sb3_demo.ipynb

{
 "cells": [
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-09T01:02:04.724524Z",
     "start_time": "2024-08-09T01:02:04.281452Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import numpy as np\n",
    "from minigrid.core.constants import COLOR_NAMES\n",
    "from minigrid.core.grid import Grid\n",
    "from minigrid.core.mission import MissionSpace\n",
    "from minigrid.core.world_object import Door, Goal, Key, Wall\n",
    "from minigrid.manual_control import ManualControl\n",
    "from minigrid.minigrid_env import MiniGridEnv"
   ],
   "id": "19be78cdf43586b1",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pygame 2.6.0 (SDL 2.28.4, Python 3.8.19)\n",
      "Hello from the pygame community. https://www.pygame.org/contribute.html\n"
     ]
    }
   ],
   "execution_count": 1
  },
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2024-08-09T01:02:04.742405Z",
     "start_time": "2024-08-09T01:02:04.726777Z"
    }
   },
   "source": [
    "class SimpleEnv(MiniGridEnv):\n",
    "    def __init__(\n",
    "        self,\n",
    "        size=10,\n",
    "        agent_start_pos=(1, 1),\n",
    "        agent_start_dir=0,\n",
    "        max_steps: int or None = None,\n",
    "        **kwargs,\n",
    "    ):\n",
    "        self.agent_start_pos = agent_start_pos\n",
    "        self.agent_start_dir = agent_start_dir\n",
    "\n",
    "        mission_space = MissionSpace(mission_func=self._gen_mission)\n",
    "\n",
    "        if max_steps is None:\n",
    "            max_steps = 4 * size**2\n",
    "\n",
    "        super().__init__(\n",
    "            mission_space=mission_space,\n",
    "            grid_size=size,\n",
    "            # Set this to True for maximum speed\n",
    "            see_through_walls=True,\n",
    "            max_steps=max_steps,\n",
    "            **kwargs,\n",
    "        )\n",
    "\n",
    "    @staticmethod\n",
    "    def _gen_mission():\n",
    "        return \"grand mission\"\n",
    "\n",
    "    def _gen_grid(self, width, height):\n",
    "        # Create an empty grid\n",
    "        self.grid = Grid(width, height)\n",
    "\n",
    "        # Generate the surrounding walls\n",
    "        self.grid.wall_rect(0, 0, width, height)\n",
    "\n",
    "        # Generate vertical separation wall\n",
    "        for i in range(0, height):\n",
    "            self.grid.set(5, i, Wall())\n",
    "        \n",
    "        # Place the door and key\n",
    "        self.grid.set(5, 6, Door(COLOR_NAMES[0], is_locked=True))\n",
    "        self.grid.set(3, 6, Key(COLOR_NAMES[0]))\n",
    "\n",
    "        # Place a goal square in the bottom-right corner\n",
    "        self.put_obj(Goal(), width - 2, height - 2)\n",
    "\n",
    "        # Place the agent\n",
    "        if self.agent_start_pos is not None:\n",
    "            self.agent_pos = self.agent_start_pos\n",
    "            self.agent_dir = self.agent_start_dir\n",
    "        else:\n",
    "            self.place_agent()\n",
    "\n",
    "        self.mission = \"grand mission\"\n"
   ],
   "outputs": [],
   "execution_count": 2
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-09T01:02:04.758419Z",
     "start_time": "2024-08-09T01:02:04.742405Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# \"left\": Actions.left,\n",
    "# \"right\": Actions.right,\n",
    "# \"up\": Actions.forward,\n",
    "# \"space\": Actions.toggle,\n",
    "# \"pageup\": Actions.pickup,\n",
    "# \"pagedown\": Actions.drop,\n",
    "# \"tab\": Actions.pickup,\n",
    "# \"left shift\": Actions.drop,\n",
    "# \"enter\": Actions.done,\n",
    "\n",
    "env = SimpleEnv(render_mode=\"human\")\n",
    "\n",
    "# enable manual control for testing\n",
    "manual_control = ManualControl(env, seed=42)\n",
    "# manual_control.start()"
   ],
   "id": "3846cc10b9e313dd",
   "outputs": [],
   "execution_count": 3
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-09T01:02:07.167682Z",
     "start_time": "2024-08-09T01:02:04.758419Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import gymnasium as gym\n",
    "from stable_baselines3.common.torch_layers import BaseFeaturesExtractor\n",
    "import torch\n",
    "import torch.nn as nn"
   ],
   "id": "6a3f7f9bfbc55a98",
   "outputs": [],
   "execution_count": 4
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-09T01:02:07.183334Z",
     "start_time": "2024-08-09T01:02:07.167682Z"
    }
   },
   "cell_type": "code",
   "source": [
    "class MinigridFeaturesExtractor(BaseFeaturesExtractor):\n",
    "    def __init__(self, observation_space: gym.Space, features_dim: int = 512, normalized_image: bool = False) -> None:\n",
    "        super().__init__(observation_space, features_dim)\n",
    "        n_input_channels = observation_space.shape[0]\n",
    "        self.cnn = nn.Sequential(\n",
    "            nn.Conv2d(n_input_channels, 16, (2, 2)),\n",
    "            nn.ReLU(),\n",
    "            nn.Conv2d(16, 32, (2, 2)),\n",
    "            nn.ReLU(),\n",
    "            nn.Conv2d(32, 64, (2, 2)),\n",
    "            nn.ReLU(),\n",
    "            nn.Flatten(),\n",
    "        )\n",
    "\n",
    "        # Compute shape by doing one forward pass\n",
    "        with torch.no_grad():\n",
    "            n_flatten = self.cnn(torch.as_tensor(observation_space.sample()[None]).float()).shape[1]\n",
    "\n",
    "        self.linear = nn.Sequential(nn.Linear(n_flatten, features_dim), nn.ReLU())\n",
    "\n",
    "    def forward(self, observations: torch.Tensor) -> torch.Tensor:\n",
    "        return self.linear(self.cnn(observations))"
   ],
   "id": "b568ded339500534",
   "outputs": [],
   "execution_count": 5
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-09T01:02:07.198948Z",
     "start_time": "2024-08-09T01:02:07.183334Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from minigrid.wrappers import ImgObsWrapper\n",
    "from stable_baselines3 import PPO"
   ],
   "id": "330eedee3c454410",
   "outputs": [],
   "execution_count": 6
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-09T01:02:16.060548Z",
     "start_time": "2024-08-09T01:02:07.198948Z"
    }
   },
   "cell_type": "code",
   "source": [
    "policy_kwargs = dict(\n",
    "    features_extractor_class=MinigridFeaturesExtractor,\n",
    "    features_extractor_kwargs=dict(features_dim=128),\n",
    ")\n",
    "\n",
    "env = SimpleEnv(render_mode=\"rgb_array\")\n",
    "env = ImgObsWrapper(env)\n",
    "\n",
    "model = PPO(\"CnnPolicy\", env, policy_kwargs=policy_kwargs, verbose=1)\n",
    "model.learn(1e3)"
   ],
   "id": "e8a9568c0bb2a44c",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using cuda device\n",
      "Wrapping the env with a `Monitor` wrapper\n",
      "Wrapping the env in a DummyVecEnv.\n",
      "Wrapping the env in a VecTransposeImage.\n",
      "---------------------------------\n",
      "| rollout/           |          |\n",
      "|    ep_len_mean     | 400      |\n",
      "|    ep_rew_mean     | 0        |\n",
      "| time/              |          |\n",
      "|    fps             | 376      |\n",
      "|    iterations      | 1        |\n",
      "|    time_elapsed    | 5        |\n",
      "|    total_timesteps | 2048     |\n",
      "---------------------------------\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<stable_baselines3.ppo.ppo.PPO at 0x13a6100aac0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 7
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-09T01:02:16.090586Z",
     "start_time": "2024-08-09T01:02:16.060548Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from minigrid.wrappers import FullyObsWrapper"
   ],
   "id": "388f79a5f5e64f50",
   "outputs": [],
   "execution_count": 8
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-09T01:02:17.344312Z",
     "start_time": "2024-08-09T01:02:16.090586Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Create and wrap the environment\n",
    "env = SimpleEnv(render_mode=None)\n",
    "env = FullyObsWrapper(env)  # Wrap the environment to get fully observable state\n",
    "\n",
    "# Reset the environment\n",
    "obs = env.reset()\n",
    "\n",
    "# Run a loop with random actions and print the observation\n",
    "done = False\n",
    "step_count = 0\n",
    "\n",
    "while not done:\n",
    "    # Select a random action\n",
    "    action = env.action_space.sample()\n",
    "    \n",
    "    # Perform the action\n",
    "    obs, reward, done, truncated, info = env.step(action)\n",
    "    \n",
    "    # Extract the image from the observation\n",
    "    image = obs['image']  # (grid_size, grid_size, 3)\n",
    "    \n",
    "    # Print the image content and format\n",
    "    print(f\"Step: {step_count}\")\n",
    "    print(f\"Image shape: {image.shape}\")\n",
    "    \n",
    "    # Print the grid with each layer separately\n",
    "    # Channel 0: Object types\n",
    "    print(\"Object Types (Channel 0):\")\n",
    "    print(image[:, :, 0].transpose(1, 0))\n",
    "\n",
    "    # Channel 1: Colors\n",
    "    print(\"Colors (Channel 1):\")\n",
    "    print(image[:, :, 1].transpose(1, 0))\n",
    "\n",
    "    # Channel 2: Additional State\n",
    "    print(\"Additional State (Channel 2):\")\n",
    "    print(image[:, :, 2].transpose(1, 0))\n",
    "    \n",
    "    direction = obs['direction']\n",
    "    mission = obs['mission']\n",
    "\n",
    "    # Print the direction and mission\n",
    "    print(f\"Direction: {direction}\")\n",
    "    print(f\"Mission: {mission}\")\n",
    "\n",
    "    step_count += 1\n",
    "\n",
    "    # Limit the number of steps to avoid infinite loops\n",
    "    if step_count > 5:\n",
    "        break\n",
    "\n",
    "# Close the environment\n",
    "env.close()"
   ],
   "id": "6fd8248db5c29d60",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step: 0\n",
      "Image shape: (10, 10, 3)\n",
      "Object Types (Channel 0):\n",
      "[[ 2  2  2  2  2  2  2  2  2  2]\n",
      " [ 2  1  1  1  1  1  1  1  1  2]\n",
      " [ 2 10  1  1  1  1  1  1  1  2]\n",
      " [ 2  1  1  1  1  1  5  1  1  2]\n",
      " [ 2  1  1  1  1  1  1  1  1  2]\n",
      " [ 2  2  2  2  2  2  4  2  2  2]\n",
      " [ 2  1  1  1  1  1  1  1  1  2]\n",
      " [ 2  1  1  1  1  1  1  1  1  2]\n",
      " [ 2  1  1  1  1  1  1  1  8  2]\n",
      " [ 2  2  2  2  2  2  2  2  2  2]]\n",
      "Colors (Channel 1):\n",
      "[[5 5 5 5 5 5 5 5 5 5]\n",
      " [5 0 0 0 0 0 0 0 0 5]\n",
      " [5 0 0 0 0 0 0 0 0 5]\n",
      " [5 0 0 0 0 0 2 0 0 5]\n",
      " [5 0 0 0 0 0 0 0 0 5]\n",
      " [5 5 5 5 5 5 2 5 5 5]\n",
      " [5 0 0 0 0 0 0 0 0 5]\n",
      " [5 0 0 0 0 0 0 0 0 5]\n",
      " [5 0 0 0 0 0 0 0 1 5]\n",
      " [5 5 5 5 5 5 5 5 5 5]]\n",
      "Additional State (Channel 2):\n",
      "[[0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 2 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0]]\n",
      "Direction: 0\n",
      "Mission: grand mission\n",
      "Step: 1\n",
      "Image shape: (10, 10, 3)\n",
      "Object Types (Channel 0):\n",
      "[[ 2  2  2  2  2  2  2  2  2  2]\n",
      " [ 2  1  1  1  1  1  1  1  1  2]\n",
      " [ 2 10  1  1  1  1  1  1  1  2]\n",
      " [ 2  1  1  1  1  1  5  1  1  2]\n",
      " [ 2  1  1  1  1  1  1  1  1  2]\n",
      " [ 2  2  2  2  2  2  4  2  2  2]\n",
      " [ 2  1  1  1  1  1  1  1  1  2]\n",
      " [ 2  1  1  1  1  1  1  1  1  2]\n",
      " [ 2  1  1  1  1  1  1  1  8  2]\n",
      " [ 2  2  2  2  2  2  2  2  2  2]]\n",
      "Colors (Channel 1):\n",
      "[[5 5 5 5 5 5 5 5 5 5]\n",
      " [5 0 0 0 0 0 0 0 0 5]\n",
      " [5 0 0 0 0 0 0 0 0 5]\n",
      " [5 0 0 0 0 0 2 0 0 5]\n",
      " [5 0 0 0 0 0 0 0 0 5]\n",
      " [5 5 5 5 5 5 2 5 5 5]\n",
      " [5 0 0 0 0 0 0 0 0 5]\n",
      " [5 0 0 0 0 0 0 0 0 5]\n",
      " [5 0 0 0 0 0 0 0 1 5]\n",
      " [5 5 5 5 5 5 5 5 5 5]]\n",
      "Additional State (Channel 2):\n",
      "[[0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 2 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0]]\n",
      "Direction: 0\n",
      "Mission: grand mission\n",
      "Step: 2\n",
      "Image shape: (10, 10, 3)\n",
      "Object Types (Channel 0):\n",
      "[[ 2  2  2  2  2  2  2  2  2  2]\n",
      " [ 2  1  1  1  1  1  1  1  1  2]\n",
      " [ 2 10  1  1  1  1  1  1  1  2]\n",
      " [ 2  1  1  1  1  1  5  1  1  2]\n",
      " [ 2  1  1  1  1  1  1  1  1  2]\n",
      " [ 2  2  2  2  2  2  4  2  2  2]\n",
      " [ 2  1  1  1  1  1  1  1  1  2]\n",
      " [ 2  1  1  1  1  1  1  1  1  2]\n",
      " [ 2  1  1  1  1  1  1  1  8  2]\n",
      " [ 2  2  2  2  2  2  2  2  2  2]]\n",
      "Colors (Channel 1):\n",
      "[[5 5 5 5 5 5 5 5 5 5]\n",
      " [5 0 0 0 0 0 0 0 0 5]\n",
      " [5 0 0 0 0 0 0 0 0 5]\n",
      " [5 0 0 0 0 0 2 0 0 5]\n",
      " [5 0 0 0 0 0 0 0 0 5]\n",
      " [5 5 5 5 5 5 2 5 5 5]\n",
      " [5 0 0 0 0 0 0 0 0 5]\n",
      " [5 0 0 0 0 0 0 0 0 5]\n",
      " [5 0 0 0 0 0 0 0 1 5]\n",
      " [5 5 5 5 5 5 5 5 5 5]]\n",
      "Additional State (Channel 2):\n",
      "[[0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 2 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0]]\n",
      "Direction: 0\n",
      "Mission: grand mission\n",
      "Step: 3\n",
      "Image shape: (10, 10, 3)\n",
      "Object Types (Channel 0):\n",
      "[[ 2  2  2  2  2  2  2  2  2  2]\n",
      " [ 2  1  1  1  1  1  1  1  1  2]\n",
      " [ 2 10  1  1  1  1  1  1  1  2]\n",
      " [ 2  1  1  1  1  1  5  1  1  2]\n",
      " [ 2  1  1  1  1  1  1  1  1  2]\n",
      " [ 2  2  2  2  2  2  4  2  2  2]\n",
