Merge branch 'mesa'

First iteration to achieve MESA compatibility.
As a side effect, we have removed `simpy`.

For a full list of changes, see `CHANGELOG.md`.

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

CHANGELOG.md

@@ -0,0 +1,126 @@
+# 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).
+
+## [0.20.0]
+### 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 That has been replaced by a scheduler within the `soil.Environment` class, similar to how `mesa` does it.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

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

Makefile

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

README.md

@@ -5,6 +5,9 @@ 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.
 
+## Citation 
+
+
 If you use Soil in your research, don't forget to cite this paper:
 
 ```bibtex
@@ -28,7 +31,24 @@ If you use Soil in your research, don't forget to cite this paper:
 
 ```
 
-@Copyright GSI - Universidad Politécnica de Madrid 2017
+## Mesa compatibility
 
-[![SOIL](logo_gsi.png)](https://www.gsi.dit.upm.es)
+Soil is in the process of becoming fully compatible with MESA.
+As of this writing, 
 
+This is a non-exhaustive list of tasks to achieve compatibility:
+
+* Environments.agents and mesa.Agent.agents are not the same. env is a property, and it only takes into account network and environment agents. Might rename environment_agents to other_agents or sth like that
+- [ ] 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.
+- [ ] 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`)
+- [ ] Document the new APIs and usage
+
+@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/configuration.rst

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

docs/example.yml

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

docs/index.rst

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

docs/installation.rst

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

docs/quickstart.rst

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

docs/quickstart.yml

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

docs/requirements.txt

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

docs/soil_tutorial.rst

@@ -26,7 +26,7 @@ But before that, let's import the soil module and networkx.
     %autoreload 2
     
     %pylab inline
-    # To display plots in the notebooed_
+    # To display plots in the notebook_
 
 
 .. parsed-literal::
@@ -47,12 +47,6 @@ There are three main elements in a soil simulation:
 -  The environment. It assigns agents to nodes in the network, and
    stores the environment parameters (shared state for all agents).
 
-Soil is based on ``simpy``, which is an event-based network simulation
-library. Soil provides several abstractions over events to make
-developing agents easier. This means you can use events (timeouts,
-delays) in soil, but for the most part we will assume your models will
-be step-based.
-
 Modeling behaviour
 ------------------
 
@@ -214,7 +208,7 @@ nodes in that network. Notice how node 0 is the only one with a TV.
     MAX_TIME = 100
     EVENT_TIME = 10
     
-    sim = soil.simulation.SoilSimulation(topology=G,
+    sim = soil.Simulation(topology=G,
                                          num_trials=1,
                                          max_time=MAX_TIME,
                                          environment_agents=[{'agent_type': NewsEnvironmentAgent,
@@ -323,7 +317,7 @@ Let's run our simulation:
 
 .. code:: ipython3
 
-    soil.simulation.run_from_config(config, dump=False)
+    soil.simulation.run_from_config(config)
 
 
 .. parsed-literal::
@@ -2531,7 +2525,7 @@ Dealing with bigger data
 
 .. parsed-literal::
 
-    267M	../rabbits/soil_output/rabbits_example/
+    267M	../rabbits/soil_output/rabbits_example/
 
 
 If we tried to load the entire history, we would probably run out of

examples/NewsSpread.ipynb

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

examples/complete.yml

@@ -1,11 +1,11 @@
 ---
 name: simple
+group: tests
 dir_path: "/tmp/"
 num_trials: 3
 max_time: 100
 interval: 1
 seed: "CompleteSeed!"
-dump: false
 network_params:
   generator: complete_graph
   n: 10
@@ -13,10 +13,11 @@ network_agents:
   - agent_type: CounterModel
     weight: 1
     state:
-      id: 0
+      state_id: 0
   - agent_type: AggregatedCounter
     weight: 0.2
 environment_agents: []
+environment_class: Environment
 environment_params:
   am_i_complete: true
 default_state:

examples/custom_generator/custom_generator.yml

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

examples/custom_generator/mymodule.py

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

examples/custom_timeouts/custom_timeouts.py

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

examples/mesa/mesa.yml

@@ -0,0 +1,21 @@
+---
+name: mesa_sim
+group: tests
+dir_path: "/tmp"
+num_trials: 3
+max_time: 100
+interval: 1
+seed: '1'
+network_params:
+  generator: social_wealth.graph_generator
+  n: 5
+network_agents:
+  - agent_type: social_wealth.SocialMoneyAgent
+    weight: 1
+environment_class: social_wealth.MoneyEnv
+environment_params:
+  num_mesa_agents: 5
+  mesa_agent_type: social_wealth.MoneyAgent
+  N: 10
+  width: 50
+  height: 50

examples/mesa/server.py

@@ -0,0 +1,105 @@
+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
+
+
+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()
+    portrayal["nodes"] = [
+        {
+            "id": agent_id,
+            "size": env.get_agent(agent_id).wealth,
+            # "color": "#CC0000" if not agents or agents[0].wealth == 0 else "#007959",
+            "color": "#CC0000",
+            "label": f"{agent_id}: {env.get_agent(agent_id).wealth}",
+        }
+        for (agent_id) in env.G.nodes
+    ]
+
+    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, library="sigma")
+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",
+    ),
+    "network_agents": [{"agent_type": SocialMoneyAgent}],
+    "height": UserSettableParameter(
+        "slider",
+        "height",
+        5,
+        5,
+        10,
+        1,
+        description="Grid height",
+        ),
+    "width": UserSettableParameter(
+        "slider",
+        "width",
+        5,
+        5,
+        10,
+        1,
+        description="Grid width",
+        ),
+    "network_params": {
+        'generator': graph_generator
+    },
+}
+
+canvas_element = CanvasGrid(gridPortrayal, model_params["width"].value, model_params["height"].value, 500, 500)
+
+
+server = ModularServer(
+    MoneyEnv, [grid, chart, canvas_element], "Money Model", model_params
+)
+server.port = 8521
+
+server.launch(open_browser=False)

examples/mesa/social_wealth.py

@@ -0,0 +1,120 @@
+'''
+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
+
+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):
+        super().__init__(unique_id=unique_id, model=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.info("Crying wolf", self.pos)
+        self.move()
+        if self.wealth > 0:
+            self.give_money()
+
+
+class SocialMoneyAgent(NetworkAgent, MoneyAgent):
+    wealth = 1
+
+    def give_money(self):
+        cellmates = set(self.model.grid.get_cell_list_contents([self.pos]))
+        friends = set(self.get_neighboring_agents())
+        self.info("Trying to give money")
+        self.debug("Cellmates: ", cellmates)
+        self.debug("Friends: ", friends)
+
+        nearby_friends = list(cellmates & friends)
+
+        if len(nearby_friends):
+            other = self.random.choice(nearby_friends)
+            other.wealth += 1
+            self.wealth -= 1
+
+
+class MoneyEnv(Environment):
+    """A model with some number of agents."""
+    def __init__(self, N, width, height, *args, network_params, **kwargs):
+
+        network_params['n'] = N
+        super().__init__(*args, network_params=network_params, **kwargs)
+        self.grid = MultiGrid(width, height, False)
+
+        # 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"})
+
+
+def graph_generator(n=5):
+    G = nx.Graph()
+    for ix in range(n):
+        G.add_edge(0, ix)
+    return G
+
+if __name__ == '__main__':
+
+
+    G = graph_generator()
+    fixed_params = {"topology": G,
+                    "width": 10,
+                    "network_agents": [{"agent_type": 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 +1,83 @@
+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.yml

@@ -6,7 +6,7 @@ environment_params:
   prob_neighbor_spread: 0.0
   prob_tv_spread: 0.01
 interval: 1
-max_time: 30
+max_time: 300
 name: Sim_all_dumb
 network_agents:
 - agent_type: DumbViewer
@@ -30,7 +30,7 @@ environment_params:
   prob_neighbor_spread: 0.0
   prob_tv_spread: 0.01
 interval: 1
-max_time: 30
+max_time: 300
 name: Sim_half_herd
 network_agents:
 - agent_type: DumbViewer
@@ -62,18 +62,18 @@ environment_params:
   prob_neighbor_spread: 0.0
   prob_tv_spread: 0.01
 interval: 1
-max_time: 30
+max_time: 300
 name: Sim_all_herd
 network_agents:
 - agent_type: HerdViewer
   state:
     has_tv: true
-    id: neutral
+    state_id: neutral
   weight: 1
 - agent_type: HerdViewer
   state:
     has_tv: true
-    id: neutral
+    state_id: neutral
   weight: 1
 network_params:
   generator: barabasi_albert_graph
@@ -89,13 +89,13 @@ environment_params:
   prob_tv_spread: 0.01
   prob_neighbor_cure: 0.1
 interval: 1
-max_time: 30
+max_time: 300
 name: Sim_wise_herd
 network_agents:
 - agent_type: HerdViewer
   state:
     has_tv: true
-    id: neutral
+    state_id: neutral
   weight: 1
 - agent_type: WiseViewer
   state:
@@ -115,13 +115,13 @@ environment_params:
   prob_tv_spread: 0.01
   prob_neighbor_cure: 0.1
 interval: 1
-max_time: 30
+max_time: 300
 name: Sim_all_wise
 network_agents:
 - agent_type: WiseViewer
   state:
     has_tv: true
-    id: neutral
+    state_id: neutral
   weight: 1
 - agent_type: WiseViewer
   state:

examples/newsspread/newsspread.py

@@ -34,8 +34,6 @@ 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()

examples/programmatic/.gitignore

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

examples/programmatic/programmatic.py

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

examples/pubcrawl/README.md

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

examples/pubcrawl/pubcrawl.py

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

examples/pubcrawl/pubcrawl.yml

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

examples/rabbits/rabbit_agents.py

@@ -1,7 +1,6 @@
-from soil.agents import FSM, state, default_state, BaseAgent
+from soil.agents import FSM, state, default_state, BaseAgent, NetworkAgent
 from enum import Enum
 from random import random, choice
-from itertools import islice
 import logging
 import math
 
@@ -22,7 +21,7 @@ class RabbitModel(FSM):
         'offspring': 0,
     }
 
-    sexual_maturity = 4*30
+    sexual_maturity = 3 #4*30
     life_expectancy = 365 * 3
     gestation = 33
     pregnancy = -1
@@ -31,9 +30,11 @@ class RabbitModel(FSM):
     @default_state
     @state
     def newborn(self):
+        self.debug(f'I am a newborn at age {self["age"]}')
         self['age'] += 1
 
         if self['age'] >= self.sexual_maturity:
+            self.debug('I am fertile!')
             return self.fertile
 
     @state
@@ -46,8 +47,7 @@ class RabbitModel(FSM):
             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):
+        for f in self.get_agents(state_id=self.fertile.id, gender=Genders.female.value, limit_neighbors=False, limit=self.max_females):
             r = random()
             if r < self['mating_prob']:
                 self.impregnate(f)
@@ -80,7 +80,7 @@ class RabbitModel(FSM):
                 self.env.add_edge(self['mate'], child.id)
                 # self.add_edge()
                 self.debug('A BABY IS COMING TO LIFE')
-                self.env['rabbits_alive'] = self.env.get('rabbits_alive', self.global_topology.number_of_nodes())+1
+                self.env['rabbits_alive'] = self.env.get('rabbits_alive', self.topology.number_of_nodes())+1
                 self.debug('Rabbits alive: {}'.format(self.env['rabbits_alive']))
                 self['offspring'] += 1
                 self.env.get_agent(self['mate'])['offspring'] += 1
@@ -97,12 +97,14 @@ class RabbitModel(FSM):
         return
 
 
-class RandomAccident(BaseAgent):
+class RandomAccident(NetworkAgent):
 
     level = logging.DEBUG
 
     def step(self):
-        rabbits_total = self.global_topology.number_of_nodes()
+        rabbits_total = self.topology.number_of_nodes()
+        if 'rabbits_alive' not in self.env:
+            self.env['rabbits_alive'] = 0
         rabbits_alive = self.env.get('rabbits_alive', rabbits_total)
         prob_death = self.env.get('prob_death', 1e-100)*math.floor(math.log10(max(1, rabbits_alive)))
         self.debug('Killing some rabbits with prob={}!'.format(prob_death))
@@ -116,5 +118,5 @@ class RandomAccident(BaseAgent):
                 self.log('Rabbits alive: {}'.format(self.env['rabbits_alive']))
                 i.set_state(i.dead)
         self.log('Rabbits alive: {}/{}'.format(rabbits_alive, rabbits_total))
-        if self.count_agents(state_id=RabbitModel.dead.id) == self.global_topology.number_of_nodes():
+        if self.count_agents(state_id=RabbitModel.dead.id) == self.topology.number_of_nodes():
             self.die()

examples/rabbits/rabbits.yml

@@ -1,7 +1,7 @@
 ---
 load_module: rabbit_agents
 name: rabbits_example
-max_time: 1200
+max_time: 150
 interval: 1
 seed: MySeed
 agent_type: RabbitModel

examples/random_delays/random_delays.py

@@ -0,0 +1,31 @@
+'''
+Example of setting a 
+Example of a fully programmatic simulation, without definition files.
+'''
+from soil import Simulation, agents
+from soil.time import Delta
+from networkx import Graph
+from random import expovariate
+import logging
+
+
+
+class MyAgent(agents.FSM):
+
+    @agents.default_state
+    @agents.state
+    def neutral(self):
+        self.info('I am running')
+        return None, Delta(expovariate(1/16))
+
+s = Simulation(name='Programmatic',
+               network_agents=[{'agent_type': MyAgent, 'id': 0}],
+               topology={'nodes': [{'id': 0}], 'links': []},
+               num_trials=1,
+               max_time=100,
+               agent_type=MyAgent,
+               dry_run=True)
+
+
+logging.basicConfig(level=logging.INFO)
+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_time: 2
+  seed: "CompleteSeed!"
+  dump: false
+  network_params:
+    generator: complete_graph
+    n: 10
+  network_agents:
+    - agent_type: CounterModel
+      weight: "{{ x1 }}"
+      state:
+        state_id: 0
+    - agent_type: AggregatedCounter
+      weight: "{{ 1 - x1 }}"
+  environment_params:
+    name: "{{ x3 }}"
+  skip_test: true
+vars:
+  bounds:
+    x1: [0, 1]
+    x2: [1, 2]
+  fixed:
+    x3: ["a", "b", "c"]

examples/terrorism/TerroristNetworkModel.py

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

examples/terrorism/TerroristNetworkModel.yml

@@ -60,3 +60,4 @@ 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/tutorial/soil_tutorial.html

