From e6c6b3b67713f15ad56deb98744729d94a45ef3f Mon Sep 17 00:00:00 2001
From: Henri Pesonen <henri.e.pesonen@gmail.com>
Date: Tue, 4 Aug 2020 09:32:24 +0200
Subject: [PATCH 1/9] Fix readthedocs build (#331)

* Add a second line to a code-block

* Remove code-blocks from quickstart.rst

* Change code to code-block

* Test whether code or code-block works

* Test ipython vs ipython3 block-type

* Pin ipython-version in requirements

* Print plugin_lexers

* Added explicit requirement on Sphinx version

* Modify conf.py

* Add IPython.sphinxext.ipython_console_highlighting to extentions in conf.py

* Update requirements.txt

* Add .readthedocs.yml to force python3.5 build on rtd

* Add requirements-file-def in .readthedocs.yml

* Modify requirements.txt

* Remove editor-specific file

* Add vscode-files to .gitignore

* Modify CHANGELOG
---
 .gitignore       |  3 +++
 .readthedocs.yml | 24 ++++++++++++++++++++++++
 CHANGELOG.rst    |  3 +++
 requirements.txt |  2 +-
 4 files changed, 31 insertions(+), 1 deletion(-)
 create mode 100644 .readthedocs.yml

diff --git a/.gitignore b/.gitignore
index 8bba4641..619ec5c4 100644
--- a/.gitignore
+++ b/.gitignore
@@ -105,3 +105,6 @@ ENV/
 *.swp
 
 notebooks/mydask.png
+
+# vscode-settings
+.vscode
\ No newline at end of file
diff --git a/.readthedocs.yml b/.readthedocs.yml
new file mode 100644
index 00000000..16a3256a
--- /dev/null
+++ b/.readthedocs.yml
@@ -0,0 +1,24 @@
+# .readthedocs.yml
+# Read the Docs configuration file
+# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details
+
+# Required
+version: 2
+
+# Build documentation in the docs/ directory with Sphinx
+sphinx:
+  configuration: docs/conf.py
+
+# Build documentation with MkDocs
+#mkdocs:
+#  configuration: mkdocs.yml
+
+# Optionally build your docs in additional formats such as PDF
+formats:
+  - pdf
+
+# Optionally set the version of Python and requirements required to build your docs
+python:
+  version: 3.5
+  install:
+    - requirements: requirements.txt
\ No newline at end of file
diff --git a/CHANGELOG.rst b/CHANGELOG.rst
index a612ff0d..f91e3008 100644
--- a/CHANGELOG.rst
+++ b/CHANGELOG.rst
@@ -1,6 +1,9 @@
 Changelog
 =========
 
+- Fix readthedocs-build by adding .readthedocs.yml and restricting the build to
+  python3.5, for now
+
 0.7.6 (2020-08-29)
 ------------------
 - Fix incompatibility with scipy>1.5 in bo.utils.stochastic_optimization
diff --git a/requirements.txt b/requirements.txt
index 113bf97c..bedbb0bf 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -5,4 +5,4 @@ GPy>=1.0.9
 networkX>=1.11,<2.0
 ipyparallel>=6
 toolz>=0.8
-scikit-learn>=0.18.1
+scikit-learn>=0.18.1
\ No newline at end of file

From accd0461745bff3edf7d6375bed12c1552c734c8 Mon Sep 17 00:00:00 2001
From: Henri Pesonen <henri.e.pesonen@gmail.com>
Date: Thu, 6 Aug 2020 19:52:42 +0200
Subject: [PATCH 2/9] Fix documentation (#332)

* Fix example docstring in adjust_posterior

* Fix docstrings in elfi.methods.bo.acquisition

* Suppress waitbar in postprocessing example which causes travis to fail
---
 CHANGELOG.rst                   |  1 +
 elfi/methods/bo/acquisition.py  | 65 ++++++++++++++++++---------------
 elfi/methods/post_processing.py | 10 ++---
 3 files changed, 41 insertions(+), 35 deletions(-)

diff --git a/CHANGELOG.rst b/CHANGELOG.rst
index f91e3008..0ced606c 100644
--- a/CHANGELOG.rst
+++ b/CHANGELOG.rst
@@ -1,6 +1,7 @@
 Changelog
 =========
 
+- Improve docstrings in elfi.methods.bo.acquisition
 - Fix readthedocs-build by adding .readthedocs.yml and restricting the build to
   python3.5, for now
 
diff --git a/elfi/methods/bo/acquisition.py b/elfi/methods/bo/acquisition.py
index 10d55c90..d11de94f 100644
--- a/elfi/methods/bo/acquisition.py
+++ b/elfi/methods/bo/acquisition.py
@@ -261,23 +261,23 @@ class MaxVar(AcquisitionBase):
     The next evaluation point is acquired in the maximiser of the variance of
     the unnormalised approximate posterior.
 
-    \theta_{t+1} = arg max Var(p(\theta) * p_a(\theta)),
+    .. math:: \theta_{t+1} = \arg \max \text{Var}(p(\theta) \cdot p_a(\theta)),
 
-    where the unnormalised likelihood p_a is defined
-    using the CDF of normal distribution, \Phi, as follows:
+    where the unnormalised likelihood :math:`p_a` is defined
+    using the CDF of normal distribution, :math:`\Phi`, as follows:
 
-    p_a(\theta) =
-        (\Phi((\epsilon - \mu_{1:t}(\theta)) / \sqrt(v_{1:t}(\theta) + \sigma2_n))),
+    .. math:: p_a(\theta) = \Phi((\epsilon - \mu_{1:t}(\theta)) /
+                             \sqrt{v_{1:t}(\theta) + \sigma^2_n}),
 
-    where \epsilon is the ABC threshold, \mu_{1:t} and v_{1:t} are
-    determined by the Gaussian process, \sigma2_n is the noise.
+    where \epsilon is the ABC threshold, :math:`\mu_{1:t}` and :math:`v_{1:t}` are
+    determined by the Gaussian process, :math:`\sigma^2_n` is the noise.
 
     References
     ----------
-    [1] Järvenpää et al. (2017). arXiv:1704.00520
-    [2] Gutmann M U, Corander J (2016). Bayesian Optimization for
-    Likelihood-Free Inference of Simulator-Based Statistical Models.
-    JMLR 17(125):1−47, 2016. http://jmlr.org/papers/v17/15-017.html
+    Järvenpää et al. (2019). Efficient Acquisition Rules for Model-Based
+    Approximate Bayesian Computation. Bayesian Analysis 14(2):595-622, 2019
+    https://projecteuclid.org/euclid.ba/1537258134
+
 
     """
 
@@ -425,22 +425,24 @@ class RandMaxVar(MaxVar):
     The next evaluation point is sampled from the density corresponding to the
     variance of the unnormalised approximate posterior (The MaxVar acquisition function).
 
