Updating for version 0.5.1

Fix #40

.github/workflows/deployment.yaml

@@ -16,7 +16,6 @@ jobs:
       run: |
         $CONDA/bin/conda install --quiet --yes conda-build anaconda-client
         $CONDA/bin/conda config --append channels conda-forge
-        $CONDA/bin/conda config --set channel_priority strict
         $CONDA/bin/conda config --set anaconda_upload yes
 
     - name: Build and upload

conda-recipe/meta.yaml

@@ -22,11 +22,9 @@ requirements:
     - numpy
     - scipy
     - h5py
-    - bokeh =2.3
-    - matplotlib
+    - bokeh =2.4
     - numba
     - lmfit
-    - uncertainties
 
 
 about:

pyzebra/__init__.py

@@ -1,7 +1,8 @@
 from pyzebra.anatric import *
 from pyzebra.ccl_io import *
-from pyzebra.h5 import *
-from pyzebra.xtal import *
 from pyzebra.ccl_process import *
+from pyzebra.h5 import *
+from pyzebra.utils import *
+from pyzebra.xtal import *
 
-__version__ = "0.3.2"
+__version__ = "0.5.1"

pyzebra/anatric.py

@@ -7,6 +7,7 @@ DATA_FACTORY_IMPLEMENTATION = [
     "morph",
     "d10",
 ]
+
 REFLECTION_PRINTER_FORMATS = [
     "rafin",
     "rafinf",
@@ -20,10 +21,11 @@ REFLECTION_PRINTER_FORMATS = [
     "oksana",
 ]
 
+ANATRIC_PATH = "/afs/psi.ch/project/sinq/rhel7/bin/anatric"
 ALGORITHMS = ["adaptivemaxcog", "adaptivedynamic"]
 
 
-def anatric(config_file, anatric_path="/afs/psi.ch/project/sinq/rhel7/bin/anatric", cwd=None):
+def anatric(config_file, anatric_path=ANATRIC_PATH, cwd=None):
     comp_proc = subprocess.run(
         [anatric_path, config_file],
         stdout=subprocess.PIPE,

pyzebra/app/app.py

@@ -2,17 +2,18 @@ import logging
 import sys
 from io import StringIO
 
+import pyzebra
 from bokeh.io import curdoc
 from bokeh.layouts import column, row
-from bokeh.models import Tabs, TextAreaInput
+from bokeh.models import Button, Panel, Tabs, TextAreaInput, TextInput
 
 import panel_ccl_integrate
 import panel_hdf_anatric
+import panel_hdf_param_study
 import panel_hdf_viewer
 import panel_param_study
 import panel_spind
 
-
 doc = curdoc()
 
 sys.stdout = StringIO()
@@ -25,16 +26,40 @@ bokeh_logger = logging.getLogger("bokeh")
 bokeh_logger.addHandler(bokeh_handler)
 bokeh_log_textareainput = TextAreaInput(title="server output:", height=150)
 
-# Final layout
-tab_hdf_viewer = panel_hdf_viewer.create()
-tab_hdf_anatric = panel_hdf_anatric.create()
-tab_ccl_integrate = panel_ccl_integrate.create()
-tab_param_study = panel_param_study.create()
-tab_spind = panel_spind.create()
+def proposal_textinput_callback(_attr, _old, _new):
+    apply_button.disabled = False
 
+proposal_textinput = TextInput(title="Proposal number:", name="")
+proposal_textinput.on_change("value_input", proposal_textinput_callback)
+doc.proposal_textinput = proposal_textinput
+
+def apply_button_callback():
+    try:
+        proposal_path = pyzebra.find_proposal_path(proposal_textinput.value)
+    except ValueError as e:
+        print(e)
+        return
+
+    proposal_textinput.name = proposal_path
+    apply_button.disabled = True
+
+apply_button = Button(label="Apply", button_type="primary")
+apply_button.on_click(apply_button_callback)
+
+# Final layout
 doc.add_root(
     column(
-        Tabs(tabs=[tab_hdf_viewer, tab_hdf_anatric, tab_ccl_integrate, tab_param_study, tab_spind]),
+        Tabs(
+            tabs=[
+                Panel(child=column(proposal_textinput, apply_button), title="user config"),
+                panel_hdf_viewer.create(),
+                panel_hdf_anatric.create(),
+                panel_ccl_integrate.create(),
+                panel_param_study.create(),
+                panel_hdf_param_study.create(),
+                panel_spind.create(),
+            ]
+        ),
         row(stdout_textareainput, bokeh_log_textareainput, sizing_mode="scale_both"),
     )
 )

pyzebra/app/cli.py

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

pyzebra/app/handler.py

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

pyzebra/app/panel_ccl_integrate.py

@@ -5,6 +5,7 @@ import tempfile
 import types
 
 import numpy as np
+from bokeh.io import curdoc
 from bokeh.layouts import column, row
 from bokeh.models import (
     BasicTicker,
@@ -28,7 +29,7 @@ from bokeh.models import (
     Panel,
     PanTool,
     Plot,
-    RadioButtonGroup,
+    RadioGroup,
     ResetTool,
     Scatter,
     Select,
@@ -37,13 +38,13 @@ from bokeh.models import (
     Spinner,
     TableColumn,
     TextAreaInput,
-    TextInput,
     WheelZoomTool,
     Whisker,
 )
 
 import pyzebra
-from pyzebra.ccl_io import AREA_METHODS
+from pyzebra.ccl_io import EXPORT_TARGETS
+from pyzebra.ccl_process import AREA_METHODS
 
 
 javaScript = """
@@ -57,7 +58,7 @@ for (let i = 0; i < js_data.data['fname'].length; i++) {
         document.body.appendChild(link);
         const url = window.URL.createObjectURL(blob);
         link.href = url;
-        link.download = js_data.data['fname'][i];
+        link.download = js_data.data['fname'][i] + js_data.data['ext'][i];
         link.click();
         window.URL.revokeObjectURL(url);
         document.body.removeChild(link);
@@ -69,22 +70,33 @@ for (let i = 0; i < js_data.data['fname'].length; i++) {
 
 
 def create():
+    doc = curdoc()
     det_data = {}
     fit_params = {}
-    js_data = ColumnDataSource(data=dict(content=["", ""], fname=["", ""]))
+    js_data = ColumnDataSource(data=dict(content=["", ""], fname=["", ""], ext=["", ""]))
 
-    def proposal_textinput_callback(_attr, _old, new):
-        proposal = new.strip()
-        year = new[:4]
-        proposal_path = f"/afs/psi.ch/project/sinqdata/{year}/zebra/{proposal}"
-        file_list = []
-        for file in os.listdir(proposal_path):
-            if file.endswith((".ccl", ".dat")):
-                file_list.append((os.path.join(proposal_path, file), file))
-        file_select.options = file_list
+    def file_select_update_for_proposal():
+        proposal_path = proposal_textinput.name
+        if proposal_path:
+            file_list = []
+            for file in os.listdir(proposal_path):
+                if file.endswith((".ccl", ".dat")):
+                    file_list.append((os.path.join(proposal_path, file), file))
+            file_select.options = file_list
+            file_open_button.disabled = False
+            file_append_button.disabled = False
+        else:
+            file_select.options = []
+            file_open_button.disabled = True
+            file_append_button.disabled = True
 
-    proposal_textinput = TextInput(title="Proposal number:", width=210)
-    proposal_textinput.on_change("value", proposal_textinput_callback)
+    doc.add_periodic_callback(file_select_update_for_proposal, 5000)
+
+    def proposal_textinput_callback(_attr, _old, _new):
+        file_select_update_for_proposal()
+
+    proposal_textinput = doc.proposal_textinput
+    proposal_textinput.on_change("name", proposal_textinput_callback)
 
     def _init_datatable():
         scan_list = [s["idx"] for s in det_data]
@@ -97,46 +109,45 @@ def create():
         scan_table_source.selected.indices = [0]
 
         merge_options = [(str(i), f"{i} ({idx})") for i, idx in enumerate(scan_list)]
-        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]
+        merge_from_select.options = merge_options
+        merge_from_select.value = merge_options[0][0]
 
     file_select = MultiSelect(title="Available .ccl/.dat files:", width=210, height=250)
 
     def file_open_button_callback():
         nonlocal det_data
-        det_data = []
-        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"])
+        for f_ind, f_path in enumerate(file_select.value):
+            with open(f_path) as file:
+                base, ext = os.path.splitext(os.path.basename(f_path))
+                file_data = pyzebra.parse_1D(file, ext)
+
+            pyzebra.normalize_dataset(file_data, monitor_spinner.value)
+
+            if f_ind == 0:  # first file
+                det_data = file_data
+                pyzebra.merge_duplicates(det_data)
+                js_data.data.update(fname=[base, base])
+            else:
+                pyzebra.merge_datasets(det_data, file_data)
 
         _init_datatable()
+        append_upload_button.disabled = False
 
-    file_open_button = Button(label="Open New", width=100)
+    file_open_button = Button(label="Open New", width=100, disabled=True)
     file_open_button.on_click(file_open_button_callback)
 
     def file_append_button_callback():
-        for f_name in file_select.value:
-            with open(f_name) as file:
-                _, ext = os.path.splitext(f_name)
-                append_data = pyzebra.parse_1D(file, ext)
+        for f_path in file_select.value:
+            with open(f_path) as file:
+                _, ext = os.path.splitext(f_path)
+                file_data = pyzebra.parse_1D(file, ext)
 
-            pyzebra.normalize_dataset(append_data, monitor_spinner.value)
-            pyzebra.merge_datasets(det_data, append_data)
+            pyzebra.normalize_dataset(file_data, monitor_spinner.value)
+            pyzebra.merge_datasets(det_data, file_data)
 
         _init_datatable()
 
-    file_append_button = Button(label="Append", width=100)
+    file_append_button = Button(label="Append", width=100, disabled=True)
     file_append_button.on_click(file_append_button_callback)
 
     def upload_button_callback(_attr, _old, new):
@@ -145,17 +156,19 @@ def create():
         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)
-                    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"])
+                file_data = pyzebra.parse_1D(file, ext)
+
+            pyzebra.normalize_dataset(file_data, monitor_spinner.value)
+
+            if not det_data:  # first file
+                det_data = file_data
+                pyzebra.merge_duplicates(det_data)
+                js_data.data.update(fname=[base, base])
+            else:
+                pyzebra.merge_datasets(det_data, file_data)
 
         _init_datatable()
+        append_upload_button.disabled = False
 
     upload_div = Div(text="or upload new .ccl/.dat files:", margin=(5, 5, 0, 5))
     upload_button = FileInput(accept=".ccl,.dat", multiple=True, width=200)
@@ -165,37 +178,40 @@ def create():
         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)
+                file_data = pyzebra.parse_1D(file, ext)
 
-            pyzebra.normalize_dataset(append_data, monitor_spinner.value)
-            pyzebra.merge_datasets(det_data, append_data)
+            pyzebra.normalize_dataset(file_data, monitor_spinner.value)
+            pyzebra.merge_datasets(det_data, file_data)
 
         _init_datatable()
 
     append_upload_div = Div(text="append extra files:", margin=(5, 5, 0, 5))
-    append_upload_button = FileInput(accept=".ccl,.dat", multiple=True, width=200)
+    append_upload_button = FileInput(accept=".ccl,.dat", multiple=True, width=200, disabled=True)
     append_upload_button.on_change("value", append_upload_button_callback)
 
     def monitor_spinner_callback(_attr, old, new):
         if det_data:
             pyzebra.normalize_dataset(det_data, new)
-            _update_plot(_get_selected_scan())
+            _update_plot()
 
     monitor_spinner = Spinner(title="Monitor:", mode="int", value=100_000, low=1, width=145)
     monitor_spinner.on_change("value", monitor_spinner_callback)
 
-    def _update_table():
+    def _update_datatable():
         fit_ok = [(1 if "fit" in scan else 0) for scan in det_data]
-        scan_table_source.data.update(fit=fit_ok)
+        export = [scan.get("active", True) for scan in det_data]
+        scan_table_source.data.update(fit=fit_ok, export=export)
 
-    def _update_plot(scan):
+    def _update_plot():
+        scan = _get_selected_scan()
         scan_motor = scan["scan_motor"]
 
-        y = scan["Counts"]
+        y = scan["counts"]
+        y_err = scan["counts_err"]
         x = scan[scan_motor]
 
         plot.axis[0].axis_label = scan_motor
-        plot_scatter_source.data.update(x=x, y=y, y_upper=y + np.sqrt(y), y_lower=y - np.sqrt(y))
+        plot_scatter_source.data.update(x=x, y=y, y_upper=y + y_err, y_lower=y - y_err)
 
         fit = scan.get("fit")
         if fit is not None:
@@ -298,13 +314,14 @@ def create():
             # skip unnecessary update caused by selection drop
             return
 
