
     lambda_textinput.on_change("value", lambda_textinput_callback)
 
     def ub_textareainput_callback(_attr, _old, new):
@@ -172,19 +173,19 @@ 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=145)
     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=145)
     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=145)
     zeroCHI_textinput.on_change("value", zeroCHI_textinput_callback)
 
     # ---- DataFactory
@@ -192,14 +193,14 @@ 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=145)
     dataFactory_dist1_textinput.on_change("value", dataFactory_dist1_textinput_callback)
 
     # ---- BackgroundProcessor
@@ -211,7 +212,7 @@ 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)
 
@@ -345,7 +346,10 @@ def create():
         with tempfile.TemporaryDirectory() as temp_dir:
             temp_file = temp_dir + "/temp.xml"
             config.save_as(temp_file)
-            pyzebra.anatric(temp_file)
+            if doc.anatric_path:
+                pyzebra.anatric(temp_file, anatric_path=doc.anatric_path)
+            else:
+                pyzebra.anatric(temp_file)
 
             with open(config.logfile) as f_log:
                 output_log.value = f_log.read()
@@ -353,57 +357,63 @@ def create():
     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_log = TextAreaInput(title="Logfile output:", height=600, disabled=True)
-    output_config = TextAreaInput(title="Current config:", height=700, width=400, disabled=True)
+    output_config = TextAreaInput(title="Current config:", height=600, width=400, disabled=True)
 
-    tab_layout = row(
+    general_params_layout = column(
-        column(
+        row(logfile_textinput, logfile_verbosity_select),
-            upload_div,
+        row(filelist_type, filelist_format_textinput),
-            upload_button,
+        filelist_datapath_textinput,
-            row(logfile_textinput, logfile_verbosity_select),
+        filelist_ranges_textareainput,
-            row(filelist_type, filelist_format_textinput),
+        crystal_sample_textinput,
-            filelist_datapath_textinput,
+        row(lambda_textinput, zeroOM_textinput),
-            filelist_ranges_textareainput,
+        row(zeroSTT_textinput, zeroCHI_textinput),
-            crystal_sample_textinput,
+        ub_textareainput,
-            row(lambda_textinput, zeroOM_textinput, zeroSTT_textinput, zeroCHI_textinput),
+        row(dataFactory_implementation_select, dataFactory_dist1_textinput),
-            ub_textareainput,
+        reflectionPrinter_format_select,
-            row(dataFactory_implementation_select, dataFactory_dist1_textinput),
+    )
-            reflectionPrinter_format_select,
+
-            process_button,
+    algorithm_params_layout = column(
-        ),
+        mode_radio_button_group,
-        column(
+        row(
-            mode_radio_button_group,
+            column(
-            row(
+                threshold_textinput,
-                column(
+                shell_textinput,
-                    threshold_textinput,
+                steepness_textinput,
-                    shell_textinput,
+                duplicateDistance_textinput,
-                    steepness_textinput,
+                maxequal_textinput,
-                    duplicateDistance_textinput,
+                aps_window_textinput,
-                    maxequal_textinput,
+            ),
-                    aps_window_textinput,
+            column(
-                ),
+                adm_window_textinput,
-                column(
+                border_textinput,
-                    adm_window_textinput,
+                minWindow_textinput,
-                    border_textinput,
+                reflectionFile_textinput,
-                    minWindow_textinput,
+                targetMonitor_textinput,
-                    reflectionFile_textinput,
+                smoothSize_textinput,
-                    targetMonitor_textinput,
+                loop_textinput,
-                    smoothSize_textinput,
+                minPeakCount_textinput,
-                    loop_textinput,
+                displacementCurve_textinput,
-                    minPeakCount_textinput,
-                    displacementCurve_textinput,
-                ),
             ),
         ),
-        output_config,
+    )
-        output_log,
+
+    tab_layout = column(
+        row(column(Spacer(height=2), upload_div), upload_button),
+        row(
+            general_params_layout,
+            algorithm_params_layout,
+            column(row(output_config, output_log), row(process_button)),
+        ),
     )
 
     async def update_config():
-        config.save_as("debug.xml")
+        with tempfile.TemporaryDirectory() as temp_dir:
-        with open("debug.xml") as f_config:
+            temp_file = temp_dir + "/debug.xml"
-            output_config.value = f_config.read()
+            config.save_as(temp_file)
+            with open(temp_file) as f_config:
+                output_config.value = f_config.read()
 
-    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_viewer.py

@@ -30,6 +30,7 @@ from bokeh.models import (
     Spacer,
     Spinner,
     TextAreaInput,
+    TextInput,
     Title,
     Toggle,
     WheelZoomTool,
@@ -40,12 +41,28 @@ import pyzebra
 
 IMAGE_W = 256
 IMAGE_H = 128
+IMAGE_PLOT_W = int(IMAGE_W * 2.5)
+IMAGE_PLOT_H = int(IMAGE_H * 2.5)
+
+PROPOSAL_PATH = "/afs/psi.ch/project/sinqdata/2020/zebra/"
 
 
 def create():
     det_data = {}
     roi_selection = {}
 
+    def proposal_textinput_callback(_attr, _old, new):
+        full_proposal_path = os.path.join(PROPOSAL_PATH, new.strip())
+        file_list = []
+        for file in os.listdir(full_proposal_path):
+            if file.endswith(".hdf"):
+                file_list.append((os.path.join(full_proposal_path, file), file))
+        filelist.options = file_list
+        filelist.value = file_list[0][0]
+
+    proposal_textinput = TextInput(title="Enter proposal number:", default_size=145)
+    proposal_textinput.on_change("value", proposal_textinput_callback)
+
     def upload_button_callback(_attr, _old, new):
         with io.StringIO(base64.b64decode(new).decode()) as file:
             h5meta_list = pyzebra.parse_h5meta(file)
@@ -53,6 +70,7 @@ def create():
             filelist.options = [(entry, os.path.basename(entry)) for entry in file_list]
             filelist.value = file_list[0]
 
+    upload_div = Div(text="or upload .cami file:", margin=(5, 5, 0, 5))
     upload_button = FileInput(accept=".cami")
     upload_button.on_change("value", upload_button_callback)
 
@@ -74,8 +92,8 @@ def create():
         image_source.data.update(image=[current_image])
 
         if auto_toggle.active:
-            im_max = int(np.max(current_image))
+            im_min = np.min(current_image)
-            im_min = int(np.min(current_image))
+            im_max = np.max(current_image)
 
             display_min_spinner.value = im_min
             display_max_spinner.value = im_max
@@ -83,11 +101,18 @@ def create():
             image_glyph.color_mapper.low = im_min
             image_glyph.color_mapper.high = im_max
 
-        magnetic_field_spinner.value = det_data["magnetic_field"][index]
+        if "mf" in det_data:
-        temperature_spinner.value = det_data["temperature"][index]
+            mf_spinner.value = det_data["mf"][index]
+        else:
+            mf_spinner.value = None
+
+        if "temp" in det_data:
+            temp_spinner.value = det_data["temp"][index]
+        else:
+            temp_spinner.value = None
 
         gamma, nu = calculate_pol(det_data, index)
-        omega = np.ones((IMAGE_H, IMAGE_W)) * det_data["rot_angle"][index]
+        omega = np.ones((IMAGE_H, IMAGE_W)) * det_data["omega"][index]
         image_source.data.update(gamma=[gamma], nu=[nu], omega=[omega])
 
     def update_overview_plot():
@@ -99,6 +124,18 @@ def create():
         overview_plot_x_image_source.data.update(image=[overview_x], dw=[n_x])
         overview_plot_y_image_source.data.update(image=[overview_y], dw=[n_y])
 
+        if proj_auto_toggle.active:
+            im_min = min(np.min(overview_x), np.min(overview_y))
+            im_max = max(np.max(overview_x), np.max(overview_y))
+
+            proj_display_min_spinner.value = im_min
+            proj_display_max_spinner.value = im_max
+
+            overview_plot_x_image_glyph.color_mapper.low = im_min
+            overview_plot_y_image_glyph.color_mapper.low = im_min
+            overview_plot_x_image_glyph.color_mapper.high = im_max
+            overview_plot_y_image_glyph.color_mapper.high = im_max
+
         if frame_button_group.active == 0:  # Frame
             overview_plot_x.axis[1].axis_label = "Frame"
             overview_plot_y.axis[1].axis_label = "Frame"
@@ -106,15 +143,16 @@ def create():
             overview_plot_x_image_source.data.update(y=[0], dh=[n_im])
             overview_plot_y_image_source.data.update(y=[0], dh=[n_im])
 
