Updating for version 0.3.0

fit_columns was deprecated in bokeh/2.2

conda-recipe/meta.yaml

@@ -15,15 +15,14 @@ build:
 
 requirements:
   build:
-    - python >=3.6
+    - python >=3.7
     - setuptools
   run:
-    - python >=3.6
+    - python >=3.7
     - numpy
     - scipy
-    - pandas
     - h5py
-    - bokeh =2.2
+    - bokeh =2.3
     - matplotlib
     - numba
     - lmfit

pyzebra/__init__.py

@@ -1,9 +1,7 @@
 from pyzebra.anatric import *
-from pyzebra.ccl_findpeaks import ccl_findpeaks
-from pyzebra.ccl_io import export_comm, load_1D, parse_1D
-from pyzebra.fit2 import fitccl
+from pyzebra.ccl_io import *
 from pyzebra.h5 import *
-from pyzebra.merge_function import add_dict, unified_merge
 from pyzebra.xtal import *
+from pyzebra.ccl_process import *
 
-__version__ = "0.2.2"
+__version__ = "0.3.0"

pyzebra/app/app.py

@@ -10,6 +10,7 @@ import panel_ccl_integrate
 import panel_hdf_anatric
 import panel_hdf_viewer
 import panel_param_study
+import panel_spind
 
 
 doc = curdoc()
@@ -29,10 +30,11 @@ tab_hdf_viewer = panel_hdf_viewer.create()
 tab_hdf_anatric = panel_hdf_anatric.create()
 tab_ccl_integrate = panel_ccl_integrate.create()
 tab_param_study = panel_param_study.create()
+tab_spind = panel_spind.create()
 
 doc.add_root(
     column(
-        Tabs(tabs=[tab_hdf_viewer, tab_hdf_anatric, tab_ccl_integrate, tab_param_study]),
+        Tabs(tabs=[tab_hdf_viewer, tab_hdf_anatric, tab_ccl_integrate, tab_param_study, tab_spind]),
         row(stdout_textareainput, bokeh_log_textareainput, sizing_mode="scale_both"),
     )
 )

pyzebra/app/cli.py

@@ -40,7 +40,7 @@ def main():
     parser.add_argument(
         "--anatric-path",
         type=str,
-        default="/afs/psi.ch/project/sinq/rhel7/bin/anatric",
+        default=None,
         help="path to anatric executable",
     )
 

pyzebra/app/panel_ccl_integrate.py

@@ -3,12 +3,10 @@ import io
 import os
 import tempfile
 import types
-from copy import deepcopy
 
 import numpy as np
 from bokeh.layouts import column, row
 from bokeh.models import (
-    Asterisk,
     BasicTicker,
     Button,
     CheckboxEditor,
@@ -20,8 +18,10 @@ from bokeh.models import (
     Dropdown,
     FileInput,
     Grid,
+    Legend,
     Line,
     LinearAxis,
+    MultiLine,
     MultiSelect,
     NumberEditor,
     Panel,
@@ -47,18 +47,19 @@ from pyzebra.ccl_io import AREA_METHODS
 
 
 javaScript = """
-setTimeout(function() {
-    const filename = 'output' + js_data.data['ext']
-    const blob = new Blob([js_data.data['cont']], {type: 'text/plain'})
+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 = filename;
+    link.download = js_data.data['fname'][i];
     link.click();
     window.URL.revokeObjectURL(url);
     document.body.removeChild(link);
-}, 500);
+}
 """
 
 PROPOSAL_PATH = "/afs/psi.ch/project/sinqdata/2020/zebra/"
@@ -67,180 +68,215 @@ PROPOSAL_PATH = "/afs/psi.ch/project/sinqdata/2020/zebra/"
 def create():
     det_data = {}
     fit_params = {}
-    peak_pos_textinput_lock = False
-    js_data = ColumnDataSource(data=dict(cont=[], ext=[]))
+    js_data = ColumnDataSource(data=dict(content=["", ""], fname=["", ""]))
 
     def proposal_textinput_callback(_attr, _old, new):
         ccl_path = os.path.join(PROPOSAL_PATH, new.strip())
         ccl_file_list = []
         for file in os.listdir(ccl_path):
-            if file.endswith(".ccl"):
+            if file.endswith((".ccl", ".dat")):
                 ccl_file_list.append((os.path.join(ccl_path, file), file))
-        ccl_file_select.options = ccl_file_list
-        ccl_file_select.value = ccl_file_list[0][0]
+        file_select.options = ccl_file_list
 
-    proposal_textinput = TextInput(title="Enter proposal number:", default_size=145)
+    proposal_textinput = TextInput(title="Proposal number:", default_size=145)
     proposal_textinput.on_change("value", proposal_textinput_callback)
 
     def _init_datatable():
-        scan_list = list(det_data["scan"].keys())
-        hkl = [
-            f'{int(m["h_index"])} {int(m["k_index"])} {int(m["l_index"])}'
-            if det_data["meta"]["indices"] == "hkl"
-            else f'{m["h_index"]} {m["k_index"]} {m["l_index"]}'
-            for m in det_data["scan"].values()
-        ]
+        scan_list = [s["idx"] for s in det_data]
+        hkl = [f'{s["h"]} {s["k"]} {s["l"]}' for s in det_data]
+        export = [s.get("active", True) for s in det_data]
         scan_table_source.data.update(
-            scan=scan_list,
-            hkl=hkl,
-            peaks=[0] * len(scan_list),
-            fit=[0] * len(scan_list),
-            export=[True] * len(scan_list),
+            scan=scan_list, hkl=hkl, fit=[0] * len(scan_list), export=export,
         )
         scan_table_source.selected.indices = []
         scan_table_source.selected.indices = [0]
 
-    def ccl_file_select_callback(_attr, _old, new):
+        merge_options = [(str(i), f"{i} ({idx})") for i, idx in enumerate(scan_list)]
+        merge_source_select.options = merge_options
+        merge_source_select.value = merge_options[0][0]
+        merge_dest_select.options = merge_options
+        merge_dest_select.value = merge_options[0][0]
+
+    def ccl_file_select_callback(_attr, _old, _new):
+        pass
+
+    file_select = MultiSelect(title="Available .ccl/.dat files:", default_size=200, height=250)
+    file_select.on_change("value", ccl_file_select_callback)
+
+    def file_open_button_callback():
         nonlocal det_data
-        with open(new) as file:
-            _, ext = os.path.splitext(new)
-            det_data = pyzebra.parse_1D(file, ext)
+        det_data = []
+        for f_name in file_select.value:
+            with open(f_name) as file:
+                base, ext = os.path.splitext(f_name)
+                if det_data:
+                    append_data = pyzebra.parse_1D(file, ext)
+                    pyzebra.normalize_dataset(append_data, monitor_spinner.value)
+                    pyzebra.merge_datasets(det_data, append_data)
+                else:
+                    det_data = pyzebra.parse_1D(file, ext)
+                    pyzebra.normalize_dataset(det_data, monitor_spinner.value)
+                    pyzebra.merge_duplicates(det_data)
+                    js_data.data.update(fname=[base + ".comm", base + ".incomm"])
 
         _init_datatable()
 
-    ccl_file_select = Select(title="Available .ccl files")
-    ccl_file_select.on_change("value", ccl_file_select_callback)
+    file_open_button = Button(label="Open New", default_size=100)
+    file_open_button.on_click(file_open_button_callback)
+
+    def file_append_button_callback():
+        for f_name in file_select.value:
+            with open(f_name) as file:
+                _, ext = os.path.splitext(f_name)
+                append_data = pyzebra.parse_1D(file, ext)
+
+            pyzebra.normalize_dataset(append_data, monitor_spinner.value)
+            pyzebra.merge_datasets(det_data, append_data)
+
+        _init_datatable()
+
+    file_append_button = Button(label="Append", default_size=100)
+    file_append_button.on_click(file_append_button_callback)
 
     def upload_button_callback(_attr, _old, new):
         nonlocal det_data
-        with io.StringIO(base64.b64decode(new).decode()) as file:
-            _, ext = os.path.splitext(upload_button.filename)
-            det_data = pyzebra.parse_1D(file, ext)
+        det_data = []
+        for f_str, f_name in zip(new, upload_button.filename):
+            with io.StringIO(base64.b64decode(f_str).decode()) as file:
+                base, ext = os.path.splitext(f_name)
+                if det_data:
+                    append_data = pyzebra.parse_1D(file, ext)
+                    pyzebra.normalize_dataset(append_data, monitor_spinner.value)
+                    pyzebra.merge_datasets(det_data, append_data)
+                else:
+                    det_data = pyzebra.parse_1D(file, ext)
+                    pyzebra.normalize_dataset(det_data, monitor_spinner.value)
+                    pyzebra.merge_duplicates(det_data)
+                    js_data.data.update(fname=[base + ".comm", base + ".incomm"])
 
         _init_datatable()
 
-    upload_button = FileInput(accept=".ccl")
+    upload_div = Div(text="or upload new .ccl/.dat files:", margin=(5, 5, 0, 5))
+    upload_button = FileInput(accept=".ccl,.dat", multiple=True, default_size=200)
     upload_button.on_change("value", upload_button_callback)
 
     def append_upload_button_callback(_attr, _old, new):
-        nonlocal det_data
-        with io.StringIO(base64.b64decode(new).decode()) as file:
-            _, ext = os.path.splitext(append_upload_button.filename)
-            append_data = pyzebra.parse_1D(file, ext)
+        for f_str, f_name in zip(new, append_upload_button.filename):
+            with io.StringIO(base64.b64decode(f_str).decode()) as file:
+                _, ext = os.path.splitext(f_name)
+                append_data = pyzebra.parse_1D(file, ext)
 
-        pyzebra.unified_merge(det_data, append_data)
+            pyzebra.normalize_dataset(append_data, monitor_spinner.value)
+            pyzebra.merge_datasets(det_data, append_data)
 
         _init_datatable()
 
-    append_upload_button = FileInput(accept=".ccl,.dat")
+    append_upload_div = Div(text="append extra files:", margin=(5, 5, 0, 5))
+    append_upload_button = FileInput(accept=".ccl,.dat", multiple=True, default_size=200)
     append_upload_button.on_change("value", append_upload_button_callback)
 
+    def monitor_spinner_callback(_attr, old, new):
+        if det_data:
+            pyzebra.normalize_dataset(det_data, new)
+            _update_plot(_get_selected_scan())
+
+    monitor_spinner = Spinner(title="Monitor:", mode="int", value=100_000, low=1, width=145)
+    monitor_spinner.on_change("value", monitor_spinner_callback)
+
     def _update_table():
-        num_of_peaks = [len(scan.get("peak_indexes", [])) for scan in det_data["scan"].values()]
-        fit_ok = [(1 if "fit" in scan else 0) for scan in det_data["scan"].values()]
-        scan_table_source.data.update(peaks=num_of_peaks, fit=fit_ok)
+        fit_ok = [(1 if "fit" in scan else 0) for scan in det_data]
+        scan_table_source.data.update(fit=fit_ok)
 
     def _update_plot(scan):
-        nonlocal peak_pos_textinput_lock
-        peak_pos_textinput_lock = True
+        scan_motor = scan["scan_motor"]
 
         y = scan["Counts"]
-        x = scan["om"]
+        x = scan[scan_motor]
 
+        plot.axis[0].axis_label = scan_motor
         plot_scatter_source.data.update(x=x, y=y, y_upper=y + np.sqrt(y), y_lower=y - np.sqrt(y))
 
-        num_of_peaks = len(scan.get("peak_indexes", []))
-        if num_of_peaks is not None and num_of_peaks > 0:
-            peak_indexes = scan["peak_indexes"]
-            if len(peak_indexes) == 1:
-                peak_pos_textinput.value = str(scan["om"][peak_indexes[0]])
-            else:
-                peak_pos_textinput.value = str([scan["om"][ind] for ind in peak_indexes])
-
-            plot_peak_source.data.update(x=scan["om"][peak_indexes], y=scan["peak_heights"])
-            plot_line_smooth_source.data.update(x=x, y=scan["smooth_peaks"])
-        else:
-            peak_pos_textinput.value = None
-            plot_peak_source.data.update(x=[], y=[])
-            plot_line_smooth_source.data.update(x=[], y=[])
-
-        peak_pos_textinput_lock = False
-
         fit = scan.get("fit")
         if fit is not None:
-            x = scan["fit"]["x_fit"]
-            plot_gauss_source.data.update(x=x, y=scan["fit"]["comps"]["gaussian"])
-            plot_bkg_source.data.update(x=x, y=scan["fit"]["comps"]["background"])
-            params = fit["result"].params
-            fit_output_textinput.value = (
-                "Gaussian: centre = %9.4f, sigma = %9.4f, area = %9.4f \n"
-                "background: slope = %9.4f, intercept = %9.4f \n"
-                "Int. area = %9.4f +/- %9.4f \n"
-                "fit area = %9.4f +/- %9.4f \n"
-                "ratio((fit-int)/fit) = %9.4f"
-                % (
-                    params["g_cen"].value,
-                    params["g_width"].value,
-                    params["g_amp"].value,
-                    params["slope"].value,
-                    params["intercept"].value,
-                    fit["int_area"].n,
-                    fit["int_area"].s,
-                    params["g_amp"].value,
-                    params["g_amp"].stderr,
-                    (params["g_amp"].value - fit["int_area"].n) / params["g_amp"].value,
-                )
-            )
-            numfit_min, numfit_max = fit["numfit"]
-            if numfit_min is None:
-                numfit_min_span.location = None
-            else:
-                numfit_min_span.location = x[numfit_min]
+            x_fit = np.linspace(x[0], x[-1], 100)
+            plot_fit_source.data.update(x=x_fit, y=fit.eval(x=x_fit))
 
-            if numfit_max is None:
-                numfit_max_span.location = None
-            else:
-                numfit_max_span.location = x[numfit_max]
+            x_bkg = []
+            y_bkg = []
+            xs_peak = []
+            ys_peak = []
+            comps = fit.eval_components(x=x_fit)
+            for i, model in enumerate(fit_params):
+                if "linear" in model:
+                    x_bkg = x_fit
+                    y_bkg = comps[f"f{i}_"]
+
+                elif any(val in model for val in ("gaussian", "voigt", "pvoigt")):
+                    xs_peak.append(x_fit)
+                    ys_peak.append(comps[f"f{i}_"])
+
+            plot_bkg_source.data.update(x=x_bkg, y=y_bkg)
+            plot_peak_source.data.update(xs=xs_peak, ys=ys_peak)
+
+            fit_output_textinput.value = fit.fit_report()
 
         else:
-            plot_gauss_source.data.update(x=[], y=[])
+            plot_fit_source.data.update(x=[], y=[])
             plot_bkg_source.data.update(x=[], y=[])
+            plot_peak_source.data.update(xs=[], ys=[])
             fit_output_textinput.value = ""
-            numfit_min_span.location = None
-            numfit_max_span.location = None
 
     # Main plot
-    plot = Plot(x_range=DataRange1d(), y_range=DataRange1d(), plot_height=400, plot_width=700)
+    plot = Plot(
+        x_range=DataRange1d(),
+        y_range=DataRange1d(only_visible=True),
+        plot_height=470,
+        plot_width=700,
+    )
 
     plot.add_layout(LinearAxis(axis_label="Counts"), place="left")
-    plot.add_layout(LinearAxis(axis_label="Omega"), place="below")
+    plot.add_layout(LinearAxis(axis_label="Scan motor"), place="below")
 
     plot.add_layout(Grid(dimension=0, ticker=BasicTicker()))
     plot.add_layout(Grid(dimension=1, ticker=BasicTicker()))
 
     plot_scatter_source = ColumnDataSource(dict(x=[0], y=[0], y_upper=[0], y_lower=[0]))
-    plot.add_glyph(plot_scatter_source, Scatter(x="x", y="y", line_color="steelblue"))
+    plot_scatter = plot.add_glyph(
+        plot_scatter_source, Scatter(x="x", y="y", line_color="steelblue")
+    )
     plot.add_layout(Whisker(source=plot_scatter_source, base="x", upper="y_upper", lower="y_lower"))
 
-    plot_line_smooth_source = ColumnDataSource(dict(x=[0], y=[0]))
-    plot.add_glyph(
-        plot_line_smooth_source, Line(x="x", y="y", line_color="steelblue", line_dash="dashed")
-    )
-
-    plot_gauss_source = ColumnDataSource(dict(x=[0], y=[0]))
-    plot.add_glyph(plot_gauss_source, Line(x="x", y="y", line_color="red", line_dash="dashed"))
+    plot_fit_source = ColumnDataSource(dict(x=[0], y=[0]))
+    plot_fit = plot.add_glyph(plot_fit_source, Line(x="x", y="y"))
 
     plot_bkg_source = ColumnDataSource(dict(x=[0], y=[0]))
-    plot.add_glyph(plot_bkg_source, Line(x="x", y="y", line_color="green", line_dash="dashed"))
+    plot_bkg = plot.add_glyph(
+        plot_bkg_source, Line(x="x", y="y", line_color="green", line_dash="dashed")
+    )
 
-    plot_peak_source = ColumnDataSource(dict(x=[], y=[]))
-    plot.add_glyph(plot_peak_source, Asterisk(x="x", y="y", size=10, line_color="red"))
+    plot_peak_source = ColumnDataSource(dict(xs=[0], ys=[0]))
+    plot_peak = plot.add_glyph(
+        plot_peak_source, MultiLine(xs="xs", ys="ys", line_color="red", line_dash="dashed")
+    )
 
-    numfit_min_span = Span(location=None, dimension="height", line_dash="dashed")
-    plot.add_layout(numfit_min_span)
+    fit_from_span = Span(location=None, dimension="height", line_dash="dashed")
+    plot.add_layout(fit_from_span)
 
-    numfit_max_span = Span(location=None, dimension="height", line_dash="dashed")
-    plot.add_layout(numfit_max_span)
+    fit_to_span = Span(location=None, dimension="height", line_dash="dashed")
+    plot.add_layout(fit_to_span)
+
+    plot.add_layout(
+        Legend(
+            items=[
+                ("data", [plot_scatter]),
+                ("best fit", [plot_fit]),
+                ("peak", [plot_peak]),
+                ("linear", [plot_bkg]),
+            ],
+            location="top_left",
+            click_policy="hide",
+        )
+    )
 
     plot.add_tools(PanTool(), WheelZoomTool(), ResetTool())
     plot.toolbar.logo = None
@@ -261,62 +297,60 @@ def create():
             # skip unnecessary update caused by selection drop
             return
 
-        _update_plot(det_data["scan"][scan_table_source.data["scan"][new[0]]])
+        _update_plot(det_data[new[0]])
 
-    scan_table_source = ColumnDataSource(dict(scan=[], hkl=[], peaks=[], fit=[], export=[]))
+    scan_table_source = ColumnDataSource(dict(scan=[], hkl=[], fit=[], export=[]))
     scan_table = DataTable(
         source=scan_table_source,
         columns=[
-            TableColumn(field="scan", title="scan"),
-            TableColumn(field="hkl", title="hkl"),
-            TableColumn(field="peaks", title="Peaks"),
-            TableColumn(field="fit", title="Fit"),
-            TableColumn(field="export", title="Export", editor=CheckboxEditor()),
+            TableColumn(field="scan", title="Scan", width=50),
+            TableColumn(field="hkl", title="hkl", width=100),
+            TableColumn(field="fit", title="Fit", width=50),
+            TableColumn(field="export", title="Export", editor=CheckboxEditor(), width=50),
         ],
-        width=250,
-        index_position=None,
+        width=310,  # +60 because of the index column
+        height=350,
+        autosize_mode="none",
         editable=True,
     )
 
     scan_table_source.selected.on_change("indices", scan_table_select_callback)
 
     def _get_selected_scan():
-        selected_index = scan_table_source.selected.indices[0]
-        selected_scan_id = scan_table_source.data["scan"][selected_index]
-        return det_data["scan"][selected_scan_id]
+        return det_data[scan_table_source.selected.indices[0]]
 
-    def peak_pos_textinput_callback(_attr, _old, new):
-        if new is not None and not peak_pos_textinput_lock:
-            scan = _get_selected_scan()
+    merge_dest_select = Select(title="destination:", width=100)
+    merge_source_select = Select(title="source:", width=100)
 
-            peak_ind = (np.abs(scan["om"] - float(new))).argmin()
-            scan["peak_indexes"] = np.array([peak_ind], dtype=np.int64)
-            scan["peak_heights"] = np.array([scan["smooth_peaks"][peak_ind]])
-            _update_table()
-            _update_plot(scan)
+    def merge_button_callback():
+        scan_dest_ind = int(merge_dest_select.value)
+        scan_source_ind = int(merge_source_select.value)
 
-    peak_pos_textinput = TextInput(title="Peak position:", default_size=145)
-    peak_pos_textinput.on_change("value", peak_pos_textinput_callback)
+        if scan_dest_ind == scan_source_ind:
+            print("WARNING: Selected scans for merging are identical")
+            return
 
-    peak_int_ratio_spinner = Spinner(
-        title="Peak intensity ratio:", value=0.8, step=0.01, low=0, high=1, default_size=145
-    )
-    peak_prominence_spinner = Spinner(title="Peak prominence:", value=50, low=0, default_size=145)
-    smooth_toggle = Toggle(label="Smooth curve", default_size=145)
-    window_size_spinner = Spinner(title="Window size:", value=7, step=2, low=1, default_size=145)
-    poly_order_spinner = Spinner(title="Poly order:", value=3, low=0, default_size=145)
+        pyzebra.merge_scans(det_data[scan_dest_ind], det_data[scan_source_ind])
+        _update_plot(_get_selected_scan())
 
-    integ_from = Spinner(title="Integrate from:", default_size=145)
-    integ_to = Spinner(title="to:", default_size=145)
+    merge_button = Button(label="Merge scans", width=145)
+    merge_button.on_click(merge_button_callback)
 
-    def fitparam_reset_button_callback():
-        ...
+    def fit_from_spinner_callback(_attr, _old, new):
+        fit_from_span.location = new
 
-    fitparam_reset_button = Button(label="Reset to defaults", default_size=145, disabled=True)
-    fitparam_reset_button.on_click(fitparam_reset_button_callback)
+    fit_from_spinner = Spinner(title="Fit from:", default_size=145)
+    fit_from_spinner.on_change("value", fit_from_spinner_callback)
+
+    def fit_to_spinner_callback(_attr, _old, new):
+        fit_to_span.location = new
+
+    fit_to_spinner = Spinner(title="to:", default_size=145)
+    fit_to_spinner.on_change("value", fit_to_spinner_callback)
 
     def fitparams_add_dropdown_callback(click):
-        new_tag = str(fitparams_select.tags[0])  # bokeh requires (str, str) for MultiSelect options
+        # bokeh requires (str, str) for MultiSelect options
+        new_tag = f"{click.item}-{fitparams_select.tags[0]}"
         fitparams_select.options.append((new_tag, click.item))
         fit_params[new_tag] = fitparams_factory(click.item)
         fitparams_select.tags[0] += 1
@@ -324,11 +358,11 @@ def create():
     fitparams_add_dropdown = Dropdown(
         label="Add fit function",
         menu=[
-            ("Background", "background"),
-            ("Gauss", "gauss"),
+            ("Linear", "linear"),
+            ("Gaussian", "gaussian"),
             ("Voigt", "voigt"),
-            ("Pseudo Voigt", "pseudovoigt"),
-            ("Pseudo Voigt1", "pseudovoigt1"),
+            ("Pseudo Voigt", "pvoigt"),
+            # ("Pseudo Voigt1", "pseudovoigt1"),
         ],
         default_size=145,
         disabled=True,
@@ -349,7 +383,7 @@ def create():
         if new:
             fitparams_table_source.data.update(fit_params[new[0]])
         else:
-            fitparams_table_source.data.update(dict(param=[], guess=[], vary=[], min=[], max=[]))
+            fitparams_table_source.data.update(dict(param=[], value=[], vary=[], min=[], max=[]))
 
     fitparams_select = MultiSelect(options=[], height=120, default_size=145)
     fitparams_select.tags = [0]
@@ -370,32 +404,32 @@ def create():
     fitparams_remove_button.on_click(fitparams_remove_button_callback)
 
     def fitparams_factory(function):
-        if function == "background":
-            params = ["slope", "offset"]
-        elif function == "gauss":
-            params = ["center", "sigma", "amplitude"]
+        if function == "linear":
+            params = ["slope", "intercept"]
+        elif function == "gaussian":
+            params = ["amplitude", "center", "sigma"]
         elif function == "voigt":
-            params = ["center", "sigma", "amplitude", "gamma"]
-        elif function == "pseudovoigt":
-            params = ["center", "sigma", "amplitude", "fraction"]
+            params = ["amplitude", "center", "sigma", "gamma"]
+        elif function == "pvoigt":
+            params = ["amplitude", "center", "sigma", "fraction"]
         elif function == "pseudovoigt1":
-            params = ["center", "g_sigma", "l_sigma", "amplitude", "fraction"]
+            params = ["amplitude", "center", "g_sigma", "l_sigma", "fraction"]
         else:
             raise ValueError("Unknown fit function")
 
         n = len(params)
         fitparams = dict(
-            param=params, guess=[None] * n, vary=[True] * n, min=[None] * n, max=[None] * n,
+            param=params, value=[None] * n, vary=[True] * n, min=[None] * n, max=[None] * n,
         )
 
         return fitparams
 
-    fitparams_table_source = ColumnDataSource(dict(param=[], guess=[], vary=[], min=[], max=[]))
+    fitparams_table_source = ColumnDataSource(dict(param=[], value=[], vary=[], min=[], max=[]))
     fitparams_table = DataTable(
         source=fitparams_table_source,
         columns=[
             TableColumn(field="param", title="Parameter"),
-            TableColumn(field="guess", title="Guess", editor=NumberEditor()),
+            TableColumn(field="value", title="Value", editor=NumberEditor()),
             TableColumn(field="vary", title="Vary", editor=CheckboxEditor()),
             TableColumn(field="min", title="Min", editor=NumberEditor()),
             TableColumn(field="max", title="Max", editor=NumberEditor()),
@@ -408,57 +442,18 @@ def create():
     )
 
     # start with `background` and `gauss` fit functions added
-    fitparams_add_dropdown_callback(types.SimpleNamespace(item="background"))
-    fitparams_add_dropdown_callback(types.SimpleNamespace(item="gauss"))
+    fitparams_add_dropdown_callback(types.SimpleNamespace(item="linear"))
+    fitparams_add_dropdown_callback(types.SimpleNamespace(item="gaussian"))
+    fitparams_select.value = ["gaussian-1"]  # add selection to gauss
 
-    fit_output_textinput = TextAreaInput(title="Fit results:", width=450, height=400)
-
-    def _get_peakfind_params():
-        return dict(
-            int_threshold=peak_int_ratio_spinner.value,
-            prominence=peak_prominence_spinner.value,
-            smooth=smooth_toggle.active,
-            window_size=window_size_spinner.value,
-            poly_order=poly_order_spinner.value,
-        )
-
-    def peakfind_all_button_callback():
-        peakfind_params = _get_peakfind_params()
-        for scan in det_data["scan"].values():
-            pyzebra.ccl_findpeaks(scan, **peakfind_params)
-
-        _update_table()
-        _update_plot(_get_selected_scan())
-
-    peakfind_all_button = Button(label="Peak Find All", button_type="primary", default_size=145)
-    peakfind_all_button.on_click(peakfind_all_button_callback)
-
-    def peakfind_button_callback():
-        scan = _get_selected_scan()
-        pyzebra.ccl_findpeaks(scan, **_get_peakfind_params())
-
-        _update_table()
-        _update_plot(scan)
-
-    peakfind_button = Button(label="Peak Find Current", default_size=145)
-    peakfind_button.on_click(peakfind_button_callback)
-
-    def _get_fit_params():
-        return dict(
-            guess=fit_params["1"]["guess"] + fit_params["0"]["guess"],
-            vary=fit_params["1"]["vary"] + fit_params["0"]["vary"],
-            constraints_min=fit_params["1"]["min"] + fit_params["0"]["min"],
-            constraints_max=fit_params["1"]["max"] + fit_params["0"]["max"],
-            numfit_min=integ_from.value,
-            numfit_max=integ_to.value,
-            binning=bin_size_spinner.value,
-        )
+    fit_output_textinput = TextAreaInput(title="Fit results:", width=750, height=200)
 
     def fit_all_button_callback():
-        fit_params = _get_fit_params()
-        for scan in det_data["scan"].values():
-            # fit_params are updated inplace within `fitccl`
-            pyzebra.fitccl(scan, **deepcopy(fit_params))
+        for scan, export in zip(det_data, scan_table_source.data["export"]):
+            if export:
+                pyzebra.fit_scan(
+                    scan, fit_params, fit_from=fit_from_spinner.value, fit_to=fit_to_spinner.value
+                )
 
         _update_plot(_get_selected_scan())
         _update_table()
@@ -468,7 +463,9 @@ def create():
 
     def fit_button_callback():
         scan = _get_selected_scan()
-        pyzebra.fitccl(scan, **_get_fit_params())
+        pyzebra.fit_scan(
+            scan, fit_params, fit_from=fit_from_spinner.value, fit_to=fit_to_spinner.value
+        )
 
         _update_plot(scan)
         _update_table()
@@ -476,115 +473,95 @@ def create():
     fit_button = Button(label="Fit Current", default_size=145)
     fit_button.on_click(fit_button_callback)
 
