100:\n start_time_est = row[ts_index]\n break\n row_list.reverse()\n for row in row_list:\n if row[views_index] > 100:\n end_time_est = row[ts_index]\n break\n \n \n \"\"\"\n BUILD THE VISUALIZATION FOR THE TEST VIEWS OF THIS CAMAPAIGN\n ============================================================ \n \"\"\"\n \n \"\"\" Read the test name \"\"\"\n ttl = DL.TestTableLoader()\n row = ttl.get_test_row(utm_campaign)\n test_name = ttl.get_test_field(row ,'test_name')\n \n \"\"\" Regenerate the data using the estimated start and end times \"\"\"\n ir = DR.IntervalReporting(was_run=False, use_labels=False, font_size=20, plot_type='line', query_type='campaign', file_path=projSet.__web_home__ + 'campaigns/static/images/')\n ir.run(start_time_est, end_time_est, interval, 'views', utm_campaign, {}, one_step=one_step)\n \n \"\"\" Determine the type of test (if not overridden) and retrieve the artifacts \"\"\"\n test_type, artifact_name_list = FDH.get_test_type(utm_campaign, start_time, end_time, DL.CampaignReportingLoader(query_type=''), test_type_var)\n \n return render_to_response('campaigns/show_campaigns.html', {'utm_campaign' : utm_campaign, 'test_name' : test_name, 'start_time' : start_time_est, 'end_time' : end_time_est, 'one_step' : one_step, \\\n 'artifacts' : artifact_name_list, 'test_type' : test_type, 'err_msg' : err_msg}, context_instance=RequestContext(request)) \n\n except Exception as inst:\n \n logging.error('Failed to correctly produce campaign diagnostics.')\n logging.error(type(inst))\n logging.error(inst.args)\n logging.error(inst)\n \n \"\"\" Return to the index page with an error \"\"\"\n err_msg = 'There is insufficient data to analyze this campaign: %s. Check to see if the <a href=\"/LML/\">impressions have been loaded</a>. <br><br>ERROR:<br><br>%s' % (utm_campaign, inst.__str__())\n \n return index(request, kwargs={'err_msg' : err_msg})","def get_queryset(self, request):\n qs = super().get_queryset(request)\n qs = qs.annotate(\n libraryrelease__count=models.Count(\n 'release', distinct=True,\n filter=models.Q(release__cd_identifier__isnull=False)))\n qs = qs.annotate(\n commercialrelease__count=models.Count(\n 'release', distinct=True,\n filter=models.Q(release__cd_identifier__isnull=True)))\n qs = qs.annotate(models.Count('recording', distinct=True))\n return qs","def summary(request):\n data = {'Yes':[], 'No':[], 'labels':{}}\n reasons = zip(list(range(RestartReason.objects.count())), \n RestartReason.objects.order_by('reason_id'))\n for i, reason in reasons:\n iv_attempts = __iv_attempts(reason.reason_id)\n data['Yes'].append(iv_attempts[True])\n data['No'].append(iv_attempts[False])\n data['labels'][i] = reason.reason_id\n return HttpResponse(simplejson.dumps(data), \n mimetype='application/javascript')","def chart(request):\n assert isinstance(request, HttpRequest)\n filename = 'ppg_RawDataSheet13.mat'\n subtitle = 'VerityDB/' + filename\n return render(\n request,\n 'research/chart.html',\n {\n 'title':'Chart',\n 'message':'Highcharts Based',\n 'year':datetime.now().year,\n #'data': content['val'][0:11]\n 'temp': models.load_data(),\n 'test': models.load_data_filename(filename),\n 'subtitle_text': subtitle,\n }\n )","def test_execute_tags_type_queries(self):\n with tenant_context(self.tenant):\n guid = AzureCostEntryLineItemDailySummary.objects.values(\"subscription_guid\")[0].get(\"subscription_guid\")\n test_cases = [\n {\"value\": \"-1\", \"unit\": \"month\", \"resolution\": \"monthly\", \"subscription_guid\": guid},\n {\"value\": \"-2\", \"unit\": \"month\", \"resolution\": \"monthly\", \"subscription_guid\": guid},\n {\"value\": \"-10\", \"unit\": \"day\", \"resolution\": \"daily\", \"subscription_guid\": guid},\n {\"value\": \"-30\", \"unit\": \"day\", \"resolution\": \"daily\", \"subscription_guid\": guid},\n ]\n\n for case in test_cases:\n url = reverse(\"azure-tags\")\n client = APIClient()\n params = {\n \"filter[resolution]\": case.get(\"resolution\"),\n \"filter[time_scope_value]\": case.get(\"value\"),\n \"filter[time_scope_units]\": case.get(\"unit\"),\n \"key_only\": False,\n \"filter[subscription_guid]\": case.get(\"subscription_guid\"),\n }\n url = url + \"?\" + urlencode(params, quote_via=quote_plus)\n response = client.get(url, **self.headers)\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n data = response.json().get(\"data\")\n self.assertTrue(data)\n self.assertTrue(isinstance(data, list))\n for tag in data:\n self.assertTrue(isinstance(tag, dict))\n self.assertIn(\"key\", tag)\n self.assertIn(\"values\", tag)\n self.assertIsNotNone(tag.get(\"key\"))\n self.assertIn(tag.get(\"values\").__class__, [list, str])\n self.assertTrue(tag.get(\"values\"))","def get_queryset(self, request):\n qs = super().get_queryset(request)\n qs = qs.annotate(models.Count('tracks', distinct=True))\n qs = qs.annotate(models.Count('works', distinct=True))\n return qs","def data_set_detail(request, chromosome_num):\n # make sure we have features on that chromosome \n try:\n traits_admin = SNP.objects.filter(snpstatus__status='A', chromosome_num=chromosome_num)\n traits_user = SNP.objects.filter(snpstatus__status='U', chromosome_num=chromosome_num)\n\n except:\n return heads_up(request)\n\n context = {'traits_admin':traits_admin, 'traits_user':traits_user, 'chromosome_num':chromosome_num}\n return render(request, 'SNP_Feature_View/data_set_detail.html', context)","def listTopicRelevance(request):\n if request.method == 'GET':\n user = request.user;\n data = [];\n for topic in Topic.objects.all():\n row = {};\n\n topicSerializer = TopicNestedSerializer(topic)\n topicSerializer.Meta.depth = 1;\n #row['topic'] = topicSerializer.data;\n user_visits = topic.visits.filter(user=user)\n visitSerializer = VisitSerializer(user_visits, many=True)\n #visitSerializer.Meta.depth = 1;\n row['visit_count'] = len(user_visits);\n if row['visit_count'] > 0:\n row['last_visit'] = user_visits.order_by('-visit_date')[0].visit_date\n else:\n row['last_visit'] = topic.created_at\n\n neighbor_visits = Visit.objects.filter(user=user, topic__relates_to__topic_to=topic)\n\n row['neighbor_visits_count'] = len(neighbor_visits);\n if row['neighbor_visits_count'] > 0:\n row['last_neighbor_visit'] = neighbor_visits.order_by('-visit_date')[0].visit_date;\n else:\n row['last_neighbor_visit'] = topic.created_at\n\n row['post_count'] = len(topic.posts.filter(user=user))\n row['like_count'] = len(topic.posts.filter(votes__user=user))\n row['relevance_score'] = 5*row['neighbor_visits_count'] - (timezone.now()-row['last_neighbor_visit']).total_seconds()/3600\n row['recommendation'] = row['relevance_score'] + topic.hotness\n\n data.append(row)\n\n print(data)\n return Response(data)","def stat(request, researcher):\n try:\n records = Record.objects.filter(researcher=researcher)\n tokens = Pubtoken.objects.filter(belonging_record__in=records)\n\n stanfordner_tokenwise_total_hit = 0\n stanfordner_tokenwise_total_miss = 0\n crfsuite_tokenwise_total_hit = 0\n crfsuite_tokenwise_total_miss = 0\n\n # stanfordner_fn_tp = 0\n # stanfordner_fn_tn = 0\n # stanfordner_fn_fp = 0\n # stanfordner_fn_fn = 0\n\n # stanfordner_ln_tp = 0\n # stanfordner_ln_tn = 0\n # stanfordner_ln_fp = 0\n # stanfordner_ln_fn = 0\n\n # stanfordner_dl_tp = 0\n # stanfordner_dl_tn = 0\n # stanfordner_dl_fp = 0\n # stanfordner_dl_fn = 0\n\n # stanfordner_ti_tp = 0\n # stanfordner_ti_tn = 0\n # stanfordner_ti_fp = 0\n # stanfordner_ti_fn = 0\n\n # stanfordner_vn_tp = 0\n # stanfordner_vn_tn = 0\n # stanfordner_vn_fp = 0\n # stanfordner_vn_fn = 0\n\n # stanfordner_yr_tp = 0\n # stanfordner_yr_tn = 0\n # stanfordner_yr_fp = 0\n # stanfordner_yr_fn = 0\n\n # crfsuite_fn_tp = 0\n # crfsuite_fn_tn = 0\n # crfsuite_fn_fp = 0\n # crfsuite_fn_fn = 0\n\n # crfsuite_ln_tp = 0\n # crfsuite_ln_tn = 0\n # crfsuite_ln_fp = 0\n # crfsuite_ln_fn = 0\n\n # crfsuite_dl_tp = 0\n # crfsuite_dl_tn = 0\n # crfsuite_dl_fp = 0\n # crfsuite_dl_fn = 0\n\n # crfsuite_ti_tp = 0\n # crfsuite_ti_tn = 0\n # crfsuite_ti_fp = 0\n # crfsuite_ti_fn = 0\n\n # crfsuite_vn_tp = 0\n # crfsuite_vn_tn = 0\n # crfsuite_vn_fp = 0\n # crfsuite_vn_fn = 0\n\n # crfsuite_yr_tp = 0\n # crfsuite_yr_tn = 0\n # crfsuite_yr_fp = 0\n # crfsuite_yr_fn = 0\n \n\n for token in tokens:\n true_labels = token.true_label.all()\n stanfordner_label = token.predicted_label.filter(label_source=1)[0]\n crfsuite_label = token.predicted_label.filter(label_source=2)[0]\n if stanfordner_label in true_labels:\n stanfordner_tokenwise_total_hit += 1\n # if stanfordner_label.label == 'FN':\n # stanfordner_fn_tp += 1\n else:\n stanfordner_tokenwise_total_miss += 1\n\n if crfsuite_label in true_labels:\n crfsuite_tokenwise_total_hit += 1\n else:\n crfsuite_tokenwise_total_miss += 1\n\n print '%s:\\nstanfordner: %s\\ncrfsuite: %s' % (researcher, float(stanfordner_tokenwise_total_hit)/(stanfordner_tokenwise_total_hit+stanfordner_tokenwise_total_miss), float(crfsuite_tokenwise_total_hit)/(crfsuite_tokenwise_total_hit+crfsuite_tokenwise_total_miss))\n return HttpResponse('%s:\\nstanfordner: %s\\ncrfsuite: %s' % (researcher, float(stanfordner_tokenwise_total_hit)/(stanfordner_tokenwise_total_hit+stanfordner_tokenwise_total_miss), float(crfsuite_tokenwise_total_hit)/(crfsuite_tokenwise_total_hit+crfsuite_tokenwise_total_miss)))\n\n except Exception, e:\n raise e","def report(request):\n return render_to_response('application/report.html',\n {'search_suggestions': _search_suggestions(),\n },\n context_instance=RequestContext(request));","def report(request):\n\tuser_docs = Document.objects.filter(user=User.objects.get(username=request.user.username))\n\t# breakpoint()\n\tdaily_uploads = user_docs.filter(created_at__day=timezone.now().strftime(\"%d\"))\n\tmonthly_uploads = user_docs.filter(created_at__month=timezone.now().strftime(\"%m\"))\n\tyearly_uploads = user_docs.filter(created_at__year=timezone.now().strftime(\"%Y\"))\n\n\tdaily_count = daily_uploads.count()\n\tmonthly_count = monthly_uploads.count()\n\tyearly_count = yearly_uploads.count()\n\t# breakpoint()\n\n\tif 'doc_name' in request.GET:\n\t\tpdf_list = user_docs.filter(name__icontains=request.GET['doc_name'])\n\telif 'month' in request.GET:\n\t\tpdf_list = user_docs.filter(created_at__month=request.GET['month'])\n\t\t# breakpoint()\n\telif 'year' in request.GET:\n\t\tpdf_list = user_docs.filter(created_at__year=request.GET['year'])\n\telif 'from' in request.GET and 'to' in request.GET:\n\t\t# breakpoint()\n\t\tpdf_list = user_docs.filter(created_at__range=[request.GET['from'],request.GET['to']])\n\n\telse:\n\t\tpdf_list = user_docs\n\tcontext = {'daily_count': daily_count, 'monthly_count': monthly_count, 'yearly_count': yearly_count, 'pdf_list':pdf_list}\n\n\treturn render(request, 'document_manager/report.html', context)","def test_context_data_no_taggedtraits(self):\n TaggedTrait.objects.all().delete()\n response = self.client.get(self.get_url(self.study.pk))\n context = response.context\n self.assertIn('study', context)\n self.assertEqual(context['study'], self.study)\n self.assertIn('tag_counts', context)\n self.assertEqual(len(context['tag_counts']), 0)\n # The button linking to this view shouldn't be present because study.get_non_archived_traits_tagged_count is 0.\n self.assertNotContains(response, self.get_url(self.study.pk))","def view_graph(request, bullet_id):\n bullet = Bullet.objects.filter(id=bullet_id).prefetch_related('results__velocity')\n velocitylist = []\n chargelist = []\n moalist = []\n for b in bullet: \n for result in b.results.all():\n chargelist.append(result.charge)\n moalist.append(result.moa)\n total_avg = 0 \n for velocity in result.velocity.all():\n total_avg += velocity.velocity\n total_avg /= 3\n velocitylist.append(total_avg) \n \n context = {'bullet': bullet, 'velocitylist': velocitylist, 'chargelist':chargelist, 'moalist':moalist} \n return render(request, 'graph.html', context)","def getTMVAResults(self):\n return self.ResultsTMVA","def monthly_report(request, site, year, month):\n siteObj = get_object_or_404(Site, pk=site)\n yearly = YearlyStatistics.objects.filter(siteId=siteObj).filter(year=year)\n monthly = MonthlyStatistics.objects.filter(siteId=siteObj).filter(year=year).filter(month=month)\n daily = DailyStatistics.objects.filter(siteId=siteObj).filter(year=year).filter(month=month)\n dailyManual = RawManualData.objects.filter(siteId=siteObj).filter(year=year).filter(month=month)\n\n if siteObj and yearly and monthly and daily and (siteObj.isPublic and\n siteObj.owner.is_active or siteObj.owner == request.user):\n climate = {'temp': Climate().TEMP_DISTRIBUTION_LIMITS, 'wind': Climate().WIND_DIRECTION_LIMITS,\n 'rh': Climate().RH_DISTRIBUTION_LIMITS}\n datasetNum = []\n a = MonthlyReport(site, year, month, monthly, yearly, daily, dailyManual)\n\n significants = {}\n for code in Weather.WEATHER_CODE:\n key = code[1]\n value = monthly[0].significants.get(code[0], 0)\n significants[key] = value\n\n return render(request, 'climate/monthly_view.html', {'site': siteObj, 'num': datasetNum, 'year': yearly,\n 'month': month, 'report': a, 'climate': climate,\n 'significants': significants})\n else:\n return render(request, 'climate/main.html', {})","def sponsored_bar():\n articles = []\n articles.extend([c.child\n for c in Content.objects\n .filter(tags__text='Sponsored First')\n .annotate(recent=Max('issue__issue_date'))\n .order_by('-recent')[:1]])\n articles.extend([c.child\n for c in Content.objects\n .filter(tags__text='Sponsored Second')\n .annotate(recent=Max('issue__issue_date'))\n .order_by('-recent')[:1]])\n articles.extend([c.child\n for c in Content.objects\n .filter(tags__text='Sponsored Third')\n .annotate(recent=Max('issue__issue_date'))\n .order_by('-recent')[:1]])\n articles.extend([c.child\n for c in Content.objects\n .filter(tags__text='Sponsored Fourth')\n .annotate(recent=Max('issue__issue_date'))\n .order_by('-recent')[:1]])\n articles.extend([c.child\n for c in Content.objects\n .filter(tags__text='Sponsored Fifth')\n .annotate(recent=Max('issue__issue_date'))\n .order_by('-recent')[:1]])\n exclude = ['Sponsored First', 'Sponsored Second', 'Sponsored Third',\n 'Sponsored Fourth', 'Sponsored Fifth']\n articles.extend([c.child\n for c in Content.objects\n .filter(tags__text='Sponsored Article')\n .exclude(tags__text__in=exclude)\n .annotate(recent=Max('issue__issue_date'))\n .order_by('-recent')[:5 - len(articles)]])\n \"\"\"\n Enable this code (and disable the above) for sponsored articles returning\n by date (without the tag preferences)\n\n articles.extend([c.child\n for c in Content.objects\n .filter(tags__text='Sponsored Article')\n .annotate(recent=Max('issue__issue_date'))\n .order_by('-recent')[:5 - len(articles)]])\n \"\"\"\n\n return {'articles': articles}","def show(request, pk, ck):\n\n project_container = get_object_or_404(ProjectContainer, id=pk)\n coding = get_object_or_404(CodingProject, id=ck)\n\n user = get_user(request)\n coder = Person.objects.using('datatracker').get(id=coding.coder)\n if project_container.code_request is None:\n mentor = coder\n else:\n mentor = Person.objects.using('datatracker').get(id=project_container.code_request.mentor)\n\n # According to model areas and working groups should come from documents\n tags = []\n keys = []\n areas = []\n if project_container.docs:\n keys = filter(None, project_container.docs.split(';'))\n docs = list(DocAlias.objects.using('datatracker').filter(name__in=keys).values_list('name', 'document__group__name',\n 'document__group__parent__name'))\n for name, gname, gparentname in docs:\n if gparentname:\n if gparentname not in areas:\n areas.append(gparentname) # use acronym?