Direct beam tab content goes here.
" +) +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) diff --git a/src/ess/loki/batch-gui-legacy/batchwidget.py b/src/ess/loki/batch-gui-legacy/batchwidget.py new file mode 100644 index 00000000..0a85e961 --- /dev/null +++ b/src/ess/loki/batch-gui-legacy/batchwidget.py @@ -0,0 +1,325 @@ +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"{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) diff --git a/src/ess/loki/batch-gui-legacy/batchwidgets.ipynb b/src/ess/loki/batch-gui-legacy/batchwidgets.ipynb new file mode 100644 index 00000000..14da5e6b --- /dev/null +++ b/src/ess/loki/batch-gui-legacy/batchwidgets.ipynb @@ -0,0 +1,36 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import batchwidget\n", + "gui = batchwidget.SansBatchReductionWidget()\n", + "display(gui.widget)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/src/ess/loki/batch-gui-legacy/tabwidget-050325.py b/src/ess/loki/batch-gui-legacy/tabwidget-050325.py new file mode 100644 index 00000000..1d52f3f0 --- /dev/null +++ b/src/ess/loki/batch-gui-legacy/tabwidget-050325.py @@ -0,0 +1,826 @@ +import os +import glob +import re +import h5py +import pandas as pd +import scipp as sc +import matplotlib.pyplot as plt +import numpy as np +import ipywidgets as widgets +from ipydatagrid import DataGrid +from IPython.display import display +from ipyfilechooser import FileChooser +from ess import sans +from ess import loki +from ess.sans.types import * +from scipp.scipy.interpolate import interp1d +import plopp as pp # used for plotting in direct beam section +import threading +import time + +# ---------------------------- +# Common Utility Functions +# ---------------------------- +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) + +def extract_run_number(filename): + m = re.search(r'(\d{4,})', filename) + if m: + return m.group(1) + return "" + +def parse_nx_details(filepath): + details = {} + with h5py.File(filepath, 'r') as f: + if 'nicos_details' in f['entry']: + grp = f['entry']['nicos_details'] + if 'runlabel' in grp: + val = grp['runlabel'][()] + details['runlabel'] = val.decode('utf8') if isinstance(val, bytes) else str(val) + if 'runtype' in grp: + val = grp['runtype'][()] + details['runtype'] = val.decode('utf8') if isinstance(val, bytes) else str(val) + return details + +# ---------------------------- +# Reduction and Plotting Functions +# ---------------------------- +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 = [] + 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") + 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 save_reduction_plots(res, sample, sample_run_file, lam_min, lam_max, lam_n, q_min, q_max, q_n, output_dir, show=True): + fig, axs = plt.subplots(1, 2, figsize=(8, 4)) + axs[0].set_box_aspect(1) + axs[1].set_box_aspect(1) + title_str = f"{sample} - {os.path.basename(sample_run_file)}" + fig.suptitle(title_str, fontsize=14) + q_bins = sc.linspace("Q", q_min, q_max, q_n, unit="1/angstrom") + x_q = 0.5 * (q_bins.values[:-1] + q_bins.values[1:]) + if res["IofQ"].variances is not None: + yerr = np.sqrt(res["IofQ"].variances) + axs[0].errorbar(x_q, res["IofQ"].values, yerr=yerr, fmt='o', linestyle='none', color='k', alpha=0.5, markerfacecolor='none') + else: + axs[0].scatter(x_q, res["IofQ"].values) + axs[0].set_xlabel("Q (Å$^{-1}$)") + axs[0].set_ylabel("I(Q)") + axs[0].set_xscale("log") + axs[0].set_yscale("log") + wavelength_bins = sc.linspace("wavelength", lam_min, lam_max, lam_n, unit="angstrom") + x_wl = 0.5 * (wavelength_bins.values[:-1] + wavelength_bins.values[1:]) + if res["transmission"].variances is not None: + yerr_tr = np.sqrt(res["transmission"].variances) + axs[1].errorbar(x_wl, res["transmission"].values, yerr=yerr_tr, fmt='^', linestyle='none', color='k', alpha=0.5, markerfacecolor='none') + else: + axs[1].scatter(x_wl, res["transmission"].values) + axs[1].set_xlabel("Wavelength (Å)") + axs[1].set_ylabel("Transmission") + plt.tight_layout() + out_png = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", "_reduced.png")) + fig.savefig(out_png, dpi=300) + if show: + display(fig) + plt.close(fig) + +# ---------------------------- +# Unified Backend Function for Reduction +# ---------------------------- +def perform_reduction_for_sample( + sample_info: dict, + input_dir: str, + output_dir: str, + reduction_params: dict, + background_run_file: str, + empty_beam_file: str, + direct_beam_file: str, + log_func: callable +): + """ + Processes a single sample reduction: + - Finds the necessary run files + - Optionally determines a mask (or finds one automatically) + - Calls the reduction and plotting routines + - Logs all steps via log_func(message) + """ + sample = sample_info.get("SAMPLE", "Unknown") + try: + sample_run_file = find_file(input_dir, str(sample_info["SANS"]), extension=".nxs") + transmission_run_file = find_file(input_dir, str(sample_info["TRANS"]), extension=".nxs") + except Exception as e: + log_func(f"Skipping sample {sample}: {e}") + return None + # Determine mask file. + mask_file = None + mask_candidate = str(sample_info.get("mask", "")).strip() + if mask_candidate: + mask_candidate_file = os.path.join(input_dir, f"{mask_candidate}.xml") + if os.path.exists(mask_candidate_file): + mask_file = mask_candidate_file + if mask_file is None: + try: + mask_file = find_mask_file(input_dir) + log_func(f"Identified mask file: {mask_file} for sample {sample}") + except Exception as e: + log_func(f"Mask file not found for sample {sample}: {e}") + return None + + log_func(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], + wavelength_min=reduction_params["wavelength_min"], + wavelength_max=reduction_params["wavelength_max"], + wavelength_n=reduction_params["wavelength_n"], + q_start=reduction_params["q_start"], + q_stop=reduction_params["q_stop"], + q_n=reduction_params["q_n"] + ) + except Exception as e: + log_func(f"Reduction failed for sample {sample}: {e}") + return None + out_xye = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", ".xye")) + try: + save_xye_pandas(res["IofQ"], out_xye) + log_func(f"Saved reduced data to {out_xye}") + except Exception as e: + log_func(f"Failed to save reduced data for {sample}: {e}") + try: + save_reduction_plots( + res, + sample, + sample_run_file, + reduction_params["wavelength_min"], + reduction_params["wavelength_max"], + reduction_params["wavelength_n"], + reduction_params["q_start"], + reduction_params["q_stop"], + reduction_params["q_n"], + output_dir, + show=True + ) + log_func(f"Saved reduction plot for sample {sample}.") + except Exception as e: + log_func(f"Failed to save reduction plot for {sample}: {e}") + log_func(f"Reduced sample {sample} and saved outputs.") + return res + +# ---------------------------- +# GUI Widgets (Refactored to use Unified Backend) +# ---------------------------- +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.wavelength_min_widget = widgets.FloatText(value=1.0, description="λ min (Å):") + self.wavelength_max_widget = widgets.FloatText(value=13.0, description="λ max (Å):") + self.wavelength_n_widget = widgets.IntText(value=201, description="λ n_bins:") + self.q_start_widget = widgets.FloatText(value=0.01, description="Q start (1/Å):") + self.q_stop_widget = widgets.FloatText(value=0.3, description="Q stop (1/Å):") + self.q_n_widget = widgets.IntText(value=101, description="Q n_bins:") + 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(lambda _: self.log_output.clear_output()) + self.clear_plots_button = widgets.Button(description="Clear Plots") + self.clear_plots_button.on_click(lambda _: self.plot_output.clear_output()) + 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]), + widgets.HBox([self.wavelength_min_widget, self.wavelength_max_widget, self.wavelength_n_widget]), + widgets.HBox([self.q_start_widget, self.q_stop_widget, self.q_n_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 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, _): + self.log_output.clear_output() + self.plot_output.clear_output() + 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 + + reduction_params = { + "wavelength_min": self.wavelength_min_widget.value, + "wavelength_max": self.wavelength_max_widget.value, + "wavelength_n": self.wavelength_n_widget.value, + "q_start": self.q_start_widget.value, + "q_stop": self.q_stop_widget.value, + "q_n": self.q_n_widget.value + } + + df = self.table.data + for idx, row in df.iterrows(): + perform_reduction_for_sample( + sample_info=row, + input_dir=input_dir, + output_dir=output_dir, + reduction_params=reduction_params, + background_run_file=background_run_file, + empty_beam_file=empty_beam_file, + direct_beam_file=direct_beam_file, + log_func=lambda msg: print(msg) + ) + + @property + def widget(self): + return self.main + +class SemiAutoReductionWidget: + def __init__(self): + 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.scan_button = widgets.Button(description="Scan Directory") + self.scan_button.on_click(self.scan_directory) + self.table = DataGrid(pd.DataFrame([]), editable=True, auto_fit_columns=True) + self.add_row_button = widgets.Button(description="Add Row") + self.add_row_button.on_click(self.add_row) + self.delete_row_button = widgets.Button(description="Delete Last Row") + self.delete_row_button.on_click(self.delete_last_row) + self.lambda_min_widget = widgets.FloatText(value=1.0, description="λ min (Å):") + self.lambda_max_widget = widgets.FloatText(value=13.0, description="λ max (Å):") + self.lambda_n_widget = widgets.IntText(value=201, description="λ n_bins:") + self.q_min_widget = widgets.FloatText(value=0.01, description="Qmin (1/Å):") + self.q_max_widget = widgets.FloatText(value=0.3, description="Qmax (1/Å):") + self.q_n_widget = widgets.IntText(value=101, description="Q n_bins:") + self.empty_beam_sans_text = widgets.Text(value="", description="Ebeam SANS:", disabled=True) + self.empty_beam_trans_text = widgets.Text(value="", description="Ebeam TRANS:", disabled=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(lambda _: self.log_output.clear_output()) + self.clear_plots_button = widgets.Button(description="Clear Plots") + self.clear_plots_button.on_click(lambda _: self.plot_output.clear_output()) + self.log_output = widgets.Output() + self.plot_output = widgets.Output() + self.processed = set() + self.main = widgets.VBox([ + widgets.HBox([self.input_dir_chooser, self.output_dir_chooser]), + self.scan_button, + self.table, + widgets.HBox([self.add_row_button, self.delete_row_button]), + widgets.HBox([self.lambda_min_widget, self.lambda_max_widget, self.lambda_n_widget]), + widgets.HBox([self.q_min_widget, self.q_max_widget, self.q_n_widget]), + widgets.HBox([self.empty_beam_sans_text, self.empty_beam_trans_text]), + widgets.HBox([self.reduce_button, self.clear_log_button, self.clear_plots_button]), + self.log_output, + self.plot_output + ]) + + def add_row(self, _): + df = self.table.data + new_row = {col: "" for col in df.columns} if not df.empty else {'SAMPLE': '', 'SANS': '', 'TRANS': ''} + df = df.append(new_row, ignore_index=True) + self.table.data = df + + def delete_last_row(self, _): + df = self.table.data + if not df.empty: + self.table.data = df.iloc[:-1] + + def scan_directory(self, _): + self.log_output.clear_output() + input_dir = self.input_dir_chooser.selected + if not input_dir or not os.path.isdir(input_dir): + with self.log_output: + print("Invalid input folder.") + return + nxs_files = glob.glob(os.path.join(input_dir, "*.nxs")) + groups = {} + for f in nxs_files: + try: + details = parse_nx_details(f) + except Exception: + continue + if 'runlabel' not in details or 'runtype' not in details: + continue + runlabel = details['runlabel'] + runtype = details['runtype'].lower() + run_number = extract_run_number(os.path.basename(f)) + if runlabel not in groups: + groups[runlabel] = {} + groups[runlabel][runtype] = run_number + table_rows = [] + for runlabel, d in groups.items(): + if 'sans' in d and 'trans' in d: + table_rows.append({'SAMPLE': runlabel, 'SANS': d['sans'], 'TRANS': d['trans']}) + df = pd.DataFrame(table_rows) + self.table.data = df + with self.log_output: + print(f"Scanned {len(nxs_files)} files. Found {len(df)} reduction entries.") + ebeam_sans_files = [] + ebeam_trans_files = [] + for f in nxs_files: + try: + details = parse_nx_details(f) + except Exception: + continue + if 'runtype' in details: + if details['runtype'].lower() == 'ebeam_sans': + ebeam_sans_files.append(f) + elif details['runtype'].lower() == 'ebeam_trans': + ebeam_trans_files.append(f) + if ebeam_sans_files: + ebeam_sans_files.sort(key=lambda x: os.path.getmtime(x), reverse=True) + self.empty_beam_sans_text.value = ebeam_sans_files[0] + else: + self.empty_beam_sans_text.value = "" + if ebeam_trans_files: + ebeam_trans_files.sort(key=lambda x: os.path.getmtime(x), reverse=True) + self.empty_beam_trans_text.value = ebeam_trans_files[0] + else: + self.empty_beam_trans_text.value = "" + + def run_reduction(self, _): + self.log_output.clear_output() + self.plot_output.clear_output() + 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 + background_run_file = self.empty_beam_sans_text.value + empty_beam_file = self.empty_beam_trans_text.value + if not background_run_file or not empty_beam_file: + with self.log_output: + print("Empty beam files not found.") + return + + reduction_params = { + "wavelength_min": self.lambda_min_widget.value, + "wavelength_max": self.lambda_max_widget.value, + "wavelength_n": self.lambda_n_widget.value, + "q_start": self.q_min_widget.value, + "q_stop": self.q_max_widget.value, + "q_n": self.q_n_widget.value + } + + df = self.table.data.copy() + for idx, row in df.iterrows(): + perform_reduction_for_sample( + sample_info=row, + input_dir=input_dir, + output_dir=output_dir, + reduction_params=reduction_params, + background_run_file=background_run_file, + empty_beam_file=empty_beam_file, + direct_beam_file=direct_beam_file, + log_func=lambda msg: print(msg) + ) + + @property + def widget(self): + return self.main + +class AutoReductionWidget: + def __init__(self): + 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.start_stop_button = widgets.Button(description="Start") + self.start_stop_button.on_click(self.toggle_running) + self.status_label = widgets.Label(value="Stopped") + self.table = DataGrid(pd.DataFrame([]), editable=False, auto_fit_columns=True) + self.log_output = widgets.Output() + self.plot_output = widgets.Output() + self.running = False + self.thread = None + self.processed = set() + self.empty_beam_sans = None + self.empty_beam_trans = None + self.main = widgets.VBox([ + widgets.HBox([self.input_dir_chooser, self.output_dir_chooser]), + widgets.HBox([self.start_stop_button, self.status_label]), + self.table, + self.log_output, + self.plot_output + ]) + + def toggle_running(self, _): + if not self.running: + self.running = True + self.start_stop_button.description = "Stop" + self.status_label.value = "Running" + self.thread = threading.Thread(target=self.background_loop, daemon=True) + self.thread.start() + else: + self.running = False + self.start_stop_button.description = "Start" + self.status_label.value = "Stopped" + + def background_loop(self): + while self.running: + 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("Invalid input folder. Waiting for valid selection...") + time.sleep(60) + continue + if not output_dir or not os.path.isdir(output_dir): + with self.log_output: + print("Invalid output folder. Waiting for valid selection...") + time.sleep(60) + continue + nxs_files = glob.glob(os.path.join(input_dir, "*.nxs")) + groups = {} + for f in nxs_files: + try: + details = parse_nx_details(f) + except Exception: + continue + if 'runlabel' not in details or 'runtype' not in details: + continue + runlabel = details['runlabel'] + runtype = details['runtype'].lower() + run_number = extract_run_number(os.path.basename(f)) + if runlabel not in groups: + groups[runlabel] = {} + groups[runlabel][runtype] = run_number + table_rows = [] + for runlabel, d in groups.items(): + if 'sans' in d and 'trans' in d: + table_rows.append({'SAMPLE': runlabel, 'SANS': d['sans'], 'TRANS': d['trans']}) + df = pd.DataFrame(table_rows) + self.table.data = df + with self.log_output: + print(f"Scanned {len(nxs_files)} files. Found {len(df)} reduction entries.") + ebeam_sans_files = [] + ebeam_trans_files = [] + for f in nxs_files: + try: + details = parse_nx_details(f) + except Exception: + continue + if 'runtype' in details: + if details['runtype'].lower() == 'ebeam_sans': + ebeam_sans_files.append(f) + elif details['runtype'].lower() == 'ebeam_trans': + ebeam_trans_files.append(f) + if ebeam_sans_files: + ebeam_sans_files.sort(key=lambda x: os.path.getmtime(x), reverse=True) + self.empty_beam_sans = ebeam_sans_files[0] + else: + self.empty_beam_sans = None + if ebeam_trans_files: + ebeam_trans_files.sort(key=lambda x: os.path.getmtime(x), reverse=True) + self.empty_beam_trans = ebeam_trans_files[0] + else: + self.empty_beam_trans = None + try: + direct_beam_file = find_direct_beam(input_dir) + except Exception as e: + with self.log_output: + print("Direct-beam file not found:", e) + time.sleep(60) + continue + for index, row in df.iterrows(): + key = (row["SAMPLE"], row["SANS"], row["TRANS"]) + if key in self.processed: + continue + try: + sample_run_file = find_file(input_dir, row["SANS"], extension=".nxs") + transmission_run_file = find_file(input_dir, row["TRANS"], extension=".nxs") + except Exception as e: + with self.log_output: + print(f"Skipping sample {row['SAMPLE']}: {e}") + continue + try: + mask_file = find_mask_file(input_dir) + with self.log_output: + print(f"Using mask file: {mask_file} for sample {row['SAMPLE']}") + except Exception as e: + with self.log_output: + print(f"Mask file not found for sample {row['SAMPLE']}: {e}") + continue + if not self.empty_beam_sans or not self.empty_beam_trans: + with self.log_output: + print("Empty beam files not found, skipping reduction for sample", row["SAMPLE"]) + continue + with self.log_output: + print(f"Reducing sample {row['SAMPLE']}...") + reduction_params = { + "wavelength_min": 1.0, + "wavelength_max": 13.0, + "wavelength_n": 201, + "q_start": 0.01, + "q_stop": 0.3, + "q_n": 101 + } + perform_reduction_for_sample( + sample_info=row, + input_dir=input_dir, + output_dir=output_dir, + reduction_params=reduction_params, + background_run_file=self.empty_beam_sans, + empty_beam_file=self.empty_beam_trans, + direct_beam_file=direct_beam_file, + log_func=lambda msg: print(msg) + ) + self.processed.add(key) + time.sleep(60) + + @property + def widget(self): + return self.main + +# ---------------------------- +# Direct Beam Functionality and Widget (unchanged) +# ---------------------------- +def compute_direct_beam_local( + mask: str, + sample_sans: str, + background_sans: str, + sample_trans: str, + background_trans: str, + empty_beam: str, + local_Iq_theory: str, + wavelength_min: float = 1.0, + wavelength_max: float = 13.0, + n_wavelength_bins: int = 50, + n_wavelength_bands: int = 50 +) -> dict: + workflow = loki.LokiAtLarmorWorkflow() + workflow = sans.with_pixel_mask_filenames(workflow, masks=[mask]) + workflow[NeXusDetectorName] = 'larmor_detector' + wl_min = sc.scalar(wavelength_min, unit='angstrom') + wl_max = sc.scalar(wavelength_max, unit='angstrom') + workflow[WavelengthBins] = sc.linspace('wavelength', wl_min, wl_max, n_wavelength_bins + 1) + workflow[WavelengthBands] = sc.linspace('wavelength', wl_min, wl_max, n_wavelength_bands + 1) + workflow[CorrectForGravity] = True + workflow[UncertaintyBroadcastMode] = UncertaintyBroadcastMode.upper_bound + workflow[ReturnEvents] = False + workflow[QBins] = sc.linspace(dim='Q', start=0.01, stop=0.3, num=101, unit='1/angstrom') + workflow[Filename[SampleRun]] = sample_sans + workflow[Filename[BackgroundRun]] = background_sans + workflow[Filename[TransmissionRun[SampleRun]]] = sample_trans + workflow[Filename[TransmissionRun[BackgroundRun]]] = background_trans + workflow[Filename[EmptyBeamRun]] = empty_beam + center = sans.beam_center_from_center_of_mass(workflow) + print("Computed beam center:", center) + workflow[BeamCenter] = center + Iq_theory = sc.io.load_hdf5(local_Iq_theory) + f = interp1d(Iq_theory, 'Q') + I0 = f(sc.midpoints(workflow.compute(QBins))).data[0] + print("Computed I0:", I0) + results = sans.direct_beam(workflow=workflow, I0=I0, niter=6) + iofq_full = results[-1]['iofq_full'] + iofq_bands = results[-1]['iofq_bands'] + direct_beam_function = results[-1]['direct_beam'] + pp.plot( + {'reference': Iq_theory, 'data': iofq_full}, + color={'reference': 'darkgrey', 'data': 'C0'}, + norm='log', + ) + print("Plotted full-range result vs. theoretical reference.") + return { + 'direct_beam_function': direct_beam_function, + 'iofq_full': iofq_full, + 'Iq_theory': Iq_theory, + } + +class DirectBeamWidget: + def __init__(self): + self.mask_text = widgets.Text(value="", placeholder="Enter mask file path", description="Mask:") + self.sample_sans_text = widgets.Text(value="", placeholder="Enter sample SANS file path", description="Sample SANS:") + self.background_sans_text = widgets.Text(value="", placeholder="Enter background SANS file path", description="Background SANS:") + self.sample_trans_text = widgets.Text(value="", placeholder="Enter sample TRANS file path", description="Sample TRANS:") + self.background_trans_text = widgets.Text(value="", placeholder="Enter background TRANS file path", description="Background TRANS:") + self.empty_beam_text = widgets.Text(value="", placeholder="Enter empty beam file path", description="Empty Beam:") + self.local_Iq_theory_text = widgets.Text(value="", placeholder="Enter I(q) Theory file path", description="I(q) Theory:") + self.db_wavelength_min_widget = widgets.FloatText(value=1.0, description="λ min (Å):") + self.db_wavelength_max_widget = widgets.FloatText(value=13.0, description="λ max (Å):") + self.db_n_wavelength_bins_widget = widgets.IntText(value=50, description="λ n_bins:") + self.db_n_wavelength_bands_widget = widgets.IntText(value=50, description="λ n_bands:") + self.compute_button = widgets.Button(description="Compute Direct Beam") + self.compute_button.on_click(self.compute_direct_beam) + self.log_output = widgets.Output() + self.plot_output = widgets.Output() + self.main = widgets.VBox([ + self.mask_text, + self.sample_sans_text, + self.background_sans_text, + self.sample_trans_text, + self.background_trans_text, + self.empty_beam_text, + self.local_Iq_theory_text, + widgets.HBox([ + self.db_wavelength_min_widget, + self.db_wavelength_max_widget, + self.db_n_wavelength_bins_widget, + self.db_n_wavelength_bands_widget + ]), + self.compute_button, + self.log_output, + self.plot_output + ]) + + def compute_direct_beam(self, _): + self.log_output.clear_output() + self.plot_output.clear_output() + mask = self.mask_text.value + sample_sans = self.sample_sans_text.value + background_sans = self.background_sans_text.value + sample_trans = self.sample_trans_text.value + background_trans = self.background_trans_text.value + empty_beam = self.empty_beam_text.value + local_Iq_theory = self.local_Iq_theory_text.value + wl_min = self.db_wavelength_min_widget.value + wl_max = self.db_wavelength_max_widget.value + n_bins = self.db_n_wavelength_bins_widget.value + n_bands = self.db_n_wavelength_bands_widget.value + with self.log_output: + print("Computing direct beam with:") + print(" Mask:", mask) + print(" Sample SANS:", sample_sans) + print(" Background SANS:", background_sans) + print(" Sample TRANS:", sample_trans) + print(" Background TRANS:", background_trans) + print(" Empty Beam:", empty_beam) + print(" I(q) Theory:", local_Iq_theory) + print(" λ min:", wl_min, "λ max:", wl_max, "n_bins:", n_bins, "n_bands:", n_bands) + try: + results = compute_direct_beam_local( + mask, + sample_sans, + background_sans, + sample_trans, + background_trans, + empty_beam, + local_Iq_theory, + wavelength_min=wl_min, + wavelength_max=wl_max, + n_wavelength_bins=n_bins, + n_wavelength_bands=n_bands + ) + with self.log_output: + print("Direct beam computation complete.") + except Exception as e: + with self.log_output: + print("Error computing direct beam:", e) + + @property + def widget(self): + return self.main + +# ---------------------------- +# Build the Tabbed Widget +# ---------------------------- +reduction_widget = SansBatchReductionWidget().widget +direct_beam_widget = DirectBeamWidget().widget +semi_auto_reduction_widget = SemiAutoReductionWidget().widget +auto_reduction_widget = AutoReductionWidget().widget + +tabs = widgets.Tab(children=[direct_beam_widget, reduction_widget, semi_auto_reduction_widget, auto_reduction_widget]) +tabs.set_title(0, "Direct Beam") +tabs.set_title(1, "Reduction (Manual)") +tabs.set_title(2, "Reduction (Smart)") +tabs.set_title(3, "Reduction (Auto)") + +# display(tabs) diff --git a/src/ess/loki/batch-gui-legacy/tabwidget.py b/src/ess/loki/batch-gui-legacy/tabwidget.py new file mode 100644 index 00000000..0c5f7c02 --- /dev/null +++ b/src/ess/loki/batch-gui-legacy/tabwidget.py @@ -0,0 +1,898 @@ +import glob +import os +import re + +import h5py +import ipywidgets as widgets +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import plopp as pp # used for plotting in direct beam section +import scipp as sc +from ipydatagrid import DataGrid +from ipyfilechooser import FileChooser +from IPython.display import display +from scipp.scipy.interpolate import interp1d + +from ess import loki, sans +from ess.sans.types import * + + +# ---------------------------- +# Reduction Functionality +# ---------------------------- +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) + + +# ---------------------------- +# Helper Functions for Semi-Auto Reduction +# ---------------------------- +def extract_run_number(filename): + m = re.search(r'(\d{4,})', filename) + if m: + return m.group(1) + return "" + + +def parse_nx_details(filepath): + details = {} + with h5py.File(filepath, 'r') as f: + if 'nicos_details' in f['entry']: + grp = f['entry']['nicos_details'] + if 'runlabel' in grp: + val = grp['runlabel'][()] + details['runlabel'] = ( + val.decode('utf8') if isinstance(val, bytes) else str(val) + ) + if 'runtype' in grp: + val = grp['runtype'][()] + details['runtype'] = ( + val.decode('utf8') if isinstance(val, bytes) else str(val) + ) + return details + + +# ---------------------------- +# Semi-Auto Reduction Widget +# ---------------------------- +class SemiAutoReductionWidget: + def __init__(self): + # Only Input and Output Folder choosers are needed. + 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.scan_button = widgets.Button(description="Scan Directory") + self.scan_button.on_click(self.scan_directory) + + # DataGrid for auto-generated reduction table; now editable. + self.table = DataGrid(pd.DataFrame([]), editable=True, auto_fit_columns=True) + + # Buttons to add or delete rows from the table. + self.add_row_button = widgets.Button(description="Add Row") + self.add_row_button.on_click(self.add_row) + self.delete_row_button = widgets.Button(description="Delete Last Row") + self.delete_row_button.on_click(self.delete_last_row) + + # Parameter widgets for reduction (lambda and Q parameters) + self.lambda_min_widget = widgets.FloatText(value=1.0, description="λ min (Å):") + self.lambda_max_widget = widgets.FloatText(value=13.0, description="λ max (Å):") + self.lambda_n_widget = widgets.IntText(value=201, description="λ n_bins:") + self.q_min_widget = widgets.FloatText(value=0.01, description="Qmin (1/Å):") + self.q_max_widget = widgets.FloatText(value=0.3, description="Qmax (1/Å):") + self.q_n_widget = widgets.IntText(value=101, description="Q n_bins:") + + # Text fields to display the automatically identified empty-beam files. + self.empty_beam_sans_text = widgets.Text( + value="", description="Ebeam SANS:", disabled=True + ) + self.empty_beam_trans_text = widgets.Text( + value="", description="Ebeam TRANS:", disabled=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(lambda _: self.log_output.clear_output()) + self.clear_plots_button = widgets.Button(description="Clear Plots") + self.clear_plots_button.on_click(lambda _: self.plot_output.clear_output()) + + self.log_output = widgets.Output() + self.plot_output = widgets.Output() + + # Build the layout. + self.main = widgets.VBox( + [ + widgets.HBox([self.input_dir_chooser, self.output_dir_chooser]), + self.scan_button, + self.table, + widgets.HBox([self.add_row_button, self.delete_row_button]), + widgets.HBox( + [ + self.lambda_min_widget, + self.lambda_max_widget, + self.lambda_n_widget, + ] + ), + widgets.HBox([self.q_min_widget, self.q_max_widget, self.q_n_widget]), + widgets.HBox([self.empty_beam_sans_text, self.empty_beam_trans_text]), + widgets.HBox( + [self.reduce_button, self.clear_log_button, self.clear_plots_button] + ), + self.log_output, + self.plot_output, + ] + ) + + def add_row(self, _): + df = self.table.data + # Create a default new row if the DataFrame is empty, otherwise add blank cells. + if df.empty: + new_row = {'SAMPLE': '', 'SANS': '', 'TRANS': ''} + else: + new_row = {col: "" for col in df.columns} + df = df.append(new_row, ignore_index=True) + self.table.data = df + + def