-        _update_plot(det_data[new[0]])
+        _update_plot()
 
     def scan_table_source_callback(_attr, _old, _new):
         _update_preview()
 
     scan_table_source = ColumnDataSource(dict(scan=[], hkl=[], fit=[], export=[]))
     scan_table_source.on_change("data", scan_table_source_callback)
+    scan_table_source.selected.on_change("indices", scan_table_select_callback)
 
     scan_table = DataTable(
         source=scan_table_source,
@@ -320,28 +337,34 @@ def create():
         editable=True,
     )
 
-    scan_table_source.selected.on_change("indices", scan_table_select_callback)
-
     def _get_selected_scan():
         return det_data[scan_table_source.selected.indices[0]]
 
-    merge_dest_select = Select(title="destination:", width=100)
-    merge_source_select = Select(title="source:", width=100)
+    merge_from_select = Select(title="scan:", width=145)
 
     def merge_button_callback():
-        scan_dest_ind = int(merge_dest_select.value)
-        scan_source_ind = int(merge_source_select.value)
+        scan_into = _get_selected_scan()
+        scan_from = det_data[int(merge_from_select.value)]
 
-        if scan_dest_ind == scan_source_ind:
+        if scan_into is scan_from:
             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())
+        pyzebra.merge_scans(scan_into, scan_from)
+        _update_datatable()
+        _update_plot()
 
-    merge_button = Button(label="Merge scans", width=145)
+    merge_button = Button(label="Merge into current", width=145)
     merge_button.on_click(merge_button_callback)
 
+    def restore_button_callback():
+        pyzebra.restore_scan(_get_selected_scan())
+        _update_datatable()
+        _update_plot()
+
+    restore_button = Button(label="Restore scan", width=145)
+    restore_button.on_click(restore_button_callback)
+
     def fit_from_spinner_callback(_attr, _old, new):
         fit_from_span.location = new
 
@@ -371,7 +394,6 @@ def create():
             # ("Pseudo Voigt1", "pseudovoigt1"),
         ],
         width=145,
-        disabled=True,
     )
     fitparams_add_dropdown.on_click(fitparams_add_dropdown_callback)
 
@@ -406,7 +428,7 @@ def create():
 
             fitparams_select.value = []
 
-    fitparams_remove_button = Button(label="Remove fit function", width=145, disabled=True)
+    fitparams_remove_button = Button(label="Remove fit function", width=145)
     fitparams_remove_button.on_click(fitparams_remove_button_callback)
 
     def fitparams_factory(function):
@@ -462,46 +484,47 @@ def create():
 
     fit_output_textinput = TextAreaInput(title="Fit results:", width=750, height=200)
 
-    def fit_all_button_callback():
+    def proc_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
                 )
+                pyzebra.get_area(
+                    scan,
+                    area_method=AREA_METHODS[area_method_radiobutton.active],
+                    lorentz=lorentz_checkbox.active,
+                )
 
-        _update_plot(_get_selected_scan())
-        _update_table()
+        _update_plot()
+        _update_datatable()
 
-    fit_all_button = Button(label="Fit All", button_type="primary", width=145)
-    fit_all_button.on_click(fit_all_button_callback)
+    proc_all_button = Button(label="Process All", button_type="primary", width=145)
+    proc_all_button.on_click(proc_all_button_callback)
 
-    def fit_button_callback():
+    def proc_button_callback():
         scan = _get_selected_scan()
         pyzebra.fit_scan(
             scan, fit_params, fit_from=fit_from_spinner.value, fit_to=fit_to_spinner.value
         )
+        pyzebra.get_area(
+            scan,
+            area_method=AREA_METHODS[area_method_radiobutton.active],
+            lorentz=lorentz_checkbox.active,
+        )
 
-        _update_plot(scan)
-        _update_table()
+        _update_plot()
+        _update_datatable()
 
-    fit_button = Button(label="Fit Current", width=145)
-    fit_button.on_click(fit_button_callback)
+    proc_button = Button(label="Process Current", width=145)
+    proc_button.on_click(proc_button_callback)
 
-    def area_method_radiobutton_callback(_handler):
-        _update_preview()
+    area_method_div = Div(text="Intensity:", margin=(5, 5, 0, 5))
+    area_method_radiobutton = RadioGroup(labels=["Function", "Area"], active=0, width=145)
 
-    area_method_radiobutton = RadioButtonGroup(
-        labels=["Fit area", "Int area"], active=0, width=145, disabled=True
-    )
-    area_method_radiobutton.on_click(area_method_radiobutton_callback)
+    lorentz_checkbox = CheckboxGroup(labels=["Lorentz Correction"], width=145, margin=(13, 5, 5, 5))
 
-    def lorentz_checkbox_callback(_handler):
-        _update_preview()
-
-    lorentz_checkbox = CheckboxGroup(labels=["Lorentz Correction"], width=145, margin=[13, 5, 5, 5])
-    lorentz_checkbox.on_click(lorentz_checkbox_callback)
-
-    export_preview_textinput = TextAreaInput(title="Export file preview:", width=500, height=400)
+    export_preview_textinput = TextAreaInput(title="Export file(s) preview:", width=500, height=400)
 
     def _update_preview():
         with tempfile.TemporaryDirectory() as temp_dir:
@@ -514,14 +537,13 @@ def create():
             pyzebra.export_1D(
                 export_data,
                 temp_file,
-                area_method=AREA_METHODS[int(area_method_radiobutton.active)],
-                lorentz=bool(lorentz_checkbox.active),
+                export_target_select.value,
                 hkl_precision=int(hkl_precision_select.value),
             )
 
             exported_content = ""
             file_content = []
-            for ext in (".comm", ".incomm"):
+            for ext in EXPORT_TARGETS[export_target_select.value]:
                 fname = temp_file + ext
                 if os.path.isfile(fname):
                     with open(fname) as f:
@@ -534,6 +556,16 @@ def create():
             js_data.data.update(content=file_content)
             export_preview_textinput.value = exported_content
 
+    def export_target_select_callback(_attr, _old, new):
+        js_data.data.update(ext=EXPORT_TARGETS[new])
+        _update_preview()
+
+    export_target_select = Select(
+        title="Export target:", options=list(EXPORT_TARGETS.keys()), value="fullprof", width=80
+    )
+    export_target_select.on_change("value", export_target_select_callback)
+    js_data.data.update(ext=EXPORT_TARGETS[export_target_select.value])
+
     def hkl_precision_select_callback(_attr, _old, _new):
         _update_preview()
 
@@ -542,28 +574,24 @@ def create():
     )
     hkl_precision_select.on_change("value", hkl_precision_select_callback)
 
-    save_button = Button(label="Download File", button_type="success", width=200)
+    save_button = Button(label="Download File(s)", button_type="success", width=200)
     save_button.js_on_click(CustomJS(args={"js_data": js_data}, code=javaScript))
 
     fitpeak_controls = row(
         column(fitparams_add_dropdown, fitparams_select, fitparams_remove_button),
         fitparams_table,
         Spacer(width=20),
-        column(
-            row(fit_from_spinner, fit_to_spinner),
-            row(area_method_radiobutton, lorentz_checkbox),
-            row(fit_button, fit_all_button),
-        ),
+        column(fit_from_spinner, lorentz_checkbox, area_method_div, area_method_radiobutton),
+        column(fit_to_spinner, proc_button, proc_all_button),
     )
 
     scan_layout = column(
         scan_table,
-        monitor_spinner,
-        row(column(Spacer(height=19), merge_button), merge_dest_select, merge_source_select),
+        row(monitor_spinner, column(Spacer(height=19), restore_button)),
+        row(column(Spacer(height=19), merge_button), merge_from_select),
     )
 
     import_layout = column(
-        proposal_textinput,
         file_select,
         row(file_open_button, file_append_button),
         upload_div,
@@ -574,7 +602,9 @@ def create():
 
     export_layout = column(
         export_preview_textinput,
-        row(hkl_precision_select, column(Spacer(height=19), row(save_button))),
+        row(
+            export_target_select, hkl_precision_select, column(Spacer(height=19), row(save_button))
+        ),
     )
 
     tab_layout = column(

pyzebra/app/panel_hdf_anatric.py

@@ -347,10 +347,7 @@ def create():
         with tempfile.TemporaryDirectory() as temp_dir:
             temp_file = temp_dir + "/config.xml"
             config.save_as(temp_file)
-            if doc.anatric_path:
-                pyzebra.anatric(temp_file, anatric_path=doc.anatric_path, cwd=temp_dir)
-            else:
-                pyzebra.anatric(temp_file, cwd=temp_dir)
+            pyzebra.anatric(temp_file, anatric_path=doc.anatric_path, cwd=temp_dir)
 
             with open(os.path.join(temp_dir, config.logfile)) as f_log:
                 output_log.value = f_log.read()

pyzebra/app/panel_hdf_param_study.py

@@ -0,0 +1,634 @@
+import base64
+import io
+import math
+import os
+
+import numpy as np
+from bokeh.io import curdoc
+from bokeh.layouts import column, gridplot, row
+from bokeh.models import (
+    BasicTicker,
+    BoxZoomTool,
+    Button,
+    CheckboxGroup,
+    ColumnDataSource,
+    DataRange1d,
+    DataTable,
+    Div,
+    FileInput,
+    Grid,
+    MultiSelect,
+    NumberEditor,
+    NumberFormatter,
+    Image,
+    LinearAxis,
+    LinearColorMapper,
+    Panel,
+    PanTool,
+    Plot,
+    Range1d,
+    ResetTool,
+    Scatter,
+    Select,
+    Spinner,
+    TableColumn,
+    Tabs,
+    Title,
+    WheelZoomTool,
+)
+from bokeh.palettes import Cividis256, Greys256, Plasma256  # pylint: disable=E0611
+from scipy.optimize import curve_fit
+
+import pyzebra
+
+IMAGE_W = 256
+IMAGE_H = 128
+IMAGE_PLOT_W = int(IMAGE_W * 2) + 52
+IMAGE_PLOT_H = int(IMAGE_H * 2) + 27
+
+
+def create():
+    doc = curdoc()
+    zebra_data = []
+    det_data = {}
+    cami_meta = {}
+
+    num_formatter = NumberFormatter(format="0.00", nan_format="")
+
+    def file_select_update():
+        if data_source.value == "proposal number":
+            proposal_path = proposal_textinput.name
+            if proposal_path:
+                file_list = []
+                for file in os.listdir(proposal_path):
+                    if file.endswith(".hdf"):
+                        file_list.append((os.path.join(proposal_path, file), file))
+                file_select.options = file_list
+            else:
+                file_select.options = []
+
+        else:  # "cami file"
+            if not cami_meta:
+                file_select.options = []
+                return
+
+            file_list = cami_meta["filelist"]
+            file_select.options = [(entry, os.path.basename(entry)) for entry in file_list]
+
+    def data_source_callback(_attr, _old, _new):
+        file_select_update()
+
+    data_source = Select(
+        title="Data Source:",
+        value="proposal number",
+        options=["proposal number", "cami file"],
+        width=210,
+    )
+    data_source.on_change("value", data_source_callback)
+
+    doc.add_periodic_callback(file_select_update, 5000)
+
+    def proposal_textinput_callback(_attr, _old, _new):
+        file_select_update()
+
+    proposal_textinput = doc.proposal_textinput
+    proposal_textinput.on_change("name", proposal_textinput_callback)
+
+    def upload_button_callback(_attr, _old, new):
+        nonlocal cami_meta
+        with io.StringIO(base64.b64decode(new).decode()) as file:
+            cami_meta = pyzebra.parse_h5meta(file)
+        data_source.value = "cami file"
+        file_select_update()
+
+    upload_div = Div(text="or upload .cami file:", margin=(5, 5, 0, 5))
+    upload_button = FileInput(accept=".cami", width=200)
+    upload_button.on_change("value", upload_button_callback)
+
+    file_select = MultiSelect(title="Available .hdf files:", width=210, height=320)
+
+    def _init_datatable():
+        file_list = []
+        for scan in zebra_data:
+            file_list.append(os.path.basename(scan["original_filename"]))
+
+        scan_table_source.data.update(
+            file=file_list,
+            param=[None] * len(zebra_data),
+            frame=[None] * len(zebra_data),
+            x_pos=[None] * len(zebra_data),
+            y_pos=[None] * len(zebra_data),
+        )
+        scan_table_source.selected.indices = []
+        scan_table_source.selected.indices = [0]
+
+        param_select.value = "user defined"
+
+    def _update_table():
+        frame = []
+        x_pos = []
+        y_pos = []
+        for scan in zebra_data:
+            if "fit" in scan:
+                framei = scan["fit"]["frame"]
+                x_posi = scan["fit"]["x_pos"]
+                y_posi = scan["fit"]["y_pos"]
+            else:
+                framei = x_posi = y_posi = None
+
+            frame.append(framei)
+            x_pos.append(x_posi)
+            y_pos.append(y_posi)
+
+        scan_table_source.data.update(frame=frame, x_pos=x_pos, y_pos=y_pos)
+
+    def file_open_button_callback():
+        nonlocal zebra_data
+        zebra_data = []
+        for f_name in file_select.value:
+            zebra_data.append(pyzebra.read_detector_data(f_name))
+
+        _init_datatable()
+
+    file_open_button = Button(label="Open New", width=100)
+    file_open_button.on_click(file_open_button_callback)
+
+    def file_append_button_callback():
+        for f_name in file_select.value:
+            zebra_data.append(pyzebra.read_detector_data(f_name))
+
+        _init_datatable()
+
+    file_append_button = Button(label="Append", width=100)
+    file_append_button.on_click(file_append_button_callback)
+
+    # Scan select
+    def scan_table_select_callback(_attr, old, new):
+        nonlocal det_data
+
+        if not new:
+            # skip empty selections
+            return
+
+        # Avoid selection of multiple indicies (via Shift+Click or Ctrl+Click)
+        if len(new) > 1:
+            # drop selection to the previous one
+            scan_table_source.selected.indices = old
+            return
+
+        if len(old) > 1:
+            # skip unnecessary update caused by selection drop
+            return
+
+        det_data = zebra_data[new[0]]
+
+        zebra_mode = det_data["zebra_mode"]
+        if zebra_mode == "nb":
+            metadata_table_source.data.update(geom=["normal beam"])
+        else:  # zebra_mode == "bi"
+            metadata_table_source.data.update(geom=["bisecting"])
+
+        if "mf" in det_data:
+            metadata_table_source.data.update(mf=[det_data["mf"][0]])
+        else:
+            metadata_table_source.data.update(mf=[None])
+
+        if "temp" in det_data:
+            metadata_table_source.data.update(temp=[det_data["temp"][0]])
+        else:
+            metadata_table_source.data.update(temp=[None])
+
+        update_overview_plot()
+
+    def scan_table_source_callback(_attr, _old, _new):
+        pass
+
+    scan_table_source = ColumnDataSource(dict(file=[], param=[], frame=[], x_pos=[], y_pos=[]))
+    scan_table_source.selected.on_change("indices", scan_table_select_callback)
+    scan_table_source.on_change("data", scan_table_source_callback)
+
+    scan_table = DataTable(
+        source=scan_table_source,
+        columns=[
+            TableColumn(field="file", title="file", width=150),
+            TableColumn(
+                field="param",
+                title="param",
+                formatter=num_formatter,
+                editor=NumberEditor(),
+                width=50,
+            ),
+            TableColumn(field="frame", title="Frame", formatter=num_formatter, width=70),
+            TableColumn(field="x_pos", title="X", formatter=num_formatter, width=70),
+            TableColumn(field="y_pos", title="Y", formatter=num_formatter, width=70),
+        ],
+        width=470,  # +60 because of the index column
+        height=420,
+        editable=True,
+        autosize_mode="none",
+    )
+
+    def param_select_callback(_attr, _old, new):
+        if new == "user defined":
+            param = [None] * len(zebra_data)
+        else:
+            # TODO: which value to take?
+            param = [scan[new][0] for scan in zebra_data]
+
+        scan_table_source.data["param"] = param
+        _update_param_plot()
+
+    param_select = Select(
+        title="Parameter:",
+        options=["user defined", "temp", "mf", "h", "k", "l"],
+        value="user defined",
+        width=145,
+    )
+    param_select.on_change("value", param_select_callback)
+
+    def update_overview_plot():
+        h5_data = det_data["data"]
+        n_im, n_y, n_x = h5_data.shape
+        overview_x = np.mean(h5_data, axis=1)
+        overview_y = np.mean(h5_data, axis=2)
+
+        # normalize for simpler colormapping
+        overview_max_val = max(np.max(overview_x), np.max(overview_y))
+        overview_x = 1000 * overview_x / overview_max_val
+        overview_y = 1000 * overview_y / overview_max_val
+
+        overview_plot_x_image_source.data.update(image=[overview_x], dw=[n_x], dh=[n_im])
+        overview_plot_y_image_source.data.update(image=[overview_y], dw=[n_y], dh=[n_im])
+
+        if proj_auto_checkbox.active:
+            im_min = min(np.min(overview_x), np.min(overview_y))
+            im_max = max(np.max(overview_x), np.max(overview_y))
+
+            proj_display_min_spinner.value = im_min
+            proj_display_max_spinner.value = im_max
+
+            overview_plot_x_image_glyph.color_mapper.low = im_min
+            overview_plot_y_image_glyph.color_mapper.low = im_min
+            overview_plot_x_image_glyph.color_mapper.high = im_max
+            overview_plot_y_image_glyph.color_mapper.high = im_max
+
+        frame_range.start = 0
+        frame_range.end = n_im
+        frame_range.reset_start = 0
+        frame_range.reset_end = n_im
+        frame_range.bounds = (0, n_im)
+
+        scan_motor = det_data["scan_motor"]
+        overview_plot_y.axis[1].axis_label = f"Scanning motor, {scan_motor}"
+
+        var = det_data[scan_motor]
+        var_start = var[0]
+        var_end = var[-1] + (var[-1] - var[0]) / (n_im - 1)
+
+        scanning_motor_range.start = var_start
+        scanning_motor_range.end = var_end
+        scanning_motor_range.reset_start = var_start
+        scanning_motor_range.reset_end = var_end
+        # handle both, ascending and descending sequences
+        scanning_motor_range.bounds = (min(var_start, var_end), max(var_start, var_end))
+
+    # shared frame ranges
+    frame_range = Range1d(0, 1, bounds=(0, 1))
+    scanning_motor_range = Range1d(0, 1, bounds=(0, 1))
+
+    det_x_range = Range1d(0, IMAGE_W, bounds=(0, IMAGE_W))
+    overview_plot_x = Plot(
+        title=Title(text="Projections on X-axis"),
+        x_range=det_x_range,
+        y_range=frame_range,
+        extra_y_ranges={"scanning_motor": scanning_motor_range},
+        plot_height=400,
+        plot_width=IMAGE_PLOT_W - 3,
+    )
+
+    # ---- tools
+    wheelzoomtool = WheelZoomTool(maintain_focus=False)
+    overview_plot_x.toolbar.logo = None
+    overview_plot_x.add_tools(
+        PanTool(), BoxZoomTool(), wheelzoomtool, ResetTool(),
+    )
+    overview_plot_x.toolbar.active_scroll = wheelzoomtool
+
+    # ---- axes
+    overview_plot_x.add_layout(LinearAxis(axis_label="Coordinate X, pix"), place="below")
+    overview_plot_x.add_layout(
+        LinearAxis(axis_label="Frame", major_label_orientation="vertical"), place="left"
+    )
+
+    # ---- grid lines
+    overview_plot_x.add_layout(Grid(dimension=0, ticker=BasicTicker()))
+    overview_plot_x.add_layout(Grid(dimension=1, ticker=BasicTicker()))
+
+    # ---- rgba image glyph
+    overview_plot_x_image_source = ColumnDataSource(
+        dict(image=[np.zeros((1, 1), dtype="float32")], x=[0], y=[0], dw=[IMAGE_W], dh=[1])
+    )
+
+    overview_plot_x_image_glyph = Image(image="image", x="x", y="y", dw="dw", dh="dh")
+    overview_plot_x.add_glyph(
+        overview_plot_x_image_source, overview_plot_x_image_glyph, name="image_glyph"
+    )
+
+    det_y_range = Range1d(0, IMAGE_H, bounds=(0, IMAGE_H))
+    overview_plot_y = Plot(
+        title=Title(text="Projections on Y-axis"),
+        x_range=det_y_range,
+        y_range=frame_range,
+        extra_y_ranges={"scanning_motor": scanning_motor_range},
+        plot_height=400,
+        plot_width=IMAGE_PLOT_H + 22,
+    )
+
+    # ---- tools
+    wheelzoomtool = WheelZoomTool(maintain_focus=False)
+    overview_plot_y.toolbar.logo = None
+    overview_plot_y.add_tools(
+        PanTool(), BoxZoomTool(), wheelzoomtool, ResetTool(),
+    )
+    overview_plot_y.toolbar.active_scroll = wheelzoomtool
+
+    # ---- axes
+    overview_plot_y.add_layout(LinearAxis(axis_label="Coordinate Y, pix"), place="below")
+    overview_plot_y.add_layout(
+        LinearAxis(
+            y_range_name="scanning_motor",
+            axis_label="Scanning motor",
+            major_label_orientation="vertical",
+        ),
+        place="right",
+    )
+
+    # ---- grid lines
+    overview_plot_y.add_layout(Grid(dimension=0, ticker=BasicTicker()))
+    overview_plot_y.add_layout(Grid(dimension=1, ticker=BasicTicker()))
+
+    # ---- rgba image glyph
+    overview_plot_y_image_source = ColumnDataSource(
+        dict(image=[np.zeros((1, 1), dtype="float32")], x=[0], y=[0], dw=[IMAGE_H], dh=[1])
+    )
+
+    overview_plot_y_image_glyph = Image(image="image", x="x", y="y", dw="dw", dh="dh")
+    overview_plot_y.add_glyph(
+        overview_plot_y_image_source, overview_plot_y_image_glyph, name="image_glyph"
+    )
+
+    cmap_dict = {
+        "gray": Greys256,
+        "gray_reversed": Greys256[::-1],
+        "plasma": Plasma256,
+        "cividis": Cividis256,
+    }
+
+    def colormap_callback(_attr, _old, new):
+        overview_plot_x_image_glyph.color_mapper = LinearColorMapper(palette=cmap_dict[new])
+        overview_plot_y_image_glyph.color_mapper = LinearColorMapper(palette=cmap_dict[new])
+
+    colormap = Select(title="Colormap:", options=list(cmap_dict.keys()), width=210)
+    colormap.on_change("value", colormap_callback)
+    colormap.value = "plasma"
+
+    PROJ_STEP = 1
+
+    def proj_auto_checkbox_callback(state):
+        if state:
+            proj_display_min_spinner.disabled = True
+            proj_display_max_spinner.disabled = True
+        else:
+            proj_display_min_spinner.disabled = False
+            proj_display_max_spinner.disabled = False
+
+        update_overview_plot()
+
+    proj_auto_checkbox = CheckboxGroup(
+        labels=["Projections Intensity Range"], active=[0], width=145, margin=[10, 5, 0, 5]
+    )
+    proj_auto_checkbox.on_click(proj_auto_checkbox_callback)
+
+    def proj_display_max_spinner_callback(_attr, _old_value, new_value):
+        proj_display_min_spinner.high = new_value - PROJ_STEP
+        overview_plot_x_image_glyph.color_mapper.high = new_value
+        overview_plot_y_image_glyph.color_mapper.high = new_value
+
+    proj_display_max_spinner = Spinner(
+        low=0 + PROJ_STEP,
+        value=1,
+        step=PROJ_STEP,
+        disabled=bool(proj_auto_checkbox.active),
+        width=100,
+        height=31,
+    )
+    proj_display_max_spinner.on_change("value", proj_display_max_spinner_callback)
+
+    def proj_display_min_spinner_callback(_attr, _old_value, new_value):
+        proj_display_max_spinner.low = new_value + PROJ_STEP
+        overview_plot_x_image_glyph.color_mapper.low = new_value
+        overview_plot_y_image_glyph.color_mapper.low = new_value
+
+    proj_display_min_spinner = Spinner(
+        low=0,
+        high=1 - PROJ_STEP,
+        value=0,
+        step=PROJ_STEP,
+        disabled=bool(proj_auto_checkbox.active),
+        width=100,
+        height=31,
+    )
+    proj_display_min_spinner.on_change("value", proj_display_min_spinner_callback)
+
+    def fit_event(scan):
+        p0 = [1.0, 0.0, 1.0]
+        maxfev = 100000
+
+        # wave = scan["wave"]
+        # ddist = scan["ddist"]
+        # cell = scan["cell"]
+
+        # gamma = scan["gamma"][0]
+        # omega = scan["omega"][0]
+        # nu = scan["nu"][0]
+        # chi = scan["chi"][0]
+        # phi = scan["phi"][0]
+
+        scan_motor = scan["scan_motor"]
+        var_angle = scan[scan_motor]
+
+        x0 = int(np.floor(det_x_range.start))
+        xN = int(np.ceil(det_x_range.end))
+        y0 = int(np.floor(det_y_range.start))
+        yN = int(np.ceil(det_y_range.end))
+        fr0 = int(np.floor(frame_range.start))
+        frN = int(np.ceil(frame_range.end))
+        data_roi = scan["data"][fr0:frN, y0:yN, x0:xN]
+
+        cnts = np.sum(data_roi, axis=(1, 2))
+        coeff, _ = curve_fit(gauss, range(len(cnts)), cnts, p0=p0, maxfev=maxfev)
+
+        # m = cnts.mean()
+        # sd = cnts.std()
+        # snr_cnts = np.where(sd == 0, 0, m / sd)
+
+        frC = fr0 + coeff[1]
+        var_F = var_angle[math.floor(frC)]
+        var_C = var_angle[math.ceil(frC)]
+        # frStep = frC - math.floor(frC)
+        var_step = var_C - var_F
+        # var_p = var_F + var_step * frStep
+
+        # if scan_motor == "gamma":
+        #     gamma = var_p
+        # elif scan_motor == "omega":
+        #     omega = var_p
+        # elif scan_motor == "nu":
+        #     nu = var_p
+        # elif scan_motor == "chi":
+        #     chi = var_p
+        # elif scan_motor == "phi":
+        #     phi = var_p
+
+        intensity = coeff[1] * abs(coeff[2] * var_step) * math.sqrt(2) * math.sqrt(np.pi)
+
+        projX = np.sum(data_roi, axis=(0, 1))
+        coeff, _ = curve_fit(gauss, range(len(projX)), projX, p0=p0, maxfev=maxfev)
+        x_pos = x0 + coeff[1]
+
+        projY = np.sum(data_roi, axis=(0, 2))
+        coeff, _ = curve_fit(gauss, range(len(projY)), projY, p0=p0, maxfev=maxfev)
+        y_pos = y0 + coeff[1]
+
+        scan["fit"] = {"frame": frC, "x_pos": x_pos, "y_pos": y_pos, "intensity": intensity}
+
+    metadata_table_source = ColumnDataSource(dict(geom=[""], temp=[None], mf=[None]))
+    metadata_table = DataTable(
+        source=metadata_table_source,
+        columns=[
+            TableColumn(field="geom", title="Geometry", width=100),
+            TableColumn(field="temp", title="Temperature", formatter=num_formatter, width=100),
+            TableColumn(field="mf", title="Magnetic Field", formatter=num_formatter, width=100),
+        ],
+        width=300,
+        height=50,
+        autosize_mode="none",
+        index_position=None,
+    )
+
+    def _update_param_plot():
+        x = []
+        y = []
+        fit_param = fit_param_select.value
+        for s, p in zip(zebra_data, scan_table_source.data["param"]):
+            if "fit" in s and fit_param:
+                x.append(p)
+                y.append(s["fit"][fit_param])
+        param_plot_scatter_source.data.update(x=x, y=y)
+
+    # Parameter plot
+    param_plot = Plot(x_range=DataRange1d(), y_range=DataRange1d(), plot_height=400, plot_width=700)
+
+    param_plot.add_layout(LinearAxis(axis_label="Fit parameter"), place="left")
+    param_plot.add_layout(LinearAxis(axis_label="Parameter"), place="below")
+
+    param_plot.add_layout(Grid(dimension=0, ticker=BasicTicker()))
+    param_plot.add_layout(Grid(dimension=1, ticker=BasicTicker()))
+
+    param_plot_scatter_source = ColumnDataSource(dict(x=[], y=[]))
+    param_plot.add_glyph(param_plot_scatter_source, Scatter(x="x", y="y"))
+
+    param_plot.add_tools(PanTool(), WheelZoomTool(), ResetTool())
+    param_plot.toolbar.logo = None
+
+    def fit_param_select_callback(_attr, _old, _new):
+        _update_param_plot()
+
+    fit_param_select = Select(title="Fit parameter", options=[], width=145)
+    fit_param_select.on_change("value", fit_param_select_callback)
+
+    def proc_all_button_callback():
+        for scan in zebra_data:
+            fit_event(scan)
+
+        _update_table()
+
+        for scan in zebra_data:
+            if "fit" in scan:
+                options = list(scan["fit"].keys())
+                fit_param_select.options = options
+                fit_param_select.value = options[0]
+                break
+
+        _update_param_plot()
+
+    proc_all_button = Button(label="Process All", button_type="primary", width=145)
+    proc_all_button.on_click(proc_all_button_callback)
+
+    def proc_button_callback():
+        fit_event(det_data)
+
+        _update_table()
+
+        for scan in zebra_data:
+            if "fit" in scan:
+                options = list(scan["fit"].keys())
+                fit_param_select.options = options
+                fit_param_select.value = options[0]
+                break
+
+        _update_param_plot()
+
+    proc_button = Button(label="Process Current", width=145)
+    proc_button.on_click(proc_button_callback)
+
+    layout_controls = row(
+        colormap,
+        column(proj_auto_checkbox, row(proj_display_min_spinner, proj_display_max_spinner)),
+        proc_button,
+        proc_all_button,
+    )
+
+    layout_overview = column(
+        gridplot(
+            [[overview_plot_x, overview_plot_y]],
+            toolbar_options=dict(logo=None),
+            merge_tools=True,
+            toolbar_location="left",
+        ),
+        layout_controls,
+    )
+
+    # Plot tabs
+    plots = Tabs(
+        tabs=[
+            Panel(child=layout_overview, title="single scan"),
+            Panel(child=column(param_plot, row(fit_param_select)), title="parameter plot"),
+        ]
+    )
+
+    # Final layout
+    import_layout = column(
+        data_source,
+        upload_div,
+        upload_button,
+        file_select,
+        row(file_open_button, file_append_button),
+    )
+
+    scan_layout = column(scan_table, row(param_select, metadata_table))
+
+    tab_layout = column(row(import_layout, scan_layout, plots))
+
+    return Panel(child=tab_layout, title="hdf param study")
+
+
+def gauss(x, *p):
+    """Defines Gaussian function
+    Args:
+        A - amplitude, mu - position of the center, sigma - width
+    Returns:
+        Gaussian function
+    """
+    A, mu, sigma = p
+    return A * np.exp(-((x - mu) ** 2) / (2.0 * sigma ** 2))

