Merge pull request #48 from eth-cscs/release-0.5.3

Release 0.5.3

.gitignore

@@ -87,3 +87,7 @@ ENV/
 
 # Rope project settings
 .ropeproject
+
+# vim backup files
+#
+**/.*.sw?

README.md

@@ -1,7 +1,7 @@
 # ABCpy [![Documentation Status](https://readthedocs.org/projects/abcpy/badge/?version=latest)](http://abcpy.readthedocs.io/en/latest/?badge=latest) [![Build Status](https://travis-ci.org/eth-cscs/abcpy.svg?branch=master)](https://travis-ci.org/eth-cscs/abcpy)
 
 ABCpy is a scientific library written in Python for Bayesian uncertainty quantification in
-absence of likelihood function, which parallelizes existing approaximate Bayesian computation (ABC) 
+absence of likelihood function, which parallelizes existing approximate Bayesian computation (ABC) 
 algorithms and other likelihood-free inference schemes. It presently includes:
 
 * RejectionABC
@@ -26,52 +26,37 @@ scientists by providing
 # Documentation
 For more information, check out the
 
-* [Documentation](http://abcpy.readthedocs.io/en/v0.5.2) 
-* [Examples](https://github.com/eth-cscs/abcpy/tree/v0.5.2/examples) directory and
-* [Reference](http://abcpy.readthedocs.io/en/v0.5.2/abcpy.html)
+* [Documentation](http://abcpy.readthedocs.io/en/v0.5.3) 
+* [Examples](https://github.com/eth-cscs/abcpy/tree/v0.5.3/examples) directory and
+* [Reference](http://abcpy.readthedocs.io/en/v0.5.3/abcpy.html)
 
 Further, we provide a
 [collection of models](https://github.com/eth-cscs/abcpy-models) for which ABCpy
 has been applied successfully. This is a good place to look at more complicated inference setups.
 
 # Author 
-ABCpy was written by [Ritabrata Dutta, Università della svizzera italiana](https://search.usi.ch/en/people/c4342228614d041dca7e2f67cbb996c9/dutta-ritabrata) 
-and [Marcel Schoengens, CSCS, ETH Zurich]([email protected]), and we're actively developing it. Please feel free 
-to submit any bugs or feature requests. We'd also love to hear about your experiences with ABCpy in general. Drop us an email!
-
-We want to thank
-[Prof. Antonietta Mira, Università della svizzera italiana](https://search.usi.ch/en/people/f8960de6d60dd08a79b6c1eb20b7442b/Mira-Antonietta),
-and
-[Prof. Jukka-Pekka Onnela, Harvard University](https://www.hsph.harvard.edu/onnela-lab/)
-for helpful contributions and advice; Avinash Ummadisinghu and Nicole Widmern
-respectively for developing dynamic-MPI backend and making ABCpy suitbale for
-hierarchical models; and finally CSCS (Swiss National Super Computing Center)
-for their generous support.
+ABCpy was written by [Ritabrata Dutta, Warwick
+University](https://warwick.ac.uk/fac/sci/statistics/staff/academic-research/dutta/)
+and [Marcel Schoengens]([email protected]), CSCS, ETH Zurich, and we're
+actively developing it. Please feel free to submit any bugs or feature requests.
+We'd also love to hear about your experiences with ABCpy in general. Drop us an
+email!
+
+We want to thank [Prof. Antonietta Mira, Università della svizzera
+italiana](https://search.usi.ch/en/people/f8960de6d60dd08a79b6c1eb20b7442b/Mira-Antonietta),
+and [Prof. Jukka-Pekka Onnela, Harvard
+University](https://www.hsph.harvard.edu/onnela-lab/) for helpful contributions
+and advice; Avinash Ummadisinghu and Nicole Widmern respectively for developing
+dynamic-MPI backend and making ABCpy suitable for hierarchical models; and
+finally CSCS (Swiss National Super Computing Center) for their generous support.
 
 ## Citation
 
-There is a paper in the proceedings of the 2017 PASC conference. We would appreciate a citation. 
-
-```
-@inproceedings{Dutta:2017:AUE:3093172.3093233,
- author = {Dutta, Ritabrata and Schoengens, Marcel and Onnela, Jukka-Pekka and Mira, Antonietta},
- title = {ABCpy: A User-Friendly, Extensible, and Parallel Library for Approximate Bayesian Computation},
- booktitle = {Proceedings of the Platform for Advanced Scientific Computing Conference},
- series = {PASC '17},
- year = {2017},
- isbn = {978-1-4503-5062-4},
- location = {Lugano, Switzerland},
- pages = {8:1--8:9},
- articleno = {8},
- numpages = {9},
- url = {http://doi.acm.org/10.1145/3093172.3093233},
- doi = {10.1145/3093172.3093233},
- acmid = {3093233},
- publisher = {ACM},
- address = {New York, NY, USA},
- keywords = {ABC, Library, Parallel, Spark},
-} 
-```
+There is a paper in the proceedings of the 2017 PASC conference. In case you use
+ABCpy for your publication, we would appreciate a citation. You can use
+[this](https://github.com/eth-cscs/abcpy/blob/v0.5.3/doc/literature/DuttaS-ABCpy-PASC-2017.bib)
+BibTex reference.
+
 
 ## Other Refernces
 

VERSION

@@ -1 +1 @@
-0.5.2
+0.5.3

abcpy/backends/mpi.py

@@ -1,10 +1,12 @@
-import numpy as np
-import cloudpickle
-import time
 import pickle
+import time
 
+import cloudpickle
+import numpy as np
 from mpi4py import MPI
-from abcpy.backends import Backend, PDS, BDS
+
+from abcpy.backends import BDS, PDS, Backend
+
 
 class BackendMPIMaster(Backend):
     """Defines the behavior of the master process
@@ -268,9 +270,11 @@ def collect(self, pds):
         all_data_indices,all_data_items = [],[]
 
         for node_data in all_data:
-            for item in node_data:
-                all_data_indices+=[item[0]]
-                all_data_items+=[item[1]]
+            for index, item in node_data:
+                if isinstance(item, Exception):
+                    raise item
+                all_data_indices.append(index)
+                all_data_items.append(item)
 
         #Sort the accumulated data according to the indices we tagged
         #them with when distributing 
@@ -490,7 +494,11 @@ def map(self, func):
             #Accumulate the indicess and *processed* chunks
             for chunk in data_chunks:
                 data_index,data_item = chunk
-                rdd+=[(data_index,func(data_item))]
+                try:
+                    result = func(data_item)
+                except Exception as e:
+                    result = e
+                rdd.append((data_index, result))
 
         pds_res = PDSMPI(rdd, pds_id_new, self)