docs(General): Fix parameters documentation format

BAMresearch · Mar 21, 2023 · b680822 · b680822
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diff --git a/src/mcsas3/mccore.py b/src/mcsas3/mccore.py
@@ -15,17 +15,28 @@
 
 class McCore:
     """
-    The core of the MC procedure. Strict requirements on input include:
-    modelFunc: SasModels function
-    measData: measurement data dictionary with Q, I, ISigma containing arrays.
-              For 2D data, Q is a two-element list with [Qx, Qy].
-              This is why it's not a Pandas Dataframe
-    pickParameters: dict of values with new random picks, named by parameter names
-    modelParameterLimits: dict of value pairs (tuples) with random pick bounds,
-                          named by parameter names
-    x0: continually updated new guess for total scaling, background values.
-    weighting: volume-weighting / compensation factor for the contributions
-    nContrib: number of contributions
+    The core of the MC procedure.
+    
+    Parameters
+    ----------
+    modelFunc:
+        SasModels function
+    measData: dict
+        measurement data dictionary with Q, I, ISigma containing arrays.
+        For 2D data, Q is a two-element list with [Qx, Qy].
+        This is why it's not a Pandas Dataframe.
+    pickParameters: dict
+        dict of values with new random picks, named by parameter names
+    modelParameterLimits: dict
+        dict of value pairs (tuples) with random pick bounds,
+        named by parameter names
+    x0:
+        continually updated new guess for total scaling, background values.
+    weighting:
+        volume-weighting / compensation factor for the contributions
+    nContrib:
+        number of contributions
+
     """
 
     _measData = None  # measurement data dict with entries for Q, I, ISigma

diff --git a/src/mcsas3/mcmodel.py b/src/mcsas3/mcmodel.py
@@ -236,21 +236,25 @@ class McModel:
     Specifies the fit parameter details and contains random pickers.
     Configuration can be alternatively loaded from an existing result file.
 
-    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'
-
-    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
+    Parameters
+    ----------
+    fitParameterLimits: dict of value pairs {"param1": (lower, upper), ... }
+        for fit parameters
+    staticParameters: dict of parameter-value pairs {"param2": value, ...}
+        to keep static during the fit
+    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: alternatively:
+    loadFromFile: str
+        A filename from a previous optimization that contains the required settings
+    loadFromRepetition: int
+        If the filename is specified, load the parameters from this particular repetition
+
     """
 
     func = None  # SasModels model instance

diff --git a/src/mcsas3/osb.py b/src/mcsas3/osb.py
@@ -7,20 +7,30 @@ class optimizeScalingAndBackground(object):
     """small class derived from the McSAS mcsas/backgroundscalingfit.py class,
     quickly provides an optimized scaling and background value for two datasets.
 
-    TODO (maybe): include a porod background contribution? If so, Q should be
+    **TODO (maybe)**: include a porod background contribution? If so, Q should be
     available to this class.
 
-    inputs:
-    *measDataI*: numpy array of measured intensities
-    *measDataISigma*: associated uncertainties
-    *modelDataI*: array of model intensities.
-    *x0* (optional): two-element tuple with initial guess for scaling and background
-    *xBounds* (optional): constraints to the optimization,
-                          speeds up when appropriate constraints are given
+    Parameters
+    ----------
+    measDataI:
+        numpy array of measured intensities
+    measDataISigma:
+        associated uncertainties
+    modelDataI:
+        array of model intensities.
+    x0:
+        optional, two-element tuple with initial guess for scaling and background
+    xBounds:
+        optional, constraints to the optimization,
+        speeds up when appropriate constraints are given
+
+    Returns
+    -------
+    x:
+        length 2 ndarray with optimized scaling parameter and background parameter
+    cs:
+        final reduced chi-squared
 
-    Returns:
-    :x: length 2 ndarray with optimized scaling parameter and background parameter
-    :cs: final reduced chi-squared
 
     Usage example: