diff --git a/env_data/tests/Test_2_Level_0.pkl b/env_data/tests/Test_2_Level_0.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..076496ad365c3556248de1c762525c18ea098fbf
Binary files /dev/null and b/env_data/tests/Test_2_Level_0.pkl differ
diff --git a/notebooks/render-episode.ipynb b/notebooks/render-episode.ipynb
index 94eba8de9e7ca9a84d4a3ffa4739f9a1aac58796..3068d46d8daa155219c16ae497144879f5429675 100644
--- a/notebooks/render-episode.ipynb
+++ b/notebooks/render-episode.ipynb
@@ -45,7 +45,8 @@
"outputs": [],
"source": [
"import pandas as pd\n",
- "import numpy as np"
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt"
]
},
{
@@ -60,7 +61,23 @@
"source": [
"import PIL\n",
"from flatland.utils.rendertools import RenderTool\n",
- "import imageio"
+ "import imageio\n",
+ "import os"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {},
+ "colab_type": "code",
+ "id": "PU5GkH271guD"
+ },
+ "outputs": [],
+ "source": [
+ "from IPython.display import clear_output\n",
+ "from IPython.core import display \n",
+ "display.display(display.HTML(\"<style>.container { width:95% !important; }</style>\"))"
]
},
{
@@ -102,7 +119,9 @@
"outputs": [],
"source": [
"# ENV FILE PATH\n",
- "env_file = \"Test_20_Level_0.pkl\"\n"
+ "#env_file = \"Test_20_Level_0.pkl\"\n",
+ "#env_file = \"../../evaluation_visualization/round2/or-0827/Test_23/Level_1.pkl\"\n",
+ "#env_file = \"../../evaluation_visualization/round2/rl-0827/Test_23/Level_1.pkl\""
]
},
{
@@ -111,7 +130,10 @@
"metadata": {},
"outputs": [],
"source": [
- "%ls"
+ "if os.path.exists(\"../env_data\"):\n",
+ " env_file = \"../env_data/tests/Test_2_Level_0.pkl\"\n",
+ "else:\n",
+ " env_file = \"./env_data/tests/Test_2_Level_0.pkl\""
]
},
{
@@ -124,9 +146,14 @@
},
"outputs": [],
"source": [
- "from IPython.display import clear_output\n",
- "from IPython.core import display \n",
- "display.display(display.HTML(\"<style>.container { width:95% !important; }</style>\"))"
+ "import pickle\n",
+ "\n",
+ "from flatland.envs.rail_env import RailEnv\n",
+ "from flatland.envs.rail_generators import sparse_rail_generator\n",
+ "from flatland.envs.schedule_generators import sparse_schedule_generator\n",
+ "from flatland.envs.malfunction_generators import malfunction_from_file, no_malfunction_generator\n",
+ "from flatland.envs.rail_generators import rail_from_file\n",
+ "from flatland.envs.schedule_generators import schedule_from_file"
]
},
{
@@ -139,15 +166,6 @@
},
"outputs": [],
"source": [
- "import pickle\n",
- "\n",
- "from flatland.envs.rail_env import RailEnv\n",
- "from flatland.envs.rail_generators import sparse_rail_generator\n",
- "from flatland.envs.schedule_generators import sparse_schedule_generator\n",
- "from flatland.envs.malfunction_generators import malfunction_from_file, no_malfunction_generator\n",
- "from flatland.envs.rail_generators import rail_from_file\n",
- "from flatland.envs.schedule_generators import schedule_from_file\n",
- "\n",
"with open(env_file, \"rb\") as fIn:\n",
" env_dict = pickle.load(fIn)\n",
"\n",
@@ -195,15 +213,6 @@
"PIL.Image.fromarray(aImg)\n"
]
},
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "#dummy= env.reset()"
- ]
- },
{
"cell_type": "code",
"execution_count": null,
@@ -226,15 +235,6 @@
" for oAg in env.agents], columns=lCols)"
]
},
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "#env_dict[\"episode\"]"
- ]
- },
{
"cell_type": "code",
"execution_count": null,
@@ -244,33 +244,6 @@
"pd.DataFrame([ vars(oAg) for oAg in env.agents])"
]
},
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "genSched = schedule_from_file(env_file)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "oSched = genSched(env.rail.grid, 3, )"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "oSched"
- ]
- },
{
"cell_type": "code",
"execution_count": null,
@@ -312,73 +285,6 @@
"episode_states = env_dict['episode']"
]
},