-    def area_method_radiobutton_callback(_attr, _old, new):
-        det_data["meta"]["area_method"] = AREA_METHODS[new]
-
     area_method_radiobutton = RadioButtonGroup(
-        labels=["Fit area", "Int area"], active=0, default_size=145
+        labels=["Fit area", "Int area"], active=0, default_size=145, disabled=True
     )
-    area_method_radiobutton.on_change("active", area_method_radiobutton_callback)
 
-    bin_size_spinner = Spinner(title="Bin size:", value=1, low=1, step=1, default_size=145)
+    bin_size_spinner = Spinner(
+        title="Bin size:", value=1, low=1, step=1, default_size=145, disabled=True
+    )
 
     lorentz_toggle = Toggle(label="Lorentz Correction", default_size=145)
 
-    preview_output_textinput = TextAreaInput(title="Export file preview:", width=450, height=400)
-
-    def preview_output_button_callback():
-        if det_data["meta"]["indices"] == "hkl":
-            ext = ".comm"
-        elif det_data["meta"]["indices"] == "real":
-            ext = ".incomm"
+    export_preview_textinput = TextAreaInput(title="Export preview:", width=500, height=400)
 
+    def preview_button_callback():
         with tempfile.TemporaryDirectory() as temp_dir:
             temp_file = temp_dir + "/temp"
-            export_data = deepcopy(det_data)
-            for s, export in zip(scan_table_source.data["scan"], scan_table_source.data["export"]):
-                if not export:
-                    del export_data["scan"][s]
-            pyzebra.export_comm(
+            export_data = []
+            for s, export in zip(det_data, scan_table_source.data["export"]):
+                if export:
+                    export_data.append(s)
+
+            pyzebra.export_1D(
                 export_data,
                 temp_file,
+                area_method=AREA_METHODS[int(area_method_radiobutton.active)],
                 lorentz=lorentz_toggle.active,
                 hkl_precision=int(hkl_precision_select.value),
             )
 
-            with open(f"{temp_file}{ext}") as f:
-                preview_output_textinput.value = f.read()
+            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)
 
-    preview_output_button = Button(label="Preview file", default_size=220)
-    preview_output_button.on_click(preview_output_button_callback)
+            js_data.data.update(content=file_content)
+            export_preview_textinput.value = exported_content
 
-    hkl_precision_select = Select(options=["2", "3", "4"], value="2", default_size=220)
+    preview_button = Button(label="Preview", default_size=200)
+    preview_button.on_click(preview_button_callback)
 
-    def export_results(det_data):
-        if det_data["meta"]["indices"] == "hkl":
-            ext = ".comm"
-        elif det_data["meta"]["indices"] == "real":
-            ext = ".incomm"
-
-        with tempfile.TemporaryDirectory() as temp_dir:
-            temp_file = temp_dir + "/temp"
-            export_data = deepcopy(det_data)
-            for s, export in zip(scan_table_source.data["scan"], scan_table_source.data["export"]):
-                if not export:
-                    del export_data["scan"][s]
-            pyzebra.export_comm(
-                export_data,
-                temp_file,
-                lorentz=lorentz_toggle.active,
-                hkl_precision=int(hkl_precision_select.value),
-            )
-
-            with open(f"{temp_file}{ext}") as f:
-                output_content = f.read()
-
-        return output_content, ext
-
-    def save_button_callback():
-        cont, ext = export_results(det_data)
-        js_data.data.update(cont=[cont], ext=[ext])
-
-    save_button = Button(label="Download file", button_type="success", default_size=220)
-    save_button.on_click(save_button_callback)
-    save_button.js_on_click(CustomJS(args={"js_data": js_data}, code=javaScript))
-
-    findpeak_controls = column(
-        row(peak_pos_textinput, column(Spacer(height=19), smooth_toggle)),
-        row(peak_int_ratio_spinner, peak_prominence_spinner),
-        row(window_size_spinner, poly_order_spinner),
-        row(peakfind_button, peakfind_all_button),
+    hkl_precision_select = Select(
+        title="hkl precision:", options=["2", "3", "4"], value="2", default_size=80
     )
 
+    save_button = Button(label="Download preview", button_type="success", default_size=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(integ_from, integ_to),
+            row(fit_from_spinner, fit_to_spinner),
             row(bin_size_spinner, column(Spacer(height=19), lorentz_toggle)),
-            row(fitparam_reset_button, area_method_radiobutton),
+            row(area_method_radiobutton),
             row(fit_button, fit_all_button),
         ),
     )
 
-    export_layout = column(
-        preview_output_textinput,
-        row(column(preview_output_button, hkl_precision_select), save_button),
+    scan_layout = column(
+        scan_table,
+        monitor_spinner,
+        row(column(Spacer(height=19), merge_button), merge_dest_select, merge_source_select),
+    )
+
+    import_layout = column(
+        proposal_textinput,
+        file_select,
+        row(file_open_button, file_append_button),
+        upload_div,
+        upload_button,
+        append_upload_div,
+        append_upload_button,
+    )
+
+    export_layout = column(
+        export_preview_textinput,
+        row(hkl_precision_select, column(Spacer(height=19), row(preview_button, save_button))),
     )
 
-    upload_div = Div(text="Or upload .ccl file:")
-    append_upload_div = Div(text="append extra .ccl/.dat files:")
     tab_layout = column(
-        row(proposal_textinput, ccl_file_select),
-        row(
-            column(Spacer(height=5), upload_div),
-            upload_button,
-            column(Spacer(height=5), append_upload_div),
-            append_upload_button,
-        ),
-        row(scan_table, plot, Spacer(width=30), fit_output_textinput, export_layout),
-        row(findpeak_controls, Spacer(width=30), fitpeak_controls),
+        row(import_layout, scan_layout, plot, Spacer(width=30), export_layout),
+        row(fitpeak_controls, fit_output_textinput),
     )
 
     return Panel(child=tab_layout, title="ccl integrate")

pyzebra/app/panel_hdf_anatric.py

@@ -12,6 +12,7 @@ from bokeh.models import (
     Panel,
     RadioButtonGroup,
     Select,
+    Spacer,
     TextAreaInput,
     TextInput,
 )
@@ -98,12 +99,11 @@ def create():
         minPeakCount_textinput.disabled = disable_adaptivedynamic
         displacementCurve_textinput.disabled = disable_adaptivedynamic
 
-    upload_div = Div(text="Open XML configuration file:")
-
     def upload_button_callback(_attr, _old, new):
         with io.BytesIO(base64.b64decode(new)) as file:
             _load_config_file(file)
 
+    upload_div = Div(text="Open XML configuration file:")
     upload_button = FileInput(accept=".xml")
     upload_button.on_change("value", upload_button_callback)
 
@@ -112,7 +112,7 @@ def create():
     def logfile_textinput_callback(_attr, _old, new):
         config.logfile = new
 
-    logfile_textinput = TextInput(title="Logfile:", value="logfile.log", width=520)
+    logfile_textinput = TextInput(title="Logfile:", value="logfile.log", width=320)
     logfile_textinput.on_change("value", logfile_textinput_callback)
 
     def logfile_verbosity_select_callback(_attr, _old, new):
@@ -133,7 +133,7 @@ def create():
     def filelist_format_textinput_callback(_attr, _old, new):
         config.filelist_format = new
 
-    filelist_format_textinput = TextInput(title="format:", width=490)
+    filelist_format_textinput = TextInput(title="format:", width=290)
     filelist_format_textinput.on_change("value", filelist_format_textinput_callback)
 
     def filelist_datapath_textinput_callback(_attr, _old, new):
@@ -161,7 +161,7 @@ def create():
     def lambda_textinput_callback(_attr, _old, new):
         config.crystal_lambda = new
 
-    lambda_textinput = TextInput(title="lambda:", width=140)
+    lambda_textinput = TextInput(title="lambda:", width=145)
     lambda_textinput.on_change("value", lambda_textinput_callback)
 
     def ub_textareainput_callback(_attr, _old, new):
@@ -173,19 +173,19 @@ def create():
     def zeroOM_textinput_callback(_attr, _old, new):
         config.crystal_zeroOM = new
 
-    zeroOM_textinput = TextInput(title="zeroOM:", width=140)
+    zeroOM_textinput = TextInput(title="zeroOM:", width=145)
     zeroOM_textinput.on_change("value", zeroOM_textinput_callback)
 
     def zeroSTT_textinput_callback(_attr, _old, new):
         config.crystal_zeroSTT = new
 
-    zeroSTT_textinput = TextInput(title="zeroSTT:", width=140)
+    zeroSTT_textinput = TextInput(title="zeroSTT:", width=145)
     zeroSTT_textinput.on_change("value", zeroSTT_textinput_callback)
 
     def zeroCHI_textinput_callback(_attr, _old, new):
         config.crystal_zeroCHI = new
 
-    zeroCHI_textinput = TextInput(title="zeroCHI:", width=140)
+    zeroCHI_textinput = TextInput(title="zeroCHI:", width=145)
     zeroCHI_textinput.on_change("value", zeroCHI_textinput_callback)
 
     # ---- DataFactory
@@ -193,14 +193,14 @@ def create():
         config.dataFactory_implementation = new
 
     dataFactory_implementation_select = Select(
-        title="DataFactory implementation:", options=DATA_FACTORY_IMPLEMENTATION, width=300,
+        title="DataFactory implement.:", options=DATA_FACTORY_IMPLEMENTATION, width=145,
     )
     dataFactory_implementation_select.on_change("value", dataFactory_implementation_select_callback)
 
     def dataFactory_dist1_textinput_callback(_attr, _old, new):
         config.dataFactory_dist1 = new
 
-    dataFactory_dist1_textinput = TextInput(title="dist1:", width=290)
+    dataFactory_dist1_textinput = TextInput(title="dist1:", width=145)
     dataFactory_dist1_textinput.on_change("value", dataFactory_dist1_textinput_callback)
 
     # ---- BackgroundProcessor
@@ -212,7 +212,7 @@ def create():
         config.reflectionPrinter_format = new
 
     reflectionPrinter_format_select = Select(
-        title="ReflectionPrinter format:", options=REFLECTION_PRINTER_FORMATS, width=300,
+        title="ReflectionPrinter format:", options=REFLECTION_PRINTER_FORMATS, width=145,
     )
     reflectionPrinter_format_select.on_change("value", reflectionPrinter_format_select_callback)
 
@@ -346,7 +346,10 @@ def create():
         with tempfile.TemporaryDirectory() as temp_dir:
             temp_file = temp_dir + "/temp.xml"
             config.save_as(temp_file)
-            pyzebra.anatric(temp_file, anatric_path=doc.anatric_path)
+            if doc.anatric_path:
+                pyzebra.anatric(temp_file, anatric_path=doc.anatric_path)
+            else:
+                pyzebra.anatric(temp_file)
 
             with open(config.logfile) as f_log:
                 output_log.value = f_log.read()
@@ -354,50 +357,54 @@ def create():
     process_button = Button(label="Process", button_type="primary")
     process_button.on_click(process_button_callback)
 
-    output_log = TextAreaInput(title="Logfile output:", height=700, disabled=True)
-    output_config = TextAreaInput(title="Current config:", height=700, width=400, disabled=True)
+    output_log = TextAreaInput(title="Logfile output:", height=600, disabled=True)
+    output_config = TextAreaInput(title="Current config:", height=600, width=400, disabled=True)
 
-    tab_layout = row(
-        column(
-            upload_div,
-            upload_button,
-            row(logfile_textinput, logfile_verbosity_select),
-            row(filelist_type, filelist_format_textinput),
-            filelist_datapath_textinput,
-            filelist_ranges_textareainput,
-            crystal_sample_textinput,
-            row(lambda_textinput, zeroOM_textinput, zeroSTT_textinput, zeroCHI_textinput),
-            ub_textareainput,
-            row(dataFactory_implementation_select, dataFactory_dist1_textinput),
-            reflectionPrinter_format_select,
-            process_button,
-        ),
-        column(
-            mode_radio_button_group,
-            row(
-                column(
-                    threshold_textinput,
-                    shell_textinput,
-                    steepness_textinput,
-                    duplicateDistance_textinput,
-                    maxequal_textinput,
-                    aps_window_textinput,
-                ),
-                column(
-                    adm_window_textinput,
-                    border_textinput,
-                    minWindow_textinput,
-                    reflectionFile_textinput,
-                    targetMonitor_textinput,
-                    smoothSize_textinput,
-                    loop_textinput,
-                    minPeakCount_textinput,
-                    displacementCurve_textinput,
-                ),
+    general_params_layout = column(
+        row(logfile_textinput, logfile_verbosity_select),
+        row(filelist_type, filelist_format_textinput),
+        filelist_datapath_textinput,
+        filelist_ranges_textareainput,
+        crystal_sample_textinput,
+        row(lambda_textinput, zeroOM_textinput),
+        row(zeroSTT_textinput, zeroCHI_textinput),
+        ub_textareainput,
+        row(dataFactory_implementation_select, dataFactory_dist1_textinput),
+        reflectionPrinter_format_select,
+    )
+
+    algorithm_params_layout = column(
+        mode_radio_button_group,
+        row(
+            column(
+                threshold_textinput,
+                shell_textinput,
+                steepness_textinput,
+                duplicateDistance_textinput,
+                maxequal_textinput,
+                aps_window_textinput,
+            ),
+            column(
+                adm_window_textinput,
+                border_textinput,
+                minWindow_textinput,
+                reflectionFile_textinput,
+                targetMonitor_textinput,
+                smoothSize_textinput,
+                loop_textinput,
+                minPeakCount_textinput,
+                displacementCurve_textinput,
             ),
         ),
-        output_config,
-        output_log,
+    )
+
+    tab_layout = column(
+        row(column(Spacer(height=2), upload_div), upload_button),
+        row(
+            general_params_layout,
+            algorithm_params_layout,
+            column(row(output_config, output_log), row(process_button)),
+        ),
     )
 
     async def update_config():

pyzebra/app/panel_hdf_viewer.py

@@ -30,6 +30,7 @@ from bokeh.models import (
     Spacer,
     Spinner,
     TextAreaInput,
+    TextInput,
     Title,
     Toggle,
     WheelZoomTool,
@@ -40,12 +41,28 @@ import pyzebra
 
