v0.20.8 fix bugs

.dockerignore

@@ -1,5 +1,7 @@
 **/soil_output
 .*
+**/.*
 **/__pycache__
 __pycache__
 *.pyc
+**/backup

.gitignore

@@ -9,3 +9,4 @@ docs/_build*
 build/*
 dist/*
 prof
+backup

CHANGELOG.md

@@ -3,15 +3,14 @@ All notable changes to this project will be documented in this file.
 
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/), and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
-## [0.3 UNRELEASED]
+## [UNRELEASED]
-### Added
+
-* Simple debugging capabilities, with a custom `pdb.Debugger` subclass that exposes commands to list agents and their status and set breakpoints on states (for FSM agents)
+## [0.20.8]
 ### Changed
-* Configuration schema is very different now. Check `soil.config` for more information. We are also using Pydantic for (de)serialization.
+* Tsih bumped to version 0.1.8
-* There may be more than one topology/network in the simulation
+### Fixed
-* Agents are split into groups now. Each group may be assigned a given set of agents or an agent distribution, and a network topology to be assigned to.
+* Mentions to `id` in docs. It should be `state_id` now.
-### Removed
+* Fixed bug: environment agents were not being added to the simulation
-* 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

README.md

@@ -5,42 +5,6 @@ Learn how to run your own simulations with our [documentation](http://soilsim.re
 
 Follow our [tutorial](examples/tutorial/soil_tutorial.ipynb) to develop your own agent models.
 
-
-# Changes in version 0.3
-
-Version 0.3 came packed with many changes to provide much better integration with MESA.
-For a long time, we tried to keep soil backwards-compatible, but it turned out to be a big endeavour and the resulting code was less readable.
-This translates to harder maintenance and a worse experience for newcomers. 
-In the end, we decided to make some breaking changes.
-
-If you have an older Soil simulation, you have two options:
-
-* Update the necessary configuration files and code. You may use the examples in the `examples` folder for reference, as well as the documentation.
-* Keep using a previous `soil` version.
-
-## Mesa compatibility
-
-Soil is in the process of becoming fully compatible with MESA.
-The idea is to provide a set of modular classes and functions that extend the functionality of mesa, whilst staying compatible.
-In the end, it should be possible to add regular mesa agents to a soil simulation, or use a soil agent within a mesa simulation/model.
-
-This is a non-exhaustive list of tasks to achieve compatibility:
-
-- [ ] Integrate `soil.Simulation` with mesa's runners:
-  - [ ] `soil.Simulation` could mimic/become a `mesa.batchrunner`
-- [ ] Integrate `soil.Environment` with `mesa.Model`:
-  - [x] `Soil.Environment` inherits from `mesa.Model`
-  - [x] `Soil.Environment` includes a Mesa-like Scheduler (see the `soil.time` module.
-  - [ ] Allow for `mesa.Model` to be used in a simulation.
-- [ ] Integrate `soil.Agent` with `mesa.Agent`:
-  - [x] Rename agent.id to unique_id?
-  - [x] mesa agents can be used in soil simulations (see `examples/mesa`)
-- [ ] Provide examples
-  - [ ] Using mesa modules in a soil simulation
-  - [ ] Using soil modules in a mesa simulation
-- [ ] Document the new APIs and usage
-
-
 ## Citation 
 
 
@@ -67,6 +31,24 @@ If you use Soil in your research, don't forget to cite this paper:
 
 ```
 
+## Mesa compatibility
+
+Soil is in the process of becoming fully compatible with MESA.
+As of this writing, 
+
+This is a non-exhaustive list of tasks to achieve compatibility:
+
+* Environments.agents and mesa.Agent.agents are not the same. env is a property, and it only takes into account network and environment agents. Might rename environment_agents to other_agents or sth like that
+- [ ] Integrate `soil.Simulation` with mesa's runners:
+  - [ ] `soil.Simulation` could mimic/become a `mesa.batchrunner`
+- [ ] Integrate `soil.Environment` with `mesa.Model`:
+  - [x] `Soil.Environment` inherits from `mesa.Model`
+  - [x] `Soil.Environment` includes a Mesa-like Scheduler (see the `soil.time` module.
+- [ ] Integrate `soil.Agent` with `mesa.Agent`:
+  - [x] Rename agent.id to unique_id?
+  - [x] mesa agents can be used in soil simulations (see `examples/mesa`)
+- [ ] Document the new APIs and usage
+
 @Copyright GSI - Universidad Politécnica de Madrid 2017-2021
 
 [![SOIL](logo_gsi.png)](https://www.gsi.upm.es)

docs/configuration.rst

@@ -13,7 +13,7 @@ Here's an example (``example.yml``).
 
 
 This example configuration will run three trials (``num_trials``) of a simulation containing a randomly generated network (``network_params``).
-The 100 nodes in the network will be SISaModel agents (``network_agents.agent_class``), which is an agent behavior that is included in Soil.
+The 100 nodes in the network will be SISaModel agents (``network_agents.agent_type``), which is an agent behavior that is included in Soil.
 10% of the agents (``weight=1``) will start in the content state, 10% in the discontent state, and the remaining 80% (``weight=8``) in the neutral state.
 All agents will have access to the environment (``environment_params``), which only contains one variable, ``prob_infected``.
 The state of the agents will be updated every 2 seconds (``interval``).
@@ -88,18 +88,9 @@ For example, the following configuration is equivalent to :code:`nx.complete_gra
 
 Environment
 ============
-
 The environment is the place where the shared state of the simulation is stored.
-That means both global parameters, such as the probability of disease outbreak.
+For instance, the probability of disease outbreak.
-But it also means other data, such as a map, or a network topology that connects multiple agents.
+The configuration file may specify the initial value of the environment parameters:
-As a result, it is also typical to add custom functions in an environment that help agents interact with each other and with the state of the simulation.
-
-Last but not least, an environment controls when and how its agents will be executed.
-By default, soil environments incorporate a ``soil.time.TimedActivation`` model for agent execution (more on this on the following section).
-
-Soil environments are very similar, and often interchangeable with, mesa models (``mesa.Model``).
-
-A configuration may specify the initial value of the environment parameters:
 
 .. code:: yaml
 
@@ -107,33 +98,23 @@ A configuration may specify the initial value of the environment parameters:
         daily_probability_of_earthquake: 0.001
         number_of_earthquakes: 0
 
-All agents have access to the environment (and its parameters).
+All agents have access to the environment parameters.
 
 In some scenarios, it is useful to have a custom environment, to provide additional methods or to control the way agents update environment state.
 For example, if our agents play the lottery, the environment could provide a method to decide whether the agent wins, instead of leaving it to the agent.
 
+
 Agents
 ======
-
 Agents are a way of modelling behavior.
-Agents can be characterized with two variables: agent type (``agent_class``) and state.
+Agents can be characterized with two variables: agent type (``agent_type``) and state.
-The agent type is a ``soil.Agent`` class, which contains the code that encapsulates the behavior of the agent.
+Only one agent is executed at a time (generally, every ``interval`` seconds), and it has access to its state and the environment parameters.
-The state is a set of variables, which may change during the simulation, and that the code may use to control the behavior.
-All agents provide a ``step`` method either explicitly or implicitly (by inheriting it from a superclass), which controls how the agent will behave in each step of the simulation.
-
-When and how agent steps are executed in a simulation depends entirely on the ``environment``.
-Most environments will internally use a scheduler (``mesa.time.BaseScheduler``), which controls the activation of agents.
-
-In soil, we generally used the ``soil.time.TimedActivation`` scheduler, which allows agents to specify when their next activation will happen, defaulting to a 
-
-When an agent's step is executed (generally, every ``interval`` seconds), the agent has access to its state and the environment.
 Through the environment, it can access the network topology and the state of other agents.
 
-There are two types of agents according to how they are added to the simulation: network agents and environment agent.
+There are three three types of agents according to how they are added to the simulation: network agents and environment agent.
 
 Network Agents
 ##############
-
 Network agents are attached to a node in the topology.
 The configuration file allows you to specify how agents will be mapped to topology nodes.
 
@@ -142,19 +123,17 @@ Hence, every node in the network will be associated to an agent of that type.
 
 .. code:: yaml
 
-   agent_class: SISaModel
+   agent_type: SISaModel
 
-It is also possible to add more than one type of agent to the simulation.
+It is also possible to add more than one type of agent to the simulation, and to control the ratio of each type (using the ``weight`` property).
-
-To control the ratio of each type (using the ``weight`` property).
 For instance, with following configuration, it is five times more likely for a node to be assigned a CounterModel type than a SISaModel type.
 
 .. code:: yaml
 
     network_agents:
-          - agent_class: SISaModel
+          - agent_type: SISaModel
             weight: 1
-          - agent_class: CounterModel
+          - agent_type: CounterModel
             weight: 5
 
 The third option is to specify the type of agent on the node itself, e.g.:
@@ -165,10 +144,10 @@ The third option is to specify the type of agent on the node itself, e.g.:
    topology:
        nodes:
            - id: first
-   agent_class: BaseAgent
+   agent_type: BaseAgent
    states:
        first:
-         agent_class: SISaModel
+         agent_type: SISaModel
           
 
 This would also work with a randomly generated network:
@@ -179,9 +158,9 @@ This would also work with a randomly generated network:
    network:
        generator: complete
        n: 5
-   agent_class: BaseAgent
+   agent_type: BaseAgent
    states:
-       - agent_class: SISaModel
+       - agent_type: SISaModel
 
               
 
@@ -192,11 +171,11 @@ e.g., to populate the network with SISaModel, roughly 10% of them with a discont
 .. code:: yaml
 
     network_agents:
-        - agent_class: SISaModel
+        - agent_type: SISaModel
             weight: 9
             state:
             id: neutral
-        - agent_class: SISaModel
+        - agent_type: SISaModel
             weight: 1
             state:
             id: discontent
@@ -206,7 +185,7 @@ For instance, to add a state for the two nodes in this configuration:
 
 .. code:: yaml
 
-   agent_class: SISaModel
+   agent_type: SISaModel
    network:
       generator: complete_graph
       n: 2
@@ -231,10 +210,10 @@ These agents are programmed in much the same way as network agents, the only dif
 .. code::
 
    environment_agents:
-       - agent_class: MyAgent
+       - agent_type: MyAgent
          state:
            mood: happy
-       - agent_class: DummyAgent
+       - agent_type: DummyAgent
 
 
 You may use environment agents to model events that a normal agent cannot control, such as natural disasters or chance.

