) {\\n Proto.StatusResponse.newBuilder()\\n .setStatus(status.protoType())\\n .build().also {\\n responseObserver.onNext(it)\\n responseObserver.onCompleted()\\n }\\n }\\n \\n fun noSuchFmuReply(id: Int, responseObserver: StreamObserver<*>) {\\n val message = \\\"No FMU with id=$id!\\\"\\n responseObserver.onError(\\n Status.INVALID_ARGUMENT\\n .augmentDescription(\\\"FmuNotFoundException\\\")\\n .withDescription(message)\\n .asRuntimeException()\\n )\\n }\\n\\n}\\n\\n\\nclass GrpcFmuServiceImpl(\\n private val fmu: Fmu\\n): FmuServiceGrpc.FmuServiceImplBase(), GrpcFmuService {\\n\\n private val modelDescription: CommonModelDescription\\n = fmu.modelDescription\\n\\n override fun getModelDescription(request: Empty, responseObserver: StreamObserver) {\\n responseObserver.onNext(modelDescription.protoType())\\n responseObserver.onCompleted()\\n }\\n\\n override fun getModelDescriptionXml(request: Empty, responseObserver: StreamObserver) {\\n\\n Proto.Str.newBuilder().setValue(fmu.modelDescriptionXml).build().also {\\n responseObserver.onNext(it)\\n responseObserver.onCompleted()\\n }\\n\\n }\\n\\n override fun canGetAndSetFMUstate(req: Proto.UInt, responseObserver: StreamObserver) {\\n val fmuId = req.value\\n Fmus.get(fmuId)?.apply {\\n val md = modelDescription\\n val canGetAndSetFMUstate = when (md) {\\n is CoSimulationModelDescription -> md.canGetAndSetFMUstate\\n is ModelExchangeModelDescription -> md.canGetAndSetFMUstate\\n else -> throw AssertionError(\\\"ModelDescription is not of type CS or ME?\\\")\\n }\\n responseObserver.onNext(canGetAndSetFMUstate.protoType())\\n responseObserver.onCompleted()\\n } ?: noSuchFmuReply(fmuId, responseObserver)\\n\\n }\\n\\n override fun getCurrentTime(req: Proto.UInt, responseObserver: StreamObserver) {\\n\\n val fmuId = req.value\\n Fmus.get(fmuId)?.apply {\\n responseObserver.onNext(currentTime.protoType())\\n responseObserver.onCompleted()\\n } ?: noSuchFmuReply(fmuId, responseObserver)\\n\\n }\\n\\n override fun isTerminated(req: Proto.UInt, responseObserver: StreamObserver) {\\n\\n val fmuId = req.value\\n Fmus.get(req.value)?.apply {\\n responseObserver.onNext(isTerminated.protoType())\\n responseObserver.onCompleted()\\n }?: noSuchFmuReply(fmuId, responseObserver)\\n\\n }\\n\\n override fun readInteger(req: Proto.ReadRequest, responseObserver: StreamObserver) {\\n\\n val fmuId = req.fmuId\\n Fmus.get(fmuId)?.apply {\\n val valueReference = req.valueReference\\n val read = variableAccessor.readInteger(valueReference)\\n responseObserver.onNext(Proto.IntRead.newBuilder()\\n .setValue(read.value)\\n .setStatus(read.status.protoType())\\n .build())\\n responseObserver.onCompleted()\\n } ?: noSuchFmuReply(fmuId, responseObserver)\\n\\n }\\n\\n override fun bulkReadInteger(req: Proto.BulkReadRequest, responseObserver: StreamObserver) {\\n\\n val fmuId = req.fmuId\\n Fmus.get(fmuId)?.apply {\\n val builder = Proto.IntListRead.newBuilder()\\n val read = variableAccessor.readInteger(req.valueReferencesList.toIntArray())\\n builder.status = read.status.protoType()\\n for (value in read.value) {\\n builder.addValues(value)\\n }\\n responseObserver.onNext(builder.build())\\n responseObserver.onCompleted()\\n }?: noSuchFmuReply(fmuId, responseObserver)\\n\\n }\\n\\n override fun readReal(req: Proto.ReadRequest, responseObserver: StreamObserver) {\\n\\n val fmuId = req.fmuId\\n Fmus.get(fmuId)?.apply {\\n val valueReference = req.valueReference\\n val read = variableAccessor.readReal(valueReference)\\n responseObserver.onNext(Proto.RealRead.newBuilder()\\n .setValue(read.value)\\n .setStatus(read.status.protoType())\\n .build())\\n responseObserver.onCompleted()\\n }?: noSuchFmuReply(fmuId, responseObserver)\\n\\n }\\n\\n override fun bulkReadReal(req: Proto.BulkReadRequest, responseObserver: StreamObserver) {\\n\\n val fmuId = req.fmuId\\n Fmus.get(fmuId)?.apply {\\n val builder = Proto.RealListRead.newBuilder()\\n val read = variableAccessor.readReal(req.valueReferencesList.toIntArray())\\n builder.status = read.status.protoType()\\n for (value in read.value) {\\n builder.addValues(value)\\n }\\n responseObserver.onNext(builder.build())\\n responseObserver.onCompleted()\\n } ?: noSuchFmuReply(fmuId, responseObserver)\\n\\n }\\n\\n override fun readString(req: Proto.ReadRequest, responseObserver: StreamObserver) {\\n\\n val fmuId = req.fmuId\\n Fmus.get(fmuId)?.apply {\\n val read = variableAccessor.readString(req.valueReference)\\n responseObserver.onNext(Proto.StrRead.newBuilder()\\n .setValue(read.value)\\n .setStatus(read.status.protoType())\\n .build())\\n responseObserver.onCompleted()\\n } ?: noSuchFmuReply(fmuId, responseObserver)\\n\\n }\\n\\n override fun bulkReadString(req: Proto.BulkReadRequest, responseObserver: StreamObserver) {\\n\\n val fmuId = req.fmuId\\n Fmus.get(fmuId)?.apply {\\n val builder = Proto.StrListRead.newBuilder()\\n val read = variableAccessor.readString(req.valueReferencesList.toIntArray())\\n builder.status = read.status.protoType()\\n for (value in read.value) {\\n builder.addValues(value)\\n }\\n responseObserver.onNext(builder.build())\\n responseObserver.onCompleted()\\n } ?: noSuchFmuReply(fmuId, responseObserver)\\n\\n }\\n\\n override fun readBoolean(req: Proto.ReadRequest, responseObserver: StreamObserver) {\\n\\n val fmuId = req.fmuId\\n Fmus.get(fmuId)?.apply {\\n val read = variableAccessor.readBoolean(req.valueReference)\\n responseObserver.onNext(Proto.BoolRead.newBuilder()\\n .setValue(read.value)\\n .setStatus(read.status.protoType())\\n .build())\\n responseObserver.onCompleted()\\n } ?: noSuchFmuReply(fmuId, responseObserver)\\n\\n }\\n\\n override fun bulkReadBoolean(req: Proto.BulkReadRequest, responseObserver: StreamObserver) {\\n\\n val fmuId = req.fmuId\\n Fmus.get(fmuId)?.apply {\\n val builder = Proto.BoolListRead.newBuilder()\\n val read = variableAccessor.readBoolean(req.valueReferencesList.toIntArray())\\n builder.status = read.status.protoType()\\n for (value in read.value) {\\n builder.addValues(value)\\n }\\n responseObserver.onNext(builder.build())\\n responseObserver.onCompleted()\\n } ?: noSuchFmuReply(fmuId, responseObserver)\\n \\n\\n }\\n\\n override fun writeInteger(req: Proto.WriteIntRequest, responseObserver: StreamObserver) {\\n\\n val fmuId = req.fmuId\\n Fmus.get(fmuId)?.apply {\\n val status = variableAccessor.writeInteger(req.valueReference, req.value)\\n statusReply(status, responseObserver)\\n } ?: noSuchFmuReply(fmuId, responseObserver)\\n\\n }\\n\\n override fun bulkWriteInteger(req: Proto.BulkWriteIntRequest, responseObserver: StreamObserver) {\\n\\n val fmuId = req.fmuId\\n Fmus.get(fmuId)?.apply {\\n val status = variableAccessor.writeInteger(req.valueReferencesList.toIntArray(), req.valuesList.toIntArray())\\n statusReply(status, responseObserver)\\n } ?: noSuchFmuReply(fmuId, responseObserver)\\n\\n }\\n\\n override fun writeReal(req: Proto.WriteRealRequest, responseObserver: StreamObserver) {\\n\\n val fmuId = req.fmuId\\n Fmus.get(fmuId)?.apply {\\n val status = variableAccessor.writeReal(req.valueReference, req.value)\\n statusReply(status, responseObserver)\\n } ?: noSuchFmuReply(fmuId, responseObserver)\\n\\n\\n }\\n\\n override fun bulkWriteReal(req: Proto.BulkWriteRealRequest, responseObserver: StreamObserver) {\\n\\n val fmuId = req.fmuId\\n Fmus.get(fmuId)?.apply {\\n val status = variableAccessor.writeReal(req.valueReferencesList.toIntArray(), req.valuesList.toDoubleArray())\\n statusReply(status, responseObserver)\\n } ?: statusReply(FmiStatus.Error, responseObserver)\\n\\n }\\n\\n override fun writeString(req: Proto.WriteStrRequest, responseObserver: StreamObserver) {\\n\\n val fmuId = req.fmuId\\n Fmus.get(fmuId)?.apply {\\n val status = variableAccessor.writeString(req.valueReference, req.value)\\n statusReply(status, responseObserver)\\n } ?: noSuchFmuReply(fmuId, responseObserver)\\n\\n\\n }\\n\\n override fun bulkWriteString(req: Proto.BulkWriteStrRequest, responseObserver: StreamObserver) {\\n\\n val fmuId = req.fmuId\\n Fmus.get(req.fmuId)?.apply {\\n val status = variableAccessor.writeString(req.valueReferencesList.toIntArray(), req.valuesList.toTypedArray())\\n statusReply(status, responseObserver)\\n } ?: noSuchFmuReply(fmuId, responseObserver)\\n\\n }\\n\\n override fun writeBoolean(req: Proto.WriteBoolRequest, responseObserver: StreamObserver) {\\n\\n val fmuId = req.fmuId\\n Fmus.get(fmuId)?.apply {\\n val status = variableAccessor.writeBoolean(req.valueReference, req.value)\\n statusReply(status, responseObserver)\\n } ?: noSuchFmuReply(fmuId, responseObserver)\\n\\n\\n }\\n\\n override fun bulkWriteBoolean(req: Proto.BulkWriteBoolRequest, responseObserver: StreamObserver) {\\n\\n val fmuId = req.fmuId\\n Fmus.get(fmuId)?.apply {\\n val status = variableAccessor.writeBoolean(req.valueReferencesList.toIntArray(), req.valuesList.toBooleanArray())\\n statusReply(status, responseObserver)\\n } ?: noSuchFmuReply(fmuId, responseObserver)\\n\\n }\\n\\n override fun init(req: Proto.InitRequest, responseObserver: StreamObserver) {\\n\\n val fmuId = req.fmuId\\n Fmus.get(fmuId)?.apply {\\n val start = req.start\\n val stop = req.stop\\n val hasStart = start > 0\\n val hasStop = stop > 0 && stop > start\\n if (hasStart && hasStop) {\\n init(start, stop)\\n } else if (hasStart && hasStop) {\\n init(start)\\n } else {\\n init()\\n }\\n statusReply(lastStatus, responseObserver)\\n } ?: noSuchFmuReply(fmuId, responseObserver)\\n\\n }\\n\\n override fun step(req: Proto.StepRequest, responseObserver: StreamObserver) {\\n \\n val fmuId = req.fmuId\\n Fmus.get(fmuId)?.apply {\\n doStep(req.stepSize)\\n Proto.StepResult.newBuilder()\\n .setSimulationTime(currentTime)\\n .setStatus(lastStatus.protoType())\\n .build().also {\\n responseObserver.onNext(it)\\n responseObserver.onCompleted()\\n }\\n\\n } ?: noSuchFmuReply(fmuId, responseObserver)\\n\\n }\\n\\n override fun terminate(req: Proto.UInt, responseObserver: StreamObserver) {\\n\\n val fmuId = req.value\\n Fmus.remove(fmuId)?.apply {\\n terminate()\\n lastStatus.also { status ->\\n LOG.debug(\\\"Terminated fmu with status: $status\\\")\\n statusReply(status, responseObserver)\\n }\\n } ?: noSuchFmuReply(fmuId, responseObserver)\\n\\n }\\n\\n override fun reset(req: Proto.UInt, responseObserver: StreamObserver) {\\n\\n val fmuId = req.value\\n Fmus.get(fmuId)?.apply {\\n reset().also {\\n statusReply(lastStatus, responseObserver)\\n }\\n } ?: noSuchFmuReply(fmuId, responseObserver)\\n\\n }\\n\\n\\n override fun createInstanceFromCS(req: Empty, responseObserver: StreamObserver) {\\n\\n Fmus.put(fmu.asCoSimulationFmu().newInstance()).also { id ->\\n Proto.UInt.newBuilder().setValue(id).build().also {\\n responseObserver.onNext(it)\\n responseObserver.onCompleted()\\n }\\n }\\n\\n }\\n\\n override fun createInstanceFromME(req: Proto.Solver, responseObserver: StreamObserver) {\\n\\n fun selectDefaultIntegrator(): FirstOrderIntegrator {\\n val stepSize = fmu.modelDescription.defaultExperiment?.stepSize ?: 1E-3\\n LOG.warn(\\\"No valid integrator found.. Defaulting to Euler with $stepSize stepSize\\\")\\n return EulerIntegrator(stepSize)\\n }\\n\\n val integrator = parseIntegrator(req.name, req.settings) ?: selectDefaultIntegrator()\\n Fmus.put(fmu.asModelExchangeFmu().newInstance(integrator)).also { id ->\\n Proto.UInt.newBuilder().setValue(id).build().also {\\n responseObserver.onNext(it)\\n responseObserver.onCompleted()\\n }\\n }\\n\\n }\\n\\n// override fun createInstanceFromME(req: Proto.Integrator, responseObserver: StreamObserver) {\\n//\\n// fun selectDefaultIntegrator(): FirstOrderIntegrator {\\n// val stepSize = fmu.modelDescription.defaultExperiment?.stepSize ?: 1E-3\\n// LOG.warn(\\\"No integrator specified.. Defaulting to Euler with $stepSize stepSize\\\")\\n// return EulerIntegrator(stepSize)\\n// }\\n//\\n// val integrator = when (req.integratorsCase) {\\n// Proto.Integrator.IntegratorsCase.GILL -> GillIntegrator(req.gill.stepSize)\\n// Proto.Integrator.IntegratorsCase.EULER -> EulerIntegrator(req.euler.stepSize)\\n// Proto.Integrator.IntegratorsCase.MID_POINT -> MidpointIntegrator(req.midPoint.stepSize)\\n// Proto.Integrator.IntegratorsCase.RUNGE_KUTTA -> ClassicalRungeKuttaIntegrator(req.rungeKutta.stepSize)\\n// Proto.Integrator.IntegratorsCase.ADAMS_BASHFORTH -> req.adamsBashforth.let { AdamsBashforthIntegrator(it.nSteps, it.minStep, it.maxStep, it.scalAbsoluteTolerance, it.scalRelativeTolerance) }\\n// Proto.Integrator.IntegratorsCase.DORMAND_PRINCE54 -> req.dormandPrince54.let { DormandPrince54Integrator(it.minStep, it.maxStep, it.scalAbsoluteTolerance, it.scalRelativeTolerance) }\\n// else -> selectDefaultIntegrator()\\n// }\\n//\\n// Fmus.put(fmu.asModelExchangeFmu().newInstance(integrator)).also { id ->\\n// Proto.UInt.newBuilder().setValue(id).build().also {\\n// responseObserver.onNext(it)\\n// responseObserver.onCompleted()\\n// }\\n// }\\n//\\n// }\\n\\n\\n private companion object {\\n val LOG: Logger = LoggerFactory.getLogger(GrpcFmuServer::class.java)\\n }\\n\\n}\\n\\n\\n\"\n }\n]"},"num_files":{"kind":"number","value":59,"string":"59"}}},{"rowIdx":2980074,"cells":{"repo_name":{"kind":"string","value":"williehammonds/TwitterDataAnalysis"},"repo_url":{"kind":"string","value":"https://github.com/williehammonds/TwitterDataAnalysis"},"snapshot_id":{"kind":"string","value":"96937ef7b042818773cdb490647aaa96971ee6ea"},"revision_id":{"kind":"string","value":"4b1d113a8b3843ccce9711102bfb013ac19b41d6"},"directory_id":{"kind":"string","value":"d1c912af79825ad0521a55ec9a033824043699af"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2021-01-16T20:46:19.726823","string":"2021-01-16T20:46:19.726823"},"revision_date":{"kind":"timestamp","value":"2015-02-23T04:03:33","string":"2015-02-23T04:03:33"},"committer_date":{"kind":"timestamp","value":"2015-02-23T04:03:33","string":"2015-02-23T04:03:33"},"github_id":{"kind":"null"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.5670995712280273,"alphanum_fraction":0.573720395565033,"avg_line_length":39.49484634399414,"blob_id":"edd6e52ae5e6bb490bb8a7f9401504c1be78b131","content_id":"7bd35a1bc7ec3307b6bf548e0dc5bdb760b7ff75","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":3927,"license_type":"permissive","max_line_length":166,"num_lines":97,"path":"/tests/test_TextTools.py","repo_name":"williehammonds/TwitterDataAnalysis","src_encoding":"UTF-8","text":"import unittest\n\nimport nltk\n\nfrom TextAnalysis.TextTools import *\n\nclass TweetTextWordBagMakerTest(unittest.TestCase):\n def setUp(self):\n self.object = TweetTextWordBagMaker()\n\n def tearDown(self):\n self.object = ''\n\n def test_add_to_ignorelist(self):\n \"\"\"\n The tested function combines the lists, removes duplicates, and converts to a tuple\n \"\"\"\n #testlist1 = ['cat', 'fish']\n #testlist2 = ['cat', 'taco', 'burrito']\n #expect = ('cat', 'fish', 'taco', 'burrito')\n\n testlist1 = [1, 2]\n testlist2 = [2, 3, 4, 5]\n expect = (1, 2, 3, 4, 5)\n \n self.object.add_to_ignorelist(testlist1)\n #make sure adds to the list\n #t1 = list(self.object.ignore).sort()\n #self.assertListEqual(t1, testlist1.sort())\n self.object.add_to_ignorelist(testlist2)\n #make sure edited out the duplicates\n self.assertTupleEqual(self.object.ignore, expect)\n \n def test__make_wordbag(self):\n test = \"The quick brown fox became a delicious taco for the hungry cat. All lived happily ever after\"\n expect = [\"the\", \"quick\", \"brown\", \"fox\", \"became\", \"a\", \"delicious\", \"taco\", \"for\", \"the\", \"hungry\", \"cat\", \".\", \"all\", \"lived\", \"happily\", \"ever\", \"after\"]\n result = self.object._make_wordbag(test)\n self.assertListEqual(result, expect)\n \n def test__filter_ignored_terms(self):\n to_remove = ['dog', 'cow']\n test = ['cat', 'dog', 'fish', 'cow']\n expect = ['cat', 'fish']\n self.object.add_to_ignorelist(to_remove)\n result = self.object._filter_ignored_terms(test)\n self.assertListEqual(result, expect)\n \n def test__filter_usernames(self):\n test = ['taco', '@burrito', 'cat', '@dog']\n expect = ['taco', 'cat']\n result = self.object._filter_usernames(test)\n self.assertListEqual(result, expect)\n \n def test__filter_urls(self):\n test = ['taco', '//t.co', 'cat', '//t.co']\n expect = ['taco', 'cat']\n result = self.object._filter_urls(test)\n self.assertListEqual(result, expect)\n \n \n def test_process(self):\n test = [{'tweetID' : 1, 'tweetText' : \"The first tweet. It has text\"},\n {'tweetID' : 2, 'tweetText' : \"The quick brown fox became a delicious taco for the hungry cat. All lived happily ever after\"}]\n expect = [\"first\", \"tweet\", \"text\", \"quick\", \"brown\", \"fox\", \"became\", \"delicious\", \"taco\", \"hungry\", \"cat\",\n \"lived\", \"happily\", \"ever\"]\n self.object.add_to_ignorelist([\".\", \",\"])\n self.object.add_to_ignorelist(nltk.corpus.stopwords.words('english'))\n self.object.process(test)\n self.assertEqual(self.object.masterbag, expect)\n self.assertTupleEqual(self.object.tweet_tuples[0], (1, [\"first\", \"tweet\", \"text\"]))\n self.assertTupleEqual(self.object.tweet_tuples[1], (2, [\"quick\", \"brown\", \"fox\", \"became\", \"delicious\", \"taco\", \"hungry\", \"cat\", \"lived\", \"happily\", \"ever\"]))\n\n\nclass WordFiltersTest(unittest.TestCase):\n def setUp(self):\n self.object = WordFilters()\n\n def tearDown(self):\n self.object = ''\n \n def test_remove_fragments(self):\n test = ['cat', 'dog', \"amp\", '...', '//t.co', \"'re'\", \"'m\", 'fish', \"'s\", 'cow']\n expect = ['cat', 'dog', 'fish', 'cow']\n result = self.object.remove_fragments(test)\n self.assertListEqual(result, expect)\n \n def test_remove_punctuation(self):\n test = ['cat', 'dog', \"&\", '.', '/', \"'\", \"!\", 'fish', \"#\", 'cow']\n expect = ['cat', 'dog', 'fish', 'cow']\n result = self.object.remove_punctuation(test)\n self.assertListEqual(result, expect)\n \n def test_filter_stopwords(self):\n test = ['taco', 'a', 'cat', 'the']\n expect = ['taco', 'cat']\n result = self.object.filter_stopwords(test)\n self.assertListEqual(result, expect)"},{"alpha_fraction":0.5550661087036133,"alphanum_fraction":0.5560826659202576,"avg_line_length":35.432098388671875,"blob_id":"bbbbf318141134fa46777556ed8db9836196113e","content_id":"e55fc568120522af1f433b51dd13da3d5b29001f","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":2951,"license_type":"permissive","max_line_length":124,"num_lines":81,"path":"/ConstantsAndUtilities.py","repo_name":"williehammonds/TwitterDataAnalysis","src_encoding":"UTF-8","text":"import string\n\n\nclass Ignore(object):\n \"\"\"\n This contains all the terms to be ignored from hashtags etc. It is inherited by other classes which do cleaning\n \n Attributes:\n words: Dictionary with categories of irrelevant terms as keys (placenames, smallwords, socialmediaterms, irrelevant)\n fragments: String fragments which should be filterd out\n \"\"\"\n fragments = [\"'s\", \"amp\", '...', '//t.co', \"'re'\", \"'m\"]\n punctuation = string.punctuation\n words = {\n 'placenames' : ['tn', 'nashville', 'memphis', 'tennessee', 'knoxville', 'fl', 'tx', 'sc', 'nc', 'co', \n 'nyc', 'va', 'ga', 'twittoma', 'team243'],\n 'smallwords' : ['no', 'be', 'my', 'the', 'like', 'in', 'i', 'a', 'you', 'is', 'of', 'and', 'it', 'to',\n 'this', 'so', 'for', 'on', 'up'], \n 'socialmediaterms' : ['hashtag', 'selfie', 'repost', 'nofilter', 'instagram', 'instamood', 'instalike',\n 'instadaily', 'picoftheday', 'photo', 'instapic', 'http', 'rt', 'mt'],\n 'irrelevant' : ['recordstoreday', 'vinyl', 'naruto', 'bread' ]\n }\n\n def __init__(self):\n self.words = Ignore.words\n wordlist = []\n [wordlist.append(word) for k in Ignore.words.keys() for word in Ignore.words[k]]\n # [wordlist.append(w) for w in wl for wl in self.words.values()]\n self.wordtuple = tuple(wordlist)\n\n\n @staticmethod\n def iterable(self):\n \"\"\"\n Iterable object for all the contents of the ignore list\n TODO Make a generator \n \"\"\"\n wordlist = []\n [wordlist.append(word) for k in words.keys() for word in words[k]]\n # [wordlist.append(w) for w in wl for wl in Ignore.words.values()]\n