merged with main branch

Author: jsalvatier

.gitignore

@@ -0,0 +1,15 @@
+*.pyc
+*.so
+build/*
+dist/*
+pymc.egg-info/
+docs/jss/
+docs/pdf/
+docs/INSTALL.tex
+docs/README.tex
+docs/UserGuide.pdf
+docs/database.tex
+develop
+testresults/*
+sphinxdoc/build/*
+*.tmproj

buildegg

@@ -1,4 +1,10 @@
 #!/bin/sh
+export MACOSX_DEPLOYMENT_TARGET=10.6
+export CFLAGS="-arch i386 -arch x86_64"
+export FFLAGS="-m32 -m64"
+export LDFLAGS="-Wall -undefined dynamic_lookup -bundle -arch i386 -arch x86_64"
+export PYTHONPATH="/Library/Python/2.6/site-packages/"
 rm -rf build
 python setup.py config_fc --fcompiler gnu95 build
-sudo python setupegg.py develop
+#sudo python setup.py install
+sudo python setupegg.py develop

buildosx

@@ -485,36 +485,6 @@ \section{Continuous distributions}
 
 \end{description}
 \end{funcdesc}
-\index{hypergeometric\_like() (in module pymc.distributions)}
-
-\begin{funcdesc}{hypergeometric\_like}{x, n, m, N}
-Hypergeometric log-likelihood. Discrete probability distribution that
-describes the number of successes in a sequence of draws from a finite
-population without replacement.
-\begin{gather}
-\begin{split}f(x \mid n, m, N) = \frac{\binom{m}{x}\binom{N-m}{n-x}}{\binom{N}{n}}\end{split}\notag
-\end{gather}\begin{description}
-\item[Parameters] \leavevmode\begin{itemize}
-\item {} 
-\emph{x} : {[}int{]} Number of successes in a sample drawn from a population.
-
-\item {} 
-\emph{n} : {[}int{]} Size of sample drawn from the population.
-
-\item {} 
-\emph{m} : {[}int{]} Number of successes in the population.
-
-\item {} 
-\emph{N} : {[}int{]} Total number of units in the population.
-
-\end{itemize}
-
-\end{description}
-
-\begin{notice}{note}{Note:}
-$E(X) = \frac{n n}{N}$
-\end{notice}
-\end{funcdesc}
 \index{inverse\_gamma\_like() (in module pymc.distributions)}
 
 \hypertarget{pymc.distributions.inverse_gamma_like}{}\begin{funcdesc}{inverse\_gamma\_like}{x, alpha, beta}

docs/distributions-module.tex

@@ -497,4 +497,8 @@ @Manual{mcmcpack
  year          = {2009},
  note          = {\proglang{R}~package version~1.0-4},
  url           = {http://CRAN.R-project.org/package=MCMCpack},
+<<<<<<< HEAD
 }
+=======
+}
+>>>>>>> gppaper

docs/jss/pymc.bib

@@ -0,0 +1,6 @@
+pymc
+numpy>=1.3
+matplotlib>=0.9
+scipy>=0.8
+nose==0.11
+pydot

pymc-requirements

@@ -175,9 +175,9 @@ def file_items(container, iterable):
         if isinstance(item, Variable):
             container.variables.add(item)
             if isinstance(item, StochasticBase):
-                if item.observed:
-                    container.observed_stochastics.add(item)
-                else:
+                if item.observed or not getattr(item, 'mask', None) is None:
+                    container.observed_stochastics.add(item)                    
+                if not item.observed:
                     container.stochastics.add(item)
             elif isinstance(item, DeterministicBase):
                 container.deterministics.add(item)

pymc/Container.py

@@ -146,7 +146,7 @@ def random(parent_name, ...):
     """
 
     def instantiate_p(__func__):
-        value, parents = _extract(__func__, kwds, keys, 'Stochastic')
+        value, parents = _extract(__func__, kwds, keys, 'Stochastic') 
         return __class__(value=value, parents=parents, **kwds)
 
     keys = ['logp','random','rseed']