docs/example.yml

@@ -8,15 +8,15 @@ network_params:
     n: 100
     m: 2
 network_agents:
-    - agent_class: SISaModel
+    - agent_type: SISaModel
       weight: 1
       state:
         id: content
-    - agent_class: SISaModel
+    - agent_type: SISaModel
       weight: 1
       state:
         id: discontent
-    - agent_class: SISaModel
+    - agent_type: SISaModel
       weight: 8
       state:
         id: neutral

docs/quickstart.yml

@@ -3,11 +3,11 @@ name: quickstart
 num_trials: 1
 max_time: 1000
 network_agents:
-  - agent_class: SISaModel
+  - agent_type: SISaModel
     state:
       id: neutral
     weight: 1
-  - agent_class: SISaModel
+  - agent_type: SISaModel
     state:
       id: content
     weight: 2

docs/requirements.txt

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

docs/soil-vs.rst

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

examples/complete.yml

@@ -1,61 +1,27 @@
 ---
-version: '2'
 name: simple
 group: tests
 dir_path: "/tmp/"
 num_trials: 3
-max_steps: 100
+max_time: 100
 interval: 1
 seed: "CompleteSeed!"
-model_class: Environment
+network_params:
-model_params:
-  am_i_complete: true
-  topologies:
-    default:
-      params:
   generator: complete_graph
   n: 10
-    another_graph:
+network_agents:
-      params:
+  - agent_type: CounterModel
-        generator: complete_graph
-        n: 2
-  environment:
-  agents:
-    agent_class: CounterModel
-    topology: default
-    state:
-      times: 1
-      # In this group we are not specifying any topology
-    fixed:
-      - name: 'Environment Agent 1'
-        agent_class: BaseAgent
-        group: environment
-        topology: null
-        hidden: true
-        state:
-          times: 10
-      - agent_class: CounterModel
-        id: 0
-        group: other_counters
-        topology: another_graph
-        state:
-          times: 1
-          total: 0
-      - agent_class: CounterModel
-        topology: another_graph
-        group: other_counters
-        id: 1
-    distribution:
-      - agent_class: CounterModel
     weight: 1
-        group: general_counters
     state:
-          times: 3
+      state_id: 0
-      - agent_class: AggregatedCounter
+  - agent_type: AggregatedCounter
     weight: 0.2
-    override:
+environment_agents: []
-      - filter:
+environment_class: Environment
-          agent_class: AggregatedCounter
+environment_params:
-        n: 2
+  am_i_complete: true
-        state:
+default_state:
-          times: 5
+  incidents: 0
+states:
+  - name: 'The first node'
+  - name: 'The second node'

examples/complete_opt2.yml

@@ -1,63 +0,0 @@
----
-version: '2'
-id: simple
-group: tests
-dir_path: "/tmp/"
-num_trials: 3
-max_steps: 100
-interval: 1
-seed: "CompleteSeed!"
-model_class: "soil.Environment"
-model_params:
-  topologies:
-    default:
-      params:
-        generator: complete_graph
-        n: 10
-    another_graph:
-      params:
-        generator: complete_graph
-        n: 2
-  agents:
-    # The values here will be used as default values for any agent
-    agent_class: CounterModel
-    topology: default
-    state:
-      times: 1
-    # This specifies a distribution of agents, each with a `weight` or an explicit number of agents
-    distribution:
-      - agent_class: CounterModel
-        weight: 1
-        # This is inherited from the default settings
-        #topology: default
-        state:
-          times: 3
-      - agent_class: AggregatedCounter
-        topology: default
-        weight: 0.2
-    fixed:
-      - name: 'Environment Agent 1'
-        # All the other agents will assigned to the 'default' group
-        group: environment
-        # Do not count this agent towards total limits
-        hidden: true
-        agent_class: soil.BaseAgent
-        topology: null
-        state:
-          times: 10
-      - agent_class: CounterModel
-        topology: another_graph
-        id: 0
-        state:
-          times: 1
-          total: 0
-      - agent_class: CounterModel
-        topology: another_graph
-        id: 1
-    override:
-      # 2 agents that match this filter will be updated to match the state {times: 5}
-      - filter:
-          agent_class: AggregatedCounter
-        n: 2
-        state:
-            times: 5

examples/custom_generator/custom_generator.yml

@@ -2,7 +2,7 @@
 name: custom-generator
 description: Using a custom generator for the network
 num_trials: 3
-max_steps: 100
+max_time: 100
 interval: 1
 network_params:
   generator: mymodule.mygenerator
@@ -10,7 +10,7 @@ network_params:
   n: 10
   n_edges: 5  
 network_agents:
-  - agent_class: CounterModel
+  - agent_type: CounterModel
     weight: 1
     state:
       state_id: 0

examples/custom_generator/mymodule.py

@@ -1,6 +1,6 @@
 from networkx import Graph
-import random
 import networkx as nx
+from random import choice
 
 def mygenerator(n=5, n_edges=5):
     '''
@@ -14,9 +14,9 @@ def mygenerator(n=5, n_edges=5):
     
     for i in range(n_edges):
         nodes = list(G.nodes)
-        n_in = random.choice(nodes)
+        n_in = choice(nodes)
         nodes.remove(n_in)  # Avoid loops
-        n_out = random.choice(nodes)
+        n_out = choice(nodes)
         G.add_edge(n_in, n_out)
     return G
     

examples/custom_timeouts/custom_timeouts.py

@@ -27,8 +27,8 @@ if __name__ == '__main__':
     import logging
     logging.basicConfig(level=logging.INFO)
     from soil import Simulation
-    s = Simulation(network_agents=[{'ids': [0], 'agent_class': Fibonacci},
+    s = Simulation(network_agents=[{'ids': [0], 'agent_type': Fibonacci},
-                                   {'ids': [1], 'agent_class': Odds}],
+                                   {'ids': [1], 'agent_type': Odds}],
                    network_params={"generator": "complete_graph", "n": 2},
                    max_time=100,
                    )

examples/mesa/mesa.yml

@@ -3,22 +3,19 @@ name: mesa_sim
 group: tests
 dir_path: "/tmp"
 num_trials: 3
-max_steps: 100
+max_time: 100
 interval: 1
 seed: '1'
-model_class: social_wealth.MoneyEnv
+network_params:
-model_params:
-  topologies:
-    default: 
-      params:
   generator: social_wealth.graph_generator
   n: 5
-  agents:
+network_agents:
-    distribution:
+  - agent_type: social_wealth.SocialMoneyAgent
-      - agent_class: social_wealth.SocialMoneyAgent
-        topology: default
     weight: 1
-  mesa_agent_class: social_wealth.MoneyAgent
+environment_class: social_wealth.MoneyEnv
+environment_params:
+  num_mesa_agents: 5
+  mesa_agent_type: social_wealth.MoneyAgent
   N: 10
   width: 50
   height: 50

examples/mesa/server.py

@@ -70,7 +70,7 @@ model_params = {
         1,
         description="Choose how many agents to include in the model",
     ),
-    "network_agents": [{"agent_class": SocialMoneyAgent}],
+    "network_agents": [{"agent_type": SocialMoneyAgent}],
     "height": UserSettableParameter(
         "slider",
         "height",

examples/mesa/social_wealth.py

@@ -71,9 +71,10 @@ class SocialMoneyAgent(NetworkAgent, MoneyAgent):
 
 class MoneyEnv(Environment):
     """A model with some number of agents."""
-    def __init__(self, width, height, *args, topologies, **kwargs):
+    def __init__(self, N, width, height, *args, network_params, **kwargs):
 
-        super().__init__(*args, topologies=topologies, **kwargs)
+        network_params['n'] = N
+        super().__init__(*args, network_params=network_params, **kwargs)
         self.grid = MultiGrid(width, height, False)
 
         # Create agents
@@ -99,7 +100,7 @@ if __name__ == '__main__':
     G = graph_generator()
     fixed_params = {"topology": G,
                     "width": 10,
-                    "network_agents": [{"agent_class": SocialMoneyAgent,
+                    "network_agents": [{"agent_type": SocialMoneyAgent,
                                        'weight': 1}],
                     "height": 10}
 

examples/newsspread/NewsSpread.ipynb

@@ -2,13 +2,12 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 1,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2017-11-08T16:22:30.732107Z",
      "start_time": "2017-11-08T17:22:30.059855+01:00"
-    },
+    }
-    "collapsed": true
    },
    "outputs": [],
    "source": [
@@ -28,24 +27,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 2,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2017-11-08T16:22:35.580593Z",
      "start_time": "2017-11-08T17:22:35.542745+01:00"
     }
    },
-   "outputs": [
+   "outputs": [],
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Populating the interactive namespace from numpy and matplotlib\n"
-     ]
-    }
-   ],
    "source": [
-    "%pylab inline\n",
+    "%matplotlib inline\n",
     "\n",
     "from soil import *"
    ]
@@ -66,7 +57,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 3,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2017-11-08T16:22:37.242327Z",
@@ -86,14 +77,14 @@
       "  prob_neighbor_spread: 0.0\r\n",
       "  prob_tv_spread: 0.01\r\n",
       "interval: 1\r\n",
-      "max_time: 30\r\n",
+      "max_time: 300\r\n",
       "name: Sim_all_dumb\r\n",
       "network_agents:\r\n",
-      "- agent_class: DumbViewer\r\n",
+      "- agent_type: DumbViewer\r\n",
       "  state:\r\n",
       "    has_tv: false\r\n",
       "  weight: 1\r\n",
-      "- agent_class: DumbViewer\r\n",
+      "- agent_type: DumbViewer\r\n",
       "  state:\r\n",
       "    has_tv: true\r\n",
       "  weight: 1\r\n",
@@ -110,22 +101,22 @@
       "  prob_neighbor_spread: 0.0\r\n",
       "  prob_tv_spread: 0.01\r\n",
       "interval: 1\r\n",
-      "max_time: 30\r\n",
+      "max_time: 300\r\n",
       "name: Sim_half_herd\r\n",
       "network_agents:\r\n",
-      "- agent_class: DumbViewer\r\n",
+      "- agent_type: DumbViewer\r\n",
       "  state:\r\n",
       "    has_tv: false\r\n",
       "  weight: 1\r\n",
-      "- agent_class: DumbViewer\r\n",
+      "- agent_type: DumbViewer\r\n",
       "  state:\r\n",
       "    has_tv: true\r\n",
       "  weight: 1\r\n",
-      "- agent_class: HerdViewer\r\n",
+      "- agent_type: HerdViewer\r\n",
       "  state:\r\n",
       "    has_tv: false\r\n",
       "  weight: 1\r\n",
-      "- agent_class: HerdViewer\r\n",
+      "- agent_type: HerdViewer\r\n",
       "  state:\r\n",
       "    has_tv: true\r\n",
       "  weight: 1\r\n",
@@ -142,18 +133,18 @@
       "  prob_neighbor_spread: 0.0\r\n",
       "  prob_tv_spread: 0.01\r\n",
       "interval: 1\r\n",
-      "max_time: 30\r\n",
+      "max_time: 300\r\n",
       "name: Sim_all_herd\r\n",
       "network_agents:\r\n",
-      "- agent_class: HerdViewer\r\n",
+      "- agent_type: HerdViewer\r\n",
       "  state:\r\n",
       "    has_tv: true\r\n",
-      "    id: neutral\r\n",
+      "    state_id: neutral\r\n",
       "  weight: 1\r\n",
-      "- agent_class: HerdViewer\r\n",
+      "- agent_type: HerdViewer\r\n",
       "  state:\r\n",
       "    has_tv: true\r\n",
-      "    id: neutral\r\n",
+      "    state_id: neutral\r\n",
       "  weight: 1\r\n",
       "network_params:\r\n",
       "  generator: barabasi_albert_graph\r\n",
@@ -169,15 +160,15 @@
       "  prob_tv_spread: 0.01\r\n",
       "  prob_neighbor_cure: 0.1\r\n",
       "interval: 1\r\n",
-      "max_time: 30\r\n",
+      "max_time: 300\r\n",
       "name: Sim_wise_herd\r\n",
       "network_agents:\r\n",
-      "- agent_class: HerdViewer\r\n",
+      "- agent_type: HerdViewer\r\n",
       "  state:\r\n",
       "    has_tv: true\r\n",
-      "    id: neutral\r\n",
+      "    state_id: neutral\r\n",
       "  weight: 1\r\n",
-      "- agent_class: WiseViewer\r\n",
+      "- agent_type: WiseViewer\r\n",
       "  state:\r\n",
       "    has_tv: true\r\n",
       "  weight: 1\r\n",
@@ -195,15 +186,15 @@
       "  prob_tv_spread: 0.01\r\n",
       "  prob_neighbor_cure: 0.1\r\n",
       "interval: 1\r\n",
-      "max_time: 30\r\n",
+      "max_time: 300\r\n",
       "name: Sim_all_wise\r\n",
       "network_agents:\r\n",
-      "- agent_class: WiseViewer\r\n",
+      "- agent_type: WiseViewer\r\n",
       "  state:\r\n",
       "    has_tv: true\r\n",
-      "    id: neutral\r\n",
+      "    state_id: neutral\r\n",
       "  weight: 1\r\n",
-      "- agent_class: WiseViewer\r\n",
+      "- agent_type: WiseViewer\r\n",
       "  state:\r\n",
       "    has_tv: true\r\n",
       "  weight: 1\r\n",
@@ -225,7 +216,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 4,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2017-11-08T18:07:46.781745Z",
@@ -233,7 +224,24 @@
     },
     "scrolled": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "No objects to concatenate",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[4], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m evodumb \u001b[38;5;241m=\u001b[39m \u001b[43manalysis\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_data\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43msoil_output/Sim_all_dumb/\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprocess\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43manalysis\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_count\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgroup\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkeys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mid\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m;\n",
+      "File \u001b[0;32m/mnt/data/home/j/git/lab.gsi/soil/soil/soil/analysis.py:14\u001b[0m, in \u001b[0;36mread_data\u001b[0;34m(group, *args, **kwargs)\u001b[0m\n\u001b[1;32m     12\u001b[0m iterable \u001b[38;5;241m=\u001b[39m _read_data(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m     13\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m group:\n\u001b[0;32m---> 14\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mgroup_trials\u001b[49m\u001b[43m(\u001b[49m\u001b[43miterable\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     15\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     16\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mlist\u001b[39m(iterable)\n",
+      "File \u001b[0;32m/mnt/data/home/j/git/lab.gsi/soil/soil/soil/analysis.py:201\u001b[0m, in \u001b[0;36mgroup_trials\u001b[0;34m(trials, aggfunc)\u001b[0m\n\u001b[1;32m    199\u001b[0m trials \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(trials)\n\u001b[1;32m    200\u001b[0m trials \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mmap\u001b[39m(\u001b[38;5;28;01mlambda\u001b[39;00m x: x[\u001b[38;5;241m1\u001b[39m] \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mtuple\u001b[39m) \u001b[38;5;28;01melse\u001b[39;00m x, trials))\n\u001b[0;32m--> 201\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mpd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconcat\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrials\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mgroupby(level\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\u001b[38;5;241m.\u001b[39magg(aggfunc)\u001b[38;5;241m.\u001b[39mreorder_levels([\u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m0\u001b[39m,\u001b[38;5;241m1\u001b[39m] ,axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n",
+      "File \u001b[0;32m/mnt/data/home/j/git/lab.gsi/soil/soil/.env-v0.20/lib/python3.8/site-packages/pandas/util/_decorators.py:331\u001b[0m, in \u001b[0;36mdeprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    325\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m>\u001b[39m num_allow_args:\n\u001b[1;32m    326\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m    327\u001b[0m         msg\u001b[38;5;241m.\u001b[39mformat(arguments\u001b[38;5;241m=\u001b[39m_format_argument_list(allow_args)),\n\u001b[1;32m    328\u001b[0m         \u001b[38;5;167;01mFutureWarning\u001b[39;00m,\n\u001b[1;32m    329\u001b[0m         stacklevel\u001b[38;5;241m=\u001b[39mfind_stack_level(),\n\u001b[1;32m    330\u001b[0m     )\n\u001b[0;32m--> 331\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/mnt/data/home/j/git/lab.gsi/soil/soil/.env-v0.20/lib/python3.8/site-packages/pandas/core/reshape/concat.py:368\u001b[0m, in \u001b[0;36mconcat\u001b[0;34m(objs, axis, join, ignore_index, keys, levels, names, verify_integrity, sort, copy)\u001b[0m\n\u001b[1;32m    146\u001b[0m \u001b[38;5;129m@deprecate_nonkeyword_arguments\u001b[39m(version\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, allowed_args\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mobjs\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[1;32m    147\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mconcat\u001b[39m(\n\u001b[1;32m    148\u001b[0m     objs: Iterable[NDFrame] \u001b[38;5;241m|\u001b[39m Mapping[HashableT, NDFrame],\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    157\u001b[0m     copy: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m    158\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m DataFrame \u001b[38;5;241m|\u001b[39m Series:\n\u001b[1;32m    159\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    160\u001b[0m \u001b[38;5;124;03m    Concatenate pandas objects along a particular axis.\u001b[39;00m\n\u001b[1;32m    161\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    366\u001b[0m \u001b[38;5;124;03m    1   3   4\u001b[39;00m\n\u001b[1;32m    367\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 368\u001b[0m     op \u001b[38;5;241m=\u001b[39m \u001b[43m_Concatenator\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    369\u001b[0m \u001b[43m        \u001b[49m\u001b[43mobjs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    370\u001b[0m \u001b[43m        \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    371\u001b[0m \u001b[43m        \u001b[49m\u001b[43mignore_index\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mignore_index\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    372\u001b[0m \u001b[43m        \u001b[49m\u001b[43mjoin\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mjoin\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    373\u001b[0m \u001b[43m        \u001b[49m\u001b[43mkeys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkeys\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    374\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlevels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlevels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    375\u001b[0m \u001b[43m        \u001b[49m\u001b[43mnames\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnames\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    376\u001b[0m \u001b[43m        \u001b[49m\u001b[43mverify_integrity\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mverify_integrity\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    377\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    378\u001b[0m \u001b[43m        \u001b[49m\u001b[43msort\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msort\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    379\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    381\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m op\u001b[38;5;241m.\u001b[39mget_result()\n",
+      "File \u001b[0;32m/mnt/data/home/j/git/lab.gsi/soil/soil/.env-v0.20/lib/python3.8/site-packages/pandas/core/reshape/concat.py:425\u001b[0m, in \u001b[0;36m_Concatenator.__init__\u001b[0;34m(self, objs, axis, join, keys, levels, names, ignore_index, verify_integrity, copy, sort)\u001b[0m\n\u001b[1;32m    422\u001b[0m     objs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(objs)\n\u001b[1;32m    424\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(objs) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m--> 425\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNo objects to concatenate\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    427\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m keys \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    428\u001b[0m     objs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(com\u001b[38;5;241m.\u001b[39mnot_none(\u001b[38;5;241m*\u001b[39mobjs))\n",
+      "\u001b[0;31mValueError\u001b[0m: No objects to concatenate"
+     ]
+    }
+   ],
    "source": [
     "evodumb = analysis.read_data('soil_output/Sim_all_dumb/', process=analysis.get_count, group=True, keys=['id']);"
    ]
@@ -721,9 +729,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "venv-soil",
    "language": "python",
-   "name": "python3"
+   "name": "venv-soil"
   },
   "language_info": {
    "codemirror_mode": {
@@ -735,7 +743,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.2"
+   "version": "3.8.10"
   },
   "toc": {
    "colors": {

examples/newsspread/NewsSpread.yml

@@ -1,18 +1,19 @@
 ---
 default_state: {}
+load_module: newsspread
 environment_agents: []
 environment_params:
   prob_neighbor_spread: 0.0
   prob_tv_spread: 0.01
 interval: 1
-max_steps: 300
+max_time: 300
 name: Sim_all_dumb
 network_agents:
-- agent_class: newsspread.DumbViewer
+- agent_type: DumbViewer
   state:
     has_tv: false
   weight: 1
-- agent_class: newsspread.DumbViewer
+- agent_type: DumbViewer
   state:
     has_tv: true
   weight: 1
@@ -23,27 +24,28 @@ network_params:
 num_trials: 50
 ---
 default_state: {}
+load_module: newsspread
 environment_agents: []
 environment_params:
   prob_neighbor_spread: 0.0
   prob_tv_spread: 0.01
 interval: 1
-max_steps: 300
+max_time: 300
 name: Sim_half_herd
 network_agents:
-- agent_class: newsspread.DumbViewer
+- agent_type: DumbViewer
   state:
     has_tv: false
   weight: 1
-- agent_class: newsspread.DumbViewer
+- agent_type: DumbViewer
   state:
     has_tv: true
   weight: 1
-- agent_class: newsspread.HerdViewer
+- agent_type: HerdViewer
   state:
     has_tv: false
   weight: 1
-- agent_class: newsspread.HerdViewer
+- agent_type: HerdViewer
   state:
     has_tv: true
   weight: 1
@@ -54,20 +56,21 @@ network_params:
 num_trials: 50
 ---
 default_state: {}
+load_module: newsspread
 environment_agents: []
 environment_params:
   prob_neighbor_spread: 0.0
   prob_tv_spread: 0.01
 interval: 1
-max_steps: 300
+max_time: 300
 name: Sim_all_herd
 network_agents:
-- agent_class: newsspread.HerdViewer
+- agent_type: HerdViewer
   state:
     has_tv: true
     state_id: neutral
   weight: 1
-- agent_class: newsspread.HerdViewer
+- agent_type: HerdViewer
   state:
     has_tv: true
     state_id: neutral
@@ -79,21 +82,22 @@ network_params:
 num_trials: 50
 ---
 default_state: {}
+load_module: newsspread
 environment_agents: []
 environment_params:
   prob_neighbor_spread: 0.0
   prob_tv_spread: 0.01
   prob_neighbor_cure: 0.1
 interval: 1
-max_steps: 300
+max_time: 300
 name: Sim_wise_herd
 network_agents:
-- agent_class: newsspread.HerdViewer
+- agent_type: HerdViewer
   state:
     has_tv: true
     state_id: neutral
   weight: 1
-- agent_class: newsspread.WiseViewer
+- agent_type: WiseViewer
   state:
     has_tv: true
   weight: 1
@@ -104,21 +108,22 @@ network_params:
 num_trials: 50
 ---
 default_state: {}
+load_module: newsspread
 environment_agents: []
 environment_params:
   prob_neighbor_spread: 0.0
   prob_tv_spread: 0.01
   prob_neighbor_cure: 0.1
 interval: 1
-max_steps: 300
+max_time: 300
 name: Sim_all_wise
 network_agents:
-- agent_class: newsspread.WiseViewer
+- agent_type: WiseViewer
   state:
     has_tv: true
     state_id: neutral
   weight: 1
-- agent_class: newsspread.WiseViewer
+- agent_type: WiseViewer
   state:
     has_tv: true
   weight: 1

examples/newsspread/newsspread.py

@@ -1,8 +1,8 @@
-from soil.agents import FSM, NetworkAgent, state, default_state, prob
+from soil.agents import FSM, state, default_state, prob
 import logging
 
 
-class DumbViewer(FSM, NetworkAgent):
+class DumbViewer(FSM):
     '''
     A viewer that gets infected via TV (if it has one) and tries to infect
     its neighbors once it's infected.
@@ -16,13 +16,13 @@ class DumbViewer(FSM, NetworkAgent):
     @state
     def neutral(self):
         if self['has_tv']:
-            if self.prob(self.model['prob_tv_spread']):
+            if prob(self.env['prob_tv_spread']):
                 return self.infected
 
     @state
     def infected(self):
         for neighbor in self.get_neighboring_agents(state_id=self.neutral.id):
-            if self.prob(self.model['prob_neighbor_spread']):
+            if prob(self.env['prob_neighbor_spread']):
                 neighbor.infect()
 
     def infect(self):
@@ -44,9 +44,9 @@ class HerdViewer(DumbViewer):
         '''Notice again that this is NOT a state. See DumbViewer.infect for reference'''
         infected = self.count_neighboring_agents(state_id=self.infected.id)
         total = self.count_neighboring_agents()
-        prob_infect = self.model['prob_neighbor_spread'] * infected/total
+        prob_infect = self.env['prob_neighbor_spread'] * infected/total
         self.debug('prob_infect', prob_infect)
-        if self.prob(prob_infect):
+        if prob(prob_infect):
             self.set_state(self.infected)
 
 
@@ -63,9 +63,9 @@ class WiseViewer(HerdViewer):
 
     @state
     def cured(self):
-        prob_cure = self.model['prob_neighbor_cure']
+        prob_cure = self.env['prob_neighbor_cure']
         for neighbor in self.get_neighboring_agents(state_id=self.infected.id):
-            if self.prob(prob_cure):
+            if prob(prob_cure):
                 try:
                     neighbor.cure()
                 except AttributeError:
@@ -80,7 +80,7 @@ class WiseViewer(HerdViewer):
                     1.0)
         infected = max(self.count_neighboring_agents(self.infected.id),
                        1.0)
-        prob_cure = self.model['prob_neighbor_cure'] * (cured/infected)
+        prob_cure = self.env['prob_neighbor_cure'] * (cured/infected)
-        if self.prob(prob_cure):
+        if prob(prob_cure):
             return self.cured
         return self.set_state(super().infected)

examples/programmatic/programmatic.py

@@ -27,7 +27,7 @@ s = Simulation(name='Programmatic',
                network_params={'generator': mygenerator},
                num_trials=1,
                max_time=100,
-               agent_class=MyAgent,
+               agent_type=MyAgent,
                dry_run=True)
 
 

examples/pubcrawl/pubcrawl.py

@@ -1,5 +1,6 @@
-from soil.agents import FSM, NetworkAgent, state, default_state
+from soil.agents import FSM, state, default_state
 from soil import Environment
+from random import random, shuffle
 from itertools import islice
 import logging
 
@@ -52,7 +53,7 @@ class CityPubs(Environment):
                 pub['occupancy'] -= 1
 
 
-class Patron(FSM, NetworkAgent):
+class Patron(FSM):
     '''Agent that looks for friends to drink with. It will do three things:
         1) Look for other patrons to drink with
         2) Look for a bar where the agent and other agents in the same group can get in.
@@ -60,10 +61,12 @@ class Patron(FSM, NetworkAgent):
     '''
     level = logging.DEBUG
 
-    pub = None
+    defaults = {
-    drunk = False
+        'pub': None,
-    pints = 0
+        'drunk': False,
-    max_pints = 3
+        'pints': 0,
+        'max_pints': 3,
+    }
 
     @default_state
     @state
@@ -87,9 +90,9 @@ class Patron(FSM, NetworkAgent):
             return self.sober_in_pub
         self.debug('I am looking for a pub')
         group = list(self.get_neighboring_agents())
-        for pub in self.model.available_pubs():
+        for pub in self.env.available_pubs():
             self.debug('We\'re trying to get into {}: total: {}'.format(pub, len(group)))
-            if self.model.enter(pub, self, *group):
+            if self.env.enter(pub, self, *group):
                 self.info('We\'re all {} getting in {}!'.format(len(group), pub))
                 return self.sober_in_pub
 
@@ -125,8 +128,8 @@ class Patron(FSM, NetworkAgent):
         Try to become friends with another agent. The chances of
         success depend on both agents' openness.
         '''
-        if force or self['openness'] > self.random.random():
+        if force or self['openness'] > random():
-            self.model.add_edge(self, other_agent)
+            self.env.add_edge(self, other_agent)
             self.info('Made some friend {}'.format(other_agent))
             return True
         return False
@@ -135,7 +138,7 @@ class Patron(FSM, NetworkAgent):
         ''' Look for random agents around me and try to befriend them'''
         befriended = False
         k = int(10*self['openness'])
-        self.random.shuffle(others)
+        shuffle(others)
         for friend in islice(others, k):  # random.choice >= 3.7
             if friend == self:
                 continue

examples/pubcrawl/pubcrawl.yml

@@ -1,25 +1,25 @@
 ---
 name: pubcrawl
 num_trials: 3
-max_steps: 10
+max_time: 10
 dump: false
 network_params:
   # Generate 100 empty nodes. They will be assigned a network agent
   generator: empty_graph
   n: 30
 network_agents:
-  - agent_class: pubcrawl.Patron
+  - agent_type: pubcrawl.Patron
     description: Extroverted patron
     state:
       openness: 1.0
     weight: 9
-  - agent_class: pubcrawl.Patron
+  - agent_type: pubcrawl.Patron
     description: Introverted patron
     state:
       openness: 0.1
     weight: 1
 environment_agents:
-  - agent_class: pubcrawl.Police
+  - agent_type: pubcrawl.Police
 environment_class: pubcrawl.CityPubs
 environment_params:
   altercations: 0

examples/rabbits/README.md

@@ -1,4 +0,0 @@
-There are two similar implementations of this simulation.
-
-- `basic`. Using simple primites
-- `improved`. Using more advanced features such as the `time` module to avoid unnecessary computations (i.e., skip steps), and generator functions.

examples/rabbits/basic/rabbit_agents.py

@@ -1,130 +0,0 @@
-from soil.agents import FSM, state, default_state, BaseAgent, NetworkAgent
-from soil.time import Delta
-from enum import Enum
-from collections import Counter
-import logging
-import math
-
-
-class RabbitModel(FSM, NetworkAgent):
-
-    sexual_maturity = 30
-    life_expectancy = 300
-
-    @default_state
-    @state
-    def newborn(self):
-        self.info('I am a newborn.')
-        self.age = 0
-        self.offspring = 0
-        return self.youngling
-
-    @state
-    def youngling(self):
-        self.age += 1
-        if self.age >= self.sexual_maturity:
-            self.info(f'I am fertile! My age is {self.age}')
-            return self.fertile
-
-    @state
-    def fertile(self):
-        raise Exception("Each subclass should define its fertile state")
-
-    @state
-    def dead(self):
-        self.die()
-
-
-class Male(RabbitModel):
-    max_females = 5
-    mating_prob = 0.001
-
-    @state
-    def fertile(self):
-        self.age += 1
-
-        if self.age > self.life_expectancy:
-            return self.dead
-
-        # Males try to mate
-        for f in self.model.agents(agent_class=Female,
-                                   state_id=Female.fertile.id,
-                                   limit=self.max_females):
-            self.debug('FOUND A FEMALE: ', repr(f), self.mating_prob)
-            if self.prob(self['mating_prob']):
-                f.impregnate(self)
-                break  # Take a break
-
-
-class Female(RabbitModel):
-    gestation = 100
-
-    @state
-    def fertile(self):
-        # Just wait for a Male
-        self.age += 1
-        if self.age > self.life_expectancy:
-            return self.dead
-
-    def impregnate(self, male):
-        self.info(f'{repr(male)} impregnating female {repr(self)}')
-        self.mate = male
-        self.pregnancy = -1
-        self.set_state(self.pregnant, when=self.now)
-        self.number_of_babies = int(8+4*self.random.random())
-        self.debug('I am pregnant')
-
-    @state
-    def pregnant(self):
-        self.age += 1
-        self.pregnancy += 1
-
-        if self.prob(self.age / self.life_expectancy):
-            return self.die()
-
-        if self.pregnancy >= self.gestation:
-            self.info('Having {} babies'.format(self.number_of_babies))
-            for i in range(self.number_of_babies):
-                state = {}
-                agent_class = self.random.choice([Male, Female])
-                child = self.model.add_node(agent_class=agent_class,
-                                            topology=self.topology,
-                                            **state)
-                child.add_edge(self)
-                try:
-                    child.add_edge(self.mate)
-                    self.model.agents[self.mate].offspring += 1
-                except ValueError:
-                    self.debug('The father has passed away')
-
-                self.offspring += 1
-            self.mate = None
-            return self.fertile
-
-    @state
-    def dead(self):
-        super().dead()
-        if 'pregnancy' in self and self['pregnancy'] > -1:
-            self.info('A mother has died carrying a baby!!')
-
-
-class RandomAccident(BaseAgent):
-
-    level = logging.INFO
-
-    def step(self):
-        rabbits_alive = self.model.topology.number_of_nodes()
-
-        if not rabbits_alive:
-            return self.die()
-
-        prob_death = self.model.get('prob_death', 1e-100)*math.floor(math.log10(max(1, rabbits_alive)))
-        self.debug('Killing some rabbits with prob={}!'.format(prob_death))
-        for i in self.iter_agents(agent_class=RabbitModel):
-            if i.state.id == i.dead.id:
-                continue
-            if self.prob(prob_death):
-                self.info('I killed a rabbit: {}'.format(i.id))
-                rabbits_alive -= 1
-                i.set_state(i.dead)
-        self.debug('Rabbits alive: {}'.format(rabbits_alive))

examples/rabbits/basic/rabbits.yml

@@ -1,41 +0,0 @@
----
-version: '2'
-name: rabbits_basic
-num_trials: 1
-seed: MySeed
-description: null
-group: null
-interval: 1.0
-max_time: 100
-model_class: soil.environment.Environment
-model_params:
-  agents:
-    topology: default
-    agent_class: rabbit_agents.RabbitModel
-    distribution:
-    - agent_class: rabbit_agents.Male
-      topology: default
-      weight: 1
-    - agent_class: rabbit_agents.Female
-      topology: default
-      weight: 1
-    fixed:
-    - agent_class: rabbit_agents.RandomAccident
-      topology: null
-      hidden: true
-      state:
-        group: environment
-    state:
-      group: network
-      mating_prob: 0.1
-  prob_death: 0.001
-  topologies:
-    default:
-      topology:
-        directed: true
-        links: []
-        nodes:
-        - id: 1
-        - id: 0
-extra:
-  visualization_params: {}

examples/rabbits/improved/rabbit_agents.py

@@ -1,130 +0,0 @@
-from soil.agents import FSM, state, default_state, BaseAgent, NetworkAgent
-from soil.time import Delta, When, NEVER
-from enum import Enum
-import logging
-import math
-
-
-class RabbitModel(FSM, NetworkAgent):
-
-    mating_prob = 0.005
-    offspring = 0
-    birth = None
-
-    sexual_maturity = 3
-    life_expectancy = 30
-
-    @default_state
-    @state
-    def newborn(self):
-        self.birth = self.now
-        self.info(f'I am a newborn.')
-        self.model['rabbits_alive'] = self.model.get('rabbits_alive', 0) + 1
-
-        # Here we can skip the `youngling` state by using a coroutine/generator. 
-        while self.age < self.sexual_maturity:
-            interval = self.sexual_maturity - self.age
-            yield Delta(interval)
-
-        self.info(f'I am fertile! My age is {self.age}')
-        return self.fertile
-
-    @property
-    def age(self):
-        return self.now - self.birth
-
-    @state
-    def fertile(self):
-        raise Exception("Each subclass should define its fertile state")
-
-    def step(self):
-        super().step()
-        if self.prob(self.age / self.life_expectancy):
-            return self.die()
-
-
-class Male(RabbitModel):
-
-    max_females = 5
-
-    @state
-    def fertile(self):
-        # Males try to mate
-        for f in self.model.agents(agent_class=Female,
-                                   state_id=Female.fertile.id,
-                                   limit=self.max_females):
-            self.debug('Found a female:', repr(f))
-            if self.prob(self['mating_prob']):
-                f.impregnate(self)
-                break  # Take a break, don't try to impregnate the rest
-
-            
-class Female(RabbitModel):
-    due_date = None
-    age_of_pregnancy = None
-    gestation = 10
-    mate = None
-
-    @state
-    def fertile(self):
-        return self.fertile, NEVER
-
-    @state
-    def pregnant(self):
-        self.info('I am pregnant')
-        if self.age > self.life_expectancy:
-            return self.dead
-
-        self.due_date = self.now + self.gestation
-
-        number_of_babies = int(8+4*self.random.random())
-
-        while self.now < self.due_date:
-            yield When(self.due_date)
-
-        self.info('Having {} babies'.format(number_of_babies))
-        for i in range(number_of_babies):
-            agent_class = self.random.choice([Male, Female])
-            child = self.model.add_node(agent_class=agent_class,
-                                        topology=self.topology)
-            self.model.add_edge(self, child)
-            self.model.add_edge(self.mate, child)
-            self.offspring += 1
-            self.model.agents[self.mate].offspring += 1
-        self.mate = None
-        self.due_date = None
-        return self.fertile
-
-    @state
-    def dead(self):
-        super().dead()
-        if self.due_date is not None:
-            self.info('A mother has died carrying a baby!!')
-
-    def impregnate(self, male):
-        self.info(f'{repr(male)} impregnating female {repr(self)}')
-        self.mate = male
-        self.set_state(self.pregnant, when=self.now)
-
-
-class RandomAccident(BaseAgent):
-
-    level = logging.INFO
-
-    def step(self):
-        rabbits_total = self.model.topology.number_of_nodes()
-        if 'rabbits_alive' not in self.model:
-            self.model['rabbits_alive'] = 0
-        rabbits_alive = self.model.get('rabbits_alive', rabbits_total)
-        prob_death = self.model.get('prob_death', 1e-100)*math.floor(math.log10(max(1, rabbits_alive)))
-        self.debug('Killing some rabbits with prob={}!'.format(prob_death))
-        for i in self.model.network_agents:
-            if i.state.id == i.dead.id:
-                continue
-            if self.prob(prob_death):
-                self.info('I killed a rabbit: {}'.format(i.id))
-                rabbits_alive = self.model['rabbits_alive'] = rabbits_alive -1
-                i.set_state(i.dead)
-        self.debug('Rabbits alive: {}/{}'.format(rabbits_alive, rabbits_total))
-        if self.model.count_agents(state_id=RabbitModel.dead.id) == self.model.topology.number_of_nodes():
-            self.die()

examples/rabbits/improved/rabbits.yml

@@ -1,41 +0,0 @@
----
-version: '2'
-name: rabbits_improved
-num_trials: 1
-seed: MySeed
-description: null
-group: null
-interval: 1.0
-max_time: 100
-model_class: soil.environment.Environment
-model_params:
-  agents:
-    topology: default
-    agent_class: rabbit_agents.RabbitModel
-    distribution:
-    - agent_class: rabbit_agents.Male
-      topology: default
-      weight: 1
-    - agent_class: rabbit_agents.Female
-      topology: default
-      weight: 1
-    fixed:
-    - agent_class: rabbit_agents.RandomAccident
-      topology: null
-      hidden: true
-      state:
-        group: environment
-    state:
-      group: network
-      mating_prob: 0.1
-  prob_death: 0.001
-  topologies:
-    default:
-      topology:
-        directed: true
-        links: []
-        nodes:
-        - id: 1
-        - id: 0
-extra:
-  visualization_params: {}

examples/rabbits/rabbit_agents.py

@@ -0,0 +1,135 @@
+from soil.agents import FSM, state, default_state, BaseAgent, NetworkAgent
+from enum import Enum
+from random import random, choice
+import logging
+import math
+
+
+class Genders(Enum):
+    male = 'male'
+    female = 'female'
+
+
+class RabbitModel(FSM):
+
+    defaults = {
+        'age': 0,
+        'gender': Genders.male.value,
+        'mating_prob': 0.001,
+        'offspring': 0,
+    }
+
+    sexual_maturity = 3 #4*30
+    life_expectancy = 365 * 3
+    gestation = 33
+    pregnancy = -1
+    max_females = 5
+
+    @default_state
+    @state
+    def newborn(self):
+        self.debug(f'I am a newborn at age {self["age"]}')
+        self['age'] += 1
+
+        if self['age'] >= self.sexual_maturity:
+            self.debug('I am fertile!')
+            return self.fertile
+    @state
+    def fertile(self):
+        raise Exception("Each subclass should define its fertile state")
+
+    @state
+    def dead(self):
+        self.info('Agent {} is dying'.format(self.id))
+        self.die()
+
+
+class Male(RabbitModel):
+
+    @state
+    def fertile(self):
+        self['age'] += 1
+        if self['age'] > self.life_expectancy:
+            return self.dead
+
+        if self['gender'] == Genders.female.value:
+            return
+
+        # Males try to mate
+        for f in self.get_agents(state_id=Female.fertile.id,
+                                 agent_type=Female,
+                                 limit_neighbors=False,
+                                 limit=self.max_females):
+            r = random()
+            if r < self['mating_prob']:
+                self.impregnate(f)
+                break # Take a break
+    def impregnate(self, whom):
+        whom['pregnancy'] = 0
+        whom['mate'] = self.id
+        whom.set_state(whom.pregnant)
+        self.debug('{} impregnating: {}. {}'.format(self.id, whom.id, whom.state))
+
+class Female(RabbitModel):
+    @state
+    def fertile(self):
+        # Just wait for a Male
+        pass
+
+    @state
+    def pregnant(self):
+        self['age'] += 1
+        if self['age'] > self.life_expectancy:
+            return self.dead
+
+        self['pregnancy'] += 1
+        self.debug('Pregnancy: {}'.format(self['pregnancy']))
+        if self['pregnancy'] >= self.gestation:
+            number_of_babies = int(8+4*random())
+            self.info('Having {} babies'.format(number_of_babies))
+            for i in range(number_of_babies):
+                state = {}
+                state['gender'] = choice(list(Genders)).value
+                child = self.env.add_node(self.__class__, state)
+                self.env.add_edge(self.id, child.id)
+                self.env.add_edge(self['mate'], child.id)
+                # self.add_edge()
+                self.debug('A BABY IS COMING TO LIFE')
+                self.env['rabbits_alive'] = self.env.get('rabbits_alive', self.topology.number_of_nodes())+1
+                self.debug('Rabbits alive: {}'.format(self.env['rabbits_alive']))
+                self['offspring'] += 1
+                self.env.get_agent(self['mate'])['offspring'] += 1
+            del self['mate']
+            self['pregnancy'] = -1
+            return self.fertile
+
+    @state
+    def dead(self):
+        super().dead()
+        if 'pregnancy' in self and self['pregnancy'] > -1:
+            self.info('A mother has died carrying a baby!!')
+
+
+class RandomAccident(NetworkAgent):
+
+    level = logging.DEBUG
+
+    def step(self):
+        rabbits_total = self.topology.number_of_nodes()
+        if 'rabbits_alive' not in self.env:
+            self.env['rabbits_alive'] = 0
+        rabbits_alive = self.env.get('rabbits_alive', rabbits_total)
+        prob_death = self.env.get('prob_death', 1e-100)*math.floor(math.log10(max(1, rabbits_alive)))
+        self.debug('Killing some rabbits with prob={}!'.format(prob_death))
+        for i in self.env.network_agents:
+            if i.state['id'] == i.dead.id:
+                continue
+            r = random()
+            if r < prob_death:
+                self.debug('I killed a rabbit: {}'.format(i.id))
+                rabbits_alive = self.env['rabbits_alive'] = rabbits_alive -1
+                self.log('Rabbits alive: {}'.format(self.env['rabbits_alive']))
+                i.set_state(i.dead)
+        self.log('Rabbits alive: {}/{}'.format(rabbits_alive, rabbits_total))
+        if self.count_agents(state_id=RabbitModel.dead.id) == self.topology.number_of_nodes():
+            self.die()

examples/rabbits/rabbits.yml

@@ -0,0 +1,21 @@
+---
+load_module: rabbit_agents
+name: rabbits_example
+max_time: 1000
+interval: 1
+seed: MySeed
+agent_type: rabbit_agents.RabbitModel
+environment_agents:
+    - agent_type: rabbit_agents.RandomAccident
+environment_params:
+  prob_death: 0.001
+default_state:
+  mating_prob: 0.1
+topology:
+  nodes:
+    - id: 1
+      agent_type: rabbit_agents.Male
+    - id: 0
+      agent_type: rabbit_agents.Female
+  directed: true
+  links: []

examples/random_delays/random_delays.py

@@ -4,6 +4,7 @@ Example of a fully programmatic simulation, without definition files.
 '''
 from soil import Simulation, agents
 from soil.time import Delta
+from random import expovariate
 import logging
 
 
@@ -19,7 +20,7 @@ class MyAgent(agents.FSM):
     @agents.state
     def ping(self):
         self.info('Ping')
-        return self.pong, Delta(self.random.expovariate(1/16))
+        return self.pong, Delta(expovariate(1/16))
 
     @agents.state
     def pong(self):
@@ -28,15 +29,15 @@ class MyAgent(agents.FSM):
         self.info(str(self.pong_counts))
         if self.pong_counts < 1:
             return self.die()
-        return None, Delta(self.random.expovariate(1/16))
+        return None, Delta(expovariate(1/16))
 
 
 s = Simulation(name='Programmatic',
-               network_agents=[{'agent_class': MyAgent, 'id': 0}],
+               network_agents=[{'agent_type': MyAgent, 'id': 0}],
                topology={'nodes': [{'id': 0}], 'links': []},
                num_trials=1,
                max_time=100,
-               agent_class=MyAgent,
+               agent_type=MyAgent,
                dry_run=True)
 
 

examples/template.yml

@@ -6,20 +6,20 @@ template:
   group: simple
   num_trials: 1
   interval: 1
-  max_steps: 2
+  max_time: 2
   seed: "CompleteSeed!"