\n else:\n areas.append(gname)\n tags += coding.tags.all()\n\n if not areas:\n areas = [constants.STRING_NONE]\n if not tags:\n tags = [constants.STRING_NONE]\n\n return render_page(request, constants.TEMPLATE_MATCHES_SHOW, {\n 'projectcontainer': project_container,\n 'coding': coding,\n 'areas': areas,\n 'tags': tags,\n 'docs': docs,\n 'coder': coder,\n 'mentor': mentor,\n 'owner': user,\n 'list_template': constants.TEMPLATE_MATCHES_LIST\n })","def allBroken(request, page=1):\n objects = im.Issue.objects.filter(resolved_state__isnull=True)\n args = utils.generatePageList(request, objects, page)\n args['issues'] = args['objects']\n issues_list = {'Issues on Unusable Machines':[]}\n for issue in args['issues']:\n iss_id = issue.item.item_id\n machine = mac.Item.objects.get(item_id=iss_id)\n\n if machine.unusable:\n issues_list['Issues on Unusable Machines'].append(issue)\n\n args['object_list'] = issues_list.items() \n args['no_results'] = args['page'].object_list.count() < 1\n return render_to_response(\"grouped_issue_list.html\", args,\n context_instance=RequestContext(request))","def run(self, date):\n\n out = self.nanoutput()\n\n beh = date.glm()\n expl = beh.explained()\n for cellgroup in expl:\n out['devexp_%s' % cellgroup] = expl[cellgroup]\n\n return out","def minority_aggregation_as_json(request):\n msa_target_lma_sum = 0\n msa_target_mma_sum = 0\n msa_target_hma_sum = 0\n\n msa_peer_lma_sum = 0\n msa_peer_mma_sum = 0\n msa_peer_hma_sum = 0\n\n\n msa_stats = {}\n\n lar_data = loan_originations_as_json(request)\n lender = get_object_or_404(Institution, pk=request.GET.get('lender'))\n metro = get_object_or_404(Geo, geo_type=Geo.METRO_TYPE, geoid=request.GET.get('metro'))\n peer_request = HttpRequest()\n peer_request.GET['lender'] = lender.institution_id\n peer_request.GET['metro']= metro.geoid\n peer_request.GET['peers'] = 'true'\n peer_lar_data = loan_originations_as_json(peer_request)\n\n msa_counties = Geo.objects.filter(geo_type=Geo.COUNTY_TYPE, cbsa=metro.cbsa, year=metro.year)\n county_stats = {}\n for county in msa_counties:\n county_tracts = Geo.objects.filter(geo_type=Geo.TRACT_TYPE, state=county.state, county=county.county, year=metro.year)\n minority_area_stats = get_minority_area_stats(lar_data, peer_lar_data, county_tracts)\n county_stats[county.geoid] = assemble_stats(*minority_area_stats)\n county_stats[county.geoid]['name'] = county.name\n #tally target msa counts\n msa_target_lma_sum += county_stats[county.geoid]['lma']\n msa_target_mma_sum += county_stats[county.geoid]['mma']\n msa_target_hma_sum += county_stats[county.geoid]['hma']\n #tally peer msa counts\n msa_peer_lma_sum += county_stats[county.geoid]['peer_lma']\n msa_peer_mma_sum += county_stats[county.geoid]['peer_mma']\n msa_peer_hma_sum += county_stats[county.geoid]['peer_hma']\n #msa\n msa_minority_area_stats = (msa_target_lma_sum, msa_target_mma_sum, msa_target_hma_sum, msa_peer_lma_sum, msa_peer_mma_sum, msa_peer_hma_sum)\n msa_stats = assemble_stats(*msa_minority_area_stats)\n \n return {\n 'msa': msa_stats,\n 'counties': county_stats,\n }","def get_summary(self, **kwargs):\n authorized_args = [\n 'begin', 'end', 'tenant_id', 'service', 'groupby', 'all_tenants']\n if kwargs.get('groupby', None):\n kwargs['groupby'] = ','.join(kwargs['groupby'])\n url = self.get_url('summary', kwargs, authorized_args)\n return self.api_client.get(url).json()","def metadata_reporter(self):\n logging.info('Creating summary report')\n header = '{}\\n'.format(','.join(self.headers))\n # Create a string to store all the results\n data = str()\n for sample in self.metadata:\n # Add the value of the appropriate attribute to the results string\n data += GenObject.returnattr(sample, 'name')\n # SampleName\n data += GenObject.returnattr(sample.run, 'SamplePlate')\n # Genus\n data += GenObject.returnattr(sample.general, 'closestrefseqgenus')\n # SamplePurity\n data += GenObject.returnattr(sample.confindr, 'num_contaminated_snvs')\n # N50\n n50 = GenObject.returnattr(sample.quast, 'N50',\n number=True)\n if n50 != '-,':\n data += n50\n else:\n data += '0,'\n # NumContigs\n data += GenObject.returnattr(sample.quast, 'num_contigs',\n number=True)\n # TotalLength\n data += GenObject.returnattr(sample.quast, 'Total_length',\n number=True)\n # MeanInsertSize\n data += GenObject.returnattr(sample.quast, 'mean_insert',\n number=True)\n # InsertSizeSTD\n data += GenObject.returnattr(sample.quast, 'std_insert',\n number=True)\n # AverageCoverageDepth\n data += GenObject.returnattr(sample.qualimap, 'MeanCoveragedata',\n number=True)\n # CoverageDepthSTD\n data += GenObject.returnattr(sample.qualimap, 'StdCoveragedata',\n number=True)\n # PercentGC\n data += GenObject.returnattr(sample.quast, 'GC',\n number=True)\n # MASH_ReferenceGenome\n data += GenObject.returnattr(sample.mash, 'closestrefseq')\n # MASH_NumMatchingHashes\n data += GenObject.returnattr(sample.mash, 'nummatches')\n # 16S_result\n data += GenObject.returnattr(sample.sixteens_full, 'sixteens_match')\n # 16S PercentID\n data += GenObject.returnattr(sample.sixteens_full, 'percent_id')\n # CoreGenesPresent\n data += GenObject.returnattr(sample.gdcs, 'coreresults')\n # rMLST_Result\n try:\n # If the number of matches to the closest reference profile is 53, return the profile number\n if sample.rmlst.matches == 53:\n if type(sample.rmlst.sequencetype) is list:\n rmlst_seq_type = ';'.join(sorted(sample.rmlst.sequencetype)).rstrip(';') + ','\n else:\n rmlst_seq_type = GenObject.returnattr(sample.rmlst, 'sequencetype')\n rmlst_seq_type = rmlst_seq_type if rmlst_seq_type != 'ND,' else 'new,'\n data += rmlst_seq_type\n else:\n # Otherwise the profile is set to new\n data += 'new,'\n except AttributeError:\n data += 'new,'\n # MLST_Result\n try:\n if sample.mlst.matches == 7:\n if type(sample.mlst.sequencetype) is list:\n mlst_seq_type = ';'.join(sorted(sample.mlst.sequencetype)).rstrip(';') + ','\n else:\n mlst_seq_type = GenObject.returnattr(sample.mlst, 'sequencetype')\n mlst_seq_type = mlst_seq_type if mlst_seq_type != 'ND,' else 'new,'\n data += mlst_seq_type\n else:\n data += 'new,'\n except AttributeError:\n data += 'new,'\n # MLST_gene_X_alleles\n try:\n # Create a set of all the genes present in the results (gene name split from allele)\n gene_set = {gene.split('_')[0] for gene in sample.mlst.combined_metadata_results}\n for gene in sorted(gene_set):\n allele_list = list()\n # Determine all the alleles that are present for each gene\n for allele in sample.mlst.combined_metadata_results:\n if gene in allele:\n allele_list.append(allele.replace(' ', '_'))\n # If there is more than one allele in the sample, add both to the string separated by a ';'\n if len(allele_list) > 1:\n data += '{},'.format(';'.join(allele_list))\n # Otherwise add the only allele\n else:\n data += allele_list[0] + ','\n # If there are fewer than seven matching alleles, add a ND for each missing result\n if len(gene_set) < 7:\n data += (7 - len(gene_set)) * 'ND,'\n except AttributeError:\n # data += '-,-,-,-,-,-,-,'\n data += 'ND,ND,ND,ND,ND,ND,ND,'\n # E_coli_Serotype\n try:\n # If no O-type was found, set the output to be O-untypeable\n if ';'.join(sample.ectyper.o_type) == '-':\n otype = 'O-untypeable'\n else:\n otype = sample.ectyper.o_type\n # Same as above for the H-type\n if ';'.join(sample.ectyper.h_type) == '-':\n htype = 'H-untypeable'\n\n else:\n htype = sample.ectyper.h_type\n serotype = '{otype}:{htype},'.format(otype=otype,\n htype=htype)\n # Add the serotype to the data string unless neither O-type not H-type were found; add ND instead\n data += serotype if serotype != 'O-untypeable:H-untypeable,' else 'ND,'\n except AttributeError:\n data += 'ND,'\n # SISTR_serovar_antigen\n data += GenObject.returnattr(sample.sistr, 'serovar_antigen').rstrip(';')\n # SISTR_serovar_cgMLST\n data += GenObject.returnattr(sample.sistr, 'serovar_cgmlst')\n # SISTR_serogroup\n data += GenObject.returnattr(sample.sistr, 'serogroup')\n # SISTR_h1\n data += GenObject.returnattr(sample.sistr, 'h1').rstrip(';')\n # SISTR_h2\n data += GenObject.returnattr(sample.sistr, 'h2').rstrip(';')\n # SISTR_serovar\n data += GenObject.returnattr(sample.sistr, 'serovar')\n # GeneSeekr_Profile\n try:\n if sample.genesippr.report_output:\n data += ';'.join(sample.genesippr.report_output) + ','\n else:\n data += 'ND,'\n except AttributeError:\n data += 'ND,'\n # Vtyper_Profile\n data += GenObject.returnattr(sample.verotoxin, 'verotoxin_subtypes_set')\n # AMR_Profile and resistant/sensitive status\n if sample.resfinder_assembled.pipelineresults:\n # Profile\n for resistance, resistance_set in sorted(sample.resfinder_assembled.pipelineresults.items()):\n data += '{res}({r_set});'.format(res=resistance.replace(',', ';'),\n r_set=';'.join(sorted(list(resistance_set))))\n data += ','\n # Resistant/Sensitive\n data += 'Resistant,'\n else:\n # Profile\n data += 'ND,'\n # Resistant/Sensitive\n data += 'Sensitive,'\n # Plasmid Result'\n if sample.mobrecon.pipelineresults:\n for plasmid, details in sorted(sample.mobrecon.pipelineresults.items()):\n data += '{plasmid}({details});'.format(plasmid=plasmid,\n details=details)\n data += ','\n else:\n data += 'ND,'\n # TotalPredictedGenes\n data += GenObject.returnattr(sample.prodigal, 'predictedgenestotal',\n number=True)\n # PredictedGenesOver3000bp\n data += GenObject.returnattr(sample.prodigal, 'predictedgenesover3000bp',\n number=True)\n # PredictedGenesOver1000bp\n data += GenObject.returnattr(sample.prodigal, 'predictedgenesover1000bp',\n number=True)\n # PredictedGenesOver500bp\n data += GenObject.returnattr(sample.prodigal, 'predictedgenesover500bp',\n number=True)\n # PredictedGenesUnder500bp\n data += GenObject.returnattr(sample.prodigal, 'predictedgenesunder500bp',\n number=True)\n # AssemblyDate\n data += datetime.now().strftime('%Y-%m-%d') + ','\n # PipelineVersion\n data += self.commit + ','\n # Name of the database used in the analyses\n data += os.path.split(self.reffilepath)[-1] + ','\n # Database download date\n data += self.download_date\n # Append a new line to the end of the results for this sample\n data += '\\n'\n # Replace any NA values with ND\n cleandata = data.replace('NA', 'ND')\n with open(os.path.join(self.reportpath, 'combinedMetadata.csv'), 'w') as metadatareport:\n metadatareport.write(header)\n metadatareport.write(cleandata)","def features(request):\n # Order features by amount of upvotes\n features_list = Feature.objects.all().order_by('-upvotes')\n \n # Pagination for features\n page = request.GET.get('page', 1)\n paginator = Paginator(features_list, 10)\n try:\n features = paginator.page(page)\n except PageNotAnInteger:\n features = paginator.page(1)\n except EmptyPage:\n features = paginator.page(paginator.num_pages)\n \n # Display graphs\n chart_total_feature = FeaturesTotalChart() \n chart_feature_daily = FeaturesDailyStatus()\n chart_feature_weekly = FeaturesWeeklyStatus()\n chart_feature_monthly = FeaturesMonthlyStatus()\n \n return render(request, \"features.html\", {\n \"features\": features,\n 'chart_total_feature': chart_total_feature,\n 'chart_feature_daily': chart_feature_daily,\n 'chart_feature_weekly': chart_feature_weekly,\n 'chart_feature_monthly': chart_feature_monthly\n })","def GatherPageData(self, mr):\n # TODO(jrobbins): Allow deep-linking into this page.\n canned_query_views = []\n if mr.project_id:\n with mr.profiler.Phase('getting canned queries'):\n canned_queries = self.services.features.GetCannedQueriesByProjectID(\n mr.cnxn, mr.project_id)\n canned_query_views = [\n savedqueries_helpers.SavedQueryView(sq, idx + 1, None, None)\n for idx, sq in enumerate(canned_queries)]\n\n saved_query_views = []\n if mr.auth.user_id and self.services.features:\n with mr.profiler.Phase('getting saved queries'):\n saved_queries = self.services.features.GetSavedQueriesByUserID(\n mr.cnxn, mr.me_user_id)\n saved_query_views = [\n savedqueries_helpers.SavedQueryView(sq, idx + 1, None, None)\n for idx, sq in enumerate(saved_queries)\n if (mr.project_id in sq.executes_in_project_ids or\n not mr.project_id)]\n\n return {\n 'issue_tab_mode': 'issueAdvSearch',\n 'page_perms': self.MakePagePerms(mr, None, permissions.CREATE_ISSUE),\n 'canned_queries': canned_query_views,\n 'saved_queries': saved_query_views,\n }","def tag_view(request, tag_id, error='', message=''):\n tag = Tag.objects.get(id=tag_id)\n return index(request=request, error=error, message=message, tag=tag, tag_id=tag_id)","def job_metrics(job_classad):\n counters = []\n\n try:\n user_name = job_classad.get(\"Owner\",\"unknown\")\n except:\n user_name = \"unknown\"\n try:\n groups = re.findall(r'(?:group_)?(\\w+)',job_classad.get(\"AccountingGroup\",\"group_unknown\"))\n exp_name = groups[0]\n subgroups = []\n if len(groups) > 1:\n # sometimes each user has an accounting group, we don't want those\n if groups[-1] == user_name:\n subgroups = groups[1:len(groups)-1]\n else:\n subgroups = groups[1:]\n except:\n exp_name = \"unknown\"\n subgroups = []\n\n if job_classad[\"JobUniverse\"] == 7:\n counters = [\".dag.totals\"]\n elif job_classad[\"JobStatus\"] == 1:\n counters = [\".idle.totals\"]\n if \"DESIRED_usage_model\" in job_classad:\n models = set(job_classad[\"DESIRED_usage_model\"].split(\",\"))\n if \"DESIRED_Sites\" in job_classad:\n sites = job_classad[\"DESIRED_Sites\"].split(\",\")\n for s in sites:\n counters.append(\".idle.sites.\"+s)\n #if \"Fermigrid\" not in sites:\n # models.discard(\"DEDICATED\")\n # models.discard(\"OPPORTUNISTIC\")\n models_sorted = list(models)\n if len(models_sorted) == 0:\n models_sorted = [\"impossible\"]\n else:\n models_sorted.sort()\n counters.append(\".idle.usage_models.\" + \"_\".join(models_sorted))\n else:\n counters.append(\".idle.usage_models.unknown\")\n elif job_classad[\"JobStatus\"] == 2:\n counters = [\".running.totals\"]\n if \"MATCH_GLIDEIN_Site\" in job_classad:\n site = job_classad[\"MATCH_GLIDEIN_Site\"]\n if site == \"FNAL\" and \"MATCH_EXP_JOBGLIDEIN_ResourceName\" in job_classad:\n site = job_classad[\"MATCH_EXP_JOBGLIDEIN_ResourceName\"]\n counters.append(\".running.sites.\" + site)\n else:\n counters.append(\".running.sites.unknown\")\n elif job_classad[\"JobStatus\"] == 5:\n counters = [\".held.totals\"]\n else:\n counters = [\".unknown.totals\"]\n\n metrics = []\n for counter in counters:\n metrics.append(\"totals\"+counter)\n metrics.append(\"experiments.\"+exp_name+\".totals\"+counter)\n metrics.append(\"experiments.\"+exp_name+\".users.\"+user_name+counter)\n if len(subgroups) > 0:\n metrics.append(\"experiments.\"+exp_name+\".subgroups.\"+\".\".join(subgroups)+'.'+counter)\n metrics.append(\"users.\"+user_name+counter)\n return metrics","def get_results_from_aggregation_sources(self, context):","def test_returns_all_studies_with_unreviewed_tagged_traits(self):\n tag = TagFactory.create()\n tagged_traits = []\n for study in self.studies:\n tmp = TaggedTraitFactory.create(trait__source_dataset__source_study_version__study=study, tag=tag)\n tagged_traits.append(tmp)\n get_data = {'q': ''}\n response = self.client.get(self.get_url(), get_data)\n pks = get_autocomplete_view_ids(response)\n self.assertEqual(sorted([study.pk for study in self.studies]), sorted(pks))","def getMetaByTags(tags):\n\n if type(tags) is str:\n tag = tags\n else:\n tag = tags[0]\n\n record = getRecordsByTags(tag)\n\n if record is not None:\n geodata_meta = parseMeta(record)\n return geodata_meta\n else:\n error = \"could not get record for tags from gnos\"\n print(error)\n return None","def getMetaByTags(tags):\n\n if type(tags) is str:\n tag = tags\n else:\n tag = tags[0]\n\n record = getRecordsByTags(tag)\n\n if record is not None:\n geodata_meta = parseMeta(record)\n return geodata_meta\n else:\n error = \"could not get record for tags from gnos\"\n print(error)\n return None","def assets_by_bom(request):\n # If this is a POST request then process the Form data\n asset_by_bom = None\n asset_by_range = None\n warehouse = None\n bom = None\n if request.method == 'POST':\n # Create a form instance and populate it\n # with data from the request(binding):\n form = AssetReportForm(request.POST)\n # Check if the form is valid:\n if form.is_valid():\n bom = form.cleaned_data['bom']\n st_date = form.cleaned_data['st_date']\n ed_date = form.cleaned_data['ed_date']\n if bom:\n bom_obj = BillOfMaterial.objects.get(bom_name=bom)\n asset_by_bom = list(\n Asset.objects.values(\n \"bom_name\",\n \"product_name__product_name\",\n )\n .filter(bom_name=bom_obj)\n .order_by('bom_name')\n .annotate(count=Count(\"bom_name\"))\n )\n else:\n asset_by_range = list(\n Asset.objects.filter(\n timestamp__gte=st_date,\n timestamp__lte=ed_date,\n ).values(\n \"bom_name__bom_name\",\n \"product_name__product_name\",\n )\n .order_by('bom_name')\n .annotate(count=Count(\"bom_name\"))\n )\n else:\n form = AssetReportForm()\n context = {\n 'form': form,\n 'bom': bom,\n 'asset_by_bom': asset_by_bom,\n 'asset_by_range': asset_by_range\n }\n # print(context)\n\n return render(request, 'inventory/assets_by_bom.html', context)","def get_queryset(self, request):\n qs = super().get_queryset(request)\n qs = qs.annotate(models.Count('work', distinct=True))\n qs = qs.annotate(models.Count('recording', distinct=True))\n return qs"],"string":"[\n \"def check_medtag_reports(request):\\n\\n json_resp = {}\\n json_resp['count'] = 0\\n medtag_arts = Report.objects.all().exclude(institute = 'PUBMED')\\n # for el in pubmed_arts:\\n # if el.id_report.startswith('PUBMED'):\\n json_resp['count'] = medtag_arts.count()\\n return JsonResponse(json_resp,safe=False)\",\n \"def usecases(request):\\n\\n context = {\\n\\n }\\n\\n return render(request, 'hydraviewer/usecases.html', context)\",\n \"def pubmed_missing_auto(request):\\n\\n usecases = UseCase.objects.all()\\n json_resp = {}\\n json_resp['annotated'] = 0\\n json_resp['tot'] = 0\\n json_resp['usecase'] = []\\n languages = ['English','english']\\n for el in usecases:\\n use = el.name\\n json_resp[use] = {}\\n batches = []\\n batches.append('all')\\n if use.lower() in ['colon','lung','uterine cervix']:\\n report = Report.objects.filter(name=el,language__in=languages,institute = 'PUBMED')\\n for el in report:\\n if el.batch not in batches:\\n batches.append(el.batch)\\n count_rep = report.count()\\n\\n if count_rep > 0:\\n json_resp['usecase'].append(str(use))\\n json_resp['tot'] = json_resp['tot'] + count_rep\\n with connection.cursor() as cursor:\\n cursor.execute(\\n \\\"SELECT COUNT(*) FROM report AS r INNER JOIN ground_truth_log_file AS g ON g.id_report = r.id_report AND r.language = g.language WHERE r.name = %s AND g.username = %s AND gt_type=%s AND institute = %s and r.language in %s;\\\",\\n [str(use),'Robot_user','labels','PUBMED',tuple(languages)]) # We could consider any of the gt_type\\n groundTruths = cursor.fetchone()[0]\\n\\n json_resp['annotated'] = json_resp['annotated'] + groundTruths\\n\\n for batch in batches:\\n\\n json_resp[use][batch] = {}\\n if batch == 'all' or batch is None:\\n report = Report.objects.filter(name=use,language__in=languages, institute='PUBMED')\\n count_rep = report.count()\\n json_resp[use][batch]['tot'] = count_rep\\n with connection.cursor() as cursor:\\n cursor.execute(\\n \\\"SELECT COUNT(*) FROM report AS r INNER JOIN ground_truth_log_file AS g ON g.id_report = r.id_report AND r.language = g.language WHERE r.name = %s AND g.username = %s AND gt_type=%s AND institute = %s and r.language in %s;\\\",\\n [str(use),'Robot_user','labels','PUBMED',tuple(languages)]) # We could consider any of the gt_type\\n groundTruths = cursor.fetchone()[0]\\n\\n if groundTruths is None:\\n json_resp[use][batch]['annotated'] = 0\\n else:\\n json_resp[use][batch]['annotated'] = groundTruths\\n else:\\n report = Report.objects.filter(name=use,language__in=languages, institute='PUBMED',batch = batch)\\n count_rep = report.count()\\n json_resp[use][batch]['tot'] = count_rep\\n with connection.cursor() as cursor:\\n cursor.execute(\\n \\\"SELECT COUNT(*) FROM report AS r INNER JOIN ground_truth_log_file AS g ON g.id_report = r.id_report AND r.language = g.language WHERE r.name = %s AND g.username = %s AND gt_type=%s AND institute = %s and batch = %s and r.language in %s;\\\",\\n [str(use), 'Robot_user', 'labels', 'PUBMED',batch,tuple(languages)]) # We could consider any of the gt_type\\n groundTruths = cursor.fetchone()[0]\\n\\n if groundTruths is None:\\n json_resp[use][batch]['annotated'] = 0\\n else:\\n json_resp[use][batch]['annotated'] = groundTruths\\n # print('risposta',json_resp)\\n return JsonResponse(json_resp)\",\n \"def summary(request, tag=''):\\n if tag:\\n if tag.startswith(\\\"@\\\"):\\n target = Target.objects.get(id=tag[1:])\\n summaries_by_value, consfield_summaries = target.getSummaries()\\n elif tag.startswith(\\\"~\\\"):\\n # TODO: is there a sensible analogous summary for users,\\n # or does it look completely different? \\n pass\\n else:\\n # TODO: other log types\\n pass\\n\\n # replace fieldnames in tag_groups with fieldsummaries in grouped_summaries\\n grouped_summaries = [ ( gtuple[0], [ summaries_by_value.pop(t) for t in gtuple[1] if t in summaries_by_value])\\n for gtuple in tag_groups ]\\n # add misc consensus fields\\n grouped_summaries.append(('other consensus fields',\\n [ summaries_by_value.pop(k) for k in summaries_by_value.keys()\\n if summaries_by_value[k].is_consensus and summaries_by_value[k].fieldname ]))\\n # add misc consensus labels\\n grouped_summaries.append(('consensus labels',\\n [ summaries_by_value.pop(k) for k in summaries_by_value.keys()\\n if summaries_by_value[k].is_consensus]))\\n grouped_summaries[-1][-1].sort(lambda x,y:cmp(x.count,y.count))\\n # add misc adhoc fields\\n grouped_summaries.append(('adhoc fields',\\n [ summaries_by_value.pop(k) for k in summaries_by_value.keys()\\n if summaries_by_value[k].fieldname ]))\\n grouped_summaries[-1][-1].sort(lambda x,y:cmp(x.count,y.count))\\n # add misc adhoc labels\\n grouped_summaries.append(('adhoc labels',\\n [ summaries_by_value.pop(k) for k in summaries_by_value.keys()]))\\n grouped_summaries[-1][-1].sort(lambda x,y:cmp(x.count,y.count))\\n \\n return render_to_response('summary.html',{'grouped_summaries':grouped_summaries, 'consfield_summaries':consfield_summaries, 'tag':tag})\",\n \"def get_stats_array_per_usecase(request):\\n\\n mode = request.GET.get('mode',None)\\n usern = request.GET.get('member',request.session['username'])\\n username = User.objects.get(username=usern, ns_id=mode)\\n language = request.GET.get('language',request.session['language'])\\n institute = request.GET.get('institute',request.session['institute'])\\n batch = request.GET.get('batch',request.session['batch'])\\n json_dict = {}\\n js = {}\\n js['original'] = {}\\n js['percent'] = {}\\n json_dict['medtag'] = get_array_per_usecase(username,mode,language,institute,batch)\\n json_dict['pubmed'] = get_array_per_usecase_PUBMED(username,mode,language,institute,batch)\\n\\n\\n # print(json_dict)\\n return JsonResponse(json_dict)\",\n \"def pubmed_reports(request):\\n\\n json_resp = {}\\n json_resp['usecase'] = []\\n reps = Report.objects.all()\\n for r in reps:\\n\\n if r.id_report.startswith('PUBMED_') and not str(r.name_id) in json_resp['usecase']:\\n json_resp['usecase'].append(str(r.name_id))\\n return JsonResponse(json_resp)\",\n \"def generate_report():\\n\\n # Fetch the top 3 most viewed articles and number of views and print them\\n articles_query = get_articles_query()\\n popular_articles = execute_query(articles_query)\\n print_top_articles(popular_articles)\\n\\n # Fetch the most popular authors and print them\\n authors_query = get_authors_query()\\n popular_authors = execute_query(authors_query)\\n print_authors(popular_authors)\\n\\n # Print the days when there were more than 1% errors in HTTP requests\\n errors_query = get_errorData_query()\\n error_data = execute_query(errors_query)\\n print_error_data(error_data)\",\n \"def index(request, error='', message='', tag=None, tag_id=None, comment_id=None, event_id=None, metric_id=None):\\n if tag:\\n metrics_to_plot = Metric.objects.filter(tags=tag)\\n else:\\n metrics_to_plot = Metric.objects.filter(source='appleweekly')\\n\\n traffic_to_plot = []\\n for metric in metrics_to_plot:\\n metric_traffic = list(Traffic.objects.filter(metric=metric))\\n if metric_traffic:\\n traffic_to_plot.append(metric_traffic)\\n\\n chart = False\\n\\n for m in metrics_to_plot:\\n if m.source == 'appleweekly':\\n try:\\n #Import Apple weekly summary metrics, but just for one-time use - don't save in db.\\n append = traffic_to_plot.append #Avoid re-calling the .append function in the middle of all those loops.\\n for w in AppleWeeklySummary.merged.all():\\n for field in AppleWeeklySummary._meta._fields(): #This grabs a list of field objects from the model specified as part of the stats app\\n if field.verbose_name == m.appleweeklyfield: #Verbose name is specified as (\\\"verbose_name\\\") in stats/models/apple_summary.py\\n append(Traffic(date=w.week_beginning, count=w.__dict__[field.name], metric=m))\\n except:\\n debug.onscreen('WARNING: Can\\\\'t find any Apple summary data. Have you imported it?')\\n elif m.source == 'itu-collection-chart':\\n try:\\n #Add the first chartrecord of the day to traffic_to_plot\\n dates = []\\n chartrecords = ItuCollectionChartScan.objects.filter(itucollection=m.itucollection).order_by('date')\\n for chartrecord in chartrecords:\\n if chartrecord.date.date() not in dates:\\n dates.append(chartrecord.date.date())\\n for date in dates:\\n chartrecords_day = []\\n for chartrecord in chartrecords:\\n if chartrecord.date.date() == date:\\n chartrecords_day.append(chartrecord)\\n traffic_to_plot.append(\\n Traffic(date=date, count=(-1 * chartrecords_day[0].position), metric=m))\\n chart = True\\n except:\\n error += 'Failed to process traffic for an itu-collection-chart.'\\n elif m.source == 'itu-item-chart':\\n try:\\n #Add the first chartrecord of the day to traffic_to_plot\\n dates = []\\n chartrecords = ItuItemChartScan.objects.filter(ituitem=m.ituitem).order_by('date')\\n for chartrecord in chartrecords:\\n if chartrecord.date.date() not in dates:\\n dates.append(chartrecord.date.date())\\n for date in dates:\\n chartrecords_day = []\\n for chartrecord in chartrecords:\\n if chartrecord.date.date() == date:\\n chartrecords_day.append(chartrecord)\\n traffic_to_plot.append(\\n Traffic(date=date, count=(-1 * chartrecords_day[0].position), metric=m))\\n chart = True\\n except:\\n error += 'Failed to process traffic for an itu-item-chart.'\\n elif m.source =='itu-#tc':\\n try:\\n dates_processed = []\\n for tc_scan in ItuScanLog.objects.filter(mode=2).order_by('time'):\\n date = tc_scan.time.date()\\n if date not in dates_processed:\\n dates_processed.append(date)\\n tc_count = ItuCollectionChartScan.objects.filter(scanlog=tc_scan,\\n itucollection__institution=m.ituinstitution).count()\\n traffic_to_plot.append(Traffic(date=date, count=tc_count, metric=m))\\n except:\\n error += 'Failed to process traffic for the # of collections in the top 200.'\\n elif m.source =='itu-#ti':\\n try:\\n dates_processed = []\\n for ti_scan in ItuScanLog.objects.filter(mode=3).order_by('time'):\\n date = ti_scan.time.date()\\n if date not in dates_processed:\\n dates_processed.append(date)\\n ti_count = ItuItemChartScan.objects.filter(scanlog=ti_scan,\\n ituitem__institution=m.ituinstitution).count()\\n traffic_to_plot.append(Traffic(date=date, count=ti_count, metric=m))\\n except:\\n error += 'Failed to process traffic for the # of collections in the top 200.'\\n\\n #NOTE: We do not need to handle the temporal range of comments and events since this is done automatically by Timeplot.\\n\\n from_itunes_u = Category.objects.get(description='From iTunes U')\\n #Create comments in the feedback database if they don't already exist.\\n for itu_comment in ItuComment.objects.filter(ituinstitution__name = 'Oxford University'):\\n comment = Comment(\\n date=itu_comment.date,\\n time=datetime.time(0,0,0),\\n source=itu_comment.itucollectionhistorical.name + ' - comment by ' + itu_comment.source,\\n detail=itu_comment.detail,\\n user_email='scan_itunes@manage.py',\\n moderated=True,\\n category=from_itunes_u,\\n itu_source=itu_comment\\n )\\n if Comment.objects.filter(detail=itu_comment.detail).count() > 0:\\n pass\\n else:\\n comment.save()\\n if tag:\\n comments_to_plot = Comment.objects.filter(moderated=True,tags=tag)\\n events_to_plot = Event.objects.filter(moderated=True,tags=tag)\\n else:\\n comments_to_plot = Comment.objects.filter(moderated=True)\\n events_to_plot = Event.objects.filter(moderated=True)\\n\\n categories_to_plot = []\\n for category in comments_to_plot.values_list('category').distinct():\\n categories_to_plot.append(Category.objects.get(id=category[0]))\\n for category in events_to_plot.values_list('category').distinct():\\n if Category.objects.get(id=category[0]) not in categories_to_plot:\\n categories_to_plot.append(Category.objects.get(id=category[0]))\\n\\n return render_to_response('feedback/index.html', {\\n 'metrics_to_plot': metrics_to_plot,\\n 'metric_textfiles': create_metric_textfiles(traffic_to_plot,metrics_to_plot),\\n 'categories_to_plot': categories_to_plot,\\n 'comments_to_plot': comments_to_plot,\\n 'events': events_to_plot,\\n 'chart': chart,\\n 'error': error,\\n 'message': message,\\n 'tag': tag, 'tag_id': tag_id, 'tags': Tag.objects.all(), 'comment_id': comment_id, 'event_id': event_id, 'metric_id': metric_id,\\n }, context_instance=RequestContext(request))\",\n \"def odag():\\r\\n document.add_heading('OnDemand Application Generation (ODAG)', 1)\\r\\n\\r\\n odag_metrics = ['enabled',\\r\\n 'maxConcurrentRequests',\\r\\n 'logLevel']\\r\\n odag = get_qlik_sense.get_odag()\\r\\n num_of_metric = len(odag_metrics)\\r\\n table = document.add_table(rows=num_of_metric+1, cols=2)\\r\\n table.style = 'Grid Table 1 Light Accent 1'\\r\\n row = table.rows[0]\\r\\n row.cells[0].text = 'details'\\r\\n\\r\\n for metric in range(len(odag_metrics)):\\r\\n row = table.rows[metric+1]\\r\\n row.cells[0].text = str(odag_metrics[metric])\\r\\n row.cells[1].text = str(odag[0][metric])\\r\\n document.add_page_break()\",\n \"def analysis_view(request):\\n return render(request, \\\"tracker/analysis.html\\\")\",\n \"def summary_list(request):\\r\\n qs = Summary.objects.all()\\r\\n context = {'summary_records': qs}\\r\\n return render(request, 'weather/summary_list.html', context)\",\n \"def views(request):\\n tag = Tag.objects.filter(name=\\\"Global\\\")\\n gauges = Gauge.objects.filter(tags=tag)\\n return render_to_response('dashboard/views.js',{'gauges': gauges} )\",\n \"def training_report_view(request, application_slug):\\n return training_report(request, application_slug, attach=False)\",\n \"def get_uses_missing_exa(request):\\n\\n use_to_ret = {}\\n use_to_ret['labels_present'] = []\\n use_to_ret['concepts_present'] = []\\n use_to_ret['labels_missing'] = []\\n use_to_ret['concepts_missing'] = []\\n uses = ['colon','uterine cervix','lung']\\n for el in uses:\\n usecase = UseCase.objects.get(name=el)\\n presence = True\\n if Report.objects.filter(name = usecase).count() > 0:\\n if not AnnotationLabel.objects.filter(name = usecase, annotation_mode = 'Manual and Automatic').exists():\\n use_to_ret['labels_missing'].append(el)\\n else:\\n use_to_ret['labels_present'].append(el)\\n\\n cursor = connection.cursor()\\n cursor.execute(\\\"SELECT c.annotation_mode FROM concept AS c INNER JOIN concept_has_uc AS hc ON c.concept_url = hc.concept_url WHERE hc.name = %s\\\",[str(el)])\\n ans = cursor.fetchall()\\n for concept in ans:\\n if concept[0] != 'Manual and Automatic':\\n presence = False\\n break\\n if len(ans) > 0:\\n if presence == False:\\n use_to_ret['concepts_missing'].append(el)\\n else:\\n use_to_ret['concepts_present'].append(el)\\n else:\\n use_to_ret['concepts_missing'].append(el)\\n\\n return JsonResponse(use_to_ret)\",\n \"def performance_analysis(request):\\n context = {}\\n\\n return render(request, 'classroom_main/performance_analysis.html', context)\",\n \"def report_missing_auto(request):\\n\\n usecases = UseCase.objects.all()\\n json_resp = {}\\n languages = ['english', 'English']\\n for el in usecases:\\n use = el.name\\n batches = []\\n batches.append('all')\\n if use.lower() in ['colon','lung','uterine cervix']:\\n report = Report.objects.filter(name=el,language__in=languages).exclude(institute = 'PUBMED')\\n count_rep = report.count()\\n for rp in report:\\n if rp.batch not in batches:\\n batches.append(rp.batch)\\n # print(el)\\n # print(count_rep)\\n\\n if count_rep > 0:\\n json_resp[use] = {}\\n for batch in batches:\\n batch = str(batch)\\n json_resp[use][batch] = {}\\n if batch == 'all':\\n json_resp[use][batch]['tot'] = count_rep\\n with connection.cursor() as cursor:\\n cursor.execute(\\n \\\"SELECT COUNT(*) FROM report AS r INNER JOIN ground_truth_log_file AS g ON g.id_report = r.id_report AND r.language = g.language WHERE r.name = %s AND g.username = %s AND gt_type=%s and institute != %s and r.language in %s;\\\",\\n [str(use),'Robot_user','labels','PUBMED',tuple(languages)]) # We could consider any of the gt_type\\n groundTruths = cursor.fetchone()[0]\\n if groundTruths is None:\\n json_resp[use][batch]['annotated'] = 0\\n else:\\n json_resp[use][batch]['annotated'] = groundTruths\\n else:\\n report_count = Report.objects.filter(name=el,batch = batch,language__in=languages).exclude(institute = 'PUBMED').count()\\n json_resp[use][batch]['tot'] = report_count\\n with connection.cursor() as cursor:\\n cursor.execute(\\n \\\"SELECT COUNT(*) FROM report AS r INNER JOIN ground_truth_log_file AS g ON g.id_report = r.id_report AND r.language = g.language WHERE r.name = %s AND g.username = %s AND gt_type=%s and institute != %s and batch = %s and r.language in %s;\\\",\\n [str(use), 'Robot_user', 'labels', 'PUBMED',batch, tuple(languages)]) # We could consider any of the gt_type\\n groundTruths = cursor.fetchone()[0]\\n if groundTruths is None:\\n json_resp[use][batch]['annotated'] = 0\\n else:\\n json_resp[use][batch]['annotated'] = groundTruths\\n # print(json_resp)\\n return JsonResponse(json_resp)\",\n \"def meta(request):\\r\\n\\r\\n\\tdatatype = request.GET.get('datatype', 'RNA')\\r\\n\\ttissue = request.GET.get('tissue', 'brain')\\r\\n\\tcategory = request.GET.get('category', 'region')\\r\\n\\tgroup = request.GET.get('group', 'PFC')\\r\\n\\tcomparison = request.GET.get('comparison', 'AD-vs-Control')\\r\\n\\tfeature_symbols_in_interest = request.GET.get('features', '').split(' ')\\r\\n\\tcollection_name = \\\"%s_%s_%s-%s_%s\\\" % (datatype,\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\ttissue,\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tcategory,\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tgroup,\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tcomparison)\\r\\n\\t\\\"\\\"\\\"\\r\\n\\t\\tWe should split POST[\\\"featureInput\\\"] here\\r\\n\\t\\\"\\\"\\\"\\r\\n\\t# import pdb; pdb.set_trace();\\r\\n\\t# feature_symbols_in_interest = split_feature_input_to_list(request.POST[\\\"featureInput\\\"])\\r\\n\\r\\n\\t# way_to_choose_probe = request.GET.get('way_to_choose_probe', 'fold change')\\r\\n\\r\\n\\trecords = list(meta_stat_client.get_all_records(collection_name))\\r\\n\\trecords_all_teststat = list(test_stat_client.get_all_records(collection_name))\\r\\n\\trecord_sample_count = test_stat_client.get_all_sample_count(collection_name)\\r\\n\\trecord_disease_state = test_stat_client.get_all_disease_state(collection_name)\\r\\n\\trecord_all_datasets = test_stat_client.get_all_datasets(collection_name)\\r\\n\\r\\n\\t# Turn into dataframe\\r\\n\\trecords = pd.DataFrame(records)\\r\\n\\trecords_all_teststat = pd.DataFrame(records_all_teststat)\\r\\n\\r\\n\\t# Select features in interest\\r\\n\\tfilt_ind = records['symb'].isin(feature_symbols_in_interest)\\r\\n\\trecords_queried = records[filt_ind]\\r\\n\\r\\n\\trecords_queried['entrez_gene_id'] = records_queried.apply(from_symbol_to_entrez_gene_id, axis=1)\\r\\n\\t\\t\\t\\t\\r\\n\\r\\n\\t# Select top 10 by meta-p-value\\r\\n\\trecords_top_10 = records.sort('pval', ascending=True).iloc[0:9, ]\\r\\n\\t# records_top_10 = records.sort('pval', ascending=True)\\r\\n\\r\\n\\trecords_top_10['entrez_gene_id'] = records_top_10.apply(from_symbol_to_entrez_gene_id, axis=1)\\r\\n\\t\\r\\n\\t# Get meta info for this collection\\r\\n\\tmeta_df = pd.DataFrame(record_sample_count, index=['sample_count'], columns=record_all_datasets)\\r\\n\\tmeta_df = pd.DataFrame.transpose(meta_df)\\r\\n\\tmeta_df['state_1_count'] = pd.Series(record_disease_state).apply(sum)\\r\\n\\tmeta_df['state_0_count'] = meta_df['sample_count'] - meta_df['state_1_count']\\r\\n\\tsymbol_count_list = []\\r\\n\\t\\r\\n\\tfor dataset in record_all_datasets:\\r\\n\\t\\tsymb_count = records_all_teststat[records_all_teststat['dataset_accession'] == dataset].shape[0]\\r\\n\\t\\tsymbol_count_list.append(symb_count)\\r\\n\\r\\n\\tmeta_df['feature_count'] = symbol_count_list\\r\\n\\tmeta_df['dataset_accession'] = meta_df.index\\r\\n\\t# import pdb;pdb.set_trace();\\r\\n\\r\\n\\t# Add string ids\\r\\n\\trecords_queried['string_id'] = from_single_symbol_to_string_id(records_queried['symb'])\\r\\n\\t# import pdb;pdb.set_trace();\\r\\n\\t# records_top_10['string_id'] = from_single_symbol_to_string_id(records_top_10['symb'])\\r\\n\\t# import pdb;pdb.set_trace();\\r\\n\\t\\r\\n\\tunion_feature_count = records.shape[0]\\r\\n\\tcheck_all_presence = lambda x : '?' not in x['eff']\\r\\n\\t\\r\\n\\tintersect_feature_count = sum(records.apply(check_all_presence, axis=1))\\r\\n\\t\\r\\n\\r\\n\\t# Output queried records to dictionary\\r\\n\\tmeta_stat_queried = records_queried.to_dict(outtype='records')\\r\\n\\tmeta_stat_top_10 = records_top_10.to_dict(outtype='records')\\r\\n\\tmeta_info = meta_df.to_dict(outtype='records')\\r\\n\\t# import pdb;pdb.set_trace();\\r\\n\\r\\n\\treturn render(request, 'meta_stat.html',\\r\\n\\t\\t\\t\\t{\\r\\n\\t\\t\\t\\t\\t'meta_stat_queried' : meta_stat_queried,\\r\\n\\t\\t\\t\\t\\t'meta_stat_top_10' : meta_stat_top_10,\\r\\n\\t\\t\\t\\t\\t'collection_name' : collection_name,\\r\\n\\t\\t\\t\\t\\t'feature_string' : '+'.join(feature_symbols_in_interest),\\r\\n\\t\\t\\t\\t\\t'meta_info' : meta_info,\\r\\n\\t\\t\\t\\t\\t'union_feature_count' : union_feature_count,\\r\\n\\t\\t\\t\\t\\t'intersect_feature_count' : intersect_feature_count\\r\\n\\t\\t\\t\\t})\",\n \"def overview(request):\\n LOGGER.info('Rendering WMT16 HIT overview for user \\\"{0}\\\".'.format(\\n request.user.username or \\\"Anonymous\\\"))\\n \\n # Re-initialise random number generator.\\n seed(None)\\n \\n # Collect available language pairs for the current user.\\n language_codes = set([x[0] for x in LANGUAGE_PAIR_CHOICES])\\n language_pairs = request.user.groups.filter(name__in=language_codes)\\n \\n # Collect available annotation projects for the current user.