chore: release v0.0.1

scripts/view.py

@@ -0,0 +1,78 @@
+# Copyright (C) 2020-2025 Motphys Technology Co., Ltd. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+import gymnasium as gym
+import numpy as np
+from absl import app, flags
+
+from motrix_envs import registry
+from motrix_envs.np.env import NpEnv
+from motrix_envs.np.renderer import NpRenderer
+
+_ENV = flags.DEFINE_string("env", "cartpole", "The env to view")
+_SIM_BACKEND = flags.DEFINE_string("sim-backend", None, "The simulation backend to use.")
+_NUM_ENVS = flags.DEFINE_integer("num-envs", 1, "Number of parallel environments.")
+
+
+class NpEnvRunner:
+    _renderer: NpRenderer
+
+    def __init__(self, env: NpEnv):
+        self._env = env
+        self._renderer = NpRenderer(env)
+
+    def _sample_random_action(self):
+        action_space = self._env.action_space
+        if isinstance(action_space, gym.spaces.Box):
+            size = (self._env.num_envs, *action_space.shape)
+            return np.random.uniform(
+                low=action_space.low,
+                high=action_space.high,
+                size=size,
+            ).astype(action_space.dtype)
+        else:
+            raise NotImplementedError("Only Box action space is supported")
+
+    def step(self):
+        actions = self._sample_random_action()
+        self._env.step(actions)
+
+    def start(self):
+        import time
+
+        env_dt = self._env.cfg.ctrl_dt
+        while True:
+            t0 = time.monotonic()
+            actions = self._sample_random_action()
+            self._env.step(actions)
+            self._renderer.render()
+            real_dt = time.monotonic() - t0
+            sleep_dt = env_dt - real_dt
+            if sleep_dt > 0:
+                time.sleep(sleep_dt)
+
+
+def main(argv):
+    env_name = _ENV.value
+    sim_backend = _SIM_BACKEND.value
+    num_envs = _NUM_ENVS.value
+    env = registry.make(env_name, sim_backend=sim_backend, num_envs=num_envs)
+
+    runner = NpEnvRunner(env)
+    runner.start()
+
+
+if __name__ == "__main__":
+    app.run(main)