Issue 27: Invalid escape sequence

dokester · Dec 9, 2024 · ce3f285 · ce3f285
1 parent acab599
commit ce3f285
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Showing 9 changed files with 46 additions and 46 deletions.
diff --git a/BayesicFitting/source/DecisionTreeModel.py b/BayesicFitting/source/DecisionTreeModel.py
@@ -13,9 +13,9 @@
 from .UniformPrior import UniformPrior
 
 __author__ = "Do Kester"
-__year__ = 2020
+__year__ = 2024
 __license__ = "GPL3"
-__version__ = "2.5.3"
+__version__ = "3.2.3"
 __url__ = "https://www.bayesicfitting.nl"
 __status__ = "Perpetual Beta"
 
@@ -34,7 +34,7 @@
 #  *
 #  * The GPL3 license can be found at <http://www.gnu.org/licenses/>.
 #  *
-#  *    2019 - 2020 Do Kester
+#  *    2019 - 2024 Do Kester
 
 
 class DecisionTreeModel( Modifiable, Dynamic, LinearModel ):
@@ -50,10 +50,10 @@ class DecisionTreeModel( Modifiable, Dynamic, LinearModel ):
     Booleans that contain unknowns should be coded as categorial.
 
         f( x:p ) = DTM
-                    \left => DTM (or None)
-                    \rite => DTM (or None)
-                    \dimension
-                    \split or \mask
+                     |-> left => DTM (or None)
+                     |-> rite => DTM (or None)
+                     |-> dimension
+                     |-> split or mask
 
     The tree is searched left to right.
 
@@ -203,7 +203,7 @@ def __init__( self, ndim=1, depth=0, split=0.5, kdim=0, itypes=[0], modifiable=T
         while True :
             try :
                 child = next( gen )
-            except :
+            except Exception :
                 break
             if child.itypes is not None :
                 del( child.itypes )
@@ -662,7 +662,7 @@ def check( self ) :
         while kl < kbranch :
             try :
                 child = next( gen )
-            except :
+            except Exception :
                 break
             if not child.isLeaf() :
                 kl += 1
@@ -700,7 +700,7 @@ def count( self ) :
         while True :
             try :
                 child = next( gen )
-            except :
+            except Exception :
                 break
 
             if child.isLeaf() :

diff --git a/BayesicFitting/source/EtalonModel.py b/BayesicFitting/source/EtalonModel.py
@@ -6,9 +6,9 @@
 from .NonLinearModel import NonLinearModel
 
 __author__ = "Do Kester"
-__year__ = 2020
+__year__ = 2024
 __license__ = "GPL3"
-__version__ = "2.6.0"
+__version__ = "3.2.3"
 __url__ = "https://www.bayesicfitting.nl"
 __status__ = "Perpetual Beta"
 
@@ -31,13 +31,13 @@
 #  * Science System (HCSS), also under GPL3.
 #  *
 #  *    2015 - 2014 Do Kester, SRON (Java code)
-#  *    2016 - 2020 Do Kester
+#  *    2016 - 2024 Do Kester
 
 class EtalonModel( NonLinearModel ):
     """
     Etalon Model.
 
-        f( x:p ) = p_0 / ( 1.0 + p_1 * sin^2( \pi p_2 x + p_3 ) )
+        f( x:p ) = p_0 / ( 1.0 + p_1 * sin^2( &pi; p_2 x + p_3 ) )
 
     where
         p_0 = amplitude

diff --git a/BayesicFitting/source/MaxLikelihoodFitter.py b/BayesicFitting/source/MaxLikelihoodFitter.py
@@ -13,9 +13,9 @@
 from .Formatter import formatter as fmt
 
 __author__ = "Do Kester"
-__year__ = 2023
+__year__ = 2024
 __license__ = "GPL3"
-__version__ = "3.1.0"
+__version__ = "3.2.3"
 __url__ = "https://www.bayesicfitting.nl"
 __status__ = "Perpetual Beta"
 
