black formatting

Documentation needs some improvement

The API has been simplified to only allow for ONE topology per
NetworkEnvironment.
This covers the main use case, and simplifies the code.

.dockerignore

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

.gitignore

@@ -8,3 +8,4 @@ soil_output
 docs/_build*
 build/*
 dist/*
+prof

.gitlab-ci.yml

@@ -0,0 +1,53 @@
+stages:
+  - test
+  - publish
+  - check_published
+
+docker:
+  stage: publish
+  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:
+  tags:
+    - docker
+  image: python:3.7
+  stage: test
+  script:
+    - pip install -r requirements.txt -r test-requirements.txt
+    - python setup.py test
+
+push_pypi:
+  only:
+    - tags
+  tags:
+    - docker
+  image: python:3.7
+  stage: publish
+  script:
+    - echo $CI_COMMIT_TAG > soil/VERSION
+    - pip install twine
+    - python setup.py sdist bdist_wheel
+    - TWINE_PASSWORD=$PYPI_PASSWORD TWINE_USERNAME=$PYPI_USERNAME python -m twine upload dist/*
+
+check_pypi:
+  only:
+    - tags
+  tags:
+    - docker
+  image: python:3.7
+  stage: check_published
+  script:
+    - pip install soil==$CI_COMMIT_TAG
+  # Allow PYPI to update its index before we try to install 
+  when: delayed
+  start_in: 2 minutes

CHANGELOG.md

@@ -0,0 +1,180 @@
+# 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).
+
+## [0.3 UNRELEASED]
+### Added
+* Simple debugging capabilities in `soil.debugging`, with a custom `pdb.Debugger` subclass that exposes commands to list agents and their status and set breakpoints on states (for FSM agents). Try it with `soil --debug <simulation file>`
+* Ability to run 
+* Ability to 
+* The `soil.exporters` module to export the results of datacollectors (model.datacollector) into files at the end of trials/simulations
+* A modular set of classes for environments/models. Now the ability to configure the agents through an agent definition and a topology through a network configuration is split into two classes (`soil.agents.BaseEnvironment` for agents, `soil.agents.NetworkEnvironment` to add topology).
+* FSM agents can now have generators as states. They work similar to normal states, with one caveat. Only `time` values can be yielded, not a state. This is because the state will not change, it will be resumed after the yield, at the appropriate time. The return value *can* be a state, or a `(state, time)` tuple, just like in normal states.
+### Changed
+* Configuration schema is very different now. Check `soil.config` for more information. We are also using Pydantic for (de)serialization.
+* There may be more than one topology/network in the simulation
+* Ability
+### Removed
+* Any `tsih` and `History` integration in the main classes. To record the state of environments/agents, just use a datacollector. In some cases this may be slower or consume more memory than the previous system. However, few cases actually used the full potential of the history, and it came at the cost of unnecessary complexity and worse performance for the majority of cases.
+
+
+## [0.20.7]
+### Changed
+* Creating a `time.When` from another `time.When` does not nest them anymore (it returns the argument)
+### Fixed
+* Bug with time.NEVER/time.INFINITY
+## [0.20.6]
+### Fixed
+* Agents now return `time.INFINITY` when dead, instead of 'inf'
+* `soil.__init__` does not re-export built-in time (change in `soil.simulation`. It used to create subtle import conflicts when importing soil.time.
+* Parallel simulations were broken because lambdas cannot be pickled properly, which is needed for multiprocessing.
+### Changed
+* Some internal simulation methods do not accept `*args` anymore, to avoid ambiguity and bugs.
+## [0.20.5]
+### Changed
+* Defaults are now set in the agent __init__, not in the environment. This decouples both classes a bit more, and it is more intuitive 
+## [0.20.4]
+### Added
+* Agents can now be given any kwargs, which will be used to set their state
+* Environments have a default logger `self.logger` and a log method, just like agents
+## [0.20.3]
+### Fixed
+* Default state values are now deepcopied again.
+* Seeds for environments only concatenate the trial id (i.e., a number), to provide repeatable results.
+* `Environment.run` now calls `Environment.step`, to allow for easy overloading of the environment step
+### Removed
+* Datacollectors are not being used for now.
+* `time.TimedActivation.step` does not use an `until` parameter anymore.
+### Changed
+* Simulations now run right up to `until` (open interval)
+* Time instants (`time.When`) don't need to be floats anymore. Now we can avoid precision issues with big numbers by using ints.
+* Rabbits simulation is more idiomatic (using subclasses)
+
+## [0.20.2]
+### Fixed
+* CI/CD testing issues
+## [0.20.1]
+### Fixed
+* Agents would run another step after dying.
+## [0.20.0]
+### Added
+* Integration with MESA
+* `not_agent_ids` parameter to get sql in history
+### Changed
+* `soil.Environment` now also inherits from `mesa.Model`
+* `soil.Agent` now also inherits from `mesa.Agent`
+* `soil.time` to replace `simpy` events, delays, duration, etc.
+* `agent.id` is not `agent.unique_id` to be compatible with `mesa`. A property `BaseAgent.id` has been added for compatibility.
+* `agent.environment` is now `agent.model`, for the same reason as above. The parameter name in `BaseAgent.__init__` has also been renamed.
+### Removed
+* `simpy` dependency and compatibility. Each agent used to be a simpy generator, but that made debugging and error handling more complex. That has been replaced by a scheduler within the `soil.Environment` class, similar to how `mesa` does it.
+* `soil.history` is now a separate package named `tsih`. The keys namedtuple uses `dict_id` instead of `agent_id`.
+### Added
+* An option to choose whether a database should be used for history 
+## [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

@@ -5,6 +5,45 @@ Learn how to run your own simulations with our [documentation](http://soilsim.re
 
 Follow our [tutorial](examples/tutorial/soil_tutorial.ipynb) to develop your own agent models.
 
+
+# Changes in version 0.3
+
+Version 0.3 came packed with many changes to provide much better integration with MESA.
+For a long time, we tried to keep soil backwards-compatible, but it turned out to be a big endeavour and the resulting code was less readable.
+This translates to harder maintenance and a worse experience for newcomers. 
+In the end, we decided to make some breaking changes.
+
+If you have an older Soil simulation, you have two options:
+
+* Update the necessary configuration files and code. You may use the examples in the `examples` folder for reference, as well as the documentation.
+* Keep using a previous `soil` version.
+
+## Mesa compatibility
+
+Soil is in the process of becoming fully compatible with MESA.
+The idea is to provide a set of modular classes and functions that extend the functionality of mesa, whilst staying compatible.
+In the end, it should be possible to add regular mesa agents to a soil simulation, or use a soil agent within a mesa simulation/model.
+
+This is a non-exhaustive list of tasks to achieve compatibility:
+
+- [ ] Integrate `soil.Simulation` with mesa's runners:
+  - [ ] `soil.Simulation` could mimic/become a `mesa.batchrunner`
+- [ ] Integrate `soil.Environment` with `mesa.Model`:
+  - [x] `Soil.Environment` inherits from `mesa.Model`
+  - [x] `Soil.Environment` includes a Mesa-like Scheduler (see the `soil.time` module.
+  - [ ] Allow for `mesa.Model` to be used in a simulation.
+- [ ] Integrate `soil.Agent` with `mesa.Agent`:
+  - [x] Rename agent.id to unique_id?
+  - [x] mesa agents can be used in soil simulations (see `examples/mesa`)
+- [ ] Provide examples
+  - [ ] Using mesa modules in a soil simulation
+  - [ ] Using soil modules in a mesa simulation
+- [ ] Document the new APIs and usage
+
+
+## Citation 
+
+
 If you use Soil in your research, don't forget to cite this paper:
 
 ```bibtex
@@ -28,7 +67,6 @@ 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)
+@Copyright GSI - Universidad Politécnica de Madrid 2017-2021
 
+[![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 +1,262 @@
+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_class``), 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.
+That means both global parameters, such as the probability of disease outbreak.
+But it also means other data, such as a map, or a network topology that connects multiple agents.
+As a result, it is also typical to add custom functions in an environment that help agents interact with each other and with the state of the simulation.
+
+Last but not least, an environment controls when and how its agents will be executed.
+By default, soil environments incorporate a ``soil.time.TimedActivation`` model for agent execution (more on this on the following section).
+
+Soil environments are very similar, and often interchangeable with, mesa models (``mesa.Model``).
+
+A configuration 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 (and its 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_class``) and state.
+The agent type is a ``soil.Agent`` class, which contains the code that encapsulates the behavior of the agent.
+The state is a set of variables, which may change during the simulation, and that the code may use to control the behavior.
+All agents provide a ``step`` method either explicitly or implicitly (by inheriting it from a superclass), which controls how the agent will behave in each step of the simulation.
+
+When and how agent steps are executed in a simulation depends entirely on the ``environment``.
+Most environments will internally use a scheduler (``mesa.time.BaseScheduler``), which controls the activation of agents.
+
+In soil, we generally used the ``soil.time.TimedActivation`` scheduler, which allows agents to specify when their next activation will happen, defaulting to a 
+
+When an agent's step is executed (generally, every ``interval`` seconds), the agent has access to its state and the environment.
+Through the environment, it can access the network topology and the state of other agents.
+
+There are two 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_class: SISaModel
+
+It is also possible to add more than one type of agent to the simulation.
+
+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_class: SISaModel
+            weight: 1
+          - agent_class: 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_class: BaseAgent
+   states:
+       first:
+         agent_class: SISaModel
+          
+
+This would also work with a randomly generated network:
+
+
+.. code:: yaml
+
+   network:
+       generator: complete
+       n: 5
+   agent_class: BaseAgent
+   states:
+       - agent_class: 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_class: SISaModel
+            weight: 9
+            state:
+            id: neutral
+        - agent_class: 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_class: 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_class: MyAgent
+         state:
+           mood: happy
+       - agent_class: 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_class: SISaModel
+      weight: 1
+      state:
+        id: content
+    - agent_class: SISaModel
+      weight: 1
+      state:
+        id: discontent
+    - agent_class: 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_class: SISaModel
+    state:
+      id: neutral
+    weight: 1
+  - agent_class: 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>=7.31.1

docs/soil-vs.rst

@@ -0,0 +1,12 @@
+### MESA
+
+Starting with version 0.3, Soil has been redesigned to complement Mesa, while remaining compatible with it.
+That means that every component in Soil (i.e., Models, Environments, etc.) can be mixed with existing mesa components.
+In fact, there are examples that show how that integration may be used, in the `examples/mesa` folder in the repository.
+
+Here are some reasons to use Soil instead of plain mesa:
+
+- Less boilerplate for common scenarios (by some definitions of common)
+- Functions to automatically populate a topology with an agent distribution (i.e., different ratios of agent class and state)
+- The `soil.Simulation` class allows you to run multiple instances of the same experiment (i.e., multiple trials with the same parameters but a different randomness seed)
+- Reporting functions that aggregate multiple

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::
@@ -47,12 +47,6 @@ There are three main elements in a soil simulation:
 -  The environment. It assigns agents to nodes in the network, and
    stores the environment parameters (shared state for all agents).
 
-Soil is based on ``simpy``, which is an event-based network simulation
-library. Soil provides several abstractions over events to make
-developing agents easier. This means you can use events (timeouts,
-delays) in soil, but for the most part we will assume your models will
-be step-based.
-
 Modeling behaviour
 ------------------
 
@@ -214,14 +208,14 @@ 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,
+                                         environment_agents=[{'agent_class': NewsEnvironmentAgent,
                                                              'state': {
                                                                  'event_time': EVENT_TIME
                                                              }}],
-                                         network_agents=[{'agent_type': NewsSpread,
+                                         network_agents=[{'agent_class': NewsSpread,
                                                           'weight': 1}],
                                          states={0: {'has_tv': True}},
                                          default_state={'has_tv': False},
@@ -291,14 +285,14 @@ For this demo, we will use a python dictionary:
         },
         'network_agents': [
             {
-                'agent_type': NewsSpread,
+                'agent_class': NewsSpread,
                 'weight': 1,
                 'state': {
                     'has_tv': False
                 }
             },
             {
-                'agent_type': NewsSpread,
+                'agent_class': NewsSpread,
                 'weight': 2,
                 'state': {
                     'has_tv': True
@@ -306,7 +300,7 @@ For this demo, we will use a python dictionary:
             }
         ],
         'environment_agents':[
-            {'agent_type': NewsEnvironmentAgent,
+            {'agent_class': NewsEnvironmentAgent,
              'state': {
                  'event_time': 10
              }
@@ -323,7 +317,7 @@ 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 +2525,7 @@ 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_class: DumbViewer\r\n",
+      "  state:\r\n",
+      "    has_tv: false\r\n",
+      "  weight: 1\r\n",
+      "- agent_class: 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_class: DumbViewer\r\n",
+      "  state:\r\n",
+      "    has_tv: false\r\n",
+      "  weight: 1\r\n",
+      "- agent_class: DumbViewer\r\n",
+      "  state:\r\n",
+      "    has_tv: true\r\n",
+      "  weight: 1\r\n",
+      "- agent_class: HerdViewer\r\n",
+      "  state:\r\n",
+      "    has_tv: false\r\n",
+      "  weight: 1\r\n",
+      "- agent_class: 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_class: HerdViewer\r\n",
+      "  state:\r\n",
+      "    has_tv: true\r\n",
+      "    id: neutral\r\n",
+      "  weight: 1\r\n",
+      "- agent_class: 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_class: HerdViewer\r\n",
+      "  state:\r\n",
+      "    has_tv: true\r\n",
+      "    id: neutral\r\n",
+      "  weight: 1\r\n",
+      "- agent_class: 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_class: WiseViewer\r\n",
+      "  state:\r\n",
+      "    has_tv: true\r\n",
+      "    id: neutral\r\n",
+      "  weight: 1\r\n",
+      "- agent_class: 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

@@ -1,26 +1,54 @@
 ---
+version: '2'
 name: simple
+group: tests
 dir_path: "/tmp/"
 num_trials: 3
-max_time: 100
+max_steps: 100
 interval: 1
 seed: "CompleteSeed!"
-dump: false
-network_params:
-  generator: complete_graph
-  n: 10
-network_agents:
-  - agent_type: CounterModel
-    weight: 1
-    state:
-      id: 0
-  - agent_type: AggregatedCounter
-    weight: 0.2
-environment_agents: []
-environment_params:
+model_class: Environment
+model_params:
   am_i_complete: true
-default_state:
-  incidents: 0
-states:
-  - name: 'The first node'
-  - name: 'The second node'
+  topology:
+    params:
+      generator: complete_graph
+      n: 12
+  environment:
+  agents:
+    agent_class: CounterModel
+    topology: true
+    state:
+      times: 1
+      # In this group we are not specifying any topology
+    fixed:
+      - name: 'Environment Agent 1'
+        agent_class: BaseAgent
+        group: environment
+        topology: false
+        hidden: true
+        state:
+          times: 10
+      - agent_class: CounterModel
+        id: 0
+        group: fixed_counters
+        state:
+          times: 1
+          total: 0
+      - agent_class: CounterModel
+        group: fixed_counters
+        id: 1
+    distribution:
+      - agent_class: CounterModel
+        weight: 1
+        group: distro_counters
+        state:
+          times: 3
+      - agent_class: AggregatedCounter
+        weight: 0.2
+    override:
+      - filter:
+          agent_class: AggregatedCounter
+        n: 2
+        state:
+          times: 5

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_steps: 100
+interval: 1
+network_params:
+  generator: mymodule.mygenerator
+# These are custom parameters
+  n: 10
+  n_edges: 5  
+network_agents:
+  - agent_class: CounterModel
+    weight: 1
+    state:
+      state_id: 0

examples/custom_generator/mymodule.py

@@ -0,0 +1,22 @@
+from networkx import Graph
+import random
+import networkx as nx
+
+
+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 = random.choice(nodes)
+        nodes.remove(n_in)  # Avoid loops
+        n_out = random.choice(nodes)
+        G.add_edge(n_in, n_out)
+    return G

examples/custom_timeouts/custom_timeouts.py

@@ -0,0 +1,38 @@
+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__":
+    from soil import Simulation
+
+    s = Simulation(
+        network_agents=[
+            {"ids": [0], "agent_class": Fibonacci},
+            {"ids": [1], "agent_class": Odds},
+        ],
+        network_params={"generator": "complete_graph", "n": 2},
+        max_time=100,
+    )
+    s.run(dry_run=True)

examples/mesa/mesa.yml

@@ -0,0 +1,19 @@
+---
+name: mesa_sim
+group: tests
+dir_path: "/tmp"
+num_trials: 3
+max_steps: 100
+interval: 1
+seed: '1'
+model_class: social_wealth.MoneyEnv
+model_params:
+  generator: social_wealth.graph_generator
+  agents:
+    topology: true
+    distribution:
+      - agent_class: social_wealth.SocialMoneyAgent
+        weight: 1
+  N: 10
+  width: 50
+  height: 50

examples/mesa/server.py

@@ -0,0 +1,114 @@
+from mesa.visualization.ModularVisualization import ModularServer
+from soil.visualization import UserSettableParameter
+from mesa.visualization.modules import ChartModule, NetworkModule, CanvasGrid
+from social_wealth import MoneyEnv, graph_generator, SocialMoneyAgent
+import networkx as nx
+
+
+class MyNetwork(NetworkModule):
+    def render(self, model):
+        return self.portrayal_method(model)
+
+
+def network_portrayal(env):
+    # The model ensures there is 0 or 1 agent per node
+
+    portrayal = dict()
+    wealths = {
+        node_id: data["agent"].wealth for (node_id, data) in env.G.nodes(data=True)
+    }
+    portrayal["nodes"] = [
+        {
+            "id": node_id,
+            "size": 2 * (wealth + 1),
+            "color": "#CC0000" if wealth == 0 else "#007959",
+            # "color": "#CC0000",
+            "label": f"{node_id}: {wealth}",
+        }
+        for (node_id, wealth) in wealths.items()
+    ]
+
+    portrayal["edges"] = [
+        {"id": edge_id, "source": source, "target": target, "color": "#000000"}
+        for edge_id, (source, target) in enumerate(env.G.edges)
+    ]
+
+    return portrayal
+
+
+def gridPortrayal(agent):
+    """
+    This function is registered with the visualization server to be called
+    each tick to indicate how to draw the agent in its current state.
+    :param agent:  the agent in the simulation
+    :return: the portrayal dictionary
+    """
+    color = max(10, min(agent.wealth * 10, 100))
+    return {
+        "Shape": "rect",
+        "w": 1,
+        "h": 1,
+        "Filled": "true",
+        "Layer": 0,
+        "Label": agent.unique_id,
+        "Text": agent.unique_id,
+        "x": agent.pos[0],
+        "y": agent.pos[1],
+        "Color": f"rgba(31, 10, 255, 0.{color})",
+    }
+
+
+grid = MyNetwork(network_portrayal, 500, 500)
+chart = ChartModule(
+    [{"Label": "Gini", "Color": "Black"}], data_collector_name="datacollector"
+)
+
+model_params = {
+    "N": UserSettableParameter(
+        "slider",
+        "N",
+        5,
+        1,
+        10,
+        1,
+        description="Choose how many agents to include in the model",
+    ),
+    "height": UserSettableParameter(
+        "slider",
+        "height",
+        5,
+        5,
+        10,
+        1,
+        description="Grid height",
+    ),
+    "width": UserSettableParameter(
+        "slider",
+        "width",
+        5,
+        5,
+        10,
+        1,
+        description="Grid width",
+    ),
+    "agent_class": UserSettableParameter(
+        "choice",
+        "Agent class",
+        value="MoneyAgent",
+        choices=["MoneyAgent", "SocialMoneyAgent"],
+    ),
+    "generator": graph_generator,
+}
+
+
+canvas_element = CanvasGrid(
+    gridPortrayal, model_params["width"].value, model_params["height"].value, 500, 500
+)
+
+
+server = ModularServer(
+    MoneyEnv, [grid, chart, canvas_element], "Money Model", model_params
+)
+server.port = 8521
+
+server.launch(open_browser=False)

examples/mesa/social_wealth.py

@@ -0,0 +1,137 @@
+"""
+This is an example that adds soil agents and environment in a normal
+mesa workflow.
+"""
+from mesa import Agent as MesaAgent
+from mesa.space import MultiGrid
+
+# from mesa.time import RandomActivation
+from mesa.datacollection import DataCollector
+from mesa.batchrunner import BatchRunner
+
+import networkx as nx
+
+from soil import NetworkAgent, Environment, serialization
+
+
+def compute_gini(model):
+    agent_wealths = [agent.wealth for agent in model.agents]
+    x = sorted(agent_wealths)
+    N = len(list(model.agents))
+    B = sum(xi * (N - i) for i, xi in enumerate(x)) / (N * sum(x))
+    return 1 + (1 / N) - 2 * B
+
+
+class MoneyAgent(MesaAgent):
+    """
+    A MESA agent with fixed initial wealth.
+    It will only share wealth with neighbors based on grid proximity
+    """
+
+    def __init__(self, unique_id, model, wealth=1):
+        super().__init__(unique_id=unique_id, model=model)
+        self.wealth = wealth
+
+    def move(self):
+        possible_steps = self.model.grid.get_neighborhood(
+            self.pos, moore=True, include_center=False
+        )
+        new_position = self.random.choice(possible_steps)
+        self.model.grid.move_agent(self, new_position)
+
+    def give_money(self):
+        cellmates = self.model.grid.get_cell_list_contents([self.pos])
+        if len(cellmates) > 1:
+            other = self.random.choice(cellmates)
+            other.wealth += 1
+            self.wealth -= 1
+
+    def step(self):
+        print("Crying wolf", self.pos)
+        self.move()
+        if self.wealth > 0:
+            self.give_money()
+
+
+class SocialMoneyAgent(NetworkAgent, MoneyAgent):
+    wealth = 1
+
+    def give_money(self):
+        cellmates = set(self.model.grid.get_cell_list_contents([self.pos]))
+        friends = set(self.get_neighboring_agents())
+        self.info("Trying to give money")
+        self.info("Cellmates: ", cellmates)
+        self.info("Friends: ", friends)
+
+        nearby_friends = list(cellmates & friends)
+
+        if len(nearby_friends):
+            other = self.random.choice(nearby_friends)
+            other.wealth += 1
+            self.wealth -= 1
+
+
+def graph_generator(n=5):
+    G = nx.Graph()
+    for ix in range(n):
+        G.add_edge(0, ix)
+    return G
+
+
+class MoneyEnv(Environment):
+    """A model with some number of agents."""
