diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml index 31104b3..e438835 100644 --- a/.gitlab-ci.yml +++ b/.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 diff --git a/CHANGELOG.md b/CHANGELOG.md index 90a713a..5c2bfaa 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -3,7 +3,30 @@ All notable changes to this project will be documented in this file. The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/), and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html). -## [UNRELEASED] +## [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 +* 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). +* 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 ` +### Changed +* Configuration schema (`Simulation`) is very simplified. All simulations should be checked +* Model / environment variables are expected (but not enforced) to be a single value. This is done to more closely align with mesa +* `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. ## [0.20.8] ### Changed diff --git a/README.md b/README.md index 714d4df..5930c74 100644 --- a/README.md +++ b/README.md @@ -1,10 +1,65 @@ # [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. +> **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) + +## Features + +* Integration with (social) networks (through `networkx`) +* Convenience functions and methods to easily assign agents to your model (and optionally to its network): + * Following a given distribution (e.g., 2 agents of type `Foo`, 10% of the network should be agents of type `Bar`) + * Based on the topology of the network +* **Several types of abstractions for agents**: + * Finite state machine, where methods can be turned into a state + * Network agents, which have convenience methods to access the model's topology + * Generator-based agents, whose state is paused though a `yield` and resumed on the next step +* **Reporting and data collection**: + * Soil models include data collection and record some data by default (# of agents, state of each agent, etc.) + * All data collected are exported by default to a SQLite database and a description file + * Options to export to other formats, such as CSV, or defining your own exporters + * A summary of the data collected is shown in the command line, for easy inspection +* **An event-based scheduler** + * Agents can be explicit about when their next time/step should be, and not all agents run in every step. This avoids unnecessary computation. + * Time intervals between each step are flexible. + * There are primitives to specify when the next execution of an agent should be (or conditions) +* **Actor-inspired** message-passing +* A simulation runner (`soil.Simulation`) that can: + * Run models in parallel + * Save results to different formats +* Simulation configuration files +* A command line interface (`soil`), to quickly run simulations with different parameters +* An integrated debugger (`soil --debug`) with custom functions to print agent states and break at specific states + +## 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 might yield surprising results. +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 may not work. + + +## Changes in version 0.3 + +Version 0.3 came packed with many changes to provide much better integration with MESA. +For a long time, we tried to keep soil backwards-compatible, but it turned out to be a big endeavour and the resulting code was less readable. +This translates to harder maintenance and a worse experience for newcomers. +In the end, we decided to make some breaking changes. + +If you have an older Soil simulation, you have two options: + +* Update the necessary configuration files and code. You may use the examples in the `examples` folder for reference, as well as the documentation. +* Keep using a previous `soil` version. + + + ## Citation @@ -31,24 +86,6 @@ 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) diff --git a/docs/configuration.rst b/docs/configuration.rst deleted file mode 100644 index c829a2f..0000000 --- a/docs/configuration.rst +++ /dev/null @@ -1,241 +0,0 @@ -Configuring a simulation ------------------------- - -There are two ways to configure a simulation: programmatically and with a configuration file. -In both cases, the parameters used are the same. -The advantage of a configuration file is that it is a clean declarative description, and it makes it easier to reproduce. - -Simulation configuration files can be formatted in ``json`` or ``yaml`` and they define all the parameters of a simulation. -Here's an example (``example.yml``). - -.. literalinclude:: example.yml - :language: yaml - - -This example configuration will run three trials (``num_trials``) of a simulation containing a randomly generated network (``network_params``). -The 100 nodes in the network will be SISaModel agents (``network_agents.agent_type``), which is an agent behavior that is included in Soil. -10% of the agents (``weight=1``) will start in the content state, 10% in the discontent state, and the remaining 80% (``weight=8``) in the neutral state. -All agents will have access to the environment (``environment_params``), which only contains one variable, ``prob_infected``. -The state of the agents will be updated every 2 seconds (``interval``). - -Now run the simulation with the command line tool: - -.. code:: bash - - soil example.yml - -Once the simulation finishes, its results will be stored in a folder named ``MyExampleSimulation``. -Three types of objects are saved by default: a pickle of the simulation; a ``YAML`` representation of the simulation (which can be used to re-launch it); and for every trial, a ``sqlite`` file with the content of the state of every network node and the environment parameters at every step of the simulation. - - -.. code:: - - soil_output - └── MyExampleSimulation - ├── MyExampleSimulation.dumped.yml - ├── MyExampleSimulation.simulation.pickle - ├── MyExampleSimulation_trial_0.db.sqlite - ├── MyExampleSimulation_trial_1.db.sqlite - └── MyExampleSimulation_trial_2.db.sqlite - - -You may also ask soil to export the states in a ``csv`` file, and the network in gephi format (``gexf``). - -Network -======= - -The network topology for the simulation can be loaded from an existing network file or generated with one of the random network generation methods from networkx. - -Loading a network -################# - -To load an existing network, specify its path in the configuration: - -.. code:: yaml - - --- - network_params: - path: /tmp/mynetwork.gexf - -Soil will try to guess what networkx method to use to read the file based on its extension. -However, we only test using ``gexf`` files. - -For simple networks, you may also include them in the configuration itself using , using the ``topology`` parameter like so: - -.. code:: yaml - - --- - topology: - nodes: - - id: First - - id: Second - links: - - source: First - target: Second - - -Generating a random network -########################### - -To generate a random network using one of networkx's built-in methods, specify the `graph generation algorithm `_ and other parameters. -For example, the following configuration is equivalent to :code:`nx.complete_graph(n=100)`: - -.. code:: yaml - - network_params: - generator: complete_graph - n: 100 - -Environment -============ -The environment is the place where the shared state of the simulation is stored. -For instance, the probability of disease outbreak. -The configuration file may specify the initial value of the environment parameters: - -.. code:: yaml - - environment_params: - daily_probability_of_earthquake: 0.001 - number_of_earthquakes: 0 - -All agents have access to the environment parameters. - -In some scenarios, it is useful to have a custom environment, to provide additional methods or to control the way agents update environment state. -For example, if our agents play the lottery, the environment could provide a method to decide whether the agent wins, instead of leaving it to the agent. - - -Agents -====== -Agents are a way of modelling behavior. -Agents can be characterized with two variables: agent type (``agent_type``) and state. -Only one agent is executed at a time (generally, every ``interval`` seconds), and it has access to its state and the environment parameters. -Through the environment, it can access the network topology and the state of other agents. - -There are three three types of agents according to how they are added to the simulation: network agents and environment agent. - -Network Agents -############## -Network agents are attached to a node in the topology. -The configuration file allows you to specify how agents will be mapped to topology nodes. - -The simplest way is to specify a single type of agent. -Hence, every node in the network will be associated to an agent of that type. - -.. code:: yaml - - agent_type: SISaModel - -It is also possible to add more than one type of agent to the simulation, and to control the ratio of each type (using the ``weight`` property). -For instance, with following configuration, it is five times more likely for a node to be assigned a CounterModel type than a SISaModel type. - -.. code:: yaml - - network_agents: - - agent_type: SISaModel - weight: 1 - - agent_type: CounterModel - weight: 5 - -The third option is to specify the type of agent on the node itself, e.g.: - - -.. code:: yaml - - topology: - nodes: - - id: first - agent_type: BaseAgent - states: - first: - agent_type: SISaModel - - -This would also work with a randomly generated network: - - -.. code:: yaml - - network: - generator: complete - n: 5 - agent_type: BaseAgent - states: - - agent_type: SISaModel - - - -In addition to agent type, you may add a custom initial state to the distribution. -This is very useful to add the same agent type with different states. -e.g., to populate the network with SISaModel, roughly 10% of them with a discontent state: - -.. code:: yaml - - network_agents: - - agent_type: SISaModel - weight: 9 - state: - id: neutral - - agent_type: SISaModel - weight: 1 - state: - id: discontent - -Lastly, the configuration may include initial state for one or more nodes. -For instance, to add a state for the two nodes in this configuration: - -.. code:: yaml - - agent_type: SISaModel - network: - generator: complete_graph - n: 2 - states: - - id: content - - id: discontent - - -Or to add state only to specific nodes (by ``id``). -For example, to apply special skills to Linux Torvalds in a simulation: - -.. literalinclude:: ../examples/torvalds.yml - :language: yaml - - -Environment Agents -################## -In addition to network agents, more agents can be added to the simulation. -These agents are programmed in much the same way as network agents, the only difference is that they will not be assigned to network nodes. - - -.. code:: - - environment_agents: - - agent_type: MyAgent - state: - mood: happy - - agent_type: DummyAgent - - -You may use environment agents to model events that a normal agent cannot control, such as natural disasters or chance. -They are also useful to add behavior that has little to do with the network and the interactions within that network. - -Templating -========== - -Sometimes, it is useful to parameterize a simulation and run it over a range of values in order to compare each run and measure the effect of those parameters in the simulation. -For instance, you may want to run a simulation with different agent distributions. - -This can be done in Soil using **templates**. -A template is a configuration where some of the values are specified with a variable. -e.g., ``weight: "{{ var1 }}"`` instead of ``weight: 1``. -There are two types of variables, depending on how their values are decided: - -* Fixed. A list of values is provided, and a new simulation is run for each possible value. If more than a variable is given, a new simulation will be run per combination of values. -* Bounded/Sampled. The bounds of the variable are provided, along with a sampler method, which will be used to compute all the configuration combinations. - -When fixed and bounded variables are mixed, Soil generates a new configuration per combination of fixed values and bounded values. - -Here is an example with a single fixed variable and two bounded variable: - -.. literalinclude:: ../examples/template.yml - :language: yaml diff --git a/docs/example.yml b/docs/example.yml index 4ef6ef2..45661b3 100644 --- a/docs/example.yml +++ b/docs/example.yml @@ -3,33 +3,38 @@ name: MyExampleSimulation max_time: 50 num_trials: 3 interval: 2 -network_params: - generator: barabasi_albert_graph - n: 100 - m: 2 -network_agents: - - agent_type: SISaModel - weight: 1 +model_params: + topology: + params: + generator: barabasi_albert_graph + n: 100 + m: 2 + agents: + distribution: + - agent_class: SISaModel + topology: True + ratio: 0.1 state: - id: content - - agent_type: SISaModel - weight: 1 + state_id: content + - agent_class: SISaModel + topology: True + ratio: .1 state: - id: discontent - - agent_type: SISaModel - weight: 8 + state_id: discontent + - agent_class: SISaModel + topology: True + ratio: 0.8 state: - id: neutral -environment_params: - prob_infect: 0.075 - neutral_discontent_spon_prob: 0.1 - neutral_discontent_infected_prob: 0.3 - neutral_content_spon_prob: 0.3 - neutral_content_infected_prob: 0.4 - discontent_neutral: 0.5 - discontent_content: 0.5 - variance_d_c: 0.2 - content_discontent: 0.2 - variance_c_d: 0.2 - content_neutral: 0.2 - standard_variance: 1 + state_id: neutral + prob_infect: 0.075 + neutral_discontent_spon_prob: 0.1 + neutral_discontent_infected_prob: 0.3 + neutral_content_spon_prob: 0.3 + neutral_content_infected_prob: 0.4 + discontent_neutral: 0.5 + discontent_content: 0.5 + variance_d_c: 0.2 + content_discontent: 0.2 + variance_c_d: 0.2 + content_neutral: 0.2 + standard_variance: 1 \ No newline at end of file diff --git a/docs/index.rst b/docs/index.rst index 92896ce..b589c06 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -1,12 +1,20 @@ -.. Soil documentation master file, created by - sphinx-quickstart on Tue Apr 25 12:48:56 2017. - You can adapt this file completely to your liking, but it should at least - contain the root `toctree` directive. - Welcome to Soil's documentation! ================================ -Soil is an Agent-based Social Simulator in Python 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 ` and the `examples on GitHub . If you use Soil in your research, do not forget to cite this paper: @@ -38,8 +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 .. diff --git a/docs/installation.rst b/docs/installation.rst index 9d63bca..831e060 100644 --- a/docs/installation.rst +++ b/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 @@ -14,6 +17,10 @@ Now test that it worked by running the command line tool soil --help + #or + + python -m soil --help + Or, if you're using using soil programmatically: .. code:: python @@ -21,4 +28,38 @@ Or, if you're using using soil programmatically: import soil print(soil.__version__) -The latest version can be installed through `GitLab `_ or `GitHub `_. + + +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 `_ 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 . \ No newline at end of file diff --git a/docs/make.bat b/docs/make.bat index 3a6121c..8c57d1f 100644 --- a/docs/make.bat +++ b/docs/make.bat @@ -12,7 +12,7 @@ set BUILDDIR=_build set SPHINXPROJ=Soil if "%1" == "" goto help - +eE %SPHINXBUILD% >NUL 2>NUL if errorlevel 9009 ( echo. diff --git a/docs/mesa.rst b/docs/mesa.rst new file mode 100644 index 0000000..51ae1c1 --- /dev/null +++ b/docs/mesa.rst @@ -0,0 +1,22 @@ +Mesa compatibility +------------------ + +Soil is in the process of becoming fully compatible with MESA. +The idea is to provide a set of modular classes and functions that extend the functionality of mesa, whilst staying compatible. +In the end, it should be possible to add regular mesa agents to a soil simulation, or use a soil agent within a mesa simulation/model. + +This is a non-exhaustive list of tasks to achieve compatibility: + +- [ ] Integrate `soil.Simulation` with mesa's runners: + - [ ] `soil.Simulation` could mimic/become a `mesa.batchrunner` +- [ ] Integrate `soil.Environment` with `mesa.Model`: + - [x] `Soil.Environment` inherits from `mesa.Model` + - [x] `Soil.Environment` includes a Mesa-like Scheduler (see the `soil.time` module. + - [ ] Allow for `mesa.Model` to be used in a simulation. +- [ ] Integrate `soil.Agent` with `mesa.Agent`: + - [x] Rename agent.id to unique_id? + - [x] mesa agents can be used in soil simulations (see `examples/mesa`) +- [ ] Provide examples + - [ ] Using mesa modules in a soil simulation + - [ ] Using soil modules in a mesa simulation +- [ ] Document the new APIs and usage \ No newline at end of file diff --git a/docs/notes_v1.0.rst b/docs/notes_v1.0.rst new file mode 100644 index 0000000..9f83a9c --- /dev/null +++ b/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. diff --git a/docs/output_21_0.png b/docs/output_21_0.png deleted file mode 100644 index 1ebaac5..0000000 Binary files a/docs/output_21_0.png and /dev/null differ diff --git a/docs/output_30_0.png b/docs/output_30_0.png new file mode 100644 index 0000000..b5ed204 Binary files /dev/null and b/docs/output_30_0.png differ diff --git a/docs/output_34_0.png b/docs/output_34_0.png new file mode 100644 index 0000000..8ec88fe Binary files /dev/null and b/docs/output_34_0.png differ diff --git a/docs/output_49_0.png b/docs/output_49_0.png new file mode 100644 index 0000000..3392810 Binary files /dev/null and b/docs/output_49_0.png differ diff --git a/docs/output_50_0.png b/docs/output_50_0.png new file mode 100644 index 0000000..914c4dd Binary files /dev/null and b/docs/output_50_0.png differ diff --git a/docs/output_55_0.png b/docs/output_55_0.png deleted file mode 100644 index 41c5676..0000000 Binary files a/docs/output_55_0.png and /dev/null differ diff --git a/docs/output_56_0.png b/docs/output_56_0.png deleted file mode 100644 index f8b44a6..0000000 Binary files a/docs/output_56_0.png and /dev/null differ diff --git a/docs/output_72_0.png b/docs/output_72_0.png deleted file mode 100644 index 7bb315c..0000000 Binary files a/docs/output_72_0.png and /dev/null differ diff --git a/docs/quickstart.rst b/docs/quickstart.rst deleted file mode 100644 index e5c1182..0000000 --- a/docs/quickstart.rst +++ /dev/null @@ -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 ` 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 `__. -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 \ No newline at end of file diff --git a/docs/quickstart.yml b/docs/quickstart.yml deleted file mode 100644 index 76ed3e2..0000000 --- a/docs/quickstart.yml +++ /dev/null @@ -1,30 +0,0 @@ ---- -name: quickstart -num_trials: 1 -max_time: 1000 -network_agents: - - agent_type: SISaModel - state: - id: neutral - weight: 1 - - agent_type: SISaModel - state: - id: content - weight: 2 -network_params: - n: 100 - k: 5 - p: 0.2 - generator: newman_watts_strogatz_graph -environment_params: - neutral_discontent_spon_prob: 0.05 - neutral_discontent_infected_prob: 0.1 - neutral_content_spon_prob: 0.2 - neutral_content_infected_prob: 0.4 - discontent_neutral: 0.2 - discontent_content: 0.05 - content_discontent: 0.05 - variance_d_c: 0.05 - variance_c_d: 0.1 - content_neutral: 0.1 - standard_variance: 0.1 diff --git a/docs/requirements.txt b/docs/requirements.txt index 28a6674..654cff4 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -1 +1 @@ -ipython==7.31.1 +ipython>=7.31.1 diff --git a/docs/soil-vs.rst b/docs/soil-vs.rst new file mode 100644 index 0000000..53b6891 --- /dev/null +++ b/docs/soil-vs.rst @@ -0,0 +1,12 @@ +### MESA + +Starting with version 0.3, Soil has been redesigned to complement Mesa, while remaining compatible with it. +That means that every component in Soil (i.e., Models, Environments, etc.) can be mixed with existing mesa components. +In fact, there are examples that show how that integration may be used, in the `examples/mesa` folder in the repository. + +Here are some reasons to use Soil instead of plain mesa: + +- Less boilerplate for common scenarios (by some definitions of common) +- Functions to automatically populate a topology with an agent distribution (i.e., different ratios of agent class and state) +- The `soil.Simulation` class allows you to run multiple instances of the same experiment (i.e., multiple trials with the same parameters but a different randomness seed) +- Reporting functions that aggregate multiple diff --git a/docs/soil_tutorial.rst b/docs/soil_tutorial.rst index b4ea7bf..fc93303 100644 --- a/docs/soil_tutorial.rst +++ b/docs/soil_tutorial.rst @@ -24,114 +24,168 @@ But before that, let’s import the soil module and networkx. %load_ext autoreload %autoreload 2 - %matplotlib inline - # To display plots in the notebooed_ + import matplotlib.pyplot as plt Basic concepts -------------- There are three main elements in a soil simulation: +- The environment or model. It assigns agents to nodes in the network, + and stores the environment parameters (shared state for all agents). - The network topology. A simulation may use an existing NetworkX - topology, or generate one on the fly -- Agents. There are two types: 1) network agents, which are linked to a - node in the topology, and 2) environment agents, which are freely - assigned to the environment. -- The environment. It assigns agents to nodes in the network, and - stores the environment parameters (shared state for all agents). + topology, or generate one on the fly. +- Agents. There are several types of agents, depending on their + behavior and their capabilities. Some examples of built-in types of + agents are: -Soil is based on ``simpy``, which is an event-based network simulation -library. Soil provides several abstractions over events to make -developing agents easier. This means you can use events (timeouts, -delays) in soil, but for the most part we will assume your models will -be step-based. + - Network agents, which are linked to a node in the topology. They + have additional methods to access their neighbors. + - FSM (Finite state machine) agents. Their behavior is defined in + terms of states, and an agent will move from one state to another. + - Evented agents, an actor-based model of agents, which can + communicate with one another through message passing. + - For convenience, a general ``soil.Agent`` class is provided, which + inherits from Network, FSM and Evented at the same time. + +Soil provides several abstractions over events to make developing agents +easier. This means you can use events (timeouts, delays) in soil, but +for the most part we will assume your models will be step-based o. Modeling behaviour ------------------ Our first step will be to model how every person in the social network -reacts when it comes to news. We will follow a very simple model (a -finite state machine). +reacts to hearing a piece of disinformation (news). We will follow a +very simple model based on a finite state machine. -There are two types of people, those who have heard about a newsworthy -event (infected) or those who have not (neutral). A neutral person may -heard about the news either on the TV (with probability -**prob_tv_spread**) or through their friends. Once a person has heard -the news, they will spread it to their friends (with a probability -**prob_neighbor_spread**). Some users do not have a TV, so they only -rely on their friends. +A person may be in one of two states: **neutral** (the default state) +and **infected**. A neutral person may hear about a piece of +disinformation either on the TV (with probability **prob_tv_spread**) or +through their friends. Once a person has heard the news, they will +spread it to their friends (with a probability +**prob_neighbor_spread**). Some users do not have a TV, so they will +only be infected by their friends. The spreading probabilities will change over time due to different -factors. We will represent this variance using an environment agent. +factors. We will represent this variance using an additional agent which +will not be a part of the social network. -Network Agents -~~~~~~~~~~~~~~ +Modelling Agents +~~~~~~~~~~~~~~~~ -A basic network agent in Soil would typically inherit from -``soil.agents.NetworkAgent``, and define its behaviour in every step of -the simulation by implementing a ``run(self)`` method. The most -important attributes of the agent are: +The following sections will cover the basics of developing agents in +SOIL. -- ``agent.state``, a dictionary with the state of the agent. This tate - will be saved in every step of the simulation. It can be accessed - from the agent as well: +For more advanced patterns, please check the **examples** folder in the +repository. -.. code:: py +Basic agents +^^^^^^^^^^^^ - a = soil.agents.NetworkAgent(env=env) - agent.state['hours_of_sleep'] = 10 - # is the same as - a['hours_of_sleep'] = 10 +The most basic agent in Soil is ``soil.BaseAgent``. These agents +implement their behavior by overriding the ``step`` method, which will +be run in every simulation step. Only one agent will be running at any +given time, and it will be doing so until the ``step`` function returns. -The state of the agent is stored in every step of the simulation: -``py print(a['hours_of_sleep', 10]) # hours of sleep before step #10 print(a[None, 0]) # whole state of the agent before step #0`` +Agents can access their environment through their ``self.model`` +attribute. This is most commonly used to get access to the environment +parameters and methods. Here is a simple example of an agent: -- ``agent.env``, a reference to the environment. Most commonly used to - get access to the environment parameters and the topology: \```py - a.env.G.nodes() # Get all nodes ids in the topology - a.env[‘minimum_hours_of_sleep’] +.. code:: python - \``\` + class ExampleAgent(BaseAgent): + def init(self): + self.is_infected = False + self.steps_neutral = 0 + + def step(self): + # Implement agent logic + if self.is_infected: + ... # Do something, like infecting other agents + return self.die("No need to do anything else") # Stop forever + else: + ... # Do something + self.steps_neutral += 1 + if self.steps_neutral > self.model.max_steps_neutral: + self.is_infected = True -Since our model is a finite state machine, we will be basing it on -``soil.agents.FSM``. +Any kind of agent behavior can be implemented with this ``step`` +function. However, it has two main drawbacks: 1) complex behaviors can +get difficult both write and understand; 2) these behaviors are not +composable. -Agents that inherit from ``soil.agents.FSM`` do not need to specify a -``step`` method. Instead, we describe each finite state with a function. -To change to another state, a function may return the new state, or the -``id`` of a state. If no state is returned, the state remains unchanged. +FSM agents +^^^^^^^^^^ -The current state of the agent can be checked with -``agent.state['id']``. That state id can be used to look for other -networks in that specific state +One way to solve both issues is to model agents as `Finite-state +Machines `__ (FSM, +for short). FSM define a series of possible states for the agent, and +changes between these states. These states can be modelled and extended +independently. -Our agent will have of two states, ``neutral`` (default) and -``infected``. +This is modelled in Soil through the ``soil.FSM`` class. Agents that +inherit from ``soil.FSM`` do not need to specify a ``step`` method. +Instead, we describe each finite state with a function. To change to +another state, a function may return the new state, or the ``id`` of a +state. If no state is returned, the state remains unchanged. -Here’s the code: +The current state of the agent can be checked with ``agent.state_id``. +That state id can be used to look for other agents in that specific +state. -.. code:: ipython3 +Our previous example could be expressed like this: - import random - - class NewsSpread(soil.agents.FSM): - @soil.agents.default_state - @soil.agents.state - def neutral(self): - r = random.random() - if self['has_tv'] and r <= self.env['prob_tv_spread']: - return self.infected - return - - @soil.agents.state - def infected(self): - prob_infect = self.env['prob_neighbor_spread'] - for neighbor in self.get_neighboring_agents(state_id=self.neutral.id): - r = random.random() - if r < prob_infect: - neighbor.set_state(self.infected.id) - return - +.. code:: python + + class FSMExample(FSM): + + def init(self): + self.steps_neutral = 0 + + @state(default=True) + def neutral(self): + ... # Do something + self.steps_neutral += 1 + if self.steps_neutral > self.model.max_steps_neutral: + return self.infected # Change state + + @state + def infected(self): + ... # Do something + return self.die("No need to do anything else") + +Generator-based agents +^^^^^^^^^^^^^^^^^^^^^^ + +Another design pattern that can be very useful in some cases is to model +each step (or a specific state) using generators (the ``yield`` +keyword). + +.. code:: python + + class GenExample(BaseAgent): + def step(self): + for i in range(self.model.max_steps_neutral): + ... # Do something + yield # Signal the scheduler that this step is done for now + ... # Do something + return self.die("No need to do anything else") + +Telling the scheduler when to wake up an agent +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +By default, every agent will be called in every simulation step, and the +time elapsed between two steps is controlled by the ``interval`` +attribute in the environment. + +But agents may signal the scheduler when they expect to be called again. +This is especially useful when an agent is going to be dormant for a +long time. To do so, an agent can return (or ``yield``) from a ``step`` +or a ``state`` a value of type ``soil.When`` (absolute time), +``soil.Delta`` (relative time) or ``soil.Cond``, telling the scheduler +when the agent will be ready to run again. If it returns nothing (i.e., +``None``), the agent will be ready to run at the next simulation step. Environment agents ~~~~~~~~~~~~~~~~~~ @@ -145,192 +199,478 @@ spreading the rumor. .. code:: ipython3 - NEIGHBOR_FACTOR = 0.9 - TV_FACTOR = 0.5 + import logging - - class NewsEnvironmentAgent(soil.agents.NetworkAgent): + class EventGenerator(soil.BaseAgent): + level = logging.INFO + def step(self): - if self.now == self['event_time']: - self.env['prob_tv_spread'] = 1 - self.env['prob_neighbor_spread'] = 1 - elif self.now > self['event_time']: - self.env['prob_tv_spread'] = self.env['prob_tv_spread'] * TV_FACTOR - self.env['prob_neighbor_spread'] = self.env['prob_neighbor_spread'] * NEIGHBOR_FACTOR + # Do nothing until the time of the event + yield soil.When(self.model.event_time) + self.info("TV event happened") + self.model.prob_tv_spread = 0.5 + self.model.prob_neighbor_spread *= 2 + self.model.prob_neighbor_spread = min(self.model.prob_neighbor_spread, 1) + yield + self.model.prob_tv_spread = 0 + + while self.alive: + self.model.prob_neighbor_spread = self.model.prob_neighbor_spread * self.model.neighbor_factor + if self.model.prob_neighbor_spread < 0.01: + return self.die("neighbors can no longer spread the rumour") + yield -Testing the agents -~~~~~~~~~~~~~~~~~~ +Environment (Model) +~~~~~~~~~~~~~~~~~~~ -Feel free to skip this section if this is your first time with soil. - -Testing agents is not easy, and this is not a thorough testing process -for agents. Rather, this section is aimed to show you how to access -internal pats of soil so you can test your agents. - -First of all, let’s check if our network agent has the states we would -expect: +Let’s define a environment model to test our event generator agent. This +environment will have a single agent (the event generator). We will also +tell the environment to save the value of ``prob_tv_spread`` after every +step: .. code:: ipython3 - NewsSpread.states + class NewsEnv(soil.NetworkEnvironment): + + prob_tv_spread = 0.1 + prob_neighbor_spread = 0.1 + event_time = 10 + tv_factor = 0.5 + neighbor_factor = 0.9 + + + def init(self): + self.add_model_reporter("prob_tv_spread") + self.add_agent(EventGenerator) + +Once the environment has been defined, we can run a simulation + +.. code:: ipython3 + + it = NewsEnv.run(iterations=1, dump=False, max_time=14) + + it[0].model_df() + + + +.. parsed-literal:: + + HBox(children=(IntProgress(value=0, description='NewsEnv', max=1, style=ProgressStyle(description_width='initi… + + + +.. parsed-literal:: + + HBox(children=(IntProgress(value=0, max=1), HTML(value=''))) + + +.. parsed-literal:: + + + + + + +.. raw:: html + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
stepagent_countprob_tv_spread
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+ + + +As we can see, the event occurred right after ``t=10``, so by ``t=11`` +the value of ``prob_tv_spread`` was already set to ``1.0``. + +You may notice nothing happened between ``t=0`` and ``t=1``. That is +because there aren’t any other agents in the simulation, and our event +generator explicitly waited until ``t=10``. + +Network agents +~~~~~~~~~~~~~~ + +In our disinformation scenario, we will model our agents as a FSM with +two states: ``neutral`` (default) and ``infected``. + +Here’s the code: + +.. code:: ipython3 + + class NewsSpread(soil.Agent): + has_tv = False + infected_by_friends = False + + @soil.state(default=True) + def neutral(self): + if self.infected_by_friends: + return self.infected + if self.has_tv: + if self.prob(self.model.prob_tv_spread): + return self.infected + + @soil.state + def infected(self): + for neighbor in self.iter_neighbors(state_id=self.neutral.id): + if self.prob(self.model.prob_neighbor_spread): + neighbor.infected_by_friends = True + +We can check that our states are well defined, here: + +.. code:: ipython3 + + NewsSpread.states() .. parsed-literal:: - {'neutral': , - 'infected': } + ['dead', 'neutral', 'infected'] -Now, let’s run a simulation on a simple network. It is comprised of -three nodes: +Environment (Model) +~~~~~~~~~~~~~~~~~~~ + +Let’s modify our simple simulation. We will add a network of agents of +type NewsSpread. + +Only one agent (0) will have a TV (in blue). .. code:: ipython3 - G = nx.Graph() - G.add_edge(0, 1) - G.add_edge(0, 2) - G.add_edge(2, 3) - G.add_node(4) + def generate_simple(): + G = nx.Graph() + G.add_edge(0, 1) + G.add_edge(0, 2) + G.add_edge(2, 3) + G.add_node(4) + return G + + G = generate_simple() pos = nx.spring_layout(G) nx.draw_networkx(G, pos, node_color='red') nx.draw_networkx(G, pos, nodelist=[0], node_color='blue') -.. image:: output_21_0.png +.. image:: output_30_0.png -Let’s run a simple simulation that assigns a NewsSpread agent to all the -nodes in that network. Notice how node 0 is the only one with a TV. - .. code:: ipython3 - import importlib - importlib.reload(soil.agents) + class NewsEnv(soil.NetworkEnvironment): + + prob_tv_spread = 0 + prob_neighbor_spread = 0.1 + event_time = 10 + tv_factor = 0.5 + neighbor_factor = 0.9 + + + def init(self): + self.add_agent(EventGenerator) + self.G = generate_simple() + self.populate_network(NewsSpread) + self.agent(node_id=0).has_tv = True + self.add_model_reporter('prob_tv_spread') + self.add_model_reporter('prob_neighbor_spread') +.. code:: ipython3 + + it = NewsEnv.run(max_time=20) + it[0].model_df() .. parsed-literal:: - + HBox(children=(IntProgress(value=0, description='NewsEnv', max=1, style=ProgressStyle(description_width='initi… -.. code:: ipython3 +.. parsed-literal:: + + HBox(children=(IntProgress(value=0, max=1), HTML(value=''))) + + +.. parsed-literal:: - env_params = { - 'prob_tv_spread': 0, - 'prob_neighbor_spread': 0 - } - MAX_TIME = 100 - EVENT_TIME = 10 - - sim = soil.Simulation(topology=G, - num_trials=1, - max_time=MAX_TIME, - environment_agents=[{'agent_type': NewsEnvironmentAgent, - 'state': { - 'event_time': EVENT_TIME - }}], - network_agents=[{'agent_type': NewsSpread, - 'weight': 1}], - states={0: {'has_tv': True}}, - default_state={'has_tv': False}, - environment_params=env_params) - env = sim.run_simulation(dry_run=True)[0] -Now we can access the results of the simulation and compare them to our -expected results -.. code:: ipython3 - agents = list(env.network_agents) - - # Until the event, all agents are neutral - for t in range(10): - for a in agents: - assert a['state_id', t] == a.neutral.id - - # After the event, the node with a TV is infected, the rest are not - assert agents[0]['has_tv'] - assert agents[0]['state_id', 11] == NewsSpread.infected.id - assert not agents[2]['has_tv'] - assert agents[2]['state_id', 11] == NewsSpread.neutral.id - - - # At the end, the agents connected to the infected one will probably be infected, too. - assert agents[1]['state_id', MAX_TIME] == NewsSpread.infected.id - assert agents[2]['state_id', MAX_TIME] == NewsSpread.infected.id - - # But the node with no friends should not be affected - assert agents[4]['state_id', MAX_TIME] == NewsSpread.neutral.id - -Lastly, let’s see if the probabilities have decreased as expected: +.. raw:: html -.. code:: ipython3 - - assert abs(env.environment_params['prob_neighbor_spread'] - (NEIGHBOR_FACTOR**(MAX_TIME-1-10))) < 10e-4 - assert abs(env.environment_params['prob_tv_spread'] - (TV_FACTOR**(MAX_TIME-1-10))) < 10e-6 - -Running the simulation ----------------------- - -To run a simulation, we need a configuration. Soil can load -configurations from python dictionaries as well as JSON and YAML files. -For this demo, we will use a python dictionary: - -.. code:: ipython3 - - config = { - 'name': 'ExampleSimulation', - 'max_time': 20, - 'interval': 1, - 'num_trials': 1, - 'network_params': { - 'generator': 'complete_graph', - 'n': 500, - }, - 'network_agents': [ - { - 'agent_type': NewsSpread, - 'weight': 1, - 'state': { - 'has_tv': False - } - }, - { - 'agent_type': NewsSpread, - 'weight': 2, - 'state': { - 'has_tv': True - } - } - ], - 'environment_agents':[ - {'agent_type': NewsEnvironmentAgent, - 'state': { - 'event_time': 10 - } - } - ], - 'states': [ {'has_tv': True} ], - 'environment_params':{ - 'prob_tv_spread': 0.01, - 'prob_neighbor_spread': 0.5 +
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20 rows × 2504 columns

+
-Let’s run our simulation: + + +In this case, notice that the inclusion of other agents (which run every +step) means that the simulation did not skip to ``t=10``. + +Now, let’s look at the state of our agents in every step: .. code:: ipython3 - soil.simulation.run_from_config(config, dry_run=True) + soil.analysis.plot(it[0]) + + + +.. image:: output_34_0.png + + +Running in more scenarios +------------------------- In real life, you probably want to run several simulations, varying some of the parameters so that you can compare and answer your research @@ -346,32 +686,210 @@ For instance: .. code:: ipython3 - network_1 = { - 'generator': 'erdos_renyi_graph', - 'n': 500, - 'p': 0.1 - } - network_2 = { - 'generator': 'barabasi_albert_graph', - 'n': 500, - 'm': 2 - } + class NewsEnvComplete(soil.Environment): + prob_tv = 0.05 + prob_tv_spread = 0 + prob_neighbor_spread = 0 + event_time = 10 + tv_factor = 0 + neighbor_factor = 0.5 + generator = "erdos_renyi_graph" + n = 100 + + def init(self): + self.add_agent(EventGenerator) + if not self.G: + opts = {"n": self.n} + if self.generator == "erdos_renyi_graph": + opts["p"] = 0.5 + elif self.generator == "barabasi_albert_graph": + opts["m"] = 4 + self.create_network(generator=self.generator, **opts) + + self.populate_network([NewsSpread, + NewsSpread.w(has_tv=True)], + [1-self.prob_tv, self.prob_tv]) + self.add_model_reporter('prob_tv_spread') + self.add_model_reporter('prob_neighbor_spread') + self.add_agent_reporter('state_id') + +Since we do not care about previous results, we will +set\ ``overwrite=True``. + +.. code:: ipython3 + + s = soil.Simulation(model=NewsEnvComplete, iterations=5, max_time=30, dump=True, overwrite=True) + N = 100 + probabilities = [0, 0.25, 0.5, 0.75, 1.0] + generators = ["erdos_renyi_graph", "barabasi_albert_graph"] - for net in [network_1, network_2]: - for i in range(5): - prob = i / 10 - config['environment_params']['prob_neighbor_spread'] = prob - config['network_params'] = net - config['name'] = 'Spread_{}_prob_{}'.format(net['generator'], prob) - s = soil.simulation.run_from_config(config, exporters=['default', 'csv']) + it = s.run(name=f"newspread", matrix=dict(n=[N], generator=generators, prob_neighbor_spread=probabilities)) + + +.. parsed-literal:: + + [INFO ][17:29:24] Output directory: /mnt/data/home/j/git/lab.gsi/soil/soil/examples/tutorial/soil_output + + + +.. parsed-literal:: + + HBox(children=(IntProgress(value=0, description='newspread', max=10, style=ProgressStyle(description_width='in… + + +.. parsed-literal:: + + n = 100 + generator = erdos_renyi_graph + prob_neighbor_spread = 0 + + +.. image:: output_58_3.png + +.. parsed-literal:: + + HBox(children=(IntProgress(value=0, max=5), HTML(value=''))) + +.. image:: output_58_4.png + +.. parsed-literal:: + + n = 100 + generator = erdos_renyi_graph + prob_neighbor_spread = 0.25 + +.. code:: ipython3 + + analysis.plot_all('soil_output/Spread_erdos*', analysis.get_count, 'state_id'); + +.. parsed-literal:: + + HBox(children=(IntProgress(value=0, max=5), HTML(value=''))) + +.. image:: output_60_0.png + +.. parsed-literal:: + + n = 100 + generator = erdos_renyi_graph + prob_neighbor_spread = 0.5 + + + +.. parsed-literal:: + + HBox(children=(IntProgress(value=0, max=5), HTML(value=''))) + + +.. parsed-literal:: + + n = 100 + generator = erdos_renyi_graph + prob_neighbor_spread = 0.75 + + + +.. parsed-literal:: + + HBox(children=(IntProgress(value=0, max=5), HTML(value=''))) + +The previous cells were using the ``count_value`` function for +aggregation. There’s another function to plot numeral values: + +.. parsed-literal:: + + n = 100 + generator = erdos_renyi_graph + prob_neighbor_spread = 1.0 + + + +.. parsed-literal:: + + HBox(children=(IntProgress(value=0, max=5), HTML(value=''))) + + +.. parsed-literal:: + + n = 100 + generator = barabasi_albert_graph + prob_neighbor_spread = 0 + + + +.. parsed-literal:: + + HBox(children=(IntProgress(value=0, max=5), HTML(value=''))) + + +.. parsed-literal:: + + n = 100 + generator = barabasi_albert_graph + prob_neighbor_spread = 0.25 + + + +.. parsed-literal:: + + HBox(children=(IntProgress(value=0, max=5), HTML(value=''))) + + +.. parsed-literal:: + + n = 100 + generator = barabasi_albert_graph + prob_neighbor_spread = 0.5 + + + +.. parsed-literal:: + + HBox(children=(IntProgress(value=0, max=5), HTML(value=''))) + + +.. parsed-literal:: + + n = 100 + generator = barabasi_albert_graph + prob_neighbor_spread = 0.75 + + + +.. parsed-literal:: + + HBox(children=(IntProgress(value=0, max=5), HTML(value=''))) + + +.. parsed-literal:: + + n = 100 + generator = barabasi_albert_graph + prob_neighbor_spread = 1.0 + + + +.. parsed-literal:: + + HBox(children=(IntProgress(value=0, max=5), HTML(value=''))) + + +.. parsed-literal:: + + + + +.. code:: ipython3 + + assert len(it) == len(probabilities) * len(generators) * s.iterations The results are conveniently stored in sqlite (history of agent and environment state) and the configuration is saved in a YAML file. You can also export the results to GEXF format (dynamic network) and CSV -using .\ ``run_from_config(config, dump=['gexf', 'csv'])`` or the -command line flags ``--graph --csv``. +using .\ ``run(dump=['gexf', 'csv'])`` or the command line flags +``--graph --csv``. .. code:: ipython3 @@ -381,2268 +899,132 @@ command line flags ``--graph --csv``. .. parsed-literal:: - soil_output - ├── Spread_barabasi_albert_graph_prob_0.0 - │   ├── backup - │   │   ├── Spread_barabasi_albert_graph_prob_0.0.dumped.yml@2023-03-23_12.57.35 - │   │   ├── Spread_barabasi_albert_graph_prob_0.0.dumped.yml@2023-03-23_14.06.30 - │   │   ├── Spread_barabasi_albert_graph_prob_0.0.dumped.yml@2023-03-23_14.19.33 - │   │   ├── Spread_barabasi_albert_graph_prob_0.0.dumped.yml@2023-03-23_14.30.56 - │   │   ├── Spread_barabasi_albert_graph_prob_0.0.sqlite@2023-03-23_12.57.35 - │   │   ├── Spread_barabasi_albert_graph_prob_0.0.sqlite@2023-03-23_14.06.31 - │   │   ├── Spread_barabasi_albert_graph_prob_0.0.sqlite@2023-03-23_14.19.33 - │   │   ├── Spread_barabasi_albert_graph_prob_0.0.sqlite@2023-03-23_14.30.56 - │   │   ├── Spread_barabasi_albert_graph_prob_0.0_trial_0.csv@2023-03-23_12.57.35 - │   │   ├── Spread_barabasi_albert_graph_prob_0.0_trial_0.csv@2023-03-23_14.06.31 - │   │   ├── Spread_barabasi_albert_graph_prob_0.0_trial_0.csv@2023-03-23_14.19.33 - │   │   ├── Spread_barabasi_albert_graph_prob_0.0_trial_0.csv@2023-03-23_14.30.56 - │   │   ├── Spread_barabasi_albert_graph_prob_0.0_trial_0.sqlite@2023-03-23_12.57.35 - │   │   ├── Spread_barabasi_albert_graph_prob_0.0_trial_0.sqlite@2023-03-23_14.06.31 - │   │   ├── Spread_barabasi_albert_graph_prob_0.0_trial_0.sqlite@2023-03-23_14.19.33 - │   │   ├── Spread_barabasi_albert_graph_prob_0.0_trial_0.sqlite@2023-03-23_14.30.56 - │   │   ├── Spread_barabasi_albert_graph_prob_0.0_trial_0.stats.csv@2023-03-23_12.57.35 - │   │   ├── Spread_barabasi_albert_graph_prob_0.0_trial_0.stats.csv@2023-03-23_14.06.31 - │   │   ├── Spread_barabasi_albert_graph_prob_0.0_trial_0.stats.csv@2023-03-23_14.19.33 - │   │   └── Spread_barabasi_albert_graph_prob_0.0_trial_0.stats.csv@2023-03-23_14.30.56 - │   ├── Spread_barabasi_albert_graph_prob_0.0.dumped.yml - │   ├── Spread_barabasi_albert_graph_prob_0.0.sqlite - │   ├── Spread_barabasi_albert_graph_prob_0.0_trial_0.csv - │   ├── Spread_barabasi_albert_graph_prob_0.0_trial_0.sqlite - │   └── Spread_barabasi_albert_graph_prob_0.0_trial_0.stats.csv - ├── Spread_barabasi_albert_graph_prob_0.1 - │   ├── backup - │   │   ├── Spread_barabasi_albert_graph_prob_0.1.dumped.yml@2023-03-23_12.57.35 - │   │   ├── Spread_barabasi_albert_graph_prob_0.1.dumped.yml@2023-03-23_14.06.31 - │   │   ├── Spread_barabasi_albert_graph_prob_0.1.dumped.yml@2023-03-23_14.19.34 - │   │   ├── Spread_barabasi_albert_graph_prob_0.1.dumped.yml@2023-03-23_14.30.56 - │   │   ├── Spread_barabasi_albert_graph_prob_0.1.sqlite@2023-03-23_12.57.35 - │   │   ├── Spread_barabasi_albert_graph_prob_0.1.sqlite@2023-03-23_14.06.31 - │   │   ├── Spread_barabasi_albert_graph_prob_0.1.sqlite@2023-03-23_14.19.34 - │   │   ├── Spread_barabasi_albert_graph_prob_0.1.sqlite@2023-03-23_14.30.56 - │   │   ├── Spread_barabasi_albert_graph_prob_0.1_trial_0.csv@2023-03-23_12.57.35 - │   │   ├── Spread_barabasi_albert_graph_prob_0.1_trial_0.csv@2023-03-23_14.06.31 - │   │   ├── Spread_barabasi_albert_graph_prob_0.1_trial_0.csv@2023-03-23_14.19.34 - │   │   ├── Spread_barabasi_albert_graph_prob_0.1_trial_0.csv@2023-03-23_14.30.56 - │   │   ├── Spread_barabasi_albert_graph_prob_0.1_trial_0.sqlite@2023-03-23_12.57.35 - │   │   ├── Spread_barabasi_albert_graph_prob_0.1_trial_0.sqlite@2023-03-23_14.06.31 - │   │   ├── Spread_barabasi_albert_graph_prob_0.1_trial_0.sqlite@2023-03-23_14.19.34 - │   │   ├── Spread_barabasi_albert_graph_prob_0.1_trial_0.sqlite@2023-03-23_14.30.56 - │   │   ├── Spread_barabasi_albert_graph_prob_0.1_trial_0.stats.csv@2023-03-23_12.57.35 - │   │   ├── Spread_barabasi_albert_graph_prob_0.1_trial_0.stats.csv@2023-03-23_14.06.31 - │   │   ├── Spread_barabasi_albert_graph_prob_0.1_trial_0.stats.csv@2023-03-23_14.19.34 - │   │   └── Spread_barabasi_albert_graph_prob_0.1_trial_0.stats.csv@2023-03-23_14.30.56 - │   ├── Spread_barabasi_albert_graph_prob_0.1.dumped.yml - │   ├── Spread_barabasi_albert_graph_prob_0.1.sqlite - │   ├── Spread_barabasi_albert_graph_prob_0.1_trial_0.csv - │   ├── Spread_barabasi_albert_graph_prob_0.1_trial_0.sqlite - │   └── Spread_barabasi_albert_graph_prob_0.1_trial_0.stats.csv - ├── Spread_barabasi_albert_graph_prob_0.2 - │   ├── backup - │   │   ├── Spread_barabasi_albert_graph_prob_0.2.dumped.yml@2023-03-23_12.57.36 - │   │   ├── Spread_barabasi_albert_graph_prob_0.2.dumped.yml@2023-03-23_14.06.31 - │   │   ├── Spread_barabasi_albert_graph_prob_0.2.dumped.yml@2023-03-23_14.19.34 - │   │   ├── Spread_barabasi_albert_graph_prob_0.2.dumped.yml@2023-03-23_14.30.56 - │   │   ├── Spread_barabasi_albert_graph_prob_0.2.sqlite@2023-03-23_12.57.36 - │   │   ├── Spread_barabasi_albert_graph_prob_0.2.sqlite@2023-03-23_14.06.31 - │   │   ├── Spread_barabasi_albert_graph_prob_0.2.sqlite@2023-03-23_14.19.34 - │   │   ├── Spread_barabasi_albert_graph_prob_0.2.sqlite@2023-03-23_14.30.57 - │   │   ├── Spread_barabasi_albert_graph_prob_0.2_trial_0.csv@2023-03-23_12.57.36 - │   │   ├── Spread_barabasi_albert_graph_prob_0.2_trial_0.csv@2023-03-23_14.06.31 - │   │   ├── Spread_barabasi_albert_graph_prob_0.2_trial_0.csv@2023-03-23_14.19.34 - │   │   ├── Spread_barabasi_albert_graph_prob_0.2_trial_0.csv@2023-03-23_14.30.57 - │   │   ├── Spread_barabasi_albert_graph_prob_0.2_trial_0.sqlite@2023-03-23_12.57.36 - │   │   ├── Spread_barabasi_albert_graph_prob_0.2_trial_0.sqlite@2023-03-23_14.06.31 - │   │   ├── Spread_barabasi_albert_graph_prob_0.2_trial_0.sqlite@2023-03-23_14.19.34 - │   │   ├── Spread_barabasi_albert_graph_prob_0.2_trial_0.sqlite@2023-03-23_14.30.57 - │   │   ├── Spread_barabasi_albert_graph_prob_0.2_trial_0.stats.csv@2023-03-23_12.57.36 - │   │   ├── Spread_barabasi_albert_graph_prob_0.2_trial_0.stats.csv@2023-03-23_14.06.31 - │   │   ├── Spread_barabasi_albert_graph_prob_0.2_trial_0.stats.csv@2023-03-23_14.19.34 - │   │   └── Spread_barabasi_albert_graph_prob_0.2_trial_0.stats.csv@2023-03-23_14.30.57 - │   ├── Spread_barabasi_albert_graph_prob_0.2.dumped.yml - │   ├── Spread_barabasi_albert_graph_prob_0.2.sqlite - │   ├── Spread_barabasi_albert_graph_prob_0.2_trial_0.csv - │   ├── Spread_barabasi_albert_graph_prob_0.2_trial_0.sqlite - │   └── Spread_barabasi_albert_graph_prob_0.2_trial_0.stats.csv - ├── Spread_barabasi_albert_graph_prob_0.3 - │   ├── backup - │   │   ├── Spread_barabasi_albert_graph_prob_0.3.dumped.yml@2023-03-23_12.57.36 - │   │   ├── Spread_barabasi_albert_graph_prob_0.3.dumped.yml@2023-03-23_14.06.31 - │   │   ├── Spread_barabasi_albert_graph_prob_0.3.dumped.yml@2023-03-23_14.19.34 - │   │   ├── Spread_barabasi_albert_graph_prob_0.3.dumped.yml@2023-03-23_14.30.57 - │   │   ├── Spread_barabasi_albert_graph_prob_0.3.sqlite@2023-03-23_12.57.36 - │   │   ├── Spread_barabasi_albert_graph_prob_0.3.sqlite@2023-03-23_14.06.32 - │   │   ├── Spread_barabasi_albert_graph_prob_0.3.sqlite@2023-03-23_14.19.34 - │   │   ├── Spread_barabasi_albert_graph_prob_0.3.sqlite@2023-03-23_14.30.57 - │   │   ├── Spread_barabasi_albert_graph_prob_0.3_trial_0.csv@2023-03-23_12.57.36 - │   │   ├── Spread_barabasi_albert_graph_prob_0.3_trial_0.csv@2023-03-23_14.06.32 - │   │   ├── Spread_barabasi_albert_graph_prob_0.3_trial_0.csv@2023-03-23_14.19.34 - │   │   ├── Spread_barabasi_albert_graph_prob_0.3_trial_0.csv@2023-03-23_14.30.57 - │   │   ├── Spread_barabasi_albert_graph_prob_0.3_trial_0.sqlite@2023-03-23_12.57.36 - │   │   ├── Spread_barabasi_albert_graph_prob_0.3_trial_0.sqlite@2023-03-23_14.06.31 - │   │   ├── Spread_barabasi_albert_graph_prob_0.3_trial_0.sqlite@2023-03-23_14.19.34 - │   │   ├── Spread_barabasi_albert_graph_prob_0.3_trial_0.sqlite@2023-03-23_14.30.57 - │   │   ├── Spread_barabasi_albert_graph_prob_0.3_trial_0.stats.csv@2023-03-23_12.57.36 - │   │   ├── Spread_barabasi_albert_graph_prob_0.3_trial_0.stats.csv@2023-03-23_14.06.32 - │   │   ├── Spread_barabasi_albert_graph_prob_0.3_trial_0.stats.csv@2023-03-23_14.19.34 - │   │   └── Spread_barabasi_albert_graph_prob_0.3_trial_0.stats.csv@2023-03-23_14.30.57 - │   ├── Spread_barabasi_albert_graph_prob_0.3.dumped.yml - │   ├── Spread_barabasi_albert_graph_prob_0.3.sqlite - │   ├── Spread_barabasi_albert_graph_prob_0.3_trial_0.csv - │   ├── Spread_barabasi_albert_graph_prob_0.3_trial_0.sqlite - │   └── Spread_barabasi_albert_graph_prob_0.3_trial_0.stats.csv - ├── Spread_barabasi_albert_graph_prob_0.4 - │   ├── backup - │   │   ├── Spread_barabasi_albert_graph_prob_0.4.dumped.yml@2023-03-23_12.57.36 - │   │   ├── Spread_barabasi_albert_graph_prob_0.4.dumped.yml@2023-03-23_14.06.32 - │   │   ├── Spread_barabasi_albert_graph_prob_0.4.dumped.yml@2023-03-23_14.19.35 - │   │   ├── Spread_barabasi_albert_graph_prob_0.4.dumped.yml@2023-03-23_14.30.57 - │   │   ├── Spread_barabasi_albert_graph_prob_0.4.sqlite@2023-03-23_12.57.36 - │   │   ├── Spread_barabasi_albert_graph_prob_0.4.sqlite@2023-03-23_14.06.32 - │   │   ├── Spread_barabasi_albert_graph_prob_0.4.sqlite@2023-03-23_14.19.35 - │   │   ├── Spread_barabasi_albert_graph_prob_0.4.sqlite@2023-03-23_14.30.57 - │   │   ├── Spread_barabasi_albert_graph_prob_0.4_trial_0.csv@2023-03-23_12.57.36 - │   │   ├── Spread_barabasi_albert_graph_prob_0.4_trial_0.csv@2023-03-23_14.06.32 - │   │   ├── Spread_barabasi_albert_graph_prob_0.4_trial_0.csv@2023-03-23_14.19.35 - │   │   ├── Spread_barabasi_albert_graph_prob_0.4_trial_0.csv@2023-03-23_14.30.57 - │   │   ├── Spread_barabasi_albert_graph_prob_0.4_trial_0.sqlite@2023-03-23_12.57.36 - │   │   ├── Spread_barabasi_albert_graph_prob_0.4_trial_0.sqlite@2023-03-23_14.06.32 - │   │   ├── Spread_barabasi_albert_graph_prob_0.4_trial_0.sqlite@2023-03-23_14.19.35 - │   │   ├── Spread_barabasi_albert_graph_prob_0.4_trial_0.sqlite@2023-03-23_14.30.57 - │   │   ├── Spread_barabasi_albert_graph_prob_0.4_trial_0.stats.csv@2023-03-23_12.57.36 - │   │   ├── Spread_barabasi_albert_graph_prob_0.4_trial_0.stats.csv@2023-03-23_14.06.32 - │   │   ├── Spread_barabasi_albert_graph_prob_0.4_trial_0.stats.csv@2023-03-23_14.19.35 - │   │   └── Spread_barabasi_albert_graph_prob_0.4_trial_0.stats.csv@2023-03-23_14.30.57 - │   ├── Spread_barabasi_albert_graph_prob_0.4.dumped.yml - │   ├── Spread_barabasi_albert_graph_prob_0.4.sqlite - │   ├── Spread_barabasi_albert_graph_prob_0.4_trial_0.csv - │   ├── Spread_barabasi_albert_graph_prob_0.4_trial_0.sqlite - │   └── Spread_barabasi_albert_graph_prob_0.4_trial_0.stats.csv - ├── Spread_erdos_renyi_graph_prob_0.0 - │   ├── backup - │   │   ├── Spread_erdos_renyi_graph_prob_0.0.dumped.yml@2023-03-23_12.57.26 - │   │   ├── Spread_erdos_renyi_graph_prob_0.0.dumped.yml@2023-03-23_14.06.21 - │   │   ├── Spread_erdos_renyi_graph_prob_0.0.dumped.yml@2023-03-23_14.19.24 - │   │   ├── Spread_erdos_renyi_graph_prob_0.0.dumped.yml@2023-03-23_14.30.47 - │   │   ├── Spread_erdos_renyi_graph_prob_0.0.sqlite@2023-03-23_12.57.26 - │   │   ├── Spread_erdos_renyi_graph_prob_0.0.sqlite@2023-03-23_14.06.22 - │   │   ├── Spread_erdos_renyi_graph_prob_0.0.sqlite@2023-03-23_14.19.25 - │   │   ├── Spread_erdos_renyi_graph_prob_0.0.sqlite@2023-03-23_14.30.47 - │   │   ├── Spread_erdos_renyi_graph_prob_0.0_trial_0.csv@2023-03-23_12.57.26 - │   │   ├── Spread_erdos_renyi_graph_prob_0.0_trial_0.csv@2023-03-23_14.06.22 - │   │   ├── Spread_erdos_renyi_graph_prob_0.0_trial_0.csv@2023-03-23_14.19.25 - │   │   ├── Spread_erdos_renyi_graph_prob_0.0_trial_0.csv@2023-03-23_14.30.47 - │   │   ├── Spread_erdos_renyi_graph_prob_0.0_trial_0.sqlite@2023-03-23_12.57.26 - │   │   ├── Spread_erdos_renyi_graph_prob_0.0_trial_0.sqlite@2023-03-23_14.06.22 - │   │   ├── Spread_erdos_renyi_graph_prob_0.0_trial_0.sqlite@2023-03-23_14.19.25 - │   │   ├── Spread_erdos_renyi_graph_prob_0.0_trial_0.sqlite@2023-03-23_14.30.47 - │   │   ├── Spread_erdos_renyi_graph_prob_0.0_trial_0.stats.csv@2023-03-23_12.57.26 - │   │   ├── Spread_erdos_renyi_graph_prob_0.0_trial_0.stats.csv@2023-03-23_14.06.22 - │   │   ├── Spread_erdos_renyi_graph_prob_0.0_trial_0.stats.csv@2023-03-23_14.19.25 - │   │   └── Spread_erdos_renyi_graph_prob_0.0_trial_0.stats.csv@2023-03-23_14.30.47 - │   ├── Spread_erdos_renyi_graph_prob_0.0.dumped.yml - │   ├── Spread_erdos_renyi_graph_prob_0.0.sqlite - │   ├── Spread_erdos_renyi_graph_prob_0.0_trial_0.csv - │   ├── Spread_erdos_renyi_graph_prob_0.0_trial_0.sqlite - │   └── Spread_erdos_renyi_graph_prob_0.0_trial_0.stats.csv - ├── Spread_erdos_renyi_graph_prob_0.1 - │   ├── backup - │   │   ├── Spread_erdos_renyi_graph_prob_0.1.dumped.yml@2023-03-23_12.57.28 - │   │   ├── Spread_erdos_renyi_graph_prob_0.1.dumped.yml@2023-03-23_14.06.24 - │   │   ├── Spread_erdos_renyi_graph_prob_0.1.dumped.yml@2023-03-23_14.19.26 - │   │   ├── Spread_erdos_renyi_graph_prob_0.1.dumped.yml@2023-03-23_14.30.49 - │   │   ├── Spread_erdos_renyi_graph_prob_0.1.sqlite@2023-03-23_12.57.28 - │   │   ├── Spread_erdos_renyi_graph_prob_0.1.sqlite@2023-03-23_14.06.24 - │   │   ├── Spread_erdos_renyi_graph_prob_0.1.sqlite@2023-03-23_14.19.27 - │   │   ├── Spread_erdos_renyi_graph_prob_0.1.sqlite@2023-03-23_14.30.49 - │   │   ├── Spread_erdos_renyi_graph_prob_0.1_trial_0.csv@2023-03-23_12.57.28 - │   │   ├── Spread_erdos_renyi_graph_prob_0.1_trial_0.csv@2023-03-23_14.06.24 - │   │   ├── Spread_erdos_renyi_graph_prob_0.1_trial_0.csv@2023-03-23_14.19.27 - │   │   ├── Spread_erdos_renyi_graph_prob_0.1_trial_0.csv@2023-03-23_14.30.49 - │   │   ├── Spread_erdos_renyi_graph_prob_0.1_trial_0.sqlite@2023-03-23_12.57.28 - │   │   ├── Spread_erdos_renyi_graph_prob_0.1_trial_0.sqlite@2023-03-23_14.06.24 - │   │   ├── Spread_erdos_renyi_graph_prob_0.1_trial_0.sqlite@2023-03-23_14.19.27 - │   │   ├── Spread_erdos_renyi_graph_prob_0.1_trial_0.sqlite@2023-03-23_14.30.49 - │   │   ├── Spread_erdos_renyi_graph_prob_0.1_trial_0.stats.csv@2023-03-23_12.57.28 - │   │   ├── Spread_erdos_renyi_graph_prob_0.1_trial_0.stats.csv@2023-03-23_14.06.24 - │   │   ├── Spread_erdos_renyi_graph_prob_0.1_trial_0.stats.csv@2023-03-23_14.19.27 - │   │   └── Spread_erdos_renyi_graph_prob_0.1_trial_0.stats.csv@2023-03-23_14.30.49 - │   ├── Spread_erdos_renyi_graph_prob_0.1.dumped.yml - │   ├── Spread_erdos_renyi_graph_prob_0.1.sqlite - │   ├── Spread_erdos_renyi_graph_prob_0.1_trial_0.csv - │   ├── Spread_erdos_renyi_graph_prob_0.1_trial_0.sqlite - │   └── Spread_erdos_renyi_graph_prob_0.1_trial_0.stats.csv - ├── Spread_erdos_renyi_graph_prob_0.2 - │   ├── backup - │   │   ├── Spread_erdos_renyi_graph_prob_0.2.dumped.yml@2023-03-23_12.57.30 - │   │   ├── Spread_erdos_renyi_graph_prob_0.2.dumped.yml@2023-03-23_14.06.26 - │   │   ├── Spread_erdos_renyi_graph_prob_0.2.dumped.yml@2023-03-23_14.19.28 - │   │   ├── Spread_erdos_renyi_graph_prob_0.2.dumped.yml@2023-03-23_14.30.51 - │   │   ├── Spread_erdos_renyi_graph_prob_0.2.sqlite@2023-03-23_12.57.31 - │   │   ├── Spread_erdos_renyi_graph_prob_0.2.sqlite@2023-03-23_14.06.26 - │   │   ├── Spread_erdos_renyi_graph_prob_0.2.sqlite@2023-03-23_14.19.29 - │   │   ├── Spread_erdos_renyi_graph_prob_0.2.sqlite@2023-03-23_14.30.51 - │   │   ├── Spread_erdos_renyi_graph_prob_0.2_trial_0.csv@2023-03-23_12.57.31 - │   │   ├── Spread_erdos_renyi_graph_prob_0.2_trial_0.csv@2023-03-23_14.06.26 - │   │   ├── Spread_erdos_renyi_graph_prob_0.2_trial_0.csv@2023-03-23_14.19.29 - │   │   ├── Spread_erdos_renyi_graph_prob_0.2_trial_0.csv@2023-03-23_14.30.51 - │   │   ├── Spread_erdos_renyi_graph_prob_0.2_trial_0.sqlite@2023-03-23_12.57.31 - │   │   ├── Spread_erdos_renyi_graph_prob_0.2_trial_0.sqlite@2023-03-23_14.06.26 - │   │   ├── Spread_erdos_renyi_graph_prob_0.2_trial_0.sqlite@2023-03-23_14.19.29 - │   │   ├── Spread_erdos_renyi_graph_prob_0.2_trial_0.sqlite@2023-03-23_14.30.51 - │   │   ├── Spread_erdos_renyi_graph_prob_0.2_trial_0.stats.csv@2023-03-23_12.57.31 - │   │   ├── Spread_erdos_renyi_graph_prob_0.2_trial_0.stats.csv@2023-03-23_14.06.26 - │   │   ├── Spread_erdos_renyi_graph_prob_0.2_trial_0.stats.csv@2023-03-23_14.19.29 - │   │   └── Spread_erdos_renyi_graph_prob_0.2_trial_0.stats.csv@2023-03-23_14.30.51 - │   ├── Spread_erdos_renyi_graph_prob_0.2.dumped.yml - │   ├── Spread_erdos_renyi_graph_prob_0.2.sqlite - │   ├── Spread_erdos_renyi_graph_prob_0.2_trial_0.csv - │   ├── Spread_erdos_renyi_graph_prob_0.2_trial_0.sqlite - │   └── Spread_erdos_renyi_graph_prob_0.2_trial_0.stats.csv - ├── Spread_erdos_renyi_graph_prob_0.3 - │   ├── backup - │   │   ├── Spread_erdos_renyi_graph_prob_0.3.dumped.yml@2023-03-23_12.57.32 - │   │   ├── Spread_erdos_renyi_graph_prob_0.3.dumped.yml@2023-03-23_14.06.28 - │   │   ├── Spread_erdos_renyi_graph_prob_0.3.dumped.yml@2023-03-23_14.19.31 - │   │   ├── Spread_erdos_renyi_graph_prob_0.3.dumped.yml@2023-03-23_14.30.53 - │   │   ├── Spread_erdos_renyi_graph_prob_0.3.sqlite@2023-03-23_12.57.33 - │   │   ├── Spread_erdos_renyi_graph_prob_0.3.sqlite@2023-03-23_14.06.28 - │   │   ├── Spread_erdos_renyi_graph_prob_0.3.sqlite@2023-03-23_14.19.31 - │   │   ├── Spread_erdos_renyi_graph_prob_0.3.sqlite@2023-03-23_14.30.53 - │   │   ├── Spread_erdos_renyi_graph_prob_0.3_trial_0.csv@2023-03-23_12.57.33 - │   │   ├── Spread_erdos_renyi_graph_prob_0.3_trial_0.csv@2023-03-23_14.06.28 - │   │   ├── Spread_erdos_renyi_graph_prob_0.3_trial_0.csv@2023-03-23_14.19.31 - │   │   ├── Spread_erdos_renyi_graph_prob_0.3_trial_0.csv@2023-03-23_14.30.53 - │   │   ├── Spread_erdos_renyi_graph_prob_0.3_trial_0.sqlite@2023-03-23_12.57.33 - │   │   ├── Spread_erdos_renyi_graph_prob_0.3_trial_0.sqlite@2023-03-23_14.06.28 - │   │   ├── Spread_erdos_renyi_graph_prob_0.3_trial_0.sqlite@2023-03-23_14.19.31 - │   │   ├── Spread_erdos_renyi_graph_prob_0.3_trial_0.sqlite@2023-03-23_14.30.53 - │   │   ├── Spread_erdos_renyi_graph_prob_0.3_trial_0.stats.csv@2023-03-23_12.57.33 - │   │   ├── Spread_erdos_renyi_graph_prob_0.3_trial_0.stats.csv@2023-03-23_14.06.28 - │   │   ├── Spread_erdos_renyi_graph_prob_0.3_trial_0.stats.csv@2023-03-23_14.19.31 - │   │   └── Spread_erdos_renyi_graph_prob_0.3_trial_0.stats.csv@2023-03-23_14.30.53 - │   ├── Spread_erdos_renyi_graph_prob_0.3.dumped.yml - │   ├── Spread_erdos_renyi_graph_prob_0.3.sqlite - │   ├── Spread_erdos_renyi_graph_prob_0.3_trial_0.csv - │   ├── Spread_erdos_renyi_graph_prob_0.3_trial_0.sqlite - │   └── Spread_erdos_renyi_graph_prob_0.3_trial_0.stats.csv - └── Spread_erdos_renyi_graph_prob_0.4 - ├── backup - │   ├── Spread_erdos_renyi_graph_prob_0.4.dumped.yml@2023-03-23_12.57.34 - │   ├── Spread_erdos_renyi_graph_prob_0.4.dumped.yml@2023-03-23_14.06.30 - │   ├── Spread_erdos_renyi_graph_prob_0.4.dumped.yml@2023-03-23_14.19.33 - │   ├── Spread_erdos_renyi_graph_prob_0.4.dumped.yml@2023-03-23_14.30.55 - │   ├── Spread_erdos_renyi_graph_prob_0.4.sqlite@2023-03-23_12.57.35 - │   ├── Spread_erdos_renyi_graph_prob_0.4.sqlite@2023-03-23_14.06.30 - │   ├── Spread_erdos_renyi_graph_prob_0.4.sqlite@2023-03-23_14.19.33 - │   ├── Spread_erdos_renyi_graph_prob_0.4.sqlite@2023-03-23_14.30.56 - │   ├── Spread_erdos_renyi_graph_prob_0.4_trial_0.csv@2023-03-23_12.57.35 - │   ├── Spread_erdos_renyi_graph_prob_0.4_trial_0.csv@2023-03-23_14.06.30 - │   ├── Spread_erdos_renyi_graph_prob_0.4_trial_0.csv@2023-03-23_14.19.33 - │   ├── Spread_erdos_renyi_graph_prob_0.4_trial_0.csv@2023-03-23_14.30.56 - │   ├── Spread_erdos_renyi_graph_prob_0.4_trial_0.sqlite@2023-03-23_12.57.35 - │   ├── Spread_erdos_renyi_graph_prob_0.4_trial_0.sqlite@2023-03-23_14.06.30 - │   ├── Spread_erdos_renyi_graph_prob_0.4_trial_0.sqlite@2023-03-23_14.19.33 - │   ├── Spread_erdos_renyi_graph_prob_0.4_trial_0.sqlite@2023-03-23_14.30.56 - │   ├── Spread_erdos_renyi_graph_prob_0.4_trial_0.stats.csv@2023-03-23_12.57.35 - │   ├── Spread_erdos_renyi_graph_prob_0.4_trial_0.stats.csv@2023-03-23_14.06.30 - │   ├── Spread_erdos_renyi_graph_prob_0.4_trial_0.stats.csv@2023-03-23_14.19.33 - │   └── Spread_erdos_renyi_graph_prob_0.4_trial_0.stats.csv@2023-03-23_14.30.56 - ├── Spread_erdos_renyi_graph_prob_0.4.dumped.yml - ├── Spread_erdos_renyi_graph_prob_0.4.sqlite - ├── Spread_erdos_renyi_graph_prob_0.4_trial_0.csv - ├── Spread_erdos_renyi_graph_prob_0.4_trial_0.sqlite - └── Spread_erdos_renyi_graph_prob_0.4_trial_0.stats.csv - - 20 directories, 250 files - 1.3M soil_output/Spread_barabasi_albert_graph_prob_0.0/backup - 1.7M soil_output/Spread_barabasi_albert_graph_prob_0.0 - 1.3M soil_output/Spread_barabasi_albert_graph_prob_0.1/backup - 1.7M soil_output/Spread_barabasi_albert_graph_prob_0.1 - 1.3M soil_output/Spread_barabasi_albert_graph_prob_0.2/backup - 1.6M soil_output/Spread_barabasi_albert_graph_prob_0.2 - 1.3M soil_output/Spread_barabasi_albert_graph_prob_0.3/backup - 1.7M soil_output/Spread_barabasi_albert_graph_prob_0.3 - 1.3M soil_output/Spread_barabasi_albert_graph_prob_0.4/backup - 1.7M soil_output/Spread_barabasi_albert_graph_prob_0.4 - 2.7M soil_output/Spread_erdos_renyi_graph_prob_0.0/backup - 3.4M soil_output/Spread_erdos_renyi_graph_prob_0.0 - 2.7M soil_output/Spread_erdos_renyi_graph_prob_0.1/backup - 3.4M soil_output/Spread_erdos_renyi_graph_prob_0.1 - 2.7M soil_output/Spread_erdos_renyi_graph_prob_0.2/backup - 3.4M soil_output/Spread_erdos_renyi_graph_prob_0.2 - 2.7M soil_output/Spread_erdos_renyi_graph_prob_0.3/backup - 3.4M soil_output/Spread_erdos_renyi_graph_prob_0.3 - 2.7M soil_output/Spread_erdos_renyi_graph_prob_0.4/backup - 3.4M soil_output/Spread_erdos_renyi_graph_prob_0.4 + soil_output + └── newspread + ├── newspread_1681989837.124865.dumped.yml + ├── newspread_1681990513.1584163.dumped.yml + ├── newspread_1681990524.5204282.dumped.yml + ├── newspread_1681990796.858183.dumped.yml + ├── newspread_1682002299.544348.dumped.yml + ├── newspread_1682003721.597205.dumped.yml + ├── newspread_1682003784.1948986.dumped.yml + ├── newspread_1682003812.4626257.dumped.yml + ├── newspread_1682004020.182087.dumped.yml + ├── newspread_1682004044.6837814.dumped.yml + ├── newspread_1682004398.267355.dumped.yml + ├── newspread_1682004564.1052232.dumped.yml + └── newspread.sqlite + + 1 directory, 13 files + 21M soil_output/newspread Analysing the results ---------------------- +~~~~~~~~~~~~~~~~~~~~~ Loading data -~~~~~~~~~~~~ +^^^^^^^^^^^^ Once the simulations are over, we can use soil to analyse the results. -Soil allows you to load results for specific trials, or for a set of -trials if you specify a pattern. The specific methods are: +There are two main ways: directly using the iterations returned by the +``run`` method, or loading up data from the results database. This is +particularly useful to store data between sessions, and to accumulate +results over multiple runs. + +The mainThe main method to load data from the database is ``read_sql``, +which can be used in two ways: -- ``analysis.read_data()`` to load all the results - from a directory. e.g. \ ``read_data('my_simulation/')``. For each - trial it finds in each folder matching the pattern, it will return - the dumped configuration for the simulation, the results of the - trial, and the configuration itself. By default, it will try to load - data from the sqlite database. -- ``analysis.read_csv()`` to load all the results from a CSV - file. - e.g. \ ``read_csv('my_simulation/my_simulation_trial0.environment.csv')`` - ``analysis.read_sql()`` to load all the results from a - sqlite database . - e.g. \ ``read_sql('my_simulation/my_simulation_trial0.db.sqlite')`` + sqlite database . e.g. \ ``read_sql('my_simulation/file.db.sqlite')`` +- ``analysis.read_sql(name=)`` will look for the + default path for a simulation named ```` + +The result in both cases is a named tuple with four dataframes: + +- ``configuration``, which contains configuration parameters per + simulation +- ``parameters``, which shows the parameters used **in every + iteration** of every simulation +- ``env``, with the data collected from the model in each iteration (as + specified in ``model_reporters``) +- ``agents``, like ``env``, but for ``agent_reporters`` Let’s see it in action by loading the stored results into a pandas dataframe: .. code:: ipython3 - from soil import analysis - import pandas as pd - -.. code:: ipython3 - - df = analysis.read_csv('soil_output/Spread_barabasi_albert_graph_prob_0.0/Spread_barabasi_albert_graph_prob_0.0_trial_0.csv') - df - - - - -.. raw:: html - -
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
keySEEDalive...state_id
agent_idenv0110100101102103104105...90919293949596979899
t_step
0.0Spread_barabasi_albert_graph_prob_0.0_trial_0TrueTrueTrueTrueTrueTrueTrueTrueTrue...neutralneutralneutralneutralneutralneutralneutralneutralneutralneutral
1.0Spread_barabasi_albert_graph_prob_0.0_trial_0TrueTrueTrueTrueTrueTrueTrueTrueTrue...neutralneutralneutralneutralneutralneutralneutralneutralneutralneutral
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4.0Spread_barabasi_albert_graph_prob_0.0_trial_0TrueTrueTrueTrueTrueTrueTrueTrueTrue...neutralneutralneutralneutralneutralneutralneutralneutralneutralneutral
5.0Spread_barabasi_albert_graph_prob_0.0_trial_0TrueTrueTrueTrueTrueTrueTrueTrueTrue...neutralneutralneutralneutralneutralneutralneutralinfectedneutralneutral
6.0Spread_barabasi_albert_graph_prob_0.0_trial_0TrueTrueTrueTrueTrueTrueTrueTrueTrue...neutralneutralneutralneutralneutralneutralneutralinfectedneutralneutral
7.0Spread_barabasi_albert_graph_prob_0.0_trial_0TrueTrueTrueTrueTrueTrueTrueTrueTrue...neutralneutralneutralneutralneutralneutralneutralinfectedneutralneutral
8.0Spread_barabasi_albert_graph_prob_0.0_trial_0TrueTrueTrueTrueTrueTrueTrueTrueTrue...neutralneutralneutralneutralneutralneutralneutralinfectedneutralneutral
9.0Spread_barabasi_albert_graph_prob_0.0_trial_0TrueTrueTrueTrueTrueTrueTrueTrueTrue...neutralneutralneutralneutralneutralneutralneutralinfectedneutralneutral
10.0Spread_barabasi_albert_graph_prob_0.0_trial_0TrueTrueTrueTrueTrueTrueTrueTrueTrue...neutralneutralneutralneutralneutralneutralneutralinfectedneutralneutral
11.0Spread_barabasi_albert_graph_prob_0.0_trial_0TrueTrueTrueTrueTrueTrueTrueTrueTrue...infectedinfectedinfectedinfectedinfectedneutralinfectedinfectedinfectedinfected
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13.0Spread_barabasi_albert_graph_prob_0.0_trial_0TrueTrueTrueTrueTrueTrueTrueTrueTrue...infectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfected
14.0Spread_barabasi_albert_graph_prob_0.0_trial_0TrueTrueTrueTrueTrueTrueTrueTrueTrue...infectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfected
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18.0Spread_barabasi_albert_graph_prob_0.0_trial_0TrueTrueTrueTrueTrueTrueTrueTrueTrue...infectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfected
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-

20 rows × 2507 columns

-
- - - -Soil can also process the data for us and split the results into -environment attributes and agent attributes: - -.. code:: ipython3 - - env, agents = analysis.split_processed(df) - -.. code:: ipython3 - - agents - - - - -.. raw:: html - -
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
keyalive...state_id
agent_id0110100101102103104105106...90919293949596979899
t_step
0.0TrueTrueTrueTrueTrueTrueTrueTrueTrueTrue...neutralneutralneutralneutralneutralneutralneutralneutralneutralneutral
1.0TrueTrueTrueTrueTrueTrueTrueTrueTrueTrue...neutralneutralneutralneutralneutralneutralneutralneutralneutralneutral
2.0TrueTrueTrueTrueTrueTrueTrueTrueTrueTrue...neutralneutralneutralneutralneutralneutralneutralneutralneutralneutral
3.0TrueTrueTrueTrueTrueTrueTrueTrueTrueTrue...neutralneutralneutralneutralneutralneutralneutralneutralneutralneutral
4.0TrueTrueTrueTrueTrueTrueTrueTrueTrueTrue...neutralneutralneutralneutralneutralneutralneutralneutralneutralneutral
5.0TrueTrueTrueTrueTrueTrueTrueTrueTrueTrue...neutralneutralneutralneutralneutralneutralneutralinfectedneutralneutral
6.0TrueTrueTrueTrueTrueTrueTrueTrueTrueTrue...neutralneutralneutralneutralneutralneutralneutralinfectedneutralneutral
7.0TrueTrueTrueTrueTrueTrueTrueTrueTrueTrue...neutralneutralneutralneutralneutralneutralneutralinfectedneutralneutral
8.0TrueTrueTrueTrueTrueTrueTrueTrueTrueTrue...neutralneutralneutralneutralneutralneutralneutralinfectedneutralneutral
9.0TrueTrueTrueTrueTrueTrueTrueTrueTrueTrue...neutralneutralneutralneutralneutralneutralneutralinfectedneutralneutral
10.0TrueTrueTrueTrueTrueTrueTrueTrueTrueTrue...neutralneutralneutralneutralneutralneutralneutralinfectedneutralneutral
11.0TrueTrueTrueTrueTrueTrueTrueTrueTrueTrue...infectedinfectedinfectedinfectedinfectedneutralinfectedinfectedinfectedinfected
12.0TrueTrueTrueTrueTrueTrueTrueTrueTrueTrue...infectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfected
13.0TrueTrueTrueTrueTrueTrueTrueTrueTrueTrue...infectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfected
14.0TrueTrueTrueTrueTrueTrueTrueTrueTrueTrue...infectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfected
15.0TrueTrueTrueTrueTrueTrueTrueTrueTrueTrue...infectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfected
16.0TrueTrueTrueTrueTrueTrueTrueTrueTrueTrue...infectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfected
17.0TrueTrueTrueTrueTrueTrueTrueTrueTrueTrue...infectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfected
18.0TrueTrueTrueTrueTrueTrueTrueTrueTrueTrue...infectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfected
19.0TrueTrueTrueTrueTrueTrueTrueTrueTrueTrue...infectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfected
-

20 rows × 2504 columns

-
- - - -The index of the results are the simulation step. Hence, we can access -the state of the simulation at a given step (e.g., 13): - -.. code:: ipython3 - - agents.loc[13, 'state_id'] - - - - -.. parsed-literal:: - - agent_id - 0 infected - 1 infected - 10 infected - 100 infected - 101 infected - ... - 95 infected - 96 infected - 97 infected - 98 infected - 99 infected - Name: 13.0, Length: 500, dtype: object - - - -Or, we can perform more complex tasks such as showing the agents that -have changed their state between two simulation steps (2 and 1): - -.. code:: ipython3 - - (agents.loc[2]['state_id'] != agents.loc[1]['state_id']).sum() - - - - -.. parsed-literal:: - - 2 - - - -To focus on specific agents, we can swap the levels of the index: - -.. code:: ipython3 - - agents.swaplevel(axis=1) - - - - -.. raw:: html - -
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
agent_id0110100101102103104105106...90919293949596979899
keyalivealivealivealivealivealivealivealivealivealive...state_idstate_idstate_idstate_idstate_idstate_idstate_idstate_idstate_idstate_id
t_step
0.0TrueTrueTrueTrueTrueTrueTrueTrueTrueTrue...neutralneutralneutralneutralneutralneutralneutralneutralneutralneutral
1.0TrueTrueTrueTrueTrueTrueTrueTrueTrueTrue...neutralneutralneutralneutralneutralneutralneutralneutralneutralneutral
2.0TrueTrueTrueTrueTrueTrueTrueTrueTrueTrue...neutralneutralneutralneutralneutralneutralneutralneutralneutralneutral
3.0TrueTrueTrueTrueTrueTrueTrueTrueTrueTrue...neutralneutralneutralneutralneutralneutralneutralneutralneutralneutral
4.0TrueTrueTrueTrueTrueTrueTrueTrueTrueTrue...neutralneutralneutralneutralneutralneutralneutralneutralneutralneutral
5.0TrueTrueTrueTrueTrueTrueTrueTrueTrueTrue...neutralneutralneutralneutralneutralneutralneutralinfectedneutralneutral
6.0TrueTrueTrueTrueTrueTrueTrueTrueTrueTrue...neutralneutralneutralneutralneutralneutralneutralinfectedneutralneutral
7.0TrueTrueTrueTrueTrueTrueTrueTrueTrueTrue...neutralneutralneutralneutralneutralneutralneutralinfectedneutralneutral
8.0TrueTrueTrueTrueTrueTrueTrueTrueTrueTrue...neutralneutralneutralneutralneutralneutralneutralinfectedneutralneutral
9.0TrueTrueTrueTrueTrueTrueTrueTrueTrueTrue...neutralneutralneutralneutralneutralneutralneutralinfectedneutralneutral
10.0TrueTrueTrueTrueTrueTrueTrueTrueTrueTrue...neutralneutralneutralneutralneutralneutralneutralinfectedneutralneutral
11.0TrueTrueTrueTrueTrueTrueTrueTrueTrueTrue...infectedinfectedinfectedinfectedinfectedneutralinfectedinfectedinfectedinfected
12.0TrueTrueTrueTrueTrueTrueTrueTrueTrueTrue...infectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfected
13.0TrueTrueTrueTrueTrueTrueTrueTrueTrueTrue...infectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfected
14.0TrueTrueTrueTrueTrueTrueTrueTrueTrueTrue...infectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfected
15.0TrueTrueTrueTrueTrueTrueTrueTrueTrueTrue...infectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfected
16.0TrueTrueTrueTrueTrueTrueTrueTrueTrueTrue...infectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfected
17.0TrueTrueTrueTrueTrueTrueTrueTrueTrueTrue...infectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfected
18.0TrueTrueTrueTrueTrueTrueTrueTrueTrueTrue...infectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfected
19.0TrueTrueTrueTrueTrueTrueTrueTrueTrueTrue...infectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfected
-

20 rows × 2504 columns

-
- - + res = soil.read_sql(name="newspread", include_agents=True) Plotting data ~~~~~~~~~~~~~ -If you don’t want to work with pandas, you can also use some pre-defined -functions from soil to conveniently plot the results: +Once we have loaded the results from the file, we can use them just like +any other dataframe. + +Here is an example of plotting the ratio of infected users in each of +our simulations: .. code:: ipython3 - analysis.plot_all('soil_output/Spread_barabasi_albert_graph_prob_0.0/', analysis.get_count, 'state_id'); + for (g, group) in res.env.dropna().groupby("params_id"): + params = res.parameters.query(f'params_id == "{g}"').iloc[0] + title = f"{params.generator.rstrip('_graph')} {params.prob_neighbor_spread}" + prob = group.groupby(by=["step"]).prob_neighbor_spread.mean() + line = "-" + if "barabasi" in params.generator: + line = "--" + prob.rename(title).fillna(0).plot(linestyle=line) + plt.title("Mean probability for each configuration") + plt.legend(); -.. image:: output_55_0.png +.. image:: output_49_0.png .. code:: ipython3 - analysis.plot_all('soil_output/Spread_barabasi_albert_graph_prob_0.3/', analysis.get_count, 'state_id'); + for (g, group) in res.agents.dropna().groupby("params_id"): + params = res.parameters.query(f'params_id == "{g}"').iloc[0] + title = f"{params.generator.rstrip('_graph')} {params.prob_neighbor_spread}" + counts = group.groupby(by=["step", "state_id"]).value_counts().unstack() + line = "-" + if "barabasi" in params.generator: + line = "--" + (counts.infected/counts.sum(axis=1)).rename(title).fillna(0).plot(linestyle=line) + plt.legend() + plt.xlim([9, None]); + plt.title("Ratio of infected users"); -.. image:: output_56_0.png +.. image:: output_50_0.png -You can use wildcards in the results path: +Data format +----------- + +Parameters +~~~~~~~~~~ + +The ``parameters`` dataframe has three keys: + +- The identifier of the simulation. This will be shared by all + iterations launched in the same run +- The identifier of the parameters used in the simulation. This will be + shared by all iterations that have the exact same set of parameters. +- The identifier of the iteration. Each row should have a different + iteration identifier + +There will be a column per each parameter passed to the environment. In +this case, that’s three: **generator**, **n** and +**prob_neighbor_spread**. .. code:: ipython3 - analysis.plot_all('soil_output/Spread_barabasi*/', analysis.get_count, 'state_id'); - - - -.. image:: output_58_0.png - - - -.. image:: output_58_1.png - - - -.. image:: output_58_2.png - - - -.. image:: output_58_3.png - - - -.. image:: output_58_4.png - - -If we compare these results to those of the other graph model (a -fully-connected graph), we can see a stark difference: - -.. code:: ipython3 - - analysis.plot_all('soil_output/Spread_erdos*', analysis.get_count, 'state_id'); - - - -.. image:: output_60_0.png - - - -.. image:: output_60_1.png - - - -.. image:: output_60_2.png - - - -.. image:: output_60_3.png - - - -.. image:: output_60_4.png - - -The previous cells were using the ``count_value`` function for -aggregation. There’s another function to plot numeral values: - -.. code:: ipython3 - - analysis.plot_all('soil_output/Spread_erdos*', analysis.get_value, 'prob_tv_spread'); - - - -.. image:: output_62_0.png - - - -.. image:: output_62_1.png - - - -.. image:: output_62_2.png - - - -.. image:: output_62_3.png - - - -.. image:: output_62_4.png - - -Manually plotting with pandas -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Although the simplest way to visualize the results of a simulation is to -use the built-in methods in the analysis module, sometimes the setup is -more complicated and we need to explore the data a little further. - -For that, we can use native pandas over the results. - -Soil provides some convenience methods to simplify common operations: - -- ``analysis.split_df`` to separate a history dataframe into - environment and agent parameters. -- ``analysis.get_count`` to get a dataframe with the value counts for - different attributes during the simulation. -- ``analysis.get_value`` to get the evolution of the value of an - attribute during the simulation. - -And, as we saw earlier, ``analysis.process`` can turn a dataframe in -canonical form into a dataframe with a column per attribute. - -.. code:: ipython3 - - !ls soil_output/Spread_barabasi_albert_graph_prob_0.0/Spread_barabasi_albert_graph_prob_0* - - -.. parsed-literal:: - - soil_output/Spread_barabasi_albert_graph_prob_0.0/Spread_barabasi_albert_graph_prob_0.0.dumped.yml - soil_output/Spread_barabasi_albert_graph_prob_0.0/Spread_barabasi_albert_graph_prob_0.0.sqlite - soil_output/Spread_barabasi_albert_graph_prob_0.0/Spread_barabasi_albert_graph_prob_0.0_trial_0.csv - soil_output/Spread_barabasi_albert_graph_prob_0.0/Spread_barabasi_albert_graph_prob_0.0_trial_0.sqlite - soil_output/Spread_barabasi_albert_graph_prob_0.0/Spread_barabasi_albert_graph_prob_0.0_trial_0.stats.csv - - -.. code:: ipython3 - - df = analysis.read_sql('soil_output/Spread_barabasi_albert_graph_prob_0.0/Spread_barabasi_albert_graph_prob_0.0_trial_0.sqlite') - df + res.parameters.head() @@ -2659,49 +1041,131 @@ canonical form into a dataframe with a column per attribute. vertical-align: top; } - .dataframe thead tr th { - text-align: left; - } - - .dataframe thead tr:last-of-type th { + .dataframe thead th { text-align: right; } - + + + - - - - + + + - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + - + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
keySEEDalive...state_idgeneratornprob_neighbor_spread
dict_idenv0110100101102103104105...90919293949596979899iteration_idparams_idsimulation_id
039063f8newspread_1682002299.544348erdos_renyi_graph1001.0
t_step5db645dnewspread_1682002299.544348barabasi_albert_graph1000.0
8f26adbnewspread_1682002299.544348barabasi_albert_graph1000.5
cb3dbcanewspread_1682002299.544348erdos_renyi_graph1000.5
d1fe9c1newspread_1682002299.544348barabasi_albert_graph1001.0
+ + + + +Configuration +~~~~~~~~~~~~~ + +This dataset is indexed by the identifier of the simulation, and there +will be a column per each attribute of the simulation. For instance, +there is one for the number of processes used, another one for the path +where the results were stored, etc. + +.. code:: ipython3 + + res.config.head() + + + + +.. raw:: html + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + @@ -2727,979 +1191,215 @@ canonical form into a dataframe with a column per attribute. - - - - - - - - - + + + + + + + + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + +
indexversionsource_filenamedescriptiongroupbackupoverwritedry_rundump...num_processesexportersmodel_reportersagent_reporterstablesoutdirexporter_paramslevelskip_testdebug
simulation_id
0.0Spread_barabasi_albert_graph_prob_0.0_trial_0TrueTrueTrueTrueTrueTrueTruenewspread_1682002299.54434802NonenewspreadNoneFalse TrueFalse True ...neutralneutralneutralneutralneutralneutralneutralneutralneutralneutral
1.0Spread_barabasi_albert_graph_prob_0.0_trial_0TrueTrueTrueTrueTrueTrueTrueTrueTrue...neutralneutralneutralneutralneutralneutralneutralneutralneutralneutral
2.0Spread_barabasi_albert_graph_prob_0.0_trial_0TrueTrueTrueTrueTrueTrueTrueTrueTrue...neutralneutralneutralneutralneutralneutralneutralneutralneutralneutral
3.0Spread_barabasi_albert_graph_prob_0.0_trial_0TrueTrueTrueTrueTrueTrueTrueTrueTrue...neutralneutralneutralneutralneutralneutralneutralneutralneutralneutral
4.0Spread_barabasi_albert_graph_prob_0.0_trial_0TrueTrueTrueTrueTrueTrueTrueTrueTrue...neutralneutralneutralneutralneutralneutralneutralneutralneutralneutral
5.0Spread_barabasi_albert_graph_prob_0.0_trial_0TrueTrueTrueTrueTrueTrueTrueTrueTrue...neutralneutralneutralneutralneutralneutralneutralinfectedneutralneutral
6.0Spread_barabasi_albert_graph_prob_0.0_trial_0TrueTrueTrueTrueTrueTrueTrueTrueTrue...neutralneutralneutralneutralneutralneutralneutralinfectedneutralneutral
7.0Spread_barabasi_albert_graph_prob_0.0_trial_0TrueTrueTrueTrueTrueTrueTrueTrueTrue...neutralneutralneutralneutralneutralneutralneutralinfectedneutralneutral
8.0Spread_barabasi_albert_graph_prob_0.0_trial_0TrueTrueTrueTrueTrueTrueTrueTrueTrue...neutralneutralneutralneutralneutralneutralneutralinfectedneutralneutral
9.0Spread_barabasi_albert_graph_prob_0.0_trial_0TrueTrueTrueTrueTrueTrueTrueTrueTrue...neutralneutralneutralneutralneutralneutralneutralinfectedneutralneutral
10.0Spread_barabasi_albert_graph_prob_0.0_trial_0TrueTrueTrueTrueTrueTrueTrueTrueTrue...neutralneutralneutralneutralneutralneutralneutralinfectedneutralneutral
11.0Spread_barabasi_albert_graph_prob_0.0_trial_0TrueTrueTrueTrueTrueTrueTrueTrueTrue...infectedinfectedinfectedinfectedinfectedneutralinfectedinfectedinfectedinfected
12.0Spread_barabasi_albert_graph_prob_0.0_trial_0TrueTrueTrueTrueTrueTrueTrueTrueTrue...infectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfected
13.0Spread_barabasi_albert_graph_prob_0.0_trial_0TrueTrueTrueTrueTrueTrueTrueTrueTrue...infectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfected
14.0Spread_barabasi_albert_graph_prob_0.0_trial_0TrueTrueTrueTrueTrueTrueTrueTrueTrue...infectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfected
15.0Spread_barabasi_albert_graph_prob_0.0_trial_0TrueTrueTrueTrueTrueTrueTrueTrueTrue...infectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfected
16.0Spread_barabasi_albert_graph_prob_0.0_trial_0TrueTrueTrueTrueTrueTrueTrueTrueTrue...infectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfected
17.0Spread_barabasi_albert_graph_prob_0.0_trial_0TrueTrueTrueTrueTrueTrueTrueTrueTrue...infectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfected
18.0Spread_barabasi_albert_graph_prob_0.0_trial_0TrueTrueTrueTrueTrueTrueTrueTrueTrue...infectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfected
19.0Spread_barabasi_albert_graph_prob_0.0_trial_0TrueTrueTrueTrueTrueTrueTrueTrueTrue...infectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfected1[<class 'soil.exporters.default'>]{}{}{}/mnt/data/home/j/git/lab.gsi/soil/soil/example...{}20FalseFalse
-

20 rows × 3008 columns

+

1 rows × 29 columns

-Let’s look at the evolution of agent parameters in the simulation +Model reporters +~~~~~~~~~~~~~~~ + +The ``env`` dataframe includes the data collected from the model. The +keys in this case are the same as ``parameters``, and an additional one: +**step**. .. code:: ipython3 - df.plot() - - - - -.. parsed-literal:: - - - - - - -.. image:: output_68_1.png - - -As we can see, ``event_time`` and ``interval`` are cluttering our -results, - -.. code:: ipython3 - - del df['interval'] - del df['event_time'] - df.plot() - - - - -.. parsed-literal:: - - - - - - -.. image:: output_70_1.png - - -The ``soil.analysis`` module also provides convenient functions to count -the number of agents in a given state: - -.. code:: ipython3 - - analysis.get_count(agents, 'state_id').plot(); - - - -.. image:: output_72_0.png - - -Dealing with bigger data ------------------------- - -.. code:: ipython3 - - from soil import analysis - -.. code:: ipython3 - - !du -xsh ../rabbits/soil_output/rabbits_example/ - - -.. parsed-literal:: - - 1.1M ../rabbits/soil_output/rabbits_example/ - - -If we tried to load the entire history, we would probably run out of -memory. Hence, it is recommended that you also specify the attributes -you are interested in. - -.. code:: ipython3 - - p = analysis.plot_all('../rabbits/soil_output/rabbits_example/', analysis.get_count, 'state_id') - - - -.. image:: output_77_0.png - - -.. code:: ipython3 - - !ls ../rabbits/soil_output/rabbits_example - - -.. parsed-literal:: - - backup rabbits_example.sqlite - rabbits_example.dumped.yml rabbits_example_trial_0.sqlite - - -.. code:: ipython3 - - df = analysis.read_sql('../rabbits/soil_output/rabbits_example/rabbits_example_trial_0.sqlite', keys=['state_id', 'rabbits_alive']) - -.. code:: ipython3 - - df - - - - -.. raw:: html - -
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
keyrabbits_alivestate_id
dict_idenv0110100101102103104105...90919293949596979899
t_step
0.00newbornnewbornnannannannannannannan...nannannannannannannannannannan
2.00fertilefertilenannannannannannannan...nannannannannannannannannannan
16.00pregnantfertilenannannannannannannan...nannannannannannannannannannan
49.08fertilefertilenannannannannannannan...nannannannannannannannannannan
51.08fertilefertilenannannannannannannan...nannannannannannannannannannan
..................................................................
739.015fertiledeaddeaddeadfertiledeadfertiledeaddead...deadfertiledeaddeaddeadfertiledeaddeaddeaddead
742.014fertiledeaddeaddeadfertiledeadfertiledeaddead...deadfertiledeaddeaddeadfertiledeaddeaddeaddead
743.012fertiledeaddeaddeadfertiledeadfertiledeaddead...deadfertiledeaddeaddeadfertiledeaddeaddeaddead
744.010fertiledeaddeaddeadfertiledeadfertiledeaddead...deadfertiledeaddeaddeadfertiledeaddeaddeaddead
751.09fertiledeaddeaddeadfertiledeadfertiledeaddead...deadfertiledeaddeaddeadfertiledeaddeaddeaddead
-

326 rows × 349 columns

-
- - - -.. code:: ipython3 - - states = analysis.get_count(df, 'state_id') - states.plot(); + res.env.head() .. image:: output_81_0.png +.. raw:: html -.. code:: ipython3 - - alive = analysis.get_value(df, 'rabbits_alive', aggfunc='sum').apply(pd.to_numeric) - alive.plot() +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
agent_counttimeprob_tv_spreadprob_neighbor_spread
simulation_idparams_iditeration_idstep
newspread_1682002299.544348fcfc9550010100.00.0
110110.00.0
210120.00.0
310130.00.0
410140.00.0
+
+Agent reporters +~~~~~~~~~~~~~~~ -.. parsed-literal:: +This dataframe reflects the data collected for all the agents in the +simulation, in every step where data collection was invoked. + +The key in this dataframe is similar to the one in the ``parameters`` +dataframe, but there will be two more keys: the ``step`` and the +``agent_id``. There will be a column per each agent reporter added to +the model. In our case, there is only one: ``state_id``. + res.agents.head() -.. image:: output_82_1.png + +.. raw:: html + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
state_id
simulation_idparams_iditeration_idstepagent_id
newspread_1682002299.544348fcfc955000None
1neutral
2neutral
3neutral
4neutral
+
-.. code:: ipython3 - - h = pd.concat([alive, states]); - h.plot(); - - - -.. image:: output_83_0.png - diff --git a/examples/complete.yml b/examples/complete.yml deleted file mode 100644 index b3d388a..0000000 --- a/examples/complete.yml +++ /dev/null @@ -1,27 +0,0 @@ ---- -name: simple -group: tests -dir_path: "/tmp/" -num_trials: 3 -max_time: 100 -interval: 1 -seed: "CompleteSeed!" -network_params: - generator: complete_graph - n: 10 -network_agents: - - agent_type: CounterModel - weight: 1 - state: - state_id: 0 - - agent_type: AggregatedCounter - weight: 0.2 -environment_agents: [] -environment_class: Environment -environment_params: - am_i_complete: true -default_state: - incidents: 0 -states: - - name: 'The first node' - - name: 'The second node' diff --git a/examples/custom_generator/custom_generator.yml b/examples/custom_generator/custom_generator.yml deleted file mode 100644 index 8c128f3..0000000 --- a/examples/custom_generator/custom_generator.yml +++ /dev/null @@ -1,16 +0,0 @@ ---- -name: custom-generator -description: Using a custom generator for the network -num_trials: 3 -max_time: 100 -interval: 1 -network_params: - generator: mymodule.mygenerator -# These are custom parameters - n: 10 - n_edges: 5 -network_agents: - - agent_type: CounterModel - weight: 1 - state: - state_id: 0 diff --git a/examples/custom_generator/generator_sim.py b/examples/custom_generator/generator_sim.py new file mode 100644 index 0000000..c3701e7 --- /dev/null +++ b/examples/custom_generator/generator_sim.py @@ -0,0 +1,39 @@ +from networkx import Graph +import random +import networkx as nx +from soil import Simulation, Environment, CounterModel, parameters + + +def mygenerator(n=5, n_edges=5): + """ + Just a simple generator that creates a network with n nodes and + n_edges edges. Edges are assigned randomly, only avoiding self loops. + """ + G = nx.Graph() + + for i in range(n): + G.add_node(i) + + for i in range(n_edges): + nodes = list(G.nodes) + n_in = random.choice(nodes) + nodes.remove(n_in) # Avoid loops + n_out = random.choice(nodes) + G.add_edge(n_in, n_out) + return G + + +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) \ No newline at end of file diff --git a/examples/custom_generator/mymodule.py b/examples/custom_generator/mymodule.py deleted file mode 100644 index 4f80510..0000000 --- a/examples/custom_generator/mymodule.py +++ /dev/null @@ -1,27 +0,0 @@ -from networkx import Graph -import networkx as nx -from random import choice - -def mygenerator(n=5, n_edges=5): - ''' - Just a simple generator that creates a network with n nodes and - n_edges edges. Edges are assigned randomly, only avoiding self loops. - ''' - G = nx.Graph() - - for i in range(n): - G.add_node(i) - - for i in range(n_edges): - nodes = list(G.nodes) - n_in = choice(nodes) - nodes.remove(n_in) # Avoid loops - n_out = choice(nodes) - G.add_edge(n_in, n_out) - return G - - - - - - \ No newline at end of file diff --git a/examples/custom_timeouts/custom_timeouts.py b/examples/custom_timeouts/custom_timeouts.py deleted file mode 100644 index 75cfc91..0000000 --- a/examples/custom_timeouts/custom_timeouts.py +++ /dev/null @@ -1,35 +0,0 @@ -from soil.agents import FSM, state, default_state - - -class Fibonacci(FSM): - '''Agent that only executes in t_steps that are Fibonacci numbers''' - - defaults = { - 'prev': 1 - } - - @default_state - @state - def counting(self): - self.log('Stopping at {}'.format(self.now)) - prev, self['prev'] = self['prev'], max([self.now, self['prev']]) - return None, self.env.timeout(prev) - -class Odds(FSM): - '''Agent that only executes in odd t_steps''' - @default_state - @state - def odds(self): - self.log('Stopping at {}'.format(self.now)) - return None, self.env.timeout(1+self.now%2) - -if __name__ == '__main__': - import logging - logging.basicConfig(level=logging.INFO) - from soil import Simulation - s = Simulation(network_agents=[{'ids': [0], 'agent_type': Fibonacci}, - {'ids': [1], 'agent_type': Odds}], - network_params={"generator": "complete_graph", "n": 2}, - max_time=100, - ) - s.run(dry_run=True) diff --git a/examples/custom_timeouts/custom_timeouts_sim.py b/examples/custom_timeouts/custom_timeouts_sim.py new file mode 100644 index 0000000..7a80242 --- /dev/null +++ b/examples/custom_timeouts/custom_timeouts_sim.py @@ -0,0 +1,41 @@ +from soil.agents import FSM, state, default_state +from soil.time import Delta + + +class Fibonacci(FSM): + """Agent that only executes in t_steps that are Fibonacci numbers""" + prev = 1 + + @default_state + @state + def counting(self): + self.log("Stopping at {}".format(self.now)) + prev, self["prev"] = self["prev"], max([self.now, self["prev"]]) + return None, Delta(prev) + + +class Odds(FSM): + """Agent that only executes in odd t_steps""" + + @default_state + @state + def odds(self): + self.log("Stopping at {}".format(self.now)) + return None, Delta(1 + self.now % 2) + + +from soil import Environment, Simulation +from networkx import complete_graph + + +class TimeoutsEnv(Environment): + def init(self): + self.create_network(generator=complete_graph, n=2) + self.add_agent(agent_class=Fibonacci, node_id=0) + self.add_agent(agent_class=Odds, node_id=1) + + +sim = Simulation(model=TimeoutsEnv, max_steps=10, interval=1) + +if __name__ == "__main__": + sim.run(dump=False) \ No newline at end of file diff --git a/examples/events_and_messages/README.md b/examples/events_and_messages/README.md new file mode 100644 index 0000000..cd8b155 --- /dev/null +++ b/examples/events_and_messages/README.md @@ -0,0 +1,9 @@ +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. diff --git a/examples/events_and_messages/cars_sim.py b/examples/events_and_messages/cars_sim.py new file mode 100644 index 0000000..ceb86e6 --- /dev/null +++ b/examples/events_and_messages/cars_sim.py @@ -0,0 +1,231 @@ +""" +This is an example of a simplified city, where there are Passengers and Drivers that can take those passengers +from their location to their desired location. + +An example scenario could play like the following: + +- Drivers start in the `wandering` state, where they wander around the city until they have been assigned a journey +- Passenger(1) tells every driver that it wants to request a Journey. +- Each driver receives the request. + If Driver(2) is interested in providing the Journey, it asks Passenger(1) to confirm that it accepts Driver(2)'s request +- When Passenger(1) accepts the request, two things happen: + - Passenger(1) changes its state to `driving_home` + - Driver(2) starts moving towards the origin of the Journey +- Once Driver(2) reaches the origin, it starts moving itself and Passenger(1) to the destination of the Journey +- When Driver(2) reaches the destination (carrying Passenger(1) along): + - Driver(2) starts wondering again + - Passenger(1) dies, and is removed from the simulation +- If there are no more passengers available in the simulation, Drivers die +""" +from __future__ import annotations +from typing import Optional +from soil import * +from soil import events +from mesa.space import MultiGrid + + +# More complex scenarios may use more than one type of message between objects. +# A common pattern is to use `enum.Enum` to represent state changes in a request. +@dataclass +class Journey: + """ + This represents a request for a journey. Passengers and drivers exchange this object. + + A journey may have a driver assigned or not. If the driver has not been assigned, this + object is considered a "request for a journey". + """ + + origin: (int, int) + destination: (int, int) + tip: float + + passenger: Passenger + driver: Optional[Driver] = None + + +class City(EventedEnvironment): + """ + An environment with a grid where drivers and passengers will be placed. + + The number of drivers and riders is configurable through its parameters: + + :param str n_cars: The total number of drivers to add + :param str n_passengers: The number of passengers in the simulation + :param list agents: Specific agents to use in the simulation. It overrides the `n_passengers` + and `n_cars` params. + :param int height: Height of the internal grid + :param int width: Width of the internal grid + """ + 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) + + for agent in self.agents: + self.grid.place_agent(agent, (0, 0)) + self.grid.move_to_empty(agent) + + self.total_earnings = 0 + self.add_model_reporter("total_earnings") + + @report + @property + def number_passengers(self): + return self.count_agents(agent_class=Passenger) + + +class Driver(Evented, FSM): + pos = None + journey = None + earnings = 0 + + def on_receive(self, msg, sender): + """This is not a state. It will run (and block) every time check_messages is invoked""" + if self.journey is None and isinstance(msg, Journey) and msg.driver is None: + msg.driver = self + self.journey = msg + + def check_passengers(self): + """If there are no more passengers, stop forever""" + c = self.count_agents(agent_class=Passenger) + self.debug(f"Passengers left {c}") + if not c: + self.die("No more passengers") + + @default_state + @state + def wandering(self): + """Move around the city until a journey is accepted""" + target = None + self.check_passengers() + self.journey = None + while self.journey is None: # No potential journeys detected (see on_receive) + if target is None or not self.move_towards(target): + target = self.random.choice( + self.model.grid.get_neighborhood(self.pos, moore=False) + ) + + self.check_passengers() + # This will call on_receive behind the scenes, and the agent's status will be updated + self.check_messages() + yield Delta(30) # Wait at least 30 seconds before checking again + + try: + # Re-send the journey to the passenger, to confirm that we have been selected + self.journey = yield self.journey.passenger.ask(self.journey, timeout=60) + except events.TimedOut: + # No journey has been accepted. Try again + self.journey = None + return + + return self.driving + + @state + def driving(self): + """The journey has been accepted. Pick them up and take them to their destination""" + 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.debug(f"Moving { self.pos } -> { target }") + if target[0] == self.pos[0] and target[1] == self.pos[1]: + return False + + next_pos = [self.pos[0], self.pos[1]] + for idx in [0, 1]: + if self.pos[idx] < target[idx]: + next_pos[idx] += 1 + break + if self.pos[idx] > target[idx]: + next_pos[idx] -= 1 + break + self.model.grid.move_agent(self, tuple(next_pos)) + if with_passenger: + self.journey.passenger.pos = ( + self.pos + ) # This could be communicated through messages + return True + + +class Passenger(Evented, FSM): + pos = None + + def on_receive(self, msg, sender): + """This is not a state. It will be run synchronously every time `check_messages` is run""" + + if isinstance(msg, Journey): + self.journey = msg + return msg + + @default_state + @state + def asking(self): + destination = ( + self.random.randint(0, self.model.grid.height-1), + self.random.randint(0, self.model.grid.width-1), + ) + self.journey = None + journey = Journey( + origin=self.pos, + destination=destination, + tip=self.random.randint(10, 100), + passenger=self, + ) + + timeout = 60 + expiration = self.now + timeout + self.info(f"Asking for journey at: { self.pos }") + self.model.broadcast(journey, ttl=timeout, sender=self, agent_class=Driver) + while not self.journey: + 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"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 + def driving_home(self): + while ( + self.pos[0] != self.journey.destination[0] + or self.pos[1] != self.journey.destination[1] + ): + try: + yield self.received(timeout=60) + except events.TimedOut: + pass + + self.die("Got home safe!") + + +simulation = Simulation(name="RideHailing", + model=City, + seed="carsSeed", + max_time=1000, + parameters=dict(n_passengers=2)) + +if __name__ == "__main__": + easy(simulation) \ No newline at end of file diff --git a/examples/mesa/mesa.yml b/examples/mesa/mesa.yml deleted file mode 100644 index 01096eb..0000000 --- a/examples/mesa/mesa.yml +++ /dev/null @@ -1,21 +0,0 @@ ---- -name: mesa_sim -group: tests -dir_path: "/tmp" -num_trials: 3 -max_time: 100 -interval: 1 -seed: '1' -network_params: - generator: social_wealth.graph_generator - n: 5 -network_agents: - - agent_type: social_wealth.SocialMoneyAgent - weight: 1 -environment_class: social_wealth.MoneyEnv -environment_params: - num_mesa_agents: 5 - mesa_agent_type: social_wealth.MoneyAgent - N: 10 - width: 50 - height: 50 diff --git a/examples/mesa/mesa_sim.py b/examples/mesa/mesa_sim.py new file mode 100644 index 0000000..1d16c47 --- /dev/null +++ b/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() diff --git a/examples/mesa/server.py b/examples/mesa/server.py index e6afecd..5db37fd 100644 --- a/examples/mesa/server.py +++ b/examples/mesa/server.py @@ -1,7 +1,8 @@ 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 class MyNetwork(NetworkModule): @@ -13,15 +14,18 @@ def network_portrayal(env): # The model ensures there is 0 or 1 agent per node portrayal = dict() + wealths = { + node_id: data["agent"].wealth for (node_id, data) in env.G.nodes(data=True) + } portrayal["nodes"] = [ { - "id": agent_id, - "size": env.get_agent(agent_id).wealth, - # "color": "#CC0000" if not agents or agents[0].wealth == 0 else "#007959", - "color": "#CC0000", - "label": f"{agent_id}: {env.get_agent(agent_id).wealth}", + "id": node_id, + "size": 2 * (wealth + 1), + "color": "#CC0000" if wealth == 0 else "#007959", + # "color": "#CC0000", + "label": f"{node_id}: {wealth}", } - for (agent_id) in env.G.nodes + for (node_id, wealth) in wealths.items() ] portrayal["edges"] = [ @@ -29,7 +33,6 @@ def network_portrayal(env): for edge_id, (source, target) in enumerate(env.G.edges) ] - return portrayal @@ -40,7 +43,7 @@ def gridPortrayal(agent): :param agent: the agent in the simulation :return: the portrayal dictionary """ - color = max(10, min(agent.wealth*10, 100)) + color = max(10, min(agent.wealth * 10, 100)) return { "Shape": "rect", "w": 1, @@ -51,18 +54,17 @@ def gridPortrayal(agent): "Text": agent.unique_id, "x": agent.pos[0], "y": agent.pos[1], - "Color": f"rgba(31, 10, 255, 0.{color})" + "Color": f"rgba(31, 10, 255, 0.{color})", } -grid = MyNetwork(network_portrayal, 500, 500, library="sigma") +grid = MyNetwork(network_portrayal, 500, 500) chart = ChartModule( [{"Label": "Gini", "Color": "Black"}], data_collector_name="datacollector" ) -model_params = { - "N": UserSettableParameter( - "slider", +parameters = { + "N": Slider( "N", 5, 1, @@ -70,36 +72,40 @@ model_params = { 1, description="Choose how many agents to include in the model", ), - "network_agents": [{"agent_type": SocialMoneyAgent}], - "height": UserSettableParameter( - "slider", + "height": Slider( "height", 5, 5, 10, 1, description="Grid height", - ), - "width": UserSettableParameter( - "slider", + ), + "width": Slider( "width", 5, 5, 10, 1, description="Grid width", - ), - "network_params": { - 'generator': graph_generator - }, + ), + "agent_class": Choice( + "Agent class", + value="MoneyAgent", + choices=["MoneyAgent", "SocialMoneyAgent"], + ), + "generator": graph_generator, } -canvas_element = CanvasGrid(gridPortrayal, model_params["width"].value, model_params["height"].value, 500, 500) + +canvas_element = CanvasGrid( + 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 -server.launch(open_browser=False) +if __name__ == '__main__': + server.launch(open_browser=False) diff --git a/examples/mesa/social_wealth.py b/examples/mesa/social_wealth.py index 3398884..d5b8dbb 100644 --- a/examples/mesa/social_wealth.py +++ b/examples/mesa/social_wealth.py @@ -1,23 +1,26 @@ -''' +""" This is an example that adds soil agents and environment in a normal mesa workflow. -''' +""" from mesa import Agent as MesaAgent from mesa.space import MultiGrid + # from mesa.time import RandomActivation from mesa.datacollection import DataCollector from mesa.batchrunner import BatchRunner import networkx as nx -from soil import NetworkAgent, Environment +from soil import NetworkAgent, Environment, serialization + def compute_gini(model): agent_wealths = [agent.wealth for agent in model.agents] x = sorted(agent_wealths) N = len(list(model.agents)) - B = sum( xi * (N-i) for i,xi in enumerate(x) ) / (N*sum(x)) - return (1 + (1/N) - 2*B) + B = sum(xi * (N - i) for i, xi in enumerate(x)) / (N * sum(x)) + return 1 + (1 / N) - 2 * B + class MoneyAgent(MesaAgent): """ @@ -25,15 +28,14 @@ class MoneyAgent(MesaAgent): It will only share wealth with neighbors based on grid proximity """ - def __init__(self, unique_id, model): + def __init__(self, unique_id, model, wealth=1, **kwargs): super().__init__(unique_id=unique_id, model=model) - self.wealth = 1 + self.wealth = wealth def move(self): possible_steps = self.model.grid.get_neighborhood( - self.pos, - moore=True, - include_center=False) + self.pos, moore=True, include_center=False + ) new_position = self.random.choice(possible_steps) self.model.grid.move_agent(self, new_position) @@ -45,21 +47,21 @@ class MoneyAgent(MesaAgent): self.wealth -= 1 def step(self): - self.info("Crying wolf", self.pos) + print("Crying wolf", self.pos) self.move() if self.wealth > 0: self.give_money() -class SocialMoneyAgent(NetworkAgent, MoneyAgent): +class SocialMoneyAgent(MoneyAgent, NetworkAgent): wealth = 1 def give_money(self): cellmates = set(self.model.grid.get_cell_list_contents([self.pos])) - friends = set(self.get_neighboring_agents()) + friends = set(self.get_neighbors()) self.info("Trying to give money") - self.debug("Cellmates: ", cellmates) - self.debug("Friends: ", friends) + self.info("Cellmates: ", cellmates) + self.info("Friends: ", friends) nearby_friends = list(cellmates & friends) @@ -69,14 +71,35 @@ class SocialMoneyAgent(NetworkAgent, MoneyAgent): self.wealth -= 1 +def graph_generator(n=5): + G = nx.Graph() + for ix in range(n): + G.add_edge(0, ix) + return G + + class MoneyEnv(Environment): """A model with some number of agents.""" - def __init__(self, N, width, height, *args, network_params, **kwargs): - network_params['n'] = N - super().__init__(*args, network_params=network_params, **kwargs) + def __init__( + self, + width, + height, + N, + generator=graph_generator, + agent_class=SocialMoneyAgent, + topology=None, + **kwargs + ): + + generator = serialization.deserialize(generator) + agent_class = serialization.deserialize(agent_class, globs=globals()) + topology = generator(n=N) + super().__init__(topology=topology, N=N, **kwargs) self.grid = MultiGrid(width, height, False) + self.populate_network(agent_class=agent_class) + # Create agents for agent in self.agents: x = self.random.randrange(self.grid.width) @@ -84,37 +107,31 @@ class MoneyEnv(Environment): self.grid.place_agent(agent, (x, y)) self.datacollector = DataCollector( - model_reporters={"Gini": compute_gini}, - agent_reporters={"Wealth": "wealth"}) + model_reporters={"Gini": compute_gini}, agent_reporters={"Wealth": "wealth"} + ) -def graph_generator(n=5): - G = nx.Graph() - for ix in range(n): - G.add_edge(0, ix) - return G +if __name__ == "__main__": -if __name__ == '__main__': - - - G = graph_generator() - fixed_params = {"topology": G, - "width": 10, - "network_agents": [{"agent_type": SocialMoneyAgent, - 'weight': 1}], - "height": 10} + fixed_params = { + "generator": nx.complete_graph, + "width": 10, + "network_agents": [{"agent_class": SocialMoneyAgent, "weight": 1}], + "height": 10, + } variable_params = {"N": range(10, 100, 10)} - batch_run = BatchRunner(MoneyEnv, - variable_parameters=variable_params, - fixed_parameters=fixed_params, - iterations=5, - max_steps=100, - model_reporters={"Gini": compute_gini}) + batch_run = BatchRunner( + MoneyEnv, + variable_parameters=variable_params, + fixed_parameters=fixed_params, + iterations=5, + max_steps=100, + model_reporters={"Gini": compute_gini}, + ) batch_run.run_all() run_data = batch_run.get_model_vars_dataframe() run_data.head() print(run_data.Gini) - diff --git a/examples/mesa/wealth.py b/examples/mesa/wealth.py index c7934de..ca0d9bf 100644 --- a/examples/mesa/wealth.py +++ b/examples/mesa/wealth.py @@ -4,24 +4,26 @@ from mesa.time import RandomActivation from mesa.datacollection import DataCollector from mesa.batchrunner import BatchRunner + def compute_gini(model): agent_wealths = [agent.wealth for agent in model.schedule.agents] x = sorted(agent_wealths) N = model.num_agents - B = sum( xi * (N-i) for i,xi in enumerate(x) ) / (N*sum(x)) - return (1 + (1/N) - 2*B) + B = sum(xi * (N - i) for i, xi in enumerate(x)) / (N * sum(x)) + return 1 + (1 / N) - 2 * B + class MoneyAgent(Agent): - """ An agent with fixed initial wealth.""" + """An agent with fixed initial wealth.""" + def __init__(self, unique_id, model): super().__init__(unique_id, model) self.wealth = 1 def move(self): possible_steps = self.model.grid.get_neighborhood( - self.pos, - moore=True, - include_center=False) + self.pos, moore=True, include_center=False + ) new_position = self.random.choice(possible_steps) self.model.grid.move_agent(self, new_position) @@ -37,8 +39,10 @@ class MoneyAgent(Agent): if self.wealth > 0: self.give_money() + class MoneyModel(Model): """A model with some number of agents.""" + def __init__(self, N, width, height): self.num_agents = N self.grid = MultiGrid(width, height, True) @@ -55,29 +59,29 @@ class MoneyModel(Model): self.grid.place_agent(a, (x, y)) self.datacollector = DataCollector( - model_reporters={"Gini": compute_gini}, - agent_reporters={"Wealth": "wealth"}) + model_reporters={"Gini": compute_gini}, agent_reporters={"Wealth": "wealth"} + ) def step(self): self.datacollector.collect(self) self.schedule.step() -if __name__ == '__main__': +if __name__ == "__main__": - fixed_params = {"width": 10, - "height": 10} + fixed_params = {"width": 10, "height": 10} variable_params = {"N": range(10, 500, 10)} - batch_run = BatchRunner(MoneyModel, - variable_params, - fixed_params, - iterations=5, - max_steps=100, - model_reporters={"Gini": compute_gini}) + batch_run = BatchRunner( + MoneyModel, + variable_params, + fixed_params, + iterations=5, + max_steps=100, + model_reporters={"Gini": compute_gini}, + ) batch_run.run_all() run_data = batch_run.get_model_vars_dataframe() run_data.head() print(run_data.Gini) - diff --git a/examples/newsspread/NewsSpread.ipynb b/examples/newsspread/NewsSpread.ipynb index 1d22a2e..4cc29e6 100644 --- a/examples/newsspread/NewsSpread.ipynb +++ b/examples/newsspread/NewsSpread.ipynb @@ -80,11 +80,11 @@ "max_time: 300\r\n", "name: Sim_all_dumb\r\n", "network_agents:\r\n", - "- agent_type: DumbViewer\r\n", + "- agent_class: DumbViewer\r\n", " state:\r\n", " has_tv: false\r\n", " weight: 1\r\n", - "- agent_type: DumbViewer\r\n", + "- agent_class: DumbViewer\r\n", " state:\r\n", " has_tv: true\r\n", " weight: 1\r\n", @@ -104,19 +104,19 @@ "max_time: 300\r\n", "name: Sim_half_herd\r\n", "network_agents:\r\n", - "- agent_type: DumbViewer\r\n", + "- agent_class: DumbViewer\r\n", " state:\r\n", " has_tv: false\r\n", " weight: 1\r\n", - "- agent_type: DumbViewer\r\n", + "- agent_class: DumbViewer\r\n", " state:\r\n", " has_tv: true\r\n", " weight: 1\r\n", - "- agent_type: HerdViewer\r\n", + "- agent_class: HerdViewer\r\n", " state:\r\n", " has_tv: false\r\n", " weight: 1\r\n", - "- agent_type: HerdViewer\r\n", + "- agent_class: HerdViewer\r\n", " state:\r\n", " has_tv: true\r\n", " weight: 1\r\n", @@ -136,12 +136,12 @@ "max_time: 300\r\n", "name: Sim_all_herd\r\n", "network_agents:\r\n", - "- agent_type: HerdViewer\r\n", + "- agent_class: HerdViewer\r\n", " state:\r\n", " has_tv: true\r\n", " state_id: neutral\r\n", " weight: 1\r\n", - "- agent_type: HerdViewer\r\n", + "- agent_class: HerdViewer\r\n", " state:\r\n", " has_tv: true\r\n", " state_id: neutral\r\n", @@ -163,12 +163,12 @@ "max_time: 300\r\n", "name: Sim_wise_herd\r\n", "network_agents:\r\n", - "- agent_type: HerdViewer\r\n", + "- agent_class: HerdViewer\r\n", " state:\r\n", " has_tv: true\r\n", " state_id: neutral\r\n", " weight: 1\r\n", - "- agent_type: WiseViewer\r\n", + "- agent_class: WiseViewer\r\n", " state:\r\n", " has_tv: true\r\n", " weight: 1\r\n", @@ -189,12 +189,12 @@ "max_time: 300\r\n", "name: Sim_all_wise\r\n", "network_agents:\r\n", - "- agent_type: WiseViewer\r\n", + "- agent_class: WiseViewer\r\n", " state:\r\n", " has_tv: true\r\n", " state_id: neutral\r\n", " weight: 1\r\n", - "- agent_type: WiseViewer\r\n", + "- agent_class: WiseViewer\r\n", " state:\r\n", " has_tv: true\r\n", " weight: 1\r\n", diff --git a/examples/newsspread/NewsSpread.yml b/examples/newsspread/NewsSpread.yml deleted file mode 100644 index ffb1778..0000000 --- a/examples/newsspread/NewsSpread.yml +++ /dev/null @@ -1,138 +0,0 @@ ---- -default_state: {} -load_module: newsspread -environment_agents: [] -environment_params: - prob_neighbor_spread: 0.0 - prob_tv_spread: 0.01 -interval: 1 -max_time: 300 -name: Sim_all_dumb -network_agents: -- agent_type: DumbViewer - state: - has_tv: false - weight: 1 -- agent_type: DumbViewer - state: - has_tv: true - weight: 1 -network_params: - generator: barabasi_albert_graph - n: 500 - m: 5 -num_trials: 50 ---- -default_state: {} -load_module: newsspread -environment_agents: [] -environment_params: - prob_neighbor_spread: 0.0 - prob_tv_spread: 0.01 -interval: 1 -max_time: 300 -name: Sim_half_herd -network_agents: -- agent_type: DumbViewer - state: - has_tv: false - weight: 1 -- agent_type: DumbViewer - state: - has_tv: true - weight: 1 -- agent_type: HerdViewer - state: - has_tv: false - weight: 1 -- agent_type: HerdViewer - state: - has_tv: true - weight: 1 -network_params: - generator: barabasi_albert_graph - n: 500 - m: 5 -num_trials: 50 ---- -default_state: {} -load_module: newsspread -environment_agents: [] -environment_params: - prob_neighbor_spread: 0.0 - prob_tv_spread: 0.01 -interval: 1 -max_time: 300 -name: Sim_all_herd -network_agents: -- agent_type: HerdViewer - state: - has_tv: true - state_id: neutral - weight: 1 -- agent_type: 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: {} -load_module: newsspread -environment_agents: [] -environment_params: - prob_neighbor_spread: 0.0 - prob_tv_spread: 0.01 - prob_neighbor_cure: 0.1 -interval: 1 -max_time: 300 -name: Sim_wise_herd -network_agents: -- agent_type: HerdViewer - state: - has_tv: true - state_id: neutral - weight: 1 -- agent_type: WiseViewer - state: - has_tv: true - weight: 1 -network_params: - generator: barabasi_albert_graph - n: 500 - m: 5 -num_trials: 50 ---- -default_state: {} -load_module: newsspread -environment_agents: [] -environment_params: - prob_neighbor_spread: 0.0 - prob_tv_spread: 0.01 - prob_neighbor_cure: 0.1 -interval: 1 -max_time: 300 -name: Sim_all_wise -network_agents: -- agent_type: WiseViewer - state: - has_tv: true - state_id: neutral - weight: 1 -- agent_type: 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 diff --git a/examples/newsspread/newsspread.py b/examples/newsspread/newsspread.py deleted file mode 100644 index dc77f09..0000000 --- a/examples/newsspread/newsspread.py +++ /dev/null @@ -1,86 +0,0 @@ -from soil.agents import FSM, state, default_state, prob -import logging - - -class DumbViewer(FSM): - ''' - A viewer that gets infected via TV (if it has one) and tries to infect - its neighbors once it's infected. - ''' - defaults = { - 'prob_neighbor_spread': 0.5, - 'prob_tv_spread': 0.1, - } - - @default_state - @state - def neutral(self): - if self['has_tv']: - 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 prob(self.env['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.set_state(self.infected) - - -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_neighboring_agents(state_id=self.infected.id) - total = self.count_neighboring_agents() - prob_infect = self.env['prob_neighbor_spread'] * infected/total - self.debug('prob_infect', prob_infect) - if prob(prob_infect): - self.set_state(self.infected) - - -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.env['prob_neighbor_cure'] - for neighbor in self.get_neighboring_agents(state_id=self.infected.id): - if prob(prob_cure): - try: - neighbor.cure() - except AttributeError: - self.debug('Viewer {} cannot be cured'.format(neighbor.id)) - - def cure(self): - self.set_state(self.cured.id) - - @state - def infected(self): - cured = max(self.count_neighboring_agents(self.cured.id), - 1.0) - infected = max(self.count_neighboring_agents(self.infected.id), - 1.0) - prob_cure = self.env['prob_neighbor_cure'] * (cured/infected) - if prob(prob_cure): - return self.cured - return self.set_state(super().infected) diff --git a/examples/newsspread/newsspread_sim.py b/examples/newsspread/newsspread_sim.py new file mode 100644 index 0000000..4fa51f1 --- /dev/null +++ b/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.") \ No newline at end of file diff --git a/examples/programmatic/programmatic.py b/examples/programmatic/programmatic.py deleted file mode 100644 index 7c8d54a..0000000 --- a/examples/programmatic/programmatic.py +++ /dev/null @@ -1,40 +0,0 @@ -''' -Example of a fully programmatic simulation, without definition files. -''' -from soil import Simulation, agents -from networkx import Graph -import logging - - -def mygenerator(): - # Add only a node - G = Graph() - G.add_node(1) - return G - - -class MyAgent(agents.FSM): - - @agents.default_state - @agents.state - def neutral(self): - self.debug('I am running') - if agents.prob(0.2): - self.info('This runs 2/10 times on average') - - -s = Simulation(name='Programmatic', - network_params={'generator': mygenerator}, - num_trials=1, - max_time=100, - agent_type=MyAgent, - dry_run=True) - - -# By default, logging will only print WARNING logs (and above). -# You need to choose a lower logging level to get INFO/DEBUG traces -logging.basicConfig(level=logging.INFO) -envs = s.run() - -# Uncomment this to output the simulation to a YAML file -# s.dump_yaml('simulation.yaml') diff --git a/examples/programmatic/programmatic_sim.py b/examples/programmatic/programmatic_sim.py new file mode 100644 index 0000000..163dd40 --- /dev/null +++ b/examples/programmatic/programmatic_sim.py @@ -0,0 +1,53 @@ +""" +Example of a fully programmatic simulation, without definition files. +""" +from soil import Simulation, Environment, agents +from networkx import Graph +import logging + + +def mygenerator(): + # Add only a node + G = Graph() + G.add_node(1) + G.add_node(2) + return G + + +class MyAgent(agents.NetworkAgent, agents.FSM): + times_run = 0 + @agents.default_state + @agents.state + def neutral(self): + self.debug("I am running") + if self.prob(0.2): + self.times_run += 1 + self.info("This runs 2/10 times on average") + + +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=ProgrammaticEnv, + seed='Program', + iterations=1, + max_time=100, + dump=False, +) + +if __name__ == "__main__": + # By default, logging will only print WARNING logs (and above). + # You need to choose a lower logging level to get INFO/DEBUG traces + logging.basicConfig(level=logging.INFO) + envs = simulation.run() + + for agent in envs[0].agents: + print(agent.times_run) diff --git a/examples/pubcrawl/pubcrawl.py b/examples/pubcrawl/pubcrawl.py deleted file mode 100644 index 6c8d632..0000000 --- a/examples/pubcrawl/pubcrawl.py +++ /dev/null @@ -1,175 +0,0 @@ -from soil.agents import FSM, state, default_state -from soil import Environment -from random import random, shuffle -from itertools import islice -import logging - - -class CityPubs(Environment): - '''Environment with Pubs''' - level = logging.INFO - - def __init__(self, *args, number_of_pubs=3, pub_capacity=10, **kwargs): - super(CityPubs, self).__init__(*args, **kwargs) - pubs = {} - for i in range(number_of_pubs): - newpub = { - 'name': 'The awesome pub #{}'.format(i), - 'open': True, - 'capacity': pub_capacity, - 'occupancy': 0, - } - pubs[newpub['name']] = newpub - self['pubs'] = pubs - - def enter(self, pub_id, *nodes): - '''Agents will try to enter. The pub checks if it is possible''' - try: - pub = self['pubs'][pub_id] - except KeyError: - raise ValueError('Pub {} is not available'.format(pub_id)) - if not pub['open'] or (pub['capacity'] < (len(nodes) + pub['occupancy'])): - return False - pub['occupancy'] += len(nodes) - for node in nodes: - node['pub'] = pub_id - return True - - def available_pubs(self): - for pub in self['pubs'].values(): - if pub['open'] and (pub['occupancy'] < pub['capacity']): - yield pub['name'] - - def exit(self, pub_id, *node_ids): - '''Agents will notify the pub they want to leave''' - try: - pub = self['pubs'][pub_id] - except KeyError: - raise ValueError('Pub {} is not available'.format(pub_id)) - for node_id in node_ids: - node = self.get_agent(node_id) - if pub_id == node['pub']: - del node['pub'] - pub['occupancy'] -= 1 - - -class Patron(FSM): - '''Agent that looks for friends to drink with. It will do three things: - 1) Look for other patrons to drink with - 2) Look for a bar where the agent and other agents in the same group can get in. - 3) While in the bar, patrons only drink, until they get drunk and taken home. - ''' - level = logging.DEBUG - - defaults = { - 'pub': None, - 'drunk': False, - 'pints': 0, - 'max_pints': 3, - } - - @default_state - @state - def looking_for_friends(self): - '''Look for friends to drink with''' - self.info('I am looking for friends') - available_friends = list(self.get_agents(drunk=False, - pub=None, - state_id=self.looking_for_friends.id)) - if not available_friends: - self.info('Life sucks and I\'m alone!') - return self.at_home - befriended = self.try_friends(available_friends) - if befriended: - return self.looking_for_pub - - @state - def looking_for_pub(self): - '''Look for a pub that accepts me and my friends''' - if self['pub'] != None: - return self.sober_in_pub - self.debug('I am looking for a pub') - group = list(self.get_neighboring_agents()) - for pub in self.env.available_pubs(): - self.debug('We\'re trying to get into {}: total: {}'.format(pub, len(group))) - if self.env.enter(pub, self, *group): - self.info('We\'re all {} getting in {}!'.format(len(group), pub)) - return self.sober_in_pub - - @state - def sober_in_pub(self): - '''Drink up.''' - self.drink() - if self['pints'] > self['max_pints']: - return self.drunk_in_pub - - @state - def drunk_in_pub(self): - '''I'm out. Take me home!''' - self.info('I\'m so drunk. Take me home!') - self['drunk'] = True - pass # out drunk - - @state - def at_home(self): - '''The end''' - others = self.get_agents(state_id=Patron.at_home.id, limit_neighbors=True) - self.debug('I\'m home. Just like {} of my friends'.format(len(others))) - - def drink(self): - self['pints'] += 1 - self.debug('Cheers to that') - - def kick_out(self): - self.set_state(self.at_home) - - def befriend(self, other_agent, force=False): - ''' - Try to become friends with another agent. The chances of - success depend on both agents' openness. - ''' - if force or self['openness'] > random(): - self.env.add_edge(self, other_agent) - self.info('Made some friend {}'.format(other_agent)) - return True - return False - - def try_friends(self, others): - ''' Look for random agents around me and try to befriend them''' - befriended = False - k = int(10*self['openness']) - shuffle(others) - for friend in islice(others, k): # random.choice >= 3.7 - if friend == self: - continue - if friend.befriend(self): - self.befriend(friend, force=True) - self.debug('Hooray! new friend: {}'.format(friend.id)) - befriended = True - else: - self.debug('{} does not want to be friends'.format(friend.id)) - return befriended - - -class Police(FSM): - '''Simple agent to take drunk people out of pubs.''' - level = logging.INFO - - @default_state - @state - def patrol(self): - drunksters = list(self.get_agents(drunk=True, - state_id=Patron.drunk_in_pub.id)) - for drunk in drunksters: - self.info('Kicking out the trash: {}'.format(drunk.id)) - drunk.kick_out() - else: - self.info('No trash to take out. Too bad.') - - -if __name__ == '__main__': - from soil import simulation - simulation.run_from_config('pubcrawl.yml', - dry_run=True, - dump=None, - parallel=False) diff --git a/examples/pubcrawl/pubcrawl.yml b/examples/pubcrawl/pubcrawl.yml deleted file mode 100644 index 7a464a6..0000000 --- a/examples/pubcrawl/pubcrawl.yml +++ /dev/null @@ -1,26 +0,0 @@ ---- -name: pubcrawl -num_trials: 3 -max_time: 10 -dump: false -network_params: - # Generate 100 empty nodes. They will be assigned a network agent - generator: empty_graph - n: 30 -network_agents: - - agent_type: pubcrawl.Patron - description: Extroverted patron - state: - openness: 1.0 - weight: 9 - - agent_type: pubcrawl.Patron - description: Introverted patron - state: - openness: 0.1 - weight: 1 -environment_agents: - - agent_type: pubcrawl.Police -environment_class: pubcrawl.CityPubs -environment_params: - altercations: 0 - number_of_pubs: 3 diff --git a/examples/pubcrawl/pubcrawl_sim.py b/examples/pubcrawl/pubcrawl_sim.py new file mode 100644 index 0000000..dd28cd3 --- /dev/null +++ b/examples/pubcrawl/pubcrawl_sim.py @@ -0,0 +1,195 @@ +from soil.agents import FSM, NetworkAgent, state, default_state +from soil import Environment, Simulation, parameters +from itertools import islice +import networkx as nx +import logging + + +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): + self.pubs = {} + for i in range(self.number_of_pubs): + newpub = { + "name": "The awesome pub #{}".format(i), + "open": True, + "capacity": self.pub_capacity, + "occupancy": 0, + } + self.pubs[newpub["name"]] = newpub + 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""" + try: + pub = self["pubs"][pub_id] + except KeyError: + raise ValueError("Pub {} is not available".format(pub_id)) + if not pub["open"] or (pub["capacity"] < (len(nodes) + pub["occupancy"])): + return False + pub["occupancy"] += len(nodes) + for node in nodes: + node["pub"] = pub_id + return True + + def available_pubs(self): + for pub in self["pubs"].values(): + if pub["open"] and (pub["occupancy"] < pub["capacity"]): + yield pub["name"] + + def exit(self, pub_id, *node_ids): + """Agents will notify the pub they want to leave""" + try: + pub = self["pubs"][pub_id] + except KeyError: + raise ValueError("Pub {} is not available".format(pub_id)) + for node_id in node_ids: + node = self.get_agent(node_id) + if pub_id == node["pub"]: + del node["pub"] + pub["occupancy"] -= 1 + + +class Patron(FSM, NetworkAgent): + """Agent that looks for friends to drink with. It will do three things: + 1) Look for other patrons to drink with + 2) Look for a bar where the agent and other agents in the same group can get in. + 3) While in the bar, patrons only drink, until they get drunk and taken home. + """ + + level = logging.DEBUG + + pub = None + drunk = False + pints = 0 + max_pints = 3 + kicked_out = False + + @default_state + @state + def looking_for_friends(self): + """Look for friends to drink with""" + self.info("I am looking for friends") + available_friends = list( + self.get_agents(drunk=False, pub=None, state_id=self.looking_for_friends.id) + ) + if not available_friends: + self.info("Life sucks and I'm alone!") + return self.at_home + befriended = self.try_friends(available_friends) + if befriended: + return self.looking_for_pub + + @state + def looking_for_pub(self): + """Look for a pub that accepts me and my friends""" + if self["pub"] != None: + return self.sober_in_pub + self.debug("I am looking for a pub") + group = list(self.get_neighbors()) + for pub in self.model.available_pubs(): + self.debug("We're trying to get into {}: total: {}".format(pub, len(group))) + if self.model.enter(pub, self, *group): + self.info("We're all {} getting in {}!".format(len(group), pub)) + return self.sober_in_pub + + @state + def sober_in_pub(self): + """Drink up.""" + self.drink() + if self["pints"] > self["max_pints"]: + return self.drunk_in_pub + + @state + def drunk_in_pub(self): + """I'm out. Take me home!""" + self.info("I'm so drunk. Take me home!") + self["drunk"] = True + if self.kicked_out: + return self.at_home + pass # out drun + + @state + def at_home(self): + """The end""" + others = self.get_agents(state_id=Patron.at_home.id, limit_neighbors=True) + self.debug("I'm home. Just like {} of my friends".format(len(others))) + + def drink(self): + self["pints"] += 1 + self.debug("Cheers to that") + + def kick_out(self): + self.kicked_out = True + + def befriend(self, other_agent, force=False): + """ + Try to become friends with another agent. The chances of + success depend on both agents' openness. + """ + if force or self["openness"] > self.random.random(): + self.add_edge(self, other_agent) + self.info("Made some friend {}".format(other_agent)) + return True + return False + + def try_friends(self, others): + """Look for random agents around me and try to befriend them""" + befriended = False + k = int(10 * self["openness"]) + self.random.shuffle(others) + for friend in islice(others, k): # random.choice >= 3.7 + if friend == self: + continue + if friend.befriend(self): + self.befriend(friend, force=True) + self.debug("Hooray! new friend: {}".format(friend.unique_id)) + befriended = True + else: + self.debug("{} does not want to be friends".format(friend.unique_id)) + return befriended + + +class Police(FSM): + """Simple agent to take drunk people out of pubs.""" + + level = logging.INFO + + @default_state + @state + def patrol(self): + drunksters = list(self.get_agents(drunk=True, state_id=Patron.drunk_in_pub.id)) + for drunk in drunksters: + self.info("Kicking out the trash: {}".format(drunk.unique_id)) + drunk.kick_out() + else: + self.info("No trash to take out. Too bad.") + + +sim = Simulation( + 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, + ) +) + + +if __name__ == "__main__": + sim.run(parallel=False) \ No newline at end of file diff --git a/examples/rabbits/README.md b/examples/rabbits/README.md new file mode 100644 index 0000000..dfee8ef --- /dev/null +++ b/examples/rabbits/README.md @@ -0,0 +1,14 @@ +There are two similar implementations of this simulation. + +- `basic`. Using simple primites +- `improved`. Using more advanced features such as the `time` module to avoid unnecessary computations (i.e., skip steps), and generator functions. + +The examples can be run directly in the terminal, and they accept command like arguments. +For example, to enable the CSV exporter and the Summary exporter, while setting `max_time` to `100` and `seed` to `CustomSeed`: + +``` +python rabbit_agents.py --set max_time=100 --csv -e summary --set 'seed="CustomSeed"' +``` + +To learn more about how this functionality works, check out the `soil.easy` function. + diff --git a/examples/rabbits/rabbit_agents.py b/examples/rabbits/rabbit_agents.py deleted file mode 100644 index a8e6028..0000000 --- a/examples/rabbits/rabbit_agents.py +++ /dev/null @@ -1,135 +0,0 @@ -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() diff --git a/examples/rabbits/rabbit_improved_sim.py b/examples/rabbits/rabbit_improved_sim.py new file mode 100644 index 0000000..278c81d --- /dev/null +++ b/examples/rabbits/rabbit_improved_sim.py @@ -0,0 +1,153 @@ +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 RabbitsImprovedEnv(RabbitEnv): + def init(self): + """Initialize the environment with the new versions of the agents""" + a1 = self.add_node(Male) + a2 = self.add_node(Female) + a1.add_edge(a2) + self.add_agent(RandomAccident) + + +class Rabbit(FSM, NetworkAgent): + + sexual_maturity = 30 + life_expectancy = 300 + birth = None + + @property + def age(self): + if self.birth is None: + return None + return self.now - self.birth + + @default_state + @state + def newborn(self): + self.info("I am a newborn.") + self.birth = self.now + self.offspring = 0 + return self.youngling, Delta(self.sexual_maturity - self.age) + + @state + def youngling(self): + if self.age >= self.sexual_maturity: + self.info(f"I am fertile! My age is {self.age}") + return self.fertile + + @state + def fertile(self): + raise Exception("Each subclass should define its fertile state") + + @state + def dead(self): + self.die() + + +class Male(Rabbit): + max_females = 5 + mating_prob = 0.001 + + @state + def fertile(self): + if self.age > self.life_expectancy: + return self.dead + + # Males try to mate + for f in self.model.agents( + agent_class=Female, state_id=Female.fertile.id, limit=self.max_females + ): + self.debug("FOUND A FEMALE: ", repr(f), self.mating_prob) + if self.prob(self["mating_prob"]): + f.impregnate(self) + break # Do not try to impregnate other females + + +class Female(Rabbit): + gestation = 10 + conception = None + + @state + def fertile(self): + # Just wait for a Male + if self.age > self.life_expectancy: + return self.dead + if self.conception is not None: + return self.pregnant + + @property + def pregnancy(self): + if self.conception is None: + return None + return self.now - self.conception + + def impregnate(self, male): + self.info(f"impregnated by {repr(male)}") + self.mate = male + self.conception = self.now + self.number_of_babies = int(8 + 4 * self.random.random()) + + @state + def pregnant(self): + self.debug("I am pregnant") + + if self.age > self.life_expectancy: + self.info("Dying before giving birth") + return self.die() + + if self.pregnancy >= self.gestation: + self.info("Having {} babies".format(self.number_of_babies)) + for i in range(self.number_of_babies): + state = {} + agent_class = self.random.choice([Male, Female]) + child = self.model.add_node(agent_class=agent_class, **state) + child.add_edge(self) + if self.mate: + child.add_edge(self.mate) + self.mate.offspring += 1 + else: + self.debug("The father has passed away") + + self.offspring += 1 + self.mate = None + return self.fertile + + def die(self): + if self.pregnancy is not None: + self.info("A mother has died carrying a baby!!") + return super().die() + + +class RandomAccident(BaseAgent): + def step(self): + rabbits_alive = self.model.G.number_of_nodes() + + if not rabbits_alive: + return self.die() + + prob_death = self.model.get("prob_death", 1e-100) * math.floor( + math.log10(max(1, rabbits_alive)) + ) + self.debug("Killing some rabbits with prob={}!".format(prob_death)) + for i in self.iter_agents(agent_class=Rabbit): + if i.state_id == i.dead.id: + continue + if self.prob(prob_death): + self.info("I killed a rabbit: {}".format(i.id)) + rabbits_alive -= 1 + i.die() + self.debug("Rabbits alive: {}".format(rabbits_alive)) + + +sim = Simulation(model=RabbitsImprovedEnv, max_time=100, seed="MySeed", iterations=1) + +if __name__ == "__main__": + sim.run() diff --git a/examples/rabbits/rabbits.yml b/examples/rabbits/rabbits.yml deleted file mode 100644 index f5e93a4..0000000 --- a/examples/rabbits/rabbits.yml +++ /dev/null @@ -1,21 +0,0 @@ ---- -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: [] diff --git a/examples/rabbits/rabbits_basic_sim.py b/examples/rabbits/rabbits_basic_sim.py new file mode 100644 index 0000000..553eb43 --- /dev/null +++ b/examples/rabbits/rabbits_basic_sim.py @@ -0,0 +1,161 @@ +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: 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) + + +class Rabbit(NetworkAgent, FSM): + + sexual_maturity = 30 + life_expectancy = 300 + + @default_state + @state + def newborn(self): + self.info("I am a newborn.") + self.age = 0 + self.offspring = 0 + return self.youngling + + @state + def youngling(self): + self.age += 1 + if self.age >= self.sexual_maturity: + self.info(f"I am fertile! My age is {self.age}") + return self.fertile + + @state + def fertile(self): + raise Exception("Each subclass should define its fertile state") + + @state + def dead(self): + self.die() + + +class Male(Rabbit): + max_females = 5 + mating_prob = 0.001 + + @state + def fertile(self): + self.age += 1 + + if self.age > self.life_expectancy: + return self.dead + + # Males try to mate + for f in self.model.agents( + agent_class=Female, state_id=Female.fertile.id, limit=self.max_females + ): + self.debug("FOUND A FEMALE: ", repr(f), self.mating_prob) + if self.prob(self["mating_prob"]): + f.impregnate(self) + break # Take a break + + +class Female(Rabbit): + gestation = 10 + pregnancy = -1 + + @state + def fertile(self): + # Just wait for a Male + self.age += 1 + if self.age > self.life_expectancy: + return self.dead + if self.pregnancy >= 0: + return self.pregnant + + def impregnate(self, male): + self.info(f"impregnated by {repr(male)}") + self.mate = male + self.pregnancy = 0 + self.number_of_babies = int(8 + 4 * self.random.random()) + + @state + def pregnant(self): + self.info("I am pregnant") + self.age += 1 + + if self.age >= self.life_expectancy: + return self.die() + + if self.pregnancy < self.gestation: + self.pregnancy += 1 + return + + self.info("Having {} babies".format(self.number_of_babies)) + for i in range(self.number_of_babies): + state = {} + agent_class = self.random.choice([Male, Female]) + child = self.model.add_node(agent_class=agent_class, **state) + child.add_edge(self) + try: + child.add_edge(self.mate) + self.model.agents[self.mate].offspring += 1 + except ValueError: + self.debug("The father has passed away") + + self.offspring += 1 + self.mate = None + self.pregnancy = -1 + return self.fertile + + def die(self): + if "pregnancy" in self and self["pregnancy"] > -1: + self.info("A mother has died carrying a baby!!") + return super().die() + + +class RandomAccident(BaseAgent): + def step(self): + rabbits_alive = self.model.G.number_of_nodes() + + if not rabbits_alive: + return self.die() + + prob_death = self.model.prob_death * math.floor( + math.log10(max(1, rabbits_alive)) + ) + self.debug("Killing some rabbits with prob={}!".format(prob_death)) + for i in self.get_agents(agent_class=Rabbit): + if i.state_id == i.dead.id: + continue + if self.prob(prob_death): + self.info("I killed a rabbit: {}".format(i.id)) + rabbits_alive -= 1 + i.die() + self.debug("Rabbits alive: {}".format(rabbits_alive)) + + + +sim = Simulation(model=RabbitEnv, max_time=100, seed="MySeed", iterations=1) + +if __name__ == "__main__": + sim.run() \ No newline at end of file diff --git a/examples/random_delays/random_delays.py b/examples/random_delays/random_delays.py deleted file mode 100644 index c3a6961..0000000 --- a/examples/random_delays/random_delays.py +++ /dev/null @@ -1,45 +0,0 @@ -''' -Example of setting a -Example of a fully programmatic simulation, without definition files. -''' -from soil import Simulation, agents -from soil.time import Delta -from random import expovariate -import logging - - - -class MyAgent(agents.FSM): - ''' - An agent that first does a ping - ''' - - defaults = {'pong_counts': 2} - - @agents.default_state - @agents.state - def ping(self): - self.info('Ping') - return self.pong, Delta(expovariate(1/16)) - - @agents.state - def pong(self): - self.info('Pong') - self.pong_counts -= 1 - self.info(str(self.pong_counts)) - if self.pong_counts < 1: - return self.die() - return None, Delta(expovariate(1/16)) - - -s = Simulation(name='Programmatic', - network_agents=[{'agent_type': MyAgent, 'id': 0}], - topology={'nodes': [{'id': 0}], 'links': []}, - num_trials=1, - max_time=100, - agent_type=MyAgent, - dry_run=True) - - -logging.basicConfig(level=logging.INFO) -envs = s.run() diff --git a/examples/random_delays/random_delays_sim.py b/examples/random_delays/random_delays_sim.py new file mode 100644 index 0000000..e0e8759 --- /dev/null +++ b/examples/random_delays/random_delays_sim.py @@ -0,0 +1,47 @@ +""" +Example of setting a +Example of a fully programmatic simulation, without definition files. +""" +from soil import Simulation, agents, Environment +from soil.time import Delta + + +class MyAgent(agents.FSM): + """ + An agent that first does a ping + """ + + defaults = {"pong_counts": 2} + + @agents.default_state + @agents.state + def ping(self): + self.info("Ping") + return self.pong, Delta(self.random.expovariate(1 / 16)) + + @agents.state + def pong(self): + self.info("Pong") + self.pong_counts -= 1 + self.info(str(self.pong_counts)) + if self.pong_counts < 1: + return self.die() + return None, Delta(self.random.expovariate(1 / 16)) + + +class RandomEnv(Environment): + + def init(self): + self.add_agent(agent_class=MyAgent) + + +s = Simulation( + name="Programmatic", + model=RandomEnv, + iterations=1, + max_time=100, + dump=False, +) + + +envs = s.run() diff --git a/examples/template.yml b/examples/template.yml deleted file mode 100644 index f61757d..0000000 --- a/examples/template.yml +++ /dev/null @@ -1,30 +0,0 @@ ---- -sampler: - method: "SALib.sample.morris.sample" - N: 10 -template: - group: simple - num_trials: 1 - interval: 1 - max_time: 2 - seed: "CompleteSeed!" - dump: false - network_params: - generator: complete_graph - n: 10 - network_agents: - - agent_type: CounterModel - weight: "{{ x1 }}" - state: - state_id: 0 - - agent_type: AggregatedCounter - weight: "{{ 1 - x1 }}" - environment_params: - name: "{{ x3 }}" - skip_test: true -vars: - bounds: - x1: [0, 1] - x2: [1, 2] - fixed: - x3: ["a", "b", "c"] diff --git a/examples/terrorism/TerroristNetworkModel.py b/examples/terrorism/TerroristNetworkModel.py deleted file mode 100644 index 3cdc675..0000000 --- a/examples/terrorism/TerroristNetworkModel.py +++ /dev/null @@ -1,208 +0,0 @@ -import random -import networkx as nx -from soil.agents import Geo, NetworkAgent, FSM, state, default_state -from soil import Environment - - -class TerroristSpreadModel(FSM, Geo): - """ - Settings: - information_spread_intensity - - terrorist_additional_influence - - min_vulnerability (optional else zero) - - max_vulnerability - - prob_interaction - """ - - def __init__(self, model=None, unique_id=0, state=()): - super().__init__(model=model, unique_id=unique_id, state=state) - - self.information_spread_intensity = model.environment_params['information_spread_intensity'] - self.terrorist_additional_influence = model.environment_params['terrorist_additional_influence'] - self.prob_interaction = model.environment_params['prob_interaction'] - - if self['id'] == self.civilian.id: # Civilian - self.mean_belief = random.uniform(0.00, 0.5) - elif self['id'] == self.terrorist.id: # Terrorist - self.mean_belief = random.uniform(0.8, 1.00) - elif self['id'] == self.leader.id: # Leader - self.mean_belief = 1.00 - else: - raise Exception('Invalid state id: {}'.format(self['id'])) - - if 'min_vulnerability' in model.environment_params: - self.vulnerability = random.uniform( model.environment_params['min_vulnerability'], model.environment_params['max_vulnerability'] ) - else : - self.vulnerability = random.uniform( 0, model.environment_params['max_vulnerability'] ) - - - @state - def civilian(self): - neighbours = list(self.get_neighboring_agents(agent_type=TerroristSpreadModel)) - if len(neighbours) > 0: - # Only interact with some of the neighbors - interactions = list(n for n in neighbours if random.random() <= self.prob_interaction) - influence = sum( self.degree(i) for i in interactions ) - mean_belief = sum( i.mean_belief * self.degree(i) / influence for i in interactions ) - mean_belief = mean_belief * self.information_spread_intensity + self.mean_belief * ( 1 - self.information_spread_intensity ) - self.mean_belief = mean_belief * self.vulnerability + self.mean_belief * ( 1 - self.vulnerability ) - - if self.mean_belief >= 0.8: - return self.terrorist - - @state - def leader(self): - self.mean_belief = self.mean_belief ** ( 1 - self.terrorist_additional_influence ) - for neighbour in self.get_neighboring_agents(state_id=[self.terrorist.id, self.leader.id]): - if self.betweenness(neighbour) > self.betweenness(self): - return self.terrorist - - @state - def terrorist(self): - neighbours = self.get_agents(state_id=[self.terrorist.id, self.leader.id], - agent_type=TerroristSpreadModel, - limit_neighbors=True) - if len(neighbours) > 0: - influence = sum( self.degree(n) for n in neighbours ) - mean_belief = sum( n.mean_belief * self.degree(n) / influence for n in neighbours ) - mean_belief = mean_belief * self.vulnerability + self.mean_belief * ( 1 - self.vulnerability ) - self.mean_belief = self.mean_belief ** ( 1 - self.terrorist_additional_influence ) - - # Check if there are any leaders in the group - leaders = list(filter(lambda x: x.state.id == self.leader.id, neighbours)) - if not leaders: - # Check if this is the potential leader - # Stop once it's found. Otherwise, set self as leader - for neighbour in neighbours: - if self.betweenness(self) < self.betweenness(neighbour): - return - return self.leader - - -class TrainingAreaModel(FSM, Geo): - """ - Settings: - training_influence - - min_vulnerability - - Requires TerroristSpreadModel. - """ - - def __init__(self, model=None, unique_id=0, state=()): - super().__init__(model=model, unique_id=unique_id, state=state) - self.training_influence = model.environment_params['training_influence'] - if 'min_vulnerability' in model.environment_params: - self.min_vulnerability = model.environment_params['min_vulnerability'] - else: self.min_vulnerability = 0 - - @default_state - @state - def terrorist(self): - for neighbour in self.get_neighboring_agents(agent_type=TerroristSpreadModel): - if neighbour.vulnerability > self.min_vulnerability: - neighbour.vulnerability = neighbour.vulnerability ** ( 1 - self.training_influence ) - - -class HavenModel(FSM, Geo): - """ - Settings: - haven_influence - - min_vulnerability - - max_vulnerability - - Requires TerroristSpreadModel. - """ - - def __init__(self, model=None, unique_id=0, state=()): - super().__init__(model=model, unique_id=unique_id, state=state) - self.haven_influence = model.environment_params['haven_influence'] - if 'min_vulnerability' in model.environment_params: - self.min_vulnerability = model.environment_params['min_vulnerability'] - else: self.min_vulnerability = 0 - self.max_vulnerability = model.environment_params['max_vulnerability'] - - def get_occupants(self, **kwargs): - return self.get_neighboring_agents(agent_type=TerroristSpreadModel, **kwargs) - - @state - def civilian(self): - civilians = self.get_occupants(state_id=self.civilian.id) - if not civilians: - return self.terrorist - - for neighbour in self.get_occupants(): - if neighbour.vulnerability > self.min_vulnerability: - neighbour.vulnerability = neighbour.vulnerability * ( 1 - self.haven_influence ) - return self.civilian - - @state - def terrorist(self): - for neighbour in self.get_occupants(): - if neighbour.vulnerability < self.max_vulnerability: - neighbour.vulnerability = neighbour.vulnerability ** ( 1 - self.haven_influence ) - return self.terrorist - - -class TerroristNetworkModel(TerroristSpreadModel): - """ - Settings: - sphere_influence - - vision_range - - weight_social_distance - - weight_link_distance - """ - - def __init__(self, model=None, unique_id=0, state=()): - super().__init__(model=model, unique_id=unique_id, state=state) - - self.vision_range = model.environment_params['vision_range'] - self.sphere_influence = model.environment_params['sphere_influence'] - self.weight_social_distance = model.environment_params['weight_social_distance'] - self.weight_link_distance = model.environment_params['weight_link_distance'] - - @state - def terrorist(self): - self.update_relationships() - return super().terrorist() - - @state - def leader(self): - self.update_relationships() - return super().leader() - - def update_relationships(self): - if self.count_neighboring_agents(state_id=self.civilian.id) == 0: - close_ups = set(self.geo_search(radius=self.vision_range, agent_type=TerroristNetworkModel)) - step_neighbours = set(self.ego_search(self.sphere_influence, agent_type=TerroristNetworkModel, center=False)) - neighbours = set(agent.id for agent in self.get_neighboring_agents(agent_type=TerroristNetworkModel)) - search = (close_ups | step_neighbours) - neighbours - for agent in self.get_agents(search): - social_distance = 1 / self.shortest_path_length(agent.id) - spatial_proximity = ( 1 - self.get_distance(agent.id) ) - prob_new_interaction = self.weight_social_distance * social_distance + self.weight_link_distance * spatial_proximity - if agent['id'] == agent.civilian.id and random.random() < prob_new_interaction: - self.add_edge(agent) - break - - def get_distance(self, target): - source_x, source_y = nx.get_node_attributes(self.topology, 'pos')[self.id] - target_x, target_y = nx.get_node_attributes(self.topology, 'pos')[target] - dx = abs( source_x - target_x ) - dy = abs( source_y - target_y ) - return ( dx ** 2 + dy ** 2 ) ** ( 1 / 2 ) - - def shortest_path_length(self, target): - try: - return nx.shortest_path_length(self.topology, self.id, target) - except nx.NetworkXNoPath: - return float('inf') diff --git a/examples/terrorism/TerroristNetworkModel.yml b/examples/terrorism/TerroristNetworkModel.yml deleted file mode 100644 index 401b77d..0000000 --- a/examples/terrorism/TerroristNetworkModel.yml +++ /dev/null @@ -1,63 +0,0 @@ -name: TerroristNetworkModel_sim -load_module: TerroristNetworkModel -max_time: 150 -num_trials: 1 -network_params: - generator: random_geometric_graph - radius: 0.2 - # generator: geographical_threshold_graph - # theta: 20 - n: 100 -network_agents: - - agent_type: TerroristNetworkModel - weight: 0.8 - state: - id: civilian # Civilians - - agent_type: TerroristNetworkModel - weight: 0.1 - state: - id: leader # Leaders - - agent_type: TrainingAreaModel - weight: 0.05 - state: - id: terrorist # Terrorism - - agent_type: HavenModel - weight: 0.05 - state: - id: civilian # Civilian - -environment_params: - # 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. diff --git a/examples/terrorism/TerroristNetworkModel_sim.py b/examples/terrorism/TerroristNetworkModel_sim.py new file mode 100644 index 0000000..282d874 --- /dev/null +++ b/examples/terrorism/TerroristNetworkModel_sim.py @@ -0,0 +1,341 @@ +import networkx as nx +from soil.agents import Geo, NetworkAgent, FSM, custom, state, default_state +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: + information_spread_intensity + + terrorist_additional_influence + + min_vulnerability (optional else zero) + + max_vulnerability + """ + + information_spread_intensity = 0.1 + terrorist_additional_influence = 0.1 + min_vulnerability = 0 + max_vulnerability = 1 + + def init(self): + if self.state_id == self.civilian.id: # Civilian + self.mean_belief = self.model.random.uniform(0.00, 0.5) + elif self.state_id == self.terrorist.id: # Terrorist + self.mean_belief = self.random.uniform(0.8, 1.00) + elif self.state_id == self.leader.id: # Leader + self.mean_belief = 1.00 + else: + raise Exception("Invalid state id: {}".format(self["id"])) + + self.vulnerability = self.random.uniform( + self.get("min_vulnerability", 0), self.get("max_vulnerability", 1) + ) + + @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.model.prob_interaction + ) + influence = sum(self.degree(i) for i in interactions) + mean_belief = sum( + i.mean_belief * self.degree(i) / influence for i in interactions + ) + mean_belief = ( + mean_belief * self.information_spread_intensity + + self.mean_belief * (1 - self.information_spread_intensity) + ) + self.mean_belief = mean_belief * self.vulnerability + self.mean_belief * ( + 1 - self.vulnerability + ) + + if self.mean_belief >= 0.8: + return self.terrorist + + @state + def leader(self): + self.mean_belief = self.mean_belief ** (1 - self.terrorist_additional_influence) + for neighbour in self.get_neighbors( + state_id=[self.terrorist.id, self.leader.id] + ): + if self.betweenness(neighbour) > self.betweenness(self): + return self.terrorist + + @state + def terrorist(self): + neighbours = self.get_agents( + state_id=[self.terrorist.id, self.leader.id], + agent_class=TerroristSpreadModel, + limit_neighbors=True, + ) + if len(neighbours) > 0: + influence = sum(self.degree(n) for n in neighbours) + mean_belief = sum( + n.mean_belief * self.degree(n) / influence for n in neighbours + ) + mean_belief = mean_belief * self.vulnerability + self.mean_belief * ( + 1 - self.vulnerability + ) + self.mean_belief = self.mean_belief ** ( + 1 - self.terrorist_additional_influence + ) + + # Check if there are any leaders in the group + leaders = list(filter(lambda x: x.state_id == self.leader.id, neighbours)) + if not leaders: + # Check if this is the potential leader + # Stop once it's found. Otherwise, set self as leader + for neighbour in neighbours: + if self.betweenness(self) < self.betweenness(neighbour): + return + return self.leader + + def ego_search(self, steps=1, center=False, agent=None, **kwargs): + """Get a list of nodes in the ego network of *node* of radius *steps*""" + node = agent.node_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): + 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[agent.node_id] + + def betweenness(self, agent, force=False): + 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[agent.node_id] + + +class TrainingAreaModel(FSM, Geo): + """ + Settings: + training_influence + + min_vulnerability + + Requires TerroristSpreadModel. + """ + + training_influence = 0.1 + min_vulnerability = 0 + + def init(self): + self.mean_believe = 1 + self.vulnerability = 0 + + @default_state + @state + def terrorist(self): + for neighbour in self.get_neighbors(agent_class=TerroristSpreadModel): + if neighbour.vulnerability > self.min_vulnerability: + neighbour.vulnerability = neighbour.vulnerability ** ( + 1 - self.training_influence + ) + + +class HavenModel(FSM, Geo): + """ + Settings: + haven_influence + + min_vulnerability + + max_vulnerability + + Requires TerroristSpreadModel. + """ + + min_vulnerability = 0 + haven_influence = 0.1 + max_vulnerability = 0.5 + + def init(self): + self.mean_believe = 0 + self.vulnerability = 0 + + def get_occupants(self, **kwargs): + return self.get_neighbors(agent_class=TerroristSpreadModel, + **kwargs) + + @default_state + @state + def civilian(self): + civilians = self.get_occupants(state_id=self.civilian.id) + if not civilians: + return self.terrorist + + for neighbour in self.get_occupants(): + if neighbour.vulnerability > self.min_vulnerability: + neighbour.vulnerability = neighbour.vulnerability * ( + 1 - self.haven_influence + ) + return self.civilian + + @state + def terrorist(self): + for neighbour in self.get_occupants(): + if neighbour.vulnerability < self.max_vulnerability: + neighbour.vulnerability = neighbour.vulnerability ** ( + 1 - self.haven_influence + ) + return self.terrorist + + +class TerroristNetworkModel(TerroristSpreadModel): + """ + Settings: + sphere_influence + + vision_range + + weight_social_distance + + weight_link_distance + """ + + sphere_influence: float = 1 + vision_range: float = 1 + weight_social_distance: float = 0.5 + weight_link_distance: float = 0.2 + + @state + def terrorist(self): + self.update_relationships() + return super().terrorist() + + @state + def leader(self): + self.update_relationships() + return super().leader() + + def update_relationships(self): + if self.count_neighbors(state_id=self.civilian.id) == 0: + close_ups = set( + self.geo_search( + radius=self.vision_range, agent_class=TerroristNetworkModel + ) + ) + step_neighbours = set( + self.ego_search( + self.sphere_influence, + agent_class=TerroristNetworkModel, + center=False, + ) + ) + neighbours = set( + agent.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.unique_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.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.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) + return (dx**2 + dy**2) ** (1 / 2) + + def shortest_path_length(self, target): + try: + 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' \ No newline at end of file diff --git a/examples/torvalds.yml b/examples/torvalds.yml deleted file mode 100644 index e338163..0000000 --- a/examples/torvalds.yml +++ /dev/null @@ -1,14 +0,0 @@ ---- -name: torvalds_example -max_time: 10 -interval: 2 -agent_type: CounterModel -default_state: - skill_level: 'beginner' -network_params: - path: 'torvalds.edgelist' -states: - Torvalds: - skill_level: 'God' - balkian: - skill_level: 'developer' diff --git a/examples/torvalds_sim.py b/examples/torvalds_sim.py new file mode 100644 index 0000000..2ee4f22 --- /dev/null +++ b/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) \ No newline at end of file diff --git a/examples/tutorial/soil_tutorial.html b/examples/tutorial/soil_tutorial.html index f93ca03..3f98f61 100644 --- a/examples/tutorial/soil_tutorial.html +++ b/examples/tutorial/soil_tutorial.html @@ -12330,11 +12330,11 @@ Notice how node 0 is the only one with a TV.

sim = soil.Simulation(topology=G, num_trials=1, max_time=MAX_TIME, - environment_agents=[{'agent_type': NewsEnvironmentAgent, + environment_agents=[{'agent_class': NewsEnvironmentAgent, 'state': { 'event_time': EVENT_TIME }}], - network_agents=[{'agent_type': NewsSpread, + network_agents=[{'agent_class': NewsSpread, 'weight': 1}], states={0: {'has_tv': True}}, default_state={'has_tv': False}, @@ -12468,14 +12468,14 @@ For this demo, we will use a python dictionary:

}, 'network_agents': [ { - 'agent_type': NewsSpread, + 'agent_class': NewsSpread, 'weight': 1, 'state': { 'has_tv': False } }, { - 'agent_type': NewsSpread, + 'agent_class': NewsSpread, 'weight': 2, 'state': { 'has_tv': True @@ -12483,7 +12483,7 @@ For this demo, we will use a python dictionary:

} ], 'environment_agents':[ - {'agent_type': NewsEnvironmentAgent, + {'agent_class': NewsEnvironmentAgent, 'state': { 'event_time': 10 } diff --git a/examples/tutorial/soil_tutorial.ipynb b/examples/tutorial/soil_tutorial.ipynb index b448037..6b27dac 100644 --- a/examples/tutorial/soil_tutorial.ipynb +++ b/examples/tutorial/soil_tutorial.ipynb @@ -6,7 +6,9 @@ "ExecuteTime": { "end_time": "2017-10-19T12:41:48.007238Z", "start_time": "2017-10-19T14:41:47.980725+02:00" - } + }, + "hideCode": false, + "hidePrompt": false }, "source": [ "# Soil Tutorial" @@ -18,7 +20,9 @@ "ExecuteTime": { "end_time": "2017-07-02T16:44:14.120953Z", "start_time": "2017-07-02T18:44:14.117152+02:00" - } + }, + "hideCode": false, + "hidePrompt": false }, "source": [ "## Introduction" @@ -28,7 +32,9 @@ "cell_type": "markdown", "metadata": { "cell_style": "center", - "collapsed": true + "collapsed": true, + "hideCode": false, + "hidePrompt": false }, "source": [ "This notebook is an introduction to the soil agent-based social network simulation framework.\n", @@ -47,7 +53,9 @@ "ExecuteTime": { "end_time": "2017-07-03T13:38:48.052876Z", "start_time": "2017-07-03T15:38:48.044762+02:00" - } + }, + "hideCode": false, + "hidePrompt": false }, "source": [ "But before that, let's import the soil module and networkx." @@ -60,7 +68,9 @@ "ExecuteTime": { "end_time": "2017-11-03T10:58:13.451481Z", "start_time": "2017-11-03T11:58:12.643469+01:00" - } + }, + "hideCode": false, + "hidePrompt": false }, "outputs": [], "source": [ @@ -70,8 +80,7 @@ "%load_ext autoreload\n", "%autoreload 2\n", "\n", - "%matplotlib inline\n", - "# To display plots in the notebooed_" + "import matplotlib.pyplot as plt" ] }, { @@ -80,7 +89,9 @@ "ExecuteTime": { "end_time": "2017-07-03T13:41:19.788717Z", "start_time": "2017-07-03T15:41:19.785448+02:00" - } + }, + "hideCode": false, + "hidePrompt": false }, "source": [ "## Basic concepts" @@ -88,17 +99,23 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, "source": [ "There are three main elements in a soil simulation:\n", " \n", - "* The network topology. A simulation may use an existing NetworkX topology, or generate one on the fly\n", - "* Agents. There are two types: 1) network agents, which are linked to a node in the topology, and 2) environment agents, which are freely assigned to the environment.\n", - "* The environment. It assigns agents to nodes in the network, and stores the environment parameters (shared state for all agents).\n", + "* The environment or model. It assigns agents to nodes in the network, and stores the environment parameters (shared state for all agents).\n", + "* The network topology. A simulation may use an existing NetworkX topology, or generate one on the fly.\n", + "* Agents. There are several types of agents, depending on their behavior and their capabilities. Some examples of built-in types of agents are:\n", + " - Network agents, which are linked to a node in the topology. They have additional methods to access their neighbors.\n", + " - FSM (Finite state machine) agents. Their behavior is defined in terms of states, and an agent will move from one state to another.\n", + " - Evented agents, an actor-based model of agents, which can communicate with one another through message passing.\n", + " - For convenience, a general `soil.Agent` class is provided, which inherits from Network, FSM and Evented at the same time.\n", "\n", - "Soil is based on ``simpy``, which is an event-based network simulation library.\n", "Soil provides several abstractions over events to make developing agents easier.\n", - "This means you can use events (timeouts, delays) in soil, but for the most part we will assume your models will be step-based.\n" + "This means you can use events (timeouts, delays) in soil, but for the most part we will assume your models will be step-based o.\n" ] }, { @@ -107,7 +124,9 @@ "ExecuteTime": { "end_time": "2017-07-02T15:55:12.933978Z", "start_time": "2017-07-02T17:55:12.930860+02:00" - } + }, + "hideCode": false, + "hidePrompt": false }, "source": [ "## Modeling behaviour" @@ -119,26 +138,45 @@ "ExecuteTime": { "end_time": "2017-07-03T13:49:31.269687Z", "start_time": "2017-07-03T15:49:31.257850+02:00" - } + }, + "hideCode": false, + "hidePrompt": false }, "source": [ - "Our first step will be to model how every person in the social network reacts when it comes to news.\n", - "We will follow a very simple model (a finite state machine).\n", + "Our first step will be to model how every person in the social network reacts to hearing a piece of disinformation (news).\n", + "We will follow a very simple model based on a finite state machine.\n", "\n", - "There are two types of people, those who have heard about a newsworthy event (infected) or those who have not (neutral).\n", - "A neutral person may heard about the news either on the TV (with probability **prob_tv_spread**) or through their friends.\n", + "A person may be in one of two states: **neutral** (the default state) and **infected**.\n", + "A neutral person may hear about a piece of disinformation either on the TV (with probability **prob_tv_spread**) or through their friends.\n", "Once a person has heard the news, they will spread it to their friends (with a probability **prob_neighbor_spread**).\n", - "Some users do not have a TV, so they only rely on their friends.\n", + "Some users do not have a TV, so they will only be infected by their friends.\n", "\n", "The spreading probabilities will change over time due to different factors.\n", - "We will represent this variance using an environment agent." + "We will represent this variance using an additional agent which will not be a part of the social network." ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, "source": [ - "### Network Agents" + "### Modelling Agents\n", + "\n", + "The following sections will cover the basics of developing agents in SOIL.\n", + "\n", + "For more advanced patterns, please check the **examples** folder in the repository." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "source": [ + "#### Basic agents" ] }, { @@ -147,78 +185,131 @@ "ExecuteTime": { "end_time": "2017-07-03T14:03:07.171127Z", "start_time": "2017-07-03T16:03:07.165779+02:00" - } + }, + "hideCode": false, + "hidePrompt": false }, "source": [ - "A basic network agent in Soil would typically inherit from ``soil.agents.NetworkAgent``, and define its behaviour in every step of the simulation by implementing a ``run(self)`` method.\n", - "The most important attributes of the agent are:\n", + "The most basic agent in Soil is ``soil.BaseAgent``.\n", + "These agents implement their behavior by overriding the `step` method, which will be run in every simulation step.\n", + "Only one agent will be running at any given time, and it will be doing so until the `step` function returns.\n", "\n", - "* ``agent.state``, a dictionary with the state of the agent. This tate will be saved in every step of the simulation. It can be accessed from the agent as well:\n", - "```py\n", - "a = soil.agents.NetworkAgent(env=env)\n", - "agent.state['hours_of_sleep'] = 10\n", - "# is the same as\n", - "a['hours_of_sleep'] = 10\n", + "Agents can access their environment through their ``self.model`` attribute.\n", + "This is most commonly used to get access to the environment parameters and methods.\n", + "Here is a simple example of an agent:\n", + "\n", + "\n", + "```python\n", + "class ExampleAgent(BaseAgent):\n", + " def init(self):\n", + " self.is_infected = False\n", + " self.steps_neutral = 0\n", + " \n", + " def step(self):\n", + " # Implement agent logic\n", + " if self.is_infected:\n", + " ... # Do something, like infecting other agents\n", + " return self.die(\"No need to do anything else\") # Stop forever\n", + " else:\n", + " ... # Do something\n", + " self.steps_neutral += 1\n", + " if self.steps_neutral > self.model.max_steps_neutral:\n", + " self.is_infected = True\n", "```\n", - " The state of the agent is stored in every step of the simulation:\n", - " ```py\n", - " print(a['hours_of_sleep', 10]) # hours of sleep before step #10\n", - " print(a[None, 0]) # whole state of the agent before step #0\n", - " ```\n", "\n", - "* ``agent.env``, a reference to the environment. Most commonly used to get access to the environment parameters and the topology:\n", - " ```py\n", - " a.env.G.nodes() # Get all nodes ids in the topology\n", - " a.env['minimum_hours_of_sleep']\n", "\n", - " ```\n", "\n", - "Since our model is a finite state machine, we will be basing it on ``soil.agents.FSM``.\n", + "Any kind of agent behavior can be implemented with this `step` function.\n", + "However, it has two main drawbacks: 1) complex behaviors can get difficult both write and understand; 2) these behaviors are not composable." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "ExecuteTime": { + "end_time": "2017-07-03T14:03:07.171127Z", + "start_time": "2017-07-03T16:03:07.165779+02:00" + }, + "hideCode": false, + "hidePrompt": false + }, + "source": [ + "#### FSM agents\n", "\n", - "Agents that inherit from ``soil.agents.FSM`` do not need to specify a ``step`` method.\n", + "One way to solve both issues is to model agents as **[Finite-state Machines](https://en.wikipedia.org/wiki/Finite-state_machine)** (FSM, for short).\n", + "FSM define a series of possible states for the agent, and changes between these states.\n", + "These states can be modelled and extended independently.\n", + "\n", + "This is modelled in Soil through the `soil.FSM` class.\n", + "Agents that inherit from ``soil.FSM`` do not need to specify a ``step`` method.\n", "Instead, we describe each finite state with a function.\n", "To change to another state, a function may return the new state, or the ``id`` of a state.\n", "If no state is returned, the state remains unchanged.\n", "\n", - "The current state of the agent can be checked with ``agent.state['id']``. That state id can be used to look for other networks in that specific state\n", + "The current state of the agent can be checked with ``agent.state_id``.\n", + "That state id can be used to look for other agents in that specific state.\n", "\n", + "Our previous example could be expressed like this:\n", "\n", - "Our agent will have of two states, ``neutral`` (default) and ``infected``.\n", + "```python\n", + "class FSMExample(FSM):\n", "\n", - "Here's the code:" + " def init(self):\n", + " self.steps_neutral = 0\n", + " \n", + " @state(default=True)\n", + " def neutral(self):\n", + " ... # Do something\n", + " self.steps_neutral += 1\n", + " if self.steps_neutral > self.model.max_steps_neutral:\n", + " return self.infected # Change state\n", + "\n", + " @state\n", + " def infected(self):\n", + " ... # Do something\n", + " return self.die(\"No need to do anything else\")\n", + "```" ] }, { - "cell_type": "code", - "execution_count": 2, + "cell_type": "markdown", "metadata": { - "ExecuteTime": { - "end_time": "2017-11-03T10:58:16.051690Z", - "start_time": "2017-11-03T11:58:16.006044+01:00" - } + "hideCode": false, + "hidePrompt": false }, - "outputs": [], "source": [ - "import random\n", + "#### Generator-based agents\n", "\n", - "class NewsSpread(soil.agents.FSM):\n", - " @soil.agents.default_state\n", - " @soil.agents.state\n", - " def neutral(self):\n", - " r = random.random()\n", - " if self['has_tv'] and r <= self.env['prob_tv_spread']:\n", - " return self.infected\n", - " return\n", - " \n", - " @soil.agents.state\n", - " def infected(self):\n", - " prob_infect = self.env['prob_neighbor_spread']\n", - " for neighbor in self.get_neighboring_agents(state_id=self.neutral.id):\n", - " r = random.random()\n", - " if r < prob_infect:\n", - " neighbor.set_state(self.infected.id)\n", - " return\n", - " " + "Another design pattern that can be very useful in some cases is to model each step (or a specific state) using generators (the `yield` keyword).\n", + "\n", + "\n", + "\n", + "```python\n", + "class GenExample(BaseAgent):\n", + " def step(self):\n", + " for i in range(self.model.max_steps_neutral):\n", + " ... # Do something\n", + " yield # Signal the scheduler that this step is done for now\n", + " ... # Do something\n", + " return self.die(\"No need to do anything else\") \n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "source": [ + "#### Telling the scheduler when to wake up an agent\n", + "\n", + "By default, every agent will be called in every simulation step, and the time elapsed between two steps is controlled by the `interval` attribute in the environment.\n", + "\n", + "But agents may signal the scheduler when they expect to be called again.\n", + "This is especially useful when an agent is going to be dormant for a long time.\n", + "To do so, an agent can return (or `yield`) from a `step` or a `state` a value of type `soil.When` (absolute time), `soil.Delta` (relative time) or `soil.Cond`, telling the scheduler when the agent will be ready to run again.\n", + "If it returns nothing (i.e., `None`), the agent will be ready to run at the next simulation step." ] }, { @@ -227,7 +318,9 @@ "ExecuteTime": { "end_time": "2017-07-02T12:22:53.931963Z", "start_time": "2017-07-02T14:22:53.928340+02:00" - } + }, + "hideCode": false, + "hidePrompt": false }, "source": [ "### Environment agents" @@ -235,7 +328,10 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, "source": [ "Environment agents allow us to control the state of the environment.\n", "In this case, we will use an environment agent to simulate a very viral event.\n", @@ -245,121 +341,364 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 111, "metadata": { "ExecuteTime": { "end_time": "2017-11-03T10:58:17.653736Z", "start_time": "2017-11-03T11:58:17.612944+01:00" - } + }, + "hideCode": false, + "hidePrompt": false }, "outputs": [], "source": [ - "NEIGHBOR_FACTOR = 0.9\n", - "TV_FACTOR = 0.5\n", + "import logging\n", "\n", - "\n", - "class NewsEnvironmentAgent(soil.agents.NetworkAgent):\n", + "class EventGenerator(soil.BaseAgent):\n", + " level = logging.INFO\n", + " \n", " def step(self):\n", - " if self.now == self['event_time']:\n", - " self.env['prob_tv_spread'] = 1\n", - " self.env['prob_neighbor_spread'] = 1\n", - " elif self.now > self['event_time']:\n", - " self.env['prob_tv_spread'] = self.env['prob_tv_spread'] * TV_FACTOR\n", - " self.env['prob_neighbor_spread'] = self.env['prob_neighbor_spread'] * NEIGHBOR_FACTOR" + " # Do nothing until the time of the event\n", + " yield soil.When(self.model.event_time)\n", + " self.info(\"TV event happened\")\n", + " self.model.prob_tv_spread = 0.5\n", + " self.model.prob_neighbor_spread *= 2\n", + " self.model.prob_neighbor_spread = min(self.model.prob_neighbor_spread, 1)\n", + " yield\n", + " self.model.prob_tv_spread = 0\n", + "\n", + " while self.alive:\n", + " self.model.prob_neighbor_spread = self.model.prob_neighbor_spread * self.model.neighbor_factor\n", + " if self.model.prob_neighbor_spread < 0.01:\n", + " return self.die(\"neighbors can no longer spread the rumour\")\n", + " yield" ] }, { "cell_type": "markdown", "metadata": { - "ExecuteTime": { - "end_time": "2017-07-02T11:23:18.052235Z", - "start_time": "2017-07-02T13:23:18.047452+02:00" - } + "hideCode": false, + "hidePrompt": false }, "source": [ - "### Testing the agents" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "ExecuteTime": { - "end_time": "2017-07-02T16:14:54.572431Z", - "start_time": "2017-07-02T18:14:54.564095+02:00" - } - }, - "source": [ - "Feel free to skip this section if this is your first time with soil." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Testing agents is not easy, and this is not a thorough testing process for agents.\n", - "Rather, this section is aimed to show you how to access internal pats of soil so you can test your agents." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "cell_style": "split" - }, - "source": [ - "First of all, let's check if our network agent has the states we would expect:" + "### Environment (Model)\n", + "\n", + "Let's define a environment model to test our event generator agent.\n", + "This environment will have a single agent (the event generator).\n", + "We will also tell the environment to save the value of `prob_tv_spread` after every step:" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 112, "metadata": { - "ExecuteTime": { - "end_time": "2017-11-03T10:58:19.781155Z", - "start_time": "2017-11-03T11:58:19.754362+01:00" - }, - "cell_style": "split" + "hideCode": false, + "hidePrompt": false + }, + "outputs": [], + "source": [ + "class NewsEnv(soil.NetworkEnvironment):\n", + " \n", + " prob_tv_spread = 0.1\n", + " prob_neighbor_spread = 0.1\n", + " event_time = 10\n", + " tv_factor = 0.5\n", + " neighbor_factor = 0.9\n", + "\n", + " \n", + " def init(self):\n", + " self.add_model_reporter(\"prob_tv_spread\")\n", + " self.add_agent(EventGenerator)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "source": [ + "Once the environment has been defined, we can run a simulation " + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": { + "hideCode": false, + "hidePrompt": false }, "outputs": [ { "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "1d73b3b3155f4132b863b3fb996ed1f1", + "version_major": 2, + "version_minor": 0 + }, "text/plain": [ - 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" + ], + "text/plain": [ + " step agent_count prob_tv_spread\n", + "time \n", + "0 0 1 0.1\n", + "10 1 1 0.1\n", + "11 2 1 0.5\n", + "12 3 1 0.0\n", + "13 4 1 0.0\n", + "14 5 1 0.0" + ] + }, + "execution_count": 102, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "NewsSpread.states" + "it = NewsEnv.run(iterations=1, dump=False, max_time=14)\n", + "\n", + "it[0].model_df()" ] }, { "cell_type": "markdown", "metadata": { - "cell_style": "split" + "hideCode": false, + "hidePrompt": false }, "source": [ - "Now, let's run a simulation on a simple network. It is comprised of three nodes:\n" + "As we can see, the event occurred right after `t=10`, so by `t=11` the value of `prob_tv_spread` was already set to `1.0`.\n", + "\n", + "You may notice nothing happened between `t=0` and `t=1`.\n", + "That is because there aren't any other agents in the simulation, and our event generator explicitly waited until `t=10`." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "source": [ + "### Network agents" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "ExecuteTime": { + "end_time": "2017-07-03T14:03:07.171127Z", + "start_time": "2017-07-03T16:03:07.165779+02:00" + }, + "hideCode": false, + "hidePrompt": false + }, + "source": [ + "In our disinformation scenario, we will model our agents as a FSM with two states: ``neutral`` (default) and ``infected``.\n", + "\n", + "Here's the code:" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 103, "metadata": { "ExecuteTime": { - "end_time": "2017-11-03T10:58:20.791777Z", - "start_time": "2017-11-03T11:58:20.565173+01:00" + "end_time": "2017-11-03T10:58:16.051690Z", + "start_time": "2017-11-03T11:58:16.006044+01:00" }, - "cell_style": "split", - "scrolled": false + "hideCode": false, + "hidePrompt": false + }, + "outputs": [], + "source": [ + "class NewsSpread(soil.Agent):\n", + " has_tv = False\n", + " infected_by_friends = False\n", + " \n", + " @soil.state(default=True)\n", + " def neutral(self):\n", + " if self.infected_by_friends:\n", + " return self.infected\n", + " if self.has_tv:\n", + " if self.prob(self.model.prob_tv_spread):\n", + " return self.infected\n", + " \n", + " @soil.state\n", + " def infected(self):\n", + " for neighbor in self.iter_neighbors(state_id=self.neutral.id):\n", + " if self.prob(self.model.prob_neighbor_spread):\n", + " neighbor.infected_by_friends = True" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "source": [ + "We can check that our states are well defined, here:" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": { + "hideCode": false, + "hidePrompt": false }, "outputs": [ { "data": { - "image/png": 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", + "text/plain": [ + "['dead', 'neutral', 'infected']" + ] + }, + "execution_count": 104, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "NewsSpread.states()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "source": [ + "### Environment (Model)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_style": "split", + "hideCode": false, + "hidePrompt": false + }, + "source": [ + "Let's modify our simple simulation.\n", + "We will add a network of agents of type NewsSpread.\n", + "\n", + "Only one agent (0) will have a TV (in blue)." + ] + }, + { + "cell_type": "code", + "execution_count": 105, + "metadata": { + "cell_style": "split", + "hideCode": false, + "hidePrompt": false + }, + "outputs": [ + { + "data": { + "image/png": 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" ] @@ -369,273 +708,363 @@ } ], "source": [ - "G = nx.Graph()\n", - "G.add_edge(0, 1)\n", - "G.add_edge(0, 2)\n", - "G.add_edge(2, 3)\n", - "G.add_node(4)\n", + "def generate_simple():\n", + " G = nx.Graph()\n", + " G.add_edge(0, 1)\n", + " G.add_edge(0, 2)\n", + " G.add_edge(2, 3)\n", + " G.add_node(4)\n", + " return G\n", + "\n", + "G = generate_simple()\n", "pos = nx.spring_layout(G)\n", "nx.draw_networkx(G, pos, node_color='red')\n", "nx.draw_networkx(G, pos, nodelist=[0], node_color='blue')" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 106, "metadata": { - "ExecuteTime": { - "end_time": "2017-07-03T11:53:30.997756Z", - "start_time": "2017-07-03T13:53:30.989609+02:00" - }, - "cell_style": "split" + "hideCode": false, + "hidePrompt": false }, + "outputs": [], "source": [ - "Let's run a simple simulation that assigns a NewsSpread agent to all the nodes in that network.\n", - "Notice how node 0 is the only one with a TV." + "class NewsEnv(soil.NetworkEnvironment):\n", + " \n", + " prob_tv_spread = 0\n", + " prob_neighbor_spread = 0.1\n", + " event_time = 10\n", + " tv_factor = 0.5\n", + " neighbor_factor = 0.9\n", + "\n", + " \n", + " def init(self):\n", + " self.add_agent(EventGenerator)\n", + " self.G = generate_simple()\n", + " self.populate_network(NewsSpread)\n", + " self.agent(node_id=0).has_tv = True\n", + " self.add_model_reporter('prob_tv_spread')\n", + " self.add_model_reporter('prob_neighbor_spread')" ] }, { "cell_type": "code", - "execution_count": 6, - "metadata": {}, + "execution_count": 107, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, "outputs": [ { "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "dc1e3d6242e24e009601774769b9525b", + "version_major": 2, + "version_minor": 0 + }, "text/plain": [ - "" + "HBox(children=(IntProgress(value=0, description='NewsEnv', max=1, style=ProgressStyle(description_width='initi…" ] }, - "execution_count": 6, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(IntProgress(value=0, max=1), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "text/html": [ + "
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202060.00.077484
\n", + "
" + ], + "text/plain": [ + " step agent_count prob_tv_spread prob_neighbor_spread\n", + "time \n", + "0 0 6 0.0 0.100000\n", + "1 1 6 0.0 0.100000\n", + "2 2 6 0.0 0.100000\n", + "3 3 6 0.0 0.100000\n", + "4 4 6 0.0 0.100000\n", + "5 5 6 0.0 0.100000\n", + "6 6 6 0.0 0.100000\n", + "7 7 6 0.0 0.100000\n", + "8 8 6 0.0 0.100000\n", + "9 9 6 0.0 0.100000\n", + "10 10 6 0.0 0.100000\n", + "11 11 6 0.5 0.200000\n", + "12 12 6 0.0 0.180000\n", + "13 13 6 0.0 0.162000\n", + "14 14 6 0.0 0.145800\n", + "15 15 6 0.0 0.131220\n", + "16 16 6 0.0 0.118098\n", + "17 17 6 0.0 0.106288\n", + "18 18 6 0.0 0.095659\n", + "19 19 6 0.0 0.086093\n", + "20 20 6 0.0 0.077484" + ] + }, + "execution_count": 107, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "import importlib\n", - "importlib.reload(soil.agents)" + "it = NewsEnv.run(max_time=20)\n", + "it[0].model_df()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "source": [ + "In this case, notice that the inclusion of other agents (which run every step) means that the simulation did not skip to `t=10`.\n", + "\n", + "Now, let's look at the state of our agents in every step:" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 108, "metadata": { - "ExecuteTime": { - "end_time": "2017-11-03T10:58:55.517768Z", - "start_time": "2017-11-03T11:58:55.424083+01:00" - }, - "cell_style": "split" + "hideCode": false, + "hidePrompt": false }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "env_params = {\n", - " 'prob_tv_spread': 0,\n", - " 'prob_neighbor_spread': 0\n", - "}\n", - "\n", - "MAX_TIME = 100\n", - "EVENT_TIME = 10\n", - "\n", - "sim = soil.Simulation(topology=G,\n", - " num_trials=1,\n", - " max_time=MAX_TIME,\n", - " environment_agents=[{'agent_type': NewsEnvironmentAgent,\n", - " 'state': {\n", - " 'event_time': EVENT_TIME\n", - " }}],\n", - " network_agents=[{'agent_type': NewsSpread,\n", - " 'weight': 1}],\n", - " states={0: {'has_tv': True}},\n", - " default_state={'has_tv': False},\n", - " environment_params=env_params)\n", - "env = sim.run_simulation(dry_run=True)[0]" + "soil.analysis.plot(it[0])" ] }, { "cell_type": "markdown", "metadata": { - "cell_style": "split" + "deletable": false, + "editable": false, + "hideCode": false, + "hidePrompt": false, + "run_control": { + "frozen": true + } }, "source": [ - "Now we can access the results of the simulation and compare them to our expected results" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "ExecuteTime": { - "end_time": "2017-11-03T10:59:01.577474Z", - "start_time": "2017-11-03T11:59:01.414215+01:00" - }, - "cell_style": "split", - "scrolled": false - }, - "outputs": [], - "source": [ - "agents = list(env.network_agents)\n", + "## Running in more scenarios\n", "\n", - "# Until the event, all agents are neutral\n", - "for t in range(10):\n", - " for a in agents:\n", - " assert a['state_id', t] == a.neutral.id\n", - " \n", - "# After the event, the node with a TV is infected, the rest are not\n", - "assert agents[0]['has_tv']\n", - "assert agents[0]['state_id', 11] == NewsSpread.infected.id\n", - "assert not agents[2]['has_tv']\n", - "assert agents[2]['state_id', 11] == NewsSpread.neutral.id\n", - "\n", - "\n", - "# At the end, the agents connected to the infected one will probably be infected, too.\n", - "assert agents[1]['state_id', MAX_TIME] == NewsSpread.infected.id\n", - "assert agents[2]['state_id', MAX_TIME] == NewsSpread.infected.id\n", - "\n", - "# But the node with no friends should not be affected\n", - "assert agents[4]['state_id', MAX_TIME] == NewsSpread.neutral.id\n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "ExecuteTime": { - "end_time": "2017-07-02T16:41:09.110652Z", - "start_time": "2017-07-02T18:41:09.106966+02:00" - }, - "cell_style": "split" - }, - "source": [ - "Lastly, let's see if the probabilities have decreased as expected:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "ExecuteTime": { - "end_time": "2017-11-01T14:07:55.288616Z", - "start_time": "2017-11-01T15:07:55.241116+01:00" - }, - "cell_style": "split" - }, - "outputs": [], - "source": [ - "assert abs(env.environment_params['prob_neighbor_spread'] - (NEIGHBOR_FACTOR**(MAX_TIME-1-10))) < 10e-4\n", - "assert abs(env.environment_params['prob_tv_spread'] - (TV_FACTOR**(MAX_TIME-1-10))) < 10e-6" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Running the simulation" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "ExecuteTime": { - "end_time": "2017-07-03T11:20:28.566944Z", - "start_time": "2017-07-03T13:20:28.561052+02:00" - }, - "cell_style": "split" - }, - "source": [ - "To run a simulation, we need a configuration.\n", - "Soil can load configurations from python dictionaries as well as JSON and YAML files.\n", - "For this demo, we will use a python dictionary:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "ExecuteTime": { - "end_time": "2017-11-01T14:07:57.008940Z", - "start_time": "2017-11-01T15:07:56.966433+01:00" - }, - "cell_style": "split" - }, - "outputs": [], - "source": [ - "config = {\n", - " 'name': 'ExampleSimulation',\n", - " 'max_time': 20,\n", - " 'interval': 1,\n", - " 'num_trials': 1,\n", - " 'network_params': {\n", - " 'generator': 'complete_graph',\n", - " 'n': 500,\n", - " },\n", - " 'network_agents': [\n", - " {\n", - " 'agent_type': NewsSpread,\n", - " 'weight': 1,\n", - " 'state': {\n", - " 'has_tv': False\n", - " }\n", - " },\n", - " {\n", - " 'agent_type': NewsSpread,\n", - " 'weight': 2,\n", - " 'state': {\n", - " 'has_tv': True\n", - " }\n", - " }\n", - " ],\n", - " 'environment_agents':[\n", - " {'agent_type': NewsEnvironmentAgent,\n", - " 'state': {\n", - " 'event_time': 10\n", - " }\n", - " }\n", - " ],\n", - " 'states': [ {'has_tv': True} ],\n", - " 'environment_params':{\n", - " 'prob_tv_spread': 0.01,\n", - " 'prob_neighbor_spread': 0.5\n", - " }\n", - "}" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "ExecuteTime": { - "end_time": "2017-07-03T11:57:34.219618Z", - "start_time": "2017-07-03T13:57:34.213817+02:00" - }, - "cell_style": "split" - }, - "source": [ - "Let's run our simulation:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "ExecuteTime": { - "end_time": "2017-11-01T14:08:34.312637Z", - "start_time": "2017-11-01T15:07:57.774458+01:00" - }, - "cell_style": "split" - }, - "outputs": [], - "source": [ - "soil.simulation.run_from_config(config, dry_run=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "ExecuteTime": { - "end_time": "2017-07-03T12:03:32.183588Z", - "start_time": "2017-07-03T14:03:32.167797+02:00" - }, - "cell_style": "split", - "collapsed": true - }, - "source": [ "In real life, you probably want to run several simulations, varying some of the parameters so that you can compare and answer your research questions.\n", "\n", "For instance:\n", @@ -646,36 +1075,365 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 109, "metadata": { - "ExecuteTime": { - "end_time": "2017-11-01T14:10:38.099667Z", - "start_time": "2017-11-01T15:10:06.008314+01:00" - }, - "cell_style": "split", - "scrolled": true + "hideCode": false, + "hidePrompt": false }, "outputs": [], "source": [ - "network_1 = {\n", - " 'generator': 'erdos_renyi_graph',\n", - " 'n': 500,\n", - " 'p': 0.1\n", - "}\n", - "network_2 = {\n", - " 'generator': 'barabasi_albert_graph',\n", - " 'n': 500,\n", - " 'm': 2\n", - "}\n", + "class NewsEnvComplete(soil.Environment):\n", + " prob_tv = 0.05\n", + " prob_tv_spread = 0\n", + " prob_neighbor_spread = 0\n", + " event_time = 10\n", + " tv_factor = 0\n", + " neighbor_factor = 0.5\n", + " generator = \"erdos_renyi_graph\"\n", + " n = 100\n", + "\n", + " def init(self):\n", + " self.add_agent(EventGenerator)\n", + " if not self.G:\n", + " opts = {\"n\": self.n}\n", + " if self.generator == \"erdos_renyi_graph\":\n", + " opts[\"p\"] = 0.5\n", + " elif self.generator == \"barabasi_albert_graph\":\n", + " opts[\"m\"] = 4\n", + " self.create_network(generator=self.generator, **opts)\n", + "\n", + " self.populate_network([NewsSpread,\n", + " NewsSpread.w(has_tv=True)],\n", + " [1-self.prob_tv, self.prob_tv])\n", + " self.add_model_reporter('prob_tv_spread')\n", + " self.add_model_reporter('prob_neighbor_spread')\n", + " self.add_agent_reporter('state_id')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Since we do not care about previous results, we will set`overwrite=True`." + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[INFO ][17:29:24] Output directory: /mnt/data/home/j/git/lab.gsi/soil/soil/examples/tutorial/soil_output\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "5528388b3491489caecc160a2d19ee7a", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(IntProgress(value=0, description='newspread', max=10, style=ProgressStyle(description_width='in…" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "n = 100\n", + "\r", + "generator = erdos_renyi_graph\n", + "\r", + "prob_neighbor_spread = 0\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(IntProgress(value=0, max=5), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "n = 100\n", + "\r", + "generator = erdos_renyi_graph\n", + "\r", + "prob_neighbor_spread = 0.25\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(IntProgress(value=0, max=5), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "n = 100\n", + "\r", + "generator = erdos_renyi_graph\n", + "\r", + "prob_neighbor_spread = 0.5\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(IntProgress(value=0, max=5), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "n = 100\n", + "\r", + "generator = erdos_renyi_graph\n", + "\r", + "prob_neighbor_spread = 0.75\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(IntProgress(value=0, max=5), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "n = 100\n", + "\r", + "generator = erdos_renyi_graph\n", + "\r", + "prob_neighbor_spread = 1.0\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(IntProgress(value=0, max=5), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "n = 100\n", + "\r", + "generator = barabasi_albert_graph\n", + "\r", + "prob_neighbor_spread = 0\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(IntProgress(value=0, max=5), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "n = 100\n", + "\r", + "generator = barabasi_albert_graph\n", + "\r", + "prob_neighbor_spread = 0.25\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(IntProgress(value=0, max=5), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "n = 100\n", + "\r", + "generator = barabasi_albert_graph\n", + "\r", + "prob_neighbor_spread = 0.5\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(IntProgress(value=0, max=5), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "n = 100\n", + "\r", + "generator = barabasi_albert_graph\n", + "\r", + "prob_neighbor_spread = 0.75\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(IntProgress(value=0, max=5), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r", + "n = 100\n", + "\r", + "generator = barabasi_albert_graph\n", + "\r", + "prob_neighbor_spread = 1.0\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(IntProgress(value=0, max=5), HTML(value='')))" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "s = soil.Simulation(model=NewsEnvComplete, iterations=5, max_time=30, dump=True, overwrite=True)\n", + "N = 100\n", + "probabilities = [0, 0.25, 0.5, 0.75, 1.0]\n", + "generators = [\"erdos_renyi_graph\", \"barabasi_albert_graph\"]\n", "\n", "\n", - "for net in [network_1, network_2]:\n", - " for i in range(5):\n", - " prob = i / 10\n", - " config['environment_params']['prob_neighbor_spread'] = prob\n", - " config['network_params'] = net\n", - " config['name'] = 'Spread_{}_prob_{}'.format(net['generator'], prob)\n", - " s = soil.simulation.run_from_config(config, exporters=['default', 'csv'])" + "it = s.run(name=f\"newspread\", matrix=dict(n=[N], generator=generators, prob_neighbor_spread=probabilities))" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, + "outputs": [], + "source": [ + "assert len(it) == len(probabilities) * len(generators) * s.iterations" ] }, { @@ -685,329 +1443,51 @@ "end_time": "2017-07-03T11:05:18.043194Z", "start_time": "2017-07-03T13:05:18.034699+02:00" }, - "cell_style": "center" + "cell_style": "center", + "hideCode": false, + "hidePrompt": false }, "source": [ "The results are conveniently stored in sqlite (history of agent and environment state) and the configuration is saved in a YAML file.\n", "\n", - "You can also export the results to GEXF format (dynamic network) and CSV using .`run_from_config(config, dump=['gexf', 'csv'])` or the command line flags `--graph --csv`." + "You can also export the results to GEXF format (dynamic network) and CSV using .`run(dump=['gexf', 'csv'])` or the command line flags `--graph --csv`." ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 122, "metadata": { "ExecuteTime": { "end_time": "2017-11-01T14:05:56.404540Z", "start_time": "2017-11-01T15:05:56.122876+01:00" }, "cell_style": "split", - "scrolled": true + "hideCode": false, + "hidePrompt": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\u001b[01;34msoil_output\u001b[00m\r\n", - "├── \u001b[01;34mSpread_barabasi_albert_graph_prob_0.0\u001b[00m\r\n", - "│   ├── \u001b[01;34mbackup\u001b[00m\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.0.dumped.yml@2023-03-23_12.57.35\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.0.dumped.yml@2023-03-23_14.06.30\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.0.dumped.yml@2023-03-23_14.19.33\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.0.dumped.yml@2023-03-23_14.30.56\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.0.sqlite@2023-03-23_12.57.35\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.0.sqlite@2023-03-23_14.06.31\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.0.sqlite@2023-03-23_14.19.33\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.0.sqlite@2023-03-23_14.30.56\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.0_trial_0.csv@2023-03-23_12.57.35\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.0_trial_0.csv@2023-03-23_14.06.31\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.0_trial_0.csv@2023-03-23_14.19.33\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.0_trial_0.csv@2023-03-23_14.30.56\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.0_trial_0.sqlite@2023-03-23_12.57.35\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.0_trial_0.sqlite@2023-03-23_14.06.31\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.0_trial_0.sqlite@2023-03-23_14.19.33\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.0_trial_0.sqlite@2023-03-23_14.30.56\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.0_trial_0.stats.csv@2023-03-23_12.57.35\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.0_trial_0.stats.csv@2023-03-23_14.06.31\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.0_trial_0.stats.csv@2023-03-23_14.19.33\r\n", - "│   │   └── Spread_barabasi_albert_graph_prob_0.0_trial_0.stats.csv@2023-03-23_14.30.56\r\n", - "│   ├── Spread_barabasi_albert_graph_prob_0.0.dumped.yml\r\n", - "│   ├── Spread_barabasi_albert_graph_prob_0.0.sqlite\r\n", - "│   ├── Spread_barabasi_albert_graph_prob_0.0_trial_0.csv\r\n", - "│   ├── Spread_barabasi_albert_graph_prob_0.0_trial_0.sqlite\r\n", - "│   └── Spread_barabasi_albert_graph_prob_0.0_trial_0.stats.csv\r\n", - "├── \u001b[01;34mSpread_barabasi_albert_graph_prob_0.1\u001b[00m\r\n", - "│   ├── \u001b[01;34mbackup\u001b[00m\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.1.dumped.yml@2023-03-23_12.57.35\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.1.dumped.yml@2023-03-23_14.06.31\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.1.dumped.yml@2023-03-23_14.19.34\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.1.dumped.yml@2023-03-23_14.30.56\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.1.sqlite@2023-03-23_12.57.35\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.1.sqlite@2023-03-23_14.06.31\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.1.sqlite@2023-03-23_14.19.34\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.1.sqlite@2023-03-23_14.30.56\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.1_trial_0.csv@2023-03-23_12.57.35\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.1_trial_0.csv@2023-03-23_14.06.31\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.1_trial_0.csv@2023-03-23_14.19.34\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.1_trial_0.csv@2023-03-23_14.30.56\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.1_trial_0.sqlite@2023-03-23_12.57.35\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.1_trial_0.sqlite@2023-03-23_14.06.31\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.1_trial_0.sqlite@2023-03-23_14.19.34\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.1_trial_0.sqlite@2023-03-23_14.30.56\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.1_trial_0.stats.csv@2023-03-23_12.57.35\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.1_trial_0.stats.csv@2023-03-23_14.06.31\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.1_trial_0.stats.csv@2023-03-23_14.19.34\r\n", - "│   │   └── Spread_barabasi_albert_graph_prob_0.1_trial_0.stats.csv@2023-03-23_14.30.56\r\n", - "│   ├── Spread_barabasi_albert_graph_prob_0.1.dumped.yml\r\n", - "│   ├── Spread_barabasi_albert_graph_prob_0.1.sqlite\r\n", - "│   ├── Spread_barabasi_albert_graph_prob_0.1_trial_0.csv\r\n", - "│   ├── Spread_barabasi_albert_graph_prob_0.1_trial_0.sqlite\r\n", - "│   └── Spread_barabasi_albert_graph_prob_0.1_trial_0.stats.csv\r\n", - "├── \u001b[01;34mSpread_barabasi_albert_graph_prob_0.2\u001b[00m\r\n", - "│   ├── \u001b[01;34mbackup\u001b[00m\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.2.dumped.yml@2023-03-23_12.57.36\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.2.dumped.yml@2023-03-23_14.06.31\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.2.dumped.yml@2023-03-23_14.19.34\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.2.dumped.yml@2023-03-23_14.30.56\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.2.sqlite@2023-03-23_12.57.36\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.2.sqlite@2023-03-23_14.06.31\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.2.sqlite@2023-03-23_14.19.34\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.2.sqlite@2023-03-23_14.30.57\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.2_trial_0.csv@2023-03-23_12.57.36\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.2_trial_0.csv@2023-03-23_14.06.31\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.2_trial_0.csv@2023-03-23_14.19.34\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.2_trial_0.csv@2023-03-23_14.30.57\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.2_trial_0.sqlite@2023-03-23_12.57.36\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.2_trial_0.sqlite@2023-03-23_14.06.31\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.2_trial_0.sqlite@2023-03-23_14.19.34\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.2_trial_0.sqlite@2023-03-23_14.30.57\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.2_trial_0.stats.csv@2023-03-23_12.57.36\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.2_trial_0.stats.csv@2023-03-23_14.06.31\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.2_trial_0.stats.csv@2023-03-23_14.19.34\r\n", - "│   │   └── Spread_barabasi_albert_graph_prob_0.2_trial_0.stats.csv@2023-03-23_14.30.57\r\n", - "│   ├── Spread_barabasi_albert_graph_prob_0.2.dumped.yml\r\n", - "│   ├── Spread_barabasi_albert_graph_prob_0.2.sqlite\r\n", - "│   ├── Spread_barabasi_albert_graph_prob_0.2_trial_0.csv\r\n", - "│   ├── Spread_barabasi_albert_graph_prob_0.2_trial_0.sqlite\r\n", - "│   └── Spread_barabasi_albert_graph_prob_0.2_trial_0.stats.csv\r\n", - "├── \u001b[01;34mSpread_barabasi_albert_graph_prob_0.3\u001b[00m\r\n", - "│   ├── \u001b[01;34mbackup\u001b[00m\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.3.dumped.yml@2023-03-23_12.57.36\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.3.dumped.yml@2023-03-23_14.06.31\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.3.dumped.yml@2023-03-23_14.19.34\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.3.dumped.yml@2023-03-23_14.30.57\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.3.sqlite@2023-03-23_12.57.36\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.3.sqlite@2023-03-23_14.06.32\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.3.sqlite@2023-03-23_14.19.34\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.3.sqlite@2023-03-23_14.30.57\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.3_trial_0.csv@2023-03-23_12.57.36\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.3_trial_0.csv@2023-03-23_14.06.32\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.3_trial_0.csv@2023-03-23_14.19.34\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.3_trial_0.csv@2023-03-23_14.30.57\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.3_trial_0.sqlite@2023-03-23_12.57.36\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.3_trial_0.sqlite@2023-03-23_14.06.31\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.3_trial_0.sqlite@2023-03-23_14.19.34\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.3_trial_0.sqlite@2023-03-23_14.30.57\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.3_trial_0.stats.csv@2023-03-23_12.57.36\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.3_trial_0.stats.csv@2023-03-23_14.06.32\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.3_trial_0.stats.csv@2023-03-23_14.19.34\r\n", - "│   │   └── Spread_barabasi_albert_graph_prob_0.3_trial_0.stats.csv@2023-03-23_14.30.57\r\n", - "│   ├── Spread_barabasi_albert_graph_prob_0.3.dumped.yml\r\n", - "│   ├── Spread_barabasi_albert_graph_prob_0.3.sqlite\r\n", - "│   ├── Spread_barabasi_albert_graph_prob_0.3_trial_0.csv\r\n", - "│   ├── Spread_barabasi_albert_graph_prob_0.3_trial_0.sqlite\r\n", - "│   └── Spread_barabasi_albert_graph_prob_0.3_trial_0.stats.csv\r\n", - "├── \u001b[01;34mSpread_barabasi_albert_graph_prob_0.4\u001b[00m\r\n", - "│   ├── \u001b[01;34mbackup\u001b[00m\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.4.dumped.yml@2023-03-23_12.57.36\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.4.dumped.yml@2023-03-23_14.06.32\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.4.dumped.yml@2023-03-23_14.19.35\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.4.dumped.yml@2023-03-23_14.30.57\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.4.sqlite@2023-03-23_12.57.36\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.4.sqlite@2023-03-23_14.06.32\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.4.sqlite@2023-03-23_14.19.35\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.4.sqlite@2023-03-23_14.30.57\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.4_trial_0.csv@2023-03-23_12.57.36\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.4_trial_0.csv@2023-03-23_14.06.32\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.4_trial_0.csv@2023-03-23_14.19.35\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.4_trial_0.csv@2023-03-23_14.30.57\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.4_trial_0.sqlite@2023-03-23_12.57.36\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.4_trial_0.sqlite@2023-03-23_14.06.32\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.4_trial_0.sqlite@2023-03-23_14.19.35\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.4_trial_0.sqlite@2023-03-23_14.30.57\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.4_trial_0.stats.csv@2023-03-23_12.57.36\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.4_trial_0.stats.csv@2023-03-23_14.06.32\r\n", - "│   │   ├── Spread_barabasi_albert_graph_prob_0.4_trial_0.stats.csv@2023-03-23_14.19.35\r\n", - "│   │   └── Spread_barabasi_albert_graph_prob_0.4_trial_0.stats.csv@2023-03-23_14.30.57\r\n", - "│   ├── Spread_barabasi_albert_graph_prob_0.4.dumped.yml\r\n", - "│   ├── Spread_barabasi_albert_graph_prob_0.4.sqlite\r\n", - "│   ├── Spread_barabasi_albert_graph_prob_0.4_trial_0.csv\r\n", - "│   ├── Spread_barabasi_albert_graph_prob_0.4_trial_0.sqlite\r\n", - "│   └── Spread_barabasi_albert_graph_prob_0.4_trial_0.stats.csv\r\n", - "├── \u001b[01;34mSpread_erdos_renyi_graph_prob_0.0\u001b[00m\r\n", - "│   ├── \u001b[01;34mbackup\u001b[00m\r\n", - "│   │   ├── Spread_erdos_renyi_graph_prob_0.0.dumped.yml@2023-03-23_12.57.26\r\n", - "│   │   ├── Spread_erdos_renyi_graph_prob_0.0.dumped.yml@2023-03-23_14.06.21\r\n", - "│   │   ├── Spread_erdos_renyi_graph_prob_0.0.dumped.yml@2023-03-23_14.19.24\r\n", - "│   │   ├── Spread_erdos_renyi_graph_prob_0.0.dumped.yml@2023-03-23_14.30.47\r\n", - "│   │   ├── Spread_erdos_renyi_graph_prob_0.0.sqlite@2023-03-23_12.57.26\r\n", - "│   │   ├── Spread_erdos_renyi_graph_prob_0.0.sqlite@2023-03-23_14.06.22\r\n", - "│   │   ├── Spread_erdos_renyi_graph_prob_0.0.sqlite@2023-03-23_14.19.25\r\n", - "│   │   ├── Spread_erdos_renyi_graph_prob_0.0.sqlite@2023-03-23_14.30.47\r\n", - "│   │   ├── Spread_erdos_renyi_graph_prob_0.0_trial_0.csv@2023-03-23_12.57.26\r\n", - "│   │   ├── Spread_erdos_renyi_graph_prob_0.0_trial_0.csv@2023-03-23_14.06.22\r\n", - "│   │   ├── Spread_erdos_renyi_graph_prob_0.0_trial_0.csv@2023-03-23_14.19.25\r\n", - "│   │   ├── Spread_erdos_renyi_graph_prob_0.0_trial_0.csv@2023-03-23_14.30.47\r\n", - "│   │   ├── Spread_erdos_renyi_graph_prob_0.0_trial_0.sqlite@2023-03-23_12.57.26\r\n", - "│   │   ├── Spread_erdos_renyi_graph_prob_0.0_trial_0.sqlite@2023-03-23_14.06.22\r\n", - "│   │   ├── Spread_erdos_renyi_graph_prob_0.0_trial_0.sqlite@2023-03-23_14.19.25\r\n", - "│   │   ├── Spread_erdos_renyi_graph_prob_0.0_trial_0.sqlite@2023-03-23_14.30.47\r\n", - "│   │   ├── Spread_erdos_renyi_graph_prob_0.0_trial_0.stats.csv@2023-03-23_12.57.26\r\n", - "│   │   ├── Spread_erdos_renyi_graph_prob_0.0_trial_0.stats.csv@2023-03-23_14.06.22\r\n", - "│   │   ├── Spread_erdos_renyi_graph_prob_0.0_trial_0.stats.csv@2023-03-23_14.19.25\r\n", - "│   │   └── Spread_erdos_renyi_graph_prob_0.0_trial_0.stats.csv@2023-03-23_14.30.47\r\n", - "│   ├── Spread_erdos_renyi_graph_prob_0.0.dumped.yml\r\n", - "│   ├── Spread_erdos_renyi_graph_prob_0.0.sqlite\r\n", - "│   ├── Spread_erdos_renyi_graph_prob_0.0_trial_0.csv\r\n", - "│   ├── Spread_erdos_renyi_graph_prob_0.0_trial_0.sqlite\r\n", - "│   └── Spread_erdos_renyi_graph_prob_0.0_trial_0.stats.csv\r\n", - "├── \u001b[01;34mSpread_erdos_renyi_graph_prob_0.1\u001b[00m\r\n", - "│   ├── \u001b[01;34mbackup\u001b[00m\r\n", - "│   │   ├── Spread_erdos_renyi_graph_prob_0.1.dumped.yml@2023-03-23_12.57.28\r\n", - "│   │   ├── Spread_erdos_renyi_graph_prob_0.1.dumped.yml@2023-03-23_14.06.24\r\n", - "│   │   ├── Spread_erdos_renyi_graph_prob_0.1.dumped.yml@2023-03-23_14.19.26\r\n", - "│   │   ├── Spread_erdos_renyi_graph_prob_0.1.dumped.yml@2023-03-23_14.30.49\r\n", - "│   │   ├── Spread_erdos_renyi_graph_prob_0.1.sqlite@2023-03-23_12.57.28\r\n", - "│   │   ├── Spread_erdos_renyi_graph_prob_0.1.sqlite@2023-03-23_14.06.24\r\n", - "│   │   ├── Spread_erdos_renyi_graph_prob_0.1.sqlite@2023-03-23_14.19.27\r\n", - "│   │   ├── Spread_erdos_renyi_graph_prob_0.1.sqlite@2023-03-23_14.30.49\r\n", - "│   │   ├── Spread_erdos_renyi_graph_prob_0.1_trial_0.csv@2023-03-23_12.57.28\r\n", - "│   │   ├── Spread_erdos_renyi_graph_prob_0.1_trial_0.csv@2023-03-23_14.06.24\r\n", - "│   │   ├── Spread_erdos_renyi_graph_prob_0.1_trial_0.csv@2023-03-23_14.19.27\r\n", - "│   │   ├── Spread_erdos_renyi_graph_prob_0.1_trial_0.csv@2023-03-23_14.30.49\r\n", - "│   │   ├── Spread_erdos_renyi_graph_prob_0.1_trial_0.sqlite@2023-03-23_12.57.28\r\n", - "│   │   ├── Spread_erdos_renyi_graph_prob_0.1_trial_0.sqlite@2023-03-23_14.06.24\r\n", - "│   │   ├── Spread_erdos_renyi_graph_prob_0.1_trial_0.sqlite@2023-03-23_14.19.27\r\n", - "│   │   ├── Spread_erdos_renyi_graph_prob_0.1_trial_0.sqlite@2023-03-23_14.30.49\r\n", - "│   │   ├── Spread_erdos_renyi_graph_prob_0.1_trial_0.stats.csv@2023-03-23_12.57.28\r\n", - "│   │   ├── Spread_erdos_renyi_graph_prob_0.1_trial_0.stats.csv@2023-03-23_14.06.24\r\n", - "│   │   ├── Spread_erdos_renyi_graph_prob_0.1_trial_0.stats.csv@2023-03-23_14.19.27\r\n", - "│   │   └── Spread_erdos_renyi_graph_prob_0.1_trial_0.stats.csv@2023-03-23_14.30.49\r\n", - "│   ├── Spread_erdos_renyi_graph_prob_0.1.dumped.yml\r\n", - "│   ├── Spread_erdos_renyi_graph_prob_0.1.sqlite\r\n", - "│   ├── Spread_erdos_renyi_graph_prob_0.1_trial_0.csv\r\n", - "│   ├── Spread_erdos_renyi_graph_prob_0.1_trial_0.sqlite\r\n", - "│   └── Spread_erdos_renyi_graph_prob_0.1_trial_0.stats.csv\r\n", - "├── \u001b[01;34mSpread_erdos_renyi_graph_prob_0.2\u001b[00m\r\n", - "│   ├── \u001b[01;34mbackup\u001b[00m\r\n", - "│   │   ├── Spread_erdos_renyi_graph_prob_0.2.dumped.yml@2023-03-23_12.57.30\r\n", - "│   │   ├── Spread_erdos_renyi_graph_prob_0.2.dumped.yml@2023-03-23_14.06.26\r\n", - "│   │   ├── Spread_erdos_renyi_graph_prob_0.2.dumped.yml@2023-03-23_14.19.28\r\n", - "│   │   ├── Spread_erdos_renyi_graph_prob_0.2.dumped.yml@2023-03-23_14.30.51\r\n", - "│   │   ├── Spread_erdos_renyi_graph_prob_0.2.sqlite@2023-03-23_12.57.31\r\n", - "│   │   ├── Spread_erdos_renyi_graph_prob_0.2.sqlite@2023-03-23_14.06.26\r\n", - "│   │   ├── Spread_erdos_renyi_graph_prob_0.2.sqlite@2023-03-23_14.19.29\r\n", - "│   │   ├── Spread_erdos_renyi_graph_prob_0.2.sqlite@2023-03-23_14.30.51\r\n", - "│   │   ├── Spread_erdos_renyi_graph_prob_0.2_trial_0.csv@2023-03-23_12.57.31\r\n", - "│   │   ├── Spread_erdos_renyi_graph_prob_0.2_trial_0.csv@2023-03-23_14.06.26\r\n", - "│   │   ├── Spread_erdos_renyi_graph_prob_0.2_trial_0.csv@2023-03-23_14.19.29\r\n", - "│   │   ├── Spread_erdos_renyi_graph_prob_0.2_trial_0.csv@2023-03-23_14.30.51\r\n", - "│   │   ├── Spread_erdos_renyi_graph_prob_0.2_trial_0.sqlite@2023-03-23_12.57.31\r\n", - "│   │   ├── Spread_erdos_renyi_graph_prob_0.2_trial_0.sqlite@2023-03-23_14.06.26\r\n", - "│   │   ├── Spread_erdos_renyi_graph_prob_0.2_trial_0.sqlite@2023-03-23_14.19.29\r\n", - "│   │   ├── Spread_erdos_renyi_graph_prob_0.2_trial_0.sqlite@2023-03-23_14.30.51\r\n", - "│   │   ├── Spread_erdos_renyi_graph_prob_0.2_trial_0.stats.csv@2023-03-23_12.57.31\r\n", - "│   │   ├── Spread_erdos_renyi_graph_prob_0.2_trial_0.stats.csv@2023-03-23_14.06.26\r\n", - "│   │   ├── Spread_erdos_renyi_graph_prob_0.2_trial_0.stats.csv@2023-03-23_14.19.29\r\n", - "│   │   └── Spread_erdos_renyi_graph_prob_0.2_trial_0.stats.csv@2023-03-23_14.30.51\r\n", - "│   ├── Spread_erdos_renyi_graph_prob_0.2.dumped.yml\r\n", - "│   ├── Spread_erdos_renyi_graph_prob_0.2.sqlite\r\n", - "│   ├── Spread_erdos_renyi_graph_prob_0.2_trial_0.csv\r\n", - "│   ├── Spread_erdos_renyi_graph_prob_0.2_trial_0.sqlite\r\n", - "│   └── Spread_erdos_renyi_graph_prob_0.2_trial_0.stats.csv\r\n", - "├── \u001b[01;34mSpread_erdos_renyi_graph_prob_0.3\u001b[00m\r\n", - "│   ├── \u001b[01;34mbackup\u001b[00m\r\n", - "│   │   ├── Spread_erdos_renyi_graph_prob_0.3.dumped.yml@2023-03-23_12.57.32\r\n", - "│   │   ├── Spread_erdos_renyi_graph_prob_0.3.dumped.yml@2023-03-23_14.06.28\r\n", - "│   │   ├── Spread_erdos_renyi_graph_prob_0.3.dumped.yml@2023-03-23_14.19.31\r\n", - "│   │   ├── Spread_erdos_renyi_graph_prob_0.3.dumped.yml@2023-03-23_14.30.53\r\n", - "│   │   ├── Spread_erdos_renyi_graph_prob_0.3.sqlite@2023-03-23_12.57.33\r\n", - "│   │   ├── Spread_erdos_renyi_graph_prob_0.3.sqlite@2023-03-23_14.06.28\r\n", - "│   │   ├── Spread_erdos_renyi_graph_prob_0.3.sqlite@2023-03-23_14.19.31\r\n", - "│   │   ├── Spread_erdos_renyi_graph_prob_0.3.sqlite@2023-03-23_14.30.53\r\n", - "│   │   ├── Spread_erdos_renyi_graph_prob_0.3_trial_0.csv@2023-03-23_12.57.33\r\n", - "│   │   ├── Spread_erdos_renyi_graph_prob_0.3_trial_0.csv@2023-03-23_14.06.28\r\n", - "│   │   ├── Spread_erdos_renyi_graph_prob_0.3_trial_0.csv@2023-03-23_14.19.31\r\n", - "│   │   ├── Spread_erdos_renyi_graph_prob_0.3_trial_0.csv@2023-03-23_14.30.53\r\n", - "│   │   ├── Spread_erdos_renyi_graph_prob_0.3_trial_0.sqlite@2023-03-23_12.57.33\r\n", - "│   │   ├── Spread_erdos_renyi_graph_prob_0.3_trial_0.sqlite@2023-03-23_14.06.28\r\n", - "│   │   ├── Spread_erdos_renyi_graph_prob_0.3_trial_0.sqlite@2023-03-23_14.19.31\r\n", - "│   │   ├── Spread_erdos_renyi_graph_prob_0.3_trial_0.sqlite@2023-03-23_14.30.53\r\n", - "│   │   ├── Spread_erdos_renyi_graph_prob_0.3_trial_0.stats.csv@2023-03-23_12.57.33\r\n", - "│   │   ├── Spread_erdos_renyi_graph_prob_0.3_trial_0.stats.csv@2023-03-23_14.06.28\r\n", - "│   │   ├── Spread_erdos_renyi_graph_prob_0.3_trial_0.stats.csv@2023-03-23_14.19.31\r\n", - "│   │   └── Spread_erdos_renyi_graph_prob_0.3_trial_0.stats.csv@2023-03-23_14.30.53\r\n", - "│   ├── Spread_erdos_renyi_graph_prob_0.3.dumped.yml\r\n", - "│   ├── Spread_erdos_renyi_graph_prob_0.3.sqlite\r\n", - "│   ├── Spread_erdos_renyi_graph_prob_0.3_trial_0.csv\r\n", - "│   ├── Spread_erdos_renyi_graph_prob_0.3_trial_0.sqlite\r\n", - "│   └── Spread_erdos_renyi_graph_prob_0.3_trial_0.stats.csv\r\n", - "└── \u001b[01;34mSpread_erdos_renyi_graph_prob_0.4\u001b[00m\r\n", - " ├── \u001b[01;34mbackup\u001b[00m\r\n", - " │   ├── Spread_erdos_renyi_graph_prob_0.4.dumped.yml@2023-03-23_12.57.34\r\n", - " │   ├── Spread_erdos_renyi_graph_prob_0.4.dumped.yml@2023-03-23_14.06.30\r\n", - " │   ├── Spread_erdos_renyi_graph_prob_0.4.dumped.yml@2023-03-23_14.19.33\r\n", - " │   ├── Spread_erdos_renyi_graph_prob_0.4.dumped.yml@2023-03-23_14.30.55\r\n", - " │   ├── Spread_erdos_renyi_graph_prob_0.4.sqlite@2023-03-23_12.57.35\r\n", - " │   ├── Spread_erdos_renyi_graph_prob_0.4.sqlite@2023-03-23_14.06.30\r\n", - " │   ├── Spread_erdos_renyi_graph_prob_0.4.sqlite@2023-03-23_14.19.33\r\n", - " │   ├── Spread_erdos_renyi_graph_prob_0.4.sqlite@2023-03-23_14.30.56\r\n", - " │   ├── Spread_erdos_renyi_graph_prob_0.4_trial_0.csv@2023-03-23_12.57.35\r\n", - " │   ├── Spread_erdos_renyi_graph_prob_0.4_trial_0.csv@2023-03-23_14.06.30\r\n", - " │   ├── Spread_erdos_renyi_graph_prob_0.4_trial_0.csv@2023-03-23_14.19.33\r\n", - " │   ├── Spread_erdos_renyi_graph_prob_0.4_trial_0.csv@2023-03-23_14.30.56\r\n", - " │   ├── Spread_erdos_renyi_graph_prob_0.4_trial_0.sqlite@2023-03-23_12.57.35\r\n", - " │   ├── Spread_erdos_renyi_graph_prob_0.4_trial_0.sqlite@2023-03-23_14.06.30\r\n", - " │   ├── Spread_erdos_renyi_graph_prob_0.4_trial_0.sqlite@2023-03-23_14.19.33\r\n", - " │   ├── Spread_erdos_renyi_graph_prob_0.4_trial_0.sqlite@2023-03-23_14.30.56\r\n", - " │   ├── Spread_erdos_renyi_graph_prob_0.4_trial_0.stats.csv@2023-03-23_12.57.35\r\n", - " │   ├── Spread_erdos_renyi_graph_prob_0.4_trial_0.stats.csv@2023-03-23_14.06.30\r\n", - " │   ├── Spread_erdos_renyi_graph_prob_0.4_trial_0.stats.csv@2023-03-23_14.19.33\r\n", - " │   └── Spread_erdos_renyi_graph_prob_0.4_trial_0.stats.csv@2023-03-23_14.30.56\r\n", - " ├── Spread_erdos_renyi_graph_prob_0.4.dumped.yml\r\n", - " ├── Spread_erdos_renyi_graph_prob_0.4.sqlite\r\n", - " ├── Spread_erdos_renyi_graph_prob_0.4_trial_0.csv\r\n", - " ├── Spread_erdos_renyi_graph_prob_0.4_trial_0.sqlite\r\n", - " └── Spread_erdos_renyi_graph_prob_0.4_trial_0.stats.csv\r\n", - "\r\n", - "20 directories, 250 files\r\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1.3M\tsoil_output/Spread_barabasi_albert_graph_prob_0.0/backup\r\n", - "1.7M\tsoil_output/Spread_barabasi_albert_graph_prob_0.0\r\n", - "1.3M\tsoil_output/Spread_barabasi_albert_graph_prob_0.1/backup\r\n", - "1.7M\tsoil_output/Spread_barabasi_albert_graph_prob_0.1\r\n", - "1.3M\tsoil_output/Spread_barabasi_albert_graph_prob_0.2/backup\r\n", - "1.6M\tsoil_output/Spread_barabasi_albert_graph_prob_0.2\r\n", - "1.3M\tsoil_output/Spread_barabasi_albert_graph_prob_0.3/backup\r\n", - "1.7M\tsoil_output/Spread_barabasi_albert_graph_prob_0.3\r\n", - "1.3M\tsoil_output/Spread_barabasi_albert_graph_prob_0.4/backup\r\n", - "1.7M\tsoil_output/Spread_barabasi_albert_graph_prob_0.4\r\n", - "2.7M\tsoil_output/Spread_erdos_renyi_graph_prob_0.0/backup\r\n", - "3.4M\tsoil_output/Spread_erdos_renyi_graph_prob_0.0\r\n", - "2.7M\tsoil_output/Spread_erdos_renyi_graph_prob_0.1/backup\r\n", - "3.4M\tsoil_output/Spread_erdos_renyi_graph_prob_0.1\r\n", - "2.7M\tsoil_output/Spread_erdos_renyi_graph_prob_0.2/backup\r\n", - "3.4M\tsoil_output/Spread_erdos_renyi_graph_prob_0.2\r\n", - "2.7M\tsoil_output/Spread_erdos_renyi_graph_prob_0.3/backup\r\n", - "3.4M\tsoil_output/Spread_erdos_renyi_graph_prob_0.3\r\n", - "2.7M\tsoil_output/Spread_erdos_renyi_graph_prob_0.4/backup\r\n", - "3.4M\tsoil_output/Spread_erdos_renyi_graph_prob_0.4\r\n" + "\u001b[01;34msoil_output\u001b[00m\n", + "└── \u001b[01;34mnewspread\u001b[00m\n", + " ├── newspread_1681989837.124865.dumped.yml\n", + " ├── newspread_1681990513.1584163.dumped.yml\n", + " ├── newspread_1681990524.5204282.dumped.yml\n", + " ├── newspread_1681990796.858183.dumped.yml\n", + " ├── newspread_1682002299.544348.dumped.yml\n", + " ├── newspread_1682003721.597205.dumped.yml\n", + " ├── newspread_1682003784.1948986.dumped.yml\n", + " ├── newspread_1682003812.4626257.dumped.yml\n", + " ├── newspread_1682004020.182087.dumped.yml\n", + " ├── newspread_1682004044.6837814.dumped.yml\n", + " ├── newspread_1682004398.267355.dumped.yml\n", + " ├── newspread_1682004564.1052232.dumped.yml\n", + " └── newspread.sqlite\n", + "\n", + "1 directory, 13 files\n", + "21M\tsoil_output/newspread\n" ] } ], @@ -1022,30 +1502,52 @@ "ExecuteTime": { "end_time": "2017-07-02T10:40:14.384177Z", "start_time": "2017-07-02T12:40:14.381885+02:00" - } + }, + "hideCode": false, + "hidePrompt": false }, "source": [ - "## Analysing the results" + "### Analysing the results" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, "source": [ - "### Loading data" + "#### Loading data" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "hideCode": false, + "hidePrompt": false + }, "source": [ "Once the simulations are over, we can use soil to analyse the results.\n", "\n", - "Soil allows you to load results for specific trials, or for a set of trials if you specify a pattern. The specific methods are:\n", + "There are two main ways: directly using the iterations returned by the `run` method, or loading up data from the results database.\n", + "This is particularly useful to store data between sessions, and to accumulate results over multiple runs.\n", "\n", - "* `analysis.read_data()` to load all the results from a directory. e.g. `read_data('my_simulation/')`. For each trial it finds in each folder matching the pattern, it will return the dumped configuration for the simulation, the results of the trial, and the configuration itself. By default, it will try to load data from the sqlite database. \n", - "* `analysis.read_csv()` to load all the results from a CSV file. e.g. `read_csv('my_simulation/my_simulation_trial0.environment.csv')`\n", - "* `analysis.read_sql()` to load all the results from a sqlite database . e.g. `read_sql('my_simulation/my_simulation_trial0.db.sqlite')`" + "The mainThe main method to load data from the database is `read_sql`, which can be used in two ways:\n", + "\n", + "* `analysis.read_sql()` to load all the results from a sqlite database . e.g. `read_sql('my_simulation/file.db.sqlite')`\n", + "* `analysis.read_sql(name=)` will look for the default path for a simulation named ``" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The result in both cases is a named tuple with four dataframes:\n", + "\n", + "* `configuration`, which contains configuration parameters per simulation\n", + "* `parameters`, which shows the parameters used **in every iteration** of every simulation\n", + "* `env`, with the data collected from the model in each iteration (as specified in `model_reporters`)\n", + "* `agents`, like `env`, but for `agent_reporters`" ] }, { @@ -1054,7 +1556,9 @@ "ExecuteTime": { "end_time": "2017-07-03T14:44:30.978223Z", "start_time": "2017-07-03T16:44:30.971952+02:00" - } + }, + "hideCode": false, + "hidePrompt": false }, "source": [ "Let's see it in action by loading the stored results into a pandas dataframe:" @@ -1062,1415 +1566,220 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": { - "ExecuteTime": { - "end_time": "2017-10-19T15:57:43.662893Z", - "start_time": "2017-10-19T17:57:43.632252+02:00" - }, - "cell_style": "center" - }, - "outputs": [], - "source": [ - "from soil import analysis\n", - "import pandas as pd" - ] - }, - { - "cell_type": "code", - "execution_count": 15, + "execution_count": 123, "metadata": { "ExecuteTime": { "end_time": "2017-10-19T15:57:44.101253Z", "start_time": "2017-10-19T17:57:44.039710+02:00" }, + "hideCode": false, + "hidePrompt": false + }, + "outputs": [], + "source": [ + "res = soil.read_sql(name=\"newspread\", include_agents=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plotting data\n", + "\n", + "Once we have loaded the results from the file, we can use them just like any other dataframe.\n", + "\n", + "Here is an example of plotting the ratio of infected users in each of our simulations:" + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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keySEEDalive...state_id
agent_idenv0110100101102103104105...90919293949596979899
t_step
0.0Spread_barabasi_albert_graph_prob_0.0_trial_0TrueTrueTrueTrueTrueTrueTrueTrueTrue...neutralneutralneutralneutralneutralneutralneutralneutralneutralneutral
1.0Spread_barabasi_albert_graph_prob_0.0_trial_0TrueTrueTrueTrueTrueTrueTrueTrueTrue...neutralneutralneutralneutralneutralneutralneutralneutralneutralneutral
2.0Spread_barabasi_albert_graph_prob_0.0_trial_0TrueTrueTrueTrueTrueTrueTrueTrueTrue...neutralneutralneutralneutralneutralneutralneutralneutralneutralneutral
3.0Spread_barabasi_albert_graph_prob_0.0_trial_0TrueTrueTrueTrueTrueTrueTrueTrueTrue...neutralneutralneutralneutralneutralneutralneutralneutralneutralneutral
4.0Spread_barabasi_albert_graph_prob_0.0_trial_0TrueTrueTrueTrueTrueTrueTrueTrueTrue...neutralneutralneutralneutralneutralneutralneutralneutralneutralneutral
5.0Spread_barabasi_albert_graph_prob_0.0_trial_0TrueTrueTrueTrueTrueTrueTrueTrueTrue...neutralneutralneutralneutralneutralneutralneutralinfectedneutralneutral
6.0Spread_barabasi_albert_graph_prob_0.0_trial_0TrueTrueTrueTrueTrueTrueTrueTrueTrue...neutralneutralneutralneutralneutralneutralneutralinfectedneutralneutral
7.0Spread_barabasi_albert_graph_prob_0.0_trial_0TrueTrueTrueTrueTrueTrueTrueTrueTrue...neutralneutralneutralneutralneutralneutralneutralinfectedneutralneutral
8.0Spread_barabasi_albert_graph_prob_0.0_trial_0TrueTrueTrueTrueTrueTrueTrueTrueTrue...neutralneutralneutralneutralneutralneutralneutralinfectedneutralneutral
9.0Spread_barabasi_albert_graph_prob_0.0_trial_0TrueTrueTrueTrueTrueTrueTrueTrueTrue...neutralneutralneutralneutralneutralneutralneutralinfectedneutralneutral
10.0Spread_barabasi_albert_graph_prob_0.0_trial_0TrueTrueTrueTrueTrueTrueTrueTrueTrue...neutralneutralneutralneutralneutralneutralneutralinfectedneutralneutral
11.0Spread_barabasi_albert_graph_prob_0.0_trial_0TrueTrueTrueTrueTrueTrueTrueTrueTrue...infectedinfectedinfectedinfectedinfectedneutralinfectedinfectedinfectedinfected
12.0Spread_barabasi_albert_graph_prob_0.0_trial_0TrueTrueTrueTrueTrueTrueTrueTrueTrue...infectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfected
13.0Spread_barabasi_albert_graph_prob_0.0_trial_0TrueTrueTrueTrueTrueTrueTrueTrueTrue...infectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfected
14.0Spread_barabasi_albert_graph_prob_0.0_trial_0TrueTrueTrueTrueTrueTrueTrueTrueTrue...infectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfected
15.0Spread_barabasi_albert_graph_prob_0.0_trial_0TrueTrueTrueTrueTrueTrueTrueTrueTrue...infectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfected
16.0Spread_barabasi_albert_graph_prob_0.0_trial_0TrueTrueTrueTrueTrueTrueTrueTrueTrue...infectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfected
17.0Spread_barabasi_albert_graph_prob_0.0_trial_0TrueTrueTrueTrueTrueTrueTrueTrueTrue...infectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfected
18.0Spread_barabasi_albert_graph_prob_0.0_trial_0TrueTrueTrueTrueTrueTrueTrueTrueTrue...infectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfected
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\n", "text/plain": [ - "key SEED alive \\\n", - "agent_id env 0 1 10 \n", - "t_step \n", - "0.0 Spread_barabasi_albert_graph_prob_0.0_trial_0 True True True \n", - "1.0 Spread_barabasi_albert_graph_prob_0.0_trial_0 True True True \n", - "2.0 Spread_barabasi_albert_graph_prob_0.0_trial_0 True True True \n", - "3.0 Spread_barabasi_albert_graph_prob_0.0_trial_0 True True True \n", - "4.0 Spread_barabasi_albert_graph_prob_0.0_trial_0 True True True \n", - "5.0 Spread_barabasi_albert_graph_prob_0.0_trial_0 True True True \n", - "6.0 Spread_barabasi_albert_graph_prob_0.0_trial_0 True True True \n", - "7.0 Spread_barabasi_albert_graph_prob_0.0_trial_0 True True True \n", - "8.0 Spread_barabasi_albert_graph_prob_0.0_trial_0 True True True \n", - "9.0 Spread_barabasi_albert_graph_prob_0.0_trial_0 True True True \n", - "10.0 Spread_barabasi_albert_graph_prob_0.0_trial_0 True True True \n", - "11.0 Spread_barabasi_albert_graph_prob_0.0_trial_0 True True True \n", - "12.0 Spread_barabasi_albert_graph_prob_0.0_trial_0 True True True \n", - "13.0 Spread_barabasi_albert_graph_prob_0.0_trial_0 True True True \n", - "14.0 Spread_barabasi_albert_graph_prob_0.0_trial_0 True True True \n", - "15.0 Spread_barabasi_albert_graph_prob_0.0_trial_0 True True True \n", - "16.0 Spread_barabasi_albert_graph_prob_0.0_trial_0 True True True \n", - "17.0 Spread_barabasi_albert_graph_prob_0.0_trial_0 True True True \n", - "18.0 Spread_barabasi_albert_graph_prob_0.0_trial_0 True True True \n", - "19.0 Spread_barabasi_albert_graph_prob_0.0_trial_0 True True True \n", - "\n", - "key ... state_id \\\n", - "agent_id 100 101 102 103 104 105 ... 90 91 \n", - "t_step ... \n", - "0.0 True True True True True True ... neutral neutral \n", - "1.0 True True True True True True ... neutral neutral \n", - "2.0 True True True True True True ... neutral neutral \n", - "3.0 True True True True True True ... neutral neutral \n", - "4.0 True True True True True True ... neutral neutral \n", - "5.0 True True True True True True ... neutral neutral \n", - "6.0 True True True True True True ... neutral neutral \n", - "7.0 True True True True True True ... neutral neutral \n", - "8.0 True True True True True True ... neutral neutral \n", - "9.0 True True True True True True ... neutral neutral \n", - "10.0 True True True True True True ... neutral neutral \n", - "11.0 True True True True True True ... infected infected \n", - "12.0 True True True True True True ... infected infected \n", - "13.0 True True True True True True ... infected infected \n", - "14.0 True True True True True True ... infected infected \n", - "15.0 True True True True True True ... infected infected \n", - "16.0 True True True True True True ... infected infected \n", - "17.0 True True True True True True ... infected infected \n", - "18.0 True True True True True True ... infected infected \n", - "19.0 True True True True True True ... infected infected \n", - "\n", - "key \\\n", - "agent_id 92 93 94 95 96 97 \n", - "t_step \n", - "0.0 neutral neutral neutral neutral neutral neutral \n", - "1.0 neutral neutral neutral neutral neutral neutral \n", - "2.0 neutral neutral neutral neutral neutral neutral \n", - "3.0 neutral neutral neutral neutral neutral neutral \n", - "4.0 neutral neutral neutral neutral neutral neutral \n", - "5.0 neutral neutral neutral neutral neutral infected \n", - "6.0 neutral neutral neutral neutral neutral infected \n", - "7.0 neutral neutral neutral neutral neutral infected \n", - "8.0 neutral neutral neutral neutral neutral infected \n", - "9.0 neutral neutral neutral neutral neutral infected \n", - "10.0 neutral neutral neutral neutral neutral infected \n", - "11.0 infected infected infected neutral infected infected \n", - "12.0 infected infected infected infected infected infected \n", - "13.0 infected infected infected infected infected infected \n", - "14.0 infected infected infected infected infected infected \n", - "15.0 infected infected infected infected infected infected \n", - "16.0 infected infected infected infected infected infected \n", - "17.0 infected infected infected infected infected infected \n", - "18.0 infected infected infected infected infected infected \n", - "19.0 infected infected infected infected infected infected \n", - "\n", - "key \n", - "agent_id 98 99 \n", - "t_step \n", - "0.0 neutral neutral \n", - "1.0 neutral neutral \n", - "2.0 neutral neutral \n", - "3.0 neutral neutral \n", - "4.0 neutral neutral \n", - "5.0 neutral neutral \n", - "6.0 neutral neutral \n", - "7.0 neutral neutral \n", - "8.0 neutral neutral \n", - "9.0 neutral neutral \n", - "10.0 neutral neutral \n", - "11.0 infected infected \n", - "12.0 infected infected \n", - "13.0 infected infected \n", - "14.0 infected infected \n", - "15.0 infected infected \n", - "16.0 infected infected \n", - "17.0 infected infected \n", - "18.0 infected infected \n", - "19.0 infected infected \n", - "\n", - "[20 rows x 2507 columns]" + "
" ] }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "df = analysis.read_csv('soil_output/Spread_barabasi_albert_graph_prob_0.0/Spread_barabasi_albert_graph_prob_0.0_trial_0.csv')\n", - "df" + "for (g, group) in res.agents.dropna().groupby(\"params_id\"):\n", + " params = res.parameters.query(f'params_id == \"{g}\"').iloc[0]\n", + " title = f\"{params.generator.rstrip('_graph')} {params.prob_neighbor_spread}\"\n", + " counts = group.groupby(by=[\"step\", \"state_id\"]).value_counts().unstack()\n", + " line = \"-\"\n", + " if \"barabasi\" in params.generator:\n", + " line = \"--\"\n", + " (counts.infected/counts.sum(axis=1)).rename(title).fillna(0).plot(linestyle=line)\n", + "plt.legend()\n", + "plt.xlim([9, None]);\n", + "plt.title(\"Ratio of infected users\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Soil can also process the data for us and split the results into environment attributes and agent attributes:" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "env, agents = analysis.split_processed(df)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Hence, we can access the state of the simulation at a given step (e.g., 13): " + "### Parameters\n", + "\n", + "The `parameters` dataframe has three keys:\n", + "\n", + "* The identifier of the simulation. This will be shared by all iterations launched in the same run\n", + "* The identifier of the parameters used in the simulation. This will be shared by all iterations that have the exact same set of parameters.\n", + "* The identifier of the iteration. Each row should have a different iteration identifier\n", + "\n", + "There will be a column per each parameter passed to the environment. In this case, that's three: **generator**, **n** and **prob_neighbor_spread**." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { - "ExecuteTime": { - "end_time": "2017-10-19T15:57:47.132212Z", - "start_time": "2017-10-19T17:57:47.084737+02:00" - }, "scrolled": true }, "outputs": [ { "data": { + "text/html": [ + "
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" + ], "text/plain": [ - "agent_id\n", - "0 infected\n", - "1 infected\n", - "10 infected\n", - "100 infected\n", - "101 infected\n", - " ... \n", - "95 infected\n", - "96 infected\n", - "97 infected\n", - "98 infected\n", - "99 infected\n", - "Name: 13.0, Length: 500, dtype: object" + "key generator \\\n", + "iteration_id params_id simulation_id \n", + "0 39063f8 newspread_1682002299.544348 erdos_renyi_graph \n", + " 5db645d newspread_1682002299.544348 barabasi_albert_graph \n", + " 8f26adb newspread_1682002299.544348 barabasi_albert_graph \n", + " cb3dbca newspread_1682002299.544348 erdos_renyi_graph \n", + " d1fe9c1 newspread_1682002299.544348 barabasi_albert_graph \n", + "\n", + "key n prob_neighbor_spread \n", + "iteration_id params_id simulation_id \n", + "0 39063f8 newspread_1682002299.544348 100 1.0 \n", + " 5db645d newspread_1682002299.544348 100 0.0 \n", + " 8f26adb newspread_1682002299.544348 100 0.5 \n", + " cb3dbca newspread_1682002299.544348 100 0.5 \n", + " d1fe9c1 newspread_1682002299.544348 100 1.0 " ] }, "execution_count": 18, @@ -2479,14 +1788,17 @@ } ], "source": [ - "agents.loc[13, 'state_id']" + "res.parameters.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Or, we can perform more complex tasks such as showing the agents that have changed their state between two simulation steps (2 and 1):" + "### Configuration\n", + "\n", + "This dataset is indexed by the identifier of the simulation, and there will be a column per each attribute of the simulation.\n", + "For instance, there is one for the number of processes used, another one for the path where the results were stored, etc." ] }, { @@ -2496,8 +1808,128 @@ "outputs": [ { "data": { + "text/html": [ + "
