unzhakov
caa4a77ebd
- Am-241 peaks at ch 134=13.8, ch 163=17.8, ch 218=26.3 keV - Calibration: E = 0.397 + 0.0995 × ch (previously wrong) - Kurie fit endpoint 425 keV vs Q=297.5 keV — spectrum extends beyond MCA range (407 keV), simple extrapolation unreliable - Proper endpoint extraction requires theory model + detector convolution (fitter module), not simple Kurie plot
296 lines
10 KiB
Python
296 lines
10 KiB
Python
#!/usr/bin/env python3
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"""
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Analyze raw Tc-99 beta spectra for rate stability and endpoint drift.
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Data is cumulative (no reset between runs), so each file contains all
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events accumulated up to that point. To get individual ~1-hour runs,
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subtract consecutive files: run_N = cumulative_N - cumulative_(N-1).
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Produces plots of:
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- Total count rate vs run number
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- Regional count rates (low-energy vs high-energy)
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- Low-energy fraction vs run number
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- Endpoint position vs run number
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"""
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from __future__ import annotations
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import sys
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from pathlib import Path
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import numpy as np
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from scipy.stats import linregress
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from exp_data.raw_reader import (
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calibrate_from_am241,
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get_run_files,
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read_raw_spectrum,
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subtract_spectra,
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)
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# Energy calibration from Am-241 source: E = offset + gain * channel
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# Determined automatically from known Am-241 gamma lines (13.8, 17.8, 26.3 keV)
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CALIBRATION_FILE = "data/raw/CAM1.DAT"
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_calib_offset, _calib_gain = calibrate_from_am241(CALIBRATION_FILE)
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ENERGY_OFFSET = _calib_offset
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ENERGY_GAIN = _calib_gain
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print(f"Energy calibration: E = {ENERGY_OFFSET:.4f} + {ENERGY_GAIN:.4f} * ch")
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# Approximate Tc-99 endpoint for reference
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TC99_ENDPOINT = 294.0 # keV
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def channel_to_energy(ch: np.ndarray) -> np.ndarray:
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"""Convert channel index to energy in keV."""
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return ENERGY_OFFSET + ch * ENERGY_GAIN
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def estimate_endpoint_from_counts(
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counts: np.ndarray,
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energies_keV: np.ndarray,
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guess_endpoint: float = TC99_ENDPOINT,
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) -> float:
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"""Estimate beta endpoint from spectrum using Kurie plot linear extrapolation.
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For allowed beta decay, the Kurie plot K(E) = sqrt(counts / (pWF)) is
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linear near the endpoint and extrapolates to zero at E = Q-value.
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We use a simplified Kurie plot (sqrt(counts)) since the phase space
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factor is slowly varying compared to the sharp drop at the endpoint.
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Fits a line to the last ~15% of the spectrum (in the tail region
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where counts are small but above background) and finds the x-intercept.
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Parameters
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----------
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counts : np.ndarray
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Count spectrum.
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energies_keV : np.ndarray
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Energy calibration for each channel.
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guess_endpoint : float
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Expected endpoint for clamping.
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Returns
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-------
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float
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Estimated endpoint energy in keV.
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"""
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# Exclude overflow channel (last channel is always saturated)
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counts_work = counts[:-1]
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energies_work = energies_keV[:-1]
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# Estimate background from the very tail (last 5%)
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n_tail = max(20, int(len(counts_work) * 0.05))
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background = np.median(counts_work[-n_tail:])
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# Select tail region: last 15% of spectrum, above background
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n_tail_region = max(30, int(len(counts_work) * 0.15))
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E_tail = energies_work[-n_tail_region:]
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c_tail = counts_work[-n_tail_region:]
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# Only keep channels above background + noise
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noise = max(np.sqrt(background), 1.0)
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mask = c_tail > (background + noise)
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if not mask.any():
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return guess_endpoint
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E_sel = E_tail[mask]
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c_sel = c_tail[mask]
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# Kurie plot: K = sqrt(counts - background)
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K = np.sqrt(np.maximum(c_sel - background, 0.01))
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# Linear fit: K = a + b * E, endpoint = -a/b
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try:
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slope, intercept, r_val, _, _ = linregress(E_sel, K)[:5]
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if slope < 0 and abs(r_val) > 0.3:
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endpoint = -intercept / slope
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# Clamp to reasonable range (Q ± 100 keV)
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endpoint = max(guess_endpoint - 100, min(guess_endpoint + 100, endpoint))
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return float(endpoint)
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except (ValueError, ZeroDivisionError):
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pass
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# Fallback: use the last channel above background
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last_above = E_sel[np.where(c_sel > background)[0][-1]]
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return float(last_above)
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def compute_regional_rates(counts: np.ndarray, energies_keV: np.ndarray) -> dict:
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"""Compute count rates in different energy regions."""
