diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index d0b04de..dc05232 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -24,6 +24,13 @@ jobs: python openmc_model.py jupyter-nbconvert --to notebook postprocessing.ipynb --execute + - name: Run foil analysis + shell: bash -l {0} + working-directory: analysis/neutron + run: | + papermill foil_analysis.ipynb temp.ipynb -p download_from_raw False + jupyter-nbconvert --to notebook --execute temp.ipynb + - name: Run tritium model shell: bash -l {0} working-directory: analysis/tritium diff --git a/.github/workflows/process.yml b/.github/workflows/process.yml index 0159f7b..f7e7814 100644 --- a/.github/workflows/process.yml +++ b/.github/workflows/process.yml @@ -30,6 +30,13 @@ jobs: run: | python openmc_model.py jupyter-nbconvert --to notebook postprocessing.ipynb --execute + + - name: Run foil analysis + shell: bash -l {0} + working-directory: analysis/neutron + run: | + papermill foil_analysis.ipynb temp.ipynb -p download_from_raw False + jupyter-nbconvert --to notebook --execute temp.ipynb - name: Run tritium model shell: bash -l {0} diff --git a/analysis/neutron/download_raw_foil_data.py b/analysis/neutron/download_raw_foil_data.py new file mode 100644 index 0000000..70f13c8 --- /dev/null +++ b/analysis/neutron/download_raw_foil_data.py @@ -0,0 +1,39 @@ +from pathlib import Path +import zipfile +import requests + + +def download_and_extract_foil_data(url: str, extracted_path: Path): + + output_filepath = Path("../../data/neutron_detection/foil_data.zip") + + if extracted_path.exists(): + print(f"Directory already exists: {extracted_path}") + else: + # URL of the file + + # Download the file + print(f"Downloading data from {url}...") + response = requests.get(url) + if response.status_code == 200: + print("Download successful!") + # Save the file to the specified directory + with open(output_filepath, "wb") as f: + f.write(response.content) + print(f"File saved to: {output_filepath}") + else: + print(f"Failed to download file. HTTP Status Code: {response.status_code}") + + # Extract the zip file + + # Ensure the extraction directory exists + extracted_path.mkdir(parents=True, exist_ok=True) + + # Unzip the file + with zipfile.ZipFile(output_filepath, "r") as zip_ref: + zip_ref.extractall(extracted_path) + print(f"Files extracted to: {extracted_path}") + + # Delete the zip file after extraction + output_filepath.unlink(missing_ok=True) + diff --git a/analysis/neutron/foil_analysis.ipynb b/analysis/neutron/foil_analysis.ipynb new file mode 100644 index 0000000..09ff73e --- /dev/null +++ b/analysis/neutron/foil_analysis.ipynb @@ -0,0 +1,585 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "7882bbca", + "metadata": {}, + "source": [ + "# Activation Foil Analysis: 1L BABY Run #2\n", + "\n", + "This notebook processes the calibration data from NaI detectors to energy calibrate the detectors and determine total detector efficiencies. Then, NaI measurements of activation foils irradiated during the run with a D-T neutron (14.1 MeV) generator are used to determine the average neutron rate during the run. " + ] + }, + { + "cell_type": "markdown", + "id": "903d6fac", + "metadata": {}, + "source": [ + "## Obtaining the Data\n", + "First, the NaI detector measurement data is obtained from Zenodo and extracted" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "ed8f159d", + "metadata": {}, + "outputs": [], + "source": [ + "# parameters\n", + "\n", + "## keep this if statement for ci and process workflows\n", + "if 'download_from_raw' not in globals() and 'download_from_raw' not in locals():\n", + " download_from_raw = False" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "dc605b1e", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from datetime import datetime\n", + "import json" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5745f109", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "f110638e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Read in properties of Nb Packet #4 foil\n", + "Read in properties of Zr Packet #1 foil\n", + "Processing Background from h5 file...\n", + "Processing Co60 Count 1 from h5 file...\n", + "Processing Cs137 Count 1 from h5 file...\n", + "Processing Mn54 Count 1 from h5 file...\n", + "Processing Na22 Count 1 from h5 file...\n", + "Processing Nb Packet #4 Count 1 from h5 file...\n", + "Processing Zr Packet #1 Count 1 from h5 file...