   dump: false
-  model_params:
   network_params:
     generator: complete_graph
     n: 10
   network_agents:
-      - agent_class: CounterModel
+    - agent_type: CounterModel
       weight: "{{ x1 }}"
       state:
         state_id: 0
-      - agent_class: AggregatedCounter
+    - agent_type: AggregatedCounter
       weight: "{{ 1 - x1 }}"
+  environment_params:
     name: "{{ x3 }}"
   skip_test: true
 vars:

examples/terrorism/TerroristNetworkModel.py

@@ -1,3 +1,4 @@
+import random
 import networkx as nx
 from soil.agents import Geo, NetworkAgent, FSM, state, default_state
 from soil import Environment
@@ -25,26 +26,26 @@ class TerroristSpreadModel(FSM, Geo):
         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)
+            self.mean_belief = random.uniform(0.00, 0.5)
         elif self['id'] == self.terrorist.id:     # Terrorist
-            self.mean_belief = self.random.uniform(0.8, 1.00)
+            self.mean_belief = random.uniform(0.8, 1.00)
         elif self['id'] == self.leader.id:     # Leader
             self.mean_belief = 1.00
         else:
             raise Exception('Invalid state id: {}'.format(self['id']))
 
         if 'min_vulnerability' in model.environment_params:
-            self.vulnerability = self.random.uniform( model.environment_params['min_vulnerability'], model.environment_params['max_vulnerability'] )
+            self.vulnerability = random.uniform( model.environment_params['min_vulnerability'], model.environment_params['max_vulnerability'] )
         else :
-            self.vulnerability = self.random.uniform( 0, model.environment_params['max_vulnerability'] )
+            self.vulnerability = random.uniform( 0, model.environment_params['max_vulnerability'] )
 
 
     @state
     def civilian(self):
-        neighbours = list(self.get_neighboring_agents(agent_class=TerroristSpreadModel))
+        neighbours = list(self.get_neighboring_agents(agent_type=TerroristSpreadModel))
         if len(neighbours) > 0:
             # Only interact with some of the neighbors
-            interactions = list(n for n in neighbours if self.random.random() <= self.prob_interaction)
+            interactions = list(n for n in neighbours if random.random() <= self.prob_interaction)
             influence = sum( self.degree(i) for i in interactions )
             mean_belief = sum( i.mean_belief * self.degree(i) / influence for i in interactions )
             mean_belief = mean_belief * self.information_spread_intensity + self.mean_belief * ( 1 - self.information_spread_intensity )
@@ -63,7 +64,7 @@ class TerroristSpreadModel(FSM, Geo):
     @state
     def terrorist(self):
         neighbours = self.get_agents(state_id=[self.terrorist.id, self.leader.id],
-                                     agent_class=TerroristSpreadModel,
+                                     agent_type=TerroristSpreadModel,
                                      limit_neighbors=True)
         if len(neighbours) > 0:
             influence = sum( self.degree(n) for n in neighbours )
@@ -81,26 +82,6 @@ class TerroristSpreadModel(FSM, Geo):
                     return
             return self.leader
 
-    def ego_search(self, steps=1, center=False, node=None, **kwargs):
-        '''Get a list of nodes in the ego network of *node* of radius *steps*'''
-        node = as_node(node if node is not None else self)
-        G = self.subgraph(**kwargs)
-        return nx.ego_graph(G, node, center=center, radius=steps).nodes()
-
-    def degree(self, node, force=False):
-        node = as_node(node)
-        if force or (not hasattr(self.model, '_degree')) or getattr(self.model, '_last_step', 0) < self.now:
-            self.model._degree = nx.degree_centrality(self.G)
-            self.model._last_step = self.now
-        return self.model._degree[node]
-
-    def betweenness(self, node, force=False):
-        node = as_node(node)
-        if force or (not hasattr(self.model, '_betweenness')) or getattr(self.model, '_last_step', 0) < self.now:
-            self.model._betweenness = nx.betweenness_centrality(self.G)
-            self.model._last_step = self.now
-        return self.model._betweenness[node]
-
 
 class TrainingAreaModel(FSM, Geo):
     """
@@ -122,7 +103,7 @@ class TrainingAreaModel(FSM, Geo):
     @default_state
     @state
     def terrorist(self):
-        for neighbour in self.get_neighboring_agents(agent_class=TerroristSpreadModel):
+        for neighbour in self.get_neighboring_agents(agent_type=TerroristSpreadModel):
             if neighbour.vulnerability > self.min_vulnerability:
                 neighbour.vulnerability = neighbour.vulnerability ** ( 1 - self.training_influence )        
 
@@ -148,7 +129,7 @@ class HavenModel(FSM, Geo):
         self.max_vulnerability = model.environment_params['max_vulnerability']
 
     def get_occupants(self, **kwargs):
-        return self.get_neighboring_agents(agent_class=TerroristSpreadModel, **kwargs)
+        return self.get_neighboring_agents(agent_type=TerroristSpreadModel, **kwargs)
 
     @state
     def civilian(self):
@@ -201,27 +182,27 @@ class TerroristNetworkModel(TerroristSpreadModel):
 
     def update_relationships(self):
         if self.count_neighboring_agents(state_id=self.civilian.id) == 0:
-            close_ups = set(self.geo_search(radius=self.vision_range, agent_class=TerroristNetworkModel))
+            close_ups = set(self.geo_search(radius=self.vision_range, agent_type=TerroristNetworkModel))
-            step_neighbours = set(self.ego_search(self.sphere_influence, agent_class=TerroristNetworkModel, center=False))
+            step_neighbours = set(self.ego_search(self.sphere_influence, agent_type=TerroristNetworkModel, center=False))
-            neighbours = set(agent.id for agent in self.get_neighboring_agents(agent_class=TerroristNetworkModel))
+            neighbours = set(agent.id for agent in self.get_neighboring_agents(agent_type=TerroristNetworkModel))
             search = (close_ups | step_neighbours) - neighbours
             for agent in self.get_agents(search):
                 social_distance = 1 / self.shortest_path_length(agent.id)
                 spatial_proximity = ( 1 - self.get_distance(agent.id) )
                 prob_new_interaction = self.weight_social_distance * social_distance + self.weight_link_distance * spatial_proximity
-                if agent['id'] == agent.civilian.id and self.random.random() < prob_new_interaction:
+                if agent['id'] == agent.civilian.id and random.random() < prob_new_interaction:
                     self.add_edge(agent)
                     break
 
     def get_distance(self, target):
-        source_x, source_y = nx.get_node_attributes(self.G, 'pos')[self.id]
+        source_x, source_y = nx.get_node_attributes(self.topology, 'pos')[self.id]
-        target_x, target_y = nx.get_node_attributes(self.G, 'pos')[target]
+        target_x, target_y = nx.get_node_attributes(self.topology, 'pos')[target]
         dx = abs( source_x - target_x )
         dy = abs( source_y - target_y )
         return ( dx ** 2 + dy ** 2 ) ** ( 1 / 2 )
 
     def shortest_path_length(self, target):
         try:
-            return nx.shortest_path_length(self.G, self.id, target)
+            return nx.shortest_path_length(self.topology, self.id, target)
         except nx.NetworkXNoPath:
             return float('inf')

examples/terrorism/TerroristNetworkModel.yml

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

examples/torvalds.yml

@@ -1,14 +1,13 @@
 ---
 name: torvalds_example
-max_steps: 10
+max_time: 10
 interval: 2
-model_params:
+agent_type: CounterModel
-  agent_class: CounterModel
+default_state:
-  default_state:
   skill_level: 'beginner'
-  network_params:
+network_params:
   path: 'torvalds.edgelist'
-  states:
+states:
   Torvalds:
     skill_level: 'God'
   balkian:

examples/tutorial/soil_tutorial.html

@@ -12330,11 +12330,11 @@ Notice how node 0 is the only one with a TV.</p>
 <span class="n">sim</span> <span class="o">=</span> <span class="n">soil</span><span class="o">.</span><span class="n">Simulation</span><span class="p">(</span><span class="n">topology</span><span class="o">=</span><span class="n">G</span><span class="p">,</span>
                                      <span class="n">num_trials</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
                                      <span class="n">max_time</span><span class="o">=</span><span class="n">MAX_TIME</span><span class="p">,</span>
-                                     <span class="n">environment_agents</span><span class="o">=</span><span class="p">[{</span><span class="s1">&#39;agent_class&#39;</span><span class="p">:</span> <span class="n">NewsEnvironmentAgent</span><span class="p">,</span>
+                                     <span class="n">environment_agents</span><span class="o">=</span><span class="p">[{</span><span class="s1">&#39;agent_type&#39;</span><span class="p">:</span> <span class="n">NewsEnvironmentAgent</span><span class="p">,</span>
                                                          <span class="s1">&#39;state&#39;</span><span class="p">:</span> <span class="p">{</span>
                                                              <span class="s1">&#39;event_time&#39;</span><span class="p">:</span> <span class="n">EVENT_TIME</span>
                                                          <span class="p">}}],</span>
-                                     <span class="n">network_agents</span><span class="o">=</span><span class="p">[{</span><span class="s1">&#39;agent_class&#39;</span><span class="p">:</span> <span class="n">NewsSpread</span><span class="p">,</span>
+                                     <span class="n">network_agents</span><span class="o">=</span><span class="p">[{</span><span class="s1">&#39;agent_type&#39;</span><span class="p">:</span> <span class="n">NewsSpread</span><span class="p">,</span>
                                                       <span class="s1">&#39;weight&#39;</span><span class="p">:</span> <span class="mi">1</span><span class="p">}],</span>
                                      <span class="n">states</span><span class="o">=</span><span class="p">{</span><span class="mi">0</span><span class="p">:</span> <span class="p">{</span><span class="s1">&#39;has_tv&#39;</span><span class="p">:</span> <span class="kc">True</span><span class="p">}},</span>
                                      <span class="n">default_state</span><span class="o">=</span><span class="p">{</span><span class="s1">&#39;has_tv&#39;</span><span class="p">:</span> <span class="kc">False</span><span class="p">},</span>
@@ -12468,14 +12468,14 @@ For this demo, we will use a python dictionary:</p>
     <span class="p">},</span>
     <span class="s1">&#39;network_agents&#39;</span><span class="p">:</span> <span class="p">[</span>
         <span class="p">{</span>
-            <span class="s1">&#39;agent_class&#39;</span><span class="p">:</span> <span class="n">NewsSpread</span><span class="p">,</span>
+            <span class="s1">&#39;agent_type&#39;</span><span class="p">:</span> <span class="n">NewsSpread</span><span class="p">,</span>
             <span class="s1">&#39;weight&#39;</span><span class="p">:</span> <span class="mi">1</span><span class="p">,</span>
             <span class="s1">&#39;state&#39;</span><span class="p">:</span> <span class="p">{</span>
                 <span class="s1">&#39;has_tv&#39;</span><span class="p">:</span> <span class="kc">False</span>
             <span class="p">}</span>
         <span class="p">},</span>
         <span class="p">{</span>
-            <span class="s1">&#39;agent_class&#39;</span><span class="p">:</span> <span class="n">NewsSpread</span><span class="p">,</span>
+            <span class="s1">&#39;agent_type&#39;</span><span class="p">:</span> <span class="n">NewsSpread</span><span class="p">,</span>
             <span class="s1">&#39;weight&#39;</span><span class="p">:</span> <span class="mi">2</span><span class="p">,</span>
             <span class="s1">&#39;state&#39;</span><span class="p">:</span> <span class="p">{</span>
                 <span class="s1">&#39;has_tv&#39;</span><span class="p">:</span> <span class="kc">True</span>
@@ -12483,7 +12483,7 @@ For this demo, we will use a python dictionary:</p>
         <span class="p">}</span>
     <span class="p">],</span>
     <span class="s1">&#39;environment_agents&#39;</span><span class="p">:[</span>
-        <span class="p">{</span><span class="s1">&#39;agent_class&#39;</span><span class="p">:</span> <span class="n">NewsEnvironmentAgent</span><span class="p">,</span>
+        <span class="p">{</span><span class="s1">&#39;agent_type&#39;</span><span class="p">:</span> <span class="n">NewsEnvironmentAgent</span><span class="p">,</span>
          <span class="s1">&#39;state&#39;</span><span class="p">:</span> <span class="p">{</span>
              <span class="s1">&#39;event_time&#39;</span><span class="p">:</span> <span class="mi">10</span>
          <span class="p">}</span>

requirements.txt

@@ -2,9 +2,8 @@ networkx>=2.5
 numpy
 matplotlib
 pyyaml>=5.1
-pandas>=1
+pandas>=0.23
 SALib>=1.3
 Jinja2
-Mesa>=1
+Mesa>=0.8
-pydantic>=1.9
+tsih>=0.1.9
-sqlalchemy>=1.4

setup.py

@@ -49,7 +49,6 @@ setup(
     extras_require=extras_require,
     tests_require=test_reqs,
     setup_requires=['pytest-runner', ],
-    pytest_plugins = ['pytest_profiling'],
     include_package_data=True,
     entry_points={
         'console_scripts':

soil/VERSION

@@ -1 +1 @@
-0.20.7
+0.20.8

soil/__init__.py

@@ -1,10 +1,8 @@
-from __future__ import annotations
-
 import importlib
 import sys
 import os
+import pdb
 import logging
-import traceback
 
 from .version import __version__
 
@@ -18,10 +16,11 @@ from . import agents
 from .simulation import *
 from .environment import Environment
 from . import serialization
+from . import analysis
 from .utils import logger
 from .time import *
 
-def main(cfg='simulation.yml', **kwargs):
+def main():
     import argparse
     from . import simulation
 
@@ -30,22 +29,20 @@ def main(cfg='simulation.yml', **kwargs):
     parser = argparse.ArgumentParser(description='Run a SOIL simulation')
     parser.add_argument('file', type=str,
                         nargs="?",
-                        default=cfg,
+                        default='simulation.yml',
                         help='Configuration file for the simulation (e.g., YAML or JSON)')
     parser.add_argument('--version', action='store_true',
                         help='Show version info and exit')
     parser.add_argument('--module', '-m', type=str,
                         help='file containing the code of any custom agents.')