-        elif frame_button_group.active == 1:  # Omega
+        elif frame_button_group.active == 1:  # Variable angle
-            overview_plot_x.axis[1].axis_label = "Omega"
+            scan_motor = det_data["scan_motor"]
-            overview_plot_y.axis[1].axis_label = "Omega"
+            overview_plot_x.axis[1].axis_label = scan_motor
+            overview_plot_y.axis[1].axis_label = scan_motor
 
-            om = det_data["rot_angle"]
+            var = det_data[scan_motor]
-            om_start = om[0]
+            var_start = var[0]
-            om_end = (om[-1] - om[0]) * n_im / (n_im - 1)
+            var_end = (var[-1] - var[0]) * n_im / (n_im - 1)
-            overview_plot_x_image_source.data.update(y=[om_start], dh=[om_end])
+            overview_plot_x_image_source.data.update(y=[var_start], dh=[var_end])
-            overview_plot_y_image_source.data.update(y=[om_start], dh=[om_end])
+            overview_plot_y_image_source.data.update(y=[var_start], dh=[var_end])
 
     def filelist_callback(_attr, _old, new):
         nonlocal det_data
@@ -122,10 +160,17 @@ def create():
 
         index_spinner.value = 0
         index_spinner.high = det_data["data"].shape[0] - 1
+
+        zebra_mode = det_data["zebra_mode"]
+        if zebra_mode == "nb":
+            geometry_textinput.value = "normal beam"
+        else:  # zebra_mode == "bi"
+            geometry_textinput.value = "bisecting"
+
         update_image(0)
         update_overview_plot()
 
-    filelist = Select()
+    filelist = Select(title="Available .hdf files:")
     filelist.on_change("value", filelist_callback)
 
     def index_spinner_callback(_attr, _old, new):
@@ -137,8 +182,8 @@ def create():
     plot = Plot(
         x_range=Range1d(0, IMAGE_W, bounds=(0, IMAGE_W)),
         y_range=Range1d(0, IMAGE_H, bounds=(0, IMAGE_H)),
-        plot_height=IMAGE_H * 3,
+        plot_height=IMAGE_PLOT_H,
-        plot_width=IMAGE_W * 3,
+        plot_width=IMAGE_PLOT_W,
         toolbar_location="left",
     )
 
@@ -191,8 +236,8 @@ def create():
     proj_v = Plot(
         x_range=plot.x_range,
         y_range=DataRange1d(),
-        plot_height=200,
+        plot_height=150,
-        plot_width=IMAGE_W * 3,
+        plot_width=IMAGE_PLOT_W,
         toolbar_location=None,
     )
 
@@ -208,8 +253,8 @@ def create():
     proj_h = Plot(
         x_range=DataRange1d(),
         y_range=plot.y_range,
-        plot_height=IMAGE_H * 3,
+        plot_height=IMAGE_PLOT_H,
-        plot_width=200,
+        plot_width=150,
         toolbar_location=None,
     )
 
@@ -272,8 +317,8 @@ def create():
         title=Title(text="Projections on X-axis"),
         x_range=det_x_range,
         y_range=frame_range,
-        plot_height=500,
+        plot_height=400,
-        plot_width=IMAGE_W * 3,
+        plot_width=IMAGE_PLOT_W,
     )
 
     # ---- tools
@@ -309,8 +354,8 @@ def create():
         title=Title(text="Projections on Y-axis"),
         x_range=det_y_range,
         y_range=frame_range,
-        plot_height=500,
+        plot_height=400,
-        plot_width=IMAGE_H * 3,
+        plot_width=IMAGE_PLOT_H,
     )
 
     # ---- tools
@@ -344,14 +389,14 @@ def create():
     def frame_button_group_callback(_active):
         update_overview_plot()
 
-    frame_button_group = RadioButtonGroup(labels=["Frames", "Omega"], active=0)
+    frame_button_group = RadioButtonGroup(labels=["Frames", "Variable Angle"], active=0)
     frame_button_group.on_click(frame_button_group_callback)
 
     roi_avg_plot = Plot(
         x_range=DataRange1d(),
         y_range=DataRange1d(),
         plot_height=200,
-        plot_width=IMAGE_W * 3,
+        plot_width=IMAGE_PLOT_W,
         toolbar_location="left",
     )
 
@@ -385,10 +430,7 @@ def create():
     colormap.on_change("value", colormap_callback)
     colormap.value = "plasma"
 
-    radio_button_group = RadioButtonGroup(labels=["nb", "nb_bi"], active=0)
-
     STEP = 1
-
     # ---- colormap auto toggle button
     def auto_toggle_callback(state):
         if state:
@@ -400,7 +442,9 @@ def create():
 
         update_image()
 
-    auto_toggle = Toggle(label="Auto Range", active=True, button_type="default", default_size=145)
+    auto_toggle = Toggle(
+        label="Main Auto Range", active=True, button_type="default", default_size=125
+    )
     auto_toggle.on_click(auto_toggle_callback)
 
     # ---- colormap display max value
@@ -409,12 +453,12 @@ def create():
         image_glyph.color_mapper.high = new_value
 
     display_max_spinner = Spinner(
-        title="Maximal Display Value:",
+        title="Max Value:",
         low=0 + STEP,
         value=1,
         step=STEP,
         disabled=auto_toggle.active,
-        default_size=145,
+        default_size=80,
     )
     display_max_spinner.on_change("value", display_max_spinner_callback)
 
@@ -424,19 +468,69 @@ def create():
         image_glyph.color_mapper.low = new_value
 
     display_min_spinner = Spinner(
-        title="Minimal Display Value:",
+        title="Min Value:",
+        low=0,
         high=1 - STEP,
         value=0,
         step=STEP,
         disabled=auto_toggle.active,
-        default_size=145,
+        default_size=80,
     )
     display_min_spinner.on_change("value", display_min_spinner_callback)
 
+    PROJ_STEP = 0.1
+    # ---- proj colormap auto toggle button
+    def proj_auto_toggle_callback(state):
+        if state:
+            proj_display_min_spinner.disabled = True
+            proj_display_max_spinner.disabled = True
+        else:
+            proj_display_min_spinner.disabled = False
+            proj_display_max_spinner.disabled = False
+
+        update_overview_plot()
+
+    proj_auto_toggle = Toggle(
+        label="Proj Auto Range", active=True, button_type="default", default_size=125
+    )
+    proj_auto_toggle.on_click(proj_auto_toggle_callback)
+
+    # ---- proj colormap display max value
+    def proj_display_max_spinner_callback(_attr, _old_value, new_value):
+        proj_display_min_spinner.high = new_value - PROJ_STEP
+        overview_plot_x_image_glyph.color_mapper.high = new_value
+        overview_plot_y_image_glyph.color_mapper.high = new_value
+
+    proj_display_max_spinner = Spinner(
+        title="Max Value:",
+        low=0 + PROJ_STEP,
+        value=1,
+        step=PROJ_STEP,
+        disabled=proj_auto_toggle.active,
+        default_size=80,
+    )
+    proj_display_max_spinner.on_change("value", proj_display_max_spinner_callback)
+
+    # ---- proj colormap display min value
+    def proj_display_min_spinner_callback(_attr, _old_value, new_value):
+        proj_display_max_spinner.low = new_value + PROJ_STEP
+        overview_plot_x_image_glyph.color_mapper.low = new_value
+        overview_plot_y_image_glyph.color_mapper.low = new_value
+
+    proj_display_min_spinner = Spinner(
+        title="Min Value:",
+        low=0,
+        high=1 - PROJ_STEP,
+        value=0,
+        step=PROJ_STEP,
+        disabled=proj_auto_toggle.active,
+        default_size=80,
+    )
+    proj_display_min_spinner.on_change("value", proj_display_min_spinner_callback)
+
     def hkl_button_callback():
         index = index_spinner.value
-        setup_type = "nb_bi" if radio_button_group.active else "nb"
+        h, k, l = calculate_hkl(det_data, index)
-        h, k, l = calculate_hkl(det_data, index, setup_type)
         image_source.data.update(h=[h], k=[k], l=[l])
 
     hkl_button = Button(label="Calculate hkl (slow)")
@@ -466,19 +560,25 @@ def create():
     selection_button = Button(label="Add selection")
     selection_button.on_click(selection_button_callback)
 
-    magnetic_field_spinner = Spinner(
+    mf_spinner = Spinner(
         title="Magnetic field:", format="0.00", width=145, disabled=True
     )
-    temperature_spinner = Spinner(title="Temperature:", format="0.00", width=145, disabled=True)
+    temp_spinner = Spinner(title="Temperature:", format="0.00", width=145, disabled=True)
+    geometry_textinput = TextInput(title="Geometry:", disabled=True)
 
     # Final layout
     layout_image = column(gridplot([[proj_v, None], [plot, proj_h]], merge_tools=False))
     colormap_layout = column(
-        row(colormap, column(Spacer(height=19), auto_toggle)),
+        row(colormap),
-        row(display_max_spinner, display_min_spinner),
+        row(column(Spacer(height=19), auto_toggle), display_max_spinner, display_min_spinner),
+        row(
+            column(Spacer(height=19), proj_auto_toggle),
+            proj_display_max_spinner,
+            proj_display_min_spinner,
+        ),
     )
-    hkl_layout = column(radio_button_group, hkl_button)
+    hkl_layout = column(geometry_textinput, hkl_button)
-    params_layout = row(magnetic_field_spinner, temperature_spinner)
+    params_layout = row(mf_spinner, temp_spinner)
 
     layout_controls = row(
         column(selection_button, selection_list),
@@ -497,10 +597,11 @@ def create():
         ),
     )
 
-    upload_div = Div(text="Upload .cami file:")
     tab_layout = row(
         column(
-            row(column(Spacer(height=5), upload_div), upload_button, filelist),
+            row(
+                proposal_textinput, filelist, Spacer(width=100), column(upload_div, upload_button),
+            ),
             layout_overview,
             layout_controls,
         ),
@@ -510,31 +611,32 @@ def create():
     return Panel(child=tab_layout, title="hdf viewer")
 
 
-def calculate_hkl(det_data, index, setup_type="nb_bi"):
+def calculate_hkl(det_data, index):
     h = np.empty(shape=(IMAGE_H, IMAGE_W))
     k = np.empty(shape=(IMAGE_H, IMAGE_W))
     l = np.empty(shape=(IMAGE_H, IMAGE_W))
 
     wave = det_data["wave"]
     ddist = det_data["ddist"]
-    gammad = det_data["pol_angle"][index]
+    gammad = det_data["gamma"][index]
-    om = det_data["rot_angle"][index]
+    om = det_data["omega"][index]
-    nud = det_data["tlt_angle"]
+    nud = det_data["nu"]
-    ub = det_data["UB"]
+    ub = det_data["ub"]
+    geometry = det_data["zebra_mode"]
 
-    if setup_type == "nb_bi":
+    if geometry == "bi":
-        ch = det_data["chi_angle"][index]
+        chi = det_data["chi"][index]
-        ph = det_data["phi_angle"][index]
+        phi = det_data["phi"][index]
-    elif setup_type == "nb":
+    elif geometry == "nb":
-        ch = 0
+        chi = 0
-        ph = 0
+        phi = 0
     else:
-        raise ValueError(f"Unknown setup type '{setup_type}'")
+        raise ValueError(f"Unknown geometry type '{geometry}'")
 
     for xi in np.arange(IMAGE_W):
         for yi in np.arange(IMAGE_H):
             h[yi, xi], k[yi, xi], l[yi, xi] = pyzebra.ang2hkl(
-                wave, ddist, gammad, om, ch, ph, nud, ub, xi, yi
+                wave, ddist, gammad, om, chi, phi, nud, ub, xi, yi
             )
 
     return h, k, l
@@ -545,8 +647,8 @@ def calculate_pol(det_data, index):
     nu = np.empty(shape=(IMAGE_H, IMAGE_W))
 
     ddist = det_data["ddist"]
-    gammad = det_data["pol_angle"][index]
+    gammad = det_data["gamma"][index]
-    nud = det_data["tlt_angle"]
+    nud = det_data["nu"]
 
     for xi in np.arange(IMAGE_W):
         for yi in np.arange(IMAGE_H):

