{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Part one: The semantic web and a RDFLib example\n", "\n", "## Introduction\n", "\n", "Work in progress...\n", "\n", "The goal is to write down and store scientific experimental data with metadata in a machine readable intelligent form. Integrated in researchers daily workflow the following shall facilitate:\n", "* Re-use of the data (also foreign data)\n", "* Reproducibility\n", "* Building joint databases capable of querying for specific questions like: \"I need a material with bandgap in the range 2.4 - 3 eV solvable in ethanol\" or \"All spectral data on P3HT mixed with whatever\".\n", "* Easy publishing with tomorrow's citation and publishing methods" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Learn python for scientific usage\n", "The following tutorials are suited for beginners but might be interesting too for more advanced Pythonians.\n", "\n", "* [Python for Scientists](http://www.mpia.de/~robitaille/PY4SCI_SS_2015/)\n", "* [IPython Notebook Tutorial](https://youtu.be/lmoNmY-cmSI?list=PLRJx8WOUx5Xd3_dgw5xRmABUd8MWdsA_C)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Learn about the semantic web:\n", "\n", "A video for the general concept: https://www.youtube.com/watch?v=4x_xzT5eF5Q\n", "\n", "Popular Open Data datasets: http://wiki.dbpedia.org/about\n", "\n", "Some basics: http://linkeddatabook.com\n", "\n", "\n", " \n", "\n", " \n", "\n", "![LOD_Cloud_2014](https://upload.wikimedia.org/wikipedia/commons/thumb/0/08/LOD_Cloud_2014.svg/1280px-LOD_Cloud_2014.svg.png) lod-cloud.net\n", "\n", " \n", "\n", " \n", "\n", "### RDF\n", "The Resource Description Framework (RDF) is a directed, labeled graph **data model** for representing information in the Web (files can be serialized in multiple formats: JSON-LD, Turtle, N3, RDF/XML). \"RDF has features that facilitate data merging even if the underlying schemata differ, and it specifically supports the evolution of schemata over time without requiring all the data consumers to be changed\" (https://www.w3.org/2001/sw/wiki/RDF).\n", "\n", "### Learn querying a RDF database (SPARQL query language)\n", "Here is a good intro video (querying in python is still done via the SPARQL syntax):\n", "* https://www.youtube.com/watch?v=FvGndkpa4K0\n", "\n", "Then as a reference there is the W3C Recommendation which defines the syntax and semantics of the SPARQL query language for RDF:\n", "\n", "* https://www.w3.org/TR/sparql11-query/" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## RDFLib example: Linking experiment data and meta-data as graph\n", "### Import of .txt into pandas" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['high_intens.txt']" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "import pandas as pd\n", "import os\n", "import glob\n", "\n", "\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "glob.glob(\"*.txt\")" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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Wavelength [nm]Counts [1/80 ms]
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" ], "text/plain": [ " Wavelength [nm] Counts [1/80 ms]\n", "0 181.49 14.00\n", "1 181.71 10.90\n", "2 181.92 17.23\n", "3 182.14 9.45\n", "4 182.36 10.65" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Import spectrum\n", "rawData = pd.read_table(\"high_intens.txt\", names = [\"Wavelength [nm]\",\"Counts [1/80 ms]\"],\n", " header=17, decimal=\",\")\n", "rawData.head()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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AQkSkBKUyRApTICEiUoZoS4QCC5EcBRIiIgkotSHSkQIJERERqZgCCRGRhJTa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\nXvNnmI8+Cj/8YfE6n3667wi6ppuF+/b1rRJ77AG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+/IhYcOlmmDXjOGVwQ8Jx\nnK0OETkPi3nYF9gBmAO8AdyqqvPrsm+OU99wQ8JxHMdxnGrjQTiO4ziO41QbNyQcx3Ecx6k2bkg4\njuM4jlNt3JBwHMdxHKfauCHhOI7jOE61cUPCcRzHcZxq8/+SsqNJVgaLVwAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "rawData.plot(x=\"Wavelength [nm]\",y=\"Counts [1/80 ms]\",figsize=(8,6),fontsize=18)\n", "plt.title(\"A sodium spectrum\", fontsize=28)\n", "plt.xlabel(\"Wavelength [nm]\",fontsize=18)\n", "plt.ylabel(\"Counts [1/80 ms]\",fontsize=18)\n", "plt.ylim(0,1000)\n", "# plt.savefig(\"Sectrum.eps\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Go semantic!\n", "### Namespace\n", "Which ontologies/schemata?