+
+    def __init__(
+        self,
+        width,
+        height,
+        N,
+        generator=graph_generator,
+        agent_class=SocialMoneyAgent,
+        topology=None,
+        **kwargs
+    ):
+
+        generator = serialization.deserialize(generator)
+        agent_class = serialization.deserialize(agent_class, globs=globals())
+        topology = generator(n=N)
+        super().__init__(topology=topology, N=N, **kwargs)
+        self.grid = MultiGrid(width, height, False)
+
+        self.populate_network(agent_class=agent_class)
+
+        # Create agents
+        for agent in self.agents:
+            x = self.random.randrange(self.grid.width)
+            y = self.random.randrange(self.grid.height)
+            self.grid.place_agent(agent, (x, y))
+
+        self.datacollector = DataCollector(
+            model_reporters={"Gini": compute_gini}, agent_reporters={"Wealth": "wealth"}
+        )
+
+
+if __name__ == "__main__":
+
+    fixed_params = {
+        "generator": nx.complete_graph,
+        "width": 10,
+        "network_agents": [{"agent_class": SocialMoneyAgent, "weight": 1}],
+        "height": 10,
+    }
+
+    variable_params = {"N": range(10, 100, 10)}
+
+    batch_run = BatchRunner(
+        MoneyEnv,
+        variable_parameters=variable_params,
+        fixed_parameters=fixed_params,
+        iterations=5,
+        max_steps=100,
+        model_reporters={"Gini": compute_gini},
+    )
+    batch_run.run_all()
+
+    run_data = batch_run.get_model_vars_dataframe()
+    run_data.head()
+    print(run_data.Gini)

examples/mesa/wealth.py

@@ -0,0 +1,87 @@
+from mesa import Agent, Model
+from mesa.space import MultiGrid
+from mesa.time import RandomActivation
+from mesa.datacollection import DataCollector
+from mesa.batchrunner import BatchRunner
+
+
+def compute_gini(model):
+    agent_wealths = [agent.wealth for agent in model.schedule.agents]
+    x = sorted(agent_wealths)
+    N = model.num_agents
+    B = sum(xi * (N - i) for i, xi in enumerate(x)) / (N * sum(x))
+    return 1 + (1 / N) - 2 * B
+
+
+class MoneyAgent(Agent):
+    """An agent with fixed initial wealth."""
+
+    def __init__(self, unique_id, model):
+        super().__init__(unique_id, model)
+        self.wealth = 1
+
+    def move(self):
+        possible_steps = self.model.grid.get_neighborhood(
+            self.pos, moore=True, include_center=False
+        )
+        new_position = self.random.choice(possible_steps)
+        self.model.grid.move_agent(self, new_position)
+
+    def give_money(self):
+        cellmates = self.model.grid.get_cell_list_contents([self.pos])
+        if len(cellmates) > 1:
+            other = self.random.choice(cellmates)
+            other.wealth += 1
+            self.wealth -= 1
+
+    def step(self):
+        self.move()
+        if self.wealth > 0:
+            self.give_money()
+
+
+class MoneyModel(Model):
+    """A model with some number of agents."""
+
+    def __init__(self, N, width, height):
+        self.num_agents = N
+        self.grid = MultiGrid(width, height, True)
+        self.schedule = RandomActivation(self)
+        self.running = True
+
+        # Create agents
+        for i in range(self.num_agents):
+            a = MoneyAgent(i, self)
+            self.schedule.add(a)
+            # Add the agent to a random grid cell
+            x = self.random.randrange(self.grid.width)
+            y = self.random.randrange(self.grid.height)
+            self.grid.place_agent(a, (x, y))
+
+        self.datacollector = DataCollector(
+            model_reporters={"Gini": compute_gini}, agent_reporters={"Wealth": "wealth"}
+        )
+
+    def step(self):
+        self.datacollector.collect(self)
+        self.schedule.step()
+
+
+if __name__ == "__main__":
+
+    fixed_params = {"width": 10, "height": 10}
+    variable_params = {"N": range(10, 500, 10)}
+
+    batch_run = BatchRunner(
+        MoneyModel,
+        variable_params,
+        fixed_params,
+        iterations=5,
+        max_steps=100,
+        model_reporters={"Gini": compute_gini},
+    )
+    batch_run.run_all()
+
+    run_data = batch_run.get_model_vars_dataframe()
+    run_data.head()
+    print(run_data.Gini)

examples/newsspread/NewsSpread.ipynb

@@ -89,11 +89,11 @@
       "max_time: 30\r\n",
       "name: Sim_all_dumb\r\n",
       "network_agents:\r\n",
-      "- agent_type: DumbViewer\r\n",
+      "- agent_class: DumbViewer\r\n",
       "  state:\r\n",
       "    has_tv: false\r\n",
       "  weight: 1\r\n",
-      "- agent_type: DumbViewer\r\n",
+      "- agent_class: DumbViewer\r\n",
       "  state:\r\n",
       "    has_tv: true\r\n",
       "  weight: 1\r\n",
@@ -113,19 +113,19 @@
       "max_time: 30\r\n",
       "name: Sim_half_herd\r\n",
       "network_agents:\r\n",
-      "- agent_type: DumbViewer\r\n",
+      "- agent_class: DumbViewer\r\n",
       "  state:\r\n",
       "    has_tv: false\r\n",
       "  weight: 1\r\n",
-      "- agent_type: DumbViewer\r\n",
+      "- agent_class: DumbViewer\r\n",
       "  state:\r\n",
       "    has_tv: true\r\n",
       "  weight: 1\r\n",
-      "- agent_type: HerdViewer\r\n",
+      "- agent_class: HerdViewer\r\n",
       "  state:\r\n",
       "    has_tv: false\r\n",
       "  weight: 1\r\n",
-      "- agent_type: HerdViewer\r\n",
+      "- agent_class: HerdViewer\r\n",
       "  state:\r\n",
       "    has_tv: true\r\n",
       "  weight: 1\r\n",
@@ -145,12 +145,12 @@
       "max_time: 30\r\n",
       "name: Sim_all_herd\r\n",
       "network_agents:\r\n",
-      "- agent_type: HerdViewer\r\n",
+      "- agent_class: HerdViewer\r\n",
       "  state:\r\n",
       "    has_tv: true\r\n",
       "    id: neutral\r\n",
       "  weight: 1\r\n",
-      "- agent_type: HerdViewer\r\n",
+      "- agent_class: HerdViewer\r\n",
       "  state:\r\n",
       "    has_tv: true\r\n",
       "    id: neutral\r\n",
@@ -172,12 +172,12 @@
       "max_time: 30\r\n",
       "name: Sim_wise_herd\r\n",
       "network_agents:\r\n",
-      "- agent_type: HerdViewer\r\n",
+      "- agent_class: HerdViewer\r\n",
       "  state:\r\n",
       "    has_tv: true\r\n",
       "    id: neutral\r\n",
       "  weight: 1\r\n",
-      "- agent_type: WiseViewer\r\n",
+      "- agent_class: WiseViewer\r\n",
       "  state:\r\n",
       "    has_tv: true\r\n",
       "  weight: 1\r\n",
@@ -198,12 +198,12 @@
       "max_time: 30\r\n",
       "name: Sim_all_wise\r\n",
       "network_agents:\r\n",
-      "- agent_type: WiseViewer\r\n",
+      "- agent_class: WiseViewer\r\n",
       "  state:\r\n",
       "    has_tv: true\r\n",
       "    id: neutral\r\n",
       "  weight: 1\r\n",
-      "- agent_type: WiseViewer\r\n",
+      "- agent_class: WiseViewer\r\n",
       "  state:\r\n",
       "    has_tv: true\r\n",
       "  weight: 1\r\n",

examples/newsspread/NewsSpread.yml

@@ -1,19 +1,18 @@
 ---
 default_state: {}
-load_module: newsspread
 environment_agents: []
 environment_params:
   prob_neighbor_spread: 0.0
   prob_tv_spread: 0.01
 interval: 1
-max_time: 30
+max_steps: 300
 name: Sim_all_dumb
 network_agents:
-- agent_type: DumbViewer
+- agent_class: newsspread.DumbViewer
   state:
     has_tv: false
   weight: 1
-- agent_type: DumbViewer
+- agent_class: newsspread.DumbViewer
   state:
     has_tv: true
   weight: 1
@@ -24,28 +23,27 @@ network_params:
 num_trials: 50
 ---
 default_state: {}
-load_module: newsspread
 environment_agents: []
 environment_params:
   prob_neighbor_spread: 0.0
   prob_tv_spread: 0.01
 interval: 1
-max_time: 30
+max_steps: 300
 name: Sim_half_herd
 network_agents:
-- agent_type: DumbViewer
+- agent_class: newsspread.DumbViewer
   state:
     has_tv: false
   weight: 1
-- agent_type: DumbViewer
+- agent_class: newsspread.DumbViewer
   state:
     has_tv: true
   weight: 1
-- agent_type: HerdViewer
+- agent_class: newsspread.HerdViewer
   state:
     has_tv: false
   weight: 1
-- agent_type: HerdViewer
+- agent_class: newsspread.HerdViewer
   state:
     has_tv: true
   weight: 1
@@ -56,24 +54,23 @@ network_params:
 num_trials: 50
 ---
 default_state: {}
-load_module: newsspread
 environment_agents: []
 environment_params:
   prob_neighbor_spread: 0.0
   prob_tv_spread: 0.01
 interval: 1
-max_time: 30
+max_steps: 300
 name: Sim_all_herd
 network_agents:
-- agent_type: HerdViewer
+- agent_class: newsspread.HerdViewer
   state:
     has_tv: true
-    id: neutral
+    state_id: neutral
   weight: 1
-- agent_type: HerdViewer
+- agent_class: newsspread.HerdViewer
   state:
     has_tv: true
-    id: neutral
+    state_id: neutral
   weight: 1
 network_params:
   generator: barabasi_albert_graph
@@ -82,22 +79,21 @@ network_params:
 num_trials: 50
 ---
 default_state: {}
-load_module: newsspread
 environment_agents: []
 environment_params:
   prob_neighbor_spread: 0.0
   prob_tv_spread: 0.01
   prob_neighbor_cure: 0.1
 interval: 1
-max_time: 30
+max_steps: 300
 name: Sim_wise_herd
 network_agents:
-- agent_type: HerdViewer
+- agent_class: newsspread.HerdViewer
   state:
     has_tv: true
-    id: neutral
+    state_id: neutral
   weight: 1
-- agent_type: WiseViewer
+- agent_class: newsspread.WiseViewer
   state:
     has_tv: true
   weight: 1
@@ -108,22 +104,21 @@ network_params:
 num_trials: 50
 ---
 default_state: {}
-load_module: newsspread
 environment_agents: []
 environment_params:
   prob_neighbor_spread: 0.0
   prob_tv_spread: 0.01
   prob_neighbor_cure: 0.1
 interval: 1
-max_time: 30
+max_steps: 300
 name: Sim_all_wise
 network_agents:
-- agent_type: WiseViewer
+- agent_class: newsspread.WiseViewer
   state:
     has_tv: true
-    id: neutral
+    state_id: neutral
   weight: 1
-- agent_type: WiseViewer
+- agent_class: newsspread.WiseViewer
   state:
     has_tv: true
   weight: 1

examples/newsspread/newsspread.py

@@ -1,81 +1,85 @@
-from soil.agents import FSM, state, default_state, prob
+from soil.agents import FSM, NetworkAgent, state, default_state, prob
 import logging
 
 
-class DumbViewer(FSM):
-    '''
+class DumbViewer(FSM, NetworkAgent):
+    """
     A viewer that gets infected via TV (if it has one) and tries to infect
     its neighbors once it's infected.
-    '''
+    """
+
     defaults = {
-        'prob_neighbor_spread': 0.5,
-        'prob_tv_spread': 0.1,
+        "prob_neighbor_spread": 0.5,
+        "prob_tv_spread": 0.1,
     }
 
     @default_state
     @state
     def neutral(self):
-        if self['has_tv']:
-            if prob(self.env['prob_tv_spread']):
-                self.set_state(self.infected)
+        if self["has_tv"]:
+            if self.prob(self.model["prob_tv_spread"]):
+                return self.infected
 
     @state
     def infected(self):
         for neighbor in self.get_neighboring_agents(state_id=self.neutral.id):
-            if prob(self.env['prob_neighbor_spread']):
+            if self.prob(self.model["prob_neighbor_spread"]):
                 neighbor.infect()
 
     def infect(self):
+        """
+        This is not a state. It is a function that other agents can use to try to
+        infect this agent. DumbViewer always gets infected, but other agents like
+        HerdViewer might not become infected right away
+        """
+
         self.set_state(self.infected)
 
 
 class HerdViewer(DumbViewer):
-    '''
+    """
     A viewer whose probability of infection depends on the state of its neighbors.
-    '''
-
-    level = logging.DEBUG
+    """
 
     def infect(self):
+        """Notice again that this is NOT a state. See DumbViewer.infect for reference"""
         infected = self.count_neighboring_agents(state_id=self.infected.id)
         total = self.count_neighboring_agents()
-        prob_infect = self.env['prob_neighbor_spread'] * infected/total
-        self.debug('prob_infect', prob_infect)
-        if prob(prob_infect):
-            self.set_state(self.infected.id)
+        prob_infect = self.model["prob_neighbor_spread"] * infected / total
+        self.debug("prob_infect", prob_infect)
+        if self.prob(prob_infect):
+            self.set_state(self.infected)
 
 
 class WiseViewer(HerdViewer):
-    '''
+    """
     A viewer that can change its mind.
-    '''
+    """
 
     defaults = {
-        'prob_neighbor_spread': 0.5,
-        'prob_neighbor_cure': 0.25,
-        'prob_tv_spread': 0.1,
+        "prob_neighbor_spread": 0.5,
+        "prob_neighbor_cure": 0.25,
+        "prob_tv_spread": 0.1,
     }
 
     @state
     def cured(self):
-        prob_cure = self.env['prob_neighbor_cure']
+        prob_cure = self.model["prob_neighbor_cure"]
         for neighbor in self.get_neighboring_agents(state_id=self.infected.id):
-            if prob(prob_cure):
+            if self.prob(prob_cure):
                 try:
                     neighbor.cure()
                 except AttributeError:
-                    self.debug('Viewer {} cannot be cured'.format(neighbor.id))
+                    self.debug("Viewer {} cannot be cured".format(neighbor.id))
 
     def cure(self):
         self.set_state(self.cured.id)
 
     @state
     def infected(self):
-        cured = max(self.count_neighboring_agents(self.cured.id),
-                    1.0)
-        infected = max(self.count_neighboring_agents(self.infected.id),
-                       1.0)
-        prob_cure = self.env['prob_neighbor_cure'] * (cured/infected)
-        if prob(prob_cure):
-            return self.cure()
+        cured = max(self.count_neighboring_agents(self.cured.id), 1.0)
+        infected = max(self.count_neighboring_agents(self.infected.id), 1.0)
+        prob_cure = self.model["prob_neighbor_cure"] * (cured / infected)
+        if self.prob(prob_cure):
+            return self.cured
         return self.set_state(super().infected)

examples/programmatic/.gitignore

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

examples/programmatic/programmatic.py

@@ -0,0 +1,41 @@
+"""
+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.debug("I am running")
+        if agents.prob(0.2):
+            self.info("This runs 2/10 times on average")
+
+
+s = Simulation(
+    name="Programmatic",
+    network_params={"generator": mygenerator},
+    num_trials=1,
+    max_time=100,
+    agent_class=MyAgent,
+    dry_run=True,
+)
+
+
+# By default, logging will only print WARNING logs (and above).
+# You need to choose a lower logging level to get INFO/DEBUG traces
+logging.basicConfig(level=logging.INFO)
+envs = s.run()
+
+# Uncomment this to output the simulation to a YAML file
+# s.dump_yaml('simulation.yaml')

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, NetworkAgent, state, default_state
+from soil import Environment
+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, NetworkAgent):
+    """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
+
+    pub = None
+    drunk = False
+    pints = 0
+    max_pints = 3
+    kicked_out = False
+
+    @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.model.available_pubs():
+            self.debug("We're trying to get into {}: total: {}".format(pub, len(group)))
+            if self.model.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
+        if self.kicked_out:
+            return self.at_home
+        pass  # out drun
+
+    @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.kicked_out = True
+
+    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"] > self.random.random():
+            self.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"])
+        self.random.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_steps: 10
+dump: false
+network_params:
+  # Generate 100 empty nodes. They will be assigned a network agent
+  generator: empty_graph
+  n: 30
+network_agents:
+  - agent_class: pubcrawl.Patron
+    description: Extroverted patron
+    state:
+      openness: 1.0
+    weight: 9
+  - agent_class: pubcrawl.Patron
+    description: Introverted patron
+    state:
+      openness: 0.1
+    weight: 1
+environment_agents:
+  - agent_class: pubcrawl.Police
+environment_class: pubcrawl.CityPubs
+environment_params:
+  altercations: 0
+  number_of_pubs: 3

examples/rabbits/README.md

@@ -0,0 +1,14 @@
+There are two similar implementations of this simulation.
+
+- `basic`. Using simple primites
+- `improved`. Using more advanced features such as the `time` module to avoid unnecessary computations (i.e., skip steps), and generator functions.
+
+The examples can be run directly in the terminal, and they accept command like arguments.
+For example, to enable the CSV exporter and the Summary exporter, while setting `max_time` to `100` and `seed` to `CustomSeed`:
+
+```
+python rabbit_agents.py  --set max_time=100 --csv -e summary  --set 'seed="CustomSeed"'
+```
+
+To learn more about how this functionality works, check out the `soil.easy` function.
+

examples/rabbits/basic/rabbit_agents.py

@@ -0,0 +1,150 @@
+from soil import FSM, state, default_state, BaseAgent, NetworkAgent, Environment
+from collections import Counter
+import logging
+import math
+
+
+class RabbitEnv(Environment):
+    @property
+    def num_rabbits(self):
+        return self.count_agents(agent_class=Rabbit)
+
+    @property
+    def num_males(self):
+        return self.count_agents(agent_class=Male)
+
+    @property
+    def num_females(self):
+        return self.count_agents(agent_class=Female)
+
+
+class Rabbit(NetworkAgent, FSM):
+
+    sexual_maturity = 30
+    life_expectancy = 300
+
+    @default_state
+    @state
+    def newborn(self):
+        self.info("I am a newborn.")
+        self.age = 0
+        self.offspring = 0
+        return self.youngling
+
+    @state
+    def youngling(self):
+        self.age += 1
+        if self.age >= self.sexual_maturity:
+            self.info(f"I am fertile! My age is {self.age}")
+            return self.fertile
+
+    @state
+    def fertile(self):
+        raise Exception("Each subclass should define its fertile state")
+
+    @state
+    def dead(self):
+        self.die()
+
+
+class Male(Rabbit):
+    max_females = 5
+    mating_prob = 0.001
+
+    @state
+    def fertile(self):
+        self.age += 1
+
+        if self.age > self.life_expectancy:
+            return self.dead
+
+        # Males try to mate
+        for f in self.model.agents(
+            agent_class=Female, state_id=Female.fertile.id, limit=self.max_females
+        ):
+            self.debug("FOUND A FEMALE: ", repr(f), self.mating_prob)
+            if self.prob(self["mating_prob"]):
+                f.impregnate(self)
+                break  # Take a break
+
+
+class Female(Rabbit):
+    gestation = 10
+    pregnancy = -1
+
+    @state
+    def fertile(self):
+        # Just wait for a Male
+        self.age += 1
+        if self.age > self.life_expectancy:
+            return self.dead
+        if self.pregnancy >= 0:
+            return self.pregnant
+
+    def impregnate(self, male):
+        self.info(f"impregnated by {repr(male)}")
+        self.mate = male
+        self.pregnancy = 0
+        self.number_of_babies = int(8 + 4 * self.random.random())
+
+    @state
+    def pregnant(self):
+        self.info("I am pregnant")
+        self.age += 1
+
+        if self.age >= self.life_expectancy:
+            return self.die()
+
+        if self.pregnancy < self.gestation:
+            self.pregnancy += 1
+            return
+
+        self.info("Having {} babies".format(self.number_of_babies))
+        for i in range(self.number_of_babies):
+            state = {}
+            agent_class = self.random.choice([Male, Female])
+            child = self.model.add_node(agent_class=agent_class, **state)
+            child.add_edge(self)
+            try:
+                child.add_edge(self.mate)
+                self.model.agents[self.mate].offspring += 1
+            except ValueError:
+                self.debug("The father has passed away")
+
+            self.offspring += 1
+        self.mate = None
+        self.pregnancy = -1
+        return self.fertile
+
+    def die(self):
+        if "pregnancy" in self and self["pregnancy"] > -1:
+            self.info("A mother has died carrying a baby!!")