 IMAGE_W = 256
 IMAGE_H = 128
+IMAGE_PLOT_W = int(IMAGE_W * 2.5)
+IMAGE_PLOT_H = int(IMAGE_H * 2.5)
+
+PROPOSAL_PATH = "/afs/psi.ch/project/sinqdata/2020/zebra/"
 
 
 def create():
     det_data = {}
     roi_selection = {}
 
+    def proposal_textinput_callback(_attr, _old, new):
+        full_proposal_path = os.path.join(PROPOSAL_PATH, new.strip())
+        file_list = []
+        for file in os.listdir(full_proposal_path):
+            if file.endswith(".hdf"):
+                file_list.append((os.path.join(full_proposal_path, file), file))
+        filelist.options = file_list
+        filelist.value = file_list[0][0]
+
+    proposal_textinput = TextInput(title="Enter proposal number:", default_size=145)
+    proposal_textinput.on_change("value", proposal_textinput_callback)
+
     def upload_button_callback(_attr, _old, new):
         with io.StringIO(base64.b64decode(new).decode()) as file:
             h5meta_list = pyzebra.parse_h5meta(file)
@@ -53,6 +70,7 @@ def create():
             filelist.options = [(entry, os.path.basename(entry)) for entry in file_list]
             filelist.value = file_list[0]
 
+    upload_div = Div(text="or upload .cami file:", margin=(5, 5, 0, 5))
     upload_button = FileInput(accept=".cami")
     upload_button.on_change("value", upload_button_callback)
 
@@ -83,18 +101,18 @@ def create():
             image_glyph.color_mapper.low = im_min
             image_glyph.color_mapper.high = im_max
 
-        if "magnetic_field" in det_data:
-            magnetic_field_spinner.value = det_data["magnetic_field"][index]
+        if "mf" in det_data:
+            mf_spinner.value = det_data["mf"][index]
         else:
-            magnetic_field_spinner.value = None
+            mf_spinner.value = None
 
-        if "temperature" in det_data:
-            temperature_spinner.value = det_data["temperature"][index]
+        if "temp" in det_data:
+            temp_spinner.value = det_data["temp"][index]
         else:
-            temperature_spinner.value = None
+            temp_spinner.value = None
 
         gamma, nu = calculate_pol(det_data, index)
-        omega = np.ones((IMAGE_H, IMAGE_W)) * det_data["rot_angle"][index]
+        omega = np.ones((IMAGE_H, IMAGE_W)) * det_data["omega"][index]
         image_source.data.update(gamma=[gamma], nu=[nu], omega=[omega])
 
     def update_overview_plot():
@@ -125,15 +143,16 @@ def create():
             overview_plot_x_image_source.data.update(y=[0], dh=[n_im])
             overview_plot_y_image_source.data.update(y=[0], dh=[n_im])
 
-        elif frame_button_group.active == 1:  # Omega
-            overview_plot_x.axis[1].axis_label = "Omega"
-            overview_plot_y.axis[1].axis_label = "Omega"
+        elif frame_button_group.active == 1:  # Variable angle
+            scan_motor = det_data["scan_motor"]
+            overview_plot_x.axis[1].axis_label = scan_motor
+            overview_plot_y.axis[1].axis_label = scan_motor
 
-            om = det_data["rot_angle"]
-            om_start = om[0]
-            om_end = (om[-1] - om[0]) * n_im / (n_im - 1)
-            overview_plot_x_image_source.data.update(y=[om_start], dh=[om_end])
-            overview_plot_y_image_source.data.update(y=[om_start], dh=[om_end])
+            var = det_data[scan_motor]
+            var_start = var[0]
+            var_end = (var[-1] - var[0]) * n_im / (n_im - 1)
+            overview_plot_x_image_source.data.update(y=[var_start], dh=[var_end])
+            overview_plot_y_image_source.data.update(y=[var_start], dh=[var_end])
 
     def filelist_callback(_attr, _old, new):
         nonlocal det_data
@@ -141,10 +160,17 @@ def create():
 
         index_spinner.value = 0
         index_spinner.high = det_data["data"].shape[0] - 1
+
+        zebra_mode = det_data["zebra_mode"]
+        if zebra_mode == "nb":
+            geometry_textinput.value = "normal beam"
+        else:  # zebra_mode == "bi"
+            geometry_textinput.value = "bisecting"
+
         update_image(0)
         update_overview_plot()
 
-    filelist = Select()
+    filelist = Select(title="Available .hdf files:")
     filelist.on_change("value", filelist_callback)
 
     def index_spinner_callback(_attr, _old, new):
@@ -156,8 +182,8 @@ def create():
     plot = Plot(
         x_range=Range1d(0, IMAGE_W, bounds=(0, IMAGE_W)),
         y_range=Range1d(0, IMAGE_H, bounds=(0, IMAGE_H)),
-        plot_height=IMAGE_H * 3,
-        plot_width=IMAGE_W * 3,
+        plot_height=IMAGE_PLOT_H,
+        plot_width=IMAGE_PLOT_W,
         toolbar_location="left",
     )
 
@@ -210,8 +236,8 @@ def create():
     proj_v = Plot(
         x_range=plot.x_range,
         y_range=DataRange1d(),
-        plot_height=200,
-        plot_width=IMAGE_W * 3,
+        plot_height=150,
+        plot_width=IMAGE_PLOT_W,
         toolbar_location=None,
     )
 
@@ -227,8 +253,8 @@ def create():
     proj_h = Plot(
         x_range=DataRange1d(),
         y_range=plot.y_range,
-        plot_height=IMAGE_H * 3,
-        plot_width=200,
+        plot_height=IMAGE_PLOT_H,
+        plot_width=150,
         toolbar_location=None,
     )
 
@@ -291,8 +317,8 @@ def create():
         title=Title(text="Projections on X-axis"),
         x_range=det_x_range,
         y_range=frame_range,
-        plot_height=500,
-        plot_width=IMAGE_W * 3,
+        plot_height=400,
+        plot_width=IMAGE_PLOT_W,
     )
 
     # ---- tools
@@ -328,8 +354,8 @@ def create():
         title=Title(text="Projections on Y-axis"),
         x_range=det_y_range,
         y_range=frame_range,
-        plot_height=500,
-        plot_width=IMAGE_H * 3,
+        plot_height=400,
+        plot_width=IMAGE_PLOT_H,
     )
 
     # ---- tools
@@ -363,14 +389,14 @@ def create():
     def frame_button_group_callback(_active):
         update_overview_plot()
 
-    frame_button_group = RadioButtonGroup(labels=["Frames", "Omega"], active=0)
+    frame_button_group = RadioButtonGroup(labels=["Frames", "Variable Angle"], active=0)
     frame_button_group.on_click(frame_button_group_callback)
 
     roi_avg_plot = Plot(
         x_range=DataRange1d(),
         y_range=DataRange1d(),
         plot_height=200,
-        plot_width=IMAGE_W * 3,
+        plot_width=IMAGE_PLOT_W,
         toolbar_location="left",
     )
 
@@ -404,8 +430,6 @@ def create():
     colormap.on_change("value", colormap_callback)
     colormap.value = "plasma"
 
-    radio_button_group = RadioButtonGroup(labels=["normal beam", "bisecting"], active=0)
-
     STEP = 1
     # ---- colormap auto toggle button
     def auto_toggle_callback(state):
@@ -506,8 +530,7 @@ def create():
 
     def hkl_button_callback():
         index = index_spinner.value
-        geometry = "bi" if radio_button_group.active else "nb"
-        h, k, l = calculate_hkl(det_data, index, geometry)
+        h, k, l = calculate_hkl(det_data, index)
         image_source.data.update(h=[h], k=[k], l=[l])
 
     hkl_button = Button(label="Calculate hkl (slow)")
@@ -537,10 +560,11 @@ def create():
     selection_button = Button(label="Add selection")
     selection_button.on_click(selection_button_callback)
 
-    magnetic_field_spinner = Spinner(
+    mf_spinner = Spinner(
         title="Magnetic field:", format="0.00", width=145, disabled=True
     )
-    temperature_spinner = Spinner(title="Temperature:", format="0.00", width=145, disabled=True)
+    temp_spinner = Spinner(title="Temperature:", format="0.00", width=145, disabled=True)
+    geometry_textinput = TextInput(title="Geometry:", disabled=True)
 
     # Final layout
     layout_image = column(gridplot([[proj_v, None], [plot, proj_h]], merge_tools=False))
@@ -553,9 +577,8 @@ def create():
             proj_display_min_spinner,
         ),
     )
-    geometry_div = Div(text="Geometry:", margin=[5, 5, -5, 5])
-    hkl_layout = column(column(geometry_div, radio_button_group), hkl_button)
-    params_layout = row(magnetic_field_spinner, temperature_spinner)
+    hkl_layout = column(geometry_textinput, hkl_button)
+    params_layout = row(mf_spinner, temp_spinner)
 
     layout_controls = row(
         column(selection_button, selection_list),
@@ -574,10 +597,11 @@ def create():
         ),
     )
 
-    upload_div = Div(text="Upload .cami file:")
     tab_layout = row(
         column(
-            row(column(Spacer(height=5), upload_div), upload_button, filelist),
+            row(
+                proposal_textinput, filelist, Spacer(width=100), column(upload_div, upload_button),
+            ),
             layout_overview,
             layout_controls,
         ),
@@ -587,31 +611,32 @@ def create():
     return Panel(child=tab_layout, title="hdf viewer")
 
 
-def calculate_hkl(det_data, index, geometry):
+def calculate_hkl(det_data, index):
     h = np.empty(shape=(IMAGE_H, IMAGE_W))
     k = np.empty(shape=(IMAGE_H, IMAGE_W))
     l = np.empty(shape=(IMAGE_H, IMAGE_W))
 
     wave = det_data["wave"]
     ddist = det_data["ddist"]
-    gammad = det_data["pol_angle"][index]
-    om = det_data["rot_angle"][index]
-    nud = det_data["tlt_angle"]
-    ub = det_data["UB"]
+    gammad = det_data["gamma"][index]
+    om = det_data["omega"][index]
+    nud = det_data["nu"]
+    ub = det_data["ub"]
+    geometry = det_data["zebra_mode"]
 
     if geometry == "bi":
-        ch = det_data["chi_angle"][index]
-        ph = det_data["phi_angle"][index]
+        chi = det_data["chi"][index]
+        phi = det_data["phi"][index]
     elif geometry == "nb":
-        ch = 0
-        ph = 0
+        chi = 0
+        phi = 0
     else:
         raise ValueError(f"Unknown geometry type '{geometry}'")
 
     for xi in np.arange(IMAGE_W):
         for yi in np.arange(IMAGE_H):
             h[yi, xi], k[yi, xi], l[yi, xi] = pyzebra.ang2hkl(
-                wave, ddist, gammad, om, ch, ph, nud, ub, xi, yi
+                wave, ddist, gammad, om, chi, phi, nud, ub, xi, yi
             )
 
     return h, k, l
@@ -622,8 +647,8 @@ def calculate_pol(det_data, index):
     nu = np.empty(shape=(IMAGE_H, IMAGE_W))
 
     ddist = det_data["ddist"]
-    gammad = det_data["pol_angle"][index]
-    nud = det_data["tlt_angle"]
+    gammad = det_data["gamma"][index]
+    nud = det_data["nu"]
 
     for xi in np.arange(IMAGE_W):
         for yi in np.arange(IMAGE_H):

pyzebra/app/panel_param_study.py

@@ -4,12 +4,10 @@ import itertools
 import os
 import tempfile
 import types
-from copy import deepcopy
 
 import numpy as np
 from bokeh.layouts import column, row
 from bokeh.models import (
-    Asterisk,
     BasicTicker,
     Button,
     CheckboxEditor,
@@ -21,6 +19,8 @@ from bokeh.models import (
     Dropdown,
     FileInput,
     Grid,
+    HoverTool,
+    Legend,
     Line,
     LinearAxis,
     MultiLine,
@@ -44,28 +44,29 @@ from bokeh.models import (
     WheelZoomTool,
     Whisker,
 )
-from bokeh.palettes import Category10
+from bokeh.palettes import Category10, Turbo256
+from bokeh.transform import linear_cmap
 
 import pyzebra
 from pyzebra.ccl_io import AREA_METHODS
 
 javaScript = """
-setTimeout(function() {
-    const filename = 'output' + js_data.data['ext']
-    const blob = new Blob([js_data.data['cont']], {type: 'text/plain'})
+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 = filename;
+    link.download = js_data.data['fname'][i];
     link.click();
     window.URL.revokeObjectURL(url);
     document.body.removeChild(link);
-}, 3000);
+}
 """
 
 PROPOSAL_PATH = "/afs/psi.ch/project/sinqdata/2020/zebra/"
-PLOT_TYPES = ("single scan", "overview")
 
 
 def color_palette(n_colors):
@@ -74,10 +75,9 @@ def color_palette(n_colors):
 
 
 def create():
-    det_data = {}
+    det_data = []
     fit_params = {}
-    peak_pos_textinput_lock = False
-    js_data = ColumnDataSource(data=dict(cont=[], ext=[]))
+    js_data = ColumnDataSource(data=dict(content=["", ""], fname=["", ""]))
 
     def proposal_textinput_callback(_attr, _old, new):
         full_proposal_path = os.path.join(PROPOSAL_PATH, new.strip())
@@ -86,57 +86,62 @@ def create():
             if file.endswith(".dat"):
                 dat_file_list.append((os.path.join(full_proposal_path, file), file))
         file_select.options = dat_file_list
-        file_select.value = dat_file_list[0][0]
 
-    proposal_textinput = TextInput(title="Enter proposal number:", default_size=145)
+    proposal_textinput = TextInput(title="Proposal number:", default_size=200)
     proposal_textinput.on_change("value", proposal_textinput_callback)
 
     def _init_datatable():
-        scan_list = list(det_data["scan"].keys())
+        scan_list = [s["idx"] for s in det_data]
         file_list = []
-        extra_meta = det_data.get("extra_meta", {})
-        for scan_id in scan_list:
-            if scan_id in extra_meta:
-                f_path = extra_meta[scan_id]["original_filename"]
-            else:
-                f_path = det_data["meta"]["original_filename"]
-
-            _, f_name = os.path.split(f_path)
-            file_list.append(f_name)
+        for scan in det_data:
+            file_list.append(os.path.basename(scan["original_filename"]))
 
         scan_table_source.data.update(
             file=file_list,
             scan=scan_list,
-            param=[""] * len(scan_list),
-            peaks=[0] * len(scan_list),
+            param=[None] * len(scan_list),
             fit=[0] * len(scan_list),
             export=[True] * len(scan_list),
         )
         scan_table_source.selected.indices = []
         scan_table_source.selected.indices = [0]
 
+        param_select.value = "user defined"
+
     def file_select_callback(_attr, _old, _new):
         pass
 
-    file_select = Select(title="Available .dat files")
+    file_select = MultiSelect(title="Available .dat files:", default_size=200, height=250)
     file_select.on_change("value", file_select_callback)
 
     def file_open_button_callback():
         nonlocal det_data
-        with open(file_select.value) as file:
-            _, ext = os.path.splitext(file_select.value)
-            det_data = pyzebra.parse_1D(file, ext)
+        det_data = []
+        for f_name in file_select.value:
+            with open(f_name) as file:
+                base, ext = os.path.splitext(f_name)
+                if det_data:
+                    append_data = pyzebra.parse_1D(file, ext)
+                    pyzebra.normalize_dataset(append_data, monitor_spinner.value)
+                    det_data.extend(append_data)
+                else:
+                    det_data = pyzebra.parse_1D(file, ext)
+                    pyzebra.normalize_dataset(det_data, monitor_spinner.value)
+                    js_data.data.update(fname=[base + ".comm", base + ".incomm"])
 
         _init_datatable()
 
-    file_open_button = Button(label="Open", default_size=100)
+    file_open_button = Button(label="Open New", default_size=100)
     file_open_button.on_click(file_open_button_callback)
 
     def file_append_button_callback():
-        with open(file_select.value) as file:
-            _, ext = os.path.splitext(file_select.value)
-            append_data = pyzebra.parse_1D(file, ext)
-            pyzebra.add_dict(det_data, append_data)
+        for f_name in file_select.value:
+            with open(f_name) as file:
+                _, ext = os.path.splitext(f_name)
+                append_data = pyzebra.parse_1D(file, ext)
+
+            pyzebra.normalize_dataset(append_data, monitor_spinner.value)
+            det_data.extend(append_data)
 
         _init_datatable()
 
@@ -145,19 +150,23 @@ def create():
 
     def upload_button_callback(_attr, _old, new):
         nonlocal det_data
-        det_data = {}
+        det_data = []
         for f_str, f_name in zip(new, upload_button.filename):
             with io.StringIO(base64.b64decode(f_str).decode()) as file:
-                _, ext = os.path.splitext(f_name)
+                base, ext = os.path.splitext(f_name)
                 if det_data:
                     append_data = pyzebra.parse_1D(file, ext)
-                    pyzebra.add_dict(det_data, append_data)
+                    pyzebra.normalize_dataset(append_data, monitor_spinner.value)
+                    det_data.extend(append_data)
                 else:
                     det_data = pyzebra.parse_1D(file, ext)
+                    pyzebra.normalize_dataset(det_data, monitor_spinner.value)
+                    js_data.data.update(fname=[base + ".comm", base + ".incomm"])
 
         _init_datatable()
 
-    upload_button = FileInput(accept=".dat", multiple=True)
+    upload_div = Div(text="or upload new .dat files:", margin=(5, 5, 0, 5))
+    upload_button = FileInput(accept=".dat", multiple=True, default_size=200)
     upload_button.on_change("value", upload_button_callback)
 
     def append_upload_button_callback(_attr, _old, new):
@@ -165,159 +174,152 @@ def create():
             with io.StringIO(base64.b64decode(f_str).decode()) as file:
                 _, ext = os.path.splitext(f_name)
                 append_data = pyzebra.parse_1D(file, ext)
-                pyzebra.add_dict(det_data, append_data)
+
+            pyzebra.normalize_dataset(append_data, monitor_spinner.value)
+            det_data.extend(append_data)
 
         _init_datatable()
 
-    append_upload_button = FileInput(accept=".dat", multiple=True)
+    append_upload_div = Div(text="append extra files:", margin=(5, 5, 0, 5))
+    append_upload_button = FileInput(accept=".dat", multiple=True, default_size=200)
     append_upload_button.on_change("value", append_upload_button_callback)
 
+    def monitor_spinner_callback(_attr, _old, new):
+        if det_data:
+            pyzebra.normalize_dataset(det_data, new)
+            _update_plot()
+
+    monitor_spinner = Spinner(title="Monitor:", mode="int", value=100_000, low=1, width=145)
+    monitor_spinner.on_change("value", monitor_spinner_callback)
+
     def _update_table():
-        num_of_peaks = [len(scan.get("peak_indexes", [])) for scan in det_data["scan"].values()]
-        fit_ok = [(1 if "fit" in scan else 0) for scan in det_data["scan"].values()]
-        scan_table_source.data.update(peaks=num_of_peaks, fit=fit_ok)
+        fit_ok = [(1 if "fit" in scan else 0) for scan in det_data]
+        scan_table_source.data.update(fit=fit_ok)
 
     def _update_plot():
         _update_single_scan_plot(_get_selected_scan())
         _update_overview()
 
     def _update_single_scan_plot(scan):
-        nonlocal peak_pos_textinput_lock
-        peak_pos_textinput_lock = True
+        scan_motor = scan["scan_motor"]
 
         y = scan["Counts"]
-        x = scan["om"]
+        x = scan[scan_motor]
 
+        plot.axis[0].axis_label = scan_motor
         plot_scatter_source.data.update(x=x, y=y, y_upper=y + np.sqrt(y), y_lower=y - np.sqrt(y))
 
-        num_of_peaks = len(scan.get("peak_indexes", []))
-        if num_of_peaks is not None and num_of_peaks > 0:
-            peak_indexes = scan["peak_indexes"]
-            if len(peak_indexes) == 1:
-                peak_pos_textinput.value = str(x[peak_indexes[0]])
-            else:
-                peak_pos_textinput.value = str([x[ind] for ind in peak_indexes])
-
-            plot_peak_source.data.update(x=x[peak_indexes], y=scan["peak_heights"])
-            plot_line_smooth_source.data.update(x=x, y=scan["smooth_peaks"])
-        else:
-            peak_pos_textinput.value = None
-            plot_peak_source.data.update(x=[], y=[])
-            plot_line_smooth_source.data.update(x=[], y=[])
-
-        peak_pos_textinput_lock = False
-
         fit = scan.get("fit")
         if fit is not None:
-            x = scan["fit"]["x_fit"]
-            plot_gauss_source.data.update(x=x, y=scan["fit"]["comps"]["gaussian"])
-            plot_bkg_source.data.update(x=x, y=scan["fit"]["comps"]["background"])
-            params = fit["result"].params
-            fit_output_textinput.value = (
-                "Gaussian: centre = %9.4f, sigma = %9.4f, area = %9.4f \n"
-                "background: slope = %9.4f, intercept = %9.4f \n"
-                "Int. area = %9.4f +/- %9.4f \n"
-                "fit area = %9.4f +/- %9.4f \n"
-                "ratio((fit-int)/fit) = %9.4f"
-                % (
-                    params["g_cen"].value,
-                    params["g_width"].value,
-                    params["g_amp"].value,
-                    params["slope"].value,
-                    params["intercept"].value,
-                    fit["int_area"].n,
-                    fit["int_area"].s,
-                    params["g_amp"].value,
-                    params["g_amp"].stderr,
-                    (params["g_amp"].value - fit["int_area"].n) / params["g_amp"].value,
-                )
-            )
-            numfit_min, numfit_max = fit["numfit"]
-            if numfit_min is None:
-                numfit_min_span.location = None
-            else:
-                numfit_min_span.location = x[numfit_min]
+            x_fit = np.linspace(x[0], x[-1], 100)
+            plot_fit_source.data.update(x=x_fit, y=fit.eval(x=x_fit))
 
-            if numfit_max is None:
-                numfit_max_span.location = None
-            else:
-                numfit_max_span.location = x[numfit_max]
+            x_bkg = []
+            y_bkg = []
+            xs_peak = []
+            ys_peak = []
+            comps = fit.eval_components(x=x_fit)
+            for i, model in enumerate(fit_params):
+                if "linear" in model:
+                    x_bkg = x_fit
+                    y_bkg = comps[f"f{i}_"]
+
+                elif any(val in model for val in ("gaussian", "voigt", "pvoigt")):
+                    xs_peak.append(x_fit)
+                    ys_peak.append(comps[f"f{i}_"])
+
+            plot_bkg_source.data.update(x=x_bkg, y=y_bkg)
+            plot_peak_source.data.update(xs=xs_peak, ys=ys_peak)
+
+            fit_output_textinput.value = fit.fit_report()
 
         else:
-            plot_gauss_source.data.update(x=[], y=[])
+            plot_fit_source.data.update(x=[], y=[])
             plot_bkg_source.data.update(x=[], y=[])
+            plot_peak_source.data.update(xs=[], ys=[])
             fit_output_textinput.value = ""
-            numfit_min_span.location = None
-            numfit_max_span.location = None
 
     def _update_overview():
         xs = []
         ys = []
         param = []
-        for ind, p in enumerate(scan_table_source.data["param"]):
-            if p:
-                s = scan_table_source.data["scan"][ind]
-                xs.append(np.array(det_data["scan"][s]["om"]))
-                ys.append(np.array(det_data["scan"][s]["Counts"]))
+        x = []
+        y = []
+        par = []
+        for s, p in enumerate(scan_table_source.data["param"]):
+            if p is not None:
+                scan = det_data[s]
+                scan_motor = scan["scan_motor"]
+                xs.append(scan[scan_motor])
+                x.extend(scan[scan_motor])
+                ys.append(scan["Counts"])
+                y.extend([float(p)] * len(scan[scan_motor]))
                 param.append(float(p))
+                par.extend(scan["Counts"])
+
+        if det_data:
+            scan_motor = det_data[0]["scan_motor"]
+            ov_plot.axis[0].axis_label = scan_motor
+            ov_param_plot.axis[0].axis_label = scan_motor
 
         ov_plot_mline_source.data.update(xs=xs, ys=ys, param=param, color=color_palette(len(xs)))
 
+        if y:
+            mapper["transform"].low = np.min([np.min(y) for y in ys])
+            mapper["transform"].high = np.max([np.max(y) for y in ys])
+        ov_param_plot_scatter_source.data.update(x=x, y=y, param=par)
+
     # Main plot
-    plot = Plot(x_range=DataRange1d(), y_range=DataRange1d(), plot_height=400, plot_width=700)
+    plot = Plot(
+        x_range=DataRange1d(),
+        y_range=DataRange1d(only_visible=True),
+        plot_height=450,
+        plot_width=700,
+    )
 
     plot.add_layout(LinearAxis(axis_label="Counts"), place="left")
-    plot.add_layout(LinearAxis(axis_label="Omega"), place="below")
+    plot.add_layout(LinearAxis(axis_label="Scan motor"), place="below")
 
     plot.add_layout(Grid(dimension=0, ticker=BasicTicker()))
     plot.add_layout(Grid(dimension=1, ticker=BasicTicker()))
 
     plot_scatter_source = ColumnDataSource(dict(x=[0], y=[0], y_upper=[0], y_lower=[0]))
-    plot.add_glyph(
-        plot_scatter_source, Scatter(x="x", y="y", line_color="steelblue", name="single scan")
-    )
-    plot.add_layout(
-        Whisker(
-            source=plot_scatter_source,
-            base="x",
-            upper="y_upper",
-            lower="y_lower",
-            name="single scan",
-        )
+    plot_scatter = plot.add_glyph(
+        plot_scatter_source, Scatter(x="x", y="y", line_color="steelblue")
     )
+    plot.add_layout(Whisker(source=plot_scatter_source, base="x", upper="y_upper", lower="y_lower"))
 
-    plot_line_smooth_source = ColumnDataSource(dict(x=[0], y=[0]))
-    plot.add_glyph(
-        plot_line_smooth_source,
-        Line(x="x", y="y", line_color="steelblue", line_dash="dashed", name="single scan"),
-    )
-
-    plot_gauss_source = ColumnDataSource(dict(x=[0], y=[0]))
-    plot.add_glyph(
-        plot_gauss_source,
-        Line(x="x", y="y", line_color="red", line_dash="dashed", name="single scan"),
-    )
+    plot_fit_source = ColumnDataSource(dict(x=[0], y=[0]))
+    plot_fit = plot.add_glyph(plot_fit_source, Line(x="x", y="y"))
 
     plot_bkg_source = ColumnDataSource(dict(x=[0], y=[0]))
-    plot.add_glyph(
-        plot_bkg_source,
-        Line(x="x", y="y", line_color="green", line_dash="dashed", name="single scan"),
+    plot_bkg = plot.add_glyph(
+        plot_bkg_source, Line(x="x", y="y", line_color="green", line_dash="dashed")
     )
 
-    plot_peak_source = ColumnDataSource(dict(x=[], y=[]))
-    plot.add_glyph(
-        plot_peak_source, Asterisk(x="x", y="y", size=10, line_color="red", name="single scan")
+    plot_peak_source = ColumnDataSource(dict(xs=[0], ys=[0]))
+    plot_peak = plot.add_glyph(
+        plot_peak_source, MultiLine(xs="xs", ys="ys", line_color="red", line_dash="dashed")
     )
 
-    numfit_min_span = Span(
-        location=None, dimension="height", line_dash="dashed", name="single scan"
-    )
-    plot.add_layout(numfit_min_span)
+    fit_from_span = Span(location=None, dimension="height", line_dash="dashed")
+    plot.add_layout(fit_from_span)
 
-    numfit_max_span = Span(
-        location=None, dimension="height", line_dash="dashed", name="single scan"
+    fit_to_span = Span(location=None, dimension="height", line_dash="dashed")
+    plot.add_layout(fit_to_span)
+
+    plot.add_layout(
+        Legend(
+            items=[
+                ("data", [plot_scatter]),
+                ("best fit", [plot_fit]),
+                ("peak", [plot_peak]),
+                ("linear", [plot_bkg]),
+            ],
+            location="top_left",
+            click_policy="hide",
+        )
     )
-    plot.add_layout(numfit_max_span)
 
     plot.add_tools(PanTool(), WheelZoomTool(), ResetTool())
     plot.toolbar.logo = None
@@ -326,22 +328,48 @@ def create():
     ov_plot = Plot(x_range=DataRange1d(), y_range=DataRange1d(), plot_height=400, plot_width=700)
 
     ov_plot.add_layout(LinearAxis(axis_label="Counts"), place="left")
-    ov_plot.add_layout(LinearAxis(axis_label="Omega"), place="below")
+    ov_plot.add_layout(LinearAxis(axis_label="Scan motor"), place="below")
 
     ov_plot.add_layout(Grid(dimension=0, ticker=BasicTicker()))
     ov_plot.add_layout(Grid(dimension=1, ticker=BasicTicker()))
 
     ov_plot_mline_source = ColumnDataSource(dict(xs=[], ys=[], param=[], color=[]))
-    ov_plot.add_glyph(
-        ov_plot_mline_source, MultiLine(xs="xs", ys="ys", line_color="color", name="overview")
-    )
+    ov_plot.add_glyph(ov_plot_mline_source, MultiLine(xs="xs", ys="ys", line_color="color"))
+
+    hover_tool = HoverTool(tooltips=[("param", "@param")])
+    ov_plot.add_tools(PanTool(), WheelZoomTool(), hover_tool, ResetTool())
 
     ov_plot.add_tools(PanTool(), WheelZoomTool(), ResetTool())
     ov_plot.toolbar.logo = None
 