return wordlist\n\n def generator(self):\n \"\"\"\n Generator which returns ignore words\n \"\"\"\n pass\n # (word for word in self.wordtuple)\n # yield word\n\n\n @staticmethod\n def get_list(self):\n \"\"\"\n Returns a list of everything to ignore\n \"\"\"\n wordlist = []\n [wordlist.append(w) for wl in self.words.values() for w in wl]\n wordlist += self.fragments\n wordlist += self.punctuation\n return wordlist\n \n \nclass Merge(object):\n \"\"\"\n This holds terms which are to be merged together in analyzing graphs\n \n Attributes:\n toMerge: Dictionary with the master term as key and values to be merged in list as values\n \"\"\"\n toMerge = {\n 'Fibromyalgia' : ['fibro', 'fibromyalgia', 'fms', 'fm'],\n 'CRPS' : ['crps', 'rsd'], \n 'ChronicFatigue' : ['chronicfatigue', 'chronicfatiguesyndrome', 'cfs', 'mecfs', 'cfsme', 'cfids', \n 'myalgicencephalomyelitis', 'mcs' ], \n 'RheumatoidArthritis' : ['rheumatoid', 'ra', 'rheumatoidarthritis'],\n 'Endometriosis' : ['endometriosis', 'endo']\n }\n\nif __name__ == '__main__':\n pass\n"},{"alpha_fraction":0.6059828996658325,"alphanum_fraction":0.6059828996658325,"avg_line_length":21.960784912109375,"blob_id":"4c0d30c4de846db05e1a82622a2a8f2c0ed8e8f9","content_id":"23f888d2b90fdab8f632ade7a0049d1d33ef5f08","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1170,"license_type":"permissive","max_line_length":64,"num_lines":51,"path":"/TweetDAOs.py","repo_name":"williehammonds/TwitterDataAnalysis","src_encoding":"UTF-8","text":"\"\"\"\nThis contains classes for loading tweet data\n\nTHIS SHOULD INSTEAD USE THE DAO IN TwitterMining\n\"\"\"\n\nimport DAO\n\n\nclass TwitterSQLDAO(DAO.BaseDAO):\n \"\"\"\n Base database abstraction layer for twitter mysql database\n \"\"\"\n\n def __init__(self, test=False, local=True):\n if test is False:\n databaseName = 'twitter_data'\n else:\n databaseName = 'twitter_dataTEST'\n DAO.BaseDAO.__init__(self)\n if local is False:\n self.connectRemote(databaseName)\n else:\n self.connect(databaseName)\n\n\nclass TweetTextGetter(TwitterSQLDAO):\n \"\"\"\n Loads all tweetids and tweettext\n \n Args:\n test: Whether to use the test db\n local: Whether to use the local or remote db\n \n Returns:\n List of dictionaries with keys tweetID and tweetText\n \"\"\"\n\n def __init__(self, test=False, local=True):\n TwitterSQLDAO.__init__(self, test=test, local=local)\n\n\n def load_tweets(self):\n self.query = \"\"\"SELECT tweetID, tweetText FROM tweets\"\"\"\n self.val = []\n self.returnAll()\n return list(self.results)\n\n\nif __name__ == '__main__':\n pass"},{"alpha_fraction":0.5942720770835876,"alphanum_fraction":0.5990453362464905,"avg_line_length":19.950000762939453,"blob_id":"9f9d9ef35a699a1cb0528048f40a1f140679182d","content_id":"b32f53076f5fcb97747c2662cb71e2bd92225ef5","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":419,"license_type":"permissive","max_line_length":42,"num_lines":20,"path":"/tests/test_ConstantsAndUtilities.py","repo_name":"williehammonds/TwitterDataAnalysis","src_encoding":"UTF-8","text":"__author__ = 'adam'\n\nimport unittest\nfrom ConstantsAndUtilities import *\n\n\nclass IgnoreTest(unittest.TestCase):\n def setUp(self):\n self.object = Ignore()\n\n def test_generator(self):\n pass\n # # expect = Ignore.words[0][0]\n # expect = 'recordstoreday'\n # result = self.object.generator()\n # self.assertEqual(expect, result)\n\n\nif __name__ == '__main__':\n unittest.main()\n"},{"alpha_fraction":0.5718811750411987,"alphanum_fraction":0.5817821621894836,"avg_line_length":32.66222381591797,"blob_id":"217351c5334bd3ef1085aceaee6e5c6e5ecf958f","content_id":"b0808a07e207b9c63847aa59439c1edef224d81e","detected_licenses":["MIT"],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":7575,"license_type":"permissive","max_line_length":127,"num_lines":225,"path":"/TextAnalysis/TextTools.py","repo_name":"williehammonds/TwitterDataAnalysis","src_encoding":"UTF-8","text":"\nimport nltk\nfrom nltk.tokenize import word_tokenize, sent_tokenize\nimport time\n\n#Used for WordFilters\nfrom ConstantsAndUtilities import Ignore\nimport string\n\nfrom OptimizationTools import *\n\nclass ProcessingError(Exception):\n def __init__(self, identifier):\n self.identifier = identifier\n def __repr__(self):\n return \"%s went bad on %s : %s\" % (self.kind, self.identifier_type, self.identifier)\n\nclass TweetProcessingError(ProcessingError):\n def __init__(self, tweetID):\n self.kind = 'TweetProcessing'\n self.identifier_type = 'tweetID'\n ProcessingError.__init__(self, tweetID)\n\nclass StringProcessingError(ProcessingError):\n def __init__(self, string_processed):\n self.kind = 'StringProcessing'\n self.identifier_type = 'String content'\n ProcessingError.__init__(self, string_processed)\n\n\nclass TweetTextWordBagMaker(object):\n \"\"\"\n This takes a list of dictionaries containing tweetID and tweetText and processes the texts for bag of words type analyses.\n \n Before running the process command, all lists of strings to ignore should be loaded using add_to_ignorelist()\n \n \n Attributes:\n masterbag: List containing all words\n ignore: Tuple of strings to ignore through filtering\n tweet_tuples: List containing tuples with the structure (tweetID, [list of words in tweet])\n \"\"\"\n def __init__(self):\n self.ignore = ()\n self.masterbag = [] #This will hold all words\n self.tweet_tuples = []\n \n def add_to_ignorelist(self, list_to_ignore):\n \"\"\"\n Add a list of strings to the internally held tuple of strings to ignore in processing text\n \n Args:\n list_to_ignore: List of strings to ignore.\n \"\"\"\n self.ignore = list(self.ignore)\n [self.ignore.append(i) for i in list_to_ignore]\n self.ignore = set(self.ignore)\n self.ignore = tuple(self.ignore)\n \n def process(self, list_of_dicts):\n \"\"\"\n Processes the tweet texts\n Most recent execution time 599.286342144 sec for 732683 tweets\n Moved stopwords filtration first: 891.928412914 for 732683 tweets\n Merged stopwords into ignore list: 234.204810858 \n 1 loops, best of 3: 14min 56s per loop\n TODO: Change order of execution for optimization\n \n Args:\n list_of_dicts: List of dictionaries with keys tweetID and tweetText\n \"\"\"\n for t in list_of_dicts:\n tweetid = t['tweetID']\n #process text\n words = self._make_wordbag(t['tweetText'])\n #words = self._filter_stopwords(words)\n words = self._filter_ignored_terms(words)\n words = self._filter_usernames(words)\n words = self._filter_urls(words)\n #process tuple\n tweet_tuple = (tweetid, words)\n self.tweet_tuples.append(tweet_tuple)\n self.masterbag += words\n \n def new_process(self, list_of_dicts):\n \"\"\"\n Best time 225.85651803\n \"\"\"\n for t in list_of_dicts:\n tweetid = t['tweetID']\n #process text\n words = self._make_wordbag(t['tweetText'])\n words = [w for w in words if self._check_unwanted(w) and w not in self.ignore ]\n #process tuple\n tweet_tuple = (tweetid, words)\n self.tweet_tuples.append(tweet_tuple)\n self.masterbag += words\n \n def OLDprocess(self, list_of_dicts): \n for t in list(self.results):\n tweetid = t['tweetID']\n #process text\n words = [word for sent in sent_tokenize(t['tweetText']) for word in word_tokenize(sent)]\n words = [w.lower() for w in words]\n words = [w for w in words if w not in Ignore.punctuation]#remove punctuation\n words = [w for w in words if w not in Ignore.fragments]#remove fragments\n words = [w for w in words if w[0] != '@']#Get rid of usernames\n words = [w for w in words if w[0:6] != '//t.co'] #Remove some urls\n words = [w for w in words if w not in Ignore.words['socialmediaterms']] #Remove terms from social media\n words = [w for w in words if w not in nltk.corpus.stopwords.words('english')] #Remove stopwords\n #process tuple\n tweet_tuple = (tweetid, words)\n self.tweet_tuples.append(tweet_tuple)\n self.masterbag += words\n \n def _make_wordbag(self, text):\n \"\"\"\n Takes a bunch of sentences and extracts all the words, makes them lowercase, and returns them in a list\n \n Args:\n text: String text to be word tokenized\n \n Returns:\n List of words, all lower case\n \"\"\"\n bag = [word.lower() for sent in sent_tokenize(text) for word in word_tokenize(sent)]\n return bag\n\n def _filter_ignored_terms(self, wordlist):\n \"\"\"\n Remove items that are in the ignore list\n \n Args:\n wordlist: List of strings to be filtered\n \n Returns:\n Filtered list\n \"\"\"\n if len(self.ignore) == 0:\n pass\n # TODO Raise error message\n #raise\n words = [w for w in wordlist if w not in self.ignore]\n return words\n \n def _check_unwanted(self, word):\n if word[0] != '@' and word[0:6] != '//t.co':\n return True\n else:\n return False\n \n def _filter_usernames(self, wordlist):\n \"\"\"\n Gets rid of usernames by recognizing the @\n TODO: Modify to recognize cases of .@username\n \n \"\"\"\n words = [w for w in wordlist if w[0] != '@']\n return words\n \n def _filter_urls(self, wordlist):\n \"\"\"\n Removes some urls\n TODO: Make this better\n \n Args:\n wordlist: List of strings to be filtered\n \n Returns:\n Filtered list\n \"\"\"\n words = [w for w in wordlist if w[0:6] != '//t.co']\n return words\n \n\n\nclass WordFilters(object):\n \"\"\"\n This has filters for removing various strings and string components.\n \n \"\"\"\n \n @staticmethod\n def remove_fragments(wordlist):\n \"\"\"\n Filters string fragments from the list and returns the filtered list\n \n Args:\n wordlist: A list of words to have fragments removed from\n \n Returns:\n The filtered list\n \"\"\"\n wordlist = [w for w in wordlist if w not in Ignore.fragments]#remove fragments\n return wordlist\n\n @staticmethod \n def remove_punctuation(wordlist):\n \"\"\"\n Filters out punctuation from input list. Does not filter at the word level (e.g., will not remove the period in \"cat.\")\n \n Args:\n wordlist: A list of strings to be filtered for list items which are punctuation marks. \n \n Returns:\n The filtered list\n \"\"\"\n wordlist = [w for w in wordlist if w not in string.punctuation]#remove punctuation\n return wordlist\n \n @staticmethod\n def filter_stopwords(wordlist):\n \"\"\"\n Uses NLTK English stopwords corpus to remove stopwords.\n \n Args:\n wordlist: List of strings to be filtered\n \n Returns:\n Filtered list\n \"\"\"\n words = [w for w in wordlist if w not in nltk.corpus.stopwords.words('english')]\n return words\n\nif __name__ == '__main__':\n pass\n"}],"string":"[\n {\n \"alpha_fraction\": 0.5670995712280273,\n \"alphanum_fraction\": 0.573720395565033,\n \"avg_line_length\": 39.49484634399414,\n \"blob_id\": \"edd6e52ae5e6bb490bb8a7f9401504c1be78b131\",\n \"content_id\": \"7bd35a1bc7ec3307b6bf548e0dc5bdb760b7ff75\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 3927,\n \"license_type\": \"permissive\",\n \"max_line_length\": 166,\n \"num_lines\": 97,\n \"path\": \"/tests/test_TextTools.py\",\n \"repo_name\": \"williehammonds/TwitterDataAnalysis\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import unittest\\n\\nimport nltk\\n\\nfrom TextAnalysis.TextTools import *\\n\\nclass TweetTextWordBagMakerTest(unittest.TestCase):\\n def setUp(self):\\n self.object = TweetTextWordBagMaker()\\n\\n def tearDown(self):\\n self.object = ''\\n\\n def test_add_to_ignorelist(self):\\n \\\"\\\"\\\"\\n The tested function combines the lists, removes duplicates, and converts to a tuple\\n \\\"\\\"\\\"\\n #testlist1 = ['cat', 'fish']\\n #testlist2 = ['cat', 'taco', 'burrito']\\n #expect = ('cat', 'fish', 'taco', 'burrito')\\n\\n testlist1 = [1, 2]\\n testlist2 = [2, 3, 4, 5]\\n expect = (1, 2, 3, 4, 5)\\n \\n self.object.add_to_ignorelist(testlist1)\\n #make sure adds to the list\\n #t1 = list(self.object.ignore).sort()\\n #self.assertListEqual(t1, testlist1.sort())\\n self.object.add_to_ignorelist(testlist2)\\n #make sure edited out the duplicates\\n self.assertTupleEqual(self.object.ignore, expect)\\n \\n def test__make_wordbag(self):\\n test = \\\"The quick brown fox became a delicious taco for the hungry cat. All lived happily ever after\\\"\\n expect = [\\\"the\\\", \\\"quick\\\", \\\"brown\\\", \\\"fox\\\", \\\"became\\\", \\\"a\\\", \\\"delicious\\\", \\\"taco\\\", \\\"for\\\", \\\"the\\\", \\\"hungry\\\", \\\"cat\\\", \\\".\\\", \\\"all\\\", \\\"lived\\\", \\\"happily\\\", \\\"ever\\\", \\\"after\\\"]\\n result = self.object._make_wordbag(test)\\n self.assertListEqual(result, expect)\\n \\n def test__filter_ignored_terms(self):\\n to_remove = ['dog', 'cow']\\n test = ['cat', 'dog', 'fish', 'cow']\\n expect = ['cat', 'fish']\\n self.object.add_to_ignorelist(to_remove)\\n result = self.object._filter_ignored_terms(test)\\n self.assertListEqual(result, expect)\\n \\n def test__filter_usernames(self):\\n test = ['taco', '@burrito', 'cat', '@dog']\\n expect = ['taco', 'cat']\\n result = self.object._filter_usernames(test)\\n self.assertListEqual(result, expect)\\n \\n def test__filter_urls(self):\\n test = ['taco', '//t.co', 'cat', '//t.co']\\n expect = ['taco', 'cat']\\n result = self.object._filter_urls(test)\\n self.assertListEqual(result, expect)\\n \\n \\n def test_process(self):\\n test = [{'tweetID' : 1, 'tweetText' : \\\"The first tweet. It has text\\\"},\\n {'tweetID' : 2, 'tweetText' : \\\"The quick brown fox became a delicious taco for the hungry cat. All lived happily ever after\\\"}]\\n expect = [\\\"first\\\", \\\"tweet\\\", \\\"text\\\", \\\"quick\\\", \\\"brown\\\", \\\"fox\\\", \\\"became\\\", \\\"delicious\\\", \\\"taco\\\", \\\"hungry\\\", \\\"cat\\\",\\n \\\"lived\\\", \\\"happily\\\", \\\"ever\\\"]\\n self.object.add_to_ignorelist([\\\".\\\", \\\",\\\"])\\n self.object.add_to_ignorelist(nltk.corpus.stopwords.words('english'))\\n self.object.process(test)\\n self.assertEqual(self.object.masterbag, expect)\\n self.assertTupleEqual(self.object.tweet_tuples[0], (1, [\\\"first\\\", \\\"tweet\\\", \\\"text\\\"]))\\n self.assertTupleEqual(self.object.tweet_tuples[1], (2, [\\\"quick\\\", \\\"brown\\\", \\\"fox\\\", \\\"became\\\", \\\"delicious\\\", \\\"taco\\\", \\\"hungry\\\", \\\"cat\\\", \\\"lived\\\", \\\"happily\\\", \\\"ever\\\"]))\\n\\n\\nclass WordFiltersTest(unittest.TestCase):\\n def setUp(self):\\n self.object = WordFilters()\\n\\n def tearDown(self):\\n self.object = ''\\n \\n def test_remove_fragments(self):\\n test = ['cat', 'dog', \\\"amp\\\", '...', '//t.co', \\\"'re'\\\", \\\"'m\\\", 'fish', \\\"'s\\\", 'cow']\\n expect = ['cat', 'dog', 'fish', 'cow']\\n result = self.object.remove_fragments(test)\\n self.assertListEqual(result, expect)\\n \\n def test_remove_punctuation(self):\\n test = ['cat', 'dog', \\\"&\\\", '.', '/', \\\"'\\\", \\\"!\\\", 'fish', \\\"#\\\", 'cow']\\n expect = ['cat', 'dog', 'fish', 'cow']\\n result = self.object.remove_punctuation(test)\\n self.assertListEqual(result, expect)\\n \\n def test_filter_stopwords(self):\\n test = ['taco', 'a', 'cat', 'the']\\n expect = ['taco', 'cat']\\n result = self.object.filter_stopwords(test)\\n self.assertListEqual(result, expect)\"\n },\n {\n \"alpha_fraction\": 0.5550661087036133,\n \"alphanum_fraction\": 0.5560826659202576,\n \"avg_line_length\": 35.432098388671875,\n \"blob_id\": \"bbbbf318141134fa46777556ed8db9836196113e\",\n \"content_id\": \"e55fc568120522af1f433b51dd13da3d5b29001f\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 2951,\n \"license_type\": \"permissive\",\n \"max_line_length\": 124,\n \"num_lines\": 81,\n \"path\": \"/ConstantsAndUtilities.py\",\n \"repo_name\": \"williehammonds/TwitterDataAnalysis\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import string\\n\\n\\nclass Ignore(object):\\n \\\"\\\"\\\"\\n This contains all the terms to be ignored from hashtags etc. It is inherited by other classes which do cleaning\\n \\n Attributes:\\n words: Dictionary with categories of irrelevant terms as keys (placenames, smallwords, socialmediaterms, irrelevant)\\n fragments: String fragments which should be filterd out\\n \\\"\\\"\\\"\\n fragments = [\\\"'s\\\", \\\"amp\\\", '...', '//t.co', \\\"'re'\\\", \\\"'m\\\"]\\n punctuation = string.punctuation\\n words = {\\n 'placenames' : ['tn', 'nashville', 'memphis', 'tennessee', 'knoxville', 'fl', 'tx', 'sc', 'nc', 'co', \\n 'nyc', 'va', 'ga', 'twittoma', 'team243'],\\n 'smallwords' : ['no', 'be', 'my', 'the', 'like', 'in', 'i', 'a', 'you', 'is', 'of', 'and', 'it', 'to',\\n 'this', 'so', 'for', 'on', 'up'], \\n 'socialmediaterms' : ['hashtag', 'selfie', 'repost', 'nofilter', 'instagram', 'instamood', 'instalike',\\n 'instadaily', 'picoftheday', 'photo', 'instapic', 'http', 'rt', 'mt'],\\n 'irrelevant' : ['recordstoreday', 'vinyl', 'naruto', 'bread' ]\\n }\\n\\n def __init__(self):\\n self.words = Ignore.words\\n wordlist = []\\n [wordlist.append(word) for k in Ignore.words.keys() for word in Ignore.words[k]]\\n # [wordlist.append(w) for w in wl for wl in self.words.values()]\\n self.wordtuple = tuple(wordlist)\\n\\n\\n @staticmethod\\n def iterable(self):\\n \\\"\\\"\\\"\\n Iterable object for all the contents of the ignore list\\n TODO Make a generator \\n \\\"\\\"\\\"\\n wordlist = []\\n [wordlist.append(word) for k in words.keys() for word in words[k]]\\n # [wordlist.append(w) for w in wl for wl in Ignore.words.values()]\\n return wordlist\\n\\n def generator(self):\\n \\\"\\\"\\\"\\n Generator which returns ignore words\\n \\\"\\\"\\\"\\n pass\\n # (word for word in self.wordtuple)\\n # yield word\\n\\n\\n @staticmethod\\n def get_list(self):\\n \\\"\\\"\\\"\\n Returns a list of everything to ignore\\n \\\"\\\"\\\"\\n wordlist = []\\n [wordlist.append(w) for wl in self.words.values() for w in wl]\\n wordlist += self.fragments\\n wordlist += self.punctuation\\n return wordlist\\n \\n \\nclass Merge(object):\\n \\\"\\\"\\\"\\n This holds terms which are to be merged together in analyzing graphs\\n \\n Attributes:\\n toMerge: Dictionary with the master term as key and values to be merged in list as values\\n \\\"\\\"\\\"\\n toMerge = {\\n 'Fibromyalgia' : ['fibro', 'fibromyalgia', 'fms', 'fm'],\\n 'CRPS' : ['crps', 'rsd'], \\n 'ChronicFatigue' : ['chronicfatigue', 'chronicfatiguesyndrome', 'cfs', 'mecfs', 'cfsme', 'cfids', \\n 'myalgicencephalomyelitis', 'mcs' ], \\n 'RheumatoidArthritis' : ['rheumatoid', 'ra', 'rheumatoidarthritis'],\\n 'Endometriosis' : ['endometriosis', 'endo']\\n }\\n\\nif __name__ == '__main__':\\n pass\\n\"\n },\n {\n \"alpha_fraction\": 0.6059828996658325,\n \"alphanum_fraction\": 0.6059828996658325,\n \"avg_line_length\": 21.960784912109375,\n \"blob_id\": \"4c0d30c4de846db05e1a82622a2a8f2c0ed8e8f9\",\n \"content_id\": \"23f888d2b90fdab8f632ade7a0049d1d33ef5f08\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1170,\n \"license_type\": \"permissive\",\n \"max_line_length\": 64,\n \"num_lines\": 51,\n \"path\": \"/TweetDAOs.py\",\n \"repo_name\": \"williehammonds/TwitterDataAnalysis\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\\"\\\"\\\"\\nThis contains classes for loading tweet data\\n\\nTHIS SHOULD INSTEAD USE THE DAO IN TwitterMining\\n\\\"\\\"\\\"\\n\\nimport DAO\\n\\n\\nclass TwitterSQLDAO(DAO.BaseDAO):\\n \\\"\\\"\\\"\\n Base database abstraction layer for twitter mysql database\\n \\\"\\\"\\\"\\n\\n def __init__(self, test=False, local=True):\\n if test is False:\\n databaseName = 'twitter_data'\\n else:\\n databaseName = 'twitter_dataTEST'\\n DAO.BaseDAO.__init__(self)\\n if local is False:\\n self.connectRemote(databaseName)\\n else:\\n self.connect(databaseName)\\n\\n\\nclass TweetTextGetter(TwitterSQLDAO):\\n \\\"\\\"\\\"\\n Loads all tweetids and tweettext\\n \\n Args:\\n test: Whether to use the test db\\n local: Whether to use the local or remote db\\n \\n Returns:\\n List of dictionaries with keys tweetID and tweetText\\n \\\"\\\"\\\"\\n\\n def __init__(self, test=False, local=True):\\n TwitterSQLDAO.