pyzebra/app/panel_hdf_viewer.py

@@ -1,8 +1,11 @@
 import base64
 import io
+import math
 import os
 
 import numpy as np
+from bokeh.events import MouseEnter
+from bokeh.io import curdoc
 from bokeh.layouts import column, gridplot, row
 from bokeh.models import (
     BasicTicker,
@@ -34,12 +37,11 @@ from bokeh.models import (
     Spacer,
     Spinner,
     TableColumn,
-    TextAreaInput,
-    TextInput,
     Title,
     WheelZoomTool,
 )
 from bokeh.palettes import Cividis256, Greys256, Plasma256  # pylint: disable=E0611
+from scipy.optimize import curve_fit
 
 import pyzebra
 
@@ -50,27 +52,57 @@ IMAGE_PLOT_H = int(IMAGE_H * 2) + 27
 
 
 def create():
+    doc = curdoc()
     det_data = {}
-    roi_selection = {}
+    cami_meta = {}
 
-    def proposal_textinput_callback(_attr, _old, new):
-        proposal = new.strip()
-        year = new[:4]
-        proposal_path = f"/afs/psi.ch/project/sinqdata/{year}/zebra/{proposal}"
-        file_list = []
-        for file in os.listdir(proposal_path):
-            if file.endswith(".hdf"):
-                file_list.append((os.path.join(proposal_path, file), file))
-        file_select.options = file_list
+    num_formatter = NumberFormatter(format="0.00", nan_format="")
 
-    proposal_textinput = TextInput(title="Proposal number:", width=210)
-    proposal_textinput.on_change("value", proposal_textinput_callback)
+    def file_select_update():
+        if data_source.value == "proposal number":
+            proposal_path = proposal_textinput.name
+            if proposal_path:
+                file_list = []
+                for file in os.listdir(proposal_path):
+                    if file.endswith(".hdf"):
+                        file_list.append((os.path.join(proposal_path, file), file))
+                file_select.options = file_list
+            else:
+                file_select.options = []
+
+        else:  # "cami file"
+            if not cami_meta:
+                file_select.options = []
+                return
+
+            file_list = cami_meta["filelist"]
+            file_select.options = [(entry, os.path.basename(entry)) for entry in file_list]
+
+    def data_source_callback(_attr, _old, _new):
+        file_select_update()
+
+    data_source = Select(
+        title="Data Source:",
+        value="proposal number",
+        options=["proposal number", "cami file"],
+        width=210,
+    )
+    data_source.on_change("value", data_source_callback)
+
+    doc.add_periodic_callback(file_select_update, 5000)
+
+    def proposal_textinput_callback(_attr, _old, _new):
+        file_select_update()
+
+    proposal_textinput = doc.proposal_textinput
+    proposal_textinput.on_change("name", proposal_textinput_callback)
 
     def upload_button_callback(_attr, _old, new):
+        nonlocal cami_meta
         with io.StringIO(base64.b64decode(new).decode()) as file:
-            h5meta_list = pyzebra.parse_h5meta(file)
-            file_list = h5meta_list["filelist"]
-            file_select.options = [(entry, os.path.basename(entry)) for entry in file_list]
+            cami_meta = pyzebra.parse_h5meta(file)
+        data_source.value = "cami file"
+        file_select_update()
 
     upload_div = Div(text="or upload .cami file:", margin=(5, 5, 0, 5))
     upload_button = FileInput(accept=".cami", width=200)
@@ -123,6 +155,11 @@ def create():
         overview_x = np.mean(h5_data, axis=1)
         overview_y = np.mean(h5_data, axis=2)
 
+        # normalize for simpler colormapping
+        overview_max_val = max(np.max(overview_x), np.max(overview_y))
+        overview_x = 1000 * overview_x / overview_max_val
+        overview_y = 1000 * overview_y / overview_max_val
+
         overview_plot_x_image_source.data.update(image=[overview_x], dw=[n_x], dh=[n_im])
         overview_plot_y_image_source.data.update(image=[overview_y], dw=[n_y], dh=[n_im])
 
@@ -155,7 +192,8 @@ def create():
         scanning_motor_range.end = var_end
         scanning_motor_range.reset_start = var_start
         scanning_motor_range.reset_end = var_end
-        scanning_motor_range.bounds = (var_start, var_end)
+        # handle both, ascending and descending sequences
+        scanning_motor_range.bounds = (min(var_start, var_end), max(var_start, var_end))
 
     def file_select_callback(_attr, old, new):
         nonlocal det_data
@@ -173,7 +211,7 @@ def create():
             # skip unnecessary update caused by selection drop
             return
 
-        det_data = pyzebra.read_detector_data(new[0])
+        det_data = pyzebra.read_detector_data(new[0], cami_meta)
 
         index_spinner.value = 0
         index_spinner.high = det_data["data"].shape[0] - 1
@@ -255,6 +293,15 @@ def create():
     image_glyph = Image(image="image", x="x", y="y", dw="dw", dh="dh")
     plot.add_glyph(image_source, image_glyph, name="image_glyph")
 
+    # calculate hkl-indices of first mouse entry
+    def mouse_enter_callback(_event):
+        if det_data and np.array_equal(image_source.data["h"][0], np.zeros((1, 1))):
+            index = index_spinner.value
+            h, k, l = calculate_hkl(det_data, index)
+            image_source.data.update(h=[h], k=[k], l=[l])
+
+    plot.on_event(MouseEnter, mouse_enter_callback)
+
     # ---- projections
     proj_v = Plot(
         x_range=plot.x_range,
@@ -469,7 +516,7 @@ def create():
         update_image()
 
     main_auto_checkbox = CheckboxGroup(
-        labels=["Main Auto Range"], active=[0], width=145, margin=[10, 5, 0, 5]
+        labels=["Frame Intensity Range"], active=[0], width=145, margin=[10, 5, 0, 5]
     )
     main_auto_checkbox.on_click(main_auto_checkbox_callback)
 
@@ -502,7 +549,7 @@ def create():
     )
     display_min_spinner.on_change("value", display_min_spinner_callback)
 
-    PROJ_STEP = 0.1
+    PROJ_STEP = 1
 
     def proj_auto_checkbox_callback(state):
         if state:
@@ -515,7 +562,7 @@ def create():
         update_overview_plot()
 
     proj_auto_checkbox = CheckboxGroup(
-        labels=["Projections Auto Range"], active=[0], width=145, margin=[10, 5, 0, 5]
+        labels=["Projections Intensity Range"], active=[0], width=145, margin=[10, 5, 0, 5]
     )
     proj_auto_checkbox.on_click(proj_auto_checkbox_callback)
 
@@ -550,40 +597,134 @@ def create():
     )
     proj_display_min_spinner.on_change("value", proj_display_min_spinner_callback)
 
-    def hkl_button_callback():
-        index = index_spinner.value
-        h, k, l = calculate_hkl(det_data, index)
-        image_source.data.update(h=[h], k=[k], l=[l])
+    events_data = dict(
+        wave=[],
+        ddist=[],
+        cell=[],
+        frame=[],
+        x_pos=[],
+        y_pos=[],
+        intensity=[],
+        snr_cnts=[],
+        gamma=[],
+        omega=[],
+        chi=[],
+        phi=[],
+        nu=[],
+    )
+    doc.events_data = events_data
 