\n", "-> dbpedia.org identifiers (URIs)?: http://linkeddatabook.com/editions/1.0/#htoc39\n", "\n", "#### Vocabularies for laboratory reporting:\n", "* http://www.sparontologies.net/ontologies\n", "* http://aber-owl.net/ontology/BRIDG\n", "* http://aber-owl.net/ontology/SP\n", "* https://www.w3.org/TR/annotation-model/\n", "* http://xml.coverpages.org/matML.html\n", "* https://rd-alliance.github.io/metadata-directory/standards/rdf-data-cube-vocabulary.html\n", "\n", "### Data and metadata to RDF-tripples\n", "We store data values in so-called Literals and use URIs to name and describe the data." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#import rdflib\n", "from rdflib import Graph, ConjunctiveGraph, Literal, BNode, Namespace, URIRef, XSD\n", "from rdflib.namespace import DC, FOAF, RDF, RDFS\n", "from rdflib.plugins.memory import IOMemory\n", "\n", "from urllib.parse import quote" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Namespaces define the recources and vocabulary we work with" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "n = Namespace(\"http://example.org/people/\")\n", "\n", "dcterms = Namespace(\"http://purl.org/dc/terms/\")\n", "dcmi = Namespace(\"http://purl.org/dc/dcmitype/\")\n", "dbr = Namespace(\"http://dbpedia.org/resource/\")\n", "owl = Namespace(\"http://www.w3.org/2002/07/owl#\")\n", "wot = Namespace(\"http://xmlns.com/wot/0.1/\")\n", "crypto = Namespace(\"https://www.w3.org/2000/10/swap/crypto#\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Think about data and meta-data:\n", "### Experiment\n", "\n", "* The data itself with descriptions for its values\n", "* Date experiment, date of last evaluation\n", "* Temperature, pressure, material etc.\n", "* Methods\n", "\n", "\n", "\n", "### Experimentalist\n", "* Name experimentalist\n", "* Mail experimentalist\n", "* Digital signature experimentalist\n", "* Institution of the experimentalist\n", "\n", "### Intended publicity\n", "* Private/workgroup/institution/openAccess\n", "* Licensing terms\n", "\n", "### Tags\n", "Only very specific tags are useful!\n", "\n", "### A rating?\n", "http://vocab.org/review/" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "rdflib.term.URIRef('file:///home/pathToCwd/methaGraph.jsonld')" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cwd = \"/home/pathToCwd/\"# os.getcwd() get currend working directory\n", "fn = os.path.join(cwd, \"methaGraph.jsonld\")\n", "\n", "mdg = Namespace(\"file://\" + quote(fn) + \"#\") # This is the namespace for the experiment\n", "\n", "myExp = URIRef(\"file://\" + quote(fn)) # This is the identifier for the experiment\n", "myExp" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "rdflib.term.URIRef('/home/pathToCwd/high_intens.txt')" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataFn = os.path.join(cwd, \"high_intens.txt\")\n", "data = URIRef(quote(dataFn))\n", "data" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "importCode = Literal(\"\"\"\n", "import pandas as pd\n", "\n", "rawData = pd.read_table(\"high_intens.txt\", names = [\"Wavelength [nm]\",\"Counts [1/80 ms]\"],\n", "header=17, decimal=\",\")\n", "\"\"\" )\n", "\n", "# https://www.hl7.org/fhir/datatypes.html#code ?" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "index = Literal(rawData.index)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "rdflib.term.Literal(\"['Wavelength [nm]' 'Counts [1/80 ms]']\")" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "header = Literal(str(rawData.columns.values))\n", "header" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from datetime import date, datetime\n", "\n", "dateExp = Literal(date(2013,2,18))\n", "dateLastEval = Literal(datetime.now())\n", "\n", "temp = Literal(25.5)\n", "pressure = Literal(1.003)\n", "lamp = URIRef(\"http://dbpedia.org/resource/Sodium-vapor_lamp\")\n", "\n", "\n", "spectrometer = dbr.Spectrometer\n", "specType = URIRef(\"http://oceanoptics.com/product/usb4000-custom\")\n", "\n", "# Create an identifier to use as the subject for the experimentator.\n", "donna = URIRef(mdg.Donna)\n", "\n", "# Create Literal for public key (See below how to create the key itself)\n", "donna_public_keys = Literal('-----BEGIN PGP PUBLIC KEY BLOCK-----\\nVersion: GnuPG v1\\n\\nmQENBFh2cIgBCACcig8bNvxeNTI8HYY9hWU4Ut6NpSpwYnUPzZVWS9UTHKheiz+A\\nCBolg0Vr/4tnNzb+zcL9/jm2ZgFwMhOt8K5CQTifYXam8gLlvPfZvhi32lLdTW/x\\nmrW/CDQz1N1teJYqQXGcn3kJrWNZ2HGRyjqCfzeRNvOuARqjMNHYBnrioHLw8UA/\\nvB0mvmmWfY/zst75SlEF1z5ZZyrnNGvTLGTNXO43RLRNsXVkCjCB/689sGW9kWu5\\nafVvvOIaLWQYXLJDlnY5tcfRtwM/Y8izSwWMfRE90lH9biSewU1CaLQf9smyQisW\\nSIp2stHaxWPymf+f/KfSMp6Tyj1QyQs9x82HABEBAAG0NERvbm5hIEZhbGVzIChB\\nIHRlc3QgdXNlcikgPG1haWx0bzpkb25uYUBleGFtcGxlLm9yZz6JATgEEwECACIF\\nAlh2cIgCGy8GCwkIBwMCBhUIAgkKCwQWAgMBAh4BAheAAAoJEJdZONqMEJiHcUcH\\n/28H4cKsHYbM1gkrwwbRRfWzot7LoIbsbLBwpQyjtrCcX9P7R63gzH2fsY782qnf\\n4WDp2/mRJX7yNa5FbTyicf+l8I+ziPSKGX9u/ag5JqFHlM8bN2/NHvXJG/zh5WOg\\nB4G1HqoHx88BdIZF7Dx7cCDIe2S9RdlFTMsh08hwnpaC7xyK5rRSK8KlrNQMF2jc\\nmnPUvByEaOkgHlBG4vipNzeIvnY32SOkAtcwVrm6+Jq4bMBM5kg9TQpU7Db/QeAE\\nEDTC7Z458oTTW5Ttt41YpW5Q8GNj8vkfmJBR0Eg3UOd+/OcJMg8TIXdgyS7f7Ehq\\n1piBeLsZgY4zoxXcVOMbFsI=\\n=lA5q\\n-----END