\\n annotation_projects = request.user.project_set.all()\\n \\n hit_data = []\\n total = [0, 0, 0]\\n\\n for language_pair in language_pairs:\\n for annotation_project in annotation_projects:\\n hit = _compute_next_task_for_user(request.user, annotation_project, language_pair)\\n user_status = HIT.compute_status_for_user(request.user, annotation_project, language_pair)\\n for i in range(3):\\n total[i] = total[i] + user_status[i]\\n \\n if hit:\\n # Convert status seconds back into datetime.time instances.\\n for i in range(2):\\n user_status[i+1] = seconds_to_timedelta(int(user_status[i+1]))\\n \\n hit_data.append(\\n (hit.get_language_pair_display(), hit.get_absolute_url(),\\n hit.hit_id, user_status, annotation_project)\\n )\\n \\n # Convert total seconds back into datetime.timedelta instances.\\n total[1] = seconds_to_timedelta(int(total[2]) / float(int(total[0]) or 1))\\n \\n # Remove microseconds to get a nicer timedelta rendering in templates.\\n total[1] = total[1] - timedelta(microseconds=total[1].microseconds)\\n \\n total[2] = seconds_to_timedelta(int(total[2]))\\n \\n groups = _identify_groups_for_user(request.user)\\n group = None\\n if len(groups) > 1:\\n LOGGER.debug(u'User \\\"{0}\\\" assigned to multiple annotation groups: {1}'.format(\\n request.user.username or u'Anonymous',\\n u', '.join([x.name for x in groups]))\\n )\\n group = groups[0]\\n \\n if group is not None:\\n group_name = group.name\\n group_status = HIT.compute_status_for_group(group)\\n for i in range(2):\\n group_status[i+1] = seconds_to_timedelta(int(group_status[i+1]))\\n \\n else:\\n group_status = None\\n group_name = None\\n \\n LOGGER.debug(u'\\\\n\\\\nHIT data for user \\\"{0}\\\":\\\\n\\\\n{1}\\\\n'.format(\\n request.user.username or \\\"Anonymous\\\",\\n u'\\\\n'.join([u'{0}\\\\t{1}\\\\t{2}\\\\t{3}'.format(*x) for x in hit_data])))\\n\\n # Compute admin URL for super users.\\n admin_url = None\\n if request.user.is_superuser:\\n admin_url = reverse('admin:index')\\n \\n dictionary = {\\n 'active_page': \\\"OVERVIEW\\\",\\n 'hit_data': hit_data,\\n 'total': total,\\n 'group_name': group_name,\\n 'group_status': group_status,\\n 'admin_url': admin_url,\\n 'title': 'WMT16 Dashboard',\\n 'annotation_groups': [x.name for x in groups],\\n }\\n dictionary.update(BASE_CONTEXT)\\n \\n LOGGER.info(dictionary.values())\\n \\n return render(request, 'wmt16/overview.html', dictionary)\",\n \"def summaryView(request):\\n\\n alert_errors = []\\n alert_infos = []\\n alert_filters = []\\n\\n runs = get_runs_from_request_filters(\\n request, alert_errors, alert_infos, alert_filters\\n )\\n\\n summary = SummaryReport(runs)\\n\\n context = {\\n \\\"refs\\\": summary.reference_runs(),\\n \\\"runs\\\": summary.runs_checked_per_type(),\\n \\\"tk_maps\\\": summary.tracker_maps_per_type(),\\n \\\"certified_runs\\\": summary.certified_runs_per_type(),\\n \\\"sums\\\": summary.sum_of_quantities_per_type(),\\n \\\"alert_errors\\\": alert_errors,\\n \\\"alert_infos\\\": alert_infos,\\n \\\"alert_filters\\\": alert_filters,\\n }\\n\\n return render(request, \\\"certhelper/summary.html\\\", context)\",\n \"def test_get_derived_metric_tags(self):\\n pass\",\n \"def test_get_vulnerability_occurrences_summary(self):\\n pass\",\n \"def get_designs(self):\",\n \"def get_test_report(request, **kwargs): \\n\\t\\n #Fetching the details of the selected event\\n test_list = sidecar.events.test_report(project_id=kwargs['test_id'])\\n report_list = []\\n\\t\\n #Creating the list for the report\\n for tests in test_list._logs:\\n\\tjson_test = json.loads(tests['data'])\\n\\ttests['success'] = json_test['success'] \\n\\ttests['time'] = json_test['time']\\n\\ttests['test_cases'] = json_test['test_cases']\\n\\treport_list.append(tests)\\n\\n #Making the context and sending to template\\n context = {\\n \\\"page_title\\\": _(\\\"Test Results\\\"),\\n \\\"tests\\\": report_list\\n }\\n return render(request, 'rally_dashboard/events/test_detail.html', context)\",\n \"def get_data(request):\\n\\n json_resp = {}\\n # reports = Report.objects.filter(name = UseCase.objects.get(name=request.session['usecase']),institute = request.session['institute'],language = request.session['language'])\\n\\n json_resp['reports'] = []\\n institute = request.GET.get('institute',request.session['institute'])\\n usecase = request.GET.get('usecase',request.session['usecase'])\\n print(usecase)\\n language = request.GET.get('language',request.session['language'])\\n ns_human = NameSpace.objects.get(ns_id='Human')\\n ns_robot = NameSpace.objects.get(ns_id='Robot')\\n user_robot = User.objects.get(username='Robot_user', ns_id=ns_robot)\\n # usec = UseCase.objects.get(name = usecase)\\n # reports = Report.objects.filter(name = usec,institute = institute, language = language).values('id_report')\\n # gt_report = GroundTruthLogFile.objects.filter(language = language).exclude(username = user_robot,id_report__in=reports).order_by('id_report').distinct('id_report')\\n cursor = connection.cursor()\\n cursor.execute(\\\"SELECT r.id_report,r.language,r.report_json,r.name,r.institute,r.batch,COUNT(*) FROM report AS r INNER JOIN ground_truth_log_file AS g ON g.id_report = r.id_report AND r.language = g.language WHERE r.name = %s AND r.institute = %s AND r.language = %s AND g.username != %s GROUP BY (r.id_report,r.language,r.report_json,r.name,r.institute,r.batch)\\\",[usecase,institute,language,'Robot_user'])\\n gt_report_ids = []\\n indice = 0\\n st = time.time()\\n for el in cursor.fetchall():\\n\\n # report = Report.objects.get(language = language, id_report = el.id_report_id)\\n gt_report_ids.append(el[0])\\n # print(str(indice))\\n indice +=1\\n # report = Report.objects.get(id_report=el.id_report, language=el.language)\\n # language = report.language\\n\\n # user_robot = User.objects.get(username = 'Robot_user',ns_id = ns_robot)\\n gt_human = 1\\n gt_robot = 0\\n\\n rep = json.loads(el[2])\\n new_rep = {}\\n for key in rep.keys():\\n nkey = key+ '_0'\\n new_rep[nkey] = rep[key]\\n\\n total = el[6]\\n\\n new_rep['usecase'] = usecase\\n new_rep['id_report_not_hashed'] = rep.get('report_id',el[0])\\n new_rep['id_report'] = el[0]\\n new_rep['institute'] = institute\\n new_rep['language'] = language\\n new_rep['batch'] = el[5]\\n\\n json_resp['reports'].append({'total':total, 'report':new_rep,'id_report':el[0], 'language':language})\\n\\n usec = UseCase.objects.get(name = usecase)\\n reports = Report.objects.filter(institute = institute,language = language,name = usec).exclude(id_report__in=gt_report_ids)\\n # print(reports.count())\\n indice = 0\\n st = time.time()\\n for el in reports:\\n report = el\\n # print(str(indice))\\n indice += 1\\n # report = Report.objects.get(id_report=el.id_report, language=el.language)\\n # language = report.language\\n\\n # user_robot = User.objects.get(username = 'Robot_user',ns_id = ns_robot)\\n gt_human = 0\\n gt_robot = 0\\n\\n rep = report.report_json\\n new_rep = {}\\n for key in rep.keys():\\n nkey = key + '_0'\\n new_rep[nkey] = rep[key]\\n\\n total = gt_human + gt_robot\\n\\n new_rep['usecase'] = report.name_id\\n new_rep['id_report_not_hashed'] = rep.get('report_id', report.id_report)\\n new_rep['id_report'] = report.id_report\\n new_rep['institute'] = report.institute\\n new_rep['language'] = report.language\\n new_rep['batch'] = report.batch\\n\\n json_resp['reports'].append(\\n {'total': total, 'report': new_rep, 'id_report': report.id_report, 'language': report.language})\\n # print('elaboro1',str(end1-st1))\\n tot = time.time()\\n print('totale',str(tot-st))\\n\\n return JsonResponse(json_resp,safe=False)\",\n \"def drought_ag_risk_map(request):\\n \\n view_center = [-105.2, 39.0]\\n view_options = MVView(\\n projection='EPSG:4326',\\n center=view_center,\\n zoom=7.0,\\n maxZoom=12,\\n minZoom=5\\n )\\n\\n # TIGER state/county mapserver\\n tiger_boundaries = MVLayer(\\n source='TileArcGISRest',\\n options={'url': 'https://tigerweb.geo.census.gov/arcgis/rest/services/TIGERweb/State_County/MapServer'},\\n legend_title='States & Counties',\\n layer_options={'visible':True,'opacity':0.2},\\n legend_extent=[-112, 36.3, -98.5, 41.66]) \\n \\n ##### WMS Layers - Ryan\\n usdm_legend = MVLegendImageClass(value='Drought Category',\\n image_url='http://ndmc-001.unl.edu:8080/cgi-bin/mapserv.exe?map=/ms4w/apps/usdm/service/usdm_current_wms.map&version=1.3.0&service=WMS&request=GetLegendGraphic&sld_version=1.1.0&layer=usdm_current&format=image/png&STYLE=default')\\n usdm_current = MVLayer(\\n source='ImageWMS',\\n options={'url': 'http://ndmc-001.unl.edu:8080/cgi-bin/mapserv.exe?',\\n 'params': {'LAYERS':'usdm_current','FORMAT':'image/png','VERSION':'1.1.1','STYLES':'default','MAP':'/ms4w/apps/usdm/service/usdm_current_wms.map'}},\\n layer_options={'visible':False,'opacity':0.25},\\n legend_title='USDM',\\n legend_classes=[usdm_legend],\\n legend_extent=[-126, 24.5, -66.2, 49])\\n \\n # USGS Rest server for HUC watersheds \\n watersheds = MVLayer(\\n source='TileArcGISRest',\\n options={'url': 'https://hydro.nationalmap.gov/arcgis/rest/services/wbd/MapServer'},\\n legend_title='HUC Watersheds',\\n layer_options={'visible':False,'opacity':0.4},\\n legend_extent=[-112, 36.3, -98.5, 41.66])\\n \\n # Sector drought vulnerability county risk score maps -> from 2018 CO Drought Plan update\\n vuln_legend = MVLegendImageClass(value='Risk Score',\\n image_url='/static/tethys_gizmos/data/ag_vuln_legend.jpg')\\n ag_vuln_kml = MVLayer(\\n source='KML',\\n options={'url': '/static/tethys_gizmos/data/CO_Ag_vuln_score_2018.kml'},\\n layer_options={'visible':True,'opacity':0.75},\\n legend_title='Ag Risk Score',\\n feature_selection=True,\\n legend_classes=[vuln_legend],\\n legend_extent=[-109.5, 36.5, -101.5, 41.6])\\n \\n # Define GeoJSON layer\\n # Data from CoCoRaHS Condition Monitoring: https://www.cocorahs.org/maps/conditionmonitoring/\\n with open(como_cocorahs) as f:\\n data = json.load(f)\\n \\n # the section below is grouping data by 'scalebar' drought condition\\n # this is a work around for displaying each drought report classification with a unique colored icon\\n data_sd = {}; data_md ={}; data_ml={}\\n data_sd[u'type'] = data['type']; data_md[u'type'] = data['type']; data_ml[u'type'] = data['type']\\n data_sd[u'features'] = [];data_md[u'features'] = [];data_ml[u'features'] = []\\n for element in data['features']:\\n if 'Severely Dry' in element['properties']['scalebar']:\\n rdate = datetime.datetime.strptime(element['properties']['reportdate'][:10],\\\"%Y-%m-%d\\\")\\n if rdate >= week20:\\n data_sd[u'features'].append(element)\\n if 'Moderately Dry' in element['properties']['scalebar']:\\n rdate = datetime.datetime.strptime(element['properties']['reportdate'][:10],\\\"%Y-%m-%d\\\")\\n if rdate >= week20:\\n data_md[u'features'].append(element)\\n if 'Mildly Dry' in element['properties']['scalebar']:\\n rdate = datetime.datetime.strptime(element['properties']['reportdate'][:10],\\\"%Y-%m-%d\\\")\\n if rdate >= week20:\\n data_ml[u'features'].append(element)\\n \\n cocojson_sevdry = MVLayer(\\n source='GeoJSON',\\n options=data_sd,\\n legend_title='CoCoRaHS Condition Monitor',\\n legend_extent=[-112, 36.3, -98.5, 41.66],\\n feature_selection=False,\\n legend_classes=[MVLegendClass('point', 'Severely Dry', fill='#67000d')],\\n layer_options={'style': {'image': {'circle': {'radius': 6,'fill': {'color': '#67000d'},'stroke': {'color': '#ffffff', 'width': 1},}}}})\\n\\n cocojson_moddry = MVLayer(\\n source='GeoJSON',\\n options=data_md,\\n legend_title='',\\n legend_extent=[-112, 36.3, -98.5, 41.66],\\n feature_selection=False,\\n legend_classes=[MVLegendClass('point', 'Moderately Dry', fill='#a8190d')],\\n layer_options={'style': {'image': {'circle': {'radius': 6,'fill': {'color': '#a8190d'},'stroke': {'color': '#ffffff', 'width': 1},}}}})\\n\\n cocojson_mildry = MVLayer(\\n source='GeoJSON',\\n options=data_ml,\\n legend_title='',\\n legend_extent=[-112, 36.3, -98.5, 41.66],\\n feature_selection=False,\\n legend_classes=[MVLegendClass('point', 'Mildly Dry', fill='#f17d44')],\\n layer_options={'style': {'image': {'circle': {'radius': 6,'fill': {'color': '#f17d44'},'stroke': {'color': '#ffffff', 'width': 1},}}}})\\n\\n \\n # Define map view options\\n drought_ag_risk_map_view_options = MapView(\\n height='100%',\\n width='100%',\\n controls=['ZoomSlider', 'Rotate', 'ScaleLine', 'FullScreen',\\n {'MousePosition': {'projection': 'EPSG:4326'}},\\n {'ZoomToExtent': {'projection': 'EPSG:4326', 'extent': [-130, 22, -65, 54]}}],\\n layers=[tiger_boundaries,cocojson_sevdry,cocojson_moddry,cocojson_mildry,ag_vuln_kml,usdm_current,watersheds],\\n view=view_options,\\n basemap='OpenStreetMap',\\n legend=True\\n )\\n\\n context = {\\n 'drought_ag_risk_map_view_options':drought_ag_risk_map_view_options,\\n }\\n\\n return render(request, 'co_drought/drought_ag_risk.html', context)\",\n \"def getMeasures():\",\n \"def all_bugs_chart(request):\\n labels = []\\n data = []\\n\\n queryset = Bug.objects.values('title', 'id').order_by('-created').exclude(status='Resolved').annotate(\\n bug_votes=Count('votes'))[:5]\\n for entry in queryset:\\n labels.append(entry['title'])\\n data.append(entry['bug_votes'])\\n\\n return JsonResponse(data={\\n 'labels': labels,\\n 'data': data,\\n })\",\n \"def data_set_characterization(request):\\n admin_approved = Phenotype.objects.filter(phenotypestatus__status='A')\\n user_submitted = Phenotype.objects.filter(phenotypestatus__status='U')\\n sample_files = SampleFile.objects.all()\\n\\n admin_approved_count = len(admin_approved)\\n user_submitted_count = len(user_submitted)\\n\\n traits_by_chrom = SNP.objects.filter(Q(snpstatus__status='A') | Q(snpstatus__status='U')).values('chromosome_num').annotate(num_traits=Count('chromosome_num'))\\n\\n context = {'user_submitted_count':user_submitted_count,\\n 'admin_approved_count':admin_approved_count,\\n 'sample_files':sample_files,\\n 'traits_by_chrom':traits_by_chrom}\\n\\n return render(request, 'SNP_Feature_View/data_set_characterization.html', context)\",\n \"def get_queryset(self, request):\\n qs = super().get_queryset(request)\\n qs = qs.prefetch_related('writerinwork_set')\\n qs = qs.prefetch_related('writers')\\n qs = qs.prefetch_related('library_release__library')\\n qs = qs.annotate(models.Count('cwr_exports', distinct=True))\\n qs = qs.annotate(models.Count('recordings', distinct=True))\\n return qs\",\n \"def measurements():\\n measurements_for_displaying = db.session.query(Measurement).all()\\n return render_template('measurement/measurements.html', measurements=measurements_for_displaying)\",\n \"def detail(request):\\r\\n\\r\\n\\tdataset = request.GET.get('dataset', '')\\r\\n\\tdatatype = request.GET.get('datatype', 'RNA')\\r\\n\\ttissue = request.GET.get('tissue', 'brain')\\r\\n\\tcategory = request.GET.get('category', 'region')\\r\\n\\tgroup = request.GET.get('group', 'PFC')\\r\\n\\tcomparison = request.GET.get('comparison', 'AD-vs-Control')\\r\\n\\tfeature_symbols_in_interest = request.GET.get('features', '').split(' ')\\r\\n\\tcollection_name = \\\"%s_%s_%s-%s_%s\\\" % (datatype,\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\ttissue,\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tcategory,\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tgroup,\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tcomparison)\\r\\n\\t\\\"\\\"\\\"\\r\\n\\t\\tWe should split POST[\\\"featureInput\\\"] here\\r\\n\\t\\\"\\\"\\\"\\r\\n\\t# import pdb; pdb.set_trace();\\r\\n\\t# feature_symbols_in_interest = split_feature_input_to_list(request.POST[\\\"featureInput\\\"])\\r\\n\\r\\n\\tway_to_choose_probe = request.GET.get('way_to_choose_probe', 'fold change')\\r\\n\\r\\n\\tall_datasets = test_stat_client.get_all_datasets(collection_name)\\r\\n\\r\\n\\ttest_statistics = list(test_stat_client.get_all_for_this_category(collection_name))\\r\\n\\r\\n\\tdisease_state_list = test_stat_client.get_disease_state_list(collection_name)\\r\\n\\r\\n\\ttest_statistics = pd.DataFrame(test_statistics)\\r\\n\\r\\n\\t# Filter 1 - dataset accession & features in interest\\r\\n\\tfilt_ind = (test_statistics['dataset_accession'] == dataset) & (test_statistics['symb'].isin(feature_symbols_in_interest))\\r\\n\\ttest_stat_df = test_statistics[filt_ind]\\r\\n\\r\\n\\t# Filter 2 - remove duplicates\\r\\n\\t\\\"\\\"\\\"\\r\\n\\t\\tHere we provide options for user to choose how to select a probe when \\r\\n\\t\\tmultiple probes are corresponding to one feature\\r\\n\\t\\\"\\\"\\\"\\r\\n\\r\\n\\tif way_to_choose_probe == \\\"fold change\\\":\\r\\n\\t\\ttest_stat_df = filtered_duplicate_by(test_stat_df, by='fc', group_index=['symb'])\\r\\n\\r\\n\\telif way_to_choose_probe == \\\"limma p value\\\" : \\r\\n\\t\\ttest_stat_df = filtered_duplicate_by(test_stat_df, by='lp', group_index=['symb'])\\r\\n\\r\\n\\telif way_to_choose_probe == \\\"t test p value\\\" :\\r\\n\\t\\ttest_stat_df = filtered_duplicate_by(test_stat_df, by='tp', group_index=['symb'])\\r\\n\\r\\n\\t# Split dataframe for stat table display and graph display\\r\\n\\tstat_table = test_stat_df.drop(['eval', 'dsl'], axis=1)\\r\\n\\tstat_graph_exprs = test_stat_df[['symb', 'eval']]\\r\\n\\tstat_graph_ds = disease_state_list[dataset]\\r\\n\\r\\n\\t# import pdb; pdb.set_trace()\\r\\n\\tstat_table['entrez_gene_id'] = stat_table.apply(from_symbol_to_entrez_gene_id, axis=1)\\t\\t\\t\\r\\n\\tstat_table['string_id'] = from_single_symbol_to_string_id(stat_table['symb'])\\r\\n\\r\\n\\tds_1_count = sum(stat_graph_ds)\\r\\n\\tds_0_count = len(stat_graph_ds) - sum(stat_graph_ds)\\r\\n\\r\\n\\tstat_graph_ds_1 = [True if x == 1 else False for x in stat_graph_ds]\\r\\n\\tstat_graph_ds_0 = [True if x == 0 else False for x in stat_graph_ds]\\r\\n\\t# stat_graph_ds_0 = stat_graph_ds == 0\\r\\n\\r\\n\\theatmap_feature_count = test_stat_df.shape[0]\\r\\n\\theatmap_sample_count = len(stat_graph_ds)\\r\\n\\theatmap_df_row_count = heatmap_sample_count * heatmap_feature_count\\r\\n\\r\\n\\t\\r\\n\\t# import pdb;pdb.set_trace\\r\\n\\t# Generate a expression table (row as feature)\\r\\n\\texpression_table = pd.DataFrame(list(stat_graph_exprs['eval']))\\r\\n\\r\\n\\t\\r\\n\\t# import pdb;pdb.set_trace();\\r\\n\\t# Transpose table before sorting by disease state\\r\\n\\texpression_table = pd.DataFrame.transpose(expression_table)\\r\\n\\r\\n\\t# Get new expression table sorted by disease state\\r\\n\\texpression_table = expression_table[stat_graph_ds_1].append(expression_table[stat_graph_ds_0], ignore_index=True)\\r\\n\\r\\n\\t### Normalize row expression\\r\\n\\texpression_table_normalized = normalize_heatmap_row_expression(expression_table)\\r\\n\\t\\r\\n\\t# Get minimum and maximum value of expression\\r\\n\\texprs_min = np.nanmin(expression_table_normalized.values)\\r\\n\\texprs_max = np.nanmax(expression_table_normalized.values)\\r\\n\\r\\n\\r\\n\\theatmap_dataset_df = pd.DataFrame({\\r\\n\\t\\t\\t'0' : sorted(range(0, heatmap_sample_count) * heatmap_feature_count), # sample_x\\r\\n\\t\\t\\t'1' : range(0, heatmap_feature_count) * heatmap_sample_count,\\t# feature_y\\r\\n\\t\\t\\t'2' : [val for row in expression_table_normalized.values.tolist() for val in row] #expression_z\\r\\n\\t\\t})\\r\\n\\r\\n\\t# Remove NANs in heatmap data series\\r\\n\\tnot_nan_index = np.invert(np.isnan(heatmap_dataset_df['2']))\\r\\n\\theatmap_dataset_df = heatmap_dataset_df[not_nan_index]\\r\\n\\t# Prepare one dimentional scatter plot\\r\\n\\r\\n\\t# Final output\\r\\n\\t# Scatter plot\\r\\n\\tstate_1_data_series = generate_scatterplot_series(range(0, ds_1_count), 0, expression_table)\\r\\n\\tstate_0_data_series = generate_scatterplot_series(range(ds_1_count, ds_1_count+ds_0_count), 1, expression_table)\\t\\r\\n\\tstate_1_name = \\\"AD\\\"\\r\\n\\tstate_0_name = \\\"Control\\\"\\r\\n\\t# Heatmap\\r\\n\\theatmap_feature_list = [x.encode('utf-8') for x in list(stat_graph_exprs['symb'])]\\r\\n\\theatmap_sample_ds_list = ['AD'] * ds_1_count + ['Control'] * ds_0_count\\r\\n\\theatmap_datasets = heatmap_dataset_df.values.tolist()\\r\\n\\theatmap_extremes = [exprs_min, exprs_max]\\r\\n\\t# Statistic table\\r\\n\\tstat_table_output = stat_table.to_dict(outtype='records')\\r\\n\\r\\n\\treturn render(request, 'feature_stat_detail.html',\\r\\n\\t\\t\\t\\t{\\r\\n\\t\\t\\t\\t\\t'dataset_name' : dataset,\\r\\n\\t\\t\\t\\t\\t'test_stat' : stat_table_output,\\r\\n\\t\\t\\t\\t\\t'feature_list' : heatmap_feature_list,\\r\\n\\t\\t\\t\\t\\t'sample_state_list' : heatmap_sample_ds_list,\\r\\n\\t\\t\\t\\t\\t'heatmap_datasets' : heatmap_datasets,\\r\\n\\t\\t\\t\\t\\t'heatmap_extremes' : heatmap_extremes,\\r\\n\\t\\t\\t\\t\\t'state_1_data_series' : state_1_data_series,\\r\\n\\t\\t\\t\\t\\t'state_0_data_series' : state_0_data_series,\\r\\n\\t\\t\\t\\t\\t'state_1_name' : state_1_name,\\r\\n\\t\\t\\t\\t\\t'state_0_name' : state_0_name,\\r\\n\\t\\t\\t\\t})\",\n \"def test_returns_all_studies_with_tagged_traits_for_multiple_tags(self):\\n tagged_traits = []\\n for study in self.studies:\\n tmp = TaggedTraitFactory.create(trait__source_dataset__source_study_version__study=study)\\n tagged_traits.append(tmp)\\n get_data = {'q': ''}\\n response = self.client.get(self.get_url(), get_data)\\n pks = get_autocomplete_view_ids(response)\\n self.assertEqual(sorted([study.pk for study in self.studies]), sorted(pks))\",\n \"def personal_group_metrics(request):\\n\\ttotal_pms, median_pm_idx, median_pm_tuple, aggregate_pm_chats, avg_chat_per_pm, total_pgs, median_pg_idx, median_pg_tuple, aggregate_pg_chats, \\\\\\n\\tavg_chat_per_pg, pms_with_sws, pgs_with_sws = avg_num_of_chats_per_type()\\n\\t\\n\\ttotal_pms_sw, median_pm_sw_idx, median_pm_sw_tuple, aggregate_pm_sws, avg_sw_per_pm, total_pgs_sw, median_pg_sw_idx, median_pg_sw_tuple, \\\\\\n\\taggregate_pg_sws, avg_sw_per_pg = avg_num_of_switchovers_per_type()\\n\\n\\ttotal_pgs_sess, total_pms_sess, med_sess_per_user_per_pg, med_sess_per_user_per_pm, avg_sess_per_user_per_pg, avg_sess_per_user_per_pm, \\\\\\n\\tavg_users_per_pm, med_users_per_pm, avg_users_per_pg, med_users_per_pg, avg_sess_per_user_per_two_user_pm, med_sess_per_user_per_two_user_pm,\\\\\\n\\ttotal_two_user_pms, avg_users_per_two_user_pm, med_users_per_two_user_pm = avg_sessions_per_type()\\n\\n\\treturn render(request,\\\"personal_group/metrics/personal_group_metrics.html\\\",{'total_pms':total_pms,'agg_pm_chats':aggregate_pm_chats,\\\\\\n\\t\\t'avg_pm_chats':avg_chat_per_pm,'total_pgs':total_pgs,'agg_pg_chats':aggregate_pg_chats,'avg_pg_chats':avg_chat_per_pg,\\\\\\n\\t\\t'med_pm_idx':median_pm_idx,'med_pg_idx':median_pg_idx,'med_pm_tup':median_pm_tuple,'med_pg_tup':median_pg_tuple,\\\\\\n\\t\\t'total_pms_sw':total_pms_sw,'agg_pm_sws':aggregate_pm_sws,'avg_pm_sws':avg_sw_per_pm,'total_pgs_sw':total_pgs_sw,\\\\\\n\\t\\t'agg_pg_sws':aggregate_pg_sws,'avg_pg_sws':avg_sw_per_pg,'med_pm_idx_sw':median_pm_sw_idx,'med_pg_idx_sw':median_pg_sw_idx,\\\\\\n\\t\\t'med_pm_tup_sw':median_pm_sw_tuple,'med_pg_tup_sw':median_pg_sw_tuple,'avg_sess_per_user_per_pg':avg_sess_per_user_per_pg,\\\\\\n\\t\\t'avg_sess_per_user_per_pm':avg_sess_per_user_per_pm,'med_sess_per_user_per_pg':med_sess_per_user_per_pg,\\\\\\n\\t\\t'med_sess_per_user_per_pm':med_sess_per_user_per_pm,'pgs_sampled_sess':total_pgs_sess,'pms_sampled_sess':total_pms_sess,\\\\\\n\\t\\t'avg_users_per_pm':avg_users_per_pm, 'med_users_per_pm':med_users_per_pm,'avg_users_per_pg':avg_users_per_pg,\\\\\\n\\t\\t'med_users_per_pg':med_users_per_pg,'pms_with_sws':pms_with_sws,'pgs_with_sws':pgs_with_sws,'total_two_user_pms':total_two_user_pms,\\\\\\n\\t\\t'avg_sess_per_user_per_two_user_pm':avg_sess_per_user_per_two_user_pm,'med_sess_per_user_per_two_user_pm':med_sess_per_user_per_two_user_pm,\\\\\\n\\t\\t'avg_users_per_two_user_pm':avg_users_per_two_user_pm, 'med_users_per_two_user_pm':med_users_per_two_user_pm})\",\n \"def summary(request):\\n\\n # Generate counts of some of the main objects\\n num_courses = models.Course.objects.all().count()\\n num_quizzes = models.Quiz.objects.all().count()\\n num_questions = models.Question.objects.count()\\n num_students = models.User.objects.count()\\n num_visits = request.session.get('num_visits', 1)\\n request.session['num_visits'] = num_visits + 1\\n\\n context = {\\n 'num_courses': num_courses,\\n 'num_quizzes': num_quizzes,\\n 'num_questions': num_questions,\\n 'num_students': num_students,\\n 'num_visits': num_visits,\\n }\\n\\n return Response(context)\",\n \"def get_gt_list(request):\\n\\n groundTruths = 0\\n json_resp = {}\\n username =request.GET.get('username',None)\\n ins = request.GET.get('inst',None)\\n lang = request.GET.get('lang',None)\\n use = request.GET.get('use',None)\\n action = request.GET.get('action',None)\\n token = request.GET.get('token',None)\\n reptype = request.GET.get('reptype',None)\\n languages = ['English','english']\\n annotation_mode = request.GET.get('annotation_mode',None)\\n if ins == '':\\n ins = None\\n if use == '':\\n use = None\\n if lang == '':\\n lang = None\\n if reptype == '':\\n reptype = 'reports'\\n if token == 'all':\\n ns_robot = NameSpace.objects.get(ns_id='Robot')\\n ns_human = NameSpace.objects.get(ns_id='Human')\\n rob_user = User.objects.get(username='Robot_user',ns_id=ns_robot)\\n list_gt = GroundTruthLogFile.objects.filter(username = rob_user).count() + GroundTruthLogFile.objects.filter(ns_id=ns_human).count()\\n groundTruths = list_gt\\n gt_rob = GroundTruthLogFile.objects.filter(ns_id=ns_robot,username = rob_user)\\n\\n i = 0\\n # print(groundTruths)\\n for el in gt_rob:\\n gts = GroundTruthLogFile.objects.filter(ns_id=ns_robot,gt_type = el.gt_type,id_report = el.id_report_id,language = el.language).exclude(insertion_time = el.insertion_time)\\n gts_count = gts.count()\\n # print('count: '+str(i)+' '+str(gts.count()))\\n i = i+1\\n groundTruths = groundTruths + gts_count\\n\\n\\n else:\\n with connection.cursor() as cursor:\\n if reptype == 'reports':\\n if annotation_mode == 'Human':\\n cursor.execute(\\n \\\"SELECT COUNT(*) FROM report AS r INNER JOIN ground_truth_log_file AS g ON g.id_report = r.id_report AND g.language = r.language WHERE r.institute = COALESCE(%s,r.institute) AND r.name = %s AND r.language = COALESCE(%s,r.language) AND g.gt_type = %s AND g.ns_id = %s and r.institute != %s AND username = COALESCE(%s,g.username)\\\",\\n [ins, use, lang, action, 'Human','PUBMED',username])\\n groundTruths = cursor.fetchone()[0]\\n elif annotation_mode == 'Robot':\\n # CAMBIO\\n # cursor.execute(\\n # \\\"SELECT COUNT(*) FROM report AS r INNER JOIN ground_truth_log_file AS g ON g.id_report = r.id_report AND g.language = r.language INNER JOIN ground_truth_log_file AS gg ON g.id_report = gg.id_report AND g.language = gg.language AND g.ns_id = gg.ns_id AND g.gt_type = gg.gt_type WHERE r.institute = COALESCE(%s,r.institute) AND r.name = %s AND r.language = COALESCE(%s,r.language) AND g.gt_type = %s AND g.ns_id = %s AND g.username != %s AND gg.username = %s AND g.insertion_time != gg.insertion_time AND r.institute != %s\\\",\\n # [ins, use, lang, action, 'Robot', 'Robot_user', 'Robot_user','PUBMED'])\\n cursor.execute(\\n \\\"SELECT COUNT(*) FROM report AS r INNER JOIN ground_truth_log_file AS g ON g.id_report = r.id_report AND g.language = r.language WHERE r.institute = COALESCE(%s,r.institute) AND r.name = %s AND r.language = COALESCE(%s,r.language) AND g.gt_type = %s AND g.ns_id = %s AND g.username != %s AND r.institute != %s AND username = COALESCE(%s,g.username)\\\",\\n [ins, use, lang, action, 'Robot', 'Robot_user','PUBMED',username])\\n groundTruths = cursor.fetchone()[0]\\n else:\\n if annotation_mode == 'Human':\\n cursor.execute(\\n \\\"SELECT COUNT(*) FROM report AS r INNER JOIN ground_truth_log_file AS g ON g.id_report = r.id_report AND g.language = r.language WHERE r.name = %s AND r.language in %s AND g.gt_type = %s AND g.ns_id = %s and r.institute = %s AND username = COALESCE(%s,g.username)\\\",\\n [use, tuple(languages), action, 'Human','PUBMED',username])\\n groundTruths = cursor.fetchone()[0]\\n elif annotation_mode == 'Robot':\\n #CAMBIO\\n # cursor.execute(\\n # \\\"SELECT COUNT(*) FROM report AS r INNER JOIN ground_truth_log_file AS g ON g.id_report = r.id_report AND g.language = r.language INNER JOIN ground_truth_log_file AS gg ON g.id_report = gg.id_report AND g.language = gg.language AND g.ns_id = gg.ns_id AND g.gt_type = gg.gt_type WHERE r.name = %s AND r.language in %s AND g.gt_type = %s AND g.ns_id = %s AND g.username != %s AND gg.username = %s AND g.insertion_time != gg.insertion_time AND r.institute = %s\\\",\\n # [use, tuple(languages), action, 'Robot', 'Robot_user', 'Robot_user','PUBMED'])\\n # groundTruths = cursor.fetchone()[0]\\n\\n cursor.execute(\\n \\\"SELECT COUNT(*) FROM report AS r INNER JOIN ground_truth_log_file AS g ON g.id_report = r.id_report AND g.language = r.language WHERE r.name = %s AND r.language in %s AND g.gt_type = %s AND g.ns_id = %s AND g.username != %s AND r.institute = %s AND username = COALESCE(%s,g.username)\\\",\\n [use, tuple(languages), action, 'Robot', 'Robot_user','PUBMED',username])\\n groundTruths = cursor.fetchone()[0]\\n\\n\\n\\n\\n json_resp['ground_truths'] = groundTruths\\n # print(json_resp)\\n return JsonResponse(json_resp)\",\n \"def get_views_report(analytics, start_date='7daysAgo', end_date='today'):\\n return analytics.reports().batchGet(\\n body={\\n 'reportRequests': [\\n {\\n 'viewId': VIEW_ID,\\n 'dateRanges': [{'startDate': start_date, 'endDate': end_date}],\\n 'metrics': [{'expression': 'ga:pageviews'}],\\n 'dimensions': [{'name': 'ga:previousPagePath'},\\n {'name': 'ga:pagePath'},\\n {'name' : 'ga:source'},\\n {\\\"name\\\": \\\"ga:segment\\\"},\\n ],\\n \\\"segments\\\":[\\n {\\n \\\"dynamicSegment\\\":\\n {\\n \\\"name\\\": \\\"ApplicationSubbmited\\\",\\n \\\"userSegment\\\":\\n {\\n \\\"segmentFilters\\\":[\\n {\\n \\\"simpleSegment\\\":\\n {\\n \\\"orFiltersForSegment\\\":\\n {\\n \\\"segmentFilterClauses\\\": [\\n {\\n \\\"dimensionFilter\\\":\\n {\\n \\\"dimensionName\\\":\\\"ga:pagePath\\\",\\n \\\"operator\\\":\\\"PARTIAL\\\",\\n \\\"expressions\\\":[\\\"jobid=\\\"]\\n }\\n }]\\n }\\n }\\n }]\\n }\\n }\\n }\\n ]\\n }]\\n }\\n ).execute()\",\n \"def getMeasures(unique_name=None):\",\n \"def all_features_chart(request):\\n labels = []\\n data = []\\n\\n queryset = Feature.objects.values('title').order_by('-created').exclude(status='Implemented').annotate(\\n feature_purchases=Sum('purchases'))[:5]\\n for entry in queryset:\\n labels.append(entry['title'])\\n data.append(entry['feature_purchases'])\\n\\n return JsonResponse(data={\\n 'labels': labels,\\n 'data': data,\\n })\",\n \"def _get_usage(self, model, counts=False, min_count=None, extra_joins=None, extra_criteria=None, params=None):\\r\\n if min_count is not None: counts = True\\r\\n\\r\\n model_table = qn(model._meta.db_table)\\r\\n model_pk = '%s.%s' % (model_table, qn(model._meta.pk.column))\\r\\n query = \\\"\\\"\\\"\\r\\n SELECT DISTINCT %(tag)s.id, %(tag)s.name%(count_sql)s\\r\\n FROM\\r\\n %(tag)s\\r\\n INNER JOIN %(tagged_item)s\\r\\n ON %(tag)s.id = %(tagged_item)s.tag_id\\r\\n INNER JOIN %(model)s\\r\\n ON %(tagged_item)s.object_id = %(model_pk)s\\r\\n %%s\\r\\n WHERE %(tagged_item)s.content_type_id = %(content_type_id)s\\r\\n %%s\\r\\n GROUP BY %(tag)s.id, %(tag)s.name\\r\\n %%s\\r\\n ORDER BY %(tag)s.name ASC\\\"\\\"\\\" % {\\r\\n 'tag': qn(self.model._meta.db_table),\\r\\n 'count_sql': counts and (', COUNT(%s)' % model_pk) or '',\\r\\n 'tagged_item': qn(TaggedItem._meta.db_table),\\r\\n 'model': model_table,\\r\\n 'model_pk': model_pk,\\r\\n 'content_type_id': ContentType.objects.get_for_model(model).pk,\\r\\n }\\r\\n\\r\\n min_count_sql = ''\\r\\n if min_count is not None:\\r\\n min_count_sql = 'HAVING COUNT(%s) >= %%s' % model_pk\\r\\n params.append(min_count)\\r\\n\\r\\n cursor = connection.cursor()\\r\\n cursor.execute(query % (extra_joins, extra_criteria, min_count_sql), params)\\r\\n tags = []\\r\\n for row in cursor.fetchall():\\r\\n t = self.model(*row[:2])\\r\\n if counts:\\r\\n t.count = row[2]\\r\\n tags.append(t)\\r\\n return tags\",\n \"def test_export_wo_tags(self):\\r\\n self._get_good_request_wo_tags()\\r\\n\\r\\n res = self.app.get(\\r\\n '/api/v1/admin/bmarks/export?api_key={0}'.format(\\r\\n self.api_key),\\r\\n status=200)\\r\\n\\r\\n self.assertTrue(\\r\\n \\\"bmark.us\\\" in res.body,\\r\\n msg='Bmark is in the exported body: ' + res.body)\\r\\n data = json.loads(res.body)\\r\\n\\r\\n self.assertEqual(\\r\\n 1,\\r\\n data['count'],\\r\\n \\\"Should be one result: \\\" + str(data['count']))\",\n \"def get_diagnose(visit):\\r\\n return visit.diagnose.all()\",\n \"def annotation_all_stats(request):\\n\\n id_report = request.GET.get('report',None)\\n language = request.GET.get('language',None)\\n\\n json_dict = get_annotations_count(id_report,language)\\n\\n # print('annotations',json_dict)\\n return JsonResponse(json_dict)\",\n \"def covid19(request):\\n\\tip = get_ip(request, right_most_proxy=True)\\n\\tIpAddressInformation.objects.create(ip_address=ip)\\n\\n\\tcontextres =[]\\n\\t#build elasticsearch query to search data\\n\\tquery={\\\"query\\\": {\\n\\t\\t\\\"bool\\\": {\\n\\t\\t\\t\\\"must\\\": [\\n\\t\\t\\t\\t{\\\"match\\\": {\\\"Associated with COVID-19\\\": \\\"Yes\\\"}},\\n\\t\\t\\t\\t{\\\"match\\\": {\\\"UniprotKb entry status\\\": \\\"Yes\\\"}}\\n\\t\\t\\t]\\n\\t\\t}\\n\\t}\\n\\t}\\n\\t#generate random file name to store search result in json format\\n\\tnameFIle=names.get_first_name()\\n\\tjsonfilename=nameFIle+'_basic_search_covid19.json'\\n\\tjsonfilepath=os.path.join(settings.BASE_DIR, 'resultFile', 'jsonData','resultJson', 'basicsearch', 'results', jsonfilename)\\n\\tjsonfileoutput= open(jsonfilepath,'w')\\n\\tjfinaldata=[]\\n\\tes.indices.refresh(index=\\\"mrmassaydb-index\\\")\\n\\t#elasticsearch will search data\\n\\tres=helpers.scan(client=es,scroll='2m',index=\\\"mrmassaydb-index\\\", doc_type=\\\"mrmassaydb-type\\\",query=query,request_timeout=30)\\n\\tjfinaldata=[]\\n\\tpepSeqList=[]\\n\\tproteinList=[]\\n\\t#if data is valid based on uniprotkb release then it will display\\n\\tfor i in res:\\n\\t\\tjdic=i['_source']\\n\\t\\tjdic={str(tkey):force_text(tvalue) for tkey,tvalue in jdic.items()}\\n\\t\\tif jdic[\\\"UniprotKb entry status\\\"] ==\\\"Yes\\\" and jdic['UniProtKB Accession'] !='502':\\n\\t\\t\\tjdic[\\\"PPI\\\"] =\\\"View\\\"\\n\\t\\t\\tjdic[\\\"sel\\\"] =\\\"\\\"\\n\\t\\t\\tjdic[\\\"Drug Bank\\\"]=jdic[\\\"Drug Bank\\\"].replace('\\\\\\\\','')\\n\\t\\t\\tjdic[\\\"Drug Bank\\\"]=jdic[\\\"Drug Bank\\\"].replace('<br>','|')\\n\\t\\t\\tjdic[\\\"SRMAtlas URL\\\"]=jdic[\\\"SRMAtlas URL\\\"].replace('\\\\\\\\','')\\n\\t\\t\\tjdic[\\\"Passel URL\\\"]=jdic[\\\"Passel URL\\\"].replace('\\\\\\\\','')\\n\\t\\t\\tjdic[\\\"CPTAC URL\\\"]=jdic[\\\"CPTAC URL\\\"].replace('\\\\\\\\','')\\n\\t\\t\\tjdic[\\\"Panoramaweb URL\\\"]=jdic[\\\"Panoramaweb URL\\\"].replace('\\\\\\\\','')\\n\\t\\t\\tjdic[\\\"PeptideTracker URL\\\"]=jdic[\\\"PeptideTracker URL\\\"].replace('\\\\\\\\','')\\n\\t\\t\\t#if jdic[\\\"Pathway Name\\\"].lower() !