diff --git a/.vscode/settings.json b/.vscode/settings.json
index 23b056c..054715f 100644
--- a/.vscode/settings.json
+++ b/.vscode/settings.json
@@ -69,7 +69,8 @@
"cinttypes": "cpp",
"typeindex": "cpp",
"typeinfo": "cpp",
- "variant": "cpp"
+ "variant": "cpp",
+ "codecvt": "cpp"
},
"cmake.ignoreCMakeListsMissing": true,
"python.autoComplete.extraPaths": [
diff --git a/analysis/analysis.ipynb b/analysis/analysis.ipynb
index ee39612..5689ea6 100644
--- a/analysis/analysis.ipynb
+++ b/analysis/analysis.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
@@ -16,7 +16,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
@@ -27,13 +27,14 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"#experiment_folder = \"casestudy_example\"\n",
- "experiment_folder = \"casestudy_wildfire_drone\"\n",
- "experiment_name = \"cs_example_edf\"\n",
+ "#experiment_name = \"cs_example_edf\"\n",
+ "experiment_folder = \"existing_system_monitor\"\n",
+ "experiment_name = \"uas_edf\"\n",
"\n",
"experiment_file = os.path.join(results_dir, experiment_folder, experiment_name + \".json\")\n",
"if not os.path.exists(experiment_file):\n",
@@ -42,7 +43,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
@@ -52,16 +53,16 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Number of records: 12055\n",
- "First record: {'entry': {'operation': 'start_work', 'chain': 0, 'node': 'node_0', 'count': 500, 'next_release_us': 99890}, 'time': 0.0001}\n",
- "Operation types: ['wait_for_work', 'get_next_executable', 'next_deadline', 'end_work', 'start_work']\n"
+ "Number of records: 42\n",
+ "First record: {'entry': {'operation': 'start_work', 'chain': 0, 'node': 'CustomListenerNode/CustomListenerNode-wildfire_talk', 'count': 0}, 'time': 0.0}\n",
+ "Operation types: ['start_work', 'end_work']\n"
]
}
],
@@ -94,7 +95,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
@@ -141,14 +142,14 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Number of nodes: 4\n"
+ "Number of nodes: 2\n"
]
}
],
@@ -159,24 +160,24 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "eabdbc0b05594f0bbbb97b9b63edf92e",
+ "model_id": "ab61935f394e43aeaa093dc378e11680",
"version_major": 2,
"version_minor": 0
},
- "image/png": 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",