+        return super().die()
+
+
+class RandomAccident(BaseAgent):
+    def step(self):
+        rabbits_alive = self.model.G.number_of_nodes()
+
+        if not rabbits_alive:
+            return self.die()
+
+        prob_death = self.model.get("prob_death", 1e-100) * math.floor(
+            math.log10(max(1, rabbits_alive))
+        )
+        self.debug("Killing some rabbits with prob={}!".format(prob_death))
+        for i in self.iter_agents(agent_class=Rabbit):
+            if i.state_id == i.dead.id:
+                continue
+            if self.prob(prob_death):
+                self.info("I killed a rabbit: {}".format(i.id))
+                rabbits_alive -= 1
+                i.die()
+        self.debug("Rabbits alive: {}".format(rabbits_alive))
+
+
+if __name__ == "__main__":
+    from soil import easy
+
+    with easy("rabbits.yml") as sim:
+        sim.run()

examples/rabbits/basic/rabbits.yml

@@ -0,0 +1,42 @@
+---
+version: '2'
+name: rabbits_basic
+num_trials: 1
+seed: MySeed
+description: null
+group: null
+interval: 1.0
+max_time: 100
+model_class: rabbit_agents.RabbitEnv
+model_params:
+  agents:
+    topology: true
+    distribution:
+    - agent_class: rabbit_agents.Male
+      weight: 1
+    - agent_class: rabbit_agents.Female
+      weight: 1
+    fixed:
+    - agent_class: rabbit_agents.RandomAccident
+      topology: false
+      hidden: true
+      state:
+        group: environment
+    state:
+      group: network
+      mating_prob: 0.1
+  prob_death: 0.001
+  topology:
+    fixed:
+      directed: true
+      links: []
+      nodes:
+      - id: 1
+      - id: 0
+  model_reporters:
+    num_males: 'num_males'
+    num_females: 'num_females'
+    num_rabbits: |
+      py:lambda env: env.num_males + env.num_females
+extra:
+  visualization_params: {}

examples/rabbits/improved/rabbit_agents.py

@@ -0,0 +1,157 @@
+from soil import FSM, state, default_state, BaseAgent, NetworkAgent, Environment
+from soil.time import Delta
+from enum import Enum
+from collections import Counter
+import logging
+import math
+
+
+class RabbitEnv(Environment):
+    @property
+    def num_rabbits(self):
+        return self.count_agents(agent_class=Rabbit)
+
+    @property
+    def num_males(self):
+        return self.count_agents(agent_class=Male)
+
+    @property
+    def num_females(self):
+        return self.count_agents(agent_class=Female)
+
+
+class Rabbit(FSM, NetworkAgent):
+
+    sexual_maturity = 30
+    life_expectancy = 300
+    birth = None
+
+    @property
+    def age(self):
+        if self.birth is None:
+            return None
+        return self.now - self.birth
+
+    @default_state
+    @state
+    def newborn(self):
+        self.info("I am a newborn.")
+        self.birth = self.now
+        self.offspring = 0
+        return self.youngling, Delta(self.sexual_maturity - self.age)
+
+    @state
+    def youngling(self):
+        if self.age >= self.sexual_maturity:
+            self.info(f"I am fertile! My age is {self.age}")
+            return self.fertile
+
+    @state
+    def fertile(self):
+        raise Exception("Each subclass should define its fertile state")
+
+    @state
+    def dead(self):
+        self.die()
+
+
+class Male(Rabbit):
+    max_females = 5
+    mating_prob = 0.001
+
+    @state
+    def fertile(self):
+        if self.age > self.life_expectancy:
+            return self.dead
+
+        # Males try to mate
+        for f in self.model.agents(
+            agent_class=Female, state_id=Female.fertile.id, limit=self.max_females
+        ):
+            self.debug("FOUND A FEMALE: ", repr(f), self.mating_prob)
+            if self.prob(self["mating_prob"]):
+                f.impregnate(self)
+                break  # Do not try to impregnate other females
+
+
+class Female(Rabbit):
+    gestation = 10
+    conception = None
+
+    @state
+    def fertile(self):
+        # Just wait for a Male
+        if self.age > self.life_expectancy:
+            return self.dead
+        if self.conception is not None:
+            return self.pregnant
+
+    @property
+    def pregnancy(self):
+        if self.conception is None:
+            return None
+        return self.now - self.conception
+
+    def impregnate(self, male):
+        self.info(f"impregnated by {repr(male)}")
+        self.mate = male
+        self.conception = self.now
+        self.number_of_babies = int(8 + 4 * self.random.random())
+
+    @state
+    def pregnant(self):
+        self.debug("I am pregnant")
+
+        if self.age > self.life_expectancy:
+            self.info("Dying before giving birth")
+            return self.die()
+
+        if self.pregnancy >= self.gestation:
+            self.info("Having {} babies".format(self.number_of_babies))
+            for i in range(self.number_of_babies):
+                state = {}
+                agent_class = self.random.choice([Male, Female])
+                child = self.model.add_node(agent_class=agent_class, **state)
+                child.add_edge(self)
+                if self.mate:
+                    child.add_edge(self.mate)
+                    self.mate.offspring += 1
+                else:
+                    self.debug("The father has passed away")
+
+                self.offspring += 1
+            self.mate = None
+            return self.fertile
+
+    def die(self):
+        if self.pregnancy is not None:
+            self.info("A mother has died carrying a baby!!")
+        return super().die()
+
+
+class RandomAccident(BaseAgent):
+    def step(self):
+        rabbits_alive = self.model.G.number_of_nodes()
+
+        if not rabbits_alive:
+            return self.die()
+
+        prob_death = self.model.get("prob_death", 1e-100) * math.floor(
+            math.log10(max(1, rabbits_alive))
+        )
+        self.debug("Killing some rabbits with prob={}!".format(prob_death))
+        for i in self.iter_agents(agent_class=Rabbit):
+            if i.state_id == i.dead.id:
+                continue
+            if self.prob(prob_death):
+                self.info("I killed a rabbit: {}".format(i.id))
+                rabbits_alive -= 1
+                i.die()
+        self.debug("Rabbits alive: {}".format(rabbits_alive))
+
+
+if __name__ == "__main__":
+    from soil import easy
+
+    with easy("rabbits.yml") as sim:
+        sim.run()

examples/rabbits/improved/rabbits.yml

@@ -0,0 +1,42 @@
+---
+version: '2'
+name: rabbits_improved
+num_trials: 1
+seed: MySeed
+description: null
+group: null
+interval: 1.0
+max_time: 100
+model_class: rabbit_agents.RabbitEnv
+model_params:
+  agents:
+    topology: true
+    distribution:
+    - agent_class: rabbit_agents.Male
+      weight: 1
+    - agent_class: rabbit_agents.Female
+      weight: 1
+    fixed:
+    - agent_class: rabbit_agents.RandomAccident
+      topology: false
+      hidden: true
+      state:
+        group: environment
+    state:
+      group: network
+      mating_prob: 0.1
+  prob_death: 0.001
+  topology:
+    fixed:
+      directed: true
+      links: []
+      nodes:
+      - id: 1
+      - id: 0
+  model_reporters:
+    num_males: 'num_males'
+    num_females: 'num_females'
+    num_rabbits: |
+      py:lambda env: env.num_males + env.num_females
+extra:
+  visualization_params: {}

examples/rabbits/rabbit_agents.py

@@ -1,120 +0,0 @@
-from soil.agents import FSM, state, default_state, BaseAgent
-from enum import Enum
-from random import random, choice
-from itertools import islice
-import logging
-import math
-
-
-class Genders(Enum):
-    male = 'male'
-    female = 'female'
-
-
-class RabbitModel(FSM):
-
-    level = logging.INFO
-
-    defaults = {
-        'age': 0,
-        'gender': Genders.male.value,
-        'mating_prob': 0.001,
-        'offspring': 0,
-    }
-
-    sexual_maturity = 4*30
-    life_expectancy = 365 * 3
-    gestation = 33
-    pregnancy = -1
-    max_females = 5
-
-    @default_state
-    @state
-    def newborn(self):
-        self['age'] += 1
-
-        if self['age'] >= self.sexual_maturity:
-            return self.fertile
-
-    @state
-    def fertile(self):
-        self['age'] += 1
-        if self['age'] > self.life_expectancy:
-            return self.dead
-
-        if self['gender'] == Genders.female.value:
-            return
-
-        # Males try to mate
-        females = self.get_agents(state_id=self.fertile.id, gender=Genders.female.value, limit_neighbors=False)
-        for f in islice(females, self.max_females):
-            r = random()
-            if r < self['mating_prob']:
-                self.impregnate(f)
-                break # Take a break
-
-    def impregnate(self, whom):
-        if self['gender'] == Genders.female.value:
-            raise NotImplementedError('Females cannot impregnate')
-        whom['pregnancy'] = 0
-        whom['mate'] = self.id
-        whom.set_state(whom.pregnant)
-        self.debug('{} impregnating: {}. {}'.format(self.id, whom.id, whom.state))
-
-    @state
-    def pregnant(self):
-        self['age'] += 1
-        if self['age'] > self.life_expectancy:
-            return self.dead
-
-        self['pregnancy'] += 1
-        self.debug('Pregnancy: {}'.format(self['pregnancy']))
-        if self['pregnancy'] >= self.gestation:
-            number_of_babies = int(8+4*random())
-            self.info('Having {} babies'.format(number_of_babies))
-            for i in range(number_of_babies):
-                state = {}
-                state['gender'] = choice(list(Genders)).value
-                child = self.env.add_node(self.__class__, state)
-                self.env.add_edge(self.id, child.id)
-                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.debug('Rabbits alive: {}'.format(self.env['rabbits_alive']))
-                self['offspring'] += 1
-                self.env.get_agent(self['mate'])['offspring'] += 1
-            del self['mate']
-            self['pregnancy'] = -1
-            return self.fertile
-
-    @state
-    def dead(self):
-        self.info('Agent {} is dying'.format(self.id))
-        if 'pregnancy' in self and self['pregnancy'] > -1:
-            self.info('A mother has died carrying a baby!!')
-        self.die()
-        return
-
-
-class RandomAccident(BaseAgent):
-
-    level = logging.DEBUG
-
-    def step(self):
-        rabbits_total = self.global_topology.number_of_nodes()
-        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))
-        for i in self.env.network_agents:
-            if i.state['id'] == i.dead.id:
-                continue
-            r = random()
-            if r < prob_death:
-                self.debug('I killed a rabbit: {}'.format(i.id))
-                rabbits_alive = self.env['rabbits_alive'] = rabbits_alive -1
-                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():
-            self.die()

examples/rabbits/rabbits.yml

@@ -1,23 +0,0 @@
----
-load_module: rabbit_agents
-name: rabbits_example
-max_time: 1200
-interval: 1
-seed: MySeed
-agent_type: RabbitModel
-environment_agents:
-    - agent_type: RandomAccident
-environment_params:
-  prob_death: 0.001
-default_state:
-  mating_prob: 0.01
-topology:
-  nodes:
-    - id: 1
-      state:
-        gender: female
-    - id: 0
-      state:
-        gender: male
-  directed: true
-  links: []

examples/random_delays/random_delays.py

@@ -0,0 +1,43 @@
+"""
+Example of setting a 
+Example of a fully programmatic simulation, without definition files.
+"""
+from soil import Simulation, agents
+from soil.time import Delta
+
+
+class MyAgent(agents.FSM):
+    """
+    An agent that first does a ping
+    """
+
+    defaults = {"pong_counts": 2}
+
+    @agents.default_state
+    @agents.state
+    def ping(self):
+        self.info("Ping")
+        return self.pong, Delta(self.random.expovariate(1 / 16))
+
+    @agents.state
+    def pong(self):
+        self.info("Pong")
+        self.pong_counts -= 1
+        self.info(str(self.pong_counts))
+        if self.pong_counts < 1:
+            return self.die()
+        return None, Delta(self.random.expovariate(1 / 16))
+
+
+s = Simulation(
+    name="Programmatic",
+    network_agents=[{"agent_class": MyAgent, "id": 0}],
+    topology={"nodes": [{"id": 0}], "links": []},
+    num_trials=1,
+    max_time=100,
+    agent_class=MyAgent,
+    dry_run=True,
+)
+
+
+envs = s.run()

examples/template.yml

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

examples/terrorism/TerroristNetworkModel.py

@@ -0,0 +1,291 @@
+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, model=None, unique_id=0, state=()):
+        super().__init__(model=model, unique_id=unique_id, state=state)
+
+        self.information_spread_intensity = model.environment_params[
+            "information_spread_intensity"
+        ]
+        self.terrorist_additional_influence = model.environment_params[
+            "terrorist_additional_influence"
+        ]
+        self.prob_interaction = model.environment_params["prob_interaction"]
+
+        if self["id"] == self.civilian.id:  # Civilian
+            self.mean_belief = self.random.uniform(0.00, 0.5)
+        elif self["id"] == self.terrorist.id:  # Terrorist
+            self.mean_belief = self.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 model.environment_params:
+            self.vulnerability = self.random.uniform(
+                model.environment_params["min_vulnerability"],
+                model.environment_params["max_vulnerability"],
+            )
+        else:
+            self.vulnerability = self.random.uniform(
+                0, model.environment_params["max_vulnerability"]
+            )
+
+    @state
+    def civilian(self):
+        neighbours = list(self.get_neighboring_agents(agent_class=TerroristSpreadModel))
+        if len(neighbours) > 0:
+            # Only interact with some of the neighbors
+            interactions = list(
+                n for n in neighbours if self.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_class=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
+
+    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.model, "_degree"))
+            or getattr(self.model, "_last_step", 0) < self.now
+        ):
+            self.model._degree = nx.degree_centrality(self.G)
+            self.model._last_step = self.now
+        return self.model._degree[node]
+
+    def betweenness(self, node, force=False):
+        node = as_node(node)
+        if (
+            force
+            or (not hasattr(self.model, "_betweenness"))
+            or getattr(self.model, "_last_step", 0) < self.now
+        ):
+            self.model._betweenness = nx.betweenness_centrality(self.G)
+            self.model._last_step = self.now
+        return self.model._betweenness[node]
+
+
+class TrainingAreaModel(FSM, Geo):
+    """
+    Settings:
+        training_influence
+
+        min_vulnerability
+
+    Requires TerroristSpreadModel.
+    """
+
+    def __init__(self, model=None, unique_id=0, state=()):
+        super().__init__(model=model, unique_id=unique_id, state=state)
+        self.training_influence = model.environment_params["training_influence"]
+        if "min_vulnerability" in model.environment_params:
+            self.min_vulnerability = model.environment_params["min_vulnerability"]
+        else:
+            self.min_vulnerability = 0
+
+    @default_state
+    @state
+    def terrorist(self):
+        for neighbour in self.get_neighboring_agents(agent_class=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, model=None, unique_id=0, state=()):
+        super().__init__(model=model, unique_id=unique_id, state=state)
+        self.haven_influence = model.environment_params["haven_influence"]
+        if "min_vulnerability" in model.environment_params:
+            self.min_vulnerability = model.environment_params["min_vulnerability"]
+        else:
+            self.min_vulnerability = 0
+        self.max_vulnerability = model.environment_params["max_vulnerability"]
+
+    def get_occupants(self, **kwargs):
+        return self.get_neighboring_agents(agent_class=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, model=None, unique_id=0, state=()):
+        super().__init__(model=model, unique_id=unique_id, state=state)
+
+        self.vision_range = model.environment_params["vision_range"]
+        self.sphere_influence = model.environment_params["sphere_influence"]
+        self.weight_social_distance = model.environment_params["weight_social_distance"]
+        self.weight_link_distance = model.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_class=TerroristNetworkModel
+                )
+            )
+            step_neighbours = set(
+                self.ego_search(
+                    self.sphere_influence,
+                    agent_class=TerroristNetworkModel,
+                    center=False,
+                )
+            )
+            neighbours = set(
+                agent.id
+                for agent in self.get_neighboring_agents(
+                    agent_class=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 self.random.random() < prob_new_interaction
+                ):
+                    self.add_edge(agent)
+                    break
+
+    def get_distance(self, target):
+        source_x, source_y = nx.get_node_attributes(self.G, "pos")[self.id]
+        target_x, target_y = nx.get_node_attributes(self.G, "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.G, self.id, target)
+        except nx.NetworkXNoPath:
+            return float("inf")

examples/terrorism/TerroristNetworkModel.yml

@@ -0,0 +1,62 @@
+name: TerroristNetworkModel_sim
+max_steps: 150 
+num_trials: 1
+model_params:
+  network_params:
+    generator: random_geometric_graph
+    radius: 0.2
+    # generator: geographical_threshold_graph
+    # theta: 20
+    n: 100
+  network_agents:
+    - agent_class: TerroristNetworkModel.TerroristNetworkModel
+      weight: 0.8
+      state:
+        id: civilian  # Civilians
+    - agent_class: TerroristNetworkModel.TerroristNetworkModel
+      weight: 0.1
+      state:
+        id: leader    # Leaders
+    - agent_class: TerroristNetworkModel.TrainingAreaModel
+      weight: 0.05
+      state:
+        id: terrorist # Terrorism
+    - agent_class: TerroristNetworkModel.HavenModel
+      weight: 0.05
+      state:
+        id: civilian  # Civilian
+
+  # 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/torvalds.yml

@@ -1,14 +1,15 @@
 ---
 name: torvalds_example
-max_time: 10
+max_steps: 10
 interval: 2
-agent_type: CounterModel
-default_state:
-  skill_level: 'beginner'
-network_params:
-  path: 'torvalds.edgelist'
-states:
-  Torvalds:
-    skill_level: 'God'
-  balkian:
-    skill_level: 'developer'
+model_params:
+  agent_class: CounterModel
+  default_state:
+    skill_level: 'beginner'
+  network_params:
+    path: 'torvalds.edgelist'
+  states:
+    Torvalds:
+      skill_level: 'God'
+    balkian:
+      skill_level: 'developer'

examples/tutorial/soil_tutorial.html

@@ -12327,14 +12327,14 @@ 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>
+                                     <span class="n">environment_agents</span><span class="o">=</span><span class="p">[{</span><span class="s1">&#39;agent_class&#39;</span><span class="p">:</span> <span class="n">NewsEnvironmentAgent</span><span class="p">,</span>
                                                          <span class="s1">&#39;state&#39;</span><span class="p">:</span> <span class="p">{</span>
                                                              <span class="s1">&#39;event_time&#39;</span><span class="p">:</span> <span class="n">EVENT_TIME</span>
                                                          <span class="p">}}],</span>
-                                     <span class="n">network_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">NewsSpread</span><span class="p">,</span>
+                                     <span class="n">network_agents</span><span class="o">=</span><span class="p">[{</span><span class="s1">&#39;agent_class&#39;</span><span class="p">:</span> <span class="n">NewsSpread</span><span class="p">,</span>
                                                       <span class="s1">&#39;weight&#39;</span><span class="p">:</span> <span class="mi">1</span><span class="p">}],</span>
                                      <span class="n">states</span><span class="o">=</span><span class="p">{</span><span class="mi">0</span><span class="p">:</span> <span class="p">{</span><span class="s1">&#39;has_tv&#39;</span><span class="p">:</span> <span class="kc">True</span><span class="p">}},</span>
                                      <span class="n">default_state</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;has_tv&#39;</span><span class="p">:</span> <span class="kc">False</span><span class="p">},</span>
@@ -12468,14 +12468,14 @@ For this demo, we will use a python dictionary:</p>
     <span class="p">},</span>
     <span class="s1">&#39;network_agents&#39;</span><span class="p">:</span> <span class="p">[</span>
         <span class="p">{</span>
-            <span class="s1">&#39;agent_type&#39;</span><span class="p">:</span> <span class="n">NewsSpread</span><span class="p">,</span>
+            <span class="s1">&#39;agent_class&#39;</span><span class="p">:</span> <span class="n">NewsSpread</span><span class="p">,</span>
             <span class="s1">&#39;weight&#39;</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span>
             <span class="s1">&#39;state&#39;</span><span class="p">:</span> <span class="p">{</span>
                 <span class="s1">&#39;has_tv&#39;</span><span class="p">:</span> <span class="kc">False</span>
             <span class="p">}</span>
         <span class="p">},</span>
         <span class="p">{</span>
-            <span class="s1">&#39;agent_type&#39;</span><span class="p">:</span> <span class="n">NewsSpread</span><span class="p">,</span>
+            <span class="s1">&#39;agent_class&#39;</span><span class="p">:</span> <span class="n">NewsSpread</span><span class="p">,</span>
             <span class="s1">&#39;weight&#39;</span><span class="p">:</span> <span class="mi">2</span><span class="p">,</span>
             <span class="s1">&#39;state&#39;</span><span class="p">:</span> <span class="p">{</span>
                 <span class="s1">&#39;has_tv&#39;</span><span class="p">:</span> <span class="kc">True</span>
@@ -12483,7 +12483,7 @@ For this demo, we will use a python dictionary:</p>
         <span class="p">}</span>
     <span class="p">],</span>
     <span class="s1">&#39;environment_agents&#39;</span><span class="p">:[</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>
+        <span class="p">{</span><span class="s1">&#39;agent_class&#39;</span><span class="p">:</span> <span class="n">NewsEnvironmentAgent</span><span class="p">,</span>
          <span class="s1">&#39;state&#39;</span><span class="p">:</span> <span class="p">{</span>
              <span class="s1">&#39;event_time&#39;</span><span class="p">:</span> <span class="mi">10</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 +1,10 @@
-nxsim
-simpy
-networkx>=2.0
+networkx>=2.5
 numpy
 matplotlib
-pyyaml
-pandas
+pyyaml>=5.1
+pandas>=1
+SALib>=1.3
+Jinja2
+Mesa>=1.1
+pydantic>=1.9
+sqlalchemy>=1.4

setup.cfg

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

setup.py

@@ -16,6 +16,12 @@ def parse_requirements(filename):
 
 install_reqs = parse_requirements("requirements.txt")
 test_reqs = parse_requirements("test-requirements.txt")
+extras_require={
+    'mesa': ['mesa>=0.8.9'],
+    'geo': ['scipy>=1.3'],
+    'web': ['tornado']
+}
+extras_require['all'] = [dep for package in extras_require.values() for dep in package]
 
 
 setup(
@@ -40,10 +46,13 @@ setup(
         'Operating System :: POSIX',
         'Programming Language :: Python :: 3'],
     install_requires=install_reqs,
+    extras_require=extras_require,
     tests_require=test_reqs,
     setup_requires=['pytest-runner', ],
+    pytest_plugins = ['pytest_profiling'],
     include_package_data=True,
     entry_points={
         'console_scripts':
-        ['soil = soil.__init__:main']
+        ['soil = soil.__main__:main',
+        'soil-web = soil.web.__init__:main']
     })

soil/VERSION

@@ -1 +1 @@
-0.11.1
+0.20.7

soil/__init__.py

@@ -1,8 +1,11 @@
+from __future__ import annotations
+
 import importlib
 import sys
 import os
-import pdb
 import logging
+import traceback
+from contextlib import contextmanager
 
 from .version import __version__
 
@@ -11,65 +14,225 @@ try:
 except NameError:
     basestring = str
 
-logging.basicConfig()
-
+from .agents import *
 from . import agents
-from . import simulation
-from . import environment
-from . import utils
-from . import analysis
+from .simulation import *
+from .environment import Environment
+from . import serialization
+from .utils import logger
+from .time import *
 
 
-def main():
+def main(
+    cfg="simulation.yml",
+    exporters=None,
+    parallel=None,
+    output="soil_output",
+    *,
+    do_run=False,
+    debug=False,
+    pdb=False,
+    **kwargs,
+):
     import argparse
     from . import simulation
 
-    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.')
