Add: Quick start guide

PythonLinearNonlinearControl/configs/__init__.py

@@ -0,0 +1,6 @@
+from PythonLinearNonlinearControl.configs.cartpole \
+    import CartPoleConfigModule  # NOQA
+from PythonLinearNonlinearControl.configs.first_order_lag \
+    import FirstOrderLagConfigModule  # NOQA
+from PythonLinearNonlinearControl.configs.two_wheeled \
+    import TwoWheeledConfigModule  # NOQA

PythonLinearNonlinearControl/controllers/__init__.py

@@ -0,0 +1,14 @@
+from PythonLinearNonlinearControl.controllers.cem \
+    import CEM  # NOQA
+from PythonLinearNonlinearControl.controllers.mppi \
+    import MPPI  # NOQA
+from PythonLinearNonlinearControl.controllers.mppi_williams \
+    import MPPIWilliams  # NOQA
+from PythonLinearNonlinearControl.controllers.random \
+    import RandomShooting  # NOQA
+from PythonLinearNonlinearControl.controllers.ilqr \
+    import iLQR  # NOQA
+from PythonLinearNonlinearControl.controllers.ddp \
+    import DDP  # NOQA
+from PythonLinearNonlinearControl.controllers.mpc \
+    import LinearMPC  # NOQA

PythonLinearNonlinearControl/envs/__init__.py

@@ -0,0 +1,8 @@
+from PythonLinearNonlinearControl.envs.cartpole \
+    import CartPoleEnv  # NOQA
+from PythonLinearNonlinearControl.envs.first_order_lag \
+    import FirstOrderLagEnv  # NOQA
+from PythonLinearNonlinearControl.envs.two_wheeled \
+    import TwoWheeledConstEnv  # NOQA
+from PythonLinearNonlinearControl.envs.two_wheeled \
+    import TwoWheeledTrackEnv  # NOQA

PythonLinearNonlinearControl/models/__init__.py

@@ -0,0 +1,6 @@
+from PythonLinearNonlinearControl.models.cartpole \
+    import CartPoleModel  # NOQA
+from PythonLinearNonlinearControl.models.first_order_lag \
+    import FirstOrderLagModel  # NOQA
+from PythonLinearNonlinearControl.models.two_wheeled \
+    import TwoWheeledModel  # NOQA

PythonLinearNonlinearControl/planners/__init__.py

@@ -0,0 +1,4 @@
+from PythonLinearNonlinearControl.planners.const_planner \
+    import ConstantPlanner  # NOQA
+from PythonLinearNonlinearControl.planners.closest_point_planner \
+    import ClosestPointPlanner  # NOQA

PythonLinearNonlinearControl/plotters/__init__.py

@@ -0,0 +1,4 @@
+from PythonLinearNonlinearControl.plotters.animator \
+    import Animator
+from PythonLinearNonlinearControl.plotters.plot_func \
+    import plot_results

PythonLinearNonlinearControl/plotters/animator.py

@@ -10,12 +10,17 @@ logger = getLogger(__name__)
 class Animator():
     """ animation class
     """
-    def __init__(self, args, env):
+    def __init__(self, env, args=None):
         """
         """
-        self.env_name = args.env
+        self.env_name = "Env"
-        self.result_dir = args.result_dir
+        self.result_dir = "./result"
-        self.controller_type = args.controller_type
+        self.controller_type = "controller"
+
+        if args is not None:
+            self.env_name = args.env
+            self.result_dir = args.result_dir
+            self.controller_type = args.controller_type
 
         self.interval = env.config["dt"] * 1000.  # to ms
         self.plot_func = env.plot_func

PythonLinearNonlinearControl/runners/__init__.py

@@ -0,0 +1,2 @@
+from PythonLinearNonlinearControl.runners.runner \
+    import ExpRunner  # NOQA

README.md

@@ -3,9 +3,9 @@
 [![GitHub issues open](https://img.shields.io/github/issues/Shunichi09/PythonLinearNonlinearControl.svg)]()
 [![Build Status](https://travis-ci.org/Shunichi09/PythonLinearNonlinearControl.svg?branch=master&service=github)](https://travis-ci.org/Shunichi09/PythonLinearNonlinearControl)
 
-# PythonLinearNonLinearControl
+# PythonLinearNonlinearControl
 
-PythonLinearNonLinearControl is a library implementing the linear and nonlinear control theories in python.
+PythonLinearNonlinearControl is a library implementing the linear and nonlinear control theories in python.
 Due to use only basic libralies (scipy, numpy), this library is easy to extend for your own situations.
 
 <div><img src="assets/concept.png" width="500"/></div>
@@ -78,7 +78,7 @@ All states and inputs of environments are continuous.
 
