Clean-up

* Removed old/unnecessary models
* Added a `simulation.{iter_}from_py` method to load simulations from python
files
* Changed tests of examples to run programmatic simulations
* Fixed programmatic examples

.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).
+
+## [0.30 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

@@ -1,4 +1,11 @@
-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]'
 

MANIFEST.in

@@ -2,3 +2,6 @@ include requirements.txt
 include test-requirements.txt
 include README.rst
 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 +5,62 @@ 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.
 
+**Note**: Mesa 0.30 introduced many fundamental changes. Check the [documention on how to update your simulations to work with newer versions](docs/migration_0.30.rst)
+
+## SOIL vs MESA
+
+SOIL is a batteries-included platform that builds on top of MESA and provides the following out of the box:
+
+* Integration with (social) networks
+* The ability to more easily assign agents to your model (and optionally to its network):
+  * Assigning agents to nodes, and vice versa
+  * Using a description (e.g., 2 agents of type `Foo`, 10% of the network should be agents of type `Bar`)
+* **Several types of abstractions for agents**:
+  * Finite state machine, where methods can be turned into a state
+  * Network agents, which have convenience methods to access the model's topology
+  * Generator-based agents, whose state is paused though a `yield` and resumed on the next step
+* **Reporting and data collection**:
+  * Soil models include data collection and record some data by default (# of agents, state of each agent, etc.)
+  * All data collected are exported by default to a SQLite database and a description file
+  * Options to export to other formats, such as CSV, or defining your own exporters
+  * A summary of the data collected is shown in the command line, for easy inspection
+* **An event-based scheduler**
+  * Agents can be explicit about when their next time/step should be, and not all agents run in every step. This avoids unnecessary computation.
+  * Time intervals between each step are flexible.
+  * There are primitives to specify when the next execution of an agent should be (or conditions)
+* **Actor-inspired** message-passing
+* A simulation runner (`soil.Simulation`) that can:
+  * Run models in parallel
+  * Save results to different formats
+* Simulation configuration files 
+* A command line interface (`soil`), to run multiple
+* An integrated debugger (`soil --debug`) with custom functions to print agent states and break at specific states
+
+Nevertheless, most features in SOIL have been designed to integrate with plain Mesa.
+For instance, it should be possible to run a `mesa.Model` models using a `soil.Simulation` and the `soil` CLI, or to integrate the `soil.TimedActivation` scheduler on a `mesa.Model`.
+
+Note that some combinations of `mesa` and `soil` components, while technically possible, are much less useful or even wrong.
+For instance, you may add any `soil.agent` agent (except for the `soil.NetworkAgent`, as it needs a topology) on a regular `mesa.Model` with a vanilla scheduler from `mesa.time`.
+But in that case the agents will not get any of the advanced event-based scheduling, and most agent behaviors that depend on that will greatly vary. 
+
+
+## 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.
+
+
+
+## Citation 
+
+
 If you use Soil in your research, don't forget to cite this paper:
 
 ```bibtex
@@ -28,7 +84,6 @@ 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

@@ -2,6 +2,8 @@ version: '3'
 services:
   dev:
     build: .
+    environment:
+      PYTHONDONTWRITEBYTECODE: 1
     volumes:
       - .:/usr/src/app
     tty: true

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/example.yml

@@ -0,0 +1,40 @@
+---
+name: MyExampleSimulation
+max_time: 50
+num_trials: 3
+interval: 2
+model_params:
+  topology:
+    params:
+      generator: barabasi_albert_graph
+      n: 100
+      m: 2
+  agents:
+    distribution:
+    - agent_class: SISaModel
+      topology: True
+      ratio: 0.1
+      state:
+        state_id: content
+    - agent_class: SISaModel
+      topology: True
+      ratio: .1
+      state:
+        state_id: discontent
+    - agent_class: SISaModel
+      topology: True
+      ratio: 0.8
+      state:
+        state_id: neutral
+  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

@@ -1,12 +1,7 @@
-.. Soil documentation master file, created by
-   sphinx-quickstart on Tue Apr 25 12:48:56 2017.
-   You can adapt this file completely to your liking, but it should at least
-   contain the root `toctree` directive.
-
 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 +34,7 @@ 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 +14,15 @@ Now test that it worked by running the command line tool
 
    soil --help
 
-Or using soil programmatically:
+   #or
+
+   python -m soil --help
+
+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 `GitHub <https://github.com/gsi-upm/soil>`_ or `GitLab <https://lab.gsi.upm.es/soil/soil.git>`_.

docs/make.bat

@@ -12,7 +12,7 @@ set BUILDDIR=_build
 set SPHINXPROJ=Soil
 
 if "%1" == "" goto help
-
+eE
 %SPHINXBUILD% >NUL 2>NUL
 if errorlevel 9009 (
 	echo.

docs/mesa.rst

@@ -0,0 +1,22 @@
+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

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 +1,33 @@
+---
+name: quickstart
+num_trials: 1
+max_time: 1000
+model_params:
+  agents:
+    - agent_class: SISaModel
+      topology: true
+      state:
+        id: neutral
+      weight: 1
+    - agent_class: SISaModel
+      topology: true
+      state:
+        id: content
+      weight: 2
+  topology:
+    params:
+      n: 100
+      k: 5
+      p: 0.2
+      generator: newman_watts_strogatz_graph
+  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
 ------------------
 
@@ -121,13 +115,13 @@ Here's the code:
         @soil.agents.state
         def neutral(self):
             r = random.random()
-            if self['has_tv'] and r < self.env['prob_tv_spread']:
+            if self['has_tv'] and r < self.model['prob_tv_spread']:
                     return self.infected
             return
         
         @soil.agents.state
         def infected(self):
-            prob_infect = self.env['prob_neighbor_spread']
+            prob_infect = self.model['prob_neighbor_spread']
             for neighbor in self.get_neighboring_agents(state_id=self.neutral.id):
                 r = random.random()
                 if r < prob_infect:
@@ -152,11 +146,11 @@ spreading the rumor.
     class NewsEnvironmentAgent(soil.agents.BaseAgent):
         def step(self):
             if self.now == self['event_time']:
-                self.env['prob_tv_spread'] = 1
-                self.env['prob_neighbor_spread'] = 1
+                self.model['prob_tv_spread'] = 1
+                self.model['prob_neighbor_spread'] = 1
             elif self.now > self['event_time']:
-                self.env['prob_tv_spread'] = self.env['prob_tv_spread'] * TV_FACTOR
-                self.env['prob_neighbor_spread'] = self.env['prob_neighbor_spread'] * NEIGHBOR_FACTOR
+                self.model['prob_tv_spread'] = self.model['prob_tv_spread'] * TV_FACTOR
+                self.model['prob_neighbor_spread'] = self.model['prob_neighbor_spread'] * NEIGHBOR_FACTOR
 
 Testing the agents
 ~~~~~~~~~~~~~~~~~~
@@ -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::

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 +1,47 @@
+from soil.agents import FSM, state, default_state
+from soil.time import Delta
+
+
+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, Delta(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, Delta(1 + self.now % 2)
+
+
+from soil import Simulation
+
+simulation = Simulation(
+    model_params={
+        'agents':[
+            {'agent_class': Fibonacci, 'node_id': 0},
+            {'agent_class': Odds, 'node_id': 1}
+        ],
+        'topology': {
+            'params': {
+                'generator': 'complete_graph',
+                'n': 2
+            }
+        },
+    },
+    max_time=100,
+)
+
+if __name__ == "__main__":
+    simulation.run(dry_run=True)

examples/events_and_messages/README.md

@@ -0,0 +1,7 @@
+This example can be run like with command-line options, like this:
+
+```bash
+python cars.py --level DEBUG  -e summary --csv
+```
+
+This will set the `CSV` (save the agent and model data to a CSV) and `summary` (print the a summary of the data to stdout) exporters, and set the log level to DEBUG.

examples/events_and_messages/cars.py

@@ -0,0 +1,243 @@
+"""
+This is an example of a simplified city, where there are Passengers and Drivers that can take those passengers
+from their location to their desired location.
+
+An example scenario could play like the following:
+
+- Drivers start in the `wandering` state, where they wander around the city until they have been assigned a journey
+- Passenger(1) tells every driver that it wants to request a Journey.
+- Each driver receives the request.
+  If Driver(2) is interested in providing the Journey, it asks Passenger(1) to confirm that it accepts Driver(2)'s request
+- When Passenger(1) accepts the request, two things happen:
+    - Passenger(1) changes its state to `driving_home`
+    - Driver(2) starts moving towards the origin of the Journey
+- Once Driver(2) reaches the origin, it starts moving itself and Passenger(1) to the destination of the Journey
+- When Driver(2) reaches the destination (carrying Passenger(1) along):
+    - Driver(2) starts wondering again
+    - Passenger(1) dies, and is removed from the simulation
+- If there are no more passengers available in the simulation, Drivers die
+"""
+from __future__ import annotations
+from typing import Optional
+from soil import *
+from soil import events
+from mesa.space import MultiGrid
+
+
+# More complex scenarios may use more than one type of message  between objects.
+# A common pattern is to use `enum.Enum` to represent state changes in a request.
+@dataclass
+class Journey:
+    """
+    This represents a request for a journey. Passengers and drivers exchange this object.
+
+    A journey may have a driver assigned or not. If the driver has not been assigned, this
+    object is considered a "request for a journey".
+    """
+
+    origin: (int, int)
+    destination: (int, int)
+    tip: float
+
+    passenger: Passenger
+    driver: Optional[Driver] = None
+
+
+class City(EventedEnvironment):
+    """
+    An environment with a grid where drivers and passengers will be placed.
+
+    The number of drivers and riders is configurable through its parameters:
+
+    :param str n_cars: The total number of drivers to add
+    :param str n_passengers: The number of passengers in the simulation
+    :param list agents: Specific agents to use in the simulation. It overrides the `n_passengers`
+    and `n_cars` params.
+    :param int height: Height of the internal grid
+    :param int width: Width of the internal grid
+    """
+
+    def __init__(
+        self,
+        *args,
+        n_cars=1,
+        n_passengers=10,
+        height=100,
+        width=100,
+        agents=None,
+        model_reporters=None,
+        **kwargs,
+    ):
+        self.grid = MultiGrid(width=width, height=height, torus=False)
+        if agents is None:
+            agents = []
+            for i in range(n_cars):
+                agents.append({"agent_class": Driver})
+            for i in range(n_passengers):
+                agents.append({"agent_class": Passenger})
+        model_reporters = model_reporters or {
+            "earnings": "total_earnings",
+            "n_passengers": "number_passengers",
+        }
+        print("REPORTERS", model_reporters)
+        super().__init__(
+            *args, agents=agents, model_reporters=model_reporters, **kwargs
+        )
+        for agent in self.agents:
+            self.grid.place_agent(agent, (0, 0))
+            self.grid.move_to_empty(agent)
+
+    @property
+    def total_earnings(self):
+        return sum(d.earnings for d in self.agents(agent_class=Driver))
+
+    @property
+    def number_passengers(self):
+        return self.count_agents(agent_class=Passenger)
+
+
+class Driver(Evented, FSM):
+    pos = None
+    journey = None
+    earnings = 0
+
+    def on_receive(self, msg, sender):
+        """This is not a state. It will run (and block) every time check_messages is invoked"""
+        if self.journey is None and isinstance(msg, Journey) and msg.driver is None:
+            msg.driver = self
+            self.journey = msg
+
+    def check_passengers(self):
+        """If there are no more passengers, stop forever"""
+        c = self.count_agents(agent_class=Passenger)
+        self.info(f"Passengers left {c}")
+        if not c:
+            self.die()
+
+    @default_state
+    @state
+    def wandering(self):
+        """Move around the city until a journey is accepted"""
+        target = None
+        self.check_passengers()
+        self.journey = None
+        while self.journey is None:  # No potential journeys detected (see on_receive)
+            if target is None or not self.move_towards(target):
+                target = self.random.choice(
+                    self.model.grid.get_neighborhood(self.pos, moore=False)
+                )
+
+            self.check_passengers()
+            # This will call on_receive behind the scenes, and the agent's status will be updated
+            self.check_messages()
+            yield Delta(30)  # Wait at least 30 seconds before checking again
+
+        try:
+            # Re-send the journey to the passenger, to confirm that we have been selected
+            self.journey = yield self.journey.passenger.ask(self.journey, timeout=60)
+        except events.TimedOut:
+            # No journey has been accepted. Try again
+            self.journey = None
+            return
+
+        return self.driving
+
+    @state
+    def driving(self):
+        """The journey has been accepted. Pick them up and take them to their destination"""
+        while self.move_towards(self.journey.origin):
+            yield
+        while self.move_towards(self.journey.destination, with_passenger=True):
+            yield
+        self.earnings += self.journey.tip
+        self.check_passengers()
+        return self.wandering
+
+    def move_towards(self, target, with_passenger=False):
+        """Move one cell at a time towards a target"""
+        self.info(f"Moving { self.pos } -> { target }")
+        if target[0] == self.pos[0] and target[1] == self.pos[1]:
+            return False
+
+        next_pos = [self.pos[0], self.pos[1]]
+        for idx in [0, 1]:
+            if self.pos[idx] < target[idx]:
+                next_pos[idx] += 1
+                break
+            if self.pos[idx] > target[idx]:
+                next_pos[idx] -= 1
+                break
+        self.model.grid.move_agent(self, tuple(next_pos))
+        if with_passenger:
+            self.journey.passenger.pos = (
+                self.pos
+            )  # This could be communicated through messages
+        return True
+
+
+class Passenger(Evented, FSM):
+    pos = None
+
+    def on_receive(self, msg, sender):
+        """This is not a state. It will be run synchronously every time `check_messages` is run"""
+
+        if isinstance(msg, Journey):
+            self.journey = msg
+            return msg
+
+    @default_state
+    @state
+    def asking(self):
+        destination = (
+            self.random.randint(0, self.model.grid.height),
+            self.random.randint(0, self.model.grid.width),
+        )
+        self.journey = None
+        journey = Journey(
+            origin=self.pos,
+            destination=destination,
+            tip=self.random.randint(10, 100),
+            passenger=self,
+        )
+
+        timeout = 60
+        expiration = self.now + timeout
+        self.model.broadcast(journey, ttl=timeout, sender=self, agent_class=Driver)
+        while not self.journey:
+            self.info(f"Passenger at: { self.pos }. Checking for responses.")
+            try:
+                # This will call check_messages behind the scenes, and the agent's status will be updated
+                # If you want to avoid that, you can call it with: check=False
+                yield self.received(expiration=expiration)
+            except events.TimedOut:
+                self.info(f"Passenger at: { self.pos }. Asking for journey.")
+                self.model.broadcast(
+                    journey, ttl=timeout, sender=self, agent_class=Driver
+                )
+                expiration = self.now + timeout
+        return self.driving_home
+
+    @state
+    def driving_home(self):
+        while (
+            self.pos[0] != self.journey.destination[0]
+            or self.pos[1] != self.journey.destination[1]
+        ):
+            try:
+                yield self.received(timeout=60)
+            except events.TimedOut:
+                pass
+
+        self.info("Got home safe!")
+        self.die()
+
+
+simulation = Simulation(
+    name="RideHailing",
+    model_class=City,
+    model_params={"n_passengers": 2},
+    seed="carsSeed",
+)
+
+if __name__ == "__main__":
+    simulation.run()

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 +1,115 @@
+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
+
+if __name__ == '__main__':
+    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, **kwargs):
+        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_neighbors())
+        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 +1,87 @@
-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
+    has_been_infected = False
 
     @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
+        if self.has_been_infected:
+            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']):
+        for neighbor in self.get_neighbors(state_id=self.neutral.id):
+            if self.prob(self.model["prob_neighbor_spread"]):
                 neighbor.infect()
 
     def infect(self):
-        self.set_state(self.infected)
+        """
+        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.has_been_infected = True
 
 
 class HerdViewer(DumbViewer):
-    '''
+    """
     A viewer whose probability of infection depends on the state of its neighbors.
-    '''
-
-    level = logging.DEBUG
+    """
 
     def infect(self):
-        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)
+        """Notice again that this is NOT a state. See DumbViewer.infect for reference"""
+        infected = self.count_neighbors(state_id=self.infected.id)
+        total = self.count_neighbors()
+        prob_infect = self.model["prob_neighbor_spread"] * infected / total
+        self.debug("prob_infect", prob_infect)
+        if self.prob(prob_infect):
+            self.has_been_infected = True
 
 
 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']
-        for neighbor in self.get_neighboring_agents(state_id=self.infected.id):
-            if prob(prob_cure):
+        prob_cure = self.model["prob_neighbor_cure"]
+        for neighbor in self.get_neighbors(state_id=self.infected.id):
+            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)
+        self.has_been_cured = True
 
     @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()
-        return self.set_state(super().infected)
+        if self.has_been_cured:
+            return self.cured
+        cured = max(self.count_neighbors(self.cured.id), 1.0)
+        infected = max(self.count_neighbors(self.infected.id), 1.0)
+        prob_cure = self.model["prob_neighbor_cure"] * (cured / infected)
+        if self.prob(prob_cure):
+            return self.cured

examples/programmatic/.gitignore

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

examples/programmatic/programmatic.py

@@ -0,0 +1,57 @@
+"""
+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)
+    G.add_node(2)
+    return G
+
+
+class MyAgent(agents.FSM):
+    times_run = 0
+    @agents.default_state
+    @agents.state
+    def neutral(self):
+        self.debug("I am running")
+        if self.prob(0.2):
+            self.times_run += 1
+            self.info("This runs 2/10 times on average")
+
+
+simulation = Simulation(
+    name="Programmatic",
+    model_params={
+        'topology': {
+            'params': {
+                'generator': mygenerator
+            },
+        },
+        'agents': {
+            'distribution': [{
+                'agent_class': MyAgent,
+                'topology': True,
+            }]
+        }
+    },
+    seed='Program',
+    agent_reporters={'times_run': 'times_run'},
+    num_trials=1,
+    max_time=100,
+    dry_run=True,
+)
+
+if __name__ == "__main__":
+    # 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 = simulation.run()
+
+    for agent in envs[0].agents:
+        print(agent.times_run)

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_neighbors())
+        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 run_from_config
+
+    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 +1,152 @@
+from soil import FSM, state, default_state, BaseAgent, NetworkAgent, Environment
+from collections import Counter
+import logging
+import math
+
+
+class RabbitEnv(Environment):
+    prob_death = 1e-100
+
+    @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.prob_death * math.floor(
+            math.log10(max(1, rabbits_alive))
+        )
+        self.debug("Killing some rabbits with prob={}!".format(prob_death))
+        for i in self.get_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",
+    model_params={
+        'agents': [{'agent_class': MyAgent}],
+    },
+    num_trials=1,
+    max_time=100,
+    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 +1,289 @@
+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_neighbors(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_neighbors(
+            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, agent=None, **kwargs):
+        """Get a list of nodes in the ego network of *node* of radius *steps*"""
+        node = agent.node
+        G = self.subgraph(**kwargs)
+        return nx.ego_graph(G, node, center=center, radius=steps).nodes()
+
+    def degree(self, agent, force=False):
+        node = agent.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, agent, force=False):
+        node = agent.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_neighbors(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_neighbors(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_neighbors(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_neighbors(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

@@ -1,32 +1,31 @@
 name: TerroristNetworkModel_sim
-load_module: TerroristNetworkModel
-max_time: 150 
+max_steps: 150 
 num_trials: 1
-network_params:
-  generator: random_geometric_graph
-  radius: 0.2
-  # generator: geographical_threshold_graph
-  # theta: 20
-  n: 100
-network_agents:
-  - agent_type: TerroristNetworkModel
-    weight: 0.8
-    state:
-      id: civilian  # Civilians
-  - agent_type: TerroristNetworkModel
-    weight: 0.1
-    state:
-      id: leader    # Leaders
-  - agent_type: TrainingAreaModel
-    weight: 0.05
-    state:
-      id: terrorist # Terrorism
-  - agent_type: HavenModel
-    weight: 0.05
-    state:
-      id: civilian  # Civilian
+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
 
-environment_params:
   # TerroristSpreadModel
   information_spread_intensity: 0.7
   terrorist_additional_influence: 0.035
@@ -60,3 +59,4 @@ visualization_params:
   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>

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(
@@ -40,15 +46,13 @@ setup(
         'Operating System :: POSIX',
         'Programming Language :: Python :: 3'],
     install_requires=install_reqs,
-    extras_require={
-        'web': ['tornado']
-
-    },
+    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 @@
-0.12.0
+0.30.0rc4

soil/__init__.py

@@ -1,8 +1,12 @@
+from __future__ import annotations
+
 import importlib
+from importlib.resources import path
 import sys
 import os
-import pdb
 import logging
+import traceback
+from contextlib import contextmanager
 
 from .version import __version__
 
@@ -11,65 +15,242 @@ try:
 except NameError:
     basestring = str
 
-logging.basicConfig()
-
+from pathlib import Path
+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, EventedEnvironment
+from .datacollection import SoilCollector
+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,
+):
+
+    sim = None
+    if isinstance(cfg, Simulation):
+        sim = cfg
+
     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 if sim is None else "",
+        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))
+    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 sim:
+            logger.info("Loading simulation instance")
+            sim.dry_run = args.dry_run
+            sim.exporters = exporters
+            sim.parallel = parallel
+            sim.outdir = output
+            sims = [
+                sim,
+            ]
+        else:
+            logger.info("Loading config file: {}".format(args.file))
+            if not os.path.exists(args.file):
+                logger.error("Please, input a valid file")
+                return
+
+            sims = list(
+                simulation.iter_from_config(
+                    args.file,
+                    dry_run=args.dry_run,
+                    exporters=exporters,
+                    parallel=parallel,
+                    outdir=output,
+                    exporter_params=exp_params,
+                    **kwargs,
+                )
+            )
+
+        for sim in sims:
+
+            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):
+    try:
+        yield main(cfg, debug=debug, pdb=pdb, **kwargs)[0]
+    except Exception as e:
+        if os.environ.get("SOIL_POSTMORTEM"):
+            from .debugging import post_mortem
+
+            print(traceback.format_exc())
+            post_mortem()
+        raise
+
+
+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_neighbors(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

@@ -1,102 +0,0 @@
-import random
-from . import BaseAgent
-
-
-class BigMarketModel(BaseAgent):
-    """
-    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']
-        self.type = ""
-        self.number_of_enterprises = len(environment.environment_params['enterprises'])
-
-        if self.id < self.number_of_enterprises:  # Enterprises
-            self.state['id'] = self.id
-            self.type = "Enterprise"
-            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
-
-    def step(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
-            for x in aware_neighbors:
-                if 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
-
-                # Establecemos limites
-                if x.sentiment_about[self.id] > 1:
-                    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]
-
-    def userBehaviour(self):
-
-        if random.random() < self.tweet_probability:  # Tweets
-            if 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()
-                    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)
-                        elif self.sentiment_about[i] == 0:
-                            # NEUTRO
-                            pass
-                        else:
-                            # POSITIVO
-                            self.userTweets("positive",i)
-
-    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
-            elif sentiment == "negative":
-                x.sentiment_about[enterprise] -=0.003
-            else:
-                pass
-
-            # Establecemos limites
-            if x.sentiment_about[enterprise] > 1:
-                x.sentiment_about[enterprise] = 1
-            if x.sentiment_about[enterprise] < -1:
-                x.sentiment_about[enterprise] = -1
-
-            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()))
-        neighbors = len(list(self.get_neighboring_agents()))
-        self['times'] = self.get('times', 0) + 1
-        self['neighbors'] = neighbors
-        self['total'] = total
+        total = len(list(self.model.schedule._agents))
+        neighbors = len(list(self.get_neighbors()))
+        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()))
-        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["times"] += 1
+        neighbors = len(list(self.get_neighbors()))
+        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
+
+
+class Geo(NetworkAgent):
+    """In this type of network, nodes have a "pos" attribute."""
+
+    def geo_search(self, radius, agent=None, center=False, **kwargs):
+        """Get a list of nodes whose coordinates are closer than *radius* to *node*."""
+        node = agent.node
+
+        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

@@ -1,8 +1,7 @@
-import random
-from . import BaseAgent
+from . import Agent, state, default_state
 
 
-class IndependentCascadeModel(BaseAgent):
+class IndependentCascadeModel(Agent):
     """
     Settings:
         innovation_prob
@@ -10,40 +9,22 @@ 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
+    time_awareness = 0
+    sentimentCorrelation = 0
 
-    def step(self):
-        self.behaviour()
+    # Outside effects
+    @default_state
+    @state
+    def outside(self):
+        if self.prob(self.model.innovation_prob):
+            self.sentimentCorrelation = 1
+            self.time_awareness = self.model.now # To know when they have been infected
+            return self.imitate
 
-    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
-            else:
-                pass
+    @state
+    def imitate(self):
+        aware_neighbors = self.get_neighbors(state_id=1, time_awareness=self.now-1)
 
-            return
-
-        # Imitation effects
-        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):
-                    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
-            else:
-                pass
-
-            return
+        if self.prob(self.model.imitation_prob * len(aware_neighbors)):
+            self.sentimentCorrelation = 1
+            return self.outside

soil/agents/ModelM2.py

@@ -1,242 +0,0 @@
-import random
-import numpy as np
-from . import BaseAgent
-
-
-class SpreadModelM2(BaseAgent):
-    """
-    Settings:
-        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, 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_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_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
-            self.neutral_behaviour()
-        elif self.state['id'] == 1:  # Infected
-            self.infected_behaviour()
-        elif self.state['id'] == 2:  # Cured
-            self.cured_behaviour()
-        elif self.state['id'] == 3:  # Vaccinated
-            self.vaccinated_behaviour()
-
-    def neutral_behaviour(self):
-
-        # 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
-
-    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
-
-    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
-
-        # 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
-
-    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
-
-        # 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
-
-        # 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
-
-
-class ControlModelM2(BaseAgent):
-    """
-    Settings:
-        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, 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_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_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
-            self.neutral_behaviour()
-        elif self.state['id'] == 1:  # Infected
-            self.infected_behaviour()
-        elif self.state['id'] == 2:  # Cured
-            self.cured_behaviour()
-        elif self.state['id'] == 3:  # Vaccinated
-            self.vaccinated_behaviour()
-        elif self.state['id'] == 4:  # Beacon-off
-            self.beacon_off_behaviour()
-        elif self.state['id'] == 5:  # Beacon-on
-            self.beacon_on_behaviour()
-
-    def neutral_behaviour(self):
-        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
-
-    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
-
-    def cured_behaviour(self):
-
-        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
-
-        # 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
-
-    def vaccinated_behaviour(self):
-        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
-
-        # 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
-
-        # 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
-
-    def beacon_off_behaviour(self):
-        self.state['visible'] = False
-        infected_neighbors = self.get_neighboring_agents(state_id=1)
-        if len(infected_neighbors) > 0:
-            self.state['id'] == 5  # Beacon on
-
-    def beacon_on_behaviour(self):
-        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
-            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
-            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
-
-        # 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

soil/agents/SISaModel.py

@@ -1,16 +1,16 @@
-import random
 import numpy as np
-from . import FSM, state
+from hashlib import sha512
+from . import Agent, state, default_state
 
 
-class SISaModel(FSM):
+class SISaModel(Agent):
     """
     Settings:
         neutral_discontent_spon_prob
 
         neutral_discontent_infected_prob
 
-        neutral_content_spong_prob
+        neutral_content_spon_prob
 
         neutral_content_infected_prob
 
@@ -29,65 +29,82 @@ class SISaModel(FSM):
         standard_variance
     """
 
-    def __init__(self, environment=None, agent_id=0, state=()):
-        super().__init__(environment=environment, agent_id=agent_id, state=state)
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
 
-        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'])
+        seed = self.model._seed
+        if isinstance(seed, (str, bytes, bytearray)):
+            if isinstance(seed, str):
+                seed = seed.encode()
+            seed = int.from_bytes(seed + sha512(seed).digest(), 'big')
 
-        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'])
+        random = np.random.default_rng(seed=seed)
 
-        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.neutral_discontent_spon_prob = random.normal(
+            self.model.neutral_discontent_spon_prob, self.model.standard_variance
+        )
+        self.neutral_discontent_infected_prob = random.normal(
+            self.model.neutral_discontent_infected_prob, self.model.standard_variance
+        )
+        self.neutral_content_spon_prob = random.normal(
+            self.model.neutral_content_spon_prob, self.model.standard_variance
+        )
+        self.neutral_content_infected_prob = random.normal(
+            self.model.neutral_content_infected_prob, self.model.standard_variance
+        )
 
+        self.discontent_neutral = random.normal(
+            self.model.discontent_neutral, self.model.standard_variance
+        )
+        self.discontent_content = random.normal(
+            self.model.discontent_content, self.model.variance_d_c
+        )
+
+        self.content_discontent = random.normal(
+            self.model.content_discontent, self.model.variance_c_d
+        )
+        self.content_neutral = random.normal(
+            self.model.discontent_neutral, self.model.standard_variance
+        )
+
+    @default_state
     @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:
+        discontent_neighbors = self.count_neighbors(state_id=self.discontent)
+        if self.prob(discontent_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:
+        content_neighbors = self.count_neighbors(state_id=self.content.id)
+        if self.prob(content_neighbors * 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:
+        content_neighbors = self.count_neighbors(state_id=self.content.id)
+        if self.prob(content_neighbors * 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_neighbors(state_id=self.discontent.id)
+        if self.prob(discontent_neighbors * self.content_discontent):
             self.discontent
         return self.content

soil/agents/SentimentCorrelationModel.py

@@ -1,102 +0,0 @@
-import random
-from . import BaseAgent
-
-
-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'] = []
-        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
-
-    def step(self):
-        self.behaviour()
-
-    def behaviour(self):
-
-        angry_neighbors_1_time_step = []
-        joyful_neighbors_1_time_step = []
-        sad_neighbors_1_time_step = []
-        disgusted_neighbors_1_time_step = []
-
-        angry_neighbors = self.get_neighboring_agents(state_id=1)
-        for x in angry_neighbors:
-            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):
-                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):
-                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):
-                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)
-        outside_effects_prob = self.outside_effects_prob
-
-        num = random.random()
-
-        if num<outside_effects_prob:
-            self.state['id'] = 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']
-
-
-        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'] = 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'] = 4
-            self.state['sentimentCorrelation'] = 4
-            self.state['time_awareness'][self.state['id']-1] = self.env.now
-
-        self.state['sentiment'] = self.state['id']

soil/agents/__init__.py

@@ -1,108 +1,178 @@
-# networkStatus = {}  # Dict that will contain the status of every agent in the network
-# sentimentCorrelationNodeArray = []
-# for x in range(0, settings.network_params["number_of_nodes"]):
-#     sentimentCorrelationNodeArray.append({'id': x})
-# Initialize agent states. Let's assume everyone is normal.
+from __future__ import annotations
 
-
-import nxsim
 import logging
-from collections import OrderedDict
-from copy import deepcopy
-from functools import partial
-import json
+from collections import OrderedDict, defaultdict
+from collections.abc import MutableMapping, Mapping, Set
+from abc import ABCMeta
+from copy import deepcopy, copy
+from functools import partial, wraps
+from itertools import islice, chain
+import inspect
+import types
+import textwrap
+import networkx as nx
 
-from functools import wraps
+from typing import Any
 
-from .. import utils, history
+from mesa import Agent as MesaAgent
+from typing import Dict, List
 
-agent_types = {}
+from .. import serialization, utils, time, config
 
 
-class MetaAgent(type):
-    def __init__(cls, name, bases, nmspc):
-        super(MetaAgent, cls).__init__(name, bases, nmspc)
-        agent_types[name] = cls
+IGNORED_FIELDS = ("model", "logger")
 
 
-class BaseAgent(nxsim.BaseAgent, metaclass=MetaAgent):
+class MetaAgent(ABCMeta):
+    def __new__(mcls, name, bases, namespace):
+        defaults = {}
+
+        # Re-use defaults from inherited classes
+        for i in bases:
+            if isinstance(i, MetaAgent):
+                defaults.update(i._defaults)
+
+        new_nmspc = {
+            "_defaults": defaults,
+            "_last_return": None,
+            "_last_except": None,
+        }
+
+        for attr, func in namespace.items():
+            if attr == "step" and inspect.isgeneratorfunction(func):
+                orig_func = func
+                new_nmspc["_coroutine"] = None
+
+                @wraps(func)
+                def func(self):
+                    while True:
+                        if not self._coroutine:
+                            self._coroutine = orig_func(self)
+                        try:
+                            if self._last_except:
+                                return self._coroutine.throw(self._last_except)
+                            else:
+                                return self._coroutine.send(self._last_return)
+                        except StopIteration as ex:
+                            self._coroutine = None
+                            return ex.value
+                        finally:
+                            self._last_return = None
+                            self._last_except = None
+
+                func.id = name or func.__name__
+                func.is_default = False
+                new_nmspc[attr] = func
+            elif (
+                isinstance(func, types.FunctionType)
+                or isinstance(func, property)
+                or isinstance(func, classmethod)
+                or attr[0] == "_"
+            ):
+                new_nmspc[attr] = func
+            elif attr == "defaults":
+                defaults.update(func)
+            else:
+                defaults[attr] = copy(func)
+
+        return super().__new__(mcls=mcls, name=name, bases=bases, namespace=new_nmspc)
+
+
+class BaseAgent(MesaAgent, MutableMapping, metaclass=MetaAgent):
     """
-    A special simpy BaseAgent that keeps track of its state history.
+    A special type of Mesa Agent that:
+
+    * Can be used as a dictionary to access its state.
+    * Has logging built-in
+    * Can be given default arguments through a defaults class attribute,
+    which will be used on construction to initialize each agent's state
+
+    Any attribute that is not preceded by an underscore (`_`) will also be added to its state.
     """
 
-    defaults = {}
+    def __init__(self, unique_id, model, name=None, interval=None, **kwargs):
+        assert isinstance(unique_id, int)
+        super().__init__(unique_id=unique_id, model=model)
 
-    def __init__(self, environment=None, agent_id=None, state=None,
-                 name='network_process', interval=None, **state_params):
-        # Check for REQUIRED arguments
-        assert environment is not None, TypeError('__init__ missing 1 required keyword argument: \'environment\'. '
-                                                  'Cannot be NoneType.')
-        # Initialize agent parameters
-        self.id = agent_id
-        self.name = name
-        self.state_params = state_params
+        self.name = (
+            str(name) if name else "{}[{}]".format(type(self).__name__, self.unique_id)
+        )
 
-        # Global parameters
-        self.global_topology = environment.G
-        self.environment_params = environment.environment_params
-
-        # Register agent to environment
-        self.env = environment
-
-        self._neighbors = None
         self.alive = True
-        real_state = deepcopy(self.defaults)
-        real_state.update(state or {})
-        self.state = real_state
-        self.interval = interval
 
-        if not hasattr(self, 'level'):
-            self.level = logging.DEBUG
-        self.logger = logging.getLogger('{}-Agent-{}'.format(self.env.name,
-                                                             self.id))
-        self.logger.setLevel(self.level)
+        self.interval = interval or self.get("interval", 1)
+        logger = utils.logger.getChild(getattr(self.model, "id", self.model)).getChild(
+            self.name
+        )
+        self.logger = logging.LoggerAdapter(logger, {"agent_name": self.name})
 
-        # initialize every time an instance of the agent is created
-        self.action = self.env.process(self.run())
+        if hasattr(self, "level"):
+            self.logger.setLevel(self.level)
 
+        for (k, v) in self._defaults.items():
+            if not hasattr(self, k) or getattr(self, k) is None:
+                setattr(self, k, deepcopy(v))
+
+        for (k, v) in kwargs.items():
+
+            setattr(self, k, v)
+
+    def __hash__(self):
+        return hash(self.unique_id)
+
+    def prob(self, probability):
+        return prob(probability, self.model.random)
+
+    # TODO: refactor to clean up mesa compatibility
     @property
-    def state(self):
-        '''
-        Return the agent itself, which behaves as a dictionary.
-        Changes made to `agent.state` will be reflected in the history.
+    def id(self):
+        return self.unique_id
 
-        This method shouldn't be used, but is kept here for backwards compatibility.
-        '''
-        return self
-
-    @state.setter
-    def state(self, value):
-        self._state = {}
-        for k, v in value.items():
-            self[k] = v
+    @classmethod
+    def from_dict(cls, model, attrs, warn_extra=True):
+        ignored = {}
+        args = {}
+        for k, v in attrs.items():
+            if k in inspect.signature(cls).parameters:
+                args[k] = v
+            else:
+                ignored[k] = v
+        if ignored and warn_extra:
+            utils.logger.info(
+                f"Ignoring the following arguments for agent class { agent_class.__name__ }: { ignored }"
+            )
+        return cls(model=model, **args)
 
     def __getitem__(self, key):
-        if isinstance(key, tuple):
-            key, t_step = key
-            k = history.Key(key=key, t_step=t_step, agent_id=self.id)
-            return self.env[k]
-        return self._state.get(key, None)
+        try:
+            return getattr(self, key)
+        except AttributeError:
+            raise KeyError(f"key {key}  not found in agent")
 
     def __delitem__(self, key):
-        self._state[key] = None
+        return delattr(self, key)
 
     def __contains__(self, key):
-        return key in self._state
+        return hasattr(self, key)
 
     def __setitem__(self, key, value):
-        self._state[key] = value
-        k = history.Key(t_step=self.now,
-                        agent_id=self.id,
-                        key=key)
-        self.env[k] = value
+        setattr(self, key, value)
 
-    def items(self):
-        return self._state.items()
+    def __len__(self):
+        return sum(1 for n in self.keys())
+
+    def __iter__(self):
+        return self.items()
+
+    def keys(self):
+        return (k for k in self.__dict__ if k[0] != "_" and k not in IGNORED_FIELDS)
+
+    def items(self, keys=None, skip=None):
+        keys = keys if keys is not None else self.keys()
+        it = ((k, self.get(k, None)) for k in keys)
+        if skip:
+            return filter(lambda x: x[0] not in skip, it)
+        return it
 
     def get(self, key, default=None):
         return self[key] if key in self else default
@@ -110,75 +180,37 @@ class BaseAgent(nxsim.BaseAgent, metaclass=MetaAgent):
     @property
     def now(self):
         try:
-            return self.env.now
+            return self.model.now
         except AttributeError:
             # No environment
             return None
 
-    def run(self):
-        if self.interval is not None:
-            interval = self.interval
-        elif 'interval' in self:
-            interval = self['interval']
-        else:
-            interval = self.env.interval
-        while self.alive:
-            res = self.step()
-            yield res or self.env.timeout(interval)
-
-    def die(self, remove=False):
+    def die(self):
+        self.info(f"agent dying")
         self.alive = False
-        if remove:
-            super().die()
+        try:
+            self.model.schedule.remove(self)
+        except KeyError:
+            pass
+        return time.NEVER
 
     def step(self):
-        pass
-
-    def to_json(self):
-        return json.dumps(self.state)
-
-    def count_agents(self, state_id=None, limit_neighbors=False):
-        if limit_neighbors:
-            agents = self.global_topology.neighbors(self.id)
-        else:
-            agents = self.global_topology.nodes()
-        count = 0
-        for agent in agents:
-            if state_id and state_id != self.global_topology.node[agent]['agent']['id']:
-                continue
-            count += 1
-        return count
-
-    def count_neighboring_agents(self, state_id=None):
-        return len(super().get_agents(state_id, limit_neighbors=True))
-
-    def get_agents(self, state_id=None, limit_neighbors=False, iterator=False, **kwargs):
-        if limit_neighbors:
-            agents = super().get_agents(state_id, limit_neighbors)
-        else:
-            agents = filter(lambda x: state_id is None or x.state.get('id', None) == state_id,
-                            self.env.agents)
-
-        def matches_all(agent):
-            state = agent.state
-            for k, v in kwargs.items():
-                if state.get(k, None) != v:
-                    return False
-            return True
-
-        f = filter(matches_all, agents)
-        if iterator:
-            return f
-        return list(f)
+        if not self.alive:
+            raise time.DeadAgent(self.unique_id)
+        super().step()
+        return time.Delta(self.interval)
 
     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 = "\t@{:>5}:\t{}".format(self.now, message)
+        message = "[@{:>4}]\t{:>10}: {}".format(self.now, repr(self), message)
         for k, v in kwargs:
             message += " {k}={v} ".format(k, v)
         extra = {}
-        extra['now'] = self.now
-        extra['id'] = self.id
+        extra["now"] = self.now
+        extra["unique_id"] = self.unique_id
+        extra["agent_name"] = self.name
         return self.logger.log(level, message, extra=extra)
 
     def debug(self, *args, **kwargs):
@@ -187,91 +219,35 @@ class BaseAgent(nxsim.BaseAgent, metaclass=MetaAgent):
     def info(self, *args, **kwargs):
         return self.log(*args, level=logging.INFO, **kwargs)
 
+    def count_agents(self, **kwargs):
+        return len(list(self.get_agents(**kwargs)))
 
-def state(func):
-    '''
-    A state function should return either a state id, or a tuple (state_id, when)
-    The default value for state_id is the current state id.
-    The default value for when is the interval defined in the nevironment.
-    '''
+    def get_agents(self, *args, **kwargs):
+        it = self.iter_agents(*args, **kwargs)
+        return list(it)
 
-    @wraps(func)
-    def func_wrapper(self):
-        next_state = func(self)
-        when = None
-        if next_state is None:
-            return when
-        try:
-            next_state, when = next_state
-        except (ValueError, TypeError):
-            pass
-        if next_state:
-            self.set_state(next_state)
-        return when
+    def iter_agents(self, *args, **kwargs):
+        yield from filter_agents(self.model.schedule._agents, *args, **kwargs)
 
-    func_wrapper.id = func.__name__
-    func_wrapper.is_default = False
-    return func_wrapper
+    def __str__(self):
+        return self.to_str()
+
+    def to_str(self, keys=None, skip=None, pretty=False):
+        content = dict(self.items(keys=keys))
+        if pretty and content:
+            d = content
+            content = "\n"
+            for k, v in d.items():
+                content += f"- {k}: {v}\n"
+            content = textwrap.indent(content, "    ")
+        return f"{repr(self)}{content}"
+
+    def __repr__(self):
+        return f"{self.__class__.__name__}({self.unique_id})"
 
 
-def default_state(func):
-    func.is_default = True
-    return func
-
-
-class MetaFSM(MetaAgent):
-    def __init__(cls, name, bases, nmspc):
-        super(MetaFSM, cls).__init__(name, bases, nmspc)
-        states = {}
-        # Re-use states from inherited classes
-        default_state = None
-        for i in bases:
-            if isinstance(i, MetaFSM):
-                for state_id, state in i.states.items():
-                    if state.is_default:
-                        default_state = state
-                    states[state_id] = state
-
-        # Add new states
-        for name, func in nmspc.items():
-            if hasattr(func, 'id'):
-                if func.is_default:
-                    default_state = func
-                states[func.id] = func
-        cls.default_state = default_state
-        cls.states = states
-
-
-class FSM(BaseAgent, metaclass=MetaFSM):
-    def __init__(self, *args, **kwargs):
-        super(FSM, self).__init__(*args, **kwargs)
-        if 'id' not in self.state:
-            if not self.default_state:
-                raise ValueError('No default state specified for {}'.format(self.id))
-            self['id'] = self.default_state.id
-
-    def step(self):
-        if 'id' in self.state:
-            next_state = self['id']
-        elif self.default_state:
-            next_state = self.default_state.id
-        else:
-            raise Exception('{} has no valid state id or default state'.format(self))
-        if next_state not in self.states:
-            raise Exception('{} is not a valid id for {}'.format(next_state, self))
-        self.states[next_state](self)
-
-    def set_state(self, state):
-        if hasattr(state, 'id'):
-            state = state.id
-        if state not in self.states:
-            raise ValueError('{} is not a valid state'.format(state))
-        self['id'] = state
-        return state
-
-
-def prob(prob=1):
-    '''
+def prob(prob, random):
+    """
     A true/False uniform distribution with a given probability.
     To be used like this:
 
@@ -280,27 +256,26 @@ def prob(prob=1):
           if prob(0.3):
               do_something()
 
-    '''
+    """
     r = random.random()
     return r < prob
 
 
-def calculate_distribution(network_agents=None,
-                           agent_type=None):
-    '''
+def calculate_distribution(network_agents=None, agent_class=None):
+    """
     Calculate the threshold values (thresholds for a uniform distribution)
     of an agent distribution given the weights of each agent type.
 
     The input has this form: ::
 
             [
-            {'agent_type': 'agent_type_1',
+            {'agent_class': 'agent_class_1',
                 'weight': 0.2,
                 'state': {
                     'id': 0
                 }
             },
-            {'agent_type': 'agent_type_2',
+            {'agent_class': 'agent_class_2',
                 'weight': 0.8,
                 'state': {
                     'id': 1
@@ -309,81 +284,359 @@ def calculate_distribution(network_agents=None,
             ]
 
     In this example, 20% of the nodes will be marked as type
-    'agent_type_1'.
-    '''
+    'agent_class_1'.
+    """
     if network_agents:
-        network_agents = deepcopy(network_agents)
-    elif agent_type:
-        network_agents = [{'agent_type': agent_type}]
+        network_agents = [
+            deepcopy(agent) for agent in network_agents if not hasattr(agent, "id")
+        ]
+    elif agent_class:
+        network_agents = [{"agent_class": agent_class}]
     else:
-        return []
+        raise ValueError("Specify a distribution or a default agent type")
+
+    # Fix missing weights and incompatible types
+    for x in network_agents:
+        x["weight"] = float(x.get("weight", 1))
 
     # Calculate the thresholds
-    total = sum(x.get('weight', 1) for x in network_agents)
+    total = sum(x["weight"] for x in network_agents)
     acc = 0
     for v in network_agents:
-        upper = acc + (v.get('weight', 1)/total)
-        v['threshold'] = [acc, upper]
+        if "ids" in v:
+            continue
+        upper = acc + (v["weight"] / total)
+        v["threshold"] = [acc, upper]
         acc = upper
     return network_agents
 
 
-def _serialize_distribution(network_agents):
-    d = _convert_agent_types(network_agents,
-                             to_string=True)
-    '''
-    When serializing an agent distribution, remove the thresholds, in order
-    to avoid cluttering the YAML definition file.
-    '''
-    for v in d:
-        if 'threshold' in v:
-            del v['threshold']
-    return d
+def _serialize_type(agent_class, known_modules=[], **kwargs):
+    if isinstance(agent_class, str):
+        return agent_class
+    known_modules += ["soil.agents"]
+    return serialization.serialize(agent_class, known_modules=known_modules, **kwargs)[
+        1
+    ]  # Get the name of the class
 
 
-def _validate_states(states, topology):
-    '''Validate states to avoid ignoring states during initialization'''
-    states = states or []
-    if isinstance(states, dict):
-        for x in states:
-            assert x in topology.node
+def _deserialize_type(agent_class, known_modules=[]):
+    if not isinstance(agent_class, str):
+        return agent_class
+    known = known_modules + ["soil.agents", "soil.agents.custom"]
+    agent_class = serialization.deserializer(agent_class, known_modules=known)
+    return agent_class
+
+
+class AgentView(Mapping, Set):
+    """A lazy-loaded list of agents."""
+
+    __slots__ = ("_agents",)
+
+    def __init__(self, agents):
+        self._agents = agents
+
+    def __getstate__(self):
+        return {"_agents": self._agents}
+
+    def __setstate__(self, state):
+        self._agents = state["_agents"]
+
+    # Mapping methods
+    def __len__(self):
+        return len(self._agents)
+
+    def __iter__(self):
+        yield from self._agents.values()
+
+    def __getitem__(self, agent_id):
+        if isinstance(agent_id, slice):
+            raise ValueError(f"Slicing is not supported")
+        if agent_id in self._agents:
+            return self._agents[agent_id]
+        raise ValueError(f"Agent {agent_id} not found")
+
+    def filter(self, *args, **kwargs):
+        yield from filter_agents(self._agents, *args, **kwargs)
+
+    def one(self, *args, **kwargs):
+        return next(filter_agents(self._agents, *args, **kwargs))
+
+    def __call__(self, *args, **kwargs):
+        return list(self.filter(*args, **kwargs))
+
+    def __contains__(self, agent_id):
+        return agent_id in self._agents
+
+    def __str__(self):
+        return str(list(unique_id for unique_id in self.keys()))
+
+    def __repr__(self):
+        return f"{self.__class__.__name__}({self})"
+
+
+def filter_agents(
+    agents,
+    *id_args,
+    unique_id=None,
+    state_id=None,
+    agent_class=None,
+    ignore=None,
+    state=None,
+    limit=None,
+    **kwargs,
+):
+    """
+    Filter agents given as a dict, by the criteria given as arguments (e.g., certain type or state id).
+    """
+    assert isinstance(agents, dict)
+
+    ids = []
+
+    if unique_id is not None:
+        if isinstance(unique_id, list):
+            ids += unique_id
+        else:
+            ids.append(unique_id)
+
+    if id_args:
+        ids += id_args
+
+    if ids:
+        f = (agents[aid] for aid in ids if aid in agents)
     else:
-        assert len(states) <= len(topology)
-    return states
+        f = agents.values()
+
+    if state_id is not None and not isinstance(state_id, (tuple, list)):
+        state_id = tuple([state_id])
+
+    if agent_class is not None:
+        agent_class = _deserialize_type(agent_class)
+        try:
+            agent_class = tuple(agent_class)
+        except TypeError:
+            agent_class = tuple([agent_class])
+
+    if ignore:
+        f = filter(lambda x: x not in ignore, f)
+
+    if state_id is not None:
+        f = filter(lambda agent: agent.get("state_id", None) in state_id, f)
+
+    if agent_class is not None:
+        f = filter(lambda agent: isinstance(agent, agent_class), f)
+
+    state = state or dict()
+    state.update(kwargs)
+
+    for k, v in state.items():
+        f = filter(lambda agent: getattr(agent, k, None) == v, f)
+
+    if limit is not None:
+        f = islice(f, limit)
+
+    yield from f
 
 
-def _convert_agent_types(ind, to_string=False):
-    '''Convenience method to allow specifying agents by class or class name.'''
-    d = deepcopy(ind)
-    for v in d:
-        agent_type = v['agent_type']
-        if to_string and not isinstance(agent_type, str):
-            v['agent_type'] = str(agent_type.__name__)
-        elif not to_string and isinstance(agent_type, str):
-            v['agent_type'] = agent_types[agent_type]
-    return d
+def from_config(
+    cfg: config.AgentConfig, random, topology: nx.Graph = None
+) -> List[Dict[str, Any]]:
+    """
+    This function turns an agentconfig into a list of individual "agent specifications", which are just a dictionary
+    with the parameters that the environment will use to construct each agent.
+
+    This function does NOT return a list of agents, mostly because some attributes to the agent are not known at the
+    time of calling this function, such as `unique_id`.
+    """
+    default = cfg or config.AgentConfig()
+    if not isinstance(cfg, config.AgentConfig):
+        cfg = config.AgentConfig(**cfg)
+
+    agents = []
+
+    assigned_total = 0
+    assigned_network = 0
+
+    if cfg.fixed is not None:
+        agents, assigned_total, assigned_network = _from_fixed(
+            cfg.fixed, topology=cfg.topology, default=cfg
+        )
+
+    n = cfg.n
+
+    if cfg.distribution:
+        topo_size = len(topology) if topology else 0
+
+        networked = []
+        total = []
+
+        for d in cfg.distribution:
+            if d.strategy == config.Strategy.topology:
+                topo = d.topology if ("topology" in d.__fields_set__) else cfg.topology
+                if not topo:
+                    raise ValueError(
+                        'The "topology" strategy only works if the topology parameter is set to True'
+                    )
+                if not topo_size:
+                    raise ValueError(
+                        f"Topology does not have enough free nodes to assign one to the agent"
+                    )
+
+                networked.append(d)
+
+            if d.strategy == config.Strategy.total:
+                if not cfg.n:
+                    raise ValueError(
+                        'Cannot use the "total" strategy without providing the total number of agents'
+                    )
+                total.append(d)
+
+        if networked:
+            new_agents = _from_distro(
+                networked,
+                n=topo_size - assigned_network,
+                topology=topo,
+                default=cfg,
+                random=random,
+            )
+            assigned_total += len(new_agents)
+            assigned_network += len(new_agents)
+            agents += new_agents
+
+        if total:
+            remaining = n - assigned_total
+            agents += _from_distro(total, n=remaining, default=cfg, random=random)
+
+        if assigned_network < topo_size:
+            utils.logger.warn(
+                f"The total number of agents does not match the total number of nodes in "
+                "every topology. This may be due to a definition error: assigned: "
+                f"{ assigned } total size: { topo_size }"
+            )
+
+    return agents
 
 
-def _agent_from_distribution(distribution, value=-1):
-    """Used in the initialization of agents given an agent distribution."""
-    if value < 0:
-        value = random.random()
-    for d in distribution:
-        threshold = d['threshold']
-        if value >= threshold[0] and value < threshold[1]:
-            state = {}
-            if 'state' in d:
-                state = deepcopy(d['state'])
-            return d['agent_type'], state
+def _from_fixed(
+    lst: List[config.FixedAgentConfig],
+    topology: bool,
+    default: config.SingleAgentConfig,
+) -> List[Dict[str, Any]]:
+    agents = []
 
-    raise Exception('Distribution for value {} not found in: {}'.format(value, distribution))
+    counts_total = 0
+    counts_network = 0
+
+    for fixed in lst:
+        agent = {}
+        if default:
+            agent = default.state.copy()
+        agent.update(fixed.state)
+        cls = serialization.deserialize(
+            fixed.agent_class or (default and default.agent_class)
+        )
+        agent["agent_class"] = cls
+        topo = (
+            fixed.topology
+            if ("topology" in fixed.__fields_set__)
+            else topology or default.topology
+        )
+
+        if topo:
+            agent["topology"] = True
+            counts_network += 1
+        if not fixed.hidden:
+            counts_total += 1
+        agents.append(agent)
+
+    return agents, counts_total, counts_network
+
+
+def _from_distro(
+    distro: List[config.AgentDistro],
+    n: int,
+    default: config.SingleAgentConfig,
+    random,
+    topology: str = None
+) -> List[Dict[str, Any]]:
+
+    agents = []
+
+    if n is None:
+        if any(lambda dist: dist.n is None, distro):
+            raise ValueError(
+                "You must provide a total number of agents, or the number of each type"
+            )
+        n = sum(dist.n for dist in distro)
+
+    weights = list(dist.weight if dist.weight is not None else 1 for dist in distro)
+    minw = min(weights)
+    norm = list(weight / minw for weight in weights)
+    total = sum(norm)
+    chunk = n // total
+
+    # random.choices would be enough to get a weighted distribution. But it can vary a lot for smaller k
+    # So instead we calculate our own distribution to make sure the actual ratios are close to what we would expect
+
+    # Calculate how many times each has to appear
+    indices = list(
+        chain.from_iterable([idx] * int(n * chunk) for (idx, n) in enumerate(norm))
+    )
+
+    # Complete with random agents following the original weight distribution
+    if len(indices) < n:
+        indices += random.choices(
+            list(range(len(distro))),
+            weights=[d.weight for d in distro],
+            k=n - len(indices),
+        )
+
+    # Deserialize classes for efficiency
+    classes = list(
+        serialization.deserialize(i.agent_class or default.agent_class) for i in distro
+    )
+
+    # Add them in random order
+    random.shuffle(indices)
+
+    for idx in indices:
+        d = distro[idx]
+        agent = d.state.copy()
+        cls = classes[idx]
+        agent["agent_class"] = cls
+        if default:
+            agent.update(default.state)
+        topology = (
+            d.topology
+            if ("topology" in d.__fields_set__)
+            else topology or default.topology
+        )
+        if topology:
+            agent["topology"] = topology
+        agents.append(agent)
+
+    return agents
+
+
+from .network_agents import *
+from .fsm import *
+from .evented import *
+
+
+class Agent(NetworkAgent, FSM, EventedAgent):
+    """Default agent class, has both network and event capabilities"""
 
 
 from .BassModel import *
-from .BigMarketModel import *
 from .IndependentCascadeModel import *
-from .ModelM2 import *
-from .SentimentCorrelationModel import *
 from .SISaModel import *
 from .CounterModel import *
-from .DrawingAgent import *
+
+
+try:
+    import scipy
+    from .Geo import Geo
+except ImportError:
+    import sys
+
+    print("Could not load the Geo Agent, scipy is not installed", file=sys.stderr)

soil/agents/evented.py

@@ -0,0 +1,77 @@
+from . import BaseAgent
+from ..events import Message, Tell, Ask, TimedOut
+from ..time import BaseCond
+from functools import partial
+from collections import deque
+
+
+class ReceivedOrTimeout(BaseCond):
+    def __init__(
+        self, agent, expiration=None, timeout=None, check=True, ignore=False, **kwargs
+    ):
+        if expiration is None:
+            if timeout is not None:
+                expiration = agent.now + timeout
+        self.expiration = expiration
+        self.ignore = ignore
+        self.check = check
+        super().__init__(**kwargs)
+
+    def expired(self, time):
+        return self.expiration and self.expiration < time
+
+    def ready(self, agent, time):
+        return len(agent._inbox) or self.expired(time)
+
+    def return_value(self, agent):
+        if not self.ignore and self.expired(agent.now):
+            raise TimedOut("No messages received")
+        if self.check:
+            agent.check_messages()
+        return None
+
+    def schedule_next(self, time, delta, first=False):
+        if self._delta is not None:
+            delta = self._delta
+        return (time + delta, self)
+
+    def __repr__(self):
+        return f"ReceivedOrTimeout(expires={self.expiration})"
+
+
+class EventedAgent(BaseAgent):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self._inbox = deque()
+        self._processed = 0
+
+    def on_receive(self, *args, **kwargs):
+        pass
+
+    def received(self, *args, **kwargs):
+        return ReceivedOrTimeout(self, *args, **kwargs)
+
+    def tell(self, msg, sender=None):
+        self._inbox.append(Tell(timestamp=self.now, payload=msg, sender=sender))
+
+    def ask(self, msg, timeout=None, **kwargs):
+        ask = Ask(timestamp=self.now, payload=msg, sender=self)
+        self._inbox.append(ask)
+        expiration = float("inf") if timeout is None else self.now + timeout
+        return ask.replied(expiration=expiration, **kwargs)
+
+    def check_messages(self):
+        changed = False
+        while self._inbox:
+            msg = self._inbox.popleft()
+            self._processed += 1
+            if msg.expired(self.now):
+                continue
+            changed = True
+            reply = self.on_receive(msg.payload, sender=msg.sender)
+            if isinstance(msg, Ask):
+                msg.reply = reply
+        return changed
+
+
+Evented = EventedAgent

soil/agents/fsm.py

@@ -0,0 +1,140 @@
+from . import MetaAgent, BaseAgent
+
+from functools import partial, wraps
+import inspect
+
+
+def state(name=None):
+    def decorator(func, name=None):
+        """
+        A state function should return either a state id, or a tuple (state_id, when)
+        The default value for state_id is the current state id.
+        The default value for when is the interval defined in the environment.
+        """
+        if inspect.isgeneratorfunction(func):
+            orig_func = func
+
+            @wraps(func)
+            def func(self):
+                while True:
+                    if not self._coroutine:
+                        self._coroutine = orig_func(self)
+
+                    try:
+                        if self._last_except:
+                            n = self._coroutine.throw(self._last_except)
+                        else:
+                            n = self._coroutine.send(self._last_return)
+                        if n:
+                            return None, n
+                        return n
+                    except StopIteration as ex:
+                        self._coroutine = None
+                        next_state = ex.value
+                        if next_state is not None:
+                            self._set_state(next_state)
+                        return next_state
+                    finally:
+                        self._last_return = None
+                        self._last_except = None
+
+        func.id = name or func.__name__
+        func.is_default = False
+        return func
+
+    if callable(name):
+        return decorator(name)
+    else:
+        return partial(decorator, name=name)
+
+
+def default_state(func):
+    func.is_default = True
+    return func
+
+
+class MetaFSM(MetaAgent):
+    def __new__(mcls, name, bases, namespace):
+        states = {}
+        # Re-use states from inherited classes
+        default_state = None
+        for i in bases:
+            if isinstance(i, MetaFSM):
+                for state_id, state in i._states.items():
+                    if state.is_default:
+                        default_state = state
+                    states[state_id] = state
+
+        # Add new states
+        for attr, func in namespace.items():
+            if hasattr(func, "id"):
+                if func.is_default:
+                    default_state = func
+                states[func.id] = func
+
+        namespace.update(
+            {
+                "_default_state": default_state,
+                "_states": states,
+            }
+        )
+
+        return super(MetaFSM, mcls).__new__(
+            mcls=mcls, name=name, bases=bases, namespace=namespace
+        )
+
+
+class FSM(BaseAgent, metaclass=MetaFSM):
+    def __init__(self, **kwargs):
+        super(FSM, self).__init__(**kwargs)
+        if not hasattr(self, "state_id"):
+            if not self._default_state:
+                raise ValueError(
+                    "No default state specified for {}".format(self.unique_id)
+                )
+            self.state_id = self._default_state.id
+
+        self._coroutine = None
+        self._set_state(self.state_id)
+
+    def step(self):
+        self.debug(f"Agent {self.unique_id} @ state {self.state_id}")
+        default_interval = super().step()
+
+        next_state = self._states[self.state_id](self)
+
+        when = None
+        try:
+            next_state, *when = next_state
+            if not when:
+                when = None
+            elif len(when) == 1:
+                when = when[0]
+            else:
+                raise ValueError(
+                    "Too many values returned. Only state (and time) allowed"
+                )
+        except TypeError:
+            pass
+
+        if next_state is not None:
+            self._set_state(next_state)
+
+        return when or default_interval
+
+    def _set_state(self, state, when=None):
+        if hasattr(state, "id"):
+            state = state.id
+        if state not in self._states:
+            raise ValueError("{} is not a valid state".format(state))
+        self.state_id = state
+        if when is not None:
+            self.model.schedule.add(self, when=when)
+        return state
+
+    def die(self):
+        return self.dead, super().die()
+
+    @state
+    def dead(self):
+        return self.die()

soil/agents/network_agents.py

@@ -0,0 +1,88 @@
+from . import BaseAgent
+
+
+class NetworkAgent(BaseAgent):
+    def __init__(self, *args, topology, node_id, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        assert topology is not None
+        assert node_id is not None
+        self.G = topology
+        assert self.G
+        self.node_id = node_id
+
+    def count_neighbors(self, state_id=None, **kwargs):
+        return len(self.get_neighbors(state_id=state_id, **kwargs))
+
+    def iter_neighbors(self, **kwargs):
+        return self.iter_agents(limit_neighbors=True, **kwargs)
+
+    def get_neighbors(self, **kwargs):
+        return list(self.iter_neighbors())
+
+    @property
+    def node(self):
+        return self.G.nodes[self.node_id]
+
+    def iter_agents(self, unique_id=None, *, limit_neighbors=False, **kwargs):
+        unique_ids = None
+        if isinstance(unique_id, list):
+            unique_ids = set(unique_id)
+        elif unique_id is not None:
+            unique_ids = set(
+                [
+                    unique_id,
+                ]
+            )
+
+        if limit_neighbors:
+            neighbor_ids = set()
+            for node_id in self.G.neighbors(self.node_id):
+                if self.G.nodes[node_id].get("agent") is not None:
+                    neighbor_ids.add(node_id)
+            if unique_ids:
+                unique_ids = unique_ids & neighbor_ids
+            else:
+                unique_ids = neighbor_ids
+            if not unique_ids:
+                return
+            unique_ids = list(unique_ids)
+        yield from super().iter_agents(unique_id=unique_ids, **kwargs)
+
+    def subgraph(self, center=True, **kwargs):
+        include = [self] if center else []
+        G = self.G.subgraph(
+            n.node_id for n in list(self.get_agents(**kwargs) + include)
+        )
+        return G
+
+    def remove_node(self):
+        self.debug(f"Removing node for {self.unique_id}: {self.node_id}")
+        self.G.remove_node(self.node_id)
+        self.node_id = None
+
+    def add_edge(self, other, edge_attr_dict=None, *edge_attrs):
+        if self.node_id not in self.G.nodes(data=False):
+            raise ValueError(
+                "{} not in list of existing agents in the network".format(
+                    self.unique_id
+                )
+            )
+        if other.node_id not in self.G.nodes(data=False):
+            raise ValueError(
+                "{} not in list of existing agents in the network".format(other)
+            )
+
+        self.G.add_edge(
+            self.node_id, other.node_id, edge_attr_dict=edge_attr_dict, *edge_attrs
+        )
+
+    def die(self, remove=True):
+        if not self.alive:
+            return None
+        if remove:
+            self.remove_node()
+        return super().die()
+
+
+NetAgent = NetworkAgent

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 +1,267 @@
+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 NetConfig(BaseModel):
+    params: Optional[Dict[str, Any]]
+    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
+    num_processes: int = 1
+    interval: float = 1
+    seed: str = ""
+    dry_run: bool = False
+    skip_test: 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 +1,17 @@
+from mesa import DataCollector as MDC
+
+
+class SoilCollector(MDC):
+    def __init__(self, model_reporters=None, agent_reporters=None, tables=None, **kwargs):
+        model_reporters = model_reporters or {}
+        agent_reporters = agent_reporters or {}
+        tables = tables or {}
+        if 'agent_count' not in model_reporters:
+            model_reporters['agent_count'] = lambda m: m.schedule.get_agent_count()
+        if 'state_id' not in agent_reporters:
+            agent_reporters['agent_id'] = lambda agent: agent.get('state_id', None)
+
+        super().__init__(model_reporters=model_reporters,
+                         agent_reporters=agent_reporters,
+                         tables=tables,
+                         **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", [])
+        if skip_soil:
+            skip.append("soil")
+            skip.append("contextlib")
+            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

@@ -1,313 +1,349 @@
+from __future__ import annotations
+
 import os
 import sqlite3
-import time
-import csv
-import random
-import simpy
-import tempfile
-import pandas as pd
+import math
+import logging
+import inspect
+
+from typing import Any, Dict, Optional, Union, List
+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 . import agents as agentmod, config, datacollection, serialization, utils, time, network, events
 
 
-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_class=time.TimedActivation,
+        dir_path=None,
+        interval=1,
+        agent_class=None,
+        agents: List[tuple[type, Dict[str, Any]]] = {},
+        collector_class: type = datacollection.SoilCollector,
+        agent_reporters: Optional[Any] = None,
+        model_reporters: Optional[Any] = None,
+        tables: Optional[Any] = None,
+        **env_params,
+    ):
+
+        super().__init__(seed=seed)
+
+        self.current_id = -1
+
+        self.id = id
+
+        self.dir_path = dir_path or os.getcwd()
+
+        if schedule_class is None:
+            schedule_class = time.TimedActivation
+        else:
+            schedule_class = serialization.deserialize(schedule_class)
+        self.schedule = schedule_class(self)
+
+        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)
+
+        collector_class = serialization.deserialize(collector_class)
+        self.datacollector = collector_class(
+            model_reporters=model_reporters,
+            agent_reporters=agent_reporters,
+            tables=tables,
+        )
+        for (k, v) in env_params.items():
+            self[k] = v
+
+    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, List[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(
+        #     "--- Step: {:^5} - Time: {now:^5} ---", steps=self.schedule.steps, now=self.now
+        # )
+        self.schedule.step()
+        self.datacollector.collect(self)
+
+    def __getitem__(self, key):
+        try:
+            return getattr(self, key)
+        except AttributeError:
+            raise KeyError(f"key {key}  not found in environment")
+
+    def __delitem__(self, key):
+        return delattr(self, key)
+
+    def __contains__(self, key):
+        return hasattr(self, key)
+
+    def __setitem__(self, key, value):
+        setattr(self, key, value)
+
+    def __str__(self):
+        return str(dict(self))
+
+    def __len__(self):
+        return sum(1 for n in self.keys())
+
+    def __iter__(self):
+        return iter(self.agents())
+
+    def get(self, key, default=None):
+        return self[key] if key in self else default
+
+    def keys(self):
+        return (k for k in self.__dict__ if k[0] != "_")
+
+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)
+
+        if topology is None:
+            topology = nx.Graph()
+        elif not isinstance(topology, nx.Graph):
+            topology = network.from_config(topology, dir_path=self.dir_path)
+
+        self.G = 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():
+            self._init_node(agent)
+
+    def _init_node(self, agent):
+        """
+        Make sure the node for a given agent has the proper attributes.
+        """
+        if hasattr(agent, "node_id"):
+            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.pop('agent', None)
+        node_attrs.update(agent)
+        agent = node_attrs
+
+        a = super()._agent_from_dict(agent)
+        self._init_node(a)
+
+        return a
 
     @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.values():
+            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 hasattr(a, "node_id"):
+            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, attribute, value in sorted(list(history)):
-                if attribute == 'visible':
-                    nowvisible = value
-                    if nowvisible and not lastvisible:
-                        laststep = t_step
-                    if not nowvisible and lastvisible:
-                        spells.append((laststep, t_step))
+            a_class = self.random.choices(agent_class, weights)[0]
+            self.add_agent(node_id=node_id, topology=self.G,  agent_class=a_class, **agent_params)
 
-                    lastvisible = nowvisible
-                    continue
-                key = 'attr_' + attribute
-                if key not in attributes:
-                    attributes[key] = list()
-                if key not in lastattributes:
-                    lastattributes[key] = (value, t_step)
-                elif lastattributes[key][0] != value:
-                    last_value, laststep = lastattributes[key]
-                    commit_value = (last_value, laststep, t_step)
-                    if key not in attributes:
-                        attributes[key] = list()
-                    attributes[key].append(commit_value)
-                    lastattributes[key] = (value, t_step)
-            for k, v in lastattributes.items():
-                attributes[k].append((v[0], v[1], None))
-            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
+class EventedEnvironment(BaseEnvironment):
+    def broadcast(self, msg, sender=None, expiration=None, ttl=None, **kwargs):
+        for agent in self.agents(**kwargs):
+            if agent == sender:
+                continue
+            self.logger.info(f"Telling {repr(agent)}: {msg} ttl={ttl}")
+            try:
+                inbox = agent._inbox
+            except AttributeError:
+                self.logger.info(
+                    f"Agent {agent.unique_id} cannot receive events because it does not have an inbox"
+                )
+                continue
+            # Allow for AttributeError exceptions in this part of the code
+            inbox.append(
+                events.Tell(
+                    payload=msg,
+                    sender=sender,
+                    expiration=expiration if ttl is None else self.now + ttl,
+                )
+            )
 
