refactor: refine CLI options and enhance debug logging

- Remove --mode, --transition-type, and --decay-index CLI flags
  (parameters now come from ENSDF data or custom JSON input)
- Clarify energy units in all CLI help text (MeV throughout, keV for detector params)
- Enhance logging verbosity:
  - -v (INFO): shows source, component initialization, workflow steps
  - -vv (DEBUG): shows detailed internals of all spectrum components
    (parameters, evaluation counts, input/output ranges)
- Improve CSV headers with element notation (e.g. Tc99 -> Ru99)
- Add CONVENTIONS.md documenting energy unit and logging conventions
- Update TODO.md with completion status

beta_spectrum/components/fermi.py

@@ -1,4 +1,5 @@
 # components/fermi.py
+import logging
 import numpy as np
 from scipy.special import loggamma
 
@@ -8,14 +9,32 @@ from ..utils import nuclear_radius
 
 
 class FermiFunction(SpectrumComponent):
-    def __init__(self, Z: int, A: int):
+    def __init__(self, Z: int, A: int, logger: logging.Logger | None = None):
+        super().__init__(logger=logger)
         self.Z = Z
         self.A = A
         self.alphaZ = ALPHA * Z
         self.gamma = np.sqrt(1.0 - self.alphaZ**2)
         self.R = nuclear_radius(A)
+        if self._logger:
+            self._logger.debug(
+                "FermiFunction: Z=%d, A=%d, alpha*Z=%.4f, gamma=%.6f, R=%.4f",
+                Z,
+                A,
+                self.alphaZ,
+                self.gamma,
+                self.R,
+            )
 
     def __call__(self, W: np.ndarray) -> np.ndarray:
+        if self._logger:
+            self._logger.debug(
+                "FermiFunction: evaluating at %d points, W range=[%.4f, %.4f]",
+                len(W),
+                W.min(),
+                W.max(),
+            )
+
         p = np.sqrt(W**2 - 1.0)
 
         # Avoid division by zero
@@ -43,4 +62,13 @@ class FermiFunction(SpectrumComponent):
 
         logF = log_prefactor + log_power + log_gamma_abs_sq - log_gamma_denom + log_exp
 
-        return np.asarray(np.exp(logF), dtype=np.float64)
+        result = np.asarray(np.exp(logF), dtype=np.float64)
+
+        if self._logger:
+            self._logger.debug(
+                "FermiFunction: output range=[%.6e, %.6e]",
+                result.min(),
+                result.max(),
+            )
+
+        return result