-    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',
-                        help='Do not store the results of the simulation.')
-    parser.add_argument('--pdb', action='store_true',
-                        help='Use a pdb console in case of exception.')
-    parser.add_argument('--graph', '-g', action='store_true',
-                        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('--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.')
+    logger.info("Running SOIL version: {}".format(__version__))
+
+    parser = argparse.ArgumentParser(description="Run a SOIL simulation")
+    parser.add_argument(
+        "file",
+        type=str,
+        nargs="?",
+        default=cfg,
+        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",
+        help="Do not store the results of the simulation to disk, show in terminal instead.",
+    )
+    parser.add_argument(
+        "--pdb", action="store_true", help="Use a pdb console in case of exception."
+    )
+    parser.add_argument(
+        "--debug",
+        action="store_true",
+        help="Run a customized version of a pdb console to debug a simulation.",
+    )
+    parser.add_argument(
+        "--graph",
+        "-g",
+        action="store_true",
+        help="Dump each trial's network topology as a GEXF graph. Defaults to false.",
+    )
+    parser.add_argument(
+        "--csv",
+        action="store_true",
+        help="Dump all data collected in CSV format. Defaults to false.",
+    )
+    parser.add_argument("--level", type=str, help="Logging level")
+    parser.add_argument(
+        "--output",
+        "-o",
+        type=str,
+        default=output or "soil_output",
+        help="folder to write results to. It defaults to the current directory.",
+    )
+    if parallel is None:
+        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",
+        default=[],
+        help="Export environment and/or simulations using this exporter",
+    )
+
+    parser.add_argument(
+        "--only-convert",
+        "--convert",
+        action="store_true",
+        help="Do not run the simulation, only convert the configuration file(s) and output them.",
+    )
+
+    parser.add_argument(
+        "--set",
+        metavar="KEY=VALUE",
+        action="append",
+        help="Set a number of parameters that will be passed to the simulation."
+        "(do not put spaces before or after the = sign). "
+        "If a value contains spaces, you should define "
+        "it with double quotes: "
+        'foo="this is a sentence". Note that '
+        "values are always treated as strings.",
+    )
 
     args = parser.parse_args()
+    logger.setLevel(getattr(logging, (args.level or "INFO").upper()))
 
-    if args.module:
+    if args.version:
+        return
+
+    if parallel is None:
+        parallel = not args.synchronous
+
+    exporters = exporters or [
+        "default",
+    ]
+    for exp in args.exporter:
+        if exp not in exporters:
+            exporters.append(exp)
+    if args.csv:
+        exporters.append("csv")
+    if args.graph:
+        exporters.append("gexf")
+
+    if os.getcwd() not in sys.path:
         sys.path.append(os.getcwd())
+    if args.module:
         importlib.import_module(args.module)
+    if output is None:
+        output = args.output
 
-    logging.info('Loading config file: {}'.format(args.file, args.output))
+    logger.info("Loading config file: {}".format(args.file))
 
+    debug = debug or args.debug
+
+    if args.pdb or debug:
+        args.synchronous = True
+        os.environ["SOIL_POSTMORTEM"] = "true"
+
+    res = []
     try:
-        dump = []
-        if not args.dry_run:
-            if args.csv:
-                dump.append('csv')
-            if args.graph:
-                dump.append('gexf')
-        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)
+        exp_params = {}
+
+        if not os.path.exists(args.file):
+            logger.error("Please, input a valid file")
+            return
+
+        for sim in simulation.iter_from_config(
+            args.file,
+            dry_run=args.dry_run,
+            exporters=exporters,
+            parallel=parallel,
+            outdir=output,
+            exporter_params=exp_params,
+            **kwargs,
+        ):
+            if args.set:
+                for s in args.set:
+                    k, v = s.split("=", 1)[:2]
+                    v = eval(v)
+                    tail, *head = k.rsplit(".", 1)[::-1]
+                    target = sim
+                    if head:
+                        for part in head[0].split("."):
+                            try:
+                                target = getattr(target, part)
+                            except AttributeError:
+                                target = target[part]
+                    try:
+                        setattr(target, tail, v)
+                    except AttributeError:
+                        target[tail] = v
+
+            if args.only_convert:
+                print(sim.to_yaml())
+                continue
+            if do_run:
+                res.append(sim.run())
+            else:
+                print("not running")
+                res.append(sim)
+
     except Exception as ex:
         if args.pdb:
-            pdb.post_mortem()
+            from .debugging import post_mortem
+
+            print(traceback.format_exc())
+            post_mortem()
         else:
             raise
+    if debug:
+        from .debugging import set_trace
+
+        os.environ["SOIL_DEBUG"] = "true"
+        set_trace()
+    return res
 
 
-if __name__ == '__main__':
-    main()
+@contextmanager
+def easy(cfg, pdb=False, debug=False, **kwargs):
+    ex = None
+    try:
+        yield main(cfg, **kwargs)[0]
+    except Exception as e:
+        if os.environ.get("SOIL_POSTMORTEM"):
+            from .debugging import post_mortem
+
+            print(traceback.format_exc())
+            post_mortem()
+        ex = e
+    finally:
+        if ex:
+            raise ex
+
+
+if __name__ == "__main__":
+    main(do_run=True)

soil/__main__.py

@@ -1,4 +1,9 @@
-from . import main
+from . import main as init_main
 
-if __name__ == '__main__':
-    main()
+
+def main():
+    init_main(do_run=True)
+
+
+if __name__ == "__main__":
+    init_main(do_run=True)

soil/agents/BassModel.py

@@ -1,40 +1,31 @@
-import random
-from . import BaseAgent
+from . import FSM, state, default_state
 
 
-class BassModel(BaseAgent):
+class BassModel(FSM):
     """
     Settings:
         innovation_prob
         imitation_prob
     """
 
-    def __init__(self, environment, agent_id, state):
-        super().__init__(environment=environment, agent_id=agent_id, state=state)
-        env_params = environment.environment_params
-        self.state['sentimentCorrelation'] = 0
+    sentimentCorrelation = 0
 
     def step(self):
         self.behaviour()
 
-    def behaviour(self):
-        # Outside effects
-        if random.random() < self.state_params['innovation_prob']:
-            if self.state['id'] == 0:
-                self.state['id'] = 1
-                self.state['sentimentCorrelation'] = 1
-            else:
-                pass
-
-            return
-
-        # Imitation effects
-        if self.state['id'] == 0:
-            aware_neighbors = self.get_neighboring_agents(state_id=1)
+    @default_state
+    @state
+    def innovation(self):
+        if self.prob(self.innovation_prob):
+            self.sentimentCorrelation = 1
+            return self.aware
+        else:
+            aware_neighbors = self.get_neighboring_agents(state_id=self.aware.id)
             num_neighbors_aware = len(aware_neighbors)
-            if random.random() < (self.state_params['imitation_prob']*num_neighbors_aware):
-                self.state['id'] = 1
-                self.state['sentimentCorrelation'] = 1
+            if self.prob((self["imitation_prob"] * num_neighbors_aware)):
+                self.sentimentCorrelation = 1
+                return self.aware
 
-            else:
-                pass
+    @state
+    def aware(self):
+        self.die()

soil/agents/BigMarketModel.py

@@ -1,57 +1,59 @@
-import random
-from . import BaseAgent
+from . import FSM, state, default_state
 
 
-class BigMarketModel(BaseAgent):
+class BigMarketModel(FSM):
     """
     Settings:
         Names:
             enterprises [Array]
-            
+
             tweet_probability_enterprises [Array]
         Users:
             tweet_probability_users
-            
+
             tweet_relevant_probability
-            
+
             tweet_probability_about [Array]
-            
+
             sentiment_about [Array]
     """
 
-    def __init__(self, environment=None, agent_id=0, state=()):
-        super().__init__(environment=environment, agent_id=agent_id, state=state)
-        self.enterprises = environment.environment_params['enterprises']
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.enterprises = self.env.environment_params["enterprises"]
         self.type = ""
-        self.number_of_enterprises = len(environment.environment_params['enterprises'])
 
-        if self.id < self.number_of_enterprises:  # Enterprises
-            self.state['id'] = self.id
+        if self.id < len(self.enterprises):  # Enterprises
+            self.set_state(self.enterprise.id)
             self.type = "Enterprise"
-            self.tweet_probability = environment.environment_params['tweet_probability_enterprises'][self.id]
+            self.tweet_probability = environment.environment_params[
+                "tweet_probability_enterprises"
+            ][self.id]
         else:  # normal users
-            self.state['id'] = self.number_of_enterprises
             self.type = "User"
-            self.tweet_probability = environment.environment_params['tweet_probability_users']
-            self.tweet_relevant_probability = environment.environment_params['tweet_relevant_probability']
-            self.tweet_probability_about = environment.environment_params['tweet_probability_about']  # List
-            self.sentiment_about = environment.environment_params['sentiment_about']  # List
+            self.set_state(self.user.id)
+            self.tweet_probability = environment.environment_params[
+                "tweet_probability_users"
+            ]
+            self.tweet_relevant_probability = environment.environment_params[
+                "tweet_relevant_probability"
+            ]
+            self.tweet_probability_about = environment.environment_params[
+                "tweet_probability_about"
+            ]  # List
+            self.sentiment_about = environment.environment_params[
+                "sentiment_about"
+            ]  # List
 
-    def step(self):
+    @state
+    def enterprise(self):
 
-        if self.id < self.number_of_enterprises:  # Enterprise
-            self.enterpriseBehaviour()
-        else:  # Usuario
-            self.userBehaviour()
-            for i in range(self.number_of_enterprises):  # So that it never is set to 0 if there are not changes (logs)
-                self.attrs['sentiment_enterprise_%s'% self.enterprises[i]] = self.sentiment_about[i]
-
-    def enterpriseBehaviour(self):
-
-        if random.random() < self.tweet_probability:  # Tweets
-            aware_neighbors = self.get_neighboring_agents(state_id=self.number_of_enterprises)  # Nodes neighbour users
+        if self.random.random() < self.tweet_probability:  # Tweets
+            aware_neighbors = self.get_neighboring_agents(
+                state_id=self.number_of_enterprises
+            )  # Nodes neighbour users
             for x in aware_neighbors:
-                if random.uniform(0,10) < 5:
+                if self.random.uniform(0, 10) < 5:
                     x.sentiment_about[self.id] += 0.1  # Increments for enterprise
                 else:
                     x.sentiment_about[self.id] -= 0.1  # Decrements for enterprise
@@ -59,37 +61,49 @@ class BigMarketModel(BaseAgent):
                 # Establecemos limites
                 if x.sentiment_about[self.id] > 1:
                     x.sentiment_about[self.id] = 1
-                if x.sentiment_about[self.id]< -1:
+                if x.sentiment_about[self.id] < -1:
                     x.sentiment_about[self.id] = -1
 
-                x.attrs['sentiment_enterprise_%s'% self.enterprises[self.id]] = x.sentiment_about[self.id]
+                x.attrs[
+                    "sentiment_enterprise_%s" % self.enterprises[self.id]
+                ] = x.sentiment_about[self.id]
 
-    def userBehaviour(self):
-
-        if random.random() < self.tweet_probability:  # Tweets
-            if random.random() < self.tweet_relevant_probability:  # Tweets something relevant
+    @state
+    def user(self):
+        if self.random.random() < self.tweet_probability:  # Tweets
+            if (
+                self.random.random() < self.tweet_relevant_probability
+            ):  # Tweets something relevant
                 # Tweet probability per enterprise
-                for i in range(self.number_of_enterprises):
-                    random_num = random.random()
+                for i in range(len(self.enterprises)):
+                    random_num = self.random.random()
                     if random_num < self.tweet_probability_about[i]:
                         # The condition is fulfilled, sentiments are evaluated towards that enterprise
                         if self.sentiment_about[i] < 0:
                             # NEGATIVO
-                            self.userTweets("negative",i)
+                            self.userTweets("negative", i)
                         elif self.sentiment_about[i] == 0:
                             # NEUTRO
                             pass
                         else:
                             # POSITIVO
-                            self.userTweets("positive",i)
+                            self.userTweets("positive", i)
+        for i in range(
+            len(self.enterprises)
+        ):  # So that it never is set to 0 if there are not changes (logs)
+            self.attrs[
+                "sentiment_enterprise_%s" % self.enterprises[i]
+            ] = self.sentiment_about[i]
 
-    def userTweets(self,sentiment,enterprise):
-        aware_neighbors = self.get_neighboring_agents(state_id=self.number_of_enterprises)  # Nodes neighbours users
+    def userTweets(self, sentiment, enterprise):
+        aware_neighbors = self.get_neighboring_agents(
+            state_id=self.number_of_enterprises
+        )  # Nodes neighbours users
         for x in aware_neighbors:
             if sentiment == "positive":
-                x.sentiment_about[enterprise] +=0.003
+                x.sentiment_about[enterprise] += 0.003
             elif sentiment == "negative":
-                x.sentiment_about[enterprise] -=0.003
+                x.sentiment_about[enterprise] -= 0.003
             else:
                 pass
 
@@ -99,4 +113,6 @@ class BigMarketModel(BaseAgent):
             if x.sentiment_about[enterprise] < -1:
                 x.sentiment_about[enterprise] = -1
 
-            x.attrs['sentiment_enterprise_%s'% self.enterprises[enterprise]] = x.sentiment_about[enterprise]
+            x.attrs[
+                "sentiment_enterprise_%s" % self.enterprises[enterprise]
+            ] = x.sentiment_about[enterprise]

soil/agents/CounterModel.py

@@ -1,32 +1,40 @@
-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.
     """
 
+    times = 0
+    neighbors = 0
+    total = 0
+
     def step(self):
         # Outside effects
-        total = len(list(self.get_all_agents()))
+        total = len(list(self.model.schedule._agents))
         neighbors = len(list(self.get_neighboring_agents()))
-        self['times'] = self.get('times', 0) + 1
-        self['neighbors'] = neighbors
-        self['total'] = total
+        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.
     """
 
+    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.debug('Running for step: {}. Total: {}'.format(self.now, total))
+        self["neighbors"] += neighbors
+        total = len(list(self.model.schedule.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/Geo.py

@@ -0,0 +1,21 @@
+from scipy.spatial import cKDTree as KDTree
+import networkx as nx
+from . import NetworkAgent, as_node
+
+
+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)]

soil/agents/IndependentCascadeModel.py

@@ -1,4 +1,3 @@
-import random
 from . import BaseAgent
 
 
@@ -10,12 +9,12 @@ class IndependentCascadeModel(BaseAgent):
         imitation_prob
     """
 
-    def __init__(self, environment=None, agent_id=0, state=()):
-        super().__init__(environment=environment, agent_id=agent_id, state=state)
-        self.innovation_prob = environment.environment_params['innovation_prob']
-        self.imitation_prob = environment.environment_params['imitation_prob']
-        self.state['time_awareness'] = 0
-        self.state['sentimentCorrelation'] = 0
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.innovation_prob = self.env.environment_params["innovation_prob"]
+        self.imitation_prob = self.env.environment_params["imitation_prob"]
+        self.state["time_awareness"] = 0
+        self.state["sentimentCorrelation"] = 0
 
     def step(self):
         self.behaviour()
@@ -23,26 +22,28 @@ class IndependentCascadeModel(BaseAgent):
     def behaviour(self):
         aware_neighbors_1_time_step = []
         # Outside effects
-        if random.random() < self.innovation_prob:
-            if self.state['id'] == 0:
-                self.state['id'] = 1
-                self.state['sentimentCorrelation'] = 1
-                self.state['time_awareness'] = self.env.now  # To know when they have been infected
+        if self.prob(self.innovation_prob):
+            if self.state["id"] == 0:
+                self.state["id"] = 1
+                self.state["sentimentCorrelation"] = 1
+                self.state[
+                    "time_awareness"
+                ] = self.env.now  # To know when they have been infected
             else:
                 pass
 
             return
 
         # Imitation effects
-        if self.state['id'] == 0:
+        if self.state["id"] == 0:
             aware_neighbors = self.get_neighboring_agents(state_id=1)
             for x in aware_neighbors:
-                if x.state['time_awareness'] == (self.env.now-1):
+                if x.state["time_awareness"] == (self.env.now - 1):
                     aware_neighbors_1_time_step.append(x)
             num_neighbors_aware = len(aware_neighbors_1_time_step)
-            if random.random() < (self.imitation_prob*num_neighbors_aware):
-                self.state['id'] = 1
-                self.state['sentimentCorrelation'] = 1
+            if self.prob(self.imitation_prob * num_neighbors_aware):
+                self.state["id"] = 1
+                self.state["sentimentCorrelation"] = 1
             else:
                 pass
 

soil/agents/ModelM2.py

@@ -1,4 +1,3 @@
-import random
 import numpy as np
 from . import BaseAgent
 
@@ -21,36 +20,52 @@ class SpreadModelM2(BaseAgent):
         prob_generate_anti_rumor
     """
 
-    def __init__(self, environment=None, agent_id=0, state=()):
-        super().__init__(environment=environment, agent_id=agent_id, state=state)
+    def __init__(self, model=None, unique_id=0, state=()):
+        super().__init__(model=environment, unique_id=unique_id, state=state)
 
-        self.prob_neutral_making_denier = np.random.normal(environment.environment_params['prob_neutral_making_denier'],
-                                                           environment.environment_params['standard_variance'])
+        # Use a single generator with the same seed as `self.random`
+        random = np.random.default_rng(seed=self._seed)
+        self.prob_neutral_making_denier = random.normal(
+            environment.environment_params["prob_neutral_making_denier"],
+            environment.environment_params["standard_variance"],
+        )
 
-        self.prob_infect = np.random.normal(environment.environment_params['prob_infect'],
-                                            environment.environment_params['standard_variance'])
+        self.prob_infect = random.normal(
+            environment.environment_params["prob_infect"],
+            environment.environment_params["standard_variance"],
+        )
 
-        self.prob_cured_healing_infected = np.random.normal(environment.environment_params['prob_cured_healing_infected'],
-                                                            environment.environment_params['standard_variance'])
-        self.prob_cured_vaccinate_neutral = np.random.normal(environment.environment_params['prob_cured_vaccinate_neutral'],
-                                                             environment.environment_params['standard_variance'])
+        self.prob_cured_healing_infected = random.normal(
+            environment.environment_params["prob_cured_healing_infected"],
+            environment.environment_params["standard_variance"],
+        )
+        self.prob_cured_vaccinate_neutral = random.normal(
+            environment.environment_params["prob_cured_vaccinate_neutral"],
+            environment.environment_params["standard_variance"],
+        )
 
-        self.prob_vaccinated_healing_infected = np.random.normal(environment.environment_params['prob_vaccinated_healing_infected'],
-                                                                 environment.environment_params['standard_variance'])
-        self.prob_vaccinated_vaccinate_neutral = np.random.normal(environment.environment_params['prob_vaccinated_vaccinate_neutral'],
-                                                                  environment.environment_params['standard_variance'])
-        self.prob_generate_anti_rumor = np.random.normal(environment.environment_params['prob_generate_anti_rumor'],
-                                                         environment.environment_params['standard_variance'])
+        self.prob_vaccinated_healing_infected = random.normal(
+            environment.environment_params["prob_vaccinated_healing_infected"],
+            environment.environment_params["standard_variance"],
+        )
+        self.prob_vaccinated_vaccinate_neutral = random.normal(
+            environment.environment_params["prob_vaccinated_vaccinate_neutral"],
+            environment.environment_params["standard_variance"],
+        )
+        self.prob_generate_anti_rumor = random.normal(
+            environment.environment_params["prob_generate_anti_rumor"],
+            environment.environment_params["standard_variance"],
+        )
 
     def step(self):
 
-        if self.state['id'] == 0:  # Neutral
+        if self.state["id"] == 0:  # Neutral
             self.neutral_behaviour()
-        elif self.state['id'] == 1:  # Infected
+        elif self.state["id"] == 1:  # Infected
             self.infected_behaviour()
-        elif self.state['id'] == 2:  # Cured
+        elif self.state["id"] == 2:  # Cured
             self.cured_behaviour()
-        elif self.state['id'] == 3:  # Vaccinated
+        elif self.state["id"] == 3:  # Vaccinated
             self.vaccinated_behaviour()
 
     def neutral_behaviour(self):
@@ -58,50 +73,50 @@ class SpreadModelM2(BaseAgent):
         # Infected
         infected_neighbors = self.get_neighboring_agents(state_id=1)
         if len(infected_neighbors) > 0:
-            if random.random() < self.prob_neutral_making_denier:
-                self.state['id'] = 3   # Vaccinated making denier
+            if self.prob(self.prob_neutral_making_denier):
+                self.state["id"] = 3  # Vaccinated making denier
 
     def infected_behaviour(self):
 
         # Neutral
         neutral_neighbors = self.get_neighboring_agents(state_id=0)
         for neighbor in neutral_neighbors:
-            if random.random() < self.prob_infect:
-                neighbor.state['id'] = 1  # Infected
+            if self.prob(self.prob_infect):
+                neighbor.state["id"] = 1  # Infected
 
     def cured_behaviour(self):
 
         # Vaccinate
         neutral_neighbors = self.get_neighboring_agents(state_id=0)
         for neighbor in neutral_neighbors:
-            if random.random() < self.prob_cured_vaccinate_neutral:
-                neighbor.state['id'] = 3  # Vaccinated