-    \theta_{t+1} ~ q(\theta),
+    .. math:: \theta_{t+1} \thicksim q(\theta),
 
-    where q(\theta) \propto Var(p(\theta) * p_a(\theta)) and
-    the unnormalised likelihood p_a is defined
-    using the CDF of normal distribution, \Phi, as follows:
+    where :math:`q(\theta) \propto \text{Var}(p(\theta) \cdot p_a(\theta))` and
+    the unnormalised likelihood :math:`p_a` is defined
+    using the CDF of normal distribution, :math:`\Phi`, as follows:
 
-    p_a(\theta) =
-        (\Phi((\epsilon - \mu_{1:t}(\theta)) / \sqrt(\v_{1:t}(\theta) + \sigma2_n))),
+    .. math:: p_a(\theta) = \Phi((\epsilon - \mu_{1:t}(\theta)) /
+                            \sqrt{v_{1:t}(\theta) + \sigma^2_n} ),
 
-    where \epsilon is the ABC threshold, \mu_{1:t} and \v_{1:t} are
-    determined by the Gaussian process, \sigma2_n is the noise.
+    where :math:`\epsilon` is the ABC threshold, :math:`\mu_{1:t}` and :math:`v_{1:t}` are
+    determined by the Gaussian process, :math:`\sigma^2_n` is the noise.
 
 
     References
     ----------
-    [1] arXiv:1704.00520 (Järvenpää et al., 2017)
+    Järvenpää et al. (2019). Efficient Acquisition Rules for Model-Based
+    Approximate Bayesian Computation. Bayesian Analysis 14(2):595-622, 2019
+    https://projecteuclid.org/euclid.ba/1537258134
 
     """
 
@@ -565,23 +567,26 @@ class ExpIntVar(MaxVar):
     Essentially, we define a loss function that measures the overall uncertainty
     in the unnormalised ABC posterior over the parameter space.
     The value of the loss function depends on the next simulation and thus
-    the next evaluation location \theta^* is chosen to minimise the expected loss.
+    the next evaluation location :math:`\theta^*` is chosen to minimise the expected loss.
 
-    \theta_{t+1} = arg min_{\theta^* \in \Theta} L_{1:t}(\theta^*), where
+    .. math:: \theta_{t+1} = arg min_{\theta^* \in \Theta} L_{1:t}(\theta^*),
 
-    \Theta is the parameter space, and L is the expected loss function approximated as follows:
+    where :math:`\Theta` is the parameter space, and :math:`L` is the expected loss
+    function approximated as follows:
 
-    L_{1:t}(\theta^*) \approx 2 * \sum_{i=1}^s (\omega^i * p^2(\theta^i)
-                                * w_{1:t+1})(theta^i, \theta^*), where
+    .. math:: L_{1:t}(\theta^*) \approx 2 * \sum_{i=1}^s (\omega^i \cdot p^2(\theta^i)
+                                \cdot w_{1:t+1}(\theta^i, \theta^*),
 
-    \omega^i is an importance weight,
-    p^2(\theta^i) is the prior squared, and
-    w_{1:t+1})(theta^i, \theta^*) is the expected variance of the unnormalised ABC posterior
-    at \theta^i after running the simulation model with parameter \theta^*
+    where :math:`\omega^i` is an importance weight,
+    :math:`p^2(\theta^i)` is the prior squared, and
+    :math:`w_{1:t+1}(\theta^i, \theta^*)` is the expected variance of the unnormalised ABC
+    posterior at \theta^i after running the simulation model with parameter :math:`\theta^*`
 
     References
     ----------
-    [1] arXiv:1704.00520 (Järvenpää et al., 2017)
+    Järvenpää et al. (2019). Efficient Acquisition Rules for Model-Based
+    Approximate Bayesian Computation. Bayesian Analysis 14(2):595-622, 2019
+    https://projecteuclid.org/euclid.ba/1537258134
 
     """
 
diff --git a/elfi/methods/post_processing.py b/elfi/methods/post_processing.py
index 847a2e65..6d410f29 100644
--- a/elfi/methods/post_processing.py
+++ b/elfi/methods/post_processing.py
@@ -239,11 +239,11 @@ def adjust_posterior(sample, model, summary_names, parameter_names=None, adjustm
 
     Examples
     --------
-    import elfi
-    from elfi.examples import gauss
-    m = gauss.get_model()
-    res = elfi.Rejection(m['d'], output_names=['ss_mean', 'ss_var']).sample(1000)
-    adj = adjust_posterior(res, m, ['ss_mean', 'ss_var'], ['mu'], LinearAdjustment())
+    >>> import elfi
+    >>> from elfi.examples import gauss
+    >>> m = gauss.get_model()
+    >>> res = elfi.Rejection(m['d'], output_names=['ss_mean', 'ss_var']).sample(1000, bar=False)
+    >>> adj = adjust_posterior(res, m, ['ss_mean', 'ss_var'], ['mu'], LinearAdjustment())
 
     """
     adjustment = _get_adjustment(adjustment)

From 6fae9719609b73fc0ea89db62ccea1ef5d1b16f5 Mon Sep 17 00:00:00 2001
From: Henri Pesonen <henri.e.pesonen@gmail.com>
Date: Mon, 21 Sep 2020 13:12:53 +0200
Subject: [PATCH 3/9] Add networkx2 support (#333)

* Change networkx support from 1.X to 2.X, initial commit

* Modify ELFI-model to follow structure of networkx2.X

* Fix docstring-example for the tests

* Fix docstring-example, again

* Fix graphviz visualization for networkx2.X

* Fix post_processing.py docstring

* Fix post_processing.py docstring
---
 CHANGELOG.rst                        |  1 +
 elfi/client.py                       | 10 ++++++++--
 elfi/compiler.py                     | 25 ++++++++++++-------------
 elfi/executor.py                     | 15 ++++++++-------
 elfi/loader.py                       | 16 ++++++++--------
 elfi/methods/post_processing.py      |  3 ++-
 elfi/methods/utils.py                |  6 ++++--
 elfi/model/elfi_model.py             | 16 ++++++++--------
 elfi/model/graphical_model.py        | 13 ++++++-------
 elfi/visualization/visualization.py  |  6 +++---
 requirements.txt                     |  2 +-
 tests/functional/test_compilation.py |  9 ++++-----
 tests/unit/test_tools.py             |  2 +-
 13 files changed, 66 insertions(+), 58 deletions(-)

diff --git a/CHANGELOG.rst b/CHANGELOG.rst
index 0ced606c..c22a9982 100644
--- a/CHANGELOG.rst
+++ b/CHANGELOG.rst
@@ -1,6 +1,7 @@
 Changelog
 =========
 
+- Change networkx support from 1.X to 2.X
 - Improve docstrings in elfi.methods.bo.acquisition
 - Fix readthedocs-build by adding .readthedocs.yml and restricting the build to
   python3.5, for now
diff --git a/elfi/client.py b/elfi/client.py
index 071cc274..a431cf56 100644
--- a/elfi/client.py
+++ b/elfi/client.py
@@ -159,7 +159,8 @@ def submit(self, batch=None):
         loaded_net = self.client.load_data(self.compiled_net, self.context, batch_index)
         # Override
         for k, v in batch.items():
-            loaded_net.node[k] = {'output': v}
+            loaded_net.nodes[k].update({'output': v})
+            del loaded_net.nodes[k]['operation']
 
         task_id = self.client.submit(loaded_net)
         self._pending_batches[batch_index] = task_id
@@ -299,7 +300,12 @@ def compile(cls, source_net, outputs=None):
             outputs = source_net.nodes()
         if not outputs:
             logger.warning("Compiling for no outputs!")
-        outputs = outputs if isinstance(outputs, list) else [outputs]
+        if isinstance(outputs, list):
+            outputs = set(outputs)
+        elif isinstance(outputs, type(source_net.nodes())):
+            outputs = outputs
+        else:
+            outputs = [outputs]
 
         compiled_net = nx.DiGraph(
             outputs=outputs, name=source_net.graph['name'], observed=source_net.graph['observed'])
diff --git a/elfi/compiler.py b/elfi/compiler.py
index 78d80820..84f4253e 100644
--- a/elfi/compiler.py
+++ b/elfi/compiler.py
@@ -54,8 +54,8 @@ def compile(cls, source_net, compiled_net):
         compiled_net.add_edges_from(source_net.edges(data=True))
 
         # Compile the nodes to computation nodes
-        for name, data in compiled_net.nodes_iter(data=True):
-            state = source_net.node[name]
+        for name, data in compiled_net.nodes(data=True):
+            state = source_net.nodes[name]['attr_dict']
             if '_output' in state and '_operation' in state:
                 raise ValueError("Cannot compile: both _output and _operation present "
                                  "for node '{}'".format(name))
@@ -92,7 +92,7 @@ def compile(cls, source_net, compiled_net):
         uses_observed = []
 
         for node in nx.topological_sort(source_net):
-            state = source_net.node[node]
+            state = source_net.nodes[node]['attr_dict']
             if state.get('_observable'):
                 observable.append(node)
                 cls.make_observed_copy(node, compiled_net)
@@ -113,14 +113,14 @@ def compile(cls, source_net, compiled_net):
                     else:
                         link_parent = parent
 
-                    compiled_net.add_edge(link_parent, obs_node, source_net[parent][node].copy())
+                    compiled_net.add_edge(link_parent, obs_node, **source_net[parent][node].copy())
 
         # Check that there are no stochastic nodes in the ancestors
         for node in uses_observed:
             # Use the observed version to query observed ancestors in the compiled_net
             obs_node = observed_name(node)
             for ancestor_node in nx.ancestors(compiled_net, obs_node):
-                if '_stochastic' in source_net.node.get(ancestor_node, {}):
+                if '_stochastic' in source_net.nodes.get(ancestor_node, {}):
                     raise ValueError("Observed nodes must be deterministic. Observed "
                                      "data depends on a non-deterministic node {}."
                                      .format(ancestor_node))
@@ -148,11 +148,10 @@ def make_observed_copy(cls, node, compiled_net, operation=None):
             raise ValueError("Observed node {} already exists!".format(obs_node))
 
         if operation is None:
-            compiled_dict = compiled_net.node[node].copy()
+            compiled_dict = compiled_net.nodes[node].copy()
         else:
             compiled_dict = dict(operation=operation)
-
-        compiled_net.add_node(obs_node, compiled_dict)
+        compiled_net.add_node(obs_node, **compiled_dict)
         return obs_node
 
 
@@ -176,8 +175,8 @@ def compile(cls, source_net, compiled_net):
         instruction_node_map = dict(_uses_batch_size='_batch_size', _uses_meta='_meta')
 
         for instruction, _node in instruction_node_map.items():
-            for node, d in source_net.nodes_iter(data=True):
-                if d.get(instruction):
+            for node, d in source_net.nodes(data=True):
+                if d['attr_dict'].get(instruction):
                     if not compiled_net.has_node(_node):
                         compiled_net.add_node(_node)
                     compiled_net.add_edge(_node, node, param=_node[1:])
@@ -203,8 +202,8 @@ def compile(cls, source_net, compiled_net):
         logger.debug("{} compiling...".format(cls.__name__))
 
         _random_node = '_random_state'
-        for node, d in source_net.nodes_iter(data=True):
-            if '_stochastic' in d:
+        for node, d in source_net.nodes(data=True):
+            if '_stochastic' in d['attr_dict']:
                 if not compiled_net.has_node(_random_node):
                     compiled_net.add_node(_random_node)
                 compiled_net.add_edge(_random_node, node, param='random_state')
@@ -230,7 +229,7 @@ def compile(cls, source_net, compiled_net):
 
         outputs = compiled_net.graph['outputs']
         output_ancestors = nbunch_ancestors(compiled_net, outputs)
-        for node in compiled_net.nodes():
+        for node in list(compiled_net.nodes()):
             if node not in output_ancestors:
                 compiled_net.remove_node(node)
         return compiled_net
diff --git a/elfi/executor.py b/elfi/executor.py
index f8aca08f..4f20fb6f 100644
--- a/elfi/executor.py
+++ b/elfi/executor.py
@@ -57,7 +57,7 @@ def execute(cls, G):
         order = cls.get_execution_order(G)
 
         for node in order:
-            attr = G.node[node]
+            attr = G.nodes[node]
             logger.debug("Executing {}".format(node))
 
             if attr.keys() >= {'operation', 'output'}:
@@ -67,7 +67,8 @@ def execute(cls, G):
             if 'operation' in attr:
                 op = attr['operation']
                 try:
-                    G.node[node] = cls._run(op, node, G)
+                    G.nodes[node].update(cls._run(op, node, G))
+                    del G.nodes[node]['operation']
                 except Exception as exc:
                     raise exc.__class__("In executing node '{}': {}."
                                         .format(node, exc)).with_traceback(exc.__traceback__)
@@ -77,7 +78,7 @@ def execute(cls, G):
                                  '{}'.format(node))
 
         # Make a result dict based on the requested outputs
-        result = {k: G.node[k]['output'] for k in G.graph['outputs']}
+        result = {k: G.nodes[k]['output'] for k in G.graph['outputs']}
         return result
 
     @classmethod
@@ -104,7 +105,7 @@ def get_execution_order(cls, G):
 
         output_nodes = G.graph['outputs']
         # Filter those output nodes who have an operation to run
-        needed = tuple(sorted(node for node in output_nodes if 'operation' in G.node[node]))
+        needed = tuple(sorted(node for node in output_nodes if 'operation' in G.nodes[node]))
 
         if needed not in cache:
             # Resolve the nodes that need to be executed in the graph
@@ -115,9 +116,9 @@ def get_execution_order(cls, G):
             sort_order = cache['sort_order']
 
             # Resolve the dependencies of needed
-            dep_graph = nx.DiGraph(G.edges())
+            dep_graph = nx.DiGraph(G.edges)
             for node in sort_order:
-                attr = G.node[node]
+                attr = G.nodes[node]
                 if attr.keys() >= {'operation', 'output'}:
                     raise ValueError('Generative graph has both op and output present')
 
@@ -143,7 +144,7 @@ def _run(fn, node, G):
 
         for parent_name in G.predecessors(node):
             param = G[parent_name][node]['param']
-            output = G.node[parent_name]['output']
+            output = G.nodes[parent_name]['output']
             if isinstance(param, int):
                 args.append((param, output))
             else:
diff --git a/elfi/loader.py b/elfi/loader.py
index eae8688e..9b64eb80 100644
--- a/elfi/loader.py
+++ b/elfi/loader.py
@@ -52,7 +52,8 @@ def load(cls, context, compiled_net, batch_index):
             obs_name = observed_name(name)
             if not compiled_net.has_node(obs_name):
                 continue
-            compiled_net.node[obs_name] = dict(output=obs)
+            compiled_net.nodes[obs_name].update(dict(output=obs))
+            del compiled_net.nodes[obs_name]['operation']
 
         del compiled_net.graph['observed']
         return compiled_net
@@ -86,9 +87,8 @@ def load(cls, context, compiled_net, batch_index):
         details = dict(_batch_size=context.batch_size, _meta=meta_dict)
 
         for node, v in details.items():
-            if compiled_net.has_node(node):
-                compiled_net.node[node]['output'] = v
-
+            if node in compiled_net:
+                compiled_net.nodes[node]['output'] = v
         return compiled_net
 
 
@@ -119,12 +119,12 @@ def load(cls, context, compiled_net, batch_index):
             if not compiled_net.has_node(node):
                 continue
             elif node in batch:
-                compiled_net.node[node]['output'] = batch[node]
-                compiled_net.node[node].pop('operation', None)
+                compiled_net.nodes[node]['output'] = batch[node]
+                compiled_net.nodes[node].pop('operation', None)
             elif node not in compiled_net.graph['outputs']:
                 # We are missing this item from the batch so add the output to the
                 # requested outputs so that it can be stored when the results arrive
-                compiled_net.graph['outputs'].append(node)
+                compiled_net.graph['outputs'].add(node)
 
         return compiled_net
 
@@ -173,6 +173,6 @@ def load(cls, context, compiled_net, batch_index):
         # Assign the random state or its acquirer function to the corresponding node
         node_name = '_random_state'
         if compiled_net.has_node(node_name):
-            compiled_net.node[node_name][key] = random_state
+            compiled_net.nodes[node_name][key] = random_state
 
         return compiled_net
diff --git a/elfi/methods/post_processing.py b/elfi/methods/post_processing.py
index 6d410f29..b1efb741 100644
--- a/elfi/methods/post_processing.py
+++ b/elfi/methods/post_processing.py
@@ -242,7 +242,8 @@ def adjust_posterior(sample, model, summary_names, parameter_names=None, adjustm
     >>> import elfi
     >>> from elfi.examples import gauss
     >>> m = gauss.get_model()
-    >>> res = elfi.Rejection(m['d'], output_names=['ss_mean', 'ss_var']).sample(1000, bar=False)
+    >>> res = elfi.Rejection(m['d'], output_names=['ss_mean', 'ss_var'],\
+      batch_size=10).sample(500, bar=False)
     >>> adj = adjust_posterior(res, m, ['ss_mean', 'ss_var'], ['mu'], LinearAdjustment())
 
     """
diff --git a/elfi/methods/utils.py b/elfi/methods/utils.py
index fb0ddcc4..b9374534 100644
--- a/elfi/methods/utils.py
+++ b/elfi/methods/utils.py
@@ -339,7 +339,8 @@ def rvs(self, size=None, random_state=None):
 
         # Change to the correct random_state instance
         # TODO: allow passing random_state to ComputationContext seed
-        loaded_net.node['_random_state'] = {'output': random_state}
+        loaded_net.nodes['_random_state'].update({'output': random_state})
+        del loaded_net.nodes['_random_state']['operation']
 
         batch = self.client.compute(loaded_net)
         rvs = np.column_stack([batch[p] for p in self.parameter_names])
@@ -377,7 +378,8 @@ def _evaluate_pdf(self, x, log=False):
 
         # Override
         for k, v in batch.items():
-            loaded_net.node[k] = {'output': v}
+            loaded_net.nodes[k].update({'output': v})
+            del loaded_net.nodes[k]['operation']
 
         val = self.client.compute(loaded_net)[node]
         if ndim == 0 or (ndim == 1 and self.dim > 1):
diff --git a/elfi/model/elfi_model.py b/elfi/model/elfi_model.py
index 3bbb071b..26edba42 100644
--- a/elfi/model/elfi_model.py
+++ b/elfi/model/elfi_model.py
@@ -304,7 +304,7 @@ def get_reference(self, name):
         name : str
 
         """
-        cls = self.get_node(name)['_class']
+        cls = self.get_node(name)['attr_dict']['_class']
         return cls.reference(name, self)
 
     def get_state(self, name):
@@ -315,7 +315,7 @@ def get_state(self, name):
         name : str
 
         """
-        return self.source_net.node[name]
+        return self.source_net.nodes[name]
 
     def update_node(self, name, updating_name):
         """Update `node` with `updating_node` in the model.
@@ -357,7 +357,7 @@ def remove_node(self, name):
     @property
     def parameter_names(self):
         """Return a list of model parameter names in an alphabetical order."""
-        return sorted([n for n in self.nodes if '_parameter' in self.get_state(n)])
+        return sorted([n for n in self.nodes if '_parameter' in self.get_state(n)['attr_dict']])
 
     @parameter_names.setter
     def parameter_names(self, parameter_names):
@@ -374,7 +374,7 @@ def parameter_names(self, parameter_names):
         """
         parameter_names = set(parameter_names)
         for n in self.nodes:
-            state = self.get_state(n)
+            state = self.get_state(n)['attr_dict']
             if n in parameter_names:
                 parameter_names.remove(n)
                 state['_parameter'] = True
@@ -453,11 +453,11 @@ def state(self):
 
     @property
     def uses_meta(self):
-        return self.state.get('_uses_meta', False)
+        return self.state['attr_dict'].get('_uses_meta', False)
 
     @uses_meta.setter
     def uses_meta(self, val):
-        self.state['_uses_meta'] = True
+        self.state['attr_dict']['_uses_meta'] = val
 
 
 class NodeReference(InstructionsMapper):
@@ -827,7 +827,7 @@ def compile_operation(state):
     @property
     def distribution(self):
         """Return the distribution object."""
-        distribution = self['distribution']
+        distribution = self.state['attr_dict']['distribution']
         if isinstance(distribution, str):
             distribution = scipy_from_str(distribution)
         return distribution
@@ -886,7 +886,7 @@ def __init__(self, distribution, *params, size=None, **kwargs):
 
         """
         super(Prior, self).__init__(distribution, *params, size=size, **kwargs)
-        self['_parameter'] = True
+        self['attr_dict']['_parameter'] = True
 
 
 class Simulator(StochasticMixin, ObservableMixin, NodeReference):
diff --git a/elfi/model/graphical_model.py b/elfi/model/graphical_model.py
index a96eb659..32451316 100644
--- a/elfi/model/graphical_model.py
+++ b/elfi/model/graphical_model.py
@@ -38,8 +38,7 @@ def remove_node(self, name):
 
         # Remove sole private parents
         for p in parent_names:
-            if p[0] == '_' and len(self.source_net.successors(p)) == 0 \
-                    and len(self.source_net.predecessors(p)) == 0:
+            if p[0] == '_' and self.source_net.degree(p) == 0:
                 self.remove_node(p)
 
     def get_node(self, name):
@@ -50,11 +49,11 @@ def get_node(self, name):
         out : dict
 
         """
-        return self.source_net.node[name]
+        return self.source_net.nodes[name]
 
     def set_node(self, name, state):
         """Set the state of the node."""
-        self.source_net.node[name] = state
+        self.source_net.nodes[name] = state
 
     def has_node(self, name):
         """Whether the graph has a node `name`."""
@@ -101,14 +100,14 @@ def update_node(self, node, updating_node):
         updating_node : str
 
         """
-        out_edges = self.source_net.out_edges(node, data=True)
+        out_edges = list(self.source_net.edges(node, data=True))
         self.remove_node(node)
-        self.source_net.add_node(node, self.source_net.node[updating_node])
+        self.source_net.add_node(node, attr_dict=self.source_net.nodes[updating_node]['attr_dict'])
         self.source_net.add_edges_from(out_edges)
 
         # Transfer incoming edges
         for u, v, data in self.source_net.in_edges(updating_node, data=True):
-            self.source_net.add_edge(u, node, data)
+            self.source_net.add_edge(u, node, **data)
 
         self.remove_node(updating_node)
 
diff --git a/elfi/visualization/visualization.py b/elfi/visualization/visualization.py
index bc2d5cd4..bccfd649 100644
--- a/elfi/visualization/visualization.py
+++ b/elfi/visualization/visualization.py
@@ -48,8 +48,8 @@ def nx_draw(G, internal=False, param_names=False, filename=None, format=None):
 
     hidden = set()
 
-    for n, state in G.nodes_iter(data=True):
-        if not internal and n[0] == '_' and state.get('_class') == Constant:
+    for n, state in G.nodes(data=True):
+        if not internal and n[0] == '_' and state['attr_dict'].get('_class') == Constant:
             hidden.add(n)
             continue
         _format = {'shape': 'circle', 'fillcolor': 'gray80', 'style': 'solid'}
@@ -58,7 +58,7 @@ def nx_draw(G, internal=False, param_names=False, filename=None, format=None):
         dot.node(n, **_format)
 
     # add edges to graph
-    for u, v, label in G.edges_iter(data='param', default=''):
+    for u, v, label in G.edges(data='param', default=''):
         if not internal and u in hidden:
             continue
 
diff --git a/requirements.txt b/requirements.txt
index bedbb0bf..b55caf1f 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -2,7 +2,7 @@ numpy>=1.12.1
 scipy>=0.19
 matplotlib>=1.1
 GPy>=1.0.9
-networkX>=1.11,<2.0
+networkX>=2.0
 ipyparallel>=6
 toolz>=0.8
 scikit-learn>=0.18.1
\ No newline at end of file
diff --git a/tests/functional/test_compilation.py b/tests/functional/test_compilation.py
index 1e1e10b3..73537ed5 100644
--- a/tests/functional/test_compilation.py
+++ b/tests/functional/test_compilation.py
@@ -11,7 +11,7 @@ def test_meta_param(ma2):
     # Test that it is passed
     try:
         # Add to state
-        sim['_uses_meta'] = True
+        sim.uses_meta = True
         sim.generate()
         assert False, "Should raise an error"
     except TypeError:
@@ -27,12 +27,11 @@ def bi(meta):
         return meta['batch_index']
 
     # Test the correct batch_index value
-    m = elfi.ElfiModel()
-    op = elfi.Operation(bi, model=m, name='op')
-    op['_uses_meta'] = True
+    op = elfi.Operation(bi, model=ma2, name='op')
+    op.uses_meta = True
     client = elfi.get_client()
     c = elfi.ComputationContext()
-    compiled_net = client.compile(m.source_net, m.nodes)
+    compiled_net = client.compile(ma2.source_net, ma2.nodes)
     loaded_net = client.load_data(compiled_net, c, batch_index=3)
     res = client.compute(loaded_net)
 
diff --git a/tests/unit/test_tools.py b/tests/unit/test_tools.py
index e386867a..aafe2fd4 100644
--- a/tests/unit/test_tools.py
+++ b/tests/unit/test_tools.py
@@ -70,7 +70,7 @@ def test_vectorized_and_external_combined():
     with pytest.raises(Exception):
         sim.generate(3)
 
-    sim['_uses_meta'] = True
+    sim.uses_meta = True
     g = sim.generate(3)
 
     # Test uniqueness of seeds

From 7174eae9da02b36e46da937b50bd23955f88cefa Mon Sep 17 00:00:00 2001
From: Henri Pesonen <henri.e.pesonen@gmail.com>
Date: Wed, 23 Sep 2020 11:10:57 +0200
Subject: [PATCH 4/9] Modify progressbar (#334)

* Initial commit

* Modify progress bar functionality to accommodate roundwise ABC-SMC progress reporting

* Fix lint
---
 CHANGELOG.rst                       |  1 +
 elfi/methods/parameter_inference.py | 15 +++---
 elfi/visualization/visualization.py | 81 ++++++++++++++++++++++++-----
 3 files changed, 76 insertions(+), 21 deletions(-)

diff --git a/CHANGELOG.rst b/CHANGELOG.rst
index c22a9982..71e8275f 100644
--- a/CHANGELOG.rst
+++ b/CHANGELOG.rst
@@ -1,6 +1,7 @@
 Changelog
 =========
 
+- Modify progress bar to better indicate ABC-SMC inference status
 - Change networkx support from 1.X to 2.X
 - Improve docstrings in elfi.methods.bo.acquisition
 - Fix readthedocs-build by adding .readthedocs.yml and restricting the build to
diff --git a/elfi/methods/parameter_inference.py b/elfi/methods/parameter_inference.py
index ad67678a..7e1d3d7e 100644
--- a/elfi/methods/parameter_inference.py
+++ b/elfi/methods/parameter_inference.py
@@ -22,7 +22,7 @@
                                 batch_to_arr2d, ceil_to_batch_size, weighted_var)
 from elfi.model.elfi_model import ComputationContext, ElfiModel, NodeReference
 from elfi.utils import is_array
-from elfi.visualization.visualization import progress_bar
+from elfi.visualization.visualization import ProgressBar
 
 logger = logging.getLogger(__name__)
 
@@ -119,6 +119,8 @@ def __init__(self,
         # inference after an iteration.
         self.state = dict(n_sim=0, n_batches=0)
         self.objective = dict()
+        self.progress_bar = ProgressBar(prefix='Progress', suffix='Complete',
+                                        decimals=1, length=50, fill='=')
 
     @property
     def pool(self):
@@ -256,18 +258,14 @@ def infer(self, *args, vis=None, bar=True, **kwargs):
 
         self.set_objective(*args, **kwargs)
 
-        if bar:
-            progress_bar(0, self._objective_n_batches, prefix='Progress:',
-                         suffix='Complete', length=50)
-
         while not self.finished:
             self.iterate()
             if vis:
                 self.plot_state(interactive=True, **vis_opt)
 
             if bar:
-                progress_bar(self.state['n_batches'], self._objective_n_batches,
-                             prefix='Progress:', suffix='Complete', length=50)
+                self.progress_bar.update_progressbar(self.state['n_batches'],
+                                                     self._objective_n_batches)
 
         self.batches.cancel_pending()
         if vis:
@@ -723,6 +721,9 @@ def prepare_new_batch(self, batch_index):
     def _init_new_round(self):
         round = self.state['round']
 
+        reinit_msg = 'ABC-SMC Round {0} / {1}'.format(round + 1, self.objective['round'] + 1)
+        self.progress_bar.reinit_progressbar(scaling=(self.state['n_batches']),
+                                             reinit_msg=reinit_msg)
         dashes = '-' * 16
         logger.info('%s Starting round %d %s' % (dashes, round, dashes))
 
diff --git a/elfi/visualization/visualization.py b/elfi/visualization/visualization.py
index bccfd649..2109b59d 100644
--- a/elfi/visualization/visualization.py
+++ b/elfi/visualization/visualization.py
@@ -250,15 +250,11 @@ def plot_traces(result, selector=None, axes=None, **kwargs):
     return axes
 
 
-def progress_bar(iteration, total, prefix='', suffix='', decimals=1, length=100, fill='█'):
-    """Progress bar for showing the inference process.
+class ProgressBar:
+    """Progress bar monitoring the inference process.
 
-    Parameters
+    Attributes
     ----------
-    iteration : int, required
-        Current iteration
-    total : int, required
-        Total iterations
     prefix : str, optional
         Prefix string
     suffix : str, optional
@@ -269,15 +265,72 @@ def progress_bar(iteration, total, prefix='', suffix='', decimals=1, length=100,
         Character length of bar
     fill : str, optional
         Bar fill character
+    scaling : int, optional
+        Integer used to scale current iteration and total iterations of the progress bar
 
     """
-    if total > 0:
-        percent = ("{0:." + str(decimals) + "f}").format(100 * (iteration / float(total)))
-        filled_length = int(length * iteration // total)
-        bar = fill * filled_length + '-' * (length - filled_length)
-        print('\r%s |%s| %s%% %s' % (prefix, bar, percent, suffix), end='\r')
-        if iteration == total:
-            print()
+
+    def __init__(self, prefix='', suffix='', decimals=1, length=100, fill='='):
+        """Construct progressbar for monitoring.
+
+        Parameters
+        ----------
+        prefix : str, optional
+            Prefix string
+        suffix : str, optional
+            Suffix string
+        decimals : int, optional
+            Positive number of decimals in percent complete
+        length : int, optional
+            Character length of bar
+        fill : str, optional
+            Bar fill character
+
+        """
+        self.prefix = prefix
+        self.suffix = suffix
+        self.decimals = 1
+        self.length = length
+        self.fill = fill
+        self.scaling = 0
+
+    def update_progressbar(self, iteration, total):
+        """Print updated progress bar in console.
+
+        Parameters
+        ----------
+        iteration : int
+            Integer indicating completed iterations
+        total : int
+            Integer indicating total number of iterations
+
+        """
+        if total - self.scaling > 0:
+            percent = ("{0:." + str(self.decimals) + "f}").\
+                format(100 * ((iteration - self.scaling) / float(total - self.scaling)))
+            filled_length = int(self.length * (iteration - self.scaling) // (total - self.scaling))
+            bar = self.fill * filled_length + '-' * (self.length - filled_length)
+            print('%s [%s] %s%% %s' % (self.prefix, bar, percent, self.suffix), end='\r')
+            if iteration == total:
+                print()
+
+    def reinit_progressbar(self, scaling=0, reinit_msg=None):
+        """Reinitialize new round of progress bar.
+
+        Parameters
+        ----------
+        scaling : int, optional
+            Integer used to scale current and total iterations of the progress bar
+        reinit_msg : str, optional
+            Message printed before restarting an empty progess bar on a new line
+
+        """
+        self.scaling = scaling
+        if scaling == 0:
+            print(reinit_msg)
+        else:
+            self.update_progressbar(scaling + 1, scaling + 1)
+            print('\n' + reinit_msg)
 
 
 def plot_params_vs_node(node, n_samples=100, func=None, seed=None, axes=None, **kwargs):

From 03c53dba558aff722db2928f954aa39c902eb2a0 Mon Sep 17 00:00:00 2001
From: Henri Pesonen <henri.e.pesonen@gmail.com>
Date: Thu, 8 Oct 2020 10:16:38 +0200
Subject: [PATCH 5/9] Update travis tests to py36 (#336)

* Initial commit with updated CHANGELOG.rst

* Add python 3.7 to travis setups and remove python 3.6
---
 .travis.yml   | 6 +++---
 CHANGELOG.rst | 1 +
 2 files changed, 4 insertions(+), 3 deletions(-)

diff --git a/.travis.yml b/.travis.yml
index c531c385..e713a35e 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -2,10 +2,10 @@ matrix:
  include:
   - os: linux
     language: python
-    python: 3.5
+    python: 3.6
   - os: linux
     language: python
-    python: 3.6
+    python: 3.7
   - os: osx
     language: generic
     before_install:
@@ -25,6 +25,6 @@ install:
   - pip install -e .
 
 script:
-  - if [[ "$TRAVIS_OS_NAME" == "linux" ]]; then ipcluster start -n 2 --daemon ; fi
+  - if [[ "$TRAVIS_OS_NAME" == "linux" ]]; then ipcluster start -n 2 --daemonize ; fi
   #- travis_wait 20 make test
   - make test
diff --git a/CHANGELOG.rst b/CHANGELOG.rst
index 71e8275f..1e08c863 100644
--- a/CHANGELOG.rst
+++ b/CHANGELOG.rst
@@ -1,6 +1,7 @@
 Changelog
 =========
 
+- Add python 3.7 to travis tests and remove python 3.5 due to clash with dask
 - Modify progress bar to better indicate ABC-SMC inference status
 - Change networkx support from 1.X to 2.X
 - Improve docstrings in elfi.methods.bo.acquisition

From 3276381c4e0460351c4900780eb6681c4403e1ad Mon Sep 17 00:00:00 2001
From: Jan Kokko <jan.kokko@helsinki.fi>
Date: Thu, 8 Oct 2020 14:40:06 +0300
Subject: [PATCH 6/9] Add dask client (#335)

* Add dask client

* Update CHANGELOG.rst

Co-authored-by: Henri Pesonen <henri.e.pesonen@gmail.com>
---
 .gitignore           |   5 +-
 CHANGELOG.rst        |   1 +
 elfi/clients/dask.py | 111 +++++++++++++++++++++++++++++++++++++++++++
 requirements.txt     |   3 +-
 tests/conftest.py    |   3 +-
 5 files changed, 120 insertions(+), 3 deletions(-)
 create mode 100644 elfi/clients/dask.py

diff --git a/.gitignore b/.gitignore
index 619ec5c4..2f0a619d 100644
--- a/.gitignore
+++ b/.gitignore
@@ -107,4 +107,7 @@ ENV/
 notebooks/mydask.png
 
 # vscode-settings
-.vscode
\ No newline at end of file
+.vscode
+
+# dask
+dask-worker-space
diff --git a/CHANGELOG.rst b/CHANGELOG.rst
index 1e08c863..dd50f5fd 100644
--- a/CHANGELOG.rst
+++ b/CHANGELOG.rst
@@ -1,6 +1,7 @@
 Changelog
 =========
 
+- Add dask support to elfi client options
 - Add python 3.7 to travis tests and remove python 3.5 due to clash with dask
 - Modify progress bar to better indicate ABC-SMC inference status
 - Change networkx support from 1.X to 2.X
diff --git a/elfi/clients/dask.py b/elfi/clients/dask.py
new file mode 100644
index 00000000..2a84303c
--- /dev/null
+++ b/elfi/clients/dask.py
@@ -0,0 +1,111 @@
+"""This module implements a multiprocessing client using dask."""
+
+import itertools
+import os
+
+from dask.distributed import Client as DaskClient
+
+import elfi.client
+
+
+def set_as_default():
+    """Set this as the default client."""
+    elfi.client.set_client()
+    elfi.client.set_default_class(Client)
+
+
+class Client(elfi.client.ClientBase):
+    """A multiprocessing client using dask."""
+
+    def __init__(self):
+        """Initialize a dask client."""
+        self.dask_client = DaskClient()
+        self.tasks = {}
+        self._id_counter = itertools.count()
+
+    def apply(self, kallable, *args, **kwargs):
+        """Add `kallable(*args, **kwargs)` to the queue of tasks. Returns immediately.
+
+        Parameters
+        ----------
+        kallable: callable
+
+        Returns
+        -------
+        task_id: int
+
+        """
+        task_id = self._id_counter.__next__()
+        async_result = self.dask_client.submit(kallable, *args, **kwargs)
+        self.tasks[task_id] = async_result
+        return task_id
+
+    def apply_sync(self, kallable, *args, **kwargs):
+        """Call and returns the result of `kallable(*args, **kwargs)`.
+
+        Parameters
+        ----------
+        kallable: callable
+
+        """
+        return self.dask_client.run_on_scheduler(kallable, *args, **kwargs)
+
+    def get_result(self, task_id):
+        """Return the result from task identified by `task_id` when it arrives.
+
+        Parameters
+        ----------
+        task_id: int
+        
+        Returns
+        -------
+        dict
+        
+        """
+        async_result = self.tasks.pop(task_id)
+        return async_result.result()
+
+    def is_ready(self, task_id):
+        """Return whether task with identifier `task_id` is ready.
+
+        Parameters
+        ----------
+        task_id: int
+
+        Returns
+        -------
+        bool
+
+        """
+        return self.tasks[task_id].done()
+
+    def remove_task(self, task_id):
+        """Remove task with identifier `task_id` from scheduler.
+
+        Parameters
+        ----------
+        task_id: int
+
+        """
+        async_result = self.tasks.pop(task_id)
+        if not async_result.done():
+            async_result.cancel()
+
+    def reset(self):
+        """Stop all worker processes immediately and clear pending tasks."""
+        self.dask_client.shutdown()
+        self.tasks.clear()
+
+    @property
+    def num_cores(self):
+        """Return the number of processes.
+
+        Returns
+        -------
+        int
+
+        """
+        return os.cpu_count()
+
+
+set_as_default()
diff --git a/requirements.txt b/requirements.txt
index b55caf1f..601a32d1 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,3 +1,4 @@
+dask[distributed]>=1.0.0,<3.0.0
 numpy>=1.12.1
 scipy>=0.19
 matplotlib>=1.1
@@ -5,4 +6,4 @@ GPy>=1.0.9
 networkX>=2.0
 ipyparallel>=6
 toolz>=0.8
-scikit-learn>=0.18.1
\ No newline at end of file
+scikit-learn>=0.18.1
diff --git a/tests/conftest.py b/tests/conftest.py
index 8482193c..d1b81ecb 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -7,6 +7,7 @@
 import scipy.stats as ss
 
 import elfi
+import elfi.clients.dask as dask
 import elfi.clients.ipyparallel as eipp
 import elfi.clients.multiprocessing as mp
 import elfi.clients.native as native
@@ -31,7 +32,7 @@ def pytest_addoption(parser):
 """Functional fixtures"""
 
 
-@pytest.fixture(scope="session", params=[eipp, mp, native])
+@pytest.fixture(scope="session", params=[eipp, dask, mp, native])
 def client(request):
     """Provides a fixture for all the different supported clients
     """

From 5f05076b0de365519b4a46eefd75af704e5bcfb5 Mon Sep 17 00:00:00 2001
From: Henri Pesonen <henri.e.pesonen@gmail.com>
Date: Thu, 8 Oct 2020 14:09:11 +0200
Subject: [PATCH 7/9] Update docs to default py36 (#337)

* Initial commit with CHANGELOG.rst

* Change python 3.6 as the default version in docs
---
 CHANGELOG.rst         |  1 +
 CONTRIBUTING.rst      |  2 +-
 README.md             |  4 ++--
 docs/installation.rst | 10 +++++-----
 docs/quickstart.rst   |  2 +-
 5 files changed, 10 insertions(+), 9 deletions(-)

diff --git a/CHANGELOG.rst b/CHANGELOG.rst
index dd50f5fd..cdac58fe 100644
--- a/CHANGELOG.rst
+++ b/CHANGELOG.rst
@@ -1,6 +1,7 @@
 Changelog
 =========
 
+- Update documentation to setting python 3.6 as default
 - Add dask support to elfi client options
 - Add python 3.7 to travis tests and remove python 3.5 due to clash with dask
 - Modify progress bar to better indicate ABC-SMC inference status
diff --git a/CONTRIBUTING.rst b/CONTRIBUTING.rst
index b48cc59e..faa969c2 100644
--- a/CONTRIBUTING.rst
+++ b/CONTRIBUTING.rst
@@ -78,7 +78,7 @@ Ready to contribute? Here's how to set up `ELFI` for local development.
    environment. You may need to replace "3.5" in the first line with the python
    version printed in the previous step::
 
-    $ conda create -n elfi python=3.5 numpy
+    $ conda create -n elfi python=3.6 numpy
     $ source activate elfi
     $ cd elfi
     $ make dev
diff --git a/README.md b/README.md
index 15eac0bd..f0fe99bf 100644
--- a/README.md
+++ b/README.md
@@ -40,7 +40,7 @@ is preferable.
 Installation
 ------------
 
-ELFI requires Python 3.5 or greater. You can install ELFI by typing in your terminal:
+ELFI requires Python 3.6 or greater. You can install ELFI by typing in your terminal:
 
 ```
 pip install elfi
@@ -70,7 +70,7 @@ with your default Python environment and can easily use different versions of Py
 in different projects. You can create a virtual environment for ELFI using anaconda with:
 