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total = float(np.sum(counts))
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low_mask = energies_keV < 100
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high_mask = ~low_mask
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return {
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"total": total,
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"low_energy": float(np.sum(counts[low_mask])),
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"high_energy": float(np.sum(counts[high_mask])),
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"low_fraction": float(np.sum(counts[low_mask]) / total) if total > 0 else 0.0,
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}
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def main():
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"""Main analysis pipeline."""
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data_dir = Path(__file__).parent.parent / "data" / "raw"
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output_dir = Path(__file__).parent.parent / "output"
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output_dir.mkdir(exist_ok=True)
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# Get run files sorted naturally (A1, A2, ..., A10, A100, ...)
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run_files = get_run_files(data_dir, prefix="A")
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if len(run_files) < 2:
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print(f"Need at least 2 run files, found {len(run_files)}")
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sys.exit(1)
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print(f"Found {len(run_files)} cumulative run files")
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# Step 1: Read all cumulative spectra
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print("\nReading cumulative spectra...")
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cumulative_specs = []
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for i, fpath in enumerate(run_files):
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try:
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spec = read_raw_spectrum(
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fpath,
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run_id=fpath.name,
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source="Tc99",
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)
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cumulative_specs.append(spec)
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except (FileNotFoundError, ValueError) as e:
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print(f" Skipping {fpath.name}: {e}")
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continue
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if len(cumulative_specs) < 2:
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print("Need at least 2 valid spectra")
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sys.exit(1)
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print(f" Loaded {len(cumulative_specs)} cumulative spectra")
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# Step 2: Subtract consecutive runs to get individual ~1h runs
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print("\nSubtracting consecutive runs...")
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individual_runs = []
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for i in range(1, len(cumulative_specs)):
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parent_a = cumulative_specs[i].run_id
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parent_b = cumulative_specs[i - 1].run_id
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diff = subtract_spectra(
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cumulative_a=cumulative_specs[i],
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cumulative_b=cumulative_specs[i - 1],
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run_id=f"run_{i}",
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)
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individual_runs.append(diff)
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print(f" Extracted {len(individual_runs)} individual runs")
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# Step 3: Analyze each run
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print("\nAnalyzing individual runs...")
