Fix pre-release version of v1.0.0rc1

The examples weren't being properly tested in the last commit. When we fixed
that a lot of bugs in the new implementation of environment and agent were
found, which accounts for most of these changes.

The main difference is the mechanism to load simulations from a configuration
file. For that to work, we had to rework our module loading code in
`serialization` and add a `source_file` attribute to configurations (and
simulations, for that matter).

.gitlab-ci.yml

@@ -20,7 +20,7 @@ docker:
 test:
   tags:
     - docker
-  image: python:3.7
+  image: python:3.8
   stage: test
   script:
     - pip install -r requirements.txt -r test-requirements.txt
@@ -31,7 +31,7 @@ push_pypi:
     - tags
   tags:
     - docker
-  image: python:3.7
+  image: python:3.8
   stage: publish
   script:
     - echo $CI_COMMIT_TAG > soil/VERSION
@@ -44,7 +44,7 @@ check_pypi:
     - tags
   tags:
     - docker
-  image: python:3.7
+  image: python:3.8
   stage: check_published
   script:
     - pip install soil==$CI_COMMIT_TAG

CHANGELOG.md

@@ -3,22 +3,31 @@ All notable changes to this project will be documented in this file.
 
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/), and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
-## [0.30 UNRELEASED]
+## [1.0 UNRELEASED]
+
+Version 1.0 introduced multiple changes, especially on the `Simulation` class and anything related to how configuration is handled.
+For an explanation of the general changes in version 1.0, please refer to the file `docs/notes_v1.0.rst`.
+
 ### Added
-* Simple debugging capabilities in `soil.debugging`, with a custom `pdb.Debugger` subclass that exposes commands to list agents and their status and set breakpoints on states (for FSM agents). Try it with `soil --debug <simulation file>`
-* Ability to run 
-* Ability to 
-* The `soil.exporters` module to export the results of datacollectors (model.datacollector) into files at the end of trials/simulations
 * A modular set of classes for environments/models. Now the ability to configure the agents through an agent definition and a topology through a network configuration is split into two classes (`soil.agents.BaseEnvironment` for agents, `soil.agents.NetworkEnvironment` to add topology).
-* FSM agents can now have generators as states. They work similar to normal states, with one caveat. Only `time` values can be yielded, not a state. This is because the state will not change, it will be resumed after the yield, at the appropriate time. The return value *can* be a state, or a `(state, time)` tuple, just like in normal states.
+* Environments now have a class method to make them easier to use without a simulation`.run`. Notice that this is different from `run_model`, which is an instance method.
+* Ability to run simulations using mesa models
+* The `soil.exporters` module to export the results of datacollectors (`model.datacollector`) into files at the end of trials/simulations
+* Agents can now have generators as a step function or a state. They work similar to normal functions, with one caveat in the case of `FSM`: only `time` values (or None) can be yielded, not a state. This is because the state will not change, it will be resumed after the yield, at the appropriate time. The return value *can* be a state, or a `(state, time)` tuple, just like in normal states.
+* Simulations can now specify a `matrix` with possible values for every simulation parameter. The final parameters will be calculated based on the `parameters` used and a cartesian product (i.e., all possible combinations) of each parameter.
+* Simple debugging capabilities in `soil.debugging`, with a custom `pdb.Debugger` subclass that exposes commands to list agents and their status and set breakpoints on states (for FSM agents). Try it with `soil --debug <simulation file>`
 ### Changed
-* Configuration schema is very different now. Check `soil.config` for more information. We are also using Pydantic for (de)serialization.
+* Configuration schema (`Simulation`) is very simplified. All simulations should be checked
-* There may be more than one topology/network in the simulation
+* Model / environment variables are expected (but not enforced) to be a single value. This is done to more closely align with mesa
-* Ability
+* `Exporter.iteration_end` now takes two parameters: `env` (same as before) and `params` (specific parameters for this environment). We considered including a `parameters` attribute in the environment, but this would not be compatible with mesa.
+* `num_trials` renamed to `iterations`
+* General renaming of `trial` to `iteration`, to work better with `mesa`
+* `model_parameters` renamed to `parameters` in simulation
+* Simulation results for every iteration of a simulation with the same name are stored in a single `sqlite` database
+
 ### 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)

README.md

@@ -1,20 +1,20 @@
 # [SOIL](https://github.com/gsi-upm/soil)
 
+
 Soil is an extensible and user-friendly Agent-based Social Simulator for Social Networks.
 Learn how to run your own simulations with our [documentation](http://soilsim.readthedocs.io).
 
 Follow our [tutorial](examples/tutorial/soil_tutorial.ipynb) to develop your own agent models.
 
-**Note**: Mesa 0.30 introduced many fundamental changes. Check the [documention on how to update your simulations to work with newer versions](docs/migration_0.30.rst)
+> **Warning**
+> Soil 1.0 introduced many fundamental changes. Check the [documention on how to update your simulations to work with newer versions](docs/notes_v1.0.rst)
 
-## SOIL vs MESA
+## Features
 
-SOIL is a batteries-included platform that builds on top of MESA and provides the following out of the box:
+* Integration with (social) networks (through `networkx`)
-
+* Convenience functions and methods to easily assign agents to your model (and optionally to its network):
-* Integration with (social) networks
+  * Following a given distribution (e.g., 2 agents of type `Foo`, 10% of the network should be agents of type `Bar`)
-* The ability to more easily assign agents to your model (and optionally to its network):
+  * Based on the topology of the network
-  * Assigning agents to nodes, and vice versa
-  * Using a description (e.g., 2 agents of type `Foo`, 10% of the network should be agents of type `Bar`)
 * **Several types of abstractions for agents**:
   * Finite state machine, where methods can be turned into a state
   * Network agents, which have convenience methods to access the model's topology
@@ -33,15 +33,18 @@ SOIL is a batteries-included platform that builds on top of MESA and provides th
   * Run models in parallel
   * Save results to different formats
 * Simulation configuration files 
-* A command line interface (`soil`), to run multiple
+* A command line interface (`soil`), to quickly run simulations with different parameters
 * An integrated debugger (`soil --debug`) with custom functions to print agent states and break at specific states
 
-Nevertheless, most features in SOIL have been designed to integrate with plain Mesa.
+
+## Mesa compatibility
+
+SOIL has been redesigned to integrate well with [Mesa](https://github.com/projectmesa/mesa).
 For instance, it should be possible to run a `mesa.Model` models using a `soil.Simulation` and the `soil` CLI, or to integrate the `soil.TimedActivation` scheduler on a `mesa.Model`.
 
-Note that some combinations of `mesa` and `soil` components, while technically possible, are much less useful or even wrong.
+Note that some combinations of `mesa` and `soil` components, while technically possible, are much less useful or might yield surprising results.
-For instance, you may add any `soil.agent` agent (except for the `soil.NetworkAgent`, as it needs a topology) on a regular `mesa.Model` with a vanilla scheduler from `mesa.time`.
+For instance, you may add any `soil.agent` agent on a regular `mesa.Model` with a vanilla scheduler from `mesa.time`.
-But in that case the agents will not get any of the advanced event-based scheduling, and most agent behaviors that depend on that will greatly vary. 
+But in that case the agents will not get any of the advanced event-based scheduling, and most agent behaviors that depend on that may not work. 
 
 
 ## Changes in version 0.3

docs/index.rst

@@ -1,7 +1,20 @@
 Welcome to Soil's documentation!
 ================================
 
-Soil is an Agent-based Social Simulator in Python focused on Social Networks.
+Soil is an opinionated Agent-based Social Simulator in Python focused on Social Networks.
+
+.. image:: soil.png
+  :width: 80%
+  :align: center
+
+Soil can be installed through pip (see more details in the :doc:`installation` page):
+
+.. code:: bash
+
+    pip install soil
+
+
+To get started developing your own simulations and agent behaviors, check out our :doc:`Tutorial <soil_tutorial>` and the `examples on GitHub <https://github.com/gsi-upm/soil/tree/master/examples>.
 
 If you use Soil in your research, do not forget to cite this paper:
 
@@ -33,8 +46,6 @@ If you use Soil in your research, do not forget to cite this paper:
    :caption: Learn more about soil:
 
    installation
-   quickstart
-   configuration
    Tutorial <soil_tutorial>
 
 ..

docs/installation.rst

@@ -1,7 +1,10 @@
 Installation
 ------------
 
-The easiest way to install Soil is through pip, with Python >= 3.4:
+Through pip
+===========
+
+The easiest way to install Soil is through pip, with Python >= 3.8:
 
 .. code:: bash
 
@@ -25,4 +28,38 @@ Or, if you're using using soil programmatically:
    import soil
    print(soil.__version__)
 
-The latest version can be installed through `GitHub <https://github.com/gsi-upm/soil>`_ or `GitLab <https://lab.gsi.upm.es/soil/soil.git>`_.
+
+
+Web UI
+======
+
+Soil also includes a web server that allows you to upload your simulations, change parameters, and visualize the results, including a timeline of the network.
+To make it work, you have to install soil like this:
+
+.. code::
+
+  pip install soil[web]
+
+Once installed, the soil web UI can be run in two ways:
+
+.. code::
+
+  soil-web
+
+  # OR
+
+  python -m soil.web
+
+
+Development
+===========
+
+The latest version can be downloaded from `GitHub <https://github.com/gsi-upm/soil>`_ and installed manually:
+
+.. code:: bash
+
+   git clone https://github.com/gsi-upm/soil
+   cd soil
+   python -m venv .venv
+   source .venv/bin/activate
+   pip install --editable .

docs/notes_v1.0.rst

@@ -0,0 +1,35 @@
+What are the main changes in version 1.0?
+#########################################
+
+Version 1.0 is a major rewrite of the Soil system, focused on simplifying the API, aligning it with Mesa, and making it easier to use.
+Unfortunately, this comes at the cost of backwards compatibility.
+
+We drew several lessons from the previous version of Soil, and tried to address them in this version.
+Mainly:
+
+- The split between simulation configuration and simulation code was overly complicated for most use cases. As a result, most users ended up reusing configuration.
+- Storing **all** the simulation data in a database is costly and unnecessary for most use cases. For most use cases, only a handful of variables need to be stored. This fits nicely with Mesa's data collection system.
+- The API was too complex, and it was difficult to understand how to use it.
+- Most parts of the API were not aligned with Mesa, which made it difficult to use Mesa's features or to integrate Soil modules with Mesa code, especially for newcomers.
+- Many parts of the API were tightly coupled, which made it difficult to find bugs, test the system and add new features.
+
+The 0.30 rewrite should provide a middle ground between Soil's opinionated approach and Mesa's flexibility.
+The new Soil is less configuration-centric.
+It aims to provide more modular and convenient functions, most of which can be used in vanilla Mesa.
+
+How are agents assigned to nodes in the network
+###############################################
+
+The constructor of the `NetworkAgent` class has two arguments: `node_id` and `topology`.
+If `topology` is not provided, it will default to `self.model.topology`.
+This assignment might err if the model does not have a `topology` attribute, but most Soil environments derive from `NetworkEnvironment`, so they include a topology by default.
+If `node_id` is not provided, a random node will be selected from the topology, until a node with no agent is found.
+Then, the `node_id` of that node is assigned to the agent.
+If no node with no agent is found, a new node is automatically added to the topology.
+
+
+Can Soil environments include more than one network / topology?
+###############################################################
+
+Yes, but each network has to be included manually.
+Somewhere between 0.20 and 0.30 we included the ability to include multiple networks, but it was deemed too complex and was removed.

docs/quickstart.rst

@@ -1,93 +0,0 @@
-Quickstart
-----------
-
-This section shows how to run your first simulation with Soil.
-For installation instructions, see :doc:`installation`.
-
-There are mainly two parts in a simulation: agent classes and simulation configuration.
-An agent class defines how the agent will behave throughout the simulation.
-The configuration includes things such as number of agents to use and their type, network topology to use, etc.
-
-
-.. image:: soil.png
-  :width: 80%
-  :align: center
-
-
-Soil includes several agent classes in the ``soil.agents`` module, and we will use them in this quickstart.
-If you are interested in developing your own agents classes, see :doc:`soil_tutorial`.
-
-Configuration
-=============
-To get you started, we will use this configuration (:download:`download the file <quickstart.yml>` directly):
-
-.. literalinclude:: quickstart.yml
-   :language: yaml
-
-The agent type used, SISa, is a very simple model.
-It only has three states (neutral, content and discontent),
-Its parameters are the probabilities to change from one state to another, either spontaneously or because of contagion from neighboring agents.
-
-Running the simulation
-======================
-
-To see the simulation in action, simply point soil to the configuration, and tell it to store the graph and the history of agent states and environment parameters at every point.
-
-.. code::
-
-    ❯ soil --graph --csv quickstart.yml                                                          [13:35:29]
-    INFO:soil:Using config(s): quickstart
-    INFO:soil:Dumping results to soil_output/quickstart : ['csv', 'gexf']
-    INFO:soil:Starting simulation quickstart at 13:35:30.
-    INFO:soil:Starting Simulation quickstart trial 0 at 13:35:30.
-    INFO:soil:Finished Simulation quickstart trial 0 at 13:35:49 in 19.43677067756653 seconds
-    INFO:soil:Starting Dumping simulation quickstart trial 0 at 13:35:49.
-    INFO:soil:Finished Dumping simulation quickstart trial 0 at 13:35:51 in 1.7733407020568848 seconds
-    INFO:soil:Dumping results to soil_output/quickstart
-    INFO:soil:Finished simulation quickstart at 13:35:51 in 21.29862952232361 seconds
-
-
-The ``CSV`` file should look like this:
-
-.. code::
-
-   agent_id,t_step,key,value
-   env,0,neutral_discontent_spon_prob,0.05
-   env,0,neutral_discontent_infected_prob,0.1
-   env,0,neutral_content_spon_prob,0.2
-   env,0,neutral_content_infected_prob,0.4
-   env,0,discontent_neutral,0.2
-   env,0,discontent_content,0.05
-   env,0,content_discontent,0.05
-   env,0,variance_d_c,0.05
-   env,0,variance_c_d,0.1
-
-Results and visualization
-=========================
-
-The environment variables are marked as ``agent_id`` env.
-Th exported values are only stored when they change.
-To find out how to get every key and value at every point in the simulation, check out the :doc:`soil_tutorial`.
-
-The dynamic graph is exported as a .gexf file which could be visualized with
-`Gephi <https://gephi.org/users/download/>`__.
-Now it is your turn to experiment with the simulation.
-Change some of the parameters, such as the number of agents, the probability of becoming content, or the type of network, and see how the results change.
-
-
-Soil also includes a web server that allows you to upload your simulations, change parameters, and visualize the results, including a timeline of the network.
-To make it work, you have to install soil like this:
-
-.. code::
-
-  pip install soil[web]
-
-Once installed, the soil web UI can be run in two ways:
-
-.. code::
-
-  soil-web
-
-  # OR
-
-  python -m soil.web

docs/quickstart.yml

@@ -1,33 +0,0 @@
----
-name: quickstart
-num_trials: 1
-max_time: 1000
-model_params:
-  agents:
-    - agent_class: SISaModel
-      topology: true
-      state:
-        id: neutral
-      weight: 1
-    - agent_class: SISaModel
-      topology: true
-      state:
-        id: content
-      weight: 2
-  topology:
-    params:
-      n: 100
-      k: 5
-      p: 0.2
-      generator: newman_watts_strogatz_graph
-  neutral_discontent_spon_prob: 0.05
-  neutral_discontent_infected_prob: 0.1
-  neutral_content_spon_prob: 0.2
-  neutral_content_infected_prob: 0.4
-  discontent_neutral: 0.2
-  discontent_content: 0.05
-  content_discontent: 0.05
-  variance_d_c: 0.05
-  variance_c_d: 0.1
-  content_neutral: 0.1
-  standard_variance: 0.1

examples/complete.yml

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

examples/custom_generator/custom_generator.yml

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

examples/custom_generator/generator_sim.py

@@ -1,6 +1,7 @@
 from networkx import Graph
 import random
 import networkx as nx
+from soil import Simulation, Environment, CounterModel, parameters
 
 
 def mygenerator(n=5, n_edges=5):
@@ -20,3 +21,19 @@ def mygenerator(n=5, n_edges=5):
         n_out = random.choice(nodes)
         G.add_edge(n_in, n_out)
     return G
+
+
+class GeneratorEnv(Environment):
+    """Using a custom generator for the network"""
+
+    generator: parameters.function = staticmethod(mygenerator)
+
+    def init(self):
+        self.create_network(generator=self.generator, n=10, n_edges=5)
+        self.add_agents(CounterModel)
+
+
+sim = Simulation(model=GeneratorEnv, max_steps=10, interval=1)
+
+if __name__ == '__main__':
+    sim.run(dump=False)

examples/custom_timeouts/custom_timeouts_sim.py

@@ -4,8 +4,7 @@ from soil.time import Delta
 
 class Fibonacci(FSM):
     """Agent that only executes in t_steps that are Fibonacci numbers"""
-
+    prev = 1
-    defaults = {"prev": 1}
 
     @default_state
     @state
@@ -25,23 +24,18 @@ class Odds(FSM):
         return None, Delta(1 + self.now % 2)
 
 
-from soil import Simulation
+from soil import Environment, Simulation
+from networkx import complete_graph
 
-simulation = Simulation(
+
-    model_params={
+class TimeoutsEnv(Environment):
-        'agents':[
+    def init(self):
-            {'agent_class': Fibonacci, 'node_id': 0},
+        self.create_network(generator=complete_graph, n=2)
-            {'agent_class': Odds, 'node_id': 1}
+        self.add_agent(agent_class=Fibonacci, node_id=0)
-        ],
+        self.add_agent(agent_class=Odds, node_id=1)
-        'topology': {
+
-            'params': {
+
-                'generator': 'complete_graph',
+sim = Simulation(model=TimeoutsEnv, max_steps=10, interval=1)
-                'n': 2
-            }
-        },
-    },
-    max_time=100,
-)
 
 if __name__ == "__main__":
-    simulation.run(dry_run=True)
+    sim.run(dump=False)

examples/events_and_messages/README.md

@@ -2,6 +2,8 @@ This example can be run like with command-line options, like this:
 
 ```bash
 python cars.py --level DEBUG  -e summary --csv
+#or
+soil cars.py -e summary
 ```
 
 This will set the `CSV` (save the agent and model data to a CSV) and `summary` (print the a summary of the data to stdout) exporters, and set the log level to DEBUG.

examples/events_and_messages/cars_sim.py

@@ -56,41 +56,25 @@ class City(EventedEnvironment):
     :param int height: Height of the internal grid
     :param int width: Width of the internal grid
     """
+    n_cars = 1
+    n_passengers = 10
+    height = 100
+    width = 100
+
+    def init(self):
+        self.grid = MultiGrid(width=self.width, height=self.height, torus=False)
+        if not self.agents:
+            self.add_agents(Driver, k=self.n_cars)
+            self.add_agents(Passenger, k=self.n_passengers)
 
-    def __init__(
-        self,
-        *args,
-        n_cars=1,
-        n_passengers=10,
-        height=100,
-        width=100,
-        agents=None,
-        model_reporters=None,
-        **kwargs,
-    ):
-        self.grid = MultiGrid(width=width, height=height, torus=False)
-        if agents is None:
-            agents = []
-            for i in range(n_cars):
-                agents.append({"agent_class": Driver})
-            for i in range(n_passengers):
-                agents.append({"agent_class": Passenger})
-        model_reporters = model_reporters or {
-            "earnings": "total_earnings",
-            "n_passengers": "number_passengers",
-        }
-        print("REPORTERS", model_reporters)
-        super().__init__(
-            *args, agents=agents, model_reporters=model_reporters, **kwargs
-        )
         for agent in self.agents:
             self.grid.place_agent(agent, (0, 0))
             self.grid.move_to_empty(agent)
         
-    @property
+        self.total_earnings = 0
-    def total_earnings(self):
+        self.add_model_reporter("total_earnings")
-        return sum(d.earnings for d in self.agents(agent_class=Driver))
 
+    @report
     @property
     def number_passengers(self):
         return self.count_agents(agent_class=Passenger)
@@ -110,9 +94,9 @@ class Driver(Evented, FSM):
     def check_passengers(self):
         """If there are no more passengers, stop forever"""
         c = self.count_agents(agent_class=Passenger)
-        self.info(f"Passengers left {c}")
+        self.debug(f"Passengers left {c}")
         if not c:
-            self.die()
+            self.die("No more passengers")
 
     @default_state
     @state
@@ -145,17 +129,21 @@ class Driver(Evented, FSM):
     @state
     def driving(self):
         """The journey has been accepted. Pick them up and take them to their destination"""
+        self.info(f"Driving towards Passenger {self.journey.passenger.unique_id}")
         while self.move_towards(self.journey.origin):
             yield
+        self.info(f"Driving {self.journey.passenger.unique_id} from {self.journey.origin} to {self.journey.destination}")
         while self.move_towards(self.journey.destination, with_passenger=True):
             yield
+        self.info("Arrived at destination")
         self.earnings += self.journey.tip
+        self.model.total_earnings += self.journey.tip
         self.check_passengers()
         return self.wandering
 
     def move_towards(self, target, with_passenger=False):
         """Move one cell at a time towards a target"""
-        self.info(f"Moving { self.pos } -> { target }")
+        self.debug(f"Moving { self.pos } -> { target }")
         if target[0] == self.pos[0] and target[1] == self.pos[1]:
             return False
 
@@ -189,8 +177,8 @@ class Passenger(Evented, FSM):
     @state
     def asking(self):
         destination = (
-            self.random.randint(0, self.model.grid.height),
+            self.random.randint(0, self.model.grid.height-1),
-            self.random.randint(0, self.model.grid.width),
+            self.random.randint(0, self.model.grid.width-1),
         )
         self.journey = None
         journey = Journey(
@@ -202,19 +190,21 @@ class Passenger(Evented, FSM):
 
         timeout = 60
         expiration = self.now + timeout
+        self.info(f"Asking for journey at: { self.pos }")
         self.model.broadcast(journey, ttl=timeout, sender=self, agent_class=Driver)
         while not self.journey:
-            self.info(f"Passenger at: { self.pos }. Checking for responses.")
+            self.debug(f"Waiting for responses at: { self.pos }")
             try:
                 # This will call check_messages behind the scenes, and the agent's status will be updated
                 # If you want to avoid that, you can call it with: check=False
                 yield self.received(expiration=expiration)
             except events.TimedOut:
-                self.info(f"Passenger at: { self.pos }. Asking for journey.")
+                self.info(f"Still no response. Waiting at: { self.pos }")
                 self.model.broadcast(
                     journey, ttl=timeout, sender=self, agent_class=Driver
                 )
                 expiration = self.now + timeout
+        self.info(f"Got a response! Waiting for driver")
         return self.driving_home
 
     @state
@@ -228,16 +218,14 @@ class Passenger(Evented, FSM):
             except events.TimedOut:
                 pass
 
-        self.info("Got home safe!")
+        self.die("Got home safe!")
-        self.die()
 
 
-simulation = Simulation(
+simulation = Simulation(name="RideHailing",
-    name="RideHailing",
+                        model=City,
-    model_class=City,
+                        seed="carsSeed",
-    model_params={"n_passengers": 2},
+                        max_time=1000,
-    seed="carsSeed",
+                        parameters=dict(n_passengers=2))
-)
 
 if __name__ == "__main__":
-    simulation.run()
+    easy(simulation)

examples/mesa/mesa.yml

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

examples/mesa/mesa_sim.py

@@ -0,0 +1,7 @@
+from soil import Simulation
+from social_wealth import MoneyEnv, graph_generator
+
+sim = Simulation(name="mesa_sim", dump=False, max_steps=10, interval=2, model=MoneyEnv, parameters=dict(generator=graph_generator, N=10, width=50, height=50))
+
+if __name__ == "__main__":
+    sim.run()

examples/mesa/server.py

@@ -1,5 +1,5 @@
 from mesa.visualization.ModularVisualization import ModularServer
-from soil.visualization import UserSettableParameter
+from mesa.visualization.UserParam import Slider, Choice
 from mesa.visualization.modules import ChartModule, NetworkModule, CanvasGrid
 from social_wealth import MoneyEnv, graph_generator, SocialMoneyAgent
 import networkx as nx
@@ -63,9 +63,8 @@ chart = ChartModule(
     [{"Label": "Gini", "Color": "Black"}], data_collector_name="datacollector"
 )
 
-model_params = {
+parameters = {
-    "N": UserSettableParameter(
+    "N": Slider(
-        "slider",
         "N",
         5,
         1,
@@ -73,8 +72,7 @@ model_params = {
         1,
         description="Choose how many agents to include in the model",
     ),
-    "height": UserSettableParameter(
+    "height": Slider(
-        "slider",
         "height",
         5,
         5,
@@ -82,8 +80,7 @@ model_params = {
         1,
         description="Grid height",
     ),
-    "width": UserSettableParameter(
+    "width": Slider(
-        "slider",
         "width",
         5,
         5,
@@ -91,8 +88,7 @@ model_params = {
         1,
         description="Grid width",
     ),
-    "agent_class": UserSettableParameter(
+    "agent_class": Choice(
-        "choice",
         "Agent class",
         value="MoneyAgent",
         choices=["MoneyAgent", "SocialMoneyAgent"],
@@ -102,12 +98,12 @@ model_params = {
 
 
 canvas_element = CanvasGrid(
-    gridPortrayal, model_params["width"].value, model_params["height"].value, 500, 500
+    gridPortrayal, parameters["width"].value, parameters["height"].value, 500, 500
 )
 
 
 server = ModularServer(
-    MoneyEnv, [grid, chart, canvas_element], "Money Model", model_params
+    MoneyEnv, [grid, chart, canvas_element], "Money Model", parameters
 )
 server.port = 8521
 

examples/mesa/social_wealth.py

@@ -53,7 +53,7 @@ class MoneyAgent(MesaAgent):
             self.give_money()
 
 
-class SocialMoneyAgent(NetworkAgent, MoneyAgent):
+class SocialMoneyAgent(MoneyAgent, NetworkAgent):
     wealth = 1
 
     def give_money(self):

examples/newsspread/NewsSpread.yml

@@ -1,133 +0,0 @@
----
-default_state: {}
-environment_agents: []
-environment_params:
-  prob_neighbor_spread: 0.0
-  prob_tv_spread: 0.01
-interval: 1
-max_steps: 300
-name: Sim_all_dumb
-network_agents:
-- agent_class: newsspread.DumbViewer
-  state:
-    has_tv: false
-  weight: 1
-- agent_class: newsspread.DumbViewer
-  state:
-    has_tv: true
-  weight: 1
-network_params:
-  generator: barabasi_albert_graph
-  n: 500
-  m: 5
-num_trials: 50
----
-default_state: {}
-environment_agents: []
-environment_params:
-  prob_neighbor_spread: 0.0
-  prob_tv_spread: 0.01
-interval: 1
-max_steps: 300
-name: Sim_half_herd
-network_agents:
-- agent_class: newsspread.DumbViewer
-  state:
-    has_tv: false
-  weight: 1
-- agent_class: newsspread.DumbViewer
-  state:
-    has_tv: true
-  weight: 1
-- agent_class: newsspread.HerdViewer
-  state:
-    has_tv: false
-  weight: 1
-- agent_class: newsspread.HerdViewer
-  state:
-    has_tv: true
-  weight: 1
-network_params:
-  generator: barabasi_albert_graph
-  n: 500
-  m: 5
-num_trials: 50
----
-default_state: {}
-environment_agents: []
-environment_params:
-  prob_neighbor_spread: 0.0
-  prob_tv_spread: 0.01
-interval: 1
-max_steps: 300
-name: Sim_all_herd
-network_agents:
-- agent_class: newsspread.HerdViewer
-  state:
-    has_tv: true
-    state_id: neutral
-  weight: 1
-- agent_class: newsspread.HerdViewer
-  state:
-    has_tv: true
-    state_id: neutral
-  weight: 1
-network_params:
-  generator: barabasi_albert_graph
-  n: 500
-  m: 5
-num_trials: 50
----
-default_state: {}
-environment_agents: []
-environment_params:
-  prob_neighbor_spread: 0.0
-  prob_tv_spread: 0.01
-  prob_neighbor_cure: 0.1
-interval: 1
-max_steps: 300
-name: Sim_wise_herd
-network_agents:
-- agent_class: newsspread.HerdViewer
-  state:
-    has_tv: true
-    state_id: neutral
-  weight: 1
-- agent_class: newsspread.WiseViewer
-  state:
-    has_tv: true
-  weight: 1
-network_params:
-  generator: barabasi_albert_graph
-  n: 500
-  m: 5
-num_trials: 50
----
-default_state: {}
-environment_agents: []
-environment_params:
-  prob_neighbor_spread: 0.0
-  prob_tv_spread: 0.01
-  prob_neighbor_cure: 0.1
-interval: 1
-max_steps: 300
-name: Sim_all_wise
-network_agents:
-- agent_class: newsspread.WiseViewer
-  state:
-    has_tv: true
-    state_id: neutral
-  weight: 1
-- agent_class: newsspread.WiseViewer
-  state:
-    has_tv: true
-  weight: 1
-network_params:
-  generator: barabasi_albert_graph
-  n: 500
-  m: 5
-network_params:
-  generator: barabasi_albert_graph
-  n: 500
-  m: 5
-num_trials: 50

examples/newsspread/newsspread.py

@@ -1,87 +0,0 @@
-from soil.agents import FSM, NetworkAgent, state, default_state, prob
-import logging
-
-
-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.
-    """
-
-    prob_neighbor_spread = 0.5
-    prob_tv_spread = 0.1
-    has_been_infected = False
-
-    @default_state
-    @state
-    def neutral(self):
-        if self["has_tv"]:
-            if self.prob(self.model["prob_tv_spread"]):
-                return self.infected
-        if self.has_been_infected:
-            return self.infected
-
-    @state
-    def infected(self):
-        for neighbor in self.get_neighbors(state_id=self.neutral.id):
-            if self.prob(self.model["prob_neighbor_spread"]):
-                neighbor.infect()
-
-    def infect(self):
-        """
-        This is not a state. It is a function that other agents can use to try to
-        infect this agent. DumbViewer always gets infected, but other agents like
-        HerdViewer might not become infected right away
-        """
-
-        self.has_been_infected = True
-
-
-class HerdViewer(DumbViewer):
-    """
-    A viewer whose probability of infection depends on the state of its neighbors.
-    """
-
-    def infect(self):
-        """Notice again that this is NOT a state. See DumbViewer.infect for reference"""
-        infected = self.count_neighbors(state_id=self.infected.id)
-        total = self.count_neighbors()
-        prob_infect = self.model["prob_neighbor_spread"] * infected / total
-        self.debug("prob_infect", prob_infect)
-        if self.prob(prob_infect):
-            self.has_been_infected = True
-
-
-class WiseViewer(HerdViewer):
-    """
-    A viewer that can change its mind.
-    """
-
-    defaults = {
-        "prob_neighbor_spread": 0.5,
-        "prob_neighbor_cure": 0.25,
-        "prob_tv_spread": 0.1,
-    }
-
-    @state
-    def cured(self):
-        prob_cure = self.model["prob_neighbor_cure"]
-        for neighbor in self.get_neighbors(state_id=self.infected.id):
-            if self.prob(prob_cure):
-                try:
-                    neighbor.cure()
-                except AttributeError:
-                    self.debug("Viewer {} cannot be cured".format(neighbor.id))
-
-    def cure(self):
-        self.has_been_cured = True
-
-    @state
-    def infected(self):
-        if self.has_been_cured:
-            return self.cured
-        cured = max(self.count_neighbors(self.cured.id), 1.0)
-        infected = max(self.count_neighbors(self.infected.id), 1.0)
-        prob_cure = self.model["prob_neighbor_cure"] * (cured / infected)
-        if self.prob(prob_cure):
-            return self.cured

examples/newsspread/newsspread_sim.py

@@ -0,0 +1,134 @@
+from soil.agents import FSM, NetworkAgent, state, default_state, prob
+from soil.parameters import *
+import logging
+
+from soil.environment import Environment
+
+
+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.
+    """
+
+    has_been_infected: bool = False
+    has_tv: bool = False
+
+    @default_state
+    @state
+    def neutral(self):
+        if self.has_tv:
+            if self.prob(self.get("prob_tv_spread")):
+                return self.infected
+        if self.has_been_infected:
+            return self.infected
+
+    @state
+    def infected(self):
+        for neighbor in self.get_neighbors(state_id=self.neutral.id):
+            if self.prob(self.get("prob_neighbor_spread")):
+                neighbor.infect()
+
+    def infect(self):
+        """
+        This is not a state. It is a function that other agents can use to try to
+        infect this agent. DumbViewer always gets infected, but other agents like
+        HerdViewer might not become infected right away
+        """
+        self.has_been_infected = True
+
+
+class HerdViewer(DumbViewer):
+    """
+    A viewer whose probability of infection depends on the state of its neighbors.
+    """
+
+    def infect(self):
+        """Notice again that this is NOT a state. See DumbViewer.infect for reference"""
+        infected = self.count_neighbors(state_id=self.infected.id)
+        total = self.count_neighbors()
+        prob_infect = self.get("prob_neighbor_spread") * infected / total
+        self.debug("prob_infect", prob_infect)
+        if self.prob(prob_infect):
+            self.has_been_infected = True
+
+
+class WiseViewer(HerdViewer):
+    """
+    A viewer that can change its mind.
+    """
+
+    @state
+    def cured(self):
+        prob_cure = self.get("prob_neighbor_cure")
+        for neighbor in self.get_neighbors(state_id=self.infected.id):
+            if self.prob(prob_cure):
+                try:
+                    neighbor.cure()
+                except AttributeError:
+                    self.debug("Viewer {} cannot be cured".format(neighbor.id))
+
+    def cure(self):
+        self.has_been_cured = True
+
+    @state
+    def infected(self):
+        if self.has_been_cured:
+            return self.cured
+        cured = max(self.count_neighbors(self.cured.id), 1.0)
+        infected = max(self.count_neighbors(self.infected.id), 1.0)
+        prob_cure = self.get("prob_neighbor_cure") * (cured / infected)
+        if self.prob(prob_cure):
+            return self.cured
+
+
+class NewsSpread(Environment):
+    ratio_dumb: probability = 1,
+    ratio_herd: probability = 0,
+    ratio_wise: probability = 0,
+    prob_tv_spread: probability = 0.1,
+    prob_neighbor_spread: probability = 0.1,
+    prob_neighbor_cure: probability = 0.05,
+
+    def init(self):
+        self.populate_network([DumbViewer, HerdViewer, WiseViewer],
+                              [self.ratio_dumb, self.ratio_herd, self.ratio_wise])
+
+
+from itertools import product
+from soil import Simulation
+
+
+# We want to investigate the effect of different agent distributions on the spread of news.
+# To do that, we will run different simulations, with a varying ratio of DumbViewers, HerdViewers, and WiseViewers
+# Because the effect of these agents might also depend on the network structure, we will run our simulations on two different networks:
+# one with a small-world structure and one with a connected structure.
+
+counter = 0
+for [r1, r2] in product([0, 0.5, 1.0], repeat=2):
+    for (generator, netparams) in {
+        "barabasi_albert_graph": {"m": 5},
+        "erdos_renyi_graph": {"p": 0.1},
+    }.items():
+        print(r1, r2, 1-r1-r2, generator)
+        # Create new simulation
+        netparams["n"] = 500
+        Simulation(
+            name='newspread_sim',
+            model=NewsSpread,
+            parameters=dict(
+                ratio_dumb=r1,
+                ratio_herd=r2,
+                ratio_wise=1-r1-r2,
+                network_generator=generator,
+                network_params=netparams,
+                prob_neighbor_spread=0,
+            ),
+            iterations=5,
+            max_steps=300,
+            dump=False,
+        ).run()
+        counter += 1
+        # Run all the necessary instances
+ 
+print(f"A total of {counter} simulations were run.")

examples/programmatic/programmatic_sim.py

@@ -1,7 +1,7 @@
 """
 Example of a fully programmatic simulation, without definition files.
 """
-from soil import Simulation, agents
+from soil import Simulation, Environment, agents
 from networkx import Graph
 import logging
 
@@ -14,7 +14,7 @@ def mygenerator():
     return G
 
 
-class MyAgent(agents.FSM):
+class MyAgent(agents.NetworkAgent, agents.FSM):
     times_run = 0
     @agents.default_state
     @agents.state
@@ -25,26 +25,22 @@ class MyAgent(agents.FSM):
             self.info("This runs 2/10 times on average")
 
 
+class ProgrammaticEnv(Environment):
+
+    def init(self):
+        self.create_network(generator=mygenerator)
+        assert len(self.G)
+        self.populate_network(agent_class=MyAgent)
+        self.add_agent_reporter('times_run')
+
+
 simulation = Simulation(
     name="Programmatic",
-    model_params={
+    model=ProgrammaticEnv,
-        'topology': {
-            'params': {
-                'generator': mygenerator
-            },
-        },
-        'agents': {
-            'distribution': [{
-                'agent_class': MyAgent,
-                'topology': True,
-            }]
-        }
-    },
     seed='Program',
-    agent_reporters={'times_run': 'times_run'},
+    iterations=1,
-    num_trials=1,
     max_time=100,
-    dry_run=True,
+    dump=False,
 )
 
 if __name__ == "__main__":

examples/pubcrawl/pubcrawl.yml

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

examples/pubcrawl/pubcrawl_sim.py

@@ -1,6 +1,7 @@
 from soil.agents import FSM, NetworkAgent, state, default_state
-from soil import Environment
+from soil import Environment, Simulation, parameters
 from itertools import islice
+import networkx as nx
 import logging
 
 
@@ -8,19 +9,24 @@ class CityPubs(Environment):
     """Environment with Pubs"""
 
     level = logging.INFO
+    number_of_pubs: parameters.Integer = 3
+    ratio_extroverted: parameters.probability = 0.1
+    pub_capacity: parameters.Integer = 10
     
-    def __init__(self, *args, number_of_pubs=3, pub_capacity=10, **kwargs):
+    def init(self):
-        super(CityPubs, self).__init__(*args, **kwargs)
+        self.pubs = {}
-        pubs = {}
+        for i in range(self.number_of_pubs):
-        for i in range(number_of_pubs):
             newpub = {
                 "name": "The awesome pub #{}".format(i),
                 "open": True,
-                "capacity": pub_capacity,
+                "capacity": self.pub_capacity,
                 "occupancy": 0,
             }
-            pubs[newpub["name"]] = newpub
+            self.pubs[newpub["name"]] = newpub
-        self["pubs"] = pubs
+        self.add_agent(agent_class=Police)
+        self.populate_network([Patron.w(openness=0.1), Patron.w(openness=1)],
+                              [self.ratio_extroverted, 1-self.ratio_extroverted])
+        assert all(["agent" in node and isinstance(node["agent"], Patron) for (_, node) in self.G.nodes(data=True)])
 
     def enter(self, pub_id, *nodes):
         """Agents will try to enter. The pub checks if it is possible"""
@@ -146,10 +152,10 @@ class Patron(FSM, NetworkAgent):
                 continue
             if friend.befriend(self):
                 self.befriend(friend, force=True)
-                self.debug("Hooray! new friend: {}".format(friend.id))
+                self.debug("Hooray! new friend: {}".format(friend.unique_id))
                 befriended = True
             else:
-                self.debug("{} does not want to be friends".format(friend.id))
+                self.debug("{} does not want to be friends".format(friend.unique_id))
         return befriended
 
 
@@ -163,13 +169,27 @@ class Police(FSM):
     def patrol(self):
         drunksters = list(self.get_agents(drunk=True, state_id=Patron.drunk_in_pub.id))
         for drunk in drunksters:
-            self.info("Kicking out the trash: {}".format(drunk.id))
+            self.info("Kicking out the trash: {}".format(drunk.unique_id))
             drunk.kick_out()
         else:
             self.info("No trash to take out. Too bad.")
 
 
-if __name__ == "__main__":
+sim = Simulation(
-    from soil import run_from_config
+    model=CityPubs,
+    name="pubcrawl",
+    iterations=3,
+    max_steps=10,
+    dump=False,
+    parameters=dict(
+        network_generator=nx.empty_graph,
+        network_params={"n": 30},
+        model=CityPubs,
+        altercations=0,
+        number_of_pubs=3,
+    )
+)
 
-    run_from_config("pubcrawl.yml", dry_run=True, dump=None, parallel=False)
+
+if __name__ == "__main__":
+    sim.run(parallel=False)

examples/rabbits/basic/rabbits.yml

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

examples/rabbits/improved/rabbits.yml

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

examples/rabbits/rabbit_improved_sim.py

@@ -1,23 +1,20 @@
-from soil import FSM, state, default_state, BaseAgent, NetworkAgent, Environment
+from soil import FSM, state, default_state, BaseAgent, NetworkAgent, Environment, Simulation
 from soil.time import Delta
 from enum import Enum
 from collections import Counter
 import logging
 import math
 
+from rabbits_basic_sim import RabbitEnv
 
-class RabbitEnv(Environment):
-    @property
-    def num_rabbits(self):
-        return self.count_agents(agent_class=Rabbit)
 
-    @property
+class RabbitsImprovedEnv(RabbitEnv):
-    def num_males(self):
+    def init(self):
-        return self.count_agents(agent_class=Male)
+        """Initialize the environment with the new versions of the agents"""
-
+        a1 = self.add_node(Male)
-    @property
+        a2 = self.add_node(Female)
-    def num_females(self):
+        a1.add_edge(a2)
-        return self.count_agents(agent_class=Female)
+        self.add_agent(RandomAccident)
 
 
 class Rabbit(FSM, NetworkAgent):
@@ -150,8 +147,7 @@ class RandomAccident(BaseAgent):
         self.debug("Rabbits alive: {}".format(rabbits_alive))
 
 
-if __name__ == "__main__":
+sim = Simulation(model=RabbitsImprovedEnv, max_time=100, seed="MySeed", iterations=1)
-    from soil import easy
 
-    with easy("rabbits.yml") as sim:
+if __name__ == "__main__":
-        sim.run()
+    sim.run()

examples/rabbits/rabbits_basic_sim.py

@@ -1,20 +1,29 @@
-from soil import FSM, state, default_state, BaseAgent, NetworkAgent, Environment
+from soil import FSM, state, default_state, BaseAgent, NetworkAgent, Environment, Simulation, report, parameters as params
 from collections import Counter
 import logging
 import math
 
 
 class RabbitEnv(Environment):
-    prob_death = 1e-100
+    prob_death: params.probability = 1e-100
 
+    def init(self):
+        a1 = self.add_node(Male)
+        a2 = self.add_node(Female)
+        a1.add_edge(a2)
+        self.add_agent(RandomAccident)
+
+    @report
     @property
     def num_rabbits(self):
         return self.count_agents(agent_class=Rabbit)
 
+    @report
     @property
     def num_males(self):
         return self.count_agents(agent_class=Male)
 
+    @report
     @property
     def num_females(self):
         return self.count_agents(agent_class=Female)
@@ -145,8 +154,8 @@ class RandomAccident(BaseAgent):
         self.debug("Rabbits alive: {}".format(rabbits_alive))
 
 
-if __name__ == "__main__":
-    from soil import easy
 
-    with easy("rabbits.yml") as sim:
+sim = Simulation(model=RabbitEnv, max_time=100, seed="MySeed", iterations=1)
-        sim.run()
+
+if __name__ == "__main__":
+    sim.run()

examples/random_delays/random_delays_sim.py

@@ -2,7 +2,7 @@
 Example of setting a 
 Example of a fully programmatic simulation, without definition files.
 """
-from soil import Simulation, agents
+from soil import Simulation, agents, Environment
 from soil.time import Delta
 
 
@@ -29,14 +29,18 @@ class MyAgent(agents.FSM):
         return None, Delta(self.random.expovariate(1 / 16))
 
 
+class RandomEnv(Environment):
+
+    def init(self):
+        self.add_agent(agent_class=MyAgent)
+
+
 s = Simulation(
     name="Programmatic",
-    model_params={
+    model=RandomEnv,
-        'agents': [{'agent_class': MyAgent}],
+    iterations=1,
-    },
-    num_trials=1,
     max_time=100,
-    dry_run=True,
+    dump=False,
 )
 
 

examples/template.yml

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

examples/terrorism/TerroristNetworkModel.yml

@@ -1,62 +0,0 @@
-name: TerroristNetworkModel_sim
-max_steps: 150 
-num_trials: 1
-model_params:
-  network_params:
-    generator: random_geometric_graph
-    radius: 0.2
-    # generator: geographical_threshold_graph
-    # theta: 20
-    n: 100
-  network_agents:
-    - agent_class: TerroristNetworkModel.TerroristNetworkModel
-      weight: 0.8
-      state:
-        id: civilian  # Civilians
-    - agent_class: TerroristNetworkModel.TerroristNetworkModel
-      weight: 0.1
-      state:
-        id: leader    # Leaders
-    - agent_class: TerroristNetworkModel.TrainingAreaModel
-      weight: 0.05
-      state:
-        id: terrorist # Terrorism
-    - agent_class: TerroristNetworkModel.HavenModel
-      weight: 0.05
-      state:
-        id: civilian  # Civilian
-
-  # TerroristSpreadModel
-  information_spread_intensity: 0.7
-  terrorist_additional_influence: 0.035
-  max_vulnerability: 0.7
-  prob_interaction: 0.5
-
-  # TrainingAreaModel and HavenModel
-  training_influence: 0.20
-  haven_influence: 0.20
-
-  # TerroristNetworkModel
-  vision_range: 0.30
-  sphere_influence: 2
-  weight_social_distance: 0.035
-  weight_link_distance: 0.035
-
-visualization_params:
-  # Icons downloaded from https://www.iconfinder.com/
-  shape_property: agent
-  shapes:
-    TrainingAreaModel: target
-    HavenModel: home
-    TerroristNetworkModel: person
-  colors:
-    - attr_id: civilian
-      color: '#40de40'
-    - attr_id: terrorist
-      color: red
-    - attr_id: leader
-      color: '#c16a6a'
-  background_image: 'map_4800x2860.jpg'
-  background_opacity: '0.9'
-  background_filter_color: 'blue'
-skip_test: true  # This simulation takes too long for automated tests.

examples/terrorism/TerroristNetworkModel_sim.py

@@ -1,8 +1,47 @@
 import networkx as nx
-from soil.agents import Geo, NetworkAgent, FSM, state, default_state
+from soil.agents import Geo, NetworkAgent, FSM, custom, state, default_state
-from soil import Environment
+from soil import Environment, Simulation
+from soil.parameters import *
+from soil.utils import int_seed
 
 
+class TerroristEnvironment(Environment):
+    n: Integer = 100
+    radius: Float = 0.2
+
+    information_spread_intensity: probability = 0.7
+    terrorist_additional_influence: probability = 0.03
+    terrorist_additional_influence: probability = 0.035
+    max_vulnerability: probability = 0.7
+    prob_interaction: probability = 0.5
+
+    # TrainingAreaModel and HavenModel
+    training_influence: probability = 0.20
+    haven_influence: probability = 0.20
+
+    # TerroristNetworkModel
+    vision_range: Float = 0.30
+    sphere_influence: Integer = 2
+    weight_social_distance: Float = 0.035
+    weight_link_distance: Float = 0.035
+
+    ratio_civil: probability = 0.8
+    ratio_leader: probability = 0.1
+    ratio_training: probability = 0.05
+    ratio_haven: probability = 0.05
+
+    def init(self):
+        self.create_network(generator=self.generator, n=self.n, radius=self.radius)
+        self.populate_network([
+            TerroristNetworkModel.w(state_id='civilian'),
+            TerroristNetworkModel.w(state_id='leader'),
+            TrainingAreaModel,
+            HavenModel
+        ], [self.ratio_civil, self.ratio_leader, self.ratio_training, self.ratio_haven])
+
+    def generator(self, *args, **kwargs):
+        return nx.random_geometric_graph(*args, **kwargs, seed=int_seed(self._seed))
+
 class TerroristSpreadModel(FSM, Geo):
     """
     Settings:
@@ -13,47 +52,35 @@ class TerroristSpreadModel(FSM, Geo):
         min_vulnerability (optional else zero)
 
         max_vulnerability
-
-        prob_interaction
     """
 
-    def __init__(self, model=None, unique_id=0, state=()):
+    information_spread_intensity = 0.1
-        super().__init__(model=model, unique_id=unique_id, state=state)
+    terrorist_additional_influence = 0.1
+    min_vulnerability = 0
+    max_vulnerability = 1
 
-        self.information_spread_intensity = model.environment_params[
+    def init(self):
-            "information_spread_intensity"
+        if self.state_id == self.civilian.id:  # Civilian
-        ]
+            self.mean_belief = self.model.random.uniform(0.00, 0.5)
-        self.terrorist_additional_influence = model.environment_params[
+        elif self.state_id == self.terrorist.id:  # Terrorist
-            "terrorist_additional_influence"
-        ]
-        self.prob_interaction = model.environment_params["prob_interaction"]
-
-        if self["id"] == self.civilian.id:  # Civilian
-            self.mean_belief = self.random.uniform(0.00, 0.5)
-        elif self["id"] == self.terrorist.id:  # Terrorist
             self.mean_belief = self.random.uniform(0.8, 1.00)
-        elif self["id"] == self.leader.id:  # Leader
+        elif self.state_id == self.leader.id:  # Leader
             self.mean_belief = 1.00
         else:
             raise Exception("Invalid state id: {}".format(self["id"]))
 
-        if "min_vulnerability" in model.environment_params:
+        self.vulnerability = self.random.uniform(
-            self.vulnerability = self.random.uniform(
+            self.get("min_vulnerability", 0), self.get("max_vulnerability", 1)
-                model.environment_params["min_vulnerability"],
+        )
-                model.environment_params["max_vulnerability"],
-            )
-        else:
-            self.vulnerability = self.random.uniform(
-                0, model.environment_params["max_vulnerability"]
-            )
 
+    @default_state
     @state
     def civilian(self):
         neighbours = list(self.get_neighbors(agent_class=TerroristSpreadModel))
         if len(neighbours) > 0:
             # Only interact with some of the neighbors
             interactions = list(
-                n for n in neighbours if self.random.random() <= self.prob_interaction
+                n for n in neighbours if self.random.random() <= self.model.prob_interaction
             )
             influence = sum(self.degree(i) for i in interactions)
             mean_belief = sum(
@@ -99,7 +126,7 @@ class TerroristSpreadModel(FSM, Geo):
             )
 
         # Check if there are any leaders in the group
-        leaders = list(filter(lambda x: x.state.id == self.leader.id, neighbours))
+        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
@@ -110,12 +137,11 @@ class TerroristSpreadModel(FSM, Geo):
 
     def ego_search(self, steps=1, center=False, agent=None, **kwargs):
         """Get a list of nodes in the ego network of *node* of radius *steps*"""
-        node = agent.node
+        node = agent.node_id if agent else self.node_id
         G = self.subgraph(**kwargs)
         return nx.ego_graph(G, node, center=center, radius=steps).nodes()
 
     def degree(self, agent, force=False):
-        node = agent.node
         if (
             force
             or (not hasattr(self.model, "_degree"))
@@ -123,10 +149,9 @@ class TerroristSpreadModel(FSM, Geo):
         ):
             self.model._degree = nx.degree_centrality(self.G)
             self.model._last_step = self.now
-        return self.model._degree[node]
+        return self.model._degree[agent.node_id]
 
     def betweenness(self, agent, force=False):
-        node = agent.node
         if (
             force
             or (not hasattr(self.model, "_betweenness"))
@@ -134,7 +159,7 @@ class TerroristSpreadModel(FSM, Geo):
         ):
             self.model._betweenness = nx.betweenness_centrality(self.G)
             self.model._last_step = self.now
-        return self.model._betweenness[node]
+        return self.model._betweenness[agent.node_id]
 
 
 class TrainingAreaModel(FSM, Geo):
@@ -147,13 +172,12 @@ class TrainingAreaModel(FSM, Geo):
     Requires TerroristSpreadModel.
     """
 
-    def __init__(self, model=None, unique_id=0, state=()):
+    training_influence = 0.1
-        super().__init__(model=model, unique_id=unique_id, state=state)
+    min_vulnerability = 0
-        self.training_influence = model.environment_params["training_influence"]
+
-        if "min_vulnerability" in model.environment_params:
+    def init(self):
-            self.min_vulnerability = model.environment_params["min_vulnerability"]
+        self.mean_believe = 1
-        else:
+        self.vulnerability = 0
-            self.min_vulnerability = 0
 
     @default_state
     @state
@@ -177,18 +201,19 @@ class HavenModel(FSM, Geo):
     Requires TerroristSpreadModel.
     """
 
-    def __init__(self, model=None, unique_id=0, state=()):
+    min_vulnerability = 0
-        super().__init__(model=model, unique_id=unique_id, state=state)
+    haven_influence = 0.1
-        self.haven_influence = model.environment_params["haven_influence"]
+    max_vulnerability = 0.5
-        if "min_vulnerability" in model.environment_params:
+
-            self.min_vulnerability = model.environment_params["min_vulnerability"]
+    def init(self):
-        else:
+        self.mean_believe = 0
-            self.min_vulnerability = 0
+        self.vulnerability = 0
-        self.max_vulnerability = model.environment_params["max_vulnerability"]
 
     def get_occupants(self, **kwargs):
-        return self.get_neighbors(agent_class=TerroristSpreadModel, **kwargs)
+        return self.get_neighbors(agent_class=TerroristSpreadModel,
+                                  **kwargs)
 
+    @default_state
     @state
     def civilian(self):
         civilians = self.get_occupants(state_id=self.civilian.id)
@@ -224,13 +249,10 @@ class TerroristNetworkModel(TerroristSpreadModel):
         weight_link_distance
     """
 
-    def __init__(self, model=None, unique_id=0, state=()):
+    sphere_influence: float = 1
-        super().__init__(model=model, unique_id=unique_id, state=state)
+    vision_range: float = 1
-
+    weight_social_distance: float = 0.5
-        self.vision_range = model.environment_params["vision_range"]
+    weight_link_distance: float = 0.2
-        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):
@@ -257,26 +279,26 @@ class TerroristNetworkModel(TerroristSpreadModel):
                 )
             )
             neighbours = set(
-                agent.id
+                agent.unique_id
                 for agent in self.get_neighbors(agent_class=TerroristNetworkModel)
             )
             search = (close_ups | step_neighbours) - neighbours
             for agent in self.get_agents(search):
-                social_distance = 1 / self.shortest_path_length(agent.id)
+                social_distance = 1 / self.shortest_path_length(agent.unique_id)
-                spatial_proximity = 1 - self.get_distance(agent.id)
+                spatial_proximity = 1 - self.get_distance(agent.unique_id)
                 prob_new_interaction = (
                     self.weight_social_distance * social_distance
                     + self.weight_link_distance * spatial_proximity
                 )
                 if (
-                    agent["id"] == agent.civilian.id
+                    agent.state_id == "civilian"
                     and self.random.random() < prob_new_interaction
                 ):
                     self.add_edge(agent)
                     break
 
     def get_distance(self, target):
-        source_x, source_y = nx.get_node_attributes(self.G, "pos")[self.id]
+        source_x, source_y = nx.get_node_attributes(self.G, "pos")[self.unique_id]
         target_x, target_y = nx.get_node_attributes(self.G, "pos")[target]
         dx = abs(source_x - target_x)
         dy = abs(source_y - target_y)
@@ -284,6 +306,36 @@ class TerroristNetworkModel(TerroristSpreadModel):
 
     def shortest_path_length(self, target):
         try:
-            return nx.shortest_path_length(self.G, self.id, target)
+            return nx.shortest_path_length(self.G, self.unique_id, target)
         except nx.NetworkXNoPath:
             return float("inf")
+
+
+sim = Simulation(
+    model=TerroristEnvironment,
+    iterations=1,
+    name="TerroristNetworkModel_sim",
+    max_steps=150,
+    seed="default2",
+    skip_test=False,
+    dump=False,
+)
+
+# TODO: integrate visualization
+# visualization_params:
+#   # Icons downloaded from https://www.iconfinder.com/
+#   shape_property: agent
+#   shapes:
+#     TrainingAreaModel: target
+#     HavenModel: home
+#     TerroristNetworkModel: person
+#   colors:
+#     - attr_id: civilian
+#       color: '#40de40'
+#     - attr_id: terrorist
+#       color: red
+#     - attr_id: leader
+#       color: '#c16a6a'
+#   background_image: 'map_4800x2860.jpg'
+#   background_opacity: '0.9'
+#   background_filter_color: 'blue'

examples/torvalds.yml

@@ -1,15 +0,0 @@
----
-name: torvalds_example
-max_steps: 10
-interval: 2
-model_params:
-  agent_class: CounterModel
-  default_state:
-    skill_level: 'beginner'
-  network_params:
-    path: 'torvalds.edgelist'
-  states:
-    Torvalds:
-      skill_level: 'God'
-    balkian:
-      skill_level: 'developer'

examples/torvalds_sim.py

@@ -0,0 +1,25 @@
+from soil import Environment, Simulation, CounterModel, report
+
+
+# Get directory path for current file
+import os, sys, inspect
+currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
+
+class TorvaldsEnv(Environment):
+
+  def init(self):
+    self.create_network(path=os.path.join(currentdir, 'torvalds.edgelist'))
+    self.populate_network(CounterModel, skill_level='beginner')
+    self.agent(node_id="Torvalds").skill_level = 'God'
+    self.agent(node_id="balkian").skill_level = 'developer'
+    self.add_agent_reporter("times")
+
+  @report
+  def god_developers(self):
+    return self.count_agents(skill_level='God')
+  
+
+sim = Simulation(name='torvalds_example',
+                 max_steps=10,
+                 interval=2,
+                 model=TorvaldsEnv)

requirements.txt

@@ -5,6 +5,9 @@ pyyaml>=5.1
 pandas>=1
 SALib>=1.3
 Jinja2
-Mesa>=1.1
+Mesa>=1.2
 pydantic>=1.9
 sqlalchemy>=1.4
+typing-extensions>=4.4
+annotated-types>=0.4
+tqdm>=4.64

setup.cfg

@@ -1,3 +1,7 @@
+[metadata]
+long_description = file: README.md
+long_description_content_type = text/markdown
+
 [aliases]
 test=pytest
 [tool:pytest]

setup.py

@@ -44,13 +44,18 @@ setup(
         'Operating System :: MacOS :: MacOS X',
         'Operating System :: Microsoft :: Windows',
         'Operating System :: POSIX',
-        'Programming Language :: Python :: 3'],
+        "Programming Language :: Python :: 3 :: Only",
+        "Programming Language :: Python :: 3.8",
+        "Programming Language :: Python :: 3.9",
+        "Programming Language :: Python :: 3.10",
+        ],
     install_requires=install_reqs,
     extras_require=extras_require,
     tests_require=test_reqs,
     setup_requires=['pytest-runner', ],
     pytest_plugins = ['pytest_profiling'],
     include_package_data=True,
+    python_requires=">=3.8",
     entry_points={
         'console_scripts':
         ['soil = soil.__main__:main',

soil/VERSION

@@ -1 +1 @@
-0.30.0rc4
+1.0.0rc1

soil/__init__.py

@@ -16,6 +16,7 @@ except NameError:
     basestring = str
 
 from pathlib import Path
+from .analysis import *
 from .agents import *
 from . import agents
 from .simulation import *
@@ -24,15 +25,15 @@ from .datacollection import SoilCollector
 from . import serialization
 from .utils import logger
 from .time import *
+from .decorators import *
 
 
 def main(
     cfg="simulation.yml",
     exporters=None,
-    parallel=None,
+    num_processes=1,
     output="soil_output",
     *,
-    do_run=False,
     debug=False,
     pdb=False,
     **kwargs,
@@ -68,6 +69,11 @@ def main(
         "--dry-run",
         "--dry",
         action="store_true",
+        help="Do not run the simulation",
+    )
+    parser.add_argument(
+        "--no-dump",
+        action="store_true",
         help="Do not store the results of the simulation to disk, show in terminal instead.",
     )
     parser.add_argument(
@@ -82,7 +88,7 @@ def main(
         "--graph",
         "-g",
         action="store_true",
-        help="Dump each trial's network topology as a GEXF graph. Defaults to false.",
+        help="Dump each iteration's network topology as a GEXF graph. Defaults to false.",
     )
     parser.add_argument(
         "--csv",
@@ -97,12 +103,11 @@ def main(
         default=output or "soil_output",
         help="folder to write results to. It defaults to the current directory.",
     )
-    if parallel is None:
+    parser.add_argument(
-        parser.add_argument(
+        "--num-processes",
-            "--synchronous",
+        default=num_processes,
-            action="store_true",
+        help="Number of processes to use for parallel execution. Defaults to 1.",
-            help="Run trials serially and synchronously instead of in parallel. Defaults to false.",
+    )
-        )
 
     parser.add_argument(
         "-e",
@@ -111,6 +116,29 @@ def main(
         default=[],
         help="Export environment and/or simulations using this exporter",
     )
+    parser.add_argument(
+        "--max_time",
+        default="-1",
+        help="Set maximum time for the simulation to run. ",
+    )
+
+    parser.add_argument(
+        "--max_steps",
+        default="-1",
+        help="Set maximum number of steps for the simulation to run.",
+    )
+
+    parser.add_argument(
+        "--iterations",
+        default="",
+        help="Set maximum number of iterations (runs) for the simulation.",
+    )
+
+    parser.add_argument(
+        "--seed",
+        default=None,
+        help="Manually set a seed for the simulation.",
+    )
 
     parser.add_argument(
         "--only-convert",
@@ -132,14 +160,12 @@ def main(
     )
 
     args = parser.parse_args()
-    logger.setLevel(getattr(logging, (args.level or "INFO").upper()))
+    level = getattr(logging, (args.level or "INFO").upper())
+    logger.setLevel(level)
 
     if args.version:
         return
 
-    if parallel is None:
-        parallel = not args.synchronous
-
     exporters = exporters or [
         "default",
     ]
@@ -167,42 +193,49 @@ def main(
     res = []
     try:
         exp_params = {}
+        opts = dict(
+                    dry_run=args.dry_run,
+                    dump=not args.no_dump,
+                    debug=debug,
+                    exporters=exporters,
+                    num_processes=args.num_processes,
+                    level=level,
+                    outdir=output,
+                    exporter_params=exp_params,
+                    **kwargs)
+        if args.seed is not None:
+            opts["seed"] = args.seed
+        if args.iterations:
+            opts["iterations"] =int(args.iterations)
 
         if sim:
             logger.info("Loading simulation instance")
-            sim.dry_run = args.dry_run
+            for (k, v) in opts.items():
-            sim.exporters = exporters
+                setattr(sim, k, v)
-            sim.parallel = parallel
+            sims = [sim]
-            sim.outdir = output
-            sims = [
-                sim,
-            ]
         else:
             logger.info("Loading config file: {}".format(args.file))
             if not os.path.exists(args.file):
                 logger.error("Please, input a valid file")
                 return
 
+            assert opts["debug"] == debug
             sims = list(
-                simulation.iter_from_config(
+                simulation.iter_from_file(
                     args.file,
-                    dry_run=args.dry_run,
+                    **opts,
-                    exporters=exporters,
-                    parallel=parallel,
-                    outdir=output,
-                    exporter_params=exp_params,
-                    **kwargs,
                 )
             )
 
         for sim in sims:
+            assert sim.debug == debug
 
             if args.set:
                 for s in args.set:
                     k, v = s.split("=", 1)[:2]
                     v = eval(v)
                     tail, *head = k.rsplit(".", 1)[::-1]
-                    target = sim
+                    target = sim.parameters
                     if head:
                         for part in head[0].split("."):
                             try:
@@ -217,11 +250,9 @@ def main(
             if args.only_convert:
                 print(sim.to_yaml())
                 continue
-            if do_run:
+            max_time = float(args.max_time) if args.max_time != "-1" else None
-                res.append(sim.run())
+            max_steps = float(args.max_steps) if args.max_steps != "-1" else None
-            else:
+            res.append(sim.run(max_time=max_time, max_steps=max_steps))
-                print("not running")
-                res.append(sim)
 
     except Exception as ex:
         if args.pdb:
@@ -242,7 +273,7 @@ def main(
 @contextmanager
 def easy(cfg, pdb=False, debug=False, **kwargs):
     try:
-        yield main(cfg, debug=debug, pdb=pdb, **kwargs)[0]
+        return main(cfg, debug=debug, pdb=pdb, **kwargs)[0]
     except Exception as e:
         if os.environ.get("SOIL_POSTMORTEM"):
             from .debugging import post_mortem
@@ -253,4 +284,4 @@ def easy(cfg, pdb=False, debug=False, **kwargs):
 
 
 if __name__ == "__main__":
-    main(do_run=True)
+    main()

soil/__main__.py

@@ -2,8 +2,8 @@ from . import main as init_main
 
 
 def main():
-    init_main(do_run=True)
+    init_main()
 
 
 if __name__ == "__main__":
-    init_main(do_run=True)
+    init_main()

soil/agents/CounterModel.py

@@ -1,6 +1,12 @@
-from . import NetworkAgent
+from . import BaseAgent, NetworkAgent
 
 
+class Ticker(BaseAgent):
+    times = 0
+
+    def step(self):
+        self.times += 1
+
 class CounterModel(NetworkAgent):
     """
     Dummy behaviour. It counts the number of nodes in the network and neighbors

soil/agents/Geo.py

@@ -6,9 +6,9 @@ from . import NetworkAgent
 class Geo(NetworkAgent):
     """In this type of network, nodes have a "pos" attribute."""
 
-    def geo_search(self, radius, agent=None, center=False, **kwargs):
+    def geo_search(self, radius, center=False, **kwargs):
         """Get a list of nodes whose coordinates are closer than *radius* to *node*."""
-        node = agent.node
+        node = self.node_id
 
         G = self.subgraph(**kwargs)
 

soil/agents/__init__.py

@@ -11,13 +11,15 @@ import inspect
 import types
 import textwrap
 import networkx as nx
+import warnings
+import sys
 
 from typing import Any
 
-from mesa import Agent as MesaAgent
+from mesa import Agent as MesaAgent, Model
 from typing import Dict, List
 
-from .. import serialization, utils, time, config
+from .. import serialization, network, utils, time, config
 
 
 IGNORED_FIELDS = ("model", "logger")
@@ -90,7 +92,7 @@ class BaseAgent(MesaAgent, MutableMapping, metaclass=MetaAgent):
     Any attribute that is not preceded by an underscore (`_`) will also be added to its state.
     """
 
-    def __init__(self, unique_id, model, name=None, interval=None, **kwargs):
+    def __init__(self, unique_id, model, name=None, init=True, interval=None, **kwargs):
         assert isinstance(unique_id, int)
         super().__init__(unique_id=unique_id, model=model)
 
@@ -116,6 +118,11 @@ class BaseAgent(MesaAgent, MutableMapping, metaclass=MetaAgent):
         for (k, v) in kwargs.items():
 
             setattr(self, k, v)
+        if init:
+            self.init()
+
+    def init(self):
+        pass
 
     def __hash__(self):
         return hash(self.unique_id)
@@ -123,9 +130,16 @@ class BaseAgent(MesaAgent, MutableMapping, metaclass=MetaAgent):
     def prob(self, probability):
         return prob(probability, self.model.random)
 
+    @classmethod
+    def w(cls, **kwargs):
+        return custom(cls, **kwargs)
+
     # TODO: refactor to clean up mesa compatibility
     @property
     def id(self):
+        msg = "This attribute is deprecated. Use `unique_id` instead"
+        warnings.warn(msg, DeprecationWarning)
+        print(msg, file=sys.stderr)
         return self.unique_id
 
     @classmethod
@@ -175,7 +189,11 @@ class BaseAgent(MesaAgent, MutableMapping, metaclass=MetaAgent):
         return it
 
     def get(self, key, default=None):
-        return self[key] if key in self else default
+        if key in self:
+            return self[key]
+        elif key in self.model:
+            return self.model[key]
+        return default
 
     @property
     def now(self):
@@ -185,8 +203,10 @@ class BaseAgent(MesaAgent, MutableMapping, metaclass=MetaAgent):
             # No environment
             return None
 
-    def die(self):
+    def die(self, msg=None):
-        self.info(f"agent dying")
+        if msg:
+            self.info("Agent dying:", msg)
+        self.debug(f"agent dying")
         self.alive = False
         try:
             self.model.schedule.remove(self)
@@ -195,15 +215,16 @@ class BaseAgent(MesaAgent, MutableMapping, metaclass=MetaAgent):
         return time.NEVER
 
     def step(self):
+        raise NotImplementedError("Agent must implement step method")
+    
+    def _check_alive(self):
         if not self.alive:
             raise time.DeadAgent(self.unique_id)
-        super().step()
-        return time.Delta(self.interval)
 
-    def log(self, message, *args, level=logging.INFO, **kwargs):
+    def log(self, *message, level=logging.INFO, **kwargs):
         if not self.logger.isEnabledFor(level):
             return
-        message = message + " ".join(str(i) for i in args)
+        message = " ".join(str(i) for i in message)
         message = "[@{:>4}]\t{:>10}: {}".format(self.now, repr(self), message)
         for k, v in kwargs:
             message += " {k}={v} ".format(k, v)
@@ -376,7 +397,7 @@ class AgentView(Mapping, Set):
 
 
 def filter_agents(
-    agents,
+    agents: dict,
     *id_args,
     unique_id=None,
     state_id=None,
@@ -621,12 +642,16 @@ def _from_distro(
 from .network_agents import *
 from .fsm import *
 from .evented import *
+from typing import Optional
 
 
 class Agent(NetworkAgent, FSM, EventedAgent):
     """Default agent class, has both network and event capabilities"""
 
 
+from ..environment import NetworkEnvironment
+
+
 from .BassModel import *
 from .IndependentCascadeModel import *
 from .SISaModel import *
@@ -640,3 +665,8 @@ except ImportError:
     import sys
 
     print("Could not load the Geo Agent, scipy is not installed", file=sys.stderr)
+
+
+def custom(cls, **kwargs):
+    """Create a new class from a template class and keyword arguments"""
+    return type(cls.__name__, (cls,), kwargs)

soil/agents/fsm.py

@@ -1,10 +1,11 @@
 from . import MetaAgent, BaseAgent
+from ..time import Delta
 
 from functools import partial, wraps
 import inspect
 
 
-def state(name=None):
+def state(name=None, default=False):
     def decorator(func, name=None):
         """
         A state function should return either a state id, or a tuple (state_id, when)
@@ -39,7 +40,7 @@ def state(name=None):
                         self._last_except = None
 
         func.id = name or func.__name__
-        func.is_default = False
+        func.is_default = default
         return func
 
     if callable(name):
@@ -85,8 +86,8 @@ class MetaFSM(MetaAgent):
 
 
 class FSM(BaseAgent, metaclass=MetaFSM):
-    def __init__(self, **kwargs):
+    def __init__(self, init=True, **kwargs):
-        super(FSM, self).__init__(**kwargs)
+        super().__init__(**kwargs, init=False)
         if not hasattr(self, "state_id"):
             if not self._default_state:
                 raise ValueError(
@@ -95,12 +96,19 @@ class FSM(BaseAgent, metaclass=MetaFSM):
             self.state_id = self._default_state.id
 
         self._coroutine = None
+        self.default_interval = Delta(self.model.interval)
         self._set_state(self.state_id)
+        if init:
+            self.init()
+
+    @classmethod
+    def states(cls):
+        return list(cls._states.keys())
 
     def step(self):
         self.debug(f"Agent {self.unique_id} @ state {self.state_id}")
-        default_interval = super().step()
 
+        self._check_alive()
         next_state = self._states[self.state_id](self)
 
         when = None
@@ -120,7 +128,7 @@ class FSM(BaseAgent, metaclass=MetaFSM):
         if next_state is not None:
             self._set_state(next_state)
 
-        return when or default_interval
+        return when or self.default_interval
 
     def _set_state(self, state, when=None):
         if hasattr(state, "id"):
@@ -132,8 +140,8 @@ class FSM(BaseAgent, metaclass=MetaFSM):
             self.model.schedule.add(self, when=when)
         return state
 
-    def die(self):
+    def die(self, *args, **kwargs):
-        return self.dead, super().die()
+        return self.dead, super().die(*args, **kwargs)
 
     @state
     def dead(self):

soil/agents/network_agents.py

@@ -2,23 +2,37 @@ from . import BaseAgent
 
 
 class NetworkAgent(BaseAgent):
-    def __init__(self, *args, topology, node_id, **kwargs):
+    def __init__(self, *args, topology=None, init=True, node_id=None, **kwargs):
-        super().__init__(*args, **kwargs)
+        super().__init__(*args, init=False, **kwargs)
 
-        assert topology is not None
+        self.G = topology or self.model.G
-        assert node_id is not None
-        self.G = topology
         assert self.G
+        if node_id is None:
+            nodes = self.random.choices(list(self.G.nodes), k=len(self.G))
+            for n_id in nodes:
+                if "agent" not in self.G.nodes[n_id] or self.G.nodes[n_id]["agent"] is None:
+                    node_id = n_id
+                    break
+            else:
+                node_id = len(self.G)
+                self.info(f"All nodes ({len(self.G)}) have an agent assigned, adding a new node to the graph for agent {self.unique_id}")
+                self.G.add_node(node_id)
+        assert node_id is not None
+        self.G.nodes[node_id]["agent"] = self
         self.node_id = node_id
+        if init:
+            self.init()
 
     def count_neighbors(self, state_id=None, **kwargs):
         return len(self.get_neighbors(state_id=state_id, **kwargs))
+        if init:
+            self.init()
 
     def iter_neighbors(self, **kwargs):
         return self.iter_agents(limit_neighbors=True, **kwargs)
 
     def get_neighbors(self, **kwargs):
-        return list(self.iter_neighbors())
+        return list(self.iter_neighbors(**kwargs))
 
     @property
     def node(self):
@@ -26,20 +40,18 @@ class NetworkAgent(BaseAgent):
 
     def iter_agents(self, unique_id=None, *, limit_neighbors=False, **kwargs):
         unique_ids = None
-        if isinstance(unique_id, list):
+        if unique_ids is not None:
-            unique_ids = set(unique_id)
+            try:
-        elif unique_id is not None:
+                unique_ids = set(unique_id)
-            unique_ids = set(
+            except TypeError:
-                [
+                unique_ids = set([unique_id])
-                    unique_id,
-                ]
-            )
 
         if limit_neighbors:
             neighbor_ids = set()
             for node_id in self.G.neighbors(self.node_id):
-                if self.G.nodes[node_id].get("agent") is not None:
+                agent = self.G.nodes[node_id].get("agent")
-                    neighbor_ids.add(node_id)
+                if agent is not None:
+                    neighbor_ids.add(agent.unique_id)
             if unique_ids:
                 unique_ids = unique_ids & neighbor_ids
             else:

soil/analysis.py

@@ -0,0 +1,49 @@
+import os
+import sqlalchemy
+import pandas as pd
+from collections import namedtuple
+
+def plot(env, agent_df=None, model_df=None, steps=False, ignore=["agent_count", ]):
+    """Plot the model dataframe and agent dataframe together."""
+    if agent_df is None:
+        agent_df = env.agent_df()
+    if model_df is None:
+        model_df = env.model_df()
+    ignore = list(ignore)
+    if not steps:
+        ignore.append("step")
+    else:
+        ignore.append("time")
+
+    ax = model_df.drop(ignore, axis='columns').plot();
+    if not agent_df.empty:
+        agent_df.unstack().apply(lambda x: x.value_counts(),
+                                 axis=1).fillna(0).plot(ax=ax, secondary_y=True);
+
+Results = namedtuple("Results", ["config", "parameters", "env", "agents"])
+#TODO implement reading from CSV and SQLITE
+def read_sql(fpath=None, name=None, include_agents=False):
+    if not (fpath is None) ^ (name is None):
+        raise ValueError("Specify either a path or a simulation name")
+    if name:
+        fpath = os.path.join("soil_output", name, f"{name}.sqlite")
+    fpath = os.path.abspath(fpath)
+    # TODO: improve url parsing. This is a hacky way to check we weren't given a URL
+    if "://" not in fpath:
+        fpath = f"sqlite:///{fpath}"
+    engine = sqlalchemy.create_engine(fpath)
+    with engine.connect() as conn:
+        env = pd.read_sql_table("env", con=conn,
+                                index_col="step").reset_index().set_index([
+                                    "simulation_id", "params_id",
+                                    "iteration_id", "step"
+                                ])
+        agents = pd.read_sql_table("agents", con=conn, index_col=["simulation_id", "params_id", "iteration_id", "step", "agent_id"])
+        config = pd.read_sql_table("configuration", con=conn, index_col="simulation_id")
+        parameters = pd.read_sql_table("parameters", con=conn, index_col=["iteration_id", "params_id", "simulation_id"])
+        try:
+            parameters = parameters.pivot(columns="key", values="value")
+        except Exception as e:
+            print(f"warning: coult not pivot parameters: {e}")
+
+        return Results(config, parameters, env, agents)

soil/config.py

@@ -1,267 +1,2 @@
-from __future__ import annotations
+def load_config(cfg):
-
+    return cfg
-from enum import Enum
-from pydantic import BaseModel, ValidationError, validator, root_validator
-
-import yaml
-import os
-import sys
-
-
-from typing import Any, Callable, Dict, List, Optional, Union, Type
-from pydantic import BaseModel, Extra
-
-from . import environment, utils
-
-import networkx as nx
-
-
-# Could use TypeAlias in python >= 3.10
-nodeId = int
-
-
-class Node(BaseModel):
-    id: nodeId
-    state: Optional[Dict[str, Any]] = {}
-
-
-class Edge(BaseModel):
-    source: nodeId
-    target: nodeId
-    value: Optional[float] = 1
-
-
-class Topology(BaseModel):
-    nodes: List[Node]
-    directed: bool
-    links: List[Edge]
-
-
-class NetConfig(BaseModel):
-    params: Optional[Dict[str, Any]]
-    fixed: Optional[Union[Topology, nx.Graph]]
-    path: Optional[str]
-
-    class Config:
-        arbitrary_types_allowed = True
-
-    @staticmethod
-    def default():
-        return NetConfig(topology=None, params=None)
-
-    @root_validator
-    def validate_all(cls, values):
-        if "params" not in values and "topology" not in values:
-            raise ValueError(
-                "You must specify either a topology or the parameters to generate a graph"
-            )
-        return values
-
-
-class EnvConfig(BaseModel):
-    @staticmethod
-    def default():
-        return EnvConfig()
-
-
-class SingleAgentConfig(BaseModel):
-    agent_class: Optional[Union[Type, str]] = None
-    unique_id: Optional[int] = None
-    topology: Optional[bool] = False
-    node_id: Optional[Union[int, str]] = None
-    state: Optional[Dict[str, Any]] = {}
-
-
-class FixedAgentConfig(SingleAgentConfig):
-    n: Optional[int] = 1
-    hidden: Optional[bool] = False  # Do not count this agent towards total agent count
-
-    @root_validator
-    def validate_all(cls, values):
-        if values.get("unique_id", None) is not None and values.get("n", 1) > 1:
-            raise ValueError(
-                f"An unique_id can only be provided when there is only one agent ({values.get('n')} given)"
-            )
-        return values
-
-
-class OverrideAgentConfig(FixedAgentConfig):
-    filter: Optional[Dict[str, Any]] = None
-
-
-class Strategy(Enum):
-    topology = "topology"
-    total = "total"
-
-
-class AgentDistro(SingleAgentConfig):
-    weight: Optional[float] = 1
-    strategy: Strategy = Strategy.topology
-
-
-class AgentConfig(SingleAgentConfig):
-    n: Optional[int] = None
-    distribution: Optional[List[AgentDistro]] = None
-    fixed: Optional[List[FixedAgentConfig]] = None
-    override: Optional[List[OverrideAgentConfig]] = None
-
-    @staticmethod
-    def default():
-        return AgentConfig()
-
-    @root_validator
-    def validate_all(cls, values):
-        if "distribution" in values and (
-            "n" not in values and "topology" not in values
-        ):
-            raise ValueError(
-                "You need to provide the number of agents or a topology to extract the value from."
-            )
-        return values
-
-
-class Config(BaseModel, extra=Extra.allow):
-    version: Optional[str] = "1"
-
-    name: str = "Unnamed Simulation"
-    description: Optional[str] = None
-    group: str = None
-    dir_path: Optional[str] = None
-    num_trials: int = 1
-    max_time: float = 100
-    max_steps: int = -1
-    num_processes: int = 1
-    interval: float = 1
-    seed: str = ""
-    dry_run: bool = False
-    skip_test: bool = False
-
-    model_class: Union[Type, str] = environment.Environment
-    model_params: Optional[Dict[str, Any]] = {}
-
-    visualization_params: Optional[Dict[str, Any]] = {}
-
-    @classmethod
-    def from_raw(cls, cfg):
-        if isinstance(cfg, Config):
-            return cfg
-        if cfg.get("version", "1") == "1" and any(
-            k in cfg for k in ["agents", "agent_class", "topology", "environment_class"]
-        ):
-            return convert_old(cfg)
-        return Config(**cfg)
-
-
-def convert_old(old, strict=True):
-    """
-    Try to convert old style configs into the new format.
-
-    This is still a work in progress and might not work in many cases.
-    """
-
-    utils.logger.warning(
-        "The old configuration format is deprecated. The converted file MAY NOT yield the right results"
-    )
-
-    new = old.copy()
-
-    network = {}
-
-    if "topology" in old:
-        del new["topology"]
-        network["topology"] = old["topology"]
-
-    if "network_params" in old and old["network_params"]:
-        del new["network_params"]
-        for (k, v) in old["network_params"].items():
-            if k == "path":
-                network["path"] = v
-            else:
-                network.setdefault("params", {})[k] = v
-
-    topology = None
-    if network:
-        topology = network
-
-    agents = {"fixed": [], "distribution": []}
-
-    def updated_agent(agent):
-        """Convert an agent definition"""
-        newagent = dict(agent)
-        return newagent
-
-    by_weight = []
-    fixed = []
-    override = []
-
-    if "environment_agents" in new:
-
-        for agent in new["environment_agents"]:
-            agent.setdefault("state", {})["group"] = "environment"
-            if "agent_id" in agent:
-                agent["state"]["name"] = agent["agent_id"]
-                del agent["agent_id"]
-            agent["hidden"] = True
-            agent["topology"] = False
-            fixed.append(updated_agent(agent))
-        del new["environment_agents"]
-
-    if "agent_class" in old:
-        del new["agent_class"]
-        agents["agent_class"] = old["agent_class"]
-
-    if "default_state" in old:
-        del new["default_state"]
-        agents["state"] = old["default_state"]
-
-    if "network_agents" in old:
-        agents["topology"] = True
-
-        agents.setdefault("state", {})["group"] = "network"
-
-        for agent in new["network_agents"]:
-            agent = updated_agent(agent)
-            if "agent_id" in agent:
-                agent["state"]["name"] = agent["agent_id"]
-                del agent["agent_id"]
-                fixed.append(agent)
-            else:
-                by_weight.append(agent)
-        del new["network_agents"]
-
-    if "agent_class" in old and (not fixed and not by_weight):
-        agents["topology"] = True
-        by_weight = [{"agent_class": old["agent_class"], "weight": 1}]
-
-    # TODO: translate states properly
-    if "states" in old:
-        del new["states"]
-        states = old["states"]
-        if isinstance(states, dict):
-            states = states.items()
-        else:
-            states = enumerate(states)
-        for (k, v) in states:
-            override.append({"filter": {"node_id": k}, "state": v})
-
-    agents["override"] = override
-    agents["fixed"] = fixed
-    agents["distribution"] = by_weight
-
-    model_params = {}
-    if "environment_params" in new:
-        del new["environment_params"]
-        model_params = dict(old["environment_params"])
-
-    if "environment_class" in old:
-        del new["environment_class"]
-        new["model_class"] = old["environment_class"]
-
-    if "dump" in old:
-        del new["dump"]
-        new["dry_run"] = not old["dump"]
-
-    model_params["topology"] = topology
-    model_params["agents"] = agents
-
-    return Config(version="2", model_params=model_params, **new)

soil/datacollection.py

@@ -8,8 +8,10 @@ class SoilCollector(MDC):
         tables = tables or {}
         if 'agent_count' not in model_reporters:
             model_reporters['agent_count'] = lambda m: m.schedule.get_agent_count()
-        if 'state_id' not in agent_reporters:
+        if 'time' not in model_reporters:
-            agent_reporters['agent_id'] = lambda agent: agent.get('state_id', None)
+            model_reporters['time'] = lambda m: m.now
+        # if 'state_id' not in agent_reporters:
+        #     agent_reporters['state_id'] = lambda agent: getattr(agent, 'state_id', None)
 
         super().__init__(model_reporters=model_reporters,
                          agent_reporters=agent_reporters,

soil/debugging.py

@@ -8,6 +8,7 @@ from textwrap import indent
 from functools import wraps
 
 from .agents import FSM, MetaFSM
+from mesa import Model, Agent
 
 
 def wrapcmd(func):
@@ -15,14 +16,22 @@ def wrapcmd(func):
     def wrapper(self, arg: str, temporary=False):
         sys.settrace(self.trace_dispatch)
 
+        lastself = self
         known = globals()
         known.update(self.curframe.f_globals)
         known.update(self.curframe.f_locals)
-        known["agent"] = known.get("self", None)
-        known["model"] = known.get("self", {}).get("model")
         known["attrs"] = arg.strip().split()
 
-        exec(func.__code__, known, known)
+        this = known.get("self", None)
+
+        if isinstance(this, Model):
+            known["model"] = this
+        elif isinstance(this, Agent):
+            known["agent"] = this
+            known["model"] = this.model
+
+        known["self"] = lastself
+        return exec(func.__code__, known, known)
 
     return wrapper
 
@@ -57,6 +66,7 @@ class Debug(pdb.Pdb):
     do_sl = do_soil_list
 
     def do_continue_state(self, arg):
+        """Continue until next time this state is reached"""
         self.do_break_state(arg, temporary=True)
         return self.do_continue("")
 
@@ -80,6 +90,49 @@ class Debug(pdb.Pdb):
 
     do_aa = do_soil_agent
 
+    def do_break_step(self, arg: str):
+        """
+        Break before the next step.
+        """
+        try:
+            known = globals()
+            known.update(self.curframe.f_globals)
+            known.update(self.curframe.f_locals)
+            func = getattr(known["model"], "step")
+        except AttributeError as ex:
+            self.error(f"The model does not have a step function: {ex}")
+            return
+        if hasattr(func, "__func__"):
+            func = func.__func__
+
+        code = func.__code__
+        # use co_name to identify the bkpt (function names
+        # could be aliased, but co_name is invariant)
+        funcname = code.co_name
+        lineno = code.co_firstlineno
+        filename = code.co_filename
+
+        # Check for reasonable breakpoint
+        line = self.checkline(filename, lineno)
+        if not line:
+            raise ValueError("no line found")
+            # now set the break point
+
+        existing = self.get_breaks(filename, line)
+        if existing:
+            self.message("Breakpoint already exists at %s:%d" % (filename, line))
+            return
+        cond = f"self.schedule.steps > {model.schedule.steps}"
+        err = self.set_break(filename, line, True, cond, funcname)
+        if err:
+            self.error(err)
+        else:
+            bp = self.get_breaks(filename, line)[-1]
+            self.message("Breakpoint %d at %s:%d" % (bp.number, bp.file, bp.line))
+            return self.do_continue("")
+    
+    do_bstep = do_break_step
+
     def do_break_state(self, arg: str, instances=None, temporary=False):
         """
         Break before a specified state is stepped into.

soil/decorators.py

@@ -0,0 +1,6 @@
+def report(f: property):
+    if isinstance(f, property):
+        setattr(f.fget, "add_to_report", True)
+    else:
+        setattr(f, "add_to_report", True)
+    return f

soil/environment.py

@@ -6,20 +6,21 @@ import math
 import logging
 import inspect
 
-from typing import Any, Dict, Optional, Union, List
+from typing import Any, Callable, Dict, Optional, Union, List, Type
 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
 
-from mesa import Model
+from mesa import Model, Agent
 
-from . import agents as agentmod, config, datacollection, serialization, utils, time, network, events
+from . import agents as agentmod, datacollection, serialization, utils, time, network, events
 
 
+# TODO: maybe add metaclass to read attributes of a model
+
 class BaseEnvironment(Model):
     """
     The environment is key in a simulation. It controls how agents interact,
@@ -33,110 +34,112 @@ class BaseEnvironment(Model):
     :meth:`soil.environment.Environment.get` method.
     """
 
-    def __init__(
+    collector_class = datacollection.SoilCollector
-        self,
-        id="unnamed_env",
-        seed="default",
-        schedule_class=time.TimedActivation,
-        dir_path=None,
-        interval=1,
-        agent_class=None,
-        agents: List[tuple[type, Dict[str, Any]]] = {},
-        collector_class: type = datacollection.SoilCollector,
-        agent_reporters: Optional[Any] = None,
-        model_reporters: Optional[Any] = None,
-        tables: Optional[Any] = None,
-        **env_params,
-    ):
-
-        super().__init__(seed=seed)
-
-        self.current_id = -1
-
-        self.id = id
 
+    def __new__(cls,
+                *args: Any,
+                seed="default",
+                dir_path=None,
+                collector_class: type = None,
+                agent_reporters: Optional[Any] = None,
+                model_reporters: Optional[Any] = None,
+                tables: Optional[Any] = None,
+                **kwargs: Any) -> Any:
+        """Create a new model with a default seed value"""
+        self = super().__new__(cls, *args, seed=seed, **kwargs)
         self.dir_path = dir_path or os.getcwd()
-
+        collector_class = collector_class or cls.collector_class
-        if schedule_class is None:
-            schedule_class = time.TimedActivation
-        else:
-            schedule_class = serialization.deserialize(schedule_class)
-        self.schedule = schedule_class(self)
-
-        self.agent_class = agent_class or agentmod.BaseAgent
-
-        self.interval = interval
-        self.init_agents(agents)
-
-        self.logger = utils.logger.getChild(self.id)
-
         collector_class = serialization.deserialize(collector_class)
         self.datacollector = collector_class(
             model_reporters=model_reporters,
             agent_reporters=agent_reporters,
             tables=tables,
         )
+        for k in dir(cls):
+            v = getattr(cls, k)
+            if isinstance(v, property):
+                v = v.fget
+            if getattr(v, "add_to_report", False):
+                self.add_model_reporter(k, v)
+
+        return self
+
+    def __init__(
+        self,
+        *,
+        id="unnamed_env",
+        seed="default",
+        dir_path=None,
+        schedule_class=time.TimedActivation,
+        interval=1,
+        logger = None,
+        agents: Optional[Dict] = None,
+        collector_class: type = datacollection.SoilCollector,
+        agent_reporters: Optional[Any] = None,
+        model_reporters: Optional[Any] = None,
+        tables: Optional[Any] = None,
+        init: bool = True,
+        **env_params,
+    ):
+
+        super().__init__()
+
+
+        self.current_id = -1
+
+        self.id = id
+
+        if logger:
+            self.logger = logger
+        else:
+            self.logger = utils.logger.getChild(self.id)
+
+        if schedule_class is None:
+            schedule_class = time.TimedActivation
+        else:
+            schedule_class = serialization.deserialize(schedule_class)
+
+        self.interval = interval
+        self.schedule = schedule_class(self)
+
         for (k, v) in env_params.items():
             self[k] = v
 
