FitManager: make uncertainty trigger the use of weighted fit

src/silx/_version.py

@@ -1,7 +1,7 @@
 #!/usr/bin/env python3
 # /*##########################################################################
 #
-# Copyright (c) 2015-2025 European Synchrotron Radiation Facility
+# Copyright (c) 2015-2026 European Synchrotron Radiation Facility
 #
 # Permission is hereby granted, free of charge, to any person obtaining a copy
 # of this software and associated documentation files (the "Software"), to deal
@@ -48,12 +48,12 @@
 
 """
 
-from collections import namedtuple
+from typing import NamedTuple
 
 __authors__ = ["Jérôme Kieffer"]
 __license__ = "MIT"
 __copyright__ = "European Synchrotron Radiation Facility, Grenoble, France"
-__date__ = "12/12/2023"
+__date__ = "30/04/2026"
 __status__ = "production"
 __docformat__ = "restructuredtext"
 __all__ = [
@@ -78,11 +78,15 @@
 date = __date__
 
 
-_version_info = namedtuple(
-    "version_info", ["major", "minor", "micro", "releaselevel", "serial"]
-)
+class _VersionInfo(NamedTuple):
+    major: int
+    minor: int
+    micro: int
+    releaselevel: str
+    serial: int
 
-version_info = _version_info(MAJOR, MINOR, MICRO, RELEV, SERIAL)
+
+version_info = _VersionInfo(MAJOR, MINOR, MICRO, RELEV, SERIAL)
 
 strictversion = version = debianversion = "%d.%d.%d" % version_info[:3]
 if version_info.releaselevel != "final":

src/silx/math/fit/fitmanager.py

@@ -1,6 +1,6 @@
 # /*#########################################################################
 #
-# Copyright (c) 2004-2023 European Synchrotron Radiation Facility
+# Copyright (c) 2004-2026 European Synchrotron Radiation Facility
 #
 # Permission is hereby granted, free of charge, to any person obtaining a copy
 # of this software and associated documentation files (the "Software"), to deal
@@ -53,7 +53,7 @@
 
 __authors__ = ["V.A. Sole", "P. Knobel"]
 __license__ = "MIT"
-__date__ = "16/01/2017"
+__date__ = "29/04/2026"
 
 _logger = logging.getLogger(__name__)
 
@@ -78,12 +78,14 @@ class FitManager:
     :param weight_flag: If this parameter is ``True`` and ``sigmay``
         uncertainties are not specified, the square root of ``y`` is used
         as weights in the least-squares problem. If ``False``, the
-        uncertainties are set to 1.
+        uncertainties are set to 1. By default, it is set to ``True`` if
+        ``sigmay`` is provided and ``False`` otherwise.
     :type weight_flag: boolean
     """
 
-    def __init__(self, x=None, y=None, sigmay=None, weight_flag=False):
+    def __init__(self, x=None, y=None, sigmay=None, weight_flag=None):
         """ """
+        weight_flag = (sigmay is not None) if weight_flag is None else weight_flag
         self.fitconfig = {
             "WeightFlag": weight_flag,
             "fitbkg": "No Background",
@@ -747,7 +749,9 @@ def setdata(self, x, y, sigmay=None, xmin=None, xmax=None):
         :type y: Sequence or numpy array or None
         :param sigmay: The uncertainties in the ``ydata`` array. These are
             used as weights in the least-squares problem.
-            If ``None``, the uncertainties are assumed to be 1.
+            If provided, enables weighted fit.
+            If not provided (i.e. ``None``): use the square root of ``y`` if ``weight_flag`` is ``True``
+            else uncertainties are assumed to be 1.
         :type sigmay: Sequence or numpy array or None
         :param xmin: Lower value of x values to use for fitting
         :param xmax: Upper value of x values to use for fitting
@@ -777,10 +781,8 @@ def setdata(self, x, y, sigmay=None, xmin=None, xmax=None):
                     numpy.sqrt(self.ydata) if self.fitconfig["WeightFlag"] else None
                 )
             else:
-                self.sigmay0 = numpy.array(sigmay)
-                self.sigmay = (
-                    numpy.array(sigmay) if self.fitconfig["WeightFlag"] else None
-                )
+                self.fitconfig["WeightFlag"] = True
+                self.sigmay0 = self.sigmay = numpy.array(sigmay)
 
             # take the data between limits, using boolean array indexing
             if (xmin is not None or xmax is not None) and len(self.xdata):
@@ -789,7 +791,14 @@ def setdata(self, x, y, sigmay=None, xmin=None, xmax=None):
                 bool_array = (self.xdata >= xmin) & (self.xdata <= xmax)
                 self.xdata = self.xdata[bool_array]
                 self.ydata = self.ydata[bool_array]
-                self.sigmay = self.sigmay[bool_array] if sigmay is not None else None
+                if sigmay is None:
+                    self.sigmay = (
+                        self.sigmay[bool_array]
+                        if self.fitconfig["WeightFlag"]
+                        else None
+                    )
+                else:
+                    self.sigmay0 = self.sigmay = self.sigmay[bool_array]
 
         self._finite_mask = numpy.logical_and(
             numpy.all(

src/silx/math/fit/fittheories.py

@@ -726,7 +726,7 @@ def estimate_splitpvoigt2(self, x, y):
         fittedpar, cons = self.estimate_height_position_fwhm(x, y)
         npeaks = len(fittedpar) // 3
         newpar = []
-        newcons = numpy.zeros((5 * npeaks, 3), numpy.float64)
+        newcons = numpy.zeros((6 * npeaks, 3), numpy.float64)
         # find out related parameters proper index
         if not self.config["NoConstraintsFlag"]:
             if self.config["SameFwhmFlag"]: