WIP: removed stats

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

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

.dockerignore

@@ -1,2 +1,5 @@
 **/soil_output
 .*
+**/__pycache__
+__pycache__
+*.pyc

.gitignore

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

.gitlab-ci.yml

@@ -1,21 +1,53 @@
-image: python:3.7
-
-steps:
-  - build
+stages:
   - test
+  - publish
+  - check_published
 
-build:
-  stage: build
+docker:
+  stage: publish
   image:
     name: gcr.io/kaniko-project/executor:debug
     entrypoint: [""]
+  tags:
+    - docker
   script:
     - echo "{\"auths\":{\"$CI_REGISTRY\":{\"username\":\"$CI_REGISTRY_USER\",\"password\":\"$CI_REGISTRY_PASSWORD\"}}}" > /kaniko/.docker/config.json
-    - /kaniko/executor --context $CI_PROJECT_DIR --dockerfile $CI_PROJECT_DIR/Dockerfile --destination $CI_REGISTRY_IMAGE:$CI_COMMIT_TAG
+    # The skip-tls-verify flag is there because our registry certificate is self signed
+    - /kaniko/executor --context $CI_PROJECT_DIR --skip-tls-verify --dockerfile $CI_PROJECT_DIR/Dockerfile --destination $CI_REGISTRY_IMAGE:$CI_COMMIT_TAG
   only:
     - tags
 
-
 test:
+  tags:
+    - docker
+  image: python:3.7
+  stage: test
   script:
-    python setup.py test
+    - pip install -r requirements.txt -r test-requirements.txt
+    - python setup.py test
+
+push_pypi:
+  only:
+    - tags
+  tags:
+    - docker
+  image: python:3.7
+  stage: publish
+  script:
+    - echo $CI_COMMIT_TAG > soil/VERSION
+    - pip install twine
+    - python setup.py sdist bdist_wheel
+    - TWINE_PASSWORD=$PYPI_PASSWORD TWINE_USERNAME=$PYPI_USERNAME python -m twine upload dist/*
+
+check_pypi:
+  only:
+    - tags
+  tags:
+    - docker
+  image: python:3.7
+  stage: check_published
+  script:
+    - pip install soil==$CI_COMMIT_TAG
+  # Allow PYPI to update its index before we try to install 
+  when: delayed
+  start_in: 2 minutes

CHANGELOG.md

@@ -0,0 +1,171 @@
+# 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).
+
+## [UNRELEASED]
+### Changed
+* Configuration schema is very different now. Check `soil.config` for more information. We are using Pydantic for (de)serialization.
+* There may be more than one topology/network in the simulation
+* Agents are split into groups now. Each group may be assigned a given set of agents or an agent distribution, and a network topology to be assigned to.
+### Removed
+* Any `tsih` and `History` integration in the main classes. To record the state of environments/agents, just use a datacollector. In some cases this may be slower or consume more memory than the previous system. However, few cases actually used the full potential of the history, and it came at the cost of unnecessary complexity and worse performance for the majority of cases.
+## [0.20.7]
+### Changed
+* Creating a `time.When` from another `time.When` does not nest them anymore (it returns the argument)
+### Fixed
+* Bug with time.NEVER/time.INFINITY
+## [0.20.6]
+### Fixed
+* Agents now return `time.INFINITY` when dead, instead of 'inf'
+* `soil.__init__` does not re-export built-in time (change in `soil.simulation`. It used to create subtle import conflicts when importing soil.time.
+* Parallel simulations were broken because lambdas cannot be pickled properly, which is needed for multiprocessing.
+### Changed
+* Some internal simulation methods do not accept `*args` anymore, to avoid ambiguity and bugs.
+## [0.20.5]
+### Changed
+* Defaults are now set in the agent __init__, not in the environment. This decouples both classes a bit more, and it is more intuitive 
+## [0.20.4]
+### Added
+* Agents can now be given any kwargs, which will be used to set their state
+* Environments have a default logger `self.logger` and a log method, just like agents
+## [0.20.3]
+### Fixed
+* Default state values are now deepcopied again.
+* Seeds for environments only concatenate the trial id (i.e., a number), to provide repeatable results.
+* `Environment.run` now calls `Environment.step`, to allow for easy overloading of the environment step
+### Removed
+* Datacollectors are not being used for now.
+* `time.TimedActivation.step` does not use an `until` parameter anymore.
+### Changed
+* Simulations now run right up to `until` (open interval)
+* Time instants (`time.When`) don't need to be floats anymore. Now we can avoid precision issues with big numbers by using ints.
+* Rabbits simulation is more idiomatic (using subclasses)
+
+## [0.20.2]
+### Fixed
+* CI/CD testing issues
+## [0.20.1]
+### Fixed
+* Agents would run another step after dying.
+## [0.20.0]
+### Added
+* Integration with MESA
+* `not_agent_ids` parameter to get sql in history
+### Changed
+* `soil.Environment` now also inherits from `mesa.Model`
+* `soil.Agent` now also inherits from `mesa.Agent`
+* `soil.time` to replace `simpy` events, delays, duration, etc.
+* `agent.id` is not `agent.unique_id` to be compatible with `mesa`. A property `BaseAgent.id` has been added for compatibility.
+* `agent.environment` is now `agent.model`, for the same reason as above. The parameter name in `BaseAgent.__init__` has also been renamed.
+### Removed
+* `simpy` dependency and compatibility. Each agent used to be a simpy generator, but that made debugging and error handling more complex. That has been replaced by a scheduler within the `soil.Environment` class, similar to how `mesa` does it.
+* `soil.history` is now a separate package named `tsih`. The keys namedtuple uses `dict_id` instead of `agent_id`.
+### Added
+* An option to choose whether a database should be used for history 
+## [0.15.2]
+### Fixed
+* Pass the right known_modules and parameters to stats discovery in simulation
+* The configuration file must exist when launching through the CLI. If it doesn't, an error will be logged
+* Minor changes in the documentation of the CLI arguments
+### Changed
+* Stats are now exported by default
+## [0.15.1]
+### Added
+* read-only `History`
+### Fixed
+* Serialization problem with the `Environment` on parallel mode.
+* Analysis functions now work as they should in the tutorial
+## [0.15.0]
+### Added
+* Control logging level in CLI and simulation
+* `Stats` to calculate trial and simulation-wide statistics
+* Simulation statistics are stored in a separate table in history (see `History.get_stats` and `History.save_stats`, as well as `soil.stats`)
+* Aliased `NetworkAgent.G` to `NetworkAgent.topology`.
+### Changed
+* Templates in config files can be given as dictionaries in addition to strings
+* Samplers are used more explicitly
+* Removed nxsim dependency. We had already made a lot of changes, and nxsim has not been updated in 5 years.
+* Exporter methods renamed to `trial` and `end`. Added `start`.
+* `Distribution` exporter now a stats class
+* `global_topology` renamed to `topology`
+* Moved topology-related methods to `NetworkAgent`
+### Fixed
+* Temporary files used for history in dry_run mode are not longer left open 
+
+## [0.14.9]
+### Changed
+* Seed random before environment initialization
+## [0.14.8]
+### Fixed
+* Invalid directory names in Windows gsi-upm/soil#5
+## [0.14.7]
+### Changed
+* Minor change to traceback handling in async simulations
+### Fixed
+* Incomplete example in the docs (example.yml) caused an exception
+## [0.14.6]
+### Fixed
+* Bug with newer versions of networkx (0.24) where the Graph.node attribute has been removed. We have updated our calls, but the code in nxsim is not under our control, so we have pinned the networkx version until that issue is solved.
+### Changed
+* Explicit yaml.SafeLoader to avoid deprecation warnings when using yaml.load. It should not break any existing setups, but we could move to the FullLoader in the future if needed.
+
+## [0.14.4]
+### Fixed
+* Bug in `agent.get_agents()` when `state_id` is passed as a string. The tests have been modified accordingly.
+## [0.14.3]
+### Fixed
+* Incompatibility with py3.3-3.6 due to ModuleNotFoundError and TypeError in DryRunner
+## [0.14.2]
+### Fixed
+* Output path for exporters is now soil_output
+### Changed 
+* CSV output to stdout in dry_run mode
+## [0.14.1]
+### Changed
+* Exporter names in lower case
+* Add default exporter in runs
+## [0.14.0]
+### Added
+* Loading configuration from template definitions in the yaml, in preparation for SALib support.
+The definition of the variables and their possible values (i.e., a problem in SALib terms), as well as a sampler function, can be provided.
+Soil uses this definition and the template to generate a set of configurations.
+* Simulation group names, to link related simulations. For now, they are only used to group all simulations in the same group under the same folder.
+* Exporters unify exporting/dumping results and other files to disk. If `dry_run` is set to `True`, exporters will write to stdout instead of a file (useful for testing/debugging).
+* Distribution exporter, to write statistics about values and value_counts in every simulation. The results are dumped to two CSV files.
+
+### Changed
+* `dir_path` is now the directory for resources (modules, files)
+* Environments and simulations do not export or write anything by default. That task is delegated to Exporters
+
+### Removed
+* The output dir for environments and simulations (see Exporters)
+* DrawingAgent, because it wrote to disk and was not being used. We provide a partial alternative in the form of the GraphDrawing exporter. A complete alternative will be provided once the network at each state can be accessed by exporters.
+
+## Fixed
+* Modules with custom agents/environments failed to load when they were run from outside the directory of the definition file. Modules are now loaded from the directory of the simulation file in addition to the working directory
+* Memory databases (in history) can now be shared between threads.
+* Testing all examples, not just subdirectories
+
+## [0.13.8]
+### Changed
+* Moved TerroristNetworkModel to examples
+### Added
+* `get_agents` and `count_agents` methods now accept lists as inputs. They can be used to retrieve agents from node ids
+* `subgraph` in BaseAgent
+* `agents.select` method, to filter out agents
+* `skip_test` property in yaml definitions, to force skipping some examples
+* `agents.Geo`, with a search function based on postition
+* `BaseAgent.ego_search` to get nodes from the ego network of a node
+* `BaseAgent.degree` and `BaseAgent.betweenness`
+### Fixed
+
+## [0.13.7]
+### Changed
+* History now defaults to not backing up! This makes it more intuitive to load the history for examination, at the expense of rewriting something. That should not happen because History is only created in the Environment, and that has `backup=True`.
+### Added
+* Agent names are assigned based on their agent types
+* Agent logging uses the agent name.
+* FSM agents can now return a timeout in addition to a new state. e.g. `return self.idle, self.env.timeout(2)` will execute the *different_state* in 2 *units of time* (`t_step=now+2`).
+* Example of using timeouts in FSM (custom_timeouts)
+* `network_agents` entries may include an `ids` entry. If set, it should be a list of node ids that should be assigned that agent type. This complements the previous behavior of setting agent type with `weights`.

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

@@ -1,4 +1,7 @@
-test:
+quick-test:
 	docker-compose exec dev python -m pytest -s -v
 
-.PHONY: test
+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 +31,25 @@ 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)

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

@@ -8,32 +8,8 @@ The advantage of a configuration file is that it is a clean declarative descript
 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``).
 
-.. code:: yaml
-          
-    ---
-    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
+.. literalinclude:: example.yml
+   :language: yaml
 
 
 This example configuration will run three trials (``num_trials``) of a simulation containing a randomly generated network (``network_params``).
@@ -112,9 +88,18 @@ For example, the following configuration is equivalent to :code:`nx.complete_gra
 
 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:
+That means both global parameters, such as the probability of disease outbreak.
+But it also means other data, such as a map, or a network topology that connects multiple agents.
+As a result, it is also typical to add custom functions in an environment that help agents interact with each other and with the state of the simulation.
+
+Last but not least, an environment controls when and how its agents will be executed.
+By default, soil environments incorporate a ``soil.time.TimedActivation`` model for agent execution (more on this on the following section).
+
+Soil environments are very similar, and often interchangeable with, mesa models (``mesa.Model``).
+
+A configuration may specify the initial value of the environment parameters:
 
 .. code:: yaml
 
@@ -122,23 +107,33 @@ The configuration file may specify the initial value of the environment paramete
         daily_probability_of_earthquake: 0.001
         number_of_earthquakes: 0
 
-All agents have access to the environment parameters.
+All agents have access to the environment (and its parameters).
 
 In some scenarios, it is useful to have a custom environment, to provide additional methods or to control the way agents update environment state.
 For example, if our agents play the lottery, the environment could provide a method to decide whether the agent wins, instead of leaving it to the agent.
 
-
 Agents
 ======
+
 Agents are a way of modelling behavior.
 Agents can be characterized with two variables: agent type (``agent_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.
+The agent type is a ``soil.Agent`` class, which contains the code that encapsulates the behavior of the agent.
+The state is a set of variables, which may change during the simulation, and that the code may use to control the behavior.
+All agents provide a ``step`` method either explicitly or implicitly (by inheriting it from a superclass), which controls how the agent will behave in each step of the simulation.
+
+When and how agent steps are executed in a simulation depends entirely on the ``environment``.
+Most environments will internally use a scheduler (``mesa.time.BaseScheduler``), which controls the activation of agents.
+
+In soil, we generally used the ``soil.time.TimedActivation`` scheduler, which allows agents to specify when their next activation will happen, defaulting to a 
+
+When an agent's step is executed (generally, every ``interval`` seconds), the agent has access to its state and the environment.
 Through the environment, it can access the network topology and the state of other agents.
 
-There are three three types of agents according to how they are added to the simulation: network agents and environment agent.
+There are two types of agents according to how they are added to the simulation: network agents and environment agent.
 
 Network Agents
 ##############
+
 Network agents are attached to a node in the topology.
 The configuration file allows you to specify how agents will be mapped to topology nodes.
 
@@ -149,7 +144,9 @@ Hence, every node in the network will be associated to an agent of that type.
 
    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).
+It is also possible to add more than one type of agent to the simulation.
+
+To control the ratio of each type (using the ``weight`` property).
 For instance, with following configuration, it is five times more likely for a node to be assigned a CounterModel type than a SISaModel type.
 
 .. code:: yaml
@@ -242,3 +239,24 @@ These agents are programmed in much the same way as network agents, the only dif
 
 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/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/requirements.txt

@@ -0,0 +1 @@
+ipython>=7.31.1

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
 ------------------
 
@@ -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

@@ -500,7 +500,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.5"
+   "version": "3.8.5"
   },
   "toc": {
    "colors": {

examples/Untitled.ipynb

@@ -80800,7 +80800,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.5"
+   "version": "3.8.6"
   }
  },
  "nbformat": 4,

examples/complete.yml

@@ -1,28 +1,65 @@
 ---
-name: simple
-dir_path: "/tmp/"
-num_trials: 3
-dry_run: True
-max_time: 100
-interval: 1
-seed: "CompleteSeed!"
-dump: false
-network_params:
-  generator: complete_graph
-  n: 10
-network_agents:
-  - agent_type: CounterModel
-    weight: 1
+version: '2'
+general:
+  id: simple
+  group: tests
+  dir_path: "/tmp/"
+  num_trials: 3
+  max_time: 100
+  interval: 1
+  seed: "CompleteSeed!"
+topologies:
+  default:
+    params:
+      generator: complete_graph
+      n: 10
+  another_graph:
+    params:
+      generator: complete_graph
+      n: 2
+environment:
+  environment_class: Environment
+  params:
+    am_i_complete: true
+agents:
+# Agents are split several groups, each with its own definition
+  default: # This is a special group. Its values will be used as default values for the rest of the groups
+    agent_class: CounterModel
+    topology: default
     state:
-      id: 0
-  - agent_type: AggregatedCounter
-    weight: 0.2
-environment_agents: []
-environment_class: Environment
-environment_params:
-  am_i_complete: true
-default_state:
-  incidents: 0
-states:
-  - name: 'The first node'
-  - name: 'The second node'
+      times: 1
+  environment:
+    # In this group we are not specifying any topology
+    fixed:
+      - name: 'Environment Agent 1'
+        agent_class: CounterModel
+        state:
+          times: 10
+  general_counters:
+    topology: default
+    distribution:
+      - agent_class: CounterModel
+        weight: 1
+        state:
+          times: 3
+      - agent_class: AggregatedCounter
+        weight: 0.2
+    override:
+      - filter:
+          agent_class: AggregatedCounter
+        n: 2
+        state:
+          times: 5
+
+  other_counters:
+    topology: another_graph
+    fixed:
+      - agent_class: CounterModel
+        id: 0
+        state:
+          times: 1
+          total: 0
+      - agent_class: CounterModel
+        id: 1
+        # If not specified, it will use the state set in the 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 +1,20 @@
+---
+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:
+  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 +1,119 @@
+'''
+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, width, height, *args, topologies, **kwargs):
+
+        super().__init__(*args, topologies=topologies, **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

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

examples/newsspread/newsspread.py

@@ -1,8 +1,8 @@
-from soil.agents import FSM, state, default_state, prob
+from soil.agents import FSM, NetworkAgent, state, default_state, prob
 import logging
 
 
-class DumbViewer(FSM):
+class DumbViewer(FSM, NetworkAgent):
     '''
     A viewer that gets infected via TV (if it has one) and tries to infect
     its neighbors once it's infected.
@@ -17,7 +17,7 @@ class DumbViewer(FSM):
     def neutral(self):
         if self['has_tv']:
             if prob(self.env['prob_tv_spread']):
-                self.set_state(self.infected)
+                return self.infected
 
     @state
     def infected(self):
@@ -26,6 +26,12 @@ class DumbViewer(FSM):
                 neighbor.infect()
 
     def infect(self):
+        '''
+        This is not a state. It is a function that other agents can use to try to
+        infect this agent. DumbViewer always gets infected, but other agents like
+        HerdViewer might not become infected right away
+        '''
+
         self.set_state(self.infected)
 
 
@@ -34,15 +40,14 @@ class HerdViewer(DumbViewer):
     A viewer whose probability of infection depends on the state of its neighbors.
     '''
 
-    level = logging.DEBUG
-
     def infect(self):
+        '''Notice again that this is NOT a state. See DumbViewer.infect for reference'''
         infected = self.count_neighboring_agents(state_id=self.infected.id)
         total = self.count_neighboring_agents()
         prob_infect = self.env['prob_neighbor_spread'] * infected/total
         self.debug('prob_infect', prob_infect)
         if prob(prob_infect):
-            self.set_state(self.infected.id)
+            self.set_state(self.infected)
 
 
 class WiseViewer(HerdViewer):
@@ -77,5 +82,5 @@ class WiseViewer(HerdViewer):
                        1.0)
         prob_cure = self.env['prob_neighbor_cure'] * (cured/infected)
         if prob(prob_cure):
-            return self.cure()
+            return self.cured
         return self.set_state(super().infected)

examples/programmatic/.gitignore

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

examples/programmatic/programmatic.py

@@ -0,0 +1,40 @@
+'''
+Example of a fully programmatic simulation, without definition files.
+'''
+from soil import Simulation, agents
+from networkx import Graph
+import logging
+
+
+def mygenerator():
+    # Add only a node
+    G = Graph()
+    G.add_node(1)
+    return G
+
+
+class MyAgent(agents.FSM):
+
+    @agents.default_state
+    @agents.state
+    def neutral(self):
+        self.debug('I am running')
+        if agents.prob(0.2):
+            self.info('This runs 2/10 times on average')
+
+
+s = Simulation(name='Programmatic',
+               network_params={'generator': mygenerator},
+               num_trials=1,
+               max_time=100,
+               agent_type=MyAgent,
+               dry_run=True)
+
+
+# By default, logging will only print WARNING logs (and above).
+# You need to choose a lower logging level to get INFO/DEBUG traces
+logging.basicConfig(level=logging.INFO)
+envs = s.run()
+
+# Uncomment this to output the simulation to a YAML file
+# s.dump_yaml('simulation.yaml')

examples/pubcrawl/pubcrawl.py

@@ -1,4 +1,4 @@
-from soil.agents import FSM, state, default_state
+from soil.agents import FSM, NetworkAgent, state, default_state
 from soil import Environment
 from random import random, shuffle
 from itertools import islice
@@ -53,13 +53,13 @@ class CityPubs(Environment):
                 pub['occupancy'] -= 1
 
 
-class Patron(FSM):
+class Patron(FSM, NetworkAgent):
     '''Agent that looks for friends to drink with. It will do three things:
         1) Look for other patrons to drink with
         2) Look for a bar where the agent and other agents in the same group can get in.
         3) While in the bar, patrons only drink, until they get drunk and taken home.
     '''
-    level = logging.INFO
+    level = logging.DEBUG
 
     defaults = {
         'pub': None,
@@ -113,7 +113,8 @@ class Patron(FSM):
     @state
     def at_home(self):
         '''The end'''
-        self.debug('Life sucks. I\'m home!')
+        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
@@ -150,7 +151,7 @@ class Patron(FSM):
         return befriended
 
 
-class Police(FSM):
+class Police(FSM, NetworkAgent):
     '''Simple agent to take drunk people out of pubs.'''
     level = logging.INFO
 

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
 
@@ -11,9 +10,7 @@ class Genders(Enum):
     female = 'female'
 
 
-class RabbitModel(FSM):
-
-    level = logging.INFO
+class RabbitModel(FSM, NetworkAgent):
 
     defaults = {
         'age': 0,
@@ -22,7 +19,7 @@ class RabbitModel(FSM):
         'offspring': 0,
     }
 
-    sexual_maturity = 4*30
+    sexual_maturity = 3 #4*30
     life_expectancy = 365 * 3
     gestation = 33
     pregnancy = -1
@@ -31,10 +28,23 @@ 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
+    def fertile(self):
+        raise Exception("Each subclass should define its fertile state")
+
+    @state
+    def dead(self):
+        self.info('Agent {} is dying'.format(self.id))
+        self.die()
+
+
+class Male(RabbitModel):
 
     @state
     def fertile(self):
@@ -46,21 +56,26 @@ 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=Female.fertile.id,
+                                 agent_type=Female,
+                                 limit_neighbors=False,
+                                 limit=self.max_females):
             r = random()
             if r < self['mating_prob']:
                 self.impregnate(f)
                 break # Take a break
-
     def impregnate(self, whom):
-        if self['gender'] == Genders.female.value:
-            raise NotImplementedError('Females cannot impregnate')
         whom['pregnancy'] = 0
         whom['mate'] = self.id
         whom.set_state(whom.pregnant)
         self.debug('{} impregnating: {}. {}'.format(self.id, whom.id, whom.state))
 
+class Female(RabbitModel):
+    @state
+    def fertile(self):
+        # Just wait for a Male
+        pass
+
     @state
     def pregnant(self):
         self['age'] += 1
@@ -80,7 +95,7 @@ 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
@@ -90,11 +105,9 @@ class RabbitModel(FSM):
 
     @state
     def dead(self):
-        self.info('Agent {} is dying'.format(self.id))
+        super().dead()
         if 'pregnancy' in self and self['pregnancy'] > -1:
             self.info('A mother has died carrying a baby!!')
-        self.die()
-        return
 
 
 class RandomAccident(BaseAgent):
@@ -102,7 +115,9 @@ class RandomAccident(BaseAgent):
     level = logging.DEBUG
 
     def step(self):
-        rabbits_total = self.global_topology.number_of_nodes()
+        rabbits_total = self.env.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 +131,5 @@ 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.env.count_agents(state_id=RabbitModel.dead.id) == self.env.topology.number_of_nodes():
             self.die()

examples/rabbits/rabbits.yml

@@ -1,23 +1,20 @@
 ---
-load_module: rabbit_agents
 name: rabbits_example
-max_time: 500
+max_time: 100
 interval: 1
 seed: MySeed
-agent_type: RabbitModel
+agent_type: rabbit_agents.RabbitModel
 environment_agents:
-    - agent_type: RandomAccident
+    - agent_type: rabbit_agents.RandomAccident
 environment_params:
   prob_death: 0.001
 default_state:
-  mating_prob: 0.01
+  mating_prob: 0.1
 topology:
   nodes:
     - id: 1
-      state:
-        gender: female
+      agent_type: rabbit_agents.Male
     - id: 0
-      state:
-        gender: male
+      agent_type: rabbit_agents.Female
   directed: true
   links: []

examples/random_delays/random_delays.py

@@ -0,0 +1,45 @@
+'''
+Example of setting a 
+Example of a fully programmatic simulation, without definition files.
+'''
+from soil import Simulation, agents
+from soil.time import Delta
+from random import expovariate
+import logging
+
+
+
+class MyAgent(agents.FSM):
+    '''
+    An agent that first does a ping
+    '''
+
+    defaults = {'pong_counts': 2}
+
+    @agents.default_state
+    @agents.state
+    def ping(self):
+        self.info('Ping')
+        return self.pong, Delta(expovariate(1/16))
+
+    @agents.state
+    def pong(self):
+        self.info('Pong')
+        self.pong_counts -= 1
+        self.info(str(self.pong_counts))
+        if self.pong_counts < 1:
+            return self.die()
+        return None, Delta(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

@@ -1,5 +1,4 @@
 name: TerroristNetworkModel_sim
-load_module: TerroristNetworkModel
 max_time: 150 
 num_trials: 1
 network_params:
@@ -9,19 +8,19 @@ network_params:
   # theta: 20
   n: 100
 network_agents:
-  - agent_type: TerroristNetworkModel
+  - agent_type: TerroristNetworkModel.TerroristNetworkModel
     weight: 0.8
     state:
       id: civilian  # Civilians
-  - agent_type: TerroristNetworkModel
+  - agent_type: TerroristNetworkModel.TerroristNetworkModel
     weight: 0.1
     state:
       id: leader    # Leaders
-  - agent_type: TrainingAreaModel
+  - agent_type: TerroristNetworkModel.TrainingAreaModel
     weight: 0.05
     state:
       id: terrorist # Terrorism
-  - agent_type: HavenModel
+  - agent_type: TerroristNetworkModel.HavenModel
     weight: 0.05
     state:
       id: civilian  # Civilian
@@ -60,3 +59,4 @@ visualization_params:
   background_image: 'map_4800x2860.jpg'
   background_opacity: '0.9'
   background_filter_color: 'blue'
+skip_test: true  # This simulation takes too long for automated tests.

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>=0.8.9
+pydantic>=1.9

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(
@@ -40,12 +46,10 @@ setup(
         'Operating System :: POSIX',
         'Programming Language :: Python :: 3'],
     install_requires=install_reqs,
-    extras_require={
-        'web': ['tornado']
-
-    },
+    extras_require=extras_require,
     tests_require=test_reqs,
     setup_requires=['pytest-runner', ],
+    pytest_plugins = ['pytest_profiling'],
     include_package_data=True,
     entry_points={
         'console_scripts':

soil/VERSION

@@ -1 +1 @@
-0.13.1
+0.20.7

soil/__init__.py

@@ -11,59 +11,83 @@ try:
 except NameError:
     basestring = str
 
-logging.basicConfig()
-
+from .agents import *
 from . import agents
 from .simulation import *
 from .environment import Environment
-from . import utils
+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',
-                        help='Do not store the results of the simulation.')
+                        help='Do not store the results of the simulation to disk, show in terminal instead.')
     parser.add_argument('--pdb', action='store_true',
                         help='Use a pdb console in case of exception.')
     parser.add_argument('--graph', '-g', action='store_true',
-                        help='Dump GEXF graph. Defaults to false.')
+                        help='Dump each trial\'s network topology as a GEXF graph. Defaults to false.')
     parser.add_argument('--csv', action='store_true',
-                        help='Dump history in CSV format. Defaults to false.')
+                        help='Dump all data collected in CSV format. Defaults to false.')
+    parser.add_argument('--level', type=str,
+                        help='Logging level')
     parser.add_argument('--output', '-o', type=str, default="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.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))
+    logger.info('Loading config file: {}'.format(args.file))
+
+    if args.pdb:
+        args.synchronous = True
+
 
     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)
+                                   exporters=exporters,
+                                   parallel=(not args.synchronous),
+                                   outdir=args.output,
+                                   exporter_params=exp_params)
     except Exception:
         if args.pdb:
             pdb.post_mortem()

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

@@ -1,32 +1,44 @@
-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.
     """
 
+    defaults = {
+        'times': 0,
+        'neighbors': 0,
+        'total': 0
+    }
+
     def step(self):
         # Outside effects
-        total = len(list(self.get_all_agents()))
+        total = len(list(self.env.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.env.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

@@ -29,8 +29,8 @@ class SISaModel(FSM):
         standard_variance
     """
 
-    def __init__(self, environment, 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(self.env['neutral_discontent_spon_prob'],
                                                              self.env['standard_variance'])

soil/agents/SentimentCorrelationModel.py

@@ -16,8 +16,8 @@ class SentimentCorrelationModel(BaseAgent):
         disgust_prob
     """
 
-    def __init__(self, environment, 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

@@ -1,67 +1,104 @@
-# 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 collections.abc import MutableMapping, Mapping, Set
+from abc import ABCMeta
 from copy import deepcopy
-from functools import partial
+from functools import partial, wraps
+from itertools import islice, chain
 import json
+import networkx as nx
 
-from functools import wraps
+from mesa import Agent as MesaAgent
+from typing import Dict, List
 
-from .. import utils, history
+from random import shuffle
+
+from .. import serialization, utils, time, config
 
 
-class BaseAgent(nxsim.BaseAgent):
+
+def as_node(agent):
+    if isinstance(agent, BaseAgent):
+        return agent.id
+    return agent
+
+IGNORED_FIELDS = ('model', 'logger')
+
+
+class DeadAgent(Exception):
+    pass
+
+class BaseAgent(MesaAgent, MutableMapping):
     """
-    A special simpy BaseAgent that keeps track of its state history.
+    A special type of Mesa Agent that:
+
+    * Can be used as a dictionary to access its state.
+    * Has logging built-in
+    * Can be given default arguments through a defaults class attribute,
+    which will be used on construction to initialize each agent's state
+
+    Any attribute that is not preceded by an underscore (`_`) will also be added to its state.
     """
 
     defaults = {}
 
-    def __init__(self, environment, agent_id=None, state=None,
-                 name='network_process', interval=None, **state_params):
+    def __init__(self,
+                 unique_id,
+                 model,
+                 name=None,
+                 interval=None,
+                 **kwargs
+    ):
         # 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
+        if isinstance(unique_id, MesaAgent):
+            raise Exception()
+        assert isinstance(unique_id, int)
+        super().__init__(unique_id=unique_id, model=model)
 
-        # Global parameters
-        self.global_topology = environment.G
-        self.environment_params = environment.environment_params
+        self.name = str(name) if name else'{}[{}]'.format(type(self).__name__, self.unique_id)
 
-        # Register agent to environment
-        self.env = environment
 
         self._neighbors = None
         self.alive = True
-        real_state = deepcopy(self.defaults)
-        real_state.update(state or {})
-        self.state = real_state
-        self.interval = interval
 
-        if not hasattr(self, 'level'):
-            self.level = logging.DEBUG
-        self.logger = logging.getLogger('{}-Agent-{}'.format(self.env.name,
-                                                             self.id))
-        self.logger.setLevel(self.level)
+        self.interval = interval or self.get('interval', 1)
+        self.logger = logging.getLogger(self.model.id).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)
+        for (k, v) in self.defaults.items():
+            if not hasattr(self, k) or getattr(self, k) is None:
+                setattr(self, k, deepcopy(v))
+
+        for (k, v) in kwargs.items():
+
+            setattr(self, k, v)
+
+        for (k, v) in getattr(self, 'defaults', {}).items():
+            if not hasattr(self, k) or getattr(self, k) is None:
+                setattr(self, k, v)
+
+    def __hash__(self):
+        return hash(self.unique_id)
+
+    # 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):
         '''
         Return the agent itself, which behaves as a dictionary.
-        Changes made to `agent.state` will be reflected in the history.
 
         This method shouldn't be used, but is kept here for backwards compatibility.
         '''
@@ -69,32 +106,40 @@ class BaseAgent(nxsim.BaseAgent):
 
     @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 __getitem__(self, key):
-        if isinstance(key, tuple):
-            key, t_step = key
-            k = history.Key(key=key, t_step=t_step, agent_id=self.id)
-            return self.env[k]
-        return self._state.get(key, None)
+        return 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 __len__(self):
+        return sum(1 for n in self.keys())
+
+    def __iter__(self):
+        return self.items()
+
+    def keys(self):
+        return (k for k in self.__dict__ if k[0] != '_')
 
     def items(self):
-        return self._state.items()
+        return ((k, v) for (k, v) in self.__dict__.items() if k[0] != '_')
 
     def get(self, key, default=None):
         return self[key] if key in self else default
@@ -102,112 +147,153 @@ class BaseAgent(nxsim.BaseAgent):
     @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.info(f'agent {self.unique_id}  is dying')
         self.alive = False
         if remove:
-            super().die()
+            self.remove_node(self.id)
+        return time.NEVER
 
     def step(self):
-        pass
-
-    def to_json(self):
-        return json.dumps(self.state)
-
-    def count_agents(self, state_id=None, limit_neighbors=False):
-        if limit_neighbors:
-            agents = self.global_topology.neighbors(self.id)
-        else:
-            agents = self.global_topology.nodes()
-        count = 0
-        for agent in agents:
-            if state_id and state_id != self.global_topology.node[agent]['agent']['id']:
-                continue
-            count += 1
-        return count
-
-    def count_neighboring_agents(self, state_id=None):
-        return len(super().get_agents(state_id, limit_neighbors=True))
-
-    def get_agents(self, state_id=None, agent_type=None, limit_neighbors=False, iterator=False, **kwargs):
-        agents = self.env.agents
-        if limit_neighbors:
-            agents = super().get_agents(state_id, limit_neighbors)
-
-        def matches_all(agent):
-            if state_id is not None:
-                if agent.state.get('id', None) != state_id:
-                    return False
-            if agent_type is not None:
-                if type(agent) != agent_type:
-                    return False
-            state = agent.state
-            for k, v in kwargs.items():
-                if state.get(k, None) != v:
-                    return False
-            return True
-
-        f = filter(matches_all, agents)
-        if iterator:
-            return f
-        return list(f)
+        if not self.alive:
+            raise DeadAgent(self.unique_id)
+        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):
         return self.log(*args, level=logging.DEBUG, **kwargs)
 
     def info(self, *args, **kwargs):
         return self.log(*args, level=logging.INFO, **kwargs)
 
+# Alias
+# Agent = BaseAgent
 
-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.env.topology_for(self.unique_id)
+
+    @property
+    def node_id(self):
+        return self.env.node_id_for(self.unique_id)
+
+    @property
+    def G(self):
+        return self.model.topologies[self._topology]
+
+    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, unique_id=None, limit_neighbors=False, **kwargs):
+        if limit_neighbors:
+            unique_id = [self.topology.nodes[node]['agent_id'] for node in self.topology.neighbors(self.node_id)]
+            if not unique_id:
+                return
+
+        yield from self.model.agents(unique_id=unique_id, **kwargs)
+
+
+    def subgraph(self, center=True, **kwargs):
+        include = [self] if center else []
+        G = self.topology.subgraph(n.node_id for n in list(self.get_agents(**kwargs)+include))
+        return G
+
+    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):
@@ -215,7 +301,7 @@ def default_state(func):
     return func
 
 
-class MetaFSM(type):
+class MetaFSM(ABCMeta):
     def __init__(cls, name, bases, nmspc):
         super(MetaFSM, cls).__init__(name, bases, nmspc)
         states = {}
@@ -241,28 +327,32 @@ class MetaFSM(type):
 class FSM(BaseAgent, 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 self.default_state:
+                self.set_state(self.default_state.id)
+            else:
+                raise Exception('{} has no valid state id or default state'.format(self))
+        interval = self.states[self.state_id](self) or interval
+        if not self.alive:
+            return time.NEVER
+        return 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
 
 
@@ -282,7 +372,7 @@ def prob(prob=1):
 
 
 def calculate_distribution(network_agents=None,
-                           agent_type=None):
+                           agent_class=None):
     '''
     Calculate the threshold values (thresholds for a uniform distribution)
     of an agent distribution given the weights of each agent type.
@@ -290,13 +380,13 @@ def calculate_distribution(network_agents=None,
     The input has this form: ::
 
             [
-            {'agent_type': 'agent_type_1',
+            {'agent_class': 'agent_class_1',
                 'weight': 0.2,
                 'state': {
                     'id': 0
                 }
             },
-            {'agent_type': 'agent_type_2',
+            {'agent_class': 'agent_class_2',
                 'weight': 0.8,
                 'state': {
                     'id': 1
@@ -305,58 +395,64 @@ def calculate_distribution(network_agents=None,
             ]
 
     In this example, 20% of the nodes will be marked as type
-    'agent_type_1'.
+    'agent_class_1'.
     '''
     if network_agents:
-        network_agents = deepcopy(network_agents)
-    elif agent_type:
-        network_agents = [{'agent_type': agent_type}]
+        network_agents = [deepcopy(agent) for agent in network_agents if not hasattr(agent, 'id')]
+    elif agent_class:
+        network_agents = [{'agent_class': agent_class}]
     else:
-        return []
+        raise ValueError('Specify a distribution or a default agent type')
+
+    # Fix missing weights and incompatible types
+    for x in network_agents:
+        x['weight'] = float(x.get('weight', 1))
 
     # Calculate the thresholds
-    total = sum(x.get('weight', 1) for x in network_agents)
+    total = sum(x['weight'] for x in network_agents)
     acc = 0
     for v in network_agents:
-        upper = acc + (v.get('weight', 1)/total)
+        if 'ids' in v:
+            continue
+        upper = acc + (v['weight']/total)
         v['threshold'] = [acc, upper]
         acc = upper
     return network_agents
 
 
-def serialize_type(agent_type, known_modules=[], **kwargs):
-    if isinstance(agent_type, str):
-        return agent_type
+def serialize_type(agent_class, known_modules=[], **kwargs):
+    if isinstance(agent_class, str):
+        return agent_class
     known_modules += ['soil.agents']
-    return utils.serialize(agent_type, known_modules=known_modules, **kwargs)[1] # Get the name of the class
+    return serialization.serialize(agent_class, known_modules=known_modules, **kwargs)[1] # Get the name of the class
 
 
-def serialize_distribution(network_agents, known_modules=[]):
+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(network_agents)
+    d = deepcopy(list(network_agents))
     for v in d:
         if 'threshold' in v:
             del v['threshold']
-        v['agent_type'] = serialize_type(v['agent_type'],
+        v['agent_class'] = serialize_type(v['agent_class'],
                                          known_modules=known_modules)
     return d
 
 
-def deserialize_type(agent_type, known_modules=[]):
-    if not isinstance(agent_type, str):
-        return agent_type
+def deserialize_type(agent_class, known_modules=[]):
+    if not isinstance(agent_class, str):
+        return agent_class
     known = known_modules + ['soil.agents', 'soil.agents.custom' ]
-    agent_type = utils.deserializer(agent_type, known_modules=known)
-    return agent_type
+    agent_class = serialization.deserializer(agent_class, known_modules=known)
+    return agent_class
 
 
-def deserialize_distribution(ind, **kwargs):
+def deserialize_definition(ind, **kwargs):
     d = deepcopy(ind)
     for v in d:
-        v['agent_type'] = deserialize_type(v['agent_type'], **kwargs)
+        v['agent_class'] = deserialize_type(v['agent_class'], **kwargs)
     return d
 
 
@@ -365,32 +461,327 @@ 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, **kwargs):
+def _convert_agent_classs(ind, to_string=False, **kwargs):
     '''Convenience method to allow specifying agents by class or class name.'''
     if to_string:
-        return serialize_distribution(ind, **kwargs)
-    return deserialize_distribution(ind, **kwargs)
+        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
+            return d['agent_class'], 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])
+
+    id = 0
+
+    def init_agent(item, id=ix):
+        while id in agents:
+            id += 1
+
+        agent = remaining[id]
+        agent['state'].update(copy(item.get('state', {})))
+        agents[agent.unique_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
+
+
+class AgentView(Mapping, Set):
+    """A lazy-loaded list of agents.
+    """
+
+    __slots__ = ("_agents",)
+
+
+    def __init__(self, agents):
+        self._agents = agents
+
+    def __getstate__(self):
+        return {"_agents": self._agents}
+
+    def __setstate__(self, state):
+        self._agents = state["_agents"]
+
+    # Mapping methods
+    def __len__(self):
+        return sum(len(x) for x in self._agents.values())
+
+    def __iter__(self):
+        yield from iter(chain.from_iterable(g.values() for g in self._agents.values()))
+
+    def __getitem__(self, agent_id):
+        if isinstance(agent_id, slice):
+            raise ValueError(f"Slicing is not supported")
+        for group in self._agents.values():
+            if agent_id in group:
+                return group[agent_id]
+        raise ValueError(f"Agent {agent_id} not found")
+
+    def filter(self, *args, **kwargs):
+        yield from filter_groups(self._agents, *args, **kwargs)
+
+    def one(self, *args, **kwargs):
+        return next(filter_groups(self._agents, *args, **kwargs))
+
+    def __call__(self, *args, **kwargs):
+        return list(self.filter(*args, **kwargs))
+
+    def __contains__(self, agent_id):
+        return any(agent_id in g for g in self._agents)
+
+    def __str__(self):
+        return str(list(a.unique_id for a in self))
+
+    def __repr__(self):
+        return f"{self.__class__.__name__}({self})"
+
+
+def filter_groups(groups, *, group=None, **kwargs):
+    assert isinstance(groups, dict)
+
+    if group is not None and not isinstance(group, list):
+        group = [group]
+
+    if group:
+        groups = list(groups[g] for g in group if g in groups)
+    else:
+        groups = list(groups.values())
+
+    agents = chain.from_iterable(filter_group(g, **kwargs) for g in groups)
+
+    yield from agents
+
+
+def filter_group(group, *id_args, unique_id=None, state_id=None, agent_class=None, ignore=None, state=None, **kwargs):
+    '''
+    Filter agents given as a dict, by the criteria given as arguments (e.g., certain type or state id).
+    '''
+    assert isinstance(group, dict)
+
+    ids = []
+
+    if unique_id is not None:
+        if isinstance(unique_id, list):
+            ids += unique_id
+        else:
+            ids.append(unique_id)
+
+    if id_args:
+        ids += id_args
+
+    if state_id is not None and not isinstance(state_id, (tuple, list)):
+        state_id = tuple([state_id])
+
+    if agent_class is not None:
+        agent_class = deserialize_type(agent_class)
+        try:
+            agent_class = tuple(agent_class)
+        except TypeError:
+            agent_class = tuple([agent_class])
+
+    if ids:
+        agents = (group[aid] for aid in ids if aid in group)
+    else:
+        agents = (a for a in group.values())
+
+    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_class is not None:
+        f = filter(lambda agent: isinstance(agent, agent_class), f)
+
+    state = state or dict()
+    state.update(kwargs)
+
+    for k, v in state.items():
+        f = filter(lambda agent: agent.state.get(k, None) == v, f)
+
+    yield from f
+
+
+def from_config(cfg: Dict[str, config.AgentConfig], env):
+    '''
+    Agents are specified in groups.
+    Each group can be specified in two ways, either through a fixed list in which each item has
+    has the agent type, number of agents to create, and the other parameters, or through what we call
+    an `agent distribution`, which is similar but instead of number of agents, it specifies the weight
+    of each agent type.
+    '''
+    default = cfg.get('default', None)
+    return {k: _group_from_config(c, default=default, env=env) for (k, c)  in cfg.items() if k is not 'default'}
+
+
+def _group_from_config(cfg: config.AgentConfig, default: config.SingleAgentConfig, env):
+    agents = {}
+    if cfg.fixed is not None:
+        agents = _from_fixed(cfg.fixed, topology=cfg.topology, default=default, env=env)
+    if cfg.distribution:
+        n = cfg.n or len(env.topologies[cfg.topology or default.topology])
+        target = n - len(agents)
+        agents.update(_from_distro(cfg.distribution, target,
+                                   topology=cfg.topology or default.topology,
+                                   default=default,
+                                   env=env))
+        assert len(agents) == n
+    if cfg.override:
+        for attrs in cfg.override:
+            if attrs.filter:
+                filtered = list(filter_group(agents, **attrs.filter))
+            else:
+                filtered = list(agents)
+
+            if attrs.n > len(filtered):
+                raise ValueError(f'Not enough agents to sample. Got {len(filtered)}, expected >= {attrs.n}')
+            for agent in random.sample(filtered, attrs.n):
+                agent.state.update(attrs.state)
+
+    return agents
+
+
+def _from_fixed(lst: List[config.FixedAgentConfig], topology: str, default: config.SingleAgentConfig, env):
+    agents = {}
+
+    for fixed in lst:
+        agent_id = fixed.agent_id
+        if agent_id is None:
+            agent_id = env.next_id()
+
+        cls = serialization.deserialize(fixed.agent_class or default.agent_class)
+        state = fixed.state.copy()
+        state.update(default.state)
+        agent = cls(unique_id=agent_id,
+                    model=env,
+                    **state)
+        topology = fixed.topology if (fixed.topology is not None) else (topology or default.topology)
+        if topology:
+            env.agent_to_node(agent_id, topology, fixed.node_id)
+        agents[agent.unique_id] = agent
+
+    return agents
+
+
+def _from_distro(distro: List[config.AgentDistro],
+                 n: int,
+                 topology: str,
+                 default: config.SingleAgentConfig,
+                 env):
+
+    agents = {}
+
+    if n is None:
+        if any(lambda dist: dist.n is None, distro):
+            raise ValueError('You must provide a total number of agents, or the number of each type')
+        n = sum(dist.n for dist in distro)
+
+    weights = list(dist.weight if dist.weight is not None else 1 for dist in distro)
+    minw = min(weights)
+    norm = list(weight / minw for weight in weights)
+    total = sum(norm)
+    chunk = n // total
+
+    # random.choices would be enough to get a weighted distribution. But it can vary a lot for smaller k
+    # So instead we calculate our own distribution to make sure the actual ratios are close to what we would expect
+
+    # Calculate how many times each has to appear
+    indices = list(chain.from_iterable([idx] * int(n*chunk) for (idx, n) in enumerate(norm)))
+
+    # Complete with random agents following the original weight distribution
+    if len(indices) < n:
+        indices += random.choices(list(range(len(distro))), weights=[d.weight for d in distro], k=n-len(indices))
+
+    # Deserialize classes for efficiency
+    classes = list(serialization.deserialize(i.agent_class or default.agent_class) for i in distro)
+
+    # Add them in random order
+    random.shuffle(indices)
+
+
+    for idx in indices:
+        d = distro[idx]
+        cls = classes[idx]
+        agent_id = env.next_id()
+        state = d.state.copy()
+        if default:
+            state.update(default.state)
+        agent = cls(unique_id=agent_id, model=env, **state)
+        topology = d.topology if (d.topology is not None) else topology or default.topology
+        if topology:
+            env.agent_to_node(agent.unique_id, topology)
+        assert agent.name is not None
+        assert agent.name != 'None'
+        assert agent.name
+        agents[agent.unique_id] = agent
+
+    return agents
 
 
 from .BassModel import *
@@ -400,4 +791,10 @@ 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.deserialize(row['value_type'], row['value'])
+    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()}
 
@@ -119,8 +135,14 @@ 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)
@@ -130,13 +152,28 @@ def get_count(df, *keys):
     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/config.py

@@ -0,0 +1,242 @@
+from __future__ import annotations
+from pydantic import BaseModel, ValidationError, validator, root_validator
+
+import yaml
+import os
+import sys
+
+from typing import Any, Callable, Dict, List, Optional, Union, Type
+from pydantic import BaseModel, Extra
+import networkx as nx
+
+class General(BaseModel):
+    id: str = 'Unnamed Simulation'
+    group: str = None
+    dir_path: Optional[str] = None
+    num_trials: int = 1
+    max_time: float = 100
+    interval: float = 1
+    seed: str = ""
+
+    @staticmethod
+    def default():
+        return General()
+
+
+# Could use TypeAlias in python >= 3.10
+nodeId = int
+
+class Node(BaseModel):
+    id: nodeId
+    state: Optional[Dict[str, Any]] = {}
+
+
+class Edge(BaseModel):
+    source: nodeId
+    target: nodeId
+    value: Optional[float] = 1
+
+
+class Topology(BaseModel):
+    nodes: List[Node]
+    directed: bool
+    links: List[Edge]
+
+
+class NetParams(BaseModel, extra=Extra.allow):
+    generator: Union[Callable, str]
+    n: int 
+
+
+class NetConfig(BaseModel):
+    group: str = 'network'
+    params: Optional[NetParams]
+    topology: Optional[Union[Topology, nx.Graph]]
+    path: Optional[str]
+
+    class Config:
+        arbitrary_types_allowed = True
+
+    @staticmethod
+    def default():
+        return NetConfig(topology=None, params=None)
+
+    @root_validator
+    def validate_all(cls,  values):
+        if 'params' not in values and 'topology' not in values:
+            raise ValueError('You must specify either a topology or the parameters to generate a graph')
+        return values
+
+
+class EnvConfig(BaseModel):
+    environment_class: Union[Type, str] = 'soil.Environment'
+    params: Dict[str, Any] = {}
+    schedule: Union[Type, str] = 'soil.time.TimedActivation'
+
+    @staticmethod
+    def default():
+        return EnvConfig()
+
+
+class SingleAgentConfig(BaseModel):
+    agent_class: Optional[Union[Type, str]] = None
+    agent_id: Optional[int] = None
+    topology: Optional[str] = None
+    node_id: Optional[Union[int, str]] = None
+    name: Optional[str] = None
+    state: Optional[Dict[str, Any]] = {}
+
+class FixedAgentConfig(SingleAgentConfig):
+    n: Optional[int] = 1
+
+    @root_validator
+    def validate_all(cls,  values):
+        if values.get('agent_id', None) is not None and values.get('n', 1) > 1:
+            print(values)
+            raise ValueError(f"An agent_id can only be provided when there is only one agent ({values.get('n')} given)")
+        return values
+
+
+class OverrideAgentConfig(FixedAgentConfig):
+    filter: Optional[Dict[str, Any]] = None
+
+
+class AgentDistro(SingleAgentConfig):
+    weight: Optional[float] = 1
+
+
+class AgentConfig(SingleAgentConfig):
+    n: Optional[int] = None
+    topology: Optional[str] = None
+    distribution: Optional[List[AgentDistro]] = None
+    fixed: Optional[List[FixedAgentConfig]] = None
+    override: Optional[List[OverrideAgentConfig]] = None
+
+    @staticmethod
+    def default():
+        return AgentConfig()
+
+    @root_validator
+    def validate_all(cls,  values):
+        if 'distribution' in values and ('n' not in values and 'topology' not in values):
+            raise ValueError("You need to provide the number of agents or a topology to extract the value from.")
+        return values
+
+
+class Config(BaseModel, extra=Extra.forbid):
+    version: Optional[str] = '1'
+    general: General = General.default()
+    topologies: Optional[Dict[str, NetConfig]] = {}
+    environment: EnvConfig = EnvConfig.default()
+    agents: Optional[Dict[str, AgentConfig]] = {}
+
+def convert_old(old, strict=True):
+    '''
+    Try to convert old style configs into the new format.
+
+    This is still a work in progress and might not work in many cases.
+    '''
+
+
+    new = {}
+
+
+    general = {}
+    for k in ['id', 
+              'group',
+              'dir_path',
+              'num_trials',
+              'max_time',
+              'interval',
+              'seed']:
+        if k in old:
+            general[k] = old[k]
+
+    if 'name' in old:
+        general['id'] = old['name']
+
+    network = {}
+
+
+    if 'network_params' in old and old['network_params']:
+        for (k, v) in old['network_params'].items():
+            if k == 'path':
+                network['path'] = v
+            else:
+                network.setdefault('params', {})[k] = v
+
+    if 'topology' in old:
+        network['topology'] = old['topology']
+
+    agents = {
+        'network': {},
+        'default': {},
+    }
+
+    if 'agent_type' in old:
+        agents['default']['agent_class'] = old['agent_type']
+
+    if 'default_state' in old:
+        agents['default']['state'] = old['default_state']
+
+
+    def updated_agent(agent):
+        newagent = dict(agent)
+        newagent['agent_class'] = newagent['agent_type']
+        del newagent['agent_type']
+        return newagent
+
+    for agent in old.get('environment_agents', []):
+        agents['environment'] = {'distribution': [], 'fixed': []}
+        if 'agent_id' in agent:
+            agent['name'] = agent['agent_id']
+            del agent['agent_id']
+        agents['environment']['fixed'].append(updated_agent(agent))
+
+    by_weight = []
+    fixed = []
+    override = []
+
+    if 'network_agents' in old:
+        agents['network']['topology'] = 'default'
+
+        for agent in old['network_agents']:
+            agent = updated_agent(agent)
+            if 'agent_id' in agent:
+                fixed.append(agent)
+            else:
+                by_weight.append(agent)
+
+    if 'agent_type' in old and (not fixed and not by_weight):
+        agents['network']['topology'] = 'default'
+        by_weight = [{'agent_class': old['agent_type']}]
+
+    
+    # TODO: translate states properly
+    if 'states' in old:
+        states = old['states']
+        if isinstance(states, dict):
+            states = states.items()
+        else:
+            states = enumerate(states)
+        for (k, v) in states:
+            override.append({'filter': {'node_id': k},
+                             'state': v
+            })
+
+    agents['network']['override'] = override
+    agents['network']['fixed'] = fixed
+    agents['network']['distribution'] = by_weight
+
+    environment = {'params': {}}
+    if 'environment_class' in old:
+        environment['environment_class'] = old['environment_class']
+
+    for (k, v) in old.get('environment_params', {}).items():
+        environment['params'][k] = v
+
+    return Config(version='2',
+                  general=general,
+                  topologies={'default': network},
+                  environment=environment,
+                  agents=agents)

soil/datacollection.py

@@ -0,0 +1,24 @@
+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
+        raise NotImplementedError()
+
+    @property
+    def model_vars(self):
+        raise NotImplementedError()
+
+    @model_vars.setter
+    def model_vars(self, value):
+        raise NotImplementedError()
+
+    @property
+    def agent_reporters(self):
+        raise NotImplementedError()
+

soil/environment.py

@@ -1,338 +1,293 @@
+from __future__ import annotations
 import os
 import sqlite3
-import time
-import csv
+import math
 import random
-import simpy
-import tempfile
-import pandas as pd
+import logging
+
+from typing import Dict
+from collections import namedtuple
+from time import time as current_time
 from copy import deepcopy
 from networkx.readwrite import json_graph
 
+
 import networkx as nx
-import nxsim
 
-from . import utils, agents, analysis, history
+from mesa import Model
+from mesa.datacollection import DataCollector
+
+from . import serialization, agents, analysis, utils, time, config, network
 
 
-class Environment(nxsim.NetworkEnvironment):
+Record = namedtuple('Record', 'dict_id t_step key value')
+
+
+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
     params, which are used as shared state between agents.
 
     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 
+    both by using the environment as a dictionary or with the environment's
     :meth:`soil.environment.Environment.get` method.
     """
 
-    def __init__(self, name=None,
-                 network_agents=None,
-                 environment_agents=None,
-                 states=None,
-                 default_state=None,
-                 interval=1,
-                 seed=None,
-                 dry_run=False,
+    def __init__(self,
+                 env_id='unnamed_env',
+                 seed='default',
+                 schedule=None,
                  dir_path=None,
-                 topology=None,
-                 *args, **kwargs):
-        self.name = name or 'UnnamedEnvironment'
-        if isinstance(states, list):
-            states = dict(enumerate(states))
-        self.states = deepcopy(states) if states else {}
-        self.default_state = deepcopy(default_state) or {}
-        if not topology:
-            topology = nx.Graph()
-        super().__init__(*args, topology=topology, **kwargs)
-        self._env_agents = {}
-        self.dry_run = dry_run
+                 interval=1,
+                 agents: Dict[str, config.AgentConfig] = {},
+                 topologies: Dict[str, config.NetConfig] = {},
+                 agent_reporters: Optional[Any] = None,
+                 model_reporters: Optional[Any] = None,
+                 tables: Optional[Any] = None,
+                 **env_params):
+
+        super().__init__()
+        self.current_id = -1
+
+        self.seed = '{}_{}'.format(seed, env_id)
+        self.id = env_id
+
+        self.dir_path = dir_path or os.getcwd()
+
+        if schedule is None:
+            schedule = time.TimedActivation()
+        self.schedule = schedule
+
+        seed = seed or current_time()
+
+        random.seed(seed)
+
+
+        self.topologies = {}
+        self._node_ids = {}
+        for (name, cfg) in topologies.items():
+            self.set_topology(cfg=cfg,
+                              graph=name)
+        self.agents = agents or {}
+
+        self.env_params = env_params or {}
+
         self.interval = interval
-        self.dir_path = dir_path or tempfile.mkdtemp('soil-env')
-        if not dry_run:
-            self.get_path()
-        self._history = history.History(name=self.name if not dry_run else None,
-                                        dir_path=self.dir_path)
-        # Add environment agents first, so their events get
-        # executed before network agents
-        self.environment_agents = environment_agents or []
-        self.network_agents = network_agents or []
-        self['SEED'] = seed or time.time()
-        random.seed(self['SEED'])
+        self['SEED'] = seed
+
+        self.logger = utils.logger.getChild(self.id)
+        self.datacollector = DataCollector(model_reporters, agent_reporters, tables)
 
     @property
-    def agents(self):
-        yield from self.environment_agents
-        yield from self.network_agents
-
-    @property
-    def environment_agents(self):
-        for ref in self._env_agents.values():
-            yield ref
-
-    @environment_agents.setter
-    def environment_agents(self, environment_agents):
-        # Set up environmental agent
-        self._env_agents = {}
-        for item in environment_agents:
-            kwargs = deepcopy(item)
-            atype = kwargs.pop('agent_type')
-            kwargs['agent_id'] = kwargs.get('agent_id', atype.__name__)
-            kwargs['state'] = kwargs.get('state', {})
-            a = atype(environment=self, **kwargs)
-            self._env_agents[a.id] = a
+    def topology(self):
+        return self.topologies['default']
 
     @property
     def network_agents(self):
-        for i in self.G.nodes():
-            node = self.G.node[i]
-            if 'agent' in node:
-                yield node['agent']
+        yield from self.agents(agent_class=agents.NetworkAgent)
 
-    @network_agents.setter
-    def network_agents(self, network_agents):
-        if not network_agents:
-            return
-        for ix in self.G.nodes():
-            self.init_agent(ix, agent_distribution=network_agents)
+    @staticmethod
+    def from_config(conf: config.Config, trial_id, **kwargs) -> Environment:
+        '''Create an environment for a trial of the simulation'''
+        conf = conf
+        if kwargs:
+            conf = config.Config(**conf.dict(exclude_defaults=True), **kwargs)
+        seed = '{}_{}'.format(conf.general.seed, trial_id)
+        id = '{}_trial_{}'.format(conf.general.id, trial_id).replace('.', '-')
+        opts = conf.environment.params.copy()
+        dir_path = conf.general.dir_path
+        opts.update(conf)
+        opts.update(kwargs)
+        env = serialization.deserialize(conf.environment.environment_class)(env_id=id, seed=seed, dir_path=dir_path, **opts)
+        return env
 
-    def init_agent(self, agent_id, agent_distribution):
-        node = self.G.nodes[agent_id]
+    @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')
+
+
+    def topology_for(self, agent_id):
+        return self.topologies[self._node_ids[agent_id][0]]
+
+    def node_id_for(self, agent_id):
+        return self._node_ids[agent_id][1]
+
+    def set_topology(self, cfg=None, dir_path=None, graph='default'):
+        topology = cfg
+        if not isinstance(cfg, nx.Graph):
+            topology = network.from_config(cfg, dir_path=dir_path or self.dir_path)
+
+        self.topologies[graph] = topology
+
+    @property
+    def agents(self):
+        return agents.AgentView(self._agents)
+    
+    def count_agents(self, *args, **kwargs):
+        return sum(1 for i in self.find_all(*args, **kwargs))
+
+    def find_all(self, *args, **kwargs):
+        return agents.AgentView(self._agents).filter(*args, **kwargs)
+
+    def find_one(self, *args, **kwargs):
+        return agents.AgentView(self._agents).one(*args, **kwargs)
+
+    @agents.setter
+    def agents(self, agents_def: Dict[str, config.AgentConfig]):
+        self._agents = agents.from_config(agents_def, env=self)
+        for d in self._agents.values():
+            for a in d.values():
+                self.schedule.add(a)
+
+    def init_agent(self, agent_id, agent_definitions, graph='default'):
+        node = self.topologies[graph].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]
-        elif 'agent_type' in node:
-            agent_type = node['agent_type']
-        elif 'agent_type' in self.default_state:
-            agent_type = self.default_state['agent_type']
+        agent_class = None
+        if 'agent_class' in self.states.get(agent_id, {}):
+            agent_class = self.states[agent_id]['agent_class']
+        elif 'agent_class' in node:
+            agent_class = node['agent_class']
+        elif 'agent_class' in self.default_state:
+            agent_class = self.default_state['agent_class']
 
-        if agent_type:
-            agent_type = agents.deserialize_type(agent_type)
+        if agent_class:
+            agent_class = agents.deserialize_type(agent_class)
+        elif agent_definitions:
+            agent_class, state = agents._agent_from_definition(agent_definitions, unique_id=agent_id)
         else:
-            agent_type, state = agents._agent_from_distribution(agent_distribution)
-        return self.set_agent(agent_id, agent_type, state)
+            serialization.logger.debug('Skipping node {}'.format(agent_id))
+            return
+        return self.set_agent(agent_id, agent_class, state)
 
-    def set_agent(self, agent_id, agent_type, state=None):
-        node = self.G.nodes[agent_id]
+    def agent_to_node(self, agent_id, graph_name='default', node_id=None, shuffle=False):
+        #TODO: test
+        if node_id is None:
+            G = self.topologies[graph_name]
+            candidates = list(G.nodes(data=True))
+            if shuffle:
+                random.shuffle(candidates)
+            for next_id, data in candidates:
+                if data.get('agent_id', None) is None:
+                    node_id = next_id
+                    data['agent_id'] = agent_id
+                    break
+                    
+
+        self._node_ids[agent_id] = (graph_name, node_id)
+        print(self._node_ids)
+
+
+    def set_agent(self, agent_id, agent_class, state=None, graph='default'):
+        node = self.topologies[graph].nodes[agent_id]
         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
-        a = agent_type(environment=self,
-                       agent_id=agent_id,
-                       state=state)
+        a = None
+        if agent_class:
+            state = defstate
+            a = agent_class(model=self,
+                           unique_id=agent_id
+            )
+
+        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):
-        agent_id = int(len(self.G.nodes()))
-        self.G.add_node(agent_id)
-        a = self.set_agent(agent_id, agent_type, state)
+    def add_node(self, agent_class, state=None, graph='default'):
+        agent_id = int(len(self.topologies[graph].nodes()))
+        self.topologies[graph].add_node(agent_id)
+        a = self.set_agent(agent_id, agent_class, state, graph=graph)
         a['visible'] = True
         return a
 
-    def add_edge(self, agent1, agent2, attrs=None):
+    def add_edge(self, agent1, agent2, start=None, graph='default', **attrs):
         if hasattr(agent1, 'id'):
             agent1 = agent1.id
         if hasattr(agent2, 'id'):
             agent2 = agent2.id
-        return self.G.add_edge(agent1, agent2)
+        start = start or self.now
+        return self.topologies[graph].add_edge(agent1, agent2, **attrs)
 
-    def run(self, *args, **kwargs):
-        self._save_state()
-        super().run(*args, **kwargs)
-        self._history.flush_cache()
-
-    def _save_state(self, now=None):
-        # for agent in self.agents:
-        #     agent.save_state()
-        utils.logger.debug('Saving state @{}'.format(self.now))
-        self._history.save_records(self.state_to_tuples(now=now))
-
-    def save_state(self):
-        '''
-        :DEPRECATED:
-        Periodically save the state of the environment and the agents.
-        '''
-        self._save_state()
-        while self.peek() != simpy.core.Infinity:
-            delay = max(self.peek() - self.now, self.interval)
-            utils.logger.debug('Step: {}'.format(self.now))
-            ev = self.event()
-            ev._ok = True
-            # Schedule the event with minimum priority so
-            # that it executes before all agents
-            self.schedule(ev, -999, delay)
-            yield ev
-            self._save_state()
-
-    def __getitem__(self, key):
-        if isinstance(key, tuple):
-            self._history.flush_cache()
-            return self._history[key]
-
-        return self.environment_params[key]
-
-    def __setitem__(self, key, value):
-        if isinstance(key, tuple):
-            k = history.Key(*key)
-            self._history.save_record(*k,
-                                      value=value)
+    def log(self, message, *args, level=logging.INFO, **kwargs):
+        if not self.logger.isEnabledFor(level):
             return
-        self.environment_params[key] = value
-        self._history.save_record(agent_id='env',
-                                  t_step=self.now,
-                                  key=key,
-                                  value=value)
+        message = message + " ".join(str(i) for i in args)
+        message = " @{:>3}: {}".format(self.now, message)
+        for k, v in kwargs:
+            message += " {k}={v} ".format(k, v)
+        extra = {}
+        extra['now'] = self.now
+        extra['unique_id'] = self.id
+        return self.logger.log(level, message, extra=extra)
+
+    def step(self):
+        '''
+        Advance one step in the simulation, and update the data collection and scheduler appropriately
+        '''
+        super().step()
+        self.schedule.step()
+        self.datacollector.collect(self)
+
+    def run(self, until, *args, **kwargs):
+        until = until or float('inf')
+
+        while self.schedule.next_time < until:
+            self.step()
+            utils.logger.debug(f'Simulation step {self.schedule.time}/{until}. Next: {self.schedule.next_time}')
+        self.schedule.time = until
 
     def __contains__(self, key):
-        return key in self.environment_params
+        return key in self.env_params
 
     def get(self, key, default=None):
         '''
-        Get the value of an environment attribute in a
-        given point in the simulation (history).
-        If key is an attribute name, this method returns
-        the current value.
-        To get values at other times, use a
-        :meth: `soil.history.Key` tuple.
+        Get the value of an environment attribute.
+        Return `default` if the value is not set.
         '''
-        return self[key] if key in self else default
+        return self.env_params.get(key, 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 __getitem__(self, key):
+        return self.env_params.get(key)
 
-    def get_agent(self, agent_id):
-        return self.G.node[agent_id]['agent']
+    def __setitem__(self, key, value):
+        return self.env_params.__setitem__(key, value)
 
-    def get_agents(self):
-        return list(self.agents)
-
-    def dump_csv(self, dir_path=None):
-        csv_name = os.path.join(self.get_path(dir_path),
-                                '{}.environment.csv'.format(self.name))
-
-        with open(csv_name, 'w') as f:
-            cr = csv.writer(f)
-            cr.writerow(('agent_id', 't_step', 'key', 'value'))
-            for i in self.history_to_tuples():
-                cr.writerow(i)
-
-    def dump_gexf(self, dir_path=None):
-        G = self.history_to_graph()
-        graph_path = os.path.join(self.get_path(dir_path),
-                                  self.name+".gexf")
-        # Workaround for geometric models
-        # See soil/soil#4
-        for node in G.nodes():
-            if 'pos' in G.node[node]:
-                G.node[node]['viz'] = {"position": {"x": G.node[node]['pos'][0], "y": G.node[node]['pos'][1], "z": 0.0}}
-                del (G.node[node]['pos'])
-
-        nx.write_gexf(G, graph_path, version="1.2draft")
-
-    def dump(self, dir_path=None, formats=None):
-        if not formats:
-            return
-        functions = {
-            'csv': self.dump_csv,
-            'gexf': self.dump_gexf
-        }
-        for f in formats:
-            if f in functions:
-                functions[f](dir_path)
-            else:
-                raise ValueError('Unknown format: {}'.format(f))
-
-    def state_to_tuples(self, now=None):
+    def _agent_to_tuples(self, agent, now=None):
         if now is None:
             now = self.now
-        for k, v in self.environment_params.items():
-            yield history.Record(agent_id='env',
-                                 t_step=now,
-                                 key=k,
-                                 value=v)
+        for k, v in agent.state.items():
+            yield Record(dict_id=agent.id,
+                          t_step=now,
+                          key=k,
+                          value=v)
+
+    def state_to_tuples(self, agent_id=None, now=None):
+        if now is None:
+            now = self.now
+
+        if agent_id:
+            agent = self.agents[agent_id]
+            yield from self._agent_to_tuples(agent, now)
+            return
+
+        for k, v in self.env_params.items():
+            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 from self._agent_to_tuples(agent, now)
 
-    def history_to_tuples(self):
-        return self._history.to_tuples()
-
-    def history_to_graph(self):
-        G = nx.Graph(self.G)
-
-        for agent in self.network_agents:
-
-            attributes = {'agent': str(agent.__class__)}
-            lastattributes = {}
-            spells = []
-            lastvisible = False
-            laststep = None
-            history = self[agent.id, None, None]
-            if not history:
-                continue
-            for t_step, attribute, value in sorted(list(history)):
-                if attribute == 'visible':
-                    nowvisible = value
-                    if nowvisible and not lastvisible:
-                        laststep = t_step
-                    if not nowvisible and lastvisible:
-                        spells.append((laststep, t_step))
-
-                    lastvisible = nowvisible
-                    continue
-                key = 'attr_' + attribute
-                if key not in attributes:
-                    attributes[key] = list()
-                if key not in lastattributes:
-                    lastattributes[key] = (value, t_step)
-                elif lastattributes[key][0] != value:
-                    last_value, laststep = lastattributes[key]
-                    commit_value = (last_value, laststep, t_step)
-                    if key not in attributes:
-                        attributes[key] = list()
-                    attributes[key].append(commit_value)
-                    lastattributes[key] = (value, t_step)
-            for k, v in lastattributes.items():
-                attributes[k].append((v[0], v[1], None))
-            if lastvisible:
-                spells.append((laststep, None))
-            if spells:
-                G.add_node(agent.id, spells=spells, **attributes)
-            else:
-                G.add_node(agent.id, **attributes)
-
-        return G
-
-    def __getstate__(self):
-        state = self.__dict__.copy()
-        state['G'] = json_graph.node_link_data(self.G)
-        state['network_agents'] = agents.serialize_distribution(self.network_agents)
-        state['environment_agents'] = agents._convert_agent_types(self.environment_agents,
-                                                                 to_string=True)
-        return state
-
-    def __setstate__(self, state):
-        self.__dict__ = state
-        self.G = json_graph.node_link_graph(state['G'])
-        self.network_agents = self.calculate_distribution(self._convert_agent_types(self.network_agents))
-        self.environment_agents = self._convert_agent_types(self.environment_agents)
-        return state
 
 
 SoilEnvironment = Environment

soil/exporters.py

@@ -0,0 +1,232 @@
+import os
+from time import time as current_time
+from io import BytesIO
+from sqlalchemy import create_engine
+
+
+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.config.general.group or '',
+                                   simulation.config.general.id)
+        self.dry_run = dry_run
+        self.copy_to = copy_to
+
+    def sim_start(self):
+        '''Method to call when the simulation starts'''
+        pass
+
+    def sim_end(self):
+        '''Method to call when the simulation ends'''
+        pass
+
+    def trial_start(self, env):
+        '''Method to call when a trial start'''
+        pass
+
+    def trial_end(self, env):
+        '''Method to call when a trial ends'''
+        pass
+
+    def output(self, f, mode='w', **kwargs):
+        if self.dry_run:
+            f = DryRunner(f, copy_to=self.copy_to)
+        else:
+            try:
+                if not os.path.isabs(f):
+                    f = os.path.join(self.outdir, f)
+            except TypeError:
+                pass
+        return open_or_reuse(f, mode=mode, **kwargs)
+
+
+class default(Exporter):
+    '''Default exporter. Writes sqlite results, as well as the simulation YAML'''
+
+    # def sim_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_start(self, env, stats):
+    #     if not self.dry_run:
+    #         with timer('Dumping simulation {} trial {}'.format(self.simulation.name,
+    #                                                            env.name)):
+    #             engine = create_engine('sqlite:///{}.sqlite'.format(env.name), echo=False)
+
+    #             dc = env.datacollector
+    #             tables = {'env': dc.get_model_vars_dataframe(),
+    #                       'agents': dc.get_agent_vars_dataframe(),
+    #                       'agents': dc.get_agent_vars_dataframe()}
+    #             for table in dc.tables:
+    #                 tables[table] = dc.get_table_dataframe(table)
+    #             for (t, df) in tables.items():
+    #                 df.to_sql(t, con=engine)
+
+    # def sim_end(self, stats):
+    #       with timer('Dumping simulation {}\'s stats'.format(self.simulation.name)):
+    #           engine = create_engine('sqlite:///{}.sqlite'.format(self.simulation.name), echo=False)
+    #           with self.output('{}.sqlite'.format(self.simulation.name), mode='wb') as f:
+    #               self.simulation.dump_sqlite(f)
+
+
+def get_dc_dfs(dc):
+    dfs = {'env': dc.get_model_vars_dataframe(),
+        'agents': dc.get_agent_vars_dataframe }
+    for table_name in dc.tables:
+        dfs[table_name] = dc.get_table_dataframe(table_name)
+    yield from dfs.items() 
+
+
+class csv(Exporter):
+
+    '''Export the state of each environment (and its agents) in a separate CSV file'''
+    def trial_end(self, env):
+        with timer('[CSV] Dumping simulation {} trial {} @ dir {}'.format(self.simulation.name,
+                                                                          env.id,
+                                                                          self.outdir)):
+            for (df_name, df) in get_dc_dfs(env.datacollector):
+                with self.output('{}.stats.{}.csv'.format(env.id, df_name)) as f:
+                    df.to_csv(f)
+
+
+class gexf(Exporter):
+    def trial_end(self, env):
+        if self.dry_run:
+            logger.info('Not dumping GEXF in dry_run mode')
+            return
+
+        with timer('[GEXF] Dumping simulation {} trial {}'.format(self.simulation.name,
+                                                                  env.id)):
+            with self.output('{}.gexf'.format(env.id), mode='wb') as f:
+                self.dump_gexf(env, f)
+
+    def dump_gexf(self, env, f):
+        G = env.history_to_graph()
+        # Workaround for geometric models
+        # See soil/soil#4
+        for node in G.nodes():
+            if 'pos' in G.nodes[node]:
+                G.nodes[node]['viz'] = {"position": {"x": G.nodes[node]['pos'][0], "y": G.nodes[node]['pos'][1], "z": 0.0}}
+                del (G.nodes[node]['pos'])
+
+        nx.write_gexf(G, f, version="1.2draft")
+
+class dummy(Exporter):
+
+    def sim_start(self):
+        with self.output('dummy', 'w') as f:
+            f.write('simulation started @ {}\n'.format(current_time()))
+
+    def trial_start(self, env):
+        with self.output('dummy', 'w') as f:
+            f.write('trial started@ {}\n'.format(current_time()))
+
+    def trial_end(self, env):
+        with self.output('dummy', 'w') as f:
+            f.write('trial ended@ {}\n'.format(current_time()))
+
+    def sim_end(self):
+        with self.output('dummy', 'a') as f:
+            f.write('simulation ended @ {}\n'.format(current_time()))
+
+class graphdrawing(Exporter):
+
+    def trial_end(self, env):
+        # Outside effects
+        f = plt.figure()
+        nx.draw(env.G, node_size=10, width=0.2, pos=nx.spring_layout(env.G, scale=100), ax=f.add_subplot(111))
+        with open('graph-{}.png'.format(env.id)) as f:
+            f.savefig(f)
+
+'''
+Convert an environment into a NetworkX graph
+'''
+def env_to_graph(env, history=None):
+    G = nx.Graph(env.G)
+
+    for agent in env.network_agents:
+
+        attributes = {'agent': str(agent.__class__)}
+        lastattributes = {}
+        spells = []
+        lastvisible = False
+        laststep = None
+        if not history:
+            history = sorted(list(env.state_to_tuples()))
+        for _, t_step, attribute, value in history:
+            if attribute == 'visible':
+                nowvisible = value
+                if nowvisible and not lastvisible:
+                    laststep = t_step
+                if not nowvisible and lastvisible:
+                    spells.append((laststep, t_step))
+
+                lastvisible = nowvisible
+                continue
+            key = 'attr_' + attribute
+            if key not in attributes:
+                attributes[key] = list()
+            if key not in lastattributes:
+                lastattributes[key] = (value, t_step)
+            elif lastattributes[key][0] != value:
+                last_value, laststep = lastattributes[key]
+                commit_value = (last_value, laststep, t_step)
+                if key not in attributes:
+                    attributes[key] = list()
+                attributes[key].append(commit_value)
+                lastattributes[key] = (value, t_step)
+        for k, v in lastattributes.items():
+            attributes[k].append((v[0], v[1], None))
+        if lastvisible:
+            spells.append((laststep, None))
+        if spells:
+            G.add_node(agent.id, spells=spells, **attributes)
+        else:
+            G.add_node(agent.id, **attributes)
+
+    return G