-    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
+class Environment(NetworkEnvironment, EventedEnvironment):
+    """Default environment class, has both network and event capabilities"""

soil/events.py

@@ -0,0 +1,56 @@
+from .time import BaseCond
+from dataclasses import dataclass, field
+from typing import Any
+from uuid import uuid4
+
+
+class Event:
+    pass
+
+
+@dataclass
+class Message:
+    payload: Any
+    sender: Any = None
+    expiration: float = None
+    timestamp: float = None
+    id: int = field(default_factory=uuid4)
+
+    def expired(self, when):
+        return self.expiration is not None and self.expiration < when
+
+
+class Reply(Message):
+    source: Message
+
+
+class ReplyCond(BaseCond):
+    def __init__(self, ask, *args, **kwargs):
+        self._ask = ask
+        super().__init__(*args, **kwargs)
+
+    def ready(self, agent, time):
+        return self._ask.reply is not None or self._ask.expired(time)
+
+    def return_value(self, agent):
+        if self._ask.expired(agent.now):
+            raise TimedOut()
+        return self._ask.reply
+
+    def __repr__(self):
+        return f"ReplyCond({self._ask.id})"
+
+
+class Ask(Message):
+    reply: Message = None
+
+    def replied(self, expiration=None):
+        return ReplyCond(self)
+
+
+class Tell(Message):
+    pass
+
+
+class TimedOut(Exception):
+    pass

soil/exporters.py

@@ -0,0 +1,256 @@
+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 SQLite(Exporter):
+    """Writes sqlite results"""
+
+    def sim_start(self):
+        if self.dry_run:
+            logger.info("NOT dumping results")
+            return
+        self.dbpath = os.path.join(self.outdir, f"{self.simulation.name}.sqlite")
+        logger.info("Dumping results to %s", self.dbpath)
+        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):
+        msg = ""
+        for (t, df) in self.get_dfs(env):
+            if not len(df):
+                continue
+            tabs = "\t" * 2
+            description = indent(str(df.describe()), tabs)
+            last_line = indent(str(df.iloc[-1:]), tabs)
+            # value_counts = indent(str(df.value_counts()), tabs)
+            value_counts = indent(str(df.apply(lambda x: x.value_counts()).T.stack()), tabs)
+
+            msg += dedent("""
+            Dataframe {t}:
+                Last line: :
+            {last_line}
+
+                Description:
+            {description}
+
+                Value counts:
+            {value_counts}
+
+            """).format(**locals())
+        logger.info(msg)
+
+class YAML(Exporter):
+    """Writes the configuration of the simulation to a YAML file"""
+
+    def sim_start(self):
+        if self.dry_run:
+            logger.info("NOT dumping results")
+            return
+        with self.output(self.simulation.name + ".dumped.yml") as f:
+            logger.info(f"Dumping simulation configuration to {self.outdir}")
+            f.write(self.simulation.to_yaml())
+
+class default(Exporter):
+    """Default exporter. Writes sqlite results, as well as the simulation YAML"""
+
+    def __init__(self, *args, exporter_cls=[], **kwargs):
+        exporter_cls = exporter_cls or [YAML, SQLite, summary]
+        self.inner = [cls(*args, **kwargs) for cls in exporter_cls]
+
+    def sim_start(self):
+        for exporter in self.inner:
+            exporter.sim_start()
+
+    def sim_end(self):
+        for exporter in self.inner:
+            exporter.sim_end()
+
+    def trial_start(self, env):
+        for exporter in self.inner:
+            exporter.trial_start(env)
+
+
+    def trial_end(self, env):
+        for exporter in self.inner:
+            exporter.trial_end(env)

soil/history.py

@@ -1,254 +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
-
-        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 db(self):
-        try:
-            self._db.cursor()
-        except sqlite3.ProgrammingError:
-            self.db = None  # Reset the database
-        return self._db
-
-    @db.setter
-    def db(self, db_path=None):
-        db_path = db_path or self.db_path
-        if isinstance(db_path, str):
-            self._db = sqlite3.connect(db_path)
-        else:
-            self._db = db_path
-
-    @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)
-        if r.resolved:
-            return r.value()
-        return r
-
-
-
-    def read_sql(self, keys=None, agent_ids=None, t_steps=None, convert_types=False, limit=-1):
-
-        self.read_types()
-
-        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 list(self)
-
-    def __getitem__(self, k):
-        n = copy.copy(self)
-        n.filter(k)
-        if n.resolved:
-            return n.value()
-        return n
-
-    def __len__(self):
-        return len(self._df)
-
-    def __str__(self):
-        if self.resolved:
-            return str(self.value())
-        return '<Records for [{}]>'.format(self._filter)
-
-
-Key = namedtuple('Key', ['agent_id', 't_step', 'key'])
-Record = namedtuple('Record', 'agent_id t_step key value')

soil/network.py

@@ -0,0 +1,83 @@
+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 = dict(cfg.params)
+        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.
+    """
+    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 +1,231 @@
+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):
+    try:
+        d = dict(d)
+    except (ValueError, TypeError) as ex:
+        return serialize(d)[0]
+    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"""
+    objects = []
+    for name in names:
+        mod = deserialize(name, known_modules=known_modules)
+        objects.append(mod(*args, **kwargs))
+    return objects

soil/simulation.py

@@ -1,219 +1,291 @@
 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, **kwargs):
 
-        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 = 1
+    num_processes: Optional[int] = 1
+    parallel: Optional[bool] = False
+    exporters: Optional[List[str]] = field(default_factory=lambda: [exporters.default])
+    model_reporters: Optional[Dict[str, Any]] = field(default_factory=dict)
+    agent_reporters: Optional[Dict[str, Any]] = field(default_factory=dict)
+    tables: Optional[Dict[str, Any]] = field(default_factory=dict)
+    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)
+    skip_test: Optional[bool] = False
 
-        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:
+            logger.warning(f'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,
+        num_processes=1,
+        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)),
+                num_processes=num_processes,
+                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 = self.agent_reporters.copy()
+        agent_reporters.update(deserialize_reporters(params.pop("agent_reporters", {})))
+        model_reporters = self.model_reporters.copy()
+        model_reporters.update(deserialize_reporters(params.pop("model_reporters", {})))
+        tables = self.tables.copy()
+        tables.update(deserialize_reporters(params.pop("tables", {})))
+
+        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,
+            tables=tables,
+            **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)
+        return serialization.serialize_dict(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 iter_from_py(pyfile, module_name='custom_simulation'):
+    """Try to load every Simulation instance in a given Python file"""
+    import importlib
+    import inspect
+    spec = importlib.util.spec_from_file_location(module_name, pyfile)
+    module = importlib.util.module_from_spec(spec)
+    sys.modules[module_name] = module
+    spec.loader.exec_module(module)
+    # import pdb;pdb.set_trace()
+    for (_name, sim) in inspect.getmembers(module, lambda x: isinstance(x, Simulation)):
+        yield sim
+    del sys.modules[module_name]
 
 
-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))
+def from_py(pyfile):
+    return next(iter_from_py(pyfile))
 
-            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 +1,207 @@
+from mesa.time import BaseScheduler
+from queue import Empty
+from heapq import heappush, heappop
+import math
+
+from inspect import getsource
+from numbers import Number
+from textwrap import dedent
+
+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 abs(self, time):
+        return self._time
+
+    def schedule_next(self, time, delta, first=False):
+        return (self._time, None)
+
+
+NEVER = When(INFINITY)
+
+
+class Delta(When):
+    def __init__(self, delta):
+        self._delta = delta
+
+    def abs(self, time):
+        return self._time + self._delta
+
+    def __eq__(self, other):
+        if isinstance(other, Delta):
+            return self._delta == other._delta
+        return False
+
+    def schedule_next(self, time, delta, first=False):
+        return (time + self._delta, None)
+
+    def __repr__(self):
+        return str(f"Delta({self._delta})")
+
+
+class BaseCond:
+    def __init__(self, msg=None, delta=None, eager=False):
+        self._msg = msg
+        self._delta = delta
+        self.eager = eager
+
+    def schedule_next(self, time, delta, first=False):
+        if first and self.eager:
+            return (time, self)
+        if self._delta:
+            delta = self._delta
+        return (time + delta, self)
+
+    def return_value(self, agent):
+        return None
+
+    def __repr__(self):
+        return self._msg or self.__class__.__name__
+
+
+class Cond(BaseCond):
+    def __init__(self, func, *args, **kwargs):
+        self._func = func
+        super().__init__(*args, **kwargs)
+
+    def ready(self, agent, time):
+        return self._func(agent)
+
+    def __repr__(self):
+        if self._msg:
+            return self._msg
+        return str(f'Cond("{dedent(getsource(self._func)).strip()}")')
+
+
+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, shuffle=True, **kwargs):
+        super().__init__(*args, **kwargs)
+        self._next = {}
+        self._queue = []
+        self._shuffle = shuffle
+        self.step_interval = 1
+        self.logger = logger.getChild(f"time_{ self.model }")
+
+    def add(self, agent: MesaAgent, when=None):
+        if when is None:
+            when = self.time
+        elif isinstance(when, When):
+            when = when.abs()
+
+        self._schedule(agent, None, when)
+        super().add(agent)
+
+    def _schedule(self, agent, condition=None, when=None):
+        if condition:
+            if not when:
+                when, condition = condition.schedule_next(
+                    when or self.time, self.step_interval
+                )
+        else:
+            if when is None:
+                when = self.time + self.step_interval
+            condition = None
+        if self._shuffle:
+            key = (when, self.model.random.random(), condition)
+        else:
+            key = (when, agent.unique_id, condition)
+        self._next[agent.unique_id] = key
+        heappush(self._queue, (key, 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 or self.time == INFINITY:
+            return
+
+        self.logger.debug("Queue length: {ql}", ql=len(self._queue))
+
+        while self._queue:
+            ((when, _id, cond), agent) = self._queue[0]
+            if when > self.time:
+                break
+
+            heappop(self._queue)
+            if cond:
+                if not cond.ready(agent, self.time):
+                    self._schedule(agent, cond)
+                    continue
+                try:
+                    agent._last_return = cond.return_value(agent)
+                except Exception as ex:
+                    agent._last_except = ex
+            else:
+                agent._last_return = None
+                agent._last_except = None
+
+            self.logger.debug("Stepping agent {agent}", agent=agent)
+            self._next.pop(agent.unique_id, None)
+
+            try:
+                returned = agent.step()
+            except DeadAgent:
+                agent.alive = False
+                continue
+
+            # Check status for MESA agents
+            if not getattr(agent, "alive", True):
+                continue
+
+            if returned:
+                next_check = returned.schedule_next(
+                    self.time, self.step_interval, first=True
+                )
+                self._schedule(agent, when=next_check[0], condition=next_check[1])
+            else:
+                next_check = (self.time + self.step_interval, None)
+
+                self._schedule(agent)
+
+        self.steps += 1
+
+        if not self._queue:
+            self.time = INFINITY
+            self.model.running = False
+            return self.time
+
+        next_time = self._queue[0][0][0]
+        
+        if next_time < self.time:
+            raise Exception(
+                f"An agent has been scheduled for a time in the past, there is probably an error ({when} < {self.time})"
+            )
+        self.logger.debug(f"Updating time step: {self.time} -> {next_time}")
+
+        self.time = next_time
+
+
+class ShuffledTimedActivation(TimedActivation):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, shuffle=True, **kwargs)
+
+
+class OrderedTimedActivation(TimedActivation):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, shuffle=False, **kwargs)

soil/utils.py

@@ -1,105 +1,156 @@
-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, cpu_count
 
 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.DEBUG,
+    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, num_processes=1, **kwargs):
+    if num_processes > 1 and not os.environ.get("SOIL_DEBUG", None):
+        if num_processes < 1:
+            num_processes = cpu_count() - num_processes
+        p = Pool(processes=num_processes)
+        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/TerroristNetworkModel.py