-    def _agent_from_dict(self, agent):
+        if agents:
-        """
+            self.add_agents(**agents)
-        Translate an agent dictionary into an agent
+        if init:
-        """
+            self.init()
-        agent = dict(**agent)
+            self.datacollector.collect(self)
-        cls = agent.pop("agent_class", None) or self.agent_class
-        unique_id = agent.pop("unique_id", None)
-        if unique_id is None:
-            unique_id = self.next_id()
 
-        return serialization.deserialize(cls)(unique_id=unique_id, model=self, **agent)
+    def init(self):
-
+        pass
-    def init_agents(self, agents: Union[config.AgentConfig, List[Dict[str, Any]]] = {}):
-        """
-        Initialize the agents in the model from either a `soil.config.AgentConfig` or a list of
-        dictionaries that each describes an agent.
-
-        If given a list of dictionaries, an agent will be created for each dictionary. The agent
-        class can be specified through the `agent_class` key. The rest of the items will be used
-        as parameters to the agent.
-        """
-        if not agents:
-            return
-
-        lst = agents
-        override = []
-        if not isinstance(lst, list):
-            if not isinstance(agents, config.AgentConfig):
-                lst = config.AgentConfig(**agents)
-            if lst.override:
-                override = lst.override
-            lst = self._agent_dict_from_config(lst)
-
-        # TODO: check override is working again. It cannot (easily) be part of agents.from_config anymore,
-        # because it needs attribute such as unique_id, which are only present after init
-        new_agents = [self._agent_from_dict(agent) for agent in lst]
-
-        for a in new_agents:
-            self.schedule.add(a)
-
-        for rule in override:
-            for agent in agentmod.filter_agents(self.schedule._agents, **rule.filter):
-                for attr, value in rule.state.items():
-                    setattr(agent, attr, value)
-
-    def _agent_dict_from_config(self, cfg):
-        return agentmod.from_config(cfg, random=self.random)
 
     @property
     def agents(self):
         return agentmod.AgentView(self.schedule._agents)
 
-    def find_one(self, *args, **kwargs):
+    def agent(self, *args, **kwargs):
         return agentmod.AgentView(self.schedule._agents).one(*args, **kwargs)
 
     def count_agents(self, *args, **kwargs):
         return sum(1 for i in self.agents(*args, **kwargs))
     
+    def agent_df(self, steps=False):
+        df = self.datacollector.get_agent_vars_dataframe()
+        if steps:
+            df.index.rename(["step", "agent_id"], inplace=True)
+            return df
+        model_df = self.datacollector.get_model_vars_dataframe()
+        df.index = df.index.set_levels(model_df.time, level=0).rename(["time", "agent_id"])
+        return df
+
+    def model_df(self, steps=False):
+        df = self.datacollector.get_model_vars_dataframe()
+        if steps:
+            return df
+        df.index.rename("step", inplace=True)
+        return df.reset_index().set_index("time")
+
     @property
     def now(self):
         if self.schedule:
@@ -144,17 +147,34 @@ class BaseEnvironment(Model):
         raise Exception(
             "The environment has not been scheduled, so it has no sense of time"
         )
+    def init_agents(self):
+        pass
 
-    def add_agent(self, unique_id=None, **kwargs):
+    def add_agent(self, agent_class, unique_id=None, **agent):
         if unique_id is None:
             unique_id = self.next_id()
 
-        kwargs["unique_id"] = unique_id
+        agent["unique_id"] = unique_id
-        a = self._agent_from_dict(kwargs)
+
+        agent = dict(**agent)
+        unique_id = agent.pop("unique_id", None)
+        if unique_id is None:
+            unique_id = self.next_id()
+
+        a = serialization.deserialize(agent_class)(unique_id=unique_id, model=self, **agent)
 
         self.schedule.add(a)
         return a
 
+    def add_agents(self, agent_classes: List[type], k, weights: Optional[List[float]] = None, **kwargs):
+        if isinstance(agent_classes, type):
+            agent_classes = [agent_classes]
+        if weights is None:
+            weights = [1] * len(agent_classes)
+
+        for cls in self.random.choices(agent_classes, weights=weights, k=k):
+            self.add_agent(agent_class=cls, **kwargs)
+
     def log(self, message, *args, level=logging.INFO, **kwargs):
         if not self.logger.isEnabledFor(level):
             return
@@ -172,12 +192,37 @@ class BaseEnvironment(Model):
         Advance one step in the simulation, and update the data collection and scheduler appropriately
         """
         super().step()
-        # self.logger.info(
-        #     "--- Step: {:^5} - Time: {now:^5} ---", steps=self.schedule.steps, now=self.now
-        # )
         self.schedule.step()
         self.datacollector.collect(self)
 
+        if self.logger.isEnabledFor(logging.DEBUG):
+            msg = "Model data:\n"
+            max_width = max(len(k) for k in self.datacollector.model_vars.keys())
+            for (k, v) in self.datacollector.model_vars.items():
+                msg += f"\t{k:<{max_width}}: {v[-1]:>6}\n"
+            self.logger.debug(f"--- Steps: {self.schedule.steps:^5} - Time: {self.now:^5} --- " + msg)
+
+    def add_model_reporter(self, name, func=None):
+        if not func:
+            func = lambda env: getattr(env, name)
+        self.datacollector._new_model_reporter(name, func)
+
+    def add_agent_reporter(self, name, agent_type=None):
+        if agent_type:
+            reporter = lambda a: getattr(a, name) if isinstance(a, agent_type) else None
+        else:
+            reporter = lambda a: getattr(a, name, None)
+        self.datacollector._new_agent_reporter(name, reporter)
+
+    @classmethod
+    def run(cls, *,
+            iterations=1,
+            num_processes=1, **kwargs):
+        from .simulation import Simulation
+        return Simulation(name=cls.__name__,
+                          model=cls, iterations=iterations,
+                          num_processes=num_processes, **kwargs).run()
+
     def __getitem__(self, key):
         try:
             return getattr(self, key)
@@ -214,67 +259,72 @@ class NetworkEnvironment(BaseEnvironment):
     and methods to associate agents to nodes and vice versa.
     """
 
-    def __init__(
+    def __init__(self,
-        self, *args, topology: Union[config.NetConfig, nx.Graph] = None, **kwargs
+                 *args,
-    ):
+                 topology: Optional[Union[nx.Graph, str]] = None,
-        agents = kwargs.pop("agents", None)
+                 agent_class: Optional[Type[agentmod.Agent]] = None,
-        super().__init__(*args, agents=None, **kwargs)
+                 network_generator: Optional[Callable] = None,
+                 network_params: Optional[Dict] = {},
+                 init=True,
+                 **kwargs):
+        self.topology = topology
+        self.network_generator = network_generator
+        self.network_params = network_params
+        if topology or network_params or network_generator:
+            self.create_network(topology, generator=network_generator, **network_params)
+        else:
+            self.G = nx.Graph()
+        super().__init__(*args, **kwargs, init=False)
 
-        if topology is None:
+        self.agent_class = agent_class
-            topology = nx.Graph()
+        if agent_class:
-        elif not isinstance(topology, nx.Graph):
+            self.agent_class = serialization.deserialize(agent_class)
-            topology = network.from_config(topology, dir_path=self.dir_path)
+        if self.agent_class:
+            self.populate_network(self.agent_class)
+        self._check_agent_nodes()
+        if init:
+            self.init()
+            self.datacollector.collect(self)
 
+    def add_agent(self, agent_class, *args, node_id=None, topology=None, **kwargs):
+        if node_id is None and topology is None:
+            return super().add_agent(agent_class, *args, **kwargs)
+        try:
+            a = super().add_agent(agent_class, *args, node_id=node_id, **kwargs)
+        except TypeError:
+            self.logger.warning(f"Agent constructor for {agent_class} does not have a node_id attribute. Might be a bug.")
+            a = super().add_agent(agent_class, *args, **kwargs)
+        self.G.nodes[node_id]["agent"] = a
+        return a
+
+    def add_agents(self, *args, k=None, **kwargs):
+        if not k and not self.G:
+            raise ValueError("Cannot add agents to an empty network")
+        super().add_agents(*args, k=k or len(self.G), **kwargs)
+
+    def create_network(self, topology=None, generator=None, path=None, **network_params):
+        if topology is not None:
+            topology = network.from_topology(topology, dir_path=self.dir_path)
+        elif path is not None:
+            topology = network.from_topology(path, dir_path=self.dir_path)
+        elif generator is not None:
+            topology = network.from_params(generator=generator, dir_path=self.dir_path, **network_params)
+        else:
+            raise ValueError("topology must be a networkx.Graph or a string, or network_generator must be provided")
         self.G = topology
 
-        self.init_agents(agents)
-
     def init_agents(self, *args, **kwargs):
         """Initialize the agents from a"""
         super().init_agents(*args, **kwargs)
-        for agent in self.schedule._agents.values():
-            self._init_node(agent)
-
-    def _init_node(self, agent):
-        """
-        Make sure the node for a given agent has the proper attributes.
-        """
-        if hasattr(agent, "node_id"):
-            self.G.nodes[agent.node_id]["agent"] = agent
-
-    def _agent_dict_from_config(self, cfg):
-        return agentmod.from_config(cfg, topology=self.G, random=self.random)
-
-    def _agent_from_dict(self, agent, unique_id=None):
-        agent = dict(agent)
-
-        if not agent.get("topology", False):
-            return super()._agent_from_dict(agent)
-
-        if unique_id is None:
-            unique_id = self.next_id()
-        node_id = agent.get("node_id", None)
-        if node_id is None:
-            node_id = network.find_unassigned(self.G, random=self.random)
-        self.G.nodes[node_id]["agent"] = None
-        agent["node_id"] = node_id
-        agent["unique_id"] = unique_id
-        agent["topology"] = self.G
-        node_attrs = self.G.nodes[node_id]
-        node_attrs.pop('agent', None)
-        node_attrs.update(agent)
-        agent = node_attrs
-
-        a = super()._agent_from_dict(agent)
-        self._init_node(a)
-
-        return a
 
     @property
     def network_agents(self):
-        for a in self.schedule._agents.values():
+        """Return agents still alive and assigned to a node in the network."""
-            if isinstance(a, agentmod.NetworkAgent):
+        for (id, data) in self.G.nodes(data=True):
-                yield a
+            if "agent" in data:
+                agent = data["agent"]
+                if getattr(agent, "alive", True):
+                    yield agent
 
     def add_node(self, agent_class, unique_id=None, node_id=None, **kwargs):
         if unique_id is None:
@@ -290,7 +340,6 @@ class NetworkEnvironment(BaseEnvironment):
             self.G.add_node(node_id)
 
         assert "agent" not in self.G.nodes[node_id]
-        self.G.nodes[node_id]["agent"] = None  # Reserve
 
         a = self.add_agent(
             unique_id=unique_id,
@@ -302,24 +351,56 @@ class NetworkEnvironment(BaseEnvironment):
         a["visible"] = True
         return a
 
-    def add_agent(self, *args, **kwargs):
+    def _check_agent_nodes(self):
-        a = super().add_agent(*args, **kwargs)
+        """
-        if hasattr(a, "node_id"):
+        Detect nodes that have agents assigned to them.
-            assert self.G.nodes[a.node_id]["agent"] == a
+        """
-        return a
+        for (id, data) in self.G.nodes(data=True):
+            if "agent_id" in data:
+                agent = self.agents(data["agent_id"])
+                self.G.nodes[id]["agent"] = agent
+                assert not getattr(agent, "node_id", None) or agent.node_id == id
+                agent.node_id = id
+        for agent in self.agents():
+            if hasattr(agent, "node_id"):
+                node_id = agent["node_id"]
+                if node_id not in self.G.nodes:
+                    raise ValueError(f"Agent {agent} is assigned to node {agent.node_id} which is not in the network")
+                node = self.G.nodes[node_id]
+                if node.get("agent") is not None and node["agent"] != agent:
+                    raise ValueError(f"Node {node_id} already has a different agent assigned to it")
+                self.G.nodes[node_id]["agent"] = agent
+
+    def add_agents(self, agent_classes: List[type], k=None, weights: Optional[List[float]] = None, **kwargs):
+        if k is None:
+            k = len(self.G)
+            if not k:
+                raise ValueError("Cannot add agents to an empty network")
+        super().add_agents(agent_classes, k=k, weights=weights, **kwargs)
 
     def agent_for_node_id(self, node_id):
         return self.G.nodes[node_id].get("agent")
 
-    def populate_network(self, agent_class, weights=None, **agent_params):
+    def populate_network(self, agent_class: List[Model], weights: List[float] = None, **agent_params):
-        if not hasattr(agent_class, "len"):
+        if isinstance(agent_class, type):
             agent_class = [agent_class]
-            weights = None
+        else:
-        for (node_id, node) in self.G.nodes(data=True):
+            agent_class = list(agent_class)
+        if not weights:
+            weights = [1] * len(agent_class)
+        assert len(self.G)
+        classes = self.random.choices(agent_class, weights, k=len(self.G))
+        toadd = []
+        for (cls, (node_id, node)) in zip(classes, self.G.nodes(data=True)):
             if "agent" in node:
                 continue
-            a_class = self.random.choices(agent_class, weights)[0]
+            node["agent"] = None # Reserve
-            self.add_agent(node_id=node_id, topology=self.G,  agent_class=a_class, **agent_params)
+            toadd.append(dict(node_id=node_id, topology=self.G,  agent_class=cls, **agent_params))
+        for d in toadd:
+            a = self.add_agent(**d)
+            self.G.nodes[d["node_id"]]["agent"] = a
+        assert all("agent" in node for (_, node) in self.G.nodes(data=True))
+        assert len(list(self.network_agents))
 
 
 class EventedEnvironment(BaseEnvironment):
@@ -327,7 +408,7 @@ class EventedEnvironment(BaseEnvironment):
         for agent in self.agents(**kwargs):
             if agent == sender:
                 continue
-            self.logger.info(f"Telling {repr(agent)}: {msg} ttl={ttl}")
+            self.logger.debug(f"Telling {repr(agent)}: {msg} ttl={ttl}")
             try:
                 inbox = agent._inbox
             except AttributeError:

soil/exporters.py

@@ -8,9 +8,10 @@ from textwrap import dedent, indent
 
 import matplotlib.pyplot as plt
 import networkx as nx
+import pandas as pd
 
 
-from .serialization import deserialize
+from .serialization import deserialize, serialize
 from .utils import try_backup, open_or_reuse, logger, timer
 
 
@@ -38,7 +39,7 @@ class DryRunner(BytesIO):
         except UnicodeDecodeError:
             pass
         logger.info(
-            "**Not** written to {} (dry run mode):\n\n{}\n\n".format(
+            "**Not** written to {} (no_dump mode):\n\n{}\n\n".format(
                 self.__fname, content
             )
         )
@@ -51,12 +52,12 @@ class Exporter:
     if you don't plan to implement all the methods.
     """
 
-    def __init__(self, simulation, outdir=None, dry_run=None, copy_to=None):
+    def __init__(self, simulation, outdir=None, dump=True, copy_to=None):
         self.simulation = simulation
         outdir = outdir or os.path.join(os.getcwd(), "soil_output")
         self.outdir = os.path.join(outdir, simulation.group or "", simulation.name)
-        self.dry_run = dry_run
+        self.dump = dump
-        if copy_to is None and dry_run:
+        if copy_to is None and not dump:
             copy_to = sys.stdout
         self.copy_to = copy_to
 
@@ -68,16 +69,16 @@ class Exporter:
         """Method to call when the simulation ends"""
         pass
 
-    def trial_start(self, env):
+    def iteration_start(self, env):
-        """Method to call when a trial start"""
+        """Method to call when a iteration start"""
         pass
 
-    def trial_end(self, env):
+    def iteration_end(self, env, params, params_id):
-        """Method to call when a trial ends"""
+        """Method to call when a iteration ends"""
         pass
 
     def output(self, f, mode="w", **kwargs):
-        if self.dry_run:
+        if not self.dump:
             f = DryRunner(f, copy_to=self.copy_to)
         else:
             try:
@@ -85,74 +86,104 @@ class Exporter:
                     f = os.path.join(self.outdir, f)
             except TypeError:
                 pass
-        return open_or_reuse(f, mode=mode, **kwargs)
+        return open_or_reuse(f, mode=mode, backup=self.simulation.backup, **kwargs)
 
-    def get_dfs(self, env):
+    def get_dfs(self, env, **kwargs):
-        yield from get_dc_dfs(env.datacollector, trial_id=env.id)
+        yield from get_dc_dfs(env.datacollector,
+                              simulation_id=self.simulation.id,
+                              iteration_id=env.id,
+                              **kwargs)
 
 
-def get_dc_dfs(dc, trial_id=None):
+def get_dc_dfs(dc, **kwargs):
-    dfs = {
+    dfs = {}
-        "env": dc.get_model_vars_dataframe(),
+    dfe = dc.get_model_vars_dataframe()
-        "agents": dc.get_agent_vars_dataframe(),
+    dfe.index.rename("step", inplace=True)
-    }
+    dfs["env"] = dfe
+    try:
+        dfa = dc.get_agent_vars_dataframe() 
+        dfa.index.rename(["step", "agent_id"], inplace=True)
+        dfs["agents"] = dfa
+    except UserWarning:
+        pass
     for table_name in dc.tables:
         dfs[table_name] = dc.get_table_dataframe(table_name)
-    if trial_id:
+    for (name, df) in dfs.items():
-        for (name, df) in dfs.items():
+        for (k, v) in kwargs.items():
-            df["trial_id"] = trial_id
+            df[k] = v
+        df.set_index(["simulation_id", "iteration_id"], append=True, inplace=True)
+    
     yield from dfs.items()
 
 
 class SQLite(Exporter):
     """Writes sqlite results"""
+    sim_started = False
 
     def sim_start(self):
-        if self.dry_run:
+        if not self.dump:
-            logger.info("NOT dumping results")
+            logger.debug("NOT dumping results")
             return
         self.dbpath = os.path.join(self.outdir, f"{self.simulation.name}.sqlite")
         logger.info("Dumping results to %s", self.dbpath)
-        try_backup(self.dbpath, remove=True)
+        if self.simulation.backup:
+            try_backup(self.dbpath, remove=True)
         