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run_ids = []
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total_rates = []
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low_rates = []
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high_rates = []
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low_fractions = []
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endpoints = []
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energies_keV = channel_to_energy(np.arange(len(individual_runs[0].counts)))
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for i, run in enumerate(individual_runs):
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run_ids.append(i + 1)
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rates = compute_regional_rates(run.counts, energies_keV)
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endpoint = estimate_endpoint_from_counts(run.counts, energies_keV)
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total_rates.append(rates["total"])
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low_rates.append(rates["low_energy"])
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high_rates.append(rates["high_energy"])
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low_fractions.append(rates["low_fraction"])
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endpoints.append(endpoint)
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if (i + 1) % 50 == 0:
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print(f" Analyzed {i + 1}/{len(individual_runs)} runs")
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run_ids = np.array(run_ids)
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total_rates = np.array(total_rates)
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low_rates = np.array(low_rates)
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high_rates = np.array(high_rates)
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low_fractions = np.array(low_fractions)
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endpoints = np.array(endpoints)
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# Print summary
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print(f"\n{'='*60}")
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print(f"Analysis of {len(run_ids)} individual Tc-99 runs")
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print(f"{'='*60}")
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print(f"Run range: {run_ids.min()} - {run_ids.max()}")
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print(f"Total counts: {total_rates.min():.0f} - {total_rates.max():.0f}")
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print(f"Low-energy fraction: {low_fractions.min():.3f} - {low_fractions.max():.3f}")
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print(f"Endpoint estimate: {endpoints.min():.1f} - {endpoints.max():.1f} keV")
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# Endpoint drift
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if len(run_ids) > 10:
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slope, intercept, r_val, _, _ = linregress(run_ids, endpoints)
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print(f"\nEndpoint drift: {slope:.3f} keV/run (R²={r_val**2:.4f})")
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print(f" Start (run 1): {intercept + slope * run_ids[0]:.1f} keV")
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print(f" End (run {run_ids[-1]}): {intercept + slope * run_ids[-1]:.1f} keV")
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# Rate drift
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slope, intercept, r_val, _, _ = linregress(run_ids, total_rates)
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print(f"\nTotal count rate drift: {slope:.0f} counts/run (R²={r_val**2:.4f})")
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# Save results
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results_file = output_dir / "rate_analysis.csv"
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with open(results_file, "w") as f:
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f.write("run,total_counts,low_energy_counts,high_energy_counts,low_fraction,endpoint_keV\n")
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for i in range(len(run_ids)):
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f.write(
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f"{run_ids[i]},{total_rates[i]:.0f},{low_rates[i]:.0f},"
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f"{high_rates[i]:.0f},{low_fractions[i]:.4f},{endpoints[i]:.1f}\n"
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)
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print(f"\nResults saved to {results_file}")
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# Plot
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try:
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import matplotlib.pyplot as plt
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fig, axes = plt.subplots(2, 2, figsize=(14, 10))
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# Total counts
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axes[0, 0].plot(run_ids, total_rates, "k-", lw=1.5)
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axes[0, 0].set_xlabel("Run number")
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axes[0, 0].set_ylabel("Total counts")
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axes[0, 0].set_title("Total count rate per run")
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axes[0, 0].grid(True, alpha=0.3)
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# Low-energy fraction
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axes[0, 1].plot(run_ids, low_fractions, "b-", lw=1.5)
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axes[0, 1].set_xlabel("Run number")
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axes[0, 1].set_ylabel("Low-energy fraction (<100 keV)")
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axes[0, 1].set_title("Low-energy fraction drift")
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axes[0, 1].grid(True, alpha=0.3)
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# Endpoint
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axes[1, 0].plot(run_ids, endpoints, "r-", lw=1.5)
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axes[1, 0].axhline(
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TC99_ENDPOINT, color="gray", ls="--",
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label=f"Tc-99 Q ≈ {TC99_ENDPOINT} keV",
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)
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axes[1, 0].set_xlabel("Run number")
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axes[1, 0].set_ylabel("Endpoint (keV)")
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axes[1, 0].set_title("Endpoint position drift")
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axes[1, 0].legend(fontsize=9)
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axes[1, 0].grid(True, alpha=0.3)
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# Low vs high energy
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axes[1, 1].plot(run_ids, low_rates, "b-", lw=1.5, label="<100 keV")
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axes[1, 1].plot(run_ids, high_rates, "r-", lw=1.5, label=">100 keV")
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axes[1, 1].set_xlabel("Run number")
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axes[1, 1].set_ylabel("Counts")
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axes[1, 1].set_title("Regional count rates")
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axes[1, 1].legend(fontsize=9)
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axes[1, 1].grid(True, alpha=0.3)
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plt.tight_layout()
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plot_file = output_dir / "rate_analysis.png"
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fig.savefig(plot_file, dpi=150, bbox_inches="tight")
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print(f"Plot saved to {plot_file}")
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except ImportError:
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print("\nmatplotlib not available — skipping plot generation")
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if __name__ == "__main__":
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main()
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