\n" + ] + } + ], + "source": [ + "from process_foil_data import get_data\n", + "check_source_measurements, background_meas, foil_measurements = get_data(download_from_raw=download_from_raw)\n" + ] + }, + { + "cell_type": "markdown", + "id": "47436b0c", + "metadata": {}, + "source": [ + "## Energy Calibration\n", + "\n", + "Using gamma check sources like Co-60 and Cs-137, the characteristic photon peaks from these sources are used to convert the digitizer channel bins into energy (keV) bins" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "1267f6b8", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "from libra_toolbox.neutron_detection.activation_foils import compass\n", + "for name, measurement in check_source_measurements.items():\n", + " fig, ax = plt.subplots(nrows=1, ncols=len(measurement.detectors))\n", + " if not isinstance(ax, np.ndarray):\n", + " ax = [ax]\n", + " for d,detector in enumerate(measurement.detectors):\n", + " hist, bin_edges = detector.get_energy_hist()\n", + " ax[d].hist(\n", + " bin_edges[:-1],\n", + " bins=bin_edges,\n", + " weights=hist,\n", + " histtype=\"step\",\n", + " label=f\"Ch {detector.channel_nb}\",\n", + " )\n", + " peaks = measurement.get_peaks(hist)\n", + " ax[d].vlines(peaks, 0, 1.1 * np.max(hist), \n", + " colors='r', linestyles='dashed',\n", + " alpha=0.8, linewidth=1.0,\n", + " label='Peaks')\n", + "\n", + " ax[d].legend()\n", + " # plt.yscale(\"log\")\n", + " ax[d].set_ylim(top=1.1 * np.max(hist))\n", + " ax[d].set_title(f\"{measurement.name} - {detector.channel_nb}\")\n", + " ax[d].set_xlabel(\"Energy (keV)\")\n", + " ax[d].set_ylabel(\"Counts\")\n", + " fig.tight_layout()\n", + " \n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "bb516151", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "calibration_coeffs = {}\n", + "\n", + "for channel_nb in [4, 5]:\n", + " calibration_channels, calibration_energies = compass.get_calibration_data(\n", + " check_source_measurements.values(),\n", + " background_measurement=background_meas,\n", + " channel_nb=channel_nb,\n", + " )\n", + "\n", + " coeff = np.polyfit(calibration_channels, calibration_energies, 1)\n", + " calibration_coeffs[channel_nb] = coeff\n", + "\n", + " xs = np.linspace(\n", + " calibration_channels[0],\n", + " calibration_channels[-1],\n", + " )\n", + " plt.plot(\n", + " xs,\n", + " np.polyval(coeff, xs),\n", + " label=f\"Ch {channel_nb} fit\",\n", + " )\n", + " plt.scatter(\n", + " calibration_channels,\n", + " calibration_energies,\n", + " label=f\"Ch {channel_nb} data\",\n", + " alpha=0.5,\n", + " )\n", + "plt.xlabel(\"Channel number\")\n", + "plt.ylabel(\"Energy (keV)\")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "6b2bb9cb", + "metadata": {}, + "source": [ + "## Detector Efficiency\n", + "\n", + "Using these same check-sources, each with a known activity, an efficiency curve for each detector is calculated. \n", + "\n", + "Two types of efficiency curves are shown: \n", + "1. Exponent of sum of logarithms (used in https://doi.org/10.2172/1524045): $ y = \\exp(\\sum_{i=0}^n a_n \\log(E)^i) $\n", + "\n", + "2. Polynomial fit (3rd order): $ y = \\sum_{i=0}^n a_n E^i $\n", + "\n", + "**Only the polynomal fit is currently implemented in libra-toolbox, so that is the curve that will be used to calculate the efficiency of the detectors at measuring the activity of the activation foil peaks.**" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "f4fd7baa", + "metadata": {}, + "outputs": [], + "source": [ + "def eff_curve_func(E, *a):\n", + " exponent_term = 0\n", + " for i,a_n in enumerate(a):\n", + " exponent_term += a_n * (np.log(E) ** i)\n", + " return np.exp(exponent_term)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "0bd43b36", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Ch 4 \n", + "\t Energies: [ 511. 661.657 834.848 1173.228 1274.537 1332.492], \n", + "\t Efficiencies: [0.01808423 0.01872441 0.01523538 0.01009384 0.00928584 0.00968485]\n", + "[-710.91840717 313.40955584 -46.14703466 2.25577573]\n", + "Ch 5 \n", + "\t Energies: [ 511. 661.657 834.848 1173.228 1274.537 1332.492], \n", + "\t Efficiencies: [0.02966506 0.02511285 0.01964984 0.01397301 0.01285142 0.01210578]\n", + "[-210.66665078 93.05547985 -13.79958673 0.67417496]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from scipy.optimize import curve_fit\n", + "\n", + "channels = []\n", + "efficiency_coeffs = {}\n", + "measurement = list(check_source_measurements.values())[0]\n", + "search_width = 330\n", + "\n", + "for detector in measurement.detectors:\n", + " channels.append(detector.channel_nb)\n", + "\n", + "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(12, 6))\n", + "for i,ch in enumerate(channels):\n", + " background_detector = background_meas.get_detector(ch)\n", + " energies = []\n", + " efficiencies = []\n", + " for name, measurement in check_source_measurements.items():\n", + " check_source_detector = measurement.get_detector(ch)\n", + " hist, bin_edges = check_source_detector.get_energy_hist_background_substract(background_detector)\n", + " calibrated_bin_edges = np.polyval(calibration_coeffs[ch], bin_edges)\n", + " \n", + " efficiency = measurement.compute_detection_efficiency(\n", + " background_measurement=background_meas,\n", + " calibration_coeffs=calibration_coeffs[ch],\n", + " channel_nb=ch,\n", + " search_width=search_width,\n", + " )\n", + " energies += measurement.check_source.nuclide.energy\n", + " efficiencies += list(efficiency)\n", + " ax[i].scatter(\n", + " measurement.check_source.nuclide.energy,\n", + " efficiency * 100,\n", + " label=name,\n", + " )\n", + "\n", + " # Sort energies and efficiencies for fitting\n", + " sorted_indices = np.argsort(energies)\n", + " energies = np.array(energies)[sorted_indices]\n", + " efficiencies = np.array(efficiencies)[sorted_indices]\n", + " print(f\"Ch {ch} \\n\\t Energies: {energies}, \\n\\t Efficiencies: {efficiencies}\")\n", + "\n", + " # Fit the efficiency curve\n", + " popt, pcov = curve_fit(\n", + " eff_curve_func,\n", + " energies,\n", + " efficiencies,\n", + " p0=[-1, 1, 0, 0],\n", + " )\n", + "\n", + " poly_coeff = np.polyfit(energies, efficiencies, 3)\n", + " efficiency_coeffs[ch] = poly_coeff\n", + " xs = np.linspace(\n", + " energies[0],\n", + " energies[-1],\n", + " 100,\n", + " )\n", + " ax[i].plot(\n", + " xs,\n", + " eff_curve_func(xs, *popt) * 100,\n", + " label=\"Fitted efficiency curve\",\n", + " )\n", + "\n", + " ax[i].plot(\n", + " xs,\n", + " np.polyval(poly_coeff, xs) * 100,\n", + " label=\"Polyfit efficiency curve\",\n", + " )\n", + " ax[i].set_xlabel(\"Energy (keV)\")\n", + " ax[i].set_ylabel(\"Detection efficiency (%)\")\n", + " ax[i].set_title(f\"Channel {ch}\")\n", + " ax[i].legend()\n", + " # plt.ylim(bottom=0)\n", + " print(popt)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "3bdd8805", + "metadata": {}, + "source": [ + "## Calculating average neutron rate from activation foils\n", + "\n", + "First, the irradiation schedule and the foil information is collected." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "65dcc6fd", + "metadata": {}, + "outputs": [], + "source": [ + "all_neutron_rates = []\n", + "all_neutron_rates_err = []" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "f0f65bc4", + "metadata": {}, + "outputs": [], + "source": [ + "from process_foil_data import irradiations, time_generator_off" + ] + }, + { + "cell_type": "markdown", + "id": "98e297ca", + "metadata": {}, + "source": [ + "### Niobium Packet #3 Results\n", + "\n", + "The activity of Nb-92m is measured using its 934 keV gamma peak and used to determine the neutron rate during the irradiation. Nb-92m is formed from the Nb-93(n,2n) reaction, which has a threshold energy of 8.9 MeV. \n", + "\n", + "The gamma spectrum obtained from the various measurements of the Niobium Packet #3 after irradiation are used to calculate the neutron rate of the overall irradiation. " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "659310cf", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Count 1\n", + "\t Ch 4: Neutron rate: 3.110e+08 +/- 1.169e+06 n/s\n", + "\t Ch 5: Neutron rate: 3.006e+08 +/- 1.009e+06 n/s\n" + ] + } + ], + "source": [ + "from process_foil_data import calculate_neutron_rate_from_foil\n", + "\n", + "foil_name = \"Nb Packet #4\"\n", + "\n", + "neutron_rates, neutron_rate_errs = calculate_neutron_rate_from_foil(foil_measurements,\n", + " foil_name,\n", + " background_meas,\n", + " calibration_coeffs,\n", + " efficiency_coeffs,\n", + " search_width=search_width)\n", + "\n", + "for count_name in neutron_rates.keys():\n", + " print(count_name)\n", + " for ch in np.sort(list(neutron_rates[count_name].keys())):\n", + " neutron_rate = neutron_rates[count_name][ch]\n", + " neutron_rate_err = neutron_rate_errs[count_name][ch]\n", + " print(f\"\\t Ch {ch}: Neutron rate: {neutron_rate[0]:.3e} +/- {neutron_rate_err[0]:.3e} n/s\")\n", + " all_neutron_rates.append(neutron_rate[0])\n", + " all_neutron_rates_err.append(neutron_rate_err[0])\n" + ] + }, + { + "cell_type": "markdown", + "id": "f6d08e41", + "metadata": {}, + "source": [ + "### Zirconium Packet #1 Results\n", + "\n", + "The activity of Zr-89 is measured using its 909 keV gamma peak and used to determine the neutron rate during the irradiation. Zr-89 m is formed from the Zr-90(n,2n) reaction, which has a threshold energy of 12.1 MeV. \n", + "\n", + "The gamma spectrum obtained from the various measurements of the Zirconium Packet #1 after irradiation are used to calculate the neutron rate of the overall irradiation. " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "55998f31", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Count 1\n", + "\t Ch 4: Neutron rate: 2.786e+08 +/- 9.065e+05 n/s\n", + "\t Ch 5: Neutron rate: 2.664e+08 +/- 7.813e+05 n/s\n" + ] + } + ], + "source": [ + "foil_name = \"Zr Packet #1\"\n", + "\n", + "neutron_rates, neutron_rate_errs = calculate_neutron_rate_from_foil(foil_measurements,\n", + " foil_name,\n", + " background_meas,\n", + " calibration_coeffs,\n", + " efficiency_coeffs,\n", + " search_width=search_width)\n", + "\n", + "for count_name in neutron_rates.keys():\n", + " print(count_name)\n", + " for ch in np.sort(list(neutron_rates[count_name].keys())):\n", + " neutron_rate = neutron_rates[count_name][ch]\n", + " neutron_rate_err = neutron_rate_errs[count_name][ch]\n", + " print(f\"\\t Ch {ch}: Neutron rate: {neutron_rate[0]:.3e} +/- {neutron_rate_err[0]:.3e} n/s\")\n", + " all_neutron_rates.append(neutron_rate[0])\n", + " all_neutron_rates_err.append(neutron_rate_err[0])" + ] + }, + { + "cell_type": "markdown", + "id": "da08bc8d", + "metadata": {}, + "source": [ + "### Averaging foil results\n", + "\n", + "The average of the neutron rates of the Niobium and Zirconium foil packets is calculated and added to the processed_data.json file. " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "6329451c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average neutron rate: 2.892e+08 ± 2.033e+07 n/s\n" + ] + } + ], + "source": [ + "average_neutron_rate = np.mean(all_neutron_rates)\n", + "# average_neutron_rate_err = np.sqrt(np.sum(np.array(all_neutron_rates_err) ** 2)) / len(all_neutron_rates_err)\n", + "average_neutron_rate_err = np.std(all_neutron_rates, ddof=1) # Use ddof=1 for sample standard deviation\n", + "\n", + "print(f\"Average neutron rate: {average_neutron_rate:.3e} ± {average_neutron_rate_err:.3e} n/s\")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "0308ee3d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processed data stored in ../../data/processed_data.json\n" + ] + } + ], + "source": [ + "processed_data_file = \"../../data/processed_data.json\"\n", + "\n", + "processed_data = {\n", + " \"neutron_rate_used_in_model\": {\n", + " \"value\":average_neutron_rate,\n", + " \"error\": average_neutron_rate_err,\n", + " \"unit\": \"neutron / second\"\n", + " }\n", + "}\n", + "\n", + "try:\n", + " with open(processed_data_file, \"r\") as f:\n", + " existing_data = json.load(f)\n", + "except FileNotFoundError:\n", + " print(f\"Processed data file not found, creating it in {processed_data_file}\")\n", + " existing_data = {}\n", + "\n", + "existing_data.update(processed_data)\n", + "\n", + "with open(processed_data_file, \"w\") as f:\n", + " json.dump(existing_data, f, indent=4)\n", + "\n", + "print(f\"Processed data stored in {processed_data_file}\")" + ] + } + ], + "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": 5 +} diff --git a/analysis/neutron/process_foil_data.py b/analysis/neutron/process_foil_data.py new file mode 100644 index 0000000..2902664 --- /dev/null +++ b/analysis/neutron/process_foil_data.py @@ -0,0 +1,493 @@ +from pathlib import Path +from libra_toolbox.neutron_detection.activation_foils.calibration import ( + CheckSource, + co60, + cs137, + mn54, + na22, + ActivationFoil, + nb93_n2n, + zr90_n2n, +) +import libra_toolbox.neutron_detection.activation_foils.compass as compass +from libra_toolbox.neutron_detection.activation_foils.compass import ( + Measurement, + CheckSourceMeasurement, + SampleMeasurement, +) +from libra_toolbox.tritium.model import ureg +from datetime import date, datetime +import json +from zoneinfo import ZoneInfo +from download_raw_foil_data import download_and_extract_foil_data +import copy +import numpy as np + +##################################################################### +##################### CHANGE THIS FOR EVERY RUN ##################### +##################################################################### + +# Path to save the extracted files +output_path = Path("../../data/neutron_detection/") +activation_foil_path = output_path / "activation_foils" + + +################ Check Source Calibration Information ################### + + +def build_check_source_from_dict(check_source_dict: dict): + """Build a CheckSource object from a dictionary.""" + if check_source_dict["nuclide"].lower() == "co60": + nuclide = co60 + elif check_source_dict["nuclide"].lower() == "cs137": + nuclide = cs137 + elif check_source_dict["nuclide"].lower() == "mn54": + nuclide = mn54 + elif check_source_dict["nuclide"].lower() == "na22": + nuclide = na22 + elif (check_source_dict["energies"] is not None and + check_source_dict["intensities"] is not None and + check_source_dict["half_life"] is not None): + nuclide = compass.Nuclide( + energies=check_source_dict["energies"], + intensities=check_source_dict["intensities"], + half_life=(check_source_dict["half_life"]["value"] + * ureg.parse_units(check_source_dict["half_life"]["unit"]) + ).to(ureg.s).magnitude + ) + else: + raise ValueError( + f"Unknown nuclide: {check_source_dict['nuclide']}. " + "Please provide a valid nuclide or energies/intensities/half_life." + ) + activity_date = datetime.strptime( + check_source_dict["activity"]["date"], "%Y-%m-%d") + # Set the timezone to America/New_York + activity_date = activity_date.replace(tzinfo=ZoneInfo("America/New_York")) + check_source = CheckSource( + nuclide=nuclide, + activity=(check_source_dict["activity"]["value"] + * ureg.parse_units(check_source_dict["activity"]["unit"]) + ).to(ureg.Bq).magnitude, + activity_date=activity_date + ) + return check_source + + +def read_check_source_data_from_json(json_data: dict, measurement_directory_path: Path): + """Read check source data from the general.json file.""" + check_source_dict = {} + for check_source_name in json_data["check_sources"]: + check_source_data = json_data["check_sources"][check_source_name] + directory = measurement_directory_path / check_source_data["directory"] + check_source = build_check_source_from_dict(check_source_data) + check_source_dict[check_source_name] = { + "directory": directory, + "check_source": check_source, + } + return check_source_dict + + +################# Background Information ################### + +def read_background_data_from_json(json_data: dict, measurement_directory_path: Path): + """Read background data from the general.json file.""" + background_dir = measurement_directory_path / json_data["background_directory"] + return background_dir + + + +################ Foil Information ################### + +def get_distance_to_source_from_dict(foil_dict: dict): + distance_to_source_dict = foil_dict["distance_to_source"] + # unit from string with pint + unit = ureg.parse_units(distance_to_source_dict["unit"]) + return (distance_to_source_dict["value"] * unit).to(ureg.cm).magnitude + + +def get_mass_from_dict(foil_dict: dict): + foil_mass = foil_dict["mass"]["value"] + # unit from string with pint + unit = ureg.parse_units(foil_dict["mass"]["unit"]) + return (foil_mass * unit).to(ureg.g).magnitude + + +def get_thickness_from_dict(foil_dict: dict): + foil_thickness = foil_dict["thickness"]["value"] + # unit from string with pint + unit = ureg.parse_units(foil_dict["thickness"]["unit"]) + return (foil_thickness * unit).to(ureg.cm).magnitude + + +def interpolate_mass_attenuation_coefficient(foil_element_symbol, energy): + """Interpolate the mass attenuation coefficient for + a given foil element symbol and energy (keV).""" + + # Data from Table 3 of https://dx.doi.org/10.18434/T4D01F + # data is in the form of [energy (MeV), mass attenuation coefficient (cm^2/g)] + if foil_element_symbol == "Zr": + data = [ + [1.00000E-01, 9.658E-01], + [1.50000E-01, 3.790E-01], + [2.00000E-01, 2.237E-01], + [3.00000E-01, 1.318E-01], + [4.00000E-01, 1.018E-01], + [5.00000E-01, 8.693E-02], + [6.00000E-01, 7.756E-02], + [8.00000E-01, 6.571E-02], + [1.00000E+00, 5.810E-02], + [1.25000E+00, 5.150E-02], + [1.50000E+00, 4.700E-02], + [2.00000E+00, 4.146E-02] + ] + elif foil_element_symbol == "Nb": + data = [ + [1.00000E-01, 1.037E+00], + [1.50000E-01, 4.023E-01], + [2.00000E-01, 2.344E-01], + [3.00000E-01, 1.357E-01], + [4.00000E-01, 1.040E-01], + [5.00000E-01, 8.831E-02], + [6.00000E-01, 7.858E-02], + [8.00000E-01, 6.642E-02], + [1.00000E+00, 5.866E-02], + [1.25000E+00, 5.196E-02], + [1.50000E+00, 4.741E-02], + [2.00000E+00, 4.185E-02] + ] + else: + raise ValueError(f"Unsupported foil element symbol: {foil_element_symbol}") + + data = np.array(data) + # Interpolate the mass attenuation coefficient + mass_attenuation_coefficient = np.interp( + energy, + data[:, 0] * 1e3, # energy values converted to keV + data[:, 1] # mass attenuation coefficient values + ) + return mass_attenuation_coefficient # in cm^2/g + + +def get_foil(foil_element_symbol, foil_designator=None): + """Get information about a specific foil from the general data file. + Args: + foil_element_symbol (str): The chemical symbol of the foil element (e.g., "Zr" for Zirconium). + foil_designator (str, optional): The designator of the foil (e.g., "Nb Packet #1") + Returns: + ActivationFoil: An ActivationFoil object containing the foil's properties. + distance_to_source (float): The distance from the foil to the neutron source in cm. + """ + + with open("../../data/general.json", "r") as f: + general_data = json.load(f) + foils = general_data["neutron_detection"]["foils"]["materials"] + foil_of_specified_element_count = 0 + for foil in foils: + if foil["material"] == foil_element_symbol: + foil_of_specified_element_count += 1 + # if no foil_designator is provided, or if it matches the foil's designator + if foil_designator is None or foil["designator"] == foil_designator: + # Get distance to generator + distance_to_source = get_distance_to_source_from_dict(foil) + + # Get mass + foil_mass = get_mass_from_dict(foil) + + # get foil thickness + foil_thickness = get_thickness_from_dict(foil) + + # Get foil name + foil_name = foil["designator"] + if foil_name is None: + foil_name = foil_element_symbol + + if foil_of_specified_element_count == 0: + raise ValueError( + f"No foils found for element {foil_element_symbol} with designator {foil_designator}" + ) + elif foil_of_specified_element_count > 1: + print( + f"Warning: Multiple foils found for element {foil_element_symbol} with designator {foil_designator}. Using the last one found." + ) + + if foil_element_symbol == "Zr": + # density in g/cm^ + # Source: + # Arblaster, John W. (2018). + # Selected Values of the Crystallographic Properties of Elements. + # Materials Park, Ohio: ASM International. ISBN 978-1-62708-155-9. + foil_density = 6.505 + + foil_reaction = zr90_n2n + + elif foil_element_symbol == "Nb": + # density in g/cm^ + # Source: + # Arblaster, John W. (2018). + # Selected Values of the Crystallographic Properties of Elements. + # Materials Park, Ohio: ASM International. ISBN 978-1-62708-155-9. + foil_density = 8.582 + + foil_reaction = nb93_n2n + + else: + raise ValueError(f"Unsupported foil element symbol: {foil_element_symbol}") + + foil_mass_attenuation_coefficient = interpolate_mass_attenuation_coefficient( + foil_element_symbol, foil_reaction.product.energy) + + foil = ActivationFoil( + reaction=foil_reaction, + mass=foil_mass, + name=foil_name, + density=foil_density, + thickness=foil_thickness, # in cm + ) + foil.mass_attenuation_coefficient = foil_mass_attenuation_coefficient + print(f"Read in properties of {foil.name} foil") + return foil, distance_to_source + + +def get_foil_source_dict_from_json(json_data: dict, measurement_directory_path: Path): + """Read foil source data from the general.json file.""" + foils = json_data["materials"] + foil_source_dict = {} + for foil_dict in foils: + foil_element_symbol = foil_dict["material"] + foil_designator = foil_dict.get("designator", None) + foil, distance_to_source = get_foil(foil_element_symbol, foil_designator) + measurement_paths = {} + for count_num, measurement_subdirectory in enumerate(foil_dict["measurement_directory"], start=1): + measurement_paths[count_num] = ( + measurement_directory_path / measurement_subdirectory + ) + # foil.name should be the same as the designator if it exists. + # Otherwise is set to the element symbol. + foil_source_dict[foil.name] = { + "measurement_paths": measurement_paths, + "foil": foil, + "distance_to_source": distance_to_source, + } + return foil_source_dict + + + +def get_data(download_from_raw=False, url=None, + check_source_dict=None, + background_dir=None, + foil_source_dict=None, + h5_filename="activation_data.h5"): + with open("../../data/general.json", "r") as f: + general_data = json.load(f) + json_data = general_data["neutron_detection"]["foils"] + # get measurement directory path + measurement_directory_path = activation_foil_path / json_data["data_directory"] + + # Get the dictionaries for check sources, background, and foils + if check_source_dict is None: + check_source_dict = read_check_source_data_from_json(json_data, measurement_directory_path) + if background_dir is None: + background_dir = read_background_data_from_json(json_data, measurement_directory_path) + if foil_source_dict is None: + foil_source_dict = get_foil_source_dict_from_json(json_data, measurement_directory_path) + + if download_from_raw: + # Download and extract foil data if not already done + if url is None: + url = json_data["data_url"] + download_and_extract_foil_data(url, activation_foil_path) + # Process data + check_source_measurements, background_meas = read_checksources_from_directory( + check_source_dict, background_dir + ) + foil_measurements = read_foil_measurements_from_dir(foil_source_dict) + + # save spectra to h5 for future, faster use + save_measurements(check_source_measurements, + background_meas, + foil_measurements, + filepath=activation_foil_path / h5_filename) + else: + # Read measurements from h5 file + measurements = Measurement.from_h5(activation_foil_path / h5_filename) + foil_measurements = copy.deepcopy(foil_source_dict) + check_source_measurements = {} + # Get list of foil measurement names + foil_measurement_names = [] + for foil_name in foil_source_dict.keys(): + for count_num in foil_source_dict[foil_name]["measurement_paths"]: + foil_measurement_names.append(f"{foil_name} Count {count_num}") + + # Add empty measurements dictionary to foil_source_dict copy + foil_measurements[foil_name]["measurements"] = {} + + for measurement in measurements: + print(f"Processing {measurement.name} from h5 file...") + # check if measurement is a check source measurement + if measurement.name in check_source_dict.keys(): + # May want to change CheckSourceMeasurement in libra-toolbox to make this more seemless + check_source_meas = CheckSourceMeasurement(measurement.name) + check_source_meas.__dict__.update(measurement.__dict__) + check_source_meas.check_source = check_source_dict[measurement.name]["check_source"] + check_source_measurements[measurement.name] = check_source_meas + elif measurement.name == "Background": + background_meas = measurement + elif measurement.name in foil_measurement_names: + # Extract foil name and count number from measurement name + split_name = measurement.name.split(' ') + count_num = int(split_name[-1]) + foil_name = " ".join(split_name[:-2]) + + foil_meas = SampleMeasurement(measurement) + foil_meas.