From 090dbd2dbe1e34b50ecf3c0769b58c39b2f3ea81 Mon Sep 17 00:00:00 2001
From: hagrid67 <jdhwatson@gmail.com>
Date: Thu, 25 Apr 2019 15:56:54 +0100
Subject: [PATCH] update Editor notebook to work with editor.py
---
notebooks/CanvasEditor.ipynb | 888 ++---------------------------------
1 file changed, 40 insertions(+), 848 deletions(-)
diff --git a/notebooks/CanvasEditor.ipynb b/notebooks/CanvasEditor.ipynb
index 7b96f45..8888f53 100644
--- a/notebooks/CanvasEditor.ipynb
+++ b/notebooks/CanvasEditor.ipynb
@@ -65,7 +65,8 @@
"from flatland.envs.rail_env import RailEnv, random_rail_generator\n",
"from flatland.core.transitions import RailEnvTransitions\n",
"from flatland.core.env_observation_builder import TreeObsForRailEnv\n",
- "import flatland.utils.rendertools as rt"
+ "import flatland.utils.rendertools as rt\n",
+ "from flatland.utils.editor import JupEditor"
]
},
{
@@ -118,171 +119,90 @@
]
},
{
- "cell_type": "code",
- "execution_count": 8,
+ "cell_type": "markdown",
"metadata": {},
- "outputs": [
- {
- "data": {
- "image/png": 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\n",
- "text/plain": [
- "<Figure size 720x720 with 1 Axes>"
- ]
- },
- "metadata": {
- "needs_background": "light"
- },
- "output_type": "display_data"
- }
- ],
"source": [
- "oFig = plt.figure(figsize=(10,10))\n",
- "oRT.renderEnv(spacing=False, arrows=False, sRailColor=\"gray\", show=False)\n",
- "img = oRT.getImage()\n",
- "#plt.clf()\n",
- "pass"
+ "### Clear the rails"
]
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
- "# This API call is misleading - it doesn't update the env's transition map.\n",
- "oEnv.rail.set_transition((1,1,2), 1, True)"
+ "oEnv.rail.grid[:,:] = 0"
]
},
{
- "cell_type": "code",
- "execution_count": 10,
+ "cell_type": "markdown",
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "0"
- ]
- },
- "execution_count": 10,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
"source": [
- "oEnv.rail.get_transition((1,1,2), 1)"
+ "### Render the env in the usual way, and take an image snapshot as numpy array\n",
+ "If you have already edited the env in the cell below, these changes should be reflected here."
]
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 9,
"metadata": {},
"outputs": [
{
- "data": {
- "text/plain": [
- "'0b1000000000100000'"
- ]
- },
- "execution_count": 11,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "bin(oEnv.rail.grid[1,1])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "<class 'numpy.ndarray'>\n"
+ ]
+ },
{
"data": {
"text/plain": [
- "'0b1000000001100000'"
+ "<Figure size 720x720 with 0 Axes>"
]
},
- "execution_count": 12,
"metadata": {},
- "output_type": "execute_result"
+ "output_type": "display_data"
}
],
"source": [
- "cell_id = (1,1,2)\n",
- "iDir = 1\n",
- "iValCell = oEnv.rail.transitions.set_transition(oEnv.rail.grid[cell_id[0]][cell_id[1]], cell_id[2], iDir, True)\n",
- "bin(iValCell)"
+ "oFig = plt.figure(figsize=(10,10))\n",
+ "oRT.renderEnv(spacing=False, arrows=False, sRailColor=\"gray\", show=False)\n",
+ "img = oRT.getImage()\n",
+ "print(type(img))\n",
+ "plt.clf() # if you don't want the image to appear here\n",
+ "pass\n",
+ "\n",
+ "wid_img = jpy_canvas.Canvas(img)"
]
},
{
- "cell_type": "code",
- "execution_count": 13,
+ "cell_type": "markdown",
"metadata": {},
- "outputs": [],
"source": [
- "oEnv.rail.grid[cell_id[0]][cell_id[1]] = iValCell"
+ "### Update the function - in case external code updated"
]
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "'0b1000000001100000'"
+ "([], deque([]))"
]
},
- "execution_count": 14,
+ "execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "bin(oEnv.rail.grid[1,1])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {},
- "outputs": [],
- "source": [
- "#image = imat.read(\"../Jupyter_Canvas_Widget/notebooks/images/mini_1.jpg\")\n",
- "image = img\n",
- "image_b = imat.rebin(image, 0.25) \n",
- "\n",
- "H,W = image.shape[:2]\n",
- "\n",
- "L = 20\n",
- "L2 = L*2 + 1"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {},
- "outputs": [],
- "source": [
- "wid_img = jpy_canvas.Canvas(image)\n",
- "wid_sub = jpy_canvas.Canvas(image_b)\n",
- "wid_sub.width=300\n",
- "wid_sub.layout.border='black'\n",
- "\n",
- "wid_img.width = W \n",
- "wid_img.height = H \n",
- "\n",
- "# wid_sub.width = L2*3\n",
- "# wid_sub.height = L2*3\n",
- "\n",
- "# guessing these:\n",
- "xyBase = array([20,20])\n",
- "nPixCell = 70\n",
- "\n",
- "#wid_box = ipywidgets.HBox([wid_img, wid_sub])"
+ "wid_img.unregister_all()\n",
+ "oEditor = JupEditor(oEnv)\n",
+ "wid_img.register_move(oEditor.event_handler)\n",
+ "oEditor.rcHistory, oEditor.qEvents"
]
},
{
@@ -294,7 +214,7 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 11,
"metadata": {
"scrolled": false
},
@@ -302,7 +222,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "e92617af405d4215ac1f02eed0c456ae",
+ "model_id": "3216678923e047dd871b8b10cc87336c",
"version_major": 2,
"version_minor": 0
},
@@ -312,485 +232,6 @@
},
"metadata": {},
"output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[2 2] [0 1]\n",
- "[0 1] [1 1]\n",
- "iTrans: 2\n",
- "[1 1] [1 2]\n",
- "iTrans: 1\n",
- "iTransLast 2\n",
- "Set RCD: [1 1] 2 to: 1\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "<Figure size 720x720 with 0 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[1 2] [1 3]\n",
- "iTrans: 1\n",
- "iTransLast 1\n",
- "Set RCD: [1 2] 1 to: 1\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "<Figure size 720x720 with 0 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[1 3] [2 3]\n",
- "iTrans: 2\n",
- "iTransLast 1\n",
- "Set RCD: [1 3] 1 to: 2\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "<Figure size 720x720 with 0 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[2 3] [2 2]\n",
- "iTrans: 3\n",
- "iTransLast 2\n",
- "Set RCD: [2 3] 2 to: 3\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "<Figure size 720x720 with 0 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[2 2] [2 1]\n",
- "iTrans: 3\n",
- "iTransLast 3\n",
- "Set RCD: [2 2] 3 to: 3\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "<Figure size 720x720 with 0 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[2 1] [2 2]\n",
- "iTrans: 1\n",
- "iTransLast 3\n",
- "Set RCD: [2 1] 3 to: 1\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "<Figure size 720x720 with 0 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[2 2] [2 3]\n",
- "iTrans: 1\n",
- "iTransLast 1\n",
- "Set RCD: [2 2] 1 to: 1\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "<Figure size 720x720 with 0 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[2 3] [2 2]\n",
- "iTrans: 3\n",
- "iTransLast 1\n",
- "Set RCD: [2 3] 1 to: 3\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "<Figure size 720x720 with 0 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[2 2] [2 1]\n",
- "iTrans: 3\n",
- "iTransLast 3\n",
- "Set RCD: [2 2] 3 to: 3\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "<Figure size 720x720 with 0 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[2 1] [2 2]\n",
- "iTrans: 1\n",
- "iTransLast 3\n",
- "Set RCD: [2 1] 3 to: 1\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "<Figure size 720x720 with 0 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[2 2] [2 1]\n",
- "iTrans: 3\n",
- "iTransLast 1\n",
- "Set RCD: [2 2] 1 to: 3\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "<Figure size 720x720 with 0 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[2 1] [2 2]\n",
- "iTrans: 1\n",
- "iTransLast 3\n",
- "Set RCD: [2 1] 3 to: 1\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "<Figure size 720x720 with 0 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[2 2] [2 1]\n",
- "iTrans: 3\n",
- "iTransLast 1\n",
- "Set RCD: [2 2] 1 to: 3\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "<Figure size 720x720 with 0 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[2 1] [2 2]\n",
- "iTrans: 1\n",
- "iTransLast 3\n",
- "Set RCD: [2 1] 3 to: 1\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "<Figure size 720x720 with 0 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[2 2] [2 1]\n",
- "iTrans: 3\n",
- "iTransLast 1\n",
- "Set RCD: [2 2] 1 to: 3\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "<Figure size 720x720 with 0 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[2 1] [2 2]\n",
- "iTrans: 1\n",
- "iTransLast 3\n",
- "Set RCD: [2 1] 3 to: 1\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "<Figure size 720x720 with 0 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[2 2] [2 1]\n",
- "iTrans: 3\n",
- "iTransLast 1\n",
- "Set RCD: [2 2] 1 to: 3\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "<Figure size 720x720 with 0 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[2 1] [2 2]\n",
- "iTrans: 1\n",
- "iTransLast 3\n",
- "Set RCD: [2 1] 3 to: 1\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "<Figure size 720x720 with 0 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[2 2] [2 1]\n",
- "iTrans: 3\n",
- "iTransLast 1\n",
- "Set RCD: [2 2] 1 to: 3\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "<Figure size 720x720 with 0 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[2 1] [2 2]\n",
- "iTrans: 1\n",
- "iTransLast 3\n",
- "Set RCD: [2 1] 3 to: 1\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "<Figure size 720x720 with 0 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[2 2] [2 1]\n",
- "iTrans: 3\n",
- "iTransLast 1\n",
- "Set RCD: [2 2] 1 to: 3\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "<Figure size 720x720 with 0 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[2 1] [2 2]\n",
- "iTrans: 1\n",
- "iTransLast 3\n",
- "Set RCD: [2 1] 3 to: 1\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "<Figure size 720x720 with 0 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[2 2] [2 1]\n",
- "iTrans: 3\n",
- "iTransLast 1\n",
- "Set RCD: [2 2] 1 to: 3\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "<Figure size 720x720 with 0 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[2 1] [3 1]\n",
- "iTrans: 2\n",
- "iTransLast 3\n",
- "Set RCD: [2 1] 3 to: 2\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "<Figure size 720x720 with 0 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "[3 1] [1 2]\n",
- "[1 2] [1 1]\n",
- "iTrans: 3\n",
- "iTransLast 2\n",
- "Set RCD: [1 2] 2 to: 3\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "<Figure size 720x720 with 0 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
}
],
"source": [
@@ -798,269 +239,20 @@
"wid_img"
]
},
- {
- "cell_type": "code",
- "execution_count": 18,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "0"
- ]
- },
- "execution_count": 18,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "lEvDraw = deque()\n",
- "\n",
- "rcLast = array([-1,-1])\n",
- "iTransLast = -1\n",
- "\n",
- "gRCTrans = array([[-1,0], [0,1], [1,0], [0,-1]]) # NESW in RC\n",
- "rcTrans = array([1,1])\n",
- "iTrans = np.argwhere(np.all(gRCTrans - rcTrans == 0, axis=1))\n",
- "len(iTrans)\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "metadata": {},
- "outputs": [],
- "source": [
- "def work_function(wid, event):\n",
- " \"\"\"Mouse motion event handler\n",
- " \"\"\"\n",
- " global rcLast, iTransLast\n",
- " \n",
- " i = event['canvasX'] \n",
- " i0 = i-L\n",
- " i1 = i+L+1\n",
- "\n",
- " j = event['canvasY']\n",
- " j0 = j-L\n",
- " j1 = j+L+1\n",
- "\n",
- " if i0 < 0:\n",
- " i0 = 0\n",
- " \n",
- " if j0 < 0:\n",
- " j0 = 0\n",
- " \n",
- " #crop = wid.data[j0:j1, i0:i1]\n",
- " #print(event)\n",
- " #print(i0,i1,j0,j1)\n",
- " #print(wid.data[i,j])\n",
- " #print(crop.shape)\n",
- " \n",
- " if False:\n",
- " with wid_sub.hold_sync():\n",
- " wid_sub.data = crop\n",
- " wid_sub.width = crop.shape[1]*5\n",
- " wid_sub.height = crop.shape[0]*5\n",
- "\n",
- " \n",
- " if event[\"buttons\"] > 0:\n",
- " if False:\n",
- " width, height = wid.data.shape[:2]\n",
- " with wid.hold_sync():\n",
- "\n",
