Test tabular batch widget 'GUI'

.gitignore

@@ -32,7 +32,6 @@ docs/generated/
 # Data files
 *.data
 *.dat
-*.csv
 *.xye
 *.h5
 *.hdf5
@@ -45,3 +44,5 @@ docs/generated/
 *.zip
 *.sqw
 *.nxspe
+/src/ess/loki/examplefiles
+/src/ess/loki/batch-gui-legacy

src/ess/loki/batch-gui-legacy/batchwidget-tabs.py

@@ -0,0 +1,338 @@
+import glob
+import os
+
+import ipywidgets as widgets
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import scipp as sc
+from ipydatagrid import DataGrid
+from ipyfilechooser import FileChooser
+from IPython.display import display
+
+from ess import loki, sans
+from ess.sans.types import *
+
+
+def reduce_loki_batch_preliminary(
+    sample_run_file: str,
+    transmission_run_file: str,
+    background_run_file: str,
+    empty_beam_file: str,
+    direct_beam_file: str,
+    mask_files: list = None,
+    correct_for_gravity: bool = True,
+    uncertainty_mode=UncertaintyBroadcastMode.upper_bound,
+    return_events: bool = False,
+    wavelength_min: float = 1.0,
+    wavelength_max: float = 13.0,
+    wavelength_n: int = 201,
+    q_start: float = 0.01,
+    q_stop: float = 0.3,
+    q_n: int = 101,
+):
+    if mask_files is None:
+        mask_files = []
+    # Define wavelength and Q bins.
+    wavelength_bins = sc.linspace(
+        "wavelength", wavelength_min, wavelength_max, wavelength_n, unit="angstrom"
+    )
+    q_bins = sc.linspace("Q", q_start, q_stop, q_n, unit="1/angstrom")
+    # Initialize the workflow.
+    workflow = loki.LokiAtLarmorWorkflow()
+    if mask_files:
+        workflow = sans.with_pixel_mask_filenames(workflow, masks=mask_files)
+    workflow[NeXusDetectorName] = "larmor_detector"
+    workflow[WavelengthBins] = wavelength_bins
+    workflow[QBins] = q_bins
+    workflow[CorrectForGravity] = correct_for_gravity
+    workflow[UncertaintyBroadcastMode] = uncertainty_mode
+    workflow[ReturnEvents] = return_events
+    workflow[Filename[BackgroundRun]] = background_run_file
+    workflow[Filename[TransmissionRun[BackgroundRun]]] = transmission_run_file
+    workflow[Filename[EmptyBeamRun]] = empty_beam_file
+    workflow[DirectBeamFilename] = direct_beam_file
+    workflow[Filename[SampleRun]] = sample_run_file
+    workflow[Filename[TransmissionRun[SampleRun]]] = transmission_run_file
+    center = sans.beam_center_from_center_of_mass(workflow)
+    workflow[BeamCenter] = center
+    tf = workflow.compute(TransmissionFraction[SampleRun])
+    da = workflow.compute(BackgroundSubtractedIofQ)
+    return {"transmission": tf, "IofQ": da}
+
+
+def find_file(work_dir, run_number, extension=".nxs"):
+    pattern = os.path.join(work_dir, f"*{run_number}*{extension}")
+    files = glob.glob(pattern)
+    if files:
+        return files[0]
+    else:
+        raise FileNotFoundError(f"Could not find file matching pattern {pattern}")
+
+
+def find_direct_beam(work_dir):
+    pattern = os.path.join(work_dir, "*direct-beam*.h5")
+    files = glob.glob(pattern)
+    if files:
+        return files[0]
+    else:
+        raise FileNotFoundError(
+            f"Could not find direct-beam file matching pattern {pattern}"
+        )
+
+
+def find_mask_file(work_dir):
+    pattern = os.path.join(work_dir, "*mask*.xml")
+    files = glob.glob(pattern)
+    if files:
+        return files[0]
+    else:
+        raise FileNotFoundError(f"Could not find mask file matching pattern {pattern}")
+
+
+def save_xye_pandas(data_array, filename):
+    q_vals = data_array.coords["Q"].values
+    i_vals = data_array.values
+    if len(q_vals) != len(i_vals):
+        q_vals = 0.5 * (q_vals[:-1] + q_vals[1:])
+    if data_array.variances is not None:
+        err_vals = np.sqrt(data_array.variances)
+        if len(err_vals) != len(i_vals):
+            err_vals = 0.5 * (err_vals[:-1] + err_vals[1:])
+    else:
+        err_vals = np.zeros_like(i_vals)
+    df = pd.DataFrame({"Q": q_vals, "I(Q)": i_vals, "Error": err_vals})
+    df.to_csv(filename, sep=" ", index=False, header=True)
+
+
+class SansBatchReductionWidget:
+    def __init__(self):
+        self.csv_chooser = FileChooser(select_dir=False)
+        self.csv_chooser.title = "Select CSV File"
+        self.csv_chooser.filter_pattern = "*.csv"
+        self.input_dir_chooser = FileChooser(select_dir=True)
+        self.input_dir_chooser.title = "Select Input Folder"
+        self.output_dir_chooser = FileChooser(select_dir=True)
+        self.output_dir_chooser.title = "Select Output Folder"
+        self.ebeam_sans_widget = widgets.Text(
+            value="",
+            placeholder="Enter Ebeam SANS run number",
+            description="Ebeam SANS:",
+        )
+        self.ebeam_trans_widget = widgets.Text(
+            value="",
+            placeholder="Enter Ebeam TRANS run number",
+            description="Ebeam TRANS:",