soil/history.py

@@ -1,281 +0,0 @@
-import time
-import os
-import pandas as pd
-import sqlite3
-import copy
-from collections import UserDict, 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:
-            if backup and os.path.exists(db_path):
-                newname = db_path + '.backup{}.sqlite'.format(time.time())
-                os.rename(db_path, newname)
-        else:
-            db_path = ":memory:"
-        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 = []
-
-    @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):
-        self.read_types()
-        return {k:v[0] for k, v in self._dtypes.items()}
-
-    def save_tuples(self, tuples):
-        '''
-        Save a series of tuples, converting them to records if necessary
-        '''
-        self.save_records(Record(*tup) for tup in tuples)
-
-    def save_records(self, records):
-        '''
-        Save a collection of records
-        '''
-        for record in records:
-            if not isinstance(record, Record):
-                record = Record(*record)
-            self.save_record(*record)
-
-    def save_record(self, agent_id, t_step, key, value):
-        '''
-        Save a collection of records to the database.
-        Database writes are cached.
-        '''
-        value = self.convert(key, value)
-        self._tups.append(Record(agent_id=agent_id,
-                                 t_step=t_step,
-                                 key=key,
-                                 value=value))
-        if len(self._tups) > 100:
-            self.flush_cache()
-
-    def convert(self, key, value):
-        """Get the serialized value for a given key."""
-        if key not in self._dtypes:
-            self.read_types()
-            if key not in self._dtypes:
-                name = utils.name(value)
-                serializer = utils.serializer(name)
-                deserializer = utils.deserializer(name)
-                self._dtypes[key] = (name, serializer, deserializer)
-                with self.db:
-                    self.db.execute("replace into value_types (key, value_type) values (?, ?)", (key, name))
-        return self._dtypes[key][1](value)
-
-    def recover(self, key, value):
-        """Get the deserialized value for a given key, and the serialized version."""
-        if key not in self._dtypes:
-            self.read_types()
-        if key not in self._dtypes:
-            raise ValueError("Unknown datatype for {} and {}".format(key, value))
-        return self._dtypes[key][2](value)
-
-
-    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.
-        '''
-        with self.db:
-            for rec in self._tups:
-                self.db.execute("replace into history(agent_id, t_step, key, value) values (?, ?, ?, ?)", (rec.agent_id, rec.t_step, rec.key, rec.value))
-        self._tups = list()
-
-    def to_tuples(self):
-        self.flush_cache()
-        with self.db:
-            res = self.db.execute("select agent_id, t_step, key, value from history ").fetchall()
-        for r in res:
-            agent_id, t_step, key, value = r
-            value = self.recover(key, value)
-            yield agent_id, t_step, key, value
-
-    def read_types(self):
-        with self.db:
-            res = self.db.execute("select key, value_type from value_types ").fetchall()
-        for k, v in res:
-            serializer = utils.serializer(v)
-            deserializer = utils.deserializer(v)
-            self._dtypes[k] = (v, serializer, deserializer)
-
-    def __getitem__(self, key):
-        self.flush_cache()
-        key = Key(*key)
-        agent_ids = [key.agent_id] if key.agent_id is not None else []
-        t_steps = [key.t_step] if key.t_step is not None else []
-        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').astype(dtype)
-        if t_steps:
-            df_p = df_p.reindex(t_steps, method='ffill')
-        return df_p.ffill()
-
-
-class Records():
-
-    def __init__(self, df, filter=None, dtypes=None):
-        if not filter:
-            filter = Key(agent_id=None,
-                         t_step=None,
-                         key=None)
-        self._df = df
-        self._filter = filter
-        self.dtypes = dtypes or {}
-        super().__init__()
-
-    def mask(self, tup):
-        res = ()
-        for i, k in zip(tup[:-1], self._filter):
-            if k is None:
-                res = res + (i,)
-        res = res + (tup[-1],)
-        return res
-
-    def filter(self, newKey):
-        f = list(self._filter)
-        for ix, i in enumerate(f):
-            if i is None:
-                f[ix] = newKey
-        self._filter = Key(*f)
-
-    @property
-    def resolved(self):
-        return sum(1 for i in self._filter if i is not None) == 3
-
-    def __iter__(self):
-        for column, series in self._df.iteritems():
-            key, agent_id = column
-            for t_step, value in series.iteritems():
-                r = Record(t_step=t_step,
-                           agent_id=agent_id,
-                           key=key,
-                           value=value)
-                yield self.mask(r)
-
-    def value(self):
-        if self.resolved:
-            f = self._filter
-            try:
-                i = self._df[f.key][str(f.agent_id)]
-                ix = i.index.get_loc(f.t_step, method='ffill')
-                return i.iloc[ix]
-            except KeyError:
-                return self.dtypes[f.key][2]()
-        return list(self)
-
-    def __getitem__(self, k):
-        n = copy.copy(self)
-        n.filter(k)
-        if n.resolved:
-            return n.value()
-        return n
-
-    def __len__(self):
-        return len(self._df)
-
-    def __str__(self):
-        if self.resolved:
-            return str(self.value())
-        return '<Records for [{}]>'.format(self._filter)
-
-
-Key = namedtuple('Key', ['agent_id', 't_step', 'key'])
-Record = namedtuple('Record', 'agent_id t_step key value')

soil/network.py

@@ -0,0 +1,42 @@
+from typing import Dict
+import os
+import sys
+
+import networkx as nx
+
+from . import config, serialization, basestring
+
+def from_config(cfg: config.NetConfig, dir_path: str = None):
+    if not isinstance(cfg, config.NetConfig):
+        cfg = config.NetConfig(**cfg)
+
+    if cfg.path:
+        path = cfg.path
+        if dir_path and not os.path.isabs(path):
+            path = os.path.join(dir_path, path)
+        extension = os.path.splitext(path)[1][1:]
+        kwargs = {}
+        if extension == 'gexf':
+            kwargs['version'] = '1.2draft'
+            kwargs['node_type'] = int
+        try:
+            method = getattr(nx.readwrite, 'read_' + extension)
+        except AttributeError:
+            raise AttributeError('Unknown format')
+        return method(path, **kwargs)
+
+    if cfg.params:
+        net_args = cfg.params.dict()
+        net_gen = net_args.pop('generator')
+
+        if dir_path not in sys.path:
+            sys.path.append(dir_path)
+
+        method = serialization.deserializer(net_gen,
+                                            known_modules=['networkx.generators',])
+        return method(**net_args)
+
+    if isinstance(cfg.topology, basestring) or isinstance(cfg.topology, dict):
+        return nx.json_graph.node_link_graph(cfg.topology)
+
+    return nx.Graph()

soil/serialization.py

@@ -0,0 +1,235 @@
+import os
+import logging
+import ast
+import sys
+import re
+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 not network_params:
+#         return G
+
+#     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 'general' in config and 'dir_path' not in config['general']:
+                    config['general']['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')
+
+KNOWN_MODULES = ['soil', ]
+
+def name(value, known_modules=KNOWN_MODULES):
+    '''Return a name that can be imported, to serialize/deserialize an object'''
+    if value is None:
+        return 'None'
+    if not isinstance(value, type):  # Get the class name first
+        value = type(value)
+    tname = value.__name__
+    if hasattr(builtins, tname):
+        return tname
+    modname = value.__module__
+    if modname == '__main__':
+        return tname
+    if known_modules and modname in known_modules:
+        return tname
+    for kmod in known_modules:
+        if not kmod:
+            continue
+        module = importlib.import_module(kmod)
+        if hasattr(module, tname):
+            return tname
+    return '{}.{}'.format(modname, tname)
+
+
+def serializer(type_):
+    if type_ != 'str' and hasattr(builtins, type_):
+        return repr
+    return lambda x: x
+
+
+def serialize(v, known_modules=KNOWN_MODULES):
+    '''Get a text representation of an object.'''
+    tname = name(v, known_modules=known_modules)
+    func = serializer(tname)
+    return func(v), tname
+
+
+IS_CLASS = re.compile(r"<class '(.*)'>")
+
+def deserializer(type_, known_modules=KNOWN_MODULES):
+    if type(type_) != str:  # Already deserialized
+        return type_
+    if type_ == 'str':
+        return lambda x='': x
+    if type_ == 'None':
+        return lambda x=None: None
+    if hasattr(builtins, type_):  # Check if it's a builtin type
+        cls = getattr(builtins, type_)
+        return lambda x=None: ast.literal_eval(x) if x is not None else cls()
+    match = IS_CLASS.match(type_)
+    if match:
+        modname, tname = match.group(1).rsplit(".", 1)
+        module = importlib.import_module(modname)
+        cls = getattr(module, tname)
+        return getattr(cls, 'deserialize', cls)
+        
+    # Otherwise, see if we can find the module and the class
+    options = []
+
+    for mod in known_modules:
+        if mod:
+            options.append((mod, type_))
+
+    if '.' in type_:  # Fully qualified module
+        module, type_ = type_.rsplit(".", 1)
+        options.append((module, type_))
+
+    errors = []
+    for modname, tname in options:
+        try:
+            module = importlib.import_module(modname)
+            cls = getattr(module, tname)
+            return getattr(cls, 'deserialize', cls)
+        except (ImportError, AttributeError) as ex:
+            errors.append((modname, tname, ex))
+    raise 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=KNOWN_MODULES, **kwargs):
+    '''Return the list of deserialized objects'''
+    objects = []
+    for name in names:
+        mod = deserialize(name, known_modules=known_modules)
+        objects.append(mod(*args, **kwargs))
+    return objects
+

soil/simulation.py

@@ -1,197 +1,149 @@
 import os
-import time
+from time import time as current_time, strftime 
 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
-
 import pickle
 
-from nxsim import NetworkSimulation
-
-from . import utils, basestring, agents
+from . import serialization, utils, basestring, agents
 from .environment import Environment
 from .utils import logger
+from .exporters import default
+
+from .config import Config, convert_old
 
 
-class Simulation(NetworkSimulation):
+#TODO: change documentation for simulation
+class Simulation:
     """
-    Subclass of nsim.NetworkSimulation with three main differences:
-        1) agent type can be specified by name or by class.
-        2) instead of just one type, a network agents distribution can be used.
-           The distribution specifies the weight (or probability) of each
-           agent type in the topology. This is an example distribution: ::
-
-                  [
-                    {'agent_type': 'agent_type_1',
-                     'weight': 0.2,
-                     'state': {
-                         'id': 0
-                      }
-                    },
-                    {'agent_type': 'agent_type_2',
-                     'weight': 0.8,
-                     'state': {
-                         'id': 1
-                      }
-                    }
-                  ]
-
-          In this example, 20% of the nodes will be marked as type
-          'agent_type_1'.
-        3) if no initial state is given, each node's state will be set
-           to `{'id': 0}`.
-
     Parameters
     ---------
-    name : str, optional
+    config (optional): :class:`config.Config`
         name of the Simulation
-    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 where to save pickled objects
-    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'
-
 
+    kwargs: parameters to use to initialize a new configuration, if one has not been provided.
     """
 
-    def __init__(self, name=None, topology=None, network_params=None,
-                 network_agents=None, agent_type=None, states=None,
-                 default_state=None, interval=1, dump=None, dry_run=False,
-                 dir_path=None, num_trials=1, max_time=100,
-                 load_module=None, seed=None,
-                 environment_agents=None, environment_params=None,
-                 environment_class=None, **kwargs):
+    def __init__(self, config=None,
+                 **kwargs):
+        if kwargs:
+            cfg = {}
+            if config:
+                cfg.update(config.dict(include_defaults=False))
+            cfg.update(kwargs)
+            config = Config(**cfg)
+        if not config:
+            raise ValueError("You need to specify a simulation configuration")
+        self.config = config
 
-        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)
 
-        self.load_module = load_module
-        self.topology = nx.Graph(topology)
-        self.network_params = network_params
-        self.name = name or 'UnnamedSimulation'
-        self.num_trials = num_trials
-        self.max_time = max_time
-        self.default_state = default_state or {}
-        self.dir_path = dir_path or os.getcwd()
-        self.interval = interval
-        self.seed = str(seed) or str(time.time())
-        self.dump = dump
-        self.dry_run = dry_run
-
-        sys.path += [self.dir_path, os.getcwd()]
-
-        self.environment_params = environment_params or {}
-        self.environment_class = utils.deserialize(environment_class,
-                                                   known_modules=['soil.environment', ]) or Environment
-
-        environment_agents = environment_agents or []
-        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,
-                                                          known_modules=[self.load_module])
-
-        self.states = agents._validate_states(states,
-                                              self.topology)
+    @property
+    def name(self) -> str:
+        return self.config.general.id
 
     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_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_exceptions, dry_run=dry_run or self.dry_run,
-                                                         return_env=True,
-                                                         **kwargs)
-                for i in p.imap_unordered(func, range(self.num_trials)):
-                    if isinstance(i, Exception):
-                        logger.error('Trial failed:\n\t{}'.format(i.message))
-                        continue
-                    yield i
-            else:
-                for i in range(self.num_trials):
-                    yield self.run_trial(i, dry_run = dry_run or self.dry_run, **kwargs)
-            if not (dry_run or self.dry_run):
-                logger.info('Dumping results to {}'.format(self.dir_path))
-                self.dump_pickle(self.dir_path)
-                self.dump_yaml(self.dir_path)
-            else:
-                logger.info('NOT dumping results')
+    def _run_sync_or_async(self, parallel=False, **kwargs):
+        if parallel and not os.environ.get('SENPY_DEBUG', None):
+            p = Pool()
+            func = partial(self.run_trial_exceptions, **kwargs)
+            for i in p.imap_unordered(func, range(self.config.general.num_trials)):
+                if isinstance(i, Exception):
+                    logger.error('Trial failed:\n\t%s', i.message)
+                    continue
+                yield i
+        else:
+            for i in range(self.config.general.num_trials):
+                yield self.run_trial(trial_id=i,
+                                     **kwargs)
 