@@ -1,255 +0,0 @@
-import random
-import networkx as nx
-from soil.agents import BaseAgent, FSM, state, default_state
-from scipy.spatial import cKDTree as KDTree
-
-global betweenness_centrality_global
-global degree_centrality_global
-
-betweenness_centrality_global = None
-degree_centrality_global = None
-
-class TerroristSpreadModel(FSM):
-    """
-    Settings:
-        information_spread_intensity
-
-        terrorist_additional_influence
-
-        min_vulnerability (optional else zero)
-
-        max_vulnerability
-
-        prob_interaction
-    """
-
-    def __init__(self, environment=None, agent_id=0, state=()):
-        super().__init__(environment=environment, agent_id=agent_id, state=state)
-
-        global betweenness_centrality_global
-        global degree_centrality_global
-        
-        if betweenness_centrality_global == None:
-            betweenness_centrality_global = nx.betweenness_centrality(self.global_topology)
-        if degree_centrality_global == None:
-            degree_centrality_global = nx.degree_centrality(self.global_topology)
-
-        self.information_spread_intensity = environment.environment_params['information_spread_intensity']
-        self.terrorist_additional_influence = environment.environment_params['terrorist_additional_influence']
-        self.prob_interaction = environment.environment_params['prob_interaction']
-
-        if self['id'] == self.civilian.id:       # Civilian
-            self.initial_belief = random.uniform(0.00, 0.5)
-        elif self['id'] == self.terrorist.id:     # Terrorist
-            self.initial_belief = random.uniform(0.8, 1.00)
-        elif self['id'] == self.leader.id:     # Leader
-            self.initial_belief = 1.00
-        else:
-            raise Exception('Invalid state id: {}'.format(self['id']))
-
-        if 'min_vulnerability' in environment.environment_params:
-            self.vulnerability = random.uniform( environment.environment_params['min_vulnerability'], environment.environment_params['max_vulnerability'] )
-        else :
-            self.vulnerability = random.uniform( 0, environment.environment_params['max_vulnerability'] )
-
-        self.mean_belief = self.initial_belief
-        self.betweenness_centrality = betweenness_centrality_global[self.id]
-        self.degree_centrality = degree_centrality_global[self.id]
-
-        # self.state['radicalism'] = self.mean_belief
-
-    def count_neighboring_agents(self, state_id=None):
-        if isinstance(state_id, list):
-            return len(self.get_neighboring_agents(state_id))
-        else:
-            return len(super().get_agents(state_id, limit_neighbors=True))
-
-    def get_neighboring_agents(self, state_id=None):
-        if isinstance(state_id, list):
-            _list = []
-            for i in state_id:
-                _list += super().get_agents(i, limit_neighbors=True)
-            return [ neighbour for neighbour in _list if isinstance(neighbour, TerroristSpreadModel) ]
-        else:
-            _list = super().get_agents(state_id, limit_neighbors=True) 
-            return [ neighbour for neighbour in _list if isinstance(neighbour, TerroristSpreadModel) ]
-
-    @state
-    def civilian(self):
-        if self.count_neighboring_agents() > 0:
-            neighbours = []
-            for neighbour in self.get_neighboring_agents():
-                if random.random() < self.prob_interaction:
-                    neighbours.append(neighbour)
-            influence = sum( neighbour.degree_centrality for neighbour in neighbours )
-            mean_belief = sum( neighbour.mean_belief * neighbour.degree_centrality / influence for neighbour in neighbours )
-            self.initial_belief = self.mean_belief
-            mean_belief = mean_belief * self.information_spread_intensity + self.initial_belief * ( 1 - self.information_spread_intensity )
-            self.mean_belief = mean_belief * self.vulnerability + self.initial_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 )
-        if self.count_neighboring_agents(state_id=[self.terrorist.id, self.leader.id]) > 0:
-            for neighbour in self.get_neighboring_agents(state_id=[self.terrorist.id, self.leader.id]):
-                if neighbour.betweenness_centrality > self.betweenness_centrality:
-                    return self.terrorist
-
-    @state
-    def terrorist(self):
-        if self.count_neighboring_agents(state_id=[self.terrorist.id, self.leader.id]) > 0:
-            neighbours = self.get_neighboring_agents(state_id=[self.terrorist.id, self.leader.id])
-            influence = sum( neighbour.degree_centrality for neighbour in neighbours )
-            mean_belief = sum( neighbour.mean_belief * neighbour.degree_centrality / influence for neighbour in neighbours )
-            self.initial_belief = self.mean_belief
-            self.mean_belief = mean_belief * self.vulnerability + self.initial_belief * ( 1 - self.vulnerability )
-            self.mean_belief = self.mean_belief ** ( 1 - self.terrorist_additional_influence )
-
-        if self.count_neighboring_agents(state_id=self.leader.id) == 0 and self.count_neighboring_agents(state_id=self.terrorist.id) > 0:
-            max_betweenness_centrality = self
-            for neighbour in self.get_neighboring_agents(state_id=self.terrorist.id):
-                if neighbour.betweenness_centrality > max_betweenness_centrality.betweenness_centrality:
-                    max_betweenness_centrality = neighbour
-            if max_betweenness_centrality == self:
-                return self.leader
-
-    def add_edge(self, G, source, target):
-        G.add_edge(source.id, target.id, start=self.env._now)
-
-    def link_search(self, G, node, radius):
-        pos = nx.get_node_attributes(G, 'pos')
-        nodes, coords = list(zip(*pos.items()))
-        kdtree = KDTree(coords)  # Cannot provide generator.
-        edge_indexes = kdtree.query_pairs(radius, 2)
-        _list = [ edge[int(not edge.index(node))] for edge in edge_indexes if node in edge ]
-        return [ G.nodes()[index]['agent'] for index in _list ]
-
-    def social_search(self, G, node, steps):
-        nodes = list(nx.ego_graph(G, node, radius=steps).nodes())
-        nodes.remove(node)
-        return [ G.nodes()[index]['agent'] for index in nodes ]
-
-
-class TrainingAreaModel(FSM):
-    """
-    Settings:
-        training_influence
-
-        min_vulnerability
-
-    Requires TerroristSpreadModel.
-    """
-
-    def __init__(self, environment=None, agent_id=0, state=()):
-        super().__init__(environment=environment, agent_id=agent_id, state=state)
-        self.training_influence = environment.environment_params['training_influence']
-        if 'min_vulnerability' in environment.environment_params:
-            self.min_vulnerability = environment.environment_params['min_vulnerability']
-        else: self.min_vulnerability = 0
-
-    @default_state
-    @state
-    def terrorist(self):
-        for neighbour in self.get_neighboring_agents():
-            if isinstance(neighbour, TerroristSpreadModel) and neighbour.vulnerability > self.min_vulnerability:
-                neighbour.vulnerability = neighbour.vulnerability ** ( 1 - self.training_influence )        
-
-
-class HavenModel(FSM):
-    """
-    Settings:
-        haven_influence
-
-        min_vulnerability
-
-        max_vulnerability
-
-    Requires TerroristSpreadModel.
-    """
-
-    def __init__(self, environment=None, agent_id=0, state=()):
-        super().__init__(environment=environment, agent_id=agent_id, state=state)
-        self.haven_influence = environment.environment_params['haven_influence']
-        if 'min_vulnerability' in environment.environment_params:
-            self.min_vulnerability = environment.environment_params['min_vulnerability']
-        else: self.min_vulnerability = 0
-        self.max_vulnerability = environment.environment_params['max_vulnerability']
-
-    @state
-    def civilian(self):
-        for neighbour_agent in self.get_neighboring_agents():
-            if isinstance(neighbour_agent, TerroristSpreadModel) and neighbour_agent['id'] == neighbour_agent.civilian.id:
-                for neighbour in self.get_neighboring_agents():
-                    if isinstance(neighbour, TerroristSpreadModel) and neighbour.vulnerability > self.min_vulnerability:
-                        neighbour.vulnerability = neighbour.vulnerability * ( 1 - self.haven_influence )
-                return self.civilian
-        return self.terrorist
-
-    @state
-    def terrorist(self):
-        for neighbour in self.get_neighboring_agents():
-            if isinstance(neighbour, TerroristSpreadModel) and neighbour.vulnerability < self.max_vulnerability:
-                neighbour.vulnerability = neighbour.vulnerability ** ( 1 - self.haven_influence )
-        return self.terrorist
-
-        
-class TerroristNetworkModel(TerroristSpreadModel):
-    """
-    Settings:
-        sphere_influence
-
-        vision_range
-
-        weight_social_distance
-
-        weight_link_distance
-    """
-
-    def __init__(self, environment=None, agent_id=0, state=()):
-        super().__init__(environment=environment, agent_id=agent_id, state=state)
-
-        self.vision_range = environment.environment_params['vision_range']
-        self.sphere_influence = environment.environment_params['sphere_influence']
-        self.weight_social_distance = environment.environment_params['weight_social_distance']
-        self.weight_link_distance = environment.environment_params['weight_link_distance']
-
-    @state
-    def terrorist(self):
-        self.update_relationships()
-        return super().terrorist()
-
-    @state
-    def leader(self):
-        self.update_relationships()
-        return super().leader()
-
-    def update_relationships(self):
-        if self.count_neighboring_agents(state_id=self.civilian.id) == 0:
-            close_ups = self.link_search(self.global_topology, self.id, self.vision_range)
-            step_neighbours = self.social_search(self.global_topology, self.id, self.sphere_influence)
-            search = list(set(close_ups).union(step_neighbours))
-            neighbours = self.get_neighboring_agents()
-            search = [item for item in search if not item in neighbours and isinstance(item, TerroristNetworkModel)]
-            for agent in search:
-                social_distance = 1 / self.shortest_path_length(self.global_topology, self.id, agent.id)
-                spatial_proximity = ( 1 - self.get_distance(self.global_topology, self.id, agent.id) )
-                prob_new_interaction = self.weight_social_distance * social_distance + self.weight_link_distance * spatial_proximity
-                if agent['id'] == agent.civilian.id and random.random() < prob_new_interaction:
-                    self.add_edge(self.global_topology, self, agent)
-                    break
-
-    def get_distance(self, G, source, target):
-        source_x, source_y = nx.get_node_attributes(G, 'pos')[source]
-        target_x, target_y = nx.get_node_attributes(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, G, source, target):
-        try:
-            return nx.shortest_path_length(G, source, target)
-        except nx.NetworkXNoPath:
-            return float('inf')

soil/web/__init__.py

@@ -19,7 +19,8 @@ from xml.etree.ElementTree import tostring
 from tornado.concurrent import run_on_executor
 from concurrent.futures import ThreadPoolExecutor
 
-from ..simulation import SoilSimulation
+from ..simulation import Simulation
+
 logger = logging.getLogger(__name__)
 logger.setLevel(logging.INFO)
 
@@ -31,171 +32,217 @@ 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. """
+    """Handler for the HTML template which holds the visualization."""
 
     def get(self):
-        self.render('index.html', port=self.application.port,
-                    name=self.application.name)
+        self.render(
+            "index.html", port=self.application.port, name=self.application.name
+        )
 
 
 class SocketHandler(tornado.websocket.WebSocketHandler):
-    """ Handler for websocket. """
+    """Handler for websocket."""
+
     executor = ThreadPoolExecutor(max_workers=MAX_WORKERS)
 
     def open(self):
         if self.application.verbose:
-            logger.info('Socket opened!')
+            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. """
+        """Receiving a message from the websocket, parse, and act accordingly."""
 
         msg = tornado.escape.json_decode(message)
 
-        if msg['type'] == 'config_file':
+        if msg["type"] == "config_file":
 
             if self.application.verbose:
-                print(msg['data'])
+                print(msg["data"])
 
-            self.config = list(yaml.load_all(msg['data']))
+            self.config = list(yaml.load_all(msg["data"]))
 
             if len(self.config) > 1:
-                error = 'Please, provide only one configuration.'
+                error = "Please, provide only one configuration."
                 if self.application.verbose:
                     logger.error(error)
-                self.write_message({'type': 'error',
-                    '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']))
+            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']
+            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'
+            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'
+                        setting_type = "great_number"
+                elif type(self.config["environment_params"][key]) == bool:
+                    setting_type = "boolean"
                 else:
-                    setting_type = 'undefined'
+                    setting_type = "undefined"
 
-                settings.append({
-                    'label': key,
-                    'type': setting_type,
-                    'value': self.config['environment_params'][key]
-                })
+                settings.append(
+                    {
+                        "label": key,
+                        "type": setting_type,
+                        "value": self.config["environment_params"][key],
+                    }
+                )
 
-            self.write_message({'type': 'settings',
-                'data': settings})
+            self.write_message({"type": "settings", "data": settings})
 
-        elif msg['type'] == 'get_trial':
+        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']))})
+                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':
+        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']
+                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()
+        elif msg["type"] == "download_gexf":
+            G = self.trials[int(msg["data"])].history_to_graph()
             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'])
-            writer = nx.readwrite.gexf.GEXFWriter(version='1.2draft')
+                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) })
+            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()
+        elif msg["type"] == "download_json":
+            G = self.trials[int(msg["data"])].history_to_graph()
             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'])
-            self.write_message({'type': 'download_json',
-                'filename': self.config['name'] + '_trial_' + str(msg['data']),
-                'data': nx.node_link_data(G) })
+                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!')
+                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])
+            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)
-
+                tornado.ioloop.IOLoop.current().call_later(
+                    LOGGING_INTERVAL, self.update_logging
+                )
 
     def on_close(self):
         if self.application.verbose:
-            logger.info('Socket closed!')
+            logger.info("Socket closed!")
 
     def send_log(self, logger, logging):
-        self.write_message({'type': 'log',
-                            'logger': logger,
-                            'logging': logging})
+        self.write_message({"type": "log", "logger": logger, "logging": logging})
 
     @property
     def simulation_name(self):
-        return self.config.get('name', 'NoSimulationRunning')
+        return self.config.get("name", "NoSimulationRunning")
 
     @run_on_executor
     def nonblocking(self, config):
-        simulation = SoilSimulation(**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']
+        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['dir_path'] = os.path.join(self.application.dir_path, config['name'])
-                config['dump'] = self.application.dump
+                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) })
+                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)
+                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)
+                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)
+        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)
@@ -218,37 +265,40 @@ class SocketHandler(tornado.websocket.WebSocketHandler):
 
 
 class ModularServer(tornado.web.Application):
-    """ Main visualization 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': ''})
+    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'}
+    settings = {"debug": True, "template_path": ROOT + "/templates"}
 
-    def __init__(self, dump=False, dir_path='output', name='SOIL', verbose=True, *args, **kwargs):
+    def __init__(
+        self, dump=False, outdir="output", name="SOIL", verbose=True, *args, **kwargs
+    ):
 
         self.verbose = verbose
         self.name = name
         self.dump = dump
-        self.dir_path = dir_path
+        self.outdir = outdir
 
         # Initializing the application itself:
         super().__init__(self.handlers, **self.settings)
 
     def launch(self, port=None):
-        """ Run the app. """
+        """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))
+        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()
@@ -263,12 +313,22 @@ def run(*args, **kwargs):
 def main():
     import argparse
 