-    def trial_end(self, env):
+        if self.simulation.overwrite:
-        if self.dry_run:
+            if os.path.exists(self.dbpath):
-            logger.info("Running in DRY_RUN mode, the database will NOT be created")
+                os.remove(self.dbpath)
+        
+        self.engine = create_engine(f"sqlite:///{self.dbpath}", echo=False)
+
+        sim_dict = {k: serialize(v)[0] for (k,v) in self.simulation.to_dict().items()}
+        sim_dict["simulation_id"] = self.simulation.id
+        df = pd.DataFrame([sim_dict])
+        df.to_sql("configuration", con=self.engine, if_exists="append")
+
+    def iteration_end(self, env, params, params_id, *args, **kwargs):
+        if not self.dump:
+            logger.info("Running in NO DUMP mode. Results will NOT be saved to a DB.")
             return
 
         with timer(
-            "Dumping simulation {} trial {}".format(self.simulation.name, env.id)
+            "Dumping simulation {} iteration {}".format(self.simulation.name, env.id)
         ):
 
-            engine = create_engine(f"sqlite:///{self.dbpath}", echo=False)
+            pd.DataFrame([{"simulation_id": self.simulation.id,
+                           "params_id": params_id,
+                           "iteration_id": env.id,
+                           "key": k,
+                           "value": serialize(v)[0]} for (k,v) in params.items()]).to_sql("parameters", con=self.engine, if_exists="append")
 
-            for (t, df) in self.get_dfs(env):
+            for (t, df) in self.get_dfs(env, params_id=params_id):
-                df.to_sql(t, con=engine, if_exists="append")
+                df.to_sql(t, con=self.engine, if_exists="append")
 
 class csv(Exporter):
+    """Export the state of each environment (and its agents) a CSV file for the simulation"""
 
-    """Export the state of each environment (and its agents) in a separate CSV file"""
+    def sim_start(self):
+        super().sim_start()
 
-    def trial_end(self, env):
+    def iteration_end(self, env, params, params_id, *args, **kwargs):
         with timer(
-            "[CSV] Dumping simulation {} trial {} @ dir {}".format(
+            "[CSV] Dumping simulation {} iteration {} @ dir {}".format(
                 self.simulation.name, env.id, self.outdir
             )
         ):
-            for (df_name, df) in self.get_dfs(env):
+            for (df_name, df) in self.get_dfs(env, params_id=params_id):
-                with self.output("{}.{}.csv".format(env.id, df_name)) as f:
+                with self.output("{}.{}.csv".format(env.id, df_name), mode="a") as f:
                     df.to_csv(f)
 
 
 # TODO: reimplement GEXF exporting without history
 class gexf(Exporter):
-    def trial_end(self, env):
+    def iteration_end(self, env, *args, **kwargs):
-        if self.dry_run:
+        if not self.dump:
-            logger.info("Not dumping GEXF in dry_run mode")
+            logger.info("Not dumping GEXF (NO_DUMP mode)")
             return
 
         with timer(
-            "[GEXF] Dumping simulation {} trial {}".format(self.simulation.name, env.id)
+            "[GEXF] Dumping simulation {} iteration {}".format(self.simulation.name, env.id)
         ):
             with self.output("{}.gexf".format(env.id), mode="wb") as f:
                 network.dump_gexf(env.history_to_graph(), f)
@@ -164,13 +195,13 @@ class dummy(Exporter):
         with self.output("dummy", "w") as f:
             f.write("simulation started @ {}\n".format(current_time()))
 
-    def trial_start(self, env):
+    def iteration_start(self, env):
         with self.output("dummy", "w") as f:
-            f.write("trial started@ {}\n".format(current_time()))
+            f.write("iteration started@ {}\n".format(current_time()))
 
-    def trial_end(self, env):
+    def iteration_end(self, env, *args, **kwargs):
         with self.output("dummy", "w") as f:
-            f.write("trial ended@ {}\n".format(current_time()))
+            f.write("iteration ended@ {}\n".format(current_time()))
 
     def sim_end(self):
         with self.output("dummy", "a") as f:
@@ -178,7 +209,7 @@ class dummy(Exporter):
 
 
 class graphdrawing(Exporter):
-    def trial_end(self, env):
+    def iteration_end(self, env, *args, **kwargs):
         # Outside effects
         f = plt.figure()
         nx.draw(
@@ -193,9 +224,9 @@ class graphdrawing(Exporter):
 
 
 class summary(Exporter):
-    """Print a summary of each trial to sys.stdout"""
+    """Print a summary of each iteration to sys.stdout"""
 
-    def trial_end(self, env):
+    def iteration_end(self, env, *args, **kwargs):
         msg = ""
         for (t, df) in self.get_dfs(env):
             if not len(df):
@@ -224,10 +255,10 @@ class YAML(Exporter):
     """Writes the configuration of the simulation to a YAML file"""
 
     def sim_start(self):
-        if self.dry_run:
+        if not self.dump:
-            logger.info("NOT dumping results")
+            logger.debug("NOT dumping results")
             return
-        with self.output(self.simulation.name + ".dumped.yml") as f:
+        with self.output(self.simulation.id + ".dumped.yml") as f:
             logger.info(f"Dumping simulation configuration to {self.outdir}")
             f.write(self.simulation.to_yaml())
 
@@ -235,22 +266,17 @@ class default(Exporter):
     """Default exporter. Writes sqlite results, as well as the simulation YAML"""
 
     def __init__(self, *args, exporter_cls=[], **kwargs):
-        exporter_cls = exporter_cls or [YAML, SQLite, summary]
+        exporter_cls = exporter_cls or [YAML, SQLite]
         self.inner = [cls(*args, **kwargs) for cls in exporter_cls]
 
-    def sim_start(self):
+    def sim_start(self, *args, **kwargs):
         for exporter in self.inner:
-            exporter.sim_start()
+            exporter.sim_start(*args, **kwargs)
 
-    def sim_end(self):
+    def sim_end(self, *args, **kwargs):
         for exporter in self.inner:
-            exporter.sim_end()
+            exporter.sim_end(*args, **kwargs)
 
-    def trial_start(self, env):
+    def iteration_end(self, *args, **kwargs):
         for exporter in self.inner:
-            exporter.trial_start(env)
+            exporter.iteration_end(*args, **kwargs)
-
-
-    def trial_end(self, env):
-        for exporter in self.inner:
-            exporter.trial_end(env)

soil/network.py

@@ -10,47 +10,47 @@ import networkx as nx
 from . import config, serialization, basestring
 
 
-def from_config(cfg: config.NetConfig, dir_path: str = None):
+def from_topology(topology, dir_path: str = None):
-    if not isinstance(cfg, config.NetConfig):
+    if topology is None:
-        cfg = config.NetConfig(**cfg)
+        return nx.Graph()
+    if isinstance(topology, nx.Graph):
+        return topology
 
-    if cfg.path:
+    # If it's a dict, assume it's a node-link graph
-        path = cfg.path
+    if isinstance(topology, dict):
-        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)
+            return nx.json_graph.node_link_graph(topology)
-        except AttributeError:
+        except Exception as ex:
-            raise AttributeError("Unknown format")
+            raise ValueError("Unknown topology format")
-        return method(path, **kwargs)
     
-    if cfg.params:
+    # Otherwise, treat like a path
-        net_args = dict(cfg.params)
+    path = topology
-        net_gen = net_args.pop("generator")
+    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 dir_path not in sys.path:
-            sys.path.append(dir_path)
 
-        method = serialization.deserializer(
+def from_params(generator, dir_path: str = None, **params):
-            net_gen,
-            known_modules=[
-                "networkx.generators",
-            ],
-        )
-        return method(**net_args)
 
-    if isinstance(cfg.fixed, config.Topology):
+    if dir_path not in sys.path:
-        cfg = cfg.fixed.dict()
+        sys.path.append(dir_path)
 
-    if isinstance(cfg, str) or isinstance(cfg, dict):
+    method = serialization.deserializer(
-        return nx.json_graph.node_link_graph(cfg)
+        generator,
-
+        known_modules=[
-    return nx.Graph()
+            "networkx.generators",
+        ],
+    )
+    return method(**params)
 
 
 def find_unassigned(G, shuffle=False, random=random):

soil/parameters.py

@@ -0,0 +1,32 @@
+from __future__ import annotations
+
+from typing_extensions import Annotated
+import annotated_types
+from typing import *
+
+from dataclasses import dataclass
+
+class Parameter:
+    pass
+
+
+def floatrange(
+    *,
+    gt: Optional[float] = None,
+    ge: Optional[float] = None,
+    lt: Optional[float] = None,
+    le: Optional[float] = None,
+    multiple_of: Optional[float] = None,
+) -> type[float]:
+    return Annotated[ 
+        float,
+        annotated_types.Interval(gt=gt, ge=ge, lt=lt, le=le),
+        annotated_types.MultipleOf(multiple_of) if multiple_of is not None else None,
+    ]
+
+function = Annotated[Callable, Parameter]
+Integer = Annotated[int, Parameter]
+Float = Annotated[float, Parameter]
+
+
+probability = floatrange(ge=0, le=1)

soil/serialization.py

@@ -4,14 +4,15 @@ import ast
 import sys
 import re
 import importlib
+import importlib.machinery, importlib.util
 from glob import glob
 from itertools import product, chain
 
-from .config import Config
-
 import yaml
 import networkx as nx
 
+from . import config
+
 from jinja2 import Template
 
 
@@ -90,23 +91,55 @@ def load_files(*patterns, **kwargs):
         for i in glob(pattern, **kwargs, recursive=True):
             for cfg in load_file(i):
                 path = os.path.abspath(i)
-                yield Config.from_raw(cfg), path
+                yield cfg, path
 
 
 def load_config(cfg):
-    if isinstance(cfg, Config):
+    if isinstance(cfg, dict):
-        yield cfg, os.getcwd()
+        yield config.load_config(cfg), os.getcwd()
-    elif isinstance(cfg, dict):
-        yield Config.from_raw(cfg), os.getcwd()
     else:
         yield from load_files(cfg)
 
 
 builtins = importlib.import_module("builtins")
 
-KNOWN_MODULES = [
+KNOWN_MODULES = {
-    "soil",
+    'soil': None,
-]
+
+}
+
+MODULE_FILES = {}
+
+def add_source_file(file):
+    """Add a file to the list of known modules"""
+    file = os.path.abspath(file)
+    if file in MODULE_FILES:
+        logger.warning(f"File {file} already added as module {MODULE_FILES[file]}. Reloading")
+        remove_source_file(file)
+    modname = f"imported_module_{len(MODULE_FILES)}"
+    loader = importlib.machinery.SourceFileLoader(modname, file)
+    spec = importlib.util.spec_from_loader(loader.name, loader)
+    my_module = importlib.util.module_from_spec(spec)
+    loader.exec_module(my_module)
+    MODULE_FILES[file] = modname
+    KNOWN_MODULES[modname] = my_module
+
+def remove_source_file(file):
+    """Remove a file from the list of known modules"""
+    file = os.path.abspath(file)
+    modname = None
+    try:
+        modname = MODULE_FILES.pop(file)
+        KNOWN_MODULES.pop(modname)
+    except KeyError as ex:
+        raise ValueError(f"File {file} had not been added as a module: {ex}")
+
+def get_module(modname):
+    """Get a module from the list of known modules"""
+    if modname not in KNOWN_MODULES or KNOWN_MODULES[modname] is None:
+        module = importlib.import_module(modname)
+        KNOWN_MODULES[modname] = module
+    return KNOWN_MODULES[modname]
 
 
 def name(value, known_modules=KNOWN_MODULES):
@@ -124,9 +157,7 @@ def name(value, known_modules=KNOWN_MODULES):
     if known_modules and modname in known_modules:
         return tname
     for kmod in known_modules:
-        if not kmod:
+        module = get_module(kmod)
-            continue
-        module = importlib.import_module(kmod)
         if hasattr(module, tname):
             return tname
     return "{}.{}".format(modname, tname)
@@ -150,7 +181,7 @@ def serialize_dict(d, known_modules=KNOWN_MODULES):
         d = dict(d)
     except (ValueError, TypeError) as ex:
         return serialize(d)[0]
-    for (k, v) in d.items():
+    for (k, v) in reversed(list(d.items())):
         if isinstance(v, dict):
             d[k] = serialize_dict(v, known_modules=known_modules)
         elif isinstance(v, list):
@@ -177,7 +208,7 @@ def deserializer(type_, known_modules=KNOWN_MODULES):
     match = IS_CLASS.match(type_)
     if match:
         modname, tname = match.group(1).rsplit(".", 1)
-        module = importlib.import_module(modname)
+        module = get_module(modname)
         cls = getattr(module, tname)
         return getattr(cls, "deserialize", cls)
 
@@ -195,7 +226,7 @@ def deserializer(type_, known_modules=KNOWN_MODULES):
     errors = []
     for modname, tname in options:
         try:
-            module = importlib.import_module(modname)
+            module = get_module(modname)
             cls = getattr(module, tname)
             return getattr(cls, "deserialize", cls)
         except (ImportError, AttributeError) as ex:

soil/simulation.py

@@ -1,87 +1,153 @@
 import os
 from time import time as current_time, strftime
-import importlib
 import sys
 import yaml
-import traceback
+import hashlib
+
 import inspect
 import logging
 import networkx as nx
 
+from tqdm.auto import tqdm
+
 from textwrap import dedent
 
-from dataclasses import dataclass, field, asdict
+from dataclasses import dataclass, field, asdict, replace
 from typing import Any, Dict, Union, Optional, List
 
 
-from networkx.readwrite import json_graph
 from functools import partial
-import pickle
+from contextlib import contextmanager
+from itertools import product
+import json
+
 
 from . import serialization, exporters, utils, basestring, agents
 from .environment import Environment
 from .utils import logger, run_and_return_exceptions
-from .config import Config, convert_old
+from .debugging import set_trace
+
+_AVOID_RUNNING = False
+_QUEUED = []
+
+@contextmanager
+def do_not_run(): 
+    global _AVOID_RUNNING
+    _AVOID_RUNNING = True
+    try:
+        logger.debug("NOT RUNNING")
+        yield
+    finally:
+        logger.debug("RUNNING AGAIN")
+        _AVOID_RUNNING = False
+
+
+def _iter_queued():
+    while _QUEUED:
+        (cls, params) = _QUEUED.pop(0)
+        yield replace(cls, parameters=params)
 
 
 # TODO: change documentation for simulation
+# TODO: rename iterations to iterations
+# TODO: make parameters a dict of iterable/any
 @dataclass
 class Simulation:
     """
-    Parameters
+    A simulation is a collection of agents and a model. It is responsible for running the model and agents, and collecting data from them.
-    ---------
-    config (optional): :class:`config.Config`
-        name of the Simulation
 
-    kwargs: parameters to use to initialize a new configuration, if one not been provided.
+    Args:
+        version: The version of the simulation. This is used to determine how to load the simulation.
+        name: The name of the simulation.
+        description: A description of the simulation.
+        group: The group that the simulation belongs to.
+        model: The model to use for the simulation. This can be a string or a class.
+        parameters: The parameters to pass to the model.
+        matrix: A matrix of values for each parameter.
+        seed: The seed to use for the simulation.
+        dir_path: The directory path to use for the simulation.
+        max_time: The maximum time to run the simulation.
+        max_steps: The maximum number of steps to run the simulation.
+        interval: The interval to use for the simulation.
+        iterations: The number of iterations (times) to run the simulation.
+        num_processes: The number of processes to use for the simulation. If greater than one, simulations will be performed in parallel. This may make debugging and error handling difficult.
+        tables: The tables to use in the simulation datacollector
+        agent_reporters: The agent reporters to use in the datacollector
+        model_reporters: The model reporters to use in the datacollector
+        dry_run: Whether or not to run the simulation. If True, the simulation will not be run.
+        backup: Whether or not to backup the simulation. If True, the simulation files will be backed up to a different directory.
+        overwrite: Whether or not to replace existing simulation data.
+        source_file: Python file to use to find additional classes.
     """
 
     version: str = "2"
-    name: str = "Unnamed simulation"
+    source_file: Optional[str] = None
+    name: Optional[str] = None
     description: Optional[str] = ""
     group: str = None
-    model_class: Union[str, type] = "soil.Environment"
+    backup: bool = False
-    model_params: dict = field(default_factory=dict)
+    overwrite: bool = False
-    seed: str = field(default_factory=lambda: current_time())
+    dry_run: bool = False
+    dump: bool = False
+    model: Union[str, type] = "soil.Environment"
+    parameters: dict = field(default_factory=dict)
+    matrix: dict = field(default_factory=dict)
+    seed: str = "default"
     dir_path: str = field(default_factory=lambda: os.getcwd())
-    max_time: float = float("inf")
+    max_time: float = None
-    max_steps: int = -1
+    max_steps: int = None
     interval: int = 1
-    num_trials: int = 1
+    iterations: int = 1
     num_processes: Optional[int] = 1
-    parallel: Optional[bool] = False
     exporters: Optional[List[str]] = field(default_factory=lambda: [exporters.default])
     model_reporters: Optional[Dict[str, Any]] = field(default_factory=dict)
     agent_reporters: Optional[Dict[str, Any]] = field(default_factory=dict)
     tables: Optional[Dict[str, Any]] = field(default_factory=dict)
-    outdir: Optional[str] = None
+    outdir: str = field(default_factory=lambda: os.path.join(os.getcwd(), "soil_output"))
+    # outdir: Optional[str] = None
     exporter_params: Optional[Dict[str, Any]] = field(default_factory=dict)
-    dry_run: bool = False
+    level: int = logging.INFO
-    extra: Dict[str, Any] = field(default_factory=dict)
     skip_test: Optional[bool] = False
+    debug: Optional[bool] = False
 
-    @classmethod
+    def __post_init__(self):
-    def from_dict(cls, env, **kwargs):
+        if self.name is None:
+            if isinstance(self.model, str):
+                self.name = self.model
+            else:
+                self.name = self.model.__name__
+        self.logger = logger.getChild(self.name)
+        self.logger.setLevel(self.level)
 
-        ignored = {
+        if self.source_file:
-            k: v for k, v in env.items() if k not in inspect.signature(cls).parameters
+            source_file = self.source_file
-        }
+            if not os.path.isabs(source_file):
+                source_file = os.path.abspath(os.path.join(self.dir_path, source_file))
+            serialization.add_source_file(source_file)
+            self.source_file = source_file
 