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" + ], "text/plain": [ - "2" + " index version source_file name description \\\n", + "simulation_id \n", + "newspread_1682002299.544348 0 2 None newspread \n", + "\n", + " group backup overwrite dry_run dump ... \\\n", + "simulation_id ... \n", + "newspread_1682002299.544348 None False True False True ... \n", + "\n", + " num_processes exporters \\\n", + "simulation_id \n", + "newspread_1682002299.544348 1 [] \n", + "\n", + " model_reporters agent_reporters tables \\\n", + "simulation_id \n", + "newspread_1682002299.544348 {} {} {} \n", + "\n", + " outdir \\\n", + "simulation_id \n", + "newspread_1682002299.544348 /mnt/data/home/j/git/lab.gsi/soil/soil/example... \n", + "\n", + " exporter_params level skip_test debug \n", + "simulation_id \n", + "newspread_1682002299.544348 {} 20 False False \n", + "\n", + "[1 rows x 29 columns]" ] }, "execution_count": 19, @@ -2506,1035 +1938,23 @@ } ], "source": [ - "(agents.loc[2]['state_id'] != agents.loc[1]['state_id']).sum()" + "res.config.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "To focus on specific agents, we can swap the levels of the index:" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "ExecuteTime": { - "end_time": "2017-10-19T15:57:49.046261Z", - "start_time": "2017-10-19T17:57:49.019721+02:00" - }, - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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"0.0 neutral neutral neutral neutral \n", - "1.0 neutral neutral neutral neutral \n", - "2.0 neutral neutral neutral neutral \n", - "3.0 neutral neutral neutral neutral \n", - "4.0 neutral neutral neutral neutral \n", - "5.0 neutral infected neutral neutral \n", - "6.0 neutral infected neutral neutral \n", - "7.0 neutral infected neutral neutral \n", - "8.0 neutral infected neutral neutral \n", - "9.0 neutral infected neutral neutral \n", - "10.0 neutral infected neutral neutral \n", - "11.0 infected infected infected infected \n", - "12.0 infected infected infected infected \n", - "13.0 infected infected infected infected \n", - "14.0 infected infected infected infected \n", - "15.0 infected infected infected infected \n", - "16.0 infected infected infected infected \n", - "17.0 infected infected infected infected \n", - "18.0 infected infected infected infected \n", - "19.0 infected infected infected infected \n", - "\n", - "[20 rows x 2504 columns]" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "agents.swaplevel(axis=1)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "ExecuteTime": { - "end_time": "2017-10-19T10:35:40.140920Z", - "start_time": "2017-10-19T12:35:40.106265+02:00" - } - }, - "source": [ - "### Plotting data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you don't want to work with pandas, you can also use some pre-defined functions from soil to conveniently plot the results:" + "### Model reporters\n", + "\n", + "The `env` dataframe includes the data collected from the model.\n", + "The keys in this case are the same as `parameters`, and an additional one: **step**." ] }, { "cell_type": "code", "execution_count": 21, - "metadata": { - "ExecuteTime": { - "end_time": "2017-10-19T15:57:52.271094Z", - "start_time": "2017-10-19T17:57:51.102434+02:00" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "analysis.plot_all('soil_output/Spread_barabasi_albert_graph_prob_0.0/', analysis.get_count, 'state_id');" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "ExecuteTime": { - "end_time": "2017-10-19T15:57:57.982007Z", - "start_time": "2017-10-19T17:57:52.273160+02:00" - }, - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "analysis.plot_all('soil_output/Spread_barabasi_albert_graph_prob_0.3/', analysis.get_count, 'state_id');" - ] - }, - { - "cell_type": "markdown", "metadata": {}, - "source": [ - "You can use wildcards in the results path:" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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", 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k9/Te34Oi5P1eFvY7ePr0aWrWrFmglfLcuXMmLTHnz59Hr9ebNZKsVatW7NixA71eb9JRdt++fTg6Ot73b05MTAxJSUk0bdq0wLoZM2YwY8YMjhw5UqDzuihfcopHsGXLlkL/w8o7/5u/qXbPnj0m58kvX77Mb7/9Ru/evYv87y3P4MGD0Wq1TJs2rcDxFEUxDofN20/+bRRFKTBpnJ+fH61atWLp0qUmTeMbN240OS1ljtzcXL7++mvj4+zsbL7++mu8vLyM/QAKq2/fvn3s2bOn2Mcxdx+rVq3i6tWrxsf79+9n3759xkBR2P4AvvjiC5PHOp2uwGkEb29v/P39iz0MszDF/Z7dz4N+trRaLUOGDOHnn38uMMQaKHC6oyh5H463bt0qdm1geI29evVi1apVJv13zp8/z9q1a83aj0ajMek/cvHiRVatWlXo9qX5nSuNuXPnGu8risLcuXOxsbGhZ8+e931e/t/L/O/xiRMn2LBhA48++miB58ybN8/kcd4M1uYE5ieffJK4uDh++eUX47KbN2+ycuVKBgwYYBIGo6KiTFoqX3vtNX799VeTW97fgREjRvDrr7+W6elCUTzSgiJ49dVXycjI4IknnqBJkyZkZ2eze/dufvjhB4KCgozn9wGaNWtGnz59TIY8AkybNu2Bx6lfvz7//ve/mTRpEhcvXmTQoEG4uLgQHR3Nr7/+yksvvcSbb75JkyZNqF+/Pm+++SZXr17F1dWVn3/+udDz6DNnzqR///506dKFUaNGkZiYyJw5c2jatClpaWlmvxf+/v589NFHXLx4kUaNGvHDDz9w9OhRFi5caJy87LHHHuOXX37hiSeeoH///kRHR/PVV18REhJS7GOau48GDRrQpUsXxo4dS1ZWFl988QWenp689dZbALi6uvLQQw/x8ccfk5OTQ61atdiwYQPR0dEm+0lNTaV27do8+eSTtGzZEmdnZzZt2sSBAweYNWuW2e9XHnO+Z0Upzs/Whx9+yJYtW+jQoQNjxowhJCSExMREDh8+zKZNm0hMTHzgcVq1aoVWq+Wjjz4iOTkZOzs7evToYTLnT1GmTp3Khg0b6Ny5M2PHjkWn0zF37lyaNWvG0aNHi/U6+/fvz2effUbfvn157rnniI+PZ968eTRo0IC///67RO9LWbO3t2fdunWEh4fToUMH1q5dy59//sm//vWvYrXafPLJJ/Tr14+wsDBGjx5tHGbs5ubG1KlTC2wfHR3N448/Tt++fdmzZw/Lli3jueeeo2XLlsWu+cknn6Rjx46MHDmSyMhIatasyfz589HpdAXeq7yQlXc6p3Xr1gWG2Oeta9q0KYMGDSp2HaIMVfSwIWF51q5dq4waNUpp0qSJ4uzsrNja2ioNGjRQXn31VSUuLs64HaBEREQoy5YtUxo2bKjY2dkpoaGhBYZr5g0dzBuOeK+ff/5Z6dKli+Lk5KQ4OTkpTZo0USIiIpQzZ84Yt4mMjFR69eqlODs7KzVr1lTGjBmjHDt2TAGUxYsXF9hfcHCwYmdnp4SEhCi//PJLiYbJduvWTWnatKly8OBBJSwsTLG3t1cCAwOVuXPnmmyn1+uVGTNmKIGBgcb3YPXq1QWOeb9hiiXZx6xZs5SAgADFzs5O6dq1a4EhmFeuXFGeeOIJxd3dXXFzc1Oeeuop5dq1awqgTJkyRVEUw7DpiRMnKi1btlRcXFwUJycnpWXLlsr8+fNN9lWS96+437OihhkX52dLURQlLi5OiYiIUAICAhQbGxvF19dX6dmzp7Jw4ULjNnnDjFeuXFlord98841Sr149RavVmj3kePPmzUpoaKhia2ur1K9fX/n222+Vf/7zn4q9vX2hr6kwixYtMr7OJk2aKIsXLy7V+1LU79zixYsVQImOji726wsPD1ecnJyUqKgopXfv3oqjo6Pi4+OjTJkyRdHpdMbtHjQMd9OmTUrnzp0VBwcHxdXVVRkwYIASGRlZaN2RkZHKk08+qbi4uCg1atRQxo0bp9y+fbvYNedJTExURo8erXh6eiqOjo5Kt27dlAMHDhTYLjAw8IE/3zLMWH0aRSnH3lOiStFoNERERJg0/QohDMPAT548afY1Xx5Ejd+5ESNG8NNPP5WoBVKIsiR9UIQQwgz3zqdx7tw51qxZQ/fu3dUpSIgqSvqgiCovMTGR7OzsItdrtdpyGw1RFVSH9y8tLe2BLQZeXl5otVrq1atnvObRpUuXWLBgAba2tsb+QJYqOTn5gZOV+fr6VlA1xVdZ6xalJwFFVHmDBw9m27ZtRa4PDAw0a0Ko6qY6vH+ffvrpAzudRkdHExQURN++ffnf//5HbGwsdnZ2hIWFMWPGjEInVLMkr7/+OkuXLr3vNpZ4xr+y1i1KT/qgiCrv0KFD9x1N4uDgQOfOnSuwosqlOrx/Fy5ceOBsul26dDFOm14ZRUZGPvDyBqWZX6a8VNa6RelJQBFCCCGExZFOskIIIYSwOJWyD4per+fatWu4uLiUeNpqIYQQQlQsRVFITU3F39/f5JIEhamUAeXatWtyxUkhhBCikrp8+TK1a9e+7zaVMqC4uLgAhhfo6uqqcjVCCCGEKI6UlBQCAgKMn+P3UykDSt5pHVdXVwkoQgghRCVTnO4Z0klWCCGEEBZHAooQQgghLI4EFCGEEEJYHAkoQgghhLA4ElCEEEIIYXEkoAghhBDC4khAEUIIIYTFkYAihBBCCIsjAUUIIYQQFkcCihBCCCEsjlkBZerUqWg0GpNbkyZNjOszMzOJiIjA09MTZ2dnhgwZQlxcnMk+YmJi6N+/P46Ojnh7ezNx4kRyc3PL5tUIIYQQokow+1o8TZs2ZdOmTXd3YH13F2+88QZ//vknK1euxM3NjXHjxjF48GB27doFgE6no3///vj6+rJ7926uX7/O8OHDsbGxYcaMGWXwcoQQQghRFZgdUKytrfH19S2wPDk5mUWLFrF8+XJ69OgBwOLFiwkODmbv3r107NiRDRs2EBkZyaZNm/Dx8aFVq1Z88MEHvP3220ydOhVbW9vSvyIhhBBCmEVRFHJ0Clm5OrJy9WTm6HCw0eLpbKdaTWYHlHPnzuHv74+9vT1hYWHMnDmTOnXqcOjQIXJycujVq5dx2yZNmlCnTh327NlDx44d2bNnD82bN8fHx8e4TZ8+fRg7diwnT54kNDS00GNmZWWRlZVlfJySkmJu2UIIIYTF0+sVsnL1JkEh/9esnEKW5erIzDH9avqcfM/N1Zl8zf8cvWJay/CwQN4f2EydNwIzA0qHDh1YsmQJjRs35vr160ybNo2uXbty4sQJYmNjsbW1xd3d3eQ5Pj4+xMbGAhAbG2sSTvLW560rysyZM5k2bZo5pZbM2Q2wZTq4+IKzD7j4gYsPOPve/ersDVqb8q9FCCFEkRRFIVevkKtTyNXr0ekNj3V6hRyd6eO8bfI/1ukVcvR6dLp82+n1xnWGZfp8x7j72HAM08e5egWd7s4+8z3O1eerL99xTcPBnVCRoydbp1f7rQXAzlr9MTRmBZR+/foZ77do0YIOHToQGBjIjz/+iIODQ5kXl2fSpElMmDDB+DglJYWAgICyP1DiBbh+FK7fbyMNONU0DS0ud8KMs0++cOML1uo1jQkhRFlJTM/mdGwKUfFpZOboi/Vhnasr+OFd2Id1br4QoMu3PqeIoJC3zb3/7VdF1lYa7KytsLPRYn/nq12+r/b3fDW9r8XeJt8ym/zL7n41eU6+/Wg0GrVfvvmnePJzd3enUaNGnD9/nkceeYTs7Gxu3bpl0ooSFxdn7LPi6+vL/v37TfaRN8qnsH4teezs7LCzq4AP++ABUCMI0mIhNQ5Sr0NaHKTGGr6mxYE+F9JvGG5xx++/P3v3wlth7g00tk7l/9qEEOIBcnR6om6kcfp6KqdiUzh9PZXTsSnEpWQ9+MkWwtpKg7VWg7WVFVorDdZWGuNXa62V8bE233bW+R5rraywuedx3nobbd6+rPLt03Sbe49jXcSxihMyrLXqt2KoqVQBJS0tjaioKF544QXatGmDjY0NmzdvZsiQIQCcOXOGmJgYwsLCAAgLC2P69OnEx8fj7e0NwMaNG3F1dSUkJKSUL6UMuNUy3Iqi10NGwt0AkxZ7N7ykXs+3LA50WZB5y3C7cer+x7V1yRdefE1bYfK+uviCnStYQKoVQlR+8amZxgBiCCSpnI9PJUdXeNNEoKcjDb1dcLW3zvfhne9DV5v3YXznAz7fY9MP+OJ8mFvd88F+93nW94SE/KHBSoNF/OcvyoZGUZRiN5S9+eabDBgwgMDAQK5du8aUKVM4evQokZGReHl5MXbsWNasWcOSJUtwdXXl1VdfBWD37t2AYZhxq1at8Pf35+OPPyY2NpYXXniBF1980axhxikpKbi5uZGcnIyrq6uZL7kCKIohmKTmDzD5vqbG3g0yOenF36+1w93Wl4D20Kgv1G4P2lLlTCFEFZaZo+N8fBqnY1M5fT3F8DU2hZtp2YVu72JnTRM/F5r4utLEz4VgP1ca+bjgbCd/Z0TpmfP5bdZP3JUrV3j22WdJSEjAy8uLLl26sHfvXry8vAD4/PPPsbKyYsiQIWRlZdGnTx/mz59vfL5Wq2X16tWMHTuWsLAwnJycCA8P5/333y/By7RgGg041DDcvIPvv21WaiGnk+5poUmNg6xkyL0NSRcNt5g9sOtLw2mkBr0MYaVBT3D0qIAXKISwNIqiEJuSaXJ65tT1FC7cTEdXSIcNKw0E1XQi2NeVJr6GINLEz4Va7g7SCiEsglktKJbC4ltQykN2xt1+MEkXIeovOLcBbifd3UZjBQEdoGFvQ2DxDpZTQkJUQbezdZyNM7SEnLoTRE7HppJ8O6fQ7d0cbAi+0yoScieINPR2wcFWW8GVi+rOnM9vCSiVmV4HVw7C2XVwdj3EnzRd7xYAjfpAwz5QtyvYlN9IKyFE2VMUhStJtzkdmxdCDC0j0QnpFPaXW2ulob6Xk8npmWBfV3xc7aRVRFgECSjV1a3LcG69YT6X6G2Qm3l3nbUD1Ot2N7DcrzOwEKLCpWXlcuZOi0heEDkTm0pqVuHXKvN0sjWclsl3eqaBtzN21tIqIiyXBBRhOCV0cced1pUNkHLFdL1Pc2h051RQrTZgJX/UhKgoiqJwOfE2By8lcvBSEocuJnE2PrXQVhEbrYYG3i4E5wsiTXxd8XKReZZE5SMBRZhSFIg7aQgr5zbA5f1Avm+7oyc0eMQQWOr3BAd3tSoVokrKztVz8loyhy4lcfBiEodikriRWnBuER9XO+PpmRA/V5r4ulLPywmbaj4fhqg6JKCI+0tPgPObDIHl/GbDCKE8Gi0Edrrb0bZmQ+loK4SZbmVkG8LIndaRY1dukZVrOoW5jVZDs1putA2sQZvAGrQOrIG3i71KFQtRMSSgiOLT5cDlfYZOtmfXw80zputrBBmCSsPeENRFpu8X4h6KohB9M90YRg5eSiTqRsH5jWo42hiDSNtAD1rUdsPeRk6tiupFAoooucRow2mgs+vg4k7Q5ZvMycYJ6j98p6Ntb8PstkJUM5k5Ok5cTebgndM1h2OSSEwvOOlZPS8nY+tIm0AP6ns5yUgaUe1JQBFlIysNLmy9OzIo7Z4rTvu1MoSVRn3ALxSs5Dy5qHpupmVx6FLSnf4jiZy4mlLgirO21la0rO1Gm0CPO4GkBh5OtipVLITlkoAiyp5eD7F/G04DnVsPVw+ZrnfyvtNvpY+hlcXORZ06hSgFvV7h/I20u51ZLyVyMSGjwHY1nW2NQaRNoAfNarnK8F4hikECiih/afFwbqPhVFDUFshOvbvOygaCOkPYOGj4iHo1CvEAt7N1HL18i8MxhtaRwzG3Cp2NtZGPs7F1pG1gDQI9HeV0jRAlIAFFVKzcbIjZbTgNdHYdJEbdXdf8KegzE5y91KtPiDviUjJNWkdOXksh957r1NjbWNEqwJ22dwJJ6zo1cHO0UaliIaoWCShCXTfPw8H/wL4FoOgNF03sMxNaPiNDlkWFu5yYwRebzrEvOoErSbcLrPdxtTOGkTaBNQjxd5V5R4QoJxJQhGW4ehh+fw3ijhse13sYHvscPOqqW5eoNraciWf8iqPG0zYaDTTxdc03uqYGtWvI1XuFqCgSUITl0OXAnrmw9UPDtYGsHaDH/0GHsaC1Vrs6UUXp9Qpzt5zn801nURRoGeDOPx9pRGgdd1zs5XSNEGqRgCIsT0IU/PG64fpAYBii/Phs8Gupalmi6km+ncM/fzzKplPxADzXoQ5TBoTIKBshLIA5n99yolVUDM/6EP4HPD4H7N3g+lFY+DBsnAI5BfsFCFESZ2JTGTh3J5tOxWNrbcXHQ1ow44nmEk6EqIQkoIiKo9FA6+EQcQBCBoGig11fwPwwuLBN7epEJffHsWsMmreLiwkZ1HJ34KeXwxjaLkDtsoQQJSQBRVQ8Fx8YuhSeWQ4u/pAUDf99HH6LgIxEtasTlUyOTs8HqyN59X9HuJ2jo3MDT/54tQstarurXZoQohQkoAj1NOkPEfug3YuGx0eWwbz2cOIXqHxdo4QKbqRm8fy3+1i0MxqAl7vVZ+nI9jLNvBBVgHSSFZYhZq9hSHLe1ZQb9YP+n4JbbXXrEhbrcEwSryw7TGxKJk62WmYNbUnfZn5qlyWEuA/pJCsqnzod4eUd0O0dw1T5Z9fCvA6w/xvDdYCEuENRFJbtvcTTX+8hNiWT+l5O/Daus4QTIaoYCSjCcljbwcOTDEGldnvIToM1b8LivhB/Wu3qhAXIzNHx1k9/8+6qE+ToFPo29WVVRGcaeMvFKYWoaiSgCMvjHQyj1sOjn4KtM1zeB191gS0zITdL7eqESq4kZfDkV7tZeegKVhp4u28TFjzfWiZeE6KKkoAiLJOVFbQfY+hE26gv6HNg24fwVVeI2ad2daKC7Th3gwFzdnLiago1HG3476gOjO1eX6aoF6IKk4AiLJtbbXh2BTy5GJy8DJ1o/9MH/vwnZKaoXZ0oZ4qiMH/recL/s5+kjBya13Ljj1e70KVhTbVLE0KUMwkowvJpNNBsMETsh1bPAwoc+NbQifb0GrWrE+UkNTOHl5cd4uN1Z9ArMLRtbVa+HEbtGo5qlyaEqAASUETl4egBg+bB8N+gRl1IvQYrnoUfwyE1Tu3qRBk6H5/KwHm7WH8yDhuthhlPNOejIS2wt5Ep64WoLiSgiMqnXncYuxs6vw4aLUSugnnt4PB3MsFbFbD2+HUGzt3FhRvp+Lra8+M/wniuQx3pbyJENSMBRVROto7wyPvw0hbDFZEzk+H3cbB0gOHKyaLSydXpmbn2FGO/P0x6to6O9TxY/VoXQuvUULs0IYQKJKCIys2vJbz4F/T+N1g7wMUdsKAT7PgMdDlqVyeKKSEti+H/2c/X2y4AMKZrXZaN7kBNZzuVKxNCqEWmuhdVR2I0rB4PF7YaHvs0h8dnQ63WalYlHuDY5VuMXXaIa8mZONpq+WhICwa09Fe7LCFEOZCp7kX15FEXXlgFgxaAQw2IOw7f9oT1/wfZ6WpXJwrxw4EYnvpqD9eSM6lb04lVEZ0lnAghAAkooqrRaKDVcxBxAJo9CYoe9syF+R3h/Ga1qxN3ZOXqmPTL37z983GydXp6Bfvw27jONPKRKeuFEAYSUETV5OwFTy6C51aCa224FQPLBsMv/4D0BLWrq9au3brN0K/28L/9l9Fo4M3ejVj4QhtcZcp6IUQ+ElBE1daoN0TshQ4vAxr4e4VhSPLfK2VIsgp2R91kwJydHLuSjJuDDUtGtmdcj4ZYWckQYiGEKQkoouqzc4F+H8HojeAVDBkJ8MuL8P2TkBavdnXVgqIoLNwexfPf7iMhPZsQP1dWv9qFbo281C5NCGGhJKCI6iOgHfxjOzz8Lmht4fwmWP8vtauq8tKychm3/Agz1pxGr8Dg1rX4eWwnAjxkynohRNGs1S5AiAplbQvdJkLtNvDdE4aQotcbrp4sylzUjTRe/u4Q5+LTsLbSMGVACM93DJRZYYUQDyQBRVRPQQ+BrQvcToLYv8G/ldoVVTnrT8byzx+PkZaVi7eLHQueb02bQA+1yxJCVBLyb6OonrTWENTFcD9vYjdRJnR6hU/Wn+Yf3x0iLSuX9kGGKeslnAghzCEBRVRf9bobvkpAKTNJ6dmMWLyfeVsM10Ma2TmI78d0wNvFXuXKhBCVjZziEdVXXkCJ2QM5mWAjH6KlceJqMi8vO8SVpNvY21jx0ZAWDGxVS+2yhBCVlLSgiOrLqzE4+0JuJlzep3Y1ldpPh64wZMFuriTdJtDTkV9f6SzhRAhRKhJQRPWl0chpnlLK0el5b9UJ3lx5jKxcPQ839uL3iC4E+8lFPIUQpSMBRVRvElBK5ZsdF/hu7yUAxvdqyKLwdrg5ypT1QojSkz4oonqr183w9doRw5Bjhxrq1lOJ3M7W8e2OaAD+PagZz3cMVLkiIURVIi0oonpz9YeajQEFoneoXU2lsuJADInp2QR4OPBMuwC1yxFCVDESUISQ0zxmy87Vs3D7BQBe7lYfa638KRFClC35qyJE3mme6G3q1lGJrDpylevJmXi72DGkdW21yxFCVEESUIQI6gIaK0g4D7cuq12NxdPpFRZsM0zENqZrPexttCpXJISoiiSgCGHvBrXaGO5LK8oDrT1xneib6bg52PBchzpqlyOEqKIkoAgB0g+lmBRFMZnG3slOBgIKIcqHBBQhwDSgKIqalVi0rWducOp6Co62WkZ0ClK7HCFEFSYBRQiA2u3AxhHSb0B8pNrVWCRFUZi75TwAz3cMxN3RVuWKhBBVmQQUIQCs7SCwk+G+nOYp1P7oRA5dSsJWa8WLXeqqXY4QooqTgCJEHumHcl/zthr6njzVtjbernLlZyFE+SpVQPnwww/RaDSMHz/euCwzM5OIiAg8PT1xdnZmyJAhxMXFmTwvJiaG/v374+joiLe3NxMnTiQ3N7c0pQhRenkB5eIuyM1WtRRLc/xKMtvP3kBrpeEfD9VXuxwhRDVQ4oBy4MABvv76a1q0aGGy/I033uCPP/5g5cqVbNu2jWvXrjF48GDjep1OR//+/cnOzmb37t0sXbqUJUuWMHny5JK/CiHKgndTcPSEnHS4ekjtaizK/K2GviePt/SnjqejytUIIaqDEgWUtLQ0hg0bxjfffEONGncvrpacnMyiRYv47LPP6NGjB23atGHx4sXs3r2bvXv3ArBhwwYiIyNZtmwZrVq1ol+/fnzwwQfMmzeP7Gz5r1WoyMoK6t6ZVVZO8xidj09l3clYAMZ2l9YTIUTFKFFAiYiIoH///vTq1ctk+aFDh8jJyTFZ3qRJE+rUqcOePXsA2LNnD82bN8fHx8e4TZ8+fUhJSeHkyZOFHi8rK4uUlBSTmxDlQvqhFLBg6wUUBXqH+NDIx0XtcoQQ1YTZsyytWLGCw4cPc+DAgQLrYmNjsbW1xd3d3WS5j48PsbGxxm3yh5O89XnrCjNz5kymTZtmbqlCmC8voFw5AJkpYO+qajlqu5yYwaqjVwF45eEGKlcjhKhOzGpBuXz5Mq+//jrff/899vYV14t/0qRJJCcnG2+XL8v1UkQ5qREINeqCooNLu9WuRnXf7LiATq/QpUFNWgW4q12OEKIaMSugHDp0iPj4eFq3bo21tTXW1tZs27aN2bNnY21tjY+PD9nZ2dy6dcvkeXFxcfj6+gLg6+tbYFRP3uO8be5lZ2eHq6uryU2IciOneQCIT81kxQHDPwOvPCx9T4QQFcusgNKzZ0+OHz/O0aNHjbe2bdsybNgw430bGxs2b95sfM6ZM2eIiYkhLCwMgLCwMI4fP058fLxxm40bN+Lq6kpISEgZvSwhSkECCgD/2XmR7Fw9oXXcCavnqXY5Qohqxqw+KC4uLjRr1sxkmZOTE56ensblo0ePZsKECXh4eODq6sqrr75KWFgYHTt2BKB3796EhITwwgsv8PHHHxMbG8u7775LREQEdnZ2ZfSyhCiFug8BGrhxClJjwaXwlr2qLDkjh2V7LwEQ0b0BGo1G5YqEENVNmc8k+/nnn/PYY48xZMgQHnroIXx9ffnll1+M67VaLatXr0ar1RIWFsbzzz/P8OHDef/998u6FCFKxtED/Foa7l/Ypm4tKvnvnoukZeXSxNeFHk281S5HCFENaRSl8l26NSUlBTc3N5KTk6U/iigfG6fAri+g1TAYNF/taipURnYunT/8i6SMHL58phUDW9VSuyQhRBVhzue3XItHiMLUyzdhW+XL8KXyv/2XScrIIdDTkf7N/dQuRwhRTUlAEaIwdcJAawcpVyHhvNrVVJisXB3fbL8AwMvd6mOtlT8RQgh1yF8fIQpj4wB1OhjuV6PRPL8evkpsSiY+rnYMbi2ndoQQ6pGAIkRRqtlw41ydngXbogAY07UedtZalSsSQlRnElCEKEpeQIneAbpcVUupCGtOxHIpIYMajjY8276O2uUIIao5CShCFMWvFdi7QVYyXD+qdjXlSlEU5m8x9LUZ2bkuTnZmX6ZLCCHKlAQUIYpipb0zaRtwYYu6tZSzv07Hczo2FSdbLeFhQWqXI4QQElCEuC9jP5SqO2GboijMvdN68nxYIG6ONipXJIQQElCEuL96Dxu+Xt4H2Rnq1lJO9l5I5EjMLWytrRjdpa7a5QghBCABRYj786gHrrVBlw0xe9SuplzM32poPXm6bQDeLvYqVyOEEAYSUIS4H42mSg83Pnb5FjvO3URrpeGlh+qpXY4QQhhJQBHiQapwQMlrPRnYyp8AD0eVqxFCiLskoAjxIHnX5Yn9G9IT1K2lDJ2LS2X9yTg0Gnile321yxFCCBMSUIR4EGdv8G5quB9ddUbzLNhqmDW2T4gvDbxdVK5GCCFMSUARojiq2Gmey4kZ/HbsGgCvPCytJ0IIyyMBRYjiqGIB5evtUej0Cl0b1qRFbXe1yxFCiAIkoAhRHIGdwMoabl2CxGi1qymV+JRMfjx4BYCIhxuoXI0QQhROAooQxWHnDLXbG+5X8laURTujyc7V0yawBh3qeqhdjhBCFEoCihDFZby6ceXtKHsrI5tley8BEPFwfTQajcoVCSFE4SSgCFFcecONL2wDvV7dWkpo6e5LpGfraOLrwsONvdUuRwghiiQBRYjiqtUGbJ3hdiLEHVe7GrOlZ+WyeLeh/0zEww2k9UQIYdEkoAhRXFobCOpiuF8J+6H8b38MtzJyCPJ05NHmfmqXI4QQ9yUBRQhzVNLhxlm5OhZuvwDA2O710VpJ64kQwrJJQBHCHHkB5dIeyMlUtRRz/HzoKvGpWfi52fNEaG21yxFCiAeSgCKEObyagLMP5N6GK/vVrqZYcnV6vtpmmNZ+TNd62FrLr70QwvLJXyohzKHRVLrTPH8ev05MYgYeTrY80z5A7XKEEKJYJKAIYa5KFFD0eoX5WwytJ6M6B+Foa61yRUIIUTwSUIQwV90786FcOwK3b6layoNsPh3PmbhUnO2seSEsSO1yhBCi2CSgCGEut1rg2RAUPVzcqXY1RVIUhblbzgPwQlggbg42KlckhBDFJwFFiJKoBKd59kQlcOzyLeysrRjVua7a5QghhFkkoAhREpUgoMzbamg9eaZdAF4udipXI4QQ5pGAIkRJBHUBjRUknIPkK2pXU8CRmCR2nU/A2krDmIfqqV2OEEKYTQKKECXh4A7+rQ33L1je1Y3nbzWM3BkUWovaNRxVrkYIIcwnAUWIkrLQ0zxnYlPZGBmHRgMvd6uvdjlCCFEiElCEKKn8AUVR1KzExII7fU/6NfOlgbezytUIIUTJSEARoqQC2oO1A6THQ/wptasBICYhg9+PXQPgle4NVK5GCCFKTgKKECVlbQeBnQz3oy2jH8pX26PQK9CtkRfNarmpXY4QQpSYBBQhSqPenVllLaAfSlxKJj8dNIwoinhYWk+EEJWbBBQhSiOvH8rFnaDLUbWUb3dcIFunp11QDdrX9VC1FiGEKC0JKEKUhk9zcPCA7DS4eki1MpLSs/l+XwwAr0jriRCiCpCAIkRpWFlZxGmeJbsvkpGtI8TPle6NvFSrQwghyooEFCFKS+X5UNKyclmy+yJg6Hui0WhUqUMIIcqSBBQhSisvoFw5AFmpFX745fsukXw7h3o1nejbzLfCjy+EEOVBAooQpVUjyHDT58Kl3RV66MwcHd/siAbg5e710VpJ64kQomqQgCJEWVDpNM9Ph65wIzULfzd7BrWqVaHHFkKI8iQBRYiyoEJAydXp+Wqb4aKALz1UD1tr+XUWQlQd8hdNiLIQ9BCggfhISI2rkEP+8fc1riTdxtPJlqfb1amQYwohREWRgCJEWXDyBN/mhvvR28v9cHq9wvwthtaTUV3q4mCrLfdjCiFERZKAIkRZqcDTPBtPxXEuPg0XO2teCAss9+MJIURFk4AiRFnJH1AUpdwOoygK87ecB2B4p0Bc7W3K7VhCCKEWCShClJU6YaC1hZQrkBBVbofZdT6BY1eSsbexYmTnuuV2HCGEUJMEFCHKiq0jBHQw3L+wpdwOM+9O68kz7epQ09mu3I4jhBBqkoAiRFkq534ohy4lsedCAtZWGl56qF65HEMIISyBBBQhylK9hw1fo3eAXlfmu1+w1dB6Mrh1LfzdHcp8/0IIYSkkoAhRlvxbgZ0bZCXDtaNluutT11PYdCoejQZe7la/TPcthBCWRgKKEGXJSgt1uxruR28t010v2GroePtocz/qeTmX6b6FEMLSmBVQFixYQIsWLXB1dcXV1ZWwsDDWrl1rXJ+ZmUlERASenp44OzszZMgQ4uJMZ9WMiYmhf//+ODo64u3tzcSJE8nNzS2bVyOEJSiHfigXb6az+u9rALzSXVpPhBBVn1kBpXbt2nz44YccOnSIgwcP0qNHDwYOHMjJkycBeOONN/jjjz9YuXIl27Zt49q1awwePNj4fJ1OR//+/cnOzmb37t0sXbqUJUuWMHny5LJ9VUKoKS+gxOyF7Iwy2eXX26PQK/BwYy+a+ruVyT6FEMKSaRSldDNKeXh48Mknn/Dkk0/i5eXF8uXLefLJJwE4ffo0wcHB7Nmzh44dO7J27Voee+wxrl27ho+PDwBfffUVb7/9Njdu3MDW1rZYx0xJScHNzY3k5GRcXV1LU74QZU9R4POmkHIVXvgV6vco1e5ikzPp+vFf5OgUfno5jLZBHmVUqBBCVCxzPr+tS3oQnU7HypUrSU9PJywsjEOHDpGTk0OvXr2M2zRp0oQ6deoYA8qePXto3ry5MZwA9OnTh7Fjx3Ly5ElCQ0MLPVZWVhZZWVkmL1AIi6XRGFpRjn5vOM1TyoDyzY4L5OgU2tf1kHBSxel0OnJyctQuQ4gSs7GxQastm2uDmR1Qjh8/TlhYGJmZmTg7O/Prr78SEhLC0aNHsbW1xd3d3WR7Hx8fYmNjAYiNjTUJJ3nr89YVZebMmUybNs3cUoVQT/6AUgqJ6dks3xcDQMTDDUpfl7BIiqIQGxvLrVu31C5FiFJzd3fH19cXjUZTqv2YHVAaN27M0aNHSU5O5qeffiI8PJxt27aVqogHmTRpEhMmTDA+TklJISAgoFyPKUSp1O1m+Hr9b0hPMFztuASW7Irmdo6OZrVceahhzTIsUFiSvHDi7e2No6Njqf+wC6EGRVHIyMggPj4eAD8/v1Ltz+yAYmtrS4MGhv/k2rRpw4EDB/jyyy95+umnyc7O5tatWyatKHFxcfj6+gLg6+vL/v37TfaXN8onb5vC2NnZYWcnU3qLSsTFB7xDID4SLm6Hpk+YvYvUzByW7L4IQET3BvKhVUXpdDpjOPH0LFmQFcJSODgYJpCMj4/H29u7VKd7Sj0Pil6vJysrizZt2mBjY8PmzZuN686cOUNMTAxhYWEAhIWFcfz4cWO6Ati4cSOurq6EhISUthQhLEsphxuvOnqNlMxc6ns50adp0QFeVG55fU4cHR1VrkSIspH3s1za/lRmtaBMmjSJfv36UadOHVJTU1m+fDlbt25l/fr1uLm5MXr0aCZMmICHhweurq68+uqrhIWF0bFjRwB69+5NSEgIL7zwAh9//DGxsbG8++67RERESAuJqHrqdYe98+FCyU6Bbj97A4DBrWtjZSWtJ1WdtJCJqqKsfpbNCijx8fEMHz6c69ev4+bmRosWLVi/fj2PPPIIAJ9//jlWVlYMGTKErKws+vTpw/z5843P12q1rF69mrFjxxIWFoaTkxPh4eG8//77ZfJihLAogZ3AyhqSoiHpItQIKvZTc3V69kYlANBV+p4IIaohswLKokWL7rve3t6eefPmMW/evCK3CQwMZM2aNeYcVojKyc4FarWFy3sNrShtgor91L+vJpOalYubg41MzCaEqJbkWjxClKcS9kPZde4mAJ3qe6KV0zuikhoxYgQajYYPP/zQZPmqVatUP6V18eJFNBoN3t7epKammqxr1aoVU6dOVacwYSQBRYjylBdQoreBXl/sp+08bwgonRvI6R1Rudnb2/PRRx+RlJSkdimFSk1N5dNPP1W7DFEICShClKfabcHWGTISIO5EsZ6SkZ3L4RjDH/MuElBEJderVy98fX2ZOXNmkdv8/PPPNG3aFDs7O4KCgpg1a5bJ+qCgIGbMmMGoUaNwcXGhTp06LFy40GSby5cvM3ToUNzd3fHw8GDgwIFcvHjxgfW9+uqrfPbZZyajS++VlJTE8OHDqVGjBo6OjvTr149z584Z1y9ZsgR3d3fWr19PcHAwzs7O9O3bl+vXr5vs59tvvyU4OBh7e3uaNGli0kdTFCQBRYjypLWBwM6G+8U8zbM/OpEcnUItdwcCPWXoqajctFotM2bMYM6cOVy5cqXA+kOHDjF06FCeeeYZjh8/ztSpU3nvvfdYsmSJyXazZs2ibdu2HDlyhFdeeYWxY8dy5swZwDCctU+fPri4uLBjxw527dplDAnZ2dn3re/ZZ5+lQYMG9x2sMWLECA4ePMjvv//Onj17UBSFRx991GQYbUZGBp9++infffcd27dvJyYmhjfffNO4/vvvv2fy5MlMnz6dU6dOMWPGDN577z2WLl1anLexelIqoeTkZAVQkpOT1S5FiAfbPU9Rprgqyn+fKNbm/159Ugl8e7Xy1spj5VyYsAS3b99WIiMjldu3b6tdSpkLDw9XBg4cqCiKonTs2FEZNWqUoiiK8uuvvyp5Hz/PPfec8sgjj5g8b+LEiUpISIjxcWBgoPL8888bH+v1esXb21tZsGCBoiiK8t133ymNGzdW9Hq9cZusrCzFwcFBWb9+faG1RUdHK4By5MgRZd26dYqNjY1y/vx5RVEUpWXLlsqUKVMURVGUs2fPKoCya9cu43Nv3rypODg4KD/++KOiKIqyePFiBTA+X1EUZd68eYqPj4/xcf369ZXly5eb1PDBBx8oYWFhRb19ldb9fqbN+fyWFhQhylteP5RLuyE3676bAuw6bxhe3FmGF4sq5KOPPmLp0qWcOnXKZPmpU6fo3LmzybLOnTtz7tw5dDqdcVmLFi2M9zUaDb6+vsbTMseOHeP8+fO4uLjg7OyMs7MzHh4eZGZmEhUV9cDa+vTpQ5cuXXjvvfcKrDt16hTW1tZ06NDBuMzT05PGjRubvBZHR0fq169vfOzn52esLz09naioKEaPHm2sz9nZmX//+9/Fqq+6KvHVjIUQxeQdDE7ekB4Pl/dD3a5FbpqQlkXkdcPVujvVl2nPRdXx0EMP0adPHyZNmsSIESPMfr6NjY3JY41Gg/5Ox/O0tDTatGnD999/X+B5Xl5exdr/hx9+SFhYGBMnTjS7tqLqUxTFWB/AN998YxJ0gDK78m9VJAFFiPKm0RhaUY7/aBjNc5+AsvvO5GxNfF2o6SyzK4uq5cMPP6RVq1Y0btzYuCw4OJhdu3aZbLdr1y4aNWpU7A/v1q1b88MPP+Dt7Y2rq2uJamvfvj2DBw/mnXfeMVkeHBxMbm4u+/bto1OnTgAkJCRw5syZYl+ixcfHB39/fy5cuMCwYcNKVF91JKd4hKgI9e5c3fgBHWV33RleLKN3RFXUvHlzhg0bxuzZs43L/vnPf7J582Y++OADzp49y9KlS5k7d65JB9MHGTZsGDVr1mTgwIHs2LGD6Ohotm7dymuvvVZox9yiTJ8+nb/++svY+RagYcOGDBw4kDFjxrBz506OHTvG888/T61atRg4cGCx9z1t2jRmzpzJ7NmzOXv2LMePH2fx4sV89tlnxd5HdSMBRYiKUPdOQLl6CDKTC91EURR23JmgTfqfiKrq/fffN56aAUPrx48//siKFSto1qwZkydP5v333zfrNJCjoyPbt2+nTp06DB48mODgYEaPHk1mZqaxRWXr1q1oNJr7Dj1u1KgRo0aNIjMz02T54sWLadOmDY899hhhYWEoisKaNWsKnNa5nxdffJFvv/2WxYsX07x5c7p168aSJUuoW7dusfdR3WiUvJNklUhKSgpubm4kJyeXuDlPiAo3pw0knIdnlkOT/gVWX0pIp9snW7HRajg6uTdOdnIGtjrIzMwkOjqaunXrYm9vr3Y5VdbixYuZMWMGkZGRZgULYb77/Uyb8/ktLShCVJQHTHufN3tsaJ0aEk6EKGNr1qxhxowZEk4qEfkrKERFqdcdDnxbZECR/idClJ+VK1eqXYIwk7SgCFFRgrqAxgpunoXkqyar9HrFOIJHrr8jhBASUISoOA41wD/UcD96m8mqyOsp3MrIwdnOmpa13VQoTgghLIsEFCEqUhH9UPL6n3Ss54m1Vn4thRBC/hIKUZGMAWUb5BtAl9f/pHMDmT1WCCFAAooQFat2e7C2h7RYuGGYDCozR8f+6ERAOsgKIUQeCShCVCQbe6gTZrh/5zTP4UtJZOXq8Xaxo4G3s3q1CSGEBZGAIkRFu6cfys58w4s1Go06NQkhhIWRgCJERcsLKBd3gi4nX/8TOb0jKpeEhAS8vb3vO328pdJoNKxatarI9RcvXkSj0XD06NFi73Pq1Km0atXKrDoyMjIYMmQIrq6uaDQabt26Zdbzy1v37t0ZP3688XHHjh35+eefK+TYElCEqGi+LQxDjrNTSbuwn7+vGq7NIwFFVDbTp09n4MCBBAUFPXDbknzg53lQmCiJ69ev069fvzLd55tvvsnmzZvNes7SpUvZsWMHu3fv5vr167i5lX6agXtDRVl69913eeedd0yup1ReJKAIUdGsrIwXD7x+ZC2KAg28nfF1k+uwiMojIyODRYsWMXr0aLVLKRFfX1/s7OzKdJ/Ozs54epo3Ei8qKorg4GCaNWuGr6+vxZ/m7devH6mpqaxdu7bcjyUBRQg13DnNY3NpOyCjd0Tls2bNGuzs7OjYsaNxWVJSEsOGDcPLywsHBwcaNmzI4sWLAYxX7Q0NDUWj0dC9e3cADhw4wCOPPELNmjVxc3OjW7duHD582LjPvNaZJ554Ao1GY9Ja89tvv9G6dWvs7e2pV68e06ZNIzc3t1j139sqs3//fkJDQ7G3t6dt27YcOXLE7Pfk3lM8I0aMYNCgQXz66af4+fnh6elJREQEOTk5gKGlY9asWWzfvt3kPcnKyuLNN9+kVq1aODk50aFDB7Zu3WpyrF27dtG9e3ccHR2pUaMGffr0ISkpiREjRrBt2za+/PJLNBqNyRWcT5w4Qb9+/XB2dsbHx4cXXniBmzdvGveZnp7O8OHDcXZ2xs/Pj1mzZhV4jVqtlkcffZQVK1aY/f6YSwKKEGq4E1Bqp5/AkUw5vSOMFEUhIztXlZs5F7ffsWMHbdq0MVn23nvvERkZydq1azl16hQLFiygZk3Dz/b+/fsB2LRpE9evX+eXX34BIDU1lfDwcHbu3MnevXtp2LAhjz76KKmpqYAhwIDhasTXr183Pt6xYwfDhw/n9ddfJzIykq+//polS5Ywffp0s9/ztLQ0HnvsMUJCQjh06BBTp07lzTffNHs/hdmyZQtRUVFs2bKFpUuXsmTJEpYsWQLAL7/8wpgxYwgLCzN5T8aNG8eePXtYsWIFf//9N0899RR9+/bl3LlzABw9epSePXsSEhLCnj172LlzJwMGDECn0/Hll18SFhbGmDFjuH79OtevXycgIIBbt27Ro0cPQkNDOXjwIOvWrSMuLo6hQ4caa504cSLbtm3jt99+Y8OGDWzdutUkLOZp3749O3bsKJP3537kYoFCqMGjLrmudbBOiaGD9jQd6j2udkXCQtzO0REyeb0qx458vw+OtsX7WLh06RL+/v4my2JiYggNDaVt27YAJq0dXl5eAHh6euLr62tc3qNHD5N9LFy4EHd3d7Zt28Zjjz1mfJ67u7vJ86ZNm8Y777xDeHg4APXq1eODDz7grbfeYsqUKcV8xQbLly9Hr9ezaNEi7O3tadq0KVeuXGHs2LFm7acwNWrUYO7cuWi1Wpo0aUL//v3ZvHkzY8aMwcPDA0dHR2xtbY2vLSYmhsWLFxMTE2N8f998803WrVvH4sWLmTFjBh9//DFt27Zl/vz5xuM0bdrUeN/W1hZHR0eT92vu3LmEhoYyY8YM47L//Oc/BAQEcPbsWfz9/Vm0aBHLli2jZ8+egKF/TO3atQu8Jn9/fy5fvoxer8fKqvzaOSSgCKGSS+7tqZ8SwyDXc7jayyXgReVy+/Zt7O1N+02NHTuWIUOGcPjwYXr37s2gQYPo1KnTffcTFxfHu+++y9atW4mPj0en05GRkUFMTMx9n3fs2DF27dpl0mKi0+nIzMwkIyMDR0fHYr+WU6dO0aJFC5PXExYWVuzn30/Tpk3RarXGx35+fhw/frzI7Y8fP45Op6NRo0Ymy7Oysoz9W44ePcpTTz1lVh3Hjh1jy5YtODsXnGspKiqK27dvk52dTYcOHYzLPTw8aNy4cYHtHRwc0Ov1ZGVl4eDgYFYd5pCAIoRKduSGUB8I0xT9x0pUPw42WiLf76PasYurZs2aJCUlmSzr168fly5dYs2aNWzcuJGePXsSERHBp59+WuR+wsPDSUhI4MsvvyQwMBA7OzvCwsLIzs6+7/HT0tKYNm0agwcPLrDu3uCkJhsb038+NBrNfUfApKWlodVqOXTokEmwAYzhoiShIC0tjQEDBvDRRx8VWOfn58f58+eLva/ExEScnJzKNZyABBQhVKEoCsviAhkBeGech7R4cPZWuyxhATQaTbFPs6gpNDSUZcuWFVju5eVFeHg44eHhdO3alYkTJ/Lpp59ia2sLGFo58tu1axfz58/n0UcfBeDy5csmHTfB8CF/7/Nat27NmTNnaNCgQalfS3BwMN999x2ZmZnGcLN3795S77ckQkND0el0xMfH07Vr10K3adGiBZs3b2batGmFrre1tS30/fr5558JCgrC2rrgz1f9+vWxsbFh37591KlTBzB0ej579izdunUz2fbEiROEhoaW5OWZRTrJCqGCM3GpnE93IFIJMiyI3q5qPUKYq0+fPpw8edKkFWXy5Mn89ttvnD9/npMnT7J69WqCg4MB8Pb2xsHBwdg5MznZMP9Pw4YN+e677zh16hT79u1j2LBhBf4zDwoKYvPmzcTGxhqPN3nyZP773/8ybdo0Tp48yalTp1ixYgXvvvuu2a/lueeeQ6PRMGbMGCIjI1mzZs19W33KU6NGjRg2bBjDhw/nl19+ITo6mv379zNz5kz+/PNPACZNmsSBAwd45ZVX+Pvvvzl9+jQLFiwwBrugoCD27dvHxYsXuXnzJnq9noiICBITE3n22Wc5cOAAUVFRrF+/npEjR6LT6XB2dmb06NFMnDiRv/76ixMnTjBixIhC+5js2LGD3r17l/t7IQFFCBXsPGf4Q3LRtZ1hwYUtKlYjhPmaN29O69at+fHHH43LbG1tmTRpEi1atOChhx5Cq9Uah6NaW1sze/Zsvv76a/z9/Rk4cCAAixYtIikpidatW/PCCy/w2muv4e1t2po4a9YsNm7cSEBAgPE/9z59+rB69Wo2bNhAu3bt6NixI59//jmBgYFmvxZnZ2f++OMPjh8/TmhoKP/3f/9X6KmQoKAgpk6davb+zbV48WKGDx/OP//5Txo3bsygQYM4cOCAsWWjUaNGbNiwgWPHjtG+fXvCwsL47bffjC0jb775JlqtlpCQELy8vIwdbnft2oVOp6N37940b96c8ePH4+7ubgwhn3zyCV27dmXAgAH06tWLLl26FBipdfXqVXbv3s3IkSPL/X3QKOaMK7MQKSkpuLm5kZycjKurq9rlCGG2kYv3s+XMDRZ0SKLfsQhwrQ1vnAALn6RJlL3MzEyio6OpW7euRfWdKI4///yTiRMncuLEiXIdzWEJMjIy8PT0ZO3atcb5Sqqjt99+m6SkJBYuXFjkNvf7mTbn89vyT3QKUcVk5+rZF50IQFDrXnDCFlKuQOIF8KyvcnVCFF///v05d+4cV69eJSAgQO1yytWWLVvo0aNHtQ4nYDhVN2HChAo5VtWOvEJYoGNXbpGRrcPTyZbGAT4QcGdYn5zmEZXQ+PHjLS6cfP/99zg7Oxd6yz9fiDn69+9v7ANSnf3zn//Ex8enQo4lLShCVLC8/idh9T2xstJAvW5wcQdc2AbtXlS5OiEqv8cff9xkPo/87h32KyyXBBQhKtiu84aAYrz+Tr2H4a9/G0by6HVgVfy5KIQQBbm4uODi4qJ2GaKU5BSPEBUoNTOHI5dvAdy9/o5fK7BzhcxbcP2YWqUJIYRFkYAiRAXaH52ITq8Q6OlIgMedqbi11hB0Z0KmC1tVq00IISyJBBQhKtDOO6d3Cly9+M7VjSWgCCGEgQQUISpQgf4nefICSsxeyLldsUUJIYQFkoAiRAWJT8nkbFwaGg2E1fM0XVmzIbj4gy7LEFKEEKKak4AiRAXZFWVoPWnm70YNJ1vTlRqNnOYRlU5CQgLe3t5cvHhR7VLMptFoWLVqVZHrL168iEaj4ejRoxVWk1pGjBjBoEGDjI+feeYZZs2apV5Bd0hAEaKC7DqfABTS/ySPBBRRyUyfPp2BAwcSFBT0wG1L84H/oDBREtevX6dfv35lus+KMnXqVFq1alVu+3/33XeZPn268YKOapGAIkQFUBSl6P4neerduaT59WOQkVhBlQlRMhkZGSxatIjRo0erXUqJ+Pr6Ymdnp3YZ5SonJ6dEz2vWrBn169dn2bJlZVyReSSgCFEBLtxM53pyJrbWVrQNqlH4Ri6+4BUMKIaZZYWwYGvWrMHOzo6OHTsalyUlJTFs2DC8vLxwcHCgYcOGLF68GIC6desCEBoaikajMV7T5sCBAzzyyCPUrFkTNzc3unXrxuHDh437zGudeeKJJ9BoNCatNb/99hutW7fG3t6eevXqMW3aNHJzc4tV/72tMvv37yc0NBR7e3vatm3LkSNHzH5P8lo2vvvuO4KCgnBzc+OZZ54hNTXVuI1er2fmzJnUrVsXBwcHWrZsyU8//WRcv2TJEtzd3U32u2rVKjR3LiS6ZMkSpk2bxrFjx9BoNGg0GpYsWWJ8TQsWLODxxx/HycmJ6dOno9PpGD16tPF4jRs35ssvv3zgaxkwYIDxStRqkZlkhagAea0nbQNrYG9zn5li63WDG6cMp3lCBlZMccKyKArkZKhzbBvHYl9Re8eOHbRp08Zk2XvvvUdkZCRr166lZs2anD9/ntu3DaPS9u/fT/v27dm0aRNNmzbF1tbQDys1NZXw8HDmzJmDoijMmjWLRx99lHPnzuHi4sKBAwfw9vZm8eLF9O3bF61Wazz+8OHDmT17Nl27diUqKoqXXnoJgClTppj1stPS0njsscd45JFHWLZsGdHR0bz++utm7SNPVFQUq1atYvXq1SQlJTF06FA+/PBDpk+fDsDMmTNZtmwZX331FQ0bNmT79u08//zzeHl50a1btwfu/+mnn+bEiROsW7eOTZs2AeDm5mZcP3XqVD788EO++OILrK2t0ev11K5dm5UrV+Lp6cnu3bt56aWX8PPzY+jQoUUep3379kyfPp2srCzVWpokoAhRAfKuv1Nk/5M89brDvq+kH0p1lpMBM/zVOfa/roGtU7E2vXTpEv7+pnXGxMQQGhpK27ZtAUxaO7y8vADw9PTE19fXuLxHjx4m+1i4cCHu7u5s27aNxx57zPg8d3d3k+dNmzaNd955h/DwcADq1avHBx98wFtvvWV2QFm+fDl6vZ5FixZhb29P06ZNuXLlCmPHjjVrP2BoIVmyZIlxqv0XXniBzZs3Gz/sZ8yYwaZNmwgLCzPWvXPnTr7++utiBRQHBwecnZ2xtrY2eT/yPPfcc4wcOdJk2bRp04z369aty549e/jxxx/vG1D8/f3Jzs4mNjaWwMDAYr32siYBRYhylqvTs+eCoYNskf1P8gR2Bo0WEi9A0iWooc4fBiEe5Pbt29jb25ssGzt2LEOGDOHw4cP07t2bQYMG0alTp/vuJy4ujnfffZetW7cSHx+PTqcjIyODmJiY+z7v2LFj7Nq1y9gyAaDT6cjMzCQjIwNHR8div5ZTp07RokULk9eTFyDMFRQUZHIdID8/P+Lj4wE4f/48GRkZPPLIIybPyc7OJjQ0tETHu1deOMxv3rx5/Oc//yEmJobbt2+TnZ39wE62Dg4OgKGvkVokoAhRzo5fTSY1MxdXe2ua1XK7/8b2rlC7LVzeB9HboMbwiilSWA4bR0NLhlrHLqaaNWuSlJRksqxfv35cunSJNWvWsHHjRnr27ElERASffvppkfsJDw8nISGBL7/8ksDAQOzs7AgLCyM7O/u+x09LS2PatGkMHjy4wLp7g1NFuvdqyRqNBr1eDxhqBvjzzz+pVauWyXZ5p1GsrKxQFMVknTmdXZ2cTFvAVqxYwZtvvsmsWbMICwvDxcWFTz75hH379t13P4mJho76eS1YapCAIkQ5y+t/0ql+TbRWxTi/X6+7IaBc2AqtJaBUOxpNsU+zqCk0NLTQUR5eXl6Eh4cTHh5O165dmThxIp9++qmxz4lOpzPZfteuXcyfP59HH30UgMuXL3Pz5k2TbWxsbAo8r3Xr1pw5c4YGDRqU+rUEBwfz3XffkZmZaQw3e/eW/YSJISEh2NnZERMTU+TpHC8vL1JTU0lPTzeGjXuHZtva2hZ4P4qya9cuOnXqxCuvvGJcFhUV9cDnnThxgtq1a1Oz5gNafcuRjOIRopwZr7/TsJi/6Mb5ULbBnf+8hLA0ffr04eTJkyatKJMnT+a3337j/PnznDx5ktWrVxMcHAyAt7c3Dg4OrFu3jri4OOMcGw0bNuS7777j1KlT7Nu3j2HDhhlPL+QJCgpi8+bNxMbGGo83efJk/vvf/zJt2jROnjzJqVOnWLFiBe+++67Zr+W5555Do9EwZswYIiMjWbNmzX1bfUrKxcWFN998kzfeeIOlS5cSFRXF4cOHmTNnDkuXLgWgQ4cOODo68q9//YuoqCiWL19uHKWTJygoiOjoaI4ePcrNmzfJysoq8pgNGzbk4MGDrF+/nrNnz/Lee+9x4MCBB9a6Y8cOevfuXarXW1oSUIQoR7ezdRy+dAsoRv+TPLXago0TZNyE+JPlV5wQpdC8eXNat27Njz/+aFxma2vLpEmTaNGiBQ899BBardY4VNXa2prZs2fz9ddf4+/vz8CBhlFqixYtIikpidatW/PCCy/w2muv4e3tbXKsWbNmsXHjRgICAox9Nfr06cPq1avZsGED7dq1o2PHjnz++ecl6tDp7OzMH3/8wfHjxwkNDeX//u//+OijjwpsFxQUxNSpU83ef34ffPAB7733HjNnziQ4OJi+ffvy559/Godhe3h4sGzZMtasWUPz5s353//+V+CYQ4YMoW/fvjz88MN4eXnxv//9r8jj/eMf/2Dw4ME8/fTTdOjQgYSEBJPWlMJkZmayatUqxowZU6rXWloa5d6TXZVASkoKbm5uJCcn4+rqqnY5QhRp+9kbDP/Pfmq5O7Dz7YeNcxk80PdPwbkN0Pvf0OnV8i1SqCozM5Po6Gjq1q2rat+Jkvjzzz+ZOHEiJ06cwMqqav+/m5GRgaenJ2vXrjXO4VJVLViwgF9//ZUNGzaU6Pn3+5k25/O7av9ECaGyu/1PPIsfTsD0NI8QFqp///689NJLXL16Ve1Syt2WLVvo0aNHlQ8nYOjzM2fOHLXLkE6yQpSnvP4nXYrb/yRPXkC5tAtys8Ha9r6bC6GW8ePHq11CAd9//z3/+Mc/Cl0XGBjIyZPmnzrt378//fv3L21plcKLL76odgmABBQhyk1iejYnr6UAhhE8ZvEOAScvSL8BVw5AUOdyqFCIqunxxx+nQ4cOha67dxiwsFxmneKZOXMm7dq1w8XFBW9vbwYNGsSZM2dMtsnMzCQiIgJPT0+cnZ0ZMmQIcXFxJtvExMTQv39/HB0d8fb2ZuLEicW+foIQlcXuKEPrSRNfF7xczJwqWqOBuneGIUb9VcaVCVG1ubi40KBBg0Jvas2KKsxnVkDZtm0bERER7N27l40bN5KTk0Pv3r1JT083bvPGG2/wxx9/sHLlSrZt28a1a9dMJtLR6XT079+f7Oxsdu/ezdKlS1myZAmTJ08uu1clhAXI63/ywOnti9LwzhC/I8sgt+hhhEIIURWVahTPjRs38Pb2Ztu2bTz00EMkJyfj5eXF8uXLefLJJwE4ffo0wcHB7Nmzh44dO7J27Voee+wxrl27ho+PDwBfffUVb7/9Njdu3DBO5nM/MopHVAZdP/6Ly4m3WTyiHQ838X7wE+6Vmw2zW0HKVXjsC2g78kHPEJVQ3oiHoKCgAvN/CFEZ3b59m4sXL6o7iidvoh0PDw8ADh06RE5ODr169TJu06RJE+rUqcOePXsA2LNnD82bNzeGEzCMZ09JSSlRxyUhLFFMQgaXE29jbaWhfV2Pku3E2vbuEONdX4BOToNWRXl9ItS85okQZSnvZ7m0/X1K3ElWr9czfvx4OnfuTLNmzQCIjY3F1tYWd3d3k219fHyIjY01bpM/nOStz1tXmKysLJOZ8lJSUkpathAVIm/0Tus6NXCyK0Vf9NbDYfsnkHQRTv4KLZ4qmwKFxdBqtbi7uxsvKOfo6GjekHQhLISiKGRkZBAfH4+7uztarbZU+yvxX86IiAhOnDjBzp07S1VAccycOdPkctFCWLpdUaXsf5LH1gk6joW//g07P4NmQ6CKT4hVHfn6+gIYQ4oQlZm7u7vxZ7o0ShRQxo0bx+rVq9m+fTu1a9c2Lvf19SU7O5tbt26ZtKLExcUZi/X19WX//v0m+8sb5VPUC5o0aRITJkwwPk5JSSEgIKAkpQtR7vR6hd3GDrKepd9huzGw80uIj4Sz66DJo6Xfp7AoGo0GPz8/vL29zbpyrRCWxsbGptQtJ3nMCiiKovDqq6/y66+/snXrVuO1A/K0adMGGxsbNm/ezJAhQwA4c+YMMTExhIWFARAWFsb06dOJj483Xm9h48aNuLq6EhISUuhx7ezsjJeiFsLSRV5PISkjBydbLS0D3Eu/Qwd3aP8i7PwcdnwKjfsZhiGLKker1ZbZH3chKjuz2oojIiJYtmwZy5cvx8XFhdjYWGJjY7l9+zYAbm5ujB49mgkTJrBlyxYOHTrEyJEjCQsLo2PHjgD07t2bkJAQXnjhBY4dO8b69et59913iYiIkBAiqoS84cUd63lioy2j0zEdXwFre7h6CKK3l80+hRDCgpn113PBggUkJyfTvXt3/Pz8jLcffvjBuM3nn3/OY489xpAhQ3jooYfw9fXll19+Ma7XarWsXr0arVZLWFgYzz//PMOHD+f9998vu1clhIp2lnb+k8I4exs6zALsmFV2+xVCCAslVzMWogxl5uho9f4GMnP0bHjjIRr5uJTdzm/FwOxQ0OfCi5uhdtuy27cQQlQAuZqxECo5HJNEZo4eLxc7Gno7l+3O3etAi6cN93d8Vrb7FkIICyMBRYgylNf/pEuDmuUzl0Xn8YAGzvwJcZFlv38hhLAQElCEKEM7zycAZdz/JD+vRhDy+J2DfV4+xxBCCAsgAUWIMpJ8O4fjV24BZTT/SVG63JkT6MRPkBhdfscRQggVSUARoozsvZCAXoH6Xk74uZXjRd/8W0GDXqDoYdeX5XccIYRQkQQUIcrIrvIYXlyUrv80fD36PaRcL//jCSFEBZOAIkQZKZf5T4oS2AnqhIEuG/bMLf/jCSFEBZOAIkQZuHbrNhdupGOlMcwgWyHyWlEOLoaMxIo5phBCVBAJKEKUgbzTOy1qu+PmYFMxB23QC3ybQ0467Pu6Yo4phBAVRAKKEGUg//wnFUajuduKsu8ryEqtuGMLIUQ5k4AiRCkpilL+858UJfhx8GwAmbcMp3qEEKKKkIAiRCmdjUvjZloW9jZWtA50r9iDW2mhyxuG+3vmQk5mxR5fCCHKiQQUIUopb/RO+7qe2FlrK76A5kPBtTakxRmGHQshRBUgAUWIUtpt7H9SQaN37mVtC51fM9zf9QXoctWpQwghypAEFCFKIUenZ+8FQ/+TTvUruP9JfqEvgGNNuBUDJ35Wrw4hhCgjElCEKIVjl2+Rnq2jhqMNIX6u6hVi6whhrxju7/wM9Hr1ahFCiDIgAUWIUsjrf9KpQU2srDTqFtPuRbBzhRun4cwadWsRQohSkoAiRCmoMv9JUezdoP0Yw/0ds0BR1K1HCCFKQQKKECWUlpXLkZhbgIUEFIAOY8HaAa4dhgtb1a5GCCFKTAKKECW0PzqBXL1CHQ9HAjwc1S7HwNkL2oQb7u+YpW4tQghRChJQhCihnedUmj32QTq9ClbWcHEHXN6vdjVCCFEiElCEKCGL6n+Sn1ttaPmM4f6Oz9StRQghSkgCihAlEJ+ayZm4VDQaCKuv0gRt99P5DUADZ9dC7Am1qxFCCLNJQBGiBPZEGU7vhPi54uFkq3I1hajZAJoOMtzf+bmqpQghRElIQBGiBHaes9DTO/l1mWD4evIXSIhStxYhhDCTBBQhzKQoirH/icV1kM3PrwU07A2KHnZ9qXY1QghhFgkoQpgp+mY615IzsdVa0S7IQ+1y7q/rPw1fjy6HlGvq1iKEEGaQgCKEmfJaT9oE1sDBVqtyNQ9QpyMEdgZ9Duyeq3Y1QghRbBJQhDBT3vV3ujS04NM7+XW90xfl0GJIT1C3FiGEKCYJKEKYQadX2B1loRO0FaV+T/BrCTkZsO8rtasRQohikYAihBmOX00mNTMXF3trmtdyU7uc4tFo7vZF2f81ZKaoW48QQhSDBBQhzJDX/6RTfU+0VhqVqzFDkwFQsxFkJsPB/6hdjRBCPJAEFCHMYLHT2z+IlRV0ecNwf888yLmtbj1CCPEAElCEKKbb2ToOXkwCoFNlCygAzZ8CtwBIj4cjy9SuRggh7ksCihDFdPBSItk6PX5u9tSr6aR2OebT2kDn1w33d80GXY669QghxH1IQBGimHbmmz1Wo6lE/U/yC30enLwgOQaO/6R2NUIIUSQJKEIUU6Xtf5KfjQOERRju7/wM9Hp16xFCiCJIQBGiGBLTszl5zTA8t1MDT5WrKaW2o8HODW6ehdOr1a5GCCEKJQFFiGLYE5WAokBjHxe8XezVLqd07F2hw0uG+ztmgaKoW48QQhRCAooQxbCzMly92BwdxoKNI1w/ClF/qV2NEEIUIAFFiGIw9j9pWMlP7+Rx8oQ2Iwz3d3ymailCCFEYCShCPMDlxAxiEjOwttLQvm4VCSgAYePAygYu7YSYvWpXI4QQJiSgCPEAea0nrQLccbazVrmaMuRWC1o9a7gvrShCCAsjAUWIB6hy/U/y6zweNFZwbj1c/1vtaoQQwkgCihD3odcr7I5KAKBLwyoYUDzrQ9MnDPd3fq5uLUIIkY8EFCHu41RsConp2TjZamkV4K52OeWjywTD15O/ws3z6tYihBB3SEAR4j7y+p90qOeJjbaK/rr4NoNGfQEFdn2hdjVCCAFIQBHivnaeN5zeqZL9T/Lr+k/D12MrIPmKurUIIQQSUIQoUlaujv3Rd/qfVPWAEtAegrqCPgd2z1W7GiGEkIAiRFEOX7pFZo6ems52NPJxVruc8tf1Tl+UQ0sg/aaqpQghhAQUIYqwOyrv6sWeaDQalaupAPUeBv9QyL0NexeoXY0QopqTgCJEEfLmP+lU1U/v5NFo7vZF2f8NZCarW48QolqTgCJEIVIyczh2+RZQDTrI5te4P9RsDFnJcGCR2tUIIaoxCShCFGJvVAJ6BerVdKKWu4Pa5VQcK6u7fVH2zIPsDHXrEUJUWxJQhCjErqo8vf2DNBsC7nUg4yYcWaZ2NUKIakoCihCFqNLX33kQrQ10ft1wf9eXkJutbj1CiGrJ7ICyfft2BgwYgL+/PxqNhlWrVpmsVxSFyZMn4+fnh4ODA7169eLcuXMm2yQmJjJs2DBcXV1xd3dn9OjRpKWlleqFCFFWriffJupGOlYaCKvnqXY56mj1PDh5Q8oVOL5S7WqEENWQ2QElPT2dli1bMm/evELXf/zxx8yePZuvvvqKffv24eTkRJ8+fcjMzDRuM2zYME6ePMnGjRtZvXo127dv56WXXir5qxCiDO26M3ts89ruuDnaqFyNSmzsodM4w/2dn4Nep249Qohqx9rcJ/Tr149+/foVuk5RFL744gveffddBg4cCMB///tffHx8WLVqFc888wynTp1i3bp1HDhwgLZt2wIwZ84cHn30UT799FP8/f1L8XKEKL28/iddGlTT1pM8bUfBjlmQcA5O/QFNB6ldkRCiGinTPijR0dHExsbSq1cv4zI3Nzc6dOjAnj17ANizZw/u7u7GcALQq1cvrKys2LdvX1mWI4TZFEWp3v1P8rNzgQ4vG+7vmAWKom49QohqpUwDSmxsLAA+Pj4my318fIzrYmNj8fb2NllvbW2Nh4eHcZt7ZWVlkZKSYnITojycj0/jRmoWdtZWtK5TQ+1y1NfhZbBxgti/4fxmtasRQlQjlWIUz8yZM3FzczPeAgIC1C5JVFF5rSft63pgb6NVuRoL4OgBbUca7u+YpW4tQohqpUwDiq+vLwBxcXEmy+Pi4ozrfH19iY+PN1mfm5tLYmKicZt7TZo0ieTkZOPt8uXLZVm2EEbVev6TooRFgNYWYnbDpd1qVyOEqCbKNKDUrVsXX19fNm++2xSckpLCvn37CAsLAyAsLIxbt25x6NAh4zZ//fUXer2eDh06FLpfOzs7XF1dTW5ClLUcnZ69FxIB6CIB5S5Xf2j1nOH+js/UrUUIUW2YHVDS0tI4evQoR48eBQwdY48ePUpMTAwajYbx48fz73//m99//53jx48zfPhw/P39GTRoEADBwcH07duXMWPGsH//fnbt2sW4ceN45plnZASPUNXfV26RlpWLu6MNIX4Sgk10fh00VnB+I1w/pnY1QohqwOyAcvDgQUJDQwkNDQVgwoQJhIaGMnnyZADeeustXn31VV566SXatWtHWloa69atw97e3riP77//niZNmtCzZ08effRRunTpwsKFC8voJQlRMjvPGeY/6Vy/JlZWGpWrsTAe9QxT4IO0ogghKoRGUSrf2MGUlBTc3NxITk6W0z2izAz9ag/7LyYy44nmPNehjtrlWJ64k7CgE6CBcQegZkO1KxJCVDLmfH5XilE8QpS39KxcDsckAdL/pEg+TaHxo4ACO79QuxohRBUnAUUIYH90Irl6hQAPB+p4OqpdjuXqMsHw9e8VcEtG0wkhyo8EFCHIP729tJ7cV0A7qPsQ6HNh9xy1qxFCVGESUITg7gRtnepLQHmgrv80fD28FNJuqFuLEKLKkoAiqr0bqVmcjk0FoFP9an6BwOKo2w1qtYHcTNg7X+1qhBBVlAQUUe3tjjK0noT4ueLpbKdyNZWARnO3FeXAt3D7lqrlCCGqJgkootoz9j9pKKd3iq1RP/AKhqwUQ0gRQogyJgFFVGuKorDznFx/x2xWVtD1zoievfMhO0PdeoQQVY4EFFGtXUzI4FpyJrZaK9oF1VC7nMql6WBwD4SMBNj+MVS+OR+FEBZMAoqo1vJG77QOdMfR1lrlaioZrTV0e8twf+fn8PNoyE5XtyYhRJUhAUVUa7vOyfwnpdJqGPT7BKys4cTP8G0vSIhSuyohRBUgAUVUWzq9YhzBI/1PSkijgQ4vQfhqcPaB+EhY+DCcWad2ZUKISk4Ciqi2Tl5LJiUzFxc7a5rXclO7nMotMAz+sR0COkJWMvzvafhrOuh1alcmhKikJKCIaiuv/0nH+p5Ya+VXodRcfCH8D2j/D8Pj7R/D8qchI1HduoQQlZL8VRbVllx/pxxY28KjH8MTC8HaAc5vhIXdIfa42pUJISoZCSiiWsrM0XHgYhIg/U/KRcunYfQGwzDkW5fg20fg2A9qVyWEqEQkoIhq6fdj18jO1ePrak99Lye1y6ma/FrAS1uhQS/IvQ2/vgRr3oLcbLUrE0JUAhJQRLWSkJbF6yuO8NZPfwPQt5kvGo1G5aqqMEcPeO5HeOjOfCn7v4alAyA1Vt26hBAWTwKKqBYUReHnQ1fo9dk2fjt6DSsNjO5Sl7f7NlG7tKrPSgs9/g+eXQF2bnB5L3z9EMTsVbsyIYQF0yhK5ZufOiUlBTc3N5KTk3F1dVW7HGHhLidm8K9fj7PjzqRsTXxd+GhIC1oGuKtbWHWUEAU/PG+YL8XKGvrMgPYvGeZTEUJUeeZ8fsvc3qLKytXpWbL7IrM2nOV2jg5bayte79mQlx6qh40MK1aHZ314cRP8/qph5tm1b8HVQ/DYF2DrqHZ1QggLIgFFVEknryXzzs/HOX41GYCO9TyY8URz6nk5q1yZwNYJhiyCWm1gw3vw9w8QFwlPfwceddWuTghhISSgiColM0fHF5vO8c2OC+j0Ci721rzbP5ihbQOkM6wl0WggLAL8WsLKERB3HBZ2MwSXho+oXZ0QwgJIO7eoMnafv0nfL7bz1bYodHqFR5v7snlCN55uV0fCiaUK6gIvbYPa7SAzGb5/CrZ9DHq92pUJIVQmnWRFpZeckcP0NZH8ePAKAD6udnwwsBm9m/qqXJkottwsWPcOHPyP4XGjfvDEV+DgrmpZQoiyZc7ntwQUUWkpisKa47FM+f0kN9OyAHi+Yx3e6tsEV3sblasTJXLke1j9BuiywKMePL0MfJqqXZUQoozIKB5R5V1Pvs17q06w6VQ8APW9nPhwSAvaBXmoXJkoldBh4BMCPwyHxAvwbS94fA40f1LtyoQQFUwCiqhU9HqF7/dd4qN1Z0jLysVGq2Fs9wZEPFwfO2ut2uWJsuAfapgi/+dRcGEr/Dwarh6GR6aBVlrGhKguJKCISuNcXCrv/HKcQ5cMF/kLrePOR0Na0MjHReXKRJlz8oTnf4G//g07P4O98+D6UXhqCTh7q12dEKICSB8UYfGycnUs2BrFvC3nydEpONlqeatvE57vGIjWSkbnVHmn/oBfx0J2Krj4wdDvIKCd2lUJIUpA+qCIKuPQpUTe+fk45+LTAOjZxJsPBjXD391B5cpEhQkeADUbww/D4OZZWNwP+n0IbUfLFPlCVGESUIRFSs3M4ZP1Z/hu7yUUBWo62zJlQFMea+Enc5pUR16NYMxfsOoVOPU7/PlPQ7+U/rPARsKqEFWRBBRhcTZFxvHuqhPEpmQC8FSb2vxf/2DcHW1Vrkyoys4Fhv4Xds+GTVPh6PcQd8JwyqdGoNrVCSHKmAQUYTFupGYx9Y+T/Pn3dQDqeDgy44nmdGlYU+XKhMXQaKDz64Yp8n8aBdeP3Z0iv0FPtasTQpQhmepeqE5RFH48cJlen23jz7+vo7XS8I9u9Vg//iEJJ6Jw9bobpsj3D4XbSbBsCOyYBZWvz78Qoggyikeo6uLNdP7163F2RyUA0NTflY+GtKBZLTeVKxOVQk4mrJ0Ih/9reNzkMRi0AOzl74IQlkhG8QiLl6PT8+2OaL7YdJasXD32NlZMeKQRozrXxVorDXuimGzsDTPN1moDaybC6dXwzRnDFPneTdSuTghRChJQRIU7fiWZt3/+m8jrKQB0aVCT6U80I9DTSeXKRKXVZgT4NIcfX4CEc/BNDxg0H5oOUrsyIUQJySkeUWFuZ+v4fNNZvt1xAb0Cbg42vPdYCENa15Khw6JspN2An0bCxR2Gx51eg55TQCv/iwlhCcz5/Ja2dFEhdpy7Qe8vtrFwuyGcDGjpz6YJ3XiyTW0JJ6LsOHvBC6sMwQQMQ5KXPQHpN1UtSwhhPmlBEeUqKT2bD/6M5JfDVwHwd7Pn3080o0cTH5UrE1XeyV9hVQTkpINrbej0KtTpYDgVJC0qQqhCOskK1SmKwu/HrvH+H5EkpGej0UB4WBBv9mmMs5382IkK0PQJ8Ao2TJGfcB7WvW1YbuMEtdtAQAcI6Gi4ro+9jBoTwtJIC4ooUxnZuew8d5Pv98Ww7ewNABr5OPPhkBa0rlND5epEtZSZAgcXwcVdcGU/ZCbfs4EGvIMNgaVOR8PXGkFynR8hyoE5n98SUESpXU7M4K/T8Ww+Hc/eCwlk5+oBsNVa8WqPBvyjW31sraW7k7AAej3cOA2X9xluMXshKbrgds4+ENDe0MJSpyP4tgBrudSCEKUlAUWUq1ydnkOXkvjrTDx/nYo3Xmk4T4CHAz0ae/NCWBANvJ1VqlKIYkqNM7SsxOw1hJZrR0GfY7qNtT34tzb0YQnoaAgvjh6qlCtEZSYBRZS5pPRstp29webT8Ww7E09KZq5xndZKQ5vAGvRs4k2PJt408HaWkTmi8srJhGtH4PJeiLnT0nI7seB2NRuZnhbybCCnhYR4AAkootQUReFMXCqbT8Wz5XQ8h2OS0Of7SXF3tOHhxt483MSbbg29cHO0Ua9YIcqTohg62cbsvRtaEs4V3M7R807H2zs3/1DDTLdCCCMJKKJEMnN07IlKYPPpOLacvsHVW7dN1jfxdaFHE296BnvTKqAGWiv5b1FUU+kJpqeFrh4GXZbpNlpb8Gt157TQnVNDzl6qlCuEpZCAIort2q3b/HXa0EqyK+ommTl64zo7ays6N6jJw3dO3dRyd1CxUiEsWG42XD92p4XlTmhJv1FwO496d/uw1OkINRuDlXQgF9WHBBRRJJ1e4ejlJMOom1PxnI5NNVnv72bPw3daScLq1cTBVqtSpUJUYopiGB0Us+/uaaEbpwpuZ+9+Z7TQnRFDfi1kThZRpUlAESaSb+ew/ewN/jodz9Yz8SRl3B2hoNFA6zo16HGnlaSJr4t0cBWiPNxOgisH850WOgQ5GQW3c6sDPk3Bt5nhq08zQ8uLlfyzICo/CSjVnKIoRN1IM7aSHLyUhC5fD1cXe2u6NfKiZ7A33Rp54+Ek8zsIUeF0ORB7/O6cLJcPQMqVwre1dgDvJoaw4pMXXJrKUGdR6UhAqYaycnXsu5DIX6fj+et0PDGJpv+ZNfB2pmcTw6ibNoE1sNHKeW8hLE5GIsRHQtxJiDtx52sk5N4ufHsX/3ytLXeCi2cD0MqoOmGZJKBUE3EpmWy5E0h2nr9JRrbOuM5Wa0WHeh535ibxoY6no4qVCiFKTK+DpIuG1pa4k3fDy61LhW+vtQWvxoaLIua1tPg0kxFEwiJIQKlCdHqFhLQs4lOziE/NJD4li5jEDHacu8nxq6bXFPF2saPHnVaSLg1q4iQX5ROi6spMgfhTEJc/uJyE7LTCt3fyvhtYfO+El5qNwNquYusW1ZoElEogR6fnRuqd4JGSSVxqFjdSMu8EEUMYiUvJIiEty2SCtHu1DHCnR2PDqJsQP1esZG4SIaovvR6SY+6GlbxWl8QLQCF/SKysDSElr5Ul7zSRi6/MiivKhQQUFWXm6O4ED0PAiDcJHXcfJ6ZnF3ufVhqo6WyHt6sd3i72+LjaEVqnBt0be+HtIjNVCiEeIDsd4k/n69dywnArcGXnOxw8TFtafJqCVxOwkbmQROlUmoAyb948PvnkE2JjY2nZsiVz5syhffv2D3yeGgElPSvXpLUjPiXzbgtIvjCS/xo1D2JtpcHbxQ4vV3u8Xezu3AwBJC+MeLvY4elsJ7O2CiHKlqJAylXTDrmxJwzT+Cv6gttrrAzDne1cDC0vhd60hq9aG9PH97tpC9uHTcF9Gvf7oH1q8x3/3n3kLZdBAmox5/NbtU4KP/zwAxMmTOCrr76iQ4cOfPHFF/Tp04czZ87g7e2tSk2R11LYce6GsbUjLi+EpGSSnq8D6oPYWlsZQoZLvuCRF0LyhZEajrZySkYIoQ6NBtxqG26N+txdnpMJN06bdsiNOwEZCYZrElUJGvNDjznBq9DQlT8gaQ1XyLa2M7RKWdsZhpJb2xmW29jfWZ/vlresGs2Ho1oLSocOHWjXrh1z584FQK/XExAQwKuvvso777xz3+eWVwvKf/dcZPJvJ4tc72irxcfVHq98rR3ernb3hBF7XB2sZbIzIUTVoSiQFgc3zkBuJuhz8910hjld8j/W54I+557Hd2663ILP1xfyfF1hz88xf39VjZVNMcNNvuU2+dbfG3jut529Gzi4l2n5Ft+Ckp2dzaFDh5g0aZJxmZWVFb169WLPnj0Fts/KyiIr6+6FuFJSUsqlrmA/Vwa18i+0tcPb1R5nGRUjhKiONBpDx1kXX7UrMY+iGE5XlSrwFHYrKpSZsc/cTMjNgpzbhq+5mflu+ZffNg1a+hzIzoHs1KJfd1kJfQEGzi3/4xRBlU/cmzdvotPp8PHxMVnu4+PD6dOnC2w/c+ZMpk2bVu51tQvyoF2QzMwohBBVgkYDGu2dUyqVeDi1LtdwteyczHuCTGa+ZXfCTF7YKbBtEWHofs+3UXf+rErRJDBp0iQmTJhgfJySkkJAQICKFQkhhBAVRHunX4utk9qVVChVAkrNmjXRarXExcWZLI+Li8PXt2ATop2dHXZ2lTj9CiGEEMIsqoy1srW1pU2bNmzevNm4TK/Xs3nzZsLCwtQoSQghhBAWRLVTPBMmTCA8PJy2bdvSvn17vvjiC9LT0xk5cqRaJQkhhBDCQqgWUJ5++mlu3LjB5MmTiY2NpVWrVqxbt65Ax1khhBBCVD8y1b0QQgghKoQ5n98y368QQgghLI4EFCGEEEJYHAkoQgghhLA4ElCEEEIIYXEkoAghhBDC4khAEUIIIYTFkYAihBBCCIsjAUUIIYQQFkcCihBCCCEsjmpT3ZdG3uS3KSkpKlcihBBCiOLK+9wuziT2lTKgpKamAhAQEKByJUIIIYQwV2pqKm5ubvfdplJei0ev13Pt2jVcXFzQaDRluu+UlBQCAgK4fPmyXOenFOR9LBvyPpYNeR/LhryPZaM6v4+KopCamoq/vz9WVvfvZVIpW1CsrKyoXbt2uR7D1dW12v3glAd5H8uGvI9lQ97HsiHvY9moru/jg1pO8kgnWSGEEEJYHAkoQgghhLA4ElDuYWdnx5QpU7Czs1O7lEpN3seyIe9j2ZD3sWzI+1g25H0snkrZSVYIIYQQVZu0oAghhBDC4khAEUIIIYTFkYAihBBCCIsjAUUIIYQQFkcCSj7z5s0jKCgIe3t7OnTowP79+9UuqVKZOXMm7dq1w8XFBW9vbwYNGsSZM2fULqvS+/DDD9FoNIwfP17tUiqdq1ev8vzzz+Pp6YmDgwPNmzfn4MGDapdVqeh0Ot577z3q1q2Lg4MD9evX54MPPijWtVSqs+3btzNgwAD8/f3RaDSsWrXKZL2iKEyePBk/Pz8cHBzo1asX586dU6dYCyUB5Y4ffviBCRMmMGXKFA4fPkzLli3p06cP8fHxapdWaWzbto2IiAj27t3Lxo0bycnJoXfv3qSnp6tdWqV14MABvv76a1q0aKF2KZVOUlISnTt3xsbGhrVr1xIZGcmsWbOoUaOG2qVVKh999BELFixg7ty5nDp1io8++oiPP/6YOXPmqF2aRUtPT6dly5bMmzev0PUff/wxs2fP5quvvmLfvn04OTnRp08fMjMzK7hSC6YIRVEUpX379kpERITxsU6nU/z9/ZWZM2eqWFXlFh8frwDKtm3b1C6lUkpNTVUaNmyobNy4UenWrZvy+uuvq11SpfL2228rXbp0UbuMSq9///7KqFGjTJYNHjxYGTZsmEoVVT6A8uuvvxof6/V6xdfXV/nkk0+My27duqXY2dkp//vf/1So0DJJCwqQnZ3NoUOH6NWrl3GZlZUVvXr1Ys+ePSpWVrklJycD4OHhoXIllVNERAT9+/c3+bkUxff777/Ttm1bnnrqKby9vQkNDeWbb75Ru6xKp1OnTmzevJmzZ88CcOzYMXbu3Em/fv1Urqzyio6OJjY21uR3283NjQ4dOshnTj6V8mKBZe3mzZvodDp8fHxMlvv4+HD69GmVqqrc9Ho948ePp3PnzjRr1kztciqdFStWcPjwYQ4cOKB2KZXWhQsXWLBgARMmTOBf//oXBw4c4LXXXsPW1pbw8HC1y6s03nnnHVJSUmjSpAlarRadTsf06dMZNmyY2qVVWrGxsQCFfubkrRMSUEQ5iYiI4MSJE+zcuVPtUiqdy5cv8/rrr7Nx40bs7e3VLqfS0uv1tG3blhkzZgAQGhrKiRMn+OqrrySgmOHHH3/k+++/Z/ny5TRt2pSjR48yfvx4/P395X0U5UpO8QA1a9ZEq9USFxdnsjwuLg5fX1+Vqqq8xo0bx+rVq9myZQu1a9dWu5xK59ChQ8THx9O6dWusra2xtrZm27ZtzJ49G2tra3Q6ndolVgp+fn6EhISYLAsODiYmJkaliiqniRMn8s477/DMM8/QvHlzXnjhBd544w1mzpypdmmVVt7ninzm3J8EFMDW1pY2bdqwefNm4zK9Xs/mzZsJCwtTsbLKRVEUxo0bx6+//spff/1F3bp11S6pUurZsyfHjx/n6NGjxlvbtm0ZNmwYR48eRavVql1ipdC5c+cCw9zPnj1LYGCgShVVThkZGVhZmX5UaLVa9Hq9ShVVfnXr1sXX19fkMyclJYV9+/bJZ04+cornjgkTJhAeHk7btm1p3749X3zxBenp6YwcOVLt0iqNiIgIli9fzm+//YaLi4vxXKqbmxsODg4qV1d5uLi4FOi34+TkhKenp/TnMcMbb7xBp06dmDFjBkOHDmX//v0sXLiQhQsXql1apTJgwACmT59OnTp1aNq0KUeOHOGzzz5j1KhRapdm0dLS0jh//rzxcXR0NEePHsXDw4M6deowfvx4/v3vf9OwYUPq1q3Le++9h7+/P4MGDVKvaEuj9jAiSzJnzhylTp06iq2trdK+fXtl7969apdUqQCF3hYvXqx2aZWeDDMumT/++ENp1qyZYmdnpzRp0kRZuHCh2iVVOikpKcrrr7+u1KlTR7G3t1fq1aun/N///Z+SlZWldmkWbcuWLYX+PQwPD1cUxTDU+L333lN8fHwUOzs7pWfPnsqZM2fULdrCaBRFpgMUQgghhGWRPihCCCGEsDgSUIQQQghhcSSgCCGEEMLiSEARQgghhMWRgCKEEEIIiyMBRQghhBAWRwKKEEIIISyOBBQhhBBCWBwJKEKIUuvevTvjx48v9X4uXryIRqPh6NGjpd6XEKJyk4AihBBCCIsjAUUIUSojRoxg27ZtfPnll2g0GjQaDRcvXixy+6SkJIYNG4aXlxcODg40bNiQxYsXAxivgB0aGopGo6F79+7G53377bcEBwdjb29PkyZNmD9/vnFdXsvLihUr6NSpE/b29jRr1oxt27aVy2sWQpQ/uZqxEKJUvvzyS86ePUuzZs14//33AfDy8ipy+/fee4/IyEjWrl1LzZo1OX/+PLdv3wZg//79tG/fnk2bNtG0aVNsbW0B+P7775k8eTJz584lNDSUI0eOMGbMGJycnAgPDzfue+LEiXzxxReEhITw2WefMWDAAKKjo/H09CzHd0AIUR4koAghSsXNzQ1bW1scHR3x9fV94PYxMTGEhobStm1bAIKCgozr8oKNp6enyb6mTJnCrFmzGDx4MGBoaYmMjOTrr782CSjjxo1jyJAhACxYsIB169axaNEi3nrrrVK/TiFExZKAIoSoUGPHjmXIkCEcPnyY3r17M2jQIDp16lTk9unp6URFRTF69GjGjBljXJ6bm4ubm5vJtmFhYcb71tbWtG3bllOnTpX9ixBClDsJKEKICtWvXz8uXbrEmjVr2LhxIz179iQiIoJPP/200O3T0tIA+Oabb+jQoYPJOq1WW+71CiHUIZ1khRClZmtri06nK/b2Xl5ehIeHs2zZMr744gsWLlxo3A9gsi8fHx/8/f25cOECDRo0MLnldarNs3fvXuP93NxcDh06RHBwcGlemhBCJdKCIoQotaCgIPbt28fFixdxdnbGw8MDK6vC//+ZPHkybdq0oWnTpmRlZbF69WpjiPD29sbBwYF169ZRu3Zt7O3tcXNzY9q0abz22mu4ubnRt29fsrKyOHjwIElJSUyYMMG473nz5tGwYUOCg4P5/PPPSUpKYtSoURXyHgghypa0oAghSu3NN99Eq9USEhKCl5cXMTExRW5ra2vLpEmTaNGiBQ899BBarZYVK1YAhn4js2fP5uuvv8bf35+BAwcC8OKLL/Ltt9+yePFimjdvTrdu3ViyZEmBFpQPP/yQDz/8kJYtW7Jz505+//13atasWX4vXAhRbjSKoihqFyGEEKVx8eJF6taty5EjR2jVqpXa5QghyoC0oAghhBDC4khAEUKUqZdffhlnZ+dCby+//LLa5QkhKgk5xSOEKFPx8fGkpKQUus7V1RVvb+8KrkgIURlJQBFCCCGExZFTPEIIIYSwOBJQhBBCCGFxJKAIIYQQwuJIQBFCCCGExZGAIoQQQgiLIwFFCCGEEBZHAooQQgghLI4EFCGEEEJYnP8HhZTh6TE33WIAAAAASUVORK5CYII=", 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "analysis.plot_all('soil_output/Spread_barabasi*/', analysis.get_count, 'state_id');" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If we compare these results to those of the other graph model (a fully-connected graph), we can see a stark difference:" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "ExecuteTime": { - "end_time": "2017-10-19T15:58:26.903783Z", - "start_time": "2017-10-19T17:57:57.983957+02:00" - }, - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "analysis.plot_all('soil_output/Spread_erdos*', analysis.get_count, 'state_id');" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The previous cells were using the `count_value` function for aggregation. There's another function to plot numeral values:" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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mixUn8P/8849O4nH79m3cvHkT48ePf+z63333HS5fvqxzifXEiRM62yfjIX2KTEan+C+SRxVfn374ckJYWJjOdfX4+Hhs27YNPXr0eOw8AIMGDYJCocC8efNK7E8IoS2tLN7Ow22EEFi2bJnOOh4eHggKCsKPP/6o072+d+9enUtLFRESEgJ/f38sWrQI9+/fL/F8Rbqru3fvDnNzcyxfvlwn9qVLl5Zo26dPH5w8eVKnZDErKwsrV66Er6+v9hfyo+WmSqUSTZs2hRCi1PJZfbi6uqJr16749ttvdRLOYhXtoi/NyJEjIZPJMHnyZNy4cQP/93//9yShAoC29+arr77SWa7PLMGlvbeA0s9RsRUrVpS6v969e1d4v/raunWrznidkydP4sSJExXaZ58+faBWq7XzdRT7/PPPIZPJSmzjST7TxZo1a4bGjRtj5cqVOuNMvv76a8hkMgwZMkS7LD09HVeuXNH5zA4YMADm5uY651YIgW+++QZeXl7o0KFDheIgw8GeEdLbW2+9hezsbLzwwgto3Lgx8vPzcezYMaxfvx6+vr4YN26ctm3z5s3Rs2dPnTJAABW6ZODv74+PPvoIs2bNQkxMDAYOHAhbW1tER0djy5YtGD9+PKZNm4bGjRvD398f06ZNw61bt2BnZ4fNmzeXeu19/vz56Nu3Lzp16oSXX34Z9+7d087LUVpSURa5XI7vvvsOvXv3RrNmzTBu3Dh4eXnh1q1b2L9/P+zs7PDHH3+Uuw0XFxdMmzYN8+fPx/PPP48+ffogPDwcO3fuhLOzs07bmTNn4tdff0Xv3r0xadIkODo64scff0R0dDQ2b96s7Xrv0aMH3N3d0bFjR7i5ueHy5cv48ssv0bdv3xJjAipjxYoV6NSpE1q0aIHXXnsN9evXR2JiIsLCwnDz5k2cPXu2Utt1cXFBr169sHHjRjg4OJRbfltRISEhGDx4MJYuXYq7d+9qS3uvXr0KoOzLJw+zs7PD008/jc8++wwFBQXw8vLCnj17EB0dXeY60dHR6N+/P3r16oWwsDCsXbsWL774IgIDA5/4mMrSoEEDdOrUCRMmTEBeXh6WLl0KJycnTJ8+/bHr9uvXD926dcN///tfxMTEIDAwEHv27MG2bdswZcoUnUG2wJN9ph+2cOFC9O/fHz169MCIESNw4cIFfPnll3j11Vd1Sna3bNmCcePGYc2aNdp5ZurWrYspU6Zg4cKFKCgoQOvWrbF161YcPnwYP//8Myc8M0Y1Xr9DRm/nzp3i5ZdfFo0bNxY2NjZCqVSKBg0aiLfeekskJiZq2wEQEydOFGvXrhUBAQFCpVKJ4OBgsX//fp3tFZdEJicnl7q/zZs3i06dOglra2thbW0tGjduLCZOnCgiIyO1bS5duiS6d+8ubGxshLOzs3jttde0pZJr1qwpsb0mTZoIlUolmjZtKn777TcxZswYvUp7i4WHh4tBgwYJJycnoVKphI+Pjxg2bJjYt29fhY5PrVaLefPmCQ8PD2FpaSm6du0qLly4UGoZ6/Xr18WQIUOEg4ODsLCwEG3atBF//vmnTptvv/1WPP3009p4/P39xTvvvCPS09MrfEzF5aILFy4s9fnr16+L0aNHC3d3d2Fubi68vLzE888/LzZt2qRtU1zae+rUKZ119+/fLwCUeA8IIcSGDRsEADF+/PhS96tvaa8QRaWeEydOFI6OjsLGxkYMHDhQREZGCgBiwYIF2nblnaObN2+KF154QTg4OAh7e3sxdOhQcfv27TLLgy9duiSGDBkibG1tRZ06dcSbb74pcnJydLZZ/Nl40mN8+FwtXrxYeHt7C5VKJTp37qxTSixEUWmvtbV1qdvJzMwUU6dOFZ6ensLc3FwEBASIhQsX6pRlPxz34z7TFbVlyxYRFBQkVCqVqFu3rnjvvfdKTBFQ/F569HOsVqvFJ598Inx8fIRSqRTNmjUTa9eurVQcJD2ZENU4WopqNZlMpjNdM1F5tm3bhoEDB+LQoUPo3Llzte0nIiICwcHBWLt2rXYG0qrw/vvvY968eUhOTi7Rs1VdYmJi4Ofnh4ULF2LatGk1sk+i6sAxI0RkEFatWoX69etX6cyZpc03sXTpUsjlcjz99NNVth8iejIcM0L0iHv37pWYjv1hCoWiwlUDhkStVj92kKmNjU2JkuTqtm7dOpw7dw7bt2/HsmXLKjSWIyEhodznLS0tYW9vj88++wynT59Gt27dYGZmhp07d2Lnzp0YP358iZJSQ1LRc2VoDPU9RkZA6utEZLpQxnVxQ9elSxfttNelPSoztsQQFI8vKO/x8BiImgJA2NjYiFdeeUUUFBRUeJ3yHsXjLvbs2SM6duwo6tSpI8zNzYW/v794//33K7wffTxu7JM+KnquHje+p6YZ6nuMDB/HjBA94vTp0+XOgmlpaYmOHTvWYERVIzc3F0eOHCm3Tf369XXmPDFUf/31V7nPe3p66j3FvyEx1nNlrHGT9JiMEBERkaQ4gJWIiIgkZRQDWDUaDW7fvg1bW9sKDW4jIiIi6QkhkJmZCU9Pz3Lvi2QUycjt27cNeuQ7ERERlS0+Pr7EHaMfZhTJSPE01vHx8bCzs5M4GiIiIqqIjIwMeHt7P/Z2FEaRjBRfmrGzs2MyQkREZGQeN8SCA1iJiIhIUkxGiIiISFJMRoiIiEhSRjFmhIiI9KdWq1FQUCB1GGTCzM3NoVAonng7TEaIiEyMEAIJCQlIS0uTOhSqBRwcHODu7v5E84AxGSEiMjHFiYirqyusrKw4WSRVCyEEsrOzkZSUBADw8PCo9LaYjBARmRC1Wq1NRJycnKQOh0ycpaUlACApKQmurq6VvmTDAaxERCakeIyIlZWVxJFQbVH8XnuS8UlMRoiITBAvzVBNqYr3GpMRIiIikpTeycihQ4fQr18/eHp6QiaTYevWrY9d58CBA3jqqaegUqnQoEED/PDDD5UIlYiISD9du3bFlClTpA6DHkPvZCQrKwuBgYFYsWJFhdpHR0ejb9++6NatGyIiIjBlyhS8+uqr2L17t97BEhERkenRu5qmd+/e6N27d4Xbf/PNN/Dz88PixYsBAE2aNMGRI0fw+eefo2fPnvrunohqudwCNSzMn3ySJSIyHNU+ZiQsLAzdu3fXWdazZ0+EhYWVuU5eXh4yMjJ0HkREm07fRPO5u7H6SLTUoZCR2r59O+zt7fHzzz8jPj4ew4YNg4ODAxwdHTFgwADExMQAKBqSYG5ujoSEBJ31p0yZgs6dO0sQuWmr9mQkISEBbm5uOsvc3NyQkZGBnJycUteZP38+7O3ttQ9vb+/qDpOIDNz15PuYvfUCCjUC+yOTpA6HjNAvv/yCkSNH4ueff8awYcPQs2dP2Nra4vDhwzh69ChsbGzQq1cv5Ofn4+mnn0b9+vXxv//9T7t+QUEBfv75Z7z88ssSHoVpMshqmlmzZiE9PV37iI+PlzokIpJQfqEGU9ZFIKdADQCIvZstcURkbFasWIE33ngDf/zxB55//nmsX78eGo0G3333HVq0aIEmTZpgzZo1iIuLw4EDBwAAr7zyCtasWaPdxh9//IHc3FwMGzZMoqMwXdU+A6u7uzsSExN1liUmJsLOzk47c9ujVCoVVCpVdYdGREZiyd6rOH8rHVZKBbLz1biZmo38Qg2UZgb59xQZmE2bNiEpKQlHjx5F69atAQBnz55FVFQUbG1tddrm5ubi+vXrAICxY8fivffew/Hjx9GuXTv88MMPGDZsGKytrWv8GExdtScj7du3x44dO3SW7d27F+3bt6/uXRORCTh2PQXfHir6clg8NBBTN0Qgt0CDW2k58HPmlwI9XnBwMM6cOYPVq1ejVatWkMlkuH//PkJCQvDzzz+XaO/i4gIAcHV1Rb9+/bBmzRr4+flh586d2l4Tqlp6/1lx//59REREICIiAkBR6W5ERATi4uIAFF1iGT16tLb966+/jhs3bmD69Om4cuUKvvrqK2zYsAFTp06tmiMgIpOVlp2P0PVnIQQwvJU3erfwgK9TUQISczdL4ujIWPj7+2P//v3Ytm0b3nrrLQDAU089hWvXrsHV1RUNGjTQedjb22vXffXVV7F+/XqsXLkS/v7+6Nixo1SHYdL0Tkb++ecfBAcHIzg4GAAQGhqK4OBgzJkzBwBw584dbWICAH5+fti+fTv27t2LwMBALF68GN999x3LeomoXEIIzPrtPBIycuHnbI05/ZoCAHyciu6DEZvCZIQqrmHDhti/fz82b96MKVOmYNSoUXB2dsaAAQNw+PBhREdH48CBA5g0aRJu3rypXa9nz56ws7PDRx99hHHjxkl4BKZN78s0Xbt2hRCizOdLm121a9euCA8P13dXRFSLbTx9EzsvJMBMLsPS4UGwVhX9uvq3Z4SDWEk/jRo1wt9//42uXbtCoVDg0KFDmDFjBgYNGoTMzEx4eXnh2WefhZ2dnXYduVyOsWPH4pNPPtHp9aeqVe1jRoiI9BWTkoX3f78IAJj6XEMEejton/N5kIzE8jINVcCjYzyaNGmiU1Tx448/PnYbt27dQp8+feDh4VHV4dEDTEaIyKAUqDWYvC4c2flqtPVzxOtd/HWe9y2+TMOeEapm6enpOH/+PH755Rf8/vvvUodj0piMEJFBWfbXNZy9mQ47CzN8PjwICrnu7cl9HlTQxKdmo1CtgZmC5b1UPQYMGICTJ0/i9ddfx3PPPSd1OCaNyQgRGYwTN+5ixYEoAMAng1rA06HkXEQedhZQmsmRX6jB7bRc1HvQU0JU1VjGW3P4JwURGYT0nAKEbigq4x38VF0839Kz1HZyuQw+jkUJCMt7iUwDkxEikpwQAu9tvYBbaTmo52iFeQOaldueg1iJTAuTESKS3JbwW/jj7G0o5DIsHREEG1X5V5CLB7GyvJfINDAZISJJxd3NxpxtRWW8k58NwFP16jx2neJBrOwZITINTEaISDKFag2mrA/H/bxCtPatg4ndGlRoPfaMEJkWJiNEJJnlf0fhTFwabFVmWDKsZBlvWYpnYY27mw21puwZoYnIODAZISJJnI69h+V/XwMAfPRCc3g7VrxE18PeAuYKGfLVGiRk5FZXiGQCfH19sXTpUqnDMFpjx47FwIEDq30/TEaIqMZl5hZg8roIaAQwMMgTA4K89FrfTCGHdx3eMI+qn0wmw9atW6UOw+QxGSGiGjdn20XcTM1B3TqW+GBg80ptw4fjRmq1/Px8qUMwCEIIFBYWSh3GE2MyQkQ1alvELWwJvwW5DFg6PAh2FuaV2g7nGjEtXbt2xZtvvok333wT9vb2cHZ2xuzZs7V3iff19cWHH36I0aNHw87ODuPHjwcAbN68Gc2aNYNKpYKvry8WL15cYtuZmZkYOXIkrK2t4eXlhRUrVlQoJl9fXwDACy+8AJlMBl9fX1y9ehUymQxXrlzRafv555/D39+/lK3oSk1NxahRo+Di4gJLS0sEBARgzZo1AICYmBjIZDKsW7cOHTp0gIWFBZo3b46DBw9q1z9w4ABkMhl27tyJkJAQqFQqHDlyBBqNBvPnz4efnx8sLS0RGBiITZs2addTq9V45ZVXtM83atQIy5Yt04lNrVYjNDQUDg4OcHJywvTp07Wvf7UTRiA9PV0AEOnp6VKHQkRPIO5ulmg+Z5fwmfGnWLwn8om29cPRaOEz40/x2o+nqig605CTkyMuXbokcnJyhBBCaDQakZVXIMlDo9FUOO4uXboIGxsbMXnyZHHlyhWxdu1aYWVlJVauXCmEEMLHx0fY2dmJRYsWiaioKBEVFSX++ecfIZfLxQcffCAiIyPFmjVrhKWlpVizZo12uz4+PsLW1lbMnz9fREZGii+++EIoFAqxZ8+ex8aUlJQkAIg1a9aIO3fuiKSkJCGEEK1atRLvvfeeTtuQkJASy0ozceJEERQUJE6dOiWio6PF3r17xe+//y6EECI6OloAEHXr1hWbNm0Sly5dEq+++qqwtbUVKSkpQggh9u/fLwCIli1bij179oioqChx9+5d8dFHH4nGjRuLXbt2ievXr4s1a9YIlUolDhw4IIQQIj8/X8yZM0ecOnVK3LhxQ/v6rl+/Xhvbp59+KurUqSM2b94sLl26JF555RVha2srBgwYUO4xPfqee1hFv79lQtRU2lN5GRkZsLe3R3p6Ouzs7KQOh4gqoVCtwchVx3EqJhXB9Ryw8T/tn+gmdwcikzB2zSk0crPF7qlPV2Gkxi03NxfR0dHw8/ODhYUFsvML0XTObkliufRBT1gpK3YLtK5duyIpKQkXL16ETFZUVTVz5kz8/vvvuHTpEnx9fREcHIwtW7Zo1xk1ahSSk5OxZ88e7bLp06dj+/btuHixaO4aX19fNGnSBDt37tS2GTFiBDIyMrBjx47HxiWTybBlyxadQZxLly7Fl19+iaioovsoXb16FY0aNcLly5fRuHHjcrfXv39/ODs7Y/Xq1SWei4mJgZ+fHxYsWIAZM2YAAAoLC+Hn54e33noL06dPx4EDB9CtWzds3boVAwYMAADk5eXB0dERf/31F9q3b6/d3quvvors7Gz88ssvpcby5ptvIiEhQduD4unpialTp+Kdd97R2XdISEi542Yefc89rKLf37xMQ0Q14usD13EqJhXWSgWWDQ9+4rvtFpf3xt7LgoblvSahXbt22kQEANq3b49r165BrVYDAFq1aqXT/vLly+jYsaPOso4dO+qsU7ydh7Vv3x6XL1+udJwjRoxATEwMjh8/DgD4+eef8dRTTz02EQGACRMmYN26dQgKCsL06dNx7NixEm0ejtfMzAytWrUqEe/Dr0VUVBSys7Px3HPPwcbGRvv46aefcP36dW27FStWICQkBC4uLrCxscHKlSsRFxcHAEhPT8edO3fQtm3bEvuuCbxrLxFVu/C4VCzdV1TG+8GA5lVyp12vOpZQyGXILdAgKTMP7vYWj1+pFrI0V+DSBz0l23dVsra2rtLtVZa7uzueeeYZ/PLLL2jXrh1++eUXTJgwoULr9u7dG7GxsdixYwf27t2LZ599FhMnTsSiRYv0iuHh1+L+/fsAgO3bt8PLS7cyTaVSAQDWrVuHadOmYfHixWjfvj1sbW2xcOFCnDhxQq/9Vhf2jBBRtbqfV4gp6yOg1gg839IDg57Sr4y3LOYKOerWsQTAu/eWRyaTwUppJsnj4V6Oinj0i/H48eMICAiAQlF6UtOkSRMcPXpUZ9nRo0fRsGFDnXWKezAe/rlJkyYVisnc3Fynl6XYqFGjsH79eoSFheHGjRsYMWJEhbYHAC4uLhgzZgzWrl2LpUuXYuXKlSXiK1ZYWIjTp0+XG2/Tpk2hUqkQFxeHBg0a6Dy8vb0BFL0uHTp0wBtvvIHg4GA0aNBAp9fE3t4eHh4eOuegeN81gT0jRFSt3v/9ImLvZsPLwRIfv9BC7y+o8vg4WSP2bjZi72ahXX2nKtsuSSMuLg6hoaH4z3/+gzNnzmD58uWlVscUe/vtt9G6dWt8+OGHGD58OMLCwvDll1/iq6++0ml39OhRfPbZZxg4cCD27t2LjRs3Yvv27RWKydfXF/v27UPHjh2hUqlQp07RvZMGDRqECRMmYMKECejWrRs8PT0rtL05c+YgJCQEzZo1Q15eHv78888SicaKFSsQEBCAJk2a4PPPP0dqaipefvnlMrdpa2uLadOmYerUqdBoNOjUqRPS09Nx9OhR2NnZYcyYMQgICMBPP/2E3bt3w8/PD//73/9w6tQp+Pn5abczefJkLFiwAAEBAWjcuDGWLFmCtLS0Ch3Xk2IyQkTV5s9zt7Hp9E3IZMCSYYGwt6xcGW9ZfJ2scAica8RUjB49Gjk5OWjTpg0UCgUmT56sLeEtzVNPPYUNGzZgzpw5+PDDD+Hh4YEPPvgAY8eO1Wn39ttv459//sG8efNgZ2eHJUuWoGfPil26Wrx4MUJDQ7Fq1Sp4eXkhJiYGQFEC0K9fP2zYsKHUwahlUSqVmDVrFmJiYmBpaYnOnTtj3bp1Om0WLFiABQsWICIiAg0aNMDvv/8OZ2fncrf74YcfwsXFBfPnz8eNGzfg4OCAp556Cu+++y4A4D//+Q/Cw8MxfPhwyGQyjBw5Em+88YbOwN63334bd+7cwZgxYyCXy/Hyyy/jhRdeQHp6eoWPr7JYTUNE1eJ2Wg56LT2EjNxCTOzmj3d6Pn5wn76+PxKND/+8hD4t3PHVqJAq374xKq+ywZB17doVQUFBtXrq9uJqmvDwcAQFBUkdToWxmoaIDJJaIzB1fQQycgsRWNceU7o3rJb9aO/em8KeESJjxmSEiKrct4eu40T0PVgpFVg6IhjmT1jGW5aHZ2E1gk5eMjA///yzTinsw49mzZpVapuvv/56mdt8/fXXq/gITAfHjBBRlTp3Mw1L9lwFALzfrxn8nKuvHNPb0RJyGZCVr0by/Ty42hrPZQnSdeDAgRrfZ//+/XXm1XiYuXnlxjd98MEHmDZtWqnPPW6Yga+vb61NqpmMEFGVyc4vxOR1ESjUCPRp4Y6hrepW6/5UZgp4OljiZmoOYu9mMxkhvdja2sLW1rZKt+nq6gpXV9cq3WZtwMs0RFRlPvjjEqJTsuBuZ4FPqriMtyzFM7HGpHCukYfV1r+wqeZVxXuNyQgRVYldF+5g3an4ojLe4YFwsFLWyH59HgxijWV5L4B/Ly9kZ/P1oJpR/F6r7KUtgJdpiKgKJKTnYuZv5wEA45+ujw7+5c+JUJW0PSOchRUAoFAo4ODggKSkJACAlZVVjfRQUe0jhEB2djaSkpLg4OBQ5ky5FcFkhIieiEYj8PbGCKRlF6C5lx3efq5Rje6fPSMlubu7A4A2ISGqTg4ODtr3XGUxGSGiJ/LdkRs4GnUXFuZyLBsRDKVZzV799XX+t2dECMFeABTdj8bDwwOurq4oKCiQOhwyYebm5k/UI1KMyQgRVdqFW+lYuDsSADDn+Wbwd7Gp8RjqORb1jGTmFiI1uwCO1jUzVsUYKBSKKvmiIKpuHMBKRJWSk6/G5HXhKFAL9GjqhpFtvCWJw8JcAQ/7opJejhshMk5MRoioUj7afgnXk7PgaqvCgsEtJb088u+4ESYjRMaIyQgR6W3vpUT8fCIOALB4WKDkl0b+nWuEg1iJjBGTESLSS1JGLmZsPgcAeLWTHzoHuEgcke4gViIyPkxGiKjCisp4z+JeVj6aeNjhnV41W8ZbFu3de1neS2SUmIwQUYWtORaDw9dSoDKT44sRQVCZGUalxsN37yUi48NkhIgq5PKdDHy68woA4L2+TRDgVrU3GHsSxQNY07ILkJadL3E0RKQvJiNE9Fi5BUVlvPlqDZ5t7Ir/a+cjdUg6rJRmcLVVAeBMrETGiMkIET3W/B2XcTXxPpxtVPh0iLRlvGXhPWqIjBeTESIq1/4rSfgxLBYAsGhoSzjbqCSOqHS8Rw2R8WIyQkRlSs7MwzubzgIAxnbwRddGrhJHVDaW9xIZLyYjRFQqIQSmbzqLlPv5aORmi5m9G0sdUrnYM0JkvJiMEFGpfgqLxf7IZCjN5Fg2MggW5oZRxlsWX5b3EhktJiNEVMLVxEx8vOMyAGBW78Zo7G4ncUSPV9wzknI/H5m5BRJHQ0T6YDJCRDpyC9SY9Gs48gs16NLQBWM7+EodUoXYWpjD2aboHjm8VENkXJiMEJGOz3ZF4kpCJpyslVg41DDLeMviw/JeIqPEZISItA5eTcbqo9EAgM+GtISrrYXEEemHg1iJjBOTESICANy9n4dpG4vKeF9q54Nnm7hJHJH+tBOfpbBnhMiYMBkhIgghMGPzeSRn5qGBqw3+27eJ1CFVCntGiIwTkxEiws8n4vDX5UQoFXIsG2H4Zbxl4ZTwRMaJyQhRLReVlImPtl8CAEzv1QjNPO0ljqjyipORpMw8ZOcXShwNEVUUkxGiWiyvUI1Jv0Ygt0CDzgHOeLmjn9QhPRF7K3M4WJkD4KUaImPCZISoFlu85you3clAHStzLBoaCLnceMp4y+LDmViJjA6TEaJa6si1FKw8dAMAsGBwS7jZGVcZb1l8HwxijWHPCJHRqFQysmLFCvj6+sLCwgJt27bFyZMny22/dOlSNGrUCJaWlvD29sbUqVORm5tbqYCJ6MmlZuXj7Y0RAICRbeqhZzN3aQOqQj4s7yUyOnonI+vXr0doaCjmzp2LM2fOIDAwED179kRSUlKp7X/55RfMnDkTc+fOxeXLl/H9999j/fr1ePfdd584eCLSnxACM387h8SMPNR3scbs542zjLcsfs7FPSNMRoiMhd7JyJIlS/Daa69h3LhxaNq0Kb755htYWVlh9erVpbY/duwYOnbsiBdffBG+vr7o0aMHRo4c+djeFCKqHutPxWP3xUSYK2T4YkQwrJRmUodUpf4dM8LLNETGQq9kJD8/H6dPn0b37t3/3YBcju7duyMsLKzUdTp06IDTp09rk48bN25gx44d6NOnT5n7ycvLQ0ZGhs6DiJ7cjeT7mPdHURnv2z0aobmX8ZbxlqW4vPdOei5yC9QSR0NEFaFXMpKSkgK1Wg03N91pot3c3JCQkFDqOi+++CI++OADdOrUCebm5vD390fXrl3LvUwzf/582Nvbax/e3t76hElEpcgv1GDyugjkFKjRwd8J4zvXlzqkalHHyhy2FkW9PXH32DtCZAyqvZrmwIED+OSTT/DVV1/hzJkz+O2337B9+3Z8+OGHZa4za9YspKenax/x8fHVHSaRyfv8r6s4fysd9pbmWDzMNMp4SyOTyXiPGiIjo9fFYmdnZygUCiQmJuosT0xMhLt76aPxZ8+ejZdeegmvvvoqAKBFixbIysrC+PHj8d///hdyecl8SKVSQaVS6RMaEZUj7PpdfHPwOgBgwaAW8LC3lDii6uXjZIXzt9I5boTISOjVM6JUKhESEoJ9+/Zpl2k0Guzbtw/t27cvdZ3s7OwSCYdCUXTfCyGEvvESkZ7SswsQuiECQgDDWtVF7xYeUodU7XiPGiLjovcw+tDQUIwZMwatWrVCmzZtsHTpUmRlZWHcuHEAgNGjR8PLywvz588HAPTr1w9LlixBcHAw2rZti6ioKMyePRv9+vXTJiVEVD2EEHh3y3ncSc+Fr5MV5vZrJnVINYJ37yUyLnonI8OHD0dycjLmzJmDhIQEBAUFYdeuXdpBrXFxcTo9Ie+99x5kMhnee+893Lp1Cy4uLujXrx8+/vjjqjsKIirVptM3sf38HZjJZVg2IhjWKtMq4y2LrzN7RoiMiUwYwbWSjIwM2NvbIz09HXZ2dlKHQ2QUYlKy0PeLw8jKV+Odno0wsVsDqUOqMUmZuWjz8T7IZcDlD3tBZcZeWCIpVPT7m/emITJBBWoNpqyPQFa+Gm38HPF6F3+pQ6pRLjYqWCsV0Agg/l6O1OEQ0WMwGSEyQV/su4aI+DTYWpjh8+FBUJhoGW9ZZDIZ795LZESYjBCZmJPR97BifxQA4JMXWsDLwbTLeMvi68y79xIZCyYjRCYkPacAU9dHQCOAQU95oV+gp9QhSYY9I0TGg8kIkQmZs+0CbqXloJ6jFeb1rx1lvGXxdWLPCJGxYDJCZCK2hN/EtojbUMhl+Hx4EGwtzKUOSVLsGSEyHkxGiExA/L1szN56EQAw6ZkAhPjUkTgi6RXPwnozNQcFao3E0RBReZiMEBm5wgdlvPfzCtHKpw4mdqtdZbxlcbVVwcJcDrVG4FYqy3uJDBmTESIj9+X+KJyOTYWtqqiM10zBjzUAyOUy+DhyJlYiY8DfWkRG7HTsPXyx7xoA4MOBzeHtaCVxRIaF96ghMg5MRoiMVGZuAaY8KOMdEOSJgcFeUodkcHiPGiLjwGSEyEjN/f0i4u/lwMvBEh8ObC51OAapeBBrTAqTESJDxmSEyAj9fvY2fjtzC3IZsHREEOxqeRlvWXx5mYbIKDAZITIyN1Oz8d8t5wEAb3ZrgNa+jhJHZLh8HlymiU/NRiHLe4kMFpMRIiOi1giErj+LzNxCBNdzwKRnA6QOyaB52FlAaSZHgVrgTnqu1OEQURmYjBAZka8PROFkzD1YKxVYyjLex5LLZajnWDwtPMeNEBkq/iYjMhIR8Wn4/K+iMt55A5prpzun8vEeNUSGj8kIkRHIyivE5HXhUGsE+rb0wOCnWMZbUdp71LCihshgMRkhMgLz/riI2LvZ8LS3wCcDW0Amk0kdktFgzwiR4WMyQmTgdpy/gw3/3IRMBiwZHgR7K5bx6oN37yUyfExGiAzY7bQczNx8DgAwoYs/2tV3kjgi41M88VnsvWxoNELiaIioNExGiAyUWiMQuiECGbmFaFnXHlOfayh1SEbJ08ECZnIZ8gs1uJPB8l4iQ8RkhMhArTx0A8dv3IOluQLLRgTDnGW8lWKmkGvLezmIlcgw8bcbkQE6fzMdi/dEAgDe798Ufs4s430SPhzESmTQmIwQGZjs/KIy3kKNQO/m7hjWylvqkIweB7ESGTYmI0QG5sM/L+NGShbc7SwwfxDLeKvCv+W9TEaIDBGTESIDsutCAn49GVdUxjssEA5WSqlDMgnFN8zj3XuJDBOTESIDkZiRi5m/FZXxju9cHx0aOEsckekoLu+NuZsFIVjeS2RomIwQGQCNRuDtDWeRll2A5l52eLtHI6lDMileDpZQyGXILdAgKTNP6nCI6BFMRogMwOqj0TgSlQILczmWDg+G0owfzaqkNJPDy8ESABDD8l4ig8PfeEQSu3g7HZ/tKirjnf18UzRwtZE4ItNUXN7LcSNEhofJCJGEcvLVmLwuAvlqDZ5r6oYX29STOiST9fC4ESIyLExGiCT0yY7LiEq6DxdbFT4d3JJlvNXIh+W9RAaLyQiRRP66lIj/HY8FACweGghHa5bxVqfiWWxjUniZhsjQMBkhkkBSZi6mP7gb7yud/PB0QxeJIzJ9D8/CyvJeIsPCZISohmk0AtM2nsO9rHw0drfF9F4s460J3o6WkMmArHw1Uu7nSx0OET2EyQhRDfsxLAaHriZDZSbHFyODoTJTSB1SraAyU8DTvqi8l/eoITIsTEaIatCVhAzM33kFAPDfvk3Q0M1W4ohqF19n3r2XyBAxGSGqIbkFakz+NQL5hRo809gVL7XzkTqkWod37yUyTExGiGrIgp1XEJmYCWcbJT4bwjJeKfx79172jBAZEiYjRDVgf2QSfjgWAwBYODQQzjYqaQOqpdgzQmSYmIwQVbOU+3l4Z+NZAMDYDr7o1shV4ohqr+JZWKNTWN5LZEiYjBBVIyEEpm86h5T7+WjkZouZvRtLHVKtVs+x6DJNZm4h0rILJI6GiIoxGSGqRmuPx+LvK0lQmsmxbGQQLMxZxislS6UCHvYWAIBoXqohMhhMRoiqydXETHy0/TIAYGavxmjsbidxRAQ8fPdeJiNEhoLJCFE1yCtUY9Kv4cgr1KBLQxeM6+grdUj0gPbuvbxHDZHBYDJCVA0W7orElYRMOForsXAoy3gNCStqiAwPkxGiKnboajK+OxINAPhscEu42lpIHBE9jHONEBkeJiNEVeheVj7eflDG+3/t6qF7UzeJI6JHsWeEyPAwGSGqIkIIzNh8DsmZeWjgaoP/9mkqdUhUiuIBrKnZBUhneS+RQWAyQlRFfj0Zj72XEmGukGHZiCBYKlnGa4isVWZwsS2aATf2HntHiAwBkxGiKhCVdB8f/HkRADC9Z2M087SXOCIqD8eNEBkWJiNETyi/UIPJ68KRW6BBpwbOeKWTn9Qh0WNox42ksGeEyBAwGSF6Qov3RuLi7Qw4WJlj8bBAyOUs4zV07BkhMixMRoiewLGoFKw8dAMAsGBQS7jZsYzXGPg6P5j4jBU1RAaByQhRJaVm5SN0w1kIAYxs441ezd2lDokqyJflvUQGpVLJyIoVK+Dr6wsLCwu0bdsWJ0+eLLd9WloaJk6cCA8PD6hUKjRs2BA7duyoVMBEhkAIgXe3nEdCRi7qO1tj9vMs4zUm9R5cpkm5n4/MXJb3EklN72Rk/fr1CA0Nxdy5c3HmzBkEBgaiZ8+eSEpKKrV9fn4+nnvuOcTExGDTpk2IjIzEqlWr4OXl9cTBE0ll4z83sfNCAszkMiwbEQwrpZnUIZEe7CzM4WStBADEctwIkeT0/g26ZMkSvPbaaxg3bhwA4JtvvsH27duxevVqzJw5s0T71atX4969ezh27BjMzc0BAL6+vk8WNZGEolOy8P4fRWW8b/dohBZ1WcZrjHycrHA3Kx+xd7PR3IvnkEhKevWM5Ofn4/Tp0+jevfu/G5DL0b17d4SFhZW6zu+//4727dtj4sSJcHNzQ/PmzfHJJ59ArVY/WeREEihQF5XxZuer0b6+E/7zdH2pQ6JK0t69l+NGiCSnV89ISkoK1Go13Nx077fh5uaGK1eulLrOjRs38Pfff2PUqFHYsWMHoqKi8MYbb6CgoABz584tdZ28vDzk5eVpf87IyNAnTKJqs/Svqzh3Mx32lizjNXa8Rw2R4aj2ahqNRgNXV1esXLkSISEhGD58OP773//im2++KXOd+fPnw97eXvvw9vau7jCJHuv4jbv46sB1AMAnL7SAp4OlxBHRk/B15lwjRIZCr2TE2dkZCoUCiYmJOssTExPh7l56WaOHhwcaNmwIheLf+3Q0adIECQkJyM/PL3WdWbNmIT09XfuIj4/XJ0yiKpeeXYDQ9REQAhgaUhd9W3pIHRI9IfaMEBkOvZIRpVKJkJAQ7Nu3T7tMo9Fg3759aN++fanrdOzYEVFRUdBoNNplV69ehYeHB5RKZanrqFQq2NnZ6TyIpCKEwLtbz+N2ei58nazwfv9mUodEVaB4FtbEjDxk5xdKHA1R7ab3ZZrQ0FCsWrUKP/74Iy5fvowJEyYgKytLW10zevRozJo1S9t+woQJuHfvHiZPnoyrV69i+/bt+OSTTzBx4sSqOwqiavTbmVvYfu4OFHIZlo4IhrWKZbymwMFKCXvLogo/lvcSSUvv36rDhw9HcnIy5syZg4SEBAQFBWHXrl3aQa1xcXGQy//Ncby9vbF7925MnToVLVu2hJeXFyZPnowZM2ZU3VEQVZPYu1mYs+0CAGBq9wAEeTtIGxBVKV9na5yNT0Ps3Sw08WAPLJFUZEIIIXUQj5ORkQF7e3ukp6fzkg3VmAK1BkO/CUNEfBra+Dri1/HtoGD1jEmZvC4c2yJuY2bvxni9i7/U4RCZnIp+f/PeNERlWP53FCLi02BrYYYlwwOZiJggDmIlMgxMRohKcSrmHr78+xoA4OMXWqBuHSuJI6LqUDyINSaFY0aIpMRkhOgRGbkFmLIuAhoBDAr2Qv9AT6lDomrCnhEiw8BkhOgRc7ZewK20HHg7WmLeAJbxmrLinpHb6bnILeAtKoikwmSE6CFbw29ha8TtojLe4cGwtTCXOiSqRo7WStg+KNWOv8dLNURSYTJC9ED8vWzM3lpUxvvWMw0Q4lNH4oiouslkMvhwWngiyTEZIQJQqNZg6voIZOYVIsSnDt7s1kDqkKiGcNwIkfSYjBAB+OrAdfwTmwoblRmWDg+CmYIfjdpCW1HDZIRIMvyNS7XembhULNtXVMb74cBm8HZkGW9tUtwzwvJeIukwGaFaLfNBGa9aI9A/0BMDg7ykDolqmJ/zg2SEPSNEkmEyQrXa+79fQty9bHg5WOLDgc0hk3GW1drGp7i8Ny0HeYUs7yWSApMRqrX+OHsbm8/chFwGfD48SHsHV6pdXGxUsFIqoBHAzdQcqcMhqpWYjFCtdCstB+9uOQ8AmNitAdr4OUocEUlFJpOxooZIYkxGqNZRa0RRGW9uIYK8HTDp2QCpQyKJ8R41RNJiMkK1zjcHr+Nk9D1YKxVYNiII5izjrfXYM0IkLf4WplrlbHwaPt97FQDwfv9m2i8hqt3+nWuEPSNEUmAyQrVGVl4hpqyPQKFGoG8LDwwJqSt1SGQg2DNCJC0mI1RrfPDHJUSnZMHD3gKfvNCCZbyk5fvg/jQ3U3NQoNZIHA1R7cNkhGqFnefvYP0/8ZDJgCXDgmBvxTJe+pebrQVUZnIUagRup7G8l6imMRkhk3cnPQczfysq4329iz/a+ztJHBEZGrlcBt8Hl2qiU3iphqimMRkhk6bRCISuP4v0nAK0rGuPqd0bSh0SGajimVhjOYiVqMYxGSGTturwDYTduAtLcwWWDg+C0oxveSqdL+9RQyQZ/mYmk3XhVjoW7YkEAMzt1xT1XWwkjogMGXtGiKTDZIRMUnZ+ISatC0eBWqBXM3cMb+0tdUhk4IrHjLBnhKjmMRkhk/TR9su4kZwFNzsV5g9iGS89XnHPSPy9bKg1QuJoiGoXJiNkcvZcTMAvJ+IAFJXx1rFWShwRGQMPe0soFXIUqFneS1TTmIyQSUnMyMWMzecAAOOfro+ODZwljoiMhUIug7ejJQCOGyGqaUxGyGRoNALTNp5FanYBmnna4e0eLOMl/TRwLRrkfPZmmrSBENUyTEbIZKw+Go3D11JgYS7HshFBUJkppA6JjEynBz1pB68mSxwJUe3CZIRMwqXbGfhsV1EZ73t9m6KBq63EEZEx6tLQFQBwJjYVGbkFEkdDVHswGSGjl1ugxuR14chXa9C9iRtGta0ndUhkpOo5WcHP2RqFGoFjUSlSh0NUazAZIaP3yY7LuJZ0Hy62Knw6mGW89GS6NHQBwEs1RDWJyQgZtX2XE/FTWCwAYNHQQDjZqCSOiIxdl0YPkpHIZAjB+UaIagKTETJaSZm5mL6pqIz35Y5+2r9oiZ5EOz8nKM3kuJ2ei6ik+1KHQ1QrMBkhoySEwDsbz+FuVj4au9tieq9GUodEJsJSqUBbP0cAvFRDVFOYjJBR+vFYDA5eTYbKTI4vRgbDwpxlvFR1invZDkQyGSGqCUxGyOhEJmTik51XAADv9mmChm4s46Wq1bVRUYnvyeh7yM4vlDgaItPHZISMSm6BGpN+DUd+oQbdGrlgdHsfqUMiE+TvYg0vB0vkqzU4fuOu1OEQmTwmI2RUPt11BZGJmXC2UeKzIYEs46VqIZPJdKpqiKh6MRkho3EgMglrjsYAABYOCYSLLct4qfpwvhGimsNkhIxCyv08TNtYVMY7pr0PujV2lTgiMnUd/J1gJpch5m42YlKypA6HyKQxGSGDJ4TAjE3nkHI/Dw3dbDCrTxOpQ6JawNbCHK186wBg7whRdWMyQgZv7Yk47LuSBKVCjmUjWMZLNaf4xnlMRoiqF5MRMmhRSZn46M9LAIAZvRujiYedxBFRbVI8biTs+l3kFqgljobIdDEZIYOVV6jGW79GIK9Qg6cbumBcB1+pQ6JapomHLVxsVcgpUOOfmFSpwyEyWUxGyGAt2h2Jy3cy4GitxKIhLSGXs4yXapZMJnuoqiZJ4miITBeTETJIR66lYNXhaADAp4NbwtXOQuKIqLbq2oglvkTVjckIGZx7WfkI3RABABjVth6ea+ombUBUq3Vq4Ay5DLiaeB+303KkDofIJDEZIYMihMCMzeeQlJkHfxdrvNe3qdQhUS3nYKVEkLcDAPaOEFUXJiNkUNadisfeS4kwV8iwbEQwLJUs4yXpaUt8OTU8UbVgMkIG43ryfXzwR1EZ7zs9G6G5l73EEREVKb5PzdGoFBSoNRJHQ2R6mIyQQcgv1GDKugjkFKjRsYETXu1UX+qQiLRaeNmjjpU5MvMKER6XJnU4RCaHyQgZhCV7r+L8rXQ4WJlj8dAglvGSQVHIZXj6QYnvgUiW+BJVNSYjJLlj11Pw7aHrAIAFg1rC3Z5lvGR4eBdfourDZIQklZadj9D1ZyEEMKK1N3o1d5c6JKJSdQ4oSkYu3s5AUmauxNEQmRYmIyQZIQTe3XIeCRm5qO9sjTn9WMZLhsvFVoXmXkX3Rjp8NUXiaIhMC5MRkszG0zex43wCzOQyLB0RBCulmdQhEZWLl2qIqkelkpEVK1bA19cXFhYWaNu2LU6ePFmh9datWweZTIaBAwdWZrdkQmJSsvD+7xcBAKE9GqJlXQdpAyKqgK6NiuYbOXwtGWqNkDgaItOhdzKyfv16hIaGYu7cuThz5gwCAwPRs2dPJCWVP8I8JiYG06ZNQ+fOnSsdLJmGArUGk9eFIztfjXb1HfGfp/2lDomoQoK9HWBrYYbU7AKcu5kmdThEJkPvZGTJkiV47bXXMG7cODRt2hTffPMNrKyssHr16jLXUavVGDVqFObNm4f69Tl/RG237K9rOHszHXYWZlgyLAgKlvGSkTBTyNGpgTMAXqohqkp6JSP5+fk4ffo0unfv/u8G5HJ0794dYWFhZa73wQcfwNXVFa+88kqF9pOXl4eMjAydB5mGEzfuYsWBKADA/EEt4elgKXFERPrhuBGiqqdXMpKSkgK1Wg03N927qLq5uSEhIaHUdY4cOYLvv/8eq1atqvB+5s+fD3t7e+3D29tbnzDJQKXnFCB0Q1EZ75CQuujb0kPqkIj0Vjw1/Nn4NKRm5UscDZFpqNZqmszMTLz00ktYtWoVnJ2dK7zerFmzkJ6ern3Ex8dXY5RUE4QQeG/rBdxKy4GPkxXe799M6pCIKsXD3hKN3GyhEcCRKJb4ElUFvWopnZ2doVAokJiYqLM8MTER7u4lJ6u6fv06YmJi0K9fP+0yjaboJlNmZmaIjIyEv3/JwYsqlQoqlUqf0MjAbQm/hT/O3oZCLsPS4UGwUbGMl4xXl0YuiEzMxMGryegX6Cl1OERGT6+eEaVSiZCQEOzbt0+7TKPRYN++fWjfvn2J9o0bN8b58+cRERGhffTv3x/dunVDREQEL7/UEnF3szFnW1EZ75RnAxBcr47EERE9mYfHjWhY4kv0xPT+8zQ0NBRjxoxBq1at0KZNGyxduhRZWVkYN24cAGD06NHw8vLC/PnzYWFhgebNm+us7+DgAAAllpNpKlRrMGV9OO7nFaK1bx280a2B1CERPbFWvnVgaa5AcmYeLidkoJmnvdQhERk1vZOR4cOHIzk5GXPmzEFCQgKCgoKwa9cu7aDWuLg4yOWc2JWKLP87Cmfi0mCrMsPnw1nGS6ZBZaZAB38n7LuShINXk5mMED0hmRDC4PsYMzIyYG9vj/T0dNjZ2UkdDlXQ6dh7GPpNGDQCWDYiCAOCvKQOiajK/C8sBrO3XURbP0es/0/Jy9REVPHvb3ZhULXIzC3A5HUR0AjghWAvJiJkcro0LJoa/nRsKjJzCySOhsi4MRmhajFn20XcTM2Bt6MlPhjAMl4yPfWcrODnbI1CjcCx63elDofIqDEZoSq3LeIWtoTfglwGLB0eBFsLc6lDIqoWxVU1ByI5GyvRk2AyQlUq/l423ttyAQDw1jMBCPFxlDgioupTnIwcupoMIxh+R2SwmIxQlSlUaxC6IQKZeYV4qp4D3nqGZbxk2trVd4LSTI5baTm4nnxf6nCIjBaTEaoyXx+4jlMxqbBRmWHp8GCYKfj2ItNmqVSgrV9R7x8v1RBVHr8tqEqEx6Vi6b5rAIAPBjRDPScriSMiqhm8iy/Rk2MyQk/sfl4hpqyPgFoj0C/QEy8Es4yXao+uD+7ieyL6HnLy1RJHQ2ScmIzQE3v/94uIvZsNLwdLfDSwOWQyzrJKtYe/iw28HCyRX6jB8Rss8SWqDCYj9ET+PHcbm07fhFwGfD48CPaWLOOl2kUmk+FpXqoheiJMRqjSbqfl4N3fzgMA3ujaAG38WMZLtVPxpRomI0SVw2SEKkWtEZi6PgIZuYUI9HbA5O4BUodEJJkO/k4wk8sQnZKF2LtZUodDZHSYjFClfHvoOk5E34OVUoFlw4NgzjJeqsVsLcwR4lMHQNEEaESkH36DkN7O3UzDkj1XAQDv928GX2driSMikl6XRpwanqiymIyQXrLzCzF5XQQKNQJ9WrhjaEhdqUMiMgjF840cu34XeYUs8SXSB5MR0ssHf1xCdEoWPOwt8MkLLVjGS/RAUw87uNiqkFOgxj8xqVKHQ2RUmIxQhe26cAfrTsVDJgMWDwuEg5VS6pCIDIZMJuNsrESVxGSEKiQhPRczH5Tx/udpf3Twd5Y4IiLDo01GOG6ESC9MRuixNBqBtzdGIC27AC287BH6XEOpQyIySJ0aOEMuAyITM3EnPUfqcIiMBpMReqzvjtzA0ai7sDRXYOmIICjN+LYhKk0dayUCvR0AsHeESB/8VqFyXbiVjoW7IwEAc/o1hb+LjcQRERm2rg1dAXDcCJE+mIxQmXLy1Zi8LhwFaoGezdwworW31CERGbzi+UaOXEtBgVojcTRExoHJCJXpo+2XcD05C252KiwY1JJlvEQV0MLLHnWszJGZV4iI+DSpwyEyCkxGqFR7LyXi5xNxAIDFQ4NQx5plvEQVoZDL0DmAVTVE+mAyQiUkZeRixuZzAIDXOvuhUwDLeIn0UVzie+BqksSREBkHJiOko6iM9yzuZeWjqYcdpvVsJHVIREbn6QfJyIVbGUjOzJM4GiLDx2SEdKw5FoPD11KgMpPji5FBUJkppA6JyOi42KrQ3MsOAHD4Gi/VED0OkxHSunwnA5/uvAIAeO/5pmjgaitxRETGi1PDE1UckxECAOQWFJXx5qs16N7EFf/Xtp7UIREZtS4P5hs5dDUZao2QOBoiw8ZkhAAA83dcxtXE+3C2UeHTwSzjJXpSwfUcYKsyQ2p2AS7cSpc6HCKDxmSEsP9KEn4MiwUALBraEk42KokjIjJ+5gq5thLtAEt8icrFZKSWS87MwzubzgIAxnX0RddGrhJHRGQ6/h03whJfovIwGanFhBCYvuksUu7no7G7LWb0aix1SEQmpbjENyI+DWnZ+RJHQ2S4mIzUYj+FxWJ/ZDKUZnIsGxEMC3OW8RJVJU8HSzR0s4FGAEeiUqQOh8hgMRmppa4mZuLjHZcBAO/2boxG7izjJaoO2ks1HDdCVCYmI7VQboEak34NR36hBt0auWBMB1+pQyIyWcUlvgevJkMIlvgSlYbJSC302a5IXEnIhJO1Ep8NCWQZL1E1au1XB5bmCiRl5uHynUypwyEySExGapmDV5Ox+mg0AGDh0JZwsWUZL1F1Upkp0MHfCQBnYyUqC5ORWuTu/TxM21hUxju6vQ+eaewmcUREtUOXRizxJSoPk5FaQgiBGZvPIzkzDwGuNni3TxOpQyKqNYoHsf4Tk4r7eYUSR0NkeJiM1BI/n4jDX5cToVSwjJeopvk4WcPXyQqFGoFjLPElKoHJSC0QlZSJj7ZfAgBM79UITT3tJI6IqPYpnt2Y40aISmIyYuLyCtWY9GsEcgs06BzgjJc7+kkdElGtVHyp5kAkS3yJHsVkxMQt3nMVl+5koI6VORYPDYRczjJeIim0q+8ElZkct9Jy8PcVDmQlehiTERN25FoKVh66AQD4dHBLuNpZSBwRUe1lqVRgbEdfAMDHOy6jQK2RNiAiA8JkxESlZuXj7Y0RAIAX29ZDj2bu0gZERJjYrQGcrJW4kZyFX07ESR0OkcFgMmKChBCY+ds5JGbkob6LNWb3bSp1SEQEwM7CHFOfawgAWPrXVaRnF0gcEZFhYDJigtafisfui4kwV8jwxYhgWCpZxktkKEa09kZDNxukZhdg+d/XpA6HyCAwGTExN5LvY94fRWW803o0QnMve4kjIqKHmSnk+O+D3sofw2IQk5IlcURE0mMyYkLyCzWYvC4COQVqdPB3wmud60sdEhGVoktDF3Rp6IICtcCCnVekDodIckxGTMjnf13F+VvpsLc0x5JhQSzjJTJg7/VtAoVchl0XE3D8xl2pwyGSFJMRExF2/S6+OXgdALBgUAu427OMl8iQBbjZYmQbbwDAR9svQaPhRGhUezEZMQHp2QUI3RABIYDhrbzRu4WH1CERUQVM7d4QtiozXLiVgd/Cb0kdDpFkmIwYOSEE3t1yHnfSc+HnbI05/VjGS2QsnGxUePOZBgCAhbuvIDufd/Sl2onJiJHbdPomtp+/AzO5DEuHB8FaZSZ1SESkhzEdfOHtaInEjDztjMlEtQ2TESMWk5KF93+/CACY+lxDBHo7SBsQEenNwlyBmb2aAAC+PXgDCem5EkdEVPMqlYysWLECvr6+sLCwQNu2bXHy5Mky265atQqdO3dGnTp1UKdOHXTv3r3c9lQxBWoNpqyPQFa+Gm39HPF6F3+pQyKiSurTwh2tfOogp0CNhbsjpQ6HqMbpnYysX78eoaGhmDt3Ls6cOYPAwED07NkTSUml34XywIEDGDlyJPbv34+wsDB4e3ujR48euHWLg7WexBf7riEiPg12Fmb4fHgQFCzjJTJaMpkMs58vGu+1+cxNnL+ZLnFERDVLJoTQq56sbdu2aN26Nb788ksAgEajgbe3N9566y3MnDnzseur1WrUqVMHX375JUaPHl2hfWZkZMDe3h7p6emws7PTJ1yTdDL6HkasDINGAF++GIznW3pKHRIRVYGp6yOwJfwW2vg5Yv34dpDJ+EcGGbeKfn/r1TOSn5+P06dPo3v37v9uQC5H9+7dERYWVqFtZGdno6CgAI6OjmW2ycvLQ0ZGhs6DiqTnFGDq+ghoBDD4qbpMRIhMyDs9G0FlJsfJ6HvYfTFR6nCIaoxeyUhKSgrUajXc3Nx0lru5uSEhIaFC25gxYwY8PT11EppHzZ8/H/b29tqHt7e3PmGatDnbLuBWWg7qOVph3oBmUodDRFXI08ES458uuo3D/J2XkVeoljgioppRo9U0CxYswLp167BlyxZYWJQ9Q+isWbOQnp6ufcTHx9dglIZrS/hNbIu4DYVchqUjgmDDMl4ik/N6F3+42KoQezcb/wuLlTocohqhVzLi7OwMhUKBxETd7sPExES4u7uXu+6iRYuwYMEC7NmzBy1btiy3rUqlgp2dnc6jtou/l43ZW4vKeCc/G4Cn6tWROCIiqg7WKjO806MRAGDZvmu4l5UvcURE1U+vZESpVCIkJAT79u3TLtNoNNi3bx/at29f5nqfffYZPvzwQ+zatQutWrWqfLS1VOGDMt77eYVo7VsHE7s1kDokIqpGg0PqoqmHHTJzC7Hsr6tSh0NU7fS+TBMaGopVq1bhxx9/xOXLlzFhwgRkZWVh3LhxAIDRo0dj1qxZ2vaffvopZs+ejdWrV8PX1xcJCQlISEjA/fv3q+4oTNyX+6NwOjYVtiozLBnGMl4iU6eQy/Be36KJ0NaeiENUEn9fkmnTOxkZPnw4Fi1ahDlz5iAoKAgRERHYtWuXdlBrXFwc7ty5o23/9ddfIz8/H0OGDIGHh4f2sWjRoqo7ChN2OvYevth3DQDw0QvN4e1oJXFERFQTOjRwRvcmblBrBD7ZcVnqcIiqld7zjEihts4zkplbgD5fHEb8vRwMDPLE0hHBUodERDXoRvJ99Pj8EAo1Av97pQ06B7hIHRKRXqplnhGqWXN/v4j4ezmoW8cSHwxsLnU4RFTD6rvY4KX2PgCAj7dfhlpj8H87ElUKkxED9fvZ2/jtzC3IZcDS4UGwszCXOiQiksDkZwNgb2mOKwmZ2PAPpzkg08RkxADdTM3Gf7ecBwC8+UwAWvmWPVstEZk2ByslJj8bAABYvCcS9/MKJY6IqOoxGTEwao1A6PqzyMwtRHA9B0x6hmW8RLXd/7XzgZ+zNVLu5+PrA1FSh0NU5ZiMGJhvDl7HyZh7sFYqsGx4MMwUPEVEtZ3STI53+xSV+q46HI2bqdkSR0RUtfhNZ0Ai4tPw+d6iCY4+GNAc9ZxYxktERbo3cUX7+k7IL9Tgs12RUodDVKWYjBiIrLxCTF4XjkKNwPMtPTDoKS+pQyIiAyKTyfDe800gkxUNcD8Tlyp1SERVhsmIgZj3x0XE3s2Gl4MlPn6hBWQyzrJKRLqaedpjaEhdAMCHf16CEUwTRVQhTEYMwI7zd7Dhn5uQyYAlwwJhb8kyXiIq3bQejWClVCA8Lg1/nrvz+BWIjACTEYndTsvBzM3nAABvdPVH2/pOEkdERIbM1c4CE7r4AwAW7LyC3AK1xBERPTkmIxJSawRCN0QgI7cQgXXtMaV7Q6lDIiIj8Grn+vCwt8CttBysPhotdThET4zJiIRWHb6B4zfuwUqpwNIRwTBnGS8RVYClUoHpvRoBAL7afx3JmXkSR0T0ZPjtJ5HzN9OxaHdRed77/ZrBz9la4oiIyJgMCPRCYF173M8rxJIHUwIQGSsmIxLIzv+3jLd3c3cMbVVX6pCIyMjI5TK893xTAMD6U3G4kpAhcURElcdkRAIf/nkZN1Ky4G5ngfmDWMZLRJXT2tcRfVq4QyOK7urLUl8yVkxGatiuCwn49WRcURnv8EA4WCmlDomIjNjMXk2gVMhx+FoKDkQmSx0OUaUwGalBiRm5mPlbURnv+Kfro4O/s8QREZGxq+dkhXEdfQEAH22/hAK1RtqAiCqByUgN0WgE3t5wFmnZBWjuZYe3n2skdUhEZCImPtMAjtZKXE/Ows/HY6UOh0hvTEZqyOqj0TgSlQILczmWjQiG0owvPRFVDTsLc0x9rmieog+3X8aGf+IljohIP/xGrAEXb6dr77I55/lm8HexkTgiIjI1L7aphxeCvaDWCEzfdA5f7LvGAa1kNJiMVLOcfDUmr4tAvlqDHk3dMLKNt9QhEZEJUshlWDIsEBO6Fk0Vv2TvVby75TwKOYaEjACTkWr2yY7LiEq6D1dbFRYMbskyXiKqNjKZDDN6NcaHA5pBLgN+PRmP8f87jez8QqlDIyoXk5Fq9NelRPzvwWCyxcMC4WjNMl4iqn4vtffF1/8XApWZHH9fScLIlceRcp9TxpPhYjJSTZIyczH9wd14X+3kh84BLhJHRES1Sc9m7vjltbZwsDLH2ZvpGPz1McSkZEkdFlGpmIxUA41GYNrGc7iXlY8mHnZ4pxfLeImo5oX4OGLzhA7wdrRE7N1sDP76GCLi06QOi6gEJiPV4MewGBy6mgyVmRxfjAiCykwhdUhEVEv5u9hg84QOaOFlj7tZ+RixMgz7LidKHRaRDiYjVexKQgbm77wCAHivbxMEuNlKHBER1XauthZYN74dujR0QW6BBq/99A9+OREndVhEWkxGqlBugRqTf41AfqEGzzZ2xf+185E6JCIiAIC1ygzfjWmFoSF1oRHAu1vOY8meSM5FQgaByUgVWrDzCiITM+Fso8KnQ1jGS0SGxVwhx2dDWmLSswEAgC/+jsI7m87xfjYkOSYjVWR/ZBJ+OBYDAFg0tCWcbVTSBkREVAqZTIbQ5xpi/qAWUMhl2HT6Jl758R9k5XEuEpIOk5EqkHI/D+9sPAsAGNvBF10buUocERFR+Ua2qYdVo0Ngaa7AoavJGL4yDEmZuVKHRbUUk5EnJETRfSBS7uejkZstZvZuLHVIREQV8kxjN/w6vh2crJW4cCsDg746huvJ96UOi2ohJiNPaO3xWPx9JQlKMzmWjQyChTnLeInIeAR5O2DzhA7wdbLCzdQcDP76GE7H3pM6LKplmIw8gauJmfho+2UAwKzejdHY3U7iiIiI9OfrbI3NEzog0NsBadkFeHHVCey+mCB1WFSLMBmppLxCNSb9Go68Qg26NHTB2A6+UodERFRpTjYq/PpaWzzb2BV5hRpMWHsa/wuLkTosqiWYjFTSwl2RuJKQCSdrJRYOZRkvERk/K6UZvn0pBCPb1INGALO3XcSCnVeg0XAuEqpeTEYq4dDVZHx3JBoA8NmQlnC1tZA4IiKiqmGmkOOTF5rj7ecaAgC+OXgdb288i/xCzkVC1YfJiJ7uZeXj7QdlvC+188GzTdwkjoiIqGrJZDK89WwAFg5pCTO5DFvCb+HlH04hM7dA6tDIRDEZ0YMQAjM2n0NyZh4auNrgv32bSB0SEVG1GdrKG9+PbQ0rpQJHolIw9JswRCZkSh0WmSAmI3r49WQ89l5KhFIhx7IRLOMlItPXpaELNvynPZxtVLiSkImeSw/hpe9P4EBkEu9rQ1WGyUgFRSXdxwd/XgQATO/VCM087SWOiIioZjT3sseWNzqgd3N3yGXA4WspGLvmFHp8fgi/noxDboFa6hDJyMmEEaS2GRkZsLe3R3p6Ouzsan4uj/xCDV746igu3s5A5wBn/DiuDeRyVs8QUe0Tfy8ba47GYP2pOGTlFyUhjtZK/F87H7zUzgcutrwvF/2rot/fTEYqYP7Oy/j24A3UsTLHrilPw82O1TNEVLtl5BZgw6l4rDkag1tpOQAApUKOAUGeeKWzHyeBJABMRqrMsagUjPr+BIQAvn0pBD2budfo/omIDFmhWoPdFxPx/ZEbOBOXpl3eqYEzXunkhy4NXdiTXIsxGakCqVn56L3sMBIycjGyTT3MH9SixvZNRGRszsSl4vsj0dh5/g6K50nzd7HGy538MCi4LiyVHPRf2zAZeUJCCLzx8xnsvJCA+i7W+POtTrBSmtXIvomIjNnN1Gz8eCwG607GIzOvEABQx8oco9r6YHR7H7jyUnetwWTkCW04FY/pm8/BXCHDljc6orkXq2eIiPRxP6+waFzJsWjE3ysaV2KukKFfoCde6eTHqsRagMnIE4hOyULfLw4jO1+Nmb0b4/Uu/tW+TyIiU6XWCOy9lIDvDkfjn9hU7fL29Z3wSic/PNPYleNKTBSTkUoqUGsw+OtjOHczHR38nbD2lbb8kBARVZGz8Wn4/kg0tp+/A/WDgSX1na0xrpMfBj/lxcvhJobJSCUt3H0FK/Zfh72lOXZN6QwPe8tq3R8RUW10Oy0HP4bF4JcTccjMLRpXYq1UILheHQTXc8BTD/51sFJKHCk9CSYjlXDixl2MWHUcQgBfj3oKvVt4VNu+iIgIyMorxKbTN7H6aDRi72aXeL6+izWCvevgKZ+iBKWhmy0U7K02GkxG9JSeXYDeyw7hdnouhrWqi8+GBFbLfoiIqCSNRuBKQibOxKUiPC4N4XGpuJGSVaKdtVKBQO+ixOQpHwcEedeBozV7TwwVkxE9CCHw5q/h2H7uDnydrLB9UmdYq3jdkohISqlZ+QiPL0pOzsSlIiIuTTsF/cP8nK0RXM8BwfXq4Kl6DmjkZgszBW+9ZgiYjOhh8+mbeHvjWZjJZdg8oQMCvR2qfB9ERPRk1BqBa0mZOBNblJyciUvFjeSSvSdWSgVa1rUv6j15MPbEyYb3zJECk5EKir2bhT7LDiMrX413ejbCxG4NqnT7RERUfdKy8xEen4bw2FSEx6chIi5NO9Haw3ycrNDE3Q4eDhbwsLeAu70lPO0t4G5vATc7C5izJ6VaVPT7u1ZfiyhQazB5XQSy8tVo4+fI+USIiIyMg5US3Rq5olsjVwBFvSdRSfcfjD1JxZm4NEQl3Ufs3exSB8gCgEwGuNio4GFvAQ97S7jbFyUsHg6WRYmLXVHSwoSl+lQqGVmxYgUWLlyIhIQEBAYGYvny5WjTpk2Z7Tdu3IjZs2cjJiYGAQEB+PTTT9GnT59KB11Vlv8dhYj4NNhamOHz4UEcoU1EZOQUchkauduikbstRrapB6CoQCHiZhqik+/jTkYuEtJzcSctF3cycpCQnosCtUBSZh6SMvNw9mZ6qduVyQBnG5W2N8XD/kGi8tD/HazMYa0049xUlaB3MrJ+/XqEhobim2++Qdu2bbF06VL07NkTkZGRcHV1LdH+2LFjGDlyJObPn4/nn38ev/zyCwYOHIgzZ86gefPmVXIQlZGTr8aW8JsAgE9eaAEvB84nQkRkiuytzNGloQu6NHQp8ZxGI3A3Kx8J6bm4nV6UnNxJz8Wd9Bztv4npechXa5CcmYfkchIWoChpsVGawcbCDDaqf/+1szDX+dnWouhhozLXWVb8b21LavQeM9K2bVu0bt0aX375JQBAo9HA29sbb731FmbOnFmi/fDhw5GVlYU///xTu6xdu3YICgrCN998U6F9VteYkfTsAvxx7jb+r51PlW2TiIhMi0YjcC87v6g3JT0HCRm5uJ2WiwRtwlLU25Kv1lTpfm1UugmN0kwOlZkcSoUcKvOif5VmDx4Khfb/ZbeRP9JGodPG2UYFpVnVXoqqljEj+fn5OH36NGbNmqVdJpfL0b17d4SFhZW6TlhYGEJDQ3WW9ezZE1u3btVn19XC3sqciQgREZVLLpfB2UYFZxsVWtQt/eZ+QgjkFWqQmVuI+3mFyMwtwP3cQmTmFeL+g2X38wqR8WD5/QfLM7X/PmifW4jCB9PkF6+DjJo5zk2vt0crX8ea2dkj9EpGUlJSoFar4ebmprPczc0NV65cKXWdhISEUtsnJCSUuZ+8vDzk5eVpf87IqKEzQUREVAkymQwW5gpYmCvgYlv5MuLipEabrDxIVLLy1MgrVCO/UFP0UBf9m/fQz3kFGuSry2+j8/NDy/ILNVCZKarwFdGPQVbTzJ8/H/PmzZM6DCIiohr1cFLjXIvmRtHr4pCzszMUCgUSExN1licmJsLd3b3Uddzd3fVqDwCzZs1Cenq69hEfH69PmERERGRE9EpGlEolQkJCsG/fPu0yjUaDffv2oX379qWu0759e532ALB3794y2wOASqWCnZ2dzoOIiIhMk96XaUJDQzFmzBi0atUKbdq0wdKlS5GVlYVx48YBAEaPHg0vLy/Mnz8fADB58mR06dIFixcvRt++fbFu3Tr8888/WLlyZdUeCRERERklvZOR4cOHIzk5GXPmzEFCQgKCgoKwa9cu7SDVuLg4yOX/drh06NABv/zyC9577z28++67CAgIwNatWyWdY4SIiIgMR62/Nw0RERFVj4p+f3OifSIiIpIUkxEiIiKSFJMRIiIikhSTESIiIpIUkxEiIiKSFJMRIiIikhSTESIiIpIUkxEiIiKSFJMRIiIikpTe08FLoXiS2IyMDIkjISIioooq/t5+3GTvRpGMZGZmAgC8vb0ljoSIiIj0lZmZCXt7+zKfN4p702g0Gty+fRu2traQyWRVtt2MjAx4e3sjPj6+VtzzpjYdL4/VdNWm4+Wxmq7acrxCCGRmZsLT01PnJrqPMoqeEblcjrp161bb9u3s7Ez6zfCo2nS8PFbTVZuOl8dqumrD8ZbXI1KMA1iJiIhIUkxGiIiISFK1OhlRqVSYO3cuVCqV1KHUiNp0vDxW01WbjpfHarpq2/E+jlEMYCUiIiLTVat7RoiIiEh6TEaIiIhIUkxGiIiISFJMRoiIiEhSJp+MrFixAr6+vrCwsEDbtm1x8uTJcttv3LgRjRs3hoWFBVq0aIEdO3bUUKRPZv78+WjdujVsbW3h6uqKgQMHIjIystx1fvjhB8hkMp2HhYVFDUVcee+//36JuBs3blzuOsZ6Xn19fUscq0wmw8SJE0ttb2zn9NChQ+jXrx88PT0hk8mwdetWneeFEJgzZw48PDxgaWmJ7t2749q1a4/drr6f+5pQ3rEWFBRgxowZaNGiBaytreHp6YnRo0fj9u3b5W6zMp+FmvC48zp27NgScffq1eux2zXE8wo8/nhL+wzLZDIsXLiwzG0a6rmtLiadjKxfvx6hoaGYO3cuzpw5g8DAQPTs2RNJSUmltj927BhGjhyJV155BeHh4Rg4cCAGDhyICxcu1HDk+jt48CAmTpyI48ePY+/evSgoKECPHj2QlZVV7np2dna4c+eO9hEbG1tDET+ZZs2a6cR95MiRMtsa83k9deqUznHu3bsXADB06NAy1zGmc5qVlYXAwECsWLGi1Oc/++wzfPHFF/jmm29w4sQJWFtbo2fPnsjNzS1zm/p+7mtKeceanZ2NM2fOYPbs2Thz5gx+++03REZGon///o/drj6fhZryuPMKAL169dKJ+9dffy13m4Z6XoHHH+/Dx3nnzh2sXr0aMpkMgwcPLne7hnhuq40wYW3atBETJ07U/qxWq4Wnp6eYP39+qe2HDRsm+vbtq7Osbdu24j//+U+1xlkdkpKSBABx8ODBMtusWbNG2Nvb11xQVWTu3LkiMDCwwu1N6bxOnjxZ+Pv7C41GU+rzxnpOhRACgNiyZYv2Z41GI9zd3cXChQu1y9LS0oRKpRK//vprmdvR93MvhUePtTQnT54UAERsbGyZbfT9LEihtGMdM2aMGDBggF7bMYbzKkTFzu2AAQPEM888U24bYzi3Vclke0by8/Nx+vRpdO/eXbtMLpeje/fuCAsLK3WdsLAwnfYA0LNnzzLbG7L09HQAgKOjY7nt7t+/Dx8fH3h7e2PAgAG4ePFiTYT3xK5duwZPT0/Ur18fo0aNQlxcXJltTeW85ufnY+3atXj55ZfLvWGksZ7TR0VHRyMhIUHn3Nnb26Nt27ZlnrvKfO4NVXp6OmQyGRwcHMptp89nwZAcOHAArq6uaNSoESZMmIC7d++W2daUzmtiYiK2b9+OV1555bFtjfXcVobJJiMpKSlQq9Vwc3PTWe7m5oaEhIRS10lISNCrvaHSaDSYMmUKOnbsiObNm5fZrlGjRli9ejW2bduGtWvXQqPRoEOHDrh582YNRqu/tm3b4ocffsCuXbvw9ddfIzo6Gp07d0ZmZmap7U3lvG7duhVpaWkYO3ZsmW2M9ZyWpvj86HPuKvO5N0S5ubmYMWMGRo4cWe5N1PT9LBiKXr164aeffsK+ffvw6aef4uDBg+jduzfUanWp7U3lvALAjz/+CFtbWwwaNKjcdsZ6bivLKO7aS/qZOHEiLly48Njri+3bt0f79u21P3fo0AFNmjTBt99+iw8//LC6w6y03r17a//fsmVLtG3bFj4+PtiwYUOF/towVt9//z169+4NT0/PMtsY6zmlfxUUFGDYsGEQQuDrr78ut62xfhZGjBih/X+LFi3QsmVL+Pv748CBA3j22WcljKz6rV69GqNGjXrswHJjPbeVZbI9I87OzlAoFEhMTNRZnpiYCHd391LXcXd316u9IXrzzTfx559/Yv/+/ahbt65e65qbmyM4OBhRUVHVFF31cHBwQMOGDcuM2xTOa2xsLP766y+8+uqreq1nrOcUgPb86HPuKvO5NyTFiUhsbCz27t2r963lH/dZMFT169eHs7NzmXEb+3ktdvjwYURGRur9OQaM99xWlMkmI0qlEiEhIdi3b592mUajwb59+3T+cnxY+/btddoDwN69e8tsb0iEEHjzzTexZcsW/P333/Dz89N7G2q1GufPn4eHh0c1RFh97t+/j+vXr5cZtzGf12Jr1qyBq6sr+vbtq9d6xnpOAcDPzw/u7u465y4jIwMnTpwo89xV5nNvKIoTkWvXruGvv/6Ck5OT3tt43GfBUN28eRN3794tM25jPq8P+/777xESEoLAwEC91zXWc1thUo+grU7r1q0TKpVK/PDDD+LSpUti/PjxwsHBQSQkJAghhHjppZfEzJkzte2PHj0qzMzMxKJFi8Tly5fF3Llzhbm5uTh//rxUh1BhEyZMEPb29uLAgQPizp072kd2dra2zaPHO2/ePLF7925x/fp1cfr0aTFixAhhYWEhLl68KMUhVNjbb78tDhw4IKKjo8XRo0dF9+7dhbOzs0hKShJCmNZ5FaKoaqBevXpixowZJZ4z9nOamZkpwsPDRXh4uAAglixZIsLDw7UVJAsWLBAODg5i27Zt4ty5c2LAgAHCz89P5OTkaLfxzDPPiOXLl2t/ftznXirlHWt+fr7o37+/qFu3roiIiND5DOfl5Wm38eixPu6zIJXyjjUzM1NMmzZNhIWFiejoaPHXX3+Jp556SgQEBIjc3FztNozlvArx+PexEEKkp6cLKysr8fXXX5e6DWM5t9XFpJMRIYRYvny5qFevnlAqlaJNmzbi+PHj2ue6dOkixowZo9N+w4YNomHDhkKpVIpmzZqJ7du313DElQOg1MeaNWu0bR493ilTpmhfGzc3N9GnTx9x5syZmg9eT8OHDxceHh5CqVQKLy8vMXz4cBEVFaV93pTOqxBC7N69WwAQkZGRJZ4z9nO6f//+Ut+3xcek0WjE7NmzhZubm1CpVOLZZ58t8Tr4+PiIuXPn6iwr73MvlfKONTo6uszP8P79+7XbePRYH/dZkEp5x5qdnS169OghXFxchLm5ufDx8RGvvfZaiaTCWM6rEI9/HwshxLfffissLS1FWlpaqdswlnNbXWRCCFGtXS9ERERE5TDZMSNERERkHJiMEBERkaSYjBAREZGkmIwQERGRpJiMEBERkaSYjBAREZGkmIwQERGRpJiMEBERkaSYjBCR3rp27YopU6Y88XZiYmIgk8kQERHxxNsiIuPFZISIiIgkxWSEiPQyduxYHDx4EMuWLYNMJoNMJkNMTEyZ7VNTUzFq1Ci4uLjA0tISAQEBWLNmDQBo7y4dHBwMmUyGrl27atf77rvv0KRJE1hYWKBx48b46quvtM8V96isW7cOHTp0gIWFBZo3b46DBw9WyzETUfUykzoAIjIuy5Ytw9WrV9G8eXN88MEHAAAXF5cy28+ePRuXLl3Czp074ezsjKioKOTk5AAATp48iTZt2uCvv/5Cs2bNoFQqAQA///wz5syZgy+//BLBwcEIDw/Ha6+9Bmtra4wZM0a77XfeeQdLly5F06ZNsWTJEvTr1w/R0dFwcnKqxleAiKoakxEi0ou9vT2USiWsrKzg7u7+2PZxcXEIDg5Gq1atAAC+vr7a54qTGCcnJ51tzZ07F4sXL8agQYMAFPWgXLp0Cd9++61OMvLmm29i8ODBAICvv/4au3btwvfff4/p06c/8XESUc1hMkJE1WrChAkYPHgwzpw5gx49emDgwIHo0KFDme2zsrJw/fp1vPLKK3jttde0ywsLC2Fvb6/Ttn379tr/m5mZoVWrVrh8+XLVHwQRVSsmI0RUrXr37o3Y2Fjs2LEDe/fuxbPPPouJEydi0aJFpba/f/8+AGDVqlVo27atznMKhaLa4yWimscBrESkN6VSCbVaXeH2Li4uGDNmDNauXYulS5di5cqV2u0A0NmWm5sbPD09cePGDTRo0EDnUTzgtdjx48e1/y8sLMTp06fRpEmTJzk0IpIAe0aISG++vr44ceIEYmJiYGNjA0dHR8jlpf9tM2fOHISEhKBZs2bIy8vDn3/+qU0YXF1dYWlpiV27dqFu3bqwsLCAvb095s2bh0mTJsHe3h69evVCXl4e/vnnH6SmpiI0NFS77RUrViAgIABNmjTB559/jtTUVLz88ss18hoQUdVhzwgR6W3atGlQKBRo2rQpXFxcEBcXV2ZbpVKJWbNmoWXLlnj66aehUCiwbt06AEXjPL744gt8++238PT0xIABAwAAr776Kr777jusWbMGLVq0QJcuXfDDDz+U6BlZsGABFixYgMDAQBw5cgS///47nJ2dq+/AiahayIQQQuogiIj0ERMTAz8/P4SHhyMoKEjqcIjoCbFnhIiIiCTFZISInsjrr78OGxubUh+vv/661OERkRHgZRoieiJJSUnIyMgo9Tk7Ozu4urrWcEREZGyYjBAREZGkeJmGiIiIJMVkhIiIiCTFZISIiIgkxWSEiIiIJMVkhIiIiCTFZISIiIgkxWSEiIiIJMVkhIiIiCT1/ycFRTULLuKhAAAAAElFTkSuQmCC", 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", 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", 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", 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "analysis.plot_all('soil_output/Spread_erdos*', analysis.get_value, 'prob_tv_spread');" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Manually plotting with pandas" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "ExecuteTime": { - "end_time": "2017-10-19T11:00:37.003972Z", - "start_time": "2017-10-19T13:00:36.983128+02:00" - } - }, - "source": [ - "Although the simplest way to visualize the results of a simulation is to use the built-in methods in the analysis module, sometimes the setup is more complicated and we need to explore the data a little further.\n", - "\n", - "For that, we can use native pandas over the results.\n", - "\n", - "Soil provides some convenience methods to simplify common operations:\n", - "\n", - "* `analysis.split_df` to separate a history dataframe into environment and agent parameters.\n", - "* `analysis.get_count` to get a dataframe with the value counts for different attributes during the simulation.\n", - "* `analysis.get_value` to get the evolution of the value of an attribute during the simulation.\n", - "\n", - "And, as we saw earlier, `analysis.process` can turn a dataframe in canonical form into a dataframe with a column per attribute.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "soil_output/Spread_barabasi_albert_graph_prob_0.0/Spread_barabasi_albert_graph_prob_0.0.dumped.yml\r\n", - "soil_output/Spread_barabasi_albert_graph_prob_0.0/Spread_barabasi_albert_graph_prob_0.0.sqlite\r\n", - "soil_output/Spread_barabasi_albert_graph_prob_0.0/Spread_barabasi_albert_graph_prob_0.0_trial_0.csv\r\n", - "soil_output/Spread_barabasi_albert_graph_prob_0.0/Spread_barabasi_albert_graph_prob_0.0_trial_0.sqlite\r\n", - "soil_output/Spread_barabasi_albert_graph_prob_0.0/Spread_barabasi_albert_graph_prob_0.0_trial_0.stats.csv\r\n" - ] - } - ], - "source": [ - "!ls soil_output/Spread_barabasi_albert_graph_prob_0.0/Spread_barabasi_albert_graph_prob_0*" - 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keySEEDalive...state_id
agent_counttimeprob_tv_spreadprob_neighbor_spread
dict_idenv0110100101102103104105...90919293949596979899
t_stepsimulation_idparams_iditeration_idstep
0.0Spread_barabasi_albert_graph_prob_0.0_trial_0TrueTrueTrueTrueTrueTrueTrueTrueTrue...neutralneutralneutralneutralneutralneutralneutralneutralneutralneutralnewspread_1682002299.544348fcfc9550010100.00.0
1.0Spread_barabasi_albert_graph_prob_0.0_trial_0TrueTrueTrueTrueTrueTrueTrueTrueTrue...neutralneutralneutralneutralneutralneutralneutralneutralneutralneutral110110.00.0
2.0Spread_barabasi_albert_graph_prob_0.0_trial_0TrueTrueTrueTrueTrueTrueTrueTrueTrue...neutralneutralneutralneutralneutralneutralneutralneutralneutralneutral210120.00.0
3.0Spread_barabasi_albert_graph_prob_0.0_trial_0TrueTrueTrueTrueTrueTrueTrueTrueTrue...neutralneutralneutralneutralneutralneutralneutralneutralneutralneutral310130.00.0
4.0Spread_barabasi_albert_graph_prob_0.0_trial_0TrueTrueTrueTrueTrueTrueTrueTrueTrue...neutralneutralneutralneutralneutralneutralneutralneutralneutralneutral
5.0Spread_barabasi_albert_graph_prob_0.0_trial_0TrueTrueTrueTrueTrueTrueTrueTrueTrue...neutralneutralneutralneutralneutralneutralneutralinfectedneutralneutral
6.0Spread_barabasi_albert_graph_prob_0.0_trial_0TrueTrueTrueTrueTrueTrueTrueTrueTrue...neutralneutralneutralneutralneutralneutralneutralinfectedneutralneutral
7.0Spread_barabasi_albert_graph_prob_0.0_trial_0TrueTrueTrueTrueTrueTrueTrueTrueTrue...neutralneutralneutralneutralneutralneutralneutralinfectedneutralneutral
8.0Spread_barabasi_albert_graph_prob_0.0_trial_0TrueTrueTrueTrueTrueTrueTrueTrueTrue...neutralneutralneutralneutralneutralneutralneutralinfectedneutralneutral
9.0Spread_barabasi_albert_graph_prob_0.0_trial_0TrueTrueTrueTrueTrueTrueTrueTrueTrue...neutralneutralneutralneutralneutralneutralneutralinfectedneutralneutral
10.0Spread_barabasi_albert_graph_prob_0.0_trial_0TrueTrueTrueTrueTrueTrueTrueTrueTrue...neutralneutralneutralneutralneutralneutralneutralinfectedneutralneutral
11.0Spread_barabasi_albert_graph_prob_0.0_trial_0TrueTrueTrueTrueTrueTrueTrueTrueTrue...infectedinfectedinfectedinfectedinfectedneutralinfectedinfectedinfectedinfected
12.0Spread_barabasi_albert_graph_prob_0.0_trial_0TrueTrueTrueTrueTrueTrueTrueTrueTrue...infectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfected
13.0Spread_barabasi_albert_graph_prob_0.0_trial_0TrueTrueTrueTrueTrueTrueTrueTrueTrue...infectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfected
14.0Spread_barabasi_albert_graph_prob_0.0_trial_0TrueTrueTrueTrueTrueTrueTrueTrueTrue...infectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfected
15.0Spread_barabasi_albert_graph_prob_0.0_trial_0TrueTrueTrueTrueTrueTrueTrueTrueTrue...infectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfected
16.0Spread_barabasi_albert_graph_prob_0.0_trial_0TrueTrueTrueTrueTrueTrueTrueTrueTrue...infectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfected
17.0Spread_barabasi_albert_graph_prob_0.0_trial_0TrueTrueTrueTrueTrueTrueTrueTrueTrue...infectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfected
18.0Spread_barabasi_albert_graph_prob_0.0_trial_0TrueTrueTrueTrueTrueTrueTrueTrueTrue...infectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfected
19.0Spread_barabasi_albert_graph_prob_0.0_trial_0TrueTrueTrueTrueTrueTrueTrueTrueTrue...infectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfectedinfected410140.00.0
\n", - "

20 rows × 3008 columns

\n", "" ], "text/plain": [ - "key SEED alive \\\n", - "dict_id env 0 1 10 \n", - "t_step \n", - "0.0 Spread_barabasi_albert_graph_prob_0.0_trial_0 True True True \n", - "1.0 Spread_barabasi_albert_graph_prob_0.0_trial_0 True True True \n", - "2.0 Spread_barabasi_albert_graph_prob_0.0_trial_0 True True True \n", - "3.0 Spread_barabasi_albert_graph_prob_0.0_trial_0 True True True \n", - "4.0 Spread_barabasi_albert_graph_prob_0.0_trial_0 True True True \n", - "5.0 Spread_barabasi_albert_graph_prob_0.0_trial_0 True True True \n", - "6.0 Spread_barabasi_albert_graph_prob_0.0_trial_0 True True True \n", - "7.0 Spread_barabasi_albert_graph_prob_0.0_trial_0 True True True \n", - "8.0 Spread_barabasi_albert_graph_prob_0.0_trial_0 True True True \n", - "9.0 Spread_barabasi_albert_graph_prob_0.0_trial_0 True True True \n", - "10.0 Spread_barabasi_albert_graph_prob_0.0_trial_0 True True True \n", - "11.0 Spread_barabasi_albert_graph_prob_0.0_trial_0 True True True \n", - "12.0 Spread_barabasi_albert_graph_prob_0.0_trial_0 True True True \n", - "13.0 Spread_barabasi_albert_graph_prob_0.0_trial_0 True True True \n", - "14.0 Spread_barabasi_albert_graph_prob_0.0_trial_0 True True True \n", - "15.0 Spread_barabasi_albert_graph_prob_0.0_trial_0 True True True \n", - "16.0 Spread_barabasi_albert_graph_prob_0.0_trial_0 True True True \n", - "17.0 Spread_barabasi_albert_graph_prob_0.0_trial_0 True True True \n", - "18.0 Spread_barabasi_albert_graph_prob_0.0_trial_0 True True True \n", - "19.0 Spread_barabasi_albert_graph_prob_0.0_trial_0 True True True \n", + " agent_count time \\\n", + "simulation_id params_id iteration_id step \n", + "newspread_1682002299.544348 fcfc955 0 0 101 0 \n", + " 1 101 1 \n", + " 2 101 2 \n", + " 3 101 3 \n", + " 4 101 4 \n", "\n", - "key ... state_id \\\n", - "dict_id 100 101 102 103 104 105 ... 90 91 \n", - "t_step ... \n", - "0.0 True True True True True True ... neutral neutral \n", - "1.0 True True True True True True ... neutral neutral \n", - "2.0 True True True True True True ... neutral neutral \n", - "3.0 True True True True True True ... neutral neutral \n", - "4.0 True True True True True True ... neutral neutral \n", - "5.0 True True True True True True ... neutral neutral \n", - "6.0 True True True True True True ... neutral neutral \n", - "7.0 True True True True True True ... neutral neutral \n", - "8.0 True True True True True True ... neutral neutral \n", - "9.0 True True True True True True ... neutral neutral \n", - "10.0 True True True True True True ... neutral neutral \n", - "11.0 True True True True True True ... infected infected \n", - "12.0 True True True True True True ... infected infected \n", - "13.0 True True True True True True ... infected infected \n", - "14.0 True True True True True True ... infected infected \n", - "15.0 True True True True True True ... infected infected \n", - "16.0 True True True True True True ... infected infected \n", - "17.0 True True True True True True ... infected infected \n", - "18.0 True True True True True True ... infected infected \n", - "19.0 True True True True True True ... infected infected \n", + " prob_tv_spread \\\n", + "simulation_id params_id iteration_id step \n", + "newspread_1682002299.544348 fcfc955 0 0 0.0 \n", + " 1 0.0 \n", + " 2 0.0 \n", + " 3 0.0 \n", + " 4 0.0 \n", "\n", - "key \\\n", - "dict_id 92 93 94 95 96 97 98 \n", - "t_step \n", - "0.0 neutral neutral neutral neutral neutral neutral neutral \n", - "1.0 neutral neutral neutral neutral neutral neutral neutral \n", - "2.0 neutral neutral neutral neutral neutral neutral neutral \n", - "3.0 neutral neutral neutral neutral neutral neutral neutral \n", - "4.0 neutral neutral neutral neutral neutral neutral neutral \n", - "5.0 neutral neutral neutral neutral neutral infected neutral \n", - "6.0 neutral neutral neutral neutral neutral infected neutral \n", - "7.0 neutral neutral neutral neutral neutral infected neutral \n", - "8.0 neutral neutral neutral neutral neutral infected neutral \n", - "9.0 neutral neutral neutral neutral neutral infected neutral \n", - "10.0 neutral neutral neutral neutral neutral infected neutral \n", - "11.0 infected infected infected neutral infected infected infected \n", - "12.0 infected infected infected infected infected infected infected \n", - "13.0 infected infected infected infected infected infected infected \n", - "14.0 infected infected infected infected infected infected infected \n", - "15.0 infected infected infected infected infected infected infected \n", - "16.0 infected infected infected infected infected infected infected \n", - "17.0 infected infected infected infected infected infected infected \n", - "18.0 infected infected infected infected infected infected infected \n", - "19.0 infected infected infected infected infected infected infected \n", - "\n", - "key \n", - "dict_id 99 \n", - "t_step \n", - "0.0 neutral \n", - "1.0 neutral \n", - "2.0 neutral \n", - "3.0 neutral \n", - "4.0 neutral \n", - "5.0 neutral \n", - "6.0 neutral \n", - "7.0 neutral \n", - "8.0 neutral \n", - "9.0 neutral \n", - "10.0 neutral \n", - "11.0 infected \n", - "12.0 infected \n", - "13.0 infected \n", - "14.0 infected \n", - "15.0 infected \n", - "16.0 infected \n", - "17.0 infected \n", - "18.0 infected \n", - "19.0 infected \n", - "\n", - "[20 rows x 3008 columns]" + " prob_neighbor_spread \n", + "simulation_id params_id iteration_id step \n", + "newspread_1682002299.544348 fcfc955 0 0 0.0 \n", + " 1 0.0 \n", + " 2 0.0 \n", + " 3 0.0 \n", + " 4 0.0 " ] }, - "execution_count": 27, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df = analysis.read_sql('soil_output/Spread_barabasi_albert_graph_prob_0.0/Spread_barabasi_albert_graph_prob_0.0_trial_0.sqlite')\n", - "df" + "res.env.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Let's look at the evolution of agent parameters in the simulation" + "### Agent reporters\n", + "\n", + "This dataframe reflects the data collected for all the agents in the simulation, in every step where data collection was invoked.\n", + "\n", + "The key in this dataframe is similar to the one in the `parameters` dataframe, but there will be two more keys: the `step` and the `agent_id`.\n", + "There will be a column per each agent reporter added to the model. In our case, there is only one: `state_id`." ] }, { "cell_type": "code", "execution_count": 28, - "metadata": { - "ExecuteTime": { - "end_time": "2017-10-19T15:59:17.153282Z", - "start_time": "2017-10-19T17:59:16.830872+02:00" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "df.plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "ExecuteTime": { - "end_time": "2017-10-19T11:10:36.086913Z", - "start_time": "2017-10-19T13:10:36.058547+02:00" - } - }, - "source": [ - "As we can see, `event_time` and `interval` are cluttering our results, " - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": { - "ExecuteTime": { - "end_time": "2017-10-19T15:59:18.418348Z", - "start_time": "2017-10-19T17:59:18.143443+02:00" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "del df['interval']\n", - "del df['event_time']\n", - "df.plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `soil.analysis` module also provides convenient functions to count the number of agents in a given state:" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": { - "ExecuteTime": { - "end_time": "2017-10-19T15:59:51.165806Z", - "start_time": "2017-10-19T17:59:50.886780+02:00" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "analysis.get_count(agents, 'state_id').plot();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Dealing with bigger data" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": { - "ExecuteTime": { - "end_time": "2017-10-19T16:00:18.148006Z", - "start_time": "2017-10-19T18:00:18.117654+02:00" - } - }, - "outputs": [], - "source": [ - "from soil import analysis" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": { - "ExecuteTime": { - "end_time": "2017-10-19T16:00:18.636440Z", - "start_time": "2017-10-19T18:00:18.504421+02:00" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1.1M\t../rabbits/soil_output/rabbits_example/\r\n" - ] - } - ], - "source": [ - "!du -xsh ../rabbits/soil_output/rabbits_example/" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "ExecuteTime": { - "end_time": "2017-10-19T11:22:22.301765Z", - "start_time": "2017-10-19T13:22:22.281986+02:00" - } - }, - "source": [ - "If we tried to load the entire history, we would probably run out of memory. Hence, it is recommended that you also specify the attributes you are interested in." - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": { - "ExecuteTime": { - "end_time": "2017-10-19T16:00:25.080582Z", - "start_time": "2017-10-19T18:00:19.594165+02:00" - }, - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "p = analysis.plot_all('../rabbits/soil_output/rabbits_example/', analysis.get_count, 'state_id')" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "backup\t\t\t rabbits_example.sqlite\r\n", - "rabbits_example.dumped.yml rabbits_example_trial_0.sqlite\r\n" - ] - } - ], - "source": [ - "!ls ../rabbits/soil_output/rabbits_example" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": { - "ExecuteTime": { - "end_time": "2017-10-19T16:00:38.434367Z", - "start_time": "2017-10-19T18:00:33.645762+02:00" - }, - "scrolled": true - }, - "outputs": [], - "source": [ - "df = analysis.read_sql('../rabbits/soil_output/rabbits_example/rabbits_example_trial_0.sqlite', keys=['state_id', 'rabbits_alive'])" - ] - }, - { - "cell_type": "code", - "execution_count": 36, "metadata": {}, "outputs": [ { @@ -4466,491 +2104,82 @@ " vertical-align: top;\n", " }\n", "\n", - " .dataframe thead tr th {\n", - " text-align: left;\n", - " }\n", - "\n", - " .dataframe thead tr:last-of-type th {\n", + " .dataframe thead th {\n", " text-align: right;\n", " }\n", "\n", "\n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "
keyrabbits_alivestate_id
state_id
dict_idenv0110100101102103104105...90919293949596979899
t_stepsimulation_idparams_iditeration_idstepagent_id
0.00newbornnewbornnannannannannannannan...nannannannannannannannannannannewspread_1682002299.544348fcfc955000None
2.00fertilefertilenannannannannannannan...nannannannannannannannannannan1neutral
16.00pregnantfertilenannannannannannannan...nannannannannannannannannannan2neutral
49.08fertilefertilenannannannannannannan...nannannannannannannannannannan3neutral
51.08fertilefertilenannannannannannannan...nannannannannannannannannannan
..................................................................
739.015fertiledeaddeaddeadfertiledeadfertiledeaddead...deadfertiledeaddeaddeadfertiledeaddeaddeaddead
742.014fertiledeaddeaddeadfertiledeadfertiledeaddead...deadfertiledeaddeaddeadfertiledeaddeaddeaddead
743.012fertiledeaddeaddeadfertiledeadfertiledeaddead...deadfertiledeaddeaddeadfertiledeaddeaddeaddead
744.010fertiledeaddeaddeadfertiledeadfertiledeaddead...deadfertiledeaddeaddeadfertiledeaddeaddeaddead
751.09fertiledeaddeaddeadfertiledeadfertiledeaddead...deadfertiledeaddeaddeadfertiledeaddeaddeaddead4neutral
\n", - "

326 rows × 349 columns

\n", "" ], "text/plain": [ - "key rabbits_alive state_id \\\n", - "dict_id env 0 1 10 100 101 102 103 \n", - "t_step \n", - "0.0 0 newborn newborn nan nan nan nan nan \n", - "2.0 0 fertile fertile nan nan nan nan nan \n", - "16.0 0 pregnant fertile nan nan nan nan nan \n", - "49.0 8 fertile fertile nan nan nan nan nan \n", - "51.0 8 fertile fertile nan nan nan nan nan \n", - "... ... ... ... ... ... ... ... ... \n", - "739.0 15 fertile dead dead dead fertile dead fertile \n", - "742.0 14 fertile dead dead dead fertile dead fertile \n", - "743.0 12 fertile dead dead dead fertile dead fertile \n", - "744.0 10 fertile dead dead dead fertile dead fertile \n", - "751.0 9 fertile dead dead dead fertile dead fertile \n", - "\n", - "key ... \\\n", - "dict_id 104 105 ... 90 91 92 93 94 95 96 \n", - "t_step ... \n", - "0.0 nan nan ... nan nan nan nan nan nan nan \n", - "2.0 nan nan ... nan nan nan nan nan nan nan \n", - "16.0 nan nan ... nan nan nan nan nan nan nan \n", - "49.0 nan nan ... nan nan nan nan nan nan nan \n", - "51.0 nan nan ... nan nan nan nan nan nan nan \n", - "... ... ... ... ... ... ... ... ... ... ... \n", - "739.0 dead dead ... dead fertile dead dead dead fertile dead \n", - "742.0 dead dead ... dead fertile dead dead dead fertile dead \n", - "743.0 dead dead ... dead fertile dead dead dead fertile dead \n", - "744.0 dead dead ... dead fertile dead dead dead fertile dead \n", - "751.0 dead dead ... dead fertile dead dead dead fertile dead \n", - "\n", - "key \n", - "dict_id 97 98 99 \n", - "t_step \n", - "0.0 nan nan nan \n", - "2.0 nan nan nan \n", - "16.0 nan nan nan \n", - "49.0 nan nan nan \n", - "51.0 nan nan nan \n", - "... ... ... ... \n", - "739.0 dead dead dead \n", - "742.0 dead dead dead \n", - "743.0 dead dead dead \n", - "744.0 dead dead dead \n", - "751.0 dead dead dead \n", - "\n", - "[326 rows x 349 columns]" + " state_id\n", + "simulation_id params_id iteration_id step agent_id \n", + "newspread_1682002299.544348 fcfc955 0 0 0 None\n", + " 1 neutral\n", + " 2 neutral\n", + " 3 neutral\n", + " 4 neutral" ] }, - "execution_count": 36, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": { - "ExecuteTime": { - "end_time": "2017-10-19T16:00:39.160418Z", - "start_time": "2017-10-19T18:00:38.436153+02:00" - }, - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "states = analysis.get_count(df, 'state_id')\n", - "states.plot();" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": { - "ExecuteTime": { - "end_time": "2017-10-19T16:00:39.515032Z", - "start_time": "2017-10-19T18:00:39.162240+02:00" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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PueLiYhISEv5yE9zfGz9+PKNGjWLTpk0MGDCAa665hu7du1/SY+fNm8cbb7zB/v37yczMpLCwsFwLmI4ePZq77rqLd955Bw8PD2bPns0NN9yA1VoyAmTr1q2sWbOGf//737bHFBUVnfdvVRU0BkUcWlZ+2X7Qqm5BiTi3suzRs5fW/CrizCwWC97urqbcLJby7Zfl4+Nj+/rQoUMMGzaMNm3a8OWXXxIfH8/bb78NVO8A2lKDBw/m8OHDPPDAAxw/fpx+/frx4IMPXvRxa9euZfTo0QwZMoSFCxeyefNm/u///q9c1zB8+HAMw+C7777jyJEjrF69mtGjR9uOZ2Zm8tRTT7Flyxbbbdu2bezdu7fKVxRWC4o4LMMwyMorG1CqYpDs77WMLPnL5MtNR7mvX+Ny9YGLiH2Ij4+nuLiYl19+2dZS8Nlnn513XmFhIRs3brS1liQkJJCamkrz5s1t5yQmJnL8+HEiIyMBWLduHVartUwX0u+5u7tTVFR03v21a9dmzJgxjBkzhp49e/LQQw/x0ksv/eV1/Pzzz9SrV4//+7//s913+PDhi1x9WZ6enowcOZLZs2ezb98+mjVrRocOHWzHO3ToQEJCAo0bNy7X81YGvbuKw8orLLuDcWFRMfWDfS7yqIoZ2aEu76zcT+KZbN5Yto9HB8dU6c8TkcrXuHFjCgoKePPNNxk+fDhr1qxhxowZ553n5ubGvffeyxtvvIGrqysTJ06kW7dutsACJR/wY8aM4aWXXiI9PZ377ruP66677k+7d+rXr8/69es5dOgQvr6+BAUF8eSTT9KxY0datmxJXl4eCxcuLBOC/kyTJk1ITExk7ty5dO7cme+++4758+eX+99j9OjRDBs2jB07dnDzzTeXOTZ16lSGDRtGdHQ01157LVarla1bt7J9+3aeffbZcv+s8lAXjzis37eerH7kSlY9fCUB3lXbguLuauWRQSWhZEbcfl5cvFtroog4mLZt2/LKK6/wn//8h1atWjF79mymTZt23nne3t488sgj3HTTTfTo0QNfX1/mzZtX5pzGjRszcuRIhgwZwoABA2jTpk2ZgbZ/9OCDD+Li4kKLFi2oXbs2iYmJuLu7M2XKFNq0aUOvXr1wcXFh7ty5F72Oq6++mgceeICJEyfSrl07fv75Zx5//PFy/3v07duXoKAgEhISuOmmm8ocGzhwIAsXLuTHH3+kc+fOdOvWjVdffZV69eqV++eUl3YzFod1+HQWvV9cibe7CzufHlRtP9cwDN5esY+XftwDwPWdopg2sjVWa/n6xEUciXYzlkul3Yylxss814Li61G9PZUWi4WJfZvw/MjWWC0wb+MRXlmyp1prEBFxdgoo4rCy8koGmlV3QCl1Q5doXry2LQBvrdjHN1uOmVKHiDin5557Dl9f3wveBg8ebHZ5VU6DZMVhZeaVLOfsY1JAARjVsS57kjN4d9UBHvx8KwAj2tW5yKNERC5u3LhxXHfddRc85uXlVc3VVD8FFHFYmSa3oJR6eFAMJ9Jy+Xbrce6fu4XTmfnccUUDU2sSqSoOOGzRYQUFBREUFGR2GeVWWa8RdfGIw8rMLRmDYmYLCoCL1cJr17fjtu71AXh64U4W/nrc1JpEKpubW8kMuezs7IucKTVd6Wuk9DVzudSCIg6rdJqxn6f5L2Or1cITw1vg5mLh/dUHeWFRAkNbR5R7tUsRe+Xi4kJgYCApKSlAyRRcvb7l9wzDIDs7m5SUFAIDA3FxKd92BH9k/ju7yGXKyCttQanY/wSVxWKx8MBVTflkXSKJZ7LZnZRB8whNgxfnUbr4WGlIEbmQwMDAS96H6K8ooIjDysqzjy6e3/N2d6VbwyBWJJxkRUKKAoo4FYvFQkREBKGhoRQUFJhdjtghNze3CreclLKfd3aRcrJ18dhRQAEY0DKcFQkn+e9PB+neKIR2UYFmlyRSqVxcXCrtQ0jkz2iQrDisDDtsQQEY1aEujWr7cCozn7+9s4Ynv91BYVGx2WWJiDgUBRRxWFkmrSR7Me6uVub9I5aRHepgGDDr50PcPusXlu5MJif//F1MRUTkfPb1zi5SDqXTjO0toACE+HrwynXtGNQynHs/3czqvadYvfcUHq5WejYJYXTXelwZE2p2mSIidkstKOKwMu20i+f3BrQM5+sJPRgTW486gV7kFRazdFcKt8/6hTs/2sjZrHyzSxQRsUsKKOKwsvLPtaDYwToof6V5hD9PjWjFT49cyaJJPbnziga4Wi0s3ZXMC4sTzC5PRMQuKaCIw8qw4y6eC7FYLMSE+/PYsBa8PboDACsTUrR0uIjIBSigiEPKLSgiNbtkHYZQPw+Tqym/3k1r4+5q5URaLpsSU80uR0TE7iigiENKTs8FwNPNSoBXxfZ7MIOnmwsj2kYC8J8fdqsVRUTkDxRQxCGdSCsJKJEBXg67H8jkAU3xcLWy4dAZ3l99wOxyRETsigKKOKQTaTkAhAd4mlzJ5YsI8OL+/k0AeO773Xy89pC5BYmI2BEFFHFIpS0ojhxQAMb3bsSkcyHlxcUJpOVofxMREVBAEQeVdC6gRDh4QLFYLNzbtwkNQ3zIyC1k1Z6TZpckImIXFFDEIZ2wBRQvkyupOBerhX7NS1aVVUARESmhgCIOyVlaUEr1bFIbgNV7T2lGj4gICijioJxhkOzvdWkQhIerlaT0XPYkZ5pdjoiI6RRQxKEUFRv8b91hTmWW7GHjDF08ULIuSmyjYAAmztlkC2AiIjWVAoo4lNV7T/L419uBkiXua3k73iJtf+axoS0I8/dgb0omo975mX0pakkRkZpLAUUcyvHUXNvXr17fzmEXabuQxqG+fDm+Ow1r+3A8LZe7/7eRgqJis8sSETGFAoo4lNJ1QkZ1qMtVLcJMrqby1a3lzef/iCXYx50DJ7OY+8sRs0sSETGFAoo4lNSckrEngU7UtfNHwb4ethVmX/kxgaNns02uSESk+imgiENJP9eCEuiAGwSWx41domkZ6c/Z7ALu+jierLxCs0sSEalW5Qoo06ZNo3Pnzvj5+REaGso111xDQkJCmXP69OmDxWIpcxs3blyZcxITExk6dCje3t6Ehoby0EMPUVioN2C5uNTskoAS4MQtKABuLlbeu7UTIb4e7DqRzq3/3UBCUobZZYmIVJtyBZS4uDgmTJjAunXrWLJkCQUFBQwYMICsrKwy5911112cOHHCdnvhhRdsx4qKihg6dCj5+fn8/PPPfPTRR8yaNYupU6dWzhWJU7MFFCdvQQGoE+jFu7d0xNvdhfjDZxn6xmp+3JFkdlkiItXCtTwnL1q0qMz3s2bNIjQ0lPj4eHr16mW739vbm/Dw8As+x48//sjOnTtZunQpYWFhtGvXjmeeeYZHHnmEJ598End39/Mek5eXR15enu379PT08pQtTqR0kGyg9/mvE2fUsV4tlkzuzb++2kbcnpP8d81BBrS88P9bIiLOpEJjUNLS0gAICgoqc//s2bMJCQmhVatWTJkyhezs3wb5rV27ltatWxMW9tsMjIEDB5Kens6OHTsu+HOmTZtGQECA7RYVFVWRssVB7UvJZOeJknBaE1pQStUJ9OKpq1sCEH/4LPtPan0UEXF+lx1QiouLmTRpEj169KBVq1a2+2+66SY++eQTVqxYwZQpU/jf//7HzTffbDuelJRUJpwAtu+Tki7cfD1lyhTS0tJstyNHNPWyJvps42+/97q1nGMF2UtVL9ibmHA/CooMBr+2mpcWJ5CTX2R2WSIiVaZcXTy/N2HCBLZv385PP/1U5v67777b9nXr1q2JiIigX79+7N+/n0aNGl3Wz/Lw8MDDw+NySxUncSqzpJtvZPs6hPjWrNeDxWLh/Vs78fg321mZcJK3Vuzj6y3HeGJ4S6dcD0ZE5LJaUCZOnMjChQtZsWIFdevW/ctzu3btCsC+ffsACA8PJzk5ucw5pd//2bgVEYCzWSVroHRrGGxyJeaICvJm5m2dmXFzRyIDPDl6Noe7Pt7I2Fm/cOSM1koREedSroBiGAYTJ05k/vz5LF++nAYNGlz0MVu2bAEgIiICgNjYWLZt20ZKSortnCVLluDv70+LFi3KU47UMGfOzeCp5VMzBsheiMViYVCrcJb+szfj+zTCzcXCst0p9H8ljjeX7aWo2DC7RBGRSlGugDJhwgQ++eQT5syZg5+fH0lJSSQlJZGTU7Lz6v79+3nmmWeIj4/n0KFDfPvtt9x666306tWLNm3aADBgwABatGjBLbfcwtatW1m8eDGPPfYYEyZMUDeO/KXSFpQgn5ozQPbPeLu78sigGH64vxfdGwWTV1jMy0v28N6qA2aXJiJSKcoVUKZPn05aWhp9+vQhIiLCdps3bx4A7u7uLF26lAEDBhATE8M///lPRo0axYIFC2zP4eLiwsKFC3FxcSE2Npabb76ZW2+9laeffrpyr0yczm8BRUG2VONQX2bf2ZXHhjYH4J2V+8gr1OBZEXF85Rokaxh/3XwcFRVFXFzcRZ+nXr16fP/99+X50VLD5RcWk3FuufegGrIGyqWyWCzc0aMBM+IOcCozj02HU4ltVDPH6YiI89BePOIQzmaXtJ64WC34eV725DOnZbVa6NkkBIAlO5MvcraIiP1TQBGHcCy1ZJxTqJ8HVqvF5Grs07A2JQPRZ68/zIm0HJOrERGpGAUUcQiHT5fs91Qv2NvkSuxX35hQOtevRV5hMa8t2Wt2OSIiFaKAIg7h8OmSdT7qBfmYXIn9slgsPDIoBoCvtxwjI7fA5IpERC6fAoo4hMRzASVaLSh/qWO9WjQO9SWvsJgftmnnYxFxXAoo4hAOn1spVV08f81isTCyQx0AZm9I1H49IuKwFFDEIdjGoKiL56L+1r4Obi4Wth5Jpf8rcaw7cNrskkREyk0BRexeZl4hpzJLphmri+fiIgK8eO/WTtQJ9OJYag73zN5EusajiIiDUUARu1c6/qSWtxsBXlrm/lJc2SyUJZN70bC2D2ey8nlnxX6zSxIRKRcFFLF7R86eGyAbpNaT8vB2d+XRc7N6ZsTt5/65m0nJyDW5KhGRS6OAInavdKCnn6daT8rrqhZh3NWzARYLfLPlOCPeWsOe5AyzyxIRuSgFFLF7pZvfubvq5VpeFouF/xvagm8m9KBRbR9OpOVy0/vrNSZFROye3vHF7uUXFgPgoYBy2drUDeTL8d1pGOLDqcw8pq/UmBQRsW96xxe7l3cuoKgFpWICvd2ZMqQ5AP/96SDHU7Vfj4jYL73ji93LUwtKpenfPJQuDYLIKyzmpR8TzC5HRORP6R1f7F6+WlAqjcVi4f/OtaLM33yM91cdIDOv0OSqRETOp3d8sXu/taC4mFyJc2gbFcjIDnUwDPj397uInbaMhb8eN7ssEZEyFFDE7qkFpfI9P7IN00a2pmFtHzJyC3ny2x0UFxtmlyUiYqN3fLF7pdOMNQal8ri7WrmxSzSL7u+Ft7sLpzLz2Xo01eyyRERs9I4vdk8tKFXH3dVKj8YhANz0/no+WH0Aw1BLioiYT+/4Yvfyi84FFBe9XKvCE8Nb0Ll+LXIKinj2u118vy3J7JJERBRQxP7lFZwbJOumQbJVoW4tbz77Ryx39WwAwAuLd9tarUREzKKAInYlJT2XwqKyH46lLSgeakGpMhaLhUn9mxLi68Hh09lcO+NnftWYFBExkd7xxW7sOJ5Gl+eWMXHO5jL32wbJuunlWpV8PFx54drW+Hm48uvRNEZN/5kdx9PMLktEaii944vd2HIkFYBFO5L4ae8p2/22QbJqQalyfWPCWPbP3nRvFExBkcFLi7XarIiYQ+/4YjdOZeTbvn7u+122dTlsmwWqBaVahPp78tzfWgOwcs9JTqRpzx4RqX56xxe7cTIz1/b1zhPpzN98DPjdZoEuGiRbXeqH+NClfhCGAV9v1iqzIlL9FFDEbpS2oDQI8QHg5R8TyC0oUguKSUZ2qAPAl5uOam0UEal2escXu3EyMw+A+/s1ITLAk+NpuTzxzQ7Sc0s2s9MYlOo1pE0E3u4u7EvJ1NooIlLt9I4vduHImWziD58FoE4tLx4eFAPAvI1HOHUuuGgl2erl7+nG2CtK1kaZt/GIydWISE2jd3yxC7PXJ9q+jg7y5pr2dZhxc0fa1g2w3R/s425GaTXasDaRAGw4eJrcgiKTqxGRmsTV7AJEAFIySgbI9mlWmzB/TwAGtQpnYMswNiWeJa+wmNBz90v1aRrmS7i/J0npuXyz5RjXd442uyQRqSHUgiJ24WxWyQDZIa0iytxvsVjoWC+I7o1CzCirxrNYLNx5bgn8qd/s4KtNR9WSIiLVQgFF7MKZ7AIAaqkbx+7cGluffjGh5BUWM/mzrXR4Zgn/+N9GPt94xLbKr4hIZVNAEbtQ2oIS5ONmciXyR+6uVmbc0pEJVzYizN+D7PwiFu9I5qEvfuXOjzaet3eSiEhlUEARu1AaUGp5qwXFHrm5WHloYAzrpvRj4b1XcH+/Jni5ubB67yme+3632eWJiBPSIFkxXX5hMRl5JWudBKmLx65ZLBZa1QmgVZ0AYsL9GD97E/9dc5C0nAKmDIkhxNfD7BJFxEmoBUVMdza7pPXExWrB31NdPI5icOsIHjm3Xs2Xm44y6LXVnD63Zo2ISEUpoIjp0nNKBsj6ebpitVpMrkbKY3yfRnx1T3fq1vLiVGYei3ckm12SiDgJBRQxXWn3jp+nehwdUYfoWlzXKQqAuD0pJlcjIs5CAUVMl3Furx1fD3XvOKq+MaEALN2Vwi+HzphcjYg4AwUUMV3muYDi56EWFEfVqk4AA1qEUVRscON763hn5T6Ki7UDsohcPgUUMV1m3m9jUMRxvXRdW4a2iaCw2OCFRQnc8t/1ZJ7rvhMRKa9yBZRp06bRuXNn/Pz8CA0N5ZprriEhIaHMObm5uUyYMIHg4GB8fX0ZNWoUycllB84lJiYydOhQvL29CQ0N5aGHHqKwUG9kNZWti0cBxaH5e7rx1o3t+c+o1ni5ubBm32k+XnvI7LJExEGVK6DExcUxYcIE1q1bx5IlSygoKGDAgAFkZWXZznnggQdYsGABn3/+OXFxcRw/fpyRI0fajhcVFTF06FDy8/P5+eef+eijj5g1axZTp06tvKsSh1L6V7avungcnsVi4frO0UwZUjL9eGXCSZMrEhFHZTEM47I7ik+ePEloaChxcXH06tWLtLQ0ateuzZw5c7j22msB2L17N82bN2ft2rV069aNH374gWHDhnH8+HHCwsIAmDFjBo888ggnT57E3f3iC3Wlp6cTEBBAWloa/v7+l1u+2IlnF+7kg58O8o/eDZkyuLnZ5UglOHw6i94vrgRgWJsIpg5vQaifdqMWqenK8/ldoTEoaWlpAAQFBQEQHx9PQUEB/fv3t50TExNDdHQ0a9euBWDt2rW0bt3aFk4ABg4cSHp6Ojt27Ljgz8nLyyM9Pb3MTZxHaQuKBsk6j3rBPtzfrwlWCyz89QS3fLBBe/aISLlcdkApLi5m0qRJ9OjRg1atWgGQlJSEu7s7gYGBZc4NCwsjKSnJds7vw0np8dJjFzJt2jQCAgJst6ioqMstW+zQibRcQF08zuaBq5ry7cQrCPR2IyE5g8/jj5pdkog4kMsOKBMmTGD79u3MnTu3Muu5oClTppCWlma7HTlypMp/plSPPckZxO0pGafgo4DidFrVCeC+vk0AeGXJHrI0q0dELtFlBZSJEyeycOFCVqxYQd26dW33h4eHk5+fT2pqapnzk5OTCQ8Pt53zx1k9pd+XnvNHHh4e+Pv7l7mJc9hyJNX2dbeGweYVIlXm5m71iA7y5mRGHu+vPmB2OSLiIMoVUAzDYOLEicyfP5/ly5fToEGDMsc7duyIm5sby5Yts92XkJBAYmIisbGxAMTGxrJt2zZSUn5bEnvJkiX4+/vTokWLilyLOKC07JI1UK5pF0lUkLfJ1UhVcHe18vCgZgC8t+qANhQUkUtSroAyYcIEPvnkE+bMmYOfnx9JSUkkJSWRk5MDQEBAAGPHjmXy5MmsWLGC+Ph4br/9dmJjY+nWrRsAAwYMoEWLFtxyyy1s3bqVxYsX89hjjzFhwgQ8PLRVe02Tdm6jwEDvi8/eEsc1tHUEMeF+ZOcXsWy39usRkYsrV0CZPn06aWlp9OnTh4iICNtt3rx5tnNeffVVhg0bxqhRo+jVqxfh4eF89dVXtuMuLi4sXLgQFxcXYmNjufnmm7n11lt5+umnK++qxGGk5uQD4O+lfXicmcViYUCLksHwS3YmU4HVDUSkhqjQOihm0ToozmPinE0s/PUEU4e14I4rGlz8AeKwfj2aytVvrQHgjh4NeHxYcywWi8lViUh1qrZ1UEQqqrSLJ0AtKE6vTd1AnhheMs7sv2sO8uFPB02uSETsmQKKmOLo2Wy+2XKMM1klXTyB3gooNcHtPRrw+LCSkPLc97tYkaDxKCJyYQooYoonv93B/XO3sON4yarACig1xx096nN9pyiKDbhvzmb2pWSaXZKI2CEFFDHF0l1l/3IO8tEMrprCYrHwzDWt6Fy/Fhl5hdz50S+kZuebXZaI2BkFFDFFZEDJxnEhvh7c17cx9YO1BkpN4u5qZfrNHakT6MWh09n8Z9Fus0sSETujgCKmSM8tWfL883GxTB7QTLM5aqAQXw9evLYNAAu2niAnv8jkikTEniigSLUrKCq27WAcqNk7NVq3hsFEBXmRmVfIjzsvvFmoiNRMCihS7dLPTS0GLdBW01mtFv7WvmQ/r/+tPUxRscMtyyQiVUQBRapd6rmA4ufpiotVXTs13d871sXTzcrGw2d5/oddZpcjInZCAUWq3W/776j1RCAqyJuX/t4WgPdXH+SbLcdMrkhE7IECilS70h2MtXqslBrWJpJ7+zYG4JmFu8g6N0ZJRGouBRSpdum5Cihyvnv7NqFuLS9OZeZphVkRUUCR6lc6xdjPQwFFfuPuamVQy3AAVu85ZXI1ImI2BRSpdpnnAoqvp6vJlYi96dW0NgDfbz/B2SytLitSkymgSLXLzCvp4vH1UECRsno0DiEm3I+M3ELeWrHP7HJExEQKKFLtSltQ/NSCIn/gYrUwZUhzAD5ee4jDp7NMrkhEzKKAItUu49wMDbWgyIX0blqbnk1CKCgyGPbGTzz3/S6Op+aYXZaIVDMFFKl2GoMiF/PMiFY0DfMlI6+Q91YdoMd/ljP6g3V8EX9UU5BFaggFFKl2mWpBkYuoH+LDovt78d/bOhHbMBjDgDX7TvPg51sZ+sZq22tIRJyXPiGk2pV+uGgMivwVq9VC35gw+saEceRMNl9vPsZHaw9z6HQ278XtZ/KAZmaXKCJVSC0oUu1sXTxaB0UuUVSQN/f2a8Kz17QESpbET07PNbkqEalKCihSbYqLDSbO2cSBUyUzM9TFI+U1sGU4HevVIqegiFd+3GN2OSJShRRQpNrsP5nJwl9PAODl5kKdWl4mVySOxmKx8K8hMQB8Hn+E5buTTa5IRKqKAopUm9RzuxgDxD3UR3vxyGXpWC+IoW0iKDbgjlkbuffTzeQVFpldlohUMgUUqTap53Yxbls3gFB/T5OrEUf2wqg23N6jPlYLLNh6nJlrDpldkohUMgUUqTZp51pQArzdTa5EHJ2PhytPDG/J86PaAPD2in3au0fEySigSLVJzS75AAlU145UklEd6tI8wp+M3EIe/HwrRcWG2SWJSCVRQJFqk17agqKAIpXExWrhP6Na4+FqZdnuFF5cnGB2SSJSSRRQpNqUDpIN9FZAkcrTpm4gL1xb0tUzI24/P+09ZXJFIlIZFFCk2pQOklULilS2Ee3qcEu3egA89/0uitXVI+LwFFCk2pQOkvVXQJEq8MBVTfHzcGXniXReXboHw1BIEXFkCihSbTJy1YIiVSfIx52HB5cs4vbm8n28s3K/yRWJSEUooEi1yTi3B4+flriXKnJLt3o8PqwFAG8u38v2Y2kmVyQil0sBRapN6S7GvtrFWKrQHT3qE9swmNyCYm54bx37UjLMLklELoMCilSb33YxVkCRqmOxWHj31o60iwokM6+QrzYdM7skEbkMCihSLYqLDTLz1YIi1cPf041RHeoAsOtEusnViMjlUECRapFdUETppAo/Dw2SlarXPMIfgF0n1MUj4ogUUKRalHbvuFgteLrpZSdVLybCHxerhaT0XOIPnzW7HBEpJ31SSLXIzCuZYuzn6YrFYjG5GqkJfD1cbd08z32/S+uiiDgYBRSpcgdPZfH15uOABshK9Zp8VTM83azEHz7LhoNnzC5HRMpBAUWqVGp2PgNfW8VbK/YBcDw1x+SKpCYJD/BkUMtwAOL2nDS5GhEpDwUUqVJHz+aQX1hs+97VqpecVK9eTWsD8NWmYxw+nWVyNSJyqfRpIVWqdINAPw9XejQO5sW/tzG5IqlprmoRRnSQN0npuVz/7jrblgsiYt8UUKRKpebkA9A80p/Zd3ZjRLs6JlckNY2fpxtfjI+lXnBJSJkRpz16RBxBuQPKqlWrGD58OJGRkVgsFr7++usyx2+77TYsFkuZ26BBg8qcc+bMGUaPHo2/vz+BgYGMHTuWzMzMCl2I2KfSHYy1QaCYKdTPk38NaQ7AB6sPsvDX45rVI2Lnyh1QsrKyaNu2LW+//fafnjNo0CBOnDhhu3366adljo8ePZodO3awZMkSFi5cyKpVq7j77rvLX73YvdIunkAFFDHZgBZh9GwSQl5hMRPnbOaOWb+ou0fEjpV7zufgwYMZPHjwX57j4eFBeHj4BY/t2rWLRYsW8csvv9CpUycA3nzzTYYMGcJLL71EZGTkeY/Jy8sjLy/P9n16upaudhSlLSiB3gooYi6LxcL7t3Zi+sr9TI/bz4qEk9zw3jrm3t0NP0+9PkXsTZWMQVm5ciWhoaE0a9aM8ePHc/r0aduxtWvXEhgYaAsnAP3798dqtbJ+/foLPt+0adMICAiw3aKioqqibKkk8YfP8p9Fu8nMKyQtW108Yj883Vx44KqmfDW+OyG+7uw4ns70lRqTImKPKj2gDBo0iI8//phly5bxn//8h7i4OAYPHkxRUREASUlJhIaGlnmMq6srQUFBJCUlXfA5p0yZQlpamu125MiRyi5bKtFDn29l+sr99HlxJesPloTTAG93k6sS+U2rOgE897fWAHz400GOns02uSIR+aNKX9bzhhtusH3dunVr2rRpQ6NGjVi5ciX9+vW7rOf08PDAw8OjskqUKnbgVMlaE6cy8zh1buxzhL+niRWJnO+qFmF0aRDEhoNnuOn99XwytivRwd5mlyUi51T5NOOGDRsSEhLCvn0lK4mGh4eTkpJS5pzCwkLOnDnzp+NWxLGE+ZeEyb4xodzXtzHPjGhJn2a1Ta5KpCyLxcLLf29LdJA3iWeymTRvs2b2iNiRKg8oR48e5fTp00RERAAQGxtLamoq8fHxtnOWL19OcXExXbt2repypBqUztx56uqWTB7QjFti6+PqoiV3xP5EBXkz9+5ueLm5sCkxVbsei9iRcn9qZGZmsmXLFrZs2QLAwYMH2bJlC4mJiWRmZvLQQw+xbt06Dh06xLJlyxgxYgSNGzdm4MCBADRv3pxBgwZx1113sWHDBtasWcPEiRO54YYbLjiDRxxLbkEReeeWttfMHXEEkYFe9GgcAsCvR9NMrkZESpU7oGzcuJH27dvTvn17ACZPnkz79u2ZOnUqLi4u/Prrr1x99dU0bdqUsWPH0rFjR1avXl1mDMns2bOJiYmhX79+DBkyhCuuuIL33nuv8q5KTFM6rdjFatHOxeIwWkT4AbDrhJYwELEX5f4E6dOnz1/20y5evPiizxEUFMScOXPK+6PFAaT+blqxxWIxuRqRS9M8wh+ADYfOkFtQhKebi8kViYgGBkil0tL24ohiGwUT6O3G4dPZPPLlrxosK2IHFFCkUqVml2wOqIAijiTQ2513RnfA1Wrhmy3H+e+aQ2aXJFLjKaBIpTqdVRJQgny0MJs4lu6NQnh8WAsAXl2yh0Pn1vMREXMooEilOpGaA0BEgBZmE8dzS7d6tI0KJDOvkGtn/Mz+k9plXcQsCihSqU6k5QIKKOKYrFYLH9zaiRYR/pzKzOfJb3eYXZJIjaWAIpUqKb00oHiZXInI5ant58GMmzvi5mJh9d5TxO05aXZJIjWSAopUKrWgiDOIDvbm1tj6AEz7fheFRcXmFiRSAymgSKVKOhdQwhVQxMHd27cx/p6u7E7KYMKcTeQVFpldkkiNooAilSY9t4DMvEJAAUUcX6C3Oy9f1w53FyuLdyTz2tK9ZpckUqMooEilKW09CfByw9tdy9yL47uqRRiv3dAOgA9WH2Dhr8fNLUikBlFAkUpz+HQ2oPEn4lwGtwpnRLtICooM7v10MxsPnTG7JJEaQQFFKsXzP+zmro83Agoo4lwsFguvXNeOYW0iMAyY8tU2Us7NVhORqqOAIpViRtx+29fhmmIsTsbFamHq8BbU9vNgb0omo2b8zEGtNCtSpRRQpFIE/25p+95Na5tYiUjVCPXz5Mtx3akX7M2RMzlcO10hRaQqKaBIpfBwLXkpfT4ulkGtwk2uRqRqRAd788W47rSM9Od0Vj5jP/rFtoO3iFQuBRSpFBnnphdrk0BxdrX9PJh5e2ciAjw5cDKLO2b9wordKVrMTaSSKaBIhRmGQda5gOLnoenF4vxC/Tx5/9ZOeLm5EH/4LLfP+oVu05bxweoDZpcm4jQUUKTCcgqKKDZKvvZRQJEaolWdABbc24Pbutcn2MedU5n5PPvdLnYcTzO7NBGnoIAiFVa6eqzFAt7uLiZXI1J9Gof68eTVLVn3r370iwkFYP6mYyZXJeIcFFCkwrLySvYo8XV3xWKxmFyNSPVzc7FyQ5doAL7ZelzjUUQqgQKKVFhmbkkLirp3pCbr3bQ2QT7unMzI44ftSWaXI+LwFFCkwkq7eHw9FVCk5nJ3tTImtj4ALy5O0O7HIhWkgCIVVhpQ1IIiNd2dPRtQ28+DxDPZfKWxKCIVooAiFaYpxiIlfDxcua17fQCW7Uo2txgRB6eAIhWWYWtB0QwekdKtHtbuP01+oQbLilwuBRSpsCx18YjYtIjwJ9jHnaz8IjYlnjW7HBGHpYAiFaYuHpHfWK0WrmgSAsDs9YnkFmiwrMjlUECRCsvQNGORMoa1iQRgwdbjDHh1FftPZppckYjjUUCRCsvSNGORMq5qEcbbN3UgzL9kRs+/vtqmxdtEykkBRSrMtg6KWlBEbIa2ieDL8d1xd7Wy/uAZxn2yidTsfLPLEnEYCihSYbZ1UNwVUER+r24tb968sT3urlaW7kqm78txfLv1uNlliTgEBRSpMK0kK/LnBrYMZ+7d3Wga5suZrHzu+3QzW4+kml2WiN1TQJEKy1IXj8hf6hBdi+/u68ngVuEA3PT+Oj5YfYDiYsPkykTslwKKVMjpzDz2JJfMUFBAEflzbi5Wnh7RinZRgWTlF/Hsd7t4e8U+s8sSsVsKKHLZ0nML6PXCCtv3mmYs8tdq+3nw1fjuPDo4BoC3V+5j1Z6TJlclYp8UUOSyJZ7OJiu/ZBGqgS3DaBjiY3JFIvbParXwj14N6RsTSm5BMWM/+oXtx9LMLkvE7iigyGVLzy0AoHGoL+/e0gmr1WJyRSKOwWKxMOPmjvSNCaWgyODf3+3CMDQeReT3FFDkspWuIOuv2Tsi5ebuauXpES1xd7Wy9sBpViSkmF2SiF1RQJHLlp5T0oLi5+lmciUijqluLW9u71EfgGnf79ZqsyK/o4Aily29tAXFSwFF5HLd06cxgd5u7E3J5PP4o2aXI2I3FFDkspW2oKiLR+TyBXi5cW/fJgC8smSPbV0hkZpOAUUuW+kYFHXxiFTMLd3qER3kzcmMPN5YtlcDZkVQQJEKKJ3F4++lFhSRinB3tfLwoGYAvLvqADe9v54jZ7JNrkrEXOUOKKtWrWL48OFERkZisVj4+uuvyxw3DIOpU6cSERGBl5cX/fv3Z+/evWXOOXPmDKNHj8bf35/AwEDGjh1LZmZmhS5EqteBk5l8ca6/XC0oIhU3tHUEjw6OwdOtZFbPPbM3aSl8qdHKHVCysrJo27Ytb7/99gWPv/DCC7zxxhvMmDGD9evX4+Pjw8CBA8nNzbWdM3r0aHbs2MGSJUtYuHAhq1at4u677778q5Bqd8uHG2xfB3m7m1iJiHOwWCyM692IxZN64ePuwrZjaSz4VTsfS81lMSrQ2WmxWJg/fz7XXHMNUNJ6EhkZyT//+U8efPBBANLS0ggLC2PWrFnccMMN7Nq1ixYtWvDLL7/QqVMnABYtWsSQIUM4evQokZGRF/256enpBAQEkJaWhr+//+WWL5cpv7CYpo/9AJT81ffi39vg7a5uHpHK8uayvby8ZA+1vN2YeXsX2kUFml2SSKUoz+d3pY5BOXjwIElJSfTv3992X0BAAF27dmXt2rUArF27lsDAQFs4Aejfvz9Wq5X169df8Hnz8vJIT08vcxPzpGbnA2C1wJs3tlc4Ealkd/ZsSNu6AZzNLuCm99dpvx6pkSo1oCQlJQEQFhZW5v6wsDDbsaSkJEJDQ8scd3V1JSgoyHbOH02bNo2AgADbLSoqqjLLlnI6cy6g1PJ21/L2IlXAy92F2Xd1o2eTELLzi7ht5gbu/XQz245qzx6pORziT98pU6YwefJk2/fp6ekKKSY6k3UuoPho7IlIVfH1cOXDMZ2Z8tU2vtx0lAVbj7Ng63HqBHrRuk4AresGlPy3ToD+XxSnVKkBJTw8HIDk5GQiIiJs9ycnJ9OuXTvbOSkpZfecKCws5MyZM7bH/5GHhwceHh6VWapUQGlACdKbokiVcne18vJ1bbnjivp8sPogC7Ye51hqDsdSc1i047cW5w7Rgbw9ugMRAV4mVitSuSq1i6dBgwaEh4ezbNky233p6emsX7+e2NhYAGJjY0lNTSU+Pt52zvLlyykuLqZr166VWY5UkbOlAUWzd0SqRcvIAF69vh2bp17F3Lu78X9DmjO8bSQNQnwA2JSYytVvreGpBTtYujOZjHNrFIk4snK3oGRmZrJv3z7b9wcPHmTLli0EBQURHR3NpEmTePbZZ2nSpAkNGjTg8ccfJzIy0jbTp3nz5gwaNIi77rqLGTNmUFBQwMSJE7nhhhsuaQaPmCctp4B/zd/G1iOpgLp4RKqbn6cb3RoG061hsO2+I2eyGTNzAwdOZjFzzSFmrjmEi9VC27oB3NOnMf1bhP3FM4rYr3JPM165ciVXXnnlefePGTOGWbNmYRgGTzzxBO+99x6pqalcccUVvPPOOzRt2tR27pkzZ5g4cSILFizAarUyatQo3njjDXx9fS+pBk0zNsfXm48xad4W2/ePDW3OnT0bmleQiACQnV/I8t0p/Lz/NGv3n+bgqSzbsf7Nw3jp720IVIun2IHyfH5XaB0UsyigmOPjtYeY+s0OOkQHcl+/JvRoHIKbi3ZLELE3x1Jz+N/aw3yw+gCFxQZNQn355M6uhPl7ml2a1HCmrYMizq10c8DGob70aRaqcCJip+oEevHo4Bi+nXgF4f6e7E3J5M6PNpKTX2R2aSKXTJ8wcslKA4qvh/beEXEELSL9+ewfsQT5uLPtWBoPfbFVOyWLw1BAkUuWmVcyM8DX0yGWzxERIDrYm3dGd8DVamHhryeYv/mY2SWJXBIFFLlkmedaUPw8FFBEHEm3hsE8cFXJRIUpX23jzWV7yS1Qd4/YNwUUuWSZeee6eNSCIuJwxl7RgJ5NQsgrLOblJXuInbaMZxfuZP/JTLNLE7kgBRS5ZKVjUPwUUEQcjqebCx/f0YXXb2hHnUAvzmYX8MFPB+n3chw3vreOBVuPk19YbHaZIjb6pJFLZmtBURePiEOyWCyMaFeHYW0iiduTwux1iaxISGHtgdOsPXCaurW8mHlbZ5qE+ZldqohaUOTSlQYUtaCIODYXq4W+MWF8eFtnVj/Sl/v6NaG2nwdHz+Zw7Yy1nEjLMbtEEQUUuXSZmmYs4nTqBHox+aqm/DipF83C/EjLKWDh1hNmlyWigCKXrrQFxdvdxeRKRKSy1fJx5/rOUQB8uekoGw+d0ZopYioFFLkkhmGQd24AnaebAoqIM+obE4rFAruTMrh2xlp6v7iSV5bs4dDv9vYRqS4KKHJJ8n43ut/TTS8bEWdUP8SHz/8Ry6gOdfFxdyHxTDZvLNtLn5dW8rd31hC356TZJUoNok8auSR5Bb8PKGpBEXFWneoH8fJ1bfnlsf68fkM7ejetjdUCmxNTufvjjRxL1QBaqR4KKHJJcgtLVp10sVq0SaBIDeDt7sqIdnX46I4urPtXPzrWq0VeYTG3frie4wopUg30SSOXpHRZbE9XvWREappQP09e+ntbIgI82X8yi4lzNmkArVQ5fdrIJck918Xjoe4dkRqpQYgPn/0jFi83FzYlpvLD9iSzSxInp4Ail0QtKCISFeTNXb0aAvCfRbu14aBUKS0JKpdEU4xFBODuXg2Zs/4wh09n0/KJxUQGehJVy5uoWt5EB3tTt5YXUUEl34f4umOxWMwuWRyUAopcktK/lNTFI1Kz+Xq4MnV4Sx754ldyCoo4ciaHI2dygNPnndu7aW1m3d5ZIUUuiwKKXBJbF4/WQBGp8a5uG8mw1hGkZOSReCabI2eyOXI2uySsnM3m6JlsTqTnErfnJBM/3cz1naLo0TgEF6uCilw6BRS5JLnnung8NAZFRACr1UJ4gCfhAZ50aRB03vH3Vx3g39/v4rtfT/DdrycI9/dk+s0daB9dy4RqxREpoNRAy3Yl88HqgxQZBk3DfHlyeEtcL7K2yW8tKOriEZGLu6tXQzrWr8X8TcdY8OtxktJzeeiLX/noji7UCfQyuzxxAAooNdA7K/cTf/gsABsOnuHgqSwCvdwJ9nXnyphQYhsGnxdE8myzeBRQROTSdIiuRYfoWvxzQFOufGkl+1Iy6f9yHFOHt+DGLtFmlyd2TgGlBjqblQ+UDGCL23OSNft+G9z28drDeLu70LNJCP8c0IymYX7A72fxqItHRMon0Nudz8fF8q+vtrPh0Bke+3o7vh6u9Gseire7PobkwvTKqIFScwoAeHRwDCM71CEtpwDDgD3JGSzblUJSei6LdySz43g6y/7ZGw9XF3XxiEiFNA71Y94/unHnRxtZtjuFez/djLurlR6NgrmhSzQDW4abXaLYGQWUGsYwDNLOBZRa3u6MaFenzPFnrzHYcTydsR/9wtGzOTz57U7a1g1gc2IqoIAiIpfPYrHwynXteGP5Xn7cmcSRMzmsSDjJioSTvHp9W/o1D8Pf083sMsVOKKDUMJl5hRQVl+yhEeh9/huBxWKhVZ0AJl/VlEe+3ManGxL5dMNvx308FFBE5PIFeLvx+LAWPDa0OXtTMnn5xwQW70jmgXlbAWgY4kPrugG0rhNA35hQGtb2NbliMYsCSg2Tml3SeuLhav3L1pBrO0Zx4FQW+1OybPf5erhwfScNbBORirNYLDQN8+OFa9vi77mTn/ef5lhqDgdOZXHgVBbfbDnOSz8msPDeK2gc6md2uWICBZQaprR7J8Drr5tRXawWpgxuXh0liUgNFuDlxot/bwvA6cw8th1LY/uxNBb+eoLdSRlMmreFj27vQrCvh8mVSnXTlIwapjSgXKh7R0TETMG+HvRpFsrEvk2YcXNHanm7sf1YOrd8uIHComKzy5NqpoBSw5R28VysBUVExEz1Q3z4Ynx3Ar3d2HkinUe+3EZqdr7ZZUk1UkCpYTJySwKKn0bKi4ida1TblymDYwD4ctNR+r4cx2e/HKH43EB/cW4KKDVMZl4hAD4eGn4kIvbv+s7RzLu7G03DfDmTlc/DX/7KDe+t43RmntmlSRVTQKlhsvJKFlzz1XRhEXEQXRsG8919Pfm/Ic3xcXdhw6EzjJz+M/N+SST9XKuwOB8FlBomO/9cC4qWlxYRB+LmYuWuXg359t4rqBPoxeHT2Tzy5TY6P7uUCbM38c2WY6RlK6w4E31K1TDq4hERR9aoti8L7r2CTzckMn/zMfalZPLdthN8t+0ELlYLXeoHcXfvhlzZLNTsUqWC9ClVw2SdCyi+Cigi4qCCfNyZcGVj7unTiO3H0vl++wmW7kxmb0omaw+cJv7wWd68qT3dGgQToCUVHJY+pWqYzHNjUNSCIiKOzmKxlCyLXzeARwbFkHg6mwe/2MqGg2f4x//iAWgS6kvHerXo1jCYoW0icHPRyAZHod9UDZNl6+LRIFkRcS7Rwd68dVN7ru8URf1gbwD2pmQy95cjTJq3haFvrGb9gdMmVymXSn9G1zBZGiQrIk4s1M+T/1zbBoBTmXlsOnyW+MNn+WzjEfYkZ3L9e+v4W/s6TBkcQ6i/p8nVyl9RC0oNkltQRGauBsmKSM0Q4uvBgJbhTBnSnOX/7MNNXaOxWGD+5mN0f345Y2f9woKtx8ktKDK7VLkABZQaIj23gB7PL+fAqZLdiTVIVkRqklo+7jz3t9bMv6cHHevVorDYYNnuFO79dDNXv/WTbZ8ysR8KKDVEQlIGp7N+28dCY1BEpCZqFxXIl+O7s3RybyZc2YggH3f2JGdy76ebtSGhnVFAqSFOpOXavm5Y24c6tbxMrEZExFyNQ315aGAMH9/RBS83F1btOcnoD9bz9eZjtgUtxVyVHlCefPJJLBZLmVtMTIzteG5uLhMmTCA4OBhfX19GjRpFcnJyZZchf5CUlgPAiHaRLH2gNx6uakEREWlVJ4BXrmuLi9XC+oNnmDRvC52fXcptMzfwyo8JLNmZTHJ67sWfSCpdlQxEaNmyJUuXLv3th7j+9mMeeOABvvvuOz7//HMCAgKYOHEiI0eOZM2aNVVRipxT2oISEeCF1WoxuRoREfsxuHUEyyP9+WrTMeZvPkbimWxWJpxkZcJJ2zm1/Ty4slltnrq6FV7u+gOvOlRJQHF1dSU8PPy8+9PS0vjwww+ZM2cOffv2BWDmzJk0b96cdevW0a1btws+X15eHnl5v+1cmZ6eXhVlO7UTqaUBRdPqRET+qF6wDw9c1ZRJ/Zuw7VgaW4+k8uvRNLYdS2NvSiYnM/L4bONRTmXm8+4tHbXgWzWokn/hvXv3EhkZScOGDRk9ejSJiYkAxMfHU1BQQP/+/W3nxsTEEB0dzdq1a//0+aZNm0ZAQIDtFhUVVRVlO7UT55oowxVQRET+lMVioU3dQG6Jrc+Lf2/Lokm92P7kQN6/tROeblaW707hkS9+pbjYMLtUp1fpAaVr167MmjWLRYsWMX36dA4ePEjPnj3JyMggKSkJd3d3AgMDyzwmLCyMpKSkP33OKVOmkJaWZrsdOXKksst2eqVjUNSCIiJSPl7uLlzVIox3RnfAxWrhq83HmPrtds7+bmakVL5K7+IZPHiw7es2bdrQtWtX6tWrx2effYaX1+XNHPHw8MDDw6OySqxxCoqKScko6SJTC4qIyOXpGxPGi9e2YfJnW/lkXSLzfjnClc1Cub5zFH1jQrFYNL6vMlV5J1pgYCBNmzZl3759hIeHk5+fT2pqaplzkpOTLzhmRSrHyYw8DAPcXCyE+CjoiYhcrpEd6vLmje1pEeFPQZHBjzuTGfvRRm75cAMHzy2EKZWjygNKZmYm+/fvJyIigo4dO+Lm5sayZctsxxMSEkhMTCQ2NraqS6mRDMNg1Z6Skehh/p6awSMiUkHD20by/f09WTypF3de0QB3Vys/7TvF32esJT1XK9JWlkoPKA8++CBxcXEcOnSIn3/+mb/97W+4uLhw4403EhAQwNixY5k8eTIrVqwgPj6e22+/ndjY2D+dwSMV8/Hawzz61TYAwrUxlohIpWkW7sdjw1qw5IFe1A/25lRmHjNW7je7LKdR6QHl6NGj3HjjjTRr1ozrrruO4OBg1q1bR+3atQF49dVXGTZsGKNGjaJXr16Eh4fz1VdfVXYZcs6uE79Nyb4ltp6JlYiIOKd6wT78a0hzAD786SDHU3NMrsg5WAzDcLi5Uunp6QQEBJCWloa/v7/Z5di1CXM28d2vJ3hieAtu79HA7HJERJySYRhc/+46Nhw6Q4foQF78e1sa1fY1uyy7U57Pb6004+Qyc0v2lNDuxSIiVcdisTB1eAs83axsSkxl0GureGHRbnLyi8wuzWEpoDi5zLySgOLnqYAiIlKVWtUJ4MdJvekbE0pBkcE7K/fT/5U4/rf2kILKZVBAcXK/taC4mVyJiIjziw725sMxnXjvlo7UCfTiWGoOj3+zg+7PL+OVHxM4mZF38ScRoIr24hH7oRYUEZHqZbFYGNAynJ5NavPZxiN88NMBjpzJ4Y3l+5ix6gBXt41kcKtwejQOwdNNGw/+GX1qObmMc3PyfRVQRESqlZe7C2O61+fmbvVYvCOJd1cdYOuRVL6IP8oX8UfxdLNyRePa/KN3QzrXDzK7XLujTy0nZhjGby0oGiQrImIKF6uFIa0jGNwqnI2Hz7Jg63GW7kzmeFouS3clE7cnhdeub8/QNhFml2pX9KnlxHIKiijdcFMtKCIi5rJYLHSuH0Tn+kE8dXVLdp5I581l+1i0I4kHPttC26gA6tbyNrtMu6FBsk6sdICs1QJe6ucUEbEbFouFlpEBvD26A10aBJFfWMydH20kOT3X7NLshgKKE8vI+20NFO2yKSJif1ysFp77W2tCfD3YnZTB+E/iccD1U6uEAooTK21B8fPUFGMREXvVONSXL8fH4u3uwqbEVP7xv3iOabl8BRRnlpmnVWRFRBxBvWAfHh/WAherhR93JtP/5TjeWbmP/MJis0szjQKKE8soXaRNA2RFROzejV2i+f6+nnSpH0ROQREvLEqg3ysr+ejnQ2TnF5pdXrVTQHFiakEREXEszcL9mPePbrxyXVtCfD04ciaHJ77dQffnl/NyDVuJVgHFiWmRNhERx2OxWBjZoS6rH76SZ0a0pF6wN6nZBby5fB89/rOcpxfstL2/OzMFFCdmGySrFhQREYfj5e7CLbH1Wf7PPkwf3YF2UYHkFxbz3zUH6fdyHN9sOebUM34UUJyY9uEREXF8LlYLg1tHMP+e7nx0RxcahPiQkpHH/XO3cNP769mXkmF2iVVCAcWJ/bYOiqYZi4g4OovFQu+mtVk0qScPDmiKh6uVtQdOM/j11bywaDeFRc4140cBxYllahaPiIjT8XB1YWLfJiyd3Jv+zcMoKDJ4Z+V+xn2yidyCIrPLqzQKKE5MGwWKiDivqCBvPhjTibduao+7q5Wlu5K55cP1pOU4xwBaBRQnlp6jWTwiIs5uWJtI/ndHF/w8Xfnl0FneWLbX7JIqhQKKEzt6tmSp5MhAL5MrERGRqtS1YTD/GdUGgGW7kk2upnIooDip3IIiks7tilkvSNt3i4g4u55NQnC1Wjh0OpvdSelml1NhCihOKvFMNlAyxTjQW7N4REScnZ+nG/2bhwHw0uIEk6upOAUUJ3X4dElAqRfsjcViMbkaERGpDg8NaoaL1cLSXSnM33zU7HIqRAHFSR0+nQVAvSAfkysREZHq0qi2L3f0qA/AA/O2Mu37XWTlOeZGgwooTur3LSgiIlJzTBncnNu61wfg3VUH6PdyHK8t3WP7w9VRKKA4qcNnFFBERGoiq9XCk1e35INbO1G3lhdJ6bm8tnQvvV9cyQ3vrSXl3AQKe6eA4oSKiw0OnsoEIFpdPCIiNVL/FmEsndybV65rS88mIVgtsO7AGZ75bpfZpV0SBRQnk5VXyPjZ8Rw5k4Or1UKTMF+zSxIREZN4urkwskNd/je2K/Pv6YHFAgu2Hmft/tNml3ZRCihO5MiZbEZN/5nFO5Jxd7Hyn1FtCPH1MLssERGxA22jAvlb+zoAjP5gHU9+u4P0XPtdFl8BxUms3X+aq9/6id1JGYT4evDp3d0Y1bGu2WWJiIgdeWJ4S4a2iaDYgFk/H6LvS3F89+sJs8u6IAUUB2cYBv9be4hbPlzP2ewCWtcJYMG9PehYr5bZpYmIiJ0J8HLj7Zs68MnYrjQM8eFUZh4TP93EnuQMs0s7jwKKA8svLOZf87fz+Dc7KCw2GNEuks/HxRIRoL13RETkz13RJIQfJvWka4MgDAOW704xu6TzKKA4qFOZedz8wXo+3ZCIxQKPDo7htevb4enmYnZpIiLiADxcXRjcKhyAVXtOmlzN+VzNLkDKb8fxNO7+OJ5jqTn4ebjy+o3t6BsTZnZZIiLiYHo2rQ3AxkNnyc4vxNvdfmKB/VQil+S7X0/w4OdbySkookGID+/f2pHGoX5mlyUiIg6oYYgPdQK9OJaaw43vraN309p0bxxC++hAPFzNbZG3GIZhmFrBZUhPTycgIIC0tDT8/f3NLqdaFBcbvLZ0D28s3wdAr6a1efOG9gRop2IREamAN5ft5eUle8rc5+lm5cYu0TwxvGWl/qzyfH6rBcVEhmGwdv9pZv18iH0pmX95bm5BEcfTSpYnvqtnAx4ZFIOri4YQiYhIxdzbrwnXtK/Dz/tPsWbfaX7ef5pTmXl4mTymUQHFBHmFRXyz5Tj//ekgu5MufWqXu4uVaSNba30TERGpVFFB3lwfFM31naMxDIO9KZl4uyug1BinMvP4ZN1hPll3mFOZ+QB4ublwbce6DG4VftEWkfrB3oT6e1ZHqSIiUkNZLBaahpk/tlEBpRrsTkrnw9UH+WbLcfKLigGICPBkTPf63NA5ikBvd5MrFBERsS8KKFWkuNhg5Z4UPvzpIGv2/bYpU9uoQMZe0YDBrcJx0xgSERGRC1JAqWTZ+YV8GX+UmWsOceBUFgBWCwxuFcEdVzTQEvQiIiKXwNSA8vbbb/Piiy+SlJRE27ZtefPNN+nSpYuZJV2246k5fLT2EJ+uTyQ9txAAP09XbuwSza2x9ahby9vkCkVERByHaQFl3rx5TJ48mRkzZtC1a1dee+01Bg4cSEJCAqGhoWaVVW5bjqTy4U8H+X7bCYqKS5aUqRfsze3d63Ntpyh8PdRIJSIiUl6mLdTWtWtXOnfuzFtvvQVAcXExUVFR3HvvvTz66KN/+diqWqgtM6+Q1Oz8Szp365E0PvzpAJsSU233dWsYxNgrGtI3JhQXq6XS6hIREXEGdr9QW35+PvHx8UyZMsV2n9VqpX///qxdu/a88/Py8sjLy7N9n56eXiV1fbvlOP+av61cj3FzsXB12zrc3qM+reoEVEldIiIiNY0pAeXUqVMUFRURFlZ2g7uwsDB279593vnTpk3jqaeeqvK6XKzg4XppM2sCvd24vlMUN8fWI9RPa5OIiIhUJocYIDFlyhQmT55s+z49PZ2oqKhK/znXdy5ZRU9ERETMZUpACQkJwcXFheTk5DL3JycnEx4eft75Hh4eeHh4VFd5IiIiYjJTVgpzd3enY8eOLFu2zHZfcXExy5YtIzY21oySRERExI6Y1sUzefJkxowZQ6dOnejSpQuvvfYaWVlZ3H777WaVJCIiInbCtIBy/fXXc/LkSaZOnUpSUhLt2rVj0aJF5w2cFRERkZrHtHVQKqKq1kERERGRqlOez2/tViciIiJ2RwFFRERE7I4CioiIiNgdBRQRERGxOwooIiIiYncUUERERMTuKKCIiIiI3VFAEREREbujgCIiIiJ2x7Sl7iuidPHb9PR0kysRERGRS1X6uX0pi9g7ZEDJyMgAICoqyuRKREREpLwyMjIICAj4y3Mcci+e4uJijh8/jp+fHxaLpVKfOz09naioKI4cOVIj9vnR9To3Xa9z0/U6N2e8XsMwyMjIIDIyEqv1r0eZOGQLitVqpW7dulX6M/z9/Z3mBXEpdL3OTdfr3HS9zs3ZrvdiLSelNEhWRERE7I4CioiIiNgdBZQ/8PDw4IknnsDDw8PsUqqFrte56Xqdm67XudW06/0jhxwkKyIiIs5NLSgiIiJidxRQRERExO4ooIiIiIjdUUARERERu6OA8jtvv/029evXx9PTk65du7JhwwazS7osq1atYvjw4URGRmKxWPj666/LHDcMg6lTpxIREYGXlxf9+/dn7969Zc45c+YMo0ePxt/fn8DAQMaOHUtmZmY1XsWlmzZtGp07d8bPz4/Q0FCuueYaEhISypyTm5vLhAkTCA4OxtfXl1GjRpGcnFzmnMTERIYOHYq3tzehoaE89NBDFBYWVuelXJLp06fTpk0b2+JNsbGx/PDDD7bjznStf/T8889jsViYNGmS7T5nu94nn3wSi8VS5hYTE2M77mzXC3Ds2DFuvvlmgoOD8fLyonXr1mzcuNF23Jnes+rXr3/e79disTBhwgTAOX+/l80QwzAMY+7cuYa7u7vx3//+19ixY4dx1113GYGBgUZycrLZpZXb999/b/zf//2f8dVXXxmAMX/+/DLHn3/+eSMgIMD4+uuvja1btxpXX3210aBBAyMnJ8d2zqBBg4y2bdsa69atM1avXm00btzYuPHGG6v5Si7NwIEDjZkzZxrbt283tmzZYgwZMsSIjo42MjMzbeeMGzfOiIqKMpYtW2Zs3LjR6Natm9G9e3fb8cLCQqNVq1ZG//79jc2bNxvff/+9ERISYkyZMsWMS/pL3377rfHdd98Ze/bsMRISEox//etfhpubm7F9+3bDMJzrWn9vw4YNRv369Y02bdoY999/v+1+Z7veJ554wmjZsqVx4sQJ2+3kyZO24852vWfOnDHq1atn3Hbbbcb69euNAwcOGIsXLzb27dtnO8eZ3rNSUlLK/G6XLFliAMaKFSsMw3C+329FKKCc06VLF2PChAm274uKiozIyEhj2rRpJlZVcX8MKMXFxUZ4eLjx4osv2u5LTU01PDw8jE8//dQwDMPYuXOnARi//PKL7ZwffvjBsFgsxrFjx6qt9suVkpJiAEZcXJxhGCXX5+bmZnz++ee2c3bt2mUAxtq1aw3DKAl1VqvVSEpKsp0zffp0w9/f38jLy6veC7gMtWrVMj744AOnvdaMjAyjSZMmxpIlS4zevXvbAoozXu8TTzxhtG3b9oLHnPF6H3nkEeOKK6740+PO/p51//33G40aNTKKi4ud8vdbEeriAfLz84mPj6d///62+6xWK/3792ft2rUmVlb5Dh48SFJSUplrDQgIoGvXrrZrXbt2LYGBgXTq1Ml2Tv/+/bFaraxfv77aay6vtLQ0AIKCggCIj4+noKCgzDXHxMQQHR1d5ppbt25NWFiY7ZyBAweSnp7Ojh07qrH68ikqKmLu3LlkZWURGxvrtNc6YcIEhg4dWua6wHl/t3v37iUyMpKGDRsyevRoEhMTAee83m+//ZZOnTrx97//ndDQUNq3b8/7779vO+7M71n5+fl88skn3HHHHVgsFqf8/VaEAgpw6tQpioqKyvzCAcLCwkhKSjKpqqpRej1/da1JSUmEhoaWOe7q6kpQUJDd/3sUFxczadIkevToQatWrYCS63F3dycwMLDMuX+85gv9m5Qeszfbtm3D19cXDw8Pxo0bx/z582nRooVTXuvcuXPZtGkT06ZNO++YM15v165dmTVrFosWLWL69OkcPHiQnj17kpGR4ZTXe+DAAaZPn06TJk1YvHgx48eP57777uOjjz4CnPs96+uvvyY1NZXbbrsNcM7Xc0U45G7GIn9mwoQJbN++nZ9++snsUqpUs2bN2LJlC2lpaXzxxReMGTOGuLg4s8uqdEeOHOH+++9nyZIleHp6ml1OtRg8eLDt6zZt2tC1a1fq1avHZ599hpeXl4mVVY3i4mI6derEc889B0D79u3Zvn07M2bMYMyYMSZXV7U+/PBDBg8eTGRkpNml2CW1oAAhISG4uLicN1I6OTmZ8PBwk6qqGqXX81fXGh4eTkpKSpnjhYWFnDlzxq7/PSZOnMjChQtZsWIFdevWtd0fHh5Ofn4+qampZc7/4zVf6N+k9Ji9cXd3p3HjxnTs2JFp06bRtm1bXn/9dae71vj4eFJSUujQoQOurq64uroSFxfHG2+8gaurK2FhYU51vRcSGBhI06ZN2bdvn9P9fgEiIiJo0aJFmfuaN29u69Zy1vesw4cPs3TpUu68807bfc74+60IBRRK3uw7duzIsmXLbPcVFxezbNkyYmNjTays8jVo0IDw8PAy15qens769ett1xobG0tqairx8fG2c5YvX05xcTFdu3at9povxjAMJk6cyPz581m+fDkNGjQoc7xjx464ubmVueaEhAQSExPLXPO2bdvKvMktWbIEf3//89487VFxcTF5eXlOd639+vVj27ZtbNmyxXbr1KkTo0ePtn3tTNd7IZmZmezfv5+IiAin+/0C9OjR47xlAfbs2UO9evUA53zPApg5cyahoaEMHTrUdp8z/n4rxOxRuvZi7ty5hoeHhzFr1ixj586dxt13320EBgaWGSntKDIyMozNmzcbmzdvNgDjlVdeMTZv3mwcPnzYMIySKXuBgYHGN998Y/z666/GiBEjLjhlr3379sb69euNn376yWjSpIldTtkzDMMYP368ERAQYKxcubLM9L3s7GzbOePGjTOio6ON5cuXGxs3bjRiY2ON2NhY2/HSqXsDBgwwtmzZYixatMioXbu2XU7de/TRR424uDjj4MGDxq+//mo8+uijhsViMX788UfDMJzrWi/k97N4DMP5rvef//ynsXLlSuPgwYPGmjVrjP79+xshISFGSkqKYRjOd70bNmwwXF1djX//+9/G3r17jdmzZxve3t7GJ598YjvH2d6zioqKjOjoaOORRx4575iz/X4rQgHld958800jOjracHd3N7p06WKsW7fO7JIuy4oVKwzgvNuYMWMMwyiZtvf4448bYWFhhoeHh9GvXz8jISGhzHOcPn3auPHGGw1fX1/D39/fuP32242MjAwTrubiLnStgDFz5kzbOTk5OcY999xj1KpVy/D29jb+9re/GSdOnCjzPIcOHTIGDx5seHl5GSEhIcY///lPo6CgoJqv5uLuuOMOo169eoa7u7tRu3Zto1+/frZwYhjOda0X8seA4mzXe/311xsRERGGu7u7UadOHeP6668vsyaIs12vYRjGggULjFatWhkeHh5GTEyM8d5775U57mzvWYsXLzaA867BMJzz93u5LIZhGKY03YiIiIj8CY1BEREREbujgCIiIiJ2RwFFRERE7I4CioiIiNgdBRQRERGxOwooIiIiYncUUERERMTuKKCIiIiI3VFAEREREbujgCIiFdanTx8mTZpU4ec5dOgQFouFLVu2VPi5RMSxKaCIiIiI3VFAEZEKue2224iLi+P111/HYrFgsVg4dOjQn55/9uxZRo8eTe3atfHy8qJJkybMnDkTgAYNGgDQvn17LBYLffr0sT3ugw8+oHnz5nh6ehITE8M777xjO1ba8jJ37ly6d++Op6cnrVq1Ii4urkquWUSqnqvZBYiIY3v99dfZs2cPrVq14umnnwagdu3af3r+448/zs6dO/nhhx8ICQlh37595OTkALBhwwa6dOnC0qVLadmyJe7u7gDMnj2bqVOn8tZbb9G+fXs2b97MXXfdhY+PD2PGjLE990MPPcRrr71GixYteOWVVxg+fDgHDx4kODi4Cv8FRKQqKKCISIUEBATg7u6Ot7c34eHhFz0/MTGR9u3b06lTJwDq169vO1YabIKDg8s81xNPPMHLL7/MyJEjgZKWlp07d/Luu++WCSgTJ05k1KhRAEyfPp1Fixbx4Ycf8vDDD1f4OkWkeimgiEi1Gj9+PKNGjWLTpk0MGDCAa665hu7du//p+VlZWezfv5+xY8dy11132e4vLCwkICCgzLmxsbG2r11dXenUqRO7du2q/IsQkSqngCIi1Wrw4MEcPnyY77//niVLltCvXz8mTJjASy+9dMHzMzMzAXj//ffp2rVrmWMuLi5VXq+ImEODZEWkwtzd3SkqKrrk82vXrs2YMWP45JNPeO2113jvvfdszwOUea6wsDAiIyM5cOAAjRs3LnMrHVRbat26dbavCwsLiY+Pp3nz5hW5NBExiVpQRKTC6tevz/r16zl06BC+vr4EBQVhtV7475+pU6fSsWNHWrZsSV5eHgsXLrSFiNDQULy8vFi0aBF169bF09OTgIAAnnrqKe677z4CAgIYNGgQeXl5bNy4kbNnzzJ58mTbc7/99ts0adKE5s2b8+qrr3L27FnuuOOOavk3EJHKpRYUEamwBx98EBcXF1q0aEHt2rVJTEz803Pd3d2ZMmUKbdq0oVevXri4uDB37lygZNzIG2+8wbvvvktkZCQjRowA4M477+SDDz5g5syZtG7dmt69ezNr1qzzWlCef/55nn/+edq2bctPP/3Et99+S0hISNVduIhUGYthGIbZRYiIVMShQ4do0KABmzdvpl27dmaXIyKVQC0oIiIiYncUUESkUo0bNw5fX98L3saNG2d2eSLiINTFIyKVKiUlhfT09Ase8/f3JzQ0tJorEhFHpIAiIiIidkddPCIiImJ3FFBERETE7iigiIiIiN1RQBERERG7o4AiIiIidkcBRUREROyOAoqIiIjYnf8H5EGoRfm5Yg4AAAAASUVORK5CYII=", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "alive = analysis.get_value(df, 'rabbits_alive', aggfunc='sum').apply(pd.to_numeric)\n", - "alive.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": { - "ExecuteTime": { - "end_time": "2017-10-19T16:00:58.815038Z", - "start_time": "2017-10-19T18:00:58.566807+02:00" - } - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "h = pd.concat([alive, states]);\n", - "h.plot();" + "res.agents.head()" ] } ], "metadata": { + "hide_code_all_hidden": false, "kernelspec": { - "display_name": "venv-soil", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "venv-soil" }, @@ -4996,6 +2225,11 @@ "toc_section_display": "block", "toc_window_display": false, "widenNotebook": false + }, + "vscode": { + "interpreter": { + "hash": "3581132406f7320837865a422f37590c78ed7dabfbcb5bc7771b9d116b13a5cf" + } } }, "nbformat": 4, diff --git a/requirements.txt b/requirements.txt index c05f883..443778f 100644 --- a/requirements.txt +++ b/requirements.txt @@ -2,8 +2,12 @@ networkx>=2.5 numpy matplotlib pyyaml>=5.1 -pandas>=0.23 +pandas>=1 SALib>=1.3 Jinja2 -Mesa>=0.8 -tsih>=0.1.9 +Mesa>=1.2 +pydantic>=1.9 +sqlalchemy>=1.4 +typing-extensions>=4.4 +annotated-types>=0.4 +tqdm>=4.64 diff --git a/setup.cfg b/setup.cfg index 970eadf..398b4cb 100644 --- a/setup.cfg +++ b/setup.cfg @@ -1,3 +1,7 @@ +[metadata] +long_description = file: README.md +long_description_content_type = text/markdown + [aliases] test=pytest [tool:pytest] diff --git a/setup.py b/setup.py index 7748e28..a64aa41 100644 --- a/setup.py +++ b/setup.py @@ -44,14 +44,20 @@ 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.__init__:main', + ['soil = soil.__main__:main', 'soil-web = soil.web.__init__:main'] }) diff --git a/soil/VERSION b/soil/VERSION index 84717d7..8862dba 100644 --- a/soil/VERSION +++ b/soil/VERSION @@ -1 +1 @@ -0.20.8 \ No newline at end of file +1.0.0rc2 diff --git a/soil/__init__.py b/soil/__init__.py index dc79354..8aadde8 100644 --- a/soil/__init__.py +++ b/soil/__init__.py @@ -1,8 +1,12 @@ +from __future__ import annotations + import importlib +from importlib.resources import path import sys import os -import pdb import logging +import traceback +from contextlib import contextmanager from .version import __version__ @@ -11,89 +15,273 @@ try: except NameError: basestring = str +from pathlib import Path +from .analysis import * from .agents import * from . import agents from .simulation import * -from .environment import Environment +from .environment import Environment, EventedEnvironment +from .datacollection import SoilCollector from . import serialization -from . import analysis from .utils import logger from .time import * +from .decorators import * + + +def main( + cfg="simulation.yml", + exporters=None, + num_processes=1, + output="soil_output", + *, + debug=False, + pdb=False, + **kwargs, +): + + sim = None + if isinstance(cfg, Simulation): + sim = cfg -def main(): import argparse from . import simulation - logger.info('Running SOIL version: {}'.format(__version__)) + logger.info("Running SOIL version: {}".format(__version__)) - parser = argparse.ArgumentParser(description='Run a SOIL simulation') - parser.add_argument('file', type=str, - nargs="?", - default='simulation.yml', - help='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.') - parser.add_argument('--pdb', action='store_true', - help='Use a pdb console in case of exception.') - parser.add_argument('--graph', '-g', action='store_true', - help='Dump GEXF graph. Defaults to false.') - parser.add_argument('--csv', action='store_true', - help='Dump history in CSV format. Defaults to false.') - parser.add_argument('--level', type=str, - help='Logging level') - parser.add_argument('--output', '-o', type=str, default="soil_output", - help='folder to write results to. It defaults to the current directory.') - parser.add_argument('--synchronous', action='store_true', - help='Run trials serially and synchronously instead of in parallel. Defaults to false.') - parser.add_argument('-e', '--exporter', action='append', - help='Export environment and/or simulations using this exporter') + parser = argparse.ArgumentParser(description="Run a SOIL simulation") + parser.add_argument( + "file", + type=str, + nargs="?", + default=cfg if sim is None else "", + help="Configuration file for the simulation (e.g., YAML or JSON)", + ) + parser.add_argument( + "--version", action="store_true", help="Show version info and exit" + ) + parser.add_argument( + "--module", + "-m", + type=str, + help="file containing the code of any custom agents.", + ) + parser.add_argument( + "--dry-run", + "--dry", + action="store_true", + help="Do not 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( + "--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 iteration's network topology as a GEXF graph. Defaults to false.", + ) + parser.add_argument( + "--csv", + action="store_true", + help="Dump all data collected in CSV format. Defaults to false.", + ) + parser.add_argument("--level", type=str, help="Logging level") + parser.add_argument( + "--output", + "-o", + type=str, + default=output or "soil_output", + help="folder to write results to. It defaults to the current directory.", + ) + parser.add_argument( + "--num-processes", + default=num_processes, + help="Number of processes to use for parallel execution. Defaults to 1.", + ) + + parser.add_argument( + "-e", + "--exporter", + action="append", + 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", + "--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() - logging.basicConfig(level=getattr(logging, (args.level or 'INFO').upper())) + level = getattr(logging, (args.level or "INFO").upper()) + logger.setLevel(level) if args.version: return + exporters = exporters or [ + "default", + ] + for exp in args.exporter: + if exp not in exporters: + exporters.append(exp) + if args.csv: + exporters.append("csv") + if args.graph: + exporters.append("gexf") + if os.getcwd() not in sys.path: sys.path.append(os.getcwd()) if args.module: importlib.import_module(args.module) + if output is None: + output = args.output - logger.info('Loading config file: {}'.format(args.file)) + debug = debug or args.debug - if args.pdb: + if args.pdb or debug: args.synchronous = True + os.environ["SOIL_POSTMORTEM"] = "true" - + res = [] try: - exporters = list(args.exporter or ['default', ]) - if args.csv: - exporters.append('csv') - if args.graph: - exporters.append('gexf') exp_params = {} - if args.dry_run: - exp_params['copy_to'] = sys.stdout + 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 not os.path.exists(args.file): - logger.error('Please, input a valid file') - return - simulation.run_from_config(args.file, - dry_run=args.dry_run, - exporters=exporters, - parallel=(not args.synchronous), - outdir=args.output, - exporter_params=exp_params) - except Exception: + if sim: + logger.info("Loading simulation instance") + for (k, v) in opts.items(): + setattr(sim, k, v) + 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_file( + args.file, + **opts, + ) + ) + + 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.parameters + 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 + max_time = float(args.max_time) if args.max_time != "-1" else None + max_steps = float(args.max_steps) if args.max_steps != "-1" else None + res.append(sim.run(max_time=max_time, max_steps=max_steps)) + + except Exception as ex: if args.pdb: - pdb.post_mortem() + from .debugging import post_mortem + + print(traceback.format_exc()) + post_mortem() else: raise + if debug: + from .debugging import set_trace + + os.environ["SOIL_DEBUG"] = "true" + set_trace() + return res -if __name__ == '__main__': +@contextmanager +def easy(cfg, pdb=False, debug=False, **kwargs): + try: + return main(cfg, debug=debug, pdb=pdb, **kwargs)[0] + except Exception as e: + if os.environ.get("SOIL_POSTMORTEM"): + from .debugging import post_mortem + + print(traceback.format_exc()) + post_mortem() + raise + + +if __name__ == "__main__": main() diff --git a/soil/__main__.py b/soil/__main__.py index c7c70d0..0c40ec3 100644 --- a/soil/__main__.py +++ b/soil/__main__.py @@ -1,4 +1,9 @@ -from . import main +from . import main as init_main -if __name__ == '__main__': - main() + +def main(): + init_main() + + +if __name__ == "__main__": + init_main() diff --git a/soil/agents/BassModel.py b/soil/agents/BassModel.py index cba6790..6049bd5 100644 --- a/soil/agents/BassModel.py +++ b/soil/agents/BassModel.py @@ -1,4 +1,3 @@ -import random from . import FSM, state, default_state @@ -8,6 +7,7 @@ class BassModel(FSM): innovation_prob imitation_prob """ + sentimentCorrelation = 0 def step(self): @@ -16,16 +16,16 @@ class BassModel(FSM): @default_state @state def innovation(self): - if random.random() < self.innovation_prob: + if self.prob(self.innovation_prob): self.sentimentCorrelation = 1 return self.aware else: - aware_neighbors = self.get_neighboring_agents(state_id=self.aware.id) + aware_neighbors = self.get_neighbors(state_id=self.aware.id) num_neighbors_aware = len(aware_neighbors) - if random.random() < (self['imitation_prob']*num_neighbors_aware): + if self.prob((self.imitation_prob * num_neighbors_aware)): self.sentimentCorrelation = 1 return self.aware @state def aware(self): - self.die() + self.die() \ No newline at end of file diff --git a/soil/agents/BigMarketModel.py b/soil/agents/BigMarketModel.py deleted file mode 100644 index fbc3ba5..0000000 --- a/soil/agents/BigMarketModel.py +++ /dev/null @@ -1,95 +0,0 @@ -import random -from . import FSM, state, default_state - - -class BigMarketModel(FSM): - """ - Settings: - Names: - enterprises [Array] - - tweet_probability_enterprises [Array] - Users: - tweet_probability_users - - tweet_relevant_probability - - tweet_probability_about [Array] - - sentiment_about [Array] - """ - - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - self.enterprises = self.env.environment_params['enterprises'] - self.type = "" - - if self.id < len(self.enterprises): # Enterprises - self.set_state(self.enterprise.id) - self.type = "Enterprise" - self.tweet_probability = environment.environment_params['tweet_probability_enterprises'][self.id] - else: # normal users - self.type = "User" - self.set_state(self.user.id) - self.tweet_probability = environment.environment_params['tweet_probability_users'] - self.tweet_relevant_probability = environment.environment_params['tweet_relevant_probability'] - self.tweet_probability_about = environment.environment_params['tweet_probability_about'] # List - self.sentiment_about = environment.environment_params['sentiment_about'] # List - - @state - def enterprise(self): - - if random.random() < self.tweet_probability: # Tweets - aware_neighbors = self.get_neighboring_agents(state_id=self.number_of_enterprises) # Nodes neighbour users - for x in aware_neighbors: - if random.uniform(0,10) < 5: - x.sentiment_about[self.id] += 0.1 # Increments for enterprise - else: - x.sentiment_about[self.id] -= 0.1 # Decrements for enterprise - - # Establecemos limites - if x.sentiment_about[self.id] > 1: - x.sentiment_about[self.id] = 1 - if x.sentiment_about[self.id]< -1: - x.sentiment_about[self.id] = -1 - - x.attrs['sentiment_enterprise_%s'% self.enterprises[self.id]] = x.sentiment_about[self.id] - - @state - def user(self): - if random.random() < self.tweet_probability: # Tweets - if random.random() < self.tweet_relevant_probability: # Tweets something relevant - # Tweet probability per enterprise - for i in range(len(self.enterprises)): - random_num = random.random() - if random_num < self.tweet_probability_about[i]: - # The condition is fulfilled, sentiments are evaluated towards that enterprise - if self.sentiment_about[i] < 0: - # NEGATIVO - self.userTweets("negative",i) - elif self.sentiment_about[i] == 0: - # NEUTRO - pass - else: - # POSITIVO - self.userTweets("positive",i) - for i in range(len(self.enterprises)): # So that it never is set to 0 if there are not changes (logs) - self.attrs['sentiment_enterprise_%s'% self.enterprises[i]] = self.sentiment_about[i] - - def userTweets(self, sentiment,enterprise): - aware_neighbors = self.get_neighboring_agents(state_id=self.number_of_enterprises) # Nodes neighbours users - for x in aware_neighbors: - if sentiment == "positive": - x.sentiment_about[enterprise] +=0.003 - elif sentiment == "negative": - x.sentiment_about[enterprise] -=0.003 - else: - pass - - # Establecemos limites - if x.sentiment_about[enterprise] > 1: - x.sentiment_about[enterprise] = 1 - if x.sentiment_about[enterprise] < -1: - x.sentiment_about[enterprise] = -1 - - x.attrs['sentiment_enterprise_%s'% self.enterprises[enterprise]] = x.sentiment_about[enterprise] diff --git a/soil/agents/CounterModel.py b/soil/agents/CounterModel.py index 528b957..96bbfd3 100644 --- a/soil/agents/CounterModel.py +++ b/soil/agents/CounterModel.py @@ -1,19 +1,29 @@ -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 in each step and adds it to its state. """ + times = 0 + neighbors = 0 + total = 0 + def step(self): # Outside effects - total = len(list(self.get_agents())) - neighbors = len(list(self.get_neighboring_agents())) - self['times'] = self.get('times', 0) + 1 - self['neighbors'] = neighbors - self['total'] = total + total = len(list(self.model.schedule._agents)) + neighbors = len(list(self.get_neighbors())) + self["times"] = self.get("times", 0) + 1 + self["neighbors"] = neighbors + self["total"] = total class AggregatedCounter(NetworkAgent): @@ -22,17 +32,15 @@ class AggregatedCounter(NetworkAgent): in each step and adds it to its state. """ - defaults = { - 'times': 0, - 'neighbors': 0, - 'total': 0 - } + times = 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.get_agents())) - self['total'] += total - self.debug('Running for step: {}. Total: {}'.format(self.now, total)) + self["times"] += 1 + neighbors = len(list(self.get_neighbors())) + self["neighbors"] += neighbors + total = len(list(self.model.schedule.agents)) + self["total"] += total + self.debug("Running for step: {}. Total: {}".format(self.now, total)) diff --git a/soil/agents/Geo.py b/soil/agents/Geo.py index bf505bf..0500802 100644 --- a/soil/agents/Geo.py +++ b/soil/agents/Geo.py @@ -1,21 +1,21 @@ from scipy.spatial import cKDTree as KDTree import networkx as nx -from . import NetworkAgent, as_node +from . import NetworkAgent + class Geo(NetworkAgent): - '''In this type of network, nodes have a "pos" attribute.''' + """In this type of network, nodes have a "pos" attribute.""" - def geo_search(self, radius, node=None, center=False, **kwargs): - '''Get a list of nodes whose coordinates are closer than *radius* to *node*.''' - node = as_node(node if node is not None else self) + def geo_search(self, radius, center=False, **kwargs): + """Get a list of nodes whose coordinates are closer than *radius* to *node*.""" + node = self.node_id G = self.subgraph(**kwargs) - pos = nx.get_node_attributes(G, 'pos') + pos = nx.get_node_attributes(G, "pos") if not pos: return [] nodes, coords = list(zip(*pos.items())) kdtree = KDTree(coords) # Cannot provide generator. indices = kdtree.query_ball_point(pos[node], radius) - return [nodes[i] for i in indices if center or (nodes[i] != node)] - + return [nodes[i] for i in indices if center or (nodes[i] != node)] \ No newline at end of file diff --git a/soil/agents/IndependentCascadeModel.py b/soil/agents/IndependentCascadeModel.py index ab5a8a8..890a54e 100644 --- a/soil/agents/IndependentCascadeModel.py +++ b/soil/agents/IndependentCascadeModel.py @@ -1,8 +1,7 @@ -import random -from . import BaseAgent +from . import Agent, state, default_state -class IndependentCascadeModel(BaseAgent): +class IndependentCascadeModel(Agent): """ Settings: innovation_prob @@ -10,40 +9,22 @@ class IndependentCascadeModel(BaseAgent): imitation_prob """ - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - self.innovation_prob = self.env.environment_params['innovation_prob'] - self.imitation_prob = self.env.environment_params['imitation_prob'] - self.state['time_awareness'] = 0 - self.state['sentimentCorrelation'] = 0 + time_awareness = 0 + sentimentCorrelation = 0 - def step(self): - self.behaviour() + # Outside effects + @default_state + @state + def outside(self): + if self.prob(self.model.innovation_prob): + self.sentimentCorrelation = 1 + self.time_awareness = self.model.now # To know when they have been infected + return self.imitate - def behaviour(self): - aware_neighbors_1_time_step = [] - # Outside effects - if random.random() < self.innovation_prob: - if self.state['id'] == 0: - self.state['id'] = 1 - self.state['sentimentCorrelation'] = 1 - self.state['time_awareness'] = self.env.now # To know when they have been infected - else: - pass + @state + def imitate(self): + aware_neighbors = self.get_neighbors(state_id=1, time_awareness=self.now-1) - return - - # Imitation effects - if self.state['id'] == 0: - aware_neighbors = self.get_neighboring_agents(state_id=1) - for x in aware_neighbors: - if x.state['time_awareness'] == (self.env.now-1): - aware_neighbors_1_time_step.append(x) - num_neighbors_aware = len(aware_neighbors_1_time_step) - if random.random() < (self.imitation_prob*num_neighbors_aware): - self.state['id'] = 1 - self.state['sentimentCorrelation'] = 1 - else: - pass - - return + if self.prob(self.model.imitation_prob * len(aware_neighbors)): + self.sentimentCorrelation = 1 + return self.outside \ No newline at end of file diff --git a/soil/agents/ModelM2.py b/soil/agents/ModelM2.py deleted file mode 100644 index ec0f98d..0000000 --- a/soil/agents/ModelM2.py +++ /dev/null @@ -1,242 +0,0 @@ -import random -import numpy as np -from . import BaseAgent - - -class SpreadModelM2(BaseAgent): - """ - Settings: - prob_neutral_making_denier - - prob_infect - - prob_cured_healing_infected - - prob_cured_vaccinate_neutral - - prob_vaccinated_healing_infected - - prob_vaccinated_vaccinate_neutral - - prob_generate_anti_rumor - """ - - def __init__(self, model=None, unique_id=0, state=()): - super().__init__(model=environment, unique_id=unique_id, state=state) - - self.prob_neutral_making_denier = np.random.normal(environment.environment_params['prob_neutral_making_denier'], - environment.environment_params['standard_variance']) - - self.prob_infect = np.random.normal(environment.environment_params['prob_infect'], - environment.environment_params['standard_variance']) - - self.prob_cured_healing_infected = np.random.normal(environment.environment_params['prob_cured_healing_infected'], - environment.environment_params['standard_variance']) - self.prob_cured_vaccinate_neutral = np.random.normal(environment.environment_params['prob_cured_vaccinate_neutral'], - environment.environment_params['standard_variance']) - - self.prob_vaccinated_healing_infected = np.random.normal(environment.environment_params['prob_vaccinated_healing_infected'], - environment.environment_params['standard_variance']) - self.prob_vaccinated_vaccinate_neutral = np.random.normal(environment.environment_params['prob_vaccinated_vaccinate_neutral'], - environment.environment_params['standard_variance']) - self.prob_generate_anti_rumor = np.random.normal(environment.environment_params['prob_generate_anti_rumor'], - environment.environment_params['standard_variance']) - - def step(self): - - if self.state['id'] == 0: # Neutral - self.neutral_behaviour() - elif self.state['id'] == 1: # Infected - self.infected_behaviour() - elif self.state['id'] == 2: # Cured - self.cured_behaviour() - elif self.state['id'] == 3: # Vaccinated - self.vaccinated_behaviour() - - def neutral_behaviour(self): - - # Infected - infected_neighbors = self.get_neighboring_agents(state_id=1) - if len(infected_neighbors) > 0: - if random.random() < self.prob_neutral_making_denier: - self.state['id'] = 3 # Vaccinated making denier - - def infected_behaviour(self): - - # Neutral - neutral_neighbors = self.get_neighboring_agents(state_id=0) - for neighbor in neutral_neighbors: - if random.random() < self.prob_infect: - neighbor.state['id'] = 1 # Infected - - def cured_behaviour(self): - - # Vaccinate - neutral_neighbors = self.get_neighboring_agents(state_id=0) - for neighbor in neutral_neighbors: - if random.random() < self.prob_cured_vaccinate_neutral: - neighbor.state['id'] = 3 # Vaccinated - - # Cure - infected_neighbors = self.get_neighboring_agents(state_id=1) - for neighbor in infected_neighbors: - if random.random() < self.prob_cured_healing_infected: - neighbor.state['id'] = 2 # Cured - - def vaccinated_behaviour(self): - - # Cure - infected_neighbors = self.get_neighboring_agents(state_id=1) - for neighbor in infected_neighbors: - if random.random() < self.prob_cured_healing_infected: - neighbor.state['id'] = 2 # Cured - - # Vaccinate - neutral_neighbors = self.get_neighboring_agents(state_id=0) - for neighbor in neutral_neighbors: - if random.random() < self.prob_cured_vaccinate_neutral: - neighbor.state['id'] = 3 # Vaccinated - - # Generate anti-rumor - infected_neighbors_2 = self.get_neighboring_agents(state_id=1) - for neighbor in infected_neighbors_2: - if random.random() < self.prob_generate_anti_rumor: - neighbor.state['id'] = 2 # Cured - - -class ControlModelM2(BaseAgent): - """ - Settings: - prob_neutral_making_denier - - prob_infect - - prob_cured_healing_infected - - prob_cured_vaccinate_neutral - - prob_vaccinated_healing_infected - - prob_vaccinated_vaccinate_neutral - - prob_generate_anti_rumor - """ - - - def __init__(self, model=None, unique_id=0, state=()): - super().__init__(model=environment, unique_id=unique_id, state=state) - - self.prob_neutral_making_denier = np.random.normal(environment.environment_params['prob_neutral_making_denier'], - environment.environment_params['standard_variance']) - - self.prob_infect = np.random.normal(environment.environment_params['prob_infect'], - environment.environment_params['standard_variance']) - - self.prob_cured_healing_infected = np.random.normal(environment.environment_params['prob_cured_healing_infected'], - environment.environment_params['standard_variance']) - self.prob_cured_vaccinate_neutral = np.random.normal(environment.environment_params['prob_cured_vaccinate_neutral'], - environment.environment_params['standard_variance']) - - self.prob_vaccinated_healing_infected = np.random.normal(environment.environment_params['prob_vaccinated_healing_infected'], - environment.environment_params['standard_variance']) - self.prob_vaccinated_vaccinate_neutral = np.random.normal(environment.environment_params['prob_vaccinated_vaccinate_neutral'], - environment.environment_params['standard_variance']) - self.prob_generate_anti_rumor = np.random.normal(environment.environment_params['prob_generate_anti_rumor'], - environment.environment_params['standard_variance']) - - def step(self): - - if self.state['id'] == 0: # Neutral - self.neutral_behaviour() - elif self.state['id'] == 1: # Infected - self.infected_behaviour() - elif self.state['id'] == 2: # Cured - self.cured_behaviour() - elif self.state['id'] == 3: # Vaccinated - self.vaccinated_behaviour() - elif self.state['id'] == 4: # Beacon-off - self.beacon_off_behaviour() - elif self.state['id'] == 5: # Beacon-on - self.beacon_on_behaviour() - - def neutral_behaviour(self): - self.state['visible'] = False - - # Infected - infected_neighbors = self.get_neighboring_agents(state_id=1) - if len(infected_neighbors) > 0: - if random.random() < self.prob_neutral_making_denier: - self.state['id'] = 3 # Vaccinated making denier - - def infected_behaviour(self): - - # Neutral - neutral_neighbors = self.get_neighboring_agents(state_id=0) - for neighbor in neutral_neighbors: - if random.random() < self.prob_infect: - neighbor.state['id'] = 1 # Infected - self.state['visible'] = False - - def cured_behaviour(self): - - self.state['visible'] = True - # Vaccinate - neutral_neighbors = self.get_neighboring_agents(state_id=0) - for neighbor in neutral_neighbors: - if random.random() < self.prob_cured_vaccinate_neutral: - neighbor.state['id'] = 3 # Vaccinated - - # Cure - infected_neighbors = self.get_neighboring_agents(state_id=1) - for neighbor in infected_neighbors: - if random.random() < self.prob_cured_healing_infected: - neighbor.state['id'] = 2 # Cured - - def vaccinated_behaviour(self): - self.state['visible'] = True - - # Cure - infected_neighbors = self.get_neighboring_agents(state_id=1) - for neighbor in infected_neighbors: - if random.random() < self.prob_cured_healing_infected: - neighbor.state['id'] = 2 # Cured - - # Vaccinate - neutral_neighbors = self.get_neighboring_agents(state_id=0) - for neighbor in neutral_neighbors: - if random.random() < self.prob_cured_vaccinate_neutral: - neighbor.state['id'] = 3 # Vaccinated - - # Generate anti-rumor - infected_neighbors_2 = self.get_neighboring_agents(state_id=1) - for neighbor in infected_neighbors_2: - if random.random() < self.prob_generate_anti_rumor: - neighbor.state['id'] = 2 # Cured - - def beacon_off_behaviour(self): - self.state['visible'] = False - infected_neighbors = self.get_neighboring_agents(state_id=1) - if len(infected_neighbors) > 0: - self.state['id'] == 5 # Beacon on - - def beacon_on_behaviour(self): - self.state['visible'] = False - # Cure (M2 feature added) - infected_neighbors = self.get_neighboring_agents(state_id=1) - for neighbor in infected_neighbors: - if random.random() < self.prob_generate_anti_rumor: - neighbor.state['id'] = 2 # Cured - neutral_neighbors_infected = neighbor.get_neighboring_agents(state_id=0) - for neighbor in neutral_neighbors_infected: - if random.random() < self.prob_generate_anti_rumor: - neighbor.state['id'] = 3 # Vaccinated - infected_neighbors_infected = neighbor.get_neighboring_agents(state_id=1) - for neighbor in infected_neighbors_infected: - if random.random() < self.prob_generate_anti_rumor: - neighbor.state['id'] = 2 # Cured - - # Vaccinate - neutral_neighbors = self.get_neighboring_agents(state_id=0) - for neighbor in neutral_neighbors: - if random.random() < self.prob_cured_vaccinate_neutral: - neighbor.state['id'] = 3 # Vaccinated diff --git a/soil/agents/SISaModel.py b/soil/agents/SISaModel.py index 4b66087..b5dbbe3 100644 --- a/soil/agents/SISaModel.py +++ b/soil/agents/SISaModel.py @@ -1,93 +1,110 @@ -import random import numpy as np -from . import FSM, state +from hashlib import sha512 +from . import Agent, state, default_state -class SISaModel(FSM): +class SISaModel(Agent): """ Settings: neutral_discontent_spon_prob - + neutral_discontent_infected_prob - + neutral_content_spon_prob - + neutral_content_infected_prob - + discontent_neutral - + discontent_content - + variance_d_c - + content_discontent - + variance_c_d - + content_neutral - + standard_variance """ - def __init__(self, environment, unique_id=0, state=()): - super().__init__(model=environment, unique_id=unique_id, state=state) + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) - self.neutral_discontent_spon_prob = np.random.normal(self.env['neutral_discontent_spon_prob'], - self.env['standard_variance']) - self.neutral_discontent_infected_prob = np.random.normal(self.env['neutral_discontent_infected_prob'], - self.env['standard_variance']) - self.neutral_content_spon_prob = np.random.normal(self.env['neutral_content_spon_prob'], - self.env['standard_variance']) - self.neutral_content_infected_prob = np.random.normal(self.env['neutral_content_infected_prob'], - self.env['standard_variance']) + seed = self.model._seed + if isinstance(seed, (str, bytes, bytearray)): + if isinstance(seed, str): + seed = seed.encode() + seed = int.from_bytes(seed + sha512(seed).digest(), 'big') - self.discontent_neutral = np.random.normal(self.env['discontent_neutral'], - self.env['standard_variance']) - self.discontent_content = np.random.normal(self.env['discontent_content'], - self.env['variance_d_c']) + random = np.random.default_rng(seed=seed) - self.content_discontent = np.random.normal(self.env['content_discontent'], - self.env['variance_c_d']) - self.content_neutral = np.random.normal(self.env['content_neutral'], - self.env['standard_variance']) + self.neutral_discontent_spon_prob = random.normal( + self.model.neutral_discontent_spon_prob, self.model.standard_variance + ) + self.neutral_discontent_infected_prob = random.normal( + self.model.neutral_discontent_infected_prob, self.model.standard_variance + ) + self.neutral_content_spon_prob = random.normal( + self.model.neutral_content_spon_prob, self.model.standard_variance + ) + self.neutral_content_infected_prob = random.normal( + self.model.neutral_content_infected_prob, self.model.standard_variance + ) + self.discontent_neutral = random.normal( + self.model.discontent_neutral, self.model.standard_variance + ) + self.discontent_content = random.normal( + self.model.discontent_content, self.model.variance_d_c + ) + + self.content_discontent = random.normal( + self.model.content_discontent, self.model.variance_c_d + ) + self.content_neutral = random.normal( + self.model.discontent_neutral, self.model.standard_variance + ) + + @default_state @state def neutral(self): # Spontaneous effects - if random.random() < self.neutral_discontent_spon_prob: + if self.prob(self.neutral_discontent_spon_prob): return self.discontent - if random.random() < self.neutral_content_spon_prob: + if self.prob(self.neutral_content_spon_prob): return self.content # Infected - discontent_neighbors = self.count_neighboring_agents(state_id=self.discontent) - if random.random() < discontent_neighbors * self.neutral_discontent_infected_prob: + discontent_neighbors = self.count_neighbors(state_id=self.discontent) + if self.prob(discontent_neighbors * self.neutral_discontent_infected_prob): return self.discontent - content_neighbors = self.count_neighboring_agents(state_id=self.content.id) - if random.random() < content_neighbors * self.neutral_content_infected_prob: + content_neighbors = self.count_neighbors(state_id=self.content.id) + if self.prob(content_neighbors * self.neutral_content_infected_prob): return self.content return self.neutral @state def discontent(self): # Healing - if random.random() < self.discontent_neutral: + if self.prob(self.discontent_neutral): return self.neutral # Superinfected - content_neighbors = self.count_neighboring_agents(state_id=self.content.id) - if random.random() < content_neighbors * self.discontent_content: + content_neighbors = self.count_neighbors(state_id=self.content.id) + if self.prob(content_neighbors * self.discontent_content): return self.content return self.discontent @state def content(self): # Healing - if random.random() < self.content_neutral: + if self.prob(self.content_neutral): return self.neutral # Superinfected - discontent_neighbors = self.count_neighboring_agents(state_id=self.discontent.id) - if random.random() < discontent_neighbors * self.content_discontent: + discontent_neighbors = self.count_neighbors(state_id=self.discontent.id) + if self.prob(discontent_neighbors * self.content_discontent): self.discontent return self.content diff --git a/soil/agents/SentimentCorrelationModel.py b/soil/agents/SentimentCorrelationModel.py deleted file mode 100644 index 7c12d7b..0000000 --- a/soil/agents/SentimentCorrelationModel.py +++ /dev/null @@ -1,102 +0,0 @@ -import random -from . import BaseAgent - - -class SentimentCorrelationModel(BaseAgent): - """ - Settings: - outside_effects_prob - - anger_prob - - joy_prob - - sadness_prob - - disgust_prob - """ - - def __init__(self, environment, unique_id=0, state=()): - super().__init__(model=environment, unique_id=unique_id, state=state) - self.outside_effects_prob = environment.environment_params['outside_effects_prob'] - self.anger_prob = environment.environment_params['anger_prob'] - self.joy_prob = environment.environment_params['joy_prob'] - self.sadness_prob = environment.environment_params['sadness_prob'] - self.disgust_prob = environment.environment_params['disgust_prob'] - self.state['time_awareness'] = [] - for i in range(4): # In this model we have 4 sentiments - self.state['time_awareness'].append(0) # 0-> Anger, 1-> joy, 2->sadness, 3 -> disgust - self.state['sentimentCorrelation'] = 0 - - def step(self): - self.behaviour() - - def behaviour(self): - - angry_neighbors_1_time_step = [] - joyful_neighbors_1_time_step = [] - sad_neighbors_1_time_step = [] - disgusted_neighbors_1_time_step = [] - - angry_neighbors = self.get_neighboring_agents(state_id=1) - for x in angry_neighbors: - if x.state['time_awareness'][0] > (self.env.now-500): - angry_neighbors_1_time_step.append(x) - num_neighbors_angry = len(angry_neighbors_1_time_step) - - joyful_neighbors = self.get_neighboring_agents(state_id=2) - for x in joyful_neighbors: - if x.state['time_awareness'][1] > (self.env.now-500): - joyful_neighbors_1_time_step.append(x) - num_neighbors_joyful = len(joyful_neighbors_1_time_step) - - sad_neighbors = self.get_neighboring_agents(state_id=3) - for x in sad_neighbors: - if x.state['time_awareness'][2] > (self.env.now-500): - sad_neighbors_1_time_step.append(x) - num_neighbors_sad = len(sad_neighbors_1_time_step) - - disgusted_neighbors = self.get_neighboring_agents(state_id=4) - for x in disgusted_neighbors: - if x.state['time_awareness'][3] > (self.env.now-500): - disgusted_neighbors_1_time_step.append(x) - num_neighbors_disgusted = len(disgusted_neighbors_1_time_step) - - anger_prob = self.anger_prob+(len(angry_neighbors_1_time_step)*self.anger_prob) - joy_prob = self.joy_prob+(len(joyful_neighbors_1_time_step)*self.joy_prob) - sadness_prob = self.sadness_prob+(len(sad_neighbors_1_time_step)*self.sadness_prob) - disgust_prob = self.disgust_prob+(len(disgusted_neighbors_1_time_step)*self.disgust_prob) - outside_effects_prob = self.outside_effects_prob - - num = random.random() - - if numanger_prob): - - self.state['id'] = 2 - self.state['sentimentCorrelation'] = 2 - self.state['time_awareness'][self.state['id']-1] = self.env.now - elif (numjoy_prob+anger_prob): - - self.state['id'] = 3 - self.state['sentimentCorrelation'] = 3 - self.state['time_awareness'][self.state['id']-1] = self.env.now - elif (numsadness_prob+anger_prob+joy_prob): - - self.state['id'] = 4 - self.state['sentimentCorrelation'] = 4 - self.state['time_awareness'][self.state['id']-1] = self.env.now - - self.state['sentiment'] = self.state['id'] diff --git a/soil/agents/__init__.py b/soil/agents/__init__.py index 27903fb..df2fd15 100644 --- a/soil/agents/__init__.py +++ b/soil/agents/__init__.py @@ -1,121 +1,167 @@ +from __future__ import annotations + import logging from collections import OrderedDict, defaultdict -from copy import deepcopy +from collections.abc import MutableMapping, Mapping, Set +from abc import ABCMeta +from copy import deepcopy, copy from functools import partial, wraps -from itertools import islice -import json +from itertools import islice, chain +import inspect +import types +import textwrap import networkx as nx +import warnings +import sys -from .. import serialization, utils, time +from typing import Any -from tsih import Key +from mesa import Agent as MesaAgent, Model +from typing import Dict, List -from mesa import Agent +from .. import serialization, network, utils, time, config -def as_node(agent): - if isinstance(agent, BaseAgent): - return agent.id - return agent - -IGNORED_FIELDS = ('model', 'logger') +IGNORED_FIELDS = ("model", "logger") -class DeadAgent(Exception): - pass +class MetaAgent(ABCMeta): + def __new__(mcls, name, bases, namespace): + defaults = {} -class BaseAgent(Agent): + # Re-use defaults from inherited classes + for i in bases: + if isinstance(i, MetaAgent): + defaults.update(i._defaults) + + new_nmspc = { + "_defaults": defaults, + "_last_return": None, + "_last_except": None, + } + + for attr, func in namespace.items(): + if attr == "step" and inspect.isgeneratorfunction(func): + orig_func = func + new_nmspc["_coroutine"] = None + + @wraps(func) + def func(self): + while True: + if not self._coroutine: + self._coroutine = orig_func(self) + try: + if self._last_except: + return self._coroutine.throw(self._last_except) + else: + return self._coroutine.send(self._last_return) + except StopIteration as ex: + self._coroutine = None + return ex.value + finally: + self._last_return = None + self._last_except = None + + func.id = name or func.__name__ + func.is_default = False + new_nmspc[attr] = func + elif ( + isinstance(func, types.FunctionType) + or isinstance(func, property) + or isinstance(func, classmethod) + or attr[0] == "_" + ): + new_nmspc[attr] = func + elif attr == "defaults": + defaults.update(func) + else: + defaults[attr] = copy(func) + + return super().__new__(mcls, name, bases, new_nmspc) + + +class BaseAgent(MesaAgent, MutableMapping, metaclass=MetaAgent): """ - A special Agent that keeps track of its state history. + A special type of Mesa Agent that: + + * Can be used as a dictionary to access its state. + * Has logging built-in + * Can be given default arguments through a defaults class attribute, + which will be used on construction to initialize each agent's state + + Any attribute that is not preceded by an underscore (`_`) will also be added to its state. """ - defaults = {} - - def __init__(self, - unique_id, - model, - name=None, - interval=None, - **kwargs - ): - # Check for REQUIRED arguments - # Initialize agent parameters - if isinstance(unique_id, Agent): - raise Exception() - self._saved = set() + 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) - self.name = name or '{}[{}]'.format(type(self).__name__, self.unique_id) - self._neighbors = None + self.name = ( + str(name) if name else "{}[{}]".format(type(self).__name__, self.unique_id) + ) + self.alive = True - self.interval = interval or self.get('interval', 1) - self.logger = logging.getLogger(self.model.name).getChild(self.name) + self.interval = interval or self.get("interval", 1) + logger = utils.logger.getChild(getattr(self.model, "id", self.model)).getChild( + self.name + ) + self.logger = logging.LoggerAdapter(logger, {"agent_name": self.name}) - if hasattr(self, 'level'): + if hasattr(self, "level"): self.logger.setLevel(self.level) - for (k, v) in self.defaults.items(): + + for (k, v) in self._defaults.items(): if not hasattr(self, k) or getattr(self, k) is None: setattr(self, k, deepcopy(v)) for (k, v) in kwargs.items(): - setattr(self, k, v) + setattr(self, k, v) + if init: + self.init() + + def init(self): + pass + + def __hash__(self): + return hash(self.unique_id) + + 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 - @property - def env(self): - return self.model - - @env.setter - def env(self, model): - self.model = model - - @property - def state(self): - ''' - Return the agent itself, which behaves as a dictionary. - Changes made to `agent.state` will be reflected in the history. - - This method shouldn't be used, but is kept here for backwards compatibility. - ''' - return self - - @state.setter - def state(self, value): - for k, v in value.items(): - self[k] = v - - @property - def environment_params(self): - return self.model.environment_params - - @environment_params.setter - def environment_params(self, value): - self.model.environment_params = value - - def __setattr__(self, key, value): - if not key.startswith('_') and key not in IGNORED_FIELDS: - try: - k = Key(t_step=self.now, - dict_id=self.unique_id, - key=key) - self._saved.add(key) - self.model[k] = value - except AttributeError: - pass - super().__setattr__(key, value) + @classmethod + def from_dict(cls, model, attrs, warn_extra=True): + ignored = {} + args = {} + for k, v in attrs.items(): + if k in inspect.signature(cls).parameters: + args[k] = v + else: + ignored[k] = v + if ignored and warn_extra: + utils.logger.info( + f"Ignoring the following arguments for agent class { agent_class.__name__ }: { ignored }" + ) + return cls(model=model, **args) def __getitem__(self, key): - if isinstance(key, tuple): - key, t_step = key - k = Key(key=key, t_step=t_step, dict_id=self.unique_id) - return self.model[k] - return getattr(self, key) + try: + return getattr(self, key) + except AttributeError: + raise KeyError(f"key {key} not found in agent") def __delitem__(self, key): return delattr(self, key) @@ -126,11 +172,28 @@ class BaseAgent(Agent): def __setitem__(self, key, value): setattr(self, key, value) - def items(self): - return ((k, getattr(self, k)) for k in self._saved) + def __len__(self): + return sum(1 for n in self.keys()) + + def __iter__(self): + return self.items() + + def keys(self): + return (k for k in self.__dict__ if k[0] != "_" and k not in IGNORED_FIELDS) + + def items(self, keys=None, skip=None): + keys = keys if keys is not None else self.keys() + it = ((k, self.get(k, None)) for k in keys) + if skip: + return filter(lambda x: x[0] not in skip, it) + return it def get(self, key, default=None): - return self[key] if key in self else default + if key in self: + return self[key] + elif key in self.model: + return self.model[key] + return default @property def now(self): @@ -140,29 +203,35 @@ class BaseAgent(Agent): # No environment return None - def die(self, remove=False): - self.info(f'agent {self.unique_id} is dying') + def die(self, msg=None): + if msg: + self.info("Agent dying:", msg) + self.debug(f"agent dying") self.alive = False - if remove: - self.remove_node(self.id) + try: + self.model.schedule.remove(self) + except KeyError: + pass return time.NEVER def step(self): + raise NotImplementedError("Agent must implement step method") + + def _check_alive(self): if not self.alive: - raise DeadAgent(self.unique_id) - return super().step() or time.Delta(self.interval) + raise time.DeadAgent(self.unique_id) - 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 = " @{:>3}: {}".format(self.now, message) + 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) extra = {} - extra['now'] = self.now - extra['unique_id'] = self.unique_id - extra['agent_name'] = self.name + extra["now"] = self.now + extra["unique_id"] = self.unique_id + extra["agent_name"] = self.name return self.logger.log(level, message, extra=extra) def debug(self, *args, **kwargs): @@ -171,200 +240,63 @@ class BaseAgent(Agent): def info(self, *args, **kwargs): return self.log(*args, level=logging.INFO, **kwargs) - -class NetworkAgent(BaseAgent): - - @property - def topology(self): - return self.model.G - - @property - def G(self): - return self.model.G - def count_agents(self, **kwargs): return len(list(self.get_agents(**kwargs))) - def count_neighboring_agents(self, state_id=None, **kwargs): - return len(self.get_neighboring_agents(state_id=state_id, **kwargs)) - - def get_neighboring_agents(self, state_id=None, **kwargs): - return self.get_agents(limit_neighbors=True, state_id=state_id, **kwargs) - - def get_agents(self, *args, limit=None, **kwargs): + def get_agents(self, *args, **kwargs): it = self.iter_agents(*args, **kwargs) - if limit is not None: - it = islice(it, limit) return list(it) - def iter_agents(self, agents=None, limit_neighbors=False, **kwargs): - if limit_neighbors: - agents = self.topology.neighbors(self.unique_id) + def iter_agents(self, *args, **kwargs): + yield from filter_agents(self.model.schedule._agents, *args, **kwargs) - agents = self.model.get_agents(agents) - return select(agents, **kwargs) + def __str__(self): + return self.to_str() - def subgraph(self, center=True, **kwargs): - include = [self] if center else [] - return self.topology.subgraph(n.unique_id for n in list(self.get_agents(**kwargs))+include) + def to_str(self, keys=None, skip=None, pretty=False): + content = dict(self.items(keys=keys)) + if pretty and content: + d = content + content = "\n" + for k, v in d.items(): + content += f"- {k}: {v}\n" + content = textwrap.indent(content, " ") + return f"{repr(self)}{content}" - def remove_node(self, unique_id): - self.topology.remove_node(unique_id) - - def add_edge(self, other, edge_attr_dict=None, *edge_attrs): - # return super(NetworkAgent, self).add_edge(node1=self.id, node2=other, **kwargs) - if self.unique_id not in self.topology.nodes(data=False): - raise ValueError('{} not in list of existing agents in the network'.format(self.unique_id)) - if other.unique_id not in self.topology.nodes(data=False): - raise ValueError('{} not in list of existing agents in the network'.format(other)) - - self.topology.add_edge(self.unique_id, other.unique_id, edge_attr_dict=edge_attr_dict, *edge_attrs) + def __repr__(self): + return f"{self.__class__.__name__}({self.unique_id})" - def ego_search(self, steps=1, center=False, node=None, **kwargs): - '''Get a list of nodes in the ego network of *node* of radius *steps*''' - node = as_node(node if node is not None else self) - G = self.subgraph(**kwargs) - return nx.ego_graph(G, node, center=center, radius=steps).nodes() - - def degree(self, node, force=False): - node = as_node(node) - if force or (not hasattr(self.model, '_degree')) or getattr(self.model, '_last_step', 0) < self.now: - self.model._degree = nx.degree_centrality(self.topology) - self.model._last_step = self.now - return self.model._degree[node] - - def betweenness(self, node, force=False): - node = as_node(node) - if force or (not hasattr(self.model, '_betweenness')) or getattr(self.model, '_last_step', 0) < self.now: - self.model._betweenness = nx.betweenness_centrality(self.topology) - self.model._last_step = self.now - return self.model._betweenness[node] - - -def state(name=None): - def decorator(func, name=None): - ''' - A state function should return either a state id, or a tuple (state_id, when) - The default value for state_id is the current state id. - The default value for when is the interval defined in the environment. - ''' - - @wraps(func) - def func_wrapper(self): - next_state = func(self) - when = None - if next_state is None: - return when - try: - next_state, when = next_state - except (ValueError, TypeError): - pass - if next_state: - self.set_state(next_state) - return when - - func_wrapper.id = name or func.__name__ - func_wrapper.is_default = False - return func_wrapper - - if callable(name): - return decorator(name) - else: - return partial(decorator, name=name) - - -def default_state(func): - func.is_default = True - return func - - -class MetaFSM(type): - def __init__(cls, name, bases, nmspc): - super().__init__(name, bases, nmspc) - states = {} - # Re-use states from inherited classes - default_state = None - for i in bases: - if isinstance(i, MetaFSM): - for state_id, state in i.states.items(): - if state.is_default: - default_state = state - states[state_id] = state - - # Add new states - for name, func in nmspc.items(): - if hasattr(func, 'id'): - if func.is_default: - default_state = func - states[func.id] = func - cls.default_state = default_state - cls.states = states - - -class FSM(NetworkAgent, metaclass=MetaFSM): - def __init__(self, *args, **kwargs): - super(FSM, self).__init__(*args, **kwargs) - if not hasattr(self, 'state_id'): - if not self.default_state: - raise ValueError('No default state specified for {}'.format(self.unique_id)) - self.state_id = self.default_state.id - - self.set_state(self.state_id) - - def step(self): - self.debug(f'Agent {self.unique_id} @ state {self.state_id}') - interval = super().step() - if 'id' not in self.state: - if self.default_state: - self.set_state(self.default_state.id) - else: - raise Exception('{} has no valid state id or default state'.format(self)) - interval = self.states[self.state_id](self) or interval - if not self.alive: - return time.NEVER - return interval - - def set_state(self, state): - if hasattr(state, 'id'): - state = state.id - if state not in self.states: - raise ValueError('{} is not a valid state'.format(state)) - self.state_id = state - return state - - -def prob(prob=1): - ''' +def prob(prob, random): + """ A true/False uniform distribution with a given probability. To be used like this: .. code-block:: python - + if prob(0.3): do_something() - ''' + """ r = random.random() return r < prob -def calculate_distribution(network_agents=None, - agent_type=None): - ''' +def calculate_distribution(network_agents=None, agent_class=None): + """ Calculate the threshold values (thresholds for a uniform distribution) of an agent distribution given the weights of each agent type. The input has this form: :: [ - {'agent_type': 'agent_type_1', + {'agent_class': 'agent_class_1', 'weight': 0.2, 'state': { 'id': 0 } }, - {'agent_type': 'agent_type_2', + {'agent_class': 'agent_class_2', 'weight': 0.8, 'state': { 'id': 1 @@ -373,201 +305,368 @@ def calculate_distribution(network_agents=None, ] In this example, 20% of the nodes will be marked as type - 'agent_type_1'. - ''' + 'agent_class_1'. + """ if network_agents: - network_agents = [deepcopy(agent) for agent in network_agents if not hasattr(agent, 'id')] - elif agent_type: - network_agents = [{'agent_type': agent_type}] + network_agents = [ + deepcopy(agent) for agent in network_agents if not hasattr(agent, "id") + ] + elif agent_class: + network_agents = [{"agent_class": agent_class}] else: - raise ValueError('Specify a distribution or a default agent type') + raise ValueError("Specify a distribution or a default agent type") # Fix missing weights and incompatible types for x in network_agents: - x['weight'] = float(x.get('weight', 1)) + x["weight"] = float(x.get("weight", 1)) # Calculate the thresholds - total = sum(x['weight'] for x in network_agents) + total = sum(x["weight"] for x in network_agents) acc = 0 for v in network_agents: - if 'ids' in v: + if "ids" in v: continue - upper = acc + (v['weight']/total) - v['threshold'] = [acc, upper] + upper = acc + (v["weight"] / total) + v["threshold"] = [acc, upper] acc = upper return network_agents -def serialize_type(agent_type, known_modules=[], **kwargs): - if isinstance(agent_type, str): - return agent_type - known_modules += ['soil.agents'] - return serialization.serialize(agent_type, known_modules=known_modules, **kwargs)[1] # Get the name of the class +def _serialize_type(agent_class, known_modules=[], **kwargs): + if isinstance(agent_class, str): + return agent_class + known_modules += ["soil.agents"] + return serialization.serialize(agent_class, known_modules=known_modules, **kwargs)[ + 1 + ] # Get the name of the class -def serialize_definition(network_agents, known_modules=[]): - ''' - When serializing an agent distribution, remove the thresholds, in order - to avoid cluttering the YAML definition file. - ''' - d = deepcopy(list(network_agents)) - for v in d: - if 'threshold' in v: - del v['threshold'] - v['agent_type'] = serialize_type(v['agent_type'], - known_modules=known_modules) - return d +def _deserialize_type(agent_class, known_modules=[]): + if not isinstance(agent_class, str): + return agent_class + known = known_modules + ["soil.agents", "soil.agents.custom"] + agent_class = serialization.deserializer(agent_class, known_modules=known) + return agent_class -def deserialize_type(agent_type, known_modules=[]): - if not isinstance(agent_type, str): - return agent_type - known = known_modules + ['soil.agents', 'soil.agents.custom' ] - agent_type = serialization.deserializer(agent_type, known_modules=known) - return agent_type +class AgentView(Mapping, Set): + """A lazy-loaded list of agents.""" + + __slots__ = ("_agents",) + + def __init__(self, agents): + self._agents = agents + + def __getstate__(self): + return {"_agents": self._agents} + + def __setstate__(self, state): + self._agents = state["_agents"] + + # Mapping methods + def __len__(self): + return len(self._agents) + + def __iter__(self): + yield from self._agents.values() + + def __getitem__(self, agent_id): + if isinstance(agent_id, slice): + raise ValueError(f"Slicing is not supported") + if agent_id in self._agents: + return self._agents[agent_id] + raise ValueError(f"Agent {agent_id} not found") + + def filter(self, *args, **kwargs): + yield from filter_agents(self._agents, *args, **kwargs) + + def one(self, *args, **kwargs): + return next(filter_agents(self._agents, *args, **kwargs)) + + def __call__(self, *args, **kwargs): + return list(self.filter(*args, **kwargs)) + + def __contains__(self, agent_id): + return agent_id in self._agents + + def __str__(self): + return str(list(unique_id for unique_id in self.keys())) + + def __repr__(self): + return f"{self.__class__.__name__}({self})" -def deserialize_definition(ind, **kwargs): - d = deepcopy(ind) - for v in d: - v['agent_type'] = deserialize_type(v['agent_type'], **kwargs) - return d +def filter_agents( + agents: dict, + *id_args, + unique_id=None, + state_id=None, + agent_class=None, + ignore=None, + state=None, + limit=None, + **kwargs, +): + """ + Filter agents given as a dict, by the criteria given as arguments (e.g., certain type or state id). + """ + assert isinstance(agents, dict) + ids = [] -def _validate_states(states, topology): - '''Validate states to avoid ignoring states during initialization''' - states = states or [] - if isinstance(states, dict): - for x in states: - assert x in topology.nodes + if unique_id is not None: + if isinstance(unique_id, list): + ids += unique_id + else: + ids.append(unique_id) + + if id_args: + ids += id_args + + if ids: + f = (agents[aid] for aid in ids if aid in agents) else: - assert len(states) <= len(topology) - return states - - -def _convert_agent_types(ind, to_string=False, **kwargs): - '''Convenience method to allow specifying agents by class or class name.''' - if to_string: - return serialize_definition(ind, **kwargs) - return deserialize_definition(ind, **kwargs) - - -def _agent_from_definition(definition, value=-1, unique_id=None): - """Used in the initialization of agents given an agent distribution.""" - if value < 0: - value = random.random() - for d in sorted(definition, key=lambda x: x.get('threshold')): - threshold = d.get('threshold', (-1, -1)) - # Check if the definition matches by id (first) or by threshold - if (unique_id is not None and unique_id in d.get('ids', [])) or \ - (value >= threshold[0] and value < threshold[1]): - state = {} - if 'state' in d: - state = deepcopy(d['state']) - return d['agent_type'], state - - raise Exception('Definition for value {} not found in: {}'.format(value, definition)) - - -def _definition_to_dict(definition, size=None, default_state=None): - state = default_state or {} - agents = {} - remaining = {} - if size: - for ix in range(size): - remaining[ix] = copy(state) - else: - remaining = defaultdict(lambda x: copy(state)) - - distro = sorted([item for item in definition if 'weight' in item]) - - ix = 0 - - def init_agent(item, id=ix): - while id in agents: - id += 1 - - agent = remaining[id] - agent['state'].update(copy(item.get('state', {}))) - agents[id] = agent - del remaining[id] - return agent - - for item in definition: - if 'ids' in item: - ids = item['ids'] - del item['ids'] - for id in ids: - agent = init_agent(item, id) - - for item in definition: - if 'number' in item: - times = item['number'] - del item['number'] - for times in range(times): - if size: - ix = random.choice(remaining.keys()) - agent = init_agent(item, id) - else: - agent = init_agent(item) - if not size: - return agents - - if len(remaining) < 0: - raise Exception('Invalid definition. Too many agents to add') - - - total_weight = float(sum(s['weight'] for s in distro)) - unit = size / total_weight - - for item in distro: - times = unit * item['weight'] - del item['weight'] - for times in range(times): - ix = random.choice(remaining.keys()) - agent = init_agent(item, id) - return agents - - -def select(agents, state_id=None, agent_type=None, ignore=None, iterator=False, **kwargs): + f = agents.values() if state_id is not None and not isinstance(state_id, (tuple, list)): state_id = tuple([state_id]) - if agent_type is not None: - try: - agent_type = tuple(agent_type) - except TypeError: - agent_type = tuple([agent_type]) - f = agents + if agent_class is not None: + agent_class = _deserialize_type(agent_class) + try: + agent_class = tuple(agent_class) + except TypeError: + agent_class = tuple([agent_class]) if ignore: f = filter(lambda x: x not in ignore, f) if state_id is not None: - f = filter(lambda agent: agent.get('state_id', None) in state_id, f) + f = filter(lambda agent: agent.get("state_id", None) in state_id, f) - if agent_type is not None: - f = filter(lambda agent: isinstance(agent, agent_type), f) - for k, v in kwargs.items(): - f = filter(lambda agent: agent.state.get(k, None) == v, f) + if agent_class is not None: + f = filter(lambda agent: isinstance(agent, agent_class), f) - if iterator: - return f - return f + state = state or dict() + state.update(kwargs) + + for k, v in state.items(): + f = filter(lambda agent: getattr(agent, k, None) == v, f) + + if limit is not None: + f = islice(f, limit) + + yield from f + + +def from_config( + cfg: config.AgentConfig, random, topology: nx.Graph = None +) -> List[Dict[str, Any]]: + """ + This function turns an agentconfig into a list of individual "agent specifications", which are just a dictionary + with the parameters that the environment will use to construct each agent. + + This function does NOT return a list of agents, mostly because some attributes to the agent are not known at the + time of calling this function, such as `unique_id`. + """ + default = cfg or config.AgentConfig() + if not isinstance(cfg, config.AgentConfig): + cfg = config.AgentConfig(**cfg) + + agents = [] + + assigned_total = 0 + assigned_network = 0 + + if cfg.fixed is not None: + agents, assigned_total, assigned_network = _from_fixed( + cfg.fixed, topology=cfg.topology, default=cfg + ) + + n = cfg.n + + if cfg.distribution: + topo_size = len(topology) if topology else 0 + + networked = [] + total = [] + + for d in cfg.distribution: + if d.strategy == config.Strategy.topology: + topo = d.topology if ("topology" in d.__fields_set__) else cfg.topology + if not topo: + raise ValueError( + 'The "topology" strategy only works if the topology parameter is set to True' + ) + if not topo_size: + raise ValueError( + f"Topology does not have enough free nodes to assign one to the agent" + ) + + networked.append(d) + + if d.strategy == config.Strategy.total: + if not cfg.n: + raise ValueError( + 'Cannot use the "total" strategy without providing the total number of agents' + ) + total.append(d) + + if networked: + new_agents = _from_distro( + networked, + n=topo_size - assigned_network, + topology=topo, + default=cfg, + random=random, + ) + assigned_total += len(new_agents) + assigned_network += len(new_agents) + agents += new_agents + + if total: + remaining = n - assigned_total + agents += _from_distro(total, n=remaining, default=cfg, random=random) + + if assigned_network < topo_size: + utils.logger.warn( + f"The total number of agents does not match the total number of nodes in " + "every topology. This may be due to a definition error: assigned: " + f"{ assigned } total size: { topo_size }" + ) + + return agents + + +def _from_fixed( + lst: List[config.FixedAgentConfig], + topology: bool, + default: config.SingleAgentConfig, +) -> List[Dict[str, Any]]: + agents = [] + + counts_total = 0 + counts_network = 0 + + for fixed in lst: + agent = {} + if default: + agent = default.state.copy() + agent.update(fixed.state) + cls = serialization.deserialize( + fixed.agent_class or (default and default.agent_class) + ) + agent["agent_class"] = cls + topo = ( + fixed.topology + if ("topology" in fixed.__fields_set__) + else topology or default.topology + ) + + if topo: + agent["topology"] = True + counts_network += 1 + if not fixed.hidden: + counts_total += 1 + agents.append(agent) + + return agents, counts_total, counts_network + + +def _from_distro( + distro: List[config.AgentDistro], + n: int, + default: config.SingleAgentConfig, + random, + topology: str = None +) -> List[Dict[str, Any]]: + + agents = [] + + if n is None: + if any(lambda dist: dist.n is None, distro): + raise ValueError( + "You must provide a total number of agents, or the number of each type" + ) + n = sum(dist.n for dist in distro) + + weights = list(dist.weight if dist.weight is not None else 1 for dist in distro) + minw = min(weights) + norm = list(weight / minw for weight in weights) + total = sum(norm) + chunk = n // total + + # random.choices would be enough to get a weighted distribution. But it can vary a lot for smaller k + # So instead we calculate our own distribution to make sure the actual ratios are close to what we would expect + + # Calculate how many times each has to appear + indices = list( + chain.from_iterable([idx] * int(n * chunk) for (idx, n) in enumerate(norm)) + ) + + # Complete with random agents following the original weight distribution + if len(indices) < n: + indices += random.choices( + list(range(len(distro))), + weights=[d.weight for d in distro], + k=n - len(indices), + ) + + # Deserialize classes for efficiency + classes = list( + serialization.deserialize(i.agent_class or default.agent_class) for i in distro + ) + + # Add them in random order + random.shuffle(indices) + + for idx in indices: + d = distro[idx] + agent = d.state.copy() + cls = classes[idx] + agent["agent_class"] = cls + if default: + agent.update(default.state) + topology = ( + d.topology + if ("topology" in d.__fields_set__) + else topology or default.topology + ) + if topology: + agent["topology"] = topology + agents.append(agent) + + return agents + + +from .network_agents import * +from .fsm import * +from .evented import * +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 .BigMarketModel import * from .IndependentCascadeModel import * -from .ModelM2 import * -from .SentimentCorrelationModel import * from .SISaModel import * from .CounterModel import * + try: import scipy from .Geo import Geo except ImportError: import sys - print('Could not load the Geo Agent, scipy is not installed', file=sys.stderr) + + 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) diff --git a/soil/agents/evented.py b/soil/agents/evented.py new file mode 100644 index 0000000..22e1191 --- /dev/null +++ b/soil/agents/evented.py @@ -0,0 +1,77 @@ +from . import BaseAgent +from ..events import Message, Tell, Ask, TimedOut +from ..time import BaseCond +from functools import partial +from collections import deque + + +class ReceivedOrTimeout(BaseCond): + def __init__( + self, agent, expiration=None, timeout=None, check=True, ignore=False, **kwargs + ): + if expiration is None: + if timeout is not None: + expiration = agent.now + timeout + self.expiration = expiration + self.ignore = ignore + self.check = check + super().__init__(**kwargs) + + def expired(self, time): + return self.expiration and self.expiration < time + + def ready(self, agent, time): + return len(agent._inbox) or self.expired(time) + + def return_value(self, agent): + if not self.ignore and self.expired(agent.now): + raise TimedOut("No messages received") + if self.check: + agent.check_messages() + return None + + def schedule_next(self, time, delta, first=False): + if self._delta is not None: + delta = self._delta + return (time + delta, self) + + def __repr__(self): + return f"ReceivedOrTimeout(expires={self.expiration})" + + +class EventedAgent(BaseAgent): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self._inbox = deque() + self._processed = 0 + + def on_receive(self, *args, **kwargs): + pass + + def received(self, *args, **kwargs): + return ReceivedOrTimeout(self, *args, **kwargs) + + def tell(self, msg, sender=None): + self._inbox.append(Tell(timestamp=self.now, payload=msg, sender=sender)) + + def ask(self, msg, timeout=None, **kwargs): + ask = Ask(timestamp=self.now, payload=msg, sender=self) + self._inbox.append(ask) + expiration = float("inf") if timeout is None else self.now + timeout + return ask.replied(expiration=expiration, **kwargs) + + def check_messages(self): + changed = False + while self._inbox: + msg = self._inbox.popleft() + self._processed += 1 + if msg.expired(self.now): + continue + changed = True + reply = self.on_receive(msg.payload, sender=msg.sender) + if isinstance(msg, Ask): + msg.reply = reply + return changed + + +Evented = EventedAgent diff --git a/soil/agents/fsm.py b/soil/agents/fsm.py new file mode 100644 index 0000000..03070ac --- /dev/null +++ b/soil/agents/fsm.py @@ -0,0 +1,148 @@ +from . import MetaAgent, BaseAgent +from ..time import Delta + +from functools import partial, wraps +import inspect + + +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) + The default value for state_id is the current state id. + The default value for when is the interval defined in the environment. + """ + if inspect.isgeneratorfunction(func): + orig_func = func + + @wraps(func) + def func(self): + while True: + if not self._coroutine: + self._coroutine = orig_func(self) + + try: + if self._last_except: + n = self._coroutine.throw(self._last_except) + else: + n = self._coroutine.send(self._last_return) + if n: + return None, n + return n + except StopIteration as ex: + self._coroutine = None + next_state = ex.value + if next_state is not None: + self._set_state(next_state) + return next_state + finally: + self._last_return = None + self._last_except = None + + func.id = name or func.__name__ + func.is_default = default + return func + + if callable(name): + return decorator(name) + else: + return partial(decorator, name=name) + + +def default_state(func): + func.is_default = True + return func + + +class MetaFSM(MetaAgent): + def __new__(mcls, name, bases, namespace): + states = {} + # Re-use states from inherited classes + default_state = None + for i in bases: + if isinstance(i, MetaFSM): + for state_id, state in i._states.items(): + if state.is_default: + default_state = state + states[state_id] = state + + # Add new states + for attr, func in namespace.items(): + if hasattr(func, "id"): + if func.is_default: + default_state = func + states[func.id] = func + + namespace.update( + { + "_default_state": default_state, + "_states": states, + } + ) + + return super(MetaFSM, mcls).__new__( + mcls=mcls, name=name, bases=bases, namespace=namespace + ) + + +class FSM(BaseAgent, metaclass=MetaFSM): + def __init__(self, init=True, **kwargs): + super().__init__(**kwargs, init=False) + if not hasattr(self, "state_id"): + if not self._default_state: + raise ValueError( + "No default state specified for {}".format(self.unique_id) + ) + self.state_id = self._default_state.id + + self._coroutine = None + self.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}") + + self._check_alive() + next_state = self._states[self.state_id](self) + + when = None + try: + next_state, *when = next_state + if not when: + when = None + elif len(when) == 1: + when = when[0] + else: + raise ValueError( + "Too many values returned. Only state (and time) allowed" + ) + except TypeError: + pass + + if next_state is not None: + self._set_state(next_state) + + return when or self.default_interval + + def _set_state(self, state, when=None): + if hasattr(state, "id"): + state = state.id + if state not in self._states: + raise ValueError("{} is not a valid state".format(state)) + self.state_id = state + if when is not None: + self.model.schedule.add(self, when=when) + return state + + def die(self, *args, **kwargs): + return self.dead, super().die(*args, **kwargs) + + @state + def dead(self): + return self.die() diff --git a/soil/agents/network_agents.py b/soil/agents/network_agents.py new file mode 100644 index 0000000..1633976 --- /dev/null +++ b/soil/agents/network_agents.py @@ -0,0 +1,100 @@ +from . import BaseAgent + + +class NetworkAgent(BaseAgent): + def __init__(self, *args, topology=None, init=True, node_id=None, **kwargs): + super().__init__(*args, init=False, **kwargs) + + self.G = topology or self.model.G + 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(**kwargs)) + + @property + def node(self): + return self.G.nodes[self.node_id] + + def iter_agents(self, unique_id=None, *, limit_neighbors=False, **kwargs): + unique_ids = None + if unique_ids is not None: + try: + unique_ids = set(unique_id) + except TypeError: + unique_ids = set([unique_id]) + + if limit_neighbors: + neighbor_ids = set() + for node_id in self.G.neighbors(self.node_id): + agent = self.G.nodes[node_id].get("agent") + if agent is not None: + neighbor_ids.add(agent.unique_id) + if unique_ids: + unique_ids = unique_ids & neighbor_ids + else: + unique_ids = neighbor_ids + if not unique_ids: + return + unique_ids = list(unique_ids) + yield from super().iter_agents(unique_id=unique_ids, **kwargs) + + def subgraph(self, center=True, **kwargs): + include = [self] if center else [] + G = self.G.subgraph( + n.node_id for n in list(self.get_agents(**kwargs) + include) + ) + return G + + def remove_node(self): + self.debug(f"Removing node for {self.unique_id}: {self.node_id}") + self.G.remove_node(self.node_id) + self.node_id = None + + def add_edge(self, other, edge_attr_dict=None, *edge_attrs): + if self.node_id not in self.G.nodes(data=False): + raise ValueError( + "{} not in list of existing agents in the network".format( + self.unique_id + ) + ) + if other.node_id not in self.G.nodes(data=False): + raise ValueError( + "{} not in list of existing agents in the network".format(other) + ) + + self.G.add_edge( + self.node_id, other.node_id, edge_attr_dict=edge_attr_dict, *edge_attrs + ) + + def die(self, remove=True): + if not self.alive: + return None + if remove: + self.remove_node() + return super().die() + + +NetAgent = NetworkAgent diff --git a/soil/analysis.py b/soil/analysis.py index 8c9cb11..ae259e1 100644 --- a/soil/analysis.py +++ b/soil/analysis.py @@ -1,206 +1,49 @@ +import os +import sqlalchemy import pandas as pd +from collections import namedtuple -import glob -import yaml -from os.path import join - -from . import serialization -from tsih import History - - -def read_data(*args, group=False, **kwargs): - iterable = _read_data(*args, **kwargs) - if group: - return group_trials(iterable) +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: - return list(iterable) + 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); -def _read_data(pattern, *args, from_csv=False, process_args=None, **kwargs): - if not process_args: - process_args = {} - for folder in glob.glob(pattern): - config_file = glob.glob(join(folder, '*.yml'))[0] - config = yaml.load(open(config_file), Loader=yaml.SafeLoader) - df = None - if from_csv: - for trial_data in sorted(glob.glob(join(folder, - '*.environment.csv'))): - df = read_csv(trial_data, **kwargs) - yield config_file, df, config - else: - for trial_data in sorted(glob.glob(join(folder, '*.sqlite'))): - df = read_sql(trial_data, **kwargs) - yield config_file, df, config +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}") - -def read_sql(db, *args, **kwargs): - h = History(db_path=db, backup=False, readonly=True) - df = h.read_sql(*args, **kwargs) - return df - - -def read_csv(filename, keys=None, convert_types=False, **kwargs): - ''' - Read a CSV in canonical form: :: - - - - ''' - df = pd.read_csv(filename) - if convert_types: - df = convert_types_slow(df) - if keys: - df = df[df['key'].isin(keys)] - df = process_one(df) - return df - - -def convert_row(row): - row['value'] = serialization.deserialize(row['value_type'], row['value']) - return row - - -def convert_types_slow(df): - ''' - Go over every column in a dataframe and convert it to the type determined by the `get_types` - function. - - This is a slow operation. - ''' - dtypes = get_types(df) - for k, v in dtypes.items(): - t = df[df['key']==k] - t['value'] = t['value'].astype(v) - df = df.apply(convert_row, axis=1) - return df - - -def split_processed(df): - env = df.loc[:, df.columns.get_level_values(1).isin(['env', 'stats'])] - agents = df.loc[:, ~df.columns.get_level_values(1).isin(['env', 'stats'])] - return env, agents - - -def split_df(df): - ''' - Split a dataframe in two dataframes: one with the history of agents, - and one with the environment history - ''' - envmask = (df['agent_id'] == 'env') - n_env = envmask.sum() - if n_env == len(df): - return df, None - elif n_env == 0: - return None, df - agents, env = [x for _, x in df.groupby(envmask)] - return env, agents - - -def process(df, **kwargs): - ''' - Process a dataframe in canonical form ``(t_step, agent_id, key, value, value_type)`` into - two dataframes with a column per key: one with the history of the agents, and one for the - history of the environment. - ''' - env, agents = split_df(df) - return process_one(env, **kwargs), process_one(agents, **kwargs) - - -def get_types(df): - ''' - Get the value type for every key stored in a raw history dataframe. - ''' - dtypes = df.groupby(by=['key'])['value_type'].unique() - return {k:v[0] for k,v in dtypes.items()} - - -def process_one(df, *keys, columns=['key', 'agent_id'], values='value', - fill=True, index=['t_step',], - aggfunc='first', **kwargs): - ''' - Process a dataframe in canonical form ``(t_step, agent_id, key, value, value_type)`` into - a dataframe with a column per key - ''' - if df is None: - return df - if keys: - df = df[df['key'].isin(keys)] - - df = df.pivot_table(values=values, index=index, columns=columns, - aggfunc=aggfunc, **kwargs) - if fill: - df = fillna(df) - return df - - -def get_count(df, *keys): - ''' - For every t_step and key, get the value count. - - The result is a dataframe with `t_step` as index, an a multiindex column based on `key` and the values found for each `key`. - ''' - if keys: - df = df[list(keys)] - df.columns = df.columns.remove_unused_levels() - counts = pd.DataFrame() - for key in df.columns.levels[0]: - g = df[[key]].apply(pd.Series.value_counts, axis=1).fillna(0) - for value, series in g.items(): - counts[key, value] = series - counts.columns = pd.MultiIndex.from_tuples(counts.columns) - return counts - - -def get_majority(df, *keys): - ''' - For every t_step and key, get the value of the majority of agents - - The result is a dataframe with `t_step` as index, and columns based on `key`. - ''' - df = get_count(df, *keys) - return df.stack(level=0).idxmax(axis=1).unstack() - - -def get_value(df, *keys, aggfunc='sum'): - ''' - For every t_step and key, get the value of *numeric columns*, aggregated using a specific function. - ''' - if keys: - df = df[list(keys)] - df.columns = df.columns.remove_unused_levels() - df = df.select_dtypes('number') - return df.groupby(level='key', axis=1).agg(aggfunc) - - -def plot_all(*args, plot_args={}, **kwargs): - ''' - Read all the trial data and plot the result of applying a function on them. - ''' - dfs = do_all(*args, **kwargs) - ps = [] - for line in dfs: - f, df, config = line - if len(df) < 1: - continue - df.plot(title=config['name'], **plot_args) - ps.append(df) - return ps - -def do_all(pattern, func, *keys, include_env=False, **kwargs): - for config_file, df, config in read_data(pattern, keys=keys): - if len(df) < 1: - continue - p = func(df, *keys, **kwargs) - yield config_file, p, config - - -def group_trials(trials, aggfunc=['mean', 'min', 'max', 'std']): - trials = list(trials) - trials = list(map(lambda x: x[1] if isinstance(x, tuple) else x, trials)) - return pd.concat(trials).groupby(level=0).agg(aggfunc).reorder_levels([2, 0,1] ,axis=1) - - -def fillna(df): - new_df = df.ffill(axis=0) - return new_df + return Results(config, parameters, env, agents) diff --git a/soil/config.py b/soil/config.py new file mode 100644 index 0000000..b99b559 --- /dev/null +++ b/soil/config.py @@ -0,0 +1,2 @@ +def load_config(cfg): + return cfg \ No newline at end of file diff --git a/soil/datacollection.py b/soil/datacollection.py index 075d988..79bdc44 100644 --- a/soil/datacollection.py +++ b/soil/datacollection.py @@ -1,26 +1,19 @@ from mesa import DataCollector as MDC -class SoilDataCollector(MDC): +class SoilCollector(MDC): + def __init__(self, model_reporters=None, agent_reporters=None, tables=None, **kwargs): + model_reporters = model_reporters or {} + agent_reporters = agent_reporters or {} + tables = tables or {} + if 'agent_count' not in model_reporters: + model_reporters['agent_count'] = lambda m: m.schedule.get_agent_count() + if 'time' not in model_reporters: + 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) - def __init__(self, environment, *args, **kwargs): - super().__init__(*args, **kwargs) - # Populate model and env reporters so they have a key per - # So they can be shown in the web interface - self.environment = environment - - - @property - def model_vars(self): - pass - - @model_vars.setter - def model_vars(self, value): - pass - - @property - def agent_reporters(self): - self.model._history._ - - pass - + super().__init__(model_reporters=model_reporters, + agent_reporters=agent_reporters, + tables=tables, + **kwargs) diff --git a/soil/debugging.py b/soil/debugging.py new file mode 100644 index 0000000..5d80f39 --- /dev/null +++ b/soil/debugging.py @@ -0,0 +1,243 @@ +from __future__ import annotations + +import pdb +import sys +import os + +from textwrap import indent +from functools import wraps + +from .agents import FSM, MetaFSM +from mesa import Model, Agent + + +def wrapcmd(func): + @wraps(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["attrs"] = arg.strip().split() + + 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 + + +class Debug(pdb.Pdb): + def __init__(self, *args, skip_soil=False, **kwargs): + skip = kwargs.get("skip", []) + if skip_soil: + skip.append("soil") + skip.append("contextlib") + skip.append("soil.*") + skip.append("mesa.*") + super(Debug, self).__init__(*args, skip=skip, **kwargs) + self.prompt = "[soil-pdb] " + + @staticmethod + def _soil_agents(model, attrs=None, pretty=True, **kwargs): + for agent in model.agents(**kwargs): + d = agent + print(" - " + indent(agent.to_str(keys=attrs, pretty=pretty), " ")) + + @wrapcmd + def do_soil_agents(): + return Debug._soil_agents(model, attrs=attrs or None) + + do_sa = do_soil_agents + + @wrapcmd + def do_soil_list(): + return Debug._soil_agents(model, attrs=["state_id"], pretty=False) + + do_sl = do_soil_list + + def do_continue_state(self, arg): + """Continue until next time this state is reached""" + self.do_break_state(arg, temporary=True) + return self.do_continue("") + + do_cs = do_continue_state + + @wrapcmd + def do_soil_agent(): + if not agent: + print("No agent available") + return + + keys = None + if attrs: + keys = [] + for k in attrs: + for key in agent.keys(): + if key.startswith(k): + keys.append(key) + + print(agent.to_str(pretty=True, keys=keys)) + + do_aa = do_soil_agent + + def do_break_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. + """ + + klass = None + state = arg + if not state: + self.error("Specify at least a state name") + return + + state, *tokens = state.lstrip().split() + if tokens: + instances = list(eval(token) for token in tokens) + + colon = state.find(":") + + if colon > 0: + klass = state[:colon].rstrip() + state = state[colon + 1 :].strip() + + print(klass, state, tokens) + klass = eval(klass, self.curframe.f_globals, self.curframe_locals) + + if klass: + klasses = [klass] + else: + klasses = [ + k + for k in self.curframe.f_globals.values() + if isinstance(k, type) and issubclass(k, FSM) + ] + + if not klasses: + self.error("No agent classes found") + + for klass in klasses: + try: + func = getattr(klass, state) + except AttributeError: + self.error(f"State {state} not found in class {klass}") + continue + if hasattr(func, "__func__"): + func = func.__func__ + + code = func.__code__ + # use co_name to identify the bkpt (function names + # could be aliased, but co_name is invariant) + funcname = code.co_name + lineno = code.co_firstlineno + filename = code.co_filename + + # Check for reasonable breakpoint + line = self.checkline(filename, lineno) + if not line: + raise ValueError("no line found") + # now set the break point + cond = None + if instances: + cond = f"self.unique_id in { repr(instances) }" + + existing = self.get_breaks(filename, line) + if existing: + self.message("Breakpoint already exists at %s:%d" % (filename, line)) + continue + err = self.set_break(filename, line, temporary, cond, funcname) + if err: + self.error(err) + else: + bp = self.get_breaks(filename, line)[-1] + self.message("Breakpoint %d at %s:%d" % (bp.number, bp.file, bp.line)) + + do_bs = do_break_state + + def do_break_state_self(self, arg: str, temporary=False): + """ + Break before a specified state is stepped into, for the current agent + """ + agent = self.curframe.f_locals.get("self") + if not agent: + self.error("No current agent.") + self.error("Try this again when the debugger is stopped inside an agent") + return + + arg = f"{agent.__class__.__name__}:{ arg } {agent.unique_id}" + return self.do_break_state(arg) + + do_bss = do_break_state_self + + +debugger = None + + +def set_trace(frame=None, **kwargs): + global debugger + if debugger is None: + debugger = Debug(**kwargs) + frame = frame or sys._getframe().f_back + debugger.set_trace(frame) + + +def post_mortem(traceback=None, **kwargs): + global debugger + if debugger is None: + debugger = Debug(**kwargs) + t = sys.exc_info()[2] + debugger.reset() + debugger.interaction(None, t) diff --git a/soil/decorators.py b/soil/decorators.py new file mode 100644 index 0000000..94a4b08 --- /dev/null +++ b/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 \ No newline at end of file diff --git a/soil/environment.py b/soil/environment.py index a33b218..63055d4 100644 --- a/soil/environment.py +++ b/soil/environment.py @@ -1,208 +1,179 @@ +from __future__ import annotations + import os import sqlite3 -import csv import math -import random -import yaml -import tempfile import logging -import pandas as pd +import inspect + +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 tsih import History, Record, Key, NoHistory +from mesa import Model, Agent -from mesa import Model +from . import agents as agentmod, datacollection, serialization, utils, time, network, events -from . import serialization, agents, analysis, utils, time -# These properties will be copied when pickling/unpickling the environment -_CONFIG_PROPS = [ 'name', - 'states', - 'default_state', - 'interval', - ] +# TODO: maybe add metaclass to read attributes of a model -class Environment(Model): +class BaseEnvironment(Model): """ - The environment is key in a simulation. It contains the network topology, - a reference to network and environment agents, as well as the environment - params, which are used as shared state between agents. + The environment is key in a simulation. It controls how agents interact, + and what information is available to them. + + This is an opinionated version of `mesa.Model` class, which adds many + convenience methods and abstractions. The environment parameters and the state of every agent can be accessed - both by using the environment as a dictionary or with the environment's + both by using the environment as a dictionary and with the environment's :meth:`soil.environment.Environment.get` method. """ - def __init__(self, name=None, - network_agents=None, - environment_agents=None, - states=None, - default_state=None, - interval=1, - network_params=None, - seed=None, - topology=None, - schedule=None, - initial_time=0, - environment_params=None, - history=True, - dir_path=None, - **kwargs): + collector_class = datacollection.SoilCollector + 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 + 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.schedule = schedule - if schedule is None: - self.schedule = time.TimedActivation() - self.name = name or 'UnnamedEnvironment' - seed = seed or current_time() - random.seed(seed) - if isinstance(states, list): - states = dict(enumerate(states)) - self.states = deepcopy(states) if states else {} - self.default_state = deepcopy(default_state) or {} + self.current_id = -1 - if topology is None: - network_params = network_params or {} - topology = serialization.load_network(network_params, - dir_path=dir_path) - if not topology: - topology = nx.Graph() - self.G = nx.Graph(topology) + self.id = id + if logger: + self.logger = logger + else: + self.logger = utils.logger.getChild(self.id) - self.environment_params = environment_params or {} - self.environment_params.update(kwargs) + if schedule_class is None: + schedule_class = time.TimedActivation + else: + schedule_class = serialization.deserialize(schedule_class) - self._env_agents = {} self.interval = interval - if history: - history = History - else: - history = NoHistory - self._history = history(name=self.name, - backup=True) - self['SEED'] = seed + self.schedule = schedule_class(self) - if network_agents: - distro = agents.calculate_distribution(network_agents) - self.network_agents = agents._convert_agent_types(distro) - else: - self.network_agents = [] + for (k, v) in env_params.items(): + self[k] = v - environment_agents = environment_agents or [] - if environment_agents: - distro = agents.calculate_distribution(environment_agents) - environment_agents = agents._convert_agent_types(distro) - self.environment_agents = environment_agents + if agents: + self.add_agents(**agents) + if init: + self.init() + self.datacollector.collect(self) - self.logger = utils.logger.getChild(self.name) + def init(self): + pass + + @property + def agents(self): + return agentmod.AgentView(self.schedule._agents) + + 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: return self.schedule.time - raise Exception('The environment has not been scheduled, so it has no sense of time') + raise Exception( + "The environment has not been scheduled, so it has no sense of time" + ) + def init_agents(self): + pass - @property - def agents(self): - yield from self.environment_agents - yield from self.network_agents + def add_agent(self, agent_class, unique_id=None, **agent): + if unique_id is None: + unique_id = self.next_id() - @property - def environment_agents(self): - for ref in self._env_agents.values(): - yield ref + agent["unique_id"] = unique_id - @environment_agents.setter - def environment_agents(self, environment_agents): - self._environment_agents = environment_agents + agent = dict(**agent) + unique_id = agent.pop("unique_id", None) + if unique_id is None: + unique_id = self.next_id() - for (ix, agent) in enumerate(self._environment_agents): - self.init_agent(len(self.G) + ix, agent_definitions=environment_agents, with_node=False) + a = serialization.deserialize(agent_class)(unique_id=unique_id, model=self, **agent) - @property - def network_agents(self): - for i in self.G.nodes(): - node = self.G.nodes[i] - if 'agent' in node: - yield node['agent'] - - @network_agents.setter - def network_agents(self, network_agents): - self._network_agents = network_agents - for ix in self.G.nodes(): - self.init_agent(ix, agent_definitions=network_agents) - - def init_agent(self, agent_id, agent_definitions, with_node=True): - init = False - - state = {} - if with_node: - node = self.G.nodes[agent_id] - state = dict(node) - state.update(self.states.get(agent_id, {})) - - agent_type = None - if 'agent_type' in state: - agent_type = state['agent_type'] - elif with_node and 'agent_type' in node: - agent_type = node['agent_type'] - elif 'agent_type' in self.default_state: - agent_type = self.default_state['agent_type'] - - if agent_type: - agent_type = agents.deserialize_type(agent_type) - elif agent_definitions: - agent_type, state = agents._agent_from_definition(agent_definitions, unique_id=agent_id) - else: - serialization.logger.debug('Skipping agent {}'.format(agent_id)) - return - return self.set_agent(agent_id, agent_type, state, with_node=with_node) - - def set_agent(self, agent_id, agent_type, state=None, with_node=True): - defstate = deepcopy(self.default_state) or {} - defstate.update(self.states.get(agent_id, {})) - if with_node: - node = self.G.nodes[agent_id] - defstate.update(node.get('state', {})) - if state: - defstate.update(state) - a = None - if agent_type: - state = defstate - a = agent_type(model=self, - unique_id=agent_id - ) - - for (k, v) in state.items(): - setattr(a, k, v) - - if with_node: - node['agent'] = a self.schedule.add(a) return a - def add_node(self, agent_type, state=None): - agent_id = int(len(self.G.nodes())) - self.G.add_node(agent_id) - a = self.set_agent(agent_id, agent_type, state) - a['visible'] = True - 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) - def add_edge(self, agent1, agent2, start=None, **attrs): - if hasattr(agent1, 'id'): - agent1 = agent1.id - if hasattr(agent2, 'id'): - agent2 = agent2.id - start = start or self.now - return self.G.add_edge(agent1, agent2, **attrs) + 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): @@ -212,185 +183,248 @@ class Environment(Model): for k, v in kwargs: message += " {k}={v} ".format(k, v) extra = {} - extra['now'] = self.now - extra['unique_id'] = self.name + extra["now"] = self.now + extra["id"] = self.id return self.logger.log(level, message, extra=extra) def step(self): + """ + Advance one step in the simulation, and update the data collection and scheduler appropriately + """ super().step() self.schedule.step() + self.datacollector.collect(self) - def run(self, until, *args, **kwargs): - self._save_state() + 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) - while self.schedule.next_time < until: - self.step() - utils.logger.debug(f'Simulation step {self.schedule.time}/{until}. Next: {self.schedule.next_time}') - self.schedule.time = until - self._history.flush_cache() + 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 _save_state(self, now=None): - serialization.logger.debug('Saving state @{}'.format(self.now)) - self._history.save_records(self.state_to_tuples(now=now)) + 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): - if isinstance(key, tuple): - self._history.flush_cache() - return self._history[key] + try: + return getattr(self, key) + except AttributeError: + raise KeyError(f"key {key} not found in environment") - return self.environment_params[key] - - def __setitem__(self, key, value): - if isinstance(key, tuple): - k = Key(*key) - self._history.save_record(*k, - value=value) - return - self.environment_params[key] = value - self._history.save_record(dict_id='env', - t_step=self.now, - key=key, - value=value) + def __delitem__(self, key): + return delattr(self, key) def __contains__(self, key): - return key in self.environment_params + return hasattr(self, key) + + def __setitem__(self, key, value): + setattr(self, key, value) + + def __str__(self): + return str(dict(self)) + + def __len__(self): + return sum(1 for n in self.keys()) + + def __iter__(self): + return iter(self.agents()) def get(self, key, default=None): - ''' - Get the value of an environment attribute in a - given point in the simulation (history). - If key is an attribute name, this method returns - the current value. - To get values at other times, use a - :meth: `soil.history.Key` tuple. - ''' return self[key] if key in self else default - def get_agent(self, agent_id): - return self.G.nodes[agent_id]['agent'] + def keys(self): + return (k for k in self.__dict__ if k[0] != "_") - def get_agents(self, nodes=None): - if nodes is None: - return self.agents - return (self.G.nodes[i]['agent'] for i in nodes) +class NetworkEnvironment(BaseEnvironment): + """ + The NetworkEnvironment is an environment that includes one or more networkx.Graph intances + and methods to associate agents to nodes and vice versa. + """ - def dump_csv(self, f): - with utils.open_or_reuse(f, 'w') as f: - cr = csv.writer(f) - cr.writerow(('agent_id', 't_step', 'key', 'value')) - for i in self.history_to_tuples(): - cr.writerow(i) + def __init__(self, + *args, + topology: Optional[Union[nx.Graph, str]] = None, + agent_class: Optional[Type[agentmod.Agent]] = None, + 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) - def dump_gexf(self, f): - G = self.history_to_graph() - # Workaround for geometric models - # See soil/soil#4 - for node in G.nodes(): - if 'pos' in G.nodes[node]: - G.nodes[node]['viz'] = {"position": {"x": G.nodes[node]['pos'][0], "y": G.nodes[node]['pos'][1], "z": 0.0}} - del (G.nodes[node]['pos']) + self.agent_class = agent_class + if agent_class: + self.agent_class = serialization.deserialize(agent_class) + if self.agent_class: + self.populate_network(self.agent_class) + self._check_agent_nodes() + if init: + self.init() + self.datacollector.collect(self) - nx.write_gexf(G, f, version="1.2draft") + 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 dump(self, *args, formats=None, **kwargs): - if not formats: - return - functions = { - 'csv': self.dump_csv, - 'gexf': self.dump_gexf - } - for f in formats: - if f in functions: - functions[f](*args, **kwargs) - else: - raise ValueError('Unknown format: {}'.format(f)) + 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 dump_sqlite(self, f): - return self._history.dump(f) + 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 - def state_to_tuples(self, now=None): - if now is None: - now = self.now - for k, v in self.environment_params.items(): - yield Record(dict_id='env', - t_step=now, - key=k, - value=v) - for agent in self.agents: - for k, v in agent.state.items(): - yield Record(dict_id=agent.id, - t_step=now, - key=k, - value=v) + def init_agents(self, *args, **kwargs): + """Initialize the agents from a""" + super().init_agents(*args, **kwargs) - def history_to_tuples(self): - return self._history.to_tuples() + @property + def network_agents(self): + """Return agents still alive and assigned to a node in the network.""" + for (id, data) in self.G.nodes(data=True): + if "agent" in data: + agent = data["agent"] + if getattr(agent, "alive", True): + yield agent - def history_to_graph(self): - G = nx.Graph(self.G) + def add_node(self, agent_class, unique_id=None, node_id=None, **kwargs): + if unique_id is None: + unique_id = self.next_id() + if node_id is None: + node_id = network.find_unassigned( + G=self.G, shuffle=True, random=self.random + ) + if node_id is None: + node_id = f"node_for_{unique_id}" - for agent in self.network_agents: + if node_id not in self.G.nodes: + self.G.add_node(node_id) - attributes = {'agent': str(agent.__class__)} - lastattributes = {} - spells = [] - lastvisible = False - laststep = None - history = self[agent.id, None, None] - if not history: + assert "agent" not in self.G.nodes[node_id] + + a = self.add_agent( + unique_id=unique_id, + agent_class=agent_class, + topology=self.G, + node_id=node_id, + **kwargs, + ) + a["visible"] = True + return a + + def _check_agent_nodes(self): + """ + Detect nodes that have agents assigned to them. + """ + 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: List[Model], weights: List[float] = None, **agent_params): + if isinstance(agent_class, type): + agent_class = [agent_class] + else: + 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 - for t_step, attribute, value in sorted(list(history)): - if attribute == 'visible': - nowvisible = value - if nowvisible and not lastvisible: - laststep = t_step - if not nowvisible and lastvisible: - spells.append((laststep, t_step)) - - lastvisible = nowvisible - continue - key = 'attr_' + attribute - if key not in attributes: - attributes[key] = list() - if key not in lastattributes: - lastattributes[key] = (value, t_step) - elif lastattributes[key][0] != value: - last_value, laststep = lastattributes[key] - commit_value = (last_value, laststep, t_step) - if key not in attributes: - attributes[key] = list() - attributes[key].append(commit_value) - lastattributes[key] = (value, t_step) - for k, v in lastattributes.items(): - attributes[k].append((v[0], v[1], None)) - if lastvisible: - spells.append((laststep, None)) - if spells: - G.add_node(agent.id, spells=spells, **attributes) - else: - G.add_node(agent.id, **attributes) - - return G - - def __getstate__(self): - state = {} - for prop in _CONFIG_PROPS: - state[prop] = self.__dict__[prop] - state['G'] = json_graph.node_link_data(self.G) - state['environment_agents'] = self._env_agents - state['history'] = self._history - state['schedule'] = self.schedule - return state - - def __setstate__(self, state): - for prop in _CONFIG_PROPS: - self.__dict__[prop] = state[prop] - self._env_agents = state['environment_agents'] - self.G = json_graph.node_link_graph(state['G']) - self._history = state['history'] - # self._env = None - self.schedule = state['schedule'] - self._queue = [] + node["agent"] = None # Reserve + 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)) -SoilEnvironment = Environment +class EventedEnvironment(BaseEnvironment): + def broadcast(self, msg, sender=None, expiration=None, ttl=None, **kwargs): + for agent in self.agents(**kwargs): + if agent == sender: + continue + self.logger.debug(f"Telling {repr(agent)}: {msg} ttl={ttl}") + try: + inbox = agent._inbox + except AttributeError: + self.logger.info( + f"Agent {agent.unique_id} cannot receive events because it does not have an inbox" + ) + continue + # Allow for AttributeError exceptions in this part of the code + inbox.append( + events.Tell( + payload=msg, + sender=sender, + expiration=expiration if ttl is None else self.now + ttl, + ) + ) + + +class Environment(NetworkEnvironment, EventedEnvironment): + """Default environment class, has both network and event capabilities""" diff --git a/soil/events.py b/soil/events.py new file mode 100644 index 0000000..82beaff --- /dev/null +++ b/soil/events.py @@ -0,0 +1,56 @@ +from .time import BaseCond +from dataclasses import dataclass, field +from typing import Any +from uuid import uuid4 + + +class Event: + pass + + +@dataclass +class Message: + payload: Any + sender: Any = None + expiration: float = None + timestamp: float = None + id: int = field(default_factory=uuid4) + + def expired(self, when): + return self.expiration is not None and self.expiration < when + + +class Reply(Message): + source: Message + + +class ReplyCond(BaseCond): + def __init__(self, ask, *args, **kwargs): + self._ask = ask + super().__init__(*args, **kwargs) + + def ready(self, agent, time): + return self._ask.reply is not None or self._ask.expired(time) + + def return_value(self, agent): + if self._ask.expired(agent.now): + raise TimedOut() + return self._ask.reply + + def __repr__(self): + return f"ReplyCond({self._ask.id})" + + +class Ask(Message): + reply: Message = None + + def replied(self, expiration=None): + return ReplyCond(self) + + +class Tell(Message): + pass + + +class TimedOut(Exception): + pass diff --git a/soil/exporters.py b/soil/exporters.py index b526b60..d4debdd 100644 --- a/soil/exporters.py +++ b/soil/exporters.py @@ -1,17 +1,21 @@ import os -import csv as csvlib -import time +import sys +from time import time as current_time from io import BytesIO +from sqlalchemy import create_engine +from textwrap import dedent, indent + import matplotlib.pyplot as plt import networkx as nx +import pandas as pd -from .serialization import deserialize -from .utils import open_or_reuse, logger, timer +from .serialization import deserialize, serialize +from .utils import try_backup, open_or_reuse, logger, timer -from . import utils +from . import utils, network class DryRunner(BytesIO): @@ -22,51 +26,59 @@ class DryRunner(BytesIO): def write(self, txt): if self.__copy_to: - self.__copy_to.write('{}:::{}'.format(self.__fname, txt)) + self.__copy_to.write("{}:::{}".format(self.__fname, txt)) try: super().write(txt) except TypeError: - super().write(bytes(txt, 'utf-8')) + super().write(bytes(txt, "utf-8")) def close(self): - content = '(binary data not shown)' + content = "(binary data not shown)" try: content = self.getvalue().decode() except UnicodeDecodeError: pass - logger.info('**Not** written to {} (dry run mode):\n\n{}\n\n'.format(self.__fname, content)) + logger.info( + "**Not** written to {} (no_dump mode):\n\n{}\n\n".format( + self.__fname, content + ) + ) super().close() class Exporter: - ''' + """ Interface for all exporters. It is not necessary, but it is useful if you don't plan to implement all the methods. - ''' + """ - def __init__(self, simulation, outdir=None, dry_run=None, copy_to=None): + 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 + outdir = outdir or os.path.join(os.getcwd(), "soil_output") + self.outdir = os.path.join(outdir, simulation.group or "", simulation.name) + self.dump = dump + if copy_to is None and not dump: + copy_to = sys.stdout self.copy_to = copy_to - def start(self): - '''Method to call when the simulation starts''' + def sim_start(self): + """Method to call when the simulation starts""" pass - def end(self, stats): - '''Method to call when the simulation ends''' + def sim_end(self): + """Method to call when the simulation ends""" pass - def trial(self, env, stats): - '''Method to call when a trial ends''' + def iteration_start(self, env): + """Method to call when a iteration start""" pass - def output(self, f, mode='w', **kwargs): - if self.dry_run: + def iteration_end(self, env, params, params_id): + """Method to call when a iteration ends""" + pass + + def output(self, f, mode="w", **kwargs): + if not self.dump: f = DryRunner(f, copy_to=self.copy_to) else: try: @@ -74,85 +86,197 @@ 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, **kwargs): + yield from get_dc_dfs(env.datacollector, + simulation_id=self.simulation.id, + iteration_id=env.id, + **kwargs) -class default(Exporter): - '''Default exporter. Writes sqlite results, as well as the simulation YAML''' - - def start(self): - if not self.dry_run: - logger.info('Dumping results to %s', self.outdir) - self.simulation.dump_yaml(outdir=self.outdir) - else: - logger.info('NOT dumping results') - - def trial(self, env, stats): - if not self.dry_run: - with timer('Dumping simulation {} trial {}'.format(self.simulation.name, - env.name)): - with self.output('{}.sqlite'.format(env.name), mode='wb') as f: - env.dump_sqlite(f) - - def end(self, stats): - with timer('Dumping simulation {}\'s stats'.format(self.simulation.name)): - with self.output('{}.sqlite'.format(self.simulation.name), mode='wb') as f: - self.simulation.dump_sqlite(f) +def get_dc_dfs(dc, **kwargs): + dfs = {} + dfe = dc.get_model_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) + for (name, df) in dfs.items(): + for (k, v) in kwargs.items(): + 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 -class csv(Exporter): - '''Export the state of each environment (and its agents) in a separate CSV file''' - def trial(self, env, stats): - with timer('[CSV] Dumping simulation {} trial {} @ dir {}'.format(self.simulation.name, - env.name, - self.outdir)): - with self.output('{}.csv'.format(env.name)) as f: - env.dump_csv(f) + def sim_start(self): + if not self.dump: + 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) + if self.simulation.backup: + try_backup(self.dbpath, remove=True) + + if self.simulation.overwrite: + if os.path.exists(self.dbpath): + os.remove(self.dbpath) + + self.engine = create_engine(f"sqlite:///{self.dbpath}", echo=False) - with self.output('{}.stats.csv'.format(env.name)) as f: - statwriter = csvlib.writer(f, delimiter='\t', quotechar='"', quoting=csvlib.QUOTE_ALL) + 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") - for stat in stats: - statwriter.writerow(stat) - - -class gexf(Exporter): - def trial(self, env, stats): - if self.dry_run: - logger.info('Not dumping GEXF in dry_run mode') + 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('[GEXF] Dumping simulation {} trial {}'.format(self.simulation.name, - env.name)): - with self.output('{}.gexf'.format(env.name), mode='wb') as f: - env.dump_gexf(f) + with timer( + "Dumping simulation {} iteration {}".format(self.simulation.name, env.id) + ): + + 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, params_id=params_id): + 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""" + + def sim_start(self): + super().sim_start() + + def iteration_end(self, env, params, params_id, *args, **kwargs): + with timer( + "[CSV] Dumping simulation {} iteration {} @ dir {}".format( + self.simulation.name, env.id, self.outdir + ) + ): + for (df_name, df) in self.get_dfs(env, params_id=params_id): + 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 iteration_end(self, env, *args, **kwargs): + if not self.dump: + logger.info("Not dumping GEXF (NO_DUMP mode)") + return + + with timer( + "[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) + self.dump_gexf(env, f) class dummy(Exporter): + def sim_start(self): + with self.output("dummy", "w") as f: + f.write("simulation started @ {}\n".format(current_time())) - def start(self): - with self.output('dummy', 'w') as f: - f.write('simulation started @ {}\n'.format(time.time())) + def iteration_start(self, env): + with self.output("dummy", "w") as f: + f.write("iteration started@ {}\n".format(current_time())) - def trial(self, env, stats): - with self.output('dummy', 'w') as f: - for i in env.history_to_tuples(): - f.write(','.join(map(str, i))) - f.write('\n') - - def sim(self, stats): - with self.output('dummy', 'a') as f: - f.write('simulation ended @ {}\n'.format(time.time())) + def iteration_end(self, env, *args, **kwargs): + with self.output("dummy", "w") as f: + f.write("iteration ended@ {}\n".format(current_time())) + def sim_end(self): + with self.output("dummy", "a") as f: + f.write("simulation ended @ {}\n".format(current_time())) class graphdrawing(Exporter): - - def trial(self, env, stats): + def iteration_end(self, env, *args, **kwargs): # Outside effects f = plt.figure() - nx.draw(env.G, node_size=10, width=0.2, pos=nx.spring_layout(env.G, scale=100), ax=f.add_subplot(111)) - with open('graph-{}.png'.format(env.name)) as f: + nx.draw( + env.G, + node_size=10, + width=0.2, + pos=nx.spring_layout(env.G, scale=100), + ax=f.add_subplot(111), + ) + with open("graph-{}.png".format(env.id)) as f: f.savefig(f) + +class summary(Exporter): + """Print a summary of each iteration to sys.stdout""" + + def iteration_end(self, env, *args, **kwargs): + msg = "" + for (t, df) in self.get_dfs(env): + if not len(df): + continue + tabs = "\t" * 2 + description = indent(str(df.describe()), tabs) + last_line = indent(str(df.iloc[-1:]), tabs) + # value_counts = indent(str(df.value_counts()), tabs) + value_counts = indent(str(df.apply(lambda x: x.value_counts()).T.stack()), tabs) + + msg += dedent(""" + Dataframe {t}: + Last line: : + {last_line} + + Description: + {description} + + Value counts: + {value_counts} + + """).format(**locals()) + logger.info(msg) + +class YAML(Exporter): + """Writes the configuration of the simulation to a YAML file""" + + def sim_start(self): + if not self.dump: + logger.debug("NOT dumping results") + return + 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()) + +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] + self.inner = [cls(*args, **kwargs) for cls in exporter_cls] + + def sim_start(self, *args, **kwargs): + for exporter in self.inner: + exporter.sim_start(*args, **kwargs) + + def sim_end(self, *args, **kwargs): + for exporter in self.inner: + exporter.sim_end(*args, **kwargs) + + def iteration_end(self, *args, **kwargs): + for exporter in self.inner: + exporter.iteration_end(*args, **kwargs) diff --git a/soil/network.py b/soil/network.py new file mode 100644 index 0000000..6fac221 --- /dev/null +++ b/soil/network.py @@ -0,0 +1,83 @@ +from __future__ import annotations + +from typing import Dict +import os +import sys +import random + +import networkx as nx + +from . import config, serialization, basestring + + +def from_topology(topology, dir_path: str = None): + if topology is None: + return nx.Graph() + if isinstance(topology, nx.Graph): + return topology + + # If it's a dict, assume it's a node-link graph + if isinstance(topology, dict): + try: + return nx.json_graph.node_link_graph(topology) + except Exception as ex: + raise ValueError("Unknown topology format") + + # Otherwise, treat like a path + path = topology + 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) + + +def from_params(generator, dir_path: str = None, **params): + + if dir_path not in sys.path: + sys.path.append(dir_path) + + method = serialization.deserializer( + generator, + known_modules=[ + "networkx.generators", + ], + ) + return method(**params) + + +def find_unassigned(G, shuffle=False, random=random): + """ + Link an agent to a node in a topology. + + If node_id is None, a node without an agent_id will be found. + """ + candidates = list(G.nodes(data=True)) + if shuffle: + random.shuffle(candidates) + for next_id, data in candidates: + if "agent" not in data: + return next_id + return None + + +def dump_gexf(G, f): + for node in G.nodes(): + if "pos" in G.nodes[node]: + G.nodes[node]["viz"] = { + "position": { + "x": G.nodes[node]["pos"][0], + "y": G.nodes[node]["pos"][1], + "z": 0.0, + } + } + del G.nodes[node]["pos"] + + nx.write_gexf(G, f, version="1.2draft") diff --git a/soil/parameters.py b/soil/parameters.py new file mode 100644 index 0000000..fddbb17 --- /dev/null +++ b/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) \ No newline at end of file diff --git a/soil/serialization.py b/soil/serialization.py index 76c60fc..34e7768 100644 --- a/soil/serialization.py +++ b/soil/serialization.py @@ -2,58 +2,28 @@ import os import logging import ast import sys +import re import importlib +import importlib.machinery, importlib.util from glob import glob from itertools import product, chain import yaml import networkx as nx +from . import config + from jinja2 import Template -logger = logging.getLogger('soil') - - -def load_network(network_params, dir_path=None): - G = nx.Graph() - - if 'path' in network_params: - path = network_params['path'] - if dir_path and not os.path.isabs(path): - path = os.path.join(dir_path, path) - extension = os.path.splitext(path)[1][1:] - kwargs = {} - if extension == 'gexf': - kwargs['version'] = '1.2draft' - kwargs['node_type'] = int - try: - method = getattr(nx.readwrite, 'read_' + extension) - except AttributeError: - raise AttributeError('Unknown format') - G = method(path, **kwargs) - - elif 'generator' in network_params: - net_args = network_params.copy() - net_gen = net_args.pop('generator') - - if dir_path not in sys.path: - sys.path.append(dir_path) - - method = deserializer(net_gen, - known_modules=['networkx.generators',]) - G = method(**net_args) - - return G - - +logger = logging.getLogger("soil") def load_file(infile): folder = os.path.dirname(infile) if folder not in sys.path: sys.path.append(folder) - with open(infile, 'r') as f: + with open(infile, "r") as f: return list(chain.from_iterable(map(expand_template, load_string(f)))) @@ -62,14 +32,15 @@ def load_string(string): def expand_template(config): - if 'template' not in config: + if "template" not in config: yield config return - if 'vars' not in config: - raise ValueError(('You must provide a definition of variables' - ' for the template.')) + if "vars" not in config: + raise ValueError( + ("You must provide a definition of variables" " for the template.") + ) - template = config['template'] + template = config["template"] if not isinstance(template, str): template = yaml.dump(template) @@ -81,9 +52,9 @@ def expand_template(config): blank_str = template.render({k: 0 for k in params[0].keys()}) blank = list(load_string(blank_str)) if len(blank) > 1: - raise ValueError('Templates must not return more than one configuration') - if 'name' in blank[0]: - raise ValueError('Templates cannot be named, use group instead') + raise ValueError("Templates must not return more than one configuration") + if "name" in blank[0]: + raise ValueError("Templates cannot be named, use group instead") for ps in params: string = template.render(ps) @@ -92,131 +63,200 @@ def expand_template(config): def params_for_template(config): - sampler_config = config.get('sampler', {'N': 100}) - sampler = sampler_config.pop('method', 'SALib.sample.morris.sample') + sampler_config = config.get("sampler", {"N": 100}) + sampler = sampler_config.pop("method", "SALib.sample.morris.sample") sampler = deserializer(sampler) - bounds = config['vars']['bounds'] + bounds = config["vars"]["bounds"] problem = { - 'num_vars': len(bounds), - 'names': list(bounds.keys()), - 'bounds': list(v for v in bounds.values()) + "num_vars": len(bounds), + "names": list(bounds.keys()), + "bounds": list(v for v in bounds.values()), } samples = sampler(problem, **sampler_config) - lists = config['vars'].get('lists', {}) + lists = config["vars"].get("lists", {}) names = list(lists.keys()) values = list(lists.values()) combs = list(product(*values)) - allnames = names + problem['names'] - allvalues = [(list(i[0])+list(i[1])) for i in product(combs, samples)] + allnames = names + problem["names"] + allvalues = [(list(i[0]) + list(i[1])) for i in product(combs, samples)] params = list(map(lambda x: dict(zip(allnames, x)), allvalues)) return params def load_files(*patterns, **kwargs): for pattern in patterns: - for i in glob(pattern, **kwargs): - for config in load_file(i): + for i in glob(pattern, **kwargs, recursive=True): + for cfg in load_file(i): path = os.path.abspath(i) - if 'dir_path' not in config: - config['dir_path'] = os.path.dirname(path) - yield config, path + yield cfg, path -def load_config(config): - if isinstance(config, dict): - yield config, os.getcwd() +def load_config(cfg): + if isinstance(cfg, dict): + yield config.load_config(cfg), os.getcwd() else: - yield from load_files(config) + yield from load_files(cfg) -builtins = importlib.import_module('builtins') +builtins = importlib.import_module("builtins") -def name(value, known_modules=[]): - '''Return a name that can be imported, to serialize/deserialize an object''' +KNOWN_MODULES = { + '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): + """Return a name that can be imported, to serialize/deserialize an object""" if value is None: - return 'None' + return "None" if not isinstance(value, type): # Get the class name first value = type(value) tname = value.__name__ if hasattr(builtins, tname): return tname modname = value.__module__ - if modname == '__main__': + if modname == "__main__": return tname if known_modules and modname in known_modules: return tname for kmod in known_modules: - if not kmod: - continue - module = importlib.import_module(kmod) + module = get_module(kmod) if hasattr(module, tname): return tname - return '{}.{}'.format(modname, tname) + return "{}.{}".format(modname, tname) def serializer(type_): - if type_ != 'str' and hasattr(builtins, type_): + if type_ != "str" and hasattr(builtins, type_): return repr return lambda x: x -def serialize(v, known_modules=[]): - '''Get a text representation of an object.''' +def serialize(v, known_modules=KNOWN_MODULES): + """Get a text representation of an object.""" tname = name(v, known_modules=known_modules) func = serializer(tname) return func(v), tname -def deserializer(type_, known_modules=[]): + +def serialize_dict(d, known_modules=KNOWN_MODULES): + try: + d = dict(d) + except (ValueError, TypeError) as ex: + return serialize(d)[0] + for (k, v) in reversed(list(d.items())): + if isinstance(v, dict): + d[k] = serialize_dict(v, known_modules=known_modules) + elif isinstance(v, list): + for ix in range(len(v)): + v[ix] = serialize_dict(v[ix], known_modules=known_modules) + elif isinstance(v, type): + d[k] = serialize(v, known_modules=known_modules)[1] + return d + + +IS_CLASS = re.compile(r"") + + +def deserializer(type_, known_modules=KNOWN_MODULES): if type(type_) != str: # Already deserialized return type_ - if type_ == 'str': - return lambda x='': x - if type_ == 'None': + if type_ == "str": + return lambda x="": x + if type_ == "None": return lambda x=None: None if hasattr(builtins, type_): # Check if it's a builtin type cls = getattr(builtins, type_) return lambda x=None: ast.literal_eval(x) if x is not None else cls() + match = IS_CLASS.match(type_) + if match: + modname, tname = match.group(1).rsplit(".", 1) + module = get_module(modname) + cls = getattr(module, tname) + return getattr(cls, "deserialize", cls) + # Otherwise, see if we can find the module and the class - modules = known_modules or [] options = [] - for mod in modules: + for mod in known_modules: if mod: options.append((mod, type_)) - if '.' in type_: # Fully qualified module + if "." in type_: # Fully qualified module module, type_ = type_.rsplit(".", 1) - options.append ((module, type_)) + options.append((module, type_)) 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) + return getattr(cls, "deserialize", cls) except (ImportError, AttributeError) as ex: errors.append((modname, tname, ex)) - raise Exception('Could not find type {}. Tried: {}'.format(type_, errors)) + raise ValueError('Could not find type "{}". Tried: {}'.format(type_, errors)) -def deserialize(type_, value=None, **kwargs): - '''Get an object from a text representation''' +def deserialize(type_, value=None, globs=None, **kwargs): + """Get an object from a text representation""" if not isinstance(type_, str): return type_ - des = deserializer(type_, **kwargs) + if globs and type_ in globs: + des = globs[type_] + else: + try: + des = deserializer(type_, **kwargs) + except ValueError as ex: + try: + des = eval(type_) + except Exception: + raise ex if value is None: return des return des(value) -def deserialize_all(names, *args, known_modules=['soil'], **kwargs): - '''Return the set of exporters for a simulation, given the exporter names''' - exporters = [] +def deserialize_all(names, *args, known_modules=KNOWN_MODULES, **kwargs): + """Return the list of deserialized objects""" + objects = [] for name in names: mod = deserialize(name, known_modules=known_modules) - exporters.append(mod(*args, **kwargs)) - return exporters - + objects.append(mod(*args, **kwargs)) + return objects diff --git a/soil/simulation.py b/soil/simulation.py index 39d909d..636769e 100644 --- a/soil/simulation.py +++ b/soil/simulation.py @@ -1,355 +1,395 @@ import os -import importlib +from time import time as current_time, strftime import sys import yaml -import traceback +import hashlib + +import inspect import logging import networkx as nx -from time import strftime -from networkx.readwrite import json_graph -from multiprocessing import Pool +from tqdm.auto import tqdm + +from textwrap import dedent + +from dataclasses import dataclass, field, asdict, replace +from typing import Any, Dict, Union, Optional, List + + from functools import partial -from tsih import History +from contextlib import contextmanager +from itertools import product +import json -import pickle -from . import serialization, utils, basestring, agents +from . import serialization, exporters, utils, basestring, agents from .environment import Environment -from .utils import logger -from .exporters import default -from .stats import defaultStats +from .utils import logger, run_and_return_exceptions +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 -#TODO: change documentation for simulation +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: """ - Similar to nsim.NetworkSimulation with three main differences: - 1) agent type can be specified by name or by class. - 2) instead of just one type, a network agents distribution can be used. - The distribution specifies the weight (or probability) of each - agent type in the topology. This is an example distribution: :: - - [ - {'agent_type': 'agent_type_1', - 'weight': 0.2, - 'state': { - 'id': 0 - } - }, - {'agent_type': 'agent_type_2', - 'weight': 0.8, - 'state': { - 'id': 1 - } - } - ] - - In this example, 20% of the nodes will be marked as type - 'agent_type_1'. - 3) if no initial state is given, each node's state will be set - to `{'id': 0}`. - - Parameters - --------- - name : str, optional - name of the Simulation - group : str, optional - a group name can be used to link simulations - topology : networkx.Graph instance, optional - network_params : dict - parameters used to create a topology with networkx, if no topology is given - network_agents : dict - definition of agents to populate the topology with - agent_type : NetworkAgent subclass, optional - Default type of NetworkAgent to use for nodes not specified in network_agents - states : list, optional - List of initial states corresponding to the nodes in the topology. Basic form is a list of integers - whose value indicates the state - dir_path: str, optional - Directory path to load simulation assets (files, modules...) - seed : str, optional - Seed to use for the random generator - num_trials : int, optional - Number of independent simulation runs - max_time : int, optional - Time how long the simulation should run - environment_params : dict, optional - Dictionary of globally-shared environmental parameters - environment_agents: dict, optional - Similar to network_agents. Distribution of Agents that control the environment - environment_class: soil.environment.Environment subclass, optional - Class for the environment. It defailts to soil.environment.Environment - load_module : str, module name, deprecated - If specified, soil will load the content of this module under 'soil.agents.custom' - + A simulation is a collection of agents and a model. It is responsible for running the model and agents, and collecting data from them. + 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. """ - def __init__(self, name=None, group=None, topology=None, network_params=None, - network_agents=None, agent_type=None, states=None, - default_state=None, interval=1, num_trials=1, - max_time=100, load_module=None, seed=None, - dir_path=None, environment_agents=None, - environment_params=None, environment_class=None, - **kwargs): + version: str = "2" + source_file: Optional[str] = None + name: Optional[str] = None + description: Optional[str] = "" + group: str = None + backup: bool = False + overwrite: bool = False + 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 = None + max_steps: int = None + interval: int = 1 + iterations: int = 1 + num_processes: Optional[int] = 1 + 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: 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) + level: int = logging.INFO + skip_test: Optional[bool] = False + debug: Optional[bool] = False - self.load_module = load_module - self.network_params = network_params - self.name = name or 'Unnamed' - self.seed = str(seed or name) - self._id = '{}_{}'.format(self.name, strftime("%Y-%m-%d_%H.%M.