@@ -38,7 +38,7 @@
 #  * Science System (HCSS), also under GPL3.
 #  *
 #  *    2003 - 2014 Do Kester, SRON ( Java code )
-#  *    2016 - 2023 Do Kester
+#  *    2016 - 2024 Do Kester
 
 class MaxLikelihoodFitter( IterativeFitter ):
     """
@@ -238,8 +238,8 @@ def func( self, par ):
         """
         Return the function to be minimized.
         In this case
-        .. math::
-            \chi^2 = \sum( D_i - F(x_i:p) )^2
+
+            chi^2 = sum( D_i - F(x_i:p) )^2
 
         """
         param = self._outer.insertParameters( par, index=self._index )
@@ -256,8 +256,8 @@ def userdfunc( self, par ):
     def dfunc( self, par ):
         """
         Return the gradient of func to the parameters p.
-        .. math::
-            d\chi^2/dp = -2 \sum( D_i - F_i ) dF_i/dp
+
+            d chi^2/dp = -2 sum( D_i - F_i ) dF_i/dp
         """
         param = self._outer.insertParameters( par, index=self._index )
         res = numpy.subtract( self._outer.model.result( self._outer.xdata, param ), self._data )

diff --git a/BayesicFitting/source/NeuralNetUtilities.py b/BayesicFitting/source/NeuralNetUtilities.py
@@ -7,9 +7,9 @@
 
 
 __author__ = "Do Kester"
-__year__ = 2022
+__year__ = 2024
 __license__ = "GPL3"
-__version__ = "3.0.0"
+__version__ = "3.2.3"
 __url__ = "https://www.bayesicfitting.nl"
 __status__ = "Alpha"
 
@@ -63,7 +63,7 @@ def pipe( self, dfdr, drdp ) :
 
 
 class Connect( BaseNeuralNetworkUtilities ):
-    """
+    r"""
     Connects one layer to the next.
 
         0       0       0       0       0       0   K inputs

diff --git a/BayesicFitting/source/RobustShell.py b/BayesicFitting/source/RobustShell.py
@@ -6,9 +6,9 @@
 from .kernels.Biweight import Biweight
 
 __author__ = "Do Kester"
-__year__ = 2023
+__year__ = 2024
 __license__ = "GPL3"
-__version__ = "3.2.0"
+__version__ = "3.2.3"
 __url__ = "https://www.bayesicfitting.nl"
 __status__ = "Perpetual Beta"
 
@@ -31,7 +31,7 @@
 #  * Science System (HCSS), also under GPL3.
 #  *
 #  *    2008 - 2014 Do Kester, SRON (Java code)
-#  *    2017 - 2023 Do Kester
+#  *    2017 - 2024 Do Kester
 
 
 class RobustShell( IterativeFitter ):
@@ -46,7 +46,7 @@ class RobustShell( IterativeFitter ):
     downweighted. "Normal" points keep their weights, more or less.
 
     Apart from methods specific to the robustification, RobustShell has a fit method
-    and little else from the Fitter family. Methods to get the :math:`\chi^2`,
+    and little else from the Fitter family. Methods to get chi^2,
     the covariance matrix, the evidence, the noise scale etc. should be taken from
     the embedded fitter.
 

diff --git a/BayesicFitting/source/SampleList.py b/BayesicFitting/source/SampleList.py
@@ -5,9 +5,9 @@
 from .Sample import Sample
 
 __author__ = "Do Kester"
-__year__ = 2023
+__year__ = 2024
 __license__ = "GPL3"
-__version__ = "3.2.0"
+__version__ = "3.2.3"
 __url__ = "https://www.bayesicfitting.nl"
 __status__ = "Perpetual Beta"
 
@@ -541,7 +541,7 @@ def average( self, xdata ):
     # ===== MONTE CARLO ERRORS ===================================================
     def monteCarloError( self, xdata ):
         """
-        Calculates 1-\sigma-confidence regions on the model given some inputs.
+        Calculates 1-sigma-confidence regions on the model given some inputs.
 