+        )
+        self.load_csv_button = widgets.Button(description="Load CSV")
+        self.load_csv_button.on_click(self.load_csv)
+        self.table = DataGrid(pd.DataFrame([]), editable=True, auto_fit_columns=True)
+        self.reduce_button = widgets.Button(description="Reduce")
+        self.reduce_button.on_click(self.run_reduction)
+        self.clear_log_button = widgets.Button(description="Clear Log")
+        self.clear_log_button.on_click(self.clear_log)
+        self.clear_plots_button = widgets.Button(description="Clear Plots")
+        self.clear_plots_button.on_click(self.clear_plots)
+        self.log_output = widgets.Output()
+        self.plot_output = widgets.Output()
+        self.main = widgets.VBox(
+            [
+                widgets.HBox(
+                    [self.csv_chooser, self.input_dir_chooser, self.output_dir_chooser]
+                ),
+                widgets.HBox([self.ebeam_sans_widget, self.ebeam_trans_widget]),
+                self.load_csv_button,
+                self.table,
+                widgets.HBox(
+                    [self.reduce_button, self.clear_log_button, self.clear_plots_button]
+                ),
+                self.log_output,
+                self.plot_output,
+            ]
+        )
+
+    def clear_log(self, _):
+        self.log_output.clear_output()
+
+    def clear_plots(self, _):
+        self.plot_output.clear_output()
+
+    def load_csv(self, _):
+        csv_path = self.csv_chooser.selected
+        if not csv_path or not os.path.exists(csv_path):
+            with self.log_output:
+                print("CSV file not selected or does not exist.")
+            return
+        df = pd.read_csv(csv_path)
+        self.table.data = df
+        with self.log_output:
+            print(f"Loaded reduction table with {len(df)} rows from {csv_path}.")
+
+    def run_reduction(self, _):
+        input_dir = self.input_dir_chooser.selected
+        output_dir = self.output_dir_chooser.selected
+        if not input_dir or not os.path.isdir(input_dir):
+            with self.log_output:
+                print("Input folder is not valid.")
+            return
+        if not output_dir or not os.path.isdir(output_dir):
+            with self.log_output:
+                print("Output folder is not valid.")
+            return
+        try:
+            direct_beam_file = find_direct_beam(input_dir)
+            with self.log_output:
+                print("Using direct-beam file:", direct_beam_file)
+        except Exception as e:
+            with self.log_output:
+                print("Direct-beam file not found:", e)
+            return
+        try:
+            background_run_file = find_file(
+                input_dir, self.ebeam_sans_widget.value, extension=".nxs"
+            )
+            empty_beam_file = find_file(
+                input_dir, self.ebeam_trans_widget.value, extension=".nxs"
+            )
+            with self.log_output:
+                print("Using empty-beam files:")
+                print("  Background (Ebeam SANS):", background_run_file)
+                print("  Empty-beam (Ebeam TRANS):", empty_beam_file)
+        except Exception as e:
+            with self.log_output:
+                print("Error finding empty-beam files:", e)
+            return
+        df = self.table.data
+        for idx, row in df.iterrows():
+            sample = row["SAMPLE"]
+            try:
+                sample_run_file = find_file(
+                    input_dir, str(row["SANS"]), extension=".nxs"
+                )
+                transmission_run_file = find_file(
+                    input_dir, str(row["TRANS"]), extension=".nxs"
+                )
+            except Exception as e:
+                with self.log_output:
+                    print(f"Skipping sample {sample}: {e}")
+                continue
+            mask_candidate = str(row.get("mask", "")).strip()
+            mask_file = None
+            if mask_candidate:
+                mask_file_candidate = os.path.join(input_dir, f"{mask_candidate}.xml")
+                if os.path.exists(mask_file_candidate):
+                    mask_file = mask_file_candidate
+            if mask_file is None:
+                try:
+                    mask_file = find_mask_file(input_dir)
+                    with self.log_output:
+                        print(
+                            f"Using global mask file: {mask_file} for sample {sample}"
+                        )
+                except Exception as e:
+                    with self.log_output:
+                        print(f"Mask file not found for sample {sample}: {e}")
+                    continue
+            with self.log_output:
+                print(f"Reducing sample {sample}...")