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "#[ (i, l) for i,l in enumerate(zip(episode_states, expert_actions)) ][:3]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 34
- },
- "colab_type": "code",
- "id": "IXQxfUXF2U33",
- "outputId": "2a94ffec-c6b7-4cc3-d779-8c430d66918d"
- },
- "outputs": [],
- "source": [
- "#dAct = expert_actions[1]\n",
- "#print(dAct)\n",
- "#env.step(dAct)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "from gym.utils import seeding"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "np_random, seed2 = seeding.np_random(123)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "for i in range(10000):\n",
- " oMF = env.malfunction_generator(env.agents[0], np_random) \n",
- " if oMF.num_broken_steps > 0:\n",
- " print(i, oMF)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "\"generate\" in dir(env.malfunction_generator)"
- ]
- },
{
"cell_type": "code",
"execution_count": null,
@@ -424,6 +330,7 @@
"\n",
"step = 0\n",
"all_done = False\n",
+ "print(\"Processing episode steps:\")\n",
"while not all_done and step < max_steps:\n",
" print(step, end=\", \")\n",
" \"\"\"\n",
@@ -462,20 +369,21 @@
"\n",
" \n",
" # Force agent states from the recorded states\n",
- " for idx, agent in enumerate(env.agents):\n",
- " #print(episode_states[step][idx])\n",
- " rcPos = episode_states[step][idx][0:2]\n",
- " #print(idx, rcPos)\n",
- " if rcPos == [0,0]:\n",
- " agent.position = None\n",
- " else:\n",
- " agent.position = (*rcPos,) # episode_states[step][idx][0], episode_states[step][idx][1]#, episode_states[step][idx][2]\n",
- " \n",
- " agent.malfunction_data[\"malfunction\"] = episode_states[step][idx][3]\n",
- " agent.direction = int(episode_states[step][idx][2])\n",
+ " if False:\n",
+ " for idx, agent in enumerate(env.agents):\n",
+ " #print(episode_states[step][idx])\n",
+ " rcPos = episode_states[step][idx][0:2]\n",
+ " #print(idx, rcPos)\n",
+ " if rcPos == [0,0]:\n",
+ " agent.position = None\n",
+ " else:\n",
+ " agent.position = (*rcPos,) # episode_states[step][idx][0], episode_states[step][idx][1]#, episode_states[step][idx][2]\n",
+ "\n",
+ " agent.malfunction_data[\"malfunction\"] = episode_states[step][idx][3]\n",
+ " agent.direction = int(episode_states[step][idx][2])\n",
"\n",
- " agent.old_direction = int(episode_states[step-1][idx][2])\n",
- " agent.old_position = episode_states[step-1][idx][:2]\n",
+ " agent.old_direction = int(episode_states[step-1][idx][2])\n",
+ " agent.old_position = episode_states[step-1][idx][:2]\n",
"\n",
" statuses = []\n",
" for a in range(n_agents):\n",
@@ -542,7 +450,7 @@
"outputs": [],
"source": [
"sfImg = env_file.replace(\"pkl\", \"gif\")\n",
- "imageio.mimsave(sfImg, [d[\"image\"] for d in frames])"
+ "imageio.mimsave(sfImg, [d[\"image\"] for d in frames], subrectangles=True)"
]
},
{
@@ -606,18 +514,19 @@
" display.display(frame['image'])\n",
" #print(frame['statuses'])\n",
"\n",
- "slider = widgets.FloatSlider(value=0, min=0, max=max_steps, step=1)\n",
- "interact(plot_func, frame_idx = slider)\n",
+ "if True:\n",
+ " slider = widgets.FloatSlider(value=0, min=0, max=max_steps, step=1)\n",
+ " interact(plot_func, frame_idx = slider)\n",
"\n",
- "play = Play(\n",
- " max=max_steps,\n",
- " value=0,\n",
- " step=1,\n",
- " interval=250\n",
- ")\n",
+ " play = Play(\n",
+ " max=max_steps,\n",
+ " value=0,\n",
+ " step=1,\n",
+ " interval=250\n",
+ " )\n",
"\n",
- "widgets.link((play, 'value'), (slider, 'value'))\n",
- "widgets.VBox([play])"
+ " widgets.link((play, 'value'), (slider, 'value'))\n",
+ " widgets.VBox([play])"
]
},
{
@@ -629,6 +538,34 @@
"id": "j9_J2f6K64Jb"
},
"outputs": [],
+ "source": [
+ "import numpy as np"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "g3Ep = np.array(episode_states)\n",
+ "np.sum(g3Ep[:,:,3] > 0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "plt.plot(np.sum(g3Ep[:,:,3]>0, axis=1))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": []
}
],