pyzebra/app/panel_param_study.py

@@ -0,0 +1,649 @@
+import base64
+import io
+import itertools
+import os
+import tempfile
+import types
+
+import numpy as np
+from bokeh.layouts import column, row
+from bokeh.models import (
+    BasicTicker,
+    Button,
+    CheckboxEditor,
+    ColumnDataSource,
+    CustomJS,
+    DataRange1d,
+    DataTable,
+    Div,
+    Dropdown,
+    FileInput,
+    Grid,
+    HoverTool,
+    Legend,
+    Line,
+    LinearAxis,
+    MultiLine,
+    MultiSelect,
+    NumberEditor,
+    Panel,
+    PanTool,
+    Plot,
+    RadioButtonGroup,
+    ResetTool,
+    Scatter,
+    Select,
+    Spacer,
+    Span,
+    Spinner,
+    TableColumn,
+    Tabs,
+    TextAreaInput,
+    TextInput,
+    Toggle,
+    WheelZoomTool,
+    Whisker,
+)
+from bokeh.palettes import Category10, Turbo256
+from bokeh.transform import linear_cmap
+
+import pyzebra
+from pyzebra.ccl_io import AREA_METHODS
+
+javaScript = """
+for (let i = 0; i < js_data.data['fname'].length; i++) {
+    if (js_data.data['content'][i] === "") continue;
+
+    const blob = new Blob([js_data.data['content'][i]], {type: 'text/plain'})
+    const link = document.createElement('a');
+    document.body.appendChild(link);
+    const url = window.URL.createObjectURL(blob);
+    link.href = url;
+    link.download = js_data.data['fname'][i];
+    link.click();
+    window.URL.revokeObjectURL(url);
+    document.body.removeChild(link);
+}
+"""
+
+PROPOSAL_PATH = "/afs/psi.ch/project/sinqdata/2020/zebra/"
+
+
+def color_palette(n_colors):
+    palette = itertools.cycle(Category10[10])
+    return list(itertools.islice(palette, n_colors))
+
+
+def create():
+    det_data = []
+    fit_params = {}
+    js_data = ColumnDataSource(data=dict(content=["", ""], fname=["", ""]))
+
+    def proposal_textinput_callback(_attr, _old, new):
+        full_proposal_path = os.path.join(PROPOSAL_PATH, new.strip())
+        dat_file_list = []
+        for file in os.listdir(full_proposal_path):
+            if file.endswith(".dat"):
+                dat_file_list.append((os.path.join(full_proposal_path, file), file))
+        file_select.options = dat_file_list
+
+    proposal_textinput = TextInput(title="Proposal number:", default_size=200)
+    proposal_textinput.on_change("value", proposal_textinput_callback)
+
+    def _init_datatable():
+        scan_list = [s["idx"] for s in det_data]
+        file_list = []
+        for scan in det_data:
+            file_list.append(os.path.basename(scan["original_filename"]))
+
+        scan_table_source.data.update(
+            file=file_list,
+            scan=scan_list,
+            param=[None] * len(scan_list),
+            fit=[0] * len(scan_list),
+            export=[True] * len(scan_list),
+        )
+        scan_table_source.selected.indices = []
+        scan_table_source.selected.indices = [0]
+
+        param_select.value = "user defined"
+
+    def file_select_callback(_attr, _old, _new):
+        pass
+
+    file_select = MultiSelect(title="Available .dat files:", default_size=200, height=250)
+    file_select.on_change("value", file_select_callback)
+
+    def file_open_button_callback():
+        nonlocal det_data
+        det_data = []
+        for f_name in file_select.value:
+            with open(f_name) as file:
+                base, ext = os.path.splitext(f_name)
+                if det_data:
+                    append_data = pyzebra.parse_1D(file, ext)
+                    pyzebra.normalize_dataset(append_data, monitor_spinner.value)
+                    det_data.extend(append_data)
+                else:
+                    det_data = pyzebra.parse_1D(file, ext)
+                    pyzebra.normalize_dataset(det_data, monitor_spinner.value)
+                    js_data.data.update(fname=[base + ".comm", base + ".incomm"])
+
+        _init_datatable()
+
+    file_open_button = Button(label="Open New", default_size=100)
+    file_open_button.on_click(file_open_button_callback)
+
+    def file_append_button_callback():
+        for f_name in file_select.value:
+            with open(f_name) as file:
+                _, ext = os.path.splitext(f_name)
+                append_data = pyzebra.parse_1D(file, ext)
+
+            pyzebra.normalize_dataset(append_data, monitor_spinner.value)
+            det_data.extend(append_data)
+
+        _init_datatable()
+
+    file_append_button = Button(label="Append", default_size=100)
+    file_append_button.on_click(file_append_button_callback)
+
+    def upload_button_callback(_attr, _old, new):
+        nonlocal det_data
+        det_data = []
+        for f_str, f_name in zip(new, upload_button.filename):
+            with io.StringIO(base64.b64decode(f_str).decode()) as file:
+                base, ext = os.path.splitext(f_name)
+                if det_data:
+                    append_data = pyzebra.parse_1D(file, ext)
+                    pyzebra.normalize_dataset(append_data, monitor_spinner.value)
+                    det_data.extend(append_data)
+                else:
+                    det_data = pyzebra.parse_1D(file, ext)
+                    pyzebra.normalize_dataset(det_data, monitor_spinner.value)
+                    js_data.data.update(fname=[base + ".comm", base + ".incomm"])
+
+        _init_datatable()
+
+    upload_div = Div(text="or upload new .dat files:", margin=(5, 5, 0, 5))
+    upload_button = FileInput(accept=".dat", multiple=True, default_size=200)
+    upload_button.on_change("value", upload_button_callback)
+
+    def append_upload_button_callback(_attr, _old, new):
+        for f_str, f_name in zip(new, append_upload_button.filename):
+            with io.StringIO(base64.b64decode(f_str).decode()) as file:
+                _, ext = os.path.splitext(f_name)
+                append_data = pyzebra.parse_1D(file, ext)
+
+            pyzebra.normalize_dataset(append_data, monitor_spinner.value)
+            det_data.extend(append_data)
+
+        _init_datatable()
+
+    append_upload_div = Div(text="append extra files:", margin=(5, 5, 0, 5))
+    append_upload_button = FileInput(accept=".dat", multiple=True, default_size=200)
+    append_upload_button.on_change("value", append_upload_button_callback)
+
+    def monitor_spinner_callback(_attr, _old, new):
+        if det_data:
+            pyzebra.normalize_dataset(det_data, new)
+            _update_plot()
+
+    monitor_spinner = Spinner(title="Monitor:", mode="int", value=100_000, low=1, width=145)
+    monitor_spinner.on_change("value", monitor_spinner_callback)
+
+    def _update_table():
+        fit_ok = [(1 if "fit" in scan else 0) for scan in det_data]
+        scan_table_source.data.update(fit=fit_ok)
+
+    def _update_plot():
+        _update_single_scan_plot(_get_selected_scan())
+        _update_overview()
+
+    def _update_single_scan_plot(scan):
+        scan_motor = scan["scan_motor"]
+
+        y = scan["Counts"]
+        x = scan[scan_motor]
+
+        plot.axis[0].axis_label = scan_motor
+        plot_scatter_source.data.update(x=x, y=y, y_upper=y + np.sqrt(y), y_lower=y - np.sqrt(y))
+
+        fit = scan.get("fit")
+        if fit is not None:
+            x_fit = np.linspace(x[0], x[-1], 100)
+            plot_fit_source.data.update(x=x_fit, y=fit.eval(x=x_fit))
+
+            x_bkg = []
+            y_bkg = []
+            xs_peak = []
+            ys_peak = []
+            comps = fit.eval_components(x=x_fit)
+            for i, model in enumerate(fit_params):
+                if "linear" in model:
+                    x_bkg = x_fit
+                    y_bkg = comps[f"f{i}_"]
+
+                elif any(val in model for val in ("gaussian", "voigt", "pvoigt")):
+                    xs_peak.append(x_fit)
+                    ys_peak.append(comps[f"f{i}_"])
+
+            plot_bkg_source.data.update(x=x_bkg, y=y_bkg)
+            plot_peak_source.data.update(xs=xs_peak, ys=ys_peak)
+
+            fit_output_textinput.value = fit.fit_report()
+
+        else:
+            plot_fit_source.data.update(x=[], y=[])
+            plot_bkg_source.data.update(x=[], y=[])
+            plot_peak_source.data.update(xs=[], ys=[])
+            fit_output_textinput.value = ""
+
+    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 = det_data[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 det_data:
+            scan_motor = det_data[0]["scan_motor"]
+            ov_plot.axis[0].axis_label = scan_motor
+            ov_param_plot.axis[0].axis_label = scan_motor
+
+        ov_plot_mline_source.data.update(xs=xs, ys=ys, param=param, color=color_palette(len(xs)))
+
+        if y:
+            mapper["transform"].low = np.min([np.min(y) for y in ys])
+            mapper["transform"].high = np.max([np.max(y) for y in ys])
+        ov_param_plot_scatter_source.data.update(x=x, y=y, param=par)
+
+    # Main plot
+    plot = Plot(
+        x_range=DataRange1d(),
+        y_range=DataRange1d(only_visible=True),
+        plot_height=450,
+        plot_width=700,
+    )
+
+    plot.add_layout(LinearAxis(axis_label="Counts"), place="left")
+    plot.add_layout(LinearAxis(axis_label="Scan motor"), place="below")
+
+    plot.add_layout(Grid(dimension=0, ticker=BasicTicker()))
+    plot.add_layout(Grid(dimension=1, ticker=BasicTicker()))
+
+    plot_scatter_source = ColumnDataSource(dict(x=[0], y=[0], y_upper=[0], y_lower=[0]))
+    plot_scatter = plot.add_glyph(
+        plot_scatter_source, Scatter(x="x", y="y", line_color="steelblue")
+    )
+    plot.add_layout(Whisker(source=plot_scatter_source, base="x", upper="y_upper", lower="y_lower"))
+
+    plot_fit_source = ColumnDataSource(dict(x=[0], y=[0]))
+    plot_fit = plot.add_glyph(plot_fit_source, Line(x="x", y="y"))
+
+    plot_bkg_source = ColumnDataSource(dict(x=[0], y=[0]))
+    plot_bkg = plot.add_glyph(
+        plot_bkg_source, Line(x="x", y="y", line_color="green", line_dash="dashed")
+    )
+
+    plot_peak_source = ColumnDataSource(dict(xs=[0], ys=[0]))
+    plot_peak = plot.add_glyph(
+        plot_peak_source, MultiLine(xs="xs", ys="ys", line_color="red", line_dash="dashed")
+    )
+
+    fit_from_span = Span(location=None, dimension="height", line_dash="dashed")
+    plot.add_layout(fit_from_span)
+
+    fit_to_span = Span(location=None, dimension="height", line_dash="dashed")
+    plot.add_layout(fit_to_span)
+
+    plot.add_layout(
+        Legend(
+            items=[
+                ("data", [plot_scatter]),
+                ("best fit", [plot_fit]),
+                ("peak", [plot_peak]),
+                ("linear", [plot_bkg]),
+            ],
+            location="top_left",
+            click_policy="hide",
+        )
+    )
+
+    plot.add_tools(PanTool(), WheelZoomTool(), ResetTool())
+    plot.toolbar.logo = None
+
+    # Overview multilines plot
+    ov_plot = Plot(x_range=DataRange1d(), y_range=DataRange1d(), plot_height=400, plot_width=700)
+
+    ov_plot.add_layout(LinearAxis(axis_label="Counts"), place="left")
+    ov_plot.add_layout(LinearAxis(axis_label="Scan motor"), place="below")
+
+    ov_plot.add_layout(Grid(dimension=0, ticker=BasicTicker()))