+    # Overview perams plot
+    ov_param_plot = Plot(
+        x_range=DataRange1d(), y_range=DataRange1d(), plot_height=400, plot_width=700
+    )
+
+    ov_param_plot.add_layout(LinearAxis(axis_label="Param"), place="left")
+    ov_param_plot.add_layout(LinearAxis(axis_label="Scan motor"), place="below")
+
+    ov_param_plot.add_layout(Grid(dimension=0, ticker=BasicTicker()))
+    ov_param_plot.add_layout(Grid(dimension=1, ticker=BasicTicker()))
+
+    ov_param_plot_scatter_source = ColumnDataSource(dict(x=[], y=[], param=[]))
+    mapper = linear_cmap(field_name="param", palette=Turbo256, low=0, high=50)
+    ov_param_plot.add_glyph(
+        ov_param_plot_scatter_source,
+        Scatter(x="x", y="y", line_color=mapper, fill_color=mapper, size=10),
+    )
+
+    ov_param_plot.add_tools(PanTool(), WheelZoomTool(), ResetTool())
+    ov_param_plot.toolbar.logo = None
+
     # Plot tabs
     plots = Tabs(
-        tabs=[Panel(child=plot, title="single scan"), Panel(child=ov_plot, title="overview")]
+        tabs=[
+            Panel(child=plot, title="single scan"),
+            Panel(child=ov_plot, title="overview"),
+            Panel(child=ov_param_plot, title="overview map"),
+        ]
     )
 
     # Scan select
@@ -362,23 +390,19 @@ def create():
 
         _update_plot()
 
-    scan_table_source = ColumnDataSource(
-        dict(file=[], scan=[], param=[], peaks=[], fit=[], export=[])
-    )
+    scan_table_source = ColumnDataSource(dict(file=[], scan=[], param=[], fit=[], export=[]))
     scan_table = DataTable(
         source=scan_table_source,
         columns=[
             TableColumn(field="file", title="file", width=150),
             TableColumn(field="scan", title="scan", width=50),
-            TableColumn(field="param", title="param", width=50),
-            TableColumn(field="peaks", title="Peaks", width=50),
+            TableColumn(field="param", title="param", editor=NumberEditor(), width=50),
             TableColumn(field="fit", title="Fit", width=50),
             TableColumn(field="export", title="Export", editor=CheckboxEditor(), width=50),
         ],
-        width=400,
-        index_position=None,
+        width=410,  # +60 because of the index column
         editable=True,
-        fit_columns=False,
+        autosize_mode="none",
     )
 
     def scan_table_source_callback(_attr, _old, _new):
@@ -389,42 +413,39 @@ def create():
     scan_table_source.on_change("data", scan_table_source_callback)
 
     def _get_selected_scan():
-        selected_index = scan_table_source.selected.indices[0]
-        selected_scan_id = scan_table_source.data["scan"][selected_index]
-        return det_data["scan"][selected_scan_id]
+        return det_data[scan_table_source.selected.indices[0]]
 
-    def peak_pos_textinput_callback(_attr, _old, new):
-        if new is not None and not peak_pos_textinput_lock:
-            scan = _get_selected_scan()
+    def param_select_callback(_attr, _old, new):
+        if new == "user defined":
+            param = [None] * len(det_data)
+        else:
+            param = [scan[new] for scan in det_data]
 
-            peak_ind = (np.abs(scan["om"] - float(new))).argmin()
-            scan["peak_indexes"] = np.array([peak_ind], dtype=np.int64)
-            scan["peak_heights"] = np.array([scan["smooth_peaks"][peak_ind]])
-            _update_table()
-            _update_plot()
+        scan_table_source.data["param"] = param
 
-    peak_pos_textinput = TextInput(title="Peak position:", default_size=145)
-    peak_pos_textinput.on_change("value", peak_pos_textinput_callback)
-
-    peak_int_ratio_spinner = Spinner(
-        title="Peak intensity ratio:", value=0.8, step=0.01, low=0, high=1, default_size=145
+    param_select = Select(
+        title="Parameter:",
+        options=["user defined", "temp", "mf", "h", "k", "l"],
+        value="user defined",
+        default_size=145,
     )
-    peak_prominence_spinner = Spinner(title="Peak prominence:", value=50, low=0, default_size=145)
-    smooth_toggle = Toggle(label="Smooth curve", default_size=145)
-    window_size_spinner = Spinner(title="Window size:", value=7, step=2, low=1, default_size=145)
-    poly_order_spinner = Spinner(title="Poly order:", value=3, low=0, default_size=145)
+    param_select.on_change("value", param_select_callback)
 
-    integ_from = Spinner(title="Integrate from:", default_size=145)
-    integ_to = Spinner(title="to:", default_size=145)
+    def fit_from_spinner_callback(_attr, _old, new):
+        fit_from_span.location = new
 
-    def fitparam_reset_button_callback():
-        ...
+    fit_from_spinner = Spinner(title="Fit from:", default_size=145)
+    fit_from_spinner.on_change("value", fit_from_spinner_callback)
 
-    fitparam_reset_button = Button(label="Reset to defaults", default_size=145, disabled=True)
-    fitparam_reset_button.on_click(fitparam_reset_button_callback)
+    def fit_to_spinner_callback(_attr, _old, new):
+        fit_to_span.location = new
+
+    fit_to_spinner = Spinner(title="to:", default_size=145)
+    fit_to_spinner.on_change("value", fit_to_spinner_callback)
 
     def fitparams_add_dropdown_callback(click):
-        new_tag = str(fitparams_select.tags[0])  # bokeh requires (str, str) for MultiSelect options
+        # bokeh requires (str, str) for MultiSelect options
+        new_tag = f"{click.item}-{fitparams_select.tags[0]}"
         fitparams_select.options.append((new_tag, click.item))
         fit_params[new_tag] = fitparams_factory(click.item)
         fitparams_select.tags[0] += 1
@@ -432,11 +453,11 @@ def create():
     fitparams_add_dropdown = Dropdown(
         label="Add fit function",
         menu=[
-            ("Background", "background"),
-            ("Gauss", "gauss"),
+            ("Linear", "linear"),
+            ("Gaussian", "gaussian"),
             ("Voigt", "voigt"),
-            ("Pseudo Voigt", "pseudovoigt"),
-            ("Pseudo Voigt1", "pseudovoigt1"),
+            ("Pseudo Voigt", "pvoigt"),
+            # ("Pseudo Voigt1", "pseudovoigt1"),
         ],
         default_size=145,
     )
@@ -456,7 +477,7 @@ def create():
         if new:
             fitparams_table_source.data.update(fit_params[new[0]])
         else:
-            fitparams_table_source.data.update(dict(param=[], guess=[], vary=[], min=[], max=[]))
+            fitparams_table_source.data.update(dict(param=[], value=[], vary=[], min=[], max=[]))
 
     fitparams_select = MultiSelect(options=[], height=120, default_size=145)
     fitparams_select.tags = [0]
@@ -477,32 +498,32 @@ def create():
     fitparams_remove_button.on_click(fitparams_remove_button_callback)
 
     def fitparams_factory(function):
-        if function == "background":
-            params = ["slope", "offset"]
-        elif function == "gauss":
-            params = ["center", "sigma", "amplitude"]
+        if function == "linear":
+            params = ["slope", "intercept"]
+        elif function == "gaussian":
+            params = ["amplitude", "center", "sigma"]
         elif function == "voigt":
-            params = ["center", "sigma", "amplitude", "gamma"]
-        elif function == "pseudovoigt":
-            params = ["center", "sigma", "amplitude", "fraction"]
+            params = ["amplitude", "center", "sigma", "gamma"]
+        elif function == "pvoigt":
+            params = ["amplitude", "center", "sigma", "fraction"]
         elif function == "pseudovoigt1":
-            params = ["center", "g_sigma", "l_sigma", "amplitude", "fraction"]
+            params = ["amplitude", "center", "g_sigma", "l_sigma", "fraction"]
         else:
             raise ValueError("Unknown fit function")
 
         n = len(params)
         fitparams = dict(
-            param=params, guess=[None] * n, vary=[True] * n, min=[None] * n, max=[None] * n,
+            param=params, value=[None] * n, vary=[True] * n, min=[None] * n, max=[None] * n,
         )
 
         return fitparams
 
-    fitparams_table_source = ColumnDataSource(dict(param=[], guess=[], vary=[], min=[], max=[]))
+    fitparams_table_source = ColumnDataSource(dict(param=[], value=[], vary=[], min=[], max=[]))
     fitparams_table = DataTable(
         source=fitparams_table_source,
         columns=[
             TableColumn(field="param", title="Parameter"),
-            TableColumn(field="guess", title="Guess", editor=NumberEditor()),
+            TableColumn(field="value", title="Value", editor=NumberEditor()),
             TableColumn(field="vary", title="Vary", editor=CheckboxEditor()),
             TableColumn(field="min", title="Min", editor=NumberEditor()),
             TableColumn(field="max", title="Max", editor=NumberEditor()),
@@ -515,57 +536,18 @@ def create():
     )
 
     # start with `background` and `gauss` fit functions added
-    fitparams_add_dropdown_callback(types.SimpleNamespace(item="background"))
-    fitparams_add_dropdown_callback(types.SimpleNamespace(item="gauss"))
+    fitparams_add_dropdown_callback(types.SimpleNamespace(item="linear"))
+    fitparams_add_dropdown_callback(types.SimpleNamespace(item="gaussian"))
+    fitparams_select.value = ["gaussian-1"]  # add selection to gauss
 
-    fit_output_textinput = TextAreaInput(title="Fit results:", width=450, height=400)
-
-    def _get_peakfind_params():
-        return dict(
-            int_threshold=peak_int_ratio_spinner.value,
-            prominence=peak_prominence_spinner.value,
-            smooth=smooth_toggle.active,
-            window_size=window_size_spinner.value,
-            poly_order=poly_order_spinner.value,
-        )
-
-    def peakfind_all_button_callback():
-        peakfind_params = _get_peakfind_params()
-        for scan in det_data["scan"].values():
-            pyzebra.ccl_findpeaks(scan, **peakfind_params)
-
-        _update_table()
-        _update_plot()
-
-    peakfind_all_button = Button(label="Peak Find All", button_type="primary", default_size=145)
-    peakfind_all_button.on_click(peakfind_all_button_callback)
-
-    def peakfind_button_callback():
-        scan = _get_selected_scan()
-        pyzebra.ccl_findpeaks(scan, **_get_peakfind_params())
-
-        _update_table()
-        _update_plot()
-
-    peakfind_button = Button(label="Peak Find Current", default_size=145)
-    peakfind_button.on_click(peakfind_button_callback)
-
-    def _get_fit_params():
-        return dict(
-            guess=fit_params["1"]["guess"] + fit_params["0"]["guess"],
-            vary=fit_params["1"]["vary"] + fit_params["0"]["vary"],
-            constraints_min=fit_params["1"]["min"] + fit_params["0"]["min"],
-            constraints_max=fit_params["1"]["max"] + fit_params["0"]["max"],
-            numfit_min=integ_from.value,
-            numfit_max=integ_to.value,
-            binning=bin_size_spinner.value,
-        )
+    fit_output_textinput = TextAreaInput(title="Fit results:", width=750, height=200)
 
     def fit_all_button_callback():
-        fit_params = _get_fit_params()
-        for scan in det_data["scan"].values():
-            # fit_params are updated inplace within `fitccl`
-            pyzebra.fitccl(scan, **deepcopy(fit_params))
+        for scan, export in zip(det_data, scan_table_source.data["export"]):
+            if export:
+                pyzebra.fit_scan(
+                    scan, fit_params, fit_from=fit_from_spinner.value, fit_to=fit_to_spinner.value
+                )
 
         _update_plot()
         _update_table()
@@ -575,7 +557,9 @@ def create():
 
     def fit_button_callback():
         scan = _get_selected_scan()
-        pyzebra.fitccl(scan, **_get_fit_params())
+        pyzebra.fit_scan(
+            scan, fit_params, fit_from=fit_from_spinner.value, fit_to=fit_to_spinner.value
+        )
 
         _update_plot()
         _update_table()
@@ -583,104 +567,83 @@ def create():
     fit_button = Button(label="Fit Current", default_size=145)
     fit_button.on_click(fit_button_callback)
 
-    def area_method_radiobutton_callback(_attr, _old, new):
-        det_data["meta"]["area_method"] = AREA_METHODS[new]
-
     area_method_radiobutton = RadioButtonGroup(
-        labels=["Fit area", "Int area"], active=0, default_size=145,
+        labels=["Fit area", "Int area"], active=0, default_size=145, disabled=True
     )
-    area_method_radiobutton.on_change("active", area_method_radiobutton_callback)
 
-    bin_size_spinner = Spinner(title="Bin size:", value=1, low=1, step=1, default_size=145)
+    bin_size_spinner = Spinner(
+        title="Bin size:", value=1, low=1, step=1, default_size=145, disabled=True
+    )
 
     lorentz_toggle = Toggle(label="Lorentz Correction", default_size=145)
 
-    preview_output_textinput = TextAreaInput(title="Export file preview:", width=450, height=400)
-
-    def preview_output_button_callback():
-        if det_data["meta"]["indices"] == "hkl":
-            ext = ".comm"
-        elif det_data["meta"]["indices"] == "real":
-            ext = ".incomm"
+    export_preview_textinput = TextAreaInput(title="Export preview:", width=450, height=400)
 
+    def preview_button_callback():
         with tempfile.TemporaryDirectory() as temp_dir:
             temp_file = temp_dir + "/temp"
-            export_data = deepcopy(det_data)
-            for s, export in zip(scan_table_source.data["scan"], scan_table_source.data["export"]):
-                if not export:
-                    del export_data["scan"][s]
-            pyzebra.export_comm(export_data, temp_file, lorentz=lorentz_toggle.active)
+            export_data = []
+            for s, export in zip(det_data, scan_table_source.data["export"]):
+                if export:
+                    export_data.append(s)
 
-            with open(f"{temp_file}{ext}") as f:
-                preview_output_textinput.value = f.read()
+            pyzebra.export_1D(
+                export_data,
+                temp_file,
+                area_method=AREA_METHODS[int(area_method_radiobutton.active)],
+                lorentz=lorentz_toggle.active,
+            )
 
-    preview_output_button = Button(label="Preview file", default_size=220)
-    preview_output_button.on_click(preview_output_button_callback)
+            exported_content = ""
+            file_content = []
+            for ext in (".comm", ".incomm"):
+                fname = temp_file + ext
+                if os.path.isfile(fname):
+                    with open(fname) as f:
+                        content = f.read()
+                        exported_content += f"{ext} file:\n" + content
+                else:
+                    content = ""
+                file_content.append(content)
 
-    def export_results(det_data):
-        if det_data["meta"]["indices"] == "hkl":
-            ext = ".comm"
-        elif det_data["meta"]["indices"] == "real":
-            ext = ".incomm"
+            js_data.data.update(content=file_content)
+            export_preview_textinput.value = exported_content
 