delete_last_row(self, _): + df = self.table.data + if not df.empty: + df = df.iloc[:-1] + self.table.data = df + + def scan_directory(self, _): + self.log_output.clear_output() + input_dir = self.input_dir_chooser.selected + if not input_dir or not os.path.isdir(input_dir): + with self.log_output: + print("Invalid input folder.") + return + nxs_files = glob.glob(os.path.join(input_dir, "*.nxs")) + groups = {} + for f in nxs_files: + try: + details = parse_nx_details(f) + except Exception: + continue + if 'runlabel' not in details or 'runtype' not in details: + continue + runlabel = details['runlabel'] + runtype = details['runtype'].lower() + run_number = extract_run_number(os.path.basename(f)) + if runlabel not in groups: + groups[runlabel] = {} + groups[runlabel][runtype] = run_number + table_rows = [] + for runlabel, d in groups.items(): + if 'sans' in d and 'trans' in d: + table_rows.append( + {'SAMPLE': runlabel, 'SANS': d['sans'], 'TRANS': d['trans']} + ) + df = pd.DataFrame(table_rows) + self.table.data = df + with self.log_output: + print(f"Scanned {len(nxs_files)} files. Found {len(df)} reduction entries.") + # Identify empty beam files: + ebeam_sans_files = [] + ebeam_trans_files = [] + for f in nxs_files: + try: + details = parse_nx_details(f) + except Exception: + continue + if 'runtype' in details: + if details['runtype'].lower() == 'ebeam_sans': + ebeam_sans_files.append(f) + elif details['runtype'].lower() == 'ebeam_trans': + ebeam_trans_files.append(f) + if ebeam_sans_files: + ebeam_sans_files.sort(key=lambda x: os.path.getmtime(x), reverse=True) + self.empty_beam_sans_text.value = ebeam_sans_files[0] + else: + self.empty_beam_sans_text.value = "" + if ebeam_trans_files: + ebeam_trans_files.sort(key=lambda x: os.path.getmtime(x), reverse=True) + self.empty_beam_trans_text.value = ebeam_trans_files[0] + else: + self.empty_beam_trans_text.value = "" + + def run_reduction(self, _): + self.log_output.clear_output() + self.plot_output.clear_output() + 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 + background_run_file = self.empty_beam_sans_text.value + empty_beam_file = self.empty_beam_trans_text.value + if not background_run_file or not empty_beam_file: + with self.log_output: + print("Empty beam files not found.") + return + # Retrieve reduction parameters from widgets. + lam_min = self.lambda_min_widget.value + lam_max = self.lambda_max_widget.value + lam_n = self.lambda_n_widget.value + q_min = self.q_min_widget.value + q_max = self.q_max_widget.value + q_n = self.q_n_widget.value + + df = self.table.data + for idx, row in df.iterrows(): + sample = row["SAMPLE"] + sans_run = row["SANS"] + trans_run = row["TRANS"] + try: + sample_run_file = find_file(input_dir, sans_run, extension=".nxs") + transmission_run_file = find_file( + input_dir, trans_run, extension=".nxs" + ) + except Exception as e: + with self.log_output: + print(f"Skipping sample {sample}: {e}") + continue + try: + mask_file = find_mask_file(input_dir) + with self.log_output: + print(f"Using 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], + wavelength_min=lam_min, + wavelength_max=lam_max, + wavelength_n=lam_n, + q_start=q_min, + q_stop=q_max, + q_n=q_n, + ) + 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}") + wavelength_bins = sc.linspace( + "wavelength", lam_min, lam_max, lam_n, 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) + q_bins = sc.linspace("Q", q_min, q_max, q_n, 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 + + +# ---------------------------- +# Direct Beam Functionality +# ---------------------------- +def compute_direct_beam_local( + mask: str, + sample_sans: str, + background_sans: str, + sample_trans: str, + background_trans: str, + empty_beam: str, + local_Iq_theory: str, + wavelength_min: float = 1.0, + wavelength_max: float = 13.0, + n_wavelength_bins: int = 50, + n_wavelength_bands: int = 50, +) -> dict: + """ + Compute the direct beam function for the LoKI detectors using locally stored data. + """ + workflow = loki.LokiAtLarmorWorkflow() + workflow = sans.with_pixel_mask_filenames(workflow, masks=[mask]) + workflow[NeXusDetectorName] = 'larmor_detector' + + wl_min = sc.scalar(wavelength_min, unit='angstrom') + wl_max = sc.scalar(wavelength_max, unit='angstrom') + workflow[WavelengthBins] = sc.linspace( + 'wavelength', wl_min, wl_max, n_wavelength_bins + 1 + ) + workflow[WavelengthBands] = sc.linspace( + 'wavelength', wl_min, wl_max, n_wavelength_bands + 1 + ) + workflow[CorrectForGravity] = True + workflow[UncertaintyBroadcastMode] = UncertaintyBroadcastMode.upper_bound + workflow[ReturnEvents] = False + workflow[QBins] = sc.linspace( + dim='Q', start=0.01, stop=0.3, num=101, unit='1/angstrom' + ) + + workflow[Filename[SampleRun]] = sample_sans + workflow[Filename[BackgroundRun]] = background_sans + workflow[Filename[TransmissionRun[SampleRun]]] = sample_trans + workflow[Filename[TransmissionRun[BackgroundRun]]] = background_trans + workflow[Filename[EmptyBeamRun]] = empty_beam + + center = sans.beam_center_from_center_of_mass(workflow) + print("Computed beam center:", center) + workflow[BeamCenter] = center + + Iq_theory = sc.io.load_hdf5(local_Iq_theory) + f = interp1d(Iq_theory, 'Q') + I0 = f(sc.midpoints(workflow.compute(QBins))).data[0] + print("Computed I0:", I0) + + results = sans.direct_beam(workflow=workflow, I0=I0, niter=6) + + iofq_full = results[-1]['iofq_full'] + iofq_bands = results[-1]['iofq_bands'] + direct_beam_function = results[-1]['direct_beam'] + + pp.plot( + {'reference': Iq_theory, 'data': iofq_full}, + color={'reference': 'darkgrey', 'data': 'C0'}, + norm='log', + ) + print("Plotted full-range result vs. theoretical reference.") + + return { + 'direct_beam_function': direct_beam_function, + 'iofq_full': iofq_full, + 'Iq_theory': Iq_theory, + } + + +# ---------------------------- +# Widgets for Reduction and Direct Beam +# ---------------------------- +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:", + ) + # Add GUI widgets for reduction parameters: + self.wavelength_min_widget = widgets.FloatText( + value=1.0, description="λ min (Å):" + ) + self.wavelength_max_widget = widgets.FloatText( + value=13.0, description="λ max (Å):" + ) + self.wavelength_n_widget = widgets.IntText(value=201, description="λ n_bins:") + self.q_start_widget = widgets.FloatText( + value=0.01, description="Q start (1/Å):" + ) + self.q_stop_widget = widgets.FloatText(value=0.3, description="Q stop (1/Å):") + self.q_n_widget = widgets.IntText(value=101, description="Q n_bins:") + + 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]), + # Reduction parameters: + widgets.HBox( + [ + self.wavelength_min_widget, + self.wavelength_max_widget, + self.wavelength_n_widget, + ] + ), + widgets.HBox( + [self.q_start_widget, self.q_stop_widget, self.q_n_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 + # Retrieve reduction parameters from widgets. + wl_min = self.wavelength_min_widget.value + wl_max = self.wavelength_max_widget.value + wl_n = self.wavelength_n_widget.value + q_start = self.q_start_widget.value + q_stop = self.q_stop_widget.value + q_n = self.q_n_widget.value + 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"Identified 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], + wavelength_min=wl_min, + wavelength_max=wl_max, + wavelength_n=wl_n, + q_start=q_start, + q_stop=q_stop, + q_n=q_n, + ) + 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}") + wavelength_bins = sc.linspace( + "wavelength", wl_min, wl_max, wl_n, 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) + q_bins = sc.linspace("Q", q_start, q_stop, q_n, 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 + + +# ---------------------------- +# Direct Beam Widget +# ---------------------------- +class DirectBeamWidget: + def __init__(self): + self.mask_text = widgets.Text( + value="", placeholder="Enter mask file path", description="Mask:" + ) + self.sample_sans_text = widgets.Text( + value="", + placeholder="Enter sample SANS file path", + description="Sample SANS:", + ) + self.background_sans_text = widgets.Text( + value="", + placeholder="Enter background SANS file path", + description="Background SANS:", + ) + self.sample_trans_text = widgets.Text( + value="", + placeholder="Enter sample TRANS file path", + description="Sample TRANS:", + ) + self.background_trans_text = widgets.Text( + value="", + placeholder="Enter background TRANS file path", + description="Background TRANS:", + ) + self.empty_beam_text = widgets.Text( + value="", + placeholder="Enter empty beam file path", + description="Empty Beam:", + ) + self.local_Iq_theory_text = widgets.Text( + value="", + placeholder="Enter I(q) theory file path", + description="I(q) Theory:", + ) + # GUI widgets for direct beam parameters: + self.db_wavelength_min_widget = widgets.FloatText( + value=1.0, description="λ min (Å):" + ) + self.db_wavelength_max_widget = widgets.FloatText( + value=13.0, description="λ max (Å):" + ) + self.db_n_wavelength_bins_widget = widgets.IntText( + value=50, description="λ n_bins:" + ) + self.db_n_wavelength_bands_widget = widgets.IntText( + value=50, description="λ n_bands:" + ) + + self.compute_button = widgets.Button(description="Compute Direct Beam") + self.compute_button.on_click(self.compute_direct_beam) + self.log_output = widgets.Output() + self.plot_output = widgets.Output() + self.main = widgets.VBox( + [ + self.mask_text, + self.sample_sans_text, + self.background_sans_text, + self.sample_trans_text, + self.background_trans_text, + self.empty_beam_text, + self.local_Iq_theory_text, + widgets.HBox( + [ + self.db_wavelength_min_widget, + self.db_wavelength_max_widget, + self.db_n_wavelength_bins_widget, + self.db_n_wavelength_bands_widget, + ] + ), + self.compute_button, + self.log_output, + self.plot_output, + ] + ) + + def compute_direct_beam(self, _): + self.log_output.clear_output() + self.plot_output.clear_output() + mask = self.mask_text.value + sample_sans = self.sample_sans_text.value + background_sans = self.background_sans_text.value + sample_trans = self.sample_trans_text.value + background_trans = self.background_trans_text.value + empty_beam = self.empty_beam_text.value + local_Iq_theory = self.local_Iq_theory_text.value + wl_min = self.db_wavelength_min_widget.value + wl_max = self.db_wavelength_max_widget.value + n_bins = self.db_n_wavelength_bins_widget.value + n_bands = self.db_n_wavelength_bands_widget.value + with self.log_output: + print("Computing direct beam with:") + print(" Mask:", mask) + print(" Sample SANS:", sample_sans) + print(" Background SANS:", background_sans) + print(" Sample TRANS:", sample_trans) + print(" Background TRANS:", background_trans) + print(" Empty Beam:", empty_beam) + print(" I(q) Theory:", local_Iq_theory) + print( + " λ min:", + wl_min, + "λ max:", + wl_max, + "n_bins:", + n_bins, + "n_bands:", + n_bands, + ) + try: + results = compute_direct_beam_local( + mask, + sample_sans, + background_sans, + sample_trans, + background_trans, + empty_beam, + local_Iq_theory, + wavelength_min=wl_min, + wavelength_max=wl_max, + n_wavelength_bins=n_bins, + n_wavelength_bands=n_bands, + ) + with self.log_output: + print("Direct beam computation complete.") + except Exception as e: + with self.log_output: + print("Error computing direct beam:", e) + + @property + def widget(self): + return self.main + + +# ---------------------------- +# Build Tabbed Widget +# ---------------------------- +reduction_widget = SansBatchReductionWidget().widget +direct_beam_widget = DirectBeamWidget().widget +semi_auto_reduction_widget = SemiAutoReductionWidget().widget +tabs = widgets.Tab( + children=[direct_beam_widget, reduction_widget, semi_auto_reduction_widget] +) +tabs.set_title(0, "Direct Beam") +tabs.set_title(1, "Reduction (Manual)") +tabs.set_title(2, "Reduction (Smart)") +# tabs.set_title(3, "Reduction (Auto)") + +# Display the tab widget. +# display(tabs) diff --git a/src/ess/loki/batch-gui-legacy/tabwidget050325.py b/src/ess/loki/batch-gui-legacy/tabwidget050325.py new file mode 100644 index 00000000..2c723c51 --- /dev/null +++ b/src/ess/loki/batch-gui-legacy/tabwidget050325.py @@ -0,0 +1,826 @@ +import os +import glob +import re +import h5py +import pandas as pd +import scipp as sc +import matplotlib.pyplot as plt +import numpy as np +import ipywidgets as widgets +from ipydatagrid import DataGrid +from IPython.display import display +from ipyfilechooser import FileChooser +from ess import sans +from ess import loki +from ess.sans.types import * +from scipp.scipy.interpolate import interp1d +import plopp as pp # used for plotting in direct beam section +import threading +import time + +# ---------------------------- +# Common Utility Functions +# ---------------------------- +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) + +def extract_run_number(filename): + m = re.search(r'(\d{4,})', filename) + if m: + return m.group(1) + return "" + +def parse_nx_details(filepath): + details = {} + with h5py.File(filepath, 'r') as f: + if 'nicos_details' in f['entry']: + grp = f['entry']['nicos_details'] + if 'runlabel' in grp: + val = grp['runlabel'][()] + details['runlabel'] = val.decode('utf8') if isinstance(val, bytes) else str(val) + if 'runtype' in grp: + val = grp['runtype'][()] + details['runtype'] = val.decode('utf8') if isinstance(val, bytes) else str(val) + return details + +# ---------------------------- +# Reduction and Plotting Functions +# ---------------------------- +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 = [] + 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") + 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 save_reduction_plots(res, sample, sample_run_file, wavelength_min, wavelength_max, wavelength_n, q_min, q_max, q_n, output_dir, show=True): + fig, axs = plt.subplots(1, 2, figsize=(8, 4)) + axs[0].set_box_aspect(1) + axs[1].set_box_aspect(1) + title_str = f"{sample} - {os.path.basename(sample_run_file)}" + fig.suptitle(title_str, fontsize=14) + q_bins = sc.linspace("Q", q_min, q_max, q_n, unit="1/angstrom") + x_q = 0.5 * (q_bins.values[:-1] + q_bins.values[1:]) + if res["IofQ"].variances is not None: + yerr = np.sqrt(res["IofQ"].variances) + axs[0].errorbar(x_q, res["IofQ"].values, yerr=yerr, fmt='o', linestyle='none', color='k', alpha=0.5, markerfacecolor='none') + else: + axs[0].scatter(x_q, res["IofQ"].values) + axs[0].set_xlabel("Q (Å$^{-1}$)") + axs[0].set_ylabel("I(Q)") + axs[0].set_xscale("log") + axs[0].set_yscale("log") + wavelength_bins = sc.linspace("wavelength", wavelength_min, wavelength_max, wavelength_n, unit="angstrom") + x_wl = 0.5 * (wavelength_bins.values[:-1] + wavelength_bins.values[1:]) + if res["transmission"].variances is not None: + yerr_tr = np.sqrt(res["transmission"].variances) + axs[1].errorbar(x_wl, res["transmission"].values, yerr=yerr_tr, fmt='^', linestyle='none', color='k', alpha=0.5, markerfacecolor='none') + else: + axs[1].scatter(x_wl, res["transmission"].values) + axs[1].set_xlabel("Wavelength (Å)") + axs[1].set_ylabel("Transmission") + plt.tight_layout() + out_png = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", "_reduced.png")) + fig.savefig(out_png, dpi=300) + if show: + display(fig) + plt.close(fig) + +# ---------------------------- +# Unified Backend Function for Reduction +# ---------------------------- +def perform_reduction_for_sample( + sample_info: dict, + input_dir: str, + output_dir: str, + reduction_params: dict, + background_run_file: str, + empty_beam_file: str, + direct_beam_file: str, + log_func: callable +): + """ + Processes a single sample reduction: + - Finds the necessary run files + - Optionally determines a mask (or finds one automatically) + - Calls the reduction and plotting routines + - Logs all steps via log_func(message) + """ + sample = sample_info.get("SAMPLE", "Unknown") + try: + sample_run_file = find_file(input_dir, str(sample_info["SANS"]), extension=".nxs") + transmission_run_file = find_file(input_dir, str(sample_info["TRANS"]), extension=".nxs") + except Exception as e: + log_func(f"Skipping sample {sample}: {e}") + return None + # Determine mask file. + mask_file = None + mask_candidate = str(sample_info.get("mask", "")).strip() + if mask_candidate: + mask_candidate_file = os.path.join(input_dir, f"{mask_candidate}.xml") + if os.path.exists(mask_candidate_file): + mask_file = mask_candidate_file + if mask_file is None: + try: + mask_file = find_mask_file(input_dir) + log_func(f"Identified mask file: {mask_file} for sample {sample}") + except Exception as e: + log_func(f"Mask file not found for sample {sample}: {e}") + return None + + log_func(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], + wavelength_min=reduction_params["wavelength_min"], + wavelength_max=reduction_params["wavelength_max"], + wavelength_n=reduction_params["wavelength_n"], + q_start=reduction_params["q_start"], + q_stop=reduction_params["q_stop"], + q_n=reduction_params["q_n"] + ) + except Exception as e: + log_func(f"Reduction failed for sample {sample}: {e}") + return None + out_xye = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", ".xye")) + try: + save_xye_pandas(res["IofQ"], out_xye) + log_func(f"Saved reduced data to {out_xye}") + except Exception as e: + log_func(f"Failed to save reduced data for {sample}: {e}") + try: + save_reduction_plots( + res, + sample, + sample_run_file, + reduction_params["wavelength_min"], + reduction_params["wavelength_max"], + reduction_params["wavelength_n"], + reduction_params["q_start"], + reduction_params["q_stop"], + reduction_params["q_n"], + output_dir, + show=True + ) + log_func(f"Saved reduction plot for sample {sample}.") + except Exception as e: + log_func(f"Failed to save reduction plot for {sample}: {e}") + log_func(f"Reduced sample {sample} and saved outputs.") + return res + +# ---------------------------- +# GUI Widgets (Refactored to use Unified Backend) +# ---------------------------- +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.wavelength_min_widget = widgets.FloatText(value=1.0, description="λ min (Å):") + self.wavelength_max_widget = widgets.FloatText(value=13.0, description="λ max (Å):") + self.wavelength_n_widget = widgets.IntText(value=201, description="λ n_bins:") + self.q_start_widget = widgets.FloatText(value=0.01, description="Q start (1/Å):") + self.q_stop_widget = widgets.FloatText(value=0.3, description="Q stop (1/Å):") + self.q_n_widget = widgets.IntText(value=101, description="Q n_bins:") + 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(lambda _: self.log_output.clear_output()) + self.clear_plots_button = widgets.Button(description="Clear Plots") + self.clear_plots_button.on_click(lambda _: self.plot_output.clear_output()) + 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]), + widgets.HBox([self.wavelength_min_widget, self.wavelength_max_widget, self.wavelength_n_widget]), + widgets.HBox([self.q_start_widget, self.q_stop_widget, self.q_n_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 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, _): + self.log_output.clear_output() + self.plot_output.clear_output() + 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 + + reduction_params = { + "wavelength_min": self.wavelength_min_widget.value, + "wavelength_max": self.wavelength_max_widget.value, + "wavelength_n": self.wavelength_n_widget.value, + "q_start": self.q_start_widget.value, + "q_stop": self.q_stop_widget.value, + "q_n": self.q_n_widget.value + } + + df = self.table.data + for idx, row in df.iterrows(): + perform_reduction_for_sample( + sample_info=row, + input_dir=input_dir, + output_dir=output_dir, + reduction_params=reduction_params, + background_run_file=background_run_file, + empty_beam_file=empty_beam_file, + direct_beam_file=direct_beam_file, + log_func=lambda msg: print(msg) + ) + + @property + def widget(self): + return self.main + +class SemiAutoReductionWidget: + def __init__(self): + 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.scan_button = widgets.Button(description="Scan Directory") + self.scan_button.on_click(self.scan_directory) + self.table = DataGrid(pd.DataFrame([]), editable=True, auto_fit_columns=True) + self.add_row_button = widgets.Button(description="Add Row") + self.add_row_button.on_click(self.add_row) + self.delete_row_button = widgets.Button(description="Delete Last Row") + self.delete_row_button.on_click(self.delete_last_row) + self.lambda_min_widget = widgets.FloatText(value=1.0, description="λ min (Å):") + self.lambda_max_widget = widgets.FloatText(value=13.0, description="λ max (Å):") + self.lambda_n_widget = widgets.IntText(value=201, description="λ n_bins:") + self.q_min_widget = widgets.FloatText(value=0.01, description="Qmin (1/Å):") + self.q_max_widget = widgets.FloatText(value=0.3, description="Qmax (1/Å):") + self.q_n_widget = widgets.IntText(value=101, description="Q n_bins:") + self.empty_beam_sans_text = widgets.Text(value="", description="Ebeam SANS:", disabled=True) + self.empty_beam_trans_text = widgets.Text(value="", description="Ebeam TRANS:", disabled=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(lambda _: self.log_output.clear_output()) + self.clear_plots_button = widgets.Button(description="Clear Plots") + self.clear_plots_button.on_click(lambda _: self.plot_output.clear_output()) + self.log_output = widgets.Output() + self.plot_output = widgets.Output() + self.processed = set() + self.main = widgets.VBox([ + widgets.HBox([self.input_dir_chooser, self.output_dir_chooser]), + self.scan_button, + self.table, + widgets.HBox([self.add_row_button, self.delete_row_button]), + widgets.HBox([self.lambda_min_widget, self.lambda_max_widget, self.lambda_n_widget]), + widgets.HBox([self.q_min_widget, self.q_max_widget, self.q_n_widget]), + widgets.HBox([self.empty_beam_sans_text, self.empty_beam_trans_text]), + widgets.HBox([self.reduce_button, self.clear_log_button, self.clear_plots_button]), + self.log_output, + self.plot_output + ]) + + def add_row(self, _): + df = self.table.data + new_row = {col: "" for col in df.columns} if not df.empty else {'SAMPLE': '', 'SANS': '', 'TRANS': ''} + df = df.append(new_row, ignore_index=True) + self.table.data = df + + def delete_last_row(self, _): + df = self.table.data + if not df.empty: + self.table.data = df.iloc[:-1] + + def scan_directory(self, _): + self.log_output.clear_output() + input_dir = self.input_dir_chooser.selected + if not input_dir or not os.path.isdir(input_dir): + with self.log_output: + print("Invalid input folder.") + return + nxs_files = glob.glob(os.path.join(input_dir, "*.nxs")) + groups = {} + for f in nxs_files: + try: + details = parse_nx_details(f) + except Exception: + continue + if 'runlabel' not in details or 'runtype' not in details: + continue + runlabel = details['runlabel'] + runtype = details['runtype'].lower() + run_number = extract_run_number(os.path.basename(f)) + if runlabel not in groups: + groups[runlabel] = {} + groups[runlabel][runtype] = run_number + table_rows = [] + for runlabel, d in groups.items(): + if 'sans' in d and 'trans' in d: + table_rows.append({'SAMPLE': runlabel, 'SANS': d['sans'], 'TRANS': d['trans']}) + df = pd.DataFrame(table_rows) + self.table.data = df + with self.log_output: + print(f"Scanned {len(nxs_files)} files. Found {len(df)} reduction entries.") + ebeam_sans_files = [] + ebeam_trans_files = [] + for f in nxs_files: + try: + details = parse_nx_details(f) + except Exception: + continue + if 'runtype' in details: + if details['runtype'].lower() == 'ebeam_sans': + ebeam_sans_files.append(f) + elif details['runtype'].lower() == 'ebeam_trans': + ebeam_trans_files.append(f) + if ebeam_sans_files: + ebeam_sans_files.sort(key=lambda x: os.path.getmtime(x), reverse=True) + self.empty_beam_sans_text.value = ebeam_sans_files[0] + else: + self.empty_beam_sans_text.value = "" + if ebeam_trans_files: + ebeam_trans_files.sort(key=lambda x: os.path.getmtime(x), reverse=True) + self.empty_beam_trans_text.value = ebeam_trans_files[0] + else: + self.empty_beam_trans_text.value = "" + + def run_reduction(self, _): + self.log_output.clear_output() + self.plot_output.clear_output() + 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 + background_run_file = self.empty_beam_sans_text.value + empty_beam_file = self.empty_beam_trans_text.value + if not background_run_file or not empty_beam_file: + with self.log_output: + print("Empty beam files not found.") + return + + reduction_params = { + "wavelength_min": self.lambda_min_widget.value, + "wavelength_max": self.lambda_max_widget.value, + "wavelength_n": self.lambda_n_widget.value, + "q_start": self.q_min_widget.value, + "q_stop": self.q_max_widget.value, + "q_n": self.q_n_widget.value + } + + df = self.table.data.copy() + for idx, row in df.iterrows(): + perform_reduction_for_sample( + sample_info=row, + input_dir=input_dir, + output_dir=output_dir, + reduction_params=reduction_params, + background_run_file=background_run_file, + empty_beam_file=empty_beam_file, + direct_beam_file=direct_beam_file, + log_func=lambda msg: print(msg) + ) + + @property + def widget(self): + return self.main + +class AutoReductionWidget: + def __init__(self): + 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.start_stop_button = widgets.Button(description="Start") + self.start_stop_button.on_click(self.toggle_running) + self.status_label = widgets.Label(value="Stopped") + self.table = DataGrid(pd.DataFrame([]), editable=False, auto_fit_columns=True) + self.log_output = widgets.Output() + self.plot_output = widgets.Output() + self.running = False + self.thread = None + self.processed = set() + self.empty_beam_sans = None + self.empty_beam_trans = None + self.main = widgets.VBox([ + widgets.HBox([self.input_dir_chooser, self.output_dir_chooser]), + widgets.HBox([self.start_stop_button, self.status_label]), + self.table, + self.log_output, + self.plot_output + ]) + + def toggle_running(self, _): + if not self.running: + self.running = True + self.start_stop_button.description = "Stop" + self.status_label.value = "Running" + self.thread = threading.Thread(target=self.background_loop, daemon=True) + self.thread.start() + else: + self.running = False + self.start_stop_button.description = "Start" + self.status_label.value = "Stopped" + + def background_loop(self): + while self.running: + 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("Invalid input folder. Waiting for valid selection...") + time.sleep(60) + continue + if not output_dir or not os.path.isdir(output_dir): + with self.log_output: + print("Invalid output folder. Waiting for valid selection...") + time.sleep(60) + continue + nxs_files = glob.glob(os.path.join(input_dir, "*.nxs")) + groups = {} + for f in nxs_files: + try: + details = parse_nx_details(f) + except Exception: + continue + if 'runlabel' not in details or 'runtype' not in details: + continue + runlabel = details['runlabel'] + runtype = details['runtype'].lower() + run_number = extract_run_number(os.path.basename(f)) + if runlabel not in groups: + groups[runlabel] = {} + groups[runlabel][runtype] = run_number + table_rows = [] + for runlabel, d in groups.items(): + if 'sans' in d and 'trans' in d: + table_rows.append({'SAMPLE': runlabel, 'SANS': d['sans'], 'TRANS': d['trans']}) + df = pd.DataFrame(table_rows) + self.table.data = df + with self.log_output: + print(f"Scanned {len(nxs_files)} files. Found {len(df)} reduction entries.") + ebeam_sans_files = [] + ebeam_trans_files = [] + for f in nxs_files: + try: + details = parse_nx_details(f) + except Exception: + continue + if 'runtype' in details: + if details['runtype'].lower() == 'ebeam_sans': + ebeam_sans_files.append(f) + elif details['runtype'].lower() == 'ebeam_trans': + ebeam_trans_files.append(f) + if ebeam_sans_files: + ebeam_sans_files.sort(key=lambda x: os.path.getmtime(x), reverse=True) + self.empty_beam_sans = ebeam_sans_files[0] + else: + self.empty_beam_sans = None + if ebeam_trans_files: + ebeam_trans_files.sort(key=lambda x: os.path.getmtime(x), reverse=True) + self.empty_beam_trans = ebeam_trans_files[0] + else: + self.empty_beam_trans = None + try: + direct_beam_file = find_direct_beam(input_dir) + except Exception as e: + with self.log_output: + print("Direct-beam file not found:", e) + time.sleep(60) + continue + for index, row in df.iterrows(): + key = (row["SAMPLE"], row["SANS"], row["TRANS"]) + if key in self.processed: + continue + try: + sample_run_file = find_file(input_dir, row["SANS"], extension=".nxs") + transmission_run_file = find_file(input_dir, row["TRANS"], extension=".nxs") + except Exception as e: + with self.log_output: + print(f"Skipping sample {row['SAMPLE']}: {e}") + continue + try: + mask_file = find_mask_file(input_dir) + with self.log_output: + print(f"Using mask file: {mask_file} for sample {row['SAMPLE']}") + except Exception as e: + with self.log_output: + print(f"Mask file not found for sample {row['SAMPLE']}: {e}") + continue + if not self.empty_beam_sans or not self.empty_beam_trans: + with self.log_output: + print("Empty beam files not found, skipping reduction for sample", row["SAMPLE"]) + continue + with self.log_output: + print(f"Reducing sample {row['SAMPLE']}...") + reduction_params = { + "wavelength_min": 1.0, + "wavelength_max": 