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..7b28a6f --- /dev/null +++ b/analysis/neutron/foil_analysis.ipynb @@ -0,0 +1,623 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "7882bbca", + "metadata": {}, + "source": [ + "# Activation Foil Analysis: 1L BABY Run #6\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 = True" + ] + }, + { + "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 #5 foil\n", + "Read in properties of Zr Packet #2 foil\n", + "Directory already exists: ../../data/neutron_detection/activation_foils\n", + "Processing Co60 Count 1...\n", + "Processing Co60 Count 2...\n", + "Processing Cs137 Count 1...\n", + "Processing Cs137 Count 2...\n", + "Processing Mn54 Count 1...\n", + "Processing Mn54 Count 2...\n", + "Processing Na22 Count 1...\n", + "Processing Na22 Count 2...\n", + "Processing background...\n", + "Processing Nb Packet #5 Count 1...\n", + "Processing Nb Packet #5 Count 2...\n", + "Processing Nb Packet #5 Count 3...\n", + "Processing Zr Packet #2 Count 1...\n", + "Processing Zr Packet #2 Count 2...\n", + "Processing Zr Packet #2 Count 3...\n", + "Processing Zr Packet #2 Count 4...\n", + "Saving measurements to ../../data/neutron_detection/activation_foils/activation_data.h5...\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, url=\"google.com\")" + ] + }, + { + "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": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[, ]\n", + "\n", + "None\n", + "\n", + "None\n" + ] + }, + { + "data": { + "image/png": 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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", + "print(check_source_measurements[\"Co60 Count 1\"].detectors)\n", + "for detector in check_source_measurements[\"Co60 Count 1\"].detectors:\n", + " print(detector)\n", + " print(detector._spectrum)\n", + " hist, bin_edges = detector.get_energy_hist()\n", + "\n", + " plt.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 = check_source_measurements[\"Co60 Count 2\"].get_peaks(hist)\n", + "\n", + " from scipy.signal import find_peaks\n", + " import numpy as np\n", + "\n", + " start_index = 400\n", + " height = 0.60 * np.max(hist[start_index:])\n", + " prominence = None\n", + " width = [10, 150]\n", + " distance = 30\n", + " peaks, peak_data = find_peaks(\n", + " hist[start_index:],\n", + " prominence=prominence,\n", + " height=height,\n", + " width=width,\n", + " distance=distance,\n", + " )\n", + " plt.plot(bin_edges[start_index:][peaks], peak_data[\"peak_heights\"], \".\", ms=10)\n", + "\n", + " for i, p in enumerate(peaks):\n", + " width = peak_data[\"widths\"][i]\n", + " plt.axvspan(\n", + " bin_edges[start_index:][p] - width,\n", + " bin_edges[start_index:][p] + width,\n", + " color=\"red\",\n", + " alpha=0.2,\n", + " label=\"Peak range\",\n", + " )\n", + "\n", + "plt.legend()\n", + "# plt.yscale(\"log\")\n", + "plt.ylim(top=2100)\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. 511. 661.657 661.657 834.848 834.848 1173.228 1173.228\n", + " 1274.537 1274.537 1332.492 1332.492], \n", + "\t Efficiencies: [0.02136112 0.02675772 0.02028089 0.02096381 0.01518903 0.01588848\n", + " 0.00992723 0.01086226 0.01094217 0.01111191 0.00959152 0.01028617]\n", + "[-569.86848844 255.37387718 -38.22581378 1.89747027]\n", + "Ch 5 \n", + "\t Energies: [ 511. 511. 661.657 661.657 834.848 834.848 1173.228 1173.228\n", + " 1274.537 1274.537 1332.492 1332.492], \n", + "\t Efficiencies: [0.03535765 0.02826532 0.02717635 0.02634508 0.02088731 0.02143481\n", + " 0.0140935 0.0140854 0.01476166 0.01449763 0.01268972 0.01211864]\n", + "[-229.53747809 101.58919373 -15.07779204 0.73780352]\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": 11, + "id": "659310cf", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/cdunn314/libra/libra-toolbox/libra_toolbox/neutron_detection/activation_foils/compass.py:574: RuntimeWarning: invalid value encountered in sqrt\n", + " nb_counts_measured_err = np.sqrt(nb_counts_measured)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Count 1\n", + "\t Ch 4: Neutron rate: 8.002e+05 +/- 8.748e+03 n/s\n", + "\t Ch 5: Neutron rate: -2.281e+06 +/- nan n/s\n", + "Count 2\n", + "\t Ch 4: Neutron rate: 7.991e+05 +/- 7.475e+03 n/s\n", + "\t Ch 5: Neutron rate: 8.141e+05 +/- 6.454e+03 n/s\n", + "Count 3\n", + "\t Ch 4: Neutron rate: 9.303e+05 +/- 6.932e+03 n/s\n", + "\t Ch 5: Neutron rate: 7.921e+05 +/- 5.240e+03 n/s\n" + ] + } + ], + "source": [ + "from process_foil_data import calculate_neutron_rate_from_foil\n", + "\n", + "foil_name = \"Nb Packet #5\"\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": 12, + "id": "55998f31", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Count 1\n", + "\t Ch 4: Neutron rate: 8.478e+05 +/- 6.254e+03 n/s\n", + "\t