      " [ 2  1  1  1  1  1  1  1  1  2]\n",
      " [ 2  1  1  1  1  1  1  1  1  2]\n",
      " [ 2  1  1  1  1  1  1  1  8  2]\n",
      " [ 2  2  2  2  2  2  2  2  2  2]]\n",
      "Colors (Channel 1):\n",
      "[[5 5 5 5 5 5 5 5 5 5]\n",
      " [5 0 0 0 0 0 0 0 0 5]\n",
      " [5 0 0 0 0 0 0 0 0 5]\n",
      " [5 0 0 0 0 0 2 0 0 5]\n",
      " [5 0 0 0 0 0 0 0 0 5]\n",
      " [5 5 5 5 5 5 2 5 5 5]\n",
      " [5 0 0 0 0 0 0 0 0 5]\n",
      " [5 0 0 0 0 0 0 0 0 5]\n",
      " [5 0 0 0 0 0 0 0 1 5]\n",
      " [5 5 5 5 5 5 5 5 5 5]]\n",
      "Additional State (Channel 2):\n",
      "[[0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 2 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0]]\n",
      "Direction: 0\n",
      "Mission: grand mission\n",
      "Step: 4\n",
      "Image shape: (10, 10, 3)\n",
      "Object Types (Channel 0):\n",
      "[[ 2  2  2  2  2  2  2  2  2  2]\n",
      " [ 2  1  1  1  1  1  1  1  1  2]\n",
      " [ 2 10  1  1  1  1  1  1  1  2]\n",
      " [ 2  1  1  1  1  1  5  1  1  2]\n",
      " [ 2  1  1  1  1  1  1  1  1  2]\n",
      " [ 2  2  2  2  2  2  4  2  2  2]\n",
      " [ 2  1  1  1  1  1  1  1  1  2]\n",
      " [ 2  1  1  1  1  1  1  1  1  2]\n",
      " [ 2  1  1  1  1  1  1  1  8  2]\n",
      " [ 2  2  2  2  2  2  2  2  2  2]]\n",
      "Colors (Channel 1):\n",
      "[[5 5 5 5 5 5 5 5 5 5]\n",
      " [5 0 0 0 0 0 0 0 0 5]\n",
      " [5 0 0 0 0 0 0 0 0 5]\n",
      " [5 0 0 0 0 0 2 0 0 5]\n",
      " [5 0 0 0 0 0 0 0 0 5]\n",
      " [5 5 5 5 5 5 2 5 5 5]\n",
      " [5 0 0 0 0 0 0 0 0 5]\n",
      " [5 0 0 0 0 0 0 0 0 5]\n",
      " [5 0 0 0 0 0 0 0 1 5]\n",
      " [5 5 5 5 5 5 5 5 5 5]]\n",
      "Additional State (Channel 2):\n",
      "[[0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 2 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0]]\n",
      "Direction: 0\n",
      "Mission: grand mission\n",
      "Step: 5\n",
      "Image shape: (10, 10, 3)\n",
      "Object Types (Channel 0):\n",
      "[[ 2  2  2  2  2  2  2  2  2  2]\n",
      " [ 2  1  1  1  1  1  1  1  1  2]\n",
      " [ 2  1  1  1  1  1  1  1  1  2]\n",
      " [ 2 10  1  1  1  1  5  1  1  2]\n",
      " [ 2  1  1  1  1  1  1  1  1  2]\n",
      " [ 2  2  2  2  2  2  4  2  2  2]\n",
      " [ 2  1  1  1  1  1  1  1  1  2]\n",
      " [ 2  1  1  1  1  1  1  1  1  2]\n",
      " [ 2  1  1  1  1  1  1  1  8  2]\n",
      " [ 2  2  2  2  2  2  2  2  2  2]]\n",
      "Colors (Channel 1):\n",
      "[[5 5 5 5 5 5 5 5 5 5]\n",
      " [5 0 0 0 0 0 0 0 0 5]\n",
      " [5 0 0 0 0 0 0 0 0 5]\n",
      " [5 0 0 0 0 0 2 0 0 5]\n",
      " [5 0 0 0 0 0 0 0 0 5]\n",
      " [5 5 5 5 5 5 2 5 5 5]\n",
      " [5 0 0 0 0 0 0 0 0 5]\n",
      " [5 0 0 0 0 0 0 0 0 5]\n",
      " [5 0 0 0 0 0 0 0 1 5]\n",
      " [5 5 5 5 5 5 5 5 5 5]]\n",
      "Additional State (Channel 2):\n",
      "[[0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 2 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0]\n",
      " [0 0 0 0 0 0 0 0 0 0]]\n",
      "Direction: 0\n",
      "Mission: grand mission\n"
     ]
    }
   ],
   "execution_count": 9
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-09T01:05:03.469219Z",
     "start_time": "2024-08-09T01:05:01.667594Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from minigrid.wrappers import FullyObsWrapper\n",
    "\n",
    "# Create and wrap the environment\n",
    "env = SimpleEnv(render_mode=None)\n",
    "env = FullyObsWrapper(env)  # Wrap the environment to get fully observable state\n",
    "\n",
    "# Reset the environment\n",
    "obs, _ = env.reset()\n",
    "\n",
    "print(obs)\n",
    "\n",
    "# Function to display the environment and get manual input\n",
    "def display_and_get_action(image):\n",
    "    plt.imshow(image)\n",
    "    plt.title(\"Click on the plot to move: left, right, up, down\")\n",
    "    plt.axis('off')\n",
    "    plt.show()\n",
    "    print(\"Click on the plot to move: left, right, up, down\")\n",
    "    clicks = plt.ginput(1)\n",
    "    plt.close()\n",
    "\n",
    "    if not clicks:\n",
    "        return env.action_space.sample()  # If no click, do random action\n",
    "\n",
    "    x, y = clicks[0]\n",
    "    if x < image.shape[1] / 4:\n",
    "        return 0  # Left\n",
    "    elif x > 3 * image.shape[1] / 4:\n",
    "        return 1  # Right\n",
    "    elif y < image.shape[0] / 4:\n",
    "        return 2  # Up\n",
    "    else:\n",
    "        return 3  # Down\n",
    "\n",
    "# Run a loop with manual actions\n",
    "done = False\n",