13.0, + "wavelength_n": 201, + "q_start": 0.01, + "q_stop": 0.3, + "q_n": 101 + } + perform_reduction_for_sample( + sample_info=row, + input_dir=input_dir, + output_dir=output_dir, + reduction_params=reduction_params, + background_run_file=self.empty_beam_sans, + empty_beam_file=self.empty_beam_trans, + direct_beam_file=direct_beam_file, + log_func=lambda msg: print(msg) + ) + self.processed.add(key) + time.sleep(60) + + @property + def widget(self): + return self.main + +# ---------------------------- +# Direct Beam Functionality and Widget (unchanged) +# ---------------------------- +def compute_direct_beam_local( + mask: str, + sample_sans: str, + background_sans: str, + sample_trans: str, + background_trans: str, + empty_beam: str, + local_Iq_theory: str, + wavelength_min: float = 1.0, + wavelength_max: float = 13.0, + n_wavelength_bins: int = 50, + n_wavelength_bands: int = 50 +) -> dict: + workflow = loki.LokiAtLarmorWorkflow() + workflow = sans.with_pixel_mask_filenames(workflow, masks=[mask]) + workflow[NeXusDetectorName] = 'larmor_detector' + wl_min = sc.scalar(wavelength_min, unit='angstrom') + wl_max = sc.scalar(wavelength_max, unit='angstrom') + workflow[WavelengthBins] = sc.linspace('wavelength', wl_min, wl_max, n_wavelength_bins + 1) + workflow[WavelengthBands] = sc.linspace('wavelength', wl_min, wl_max, n_wavelength_bands + 1) + workflow[CorrectForGravity] = True + workflow[UncertaintyBroadcastMode] = UncertaintyBroadcastMode.upper_bound + workflow[ReturnEvents] = False + workflow[QBins] = sc.linspace(dim='Q', start=0.01, stop=0.3, num=101, unit='1/angstrom') + workflow[Filename[SampleRun]] = sample_sans + workflow[Filename[BackgroundRun]] = background_sans + workflow[Filename[TransmissionRun[SampleRun]]] = sample_trans + workflow[Filename[TransmissionRun[BackgroundRun]]] = background_trans + workflow[Filename[EmptyBeamRun]] = empty_beam + center = sans.beam_center_from_center_of_mass(workflow) + print("Computed beam center:", center) + workflow[BeamCenter] = center + Iq_theory = sc.io.load_hdf5(local_Iq_theory) + f = interp1d(Iq_theory, 'Q') + I0 = f(sc.midpoints(workflow.compute(QBins))).data[0] + print("Computed I0:", I0) + results = sans.direct_beam(workflow=workflow, I0=I0, niter=6) + iofq_full = results[-1]['iofq_full'] + iofq_bands = results[-1]['iofq_bands'] + direct_beam_function = results[-1]['direct_beam'] + pp.plot( + {'reference': Iq_theory, 'data': iofq_full}, + color={'reference': 'darkgrey', 'data': 'C0'}, + norm='log', + ) + print("Plotted full-range result vs. theoretical reference.") + return { + 'direct_beam_function': direct_beam_function, + 'iofq_full': iofq_full, + 'Iq_theory': Iq_theory, + } + +class DirectBeamWidget: + def __init__(self): + self.mask_text = widgets.Text(value="", placeholder="Enter mask file path", description="Mask:") + self.sample_sans_text = widgets.Text(value="", placeholder="Enter sample SANS file path", description="Sample SANS:") + self.background_sans_text = widgets.Text(value="", placeholder="Enter background SANS file path", description="Background SANS:") + self.sample_trans_text = widgets.Text(value="", placeholder="Enter sample TRANS file path", description="Sample TRANS:") + self.background_trans_text = widgets.Text(value="", placeholder="Enter background TRANS file path", description="Background TRANS:") + self.empty_beam_text = widgets.Text(value="", placeholder="Enter empty beam file path", description="Empty Beam:") + self.local_Iq_theory_text = widgets.Text(value="", placeholder="Enter I(q) Theory file path", description="I(q) Theory:") + self.db_wavelength_min_widget = widgets.FloatText(value=1.0, description="λ min (Å):") + self.db_wavelength_max_widget = widgets.FloatText(value=13.0, description="λ max (Å):") + self.db_n_wavelength_bins_widget = widgets.IntText(value=50, description="λ n_bins:") + self.db_n_wavelength_bands_widget = widgets.IntText(value=50, description="λ n_bands:") + self.compute_button = widgets.Button(description="Compute Direct Beam") + self.compute_button.on_click(self.compute_direct_beam) + self.log_output = widgets.Output() + self.plot_output = widgets.Output() + self.main = widgets.VBox([ + self.mask_text, + self.sample_sans_text, + self.background_sans_text, + self.sample_trans_text, + self.background_trans_text, + self.empty_beam_text, + self.local_Iq_theory_text, + widgets.HBox([ + self.db_wavelength_min_widget, + self.db_wavelength_max_widget, + self.db_n_wavelength_bins_widget, + self.db_n_wavelength_bands_widget + ]), + self.compute_button, + self.log_output, + self.plot_output + ]) + + def compute_direct_beam(self, _): + self.log_output.clear_output() + self.plot_output.clear_output() + mask = self.mask_text.value + sample_sans = self.sample_sans_text.value + background_sans = self.background_sans_text.value + sample_trans = self.sample_trans_text.value + background_trans = self.background_trans_text.value + empty_beam = self.empty_beam_text.value + local_Iq_theory = self.local_Iq_theory_text.value + wl_min = self.db_wavelength_min_widget.value + wl_max = self.db_wavelength_max_widget.value + n_bins = self.db_n_wavelength_bins_widget.value + n_bands = self.db_n_wavelength_bands_widget.value + with self.log_output: + print("Computing direct beam with:") + print(" Mask:", mask) + print(" Sample SANS:", sample_sans) + print(" Background SANS:", background_sans) + print(" Sample TRANS:", sample_trans) + print(" Background TRANS:", background_trans) + print(" Empty Beam:", empty_beam) + print(" I(q) Theory:", local_Iq_theory) + print(" λ min:", wl_min, "λ max:", wl_max, "n_bins:", n_bins, "n_bands:", n_bands) + try: + results = compute_direct_beam_local( + mask, + sample_sans, + background_sans, + sample_trans, + background_trans, + empty_beam, + local_Iq_theory, + wavelength_min=wl_min, + wavelength_max=wl_max, + n_wavelength_bins=n_bins, + n_wavelength_bands=n_bands + ) + with self.log_output: + print("Direct beam computation complete.") + except Exception as e: + with self.log_output: + print("Error computing direct beam:", e) + + @property + def widget(self): + return self.main + +# ---------------------------- +# Build the Tabbed Widget +# ---------------------------- +reduction_widget = SansBatchReductionWidget().widget +direct_beam_widget = DirectBeamWidget().widget +semi_auto_reduction_widget = SemiAutoReductionWidget().widget +auto_reduction_widget = AutoReductionWidget().widget + +tabs = widgets.Tab(children=[direct_beam_widget, reduction_widget, semi_auto_reduction_widget, auto_reduction_widget]) +tabs.set_title(0, "Direct Beam") +tabs.set_title(1, "Reduction (Manual)") +tabs.set_title(2, "Reduction (Smart)") +tabs.set_title(3, "Reduction (Auto)") + +# display(tabs) diff --git a/src/ess/loki/batch-gui-legacy/tabwidgetauto-040325.py b/src/ess/loki/batch-gui-legacy/tabwidgetauto-040325.py new file mode 100644 index 00000000..cad8b49f --- /dev/null +++ b/src/ess/loki/batch-gui-legacy/tabwidgetauto-040325.py @@ -0,0 +1,926 @@ +import os +import glob +import re +import h5py +import pandas as pd +import scipp as sc +import matplotlib.pyplot as plt +import numpy as np +import ipywidgets as widgets +from ipydatagrid import DataGrid +from IPython.display import display +from ipyfilechooser import FileChooser +from ess import sans +from ess import loki +from ess.sans.types import * +from scipp.scipy.interpolate import interp1d +import plopp as pp # used for plotting in direct beam section +import threading +import time + + +# ---------------------------- +# Reduction Functionality +# ---------------------------- +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) + +# ---------------------------- +# Helper Functions for Semi-Auto Reduction +# ---------------------------- +def extract_run_number(filename): + m = re.search(r'(\d{4,})', filename) + if m: + return m.group(1) + return "" + +def parse_nx_details(filepath): + details = {} + with h5py.File(filepath, 'r') as f: + if 'nicos_details' in f['entry']: + grp = f['entry']['nicos_details'] + if 'runlabel' in grp: + val = grp['runlabel'][()] + details['runlabel'] = val.decode('utf8') if isinstance(val, bytes) else str(val) + if 'runtype' in grp: + val = grp['runtype'][()] + details['runtype'] = val.decode('utf8') if isinstance(val, bytes) else str(val) + return details + +# ---------------------------- +# Common Plotting Function +# ---------------------------- + +def save_reduction_plots(res, sample, sample_run_file, lam_min, lam_max, lam_n, q_min, q_max, q_n, output_dir, show=True): + """ + Creates a figure with 1 row x 2 columns: + - Left subplot: I(Q) (scatter with errorbars, log-log). + - Right subplot: Transmission fraction vs. wavelength (scatter with errorbars). + A single centered title (filename and sample ID) is added above the subplots. + The figure is saved to output_dir with a filename based on sample_run_file. + If show is True, the figure is displayed in the current output. + """ + # Create a figure with two subplots side by side. + fig, axs = plt.subplots(1, 2, figsize=(8, 4)) + + # Force each axis to be square (requires Matplotlib>=3.3) + axs[0].set_box_aspect(1) + axs[1].set_box_aspect(1) + + # Set a centered overall title (filename and sample) + title_str = f"{sample} - {os.path.basename(sample_run_file)}" + fig.suptitle(title_str, fontsize=14) + + # Subplot A: I(Q) + q_bins = sc.linspace("Q", q_min, q_max, q_n, unit="1/angstrom") + x_q = 0.5 * (q_bins.values[:-1] + q_bins.values[1:]) + if res["IofQ"].variances is not None: + yerr = np.sqrt(res["IofQ"].variances) + axs[0].errorbar(x_q, res["IofQ"].values, yerr=yerr, fmt='o', linestyle='none', color='k', alpha=0.5, markerfacecolor='none') + else: + axs[0].scatter(x_q, res["IofQ"].values) + axs[0].set_xlabel("Q (Å$^{-1}$)") + axs[0].set_ylabel("I(Q)") + axs[0].set_xscale("log") + axs[0].set_yscale("log") + + # Subplot B: Transmission vs. Wavelength + wavelength_bins = sc.linspace("wavelength", lam_min, lam_max, lam_n, unit="angstrom") + x_wl = 0.5 * (wavelength_bins.values[:-1] + wavelength_bins.values[1:]) + if res["transmission"].variances is not None: + yerr_tr = np.sqrt(res["transmission"].variances) + axs[1].errorbar(x_wl, res["transmission"].values, yerr=yerr_tr, fmt='^', linestyle='none', color='k', alpha=0.5, markerfacecolor='none') + else: + axs[1].scatter(x_wl, res["transmission"].values) + axs[1].set_xlabel("Wavelength (Å)") + axs[1].set_ylabel("Transmission") + + # Adjust layout to leave room for the suptitle. + plt.tight_layout()#rect=[0, 0, 1, 0.90]) + + # Save the figure. + out_png = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", "_reduced.png")) + fig.savefig(out_png, dpi=300) + + # Display the figure in the GUI if requested. + if show: + display(fig) + + # Close the figure so memory is released. + plt.close(fig) + + + + +# ---------------------------- +# SansBatchReductionWidget (Updated) +# ---------------------------- +class SansBatchReductionWidget: + def __init__(self): + # CSV chooser for pre-loaded reduction table. + self.csv_chooser = FileChooser(select_dir=False) + self.csv_chooser.title = "Select CSV File" + self.csv_chooser.filter_pattern = "*.csv" + # Folder choosers. + 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" + # Empty-beam run number widgets. + 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:") + # Reduction parameter widgets. + self.wavelength_min_widget = widgets.FloatText(value=1.0, description="λ min (Å):") + self.wavelength_max_widget = widgets.FloatText(value=13.0, description="λ max (Å):") + self.wavelength_n_widget = widgets.IntText(value=201, description="λ n_bins:") + self.q_start_widget = widgets.FloatText(value=0.01, description="Q start (1/Å):") + self.q_stop_widget = widgets.FloatText(value=0.3, description="Q stop (1/Å):") + self.q_n_widget = widgets.IntText(value=101, description="Q n_bins:") + # Button to load CSV. + self.load_csv_button = widgets.Button(description="Load CSV") + self.load_csv_button.on_click(self.load_csv) + # DataGrid for the reduction table. + self.table = DataGrid(pd.DataFrame([]), editable=True, auto_fit_columns=True) + # Reduction and clear buttons. + 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(lambda _: self.log_output.clear_output()) + self.clear_plots_button = widgets.Button(description="Clear Plots") + self.clear_plots_button.on_click(lambda _: self.plot_output.clear_output()) + # Output widgets. + self.log_output = widgets.Output() + self.plot_output = widgets.Output() + # Build layout. + 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]), + widgets.HBox([self.wavelength_min_widget, self.wavelength_max_widget, self.wavelength_n_widget]), + widgets.HBox([self.q_start_widget, self.q_stop_widget, self.q_n_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 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, _): + self.log_output.clear_output() + self.plot_output.clear_output() + 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 + wl_min = self.wavelength_min_widget.value + wl_max = self.wavelength_max_widget.value + wl_n = self.wavelength_n_widget.value + q_start = self.q_start_widget.value + q_stop = self.q_stop_widget.value + q_n = self.q_n_widget.value + + 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"Identified 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], + wavelength_min=wl_min, + wavelength_max=wl_max, + wavelength_n=wl_n, + q_start=q_start, + q_stop=q_stop, + q_n=q_n + ) + 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}") + try: + with self.plot_output: + save_reduction_plots(res, sample, sample_run_file, lam_min, lam_max, lam_n, q_min, q_max, q_n, output_dir, show=True) + with self.log_output: + print(f"Saved combined reduction plot for sample {sample}.") + except Exception as e: + with self.log_output: + print(f"Failed to save reduction plot for {sample}: {e}") + + #try: + # save_reduction_plots(res, sample, sample_run_file, wl_min, wl_max, wl_n, q_start, q_stop, q_n, output_dir)#, n_bands=5) + # with self.log_output: + # print(f"Saved combined reduction plot for sample {sample}.") + #except Exception as e: + # with self.log_output: + # print(f"Failed to save reduction plot for {sample}: {e}") + with self.log_output: + print(f"Reduced sample {sample} and saved outputs.") + + @property + def widget(self): + return self.main + +# ---------------------------- +# Semi-Auto Reduction Widget (unchanged) +# ---------------------------- +class SemiAutoReductionWidget: + def __init__(self): + 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.scan_button = widgets.Button(description="Scan Directory") + self.scan_button.on_click(self.scan_directory) + self.table = DataGrid(pd.DataFrame([]), editable=True, auto_fit_columns=True) + self.add_row_button = widgets.Button(description="Add Row") + self.add_row_button.on_click(self.add_row) + self.delete_row_button = widgets.Button(description="Delete Last Row") + self.delete_row_button.on_click(self.delete_last_row) + self.lambda_min_widget = widgets.FloatText(value=1.0, description="λ min (Å):") + self.lambda_max_widget = widgets.FloatText(value=13.0, description="λ max (Å):") + self.lambda_n_widget = widgets.IntText(value=201, description="λ n_bins:") + self.q_min_widget = widgets.FloatText(value=0.01, description="Qmin (1/Å):") + self.q_max_widget = widgets.FloatText(value=0.3, description="Qmax (1/Å):") + self.q_n_widget = widgets.IntText(value=101, description="Q n_bins:") + self.empty_beam_sans_text = widgets.Text(value="", description="Ebeam SANS:", disabled=True) + self.empty_beam_trans_text = widgets.Text(value="", description="Ebeam TRANS:", disabled=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(lambda _: self.log_output.clear_output()) + self.clear_plots_button = widgets.Button(description="Clear Plots") + self.clear_plots_button.on_click(lambda _: self.plot_output.clear_output()) + self.log_output = widgets.Output() + self.plot_output = widgets.Output() + + # Add the processed set here: + self.processed = set() + + self.main = widgets.VBox([ + widgets.HBox([self.input_dir_chooser, self.output_dir_chooser]), + self.scan_button, + self.table, + widgets.HBox([self.add_row_button, self.delete_row_button]), + widgets.HBox([self.lambda_min_widget, self.lambda_max_widget, self.lambda_n_widget]), + widgets.HBox([self.q_min_widget, self.q_max_widget, self.q_n_widget]), + widgets.HBox([self.empty_beam_sans_text, self.empty_beam_trans_text]), + widgets.HBox([self.reduce_button, self.clear_log_button, self.clear_plots_button]), + self.log_output, + self.plot_output + ]) + + + def add_row(self, _): + df = self.table.data + if df.empty: + new_row = {'SAMPLE': '', 'SANS': '', 'TRANS': ''} + else: + new_row = {col: "" for col in df.columns} + df = df.append(new_row, ignore_index=True) + self.table.data = df + + def delete_last_row(self, _): + df = self.table.data + if not df.empty: + df = df.iloc[:-1] + self.table.data = df + + def scan_directory(self, _): + self.log_output.clear_output() + input_dir = self.input_dir_chooser.selected + if not input_dir or not os.path.isdir(input_dir): + with self.log_output: + print("Invalid input folder.") + return + nxs_files = glob.glob(os.path.join(input_dir, "*.nxs")) + groups = {} + for f in nxs_files: + try: + details = parse_nx_details(f) + except Exception: + continue + if 'runlabel' not in details or 'runtype' not in details: + continue + runlabel = details['runlabel'] + runtype = details['runtype'].lower() + run_number = extract_run_number(os.path.basename(f)) + if runlabel not in groups: + groups[runlabel] = {} + groups[runlabel][runtype] = run_number + table_rows = [] + for runlabel, d in groups.items(): + if 'sans' in d and 'trans' in d: + table_rows.append({'SAMPLE': runlabel, 'SANS': d['sans'], 'TRANS': d['trans']}) + df = pd.DataFrame(table_rows) + self.table.data = df + with self.log_output: + print(f"Scanned {len(nxs_files)} files. Found {len(df)} reduction entries.") + ebeam_sans_files = [] + ebeam_trans_files = [] + for f in nxs_files: + try: + details = parse_nx_details(f) + except Exception: + continue + if 'runtype' in details: + if details['runtype'].lower() == 'ebeam_sans': + ebeam_sans_files.append(f) + elif details['runtype'].lower() == 'ebeam_trans': + ebeam_trans_files.append(f) + if ebeam_sans_files: + ebeam_sans_files.sort(key=lambda x: os.path.getmtime(x), reverse=True) + self.empty_beam_sans_text.value = ebeam_sans_files[0] + else: + self.empty_beam_sans_text.value = "" + if ebeam_trans_files: + ebeam_trans_files.sort(key=lambda x: os.path.getmtime(x), reverse=True) + self.empty_beam_trans_text.value = ebeam_trans_files[0] + else: + self.empty_beam_trans_text.value = "" + + def run_reduction(self, _): + self.log_output.clear_output() + self.plot_output.clear_output() + 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 + background_run_file = self.empty_beam_sans_text.value + empty_beam_file = self.empty_beam_trans_text.value + if not background_run_file or not empty_beam_file: + with self.log_output: + print("Empty beam files not found.") + return + lam_min = self.lambda_min_widget.value + lam_max = self.lambda_max_widget.value + lam_n = self.lambda_n_widget.value + q_min = self.q_min_widget.value + q_max = self.q_max_widget.value + q_n = self.q_n_widget.value + + #df = self.table.data + df = self.table.data.copy() + for idx, row in df.iterrows(): + sample = row["SAMPLE"] + sans_run = row["SANS"] + trans_run = row["TRANS"] + try: + sample_run_file = find_file(input_dir, sans_run, extension=".nxs") + transmission_run_file = find_file(input_dir, trans_run, extension=".nxs") + except Exception as e: + with self.log_output: + print(f"Skipping sample {sample}: {e}") + continue + try: + mask_file = find_mask_file(input_dir) + with self.log_output: + print(f"Using 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], + wavelength_min=lam_min, + wavelength_max=lam_max, + wavelength_n=lam_n, + q_start=q_min, + q_stop=q_max, + q_n=q_n + ) + 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}") + try: + save_reduction_plots(res, sample, sample_run_file, lam_min, lam_max, lam_n, q_min, q_max, q_n, output_dir)#, n_bands=5) + with self.log_output: + print(f"Saved combined reduction plot for sample {sample}.") + except Exception as e: + with self.log_output: + print(f"Failed to save reduction plot for {sample}: {e}") + with self.log_output: + print(f"Reduced sample {sample} and saved outputs.") + self.processed.add((row["SAMPLE"], row["SANS"], row["TRANS"])) + #time.sleep(1) # small delay between rows + #time.sleep(60) + + @property + def widget(self): + return self.main + +# ---------------------------- +# Direct Beam Functionality and Widget (unchanged) +# ---------------------------- +def compute_direct_beam_local( + mask: str, + sample_sans: str, + background_sans: str, + sample_trans: str, + background_trans: str, + empty_beam: str, + local_Iq_theory: str, + wavelength_min: float = 1.0, + wavelength_max: float = 13.0, + n_wavelength_bins: int = 50, + n_wavelength_bands: int = 50 +) -> dict: + workflow = loki.LokiAtLarmorWorkflow() + workflow = sans.with_pixel_mask_filenames(workflow, masks=[mask]) + workflow[NeXusDetectorName] = 'larmor_detector' + wl_min = sc.scalar(wavelength_min, unit='angstrom') + wl_max = sc.scalar(wavelength_max, unit='angstrom') + workflow[WavelengthBins] = sc.linspace('wavelength', wl_min, wl_max, n_wavelength_bins + 1) + workflow[WavelengthBands] = sc.linspace('wavelength', wl_min, wl_max, n_wavelength_bands + 1) + workflow[CorrectForGravity] = True + workflow[UncertaintyBroadcastMode] = UncertaintyBroadcastMode.upper_bound + workflow[ReturnEvents] = False + workflow[QBins] = sc.linspace(dim='Q', start=0.01, stop=0.3, num=101, unit='1/angstrom') + workflow[Filename[SampleRun]] = sample_sans + workflow[Filename[BackgroundRun]] = background_sans + workflow[Filename[TransmissionRun[SampleRun]]] = sample_trans + workflow[Filename[TransmissionRun[BackgroundRun]]] = background_trans + workflow[Filename[EmptyBeamRun]] = empty_beam + center = sans.beam_center_from_center_of_mass(workflow) + print("Computed beam center:", center) + workflow[BeamCenter] = center + Iq_theory = sc.io.load_hdf5(local_Iq_theory) + f = interp1d(Iq_theory, 'Q') + I0 = f(sc.midpoints(workflow.compute(QBins))).data[0] + print("Computed I0:", I0) + results = sans.direct_beam(workflow=workflow, I0=I0, niter=6) + iofq_full = results[-1]['iofq_full'] + iofq_bands = results[-1]['iofq_bands'] + direct_beam_function = results[-1]['direct_beam'] + pp.plot( + {'reference': Iq_theory, 'data': iofq_full}, + color={'reference': 'darkgrey', 'data': 'C0'}, + norm='log', + ) + print("Plotted full-range result vs. theoretical reference.") + return { + 'direct_beam_function': direct_beam_function, + 'iofq_full': iofq_full, + 'Iq_theory': Iq_theory, + } + +class DirectBeamWidget: + def __init__(self): + self.mask_text = widgets.Text(value="", placeholder="Enter mask file path", description="Mask:") + self.sample_sans_text = widgets.Text(value="", placeholder="Enter sample SANS file path", description="Sample SANS:") + self.background_sans_text = widgets.Text(value="", placeholder="Enter background SANS file path", description="Background SANS:") + self.sample_trans_text = widgets.Text(value="", placeholder="Enter sample TRANS file path", description="Sample TRANS:") + self.background_trans_text = widgets.Text(value="", placeholder="Enter background TRANS file path", description="Background TRANS:") + self.empty_beam_text = widgets.Text(value="", placeholder="Enter empty beam file path", description="Empty Beam:") + self.local_Iq_theory_text = widgets.Text(value="", placeholder="Enter I(q) Theory file path", description="I(q) Theory:") + self.db_wavelength_min_widget = widgets.FloatText(value=1.0, description="λ min (Å):") + self.db_wavelength_max_widget = widgets.FloatText(value=13.0, description="λ max (Å):") + self.db_n_wavelength_bins_widget = widgets.IntText(value=50, description="λ n_bins:") + self.db_n_wavelength_bands_widget = widgets.IntText(value=50, description="λ n_bands:") + self.compute_button = widgets.Button(description="Compute Direct Beam") + self.compute_button.on_click(self.compute_direct_beam) + self.log_output = widgets.Output() + self.plot_output = widgets.Output() + self.main = widgets.VBox([ + self.mask_text, + self.sample_sans_text, + self.background_sans_text, + self.sample_trans_text, + self.background_trans_text, + self.empty_beam_text, + self.local_Iq_theory_text, + widgets.HBox([ + self.db_wavelength_min_widget, + self.db_wavelength_max_widget, + self.db_n_wavelength_bins_widget, + self.db_n_wavelength_bands_widget + ]), + self.compute_button, + self.log_output, + self.plot_output + ]) + + def compute_direct_beam(self, _): + self.log_output.clear_output() + self.plot_output.clear_output() + mask = self.mask_text.value + sample_sans = self.sample_sans_text.value + background_sans = self.background_sans_text.value + sample_trans = self.sample_trans_text.value + background_trans = self.background_trans_text.value + empty_beam = self.empty_beam_text.value + local_Iq_theory = self.local_Iq_theory_text.value + wl_min = self.db_wavelength_min_widget.value + wl_max = self.db_wavelength_max_widget.value + n_bins = self.db_n_wavelength_bins_widget.value + n_bands = self.db_n_wavelength_bands_widget.value + with self.log_output: + print("Computing direct beam with:") + print(" Mask:", mask) + print(" Sample SANS:", sample_sans) + print(" Background SANS:", background_sans) + print(" Sample TRANS:", sample_trans) + print(" Background TRANS:", background_trans) + print(" Empty Beam:", empty_beam) + print(" I(q) Theory:", local_Iq_theory) + print(" λ min:", wl_min, "λ max:", wl_max, "n_bins:", n_bins, "n_bands:", n_bands) + try: + results = compute_direct_beam_local( + mask, + sample_sans, + background_sans, + sample_trans, + background_trans, + empty_beam, + local_Iq_theory, + wavelength_min=wl_min, + wavelength_max=wl_max, + n_wavelength_bins=n_bins, + n_wavelength_bands=n_bands + ) + with self.log_output: + print("Direct beam computation complete.") + except Exception as e: + with self.log_output: + print("Error computing direct beam:", e) + + @property + def widget(self): + return self.main + +# ---------------------------- +# Auto Reduction Widget (unchanged, with common plotting call) +# ---------------------------- +class AutoReductionWidget: + def __init__(self): + 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.start_stop_button = widgets.Button(description="Start") + self.start_stop_button.on_click(self.toggle_running) + self.status_label = widgets.Label(value="Stopped") + self.table = DataGrid(pd.DataFrame([]), editable=False, auto_fit_columns=True) + self.log_output = widgets.Output() + self.running = False + self.thread = None + self.processed = set() # Track already reduced entries. + self.empty_beam_sans = None + self.empty_beam_trans = None + self.main = widgets.VBox([ + widgets.HBox([self.input_dir_chooser, self.output_dir_chooser]), + widgets.HBox([self.start_stop_button, self.status_label]), + self.table, + self.log_output + ]) + + def toggle_running(self, _): + if not self.running: + self.running = True + self.start_stop_button.description = "Stop" + self.status_label.value = "Running" + self.thread = threading.Thread(target=self.background_loop, daemon=True) + self.thread.start() + else: + self.running = False + self.start_stop_button.description = "Start" + self.status_label.value = "Stopped" + + def background_loop(self): + while self.running: + 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("Invalid input folder. Waiting for valid selection...") + time.sleep(60) + continue + if not output_dir or not os.path.isdir(output_dir): + with self.log_output: + print("Invalid output folder. Waiting for valid selection...") + time.sleep(60) + continue + nxs_files = glob.glob(os.path.join(input_dir, "*.nxs")) + groups = {} + for f in nxs_files: + try: + details = parse_nx_details(f) + except Exception: + continue + if 'runlabel' not in details or 'runtype' not in details: + continue + runlabel = details['runlabel'] + runtype = details['runtype'].lower() + run_number = extract_run_number(os.path.basename(f)) + if runlabel not in groups: + groups[runlabel] = {} + groups[runlabel][runtype] = run_number + table_rows = [] + for runlabel, d in groups.items(): + if 'sans' in d and 'trans' in d: + table_rows.append({'SAMPLE': runlabel, 'SANS': d['sans'], 'TRANS': d['trans']}) + df = pd.DataFrame(table_rows) + self.table.data = df + with self.log_output: + print(f"Scanned {len(nxs_files)} files. Found {len(df)} reduction entries.") + ebeam_sans_files = [] + ebeam_trans_files = [] + for f in nxs_files: + try: + details = parse_nx_details(f) + except Exception: + continue + if 'runtype' in details: + if details['runtype'].lower() == 'ebeam_sans': + ebeam_sans_files.append(f) + elif details['runtype'].lower() == 'ebeam_trans': + ebeam_trans_files.append(f) + if ebeam_sans_files: + ebeam_sans_files.sort(key=lambda x: os.path.getmtime(x), reverse=True) + self.empty_beam_sans = ebeam_sans_files[0] + else: + self.empty_beam_sans = None + if ebeam_trans_files: + ebeam_trans_files.sort(key=lambda x: os.path.getmtime(x), reverse=True) + self.empty_beam_trans = ebeam_trans_files[0] + else: + self.empty_beam_trans = None + try: + direct_beam_file = find_direct_beam(input_dir) + except Exception as e: + with self.log_output: + print("Direct-beam file not found:", e) + time.sleep(60) + continue + for index, row in df.iterrows(): + key = (row["SAMPLE"], row["SANS"], row["TRANS"]) + if key in self.processed: + continue + try: + sample_run_file = find_file(input_dir, row["SANS"], extension=".nxs") + transmission_run_file = find_file(input_dir, row["TRANS"], extension=".nxs") + except Exception as e: + with self.log_output: + print(f"Skipping sample {row['SAMPLE']}: {e}") + continue + try: + mask_file = find_mask_file(input_dir) + with self.log_output: + print(f"Using mask file: {mask_file} for sample {row['SAMPLE']}") + except Exception as e: + with self.log_output: + print(f"Mask file not found for sample {row['SAMPLE']}: {e}") + continue + if not self.empty_beam_sans or not self.empty_beam_trans: + with self.log_output: + print("Empty beam files not found, skipping reduction for sample", row["SAMPLE"]) + continue + with self.log_output: + print(f"Reducing sample {row['SAMPLE']}...") + try: + res = reduce_loki_batch_preliminary( + sample_run_file=sample_run_file, + transmission_run_file=transmission_run_file, + background_run_file=self.empty_beam_sans, + empty_beam_file=self.empty_beam_trans, + direct_beam_file=direct_beam_file, + mask_files=[mask_file], + wavelength_min=1.0, + wavelength_max=13.0, + wavelength_n=201, + q_start=0.01, + q_stop=0.3, + q_n=101 + ) + except Exception as e: + with self.log_output: + print(f"Reduction failed for sample {row['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 {row['SAMPLE']}: {e}") + try: + with self.plot_output: + save_reduction_plots(res, sample, sample_run_file, lam_min, lam_max, lam_n, q_min, q_max, q_n, output_dir, show=True) + with self.log_output: + print(f"Saved combined reduction plot for sample {sample}.") + except Exception as e: + with self.log_output: + print(f"Failed to save reduction plot for {sample}: {e}") + + #try: + # save_reduction_plots(res, row["SAMPLE"], sample_run_file, 1.0, 13.0, 201, 0.01, 0.3, 101, output_dir)#, n_bands=5) + # with self.log_output: + # print(f"Saved combined reduction plot for sample {row['SAMPLE']}.") + #except Exception as e: + # with self.log_output: + # print(f"Failed to save reduction plot for {row['SAMPLE']}: {e}") + with self.log_output: + print(f"Reduced sample {row['SAMPLE']} and saved outputs.") + self.processed.add(key) + time.sleep(60) + + @property + def widget(self): + return self.main + +# ---------------------------- +# Build the Tabbed Widget +# ---------------------------- +reduction_widget = SansBatchReductionWidget().widget +direct_beam_widget = DirectBeamWidget().widget +semi_auto_reduction_widget = SemiAutoReductionWidget().widget +auto_reduction_widget = AutoReductionWidget().widget + +tabs = widgets.Tab(children=[direct_beam_widget, reduction_widget, semi_auto_reduction_widget, auto_reduction_widget]) +tabs.set_title(0, "Direct Beam") +tabs.set_title(1, "Reduction (Manual)") +tabs.set_title(2, "Reduction (Smart)") +tabs.set_title(3, "Reduction (Auto)") + +#display(tabs) diff --git a/src/ess/loki/batch-gui-legacy/tabwidgetauto.py b/src/ess/loki/batch-gui-legacy/tabwidgetauto.py new file mode 100644 index 00000000..e9527b3b --- /dev/null +++ b/src/ess/loki/batch-gui-legacy/tabwidgetauto.py @@ -0,0 +1,926 @@ +import os +import glob +import re +import h5py +import pandas as pd +import scipp as sc +import matplotlib.pyplot as plt +import numpy as np +import ipywidgets as widgets +from ipydatagrid import DataGrid +from IPython.display import display +from ipyfilechooser import FileChooser +from ess import sans +from ess import loki +from ess.sans.types import * +from scipp.scipy.interpolate import interp1d +import plopp as pp # used for plotting in direct beam section +import threading +import time + + +# ---------------------------- +# Reduction Functionality +# ---------------------------- +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) + +# ---------------------------- +# Helper Functions for Semi-Auto Reduction +# ---------------------------- +def extract_run_number(filename): + m = re.search(r'(\d{4,})', filename) + if m: + return m.group(1) + return "" + +def parse_nx_details(filepath): + details = {} + with h5py.File(filepath, 'r') as f: + if 'nicos_details' in f['entry']: + grp = f['entry']['nicos_details'] + if 'runlabel' in grp: + val = grp['runlabel'][()] + details['runlabel'] = val.decode('utf8') if isinstance(val, bytes) else str(val) + if 'runtype' in grp: + val = grp['runtype'][()] + details['runtype'] = val.decode('utf8') if isinstance(val, bytes) else str(val) + return details + +# ---------------------------- +# Common Plotting Function +# ---------------------------- + +def save_reduction_plots(res, sample, sample_run_file, lam_min, lam_max, lam_n, q_min, q_max, q_n, output_dir, show=True): + """ + Creates a figure with 1 row x 2 columns: + - Left subplot: I(Q) (scatter with errorbars, log-log). + - Right subplot: Transmission fraction vs. wavelength (scatter with errorbars). + A single centered title (filename and sample ID) is added above the subplots. + The figure is saved to output_dir with a filename based on sample_run_file. + If show is True, the figure is displayed in the current output. + """ + # Create a figure with two subplots side by side. + fig, axs = plt.subplots(1, 2, figsize=(8, 4)) + + # Force each axis to be square (requires Matplotlib>=3.3) + axs[0].set_box_aspect(1) + axs[1].set_box_aspect(1) + + # Set a centered overall title (filename and sample) + title_str = f"{sample} - {os.path.basename(sample_run_file)}" + fig.suptitle(title_str, fontsize=14) + + # Subplot A: I(Q) + q_bins = sc.linspace("Q", q_min, q_max, q_n, unit="1/angstrom") + x_q = 0.5 * (q_bins.values[:-1] + q_bins.values[1:]) + if res["IofQ"].variances is not None: + yerr = np.sqrt(res["IofQ"].variances) + axs[0].errorbar(x_q, res["IofQ"].values, yerr=yerr, fmt='o', linestyle='none', color='k', alpha=0.5, markerfacecolor='none') + else: + axs[0].scatter(x_q, res["IofQ"].values) + axs[0].set_xlabel("Q (Å$^{-1}$)") + axs[0].set_ylabel("I(Q)") + axs[0].set_xscale("log") + axs[0].set_yscale("log") + + # Subplot B: Transmission vs. Wavelength + wavelength_bins = sc.linspace("wavelength", lam_min, lam_max, lam_n, unit="angstrom") + x_wl = 0.5 * (wavelength_bins.values[:-1] + wavelength_bins.values[1:]) + if res["transmission"].variances is not None: + yerr_tr = np.sqrt(res["transmission"].variances) + axs[1].errorbar(x_wl, res["transmission"].values, yerr=yerr_tr, fmt='^', linestyle='none', color='k', alpha=0.5, markerfacecolor='none') + else: + axs[1].scatter(x_wl, res["transmission"].values) + axs[1].set_xlabel("Wavelength (Å)") + axs[1].set_ylabel("Transmission") + + # Adjust layout to leave room for the suptitle. + plt.tight_layout(rect=[0, 0, 1, 0.90]) + + # Save the figure. + out_png = os.path.join(output_dir, os.path.basename(sample_run_file).replace(".nxs", "_reduced.png")) + fig.savefig(out_png, dpi=300) + + # Display the figure in the GUI if requested. + if show: + display(fig) + + # Close the figure so memory is released. + plt.close(fig) + + + + +# ---------------------------- +# SansBatchReductionWidget (Updated) +# ---------------------------- +class SansBatchReductionWidget: + def __init__(self): + # CSV chooser for pre-loaded reduction table. + self.csv_chooser = FileChooser(select_dir=False) + self.csv_chooser.title = "Select CSV File" + self.csv_chooser.filter_pattern = "*.csv" + # Folder choosers. + 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" + # Empty-beam run number widgets. + 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:") + # Reduction parameter widgets. + self.wavelength_min_widget = widgets.FloatText(value=1.0, description="λ min (Å):") + self.wavelength_max_widget = widgets.FloatText(value=13.0, description="λ max (Å):") + self.wavelength_n_widget = widgets.IntText(value=201, description="λ n_bins:") + self.q_start_widget = widgets.FloatText(value=0.01, description="Q start (1/Å):") + self.q_stop_widget = widgets.FloatText(value=0.3, description="Q stop (1/Å):") + self.q_n_widget = widgets.IntText(value=101, description="Q n_bins:") + # Button to load CSV. + self.load_csv_button = widgets.Button(description="Load CSV") + self.load_csv_button.on_click(self.load_csv) + # DataGrid for the reduction table. + self.table = DataGrid(pd.DataFrame([]), editable=True, auto_fit_columns=True) + # Reduction and clear buttons. + 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(lambda _: self.log_output.clear_output()) + self.clear_plots_button = widgets.Button(description="Clear Plots") + self.clear_plots_button.on_click(lambda _: self.plot_output.clear_output()) + # Output widgets. + self.log_output = widgets.Output() + self.plot_output = widgets.Output() + # Build layout. + 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]), + widgets.HBox([self.wavelength_min_widget, self.wavelength_max_widget, self.wavelength_n_widget]), + widgets.HBox([self.q_start_widget, self.q_stop_widget, self.q_n_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 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, _): + self.log_output.clear_output() + self.plot_output.clear_output() + 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 + wl_min = self.wavelength_min_widget.value + wl_max = self.wavelength_max_widget.value + wl_n = self.wavelength_n_widget.value + q_start = self.q_start_widget.value + q_stop = self.q_stop_widget.value + q_n = self.q_n_widget.value + + 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"Identified 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], + wavelength_min=wl_min, + wavelength_max=wl_max, + wavelength_n=wl_n, + q_start=q_start, + q_stop=q_stop, + q_n=q_n + ) + 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}") + try: + with self.plot_output: + save_reduction_plots(res, sample, sample_run_file, lam_min, lam_max, lam_n, q_min, q_max, q_n, output_dir, show=True) + with self.log_output: + print(f"Saved combined reduction plot for sample {sample}.") + except Exception as e: + with self.log_output: + print(f"Failed to save reduction plot for {sample}: {e}") + + #try: + # save_reduction_plots(res, sample, sample_run_file, wl_min, wl_max, wl_n, q_start, q_stop, q_n, output_dir)#, n_bands=5) + # with self.log_output: + # print(f"Saved combined reduction plot for sample {sample}.") + #except Exception as e: + # with self.log_output: + # print(f"Failed to save reduction plot for {sample}: {e}") + with self.log_output: + print(f"Reduced sample {sample} and saved outputs.") + + @property + def widget(self): + return self.main + +# ---------------------------- +# Semi-Auto Reduction Widget (unchanged) +# ---------------------------- +class SemiAutoReductionWidget: + def __init__(self): + 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.scan_button = widgets.Button(description="Scan Directory") + self.scan_button.on_click(self.scan_directory) + self.table = DataGrid(pd.DataFrame([]), editable=True, auto_fit_columns=True) + self.add_row_button = widgets.Button(description="Add Row") + self.add_row_button.on_click(self.add_row) + self.delete_row_button = widgets.Button(description="Delete Last Row") + self.delete_row_button.on_click(self.delete_last_row) + self.lambda_min_widget = widgets.FloatText(value=1.0, description="λ min (Å):") + self.lambda_max_widget = widgets.FloatText(value=13.0, description="λ max (Å):") + self.lambda_n_widget = widgets.IntText(value=201, description="λ n_bins:") + self.q_min_widget = widgets.FloatText(value=0.01, description="Qmin (1/Å):") + self.q_max_widget = widgets.FloatText(value=0.3, description="Qmax (1/Å):") + self.q_n_widget = widgets.IntText(value=101, description="Q n_bins:") + self.empty_beam_sans_text = widgets.Text(value="", description="Ebeam SANS:", disabled=True) + self.empty_beam_trans_text = widgets.Text(value="", description="Ebeam TRANS:", disabled=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(lambda _: self.log_output.clear_output()) + self.clear_plots_button = widgets.Button(description="Clear Plots") + self.clear_plots_button.on_click(lambda _: self.plot_output.clear_output()) + self.log_output = widgets.Output() + self.plot_output = widgets.Output() + + # Add the processed set here: + self.processed = set() + + self.main = widgets.VBox([ + widgets.HBox([self.input_dir_chooser, self.output_dir_chooser]), + self.scan_button, + self.table, + widgets.HBox([self.add_row_button, self.delete_row_button]), + widgets.HBox([self.lambda_min_widget, self.lambda_max_widget, self.lambda_n_widget]), + widgets.HBox([self.q_min_widget, self.q_max_widget, self.q_n_widget]), + widgets.HBox([self.empty_beam_sans_text, self.empty_beam_trans_text]), + widgets.HBox([self.reduce_button, self.clear_log_button, self.clear_plots_button]), + self.log_output, + self.plot_output + ]) + + + def add_row(self, _): + df = self.table.data + if df.empty: + new_row = {'SAMPLE': '', 'SANS': '', 'TRANS': ''} + else: + new_row = {col: "" for col in df.columns} + df = df.append(new_row, ignore_index=True) + self.table.data = df + + def delete_last_row(self, _): + df = self.table.data + if not df.empty: + df = df.iloc[:-1] + self.table.data = df + + def scan_directory(self, _): + self.log_output.clear_output() + input_dir = self.input_dir_chooser.selected + if not input_dir or not os.path.isdir(input_dir): + with self.log_output: + print("Invalid input folder.") + return + nxs_files = glob.glob(os.path.join(input_dir, "*.nxs")) + groups = {} + for f in nxs_files: + try: + details = parse_nx_details(f) + except Exception: + continue + if 'runlabel' not in details or 'runtype' not in details: + continue + runlabel = details['runlabel'] + runtype = details['runtype'].lower() + run_number = extract_run_number(os.path.basename(f)) + if runlabel not in groups: + groups[runlabel] = {} + groups[runlabel][runtype] = run_number + table_rows = [] + for runlabel, d in groups.items(): + if 'sans' in d and 'trans' in d: + table_rows.append({'SAMPLE': runlabel, 'SANS': d['sans'], 'TRANS': d['trans']}) + df = pd.DataFrame(table_rows) + self.table.data = df + with self.log_output: + print(f"Scanned {len(nxs_files)} files. Found {len(df)} reduction entries.") + ebeam_sans_files = [] + ebeam_trans_files = [] + for f in nxs_files: + try: + details = parse_nx_details(f) + except Exception: + continue + if 'runtype' in details: + if details['runtype'].lower() == 'ebeam_sans': + ebeam_sans_files.append(f) + elif details['runtype'].lower() == 'ebeam_trans': + ebeam_trans_files.append(f) + if