__dict__.update(measurement.__dict__) + foil_meas.foil = foil_source_dict[foil_name]["foil"] + foil_measurements[foil_name]["measurements"][count_num] = foil_meas + else: + print(f"Extra measurement included in h5 file: {measurement.name}") + + return check_source_measurements, background_meas, foil_measurements + + +def save_measurements(check_source_measurements, + background_meas, + foil_measurements, + filepath=activation_foil_path / "activation_data.h5"): + """Save measurements to an h5 file.""" + print(f"Saving measurements to {filepath}...") + # Ensure the directory exists + filepath.parent.mkdir(parents=True, exist_ok=True) + measurements = list(check_source_measurements.values()) + # Add background measurement to the list + measurements.append(background_meas) + # Add foil measurements to the list + for foil_name in foil_measurements.keys(): + for count_num in foil_measurements[foil_name]["measurements"].keys(): + measurements.append(foil_measurements[foil_name]["measurements"][count_num]) + + for i,measurement in enumerate(measurements): + if i==0: + mode = 'w' + else: + mode = 'a' + measurement.to_h5( + filename= filepath, + mode=mode, + spectrum_only=True + ) + + +def read_checksources_from_directory( + check_source_measurements: dict, background_dir: Path +): + + measurements = {} + + for name, values in check_source_measurements.items(): + print(f"Processing {name}...") + meas = CheckSourceMeasurement.from_directory(values["directory"], name=name) + meas.check_source = values["check_source"] + measurements[name] = meas + + print(f"Processing background...") + background_meas = Measurement.from_directory( + background_dir, + name="Background", + info_file_optional=True, + ) + return measurements, background_meas + + +def read_foil_measurements_from_dir( + foil_measurements: dict +): + + for foil_name in foil_measurements.keys(): + foil_measurements[foil_name]["measurements"] = {} + foil = foil_measurements[foil_name]["foil"] + for count_num, measurement_path in foil_measurements[foil_name]["measurement_paths"].items(): + measurement_name = f"{foil_name} Count {count_num}" + print(f"Processing {measurement_name}...") + measurement = SampleMeasurement.from_directory( + source_dir=measurement_path, + name=measurement_name + ) + measurement.foil = foil + foil_measurements[foil_name]["measurements"][count_num] = measurement + + return foil_measurements + + +# Get the irradiation schedule + +with open("../../data/general.json", "r") as f: + general_data = json.load(f) +irradiations = [] +for generator in general_data["generators"]: + if generator["enabled"] is False: + continue + for i, irradiation_period in enumerate(generator["periods"]): + if i == 0: + overall_start_time = datetime.strptime( + irradiation_period["start"], "%m/%d/%Y %H:%M" + ) + start_time = datetime.strptime(irradiation_period["start"], "%m/%d/%Y %H:%M") + end_time = datetime.strptime(irradiation_period["end"], "%m/%d/%Y %H:%M") + irradiations.append( + { + "t_on": (start_time - overall_start_time).total_seconds(), + "t_off": (end_time - overall_start_time).total_seconds(), + } + ) +time_generator_off = end_time +time_generator_off = time_generator_off.replace(tzinfo=ZoneInfo("America/New_York")) + + + +def calculate_neutron_rate_from_foil(foil_measurements, + foil_name, + background_meas, + calibration_coeffs, + efficiency_coeffs, + search_width=330, + irradiations=irradiations, + time_generator_off=time_generator_off): + neutron_rates = {} + neutron_rate_errs = {} + + for count_num, measurement in foil_measurements[foil_name]["measurements"].items(): + + neutron_rates[f"Count {count_num}"] = {} + neutron_rate_errs[f"Count {count_num}"] = {} + + for detector in measurement.detectors: + ch = detector.channel_nb + + gamma_emitted, gamma_emitted_err = measurement.get_gamma_emitted( + background_measurement=background_meas, + calibration_coeffs=calibration_coeffs[ch], + efficiency_coeffs=efficiency_coeffs[ch], + channel_nb=ch, + search_width=search_width) + + neutron_rate = measurement.get_neutron_rate( + channel_nb=ch, + photon_counts=gamma_emitted, + irradiations=irradiations, + distance=foil_measurements[foil_name]["distance_to_source"], + time_generator_off=time_generator_off, + branching_ratio=foil_measurements[foil_name]["foil"].reaction.product.intensity + ) + + neutron_rate_err = measurement.get_neutron_rate( + channel_nb=ch, + photon_counts=gamma_emitted_err, + irradiations=irradiations, + distance=foil_measurements[foil_name]["distance_to_source"], + time_generator_off=time_generator_off, + branching_ratio=foil_measurements[foil_name]["foil"].reaction.product.intensity + ) + neutron_rates[f"Count {count_num}"][ch] = neutron_rate + neutron_rate_errs[f"Count {count_num}"][ch] = neutron_rate_err + + return neutron_rates, neutron_rate_errs diff --git