__init__(self, test=test, local=local)\\n\\n\\n def load_tweets(self):\\n self.query = \\\"\\\"\\\"SELECT tweetID, tweetText FROM tweets\\\"\\\"\\\"\\n self.val = []\\n self.returnAll()\\n return list(self.results)\\n\\n\\nif __name__ == '__main__':\\n pass\"\n },\n {\n \"alpha_fraction\": 0.5942720770835876,\n \"alphanum_fraction\": 0.5990453362464905,\n \"avg_line_length\": 19.950000762939453,\n \"blob_id\": \"9f9d9ef35a699a1cb0528048f40a1f140679182d\",\n \"content_id\": \"b32f53076f5fcb97747c2662cb71e2bd92225ef5\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 419,\n \"license_type\": \"permissive\",\n \"max_line_length\": 42,\n \"num_lines\": 20,\n \"path\": \"/tests/test_ConstantsAndUtilities.py\",\n \"repo_name\": \"williehammonds/TwitterDataAnalysis\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"__author__ = 'adam'\\n\\nimport unittest\\nfrom ConstantsAndUtilities import *\\n\\n\\nclass IgnoreTest(unittest.TestCase):\\n def setUp(self):\\n self.object = Ignore()\\n\\n def test_generator(self):\\n pass\\n # # expect = Ignore.words[0][0]\\n # expect = 'recordstoreday'\\n # result = self.object.generator()\\n # self.assertEqual(expect, result)\\n\\n\\nif __name__ == '__main__':\\n unittest.main()\\n\"\n },\n {\n \"alpha_fraction\": 0.5718811750411987,\n \"alphanum_fraction\": 0.5817821621894836,\n \"avg_line_length\": 32.66222381591797,\n \"blob_id\": \"217351c5334bd3ef1085aceaee6e5c6e5ecf958f\",\n \"content_id\": \"b0808a07e207b9c63847aa59439c1edef224d81e\",\n \"detected_licenses\": [\n \"MIT\"\n ],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 7575,\n \"license_type\": \"permissive\",\n \"max_line_length\": 127,\n \"num_lines\": 225,\n \"path\": \"/TextAnalysis/TextTools.py\",\n \"repo_name\": \"williehammonds/TwitterDataAnalysis\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"\\nimport nltk\\nfrom nltk.tokenize import word_tokenize, sent_tokenize\\nimport time\\n\\n#Used for WordFilters\\nfrom ConstantsAndUtilities import Ignore\\nimport string\\n\\nfrom OptimizationTools import *\\n\\nclass ProcessingError(Exception):\\n def __init__(self, identifier):\\n self.identifier = identifier\\n def __repr__(self):\\n return \\\"%s went bad on %s : %s\\\" % (self.kind, self.identifier_type, self.identifier)\\n\\nclass TweetProcessingError(ProcessingError):\\n def __init__(self, tweetID):\\n self.kind = 'TweetProcessing'\\n self.identifier_type = 'tweetID'\\n ProcessingError.__init__(self, tweetID)\\n\\nclass StringProcessingError(ProcessingError):\\n def __init__(self, string_processed):\\n self.kind = 'StringProcessing'\\n self.identifier_type = 'String content'\\n ProcessingError.__init__(self, string_processed)\\n\\n\\nclass TweetTextWordBagMaker(object):\\n \\\"\\\"\\\"\\n This takes a list of dictionaries containing tweetID and tweetText and processes the texts for bag of words type analyses.\\n \\n Before running the process command, all lists of strings to ignore should be loaded using add_to_ignorelist()\\n \\n \\n Attributes:\\n masterbag: List containing all words\\n ignore: Tuple of strings to ignore through filtering\\n tweet_tuples: List containing tuples with the structure (tweetID, [list of words in tweet])\\n \\\"\\\"\\\"\\n def __init__(self):\\n self.ignore = ()\\n self.masterbag = [] #This will hold all words\\n self.tweet_tuples = []\\n \\n def add_to_ignorelist(self, list_to_ignore):\\n \\\"\\\"\\\"\\n Add a list of strings to the internally held tuple of strings to ignore in processing text\\n \\n Args:\\n list_to_ignore: List of strings to ignore.\\n \\\"\\\"\\\"\\n self.ignore = list(self.ignore)\\n [self.ignore.append(i) for i in list_to_ignore]\\n self.ignore = set(self.ignore)\\n self.ignore = tuple(self.ignore)\\n \\n def process(self, list_of_dicts):\\n \\\"\\\"\\\"\\n Processes the tweet texts\\n Most recent execution time 599.286342144 sec for 732683 tweets\\n Moved stopwords filtration first: 891.928412914 for 732683 tweets\\n Merged stopwords into ignore list: 234.204810858 \\n 1 loops, best of 3: 14min 56s per loop\\n TODO: Change order of execution for optimization\\n \\n Args:\\n list_of_dicts: List of dictionaries with keys tweetID and tweetText\\n \\\"\\\"\\\"\\n for t in list_of_dicts:\\n tweetid = t['tweetID']\\n #process text\\n words = self._make_wordbag(t['tweetText'])\\n #words = self._filter_stopwords(words)\\n words = self._filter_ignored_terms(words)\\n words = self._filter_usernames(words)\\n words = self._filter_urls(words)\\n #process tuple\\n tweet_tuple = (tweetid, words)\\n self.tweet_tuples.append(tweet_tuple)\\n self.masterbag += words\\n \\n def new_process(self, list_of_dicts):\\n \\\"\\\"\\\"\\n Best time 225.85651803\\n \\\"\\\"\\\"\\n for t in list_of_dicts:\\n tweetid = t['tweetID']\\n #process text\\n words = self._make_wordbag(t['tweetText'])\\n words = [w for w in words if self._check_unwanted(w) and w not in self.ignore ]\\n #process tuple\\n tweet_tuple = (tweetid, words)\\n self.tweet_tuples.append(tweet_tuple)\\n self.masterbag += words\\n \\n def OLDprocess(self, list_of_dicts): \\n for t in list(self.results):\\n tweetid = t['tweetID']\\n #process text\\n words = [word for sent in sent_tokenize(t['tweetText']) for word in word_tokenize(sent)]\\n words = [w.lower() for w in words]\\n words = [w for w in words if w not in Ignore.punctuation]#remove punctuation\\n words = [w for w in words if w not in Ignore.fragments]#remove fragments\\n words = [w for w in words if w[0] != '@']#Get rid of usernames\\n words = [w for w in words if w[0:6] != '//t.co'] #Remove some urls\\n words = [w for w in words if w not in Ignore.words['socialmediaterms']] #Remove terms from social media\\n words = [w for w in words if w not in nltk.corpus.stopwords.words('english')] #Remove stopwords\\n #process tuple\\n tweet_tuple = (tweetid, words)\\n self.tweet_tuples.append(tweet_tuple)\\n self.masterbag += words\\n \\n def _make_wordbag(self, text):\\n \\\"\\\"\\\"\\n Takes a bunch of sentences and extracts all the words, makes them lowercase, and returns them in a list\\n \\n Args:\\n text: String text to be word tokenized\\n \\n Returns:\\n List of words, all lower case\\n \\\"\\\"\\\"\\n bag = [word.lower() for sent in sent_tokenize(text) for word in word_tokenize(sent)]\\n return bag\\n\\n def _filter_ignored_terms(self, wordlist):\\n \\\"\\\"\\\"\\n Remove items that are in the ignore list\\n \\n Args:\\n wordlist: List of strings to be filtered\\n \\n Returns:\\n Filtered list\\n \\\"\\\"\\\"\\n if len(self.ignore) == 0:\\n pass\\n # TODO Raise error message\\n #raise\\n words = [w for w in wordlist if w not in self.ignore]\\n return words\\n \\n def _check_unwanted(self, word):\\n if word[0] != '@' and word[0:6] != '//t.co':\\n return True\\n else:\\n return False\\n \\n def _filter_usernames(self, wordlist):\\n \\\"\\\"\\\"\\n Gets rid of usernames by recognizing the @\\n TODO: Modify to recognize cases of .@username\\n \\n \\\"\\\"\\\"\\n words = [w for w in wordlist if w[0] != '@']\\n return words\\n \\n def _filter_urls(self, wordlist):\\n \\\"\\\"\\\"\\n Removes some urls\\n TODO: Make this better\\n \\n Args:\\n wordlist: List of strings to be filtered\\n \\n Returns:\\n Filtered list\\n \\\"\\\"\\\"\\n words = [w for w in wordlist if w[0:6] != '//t.co']\\n return words\\n \\n\\n\\nclass WordFilters(object):\\n \\\"\\\"\\\"\\n This has filters for removing various strings and string components.\\n \\n \\\"\\\"\\\"\\n \\n @staticmethod\\n def remove_fragments(wordlist):\\n \\\"\\\"\\\"\\n Filters string fragments from the list and returns the filtered list\\n \\n Args:\\n wordlist: A list of words to have fragments removed from\\n \\n Returns:\\n The filtered list\\n \\\"\\\"\\\"\\n wordlist = [w for w in wordlist if w not in Ignore.fragments]#remove fragments\\n return wordlist\\n\\n @staticmethod \\n def remove_punctuation(wordlist):\\n \\\"\\\"\\\"\\n Filters out punctuation from input list. Does not filter at the word level (e.g., will not remove the period in \\\"cat.\\\")\\n \\n Args:\\n wordlist: A list of strings to be filtered for list items which are punctuation marks. \\n \\n Returns:\\n The filtered list\\n \\\"\\\"\\\"\\n wordlist = [w for w in wordlist if w not in string.punctuation]#remove punctuation\\n return wordlist\\n \\n @staticmethod\\n def filter_stopwords(wordlist):\\n \\\"\\\"\\\"\\n Uses NLTK English stopwords corpus to remove stopwords.\\n \\n Args:\\n wordlist: List of strings to be filtered\\n \\n Returns:\\n Filtered list\\n \\\"\\\"\\\"\\n words = [w for w in wordlist if w not in nltk.corpus.stopwords.words('english')]\\n return words\\n\\nif __name__ == '__main__':\\n pass\\n\"\n }\n]"},"num_files":{"kind":"number","value":5,"string":"5"}}},{"rowIdx":2980075,"cells":{"repo_name":{"kind":"string","value":"bryand1/textsummarization"},"repo_url":{"kind":"string","value":"https://github.com/bryand1/textsummarization"},"snapshot_id":{"kind":"string","value":"2c6057d89fc5e21deeff8fe63b90deaaa71e49fb"},"revision_id":{"kind":"string","value":"db22bebb5ee20eb9f35da19eb1baabf2e35a53b0"},"directory_id":{"kind":"string","value":"6a9e84af14cc2485830ad8cb80ee455c2eb253b0"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2022-12-16T05:58:37.469921","string":"2022-12-16T05:58:37.469921"},"revision_date":{"kind":"timestamp","value":"2018-05-03T18:26:56","string":"2018-05-03T18:26:56"},"committer_date":{"kind":"timestamp","value":"2018-05-03T18:26:56","string":"2018-05-03T18:26:56"},"github_id":{"kind":"number","value":132033886,"string":"132,033,886"},"star_events_count":{"kind":"number","value":1,"string":"1"},"fork_events_count":{"kind":"number","value":1,"string":"1"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"timestamp","value":"2018-05-03T18:21:09","string":"2018-05-03T18:21:09"},"gha_updated_at":{"kind":"timestamp","value":"2019-08-09T11:43:54","string":"2019-08-09T11:43:54"},"gha_pushed_at":{"kind":"timestamp","value":"2022-12-08T00:01:14","string":"2022-12-08T00:01:14"},"gha_language":{"kind":"string","value":"Python"},"files":{"kind":"list like","value":[{"alpha_fraction":0.7527114748954773,"alphanum_fraction":0.7527114748954773,"avg_line_length":26.117647171020508,"blob_id":"2f03d7dbab6fa0a25239bd674fb363c646927fc3","content_id":"c88bf7b4644d3173c456f817be2fcacb84028ed1","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":461,"license_type":"no_license","max_line_length":72,"num_lines":17,"path":"/util.py","repo_name":"bryand1/textsummarization","src_encoding":"UTF-8","text":"import config\nimport logging\nimport sys\nimport time\n\n\ndef get_logger(name):\n logger = logging.getLogger(name)\n logger.setLevel(config.LOGLEVEL)\n formatter = logging.Formatter(config.LOGFMT, datefmt=config.DATEFMT)\n formatter.converter = time.gmtime\n handler = logging.StreamHandler(sys.stdout)\n handler.setLevel(config.LOGLEVEL)\n handler.setFormatter(formatter)\n logger.addHandler(handler)\n logger.propagate = False\n return logger\n"},{"alpha_fraction":0.5921908617019653,"alphanum_fraction":0.5965293049812317,"avg_line_length":28.74193572998047,"blob_id":"63e208c7c4d23cfc9f127e7e300f6b8e0f4bf932","content_id":"7994945ed738fd77cce4e22ebd621563608a5809","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1844,"license_type":"no_license","max_line_length":89,"num_lines":62,"path":"/main.py","repo_name":"bryand1/textsummarization","src_encoding":"UTF-8","text":"# -*- coding: utf-8 -*-\n\"\"\"\nSee and compare the output of popular extractive summarization APIs.