The examples weren't being properly tested in the last commit. When we fixed
that a lot of bugs in the new implementation of environment and agent were
found, which accounts for most of these changes.
The main difference is the mechanism to load simulations from a configuration
file. For that to work, we had to rework our module loading code in
`serialization` and add a `source_file` attribute to configurations (and
simulations, for that matter).
* Removed old/unnecessary models
* Added a `simulation.{iter_}from_py` method to load simulations from python
files
* Changed tests of examples to run programmatic simulations
* Fixed programmatic examples
Treating time and conditions as the same entity was getting confusing, and it
added a lot of unnecessary abstraction in a critical part (the scheduler).
The scheduling queue now has the time as a floating number (faster), the agent
id (for ties) and the condition, as well as the agent. The first three
elements (time, id, condition) can be considered as the "key" for the event.
To allow for agent execution to be "randomized" within every step, a new
parameter has been added to the scheduler, which makes it add a random number to
the key in order to change the ordering.
`EventedAgent.received` now checks the messages before returning control to the
user by default.
Documentation needs some improvement
The API has been simplified to only allow for ONE topology per
NetworkEnvironment.
This covers the main use case, and simplifies the code.
See CHANGELOG.md for a full list of changes
* Removed nxsim
* Refactored `agents.NetworkAgent` and `agents.BaseAgent`
* Refactored exporters
* Added stats to history