='na':\\n\\t\\t\\t#\\tjdic[\\\"Pathway Name\\\"]=re.sub(r\\\"(\\\\w)([A-Z])\\\",r\\\"\\\\1|\\\\2\\\",jdic[\\\"Pathway Name\\\"])\\n\\t\\t\\tjdic[\\\"Mean Concentration\\\"] =jdic[\\\"Mean Concentration\\\"].replace('fmol/','fmol/µ')\\n\\t\\t\\tjdic[\\\"Concentration\\\"] =jdic[\\\"Concentration\\\"].replace('fmol/','fmol/µ')\\n\\t\\t\\tif str(jdic[\\\"Associated with COVID-19\\\"]).strip().upper() =='YES':\\n\\t\\t\\t\\tpepSeqList.append(jdic[\\\"Peptide Sequence\\\"].strip())\\n\\t\\t\\t\\tproteinList.append(jdic[\\\"UniProtKB Accession\\\"].strip().split('-')[0])\\n\\t\\t\\tjfinaldata.append(jdic)\\n\\tes.indices.refresh(index=\\\"mrmassaydb-index\\\")\\n\\t#checking any result generated by database\\n\\tfoundHits=len(jfinaldata)\\n\\t#storing only 10000 rows in json format\\n\\tjson.dump(jfinaldata[:10000],jsonfileoutput)\\n\\tjsonfileoutput.close()\\n\\t# if result found then do other job\\n\\tif foundHits >0:\\n\\t\\tstatsummary=summaryStatcal(jfinaldata) # sent data to this funcation for generating stat\\n\\t\\tpathwaychart=statsummary['pathwaychart']\\n\\t\\tpathwaychart=[i[:2] for i in pathwaychart]\\n\\t\\tspecieslist=statsummary['specieslist']\\n\\t\\ttotallist=statsummary['total']\\n\\t\\tsubcell=statsummary['subcell']\\n\\t\\tgodic=statsummary['godic']\\n\\t\\tjvennprot=statsummary['jevennstat'][0]\\n\\t\\tjvennpep=statsummary['jevennstat'][1]\\n\\t\\tmrmdatabase=statsummary['jevennstat'][2]\\n\\t\\tsortedgodic=OrderedDict(sorted(godic.items(), key=lambda t: t[1])) # sorting GO data\\n\\t\\tupdatedgodic=dict(list(sortedgodic.items()))\\n\\t\\tpepseqdataseries=ast.literal_eval(json.dumps(statsummary['pepseqdataseries'])) #dumping data into json format\\n\\t\\tprodataseries=statsummary['prodataseries']\\n\\t\\tunqisostat=statsummary['unqisostat']\\n\\t\\tjsonfilepathStat=os.path.join(settings.BASE_DIR, 'resultFile', 'jsonData','resultJson', 'basicsearch', 'statsummary', jsonfilename) #storing stat result in json format\\n\\t\\tjsonfileoutputStat= open(jsonfilepathStat,'w')\\n\\t\\tjson.dump(statsummary,jsonfileoutputStat)\\n\\t\\tjsonfileoutputStat.close()\\n\\t\\turlname=\\\"'/resultFile/jsonData/resultJson/basicsearch/results/\\\"+jsonfilename+\\\"'\\\"\\n\\n\\t\\tcontextindex={\\n\\t\\t\\t\\\"filename\\\":urlname,\\\"colname\\\":json.dumps(colname),'foundHits':foundHits,\\n\\t\\t\\t'pathwaychart':pathwaychart[:11],'specieslist':specieslist,\\n\\t\\t\\t'totallist':totallist,'subcell':subcell,\\n\\t\\t\\t'updatedgodic':updatedgodic,'pepseqdataseries':pepseqdataseries,\\n\\t\\t\\t'prodataseries':prodataseries,'unqisostat':unqisostat,\\n\\t\\t\\t'uniquePepSeq':len(set(pepSeqList)),'uniqueProtein':len(set(proteinList)),\\n\\t\\t\\t'jvennprot':json.dumps(jvennprot),'jvennpep':json.dumps(jvennpep),'jvennmrmdb':json.dumps(mrmdatabase)\\n\\t\\t\\t}\\n\\t\\treturn render(request,'covid19.html',contextindex)\\n\\telse:\\n\\t\\treturn render(request,'covid19.html',{'foundHits':foundHits})\",\n \"def get_queryset(self, request):\\n qs = super().get_queryset(request)\\n qs = qs.annotate(models.Count('works'))\\n return qs\",\n \"def get_ensembles_summary():\\n\\tregions = request.args.get('region', '').split()\\n\\tregions = [ region.lower() for region in regions ]\\n\\t# Remove suffix after \\\"-\\\" -- for compatibility with Dong lab iConnectome\\n\\tregions = [ re.sub(r'-.*','',region) for region in regions ]\\n\\tregions_tgt = request.args.get('region_tgt', '').split()\\n\\tregions_tgt = [ region_tgt.lower() for region_tgt in regions_tgt ]\\n\\t\\n\\tensemble_list=[]\\n\\tensemble_list = db.get_engine(current_app, 'methylation_data').execute(\\\"SELECT * FROM ensembles\\\").fetchall()\\n\\n\\ttotal_methylation_cell_each_dataset = db.get_engine(current_app, 'methylation_data').execute(\\\"SELECT dataset, COUNT(*) as `num` FROM cells GROUP BY dataset\\\").fetchall()\\n\\ttotal_snATAC_cell_each_dataset = db.get_engine(current_app, 'snATAC_data').execute(\\\"SELECT dataset, COUNT(*) as `num` FROM cells GROUP BY dataset\\\").fetchall()\\n\\ttotal_methylation_cell_each_dataset = pd.DataFrame(total_methylation_cell_each_dataset, columns=['dataset','num']).set_index('dataset')\\n\\ttotal_snATAC_cell_each_dataset = pd.DataFrame(total_snATAC_cell_each_dataset, columns=['dataset','num']).set_index('dataset')\\n\\n\\tensembles_cell_counts = []\\n\\tfor ensemble in ensemble_list:\\n\\t\\tdatasets = ensemble['datasets'].split(',')\\n\\t\\tmethylation_cell_counts=total_methylation_cell_each_dataset.filter(datasets,axis=0)['num'].to_dict()\\n\\t\\tsnATAC_cell_counts=total_snATAC_cell_each_dataset.filter(datasets,axis=0)['num'].to_dict()\\n\\t\\tannoj_exists = ensemble_annoj_exists(ensemble['ensemble_id'])\\n\\t\\tensembles_cell_counts.append( {\\\"id\\\": ensemble['ensemble_id'],\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t \\\"ensemble\\\": ensemble['ensemble_name'],\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t \\\"ens_methylation_counts\\\": methylation_cell_counts,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t \\\"ens_snATAC_counts\\\": snATAC_cell_counts,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t \\\"public_access\\\": ensemble['public_access'],\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t \\\"description\\\": ensemble['description'],\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t \\\"annoj_exists\\\": annoj_exists,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t \\\"datasets\\\": datasets\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t })\\n\\n\\t# aba_regions = db.get_engine(current_app, 'methylation_data').execute('SELECT * FROM ABA_regions').fetchall()\\n\\taba_regions = pd.read_sql('SELECT * FROM ABA_regions', db.get_engine(current_app, 'methylation_data'))\\n\\taba_regions = aba_regions\\n\\t\\n\\tensembles_json_list = []\\n\\tfor ens in ensembles_cell_counts:\\n\\t\\ttotal_methylation_cells = 0\\n\\t\\ttotal_snATAC_cells = 0\\n\\t\\tdatasets_in_ensemble_cell_count = []\\n\\t\\tdatasets_in_ensemble = []\\n\\t\\tsnATAC_datasets_in_ensemble = []\\n\\t\\tens_dict = {}\\n\\t\\tif ens['ens_methylation_counts'] is not None:\\n\\t\\t\\tfor dataset, count in ens['ens_methylation_counts'].items():\\n\\t\\t\\t\\tens_dict[dataset] = str(count)\\n\\t\\t\\t\\ttotal_methylation_cells += count\\n\\t\\t\\t\\tdatasets_in_ensemble.append(dataset)\\n\\t\\t\\t\\tdatasets_in_ensemble_cell_count.append(dataset+\\\" (\\\"+str(count)+\\\" cells)\\\")\\n\\t\\t\\t\\t# ens_dict[dataset] = str(count) + '/' + str(total_methylation_cell_each_dataset[dataset])\\n\\t\\tif ens['ens_snATAC_counts'] is not None:\\n\\t\\t\\tfor dataset, count in ens['ens_snATAC_counts'].items():\\n\\t\\t\\t\\ttotal_snATAC_cells += count\\n\\t\\t\\t\\tdatasets_in_ensemble.append('CEMBA_'+dataset)\\n\\t\\t\\t\\tsnATAC_datasets_in_ensemble.append(dataset+\\\" (\\\"+str(count)+\\\" cells)\\\")\\n\\n\\n\\t\\t# Do not display ensembles that contain less than 200 total cells. (mainly RS2 data)\\n\\t\\tif total_methylation_cells>0 or total_snATAC_cells>0:\\n\\n\\t\\t\\tens_dict[\\\"ensemble_id\\\"] = ens['id']\\n\\t\\t\\tens_dict[\\\"ensemble_name\\\"] = ens['ensemble']\\n\\t\\t\\tens_dict[\\\"description\\\"] = ens['description']\\n\\t\\t\\tens_dict[\\\"datasets_rs1\\\"] = \\\", \\\".join(sorted([x for x in datasets_in_ensemble_cell_count if 'RS2' not in x]))\\n\\t\\t\\tens_dict[\\\"datasets_rs2\\\"] = \\\", \\\".join(sorted([x for x in datasets_in_ensemble_cell_count if 'RS2' in x]))\\n\\t\\t\\trs2_datasets_in_ensemble = sorted([x for x in datasets_in_ensemble if 'RS2' in x])\\n\\t\\t\\tens_dict[\\\"target_regions_rs2_acronym\\\"] = \\\"\\\"\\n\\t\\t\\tens_dict[\\\"target_regions_rs2_descriptive\\\"] = \\\"\\\"\\n\\t\\t\\tens_dict[\\\"ABA_regions_acronym\\\"] = ''\\n\\t\\t\\tens_dict[\\\"ABA_regions_description\\\"] = ''\\n\\n\\t\\t\\t# TODO: \\n\\t\\t\\tif len(rs2_datasets_in_ensemble) != 0:\\n\\t\\t\\t\\ttarget_regions_query = \\\"SELECT DISTINCT datasets.target_region, ABA_regions.ABA_description \\\\\\n\\t\\t\\t\\t\\tFROM datasets \\\\\\n\\t\\t\\t\\t\\tINNER JOIN ABA_regions ON ABA_regions.ABA_acronym=datasets.target_region \\\\\\n\\t\\t\\t\\t\\tAND datasets.dataset in (\\\" + \\\",\\\".join((\\\"%s\\\",) * len(rs2_datasets_in_ensemble)) + \\\")\\\"\\n\\t\\t\\t\\ttarget_regions_result = db.get_engine(current_app, 'methylation_data').execute(target_regions_query, tuple(rs2_datasets_in_ensemble,)).fetchall()\\n\\t\\t\\t\\tens_dict[\\\"target_regions_rs2_acronym\\\"] = \\\", \\\".join([ x.target_region for x in target_regions_result ])\\n\\t\\t\\t\\tens_dict[\\\"target_regions_rs2_descriptive\\\"] = \\\", \\\".join([ x.ABA_description for x in target_regions_result ])\\n\\n\\t\\t\\t\\t# target_regions_result = aba_regions.loc[rs2_datasets_in_ensemble,'ABA_description']\\n\\n\\t\\t\\tens_dict[\\\"snATAC_datasets_rs1\\\"] = \\\", \\\".join(sorted([x for x in snATAC_datasets_in_ensemble if 'RS2' not in x]))\\n\\t\\t\\tens_dict[\\\"snATAC_datasets_rs2\\\"] = \\\", \\\".join(sorted([x for x in snATAC_datasets_in_ensemble if 'RS2' in x]))\\n\\t\\t\\tens_dict[\\\"num_datasets\\\"] = len(datasets_in_ensemble_cell_count)+len(snATAC_datasets_in_ensemble)\\n\\n\\t\\t\\tslices_list_rs1 = re.findall('CEMBA_([0-9]+[A-Z])',','.join(ens['datasets']))\\n\\t\\t\\tslices_list_rs2 = re.findall('CEMBA_RS2_[A-Z][mf]([0-9]+[A-Z])',','.join(ens['datasets']))\\n\\t\\t\\tslices_set = set(slices_list_rs1)\\n\\t\\t\\tslices_set.update(slices_list_rs2)\\n\\t\\t\\tens_dict[\\\"slices\\\"] = \\\", \\\".join(sorted(list(slices_set)))\\n\\t\\t\\tens_dict[\\\"total_methylation_cells\\\"] = total_methylation_cells\\n\\t\\t\\tens_dict[\\\"total_snATAC_cells\\\"] = total_snATAC_cells\\n\\n\\t\\t\\tif slices_set:\\n\\t\\t\\t\\tens_regions_acronyms = aba_regions.set_index('code').loc[list(slices_set),'ABA_acronym'].values\\n\\t\\t\\t\\tens_regions_descriptions = aba_regions.set_index('code').loc[list(slices_set),'ABA_description'].values\\n\\t\\t\\t\\tens_dict[\\\"ABA_regions_acronym\\\"] = \\\", \\\".join(ens_regions_acronyms).replace('+',', ')\\n\\t\\t\\t\\tens_dict[\\\"ABA_regions_description\\\"] = \\\", \\\".join(ens_regions_descriptions).replace('+',', ')\\n\\n\\t\\t\\tif ens['public_access'] == 0:\\n\\t\\t\\t\\tens_dict[\\\"public_access_icon\\\"] = \\\"fas fa-lock\\\"\\n\\t\\t\\t\\tens_dict[\\\"public_access_color\\\"] = \\\"black\\\"\\n\\t\\t\\telse:\\n\\t\\t\\t\\tens_dict[\\\"public_access_icon\\\"] = \\\"fas fa-lock-open\\\"\\n\\t\\t\\t\\tens_dict[\\\"public_access_color\\\"] = \\\"green\\\"\\n\\n\\t\\t\\tens_dict[\\\"annoj_exists\\\"] = ens['annoj_exists']\\n\\n\\n\\t\\t\\tuse_region=True\\n\\t\\t\\tif regions!=['none']:\\n\\t\\t\\t\\tuse_region = use_region and (len([region for region in regions if region in ens_dict[\\\"ABA_regions_acronym\\\"].lower()])>0)\\n\\t\\t\\tif regions_tgt!=['none']:\\n\\t\\t\\t\\tuse_region = use_region and (len([region_tgt for region_tgt in regions_tgt if region_tgt in ens_dict[\\\"target_regions_rs2_acronym\\\"].lower()])>0)\\n\\t\\t\\tif use_region:\\n\\t\\t\\t\\tensembles_json_list.append(ens_dict)\\n\\n\\tens_json = json.dumps(ensembles_json_list)\\n\\n\\treturn ens_json\",\n \"def AppMetricsView(request): # pylint: disable=invalid-name\\n return ExportToDjangoView(request, view=\\\"app-metrics\\\")\",\n \"def metrics(_):\\r\\n collector = BuildsCollector()\\r\\n build_metrics, headers = collector.get_metrics_table()\\r\\n print(tabulate(build_metrics, headers=headers))\",\n \"def get_reports(request):\\n\\n inst = request.GET.get('institute',None)\\n use = request.GET.get('usec',None)\\n print(use)\\n lang = request.GET.get('lang',None)\\n batch = request.GET.get('batch',None)\\n all = request.GET.get('all',None)\\n actual_report = request.GET.get('actual_report',None)\\n if all == 'all':\\n # All the reports are returned independently of the usecase, the language or institute.\\n use_obj = UseCase.objects.get(name = use)\\n reps = Report.objects.filter(institute = inst,name = use_obj,language = lang).values('id_report','report_json','language')\\n json_resp = {}\\n json_resp['report'] = []\\n\\n for report in reps:\\n json_rep = {}\\n json_rep['id_report'] = report['id_report']\\n json_rep['language'] = report['language']\\n json_rep['report_json'] = report['report_json']\\n json_resp['report'].append(json_rep)\\n return JsonResponse(json_resp)\\n\\n if(inst != None and use != None and lang != None and batch != None):\\n\\n \\\"\\\"\\\" It is used in the options modal: if the reuqired combination of institute, language and usecase has 0 reports\\n associated, a message is returned. In this case this view returns the number of reports associated to a specific \\n configuration required \\\"\\\"\\\"\\n\\n rep = Report.objects.filter(institute = inst, name = use, language = lang, batch = batch)\\n json_count = {'count':len(rep)}\\n return JsonResponse(json_count)\\n\\n usecase = request.session.get('usecase',None)\\n mode1 = request.session.get('mode',None)\\n mode = NameSpace.objects.get(ns_id=mode1)\\n language = request.session.get('language',None)\\n institute = request.session.get('institute',None)\\n username = request.session['username']\\n batch = request.session['batch']\\n token = request.GET.get('configure',None) # This parameter is set when\\n\\n jsonError = {'error':'something wrong with params!'}\\n if usecase is not None and language is not None and institute is not None and batch is not None:\\n # Get the reports associated to the usecase, language and institute of the SESSION\\n reports1 = Report.objects.filter(name = usecase, language = language, institute = institute,batch=batch)\\n if mode1 == 'Robot':\\n # gts_r = GroundTruthLogFile.objects.filter(language = language,ns_id=mode).values('id_report')\\n # gts_r1 = GroundTruthLogFile.objects.filter(language=language, ns_id=mode).order_by(\\n # 'id_report').distinct('id_report').values('id_report')\\n # ids1 = [el['id_report'] for el in gts_r1]\\n # print(len(ids1))\\n gts_r1 = GroundTruthLogFile.objects.filter(id_report__in = reports1,language = language,ns_id=mode).order_by('id_report').distinct('id_report').values('id_report')\\n ids = [el['id_report'] for el in gts_r1]\\n # print(len(ids))\\n # print(ids == ids1)\\n # for el in gts_r1:\\n # # if el['id_report'] not in ids and Report.objects.filter(language = language, id_report = el['id_report'], batch = batch).exists():\\n # ids.append(el['id_report'])\\n\\n reports1 = Report.objects.filter(id_report__in=ids,name = usecase, language = language, institute = institute,batch = batch)\\n\\n json_resp = {}\\n json_resp['report'] = []\\n if reports1.exists():\\n reports = reports1.values('id_report','report_json','language')\\n for report in reports:\\n json_rep = {}\\n json_rep['id_report'] = report['id_report']\\n json_rep['language'] = report['language']\\n json_rep['report_json'] = report['report_json']\\n json_resp['report'].append(json_rep)\\n\\n json_resp['report'].sort(key=lambda json: json['id_report'], reverse=False) # Reports are sorted by ID\\n # json_resp['report'].sort(key=lambda json: json['report_json']['report_id'], reverse=False) # Reports are sorted by ID\\n json_resp['index'] = 0\\n\\n if token is not None:\\n # Get the last ground truth given the session parameters.\\n gt = get_last_groundtruth(username, usecase, language, institute,mode,batch)\\n else:\\n # Get the last ground truth of the user.\\n gt = get_last_groundtruth(username,None, None, None,mode,batch)\\n\\n if gt is not None:\\n # The index is updated and it characterizes the first report of the list shown to the user.\\n id_report = gt['id_report']\\n use = gt['use_case']\\n lang = gt['language']\\n institute = gt['institute']\\n report_json = Report.objects.get(id_report = id_report, name = use, language = lang, institute = institute)\\n rep_json = report_json.report_json\\n index = json_resp['report'].index({'id_report':id_report,'language':lang,'report_json':rep_json})\\n json_resp['index'] = int(index)\\n if actual_report is not None:\\n index = json_resp['report'].index(actual_report)\\n json_resp['index'] = int(index)\\n\\n return JsonResponse(json_resp)\\n else:\\n return JsonResponse(jsonError,status=500)\",\n \"def get_primaries(exclude_group):\",\n \"def check_PUBMED_reports(request):\\n\\n json_resp = {}\\n json_resp['count'] = 0\\n pubmed_arts = Report.objects.filter(institute = 'PUBMED')\\n for el in pubmed_arts:\\n if el.id_report.startswith('PUBMED'):\\n json_resp['count'] += 1\\n return JsonResponse(json_resp,safe=False)\",\n \"def dvi():\\n\\n return M(c=\\\"dvi\\\")(\\n #M(\\\"Home\\\", f=\\\"index\\\"),\\n M(\\\"Recovery Requests\\\", f=\\\"recreq\\\")(\\n M(\\\"New Request\\\", m=\\\"create\\\"),\\n M(\\\"List Current\\\",\\n vars={\\\"recreq.status\\\":\\\"1,2,3\\\"}),\\n ),\\n M(\\\"Dead Bodies\\\", f=\\\"body\\\")(\\n M(\\\"Add\\\", m=\\\"create\\\"),\\n M(\\\"List unidentified\\\",\\n vars={\\\"identification.status\\\": \\\"None\\\"}),\\n M(\\\"Report by Age/Gender\\\", m=\\\"report\\\",\\n vars=dict(rows=\\\"age_group\\\",\\n cols=\\\"gender\\\",\\n fact=\\\"count(pe_label)\\\",\\n ),\\n ),\\n ),\\n #M(\\\"Missing Persons\\\", f=\\\"person\\\")(\\n # M(\\\"List all\\\"),\\n #),\\n M(\\\"Morgues\\\", f=\\\"morgue\\\")(\\n M(\\\"Create\\\", m=\\\"create\\\"),\\n ),\\n M(\\\"Dashboard\\\", f=\\\"index\\\"),\\n )\",\n \"def tags():\",\n \"def metagenome(args):\\n set_quiet(args.quiet)\\n\\n # first, load taxonomic_assignments\\n try:\\n tax_assign = MultiLineageDB.load(args.taxonomy_csv,\\n keep_full_identifiers=args.keep_full_identifiers,\\n keep_identifier_versions=args.keep_identifier_versions,\\n force=args.force, lins=args.lins)\\n available_ranks = tax_assign.available_ranks\\n except ValueError as exc:\\n error(f\\\"ERROR: {str(exc)}\\\")\\n sys.exit(-1)\\n\\n if not tax_assign:\\n error(f'ERROR: No taxonomic assignments loaded from {\\\",\\\".join(args.taxonomy_csv)}. Exiting.')\\n sys.exit(-1)\\n\\n if args.rank and args.rank not in available_ranks:\\n error(f\\\"ERROR: No taxonomic information provided for rank {args.rank}: cannot summarize at this rank\\\")\\n sys.exit(-1)\\n\\n # next, collect and load gather results\\n gather_csvs = tax_utils.collect_gather_csvs(args.gather_csv, from_file= args.from_file)\\n try:\\n query_gather_results = tax_utils.check_and_load_gather_csvs(gather_csvs, tax_assign, force=args.force,\\n fail_on_missing_taxonomy=args.fail_on_missing_taxonomy,\\n keep_full_identifiers=args.keep_full_identifiers,\\n keep_identifier_versions = args.keep_identifier_versions,\\n lins=args.lins,\\n )\\n except ValueError as exc:\\n error(f\\\"ERROR: {str(exc)}\\\")\\n sys.exit(-1)\\n\\n if not query_gather_results:\\n notify('No gather results loaded. Exiting.')\\n sys.exit(-1)\\n\\n single_query_output_formats = ['csv_summary', 'kreport']\\n desired_single_outputs = []\\n if len(query_gather_results) > 1: # working with multiple queries\\n desired_single_outputs = [x for x in args.output_format if x in single_query_output_formats]\\n if desired_single_outputs:\\n notify(f\\\"WARNING: found results for multiple gather queries. Can only output multi-query result formats: skipping {', '.join(desired_single_outputs)}\\\")\\n # remove single query outputs from output format\\n args.output_format = [x for x in args.output_format if x not in single_query_output_formats]\\n if not args.output_format: # or do we want to insert `human` here so we always report something?\\n error(f\\\"ERROR: No output formats remaining.\\\")\\n sys.exit(-1)\\n\\n # for each queryResult, actually summarize at rank, reporting any errors that occur.\\n for queryResult in query_gather_results:\\n try:\\n queryResult.build_summarized_result()\\n except ValueError as exc:\\n error(f\\\"ERROR: {str(exc)}\\\")\\n sys.exit(-1)\\n\\n # write summarized output in human-readable format\\n if \\\"lineage_summary\\\" in args.output_format:\\n lineage_outfile, limit_float = make_outfile(args.output_base, \\\"lineage_summary\\\", output_dir=args.output_dir)\\n\\n ## aggregate by lineage by query\\n lineageD, query_names= tax_utils.aggregate_by_lineage_at_rank(query_gather_results=query_gather_results,\\n rank=args.rank, by_query=True)\\n\\n with FileOutputCSV(lineage_outfile) as out_fp:\\n tax_utils.write_lineage_sample_frac(query_names, lineageD, out_fp, sep='\\\\t')\\n\\n # write summarized --> krona output tsv\\n if \\\"krona\\\" in args.output_format:\\n krona_results, header = tax_utils.format_for_krona(query_gather_results, rank=args.rank)\\n\\n krona_outfile, limit_float = make_outfile(args.output_base, \\\"krona\\\", output_dir=args.output_dir)\\n with FileOutputCSV(krona_outfile) as out_fp:\\n tax_utils.write_krona(header, krona_results, out_fp)\\n\\n if \\\"human\\\" in args.output_format:\\n summary_outfile, limit_float = make_outfile(args.output_base, \\\"human\\\", output_dir=args.output_dir)\\n\\n with FileOutput(summary_outfile) as out_fp:\\n human_display_rank = args.rank or \\\"species\\\"\\n if args.lins and not args.rank:\\n human_display_rank = query_gather_results[0].ranks[-1] # lowest rank\\n\\n tax_utils.write_human_summary(query_gather_results, out_fp, human_display_rank)\\n\\n # write summarized output csv\\n single_query_results = query_gather_results[0]\\n if \\\"csv_summary\\\" in args.output_format:\\n summary_outfile, limit_float = make_outfile(args.output_base, \\\"csv_summary\\\", output_dir=args.output_dir)\\n with FileOutputCSV(summary_outfile) as out_fp:\\n tax_utils.write_summary(query_gather_results, out_fp, limit_float_decimals=limit_float)\\n\\n # write summarized --> kreport output tsv\\n if \\\"kreport\\\" in args.output_format:\\n kreport_outfile, limit_float = make_outfile(args.output_base, \\\"kreport\\\", output_dir=args.output_dir)\\n\\n with FileOutputCSV(kreport_outfile) as out_fp:\\n header, kreport_results = single_query_results.make_kreport_results()\\n tax_utils.write_output(header, kreport_results, out_fp, sep=\\\"\\\\t\\\", write_header=False)\\n\\n # write summarized --> LINgroup output tsv\\n if \\\"lingroup\\\" in args.output_format:\\n try:\\n lingroups = tax_utils.read_lingroups(args.lingroup)\\n except ValueError as exc:\\n error(f\\\"ERROR: {str(exc)}\\\")\\n sys.exit(-1)\\n\\n lingroupfile, limit_float = make_outfile(args.output_base, \\\"lingroup\\\", output_dir=args.output_dir)\\n\\n with FileOutputCSV(lingroupfile) as out_fp:\\n header, lgreport_results = single_query_results.make_lingroup_results(LINgroupsD = lingroups)\\n tax_utils.write_output(header, lgreport_results, out_fp, sep=\\\"\\\\t\\\", write_header=True)\\n\\n # write cami bioboxes format\\n if \\\"bioboxes\\\" in args.output_format:\\n bbfile, limit_float = make_outfile(args.output_base, \\\"bioboxes\\\", output_dir=args.output_dir)\\n\\n with FileOutputCSV(bbfile) as out_fp:\\n header_lines, bb_results = single_query_results.make_cami_bioboxes()\\n tax_utils.write_bioboxes(header_lines, bb_results, out_fp, sep=\\\"\\\\t\\\")\",\n \"def dataset_summary(request, project, data_type, week):\\n logger = logging.getLogger('ibdmdb')\\n\\n file_cache_dict = map(str.strip, open(public_cache_file).readlines())\\n summary_groups = itertools.groupby(file_cache_dict, lambda x: 'summary' in x and project in x and data_type in x and week in x)\\n\\n anadama2_static_base = settings.STATIC_ROOT\\n\\n logger.info(\\\"Summary groups:\\\", summary_groups)\\n\\n # If this is a list > 1 than something went wrong here\\n summary_files = []\\n\\n for (group, items) in summary_groups:\\n if not group:\\n continue\\n\\n summary_files.extend(list(items))\\n\\n if summary_files:\\n # We should have a summary.html file, a summary.json file and a\\n # collection of images here that we will want to handle.\\n #\\n # The images and json file should go to our AnADAMA2 static directory\\n # to be served with our static content.\\n #\\n # Quick note here over the adding of the \\\"/tunnel/dataset_summary\\\"\\n # If we are operating in our current production setting we are proxy'ing \\n # requests between a protected server and an unprotected server so we \\n # need to add these all for the requests to pass through to the internal \\n # server. Messy but needed for the time being.\\n anadama2_static_dir = os.path.join(anadama2_static_base,\\n 'tunnel',\\n 'dataset_summary',\\n project,\\n data_type, week)\\n\\n if not os.path.isdir(anadama2_static_dir):\\n logger.info('Static dir being created %s' % anadama2_static_dir)\\n os.makedirs(anadama2_static_dir)\\n \\n complete_file = os.path.join(anadama2_static_dir, 'summary', 'complete')\\n if not os.path.exists(complete_file):\\n logger.info('Creating symylink of all files')\\n\\n # Pull out the first file; get the basename and symlink that into the static dir folder\\n # TODO: Add a check here to see if we retrieve nothing -- which means something bad happened..\\n summary_html = next((x for x in summary_files if \\\"summary.html\\\" in x), None)\\n summary_base = os.path.dirname(summary_html)\\n os.symlink(summary_base, os.path.join(anadama2_static_dir, 'summary'))\\n open(complete_file, 'a').close()\\n\\n # Need to add a check in here to make sure that our summary.html file does exist.\\n summary_url = os.path.join('/tunnel', 'dataset_summary', \\n project, data_type, week, 'summary', 'summary.html')\\n\\n return HttpResponseRedirect(summary_url)\\n return HttpResponse(status=500)\",\n \"def survivor_reports(request):\\n\\n if request.method == 'GET':\\n data = {}\\n total_survivors = infected = non_infected = water = food = medication = ammunition = pointslost = 0\\n for i in Survivor.objects.all():\\n total_survivors += 1\\n if i.infected is False:\\n non_infected += 1\\n water += i.water\\n food += i.food\\n medication += i.medication\\n ammunition += i.ammunition\\n if i.infected is True:\\n infected += 1\\n pointslost += (4 * i.water)\\n pointslost += (3 * i.food)\\n pointslost += (2 * i.medication)\\n pointslost += (1 * i.ammunition)\\n\\n if total_survivors != 0:\\n data['Percentage of infected survivors'] = str(round((infected/total_survivors), 2) * 100) + '%'\\n data['Percentage of non-infected survivors'] = str(round((non_infected/total_survivors), 2) * 100) + '%'\\n data['Average amount of water by survivor'] = round(water/non_infected,1)\\n data['Average amount of food by survivor'] = round(food/non_infected,1)\\n data['Average amount of medication by survivor'] = round(medication/non_infected,1)\\n data['Average amount of ammunition by survivor'] = round(ammunition/non_infected,1)\\n data['Points lost because of infected survivor'] = pointslost\\n else:\\n data['Percentage of infected survivors'] = '0.0%'\\n data['Percentage of non-infected survivors'] = '0.0%'\\n data['Average amount of water by survivor'] = 0\\n data['Average amount of food by survivor'] = 0\\n data['Average amount of medication by survivor'] = 0\\n data['Average amount of ammunition by survivor'] = 0\\n data['Points lost because of infected survivor'] = 0\\n return Response(data, status=status.HTTP_200_OK)\",\n \"def get(self, request):\\n issue_list = Issue.objects.order_by('-created_at')\\n open_issues = Issue.objects.filter(status='O').count()\\n closed_issues = Issue.objects.filter(status='C').count()\\n \\n return render(request, 'issue.html', {\\n 'issue_list': issue_list,\\n 'open_issues': open_issues,\\n 'closed_issues': closed_issues,\\n })\",\n \"def get_queryset(self, request):\\n qs = super().get_queryset(request)\\n qs = qs.annotate(\\n work__count=models.Count('release__works', distinct=True))\\n qs = qs.annotate(\\n release__count=models.Count('release__id', distinct=True))\\n return qs\",\n \"def summary(self, *args, **kwargs):\\n article = self.get_object()\\n summary_data = self.get_serializer(article).data\\n\\n keywords = summary_data['keywords']\\n related_articles = \\\\\\n Article.objects.filter(Q(keywords__contains=keywords[:1])\\n | Q(keywords__contains=keywords[1:2])\\n | Q(keywords__contains=keywords[2:3])) \\\\\\n .order_by('-publish_time')[:11] \\\\\\n .values('identifier', 'title', 'images', 'site_name', 'domain', 'publish_time')\\n\\n related_articles = [related for related in list(related_articles)\\n if related['identifier'] != article.identifier]\\n\\n summary_data['related'] = related_articles\\n\\n return Response(summary_data)\",\n \"def get_medicines(visit):\\r\\n return visit.medicines.all()\",\n \"def tagged(tag = ''):\\n\\tresults = queries.tagged(tag)\\n\\ttags = queries.tags()\\n\\treturn render_template('index.html', packages=results, tags=tags, currentFilter=tag)\",\n \"def summary_page() :\\r\\n logger.debug(\\\"\\\")\\r\\n model = session_info.get_user_model(session)\\r\\n return render_template( \\\"summary_page.html\\\" , model=model ,\\r\\n stat_types=param_stats.StatTypes )\",\n \"def get_user_activity_report(request, ids, index, value, start_date, end_date, stats): \\n\\n domain = request.user.selected_domain\\n if not ids: raise Exception(\\\"The requested form was not found\\\")\\n \\n _report = Report(\\\"CHW Group Total Activity Report\\\")\\n _report.generating_url = request.path\\n metadata = Metadata.objects.filter(timestart__gte=start_date)\\n # the query below is used if you want to query by submission time (instead of form completion time)\\n #metadata = Metadata.objects.filter(attachment__submission__submit_time__gte=start_date)\\n \\n # since we are working at a granularity of 'days', we want to make sure include \\n # complete days in our queries, so we round up\\n timespan = get_timespan(start_date, end_date)\\n delta = timedelta(days=timespan.days+1)\\n metadata = metadata.filter(timeend__lt=start_date+delta)\\n # the query below is used if you want to query by submission time (instead of form completion time)\\n #metadata = metadata.filter(attachment__submission__submit_time__lte=end_date)\\n \\n dataset = DataSet( unicode(value[0]) + \\\" per \\\" + unicode(index) )\\n dataset.indices = unicode(index)\\n dataset.params = request.GET\\n\\n # when 'organization' is properly populated, we can start using that\\n # member_list = utils.get_members(organization)\\n # for now, just use domain\\n member_list = [r.chw_username for r in ReporterProfile.objects.filter(domain=domain).order_by(\\\"chw_username\\\")]\\n\\n # get a sum of all forms\\n visits_per_member = Values( \\\"visits\\\" )\\n for member in member_list:\\n visits_per_member.append( (member, metadata.filter(username=member).count()) )\\n visits_per_member.run_stats(stats)\\n visits_per_member.sort(key=operator.itemgetter(1), reverse=True) \\n dataset.valuesets.append( visits_per_member )\\n \\n # this report only requires the first form. you can imagine other reports doing \\n # this iteration: for id in ids:\\n form_per_member = Values( unicode(value[0]) )\\n form_metadata = metadata.filter(formdefmodel=ids[0])\\n for member in member_list:\\n # values are tuples of dates and counts\\n form_per_member.append( (member, form_metadata.filter(username=member).count()) )\\n form_per_member.run_stats(stats)\\n form_per_member.sort(key=operator.itemgetter(1), reverse=True) \\n dataset.valuesets.append( form_per_member )\\n \\n _report.datasets.append(dataset)\\n return _report\",\n \"def get_outage(self):\\r\\n try:\\r\\n assert self._db_connection, {\\r\\n STATUS_KEY: HTTP_500_INTERNAL_SERVER_ERROR,\\r\\n MESSAGE_KEY: DB_ERROR}\\r\\n\\r\\n if self.equipment == COKE_DRUM_VALUE and self.module == OUTAGE_VALUE:\\r\\n \\\"\\\"\\\"\\r\\n This will return the graph data for the selected outage module\\r\\n \\\"\\\"\\\"\\r\\n query_params = {\\r\\n TAG_NAME_REQUEST: self.query_params.GET[TAG_NAME_REQUEST],\\r\\n START_DATE_REQUEST: self.query_params.GET[START_DATE_REQUEST],\\r\\n END_DATE_REQUEST: self.query_params.GET[END_DATE_REQUEST]\\r\\n }\\r\\n MODULE_LEVEL_MULTILINE_TAG = tuple(LIST_OF_OUTAGE_MODULE_LEVEL_MULTILINE_TAGS_GRAPH)\\r\\n if MULTILINE_REQUEST in self.query_params.GET:\\r\\n \\\"\\\"\\\"\\r\\n This will return the graph data for the actual and predicted tags for the selected outage module \\r\\n \\\"\\\"\\\"\\r\\n query_params[MULTILINE_REQUEST] = self.query_params.GET[MULTILINE_REQUEST]\\r\\n\\r\\n if query_params:\\r\\n if START_DATE_REQUEST not in query_params or not query_params[START_DATE_REQUEST] and \\\\\\r\\n MULTILINE_REQUEST not in query_params:\\r\\n graph_data = django_search_query_all(\\r\\n DETAILED_OUTAGE_GRAPH_NULL_START_DATE.format(\\r\\n self.module,\\r\\n query_params[TAG_NAME_REQUEST],\\r\\n query_params[END_DATE_REQUEST]))\\r\\n elif query_params[START_DATE_REQUEST] and MULTILINE_REQUEST not in query_params:\\r\\n graph_data = django_search_query_all(\\r\\n DETAILED_OUTAGE_GRAPH.format(\\r\\n self.module,\\r\\n query_params[TAG_NAME_REQUEST],\\r\\n query_params[START_DATE_REQUEST],\\r\\n query_params[END_DATE_REQUEST]))\\r\\n elif query_params[START_DATE_REQUEST] and query_params[MULTILINE_REQUEST]:\\r\\n if query_params[TAG_NAME_REQUEST] in LIST_OF_OUTAGE_MODULE_LEVEL_MULTILINE_TAGS_GRAPH:\\r\\n graph_data = django_search_query_all(\\r\\n DETAILED_OUTAGE_MODULE_MULTILINE_GRAPH.format(\\r\\n self.module,\\r\\n MODULE_LEVEL_MULTILINE_TAG,\\r\\n query_params[START_DATE_REQUEST],\\r\\n query_params[END_DATE_REQUEST]))\\r\\n\\r\\n else:\\r\\n graph_data = django_search_query_all(\\r\\n DETAILED_OUTAGE_GRAPH.format(\\r\\n self.module,\\r\\n query_params[TAG_NAME_REQUEST],\\r\\n query_params[START_DATE_REQUEST],\\r\\n query_params[END_DATE_REQUEST]))\\r\\n\\r\\n df_data = pd.DataFrame(graph_data)\\r\\n min_max = django_search_query_all(\\r\\n MIN_MAX_DATA.format(\\r\\n self.module,\\r\\n query_params[TAG_NAME_REQUEST]\\r\\n ))\\r\\n df_min_max_data = pd.DataFrame(min_max)\\r\\n graph = []\\r\\n\\r\\n if not df_data.empty:\\r\\n df_data = df_data.where(pd.notnull(df_data) == True, None)\\r\\n df_data.sort_values(TIMESTAMP_KEY, ascending=True, inplace=True)\\r\\n df_unit = df_data[UNIT].iloc[0]\\r\\n df_description = df_data[DESCRIPTION].iloc[0]\\r\\n df_timestamp = list(dict.fromkeys(list(df_data[TIMESTAMP_KEY])))\\r\\n\\r\\n if query_params[TAG_NAME_REQUEST] in LIST_OF_OUTAGE_MODULE_LEVEL_MULTILINE_TAGS_GRAPH:\\r\\n df_result = df_data.groupby(TAG_NAME_REQUEST)\\r\\n actual_north_data = []\\r\\n predicted_north_data = []\\r\\n actual_south_data = []\\r\\n predicted_south_data = []\\r\\n if len(df_result) == 2:\\r\\n df_description = \\\\\\r\\n df_data[df_data[TAG_NAME_REQUEST] == query_params[TAG_NAME_REQUEST]][\\r\\n DESCRIPTION].iloc[0]\\r\\n df_north_actual = df_result.get_group(OUTAGE_MODULE_LEVEL_ACTUAL_TAG)\\r\\n actual_north_data = list(df_north_actual['north_drum_tag_value'])\\r\\n df_north_predicted = df_result.get_group(OUTAGE_MODULE_LEVEL_PREDICTED_TAG)\\r\\n predicted_north_data = list(df_north_predicted['north_drum_tag_value'])\\r\\n df_south_actual = df_result.get_group(OUTAGE_MODULE_LEVEL_ACTUAL_TAG)\\r\\n actual_south_data = list(df_south_actual['south_drum_tag_value'])\\r\\n df_south_predicted = df_result.get_group(OUTAGE_MODULE_LEVEL_PREDICTED_TAG)\\r\\n predicted_south_data = list(df_south_predicted['south_drum_tag_value'])\\r\\n elif len(df_result) == 1:\\r\\n\\r\\n if df_result[TAG_NAME_REQUEST] == OUTAGE_MODULE_LEVEL_ACTUAL_TAG:\\r\\n df_description = \\\\\\r\\n df_data[df_data[TAG_NAME_REQUEST] == OUTAGE_MODULE_LEVEL_ACTUAL_TAG][\\r\\n DESCRIPTION].iloc[0]\\r\\n df_north_actual = df_result.get_group(OUTAGE_MODULE_LEVEL_ACTUAL_TAG)\\r\\n actual_north_data = list(df_north_actual['north_drum_tag_value'])\\r\\n df_south_actual = df_result.get_group(OUTAGE_MODULE_LEVEL_ACTUAL_TAG)\\r\\n actual_south_data = list(df_south_actual['south_drum_tag_value'])\\r\\n\\r\\n elif df_result[TAG_NAME_REQUEST] != OUTAGE_MODULE_LEVEL_ACTUAL_TAG:\\r\\n df_description = \\\\\\r\\n df_data[df_data[TAG_NAME_REQUEST] == OUTAGE_MODULE_LEVEL_PREDICTED_TAG][\\r\\n DESCRIPTION].iloc[0]\\r\\n df_north_predicted = df_result.get_group(OUTAGE_MODULE_LEVEL_PREDICTED_TAG)\\r\\n predicted_north_data = list(df_north_predicted['north_drum_tag_value'])\\r\\n df_south_predicted = df_result.get_group(OUTAGE_MODULE_LEVEL_PREDICTED_TAG)\\r\\n predicted_south_data = list(df_south_predicted['south_drum_tag_value'])\\r\\n\\r\\n temp = {\\\"north_actual\\\": actual_north_data, \\\"north_predicted\\\": predicted_north_data,\\r\\n \\\"south_actual\\\": actual_south_data, \\\"south_predicted\\\": predicted_south_data,\\r\\n \\\"x_axis\\\": df_timestamp,\\r\\n \\\"unit\\\": df_unit,\\r\\n \\\"description\\\": df_description}\\r\\n\\r\\n else:\\r\\n temp = {\\\"y_axis\\\": list(df_data[TAG_VALUE]), \\\"x_axis\\\": df_timestamp,\\r\\n \\\"unit\\\": df_unit, \\\"description\\\": df_description}\\r\\n if not df_min_max_data.empty:\\r\\n temp[\\\"min_data\\\"] = df_min_max_data[MIN_VALUE].iloc[0]\\r\\n temp[\\\"max_data\\\"] = df_min_max_data[MAX_VALUE].iloc[0]\\r\\n else:\\r\\n temp[\\\"min_data\\\"] = None\\r\\n temp[\\\"max_data\\\"] = None\\r\\n graph.append(temp)\\r\\n\\r\\n return graph\\r\\n\\r\\n except AssertionError as e:\\r\\n log_error(\\\"Exception due to : %s\\\" + str(e))\\r\\n return asert_res(e)\\r\\n except Exception as e:\\r\\n log_error(\\\"Exception due to : %s\\\" + str(e))\\r\\n return json_InternalServerError\",\n \"def get_results(self):\\n summary = self.handle.get_summary_data(self.group_name)\\n results = {'template': {'status': 'no data'},\\n 'complement': {'status': 'no data'},\\n '2d': {'status': 'no data'}}\\n if 'genome_mapping_template' in summary:\\n results['template'] = self._get_results(summary['genome_mapping_template'])\\n if 'genome_mapping_complement' in summary:\\n results['complement'] = self._get_results(summary['genome_mapping_complement'])\\n if 'genome_mapping_2d' in summary:\\n results['2d'] = self._get_results(summary['genome_mapping_2d'])\\n return results\",\n \"def test_showWithCustomerTag(self):\\n expectedOut = [r\\\"ID\\\\s*NAME\\\\s*STATUS\\\\s*SEVERITY\\\"]\\n allAlerts = util.allAlerts\\n # Only this alert has both tags.