-    hkl_button = Button(label="Calculate hkl (slow)", width=210)
-    hkl_button.on_click(hkl_button_callback)
+    events_table_source = ColumnDataSource(events_data)
+    events_table = DataTable(
+        source=events_table_source,
+        columns=[
+            TableColumn(field="frame", title="Frame", formatter=num_formatter, width=70),
+            TableColumn(field="x_pos", title="X", formatter=num_formatter, width=70),
+            TableColumn(field="y_pos", title="Y", formatter=num_formatter, width=70),
+            TableColumn(field="intensity", title="Intensity", formatter=num_formatter, width=70),
+            TableColumn(field="gamma", title="Gamma", formatter=num_formatter, width=70),
+            TableColumn(field="omega", title="Omega", formatter=num_formatter, width=70),
+            TableColumn(field="chi", title="Chi", formatter=num_formatter, width=70),
+            TableColumn(field="phi", title="Phi", formatter=num_formatter, width=70),
+            TableColumn(field="nu", title="Nu", formatter=num_formatter, width=70),
+        ],
+        height=150,
+        width=630,
+        autosize_mode="none",
+        index_position=None,
+    )
 
-    selection_list = TextAreaInput(rows=7)
+    def add_event_button_callback():
+        p0 = [1.0, 0.0, 1.0]
+        maxfev = 100000
 
-    def selection_button_callback():
-        nonlocal roi_selection
-        selection = [
-            int(np.floor(det_x_range.start)),
-            int(np.ceil(det_x_range.end)),
-            int(np.floor(det_y_range.start)),
-            int(np.ceil(det_y_range.end)),
-            int(np.floor(frame_range.start)),
-            int(np.ceil(frame_range.end)),
-        ]
+        wave = det_data["wave"]
+        ddist = det_data["ddist"]
+        cell = det_data["cell"]
 
-        filename_id = file_select.value[0][-8:-4]
-        if filename_id in roi_selection:
-            roi_selection[f"{filename_id}"].append(selection)
-        else:
-            roi_selection[f"{filename_id}"] = [selection]
+        gamma = det_data["gamma"][0]
+        omega = det_data["omega"][0]
+        nu = det_data["nu"][0]
+        chi = det_data["chi"][0]
+        phi = det_data["phi"][0]
 
-        selection_list.value = str(roi_selection)
+        scan_motor = det_data["scan_motor"]
+        var_angle = det_data[scan_motor]
 
-    selection_button = Button(label="Add selection")
-    selection_button.on_click(selection_button_callback)
+        x0 = int(np.floor(det_x_range.start))
+        xN = int(np.ceil(det_x_range.end))
+        y0 = int(np.floor(det_y_range.start))
+        yN = int(np.ceil(det_y_range.end))
+        fr0 = int(np.floor(frame_range.start))
+        frN = int(np.ceil(frame_range.end))
+        data_roi = det_data["data"][fr0:frN, y0:yN, x0:xN]
+
+        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]
+
+        events_data["wave"].append(wave)
+        events_data["ddist"].append(ddist)
+        events_data["cell"].append(cell)
+        events_data["frame"].append(frC)
+        events_data["x_pos"].append(x_pos)
+        events_data["y_pos"].append(y_pos)
+        events_data["intensity"].append(intensity)
+        events_data["snr_cnts"].append(snr_cnts)
+        events_data["gamma"].append(gamma)
+        events_data["omega"].append(omega)
+        events_data["chi"].append(chi)
+        events_data["phi"].append(phi)
+        events_data["nu"].append(nu)
+
+        events_table_source.data = events_data
+
+    add_event_button = Button(label="Add spind event", width=145)
+    add_event_button.on_click(add_event_button_callback)
+
+    def remove_event_button_callback():
+        ind2remove = events_table_source.selected.indices
+        for value in events_data.values():
+            for ind in reversed(ind2remove):
+                del value[ind]
+
+        events_table_source.data = events_data
+
+    remove_event_button = Button(label="Remove spind event", width=145)
+    remove_event_button.on_click(remove_event_button_callback)
 
     metadata_table_source = ColumnDataSource(dict(geom=[""], temp=[None], mf=[None]))
-    num_formatter = NumberFormatter(format="0.00", nan_format="")
     metadata_table = DataTable(
         source=metadata_table_source,
         columns=[
@@ -598,7 +739,7 @@ def create():
     )
 
     # Final layout
-    import_layout = column(proposal_textinput, upload_div, upload_button, file_select)
+    import_layout = column(data_source, upload_div, upload_button, file_select)
     layout_image = column(gridplot([[proj_v, None], [plot, proj_h]], merge_tools=False))
     colormap_layout = column(
         colormap,
@@ -608,12 +749,9 @@ def create():
         row(proj_display_min_spinner, proj_display_max_spinner),
     )
 
-    layout_controls = row(
-        column(selection_button, selection_list),
-        Spacer(width=20),
-        column(
-            row(index_spinner, column(Spacer(height=25), index_slider)), metadata_table, hkl_button
-        ),
+    layout_controls = column(
+        row(metadata_table, index_spinner, column(Spacer(height=25), index_slider)),
+        row(column(add_event_button, remove_event_button), events_table),
     )
 
     layout_overview = column(
@@ -634,6 +772,17 @@ def create():
     return Panel(child=tab_layout, title="hdf viewer")
 
 
+def gauss(x, *p):
+    """Defines Gaussian function
+    Args:
+        A - amplitude, mu - position of the center, sigma - width
+    Returns:
+        Gaussian function
+    """
+    A, mu, sigma = p
+    return A * np.exp(-((x - mu) ** 2) / (2.0 * sigma ** 2))
+
+
 def calculate_hkl(det_data, index):
     h = np.empty(shape=(IMAGE_H, IMAGE_W))
     k = np.empty(shape=(IMAGE_H, IMAGE_W))

pyzebra/app/panel_param_study.py

@@ -6,6 +6,7 @@ import tempfile
 import types
 
 import numpy as np
+from bokeh.io import curdoc
 from bokeh.layouts import column, row
 from bokeh.models import (
     BasicTicker,
@@ -21,6 +22,7 @@ from bokeh.models import (
     FileInput,
     Grid,
     HoverTool,
+    Image,
     Legend,
     Line,
     LinearAxis,
@@ -30,7 +32,7 @@ from bokeh.models import (
     Panel,
     PanTool,
     Plot,
-    RadioButtonGroup,
+    RadioGroup,
     ResetTool,
     Scatter,
     Select,
@@ -40,29 +42,34 @@ from bokeh.models import (
     TableColumn,
     Tabs,
     TextAreaInput,
-    TextInput,
     WheelZoomTool,
     Whisker,
 )
 from bokeh.palettes import Category10, Turbo256
 from bokeh.transform import linear_cmap
+from scipy import interpolate
 
 import pyzebra
-from pyzebra.ccl_io import AREA_METHODS
+from pyzebra.ccl_process import AREA_METHODS
 
 javaScript = """
+let j = 0;
 for (let i = 0; i < js_data.data['fname'].length; i++) {
     if (js_data.data['content'][i] === "") continue;
 
-    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);
+    setTimeout(function() {
+        const blob = new Blob([js_data.data['content'][i]], {type: 'text/plain'})
+        const link = document.createElement('a');
+        document.body.appendChild(link);
+        const url = window.URL.createObjectURL(blob);
+        link.href = url;
+        link.download = js_data.data['fname'][i] + js_data.data['ext'][i];
+        link.click();
+        window.URL.revokeObjectURL(url);
+        document.body.removeChild(link);
+    }, 100 * j)
+
+    j++;
 }
 """
 
@@ -73,22 +80,33 @@ def color_palette(n_colors):
 
 
 def create():
+    doc = curdoc()
     det_data = []
     fit_params = {}
-    js_data = ColumnDataSource(data=dict(content=["", ""], fname=["", ""]))
+    js_data = ColumnDataSource(data=dict(content=[""], fname=[""], ext=[""]))
 
-    def proposal_textinput_callback(_attr, _old, new):
-        proposal = new.strip()
-        year = new[:4]
-        proposal_path = f"/afs/psi.ch/project/sinqdata/{year}/zebra/{proposal}"
-        file_list = []
-        for file in os.listdir(proposal_path):
-            if file.endswith((".ccl", ".dat")):
-                file_list.append((os.path.join(proposal_path, file), file))
-        file_select.options = file_list
+    def file_select_update_for_proposal():
+        proposal_path = proposal_textinput.name
+        if proposal_path:
+            file_list = []
+            for file in os.listdir(proposal_path):
+                if file.endswith((".ccl", ".dat")):
+                    file_list.append((os.path.join(proposal_path, file), file))
+            file_select.options = file_list
+            file_open_button.disabled = False
+            file_append_button.disabled = False
+        else:
+            file_select.options = []
+            file_open_button.disabled = True
+            file_append_button.disabled = True
 
-    proposal_textinput = TextInput(title="Proposal number:", width=210)
-    proposal_textinput.on_change("value", proposal_textinput_callback)
+    doc.add_periodic_callback(file_select_update_for_proposal, 5000)
+
+    def proposal_textinput_callback(_attr, _old, _new):
+        file_select_update_for_proposal()
+
+    proposal_textinput = doc.proposal_textinput
+    proposal_textinput.on_change("name", proposal_textinput_callback)
 
     def _init_datatable():
         scan_list = [s["idx"] for s in det_data]
@@ -106,42 +124,46 @@ def create():
         scan_table_source.selected.indices = []
         scan_table_source.selected.indices = [0]
 
+        scan_motor_select.options = det_data[0]["scan_motors"]
+        scan_motor_select.value = det_data[0]["scan_motor"]
         param_select.value = "user defined"
 
     file_select = MultiSelect(title="Available .ccl/.dat files:", width=210, height=250)
 
     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"])
+        for f_ind, f_path in enumerate(file_select.value):
+            with open(f_path) as file:
+                base, ext = os.path.splitext(os.path.basename(f_path))
+                file_data = pyzebra.parse_1D(file, ext)
+
+            pyzebra.normalize_dataset(file_data, monitor_spinner.value)
+
+            if f_ind == 0:  # first file
+                det_data = file_data
+                pyzebra.merge_duplicates(det_data)
+                js_data.data.update(fname=[base])
+            else:
+                pyzebra.merge_datasets(det_data, file_data)
 
         _init_datatable()
+        append_upload_button.disabled = False
 
-    file_open_button = Button(label="Open New", width=100)
+    file_open_button = Button(label="Open New", width=100, disabled=True)
     file_open_button.on_click(file_open_button_callback)
 
     def file_append_button_callback():
-        for f_name in file_select.value:
-            with open(f_name) as file:
-                _, ext = os.path.splitext(f_name)
-                append_data = pyzebra.parse_1D(file, ext)
+        for f_path in file_select.value:
+            with open(f_path) as file:
+                _, ext = os.path.splitext(f_path)
+                file_data = pyzebra.parse_1D(file, ext)
 
-            pyzebra.normalize_dataset(append_data, monitor_spinner.value)
-            det_data.extend(append_data)
+            pyzebra.normalize_dataset(file_data, monitor_spinner.value)
+            pyzebra.merge_datasets(det_data, file_data)
 
         _init_datatable()
 
-    file_append_button = Button(label="Append", width=100)
+    file_append_button = Button(label="Append", width=100, disabled=True)
     file_append_button.on_click(file_append_button_callback)
 
     def upload_button_callback(_attr, _old, new):
@@ -150,16 +172,19 @@ def create():
         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"])
+                file_data = pyzebra.parse_1D(file, ext)
+
+            pyzebra.normalize_dataset(file_data, monitor_spinner.value)
+
+            if not det_data:  # first file
+                det_data = file_data
+                pyzebra.merge_duplicates(det_data)
+                js_data.data.update(fname=[base])
+            else:
+                pyzebra.merge_datasets(det_data, file_data)
 
         _init_datatable()
+        append_upload_button.disabled = False
 
     upload_div = Div(text="or upload new .ccl/.dat files:", margin=(5, 5, 0, 5))
     upload_button = FileInput(accept=".ccl,.dat", multiple=True, width=200)
@@ -169,15 +194,15 @@ def create():
         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)
+                file_data = pyzebra.parse_1D(file, ext)
 
-            pyzebra.normalize_dataset(append_data, monitor_spinner.value)
-            det_data.extend(append_data)
+            pyzebra.normalize_dataset(file_data, monitor_spinner.value)
+            pyzebra.merge_datasets(det_data, file_data)
 
         _init_datatable()
 
     append_upload_div = Div(text="append extra files:", margin=(5, 5, 0, 5))
-    append_upload_button = FileInput(accept=".ccl,.dat", multiple=True, width=200)
+    append_upload_button = FileInput(accept=".ccl,.dat", multiple=True, width=200, disabled=True)
     append_upload_button.on_change("value", append_upload_button_callback)
 
     def monitor_spinner_callback(_attr, _old, new):
@@ -188,22 +213,33 @@ def create():
     monitor_spinner = Spinner(title="Monitor:", mode="int", value=100_000, low=1, width=145)
     monitor_spinner.on_change("value", monitor_spinner_callback)
 
+    def scan_motor_select_callback(_attr, _old, new):
+        if det_data:
+            for scan in det_data:
+                scan["scan_motor"] = new
+            _update_plot()
+
+    scan_motor_select = Select(title="Scan motor:", options=[], width=145)
+    scan_motor_select.on_change("value", scan_motor_select_callback)
+
     def _update_table():
         fit_ok = [(1 if "fit" in scan else 0) for scan in det_data]
         scan_table_source.data.update(fit=fit_ok)
 
     def _update_plot():
-        _update_single_scan_plot(_get_selected_scan())
+        _update_single_scan_plot()
         _update_overview()
 
