v0.15.2

See CHANGELOG.md for a complete list of changes

.dockerignore

@@ -0,0 +1,5 @@
+**/soil_output
+.*
+**/__pycache__
+__pycache__
+*.pyc

.gitlab-ci.yml

@@ -0,0 +1,28 @@
+stages:
+  - test
+  - build
+
+build:
+  stage: build
+  image:
+    name: gcr.io/kaniko-project/executor:debug
+    entrypoint: [""]
+  tags:
+    - docker
+  script:
+    - echo "{\"auths\":{\"$CI_REGISTRY\":{\"username\":\"$CI_REGISTRY_USER\",\"password\":\"$CI_REGISTRY_PASSWORD\"}}}" > /kaniko/.docker/config.json
+    # The skip-tls-verify flag is there because our registry certificate is self signed
+    - /kaniko/executor --context $CI_PROJECT_DIR --skip-tls-verify --dockerfile $CI_PROJECT_DIR/Dockerfile --destination $CI_REGISTRY_IMAGE:$CI_COMMIT_TAG
+  only:
+    - tags
+
+
+test:
+  except:
+    - tags  # Avoid running tests for tags, because they are already run for the branch
+  tags:
+    - docker
+  image: python:3.7
+  stage: test
+  script:
+    - python setup.py test

CHANGELOG.md

@@ -0,0 +1,111 @@
+# Changelog
+All notable changes to this project will be documented in this file.
+
+The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/), and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
+
+## [0.15.2]
+### Fixed
+* Pass the right known_modules and parameters to stats discovery in simulation
+* The configuration file must exist when launching through the CLI. If it doesn't, an error will be logged
+* Minor changes in the documentation of the CLI arguments
+### Changed
+* Stats are now exported by default
+## [0.15.1]
+### Added
+* read-only `History`
+### Fixed
+* Serialization problem with the `Environment` on parallel mode.
+* Analysis functions now work as they should in the tutorial
+## [0.15.0]
+### Added
+* Control logging level in CLI and simulation
+* `Stats` to calculate trial and simulation-wide statistics
+* Simulation statistics are stored in a separate table in history (see `History.get_stats` and `History.save_stats`, as well as `soil.stats`)
+* Aliased `NetworkAgent.G` to `NetworkAgent.topology`.
+### Changed
+* Templates in config files can be given as dictionaries in addition to strings
+* Samplers are used more explicitly
+* Removed nxsim dependency. We had already made a lot of changes, and nxsim has not been updated in 5 years.
+* Exporter methods renamed to `trial` and `end`. Added `start`.
+* `Distribution` exporter now a stats class
+* `global_topology` renamed to `topology`
+* Moved topology-related methods to `NetworkAgent`
+### Fixed
+* Temporary files used for history in dry_run mode are not longer left open 
+
+## [0.14.9]
+### Changed
+* Seed random before environment initialization
+## [0.14.8]
+### Fixed
+* Invalid directory names in Windows gsi-upm/soil#5
+## [0.14.7]
+### Changed
+* Minor change to traceback handling in async simulations
+### Fixed
+* Incomplete example in the docs (example.yml) caused an exception
+## [0.14.6]
+### Fixed
+* Bug with newer versions of networkx (0.24) where the Graph.node attribute has been removed. We have updated our calls, but the code in nxsim is not under our control, so we have pinned the networkx version until that issue is solved.
+### Changed
+* Explicit yaml.SafeLoader to avoid deprecation warnings when using yaml.load. It should not break any existing setups, but we could move to the FullLoader in the future if needed.
+
+## [0.14.4]
+### Fixed
+* Bug in `agent.get_agents()` when `state_id` is passed as a string. The tests have been modified accordingly.
+## [0.14.3]
+### Fixed
+* Incompatibility with py3.3-3.6 due to ModuleNotFoundError and TypeError in DryRunner
+## [0.14.2]
+### Fixed
+* Output path for exporters is now soil_output
+### Changed 
+* CSV output to stdout in dry_run mode
+## [0.14.1]
+### Changed
+* Exporter names in lower case
+* Add default exporter in runs
+## [0.14.0]
+### Added
+* Loading configuration from template definitions in the yaml, in preparation for SALib support.
+The definition of the variables and their possible values (i.e., a problem in SALib terms), as well as a sampler function, can be provided.
+Soil uses this definition and the template to generate a set of configurations.
+* Simulation group names, to link related simulations. For now, they are only used to group all simulations in the same group under the same folder.
+* Exporters unify exporting/dumping results and other files to disk. If `dry_run` is set to `True`, exporters will write to stdout instead of a file (useful for testing/debugging).
+* Distribution exporter, to write statistics about values and value_counts in every simulation. The results are dumped to two CSV files.
+
+### Changed
+* `dir_path` is now the directory for resources (modules, files)
+* Environments and simulations do not export or write anything by default. That task is delegated to Exporters
+
+### Removed
+* The output dir for environments and simulations (see Exporters)
+* DrawingAgent, because it wrote to disk and was not being used. We provide a partial alternative in the form of the GraphDrawing exporter. A complete alternative will be provided once the network at each state can be accessed by exporters.
+
+## Fixed
+* Modules with custom agents/environments failed to load when they were run from outside the directory of the definition file. Modules are now loaded from the directory of the simulation file in addition to the working directory
+* Memory databases (in history) can now be shared between threads.
+* Testing all examples, not just subdirectories
+
+## [0.13.8]
+### Changed
+* Moved TerroristNetworkModel to examples
+### Added
+* `get_agents` and `count_agents` methods now accept lists as inputs. They can be used to retrieve agents from node ids
+* `subgraph` in BaseAgent
+* `agents.select` method, to filter out agents
+* `skip_test` property in yaml definitions, to force skipping some examples
+* `agents.Geo`, with a search function based on postition
+* `BaseAgent.ego_search` to get nodes from the ego network of a node
+* `BaseAgent.degree` and `BaseAgent.betweenness`
+### Fixed
+
+## [0.13.7]
+### Changed
+* History now defaults to not backing up! This makes it more intuitive to load the history for examination, at the expense of rewriting something. That should not happen because History is only created in the Environment, and that has `backup=True`.
+### Added
+* Agent names are assigned based on their agent types
+* Agent logging uses the agent name.
+* FSM agents can now return a timeout in addition to a new state. e.g. `return self.idle, self.env.timeout(2)` will execute the *different_state* in 2 *units of time* (`t_step=now+2`).
+* Example of using timeouts in FSM (custom_timeouts)
+* `network_agents` entries may include an `ids` entry. If set, it should be a list of node ids that should be assigned that agent type. This complements the previous behavior of setting agent type with `weights`.

Dockerfile

@@ -1,3 +1,12 @@
-FROM python:3.4-onbuild
+FROM python:3.7
+
+WORKDIR /usr/src/app
+
+COPY test-requirements.txt requirements.txt /usr/src/app/
+RUN pip install --no-cache-dir -r test-requirements.txt -r requirements.txt
+
+COPY ./ /usr/src/app
+
+RUN pip install '.[web]'
 
 ENTRYPOINT ["python", "-m", "soil"]

MANIFEST.in

@@ -1,4 +1,7 @@
 include requirements.txt
 include test-requirements.txt
 include README.rst
-graft soil
+graft soil
+global-exclude __pycache__
+global-exclude soil_output
+global-exclude *.py[co]

Makefile

@@ -0,0 +1,7 @@
+quick-test:
+	docker-compose exec dev python -m pytest -s -v
+
+test:
+	docker run -t -v $$PWD:/usr/src/app -w /usr/src/app python:3.7 python setup.py test
+
+.PHONY: test

README.md

@@ -30,5 +30,5 @@ If you use Soil in your research, don't forget to cite this paper:
 
 @Copyright GSI - Universidad Politécnica de Madrid 2017
 
-[![SOIL](logo_gsi.png)](https://www.gsi.dit.upm.es)
+[![SOIL](logo_gsi.png)](https://www.gsi.upm.es)
 

docker-compose.yml

@@ -2,7 +2,11 @@ version: '3'
 services:
   dev:
     build: .
+    environment:
+      PYTHONDONTWRITEBYTECODE: 1
     volumes:
       - .:/usr/src/app
     tty: true
     entrypoint: /bin/bash
+    ports:
+      - '8001:8001'

docs/conf.py

@@ -31,7 +31,7 @@
 # Add any Sphinx extension module names here, as strings. They can be
 # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
 # ones.
-extensions = []
+extensions = ['IPython.sphinxext.ipython_console_highlighting']
 
 # Add any paths that contain templates here, relative to this directory.
 templates_path = ['_templates']
@@ -69,7 +69,7 @@ language = None
 # List of patterns, relative to source directory, that match files and
 # directories to ignore when looking for source files.
 # This patterns also effect to html_static_path and html_extra_path
-exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']
+exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store', '**.ipynb_checkpoints']
 
 # The name of the Pygments (syntax highlighting) style to use.
 pygments_style = 'sphinx'

docs/configuration.rst

@@ -0,0 +1,241 @@
+Configuring a simulation
+------------------------
+
+There are two ways to configure a simulation: programmatically and with a configuration file.
+In both cases, the parameters used are the same.
+The advantage of a configuration file is that it is a clean declarative description, and it makes it easier to reproduce.
+
+Simulation configuration files can be formatted in ``json`` or ``yaml`` and they define all the parameters of a simulation.
+Here's an example (``example.yml``).
+
+.. literalinclude:: example.yml
+   :language: yaml
+
+
+This example configuration will run three trials (``num_trials``) of a simulation containing a randomly generated network (``network_params``).
+The 100 nodes in the network will be SISaModel agents (``network_agents.agent_type``), which is an agent behavior that is included in Soil.
+10% of the agents (``weight=1``) will start in the content state, 10% in the discontent state, and the remaining 80% (``weight=8``) in the neutral state.
+All agents will have access to the environment (``environment_params``), which only contains one variable, ``prob_infected``.
+The state of the agents will be updated every 2 seconds (``interval``).
+
+Now run the simulation with the command line tool:
+
+.. code:: bash
+
+   soil example.yml
+
+Once the simulation finishes, its results will be stored in a folder named ``MyExampleSimulation``.
+Three types of objects are saved by default: a pickle of the simulation; a ``YAML`` representation of the simulation (which can be used to re-launch it); and for every trial, a ``sqlite`` file with the content of the state of every network node and the environment parameters at every step of the simulation.
+
+
+.. code::
+
+    soil_output
+    └── MyExampleSimulation
+        ├── MyExampleSimulation.dumped.yml
+        ├── MyExampleSimulation.simulation.pickle
+        ├── MyExampleSimulation_trial_0.db.sqlite
+        ├── MyExampleSimulation_trial_1.db.sqlite
+        └── MyExampleSimulation_trial_2.db.sqlite
+
+
+You may also ask soil to export the states in a ``csv`` file, and the network in gephi format (``gexf``).
+
+Network
+=======
+
+The network topology for the simulation can be loaded from an existing network file or generated with one of the random network generation methods from networkx.
+
+Loading a network
+#################
+
+To load an existing network, specify its path in the configuration:
+
+.. code:: yaml
+
+   ---
+   network_params:
+      path: /tmp/mynetwork.gexf
+
+Soil will try to guess what networkx method to use to read the file based on its extension.
+However, we only test using ``gexf`` files.
+
+For simple networks, you may also include them in the configuration itself using , using the ``topology`` parameter like so:
+
+.. code:: yaml
+
+   ---
+   topology:
+       nodes:
+          - id: First
+          - id: Second
+       links:
+          - source: First
+            target: Second
+
+
+Generating a random network
+###########################
+
+To generate a random network using one of networkx's built-in methods, specify the `graph generation algorithm <https://networkx.github.io/documentation/development/reference/generators.html>`_ and other parameters.
+For example, the following configuration is equivalent to :code:`nx.complete_graph(n=100)`:
+
+.. code:: yaml
+
+    network_params:
+        generator: complete_graph
+        n: 100
+
+Environment
+============
+The environment is the place where the shared state of the simulation is stored.
+For instance, the probability of disease outbreak.
+The configuration file may specify the initial value of the environment parameters:
+
+.. code:: yaml
+
+    environment_params:
+        daily_probability_of_earthquake: 0.001
+        number_of_earthquakes: 0
+
+All agents have access to the environment parameters.
+
+In some scenarios, it is useful to have a custom environment, to provide additional methods or to control the way agents update environment state.
+For example, if our agents play the lottery, the environment could provide a method to decide whether the agent wins, instead of leaving it to the agent.
+
+
+Agents
+======
+Agents are a way of modelling behavior.
+Agents can be characterized with two variables: agent type (``agent_type``) and state.
+Only one agent is executed at a time (generally, every ``interval`` seconds), and it has access to its state and the environment parameters.
+Through the environment, it can access the network topology and the state of other agents.
+
+There are three three types of agents according to how they are added to the simulation: network agents and environment agent.
+
+Network Agents
+##############
+Network agents are attached to a node in the topology.
+The configuration file allows you to specify how agents will be mapped to topology nodes.
+
+The simplest way is to specify a single type of agent.
+Hence, every node in the network will be associated to an agent of that type.
+
+.. code:: yaml
+
+   agent_type: SISaModel
+
+It is also possible to add more than one type of agent to the simulation, and to control the ratio of each type (using the ``weight`` property).
+For instance, with following configuration, it is five times more likely for a node to be assigned a CounterModel type than a SISaModel type.
+
+.. code:: yaml
+
+    network_agents:
+          - agent_type: SISaModel
+            weight: 1
+          - agent_type: CounterModel
+            weight: 5
+
+The third option is to specify the type of agent on the node itself, e.g.:
+
+
+.. code:: yaml
+
+   topology:
+       nodes:
+           - id: first
+   agent_type: BaseAgent
+   states:
+       first:
+         agent_type: SISaModel
+          
+
+This would also work with a randomly generated network:
+
+
+.. code:: yaml
+
+   network:
+       generator: complete
+       n: 5
+   agent_type: BaseAgent
+   states:
+       - agent_type: SISaModel
+
+              
+
+In addition to agent type, you may add a custom initial state to the distribution.
+This is very useful to add the same agent type with different states.
+e.g., to populate the network with SISaModel, roughly 10% of them with a discontent state:
+
+.. code:: yaml
+
+    network_agents:
+        - agent_type: SISaModel
+            weight: 9
+            state:
+            id: neutral
+        - agent_type: SISaModel
+            weight: 1
+            state:
+            id: discontent
+
+Lastly, the configuration may include initial state for one or more nodes.
+For instance, to add a state for the two nodes in this configuration:
+
+.. code:: yaml
+
+   agent_type: SISaModel
+   network:
+      generator: complete_graph
+      n: 2
+   states:
+     - id: content
+     - id: discontent
+
+
+Or to add state only to specific nodes (by ``id``).
+For example, to apply special skills to Linux Torvalds in a simulation:
+
+.. literalinclude:: ../examples/torvalds.yml
+   :language: yaml
+
+
+Environment Agents
+##################
+In addition to network agents, more agents can be added to the simulation.
+These agents are programmed in much the same way as network agents, the only difference is that they will not be assigned to network nodes.
+
+
+.. code::
+
+   environment_agents:
+       - agent_type: MyAgent
+         state:
+           mood: happy
+       - agent_type: DummyAgent
+
+
+You may use environment agents to model events that a normal agent cannot control, such as natural disasters or chance.
+They are also useful to add behavior that has little to do with the network and the interactions within that network.
+
+Templating
+==========
+
+Sometimes, it is useful to parameterize a simulation and run it over a range of values in order to compare each run and measure the effect of those parameters in the simulation.
+For instance, you may want to run a simulation with different agent distributions.
+
+This can be done in Soil using **templates**.
+A template is a configuration where some of the values are specified with a variable.
+e.g.,  ``weight: "{{ var1 }}"`` instead of ``weight: 1``.
+There are two types of variables, depending on how their values are decided:
+
+* Fixed. A list of values is provided, and a new simulation is run for each possible value. If more than a variable is given, a new simulation will be run per combination of values.
+* Bounded/Sampled. The bounds of the variable are provided, along with a sampler method, which will be used to compute all the configuration combinations.
+
+When fixed and bounded variables are mixed, Soil generates a new configuration per combination of fixed values and bounded values.
+
+Here is an example with a single fixed variable and two bounded variable:
+
+.. literalinclude:: ../examples/template.yml
+   :language: yaml

docs/example.yml

@@ -0,0 +1,35 @@
+---
+name: MyExampleSimulation
+max_time: 50
+num_trials: 3
+interval: 2
+network_params:
+    generator: barabasi_albert_graph
+    n: 100
+    m: 2
+network_agents:
+    - agent_type: SISaModel
+      weight: 1
+      state:
+        id: content
+    - agent_type: SISaModel
+      weight: 1
+      state:
+        id: discontent
+    - agent_type: SISaModel
+      weight: 8
+      state:
+        id: neutral
+environment_params:
+    prob_infect: 0.075
+    neutral_discontent_spon_prob: 0.1
+    neutral_discontent_infected_prob: 0.3
+    neutral_content_spon_prob: 0.3
+    neutral_content_infected_prob: 0.4
+    discontent_neutral: 0.5
+    discontent_content: 0.5
+    variance_d_c: 0.2
+    content_discontent: 0.2
+    variance_c_d: 0.2
+    content_neutral: 0.2
+    standard_variance: 1

docs/index.rst

@@ -6,7 +6,7 @@
 Welcome to Soil's documentation!
 ================================
 
-Soil is an Agent-based Social Simulator in Python for modelling and simulation of Social Networks.
+Soil is an Agent-based Social Simulator in Python focused on Social Networks.
 
 If you use Soil in your research, do not forget to cite this paper:
 
@@ -39,6 +39,7 @@ If you use Soil in your research, do not forget to cite this paper:
 
    installation
    quickstart
+   configuration
    Tutorial <soil_tutorial>
 
 ..

docs/installation.rst

@@ -14,11 +14,11 @@ Now test that it worked by running the command line tool
 
    soil --help
 
-Or using soil programmatically:
+Or, if you're using using soil programmatically:
 
 .. code:: python
 
    import soil
    print(soil.__version__)
 
-The latest version can be installed through `GitLab <https://lab.cluster.gsi.dit.upm.es/soil/soil.git>`_.
+The latest version can be installed through `GitLab <https://lab.gsi.upm.es/soil/soil.git>`_ or `GitHub <https://github.com/gsi-upm/soil>`_.

docs/quickstart.rst

@@ -1,197 +1,93 @@
 Quickstart
 ----------
 
-This section shows how to run simulations from simulation configuration files.
-First of all, you need to install the package (See :doc:`installation`)
+This section shows how to run your first simulation with Soil.
+For installation instructions, see :doc:`installation`.
 
-Simulation configuration files are ``json`` or ``yaml`` files that define all the parameters of a simulation.
-Here's an example (``example.yml``).
-
-.. code:: yaml
-          
-    ---
-    name: MyExampleSimulation
-    max_time: 50
-    num_trials: 3
-    interval: 2
-    network_params:
-        network_type: barabasi_albert_graph
-        n: 100
-        m: 2
-    agent_distribution:
-        - agent_type: SISaModel
-            weight: 1
-            state:
-            id: content
-        - agent_type: SISaModel
-            weight: 1
-            state:
-            id: discontent
-        - agent_type: SISaModel
-            weight: 8
-            state:
-            id: neutral
-    environment_params:
-        prob_infect: 0.075
+There are mainly two parts in a simulation: agent classes and simulation configuration.
+An agent class defines how the agent will behave throughout the simulation.
+The configuration includes things such as number of agents to use and their type, network topology to use, etc.
 
 
-This example configuration will run three trials of a simulation containing a randomly generated network.
-The 100 nodes in the network will be SISaModel agents, 10% of them will start in the content state, 10% in the discontent state, and the remaining 80% in the neutral state.
-All agents will have access to the environment, which only contains one variable, ``prob_infected``.
-The state of the agents will be updated every 2 seconds (``interval``).
-
-Now run the simulation with the command line tool:
-
-.. code:: bash
-
-   soil example.yml
-
-Once the simulation finishes, its results will be stored in a folder named ``MyExampleSimulation``.
-Four types of objects are saved by default: a pickle of the simulation; a ``YAML`` representation of the simulation (which can be used to re-launch it); and for every trial, a csv file with the content of the state of every network node and the environment parameters at every step of the simulation, as well as the network in gephi format (``gexf``).
+.. image:: soil.png
+  :width: 80%
+  :align: center
 
 
-.. code::
+Soil includes several agent classes in the ``soil.agents`` module, and we will use them in this quickstart.
+If you are interested in developing your own agents classes, see :doc:`soil_tutorial`.
 
-    soil_output
-    ├── Sim_prob_0
-    │   ├── Sim_prob_0.dumped.yml
-    │   ├── Sim_prob_0.simulation.pickle
-    │   ├── Sim_prob_0_trial_0.environment.csv
-    │   └── Sim_prob_0_trial_0.gexf
+Configuration
+=============
+To get you started, we will use this configuration (:download:`download the file <quickstart.yml>` directly):
 
-
-Network
-=======
-
-The network topology for the simulation can be loaded from an existing network file or generated with one of the random network generation methods from networkx.
-
-Loading a network
-#################
-
-To load an existing network, specify its path in the configuration:
-
-.. code:: yaml
-
-   ---
-   network_params:
-      path: /tmp/mynetwork.gexf
-
-Soil will try to guess what networkx method to use to read the file based on its extension.
-However, we only test using ``gexf`` files.
-
-Generating a random network
-###########################
-
-To generate a random network using one of networkx's built-in methods, specify the `graph generation algorithm <https://networkx.github.io/documentation/development/reference/generators.html>`_ and other parameters.
-For example, the following configuration is equivalent to :code:`nx.complete_graph(100)`:
-
-.. code:: yaml
-
-    network_params:
-        network_type: complete_graph
-        n: 100
-
-Environment
-============
-The environment is the place where the shared state of the simulation is stored.
-For instance, the probability of disease outbreak.
-The configuration file may specify the initial value of the environment parameters:
-
-.. code:: yaml
-
-    environment_params:
-        daily_probability_of_earthquake: 0.001
-        number_of_earthquakes: 0
-
-Any agent has unrestricted access to the environment.
-However, for the sake of simplicity, we recommend limiting environment updates to environment agents.
-
-Agents
-======
-Agents are a way of modelling behavior.
-Agents can be characterized with two variables: an agent type (``agent_type``) and its state.
-Only one agent is executed at a time (generally, every ``interval`` seconds), and it has access to its state and the environment parameters.
-Through the environment, it can access the network topology and the state of other agents.
-
-There are three three types of agents according to how they are added to the simulation: network agents and environment agent.
-
-Network Agents
-##############
-Network agents are attached to a node in the topology.
-The configuration file allows you to specify how agents will be mapped to topology nodes.
-
-The simplest way is to specify a single type of agent.
-Hence, every node in the network will be associated to an agent of that type.
-
-.. code:: yaml
-
-   agent_type: SISaModel
-
-It is also possible to add more than one type of agent to the simulation, and to control the ratio of each type (using the ``weight`` property).
-For instance, with following configuration, it is five times more likely for a node to be assigned a CounterModel type than a SISaModel type.
-
-.. code:: yaml
-
-   agent_distribution:
-      - agent_type: SISaModel
-        weight: 1
-      - agent_type: CounterModel
-        weight: 5
-
-In addition to agent type, you may also add a custom initial state to the distribution.
-This is very useful to add the same agent type with different states.
-e.g., to populate the network with SISaModel, roughly 10% of them with a discontent state:
-
-.. code:: yaml
-
-    agent_distribution:
-        - agent_type: SISaModel
-            weight: 9
-            state:
-            id: neutral
-        - agent_type: SISaModel
-            weight: 1
-            state:
-            id: discontent
-
-Lastly, the configuration may include initial state for one or more nodes.
-For instance, to add a state for the two nodes in this configuration:
-
-.. code:: yaml
-
-   agent_type: SISaModel
-   network:
-      network_type: complete_graph
-      n: 2
-   states:
-     - id: content
-     - id: discontent
-
-
-Or to add state only to specific nodes (by ``id``).
-For example, to apply special skills to Linux Torvalds in a simulation:
-
-.. literalinclude:: ../examples/torvalds.yml
+.. literalinclude:: quickstart.yml
    :language: yaml
 
+The agent type used, SISa, is a very simple model.
+It only has three states (neutral, content and discontent),
+Its parameters are the probabilities to change from one state to another, either spontaneously or because of contagion from neighboring agents.
 
-Environment Agents
-##################
-In addition to network agents, more agents can be added to the simulation.
-These agens are programmed in much the same way as network agents, the only difference is that they will not be assigned to network nodes.
+Running the simulation
+======================
 
+To see the simulation in action, simply point soil to the configuration, and tell it to store the graph and the history of agent states and environment parameters at every point.
 
 .. code::
 
-   environment_agents:
-       - agent_type: MyAgent
-         state:
-           mood: happy
-       - agent_type: DummyAgent
+    ❯ soil --graph --csv quickstart.yml                                                          [13:35:29]
+    INFO:soil:Using config(s): quickstart
+    INFO:soil:Dumping results to soil_output/quickstart : ['csv', 'gexf']
+    INFO:soil:Starting simulation quickstart at 13:35:30.
+    INFO:soil:Starting Simulation quickstart trial 0 at 13:35:30.
+    INFO:soil:Finished Simulation quickstart trial 0 at 13:35:49 in 19.43677067756653 seconds
+    INFO:soil:Starting Dumping simulation quickstart trial 0 at 13:35:49.
+    INFO:soil:Finished Dumping simulation quickstart trial 0 at 13:35:51 in 1.7733407020568848 seconds
+    INFO:soil:Dumping results to soil_output/quickstart
+    INFO:soil:Finished simulation quickstart at 13:35:51 in 21.29862952232361 seconds
 
 
-Visualizing the results
-=======================
+The ``CSV`` file should look like this:
 
-The simulation will return a dynamic graph .gexf file which could be visualized with
+.. code::
+
+   agent_id,t_step,key,value
+   env,0,neutral_discontent_spon_prob,0.05
+   env,0,neutral_discontent_infected_prob,0.1
+   env,0,neutral_content_spon_prob,0.2
+   env,0,neutral_content_infected_prob,0.4
+   env,0,discontent_neutral,0.2
+   env,0,discontent_content,0.05
+   env,0,content_discontent,0.05
+   env,0,variance_d_c,0.05
+   env,0,variance_c_d,0.1
+
+Results and visualization
+=========================
+
+The environment variables are marked as ``agent_id`` env.
+Th exported values are only stored when they change.
+To find out how to get every key and value at every point in the simulation, check out the :doc:`soil_tutorial`.
+
+The dynamic graph is exported as a .gexf file which could be visualized with
 `Gephi <https://gephi.org/users/download/>`__.
+Now it is your turn to experiment with the simulation.
+Change some of the parameters, such as the number of agents, the probability of becoming content, or the type of network, and see how the results change.
+
+
+Soil also includes a web server that allows you to upload your simulations, change parameters, and visualize the results, including a timeline of the network.
+To make it work, you have to install soil like this:
+
+.. code::
+
+  pip install soil[web]
+
+Once installed, the soil web UI can be run in two ways:
+
+.. code::
+
+  soil-web
+
+  # OR
+
+  python -m soil.web

docs/quickstart.yml

@@ -0,0 +1,30 @@
+---
+name: quickstart
+num_trials: 1
+max_time: 1000
+network_agents:
+  - agent_type: SISaModel
+    state:
+      id: neutral
+    weight: 1
+  - agent_type: SISaModel
+    state:
+      id: content
+    weight: 2
+network_params:
+  n: 100
+  k: 5
+  p: 0.2
+  generator: newman_watts_strogatz_graph
+environment_params:
+    neutral_discontent_spon_prob: 0.05
+    neutral_discontent_infected_prob: 0.1
+    neutral_content_spon_prob: 0.2
+    neutral_content_infected_prob: 0.4
+    discontent_neutral: 0.2
+    discontent_content: 0.05
+    content_discontent: 0.05
+    variance_d_c: 0.05
+    variance_c_d: 0.1
+    content_neutral: 0.1
+    standard_variance: 0.1

docs/requirements.txt

@@ -0,0 +1 @@
+ipython==7.23

docs/soil_tutorial.rst

@@ -26,7 +26,7 @@ But before that, let's import the soil module and networkx.
     %autoreload 2
     
     %pylab inline
-    # To display plots in the notebooed_
+    # To display plots in the notebook_
 
 
 .. parsed-literal::
@@ -214,7 +214,7 @@ nodes in that network. Notice how node 0 is the only one with a TV.
     MAX_TIME = 100
     EVENT_TIME = 10
     
-    sim = soil.simulation.SoilSimulation(topology=G,
+    sim = soil.Simulation(topology=G,
                                          num_trials=1,
                                          max_time=MAX_TIME,
                                          environment_agents=[{'agent_type': NewsEnvironmentAgent,
@@ -323,7 +323,7 @@ Let's run our simulation:
 
 .. code:: ipython3
 
-    soil.simulation.run_from_config(config, dump=False)
+    soil.simulation.run_from_config(config)
 
 
 .. parsed-literal::
@@ -2531,7 +2531,7 @@ Dealing with bigger data
 
 .. parsed-literal::
 
-    267M	../rabbits/soil_output/rabbits_example/
+    267M	../rabbits/soil_output/rabbits_example/
 
 
 If we tried to load the entire history, we would probably run out of

examples/NewsSpread.ipynb

@@ -2,14 +2,22 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {
     "ExecuteTime": {
      "start_time": "2017-11-02T09:48:41.843Z"
     },
     "scrolled": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Populating the interactive namespace from numpy and matplotlib\n"
+     ]
+    }
+   ],
    "source": [
     "import soil\n",
     "import networkx as nx\n",
@@ -39,26 +47,216 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "total 288K\r\n",
+      "drwxr-xr-x  7 j users 4.0K May 23 12:48 .\r\n",
+      "drwxr-xr-x 15 j users  20K May  7 18:59 ..\r\n",
+      "-rw-r--r--  1 j users  451 Oct 17  2017 complete.yml\r\n",
+      "drwxr-xr-x  2 j users 4.0K Feb 18 11:22 .ipynb_checkpoints\r\n",
+      "drwxr-xr-x  2 j users 4.0K Oct 17  2017 long_running\r\n",
+      "-rw-r--r--  1 j users 1.2K May 23 12:49 .nbgrader.log\r\n",
+      "drwxr-xr-x  4 j users 4.0K May  4 11:23 newsspread\r\n",
+      "-rw-r--r--  1 j users 225K May  4 11:23 NewsSpread.ipynb\r\n",
+      "drwxr-xr-x  4 j users 4.0K May  4 11:21 rabbits\r\n",
+      "-rw-r--r--  1 j users   42 Jul  3  2017 torvalds.edgelist\r\n",
+      "-rw-r--r--  1 j users  245 Oct 13  2017 torvalds.yml\r\n",
+      "drwxr-xr-x  4 j users 4.0K May  4 11:23 tutorial\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!ls "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
    "metadata": {
     "ExecuteTime": {
      "start_time": "2017-11-02T09:48:43.440Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "---\r\n",
+      "default_state: {}\r\n",
+      "load_module: newsspread\r\n",
+      "environment_agents: []\r\n",
+      "environment_params:\r\n",
+      "  prob_neighbor_spread: 0.0\r\n",
+      "  prob_tv_spread: 0.01\r\n",
+      "interval: 1\r\n",
+      "max_time: 30\r\n",
+      "name: Sim_all_dumb\r\n",
+      "network_agents:\r\n",
+      "- agent_type: DumbViewer\r\n",
+      "  state:\r\n",
+      "    has_tv: false\r\n",
+      "  weight: 1\r\n",
+      "- agent_type: DumbViewer\r\n",
+      "  state:\r\n",
+      "    has_tv: true\r\n",
+      "  weight: 1\r\n",
+      "network_params:\r\n",
+      "  generator: barabasi_albert_graph\r\n",
+      "  n: 500\r\n",
+      "  m: 5\r\n",
+      "num_trials: 50\r\n",
+      "---\r\n",
+      "default_state: {}\r\n",
+      "load_module: newsspread\r\n",
+      "environment_agents: []\r\n",
+      "environment_params:\r\n",
+      "  prob_neighbor_spread: 0.0\r\n",
+      "  prob_tv_spread: 0.01\r\n",
+      "interval: 1\r\n",
+      "max_time: 30\r\n",
+      "name: Sim_half_herd\r\n",
+      "network_agents:\r\n",
+      "- agent_type: DumbViewer\r\n",
+      "  state:\r\n",
+      "    has_tv: false\r\n",
+      "  weight: 1\r\n",
+      "- agent_type: DumbViewer\r\n",
+      "  state:\r\n",
+      "    has_tv: true\r\n",
+      "  weight: 1\r\n",
+      "- agent_type: HerdViewer\r\n",
+      "  state:\r\n",
+      "    has_tv: false\r\n",
+      "  weight: 1\r\n",
+      "- agent_type: HerdViewer\r\n",
+      "  state:\r\n",
+      "    has_tv: true\r\n",
+      "  weight: 1\r\n",
+      "network_params:\r\n",
+      "  generator: barabasi_albert_graph\r\n",
+      "  n: 500\r\n",
+      "  m: 5\r\n",
+      "num_trials: 50\r\n",
+      "---\r\n",
+      "default_state: {}\r\n",
+      "load_module: newsspread\r\n",
+      "environment_agents: []\r\n",
+      "environment_params:\r\n",
+      "  prob_neighbor_spread: 0.0\r\n",
+      "  prob_tv_spread: 0.01\r\n",
+      "interval: 1\r\n",
+      "max_time: 30\r\n",
+      "name: Sim_all_herd\r\n",
+      "network_agents:\r\n",
+      "- agent_type: HerdViewer\r\n",
+      "  state:\r\n",
+      "    has_tv: true\r\n",
+      "    id: neutral\r\n",
+      "  weight: 1\r\n",
+      "- agent_type: HerdViewer\r\n",
+      "  state:\r\n",
+      "    has_tv: true\r\n",
+      "    id: neutral\r\n",
+      "  weight: 1\r\n",
+      "network_params:\r\n",
+      "  generator: barabasi_albert_graph\r\n",
+      "  n: 500\r\n",
+      "  m: 5\r\n",
+      "num_trials: 50\r\n",
+      "---\r\n",
+      "default_state: {}\r\n",
+      "load_module: newsspread\r\n",
+      "environment_agents: []\r\n",
+      "environment_params:\r\n",
+      "  prob_neighbor_spread: 0.0\r\n",
+      "  prob_tv_spread: 0.01\r\n",
+      "  prob_neighbor_cure: 0.1\r\n",
+      "interval: 1\r\n",
+      "max_time: 30\r\n",
+      "name: Sim_wise_herd\r\n",
+      "network_agents:\r\n",
+      "- agent_type: HerdViewer\r\n",
+      "  state:\r\n",
+      "    has_tv: true\r\n",
+      "    id: neutral\r\n",
+      "  weight: 1\r\n",
+      "- agent_type: WiseViewer\r\n",
+      "  state:\r\n",
+      "    has_tv: true\r\n",
+      "  weight: 1\r\n",
+      "network_params:\r\n",
+      "  generator: barabasi_albert_graph\r\n",
+      "  n: 500\r\n",
+      "  m: 5\r\n",
+      "num_trials: 50\r\n",
+      "---\r\n",
+      "default_state: {}\r\n",
+      "load_module: newsspread\r\n",
+      "environment_agents: []\r\n",
+      "environment_params:\r\n",
+      "  prob_neighbor_spread: 0.0\r\n",
+      "  prob_tv_spread: 0.01\r\n",
+      "  prob_neighbor_cure: 0.1\r\n",
+      "interval: 1\r\n",
+      "max_time: 30\r\n",
+      "name: Sim_all_wise\r\n",
+      "network_agents:\r\n",
+      "- agent_type: WiseViewer\r\n",
+      "  state:\r\n",
+      "    has_tv: true\r\n",
+      "    id: neutral\r\n",
+      "  weight: 1\r\n",
+      "- agent_type: WiseViewer\r\n",
+      "  state:\r\n",
+      "    has_tv: true\r\n",
+      "  weight: 1\r\n",
+      "network_params:\r\n",
+      "  generator: barabasi_albert_graph\r\n",
+      "  n: 500\r\n",
+      "  m: 5\r\n",
+      "network_params:\r\n",
+      "  generator: barabasi_albert_graph\r\n",
+      "  n: 500\r\n",
+      "  m: 5\r\n",
+      "num_trials: 50\r\n"
+     ]
+    }
+   ],
    "source": [
-    "!cat NewsSpread.yml"
+    "!cat newsspread/NewsSpread.yml"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {
     "ExecuteTime": {
      "start_time": "2017-11-02T09:48:43.879Z"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "No objects to concatenate",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m----------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                           Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-10-bae848826594>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mevodumb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0manalysis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'soil_output/Sim_all_dumb/'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgroup\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprocess\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0manalysis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_count\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeys\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'id'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m~/git/lab.gsi/soil/soil/soil/analysis.py\u001b[0m in \u001b[0;36mread_data\u001b[0;34m(group, *args, **kwargs)\u001b[0m\n\u001b[1;32m     11\u001b[0m     \u001b[0miterable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_read_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mgroup\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mgroup_trials\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     14\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/git/lab.gsi/soil/soil/soil/analysis.py\u001b[0m in \u001b[0;36mgroup_trials\u001b[0;34m(trials, aggfunc)\u001b[0m\n\u001b[1;32m    159\u001b[0m     \u001b[0mtrials\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrials\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    160\u001b[0m     \u001b[0mtrials\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrials\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 161\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrials\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0magg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maggfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreorder_levels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m,\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    162\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    163\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.local/lib/python3.6/site-packages/pandas/core/reshape/concat.py\u001b[0m in \u001b[0;36mconcat\u001b[0;34m(objs, axis, join, join_axes, ignore_index, keys, levels, names, verify_integrity, copy)\u001b[0m\n\u001b[1;32m    210\u001b[0m                        \u001b[0mkeys\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnames\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    211\u001b[0m                        \u001b[0mverify_integrity\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverify_integrity\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 212\u001b[0;31m                        copy=copy)\n\u001b[0m\u001b[1;32m    213\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    214\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.local/lib/python3.6/site-packages/pandas/core/reshape/concat.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, objs, axis, join, join_axes, keys, levels, names, ignore_index, verify_integrity, copy)\u001b[0m\n\u001b[1;32m    243\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    244\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobjs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 245\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'No objects to concatenate'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    246\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    247\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mkeys\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: No objects to concatenate"
+     ]
+    }
+   ],
    "source": [
     "evodumb = analysis.read_data('soil_output/Sim_all_dumb/', group=True, process=analysis.get_count, keys=['id']);"
    ]
@@ -302,7 +500,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.2"
+   "version": "3.8.5"
   },
   "toc": {
    "colors": {

examples/complete.yml

@@ -1,11 +1,11 @@
 ---
 name: simple
+group: tests
 dir_path: "/tmp/"
 num_trials: 3
 max_time: 100
 interval: 1
 seed: "CompleteSeed!"
-dump: false
 network_params:
   generator: complete_graph
   n: 10
@@ -17,6 +17,7 @@ network_agents:
   - agent_type: AggregatedCounter
     weight: 0.2
 environment_agents: []
+environment_class: Environment
 environment_params:
   am_i_complete: true
 default_state:

examples/custom_generator/custom_generator.yml

@@ -0,0 +1,16 @@
+---
+name: custom-generator
+description: Using a custom generator for the network
+num_trials: 3
+max_time: 100
+interval: 1
+network_params:
+  generator: mymodule.mygenerator
+# These are custom parameters
+  n: 10
+  n_edges: 5  
+network_agents:
+  - agent_type: CounterModel
+    weight: 1
+    state:
+      id: 0

examples/custom_generator/mymodule.py

@@ -0,0 +1,27 @@
+from networkx import Graph
+import networkx as nx
+from random import choice
+
+def mygenerator(n=5, n_edges=5):
+    '''
+    Just a simple generator that creates a network with n nodes and
+    n_edges edges. Edges are assigned randomly, only avoiding self loops.
+    '''
+    G = nx.Graph()
+
+    for i in range(n):
+        G.add_node(i)
+    
+    for i in range(n_edges):
+        nodes = list(G.nodes)
+        n_in = choice(nodes)
+        nodes.remove(n_in)  # Avoid loops
+        n_out = choice(nodes)
+        G.add_edge(n_in, n_out)
+    return G
+    
+
+
+
+
+    

examples/custom_timeouts/custom_timeouts.py

@@ -0,0 +1,35 @@
+from soil.agents import FSM, state, default_state
+
+
+class Fibonacci(FSM):
+    '''Agent that only executes in t_steps that are Fibonacci numbers'''
+
+    defaults = {
+        'prev': 1
+    }
+
+    @default_state
+    @state
+    def counting(self):
+        self.log('Stopping at {}'.format(self.now))
+        prev, self['prev'] = self['prev'], max([self.now, self['prev']])
+        return None, self.env.timeout(prev)
+
+class Odds(FSM):
+    '''Agent that only executes in odd t_steps'''
+    @default_state
+    @state
+    def odds(self):
+        self.log('Stopping at {}'.format(self.now))
+        return None, self.env.timeout(1+self.now%2)
+
+if __name__ == '__main__':
+    import logging
+    logging.basicConfig(level=logging.INFO)
+    from soil import Simulation
+    s = Simulation(network_agents=[{'ids': [0], 'agent_type': Fibonacci},
+                                   {'ids': [1], 'agent_type': Odds}],
+                   network_params={"generator": "complete_graph", "n": 2},
+                   max_time=100,
+                   )
+    s.run(dry_run=True)

examples/newsspread/NewsSpread.yml

@@ -6,7 +6,7 @@ environment_params:
   prob_neighbor_spread: 0.0
   prob_tv_spread: 0.01
 interval: 1
-max_time: 30
+max_time: 300
 name: Sim_all_dumb
 network_agents:
 - agent_type: DumbViewer
@@ -30,7 +30,7 @@ environment_params:
   prob_neighbor_spread: 0.0
   prob_tv_spread: 0.01
 interval: 1
-max_time: 30
+max_time: 300
 name: Sim_half_herd
 network_agents:
 - agent_type: DumbViewer
@@ -62,7 +62,7 @@ environment_params:
   prob_neighbor_spread: 0.0
   prob_tv_spread: 0.01
 interval: 1
-max_time: 30
+max_time: 300
 name: Sim_all_herd
 network_agents:
 - agent_type: HerdViewer
@@ -89,7 +89,7 @@ environment_params:
   prob_tv_spread: 0.01
   prob_neighbor_cure: 0.1
 interval: 1
-max_time: 30
+max_time: 300
 name: Sim_wise_herd
 network_agents:
 - agent_type: HerdViewer
@@ -115,7 +115,7 @@ environment_params:
   prob_tv_spread: 0.01
   prob_neighbor_cure: 0.1
 interval: 1
-max_time: 30
+max_time: 300
 name: Sim_all_wise
 network_agents:
 - agent_type: WiseViewer

examples/programmatic/.gitignore

@@ -0,0 +1 @@
+Programmatic*

examples/programmatic/programmatic.py

@@ -0,0 +1,38 @@
+'''
+Example of a fully programmatic simulation, without definition files.
+'''
+from soil import Simulation, agents
+from networkx import Graph
+import logging
+
+
+def mygenerator():
+    # Add only a node
+    G = Graph()
+    G.add_node(1)
+    return G
+
+
+class MyAgent(agents.FSM):
+
+    @agents.default_state
+    @agents.state
+    def neutral(self):
+        self.info('I am running')
+
+
+s = Simulation(name='Programmatic',
+               network_params={'generator': mygenerator},
+               num_trials=1,
+               max_time=100,
+               agent_type=MyAgent,
+               dry_run=True)
+
+
+logging.basicConfig(level=logging.INFO)
+envs = s.run()
+
+s.dump_yaml()
+
+for env in envs:
+    env.dump_csv()

examples/pubcrawl/README.md

@@ -0,0 +1,10 @@
+Simulation of pubs and drinking pals that go from pub to pub.
+
+Th custom environment includes a list of pubs and methods to allow agents to discover and enter pubs.
+There are two types of agents:
+
+* Patron. A patron will do three things, in this order:
+    * Look for other patrons to drink with
+    * Look for a pub where the agent and other agents in the same group can get in.
+    * While in the pub, patrons only drink, until they get drunk and taken home.
+* Police. There is only one police agent that will take any drunk patrons home (kick them out of the pub).

examples/pubcrawl/pubcrawl.py

@@ -0,0 +1,175 @@
+from soil.agents import FSM, state, default_state
+from soil import Environment
+from random import random, shuffle
+from itertools import islice
+import logging
+
+
+class CityPubs(Environment):
+    '''Environment with Pubs'''
+    level = logging.INFO
+
+    def __init__(self, *args, number_of_pubs=3, pub_capacity=10, **kwargs):
+        super(CityPubs, self).__init__(*args, **kwargs)
+        pubs = {}
+        for i in range(number_of_pubs):
+            newpub = {
+                'name': 'The awesome pub #{}'.format(i),
+                'open': True,
+                'capacity': pub_capacity,
+                'occupancy': 0,
+            }
+            pubs[newpub['name']] = newpub
+        self['pubs'] = pubs
+
+    def enter(self, pub_id, *nodes):
+        '''Agents will try to enter. The pub checks if it is possible'''
+        try:
+            pub = self['pubs'][pub_id]
+        except KeyError:
+            raise ValueError('Pub {} is not available'.format(pub_id))
+        if not pub['open'] or (pub['capacity'] < (len(nodes) + pub['occupancy'])):
+            return False
+        pub['occupancy'] += len(nodes)
+        for node in nodes:
+            node['pub'] = pub_id
+        return True
+
+    def available_pubs(self):
+        for pub in self['pubs'].values():
+            if pub['open'] and (pub['occupancy'] < pub['capacity']):
+                yield pub['name']
+
+    def exit(self, pub_id, *node_ids):
+        '''Agents will notify the pub they want to leave'''
+        try:
+            pub = self['pubs'][pub_id]
+        except KeyError:
+            raise ValueError('Pub {} is not available'.format(pub_id))
+        for node_id in node_ids:
+            node = self.get_agent(node_id)
+            if pub_id == node['pub']:
+                del node['pub']
+                pub['occupancy'] -= 1
+
+
+class Patron(FSM):
+    '''Agent that looks for friends to drink with. It will do three things:
+        1) Look for other patrons to drink with
+        2) Look for a bar where the agent and other agents in the same group can get in.
+        3) While in the bar, patrons only drink, until they get drunk and taken home.
+    '''
+    level = logging.DEBUG
+
+    defaults = {
+        'pub': None,
+        'drunk': False,
+        'pints': 0,
+        'max_pints': 3,
+    }
+
+    @default_state
+    @state
+    def looking_for_friends(self):
+        '''Look for friends to drink with'''
+        self.info('I am looking for friends')
+        available_friends = list(self.get_agents(drunk=False,
+                                                 pub=None,
+                                                 state_id=self.looking_for_friends.id))
+        if not available_friends:
+            self.info('Life sucks and I\'m alone!')
+            return self.at_home
+        befriended = self.try_friends(available_friends)
+        if befriended:
+            return self.looking_for_pub
+
+    @state
+    def looking_for_pub(self):
+        '''Look for a pub that accepts me and my friends'''
+        if self['pub'] != None:
+            return self.sober_in_pub
+        self.debug('I am looking for a pub')
+        group = list(self.get_neighboring_agents())
+        for pub in self.env.available_pubs():
+            self.debug('We\'re trying to get into {}: total: {}'.format(pub, len(group)))
+            if self.env.enter(pub, self, *group):
+                self.info('We\'re all {} getting in {}!'.format(len(group), pub))
+                return self.sober_in_pub
+
+    @state
+    def sober_in_pub(self):
+        '''Drink up.'''
+        self.drink()
+        if self['pints'] > self['max_pints']:
+            return self.drunk_in_pub
+
+    @state
+    def drunk_in_pub(self):
+        '''I'm out. Take me home!'''
+        self.info('I\'m so drunk. Take me home!')
+        self['drunk'] = True
+        pass  # out drunk
+
+    @state
+    def at_home(self):
+        '''The end'''
+        others = self.get_agents(state_id=Patron.at_home.id, limit_neighbors=True)
+        self.debug('I\'m home. Just like {} of my friends'.format(len(others)))
+    
+    def drink(self):
+        self['pints'] += 1
+        self.debug('Cheers to that')
+    
+    def kick_out(self):
+        self.set_state(self.at_home)
+
+    def befriend(self, other_agent, force=False):
+        '''
+        Try to become friends with another agent. The chances of
+        success depend on both agents' openness.
+        '''
+        if force or self['openness'] > random():
+            self.env.add_edge(self, other_agent)
+            self.info('Made some friend {}'.format(other_agent))
+            return True
+        return False
+
+    def try_friends(self, others):
+        ''' Look for random agents around me and try to befriend them'''
+        befriended = False
+        k = int(10*self['openness'])
+        shuffle(others)
+        for friend in islice(others, k):  # random.choice >= 3.7
+            if friend == self:
+                continue
+            if friend.befriend(self):
+                self.befriend(friend, force=True)
+                self.debug('Hooray! new friend: {}'.format(friend.id))
+                befriended = True
+            else:
+                self.debug('{} does not want to be friends'.format(friend.id))
+        return befriended
+
+
+class Police(FSM):
+    '''Simple agent to take drunk people out of pubs.'''
+    level = logging.INFO
+
+    @default_state
+    @state
+    def patrol(self):
+        drunksters = list(self.get_agents(drunk=True,
+                                          state_id=Patron.drunk_in_pub.id))
+        for drunk in drunksters:
+            self.info('Kicking out the trash: {}'.format(drunk.id))
+            drunk.kick_out()
+        else:
+            self.info('No trash to take out. Too bad.')
+
+
+if __name__ == '__main__':
+    from soil import simulation
+    simulation.run_from_config('pubcrawl.yml',
+                               dry_run=True,
+                               dump=None,
+                               parallel=False)

examples/pubcrawl/pubcrawl.yml

@@ -0,0 +1,26 @@
+---
+name: pubcrawl
+num_trials: 3
+max_time: 10
+dump: false
+network_params:
+  # Generate 100 empty nodes. They will be assigned a network agent
+  generator: empty_graph
+  n: 30
+network_agents:
+  - agent_type: pubcrawl.Patron
+    description: Extroverted patron
+    state:
+      openness: 1.0
+    weight: 9
+  - agent_type: pubcrawl.Patron
+    description: Introverted patron
+    state:
+      openness: 0.1
+    weight: 1
+environment_agents:
+  - agent_type: pubcrawl.Police
+environment_class: pubcrawl.CityPubs
+environment_params:
+  altercations: 0
+  number_of_pubs: 3

examples/rabbits/rabbit_agents.py

@@ -1,4 +1,4 @@
-from soil.agents import FSM, state, default_state, BaseAgent
+from soil.agents import FSM, state, default_state, BaseAgent, NetworkAgent
 from enum import Enum
 from random import random, choice
 from itertools import islice
@@ -80,7 +80,7 @@ class RabbitModel(FSM):
                 self.env.add_edge(self['mate'], child.id)
                 # self.add_edge()
                 self.debug('A BABY IS COMING TO LIFE')
-                self.env['rabbits_alive'] = self.env.get('rabbits_alive', self.global_topology.number_of_nodes())+1
+                self.env['rabbits_alive'] = self.env.get('rabbits_alive', self.topology.number_of_nodes())+1
                 self.debug('Rabbits alive: {}'.format(self.env['rabbits_alive']))
                 self['offspring'] += 1
                 self.env.get_agent(self['mate'])['offspring'] += 1
@@ -97,12 +97,14 @@ class RabbitModel(FSM):
         return
 
 
-class RandomAccident(BaseAgent):
+class RandomAccident(NetworkAgent):
 
     level = logging.DEBUG
 
     def step(self):
-        rabbits_total = self.global_topology.number_of_nodes()
+        rabbits_total = self.topology.number_of_nodes()
+        if 'rabbits_alive' not in self.env:
+            self.env['rabbits_alive'] = 0
         rabbits_alive = self.env.get('rabbits_alive', rabbits_total)
         prob_death = self.env.get('prob_death', 1e-100)*math.floor(math.log10(max(1, rabbits_alive)))
         self.debug('Killing some rabbits with prob={}!'.format(prob_death))
@@ -116,5 +118,5 @@ class RandomAccident(BaseAgent):
                 self.log('Rabbits alive: {}'.format(self.env['rabbits_alive']))
                 i.set_state(i.dead)
         self.log('Rabbits alive: {}/{}'.format(rabbits_alive, rabbits_total))
-        if self.count_agents(state_id=RabbitModel.dead.id) == self.global_topology.number_of_nodes():
+        if self.count_agents(state_id=RabbitModel.dead.id) == self.topology.number_of_nodes():
             self.die()

examples/rabbits/rabbits.yml

@@ -1,7 +1,7 @@
 ---
 load_module: rabbit_agents
 name: rabbits_example
-max_time: 1200
+max_time: 500
 interval: 1
 seed: MySeed
 agent_type: RabbitModel

examples/template.yml

@@ -0,0 +1,30 @@
+---
+sampler:
+  method: "SALib.sample.morris.sample"
+  N: 10
+template:
+  group: simple
+  num_trials: 1
+  interval: 1
+  max_time: 2
+  seed: "CompleteSeed!"
+  dump: false
+  network_params:
+    generator: complete_graph
+    n: 10
+  network_agents:
+    - agent_type: CounterModel
+      weight: "{{ x1 }}"
+      state:
+        id: 0
+    - agent_type: AggregatedCounter
+      weight: "{{ 1 - x1 }}"
+  environment_params:
+    name: "{{ x3 }}"
+  skip_test: true
+vars:
+  bounds:
+    x1: [0, 1]
+    x2: [1, 2]
+  fixed:
+    x3: ["a", "b", "c"]

examples/terrorism/TerroristNetworkModel.py

@@ -0,0 +1,208 @@
+import random
+import networkx as nx
+from soil.agents import Geo, NetworkAgent, FSM, state, default_state
+from soil import Environment
+
+
+class TerroristSpreadModel(FSM, Geo):
+    """
+    Settings:
+        information_spread_intensity
+
+        terrorist_additional_influence
+
+        min_vulnerability (optional else zero)
+
+        max_vulnerability
+
+        prob_interaction
+    """
+
+    def __init__(self, environment=None, agent_id=0, state=()):
+        super().__init__(environment=environment, agent_id=agent_id, state=state)
+
+        self.information_spread_intensity = environment.environment_params['information_spread_intensity']
+        self.terrorist_additional_influence = environment.environment_params['terrorist_additional_influence']
+        self.prob_interaction = environment.environment_params['prob_interaction']
+
+        if self['id'] == self.civilian.id:       # Civilian
+            self.mean_belief = random.uniform(0.00, 0.5)
+        elif self['id'] == self.terrorist.id:     # Terrorist
+            self.mean_belief = random.uniform(0.8, 1.00)
+        elif self['id'] == self.leader.id:     # Leader
+            self.mean_belief = 1.00
+        else:
+            raise Exception('Invalid state id: {}'.format(self['id']))
+
+        if 'min_vulnerability' in environment.environment_params:
+            self.vulnerability = random.uniform( environment.environment_params['min_vulnerability'], environment.environment_params['max_vulnerability'] )
+        else :
+            self.vulnerability = random.uniform( 0, environment.environment_params['max_vulnerability'] )
+
+
+    @state
+    def civilian(self):
+        neighbours = list(self.get_neighboring_agents(agent_type=TerroristSpreadModel))
+        if len(neighbours) > 0:
+            # Only interact with some of the neighbors
+            interactions = list(n for n in neighbours if random.random() <= self.prob_interaction)
+            influence = sum( self.degree(i) for i in interactions )
+            mean_belief = sum( i.mean_belief * self.degree(i) / influence for i in interactions )
+            mean_belief = mean_belief * self.information_spread_intensity + self.mean_belief * ( 1 - self.information_spread_intensity )
+            self.mean_belief = mean_belief * self.vulnerability + self.mean_belief * ( 1 - self.vulnerability )
+        
+        if self.mean_belief >= 0.8:
+            return self.terrorist
+
+    @state
+    def leader(self):
+        self.mean_belief = self.mean_belief ** ( 1 - self.terrorist_additional_influence )
+        for neighbour in self.get_neighboring_agents(state_id=[self.terrorist.id, self.leader.id]):
+            if self.betweenness(neighbour) > self.betweenness(self):
+                return self.terrorist
+
+    @state
+    def terrorist(self):
+        neighbours = self.get_agents(state_id=[self.terrorist.id, self.leader.id],
+                                     agent_type=TerroristSpreadModel,
+                                     limit_neighbors=True)
+        if len(neighbours) > 0:
+            influence = sum( self.degree(n) for n in neighbours )
+            mean_belief = sum( n.mean_belief * self.degree(n) / influence for n in neighbours )
+            mean_belief = mean_belief * self.vulnerability + self.mean_belief * ( 1 - self.vulnerability )
+            self.mean_belief = self.mean_belief ** ( 1 - self.terrorist_additional_influence )
+
+        # Check if there are any leaders in the group
+        leaders = list(filter(lambda x: x.state.id == self.leader.id, neighbours))
+        if not leaders:
+            # Check if this is the potential leader
+            # Stop once it's found. Otherwise, set self as leader
+            for neighbour in neighbours:
+                if self.betweenness(self) < self.betweenness(neighbour):
+                    return
+            return self.leader
+
+
+class TrainingAreaModel(FSM, Geo):
+    """
+    Settings:
+        training_influence
+
+        min_vulnerability
+
+    Requires TerroristSpreadModel.
+    """
+
+    def __init__(self, environment=None, agent_id=0, state=()):
+        super().__init__(environment=environment, agent_id=agent_id, state=state)
+        self.training_influence = environment.environment_params['training_influence']
+        if 'min_vulnerability' in environment.environment_params:
+            self.min_vulnerability = environment.environment_params['min_vulnerability']
+        else: self.min_vulnerability = 0
+
+    @default_state
+    @state
+    def terrorist(self):
+        for neighbour in self.get_neighboring_agents(agent_type=TerroristSpreadModel):
+            if neighbour.vulnerability > self.min_vulnerability:
+                neighbour.vulnerability = neighbour.vulnerability ** ( 1 - self.training_influence )        
+
+
+class HavenModel(FSM, Geo):
+    """
+    Settings:
+        haven_influence
+
+        min_vulnerability
+
+        max_vulnerability
+
+    Requires TerroristSpreadModel.
+    """
+
+    def __init__(self, environment=None, agent_id=0, state=()):
+        super().__init__(environment=environment, agent_id=agent_id, state=state)
+        self.haven_influence = environment.environment_params['haven_influence']
+        if 'min_vulnerability' in environment.environment_params:
+            self.min_vulnerability = environment.environment_params['min_vulnerability']
+        else: self.min_vulnerability = 0
+        self.max_vulnerability = environment.environment_params['max_vulnerability']
+
+    def get_occupants(self, **kwargs):
+        return self.get_neighboring_agents(agent_type=TerroristSpreadModel, **kwargs)
+
+    @state
+    def civilian(self):
+        civilians = self.get_occupants(state_id=self.civilian.id)
+        if not civilians:
+            return self.terrorist
+
+        for neighbour in self.get_occupants():
+            if neighbour.vulnerability > self.min_vulnerability:
+                neighbour.vulnerability = neighbour.vulnerability * ( 1 - self.haven_influence )
+        return self.civilian
+
+    @state
+    def terrorist(self):
+        for neighbour in self.get_occupants():
+            if neighbour.vulnerability < self.max_vulnerability:
+                neighbour.vulnerability = neighbour.vulnerability ** ( 1 - self.haven_influence )
+        return self.terrorist
+
+
+class TerroristNetworkModel(TerroristSpreadModel):
+    """
+    Settings:
+        sphere_influence
+
+        vision_range
+
+        weight_social_distance
+
+        weight_link_distance
+    """
+
+    def __init__(self, environment=None, agent_id=0, state=()):
+        super().__init__(environment=environment, agent_id=agent_id, state=state)
+
+        self.vision_range = environment.environment_params['vision_range']
+        self.sphere_influence = environment.environment_params['sphere_influence']
+        self.weight_social_distance = environment.environment_params['weight_social_distance']
+        self.weight_link_distance = environment.environment_params['weight_link_distance']
+
+    @state
+    def terrorist(self):
+        self.update_relationships()
+        return super().terrorist()
+
+    @state
+    def leader(self):
+        self.update_relationships()
+        return super().leader()
+
+    def update_relationships(self):
+        if self.count_neighboring_agents(state_id=self.civilian.id) == 0:
+            close_ups = set(self.geo_search(radius=self.vision_range, agent_type=TerroristNetworkModel))
+            step_neighbours = set(self.ego_search(self.sphere_influence, agent_type=TerroristNetworkModel, center=False))
+            neighbours = set(agent.id for agent in self.get_neighboring_agents(agent_type=TerroristNetworkModel))
+            search = (close_ups | step_neighbours) - neighbours
+            for agent in self.get_agents(search):
+                social_distance = 1 / self.shortest_path_length(agent.id)
+                spatial_proximity = ( 1 - self.get_distance(agent.id) )
+                prob_new_interaction = self.weight_social_distance * social_distance + self.weight_link_distance * spatial_proximity
+                if agent['id'] == agent.civilian.id and random.random() < prob_new_interaction:
+                    self.add_edge(agent)
+                    break
+
+    def get_distance(self, target):
+        source_x, source_y = nx.get_node_attributes(self.topology, 'pos')[self.id]
+        target_x, target_y = nx.get_node_attributes(self.topology, 'pos')[target]
+        dx = abs( source_x - target_x )
+        dy = abs( source_y - target_y )
+        return ( dx ** 2 + dy ** 2 ) ** ( 1 / 2 )
+
+    def shortest_path_length(self, target):
+        try:
+            return nx.shortest_path_length(self.topology, self.id, target)
+        except nx.NetworkXNoPath:
+            return float('inf')

examples/terrorism/TerroristNetworkModel.yml

@@ -0,0 +1,63 @@
+name: TerroristNetworkModel_sim
+load_module: TerroristNetworkModel
+max_time: 150 
+num_trials: 1
+network_params:
+  generator: random_geometric_graph
+  radius: 0.2
+  # generator: geographical_threshold_graph
+  # theta: 20
+  n: 100
+network_agents:
+  - agent_type: TerroristNetworkModel
+    weight: 0.8
+    state:
+      id: civilian  # Civilians
+  - agent_type: TerroristNetworkModel
+    weight: 0.1
+    state:
+      id: leader    # Leaders
+  - agent_type: TrainingAreaModel
+    weight: 0.05
+    state:
+      id: terrorist # Terrorism
+  - agent_type: HavenModel
+    weight: 0.05
+    state:
+      id: civilian  # Civilian
+
+environment_params:
+  # TerroristSpreadModel
+  information_spread_intensity: 0.7
+  terrorist_additional_influence: 0.035
+  max_vulnerability: 0.7
+  prob_interaction: 0.5
+
+  # TrainingAreaModel and HavenModel
+  training_influence: 0.20
+  haven_influence: 0.20
+
+  # TerroristNetworkModel
+  vision_range: 0.30
+  sphere_influence: 2
+  weight_social_distance: 0.035
+  weight_link_distance: 0.035
+
+visualization_params:
+  # Icons downloaded from https://www.iconfinder.com/
+  shape_property: agent
+  shapes:
+    TrainingAreaModel: target
+    HavenModel: home
+    TerroristNetworkModel: person
+  colors:
+    - attr_id: civilian
+      color: '#40de40'
+    - attr_id: terrorist
+      color: red
+    - attr_id: leader
+      color: '#c16a6a'
+  background_image: 'map_4800x2860.jpg'
+  background_opacity: '0.9'
+  background_filter_color: 'blue'
+skip_test: true  # This simulation takes too long for automated tests.

examples/tutorial/soil_tutorial.html

@@ -12327,7 +12327,7 @@ Notice how node 0 is the only one with a TV.</p>
 <span class="n">MAX_TIME</span> <span class="o">=</span> <span class="mi">100</span>
 <span class="n">EVENT_TIME</span> <span class="o">=</span> <span class="mi">10</span>
 
-<span class="n">sim</span> <span class="o">=</span> <span class="n">soil</span><span class="o">.</span><span class="n">simulation</span><span class="o">.</span><span class="n">SoilSimulation</span><span class="p">(</span><span class="n">topology</span><span class="o">=</span><span class="n">G</span><span class="p">,</span>
+<span class="n">sim</span> <span class="o">=</span> <span class="n">soil</span><span class="o">.</span><span class="n">Simulation</span><span class="p">(</span><span class="n">topology</span><span class="o">=</span><span class="n">G</span><span class="p">,</span>
                                      <span class="n">num_trials</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
                                      <span class="n">max_time</span><span class="o">=</span><span class="n">MAX_TIME</span><span class="p">,</span>
                                      <span class="n">environment_agents</span><span class="o">=</span><span class="p">[{</span><span class="s1">&#39;agent_type&#39;</span><span class="p">:</span> <span class="n">NewsEnvironmentAgent</span><span class="p">,</span>
@@ -21883,7 +21883,7 @@ bgAAAABJRU5ErkJggg==
 
 
 <div class="output_subarea output_stream output_stdout output_text">
-<pre>267M	../rabbits/soil_output/rabbits_example/
+<pre>267M	../rabbits/soil_output/rabbits_example/
 </pre>
 </div>
 </div>

requirements.txt

@@ -1,7 +1,9 @@
-nxsim
-simpy
-networkx>=2.0
+simpy>=4.0
+networkx>=2.5
 numpy
 matplotlib
-pyyaml
-pandas
+pyyaml>=5.1
+pandas>=0.23
+scipy>=1.3
+SALib>=1.3
+Jinja2

setup.cfg

@@ -0,0 +1,4 @@
+[aliases]
+test=pytest
+[tool:pytest]
+addopts = --verbose

setup.py

@@ -40,10 +40,15 @@ setup(
         'Operating System :: POSIX',
         'Programming Language :: Python :: 3'],
     install_requires=install_reqs,
+    extras_require={
+        'web': ['tornado']
+
+    },
     tests_require=test_reqs,
     setup_requires=['pytest-runner', ],
     include_package_data=True,
     entry_points={
         'console_scripts':
-        ['soil = soil.__init__:main']
+        ['soil = soil.__init__:main',
+        'soil-web = soil.web.__init__:main']
     })

soil/VERSION

@@ -1 +1 @@
-0.11.1
+0.15.2

soil/__init__.py

@@ -11,24 +11,27 @@ try:
 except NameError:
     basestring = str
 
-logging.basicConfig()
-
 from . import agents
-from . import simulation
-from . import environment
-from . import utils
+from .simulation import *
+from .environment import Environment
+from .history import History
+from . import serialization
 from . import analysis
-
+from .utils import logger
 
 def main():
     import argparse
     from . import simulation
 
+    logger.info('Running SOIL version: {}'.format(__version__))
+
     parser = argparse.ArgumentParser(description='Run a SOIL simulation')
     parser.add_argument('file', type=str,
                         nargs="?",
                         default='simulation.yml',
-                        help='python module containing the simulation configuration.')
+                        help='Configuration file for the simulation (e.g., YAML or JSON)')
+    parser.add_argument('--version', action='store_true',
+                        help='Show version info and exit')
     parser.add_argument('--module', '-m', type=str,
                         help='file containing the code of any custom agents.')
     parser.add_argument('--dry-run', '--dry', action='store_true',
@@ -39,32 +42,48 @@ def main():
                         help='Dump GEXF graph. Defaults to false.')
     parser.add_argument('--csv', action='store_true',
                         help='Dump history in CSV format. Defaults to false.')
+    parser.add_argument('--level', type=str,
+                        help='Logging level')
     parser.add_argument('--output', '-o', type=str, default="soil_output",
                         help='folder to write results to. It defaults to the current directory.')
     parser.add_argument('--synchronous', action='store_true',
                         help='Run trials serially and synchronously instead of in parallel. Defaults to false.')
+    parser.add_argument('-e', '--exporter', action='append',
+                        help='Export environment and/or simulations using this exporter')
 
     args = parser.parse_args()
+    logging.basicConfig(level=getattr(logging, (args.level or 'INFO').upper()))
 
-    if args.module:
+    if args.version:
+        return
+
+    if os.getcwd() not in sys.path:
         sys.path.append(os.getcwd())
+    if args.module:
         importlib.import_module(args.module)
 
-    logging.info('Loading config file: {}'.format(args.file, args.output))
+    logger.info('Loading config file: {}'.format(args.file))
 
     try:
-        dump = []
-        if not args.dry_run:
-            if args.csv:
-                dump.append('csv')
-            if args.graph:
-                dump.append('gexf')
+        exporters = list(args.exporter or ['default', ])
+        if args.csv:
+            exporters.append('csv')
+        if args.graph:
+            exporters.append('gexf')
+        exp_params = {}
+        if args.dry_run:
+            exp_params['copy_to'] = sys.stdout
+
+        if not os.path.exists(args.file):
+            logger.error('Please, input a valid file')
+            return
         simulation.run_from_config(args.file,
                                    dry_run=args.dry_run,
-                                   dump=dump,
-                                   parallel=(not args.synchronous and not args.pdb),
-                                   results_dir=args.output)
-    except Exception as ex:
+                                   exporters=exporters,
+                                   parallel=(not args.synchronous),
+                                   outdir=args.output,
+                                   exporter_params=exp_params)
+    except Exception:
         if args.pdb:
             pdb.post_mortem()
         else:

soil/agents/BassModel.py

@@ -9,7 +9,7 @@ class BassModel(BaseAgent):
         imitation_prob
     """
 
-    def __init__(self, environment, agent_id, state):
+    def __init__(self, environment, agent_id, state, **kwargs):
         super().__init__(environment=environment, agent_id=agent_id, state=state)
         env_params = environment.environment_params
         self.state['sentimentCorrelation'] = 0
@@ -19,7 +19,7 @@ class BassModel(BaseAgent):
 
     def behaviour(self):
         # Outside effects
-        if random.random() < self.state_params['innovation_prob']:
+        if random.random() < self['innovation_prob']:
             if self.state['id'] == 0:
                 self.state['id'] = 1
                 self.state['sentimentCorrelation'] = 1
@@ -32,7 +32,7 @@ class BassModel(BaseAgent):
         if self.state['id'] == 0:
             aware_neighbors = self.get_neighboring_agents(state_id=1)
             num_neighbors_aware = len(aware_neighbors)
-            if random.random() < (self.state_params['imitation_prob']*num_neighbors_aware):
+            if random.random() < (self['imitation_prob']*num_neighbors_aware):
                 self.state['id'] = 1
                 self.state['sentimentCorrelation'] = 1
 

soil/agents/CounterModel.py

@@ -1,7 +1,7 @@
-from . import BaseAgent
+from . import NetworkAgent
 
 
-class CounterModel(BaseAgent):
+class CounterModel(NetworkAgent):
     """
     Dummy behaviour. It counts the number of nodes in the network and neighbors
     in each step and adds it to its state.
@@ -9,24 +9,30 @@ class CounterModel(BaseAgent):
 
     def step(self):
         # Outside effects
-        total = len(list(self.get_all_agents()))
+        total = len(list(self.get_agents()))
         neighbors = len(list(self.get_neighboring_agents()))
         self['times'] = self.get('times', 0) + 1
         self['neighbors'] = neighbors
         self['total'] = total
 
 
-class AggregatedCounter(BaseAgent):
+class AggregatedCounter(NetworkAgent):
     """
     Dummy behaviour. It counts the number of nodes in the network and neighbors
     in each step and adds it to its state.
     """
 
+    defaults = {
+        'times': 0,
+        'neighbors': 0,
+        'total': 0
+    }
+
     def step(self):
         # Outside effects
-        total = len(list(self.get_all_agents()))
+        self['times'] += 1
         neighbors = len(list(self.get_neighboring_agents()))
-        self['times'] = self.get('times', 0) + 1
-        self['neighbors'] = self.get('neighbors', 0) + neighbors
-        self['total'] = total = self.get('total', 0) + total
+        self['neighbors'] += neighbors
+        total = len(list(self.get_agents()))
+        self['total'] += total
         self.debug('Running for step: {}. Total: {}'.format(self.now, total))

soil/agents/DrawingAgent.py

@@ -1,18 +0,0 @@
-from . import BaseAgent
-
-import os.path
-import matplotlib
-import matplotlib.pyplot as plt
-import networkx as nx
-
-
-class DrawingAgent(BaseAgent):
-    """
-    Agent that draws the state of the network.
-    """
-
-    def step(self):
-        # Outside effects
-        f = plt.figure()
-        nx.draw(self.env.G, node_size=10, width=0.2, pos=nx.spring_layout(self.env.G, scale=100), ax=f.add_subplot(111))
-        f.savefig(os.path.join(self.env.get_path(), "graph-"+str(self.env.now)+".png"))

soil/agents/SISaModel.py

@@ -10,7 +10,7 @@ class SISaModel(FSM):
         
         neutral_discontent_infected_prob
         
-        neutral_content_spong_prob
+        neutral_content_spon_prob
         
         neutral_content_infected_prob
         
@@ -29,27 +29,27 @@ class SISaModel(FSM):
         standard_variance
     """
 
-    def __init__(self, environment=None, agent_id=0, state=()):
+    def __init__(self, environment, agent_id=0, state=()):
         super().__init__(environment=environment, agent_id=agent_id, state=state)
 
-        self.neutral_discontent_spon_prob = np.random.normal(environment.environment_params['neutral_discontent_spon_prob'],
-                                                             environment.environment_params['standard_variance'])
-        self.neutral_discontent_infected_prob = np.random.normal(environment.environment_params['neutral_discontent_infected_prob'],
-                                                                 environment.environment_params['standard_variance'])
-        self.neutral_content_spon_prob = np.random.normal(environment.environment_params['neutral_content_spon_prob'],
-                                                          environment.environment_params['standard_variance'])
-        self.neutral_content_infected_prob = np.random.normal(environment.environment_params['neutral_content_infected_prob'],
-                                                              environment.environment_params['standard_variance'])
+        self.neutral_discontent_spon_prob = np.random.normal(self.env['neutral_discontent_spon_prob'],
+                                                             self.env['standard_variance'])
+        self.neutral_discontent_infected_prob = np.random.normal(self.env['neutral_discontent_infected_prob'],
+                                                                 self.env['standard_variance'])
+        self.neutral_content_spon_prob = np.random.normal(self.env['neutral_content_spon_prob'],
+                                                          self.env['standard_variance'])
+        self.neutral_content_infected_prob = np.random.normal(self.env['neutral_content_infected_prob'],
+                                                              self.env['standard_variance'])
 
-        self.discontent_neutral = np.random.normal(environment.environment_params['discontent_neutral'],
-                                                   environment.environment_params['standard_variance'])
-        self.discontent_content = np.random.normal(environment.environment_params['discontent_content'],
-                                                   environment.environment_params['variance_d_c'])
+        self.discontent_neutral = np.random.normal(self.env['discontent_neutral'],
+                                                   self.env['standard_variance'])
+        self.discontent_content = np.random.normal(self.env['discontent_content'],
+                                                   self.env['variance_d_c'])
 
-        self.content_discontent = np.random.normal(environment.environment_params['content_discontent'],
-                                                   environment.environment_params['variance_c_d'])
-        self.content_neutral = np.random.normal(environment.environment_params['content_neutral'],
-                                                environment.environment_params['standard_variance'])
+        self.content_discontent = np.random.normal(self.env['content_discontent'],
+                                                   self.env['variance_c_d'])
+        self.content_neutral = np.random.normal(self.env['content_neutral'],
+                                                self.env['standard_variance'])
 
     @state
     def neutral(self):

soil/agents/SentimentCorrelationModel.py

@@ -16,7 +16,7 @@ class SentimentCorrelationModel(BaseAgent):
         disgust_prob
     """
 
-    def __init__(self, environment=None, agent_id=0, state=()):
+    def __init__(self, environment, agent_id=0, state=()):
         super().__init__(environment=environment, agent_id=agent_id, state=state)
         self.outside_effects_prob = environment.environment_params['outside_effects_prob']
         self.anger_prob = environment.environment_params['anger_prob']

soil/agents/__init__.py

@@ -3,48 +3,42 @@
 # for x in range(0, settings.network_params["number_of_nodes"]):
 #     sentimentCorrelationNodeArray.append({'id': x})
 # Initialize agent states. Let's assume everyone is normal.
-    
 
-import nxsim
+
 import logging
 from collections import OrderedDict
 from copy import deepcopy
 from functools import partial
+from scipy.spatial import cKDTree as KDTree
 import json
+import simpy
 
 from functools import wraps
 
-from .. import utils, history
-
-agent_types = {}
+from .. import serialization, history, utils
 
 
-class MetaAgent(type):
-    def __init__(cls, name, bases, nmspc):
-        super(MetaAgent, cls).__init__(name, bases, nmspc)
-        agent_types[name] = cls
+def as_node(agent):
+    if isinstance(agent, BaseAgent):
+        return agent.id
+    return agent
 
 
-class BaseAgent(nxsim.BaseAgent, metaclass=MetaAgent):
+class BaseAgent:
     """
     A special simpy BaseAgent that keeps track of its state history.
     """
 
     defaults = {}
 
-    def __init__(self, environment=None, agent_id=None, state=None,
-                 name='network_process', interval=None, **state_params):
+    def __init__(self, environment, agent_id, state=None,
+                 name=None, interval=None):
         # Check for REQUIRED arguments
         assert environment is not None, TypeError('__init__ missing 1 required keyword argument: \'environment\'. '
                                                   'Cannot be NoneType.')
         # Initialize agent parameters
         self.id = agent_id
-        self.name = name
-        self.state_params = state_params
-
-        # Global parameters
-        self.global_topology = environment.G
-        self.environment_params = environment.environment_params
+        self.name = name or '{}[{}]'.format(type(self).__name__, self.id)
 
         # Register agent to environment
         self.env = environment
@@ -53,47 +47,64 @@ class BaseAgent(nxsim.BaseAgent, metaclass=MetaAgent):
         self.alive = True
         real_state = deepcopy(self.defaults)
         real_state.update(state or {})
-        self._state = real_state
+        self.state = real_state
         self.interval = interval
 
-        if not hasattr(self, 'level'):
-            self.level = logging.DEBUG
-        self.logger = logging.getLogger('{}-Agent-{}'.format(self.env.name,
-                                                             self.id))
-        self.logger.setLevel(self.level)
+        self.logger = logging.getLogger(self.env.name).getChild(self.name)
+
+        if hasattr(self, 'level'):
+            self.logger.setLevel(self.level)
 
         # initialize every time an instance of the agent is created
         self.action = self.env.process(self.run())
 
     @property
     def state(self):
-        return self._state
+        '''
+        Return the agent itself, which behaves as a dictionary.
+        Changes made to `agent.state` will be reflected in the history.
+
+        This method shouldn't be used, but is kept here for backwards compatibility.
+        '''
+        return self
 
     @state.setter
     def state(self, value):
+        self._state = {}
         for k, v in value.items():
             self[k] = v
 
+    @property
+    def environment_params(self):
+        return self.env.environment_params
+
+    @environment_params.setter
+    def environment_params(self, value):
+        self.env.environment_params = value
+
     def __getitem__(self, key):
         if isinstance(key, tuple):
             key, t_step = key
             k = history.Key(key=key, t_step=t_step, agent_id=self.id)
             return self.env[k]
-        return self.state.get(key, None)
+        return self._state.get(key, None)
 
     def __delitem__(self, key):
-        self.state[key] = None
+        self._state[key] = None
 
     def __contains__(self, key):
-        return key in self.state
+        return key in self._state
 
     def __setitem__(self, key, value):
-        self.state[key] = value
+        self._state[key] = value
         k = history.Key(t_step=self.now,
                         agent_id=self.id,
                         key=key)
         self.env[k] = value
 
+    def items(self):
+        return self._state.items()
+
     def get(self, key, default=None):
         return self[key] if key in self else default
 
@@ -119,57 +130,10 @@ class BaseAgent(nxsim.BaseAgent, metaclass=MetaAgent):
     def die(self, remove=False):
         self.alive = False
         if remove:
-            super().die()
+            self.remove_node(self.id)
 
     def step(self):
-        pass
-
-    def to_json(self):
-        return json.dumps(self.state)
-
-    def count_agents(self, state_id=None, limit_neighbors=False):
-        if limit_neighbors:
-            agents = self.global_topology.neighbors(self.id)
-        else:
-            agents = self.global_topology.nodes()
-        count = 0
-        for agent in agents:
-            if state_id and state_id != self.global_topology.node[agent]['agent']['id']:
-                continue
-            count += 1
-        return count
-
-    def count_neighboring_agents(self, state_id=None):
-        return len(super().get_agents(state_id, limit_neighbors=True))
-
-    def get_agents(self, state_id=None, limit_neighbors=False, iterator=False, **kwargs):
-        if limit_neighbors:
-            agents = super().get_agents(state_id, limit_neighbors)
-        else:
-            agents = filter(lambda x: state_id is None or x.state.get('id', None) == state_id,
-                            self.env.agents)
-
-        def matches_all(agent):
-            state = agent.state
-            for k, v in kwargs.items():
-                if state.get(k, None) != v:
-                    return False
-            return True
-
-        f = filter(matches_all, agents)
-        if iterator:
-            return f
-        return list(f)
-
-    def log(self, message, *args, level=logging.INFO, **kwargs):
-        message = message + " ".join(str(i) for i in args)
-        message = "\t@{:>5}:\t{}".format(self.now, message)
-        for k, v in kwargs:
-            message += " {k}={v} ".format(k, v)
-        extra = {}
-        extra['now'] = self.now
-        extra['id'] = self.id
-        return self.logger.log(level, message, extra=extra)
+        return
 
     def debug(self, *args, **kwargs):
         return self.log(*args, level=logging.DEBUG, **kwargs)
@@ -177,31 +141,131 @@ class BaseAgent(nxsim.BaseAgent, metaclass=MetaAgent):
     def info(self, *args, **kwargs):
         return self.log(*args, level=logging.INFO, **kwargs)
 
+    def __getstate__(self):
+        '''
+        Serializing an agent will lose all its running information (you cannot
+        serialize an iterator), but it keeps the state and link to the environment,
+        so it can be used for inspection and dumping to a file
+        '''
+        state = {}
+        state['id'] = self.id
+        state['environment'] = self.env
+        state['_state'] = self._state
+        return state
 
-def state(func):
-    '''
-    A state function should return either a state id, or a tuple (state_id, when)
-    The default value for state_id is the current state id.
-    The default value for when is the interval defined in the nevironment.
-    '''
+    def __setstate__(self, state):
+        '''
+        Get back a serialized agent and try to re-compose it
+        '''
+        self.id = state['id']
+        self._state = state['_state']
+        self.env = state['environment']
 
-    @wraps(func)
-    def func_wrapper(self):
-        next_state = func(self)
-        when = None
-        if next_state is None:
+class NetworkAgent(BaseAgent):
+
+    @property
+    def topology(self):
+        return self.env.G
+
+    @property
+    def G(self):
+        return self.env.G
+
+    def count_agents(self, **kwargs):
+        return len(list(self.get_agents(**kwargs)))
+
+    def count_neighboring_agents(self, state_id=None, **kwargs):
+        return len(self.get_neighboring_agents(state_id=state_id, **kwargs))
+
+    def get_neighboring_agents(self, state_id=None, **kwargs):
+        return self.get_agents(limit_neighbors=True, state_id=state_id, **kwargs)
+
+    def get_agents(self, agents=None, limit_neighbors=False, **kwargs):
+        if limit_neighbors:
+            agents = self.topology.neighbors(self.id)
+
+        agents = self.env.get_agents(agents)
+        return select(agents, **kwargs)
+
+    def log(self, message, *args, level=logging.INFO, **kwargs):
+        message = message + " ".join(str(i) for i in args)
+        message = " @{:>3}: {}".format(self.now, message)
+        for k, v in kwargs:
+            message += " {k}={v} ".format(k, v)
+        extra = {}
+        extra['now'] = self.now
+        extra['agent_id'] = self.id
+        extra['agent_name'] = self.name
+        return self.logger.log(level, message, extra=extra)
+
+    def subgraph(self, center=True, **kwargs):
+        include = [self] if center else []
+        return self.topology.subgraph(n.id for n in self.get_agents(**kwargs)+include)
+
+    def remove_node(self, agent_id):
+        self.topology.remove_node(agent_id)
+
+    def add_edge(self, other, edge_attr_dict=None, *edge_attrs):
+        # return super(NetworkAgent, self).add_edge(node1=self.id, node2=other, **kwargs)
+        if self.id not in self.topology.nodes(data=False):
+            raise ValueError('{} not in list of existing agents in the network'.format(self.id))
+        if other not in self.topology.nodes(data=False):
+            raise ValueError('{} not in list of existing agents in the network'.format(other))
+
+        self.topology.add_edge(self.id, other, edge_attr_dict=edge_attr_dict, *edge_attrs)
+
+
+    def ego_search(self, steps=1, center=False, node=None, **kwargs):
+        '''Get a list of nodes in the ego network of *node* of radius *steps*'''
+        node = as_node(node if node is not None else self)
+        G = self.subgraph(**kwargs)
+        return nx.ego_graph(G, node, center=center, radius=steps).nodes()
+
+    def degree(self, node, force=False):
+        node = as_node(node)
+        if force or (not hasattr(self.env, '_degree')) or getattr(self.env, '_last_step', 0) < self.now:
+            self.env._degree = nx.degree_centrality(self.topology)
+            self.env._last_step = self.now
+        return self.env._degree[node]
+
+    def betweenness(self, node, force=False):
+        node = as_node(node)
+        if force or (not hasattr(self.env, '_betweenness')) or getattr(self.env, '_last_step', 0) < self.now:
+            self.env._betweenness = nx.betweenness_centrality(self.topology)
+            self.env._last_step = self.now
+        return self.env._betweenness[node]
+
+
+def state(name=None):
+    def decorator(func, name=None):
+        '''
+        A state function should return either a state id, or a tuple (state_id, when)
+        The default value for state_id is the current state id.
+        The default value for when is the interval defined in the environment.
+        '''
+
+        @wraps(func)
+        def func_wrapper(self):
+            next_state = func(self)
+            when = None
+            if next_state is None:
+                return when
+            try:
+                next_state, when = next_state
+            except (ValueError, TypeError):
+                pass
+            if next_state:
+                self.set_state(next_state)
             return when
-        try:
-            next_state, when = next_state
-        except (ValueError, TypeError):
-            pass
-        if next_state:
-            self.set_state(next_state)
-        return when
 
-    func_wrapper.id = func.__name__
-    func_wrapper.is_default = False
-    return func_wrapper
+        func_wrapper.id = name or func.__name__
+        func_wrapper.is_default = False
+        return func_wrapper
+
+    if callable(name):
+        return decorator(name)
+    else:
+        return partial(decorator, name=name)
 
 
 def default_state(func):
@@ -209,7 +273,7 @@ def default_state(func):
     return func
 
 
-class MetaFSM(MetaAgent):
+class MetaFSM(type):
     def __init__(cls, name, bases, nmspc):
         super(MetaFSM, cls).__init__(name, bases, nmspc)
         states = {}
@@ -232,16 +296,22 @@ class MetaFSM(MetaAgent):
         cls.states = states
 
 
-class FSM(BaseAgent, metaclass=MetaFSM):
+class FSM(NetworkAgent, metaclass=MetaFSM):
     def __init__(self, *args, **kwargs):
         super(FSM, self).__init__(*args, **kwargs)
         if 'id' not in self.state:
             if not self.default_state:
                 raise ValueError('No default state specified for {}'.format(self.id))
             self['id'] = self.default_state.id
+        self._next_change = simpy.core.Infinity
+        self._next_state = self.state
 
     def step(self):
-        if 'id' in self.state:
+        if self._next_change < self.now:
+            next_state = self._next_state
+            self._next_change = simpy.core.Infinity
+            self['id'] = next_state
+        elif 'id' in self.state:
             next_state = self['id']
         elif self.default_state:
             next_state = self.default_state.id
@@ -249,7 +319,11 @@ class FSM(BaseAgent, metaclass=MetaFSM):
             raise Exception('{} has no valid state id or default state'.format(self))
         if next_state not in self.states:
             raise Exception('{} is not a valid id for {}'.format(next_state, self))
-        self.states[next_state](self)
+        return self.states[next_state](self)
+
+    def next_state(self, state):
+        self._next_change = self.now
+        self._next_state = state
 
     def set_state(self, state):
         if hasattr(state, 'id'):
@@ -275,6 +349,9 @@ def prob(prob=1):
     return r < prob
 
 
+STATIC_THRESHOLD = (-1, -1)
+
+
 def calculate_distribution(network_agents=None,
                            agent_type=None):
     '''
@@ -306,28 +383,58 @@ def calculate_distribution(network_agents=None,
     elif agent_type:
         network_agents = [{'agent_type': agent_type}]
     else:
-        return []
+        raise ValueError('Specify a distribution or a default agent type')
+
+    # Fix missing weights and incompatible types
+    for x in network_agents:
+        x['weight'] = float(x.get('weight', 1))
 
     # Calculate the thresholds
-    total = sum(x.get('weight', 1) for x in network_agents)
+    total = sum(x['weight'] for x in network_agents)
     acc = 0
     for v in network_agents:
-        upper = acc + (v.get('weight', 1)/total)
+        if 'ids' in v:
+            v['threshold'] = STATIC_THRESHOLD
+            continue
+        upper = acc + (v['weight']/total)
         v['threshold'] = [acc, upper]
         acc = upper
     return network_agents
 
 
-def _serialize_distribution(network_agents):
-    d = _convert_agent_types(network_agents,
-                             to_string=True)
+def serialize_type(agent_type, known_modules=[], **kwargs):
+    if isinstance(agent_type, str):
+        return agent_type
+    known_modules += ['soil.agents']
+    return serialization.serialize(agent_type, known_modules=known_modules, **kwargs)[1] # Get the name of the class
+
+
+def serialize_distribution(network_agents, known_modules=[]):
     '''
     When serializing an agent distribution, remove the thresholds, in order
     to avoid cluttering the YAML definition file.
     '''
+    d = deepcopy(list(network_agents))
     for v in d:
         if 'threshold' in v:
             del v['threshold']
+        v['agent_type'] = serialize_type(v['agent_type'],
+                                         known_modules=known_modules)
+    return d
+
+
+def deserialize_type(agent_type, known_modules=[]):
+    if not isinstance(agent_type, str):
+        return agent_type
+    known = known_modules + ['soil.agents', 'soil.agents.custom' ]
+    agent_type = serialization.deserializer(agent_type, known_modules=known)
+    return agent_type
+
+
+def deserialize_distribution(ind, **kwargs):
+    d = deepcopy(ind)
+    for v in d:
+        v['agent_type'] = deserialize_type(v['agent_type'], **kwargs)
     return d
 
 
@@ -336,39 +443,86 @@ def _validate_states(states, topology):
     states = states or []
     if isinstance(states, dict):
         for x in states:
-            assert x in topology.node
+            assert x in topology.nodes
     else:
         assert len(states) <= len(topology)
     return states
 
 
-def _convert_agent_types(ind, to_string=False):
+def _convert_agent_types(ind, to_string=False, **kwargs):
     '''Convenience method to allow specifying agents by class or class name.'''
-    d = deepcopy(ind)
-    for v in d:
-        agent_type = v['agent_type']
-        if to_string and not isinstance(agent_type, str):
-            v['agent_type'] = str(agent_type.__name__)
-        elif not to_string and isinstance(agent_type, str):
-            v['agent_type'] = agent_types[agent_type]
-    return d
+    if to_string:
+        return serialize_distribution(ind, **kwargs)
+    return deserialize_distribution(ind, **kwargs)
 
 
-def _agent_from_distribution(distribution, value=-1):
+def _agent_from_distribution(distribution, value=-1, agent_id=None):
     """Used in the initialization of agents given an agent distribution."""
     if value < 0:
         value = random.random()
-    for d in distribution:
+    for d in sorted(distribution, key=lambda x: x['threshold']):
         threshold = d['threshold']
-        if value >= threshold[0] and value < threshold[1]:
-            state = {}
-            if 'state' in d:
-                state = deepcopy(d['state'])
-            return d['agent_type'], state
+        # Check if the definition matches by id (first) or by threshold
+        if not ((agent_id is not None and threshold == STATIC_THRESHOLD and agent_id in d['ids']) or \
+                (value >= threshold[0] and value < threshold[1])):
+            continue
+        state = {}
+        if 'state' in d:
+            state = deepcopy(d['state'])
+        return d['agent_type'], state
 
     raise Exception('Distribution for value {} not found in: {}'.format(value, distribution))
 
 
+class Geo(NetworkAgent):
+    '''In this type of network, nodes have a "pos" attribute.'''
+
+    def geo_search(self, radius, node=None, center=False, **kwargs):
+        '''Get a list of nodes whose coordinates are closer than *radius* to *node*.'''
+        node = as_node(node if node is not None else self)
+
+        G = self.subgraph(**kwargs)
+
+        pos = nx.get_node_attributes(G, 'pos')
+        if not pos:
+            return []
+        nodes, coords = list(zip(*pos.items()))
+        kdtree = KDTree(coords)  # Cannot provide generator.
+        indices = kdtree.query_ball_point(pos[node], radius)
+        return [nodes[i] for i in indices if center or (nodes[i] != node)]
+
+
+def select(agents, state_id=None, agent_type=None, ignore=None, iterator=False, **kwargs):
+
+    if state_id is not None and not isinstance(state_id, (tuple, list)):
+        state_id = tuple([state_id])
+    if agent_type is not None:
+        try:
+            agent_type = tuple(agent_type)
+        except TypeError:
+            agent_type = tuple([agent_type])
+
+    def matches_all(agent):
+        if state_id is not None:
+            if agent.state.get('id', None) not in state_id:
+                return False
+        if agent_type is not None:
+            if not isinstance(agent, agent_type):
+                return False
+        state = agent.state
+        for k, v in kwargs.items():
+            if state.get(k, None) != v:
+                return False
+        return True
+
+    f = filter(matches_all, agents)
+    if ignore:
+        f = filter(lambda x: x not in ignore, f)
+    if iterator:
+        return f
+    return list(f)
+
+
 from .BassModel import *
 from .BigMarketModel import *
 from .IndependentCascadeModel import *
@@ -376,4 +530,3 @@ from .ModelM2 import *
 from .SentimentCorrelationModel import *
 from .SISaModel import *
 from .CounterModel import *
-from .DrawingAgent import *

soil/analysis.py

@@ -4,7 +4,7 @@ import glob
 import yaml
 from os.path import join
 
-from . import utils, history
+from . import serialization, history
 
 
 def read_data(*args, group=False, **kwargs):
@@ -20,7 +20,7 @@ def _read_data(pattern, *args, from_csv=False, process_args=None, **kwargs):
         process_args = {}
     for folder in glob.glob(pattern):
         config_file = glob.glob(join(folder, '*.yml'))[0]
-        config = yaml.load(open(config_file))
+        config = yaml.load(open(config_file), Loader=yaml.SafeLoader)
         df = None
         if from_csv:
             for trial_data in sorted(glob.glob(join(folder,
@@ -28,13 +28,13 @@ def _read_data(pattern, *args, from_csv=False, process_args=None, **kwargs):
                 df = read_csv(trial_data, **kwargs)
                 yield config_file, df, config
         else:
-            for trial_data in sorted(glob.glob(join(folder, '*.db.sqlite'))):
+            for trial_data in sorted(glob.glob(join(folder, '*.sqlite'))):
                 df = read_sql(trial_data, **kwargs)
                 yield config_file, df, config
 
 
 def read_sql(db, *args, **kwargs):
-    h = history.History(db, backup=False)
+    h = history.History(db_path=db, backup=False, readonly=True)
     df = h.read_sql(*args, **kwargs)
     return df
 
@@ -56,7 +56,7 @@ def read_csv(filename, keys=None, convert_types=False, **kwargs):
 
 
 def convert_row(row):
-    row['value'] = utils.convert(row['value'], row['value_type'])
+    row['value'] = serialization.deserialize(row['value_type'], row['value'])
     return row
 
 
@@ -69,6 +69,13 @@ def convert_types_slow(df):
     df = df.apply(convert_row, axis=1)
     return df
 
+
+def split_processed(df):
+    env = df.loc[:, df.columns.get_level_values(1).isin(['env', 'stats'])]
+    agents = df.loc[:, ~df.columns.get_level_values(1).isin(['env', 'stats'])]
+    return env, agents
+
+
 def split_df(df):
     '''
     Split a dataframe in two dataframes: one with the history of agents,
@@ -123,7 +130,7 @@ def get_count(df, *keys):
         df = df[list(keys)]
     counts = pd.DataFrame()
     for key in df.columns.levels[0]:
-        g = df[key].apply(pd.Series.value_counts, axis=1).fillna(0)
+        g = df[[key]].apply(pd.Series.value_counts, axis=1).fillna(0)
         for value, series in g.iteritems():
             counts[key, value] = series
     counts.columns = pd.MultiIndex.from_tuples(counts.columns)
@@ -133,10 +140,10 @@ def get_count(df, *keys):
 def get_value(df, *keys, aggfunc='sum'):
     if keys:
         df = df[list(keys)]
-    return df.groupby(axis=1, level=0).agg(aggfunc, axis=1)
+    return df.groupby(axis=1, level=0).agg(aggfunc)
 
 
-def plot_all(*args, **kwargs):
+def plot_all(*args, plot_args={}, **kwargs):
     '''
     Read all the trial data and plot the result of applying a function on them.
     '''
@@ -144,14 +151,17 @@ def plot_all(*args, **kwargs):
     ps = []
     for line in dfs:
         f, df, config = line
-        df.plot(title=config['name'])
+        if len(df) < 1:
+            continue
+        df.plot(title=config['name'], **plot_args)
         ps.append(df)
     return ps
 
 def do_all(pattern, func, *keys, include_env=False, **kwargs):
     for config_file, df, config in read_data(pattern, keys=keys):
+        if len(df) < 1:
+            continue
         p = func(df, *keys, **kwargs)
-        p.plot(title=config['name'])
         yield config_file, p, config
 
 

soil/environment.py

@@ -4,18 +4,25 @@ import time
 import csv
 import random
 import simpy
+import yaml
 import tempfile
 import pandas as pd
 from copy import deepcopy
 from networkx.readwrite import json_graph
 
 import networkx as nx
-import nxsim
+import simpy
 
-from . import utils, agents, analysis, history
+from . import serialization, agents, analysis, history, utils
 
+# These properties will be copied when pickling/unpickling the environment
+_CONFIG_PROPS = [ 'name',
+                 'states',
+                 'default_state',
+                 'interval',
+                 ]
 
-class SoilEnvironment(nxsim.NetworkEnvironment):
+class Environment(simpy.Environment):
     """
     The environment is key in a simulation. It contains the network topology,
     a reference to network and environment agents, as well as the environment
@@ -23,7 +30,7 @@ class SoilEnvironment(nxsim.NetworkEnvironment):
 
     The environment parameters and the state of every agent can be accessed
     both by using the environment as a dictionary or with the environment's 
-    :meth:`soil.environment.SoilEnvironment.get` method.
+    :meth:`soil.environment.Environment.get` method.
     """
 
     def __init__(self, name=None,
@@ -33,31 +40,34 @@ class SoilEnvironment(nxsim.NetworkEnvironment):
                  default_state=None,
                  interval=1,
                  seed=None,
-                 dry_run=False,
-                 dir_path=None,
                  topology=None,
-                 *args, **kwargs):
+                 initial_time=0,
+                 **environment_params):
+
+
         self.name = name or 'UnnamedEnvironment'
+        seed = seed or time.time()
+        random.seed(seed)
         if isinstance(states, list):
             states = dict(enumerate(states))
         self.states = deepcopy(states) if states else {}
         self.default_state = deepcopy(default_state) or {}
         if not topology:
             topology = nx.Graph()
-        super().__init__(*args, topology=topology, **kwargs)
+        self.G = nx.Graph(topology) 
+
+        super().__init__(initial_time=initial_time)
+        self.environment_params = environment_params
+
         self._env_agents = {}
-        self.dry_run = dry_run
         self.interval = interval
-        self.dir_path = dir_path or tempfile.mkdtemp('soil-env')
-        self.get_path()
-        self._history = history.History(name=self.name if not dry_run else None,
-                                        dir_path=self.dir_path)
+        self._history = history.History(name=self.name,
+                                        backup=True)
+        self['SEED'] = seed
         # Add environment agents first, so their events get
         # executed before network agents
         self.environment_agents = environment_agents or []
         self.network_agents = network_agents or []
-        self['SEED'] = seed or time.time()
-        random.seed(self['SEED'])
 
     @property
     def agents(self):
@@ -84,29 +94,51 @@ class SoilEnvironment(nxsim.NetworkEnvironment):
     @property
     def network_agents(self):
         for i in self.G.nodes():
-            node = self.G.node[i]
+            node = self.G.nodes[i]
             if 'agent' in node:
                 yield node['agent']
 
     @network_agents.setter
     def network_agents(self, network_agents):
-        if not network_agents:
-            return
+        self._network_agents = network_agents
         for ix in self.G.nodes():
-            agent, state = agents._agent_from_distribution(network_agents)
-            self.set_agent(ix, agent_type=agent, state=state)
+            self.init_agent(ix, agent_distribution=network_agents)
+
+    def init_agent(self, agent_id, agent_distribution):
+        node = self.G.nodes[agent_id]
+        init = False
+        state = dict(node)
+
+        agent_type = None
+        if 'agent_type' in self.states.get(agent_id, {}):
+            agent_type = self.states[agent_id]['agent_type']
+        elif 'agent_type' in node:
+            agent_type = node['agent_type']
+        elif 'agent_type' in self.default_state:
+            agent_type = self.default_state['agent_type']
+
+        if agent_type:
+            agent_type = agents.deserialize_type(agent_type)
+        elif agent_distribution:
+            agent_type, state = agents._agent_from_distribution(agent_distribution, agent_id=agent_id)
+        else:
+            serialization.logger.debug('Skipping node {}'.format(agent_id))
+            return
+        return self.set_agent(agent_id, agent_type, state)
 
     def set_agent(self, agent_id, agent_type, state=None):
         node = self.G.nodes[agent_id]
-        defstate = deepcopy(self.default_state)
+        defstate = deepcopy(self.default_state) or {}
         defstate.update(self.states.get(agent_id, {}))
+        defstate.update(node.get('state', {}))
         if state:
             defstate.update(state)
-        state = defstate
-        state.update(node.get('state', {}))
-        a = agent_type(environment=self,
-                       agent_id=agent_id,
-                       state=state)
+        a = None
+        if agent_type:
+            state = defstate
+            a = agent_type(environment=self,
+                           agent_id=agent_id,
+                           state=state)
         node['agent'] = a
         return a
 
@@ -117,18 +149,21 @@ class SoilEnvironment(nxsim.NetworkEnvironment):
         a['visible'] = True
         return a
 
-    def add_edge(self, agent1, agent2, attrs=None):
-        return self.G.add_edge(agent1, agent2)
+    def add_edge(self, agent1, agent2, start=None, **attrs):
+        if hasattr(agent1, 'id'):
+            agent1 = agent1.id
+        if hasattr(agent2, 'id'):
+            agent2 = agent2.id
+        start = start or self.now
+        return self.G.add_edge(agent1, agent2, **attrs)
 
-    def run(self, *args, **kwargs):
+    def run(self, until, *args, **kwargs):
         self._save_state()
-        super().run(*args, **kwargs)
+        super().run(until, *args, **kwargs)
         self._history.flush_cache()
 
     def _save_state(self, now=None):
-        # for agent in self.agents:
-        #     agent.save_state()
-        utils.logger.debug('Saving state @{}'.format(self.now))
+        serialization.logger.debug('Saving state @{}'.format(self.now))
         self._history.save_records(self.state_to_tuples(now=now))
 
     def save_state(self):
@@ -139,7 +174,7 @@ class SoilEnvironment(nxsim.NetworkEnvironment):
         self._save_state()
         while self.peek() != simpy.core.Infinity:
             delay = max(self.peek() - self.now, self.interval)
-            utils.logger.debug('Step: {}'.format(self.now))
+            serialization.logger.debug('Step: {}'.format(self.now))
             ev = self.event()
             ev._ok = True
             # Schedule the event with minimum priority so
@@ -181,45 +216,33 @@ class SoilEnvironment(nxsim.NetworkEnvironment):
         '''
         return self[key] if key in self else default
 
-    def get_path(self, dir_path=None):
-        dir_path = dir_path or self.dir_path
-        if not os.path.exists(dir_path):
-            try:
-                os.makedirs(dir_path)
-            except FileExistsError:
-                pass
-        return dir_path
-
     def get_agent(self, agent_id):
-        return self.G.node[agent_id]['agent']
+        return self.G.nodes[agent_id]['agent']
 
-    def get_agents(self):
-        return list(self.agents)
+    def get_agents(self, nodes=None):
+        if nodes is None:
+            return list(self.agents)
+        return [self.G.nodes[i]['agent'] for i in nodes]
 
-    def dump_csv(self, dir_path=None):
-        csv_name = os.path.join(self.get_path(dir_path),
-                                '{}.environment.csv'.format(self.name))
-
-        with open(csv_name, 'w') as f:
+    def dump_csv(self, f):
+        with utils.open_or_reuse(f, 'w') as f:
             cr = csv.writer(f)
-            cr.writerow(('agent_id', 't_step', 'key', 'value', 'value_type'))
+            cr.writerow(('agent_id', 't_step', 'key', 'value'))
             for i in self.history_to_tuples():
                 cr.writerow(i)
 
-    def dump_gexf(self, dir_path=None):
+    def dump_gexf(self, f):
         G = self.history_to_graph()
-        graph_path = os.path.join(self.get_path(dir_path),
-                                  self.name+".gexf")
         # Workaround for geometric models
         # See soil/soil#4
         for node in G.nodes():
-            if 'pos' in G.node[node]:
-                G.node[node]['viz'] = {"position": {"x": G.node[node]['pos'][0], "y": G.node[node]['pos'][1], "z": 0.0}}
-                del (G.node[node]['pos'])
+            if 'pos' in G.nodes[node]:
+                G.nodes[node]['viz'] = {"position": {"x": G.nodes[node]['pos'][0], "y": G.nodes[node]['pos'][1], "z": 0.0}}
+                del (G.nodes[node]['pos'])
 
-        nx.write_gexf(G, graph_path, version="1.2draft")
+        nx.write_gexf(G, f, version="1.2draft")
 
-    def dump(self, dir_path=None, formats=None):
+    def dump(self, *args, formats=None, **kwargs):
         if not formats:
             return
         functions = {
@@ -228,10 +251,13 @@ class SoilEnvironment(nxsim.NetworkEnvironment):
         }
         for f in formats:
             if f in functions:
-                functions[f](dir_path)
+                functions[f](*args, **kwargs)
             else:
                 raise ValueError('Unknown format: {}'.format(f))
 
+    def dump_sqlite(self, f):
+        return self._history.dump(f)
+
     def state_to_tuples(self, now=None):
         if now is None:
             now = self.now
@@ -263,31 +289,30 @@ class SoilEnvironment(nxsim.NetworkEnvironment):
             history = self[agent.id, None, None]
             if not history:
                 continue
-            for t_step, state in reversed(sorted(list(history.items()))):
-                for attribute, value in state.items():
-                    if attribute == 'visible':
-                        nowvisible = state[attribute]
-                        if nowvisible and not lastvisible:
-                            laststep = t_step
-                        if not nowvisible and lastvisible:
-                            spells.append((laststep, t_step))
+            for t_step, attribute, value in sorted(list(history)):
+                if attribute == 'visible':
+                    nowvisible = value
+                    if nowvisible and not lastvisible:
+                        laststep = t_step
+                    if not nowvisible and lastvisible:
+                        spells.append((laststep, t_step))
 
-                        lastvisible = nowvisible
-                    else:
-                        key = 'attr_' + attribute
-                        if key not in attributes:
-                            attributes[key] = list()
-                        if key not in lastattributes:
-                            lastattributes[key] = (state[attribute], t_step)
-                        elif lastattributes[key][0] != value:
-                            last_value, laststep = lastattributes[key]
-                            value = (last_value, t_step, laststep)
-                            if key not in attributes:
-                                attributes[key] = list()
-                            attributes[key].append(value)
-                            lastattributes[key] = (state[attribute], t_step)
+                    lastvisible = nowvisible
+                    continue
+                key = 'attr_' + attribute
+                if key not in attributes:
+                    attributes[key] = list()
+                if key not in lastattributes:
+                    lastattributes[key] = (value, t_step)
+                elif lastattributes[key][0] != value:
+                    last_value, laststep = lastattributes[key]
+                    commit_value = (last_value, laststep, t_step)
+                    if key not in attributes:
+                        attributes[key] = list()
+                    attributes[key].append(commit_value)
+                    lastattributes[key] = (value, t_step)
             for k, v in lastattributes.items():
-                attributes[k].append((v[0], 0, v[1]))
+                attributes[k].append((v[0], v[1], None))
             if lastvisible:
                 spells.append((laststep, None))
             if spells:
@@ -298,17 +323,23 @@ class SoilEnvironment(nxsim.NetworkEnvironment):
         return G
 
     def __getstate__(self):
-        state = self.__dict__.copy()
+        state = {}
+        for prop in _CONFIG_PROPS:
+            state[prop] = self.__dict__[prop]
         state['G'] = json_graph.node_link_data(self.G)
-        state['network_agents'] = agents._serialize_distribution(self.network_agents)
-        state['environment_agents'] = agents._convert_agent_types(self.environment_agents,
-                                                                 to_string=True)
-        del state['_queue']
+        state['environment_agents'] = self._env_agents
+        state['history'] = self._history
+        state['_now'] = self._now
         return state
 
     def __setstate__(self, state):
-        self.__dict__ = state
+        for prop in _CONFIG_PROPS:
+            self.__dict__[prop] = state[prop]
+        self._env_agents = state['environment_agents']
         self.G = json_graph.node_link_graph(state['G'])
-        self.network_agents = self.calculate_distribution(self._convert_agent_types(self.network_agents))
-        self.environment_agents = self._convert_agent_types(self.environment_agents)
-        return state
+        self._history = state['history']
+        self._now = state['_now']
+        self._queue = []
+
+
+SoilEnvironment = Environment

soil/exporters.py

@@ -0,0 +1,158 @@
+import os
+import csv as csvlib
+import time
+from io import BytesIO
+
+import matplotlib.pyplot as plt
+import networkx as nx
+
+
+from .serialization import deserialize
+from .utils import open_or_reuse, logger, timer
+
+
+from . import utils
+
+
+class DryRunner(BytesIO):
+    def __init__(self, fname, *args, copy_to=None, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.__fname = fname
+        self.__copy_to = copy_to
+
+    def write(self, txt):
+        if self.__copy_to:
+            self.__copy_to.write('{}:::{}'.format(self.__fname, txt))
+        try:
+            super().write(txt)
+        except TypeError:
+            super().write(bytes(txt, 'utf-8'))
+
+    def close(self):
+        content = '(binary data not shown)'
+        try:
+            content = self.getvalue().decode()
+        except UnicodeDecodeError:
+            pass
+        logger.info('**Not** written to {} (dry run mode):\n\n{}\n\n'.format(self.__fname, content))
+        super().close()
+
+
+class Exporter:
+    '''
+    Interface for all exporters. It is not necessary, but it is useful
+    if you don't plan to implement all the methods.
+    '''
+
+    def __init__(self, simulation, outdir=None, dry_run=None, copy_to=None):
+        self.simulation = simulation
+        outdir = outdir or os.path.join(os.getcwd(), 'soil_output')
+        self.outdir = os.path.join(outdir,
+                                   simulation.group or '',
+                                   simulation.name)
+        self.dry_run = dry_run
+        self.copy_to = copy_to
+
+    def start(self):
+        '''Method to call when the simulation starts'''
+        pass
+
+    def end(self, stats):
+        '''Method to call when the simulation ends'''
+        pass
+
+    def trial(self, env, stats):
+        '''Method to call when a trial ends'''
+        pass
+
+    def output(self, f, mode='w', **kwargs):
+        if self.dry_run:
+            f = DryRunner(f, copy_to=self.copy_to)
+        else:
+            try:
+                if not os.path.isabs(f):
+                    f = os.path.join(self.outdir, f)
+            except TypeError:
+                pass
+        return open_or_reuse(f, mode=mode, **kwargs)
+
+
+class default(Exporter):
+    '''Default exporter. Writes sqlite results, as well as the simulation YAML'''
+
+    def start(self):
+        if not self.dry_run:
+            logger.info('Dumping results to %s', self.outdir)
+            self.simulation.dump_yaml(outdir=self.outdir)
+        else:
+            logger.info('NOT dumping results')
+
+    def trial(self, env, stats):
+        if not self.dry_run:
+            with timer('Dumping simulation {} trial {}'.format(self.simulation.name,
+                                                               env.name)):
+                with self.output('{}.sqlite'.format(env.name), mode='wb') as f:
+                    env.dump_sqlite(f)
+
+    def end(self, stats):
+          with timer('Dumping simulation {}\'s stats'.format(self.simulation.name)):
+              with self.output('{}.sqlite'.format(self.simulation.name), mode='wb') as f:
+                  self.simulation.dump_sqlite(f)
+
+
+
+class csv(Exporter):
+    '''Export the state of each environment (and its agents) in a separate CSV file'''
+    def trial(self, env, stats):
+        with timer('[CSV] Dumping simulation {} trial {} @ dir {}'.format(self.simulation.name,
+                                                                          env.name,
+                                                                          self.outdir)):
+            with self.output('{}.csv'.format(env.name)) as f:
+                env.dump_csv(f)
+
+            with self.output('{}.stats.csv'.format(env.name)) as f:
+                statwriter = csvlib.writer(f, delimiter='\t', quotechar='"', quoting=csvlib.QUOTE_ALL)
+
+                for stat in stats:
+                    statwriter.writerow(stat)
+
+
+class gexf(Exporter):
+    def trial(self, env, stats):
+        if self.dry_run:
+            logger.info('Not dumping GEXF in dry_run mode')
+            return
+
+        with timer('[GEXF] Dumping simulation {} trial {}'.format(self.simulation.name,
+                                                                  env.name)):
+            with self.output('{}.gexf'.format(env.name), mode='wb') as f:
+                env.dump_gexf(f)
+
+
+class dummy(Exporter):
+
+    def start(self):
+        with self.output('dummy', 'w') as f:
+            f.write('simulation started @ {}\n'.format(time.time()))
+
+    def trial(self, env, stats):
+        with self.output('dummy', 'w') as f:
+            for i in env.history_to_tuples():
+                f.write(','.join(map(str, i)))
+                f.write('\n')
+
+    def sim(self, stats):
+        with self.output('dummy', 'a') as f:
+            f.write('simulation ended @ {}\n'.format(time.time()))
+
+
+
+class graphdrawing(Exporter):
+
+    def trial(self, env, stats):
+        # Outside effects
+        f = plt.figure()
+        nx.draw(env.G, node_size=10, width=0.2, pos=nx.spring_layout(env.G, scale=100), ax=f.add_subplot(111))
+        with open('graph-{}.png'.format(env.name)) as f:
+            f.savefig(f)
+

soil/history.py

@@ -3,9 +3,15 @@ import os
 import pandas as pd
 import sqlite3
 import copy
-from collections import UserDict, Iterable, namedtuple
+import logging
+import tempfile
 
-from . import utils
+logger = logging.getLogger(__name__)
+
+from collections import UserDict, namedtuple
+
+from . import serialization
+from .utils import open_or_reuse, unflatten_dict
 
 
 class History:
@@ -13,57 +19,158 @@ class History:
     Store and retrieve values from a sqlite database.
     """
 
-    def __init__(self, db_path=None, name=None, dir_path=None, backup=True):
-        if db_path is None and name:
-            db_path = os.path.join(dir_path or os.getcwd(),
-                                   '{}.db.sqlite'.format(name))
-        if db_path is None:
-            db_path = ":memory:"
-        else:
-            if backup and os.path.exists(db_path):
+    def __init__(self, name=None, db_path=None, backup=False, readonly=False):
+        if readonly and (not os.path.exists(db_path)):
+            raise Exception('The DB file does not exist. Cannot open in read-only mode')
+
+        self._db = None
+        self._temp = db_path is None
+        self._stats_columns = None
+        self.readonly = readonly
+
+        if self._temp:
+            if not name:
+                name = time.time()
+            # The file will be deleted as soon as it's closed
+            # Normally, that will be on destruction
+            db_path = tempfile.NamedTemporaryFile(suffix='{}.sqlite'.format(name)).name
+
+
+        if backup and os.path.exists(db_path):
                 newname = db_path + '.backup{}.sqlite'.format(time.time())
                 os.rename(db_path, newname)
-        self._db_path = db_path
-        if isinstance(db_path, str):
-            self._db = sqlite3.connect(db_path)
-        else:
-            self._db = db_path
 
-        with self._db:
-            self._db.execute('''CREATE TABLE IF NOT EXISTS history (agent_id text, t_step int, key text, value text text)''')
-            self._db.execute('''CREATE TABLE IF NOT EXISTS value_types (key text, value_type text)''')
-            self._db.execute('''CREATE UNIQUE INDEX IF NOT EXISTS idx_history ON history (agent_id, t_step, key);''')
+        self.db_path = db_path
+
+        self.db = db_path
         self._dtypes = {}
         self._tups = []
 
-    def conversors(self, key):
-        """Get the serializer and deserializer for a given key."""
-        if key not in self._dtypes:
-            self.read_types()
-        return self._dtypes[key]
+
+        if self.readonly:
+            return
+
+        with self.db:
+            logger.debug('Creating database {}'.format(self.db_path))
+            self.db.execute('''CREATE TABLE IF NOT EXISTS history (agent_id text, t_step int, key text, value text)''')
+            self.db.execute('''CREATE TABLE IF NOT EXISTS value_types (key text, value_type text)''')
+            self.db.execute('''CREATE TABLE IF NOT EXISTS stats (trial_id text)''')
+            self.db.execute('''CREATE UNIQUE INDEX IF NOT EXISTS idx_history ON history (agent_id, t_step, key);''')
+
+    @property
+    def db(self):
+        try:
+            self._db.cursor()
+        except (sqlite3.ProgrammingError, AttributeError):
+            self.db = None  # Reset the database
+        return self._db
+
+    @db.setter
+    def db(self, db_path=None):
+        self._close()
+        db_path = db_path or self.db_path
+        if isinstance(db_path, str):
+            logger.debug('Connecting to database {}'.format(db_path))
+            self._db = sqlite3.connect(db_path)
+            self._db.row_factory = sqlite3.Row
+        else:
+            self._db = db_path
+
+    def _close(self):
+        if self._db is None:
+            return
+        self.flush_cache()
+        self._db.close()
+        self._db = None
+
+    def save_stats(self, stat):
+        if self.readonly:
+            print('DB in readonly mode')
+            return
+        if not stat:
+            return
+        with self.db:
+            if not self._stats_columns:
+                self._stats_columns = list(c['name'] for c in self.db.execute('PRAGMA table_info(stats)'))
+
+            for column, value in stat.items():
+                if column in self._stats_columns:
+                    continue
+                dtype = 'text'
+                if not isinstance(value, str):
+                    try:
+                        float(value)
+                        dtype = 'real'
+                        int(value)
+                        dtype = 'int'
+                    except ValueError:
+                        pass
+                self.db.execute('ALTER TABLE stats ADD "{}" "{}"'.format(column, dtype))
+                self._stats_columns.append(column)
+
+            columns = ", ".join(map(lambda x: '"{}"'.format(x), stat.keys()))
+            values = ", ".join(['"{0}"'.format(col) for col in stat.values()])
+            query = "INSERT INTO stats ({columns}) VALUES ({values})".format(
+                columns=columns,
+                values=values
+            )
+            self.db.execute(query)
+
+    def get_stats(self, unflatten=True):
+        rows = self.db.execute("select * from stats").fetchall()
+        res = []
+        for row in rows:
+            d = {}
+            for k in row.keys():
+                if row[k] is None:
+                    continue
+                d[k] = row[k]
+            if unflatten:
+                d = unflatten_dict(d)
+            res.append(d)
+        return res
 
     @property
     def dtypes(self):
+        self._read_types()
         return {k:v[0] for k, v in self._dtypes.items()}
 
     def save_tuples(self, tuples):
+        '''
+        Save a series of tuples, converting them to records if necessary
+        '''
         self.save_records(Record(*tup) for tup in tuples)
 
     def save_records(self, records):
-        with self._db:
-            for rec in records:
-                if not isinstance(rec, Record):
-                    rec = Record(*rec)
-                if rec.key not in self._dtypes:
-                    name = utils.name(rec.value)
-                    serializer = utils.serializer(name)
-                    deserializer = utils.deserializer(name)
-                    self._dtypes[rec.key] = (name, serializer, deserializer)
-                    self._db.execute("replace into value_types (key, value_type) values (?, ?)", (rec.key, name))
-                self._db.execute("replace into history(agent_id, t_step, key, value) values (?, ?, ?, ?)", (rec.agent_id, rec.t_step, rec.key, rec.value))
+        '''
+        Save a collection of records
+        '''
+        for record in records:
+            if not isinstance(record, Record):
+                record = Record(*record)
+            self.save_record(*record)
 
-    def save_record(self, *args, **kwargs):
-        self._tups.append(Record(*args, **kwargs))
+    def save_record(self, agent_id, t_step, key, value):
+        '''
+        Save a collection of records to the database.
+        Database writes are cached.
+        '''
+        if self.readonly:
+            raise Exception('DB in readonly mode')
+        if key not in self._dtypes:
+            self._read_types()
+            if key not in self._dtypes:
+                name = serialization.name(value)
+                serializer = serialization.serializer(name)
+                deserializer = serialization.deserializer(name)
+                self._dtypes[key] = (name, serializer, deserializer)
+                with self.db:
+                    self.db.execute("replace into value_types (key, value_type) values (?, ?)", (key, name))
+        value = self._dtypes[key][1](value)
+        self._tups.append(Record(agent_id=agent_id,
+                                 t_step=t_step,
+                                 key=key,
+                                 value=value))
         if len(self._tups) > 100:
             self.flush_cache()
 
@@ -72,27 +179,37 @@ class History:
         Use a cache to save state changes to avoid opening a session for every change.
         The cache will be flushed at the end of the simulation, and when history is accessed.
         '''
-        self.save_records(self._tups)
+        if self.readonly:
+            raise Exception('DB in readonly mode')
+        logger.debug('Flushing cache {}'.format(self.db_path))
+        with self.db:
+            for rec in self._tups:
+                self.db.execute("replace into history(agent_id, t_step, key, value) values (?, ?, ?, ?)", (rec.agent_id, rec.t_step, rec.key, rec.value))
         self._tups = list()
 
     def to_tuples(self):
-            self.flush_cache()
-            with self._db:
-                res = self._db.execute("select agent_id, t_step, key, value from history ").fetchall()
-            for r in res:
-                agent_id, t_step, key, value = r
-                _, _ , des = self.conversors(key)
-                yield agent_id, t_step, key, des(value)
+        self.flush_cache()
+        with self.db:
+            res = self.db.execute("select agent_id, t_step, key, value from history ").fetchall()
+        for r in res:
+            agent_id, t_step, key, value = r
+            if key not in self._dtypes:
+                self._read_types()
+            if key not in self._dtypes:
+                raise ValueError("Unknown datatype for {} and {}".format(key, value))
+            value = self._dtypes[key][2](value)
+            yield agent_id, t_step, key, value
 
-    def read_types(self):
-            with self._db:
-                res = self._db.execute("select key, value_type from value_types ").fetchall()
-            for k, v in res:
-                serializer = utils.serializer(v)
-                deserializer = utils.deserializer(v)
-                self._dtypes[k] = (v, serializer, deserializer)
+    def _read_types(self):
+        with self.db:
+            res = self.db.execute("select key, value_type from value_types ").fetchall()
+        for k, v in res:
+            serializer = serialization.serializer(v)
+            deserializer = serialization.deserializer(v)
+            self._dtypes[k] = (v, serializer, deserializer)
 
     def __getitem__(self, key):
+        self.flush_cache()
         key = Key(*key)
         agent_ids = [key.agent_id] if key.agent_id is not None else []
         t_steps = [key.t_step] if key.t_step is not None else []
@@ -102,13 +219,13 @@ class History:
                            t_steps=t_steps,
                            keys=keys)
         r = Records(df, filter=key, dtypes=self._dtypes)
-        return r.value()
-
-
+        if r.resolved:
+            return r.value()
+        return r
 
     def read_sql(self, keys=None, agent_ids=None, t_steps=None, convert_types=False, limit=-1):
 
-        self.read_types()
+        self._read_types()
 
         def escape_and_join(v):
             if v is None:
@@ -122,7 +239,13 @@ class History:
 
         last_df = None
         if t_steps:
-            # Look for the last value before the minimum step in the query
+            # Convert negative indices into positive
+            if any(x<0 for x in t_steps):
+                max_t = int(self.db.execute("select max(t_step) from history").fetchone()[0])
+                t_steps = [t if t>0 else max_t+1+t for t in t_steps]
+
+            # We will be doing ffill interpolation, so we need to look for
+            # the last value before the minimum step in the query
             min_step = min(t_steps)
             last_filters = ['t_step < {}'.format(min_step),]
             last_filters = last_filters + filters
@@ -141,7 +264,7 @@ class History:
                h1.key      = h2.key       and
                h1.t_step   = h2.t_step
             '''.format(condition=condition)
-            last_df = pd.read_sql_query(last_query, self._db)
+            last_df = pd.read_sql_query(last_query, self.db)
 
             filters.append("t_step >= '{}' and t_step <= '{}'".format(min_step, max(t_steps)))
 
@@ -149,7 +272,7 @@ class History:
         if filters:
             condition = 'where {} '.format(' and '.join(filters))
         query = 'select * from history {} limit {}'.format(condition, limit)
-        df = pd.read_sql_query(query, self._db)
+        df = pd.read_sql_query(query, self.db)
         if last_df is not None:
             df = pd.concat([df, last_df])
 
@@ -160,11 +283,31 @@ class History:
         for k, v in self._dtypes.items():
             if k in df_p:
                 dtype, _, deserial = v
-                df_p[k] = df_p[k].fillna(method='ffill').fillna(deserial()).astype(dtype)
+                try:
+                    df_p[k] = df_p[k].fillna(method='ffill').astype(dtype)
+                except (TypeError, ValueError):
+                    # Avoid forward-filling unknown/incompatible types
+                    continue
         if t_steps:
             df_p = df_p.reindex(t_steps, method='ffill')
         return df_p.ffill()
 
+    def __getstate__(self):
+        state = dict(**self.__dict__)
+        del state['_db']
+        del state['_dtypes']
+        return state
+
+    def __setstate__(self, state):
+        self.__dict__ = state
+        self._dtypes = {}
+        self._db = None
+
+    def dump(self, f):
+        self._close()
+        for line in open_or_reuse(self.db_path, 'rb'):
+            f.write(line)
+
 
 class Records():
 
@@ -214,18 +357,29 @@ class Records():
                 i = self._df[f.key][str(f.agent_id)]
                 ix = i.index.get_loc(f.t_step, method='ffill')
                 return i.iloc[ix]
-            except KeyError:
+            except KeyError as ex:
                 return self.dtypes[f.key][2]()
-        return self
+        return list(self)
+
+    def df(self):
+        return self._df
 
     def __getitem__(self, k):
         n = copy.copy(self)
         n.filter(k)
-        return n.value()
+        if n.resolved:
+            return n.value()
+        return n
 
     def __len__(self):
         return len(self._df)
 
+    def __str__(self):
+        if self.resolved:
+            return str(self.value())
+        return '<Records for [{}]>'.format(self._filter)
 
 Key = namedtuple('Key', ['agent_id', 't_step', 'key'])
 Record = namedtuple('Record', 'agent_id t_step key value')
+
+Stat = namedtuple('Stat', 'trial_id')

soil/serialization.py

@@ -0,0 +1,220 @@
+import os
+import logging
+import ast
+import sys
+import importlib
+from glob import glob
+from itertools import product, chain
+
+import yaml
+import networkx as nx
+
+from jinja2 import Template
+
+
+logger = logging.getLogger('soil')
+logger.setLevel(logging.INFO)
+
+
+def load_network(network_params, dir_path=None):
+    G = nx.Graph()
+
+    if 'path' in network_params:
+        path = network_params['path']
+        if dir_path and not os.path.isabs(path):
+            path = os.path.join(dir_path, path)
+        extension = os.path.splitext(path)[1][1:]
+        kwargs = {}
+        if extension == 'gexf':
+            kwargs['version'] = '1.2draft'
+            kwargs['node_type'] = int
+        try:
+            method = getattr(nx.readwrite, 'read_' + extension)
+        except AttributeError:
+            raise AttributeError('Unknown format')
+        G = method(path, **kwargs)
+
+    elif 'generator' in network_params:
+        net_args = network_params.copy()
+        net_gen = net_args.pop('generator')
+
+        if dir_path not in sys.path:
+            sys.path.append(dir_path)
+
+        method = deserializer(net_gen,
+                              known_modules=['networkx.generators',])
+        G = method(**net_args)
+
+    return G
+
+
+
+
+def load_file(infile):
+    with open(infile, 'r') as f:
+        return list(chain.from_iterable(map(expand_template, load_string(f))))
+
+
+def load_string(string):
+    yield from yaml.load_all(string, Loader=yaml.FullLoader)
+
+
+def expand_template(config):
+    if 'template' not in config:
+        yield config
+        return
+    if 'vars' not in config:
+        raise ValueError(('You must provide a definition of variables'
+                          ' for the template.'))
+
+    template = config['template']
+
+    if not isinstance(template, str):
+        template = yaml.dump(template)
+
+    template = Template(template)
+
+    params = params_for_template(config)
+
+    blank_str = template.render({k: 0 for k in params[0].keys()})
+    blank = list(load_string(blank_str))
+    if len(blank) > 1:
+        raise ValueError('Templates must not return more than one configuration')
+    if 'name' in blank[0]:
+        raise ValueError('Templates cannot be named, use group instead')
+
+    for ps in params:
+        string = template.render(ps)
+        for c in load_string(string):
+            yield c
+
+
+def params_for_template(config):
+    sampler_config = config.get('sampler', {'N': 100})
+    sampler = sampler_config.pop('method', 'SALib.sample.morris.sample')
+    sampler = deserializer(sampler)
+    bounds = config['vars']['bounds']
+
+    problem = {
+        'num_vars': len(bounds),
+        'names': list(bounds.keys()),
+        'bounds': list(v for v in bounds.values())
+    }
+    samples = sampler(problem, **sampler_config)
+
+    lists = config['vars'].get('lists', {})
+    names = list(lists.keys())
+    values = list(lists.values())
+    combs = list(product(*values))
+
+    allnames = names + problem['names']
+    allvalues = [(list(i[0])+list(i[1])) for i in product(combs, samples)]
+    params = list(map(lambda x: dict(zip(allnames, x)), allvalues))
+    return params
+
+
+def load_files(*patterns, **kwargs):
+    for pattern in patterns:
+        for i in glob(pattern, **kwargs):
+            for config in load_file(i):
+                path = os.path.abspath(i)
+                if 'dir_path' not in config:
+                    config['dir_path'] = os.path.dirname(path)
+                yield config, path
+
+
+def load_config(config):
+    if isinstance(config, dict):
+        yield config, os.getcwd()
+    else:
+        yield from load_files(config)
+
+
+builtins = importlib.import_module('builtins')
+
+def name(value, known_modules=[]):
+    '''Return a name that can be imported, to serialize/deserialize an object'''
+    if value is None:
+        return 'None'
+    if not isinstance(value, type):  # Get the class name first
+        value = type(value)
+    tname = value.__name__
+    if hasattr(builtins, tname):
+        return tname
+    modname = value.__module__
+    if modname == '__main__':
+        return tname
+    if known_modules and modname in known_modules:
+        return tname
+    for kmod in known_modules:
+        if not kmod:
+            continue
+        module = importlib.import_module(kmod)
+        if hasattr(module, tname):
+            return tname
+    return '{}.{}'.format(modname, tname)
+
+
+def serializer(type_):
+    if type_ != 'str' and hasattr(builtins, type_):
+        return repr
+    return lambda x: x
+
+
+def serialize(v, known_modules=[]):
+    '''Get a text representation of an object.'''
+    tname = name(v, known_modules=known_modules)
+    func = serializer(tname)
+    return func(v), tname
+
+def deserializer(type_, known_modules=[]):
+    if type(type_) != str:  # Already deserialized
+        return type_
+    if type_ == 'str':
+        return lambda x='': x
+    if type_ == 'None':
+        return lambda x=None: None
+    if hasattr(builtins, type_):  # Check if it's a builtin type
+        cls = getattr(builtins, type_)
+        return lambda x=None: ast.literal_eval(x) if x is not None else cls()
+    # Otherwise, see if we can find the module and the class
+    modules = known_modules or []
+    options = []
+
+    for mod in modules:
+        if mod:
+            options.append((mod, type_))
+
+    if '.' in type_:  # Fully qualified module
+        module, type_ = type_.rsplit(".", 1)
+        options.append ((module, type_))
+
+    errors = []
+    for modname, tname in options:
+        try:
+            module = importlib.import_module(modname)
+            cls = getattr(module, tname)
+            return getattr(cls, 'deserialize', cls)
+        except (ImportError, AttributeError) as ex:
+            errors.append((modname, tname, ex))
+    raise Exception('Could not find type {}. Tried: {}'.format(type_, errors))
+
+
+def deserialize(type_, value=None, **kwargs):
+    '''Get an object from a text representation'''
+    if not isinstance(type_, str):
+        return type_
+    des = deserializer(type_, **kwargs)
+    if value is None:
+        return des
+    return des(value)
+
+
+def deserialize_all(names, *args, known_modules=['soil'], **kwargs):
+    '''Return the set of exporters for a simulation, given the exporter names'''
+    exporters = []
+    for name in names:
+        mod = deserialize(name, known_modules=known_modules)
+        exporters.append(mod(*args, **kwargs))
+    return exporters
+

soil/simulation.py

@@ -1,8 +1,10 @@
 import os
 import time
-import imp
+import importlib
 import sys
 import yaml
+import traceback
+import logging
 import networkx as nx
 from networkx.readwrite import json_graph
 from multiprocessing import Pool
@@ -10,15 +12,19 @@ from functools import partial
 
 import pickle
 
-from nxsim import NetworkSimulation
-
-from . import utils, environment, basestring, agents
+from . import serialization, utils, basestring, agents
+from .environment import Environment
 from .utils import logger
+from .exporters import default
+from .stats import defaultStats
+from .history import History
 
 
-class SoilSimulation(NetworkSimulation):
+#TODO: change documentation for simulation
+
+class Simulation:
     """
-    Subclass of nsim.NetworkSimulation with three main differences:
+    Similar to nsim.NetworkSimulation with three main differences:
         1) agent type can be specified by name or by class.
         2) instead of just one type, a network agents distribution can be used.
            The distribution specifies the weight (or probability) of each
@@ -43,112 +49,224 @@ class SoilSimulation(NetworkSimulation):
           'agent_type_1'.
         3) if no initial state is given, each node's state will be set
            to `{'id': 0}`.
-    """
-    def __init__(self, name=None, topology=None, network_params=None,
-                 network_agents=None, agent_type=None, states=None,
-                 default_state=None, interval=1, dump=None, dry_run=False,
-                 dir_path=None, num_trials=1, max_time=100,
-                 agent_module=None, load_module=None, seed=None,
-                 environment_agents=None, environment_params=None):
 
-        if topology is None:
-            topology = utils.load_network(network_params,
-                                          dir_path=dir_path)
-        elif isinstance(topology, basestring) or isinstance(topology, dict):
-            topology = json_graph.node_link_graph(topology)
+    Parameters
+    ---------
+    name : str, optional
+        name of the Simulation
+    group : str, optional
+        a group name can be used to link simulations
+    topology : networkx.Graph instance, optional
+    network_params : dict
+        parameters used to create a topology with networkx, if no topology is given
+    network_agents : dict
+        definition of agents to populate the topology with
+    agent_type : NetworkAgent subclass, optional
+        Default type of NetworkAgent to use for nodes not specified in network_agents
+    states : list, optional
+        List of initial states corresponding to the nodes in the topology. Basic form is a list of integers
+        whose value indicates the state
+    dir_path: str, optional
+        Directory path to load simulation assets (files, modules...)
+    seed : str, optional
+        Seed to use for the random generator
+    num_trials : int, optional
+        Number of independent simulation runs
+    max_time : int, optional
+        Time how long the simulation should run
+    environment_params : dict, optional
+        Dictionary of globally-shared environmental parameters
+    environment_agents: dict, optional
+        Similar to network_agents. Distribution of Agents that control the environment
+    environment_class: soil.environment.Environment subclass, optional
+        Class for the environment. It defailts to soil.environment.Environment
+    load_module : str, module name, deprecated
+        If specified, soil will load the content of this module under 'soil.agents.custom'
+
+
+    """
+
+    def __init__(self, name=None, group=None, topology=None, network_params=None,
+                 network_agents=None, agent_type=None, states=None,
+                 default_state=None, interval=1, num_trials=1,
+                 max_time=100, load_module=None, seed=None,
+                 dir_path=None, environment_agents=None,
+                 environment_params=None, environment_class=None,
+                 **kwargs):
 
         self.load_module = load_module
-        self.topology = nx.Graph(topology)
         self.network_params = network_params
-        self.name = name or 'UnnamedSimulation'
+        self.name = name or 'Unnamed'
+        self.seed = str(seed or name)
+        self._id = '{}_{}'.format(self.name, time.strftime("%Y-%m-%d_%H.%M.%S"))
+        self.group = group or ''
         self.num_trials = num_trials
         self.max_time = max_time
         self.default_state = default_state or {}
         self.dir_path = dir_path or os.getcwd()
         self.interval = interval
-        self.seed = str(seed) or str(time.time())
-        self.dump = dump
-        self.dry_run = dry_run
-        self.environment_params = environment_params or {}
 
-        if load_module:
-            path = sys.path + [self.dir_path, os.getcwd()]
-            f, fp, desc = imp.find_module(load_module, path)
-            imp.load_module('soil.agents.custom', f, fp, desc)
+        sys.path += list(x for x in [os.getcwd(), self.dir_path] if x not in sys.path)
+
+        if topology is None:
+            topology = serialization.load_network(network_params,
+                                                  dir_path=self.dir_path)
+        elif isinstance(topology, basestring) or isinstance(topology, dict):
+            topology = json_graph.node_link_graph(topology)
+        self.topology = nx.Graph(topology)
+
+
+        self.environment_params = environment_params or {}
+        self.environment_class = serialization.deserialize(environment_class,
+                                                   known_modules=['soil.environment', ]) or Environment
 
         environment_agents = environment_agents or []
-        self.environment_agents = agents._convert_agent_types(environment_agents)
+        self.environment_agents = agents._convert_agent_types(environment_agents,
+                                                              known_modules=[self.load_module])
 
         distro = agents.calculate_distribution(network_agents,
                                                agent_type)
-        self.network_agents = agents._convert_agent_types(distro)
+        self.network_agents = agents._convert_agent_types(distro,
+                                                          known_modules=[self.load_module])
 
         self.states = agents._validate_states(states,
                                               self.topology)
 
+        self._history = History(name=self.name,
+                                backup=False)
+
     def run_simulation(self, *args, **kwargs):
         return self.run(*args, **kwargs)
 
     def run(self, *args, **kwargs):
-        return list(self.run_simulation_gen(*args, **kwargs))
+        '''Run the simulation and return the list of resulting environments'''
+        return list(self.run_gen(*args, **kwargs))
+
+    def _run_sync_or_async(self, parallel=False, *args, **kwargs):
+        if parallel:
+            p = Pool()
+            func = partial(self.run_trial_exceptions,
+                           *args,
+                           **kwargs)
+            for i in p.imap_unordered(func, range(self.num_trials)):
+                if isinstance(i, Exception):
+                    logger.error('Trial failed:\n\t%s', i.message)
+                    continue
+                yield i
+        else:
+            for i in range(self.num_trials):
+                yield self.run_trial(*args,
+                                     **kwargs)
+
+    def run_gen(self, *args, parallel=False, dry_run=False,
+                exporters=[default, ], stats=[defaultStats], outdir=None, exporter_params={},
+                stats_params={}, log_level=None,
+                **kwargs):
+        '''Run the simulation and yield the resulting environments.'''
+        if log_level:
+            logger.setLevel(log_level)
+        logger.info('Using exporters: %s', exporters or [])
+        logger.info('Output directory: %s', outdir)
+        exporters = serialization.deserialize_all(exporters,
+                                                  simulation=self,
+                                                  known_modules=['soil.exporters',],
+                                                  dry_run=dry_run,
+                                                  outdir=outdir,
+                                                  **exporter_params)
+        stats = serialization.deserialize_all(simulation=self,
+                                              names=stats,
+                                              known_modules=['soil.stats',],
+                                              **stats_params)
 
-    def run_simulation_gen(self, *args, parallel=False, dry_run=False,
-                           **kwargs):
-        p = Pool()
         with utils.timer('simulation {}'.format(self.name)):
-            if parallel:
-                func = partial(self.run_trial, dry_run=dry_run or self.dry_run,
-                               return_env=not parallel, **kwargs)
-                for i in p.imap_unordered(func, range(self.num_trials)):
-                    yield i
-            else:
-                for i in range(self.num_trials):
-                    yield self.run_trial(i, dry_run=dry_run or self.dry_run, **kwargs)
-            if not (dry_run or self.dry_run):
-                logger.info('Dumping results to {}'.format(self.dir_path))
-                self.dump_pickle(self.dir_path)
-                self.dump_yaml(self.dir_path)
-            else:
-                logger.info('NOT dumping results')
+            for stat in stats:
+                stat.start()
+
+            for exporter in exporters:
+                exporter.start()
+            for env in self._run_sync_or_async(*args,
+                                               parallel=parallel,
+                                               log_level=log_level,
+                                               **kwargs):
+
+                collected = list(stat.trial(env) for stat in stats)
+
+                saved = self.save_stats(collected, t_step=env.now, trial_id=env.name)
+
+                for exporter in exporters:
+                    exporter.trial(env, saved)
+
+                yield env
+
+
+            collected = list(stat.end() for stat in stats)
+            saved = self.save_stats(collected)
+
+            for exporter in exporters:
+                exporter.end(saved)
+
+
+    def save_stats(self, collection, **kwargs):
+        stats = dict(kwargs)
+        for stat in collection:
+            stats.update(stat)
+        self._history.save_stats(utils.flatten_dict(stats))
+        return stats
+
+    def get_stats(self, **kwargs):
+        return self._history.get_stats(**kwargs)
+
+    def log_stats(self, stats):
+        logger.info('Stats: \n{}'.format(yaml.dump(stats, default_flow_style=False)))
+    
 
     def get_env(self, trial_id=0, **kwargs):
+        '''Create an environment for a trial of the simulation'''
         opts = self.environment_params.copy()
-        env_name = '{}_trial_{}'.format(self.name, trial_id)
         opts.update({
-            'name': env_name,
+            'name': trial_id,
             'topology': self.topology.copy(),
-            'seed': self.seed+env_name,
+            'seed': '{}_trial_{}'.format(self.seed, trial_id),
             'initial_time': 0,
-            'dry_run': self.dry_run,
             'interval': self.interval,
             'network_agents': self.network_agents,
+            'initial_time': 0,
             'states': self.states,
             'default_state': self.default_state,
             'environment_agents': self.environment_agents,
-            'dir_path': self.dir_path,
         })
         opts.update(kwargs)
-        env = environment.SoilEnvironment(**opts)
+        env = self.environment_class(**opts)
         return env
 
-    def run_trial(self, trial_id=0, until=None, return_env=True, **opts):
-        """Run a single trial of the simulation
-
-        Parameters
-        ----------
-        trial_id : int
+    def run_trial(self, until=None, log_level=logging.INFO, **opts):
         """
+        Run a single trial of the simulation
+
+        """
+        trial_id = '{}_trial_{}'.format(self.name, time.time()).replace('.', '-')
+        if log_level:
+            logger.setLevel(log_level)
         # Set-up trial environment and graph
         until = until or self.max_time
         env = self.get_env(trial_id=trial_id, **opts)
         # Set up agents on nodes
         with utils.timer('Simulation {} trial {}'.format(self.name, trial_id)):
             env.run(until)
-        if self.dump and not self.dry_run:
-            with utils.timer('Dumping simulation {} trial {}'.format(self.name, trial_id)):
-                env.dump(formats=self.dump)
-        if return_env:
-            return env
+        return env
+
+    def run_trial_exceptions(self, *args, **kwargs):
+        '''
+        A wrapper for run_trial that catches exceptions and returns them.
+        It is meant for async simulations
+        '''
+        try:
+            return self.run_trial(*args, **kwargs)
+        except Exception as ex:
+            if ex.__cause__ is not None:
+                ex = ex.__cause__
+            ex.message = ''.join(traceback.format_exception(type(ex), ex, ex.__traceback__)[:])
+            return ex
 
     def to_dict(self):
         return self.__getstate__()
@@ -156,64 +274,84 @@ class SoilSimulation(NetworkSimulation):
     def to_yaml(self):
         return yaml.dump(self.to_dict())
 
-    def dump_yaml(self, dir_path=None, file_name=None):
-        dir_path = dir_path or self.dir_path
-        if not os.path.exists(dir_path):
-            os.makedirs(dir_path)
-        if not file_name:
-            file_name = os.path.join(dir_path,
-                                     '{}.dumped.yml'.format(self.name))
-        with open(file_name, 'w') as f:
+
+    def dump_yaml(self, f=None, outdir=None):
+        if not f and not outdir:
+            raise ValueError('specify a file or an output directory')
+
+        if not f:
+            f = os.path.join(outdir, '{}.dumped.yml'.format(self.name))
+
+        with utils.open_or_reuse(f, 'w') as f:
             f.write(self.to_yaml())
 
-    def dump_pickle(self, dir_path=None, pickle_name=None):
-        dir_path = dir_path or self.dir_path
-        if not os.path.exists(dir_path):
-            os.makedirs(dir_path)
-        if not pickle_name:
-            pickle_name = os.path.join(dir_path,
-                                       '{}.simulation.pickle'.format(self.name))
-        with open(pickle_name, 'wb') as f:
+    def dump_pickle(self, f=None, outdir=None):
+        if not outdir and not f:
+            raise ValueError('specify a file or an output directory')
+
+        if not f:
+            f = os.path.join(outdir,
+                             '{}.simulation.pickle'.format(self.name))
+        with utils.open_or_reuse(f, 'wb') as f:
             pickle.dump(self, f)
 
+    def dump_sqlite(self, f):
+        return self._history.dump(f)
+
     def __getstate__(self):
-        state = self.__dict__.copy()
-        state['topology'] = json_graph.node_link_data(self.topology)
-        state['network_agents'] = agents._serialize_distribution(self.network_agents)
-        state['environment_agents'] = agents._convert_agent_types(self.environment_agents,
-                                                                 to_string=True)
+        state={}
+        for k, v in self.__dict__.items():
+            if k[0] != '_':
+                state[k] = v
+                state['topology'] = json_graph.node_link_data(self.topology)
+                state['network_agents'] = agents.serialize_distribution(self.network_agents,
+                                                                        known_modules = [])
+                state['environment_agents'] = agents.serialize_distribution(self.environment_agents,
+                                                                            known_modules = [])
+                state['environment_class'] = serialization.serialize(self.environment_class,
+                                                                     known_modules=['soil.environment'])[1]  # func, name
+        if state['load_module'] is None:
+            del state['load_module']
         return state
 
     def __setstate__(self, state):
         self.__dict__ = state
+        self.load_module = getattr(self, 'load_module', None)
+        if self.dir_path not in sys.path:
+            sys.path += [self.dir_path, os.getcwd()]
         self.topology = json_graph.node_link_graph(state['topology'])
         self.network_agents = agents.calculate_distribution(agents._convert_agent_types(self.network_agents))
-        self.environment_agents = agents._convert_agent_types(self.environment_agents)
+        self.environment_agents = agents._convert_agent_types(self.environment_agents,
+                                                              known_modules=[self.load_module])
+        self.environment_class = serialization.deserialize(self.environment_class,
+                                                   known_modules=[self.load_module, 'soil.environment', ])  # func, name
         return state
 
 
-def from_config(config):
-    config = list(utils.load_config(config))
+def all_from_config(config):
+    configs = list(serialization.load_config(config))
+    for config, _ in configs:
+        sim = Simulation(**config)
+        yield sim
+
+
+def from_config(conf_or_path):
+    config = list(serialization.load_config(conf_or_path))
     if len(config) > 1:
         raise AttributeError('Provide only one configuration')
     config = config[0][0]
-    sim = SoilSimulation(**config)
+    sim = Simulation(**config)
     return sim
 
 
-def run_from_config(*configs, results_dir='soil_output', dry_run=False, dump=None, timestamp=False,  **kwargs):
+def run_from_config(*configs, **kwargs):
     for config_def in configs:
         # logger.info("Found {} config(s)".format(len(ls)))
-        for config, _ in utils.load_config(config_def):
+        for config, path in serialization.load_config(config_def):
             name = config.get('name', 'unnamed')
             logger.info("Using config(s): {name}".format(name=name))
 
-            if timestamp:
-                sim_folder = '{}_{}'.format(name,
-                                            time.strftime("%Y-%m-%d_%H:%M:%S"))
-            else:
-                sim_folder = name
-            dir_path = os.path.join(results_dir, sim_folder)
-            sim = SoilSimulation(dir_path=dir_path, dump=dump, **config)
-            logger.info('Dumping results to {} : {}'.format(sim.dir_path, sim.dump))
+            dir_path = config.pop('dir_path', os.path.dirname(path))
+            sim = Simulation(dir_path=dir_path,
+                             **config)
             sim.run_simulation(**kwargs)

soil/stats.py

@@ -0,0 +1,106 @@
+import pandas as pd
+
+from collections import Counter
+
+class Stats:
+    '''
+    Interface for all stats. It is not necessary, but it is useful
+    if you don't plan to implement all the methods.
+    '''
+
+    def __init__(self, simulation):
+        self.simulation = simulation
+
+    def start(self):
+        '''Method to call when the simulation starts'''
+        pass
+
+    def end(self):
+        '''Method to call when the simulation ends'''
+        return {}
+
+    def trial(self, env):
+        '''Method to call when a trial ends'''
+        return {}
+
+
+class distribution(Stats):
+    '''
+    Calculate the distribution of agent states at the end of each trial,
+    the mean value, and its deviation.
+    '''
+
+    def start(self):
+        self.means = []
+        self.counts = []
+
+    def trial(self, env):
+        df = env[None, None, None].df()
+        df = df.drop('SEED', axis=1)
+        ix = df.index[-1]
+        attrs = df.columns.get_level_values(0)
+        vc = {}
+        stats = {
+            'mean': {},
+            'count': {},
+        }
+        for a in attrs:
+            t = df.loc[(ix, a)]
+            try:
+                stats['mean'][a] = t.mean()
+                self.means.append(('mean', a, t.mean()))
+            except TypeError:
+                pass
+
+            for name, count in t.value_counts().iteritems():
+                if a not in stats['count']:
+                    stats['count'][a] = {}
+                stats['count'][a][name] = count
+                self.counts.append(('count', a, name, count))
+
+        return stats
+
+    def end(self):
+        dfm = pd.DataFrame(self.means, columns=['metric', 'key', 'value'])
+        dfc = pd.DataFrame(self.counts, columns=['metric', 'key', 'value', 'count'])
+
+        count = {}
+        mean = {}
+
+        if self.means:
+            res = dfm.groupby(by=['key']).agg(['mean', 'std', 'count', 'median', 'max', 'min'])
+            mean = res['value'].to_dict()
+        if self.counts:
+            res = dfc.groupby(by=['key', 'value']).agg(['mean', 'std', 'count', 'median', 'max', 'min'])
+            for k,v in res['count'].to_dict().items():
+                if k not in count:
+                    count[k] = {}
+                for tup, times in v.items():
+                    subkey, subcount = tup
+                    if subkey not in count[k]:
+                        count[k][subkey] = {}
+                    count[k][subkey][subcount] = times
+
+
+        return {'count': count, 'mean': mean}
+
+
+class defaultStats(Stats):
+
+    def trial(self, env):
+        c = Counter()
+        c.update(a.__class__.__name__ for a in env.network_agents)
+
+        c2 = Counter()
+        c2.update(a['id'] for a in env.network_agents)
+
+        return {
+            'network ': {
+                'n_nodes': env.G.number_of_nodes(),
+                'n_edges': env.G.number_of_nodes(),
+            },
+            'agents': {
+                'model_count': dict(c),
+                'state_count': dict(c2),
+            }
+        }

soil/utils.py

@@ -1,105 +1,87 @@
-import os
-import yaml
 import logging
-import importlib
-from time import time
-from glob import glob
-from random import random
-from copy import deepcopy
+import time
+import os
 
-import networkx as nx
+from shutil import copyfile
 
 from contextlib import contextmanager
 
-
 logger = logging.getLogger('soil')
+logging.basicConfig()
 logger.setLevel(logging.INFO)
 
 
-def load_network(network_params, dir_path=None):
-    if network_params is None:
-        return nx.Graph()
-    path = network_params.get('path', None)
-    if path:
-        if dir_path and not os.path.isabs(path):
-            path = os.path.join(dir_path, path)
-        extension = os.path.splitext(path)[1][1:]
-        kwargs = {}
-        if extension == 'gexf':
-            kwargs['version'] = '1.2draft'
-            kwargs['node_type'] = int
-        try:
-            method = getattr(nx.readwrite, 'read_' + extension)
-        except AttributeError:
-            raise AttributeError('Unknown format')
-        return method(path, **kwargs)
-
-    net_args = network_params.copy()
-    net_type = net_args.pop('generator')
-
-    method = getattr(nx.generators, net_type)
-    return method(**net_args)
-
-
-def load_file(infile):
-    with open(infile, 'r') as f:
-        return list(yaml.load_all(f))
-
-
-def load_files(*patterns):
-    for pattern in patterns:
-        for i in glob(pattern):
-            for config in load_file(i):
-                yield config, os.path.abspath(i)
-
-
-def load_config(config):
-    if isinstance(config, dict):
-        yield config, None
-    else:
-        yield from load_files(config)
-
-
 @contextmanager
 def timer(name='task', pre="", function=logger.info, to_object=None):
-    start = time()
-    function('{}Starting {} at {}.'.format(pre, name, start))
+    start = time.time()
+    function('{}Starting {} at {}.'.format(pre, name,
+                                           time.strftime("%X", time.gmtime(start))))
     yield start
-    end = time()
-    function('{}Finished {} in {} seconds'.format(pre, name, str(end-start)))
+    end = time.time()
+    function('{}Finished {} at {} in {} seconds'.format(pre, name,
+                                                        time.strftime("%X", time.gmtime(end)),
+                                                        str(end-start)))
     if to_object:
         to_object.start = start
         to_object.end = end
 
 
-def repr(v):
-    func = serializer(v)
-    tname = name(v)
-    return func(v), tname
+def safe_open(path, mode='r', backup=True, **kwargs):
+    outdir = os.path.dirname(path)
+    if outdir and not os.path.exists(outdir):
+        os.makedirs(outdir)
+    if backup and 'w' in mode and os.path.exists(path):
+        creation = os.path.getctime(path)
+        stamp = time.strftime('%Y-%m-%d_%H.%M.%S', time.localtime(creation))
+
+        backup_dir = os.path.join(outdir, 'backup')
+        if not os.path.exists(backup_dir):
+            os.makedirs(backup_dir)
+        newpath = os.path.join(backup_dir, '{}@{}'.format(os.path.basename(path),
+                                                               stamp))
+        copyfile(path, newpath)
+    return open(path, mode=mode, **kwargs)
 
 
-def name(v):
-    return type(v).__name__
-
-
-def serializer(type_):
-    if type_ == 'bool':
-        return lambda x:  "true" if x else ""
-    return lambda x: x
-
-
-def deserializer(type_):
+def open_or_reuse(f, *args, **kwargs):
     try:
-        # Check if it's a builtin type
-        module = importlib.import_module('builtins')
-        cls = getattr(module, type_)
-    except AttributeError:
-        # if not, separate module and class
-        module, type_ = type_.rsplit(".", 1)
-        module = importlib.import_module(module)
-        cls = getattr(module, type_)
-    return cls
+        return safe_open(f, *args, **kwargs)
+    except (AttributeError, TypeError):
+        return f
+
+def flatten_dict(d):
+    if not isinstance(d, dict):
+        return d
+    return dict(_flatten_dict(d))
+
+def _flatten_dict(d, prefix=''):
+    if not isinstance(d, dict):
+        # print('END:', prefix, d)
+        yield prefix, d
+        return
+    if prefix:
+        prefix = prefix + '.'
+    for k, v in d.items():
+        # print(k, v)
+        res = list(_flatten_dict(v, prefix='{}{}'.format(prefix, k)))
+        # print('RES:', res)
+        yield from res
 
 
-def convert(value, type_):
-    return deserializer(type_)(value)
+def unflatten_dict(d):
+    out = {}
+    for k, v in d.items():
+        target = out
+        if not isinstance(k, str):
+            target[k] = v
+            continue
+        tokens = k.split('.')
+        if len(tokens) < 2:
+            target[k] = v
+            continue
+        for token in tokens[:-1]:
+            if token not in target:
+                target[token] = {}
+            target = target[token]
+        target[tokens[-1]] = v
+    return out

soil/web/.gitignore

@@ -0,0 +1,4 @@
+__pycache__/
+output/
+tests/
+soil_output/

soil/web/README.md

@@ -0,0 +1,59 @@
+# Graph Visualization with D3.js
+
+The aim of this software is to provide a useful tool for visualising and analysing the result of different simulations based on graph. Once you run the simulation, you will be able to interact with the simulation in real time.
+
+For this purpose, a model which tries to simulate the spread of information to comprehend the radicalism spread in a society is included. Whith all this, the main project goals could be divided in five as it is shown in the following.
+
+* Simulate the spread of information through a network applied to radicalism.
+* Visualize the results of the simulation.
+* Interact with the simulation in real time.
+* Extract data from the results.
+* Show data in a right way for its research.
+
+## Deploying the server
+
+For deploying the application, you will only need to run the following command.
+
+`python3 run.py [--name NAME] [--dump] [--port PORT] [--verbose]`
+
+Where the options are detailed in the following table.
+
+| Option | Description |
+| --- | --- |
+| `--name NAME` | The name of the simulation. It will appear on the app. |
+| `--dump` | For dumping the results in server side. |
+| `--port PORT` | The port where the server will listen. |
+| `--verbose` | Verbose mode. |
+
+> You can dump the results of the simulation in server side. Anyway, you will be able to download them in GEXF or JSON Graph format directly from the browser.
+
+## Visualization Params
+
+The configuration of the simulation is based on the simulator configuration. In this case, it follows the [SOIL](https://github.com/gsi-upm/soil) configuration syntax and for visualising the results in a more comfortable way, more params can be added in `visualization_params` dictionary.
+
+* For setting a background image, the tag needed is `background image`. You can also add a `background_opacity` and `background_filter_color` if the image is so clear than you can difficult view the nodes.
+* For setting colors to the nodes, you can do it based on their properties values. Using the `color` tag, you will need to indicate the attribute key and value, and then the color you want to apply.
+* The shapes applied to a group of nodes are always the same. This means than it won't change dynamically, so you will have to indicate the property with the `shape_property` tag and add a dictionary called `shapes` in which for each value, you indicate the shape.
+  All shapes have to had been downloaded before in SVG format and added to the server.
+
+An example of this configuration applied to the TerroristNetworkModel is presented.
+
+```yaml
+visualization_params:
+  # Icons downloaded from https://www.iconfinder.com/
+  shape_property: agent
+  shapes:
+    TrainingAreaModel: target
+    HavenModel: home
+    TerroristNetworkModel: person
+  colors:
+    - attr_id: 0
+      color: '#40de40'
+    - attr_id: 1
+      color: red
+    - attr_id: 2
+      color: '#c16a6a'
+  background_image: 'map_4800x2860.jpg'
+  background_opacity: '0.9'
+  background_filter_color: 'blue'
+```

soil/web/__init__.py

@@ -0,0 +1,274 @@
+import io
+import threading
+import asyncio
+import logging
+import networkx as nx
+import os
+import sys
+import tornado.ioloop
+import tornado.web
+import tornado.websocket
+import tornado.escape
+import tornado.gen
+import yaml
+import webbrowser
+from contextlib import contextmanager
+from time import sleep
+from xml.etree.ElementTree import tostring
+
+from tornado.concurrent import run_on_executor
+from concurrent.futures import ThreadPoolExecutor
+
+from ..simulation import Simulation
+logger = logging.getLogger(__name__)
+logger.setLevel(logging.INFO)
+
+ROOT = os.path.abspath(os.path.dirname(__file__))
+
+MAX_WORKERS = 4
+LOGGING_INTERVAL = 0.5
+
+# Workaround to let Soil load the required modules
+sys.path.append(ROOT)
+
+class PageHandler(tornado.web.RequestHandler):
+    """ Handler for the HTML template which holds the visualization. """
+
+    def get(self):
+        self.render('index.html', port=self.application.port,
+                    name=self.application.name)
+
+
+class SocketHandler(tornado.websocket.WebSocketHandler):
+    """ Handler for websocket. """
+    executor = ThreadPoolExecutor(max_workers=MAX_WORKERS)
+
+    def open(self):
+        if self.application.verbose:
+            logger.info('Socket opened!')
+
+    def check_origin(self, origin):
+        return True
+
+    def on_message(self, message):
+        """ Receiving a message from the websocket, parse, and act accordingly. """
+
+        msg = tornado.escape.json_decode(message)
+
+        if msg['type'] == 'config_file':
+
+            if self.application.verbose:
+                print(msg['data'])
+
+            self.config = list(yaml.load_all(msg['data']))
+
+            if len(self.config) > 1:
+                error = 'Please, provide only one configuration.'
+                if self.application.verbose:
+                    logger.error(error)
+                self.write_message({'type': 'error',
+                    'error': error})
+                return
+
+            self.config = self.config[0]
+            self.send_log('INFO.' + self.simulation_name,
+                          'Using config: {name}'.format(name=self.config['name']))
+
+            if 'visualization_params' in self.config:
+                self.write_message({'type': 'visualization_params',
+                    'data': self.config['visualization_params']})
+            self.name = self.config['name']
+            self.run_simulation()
+
+            settings = []
+            for key in self.config['environment_params']: 
+                if type(self.config['environment_params'][key]) == float or type(self.config['environment_params'][key]) == int:
+                    if self.config['environment_params'][key] <= 1:
+                        setting_type = 'number'
+                    else:
+                        setting_type = 'great_number'
+                elif type(self.config['environment_params'][key]) == bool:
+                    setting_type = 'boolean'
+                else:
+                    setting_type = 'undefined'
+
+                settings.append({
+                    'label': key,
+                    'type': setting_type,
+                    'value': self.config['environment_params'][key]
+                })
+
+            self.write_message({'type': 'settings',
+                'data': settings})
+
+        elif msg['type'] == 'get_trial':
+            if self.application.verbose:
+                logger.info('Trial {} requested!'.format(msg['data']))
+            self.send_log('INFO.' + __name__, 'Trial {} requested!'.format(msg['data']))
+            self.write_message({'type': 'get_trial',
+                                'data': self.get_trial(int(msg['data']))})
+
+        elif msg['type'] == 'run_simulation':
+            if self.application.verbose:
+                logger.info('Running new simulation for {name}'.format(name=self.config['name']))
+            self.send_log('INFO.' + self.simulation_name, 'Running new simulation for {name}'.format(name=self.config['name']))
+            self.config['environment_params'] = msg['data']
+            self.run_simulation()
+
+        elif msg['type'] == 'download_gexf':
+            G = self.trials[ int(msg['data']) ].history_to_graph()
+            for node in G.nodes():
+                if 'pos' in G.nodes[node]:
+                    G.nodes[node]['viz'] = {"position": {"x": G.nodes[node]['pos'][0], "y": G.nodes[node]['pos'][1], "z": 0.0}}
+                    del (G.nodes[node]['pos'])
+            writer = nx.readwrite.gexf.GEXFWriter(version='1.2draft')
+            writer.add_graph(G)
+            self.write_message({'type': 'download_gexf',
+                'filename': self.config['name'] + '_trial_' + str(msg['data']),
+                'data': tostring(writer.xml).decode(writer.encoding) })
+
+        elif msg['type'] == 'download_json':
+            G = self.trials[ int(msg['data']) ].history_to_graph()
+            for node in G.nodes():
+                if 'pos' in G.nodes[node]:
+                    G.nodes[node]['viz'] = {"position": {"x": G.nodes[node]['pos'][0], "y": G.nodes[node]['pos'][1], "z": 0.0}}
+                    del (G.nodes[node]['pos'])
+            self.write_message({'type': 'download_json',
+                'filename': self.config['name'] + '_trial_' + str(msg['data']),
+                'data': nx.node_link_data(G) })
+
+        else:
+            if self.application.verbose:
+                logger.info('Unexpected message!')
+
+    def update_logging(self):
+        try:
+            if (not self.log_capture_string.closed and self.log_capture_string.getvalue()):
+                for i in range(len(self.log_capture_string.getvalue().split('\n')) - 1):
+                    self.send_log('INFO.' + self.simulation_name, self.log_capture_string.getvalue().split('\n')[i])
+                self.log_capture_string.truncate(0)
+                self.log_capture_string.seek(0)
+        finally:
+            if self.capture_logging:
+                tornado.ioloop.IOLoop.current().call_later(LOGGING_INTERVAL, self.update_logging)
+
+
+    def on_close(self):
+        if self.application.verbose:
+            logger.info('Socket closed!')
+
+    def send_log(self, logger, logging):
+        self.write_message({'type': 'log',
+                            'logger': logger,
+                            'logging': logging})
+
+    @property
+    def simulation_name(self):
+        return self.config.get('name', 'NoSimulationRunning')
+
+    @run_on_executor
+    def nonblocking(self, config):
+        simulation = Simulation(**config)
+        return simulation.run()
+
+    @tornado.gen.coroutine
+    def run_simulation(self):
+        # Run simulation and capture logs
+        logger.info('Running simulation!')
+        if 'visualization_params' in self.config:
+            del self.config['visualization_params']
+        with self.logging(self.simulation_name):
+            try:
+                config = dict(**self.config)
+                config['outdir'] = os.path.join(self.application.outdir, config['name'])
+                config['dump'] = self.application.dump
+                self.trials = yield self.nonblocking(config)
+
+                self.write_message({'type': 'trials',
+                    'data': list(trial.name for trial in self.trials) })
+            except Exception as ex:
+                error = 'Something went wrong:\n\t{}'.format(ex)
+                logging.info(error)
+                self.write_message({'type': 'error',
+                                    'error': error})
+                self.send_log('ERROR.' + self.simulation_name, error)
+
+    def get_trial(self, trial):
+        logger.info('Available trials: %s ' % len(self.trials))
+        logger.info('Ask for : %s' % trial)
+        trial = self.trials[trial]
+        G = trial.history_to_graph()
+        return nx.node_link_data(G)
+
+    @contextmanager
+    def logging(self, logger):
+        self.capture_logging = True
+        self.logger_application = logging.getLogger(logger)
+        self.log_capture_string = io.StringIO()
+        ch = logging.StreamHandler(self.log_capture_string)
+        self.logger_application.addHandler(ch)
+        self.update_logging()
+        yield self.capture_logging
+
+        sleep(0.2)
+        self.log_capture_string.close()
+        self.logger_application.removeHandler(ch)
+        self.capture_logging = False
+        return self.capture_logging
+    
+
+class ModularServer(tornado.web.Application):
+    """ Main visualization application. """
+
+    port = 8001
+    page_handler = (r'/', PageHandler)
+    socket_handler = (r'/ws', SocketHandler)
+    static_handler = (r'/(.*)', tornado.web.StaticFileHandler,
+                      {'path': os.path.join(ROOT, 'static')})
+    local_handler = (r'/local/(.*)', tornado.web.StaticFileHandler,
+                     {'path': ''})
+
+    handlers = [page_handler, socket_handler, static_handler, local_handler]
+    settings = {'debug': True,
+                'template_path': ROOT + '/templates'}
+
+    def __init__(self, dump=False, outdir='output', name='SOIL', verbose=True, *args, **kwargs):
+        
+        self.verbose = verbose
+        self.name = name
+        self.dump = dump
+        self.outdir = outdir
+
+        # Initializing the application itself:
+        super().__init__(self.handlers, **self.settings)
+
+    def launch(self, port=None):
+        """ Run the app. """
+        
+        if port is not None:
+            self.port = port
+        url = 'http://127.0.0.1:{PORT}'.format(PORT=self.port)
+        print('Interface starting at {url}'.format(url=url))
+        self.listen(self.port)
+        # webbrowser.open(url)
+        tornado.ioloop.IOLoop.instance().start()
+
+
+def run(*args, **kwargs):
+    asyncio.set_event_loop(asyncio.new_event_loop())
+    server = ModularServer(*args, **kwargs)
+    server.launch()
+
+
+def main():
+    import argparse
+
+    parser = argparse.ArgumentParser(description='Visualization of a Graph Model')
+
+    parser.add_argument('--name', '-n', nargs=1, default='SOIL', help='name of the simulation')
+    parser.add_argument('--dump', '-d', help='dumping results in folder output', action='store_true')
+    parser.add_argument('--port', '-p', nargs=1, default=8001, help='port for launching the server')
+    parser.add_argument('--verbose', '-v', help='verbose mode', action='store_true')
+    args = parser.parse_args()
+
+    run(name=args.name, port=(args.port[0] if isinstance(args.port, list) else args.port), verbose=args.verbose)

soil/web/__main__.py

@@ -0,0 +1,5 @@
+from . import main
+
+
+if __name__ == "__main__":
+    main()

soil/web/config.yml

@@ -0,0 +1,25 @@
+name: ControlModelM2_sim
+max_time: 50
+num_trials: 2
+network_params:
+  generator: barabasi_albert_graph
+  n: 100
+  m: 2
+network_agents:
+  - agent_type: ControlModelM2
+    weight: 0.1
+    state:
+      id: 1
+  - agent_type: ControlModelM2
+    weight: 0.9
+    state:
+      id: 0
+environment_params:
+  prob_neutral_making_denier: 0.035
+  prob_infect: 0.075
+  prob_cured_healing_infected: 0.035
+  prob_cured_vaccinate_neutral: 0.035
+  prob_vaccinated_healing_infected: 0.035
+  prob_vaccinated_vaccinate_neutral: 0.035
+  prob_generate_anti_rumor: 0.035
+  standard_variance: 0.055

soil/web/run.py

@@ -0,0 +1,23 @@
+import argparse
+from server import ModularServer
+from simulator import Simulator
+
+
+def run(simulator, name="SOIL", port=8001, verbose=False):
+    server = ModularServer(simulator, name=(name[0] if isinstance(name, list) else name), verbose=verbose)
+    server.port = port
+    server.launch()
+
+
+if __name__ == "__main__":
+
+    parser = argparse.ArgumentParser(description='Visualization of a Graph Model')
+
+    parser.add_argument('--name', '-n', nargs=1, default='SOIL', help='name of the simulation')
+    parser.add_argument('--dump', '-d', help='dumping results in folder output', action='store_true')
+    parser.add_argument('--port', '-p', nargs=1, default=8001, help='port for launching the server')
+    parser.add_argument('--verbose', '-v', help='verbose mode', action='store_true')
+    args = parser.parse_args()
+
+    soil = Simulator(dump=args.dump)
+    run(soil, name=args.name, port=(args.port[0] if isinstance(args.port, list) else args.port), verbose=args.verbose)

soil/web/static/css/main.css

@@ -0,0 +1,431 @@
+
+html, body {
+  height: 100%;
+}
+
+.carousel {
+  height: calc(100% - 150px);
+}
+
+.carousel-inner {
+  height: calc(100% - 50px) !important;
+}
+
+.carousel-inner .item,
+.carousel-inner .item .container-fluid {
+  height: 100%;
+}
+
+.navbar {
+  box-shadow: 0px 0px 5px 2px rgba(0, 0, 0, .2)
+}
+
+.nav.navbar-right {
+  margin-right: 10px !important;
+}
+
+.nav.navbar-right a {
+  outline: none !important;
+}
+
+.dropdown-menu > li > a:hover {
+  background-color: #d4d3d3;
+  cursor: pointer;
+}
+
+.wrapper-heading {
+  display: flex;
+  flex-direction: row;
+  padding: 0 !important;
+}
+
+.soil_logo {
+  padding: 0 !important;
+  border-left: none !important;
+  border-right: none !important;
+  display: flex;
+  justify-content: flex-end;
+  background-color: rgb(88, 88, 88);
+}
+
+.soil_logo > img {
+  max-height: 100%;
+}
+
+.node {
+  stroke: #fff;
+  stroke-width: 1.5px;
+}
+
+.link {
+  stroke: #999;
+  stroke-opacity: .6;
+}
+
+svg#graph, #configuration {
+	background-color: white;
+	margin-top: 15px;
+	border-style: double;
+	border-color: rgba(0, 0, 0, 0.35);
+	border-radius: 5px;
+	padding: 0px;
+}
+
+#timeline {
+	padding: 0;
+	margin-top: 20px;
+}
+
+#configuration {
+	margin-top: 15px;
+	padding: 15px;
+	border-left: none !important;
+  overflow: auto;
+  display: flex;
+  flex-direction: column;
+  align-items: inherit;
+  justify-content: space-evenly;
+}
+
+button {
+	outline: none !important;
+}
+
+.btn-toolbar.controls {
+	position: absolute;
+	right: 0;
+}
+
+.controls > .btn {
+	margin-left: 10px !important;
+}
+
+button.pressed {
+	background-color: rgb(167, 242, 168);
+	-webkit-animation: background 1s cubic-bezier(1,0,0,1) infinite;
+	animation: background 1s cubic-bezier(1,0,0,1) infinite;
+	cursor: default !important;
+}
+
+@-webkit-keyframes background {
+  50% { background-color: #dddddd; }
+  100% { background-color: rgb(167, 242, 168); }
+}
+
+@keyframes background { 
+  50% { background-color: #dddddd; }
+  100% { background-color: rgb(167, 242, 168); }
+}
+
+#slider3 {
+	background: repeating-linear-gradient( 90deg, white 27px, white 30px, #fff 32px, #aaa 33px );
+	background-color: white;
+}
+
+hr {
+	margin-top: 15px !important;
+  margin-bottom: 15px !important;
+  width: 100%;
+}
+
+#update .config-item {
+  margin-top: 15px !important;
+}
+
+/** LOADER **/
+#load {
+  position: absolute;
+  font-weight: bold;
+}
+
+#load.loader {
+  border: 5px solid #f3f3f3;
+  border-radius: 50%;
+  border-top: 5px solid #3498db;
+  border-bottom: 5px solid #3498db;
+  width: 30px;
+  height: 30px;
+  -webkit-animation: spin 1s linear infinite;
+  animation: spin 1s linear infinite;
+  position: absolute;
+}
+
+#load:before {
+  content: 'No file'
+}
+
+#load.loader:before {
+  content: '' !important;
+}
+
+@-webkit-keyframes spin {
+  0% { -webkit-transform: rotate(0deg); }
+  100% { -webkit-transform: rotate(360deg); }
+}
+
+@keyframes spin {
+  0% { transform: rotate(0deg); }
+  100% { transform: rotate(360deg); }
+}
+
+/** ALERT **/
+.alert-danger {
+	position: absolute;
+	margin-top: 20px;
+	margin-left: 5px;
+}
+
+/** FILE BROWSER **/
+.custom-file {
+	position: relative;
+  display: inline-block;
+  width: 100%;
+  height: 35px;
+  margin-bottom: 0;
+  cursor: pointer;
+}
+
+.custom-file-input {
+	min-width: 14rem;
+  max-width: 100%;
+  height: 35px;
+  margin: 0;
+  filter: alpha(opacity=0);
+  opacity: 0;
+}
+
+.custom-file-control {
+  position: absolute;
+  top: 0;
+  right: 0;
+  left: 0;
+  z-index: 5;
+  height: 35px;
+  padding: .5rem 1rem;
+  overflow: hidden;
+  line-height: 1.5;
+  color: #464a4c;
+  pointer-events: none;
+  -webkit-user-select: none;
+  -moz-user-select: none;
+  -ms-user-select: none;
+  user-select: none;
+  background-color: #fff;
+  border: 1px solid rgba(0,0,0,.15);
+  border-radius: .25rem;
+}
+
+.custom-file-control::before {
+	content: "Browse";
+	position: absolute;
+  top: -1px;
+  right: -1px;
+  bottom: -1px;
+  z-index: 6;
+  display: block;
+  height: 35px;
+  padding: .5rem 1rem;
+  line-height: 1.5;
+  color: #464a4c;
+  background-color: #eceeef;
+  border: 1px solid rgba(0,0,0,.15);
+  border-radius: 0 .25rem .25rem 0;
+}
+
+.custom-file-control::after {
+	content: attr(data-content);
+}
+
+/** TABLES **/
+#percentTable {
+	height: 150px !important;
+	width: 100% !important;
+}
+
+#percentTable tr {
+	padding: 5px 2px;
+}
+
+#percentTable .no-data-table {
+  font-size: 10px;
+  justify-content: center;
+  align-items: center;
+  display: flex;
+  flex: 1;
+  height: 100%;
+  font-weight: 100;
+}
+
+hr {
+	margin-top: 15px !important;
+	margin-bottom: 15px !important;
+}
+
+#info-graph {
+	width: 70% !important;
+}
+
+.logo {
+	margin-top: -40px;
+  position: absolute;
+  right: 15px;
+}
+
+/** SLIDER **/
+.speed-slider, 
+.link-distance-slider {
+	padding: 0 10px !important;
+	margin-top: 5px !important;
+	width: 100% !important;
+}
+
+.slider {
+	width: 100% !important;
+}
+
+.slider .slider-selection {
+	background-image: linear-gradient(to bottom, 
+                    rgba(36, 110, 162, 0.5) 0%, 
+                    rgba(3, 169, 224, 0.5) 100%) !important;
+}
+
+.slider-disabled .slider-selection {
+  opacity: 0.5;
+}
+
+.slider.slider-disabled .slider-track {
+	cursor: default !important;
+}
+
+table#speed,
+table#link-distance {
+	width: 100%;
+}
+
+table#speed .min,
+table#speed .max,
+table#link-distance .min,
+table#link-distance .max {
+	font-weight: normal !important;
+}
+
+/* Console */
+
+#update, .console, .soil_logo  {
+  padding: 10px 15px;
+  height: 135px;
+  border: 1px solid #585858;
+}
+
+#update {
+  border-top-right-radius: 5px;
+  border-bottom-right-radius: 5px;
+}
+
+.container-fluid.fixed {
+  padding-top: 15px;
+}
+
+.console {
+  background-color: rgb(88,88,88);
+  font-family: "Ubuntu Mono";
+  font-size: 14px;
+  font-weight: 500;
+  color: white;
+  line-height: 14px;
+  overflow: auto;
+  border-top-left-radius: 5px;
+  border-bottom-left-radius: 5px;
+  width: 100%;
+}
+
+.console::-webkit-scrollbar {
+  width: 6px;
+  background-color: #F5F5F5;
+}
+
+.console::-webkit-scrollbar-thumb {
+  -webkit-box-shadow: inset 0 0 6px rgba(0,0,0,.3);
+  background-color: #555;
+}
+
+/** FORMS **/
+.checkbox {
+  margin-left: 10px !important;
+}
+
+#wrapper-settings {
+  padding: 15px !important;
+  height: 100%;
+  overflow-y: auto;
+  overflow-x: hidden;
+}
+
+#wrapper-settings.none {
+  font-weight: bold;
+  display: flex;
+  flex: 1;
+  justify-content: center;
+  align-items: center;
+}
+
+#wrapper-settings.none:before {
+  content: 'No configuration provided';
+}
+
+#wrapper-settings .btn-group button:focus {
+  background: initial;
+  border-color: #ccc;
+}
+
+#wrapper-settings .btn-group button {
+  font-size: xx-small;
+  padding: 3px 6px;
+}
+
+.item.settings .container-fluid {
+  padding-top: 10px !important;
+}
+
+#wrapper-settings::-webkit-scrollbar {
+  width: 6px;
+  background-color: #F5F5F5;
+}
+
+#wrapper-settings::-webkit-scrollbar-thumb {
+  -webkit-box-shadow: inset 0 0 6px rgba(0,0,0,.3);
+  background-color: #ccc;
+}
+
+/** CHARTS **/
+#charts {
+  height: 100%;
+  padding-left: 0 !important;
+  padding-top: 15px !important;
+  padding-bottom: 15px !important;
+}
+
+.chart {
+  height: 50%;
+}
+
+.chart.no-data:before {
+  content: 'No data';
+  position: absolute;
+  font-size: 10px;
+  padding-bottom: 35px;
+}
+
+.chart.no-data {
+  font-weight: bold;
+  display: flex;
+  flex: 1;
+  justify-content: center;
+  align-items: center;
+}
+
+/** MODAL **/
+.modal-footer, 
+.modal-header {
+  border: none !important;
+}

soil/web/static/css/timeline.css

@@ -0,0 +1,72 @@
+#slider3 {
+  margin: 0 0 10px 0;
+}
+
+.d3-slider {
+    position: relative;
+    font-family: Verdana,Arial,sans-serif;
+    font-size: 1.1em;
+    border: 1px solid #aaaaaa;
+    z-index: 2;
+}
+
+.d3-slider-horizontal {
+    height: 40px;
+}  
+
+.d3-slider-range {
+  background:#2980b9;
+  left:0px;
+  right:0px;
+  height: 0.8em;
+  position: absolute;
+}
+  
+.d3-slider-handle {
+    position: absolute;
+    width: .8em;
+    height: 48px;
+    border: 1px solid #d3d3d3;
+    border-radius: 4px;
+    background: #eee;
+    background: linear-gradient(to bottom, #eee 0%, #ddd 100%);
+    z-index: 3;
+}
+
+.d3-slider-handle:hover {
+    border: 1px solid #999999;
+}
+
+.d3-slider-horizontal .d3-slider-handle {
+    top: -.3em;
+    margin-left: -.4em;
+}
+
+.d3-slider-axis {
+    position: relative;
+    z-index: 1;    
+}
+
+.d3-slider-axis-bottom {
+    top: 38px;
+}
+
+.d3-slider-axis-right {
+    left: .8em;
+}
+
+.d3-slider-axis path {
+    stroke-width: 0;
+    fill: none;
+}
+
+.d3-slider-axis line {
+    fill: none;
+    stroke: #aaa;
+    shape-rendering: crispEdges;
+    stroke-dasharray: 2;
+}
+
+.d3-slider-axis text {
+    font-size: 11px;
+}

soil/web/static/img/logo_gsi.svg

@@ -0,0 +1,140 @@
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+<!-- Created with Inkscape (http://www.inkscape.org/) -->
+
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+   xmlns:svg="http://www.w3.org/2000/svg"
+   xmlns="http://www.w3.org/2000/svg"
+   xmlns:sodipodi="http://sodipodi.sourceforge.net/DTD/sodipodi-0.dtd"
+   xmlns:inkscape="http://www.inkscape.org/namespaces/inkscape"
+   width="360"
+   height="330"
+   id="svg3752"
+   version="1.1"
+   inkscape:version="0.48.4 r9939"
+   sodipodi:docname="logo_gsi_nuevo.svg"
+   inkscape:export-filename="/home/cif/GoogleDrive/docs/gsi/corporativo/logos-fondos/logos/logo_gsi_nuevo_740_671.png"
+   inkscape:export-xdpi="185.6273"
+   inkscape:export-ydpi="185.6273">
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+       sodipodi:type="inkscape:persp3d"
+       inkscape:vp_x="0 : 526.18109 : 1"
+       inkscape:vp_y="0 : 1000 : 0"
+       inkscape:vp_z="744.09448 : 526.18109 : 1"
+       inkscape:persp3d-origin="372.04724 : 350.78739 : 1"
+       id="perspective3760" />
+    <inkscape:perspective
+       id="perspective3730"
+       inkscape:persp3d-origin="0.5 : 0.33333333 : 1"
+       inkscape:vp_z="1 : 0.5 : 1"
+       inkscape:vp_y="0 : 1000 : 0"
+       inkscape:vp_x="0 : 0.5 : 1"
+       sodipodi:type="inkscape:persp3d" />
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+       inkscape:collect="always"
+       id="filter3757"
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+           d="m 633.2666,634.51772 c -3e-5,0.13672 -0.0411,0.25976 -0.12305,0.36914 -0.0684,0.10937 -0.20511,0.19824 -0.41015,0.2666 -0.19144,0.0684 -0.45121,0.12305 -0.7793,0.16406 -0.32816,0.041 -0.74515,0.0615 -1.25098,0.0615 -0.49221,0 -0.90237,-0.0205 -1.23046,-0.0615 -0.32816,-0.041 -0.58792,-0.0957 -0.7793,-0.16406 -0.19144,-0.0684 -0.32815,-0.15723 -0.41016,-0.2666 -0.0821,-0.10938 -0.12307,-0.23242 -0.12304,-0.36914 l 0,-21.59473 -0.041,0 -7.69043,21.57422 c -0.0547,0.17774 -0.14357,0.32813 -0.2666,0.45117 -0.12307,0.10938 -0.29397,0.19825 -0.5127,0.2666 -0.20509,0.0684 -0.4717,0.10938 -0.7998,0.12305 -0.32814,0.0273 -0.72463,0.041 -1.18945,0.041 -0.46487,0 -0.86135,-0.0205 -1.18946,-0.0615 -0.32814,-0.0273 -0.60158,-0.0752 -0.82031,-0.14355 -0.20509,-0.082 -0.36916,-0.17774 -0.49219,-0.28711 -0.12306,-0.10938 -0.20509,-0.23926 -0.24609,-0.38965 l -7.42383,-21.57422 -0.041,0 0,21.59473 c -1e-5,0.13672 -0.041,0.25976 -0.12305,0.36914 -0.0684,0.10937 -0.20509,0.19824 -0.41016,0.2666 -0.20508,0.0684 -0.47168,0.12305 -0.7998,0.16406 -0.31446,0.041 -0.72462,0.0615 -1.23047,0.0615 -0.49219,0 -0.90235,-0.0205 -1.23047,-0.0615 -0.32813,-0.041 -0.59473,-0.0957 -0.7998,-0.16406 -0.19141,-0.0684 -0.32813,-0.15723 -0.41016,-0.2666 -0.0684,-0.10938 -0.10254,-0.23242 -0.10254,-0.36914 l 0,-23.64551 c 0,-0.69724 0.18457,-1.23044 0.55371,-1.59961 0.36914,-0.36911 0.86133,-0.55368 1.47656,-0.55371 l 3.52735,0 c 0.62889,3e-5 1.16893,0.0547 1.62011,0.16406 0.45117,0.0957 0.84081,0.26663 1.16895,0.5127 0.32811,0.23245 0.60155,0.5469 0.82031,0.94336 0.21874,0.38283 0.41015,0.86135 0.57422,1.43554 l 5.74219,15.81153 0.082,0 5.94727,-15.77051 c 0.17771,-0.57419 0.36911,-1.05955 0.57421,-1.45605 0.21873,-0.39646 0.46482,-0.71775 0.73829,-0.96387 0.28708,-0.24607 0.62204,-0.41697 1.00488,-0.5127 0.38278,-0.10935 0.82712,-0.16403 1.33301,-0.16406 l 3.62988,0 c 0.36911,3e-5 0.68356,0.0479 0.94336,0.14356 0.2734,0.0957 0.49215,0.23928 0.65625,0.43066 0.1777,0.17776 0.30758,0.40335 0.38965,0.67676 0.0957,0.25979 0.14352,0.56057 0.14355,0.90234 l 0,23.64551"
+           id="path3818" />
+      </g>
+      <path
+         transform="matrix(1.6,0,0,1.6,-233,-3460.4252)"
+         style="fill:#ffffff;fill-opacity:0.98431373;filter:url(#filter3757)"
+         d="m 445,510.49219 c 0,1.38071 -1.11929,2.5 -2.5,2.5 -1.38071,0 -2.5,-1.11929 -2.5,-2.5 0,-1.38071 1.11929,-2.5 2.5,-2.5 1.38071,0 2.5,1.11929 2.5,2.5 z"
+         id="path3735" />
+    </g>
+  </g>
+</svg>

soil/web/static/img/svg/home.svg

@@ -0,0 +1 @@
+<?xml version="1.0" ?><svg height="1792" viewBox="0 0 1792 1792" width="1792" xmlns="http://www.w3.org/2000/svg"><path d="M1472 992v480q0 26-19 45t-45 19h-384v-384h-256v384h-384q-26 0-45-19t-19-45v-480q0-1 .5-3t.5-3l575-474 575 474q1 2 1 6zm223-69l-62 74q-8 9-21 11h-3q-13 0-21-7l-692-577-692 577q-12 8-24 7-13-2-21-11l-62-74q-8-10-7-23.5t11-21.5l719-599q32-26 76-26t76 26l244 204v-195q0-14 9-23t23-9h192q14 0 23 9t9 23v408l219 182q10 8 11 21.5t-7 23.5z"/></svg>

soil/web/static/img/svg/person.svg

@@ -0,0 +1 @@
+<?xml version="1.0" ?><!DOCTYPE svg  PUBLIC '-//W3C//DTD SVG 1.1//EN'  'http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd'><svg height="512px" id="Layer_1" style="enable-background:new 0 0 512 512;" version="1.1" viewBox="0 0 512 512" width="512px" xml:space="preserve" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><path d="M448,448c0,0,0-26.4-2.2-40.2c-1.8-10.9-16.9-25.3-81.1-48.9c-63.2-23.2-59.3-11.9-59.3-54.6c0-27.7,14.1-11.6,23.1-64.2  c3.5-20.7,6.3-6.9,13.9-40.1c4-17.4-2.7-18.7-1.9-27c0.8-8.3,1.6-15.7,3.1-32.7C345.4,119.3,325.9,64,256,64  c-69.9,0-89.4,55.3-87.5,76.4c1.5,16.9,2.3,24.4,3.1,32.7c0.8,8.3-5.9,9.6-1.9,27c7.6,33.1,10.4,19.3,13.9,40.1  c9,52.6,23.1,36.5,23.1,64.2c0,42.8,3.9,31.5-59.3,54.6c-64.2,23.5-79.4,38-81.1,48.9C64,421.6,64,448,64,448h192H448z"/></svg>

soil/web/static/img/svg/plus.svg

@@ -0,0 +1 @@
+<?xml version="1.0" ?><!DOCTYPE svg  PUBLIC '-//W3C//DTD SVG 1.0//EN'  'http://www.w3.org/TR/2001/REC-SVG-20010904/DTD/svg10.dtd'><svg enable-background="new 0 0 91.8 92.6" id="Layer_1" version="1.0" viewBox="0 0 91.8 92.6" xml:space="preserve" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><path d="M46.3,3.6c-23.5,0-42.5,19-42.5,42.5s19,42.5,42.5,42.5c23.5,0,42.5-19,42.5-42.5S69.8,3.6,46.3,3.6z   M72.8,52.9H53v19.8c0,2-1.6,3.6-3.6,3.6h-6.2c-2,0-3.6-1.6-3.6-3.6V52.9H19.8c-2,0-3.6-1.6-3.6-3.6v-6.2c0-2,1.6-3.6,3.6-3.6h19.8  V19.7c0-2,1.6-3.6,3.6-3.6h6.2c2,0,3.6,1.6,3.6,3.6v19.8h19.8c2,0,3.6,1.6,3.6,3.6v6.2C76.4,51.2,74.8,52.9,72.8,52.9z" fill="#1E1E1E"/></svg>

soil/web/static/img/svg/target.svg

@@ -0,0 +1 @@
+<?xml version="1.0" ?><svg height="16px" version="1.1" viewBox="0 0 16 16" width="16px" xmlns="http://www.w3.org/2000/svg" xmlns:sketch="http://www.bohemiancoding.com/sketch/ns" xmlns:xlink="http://www.w3.org/1999/xlink"><title/><defs/><g fill="none" fill-rule="evenodd" id="Icons with numbers" stroke="none" stroke-width="1"><g fill="#000000" id="Group" transform="translate(-192.000000, -192.000000)"><path d="M201,205.917042 C203.512502,205.49553 205.495527,203.512505 205.917042,201 L203,201 L203,199 L205.917042,199 C205.495527,196.487495 203.512502,194.50447 201,194.082958 L201,197 L199,197 L199,194.082958 C196.487498,194.50447 194.504473,196.487495 194.082958,199 L197,199 L197,201 L194.082958,201 C194.504473,203.512505 196.487498,205.49553 199,205.917042 L199,203 L201,203 Z M200,208 C195.581722,208 192,204.418278 192,200 C192,195.581722 195.581722,192 200,192 C204.418278,192 208,195.581722 208,200 C208,204.418278 204.418278,208 200,208 Z M200,208" id="Oval 163"/></g></g></svg>

soil/web/static/img/svg/time.svg

@@ -0,0 +1 @@
+<?xml version="1.0" ?><!DOCTYPE svg  PUBLIC '-//W3C//DTD SVG 1.1//EN'  'http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd'><svg enable-background="new 0 0 64 64" height="64px" id="Layer_1" version="1.1" viewBox="0 0 64 64" width="64px" xml:space="preserve" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g><g><path d="M52.419,15.975c0,0,1.013,1.019,1.727,0.002l1.363-1.953c0.476-0.687-0.139-1.162-0.202-1.209    l-8.265-5.775H47.04c-0.509-0.354-0.847-0.139-1.024,0.06l-0.148,0.213l-1.259,1.802c-0.006,0.007-0.71,1.119,0.416,1.707v0.001    c1.61,0.792,4.563,2.462,7.392,5.158L52.419,15.975z" fill="#241F20"/></g><g><path d="M38.512,0.071H25.488c-1.011,0-1.839,0.812-1.839,1.839v1.518c0,1.026,0.828,1.854,1.839,1.854h0.644    v1.072c0.001,1.541,0.974,1.669,1.462,1.636c0.083-0.012,0.169-0.025,0.26-0.037c0.001,0,0.013-0.003,0.013-0.003L27.866,7.95    c1.734-0.237,4.605-0.464,7.898-0.045l0.002-0.003c0,0,2.109,0.391,2.103-1.549V5.281h0.644c1.012,0,1.839-0.827,1.839-1.854V1.91    C40.351,0.884,39.523,0.071,38.512,0.071z" fill="#241F20"/></g><path d="M32,10.301c-14.808,0-26.812,12.005-26.812,26.815c0,14.807,12.004,26.812,26.812,26.812   c14.809,0,26.812-12.006,26.812-26.812C58.812,22.306,46.809,10.301,32,10.301z M33.717,37.108   c-1.575,0.002-1.709-1.094-1.717-1.41V17.155c0.046-0.645,0.381-1.86,2.248-1.546c0.037,0.005,0.072,0.009,0.111,0.014   c0.12,0.02,0.233,0.036,0.32,0.043c5.44,0.764,17.373,4.302,18.864,20.343c-0.042,0.446-0.295,1.096-1.412,1.103   C42.529,37.085,36.454,37.097,33.717,37.108z" fill="#241F20"/></g></svg>

soil/web/static/js/socket.js

@@ -0,0 +1,461 @@
+
+// Open the websocket connection
+var ws = new WebSocket((window.location.protocol === 'https:' ? 'wss://' : 'ws://') + window.location.host + '/ws'); 
+
+// Open conection with Socket
+ws.onopen = function() {
+    console.log('Connection opened!');
+};
+
+// Receive data from server
+ws.onmessage = function(message) {
+    //console.log('Message received!');
+
+    var msg = JSON.parse(message.data);
+
+    switch(msg['type']) {
+        case 'trials':
+            reset_trials();
+            set_trials(msg['data']);
+            // $('#load').removeClass('loader');
+            break;
+
+        case 'get_trial':
+            console.log(msg['data']);
+
+            self.GraphVisualization.import(convertJSON(msg['data']), function() {
+                reset_configuration();
+                set_configuration();
+                // $('#home_menu').click(function() {
+                //     setTimeout(function() {
+                //         reset_timeline();
+                //         set_timeline(msg['data']);
+                //     }, 1000);
+                // });
+                reset_timeline();
+                set_timeline(msg['data']);
+                $('#load').hide();
+            });
+            $('#charts .chart').removeClass('no-data');
+            set_chart_nodes(msg['data'], chart_nodes)
+            set_chart_attrs(msg['data'], chart_attrs, $('.config-item #properties').val())
+            $('.config-item #properties').change(function() {
+                chart_attrs.destroy();
+                chart_attrs = create_chart(width_chart, height_chart, 'Time', 'Attributes', '#chart_attrs');
+                set_chart_attrs(msg['data'], chart_attrs, $('.config-item #properties').val())
+            });
+            break;
+
+        case 'settings':
+            $('#wrapper-settings').empty().removeClass('none');
+            initGUI(msg['data']);
+            break;
+
+        case 'error':
+            console.error(msg['error']);
+            _socket.error(msg['error']);
+            $('#load').removeClass('loader');
+            break;
+
+        case 'log':
+            $('.console').append('$ ' + msg['logger'] + ': ' + msg['logging'] + '<br/>');
+            $('.console').animate({ scrollTop: $('.console')[0].scrollHeight }, 'fast');
+            break;
+
+        case 'visualization_params':
+            console.log(msg['data']);
+            self.GraphVisualization.set_params(msg['data']['shape_property'], msg['data']['shapes'], msg['data']['colors']);
+
+            if ( msg['data']['background_image'] ) {
+                // $('svg#graph').css('background-image', 'linear-gradient(to bottom, rgba(0,0,0,0.4) 0%,rgba(0,0,0,0.4) 100%), url(img/background/' + msg['data']['background_image'])
+                //               .css('background-size', '130%').css('background-position', '5% 30%').css('background-repeat', 'no-repeat');
+                $('<style>').text('svg line.link { stroke: white !important; stroke-width: 1.5px !important; }').appendTo($('html > head'));
+                $('<style>').text('svg circle.node { stroke-width: 2.5px !important; }').appendTo($('html > head'));
+                self.GraphVisualization.set_background('img/background/' + msg['data']['background_image'], msg['data']['background_opacity'], msg['data']['background_filter_color']);
+            }
+            break;
+
+        case 'download_gexf':
+            var xml_declaration = '<?xml version="1.0" encoding="utf-8"?>';
+            download(msg['filename'] + '.gexf', 'xml', xml_declaration + msg['data']);
+            break;
+
+        case 'download_json':
+            download(msg['filename'] + '.json', 'json', JSON.stringify(msg['data'], null, 4));
+            break;
+
+        default:
+            console.warn('Unexpected message!')
+    }
+
+}
+
+var _socket = {
+    send: function(message, type) {
+        var json = {}
+        json['type'] = type
+        json['data'] = message
+        ws.send(JSON.stringify(json))
+    },
+    error: function(message) {
+        $('#error-message').text(message);
+        $('.alert.alert-danger').show();
+    },
+    current_trial: undefined
+};
+
+var set_trials = function(trials) {
+    for ( i in trials ) {
+        var list_item = $('<li>').appendTo('.dropdown#trials .dropdown-menu');
+        $('<a>').val(i).text(trials[i]).appendTo(list_item);
+    }
+    // Select 'trials'
+    $('.dropdown#trials li a').click(function() {
+        var a = $('.dropdown-toggle .caret');
+        $('.dropdown-toggle').text($(this).text() + ' ').append(a);
+        _socket.send($(this).val(), 'get_trial');
+        _socket.current_trial = $(this).val();
+    });
+    // Request first trial as default
+    _socket.send(0, 'get_trial')
+    _socket.current_trial = 0
+};
+
+var reset_trials = function() {
+    // 'Trials' selector
+    $('.dropdown-menu').empty();
+    var a = $('.dropdown-toggle .caret');
+    $('.dropdown-toggle').text('Trials ').append(a);
+}
+
+var convertJSON = function(json) {
+    // For NetworkX Geometric Graphs get positions
+    json.nodes.forEach(function(node) {
+        var scx = d3.scale.linear().domain([0, 1]).range([0, width]);
+        var scy = d3.scale.linear().domain([0, 1]).range([width, 0]);
+        if ( node.pos ) {
+            node.scx = scx(node.pos[0]);
+            node.scy = scy(node.pos[1]);
+        }
+        delete node.pos;
+    });
+    json.links.forEach(function(link) {
+        link.source = json.nodes[link.source]
+        link.target = json.nodes[link.target]
+    });
+    // Fix spells for nodes
+    json.nodes.forEach(function(node) {
+        for (i in node.spells) {
+            if (node.spells[i][0] > node.spells[i][1]) {
+                aux = node.spells[i][0];
+                node.spells[i][0] = node.spells[i][1];
+                node.spells[i][1] = aux;
+            }
+        }
+    });
+    return json;
+}
+
+var update_statistics_table = function() {
+
+    $('#percentTable tbody').empty()
+
+    var statisticsSorted = Object.keys(self.GraphVisualization.statistics).sort(function(a,b) {
+        return self.GraphVisualization.statistics[b] - self.GraphVisualization.statistics[a];
+    });
+
+    for ( var i in statisticsSorted ) {
+        if ( i <= 5 ) {
+            // Draw table
+            var appendTo = '#percentTable > tbody tr:nth-child(' + Number(parseInt(i) + 1) + ')';
+            var propertyName = (statisticsSorted[i].includes('class')) ? 
+                                        statisticsSorted[i].split('.').pop().split('\'')[0] : statisticsSorted[i];
+
+            $('<tr>').addClass('col-sm-12').appendTo('#percentTable > tbody');
+            $('<td>').css('background-color', self.GraphVisualization.color($('.config-item #properties').val(), statisticsSorted[i])).addClass('col-sm-1').appendTo(appendTo);
+            $('<td>').addClass('text-left col-sm-4').text(self.GraphVisualization.statistics[statisticsSorted[i]] + ' %').appendTo(appendTo);
+            $('<td>').addClass('text-right col-sm-6 property-name').text(propertyName).appendTo(appendTo);
+        }
+    }
+}
+
+var set_configuration = function() {
+    // Number of nodes and links info table
+    $('<tr>').appendTo('#info-graph > tbody');
+    $('<th>').text('Nodes:').appendTo('#info-graph > tbody tr:nth-child(1)');
+    $('<th>').text(self.GraphVisualization.nodes).addClass('text-right').appendTo('#info-graph > tbody tr:nth-child(1)');
+    
+    $('<tr>').appendTo('#info-graph > tbody');
+    $('<th>').text('Links:').appendTo('#info-graph > tbody tr:nth-child(2)');
+    $('<th>').text(self.GraphVisualization.links).addClass('text-right').appendTo('#info-graph > tbody tr:nth-child(2)');
+
+    // Options of 'Select'
+    for ( var i in self.GraphVisualization.model['dynamic'] ) {
+        $('<option>').val(self.GraphVisualization.model['dynamic'][i].title)
+                     .text(self.GraphVisualization.model['dynamic'][i].title).appendTo('#properties-dynamic');
+    }
+    for ( var i in self.GraphVisualization.model['static'] ) {
+        $('<option>').val(self.GraphVisualization.model['static'][i].title)
+                     .text(self.GraphVisualization.model['static'][i].title).appendTo('#properties-static');
+    }
+
+    // Hide optgroups if they are empty
+    if ( $('#properties-dynamic').children().length === 0 ) $('#properties-dynamic').hide();
+    if ( $('#properties-static').children().length === 0 )  $('#properties-static').hide();
+
+    update_statistics_table();
+
+    // Enable 'Link Distance' slider
+    $('#link-distance-slider').slider('enable').on('change', function(value) {
+        self.GraphVisualization.set_link_distance(value.value.newValue);
+    });
+
+    // Enable 'Run configuration' button
+    $('#run_simulation').attr('data-toggle', 'modal').attr('data-target', '#simulation_modal');
+
+    // Enable 'Download' buttons
+    $('#download_modal .btn-success').prop('disabled', false);
+    $('#download_gexf').on('click', function() {
+        _socket.send(_socket.current_trial, 'download_gexf')
+    });
+    $('#download_json').on('click', function() {
+        _socket.send(_socket.current_trial, 'download_json')
+    });
+}
+
+var reset_configuration = function() {
+    // Information table about the graph
+    $('#info-graph > tbody').empty();
+
+    // 'Select' for properties
+    $('#properties-dynamic').empty().show();
+    $('#properties-static').empty().show();
+
+    // 'Link Distance' slider
+    $('#link-distance-slider').slider('disable').slider('setValue', 30);
+
+    // 'Download' buttons
+    $('#download_gexf').off();
+    $('#download_json').off();
+}
+
+var slider;
+
+var set_timeline = function(graph) {
+    // 'Timeline' slider
+    var [min, max] = get_limits(graph);
+
+    var stepUnix = 1;
+    var minUnix  = (min !== Math.min()) ? min : 0;
+    var maxUnix  = (max !== Math.max()) ? max : minUnix + 20;
+
+    slider = d3.slider();
+    d3.select('#slider3').attr('width', width).call(
+        slider.axis(true).min(minUnix).max(maxUnix).step(stepUnix).value(minUnix)
+        .on('slide', function(evt, value) {
+            self.GraphVisualization.update_graph($('.config-item #properties').val(), value, function() {
+                update_statistics_table();
+            });
+        })
+    );
+
+    // Draw graph for the first time
+    self.GraphVisualization.update_graph($('.config-item #properties').val(), maxUnix, function() {
+        update_statistics_table();
+        setTimeout(function() {           
+            self.GraphVisualization.fit(); 
+            if ( $('svg #root > image').length !== 0 ) {
+                $('svg #root > image').attr('height', d3.select('#root').node().getBBox().height * 1.2);
+                var dx = d3.select('#graph-wrapper').node().getBBox().width - d3.select('svg #root > image').node().getBBox().width;
+                var dy = d3.select('#graph-wrapper').node().getBBox().height - d3.select('svg #root > image').node().getBBox().height;
+                $('svg #root > image').attr('transform', 'translate(' + (dx / 2) + ',' + (dy / 2) + ')');
+                $('svg #root > rect').attr('transform', 'translate(' + (dx / 2) + ',' + (dy / 2) + ')')
+                                     .attr('width', d3.select('svg #root > image').node().getBBox().width)
+                                     .attr('height', d3.select('svg #root > image').node().getBBox().height);
+            }
+        }, 1000);
+    });
+
+    // 'Speed' slider
+    $('#speed-slider').slider('enable').on('change', function(value) {
+        speed = value.value.newValue;
+    });
+
+    // Button 'Play'
+    $('button#button_play').on('click', function() {
+        play();
+
+    });
+
+    // Button 'Pause'
+    $('button#button_pause').on('click', function() {
+        stop();
+        $('button#button_play').removeClass('pressed').prop("disabled", false);
+    });
+
+    // Button 'Zoom to Fit'
+    $('button#button_zoomFit').click(function() { self.GraphVisualization.fit(); });
+}
+
+var player;
+
+function play(){
+    $('button#button_play').addClass('pressed').prop("disabled", true);
+
+    if (slider.value() >= slider.max()) {
+        slider.value(slider.min());
+    }
+
+    var FRAME_INTERVAL = 100;
+    var speed_ratio = FRAME_INTERVAL / 1000 // speed=1 => 1 step per second
+    
+    nextStep = function() {
+        newvalue = Math.min(slider.value() + speed*speed_ratio, slider.max());
+        console.log("new time value", newvalue);
+        slider.value(newvalue);
+
+        self.GraphVisualization.update_graph($('.config-item #properties').val(), slider.value(), function () {
+            update_statistics_table();
+        });
+
+        if (newvalue < slider.max()) {
+            player = setTimeout(nextStep, FRAME_INTERVAL);
+        } else {
+            $('button#button_play').removeClass('pressed').prop("disabled", false);
+        }
+    }
+
+    player = setTimeout(nextStep, FRAME_INTERVAL);
+}
+
+function stop() {
+    clearTimeout(player);
+}
+
+var reset_timeline = function() {
+    // 'Timeline' slider
+    $('#slider3').html('');
+
+    // 'Speed' slider
+    // $('#speed-slider').slider('disable').slider('setValue', 1000);
+
+    // Buttons
+    stop();
+    $('button#button_play').off().removeClass('pressed').prop("disabled", false);
+    $('button#button_pause').off();
+    $('button#button_zoomFit').off();
+}
+
+var get_limits = function(graph) {
+    var max = Math.max();
+    var min = Math.min()
+    graph.links.forEach(function(link) {
+        if (link.end    > max) max = link.end
+        if (link.start  > max) max = link.start
+        if (link.end    < min) min = link.end
+        if (link.start  < min) min = link.start
+    });
+    graph.nodes.forEach(function(node) {
+        for (property in node) {
+            if ( Array.isArray(node[property]) ) {
+
+                for (i in node[property]) {
+                    for (j in node[property][i]) {
+                        if (node[property][i][j] > max) max = node[property][i][j];
+                        if (node[property][i][j] < min) min = node[property][i][j];
+                    }
+                }
+
+            }
+        }
+    })
+    return [min, max];
+}
+
+var set_chart_nodes = function(graph, chart) {
+    var [min, max] = get_limits(graph);
+    var data = ['nodes']
+    for (var i = min; i <= max; i++) {
+        data.push(this.GraphVisualization.get_nodes(i));
+    }
+    chart.load({
+        unload: true,
+        columns: [data]
+    });
+}
+
+var set_chart_attrs = function(graph, chart, property) {
+    var [min, max] = get_limits(graph);
+    var data_tmp = {}
+    for (var i = min; i <= max; i++) {
+        this.GraphVisualization.get_attributes(property, i, function(object) {
+            for (var value in object) {
+                if (!data_tmp[value]) {
+                    var time = 0
+                    for (var done in data_tmp)
+                        time = (data_tmp[done].length > time) ? data_tmp[done].length - 1 : time
+                    data_tmp[value] = Array(time).fill(0);
+                }
+                data_tmp[value].push(object[value]);
+            }
+        });
+    }
+    var data = $.map(data_tmp, function(value, index) {
+        value.splice(0,0,index);
+        return [value];
+    });
+    chart.load({
+        unload: true,
+        columns: data
+    });
+    chart.axis.labels({y: property});
+}
+
+var create_chart = function(width, height, label_x, label_y, bind_to) {
+    return c3.generate({
+        size: {
+            width: width,
+            height: height
+        },
+        data: {
+            columns: [],
+            type: 'area-spline'
+        },
+        axis: {
+            x: { label: label_x },
+            y: { label: label_y }
+        },
+        point: { show: false },
+        bindto: bind_to
+    });
+}
+
+var run_simulation = function() {
+    var environment_variables = {}
+    $('#wrapper-settings input').each(function() {
+        switch(this.type) {
+            case 'text':
+                environment_variables[this.id] = Number(this.value);
+                break;
+            case 'checkbox':
+                environment_variables[this.id] = ($(this).is(':checked')) ? true : false;
+                break;
+            case 'number':
+                environment_variables[this.id] = Number(this.value);
+                break;
+            default:
+                console.warn(this.id +  ' not defined when running simulation!');
+                break;
+        }
+        
+    });
+    return environment_variables;
+}
+
+var download = function(filename, filetype, content) {
+    var file = document.createElement('a');
+    file.setAttribute('href', 'data:text/' + filetype + ';charset=utf-8,' + encodeURIComponent(content));
+    file.setAttribute('download', filename);
+    file.click();
+    delete file;
+}

soil/web/static/js/template.js

@@ -0,0 +1,99 @@
+// Add model parameters that can be edited prior to a model run
+var initGUI = function(model_params) {
+
+    var addBooleanInput = function(name, value) {
+        var checked = (value) ? 'checked' : 'value';
+
+        var wrapper     = $('<div>').attr('class', 'col-sm-6').height('110px');
+        var input_group = $('<div>').attr('class', 'input-group').appendTo(wrapper);
+        var label       = $('<label>').attr('for', name).attr('class', 'checkbox').appendTo(input_group);
+        var input       = $('<input>') .attr('class', 'form-check-input').attr('id', name).attr('type', 'checkbox').attr(checked, checked).appendTo(label);
+
+        input.after(name);
+        $('#wrapper-settings').append(wrapper);
+    };
+
+    var addSliderInput = function(name, value) {
+
+        var wrapper = $('<div>').attr('class', 'col-sm-6').height('110px');
+        var label   = $('<div>').width('100%').text(name).css('text-align', 'center').css('font-weight', 'bolder').appendTo(wrapper);
+        var input   = $('<input>').attr('id', name).attr('type', 'text').attr('data-slider-min', '0.001').attr('data-slider-max', '1').attr('data-slider-step', '0.001').attr('data-slider-value', value).attr('data-slider-tooltip', 'hide').css('padding', '0 10px').appendTo(wrapper);
+        
+        var span          = $('<div>').attr('id', name + '_value').text('Current value: ').width('100%').css('padding-top', '10px').appendTo(wrapper);
+        var current_value = $('<span>').attr('id', name + '_number').text(value).appendTo(span);
+
+        var button_group  = $('<div>').attr('class', 'btn-group').attr('role', 'group').css('position', 'absolute').css('right', '15px').appendTo(span);
+        var button_down   = $('<button>').attr('type', 'button').attr('class', 'btn btn-default btn-default-down').appendTo(button_group);
+        var button_up     = $('<button>').attr('type', 'button').attr('class', 'btn btn-default btn-default-down').appendTo(button_group);
+
+        $('<span>').attr('class', 'glyphicon glyphicon-chevron-down').attr('aria-hidden', 'true').appendTo(button_down);
+        $('<span>').attr('class', 'glyphicon glyphicon-chevron-up').attr('aria-hidden', 'true').appendTo(button_up);
+
+        $('#wrapper-settings').append(wrapper);
+        input.slider().on('change', function(slideEvt) {
+            current_value.text(slideEvt.value.newValue);
+        });
+        var timeout, interval;
+        button_down.on('mousedown', function() {
+            input.slider('setValue', input.slider('getValue') - 0.001);
+            current_value.text(input.slider('getValue'));
+            timeout = setTimeout(function() {
+                interval = setInterval(function() {
+                    input.slider('setValue', input.slider('getValue') - 0.001);
+                    current_value.text(input.slider('getValue'));
+                }, 30);
+            }, 500);
+        });
+        button_down.on('mouseup', function() {
+            clearTimeout(timeout);
+            clearInterval(interval);
+        });
+        button_up.on('mousedown', function() {
+            input.slider('setValue', input.slider('getValue') + 0.001);
+            current_value.text(input.slider('getValue'));
+            timeout = setTimeout(function() {
+                interval = setInterval(function() {
+                    input.slider('setValue', input.slider('getValue') + 0.001);
+                    current_value.text(input.slider('getValue'));
+                }, 30);
+            }, 500);
+        });
+        button_up.on('mouseup', function() {
+            clearTimeout(timeout);
+            clearInterval(interval);
+        });
+    };
+
+    var addNumberInput = function(name, value) {
+        var wrapper = $('<div>').attr('class', 'col-sm-6').height('110px');
+        var label   = $('<div>').width('100%').text(name).css('text-align', 'center').css('font-weight', 'bolder').appendTo(wrapper);
+        var input   = $('<input>').attr('id', name).attr('type', 'number').attr('class', 'form-control').attr('value', value).attr('min', 0).css('margin-top', '18px').appendTo(wrapper);
+        $('#wrapper-settings').append(wrapper);
+    }
+
+    var addTextBox = function(param, obj) {
+        var well = $('<div class="well">' + obj.value + '</div>')[0];
+        sidebar.append(well);
+    };
+
+    for (var option in model_params) {
+        
+        var type = typeof(model_params[option]);
+        var param_str = String(option);
+
+        switch (model_params[option]['type']) {
+            case 'boolean':
+                addBooleanInput(model_params[option]['label'], model_params[option]['value']);
+                break;
+            case 'number':
+                addSliderInput(model_params[option]['label'], model_params[option]['value']);
+                break;
+            case 'great_number':
+                addNumberInput(model_params[option]['label'], model_params[option]['value']);
+                break;
+            default:
+                console.warn(model_params[option]['label'] + ' not defined!');
+                break;
+        }
+    }
+};

soil/web/static/js/timeline.js

@@ -0,0 +1,429 @@
+/*
+    D3.js Slider
+    Inspired by jQuery UI Slider
+    Copyright (c) 2013, Bjorn Sandvik - http://blog.thematicmapping.org
+    BSD license: http://opensource.org/licenses/BSD-3-Clause
+*/
+(function (root, factory) {
+  if (typeof define === 'function' && define.amd) {
+    // AMD. Register as an anonymous module.
+    define(['d3'], factory);
+  } else if (typeof exports === 'object') {
+    if (process.browser) {
+      // Browserify. Import css too using cssify.
+      require('./d3.slider.css');
+    }
+    // Node. Does not work with strict CommonJS, but
+    // only CommonJS-like environments that support module.exports,
+    // like Node.
+    module.exports = factory(require('d3'));
+  } else {
+    // Browser globals (root is window)
+    root.d3.slider = factory(root.d3);
+  }
+}(this, function (d3) {
+return function module() {
+  "use strict";
+
+  // Public variables width default settings
+  var min = 0,
+      max = 100,
+      step = 0.01,
+      animate = true,
+      orientation = "horizontal",
+      axis = false,
+      margin = 50,
+      value,
+      active = 1,
+      snap = false,
+      scale;
+
+  // Private variables
+  var axisScale,
+      dispatch = d3.dispatch("slide", "slideend"),
+      formatPercent = d3.format(".2%"),
+      tickPadding = 5,
+      tickFormat = d3.format(".0"),
+      handle1,
+      handle2 = null,
+      divRange,
+      sliderLength;
+
+  function slider(selection) {
+    selection.each(function() {
+
+      // Create scale if not defined by user
+      if (!scale) {
+        scale = d3.scale.linear().domain([min, max]);
+      }
+
+      // Start value
+      value = value || scale.domain()[0];
+
+      // DIV container
+      var div = d3.select(this).classed("d3-slider d3-slider-" + orientation, true);
+      
+      var drag = d3.behavior.drag();
+      drag.on('dragend', function () {
+        dispatch.slideend(d3.event, value);
+      })
+
+      // Slider handle
+      //if range slider, create two
+      // var divRange;
+
+      if (toType(value) == "array" && value.length == 2) {
+        handle1 = div.append("a")
+          .classed("d3-slider-handle", true)
+          .attr("xlink:href", "#")
+          .attr('id', "handle-one")
+          .on("click", stopPropagation)
+          .call(drag);
+        handle2 = div.append("a")
+          .classed("d3-slider-handle", true)
+          .attr('id', "handle-two")
+          .attr("xlink:href", "#")
+          .on("click", stopPropagation)
+          .call(drag);
+      } else {
+        handle1 = div.append("a")
+          .classed("d3-slider-handle", true)
+          .attr("xlink:href", "#")
+          .attr('id', "handle-one")
+          .on("click", stopPropagation)
+          .call(drag);
+      }
+      
+      // Horizontal slider
+      if (orientation === "horizontal") {
+
+        div.on("click", onClickHorizontal);
+        
+        if (toType(value) == "array" && value.length == 2) {
+          divRange = d3.select(this).append('div').classed("d3-slider-range", true);
+
+          handle1.style("left", formatPercent(scale(value[ 0 ])));
+          divRange.style("left", formatPercent(scale(value[ 0 ])));
+          drag.on("drag", onDragHorizontal);
+
+          var width = 100 - parseFloat(formatPercent(scale(value[ 1 ])));
+          handle2.style("left", formatPercent(scale(value[ 1 ])));
+          divRange.style("right", width+"%");
+          drag.on("drag", onDragHorizontal);
+
+        } else {
+          handle1.style("left", formatPercent(scale(value)));
+          drag.on("drag", onDragHorizontal);
+        }
+        
+        sliderLength = parseInt(div.style("width"), 10);
+
+      } else { // Vertical
+
+        div.on("click", onClickVertical);
+        drag.on("drag", onDragVertical);
+        if (toType(value) == "array" && value.length == 2) {
+          divRange = d3.select(this).append('div').classed("d3-slider-range-vertical", true);
+
+          handle1.style("bottom", formatPercent(scale(value[ 0 ])));
+          divRange.style("bottom", formatPercent(scale(value[ 0 ])));
+          drag.on("drag", onDragVertical);
+
+          var top = 100 - parseFloat(formatPercent(scale(value[ 1 ])));
+          handle2.style("bottom", formatPercent(scale(value[ 1 ])));
+          divRange.style("top", top+"%");
+          drag.on("drag", onDragVertical);
+
+        } else {
+          handle1.style("bottom", formatPercent(scale(value)));
+          drag.on("drag", onDragVertical);
+        }
+        
+        sliderLength = parseInt(div.style("height"), 10);
+
+      }
+      
+      if (axis) {
+        createAxis(div);
+      }
+
+
+      function createAxis(dom) {
+
+        // Create axis if not defined by user
+        if (typeof axis === "boolean") {
+
+          axis = d3.svg.axis()
+              .ticks(Math.round(sliderLength) / 100)
+              .tickFormat(tickFormat)
+              .tickPadding(tickPadding)
+              .orient((orientation === "horizontal") ? "bottom" :  "right");
+
+        }
+
+        // Copy slider scale to move from percentages to pixels
+        axisScale = scale.ticks ? scale.copy().range([0, sliderLength]) : scale.copy().rangePoints([0, sliderLength], 0.5);
+          axis.scale(axisScale);
+
+          // Create SVG axis container
+        var svg = dom.append("svg")
+            .classed("d3-slider-axis d3-slider-axis-" + axis.orient(), true)
+            .on("click", stopPropagation);
+
+        var g = svg.append("g");
+
+        // Horizontal axis
+        if (orientation === "horizontal") {
+
+          svg.style("margin-left", -margin - 16 + "px");
+
+          svg.attr({
+            width: sliderLength + margin * 2,
+            height: margin + 30
+          });
+
+          if (axis.orient() === "top") {
+            svg.style("top", -margin + "px");
+            g.attr("transform", "translate(" + margin + "," + margin + ")");
+          } else { // bottom
+            g.attr("transform", "translate(" + margin + ",0)");
+          }
+
+        } else { // Vertical
+
+          svg.style("top", -margin + "px");
+
+          svg.attr({
+            width: margin,
+            height: sliderLength + margin * 2
+          });
+
+          if (axis.orient() === "left") {
+            svg.style("left", -margin + "px");
+            g.attr("transform", "translate(" + margin + "," + margin + ")");
+          } else { // right          
+            g.attr("transform", "translate(" + 0 + "," + margin + ")");
+          }
+
+        }
+
+        g.call(axis);
+
+      }
+
+      function onClickHorizontal() {
+        if (toType(value) != "array") {
+          var pos = Math.max(0, Math.min(sliderLength, d3.event.offsetX || d3.event.layerX));
+          moveHandle(scale.invert ? 
+                      stepValue(scale.invert(pos / sliderLength))
+                    : nearestTick(pos / sliderLength));
+        }
+      }
+
+      function onClickVertical() {
+        if (toType(value) != "array") {
+          var pos = sliderLength - Math.max(0, Math.min(sliderLength, d3.event.offsetY || d3.event.layerY));
+          moveHandle(scale.invert ? 
+                      stepValue(scale.invert(pos / sliderLength))
+                    : nearestTick(pos / sliderLength));
+        }
+      }
+
+      function onDragHorizontal() {
+        if ( d3.event.sourceEvent.target.id === "handle-one") {
+          active = 1;
+        } else if ( d3.event.sourceEvent.target.id == "handle-two" ) {
+          active = 2;
+        }
+        var pos = Math.max(0, Math.min(sliderLength, d3.event.x));
+        moveHandle(scale.invert ? 
+                    stepValue(scale.invert(pos / sliderLength))
+                  : nearestTick(pos / sliderLength));
+      }
+
+      function onDragVertical() {
+        if ( d3.event.sourceEvent.target.id === "handle-one") {
+          active = 1;
+        } else if ( d3.event.sourceEvent.target.id == "handle-two" ) {
+          active = 2;
+        }
+        var pos = sliderLength - Math.max(0, Math.min(sliderLength, d3.event.y))
+        moveHandle(scale.invert ? 
+                    stepValue(scale.invert(pos / sliderLength))
+                  : nearestTick(pos / sliderLength));
+      }
+
+      function stopPropagation() {
+        d3.event.stopPropagation();
+      }
+
+    });
+
+  }
+
+  // Move slider handle on click/drag
+  function moveHandle(newValue) {
+    var currentValue = toType(value) == "array"  && value.length == 2 ? value[active - 1]: value,
+        oldPos = formatPercent(scale(stepValue(currentValue))),
+        newPos = formatPercent(scale(stepValue(newValue))),
+        position = (orientation === "horizontal") ? "left" : "bottom";
+    if (oldPos !== newPos) {
+
+      if (toType(value) == "array" && value.length == 2) {
+        value[ active - 1 ] = newValue;
+        if (d3.event) {
+          dispatch.slide(d3.event, value );
+        };
+      } else {
+        if (d3.event) {
+          dispatch.slide(d3.event.sourceEvent || d3.event, value = newValue);
+        };
+      }
+
+      if ( value[ 0 ] >= value[ 1 ] ) return;
+      if ( active === 1 ) {
+        if (toType(value) == "array" && value.length == 2) {
+          (position === "left") ? divRange.style("left", newPos) : divRange.style("bottom", newPos);
+        }
+
+        if (animate) {
+          handle1.transition()
+              .styleTween(position, function() { return d3.interpolate(oldPos, newPos); })
+              .duration((typeof animate === "number") ? animate : 250);
+        } else {
+          handle1.style(position, newPos);
+        }
+      } else {
+        
+        var width = 100 - parseFloat(newPos);
+        var top = 100 - parseFloat(newPos);
+
+        (position === "left") ? divRange.style("right", width + "%") : divRange.style("top", top + "%");
+        
+        if (animate) {
+          handle2.transition()
+              .styleTween(position, function() { return d3.interpolate(oldPos, newPos); })
+              .duration((typeof animate === "number") ? animate : 250);
+        } else {
+          handle2.style(position, newPos);
+        }
+      }
+    }
+  }
+
+  // Calculate nearest step value
+  function stepValue(val) {
+
+    if (val === scale.domain()[0] || val === scale.domain()[1]) {
+      return val;
+    }
+
+    var alignValue = val;
+    if (snap) {
+      alignValue = nearestTick(scale(val));
+    } else{
+      var valModStep = (val - scale.domain()[0]) % step;
+      alignValue = val - valModStep;
+
+      if (Math.abs(valModStep) * 2 >= step) {
+        alignValue += (valModStep > 0) ? step : -step;
+      }
+    };
+
+    return alignValue;
+
+  }
+
+  // Find the nearest tick
+  function nearestTick(pos) {
+    var ticks = scale.ticks ? scale.ticks() : scale.domain();
+    var dist = ticks.map(function(d) {return pos - scale(d);});
+    var i = -1,
+        index = 0,
+        r = scale.ticks ? scale.range()[1] : scale.rangeExtent()[1];
+    do {
+        i++;
+        if (Math.abs(dist[i]) < r) {
+          r = Math.abs(dist[i]);
+          index = i;
+        };
+    } while (dist[i] > 0 && i < dist.length - 1);
+
+    return ticks[index];
+  };
+
+  // Return the type of an object
+  function toType(v) {
+    return ({}).toString.call(v).match(/\s([a-zA-Z]+)/)[1].toLowerCase();
+  };
+
+  // Getter/setter functions
+  slider.min = function(_) {
+    if (!arguments.length) return min;
+    min = _;
+    return slider;
+  };
+
+  slider.max = function(_) {
+    if (!arguments.length) return max;
+    max = _;
+    return slider;
+  };
+
+  slider.step = function(_) {
+    if (!arguments.length) return step;
+    step = _;
+    return slider;
+  };
+
+  slider.animate = function(_) {
+    if (!arguments.length) return animate;
+    animate = _;
+    return slider;
+  };
+
+  slider.orientation = function(_) {
+    if (!arguments.length) return orientation;
+    orientation = _;
+    return slider;
+  };
+
+  slider.axis = function(_) {
+    if (!arguments.length) return axis;
+    axis = _;
+    return slider;
+  };
+
+  slider.margin = function(_) {
+    if (!arguments.length) return margin;
+    margin = _;
+    return slider;
+  };
+
+  slider.value = function(_) {
+    if (!arguments.length) return value;
+    if (value) {
+      moveHandle(stepValue(_));
+    };
+    value = _;
+    return slider;
+  };
+
+  slider.snap = function(_) {
+    if (!arguments.length) return snap;
+    snap = _;
+    return slider;
+  };
+
+  slider.scale = function(_) {
+    if (!arguments.length) return scale;
+    scale = _;
+    return slider;
+  };
+
+  d3.rebind(slider, dispatch, "on");
+
+  return slider;
+
+}
+}));

soil/web/static/js/visualization.js

@@ -0,0 +1,686 @@
+
+;(function(undefined) {
+  "use strict";
+
+  /**
+   * Graph Visualization
+   * ===================
+   *
+   * Author: Tasio Méndez (tasiomendez)
+   * URL: https://github.com/tasiomendez/
+   * Version: 0.1
+   */
+
+  // Private constants
+  var focus_opacity = 0.1,
+      radius = 8,
+      shape_size = 16,
+      required_node = ['id', 'index', 'label', 'px', 'py', 'spells', 'weight', 'x', 'y', 'pos', 'scx', 'scy'];
+
+  // Private variables
+  var width,
+      height,
+      graph,              // JSON data for the graph
+      model,              // Definition of the attributes of the nodes
+      linkedByIndex,      // Nodes linked by index
+      name,               // Name of the graph (id for svg item)
+      svg,                // Svg item
+      force,              // Set up the force layout
+      color,              // Color for nodes
+      zoom,               // Zoom
+
+      groot,              // Append sections to svg to have nodes and edges separately
+      graph_wrapper,
+      glinks,
+      gnodes,
+      background_image,
+      background_opacity,
+      background_filter_color,
+      data_node,          // Actual node data for the graph
+      data_link,          // Actual link data for the graph
+
+      link,               // Line svgs
+      node,               // Circles for the nodes
+      shape_property,     // Property to whom the shape will be applied
+      shapes,             // Dictionary of shapes for nodes
+      colors,             // Dictionary of colors for nodes
+      background;         // Background of the graph
+
+  Number.prototype.between = function(min, max) {
+    var min = (min || min === 0) ? min : Math.max(),
+        max = (max || max === 0) ? max : Math.min();
+
+    return ( this > min && this <= max ) || ( min === 0 && this === 0 );
+  };
+
+  Number.prototype.is_type = function() {
+    if ( typeof(this) === 'number' )
+      return ( Number.isInteger(this) ) ? 'int' :  'float';
+    else 
+      return false;
+  }
+
+  String.prototype.is_type = function() {
+    return "string";
+  }
+
+  var lastFocusNode;
+  var _helpers = {
+    set_node: function(node, property, time) {
+      // Add nodes if data has more nodes than before
+      node.enter().append('circle')
+          .attr('class', 'node')
+          .attr('r', radius)
+          .style('fill', function (d) {
+              if ( Array.isArray(d[property]) ) {
+                var color_node = _helpers.set_color(property, d[property][0][0]);
+                d[property].forEach(function(p) {
+                  if ( time.between(p[1], p[2]) ) color_node = _helpers.set_color(property, p[0]);
+                });
+                return color_node;
+              } else {
+                return _helpers.set_color(property, d[property]);
+              }
+          })
+          .style('stroke', function(d) {
+              if (_helpers.set_shape(d[shape_property]) !== (-1))
+                if ( Array.isArray(d[property]) ) {
+                  var color_node = _helpers.set_color(property, d[property][0][0]);
+                  d[property].forEach(function(p) {
+                    if ( time.between(p[1], p[2]) ) color_node = _helpers.set_color(property, p[0]);
+                  });
+                  return color_node;
+                } else {
+                  return _helpers.set_color(property, d[property]);
+                }
+              else 
+                return '#ffffff';
+          })
+          // Cancel zoom movement so you can move the node
+          .on('mousedown', function(d) {
+              d3.event.stopPropagation();
+          })
+          // Double-click to focus neighbours
+          .on('dblclick', function(d) {
+              d3.event.stopPropagation();
+              if (d === lastFocusNode) {
+                  lastFocusNode = undefined;
+                  node.style('opacity', 1);
+                  link.style('opacity', 1);
+              } else {
+                  lastFocusNode = d;
+                  _helpers.set_focus(d);
+              }
+          }).call(force.drag);
+
+      // Remove nodes if data has less nodes than before
+      node.exit().remove();
+
+      // Update existing nodes
+      node.attr('class', 'node')
+          .attr('r', radius)
+          .style('fill', function (d) {
+              if (_helpers.set_shape(d[shape_property]) !== (-1)) {
+                return 'url(#' + _helpers.set_shape(d[shape_property]) + ')';
+              }
+              if ( Array.isArray(d[property]) ) {
+                var color_node = _helpers.set_color(property, d[property][0][0]);
+                d[property].forEach(function(p) {
+                  if ( time.between(p[1], p[2]) ) color_node = _helpers.set_color(property, p[0]);
+                });
+                return color_node;
+              } else {
+                return _helpers.set_color(property, d[property]);
+              }
+          })
+          .style('stroke', function(d) {
+              if (_helpers.set_shape(d[shape_property]) !== (-1))
+                if ( Array.isArray(d[property]) ) {
+                  var color_node = _helpers.set_color(property, d[property][0][0]);
+                  d[property].forEach(function(p) {
+                    if ( time.between(p[1], p[2]) ) color_node = _helpers.set_color(property, p[0]);
+                  });
+                  return color_node;
+                } else {
+                  return _helpers.set_color(property, d[property]);
+                }
+              else 
+                return '#ffffff';
+          })
+          .on('dblclick', function(d) {
+            d3.event.stopPropagation();
+            if (d === lastFocusNode) {
+                lastFocusNode = undefined;
+                node.style('opacity', 1);
+                link.style('opacity', 1);
+            } else {
+                lastFocusNode = d;
+                _helpers.set_focus(d);
+            }
+          });
+    },
+    set_link: function(link) {
+      // Remove links if data has more links than before
+      link.enter().append('line')
+          .attr('class', 'link')
+          .style('stroke-width', function (d) {
+              return Math.sqrt(d.value);
+          });
+
+      // Remove links if data has less links than before
+      link.exit().remove();
+    },
+    isConnected: function(source, neighbour) {
+      return linkedByIndex[source.id + ',' + neighbour.id] || 
+              linkedByIndex[neighbour.id + ',' + source.id];
+    },
+    set_focus: function(d) {
+      node.style('opacity', function(o) {
+          return _helpers.isConnected(d,o) || d.index == o.index ? 1 : focus_opacity;
+      });
+      link.style('opacity', function(o) {
+          return o.source.index == d.index || o.target.index == d.index ? 1 : focus_opacity;
+      });
+    },
+    push_once: function(array, item, key) {
+      for (var i in array) {
+        if ( array[i][key] == item[key] ) return false;
+      }
+      array.push(item);
+      return true;
+    },
+    set_color: function(property, value) {
+      if ( colors instanceof Array ) {
+        for ( var c in colors ) {
+          if ( colors[c][property] == value ) { return colors[c]['color']; }
+        }
+        return color(value);
+      } else {
+        return color(value);
+      }
+    },
+    set_shape: function(value) {
+      if ( shapes instanceof Object && shape_property ) {
+        for ( var s in shapes ) {
+          var str_value = (value.includes('class')) ? value.split('.').pop().split('\'')[0] : value;
+          if ( str_value == s ) return shapes[s];
+        }
+        return (-1);
+      } else {
+        return (-1);
+      }
+    }
+  }
+
+
+  /**
+   * Graph Visualization Core Functions
+   * ----------------------------------
+   *
+   * The graph visualization functions themselves.
+   */
+
+  function Graph() {
+    // Color
+    color = d3.scale.category20();
+
+    // Set up the force layout
+    force = d3.layout.force()
+                     .charge(-500)
+                     .linkDistance(30)
+                     .size([width, height]);
+
+    // Append sections to svg to have nodes and edges separately
+    groot  = svg.append('g').attr('id', 'root');
+
+    // Set background
+    if ( background !== undefined ) {
+      var rect = groot.append('rect').attr('fill', background_filter_color);
+      background_image = groot.append('image').attr('href', background).style('opacity', background_opacity);
+      graph_wrapper = groot.append('g')   .attr('id', 'graph-wrapper');
+      glinks = graph_wrapper.append('g')  .attr('id', 'links');
+      gnodes = graph_wrapper.append('g')  .attr('id', 'nodes');
+    } else {
+      glinks = groot.append('g')  .attr('id', 'links');
+      gnodes = groot.append('g')  .attr('id', 'nodes');
+    }
+
+    // Add patterns for shapes
+    var defs = [];
+    for ( var i in shapes )
+      if (!defs.includes(shapes[i])) defs.push(shapes[i])
+    
+    svg.append('defs')
+       .selectAll('pattern')
+       .data(defs)
+       .enter()
+       .append('pattern')
+       .attr('id', function(d, i) {
+          return d;
+       })
+       .attr('patternUnits', 'objectBoundingBox')
+       .attr('width', 1)
+       .attr('height', 1)
+       .append('image')
+       .attr('href', function(d) {
+          return window.location.protocol + '//' + window.location.host + '/img/svg/' + d + '.svg';
+       })
+       .attr('width', shape_size)
+       .attr('height', shape_size);
+
+    // Zoom
+    zoom = d3.behavior
+             .zoom()
+             .scaleExtent([1/5, 10])
+             .on('zoom', function () {
+                //console.trace("zoom", d3.event.translate, d3.event.scale);
+                groot.attr('transform',
+                        'translate(' + d3.event.translate + ')scale(' + d3.event.scale     + ')');
+            });
+
+    // Activate zoom for the svg item
+    svg.style('background-color', 'rgb(255,255,255)')
+       .call(zoom);
+
+    // Update linkedByIndex 
+    linkedByIndex = {};
+    graph.links.forEach(function(d) {
+        linkedByIndex[d.source.id + ',' + d.target.id] = true;
+    });
+
+    // Creates the graph data structure out of the json data
+    force.nodes(graph.nodes)
+         .links(graph.links)
+         .start();
+
+    // Now we are giving the SVGs coordinates - the force layout is generating the coordinates 
+    // which this code is using to update the attributes of the SVG elements
+    force.on('tick', function () {
+
+        link.attr('x1', function (d) {
+            if ( d.source.scx ) return d.source.scx;
+            else return d.source.x;
+        }).attr('y1', function (d) {
+            if ( d.source.scy ) return d.source.scy;
+            else return d.source.y;
+        }).attr('x2', function (d) {
+            if ( d.target.scx ) return d.target.scx;
+            else return d.target.x;
+        }).attr('y2', function (d) {
+            if ( d.target.scy ) return d.target.scy;
+            else return d.target.y;
+        });
+
+        node.attr('transform',  function translate(d) {
+            if ( d.scx || d.scy ) return 'translate(' + d.scx + ',' + d.scy + ')';
+            else return 'translate(' + d.x + ',' + d.y + ')';
+        });
+    });
+
+  }
+
+  function update_data(property, time) {
+
+    // Reset data
+    var delete_links = true;
+    data_node = [];
+    data_link = graph.links.slice();
+
+    // Nodes 
+    graph.nodes.forEach(function(node) {
+      if (Array.isArray(node.spells)) {
+          node.spells.forEach( function(d) {
+            if ( time.between(d[0], d[1]) ) {
+              data_node.push(node);
+            } else {
+              graph.links.forEach(function(link) {
+                if (link.source === node || link.target === node)
+                    data_link.splice(data_link.indexOf(link), 1);
+              });
+            }
+          });
+
+      } else {
+          data_node.push(node);
+      }
+    });
+
+    // Links
+    graph.links.forEach(function(link) {
+      if ( !(time.between(link.start, link.end)) && data_link.includes(link) )
+          data_link.splice(data_link.indexOf(link), 1);
+    });
+
+    // Reset force
+    force.stop()
+         .nodes(data_node)
+         .links(data_link)
+         .start();
+
+    // Create all the line svgs but without locations
+    link = glinks.selectAll('.link').data(data_link);
+    _helpers.set_link(link);
+
+    // Do the same with the circles for the nodes - no
+    node = gnodes.selectAll('.node').data(data_node);
+    _helpers.set_node(node, property, time);
+
+    // Node Attributes
+    var statistics = {}
+    self.GraphVisualization.statistics = {};
+    data_node.forEach(function(n) {
+      // Count node properties
+      if ( Array.isArray(n[property]) ) {
+        n[property].forEach(function(p) {
+          if ( time.between(p[1], p[2]) ) statistics[p[0]] = (!statistics[p[0]]) ? 1 : statistics[p[0]] + 1;
+        });
+      } else {  statistics[n[property]] = (!statistics[n[property]]) ? 1 : statistics[n[property]] + 1; }
+    });
+    for ( i in statistics ) {
+      statistics[i] = (statistics[i] / data_node.length * 100).toFixed(2);
+    }
+    self.GraphVisualization.statistics = statistics
+  }
+
+  function get_models(graph) {
+
+    var models = { 'dynamic': [], 'static': [] }
+
+    graph['nodes'].forEach(function(node) {
+      for ( var att in node ) {
+        if (!required_node.includes(att)) {
+          if ( Array.isArray(node[att]) ) _helpers.push_once(models['dynamic'], { 'title': att, 'type': node[att][0][0].is_type() }, 'title');
+          else _helpers.push_once(models['static'], { 'title': att, 'type': typeof(node[att]) }, 'title');
+        }
+      }
+    });
+
+    return models;
+  }
+
+
+  /**
+   * Public API
+   * -----------
+   *
+   * User-accessible functions.
+   */
+
+  /**
+   * Create the space where the graph will we drawn.
+   * A function that identifies the svg item.
+   *
+   * @param   {object}    id            The id of the svg item.
+   * @return  {object}                  This class.
+   */
+  function create(id, n_height, n_width, callback) {
+    name = id;
+    svg = d3.select('svg#' + name)
+            .attr('width', n_width)
+            .attr('height', n_height)
+            .style('background-color', 'rgba(128,128,128,0.1)');
+
+    height  = n_height;
+    width   = n_width
+
+    if (callback) { callback(this.GraphVisualization);  }
+    else          { return this.GraphVisualization      }
+  }
+
+  /**
+   * Import JSON and attributes.
+   * A function that imports the graph and the attributes of all the nodes.
+   *
+   * @param   {object}    json          The json structure of the graph.
+   * @param   {object}    callback      A function called at the end.
+   */
+  function importJSON(json, callback) {
+    reset()
+    graph = json;
+
+    // Create the graph itself
+    Graph();
+
+    self.GraphVisualization.nodes = graph.nodes.length;
+    self.GraphVisualization.links = graph.links.length;
+    self.GraphVisualization.model = get_models(json);
+
+    // Draw graph with default property and time for the first time
+    update_data(self.GraphVisualization.model.dynamic[0].title, 0);
+
+    if (callback) { callback(); }
+  }
+
+  /**
+   * Set link distance.
+   * A function that set the link distance. If it is not called, it uses 30 as default
+   *
+   * @param   {object}    distance      Distance.
+   * @param   {object}    callback      A function called at the end.
+   */
+  function set_link_distance(distance, callback) {
+    if (graph) {
+      force.stop().linkDistance(distance).start();
+
+      // Update radius of the nodes to see them better
+      var r = d3.scale.linear().domain([30, 1000]).range([8, 24]);
+      radius = r(distance);
+      node.attr('r', radius);
+
+      var s = d3.scale.linear().domain([30, 1000]).range([16, 48]);
+      if ( shapes instanceof Object && shape_property ) {
+        svg.selectAll('pattern image').attr('width', s(distance)).attr('height', s(distance));
+      }
+
+      if (callback) { callback(radius); }
+    }
+  }
+
+  /**
+   * Set background image.
+   * A function that set a background image.
+   *
+   * @param   {object}    image         Path to image.
+   */
+  function set_background(image, set_opacity, set_color) {
+    background = image;
+    background_opacity = set_opacity || 0.8;
+    background_filter_color = set_color || 'white';
+  }
+
+  /**
+   * Set property and instant of time.
+   * A function that draws the graph depends on the property and instant of time selected.
+   *
+   * @param   {object}    property      Property to show.
+   * @param   {object}    time          Instant of time.
+   * @param   {object}    callback      A function called at the end.
+   */
+  function update_graph(property, time, callback) {
+    if (graph) {
+      update_data(property, time);
+
+      if (callback) { callback(); }
+    }
+  }
+
+  /**
+   * Set shapes and color of graph.
+   * A function that set the shapes and colors of the nodes depending on their status.
+   *
+   * @param   {object}    set_shapes    Shapes for nodes.
+   * @param   {object}    set_colors    Colors for nodes.
+   * @param   {object}    callback      A function called at the end.
+   */
+  function set_params(set_shape_property, set_shapes, set_colors, callback) {
+    shape_property = set_shape_property;
+    shapes = set_shapes;
+    colors = set_colors;
+
+    self.GraphVisualization.shapes = shapes;
+    self.GraphVisualization.colors = colors;
+
+    if (callback) { callback(); }
+  }  
+
+  /**
+   * Adjust the graph to the whole area.
+   * A function that adjust the graph to the svg item.
+   *
+   * @param   {object}    padding       Space from the graph to the border.
+   *                                    85% by default.
+   * @param   {object}    transition    Duration of the zoom action.
+   *                                    750 milliseconds by default.
+   * @param   {object}    callback      A function called at the end.
+   */
+  function zoom_to_fit(padding, transition, callback) {
+
+    var bounds = groot.node().getBBox();
+    var parent = groot.node().parentElement;
+    var fullWidth = parent.clientWidth,
+        fullHeight = parent.clientHeight;
+    var widthBounds = bounds.width,
+        heightBounds = bounds.height;
+    var midX = bounds.x + widthBounds / 2,
+        midY = bounds.y + heightBounds / 2;
+    if (widthBounds == 0 || heightBounds == 0) return; // nothing to fit
+    var scale = (padding || 0.85) / Math.max(widthBounds / fullWidth, heightBounds / fullHeight);
+    var translate = [fullWidth / 2 - scale * midX, fullHeight / 2 - scale * midY];
+
+    //console.trace("zoomFit", translate, scale);
+    groot
+        .transition()
+        .duration(transition || 750) // milliseconds
+        .call(zoom.translate(translate).scale(scale).event);
+
+    if (callback) { callback(); }
+  }
+
+  /**
+   * Reset the whole graph.
+   * A function that reset the svg item.
+   *
+   */
+  function reset() {
+    d3.select('svg#' + name)
+      .html('')
+      .attr('width', width)
+      .attr('height', height)
+      .style('background-color', 'rgba(128,128,128,0.1)');
+  }
+
+  /**
+   * Get color for a value.
+   * A function that get the color of a node or a group of nodes.
+   *
+   * @param   {object}    value         Value.
+   * @return  {object}    color         The color in hexadecimal.
+   */
+  function color(property, value) {
+    if (graph) {
+      return _helpers.set_color(property, value);
+    }
+  }
+
+  /**
+   * Get attributes at one moment given.
+   * A function that get the attributes of all nodes at a specific time.
+   *
+   * @param   {object}    time          Instant of time.
+   * @param   {object}    callback      A function called at the end.
+   * @return  {object}    object        An object with the number of nodes.
+   */
+  function get_attributes(property, time, callback) {
+    var attrs = {}
+
+    graph.nodes.forEach(function(node) {
+
+      if (Array.isArray(node.spells)) {
+          node.spells.forEach( function(d) {
+            if ( time.between(d[0], d[1]) ) { 
+
+              if (Array.isArray(node[property])) {
+                node[property].forEach( function(p) {
+                  if ( time.between(p[1], p[2]) ) attrs[p[0]] = (!attrs[p[0]]) ? 1 : attrs[p[0]] + 1;
+                });
+              } else { attrs[node[property]] = (!attrs[node[property]]) ? 1 : attrs[node[property]] + 1;  }
+
+            } 
+          });
+
+      } else {
+
+        if (Array.isArray(node[property])) {
+          node[property].forEach( function(p) {
+            if ( time.between(p[1], p[2]) ) attrs[p[0]] = (!attrs[p[0]]) ? 1 : attrs[p[0]] + 1;
+          });
+        } else { attrs[node[property]] = (!attrs[node[property]]) ? 1 : attrs[node[property]] + 1; }
+
+      }
+    });
+
+    if (callback) { callback(attrs); }
+    else { return attrs }
+  }
+
+  /**
+   * Get nodes at one moment given.
+   * A function that get the number of nodes at a specific time.
+   *
+   * @param   {object}    time          Instant of time.
+   * @param   {object}    callback      A function called at the end.
+   * @return  {object}    number        The number of nodes.
+   */
+  function get_nodes(time, callback) {
+    var total_nodes = 0;
+    graph.nodes.forEach(function(node) {
+      if (Array.isArray(node.spells)) {
+          node.spells.forEach( function(d) {
+            if ( time.between(d[0], d[1]) ) { total_nodes++; } 
+          });
+      } else {
+          total_nodes++;
+      }
+    });
+
+    if (callback) { callback(total_nodes); }
+    else { return total_nodes }
+  }
+
+
+  /**
+   * Exporting
+   * ---------
+   */
+  this.GraphVisualization = {
+
+    // Functions
+    create: create,
+    import: importJSON,
+    update_graph: update_graph,
+    set_params: set_params,
+    set_link_distance: set_link_distance,
+    set_background: set_background,
+    fit: zoom_to_fit,
+    reset: reset,
+
+    // Attributes
+    model: {},
+    nodes: undefined,
+    links: undefined,
+
+    // Getters
+    color: color,
+    shapes: shapes,
+    colors: colors,
+    get_attributes: get_attributes,
+    get_nodes: get_nodes,
+
+    // Statistics
+    statistics: {},
+
+    // Version
+    version: '0.1'
+  };
+
+}).call(this);

soil/web/templates/index.html

@@ -0,0 +1,378 @@
+<!DOCTYPE html>
+
+<head>
+  <!-- FAVICON -->
+  <link rel="shortcut icon" href="http://gsi.dit.upm.es/templates/purity_iii/favicon.ico" type="image/x-icon">
+  <link rel="icon" href="http://gsi.dit.upm.es/templates/purity_iii/favicon.ico" type="image/x-icon">
+
+  <!-- JQUERY -->
+  <script src="https://code.jquery.com/jquery-3.2.1.min.js"></script>
+
+  <!-- BOOTSTRAP 3.3.7 -->
+  <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.7/css/bootstrap.min.css">
+  <script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.7/js/bootstrap.min.js"></script>
+
+  <!-- BOOTSTRAP SLIDER -->
+  <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/bootstrap-slider/9.9.0/css/bootstrap-slider.css" />
+  <script src="https://cdnjs.cloudflare.com/ajax/libs/bootstrap-slider/9.9.0/bootstrap-slider.js"></script>
+
+  <!-- D3.js // DATA-DRIVEN DOCUMENTS -->
+  <script type="text/javascript" src="http://d3js.org/d3.v3.js"></script>
+  <script type="text/javascript" src="http://labratrevenge.com/d3-tip/javascripts/d3.tip.v0.6.3.js"></script>
+
+  <!-- C3.js // D3-based reusable chart library -->
+  <link href="https://cdnjs.cloudflare.com/ajax/libs/c3/0.4.18/c3.css" rel="stylesheet">
+  <script src="https://cdnjs.cloudflare.com/ajax/libs/c3/0.4.18/c3.min.js"></script>
+
+  <!-- JAVASCRIPTS -->
+  <script type="text/javascript" src="js/visualization.js"></script>
+  <script type="text/javascript" src="js/timeline.js"></script>
+  <script type="text/javascript" src="js/socket.js"></script>
+  <script type="text/javascript" src="js/template.js"></script>
+
+  <!-- STYLESHEETS -->
+  <link rel="stylesheet" type="text/css" href="css/main.css">
+  <link rel="stylesheet" type="text/css" href="css/timeline.css">
+  <link rel="stylesheet" type="text/css" href="//fonts.googleapis.com/css?family=Ubuntu+Mono" />
+  
+  <title>{{ name }}</title>
+
+  <script type="text/javascript">//<![CDATA[
+
+    var width   = window.innerWidth * 0.75,
+        height  = window.innerHeight * 3 / 5,
+        speed   = 1,
+        play,
+        slider;
+
+    var width_chart  = (window.innerWidth - 30) / 2 - 15,
+        height_chart = (window.innerHeight - 230) / 2,
+        chart_nodes,
+        chart_attrs;
+
+    window.onload = function() {
+        "use strict";
+
+        // Create svg, timeline and settings
+        self.GraphVisualization.create('graph', height, width);
+        $('<div>').attr('id', 'load').appendTo('#graph_container').css('left', width / 2 - 25).css('top', height / 2);
+        $('#configuration').css("height", height);
+        d3.select('#slider3').attr("width", width).call(
+            d3.slider().axis(true).min(0).max(100)
+        );
+
+        // Load a file
+        $('#update #file').change(function() { 
+
+            var file = $('#file')[0].files[0];
+            $('.console').append('<br/>');
+            self.GraphVisualization.reset();
+            $('#load').show();
+
+            $('.custom-file-control').attr("data-content", 
+                file['name'] || "Choose file..."
+            );
+
+            if ( file['type'] !== "application/x-yaml" ) {
+                console.error('File format not supported. Sorry for the inconvenience.');
+                _socket.error('File format not supported. Sorry for the inconvenience.');
+                return;
+            } else {
+                $('.alert.alert-danger').hide();
+            }
+
+            var fileReader = new FileReader();
+            if (fileReader && file) {
+                fileReader.readAsText(file);
+                fileReader.onload = function () {
+                    var content = fileReader.result;
+                    $('#load').show().addClass('loader');
+                    _socket.send(content, 'config_file');
+                };
+            }
+
+        });
+
+        // Select 'attributes'
+        $('.config-item #properties').change(function() {
+            self.GraphVisualization.update_graph($(this).val(), slider.value(), function() {
+                update_statistics_table();
+            });
+        });
+
+        // Run simulation
+        $('#simulation_modal .btn-success').click(function() {
+            if ( !jQuery.isEmptyObject(run_simulation()) ) {
+                self.GraphVisualization.reset();
+                $('#load').show().addClass('loader');
+                _socket.send(run_simulation(), 'run_simulation');
+                $('.console').append('<br/>');
+            }
+            $('#simulation_modal').modal('hide')
+        });
+
+        chart_nodes = create_chart(width_chart, height_chart, 'Time', 'Number of nodes', '#chart_nodes');
+        chart_attrs = create_chart(width_chart, height_chart, 'Time', 'Attributes', '#chart_attrs');
+
+        // Fill modal window
+        $('#simulation_modal').on('show.bs.modal', function(e) {
+            var variables = run_simulation()
+            var x = 0,
+                row;
+            for (var i in variables) {
+                if ( x % 2 === 0 ) row = $('<tr>').appendTo('#simulation_modal table tbody');
+                $('<td>').text(i).appendTo(row);
+                $('<td>').text(variables[i]).appendTo(row);
+                x++;
+            }
+        });
+
+        $('#simulation_modal').on('hide.bs.modal', function(e) {
+            $('#simulation_modal table tbody').empty();
+        });
+    }
+
+    ///]]
+
+  </script>
+
+</head>
+
+<body>
+
+<div class="container-fluid fixed">
+
+    <div class="col-sm-9 wrapper-heading">
+        <!-- CONSOLE -->
+        <div class="console">
+            Please, upload a YAML file that defines all the parameters of a simulation. <br/>
+            If you don't know how to write the file, please visit this page:<br/>
+            http://soilsim.readthedocs.io/en/latest/quickstart.html<br/>
+        </div>
+        <!-- //CONSOLE -->
+
+        <!-- SOIL Logo -->
+        <div class="soil_logo" >
+            <img src="img/logo_soil.png" />
+        </div>
+        <!-- //SOIL Logo -->
+    </div>
+
+    <div id="update" class="col-sm-3">
+        <!-- Load File -->
+        <form enctype="multipart/form-data">
+            <label class="custom-file">
+                <input type="file" id="file" name="file" class="custom-file-input">
+                <span class="custom-file-control" data-content="Choose file..."></span>
+            </label>
+        </form>  
+        <!-- //Load File -->
+
+        <!-- Atributos -->
+        <div class="config-item">
+            Attributes: 
+            <select id="properties" class="form-control form-control-sm">
+                <optgroup id="properties-dynamic" label="Dynamics"></optgroup>
+                <optgroup id="properties-static" label="Statics"></optgroup>
+            </select>
+        </div>
+        <!-- //Atributos -->
+    </div>
+
+</div>
+
+<nav class="navbar navbar-default navbar-fixed-bottom">
+    <div class="container-fluid">
+        <div class="navbar-header">
+            <a class="navbar-brand" href="#">{{ name }}</a>
+        </div>
+        <div class="collapse navbar-collapse">
+            <ul class="nav navbar-nav">
+                <li data-target="#myCarousel" data-slide-to="0" class="active" id="home_menu"><a href='#'>Home</a></li>
+                <li data-target="#myCarousel" data-slide-to="1" id="settings_menu"><a href="#">Settings &amp; Charts</a></li>
+                <li class="dropdown" id="trials">
+                    <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-haspopup="true" aria-expanded="false">Trials <span class="caret"></span></a>
+                    <ul class="dropdown-menu"></ul>
+                </li>
+            </ul>
+            <ul class="nav navbar-nav navbar-right">
+                <li><a href="#" id="run_simulation" role="button">Run simulation</a></li>
+                <li><a href="#" id="download_simulation" role="button" data-toggle="modal" data-target="#download_modal">Download</a></li>
+            </ul>
+        </div>
+        <script type="text/javascript">
+            $('.nav li[data-target="#myCarousel"]').click(function() {
+                $('.nav li').removeClass('active');
+                $(this).addClass('active');
+            });
+        </script>
+    </div>
+</nav>
+
+<div id="myCarousel" class="carousel slide">
+
+    <!-- Wrapper for slides -->
+    <div class="carousel-inner">
+
+        <!-- Wrapper Graph Container -->
+        <div class="item active">
+            <div class="container-fluid">
+
+                <div id="graph_container">
+                    <svg id="graph" class="col-sm-9" xmlns="http://www.w3.org/2000/svg"></svg>
+                </div>
+
+                <div id="configuration" class="col-sm-3">
+                    <!-- Graph Info -->
+                    <div class="config-item" style="margin-top: 0 !important;">
+                        <table id="info-graph">
+                            <tbody>
+                                <tr id="nodes"><th>Nodes:</th></tr>
+                                <tr id="links"><th>Links:</th></tr>
+                            </tbody>
+                        </table>
+                        <div class="logo pull-right">
+                            <img src="img/logo_gsi.svg" style="height: 40px;">
+                        </div>
+                    </div>
+                    <hr />
+                    <!-- //Graph Info --> 
+                    
+                    <!-- PROPIEDADES -->
+                    <div class="config-item">
+                        <table id="percentTable">
+                            <tbody><tr><th class="no-data-table">No data</th></tr></tbody>
+                        </table>
+                    </div>
+                    <hr />
+                    <!-- //PROPIEDADES -->
+
+                    <!-- SPEED -->
+                    <div class="config-item">
+                        <table id="speed"><tbody><tr>
+                            <th class="text-left min">min</th>
+                            <th class="text-center">Speed</th>
+                            <th class="text-right max">max</th>
+                        </tr></tbody></table>
+                        <div class="speed-slider">
+                            <input id="speed-slider" type="text" data-slider-min="0.1" data-slider-max="100" data-slider-step="0.1" data-slider-value="1" data-slider-tooltip="hide" data-slider-enabled="false" data-slider-scale="logarithmic"/> 
+                        </div>
+                    </div>
+                    <hr />
+                    <script type="text/javascript">
+                        $('#speed-slider').slider();
+                    </script>
+                    <!-- //SPEED -->
+
+                    <!-- LINK DISTANCE -->
+                    <div class="config-item">
+                        <table id="link-distance"><tbody><tr>
+                            <th class="text-left min">min</th>
+                            <th class="text-center">Link Distance</th>
+                            <th class="text-right max">max</th>
+                        </tr></tbody></table>
+                        <div class="link-distance-slider">
+                            <input id="link-distance-slider" type="text" data-slider-min="30" data-slider-max="1000" data-slider-step="0.01" data-slider-value="30" data-slider-tooltip="hide" data-slider-reversed="false" data-slider-enabled="false" data-slider-scale="logarithmic"/> 
+                        </div>
+                    </div>
+                    <script type="text/javascript">
+                        $('#link-distance-slider').slider();
+                    </script>
+                    <!-- //LINK DISTANCE -->
+                </div>
+                
+                <!-- TIMELINE -->
+                <div id="timeline" class="col-sm-12">
+                    <div id="slider3" class="pull-left col-sm-9"></div>
+
+                    <div class="btn-toolbar controls">
+                        <button type="button" id="button_play" class="btn btn-lg">
+                            <span class="glyphicon glyphicon-play" aria-hidden="true"></span>
+                        </button>
+                        <button type="button" id="button_pause" class="btn btn-lg">
+                            <span class="glyphicon glyphicon-pause" aria-hidden="true"></span>
+                        </button>
+                        <button type="button" id="button_zoomFit" class="btn btn-lg">
+                            <span class="glyphicon glyphicon-fullscreen" aria-hidden="true"></span>
+                        </button>
+                    </div>
+                </div>
+                <!-- //TIMELINE -->
+
+                <!-- ERROR ALERT -->
+                <div class="alert alert-danger alert-dismissable fade in" style="display: none;">
+                    <a href="#" class="close" data-dismiss="alert" aria-label="close">&times;</a>
+                    <strong>Error! </strong><span id="error-message"></span>
+                </div>
+                <!-- //ERROR ALERT -->
+
+            </div>
+        </div>
+        <!-- //Wrapper Graph Container -->
+
+        <!-- Wrapper Settings -->
+        <div class="item" id="settings">
+            <div class="container-fluid">
+                <div class="col-sm-6" id="charts">
+                    <div id="chart_nodes" class="chart no-data"></div>
+                    <div id="chart_attrs" class="chart no-data"></div>
+                </div>
+                <div class="col-sm-6 none" id="wrapper-settings"></div>
+            </div>
+        </div>
+
+    </div>
+    <!-- //Wrapper for slides -->
+
+</div>
+
+<div class="modal fade" tabindex="-1" role="dialog" id="simulation_modal">
+    <div class="modal-dialog" role="document">
+        <div class="modal-content">
+            <div class="modal-header">
+                <button type="button" class="close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">&times;</span></button>
+                <h4 class="modal-title">New simulation</h4>
+            </div>
+            <div class="modal-body text-justify">
+                <p>You are going to run a new simulation, all charts and trials are going to be lost. A new ones will be available after the simulation.</p>
+                <p>Check your new environment variables for this simulation.</p>
+                <table class="table">
+                    <thead><tr><th>Variable</th><th>Value</th><th>Variable</th><th>Value</th></tr></thead>
+                    <tbody></tbody>
+                </table>
+            </div>
+            <div class="modal-footer">
+                <button type="button" class="btn btn-success">Run</button>
+                <button type="button" class="btn btn-danger" data-dismiss="modal">Cancel</button>
+            </div>
+        </div><!-- /.modal-content -->
+    </div><!-- /.modal-dialog -->
+</div><!-- /.modal -->
+
+<div class="modal fade" tabindex="-1" role="dialog" id="download_modal">
+    <div class="modal-dialog" role="document">
+        <div class="modal-content">
+            <div class="modal-header">
+                <button type="button" class="close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">&times;</span></button>
+                <h4 class="modal-title">Download Simulation Results</h4>
+            </div>
+            <div class="modal-body text-justify">
+                <p>You can download the results of the selected trial in GEXF or JSON Graph format for your personal purposes.</p>
+                <ul >
+                    <li><b>GEXF</b> <i>(Graph Exchange XML Format)</i> is a language for describing complex network structures, their associated data and dynamics. It can be used to visualize the simulation with Gephi.</li>
+                    <li><b>JSON Graph</b> generate and parse JSON serializable data for NetworkX graphs. It is a convention for modeling graph information as a JSON object that can be parsed by any JSON parser.</li>
+                </ul>
+                <p>For downloading the results of the other trials simulated, please first select them in menu.</p>
+            </div>
+            <div class="modal-footer">
+                <button type="button" class="btn btn-success" disabled="disabled" id="download_gexf">GEXF</button>
+                <button type="button" class="btn btn-success" disabled="disabled" id="download_json">JSON Graph</button>
+                <button type="button" class="btn btn-danger" data-dismiss="modal">Cancel</button>
+            </div>
+        </div><!-- /.modal-content -->
+    </div><!-- /.modal-dialog -->
+</div><!-- /.modal -->
+
+
+</body>
+</html>

test-requirements.txt

@@ -0,0 +1 @@
+pytest

tests/test.gexf

@@ -0,0 +1,12 @@
+<?xml version='1.0' encoding='utf-8'?>
+<gexf version="1.2" xmlns="http://www.gexf.net/1.2draft" xmlns:viz="http://www.gexf.net/1.2draft/viz" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.w3.org/2001/XMLSchema-instance">
+  <graph defaultedgetype="undirected" mode="static">
+    <nodes>
+      <node id="0" label="0" />
+      <node id="1" label="1" />
+    </nodes>
+    <edges>
+      <edge id="0" source="0" target="1" />
+    </edges>
+  </graph>
+</gexf>

tests/test_analysis.py

@@ -39,7 +39,6 @@ class TestAnalysis(TestCase):
         agent should be able to update its state."""
         config = {
             'name': 'analysis',
-            'dry_run': True,
             'seed': 'seed',
             'network_params': {
                 'generator': 'complete_graph',
@@ -53,7 +52,7 @@ class TestAnalysis(TestCase):
             }
         }
         s = simulation.from_config(config)
-        self.env = s.run_simulation()[0]
+        self.env = s.run_simulation(dry_run=True)[0]
 
     def test_saved(self):
         env = self.env
@@ -65,10 +64,10 @@ class TestAnalysis(TestCase):
 
     def test_count(self):
         env = self.env
-        df = analysis.read_sql(env._history._db)
+        df = analysis.read_sql(env._history.db_path)
         res = analysis.get_count(df, 'SEED', 'id')
-        assert res['SEED']['seedanalysis_trial_0'].iloc[0] == 1
-        assert res['SEED']['seedanalysis_trial_0'].iloc[-1] == 1
+        assert res['SEED'][self.env['SEED']].iloc[0] == 1
+        assert res['SEED'][self.env['SEED']].iloc[-1] == 1
         assert res['id']['odd'].iloc[0] == 2
         assert res['id']['even'].iloc[0] == 0
         assert res['id']['odd'].iloc[-1] == 1
@@ -76,7 +75,7 @@ class TestAnalysis(TestCase):
 
     def test_value(self):
         env = self.env
-        df = analysis.read_sql(env._history._db)
+        df = analysis.read_sql(env._history.db_path)
         res_sum = analysis.get_value(df, 'count')
 
         assert res_sum['count'].iloc[0] == 2
@@ -87,4 +86,4 @@ class TestAnalysis(TestCase):
 
         res_total = analysis.get_value(df)
 
-        res_total['SEED'].iloc[0] == 'seedanalysis_trial_0'
+        res_total['SEED'].iloc[0] == self.env['SEED']

tests/test_examples.py

@@ -0,0 +1,54 @@
+from unittest import TestCase
+import os
+from os.path import join
+
+from soil import serialization, simulation
+
+ROOT = os.path.abspath(os.path.dirname(__file__))
+EXAMPLES = join(ROOT, '..', 'examples')
+
+FORCE_TESTS = os.environ.get('FORCE_TESTS', '')
+
+
+class TestExamples(TestCase):
+    pass
+
+
+def make_example_test(path, config):
+    def wrapped(self):
+        root = os.getcwd()
+        for s in simulation.all_from_config(path):
+            iterations = s.max_time * s.num_trials
+            if iterations > 1000:
+                s.max_time = 100
+                s.num_trials = 1
+            if config.get('skip_test', False) and not FORCE_TESTS:
+                self.skipTest('Example ignored.')
+            envs = s.run_simulation(dry_run=True)
+            assert envs
+            for env in envs:
+                assert env
+                try:
+                    n = config['network_params']['n']
+                    assert len(list(env.network_agents)) == n
+                    assert env.now > 0  # It has run
+                    assert env.now <= config['max_time']  # But not further than allowed
+                except KeyError:
+                    pass
+    return wrapped
+
+
+def add_example_tests():
+    for config, path in serialization.load_files(
+            join(EXAMPLES, '*', '*.yml'),
+            join(EXAMPLES, '*.yml'),
+    ):
+        p = make_example_test(path=path, config=config)
+        fname = os.path.basename(path)
+        p.__name__ = 'test_example_file_%s' % fname
+        p.__doc__ = '%s should be a valid configuration' % fname
+        setattr(TestExamples, p.__name__, p)
+        del p
+
+
+add_example_tests()

tests/test_exporters.py

@@ -0,0 +1,101 @@
+import os
+import io
+import tempfile
+import shutil
+from time import time
+
+from unittest import TestCase
+from soil import exporters
+from soil import simulation
+
+from soil.stats import distribution
+
+class Dummy(exporters.Exporter):
+    started = False
+    trials = 0
+    ended = False
+    total_time = 0
+    called_start = 0
+    called_trial = 0
+    called_end = 0
+
+    def start(self):
+        self.__class__.called_start += 1
+        self.__class__.started = True
+
+    def trial(self, env, stats):
+        assert env
+        self.__class__.trials += 1
+        self.__class__.total_time += env.now
+        self.__class__.called_trial += 1
+
+    def end(self, stats):
+        self.__class__.ended = True
+        self.__class__.called_end += 1
+
+
+class Exporters(TestCase):
+    def test_basic(self):
+        config = {
+            'name': 'exporter_sim',
+            'network_params': {},
+            'agent_type': 'CounterModel',
+            'max_time': 2,
+            'num_trials': 5,
+            'environment_params': {}
+        }
+        s = simulation.from_config(config)
+        for env in s.run_simulation(exporters=[Dummy], dry_run=True):
+            assert env.now <= 2
+
+        assert Dummy.started
+        assert Dummy.ended
+        assert Dummy.called_start == 1
+        assert Dummy.called_end == 1
+        assert Dummy.called_trial == 5
+        assert Dummy.trials == 5
+        assert Dummy.total_time == 2*5
+
+    def test_writing(self):
+        '''Try to write CSV, GEXF, sqlite and YAML (without dry_run)'''
+        n_trials = 5
+        config = {
+            'name': 'exporter_sim',
+            'network_params': {
+                'generator': 'complete_graph',
+                'n': 4
+            },
+            'agent_type': 'CounterModel',
+            'max_time': 2,
+            'num_trials': n_trials,
+            'environment_params': {}
+        }
+        output = io.StringIO()
+        s = simulation.from_config(config)
+        tmpdir = tempfile.mkdtemp()
+        envs = s.run_simulation(exporters=[
+                                    exporters.default,
+                                    exporters.csv,
+                                    exporters.gexf,
+                                ],
+                                stats=[distribution,],
+                                outdir=tmpdir,
+                                exporter_params={'copy_to': output})
+        result = output.getvalue()
+
+        simdir = os.path.join(tmpdir, s.group or '', s.name)
+        with open(os.path.join(simdir, '{}.dumped.yml'.format(s.name))) as f:
+            result = f.read()
+            assert result
+
+        try:
+            for e in envs:
+                with open(os.path.join(simdir, '{}.gexf'.format(e.name))) as f:
+                    result = f.read()
+                    assert result
+
+                with open(os.path.join(simdir, '{}.csv'.format(e.name))) as f:
+                    result = f.read()
+                    assert result
+        finally:
+            shutil.rmtree(tmpdir)

tests/test_history.py

@@ -5,6 +5,7 @@ import shutil
 from glob import glob
 
 from soil import history
+from soil import utils
 
 
 ROOT = os.path.abspath(os.path.dirname(__file__))
@@ -116,18 +117,87 @@ class TestHistory(TestCase):
         db_path = os.path.join(DBROOT, 'test')
         h = history.History(db_path=db_path)
         h.save_tuples(tuples)
+        h.flush_cache()
         assert os.path.exists(db_path)
 
         # Recover the data
-        recovered = history.History(db_path=db_path, backup=False)
+        recovered = history.History(db_path=db_path)
         assert recovered['a_1', 0, 'id'] == 'v'
         assert recovered['a_1', 4, 'id'] == 'e'
 
-        # Using the same name should create a backup copy
+        # Using backup=True should create a backup copy, and initialize an empty history
         newhistory = history.History(db_path=db_path, backup=True)
         backuppaths = glob(db_path + '.backup*.sqlite')
         assert len(backuppaths) == 1
         backuppath = backuppaths[0]
-        assert newhistory._db_path == h._db_path
+        assert newhistory.db_path == h.db_path
         assert os.path.exists(backuppath)
-        assert not len(newhistory[None, None, None])
+        assert len(newhistory[None, None, None]) == 0
+
+    def test_history_tuples(self):
+        """
+        The data recovered should be equal to the one recorded.
+        """
+        tuples = (
+            ('a_1', 0, 'id', 'v'),
+            ('a_1', 1, 'id', 'a'),
+            ('a_1', 2, 'id', 'l'),
+            ('a_1', 3, 'id', 'u'),
+            ('a_1', 4, 'id', 'e'),
+            ('env', 1, 'prob', 1),
+            ('env', 2, 'prob', 2),
+            ('env', 3, 'prob', 3),
+            ('a_2', 7, 'finished', True),
+        )
+        h = history.History()
+        h.save_tuples(tuples)
+        recovered = list(h.to_tuples())
+        assert recovered
+        for i in recovered:
+            assert i in tuples
+
+    def test_stats(self):
+        """
+        The data recovered should be equal to the one recorded.
+        """
+        tuples = (
+            ('a_1', 0, 'id', 'v'),
+            ('a_1', 1, 'id', 'a'),
+            ('a_1', 2, 'id', 'l'),
+            ('a_1', 3, 'id', 'u'),
+            ('a_1', 4, 'id', 'e'),
+            ('env', 1, 'prob', 1),
+            ('env', 2, 'prob', 2),
+            ('env', 3, 'prob', 3),
+            ('a_2', 7, 'finished', True),
+        )
+        stat_tuples = [
+            {'num_infected': 5, 'runtime': 0.2},
+            {'num_infected': 5, 'runtime': 0.2},
+            {'new': '40'},
+        ]
+        h = history.History()
+        h.save_tuples(tuples)
+        for stat in stat_tuples:
+            h.save_stats(stat)
+        recovered = h.get_stats()
+        assert recovered
+        assert recovered[0]['num_infected'] == 5
+        assert recovered[1]['runtime'] == 0.2
+        assert recovered[2]['new'] == '40'
+
+    def test_unflatten(self):
+        ex = {'count.neighbors.3': 4,
+              'count.times.2': 4,
+              'count.total.4': 4,
+              'mean.neighbors': 3,
+              'mean.times': 2,
+              'mean.total': 4,
+              't_step': 2,
+              'trial_id': 'exporter_sim_trial_1605817956-4475424'}
+        res = utils.unflatten_dict(ex)
+
+        assert 'count' in res
+        assert 'mean' in res
+        assert 't_step' in res
+        assert 'trial_id' in res

tests/test_main.py

@@ -1,19 +1,31 @@
 from unittest import TestCase
 
 import os
+import io
 import yaml
+import pickle
 import networkx as nx
 from functools import partial
 
 from os.path import join
-from soil import simulation, environment, agents, utils
+from soil import (simulation, Environment, agents, serialization,
+                  history, utils)
 
 
 ROOT = os.path.abspath(os.path.dirname(__file__))
-
 EXAMPLES = join(ROOT, '..', 'examples')
 
 
+class CustomAgent(agents.FSM):
+    @agents.default_state
+    @agents.state
+    def normal(self):
+        self.state['neighbors'] = self.count_agents(state_id='normal',
+                                                    limit_neighbors=True)
+    @agents.state
+    def unreachable(self):
+        return
+
 class TestMain(TestCase):
 
     def test_load_graph(self):
@@ -22,22 +34,20 @@ class TestMain(TestCase):
         Raise an exception otherwise.
         """
         config = {
-            'dry_run': True,
             'network_params': {
                 'path': join(ROOT, 'test.gexf')
             }
         }
-        G = utils.load_network(config['network_params'])
+        G = serialization.load_network(config['network_params'])
         assert G
         assert len(G) == 2
         with self.assertRaises(AttributeError):
             config = {
-            'dry_run': True,
                 'network_params': {
                     'path': join(ROOT, 'unknown.extension')
                 }
             }
-            G = utils.load_network(config['network_params'])
+            G = serialization.load_network(config['network_params'])
             print(G)
 
     def test_generate_barabasi(self):
@@ -46,22 +56,20 @@ class TestMain(TestCase):
         should be used to generate a network
         """
         config = {
-            'dry_run': True,
             'network_params': {
                 'generator': 'barabasi_albert_graph'
             }
         }
         with self.assertRaises(TypeError):
-            G = utils.load_network(config['network_params'])
+            G = serialization.load_network(config['network_params'])
         config['network_params']['n'] = 100
         config['network_params']['m'] = 10
-        G = utils.load_network(config['network_params'])
+        G = serialization.load_network(config['network_params'])
         assert len(G) == 100
 
     def test_empty_simulation(self):
         """A simulation with a base behaviour should do nothing"""
         config = {
-            'dry_run': True,
             'network_params': {
                 'path': join(ROOT, 'test.gexf')
             },
@@ -78,7 +86,6 @@ class TestMain(TestCase):
         agent should be able to update its state."""
         config = {
             'name': 'CounterAgent',
-            'dry_run': True,
             'network_params': {
                 'path': join(ROOT, 'test.gexf')
             },
@@ -102,7 +109,6 @@ class TestMain(TestCase):
         """
         config = {
             'name': 'CounterAgent',
-            'dry_run': True,
             'network_params': {
                 'path': join(ROOT, 'test.gexf')
             },
@@ -127,19 +133,13 @@ class TestMain(TestCase):
 
     def test_custom_agent(self):
         """Allow for search of neighbors with a certain state_id"""
-        class CustomAgent(agents.BaseAgent):
-            def step(self):
-                self.state['neighbors'] = self.count_agents(state_id=0,
-                                                            limit_neighbors=True)
         config = {
-            'dry_run': True,
             'network_params': {
                 'path': join(ROOT, 'test.gexf')
             },
             'network_agents': [{
                 'agent_type': CustomAgent,
-                'weight': 1,
-                'state': {'id': 0}
+                'weight': 1
 
             }],
             'max_time': 10,
@@ -149,15 +149,17 @@ class TestMain(TestCase):
         s = simulation.from_config(config)
         env = s.run_simulation(dry_run=True)[0]
         assert env.get_agent(0).state['neighbors'] == 1
+        assert env.get_agent(0).state['neighbors'] == 1
+        assert env.get_agent(1).count_agents(state_id='normal') == 2
+        assert env.get_agent(1).count_agents(state_id='normal', limit_neighbors=True) == 1
 
     def test_torvalds_example(self):
         """A complete example from a documentation should work."""
-        config = utils.load_file(join(EXAMPLES, 'torvalds.yml'))[0]
+        config = serialization.load_file(join(EXAMPLES, 'torvalds.yml'))[0]
         config['network_params']['path'] = join(EXAMPLES,
                                                 config['network_params']['path'])
         s = simulation.from_config(config)
-        s.dry_run = True
-        env = s.run_simulation()[0]
+        env = s.run_simulation(dry_run=True)[0]
         for a in env.network_agents:
             skill_level = a.state['skill_level']
             if a.id == 'Torvalds':
@@ -179,17 +181,14 @@ class TestMain(TestCase):
         should be equivalent to the configuration file used
         """
         with utils.timer('loading'):
-            config = utils.load_file(join(EXAMPLES, 'complete.yml'))[0]
+            config = serialization.load_file(join(EXAMPLES, 'complete.yml'))[0]
             s = simulation.from_config(config)
-            s.dry_run = True
         with utils.timer('serializing'):
             serial = s.to_yaml()
         with utils.timer('recovering'):
-            recovered = yaml.load(serial)
+            recovered = yaml.load(serial, Loader=yaml.SafeLoader)
         with utils.timer('deleting'):
             del recovered['topology']
-            del recovered['load_module']
-            del recovered['dry_run']
         assert config == recovered
 
     def test_configuration_changes(self):
@@ -197,25 +196,16 @@ class TestMain(TestCase):
         The configuration should not change after running
          the simulation.
         """
-        config = utils.load_file('examples/complete.yml')[0]
+        config = serialization.load_file(join(EXAMPLES, 'complete.yml'))[0]
         s = simulation.from_config(config)
-        s.dry_run = True
         for i in range(5):
             s.run_simulation(dry_run=True)
             nconfig = s.to_dict()
             del nconfig['topology']
-            del nconfig['dry_run']
-            del nconfig['load_module']
             assert config == nconfig
 
-    def test_examples(self):
-        """
-        Make sure all examples in the examples folder are correct
-        """
-        pass
-
     def test_row_conversion(self):
-        env = environment.SoilEnvironment(dry_run=True)
+        env = Environment()
         env['test'] = 'test_value'
 
         res = list(env.history_to_tuples())
@@ -233,37 +223,136 @@ class TestMain(TestCase):
         There is a bug in networkx that prevents it from creating a GEXF file 
         from geometric models. We should work around it.
         """
-        G = nx.random_geometric_graph(20,0.1)
-        env = environment.SoilEnvironment(topology=G, dry_run=True)
-        env.dump_gexf('/tmp/dump-gexf')
+        G = nx.random_geometric_graph(20, 0.1)
+        env = Environment(topology=G)
+        f = io.BytesIO()
+        env.dump_gexf(f)
 
+    def test_save_graph(self):
+        '''
+        The history_to_graph method should return a valid networkx graph.
 
-def make_example_test(path, config):
-    def wrapped(self):
-        root = os.getcwd()
-        os.chdir(os.path.dirname(path))
+        The state of the agent should be encoded as intervals in the nx graph.
+        '''
+        G = nx.cycle_graph(5)
+        distribution = agents.calculate_distribution(None, agents.BaseAgent)
+        env = Environment(topology=G, network_agents=distribution)
+        env[0, 0, 'testvalue'] = 'start'
+        env[0, 10, 'testvalue'] = 'finish'
+        nG = env.history_to_graph()
+        values = nG.nodes[0]['attr_testvalue']
+        assert ('start', 0, 10) in values
+        assert ('finish', 10, None) in values
+
+    def test_serialize_class(self):
+        ser, name = serialization.serialize(agents.BaseAgent)
+        assert name == 'soil.agents.BaseAgent'
+        assert ser == agents.BaseAgent
+
+        ser, name = serialization.serialize(CustomAgent)
+        assert name == 'test_main.CustomAgent'
+        assert ser == CustomAgent
+        pickle.dumps(ser)
+
+    def test_serialize_builtin_types(self):
+
+        for i in [1, None, True, False, {}, [], list(), dict()]:
+            ser, name = serialization.serialize(i)
+            assert type(ser) == str
+            des = serialization.deserialize(name, ser)
+            assert i == des
+
+    def test_serialize_agent_type(self):
+        '''A class from soil.agents should be serialized without the module part'''
+        ser = agents.serialize_type(CustomAgent)
+        assert ser == 'test_main.CustomAgent'
+        ser = agents.serialize_type(agents.BaseAgent)
+        assert ser == 'BaseAgent'
+        pickle.dumps(ser)
+    
+    def test_deserialize_agent_distribution(self):
+        agent_distro = [
+            {
+                'agent_type': 'CounterModel',
+                'weight': 1
+            },
+            {
+                'agent_type': 'test_main.CustomAgent',
+                'weight': 2
+            },
+        ]
+        converted = agents.deserialize_distribution(agent_distro)
+        assert converted[0]['agent_type'] == agents.CounterModel
+        assert converted[1]['agent_type'] == CustomAgent
+        pickle.dumps(converted)
+
+    def test_serialize_agent_distribution(self):
+        agent_distro = [
+            {
+                'agent_type': agents.CounterModel,
+                'weight': 1
+            },
+            {
+                'agent_type': CustomAgent,
+                'weight': 2
+            },
+        ]
+        converted = agents.serialize_distribution(agent_distro)
+        assert converted[0]['agent_type'] == 'CounterModel'
+        assert converted[1]['agent_type'] == 'test_main.CustomAgent'
+        pickle.dumps(converted)
+
+    def test_pickle_agent_environment(self):
+        env = Environment(name='Test')
+        a = agents.BaseAgent(environment=env, agent_id=25)
+
+        a['key'] = 'test'
+
+        pickled = pickle.dumps(a)
+        recovered = pickle.loads(pickled)
+
+        assert recovered.env.name == 'Test'
+        assert list(recovered.env._history.to_tuples())
+        assert recovered['key', 0] == 'test'
+        assert recovered['key'] == 'test'
+
+    def test_history(self):
+        '''Test storing in and retrieving from history (sqlite)'''
+        h = history.History()
+        h.save_record(agent_id=0, t_step=0, key="test", value="hello")
+        assert h[0, 0, "test"] == "hello"
+
+    def test_subgraph(self):
+        '''An agent should be able to subgraph the global topology'''
+        G = nx.Graph()
+        G.add_node(3)
+        G.add_edge(1, 2)
+        distro = agents.calculate_distribution(agent_type=agents.NetworkAgent)
+        env = Environment(name='Test', topology=G, network_agents=distro)
+        lst = list(env.network_agents)
+
+        a2 = env.get_agent(2)
+        a3 = env.get_agent(3)
+        assert len(a2.subgraph(limit_neighbors=True)) == 2
+        assert len(a3.subgraph(limit_neighbors=True)) == 1
+        assert len(a3.subgraph(limit_neighbors=True, center=False)) == 0
+        assert len(a3.subgraph(agent_type=agents.NetworkAgent)) == 3
+
+    def test_templates(self):
+        '''Loading a template should result in several configs'''
+        configs = serialization.load_file(join(EXAMPLES, 'template.yml'))
+        assert len(configs) > 0
+
+    def test_until(self):
+        config = {
+            'name': 'exporter_sim',
+            'network_params': {},
+            'agent_type': 'CounterModel',
+            'max_time': 2,
+            'num_trials': 100,
+            'environment_params': {}
+        }
         s = simulation.from_config(config)
-        envs = s.run_simulation(dry_run=True)
-        assert envs
-        for env in envs:
-            assert env
-            try:
-                n = config['network_params']['n']
-                assert len(env.get_agents()) == n
-            except KeyError:
-                pass
-        os.chdir(root)
-    return wrapped
-
-
-def add_example_tests():
-    for config, path in utils.load_config(join(EXAMPLES, '*.yml')):
-        p = make_example_test(path=path, config=config)
-        fname = os.path.basename(path)
-        p.__name__ = 'test_example_file_%s' % fname
-        p.__doc__ = '%s should be a valid configuration' % fname
-        setattr(TestMain, p.__name__, p)
-        del p
-
-
-add_example_tests()
+        runs = list(s.run_simulation(dry_run=True))
+        over = list(x.now for x in runs if x.now>2)
+        assert len(over) == 0

tests/test_stats.py

@@ -0,0 +1,34 @@
+from unittest import TestCase
+
+from soil import simulation, stats
+from soil.utils import unflatten_dict
+
+class Stats(TestCase):
+
+    def test_distribution(self):
+        '''The distribution exporter should write the number of agents in each state'''
+        config = {
+            'name': 'exporter_sim',
+            'network_params': {
+                'generator': 'complete_graph',
+                'n': 4
+            },
+            'agent_type': 'CounterModel',
+            'max_time': 2,
+            'num_trials': 5,
+            'environment_params': {}
+        }
+        s = simulation.from_config(config)
+        for env in s.run_simulation(stats=[stats.distribution]):
+            pass
+            # stats_res = unflatten_dict(dict(env._history['stats', -1, None]))
+        allstats = s.get_stats()
+        for stat in allstats:
+            assert 'count' in stat
+            assert 'mean' in stat
+            if 'trial_id' in stat:
+                assert stat['mean']['neighbors'] == 3
+                assert stat['count']['total']['4'] == 4
+            else:
+                assert stat['count']['count']['neighbors']['3'] == 20
+                assert stat['mean']['min']['neighbors'] == stat['mean']['max']['neighbors']