Change setup.py

.flake8

@@ -0,0 +1,5 @@
+[flake8]
+exclude = 
+  venv
+max-line-length = 88
+extend-ignore = E231

.github/workflows/pythonapp.yml

@@ -15,13 +15,10 @@ jobs:
 
     steps:
     - uses: actions/checkout@v1
-    - name: setup PYTHONPATH
-      run: |
-        export PYTHONPATH=${PWD}:${PYTHONPATH}
     - name: Lint with flake8
       run: |
         # stop the build if flake8 fails.
-        flake8 . --count --show-source --statistics
+        flake8 . --extend-ignore=E231 --count --show-source --statistics
         # exit-zero treats all errors as warnings. 
         flake8 . --count --exit-zero --max-complexity=10 --statistics
     - name: Test with python unittest

README.md

@@ -16,21 +16,15 @@ We can synthesize neural-network controllers with Lyapunov stability guarantees.
 ## Python requirements
 We use python 3 in this project. You could first install the packages in requirements.txt.
 
-## Install gurobi
-Please download gurobi from https://www.gurobi.com/products/gurobi-optimizer/. We require at least gurobi 9.5. After downloading the software, please install its Python API by following https://www.gurobi.com/documentation/9.0/quickstart_mac/the_grb_python_interface_f.html
-
-To check your gurobi installation, type the following command in your terminal:
+## Installation
+Please run
 ```
-$ python3 -c "import gurobipy"
+pip install -e .
 ```
-There should be no error thrown when executing the command.
+to install the package together with the dependencies.
 
-## Setup environment variable
-In the terminal, please run
-```
-$ python3 setup.py
-```
-It will print out the command to setup the environment variables. Execute that command in your terminal.
+## Setup gurobi
+Please refer to https://support.gurobi.com/hc/en-us/articles/360044290292-How-do-I-install-Gurobi-for-Python- on setting up Gurobi. Note that by default Gurobi ships with a limited license that has size limits on the optimization problem. Please refer to https://support.gurobi.com/hc/en-us/articles/360051597492 on using your own academic or commential license.
 
 ## Run a toy example
 You could run

neural_network_lyapunov/examples/car/train_acceleration_car_demo.py

@@ -73,9 +73,10 @@ def compute_delta_pos(model, network_input):
 
 
 if __name__ == "__main__":
-    raise Exception("Dubins car cannot be stabilized by a continuous controller, refer" +
-                    "to https://arxiv.org/pdf/math/9902026.pdf for a proof. Using" +
-                    "neural-network controllers is doomed to fail for Dubins car.")
+    raise Exception("Dubins car cannot be stabilized by a continuous controller," +
+                    "referto https://arxiv.org/pdf/math/9902026.pdf for a proof." +
+                    "Using neural-network controllers is doomed to fail for Dubins" +
+                    "car.")
     parser = argparse.ArgumentParser(
         description="Acceleration car training demo")
     parser.add_argument("--generate_dynamics_data", action="store_true")

neural_network_lyapunov/examples/car/train_unicycle_demo.py

@@ -153,9 +153,10 @@ def compute_v(model, x):
 
 
 if __name__ == "__main__":
-    raise Exception("Dubins car cannot be stabilized by a continuous controller, refer" +
-                    "to https://arxiv.org/pdf/math/9902026.pdf for a proof. Using" +
-                    "neural-network controllers is doomed to fail for Dubins car.")
+    raise Exception("Dubins car cannot be stabilized by a continuous controller," +
+                    " refer to https://arxiv.org/pdf/math/9902026.pdf for a proof." +
+                    " Using neural-network controllers is doomed to fail for Dubins" +
+                    " car.")
     parser = argparse.ArgumentParser(description="Unicycle training demo")
     parser.add_argument("--generate_dynamics_data", action="store_true")
     parser.add_argument("--load_dynamics_data",

neural_network_lyapunov/examples/quadrotor3d/visualize_quadrotor3d.py

@@ -90,10 +90,10 @@ def get_mesh_markers(traj, num_markers):
     args = parser.parse_args()
 
     traj = np.load(args.file)
-    assert(isinstance(traj, np.ndarray))
-    assert(len(traj.shape) == 2)
-    assert(traj.shape[0] >= 6)
-    assert(traj.shape[1] >= 2)
+    assert (isinstance(traj, np.ndarray))
+    assert (len(traj.shape) == 2)
+    assert (traj.shape[0] >= 6)
+    assert (traj.shape[1] >= 2)
 
     mesh_pub = rospy.Publisher('animated_mesh', Marker, queue_size=10)
     traj_line_pub = rospy.Publisher('traj_line', Marker, queue_size=10)

neural_network_lyapunov/examples/rl/td3.py

@@ -323,7 +323,7 @@ def test_agent(epoch):
         mean_ret = 0.
         for j in range(num_test_episodes):
             o, d, ep_ret, ep_len = test_env.reset(), False, 0, 0
-            while not(d or (ep_len == max_ep_len)):
+            while not (d or (ep_len == max_ep_len)):
                 # Take deterministic actions at test time (noise_scale=0)
                 o, r, d, _ = test_env.step(get_action(o, 0))
                 ep_ret += r

neural_network_lyapunov/examples/tinydiffsim/learn_ca_dyn.py

@@ -65,7 +65,7 @@ def generate_dataset(cfg, mb, actuation_mask):
                 j += 1
             else:
                 tau[i] = 0
-        assert(j == u_dim)
+        assert (j == u_dim)
 
         mb.q = q
         mb.qd = qd

neural_network_lyapunov/utils.py

@@ -906,7 +906,6 @@ def leaky_relu_interval(negative_slope, x_lo, x_up):
     [output_lo, output_up]
     """
     assert (x_up > x_lo)
-    assert (type(x_lo) == type(x_up))
     if (negative_slope >= 0):
         if x_lo >= 0:
             if isinstance(x_lo, torch.Tensor):

requirements.txt

@@ -1,6 +1,10 @@
-pytorch >= 1.8
+cvxpy >= 1.1
+flake8
+gurobipy == 10.0 
+matplotlib
 numpy >= 1.18
+pybullet
+torch 
 scipy >= 1.4
 tensorboard >= 2.3
-pybullet
-cvxpy >= 1.1
+wandb

setup.py

@@ -1,24 +1,12 @@
-import os
-
-from os.path import dirname, abspath, isdir
-
-
-def main():
-    cur_dir = dirname(abspath(__file__))
-    # Now add this folder to PYTHONPATH
-    setup_script = cur_dir + "/config/setup_environments.sh"
-    if not isdir(cur_dir+"/config"):
-        print("create config folder")
-        os.mkdir(cur_dir+"/config")
-    with open(setup_script, "a") as f:
-        f.write(f"""
-        export PYTHONPATH={cur_dir}:${{PYTHONPATH}}
-        """)
-    print("""
-    To use neural_network_lyapunov functionality:
-        source ./config/setup_environments.sh
-    """)
-
-
-if __name__ == "__main__":
-    main()
+from setuptools import setup, find_packages
+
+# Read the contents of requirements file
+with open("requirements.txt") as f:
+    requirements = f.read().splitlines()
+
+setup(
+    name="neural-network-lyapunov",
+    version="0.1",
+    packages=find_packages(),
+    install_requires=requirements,
+)