PGP PUBLIC KEY BLOCK-----\\n')\n", "\n", "publicity = Literal(\"Workgroup\")\n", "\n", "tag1 = Literal(\"SPAM SPAM SPAM EGG\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Make list of triples" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "experiment = [(myExp, RDF.type, dbr.Experiment),\n", " (myExp, RDFS.label, Literal(\"myExperimentGraph\")),\n", " (myExp, dcterms.date, dateExp),\n", " (myExp, dcterms.modifed, dateLastEval ),\n", " (myExp, dcmi.Dataset, data),\n", " (data, RDFS.label, header),\n", " (myExp, dcterms.accrualMethod, spectrometer),\n", " (spectrometer, RDF.type, specType),\n", " (myExp, dbr.Light,lamp),\n", " ]\n", "\n", "experimentator = [(donna, RDF.type, FOAF.Person),\n", " (donna, FOAF.name, Literal(\"Donna Fales\")),\n", " (donna, FOAF.mbox, URIRef(\"mailto:donna@example.org\")),\n", " (donna, dcterms.creator, myExp),\n", " (donna, wot.hasKey, donna_public_keys)\n", " ]\n", "\n", "public = [(myExp, dcterms.accessRights, publicity)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Create graph with RDFLib" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# A Conjunctive graph is an (unnamed) aggregation of all the named graphs within a Store.\n", "# See https://github.com/RDFLib/rdflib/blob/master/examples/conjunctive_graphs.py\n", "\n", "g = ConjunctiveGraph()\n", "if os.path.exists(fn):\n", " g.load(myExp, format=\"json-ld\")\n", " g.bind(\"dc\", DC)\n", " g.bind(\"foaf\", FOAF)\n", " g.bind(\"dcterms\", dcterms)\n", " g.bind(\"dcmi\", dcmi)\n", " g.bind(\"dbr\", dbr)\n", " g.bind(\"owl\", owl)\n", " g.bind(\"wot\", wot)\n", " g.bind(\"dcterms\", dcterms)\n", " g.bind(\"mdg\", mdg)\n", " g.bind(\"crypto\", crypto)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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SubjectsPredicatesObjects
0file:///home/pathToCwd/methaGraph.jsonldhttp://purl.org/dc/terms/accrualMethodhttp://dbpedia.org/resource/Spectrometer
1http://dbpedia.org/resource/Spectrometerhttp://www.w3.org/1999/02/22-rdf-syntax-ns#typehttp://oceanoptics.com/product/usb4000-custom
2file:///home/pathToCwd/methaGraph.jsonldhttp://www.w3.org/1999/02/22-rdf-syntax-ns#typehttp://dbpedia.org/resource/Experiment
3file:///home/pathToCwd/methaGraph.jsonld#Donnahttp://www.w3.org/1999/02/22-rdf-syntax-ns#typehttp://xmlns.com/foaf/0.1/Person
4file:///home/pathToCwd/methaGraph.jsonld#Donnahttp://purl.org/dc/terms/creatorfile:///home/pathToCwd/methaGraph.jsonld
5file:///home/pathToCwd/methaGraph.jsonldhttp://purl.org/dc/terms/accessRightsWorkgroup
6/home/pathToCwd/high_intens.txthttp://www.w3.org/2000/01/rdf-schema#label['Wavelength [nm]' 'Counts [1/80 ms]']
7file:///home/pathToCwd/methaGraph.jsonldhttp://purl.org/dc/dcmitype/Dataset/home/pathToCwd/high_intens.txt
8file:///home/pathToCwd/methaGraph.jsonldhttp://purl.org/dc/terms/modifed2017-01-16T21:26:03.401966
9file:///home/pathToCwd/methaGraph.jsonld#Donnahttp://xmlns.com/wot/0.1/hasKey-----BEGIN PGP PUBLIC KEY BLOCK-----\\nVersion:...
10file:///home/pathToCwd/methaGraph.jsonldhttp://dbpedia.org/resource/Lighthttp://dbpedia.org/resource/Sodium-vapor_lamp
11file:///home/pathToCwd/methaGraph.jsonld#Donnahttp://xmlns.com/foaf/0.1/mboxmailto:donna@example.org
12file:///home/pathToCwd/methaGraph.jsonldhttp://purl.org/dc/terms/date2013-02-18
13file:///home/pathToCwd/methaGraph.jsonldhttp://www.w3.org/2000/01/rdf-schema#labelmyExperimentGraph
14file:///home/pathToCwd/methaGraph.jsonld#Donnahttp://xmlns.com/foaf/0.1/nameDonna Fales
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" ], "text/plain": [ " Subjects \\\n", "0 file:///home/pathToCwd/methaGraph.jsonld \n", "1 http://dbpedia.org/resource/Spectrometer \n", "2 file:///home/pathToCwd/methaGraph.jsonld \n", "3 file:///home/pathToCwd/methaGraph.jsonld#Donna \n", "4 file:///home/pathToCwd/methaGraph.jsonld#Donna \n", "5 file:///home/pathToCwd/methaGraph.jsonld \n", "6 /home/pathToCwd/high_intens.txt \n", "7 file:///home/pathToCwd/methaGraph.jsonld \n", "8 file:///home/pathToCwd/methaGraph.jsonld \n", "9 file:///home/pathToCwd/methaGraph.jsonld#Donna \n", "10 file:///home/pathToCwd/methaGraph.jsonld \n", "11 file:///home/pathToCwd/methaGraph.jsonld#Donna \n", "12 file:///home/pathToCwd/methaGraph.jsonld \n", "13 