-        with tempfile.TemporaryDirectory() as temp_dir:
-            temp_file = temp_dir + "/temp"
-            export_data = deepcopy(det_data)
-            for s, export in zip(scan_table_source.data["scan"], scan_table_source.data["export"]):
-                if not export:
-                    del export_data["scan"][s]
-            pyzebra.export_comm(export_data, temp_file, lorentz=lorentz_toggle.active)
+    preview_button = Button(label="Preview", default_size=220)
+    preview_button.on_click(preview_button_callback)
 
-            with open(f"{temp_file}{ext}") as f:
-                output_content = f.read()
-
-        return output_content, ext
-
-    def save_button_callback():
-        cont, ext = export_results(det_data)
-        js_data.data.update(cont=[cont], ext=[ext])
-
-    save_button = Button(label="Download file", button_type="success", default_size=220)
-    save_button.on_click(save_button_callback)
+    save_button = Button(label="Download preview", button_type="success", default_size=220)
     save_button.js_on_click(CustomJS(args={"js_data": js_data}, code=javaScript))
 
-    findpeak_controls = column(
-        row(peak_pos_textinput, column(Spacer(height=19), smooth_toggle)),
-        row(peak_int_ratio_spinner, peak_prominence_spinner),
-        row(window_size_spinner, poly_order_spinner),
-        row(peakfind_button, peakfind_all_button),
-    )
-
     fitpeak_controls = row(
         column(fitparams_add_dropdown, fitparams_select, fitparams_remove_button),
         fitparams_table,
         Spacer(width=20),
         column(
-            row(integ_from, integ_to),
+            row(fit_from_spinner, fit_to_spinner),
             row(bin_size_spinner, column(Spacer(height=19), lorentz_toggle)),
-            row(fitparam_reset_button, area_method_radiobutton),
+            row(area_method_radiobutton),
             row(fit_button, fit_all_button),
         ),
     )
 
-    export_layout = column(preview_output_textinput, row(preview_output_button, save_button))
+    scan_layout = column(scan_table, row(monitor_spinner, param_select))
+
+    import_layout = column(
+        proposal_textinput,
+        file_select,
+        row(file_open_button, file_append_button),
+        upload_div,
+        upload_button,
+        append_upload_div,
+        append_upload_button,
+    )
+
+    export_layout = column(export_preview_textinput, row(preview_button, save_button))
 
-    upload_div = Div(text="Or upload .dat files:")
-    append_upload_div = Div(text="append extra .dat files:")
     tab_layout = column(
-        row(
-            proposal_textinput,
-            file_select,
-            column(Spacer(height=19), row(file_open_button, file_append_button)),
-        ),
-        row(
-            column(Spacer(height=5), upload_div),
-            upload_button,
-            column(Spacer(height=5), append_upload_div),
-            append_upload_button,
-        ),
-        row(scan_table, plots, Spacer(width=30), fit_output_textinput, export_layout),
-        row(findpeak_controls, Spacer(width=30), fitpeak_controls),
+        row(import_layout, scan_layout, plots, Spacer(width=30), export_layout),
+        row(fitpeak_controls, fit_output_textinput),
     )
 
     return Panel(child=tab_layout, title="param study")

pyzebra/app/panel_spind.py

@@ -0,0 +1,253 @@
+import ast
+import math
+import os
+import subprocess
+import tempfile
+from collections import defaultdict
+
+import numpy as np
+from bokeh.layouts import column, row
+from bokeh.models import (
+    Button,
+    ColumnDataSource,
+    DataTable,
+    Panel,
+    Spinner,
+    TableColumn,
+    TextAreaInput,
+    TextInput,
+)
+from scipy.optimize import curve_fit
+
+import pyzebra
+
+
+def create():
+    path_prefix_textinput = TextInput(title="Path prefix:", value="")
+    selection_list = TextAreaInput(title="ROIs:", rows=7)
+    lattice_const_textinput = TextInput(
+        title="Lattice constants:", value="8.3211,8.3211,8.3211,90.00,90.00,90.00"
+    )
+    max_res_spinner = Spinner(title="max-res", value=2, step=0.01)
+    seed_pool_size_spinner = Spinner(title="seed-pool-size", value=5, step=0.01)
+    seed_len_tol_spinner = Spinner(title="seed-len-tol", value=0.02, step=0.01)
+    seed_angle_tol_spinner = Spinner(title="seed-angle-tol", value=1, step=0.01)
+    eval_hkl_tol_spinner = Spinner(title="eval-hkl-tol", value=0.15, step=0.01)
+
+    diff_vec = []
+
+    def process_button_callback():
+        nonlocal diff_vec
+        with tempfile.TemporaryDirectory() as temp_dir:
+            temp_peak_list_dir = os.path.join(temp_dir, "peak_list")
+            os.mkdir(temp_peak_list_dir)
+            temp_event_file = os.path.join(temp_peak_list_dir, "event-0.txt")
+            temp_hkl_file = os.path.join(temp_dir, "hkl.h5")
+            roi_dict = ast.literal_eval(selection_list.value)
+
+            comp_proc = subprocess.run(
+                [
+                    "mpiexec",
+                    "-n",
+                    "2",
+                    "python",
+                    "spind/gen_hkl_table.py",
+                    lattice_const_textinput.value,
+                    "--max-res",
+                    str(max_res_spinner.value),
+                    "-o",
+                    temp_hkl_file,
+                ],
+                check=True,
+                stdout=subprocess.PIPE,
+                stderr=subprocess.STDOUT,
+                text=True,
+            )
+            print(" ".join(comp_proc.args))
+            print(comp_proc.stdout)
+
+            diff_vec = prepare_event_file(temp_event_file, roi_dict, path_prefix_textinput.value)
+
+            comp_proc = subprocess.run(
+                [
+                    "mpiexec",
+                    "-n",
+                    "2",
+                    "python",
+                    "spind/SPIND.py",
+                    temp_peak_list_dir,
+                    temp_hkl_file,
+                    "-o",
+                    temp_dir,
+                    "--seed-pool-size",
+                    str(seed_pool_size_spinner.value),
+                    "--seed-len-tol",
+                    str(seed_len_tol_spinner.value),
+                    "--seed-angle-tol",
+                    str(seed_angle_tol_spinner.value),
+                    "--eval-hkl-tol",
+                    str(eval_hkl_tol_spinner.value),
+                ],
+                check=True,
+                stdout=subprocess.PIPE,
+                stderr=subprocess.STDOUT,
+                text=True,
+            )
+            print(" ".join(comp_proc.args))
+            print(comp_proc.stdout)
+
+            try:
+                with open(os.path.join(temp_dir, "spind.txt")) as f_out:
+                    spind_res = defaultdict(list)
+                    for line in f_out:
+                        c1, c2, c3, c4, c5, *c_rest = line.split()
+                        spind_res["label"].append(c1)
+                        spind_res["crystal_id"].append(c2)
+                        spind_res["match_rate"].append(c3)
+                        spind_res["matched_peaks"].append(c4)
+                        spind_res["column_5"].append(c5)
+
+                        # last digits are spind UB matrix
+                        vals = list(map(float, c_rest))
+                        ub_matrix_spind = np.array(vals).reshape(3, 3)
+                        ub_matrix = np.linalg.inv(np.transpose(ub_matrix_spind)) * 1e10
+                        spind_res["ub_matrix"].append(ub_matrix)
+
+                    results_table_source.data.update(spind_res)
+
+            except FileNotFoundError:
+                print("No results from spind")
+
+    process_button = Button(label="Process", button_type="primary")
+    process_button.on_click(process_button_callback)
+
+    hkl_textareainput = TextAreaInput(title="hkl values:", rows=7)
+
+    def results_table_select_callback(_attr, old, new):
+        if new:
+            ind = new[0]
+            ub_matrix = results_table_source.data["ub_matrix"][ind]
+            res = ""
+            for vec in diff_vec:
+                res += f"{vec @ ub_matrix}\n"
+            hkl_textareainput.value = res
+        else:
+            hkl_textareainput.value = None
+
+    results_table_source = ColumnDataSource(dict())
+    results_table = DataTable(
+        source=results_table_source,
+        columns=[
+            TableColumn(field="label", title="Label", width=50),
+            TableColumn(field="crystal_id", title="Crystal ID", width=100),
+            TableColumn(field="match_rate", title="Match Rate", width=100),
+            TableColumn(field="matched_peaks", title="Matched Peaks", width=100),
+            TableColumn(field="column_5", title="", width=100),
+            TableColumn(field="ub_matrix", title="UB Matrix", width=250),
+        ],
+        height=300,
+        width=700,
+        autosize_mode="none",
+        index_position=None,
+    )
+
+    results_table_source.selected.on_change("indices", results_table_select_callback)
+
+    tab_layout = row(
+        column(
+            path_prefix_textinput,
+            selection_list,
+            lattice_const_textinput,
+            max_res_spinner,
+            seed_pool_size_spinner,
+            seed_len_tol_spinner,
+            seed_angle_tol_spinner,
+            eval_hkl_tol_spinner,
+            process_button,
+        ),
+        column(results_table, row(hkl_textareainput)),
+    )
+
+    return Panel(child=tab_layout, title="spind")
+
+
+def gauss(x, *p):
+    """Defines Gaussian function
+    Args:
+        A - amplitude, mu - position of the center, sigma - width
+    Returns:
+        Gaussian function
+    """
+    A, mu, sigma = p
+    return A * np.exp(-((x - mu) ** 2) / (2.0 * sigma ** 2))
+
+
+def prepare_event_file(export_filename, roi_dict, path_prefix=""):
+    diff_vec = []
+    p0 = [1.0, 0.0, 1.0]
+    maxfev = 100000
+    with open(export_filename, "w") as f:
+        for file, rois in roi_dict.items():
+            dat = pyzebra.read_detector_data(path_prefix + file + ".hdf")
+
+            wave = dat["wave"]
+            ddist = dat["ddist"]
+
+            gamma = dat["gamma"][0]
+            omega = dat["omega"][0]
+            nu = dat["nu"][0]
+            chi = dat["chi"][0]
+            phi = dat["phi"][0]
+
+            scan_motor = dat["scan_motor"]
+            var_angle = dat[scan_motor]
+
+            for roi in rois:
+                x0, xN, y0, yN, fr0, frN = roi
+                data_roi = dat["data"][fr0:frN, y0:yN, x0:xN]
+
+                cnts = np.sum(data_roi, axis=(1, 2))
+                coeff, _ = curve_fit(gauss, range(len(cnts)), cnts, p0=p0, maxfev=maxfev)
+
+                m = cnts.mean()
+                sd = cnts.std()
+                snr_cnts = np.where(sd == 0, 0, m / sd)
+
+                frC = fr0 + coeff[1]
+                var_F = var_angle[math.floor(frC)]
+                var_C = var_angle[math.ceil(frC)]
+                frStep = frC - math.floor(frC)
+                var_step = var_C - var_F
+                var_p = var_F + var_step * frStep
+
+                if scan_motor == "gamma":
+                    gamma = var_p
+                elif scan_motor == "omega":
+                    omega = var_p
+                elif scan_motor == "nu":
+                    nu = var_p
+                elif scan_motor == "chi":
+                    chi = var_p
+                elif scan_motor == "phi":
+                    phi = var_p
+
+                intensity = coeff[1] * abs(coeff[2] * var_step) * math.sqrt(2) * math.sqrt(np.pi)
+
+                projX = np.sum(data_roi, axis=(0, 1))
+                coeff, _ = curve_fit(gauss, range(len(projX)), projX, p0=p0, maxfev=maxfev)
+                x_pos = x0 + coeff[1]
+
+                projY = np.sum(data_roi, axis=(0, 2))
+                coeff, _ = curve_fit(gauss, range(len(projY)), projY, p0=p0, maxfev=maxfev)
+                y_pos = y0 + coeff[1]
+
+                ga, nu = pyzebra.det2pol(ddist, gamma, nu, x_pos, y_pos)
+                diff_vector = pyzebra.z1frmd(wave, ga, omega, chi, phi, nu)
+                d_spacing = float(pyzebra.dandth(wave, diff_vector)[0])
+                dv1, dv2, dv3 = diff_vector.flatten() * 1e10
+
+                diff_vec.append(diff_vector.flatten())
+
+                f.write(f"{x_pos} {y_pos} {intensity} {snr_cnts} {dv1} {dv2} {dv3} {d_spacing}\n")
+
+    return diff_vec

pyzebra/ccl_findpeaks.py

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

pyzebra/ccl_io.py

@@ -58,37 +58,22 @@ META_VARS_FLOAT = (
 
 META_UB_MATRIX = ("ub1j", "ub2j", "ub3j")
 
-CCL_FIRST_LINE = (
-    ("scan_number", int),
-    ("h_index", float),
-    ("k_index", float),
-    ("l_index", float),
-)
+CCL_FIRST_LINE = (("idx", int), ("h", float), ("k", float), ("l", float))
 
 CCL_ANGLES = {
-    "bi": (
-        ("twotheta_angle", float),
-        ("omega_angle", float),
-        ("chi_angle", float),
-        ("phi_angle", float),
-    ),
-    "nb": (
-        ("gamma_angle", float),
-        ("omega_angle", float),
-        ("nu_angle", float),
-        ("unkwn_angle", float),
-    ),
+    "bi": (("twotheta", float), ("omega", float), ("chi", float), ("phi", float)),
+    "nb": (("gamma", float), ("omega", float), ("nu", float), ("skip_angle", float)),
 }
 
 CCL_SECOND_LINE = (
     ("n_points", int),
     ("angle_step", float),
     ("monitor", float),
-    ("temperature", float),
-    ("mag_field", float),
+    ("temp", float),
+    ("mf", float),
     ("date", str),
     ("time", str),
-    ("scan_type", str),
+    ("scan_motor", str),
 )
 
 AREA_METHODS = ("fit_area", "int_area")
@@ -114,61 +99,106 @@ def load_1D(filepath):
 
 
 def parse_1D(fileobj, data_type):
+    metadata = {"data_type": data_type}
+
     # read metadata
-    metadata = {}
     for line in fileobj:
         if "=" in line:
-            variable, value = line.split("=")
+            variable, value = line.split("=", 1)
             variable = variable.strip()
-            if variable in META_VARS_FLOAT:
+            value = value.strip()
+
+            if variable in META_VARS_STR:
+                metadata[variable] = value
+
+            elif variable in META_VARS_FLOAT:
+                if variable == "2-theta":  # fix that angle name not to be an expression
+                    variable = "twotheta"
+                if variable in ("a", "b", "c", "alpha", "beta", "gamma"):
+                    variable += "_cell"
                 metadata[variable] = float(value)
-            elif variable in META_VARS_STR:
-                metadata[variable] = str(value)[:-1].strip()
+
             elif variable in META_UB_MATRIX:
-                metadata[variable] = re.findall(r"[-+]?\d*\.\d+|\d+", str(value))
+                if "ub" not in metadata:
+                    metadata["ub"] = np.zeros((3, 3))
+                row = int(variable[-2]) - 1
+                metadata["ub"][row, :] = list(map(float, value.split()))
 
         if "#data" in line:
             # this is the end of metadata and the start of data section
             break
 
