Version 3.2.4

Author: dokester

BayesicFitting/__init__.py

@@ -107,6 +107,7 @@
 from .source.PadeModel import PadeModel
 from .source.PhantomCollection import PhantomCollection
 from .source.PhantomSampler import PhantomSampler
+#from .source.PixelModel import PixelModel
 from .source.PoissonErrorDistribution import PoissonErrorDistribution
 from .source.PolySineAmpModel import PolySineAmpModel
 from .source.PolySurfaceModel import PolySurfaceModel

BayesicFitting/source/AmoebaFitter.py

@@ -6,9 +6,9 @@
 from .AnnealingAmoeba import AnnealingAmoeba
 
 __author__ = "Do Kester"
-__year__ = 2023
+__year__ = 2025
 __license__ = "GPL3"
-__version__ = "3.1.0"
+__version__ = "3.2.4"
 __url__ = "https://www.bayesicfitting.nl"
 __status__ = "Perpetual Beta"
 
@@ -31,7 +31,7 @@
 #  * Science System (HCSS), also under GPL3.
 #  *
 #  *    2003 - 2014 Do Kester, SRON (Java code)
-#  *    2017 - 2023 Do Kester
+#  *    2017 - 2025 Do Kester
 
 class AmoebaFitter( MaxLikelihoodFitter ):
     """
@@ -43,7 +43,7 @@ class AmoebaFitter( MaxLikelihoodFitter ):
 
     Examples
     --------
-    # assume x and y are Double1d data arrays.
+    >>> # assume x and y are Double1d data arrays.
     >>> x = numpy.arange( 100, dtype=float ) / 10
     >>> y = 3.5 * SIN( x + 0.4 )                    # make sine
     >>> numpy.random.seed( 12345L )                 # Gaussian random number generator
@@ -60,7 +60,7 @@ class AmoebaFitter( MaxLikelihoodFitter ):
     >>> yfit  = amfit.getResult( )                # fitted values
     >>> yfit  = sine( x )                         # fitted values ( same as previous )
     >>> yband = amfit.monteCarloError( )               # 1 sigma confidence region
-    # for diagnostics ( or just for fun )
+    >>> # for diagnostics ( or just for fun )
     >>> amfit = AmoebaFitter( x, sine )
     >>> amfit.setTemperature( 10 )                # set a temperature to escape local minima
     >>> amfit.setVerbose( 10 )                    # report every 10th iteration

BayesicFitting/source/AnnealingAmoeba.py

@@ -6,9 +6,9 @@
 from .ConvergenceError import ConvergenceError
 
 __author__ = "Do Kester"
-__year__ = 2020
+__year__ = 2025
 __license__ = "GPL3"
-__version__ = "2.5.3"
+__version__ = "3.2.4"
 __url__ = "https://www.bayesicfitting.nl"
 __status__ = "Perpetual Beta"
 
@@ -31,7 +31,7 @@
 #  * Science System (HCSS), also under GPL3.
 #  *
 #  *    2003 - 2014 Do Kester, SRON (Java code)
-#  *    2017 - 2020 Do Kester
+#  *    2017 - 2025 Do Kester
 
 class AnnealingAmoeba( object ):
     """
@@ -66,7 +66,7 @@ class AnnealingAmoeba( object ):
     Iteration continues until the relative difference between the low and high
     points within the simplex is less than reltol
         |yhi - ylo| / ( |yhi| + |ylo| ) < reltol
-    and/or the absolute difference is less than abstol
+     and/or the absolute difference is less than abstol
         |yhi - ylo| < abstol.
 
     AnnealingAmoeba can be used with limits set to one or more of the input values.
@@ -87,12 +87,10 @@ class AnnealingAmoeba( object ):
         the best of the above values
     xopt : ndarray
         copy of the simplex point that has the best value (nx)
-
     rng : RandomState
         random number generator
     seed : int
         seed of rng
-
     reltol : float
         Relative tolerance. Program stops when ( |yhi-ylo| / (|yhi|+|ylo|) ) < reltol
     abstol : float
@@ -103,14 +101,12 @@ class AnnealingAmoeba( object ):
         iteration counter
     ncalls : int (read only)
         numbers of calls to func
-
     temp : float
         annealing temperature (default: 0)
     cooling : float (non existent when temp=0)
         cooling factor (default: 0.95)
     steps : int (non existent when temp=0)
         number of steps per cooling cycle (default: 10)
-
     verbose : int
         0  : silent
         1 : print results to output
@@ -119,7 +115,6 @@ class AnnealingAmoeba( object ):
     callback : callable
         function to be called every iteration of form :
         xopt = callback( xopt )
-
     simplex : ndarray
         the simplex has shape = (nx+1, nx); nx is the size of x
     values : ndarray