file:///home/pathToCwd/methaGraph.jsonld \n", "14 file:///home/pathToCwd/methaGraph.jsonld#Donna \n", "\n", " Predicates \\\n", "0 http://purl.org/dc/terms/accrualMethod \n", "1 http://www.w3.org/1999/02/22-rdf-syntax-ns#type \n", "2 http://www.w3.org/1999/02/22-rdf-syntax-ns#type \n", "3 http://www.w3.org/1999/02/22-rdf-syntax-ns#type \n", "4 http://purl.org/dc/terms/creator \n", "5 http://purl.org/dc/terms/accessRights \n", "6 http://www.w3.org/2000/01/rdf-schema#label \n", "7 http://purl.org/dc/dcmitype/Dataset \n", "8 http://purl.org/dc/terms/modifed \n", "9 http://xmlns.com/wot/0.1/hasKey \n", "10 http://dbpedia.org/resource/Light \n", "11 http://xmlns.com/foaf/0.1/mbox \n", "12 http://purl.org/dc/terms/date \n", "13 http://www.w3.org/2000/01/rdf-schema#label \n", "14 http://xmlns.com/foaf/0.1/name \n", "\n", " Objects \n", "0 http://dbpedia.org/resource/Spectrometer \n", "1 http://oceanoptics.com/product/usb4000-custom \n", "2 http://dbpedia.org/resource/Experiment \n", "3 http://xmlns.com/foaf/0.1/Person \n", "4 file:///home/pathToCwd/methaGraph.jsonld \n", "5 Workgroup \n", "6 ['Wavelength [nm]' 'Counts [1/80 ms]'] \n", "7 /home/pathToCwd/high_intens.txt \n", "8 2017-01-16T21:26:03.401966 \n", "9 -----BEGIN PGP PUBLIC KEY BLOCK-----\\nVersion:... \n", "10 http://dbpedia.org/resource/Sodium-vapor_lamp \n", "11 mailto:donna@example.org \n", "12 2013-02-18 \n", "13 myExperimentGraph \n", "14 Donna Fales " ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Add triples to named graphs\n", "gExp = Graph(store=g.store, identifier=\"Experiment\")\n", "gExpTator = Graph(store=g.store, identifier=\"Experimentator\")\n", "gPub = Graph(store=g.store, identifier=\"Publicity\")\n", "\n", "for triple in experiment:\n", " gExp.add(triple)\n", " \n", "for triple in experimentator:\n", " gExpTator.add(triple)\n", " \n", "for triple in public:\n", " gPub.add(triple)\n", "\n", "\n", "# Create context for adressing the whole experiment data and metadata\n", "gExpGraph = Graph(store=g.store, identifier=\"myExperimentGraph\")\n", "\n", "gExpGraph += gExp + gExpTator + gPub\n", "\n", "pd.DataFrame(list(g.triples((None, None, None))), columns=[\"Subjects\", \"Predicates\", \"Objects\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Visualize graph" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import networkx as nx\n", "\n", "def drwGraph(g, color):\n", " G = nx.Graph()\n", "\n", " # In NetworkX subjects and objects are nodes and predicates are edges\n", " for s, p, o in g:\n", " # Compaction\n", " su = s.split(\"/\")[-1].split(\"#\")[-1]\n", " pr = p.split(\"/\")[-1].split(\"#\")[-1]\n", " ob = o.split(\"/\")[-1].split(\"#\")[-1]\n", "\n", " G.add_edge(su, ob, {pr: \"\" })\n", "\n", "\n", " plt.figure(figsize=(14,10))\n", " node_sizes = [200*len(G.edges(n)) for n in G.nodes()] # Bigger for more edges\n", " pos = nx.spring_layout(G)\n", "\n", " nx.draw_networkx(G, pos=pos, node_size=node_sizes, linewidths=0, node_color=color)\n", " nx.draw_networkx_edge_labels(G,pos)\n", " plt.box(\"off\")\n", " plt.axis(\"off\")\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/m/Schreibtisch/Interactive Labbook/MathVenv/lib/python3.4/site-packages/matplotlib/collections.py:631: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", " if self._edgecolors_original != str('face'):\n", "/home/m/Schreibtisch/Interactive Labbook/MathVenv/lib/python3.4/site-packages/matplotlib/collections.py:571: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", " if self._edgecolors == str('face'):\n" ] }, { "data": { "image/png": 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k/ec6PGbimQx8BHSqvO6KSBX5yNbcy+Uf8fga82Dd/Q3gbdYMPBy4KK5vWUCY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GZnGitWk3M7Kzs4mLi+P7778nKysr8jkZwFTgkz28HxERkTxWxL4hIlKErLTU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\nqhXly5fPe9Ofm5tbMAAoh29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+wHx3LQaW42s3vg4/aGYN8ZmY+51zWc65r4FPijtPfJDS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0796dxx9/nMzMTCZNmsQXX3xBRkYGvXr1Ii8vj7lz5zJ16lRisRh3330333//PYMH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\nRK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "drwGraph(gExpTator, \"b\")\n", "drwGraph(gExpGraph, \"sandybrown\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Sign graph and store it\n", "* Digital signature experimentator\n", " * If you are not at all fammiliar with assymetric key cryptography: https://www.youtube.com/watch?v=CR8ZFRVmQLg\n", " * https://pythonhosted.org/python-gnupg/\n", " * https://www.w3.org/2005/Incubator/webid/wiki/Main_Page ?\n", " * https://github.com/detrout/python-webid\n", " \n", "### Create GPG keys" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import gnupg\n", "gpg = gnupg.GPG()\n", "\n", "# Create a key pair\n", "input_data = gpg.gen_key_input(key_type=\"RSA\", key_length=2048, name_real=\"Donna Fales\"\n", " ,name_comment=\"A test user\", name_email=\"mailto:donna@example.org\")\n", "\n", "# only once:\n", "# key = gpg.gen_key(input_data) # See https://pythonhosted.org/python-gnupg/#performance-issues" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "[{'algo': '1',\n", " 'date': '1484157064',\n", " 'dummy': '',\n", " 'expires': '',\n", " 'fingerprint': '92B19ACC9C85FA69DF81A5C4975938DA8C109887',\n", " 'keyid': '975938DA8C109887',\n", " 'length': '2048',\n", " 'ownertrust': 'u',\n", " 'sig': '',\n", " 'sigs': [],\n", " 'subkeys': [],\n", " 'trust': 'u',\n", " 'type': 'pub',\n", " 'uids': ['Donna Fales (A test user) ']}]\n", " \n" ] } ], "source": [ "# List public keys\n", "keyData = gpg.list_keys() # private keys: gpg.list_keys(True)\n", "\n", "output = \"\"\"\n", "[{'algo': '1',\n", " 'date': '1484157064',\n", " 'dummy': '',\n", " 'expires': '',\n", " 'fingerprint': '92B19ACC9C85FA69DF81A5C4975938DA8C109887',\n", " 'keyid': '975938DA8C109887',\n", " 'length': '2048',\n", " 'ownertrust': 'u',\n", " 'sig': '',\n", " 'sigs': [],\n", " 'subkeys': [],\n", " 'trust': 'u',\n", " 'type': 'pub',\n", " 'uids': ['Donna Fales (A test user) ']}]\n", " \"\"\"\n", "\n", "# The list should contain something like this (depending on your existing keys)\n", "print(output)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-----BEGIN PGP PUBLIC KEY BLOCK-----\n", "Version: GnuPG v1\n", "\n", "mQENBFh2cIgBCACcig8bNvxeNTI8HYY9hWU4Ut6NpSpwYnUPzZVWS9UTHKheiz+A\n", "CBolg0Vr/4tnNzb+zcL9/jm2ZgFwMhOt8K5CQTifYXam8gLlvPfZvhi32lLdTW/x\n", "mrW/CDQz1N1teJYqQXGcn3kJrWNZ2HGRyjqCfzeRNvOuARqjMNHYBnrioHLw8UA/\n", "vB0mvmmWfY/zst75SlEF1z5ZZyrnNGvTLGTNXO43RLRNsXVkCjCB/689sGW9kWu5\n", "afVvvOIaLWQYXLJDlnY5tcfRtwM/Y8izSwWMfRE90lH9biSewU1CaLQf9smyQisW\n", "SIp2stHaxWPymf+f/KfSMp6Tyj1QyQs9x82HABEBAAG0NERvbm5hIEZhbGVzIChB\n", "IHRlc3QgdXNlcikgPG1haWx0bzpkb25uYUBleGFtcGxlLm9yZz6JATgEEwECACIF\n", "Alh2cIgCGy8GCwkIBwMCBhUIAgkKCwQWAgMBAh4BAheAAAoJEJdZONqMEJiHcUcH\n", "/28H4cKsHYbM1gkrwwbRRfWzot7LoIbsbLBwpQyjtrCcX9P7R63gzH2fsY782qnf\n", "4WDp2/mRJX7yNa5FbTyicf+l8I+ziPSKGX9u/ag5JqFHlM8bN2/NHvXJG/zh5WOg\n", "B4G1HqoHx88BdIZF7Dx7cCDIe2S9RdlFTMsh08hwnpaC7xyK5rRSK8KlrNQMF2jc\n", "mnPUvByEaOkgHlBG4vipNzeIvnY32SOkAtcwVrm6+Jq4bMBM5kg9TQpU7Db/QeAE\n", "EDTC7Z458oTTW5Ttt41YpW5Q8GNj8vkfmJBR0Eg3UOd+/OcJMg8TIXdgyS7f7Ehq\n", "1piBeLsZgY4zoxXcVOMbFsI=\n", "=lA5q\n", "-----END PGP PUBLIC KEY BLOCK-----\n", "\n" ] } ], "source": [ "# Export key\n", "keyid = '975938DA8C109887'\n", "\n", "ascii_donna_public_keys = gpg.export_keys(keyid)\n", "donna_public_keys = Literal(ascii_donna_public_keys)\n", "\n", "print(ascii_donna_public_keys)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Hash data\n", "http://pythoncentral.io/hashing-files-with-python/" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import hashlib\n", "filename = \"high_intens.txt\"\n", "\n", "BLOCKSIZE = 65536\n", "hasher = hashlib.sha1()\n", "\n", "with open(filename, \"rb\") as stream:\n", " buf = stream.read(BLOCKSIZE)\n", " while len(buf) > 0:\n", " hasher.update(buf)\n", " buf = stream.read(BLOCKSIZE)\n", "\n", "dataHash = Literal(hasher.hexdigest())\n", "\n", "gExp.add((data,crypto.sha, dataHash))" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-----BEGIN PGP SIGNED MESSAGE-----\n", "Hash: SHA1\n", "\n", "177,54\t13,20\r\n", "177,75\t13,20\r\n", "177,97\t13,20\r\n", "178,19\t11,52\r\n", "178,41\t11,72\r\n", "178,63\t13,68\r\n", "178,85\t10,73\r\n", "179,07\t12,98\r\n", "179,29\t15,51\r\n", "179,51\t16,87\r\n", "179,73\t10,66\r\n", "179,95\t12,67\r\n", "180,17\t17,56\r\n", "180,39\t17,04\r\n", "180,61\t12,33\r\n", "180,83\t12,68\r\n", "181,05\t13,23\r\n", "181,27\t11,28\r\n", "1\n", "\n", "...\n", "...\n" ] } ], "source": [ "# You can sign data with gpg directly but we will sign a hash of the\n", "# graph including the hash of the data as a Literal object\n", "\n", "with open(filename, \"rb\") as stream:\n", " signed_data = gpg.sign_file(stream, keyid=\"975938DA8C109887\")\n", "\n", "print(str(signed_data)[0:300] + \"\\n\\n...\\n...