-    parser = argparse.ArgumentParser(description='Visualization of a Graph Model')
+    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')
+    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)
+    run(
+        name=args.name,
+        port=(args.port[0] if isinstance(args.port, list) else args.port),
+        verbose=args.verbose,
+    )

soil/web/config.yml

@@ -6,11 +6,11 @@ network_params:
   n: 100
   m: 2
 network_agents:
-  - agent_type: ControlModelM2
+  - agent_class: ControlModelM2
     weight: 0.1
     state:
       id: 1
-  - agent_type: ControlModelM2
+  - agent_class: ControlModelM2
     weight: 0.9
     state:
       id: 0

soil/web/run.py

@@ -4,20 +4,33 @@ 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 = 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 = 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')
+    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)
+    run(
+        soil,
+        name=args.name,
+        port=(args.port[0] if isinstance(args.port, list) else args.port),
+        verbose=args.verbose,
+    )

test-requirements.txt

@@ -0,0 +1,4 @@
+pytest
+pytest-profiling
+scipy>=1.3
+tornado

tests/complete_converted.yml

@@ -0,0 +1,49 @@
+---
+version: '2'
+name: simple
+group: tests
+dir_path: "/tmp/"
+num_trials: 3
+max_time: 100
+interval: 1
+seed: "CompleteSeed!"
+model_class: Environment
+model_params:
+  topology:
+    params:
+      generator: complete_graph
+      n: 4
+  agents:
+    agent_class: CounterModel
+    state:
+      group: network
+      times: 1
+    topology: true
+    distribution:
+      - agent_class: CounterModel
+        weight: 0.25
+        state:
+          state_id: 0
+          times: 1
+      - agent_class: AggregatedCounter
+        weight: 0.5
+        state:
+          times: 2
+    override:
+      - filter:
+          node_id: 1
+        state:
+          name: 'Node 1'
+      - filter:
+          node_id: 2
+        state:
+          name: 'Node 2'
+    fixed:
+      - agent_class: BaseAgent
+        hidden: true
+        topology: false
+        state:
+          name: 'Environment Agent 1'
+          times: 10
+          group: environment
+  am_i_complete: true

tests/old_complete.yml

@@ -0,0 +1,37 @@
+---
+name: simple
+group: tests
+dir_path: "/tmp/"
+num_trials: 3
+max_time: 100
+interval: 1
+seed: "CompleteSeed!"
+network_params:
+  generator: complete_graph
+  n: 4
+network_agents:
+  - agent_class: CounterModel
+    weight: 0.25
+    state:
+      state_id: 0
+      times: 1
+  - agent_class: AggregatedCounter
+    weight: 0.5
+    state:
+      times: 2
+environment_agents:
+  - agent_id: 'Environment Agent 1'
+    agent_class: BaseAgent
+    state:
+      times: 10
+environment_class: Environment
+environment_params:
+  am_i_complete: true
+agent_class: CounterModel
+default_state:
+  times: 1
+states:
+  1:
+    name: 'Node 1'
+  2:
+    name: 'Node 2'

tests/test.gexf

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

tests/test_agents.py

@@ -0,0 +1,163 @@
+from unittest import TestCase
+import pytest
+
+from soil import agents, environment
+from soil import time as stime
+
+
+class Dead(agents.FSM):
+    @agents.default_state
+    @agents.state
+    def only(self):
+        return self.die()
+
+
+class TestAgents(TestCase):
+    def test_die_returns_infinity(self):
+        """The last step of a dead agent should return time.INFINITY"""
+        d = Dead(unique_id=0, model=environment.Environment())
+        ret = d.step()
+        assert ret == stime.NEVER
+
+    def test_die_raises_exception(self):
+        """A dead agent should raise an exception if it is stepped after death"""
+        d = Dead(unique_id=0, model=environment.Environment())
+        d.step()
+        with pytest.raises(stime.DeadAgent):
+            d.step()
+
+    def test_agent_generator(self):
+        """
+        The step function of an agent could be a generator. In that case, the state of the
+        agent will be resumed after every call to step.
+        """
+        a = 0
+
+        class Gen(agents.BaseAgent):
+            def step(self):
+                nonlocal a
+                for i in range(5):
+                    yield
+                    a += 1
+
+        e = environment.Environment()
+        g = Gen(model=e, unique_id=e.next_id())
+        e.schedule.add(g)
+
+        for i in range(5):
+            e.step()
+            assert a == i
+
+    def test_state_decorator(self):
+        class MyAgent(agents.FSM):
+            run = 0
+
+            @agents.default_state
+            @agents.state("original")
+            def root(self):
+                self.run += 1
+                return self.other
+
+            @agents.state
+            def other(self):
+                self.run += 1
+
+        e = environment.Environment()
+        a = MyAgent(model=e, unique_id=e.next_id())
+        a.step()
+        assert a.run == 1
+        a.step()
+
+    def test_broadcast(self):
+        """
+        An agent should be able to broadcast messages to every other agent, AND each receiver should be able
+        to process it
+        """
+
+        class BCast(agents.Evented):
+            pings_received = 0
+
+            def step(self):
+                print(self.model.broadcast)
+                try:
+                    self.model.broadcast("PING")
+                except Exception as ex:
+                    print(ex)
+                while True:
+                    self.check_messages()
+                    yield
+
+            def on_receive(self, msg, sender=None):
+                self.pings_received += 1
+
+        e = environment.EventedEnvironment()
+
+        for i in range(10):
+            e.add_agent(agent_class=BCast)
+        e.step()
+        pings_received = lambda: [a.pings_received for a in e.agents]
+        assert sorted(pings_received()) == list(range(1, 11))
+        e.step()
+        assert all(x == 10 for x in pings_received())
+
+    def test_ask_messages(self):
+        """
+        An agent should be able to ask another agent, and wait for a response.
+        """
+
+        # There are two agents, they try to send pings
+        # This is arguably a very contrived example. In practice, the or
+        # There should be a delay of one step between agent 0 and 1
+        # On the first step:
+        #   Agent 0 sends a PING, but blocks before a PONG
+        #   Agent 1 detects the PING, responds with a PONG, and blocks after its own PING
+        # After that step, every agent can both receive (there are pending messages) and send.
+        # In each step, for each agent, one message is sent, and another one is received
+        # (although not necessarily in that order).
+
+        # Results depend on ordering (agents are normally shuffled)
+        # so we force the timedactivation not to be shuffled
+
+        pings = []
+        pongs = []
+        responses = []
+
+        class Ping(agents.EventedAgent):
+            def step(self):
+                target_id = (self.unique_id + 1) % self.count_agents()
+                target = self.model.agents[target_id]
+                print("starting")
+                while True:
+                    if pongs or not pings:  # First agent, or anyone after that
+                        pings.append(self.now)
+                        response = yield target.ask("PING")
+                        responses.append(response)
+                    else:
+                        print("NOT sending ping")
+                    print("Checking msgs")
+                    # Do not block if we have already received a PING
+                    if not self.check_messages():
+                        yield self.received()
+                print("done")
+
+            def on_receive(self, msg, sender=None):
+                if msg == "PING":
+                    pongs.append(self.now)
+                    return "PONG"
+                raise Exception("This should never happen")
+
+        e = environment.EventedEnvironment(schedule_class=stime.OrderedTimedActivation)
+        for i in range(2):
+            e.add_agent(agent_class=Ping)
+        assert e.now == 0
+
+        for i in range(5):
+            e.step()
+            time = i + 1
+            assert e.now == time
+            assert len(pings) == 2 * time
+            assert len(pongs) == (2 * time) - 1
+            # Every step between 0 and t appears twice
+            assert sum(pings) == sum(range(time)) * 2
+            # It is the same as pings, without the leading 0
+            assert sum(pongs) == sum(range(time)) * 2

tests/test_analysis.py

@@ -1,90 +0,0 @@
-from unittest import TestCase
-
-import os
-import pandas as pd
-import yaml
-from functools import partial
-
-from os.path import join
-from soil import simulation, analysis, agents
-
-
-ROOT = os.path.abspath(os.path.dirname(__file__))
-
-
-class Ping(agents.FSM):
-
-    defaults = {
-        'count': 0,
-    }
-
-    @agents.default_state
-    @agents.state
-    def even(self):
-        self['count'] += 1
-        return self.odd
-
-    @agents.state
-    def odd(self):
-        self['count'] += 1
-        return self.even
-
-
-class TestAnalysis(TestCase):
-
-    # Code to generate a simple sqlite history
-    def setUp(self):
-        """
-        The initial states should be applied to the agent and the
-        agent should be able to update its state."""
-        config = {
-            'name': 'analysis',
-            'dry_run': True,
-            'seed': 'seed',
-            'network_params': {
-                'generator': 'complete_graph',
-                'n': 2
-            },
-            'agent_type': Ping,
-            'states': [{'interval': 1}, {'interval': 2}],
-            'max_time': 30,
-            'num_trials': 1,
-            'environment_params': {
-            }
-        }
-        s = simulation.from_config(config)
-        self.env = s.run_simulation()[0]
-
-    def test_saved(self):
-        env = self.env
-        assert env.get_agent(0)['count', 0] == 1
-        assert env.get_agent(0)['count', 29] == 30
-        assert env.get_agent(1)['count', 0] == 1
-        assert env.get_agent(1)['count', 29] == 15
-        assert env['env', 29, None]['SEED'] == env['env', 29, 'SEED']
-
-    def test_count(self):
-        env = self.env
-        df = analysis.read_sql(env._history._db)
-        res = analysis.get_count(df, 'SEED', 'id')
-        assert res['SEED']['seedanalysis_trial_0'].iloc[0] == 1
-        assert res['SEED']['seedanalysis_trial_0'].iloc[-1] == 1
-        assert res['id']['odd'].iloc[0] == 2
-        assert res['id']['even'].iloc[0] == 0
-        assert res['id']['odd'].iloc[-1] == 1
-        assert res['id']['even'].iloc[-1] == 1
-
-    def test_value(self):
-        env = self.env
-        df = analysis.read_sql(env._history._db)
-        res_sum = analysis.get_value(df, 'count')
-
-        assert res_sum['count'].iloc[0] == 2
-
-        import numpy as np
-        res_mean = analysis.get_value(df, 'count', aggfunc=np.mean)
-        assert res_mean['count'].iloc[0] == 1
-
-        res_total = analysis.get_value(df)
-
-        res_total['SEED'].iloc[0] == 'seedanalysis_trial_0'

tests/test_config.py

@@ -0,0 +1,146 @@
+from unittest import TestCase
+import os
+import yaml
+import copy
+from os.path import join
+
+from soil import simulation, serialization, config, network, agents, utils
+
+ROOT = os.path.abspath(os.path.dirname(__file__))
+EXAMPLES = join(ROOT, "..", "examples")
+
+FORCE_TESTS = os.environ.get("FORCE_TESTS", "")
+
+
+def isequal(a, b):
+    if isinstance(a, dict):
+        for (k, v) in a.items():
+            if v:
+                isequal(a[k], b[k])
+            else:
+                assert not b.get(k, None)
+        return
+    assert a == b
+
+
+class TestConfig(TestCase):
+    def test_conversion(self):
+        expected = serialization.load_file(join(ROOT, "complete_converted.yml"))[0]
+        old = serialization.load_file(join(ROOT, "old_complete.yml"))[0]
+        converted_defaults = config.convert_old(old, strict=False)
+        converted = converted_defaults.dict(exclude_unset=True)
+
+        isequal(converted, expected)
+
+    def test_configuration_changes(self):
+        """
+        The configuration should not change after running
+         the simulation.
+        """
+        config = serialization.load_file(join(EXAMPLES, "complete.yml"))[0]
+        s = simulation.from_config(config)
+        init_config = copy.copy(s.to_dict())
+
+        s.run_simulation(dry_run=True)
+        nconfig = s.to_dict()
+        # del nconfig['to
+        isequal(init_config, nconfig)
+
+    def test_topology_config(self):
+        netconfig = config.NetConfig(**{"path": join(ROOT, "test.gexf")})
+        net = network.from_config(netconfig, dir_path=ROOT)
+        assert len(net.nodes) == 2
+        assert len(net.edges) == 1
+
+    def test_env_from_config(self):
+        """
+        Simple configuration that tests that the graph is loaded, and that
+        network agents are initialized properly.
+        """
+        cfg = {
+            "name": "CounterAgent",
+            "network_params": {"path": join(ROOT, "test.gexf")},
+            "agent_class": "CounterModel",
+            # 'states': [{'times': 10}, {'times': 20}],
+            "max_time": 2,
+            "dry_run": True,
+            "num_trials": 1,
+            "environment_params": {},
+        }
+        conf = config.convert_old(cfg)
+        s = simulation.from_config(conf)
+
+        env = s.get_env()
+        assert len(env.G.nodes) == 2
+        assert len(env.G.edges) == 1
+        assert len(env.agents) == 2
+        assert env.agents[0].G == env.G
+
+    def test_agents_from_config(self):
+        """We test that the known complete configuration produces
+        the right agents in the right groups"""
+        cfg = serialization.load_file(join(ROOT, "complete_converted.yml"))[0]
+        s = simulation.from_config(cfg)
+        env = s.get_env()
+        assert len(env.G.nodes) == 4
+        assert len(env.agents(group="network")) == 4
+        assert len(env.agents(group="environment")) == 1
+
+    def test_yaml(self):
+        """
+        The YAML version of a newly created configuration should be equivalent
+        to the configuration file used.
+        Values not present in the original config file should have reasonable
+        defaults.
+        """
+        with utils.timer("loading"):
+            config = serialization.load_file(join(EXAMPLES, "complete.yml"))[0]
+            s = simulation.from_config(config)
+        with utils.timer("serializing"):
+            serial = s.to_yaml()
+        with utils.timer("recovering"):
+            recovered = yaml.load(serial, Loader=yaml.FullLoader)
+        for (k, v) in config.items():
+            assert recovered[k] == v
+
+
+def make_example_test(path, cfg):
+    def wrapped(self):
+        root = os.getcwd()
+        print(path)
+        s = simulation.from_config(cfg)
+        iterations = s.max_time * s.num_trials
+        if iterations > 1000:
+            s.max_time = 100
+            s.num_trials = 1
+        if cfg.skip_test and not FORCE_TESTS:
+            self.skipTest('Example ignored.')
+        envs = s.run_simulation(dry_run=True)
+        assert envs
+        for env in envs:
+            assert env
+            try:
+                n = cfg.model_params['topology']['params']['n']
+                assert len(list(env.network_agents)) == n
+                assert env.now > 0  # It has run
+                assert env.now <= cfg.max_time  # But not further than allowed
+            except KeyError:
+                pass
+
+    return wrapped
+
+
+def add_example_tests():
+    for config, path in serialization.load_files(
+        join(EXAMPLES, "*", "*.yml"),
+        join(EXAMPLES, "*.yml"),
+    ):
+        p = make_example_test(path=path, cfg=config)
+        fname = os.path.basename(path)
+        p.__name__ = "test_example_file_%s" % fname
+        p.__doc__ = "%s should be a valid configuration" % fname
+        setattr(TestConfig, p.__name__, p)
+        del p
+
+
+add_example_tests()