BayesicFitting/source/ArctanModel.py

@@ -4,9 +4,9 @@
 from .NonLinearModel import NonLinearModel
 
 __author__ = "Do Kester"
-__year__ = 2020
+__year__ = 2025
 __license__ = "GPL3"
-__version__ = "2.5.3"
+__version__ = "3.2.4"
 __url__ = "https://www.bayesicfitting.nl"
 __status__ = "Perpetual Beta"
 
@@ -28,18 +28,20 @@
 #  * Science System (HCSS), also under GPL3.
 #  *
 #  *    2007 - 2014 Do Kester, SRON (JAVA code)
-#  *    2016 - 2020 Do Kester
+#  *    2016 - 2025 Do Kester
 
 class ArctanModel( NonLinearModel ):
     """
     Arctangus Model.
 
         f( x:p ) = p_0 * arctan( p_2 * ( x - p_1 ) )
-        p_0 = amplitude;  p_1 = center; p_2 = slope.
+            p_0 = amplitude  
+            p_1 = center 
+            p_2 = slope.
 
     As always x = input.
 
-    The parameters are initialized at {2/pi, 0.0, 1.0}. It is a non-linear model.
+    The parameters are initialized at [2/pi, 0.0, 1.0]. It is a non-linear model.
 
     Attributes from Model
     --------------------------
@@ -57,7 +59,7 @@ class ArctanModel( NonLinearModel ):
     -------
     >>> arct = ArctanModel( )
     >>> print( arct.getNumberOfParameters( ) )
-    3
+    >>> 3
 
 
     Author:      Do Kester

BayesicFitting/source/AstropyModel.py

@@ -10,9 +10,9 @@
 from .Model import Model
 
 __author__ = "Do Kester"
-__year__ = 2024
+__year__ = 2025
 __license__ = "GPL3"
-__version__ = "3.2.1"
+__version__ = "3.2.4"
 __url__ = "https://www.bayesicfitting.nl"
 __status__ = "Perpetual Beta"
 
@@ -45,13 +45,13 @@ class AstropyModel( Model ):
     >>>
     >>> gauss = AstropyModel( gm )
     >>> print( gauss )
-    AstropyModel( Gauss1D )
+    >>> AstropyModel( Gauss1D )
     >>> print( gauss.getNumberOfParameters( ) )
-    3
+    >>> 3
     >>> print( gauss( numpy.linspace( -5, 5, 11 ) ) )
-    [  3.72665317e-06   3.35462628e-04   1.11089965e-02   1.35335283e-01
-       6.06530660e-01   1.00000000e+00   6.06530660e-01   1.35335283e-01
-       1.11089965e-02   3.35462628e-04   3.72665317e-06]
+    >>> [  3.72665317e-06   3.35462628e-04   1.11089965e-02   1.35335283e-01
+    >>>    6.06530660e-01   1.00000000e+00   6.06530660e-01   1.35335283e-01
+    >>>    1.11089965e-02   3.35462628e-04   3.72665317e-06]
 
     Attributes
     ----------

BayesicFitting/source/BSplinesModel.py

@@ -9,9 +9,9 @@
 from .LinearModel import LinearModel
 
 __author__ = "Do Kester"
-__year__ = 2022
+__year__ = 2025
 __license__ = "GPL3"
-__version__ = "3.0.0"
+__version__ = "3.2.4"
 __url__ = "https://www.bayesicfitting.nl"
 __status__ = "Perpetual Beta"
 
@@ -29,7 +29,7 @@
 #  *
 #  * The GPL3 license can be found at <http://www.gnu.org/licenses/>.
 #  *
-#  *    2017 - 2022 Do Kester
+#  *    2017 - 2025 Do Kester
 
 class BSplinesModel( LinearModel ):
     """
@@ -46,12 +46,13 @@ class BSplinesModel( LinearModel ):
 
     It is a linear model.
 
-    order   behaviour between knots     continuity at knots
-      0     piecewise constant          not continuous at all
-      1     piecewise linear            lines are continuous
-      2     parabolic pieces            1st derivatives are also continuous
-      3     cubic pieces                2nd derivatives are also continuous
-     n>3    n-th order polynomials      (n-1)-th derivatives are also continuous
+    | order |behaviour between knots | continuity at knots                |
+    |:-----:|:-----------------------|:-----------------------------------|
+    |   0   | piecewise constant     | not continuous at all              |
+    |   1   | piecewise linear       | lines are continuous               |
+    |   2   | parabolic pieces       | 1st derivatives are also continuous|
+    |   3   | cubic pieces           | 2nd derivatives are also continuous|
+    |  n>3  | n-th order polynomials | (n-1)th derivatives are continuous |
 