+    ov_plot.add_layout(Grid(dimension=1, ticker=BasicTicker()))
+
+    ov_plot_mline_source = ColumnDataSource(dict(xs=[], ys=[], param=[], color=[]))
+    ov_plot.add_glyph(ov_plot_mline_source, MultiLine(xs="xs", ys="ys", line_color="color"))
+
+    hover_tool = HoverTool(tooltips=[("param", "@param")])
+    ov_plot.add_tools(PanTool(), WheelZoomTool(), hover_tool, ResetTool())
+
+    ov_plot.add_tools(PanTool(), WheelZoomTool(), ResetTool())
+    ov_plot.toolbar.logo = None
+
+    # Overview perams plot
+    ov_param_plot = Plot(
+        x_range=DataRange1d(), y_range=DataRange1d(), plot_height=400, plot_width=700
+    )
+
+    ov_param_plot.add_layout(LinearAxis(axis_label="Param"), place="left")
+    ov_param_plot.add_layout(LinearAxis(axis_label="Scan motor"), place="below")
+
+    ov_param_plot.add_layout(Grid(dimension=0, ticker=BasicTicker()))
+    ov_param_plot.add_layout(Grid(dimension=1, ticker=BasicTicker()))
+
+    ov_param_plot_scatter_source = ColumnDataSource(dict(x=[], y=[], param=[]))
+    mapper = linear_cmap(field_name="param", palette=Turbo256, low=0, high=50)
+    ov_param_plot.add_glyph(
+        ov_param_plot_scatter_source,
+        Scatter(x="x", y="y", line_color=mapper, fill_color=mapper, size=10),
+    )
+
+    ov_param_plot.add_tools(PanTool(), WheelZoomTool(), ResetTool())
+    ov_param_plot.toolbar.logo = None
+
+    # 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"),
+        ]
+    )
+
+    # 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()
+
+    scan_table_source = ColumnDataSource(dict(file=[], scan=[], param=[], fit=[], export=[]))
+    scan_table = DataTable(
+        source=scan_table_source,
+        columns=[
+            TableColumn(field="file", title="file", width=150),
+            TableColumn(field="scan", title="scan", width=50),
+            TableColumn(field="param", title="param", editor=NumberEditor(), width=50),
+            TableColumn(field="fit", title="Fit", width=50),
+            TableColumn(field="export", title="Export", editor=CheckboxEditor(), width=50),
+        ],
+        width=410,  # +60 because of the index column
+        editable=True,
+        autosize_mode="none",
+    )
+
+    def scan_table_source_callback(_attr, _old, _new):
+        if scan_table_source.selected.indices:
+            _update_plot()
+
+    scan_table_source.selected.on_change("indices", scan_table_select_callback)
+    scan_table_source.on_change("data", scan_table_source_callback)
+
+    def _get_selected_scan():
+        return det_data[scan_table_source.selected.indices[0]]
+
+    def param_select_callback(_attr, _old, new):
+        if new == "user defined":
+            param = [None] * len(det_data)
+        else:
+            param = [scan[new] for scan in det_data]
+
+        scan_table_source.data["param"] = param
+
+    param_select = Select(
+        title="Parameter:",
+        options=["user defined", "temp", "mf", "h", "k", "l"],
+        value="user defined",
+        default_size=145,
+    )
+    param_select.on_change("value", param_select_callback)
+
+    def fit_from_spinner_callback(_attr, _old, new):
+        fit_from_span.location = new
+
+    fit_from_spinner = Spinner(title="Fit from:", default_size=145)
+    fit_from_spinner.on_change("value", fit_from_spinner_callback)
+
+    def fit_to_spinner_callback(_attr, _old, new):
+        fit_to_span.location = new
+
+    fit_to_spinner = Spinner(title="to:", default_size=145)
+    fit_to_spinner.on_change("value", fit_to_spinner_callback)
+
+    def fitparams_add_dropdown_callback(click):
+        # bokeh requires (str, str) for MultiSelect options
+        new_tag = f"{click.item}-{fitparams_select.tags[0]}"
+        fitparams_select.options.append((new_tag, click.item))
+        fit_params[new_tag] = fitparams_factory(click.item)
+        fitparams_select.tags[0] += 1
+
+    fitparams_add_dropdown = Dropdown(
+        label="Add fit function",
+        menu=[
+            ("Linear", "linear"),
+            ("Gaussian", "gaussian"),
+            ("Voigt", "voigt"),
+            ("Pseudo Voigt", "pvoigt"),
+            # ("Pseudo Voigt1", "pseudovoigt1"),
+        ],
+        default_size=145,
+    )
+    fitparams_add_dropdown.on_click(fitparams_add_dropdown_callback)
+
+    def fitparams_select_callback(_attr, old, new):
+        # Avoid selection of multiple indicies (via Shift+Click or Ctrl+Click)
+        if len(new) > 1:
+            # drop selection to the previous one
+            fitparams_select.value = old
+            return
+
+        if len(old) > 1:
+            # skip unnecessary update caused by selection drop
+            return
+
+        if new:
+            fitparams_table_source.data.update(fit_params[new[0]])
+        else:
+            fitparams_table_source.data.update(dict(param=[], value=[], vary=[], min=[], max=[]))
+
+    fitparams_select = MultiSelect(options=[], height=120, default_size=145)
+    fitparams_select.tags = [0]
+    fitparams_select.on_change("value", fitparams_select_callback)
+
+    def fitparams_remove_button_callback():
+        if fitparams_select.value:
+            sel_tag = fitparams_select.value[0]
+            del fit_params[sel_tag]
+            for elem in fitparams_select.options:
+                if elem[0] == sel_tag:
+                    fitparams_select.options.remove(elem)
+                    break
+
+            fitparams_select.value = []
+
+    fitparams_remove_button = Button(label="Remove fit function", default_size=145)
+    fitparams_remove_button.on_click(fitparams_remove_button_callback)
+
+    def fitparams_factory(function):
+        if function == "linear":
+            params = ["slope", "intercept"]
+        elif function == "gaussian":
+            params = ["amplitude", "center", "sigma"]
+        elif function == "voigt":
+            params = ["amplitude", "center", "sigma", "gamma"]
+        elif function == "pvoigt":
+            params = ["amplitude", "center", "sigma", "fraction"]
+        elif function == "pseudovoigt1":
+            params = ["amplitude", "center", "g_sigma", "l_sigma", "fraction"]
+        else:
+            raise ValueError("Unknown fit function")
+
+        n = len(params)
+        fitparams = dict(
+            param=params, value=[None] * n, vary=[True] * n, min=[None] * n, max=[None] * n,
+        )
+
+        return fitparams
+
+    fitparams_table_source = ColumnDataSource(dict(param=[], value=[], vary=[], min=[], max=[]))
+    fitparams_table = DataTable(
+        source=fitparams_table_source,
+        columns=[
+            TableColumn(field="param", title="Parameter"),
+            TableColumn(field="value", title="Value", editor=NumberEditor()),
+            TableColumn(field="vary", title="Vary", editor=CheckboxEditor()),
+            TableColumn(field="min", title="Min", editor=NumberEditor()),
+            TableColumn(field="max", title="Max", editor=NumberEditor()),
+        ],
+        height=200,
+        width=350,
+        index_position=None,
+        editable=True,
+        auto_edit=True,
+    )
+
+    # start with `background` and `gauss` fit functions added
+    fitparams_add_dropdown_callback(types.SimpleNamespace(item="linear"))
+    fitparams_add_dropdown_callback(types.SimpleNamespace(item="gaussian"))
+    fitparams_select.value = ["gaussian-1"]  # add selection to gauss
+
+    fit_output_textinput = TextAreaInput(title="Fit results:", width=750, height=200)
+
+    def fit_all_button_callback():
+        for scan, export in zip(det_data, scan_table_source.data["export"]):
+            if export:
+                pyzebra.fit_scan(
+                    scan, fit_params, fit_from=fit_from_spinner.value, fit_to=fit_to_spinner.value
+                )
+
+        _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)
+
+    def fit_button_callback():
+        scan = _get_selected_scan()
+        pyzebra.fit_scan(
+            scan, fit_params, fit_from=fit_from_spinner.value, fit_to=fit_to_spinner.value
+        )
+
+        _update_plot()
+        _update_table()
+
+    fit_button = Button(label="Fit Current", default_size=145)
+    fit_button.on_click(fit_button_callback)
+
+    area_method_radiobutton = RadioButtonGroup(
+        labels=["Fit area", "Int area"], active=0, default_size=145, disabled=True
+    )
+
+    bin_size_spinner = Spinner(
+        title="Bin size:", value=1, low=1, step=1, default_size=145, disabled=True
+    )
+
+    lorentz_toggle = Toggle(label="Lorentz Correction", default_size=145)
+
+    export_preview_textinput = TextAreaInput(title="Export preview:", width=450, height=400)
+
+    def preview_button_callback():
+        with tempfile.TemporaryDirectory() as temp_dir:
+            temp_file = temp_dir + "/temp"
+            export_data = []
+            for s, export in zip(det_data, scan_table_source.data["export"]):
+                if export:
+                    export_data.append(s)
+
+            pyzebra.export_1D(
+                export_data,
+                temp_file,
+                area_method=AREA_METHODS[int(area_method_radiobutton.active)],
+                lorentz=lorentz_toggle.active,
+            )
+
+            exported_content = ""
+            file_content = []
+            for ext in (".comm", ".incomm"):
+                fname = temp_file + ext
+                if os.path.isfile(fname):
+                    with open(fname) as f:
+                        content = f.read()
+                        exported_content += f"{ext} file:\n" + content
+                else:
+                    content = ""
+                file_content.append(content)
+
+            js_data.data.update(content=file_content)
+            export_preview_textinput.value = exported_content
+
+    preview_button = Button(label="Preview", default_size=220)
+    preview_button.on_click(preview_button_callback)
+
+    save_button = Button(label="Download preview", button_type="success", default_size=220)
+    save_button.js_on_click(CustomJS(args={"js_data": js_data}, code=javaScript))
+
+    fitpeak_controls = row(
+        column(fitparams_add_dropdown, fitparams_select, fitparams_remove_button),
+        fitparams_table,
+        Spacer(width=20),
+        column(
+            row(fit_from_spinner, fit_to_spinner),
+            row(bin_size_spinner, column(Spacer(height=19), lorentz_toggle)),
+            row(area_method_radiobutton),
+            row(fit_button, fit_all_button),
+        ),
+    )
+
+    scan_layout = column(scan_table, row(monitor_spinner, param_select))
+
+    import_layout = column(
+        proposal_textinput,
+        file_select,
+        row(file_open_button, file_append_button),
+        upload_div,
+        upload_button,
+        append_upload_div,
+        append_upload_button,
+    )
+
+    export_layout = column(export_preview_textinput, row(preview_button, save_button))
+
+    tab_layout = column(
+        row(import_layout, scan_layout, plots, Spacer(width=30), export_layout),
+        row(fitpeak_controls, fit_output_textinput),
+    )
+
+    return Panel(child=tab_layout, title="param study")