+            if self.prob(self.prob_cured_vaccinate_neutral):
+                neighbor.state["id"] = 3  # Vaccinated
 
         # Cure
         infected_neighbors = self.get_neighboring_agents(state_id=1)
         for neighbor in infected_neighbors:
-            if random.random() < self.prob_cured_healing_infected:
-                neighbor.state['id'] = 2  # Cured
+            if self.prob(self.prob_cured_healing_infected):
+                neighbor.state["id"] = 2  # Cured
 
     def vaccinated_behaviour(self):
 
         # Cure
         infected_neighbors = self.get_neighboring_agents(state_id=1)
         for neighbor in infected_neighbors:
-            if random.random() < self.prob_cured_healing_infected:
-                neighbor.state['id'] = 2  # Cured
+            if self.prob(self.prob_cured_healing_infected):
+                neighbor.state["id"] = 2  # Cured
 
         # Vaccinate
         neutral_neighbors = self.get_neighboring_agents(state_id=0)
         for neighbor in neutral_neighbors:
-            if random.random() < self.prob_cured_vaccinate_neutral:
-                neighbor.state['id'] = 3  # Vaccinated
+            if self.prob(self.prob_cured_vaccinate_neutral):
+                neighbor.state["id"] = 3  # Vaccinated
 
         # Generate anti-rumor
         infected_neighbors_2 = self.get_neighboring_agents(state_id=1)
         for neighbor in infected_neighbors_2:
-            if random.random() < self.prob_generate_anti_rumor:
-                neighbor.state['id'] = 2  # Cured
+            if self.prob(self.prob_generate_anti_rumor):
+                neighbor.state["id"] = 2  # Cured
 
 
 class ControlModelM2(BaseAgent):
@@ -110,133 +125,146 @@ class ControlModelM2(BaseAgent):
         prob_neutral_making_denier
 
         prob_infect
-        
+
         prob_cured_healing_infected
-        
+
         prob_cured_vaccinate_neutral
-        
+
         prob_vaccinated_healing_infected
-        
+
         prob_vaccinated_vaccinate_neutral
-        
+
         prob_generate_anti_rumor
     """
 
+    def __init__(self, model=None, unique_id=0, state=()):
+        super().__init__(model=environment, unique_id=unique_id, state=state)
 
-    def __init__(self, environment=None, agent_id=0, state=()):
-        super().__init__(environment=environment, agent_id=agent_id, state=state)
+        self.prob_neutral_making_denier = np.random.normal(
+            environment.environment_params["prob_neutral_making_denier"],
+            environment.environment_params["standard_variance"],
+        )
 
-        self.prob_neutral_making_denier = np.random.normal(environment.environment_params['prob_neutral_making_denier'],
-                                                           environment.environment_params['standard_variance'])
+        self.prob_infect = np.random.normal(
+            environment.environment_params["prob_infect"],
+            environment.environment_params["standard_variance"],
+        )
 
-        self.prob_infect = np.random.normal(environment.environment_params['prob_infect'],
-                                            environment.environment_params['standard_variance'])
+        self.prob_cured_healing_infected = np.random.normal(
+            environment.environment_params["prob_cured_healing_infected"],
+            environment.environment_params["standard_variance"],
+        )
+        self.prob_cured_vaccinate_neutral = np.random.normal(
+            environment.environment_params["prob_cured_vaccinate_neutral"],
+            environment.environment_params["standard_variance"],
+        )
 
-        self.prob_cured_healing_infected = np.random.normal(environment.environment_params['prob_cured_healing_infected'],
-                                                            environment.environment_params['standard_variance'])
-        self.prob_cured_vaccinate_neutral = np.random.normal(environment.environment_params['prob_cured_vaccinate_neutral'],
-                                                             environment.environment_params['standard_variance'])
-
-        self.prob_vaccinated_healing_infected = np.random.normal(environment.environment_params['prob_vaccinated_healing_infected'],
-                                                                 environment.environment_params['standard_variance'])
-        self.prob_vaccinated_vaccinate_neutral = np.random.normal(environment.environment_params['prob_vaccinated_vaccinate_neutral'],
-                                                                  environment.environment_params['standard_variance'])
-        self.prob_generate_anti_rumor = np.random.normal(environment.environment_params['prob_generate_anti_rumor'],
-                                                         environment.environment_params['standard_variance'])
+        self.prob_vaccinated_healing_infected = np.random.normal(
+            environment.environment_params["prob_vaccinated_healing_infected"],
+            environment.environment_params["standard_variance"],
+        )
+        self.prob_vaccinated_vaccinate_neutral = np.random.normal(
+            environment.environment_params["prob_vaccinated_vaccinate_neutral"],
+            environment.environment_params["standard_variance"],
+        )
+        self.prob_generate_anti_rumor = np.random.normal(
+            environment.environment_params["prob_generate_anti_rumor"],
+            environment.environment_params["standard_variance"],
+        )
 
     def step(self):
 
-        if self.state['id'] == 0:  # Neutral
+        if self.state["id"] == 0:  # Neutral
             self.neutral_behaviour()
-        elif self.state['id'] == 1:  # Infected
+        elif self.state["id"] == 1:  # Infected
             self.infected_behaviour()
-        elif self.state['id'] == 2:  # Cured
+        elif self.state["id"] == 2:  # Cured
             self.cured_behaviour()
-        elif self.state['id'] == 3:  # Vaccinated
+        elif self.state["id"] == 3:  # Vaccinated
             self.vaccinated_behaviour()
-        elif self.state['id'] == 4:  # Beacon-off
+        elif self.state["id"] == 4:  # Beacon-off
             self.beacon_off_behaviour()
-        elif self.state['id'] == 5:  # Beacon-on
+        elif self.state["id"] == 5:  # Beacon-on
             self.beacon_on_behaviour()
 
     def neutral_behaviour(self):
-        self.state['visible'] = False
+        self.state["visible"] = False
 
         # Infected
         infected_neighbors = self.get_neighboring_agents(state_id=1)
         if len(infected_neighbors) > 0:
-            if random.random() < self.prob_neutral_making_denier:
-                self.state['id'] = 3   # Vaccinated making denier
+            if self.random(self.prob_neutral_making_denier):
+                self.state["id"] = 3  # Vaccinated making denier
 
     def infected_behaviour(self):
 
         # Neutral
         neutral_neighbors = self.get_neighboring_agents(state_id=0)
         for neighbor in neutral_neighbors:
-            if random.random() < self.prob_infect:
-                neighbor.state['id'] = 1  # Infected
-        self.state['visible'] = False
+            if self.prob(self.prob_infect):
+                neighbor.state["id"] = 1  # Infected
+        self.state["visible"] = False
 
     def cured_behaviour(self):
 
-        self.state['visible'] = True
+        self.state["visible"] = True
         # Vaccinate
         neutral_neighbors = self.get_neighboring_agents(state_id=0)
         for neighbor in neutral_neighbors:
-            if random.random() < self.prob_cured_vaccinate_neutral:
-                neighbor.state['id'] = 3  # Vaccinated
+            if self.prob(self.prob_cured_vaccinate_neutral):
+                neighbor.state["id"] = 3  # Vaccinated
 
         # Cure
         infected_neighbors = self.get_neighboring_agents(state_id=1)
         for neighbor in infected_neighbors:
-            if random.random() < self.prob_cured_healing_infected:
-                neighbor.state['id'] = 2  # Cured
+            if self.prob(self.prob_cured_healing_infected):
+                neighbor.state["id"] = 2  # Cured
 
     def vaccinated_behaviour(self):
-        self.state['visible'] = True
+        self.state["visible"] = True
 
         # Cure
         infected_neighbors = self.get_neighboring_agents(state_id=1)
         for neighbor in infected_neighbors:
-            if random.random() < self.prob_cured_healing_infected:
-                neighbor.state['id'] = 2  # Cured
+            if self.prob(self.prob_cured_healing_infected):
+                neighbor.state["id"] = 2  # Cured
 
         # Vaccinate
         neutral_neighbors = self.get_neighboring_agents(state_id=0)
         for neighbor in neutral_neighbors:
-            if random.random() < self.prob_cured_vaccinate_neutral:
-                neighbor.state['id'] = 3  # Vaccinated
+            if self.prob(self.prob_cured_vaccinate_neutral):
+                neighbor.state["id"] = 3  # Vaccinated
 
         # Generate anti-rumor
         infected_neighbors_2 = self.get_neighboring_agents(state_id=1)
         for neighbor in infected_neighbors_2:
-            if random.random() < self.prob_generate_anti_rumor:
-                neighbor.state['id'] = 2  # Cured
+            if self.prob(self.prob_generate_anti_rumor):
+                neighbor.state["id"] = 2  # Cured
 
     def beacon_off_behaviour(self):
-        self.state['visible'] = False
+        self.state["visible"] = False
         infected_neighbors = self.get_neighboring_agents(state_id=1)
         if len(infected_neighbors) > 0:
-            self.state['id'] == 5  # Beacon on
+            self.state["id"] == 5  # Beacon on
 
     def beacon_on_behaviour(self):
-        self.state['visible'] = False
+        self.state["visible"] = False
         # Cure (M2 feature added)
         infected_neighbors = self.get_neighboring_agents(state_id=1)
         for neighbor in infected_neighbors:
-            if random.random() < self.prob_generate_anti_rumor:
-                neighbor.state['id'] = 2  # Cured
+            if self.prob(self.prob_generate_anti_rumor):
+                neighbor.state["id"] = 2  # Cured
             neutral_neighbors_infected = neighbor.get_neighboring_agents(state_id=0)
             for neighbor in neutral_neighbors_infected:
-                if random.random() < self.prob_generate_anti_rumor:
-                    neighbor.state['id'] = 3  # Vaccinated
+                if self.prob(self.prob_generate_anti_rumor):
+                    neighbor.state["id"] = 3  # Vaccinated
             infected_neighbors_infected = neighbor.get_neighboring_agents(state_id=1)
             for neighbor in infected_neighbors_infected:
-                if random.random() < self.prob_generate_anti_rumor:
-                    neighbor.state['id'] = 2  # Cured
+                if self.prob(self.prob_generate_anti_rumor):
+                    neighbor.state["id"] = 2  # Cured
 
         # Vaccinate
         neutral_neighbors = self.get_neighboring_agents(state_id=0)
         for neighbor in neutral_neighbors:
-            if random.random() < self.prob_cured_vaccinate_neutral:
-                neighbor.state['id'] = 3  # Vaccinated
+            if self.prob(self.prob_cured_vaccinate_neutral):
+                neighbor.state["id"] = 3  # Vaccinated

soil/agents/SISaModel.py

@@ -1,4 +1,3 @@
-import random
 import numpy as np
 from . import FSM, state
 
@@ -7,87 +6,99 @@ class SISaModel(FSM):
     """
     Settings:
         neutral_discontent_spon_prob
-        
+
         neutral_discontent_infected_prob
-        
-        neutral_content_spong_prob
-        
+
+        neutral_content_spon_prob
+
         neutral_content_infected_prob
-        
+
         discontent_neutral
-        
+
         discontent_content
-        
+
         variance_d_c
-        
+
         content_discontent
-        
+
         variance_c_d
-        
+
         content_neutral
-        
+
         standard_variance
     """
 
-    def __init__(self, environment=None, agent_id=0, state=()):
-        super().__init__(environment=environment, agent_id=agent_id, state=state)
+    def __init__(self, environment, unique_id=0, state=()):
+        super().__init__(model=environment, unique_id=unique_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'])
+        random = np.random.default_rng(seed=self._seed)
 
-        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.neutral_discontent_spon_prob = random.normal(
+            self.env["neutral_discontent_spon_prob"], self.env["standard_variance"]
+        )
+        self.neutral_discontent_infected_prob = random.normal(
+            self.env["neutral_discontent_infected_prob"], self.env["standard_variance"]
+        )
+        self.neutral_content_spon_prob = random.normal(
+            self.env["neutral_content_spon_prob"], self.env["standard_variance"]
+        )
+        self.neutral_content_infected_prob = random.normal(
+            self.env["neutral_content_infected_prob"], self.env["standard_variance"]
+        )
 
-        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.discontent_neutral = random.normal(
+            self.env["discontent_neutral"], self.env["standard_variance"]
+        )
+        self.discontent_content = random.normal(
+            self.env["discontent_content"], self.env["variance_d_c"]
+        )
+
+        self.content_discontent = random.normal(
+            self.env["content_discontent"], self.env["variance_c_d"]
+        )
+        self.content_neutral = random.normal(
+            self.env["content_neutral"], self.env["standard_variance"]
+        )
 
     @state
     def neutral(self):
         # Spontaneous effects
-        if random.random() < self.neutral_discontent_spon_prob:
+        if self.prob(self.neutral_discontent_spon_prob):
             return self.discontent
-        if random.random() < self.neutral_content_spon_prob:
+        if self.prob(self.neutral_content_spon_prob):
             return self.content
 
         # Infected
         discontent_neighbors = self.count_neighboring_agents(state_id=self.discontent)
-        if random.random() < discontent_neighbors * self.neutral_discontent_infected_prob:
+        if self.prob(scontent_neighbors * self.neutral_discontent_infected_prob):
             return self.discontent
         content_neighbors = self.count_neighboring_agents(state_id=self.content.id)
-        if random.random() < content_neighbors * self.neutral_content_infected_prob:
+        if self.prob(s * self.neutral_content_infected_prob):
             return self.content
         return self.neutral
 
     @state
     def discontent(self):
         # Healing
-        if random.random() < self.discontent_neutral:
+        if self.prob(self.discontent_neutral):
             return self.neutral
 
         # Superinfected
         content_neighbors = self.count_neighboring_agents(state_id=self.content.id)
-        if random.random() < content_neighbors * self.discontent_content:
+        if self.prob(s * self.discontent_content):
             return self.content
         return self.discontent
 
     @state
     def content(self):
         # Healing
-        if random.random() < self.content_neutral:
+        if self.prob(self.content_neutral):
             return self.neutral
 
         # Superinfected
-        discontent_neighbors = self.count_neighboring_agents(state_id=self.discontent.id)
-        if random.random() < discontent_neighbors * self.content_discontent:
+        discontent_neighbors = self.count_neighboring_agents(
+            state_id=self.discontent.id
+        )
+        if self.prob(scontent_neighbors * self.content_discontent):
             self.discontent
         return self.content

soil/agents/SentimentCorrelationModel.py

@@ -1,4 +1,3 @@
-import random
 from . import BaseAgent
 
 
@@ -6,27 +5,31 @@ class SentimentCorrelationModel(BaseAgent):
     """
     Settings:
         outside_effects_prob
-        
+
         anger_prob
-        
+
         joy_prob
-        
+
         sadness_prob
-        
+
         disgust_prob
     """
 
-    def __init__(self, environment=None, 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']
-        self.joy_prob = environment.environment_params['joy_prob']
-        self.sadness_prob = environment.environment_params['sadness_prob']
-        self.disgust_prob = environment.environment_params['disgust_prob']
-        self.state['time_awareness'] = []
+    def __init__(self, environment, unique_id=0, state=()):
+        super().__init__(model=environment, unique_id=unique_id, state=state)
+        self.outside_effects_prob = environment.environment_params[
+            "outside_effects_prob"
+        ]
+        self.anger_prob = environment.environment_params["anger_prob"]
+        self.joy_prob = environment.environment_params["joy_prob"]
+        self.sadness_prob = environment.environment_params["sadness_prob"]
+        self.disgust_prob = environment.environment_params["disgust_prob"]
+        self.state["time_awareness"] = []
         for i in range(4):  # In this model we have 4 sentiments
-            self.state['time_awareness'].append(0)  # 0-> Anger, 1-> joy, 2->sadness, 3 -> disgust
-        self.state['sentimentCorrelation'] = 0
+            self.state["time_awareness"].append(
+                0
+            )  # 0-> Anger, 1-> joy, 2->sadness, 3 -> disgust
+        self.state["sentimentCorrelation"] = 0
 
     def step(self):
         self.behaviour()
@@ -40,63 +43,73 @@ class SentimentCorrelationModel(BaseAgent):
 
         angry_neighbors = self.get_neighboring_agents(state_id=1)
         for x in angry_neighbors:
-            if x.state['time_awareness'][0] > (self.env.now-500):
+            if x.state["time_awareness"][0] > (self.env.now - 500):
                 angry_neighbors_1_time_step.append(x)
         num_neighbors_angry = len(angry_neighbors_1_time_step)
 
         joyful_neighbors = self.get_neighboring_agents(state_id=2)
         for x in joyful_neighbors:
-            if x.state['time_awareness'][1] > (self.env.now-500):
+            if x.state["time_awareness"][1] > (self.env.now - 500):
                 joyful_neighbors_1_time_step.append(x)
         num_neighbors_joyful = len(joyful_neighbors_1_time_step)
 
         sad_neighbors = self.get_neighboring_agents(state_id=3)
         for x in sad_neighbors:
-            if x.state['time_awareness'][2] > (self.env.now-500):
+            if x.state["time_awareness"][2] > (self.env.now - 500):
                 sad_neighbors_1_time_step.append(x)
         num_neighbors_sad = len(sad_neighbors_1_time_step)
 
         disgusted_neighbors = self.get_neighboring_agents(state_id=4)
         for x in disgusted_neighbors:
-            if x.state['time_awareness'][3] > (self.env.now-500):
+            if x.state["time_awareness"][3] > (self.env.now - 500):
                 disgusted_neighbors_1_time_step.append(x)
         num_neighbors_disgusted = len(disgusted_neighbors_1_time_step)
 
-        anger_prob = self.anger_prob+(len(angry_neighbors_1_time_step)*self.anger_prob)
-        joy_prob = self.joy_prob+(len(joyful_neighbors_1_time_step)*self.joy_prob)
-        sadness_prob = self.sadness_prob+(len(sad_neighbors_1_time_step)*self.sadness_prob)
-        disgust_prob = self.disgust_prob+(len(disgusted_neighbors_1_time_step)*self.disgust_prob)
+        anger_prob = self.anger_prob + (
+            len(angry_neighbors_1_time_step) * self.anger_prob
+        )
+        joy_prob = self.joy_prob + (len(joyful_neighbors_1_time_step) * self.joy_prob)
+        sadness_prob = self.sadness_prob + (
+            len(sad_neighbors_1_time_step) * self.sadness_prob
+        )
+        disgust_prob = self.disgust_prob + (
+            len(disgusted_neighbors_1_time_step) * self.disgust_prob
+        )
         outside_effects_prob = self.outside_effects_prob
 
-        num = random.random()
+        num = self.random.random()
 
-        if num<outside_effects_prob:
-            self.state['id'] = random.randint(1, 4)
+        if num < outside_effects_prob:
+            self.state["id"] = self.random.randint(1, 4)
 
-            self.state['sentimentCorrelation'] = self.state['id'] # It is stored when it has been infected for the dynamic network
-            self.state['time_awareness'][self.state['id']-1] = self.env.now
-            self.state['sentiment'] = self.state['id']
+            self.state["sentimentCorrelation"] = self.state[
+                "id"
+            ]  # It is stored when it has been infected for the dynamic network
+            self.state["time_awareness"][self.state["id"] - 1] = self.env.now
+            self.state["sentiment"] = self.state["id"]
 
+        if num < anger_prob:
 
-        if(num<anger_prob):
+            self.state["id"] = 1
+            self.state["sentimentCorrelation"] = 1
+            self.state["time_awareness"][self.state["id"] - 1] = self.env.now
+        elif num < joy_prob + anger_prob and num > anger_prob:
 
-            self.state['id'] = 1
-            self.state['sentimentCorrelation'] = 1
-            self.state['time_awareness'][self.state['id']-1] = self.env.now
-        elif (num<joy_prob+anger_prob and num>anger_prob):
+            self.state["id"] = 2
+            self.state["sentimentCorrelation"] = 2
+            self.state["time_awareness"][self.state["id"] - 1] = self.env.now
+        elif num < sadness_prob + anger_prob + joy_prob and num > joy_prob + anger_prob:
 
-            self.state['id'] = 2
-            self.state['sentimentCorrelation'] = 2
-            self.state['time_awareness'][self.state['id']-1] = self.env.now
-        elif (num<sadness_prob+anger_prob+joy_prob and num>joy_prob+anger_prob):
+            self.state["id"] = 3
+            self.state["sentimentCorrelation"] = 3
+            self.state["time_awareness"][self.state["id"] - 1] = self.env.now
+        elif (
+            num < disgust_prob + sadness_prob + anger_prob + joy_prob
+            and num > sadness_prob + anger_prob + joy_prob
+        ):
 
-            self.state['id'] = 3
-            self.state['sentimentCorrelation'] = 3
-            self.state['time_awareness'][self.state['id']-1] = self.env.now
-        elif (num<disgust_prob+sadness_prob+anger_prob+joy_prob and num>sadness_prob+anger_prob+joy_prob):
+            self.state["id"] = 4
+            self.state["sentimentCorrelation"] = 4
+            self.state["time_awareness"][self.state["id"] - 1] = self.env.now
 
-            self.state['id'] = 4
-            self.state['sentimentCorrelation'] = 4
-            self.state['time_awareness'][self.state['id']-1] = self.env.now
-
-        self.state['sentiment'] = self.state['id']
+        self.state["sentiment"] = self.state["id"]

soil/analysis.py

@@ -1,166 +0,0 @@
-import pandas as pd
-
-import glob
-import yaml
-from os.path import join
-
-from . import utils, history
-
-
-def read_data(*args, group=False, **kwargs):
-    iterable = _read_data(*args, **kwargs)
-    if group:
-        return group_trials(iterable)
-    else:
-        return list(iterable)
-
-
-def _read_data(pattern, *args, from_csv=False, process_args=None, **kwargs):
-    if not process_args:
-        process_args = {}
-    for folder in glob.glob(pattern):
-        config_file = glob.glob(join(folder, '*.yml'))[0]
-        config = yaml.load(open(config_file))
-        df = None
-        if from_csv:
-            for trial_data in sorted(glob.glob(join(folder,
-                                                    '*.environment.csv'))):
-                df = read_csv(trial_data, **kwargs)
-                yield config_file, df, config
-        else:
-            for trial_data in sorted(glob.glob(join(folder, '*.db.sqlite'))):
-                df = read_sql(trial_data, **kwargs)
-                yield config_file, df, config
-
-
-def read_sql(db, *args, **kwargs):
-    h = history.History(db, backup=False)
-    df = h.read_sql(*args, **kwargs)
-    return df
-
-
-def read_csv(filename, keys=None, convert_types=False, **kwargs):
-    '''
-    Read a CSV in canonical form: ::
-
-        <agent_id, t_step, key, value, value_type>
-
-    '''
-    df = pd.read_csv(filename)
-    if convert_types:
-        df = convert_types_slow(df)
-    if keys:
-        df = df[df['key'].isin(keys)]
-    df = process_one(df)
-    return df
-
-
-def convert_row(row):
-    row['value'] = utils.convert(row['value'], row['value_type'])
-    return row
-
-
-def convert_types_slow(df):
-    '''This is a slow operation.'''