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

requirements.txt

@@ -1,7 +1,9 @@
-nxsim
-simpy
-networkx>=2.0
+networkx>=2.5
 numpy
 matplotlib
-pyyaml
-pandas
+pyyaml>=5.1
+pandas>=0.23
+SALib>=1.3
+Jinja2
+Mesa>=0.8
+tsih>=0.1.5

setup.cfg

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

setup.py

@@ -16,6 +16,12 @@ def parse_requirements(filename):
 
 install_reqs = parse_requirements("requirements.txt")
 test_reqs = parse_requirements("test-requirements.txt")
+extras_require={
+    'mesa': ['mesa>=0.8.9'],
+    'geo': ['scipy>=1.3'],
+    'web': ['tornado']
+}
+extras_require['all'] = [dep for package in extras_require.values() for dep in package]
 
 
 setup(
@@ -40,10 +46,7 @@ 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', ],
     include_package_data=True,

soil/VERSION

@@ -1 +1 @@
-0.12.0
+0.20.0

soil/__init__.py

@@ -11,24 +11,28 @@ try:
 except NameError:
     basestring = str
 
-logging.basicConfig()
-
+from .agents import *
 from . import agents
-from . import simulation
-from . import environment
-from . import utils
+from .simulation import *
+from .environment import Environment
+from . import serialization
 from . import analysis
-
+from .utils import logger
+from .time import *
 
 def main():
     import argparse
     from . import simulation
 
+    logger.info('Running SOIL version: {}'.format(__version__))
+
     parser = argparse.ArgumentParser(description='Run a SOIL simulation')
     parser.add_argument('file', type=str,
                         nargs="?",
                         default='simulation.yml',
-                        help='python module containing the simulation configuration.')
+                        help='Configuration file for the simulation (e.g., YAML or JSON)')
+    parser.add_argument('--version', action='store_true',
+                        help='Show version info and exit')
     parser.add_argument('--module', '-m', type=str,
                         help='file containing the code of any custom agents.')
     parser.add_argument('--dry-run', '--dry', action='store_true',
@@ -39,32 +43,48 @@ def main():
                         help='Dump GEXF graph. Defaults to false.')
     parser.add_argument('--csv', action='store_true',
                         help='Dump history in CSV format. Defaults to false.')
+    parser.add_argument('--level', type=str,
+                        help='Logging level')
     parser.add_argument('--output', '-o', type=str, default="soil_output",
                         help='folder to write results to. It defaults to the current directory.')
     parser.add_argument('--synchronous', action='store_true',
                         help='Run trials serially and synchronously instead of in parallel. Defaults to false.')
+    parser.add_argument('-e', '--exporter', action='append',
+                        help='Export environment and/or simulations using this exporter')
 
     args = parser.parse_args()
+    logging.basicConfig(level=getattr(logging, (args.level or 'INFO').upper()))
 
-    if args.module:
+    if args.version:
+        return
+
+    if os.getcwd() not in sys.path:
         sys.path.append(os.getcwd())
+    if args.module:
         importlib.import_module(args.module)
 
-    logging.info('Loading config file: {}'.format(args.file, args.output))
+    logger.info('Loading config file: {}'.format(args.file))
 
     try:
-        dump = []
-        if not args.dry_run:
-            if args.csv:
-                dump.append('csv')
-            if args.graph:
-                dump.append('gexf')
+        exporters = list(args.exporter or ['default', ])
+        if args.csv:
+            exporters.append('csv')
+        if args.graph:
+            exporters.append('gexf')
+        exp_params = {}
+        if args.dry_run:
+            exp_params['copy_to'] = sys.stdout
+
+        if not os.path.exists(args.file):
+            logger.error('Please, input a valid file')
+            return
         simulation.run_from_config(args.file,
                                    dry_run=args.dry_run,
-                                   dump=dump,
-                                   parallel=(not args.synchronous and not args.pdb),
-                                   results_dir=args.output)
-    except Exception as ex:
+                                   exporters=exporters,
+                                   parallel=(not args.synchronous),
+                                   outdir=args.output,
+                                   exporter_params=exp_params)
+    except Exception:
         if args.pdb:
             pdb.post_mortem()
         else:

soil/agents/BassModel.py

@@ -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 random.random() < self.innovation_prob:
+            self.sentimentCorrelation = 1
+            return self.aware
+        else:
+            aware_neighbors = self.get_neighboring_agents(state_id=self.aware.id)
             num_neighbors_aware = len(aware_neighbors)
-            if random.random() < (self.state_params['imitation_prob']*num_neighbors_aware):
-                self.state['id'] = 1
-                self.state['sentimentCorrelation'] = 1
+            if random.random() < (self['imitation_prob']*num_neighbors_aware):
+                self.sentimentCorrelation = 1
+                return self.aware
 
-            else:
-                pass
+    @state
+    def aware(self):
+        self.die()

soil/agents/BigMarketModel.py

@@ -1,8 +1,8 @@
 import random
-from . import BaseAgent
+from . import FSM, state, default_state
 
 
-class BigMarketModel(BaseAgent):
+class BigMarketModel(FSM):
     """
     Settings:
         Names:
@@ -19,34 +19,25 @@ class BigMarketModel(BaseAgent):
             sentiment_about [Array]
     """
 
-    def __init__(self, environment=None, agent_id=0, state=()):
-        super().__init__(environment=environment, agent_id=agent_id, state=state)
-        self.enterprises = environment.environment_params['enterprises']
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.enterprises = self.env.environment_params['enterprises']
         self.type = ""
-        self.number_of_enterprises = len(environment.environment_params['enterprises'])
 
-        if self.id < self.number_of_enterprises:  # Enterprises
-            self.state['id'] = self.id
+        if self.id < len(self.enterprises):  # Enterprises
+            self.set_state(self.enterprise.id)
             self.type = "Enterprise"
             self.tweet_probability = environment.environment_params['tweet_probability_enterprises'][self.id]
         else:  # normal users
-            self.state['id'] = self.number_of_enterprises
             self.type = "User"
+            self.set_state(self.user.id)
             self.tweet_probability = environment.environment_params['tweet_probability_users']
             self.tweet_relevant_probability = environment.environment_params['tweet_relevant_probability']
             self.tweet_probability_about = environment.environment_params['tweet_probability_about']  # List
             self.sentiment_about = environment.environment_params['sentiment_about']  # List
 
-    def step(self):
-
-        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):
+    @state
+    def enterprise(self):
 
         if random.random() < self.tweet_probability:  # Tweets
             aware_neighbors = self.get_neighboring_agents(state_id=self.number_of_enterprises)  # Nodes neighbour users
@@ -64,12 +55,12 @@ class BigMarketModel(BaseAgent):
 
                 x.attrs['sentiment_enterprise_%s'% self.enterprises[self.id]] = x.sentiment_about[self.id]
 
-    def userBehaviour(self):
-
+    @state
+    def user(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):
+                for i in range(len(self.enterprises)):
                     random_num = random.random()
                     if random_num < self.tweet_probability_about[i]:
                         # The condition is fulfilled, sentiments are evaluated towards that enterprise
@@ -82,8 +73,10 @@ class BigMarketModel(BaseAgent):
                         else:
                             # POSITIVO
                             self.userTweets("positive",i)
+        for i in range(len(self.enterprises)):  # So that it never is set to 0 if there are not changes (logs)
+            self.attrs['sentiment_enterprise_%s'% self.enterprises[i]] = self.sentiment_about[i]
 
-    def userTweets(self,sentiment,enterprise):
+    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":

soil/agents/CounterModel.py

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

soil/agents/DrawingAgent.py

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

soil/agents/Geo.py

@@ -0,0 +1,21 @@
+from scipy.spatial import cKDTree as KDTree
+import networkx as nx
+from . import NetworkAgent, as_node
+
+class Geo(NetworkAgent):
+    '''In this type of network, nodes have a "pos" attribute.'''
+
+    def geo_search(self, radius, node=None, center=False, **kwargs):
+        '''Get a list of nodes whose coordinates are closer than *radius* to *node*.'''
+        node = as_node(node if node is not None else self)
+
+        G = self.subgraph(**kwargs)
+
+        pos = nx.get_node_attributes(G, 'pos')
+        if not pos:
+            return []
+        nodes, coords = list(zip(*pos.items()))
+        kdtree = KDTree(coords)  # Cannot provide generator.
+        indices = kdtree.query_ball_point(pos[node], radius)
+        return [nodes[i] for i in indices if center or (nodes[i] != node)]
+

soil/agents/IndependentCascadeModel.py

@@ -10,10 +10,10 @@ 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']
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.innovation_prob = self.env.environment_params['innovation_prob']
+        self.imitation_prob = self.env.environment_params['imitation_prob']
         self.state['time_awareness'] = 0
         self.state['sentimentCorrelation'] = 0
 

soil/agents/ModelM2.py

@@ -21,8 +21,8 @@ class SpreadModelM2(BaseAgent):
         prob_generate_anti_rumor
     """
 
-    def __init__(self, environment=None, agent_id=0, state=()):
-        super().__init__(environment=environment, agent_id=agent_id, state=state)
+    def __init__(self, model=None, unique_id=0, state=()):
+        super().__init__(model=environment, unique_id=unique_id, state=state)
 
         self.prob_neutral_making_denier = np.random.normal(environment.environment_params['prob_neutral_making_denier'],
                                                            environment.environment_params['standard_variance'])
@@ -123,8 +123,8 @@ class ControlModelM2(BaseAgent):
     """
 
 
-    def __init__(self, environment=None, agent_id=0, state=()):
-        super().__init__(environment=environment, agent_id=agent_id, state=state)
+    def __init__(self, model=None, unique_id=0, state=()):
+        super().__init__(model=environment, unique_id=unique_id, state=state)
 
         self.prob_neutral_making_denier = np.random.normal(environment.environment_params['prob_neutral_making_denier'],
                                                            environment.environment_params['standard_variance'])

soil/agents/SISaModel.py

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

soil/agents/SentimentCorrelationModel.py

@@ -16,8 +16,8 @@ class SentimentCorrelationModel(BaseAgent):
         disgust_prob
     """
 
-    def __init__(self, environment=None, agent_id=0, state=()):
-        super().__init__(environment=environment, agent_id=agent_id, state=state)
+    def __init__(self, environment, unique_id=0, state=()):
+        super().__init__(model=environment, unique_id=unique_id, state=state)
         self.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']

soil/agents/__init__.py

@@ -1,69 +1,67 @@
-# 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.
-    
-
-import nxsim
 import logging
-from collections import OrderedDict
+from collections import OrderedDict, defaultdict
 from copy import deepcopy
-from functools import partial
+from functools import partial, wraps
+from itertools import islice
 import json
+import networkx as nx
 
-from functools import wraps
+from .. import serialization, utils, time
 
-from .. import utils, history
+from tsih import Key
 
-agent_types = {}
+from mesa import Agent
 
 
-class MetaAgent(type):
-    def __init__(cls, name, bases, nmspc):
-        super(MetaAgent, cls).__init__(name, bases, nmspc)
-        agent_types[name] = cls
+def as_node(agent):
+    if isinstance(agent, BaseAgent):
+        return agent.id
+    return agent
 
+IGNORED_FIELDS = ('model', 'logger')
 
-class BaseAgent(nxsim.BaseAgent, metaclass=MetaAgent):
+class BaseAgent(Agent):
     """
-    A special simpy BaseAgent that keeps track of its state history.
+    A special Agent that keeps track of its state history.
     """
 
     defaults = {}
 
-    def __init__(self, environment=None, agent_id=None, state=None,
-                 name='network_process', interval=None, **state_params):
+    def __init__(self,
+                 unique_id,
+                 model,
+                 name=None,
+                 interval=None):
         # Check for REQUIRED arguments
-        assert environment is not None, TypeError('__init__ missing 1 required keyword argument: \'environment\'. '
-                                                  'Cannot be NoneType.')
         # Initialize agent parameters
-        self.id = agent_id
-        self.name = name
-        self.state_params = state_params
-
-        # Global parameters
-        self.global_topology = environment.G
-        self.environment_params = environment.environment_params
-
-        # Register agent to environment
-        self.env = environment
+        if isinstance(unique_id, Agent):
+            raise Exception()
+        self._saved = set()
+        super().__init__(unique_id=unique_id, model=model)
+        self.name = name or '{}[{}]'.format(type(self).__name__, self.unique_id)
 
         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)
+        self.logger = logging.getLogger(self.model.name).getChild(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)
+
+
+    # TODO: refactor to clean up mesa compatibility
+    @property
+    def id(self):
+        return self.unique_id
+
+    @property
+    def env(self):
+        return self.model
+
+    @env.setter
+    def env(self, model):
+        self.model = model
 
     @property
     def state(self):
@@ -77,32 +75,47 @@ class BaseAgent(nxsim.BaseAgent, metaclass=MetaAgent):
 
     @state.setter
     def state(self, value):
-        self._state = {}
         for k, v in value.items():
             self[k] = v
 
+    @property
+    def environment_params(self):
+        return self.model.environment_params
+
+    @environment_params.setter
+    def environment_params(self, value):
+        self.model.environment_params = value
+
+    def __setattr__(self, key, value):
+        if not key.startswith('_') and key not in IGNORED_FIELDS:
+            try:
+                k = Key(t_step=self.now,
+                        dict_id=self.unique_id,
+                        key=key)
+                self._saved.add(key)
+                self.model[k] = value
+            except AttributeError:
+                pass
+        super().__setattr__(key, value)
+
     def __getitem__(self, key):
         if isinstance(key, tuple):
             key, t_step = key
-            k = history.Key(key=key, t_step=t_step, agent_id=self.id)
-            return self.env[k]
-        return self._state.get(key, None)
+            k = Key(key=key, t_step=t_step, dict_id=self.unique_id)
+            return self.model[k]
+        return getattr(self, key)
 
     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()
+        return ((k, getattr(self, k)) for k in self._saved)
 
     def get(self, key, default=None):
         return self[key] if key in self else default
@@ -110,75 +123,32 @@ 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):
         self.alive = False
         if remove:
-            super().die()
+            self.remove_node(self.id)
 
     def step(self):
-        pass
-
-    def to_json(self):
-        return json.dumps(self.state)
-
-    def count_agents(self, state_id=None, limit_neighbors=False):
-        if limit_neighbors:
-            agents = self.global_topology.neighbors(self.id)
-        else:
-            agents = self.global_topology.nodes()
-        count = 0
-        for agent in agents:
-            if state_id and state_id != self.global_topology.node[agent]['agent']['id']:
-                continue
-            count += 1
-        return count
-
-    def count_neighboring_agents(self, state_id=None):
-        return len(super().get_agents(state_id, limit_neighbors=True))
-
-    def get_agents(self, state_id=None, limit_neighbors=False, iterator=False, **kwargs):
-        if limit_neighbors:
-            agents = super().get_agents(state_id, limit_neighbors)
-        else:
-            agents = filter(lambda x: state_id is None or x.state.get('id', None) == state_id,
-                            self.env.agents)
-
-        def matches_all(agent):
-            state = agent.state
-            for k, v in kwargs.items():
-                if state.get(k, None) != v:
-                    return False
-            return True
-
-        f = filter(matches_all, agents)
-        if iterator:
-            return f
-        return list(f)
+        if not self.alive:
+            return time.When('inf')
+        return super().step() or 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 = " @{:>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
+        extra['unique_id'] = self.unique_id
+        extra['agent_name'] = self.name
         return self.logger.log(level, message, extra=extra)
 
     def debug(self, *args, **kwargs):
@@ -188,30 +158,106 @@ class BaseAgent(nxsim.BaseAgent, metaclass=MetaAgent):
         return self.log(*args, level=logging.INFO, **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.
-    '''
+class NetworkAgent(BaseAgent):
 
-    @wraps(func)
-    def func_wrapper(self):
-        next_state = func(self)
-        when = None
-        if next_state is None:
+    @property
+    def topology(self):
+        return self.model.G
+
+    @property
+    def G(self):
+        return self.model.G
+
+    def count_agents(self, **kwargs):
+        return len(list(self.get_agents(**kwargs)))
+
+    def count_neighboring_agents(self, state_id=None, **kwargs):
+        return len(self.get_neighboring_agents(state_id=state_id, **kwargs))
+
+    def get_neighboring_agents(self, state_id=None, **kwargs):
+        return self.get_agents(limit_neighbors=True, state_id=state_id, **kwargs)
+
+    def get_agents(self, *args, limit=None, **kwargs):
+        it = self.iter_agents(*args, **kwargs)
+        if limit is not None:
+            it = islice(it, limit)
+        return list(it)
+
+    def iter_agents(self, agents=None, limit_neighbors=False, **kwargs):
+        if limit_neighbors:
+            agents = self.topology.neighbors(self.unique_id)
+
+        agents = self.model.get_agents(agents)
+        return select(agents, **kwargs)
+
+    def subgraph(self, center=True, **kwargs):
+        include = [self] if center else []
+        return self.topology.subgraph(n.unique_id for n in list(self.get_agents(**kwargs))+include)
+
+    def remove_node(self, unique_id):
+        self.topology.remove_node(unique_id)
+
+    def add_edge(self, other, edge_attr_dict=None, *edge_attrs):
+        # return super(NetworkAgent, self).add_edge(node1=self.id, node2=other, **kwargs)
+        if self.unique_id not in self.topology.nodes(data=False):
+            raise ValueError('{} not in list of existing agents in the network'.format(self.unique_id))
+        if other.unique_id not in self.topology.nodes(data=False):
+            raise ValueError('{} not in list of existing agents in the network'.format(other))
+
+        self.topology.add_edge(self.unique_id, other.unique_id, edge_attr_dict=edge_attr_dict, *edge_attrs)
+
+
+    def ego_search(self, steps=1, center=False, node=None, **kwargs):
+        '''Get a list of nodes in the ego network of *node* of radius *steps*'''
+        node = as_node(node if node is not None else self)
+        G = self.subgraph(**kwargs)
+        return nx.ego_graph(G, node, center=center, radius=steps).nodes()
+
+    def degree(self, node, force=False):
+        node = as_node(node)
+        if force or (not hasattr(self.model, '_degree')) or getattr(self.model, '_last_step', 0) < self.now:
+            self.model._degree = nx.degree_centrality(self.topology)
+            self.model._last_step = self.now
+        return self.model._degree[node]
+
+    def betweenness(self, node, force=False):
+        node = as_node(node)
+        if force or (not hasattr(self.model, '_betweenness')) or getattr(self.model, '_last_step', 0) < self.now:
+            self.model._betweenness = nx.betweenness_centrality(self.topology)
+            self.model._last_step = self.now
+        return self.model._betweenness[node]
+
+
+def state(name=None):
+    def decorator(func, name=None):
+        '''
+        A state function should return either a state id, or a tuple (state_id, when)
+        The default value for state_id is the current state id.
+        The default value for when is the interval defined in the environment.
+        '''
+
+        @wraps(func)
+        def func_wrapper(self):
+            next_state = func(self)
+            when = None
+            if next_state is None:
+                return when
+            try:
+                next_state, when = next_state
+            except (ValueError, TypeError):
+                pass
+            if next_state:
+                self.set_state(next_state)
             return when
-        try:
-            next_state, when = next_state
-        except (ValueError, TypeError):
-            pass
-        if next_state:
-            self.set_state(next_state)
-        return when
 
-    func_wrapper.id = func.__name__
-    func_wrapper.is_default = False
-    return func_wrapper
+        func_wrapper.id = name or func.__name__
+        func_wrapper.is_default = False
+        return func_wrapper
+
+    if callable(name):
+        return decorator(name)
+    else:
+        return partial(decorator, name=name)
 
 
 def default_state(func):
@@ -219,7 +265,7 @@ def default_state(func):
     return func
 
 
-class MetaFSM(MetaAgent):
+class MetaFSM(type):
     def __init__(cls, name, bases, nmspc):
         super(MetaFSM, cls).__init__(name, bases, nmspc)
         states = {}
@@ -242,31 +288,34 @@ class MetaFSM(MetaAgent):
         cls.states = states
 
 
-class FSM(BaseAgent, metaclass=MetaFSM):
+class FSM(NetworkAgent, metaclass=MetaFSM):
     def __init__(self, *args, **kwargs):
         super(FSM, self).__init__(*args, **kwargs)
-        if 'id' not in self.state:
+        if not hasattr(self, 'state_id'):
             if not self.default_state:
-                raise ValueError('No default state specified for {}'.format(self.id))
-            self['id'] = self.default_state.id
+                raise ValueError('No default state specified for {}'.format(self.unique_id))
+            self.state_id = self.default_state.id
+
+        self.set_state(self.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)
+        self.debug(f'Agent {self.unique_id} @ state {self.state_id}')
+        interval = super().step()
+        if 'id' not in self.state:
+            # if 'id' in self.state:
+            #     self.set_state(self.state['id'])
+            if self.default_state:
+                self.set_state(self.default_state.id)
+            else:
+                raise Exception('{} has no valid state id or default state'.format(self))
+        return self.states[self.state_id](self) or interval
 
     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
+        self.state_id = state
         return state
 
 
@@ -312,32 +361,61 @@ def calculate_distribution(network_agents=None,
     'agent_type_1'.
     '''
     if network_agents:
-        network_agents = deepcopy(network_agents)
+        network_agents = [deepcopy(agent) for agent in network_agents if not hasattr(agent, 'id')]
     elif agent_type:
         network_agents = [{'agent_type': agent_type}]
     else:
-        return []
+        raise ValueError('Specify a distribution or a default agent type')
+
+    # Fix missing weights and incompatible types
+    for x in network_agents:
+        x['weight'] = float(x.get('weight', 1))
 
     # Calculate the thresholds
-    total = sum(x.get('weight', 1) for x in network_agents)
+    total = sum(x['weight'] for x in network_agents)
     acc = 0
     for v in network_agents:
-        upper = acc + (v.get('weight', 1)/total)
+        if 'ids' in v:
+            continue
+        upper = acc + (v['weight']/total)
         v['threshold'] = [acc, upper]
         acc = upper
     return network_agents
 
 
-def _serialize_distribution(network_agents):
-    d = _convert_agent_types(network_agents,
-                             to_string=True)
+def serialize_type(agent_type, known_modules=[], **kwargs):
+    if isinstance(agent_type, str):
+        return agent_type
+    known_modules += ['soil.agents']
+    return serialization.serialize(agent_type, known_modules=known_modules, **kwargs)[1] # Get the name of the class
+
+
+def serialize_definition(network_agents, known_modules=[]):
     '''
     When serializing an agent distribution, remove the thresholds, in order
     to avoid cluttering the YAML definition file.
     '''
+    d = deepcopy(list(network_agents))
     for v in d:
         if 'threshold' in v:
             del v['threshold']
+        v['agent_type'] = serialize_type(v['agent_type'],
+                                         known_modules=known_modules)
+    return d
+
+
+def deserialize_type(agent_type, known_modules=[]):
+    if not isinstance(agent_type, str):
+        return agent_type
+    known = known_modules + ['soil.agents', 'soil.agents.custom' ]
+    agent_type = serialization.deserializer(agent_type, known_modules=known)
+    return agent_type
+
+
+def deserialize_definition(ind, **kwargs):
+    d = deepcopy(ind)
+    for v in d:
+        v['agent_type'] = deserialize_type(v['agent_type'], **kwargs)
     return d
 
 
@@ -346,37 +424,121 @@ def _validate_states(states, topology):
     states = states or []
     if isinstance(states, dict):
         for x in states:
-            assert x in topology.node
+            assert x in topology.nodes
     else:
         assert len(states) <= len(topology)
     return states
 
 
-def _convert_agent_types(ind, to_string=False):
+def _convert_agent_types(ind, to_string=False, **kwargs):
     '''Convenience method to allow specifying agents by class or class name.'''
-    d = deepcopy(ind)
-    for v in d:
-        agent_type = v['agent_type']
-        if to_string and not isinstance(agent_type, str):
-            v['agent_type'] = str(agent_type.__name__)
-        elif not to_string and isinstance(agent_type, str):
-            v['agent_type'] = agent_types[agent_type]
-    return d
+    if to_string:
+        return serialize_definition(ind, **kwargs)
+    return deserialize_definition(ind, **kwargs)
 
 
-def _agent_from_distribution(distribution, value=-1):
+def _agent_from_definition(definition, value=-1, unique_id=None):
     """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]:
+    for d in sorted(definition, key=lambda x: x.get('threshold')):
+        threshold = d.get('threshold', (-1, -1))
+        # Check if the definition matches by id (first) or by threshold
+        if (unique_id is not None and unique_id in d.get('ids', [])) or \
+           (value >= threshold[0] and value < threshold[1]):
             state = {}
             if 'state' in d:
                 state = deepcopy(d['state'])
             return d['agent_type'], state
 
-    raise Exception('Distribution for value {} not found in: {}'.format(value, distribution))
+    raise Exception('Definition for value {} not found in: {}'.format(value, definition))
+
+
+def _definition_to_dict(definition, size=None, default_state=None):
+    state = default_state or {}
+    agents = {}
+    remaining = {}
+    if size:
+        for ix in range(size):
+            remaining[ix] = copy(state)
+    else:
+        remaining = defaultdict(lambda x: copy(state))
+
+    distro = sorted([item for item in definition if 'weight' in item])
+
+    ix = 0
+    def init_agent(item, id=ix):
+        while id in agents:
+            id += 1
+
+        agent = remaining[id]
+        agent['state'].update(copy(item.get('state', {})))
+        agents[id] = agent
+        del remaining[id]
+        return agent
+
+    for item in definition:
+        if 'ids' in item:
+            ids = item['ids']
+            del item['ids']
+            for id in ids:
+                agent = init_agent(item, id)
+
+    for item in definition:
+        if 'number' in item:
+            times = item['number']
+            del item['number']
+            for times in range(times):
+                if size:
+                    ix = random.choice(remaining.keys())
+                    agent = init_agent(item, id)
+                else:
+                    agent = init_agent(item)
+    if not size:
+        return agents
+
+    if len(remaining) < 0:
+        raise Exception('Invalid definition. Too many agents to add')
+
+
+    total_weight = float(sum(s['weight'] for s in distro))
+    unit = size / total_weight
+
+    for item in distro:
+        times = unit * item['weight']
+        del item['weight']
+        for times in range(times):
+            ix = random.choice(remaining.keys())
+            agent = init_agent(item, id)
+    return agents
+
+
+def select(agents, state_id=None, agent_type=None, ignore=None, iterator=False, **kwargs):
+
+    if state_id is not None and not isinstance(state_id, (tuple, list)):
+        state_id = tuple([state_id])
+    if agent_type is not None:
+        try:
+            agent_type = tuple(agent_type)
+        except TypeError:
+            agent_type = tuple([agent_type])
+
+    f = agents
+
+    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_type is not None:
+        f = filter(lambda agent: isinstance(agent, agent_type), f)
+    for k, v in kwargs.items():
+        f = filter(lambda agent: agent.state.get(k, None) == v, f)
+
+    if iterator:
+        return f
+    return f
 
 
 from .BassModel import *
@@ -386,4 +548,10 @@ 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/analysis.py

@@ -4,7 +4,8 @@ import glob
 import yaml
 from os.path import join
 
-from . import utils, history
+from . import serialization
+from tsih import History
 
 
 def read_data(*args, group=False, **kwargs):
@@ -20,7 +21,7 @@ def _read_data(pattern, *args, from_csv=False, process_args=None, **kwargs):
         process_args = {}
     for folder in glob.glob(pattern):
         config_file = glob.glob(join(folder, '*.yml'))[0]
-        config = yaml.load(open(config_file))
+        config = yaml.load(open(config_file), Loader=yaml.SafeLoader)
         df = None
         if from_csv:
             for trial_data in sorted(glob.glob(join(folder,
@@ -28,13 +29,13 @@ def _read_data(pattern, *args, from_csv=False, process_args=None, **kwargs):
                 df = read_csv(trial_data, **kwargs)
                 yield config_file, df, config
         else:
-            for trial_data in sorted(glob.glob(join(folder, '*.db.sqlite'))):
+            for trial_data in sorted(glob.glob(join(folder, '*.sqlite'))):
                 df = read_sql(trial_data, **kwargs)
                 yield config_file, df, config
 
 
 def read_sql(db, *args, **kwargs):
-    h = history.History(db, backup=False)
+    h = History(db_path=db, backup=False, readonly=True)
     df = h.read_sql(*args, **kwargs)
     return df
 
@@ -56,12 +57,17 @@ def read_csv(filename, keys=None, convert_types=False, **kwargs):
 
 
 def convert_row(row):
-    row['value'] = utils.convert(row['value'], row['value_type'])
+    row['value'] = serialization.deserialize(row['value_type'], row['value'])
     return row
 
 
 def convert_types_slow(df):
-    '''This is a slow operation.'''
+    '''
+    Go over every column in a dataframe and convert it to the type determined by the `get_types`
+    function.
+
+    This is a slow operation.
+    '''
     dtypes = get_types(df)
     for k, v in dtypes.items():
         t = df[df['key']==k]
@@ -69,6 +75,13 @@ def convert_types_slow(df):
     df = df.apply(convert_row, axis=1)
     return df
 
+
+def split_processed(df):
+    env = df.loc[:, df.columns.get_level_values(1).isin(['env', 'stats'])]
+    agents = df.loc[:, ~df.columns.get_level_values(1).isin(['env', 'stats'])]
+    return env, agents
+
+
 def split_df(df):
     '''
     Split a dataframe in two dataframes: one with the history of agents,
@@ -95,6 +108,9 @@ def process(df, **kwargs):
 
 
 def get_types(df):
+    '''
+    Get the value type for every key stored in a raw history dataframe.
+    '''
     dtypes = df.groupby(by=['key'])['value_type'].unique()
     return {k:v[0] for k,v in dtypes.iteritems()}
 
@@ -119,24 +135,45 @@ def process_one(df, *keys, columns=['key', 'agent_id'], values='value',
 
 
 def get_count(df, *keys):
+    '''
+    For every t_step and key, get the value count.
+
+    The result is a dataframe with `t_step` as index, an a multiindex column based on `key` and the values found for each `key`.
+    '''
     if keys:
         df = df[list(keys)]
+        df.columns = df.columns.remove_unused_levels()
     counts = pd.DataFrame()
     for key in df.columns.levels[0]:
-        g = df[key].apply(pd.Series.value_counts, axis=1).fillna(0)
+        g = df[[key]].apply(pd.Series.value_counts, axis=1).fillna(0)
         for value, series in g.iteritems():
             counts[key, value] = series
     counts.columns = pd.MultiIndex.from_tuples(counts.columns)
     return counts
 
 
+def get_majority(df, *keys):
+    '''
+    For every t_step and key, get the value of the majority of agents
+
+    The result is a dataframe with `t_step` as index, and columns based on `key`.
+    '''
+    df = get_count(df, *keys)
+    return df.stack(level=0).idxmax(axis=1).unstack()
+
+
 def get_value(df, *keys, aggfunc='sum'):
+    '''
+    For every t_step and key, get the value of *numeric columns*, aggregated using a specific function.
+    '''
     if keys:
         df = df[list(keys)]
-    return df.groupby(axis=1, level=0).agg(aggfunc, axis=1)
+        df.columns = df.columns.remove_unused_levels()
+    df = df.select_dtypes('number')
+    return df.groupby(level='key', axis=1).agg(aggfunc)
 
 
-def plot_all(*args, **kwargs):
+def plot_all(*args, plot_args={}, **kwargs):
     '''
     Read all the trial data and plot the result of applying a function on them.
     '''
@@ -144,14 +181,17 @@ def plot_all(*args, **kwargs):
     ps = []
     for line in dfs:
         f, df, config = line
-        df.plot(title=config['name'])
+        if len(df) < 1:
+            continue
+        df.plot(title=config['name'], **plot_args)
         ps.append(df)
     return ps
 
 def do_all(pattern, func, *keys, include_env=False, **kwargs):
     for config_file, df, config in read_data(pattern, keys=keys):
+        if len(df) < 1:
+            continue
         p = func(df, *keys, **kwargs)
-        p.plot(title=config['name'])
         yield config_file, p, config
 
 

soil/datacollection.py

@@ -0,0 +1,26 @@
+from mesa import DataCollector as MDC
+
+class SoilDataCollector(MDC):
+
+
+    def __init__(self, environment, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        # Populate model and env reporters so they have a key per 
+        # So they can be shown in the web interface
+        self.environment = environment
+
+
+    @property
+    def model_vars(self):
+        pass
+
+    @model_vars.setter
+    def model_vars(self, value):
+        pass
+
+    @property
+    def agent_reporters(self):
+        self.model._history._
+
+        pass
+

soil/environment.py

@@ -1,21 +1,31 @@
 import os
 import sqlite3
-import time
 import csv
+import math
 import random
-import simpy
+import yaml
 import tempfile
 import pandas as pd
+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 tsih import History, Record, Key, NoHistory
 
+from mesa import Model
 
-class SoilEnvironment(nxsim.NetworkEnvironment):
+from . import serialization, agents, analysis, utils, time
+
+# These properties will be copied when pickling/unpickling the environment
+_CONFIG_PROPS = [ 'name',
+                  'states',
+                  'default_state',
+                  'interval',
+                 ]
+
+class Environment(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
@@ -23,7 +33,7 @@ class SoilEnvironment(nxsim.NetworkEnvironment):
 
     The environment parameters and the state of every agent can be accessed
     both by using the environment as a dictionary or with the environment's 
-    :meth:`soil.environment.SoilEnvironment.get` method.
+    :meth:`soil.environment.Environment.get` method.
     """
 
     def __init__(self, name=None,
@@ -32,32 +42,70 @@ class SoilEnvironment(nxsim.NetworkEnvironment):
                  states=None,
                  default_state=None,
                  interval=1,
+                 network_params=None,
                  seed=None,
-                 dry_run=False,
-                 dir_path=None,
                  topology=None,
-                 *args, **kwargs):
+                 schedule=None,
+                 initial_time=0,
+                 environment_params=None,
+                 history=True,
+                 dir_path=None,
+                 **kwargs):
+
+
+        super().__init__()
+
+        self.schedule = schedule
+        if schedule is None:
+            self.schedule = time.TimedActivation()
+
         self.name = name or 'UnnamedEnvironment'
+        seed = seed or current_time()
+        random.seed(seed)
         if isinstance(states, list):
             states = dict(enumerate(states))
         self.states = deepcopy(states) if states else {}
         self.default_state = deepcopy(default_state) or {}
+
+        if topology is None:
+            network_params = network_params or {}
+            topology = serialization.load_network(network_params,
+                                                  dir_path=dir_path)
         if not topology:
             topology = nx.Graph()
-        super().__init__(*args, topology=topology, **kwargs)
+        self.G = nx.Graph(topology) 
+
+
+        self.environment_params = environment_params or {}
+        self.environment_params.update(kwargs)
+
         self._env_agents = {}
-        self.dry_run = dry_run
         self.interval = interval
-        self.dir_path = dir_path or tempfile.mkdtemp('soil-env')
-        self.get_path()
-        self._history = history.History(name=self.name if not dry_run else None,
-                                        dir_path=self.dir_path)
-        # 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'])
+        if history:
+            history = History
+        else:
+            history = NoHistory
+        self._history = history(name=self.name,
+                                backup=True)
+        self['SEED'] = seed
+
+        if network_agents:
+            distro = agents.calculate_distribution(network_agents)
+            self.network_agents = agents._convert_agent_types(distro)
+        else:
+            self.network_agents = []
+
+        environment_agents = environment_agents or []
+        if environment_agents:
+            distro = agents.calculate_distribution(environment_agents)
+            environment_agents = agents._convert_agent_types(distro)
+        self.environment_agents = environment_agents
+
+    @property
+    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')
 
     @property
     def agents(self):
@@ -71,43 +119,67 @@ class SoilEnvironment(nxsim.NetworkEnvironment):
 
     @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
+        self._environment_agents = environment_agents
+
+        self._env_agents = agents._definition_to_dict(definition=environment_agents)
 
     @property
     def network_agents(self):
         for i in self.G.nodes():
-            node = self.G.node[i]
+            node = self.G.nodes[i]
             if 'agent' in node:
                 yield node['agent']
 
     @network_agents.setter
     def network_agents(self, network_agents):
-        if not network_agents:
-            return
+        self._network_agents = network_agents
         for ix in self.G.nodes():
-            agent, state = agents._agent_from_distribution(network_agents)
-            self.set_agent(ix, agent_type=agent, state=state)
+            self.init_agent(ix, agent_definitions=network_agents)
+
+    def init_agent(self, agent_id, agent_definitions):
+        node = self.G.nodes[agent_id]
+        init = False
+        state = dict(node)
+
+        agent_type = None
+        if 'agent_type' in self.states.get(agent_id, {}):
+            agent_type = self.states[agent_id]['agent_type']
+        elif 'agent_type' in node:
+            agent_type = node['agent_type']
+        elif 'agent_type' in self.default_state:
+            agent_type = self.default_state['agent_type']
+
+        if agent_type:
+            agent_type = agents.deserialize_type(agent_type)
+        elif agent_definitions:
+            agent_type, state = agents._agent_from_definition(agent_definitions, unique_id=agent_id)
+        else:
+            serialization.logger.debug('Skipping node {}'.format(agent_id))
+            return
+        return self.set_agent(agent_id, agent_type, state)
 
     def set_agent(self, agent_id, agent_type, state=None):
         node = self.G.nodes[agent_id]
-        defstate = deepcopy(self.default_state)
+        defstate = deepcopy(self.default_state) or {}
         defstate.update(self.states.get(agent_id, {}))
+        defstate.update(node.get('state', {}))
         if state:
             defstate.update(state)
-        state = defstate
-        state.update(node.get('state', {}))
-        a = agent_type(environment=self,
-                       agent_id=agent_id,
-                       state=state)
+        a = None
+        if agent_type:
+            state = defstate
+            a = agent_type(model=self,
+                           unique_id=agent_id)
+
+        for (k, v) in getattr(a, 'defaults', {}).items():
+            if not hasattr(a, k) or getattr(a, k) is None:
+                setattr(a, k, v)
+
+        for (k, v) in state.items():
+            setattr(a, k, v)
+
         node['agent'] = a
+        self.schedule.add(a)
         return a
 
     def add_node(self, agent_type, state=None):
@@ -117,37 +189,31 @@ class SoilEnvironment(nxsim.NetworkEnvironment):
         a['visible'] = True
         return a
 
-    def add_edge(self, agent1, agent2, attrs=None):
-        return self.G.add_edge(agent1, agent2)
+    def add_edge(self, agent1, agent2, start=None, **attrs):
+        if hasattr(agent1, 'id'):
+            agent1 = agent1.id
+        if hasattr(agent2, 'id'):
+            agent2 = agent2.id
+        start = start or self.now
+        return self.G.add_edge(agent1, agent2, **attrs)
 
-    def run(self, *args, **kwargs):
+    def step(self):
+        super().step()
+        self.datacollector.collect(self)
+        self.schedule.step()
+
+    def run(self, until, *args, **kwargs):
         self._save_state()
-        super().run(*args, **kwargs)
+
+        while self.schedule.next_time <= until and not math.isinf(self.schedule.next_time):
+            self.schedule.step(until=until)
+            utils.logger.debug(f'Simulation step {self.schedule.time}/{until}. Next: {self.schedule.next_time}')
         self._history.flush_cache()
 
     def _save_state(self, now=None):
-        # for agent in self.agents:
-        #     agent.save_state()
-        utils.logger.debug('Saving state @{}'.format(self.now))
+        serialization.logger.debug('Saving state @{}'.format(self.now))
         self._history.save_records(self.state_to_tuples(now=now))
 
-    def save_state(self):
-        '''
-        :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()
@@ -157,12 +223,12 @@ class SoilEnvironment(nxsim.NetworkEnvironment):
 
     def __setitem__(self, key, value):
         if isinstance(key, tuple):
-            k = history.Key(*key)
+            k = Key(*key)
             self._history.save_record(*k,
                                       value=value)
             return
         self.environment_params[key] = value
-        self._history.save_record(agent_id='env',
+        self._history.save_record(dict_id='env',
                                   t_step=self.now,
                                   key=key,
                                   value=value)
@@ -181,45 +247,33 @@ class SoilEnvironment(nxsim.NetworkEnvironment):
         '''
         return self[key] if key in self else default
 
-    def get_path(self, dir_path=None):
-        dir_path = dir_path or self.dir_path
-        if not os.path.exists(dir_path):
-            try:
-                os.makedirs(dir_path)
-            except FileExistsError:
-                pass
-        return dir_path
-
     def get_agent(self, agent_id):
-        return self.G.node[agent_id]['agent']
+        return self.G.nodes[agent_id]['agent']
 
-    def get_agents(self):
-        return list(self.agents)
+    def get_agents(self, nodes=None):
+        if nodes is None:
+            return self.agents
+        return (self.G.nodes[i]['agent'] for i in nodes)
 
-    def dump_csv(self, dir_path=None):
-        csv_name = os.path.join(self.get_path(dir_path),
-                                '{}.environment.csv'.format(self.name))
-
-        with open(csv_name, 'w') as f:
+    def dump_csv(self, f):
+        with utils.open_or_reuse(f, 'w') as f:
             cr = csv.writer(f)
-            cr.writerow(('agent_id', 't_step', 'key', 'value', 'value_type'))
+            cr.writerow(('agent_id', 't_step', 'key', 'value'))
             for i in self.history_to_tuples():
                 cr.writerow(i)
 
-    def dump_gexf(self, dir_path=None):
+    def dump_gexf(self, f):
         G = self.history_to_graph()
-        graph_path = os.path.join(self.get_path(dir_path),
-                                  self.name+".gexf")
         # Workaround for geometric models
         # See soil/soil#4
         for node in G.nodes():
-            if 'pos' in G.node[node]:
-                G.node[node]['viz'] = {"position": {"x": G.node[node]['pos'][0], "y": G.node[node]['pos'][1], "z": 0.0}}
-                del (G.node[node]['pos'])
+            if 'pos' in G.nodes[node]:
+                G.nodes[node]['viz'] = {"position": {"x": G.nodes[node]['pos'][0], "y": G.nodes[node]['pos'][1], "z": 0.0}}
+                del (G.nodes[node]['pos'])
 
-        nx.write_gexf(G, graph_path, version="1.2draft")
+        nx.write_gexf(G, f, version="1.2draft")
 
-    def dump(self, dir_path=None, formats=None):
+    def dump(self, *args, formats=None, **kwargs):
         if not formats:
             return
         functions = {
@@ -228,24 +282,27 @@ class SoilEnvironment(nxsim.NetworkEnvironment):
         }
         for f in formats:
             if f in functions:
-                functions[f](dir_path)
+                functions[f](*args, **kwargs)
             else:
                 raise ValueError('Unknown format: {}'.format(f))
 
+    def dump_sqlite(self, f):
+        return self._history.dump(f)
+
     def state_to_tuples(self, now=None):
         if now is None:
             now = self.now
         for k, v in self.environment_params.items():
-            yield history.Record(agent_id='env',
-                                 t_step=now,
-                                 key=k,
-                                 value=v)
+            yield Record(dict_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)
+                yield Record(dict_id=agent.id,
+                             t_step=now,
+                             key=k,
+                             value=v)
 
     def history_to_tuples(self):
         return self._history.to_tuples()
@@ -297,17 +354,24 @@ class SoilEnvironment(nxsim.NetworkEnvironment):
         return G
 
     def __getstate__(self):
-        state = self.__dict__.copy()
+        state = {}
+        for prop in _CONFIG_PROPS:
+            state[prop] = self.__dict__[prop]
         state['G'] = json_graph.node_link_data(self.G)
-        state['network_agents'] = agents._serialize_distribution(self.network_agents)
-        state['environment_agents'] = agents._convert_agent_types(self.environment_agents,
-                                                                 to_string=True)
-        del state['_queue']
+        state['environment_agents'] = self._env_agents
+        state['history'] = self._history
+        state['schedule'] = self.schedule
         return state
 
     def __setstate__(self, state):
-        self.__dict__ = state
+        for prop in _CONFIG_PROPS:
+            self.__dict__[prop] = state[prop]
+        self._env_agents = state['environment_agents']
         self.G = json_graph.node_link_graph(state['G'])
-        self.network_agents = self.calculate_distribution(self._convert_agent_types(self.network_agents))
-        self.environment_agents = self._convert_agent_types(self.environment_agents)
-        return state
+        self._history = state['history']
+        # self._env = None
+        self.schedule = state['schedule']
+        self._queue = []
+
+
+SoilEnvironment = Environment

soil/exporters.py

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

soil/history.py

@@ -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/serialization.py

@@ -0,0 +1,222 @@
+import os
+import logging
+import ast
+import sys
+import importlib
+from glob import glob
+from itertools import product, chain
+
+import yaml
+import networkx as nx
+
+from jinja2 import Template
+
+
+logger = logging.getLogger('soil')
+
+
+def load_network(network_params, dir_path=None):
+    G = nx.Graph()
+
+    if 'path' in network_params:
+        path = network_params['path']
+        if dir_path and not os.path.isabs(path):
+            path = os.path.join(dir_path, path)
+        extension = os.path.splitext(path)[1][1:]
+        kwargs = {}
+        if extension == 'gexf':
+            kwargs['version'] = '1.2draft'
+            kwargs['node_type'] = int
+        try:
+            method = getattr(nx.readwrite, 'read_' + extension)
+        except AttributeError:
+            raise AttributeError('Unknown format')
+        G = method(path, **kwargs)
+
+    elif 'generator' in network_params:
+        net_args = network_params.copy()
+        net_gen = net_args.pop('generator')
+
+        if dir_path not in sys.path:
+            sys.path.append(dir_path)
+
+        method = deserializer(net_gen,
+                              known_modules=['networkx.generators',])
+        G = method(**net_args)
+
+    return G
+
+
+
+
+def load_file(infile):
+    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):
+            for config in load_file(i):
+                path = os.path.abspath(i)
+                if 'dir_path' not in config:
+                    config['dir_path'] = os.path.dirname(path)
+                yield config, path
+
+
+def load_config(config):
+    if isinstance(config, dict):
+        yield config, os.getcwd()
+    else:
+        yield from load_files(config)
+
+
+builtins = importlib.import_module('builtins')
+
+def name(value, known_modules=[]):
+    '''Return a name that can be imported, to serialize/deserialize an object'''
+    if value is None:
+        return 'None'
+    if not isinstance(value, type):  # Get the class name first
+        value = type(value)
+    tname = value.__name__
+    if hasattr(builtins, tname):
+        return tname
+    modname = value.__module__
+    if modname == '__main__':
+        return tname
+    if known_modules and modname in known_modules:
+        return tname
+    for kmod in known_modules:
+        if not kmod:
+            continue
+        module = importlib.import_module(kmod)
+        if hasattr(module, tname):
+            return tname
+    return '{}.{}'.format(modname, tname)
+
+
+def serializer(type_):
+    if type_ != 'str' and hasattr(builtins, type_):
+        return repr
+    return lambda x: x
+
+
+def serialize(v, known_modules=[]):
+    '''Get a text representation of an object.'''
+    tname = name(v, known_modules=known_modules)
+    func = serializer(tname)
+    return func(v), tname
+
+def deserializer(type_, known_modules=[]):
+    if type(type_) != str:  # Already deserialized
+        return type_
+    if type_ == 'str':
+        return lambda x='': x
+    if type_ == 'None':
+        return lambda x=None: None
+    if hasattr(builtins, type_):  # Check if it's a builtin type
+        cls = getattr(builtins, type_)
+        return lambda x=None: ast.literal_eval(x) if x is not None else cls()
+    # Otherwise, see if we can find the module and the class
+    modules = known_modules or []
+    options = []
+
+    for mod in modules:
+        if mod:
+            options.append((mod, type_))
+
+    if '.' in type_:  # Fully qualified module
+        module, type_ = type_.rsplit(".", 1)
+        options.append ((module, type_))
+
+    errors = []
+    for modname, tname in options:
+        try:
+            module = importlib.import_module(modname)
+            cls = getattr(module, tname)
+            return getattr(cls, 'deserialize', cls)
+        except (ImportError, AttributeError) as ex:
+            errors.append((modname, tname, ex))
+    raise Exception('Could not find type {}. Tried: {}'.format(type_, errors))
+
+
+def deserialize(type_, value=None, **kwargs):
+    '''Get an object from a text representation'''
+    if not isinstance(type_, str):
+        return type_
+    des = deserializer(type_, **kwargs)
+    if value is None:
+        return des
+    return des(value)
+
+
+def deserialize_all(names, *args, known_modules=['soil'], **kwargs):
+    '''Return the set of exporters for a simulation, given the exporter names'''
+    exporters = []
+    for name in names:
+        mod = deserialize(name, known_modules=known_modules)
+        exporters.append(mod(*args, **kwargs))
+    return exporters
+

soil/simulation.py

@@ -1,24 +1,30 @@
 import os
 import time
-import imp
+import importlib
 import sys
 import yaml
+import traceback
+import logging
 import networkx as nx
 from networkx.readwrite import json_graph
 from multiprocessing import Pool
 from functools import partial
+from tsih import History
 
 import pickle
 
-from nxsim import NetworkSimulation
-
-from . import utils, environment, basestring, agents
+from . import serialization, utils, basestring, agents
+from .environment import Environment
 from .utils import logger
+from .exporters import default
+from .stats import defaultStats
 
 
-class SoilSimulation(NetworkSimulation):
+#TODO: change documentation for simulation
+
+class Simulation:
     """
-    Subclass of nsim.NetworkSimulation with three main differences:
+    Similar to nsim.NetworkSimulation with three main differences:
         1) agent type can be specified by name or by class.
         2) instead of just one type, a network agents distribution can be used.
            The distribution specifies the weight (or probability) of each
@@ -43,112 +49,226 @@ class SoilSimulation(NetworkSimulation):
           'agent_type_1'.
         3) if no initial state is given, each node's state will be set
            to `{'id': 0}`.
-    """
-    def __init__(self, name=None, topology=None, network_params=None,
-                 network_agents=None, agent_type=None, states=None,
-                 default_state=None, interval=1, dump=None, dry_run=False,
-                 dir_path=None, num_trials=1, max_time=100,
-                 agent_module=None, load_module=None, seed=None,
-                 environment_agents=None, environment_params=None, **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)
+    Parameters
+    ---------
+    name : str, optional
+        name of the Simulation
+    group : str, optional
+        a group name can be used to link simulations
+    topology : networkx.Graph instance, optional
+    network_params : dict
+        parameters used to create a topology with networkx, if no topology is given
+    network_agents : dict
+        definition of agents to populate the topology with
+    agent_type : NetworkAgent subclass, optional
+        Default type of NetworkAgent to use for nodes not specified in network_agents
+    states : list, optional
+        List of initial states corresponding to the nodes in the topology. Basic form is a list of integers
+        whose value indicates the state
+    dir_path: str, optional
+        Directory path to load simulation assets (files, modules...)
+    seed : str, optional
+        Seed to use for the random generator
+    num_trials : int, optional
+        Number of independent simulation runs
+    max_time : int, optional
+        Time how long the simulation should run
+    environment_params : dict, optional
+        Dictionary of globally-shared environmental parameters
+    environment_agents: dict, optional
+        Similar to network_agents. Distribution of Agents that control the environment
+    environment_class: soil.environment.Environment subclass, optional
+        Class for the environment. It defailts to soil.environment.Environment
+    load_module : str, module name, deprecated
+        If specified, soil will load the content of this module under 'soil.agents.custom'
+
+
+    """
+
+    def __init__(self, name=None, group=None, topology=None, network_params=None,
+                 network_agents=None, agent_type=None, states=None,
+                 default_state=None, interval=1, num_trials=1,
+                 max_time=100, load_module=None, seed=None,
+                 dir_path=None, environment_agents=None,
+                 environment_params=None, environment_class=None,
+                 **kwargs):
 
         self.load_module = load_module
-        self.topology = nx.Graph(topology)
         self.network_params = network_params
-        self.name = name or 'UnnamedSimulation'
+        self.name = name or 'Unnamed'
+        self.seed = str(seed or name)
+        self._id = '{}_{}'.format(self.name, time.strftime("%Y-%m-%d_%H.%M.%S"))
+        self.group = group or ''
         self.num_trials = num_trials
         self.max_time = max_time
         self.default_state = default_state or {}
         self.dir_path = dir_path or os.getcwd()
         self.interval = interval
-        self.seed = str(seed) or str(time.time())
-        self.dump = dump
-        self.dry_run = dry_run
-        self.environment_params = environment_params or {}
 
-        if load_module:
-            path = sys.path + [self.dir_path, os.getcwd()]
-            f, fp, desc = imp.find_module(load_module, path)
-            imp.load_module('soil.agents.custom', f, fp, desc)
+        sys.path += list(x for x in [os.getcwd(), self.dir_path] if x not in sys.path)
+
+        if topology is None:
+            topology = serialization.load_network(network_params,
+                                                  dir_path=self.dir_path)
+        elif isinstance(topology, basestring) or isinstance(topology, dict):
+            topology = json_graph.node_link_graph(topology)
+        self.topology = nx.Graph(topology)
+
+
+        self.environment_params = environment_params or {}
+        self.environment_class = serialization.deserialize(environment_class,
+                                                   known_modules=['soil.environment', ]) or Environment
 
         environment_agents = environment_agents or []
-        self.environment_agents = agents._convert_agent_types(environment_agents)
+        self.environment_agents = agents._convert_agent_types(environment_agents,
+                                                              known_modules=[self.load_module])
 
         distro = agents.calculate_distribution(network_agents,
                                                agent_type)
-        self.network_agents = agents._convert_agent_types(distro)
+        self.network_agents = agents._convert_agent_types(distro,
+                                                          known_modules=[self.load_module])
 
         self.states = agents._validate_states(states,
                                               self.topology)
 
+        self._history = History(name=self.name,
+                                backup=False)
+
     def run_simulation(self, *args, **kwargs):
         return self.run(*args, **kwargs)
 
     def run(self, *args, **kwargs):
-        return list(self.run_simulation_gen(*args, **kwargs))
+        '''Run the simulation and return the list of resulting environments'''
+        return list(self.run_gen(*args, **kwargs))
+
+    def _run_sync_or_async(self, parallel=False, *args, **kwargs):
+        if parallel and not os.environ.get('SENPY_DEBUG', None):
+            p = Pool()
+            func = partial(self.run_trial_exceptions,
+                           *args,
+                           **kwargs)
+            for i in p.imap_unordered(func, range(self.num_trials)):
+                if isinstance(i, Exception):
+                    logger.error('Trial failed:\n\t%s', i.message)
+                    continue
+                yield i
+        else:
+            for i in range(self.num_trials):
+                yield self.run_trial(*args,
+                                     **kwargs)
+
+    def run_gen(self, *args, parallel=False, dry_run=False,
+                exporters=[default, ], stats=[], outdir=None, exporter_params={},
+                stats_params={}, log_level=None,
+                **kwargs):
+        '''Run the simulation and yield the resulting environments.'''
+        if log_level:
+            logger.setLevel(log_level)
+        logger.info('Using exporters: %s', exporters or [])
+        logger.info('Output directory: %s', outdir)
+        exporters = serialization.deserialize_all(exporters,
+                                                  simulation=self,
+                                                  known_modules=['soil.exporters',],
+                                                  dry_run=dry_run,
+                                                  outdir=outdir,
+                                                  **exporter_params)
+        stats = serialization.deserialize_all(simulation=self,
+                                              names=stats,
+                                              known_modules=['soil.stats',],
+                                              **stats_params)
 
-    def run_simulation_gen(self, *args, parallel=False, dry_run=False,
-                           **kwargs):
-        p = Pool()
         with utils.timer('simulation {}'.format(self.name)):
-            if parallel:
-                func = partial(self.run_trial, dry_run=dry_run or self.dry_run,
-                               return_env=not parallel, **kwargs)
-                for i in p.imap_unordered(func, range(self.num_trials)):
-                    yield i
-            else:
-                for i in range(self.num_trials):
-                    yield self.run_trial(i, dry_run=dry_run or self.dry_run, **kwargs)
-            if not (dry_run or self.dry_run):
-                logger.info('Dumping results to {}'.format(self.dir_path))
-                self.dump_pickle(self.dir_path)
-                self.dump_yaml(self.dir_path)
-            else:
-                logger.info('NOT dumping results')
+            for stat in stats:
+                stat.start()
+
+            for exporter in exporters:
+                exporter.start()
+            for env in self._run_sync_or_async(*args,
+                                               parallel=parallel,
+                                               log_level=log_level,
+                                               **kwargs):
+
+                collected = list(stat.trial(env) for stat in stats)
+
+                saved = self.save_stats(collected, t_step=env.now, trial_id=env.name)
+
+                for exporter in exporters:
+                    exporter.trial(env, saved)
+
+                yield env
+
+
+            collected = list(stat.end() for stat in stats)
+            saved = self.save_stats(collected)
+
+            for exporter in exporters:
+                exporter.end(saved)
+
+
+    def save_stats(self, collection, **kwargs):
+        stats = dict(kwargs)
+        for stat in collection:
+            stats.update(stat)
+        self._history.save_stats(utils.flatten_dict(stats))
+        return stats
+
+    def get_stats(self, **kwargs):
+        return self._history.get_stats(**kwargs)
+
+    def log_stats(self, stats):
+        logger.info('Stats: \n{}'.format(yaml.dump(stats, default_flow_style=False)))
+    
 
     def get_env(self, trial_id=0, **kwargs):
+        '''Create an environment for a trial of the simulation'''
         opts = self.environment_params.copy()
-        env_name = '{}_trial_{}'.format(self.name, trial_id)
         opts.update({
-            'name': env_name,
+            'name': trial_id,
             'topology': self.topology.copy(),
-            'seed': self.seed+env_name,
+            'network_params': self.network_params,
+            'seed': '{}_trial_{}'.format(self.seed, trial_id),
             'initial_time': 0,
-            'dry_run': self.dry_run,
             'interval': self.interval,
             'network_agents': self.network_agents,
+            'initial_time': 0,
             'states': self.states,
+            'dir_path': self.dir_path,
             'default_state': self.default_state,
             'environment_agents': self.environment_agents,
-            'dir_path': self.dir_path,
         })
         opts.update(kwargs)
-        env = environment.SoilEnvironment(**opts)
+        env = self.environment_class(**opts)
         return env
 
-    def run_trial(self, trial_id=0, until=None, return_env=True, **opts):
-        """Run a single trial of the simulation
-
-        Parameters
-        ----------
-        trial_id : int
+    def run_trial(self, until=None, log_level=logging.INFO, **opts):
         """
+        Run a single trial of the simulation
+
+        """
+        trial_id = '{}_trial_{}'.format(self.name, time.time()).replace('.', '-')
+        if log_level:
+            logger.setLevel(log_level)
         # Set-up trial environment and graph
         until = until or self.max_time
         env = self.get_env(trial_id=trial_id, **opts)
         # Set up agents on nodes
         with utils.timer('Simulation {} trial {}'.format(self.name, trial_id)):
             env.run(until)
-        if self.dump and not self.dry_run:
-            with utils.timer('Dumping simulation {} trial {}'.format(self.name, trial_id)):
-                env.dump(formats=self.dump)
-        if return_env:
-            return env
+        return env
+
+    def run_trial_exceptions(self, *args, **kwargs):
+        '''
+        A wrapper for run_trial that catches exceptions and returns them.
+        It is meant for async simulations
+        '''
+        try:
+            return self.run_trial(*args, **kwargs)
+        except Exception as ex:
+            if ex.__cause__ is not None:
+                ex = ex.__cause__
+            ex.message = ''.join(traceback.format_exception(type(ex), ex, ex.__traceback__)[:])
+            return ex
 
     def to_dict(self):
         return self.__getstate__()
@@ -156,64 +276,83 @@ class SoilSimulation(NetworkSimulation):
     def to_yaml(self):
         return yaml.dump(self.to_dict())
 
-    def dump_yaml(self, dir_path=None, file_name=None):
-        dir_path = dir_path or self.dir_path
-        if not os.path.exists(dir_path):
-            os.makedirs(dir_path)
-        if not file_name:
-            file_name = os.path.join(dir_path,
-                                     '{}.dumped.yml'.format(self.name))
-        with open(file_name, 'w') as f:
+
+    def dump_yaml(self, f=None, outdir=None):
+        if not f and not outdir:
+            raise ValueError('specify a file or an output directory')
+
+        if not f:
+            f = os.path.join(outdir, '{}.dumped.yml'.format(self.name))
+
+        with utils.open_or_reuse(f, 'w') as f:
             f.write(self.to_yaml())
 
-    def dump_pickle(self, dir_path=None, pickle_name=None):
-        dir_path = dir_path or self.dir_path
-        if not os.path.exists(dir_path):
-            os.makedirs(dir_path)
-        if not pickle_name:
-            pickle_name = os.path.join(dir_path,
-                                       '{}.simulation.pickle'.format(self.name))
-        with open(pickle_name, 'wb') as f:
+    def dump_pickle(self, f=None, outdir=None):
+        if not outdir and not f:
+            raise ValueError('specify a file or an output directory')
+
+        if not f:
+            f = os.path.join(outdir,
+                             '{}.simulation.pickle'.format(self.name))
+        with utils.open_or_reuse(f, 'wb') as f:
             pickle.dump(self, f)
 
+    def dump_sqlite(self, f):
+        return self._history.dump(f)
+
     def __getstate__(self):
-        state = self.__dict__.copy()
-        state['topology'] = json_graph.node_link_data(self.topology)
-        state['network_agents'] = agents._serialize_distribution(self.network_agents)
-        state['environment_agents'] = agents._convert_agent_types(self.environment_agents,
-                                                                 to_string=True)
+        state={}
+        for k, v in self.__dict__.items():
+            if k[0] != '_':
+                state[k] = v
+                state['topology'] = json_graph.node_link_data(self.topology)
+                state['network_agents'] = agents.serialize_definition(self.network_agents,
+                                                                      known_modules = [])
+                state['environment_agents'] = agents.serialize_definition(self.environment_agents,
+                                                                          known_modules = [])
+                state['environment_class'] = serialization.serialize(self.environment_class,
+                                                                     known_modules=['soil.environment'])[1]  # func, name
+        if state['load_module'] is None:
+            del state['load_module']
         return state
 
     def __setstate__(self, state):
         self.__dict__ = state
+        self.load_module = getattr(self, 'load_module', None)
+        if self.dir_path not in sys.path:
+            sys.path += [self.dir_path, os.getcwd()]
         self.topology = json_graph.node_link_graph(state['topology'])
         self.network_agents = agents.calculate_distribution(agents._convert_agent_types(self.network_agents))
-        self.environment_agents = agents._convert_agent_types(self.environment_agents)
-        return state
+        self.environment_agents = agents._convert_agent_types(self.environment_agents,
+                                                              known_modules=[self.load_module])
+        self.environment_class = serialization.deserialize(self.environment_class,
+                                                   known_modules=[self.load_module, 'soil.environment', ])  # func, name
 
 
-def from_config(config):
-    config = list(utils.load_config(config))
+def all_from_config(config):
+    configs = list(serialization.load_config(config))
+    for config, _ in configs:
+        sim = Simulation(**config)
+        yield sim
+
+
+def from_config(conf_or_path):
+    config = list(serialization.load_config(conf_or_path))
     if len(config) > 1:
         raise AttributeError('Provide only one configuration')
     config = config[0][0]
-    sim = SoilSimulation(**config)
+    sim = Simulation(**config)
     return sim
 
 
-def run_from_config(*configs, results_dir='soil_output', dry_run=False, dump=None, timestamp=False,  **kwargs):
+def run_from_config(*configs, **kwargs):
     for config_def in configs:
         # logger.info("Found {} config(s)".format(len(ls)))
-        for config, _ in utils.load_config(config_def):
+        for config, path in serialization.load_config(config_def):
             name = config.get('name', 'unnamed')
             logger.info("Using config(s): {name}".format(name=name))
 
-            if timestamp:
-                sim_folder = '{}_{}'.format(name,
-                                            time.strftime("%Y-%m-%d_%H:%M:%S"))
-            else:
-                sim_folder = name
-            dir_path = os.path.join(results_dir, sim_folder)
-            sim = SoilSimulation(dir_path=dir_path, dump=dump, **config)
-            logger.info('Dumping results to {} : {}'.format(sim.dir_path, sim.dump))
+            dir_path = config.pop('dir_path', os.path.dirname(path))
+            sim = Simulation(dir_path=dir_path,
+                             **config)
             sim.run_simulation(**kwargs)

soil/stats.py

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

soil/time.py

@@ -0,0 +1,87 @@
+from mesa.time import BaseScheduler
+from queue import Empty
+from heapq import heappush, heappop
+import math
+from .utils import logger
+from mesa import Agent
+
+
+class When:
+    def __init__(self, time):
+        self._time = float(time)
+
+    def abs(self, time):
+        return self._time
+
+
+class Delta:
+    def __init__(self, delta):
+        self._delta = delta
+
+    def __eq__(self, other):
+        return self._delta == other._delta
+
+    def abs(self, time):
+        return time + self._delta
+
+
+class TimedActivation(BaseScheduler):
+    """A scheduler which activates each agent when the agent requests.
+    In each activation, each agent will update its 'next_time'.
+    """
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(self)
+        self._queue = []
+        self.next_time = 0
+
+    def add(self, agent: Agent):
+        if agent.unique_id not in self._agents:
+            heappush(self._queue, (self.time, agent.unique_id))
+            super().add(agent)
+
+    def step(self, until: float =float('inf')) -> None:
+        """
+        Executes agents in order, one at a time. After each step,
+        an agent will signal when it wants to be scheduled next.
+        """
+
+        when = None
+        agent_id = None
+        unsched = []
+        until = until or float('inf')
+
+        if not self._queue:
+            self.time = until
+            self.next_time = float('inf')
+            return
+
+        (when, agent_id) = self._queue[0]
+
+        if until and when > until:
+            self.time = until
+            self.next_time = when
+            return
+
+        self.time = when
+        next_time = float("inf")
+
+        while when == self.time:
+            heappop(self._queue)
+            logger.debug(f'Stepping agent {agent_id}')
+            when = (self._agents[agent_id].step() or Delta(1)).abs(self.time)
+            heappush(self._queue, (when, agent_id))
+            if when < next_time:
+                next_time = when
+
+            if not self._queue or self._queue[0][0] > self.time:
+                agent_id = None
+                break
+            else:
+                (when, agent_id) = self._queue[0]
+
+        if when and when < self.time:
+            raise Exception("Invalid scheduling time")
+
+        self.next_time = next_time
+        self.steps += 1

soil/utils.py

@@ -1,105 +1,89 @@
-import os
-import yaml
 import logging
-import importlib
-from time import time
-from glob import glob
-from random import random
-from copy import deepcopy
+import time
+import os
 