     The user lays out a number ( << datapoints ) of knots on the x-axis at
     arbitrary position, generally more knots where the curvature is higher.
@@ -72,17 +73,17 @@ class BSplinesModel( LinearModel ):
     >>> knots = numpy.arange( 17, dtype=float ) * 10    # make equidistant knots from 0 to 160
     >>> csm = BSplinesModel( knots=knots, order=2 )
     >>> print csm.getNumberOfParameters( )
-    18
-    # or alternatively:
+    >>> 18
+    >>> # or alternatively:
     >>> csm = BSplinesModel( nrknots=17, order=2, min=0, max=160 )    # automatic layout of knots
     >>> print csm.getNumberOfParameters( )
-    18
-    # or alternatively:
+    >>> 18
+    >>> # or alternatively:
     >>> npt = 161                                               # to include both 0 and 160.
     >>> x = numpy.arange( npt, dtype=float )                    # x-values
     >>> csm = BSplinesModel( nrknots=17, order=2, xrange=x )     # automatic layout of knots
     >>> print csm.getNumberOfParameters( )
-    18
+    >>> 18
 
     Attributes
     ----------

BayesicFitting/source/BaseFitter.py

@@ -12,9 +12,9 @@
 from .Formatter import formatter as fmt
 
 __author__ = "Do Kester"
-__year__ = 2023
+__year__ = 2025
 __license__ = "GPL3"
-__version__ = "3.2.2"
+__version__ = "3.2.4"
 __url__ = "https://www.bayesicfitting.nl"
 __status__ = "Perpetual Beta"
 
@@ -36,7 +36,7 @@
 #  * Science System (HCSS), also under GPL3.
 #  *
 #  *    2003 - 2014 Do Kester, SRON (JAVA code)
-#  *    2016 - 2023 Do Kester
+#  *    2016 - 2025 Do Kester
 
 class BaseFitter( object ):
     """
@@ -56,7 +56,7 @@ class BaseFitter( object ):
 
     Examples
     --------
-    # It is not possible to use this class. User Fitter, CurveFitter etc. in stead
+    It is not possible to use this class. Use Fitter, CurveFitter etc. in stead
 
     Note Also
     ---------
@@ -90,7 +90,6 @@ class BaseFitter( object ):
         the fixed noise scale.
         The presence of `fixedScale` has consequences for the definitions of `chisq`,
         `(co)variance`, `stdevs` and `evidence`
-
     minimumScale : float
         introduce a minimum value for the noise scale
     design : matrix (read only)

BayesicFitting/source/BaseModel.py

@@ -9,9 +9,9 @@
 from .UniformPrior import UniformPrior
 
 __author__ = "Do Kester"
-__year__ = 2024
+__year__ = 2025
 __license__ = "GPL3"
-__version__ = "3.2.1"
+__version__ = "3.2.4"
 __url__ = "https://www.bayesicfitting.nl"
 __status__ = "Perpetual Beta"
 
@@ -33,7 +33,7 @@
 #  * Science System (HCSS), also under GPL3.
 #  *
 #  *    2011 - 2014 Do Kester, SRON (Java code)
-#  *    2016 - 202024 Do Kester
+#  *    2016 - 202025 Do Kester
 
 class BaseModel( object ):
     """
@@ -83,12 +83,12 @@ class BaseModel( object ):
     parNames : list of str
         list of parameter names. (default : "parameter_k")
 
-    Author :         Do Kester
+    Author          Do Kester
 
     """
 
     #  *************************************************************************
-    def __init__( self, nparams=0, ndim=1, copy=None, posIndex=[], nonZero=[], **kwargs ):
+    def __init__( self, nparams=0, ndim=1, ndout=None, copy=None, posIndex=[], nonZero=[], **kwargs ):
         """
         BaseModel Constructor.
         <br>
@@ -113,6 +113,8 @@ def __init__( self, nparams=0, ndim=1, copy=None, posIndex=[], nonZero=[], **kwa
         if copy is None :
             setatt( self, "npbase", nparams )
             setatt( self, "ndim", ndim )
+            if ndout is not None :
+                setatt( self, "ndout", ndout, type=int )
             setatt( self, "priors", None )
             setatt( self, "posIndex", numpy.asarray( posIndex, dtype=int ) )
             setatt( self, "nonZero", numpy.asarray( nonZero, dtype=int ) )
@@ -123,6 +125,8 @@ def __init__( self, nparams=0, ndim=1, copy=None, posIndex=[], nonZero=[], **kwa
         else :
             setatt( self, "npbase", copy.npbase )
             setatt( self, "ndim", copy.ndim )
+            if "ndout" in vars( copy ) :
+                setatt( self, "ndout", copy.ndout, type=int )
             setatt( self, "priors", None if copy.priors is None else copy.priors.copy() )
             setatt( self, "posIndex", copy.posIndex )
             setatt( self, "nonZero", copy.nonZero )