-    dtypes = get_types(df)
-    for k, v in dtypes.items():
-        t = df[df['key']==k]
-        t['value'] = t['value'].astype(v)
-    df = df.apply(convert_row, axis=1)
-    return df
-
-def split_df(df):
-    '''
-    Split a dataframe in two dataframes: one with the history of agents,
-    and one with the environment history
-    '''
-    envmask = (df['agent_id'] == 'env')
-    n_env = envmask.sum()
-    if n_env == len(df):
-        return df, None
-    elif n_env == 0:
-        return None, df
-    agents, env = [x for _, x in df.groupby(envmask)]
-    return env, agents
-
-
-def process(df, **kwargs):
-    '''
-    Process a dataframe in canonical form ``(t_step, agent_id, key, value, value_type)`` into
-    two dataframes with a column per key: one with the history of the agents, and one for the
-    history of the environment.
-    '''
-    env, agents = split_df(df)
-    return process_one(env, **kwargs), process_one(agents, **kwargs)
-
-
-def get_types(df):
-    dtypes = df.groupby(by=['key'])['value_type'].unique()
-    return {k:v[0] for k,v in dtypes.iteritems()}
-
-
-def process_one(df, *keys, columns=['key', 'agent_id'], values='value',
-                fill=True, index=['t_step',],
-                aggfunc='first', **kwargs):
-    '''
-    Process a dataframe in canonical form ``(t_step, agent_id, key, value, value_type)`` into
-    a dataframe with a column per key
-    '''
-    if df is None:
-        return df
-    if keys:
-        df = df[df['key'].isin(keys)]
-
-    df = df.pivot_table(values=values, index=index, columns=columns,
-                        aggfunc=aggfunc, **kwargs)
-    if fill:
-        df = fillna(df)
-    return df
-
-
-def get_count(df, *keys):
-    if 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)
-        for value, series in g.iteritems():
-            counts[key, value] = series
-    counts.columns = pd.MultiIndex.from_tuples(counts.columns)
-    return counts
-
-
-def get_value(df, *keys, aggfunc='sum'):
-    if keys:
-        df = df[list(keys)]
-    return df.groupby(axis=1, level=0).agg(aggfunc, axis=1)
-
-
-def plot_all(*args, **kwargs):
-    '''
-    Read all the trial data and plot the result of applying a function on them.
-    '''
-    dfs = do_all(*args, **kwargs)
-    ps = []
-    for line in dfs:
-        f, df, config = line
-        df.plot(title=config['name'])
-        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):
-        p = func(df, *keys, **kwargs)
-        p.plot(title=config['name'])
-        yield config_file, p, config
-
-
-def group_trials(trials, aggfunc=['mean', 'min', 'max', 'std']):
-    trials = list(trials)
-    trials = list(map(lambda x: x[1] if isinstance(x, tuple) else x, trials))
-    return pd.concat(trials).groupby(level=0).agg(aggfunc).reorder_levels([2, 0,1] ,axis=1)
-
-
-def fillna(df):
-    new_df = df.ffill(axis=0)
-    return new_df

soil/config.py

@@ -0,0 +1,270 @@
+from __future__ import annotations
+
+from enum import Enum
+from pydantic import BaseModel, ValidationError, validator, root_validator
+
+import yaml
+import os
+import sys
+
+
+from typing import Any, Callable, Dict, List, Optional, Union, Type
+from pydantic import BaseModel, Extra
+
+from . import environment, utils
+
+import networkx as nx
+
+
+# Could use TypeAlias in python >= 3.10
+nodeId = int
+
+
+class Node(BaseModel):
+    id: nodeId
+    state: Optional[Dict[str, Any]] = {}
+
+
+class Edge(BaseModel):
+    source: nodeId
+    target: nodeId
+    value: Optional[float] = 1
+
+
+class Topology(BaseModel):
+    nodes: List[Node]
+    directed: bool
+    links: List[Edge]
+
+
+class NetParams(BaseModel, extra=Extra.allow):
+    generator: Union[Callable, str]
+    n: int
+
+
+class NetConfig(BaseModel):
+    params: Optional[NetParams]
+    fixed: Optional[Union[Topology, nx.Graph]]
+    path: Optional[str]
+
+    class Config:
+        arbitrary_types_allowed = True
+
+    @staticmethod
+    def default():
+        return NetConfig(topology=None, params=None)
+
+    @root_validator
+    def validate_all(cls, values):
+        if "params" not in values and "topology" not in values:
+            raise ValueError(
+                "You must specify either a topology or the parameters to generate a graph"
+            )
+        return values
+
+
+class EnvConfig(BaseModel):
+    @staticmethod
+    def default():
+        return EnvConfig()
+
+
+class SingleAgentConfig(BaseModel):
+    agent_class: Optional[Union[Type, str]] = None
+    unique_id: Optional[int] = None
+    topology: Optional[bool] = False
+    node_id: Optional[Union[int, str]] = None
+    state: Optional[Dict[str, Any]] = {}
+
+
+class FixedAgentConfig(SingleAgentConfig):
+    n: Optional[int] = 1
+    hidden: Optional[bool] = False  # Do not count this agent towards total agent count
+
+    @root_validator
+    def validate_all(cls, values):
+        if values.get("unique_id", None) is not None and values.get("n", 1) > 1:
+            raise ValueError(
+                f"An unique_id can only be provided when there is only one agent ({values.get('n')} given)"
+            )
+        return values
+
+
+class OverrideAgentConfig(FixedAgentConfig):
+    filter: Optional[Dict[str, Any]] = None
+
+
+class Strategy(Enum):
+    topology = "topology"
+    total = "total"
+
+
+class AgentDistro(SingleAgentConfig):
+    weight: Optional[float] = 1
+    strategy: Strategy = Strategy.topology
+
+
+class AgentConfig(SingleAgentConfig):
+    n: Optional[int] = None
+    distribution: Optional[List[AgentDistro]] = None
+    fixed: Optional[List[FixedAgentConfig]] = None
+    override: Optional[List[OverrideAgentConfig]] = None
+
+    @staticmethod
+    def default():
+        return AgentConfig()
+
+    @root_validator
+    def validate_all(cls, values):
+        if "distribution" in values and (
+            "n" not in values and "topology" not in values
+        ):
+            raise ValueError(
+                "You need to provide the number of agents or a topology to extract the value from."
+            )
+        return values
+
+
+class Config(BaseModel, extra=Extra.allow):
+    version: Optional[str] = "1"
+
+    name: str = "Unnamed Simulation"
+    description: Optional[str] = None
+    group: str = None
+    dir_path: Optional[str] = None
+    num_trials: int = 1
+    max_time: float = 100
+    max_steps: int = -1
+    interval: float = 1
+    seed: str = ""
+    dry_run: bool = False
+
+    model_class: Union[Type, str] = environment.Environment
+    model_params: Optional[Dict[str, Any]] = {}
+
+    visualization_params: Optional[Dict[str, Any]] = {}
+
+    @classmethod
+    def from_raw(cls, cfg):
+        if isinstance(cfg, Config):
+            return cfg
+        if cfg.get("version", "1") == "1" and any(
+            k in cfg for k in ["agents", "agent_class", "topology", "environment_class"]
+        ):
+            return convert_old(cfg)
+        return Config(**cfg)
+
+
+def convert_old(old, strict=True):
+    """
+    Try to convert old style configs into the new format.
+
+    This is still a work in progress and might not work in many cases.
+    """
+
+    utils.logger.warning(
+        "The old configuration format is deprecated. The converted file MAY NOT yield the right results"
+    )
+
+    new = old.copy()
+
+    network = {}
+
+    if "topology" in old:
+        del new["topology"]
+        network["topology"] = old["topology"]
+
+    if "network_params" in old and old["network_params"]:
+        del new["network_params"]
+        for (k, v) in old["network_params"].items():
+            if k == "path":
+                network["path"] = v
+            else:
+                network.setdefault("params", {})[k] = v
+
+    topology = None
+    if network:
+        topology = network
+
+    agents = {"fixed": [], "distribution": []}
+
+    def updated_agent(agent):
+        """Convert an agent definition"""
+        newagent = dict(agent)
+        return newagent
+
+    by_weight = []
+    fixed = []
+    override = []
+
+    if "environment_agents" in new:
+
+        for agent in new["environment_agents"]:
+            agent.setdefault("state", {})["group"] = "environment"
+            if "agent_id" in agent:
+                agent["state"]["name"] = agent["agent_id"]
+                del agent["agent_id"]
+            agent["hidden"] = True
+            agent["topology"] = False
+            fixed.append(updated_agent(agent))
+        del new["environment_agents"]
+
+    if "agent_class" in old:
+        del new["agent_class"]
+        agents["agent_class"] = old["agent_class"]
+
+    if "default_state" in old:
+        del new["default_state"]
+        agents["state"] = old["default_state"]
+
+    if "network_agents" in old:
+        agents["topology"] = True
+
+        agents.setdefault("state", {})["group"] = "network"
+
+        for agent in new["network_agents"]:
+            agent = updated_agent(agent)
+            if "agent_id" in agent:
+                agent["state"]["name"] = agent["agent_id"]
+                del agent["agent_id"]
+                fixed.append(agent)
+            else:
+                by_weight.append(agent)
+        del new["network_agents"]
+
+    if "agent_class" in old and (not fixed and not by_weight):
+        agents["topology"] = True
+        by_weight = [{"agent_class": old["agent_class"], "weight": 1}]
+
+    # TODO: translate states properly
+    if "states" in old:
+        del new["states"]
+        states = old["states"]
+        if isinstance(states, dict):
+            states = states.items()
+        else:
+            states = enumerate(states)
+        for (k, v) in states:
+            override.append({"filter": {"node_id": k}, "state": v})
+
+    agents["override"] = override
+    agents["fixed"] = fixed
+    agents["distribution"] = by_weight
+
+    model_params = {}
+    if "environment_params" in new:
+        del new["environment_params"]
+        model_params = dict(old["environment_params"])
+
+    if "environment_class" in old:
+        del new["environment_class"]
+        new["model_class"] = old["environment_class"]
+
+    if "dump" in old:
+        del new["dump"]
+        new["dry_run"] = not old["dump"]
+
+    model_params["topology"] = topology
+    model_params["agents"] = agents
+
+    return Config(version="2", model_params=model_params, **new)

soil/datacollection.py

@@ -0,0 +1,6 @@
+from mesa import DataCollector as MDC
+
+
+class SoilDataCollector(MDC):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)

soil/debugging.py

@@ -0,0 +1,190 @@
+from __future__ import annotations
+
+import pdb
+import sys
+import os
+
+from textwrap import indent
+from functools import wraps
+
+from .agents import FSM, MetaFSM
+
+
+def wrapcmd(func):
+    @wraps(func)
+    def wrapper(self, arg: str, temporary=False):
+        sys.settrace(self.trace_dispatch)
+
+        known = globals()
+        known.update(self.curframe.f_globals)
+        known.update(self.curframe.f_locals)
+        known["agent"] = known.get("self", None)
+        known["model"] = known.get("self", {}).get("model")
+        known["attrs"] = arg.strip().split()
+
+        exec(func.__code__, known, known)
+
+    return wrapper
+
+
+class Debug(pdb.Pdb):
+    def __init__(self, *args, skip_soil=False, **kwargs):
+        skip = kwargs.get("skip", [])
+        skip.append("soil")
+        skip.append("contextlib")
+        if skip_soil:
+            skip.append("soil.*")
+            skip.append("mesa.*")
+        super(Debug, self).__init__(*args, skip=skip, **kwargs)
+        self.prompt = "[soil-pdb] "
+
+    @staticmethod
+    def _soil_agents(model, attrs=None, pretty=True, **kwargs):
+        for agent in model.agents(**kwargs):
+            d = agent
+            print(" - " + indent(agent.to_str(keys=attrs, pretty=pretty), "  "))
+
+    @wrapcmd
+    def do_soil_agents():
+        return Debug._soil_agents(model, attrs=attrs or None)
+
+    do_sa = do_soil_agents
+
+    @wrapcmd
+    def do_soil_list():
+        return Debug._soil_agents(model, attrs=["state_id"], pretty=False)
+
+    do_sl = do_soil_list
+
+    def do_continue_state(self, arg):
+        self.do_break_state(arg, temporary=True)
+        return self.do_continue("")
+
+    do_cs = do_continue_state
+
+    @wrapcmd
+    def do_soil_agent():
+        if not agent:
+            print("No agent available")
+            return
+
+        keys = None
+        if attrs:
+            keys = []
+            for k in attrs:
+                for key in agent.keys():
+                    if key.startswith(k):
+                        keys.append(key)
+
+        print(agent.to_str(pretty=True, keys=keys))
+
+    do_aa = do_soil_agent
+
+    def do_break_state(self, arg: str, instances=None, temporary=False):
+        """
+        Break before a specified state is stepped into.
+        """
+
+        klass = None
+        state = arg
+        if not state:
+            self.error("Specify at least a state name")
+            return
+
+        state, *tokens = state.lstrip().split()
+        if tokens:
+            instances = list(eval(token) for token in tokens)
+
+        colon = state.find(":")
+
+        if colon > 0:
+            klass = state[:colon].rstrip()
+            state = state[colon + 1 :].strip()
+
+            print(klass, state, tokens)
+            klass = eval(klass, self.curframe.f_globals, self.curframe_locals)
+
+        if klass:
+            klasses = [klass]
+        else:
+            klasses = [
+                k
+                for k in self.curframe.f_globals.values()
+                if isinstance(k, type) and issubclass(k, FSM)
+            ]
+
+        if not klasses:
+            self.error("No agent classes found")
+
+        for klass in klasses:
+            try:
+                func = getattr(klass, state)
+            except AttributeError:
+                self.error(f"State {state} not found in class {klass}")
+                continue
+            if hasattr(func, "__func__"):
+                func = func.__func__
+
+            code = func.__code__
+            # use co_name to identify the bkpt (function names
+            # could be aliased, but co_name is invariant)
+            funcname = code.co_name
+            lineno = code.co_firstlineno
+            filename = code.co_filename
+
+            # Check for reasonable breakpoint
+            line = self.checkline(filename, lineno)
+            if not line:
+                raise ValueError("no line found")
+                # now set the break point
+            cond = None
+            if instances:
+                cond = f"self.unique_id in { repr(instances) }"
+
+            existing = self.get_breaks(filename, line)
+            if existing:
+                self.message("Breakpoint already exists at %s:%d" % (filename, line))
+                continue
+            err = self.set_break(filename, line, temporary, cond, funcname)
+            if err:
+                self.error(err)
+            else:
+                bp = self.get_breaks(filename, line)[-1]
+                self.message("Breakpoint %d at %s:%d" % (bp.number, bp.file, bp.line))
+
+    do_bs = do_break_state
+
+    def do_break_state_self(self, arg: str, temporary=False):
+        """
+        Break before a specified state is stepped into, for the current agent
+        """
+        agent = self.curframe.f_locals.get("self")
+        if not agent:
+            self.error("No current agent.")
+            self.error("Try this again when the debugger is stopped inside an agent")
+            return
+
+        arg = f"{agent.__class__.__name__}:{ arg } {agent.unique_id}"
+        return self.do_break_state(arg)
+
+    do_bss = do_break_state_self
+
+
+debugger = None
+
+
+def set_trace(frame=None, **kwargs):
+    global debugger
+    if debugger is None:
+        debugger = Debug(**kwargs)
+    frame = frame or sys._getframe().f_back
+    debugger.set_trace(frame)
+
+
+def post_mortem(traceback=None, **kwargs):
+    global debugger
+    if debugger is None:
+        debugger = Debug(**kwargs)
+    t = sys.exc_info()[2]
+    debugger.reset()
+    debugger.interaction(None, t)

soil/environment.py

@@ -1,314 +1,318 @@
+from __future__ import annotations
+
 import os
 import sqlite3
-import time
-import csv
+import math
 import random
-import simpy
-import tempfile
-import pandas as pd
+import logging
+import inspect
+
+from typing import Any, Dict, Optional, Union
+from collections import namedtuple
+from time import time as current_time
 from copy import deepcopy
 from networkx.readwrite import json_graph
 
+
 import networkx as nx
-import nxsim
 
-from . import utils, agents, analysis, history
+from mesa import Model
+from mesa.datacollection import DataCollector
+
+from . import agents as agentmod, config, serialization, utils, time, network
 
 
-class SoilEnvironment(nxsim.NetworkEnvironment):
+class BaseEnvironment(Model):
     """
-    The environment is key in a simulation. It contains the network topology,
-    a reference to network and environment agents, as well as the environment
-    params, which are used as shared state between agents.
+    The environment is key in a simulation. It controls how agents interact,
+    and what information is available to them.
+
+    This is an opinionated version of `mesa.Model` class, which adds many
+    convenience methods and abstractions.
 
     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.
+    both by using the environment as a dictionary and with the environment's
+    :meth:`soil.environment.Environment.get` method.
     """
 
-    def __init__(self, name=None,
-                 network_agents=None,
-                 environment_agents=None,
-                 states=None,
-                 default_state=None,
-                 interval=1,
-                 seed=None,
-                 dry_run=False,
-                 dir_path=None,
-                 topology=None,
-                 *args, **kwargs):
-        self.name = name or 'UnnamedEnvironment'
-        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._env_agents = {}
-        self.dry_run = dry_run
+    def __init__(
+        self,
+        id="unnamed_env",
+        seed="default",
+        schedule=None,
+        dir_path=None,
+        interval=1,
+        agent_class=None,
+        agents: [tuple[type, Dict[str, Any]]] = {},
+        agent_reporters: Optional[Any] = None,
+        model_reporters: Optional[Any] = None,
+        tables: Optional[Any] = None,
+        **env_params,
+    ):
+
+        super().__init__(seed=seed)
+        self.env_params = env_params or {}
+
+        self.current_id = -1
+
+        self.id = id
+
+        self.dir_path = dir_path or os.getcwd()
+
+        if schedule is None:
+            schedule = time.TimedActivation(self)
+        self.schedule = schedule
+
+        self.agent_class = agent_class or agentmod.BaseAgent
+
         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)
-        # 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'])
+        self.init_agents(agents)
+
+        self.logger = utils.logger.getChild(self.id)
+
+        self.datacollector = DataCollector(
+            model_reporters=model_reporters,
+            agent_reporters=agent_reporters,
+            tables=tables,
+        )
+
+    def _agent_from_dict(self, agent):
+        """
+        Translate an agent dictionary into an agent
+        """
+        agent = dict(**agent)
+        cls = agent.pop("agent_class", None) or self.agent_class
+        unique_id = agent.pop("unique_id", None)
+        if unique_id is None:
+            unique_id = self.next_id()
+
+        return serialization.deserialize(cls)(unique_id=unique_id, model=self, **agent)
+
+    def init_agents(self, agents: Union[config.AgentConfig, [Dict[str, Any]]] = {}):
+        """
+        Initialize the agents in the model from either a `soil.config.AgentConfig` or a list of
+        dictionaries that each describes an agent.
+
+        If given a list of dictionaries, an agent will be created for each dictionary. The agent
+        class can be specified through the `agent_class` key. The rest of the items will be used
+        as parameters to the agent.
+        """
+        if not agents:
+            return
+
+        lst = agents
+        override = []
+        if not isinstance(lst, list):
+            if not isinstance(agents, config.AgentConfig):
+                lst = config.AgentConfig(**agents)
+            if lst.override:
+                override = lst.override
+            lst = self._agent_dict_from_config(lst)
+
+        # TODO: check override is working again. It cannot (easily) be part of agents.from_config anymore,
+        # because it needs attribute such as unique_id, which are only present after init
+        new_agents = [self._agent_from_dict(agent) for agent in lst]
+
+        for a in new_agents:
+            self.schedule.add(a)
+
+        for rule in override:
+            for agent in agentmod.filter_agents(self.schedule._agents, **rule.filter):
+                for attr, value in rule.state.items():
+                    setattr(agent, attr, value)
+
+    def _agent_dict_from_config(self, cfg):
+        return agentmod.from_config(cfg, random=self.random)
 
     @property
     def agents(self):
-        yield from self.environment_agents
-        yield from self.network_agents
+        return agentmod.AgentView(self.schedule._agents)
+
+    def find_one(self, *args, **kwargs):
+        return agentmod.AgentView(self.schedule._agents).one(*args, **kwargs)
+
+    def count_agents(self, *args, **kwargs):
+        return sum(1 for i in self.agents(*args, **kwargs))
 
     @property
-    def environment_agents(self):
-        for ref in self._env_agents.values():
-            yield ref
+    def now(self):
+        if self.schedule:
+            return self.schedule.time
+        raise Exception(
+            "The environment has not been scheduled, so it has no sense of time"
+        )
 
-    @environment_agents.setter
-    def environment_agents(self, environment_agents):
-        # Set up environmental agent
-        self._env_agents = {}
-        for item in environment_agents:
-            kwargs = deepcopy(item)
-            atype = kwargs.pop('agent_type')
-            kwargs['agent_id'] = kwargs.get('agent_id', atype.__name__)
-            kwargs['state'] = kwargs.get('state', {})
-            a = atype(environment=self, **kwargs)
-            self._env_agents[a.id] = a
+    def add_agent(self, unique_id=None, **kwargs):
+        if unique_id is None:
+            unique_id = self.next_id()
+
+        kwargs["unique_id"] = unique_id
+        a = self._agent_from_dict(kwargs)
+
+        self.schedule.add(a)
+        return a
+
+    def log(self, message, *args, level=logging.INFO, **kwargs):
+        if not self.logger.isEnabledFor(level):
+            return
+        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["id"] = self.id
+        return self.logger.log(level, message, extra=extra)
+
+    def step(self):
+        """
+        Advance one step in the simulation, and update the data collection and scheduler appropriately
+        """
+        super().step()
+        self.logger.info(
+            f"--- Step: {self.schedule.steps:^5} - Time: {self.now:^5} ---"
+        )
+        self.schedule.step()
+        self.datacollector.collect(self)
+
+    def __contains__(self, key):
+        return key in self.env_params
+
+    def get(self, key, default=None):
+        """
+        Get the value of an environment attribute.
+        Return `default` if the value is not set.