-    def _update_single_scan_plot(scan):
+    def _update_single_scan_plot():
+        scan = _get_selected_scan()
         scan_motor = scan["scan_motor"]
 
-        y = scan["Counts"]
+        y = scan["counts"]
+        y_err = scan["counts_err"]
         x = scan[scan_motor]
 
         plot.axis[0].axis_label = scan_motor
-        plot_scatter_source.data.update(x=x, y=y, y_upper=y + np.sqrt(y), y_lower=y - np.sqrt(y))
+        plot_scatter_source.data.update(x=x, y=y, y_upper=y + y_err, y_lower=y - y_err)
 
         fit = scan.get("fit")
         if fit is not None:
@@ -248,10 +284,10 @@ def create():
                 scan_motor = scan["scan_motor"]
                 xs.append(scan[scan_motor])
                 x.extend(scan[scan_motor])
-                ys.append(scan["Counts"])
+                ys.append(scan["counts"])
                 y.extend([float(p)] * len(scan[scan_motor]))
                 param.append(float(p))
-                par.extend(scan["Counts"])
+                par.extend(scan["counts"])
 
         if det_data:
             scan_motor = det_data[0]["scan_motor"]
@@ -265,6 +301,38 @@ def create():
             mapper["transform"].high = np.max([np.max(y) for y in ys])
         ov_param_plot_scatter_source.data.update(x=x, y=y, param=par)
 
+        if y:
+            interp_f = interpolate.interp2d(x, y, par)
+            x1, x2 = min(x), max(x)
+            y1, y2 = min(y), max(y)
+            image = interp_f(
+                np.linspace(x1, x2, ov_param_plot.inner_width // 10),
+                np.linspace(y1, y2, ov_param_plot.inner_height // 10),
+                assume_sorted=True,
+            )
+            ov_param_plot_image_source.data.update(
+                image=[image], x=[x1], y=[y1], dw=[x2 - x1], dh=[y2 - y1]
+            )
+        else:
+            ov_param_plot_image_source.data.update(image=[], x=[], y=[], dw=[], dh=[])
+
+    def _update_param_plot():
+        x = []
+        y = []
+        y_lower = []
+        y_upper = []
+        fit_param = fit_param_select.value
+        for s, p in zip(det_data, scan_table_source.data["param"]):
+            if "fit" in s and fit_param:
+                x.append(p)
+                param_fit_val = s["fit"].params[fit_param].value
+                param_fit_std = s["fit"].params[fit_param].stderr
+                y.append(param_fit_val)
+                y_lower.append(param_fit_val - param_fit_std)
+                y_upper.append(param_fit_val + param_fit_std)
+
+        param_plot_scatter_source.data.update(x=x, y=y, y_lower=y_lower, y_upper=y_upper)
+
     # Main plot
     plot = Plot(
         x_range=DataRange1d(),
@@ -321,7 +389,7 @@ def create():
     plot.toolbar.logo = None
 
     # Overview multilines plot
-    ov_plot = Plot(x_range=DataRange1d(), y_range=DataRange1d(), plot_height=400, plot_width=700)
+    ov_plot = Plot(x_range=DataRange1d(), y_range=DataRange1d(), plot_height=450, plot_width=700)
 
     ov_plot.add_layout(LinearAxis(axis_label="Counts"), place="left")
     ov_plot.add_layout(LinearAxis(axis_label="Scan motor"), place="below")
@@ -340,7 +408,7 @@ def create():
 
     # Overview perams plot
     ov_param_plot = Plot(
-        x_range=DataRange1d(), y_range=DataRange1d(), plot_height=400, plot_width=700
+        x_range=DataRange1d(), y_range=DataRange1d(), plot_height=450, plot_width=700
     )
 
     ov_param_plot.add_layout(LinearAxis(axis_label="Param"), place="left")
@@ -349,6 +417,11 @@ def create():
     ov_param_plot.add_layout(Grid(dimension=0, ticker=BasicTicker()))
     ov_param_plot.add_layout(Grid(dimension=1, ticker=BasicTicker()))
 
+    ov_param_plot_image_source = ColumnDataSource(dict(image=[], x=[], y=[], dw=[], dh=[]))
+    ov_param_plot.add_glyph(
+        ov_param_plot_image_source, Image(image="image", x="x", y="y", dw="dw", dh="dh")
+    )
+
     ov_param_plot_scatter_source = ColumnDataSource(dict(x=[], y=[], param=[]))
     mapper = linear_cmap(field_name="param", palette=Turbo256, low=0, high=50)
     ov_param_plot.add_glyph(
@@ -359,12 +432,37 @@ def create():
     ov_param_plot.add_tools(PanTool(), WheelZoomTool(), ResetTool())
     ov_param_plot.toolbar.logo = None
 
+    # Parameter plot
+    param_plot = Plot(x_range=DataRange1d(), y_range=DataRange1d(), plot_height=400, plot_width=700)
+
+    param_plot.add_layout(LinearAxis(axis_label="Fit parameter"), place="left")
+    param_plot.add_layout(LinearAxis(axis_label="Parameter"), place="below")
+
+    param_plot.add_layout(Grid(dimension=0, ticker=BasicTicker()))
+    param_plot.add_layout(Grid(dimension=1, ticker=BasicTicker()))
+
+    param_plot_scatter_source = ColumnDataSource(dict(x=[], y=[], y_upper=[], y_lower=[]))
+    param_plot.add_glyph(param_plot_scatter_source, Scatter(x="x", y="y"))
+    param_plot.add_layout(
+        Whisker(source=param_plot_scatter_source, base="x", upper="y_upper", lower="y_lower")
+    )
+
+    param_plot.add_tools(PanTool(), WheelZoomTool(), ResetTool())
+    param_plot.toolbar.logo = None
+
+    def fit_param_select_callback(_attr, _old, _new):
+        _update_param_plot()
+
+    fit_param_select = Select(title="Fit parameter", options=[], width=145)
+    fit_param_select.on_change("value", fit_param_select_callback)
+
     # Plot tabs
     plots = Tabs(
         tabs=[
             Panel(child=plot, title="single scan"),
             Panel(child=ov_plot, title="overview"),
             Panel(child=ov_param_plot, title="overview map"),
+            Panel(child=column(param_plot, row(fit_param_select)), title="parameter plot"),
         ]
     )
 
@@ -391,6 +489,7 @@ def create():
 
     scan_table_source = ColumnDataSource(dict(file=[], scan=[], param=[], fit=[], export=[]))
     scan_table_source.on_change("data", scan_table_source_callback)
+    scan_table_source.selected.on_change("indices", scan_table_select_callback)
 
     scan_table = DataTable(
         source=scan_table_source,
@@ -406,13 +505,6 @@ def create():
         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]]
 
@@ -423,6 +515,7 @@ def create():
             param = [scan[new] for scan in det_data]
 
         scan_table_source.data["param"] = param
+        _update_param_plot()
 
     param_select = Select(
         title="Parameter:",
@@ -551,44 +644,61 @@ def create():
 
     fit_output_textinput = TextAreaInput(title="Fit results:", width=750, height=200)
 
-    def fit_all_button_callback():
+    def proc_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
                 )
+                pyzebra.get_area(
+                    scan,
+                    area_method=AREA_METHODS[area_method_radiobutton.active],
+                    lorentz=lorentz_checkbox.active,
+                )
 
         _update_plot()
         _update_table()
 
-    fit_all_button = Button(label="Fit All", button_type="primary", width=145)
-    fit_all_button.on_click(fit_all_button_callback)
+        for scan in det_data:
+            if "fit" in scan:
+                options = list(scan["fit"].params.keys())
+                fit_param_select.options = options
+                fit_param_select.value = options[0]
+                break
+        _update_param_plot()
 
-    def fit_button_callback():
+    proc_all_button = Button(label="Process All", button_type="primary", width=145)
+    proc_all_button.on_click(proc_all_button_callback)
+
+    def proc_button_callback():
         scan = _get_selected_scan()
         pyzebra.fit_scan(
             scan, fit_params, fit_from=fit_from_spinner.value, fit_to=fit_to_spinner.value
         )
+        pyzebra.get_area(
+            scan,
+            area_method=AREA_METHODS[area_method_radiobutton.active],
+            lorentz=lorentz_checkbox.active,
+        )
 
         _update_plot()
         _update_table()
 
-    fit_button = Button(label="Fit Current", width=145)
-    fit_button.on_click(fit_button_callback)
+        for scan in det_data:
+            if "fit" in scan:
+                options = list(scan["fit"].params.keys())
+                fit_param_select.options = options
+                fit_param_select.value = options[0]
+                break
+        _update_param_plot()
 
-    def area_method_radiobutton_callback(_handler):
-        _update_preview()
+    proc_button = Button(label="Process Current", width=145)
+    proc_button.on_click(proc_button_callback)
 
-    area_method_radiobutton = RadioButtonGroup(
-        labels=["Fit area", "Int area"], active=0, width=145, disabled=True
-    )
-    area_method_radiobutton.on_click(area_method_radiobutton_callback)
+    area_method_div = Div(text="Intensity:", margin=(5, 5, 0, 5))
+    area_method_radiobutton = RadioGroup(labels=["Function", "Area"], active=0, width=145)
 
-    def lorentz_checkbox_callback(_handler):
-        _update_preview()
-
-    lorentz_checkbox = CheckboxGroup(labels=["Lorentz Correction"], width=145, margin=[13, 5, 5, 5])
-    lorentz_checkbox.on_click(lorentz_checkbox_callback)
+    lorentz_checkbox = CheckboxGroup(labels=["Lorentz Correction"], width=145, margin=(13, 5, 5, 5))
 
     export_preview_textinput = TextAreaInput(title="Export file preview:", width=450, height=400)
 
@@ -596,28 +706,27 @@ def create():
         with tempfile.TemporaryDirectory() as temp_dir:
             temp_file = temp_dir + "/temp"
             export_data = []
-            for s, export in zip(det_data, scan_table_source.data["export"]):
+            param_data = []
+            for s, p, export in zip(
+                det_data, scan_table_source.data["param"], scan_table_source.data["export"]
+            ):
                 if export:
                     export_data.append(s)
+                    param_data.append(p)
 
-            pyzebra.export_1D(
-                export_data,
-                temp_file,
-                area_method=AREA_METHODS[int(area_method_radiobutton.active)],
-                lorentz=bool(lorentz_checkbox.active),
-            )
+            pyzebra.export_param_study(export_data, param_data, temp_file)
 
             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)
+
+            fname = temp_file
+            if os.path.isfile(fname):
+                with open(fname) as f:
+                    content = f.read()
+                    exported_content += content
+            else:
+                content = ""
+            file_content.append(content)
 
             js_data.data.update(content=file_content)
             export_preview_textinput.value = exported_content
@@ -629,17 +738,13 @@ def create():
         column(fitparams_add_dropdown, fitparams_select, fitparams_remove_button),
         fitparams_table,
         Spacer(width=20),
-        column(
-            row(fit_from_spinner, fit_to_spinner),
-            row(area_method_radiobutton, lorentz_checkbox),
-            row(fit_button, fit_all_button),
-        ),
+        column(fit_from_spinner, lorentz_checkbox, area_method_div, area_method_radiobutton),
+        column(fit_to_spinner, proc_button, proc_all_button),
     )
 
-    scan_layout = column(scan_table, row(monitor_spinner, param_select))
+    scan_layout = column(scan_table, row(monitor_spinner, scan_motor_select, param_select))
 
     import_layout = column(
-        proposal_textinput,
         file_select,
         row(file_open_button, file_append_button),
         upload_div,

pyzebra/app/panel_spind.py

@@ -1,10 +1,9 @@
-import ast
-import math
 import os
 import subprocess
 import tempfile
 
 import numpy as np
+from bokeh.io import curdoc
 from bokeh.layouts import column, row
 from bokeh.models import (
     Button,
@@ -16,17 +15,16 @@ from bokeh.models import (
     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"
-    )
+    doc = curdoc()
+    events_data = doc.events_data
+
+    npeaks_spinner = Spinner(title="Number of peaks from hdf_view panel:", disabled=True)
+    lattice_const_textinput = TextInput(title="Lattice constants:")
     max_res_spinner = Spinner(title="max-res:", value=2, step=0.01, width=145)
     seed_pool_size_spinner = Spinner(title="seed-pool-size:", value=5, step=0.01, width=145)
     seed_len_tol_spinner = Spinner(title="seed-len-tol:", value=0.02, step=0.01, width=145)
@@ -46,7 +44,6 @@ def create():
             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(
                 [
@@ -54,7 +51,7 @@ def create():
                     "-n",
                     "2",
                     "python",
-                    os.path.expanduser("~/spind/gen_hkl_table.py"),
+                    os.path.join(doc.spind_path, "gen_hkl_table.py"),
                     lattice_const_textinput.value,
                     "--max-res",
                     str(max_res_spinner.value),
@@ -69,7 +66,33 @@ def create():
             print(" ".join(comp_proc.args))
             print(comp_proc.stdout)
 
