fix: raise RuntimeError if fit_roi fails

pdf_agents/peakfit.py

@@ -194,25 +194,14 @@ def fit_roi(self, xroi: tuple, fit_func: str, pos_percent_lim: float, maxcycles:
 
                 popt, pcov = curve_fit(self.voigt, x_roi, y_roi, p0=p0, bounds=bounds, max_nfev=maxcycles)
 
-            except RuntimeError:  # if fitting fails, return None for parameters
-                popt = None
-
-            if popt is not None:
-                peak_amplitude, peak_position, peak_sigma, peak_gamma = popt
-                peak_fwhm = self.voigt_fwhm(peak_sigma, peak_gamma)
+            except RuntimeError:
+                raise RuntimeError("PeakFitAgent fit failed to converge")
 
-                ycalc = self.voigt(x_roi, *popt)
-                ydiff = y_roi - self.voigt(x_roi, *popt)
+            peak_amplitude, peak_position, peak_sigma, peak_gamma = popt
+            peak_fwhm = self.voigt_fwhm(peak_sigma, peak_gamma)
 
-            else:
-                peak_amplitude = None
-                peak_position = None
-                peak_sigma = None
-                peak_gamma = None
-                peak_fwhm = None
-                ycalc = None
-                ydiff = None
-                x_roi = None
+            ycalc = self.voigt(x_roi, *popt)
+            ydiff = y_roi - self.voigt(x_roi, *popt)
 
             return peak_amplitude, peak_position, peak_sigma, peak_gamma, peak_fwhm, ycalc, ydiff, x_roi
 
@@ -234,23 +223,13 @@ def fit_roi(self, xroi: tuple, fit_func: str, pos_percent_lim: float, maxcycles:
                 popt, pcov = curve_fit(self.gaussian, x_roi, y_roi, p0=p0, bounds=bounds, max_nfev=maxcycles)
 
             except RuntimeError:
-                # If fitting fails, return None for parameters
-                popt = None
-
-            if popt is not None:
-                peak_amplitude, peak_position, peak_width = popt
-                peak_fwhm = self.gaussian_fwhm(peak_width)
+                raise RuntimeError("PeakFitAgent fit failed to converge")
 
-                ycalc = self.gaussian(x_roi, *popt)
-                ydiff = y_roi - self.gaussian(x_roi, *popt)
+            peak_amplitude, peak_position, peak_width = popt
+            peak_fwhm = self.gaussian_fwhm(peak_width)
 
-            else:
-                peak_amplitude = None
-                peak_position = None
-                peak_fwhm = None
-                ycalc = None
-                ydiff = None
-                x_roi = None
+            ycalc = self.gaussian(x_roi, *popt)
+            ydiff = y_roi - self.gaussian(x_roi, *popt)
 
             return peak_amplitude, peak_position, peak_fwhm, ycalc, ydiff, x_roi
 
@@ -272,26 +251,16 @@ def fit_roi(self, xroi: tuple, fit_func: str, pos_percent_lim: float, maxcycles:
                 popt, pcov = curve_fit(self.lorentzian, x_roi, y_roi, p0=p0, bounds=bounds, max_nfev=1000)
 
             except RuntimeError:
-                # If fitting fails, return None for parameters
-                popt = None
-                print(f"Fitting failed for ROI = {x_roi[0]} - {x_roi[-1]}\n")
-
-            if popt is not None:
-                peak_amplitude, peak_position, peak_width = popt
-                peak_fwhm = self.lorentzian_fwhm(peak_width)
+                raise RuntimeError("PeakFitAgent fit failed to converge")
 
-                ycalc = self.lorentzian(x_roi, *popt)
-                ydiff = y_roi - self.lorentzian(x_roi, *popt)
+            peak_amplitude, peak_position, peak_width = popt
+            peak_fwhm = self.lorentzian_fwhm(peak_width)
 
-            else:
-                peak_amplitude = None
-                peak_position = None
-                peak_fwhm = None
-                ycalc = None
-                ydiff = None
-                x_roi = None
+            ycalc = self.lorentzian(x_roi, *popt)
+            ydiff = y_roi - self.lorentzian(x_roi, *popt)
 
             return peak_amplitude, peak_position, peak_fwhm, ycalc, ydiff, x_roi
+
         else:
             raise NameError(
                 f"fit_func not recognized - must be 'voigt', 'gaussian', or 'lorentzian'. Input is {fit_func}"
@@ -304,8 +273,6 @@ def report(self) -> Dict[str, ArrayLike]:
         )  # this is what kmeans will likely consume - possibly better to handle this upstream?
         peak_amplitudes = []
         peak_positions = []
-        peak_sigmas = []  # not used for gaussian / lorentzian case, None is a placeholder
-        peak_gammas = []  # not used for gaussian / lorentzian case, None is a placeholder
         peak_fwhms = []
         ycalcs = []
         ydiffs = []
@@ -320,8 +287,6 @@ def report(self) -> Dict[str, ArrayLike]:
                 peak_amps_poss_fwhms.append([peak_amplitude, peak_position, peak_fwhm])
                 peak_amplitudes.append(peak_amplitude)
                 peak_positions.append(peak_position)
-                peak_sigmas.append(peak_sigma)
-                peak_gammas.append(peak_gamma)
                 peak_fwhms.append(peak_fwhm)
                 ycalcs.append(ycalc)
                 ydiffs.append(ydiff)
@@ -336,8 +301,6 @@ def report(self) -> Dict[str, ArrayLike]:
                 peak_amps_poss_fwhms.append([peak_amplitude, peak_position, peak_fwhm])
                 peak_amplitudes.append(peak_amplitude)
                 peak_positions.append(peak_position)
-                peak_sigmas.append(None)
-                peak_gammas.append(None)
                 peak_fwhms.append(peak_fwhm)
                 ycalcs.append(ycalc)
                 ydiffs.append(ydiff)
@@ -358,8 +321,6 @@ def report(self) -> Dict[str, ArrayLike]:
             peak_amps_poss_fwhms=np.array(peak_amps_poss_fwhms),
             peak_amplitudes=np.array(peak_amplitudes),
             peak_positions=np.array(peak_positions),
-            peak_sigmas=np.array(peak_sigmas),
-            peak_gammas=np.array(peak_gammas),
             peak_fwhms=np.array(peak_fwhms),
             ycalc=np.array(ycalcs),
             ydiffs=np.array(ydiffs),