\n@author Bryan Andrade \n@version v0.0.1\n\nAPIs tested\n1. TextAnalysis Text Summarization\n2. SMMRY\n\nUsage: python3 main.py [--api textsummarization | smmry]\n\nArgs:\n--api Choose which API to test. Default is to run all APIs\n\"\"\"\nimport api\nimport argparse\nimport config\nimport os\nimport util\n\nlogger = util.get_logger(\"main\")\n\nif __name__ == '__main__':\n p = argparse.ArgumentParser()\n p.add_argument('--api', type=str)\n cmdargs = p.parse_args()\n # If cmdargs.api is None, user intends to run all summary APIs\n article = 'uber-vs-lyft.txt'\n with open(os.path.join(config.WD, 'article', article)) as fh:\n text = fh.read()\n logger.info(\"Loaded article %s\", article)\n # TextAnalysis Text Summarization API\n if cmdargs.api == 'textsummarization' or cmdargs.api is None:\n args = (\n config.TEXTSUMMARIZATION_URL,\n config.X_MASHAPE_KEY,\n config.SENTNUM,\n text\n )\n resp = api.textsummarization.summarize(*args)\n js = resp.json()\n output = []\n for i, sentence in enumerate(js['sentences']):\n output.append(f\"{i + 1}: {sentence}\")\n with open(os.path.join(config.WD, 'output', 'textsummarization.txt'), 'w') as fh:\n fh.write('\\n'.join(output))\n logger.info(\"textsummarization done\")\n\n # SMMRY\n if cmdargs.api == 'smmry' or cmdargs.api is None:\n args = (\n config.SMMRY_URL,\n config.SMMRY_KEY,\n config.SENTNUM,\n text\n )\n resp = api.smmry.summarize(*args)\n js = resp.json()\n with open(os.path.join(config.WD, 'output', 'smmry.txt'), 'w') as fh:\n fh.write(js['sm_api_content'])\n logger.info(\"smmry done\")\n"},{"alpha_fraction":0.6462196707725525,"alphanum_fraction":0.6462196707725525,"avg_line_length":27.040000915527344,"blob_id":"21bbeeaf49e5e1828111000a3c73b875fcdf95bd","content_id":"10bfe75d4c907898c4efb43812bc0894e748f55a","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":701,"license_type":"no_license","max_line_length":71,"num_lines":25,"path":"/api/textsummarization.py","repo_name":"bryand1/textsummarization","src_encoding":"UTF-8","text":"import requests\n\n\ndef summarize(api_endpoint, api_key, num_sentences, text_to_summarize):\n \"\"\"\n TextAnalysis Text Summarization API - provided by Mashape\n :param api_endpoint: string url\n :param api_key: string credential\n :param text_to_summarize: plain text article\n :param num_sentences: int of size of the summary\n :return: requests.Response\n \"\"\"\n headers = {\n 'X-Mashape-Key': api_key,\n 'Content-Type': 'application/x-www-form-urlencoded',\n 'Accept': 'application/json'\n }\n\n data = {\n 'sentnum': num_sentences,\n 'text': text_to_summarize\n }\n\n resp = requests.post(api_endpoint, headers=headers, data=data)\n return resp\n"},{"alpha_fraction":0.7066380977630615,"alphanum_fraction":0.7109207510948181,"avg_line_length":28.1875,"blob_id":"d6f9b14a36890b5182b8841404cb4273c4936256","content_id":"22bd4ad91538367cfa31e8aa2b17f2782ee496ca","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":467,"license_type":"no_license","max_line_length":100,"num_lines":16,"path":"/config.py","repo_name":"bryand1/textsummarization","src_encoding":"UTF-8","text":"import os\nimport logging\n\nWD = os.path.dirname(os.path.abspath(__file__))\n\nLOGLEVEL = logging.INFO\nLOGFMT = \"%(asctime)s %(name)s %(levelname)s %(message)s\"\nDATEFMT = \"%Y-%m-%d %H:%M:%S\"\n\nX_MASHAPE_KEY = os.environ.get('X_MASHAPE_KEY')\n\nTEXTSUMMARIZATION_URL = 'https://textanalysis-text-summarization.p.mashape.com/text-summarizer-text'\nSMMRY_URL = 'https://api.smmry.com'\nSMMRY_KEY = os.environ.get('SMMRY_KEY')\n# Number of sentences in summary output\nSENTNUM = 10\n"},{"alpha_fraction":0.6612021923065186,"alphanum_fraction":0.6612021923065186,"avg_line_length":31.294116973876953,"blob_id":"b37f6234a1c784165e26c13079ceb9a7c47e71ee","content_id":"2859d991cd3b5bb4f046fbfd9451be5826d7d2ef","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":549,"license_type":"no_license","max_line_length":71,"num_lines":17,"path":"/api/smmry.py","repo_name":"bryand1/textsummarization","src_encoding":"UTF-8","text":"import requests\n\n\ndef summarize(api_endpoint, api_key, num_sentences, text_to_summarize):\n \"\"\"\n SMMRY - text summarization API\n :param api_endpoint: string url\n :param api_key: string credential\n :param text_to_summarize: plain text article\n :param num_sentences: int of size of the summary\n :return: requests.Response\n \"\"\"\n url = \"{}/?SM_API_KEY={}&SM_LENGTH={}\".format(\n api_endpoint, api_key, num_sentences)\n data = {'sm_api_input': text_to_summarize}\n resp = requests.post(url, data=data)\n return resp\n"},{"alpha_fraction":0.7235862016677856,"alphanum_fraction":0.7263448238372803,"avg_line_length":36.76041793823242,"blob_id":"bf1bd1456246e73bc1f1590aca15cb3db477ca69","content_id":"c062db06db1de2ac6546f9aee41d1e903a1dfd4a","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":3625,"license_type":"no_license","max_line_length":570,"num_lines":96,"path":"/README.md","repo_name":"bryand1/textsummarization","src_encoding":"UTF-8","text":"# Text Summarization Comparison\n\n### Objective\n\nSee and compare the output of popular extractive summarization APIs.\n\n### Getting started\n\n```bash\ngit clone https://github.com/bryand1/textsummarization.git\ncd textsummarization\npip3 install -r requirements.txt\ntouch apikeys.env\n# Edit apikeys.env with your API keys\n# export X_MASHAPE_KEY=[YOUR KEY]\n# export SMMRY_KEY=[YOUR KEY]\nbash run.sh\n```\n\n### APIs Tested\n\n+ [TextAnalysis Text Summarization](https://market.mashape.com/textanalysis/text-summarization)\n+ [SMMRY](https://smmry.com/)\n\n#### Text Summarization\n\n```python\n# request format\n# These code snippets use an open-source library. http://unirest.io/python\nresponse = unirest.post(\"https://textanalysis-text-summarization.p.mashape.com/text-summarizer-text\",\n headers={\n \"X-Mashape-Key\": \"[ENTER KEY HERE]\",\n \"Content-Type\": \"application/x-www-form-urlencoded\",\n \"Accept\": \"application/json\"\n },\n params={\n \"sentnum\": 5,\n \"text\": \"Automatic summarization is the process of reducing a text document with a computer program in order to create a summary that retains the most important points of the original document. As the problem of information overload has grown, and as the quantity of data has increased, so has interest in automatic summarization. Technologies that can make a coherent summary take into account variables such as length, writing style and syntax. An example of the use of summarization technology is search engines such as Google. Document summarization is another.\"\n }\n)\n```\n\nText Summarization returns a JSON response.\n\n```javascript\n{\n \"sentences\": [\n \"Text of sentence #1...\",\n \"Text of sentence #2...\",\n // etc.\n ]\n}\n```\n\n#### SMMRY\n\nExcerpts from [SMMRY API documentation](https://smmry.com/api)\n\nThe API request must be made to https://api.smmry.com. The returned response will be encoded in JSON.\n\nHere are the possible parameters placed in the request URL.\n \n+ SM\\_API\\_KEY=N Required, your API key.\n+ SM\\_URL=X Optional, the webpage to summarize.\n+ SM\\_LENGTH=N Optional, the number of sentences returned, default 7.\n+ SM\\_KEYWORD_COUNT=N Optional, N the number of keywords to return.\n+ SM\\_WITH\\_BREAK Optional, inserts the string [BREAK] between sentences.\n+ SM\\_WITH\\_ENCODE Optional, converts HTML entities to their applicable chars.\n+ SM\\_IGNORE\\_LENGTH Optional, returns summary regardless of quality or length.\n+ SM\\_QUOTE\\_AVOID Optional, sentences with quotations will be excluded.\n+ SM\\_QUESTION\\_AVOID Optional, sentences with question will be excluded.\n+ SM\\_EXCLAMATION\\_AVOID Optional, sentences with exclamation marks will be excluded.\n\nHere are the possible indexes of the array returned in a JSON array.\n \n+ sm\\_api\\_message Contains notices, warnings, and error messages.\n+ sm\\_api\\_character_count Contains the amount of characters returned.\n+ sm\\_api\\_title Contains the title when available.\n+ sm\\_api\\_content Contains the summary.\n+ sm\\_api\\_keyword\\_array Contains top ranked keywords in descending order.\n+ sm\\_api\\_error Contains error code.\n\n```php\n$text = \"Your long text goes here...\";\n\n$ch = curl_init(\"http://api.smmry.com/&SM_API_KEY=X\");\ncurl_setopt($ch, CURLOPT_HTTPHEADER, array(\"Expect:\")); // See Note\ncurl_setopt($ch, CURLOPT_POST, true); \ncurl_setopt($ch, CURLOPT_POSTFIELDS, \"sm_api_input=\".$text);\ncurl_setopt($ch, CURLOPT_FOLLOWLOCATION, true);\ncurl_setopt($ch, CURLOPT_RETURNTRANSFER, true);\ncurl_setopt($ch, CURLOPT_CONNECTTIMEOUT, 20);\ncurl_setopt($ch, CURLOPT_TIMEOUT, 20);\n$return = json_decode(curl_exec($ch), true);\ncurl_close($ch);\n```\n"},{"alpha_fraction":0.807692289352417,"alphanum_fraction":0.807692289352417,"avg_line_length":25,"blob_id":"b1a02d58dbfbb8dd7827a68cbb032453f77f2b6a","content_id":"50ba5f571383f5956abdba89e9ecb97e4b419888","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":52,"license_type":"no_license","max_line_length":31,"num_lines":2,"path":"/api/__init__.py","repo_name":"bryand1/textsummarization","src_encoding":"UTF-8","text":"from . import smmry\nfrom . import textsummarization\n"},{"alpha_fraction":0.3921568691730499,"alphanum_fraction":0.6764705777168274,"avg_line_length":13.714285850524902,"blob_id":"0eeac963ac580cddadaf7c12c9dc8d4c6475f8cf","content_id":"38b3f0049daf19d739d0b40d9e814d5cc6a4a6c2","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Text","length_bytes":102,"license_type":"no_license","max_line_length":18,"num_lines":7,"path":"/requirements.txt","repo_name":"bryand1/textsummarization","src_encoding":"UTF-8","text":"attrs==17.4.0\ncertifi==2017.4.17\nchardet==3.0.3\nidna==2.5\nrequests==2.17.3\nsix==1.11.0\nurllib3==1.21.1"}],"string":"[\n {\n \"alpha_fraction\": 0.7527114748954773,\n \"alphanum_fraction\": 0.7527114748954773,\n \"avg_line_length\": 26.117647171020508,\n \"blob_id\": \"2f03d7dbab6fa0a25239bd674fb363c646927fc3\",\n \"content_id\": \"c88bf7b4644d3173c456f817be2fcacb84028ed1\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 461,\n \"license_type\": \"no_license\",\n \"max_line_length\": 72,\n \"num_lines\": 17,\n \"path\": \"/util.py\",\n \"repo_name\": \"bryand1/textsummarization\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import config\\nimport logging\\nimport sys\\nimport time\\n\\n\\ndef get_logger(name):\\n logger = logging.getLogger(name)\\n logger.setLevel(config.LOGLEVEL)\\n formatter = logging.Formatter(config.LOGFMT, datefmt=config.DATEFMT)\\n formatter.converter = time.gmtime\\n handler = logging.StreamHandler(sys.stdout)\\n handler.setLevel(config.LOGLEVEL)\\n handler.setFormatter(formatter)\\n logger.addHandler(handler)\\n logger.propagate = False\\n return logger\\n\"\n },\n {\n \"alpha_fraction\": 0.5921908617019653,\n \"alphanum_fraction\": 0.5965293049812317,\n \"avg_line_length\": 28.74193572998047,\n \"blob_id\": \"63e208c7c4d23cfc9f127e7e300f6b8e0f4bf932\",\n \"content_id\": \"7994945ed738fd77cce4e22ebd621563608a5809\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1844,\n \"license_type\": \"no_license\",\n \"max_line_length\": 89,\n \"num_lines\": 62,\n \"path\": \"/main.py\",\n \"repo_name\": \"bryand1/textsummarization\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# -*- coding: utf-8 -*-\\n\\\"\\\"\\\"\\nSee and compare the output of popular extractive summarization APIs.\\n@author Bryan Andrade \\n@version v0.0.1\\n\\nAPIs tested\\n1. TextAnalysis Text Summarization\\n2. SMMRY\\n\\nUsage: python3 main.py [--api textsummarization | smmry]\\n\\nArgs:\\n--api Choose which API to test. Default is to run all APIs\\n\\\"\\\"\\\"\\nimport api\\nimport argparse\\nimport config\\nimport os\\nimport util\\n\\nlogger = util.get_logger(\\\"main\\\")\\n\\nif __name__ == '__main__':\\n p = argparse.ArgumentParser()\\n p.add_argument('--api', type=str)\\n cmdargs = p.parse_args()\\n # If cmdargs.api is None, user intends to run all summary APIs\\n article = 'uber-vs-lyft.txt'\\n with open(os.path.join(config.WD, 'article', article)) as fh:\\n text = fh.read()\\n logger.info(\\\"Loaded article %s\\\", article)\\n # TextAnalysis Text Summarization API\\n if cmdargs.api == 'textsummarization' or cmdargs.api is None:\\n args = (\\n config.TEXTSUMMARIZATION_URL,\\n config.X_MASHAPE_KEY,\\n config.SENTNUM,\\n text\\n )\\n resp = api.textsummarization.summarize(*args)\\n js = resp.json()\\n output = []\\n for i, sentence in enumerate(js['sentences']):\\n output.append(f\\\"{i + 1}: {sentence}\\\")\\n with open(os.path.join(config.WD, 'output', 'textsummarization.txt'), 'w') as fh:\\n fh.write('\\\\n'.join(output))\\n logger.info(\\\"textsummarization done\\\")\\n\\n # SMMRY\\n if cmdargs.api == 'smmry' or cmdargs.api is None:\\n args = (\\n config.SMMRY_URL,\\n config.SMMRY_KEY,\\n config.SENTNUM,\\n text\\n )\\n resp = api.smmry.summarize(*args)\\n js = resp.json()\\n with open(os.path.join(config.WD, 'output', 'smmry.txt'), 'w') as fh:\\n fh.write(js['sm_api_content'])\\n logger.info(\\\"smmry done\\\")\\n\"\n },\n {\n \"alpha_fraction\": 0.6462196707725525,\n \"alphanum_fraction\": 0.6462196707725525,\n \"avg_line_length\": 27.040000915527344,\n \"blob_id\": \"21bbeeaf49e5e1828111000a3c73b875fcdf95bd\",\n \"content_id\": \"10bfe75d4c907898c4efb43812bc0894e748f55a\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 701,\n \"license_type\": \"no_license\",\n \"max_line_length\": 71,\n \"num_lines\": 25,\n \"path\": \"/api/textsummarization.py\",\n \"repo_name\": \"bryand1/textsummarization\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import requests\\n\\n\\ndef summarize(api_endpoint, api_key, num_sentences, text_to_summarize):\\n \\\"\\\"\\\"\\n TextAnalysis Text Summarization API - provided by Mashape\\n :param api_endpoint: string url\\n :param api_key: string credential\\n :param text_to_summarize: plain text article\\n :param num_sentences: int of size of the summary\\n :return: requests.Response\\n \\\"\\\"\\\"\\n headers = {\\n 'X-Mashape-Key': api_key,\\n 'Content-Type': 'application/x-www-form-urlencoded',\\n 'Accept': 'application/json'\\n }\\n\\n data = {\\n 'sentnum': num_sentences,\\n 'text': text_to_summarize\\n }\\n\\n resp = requests.post(api_endpoint, headers=headers, data=data)\\n return resp\\n\"\n },\n {\n \"alpha_fraction\": 0.7066380977630615,\n \"alphanum_fraction\": 0.7109207510948181,\n \"avg_line_length\": 28.1875,\n \"blob_id\": \"d6f9b14a36890b5182b8841404cb4273c4936256\",\n \"content_id\": \"22bd4ad91538367cfa31e8aa2b17f2782ee496ca\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 467,\n \"license_type\": \"no_license\",\n \"max_line_length\": 100,\n \"num_lines\": 16,\n \"path\": \"/config.py\",\n \"repo_name\": \"bryand1/textsummarization\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import os\\nimport logging\\n\\nWD = os.path.dirname(os.path.abspath(__file__))\\n\\nLOGLEVEL = logging.INFO\\nLOGFMT = \\\"%(asctime)s %(name)s %(levelname)s %(message)s\\\"\\nDATEFMT = \\\"%Y-%m-%d %H:%M:%S\\\"\\n\\nX_MASHAPE_KEY = os.environ.get('X_MASHAPE_KEY')\\n\\nTEXTSUMMARIZATION_URL = 'https://textanalysis-text-summarization.p.mashape.com/text-summarizer-text'\\nSMMRY_URL = 'https://api.smmry.com'\\nSMMRY_KEY = os.environ.get('SMMRY_KEY')\\n# Number of sentences in summary output\\nSENTNUM = 10\\n\"\n },\n {\n \"alpha_fraction\": 0.6612021923065186,\n \"alphanum_fraction\": 0.6612021923065186,\n \"avg_line_length\": 31.294116973876953,\n \"blob_id\": \"b37f6234a1c784165e26c13079ceb9a7c47e71ee\",\n \"content_id\": \"2859d991cd3b5bb4f046fbfd9451be5826d7d2ef\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 549,\n \"license_type\": \"no_license\",\n \"max_line_length\": 71,\n \"num_lines\": 17,\n \"path\": \"/api/smmry.py\",\n \"repo_name\": \"bryand1/textsummarization\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"import requests\\n\\n\\ndef summarize(api_endpoint, api_key, num_sentences, text_to_summarize):\\n \\\"\\\"\\\"\\n SMMRY - text summarization API\\n :param api_endpoint: string url\\n :param api_key: string credential\\n :param text_to_summarize: plain text article\\n :param num_sentences: int of size of the summary\\n :return: requests.Response\\n \\\"\\\"\\\"\\n url = \\\"{}/?SM_API_KEY={}&SM_LENGTH={}\\\".format(\\n api_endpoint, api_key, num_sentences)\\n data = {'sm_api_input': text_to_summarize}\\n resp = requests.post(url, data=data)\\n return resp\\n\"\n },\n {\n \"alpha_fraction\": 0.7235862016677856,\n \"alphanum_fraction\": 0.7263448238372803,\n \"avg_line_length\": 36.76041793823242,\n \"blob_id\": \"bf1bd1456246e73bc1f1590aca15cb3db477ca69\",\n \"content_id\": \"c062db06db1de2ac6546f9aee41d1e903a1dfd4a\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Markdown\",\n \"length_bytes\": 3625,\n \"license_type\": \"no_license\",\n \"max_line_length\": 570,\n \"num_lines\": 96,\n \"path\": \"/README.md\",\n \"repo_name\": \"bryand1/textsummarization\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"# Text Summarization Comparison\\n\\n### Objective\\n\\nSee and compare the output of popular extractive summarization APIs.\\n\\n### Getting started\\n\\n```bash\\ngit clone https://github.com/bryand1/textsummarization.git\\ncd textsummarization\\npip3 install -r requirements.txt\\ntouch apikeys.env\\n# Edit apikeys.env with your API keys\\n# export X_MASHAPE_KEY=[YOUR KEY]\\n# export SMMRY_KEY=[YOUR KEY]\\nbash run.sh\\n```\\n\\n### APIs Tested\\n\\n+ [TextAnalysis Text Summarization](https://market.mashape.com/textanalysis/text-summarization)\\n+ [SMMRY](https://smmry.com/)\\n\\n#### Text Summarization\\n\\n```python\\n# request format\\n# These code snippets use an open-source library. http://unirest.io/python\\nresponse = unirest.post(\\\"https://textanalysis-text-summarization.p.mashape.com/text-summarizer-text\\\",\\n headers={\\n \\\"X-Mashape-Key\\\": \\\"[ENTER KEY HERE]\\\",\\n \\\"Content-Type\\\": \\\"application/x-www-form-urlencoded\\\",\\n \\\"Accept\\\": \\\"application/json\\\"\\n },\\n params={\\n \\\"sentnum\\\": 5,\\n \\\"text\\\": \\\"Automatic summarization is the process of reducing a text document with a computer program in order to create a summary that retains the most important points of the original document. As the problem of information overload has grown, and as the quantity of data has increased, so has interest in automatic summarization. Technologies that can make a coherent summary take into account variables such as length, writing style and syntax. An example of the use of summarization technology is search engines such as Google. Document summarization is another.\\\"\\n }\\n)\\n```\\n\\nText Summarization returns a JSON response.\\n\\n```javascript\\n{\\n \\\"sentences\\\": [\\n \\\"Text of sentence #1...\\\",\\n \\\"Text of sentence #2...\\\",\\n // etc.\\n ]\\n}\\n```\\n\\n#### SMMRY\\n\\nExcerpts from [SMMRY API documentation](https://smmry.com/api)\\n\\nThe API request must be made to https://api.smmry.com. The returned response will be encoded in JSON.