     parser.add_argument('--dry-run', '--dry', action='store_true',
-                        help='Do not store the results of the simulation to disk, show in terminal instead.')
+                        help='Do not store the results of the simulation.')
     parser.add_argument('--pdb', action='store_true',
                         help='Use a pdb console in case of exception.')
-    parser.add_argument('--debug', action='store_true',
-                        help='Run a customized version of a pdb console to debug a simulation.')
     parser.add_argument('--graph', '-g', action='store_true',
-                        help='Dump each trial\'s network topology as a GEXF graph. Defaults to false.')
+                        help='Dump GEXF graph. Defaults to false.')
     parser.add_argument('--csv', action='store_true',
-                        help='Dump all data collected in CSV format. Defaults to false.')
+                        help='Dump history in CSV format. Defaults to false.')
     parser.add_argument('--level', type=str,
                         help='Logging level')
     parser.add_argument('--output', '-o', type=str, default="soil_output",
@@ -54,22 +51,9 @@ def main(cfg='simulation.yml', **kwargs):
                         help='Run trials serially and synchronously instead of in parallel. Defaults to false.')
     parser.add_argument('-e', '--exporter', action='append',
                         help='Export environment and/or simulations using this exporter')
-    parser.add_argument('--only-convert', '--convert', action='store_true',
-                        help='Do not run the simulation, only convert the configuration file(s) and output them.')
-
-
-    parser.add_argument("--set",
-                        metavar="KEY=VALUE",
-                        action='append',
-                        help="Set a number of parameters that will be passed to the simulation."
-                        "(do not put spaces before or after the = sign). "
-                        "If a value contains spaces, you should define "
-                        "it with double quotes: "
-                        'foo="this is a sentence". Note that '
-                        "values are always treated as strings.")
 
     args = parser.parse_args()
-    logger.setLevel(getattr(logging, (args.level or 'INFO').upper()))
+    logging.basicConfig(level=getattr(logging, (args.level or 'INFO').upper()))
 
     if args.version:
         return
@@ -81,10 +65,9 @@ def main(cfg='simulation.yml', **kwargs):
 
     logger.info('Loading config file: {}'.format(args.file))
 
-    if args.pdb or args.debug:
+    if args.pdb:
         args.synchronous = True
-    if args.debug:
+
-        os.environ['SOIL_DEBUG'] = 'true'
 
     try:
         exporters = list(args.exporter or ['default', ])
@@ -99,48 +82,18 @@ def main(cfg='simulation.yml', **kwargs):
         if not os.path.exists(args.file):
             logger.error('Please, input a valid file')
             return
-        for sim in simulation.iter_from_config(args.file):
+        simulation.run_from_config(args.file,
-            if args.set:
+                                   dry_run=args.dry_run,
-                for s in args.set:
-                    k, v = s.split('=', 1)[:2]
-                    v = eval(v)
-                    tail, *head = k.rsplit('.', 1)[::-1]
-                    target = sim
-                    if head:
-                        for part in head[0].split('.'):
-                            try:
-                                target = getattr(target, part)
-                            except AttributeError:
-                                target = target[part]
-                    try:
-                        setattr(target, tail, v)
-                    except AttributeError:
-                        target[tail] = v
-
-                if args.only_convert:
-                    print(sim.to_yaml())
-                    continue
-
-            sim.run_simulation(dry_run=args.dry_run,
                                    exporters=exporters,
                                    parallel=(not args.synchronous),
                                    outdir=args.output,
-                               exporter_params=exp_params,
+                                   exporter_params=exp_params)
-                               **kwargs)
+    except Exception:
-
-    except Exception as ex:
         if args.pdb:
-            from .debugging import post_mortem
+            pdb.post_mortem()
-            print(traceback.format_exc())
-            post_mortem()
         else:
             raise
 
-def easy(cfg, debug=False):
+
-    sim = simulation.from_config(cfg)
-    if debug or os.environ.get('SOIL_DEBUG'):
-        from .debugging import setup
-        setup(sys._getframe().f_back)
-    return sim
 if __name__ == '__main__':
     main()

soil/agents/BassModel.py

@@ -1,3 +1,4 @@
+import random
 from . import FSM, state, default_state
 
 
@@ -15,13 +16,13 @@ class BassModel(FSM):
     @default_state
     @state
     def innovation(self):
-        if self.prob(self.innovation_prob):
+        if random.random() < self.innovation_prob:
             self.sentimentCorrelation = 1
             return self.aware
         else:
             aware_neighbors = self.get_neighboring_agents(state_id=self.aware.id)
             num_neighbors_aware = len(aware_neighbors)
-            if self.prob((self['imitation_prob']*num_neighbors_aware)):
+            if random.random() < (self['imitation_prob']*num_neighbors_aware):
                 self.sentimentCorrelation = 1
                 return self.aware
 

soil/agents/BigMarketModel.py

@@ -1,3 +1,4 @@
+import random
 from . import FSM, state, default_state
 
 
@@ -38,10 +39,10 @@ class BigMarketModel(FSM):
     @state
     def enterprise(self):
 
-        if self.random.random() < self.tweet_probability:  # Tweets
+        if random.random() < self.tweet_probability:  # Tweets
             aware_neighbors = self.get_neighboring_agents(state_id=self.number_of_enterprises)  # Nodes neighbour users
             for x in aware_neighbors:
-                if self.random.uniform(0,10) < 5:
+                if random.uniform(0,10) < 5:
                     x.sentiment_about[self.id] += 0.1  # Increments for enterprise
                 else:
                     x.sentiment_about[self.id] -= 0.1  # Decrements for enterprise
@@ -56,11 +57,11 @@ class BigMarketModel(FSM):
 
     @state
     def user(self):
-        if self.random.random() < self.tweet_probability:  # Tweets
+        if random.random() < self.tweet_probability:  # Tweets
-            if self.random.random() < self.tweet_relevant_probability:  # Tweets something relevant
+            if random.random() < self.tweet_relevant_probability:  # Tweets something relevant
                 # Tweet probability per enterprise
                 for i in range(len(self.enterprises)):
-                    random_num = self.random.random()
+                    random_num = random.random()
                     if random_num < self.tweet_probability_about[i]:
                         # The condition is fulfilled, sentiments are evaluated towards that enterprise
                         if self.sentiment_about[i] < 0:

soil/agents/CounterModel.py

@@ -7,13 +7,9 @@ class CounterModel(NetworkAgent):
     in each step and adds it to its state.
     """
 
-    times = 0
-    neighbors = 0
-    total = 0
-
     def step(self):
         # Outside effects
-        total = len(list(self.model.schedule._agents))
+        total = len(list(self.get_agents()))
         neighbors = len(list(self.get_neighboring_agents()))
         self['times'] = self.get('times', 0) + 1
         self['neighbors'] = neighbors
@@ -26,15 +22,17 @@ class AggregatedCounter(NetworkAgent):
     in each step and adds it to its state.
     """
 
-    times = 0
+    defaults = {
-    neighbors = 0
+        'times': 0,
-    total = 0
+        'neighbors': 0,
+        'total': 0
+    }
 
     def step(self):
         # Outside effects
         self['times'] += 1
         neighbors = len(list(self.get_neighboring_agents()))
         self['neighbors'] += neighbors
-        total = len(list(self.model.schedule.agents))
+        total = len(list(self.get_agents()))
         self['total'] += total
         self.debug('Running for step: {}. Total: {}'.format(self.now, total))