-        d = {k: v for k, v in env.items() if k not in ignored}
+        if isinstance(self.model, str):
-        if ignored:
+            self.model = serialization.deserialize(self.model)
-            d.setdefault("extra", {}).update(ignored)
-        if ignored:
-            logger.warning(f'Ignoring these parameters (added to "extra"): { ignored }')
-        d.update(kwargs)
 
-        return cls(**d)
+        def deserialize_reporters(reporters):
+            for (k, v) in reporters.items():
+                if isinstance(v, str) and v.startswith("py:"):
+                    reporters[k] = serialization.deserialize(v.split(":", 1)[1])
+            return reporters
 
-    def run_simulation(self, *args, **kwargs):
+        self.agent_reporters = deserialize_reporters(self.agent_reporters)
-        return self.run(*args, **kwargs)
+        self.model_reporters = deserialize_reporters(self.model_reporters)
+        self.tables = deserialize_reporters(self.tables)
+        if self.source_file:
+            serialization.remove_source_file(self.source_file)
+        self.id = f"{self.name}_{current_time()}"
 
-    def run(self, *args, **kwargs):
+    def run(self, **kwargs):
         """Run the simulation and return the list of resulting environments"""
-        logger.info(
+        if kwargs:
+            return replace(self, **kwargs).run()
+
+        self.logger.debug(
             dedent(
                 """
         Simulation:
@@ -90,156 +156,157 @@ class Simulation:
             )
             + self.to_yaml()
         )
-        return list(self.run_gen(*args, **kwargs))
+        param_combinations = self._collect_params(**kwargs)
+        if _AVOID_RUNNING:
+            _QUEUED.extend((self, param) for param in param_combinations)
+            return []
 
-    def run_gen(
+        self.logger.debug("Using exporters: %s", self.exporters or [])
-        self,
-        num_processes=1,
-        dry_run=None,
-        exporters=None,
-        outdir=None,
-        exporter_params={},
-        log_level=None,
-        **kwargs,
-    ):
-        """Run the simulation and yield the resulting environments."""
-        if log_level:
-            logger.setLevel(log_level)
-        outdir = outdir or self.outdir
-        logger.info("Using exporters: %s", exporters or [])
-        logger.info("Output directory: %s", outdir)
-        if dry_run is None:
-            dry_run = self.dry_run
-        if exporters is None:
-            exporters = self.exporters
-        if not exporter_params:
-            exporter_params = self.exporter_params
 
         exporters = serialization.deserialize_all(
-            exporters,
+            self.exporters,
             simulation=self,
             known_modules=[
                 "soil.exporters",
             ],
-            dry_run=dry_run,
+            dump=self.dump and not self.dry_run,
-            outdir=outdir,
+            outdir=self.outdir,
-            **exporter_params,
+            **self.exporter_params,
         )
 
-        with utils.timer("simulation {}".format(self.name)):
+        results = []
-            for exporter in exporters:
+        for exporter in exporters:
-                exporter.sim_start()
+            exporter.sim_start()
-
-            for env in utils.run_parallel(
-                func=self.run_trial,
-                iterable=range(int(self.num_trials)),
-                num_processes=num_processes,
-                log_level=log_level,
-                **kwargs,
-            ):
 
+        for params in tqdm(param_combinations, desc=self.name, unit="configuration"):
+            for (k, v) in params.items():
+                tqdm.write(f"{k} = {v}")
+            sha = hashlib.sha256()
+            sha.update(repr(sorted(params.items())).encode())
+            params_id = sha.hexdigest()[:7]
+            for env in self._run_iters_for_params(params):
                 for exporter in exporters:
-                    exporter.trial_start(env)
+                    exporter.iteration_end(env, params, params_id)
+                results.append(env)
 
-                for exporter in exporters:
+        for exporter in exporters:
-                    exporter.trial_end(env)
+            exporter.sim_end()
 
-                yield env
+        return results
 
-            for exporter in exporters:
+    def _collect_params(self):
-                exporter.sim_end()
 
-    def get_env(self, trial_id=0, model_params=None, **kwargs):
+        parameters = []
-        """Create an environment for a trial of the simulation"""
+        if self.parameters:
+            parameters.append(self.parameters)
+        if self.matrix:
+            assert isinstance(self.matrix, dict)
+            for values in product(*(self.matrix.values())):
+                parameters.append(dict(zip(self.matrix.keys(), values)))
 
-        def deserialize_reporters(reporters):
+        if not parameters:
-            for (k, v) in reporters.items():
+            parameters = [{}]
-                if isinstance(v, str) and v.startswith("py:"):
+        if self.dump:
-                    reporters[k] = serialization.deserialize(v.split(":", 1)[1])
+            self.logger.info("Output directory: %s", self.outdir)
-            return reporters
 
-        params = self.model_params.copy()
+        return parameters
-        if model_params:
-            params.update(model_params)
-        params.update(kwargs)
 
-        agent_reporters = self.agent_reporters.copy()
+    def _run_iters_for_params(
-        agent_reporters.update(deserialize_reporters(params.pop("agent_reporters", {})))
+        self,
-        model_reporters = self.model_reporters.copy()
+        params
-        model_reporters.update(deserialize_reporters(params.pop("model_reporters", {})))
+    ):
-        tables = self.tables.copy()
+        """Run the simulation and yield the resulting environments."""
-        tables.update(deserialize_reporters(params.pop("tables", {})))
 
-        env = serialization.deserialize(self.model_class)
+        try:
-        return env(
+            if self.source_file:
-            id=f"{self.name}_trial_{trial_id}",
+                serialization.add_source_file(self.source_file)
-            seed=f"{self.seed}_trial_{trial_id}",
+
+            with utils.timer(f"running for config {params}"):
+                if self.dry_run:
+                    def func(*args, **kwargs):
+                        return None
+                else:
+                    func = self._run_model
+
+                for env in tqdm(utils.run_parallel(
+                    func=func,
+                    iterable=range(self.iterations),
+                    **params,
+                ), total=self.iterations, leave=False):
+                    if env is None and self.dry_run:
+                        continue
+
+                    yield env
+        finally:
+            if self.source_file:
+                serialization.remove_source_file(self.source_file)
+
+    def _get_env(self, iteration_id, params):
+        """Create an environment for a iteration of the simulation"""
+
+        iteration_id = str(iteration_id)
+
+        agent_reporters = self.agent_reporters
+        agent_reporters.update(params.pop("agent_reporters", {}))
+        model_reporters = self.model_reporters
+        model_reporters.update(params.pop("model_reporters", {}))
+
+        return self.model(
+            id=iteration_id,
+            seed=f"{self.seed}_iteration_{iteration_id}",
             dir_path=self.dir_path,
+            interval=self.interval,
+            logger=self.logger.getChild(iteration_id),
             agent_reporters=agent_reporters,
             model_reporters=model_reporters,
-            tables=tables,
+            tables=self.tables,
             **params,
         )
 
-    def run_trial(
+    def _run_model(self, iteration_id, **params):
-        self, trial_id=None, until=None, log_file=False, log_level=logging.INFO, **opts
-    ):
         """
-        Run a single trial of the simulation
+        Run a single iteration of the simulation
 
         """
-        if log_level:
+        # Set-up iteration environment and graph
-            logger.setLevel(log_level)
+        model = self._get_env(iteration_id, params)
-        model = self.get_env(trial_id, **opts)
+        with utils.timer("Simulation {} iteration {}".format(self.name, iteration_id)):
-        trial_id = trial_id if trial_id is not None else current_time()
+
-        with utils.timer("Simulation {} trial {}".format(self.name, trial_id)):
+            max_time = self.max_time 
-            return self.run_model(
+            max_steps = self.max_steps
-                model=model, trial_id=trial_id, until=until, log_level=log_level
+
+            if (max_time is not None) and (max_steps is not None):
+                is_done = lambda model: (not model.running) or (model.schedule.time >= max_time) or (model.schedule.steps >= max_steps)
+            elif max_time is not None:
+                is_done = lambda model: (not model.running) or (model.schedule.time >= max_time)
+            elif max_steps is not None:
+                is_done = lambda model: (not model.running) or (model.schedule.steps >= max_steps)
+            else:
+                is_done = lambda model: not model.running
+            
+            if not model.schedule.agents:
+                raise Exception("No agents in model. This is probably a bug. Make sure that the model has agents scheduled after its initialization.")
+
+            newline = "\n"
+            self.logger.debug(
+                dedent(
+                    f"""
+    Model stats:
+    Agent count: { model.schedule.get_agent_count() }):
+    Topology size: { len(model.G) if hasattr(model, "G") else 0 }
+            """
+                )
             )
 
-    def run_model(self, model, until=None, **opts):
+            if self.debug:
-        # Set-up trial environment and graph
+                set_trace()
-        until = float(until or self.max_time or "inf")
 
-        # Set up agents on nodes
+            while not is_done(model):
-        def is_done():
+                self.logger.debug(
-            return not model.running
+                    f'Simulation time {model.schedule.time}/{max_time}.'
+                )
+                model.step()
 
-        if until and hasattr(model.schedule, "time"):
-            prev = is_done
-
-            def is_done():
-                return prev() or model.schedule.time >= until
-
-        if self.max_steps and self.max_steps > 0 and hasattr(model.schedule, "steps"):
-            prev_steps = is_done
-
-            def is_done():
-                return prev_steps() or model.schedule.steps >= self.max_steps
-
-        newline = "\n"
-        logger.info(
-            dedent(
-                f"""
-Model stats:
-  Agents (total: { model.schedule.get_agent_count() }):
-      - { (newline + '      - ').join(str(a) for a in model.schedule.agents) }
-
-  Topology size: { len(model.G) if hasattr(model, "G") else 0 }
-        """
-            )
-        )
-
-        while not is_done():
-            utils.logger.debug(
-                f'Simulation time {model.schedule.time}/{until}. Next: {getattr(model.schedule, "next_time", model.schedule.time + self.interval)}'
-            )
-            model.step()
-
-        if (
-            model.schedule.time < until
-        ):  # Simulation ended (no more steps) before the expected time
-            model.schedule.time = until
         return model
 
     def to_dict(self):
@@ -250,14 +317,27 @@ Model stats:
         return yaml.dump(self.to_dict())
 
 
+def iter_from_file(*files, **kwargs):
+    for f in files:
+        try:
+            yield from iter_from_py(f, **kwargs)
+        except ValueError as ex:
+            yield from iter_from_config(f, **kwargs)
+
+
+def from_file(*args, **kwargs):
+    return list(iter_from_file(*args, **kwargs))
+
+
 def iter_from_config(*cfgs, **kwargs):
     for config in cfgs:
         configs = list(serialization.load_config(config))
         for config, path in configs:
             d = dict(config)
+            d.update(kwargs)
             if "dir_path" not in d:
                 d["dir_path"] = os.path.dirname(path)
-            yield Simulation.from_dict(d, **kwargs)
+            yield Simulation(**d)
 
 
 def from_config(conf_or_path):
@@ -266,26 +346,50 @@ def from_config(conf_or_path):
         raise AttributeError("Provide only one configuration")
     return lst[0]
 
-def iter_from_py(pyfile, module_name='custom_simulation'):
+
+def iter_from_py(pyfile, module_name='imported_file', **kwargs):
     """Try to load every Simulation instance in a given Python file"""
     import importlib
-    import inspect
+    added = False
-    spec = importlib.util.spec_from_file_location(module_name, pyfile)
+    sims = []
-    module = importlib.util.module_from_spec(spec)
+    assert not _AVOID_RUNNING
-    sys.modules[module_name] = module
+    with do_not_run():
-    spec.loader.exec_module(module)
+        assert _AVOID_RUNNING
-    # import pdb;pdb.set_trace()
+        spec = importlib.util.spec_from_file_location(module_name, pyfile)
-    for (_name, sim) in inspect.getmembers(module, lambda x: isinstance(x, Simulation)):
+        folder = os.path.dirname(pyfile)
-        yield sim
+        if folder not in sys.path:
-    del sys.modules[module_name]
+            added = True
+            sys.path.append(folder)
+        if not spec:
+            raise ValueError(f"{pyfile} does not seem to be a Python module")
+        module = importlib.util.module_from_spec(spec)
+        sys.modules[module_name] = module
+        spec.loader.exec_module(module)
+        for (_name, sim) in inspect.getmembers(module, lambda x: isinstance(x, Simulation)):
+            sims.append(sim)
+        for sim in _iter_queued():
+            sims.append(sim)
+        if not sims:
+            for (_name, sim) in inspect.getmembers(module, lambda x: inspect.isclass(x) and issubclass(x, Simulation)):
+                sims.append(sim(**kwargs))
+        del sys.modules[module_name]
+    assert not _AVOID_RUNNING
+    if not sims:
+        raise AttributeError(f"No valid configurations found in {pyfile}")
+    if added:
+        sys.path.remove(folder)
+    for sim in sims:
+        yield replace(sim, **kwargs)
 
 
 def from_py(pyfile):
     return next(iter_from_py(pyfile))
 
 
-
+def run_from_file(*files, **kwargs):
-def run_from_config(*configs, **kwargs):
+    for sim in iter_from_file(*files):
-    for sim in iter_from_config(*configs):
         logger.info(f"Using config(s): {sim.name}")
         sim.run_simulation(**kwargs)
+
+def run(env, iterations=1, num_processes=1, dump=False, name="test", **kwargs):
+    return Simulation(model=env, iterations=iterations, name=name, dump=dump, num_processes=num_processes, **kwargs).run()

soil/time.py

@@ -1,6 +1,6 @@
 from mesa.time import BaseScheduler
 from queue import Empty
-from heapq import heappush, heappop
+from heapq import heappush, heappop, heapreplace
 import math
 
 from inspect import getsource
@@ -97,8 +97,10 @@ class TimedActivation(BaseScheduler):
         self._next = {}
         self._queue = []
         self._shuffle = shuffle
-        self.step_interval = 1
+        # self.step_interval = getattr(self.model, "interval", 1)
-        self.logger = logger.getChild(f"time_{ self.model }")
+        self.step_interval = self.model.interval
+        self.logger = getattr(self.model, "logger", logger).getChild(f"time_{ self.model }")
+        self.next_time = self.time
 
     def add(self, agent: MesaAgent, when=None):
         if when is None:
@@ -109,7 +111,7 @@ class TimedActivation(BaseScheduler):
         self._schedule(agent, None, when)
         super().add(agent)
 
-    def _schedule(self, agent, condition=None, when=None):
+    def _schedule(self, agent, condition=None, when=None, replace=False):
         if condition:
             if not when:
                 when, condition = condition.schedule_next(
@@ -124,7 +126,10 @@ class TimedActivation(BaseScheduler):
         else:
             key = (when, agent.unique_id, condition)
         self._next[agent.unique_id] = key
-        heappush(self._queue, (key, agent))
+        if replace:
+            heapreplace(self._queue, (key, agent))
+        else:
+            heappush(self._queue, (key, agent))
 
     def step(self) -> None:
         """
@@ -136,17 +141,16 @@ class TimedActivation(BaseScheduler):
         if not self.model.running or self.time == INFINITY:
             return
 
-        self.logger.debug("Queue length: {ql}", ql=len(self._queue))
+        self.logger.debug(f"Queue length: %s", len(self._queue))
 
         while self._queue:
             ((when, _id, cond), agent) = self._queue[0]
             if when > self.time:
                 break
 
-            heappop(self._queue)
             if cond:
                 if not cond.ready(agent, self.time):
-                    self._schedule(agent, cond)
+                    self._schedule(agent, cond, replace=True)
                     continue
                 try:
                     agent._last_return = cond.return_value(agent)
@@ -156,35 +160,37 @@ class TimedActivation(BaseScheduler):
                 agent._last_return = None
                 agent._last_except = None
 
-            self.logger.debug("Stepping agent {agent}", agent=agent)
+            self.logger.debug("Stepping agent %s", agent)
             self._next.pop(agent.unique_id, None)
 
             try:
                 returned = agent.step()
             except DeadAgent:
                 agent.alive = False
+                heappop(self._queue)
                 continue
 
             # Check status for MESA agents
             if not getattr(agent, "alive", True):
+                heappop(self._queue)
                 continue
 
             if returned:
                 next_check = returned.schedule_next(
                     self.time, self.step_interval, first=True
                 )
-                self._schedule(agent, when=next_check[0], condition=next_check[1])
+                self._schedule(agent, when=next_check[0], condition=next_check[1], replace=True)
             else:
                 next_check = (self.time + self.step_interval, None)
 
-                self._schedule(agent)
+                self._schedule(agent, replace=True)
 
         self.steps += 1
 
         if not self._queue:
-            self.time = INFINITY
             self.model.running = False
-            return self.time
+            self.time = INFINITY
+            return
 
         next_time = self._queue[0][0][0]
 
@@ -192,7 +198,7 @@ class TimedActivation(BaseScheduler):
             raise Exception(
                 f"An agent has been scheduled for a time in the past, there is probably an error ({when} < {self.time})"
             )
-        self.logger.debug(f"Updating time step: {self.time} -> {next_time}")
+        self.logger.debug("Updating time step: %s -> %s ", self.time, next_time)
 
         self.time = next_time
 

soil/utils.py

@@ -10,7 +10,7 @@ from multiprocessing import Pool, cpu_count
 from contextlib import contextmanager
 
 logger = logging.getLogger("soil")
-logger.setLevel(logging.INFO)
+logger.setLevel(logging.WARNING)
 
 timeformat = "%H:%M:%S"
 
@@ -24,7 +24,7 @@ consoleHandler = logging.StreamHandler()
 consoleHandler.setFormatter(logFormatter)
 
 logging.basicConfig(
-    level=logging.DEBUG,
+    level=logging.INFO,
     handlers=[
         consoleHandler,
     ],
@@ -60,7 +60,7 @@ def try_backup(path, remove=False):
     if not os.path.exists(backup_dir):
         os.makedirs(backup_dir)
     newpath = os.path.join(backup_dir, "{}@{}".format(os.path.basename(path), stamp))
-    if move:
+    if remove:
         move(path, newpath)
     else:
         copyfile(path, newpath)
@@ -126,7 +126,7 @@ def unflatten_dict(d):
 
 def run_and_return_exceptions(func, *args, **kwargs):
     """
-    A wrapper for run_trial that catches exceptions and returns them.
+    A wrapper for a function that catches exceptions and returns them.
     It is meant for async simulations.
     """
     try:
@@ -154,3 +154,7 @@ def run_parallel(func, iterable, num_processes=1, **kwargs):
     else:
         for i in iterable:
             yield func(i, **kwargs)
+
+
+def int_seed(seed: str):
+    return int.from_bytes(seed.encode(), "little")

soil/visualization.py

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