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x4ojH7OzsjCuuuCI2bdoUDz/8cJxyyilHHA8AcDw7ZAA+//zzcd9998Xjjz8ev/M7vxMREX/7t38bEydOrI455ZRToq6uLurr69/yad+FCxfGRz/60ernS5YsiVtvvbV63fjx42P9+vXxzW9+8y0D8ECcnXTSSd3O+8EPfrDbuG9961vR1NQUDz30UHzkIx857PH27NkTl156aXR2dsbq1aujsbHxsGM7Ozujs7Oz+nlHR8cR5woA0B8d8ingDRs2xKBBg2LKlCnV6yZMmFB9pe/Rmjp1avXj119/PTZu3Bhz586N4cOHVy833XRTbNy48Tc6fkTE9u3bY968eXHOOedEY2NjNDQ0xJ49e97y6dwrrrgiXn/99bj//vuPGH8REV/84hejsbGxemlubv6N5wsAUCs9ehHI2zVs2LDqx3v27ImIiG9/+9vR0tLSbdzAgQN/43PMnj07fvWrX8Xtt98eY8eOjbq6umhtbY29e/ce8X4zZ86M73//+7FmzZqDHkX8/91www1x3XXXVT/v6OgQgQDAceeQAThhwoTYt29fPPXUU9WngJ977rnYuXNnj084atSoGDNmTLzwwgtx5ZVXHvX9D/zO3/79+7td/+ijj8Ydd9wRM2fOjIiIzZs3xy9/+cu3PN4nP/nJ+O3f/u247LLL4sc//nG0tbUddmxdXV3U1dUd9ZwBAPqTQwbgb/3Wb8WMGTNi/vz58Td/8zcxaNCgWLhw4RFf6HE0li5dGgsWLIjGxsaYMWNGdHZ2xpNPPhmvvfZat0fYDuXUU0+N+vr6+Nd//dc444wzYujQodHY2BjnnHNOfO9734upU6dGR0dHfPazn33b8/3zP//z2L9/f3zkIx+J++67L37v936vN5YJANAvHfZtYFasWBFjxoyJtra2+OhHPxrXXHNNnHrqqb1y0quvvjruvPPOWLFiRUyaNCna2tpi5cqV1bd4OZJBgwbF17/+9fjmN78ZY8aMicsvvzwi/vdFKq+99lq8733vi6uuuioWLFhwVPNduHBhLF26NGbOnBk//elPf+O1AQD0d5VSSqn1JI5XHR0d0djYGLt27YqGhoZaTwcAeBv8/O7hn4IDAOD40+8CcNmyZd3eHubNl2P5N4YBALLod08B79ixI3bs2HHI2+rr6+P000/v4xkdnoeQAeD44+d3H70P4NEYOXJkjBw5stbTAAA4YfW7p4ABADi2BCAAQDICEAAgGQEIAJCMAAQASEYAAgAkIwABAJIRgAAAyQhAAIBkBCAAQDICEAAgGQEIAJCMAAQASEYAAgAkIwABAJIRgAAAyQhAAIBkBCAAQDICEAAgGQEIAJCMAAQASEYAAgAkIwABAJIRgAAAyQhAAIBkBCAAQDICEAAgGQEIAJCMAAQASEYAAgAkIwABAJIRgAAAyQhAAIBkBCAAQDICEAAgGQEIAJCMAAQASEYAAgAkIwABAJIRgAAAyQhAAIBkBCAAQDICEAAgGQEIAJCMAAQASEYAAgAkIwABAJIRgAAAyQhAAIBkBCAAQDICEAAgGQEIAJCMAAQASEYAAgAkIwABAJIRgAAAyQhAAIBkBCAAQDICEAAgGQEIAJCMAAQASEYAAgAkIwABAJIRgAAAyQhAAIBkBCAAQDICEAAgGQEIAJCMAAQASEYAAgAkIwABAJIRgAAAyQhAAIBkBCAAQDICEAAgGQEIAJCMAAQASEYAAgAkIwABAJIRgAAAyQyq9QSOZ6WUiIjo6Oio8UwAgLfrwM/tAz/HMxKAPbB79+6IiGhubq7xTACAo7V79+5obGys9TRqolIy528PdXV1xZYtW2LEiBFRqVR69dgdHR3R3NwcmzdvjoaGhl49Nv+Pfe4b9rlv2Oe+YZ/7xrHc51JK7N69O8aMGRMDBuT8bTiPAPbAgAED4owzzjim52hoaPANpg/Y575hn/uGfe4b9rlvHKt9zvrI3wE5sxcAIDEBCACQjADsp+rq6mLJkiVRV1dX66mc0Oxz37DPfcM+9w373Dfs87HlRSAAAMl4BBAAIBkBCACQjAAEAEhGAAIAJCMAj5G//uu/jnHjxsXQoUOjpaUlHn/88SOO/8d//MeYMGFCDB06NCZNmhT33ntvt9tLKbF48eI47bTTor6+PqZPnx4///nPu43ZsWNHXHnlldHQ0BBNTU0xd+7c2LNnT6+vrT+pxT7ffPPNceGFF8Y73vGOaGpq6u0l9Ut9vc+bNm2KuXPnxvjx46O+vj7OOuusWLJkSezdu/eYrK+/qMXX82WXXRZnnnlmDB06NE477bS46qqrYsuWLb2+tv6kFvt8QGdnZ7z3ve+NSqUS69at660l9Vu12Otx48ZFpVLpdrnlllt6fW3HvUKvu/vuu8uQIUPK3/3d35Vnn322zJs3rzQ1NZXt27cfcvyjjz5aBg4cWL70pS+V9evXl7/8y78sgwcPLv/5n/9ZHXPLLbeUxsbG8qMf/ag8/fTT5bLLLivjx48vb7zxRnXMjBkzyvnnn1/Wrl1bHn744XL22WeXK6644pivt1Zqtc+LFy8uX/3qV8t1111XGhsbj/Uya64W+3zfffeVOXPmlH/7t38rGzduLKtWrSqnnnpq+Yu/+Is+WXMt1Orr+atf/WpZs2ZN2bRpU3n00UdLa2traW1tPebrrZVa7fMBCxYsKJdcckmJiNLe3n6sltkv1Gqvx44dW/7qr/6qbN26tXrZs2fPMV/v8UYAHgMXXHBB+bM/+7Pq5/v37y9jxowpX/ziFw85/mMf+1i59NJLu13X0tJS5s+fX0oppaurq4wePbp8+ctfrt6+c+fOUldXV/7hH/6hlFLK+vXrS0SUJ554ojrmvvvuK5VKpbzyyiu9trb+pBb7/GYrVqxIEYC13ucDvvSlL5Xx48f3ZCn9Wn/Z51WrVpVKpVL27t3bk+X0W7Xc53vvvbdMmDChPPvssykCsFZ7PXbs2HLbbbf14kpOTJ4C7mV79+6Np556KqZPn169bsCAATF9+vRYs2bNIe+zZs2abuMjIj784Q9Xx7/44ouxbdu2bmMaGxujpaWlOmbNmjXR1NQUU6dOrY6ZPn16DBgwIB577LFeW19/Uat9zqY/7fOuXbti5MiRPVlOv9Vf9nnHjh1x1113xYUXXhiDBw/u6bL6nVru8/bt22PevHnxve99L97xjnf05rL6pVp/Td9yyy1x0kknxeTJk+PLX/5y7Nu3r7eWdsIQgL3sl7/8Zezfvz9GjRrV7fpRo0bFtm3bDnmfbdu2HXH8gX/fasypp57a7fZBgwbFyJEjD3ve41mt9jmb/rLP//Vf/xXLly+P+fPn/0br6O9qvc+f//znY9iwYXHSSSfFSy+9FKtWrerRevqrWu1zKSXmzJkT1157bbf/pJ/Iavk1vWDBgrj77rtj9erVMX/+/Fi2bFl87nOf6/GaTjQCEOjXXnnllZgxY0b88R//ccybN6/W0zkhffazn4329va4//77Y+DAgTFr1qwo/khUr1m+fHns3r07brjhhlpPJYXrrrsupk2bFuedd15ce+21ceutt8by5cujs7Oz1lPrVwRgLzv55JNj4MCBsX379m7Xb9++PUaPHn3I+4wePfqI4w/8+1ZjXn311W6379u3L3bs2HHY8x7ParXP2dR6n7ds2RIXXXRRXHjhhfGtb32rR2vpz2q9zyeffHK8+93vjosvvjjuvvvuuPfee2Pt2rU9WlN/VKt9/slPfhJr1qyJurq6GDRoUJx99tkRETF16tSYPXt2zxfWD9X6a/rNWlpaYt++fbFp06ajXcYJTQD2siFDhsSUKVPiwQcfrF7X1dUVDz74YLS2th7yPq2trd3GR0Q88MAD1fHjx4+P0aNHdxvT0dERjz32WHVMa2tr7Ny5M5566qnqmJ/85CfR1dUVLS0tvba+/qJW+5xNLff5lVdeiWnTpsWUKVNixYoVMWDAifvtqj99PXd1dUVEnJCPltRqn7/+9a/H008/HevWrYt169ZV39rknnvuiZtvvrlX19hf9Kev6XXr1sWAAQMO+jWp9Gr9KpQT0d13313q6urKypUry/r168s111xTmpqayrZt20oppVx11VXl+uuvr45/9NFHy6BBg8pXvvKVsmHDhrJkyZJDvvS9qamprFq1qjzzzDPl8ssvP+TbwEyePLk89thj5ZFHHinnnHPOCf82MLXY51/84helvb29LF26tAwfPry0t7eX9vb2snv37r5bfB+qxT6//PLL5eyzzy4f+tCHyssvv9zt7RxOVLXY57Vr15bly5eX9vb2smnTpvLggw+WCy+8sJx11lnl17/+dd9uQB+p1feNN3vxxRdTvAq4Fnv905/+tNx2221l3bp1ZePGjeX73/9+OeWUU8qsWbP6dvHHAQF4jCxfvryceeaZZciQIeWCCy4oa9eurd7W1tZWZs+e3W38D37wg/Lud7+7DBkypJx77rnlxz/+cbfbu7q6yqJFi8qoUaNKXV1d+dCHPlSee+65bmN+9atflSuuuKIMHz68NDQ0lD/5kz85YaPkgFrs8+zZs0tEHHRZvXr1sVpmzfX1Pq9YseKQe3yi/5+1r/f5mWeeKRdddFEZOXJkqaurK+PGjSvXXnttefnll4/pOmutFt833ixLAJbS93v91FNPlZaWltLY2FiGDh1aJk6cWJYtW3bC/oemJyql+E1fAIBMTtxfqgEA4JAEIABAMgIQACAZAQgAkIwABABIRgACACQjAAEAkhGAAADJCEAAgGQEIABAMgIQACAZAQgAkIwABABIRgACACQjAAEAkhGAAADJCEAAgGQEIABAMgIQACAZAQgAkIwABABIRgACACQjAAEAkhGAAADJCEAAgGQEIABAMgIQACAZAQgAkIwABABIRgACACQjAAEAkvk/MkCJF/d2EQ8AAAAASUVORK5CYII=",
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