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Merge pull request #46 from nasa/develop
Release 0.2.5
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\page analyst Analyst Notebook | ||
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copied into the jupyter notebook home folder, are scripts that are made by the analyst and will be saved, even after the docker is shutdown. |
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{ | ||
"cells": [ | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": { | ||
"tags": [] | ||
}, | ||
"source": [ | ||
"# Import bagfiles to Database for Analysis\n", | ||
"\n", | ||
"The database used is ArangoDB. If you followed the Analyst notebook startup procedure [here](https://nasa.github.io/isaac/html/index.html), the local database should already be hosted in http://localhost:8529.\n", | ||
"\n", | ||
"Go ahead open the link at take a look! The username is `root` and the password `isaac`.\n", | ||
"\n", | ||
"To connect to the database:" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"from pyArango.connection import *\n", | ||
"\n", | ||
"# Connect to the database\n", | ||
"conn = Connection(arangoURL=\"http://iui_arangodb:8529\", username=\"root\", password=\"isaac\")\n", | ||
"\n", | ||
"# Open the isaac database / create it if it does not exist\n", | ||
"if not conn.hasDatabase(\"isaac\"):\n", | ||
" conn.createDatabase(name=\"isaac\")\n", | ||
"db = conn[\"isaac\"]\n", | ||
"\n", | ||
"# Create a collection\n", | ||
"if not db.hasCollection(\"analyst\"):\n", | ||
" db.createCollection(name=\"analyst\")\n", | ||
" \n", | ||
"print(\"Connected to database!\")" | ||
] | ||
}, | ||
{ | ||
"cell_type": "markdown", | ||
"metadata": {}, | ||
"source": [ | ||
"The next step is to select the bagfile to import" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"from scripts.load_bag_database import LoadBagDatabase\n", | ||
"\n", | ||
"\n", | ||
"path=\"/home/analyst/data/bags/\"\n", | ||
"\n", | ||
"LoadBagDatabase(db, path, \"/gnc/ekf\")" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [] | ||
} | ||
], | ||
"metadata": { | ||
"kernelspec": { | ||
"display_name": "Python 3 (ipykernel)", | ||
"language": "python", | ||
"name": "python3" | ||
}, | ||
"language_info": { | ||
"codemirror_mode": { | ||
"name": "ipython", | ||
"version": 3 | ||
}, | ||
"file_extension": ".py", | ||
"mimetype": "text/x-python", | ||
"name": "python", | ||
"nbconvert_exporter": "python", | ||
"pygments_lexer": "ipython3", | ||
"version": "3.8.10" | ||
} | ||
}, | ||
"nbformat": 4, | ||
"nbformat_minor": 4 | ||
} |
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{ | ||
"cells": [ | ||
{ | ||
"cell_type": "markdown", | ||
"id": "5636fde2-c963-48b5-a883-a7d520a6084e", | ||
"metadata": {}, | ||
"source": [ | ||
"# Read Various Data Types from Database" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"id": "23334b94-b585-4319-a75b-eeacece22852", | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"id": "9288a825-64f6-4177-9f65-9bff1ea74210", | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [] | ||
} | ||
], | ||
"metadata": { | ||
"kernelspec": { | ||
"display_name": "Python 3 (ipykernel)", | ||
"language": "python", | ||
"name": "python3" | ||
}, | ||
"language_info": { | ||
"codemirror_mode": { | ||
"name": "ipython", | ||
"version": 3 | ||
}, | ||
"file_extension": ".py", | ||
"mimetype": "text/x-python", | ||
"name": "python", | ||
"nbconvert_exporter": "python", | ||
"pygments_lexer": "ipython3", | ||
"version": "3.8.10" | ||
} | ||
}, | ||
"nbformat": 4, | ||
"nbformat_minor": 5 | ||
} |
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{ | ||
"cells": [ | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"id": "1b6a6b4d-99b1-47ca-8cfd-a668539fdab8", | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": null, | ||
"id": "9df2443f-50cc-49a9-9b4e-2f1018fc6904", | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": 1, | ||
"id": "097ce74d-b561-4aa4-b899-8301205457a2", | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"from pathlib import Path\n", | ||
"\n", | ||
"import matplotlib.pyplot as plt\n", | ||
"import numpy as np\n", | ||
"import torch\n", | ||
"import torch.nn.functional as F\n", | ||
"import torchvision.transforms as transforms\n", | ||
"from PIL import Image\n", | ||
"from torch import nn, optim\n", | ||
"from torchvision import datasets, models, transforms\n", | ||
"\n", | ||
"\n", | ||
"\n", | ||
"test_transforms = transforms.Compose(\n", | ||
" [\n", | ||
" transforms.Resize(224),\n", | ||
" transforms.ToTensor(),\n", | ||
" transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n", | ||
" ]\n", | ||
")" | ||
] | ||
} | ||
], | ||
"metadata": { | ||
"kernelspec": { | ||
"display_name": "Python 3 (ipykernel)", | ||
"language": "python", | ||
"name": "python3" | ||
}, | ||
"language_info": { | ||
"codemirror_mode": { | ||
"name": "ipython", | ||
"version": 3 | ||
}, | ||
"file_extension": ".py", | ||
"mimetype": "text/x-python", | ||
"name": "python", | ||
"nbconvert_exporter": "python", | ||
"pygments_lexer": "ipython3", | ||
"version": "3.9.7" | ||
} | ||
}, | ||
"nbformat": 4, | ||
"nbformat_minor": 5 | ||
} |
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