 ```
-conda create -n elfi python=3.5 numpy
+conda create -n elfi python=3.6 numpy
 source activate elfi
 pip install elfi
 ```
diff --git a/docs/installation.rst b/docs/installation.rst
index 27b888f0..c891e938 100644
--- a/docs/installation.rst
+++ b/docs/installation.rst
@@ -3,7 +3,7 @@
 Installation
 ============
 
-ELFI requires Python 3.5 or greater (see below how to install). To install ELFI, simply
+ELFI requires Python 3.6 or greater (see below how to install). To install ELFI, simply
 type in your terminal:
 
 .. code-block:: console
@@ -18,16 +18,16 @@ process.
 .. _Python installation guide: http://docs.python-guide.org/en/latest/starting/installation/
 
 
-Installing Python 3.5
+Installing Python 3.6
 ---------------------
 
 If you are new to Python, perhaps the simplest way to install it is with Anaconda_ that
-manages different Python versions. After installing Anaconda, you can create a Python 3.5.
+manages different Python versions. After installing Anaconda, you can create a Python 3.6.
 environment with ELFI:
 
 .. code-block:: console
 
-    conda create -n elfi python=3.5 numpy
+    conda create -n elfi python=3.6 numpy
     source activate elfi
     pip install elfi
 
@@ -51,7 +51,7 @@ Resolving these may sometimes go wrong:
 * If you receive an error about missing ``numpy``, please install it first.
 * If you receive an error about `yaml.load`, install ``pyyaml``.
 * On OS X with Anaconda virtual environment say `conda install python.app` and then use `pythonw` instead of `python`.
-* Note that ELFI requires Python 3.5 or greater
+* Note that ELFI requires Python 3.6 or greater
 * In some environments ``pip`` refers to Python 2.x, and you have to use ``pip3`` to use the Python 3.x version
 * Make sure your Python installation meets the versions listed in requirements_.
 
diff --git a/docs/quickstart.rst b/docs/quickstart.rst
index 3a81fa4e..f643f26c 100644
--- a/docs/quickstart.rst
+++ b/docs/quickstart.rst
@@ -3,7 +3,7 @@ Quickstart
 
 First ensure you have
 `installed <http://elfi.readthedocs.io/en/stable/installation.html>`__
-Python 3.5 (or greater) and ELFI. After installation you can start using
+Python 3.6 (or greater) and ELFI. After installation you can start using
 ELFI:
 
 .. code:: ipython3

From 305d09b823b2763bcd7d113df56a45cb263d4a53 Mon Sep 17 00:00:00 2001
From: Henri Pesonen <henri.e.pesonen@gmail.com>
Date: Mon, 12 Oct 2020 13:45:10 +0200
Subject: [PATCH 8/9] Update docs (#338)

* Initial commit

* Change python 3.5 references to 3.6
---
 CHANGELOG.rst    | 1 +
 CONTRIBUTING.rst | 4 ++--
 setup.py         | 2 +-
 3 files changed, 4 insertions(+), 3 deletions(-)

diff --git a/CHANGELOG.rst b/CHANGELOG.rst
index cdac58fe..22dbbacc 100644
--- a/CHANGELOG.rst
+++ b/CHANGELOG.rst
@@ -1,6 +1,7 @@
 Changelog
 =========
 
+- Update info to reflect setting python 3.6 as the default version
 - Update documentation to setting python 3.6 as default
 - Add dask support to elfi client options
 - Add python 3.7 to travis tests and remove python 3.5 due to clash with dask
diff --git a/CONTRIBUTING.rst b/CONTRIBUTING.rst
index faa969c2..61735334 100644
--- a/CONTRIBUTING.rst
+++ b/CONTRIBUTING.rst
@@ -75,7 +75,7 @@ Ready to contribute? Here's how to set up `ELFI` for local development.
    $ python -V
 
 4. Install your local copy and the development requirements into a conda
-   environment. You may need to replace "3.5" in the first line with the python
+   environment. You may need to replace "3.6" in the first line with the python
    version printed in the previous step::
 
     $ conda create -n elfi python=3.6 numpy
@@ -127,7 +127,7 @@ Before you submit a pull request, check that it meets these guidelines:
 2. If the pull request adds functionality, the docs should be updated. Put
    your new functionality into a function with a docstring, and add the
    feature to the list in README.rst.
-3. The pull request should work for Python 3.5 and later. Check
+3. The pull request should work for Python 3.6 and later. Check
    https://travis-ci.org/elfi-dev/elfi/pull_requests
    and make sure that the tests pass for all supported Python versions.
 
diff --git a/setup.py b/setup.py
index 5c983047..22825b64 100644
--- a/setup.py
+++ b/setup.py
@@ -31,7 +31,7 @@
     long_description=(open('docs/description.rst').read()),
     license='BSD',
     classifiers=[
-        'Programming Language :: Python :: 3.5', 'Topic :: Scientific/Engineering',
+        'Programming Language :: Python :: 3.6', 'Topic :: Scientific/Engineering',
         'Topic :: Scientific/Engineering :: Artificial Intelligence',
         'Topic :: Scientific/Engineering :: Bio-Informatics',
         'Topic :: Scientific/Engineering :: Mathematics', 'Operating System :: OS Independent',

From 9ae457fe4fddd93d5777d02380a934ebde774b0c Mon Sep 17 00:00:00 2001
From: Henri Pesonen <henri.e.pesonen@gmail.com>
Date: Mon, 12 Oct 2020 15:07:31 +0200
Subject: [PATCH 9/9] Bump version (#339)

* Initial commit with CHANGELOG.rst

* Modify relevant files for new release

* Modify post-processing dosctring

* Fix postprocessing multiple-line docstring example
---
 CHANGELOG.rst                   | 3 +++
 README.md                       | 2 +-
 elfi/__init__.py                | 2 +-
 elfi/clients/dask.py            | 4 ++--
 elfi/methods/post_processing.py | 4 ++--
 5 files changed, 9 insertions(+), 6 deletions(-)

diff --git a/CHANGELOG.rst b/CHANGELOG.rst
index 22dbbacc..0bc4ea54 100644
--- a/CHANGELOG.rst
+++ b/CHANGELOG.rst
@@ -1,6 +1,9 @@
 Changelog
 =========
 
+
+0.7.7 (2020-10-12)
+------------------
 - Update info to reflect setting python 3.6 as the default version
 - Update documentation to setting python 3.6 as default
 - Add dask support to elfi client options
diff --git a/README.md b/README.md
index f0fe99bf..1f5c3848 100644
--- a/README.md
+++ b/README.md
@@ -1,4 +1,4 @@
-**Version 0.7.6 released!** See the [CHANGELOG](CHANGELOG.rst) and [notebooks](https://github.com/elfi-dev/notebooks).
+**Version 0.7.7 released!** See the [CHANGELOG](CHANGELOG.rst) and [notebooks](https://github.com/elfi-dev/notebooks).
 
 **NOTE:** For the time being NetworkX 2 is incompatible with ELFI.
 
diff --git a/elfi/__init__.py b/elfi/__init__.py
index 6a5c7a15..7f854c2d 100644
--- a/elfi/__init__.py
+++ b/elfi/__init__.py
@@ -26,4 +26,4 @@
 __email__ = 'elfi-support@hiit.fi'
 
 # make sure __version_ is on the last non-empty line (read by setup.py)
-__version__ = '0.7.6'
+__version__ = '0.7.7'
diff --git a/elfi/clients/dask.py b/elfi/clients/dask.py
index 2a84303c..134d6a13 100644
--- a/elfi/clients/dask.py
+++ b/elfi/clients/dask.py
@@ -56,11 +56,11 @@ def get_result(self, task_id):
         Parameters
         ----------
         task_id: int
-        
+
         Returns
         -------
         dict
-        
+
         """
         async_result = self.tasks.pop(task_id)
         return async_result.result()
diff --git a/elfi/methods/post_processing.py b/elfi/methods/post_processing.py
index b1efb741..b4c9a39c 100644
--- a/elfi/methods/post_processing.py
+++ b/elfi/methods/post_processing.py
@@ -242,8 +242,8 @@ def adjust_posterior(sample, model, summary_names, parameter_names=None, adjustm
     >>> import elfi
     >>> from elfi.examples import gauss
     >>> m = gauss.get_model()
-    >>> res = elfi.Rejection(m['d'], output_names=['ss_mean', 'ss_var'],\
-      batch_size=10).sample(500, bar=False)
+    >>> res = elfi.Rejection(m['d'], output_names=['ss_mean', 'ss_var'],
+    ...                      batch_size=10).sample(500, bar=False)
     >>> adj = adjust_posterior(res, m, ['ss_mean', 'ss_var'], ['mu'], LinearAdjustment())
 
     """