pyzebra/app/panel_spind.py

@@ -0,0 +1,253 @@
+import ast
+import math
+import os
+import subprocess
+import tempfile
+from collections import defaultdict
+
+import numpy as np
+from bokeh.layouts import column, row
+from bokeh.models import (
+    Button,
+    ColumnDataSource,
+    DataTable,
+    Panel,
+    Spinner,
+    TableColumn,
+    TextAreaInput,
+    TextInput,
+)
+from scipy.optimize import curve_fit
+
+import pyzebra
+
+
+def create():
+    path_prefix_textinput = TextInput(title="Path prefix:", value="")
+    selection_list = TextAreaInput(title="ROIs:", rows=7)
+    lattice_const_textinput = TextInput(
+        title="Lattice constants:", value="8.3211,8.3211,8.3211,90.00,90.00,90.00"
+    )
+    max_res_spinner = Spinner(title="max-res", value=2, step=0.01)
+    seed_pool_size_spinner = Spinner(title="seed-pool-size", value=5, step=0.01)
+    seed_len_tol_spinner = Spinner(title="seed-len-tol", value=0.02, step=0.01)
+    seed_angle_tol_spinner = Spinner(title="seed-angle-tol", value=1, step=0.01)
+    eval_hkl_tol_spinner = Spinner(title="eval-hkl-tol", value=0.15, step=0.01)
+
+    diff_vec = []
+
+    def process_button_callback():
+        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")
+            roi_dict = ast.literal_eval(selection_list.value)
+
+            comp_proc = subprocess.run(
+                [
+                    "mpiexec",
+                    "-n",
+                    "2",
+                    "python",
+                    "spind/gen_hkl_table.py",
+                    lattice_const_textinput.value,
+                    "--max-res",
+                    str(max_res_spinner.value),
+                    "-o",
+                    temp_hkl_file,
+                ],
+                check=True,
+                stdout=subprocess.PIPE,
+                stderr=subprocess.STDOUT,
+                text=True,
+            )
+            print(" ".join(comp_proc.args))
+            print(comp_proc.stdout)
+
+            diff_vec = prepare_event_file(temp_event_file, roi_dict, path_prefix_textinput.value)
+
+            comp_proc = subprocess.run(
+                [
+                    "mpiexec",
+                    "-n",
+                    "2",
+                    "python",
+                    "spind/SPIND.py",
+                    temp_peak_list_dir,
+                    temp_hkl_file,
+                    "-o",
+                    temp_dir,
+                    "--seed-pool-size",
+                    str(seed_pool_size_spinner.value),
+                    "--seed-len-tol",
+                    str(seed_len_tol_spinner.value),
+                    "--seed-angle-tol",
+                    str(seed_angle_tol_spinner.value),
+                    "--eval-hkl-tol",
+                    str(eval_hkl_tol_spinner.value),
+                ],
+                check=True,
+                stdout=subprocess.PIPE,
+                stderr=subprocess.STDOUT,
+                text=True,
+            )
+            print(" ".join(comp_proc.args))
+            print(comp_proc.stdout)
+
+            try:
+                with open(os.path.join(temp_dir, "spind.txt")) as f_out:
+                    spind_res = defaultdict(list)
+                    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.array(vals).reshape(3, 3)
+                        ub_matrix = np.linalg.inv(np.transpose(ub_matrix_spind)) * 1e10
+                        spind_res["ub_matrix"].append(ub_matrix)
+
+                    results_table_source.data.update(spind_res)
+
+            except FileNotFoundError:
+                print("No results from spind")
+
+    process_button = Button(label="Process", button_type="primary")
+    process_button.on_click(process_button_callback)
+
+    hkl_textareainput = TextAreaInput(title="hkl values:", rows=7)
+
+    def results_table_select_callback(_attr, old, new):
+        if new:
+            ind = new[0]
+            ub_matrix = results_table_source.data["ub_matrix"][ind]
+            res = ""
+            for vec in diff_vec:
+                res += f"{vec @ ub_matrix}\n"
+            hkl_textareainput.value = res
+        else:
+            hkl_textareainput.value = None
+
+    results_table_source = ColumnDataSource(dict())
+    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=250),
+        ],
+        height=300,
+        width=700,
+        autosize_mode="none",
+        index_position=None,
+    )
+
+    results_table_source.selected.on_change("indices", results_table_select_callback)
+
+    tab_layout = row(
+        column(
+            path_prefix_textinput,
+            selection_list,
+            lattice_const_textinput,
+            max_res_spinner,
+            seed_pool_size_spinner,
+            seed_len_tol_spinner,
+            seed_angle_tol_spinner,
+            eval_hkl_tol_spinner,
+            process_button,
+        ),
+        column(results_table, row(hkl_textareainput)),
+    )
+
+    return Panel(child=tab_layout, title="spind")
+
+
+def gauss(x, *p):
+    """Defines Gaussian function
+    Args:
+        A - amplitude, mu - position of the center, sigma - width
+    Returns:
+        Gaussian function
+    """
+    A, mu, sigma = p
+    return A * np.exp(-((x - mu) ** 2) / (2.0 * sigma ** 2))
+
+
+def prepare_event_file(export_filename, roi_dict, path_prefix=""):
+    diff_vec = []
+    p0 = [1.0, 0.0, 1.0]
+    maxfev = 100000
+    with open(export_filename, "w") as f:
+        for file, rois in roi_dict.items():
+            dat = pyzebra.read_detector_data(path_prefix + file + ".hdf")
+
+            wave = dat["wave"]
+            ddist = dat["ddist"]
+
+            gamma = dat["gamma"][0]
+            omega = dat["omega"][0]
+            nu = dat["nu"][0]
+            chi = dat["chi"][0]
+            phi = dat["phi"][0]
+
+            scan_motor = dat["scan_motor"]
+            var_angle = dat[scan_motor]
+
+            for roi in rois:
+                x0, xN, y0, yN, fr0, frN = roi
+                data_roi = dat["data"][fr0:frN, y0:yN, x0:xN]
+
+                cnts = np.sum(data_roi, axis=(1, 2))
+                coeff, _ = curve_fit(gauss, range(len(cnts)), cnts, p0=p0, maxfev=maxfev)
+
+                m = cnts.mean()
+                sd = cnts.std()
+                snr_cnts = np.where(sd == 0, 0, m / sd)
+
+                frC = fr0 + coeff[1]
+                var_F = var_angle[math.floor(frC)]
+                var_C = var_angle[math.ceil(frC)]
+                frStep = frC - math.floor(frC)
+                var_step = var_C - var_F
+                var_p = var_F + var_step * frStep
+
+                if scan_motor == "gamma":
+                    gamma = var_p
+                elif scan_motor == "omega":
+                    omega = var_p
+                elif scan_motor == "nu":
+                    nu = var_p
+                elif scan_motor == "chi":
+                    chi = var_p
+                elif scan_motor == "phi":
+                    phi = var_p
+
+                intensity = coeff[1] * abs(coeff[2] * var_step) * math.sqrt(2) * math.sqrt(np.pi)
+
+                projX = np.sum(data_roi, axis=(0, 1))
+                coeff, _ = curve_fit(gauss, range(len(projX)), projX, p0=p0, maxfev=maxfev)
+                x_pos = x0 + coeff[1]
+
+                projY = np.sum(data_roi, axis=(0, 2))
+                coeff, _ = curve_fit(gauss, range(len(projY)), projY, p0=p0, maxfev=maxfev)
+                y_pos = y0 + coeff[1]
+
+                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])
+                dv1, dv2, dv3 = diff_vector.flatten() * 1e10
+
+                diff_vec.append(diff_vector.flatten())
+
+                f.write(f"{x_pos} {y_pos} {intensity} {snr_cnts} {dv1} {dv2} {dv3} {d_spacing}\n")
+
+    return diff_vec