 You could know abount our environmets more in [Environments.md](Environments.md)
 
-# Usage 
+# Installation
 
 ## To install this package
 
@@ -104,16 +104,6 @@ or
 pip install -e .
 ```
 
-## Run Experiments
-
-You can run the experiments as follows:
-
-```
-python scripts/simple_run.py --env FirstOrderLag --controller CEM
-```
-
-**figures and animations are saved in the ./result folder.**
-
 # Basic concepts
 
 When we design control systems, we should have **Model**, **Planner**, **Controller** and **Runner** as shown in the figure.
@@ -141,6 +131,71 @@ Runner runs the simulation.
 
 Please, see more detail in each scripts.
 
+# Getting started
+
+## [QuickStart Guide](scripts/quickstart/quickstart.md)
+
+This is a quickstart guide for users who just want to try PythonLinearNonlinearControl.
+If you have not installed PythonLinearNonLinearControl, please see the section of "how to setup" in README.md
+
+When we design control systems, we should have Environment, Model, Planner, Controller and Runner.
+Therefore your script contains those Modules.
+
+First, import each Modules from PythonLinearNonlinearControl.
+
+```py
+from PythonLinearNonlinearControl import configs 
+from PythonLinearNonlinearControl import envs
+from PythonLinearNonlinearControl import models
+from PythonLinearNonlinearControl import planners
+from PythonLinearNonlinearControl import controllers
+from PythonLinearNonlinearControl import runners
+```
+
+Configs contains each modules configurations such as cost functions, prediction length, ...etc.
+
+Then you can make each module. (This is example about CEM and CartPole env)
+
+```py
+config = configs.CartPoleConfigModule()
+env = envs.CartPoleEnv()
+model = models.CartPoleModel(config)
+planner = controllers.CEM(config, model)
+runner = planners.ConstantPlanner(config)
+controller = runners.ExpRunner()
+```
+
+The preparation for experiment has done!
+Please run the runner.
+
+```py
+history_x, history_u, history_g = runner.run(env, controller, planner) 
+```
+
+You can get the results of history of state, history of input and history of goal.
+Use that histories to visualize the Animation or Figures.
+(Note FirstOrderEnv does not support animation)
+
+```py
+# plot results
+plot_results(args, history_x, history_u, history_g=history_g)
+save_plot_data(args, history_x, history_u, history_g=history_g)
+
+# create animation
+animator = Animator(args, env)
+animator.draw(history_x, history_g)
+```
+
+## Run Experiments
+
+You can run the experiments as follows:
+
+```
+python scripts/simple_run.py --env CartPole --controller CEM --save_anim 1
+```
+
+**figures and animations are saved in the ./result folder.**
+
 # Old version
 
 If you are interested in the old version of this library, that was not a library just examples, please see [v1.0](https://github.com/Shunichi09/PythonLinearNonlinearControl/tree/v1.0)

scripts/quickstart/quickstart.md

@@ -0,0 +1,52 @@
+# PythonLinearnNonlinearControl Quickstart Guide
+
+This is a quickstart guide for users who just want to try PythonLinearNonlinearControl.
+If you have not installed PythonLinearNonLinearControl, please see the section of "how to setup" in README.md
+
+When we design control systems, we should have Environment, Model, Planner, Controller and Runner.
+Therefore your script contains those Modules.
+
+First, import each Modules from PythonLinearNonlinearControl.
+
+```py
+from PythonLinearNonlinearControl import configs 
+from PythonLinearNonlinearControl import envs
+from PythonLinearNonlinearControl import models
+from PythonLinearNonlinearControl import planners
+from PythonLinearNonlinearControl import controllers
+from PythonLinearNonlinearControl import runners
+```
+
+Configs contains each modules configurations such as cost functions, prediction length, ...etc.
+
+Then you can make each module. (This is example about CEM and CartPole env)
+
+```py
+config = configs.CartPoleConfigModule()
+env = envs.CartPoleEnv()
+model = models.CartPoleModel(config)
+planner = controllers.CEM(config, model)
+runner = planners.ConstantPlanner(config)
+controller = runners.ExpRunner()
+```
+
+The preparation for experiment has done!
+Please run the runner.
+
+```py
+history_x, history_u, history_g = runner.run(env, controller, planner) 
+```
+
+You can get the results of history of state, history of input and history of goal.
+Use that histories to visualize the Animation or Figures.
+(Note FirstOrderEnv does not support animation)
+
+```py
+# plot results
+plot_results(args, history_x, history_u, history_g=history_g)
+save_plot_data(args, history_x, history_u, history_g=history_g)
+
+# create animation
+animator = Animator(args, env)
+animator.draw(history_x, history_g)
+```

scripts/simple_run.py

@@ -41,7 +41,7 @@ def run(args):
     save_plot_data(args, history_x, history_u, history_g=history_g)
 
     if args.save_anim:
-        animator = Animator(args, env)
+        animator = Animator(env, args=args)
         animator.draw(history_x, history_g)
 
 def main():