Bump version and notebooks to 0.6.2 (#230)

Author: vuolleko

CHANGELOG.rst

@@ -1,8 +1,8 @@
 Changelog
 =========
 
-dev
----
+0.6.2 (2017-09-06)
+------------------
 
 - Easier saving and loading of ElfiModel
 - Renamed elfi.set_current_model to elfi.set_default_model

Makefile

@@ -69,7 +69,7 @@ docs: ## generate Sphinx HTML documentation, including API docs
 	$(MAKE) -C docs html
 	# $(BROWSER) docs/_build/html/index.html
 
-CONTENT_URL := http://research.cs.aalto.fi/pml/software/elfi/docs/0.6.1/
+CONTENT_URL := http://research.cs.aalto.fi/pml/software/elfi/docs/0.6.2/
 
 notebook-docs: ## Conver notebooks to rst docs. Assumes you have them in `notebooks` directory.
 	jupyter nbconvert --to rst ../notebooks/quickstart.ipynb --output-dir docs

README.md

@@ -1,4 +1,4 @@
-**Version 0.6.1 released!** See the CHANGELOG and [notebooks](https://github.com/elfi-dev/notebooks).
+**Version 0.6.2 released!** See the CHANGELOG and [notebooks](https://github.com/elfi-dev/notebooks).
 
 ELFI - Engine for Likelihood-Free Inference
 ===========================================

docs/quickstart.rst

@@ -7,15 +7,15 @@ First ensure you have
 Python 3.5 (or greater) and ELFI. After installation you can start using
 ELFI:
 
-.. code:: python
+.. code:: ipython3
 
     import elfi
 
 ELFI includes an easy to use generative modeling syntax, where the
 generative model is specified as a directed acyclic graph (DAG). Let’s
 create two prior nodes:
 
-.. code:: python
+.. code:: ipython3
 
     mu = elfi.Prior('uniform', -2, 4)
     sigma = elfi.Prior('uniform', 1, 4)
@@ -30,7 +30,7 @@ summary statistics for the data. As an example, lets define the
 simulator as 30 draws from a Gaussian distribution with a given mean and
 standard deviation. Let's use mean and variance as our summaries:
 
-.. code:: python
+.. code:: ipython3
 
     import scipy.stats as ss
     import numpy as np
@@ -48,7 +48,7 @@ standard deviation. Let's use mean and variance as our summaries:
 Let’s now assume we have some observed data ``y0`` (here we just create
 some with the simulator):
 
-.. code:: python
+.. code:: ipython3
 
     # Set the generating parameters that we will try to infer
     mean0 = 1
@@ -73,7 +73,7 @@ Now we have all the components needed. Let’s complete our model by
 adding the simulator, the observed data, summaries and a distance to our
 model:
 
-.. code:: python
+.. code:: ipython3
 
     # Add the simulator node and observed data to the model
     sim = elfi.Simulator(simulator, mu, sigma, observed=y0)
@@ -89,15 +89,15 @@ model:
 If you have ``graphviz`` installed to your system, you can also
 visualize the model:
 
-.. code:: python
+.. code:: ipython3
 
     # Plot the complete model (requires graphviz)
     elfi.draw(d)
 
 
 
 
-.. image:: http://research.cs.aalto.fi/pml/software/elfi/docs/0.6.1/quickstart_files/quickstart_11_0.svg
+.. image:: http://research.cs.aalto.fi/pml/software/elfi/docs/0.6.2/quickstart_files/quickstart_11_0.svg
 
 
 
@@ -108,7 +108,7 @@ We can try to infer the true generating parameters ``mean0`` and
 Rejection sampling and sample 1000 samples from the approximate
 posterior using threshold value 0.5:
 
-.. code:: python
+.. code:: ipython3
 
     rej = elfi.Rejection(d, batch_size=10000, seed=30052017)
     res = rej.sample(1000, threshold=.5)
@@ -127,13 +127,13 @@ posterior using threshold value 0.5:
 
 Let's plot also the marginal distributions for the parameters:
 
-.. code:: python
+.. code:: ipython3
 
     import matplotlib.pyplot as plt
     res.plot_marginals()
     plt.show()
 
 
 
-.. image:: http://research.cs.aalto.fi/pml/software/elfi/docs/0.6.1/quickstart_files/quickstart_16_0.png
+.. image:: http://research.cs.aalto.fi/pml/software/elfi/docs/0.6.2/quickstart_files/quickstart_16_0.png