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 +1,310 @@
+import os
+import re
+from collections import defaultdict
+
+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 = (("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),
+)
+
+AREA_METHODS = ("fit_area", "int_area")
+
+
+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):
+    metadata = {"data_type": data_type}
+
+    # read metadata
+    for line in fileobj:
+        if "=" in line:
+            variable, value = line.split("=", 1)
+            variable = variable.strip()
+            value = value.strip()
+
+            if variable in META_VARS_STR:
+                metadata[variable] = value
+
+            elif variable in META_VARS_FLOAT:
+                if variable == "2-theta":  # fix that angle name not to be an expression
+                    variable = "twotheta"
+                if variable in ("a", "b", "c", "alpha", "beta", "gamma"):
+                    variable += "_cell"
+                metadata[variable] = float(value)
+
+            elif variable in META_UB_MATRIX:
+                if "ub" not in metadata:
+                    metadata["ub"] = np.zeros((3, 3))
+                row = int(variable[-2]) - 1
+                metadata["ub"][row, :] = list(map(float, value.split()))
+
+        if "#data" in line:
+            # this is the end of metadata and the start of data section
+            break
+
+    # handle older files that don't contain "zebra_mode" metadata
+    if "zebra_mode" not in metadata:
+        metadata["zebra_mode"] = "nb"
+
+    # read data
+    scan = []
+    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
+
+            s = {}
+
+            # first line
+            for param, (param_name, param_type) in zip(line.split(), ccl_first_line):
+                s[param_name] = param_type(param)
+
+            # second line
+            next_line = next(fileobj)
+            for param, (param_name, param_type) in zip(next_line.split(), ccl_second_line):
+                s[param_name] = param_type(param)
+
+            if s["scan_motor"] != "om":
+                raise Exception("Unsupported variable name in ccl file.")
+
+            # "om" -> "omega"
+            s["scan_motor"] = "omega"
+            # overwrite metadata, because it only refers to the scan center
+            half_dist = (s["n_points"] - 1) / 2 * s["angle_step"]
+            s["omega"] = np.linspace(s["omega"] - half_dist, s["omega"] + half_dist, s["n_points"])
+
+            # subsequent lines with counts
+            counts = []
+            while len(counts) < s["n_points"]:
+                counts.extend(map(float, next(fileobj).split()))
+            s["Counts"] = np.array(counts)
+
+            if s["h"].is_integer() and s["k"].is_integer() and s["l"].is_integer():
+                s["h"], s["k"], s["l"] = map(int, (s["h"], s["k"], s["l"]))
+
+            scan.append({**metadata, **s})
+
+    elif data_type == ".dat":
+        # TODO: this might need to be adapted in the future, when "gamma" will be added to dat files
+        if metadata["zebra_mode"] == "nb":
+            metadata["gamma"] = metadata["twotheta"]
+
+        s = defaultdict(list)
+
+        match = re.search("Scanning Variables: (.*), Steps: (.*)", next(fileobj))
+        if match.group(1) == "h, k, l":
+            steps = match.group(2).split()
+            for step, ind in zip(steps, "hkl"):
+                if float(step) != 0:
+                    scan_motor = ind
+                    break
+        else:
+            scan_motor = match.group(1)
+
+        s["scan_motor"] = scan_motor
+
+        match = re.search("(.*) Points, Mode: (.*), Preset (.*)", next(fileobj))
+        if match.group(2) != "Monitor":
+            raise Exception("Unknown mode in dat file.")
+        s["monitor"] = float(match.group(3))
+
+        col_names = 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()):
+                s[name].append(float(val))
+
+        for name in col_names:
+            s[name] = np.array(s[name])
+
+        # "om" -> "omega"
+        if s["scan_motor"] == "om":
+            s["scan_motor"] = "omega"
+            s["omega"] = s["om"]
+            del s["om"]
+
+        # "tt" -> "temp"
+        elif s["scan_motor"] == "tt":
+            s["scan_motor"] = "temp"
+            s["temp"] = s["tt"]
+            del s["tt"]
+
+        # "mf" stays "mf"
+        # "phi" stays "phi"
+
+        if "h" not in s:
+            s["h"] = s["k"] = s["l"] = float("nan")
+
+        for param in ("mf", "temp"):
+            if param not in metadata:
+                s[param] = 0
+
+        s["idx"] = 1
+
+        scan.append({**metadata, **s})
+
+    else:
+        print("Unknown file extention")
+
+    return scan
+
+
+def export_1D(data, path, area_method=AREA_METHODS[0], lorentz=False, hkl_precision=2):
+    """Exports data in the .comm/.incomm format
+
+    Scans with integer/real hkl values are saved in .comm/.incomm files correspondingly. If no scans
+    are present for a particular output format, that file won't be created.
+    """
+    zebra_mode = data[0]["zebra_mode"]
+    file_content = {".comm": [], ".incomm": []}
+
+    for scan in data:
+        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:6}{k:6}{l:6}"
+        else:
+            hkl_str = f"{h:8.{hkl_precision}f}{k:8.{hkl_precision}f}{l:8.{hkl_precision}f}"
+
+        for name, param in scan["fit"].params.items():
+            if "amplitude" in name:
+                area_n = param.value
+                area_s = param.stderr
+                break
+        else:
+            area_n = 0
+            area_s = 0
+
+        if area_n is None or area_s is None:
+            print(f"Couldn't export scan: {scan['idx']}")
+            continue
+
+        # apply lorentz correction to area
+        if lorentz:
+            if 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_n = np.abs(area_n * corr_factor)
+            area_s = np.abs(area_s * corr_factor)
+
+        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]
+            ang_str = ang_str + f"{angle_center:8g}"
+
+        ref = file_content[".comm"] if hkl_are_integers else file_content[".incomm"]
+        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)