+    # handle older files that don't contain "zebra_mode" metadata
+    if "zebra_mode" not in metadata:
+        metadata["zebra_mode"] = "nb"
+
     # read data
-    scan = {}
+    scan = []
     if data_type == ".ccl":
-        ccl_first_line = (*CCL_FIRST_LINE, *CCL_ANGLES[metadata["zebra_mode"]])
+        ccl_first_line = CCL_FIRST_LINE + CCL_ANGLES[metadata["zebra_mode"]]
         ccl_second_line = CCL_SECOND_LINE
 
         for line in fileobj:
-            d = {}
+            # skip empty/whitespace lines before start of any scan
+            if not line or line.isspace():
+                continue
+
+            s = {}
 
             # first line
             for param, (param_name, param_type) in zip(line.split(), ccl_first_line):
-                d[param_name] = param_type(param)
+                s[param_name] = param_type(param)
 
             # second line
             next_line = next(fileobj)
             for param, (param_name, param_type) in zip(next_line.split(), ccl_second_line):
-                d[param_name] = param_type(param)
+                s[param_name] = param_type(param)
 
-            d["om"] = np.linspace(
-                d["omega_angle"] - (d["n_points"] / 2) * d["angle_step"],
-                d["omega_angle"] + (d["n_points"] / 2) * d["angle_step"],
-                d["n_points"],
-            )
+            if s["scan_motor"] != "om":
+                raise Exception("Unsupported variable name in ccl file.")
+
+            # "om" -> "omega"
+            s["scan_motor"] = "omega"
+            # overwrite metadata, because it only refers to the scan center
+            half_dist = (s["n_points"] - 1) / 2 * s["angle_step"]
+            s["omega"] = np.linspace(s["omega"] - half_dist, s["omega"] + half_dist, s["n_points"])
 
             # subsequent lines with counts
             counts = []
-            while len(counts) < d["n_points"]:
-                counts.extend(map(int, next(fileobj).split()))
-            d["Counts"] = counts
+            while len(counts) < s["n_points"]:
+                counts.extend(map(float, next(fileobj).split()))
+            s["Counts"] = np.array(counts)
 
-            scan[d["scan_number"]] = d
+            if s["h"].is_integer() and s["k"].is_integer() and s["l"].is_integer():
+                s["h"], s["k"], s["l"] = map(int, (s["h"], s["k"], s["l"]))
+
+            scan.append({**metadata, **s})
 
     elif data_type == ".dat":
-        # skip the first 2 rows, the third row contans the column names
-        next(fileobj)
-        next(fileobj)
+        # TODO: this might need to be adapted in the future, when "gamma" will be added to dat files
+        if metadata["zebra_mode"] == "nb":
+            metadata["gamma"] = metadata["twotheta"]
+
+        s = defaultdict(list)
+
+        match = re.search("Scanning Variables: (.*), Steps: (.*)", next(fileobj))
+        if match.group(1) == "h, k, l":
+            steps = match.group(2).split()
+            for step, ind in zip(steps, "hkl"):
+                if float(step) != 0:
+                    scan_motor = ind
+                    break
+        else:
+            scan_motor = match.group(1)
+
+        s["scan_motor"] = scan_motor
+
+        match = re.search("(.*) Points, Mode: (.*), Preset (.*)", next(fileobj))
+        if match.group(2) != "Monitor":
+            raise Exception("Unknown mode in dat file.")
+        s["monitor"] = float(match.group(3))
+
         col_names = next(fileobj).split()
-        data_cols = defaultdict(list)
 
         for line in fileobj:
             if "END-OF-DATA" in line:
@@ -176,100 +206,105 @@ def parse_1D(fileobj, data_type):
                 break
 
             for name, val in zip(col_names, line.split()):
-                data_cols[name].append(float(val))
+                s[name].append(float(val))
 
-        try:
-            data_cols["h_index"] = float(metadata["title"].split()[-3])
-            data_cols["k_index"] = float(metadata["title"].split()[-2])
-            data_cols["l_index"] = float(metadata["title"].split()[-1])
-        except (ValueError, IndexError):
-            print("seems hkl is not in title")
+        for name in col_names:
+            s[name] = np.array(s[name])
 
-        data_cols["om"] = np.array(data_cols["om"])
+        # "om" -> "omega"
+        if s["scan_motor"] == "om":
+            s["scan_motor"] = "omega"
+            s["omega"] = s["om"]
+            del s["om"]
 
-        data_cols["temperature"] = metadata["temp"]
-        try:
-            data_cols["mag_field"] = metadata["mf"]
-        except KeyError:
-            print("Mag_field not present in dat file")
+        # "tt" -> "temp"
+        elif s["scan_motor"] == "tt":
+            s["scan_motor"] = "temp"
+            s["temp"] = s["tt"]
+            del s["tt"]
 
-        data_cols["omega_angle"] = metadata["omega"]
-        data_cols["n_points"] = len(data_cols["om"])
-        data_cols["monitor"] = data_cols["Monitor1"][0]
-        data_cols["twotheta_angle"] = metadata["2-theta"]
-        data_cols["chi_angle"] = metadata["chi"]
-        data_cols["phi_angle"] = metadata["phi"]
-        data_cols["nu_angle"] = metadata["nu"]
+        # "mf" stays "mf"
+        # "phi" stays "phi"
 
-        data_cols["scan_number"] = 1
-        scan[data_cols["scan_number"]] = dict(data_cols)
+        if "h" not in s:
+            s["h"] = s["k"] = s["l"] = float("nan")
+
+        for param in ("mf", "temp"):
+            if param not in metadata:
+                s[param] = 0
+
+        s["idx"] = 1
+
+        scan.append({**metadata, **s})
 
     else:
         print("Unknown file extention")
 
-    # utility information
-    if all(
-        s["h_index"].is_integer() and s["k_index"].is_integer() and s["l_index"].is_integer()
-        for s in scan.values()
-    ):
-        metadata["indices"] = "hkl"
-    else:
-        metadata["indices"] = "real"
-
-    metadata["data_type"] = data_type
-    metadata["area_method"] = AREA_METHODS[0]
-
-    return {"meta": metadata, "scan": scan}
+    return scan
 
 
-def export_comm(data, path, lorentz=False, hkl_precision=2):
-    """exports data in the *.comm format
-    :param lorentz: perform Lorentz correction
-    :param path: path to file + name
-    :arg data - data to export, is dict after peak fitting
+def export_1D(data, path, area_method=AREA_METHODS[0], lorentz=False, hkl_precision=2):
+    """Exports data in the .comm/.incomm format
 
+    Scans with integer/real hkl values are saved in .comm/.incomm files correspondingly. If no scans
+    are present for a particular output format, that file won't be created.
     """
-    zebra_mode = data["meta"]["zebra_mode"]
-    if data["meta"]["indices"] == "hkl":
-        extension = ".comm"
-    else:  # data["meta"]["indices"] == "real":
-        extension = ".incomm"
+    zebra_mode = data[0]["zebra_mode"]
+    file_content = {".comm": [], ".incomm": []}
 
-    with open(str(path + extension), "w") as out_file:
-        for key, scan in data["scan"].items():
-            if "fit" not in scan:
-                print("Scan skipped - no fit value for:", key)
-                continue
+    for scan in data:
+        if "fit" not in scan:
+            continue
 
-            scan_str = f"{key:6}"
+        idx_str = f"{scan['idx']:6}"
 
-            h, k, l = scan["h_index"], scan["k_index"], scan["l_index"]
-            if data["meta"]["indices"] == "hkl":
-                hkl_str = f"{int(h):6}{int(k):6}{int(l):6}"
-            else:  # data["meta"]["indices"] == "real"
-                hkl_str = f"{h:8.{hkl_precision}f}{k:8.{hkl_precision}f}{l:8.{hkl_precision}f}"
+        h, k, l = scan["h"], scan["k"], scan["l"]
+        hkl_are_integers = isinstance(h, int)  # if True, other indices are of type 'int' too
+        if hkl_are_integers:
+            hkl_str = f"{h:6}{k:6}{l:6}"
+        else:
+            hkl_str = f"{h:8.{hkl_precision}f}{k:8.{hkl_precision}f}{l:8.{hkl_precision}f}"
 
-            area_method = data["meta"]["area_method"]
-            area_n = scan["fit"][area_method].n
-            area_s = scan["fit"][area_method].s
+        for name, param in scan["fit"].params.items():
+            if "amplitude" in name:
+                area_n = param.value
+                area_s = param.stderr
+                break
+        else:
+            area_n = 0
+            area_s = 0
 
-            # apply lorentz correction to area
-            if lorentz:
-                if zebra_mode == "bi":
-                    twotheta_angle = np.deg2rad(scan["twotheta_angle"])
-                    corr_factor = np.sin(twotheta_angle)
-                else:  # zebra_mode == "nb":
-                    gamma_angle = np.deg2rad(scan["gamma_angle"])
-                    nu_angle = np.deg2rad(scan["nu_angle"])
-                    corr_factor = np.sin(gamma_angle) * np.cos(nu_angle)
+        if area_n is None or area_s is None:
+            print(f"Couldn't export scan: {scan['idx']}")
+            continue
 
-                area_n = np.abs(area_n * corr_factor)
-                area_s = np.abs(area_s * corr_factor)
+        # 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_str = f"{area_n:10.2f}{area_s:10.2f}"
+            area_n = np.abs(area_n * corr_factor)
+            area_s = np.abs(area_s * corr_factor)
 
-            ang_str = ""
-            for angle, _ in CCL_ANGLES[zebra_mode]:
-                ang_str = ang_str + f"{scan[angle]:8}"
+        area_str = f"{area_n:10.2f}{area_s:10.2f}"
 
-            out_file.write(scan_str + hkl_str + area_str + ang_str + "\n")
+        ang_str = ""
+        for angle, _ in CCL_ANGLES[zebra_mode]:
+            if angle == scan["scan_motor"]:
+                angle_center = (np.min(scan[angle]) + np.max(scan[angle])) / 2
+            else:
+                angle_center = scan[angle]
+            ang_str = ang_str + f"{angle_center:8g}"
+
+        ref = file_content[".comm"] if hkl_are_integers else file_content[".incomm"]
+        ref.append(idx_str + hkl_str + area_str + ang_str + "\n")
+
+    for ext, content in file_content.items():
+        if content:
+            with open(path + ext, "w") as out_file:
+                out_file.writelines(content)

pyzebra/ccl_process.py

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

pyzebra/fit2.py

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

pyzebra/fitvol3.py

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

pyzebra/h5.py

@@ -41,7 +41,7 @@ def read_detector_data(filepath):
         filepath (str): File path of an h5 file.
 
     Returns:
-        ndarray: A 3D array of data, rot_angle, pol_angle, tilt_angle.
+        ndarray: A 3D array of data, omega, gamma, nu.
     """
     with h5py.File(filepath, "r") as h5f:
         data = h5f["/entry1/area_detector2/data"][:]
@@ -52,20 +52,37 @@ def read_detector_data(filepath):
 
         det_data = {"data": data}
 
-        det_data["rot_angle"] = h5f["/entry1/area_detector2/rotation_angle"][:]  # om, sometimes ph
-        det_data["pol_angle"] = h5f["/entry1/ZEBRA/area_detector2/polar_angle"][:]  # gammad
-        det_data["tlt_angle"] = h5f["/entry1/ZEBRA/area_detector2/tilt_angle"][:]  # nud
+        if "/entry1/zebra_mode" in h5f:
+            det_data["zebra_mode"] = h5f["/entry1/zebra_mode"][0].decode()
+        else:
+            det_data["zebra_mode"] = "nb"
+
+        # om, sometimes ph
+        if det_data["zebra_mode"] == "nb":
+            det_data["omega"] = h5f["/entry1/area_detector2/rotation_angle"][:]
+        else:  # bi
+            det_data["omega"] = h5f["/entry1/sample/rotation_angle"][:]
+
+        det_data["gamma"] = h5f["/entry1/ZEBRA/area_detector2/polar_angle"][:]  # gammad
+        det_data["nu"] = h5f["/entry1/ZEBRA/area_detector2/tilt_angle"][:]  # nud
         det_data["ddist"] = h5f["/entry1/ZEBRA/area_detector2/distance"][:]
         det_data["wave"] = h5f["/entry1/ZEBRA/monochromator/wavelength"][:]
-        det_data["chi_angle"] = h5f["/entry1/sample/chi"][:]  # ch
-        det_data["phi_angle"] = h5f["/entry1/sample/phi"][:]  # ph
-        det_data["UB"] = h5f["/entry1/sample/UB"][:].reshape(3, 3)
+        det_data["chi"] = h5f["/entry1/sample/chi"][:]  # ch
+        det_data["phi"] = h5f["/entry1/sample/phi"][:]  # ph
+        det_data["ub"] = h5f["/entry1/sample/UB"][:].reshape(3, 3)
+
+        for var in ("omega", "gamma", "nu", "chi", "phi"):
+            if abs(det_data[var][0] - det_data[var][-1]) > 0.1:
+                det_data["scan_motor"] = var
+                break
+        else:
+            raise ValueError("No angles that vary")
 
         # optional parameters
         if "/entry1/sample/magnetic_field" in h5f:
-            det_data["magnetic_field"] = h5f["/entry1/sample/magnetic_field"][:]
+            det_data["mf"] = h5f["/entry1/sample/magnetic_field"][:]
 
         if "/entry1/sample/temperature" in h5f:
-            det_data["temperature"] = h5f["/entry1/sample/temperature"][:]
+            det_data["temp"] = h5f["/entry1/sample/temperature"][:]
 
     return det_data

pyzebra/merge_function.py

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

pyzebra/param_study_moduls.py

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

pyzebra/xtal.py

@@ -407,24 +407,24 @@ def box_int(file, box):
 
     dat = pyzebra.read_detector_data(file)
 
-    sttC = dat["pol_angle"][0]
-    om = dat["rot_angle"]
-    nuC = dat["tlt_angle"][0]
+    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["rot_angle"][fr0:frN]
+    om = dat["omega"][fr0:frN]
     cnts = np.sum(dat["data"][fr0:frN, y0:yN, x0:xN], axis=(1, 2))
 
     p0 = [1.0, 0.0, 1.0]
     coeff, var_matrix = curve_fit(gauss, range(len(cnts)), cnts, p0=p0)
 
     frC = fr0 + coeff[1]
-    omF = dat["rot_angle"][math.floor(frC)]
-    omC = dat["rot_angle"][math.ceil(frC)]
+    omF = dat["omega"][math.floor(frC)]
+    omC = dat["omega"][math.ceil(frC)]
     frStep = frC - math.floor(frC)
     omStep = omC - omF
     omP = omF + omStep * frStep