         The model is run with the input for the parameters in each of the
         samples. Appropiately weighted standard deviations are calculated

diff --git a/BayesicFitting/source/SineDriftModel.py b/BayesicFitting/source/SineDriftModel.py
@@ -5,9 +5,9 @@
 from .NonLinearModel import NonLinearModel
 
 __author__ = "Do Kester"
-__year__ = 2020
+__year__ = 2024
 __license__ = "GPL3"
-__version__ = "2.5.3"
+__version__ = "3.2.3"
 __url__ = "https://www.bayesicfitting.nl"
 __status__ = "Perpetual Beta"
 
@@ -35,12 +35,12 @@
 class SineDriftModel( NonLinearModel ):
     """
     Sinusoidal Model with drifting frequency.
-    .. math::
-        f( x:p ) = p_1 * \cos( \phi ) + p_2 * sin( \phi )
-        \phi = 2 * \pi * x * ( p_0 + x * p_3 )
 
-    where :math:`p_0` = frequency, :math:`p_3` = is the drift in frequency,
-    :math:`p_1` = amplitude cosine and :math:`p_2` = amplitude sine.
+        f( x:p ) = p_1 * cos( phi ) + p_2 * sin( phi )
+           phi = 2 * pi * x * ( p_0 + x * p_3 )
+
+    where p_0 = frequency, p_3 = is the drift in frequency,
+          p_1 = amplitude cosine and p_2 = amplitude sine.
     As always x = input.
 
     The parameters are initialized at {1.0, 1.0, 1.0, 0.0}. It is a non-linear model.

diff --git a/BayesicFitting/source/SineSplineModel.py b/BayesicFitting/source/SineSplineModel.py
@@ -6,9 +6,9 @@
 from .SplinesModel import SplinesModel
 
 __author__ = "Do Kester"
-__year__ = 2020
+__year__ = 2024
 __license__ = "GPL3"
-__version__ = "2.5.3"
+__version__ = "3.2.3"
 __url__ = "https://www.bayesicfitting.nl"
 __status__ = "Perpetual Beta"
 
@@ -26,13 +26,13 @@
 #  *
 #  * The GPL3 license can be found at <http://www.gnu.org/licenses/>.
 #  *
-#  *    2017 - 2020 Do Kester
+#  *    2017 - 2024 Do Kester
 
 class SineSplineModel( LinearModel ):
     """
     Sine of fixed frequency with splineslike amplitudes/phases.
 
-        f( x:p ) = SM0 \cos( 2 &pi; &omega; x ) + SM1 sin( 2 &pi; &omega; x )
+        f( x:p ) = SM0 cos( 2 &pi; &omega; x ) + SM1 sin( 2 &pi; &omega; x )
 
     Where SM0 and SM1 are splines models with defined knots and order.
 

diff --git a/BayesicFitting/source/SoftMaxModel.py b/BayesicFitting/source/SoftMaxModel.py
@@ -10,9 +10,9 @@
 from .NonLinearModel import NonLinearModel
 
 __author__ = "Do Kester"
-__year__ = 2022
+__year__ = 2024
 __license__ = "GPL3"
-__version__ = "3.0.0"
+__version__ = "3.2.3"
 __url__ = "https://www.bayesicfitting.nl"
 __status__ = "Perpetual Beta"
 
@@ -31,11 +31,11 @@
 #  *
 #  * The GPL3 license can be found at <http://www.gnu.org/licenses/>.
 #  *
-#  *    2020 - 2022 Do Kester
+#  *    2020 - 2024 Do Kester
 
 
 class SoftMaxModel( NonLinearModel ):
-    """
+    r"""
     Softmax Model is a Logistic model if the number of outputs is 1.
     Otherwise it is generalization of the LogisticModel over multiple outputs