-            diff_vec = prepare_event_file(temp_event_file, roi_dict, path_prefix_textinput.value)
+            # prepare an event file
+            diff_vec = []
+            with open(temp_event_file, "w") as f:
+                npeaks = len(next(iter(doc.events_data.values())))
+                for ind in range(npeaks):
+                    wave = events_data["wave"][ind]
+                    ddist = events_data["ddist"][ind]
+                    x_pos = events_data["x_pos"][ind]
+                    y_pos = events_data["y_pos"][ind]
+                    intensity = events_data["intensity"][ind]
+                    snr_cnts = events_data["snr_cnts"][ind]
+                    gamma = events_data["gamma"][ind]
+                    omega = events_data["omega"][ind]
+                    chi = events_data["chi"][ind]
+                    phi = events_data["phi"][ind]
+                    nu = events_data["nu"][ind]
+
+                    ga, nu = pyzebra.det2pol(ddist, gamma, nu, x_pos, y_pos)
+                    diff_vector = pyzebra.z1frmd(wave, ga, omega, chi, phi, nu)
+                    d_spacing = float(pyzebra.dandth(wave, diff_vector)[0])
+                    diff_vector = diff_vector.flatten() * 1e10
+                    dv1, dv2, dv3 = diff_vector
+
+                    diff_vec.append(diff_vector)
+                    f.write(
+                        f"{x_pos} {y_pos} {intensity} {snr_cnts} {dv1} {dv2} {dv3} {d_spacing}\n"
+                    )
 
             print(f"Content of {temp_event_file}:")
             with open(temp_event_file) as f:
@@ -81,7 +104,7 @@ def create():
                     "-n",
                     "2",
                     "python",
-                    os.path.expanduser("~/spind/SPIND.py"),
+                    os.path.join(doc.spind_path, "SPIND.py"),
                     temp_peak_list_dir,
                     temp_hkl_file,
                     "-o",
@@ -136,6 +159,9 @@ def create():
     process_button = Button(label="Process", button_type="primary")
     process_button.on_click(process_button_callback)
 
+    if doc.spind_path is None:
+        process_button.disabled = True
+
     ub_matrix_textareainput = TextAreaInput(title="UB matrix:", rows=7, width=400)
     hkl_textareainput = TextAreaInput(title="hkl values:", rows=7, width=400)
 
@@ -175,8 +201,7 @@ def create():
 
     tab_layout = row(
         column(
-            path_prefix_textinput,
-            selection_list,
+            npeaks_spinner,
             lattice_const_textinput,
             row(max_res_spinner, seed_pool_size_spinner),
             row(seed_len_tol_spinner, seed_angle_tol_spinner),
@@ -186,87 +211,13 @@ def create():
         column(results_table, row(ub_matrix_textareainput, hkl_textareainput)),
     )
 
+    async def update_npeaks_spinner():
+        npeaks = len(next(iter(doc.events_data.values())))
+        npeaks_spinner.value = npeaks
+        # TODO: check cell parameter for consistency?
+        if npeaks:
+            lattice_const_textinput.value = ",".join(map(str, doc.events_data["cell"][0]))
+
+    doc.add_periodic_callback(update_npeaks_spinner, 1000)
+
     return Panel(child=tab_layout, title="spind")
-
-
-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])
-                diff_vector = diff_vector.flatten() * 1e10
-                dv1, dv2, dv3 = diff_vector
-
-                diff_vec.append(diff_vector)
-
-                f.write(f"{x_pos} {y_pos} {intensity} {snr_cnts} {dv1} {dv2} {dv3} {d_spacing}\n")
-
-    return diff_vec

pyzebra/ccl_io.py

@@ -76,7 +76,7 @@ CCL_SECOND_LINE = (
     ("scan_motor", str),
 )
 
-AREA_METHODS = ("fit_area", "int_area")
+EXPORT_TARGETS = {"fullprof": (".comm", ".incomm"), "jana": (".col", ".incol")}
 
 
 def load_1D(filepath):
@@ -159,6 +159,7 @@ def parse_1D(fileobj, data_type):
 
             # "om" -> "omega"
             s["scan_motor"] = "omega"
+            s["scan_motors"] = ["omega", ]
             # overwrite metadata, because it only refers to the scan center
             half_dist = (s["n_points"] - 1) / 2 * s["angle_step"]
             s["omega"] = np.linspace(s["omega"] - half_dist, s["omega"] + half_dist, s["n_points"])
@@ -167,7 +168,8 @@ def parse_1D(fileobj, data_type):
             counts = []
             while len(counts) < s["n_points"]:
                 counts.extend(map(float, next(fileobj).split()))
-            s["Counts"] = np.array(counts)
+            s["counts"] = np.array(counts)
+            s["counts_err"] = np.sqrt(s["counts"])
 
             if s["h"].is_integer() and s["k"].is_integer() and s["l"].is_integer():
                 s["h"], s["k"], s["l"] = map(int, (s["h"], s["k"], s["l"]))
@@ -182,23 +184,15 @@ def parse_1D(fileobj, data_type):
         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
+        motors = [motor.lower() for motor in match.group(1).split(", ")]
+        steps = [float(step) for step in match.group(2).split()]
 
         match = re.search("(.*) Points, Mode: (.*), Preset (.*)", next(fileobj))
         if match.group(2) != "Monitor":
             raise Exception("Unknown mode in dat file.")
         s["monitor"] = float(match.group(3))
 
-        col_names = next(fileobj).split()
+        col_names = list(map(str.lower, next(fileobj).split()))
 
         for line in fileobj:
             if "END-OF-DATA" in line:
@@ -211,21 +205,33 @@ def parse_1D(fileobj, data_type):
         for name in col_names:
             s[name] = np.array(s[name])
 
+        s["counts_err"] = np.sqrt(s["counts"])
+
+        s["scan_motors"] = []
+        for motor, step in zip(motors, steps):
+            if step == 0:
+                # it's not a scan motor, so keep only the median value
+                s[motor] = np.median(s[motor])
+            else:
+                s["scan_motors"].append(motor)
+
         # "om" -> "omega"
-        if s["scan_motor"] == "om":
-            s["scan_motor"] = "omega"
+        if "om" in s["scan_motors"]:
+            s["scan_motors"][s["scan_motors"].index("om")] = "omega"
             s["omega"] = s["om"]
             del s["om"]
 
         # "tt" -> "temp"
-        elif s["scan_motor"] == "tt":
-            s["scan_motor"] = "temp"
+        if "tt" in s["scan_motors"]:
+            s["scan_motors"][s["scan_motors"].index("tt")] = "temp"
             s["temp"] = s["tt"]
             del s["tt"]
 
         # "mf" stays "mf"
         # "phi" stays "phi"
 
+        s["scan_motor"] = s["scan_motors"][0]
+
         if "h" not in s:
             s["h"] = s["k"] = s["l"] = float("nan")
 
@@ -243,14 +249,19 @@ def parse_1D(fileobj, data_type):
     return scan
 
 
-def export_1D(data, path, area_method=AREA_METHODS[0], lorentz=False, hkl_precision=2):
-    """Exports data in the .comm/.incomm format
+def export_1D(data, path, export_target, hkl_precision=2):
+    """Exports data in the .comm/.incomm format for fullprof or .col/.incol format for jana.
 
-    Scans with integer/real hkl values are saved in .comm/.incomm files correspondingly. If no scans
-    are present for a particular output format, that file won't be created.
+    Scans with integer/real hkl values are saved in .comm/.incomm or .col/.incol files
+    correspondingly. If no scans are present for a particular output format, that file won't be
+    created.
     """
+    if export_target not in EXPORT_TARGETS:
+        raise ValueError(f"Unknown export target: {export_target}.")
+
     zebra_mode = data[0]["zebra_mode"]
-    file_content = {".comm": [], ".incomm": []}
+    exts = EXPORT_TARGETS[export_target]
+    file_content = {ext: [] for ext in exts}
 
     for scan in data:
         if "fit" not in scan:
@@ -265,34 +276,7 @@ def export_1D(data, path, area_method=AREA_METHODS[0], lorentz=False, hkl_precis
         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:
-                if param.stderr is None:
-                    area_n = np.nan
-                    area_s = np.nan
-                else:
-                    area_n = param.value
-                    area_s = param.stderr
-                # TODO: take into account multiple peaks
-                break
-        else:
-            # no peak functions in a fit model
-            area_n = np.nan
-            area_s = np.nan
-
-        # 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_n, area_s = scan["area"]
         area_str = f"{area_n:10.2f}{area_s:10.2f}"
 
         ang_str = ""
@@ -301,12 +285,47 @@ def export_1D(data, path, area_method=AREA_METHODS[0], lorentz=False, hkl_precis
                 angle_center = (np.min(scan[angle]) + np.max(scan[angle])) / 2
             else:
                 angle_center = scan[angle]
+
+            if angle == "twotheta" and export_target == "jana":
+                angle_center /= 2
+
             ang_str = ang_str + f"{angle_center:8g}"
 
-        ref = file_content[".comm"] if hkl_are_integers else file_content[".incomm"]
+        if export_target == "jana":
+            ang_str = ang_str + f"{scan['temp']:8}" + f"{scan['monitor']:8}"
+
+        ref = file_content[exts[0]] if hkl_are_integers else file_content[exts[1]]
         ref.append(idx_str + hkl_str + area_str + ang_str + "\n")
 
     for ext, content in file_content.items():
         if content:
             with open(path + ext, "w") as out_file:
                 out_file.writelines(content)
+
+
+def export_param_study(data, param_data, path):
+    file_content = []
+    for scan, param in zip(data, param_data):
+        if "fit" not in scan:
+            continue
+
+        if not file_content:
+            title_str = f"{'param':12}"
+            for fit_param_name in scan["fit"].params:
+                title_str = title_str + f"{fit_param_name:20}" + f"{'std_' + fit_param_name:20}"
+            title_str = title_str + "file"
+            file_content.append(title_str + "\n")
+
+        param_str = f"{param:<12.2f}"
+
+        fit_str = ""
+        for fit_param in scan["fit"].params.values():
+            fit_str = fit_str + f"{fit_param.value:<20.2f}" + f"{fit_param.stderr:<20.2f}"
+
+        _, fname_str = os.path.split(scan["original_filename"])
+
+        file_content.append(param_str + fit_str + fname_str + "\n")
+
+    if file_content:
+        with open(path, "w") as out_file:
+            out_file.writelines(file_content)

pyzebra/ccl_process.py

@@ -1,8 +1,8 @@
-import itertools
 import os
 
 import numpy as np
 from lmfit.models import GaussianModel, LinearModel, PseudoVoigtModel, VoigtModel
+from scipy.integrate import simpson, trapezoid
 
 from .ccl_io import CCL_ANGLES
 
@@ -22,18 +22,24 @@ MAX_RANGE_GAP = {
     "omega": 0.5,
 }
 
+AREA_METHODS = ("fit_area", "int_area")
+
 
 def normalize_dataset(dataset, monitor=100_000):
     for scan in dataset:
         monitor_ratio = monitor / scan["monitor"]
-        scan["Counts"] *= monitor_ratio
+        scan["counts"] *= monitor_ratio
+        scan["counts_err"] *= 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)
+    merged = np.zeros(len(dataset), dtype=np.bool)
+    for ind_into, scan_into in enumerate(dataset):
+        for ind_from, scan_from in enumerate(dataset[ind_into + 1 :], start=ind_into + 1):
+            if _parameters_match(scan_into, scan_from) and not merged[ind_from]:
+                merge_scans(scan_into, scan_from)
+                merged[ind_from] = True
 
 
 def _parameters_match(scan1, scan2):
@@ -61,30 +67,72 @@ def _parameters_match(scan1, scan2):
     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
+def merge_datasets(dataset_into, dataset_from):
+    merged = np.zeros(len(dataset_from), dtype=np.bool)
+    for scan_into in dataset_into:
+        for ind, scan_from in enumerate(dataset_from):
+            if _parameters_match(scan_into, scan_from) and not merged[ind]:
+                merge_scans(scan_into, scan_from)
+                merged[ind] = True
 
-        dataset1.append(scan_j)
+    for scan_from in dataset_from:
+        dataset_into.append(scan_from)
 
 
-def merge_scans(scan1, scan2):
-    omega = np.concatenate((scan1["omega"], scan2["omega"]))
-    counts = np.concatenate((scan1["Counts"], scan2["Counts"]))
+def merge_scans(scan_into, scan_from):
+    # TODO: does it need to be "scan_motor" instead of omega for a generalized solution?
+    if "init_scan" not in scan_into:
+        scan_into["init_scan"] = scan_into.copy()
 
-    index = np.argsort(omega)
+    if "merged_scans" not in scan_into:
+        scan_into["merged_scans"] = []
 