\\n\\nHere are the possible parameters placed in the request URL.\\n \\n+ SM\\\\_API\\\\_KEY=N Required, your API key.\\n+ SM\\\\_URL=X Optional, the webpage to summarize.\\n+ SM\\\\_LENGTH=N Optional, the number of sentences returned, default 7.\\n+ SM\\\\_KEYWORD_COUNT=N Optional, N the number of keywords to return.\\n+ SM\\\\_WITH\\\\_BREAK Optional, inserts the string [BREAK] between sentences.\\n+ SM\\\\_WITH\\\\_ENCODE Optional, converts HTML entities to their applicable chars.\\n+ SM\\\\_IGNORE\\\\_LENGTH Optional, returns summary regardless of quality or length.\\n+ SM\\\\_QUOTE\\\\_AVOID Optional, sentences with quotations will be excluded.\\n+ SM\\\\_QUESTION\\\\_AVOID Optional, sentences with question will be excluded.\\n+ SM\\\\_EXCLAMATION\\\\_AVOID Optional, sentences with exclamation marks will be excluded.\\n\\nHere are the possible indexes of the array returned in a JSON array.\\n \\n+ sm\\\\_api\\\\_message Contains notices, warnings, and error messages.\\n+ sm\\\\_api\\\\_character_count Contains the amount of characters returned.\\n+ sm\\\\_api\\\\_title Contains the title when available.\\n+ sm\\\\_api\\\\_content Contains the summary.\\n+ sm\\\\_api\\\\_keyword\\\\_array Contains top ranked keywords in descending order.\\n+ sm\\\\_api\\\\_error Contains error code.\\n\\n```php\\n$text = \\\"Your long text goes here...\\\";\\n\\n$ch = curl_init(\\\"http://api.smmry.com/&SM_API_KEY=X\\\");\\ncurl_setopt($ch, CURLOPT_HTTPHEADER, array(\\\"Expect:\\\")); // See Note\\ncurl_setopt($ch, CURLOPT_POST, true); \\ncurl_setopt($ch, CURLOPT_POSTFIELDS, \\\"sm_api_input=\\\".$text);\\ncurl_setopt($ch, CURLOPT_FOLLOWLOCATION, true);\\ncurl_setopt($ch, CURLOPT_RETURNTRANSFER, true);\\ncurl_setopt($ch, CURLOPT_CONNECTTIMEOUT, 20);\\ncurl_setopt($ch, CURLOPT_TIMEOUT, 20);\\n$return = json_decode(curl_exec($ch), true);\\ncurl_close($ch);\\n```\\n\"\n },\n {\n \"alpha_fraction\": 0.807692289352417,\n \"alphanum_fraction\": 0.807692289352417,\n \"avg_line_length\": 25,\n \"blob_id\": \"b1a02d58dbfbb8dd7827a68cbb032453f77f2b6a\",\n \"content_id\": \"50ba5f571383f5956abdba89e9ecb97e4b419888\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 52,\n \"license_type\": \"no_license\",\n \"max_line_length\": 31,\n \"num_lines\": 2,\n \"path\": \"/api/__init__.py\",\n \"repo_name\": \"bryand1/textsummarization\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"from . import smmry\\nfrom . import textsummarization\\n\"\n },\n {\n \"alpha_fraction\": 0.3921568691730499,\n \"alphanum_fraction\": 0.6764705777168274,\n \"avg_line_length\": 13.714285850524902,\n \"blob_id\": \"0eeac963ac580cddadaf7c12c9dc8d4c6475f8cf\",\n \"content_id\": \"38b3f0049daf19d739d0b40d9e814d5cc6a4a6c2\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Text\",\n \"length_bytes\": 102,\n \"license_type\": \"no_license\",\n \"max_line_length\": 18,\n \"num_lines\": 7,\n \"path\": \"/requirements.txt\",\n \"repo_name\": \"bryand1/textsummarization\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"attrs==17.4.0\\ncertifi==2017.4.17\\nchardet==3.0.3\\nidna==2.5\\nrequests==2.17.3\\nsix==1.11.0\\nurllib3==1.21.1\"\n }\n]"},"num_files":{"kind":"number","value":8,"string":"8"}}},{"rowIdx":2980076,"cells":{"repo_name":{"kind":"string","value":"kiradegraw/testRemoteRepo"},"repo_url":{"kind":"string","value":"https://github.com/kiradegraw/testRemoteRepo"},"snapshot_id":{"kind":"string","value":"037c9f7e09ba2fceab4ff401b7142bbd44ff2f5a"},"revision_id":{"kind":"string","value":"5be45d9a6e2db3487a1e4dffa660dcccd21868f5"},"directory_id":{"kind":"string","value":"d7f1a90afdf2b85ab9566e21776a06088c8b27ba"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2023-07-27T23:31:12.013112","string":"2023-07-27T23:31:12.013112"},"revision_date":{"kind":"timestamp","value":"2021-09-07T23:29:56","string":"2021-09-07T23:29:56"},"committer_date":{"kind":"timestamp","value":"2021-09-07T23:29:56","string":"2021-09-07T23:29:56"},"github_id":{"kind":"number","value":404149810,"string":"404,149,810"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.5218855142593384,"alphanum_fraction":0.5412458181381226,"avg_line_length":18.393442153930664,"blob_id":"19293bd2a7deaabd65409900dc3a5c2ec7159123","content_id":"be30b54a297e5221a3ec5b7b15cd00363a3dbfac","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":1188,"license_type":"no_license","max_line_length":78,"num_lines":61,"path":"/newtons_method.py","repo_name":"kiradegraw/testRemoteRepo","src_encoding":"UTF-8","text":"#! /usr/bin/env python3\n\nimport sys\n\nfrom typing import (Callable)\nimport typing\nfrom fractions import (Fraction)\n\n\nEPSILON = 10e-6\nMAX_ITERATIONS = 100\n\ndef newtons_method(f, df, x_n, eps=EPSILON):\n\n n = 0\n\n next_x_n = x_n + 1000 * eps\n while abs(x_n - next_x_n) > eps:\n x_n = next_x_n\n next_x_n = x_n - (f(x_n) / df(x_n))\n\n if n >= MAX_ITERATIONS:\n break\n\n n += 1\n\n return x_n\n\n\ndef main():\n try:\n initial_guess = float(sys.argv[1])\n\n except IndexError as error:\n print(\"Usage: {0} initial_guess\".format(*sys.argv))\n sys.exit(1)\n\n except ValueError as error:\n print(\"ERROR {0} is not a valid number\".format(*sys.argv))\n print(\" \" + str(error))\n sys.exit(2)\n\n # Function (f) and its derivative (dx)\n def f(x):\n return (x ** 2) - 1\n\n def df(x):\n return 2 * x\n\n try:\n solution_newton = newtons_method(f, df, initial_guess)\n fx_newton = f(solution_newton)\n\n print(\"x = {:.4f} | f(x) = {:.4f}\".format(solution_newton, fx_newton))\n\n except ZeroDivisionError as error:\n print(str(error))\n\n\nif __name__ == \"__main__\":\n main()\n\n\n\n\n\n"}],"string":"[\n {\n \"alpha_fraction\": 0.5218855142593384,\n \"alphanum_fraction\": 0.5412458181381226,\n \"avg_line_length\": 18.393442153930664,\n \"blob_id\": \"19293bd2a7deaabd65409900dc3a5c2ec7159123\",\n \"content_id\": \"be30b54a297e5221a3ec5b7b15cd00363a3dbfac\",\n \"detected_licenses\": [],\n \"is_generated\": false,\n \"is_vendor\": false,\n \"language\": \"Python\",\n \"length_bytes\": 1188,\n \"license_type\": \"no_license\",\n \"max_line_length\": 78,\n \"num_lines\": 61,\n \"path\": \"/newtons_method.py\",\n \"repo_name\": \"kiradegraw/testRemoteRepo\",\n \"src_encoding\": \"UTF-8\",\n \"text\": \"#! /usr/bin/env python3\\n\\nimport sys\\n\\nfrom typing import (Callable)\\nimport typing\\nfrom fractions import (Fraction)\\n\\n\\nEPSILON = 10e-6\\nMAX_ITERATIONS = 100\\n\\ndef newtons_method(f, df, x_n, eps=EPSILON):\\n\\n n = 0\\n\\n next_x_n = x_n + 1000 * eps\\n while abs(x_n - next_x_n) > eps:\\n x_n = next_x_n\\n next_x_n = x_n - (f(x_n) / df(x_n))\\n\\n if n >= MAX_ITERATIONS:\\n break\\n\\n n += 1\\n\\n return x_n\\n\\n\\ndef main():\\n try:\\n initial_guess = float(sys.argv[1])\\n\\n except IndexError as error:\\n print(\\\"Usage: {0} initial_guess\\\".format(*sys.argv))\\n sys.exit(1)\\n\\n except ValueError as error:\\n print(\\\"ERROR {0} is not a valid number\\\".format(*sys.argv))\\n print(\\\" \\\" + str(error))\\n sys.exit(2)\\n\\n # Function (f) and its derivative (dx)\\n def f(x):\\n return (x ** 2) - 1\\n\\n def df(x):\\n return 2 * x\\n\\n try:\\n solution_newton = newtons_method(f, df, initial_guess)\\n fx_newton = f(solution_newton)\\n\\n print(\\\"x = {:.4f} | f(x) = {:.4f}\\\".format(solution_newton, fx_newton))\\n\\n except ZeroDivisionError as error:\\n print(str(error))\\n\\n\\nif __name__ == \\\"__main__\\\":\\n main()\\n\\n\\n\\n\\n\\n\"\n }\n]"},"num_files":{"kind":"number","value":1,"string":"1"}}},{"rowIdx":2980077,"cells":{"repo_name":{"kind":"string","value":"CaViJun/pregnancy"},"repo_url":{"kind":"string","value":"https://github.com/CaViJun/pregnancy"},"snapshot_id":{"kind":"string","value":"051b45973528edcc9275114152d61244333f647d"},"revision_id":{"kind":"string","value":"72a872093c585cb0495a583d31638d4b0a068dcf"},"directory_id":{"kind":"string","value":"6202827680c46e2f7561b2cebb8b414b6433b541"},"branch_name":{"kind":"string","value":"refs/heads/master"},"visit_date":{"kind":"timestamp","value":"2020-05-16T23:41:24.728922","string":"2020-05-16T23:41:24.728922"},"revision_date":{"kind":"timestamp","value":"2015-04-15T04:57:05","string":"2015-04-15T04:57:05"},"committer_date":{"kind":"timestamp","value":"2015-04-15T04:57:05","string":"2015-04-15T04:57:05"},"github_id":{"kind":"number","value":28946347,"string":"28,946,347"},"star_events_count":{"kind":"number","value":0,"string":"0"},"fork_events_count":{"kind":"number","value":0,"string":"0"},"gha_license_id":{"kind":"null"},"gha_created_at":{"kind":"null"},"gha_updated_at":{"kind":"null"},"gha_pushed_at":{"kind":"null"},"gha_language":{"kind":"null"},"files":{"kind":"list like","value":[{"alpha_fraction":0.7903929948806763,"alphanum_fraction":0.7903929948806763,"avg_line_length":75,"blob_id":"e6581b50f6babf5ac9fae737dd8da528280d0128","content_id":"9b726f1c3f61c1f3ac32c3a51315a527d9d4d781","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Markdown","length_bytes":229,"license_type":"no_license","max_line_length":137,"num_lines":3,"path":"/README.md","repo_name":"CaViJun/pregnancy","src_encoding":"UTF-8","text":"# pregnancy\nA little web app to show the pregnancy procedure. Set the date pregnant,then it compute the birthday,also show how many days,weeks passed\nand left. In fact it appears in two ways:Python web app, Python command line. \n"},{"alpha_fraction":0.5689922571182251,"alphanum_fraction":0.5937984585762024,"avg_line_length":22.88888931274414,"blob_id":"a098c5085548e76e7954fd05fe6a0a353fa4931d","content_id":"6b1415da564839f63be851183e2cad6f21009d22","detected_licenses":[],"is_generated":false,"is_vendor":false,"language":"Python","length_bytes":645,"license_type":"no_license","max_line_length":148,"num_lines":27,"path":"/wsgi.py","repo_name":"CaViJun/pregnancy","src_encoding":"UTF-8","text":"# -*- coding: utf-8 -*-\nfrom bottle import static_file\n\ndef application(environ,start_response):\n if environ['PATH_INFO'] == '/favicon.ico':\n return static_file('favicon.ico',root='./')\n \n data = '''\n\n\n\n\n\n\n