- " if i>10 and i<width and j> 10 and j < height:\n",
- " writableData = np.copy(wid.data)\n",
- " writableData[j-5:j+5, i-5:i+5, :] = 255\n",
- " wid.data = writableData\n",
- " else:\n",
- " lEvDraw.append((time.time(), i,j))\n",
- " \n",
- " if len(lEvDraw) > 0:\n",
- " tNow = time.time()\n",
- " if tNow - lEvDraw[0][0] > 0.1: # wait before trying to draw\n",
- " height, width = wid.data.shape[:2]\n",
- " writableData = np.copy(wid.data)\n",
- " bRedrawn = False\n",
- " with wid.hold_sync():\n",
- " #rcLast = array([-1,-1])\n",
- " while len(lEvDraw) > 0:\n",
- " t, i, j = lEvDraw.popleft()\n",
- " #print(\"tij:\", t,i,j)\n",
- " if i>10 and i<width and j> 10 and j < height:\n",
- " writableData[j-2:j+2, i-2:i+2, :] = 0\n",
- " \n",
- " rcCell = ((array([j,i]) - xyBase) / nPixCell).astype(int)\n",
- " \n",
- " if (not np.array_equal(rcLast, array([-1,-1]))) and not np.array_equal(rcLast, rcCell):\n",
- " print (rcLast, rcCell) \n",
- " rcTrans = rcCell - rcLast\n",
- " iTrans = np.argwhere(np.all(gRCTrans - rcTrans == 0, axis=1))\n",
- " if len(iTrans) > 0:\n",
- " iTrans = iTrans[0][0]\n",
- " print(\"iTrans: \", iTrans)\n",
- " if iTransLast >= 0:\n",
- " print(\"iTransLast\", iTransLast)\n",
- " print(\"Set RCD:\", rcLast, iTransLast, \"to: \", iTrans )\n",
- " #oEnv.rail.set_transition((*rcLast, iTransLast), iTrans, True) # does nothing\n",
- " iValCell = oEnv.rail.transitions.set_transition(oEnv.rail.grid[rcLast[0], rcLast[1]], iTransLast, iTrans, True)\n",
- " oEnv.rail.grid[rcLast[0], rcLast[1]] = iValCell\n",
- " \n",
- " oFig = plt.figure(figsize=(10,10))\n",
- " oRT.renderEnv(spacing=False, arrows=False, sRailColor=\"gray\", show=False)\n",
- " img = oRT.getImage()\n",
- " plt.clf()\n",
- " wid.data = img\n",
- " bRedrawn = True\n",
- " \n",
- " \n",
- " iTransLast = iTrans\n",
- " rcLast = rcCell\n",
- " \n",
- " if not bRedrawn:\n",
- " wid.data = writableData\n",
- " #wid.width = W \n",
- " #wid.height = H"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "metadata": {},
- "outputs": [],
- "source": [
- "wid_img.register_move(work_function)"
- ]
- },
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Junk below here"
+ "### Save the image (change the filename...)"
]
},
{
"cell_type": "code",
- "execution_count": 21,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([[[255, 255, 255],\n",
- " [255, 255, 255],\n",
- " [255, 255, 255]],\n",
- "\n",
- " [[255, 255, 255],\n",
- " [255, 255, 255],\n",
- " [255, 255, 255]],\n",
- "\n",
- " [[255, 255, 255],\n",
- " [255, 255, 255],\n",
- " [255, 255, 255]]], dtype=uint8)"
- ]
- },
- "execution_count": 21,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "crop = wid_img.data[0:3, 0:3]\n",
- "crop"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 22,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "<class 'numpy.ndarray'> (720, 720, 3)\n"
- ]
- }
- ],
- "source": [
- "image2 = np.copy(image)\n",
- "print(type(image2), image2.shape)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 23,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(720, 720)"
- ]
- },
- "execution_count": 23,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "W,H\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 24,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([2, 3])"
- ]
- },
- "execution_count": 24,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "array([2,3]).astype(int)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 25,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array([[0., 0., 1., 0., 0.],\n",
- " [0., 0., 1., 0., 0.]])"
- ]
- },
- "execution_count": 25,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "gA = np.zeros((5,5))\n",
- "gA[2,2]= 1\n",
- "\n",
- "rcLast = array([2,2])\n",
- "gA[rcLast.T] \n",
- "#gA"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
+ "execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
- "\n"
+ "oEnv.rail.save_transition_map(\"../flatland/env-data/tests/test-editor.npy\")"
]
}
],
--
GitLab