pyzebra/ccl_process.py

@@ -0,0 +1,139 @@
+import itertools
+import os
+
+import numpy as np
+from lmfit.models import GaussianModel, LinearModel, PseudoVoigtModel, VoigtModel
+
+from .ccl_io import CCL_ANGLES
+
+PARAM_PRECISIONS = {
+    "twotheta": 0.1,
+    "chi": 0.1,
+    "nu": 0.1,
+    "phi": 0.05,
+    "omega": 0.05,
+    "gamma": 0.05,
+    "temp": 1,
+    "mf": 0.001,
+    "ub": 0.01,
+}
+
+MAX_RANGE_GAP = {
+    "omega": 0.5,
+}
+
+
+def normalize_dataset(dataset, monitor=100_000):
+    for scan in dataset:
+        monitor_ratio = monitor / scan["monitor"]
+        scan["Counts"] *= monitor_ratio
+        scan["monitor"] = monitor
+
+
+def merge_duplicates(dataset):
+    for scan_i, scan_j in itertools.combinations(dataset, 2):
+        if _parameters_match(scan_i, scan_j):
+            merge_scans(scan_i, scan_j)
+
+
+def _parameters_match(scan1, scan2):
+    zebra_mode = scan1["zebra_mode"]
+    if zebra_mode != scan2["zebra_mode"]:
+        return False
+
+    for param in ("ub", "temp", "mf", *(vars[0] for vars in CCL_ANGLES[zebra_mode])):
+        if param.startswith("skip"):
+            # ignore skip parameters, like the last angle in 'nb' zebra mode
+            continue
+
+        if param == scan1["scan_motor"] == scan2["scan_motor"]:
+            # check if ranges of variable parameter overlap
+            range1 = scan1[param]
+            range2 = scan2[param]
+            # maximum gap between ranges of the scanning parameter (default 0)
+            max_range_gap = MAX_RANGE_GAP.get(param, 0)
+            if max(range1[0] - range2[-1], range2[0] - range1[-1]) > max_range_gap:
+                return False
+
+        elif np.max(np.abs(scan1[param] - scan2[param])) > PARAM_PRECISIONS[param]:
+            return False
+
+    return True
+
+
+def merge_datasets(dataset1, dataset2):
+    for scan_j in dataset2:
+        for scan_i in dataset1:
+            if _parameters_match(scan_i, scan_j):
+                merge_scans(scan_i, scan_j)
+                break
+
+        dataset1.append(scan_j)
+
+
+def merge_scans(scan1, scan2):
+    omega = np.concatenate((scan1["omega"], scan2["omega"]))
+    counts = np.concatenate((scan1["Counts"], scan2["Counts"]))
+
+    index = np.argsort(omega)
+
+    scan1["omega"] = omega[index]
+    scan1["Counts"] = counts[index]
+
+    scan2["active"] = False
+
+    fname1 = os.path.basename(scan1["original_filename"])
+    fname2 = os.path.basename(scan2["original_filename"])
+    print(f'Merging scans: {scan1["idx"]} ({fname1}) <-- {scan2["idx"]} ({fname2})')
+
+
+def fit_scan(scan, model_dict, fit_from=None, fit_to=None):
+    if fit_from is None:
+        fit_from = -np.inf
+    if fit_to is None:
+        fit_to = np.inf
+
+    y_fit = scan["Counts"]
+    x_fit = scan[scan["scan_motor"]]
+
+    # apply fitting range
+    fit_ind = (fit_from <= x_fit) & (x_fit <= fit_to)
+    y_fit = y_fit[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
+
+            _model.set_param_hint(param_name, **param_hints)
+
+        if model is None:
+            model = _model
+        else:
+            model += _model
+
+    weights = [1 / np.sqrt(val) if val != 0 else 1 for val in y_fit]
+    scan["fit"] = model.fit(y_fit, x=x_fit, weights=weights)

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
-                + l_str
-                + k_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/h5.py

@@ -41,7 +41,7 @@ def read_detector_data(filepath):
         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"][:]
@@ -52,15 +52,37 @@ def read_detector_data(filepath):
 
         det_data = {"data": data}
 
-        det_data["rot_angle"] = h5f["/entry1/area_detector2/rotation_angle"][:]  # om, sometimes ph
+        if "/entry1/zebra_mode" in h5f:
-        det_data["pol_angle"] = h5f["/entry1/ZEBRA/area_detector2/polar_angle"][:]  # gammad
+            det_data["zebra_mode"] = h5f["/entry1/zebra_mode"][0].decode()
-        det_data["tlt_angle"] = h5f["/entry1/ZEBRA/area_detector2/tilt_angle"][:]  # nud
+        else:
+            det_data["zebra_mode"] = "nb"
+
+        # om, sometimes ph
+        if det_data["zebra_mode"] == "nb":
+            det_data["omega"] = h5f["/entry1/area_detector2/rotation_angle"][:]
+        else:  # bi
+            det_data["omega"] = h5f["/entry1/sample/rotation_angle"][:]
+
+        det_data["gamma"] = h5f["/entry1/ZEBRA/area_detector2/polar_angle"][:]  # gammad
+        det_data["nu"] = h5f["/entry1/ZEBRA/area_detector2/tilt_angle"][:]  # nud
         det_data["ddist"] = h5f["/entry1/ZEBRA/area_detector2/distance"][:]
         det_data["wave"] = h5f["/entry1/ZEBRA/monochromator/wavelength"][:]
-        det_data["chi_angle"] = h5f["/entry1/sample/chi"][:]  # ch
+        det_data["chi"] = h5f["/entry1/sample/chi"][:]  # ch
-        det_data["phi_angle"] = h5f["/entry1/sample/phi"][:]  # ph
+        det_data["phi"] = h5f["/entry1/sample/phi"][:]  # ph
-        det_data["UB"] = h5f["/entry1/sample/UB"][:].reshape(3, 3)
+        det_data["ub"] = h5f["/entry1/sample/UB"][:].reshape(3, 3)
-        det_data["magnetic_field"] = h5f["/entry1/sample/magnetic_field"][:]
+
-        det_data["temperature"] = h5f["/entry1/sample/temperature"][:]
+        for var in ("omega", "gamma", "nu", "chi", "phi"):
+            if abs(det_data[var][0] - det_data[var][-1]) > 0.1:
+                det_data["scan_motor"] = var
+                break
+        else:
+            raise ValueError("No angles that vary")
+
+        # optional parameters
+        if "/entry1/sample/magnetic_field" in h5f:
+            det_data["mf"] = h5f["/entry1/sample/magnetic_field"][:]
+
+        if "/entry1/sample/temperature" in h5f:
+            det_data["temp"] = h5f["/entry1/sample/temperature"][:]
 
     return det_data

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

@@ -1,202 +0,0 @@
-from load_1D import load_1D
-from ccl_dict_operation import add_dict
-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
-
-
-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"))

pyzebra/xtal.py

@@ -407,24 +407,24 @@ def box_int(file, box):
 
     dat = pyzebra.read_detector_data(file)
 
-    sttC = dat["pol_angle"][0]
+    sttC = dat["gamma"][0]
-    om = dat["rot_angle"]
+    om = dat["omega"]
-    nuC = dat["tlt_angle"][0]
+    nuC = dat["nu"][0]
     ddist = dat["ddist"]
 
     # defining indices
     x0, xN, y0, yN, fr0, frN = box
 
     # omega fit
-    om = dat["rot_angle"][fr0:frN]
+    om = dat["omega"][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)]
+    omF = dat["omega"][math.floor(frC)]
-    omC = dat["rot_angle"][math.ceil(frC)]
+    omC = dat["omega"][math.ceil(frC)]
     frStep = frC - math.floor(frC)
     omStep = omC - omF
     omP = omF + omStep * frStep

scripts/pyzebra-start.sh

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

scripts/pyzebra-test-start.sh

@@ -0,0 +1,4 @@
+source /home/pyzebra/miniconda3/etc/profile.d/conda.sh
+
+conda activate test
+python ~/pyzebra/pyzebra/app/cli.py --allow-websocket-origin=pyzebra.psi.ch:5006

scripts/pyzebra-test.service

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

scripts/pyzebra.service

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