+        """
+        return self.env_params.get(key, default)
+
+    def __getitem__(self, key):
+        return self.env_params.get(key)
+
+    def __setitem__(self, key, value):
+        return self.env_params.__setitem__(key, value)
+
+    def __str__(self):
+        return str(self.env_params)
+
+
+class NetworkEnvironment(BaseEnvironment):
+    """
+    The NetworkEnvironment is an environment that includes one or more networkx.Graph intances
+    and methods to associate agents to nodes and vice versa.
+    """
+
+    def __init__(
+        self, *args, topology: Union[config.NetConfig, nx.Graph] = None, **kwargs
+    ):
+        agents = kwargs.pop("agents", None)
+        super().__init__(*args, agents=None, **kwargs)
+
+        self._set_topology(topology)
+
+        self.init_agents(agents)
+
+    def init_agents(self, *args, **kwargs):
+        """Initialize the agents from a"""
+        super().init_agents(*args, **kwargs)
+        for agent in self.schedule._agents.values():
+            if hasattr(agent, "node_id"):
+                self._init_node(agent)
+
+    def _init_node(self, agent):
+        """
+        Make sure the node for a given agent has the proper attributes.
+        """
+        self.G.nodes[agent.node_id]["agent"] = agent
+
+    def _agent_dict_from_config(self, cfg):
+        return agentmod.from_config(cfg, topology=self.G, random=self.random)
+
+    def _agent_from_dict(self, agent, unique_id=None):
+        agent = dict(agent)
+
+        if not agent.get("topology", False):
+            return super()._agent_from_dict(agent)
+
+        if unique_id is None:
+            unique_id = self.next_id()
+        node_id = agent.get("node_id", None)
+        if node_id is None:
+            node_id = network.find_unassigned(self.G, random=self.random)
+        self.G.nodes[node_id]["agent"] = None
+        agent["node_id"] = node_id
+        agent["unique_id"] = unique_id
+        agent["topology"] = self.G
+        node_attrs = self.G.nodes[node_id]
+        node_attrs.update(agent)
+        agent = node_attrs
+
+        a = super()._agent_from_dict(agent)
+        self._init_node(a)
+
+        return a
+
+    def _set_topology(self, cfg=None, dir_path=None):
+        if cfg is None:
+            cfg = nx.Graph()
+        elif not isinstance(cfg, nx.Graph):
+            cfg = network.from_config(cfg, dir_path=dir_path or self.dir_path)
+
+        self.G = cfg
 
     @property
     def network_agents(self):
-        for i in self.G.nodes():
-            node = self.G.node[i]
-            if 'agent' in node:
-                yield node['agent']
+        for a in self.schedule._agents:
+            if isinstance(a, agentmod.NetworkAgent):
+                yield a
 
-    @network_agents.setter
-    def network_agents(self, network_agents):
-        if not network_agents:
-            return
-        for ix in self.G.nodes():
-            agent, state = agents._agent_from_distribution(network_agents)
-            self.set_agent(ix, agent_type=agent, state=state)
+    def add_node(self, agent_class, unique_id=None, node_id=None, **kwargs):
+        if unique_id is None:
+            unique_id = self.next_id()
+        if node_id is None:
+            node_id = network.find_unassigned(
+                G=self.G, shuffle=True, random=self.random
+            )
+            if node_id is None:
+                node_id = f"node_for_{unique_id}"
 
-    def set_agent(self, agent_id, agent_type, state=None):
-        node = self.G.nodes[agent_id]
-        defstate = deepcopy(self.default_state)
-        defstate.update(self.states.get(agent_id, {}))
-        if state:
-            defstate.update(state)
-        state = defstate
-        state.update(node.get('state', {}))
-        a = agent_type(environment=self,
-                       agent_id=agent_id,
-                       state=state)
-        node['agent'] = a
+        if node_id not in self.G.nodes:
+            self.G.add_node(node_id)
+
+        assert "agent" not in self.G.nodes[node_id]
+        self.G.nodes[node_id]["agent"] = None  # Reserve
+
+        a = self.add_agent(
+            unique_id=unique_id,
+            agent_class=agent_class,
+            topology=self.G,
+            node_id=node_id,
+            **kwargs,
+        )
+        a["visible"] = True
         return a
 
-    def add_node(self, agent_type, state=None):
-        agent_id = int(len(self.G.nodes()))
-        self.G.add_node(agent_id)
-        a = self.set_agent(agent_id, agent_type, state)
-        a['visible'] = True
+    def add_agent(self, *args, **kwargs):
+        a = super().add_agent(*args, **kwargs)
+        if "node_id" in a:
+            if a.node_id == 24:
+                import pdb
+
+                pdb.set_trace()
+            assert self.G.nodes[a.node_id]["agent"] == a
         return a
 
-    def add_edge(self, agent1, agent2, attrs=None):
-        return self.G.add_edge(agent1, agent2)
+    def agent_for_node_id(self, node_id):
+        return self.G.nodes[node_id].get("agent")
 
-    def run(self, *args, **kwargs):
-        self._save_state()
-        super().run(*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))
-        self._history.save_records(self.state_to_tuples(now=now))
-
-    def save_state(self):
-        '''
-        :DEPRECATED:
-        Periodically save the state of the environment and the agents.
-        '''
-        self._save_state()
-        while self.peek() != simpy.core.Infinity:
-            delay = max(self.peek() - self.now, self.interval)
-            utils.logger.debug('Step: {}'.format(self.now))
-            ev = self.event()
-            ev._ok = True
-            # Schedule the event with minimum priority so
-            # that it executes before all agents
-            self.schedule(ev, -999, delay)
-            yield ev
-            self._save_state()
-
-    def __getitem__(self, key):
-        if isinstance(key, tuple):
-            self._history.flush_cache()
-            return self._history[key]
-
-        return self.environment_params[key]
-
-    def __setitem__(self, key, value):
-        if isinstance(key, tuple):
-            k = history.Key(*key)
-            self._history.save_record(*k,
-                                      value=value)
-            return
-        self.environment_params[key] = value
-        self._history.save_record(agent_id='env',
-                                  t_step=self.now,
-                                  key=key,
-                                  value=value)
-
-    def __contains__(self, key):
-        return key in self.environment_params
-
-    def get(self, key, default=None):
-        '''
-        Get the value of an environment attribute in a
-        given point in the simulation (history).
-        If key is an attribute name, this method returns
-        the current value.
-        To get values at other times, use a
-        :meth: `soil.history.Key` tuple.
-        '''
-        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']
-
-    def get_agents(self):
-        return list(self.agents)
-
-    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:
-            cr = csv.writer(f)
-            cr.writerow(('agent_id', 't_step', 'key', 'value', 'value_type'))
-            for i in self.history_to_tuples():
-                cr.writerow(i)
-
-    def dump_gexf(self, dir_path=None):
-        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'])
-
-        nx.write_gexf(G, graph_path, version="1.2draft")
-
-    def dump(self, dir_path=None, formats=None):
-        if not formats:
-            return
-        functions = {
-            'csv': self.dump_csv,
-            'gexf': self.dump_gexf
-        }
-        for f in formats:
-            if f in functions:
-                functions[f](dir_path)
-            else:
-                raise ValueError('Unknown format: {}'.format(f))
-
-    def state_to_tuples(self, now=None):
-        if now is None:
-            now = self.now
-        for k, v in self.environment_params.items():
-            yield history.Record(agent_id='env',
-                                 t_step=now,
-                                 key=k,
-                                 value=v)
-        for agent in self.agents:
-            for k, v in agent.state.items():
-                yield history.Record(agent_id=agent.id,
-                                     t_step=now,
-                                     key=k,
-                                     value=v)
-
-    def history_to_tuples(self):
-        return self._history.to_tuples()
-
-    def history_to_graph(self):
-        G = nx.Graph(self.G)
-
-        for agent in self.network_agents:
-
-            attributes = {'agent': str(agent.__class__)}
-            lastattributes = {}
-            spells = []
-            lastvisible = False
-            laststep = None
-            history = self[agent.id, None, None]
-            if not history:
+    def populate_network(self, agent_class, weights=None, **agent_params):
+        if not hasattr(agent_class, "len"):
+            agent_class = [agent_class]
+            weights = None
+        for (node_id, node) in self.G.nodes(data=True):
+            if "agent" in node:
                 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))
+            a_class = self.random.choices(agent_class, weights)[0]
+            self.add_agent(node_id=node_id, agent_class=a_class, **agent_params)
 
-                        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)
-            for k, v in lastattributes.items():
-                attributes[k].append((v[0], 0, v[1]))
-            if lastvisible:
-                spells.append((laststep, None))
-            if spells:
-                G.add_node(agent.id, spells=spells, **attributes)
-            else:
-                G.add_node(agent.id, **attributes)
 
-        return G
-
-    def __getstate__(self):
-        state = self.__dict__.copy()
-        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']
-        return state
-
-    def __setstate__(self, state):
-        self.__dict__ = state
-        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
+Environment = NetworkEnvironment

soil/exporters.py

@@ -0,0 +1,213 @@
+import os
+import sys
+from time import time as current_time
+from io import BytesIO
+from sqlalchemy import create_engine
+from textwrap import dedent, indent
+
+
+import matplotlib.pyplot as plt
+import networkx as nx
+
+
+from .serialization import deserialize
+from .utils import try_backup, open_or_reuse, logger, timer
+
+
+from . import utils, network
+
+
+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
+        if copy_to is None and dry_run:
+            copy_to = sys.stdout
+        self.copy_to = copy_to
+
+    def sim_start(self):
+        """Method to call when the simulation starts"""
+        pass
+
+    def sim_end(self):
+        """Method to call when the simulation ends"""
+        pass
+
+    def trial_start(self, env):
+        """Method to call when a trial start"""
+        pass
+
+    def trial_end(self, env):
+        """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)
+
+    def get_dfs(self, env):
+        yield from get_dc_dfs(env.datacollector, trial_id=env.id)
+
+
+def get_dc_dfs(dc, trial_id=None):
+    dfs = {
+        "env": dc.get_model_vars_dataframe(),
+        "agents": dc.get_agent_vars_dataframe(),
+    }
+    for table_name in dc.tables:
+        dfs[table_name] = dc.get_table_dataframe(table_name)
+    if trial_id:
+        for (name, df) in dfs.items():
+            df["trial_id"] = trial_id
+    yield from dfs.items()
+
+
+class default(Exporter):
+    """Default exporter. Writes sqlite results, as well as the simulation YAML"""
+
+    def sim_start(self):
+        if self.dry_run:
+            logger.info("NOT dumping results")
+            return
+        logger.info("Dumping results to %s", self.outdir)
+        with self.output(self.simulation.name + ".dumped.yml") as f:
+            f.write(self.simulation.to_yaml())
+        self.dbpath = os.path.join(self.outdir, f"{self.simulation.name}.sqlite")
+        try_backup(self.dbpath, remove=True)
+
+    def trial_end(self, env):
+        if self.dry_run:
+            logger.info("Running in DRY_RUN mode, the database will NOT be created")
+            return
+
+        with timer(
+            "Dumping simulation {} trial {}".format(self.simulation.name, env.id)
+        ):
+
+            engine = create_engine(f"sqlite:///{self.dbpath}", echo=False)
+
+            for (t, df) in self.get_dfs(env):
+                df.to_sql(t, con=engine, if_exists="append")
+
+
+class csv(Exporter):
+
+    """Export the state of each environment (and its agents) in a separate CSV file"""
+
+    def trial_end(self, env):
+        with timer(
+            "[CSV] Dumping simulation {} trial {} @ dir {}".format(
+                self.simulation.name, env.id, self.outdir
+            )
+        ):
+            for (df_name, df) in self.get_dfs(env):
+                with self.output("{}.{}.csv".format(env.id, df_name)) as f:
+                    df.to_csv(f)
+
+
+# TODO: reimplement GEXF exporting without history
+class gexf(Exporter):
+    def trial_end(self, env):
+        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.id)
+        ):
+            with self.output("{}.gexf".format(env.id), mode="wb") as f:
+                network.dump_gexf(env.history_to_graph(), f)
+                self.dump_gexf(env, f)
+
+
+class dummy(Exporter):
+    def sim_start(self):
+        with self.output("dummy", "w") as f:
+            f.write("simulation started @ {}\n".format(current_time()))
+
+    def trial_start(self, env):
+        with self.output("dummy", "w") as f:
+            f.write("trial started@ {}\n".format(current_time()))
+
+    def trial_end(self, env):
+        with self.output("dummy", "w") as f:
+            f.write("trial ended@ {}\n".format(current_time()))
+
+    def sim_end(self):
+        with self.output("dummy", "a") as f:
+            f.write("simulation ended @ {}\n".format(current_time()))
+
+
+class graphdrawing(Exporter):
+    def trial_end(self, env):
+        # 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.id)) as f:
+            f.savefig(f)
+
+
+class summary(Exporter):
+    """Print a summary of each trial to sys.stdout"""
+
+    def trial_end(self, env):
+        for (t, df) in self.get_dfs(env):
+            if not len(df):
+                continue
+            msg = indent(str(df.describe()), "    ")
+            logger.info(
+                dedent(
+                    f"""
+            Dataframe {t}:
+            """
+                )
+                + msg
+            )

soil/history.py

@@ -1,231 +0,0 @@
-import time
-import os
-import pandas as pd
-import sqlite3
-import copy
-from collections import UserDict, Iterable, namedtuple
-
-from . import utils
-
-
-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):
-                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._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]
-
-    @property
-    def dtypes(self):
-        return {k:v[0] for k, v in self._dtypes.items()}
-
-    def save_tuples(self, tuples):
-        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))
-
-    def save_record(self, *args, **kwargs):
-        self._tups.append(Record(*args, **kwargs))
-        if len(self._tups) > 100:
-            self.flush_cache()
-
-    def flush_cache(self):
-        '''
-        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)
-        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)
-
-    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 __getitem__(self, key):
-        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 []
-        keys = [key.key] if key.key is not None else []
-
-        df = self.read_sql(agent_ids=agent_ids,
-                           t_steps=t_steps,
-                           keys=keys)
-        r = Records(df, filter=key, dtypes=self._dtypes)
-        return r.value()
-
-
-
-    def read_sql(self, keys=None, agent_ids=None, t_steps=None, convert_types=False, limit=-1):
-
-        self.read_types()
-
-        def escape_and_join(v):
-            if v is None:
-                return
-            return ",".join(map(lambda x: "\'{}\'".format(x), v))
-
-        filters = [("key in ({})".format(escape_and_join(keys)), keys),
-                   ("agent_id in ({})".format(escape_and_join(agent_ids)), agent_ids)
-        ]
-        filters = list(k[0] for k in filters if k[1])
-
-        last_df = None
-        if t_steps:
-            # 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
-            condition = ' and '.join(last_filters)
-
-            last_query = '''
-            select h1.*
-            from history h1
-            inner join (
-            select agent_id, key, max(t_step) as t_step
-            from history
-            where {condition}
-            group by agent_id, key
-            ) h2
-            on h1.agent_id = h2.agent_id  and
-               h1.key      = h2.key       and
-               h1.t_step   = h2.t_step
-            '''.format(condition=condition)
-            last_df = pd.read_sql_query(last_query, self._db)
-
-            filters.append("t_step >= '{}' and t_step <= '{}'".format(min_step, max(t_steps)))
-
-        condition = ''
-        if filters:
-            condition = 'where {} '.format(' and '.join(filters))
-        query = 'select * from history {} limit {}'.format(condition, limit)
-        df = pd.read_sql_query(query, self._db)
-        if last_df is not None:
-            df = pd.concat([df, last_df])
-
-        df_p = df.pivot_table(values='value', index=['t_step'],
-                              columns=['key', 'agent_id'],
-                              aggfunc='first')
-
-        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)
-        if t_steps:
-            df_p = df_p.reindex(t_steps, method='ffill')
-        return df_p.ffill()
-
-
-class Records():
-
-    def __init__(self, df, filter=None, dtypes=None):
-        if not filter:
-            filter = Key(agent_id=None,
-                         t_step=None,
-                         key=None)
-        self._df = df
-        self._filter = filter
-        self.dtypes = dtypes or {}
-        super().__init__()
-
-    def mask(self, tup):
-        res = ()
-        for i, k in zip(tup[:-1], self._filter):
-            if k is None:
-                res = res + (i,)
-        res = res + (tup[-1],)
-        return res
-
-    def filter(self, newKey):
-        f = list(self._filter)
-        for ix, i in enumerate(f):
-            if i is None:
-                f[ix] = newKey
-        self._filter = Key(*f)
-
-    @property
-    def resolved(self):
-        return sum(1 for i in self._filter if i is not None) == 3
-
-    def __iter__(self):
-        for column, series in self._df.iteritems():
-            key, agent_id = column
-            for t_step, value in series.iteritems():
-                r = Record(t_step=t_step,
-                           agent_id=agent_id,
-                           key=key,
-                           value=value)
-                yield self.mask(r)
-
-    def value(self):
-        if self.resolved:
-            f = self._filter
-            try:
-                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:
-                return self.dtypes[f.key][2]()
-        return self
-
-    def __getitem__(self, k):
-        n = copy.copy(self)
-        n.filter(k)
-        return n.value()
-
-    def __len__(self):
-        return len(self._df)
-
-
-Key = namedtuple('Key', ['agent_id', 't_step', 'key'])
-Record = namedtuple('Record', 'agent_id t_step key value')

soil/network.py

@@ -0,0 +1,84 @@
+from __future__ import annotations
+
+from typing import Dict
+import os
+import sys
+import random
+
+import networkx as nx
+
+from . import config, serialization, basestring
+
+
+def from_config(cfg: config.NetConfig, dir_path: str = None):
+    if not isinstance(cfg, config.NetConfig):
+        cfg = config.NetConfig(**cfg)
+
+    if cfg.path:
+        path = cfg.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)
+
+    if cfg.params:
+        net_args = cfg.params.dict()
+        net_gen = net_args.pop("generator")
+
+        if dir_path not in sys.path:
+            sys.path.append(dir_path)
+
+        method = serialization.deserializer(
+            net_gen,
+            known_modules=[
+                "networkx.generators",
+            ],
+        )
+        return method(**net_args)
+
+    if isinstance(cfg.fixed, config.Topology):
+        cfg = cfg.fixed.dict()
+
+    if isinstance(cfg, str) or isinstance(cfg, dict):
+        return nx.json_graph.node_link_graph(cfg)
+
+    return nx.Graph()
+
+
+def find_unassigned(G, shuffle=False, random=random):
+    """
+    Link an agent to a node in a topology.
+
+    If node_id is None, a node without an agent_id will be found.
+    """
+    # TODO: test
+    candidates = list(G.nodes(data=True))
+    if shuffle:
+        random.shuffle(candidates)
+    for next_id, data in candidates:
+        if "agent" not in data:
+            return next_id
+    return None
+
+
+def dump_gexf(G, f):
+    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"]
+
+    nx.write_gexf(G, f, version="1.2draft")

soil/serialization.py

@@ -0,0 +1,230 @@
+import os
+import logging
+import ast
+import sys
+import re
+import importlib
+from glob import glob
+from itertools import product, chain
+
+from .config import Config
+
+import yaml
+import networkx as nx
+
+from jinja2 import Template
+
+
+logger = logging.getLogger("soil")
+
+
+def load_file(infile):
+    folder = os.path.dirname(infile)
+    if folder not in sys.path:
+        sys.path.append(folder)
+    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, recursive=True):
+            for cfg in load_file(i):
+                path = os.path.abspath(i)
+                yield Config.from_raw(cfg), path
+
+
+def load_config(cfg):
+    if isinstance(cfg, Config):
+        yield cfg, os.getcwd()
+    elif isinstance(cfg, dict):
+        yield Config.from_raw(cfg), os.getcwd()
+    else:
+        yield from load_files(cfg)
+
+
+builtins = importlib.import_module("builtins")
+
+KNOWN_MODULES = [
+    "soil",
+]
+
+
+def name(value, known_modules=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=KNOWN_MODULES):
+    """Get a text representation of an object."""
+    tname = name(v, known_modules=known_modules)
+    func = serializer(tname)
+    return func(v), tname
+
+
+def serialize_dict(d, known_modules=KNOWN_MODULES):
+    d = dict(d)
+    for (k, v) in d.items():
+        if isinstance(v, dict):
+            d[k] = serialize_dict(v, known_modules=known_modules)
+        elif isinstance(v, list):
+            for ix in range(len(v)):
+                v[ix] = serialize_dict(v[ix], known_modules=known_modules)
+        elif isinstance(v, type):
+            d[k] = serialize(v, known_modules=known_modules)[1]
+    return d
+
+
+IS_CLASS = re.compile(r"<class '(.*)'>")
+
+
+def deserializer(type_, known_modules=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()
+    match = IS_CLASS.match(type_)
+    if match:
+        modname, tname = match.group(1).rsplit(".", 1)
+        module = importlib.import_module(modname)
+        cls = getattr(module, tname)
+        return getattr(cls, "deserialize", cls)
+
+    # Otherwise, see if we can find the module and the class
+    options = []
+
+    for mod in known_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 ValueError('Could not find type "{}". Tried: {}'.format(type_, errors))
+
+
+def deserialize(type_, value=None, globs=None, **kwargs):
+    """Get an object from a text representation"""
+    if not isinstance(type_, str):
+        return type_
+    if globs and type_ in globs:
+        des = globs[type_]
+    else:
+        try:
+            des = deserializer(type_, **kwargs)
+        except ValueError as ex:
+            try:
+                des = eval(type_)
+            except Exception:
+                raise ex
+    if value is None:
+        return des
+    return des(value)
+
+
+def deserialize_all(names, *args, known_modules=KNOWN_MODULES, **kwargs):
+    """Return the list of deserialized objects"""
+    # TODO: remove
+    print("SERIALIZATION", kwargs)
+    objects = []
+    for name in names:
+        mod = deserialize(name, known_modules=known_modules)
+        objects.append(mod(*args, **kwargs))
+    return objects

soil/simulation.py

@@ -1,219 +1,268 @@
 import os
-import time
-import imp
+from time import time as current_time, strftime
+import importlib
 import sys
 import yaml
+import traceback
+import inspect
+import logging
 import networkx as nx
-from networkx.readwrite import json_graph
-from multiprocessing import Pool
-from functools import partial
 
+from textwrap import dedent
+
+from dataclasses import dataclass, field, asdict
+from typing import Any, Dict, Union, Optional, List
+
+
+from networkx.readwrite import json_graph
+from functools import partial
 import pickle
 
-from nxsim import NetworkSimulation
-
-from . import utils, environment, basestring, agents
-from .utils import logger
+from . import serialization, exporters, utils, basestring, agents
+from .environment import Environment
+from .utils import logger, run_and_return_exceptions
+from .config import Config, convert_old
 
 
-class SoilSimulation(NetworkSimulation):
+# TODO: change documentation for simulation
+@dataclass
+class Simulation:
     """
-    Subclass of 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
-           agent type in the topology. This is an example distribution: ::
+    Parameters
+    ---------
+    config (optional): :class:`config.Config`
+        name of the Simulation
 
-                  [
-                    {'agent_type': 'agent_type_1',
-                     'weight': 0.2,
-                     'state': {
-                         'id': 0
-                      }
-                    },
-                    {'agent_type': 'agent_type_2',
-                     'weight': 0.8,
-                     'state': {
-                         'id': 1
-                      }
-                    }
-                  ]
-
-          In this example, 20% of the nodes will be marked as type
-          'agent_type_1'.
