docs(syntax): fix syntax issues with sphinx

src/mcsas3/McData.py

@@ -31,6 +31,7 @@ class McData:
     csvargs = {}  # overwritten in subclass
     qNudge = None  # can adjust/offset the q values in case of misaligned q vector, in particular visible in 2D data...
     omitQRanges = None  # to skip or omit unwanted data ranges, for example with sharp XRD peaks, must be a list of [[qmin, qmax], ...] pairs
+    resultIndex = None
     # maybe make this behave like a dict? or maybe that's a bad idea... possible method here: https://stackoverflow.com/questions/4014621/a-python-class-that-acts-like-dict
     # Q = None # links to measData
     # I = None # links to measData
@@ -69,6 +70,7 @@ def __init__(
         resultIndex:int=1,
         **kwargs:dict,
     )-> None:
+        """loadFromFile must be a previous optimization. Else, use any of the other 'from_*' functions """
 
         # reset everything so we're sure not to inherit anything from elsewhere:
         self.filename = None  # input filename
@@ -93,7 +95,6 @@ def __init__(
         # make sure we store and read from the right place.
         self.resultIndex = McHDF.ResultIndex(resultIndex) # defines the HDF5 root path
 
-        """loadFromFile must be a previous optimization. Else, use any of the other 'from_*' functions """
         if loadFromFile is not None:
             self.load(loadFromFile)
 

src/mcsas3/mcmodel.py

@@ -225,16 +225,16 @@ class McModel:
 
     parameters:
     ===
-    * fitParameterLimits *: dict of value pairs {"param1": (lower, upper), ... } for fit parameters
-    * staticParameters *: dict of parameter-value pairs to keep static during the fit {"param2": value, ...}. 
-    * seed *: random number generator seed, should vary for parallel execution
-    * nContrib *: number of individual SasModel contributions from which the total model intensity is calculated
-    * modelName *: SasModels model name to load, default 'sphere'
+    *fitParameterLimits*: dict of value pairs {"param1": (lower, upper), ... } for fit parameters
+    *staticParameters*: dict of parameter-value pairs to keep static during the fit {"param2": value, ...}. 
+    *seed*: random number generator seed, should vary for parallel execution
+    *nContrib*: number of individual SasModel contributions from which the total model intensity is calculated
+    *modelName*: SasModels model name to load, default 'sphere'
 
     or:
     ===
-    * loadFromFile *: A filename from a previous optimization that contains the required settings
-    * loadFromRepetition *: if the filename is specified, load the parameters from this particular repetition
+    *loadFromFile*: A filename from a previous optimization that contains the required settings
+    *loadFromRepetition*: if the filename is specified, load the parameters from this particular repetition
 
     """