-import networkx as nx
+from shutil import copyfile
 
 from contextlib import contextmanager
 
-
 logger = logging.getLogger('soil')
-logger.setLevel(logging.INFO)
-
-
-def load_network(network_params, dir_path=None):
-    if network_params is None:
-        return nx.Graph()
-    path = network_params.get('path', None)
-    if path:
-        if dir_path and not os.path.isabs(path):
-            path = os.path.join(dir_path, path)
-        extension = os.path.splitext(path)[1][1:]
-        kwargs = {}
-        if extension == 'gexf':
-            kwargs['version'] = '1.2draft'
-            kwargs['node_type'] = int
-        try:
-            method = getattr(nx.readwrite, 'read_' + extension)
-        except AttributeError:
-            raise AttributeError('Unknown format')
-        return method(path, **kwargs)
-
-    net_args = network_params.copy()
-    net_type = net_args.pop('generator')
-
-    method = getattr(nx.generators, net_type)
-    return method(**net_args)
-
-
-def load_file(infile):
-    with open(infile, 'r') as f:
-        return list(yaml.load_all(f))
-
-
-def load_files(*patterns):
-    for pattern in patterns:
-        for i in glob(pattern):
-            for config in load_file(i):
-                yield config, os.path.abspath(i)
-
-
-def load_config(config):
-    if isinstance(config, dict):
-        yield config, None
-    else:
-        yield from load_files(config)
+# logging.basicConfig()
+# logger.setLevel(logging.INFO)
 
 
 @contextmanager
 def timer(name='task', pre="", function=logger.info, to_object=None):
-    start = time()
-    function('{}Starting {} at {}.'.format(pre, name, start))
+    start = time.time()
+    function('{}Starting {} at {}.'.format(pre, name,
+                                           time.strftime("%X", time.gmtime(start))))
     yield start
-    end = time()
-    function('{}Finished {} in {} seconds'.format(pre, name, str(end-start)))
+    end = time.time()
+    function('{}Finished {} at {} in {} seconds'.format(pre, name,
+                                                        time.strftime("%X", time.gmtime(end)),
+                                                        str(end-start)))
     if to_object:
         to_object.start = start
         to_object.end = end
 
 
-def repr(v):
-    func = serializer(v)
-    tname = name(v)
-    return func(v), tname
 
 
-def 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 and os.path.exists(path):
+        creation = os.path.getctime(path)
+        stamp = time.strftime('%Y-%m-%d_%H.%M.%S', time.localtime(creation))
+
+        backup_dir = os.path.join(outdir, 'backup')
+        if not os.path.exists(backup_dir):
+            os.makedirs(backup_dir)
+        newpath = os.path.join(backup_dir, '{}@{}'.format(os.path.basename(path),
+                                                               stamp))
+        copyfile(path, newpath)
+    return open(path, mode=mode, **kwargs)
 
 
-def serializer(type_):
-    if type_ == 'bool':
-        return lambda x:  "true" if x else ""
-    return lambda x: x
-
-
-def deserializer(type_):
+def open_or_reuse(f, *args, **kwargs):
     try:
-        # Check if it's a builtin type
-        module = importlib.import_module('builtins')
-        cls = getattr(module, type_)
-    except AttributeError:
-        # if not, separate module and class
-        module, type_ = type_.rsplit(".", 1)
-        module = importlib.import_module(module)
-        cls = getattr(module, type_)
-    return cls
+        return safe_open(f, *args, **kwargs)
+    except (AttributeError, TypeError):
+        return f
+
+def flatten_dict(d):
+    if not isinstance(d, dict):
+        return d
+    return dict(_flatten_dict(d))
+
+def _flatten_dict(d, prefix=''):
+    if not isinstance(d, dict):
+        # print('END:', prefix, d)
+        yield prefix, d
+        return
+    if prefix:
+        prefix = prefix + '.'
+    for k, v in d.items():
+        # print(k, v)
+        res = list(_flatten_dict(v, prefix='{}{}'.format(prefix, k)))
+        # print('RES:', res)
+        yield from res
 
 
-def convert(value, type_):
-    return deserializer(type_)(value)
+def unflatten_dict(d):
+    out = {}
+    for k, v in d.items():
+        target = out
+        if not isinstance(k, str):
+            target[k] = v
+            continue
+        tokens = k.split('.')
+        if len(tokens) < 2:
+            target[k] = v
+            continue
+        for token in tokens[:-1]:
+            if token not in target:
+                target[token] = {}
+            target = target[token]
+        target[tokens[-1]] = v
+    return out

soil/visualization.py

@@ -0,0 +1,5 @@
+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 +19,7 @@ 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)
 
@@ -118,9 +118,9 @@ class SocketHandler(tornado.websocket.WebSocketHandler):
         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'])
+                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',
@@ -130,9 +130,9 @@ class SocketHandler(tornado.websocket.WebSocketHandler):
         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'])
+                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) })
@@ -168,7 +168,7 @@ class SocketHandler(tornado.websocket.WebSocketHandler):
 
     @run_on_executor
     def nonblocking(self, config):
-        simulation = SoilSimulation(**config)
+        simulation = Simulation(**config)
         return simulation.run()
 
     @tornado.gen.coroutine
@@ -180,7 +180,7 @@ class SocketHandler(tornado.websocket.WebSocketHandler):
         with self.logging(self.simulation_name):
             try:
                 config = dict(**self.config)
-                config['dir_path'] = os.path.join(self.application.dir_path, config['name'])
+                config['outdir'] = os.path.join(self.application.outdir, config['name'])
                 config['dump'] = self.application.dump
                 self.trials = yield self.nonblocking(config)
 
@@ -232,12 +232,12 @@ class ModularServer(tornado.web.Application):
     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)

test-requirements.txt

@@ -0,0 +1,4 @@
+pytest
+mesa>=0.8.9
+scipy>=1.3
+tornado

tests/test.gexf

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

tests/test_analysis.py

@@ -21,11 +21,13 @@ class Ping(agents.FSM):
     @agents.default_state
     @agents.state
     def even(self):
+        self.debug(f'Even {self["count"]}')
         self['count'] += 1
         return self.odd
 
     @agents.state
     def odd(self):
+        self.debug(f'Odd {self["count"]}')
         self['count'] += 1
         return self.even
 
@@ -39,7 +41,6 @@ class TestAnalysis(TestCase):
         agent should be able to update its state."""
         config = {
             'name': 'analysis',
-            'dry_run': True,
             'seed': 'seed',
             'network_params': {
                 'generator': 'complete_graph',
@@ -53,7 +54,7 @@ class TestAnalysis(TestCase):
             }
         }
         s = simulation.from_config(config)
-        self.env = s.run_simulation()[0]
+        self.env = s.run_simulation(dry_run=True)[0]
 
     def test_saved(self):
         env = self.env
@@ -65,26 +66,25 @@ class TestAnalysis(TestCase):
 
     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
+        df = analysis.read_sql(env._history.db_path)
+        res = analysis.get_count(df, 'SEED', 'state_id')
+        assert res['SEED'][self.env['SEED']].iloc[0] == 1
+        assert res['SEED'][self.env['SEED']].iloc[-1] == 1
+        assert res['state_id']['odd'].iloc[0] == 2
+        assert res['state_id']['even'].iloc[0] == 0
+        assert res['state_id']['odd'].iloc[-1] == 1
+        assert res['state_id']['even'].iloc[-1] == 1
 
     def test_value(self):
         env = self.env
-        df = analysis.read_sql(env._history._db)
+        df = analysis.read_sql(env._history.db_path)
         res_sum = analysis.get_value(df, 'count')
 
         assert res_sum['count'].iloc[0] == 2
 
         import numpy as np
         res_mean = analysis.get_value(df, 'count', aggfunc=np.mean)
-        assert res_mean['count'].iloc[0] == 1
+        assert res_mean['count'].iloc[15] == (16+8)/2
 
-        res_total = analysis.get_value(df)
-
-        res_total['SEED'].iloc[0] == 'seedanalysis_trial_0'
+        res_total = analysis.get_majority(df)
+        res_total['SEED'].iloc[0] == self.env['SEED']

tests/test_examples.py

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

tests/test_exporters.py

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

tests/test_history.py

@@ -1,133 +0,0 @@
-from unittest import TestCase
-
-import os
-import shutil
-from glob import glob
-
-from soil import history
-
-
-ROOT = os.path.abspath(os.path.dirname(__file__))
-DBROOT = os.path.join(ROOT, 'testdb')
-
-
-class TestHistory(TestCase):
-
-    def setUp(self):
-        if not os.path.exists(DBROOT):
-            os.makedirs(DBROOT)
-
-    def tearDown(self):
-        if os.path.exists(DBROOT):
-            shutil.rmtree(DBROOT)
-
-    def test_history(self):
-        """
-        """
-        tuples = (
-            ('a_0', 0, 'id', 'h'),
-            ('a_0', 1, 'id', 'e'),
-            ('a_0', 2, 'id', 'l'),
-            ('a_0', 3, 'id', 'l'),
-            ('a_0', 4, 'id', 'o'),
-            ('a_1', 0, 'id', 'v'),
-            ('a_1', 1, 'id', 'a'),
-            ('a_1', 2, 'id', 'l'),
-            ('a_1', 3, 'id', 'u'),
-            ('a_1', 4, 'id', 'e'),
-            ('env', 1, 'prob', 1),
-            ('env', 3, 'prob', 2),
-            ('env', 5, 'prob', 3),
-            ('a_2', 7, 'finished', True),
-        )
-        h = history.History()
-        h.save_tuples(tuples)
-        # assert h['env', 0, 'prob'] == 0
-        for i in range(1, 7):
-            assert h['env', i, 'prob'] == ((i-1)//2)+1
-
-
-        for i, k in zip(range(5), 'hello'):
-            assert h['a_0', i, 'id'] == k
-        for record, value in zip(h['a_0', None, 'id'], 'hello'):
-            t_step, val = record
-            assert val == value
-
-        for i, k in zip(range(5), 'value'):
-            assert h['a_1', i, 'id'] == k
-        for i in range(5, 8):
-            assert h['a_1', i, 'id'] == 'e'
-        for i in range(7):
-            assert h['a_2', i, 'finished'] == False
-        assert h['a_2', 7, 'finished']
-
-    def test_history_gen(self):
-        """
-        """
-        tuples = (
-            ('a_1', 0, 'id', 'v'),
-            ('a_1', 1, 'id', 'a'),
-            ('a_1', 2, 'id', 'l'),
-            ('a_1', 3, 'id', 'u'),
-            ('a_1', 4, 'id', 'e'),
-            ('env', 1, 'prob', 1),
-            ('env', 2, 'prob', 2),
-            ('env', 3, 'prob', 3),
-            ('a_2', 7, 'finished', True),
-        )
-        h = history.History()
-        h.save_tuples(tuples)
-        for t_step, key, value in h['env', None, None]:
-            assert t_step == value
-            assert key == 'prob'
-
-        records = list(h[None, 7, None])
-        assert len(records) == 3
-        for i in records:
-            agent_id, key, value = i
-            if agent_id == 'a_1':
-                assert key == 'id'
-                assert value == 'e'
-            elif agent_id == 'a_2':
-                assert key == 'finished'
-                assert value
-            else:
-                assert key == 'prob'
-                assert value == 3
-
-        records = h['a_1', 7, None]
-        assert records['id'] == 'e'
-
-    def test_history_file(self):
-        """
-        History should be saved to a file
-        """
-        tuples = (
-            ('a_1', 0, 'id', 'v'),
-            ('a_1', 1, 'id', 'a'),
-            ('a_1', 2, 'id', 'l'),
-            ('a_1', 3, 'id', 'u'),
-            ('a_1', 4, 'id', 'e'),
-            ('env', 1, 'prob', 1),
-            ('env', 2, 'prob', 2),
-            ('env', 3, 'prob', 3),
-            ('a_2', 7, 'finished', True),
-        )
-        db_path = os.path.join(DBROOT, 'test')
-        h = history.History(db_path=db_path)
-        h.save_tuples(tuples)
-        assert os.path.exists(db_path)
-
-        # Recover the data
-        recovered = history.History(db_path=db_path, backup=False)
-        assert recovered['a_1', 0, 'id'] == 'v'
-        assert recovered['a_1', 4, 'id'] == 'e'
-
-        # Using the same name should create a backup copy
-        newhistory = history.History(db_path=db_path, backup=True)
-        backuppaths = glob(db_path + '.backup*.sqlite')
-        assert len(backuppaths) == 1
-        backuppath = backuppaths[0]
-        assert newhistory._db_path == h._db_path
-        assert os.path.exists(backuppath)
-        assert not len(newhistory[None, None, None])

tests/test_main.py

@@ -1,19 +1,32 @@
 from unittest import TestCase
 
 import os
+import io
 import yaml
+import pickle
 import networkx as nx
 from functools import partial
 
 from os.path import join
-from soil import simulation, environment, agents, utils
+from soil import (simulation, Environment, agents, serialization,
+                  utils)
+from soil.time import Delta
 
 
 ROOT = os.path.abspath(os.path.dirname(__file__))
-
 EXAMPLES = join(ROOT, '..', 'examples')
 