%S")) - self.group = group or '' - self.num_trials = num_trials - self.max_time = max_time - self.default_state = default_state or {} - self.dir_path = dir_path or os.getcwd() - self.interval = interval + def __post_init__(self): + 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) - sys.path += list(x for x in [os.getcwd(), self.dir_path] if x not in sys.path) + if self.source_file: + 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 - if topology is None: - topology = serialization.load_network(network_params, - dir_path=self.dir_path) - elif isinstance(topology, basestring) or isinstance(topology, dict): - topology = json_graph.node_link_graph(topology) - self.topology = nx.Graph(topology) + if isinstance(self.model, str): + self.model = serialization.deserialize(self.model) + 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 - self.environment_params = environment_params or {} - self.environment_class = serialization.deserialize(environment_class, - known_modules=['soil.environment', ]) or Environment + self.agent_reporters = deserialize_reporters(self.agent_reporters) + 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()}" - environment_agents = environment_agents or [] - self.environment_agents = agents._convert_agent_types(environment_agents, - known_modules=[self.load_module]) + def run(self, **kwargs): + """Run the simulation and return the list of resulting environments""" + if kwargs: + return replace(self, **kwargs).run() - distro = agents.calculate_distribution(network_agents, - agent_type) - self.network_agents = agents._convert_agent_types(distro, - known_modules=[self.load_module]) + self.logger.debug( + dedent( + """ + Simulation: + --- + """ + ) + + self.to_yaml() + ) + param_combinations = self._collect_params(**kwargs) + if _AVOID_RUNNING: + _QUEUED.extend((self, param) for param in param_combinations) + return [] - self.states = agents._validate_states(states, - self.topology) + self.logger.debug("Using exporters: %s", self.exporters or []) - self._history = History(name=self.name, - backup=False) + exporters = serialization.deserialize_all( + self.exporters, + simulation=self, + known_modules=[ + "soil.exporters", + ], + dump=self.dump and not self.dry_run, + outdir=self.outdir, + **self.exporter_params, + ) - def run_simulation(self, *args, **kwargs): - return self.run(*args, **kwargs) - - def run(self, *args, **kwargs): - '''Run the simulation and return the list of resulting environments''' - return list(self.run_gen(*args, **kwargs)) - - def _run_sync_or_async(self, parallel=False, **kwargs): - if parallel and not os.environ.get('SENPY_DEBUG', None): - p = Pool() - func = partial(self.run_trial_exceptions, **kwargs) - for i in p.imap_unordered(func, range(self.num_trials)): - if isinstance(i, Exception): - logger.error('Trial failed:\n\t%s', i.message) - continue - yield i - else: - for i in range(self.num_trials): - yield self.run_trial(trial_id=i, - **kwargs) - - def run_gen(self, parallel=False, dry_run=False, - exporters=[default, ], stats=[], outdir=None, exporter_params={}, - stats_params={}, log_level=None, - **kwargs): - '''Run the simulation and yield the resulting environments.''' - if log_level: - logger.setLevel(log_level) - logger.info('Using exporters: %s', exporters or []) - logger.info('Output directory: %s', outdir) - exporters = serialization.deserialize_all(exporters, - simulation=self, - known_modules=['soil.exporters',], - dry_run=dry_run, - outdir=outdir, - **exporter_params) - stats = serialization.deserialize_all(simulation=self, - names=stats, - known_modules=['soil.stats',], - **stats_params) - - with utils.timer('simulation {}'.format(self.name)): - for stat in stats: - stat.start() - - for exporter in exporters: - exporter.start() - for env in self._run_sync_or_async(parallel=parallel, - log_level=log_level, - **kwargs): - - collected = list(stat.trial(env) for stat in stats) - - saved = self.save_stats(collected, t_step=env.now, trial_id=env.name) + results = [] + for exporter in exporters: + exporter.sim_start() + 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(env, saved) + exporter.iteration_end(env, params, params_id) + results.append(env) - yield env + for exporter in exporters: + exporter.sim_end() + return results - collected = list(stat.end() for stat in stats) - saved = self.save_stats(collected) + def _collect_params(self): - for exporter in exporters: - exporter.end(saved) + parameters = [] + 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))) + if not parameters: + parameters = [{}] + if self.dump: + self.logger.info("Output directory: %s", self.outdir) - def save_stats(self, collection, **kwargs): - stats = dict(kwargs) - for stat in collection: - stats.update(stat) - self._history.save_stats(utils.flatten_dict(stats)) - return stats + return parameters - def get_stats(self, **kwargs): - return self._history.get_stats(**kwargs) + def _run_iters_for_params( + self, + params + ): + """Run the simulation and yield the resulting environments.""" - def log_stats(self, stats): - logger.info('Stats: \n{}'.format(yaml.dump(stats, default_flow_style=False))) - - - def get_env(self, trial_id=0, **kwargs): - '''Create an environment for a trial of the simulation''' - opts = self.environment_params.copy() - opts.update({ - 'name': '{}_trial_{}'.format(self.name, trial_id), - 'topology': self.topology.copy(), - 'network_params': self.network_params, - 'seed': '{}_trial_{}'.format(self.seed, trial_id), - 'initial_time': 0, - 'interval': self.interval, - 'network_agents': self.network_agents, - 'initial_time': 0, - 'states': self.states, - 'dir_path': self.dir_path, - 'default_state': self.default_state, - 'environment_agents': self.environment_agents, - }) - opts.update(kwargs) - env = self.environment_class(**opts) - return env - - def run_trial(self, trial_id=0, until=None, log_level=logging.INFO, **opts): - """ - Run a single trial of the simulation - - """ - if log_level: - logger.setLevel(log_level) - # Set-up trial environment and graph - until = until or self.max_time - env = self.get_env(trial_id=trial_id, **opts) - # Set up agents on nodes - with utils.timer('Simulation {} trial {}'.format(self.name, trial_id)): - env.run(until) - return env - - def run_trial_exceptions(self, *args, **kwargs): - ''' - A wrapper for run_trial that catches exceptions and returns them. - It is meant for async simulations - ''' try: - return self.run_trial(*args, **kwargs) - except Exception as ex: - if ex.__cause__ is not None: - ex = ex.__cause__ - ex.message = ''.join(traceback.format_exception(type(ex), ex, ex.__traceback__)[:]) - return ex + if self.source_file: + serialization.add_source_file(self.source_file) + + 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=self.tables, + **params, + ) + + def _run_model(self, iteration_id, **params): + """ + Run a single iteration of the simulation + + """ + # Set-up iteration environment and graph + model = self._get_env(iteration_id, params) + with utils.timer("Simulation {} iteration {}".format(self.name, iteration_id)): + + max_time = self.max_time + max_steps = self.max_steps + + 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 } + """ + ) + ) + + if self.debug: + set_trace() + + while not is_done(model): + self.logger.debug( + f'Simulation time {model.schedule.time}/{max_time}.' + ) + model.step() + + return model def to_dict(self): - return self.__getstate__() + d = asdict(self) + return serialization.serialize_dict(d) def to_yaml(self): return yaml.dump(self.to_dict()) - def dump_yaml(self, f=None, outdir=None): - if not f and not outdir: - raise ValueError('specify a file or an output directory') - - if not f: - f = os.path.join(outdir, '{}.dumped.yml'.format(self.name)) - - with utils.open_or_reuse(f, 'w') as f: - f.write(self.to_yaml()) - - def dump_pickle(self, f=None, outdir=None): - if not outdir and not f: - raise ValueError('specify a file or an output directory') - - if not f: - f = os.path.join(outdir, - '{}.simulation.pickle'.format(self.name)) - with utils.open_or_reuse(f, 'wb') as f: - pickle.dump(self, f) - - def dump_sqlite(self, f): - return self._history.dump(f) - - def __getstate__(self): - state={} - for k, v in self.__dict__.items(): - if k[0] != '_': - state[k] = v - state['topology'] = json_graph.node_link_data(self.topology) - state['network_agents'] = agents.serialize_definition(self.network_agents, - known_modules = []) - state['environment_agents'] = agents.serialize_definition(self.environment_agents, - known_modules = []) - state['environment_class'] = serialization.serialize(self.environment_class, - known_modules=['soil.environment'])[1] # func, name - if state['load_module'] is None: - del state['load_module'] - return state - - def __setstate__(self, state): - self.__dict__ = state - self.load_module = getattr(self, 'load_module', None) - if self.dir_path not in sys.path: - sys.path += [self.dir_path, os.getcwd()] - self.topology = json_graph.node_link_graph(state['topology']) - self.network_agents = agents.calculate_distribution(agents._convert_agent_types(self.network_agents)) - self.environment_agents = agents._convert_agent_types(self.environment_agents, - known_modules=[self.load_module]) - self.environment_class = serialization.deserialize(self.environment_class, - known_modules=[self.load_module, 'soil.environment', ]) # func, name +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 all_from_config(config): - configs = list(serialization.load_config(config)) - for config, _ in configs: - sim = Simulation(**config) - yield sim +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(**d) def from_config(conf_or_path): - config = list(serialization.load_config(conf_or_path)) - if len(config) > 1: - raise AttributeError('Provide only one configuration') - config = config[0][0] - sim = Simulation(**config) - return sim + lst = list(iter_from_config(conf_or_path)) + if len(lst) > 1: + raise AttributeError("Provide only one configuration") + return lst[0] -def run_from_config(*configs, **kwargs): - for config_def in configs: - # logger.info("Found {} config(s)".format(len(ls))) - for config, path in serialization.load_config(config_def): - name = config.get('name', 'unnamed') - logger.info("Using config(s): {name}".format(name=name)) +def iter_from_py(pyfile, module_name='imported_file', **kwargs): + """Try to load every Simulation instance in a given Python file""" + import importlib + added = False + sims = [] + assert not _AVOID_RUNNING + with do_not_run(): + assert _AVOID_RUNNING + spec = importlib.util.spec_from_file_location(module_name, pyfile) + folder = os.path.dirname(pyfile) + if folder not in sys.path: + 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) - dir_path = config.pop('dir_path', os.path.dirname(path)) - sim = Simulation(dir_path=dir_path, - **config) - sim.run_simulation(**kwargs) + +def from_py(pyfile): + return next(iter_from_py(pyfile)) + + +def run_from_file(*files, **kwargs): + for sim in iter_from_file(*files): + 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() \ No newline at end of file diff --git a/soil/stats.py b/soil/stats.py deleted file mode 100644 index 03e48da..0000000 --- a/soil/stats.py +++ /dev/null @@ -1,106 +0,0 @@ -import pandas as pd - -from collections import Counter - -class Stats: - ''' - Interface for all stats. It is not necessary, but it is useful - if you don't plan to implement all the methods. - ''' - - def __init__(self, simulation): - self.simulation = simulation - - def start(self): - '''Method to call when the simulation starts''' - pass - - def end(self): - '''Method to call when the simulation ends''' - return {} - - def trial(self, env): - '''Method to call when a trial ends''' - return {} - - -class distribution(Stats): - ''' - Calculate the distribution of agent states at the end of each trial, - the mean value, and its deviation. - ''' - - def start(self): - self.means = [] - self.counts = [] - - def trial(self, env): - df = env[None, None, None].df() - df = df.drop('SEED', axis=1) - ix = df.index[-1] - attrs = df.columns.get_level_values(0) - vc = {} - stats = { - 'mean': {}, - 'count': {}, - } - for a in attrs: - t = df.loc[(ix, a)] - try: - stats['mean'][a] = t.mean() - self.means.append(('mean', a, t.mean())) - except TypeError: - pass - - for name, count in t.value_counts().items(): - if a not in stats['count']: - stats['count'][a] = {} - stats['count'][a][name] = count - self.counts.append(('count', a, name, count)) - - return stats - - def end(self): - dfm = pd.DataFrame(self.means, columns=['metric', 'key', 'value']) - dfc = pd.DataFrame(self.counts, columns=['metric', 'key', 'value', 'count']) - - count = {} - mean = {} - - if self.means: - res = dfm.drop('metric', axis=1).groupby(by=['key']).agg(['mean', 'std', 'count', 'median', 'max', 'min']) - mean = res['value'].to_dict() - if self.counts: - res = dfc.drop('metric', axis=1).groupby(by=['key', 'value']).agg(['mean', 'std', 'count', 'median', 'max', 'min']) - for k,v in res['count'].to_dict().items(): - if k not in count: - count[k] = {} - for tup, times in v.items(): - subkey, subcount = tup - if subkey not in count[k]: - count[k][subkey] = {} - count[k][subkey][subcount] = times - - - return {'count': count, 'mean': mean} - - -class defaultStats(Stats): - - def trial(self, env): - c = Counter() - c.update(a.__class__.__name__ for a in env.network_agents) - - c2 = Counter() - c2.update(a['id'] for a in env.network_agents) - - return { - 'network ': { - 'n_nodes': env.G.number_of_nodes(), - 'n_edges': env.G.number_of_edges(), - }, - 'agents': { - 'model_count': dict(c), - 'state_count': dict(c2), - } - } diff --git a/soil/time.py b/soil/time.py index 90ec513..b179bff 100644 --- a/soil/time.py +++ b/soil/time.py @@ -1,12 +1,22 @@ 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 +from numbers import Number +from textwrap import dedent + from .utils import logger -from mesa import Agent +from mesa import Agent as MesaAgent -INFINITY = float('inf') +INFINITY = float("inf") + + +class DeadAgent(Exception): + pass + class When: def __init__(self, time): @@ -17,6 +27,10 @@ class When: def abs(self, time): return self._time + def schedule_next(self, time, delta, first=False): + return (self._time, None) + + NEVER = When(INFINITY) @@ -24,11 +38,53 @@ class Delta(When): def __init__(self, delta): self._delta = delta - def __eq__(self, other): - return self._delta == other._delta - def abs(self, time): - return time + self._delta + return self._time + self._delta + + def __eq__(self, other): + if isinstance(other, Delta): + return self._delta == other._delta + return False + + def schedule_next(self, time, delta, first=False): + return (time + self._delta, None) + + def __repr__(self): + return str(f"Delta({self._delta})") + + +class BaseCond: + def __init__(self, msg=None, delta=None, eager=False): + self._msg = msg + self._delta = delta + self.eager = eager + + def schedule_next(self, time, delta, first=False): + if first and self.eager: + return (time, self) + if self._delta: + delta = self._delta + return (time + delta, self) + + def return_value(self, agent): + return None + + def __repr__(self): + return self._msg or self.__class__.__name__ + + +class Cond(BaseCond): + def __init__(self, func, *args, **kwargs): + self._func = func + super().__init__(*args, **kwargs) + + def ready(self, agent, time): + return self._func(agent) + + def __repr__(self): + if self._msg: + return self._msg + return str(f'Cond("{dedent(getsource(self._func)).strip()}")') class TimedActivation(BaseScheduler): @@ -36,15 +92,44 @@ class TimedActivation(BaseScheduler): In each activation, each agent will update its 'next_time'. """ - def __init__(self, *args, **kwargs): - super().__init__(self) + def __init__(self, *args, shuffle=True, **kwargs): + super().__init__(*args, **kwargs) + self._next = {} self._queue = [] - self.next_time = 0 + self._shuffle = shuffle + # self.step_interval = getattr(self.model, "interval", 1) + 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: Agent): - if agent.unique_id not in self._agents: - heappush(self._queue, (self.time, agent.unique_id)) - super().add(agent) + def add(self, agent: MesaAgent, when=None): + if when is None: + when = self.time + elif isinstance(when, When): + when = when.abs() + + self._schedule(agent, None, when) + super().add(agent) + + def _schedule(self, agent, condition=None, when=None, replace=False): + if condition: + if not when: + when, condition = condition.schedule_next( + when or self.time, self.step_interval + ) + else: + if when is None: + when = self.time + self.step_interval + condition = None + if self._shuffle: + key = (when, self.model.random.random(), condition) + else: + key = (when, agent.unique_id, condition) + self._next[agent.unique_id] = key + if replace: + heapreplace(self._queue, (key, agent)) + else: + heappush(self._queue, (key, agent)) def step(self) -> None: """ @@ -52,29 +137,77 @@ class TimedActivation(BaseScheduler): an agent will signal when it wants to be scheduled next. """ - if self.next_time == INFINITY: + self.logger.debug(f"Simulation step {self.time}") + if not self.model.running or self.time == INFINITY: return - self.time = self.next_time - when = self.time + self.logger.debug(f"Queue length: %s", len(self._queue)) - while self._queue and self._queue[0][0] == self.time: - (when, agent_id) = heappop(self._queue) - logger.debug(f'Stepping agent {agent_id}') + while self._queue: + ((when, _id, cond), agent) = self._queue[0] + if when > self.time: + break - returned = self._agents[agent_id].step() - when = (returned or Delta(1)).abs(self.time) - if when < self.time: - raise Exception("Cannot schedule an agent for a time in the past ({} < {})".format(when, self.time)) + if cond: + if not cond.ready(agent, self.time): + self._schedule(agent, cond, replace=True) + continue + try: + agent._last_return = cond.return_value(agent) + except Exception as ex: + agent._last_except = ex + else: + agent._last_return = None + agent._last_except = None - heappush(self._queue, (when, agent_id)) + 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], replace=True) + else: + next_check = (self.time + self.step_interval, None) + + self._schedule(agent, replace=True) self.steps += 1 if not self._queue: + self.model.running = False self.time = INFINITY - self.next_time = INFINITY return - self.next_time = self._queue[0][0] + next_time = self._queue[0][0][0] + if next_time < self.time: + raise Exception( + f"An agent has been scheduled for a time in the past, there is probably an error ({when} < {self.time})" + ) + self.logger.debug("Updating time step: %s -> %s ", self.time, next_time) + + self.time = next_time + + +class ShuffledTimedActivation(TimedActivation): + def __init__(self, *args, **kwargs): + super().__init__(*args, shuffle=True, **kwargs) + + +class OrderedTimedActivation(TimedActivation): + def __init__(self, *args, **kwargs): + super().__init__(*args, shuffle=False, **kwargs) diff --git a/soil/utils.py b/soil/utils.py index e95758c..0be4c40 100644 --- a/soil/utils.py +++ b/soil/utils.py @@ -1,71 +1,106 @@ import logging -import time +from time import time as current_time, strftime, gmtime, localtime import os +import traceback -from shutil import copyfile +from functools import partial +from shutil import copyfile, move +from multiprocessing import Pool, cpu_count from contextlib import contextmanager -logger = logging.getLogger('soil') -# logging.basicConfig() -# logger.setLevel(logging.INFO) +logger = logging.getLogger("soil") +logger.setLevel(logging.WARNING) + +timeformat = "%H:%M:%S" + +if os.environ.get("SOIL_VERBOSE", ""): + logformat = "[%(levelname)-5.5s][%(asctime)s][%(name)s]: %(message)s" +else: + logformat = "[%(levelname)-5.5s][%(asctime)s] %(message)s" + +logFormatter = logging.Formatter(logformat, timeformat) +consoleHandler = logging.StreamHandler() +consoleHandler.setFormatter(logFormatter) + +logging.basicConfig( + level=logging.INFO, + handlers=[ + consoleHandler, + ], +) @contextmanager -def timer(name='task', pre="", function=logger.info, to_object=None): - start = time.time() - function('{}Starting {} at {}.'.format(pre, name, - time.strftime("%X", time.gmtime(start)))) +def timer(name="task", pre="", function=logger.info, to_object=None): + start = current_time() + function("{}Starting {} at {}.".format(pre, name, strftime("%X", gmtime(start)))) yield start - end = time.time() - function('{}Finished {} at {} in {} seconds'.format(pre, name, - time.strftime("%X", time.gmtime(end)), - str(end-start))) + end = current_time() + function( + "{}Finished {} at {} in {} seconds".format( + pre, name, strftime("%X", gmtime(end)), str(end - start) + ) + ) if to_object: to_object.start = start to_object.end = end - - -def safe_open(path, mode='r', backup=True, **kwargs): +def try_backup(path, remove=False): + if not os.path.exists(path): + return None outdir = os.path.dirname(path) if outdir and not os.path.exists(outdir): os.makedirs(outdir) - if backup and 'w' in mode and os.path.exists(path): - creation = os.path.getctime(path) - stamp = time.strftime('%Y-%m-%d_%H.%M.%S', time.localtime(creation)) + creation = os.path.getctime(path) + stamp = strftime("%Y-%m-%d_%H.%M.%S", localtime(creation)) - backup_dir = os.path.join(outdir, 'backup') - if not os.path.exists(backup_dir): - os.makedirs(backup_dir) - newpath = os.path.join(backup_dir, '{}@{}'.format(os.path.basename(path), - stamp)) + backup_dir = os.path.join(outdir, "backup") + if not os.path.exists(backup_dir): + os.makedirs(backup_dir) + newpath = os.path.join(backup_dir, "{}@{}".format(os.path.basename(path), stamp)) + if remove: + move(path, newpath) + else: copyfile(path, newpath) + return newpath + + +def safe_open(path, mode="r", backup=True, **kwargs): + outdir = os.path.dirname(path) + if outdir and not os.path.exists(outdir): + os.makedirs(outdir) + if backup and "w" in mode: + try_backup(path) return open(path, mode=mode, **kwargs) +@contextmanager def open_or_reuse(f, *args, **kwargs): try: - return safe_open(f, *args, **kwargs) - except (AttributeError, TypeError): - return f + with safe_open(f, *args, **kwargs) as f: + yield f + except (AttributeError, TypeError) as ex: + yield f + def flatten_dict(d): if not isinstance(d, dict): return d return dict(_flatten_dict(d)) -def _flatten_dict(d, prefix=''): + +def _flatten_dict(d, prefix=""): if not isinstance(d, dict): # print('END:', prefix, d) yield prefix, d return if prefix: - prefix = prefix + '.' + prefix = prefix + "." for k, v in d.items(): # print(k, v) - res = list(_flatten_dict(v, prefix='{}{}'.format(prefix, k))) + res = list(_flatten_dict(v, prefix="{}{}".format(prefix, k))) # print('RES:', res) yield from res @@ -77,7 +112,7 @@ def unflatten_dict(d): if not isinstance(k, str): target[k] = v continue - tokens = k.split('.') + tokens = k.split(".") if len(tokens) < 2: target[k] = v continue @@ -87,3 +122,39 @@ def unflatten_dict(d): target = target[token] target[tokens[-1]] = v return out + + +def run_and_return_exceptions(func, *args, **kwargs): + """ + A wrapper for a function that catches exceptions and returns them. + It is meant for async simulations. + """ + try: + return func(*args, **kwargs) + except Exception as ex: + if ex.__cause__ is not None: + ex = ex.__cause__ + ex.message = "".join( + traceback.format_exception(type(ex), ex, ex.__traceback__)[:] + ) + return ex + + +def run_parallel(func, iterable, num_processes=1, **kwargs): + if num_processes > 1 and not os.environ.get("SOIL_DEBUG", None): + if num_processes < 1: + num_processes = cpu_count() - num_processes + p = Pool(processes=num_processes) + wrapped_func = partial(run_and_return_exceptions, func, **kwargs) + for i in p.imap_unordered(wrapped_func, iterable): + if isinstance(i, Exception): + logger.error("Trial failed:\n\t%s", i.message) + continue + yield i + else: + for i in iterable: + yield func(i, **kwargs) + + +def int_seed(seed: str): + return int.from_bytes(seed.encode(), "little") \ No newline at end of file diff --git a/soil/version.py b/soil/version.py index ea5b40a..ae66caa 100644 --- a/soil/version.py +++ b/soil/version.py @@ -4,7 +4,7 @@ import logging logger = logging.getLogger(__name__) ROOT = os.path.dirname(__file__) -DEFAULT_FILE = os.path.join(ROOT, 'VERSION') +DEFAULT_FILE = os.path.join(ROOT, "VERSION") def read_version(versionfile=DEFAULT_FILE): @@ -12,9 +12,10 @@ def read_version(versionfile=DEFAULT_FILE): with open(versionfile) as f: return f.read().strip() except IOError: # pragma: no cover - logger.error(('Running an unknown version of {}.' - 'Be careful!.').format(__name__)) - return '0.0' + logger.error( + ("Running an unknown version of {}." "Be careful!.").format(__name__) + ) + return "0.0" __version__ = read_version() diff --git a/soil/visualization.py b/soil/visualization.py deleted file mode 100644 index fe12aca..0000000 --- a/soil/visualization.py +++ /dev/null @@ -1,5 +0,0 @@ -from mesa.visualization.UserParam import UserSettableParameter - -class UserSettableParameter(UserSettableParameter): - def __str__(self): - return self.value diff --git a/soil/web/__init__.py b/soil/web/__init__.py index 2339288..5327703 100644 --- a/soil/web/__init__.py +++ b/soil/web/__init__.py @@ -20,6 +20,7 @@ from tornado.concurrent import run_on_executor from concurrent.futures import ThreadPoolExecutor from ..simulation import Simulation + logger = logging.getLogger(__name__) logger.setLevel(logging.INFO) @@ -31,140 +32,183 @@ LOGGING_INTERVAL = 0.5 # Workaround to let Soil load the required modules sys.path.append(ROOT) + class PageHandler(tornado.web.RequestHandler): - """ Handler for the HTML template which holds the visualization. """ + """Handler for the HTML template which holds the visualization.""" def get(self): - self.render('index.html', port=self.application.port, - name=self.application.name) + self.render( + "index.html", port=self.application.port, name=self.application.name + ) class SocketHandler(tornado.websocket.WebSocketHandler): - """ Handler for websocket. """ + """Handler for websocket.""" + executor = ThreadPoolExecutor(max_workers=MAX_WORKERS) def open(self): if self.application.verbose: - logger.info('Socket opened!') + logger.info("Socket opened!") def check_origin(self, origin): return True def on_message(self, message): - """ Receiving a message from the websocket, parse, and act accordingly. """ + """Receiving a message from the websocket, parse, and act accordingly.""" msg = tornado.escape.json_decode(message) - if msg['type'] == 'config_file': + if msg["type"] == "config_file": if self.application.verbose: - print(msg['data']) + print(msg["data"]) - self.config = list(yaml.load_all(msg['data'])) + self.config = list(yaml.load_all(msg["data"])) if len(self.config) > 1: - error = 'Please, provide only one configuration.' + error = "Please, provide only one configuration." if self.application.verbose: logger.error(error) - self.write_message({'type': 'error', - 'error': error}) + self.write_message({"type": "error", "error": error}) return self.config = self.config[0] - self.send_log('INFO.' + self.simulation_name, - 'Using config: {name}'.format(name=self.config['name'])) + self.send_log( + "INFO." + self.simulation_name, + "Using config: {name}".format(name=self.config["name"]), + ) - if 'visualization_params' in self.config: - self.write_message({'type': 'visualization_params', - 'data': self.config['visualization_params']}) - self.name = self.config['name'] + if "visualization_params" in self.config: + self.write_message( + { + "type": "visualization_params", + "data": self.config["visualization_params"], + } + ) + self.name = self.config["name"] self.run_simulation() settings = [] - for key in self.config['environment_params']: - if type(self.config['environment_params'][key]) == float or type(self.config['environment_params'][key]) == int: - if self.config['environment_params'][key] <= 1: - setting_type = 'number' + for key in self.config["environment_params"]: + if ( + type(self.config["environment_params"][key]) == float + or type(self.config["environment_params"][key]) == int + ): + if self.config["environment_params"][key] <= 1: + setting_type = "number" else: - setting_type = 'great_number' - elif type(self.config['environment_params'][key]) == bool: - setting_type = 'boolean' + setting_type = "great_number" + elif type(self.config["environment_params"][key]) == bool: + setting_type = "boolean" else: - setting_type = 'undefined' + setting_type = "undefined" - settings.append({ - 'label': key, - 'type': setting_type, - 'value': self.config['environment_params'][key] - }) + settings.append( + { + "label": key, + "type": setting_type, + "value": self.config["environment_params"][key], + } + ) - self.write_message({'type': 'settings', - 'data': settings}) + self.write_message({"type": "settings", "data": settings}) - elif msg['type'] == 'get_trial': + elif msg["type"] == "get_trial": if self.application.verbose: - logger.info('Trial {} requested!'.format(msg['data'])) - self.send_log('INFO.' + __name__, 'Trial {} requested!'.format(msg['data'])) - self.write_message({'type': 'get_trial', - 'data': self.get_trial(int(msg['data']))}) + logger.info("Trial {} requested!".format(msg["data"])) + self.send_log("INFO." + __name__, "Trial {} requested!".format(msg["data"])) + self.write_message( + {"type": "get_trial", "data": self.get_trial(int(msg["data"]))} + ) - elif msg['type'] == 'run_simulation': + elif msg["type"] == "run_simulation": if self.application.verbose: - logger.info('Running new simulation for {name}'.format(name=self.config['name'])) - self.send_log('INFO.' + self.simulation_name, 'Running new simulation for {name}'.format(name=self.config['name'])) - self.config['environment_params'] = msg['data'] + logger.info( + "Running new simulation for {name}".format(name=self.config["name"]) + ) + self.send_log( + "INFO." + self.simulation_name, + "Running new simulation for {name}".format(name=self.config["name"]), + ) + self.config["environment_params"] = msg["data"] self.run_simulation() - elif msg['type'] == 'download_gexf': - G = self.trials[ int(msg['data']) ].history_to_graph() + elif msg["type"] == "download_gexf": + G = self.trials[int(msg["data"])].history_to_graph() for node in G.nodes(): - if 'pos' in G.nodes[node]: - G.nodes[node]['viz'] = {"position": {"x": G.nodes[node]['pos'][0], "y": G.nodes[node]['pos'][1], "z": 0.0}} - del (G.nodes[node]['pos']) - writer = nx.readwrite.gexf.GEXFWriter(version='1.2draft') + if "pos" in G.nodes[node]: + G.nodes[node]["viz"] = { + "position": { + "x": G.nodes[node]["pos"][0], + "y": G.nodes[node]["pos"][1], + "z": 0.0, + } + } + del G.nodes[node]["pos"] + writer = nx.readwrite.gexf.GEXFWriter(version="1.2draft") writer.add_graph(G) - self.write_message({'type': 'download_gexf', - 'filename': self.config['name'] + '_trial_' + str(msg['data']), - 'data': tostring(writer.xml).decode(writer.encoding) }) + self.write_message( + { + "type": "download_gexf", + "filename": self.config["name"] + "_trial_" + str(msg["data"]), + "data": tostring(writer.xml).decode(writer.encoding), + } + ) - elif msg['type'] == 'download_json': - G = self.trials[ int(msg['data']) ].history_to_graph() + elif msg["type"] == "download_json": + G = self.trials[int(msg["data"])].history_to_graph() for node in G.nodes(): - if 'pos' in G.nodes[node]: - G.nodes[node]['viz'] = {"position": {"x": G.nodes[node]['pos'][0], "y": G.nodes[node]['pos'][1], "z": 0.0}} - del (G.nodes[node]['pos']) - self.write_message({'type': 'download_json', - 'filename': self.config['name'] + '_trial_' + str(msg['data']), - 'data': nx.node_link_data(G) }) + if "pos" in G.nodes[node]: + G.nodes[node]["viz"] = { + "position": { + "x": G.nodes[node]["pos"][0], + "y": G.nodes[node]["pos"][1], + "z": 0.0, + } + } + del G.nodes[node]["pos"] + self.write_message( + { + "type": "download_json", + "filename": self.config["name"] + "_trial_" + str(msg["data"]), + "data": nx.node_link_data(G), + } + ) else: if self.application.verbose: - logger.info('Unexpected message!') + logger.info("Unexpected message!") def update_logging(self): try: - if (not self.log_capture_string.closed and self.log_capture_string.getvalue()): - for i in range(len(self.log_capture_string.getvalue().split('\n')) - 1): - self.send_log('INFO.' + self.simulation_name, self.log_capture_string.getvalue().split('\n')[i]) + if ( + not self.log_capture_string.closed + and self.log_capture_string.getvalue() + ): + for i in range(len(self.log_capture_string.getvalue().split("\n")) - 1): + self.send_log( + "INFO." + self.simulation_name, + self.log_capture_string.getvalue().split("\n")[i], + ) self.log_capture_string.truncate(0) self.log_capture_string.seek(0) finally: if self.capture_logging: - tornado.ioloop.IOLoop.current().call_later(LOGGING_INTERVAL, self.update_logging) - + tornado.ioloop.IOLoop.current().call_later( + LOGGING_INTERVAL, self.update_logging + ) def on_close(self): if self.application.verbose: - logger.info('Socket closed!') + logger.info("Socket closed!") def send_log(self, logger, logging): - self.write_message({'type': 'log', - 'logger': logger, - 'logging': logging}) + self.write_message({"type": "log", "logger": logger, "logging": logging}) @property def simulation_name(self): - return self.config.get('name', 'NoSimulationRunning') + return self.config.get("name", "NoSimulationRunning") @run_on_executor def nonblocking(self, config): @@ -174,28 +218,31 @@ class SocketHandler(tornado.websocket.WebSocketHandler): @tornado.gen.coroutine def run_simulation(self): # Run simulation and capture logs - logger.info('Running simulation!') - if 'visualization_params' in self.config: - del self.config['visualization_params'] + logger.info("Running simulation!") + if "visualization_params" in self.config: + del self.config["visualization_params"] with self.logging(self.simulation_name): try: config = dict(**self.config) - config['outdir'] = os.path.join(self.application.outdir, config['name']) - config['dump'] = self.application.dump + config["outdir"] = os.path.join(self.application.outdir, config["name"]) + config["dump"] = self.application.dump self.trials = yield self.nonblocking(config) - self.write_message({'type': 'trials', - 'data': list(trial.name for trial in self.trials) }) + self.write_message( + { + "type": "trials", + "data": list(trial.name for trial in self.trials), + } + ) except Exception as ex: - error = 'Something went wrong:\n\t{}'.format(ex) + error = "Something went wrong:\n\t{}".format(ex) logging.info(error) - self.write_message({'type': 'error', - 'error': error}) - self.send_log('ERROR.' + self.simulation_name, error) + self.write_message({"type": "error", "error": error}) + self.send_log("ERROR." + self.simulation_name, error) def get_trial(self, trial): - logger.info('Available trials: %s ' % len(self.trials)) - logger.info('Ask for : %s' % trial) + logger.info("Available trials: %s " % len(self.trials)) + logger.info("Ask for : %s" % trial) trial = self.trials[trial] G = trial.history_to_graph() return nx.node_link_data(G) @@ -215,25 +262,28 @@ class SocketHandler(tornado.websocket.WebSocketHandler): self.logger_application.removeHandler(ch) self.capture_logging = False return self.capture_logging - + class ModularServer(tornado.web.Application): - """ Main visualization application. """ + """Main visualization application.""" port = 8001 - page_handler = (r'/', PageHandler) - socket_handler = (r'/ws', SocketHandler) - static_handler = (r'/(.*)', tornado.web.StaticFileHandler, - {'path': os.path.join(ROOT, 'static')}) - local_handler = (r'/local/(.*)', tornado.web.StaticFileHandler, - {'path': ''}) + page_handler = (r"/", PageHandler) + socket_handler = (r"/ws", SocketHandler) + static_handler = ( + r"/(.*)", + tornado.web.StaticFileHandler, + {"path": os.path.join(ROOT, "static")}, + ) + local_handler = (r"/local/(.*)", tornado.web.StaticFileHandler, {"path": ""}) handlers = [page_handler, socket_handler, static_handler, local_handler] - settings = {'debug': True, - 'template_path': ROOT + '/templates'} + settings = {"debug": True, "template_path": ROOT + "/templates"} + + def __init__( + self, dump=False, outdir="output", name="SOIL", verbose=True, *args, **kwargs + ): - def __init__(self, dump=False, outdir='output', name='SOIL', verbose=True, *args, **kwargs): - self.verbose = verbose self.name = name self.dump = dump @@ -243,12 +293,12 @@ class ModularServer(tornado.web.Application): super().__init__(self.handlers, **self.settings) def launch(self, port=None): - """ Run the app. """ - + """Run the app.""" + if port is not None: self.port = port - url = 'http://127.0.0.1:{PORT}'.format(PORT=self.port) - print('Interface starting at {url}'.format(url=url)) + url = "http://127.0.0.1:{PORT}".format(PORT=self.port) + print("Interface starting at {url}".format(url=url)) self.listen(self.port) # webbrowser.open(url) tornado.ioloop.IOLoop.instance().start() @@ -263,12 +313,22 @@ def run(*args, **kwargs): def main(): import argparse - parser = argparse.ArgumentParser(description='Visualization of a Graph Model') + parser = argparse.ArgumentParser(description="Visualization of a Graph Model") - parser.add_argument('--name', '-n', nargs=1, default='SOIL', help='name of the simulation') - parser.add_argument('--dump', '-d', help='dumping results in folder output', action='store_true') - parser.add_argument('--port', '-p', nargs=1, default=8001, help='port for launching the server') - parser.add_argument('--verbose', '-v', help='verbose mode', action='store_true') + parser.add_argument( + "--name", "-n", nargs=1, default="SOIL", help="name of the simulation" + ) + parser.add_argument( + "--dump", "-d", help="dumping results in folder output", action="store_true" + ) + parser.add_argument( + "--port", "-p", nargs=1, default=8001, help="port for launching the server" + ) + parser.add_argument("--verbose", "-v", help="verbose mode", action="store_true") args = parser.parse_args() - run(name=args.name, port=(args.port[0] if isinstance(args.port, list) else args.port), verbose=args.verbose) + run( + name=args.name, + port=(args.port[0] if isinstance(args.port, list) else args.port), + verbose=args.verbose, + ) diff --git a/soil/web/__main__.py b/soil/web/__main__.py index 5c211a8..29c2e0a 100644 --- a/soil/web/__main__.py +++ b/soil/web/__main__.py @@ -2,4 +2,4 @@ from . import main if __name__ == "__main__": - main() \ No newline at end of file + main() diff --git a/soil/web/config.yml b/soil/web/config.yml index 1f741eb..27e2785 100644 --- a/soil/web/config.yml +++ b/soil/web/config.yml @@ -6,11 +6,11 @@ network_params: n: 100 m: 2 network_agents: - - agent_type: ControlModelM2 + - agent_class: ControlModelM2 weight: 0.1 state: id: 1 - - agent_type: ControlModelM2 + - agent_class: ControlModelM2 weight: 0.9 state: id: 0 diff --git a/soil/web/run.py b/soil/web/run.py index a0b1416..b13ca56 100644 --- a/soil/web/run.py +++ b/soil/web/run.py @@ -4,20 +4,33 @@ from simulator import Simulator def run(simulator, name="SOIL", port=8001, verbose=False): - server = ModularServer(simulator, name=(name[0] if isinstance(name, list) else name), verbose=verbose) + server = ModularServer( + simulator, name=(name[0] if isinstance(name, list) else name), verbose=verbose + ) server.port = port server.launch() if __name__ == "__main__": - parser = argparse.ArgumentParser(description='Visualization of a Graph Model') + parser = argparse.ArgumentParser(description="Visualization of a Graph Model") - parser.add_argument('--name', '-n', nargs=1, default='SOIL', help='name of the simulation') - parser.add_argument('--dump', '-d', help='dumping results in folder output', action='store_true') - parser.add_argument('--port', '-p', nargs=1, default=8001, help='port for launching the server') - parser.add_argument('--verbose', '-v', help='verbose mode', action='store_true') + parser.add_argument( + "--name", "-n", nargs=1, default="SOIL", help="name of the simulation" + ) + parser.add_argument( + "--dump", "-d", help="dumping results in folder output", action="store_true" + ) + parser.add_argument( + "--port", "-p", nargs=1, default=8001, help="port for launching the server" + ) + parser.add_argument("--verbose", "-v", help="verbose mode", action="store_true") args = parser.parse_args() soil = Simulator(dump=args.dump) - run(soil, name=args.name, port=(args.port[0] if isinstance(args.port, list) else args.port), verbose=args.verbose) + run( + soil, + name=args.name, + port=(args.port[0] if isinstance(args.port, list) else args.port), + verbose=args.verbose, + ) diff --git a/soil/web/static/img/background/map.png b/soil/web/static/img/background/map.png deleted file mode 100644 index 84709e0..0000000 Binary files a/soil/web/static/img/background/map.png and /dev/null differ diff --git a/soil/web/static/img/background/map_4800x2860.jpg b/soil/web/static/img/background/map_4800x2860.jpg deleted file mode 100644 index b41a0ee..0000000 Binary files a/soil/web/static/img/background/map_4800x2860.jpg and /dev/null differ diff --git a/test-requirements.txt b/test-requirements.txt index cf59a7e..d95c1b1 100644 --- a/test-requirements.txt +++ b/test-requirements.txt @@ -1,4 +1,4 @@ pytest -mesa>=0.8.9 +pytest-profiling scipy>=1.3 tornado diff --git a/tests/test_agents.py b/tests/test_agents.py index e95c11c..000049a 100644 --- a/tests/test_agents.py +++ b/tests/test_agents.py @@ -4,19 +4,174 @@ import pytest from soil import agents, environment from soil import time as stime + class Dead(agents.FSM): @agents.default_state @agents.state def only(self): - self.die() + return self.die() -class TestMain(TestCase): - def test_die_raises_exception(self): + +class TestAgents(TestCase): + def test_die_returns_infinity(self): + """The last step of a dead agent should return time.INFINITY""" d = Dead(unique_id=0, model=environment.Environment()) + ret = d.step() + assert ret == stime.NEVER + + def test_die_raises_exception(self): + """A dead agent should raise an exception if it is stepped after death""" + d = Dead(unique_id=0, model=environment.Environment()) + assert d.alive d.step() - with pytest.raises(agents.DeadAgent): + assert not d.alive + with pytest.raises(stime.DeadAgent): d.step() - def test_die_returns_infinity(self): - d = Dead(unique_id=0, model=environment.Environment()) - assert d.step().abs(0) == stime.INFINITY + def test_agent_generator(self): + """ + The step function of an agent could be a generator. In that case, the state of the + agent will be resumed after every call to step. + """ + a = 0 + + class Gen(agents.BaseAgent): + def step(self): + nonlocal a + for i in range(5): + yield + a += 1 + + e = environment.Environment() + g = Gen(model=e, unique_id=e.next_id()) + e.schedule.add(g) + + for i in range(5): + e.step() + assert a == i + + def test_state_decorator(self): + class MyAgent(agents.FSM): + run = 0 + + @agents.state("original", default=True) + def root(self): + self.run += 1 + return self.other + + @agents.state + def other(self): + self.run += 1 + + e = environment.Environment() + a = e.add_agent(MyAgent) + e.step() + assert a.run == 1 + a.step() + print("DONE") + + def test_broadcast(self): + """ + An agent should be able to broadcast messages to every other agent, AND each receiver should be able + to process it + """ + + class BCast(agents.Evented): + pings_received = 0 + + def step(self): + print(self.model.broadcast) + try: + self.model.broadcast("PING") + except Exception as ex: + print(ex) + while True: + self.check_messages() + yield + + def on_receive(self, msg, sender=None): + self.pings_received += 1 + + e = environment.EventedEnvironment() + + for i in range(10): + e.add_agent(agent_class=BCast) + e.step() + pings_received = lambda: [a.pings_received for a in e.agents] + assert sorted(pings_received()) == list(range(1, 11)) + e.step() + assert all(x == 10 for x in pings_received()) + + def test_ask_messages(self): + """ + An agent should be able to ask another agent, and wait for a response. + """ + + # There are two agents, they try to send pings + # This is arguably a very contrived example. + # There should be a delay of one step between agent 0 and 1 + # On the first step: + # Agent 0 sends a PING, but blocks before a PONG + # Agent 1 detects the PING, responds with a PONG, and blocks after its own PING + # After that step, every agent can both receive (there are pending messages) and send. + # In each step, for each agent, one message is sent, and another one is received + # (although not necessarily in that order). + + # Results depend on ordering (agents are normally shuffled) + # so we force the timedactivation not to be shuffled + + pings = [] + pongs = [] + responses = [] + + class Ping(agents.EventedAgent): + def step(self): + target_id = (self.unique_id + 1) % self.count_agents() + target = self.model.agents[target_id] + print("starting") + while True: + if pongs or not pings: # First agent, or anyone after that + pings.append(self.now) + response = yield target.ask("PING") + responses.append(response) + else: + print("NOT sending ping") + print("Checking msgs") + # Do not block if we have already received a PING + if not self.check_messages(): + yield self.received() + print("done") + + def on_receive(self, msg, sender=None): + if msg == "PING": + pongs.append(self.now) + return "PONG" + raise Exception("This should never happen") + + e = environment.EventedEnvironment(schedule_class=stime.OrderedTimedActivation) + for i in range(2): + e.add_agent(agent_class=Ping) + assert e.now == 0 + + for i in range(5): + e.step() + time = i + 1 + assert e.now == time + assert len(pings) == 2 * time + assert len(pongs) == (2 * time) - 1 + # Every step between 0 and t appears twice + assert sum(pings) == sum(range(time)) * 2 + # It is the same as pings, without the leading 0 + assert sum(pongs) == sum(range(time)) * 2 + + def test_agent_filter(self): + e = environment.Environment() + e.add_agent(agent_class=agents.BaseAgent) + e.add_agent(agent_class=agents.Evented) + base = list(e.agents(agent_class=agents.BaseAgent)) + assert len(base) == 2 + ev = list(e.agents(agent_class=agents.Evented)) + assert len(ev) == 1 + assert ev[0].unique_id == 1 + null = list(e.agents(unique_ids=[0, 1], agent_class=agents.NetworkAgent)) + assert not null \ No newline at end of file diff --git a/tests/test_analysis.py b/tests/test_analysis.py deleted file mode 100644 index 47c649b..0000000 --- a/tests/test_analysis.py +++ /dev/null @@ -1,90 +0,0 @@ -from unittest import TestCase - -import os -import pandas as pd -import yaml -from functools import partial - -from os.path import join -from soil import simulation, analysis, agents - - -ROOT = os.path.abspath(os.path.dirname(__file__)) - - -class Ping(agents.FSM): - - defaults = { - 'count': 0, - } - - @agents.default_state - @agents.state - def even(self): - self.debug(f'Even {self["count"]}') - self['count'] += 1 - return self.odd - - @agents.state - def odd(self): - self.debug(f'Odd {self["count"]}') - self['count'] += 1 - return self.even - - -class TestAnalysis(TestCase): - - # Code to generate a simple sqlite history - def setUp(self): - """ - The initial states should be applied to the agent and the - agent should be able to update its state.""" - config = { - 'name': 'analysis', - 'seed': 'seed', - 'network_params': { - 'generator': 'complete_graph', - 'n': 2 - }, - 'agent_type': Ping, - 'states': [{'interval': 1}, {'interval': 2}], - 'max_time': 30, - 'num_trials': 1, - 'environment_params': { - } - } - s = simulation.from_config(config) - self.env = s.run_simulation(dry_run=True)[0] - - def test_saved(self): - env = self.env - assert env.get_agent(0)['count', 0] == 1 - assert env.get_agent(0)['count', 29] == 30 - assert env.get_agent(1)['count', 0] == 1 - assert env.get_agent(1)['count', 29] == 15 - assert env['env', 29, None]['SEED'] == env['env', 29, 'SEED'] - - def test_count(self): - env = self.env - df = analysis.read_sql(env._history.db_path) - res = analysis.get_count(df, 'SEED', 'state_id') - assert res['SEED'][self.env['SEED']].iloc[0] == 1 - assert res['SEED'][self.env['SEED']].iloc[-1] == 1 - assert res['state_id']['odd'].iloc[0] == 2 - assert res['state_id']['even'].iloc[0] == 0 - assert res['state_id']['odd'].iloc[-1] == 1 - assert res['state_id']['even'].iloc[-1] == 1 - - def test_value(self): - env = self.env - df = analysis.read_sql(env._history.db_path) - res_sum = analysis.get_value(df, 'count') - - assert res_sum['count'].iloc[0] == 2 - - import numpy as np - res_mean = analysis.get_value(df, 'count', aggfunc=np.mean) - assert res_mean['count'].iloc[15] == (16+8)/2 - - res_total = analysis.get_majority(df) - res_total['SEED'].iloc[0] == self.env['SEED'] diff --git a/tests/test_config.py b/tests/test_config.py new file mode 100644 index 0000000..9da72e7 --- /dev/null +++ b/tests/test_config.py @@ -0,0 +1,82 @@ +from unittest import TestCase, skip +import os +import yaml +import copy +from os.path import join + +from soil import simulation, serialization, config, network, agents, utils + +ROOT = os.path.abspath(os.path.dirname(__file__)) +EXAMPLES = join(ROOT, "..", "examples") + +FORCE_TESTS = os.environ.get("FORCE_TESTS", "") + + +def isequal(a, b): + if isinstance(a, dict): + for (k, v) in a.items(): + if v: + isequal(a[k], b[k]) + else: + assert not b.get(k, None) + return + assert a == b + + +# @skip("new versions of soil do not rely on configuration files") +class TestConfig(TestCase): + + def test_torvalds_config(self): + sim = simulation.from_config(os.path.join(ROOT, "test_config.yml")) + MAX_STEPS = 10 + INTERVAL = 2 + assert sim.interval == INTERVAL + assert sim.max_steps == MAX_STEPS + envs = sim.run() + assert len(envs) == 1 + env = envs[0] + assert env.interval == 2 + assert env.count_agents() == 3 + assert env.now == INTERVAL * MAX_STEPS + + +def make_example_test(path, cfg): + def wrapped(self): + root = os.getcwd() + print(path) + s = simulation.from_config(cfg) + iterations = s.max_time * s.iterations + if iterations > 1000: + s.max_time = 100 + s.iterations = 1 + if cfg.skip_test and not FORCE_TESTS: + self.skipTest('Example ignored.') + envs = s.run_simulation(dump=False) + assert envs + for env in envs: + assert env + try: + n = cfg.parameters['topology']['params']['n'] + assert len(list(env.network_agents)) == n + assert env.now > 0 # It has run + assert env.now <= cfg.max_time # But not further than allowed + except KeyError: + pass + + return wrapped + + +def add_example_tests(): + for config, path in serialization.load_files( + join(EXAMPLES, "*", "*.yml"), + join(EXAMPLES, "*.yml"), + ): + p = make_example_test(path=path, cfg=config) + fname = os.path.basename(path) + p.__name__ = "test_example_file_%s" % fname + p.__doc__ = "%s should be a valid configuration" % fname + setattr(TestConfig, p.__name__, p) + del p + + +add_example_tests() diff --git a/tests/test_config.yml b/tests/test_config.yml new file mode 100644 index 0000000..db1ffae --- /dev/null +++ b/tests/test_config.yml @@ -0,0 +1,5 @@ +--- +source_file: "../examples/torvalds_sim.py" +model: "TorvaldsEnv" +max_steps: 10 +interval: 2 \ No newline at end of file diff --git a/tests/test_examples.py b/tests/test_examples.py index 6a00367..d06a2d9 100644 --- a/tests/test_examples.py +++ b/tests/test_examples.py @@ -1,54 +1,82 @@ from unittest import TestCase +from unittest.case import SkipTest + import os from os.path import join +from glob import glob -from soil import serialization, simulation + +from soil import simulation ROOT = os.path.abspath(os.path.dirname(__file__)) -EXAMPLES = join(ROOT, '..', 'examples') +EXAMPLES = join(ROOT, "..", "examples") -FORCE_TESTS = os.environ.get('FORCE_TESTS', '') +FORCE_TESTS = os.environ.get("FORCE_TESTS", "") class TestExamples(TestCase): + """Empty class that will be populated with auto-discovery tests for every example""" pass -def make_example_test(path, config): +def get_test_for_sims(sims, path): + root = os.getcwd() + def wrapped(self): - root = os.getcwd() - for s in simulation.all_from_config(path): - iterations = s.max_time * s.num_trials - if iterations > 1000: - s.max_time = 100 - s.num_trials = 1 - if config.get('skip_test', False) and not FORCE_TESTS: - self.skipTest('Example ignored.') - envs = s.run_simulation(dry_run=True) + run = False + for sim in sims: + if sim.skip_test and not FORCE_TESTS: + continue + run = True + + if sim.max_steps is None: + sim.max_steps = 100 + + iterations = sim.max_steps * sim.iterations + if iterations < 0 or iterations > 1000: + sim.max_steps = 100 + sim.iterations = 1 + + envs = sim.run(dump=False) assert envs for env in envs: assert env + assert env.now > 0 try: - n = config['network_params']['n'] + n = sim.parameters["network_params"]["n"] assert len(list(env.network_agents)) == n - assert env.now > 0 # It has run - assert env.now <= config['max_time'] # But not further than allowed except KeyError: pass + assert env.schedule.steps > 0 # It has run + assert env.schedule.steps <= sim.max_steps # But not further than allowed + if not run: + raise SkipTest("Example ignored because all simulations are set up to be skipped.") + return wrapped def add_example_tests(): - for config, path in serialization.load_files( - join(EXAMPLES, '*', '*.yml'), - join(EXAMPLES, '*.yml'), - ): - p = make_example_test(path=path, config=config) + sim_paths = {} + for path in glob(join(EXAMPLES, '**', '*.yml')): + if "soil_output" in path: + continue + if path not in sim_paths: + sim_paths[path] = [] + for sim in simulation.iter_from_config(path): + sim_paths[path].append(sim) + for path in glob(join(EXAMPLES, '**', '*_sim.py')): + if path not in sim_paths: + sim_paths[path] = [] + for sim in simulation.iter_from_py(path): + sim_paths[path].append(sim) + + for (path, sims) in sim_paths.items(): + test_case = get_test_for_sims(sims, path) fname = os.path.basename(path) - p.__name__ = 'test_example_file_%s' % fname - p.__doc__ = '%s should be a valid configuration' % fname - setattr(TestExamples, p.__name__, p) - del p + test_case.__name__ = "test_example_file_%s" % fname + test_case.__doc__ = "%s should be a valid configuration" % fname + setattr(TestExamples, test_case.__name__, test_case) + del test_case add_example_tests() diff --git a/tests/test_exporters.py b/tests/test_exporters.py index 1583f23..3b815f9 100644 --- a/tests/test_exporters.py +++ b/tests/test_exporters.py @@ -2,100 +2,121 @@ import os import io import tempfile import shutil -from time import time +import sqlite3 from unittest import TestCase from soil import exporters +from soil import environment from soil import simulation +from soil import agents +from soil import decorators + +from mesa import Agent as MesaAgent -from soil.stats import distribution class Dummy(exporters.Exporter): started = False - trials = 0 + iterations = 0 ended = False total_time = 0 called_start = 0 - called_trial = 0 + called_iteration = 0 called_end = 0 - def start(self): + def sim_start(self): self.__class__.called_start += 1 self.__class__.started = True - def trial(self, env, stats): + def iteration_end(self, env, *args, **kwargs): assert env - self.__class__.trials += 1 + self.__class__.iterations += 1 self.__class__.total_time += env.now - self.__class__.called_trial += 1 + self.__class__.called_iteration += 1 - def end(self, stats): + def sim_end(self): self.__class__.ended = True self.__class__.called_end += 1 class Exporters(TestCase): def test_basic(self): - config = { - 'name': 'exporter_sim', - 'network_params': {}, - 'agent_type': 'CounterModel', - 'max_time': 2, - 'num_trials': 5, - 'environment_params': {} - } - s = simulation.from_config(config) - for env in s.run_simulation(exporters=[Dummy], dry_run=True): - assert env.now <= 2 + # We need to add at least one agent to make sure the scheduler + # ticks every step + class SimpleEnv(environment.Environment): + def init(self): + self.add_agent(agent_class=MesaAgent) + + iterations = 5 + max_time = 2 + s = simulation.Simulation(iterations=iterations, + max_time=max_time, name="exporter_sim", + exporters=[Dummy], dump=False, model=SimpleEnv) + + for env in s.run(): + assert len(env.agents) == 1 assert Dummy.started assert Dummy.ended assert Dummy.called_start == 1 assert Dummy.called_end == 1 - assert Dummy.called_trial == 5 - assert Dummy.trials == 5 - assert Dummy.total_time == 2*5 + assert Dummy.called_iteration == iterations + assert Dummy.iterations == iterations + assert Dummy.total_time == max_time * iterations def test_writing(self): - '''Try to write CSV, GEXF, sqlite and YAML (without dry_run)''' - n_trials = 5 - config = { - 'name': 'exporter_sim', - 'network_params': { - 'generator': 'complete_graph', - 'n': 4 - }, - 'agent_type': 'CounterModel', - 'max_time': 2, - 'num_trials': n_trials, - 'environment_params': {} - } + """Try to write CSV, sqlite and YAML (without no_dump)""" + n_iterations = 5 + n_nodes = 4 + max_time = 2 output = io.StringIO() - s = simulation.from_config(config) tmpdir = tempfile.mkdtemp() - envs = s.run_simulation(exporters=[ - exporters.default, - exporters.csv, - exporters.gexf, - ], - stats=[distribution,], - outdir=tmpdir, - exporter_params={'copy_to': output}) + + class ConstantEnv(environment.Environment): + @decorators.report + @property + def constant(self): + return 1 + + s = simulation.Simulation( + model=ConstantEnv, + name="exporter_sim", + exporters=[ + exporters.default, + exporters.csv, + ], + exporter_params={"copy_to": output}, + parameters=dict( + network_generator="complete_graph", + network_params={"n": n_nodes}, + agent_class="CounterModel", + agent_reporters={"times": "times"}, + ), + max_time=max_time, + outdir=tmpdir, + iterations=n_iterations, + dump=True, + ) + envs = s.run() result = output.getvalue() - simdir = os.path.join(tmpdir, s.group or '', s.name) - with open(os.path.join(simdir, '{}.dumped.yml'.format(s.name))) as f: + simdir = os.path.join(tmpdir, s.group or "", s.name) + with open(os.path.join(simdir, "{}.dumped.yml".format(s.id))) as f: result = f.read() assert result try: + dbpath = os.path.join(simdir, f"{s.name}.sqlite") + db = sqlite3.connect(dbpath) + cur = db.cursor() + agent_entries = cur.execute("SELECT times FROM agents WHERE times > 0").fetchall() + env_entries = cur.execute("SELECT constant from env WHERE constant == 1").fetchall() + assert len(agent_entries) == n_nodes * n_iterations * max_time + assert len(env_entries) == n_iterations * (max_time + 1) # +1 for the initial state + for e in envs: - with open(os.path.join(simdir, '{}.gexf'.format(e.name))) as f: + with open(os.path.join(simdir, "{}.env.csv".format(e.id))) as f: result = f.read() assert result - with open(os.path.join(simdir, '{}.csv'.format(e.name))) as f: - result = f.read() - assert result finally: shutil.rmtree(tmpdir) diff --git a/tests/test_main.py b/tests/test_main.py index d7dc58c..0bc7f82 100644 --- a/tests/test_main.py +++ b/tests/test_main.py @@ -1,256 +1,132 @@ from unittest import TestCase import os -import io -import yaml import pickle import networkx as nx from functools import partial from os.path import join -from soil import (simulation, Environment, agents, serialization, - utils) +from soil import simulation, Environment, agents, network, serialization, utils, config, from_file from soil.time import Delta +from mesa import Agent as MesaAgent ROOT = os.path.abspath(os.path.dirname(__file__)) -EXAMPLES = join(ROOT, '..', 'examples') +EXAMPLES = join(ROOT, "..", "examples") -class CustomAgent(agents.FSM): +class CustomAgent(agents.FSM, agents.NetworkAgent): @agents.default_state @agents.state def normal(self): - self.neighbors = self.count_agents(state_id='normal', - limit_neighbors=True) + self.neighbors = self.count_agents(state_id="normal", limit_neighbors=True) + @agents.state def unreachable(self): return + class TestMain(TestCase): - - def test_load_graph(self): - """ - Load a graph from file if the extension is known. - Raise an exception otherwise. - """ - config = { - 'network_params': { - 'path': join(ROOT, 'test.gexf') - } - } - G = serialization.load_network(config['network_params']) - assert G - assert len(G) == 2 - with self.assertRaises(AttributeError): - config = { - 'network_params': { - 'path': join(ROOT, 'unknown.extension') - } - } - G = serialization.load_network(config['network_params']) - print(G) - - def test_generate_barabasi(self): - """ - If no path is given, a generator and network parameters - should be used to generate a network - """ - config = { - 'network_params': { - 'generator': 'barabasi_albert_graph' - } - } - with self.assertRaises(TypeError): - G = serialization.load_network(config['network_params']) - config['network_params']['n'] = 100 - config['network_params']['m'] = 10 - G = serialization.load_network(config['network_params']) - assert len(G) == 100 - def test_empty_simulation(self): """A simulation with a base behaviour should do nothing""" config = { - 'network_params': { - 'path': join(ROOT, 'test.gexf') + "parameters": { + "topology": join(ROOT, "test.gexf"), + "agent_class": MesaAgent, + }, + "max_time": 1 + } + s = simulation.from_config(config) + s.run(dump=False) + + def test_network_agent(self): + """ + The initial states should be applied to the agent and the + agent should be able to update its state.""" + config = { + "name": "CounterAgent", + "iterations": 1, + "max_time": 2, + "parameters": { + "network_params": { + "generator": nx.complete_graph, + "n": 2, + }, + "agent_class": "CounterModel", + "states": { + 0: {"times": 10}, + 1: {"times": 20}, + }, }, - 'agent_type': 'BaseAgent', - 'environment_params': { - } } s = simulation.from_config(config) - s.run_simulation(dry_run=True) def test_counter_agent(self): """ The initial states should be applied to the agent and the agent should be able to update its state.""" - config = { - 'name': 'CounterAgent', - 'network_params': { - 'path': join(ROOT, 'test.gexf') - }, - 'agent_type': 'CounterModel', - 'states': [{'times': 10}, {'times': 20}], - 'max_time': 2, - 'num_trials': 1, - 'environment_params': { - } - } - s = simulation.from_config(config) - env = s.run_simulation(dry_run=True)[0] - assert env.get_agent(0)['times', 0] == 11 - assert env.get_agent(0)['times', 1] == 12 - assert env.get_agent(1)['times', 0] == 21 - assert env.get_agent(1)['times', 1] == 22 + env = Environment() + env.add_agent(agents.Ticker, times=10) + env.add_agent(agents.Ticker, times=20) - def test_counter_agent_history(self): - """ - The evolution of the state should be recorded in the logging agent - """ - config = { - 'name': 'CounterAgent', - 'network_params': { - 'path': join(ROOT, 'test.gexf') - }, - 'network_agents': [{ - 'agent_type': 'AggregatedCounter', - 'weight': 1, - 'state': {'state_id': 0} + assert isinstance(env.agents[0], agents.Ticker) + assert env.agents[0]["times"] == 10 + assert env.agents[1]["times"] == 20 + env.step() + assert env.agents[0]["times"] == 11 + assert env.agents[1]["times"] == 21 - }], - 'max_time': 10, - 'environment_params': { - } - } - s = simulation.from_config(config) - env = s.run_simulation(dry_run=True)[0] - for agent in env.network_agents: - last = 0 - assert len(agent[None, None]) == 10 - for step, total in sorted(agent['total', None]): - assert total == last + 2 - last = total - - def test_custom_agent(self): - """Allow for search of neighbors with a certain state_id""" - config = { - 'network_params': { - 'path': join(ROOT, 'test.gexf') - }, - 'network_agents': [{ - 'agent_type': CustomAgent, - 'weight': 1 - - }], - 'max_time': 10, - 'environment_params': { - } - } - s = simulation.from_config(config) - env = s.run_simulation(dry_run=True)[0] - assert env.get_agent(1).count_agents(state_id='normal') == 2 - assert env.get_agent(1).count_agents(state_id='normal', limit_neighbors=True) == 1 - assert env.get_agent(0).neighbors == 1 + def test_init_and_count_agents(self): + """Agents should be properly initialized and counting should filter them properly""" + env = Environment(topology=join(ROOT, "test.gexf")) + env.populate_network([CustomAgent.w(weight=1), CustomAgent.w(weight=3)]) + assert env.agents[0].weight == 1 + assert env.count_agents() == 2 + assert env.count_agents(weight=1) == 1 + assert env.count_agents(weight=3) == 1 + assert env.count_agents(agent_class=CustomAgent) == 2 def test_torvalds_example(self): """A complete example from a documentation should work.""" - config = serialization.load_file(join(EXAMPLES, 'torvalds.yml'))[0] - config['network_params']['path'] = join(EXAMPLES, - config['network_params']['path']) - s = simulation.from_config(config) - env = s.run_simulation(dry_run=True)[0] - for a in env.network_agents: - skill_level = a.state['skill_level'] - if a.id == 'Torvalds': - assert skill_level == 'God' - assert a.state['total'] == 3 - assert a.state['neighbors'] == 2 - elif a.id == 'balkian': - assert skill_level == 'developer' - assert a.state['total'] == 3 - assert a.state['neighbors'] == 1 - else: - assert skill_level == 'beginner' - assert a.state['total'] == 3 - assert a.state['neighbors'] == 1 - - def test_yaml(self): - """ - The YAML version of a newly created simulation - should be equivalent to the configuration file used - """ - with utils.timer('loading'): - config = serialization.load_file(join(EXAMPLES, 'complete.yml'))[0] - s = simulation.from_config(config) - with utils.timer('serializing'): - serial = s.to_yaml() - with utils.timer('recovering'): - recovered = yaml.load(serial, Loader=yaml.SafeLoader) - with utils.timer('deleting'): - del recovered['topology'] - assert config == recovered - - def test_configuration_changes(self): - """ - The configuration should not change after running - the simulation. - """ - config = serialization.load_file(join(EXAMPLES, 'complete.yml'))[0] - s = simulation.from_config(config) - - s.run_simulation(dry_run=True) - nconfig = s.to_dict() - del nconfig['topology'] - assert config == nconfig - - def test_row_conversion(self): - env = Environment() - env['test'] = 'test_value' - - res = list(env.history_to_tuples()) - assert len(res) == len(env.environment_params) - - env.schedule.time = 1 - env['test'] = 'second_value' - res = list(env.history_to_tuples()) - - assert env['env', 0, 'test' ] == 'test_value' - assert env['env', 1, 'test' ] == 'second_value' - - def test_save_geometric(self): - """ - There is a bug in networkx that prevents it from creating a GEXF file - from geometric models. We should work around it. - """ - G = nx.random_geometric_graph(20, 0.1) - env = Environment(topology=G) - f = io.BytesIO() - env.dump_gexf(f) - - def test_save_graph(self): - ''' - The history_to_graph method should return a valid networkx graph. - - The state of the agent should be encoded as intervals in the nx graph. - ''' - G = nx.cycle_graph(5) - distribution = agents.calculate_distribution(None, agents.BaseAgent) - env = Environment(topology=G, network_agents=distribution) - env[0, 0, 'testvalue'] = 'start' - env[0, 10, 'testvalue'] = 'finish' - nG = env.history_to_graph() - values = nG.nodes[0]['attr_testvalue'] - assert ('start', 0, 10) in values - assert ('finish', 10, None) in values + owd = os.getcwd() + pyfile = join(EXAMPLES, "torvalds_sim.py") + try: + os.chdir(os.path.dirname(pyfile)) + s = simulation.from_py(pyfile) + env = s.run(dump=False)[0] + for a in env.network_agents: + skill_level = a["skill_level"] + if a.node_id == "Torvalds": + assert skill_level == "God" + assert a["total"] == 3 + assert a["neighbors"] == 2 + elif a.node_id == "balkian": + assert skill_level == "developer" + assert a["total"] == 3 + assert a["neighbors"] == 1 + else: + assert skill_level == "beginner" + assert a["total"] == 3 + assert a["neighbors"] == 1 + finally: + os.chdir(owd) def test_serialize_class(self): - ser, name = serialization.serialize(agents.BaseAgent) - assert name == 'soil.agents.BaseAgent' + ser, name = serialization.serialize(agents.BaseAgent, known_modules=[]) + assert name == "soil.agents.BaseAgent" + assert ser == agents.BaseAgent + + ser, name = serialization.serialize( + agents.BaseAgent, + known_modules=[ + "soil", + ], + ) + assert name == "BaseAgent" assert ser == agents.BaseAgent ser, name = serialization.serialize(CustomAgent) - assert name == 'test_main.CustomAgent' + assert name == "test_main.CustomAgent" assert ser == CustomAgent pickle.dumps(ser) @@ -262,99 +138,37 @@ class TestMain(TestCase): des = serialization.deserialize(name, ser) assert i == des - def test_serialize_agent_type(self): - '''A class from soil.agents should be serialized without the module part''' - ser = agents.serialize_type(CustomAgent) - assert ser == 'test_main.CustomAgent' - ser = agents.serialize_type(agents.BaseAgent) - assert ser == 'BaseAgent' + def test_serialize_agent_class(self): + """A class from soil.agents should be serialized without the module part""" + ser = agents._serialize_type(CustomAgent) + assert ser == "test_main.CustomAgent" + ser = agents._serialize_type(agents.BaseAgent) + assert ser == "BaseAgent" pickle.dumps(ser) - - def test_deserialize_agent_distribution(self): - agent_distro = [ - { - 'agent_type': 'CounterModel', - 'weight': 1 - }, - { - 'agent_type': 'test_main.CustomAgent', - 'weight': 2 - }, - ] - converted = agents.deserialize_definition(agent_distro) - assert converted[0]['agent_type'] == agents.CounterModel - assert converted[1]['agent_type'] == CustomAgent - pickle.dumps(converted) - - def test_serialize_agent_distribution(self): - agent_distro = [ - { - 'agent_type': agents.CounterModel, - 'weight': 1 - }, - { - 'agent_type': CustomAgent, - 'weight': 2 - }, - ] - converted = agents.serialize_definition(agent_distro) - assert converted[0]['agent_type'] == 'CounterModel' - assert converted[1]['agent_type'] == 'test_main.CustomAgent' - pickle.dumps(converted) - - def test_pickle_agent_environment(self): - env = Environment(name='Test') - a = agents.BaseAgent(model=env, unique_id=25) - - a['key'] = 'test' - - pickled = pickle.dumps(a) - recovered = pickle.loads(pickled) - - assert recovered.env.name == 'Test' - assert list(recovered.env._history.to_tuples()) - assert recovered['key', 0] == 'test' - assert recovered['key'] == 'test' - - def test_subgraph(self): - '''An agent should be able to subgraph the global topology''' - G = nx.Graph() - G.add_node(3) - G.add_edge(1, 2) - distro = agents.calculate_distribution(agent_type=agents.NetworkAgent) - env = Environment(name='Test', topology=G, network_agents=distro) - lst = list(env.network_agents) - - a2 = env.get_agent(2) - a3 = env.get_agent(3) - assert len(a2.subgraph(limit_neighbors=True)) == 2 - assert len(a3.subgraph(limit_neighbors=True)) == 1 - assert len(a3.subgraph(limit_neighbors=True, center=False)) == 0 - assert len(a3.subgraph(agent_type=agents.NetworkAgent)) == 3 - - def test_templates(self): - '''Loading a template should result in several configs''' - configs = serialization.load_file(join(EXAMPLES, 'template.yml')) - assert len(configs) > 0 def test_until(self): - config = { - 'name': 'until_sim', - 'network_params': {}, - 'agent_type': 'CounterModel', - 'max_time': 2, - 'num_trials': 50, - 'environment_params': {} - } - s = simulation.from_config(config) - runs = list(s.run_simulation(dry_run=True)) - over = list(x.now for x in runs if x.now>2) - assert len(runs) == config['num_trials'] + n_runs = 0 + + class CheckRun(agents.BaseAgent): + def step(self): + nonlocal n_runs + n_runs += 1 + + n_trials = 50 + max_time = 2 + s = simulation.Simulation( + parameters=dict(agents=dict(agent_classes=[CheckRun], k=1)), + iterations=n_trials, + max_time=max_time, + ) + runs = list(s.run(dump=False)) + over = list(x.now for x in runs if x.now > 2) + assert len(runs) == n_trials assert len(over) == 0 - def test_fsm(self): - '''Basic state change''' + """Basic state change""" + class ToggleAgent(agents.FSM): @agents.default_state @agents.state @@ -373,7 +187,8 @@ class TestMain(TestCase): assert a.state_id == a.ping.id def test_fsm_when(self): - '''Basic state change''' + """Basic state change""" + class ToggleAgent(agents.FSM): @agents.default_state @agents.state @@ -389,3 +204,32 @@ class TestMain(TestCase): assert when == 2 when = a.step() assert when == Delta(a.interval) + + def test_load_sim(self): + """Make sure at least one of the examples can be loaded""" + sims = from_file(os.path.join(EXAMPLES, "newsspread", "newsspread_sim.py")) + assert len(sims) == 3*3*2 + for sim in sims: + assert sim + assert sim.name == "newspread_sim" + assert sim.iterations == 5 + assert sim.max_steps == 300 + assert not sim.dump + assert sim.parameters + assert "ratio_dumb" in sim.parameters + assert "ratio_herd" in sim.parameters + assert "ratio_wise" in sim.parameters + assert "network_generator" in sim.parameters + assert "network_params" in sim.parameters + assert "prob_neighbor_spread" in sim.parameters + + def test_config_matrix(self): + """It should be possible to specify a matrix of parameters""" + a = [1, 2] + b = [3, 4] + sim = simulation.Simulation(matrix=dict(a=a, b=b)) + configs = sim._collect_params() + assert len(configs) == len(a) * len(b) + for i in a: + for j in b: + assert {"a": i, "b": j} in configs \ No newline at end of file diff --git a/tests/test_mesa.py b/tests/test_mesa.py index b219de9..a0aa5a1 100644 --- a/tests/test_mesa.py +++ b/tests/test_mesa.py @@ -1,4 +1,4 @@ -''' +""" Mesa-SOIL integration tests We have to test that: @@ -8,13 +8,15 @@ We have to test that: - Mesa visualizations work with SOIL simulations -''' +""" from mesa import Agent, Model from mesa.time import RandomActivation from mesa.space import MultiGrid + class MoneyAgent(Agent): - """ An agent with fixed initial wealth.""" + """An agent with fixed initial wealth.""" + def __init__(self, unique_id, model): super().__init__(unique_id, model) self.wealth = 1 @@ -33,15 +35,15 @@ class MoneyAgent(Agent): def move(self): possible_steps = self.model.grid.get_neighborhood( - self.pos, - moore=True, - include_center=False) + self.pos, moore=True, include_center=False + ) new_position = self.random.choice(possible_steps) self.model.grid.move_agent(self, new_position) class MoneyModel(Model): """A model with some number of agents.""" + def __init__(self, N, width, height): self.num_agents = N self.grid = MultiGrid(width, height, True) @@ -58,7 +60,7 @@ class MoneyModel(Model): self.grid.place_agent(a, (x, y)) def step(self): - '''Advance the model by one step.''' + """Advance the model by one step.""" self.schedule.step() diff --git a/tests/test_network.py b/tests/test_network.py new file mode 100644 index 0000000..b995ff7 --- /dev/null +++ b/tests/test_network.py @@ -0,0 +1,87 @@ +from unittest import TestCase + +import io +import os +import networkx as nx + +from os.path import join + +from soil import config, network, environment, agents, simulation +from test_main import CustomAgent + +ROOT = os.path.abspath(os.path.dirname(__file__)) +EXAMPLES = join(ROOT, "..", "examples") + + +class TestNetwork(TestCase): + def test_load_graph(self): + """ + Load a graph from file if the extension is known. + Raise an exception otherwise. + """ + G = network.from_topology(join(ROOT, "test.gexf")) + assert G + assert len(G) == 2 + with self.assertRaises(AttributeError): + G = network.from_topology(join(ROOT, "unknown.extension")) + print(G) + + def test_generate_barabasi(self): + """ + If no path is given, a generator and network parameters + should be used to generate a network + """ + cfg = {"generator": "barabasi_albert_graph"} + with self.assertRaises(Exception): + G = network.from_params(**cfg) + cfg["n"] = 100 + cfg["m"] = 10 + G = network.from_params(**cfg) + assert len(G) == 100 + + def test_save_geometric(self): + """ + There is a bug in networkx that prevents it from creating a GEXF file + from geometric models. We should work around it. + """ + G = nx.random_geometric_graph(20, 0.1) + env = environment.NetworkEnvironment(topology=G) + f = io.BytesIO() + assert env.G + network.dump_gexf(env.G, f) + + def test_networkenvironment_creation(self): + """Networkenvironment should accept netconfig as parameters""" + env = environment.Environment(topology=join(ROOT, "test.gexf")) + env.populate_network(CustomAgent) + assert env.G + env.step() + assert len(env.G) == 2 + assert len(env.agents) == 2 + assert env.agents[1].count_agents(state_id="normal") == 2 + assert env.agents[1].count_agents(state_id="normal", limit_neighbors=True) == 1 + assert env.agents[0].count_neighbors() == 1 + + def test_custom_agent_neighbors(self): + """Allow for search of neighbors with a certain state_id""" + env = environment.Environment() + env.create_network(join(ROOT, "test.gexf")) + env.populate_network(CustomAgent) + assert env.agents[1].count_agents(state_id="normal") == 2 + assert env.agents[1].count_agents(state_id="normal", limit_neighbors=True) == 1 + assert env.agents[0].count_neighbors() == 1 + + def test_subgraph(self): + """An agent should be able to subgraph the global topology""" + G = nx.Graph() + G.add_node(3) + G.add_edge(1, 2) + env = environment.Environment(name="Test", topology=G) + env.populate_network(agents.NetworkAgent) + + a2 = env.agent(node_id=2) + a3 = env.agent(node_id=3) + assert len(a2.subgraph(limit_neighbors=True)) == 2 + assert len(a3.subgraph(limit_neighbors=True)) == 1 + assert len(a3.subgraph(limit_neighbors=True, center=False)) == 0 + assert len(a3.subgraph(agent_class=agents.NetworkAgent)) == 3 diff --git a/tests/test_stats.py b/tests/test_stats.py deleted file mode 100644 index 406e1fd..0000000 --- a/tests/test_stats.py +++ /dev/null @@ -1,34 +0,0 @@ -from unittest import TestCase - -from soil import simulation, stats -from soil.utils import unflatten_dict - -class Stats(TestCase): - - def test_distribution(self): - '''The distribution exporter should write the number of agents in each state''' - config = { - 'name': 'exporter_sim', - 'network_params': { - 'generator': 'complete_graph', - 'n': 4 - }, - 'agent_type': 'CounterModel', - 'max_time': 2, - 'num_trials': 5, - 'environment_params': {} - } - s = simulation.from_config(config) - for env in s.run_simulation(stats=[stats.distribution]): - pass - # stats_res = unflatten_dict(dict(env._history['stats', -1, None])) - allstats = s.get_stats() - for stat in allstats: - assert 'count' in stat - assert 'mean' in stat - if 'trial_id' in stat: - assert stat['mean']['neighbors'] == 3 - assert stat['count']['total']['4'] == 4 - else: - assert stat['count']['count']['neighbors']['3'] == 20 - assert stat['mean']['min']['neighbors'] == stat['mean']['max']['neighbors'] diff --git a/tests/test_time.py b/tests/test_time.py new file mode 100644 index 0000000..27d1765 --- /dev/null +++ b/tests/test_time.py @@ -0,0 +1,75 @@ +from unittest import TestCase + +from soil import time, agents, environment + + +class TestMain(TestCase): + def test_cond(self): + """ + A condition should match a When if the concition is True + """ + + t = time.Cond(lambda t: True) + f = time.Cond(lambda t: False) + for i in range(10): + w = time.When(i) + assert w == t + assert w is not f + + def test_cond(self): + """ + Comparing a Cond to a Delta should always return False + """ + + c = time.Cond(lambda t: False) + d = time.Delta(1) + assert c is not d + + def test_cond_env(self): + """ """ + + times_started = [] + times_awakened = [] + times_asleep = [] + times = [] + done = [] + + class CondAgent(agents.BaseAgent): + def step(self): + nonlocal done + times_started.append(self.now) + while True: + times_asleep.append(self.now) + yield time.Cond(lambda agent: agent.now >= 10, delta=2) + times_awakened.append(self.now) + if self.now >= 10: + break + done.append(self.now) + + env = environment.Environment() + env.add_agent(CondAgent) + + while env.schedule.time < 11: + times.append(env.now) + env.step() + + assert env.schedule.time == 11 + assert times_started == [0] + assert times_awakened == [10] + assert done == [10] + # The first time will produce the Cond. + assert env.schedule.steps == 6 + assert len(times) == 6 + + while env.schedule.time < 13: + times.append(env.now) + env.step() + + assert times == [0, 2, 4, 6, 8, 10, 11] + assert env.schedule.time == 13 + assert times_started == [0, 11] + assert times_awakened == [10] + assert done == [10] + # Once more to yield the cond, another one to continue + assert env.schedule.steps == 7 + assert len(times) == 7