ebeam_sans_files: + ebeam_sans_files.sort(key=lambda x: os.path.getmtime(x), reverse=True) + self.empty_beam_sans_text.value = ebeam_sans_files[0] + else: + self.empty_beam_sans_text.value = "" + if ebeam_trans_files: + ebeam_trans_files.sort(key=lambda x: os.path.getmtime(x), reverse=True) + self.empty_beam_trans_text.value = ebeam_trans_files[0] + else: + self.empty_beam_trans_text.value = "" + + def run_reduction(self, _): + self.log_output.clear_output() + self.plot_output.clear_output() + 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 + background_run_file = self.empty_beam_sans_text.value + empty_beam_file = self.empty_beam_trans_text.value + if not background_run_file or not empty_beam_file: + with self.log_output: + print("Empty beam files not found.") + return + lam_min = self.lambda_min_widget.value + lam_max = self.lambda_max_widget.value + lam_n = self.lambda_n_widget.value + q_min = self.q_min_widget.value + q_max = self.q_max_widget.value + q_n = self.q_n_widget.value + + #df = self.table.data + df = self.table.data.copy() + for idx, row in df.iterrows(): + sample = row["SAMPLE"] + sans_run = row["SANS"] + trans_run = row["TRANS"] + try: + sample_run_file = find_file(input_dir, sans_run, extension=".nxs") + transmission_run_file = find_file(input_dir, trans_run, extension=".nxs") + except Exception as e: + with self.log_output: + print(f"Skipping sample {sample}: {e}") + continue + try: + mask_file = find_mask_file(input_dir) + with self.log_output: + print(f"Using 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], + wavelength_min=lam_min, + wavelength_max=lam_max, + wavelength_n=lam_n, + q_start=q_min, + q_stop=q_max, + q_n=q_n + ) + 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}") + try: + save_reduction_plots(res, sample, sample_run_file, lam_min, lam_max, lam_n, q_min, q_max, q_n, output_dir)#, n_bands=5) + with self.log_output: + print(f"Saved combined reduction plot for sample {sample}.") + except Exception as e: + with self.log_output: + print(f"Failed to save reduction plot for {sample}: {e}") + with self.log_output: + print(f"Reduced sample {sample} and saved outputs.") + self.processed.add((row["SAMPLE"], row["SANS"], row["TRANS"])) + #time.sleep(1) # small delay between rows + #time.sleep(60) + + @property + def widget(self): + return self.main + +# ---------------------------- +# Direct Beam Functionality and Widget (unchanged) +# ---------------------------- +def compute_direct_beam_local( + mask: str, + sample_sans: str, + background_sans: str, + sample_trans: str, + background_trans: str, + empty_beam: str, + local_Iq_theory: str, + wavelength_min: float = 1.0, + wavelength_max: float = 13.0, + n_wavelength_bins: int = 50, + n_wavelength_bands: int = 50 +) -> dict: + workflow = loki.LokiAtLarmorWorkflow() + workflow = sans.with_pixel_mask_filenames(workflow, masks=[mask]) + workflow[NeXusDetectorName] = 'larmor_detector' + wl_min = sc.scalar(wavelength_min, unit='angstrom') + wl_max = sc.scalar(wavelength_max, unit='angstrom') + workflow[WavelengthBins] = sc.linspace('wavelength', wl_min, wl_max, n_wavelength_bins + 1) + workflow[WavelengthBands] = sc.linspace('wavelength', wl_min, wl_max, n_wavelength_bands + 1) + workflow[CorrectForGravity] = True + workflow[UncertaintyBroadcastMode] = UncertaintyBroadcastMode.upper_bound + workflow[ReturnEvents] = False + workflow[QBins] = sc.linspace(dim='Q', start=0.01, stop=0.3, num=101, unit='1/angstrom') + workflow[Filename[SampleRun]] = sample_sans + workflow[Filename[BackgroundRun]] = background_sans + workflow[Filename[TransmissionRun[SampleRun]]] = sample_trans + workflow[Filename[TransmissionRun[BackgroundRun]]] = background_trans + workflow[Filename[EmptyBeamRun]] = empty_beam + center = sans.beam_center_from_center_of_mass(workflow) + print("Computed beam center:", center) + workflow[BeamCenter] = center + Iq_theory = sc.io.load_hdf5(local_Iq_theory) + f = interp1d(Iq_theory, 'Q') + I0 = f(sc.midpoints(workflow.compute(QBins))).data[0] + print("Computed I0:", I0) + results = sans.direct_beam(workflow=workflow, I0=I0, niter=6) + iofq_full = results[-1]['iofq_full'] + iofq_bands = results[-1]['iofq_bands'] + direct_beam_function = results[-1]['direct_beam'] + pp.plot( + {'reference': Iq_theory, 'data': iofq_full}, + color={'reference': 'darkgrey', 'data': 'C0'}, + norm='log', + ) + print("Plotted full-range result vs. theoretical reference.") + return { + 'direct_beam_function': direct_beam_function, + 'iofq_full': iofq_full, + 'Iq_theory': Iq_theory, + } + +class DirectBeamWidget: + def __init__(self): + self.mask_text = widgets.Text(value="", placeholder="Enter mask file path", description="Mask:") + self.sample_sans_text = widgets.Text(value="", placeholder="Enter sample SANS file path", description="Sample SANS:") + self.background_sans_text = widgets.Text(value="", placeholder="Enter background SANS file path", description="Background SANS:") + self.sample_trans_text = widgets.Text(value="", placeholder="Enter sample TRANS file path", description="Sample TRANS:") + self.background_trans_text = widgets.Text(value="", placeholder="Enter background TRANS file path", description="Background TRANS:") + self.empty_beam_text = widgets.Text(value="", placeholder="Enter empty beam file path", description="Empty Beam:") + self.local_Iq_theory_text = widgets.Text(value="", placeholder="Enter I(q) Theory file path", description="I(q) Theory:") + self.db_wavelength_min_widget = widgets.FloatText(value=1.0, description="λ min (Å):") + self.db_wavelength_max_widget = widgets.FloatText(value=13.0, description="λ max (Å):") + self.db_n_wavelength_bins_widget = widgets.IntText(value=50, description="λ n_bins:") + self.db_n_wavelength_bands_widget = widgets.IntText(value=50, description="λ n_bands:") + self.compute_button = widgets.Button(description="Compute Direct Beam") + self.compute_button.on_click(self.compute_direct_beam) + self.log_output = widgets.Output() + self.plot_output = widgets.Output() + self.main = widgets.VBox([ + self.mask_text, + self.sample_sans_text, + self.background_sans_text, + self.sample_trans_text, + self.background_trans_text, + self.empty_beam_text, + self.local_Iq_theory_text, + widgets.HBox([ + self.db_wavelength_min_widget, + self.db_wavelength_max_widget, + self.db_n_wavelength_bins_widget, + self.db_n_wavelength_bands_widget + ]), + self.compute_button, + self.log_output, + self.plot_output + ]) + + def compute_direct_beam(self, _): + self.log_output.clear_output() + self.plot_output.clear_output() + mask = self.mask_text.value + sample_sans = self.sample_sans_text.value + background_sans = self.background_sans_text.value + sample_trans = self.sample_trans_text.value + background_trans = self.background_trans_text.value + empty_beam = self.empty_beam_text.value + local_Iq_theory = self.local_Iq_theory_text.value + wl_min = self.db_wavelength_min_widget.value + wl_max = self.db_wavelength_max_widget.value + n_bins = self.db_n_wavelength_bins_widget.value + n_bands = self.db_n_wavelength_bands_widget.value + with self.log_output: + print("Computing direct beam with:") + print(" Mask:", mask) + print(" Sample SANS:", sample_sans) + print(" Background SANS:", background_sans) + print(" Sample TRANS:", sample_trans) + print(" Background TRANS:", background_trans) + print(" Empty Beam:", empty_beam) + print(" I(q) Theory:", local_Iq_theory) + print(" λ min:", wl_min, "λ max:", wl_max, "n_bins:", n_bins, "n_bands:", n_bands) + try: + results = compute_direct_beam_local( + mask, + sample_sans, + background_sans, + sample_trans, + background_trans, + empty_beam, + local_Iq_theory, + wavelength_min=wl_min, + wavelength_max=wl_max, + n_wavelength_bins=n_bins, + n_wavelength_bands=n_bands + ) + with self.log_output: + print("Direct beam computation complete.") + except Exception as e: + with self.log_output: + print("Error computing direct beam:", e) + + @property + def widget(self): + return self.main + +# ---------------------------- +# Auto Reduction Widget (unchanged, with common plotting call) +# ---------------------------- +class AutoReductionWidget: + def __init__(self): + 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.start_stop_button = widgets.Button(description="Start") + self.start_stop_button.on_click(self.toggle_running) + self.status_label = widgets.Label(value="Stopped") + self.table = DataGrid(pd.DataFrame([]), editable=False, auto_fit_columns=True) + self.log_output = widgets.Output() + self.running = False + self.thread = None + self.processed = set() # Track already reduced entries. + self.empty_beam_sans = None + self.empty_beam_trans = None + self.main = widgets.VBox([ + widgets.HBox([self.input_dir_chooser, self.output_dir_chooser]), + widgets.HBox([self.start_stop_button, self.status_label]), + self.table, + self.log_output + ]) + + def toggle_running(self, _): + if not self.running: + self.running = True + self.start_stop_button.description = "Stop" + self.status_label.value = "Running" + self.thread = threading.Thread(target=self.background_loop, daemon=True) + self.thread.start() + else: + self.running = False + self.start_stop_button.description = "Start" + self.status_label.value = "Stopped" + + def background_loop(self): + while self.running: + 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("Invalid input folder. Waiting for valid selection...") + time.sleep(60) + continue + if not output_dir or not os.path.isdir(output_dir): + with self.log_output: + print("Invalid output folder. Waiting for valid selection...") + time.sleep(60) + continue + nxs_files = glob.glob(os.path.join(input_dir, "*.nxs")) + groups = {} + for f in nxs_files: + try: + details = parse_nx_details(f) + except Exception: + continue + if 'runlabel' not in details or 'runtype' not in details: + continue + runlabel = details['runlabel'] + runtype = details['runtype'].lower() + run_number = extract_run_number(os.path.basename(f)) + if runlabel not in groups: + groups[runlabel] = {} + groups[runlabel][runtype] = run_number + table_rows = [] + for runlabel, d in groups.items(): + if 'sans' in d and 'trans' in d: + table_rows.append({'SAMPLE': runlabel, 'SANS': d['sans'], 'TRANS': d['trans']}) + df = pd.DataFrame(table_rows) + self.table.data = df + with self.log_output: + print(f"Scanned {len(nxs_files)} files. Found {len(df)} reduction entries.") + ebeam_sans_files = [] + ebeam_trans_files = [] + for f in nxs_files: + try: + details = parse_nx_details(f) + except Exception: + continue + if 'runtype' in details: + if details['runtype'].lower() == 'ebeam_sans': + ebeam_sans_files.append(f) + elif details['runtype'].lower() == 'ebeam_trans': + ebeam_trans_files.append(f) + if ebeam_sans_files: + ebeam_sans_files.sort(key=lambda x: os.path.getmtime(x), reverse=True) + self.empty_beam_sans = ebeam_sans_files[0] + else: + self.empty_beam_sans = None + if ebeam_trans_files: + ebeam_trans_files.sort(key=lambda x: os.path.getmtime(x), reverse=True) + self.empty_beam_trans = ebeam_trans_files[0] + else: + self.empty_beam_trans = None + try: + direct_beam_file = find_direct_beam(input_dir) + except Exception as e: + with self.log_output: + print("Direct-beam file not found:", e) + time.sleep(60) + continue + for index, row in df.iterrows(): + key = (row["SAMPLE"], row["SANS"], row["TRANS"]) + if key in self.processed: + continue + try: + sample_run_file = find_file(input_dir, row["SANS"], extension=".nxs") + transmission_run_file = find_file(input_dir, row["TRANS"], extension=".nxs") + except Exception as e: + with self.log_output: + print(f"Skipping sample {row['SAMPLE']}: {e}") + continue + try: + mask_file = find_mask_file(input_dir) + with self.log_output: + print(f"Using mask file: {mask_file} for sample {row['SAMPLE']}") + except Exception as e: + with self.log_output: + print(f"Mask file not found for sample {row['SAMPLE']}: {e}") + continue + if not self.empty_beam_sans or not self.empty_beam_trans: + with self.log_output: + print("Empty beam files not found, skipping reduction for sample", row["SAMPLE"]) + continue + with self.log_output: + print(f"Reducing sample {row['SAMPLE']}...") + try: + res = reduce_loki_batch_preliminary( + sample_run_file=sample_run_file, + transmission_run_file=transmission_run_file, + background_run_file=self.empty_beam_sans, + empty_beam_file=self.empty_beam_trans, + direct_beam_file=direct_beam_file, + mask_files=[mask_file], + wavelength_min=1.0, + wavelength_max=13.0, + wavelength_n=201, + q_start=0.01, + q_stop=0.3, + q_n=101 + ) + except Exception as e: + with self.log_output: + print(f"Reduction failed for sample {row['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 {row['SAMPLE']}: {e}") + try: + with self.plot_output: + save_reduction_plots(res, sample, sample_run_file, lam_min, lam_max, lam_n, q_min, q_max, q_n, output_dir, show=True) + with self.log_output: + print(f"Saved combined reduction plot for sample {sample}.") + except Exception as e: + with self.log_output: + print(f"Failed to save reduction plot for {sample}: {e}") + + #try: + # save_reduction_plots(res, row["SAMPLE"], sample_run_file, 1.0, 13.0, 201, 0.01, 0.3, 101, output_dir)#, n_bands=5) + # with self.log_output: + # print(f"Saved combined reduction plot for sample {row['SAMPLE']}.") + #except Exception as e: + # with self.log_output: + # print(f"Failed to save reduction plot for {row['SAMPLE']}: {e}") + with self.log_output: + print(f"Reduced sample {row['SAMPLE']} and saved outputs.") + self.processed.add(key) + time.sleep(60) + + @property + def widget(self): + return self.main + +# ---------------------------- +# Build the Tabbed Widget +# ---------------------------- +reduction_widget = SansBatchReductionWidget().widget +direct_beam_widget = DirectBeamWidget().widget +semi_auto_reduction_widget = SemiAutoReductionWidget().widget +auto_reduction_widget = AutoReductionWidget().widget + +tabs = widgets.Tab(children=[direct_beam_widget, reduction_widget, semi_auto_reduction_widget, auto_reduction_widget]) +tabs.set_title(0, "Direct Beam") +tabs.set_title(1, "Reduction (Manual)") +tabs.set_title(2, "Reduction (Smart)") +tabs.set_title(3, "Reduction (Auto)") + +#display(tabs) diff --git a/src/ess/loki/examplefiles/.DS_Store b/src/ess/loki/examplefiles/.DS_Store new file mode 100644 index 00000000..3d9d7722 Binary files /dev/null and b/src/ess/loki/examplefiles/.DS_Store differ diff --git a/src/ess/loki/examplefiles/mask_new_July2022.xml b/src/ess/loki/examplefiles/mask_new_July2022.xml new file mode 100644 index 00000000..8c9e06ac --- /dev/null +++ b/src/ess/loki/examplefiles/mask_new_July2022.xml @@ -0,0 +1,6 @@ + +