+            try:
+                res = reduce_loki_batch_preliminary(
+                    sample_run_file=sample_run_file,
+                    transmission_run_file=transmission_run_file,
+                    background_run_file=background_run_file,
+                    empty_beam_file=empty_beam_file,
+                    direct_beam_file=direct_beam_file,
+                    mask_files=[mask_file],
+                )
+            except Exception as e:
+                with self.log_output:
+                    print(f"Reduction failed for sample {sample}: {e}")
+                continue
+            out_xye = os.path.join(
+                output_dir, os.path.basename(sample_run_file).replace(".nxs", ".xye")
+            )
+            try:
+                save_xye_pandas(res["IofQ"], out_xye)
+                with self.log_output:
+                    print(f"Saved reduced data to {out_xye}")
+            except Exception as e:
+                with self.log_output:
+                    print(f"Failed to save reduced data for {sample}: {e}")
+            # Generate and display Transmission plot.
+            wavelength_bins = sc.linspace("wavelength", 1.0, 13.0, 201, unit="angstrom")
+            x_wl = 0.5 * (wavelength_bins.values[:-1] + wavelength_bins.values[1:])
+            fig_trans, ax_trans = plt.subplots()
+            ax_trans.plot(x_wl, res["transmission"].values, marker='o', linestyle='-')
+            ax_trans.set_title(
+                f"Transmission: {sample} {os.path.basename(sample_run_file)}"
+            )
+            ax_trans.set_xlabel("Wavelength (Å)")
+            ax_trans.set_ylabel("Transmission")
+            plt.tight_layout()
+            with self.plot_output:
+                display(fig_trans)
+            trans_png = os.path.join(
+                output_dir,
+                os.path.basename(sample_run_file).replace(".nxs", "_transmission.png"),
+            )
+            fig_trans.savefig(trans_png, dpi=300)
+            plt.close(fig_trans)
+            # Generate and display I(Q) plot.
+            q_bins = sc.linspace("Q", 0.01, 0.3, 101, unit="1/angstrom")
+            x_q = 0.5 * (q_bins.values[:-1] + q_bins.values[1:])
+            fig_iq, ax_iq = plt.subplots()
+            if res["IofQ"].variances is not None:
+                yerr = np.sqrt(res["IofQ"].variances)
+                ax_iq.errorbar(
+                    x_q, res["IofQ"].values, yerr=yerr, marker='o', linestyle='-'
+                )
+            else:
+                ax_iq.plot(x_q, res["IofQ"].values, marker='o', linestyle='-')
+            ax_iq.set_title(f"I(Q): {os.path.basename(sample_run_file)} ({sample})")
+            ax_iq.set_xlabel("Q (Å$^{-1}$)")
+            ax_iq.set_ylabel("I(Q)")
+            ax_iq.set_xscale("log")
+            ax_iq.set_yscale("log")
+            plt.tight_layout()
+            with self.plot_output:
+                display(fig_iq)
+            iq_png = os.path.join(
+                output_dir,
+                os.path.basename(sample_run_file).replace(".nxs", "_IofQ.png"),
+            )
+            fig_iq.savefig(iq_png, dpi=300)
+            plt.close(fig_iq)
+            with self.log_output:
+                print(f"Reduced sample {sample} and saved outputs.")
+
+        @property
+        def widget(self):
+            return self.main
+
+
+def save_xye_pandas(data_array, filename):
+    q_vals = data_array.coords["Q"].values
+    i_vals = data_array.values
+    if len(q_vals) != len(i_vals):
+        q_vals = 0.5 * (q_vals[:-1] + q_vals[1:])
+    if data_array.variances is not None:
+        err_vals = np.sqrt(data_array.variances)
+        if len(err_vals) != len(i_vals):
+            err_vals = 0.5 * (err_vals[:-1] + err_vals[1:])
+    else:
+        err_vals = np.zeros_like(i_vals)
+    df = pd.DataFrame({"Q": q_vals, "I(Q)": i_vals, "Error": err_vals})
+    df.to_csv(filename, sep=" ", index=False, header=True)
+
+
+# Build the main tabbed widget.
+reduction_widget = SansBatchReductionWidget().widget
+direct_beam_widget = widgets.HTML(
+    "<h3>Direct Beam</h3><p>Direct beam tab content goes here.</p>"
+)
+tabs = widgets.Tab(children=[reduction_widget, direct_beam_widget])
+tabs.set_title(0, "Reduction")
+tabs.set_title(1, "Direct Beam")
+
+# Display the tab widget.
+display(tabs)