\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Hash graph\n", "RDFLib.RGDA1: The RDF Graph Digest Algorithm 1 (RGDA1) produces cryptographically secure hashes for all RDF graphs\n", " * create platform-independent, cryptographically secure, reproducible identifiers for all RDF graphs, (from: WEBSIG: A DIGITAL SIGNATURE FRAMEWORK FOR THE WEB)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'1000973175714773174394657975727339334874012862822329149053392935196204522504018'" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from rdflib.compare import isomorphic, _TripleCanonicalizer\n", "# Warning: the time to canonicalize bnodes may increase exponentially on degenerate larger graphs\n", "\n", "\n", "# We use the context to specify which part of the graph we want to hash and sign\n", "gHash = str(_TripleCanonicalizer(gExpGraph).to_hash())\n", "gHash" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Sign hash" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-----BEGIN PGP SIGNED MESSAGE-----\n", "Hash: SHA1\n", "\n", "1000973175714773174394657975727339334874012862822329149053392935196204522504018\n", "-----BEGIN PGP SIGNATURE-----\n", "Version: GnuPG v1\n", "\n", "iQEcBAEBAgAGBQJYfTqAAAoJEJdZONqMEJiH0MUH/A5QyhEcp07UDk0KzUwv4h7w\n", "h36sr2NjMTymkfYBDCYXMdYVQRL3GSL0jHbT6J57H7eyVFLqCf7tk5pBDeGNz+d4\n", "WRr850N+UJVx56sX1bkXRlmZm5mhqBXkWlwx95GGzHGsm52Ct7B0kN20dyyJx3I2\n", "xWSNay8gOvtrXs0KWdnpZTkyQP3nVJ+TF3m26UBgDjnxAY+ne1OpipLGwB5DVWZl\n", "LSC2pKMZdq7WapFKJ5WkXW7G9gV9bfFsaY65Q7c5QH7AhgtAXi6HIuqCLNCMgiTk\n", "J8ERS7IqFXLHUdeWty+bZNfaral4VGhYxo24qUxdTQDtmxsd8PXQP0I7WrFGWTY=\n", "=OdGv\n", "-----END PGP SIGNATURE-----\n", "\n" ] } ], "source": [ "sH = gpg.sign(gHash, keyid=\"975938DA8C109887\") # gpg object\n", "signedHash = Literal(str(sH))\n", "sigDate = Literal(datetime.now())\n", "\n", "print(signedHash)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Add signature to a new named graph \"signatures\"" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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SubjectsPredicatesObjects
0file:///home/pathToCwd/methaGraph.jsonldhttp://xmlns.com/wot/0.1/sigdate2017-01-16T21:26:24.740440
1file:///home/pathToCwd/methaGraph.jsonld#Donnahttp://xmlns.com/wot/0.1/signed-----BEGIN PGP SIGNED MESSAGE-----\\nHash: SHA1...
\n", "
" ], "text/plain": [ " Subjects \\\n", "0 file:///home/pathToCwd/methaGraph.jsonld \n", "1 file:///home/pathToCwd/methaGraph.jsonld#Donna \n", "\n", " Predicates \\\n", "0 http://xmlns.com/wot/0.1/sigdate \n", "1 http://xmlns.com/wot/0.1/signed \n", "\n", " Objects \n", "0 2017-01-16T21:26:24.740440 \n", "1 -----BEGIN PGP SIGNED MESSAGE-----\\nHash: SHA1... " ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sig = Graph(store=g.store, identifier=\"Signatures\")\n", "\n", "# Remove old signatures\n", "sig.remove((None, None, None))\n", "\n", "sig.add((donna, wot.signed, signedHash)) # the wot.signed is used wrong in this examle\n", "sig.add((myExp, wot.sigdate, sigDate))\n", "\n", "pd.DataFrame(list(sig.triples((None, None, None))), columns=[\"Subjects\", \"Predicates\", \"Objects\"])" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " a rdfg:Graph;rdflib:storage [a rdflib:Store;rdfs:label 'IOMemory'].\n", " a rdfg:Graph;rdflib:storage [a rdflib:Store;rdfs:label 'IOMemory'].\n", " a rdfg:Graph;rdflib:storage [a rdflib:Store;rdfs:label 'IOMemory'].\n", " a rdfg:Graph;rdflib:storage [a rdflib:Store;rdfs:label 'IOMemory'].\n", " a rdfg:Graph;rdflib:storage [a rdflib:Store;rdfs:label 'IOMemory'].\n" ] } ], "source": [ "# Print out names/contexts of all graphs\n", "for c in g.contexts():\n", " print(c)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Store in json-ld file" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import rdflib_jsonld\n", "g.serialize(\"methaGraph.jsonld\", format='json-ld')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Open Graph again and look into data" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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SubjectsPredicatesObjects
0file:///home/pathToCwd/methaGraph.jsonldhttp://purl.org/dc/terms/accrualMethodhttp://dbpedia.org/resource/Spectrometer
1http://dbpedia.org/resource/Spectrometerhttp://www.w3.org/1999/02/22-rdf-syntax-ns#typehttp://oceanoptics.com/product/usb4000-custom
2file:///home/pathToCwd/methaGraph.jsonldhttp://www.w3.org/2000/01/rdf-schema#labelmyExperimentGraph
3file:///home/pathToCwd/methaGraph.jsonld#Donnahttp://xmlns.com/foaf/0.1/mboxmailto:donna@example.org
4file:///home/pathToCwd/methaGraph.jsonld#Donnahttp://www.w3.org/1999/02/22-rdf-syntax-ns#typehttp://xmlns.com/foaf/0.1/Person
5file:///home/pathToCwd/methaGraph.jsonldhttp://purl.org/dc/terms/modifed2017-01-16T21:26:03.401966
6file:///home/pathToCwd/methaGraph.jsonld#Donnahttp://xmlns.com/wot/0.1/signed-----BEGIN PGP SIGNED MESSAGE-----\\nHash: SHA1...