    "step_count = 0\n",
    "\n",
    "while not done:\n",
    "    # Display the image and get manual action\n",
    "    image = np.transpose(obs['image'], (1, 2, 0))  # Transpose the image for display\n",
    "    action = display_and_get_action(image)\n",
    "\n",
    "    # Perform the action\n",
    "    obs, reward, done, truncated, info = env.step(action)\n",
    "\n",
    "    # Print the image content and format\n",
    "    print(f\"Step: {step_count}\")\n",
    "    print(f\"Image shape: {image.shape}\")\n",
    "\n",
    "    # Print the grid with each layer separately\n",
    "    # Channel 0: Object types\n",
    "    print(\"Object Types (Channel 0):\")\n",
    "    print(obs['image'][:, :, 0])\n",
    "\n",
    "    # Channel 1: Colors\n",
    "    print(\"Colors (Channel 1):\")\n",
    "    print(obs['image'][:, :, 1])\n",
    "\n",
    "    # Channel 2: Additional State\n",
    "    print(\"Additional State (Channel 2):\")\n",
    "    print(obs['image'][:, :, 2])\n",
    "    \n",
    "    direction = obs['direction']\n",
    "    mission = obs['mission']\n",
    "\n",
    "    # Print the direction and mission\n",
    "    print(f\"Direction: {direction}\")\n",
    "    print(f\"Mission: {mission}\")\n",
    "\n",
    "    step_count += 1\n",
    "\n",
    "    # Limit the number of steps to avoid infinite loops\n",
    "    if step_count > 5:\n",
    "        break\n",
    "\n",
    "# Close the environment\n",
    "env.close()"
   ],
   "id": "188d84a2042af07d",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'image': array([[[ 2,  5,  0],\n",
      "        [ 2,  5,  0],\n",
      "        [ 2,  5,  0],\n",
      "        [ 2,  5,  0],\n",
      "        [ 2,  5,  0],\n",
      "        [ 2,  5,  0],\n",
      "        [ 2,  5,  0],\n",
      "        [ 2,  5,  0],\n",
      "        [ 2,  5,  0],\n",
      "        [ 2,  5,  0]],\n",
      "\n",
      "       [[ 2,  5,  0],\n",
      "        [10,  0,  0],\n",
      "        [ 1,  0,  0],\n",
      "        [ 1,  0,  0],\n",
      "        [ 1,  0,  0],\n",
      "        [ 1,  0,  0],\n",
      "        [ 1,  0,  0],\n",
      "        [ 1,  0,  0],\n",
      "        [ 1,  0,  0],\n",
      "        [ 2,  5,  0]],\n",
      "\n",
      "       [[ 2,  5,  0],\n",
      "        [ 1,  0,  0],\n",
      "        [ 1,  0,  0],\n",
      "        [ 1,  0,  0],\n",
      "        [ 1,  0,  0],\n",
      "        [ 1,  0,  0],\n",
      "        [ 1,  0,  0],\n",
      "        [ 1,  0,  0],\n",
      "        [ 1,  0,  0],\n",
      "        [ 2,  5,  0]],\n",
      "\n",
      "       [[ 2,  5,  0],\n",
      "        [ 1,  0,  0],\n",
      "        [ 1,  0,  0],\n",
      "        [ 1,  0,  0],\n",
      "        [ 1,  0,  0],\n",
      "        [ 1,  0,  0],\n",
      "        [ 5,  2,  0],\n",
      "        [ 1,  0,  0],\n",
      "        [ 1,  0,  0],\n",
      "        [ 2,  5,  0]],\n",
      "\n",
      "       [[ 2,  5,  0],\n",
      "        [ 1,  0,  0],\n",
      "        [ 1,  0,  0],\n",
      "        [ 1,  0,  0],\n",
      "        [ 1,  0,  0],\n",
      "        [ 1,  0,  0],\n",
      "        [ 1,  0,  0],\n",
      "        [ 1,  0,  0],\n",
      "        [ 1,  0,  0],\n",
      "        [ 2,  5,  0]],\n",
      "\n",
      "       [[ 2,  5,  0],\n",
      "        [ 2,  5,  0],\n",
      "        [ 2,  5,  0],\n",
      "        [ 2,  5,  0],\n",
      "        [ 2,  5,  0],\n",
      "        [ 2,  5,  0],\n",
      "        [ 4,  2,  2],\n",
      "        [ 2,  5,  0],\n",
      "        [ 2,  5,  0],\n",
      "        [ 2,  5,  0]],\n",
      "\n",
      "       [[ 2,  5,  0],\n",
      "        [ 1,  0,  0],\n",
      "        [ 1,  0,  0],\n",
      "        [ 1,  0,  0],\n",
      "        [ 1,  0,  0],\n",
      "        [ 1,  0,  0],\n",
      "        [ 1,  0,  0],\n",
      "        [ 1,  0,  0],\n",
      "        [ 1,  0,  0],\n",
      "        [ 2,  5,  0]],\n",
      "\n",
      "       [[ 2,  5,  0],\n",
      "        [ 1,  0,  0],\n",
      "        [ 1,  0,  0],\n",
      "        [ 1,  0,  0],\n",
      "        [ 1,  0,  0],\n",
      "        [ 1,  0,  0],\n",
      "        [ 1,  0,  0],\n",
      "        [ 1,  0,  0],\n",
      "        [ 1,  0,  0],\n",
      "        [ 2,  5,  0]],\n",
      "\n",
      "       [[ 2,  5,  0],\n",
      "        [ 1,  0,  0],\n",
      "        [ 1,  0,  0],\n",
      "        [ 1,  0,  0],\n",
      "        [ 1,  0,  0],\n",
      "        [ 1,  0,  0],\n",
      "        [ 1,  0,  0],\n",