-    def get_env(self, trial_id = 0, **kwargs):
-        opts=self.environment_params.copy()
-        env_name='{}_trial_{}'.format(self.name, trial_id)
-        opts.update({
-            'name': env_name,
-            'topology': self.topology.copy(),
-            'seed': self.seed+env_name,
-            'initial_time': 0,
-            'dry_run': self.dry_run,
-            'interval': self.interval,
-            'network_agents': self.network_agents,
-            'states': self.states,
-            'default_state': self.default_state,
-            'environment_agents': self.environment_agents,
-            'dir_path': self.dir_path,
-        })
-        opts.update(kwargs)
-        env=self.environment_class(**opts)
+    def run_gen(self, parallel=False, dry_run=False,
+                exporters=[default, ], outdir=None, exporter_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)
+
+        with utils.timer('simulation {}'.format(self.config.general.id)):
+            for exporter in exporters:
+                exporter.sim_start()
+
+            for env in self._run_sync_or_async(parallel=parallel,
+                                               log_level=log_level,
+                                               **kwargs):
+
+                for exporter in exporters:
+                    exporter.trial_start(env)
+
+                for exporter in exporters:
+                    exporter.trial_end(env)
+
+                yield env
+
+            for exporter in exporters:
+                exporter.sim_end()
+
+    def get_env(self, trial_id=0, **kwargs):
+        '''Create an environment for a trial of the simulation'''
+        # opts = self.environment_params.copy()
+        # opts.update({
+        #     'name': '{}_trial_{}'.format(self.name, trial_id),
+        #     'topology': self.topology.copy(),
+        #     'network_params': self.network_params,
+        #     'seed': '{}_trial_{}'.format(self.seed, trial_id),
+        #     'initial_time': 0,
+        #     'interval': self.interval,
+        #     'network_agents': self.network_agents,
+        #     'initial_time': 0,
+        #     'states': self.states,
+        #     'dir_path': self.dir_path,
+        #     'default_state': self.default_state,
+        #     'history': bool(self._history),
+        #     'environment_agents': self.environment_agents,
+        # })
+        # opts.update(kwargs)
+        print(self.config)
+        env = Environment.from_config(self.config, trial_id=trial_id, **kwargs)
         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, trial_id=None, until=None, log_level=logging.INFO, **opts):
         """
+        Run a single trial of the simulation
+
+        """
+        trial_id = trial_id if trial_id is not None else current_time()
+        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)
+        until = until or self.config.general.max_time
+
+        env = self.get_env(trial_id, **opts)
         # Set up agents on nodes
-        with utils.timer('Simulation {} trial {}'.format(self.name, trial_id)):
+        with utils.timer('Simulation {} trial {}'.format(self.config.general.id, 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.
@@ -200,90 +152,47 @@ class Simulation(NetworkSimulation):
         try:
             return self.run_trial(*args, **kwargs)
         except Exception as ex:
-            c = ex.__cause__
-            c.message = ''.join(traceback.format_tb(c.__traceback__)[3:])
-            return c
+            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__()
+        return self.config.dict()
 
     def to_yaml(self):
-        return yaml.dump(self.to_dict())
-
-    def dump_yaml(self, dir_path = None, file_name = None):
-        dir_path=dir_path or self.dir_path
-        if not os.path.exists(dir_path):
-            os.makedirs(dir_path)
-        if not file_name:
-            file_name=os.path.join(dir_path,
-                                     '{}.dumped.yml'.format(self.name))
-        with open(file_name, 'w') as f:
-            f.write(self.to_yaml())
-
-    def dump_pickle(self, dir_path = None, pickle_name = None):
-        dir_path=dir_path or self.dir_path
-        if not os.path.exists(dir_path):
-            os.makedirs(dir_path)
-        if not pickle_name:
-            pickle_name=os.path.join(dir_path,
-                                       '{}.simulation.pickle'.format(self.name))
-        with open(pickle_name, 'wb') as f:
-            pickle.dump(self, f)
-
-    def __getstate__(self):
-        state={}
-        for k, v in self.__dict__.items():
-            if k[0] != '_':
-                state[k]=v
-        state['topology']=json_graph.node_link_data(self.topology)
-        state['network_agents']=agents.serialize_distribution(self.network_agents,
-                                                                known_modules = [])
-        state['environment_agents']=agents.serialize_distribution(self.environment_agents,
-                                                                    known_modules = [])
-        state['environment_class']=utils.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,
-                                                              known_modules=[self.load_module])
-        self.environment_class = utils.deserialize(self.environment_class,
-                                                   known_modules=[self.load_module, 'soil.environment', ])  # func, name
-        return state
+        return yaml.dump(self.config.dict())
 
 
-def from_config(config):
-    config = list(utils.load_config(config))
+def all_from_config(config):
+    configs = list(serialization.load_config(config))
+    for config, path in configs:
+        if config.get('version', '1') == '1':
+            config = convert_old(config)
+        if not isinstance(config, Config):
+            config = Config(**config)
+        if not config.general.dir_path:
+            config.general.dir_path = os.path.dirname(path)
+        sim = Simulation(config=config)
+        yield sim
+
+
+def from_config(conf_or_path):
+    lst = list(all_from_config(conf_or_path))
+    if len(lst) > 1:
+        raise AttributeError('Provide only one configuration')
+    return lst[0]
+
+def from_old_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 = Simulation(**config)
-    return sim
+    config = convert_old(config[0][0])
+    return Simulation(config)
 
 
-def run_from_config(*configs, results_dir='soil_output', dump=None, timestamp=False,  **kwargs):
-    for config_def in configs:
-        # logger.info("Found {} config(s)".format(len(ls)))
-        for config, _ in utils.load_config(config_def):
-            name = config.get('name', 'unnamed')
-            logger.info("Using config(s): {name}".format(name=name))
-
-            if timestamp:
-                sim_folder = '{}_{}'.format(name,
-                                            time.strftime("%Y-%m-%d_%H:%M:%S"))
-            else:
-                sim_folder = name
-            dir_path = os.path.join(results_dir, sim_folder)
-            if dump is not None:
-                config['dump'] = dump
-            sim = Simulation(dir_path=dir_path, **config)
-            logger.info('Dumping results to {} : {}'.format(sim.dir_path, sim.dump))
-            sim.run_simulation(**kwargs)
+def run_from_config(*configs, **kwargs):
+    for sim in all_from_config(configs):
+        name = config.general.id
+        logger.info("Using config(s): {name}".format(name=name))
+        sim.run_simulation(**kwargs)

soil/time.py

@@ -0,0 +1,80 @@
+from mesa.time import BaseScheduler
+from queue import Empty
+from heapq import heappush, heappop
+import math
+from .utils import logger
+from mesa import Agent as MesaAgent
+
+
+INFINITY = float('inf')
+
+class When:
+    def __init__(self, time):
+        if isinstance(time, When):
+            return time
+        self._time = time
+
+    def abs(self, time):
+        return self._time
+
+NEVER = When(INFINITY)
+
+
+class Delta(When):
+    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: MesaAgent):
+        if agent.unique_id not in self._agents:
+            heappush(self._queue, (self.time, agent.unique_id))
+            super().add(agent)
+
+    def step(self) -> None:
+        """
+        Executes agents in order, one at a time. After each step,
+        an agent will signal when it wants to be scheduled next.
+        """
+
+        if self.next_time == INFINITY:
+            return
+
+        self.time = self.next_time
+        when = self.time
+
+        while self._queue and self._queue[0][0] == self.time:
+            (when, agent_id) = heappop(self._queue)
+            logger.debug(f'Stepping agent {agent_id}')
+
+            returned = self._agents[agent_id].step()
+            when = (returned or Delta(1)).abs(self.time)
+            if when < self.time:
+                raise Exception("Cannot schedule an agent for a time in the past ({} < {})".format(when, self.time))
+
+            heappush(self._queue, (when, agent_id))
+
+        self.steps += 1
+
+        if not self._queue:
+            self.time = INFINITY
+            self.next_time = INFINITY
+            return
+
+        self.next_time = self._queue[0][0]
+

soil/utils.py

@@ -1,154 +1,91 @@
-import os
-import ast
-import yaml
 import logging
-import importlib
-import time
-from glob import glob
-from random import random
-from copy import deepcopy
+from time import time as current_time, strftime, gmtime, localtime
+import os
 
-import 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.time()
+    start = current_time()
     function('{}Starting {} at {}.'.format(pre, name,
-                                           time.strftime("%X", time.gmtime(start))))
+                                           strftime("%X", gmtime(start))))
     yield start
-    end = time.time()
+    end = current_time()
     function('{}Finished {} at {} in {} seconds'.format(pre, name,
-                                                        time.strftime("%X", time.gmtime(end)),
+                                                        strftime("%X", gmtime(end)),
                                                         str(end-start)))
     if to_object:
         to_object.start = start
         to_object.end = end
 
 
-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:
+
+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 = strftime('%Y-%m-%d_%H.%M.%S', localtime(creation))
+
+        backup_dir = os.path.join(outdir, 'backup')
+        if not os.path.exists(backup_dir):
+            os.makedirs(backup_dir)
+        newpath = os.path.join(backup_dir, '{}@{}'.format(os.path.basename(path),
+                                                               stamp))
+        copyfile(path, newpath)
+    return open(path, mode=mode, **kwargs)
+
+
+@contextmanager
+def open_or_reuse(f, *args, **kwargs):
+    try:
+        with safe_open(f, *args, **kwargs) as f:
+            yield f
+    except (AttributeError, TypeError):
+        yield 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 unflatten_dict(d):
+    out = {}
+    for k, v in d.items():
+        target = out
+        if not isinstance(k, str):
+            target[k] = v
             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_ == '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)
+        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)
@@ -271,4 +271,4 @@ def main():
     parser.add_argument('--verbose', '-v', help='verbose mode', action='store_true')
     args = parser.parse_args()
 
-    run(name=args.name, port=(args.port[0] if isinstance(args.port, list) else args.port), verbose=args.verbose)
+    run(name=args.name, port=(args.port[0] if isinstance(args.port, list) else args.port), verbose=args.verbose)

test-requirements.txt

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

tests/complete_converted.yml

@@ -0,0 +1,49 @@
+---
+version: '2'
+general:
+  id: simple
+  group: tests
+  dir_path: "/tmp/"
+  num_trials: 3
+  max_time: 100
+  interval: 1
+  seed: "CompleteSeed!"
+topologies:
+  default:
+    params:
+      generator: complete_graph
+      n: 10
+agents:
+  default:
+    agent_class: CounterModel
+    state:
+      times: 1
+  network:
+    topology: 'default'
+    distribution:
+      - agent_class: CounterModel
+        weight: 0.4
+        state:
+          state_id: 0
+      - agent_class: AggregatedCounter
+        weight: 0.6
+    override:
+      - filter:
+          node_id: 0
+        state:
+          name: 'The first node'
+      - filter:
+          node_id: 1
+        state:
+          name: 'The second node'
+
+  environment:
+    fixed:
+      - name: 'Environment Agent 1'
+        agent_class: CounterModel
+        state:
+          times: 10
+environment:
+  environment_class: Environment
+  params:
+    am_i_complete: true

tests/old_complete.yml

@@ -0,0 +1,32 @@
+---
+name: simple
+group: tests
+dir_path: "/tmp/"
+num_trials: 3
+max_time: 100
+interval: 1
+seed: "CompleteSeed!"
+network_params:
+  generator: complete_graph
+  n: 10
+network_agents:
+  - agent_type: CounterModel
+    weight: 0.4
+    state:
+      state_id: 0
+  - agent_type: AggregatedCounter
+    weight: 0.6
+environment_agents:
+  - agent_id: 'Environment Agent 1'
+    agent_type: CounterModel
+    state:
+      times: 10
+environment_class: Environment
+environment_params:
+  am_i_complete: true
+agent_type: CounterModel
+default_state:
+  times: 1
+states:
+  - name: 'The first node'
+  - name: 'The second node'

tests/test_agents.py

@@ -0,0 +1,22 @@
+from unittest import TestCase
+import pytest
+
+from soil import agents, environment
+from soil import time as stime
+
+class Dead(agents.FSM):
+    @agents.default_state
+    @agents.state
+    def only(self):
+        self.die()
+
+class TestMain(TestCase):
+    def test_die_raises_exception(self):
+        d = Dead(unique_id=0, model=environment.Environment())
+        d.step()
+        with pytest.raises(agents.DeadAgent):
+            d.step()
+
+    def test_die_returns_infinity(self):
+        d = Dead(unique_id=0, model=environment.Environment())
+        assert d.step().abs(0) == stime.INFINITY

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',
@@ -49,11 +50,12 @@ class TestAnalysis(TestCase):
             'states': [{'interval': 1}, {'interval': 2}],
             'max_time': 30,
             'num_trials': 1,
+            'history': True,
             'environment_params': {
             }
         }
         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 +67,25 @@ 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_config.py

@@ -0,0 +1,119 @@
+from unittest import TestCase
+import os
+from os.path import join
+
+from soil import simulation, serialization, config, network, agents
+
+ROOT = os.path.abspath(os.path.dirname(__file__))
+EXAMPLES = join(ROOT, '..', 'examples')
+
+FORCE_TESTS = os.environ.get('FORCE_TESTS', '')
+
+
+class TestConfig(TestCase):
+
+    def test_conversion(self):
+        expected = serialization.load_file(join(ROOT, "complete_converted.yml"))[0]
+        old = serialization.load_file(join(ROOT, "old_complete.yml"))[0]
+        converted_defaults = config.convert_old(old, strict=False)
+        converted = converted_defaults.dict(skip_defaults=True)
+
+        def isequal(a, b):
+            if isinstance(a, dict):
+                for (k, v) in a.items():
+                    if v:
+                        isequal(a[k], b[k])
+                    else:
+                        assert not b.get(k, None)
+                return
+            assert a == b
+
+        isequal(converted, expected)
+
+    def test_topology_config(self):
+        netconfig = config.NetConfig(**{
+            'path': join(ROOT, 'test.gexf')
+        })
+        net = network.from_config(netconfig, dir_path=ROOT)
+        assert len(net.nodes) == 2
+        assert len(net.edges) == 1
+
+    def test_env_from_config(self):
+        """
+        Simple configuration that tests that the graph is loaded, and that
+        network agents are initialized properly.
+        """
+        config = {
+            'name': 'CounterAgent',
+            'network_params': {
+                'path': join(ROOT, 'test.gexf')
+            },
+            'agent_type': 'CounterModel',
+            # 'states': [{'times': 10}, {'times': 20}],
+            'max_time': 2,
+            'dry_run': True,
+            'num_trials': 1,
+            'environment_params': {
+            }
+        }
+        s = simulation.from_old_config(config)
+        env = s.get_env()
+        assert len(env.topologies['default'].nodes) == 2
+        assert len(env.topologies['default'].edges) == 1
+        assert len(env.agents) == 2
+        assert env.agents[0].topology == env.topologies['default']
+
+
+    def test_agents_from_config(self):
+        '''We test that the known complete configuration produces
+        the right agents in the right groups'''
+        cfg = serialization.load_file(join(ROOT, "complete_converted.yml"))[0]
+        s = simulation.from_config(cfg)
+        env = s.get_env()
+        assert len(env.topologies['default'].nodes) == 10
+        assert len(env.agents(group='network')) == 10
+        assert len(env.agents(group='environment')) == 1
+        
+        assert sum(1 for a in env.agents(group='network', agent_type=agents.CounterModel)) == 4
+        assert sum(1 for a in env.agents(group='network', agent_type=agents.AggregatedCounter)) == 6
+
+def make_example_test(path, cfg):
+    def wrapped(self):
+        root = os.getcwd()
+        print(path)
+        s = simulation.from_config(cfg)
+        # for s in simulation.all_from_config(path):
+        #     iterations = s.config.max_time * s.config.num_trials
+        #     if iterations > 1000:
+        #         s.config.max_time = 100
+        #         s.config.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, cfg=config)
+        fname = os.path.basename(path)
+        p.__name__ = 'test_example_file_%s' % fname
+        p.__doc__ = '%s should be a valid configuration' % fname
+        setattr(TestConfig, p.__name__, p)
+        del p
+
+
+add_example_tests()

tests/test_examples.py

@@ -2,11 +2,13 @@ from unittest import TestCase
 import os
 from os.path import join
 
-from soil import utils, simulation
+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
@@ -15,28 +17,32 @@ class TestExamples(TestCase):
 def make_example_test(path, config):
     def wrapped(self):
         root = os.getcwd()
-        os.chdir(os.path.dirname(path))
-        s = simulation.from_config(config)
-        iterations = s.max_time * s.num_trials
-        if iterations > 1000:
-            self.skipTest('This example would probably take too long')
-        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 > 2  # It has run
-                assert env.now <= config['max_time']  # But not further than allowed
-            except KeyError:
-                pass
-        os.chdir(root)
+        for s in simulation.all_from_config(path):
+            iterations = s.config.general.max_time * s.config.general.num_trials
+            if iterations > 1000:
+                s.config.general.max_time = 100
+                s.config.general.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 utils.load_config(join(EXAMPLES, '**', '*.yml')):
+    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

tests/test_exporters.py

@@ -0,0 +1,99 @@
+import os
+import io
+import tempfile
+import shutil
+
+from unittest import TestCase
+from soil import exporters
+from soil import simulation
+
+class Dummy(exporters.Exporter):
+    started = False
+    trials = 0
+    ended = False
+    total_time = 0
+    called_start = 0
+    called_trial = 0
+    called_end = 0
+
+    def sim_start(self):
+        self.__class__.called_start += 1
+        self.__class__.started = True
+
+    def trial_end(self, env):
+        assert env
+        self.__class__.trials += 1
+        self.__class__.total_time += env.now
+        self.__class__.called_trial += 1
+
+    def sim_end(self):
+        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,
+            'dry_run': False,
+            'environment_params': {}
+        }
+        output = io.StringIO()
+        s = simulation.from_config(config)
+        tmpdir = tempfile.mkdtemp()
+        envs = s.run_simulation(exporters=[
+                                    exporters.default,
+                                    exporters.csv,
+                                    exporters.gexf,
+                                ],
+                                dry_run=False,
+                                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