Ch 5: Neutron rate: 8.096e+05 +/- 5.241e+03 n/s\n", + "Count 2\n", + "\t Ch 4: Neutron rate: 8.249e+05 +/- 3.894e+03 n/s\n", + "\t Ch 5: Neutron rate: 8.142e+05 +/- 3.317e+03 n/s\n", + "Count 3\n", + "\t Ch 4: Neutron rate: 8.427e+05 +/- 5.383e+03 n/s\n", + "\t Ch 5: Neutron rate: 7.846e+05 +/- 4.451e+03 n/s\n", + "Count 4\n", + "\t Ch 4: Neutron rate: 8.217e+05 +/- 6.909e+03 n/s\n", + "\t Ch 5: Neutron rate: 7.772e+05 +/- 5.759e+03 n/s\n" + ] + } + ], + "source": [ + "foil_name = \"Zr Packet #2\"\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": 13, + "id": "6329451c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Average neutron rate: 5.984e+05 ± 8.297e+05 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": 14, + "id": "0308ee3d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processed data file not found, creating it in ../../data/processed_data.json\n", + "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/data/general.json b/data/general.json index a7a55c4..a200a70 100644 --- a/data/general.json +++ b/data/general.json @@ -87,55 +87,191 @@ "foils": { "enabled": true, "position": "on generators, at target plane, facing top and bottom", + "data_url": "https://zenodo.org/records/16473161/files/250603_1L_BABY_Run7.zip?download=1", + "data_directory": "250603_1L_BABY_Run7/DAQ", "materials": [ { "position": "Above nGen tip", "material": "Nb", - "designator": null, + "designator": "Nb Packet #5", "irradiation_date": "6/3/2025", - "number_of_foils": null, + "number_of_foils": 2, "distance_to_source": { "value": 5.5, "unit": "cm", "note": "To generator axis at target plane" }, "mass": { - "value": null, + "value": 999, "unit": "g" }, "mass_err": { - "value": null, + "value": 99, "unit": "g" }, "thickness": { - "value": null, + "value": 0.02, "unit": "inch" - } + }, + "measurement_directory":[ + "Niobium5_20250604_1322_count1/UNFILTERED", + "Niobium5_20250605_1916_count2/UNFILTERED", + "Niobium5_20250606_1124_count3/UNFILTERED" + ] }, { "position": "Under nGen tip", "material": "Zr", - "designator": null, + "designator": "Zr Packet #2", "irradiation_date": "6/3/2025", - "number_of_foils": 8, + "number_of_foils": 6, "distance_to_source": { "value": 4.5, "unit": "cm" }, "mass": { - "value": null, + "value": 999, "unit": "g" }, "mass_err": { - "value": null, + "value": 99, "unit": "g" }, "thickness": { - "value": null, + "value": 0.03, "unit": "inch" + }, + "measurement_directory": [ + "Zirconium2_20250603_1347_count1/UNFILTERED", + "Zirconium2_20250603_2001_count2/UNFILTERED", + "Zirconium2_20250605_0009_count3/UNFILTERED", + "Zirconium2_20250605_1145_count4/UNFILTERED" + ] + } + ], + "background_directory": "Background_20250607_count1/UNFILTERED", + "check_sources": { + "Co60 Count 1": { + "directory": "Co60_0_872uCi_2014Mar19_count1/UNFILTERED", + "nuclide": "Co60", + "activity": { + "value": 0.872, + "unit": "uCi", + "date": "2014-03-19" + }, + "energies": null, + "intensities": null, + "half_life": { + "value": null, + "unit": null + } + }, + "Co60 Count 2": { + "directory": "Co60_0_872uCi_2014Mar19_count2/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_9_38uCi_2023Sep29_count1/UNFILTERED", + "nuclide": "Cs137", + "activity": { + "value": 9.38, + "unit": "uCi", + "date": "2023-09-29" + }, + "energies": null, + "intensities": null, + "half_life": { + "value": null, + "unit": null + } + }, + "Cs137 Count 2": { + "directory": "Cs137_9_38uCi_2023Sep29_count2/UNFILTERED", + "nuclide": "Cs137", + "activity": { + "value": 9.38, + "unit": "uCi", + "date": "2023-09-29" + }, + "energies": null, + "intensities": null, + "half_life": { + "value": null, + "unit": null + } + }, + "Mn54 Count 1": { + "directory": "Mn54_6_27uCi_2016May2_count1/UNFILTERED", + "nuclide": "Mn54", + "activity": { + "value": 6.27, + "unit": "uCi", + "date": "2016-05-02" + }, + "energies": null, + "intensities": null, + "half_life": { + "value": null, + "unit": null + } + }, + "Mn54 Count 2": { + "directory": "Mn54_6_27uCi_2016May2_count2/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_9_98uCi_2023Sep29_count1/UNFILTERED", + "nuclide": "Na22", + "activity": { + "value": 9.98, + "unit": "uCi", + "date": "2023-09-29" + }, + "energies": null, + "intensities": null, + "half_life": { + "value": null, + "unit": null + } + }, + "Na22 Count 2": { + "directory": "Na22_9_98uCi_2023Sep29_count2/UNFILTERED", + "nuclide": "Na22", + "activity": { + "value": 9.98, + "unit": "uCi", + "date": "2023-09-29" + }, + "energies": null, + "intensities": null, + "half_life": { + "value": null, + "unit": null } } - ] + } }, "diamond": { "enabled": false, 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..472506c Binary files /dev/null and b/data/neutron_detection/activation_foils/activation_data.h5 differ