      "        [ 1,  0,  0],\n",
      "        [ 8,  1,  0],\n",
      "        [ 2,  5,  0]],\n",
      "\n",
      "       [[ 2,  5,  0],\n",
      "        [ 2,  5,  0],\n",
      "        [ 2,  5,  0],\n",
      "        [ 2,  5,  0],\n",
      "        [ 2,  5,  0],\n",
      "        [ 2,  5,  0],\n",
      "        [ 2,  5,  0],\n",
      "        [ 2,  5,  0],\n",
      "        [ 2,  5,  0],\n",
      "        [ 2,  5,  0]]], dtype=uint8), 'direction': 0, 'mission': 'grand mission'}\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "Invalid shape (10, 3, 10) for image data",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mTypeError\u001B[0m                                 Traceback (most recent call last)",
      "Cell \u001B[1;32mIn[12], line 43\u001B[0m\n\u001B[0;32m     40\u001B[0m \u001B[38;5;28;01mwhile\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m done:\n\u001B[0;32m     41\u001B[0m     \u001B[38;5;66;03m# Display the image and get manual action\u001B[39;00m\n\u001B[0;32m     42\u001B[0m     image \u001B[38;5;241m=\u001B[39m np\u001B[38;5;241m.\u001B[39mtranspose(obs[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mimage\u001B[39m\u001B[38;5;124m'\u001B[39m], (\u001B[38;5;241m1\u001B[39m, \u001B[38;5;241m2\u001B[39m, \u001B[38;5;241m0\u001B[39m))  \u001B[38;5;66;03m# Transpose the image for display\u001B[39;00m\n\u001B[1;32m---> 43\u001B[0m     action \u001B[38;5;241m=\u001B[39m \u001B[43mdisplay_and_get_action\u001B[49m\u001B[43m(\u001B[49m\u001B[43mimage\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m     45\u001B[0m     \u001B[38;5;66;03m# Perform the action\u001B[39;00m\n\u001B[0;32m     46\u001B[0m     obs, reward, done, truncated, info \u001B[38;5;241m=\u001B[39m env\u001B[38;5;241m.\u001B[39mstep(action)\n",
      "Cell \u001B[1;32mIn[12], line 15\u001B[0m, in \u001B[0;36mdisplay_and_get_action\u001B[1;34m(image)\u001B[0m\n\u001B[0;32m     14\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mdisplay_and_get_action\u001B[39m(image):\n\u001B[1;32m---> 15\u001B[0m     \u001B[43mplt\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mimshow\u001B[49m\u001B[43m(\u001B[49m\u001B[43mimage\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m     16\u001B[0m     plt\u001B[38;5;241m.\u001B[39mtitle(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mClick on the plot to move: left, right, up, down\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[0;32m     17\u001B[0m     plt\u001B[38;5;241m.\u001B[39maxis(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124moff\u001B[39m\u001B[38;5;124m'\u001B[39m)\n",
      "File \u001B[1;32m~\\anaconda3\\envs\\mini-grid-generation\\lib\\site-packages\\matplotlib\\pyplot.py:2695\u001B[0m, in \u001B[0;36mimshow\u001B[1;34m(X, cmap, norm, aspect, interpolation, alpha, vmin, vmax, origin, extent, interpolation_stage, filternorm, filterrad, resample, url, data, **kwargs)\u001B[0m\n\u001B[0;32m   2689\u001B[0m \u001B[38;5;129m@_copy_docstring_and_deprecators\u001B[39m(Axes\u001B[38;5;241m.\u001B[39mimshow)\n\u001B[0;32m   2690\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mimshow\u001B[39m(\n\u001B[0;32m   2691\u001B[0m         X, cmap\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, norm\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, \u001B[38;5;241m*\u001B[39m, aspect\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, interpolation\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m,\n\u001B[0;32m   2692\u001B[0m         alpha\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, vmin\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, vmax\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, origin\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, extent\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m,\n\u001B[0;32m   2693\u001B[0m         interpolation_stage\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, filternorm\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m, filterrad\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m4.0\u001B[39m,\n\u001B[0;32m   2694\u001B[0m         resample\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, url\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, data\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs):\n\u001B[1;32m-> 2695\u001B[0m     __ret \u001B[38;5;241m=\u001B[39m \u001B[43mgca\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mimshow\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m   2696\u001B[0m \u001B[43m        \u001B[49m\u001B[43mX\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcmap\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mcmap\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mnorm\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mnorm\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43maspect\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43maspect\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   2697\u001B[0m \u001B[43m        \u001B[49m\u001B[43minterpolation\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43minterpolation\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43malpha\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43malpha\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mvmin\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mvmin\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   2698\u001B[0m \u001B[43m        \u001B[49m\u001B[43mvmax\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mvmax\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43morigin\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43morigin\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mextent\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mextent\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   2699\u001B[0m \u001B[43m        \u001B[49m\u001B[43minterpolation_stage\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43minterpolation_stage\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   2700\u001B[0m \u001B[43m        \u001B[49m\u001B[43mfilternorm\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mfilternorm\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mfilterrad\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mfilterrad\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mresample\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mresample\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   2701\u001B[0m \u001B[43m        \u001B[49m\u001B[43murl\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43murl\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43m{\u001B[49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mdata\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m:\u001B[49m\u001B[43m \u001B[49m\u001B[43mdata\u001B[49m\u001B[43m}\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mif\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43mdata\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;129;43;01mis\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[38;5;129;43;01mnot\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[38;5;28;43;01mNone\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[38;5;28;43;01melse\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43m{\u001B[49m\u001B[43m}\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m   2702\u001B[0m \u001B[43m        \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m   2703\u001B[0m     sci(__ret)\n\u001B[0;32m   2704\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m __ret\n",
      "File \u001B[1;32m~\\anaconda3\\envs\\mini-grid-generation\\lib\\site-packages\\matplotlib\\__init__.py:1474\u001B[0m, in \u001B[0;36m_preprocess_data.<locals>.inner\u001B[1;34m(ax, data, *args, **kwargs)\u001B[0m\n\u001B[0;32m   1471\u001B[0m \u001B[38;5;129m@functools\u001B[39m\u001B[38;5;241m.\u001B[39mwraps(func)\n\u001B[0;32m   1472\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21minner\u001B[39m(ax, \u001B[38;5;241m*\u001B[39margs, data\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs):\n\u001B[0;32m   1473\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m data \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[1;32m-> 1474\u001B[0m         \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mfunc\u001B[49m\u001B[43m(\u001B[49m\u001B[43max\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;28;43mmap\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43msanitize_sequence\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43margs\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m   1476\u001B[0m     bound \u001B[38;5;241m=\u001B[39m new_sig\u001B[38;5;241m.\u001B[39mbind(ax, \u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n\u001B[0;32m   1477\u001B[0m     auto_label \u001B[38;5;241m=\u001B[39m (bound\u001B[38;5;241m.\u001B[39marguments\u001B[38;5;241m.\u001B[39mget(label_namer)\n\u001B[0;32m   1478\u001B[0m                   \u001B[38;5;129;01mor\u001B[39;00m bound\u001B[38;5;241m.\u001B[39mkwargs\u001B[38;5;241m.\u001B[39mget(label_namer))\n",