-        3) if no initial state is given, each node's state will be set
-           to `{'id': 0}`.
+    kwargs: parameters to use to initialize a new configuration, if one not been provided.
     """
-    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)
+    version: str = "2"
+    name: str = "Unnamed simulation"
+    description: Optional[str] = ""
+    group: str = None
+    model_class: Union[str, type] = "soil.Environment"
+    model_params: dict = field(default_factory=dict)
+    seed: str = field(default_factory=lambda: current_time())
+    dir_path: str = field(default_factory=lambda: os.getcwd())
+    max_time: float = float("inf")
+    max_steps: int = -1
+    interval: int = 1
+    num_trials: int = 3
+    parallel: Optional[bool] = None
+    exporters: Optional[List[str]] = field(default_factory=list)
+    outdir: Optional[str] = None
+    exporter_params: Optional[Dict[str, Any]] = field(default_factory=dict)
+    dry_run: bool = False
+    extra: Dict[str, Any] = field(default_factory=dict)
 
-        self.load_module = load_module
-        self.topology = nx.Graph(topology)
-        self.network_params = network_params
-        self.name = name or 'UnnamedSimulation'
-        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 {}
+    @classmethod
+    def from_dict(cls, env, **kwargs):
 
-        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)
+        ignored = {
+            k: v for k, v in env.items() if k not in inspect.signature(cls).parameters
+        }
 
-        environment_agents = environment_agents or []
-        self.environment_agents = agents._convert_agent_types(environment_agents)
+        d = {k: v for k, v in env.items() if k not in ignored}
+        if ignored:
+            d.setdefault("extra", {}).update(ignored)
+        if ignored:
+            print(f'Warning: Ignoring these parameters (added to "extra"): { ignored }')
+        d.update(kwargs)
 
-        distro = agents.calculate_distribution(network_agents,
-                                               agent_type)
-        self.network_agents = agents._convert_agent_types(distro)
-
-        self.states = agents._validate_states(states,
-                                              self.topology)
+        return cls(**d)
 
     def run_simulation(self, *args, **kwargs):
         return self.run(*args, **kwargs)
 
     def run(self, *args, **kwargs):
-        return list(self.run_simulation_gen(*args, **kwargs))
-
-    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')
-
-    def get_env(self, trial_id=0, **kwargs):
-        opts = self.environment_params.copy()
-        env_name = '{}_trial_{}'.format(self.name, trial_id)
-        opts.update({
-            'name': env_name,
-            'topology': self.topology.copy(),
-            'seed': self.seed+env_name,
-            'initial_time': 0,
-            'dry_run': self.dry_run,
-            'interval': self.interval,
-            'network_agents': self.network_agents,
-            '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)
-        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
+        """Run the simulation and return the list of resulting environments"""
+        logger.info(
+            dedent(
+                """
+        Simulation:
+        ---
         """
+            )
+            + self.to_yaml()
+        )
+        return list(self.run_gen(*args, **kwargs))
+
+    def run_gen(
+        self,
+        parallel=False,
+        dry_run=None,
+        exporters=None,
+        outdir=None,
+        exporter_params={},
+        log_level=None,
+        **kwargs,
+    ):
+        """Run the simulation and yield the resulting environments."""
+        if log_level:
+            logger.setLevel(log_level)
+        outdir = outdir or self.outdir
+        logger.info("Using exporters: %s", exporters or [])
+        logger.info("Output directory: %s", outdir)
+        if dry_run is None:
+            dry_run = self.dry_run
+        if exporters is None:
+            exporters = self.exporters
+        if not exporter_params:
+            exporter_params = self.exporter_params
+
+        exporters = serialization.deserialize_all(
+            exporters,
+            simulation=self,
+            known_modules=[
+                "soil.exporters",
+            ],
+            dry_run=dry_run,
+            outdir=outdir,
+            **exporter_params,
+        )
+
+        with utils.timer("simulation {}".format(self.name)):
+            for exporter in exporters:
+                exporter.sim_start()
+
+            for env in utils.run_parallel(
+                func=self.run_trial,
+                iterable=range(int(self.num_trials)),
+                parallel=parallel,
+                log_level=log_level,
+                **kwargs,
+            ):
+
+                for exporter in exporters:
+                    exporter.trial_start(env)
+
+                for exporter in exporters:
+                    exporter.trial_end(env)
+
+                yield env
+
+            for exporter in exporters:
+                exporter.sim_end()
+
+    def get_env(self, trial_id=0, model_params=None, **kwargs):
+        """Create an environment for a trial of the simulation"""
+
+        def deserialize_reporters(reporters):
+            for (k, v) in reporters.items():
+                if isinstance(v, str) and v.startswith("py:"):
+                    reporters[k] = serialization.deserialize(v.split(":", 1)[1])
+            return reporters
+
+        params = self.model_params.copy()
+        if model_params:
+            params.update(model_params)
+        params.update(kwargs)
+
+        agent_reporters = deserialize_reporters(params.pop("agent_reporters", {}))
+        model_reporters = deserialize_reporters(params.pop("model_reporters", {}))
+
+        env = serialization.deserialize(self.model_class)
+        return env(
+            id=f"{self.name}_trial_{trial_id}",
+            seed=f"{self.seed}_trial_{trial_id}",
+            dir_path=self.dir_path,
+            agent_reporters=agent_reporters,
+            model_reporters=model_reporters,
+            **params,
+        )
+
+    def run_trial(
+        self, trial_id=None, until=None, log_file=False, log_level=logging.INFO, **opts
+    ):
+        """
+        Run a single trial of the simulation
+
+        """
+        if log_level:
+            logger.setLevel(log_level)
+        model = self.get_env(trial_id, **opts)
+        trial_id = trial_id if trial_id is not None else current_time()
+        with utils.timer("Simulation {} trial {}".format(self.name, trial_id)):
+            return self.run_model(
+                model=model, trial_id=trial_id, until=until, log_level=log_level
+            )
+
+    def run_model(self, model, until=None, **opts):
         # Set-up trial environment and graph
-        until = until or self.max_time
-        env = self.get_env(trial_id=trial_id, **opts)
+        until = float(until or self.max_time or "inf")
+
         # 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
+        def is_done():
+            return not model.running
+
+        if until and hasattr(model.schedule, "time"):
+            prev = is_done
+
+            def is_done():
+                return prev() or model.schedule.time >= until
+
+        if self.max_steps and self.max_steps > 0 and hasattr(model.schedule, "steps"):
+            prev_steps = is_done
+
+            def is_done():
+                return prev_steps() or model.schedule.steps >= self.max_steps
+
+        newline = "\n"
+        logger.info(
+            dedent(
+                f"""
+Model stats:
+  Agents (total: { model.schedule.get_agent_count() }):
+      - { (newline + '      - ').join(str(a) for a in model.schedule.agents) }
+
+  Topology size: { len(model.G) if hasattr(model, "G") else 0 }
+        """
+            )
+        )
+
+        while not is_done():
+            utils.logger.debug(
+                f'Simulation time {model.schedule.time}/{until}. Next: {getattr(model.schedule, "next_time", model.schedule.time + self.interval)}'
+            )
+            model.step()
+
+        if (
+            model.schedule.time < until
+        ):  # Simulation ended (no more steps) before the expected time
+            model.schedule.time = until
+        return model
 
     def to_dict(self):
-        return self.__getstate__()
+        d = asdict(self)
+        if not isinstance(d["model_class"], str):
+            d["model_class"] = serialization.name(d["model_class"])
+        d["model_params"] = serialization.serialize_dict(d["model_params"])
+        d["dir_path"] = str(d["dir_path"])
+        d["version"] = "2"
+        return d
 
     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:
-            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:
-            pickle.dump(self, 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)
-        return state
-
-    def __setstate__(self, state):
-        self.__dict__ = state
-        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)
-        return state
+def iter_from_config(*cfgs, **kwargs):
+    for config in cfgs:
+        configs = list(serialization.load_config(config))
+        for config, path in configs:
+            d = dict(config)
+            if "dir_path" not in d:
+                d["dir_path"] = os.path.dirname(path)
+            yield Simulation.from_dict(d, **kwargs)
 
 
-def from_config(config):
-    config = list(utils.load_config(config))
-    if len(config) > 1:
-        raise AttributeError('Provide only one configuration')
-    config = config[0][0]
-    sim = SoilSimulation(**config)
-    return sim
+def from_config(conf_or_path):
+    lst = list(iter_from_config(conf_or_path))
+    if len(lst) > 1:
+        raise AttributeError("Provide only one configuration")
+    return lst[0]
 
 
-def run_from_config(*configs, results_dir='soil_output', dry_run=False, dump=None, timestamp=False,  **kwargs):
-    for config_def in configs:
-        # logger.info("Found {} config(s)".format(len(ls)))
-        for config, _ in utils.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))
-            sim.run_simulation(**kwargs)
+def run_from_config(*configs, **kwargs):
+    for sim in iter_from_config(*configs):
+        logger.info(f"Using config(s): {sim.name}")
+        sim.run_simulation(**kwargs)

soil/time.py

@@ -0,0 +1,201 @@
+from mesa.time import BaseScheduler
+from queue import Empty
+from heapq import heappush, heappop, heapify
+import math
+
+from inspect import getsource
+from numbers import Number
+
+from .utils import logger
+from mesa import Agent as MesaAgent
+
+
+INFINITY = float("inf")
+
+
+class DeadAgent(Exception):
+    pass
+
+
+class When:
+    def __init__(self, time):
+        if isinstance(time, When):
+            return time
+        self._time = time
+
+    def next(self, time):
+        return self._time
+
+    def abs(self, time):
+        return self
+
+    def __repr__(self):
+        return str(f"When({self._time})")
+
+    def __lt__(self, other):
+        if isinstance(other, Number):
+            return self._time < other
+        return self._time < other.next(self._time)
+
+    def __gt__(self, other):
+        if isinstance(other, Number):
+            return self._time > other
+        return self._time > other.next(self._time)
+
+    def ready(self, agent):
+        return self._time <= agent.model.schedule.time
+
+
+class Cond(When):
+    def __init__(self, func, delta=1):
+        self._func = func
+        self._delta = delta
+        self._checked = False
+
+    def next(self, time):
+        if self._checked:
+            return time + self._delta
+        return time
+
+    def abs(self, time):
+        return self
+
+    def ready(self, agent):
+        self._checked = True
+        return self._func(agent)
+
+    def __eq__(self, other):
+        return False
+
+    def __lt__(self, other):
+        return True
+
+    def __gt__(self, other):
+        return False
+
+    def __repr__(self):
+        return str(f'Cond("{getsource(self._func)}")')
+
+
+NEVER = When(INFINITY)
+
+
+class Delta(When):
+    def __init__(self, delta):
+        self._delta = delta
+
+    def __eq__(self, other):
+        if isinstance(other, Delta):
+            return self._delta == other._delta
+        return False
+
+    def abs(self, time):
+        return When(self._delta + time)
+
+    def next(self, time):
+        return time + self._delta
+
+    def __repr__(self):
+        return str(f"Delta({self._delta})")
+
+
+class TimedActivation(BaseScheduler):
+    """A scheduler which activates each agent when the agent requests.
+    In each activation, each agent will update its 'next_time'.
+    """
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self._next = {}
+        self._queue = []
+        self.next_time = 0
+        self.logger = logger.getChild(f"time_{ self.model }")
+
+    def add(self, agent: MesaAgent, when=None):
+        if when is None:
+            when = When(self.time)
+        elif not isinstance(when, When):
+            when = When(when)
+        if agent.unique_id in self._agents:
+            del self._agents[agent.unique_id]
+            if agent.unique_id in self._next:
+                self._queue.remove((self._next[agent.unique_id], agent))
+                heapify(self._queue)
+
+        self._next[agent.unique_id] = when
+        heappush(self._queue, (when, agent))
+        super().add(agent)
+
+    def step(self) -> None:
+        """
+        Executes agents in order, one at a time. After each step,
+        an agent will signal when it wants to be scheduled next.
+        """
+
+        self.logger.debug(f"Simulation step {self.time}")
+        if not self.model.running:
+            return
+
+        when = NEVER
+
+        to_process = []
+        skipped = []
+        next_time = INFINITY
+
+        ix = 0
+
+        while self._queue:
+            (when, agent) = self._queue[0]
+            if when > self.time:
+                break
+            heappop(self._queue)
+            if when.ready(agent):
+                to_process.append(agent)
+                self._next.pop(agent.unique_id, None)
+                continue
+
+            next_time = min(next_time, when.next(self.time))
+            self._next[agent.unique_id] = next_time
+            skipped.append((when, agent))
+
+        if self._queue:
+            next_time = min(next_time, self._queue[0][0].next(self.time))
+
+        self._queue = [*skipped, *self._queue]
+
+        for agent in to_process:
+            self.logger.debug(f"Stepping agent {agent}")
+
+            try:
+                returned = ((agent.step() or Delta(1))).abs(self.time)
+            except DeadAgent:
+                if agent.unique_id in self._next:
+                    del self._next[agent.unique_id]
+                agent.alive = False
+                continue
+
+            if not getattr(agent, "alive", True):
+                self.remove(agent)
+                continue
+
+            value = returned.next(self.time)
+
+            if value < self.time:
+                raise Exception(
+                    f"Cannot schedule an agent for a time in the past ({when} < {self.time})"
+                )
+            if value < INFINITY:
+                next_time = min(value, next_time)
+
+                self._next[agent.unique_id] = returned
+                heappush(self._queue, (returned, agent))
+            else:
+                assert not self._next[agent.unique_id]
+
+        self.steps += 1
+        self.logger.debug(f"Updating time step: {self.time} -> {next_time}")
+        self.time = next_time
+
+        if not self._queue or next_time == INFINITY:
+            self.model.running = False
+            return self.time

soil/utils.py

@@ -1,105 +1,154 @@
-import os
-import yaml
 import logging
-import importlib
-from time import time
-from glob import glob
-from random import random
-from copy import deepcopy
+from time import time as current_time, strftime, gmtime, localtime
+import os
+import traceback
 
-import networkx as nx
+from functools import partial
+from shutil import copyfile, move
+from multiprocessing import Pool
 
 from contextlib import contextmanager
 
-
-logger = logging.getLogger('soil')
+logger = logging.getLogger("soil")
 logger.setLevel(logging.INFO)
 
+timeformat = "%H:%M:%S"
 
-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)
+if os.environ.get("SOIL_VERBOSE", ""):
+    logformat = "[%(levelname)-5.5s][%(asctime)s][%(name)s]:  %(message)s"
+else:
+    logformat = "[%(levelname)-5.5s][%(asctime)s] %(message)s"
 
-    net_args = network_params.copy()
-    net_type = net_args.pop('generator')
+logFormatter = logging.Formatter(logformat, timeformat)
+consoleHandler = logging.StreamHandler()
+consoleHandler.setFormatter(logFormatter)
 
-    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)
+logging.basicConfig(
+    level=logging.INFO,
+    handlers=[
+        consoleHandler,
+    ],
+)
 
 
 @contextmanager
-def timer(name='task', pre="", function=logger.info, to_object=None):
-    start = time()
-    function('{}Starting {} at {}.'.format(pre, name, start))
+def timer(name="task", pre="", function=logger.info, to_object=None):
+    start = current_time()
+    function("{}Starting {} at {}.".format(pre, name, strftime("%X", gmtime(start))))
     yield start
-    end = time()
-    function('{}Finished {} in {} seconds'.format(pre, name, str(end-start)))
+    end = current_time()
+    function(
+        "{}Finished {} at {} in {} seconds".format(
+            pre, name, strftime("%X", 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 try_backup(path, remove=False):
+    if not os.path.exists(path):
+        return None
+    outdir = os.path.dirname(path)
+    if outdir and not os.path.exists(outdir):
+        os.makedirs(outdir)
+    creation = os.path.getctime(path)
+    stamp = strftime("%Y-%m-%d_%H.%M.%S", 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))
+    if move:
+        move(path, newpath)
+    else:
+        copyfile(path, newpath)
+    return newpath
 
 
-def name(v):
-    return type(v).__name__
+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:
+        try_backup(path)
+    return open(path, mode=mode, **kwargs)
 
 
-def serializer(type_):
-    if type_ == 'bool':
-        return lambda x:  "true" if x else ""
-    return lambda x: x
-
-
-def deserializer(type_):
+@contextmanager
+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
+        with safe_open(f, *args, **kwargs) as f:
+            yield f
+    except (AttributeError, TypeError) as ex:
+        yield f
 
 
-def convert(value, type_):
-    return deserializer(type_)(value)
+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 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
+
+
+def run_and_return_exceptions(func, *args, **kwargs):
+    """
+    A wrapper for run_trial that catches exceptions and returns them.
+    It is meant for async simulations.
+    """
+    try:
+        return func(*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 run_parallel(func, iterable, parallel=False, **kwargs):
+    if parallel and not os.environ.get("SOIL_DEBUG", None):
+        p = Pool()
+        wrapped_func = partial(run_and_return_exceptions, func, **kwargs)
+        for i in p.imap_unordered(wrapped_func, iterable):
+            if isinstance(i, Exception):
+                logger.error("Trial failed:\n\t%s", i.message)
+                continue
+            yield i
+    else:
+        for i in iterable:
+            yield func(i, **kwargs)

soil/version.py

@@ -4,7 +4,7 @@ import logging
 logger = logging.getLogger(__name__)
 
 ROOT = os.path.dirname(__file__)
-DEFAULT_FILE = os.path.join(ROOT, 'VERSION')
+DEFAULT_FILE = os.path.join(ROOT, "VERSION")
 
 
 def read_version(versionfile=DEFAULT_FILE):
@@ -12,9 +12,10 @@ def read_version(versionfile=DEFAULT_FILE):
         with open(versionfile) as f:
             return f.read().strip()
     except IOError:  # pragma: no cover
-        logger.error(('Running an unknown version of {}.'
-                      'Be careful!.').format(__name__))
-        return '0.0'
+        logger.error(
+            ("Running an unknown version of {}." "Be careful!.").format(__name__)
+        )
+        return "0.0"
 
 
 __version__ = read_version()

soil/visualization.py

@@ -0,0 +1,6 @@
+from mesa.visualization.UserParam import UserSettableParameter
+
+
+class UserSettableParameter(UserSettableParameter):
+    def __str__(self):
+        return self.value

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 +1,334 @@
+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_class: ControlModelM2
+    weight: 0.1
+    state:
+      id: 1
+  - agent_class: 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 +1,36 @@
+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 @@
+<?xml version="1.0" encoding="UTF-8" standalone="no"?>
+<!-- Created with Inkscape (http://www.inkscape.org/) -->
+
+<svg
+   xmlns:dc="http://purl.org/dc/elements/1.1/"
+   xmlns:cc="http://creativecommons.org/ns#"
+   xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
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+           d="m 598.11621,616.77846 c -2e-5,1.49025 -0.23244,2.80959 -0.69726,3.95801 -0.46487,1.14845 -1.14163,2.11915 -2.03028,2.91211 -0.88869,0.77931 -1.98244,1.37403 -3.28125,1.78418 -1.28517,0.41016 -2.80274,0.61524 -4.55273,0.61523 l -2.21485,0 0,8.46973 c 0,0.13672 -0.0479,0.25976 -0.14355,0.36914 -0.082,0.10937 -0.22559,0.19824 -0.43067,0.2666 -0.20508,0.0684 -0.47852,0.12305 -0.82031,0.16406 -0.3418,0.041 -0.7793,0.0615 -1.3125,0.0615 -0.51953,0 -0.95703,-0.0205 -1.3125,-0.0615 -0.3418,-0.041 -0.61524,-0.0957 -0.82031,-0.16406 -0.20508,-0.0684 -0.34864,-0.15723 -0.43066,-0.2666 -0.082,-0.10938 -0.12305,-0.23242 -0.12305,-0.36914 l 0,-23.8711 c 0,-0.64255 0.16406,-1.12106 0.49219,-1.43554 0.34179,-0.3281 0.78613,-0.49216 1.333,-0.49219 l 6.25489,0 c 0.62889,3e-5 1.22362,0.0274 1.78418,0.082 0.5742,0.041 1.2578,0.14358 2.05078,0.30762 0.79295,0.15042 1.59276,0.43752 2.39941,0.86133 0.8203,0.42385 1.51756,0.96389 2.0918,1.62011 0.5742,0.64261 1.0117,1.40139 1.3125,2.27637 0.30076,0.86135 0.45115,1.83205 0.45117,2.91211 m -5.63965,0.38965 c -10e-6,-0.92967 -0.16408,-1.69529 -0.49219,-2.29688 -0.32813,-0.60154 -0.73145,-1.04587 -1.20996,-1.333 -0.47852,-0.28709 -0.98438,-0.46483 -1.51757,-0.53321 -0.51955,-0.082 -1.05959,-0.12302 -1.62012,-0.12304 l -2.29688,0 0,9.00293 2.41993,0 c 0.86131,1e-5 1.57908,-0.1162 2.15332,-0.34864 0.58787,-0.2324 1.06639,-0.55369 1.43554,-0.96386 0.36913,-0.42382 0.6494,-0.92284 0.84082,-1.49707 0.19139,-0.58788 0.2871,-1.22362 0.28711,-1.90723"
+           id="path3816" />
+        <path
+           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;
+}