 
+class CustomAgent(agents.FSM):
+    @agents.default_state
+    @agents.state
+    def normal(self):
+        self.neighbors = self.count_agents(state_id='normal',
+                                           limit_neighbors=True)
+    @agents.state
+    def unreachable(self):
+        return
+
 class TestMain(TestCase):
 
     def test_load_graph(self):
@@ -22,22 +35,20 @@ class TestMain(TestCase):
         Raise an exception otherwise.
         """
         config = {
-            'dry_run': True,
             'network_params': {
                 'path': join(ROOT, 'test.gexf')
             }
         }
-        G = utils.load_network(config['network_params'])
+        G = serialization.load_network(config['network_params'])
         assert G
         assert len(G) == 2
         with self.assertRaises(AttributeError):
             config = {
-            'dry_run': True,
                 'network_params': {
                     'path': join(ROOT, 'unknown.extension')
                 }
             }
-            G = utils.load_network(config['network_params'])
+            G = serialization.load_network(config['network_params'])
             print(G)
 
     def test_generate_barabasi(self):
@@ -46,22 +57,20 @@ class TestMain(TestCase):
         should be used to generate a network
         """
         config = {
-            'dry_run': True,
             'network_params': {
                 'generator': 'barabasi_albert_graph'
             }
         }
         with self.assertRaises(TypeError):
-            G = utils.load_network(config['network_params'])
+            G = serialization.load_network(config['network_params'])
         config['network_params']['n'] = 100
         config['network_params']['m'] = 10
-        G = utils.load_network(config['network_params'])
+        G = serialization.load_network(config['network_params'])
         assert len(G) == 100
 
     def test_empty_simulation(self):
         """A simulation with a base behaviour should do nothing"""
         config = {
-            'dry_run': True,
             'network_params': {
                 'path': join(ROOT, 'test.gexf')
             },
@@ -78,7 +87,6 @@ class TestMain(TestCase):
         agent should be able to update its state."""
         config = {
             'name': 'CounterAgent',
-            'dry_run': True,
             'network_params': {
                 'path': join(ROOT, 'test.gexf')
             },
@@ -102,14 +110,13 @@ class TestMain(TestCase):
         """
         config = {
             'name': 'CounterAgent',
-            'dry_run': True,
             'network_params': {
                 'path': join(ROOT, 'test.gexf')
             },
             'network_agents': [{
                 'agent_type': 'AggregatedCounter',
                 'weight': 1,
-                'state': {'id': 0}
+                'state': {'state_id': 0}
 
             }],
             'max_time': 10,
@@ -120,26 +127,20 @@ class TestMain(TestCase):
         env = s.run_simulation(dry_run=True)[0]
         for agent in env.network_agents:
             last = 0
-            assert len(agent[None, None]) == 10
+            assert len(agent[None, None]) == 11
             for step, total in sorted(agent['total', None]):
                 assert total == last + 2
                 last = total
 
     def test_custom_agent(self):
         """Allow for search of neighbors with a certain state_id"""
-        class CustomAgent(agents.BaseAgent):
-            def step(self):
-                self.state['neighbors'] = self.count_agents(state_id=0,
-                                                            limit_neighbors=True)
         config = {
-            'dry_run': True,
             'network_params': {
                 'path': join(ROOT, 'test.gexf')
             },
             'network_agents': [{
                 'agent_type': CustomAgent,
-                'weight': 1,
-                'state': {'id': 0}
+                'weight': 1
 
             }],
             'max_time': 10,
@@ -148,16 +149,17 @@ class TestMain(TestCase):
         }
         s = simulation.from_config(config)
         env = s.run_simulation(dry_run=True)[0]
-        assert env.get_agent(0).state['neighbors'] == 1
+        assert env.get_agent(1).count_agents(state_id='normal') == 2
+        assert env.get_agent(1).count_agents(state_id='normal', limit_neighbors=True) == 1
+        assert env.get_agent(0).neighbors == 1
 
     def test_torvalds_example(self):
         """A complete example from a documentation should work."""
-        config = utils.load_file(join(EXAMPLES, 'torvalds.yml'))[0]
+        config = serialization.load_file(join(EXAMPLES, 'torvalds.yml'))[0]
         config['network_params']['path'] = join(EXAMPLES,
                                                 config['network_params']['path'])
         s = simulation.from_config(config)
-        s.dry_run = True
-        env = s.run_simulation()[0]
+        env = s.run_simulation(dry_run=True)[0]
         for a in env.network_agents:
             skill_level = a.state['skill_level']
             if a.id == 'Torvalds':
@@ -179,17 +181,14 @@ class TestMain(TestCase):
         should be equivalent to the configuration file used
         """
         with utils.timer('loading'):
-            config = utils.load_file(join(EXAMPLES, 'complete.yml'))[0]
+            config = serialization.load_file(join(EXAMPLES, 'complete.yml'))[0]
             s = simulation.from_config(config)
-            s.dry_run = True
         with utils.timer('serializing'):
             serial = s.to_yaml()
         with utils.timer('recovering'):
-            recovered = yaml.load(serial)
+            recovered = yaml.load(serial, Loader=yaml.SafeLoader)
         with utils.timer('deleting'):
             del recovered['topology']
-            del recovered['load_module']
-            del recovered['dry_run']
         assert config == recovered
 
     def test_configuration_changes(self):
@@ -197,31 +196,22 @@ class TestMain(TestCase):
         The configuration should not change after running
          the simulation.
         """
-        config = utils.load_file('examples/complete.yml')[0]
+        config = serialization.load_file(join(EXAMPLES, 'complete.yml'))[0]
         s = simulation.from_config(config)
-        s.dry_run = True
-        for i in range(5):
-            s.run_simulation(dry_run=True)
-            nconfig = s.to_dict()
-            del nconfig['topology']
-            del nconfig['dry_run']
-            del nconfig['load_module']
-            assert config == nconfig
 
-    def test_examples(self):
-        """
-        Make sure all examples in the examples folder are correct
-        """
-        pass
+        s.run_simulation(dry_run=True)
+        nconfig = s.to_dict()
+        del nconfig['topology']
+        assert config == nconfig
 
     def test_row_conversion(self):
-        env = environment.SoilEnvironment(dry_run=True)
+        env = Environment()
         env['test'] = 'test_value'
 
         res = list(env.history_to_tuples())
         assert len(res) == len(env.environment_params)
 
-        env._now = 1
+        env.schedule.time = 1
         env['test'] = 'second_value'
         res = list(env.history_to_tuples())
 
@@ -234,8 +224,9 @@ class TestMain(TestCase):
         from geometric models. We should work around it.
         """
         G = nx.random_geometric_graph(20, 0.1)
-        env = environment.SoilEnvironment(topology=G, dry_run=True)
-        env.dump_gexf('/tmp/dump-gexf')
+        env = Environment(topology=G)
+        f = io.BytesIO()
+        env.dump_gexf(f)
 
     def test_save_graph(self):
         '''
@@ -245,41 +236,156 @@ class TestMain(TestCase):
         '''
         G = nx.cycle_graph(5)
         distribution = agents.calculate_distribution(None, agents.BaseAgent)
-        env = environment.SoilEnvironment(topology=G, network_agents=distribution, dry_run=True)
+        env = Environment(topology=G, network_agents=distribution)
         env[0, 0, 'testvalue'] = 'start'
         env[0, 10, 'testvalue'] = 'finish'
         nG = env.history_to_graph()
-        values = nG.node[0]['attr_testvalue']
+        values = nG.nodes[0]['attr_testvalue']
         assert ('start', 0, 10) in values
         assert ('finish', 10, None) in values
 
+    def test_serialize_class(self):
+        ser, name = serialization.serialize(agents.BaseAgent)
+        assert name == 'soil.agents.BaseAgent'
+        assert ser == agents.BaseAgent
 
-def make_example_test(path, config):
-    def wrapped(self):
-        root = os.getcwd()
-        os.chdir(os.path.dirname(path))
+        ser, name = serialization.serialize(CustomAgent)
+        assert name == 'test_main.CustomAgent'
+        assert ser == CustomAgent
+        pickle.dumps(ser)
+
+    def test_serialize_builtin_types(self):
+
+        for i in [1, None, True, False, {}, [], list(), dict()]:
+            ser, name = serialization.serialize(i)
+            assert type(ser) == str
+            des = serialization.deserialize(name, ser)
+            assert i == des
+
+    def test_serialize_agent_type(self):
+        '''A class from soil.agents should be serialized without the module part'''
+        ser = agents.serialize_type(CustomAgent)
+        assert ser == 'test_main.CustomAgent'
+        ser = agents.serialize_type(agents.BaseAgent)
+        assert ser == 'BaseAgent'
+        pickle.dumps(ser)
+    
+    def test_deserialize_agent_distribution(self):
+        agent_distro = [
+            {
+                'agent_type': 'CounterModel',
+                'weight': 1
+            },
+            {
+                'agent_type': 'test_main.CustomAgent',
+                'weight': 2
+            },
+        ]
+        converted = agents.deserialize_definition(agent_distro)
+        assert converted[0]['agent_type'] == agents.CounterModel
+        assert converted[1]['agent_type'] == CustomAgent
+        pickle.dumps(converted)
+
+    def test_serialize_agent_distribution(self):
+        agent_distro = [
+            {
+                'agent_type': agents.CounterModel,
+                'weight': 1
+            },
+            {
+                'agent_type': CustomAgent,
+                'weight': 2
+            },
+        ]
+        converted = agents.serialize_definition(agent_distro)
+        assert converted[0]['agent_type'] == 'CounterModel'
+        assert converted[1]['agent_type'] == 'test_main.CustomAgent'
+        pickle.dumps(converted)
+
+    def test_pickle_agent_environment(self):
+        env = Environment(name='Test')
+        a = agents.BaseAgent(model=env, unique_id=25)
+
+        a['key'] = 'test'
+
+        pickled = pickle.dumps(a)
+        recovered = pickle.loads(pickled)
+
+        assert recovered.env.name == 'Test'
+        assert list(recovered.env._history.to_tuples())
+        assert recovered['key', 0] == 'test'
+        assert recovered['key'] == 'test'
+
+    def test_subgraph(self):
+        '''An agent should be able to subgraph the global topology'''
+        G = nx.Graph()
+        G.add_node(3)
+        G.add_edge(1, 2)
+        distro = agents.calculate_distribution(agent_type=agents.NetworkAgent)
+        env = Environment(name='Test', topology=G, network_agents=distro)
+        lst = list(env.network_agents)
+
+        a2 = env.get_agent(2)
+        a3 = env.get_agent(3)
+        assert len(a2.subgraph(limit_neighbors=True)) == 2
+        assert len(a3.subgraph(limit_neighbors=True)) == 1
+        assert len(a3.subgraph(limit_neighbors=True, center=False)) == 0
+        assert len(a3.subgraph(agent_type=agents.NetworkAgent)) == 3
+
+    def test_templates(self):
+        '''Loading a template should result in several configs'''
+        configs = serialization.load_file(join(EXAMPLES, 'template.yml'))
+        assert len(configs) > 0
+
+    def test_until(self):
+        config = {
+            'name': 'until_sim',
+            'network_params': {},
+            'agent_type': 'CounterModel',
+            'max_time': 2,
+            'num_trials': 50,
+            'environment_params': {}
+        }
         s = simulation.from_config(config)
-        envs = s.run_simulation(dry_run=True)
-        assert envs
-        for env in envs:
-            assert env
-            try:
-                n = config['network_params']['n']
-                assert len(env.get_agents()) == n
-            except KeyError:
-                pass
-        os.chdir(root)
-    return wrapped
+        runs = list(s.run_simulation(dry_run=True))
+        over = list(x.now for x in runs if x.now>2)
+        assert len(runs) == config['num_trials']
+        assert len(over) == 0
 
 
-def add_example_tests():
-    for config, path in utils.load_config(join(EXAMPLES, '*.yml')):
-        p = make_example_test(path=path, config=config)
-        fname = os.path.basename(path)
-        p.__name__ = 'test_example_file_%s' % fname
-        p.__doc__ = '%s should be a valid configuration' % fname
-        setattr(TestMain, p.__name__, p)
-        del p
+    def test_fsm(self):
+        '''Basic state change'''
+        class ToggleAgent(agents.FSM):
+            @agents.default_state
+            @agents.state
+            def ping(self):
+                return self.pong
 
+            @agents.state
+            def pong(self):
+                return self.ping
 
-add_example_tests()
+        a = ToggleAgent(unique_id=1, model=Environment())
+        assert a.state_id == a.ping.id
+        a.step()
+        assert a.state_id == a.pong.id
+        a.step()
+        assert a.state_id == a.ping.id
+
+    def test_fsm_when(self):
+        '''Basic state change'''
+        class ToggleAgent(agents.FSM):
+            @agents.default_state
+            @agents.state
+            def ping(self):
+                return self.pong, 2
+
+            @agents.state
+            def pong(self):
+                return self.ping
+
+        a = ToggleAgent(unique_id=1, model=Environment())
+        when = a.step()
+        assert when == 2
+        when = a.step()
+        assert when == Delta(a.interval)

tests/test_mesa.py

@@ -0,0 +1,69 @@
+'''
+Mesa-SOIL integration tests
+
+We have to test that:
+- Mesa agents can be used in SOIL
+- Simplified soil agents can be used in mesa simulations
+- Mesa and soil agents can interact in a simulation
+
+- Mesa visualizations work with SOIL simulations
+
+'''
+from mesa import Agent, Model
+from mesa.time import RandomActivation
+from mesa.space import MultiGrid
+
+class MoneyAgent(Agent):
+    """ An agent with fixed initial wealth."""
+    def __init__(self, unique_id, model):
+        super().__init__(unique_id, model)
+        self.wealth = 1
+
+    def step(self):
+        self.move()
+        if self.wealth > 0:
+            self.give_money()
+
+    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 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)
+
+
+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)
+
+        # 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))
+
+    def step(self):
+        '''Advance the model by one step.'''
+        self.schedule.step()
+
+
+# model = MoneyModel(10)
+# for i in range(10):
+#     model.step()
+
+# agent_wealth = [a.wealth for a in model.schedule.agents]

tests/test_stats.py

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