7file:///home/pathToCwd/high_intens.txthttp://www.w3.org/2000/01/rdf-schema#label['Wavelength [nm]' 'Counts [1/80 ms]']
8file:///home/pathToCwd/methaGraph.jsonldhttp://www.w3.org/1999/02/22-rdf-syntax-ns#typehttp://dbpedia.org/resource/Experiment
9file:///home/pathToCwd/methaGraph.jsonld#Donnahttp://xmlns.com/wot/0.1/hasKey-----BEGIN PGP PUBLIC KEY BLOCK-----\\nVersion:...
10file:///home/pathToCwd/methaGraph.jsonldhttp://purl.org/dc/terms/date2013-02-18
11file:///home/pathToCwd/methaGraph.jsonld#Donnahttp://purl.org/dc/terms/creatorfile:///home/pathToCwd/methaGraph.jsonld
12file:///home/pathToCwd/methaGraph.jsonldhttp://xmlns.com/wot/0.1/sigdate2017-01-16T21:26:24.740440
13file:///home/pathToCwd/methaGraph.jsonldhttp://purl.org/dc/dcmitype/Datasetfile:///home/pathToCwd/high_intens.txt
14file:///home/pathToCwd/methaGraph.jsonldhttp://purl.org/dc/terms/accessRightsWorkgroup
15file:///home/pathToCwd/methaGraph.jsonldhttp://dbpedia.org/resource/Lighthttp://dbpedia.org/resource/Sodium-vapor_lamp
16file:///home/pathToCwd/high_intens.txthttps://www.w3.org/2000/10/swap/crypto#sha5657eb62368fbe9b96b169889489249b5fd63356
17file:///home/pathToCwd/methaGraph.jsonld#Donnahttp://xmlns.com/foaf/0.1/nameDonna Fales
\n", "
" ], "text/plain": [ " Subjects \\\n", "0 file:///home/pathToCwd/methaGraph.jsonld \n", "1 http://dbpedia.org/resource/Spectrometer \n", "2 file:///home/pathToCwd/methaGraph.jsonld \n", "3 file:///home/pathToCwd/methaGraph.jsonld#Donna \n", "4 file:///home/pathToCwd/methaGraph.jsonld#Donna \n", "5 file:///home/pathToCwd/methaGraph.jsonld \n", "6 file:///home/pathToCwd/methaGraph.jsonld#Donna \n", "7 file:///home/pathToCwd/high_intens.txt \n", "8 file:///home/pathToCwd/methaGraph.jsonld \n", "9 file:///home/pathToCwd/methaGraph.jsonld#Donna \n", "10 file:///home/pathToCwd/methaGraph.jsonld \n", "11 file:///home/pathToCwd/methaGraph.jsonld#Donna \n", "12 file:///home/pathToCwd/methaGraph.jsonld \n", "13 file:///home/pathToCwd/methaGraph.jsonld \n", "14 file:///home/pathToCwd/methaGraph.jsonld \n", "15 file:///home/pathToCwd/methaGraph.jsonld \n", "16 file:///home/pathToCwd/high_intens.txt \n", "17 file:///home/pathToCwd/methaGraph.jsonld#Donna \n", "\n", " Predicates \\\n", "0 http://purl.org/dc/terms/accrualMethod \n", "1 http://www.w3.org/1999/02/22-rdf-syntax-ns#type \n", "2 http://www.w3.org/2000/01/rdf-schema#label \n", "3 http://xmlns.com/foaf/0.1/mbox \n", "4 http://www.w3.org/1999/02/22-rdf-syntax-ns#type \n", "5 http://purl.org/dc/terms/modifed \n", "6 http://xmlns.com/wot/0.1/signed \n", "7 http://www.w3.org/2000/01/rdf-schema#label \n", "8 http://www.w3.org/1999/02/22-rdf-syntax-ns#type \n", "9 http://xmlns.com/wot/0.1/hasKey \n", "10 http://purl.org/dc/terms/date \n", "11 http://purl.org/dc/terms/creator \n", "12 http://xmlns.com/wot/0.1/sigdate \n", "13 http://purl.org/dc/dcmitype/Dataset \n", "14 http://purl.org/dc/terms/accessRights \n", "15 http://dbpedia.org/resource/Light \n", "16 https://www.w3.org/2000/10/swap/crypto#sha \n", "17 http://xmlns.com/foaf/0.1/name \n", "\n", " Objects \n", "0 http://dbpedia.org/resource/Spectrometer \n", "1 http://oceanoptics.com/product/usb4000-custom \n", "2 myExperimentGraph \n", "3 mailto:donna@example.org \n", "4 http://xmlns.com/foaf/0.1/Person \n", "5 2017-01-16T21:26:03.401966 \n", "6 -----BEGIN PGP SIGNED MESSAGE-----\\nHash: SHA1... \n", "7 ['Wavelength [nm]' 'Counts [1/80 ms]'] \n", "8 http://dbpedia.org/resource/Experiment \n", "9 -----BEGIN PGP PUBLIC KEY BLOCK-----\\nVersion:... \n", "10 2013-02-18 \n", "11 file:///home/pathToCwd/methaGraph.jsonld \n", "12 2017-01-16T21:26:24.740440 \n", "13 file:///home/pathToCwd/high_intens.txt \n", "14 Workgroup \n", "15 http://dbpedia.org/resource/Sodium-vapor_lamp \n", "16 5657eb62368fbe9b96b169889489249b5fd63356 \n", "17 Donna Fales " ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "newGraph = ConjunctiveGraph()\n", "\n", "newGraph.load(\"methaGraph.jsonld\", format='json-ld')\n", "\n", "pd.DataFrame(list(newGraph.triples((None, None, None))), columns=[\"Subjects\", \"Predicates\", \"Objects\"])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Print out names/contexts of all graphs\n", "for