      "File \u001B[1;32m~\\anaconda3\\envs\\mini-grid-generation\\lib\\site-packages\\matplotlib\\axes\\_axes.py:5663\u001B[0m, in \u001B[0;36mAxes.imshow\u001B[1;34m(self, X, cmap, norm, aspect, interpolation, alpha, vmin, vmax, origin, extent, interpolation_stage, filternorm, filterrad, resample, url, **kwargs)\u001B[0m\n\u001B[0;32m   5655\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mset_aspect(aspect)\n\u001B[0;32m   5656\u001B[0m im \u001B[38;5;241m=\u001B[39m mimage\u001B[38;5;241m.\u001B[39mAxesImage(\u001B[38;5;28mself\u001B[39m, cmap\u001B[38;5;241m=\u001B[39mcmap, norm\u001B[38;5;241m=\u001B[39mnorm,\n\u001B[0;32m   5657\u001B[0m                       interpolation\u001B[38;5;241m=\u001B[39minterpolation, origin\u001B[38;5;241m=\u001B[39morigin,\n\u001B[0;32m   5658\u001B[0m                       extent\u001B[38;5;241m=\u001B[39mextent, filternorm\u001B[38;5;241m=\u001B[39mfilternorm,\n\u001B[0;32m   5659\u001B[0m                       filterrad\u001B[38;5;241m=\u001B[39mfilterrad, resample\u001B[38;5;241m=\u001B[39mresample,\n\u001B[0;32m   5660\u001B[0m                       interpolation_stage\u001B[38;5;241m=\u001B[39minterpolation_stage,\n\u001B[0;32m   5661\u001B[0m                       \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n\u001B[1;32m-> 5663\u001B[0m \u001B[43mim\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mset_data\u001B[49m\u001B[43m(\u001B[49m\u001B[43mX\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m   5664\u001B[0m im\u001B[38;5;241m.\u001B[39mset_alpha(alpha)\n\u001B[0;32m   5665\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m im\u001B[38;5;241m.\u001B[39mget_clip_path() \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[0;32m   5666\u001B[0m     \u001B[38;5;66;03m# image does not already have clipping set, clip to axes patch\u001B[39;00m\n",
      "File \u001B[1;32m~\\anaconda3\\envs\\mini-grid-generation\\lib\\site-packages\\matplotlib\\image.py:710\u001B[0m, in \u001B[0;36m_ImageBase.set_data\u001B[1;34m(self, A)\u001B[0m\n\u001B[0;32m    706\u001B[0m     \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_A \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_A[:, :, \u001B[38;5;241m0\u001B[39m]\n\u001B[0;32m    708\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m (\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_A\u001B[38;5;241m.\u001B[39mndim \u001B[38;5;241m==\u001B[39m \u001B[38;5;241m2\u001B[39m\n\u001B[0;32m    709\u001B[0m         \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_A\u001B[38;5;241m.\u001B[39mndim \u001B[38;5;241m==\u001B[39m \u001B[38;5;241m3\u001B[39m \u001B[38;5;129;01mand\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_A\u001B[38;5;241m.\u001B[39mshape[\u001B[38;5;241m-\u001B[39m\u001B[38;5;241m1\u001B[39m] \u001B[38;5;129;01min\u001B[39;00m [\u001B[38;5;241m3\u001B[39m, \u001B[38;5;241m4\u001B[39m]):\n\u001B[1;32m--> 710\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mTypeError\u001B[39;00m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mInvalid shape \u001B[39m\u001B[38;5;132;01m{}\u001B[39;00m\u001B[38;5;124m for image data\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m    711\u001B[0m                     \u001B[38;5;241m.\u001B[39mformat(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_A\u001B[38;5;241m.\u001B[39mshape))\n\u001B[0;32m    713\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_A\u001B[38;5;241m.\u001B[39mndim \u001B[38;5;241m==\u001B[39m \u001B[38;5;241m3\u001B[39m:\n\u001B[0;32m    714\u001B[0m     \u001B[38;5;66;03m# If the input data has values outside the valid range (after\u001B[39;00m\n\u001B[0;32m    715\u001B[0m     \u001B[38;5;66;03m# normalisation), we issue a warning and then clip X to the bounds\u001B[39;00m\n\u001B[0;32m    716\u001B[0m     \u001B[38;5;66;03m# - otherwise casting wraps extreme values, hiding outliers and\u001B[39;00m\n\u001B[0;32m    717\u001B[0m     \u001B[38;5;66;03m# making reliable interpretation impossible.\u001B[39;00m\n\u001B[0;32m    718\u001B[0m     high \u001B[38;5;241m=\u001B[39m \u001B[38;5;241m255\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m np\u001B[38;5;241m.\u001B[39missubdtype(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_A\u001B[38;5;241m.\u001B[39mdtype, np\u001B[38;5;241m.\u001B[39minteger) \u001B[38;5;28;01melse\u001B[39;00m \u001B[38;5;241m1\u001B[39m\n",
      "\u001B[1;31mTypeError\u001B[0m: Invalid shape (10, 3, 10) for image data"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 12
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}