a/analysis/tritium/tritium_model.py b/analysis/tritium/tritium_model.py index 4d31f71..41ea471 100644 --- a/analysis/tritium/tritium_model.py +++ b/analysis/tritium/tritium_model.py @@ -8,7 +8,7 @@ LSCSample, LIBRASample, ) - +from pathlib import Path from datetime import datetime @@ -159,13 +159,34 @@ def create_sample(label: str, filename: str) -> LSCSample: irradiations.append([irr_start_time, irr_stop_time]) # Neutron rate -neutron_rate_relative_uncertainty = ( - 0.089 # TODO check with Collin what is the uncertainty on this measurement -) -neutron_rate = ( - np.mean([3.377e8, 3.592e8]) * ureg.neutron * ureg.s**-1 -) # TODO from Collin's foil analysis, replace with more robust method +# check if neutron rate is provided in processed_data.json +processed_data_file = Path("../../data/processed_data.json") +neutron_rate = None +if processed_data_file.exists(): + with open(processed_data_file, "r") as f: + processed_data = json.load(f) + if "neutron_rate_used_in_model" in processed_data: + if processed_data["neutron_rate_used_in_model"]["value"] is not None: + neutron_rate = processed_data["neutron_rate_used_in_model"]["value"] * ureg( + processed_data["neutron_rate_used_in_model"]["unit"] + ) + neutron_rate_uncertainty = processed_data["neutron_rate_used_in_model"][ + "error" + ] * ureg(processed_data["neutron_rate_used_in_model"]["unit"]) + print( + f"Using neutron rate from processed_data.json: {neutron_rate} ± {neutron_rate_uncertainty}" + ) +if neutron_rate is None: + neutron_rate = ( + 1.0e08 * ureg.neutron * ureg.s**-1 + ) # based on manufacturer test data for generator settings + neutron_rate_uncertainty = 1.0e07 * ureg.neutron * ureg.s**-1 + print(f"Using default neutron rate: {neutron_rate} ± {neutron_rate_uncertainty}") + +neutron_rate_relative_uncertainty = (neutron_rate_uncertainty / neutron_rate).to( + ureg.dimensionless +) # TBR from OpenMC @@ -240,6 +261,7 @@ def create_sample(label: str, filename: str) -> LSCSample: "neutron_rate_used_in_model": { "value": baby_model.neutron_rate.magnitude, "unit": str(baby_model.neutron_rate.units), + "error": neutron_rate_uncertainty.magnitude, }, "measured_TBR": { "value": measured_TBR.magnitude, diff --git a/data/general.json b/data/general.json index 9895fee..b772040 100644 --- a/data/general.json +++ b/data/general.json @@ -133,6 +133,8 @@ "foils": { "enabled": true, "position": "on generators, at target plane, facing top and bottom", + "data_url": "https://zenodo.org/records/15446933/files/241212_BABY_1L_run2.zip?download=1", + "data_directory": "241212_BABY_1L_run2/DAQ/", "materials": [ { "position": "P-383 - top", @@ -154,7 +156,10 @@ "thickness": { "value": 0.02, "unit": "inch" - } + }, + "measurement_directory":[ + "Niobium4_20241214_1329_4in/UNFILTERED" + ] }, { "position": "P-383 - bottom", @@ -176,9 +181,75 @@ "thickness": { "value": 0.04, "unit": "inch" + }, + "measurement_directory":[ + "Zirconium_20241213_2155_4in/UNFILTERED" + ] + } + ], + "background_directory": "Background_20241215_1800_4in/UNFILTERED", + "check_sources": { + "Co60 Count 1": { + "directory": "Co60_0_872uCi_19Mar2014_20241213_4in/UNFILTERED", + "nuclide": "Co60", + "activity": { + "value": 0.872, + "unit": "uCi", + "date": "2014-03-19" + }, + "energies": null, + "intensities": null, + "half_life": { + "value": null, + "unit": null + } + }, + "Cs137 Count 1": { + "directory": "Cs137_4_66uCi_19Mar2014_20241213_4in/UNFILTERED", + "nuclide": "Cs137", + "activity": { + "value": 4.66, + "unit": "uCi", + "date": "2014-03-19" + }, + "energies": null, + "intensities": null, + "half_life": { + "value": null, + "unit": null + } + }, + "Mn54 Count 1": { + "directory": "Mn54_6_27uCi_2May2016_20241213_4in/UNFILTERED", + "nuclide": "Mn54", + "activity": { + "value": 6.27, + "unit": "uCi", + "date": "2016-05-02" + }, + "energies": null, + "intensities": null, + "half_life": { + "value": null, + "unit": null + } + }, + "Na22 Count 1": { + "directory": "Na22_7_33uCi_2May2016_20241213_4in/UNFILTERED", + "nuclide": "Na22", + "activity": { + "value": 7.33, + "unit": "uCi", + "date": "2016-05-02" + }, + "energies": null, + "intensities": null, + "half_life": { + "value": null, + "unit": null } } - ] + } }, "diamond": { "enabled": true, diff --git a/data/neutron_detection/activation_foils/activation_data.h5 b/data/neutron_detection/activation_foils/activation_data.h5 new file mode 100644 index 0000000..852aecc Binary files /dev/null and b/data/neutron_detection/activation_foils/activation_data.h5 differ diff --git a/environment.yml b/environment.yml index 1a045cb..9094ac1 100644 --- a/environment.yml +++ b/environment.yml @@ -14,9 +14,10 @@ dependencies: - nbconvert - ipykernel - pint + - papermill - pip - pip: - - git+https://github.com/libra-project/libra-toolbox@v0.4.1 + - libra-toolbox==0.8 - openmc_data_downloader==0.6.0 - matplotlib-label-lines - h-transport-materials~=0.17.0