c in newGraph.contexts():\n", " print(c)\n", "\n", "c.identifier" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Verification of a graph\n" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1000973175714773174394657975727339334874012862822329149053392935196204522504018\n", "1000973175714773174394657975727339334874012862822329149053392935196204522504018\n", "Graph verified\n" ] } ], "source": [ "# Quick and dirty\n", "for h in newGraph.objects(donna, wot.signed):\n", " # First verify the signature\n", " if gpg.verify(str(h)):\n", " # Second compare hash\n", " sigHash = gpg.decrypt(h).data.decode(\"utf-8\").strip() # gpg.decrypt(h): bytes --> string\n", " \n", " identifier = str(g.value(myExp, RDFS.label))\n", " signedG = g.get_context(identifier)\n", " realHash = str(_TripleCanonicalizer(signedG).to_hash()) # What happens if there are two\n", " # equal identifiers?\n", " print(sigHash)\n", " print(realHash)\n", " \n", " if sigHash == realHash:\n", " print(\"Graph verified\")\n", " \n", " else:\n", " print(\"Signature verified but graph has changed\")\n", " \n", " else:\n", " print(\"Signature verification failed\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Is this really a safe verification ?\n", "\n", "to_isomorphic(signedG).internal_hash() # The conjunctive graph is hashed instead of the named graph ?? --> ISOMORPHIC\n", "isomorphic(to_isomorphic(signedG) == to_isomorphic(g))\n", "\n", "# !!!\n", "_TripleCanonicalizer(signedG).to_hash() == _TripleCanonicalizer(myG).to_hash()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Store graph with sleepycat" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# A Conjunctive graph is an (unnamed) aggregation of all the named graphs within a Store.\n", "# See https://github.com/RDFLib/rdflib/blob/master/examples/conjunctive_graphs.py\n", "from rdflib.store import NO_STORE, VALID_STORE\n", "\n", "path = \"myRDFLibStore\"\n", "store = 'Sleepycat'\n", "\n", "gS = ConjunctiveGraph(store=store, identifier=\"myRDFLibStore\")\n", "\n", "# We want to store the graph after use on the disk\n", "rt = gS.open(path, create=False)\n", "\n", "if rt == NO_STORE:\n", " # There is no underlying Sleepycat infrastructure, create it\n", " gS.open(path, create=True)\n", "else:\n", " assert rt == VALID_STORE, 'The underlying store is corrupt'\n", "\n", "\n", "# Add tripples\n", "gS += g\n", "\n", "# After use close!\n", "gS.close()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Create class for automated integration in workflow\n", "* https://docs.python.org/3.4/tutorial/classes.html\n", "* https://github.com/RDFLib/rdflib/blob/master/examples/film.py" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Search through ontologies" ] }, { "cell_type": "code", "execution_count": 108, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "----------\n", "Reading: \n", ".. trying rdf serialization: \n", "..... success!\n", "----------\n", "Loaded 35 triples.\n", "----------\n", "RDF sources loaded successfully: 1 of 1.\n", "----------\n", "-> https://www.w3.org/2000/10/swap/crypto#\n", "Scanning entities...\n", "----------\n", "Ontologies.........: 0\n", "Classes............: 2\n", "Properties.........: 5\n", "..annotation.......: 0\n", "..datatype.........: 0\n", "..object...........: 2\n", "Concepts (SKOS)....: 0\n", "----------\n" ] }, { "data": { "text/plain": [ "[,\n", " ]" ] }, "execution_count": 108, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import ontospy\n", "\n", "model = ontospy.Ontospy(\"https://www.w3.org/2000/10/swap/crypto#\")\n", "model.classes" ] }, { "cell_type": "code", "execution_count": 109, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[,\n", " ,\n", " ,\n", " ,\n", " ]" ] }, "execution_count": 109, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.properties" ] }, { "cell_type": "code", "execution_count": 110, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "http://www.w3.org/2000/10/swap/crypto#HashFunction\n", "http://www.w3.org/2000/10/swap/crypto#PublicKeyObject\n" ] } ], "source": [ "model.printClassTree()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.4.2" } }, "nbformat": 4, "nbformat_minor": 0 }