@@ -1,156 +1,128 @@
 from unittest import TestCase
 
 import os
-import shutil
-from glob import glob
+import io
+import yaml
+import copy
+import pickle
+import networkx as nx
+from functools import partial
 
-from soil import history
+from os.path import join
+from soil import (simulation, Environment, agents, serialization,
+                  utils)
+from soil.time import Delta
+from tsih import NoHistory, History
 
 
 ROOT = os.path.abspath(os.path.dirname(__file__))
-DBROOT = os.path.join(ROOT, 'testdb')
+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 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):
+    def test_counter_agent_history(self):
         """
+        The evolution of the state should be recorded in the logging agent
         """
-        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
+        config = {
+            'name': 'CounterAgent',
+            'network_params': {
+                'path': join(ROOT, 'test.gexf')
+            },
+            'network_agents': [{
+                'agent_type': 'AggregatedCounter',
+                'weight': 1,
+                'state': {'state_id': 0}
 
+            }],
+            'max_time': 10,
+            'environment_params': {
+            }
+        }
+        s = simulation.from_config(config)
+        env = s.run_simulation(dry_run=True)[0]
+        for agent in env.network_agents:
+            last = 0
+            assert len(agent[None, None]) == 11
+            for step, total in sorted(agent['total', None]):
+                assert total == last + 2
+                last = total
 
-        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
+    def test_row_conversion(self):
+        env = Environment(history=True)
+        env['test'] = 'test_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']
+        res = list(env.history_to_tuples())
+        assert len(res) == len(env.environment_params)
 
-    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'
+        env.schedule.time = 1
+        env['test'] = 'second_value'
+        res = list(env.history_to_tuples())
 
-        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
+        assert env['env', 0, 'test' ] == 'test_value'
+        assert env['env', 1, 'test' ] == 'second_value'
 
-        records = h['a_1', 7, None]
-        assert records['id'] == 'e'
+    def test_nohistory(self):
+        '''
+        Make sure that no history(/sqlite) is used by default
+        '''
+        env = Environment(topology=nx.Graph(), network_agents=[])
+        assert isinstance(env._history, NoHistory)
 
-    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)
-        h.flush_cache()
-        assert os.path.exists(db_path)
+    def test_save_graph_history(self):
+        '''
+        The history_to_graph method should return a valid networkx graph.
 
-        # 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'
+        The state of the agent should be encoded as intervals in the nx graph.
+        '''
+        G = nx.cycle_graph(5)
+        distribution = agents.calculate_distribution(None, agents.BaseAgent)
+        env = Environment(topology=G, network_agents=distribution, history=True)
+        env[0, 0, 'testvalue'] = 'start'
+        env[0, 10, 'testvalue'] = 'finish'
+        nG = env.history_to_graph()
+        values = nG.nodes[0]['attr_testvalue']
+        assert ('start', 0, 10) in values
+        assert ('finish', 10, None) in values
 
-        # 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])
+    def test_save_graph_nohistory(self):
+        '''
+        The history_to_graph method should return a valid networkx graph.
 
-    def test_history_tuples(self):
-        """
-        The data recovered should be equal to the one recorded.
-        """
-        tuples = (
-            ('a_1', 0, 'id', 'v'),
-            ('a_1', 1, 'id', 'a'),
-            ('a_1', 2, 'id', 'l'),
-            ('a_1', 3, 'id', 'u'),
-            ('a_1', 4, 'id', 'e'),
-            ('env', 1, 'prob', 1),
-            ('env', 2, 'prob', 2),
-            ('env', 3, 'prob', 3),
-            ('a_2', 7, 'finished', True),
-        )
-        h = history.History()
-        h.save_tuples(tuples)
-        recovered = list(h.to_tuples())
-        assert recovered
-        for i in recovered:
-            assert i in tuples
+        When NoHistory is used, only the last known value is known
+        '''
+        G = nx.cycle_graph(5)
+        distribution = agents.calculate_distribution(None, agents.BaseAgent)
+        env = Environment(topology=G, network_agents=distribution, history=False)
+        env.get_agent(0)['testvalue'] = 'start'
+        env.schedule.time = 10
+        env.get_agent(0)['testvalue'] = 'finish'
+        nG = env.history_to_graph()
+        values = nG.nodes[0]['attr_testvalue']
+        assert ('start', 0, None) not in values
+        assert ('finish', 10, None) in values
+
+    def test_pickle_agent_environment(self):
+        env = Environment(name='Test', history=True)
+        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'

tests/test_main.py

@@ -1,22 +1,31 @@
 from unittest import TestCase
 
 import os
+import io
 import yaml
+import copy
+import pickle
 import networkx as nx
 from functools import partial
 
 from os.path import join
-from soil import simulation, Environment, agents, utils, history
-
+from soil import (simulation, Environment, agents, network, serialization,
+                  utils, config)
+from soil.time import Delta
 
 ROOT = os.path.abspath(os.path.dirname(__file__))
 EXAMPLES = join(ROOT, '..', 'examples')
 
 
-class CustomAgent(agents.BaseAgent):
-    def step(self):
-        self.state['neighbors'] = self.count_agents(state_id=0,
-                                                    limit_neighbors=True)
+class CustomAgent(agents.FSM, agents.NetworkAgent):
+    @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):
 
@@ -26,22 +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 = network.from_config(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 = network.from_config(config['network_params'])
             print(G)
 
     def test_generate_barabasi(self):
@@ -49,23 +56,21 @@ class TestMain(TestCase):
         If no path is given, a generator and network parameters
         should be used to generate a network
         """
-        config = {
-            'dry_run': True,
-            'network_params': {
+        cfg = {
+            'params': {
                 'generator': 'barabasi_albert_graph'
             }
         }
-        with self.assertRaises(TypeError):
-            G = utils.load_network(config['network_params'])
-        config['network_params']['n'] = 100
-        config['network_params']['m'] = 10
-        G = utils.load_network(config['network_params'])
+        with self.assertRaises(Exception):
+            G = network.from_config(cfg)
+        cfg['params']['n'] = 100
+        cfg['params']['m'] = 10
+        G = network.from_config(cfg)
         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')
             },
@@ -73,91 +78,97 @@ class TestMain(TestCase):
             'environment_params': {
             }
         }
-        s = simulation.from_config(config)
+        s = simulation.from_old_config(config)
         s.run_simulation(dry_run=True)
 
+
+    def test_network_agent(self):
+        """
+        The initial states should be applied to the agent and the
+        agent should be able to update its state."""
+        config = {
+            'name': 'CounterAgent',
+            'network_params': {
+                'generator': nx.complete_graph,
+                'n': 2,
+            },
+            'agent_type': 'CounterModel',
+            'states': {
+                0: {'times': 10},
+                1: {'times': 20},
+            },
+            'max_time': 2,
+            'num_trials': 1,
+            'environment_params': {
+            }
+        }
+        s = simulation.from_old_config(config)
+
     def test_counter_agent(self):
         """
         The initial states should be applied to the agent and the
         agent should be able to update its state."""
         config = {
-            'name': 'CounterAgent',
-            'dry_run': True,
-            'network_params': {
-                'path': join(ROOT, 'test.gexf')
+            'version': '2',
+            'general': {
+                'name': 'CounterAgent',
+                'max_time': 2,
+                'dry_run': True,
+                'num_trials': 1,
             },
-            'agent_type': 'CounterModel',
-            'states': [{'times': 10}, {'times': 20}],
-            'max_time': 2,
-            'num_trials': 1,
-            'environment_params': {
+            'topologies': {
+                'default': {
+                    'path': join(ROOT, 'test.gexf')
+                }
+            },
+            'agents': {
+                'default': {
+                    'agent_class': 'CounterModel',
+                },
+                'counters': {
+                    'topology': 'default',
+                    'fixed': [{'state': {'times': 10}}, {'state': {'times': 20}}],
+                }
             }
         }
         s = simulation.from_config(config)
-        env = s.run_simulation(dry_run=True)[0]
-        assert env.get_agent(0)['times', 0] == 11
-        assert env.get_agent(0)['times', 1] == 12
-        assert env.get_agent(1)['times', 0] == 21
-        assert env.get_agent(1)['times', 1] == 22
-
-    def test_counter_agent_history(self):
-        """
-        The evolution of the state should be recorded in the logging agent
-        """
-        config = {
-            'name': 'CounterAgent',
-            'dry_run': True,
-            'network_params': {
-                'path': join(ROOT, 'test.gexf')
-            },
-            'network_agents': [{
-                'agent_type': 'AggregatedCounter',
-                'weight': 1,
-                'state': {'id': 0}
-
-            }],
-            'max_time': 10,
-            'environment_params': {
-            }
-        }
-        s = simulation.from_config(config)
-        env = s.run_simulation(dry_run=True)[0]
-        for agent in env.network_agents:
-            last = 0
-            assert len(agent[None, None]) == 10
-            for step, total in sorted(agent['total', None]):
-                assert total == last + 2
-                last = total
+        env = s.get_env()
+        assert isinstance(env.agents[0], agents.CounterModel)
+        assert env.agents[0].topology == env.topologies['default']
+        assert env.agents[0]['times'] == 10
+        assert env.agents[0]['times'] == 10
+        env.step()
+        assert env.agents[0]['times'] == 11
+        assert env.agents[1]['times'] == 21
 
     def test_custom_agent(self):
         """Allow for search of neighbors with a certain state_id"""
         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,
             'environment_params': {
             }
         }
-        s = simulation.from_config(config)
+        s = simulation.from_old_config(config)
         env = s.run_simulation(dry_run=True)[0]
-        assert env.get_agent(0).state['neighbors'] == 1
+        assert env.agents[1].count_agents(state_id='normal') == 2
+        assert env.agents[1].count_agents(state_id='normal', limit_neighbors=True) == 1
+        assert env.agents[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]
+        s = simulation.from_old_config(config)
+        env = s.run_simulation(dry_run=True)[0]
         for a in env.network_agents:
             skill_level = a.state['skill_level']
             if a.id == 'Torvalds':
@@ -175,48 +186,34 @@ class TestMain(TestCase):
 
     def test_yaml(self):
         """
-        The YAML version of a newly created simulation
-        should be equivalent to the configuration file used
+        The YAML version of a newly created configuration should be equivalent
+        to the configuration file used.
+        Values not present in the original config file should have reasonable
+        defaults.
         """
         with utils.timer('loading'):
-            config = utils.load_file(join(EXAMPLES, 'complete.yml'))[0]
-            s = simulation.from_config(config)
-            s.dry_run = True
+            config = serialization.load_file(join(EXAMPLES, 'complete.yml'))[0]
+            s = simulation.from_old_config(config)
         with utils.timer('serializing'):
             serial = s.to_yaml()
         with utils.timer('recovering'):
-            recovered = yaml.load(serial)
-        with utils.timer('deleting'):
-            del recovered['topology']
-        assert config == recovered
+            recovered = yaml.load(serial, Loader=yaml.SafeLoader)
+        for (k, v) in config.items():
+            assert recovered[k] == v
 
     def test_configuration_changes(self):
         """
         The configuration should not change after running
          the simulation.
         """
-        config = utils.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']
-            assert config == nconfig
+        config = serialization.load_file(join(EXAMPLES, 'complete.yml'))[0]
+        s = simulation.from_old_config(config)
+        init_config = copy.copy(s.config)
 
-    def test_row_conversion(self):
-        env = Environment(dry_run=True)
-        env['test'] = 'test_value'
-
-        res = list(env.history_to_tuples())
-        assert len(res) == len(env.environment_params)
-
-        env._now = 1
-        env['test'] = 'second_value'
-        res = list(env.history_to_tuples())
-
-        assert env['env', 0, 'test' ] == 'test_value'
-        assert env['env', 1, 'test' ] == 'second_value'
+        s.run_simulation(dry_run=True)
+        nconfig = s.config
+        # del nconfig['to
+        assert init_config == nconfig
 
     def test_save_geometric(self):
         """
@@ -224,43 +221,30 @@ class TestMain(TestCase):
         from geometric models. We should work around it.
         """
         G = nx.random_geometric_graph(20, 0.1)
-        env = Environment(topology=G, dry_run=True)
-        env.dump_gexf('/tmp/dump-gexf')
-
-    def test_save_graph(self):
-        '''
-        The history_to_graph method should return a valid networkx graph.
-
-        The state of the agent should be encoded as intervals in the nx graph.
-        '''
-        G = nx.cycle_graph(5)
-        distribution = agents.calculate_distribution(None, agents.BaseAgent)
-        env = Environment(topology=G, network_agents=distribution, dry_run=True)
-        env[0, 0, 'testvalue'] = 'start'
-        env[0, 10, 'testvalue'] = 'finish'
-        nG = env.history_to_graph()
-        values = nG.node[0]['attr_testvalue']
-        assert ('start', 0, 10) in values
-        assert ('finish', 10, None) in values
+        env = Environment(topology=G)
+        f = io.BytesIO()
+        env.dump_gexf(f)
 
     def test_serialize_class(self):
-        ser, name = utils.serialize(agents.BaseAgent)
+        ser, name = serialization.serialize(agents.BaseAgent, known_modules=[])
         assert name == 'soil.agents.BaseAgent'
         assert ser == agents.BaseAgent
 
-        class CustomAgent(agents.BaseAgent):
-            pass
+        ser, name = serialization.serialize(agents.BaseAgent, known_modules=['soil', ])
+        assert name == 'BaseAgent'
+        assert ser == agents.BaseAgent
 
-        ser, name = utils.serialize(CustomAgent)
+        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 = utils.serialize(i)
+            ser, name = serialization.serialize(i)
             assert type(ser) == str
-            des = utils.deserialize(name, ser)
+            des = serialization.deserialize(name, ser)
             assert i == des
 
     def test_serialize_agent_type(self):
@@ -269,7 +253,8 @@ class TestMain(TestCase):
         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 = [
             {
@@ -281,9 +266,10 @@ class TestMain(TestCase):
                 'weight': 2
             },
         ]
-        converted = agents.deserialize_distribution(agent_distro)
+        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 = [
@@ -296,12 +282,83 @@ class TestMain(TestCase):
                 'weight': 2
             },
         ]
-        converted = agents.serialize_distribution(agent_distro)
+        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_history(self):
-        '''Test storing in and retrieving from history (sqlite)'''
-        h = history.History()
-        h.save_record(agent_id=0, t_step=0, key="test", value="hello")
-        assert h[0, 0, "test"] == "hello"
+    def test_subgraph(self):
+        '''An agent should be able to subgraph the global topology'''
+        G = nx.Graph()
+        G.add_node(3)
+        G.add_edge(1, 2)
+        distro = agents.calculate_distribution(agent_type=agents.NetworkAgent)
+        distro[0]['topology'] = 'default'
+        aconfig = config.AgentConfig(distribution=distro, topology='default')
+        env = Environment(name='Test', topologies={'default': G}, agents={'network': aconfig})
+        lst = list(env.network_agents)
+
+        a2 = env.find_one(node_id=2)
+        a3 = env.find_one(node_id=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_old_config(config)
+        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 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
+
+        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]