-    scan1["omega"] = omega[index]
-    scan1["Counts"] = counts[index]
+    if scan_from in scan_into["merged_scans"]:
+        return
 
-    scan2["active"] = False
+    scan_into["merged_scans"].append(scan_from)
 
-    fname1 = os.path.basename(scan1["original_filename"])
-    fname2 = os.path.basename(scan2["original_filename"])
-    print(f'Merging scans: {scan1["idx"]} ({fname1}) <-- {scan2["idx"]} ({fname2})')
+    if (
+        scan_into["omega"].shape == scan_from["omega"].shape
+        and np.max(np.abs(scan_into["omega"] - scan_from["omega"])) < 0.0005
+    ):
+        counts_tmp = 0
+        counts_err_tmp = 0
+
+        for scan in [scan_into["init_scan"], *scan_into["merged_scans"]]:
+            counts_tmp += scan["counts"]
+            counts_err_tmp += scan["counts_err"] ** 2
+
+        scan_into["counts"] = counts_tmp / (1 + len(scan_into["merged_scans"]))
+        scan_into["counts_err"] = np.sqrt(counts_err_tmp)
+
+    else:
+        omega = np.concatenate((scan_into["omega"], scan_from["omega"]))
+        counts = np.concatenate((scan_into["counts"], scan_from["counts"]))
+        counts_err = np.concatenate((scan_into["counts_err"], scan_from["counts_err"]))
+
+        index = np.argsort(omega)
+
+        scan_into["omega"] = omega[index]
+        scan_into["counts"] = counts[index]
+        scan_into["counts_err"] = counts_err[index]
+
+    scan_from["active"] = False
+
+    fname1 = os.path.basename(scan_into["original_filename"])
+    fname2 = os.path.basename(scan_from["original_filename"])
+    print(f'Merging scans: {scan_into["idx"]} ({fname1}) <-- {scan_from["idx"]} ({fname2})')
+
+
+def restore_scan(scan):
+    if "merged_scans" in scan:
+        for merged_scan in scan["merged_scans"]:
+            merged_scan["active"] = True
+
+    if "init_scan" in scan:
+        tmp = scan["init_scan"]
+        scan.clear()
+        scan.update(tmp)
 
 
 def fit_scan(scan, model_dict, fit_from=None, fit_to=None):
@@ -93,12 +141,14 @@ def fit_scan(scan, model_dict, fit_from=None, fit_to=None):
     if fit_to is None:
         fit_to = np.inf
 
-    y_fit = scan["Counts"]
+    y_fit = scan["counts"]
+    y_err = scan["counts_err"]
     x_fit = scan[scan["scan_motor"]]
 
     # apply fitting range
     fit_ind = (fit_from <= x_fit) & (x_fit <= fit_to)
     y_fit = y_fit[fit_ind]
+    y_err = y_err[fit_ind]
     x_fit = x_fit[fit_ind]
 
     model = None
@@ -146,5 +196,44 @@ def fit_scan(scan, model_dict, fit_from=None, fit_to=None):
         else:
             model += _model
 
-    weights = [1 / np.sqrt(val) if val != 0 else 1 for val in y_fit]
+    weights = [1 / y_err if y_err != 0 else 1 for y_err in y_err]
     scan["fit"] = model.fit(y_fit, x=x_fit, weights=weights)
+
+
+def get_area(scan, area_method, lorentz):
+    if area_method not in AREA_METHODS:
+        raise ValueError(f"Unknown area method: {area_method}.")
+
+    if area_method == "fit_area":
+        area_v = 0
+        area_s = 0
+        for name, param in scan["fit"].params.items():
+            if "amplitude" in name:
+                if param.stderr is None:
+                    area_v = np.nan
+                    area_s = np.nan
+                else:
+                    area_v += param.value
+                    area_s += param.stderr
+
+    else:  # area_method == "int_area"
+        y_val = scan["counts"]
+        x_val = scan[scan["scan_motor"]]
+        y_bkg = scan["fit"].eval_components(x=x_val)["f0_"]
+        area_v = simpson(y_val, x=x_val) - trapezoid(y_bkg, x=x_val)
+        area_s = np.sqrt(area_v)
+
+    if lorentz:
+        # lorentz correction to area
+        if scan["zebra_mode"] == "bi":
+            twotheta = np.deg2rad(scan["twotheta"])
+            corr_factor = np.sin(twotheta)
+        else:  # zebra_mode == "nb":
+            gamma = np.deg2rad(scan["gamma"])
+            nu = np.deg2rad(scan["nu"])
+            corr_factor = np.sin(gamma) * np.cos(nu)
+
+        area_v = np.abs(area_v * corr_factor)
+        area_s = np.abs(area_s * corr_factor)
+
+    scan["area"] = (area_v, area_s)

pyzebra/h5.py

@@ -1,6 +1,11 @@
 import h5py
+import numpy as np
 
 
+META_MATRIX = ("UB")
+META_CELL = ("cell")
+META_STR = ("name")
+
 def read_h5meta(filepath):
     """Open and parse content of a h5meta file.
 
@@ -23,18 +28,37 @@ def parse_h5meta(file):
         line = line.strip()
         if line.startswith("#begin "):
             section = line[len("#begin ") :]
-            content[section] = []
+            if section in ("detector parameters", "crystal"):
+                content[section] = {}
+            else:
+                content[section] = []
 
         elif line.startswith("#end"):
             section = None
 
         elif section:
-            content[section].append(line)
+            if section in ("detector parameters", "crystal"):
+                if "=" in line:
+                    variable, value = line.split("=", 1)
+                    variable = variable.strip()
+                    value = value.strip()
+
+                    if variable in META_STR:
+                        pass
+                    elif variable in META_CELL:
+                        value = np.array(value.split(",")[:6], dtype=np.float)
+                    elif variable in META_MATRIX:
+                        value = np.array(value.split(",")[:9], dtype=np.float).reshape(3, 3)
+                    else:  # default is a single float number
+                        value = float(value)
+                    content[section][variable] = value
+            else:
+                content[section].append(line)
 
     return content
 
 
-def read_detector_data(filepath):
+def read_detector_data(filepath, cami_meta=None):
     """Read detector data and angles from an h5 file.
 
     Args:
@@ -51,12 +75,18 @@ def read_detector_data(filepath):
         data = data.reshape(n, rows, cols)
 
         det_data = {"data": data}
+        det_data["original_filename"] = filepath
 
         if "/entry1/zebra_mode" in h5f:
             det_data["zebra_mode"] = h5f["/entry1/zebra_mode"][0].decode()
         else:
             det_data["zebra_mode"] = "nb"
 
+        # overwrite zebra_mode from cami
+        if cami_meta is not None:
+            if "zebra_mode" in cami_meta:
+                det_data["zebra_mode"] = cami_meta["zebra_mode"][0]
+
         # om, sometimes ph
         if det_data["zebra_mode"] == "nb":
             det_data["omega"] = h5f["/entry1/area_detector2/rotation_angle"][:]
@@ -70,6 +100,8 @@ def read_detector_data(filepath):
         det_data["chi"] = h5f["/entry1/sample/chi"][:]  # ch
         det_data["phi"] = h5f["/entry1/sample/phi"][:]  # ph
         det_data["ub"] = h5f["/entry1/sample/UB"][:].reshape(3, 3)
+        det_data["name"] = h5f["/entry1/sample/name"][0].decode()
+        det_data["cell"] = h5f["/entry1/sample/cell"][:]
 
         for var in ("omega", "gamma", "nu", "chi", "phi"):
             if abs(det_data[var][0] - det_data[var][-1]) > 0.1:
@@ -85,4 +117,22 @@ def read_detector_data(filepath):
         if "/entry1/sample/temperature" in h5f:
             det_data["temp"] = h5f["/entry1/sample/temperature"][:]
 
+        # overwrite metadata from .cami
+        if cami_meta is not None:
+            if "crystal" in cami_meta:
+                cami_meta_crystal = cami_meta["crystal"]
+                if "name" in cami_meta_crystal:
+                    det_data["name"] = cami_meta_crystal["name"]
+                if "UB" in cami_meta_crystal:
+                    det_data["ub"] = cami_meta_crystal["UB"]
+                if "cell" in cami_meta_crystal:
+                    det_data["cell"] = cami_meta_crystal["cell"]
+                if "lambda" in cami_meta_crystal:
+                    det_data["wave"] = cami_meta_crystal["lambda"]
+
+            if "detector parameters" in cami_meta:
+                cami_meta_detparam = cami_meta["detector parameters"]
+                if "dist1" in cami_meta_detparam:
+                    det_data["ddist"] = cami_meta_detparam["dist1"]
+
     return det_data

pyzebra/utils.py

@@ -0,0 +1,20 @@
+import os
+
+ZEBRA_PROPOSALS_PATHS = [
+    f"/afs/psi.ch/project/sinqdata/{year}/zebra/" for year in (2016, 2017, 2018, 2020, 2021)
+]
+
+def find_proposal_path(proposal):
+    proposal = proposal.strip()
+    if proposal:
+        for zebra_proposals_path in ZEBRA_PROPOSALS_PATHS:
+            proposal_path = os.path.join(zebra_proposals_path, proposal)
+            if os.path.isdir(proposal_path):
+                # found it
+                break
+        else:
+            raise ValueError(f"Can not find data for proposal '{proposal}'.")
+    else:
+        proposal_path = ""
+
+    return proposal_path

pyzebra/xtal.py

@@ -1,15 +1,5 @@
-import math
-
 import numpy as np
 from numba import njit
-from scipy.optimize import curve_fit
-
-import pyzebra
-
-try:
-    from matplotlib import pyplot as plt
-except ImportError:
-    print("matplotlib is not available")
 
 pi_r = 180 / np.pi
 
@@ -393,84 +383,3 @@ def gauss(x, *p):
     """
     A, mu, sigma = p
     return A * np.exp(-((x - mu) ** 2) / (2.0 * sigma ** 2))
-
-
-def box_int(file, box):
-    """Calculates center of the peak in the NB-geometry angles and Intensity of the peak
-
-    Args:
-        file name, box size [x0:xN, y0:yN, fr0:frN]
-
-    Returns:
-        gamma, omPeak, nu polar angles, Int and data for 3 fit plots
-    """
-
-    dat = pyzebra.read_detector_data(file)
-
-    sttC = dat["gamma"][0]
-    om = dat["omega"]
-    nuC = dat["nu"][0]
-    ddist = dat["ddist"]
-
-    # defining indices
-    x0, xN, y0, yN, fr0, frN = box
-
-    # omega fit
-    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["omega"][math.floor(frC)]
-    omC = dat["omega"][math.ceil(frC)]
-    frStep = frC - math.floor(frC)
-    omStep = omC - omF
-    omP = omF + omStep * frStep
-    Int = coeff[1] * abs(coeff[2] * omStep) * math.sqrt(2) * math.sqrt(np.pi)
-    # omega plot
-    x_fit = np.linspace(0, len(cnts), 100)
-    y_fit = gauss(x_fit, *coeff)
-    plt.figure()
-    plt.subplot(131)
-    plt.plot(range(len(cnts)), cnts)
-    plt.plot(x_fit, y_fit)
-    plt.ylabel("Intensity in the box")
-    plt.xlabel("Frame N of the box")
-    label = "om"
-    # gamma fit
-    sliceXY = dat["data"][fr0:frN, y0:yN, x0:xN]
-    sliceXZ = np.sum(sliceXY, axis=1)
-    sliceYZ = np.sum(sliceXY, axis=2)
-
-    projX = np.sum(sliceXZ, axis=0)
-    p0 = [1.0, 0.0, 1.0]
-    coeff, var_matrix = curve_fit(gauss, range(len(projX)), projX, p0=p0)
-    x = x0 + coeff[1]
-    # gamma plot
-    x_fit = np.linspace(0, len(projX), 100)
-    y_fit = gauss(x_fit, *coeff)
-    plt.subplot(132)
-    plt.plot(range(len(projX)), projX)
-    plt.plot(x_fit, y_fit)
-    plt.ylabel("Intensity in the box")
-    plt.xlabel("X-pixel of the box")
-
-    # nu fit
-    projY = np.sum(sliceYZ, axis=0)
-    p0 = [1.0, 0.0, 1.0]
-    coeff, var_matrix = curve_fit(gauss, range(len(projY)), projY, p0=p0)
-    y = y0 + coeff[1]
-    # nu plot
-    x_fit = np.linspace(0, len(projY), 100)
-    y_fit = gauss(x_fit, *coeff)
-    plt.subplot(133)
-    plt.plot(range(len(projY)), projY)
-    plt.plot(x_fit, y_fit)
-    plt.ylabel("Intensity in the box")
-    plt.xlabel("Y-pixel of the box")
-
-    ga, nu = pyzebra.det2pol(ddist, sttC, nuC, x, y)
-
-    return ga[0], omP, nu[0], Int

scripts/pyzebra-start.sh

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

scripts/pyzebra-test-start.sh

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