\\n expectedAlerts = [util.Alert.kubernetesSkynetTag]\\n for r in expectedAlerts:\\n expectedOut.append(\\n util.SummaryLineProcessor.expectedAlertSummaryLineRegex(r))\\n self.summaryRsrcs(\\\"alert\\\",\\n allAlerts,\\n expectedOut,\\n rsrcAdditionalParams=[\\n \\\"--customerTag\\\", \\\"kubernetes\\\",\\n \\\"--customerTag\\\", \\\"skynet\\\"])\\n\\n expectedOut = [r\\\"ID\\\\s*NAME\\\\s*DESCRIPTION\\\"]\\n allDashboards = util.allDashboards\\n # Only this dashboard has both tags.\\n expectedDashboards = [util.Dashboard.skynetApplier]\\n for r in expectedDashboards:\\n expectedOut.append(\\n util.SummaryLineProcessor.expectedDashboardSummaryLineRegex(r))\\n self.summaryRsrcs(\\\"dashboard\\\",\\n allDashboards,\\n expectedOut,\\n rsrcAdditionalParams=[\\n \\\"--customerTag\\\", \\\"kubernetes\\\",\\n \\\"--customerTag\\\", \\\"skynet\\\"])\",\n \"def get(self, request, unit_id):\\n start_date = get_start_date(request)\\n end_date = get_end_date(request)\\n months = {}\\n while(start_date <= end_date):\\n first_of_month = start_date.replace(day=1)\\n last_of_month = start_date.replace(\\n day=1) + relativedelta(months=1) - relativedelta(days=1)\\n readings_sum = MeterReading.objects.filter(\\n date__gte=first_of_month,\\n date__lte=last_of_month,\\n unit=unit_id,\\n reading_type='GAS'\\n ).aggregate(Sum('usage'))\\n months[start_date.strftime(\\n \\\"%Y-%m-%d\\\")] = round(readings_sum['usage__sum']*.06, 0)\\n start_date += relativedelta(months=1)\\n return JsonResponse(months, safe=False)\",\n \"def display_report(request, **kwargs):\\n\\n #Getting the report of the tests \\n try:\\n outputStr = sidecar.events.test_logs(project_id=kwargs['project_id'])\\n outputStr = outputStr.results\\n except Exception, e:\\n outputStr = \\\"Updating the logs...\\\"\\n \\n #Making the output\\n context = {\\n \\\"page_title\\\": _(\\\"Test Report\\\"),\\n \\\"test_report\\\": outputStr\\n }\\n return render(request, 'rally_dashboard/events/view_report.html', context)\",\n \"def cross_study(request):\\r\\n\\tform = tissueDataTypeSelectionForm()\\r\\n\\tcollection_names = []\\r\\n\\tcomm_deg_stat_list = []\\r\\n\\tDEG_num_list = []\\r\\n\\tcommon_deg_names = []\\r\\n\\r\\n\\t# import pdb;pdb.set_trace();\\r\\n\\tif request.method == 'POST':\\r\\n\\t\\tform = tissueDataTypeSelectionForm(request.POST)\\r\\n\\t\\t# import pdb;pdb.set_trace();\\r\\n\\t\\tif form.is_valid():\\r\\n\\t\\t\\tform = tissueDataTypeSelectionForm()\\r\\n\\t\\t\\t# import pdb;pdb.set_trace();\\r\\n\\t\\t\\tchecked_fields = list(request.POST.viewkeys())\\r\\n\\t\\t\\t# remove form token\\r\\n\\t\\t\\tchecked_fields.remove('csrfmiddlewaretoken')\\r\\n\\t\\t\\t# if it is null then return original form\\r\\n\\t\\t\\tif not checked_fields:\\r\\n\\t\\t\\t\\treturn render(request, 'cross_study.html', {\\r\\n\\t\\t\\t\\t\\t\\t'form' : form, \\r\\n\\t\\t\\t\\t\\t\\t'message' : 'Please select at least one group you want to look into'\\r\\n\\t\\t\\t\\t\\t})\\r\\n\\t\\t\\t# replace double underscore \\\"__\\\" to hyphen \\\"-\\\"\\r\\n\\t\\t\\t# and those been replaced are collection names\\r\\n\\t\\t\\tcollection_names = [x.replace('__', '-').encode('utf-8') for x in checked_fields]\\r\\n\\t\\t\\t# import pdb;pdb.set_trace();\\r\\n\\t\\t\\t\\r\\n\\t\\t\\t# extract tissue, data type, and region\\r\\n\\t\\t\\ttable_header_names = [' '.join([x.split('_')[0].capitalize(), x.split('_')[1].capitalize(), x.split('_')[2].capitalize()]) for x in collection_names]\\r\\n\\r\\n\\t\\t\\t# Get collection names\\r\\n\\t\\t\\tmeta_collections = meta_stat_client.get_all_collections()\\r\\n\\t\\t\\tstat_collections = test_stat_client.get_all_collections()\\r\\n\\r\\n\\t\\t\\t# Variables to return \\r\\n\\t\\t\\tcomm_deg_df = pd.DataFrame()\\r\\n\\r\\n\\t\\t\\t# Remove duplicate columns\\r\\n\\t\\t\\tcols_to_use = ['symb']\\r\\n\\r\\n\\t\\t\\tcomm_deg_df, DEG_num_list, unique_symbol_num_list = get_deg_tables_from_collection(collection_names, meta_collections, meta_stat_client, test_stat_client)\\r\\n\\r\\n\\t\\t\\t# Filter out those records with at least one NaN (They do not appear in all collections) \\r\\n\\t\\t\\t# Add one column to indicate NaN number\\r\\n\\t\\t\\t\\r\\n\\t\\t\\tcomm_deg_df['no_nan'] = comm_deg_df.apply(check_nan_in_a_row, axis=1)\\r\\n\\t\\t\\t\\r\\n\\t\\t\\t# import pdb;pdb.set_trace();\\r\\n\\t\\t\\t\\r\\n\\t\\t\\t# Select those without nan and drop indicator column\\r\\n\\t\\t\\tcomm_deg_df = comm_deg_df[comm_deg_df['no_nan']].drop(['no_nan'], axis=1)\\r\\n\\t\\t\\t# Turn into a list of dict\\r\\n\\t\\t\\t# comm_deg_stat_list = comm_deg_df.to_dict(orient='records')[0:9]\\r\\n\\t\\t\\t# import pdb;pdb.set_trace();\\r\\n\\t\\t\\tcomm_deg_stat_list = comm_deg_df.to_dict('records')\\r\\n\\t\\t\\tcommon_deg_names = list(comm_deg_df.index)\\r\\n\\t\\t\\t# print common_deg_names\\r\\n\\t\\t\\t# print comm_deg_df.iloc[0:9, ]\\r\\n\\r\\n\\r\\n\\t\\t\\treturn render(request, 'cross_study.html', {\\r\\n\\t\\t\\t\\t\\t'form' : form,\\r\\n\\t\\t\\t\\t\\t'table_header_names' : table_header_names,\\r\\n\\t\\t\\t\\t\\t'common_deg_names' : common_deg_names,\\r\\n\\t\\t\\t\\t\\t'collection_names' : collection_names,\\r\\n\\t\\t\\t\\t\\t'csv_url_part' : '+'.join(collection_names),\\r\\n\\t\\t\\t\\t\\t'common_deg_stat' : comm_deg_stat_list,\\r\\n\\t\\t\\t\\t\\t'common_deg_number_list' : DEG_num_list, \\r\\n\\t\\t\\t\\t\\t'unique_symbol_num_list' : unique_symbol_num_list\\r\\n\\t\\t\\t\\t})\\r\\n\\t\\t\\r\\n\\t\\treturn render(request, 'cross_study.html', {\\r\\n\\t\\t\\t\\t'form' : form\\r\\n\\t\\t\\t})\\r\\n\\r\\n\\telse:\\r\\n\\t\\treturn render(request, 'cross_study.html', {\\r\\n\\t\\t\\t\\t'form' : form\\r\\n\\t\\t\\t})\",\n \"def show_campaigns(request, utm_campaign, **kwargs):\\n \\n err_msg = ''\\n try:\\n err_msg = str(kwargs['kwargs']['err_msg'])\\n except:\\n pass\\n \\n test_type_override = ''\\n try:\\n test_type_override = MySQLdb._mysql.escape_string(request.POST['test_type_override'])\\n \\n if test_type_override == 'Banner':\\n test_type_var = FDH._TESTTYPE_BANNER_\\n elif test_type_override == 'Landing Page':\\n test_type_var = FDH._TESTTYPE_LP_\\n elif test_type_override == 'Banner and LP':\\n test_type_var = FDH._TESTTYPE_BANNER_LP_\\n \\n except:\\n test_type_var = ''\\n pass\\n \\n try:\\n \\\"\\\"\\\" Find the earliest and latest page views for a given campaign \\\"\\\"\\\"\\n lptl = DL.LandingPageTableLoader()\\n ccrml = DL.CiviCRMLoader()\\n \\n start_time = ccrml.get_earliest_donation(utm_campaign)\\n end_time = ccrml.get_latest_donation(utm_campaign)\\n \\n one_step = lptl.is_one_step(start_time, end_time, utm_campaign) \\n \\n if not(one_step): \\n start_time = lptl.get_earliest_campaign_view(utm_campaign)\\n end_time = lptl.get_latest_campaign_view(utm_campaign) \\n\\n interval = 1\\n \\n \\\"\\\"\\\" Create reporting object to retrieve campaign data and write plots to image repo on disk \\\"\\\"\\\"\\n ir = DR.IntervalReporting(was_run=False, use_labels=False, font_size=20, plot_type='line', query_type='campaign', file_path=projSet.__web_home__ + 'campaigns/static/images/')\\n \\n \\\"\\\"\\\" Produce analysis on the campaign view data \\\"\\\"\\\" \\n ir.run(start_time, end_time, interval, 'views', utm_campaign, {}, one_step=one_step)\\n \\n \\\"\\\"\\\" \\n ESTIMATE THE START AND END TIME OF THE CAMPAIGN\\n ===============================================\\n \\n Search for the first instance when more than 10 views are observed over a sampling period\\n \\\"\\\"\\\"\\n \\n col_names = ir._data_loader_.get_column_names()\\n \\n views_index = col_names.index('views')\\n ts_index = col_names.index('ts')\\n \\n row_list = list(ir._data_loader_._results_) # copy the query results\\n for row in row_list:\\n if row[views_index] > 100:\\n start_time_est = row[ts_index]\\n break\\n row_list.reverse()\\n for row in row_list:\\n if row[views_index] > 100:\\n end_time_est = row[ts_index]\\n break\\n \\n \\n \\\"\\\"\\\"\\n BUILD THE VISUALIZATION FOR THE TEST VIEWS OF THIS CAMAPAIGN\\n ============================================================ \\n \\\"\\\"\\\"\\n \\n \\\"\\\"\\\" Read the test name \\\"\\\"\\\"\\n ttl = DL.TestTableLoader()\\n row = ttl.get_test_row(utm_campaign)\\n test_name = ttl.get_test_field(row ,'test_name')\\n \\n \\\"\\\"\\\" Regenerate the data using the estimated start and end times \\\"\\\"\\\"\\n ir = DR.IntervalReporting(was_run=False, use_labels=False, font_size=20, plot_type='line', query_type='campaign', file_path=projSet.__web_home__ + 'campaigns/static/images/')\\n ir.run(start_time_est, end_time_est, interval, 'views', utm_campaign, {}, one_step=one_step)\\n \\n \\\"\\\"\\\" Determine the type of test (if not overridden) and retrieve the artifacts \\\"\\\"\\\"\\n test_type, artifact_name_list = FDH.get_test_type(utm_campaign, start_time, end_time, DL.CampaignReportingLoader(query_type=''), test_type_var)\\n \\n return render_to_response('campaigns/show_campaigns.html', {'utm_campaign' : utm_campaign, 'test_name' : test_name, 'start_time' : start_time_est, 'end_time' : end_time_est, 'one_step' : one_step, \\\\\\n 'artifacts' : artifact_name_list, 'test_type' : test_type, 'err_msg' : err_msg}, context_instance=RequestContext(request)) \\n\\n except Exception as inst:\\n \\n logging.error('Failed to correctly produce campaign diagnostics.')\\n logging.error(type(inst))\\n logging.error(inst.args)\\n logging.error(inst)\\n \\n \\\"\\\"\\\" Return to the index page with an error \\\"\\\"\\\"\\n err_msg = 'There is insufficient data to analyze this campaign: %s. Check to see if the <a href=\\\"/LML/\\\">impressions have been loaded</a>. <br><br>ERROR:<br><br>%s' % (utm_campaign, inst.__str__())\\n \\n return index(request, kwargs={'err_msg' : err_msg})\",\n \"def get_queryset(self, request):\\n qs = super().get_queryset(request)\\n qs = qs.annotate(\\n libraryrelease__count=models.Count(\\n 'release', distinct=True,\\n filter=models.Q(release__cd_identifier__isnull=False)))\\n qs = qs.annotate(\\n commercialrelease__count=models.Count(\\n 'release', distinct=True,\\n filter=models.Q(release__cd_identifier__isnull=True)))\\n qs = qs.annotate(models.Count('recording', distinct=True))\\n return qs\",\n \"def summary(request):\\n data = {'Yes':[], 'No':[], 'labels':{}}\\n reasons = zip(list(range(RestartReason.objects.count())), \\n RestartReason.objects.order_by('reason_id'))\\n for i, reason in reasons:\\n iv_attempts = __iv_attempts(reason.reason_id)\\n data['Yes'].append(iv_attempts[True])\\n data['No'].append(iv_attempts[False])\\n data['labels'][i] = reason.reason_id\\n return HttpResponse(simplejson.dumps(data), \\n mimetype='application/javascript')\",\n \"def chart(request):\\n assert isinstance(request, HttpRequest)\\n filename = 'ppg_RawDataSheet13.mat'\\n subtitle = 'VerityDB/' + filename\\n return render(\\n request,\\n 'research/chart.html',\\n {\\n 'title':'Chart',\\n 'message':'Highcharts Based',\\n 'year':datetime.now().year,\\n #'data': content['val'][0:11]\\n 'temp': models.load_data(),\\n 'test': models.load_data_filename(filename),\\n 'subtitle_text': subtitle,\\n }\\n )\",\n \"def test_execute_tags_type_queries(self):\\n with tenant_context(self.tenant):\\n guid = AzureCostEntryLineItemDailySummary.objects.values(\\\"subscription_guid\\\")[0].get(\\\"subscription_guid\\\")\\n test_cases = [\\n {\\\"value\\\": \\\"-1\\\", \\\"unit\\\": \\\"month\\\", \\\"resolution\\\": \\\"monthly\\\", \\\"subscription_guid\\\": guid},\\n {\\\"value\\\": \\\"-2\\\", \\\"unit\\\": \\\"month\\\", \\\"resolution\\\": \\\"monthly\\\", \\\"subscription_guid\\\": guid},\\n {\\\"value\\\": \\\"-10\\\", \\\"unit\\\": \\\"day\\\", \\\"resolution\\\": \\\"daily\\\", \\\"subscription_guid\\\": guid},\\n {\\\"value\\\": \\\"-30\\\", \\\"unit\\\": \\\"day\\\", \\\"resolution\\\": \\\"daily\\\", \\\"subscription_guid\\\": guid},\\n ]\\n\\n for case in test_cases:\\n url = reverse(\\\"azure-tags\\\")\\n client = APIClient()\\n params = {\\n \\\"filter[resolution]\\\": case.get(\\\"resolution\\\"),\\n \\\"filter[time_scope_value]\\\": case.get(\\\"value\\\"),\\n \\\"filter[time_scope_units]\\\": case.get(\\\"unit\\\"),\\n \\\"key_only\\\": False,\\n \\\"filter[subscription_guid]\\\": case.get(\\\"subscription_guid\\\"),\\n }\\n url = url + \\\"?\\\" + urlencode(params, quote_via=quote_plus)\\n response = client.get(url, **self.headers)\\n self.assertEqual(response.status_code, status.HTTP_200_OK)\\n data = response.json().get(\\\"data\\\")\\n self.assertTrue(data)\\n self.assertTrue(isinstance(data, list))\\n for tag in data:\\n self.assertTrue(isinstance(tag, dict))\\n self.assertIn(\\\"key\\\", tag)\\n self.assertIn(\\\"values\\\", tag)\\n self.assertIsNotNone(tag.get(\\\"key\\\"))\\n self.assertIn(tag.get(\\\"values\\\").__class__, [list, str])\\n self.assertTrue(tag.get(\\\"values\\\"))\",\n \"def get_queryset(self, request):\\n qs = super().get_queryset(request)\\n qs = qs.annotate(models.Count('tracks', distinct=True))\\n qs = qs.annotate(models.Count('works', distinct=True))\\n return qs\",\n \"def data_set_detail(request, chromosome_num):\\n # make sure we have features on that chromosome \\n try:\\n traits_admin = SNP.objects.filter(snpstatus__status='A', chromosome_num=chromosome_num)\\n traits_user = SNP.objects.filter(snpstatus__status='U', chromosome_num=chromosome_num)\\n\\n except:\\n return heads_up(request)\\n\\n context = {'traits_admin':traits_admin, 'traits_user':traits_user, 'chromosome_num':chromosome_num}\\n return render(request, 'SNP_Feature_View/data_set_detail.html', context)\",\n \"def listTopicRelevance(request):\\n if request.method == 'GET':\\n user = request.user;\\n data = [];\\n for topic in Topic.objects.all():\\n row = {};\\n\\n topicSerializer = TopicNestedSerializer(topic)\\n topicSerializer.Meta.depth = 1;\\n #row['topic'] = topicSerializer.data;\\n user_visits = topic.visits.filter(user=user)\\n visitSerializer = VisitSerializer(user_visits, many=True)\\n #visitSerializer.Meta.depth = 1;\\n row['visit_count'] = len(user_visits);\\n if row['visit_count'] > 0:\\n row['last_visit'] = user_visits.order_by('-visit_date')[0].visit_date\\n else:\\n row['last_visit'] = topic.created_at\\n\\n neighbor_visits = Visit.objects.filter(user=user, topic__relates_to__topic_to=topic)\\n\\n row['neighbor_visits_count'] = len(neighbor_visits);\\n if row['neighbor_visits_count'] > 0:\\n row['last_neighbor_visit'] = neighbor_visits.order_by('-visit_date')[0].visit_date;\\n else:\\n row['last_neighbor_visit'] = topic.created_at\\n\\n row['post_count'] = len(topic.posts.filter(user=user))\\n row['like_count'] = len(topic.posts.filter(votes__user=user))\\n row['relevance_score'] = 5*row['neighbor_visits_count'] - (timezone.now()-row['last_neighbor_visit']).total_seconds()/3600\\n row['recommendation'] = row['relevance_score'] + topic.hotness\\n\\n data.append(row)\\n\\n print(data)\\n return Response(data)\",\n \"def stat(request, researcher):\\n try:\\n records = Record.objects.filter(researcher=researcher)\\n tokens = Pubtoken.objects.filter(belonging_record__in=records)\\n\\n stanfordner_tokenwise_total_hit = 0\\n stanfordner_tokenwise_total_miss = 0\\n crfsuite_tokenwise_total_hit = 0\\n crfsuite_tokenwise_total_miss = 0\\n\\n # stanfordner_fn_tp = 0\\n # stanfordner_fn_tn = 0\\n # stanfordner_fn_fp = 0\\n # stanfordner_fn_fn = 0\\n\\n # stanfordner_ln_tp = 0\\n # stanfordner_ln_tn = 0\\n # stanfordner_ln_fp = 0\\n # stanfordner_ln_fn = 0\\n\\n # stanfordner_dl_tp = 0\\n # stanfordner_dl_tn = 0\\n # stanfordner_dl_fp = 0\\n # stanfordner_dl_fn = 0\\n\\n # stanfordner_ti_tp = 0\\n # stanfordner_ti_tn = 0\\n # stanfordner_ti_fp = 0\\n # stanfordner_ti_fn = 0\\n\\n # stanfordner_vn_tp = 0\\n # stanfordner_vn_tn = 0\\n # stanfordner_vn_fp = 0\\n # stanfordner_vn_fn = 0\\n\\n # stanfordner_yr_tp = 0\\n # stanfordner_yr_tn = 0\\n # stanfordner_yr_fp = 0\\n # stanfordner_yr_fn = 0\\n\\n # crfsuite_fn_tp = 0\\n # crfsuite_fn_tn = 0\\n # crfsuite_fn_fp = 0\\n # crfsuite_fn_fn = 0\\n\\n # crfsuite_ln_tp = 0\\n # crfsuite_ln_tn = 0\\n # crfsuite_ln_fp = 0\\n # crfsuite_ln_fn = 0\\n\\n # crfsuite_dl_tp = 0\\n # crfsuite_dl_tn = 0\\n # crfsuite_dl_fp = 0\\n # crfsuite_dl_fn = 0\\n\\n # crfsuite_ti_tp = 0\\n # crfsuite_ti_tn = 0\\n # crfsuite_ti_fp = 0\\n # crfsuite_ti_fn = 0\\n\\n # crfsuite_vn_tp = 0\\n # crfsuite_vn_tn = 0\\n # crfsuite_vn_fp = 0\\n # crfsuite_vn_fn = 0\\n\\n # crfsuite_yr_tp = 0\\n # crfsuite_yr_tn = 0\\n # crfsuite_yr_fp = 0\\n # crfsuite_yr_fn = 0\\n \\n\\n for token in tokens:\\n true_labels = token.true_label.all()\\n stanfordner_label = token.predicted_label.filter(label_source=1)[0]\\n crfsuite_label = token.predicted_label.filter(label_source=2)[0]\\n if stanfordner_label in true_labels:\\n stanfordner_tokenwise_total_hit += 1\\n # if stanfordner_label.label == 'FN':\\n # stanfordner_fn_tp += 1\\n else:\\n stanfordner_tokenwise_total_miss += 1\\n\\n if crfsuite_label in true_labels:\\n crfsuite_tokenwise_total_hit += 1\\n else:\\n crfsuite_tokenwise_total_miss += 1\\n\\n print '%s:\\\\nstanfordner: %s\\\\ncrfsuite: %s' % (researcher, float(stanfordner_tokenwise_total_hit)/(stanfordner_tokenwise_total_hit+stanfordner_tokenwise_total_miss), float(crfsuite_tokenwise_total_hit)/(crfsuite_tokenwise_total_hit+crfsuite_tokenwise_total_miss))\\n return HttpResponse('%s:\\\\nstanfordner: %s\\\\ncrfsuite: %s' % (researcher, float(stanfordner_tokenwise_total_hit)/(stanfordner_tokenwise_total_hit+stanfordner_tokenwise_total_miss), float(crfsuite_tokenwise_total_hit)/(crfsuite_tokenwise_total_hit+crfsuite_tokenwise_total_miss)))\\n\\n except Exception, e:\\n raise e\",\n \"def report(request):\\n return render_to_response('application/report.html',\\n {'search_suggestions': _search_suggestions(),\\n },\\n context_instance=RequestContext(request));\",\n \"def report(request):\\n\\tuser_docs = Document.objects.filter(user=User.objects.get(username=request.user.username))\\n\\t# breakpoint()\\n\\tdaily_uploads = user_docs.filter(created_at__day=timezone.now().strftime(\\\"%d\\\"))\\n\\tmonthly_uploads = user_docs.filter(created_at__month=timezone.now().strftime(\\\"%m\\\"))\\n\\tyearly_uploads = user_docs.filter(created_at__year=timezone.now().strftime(\\\"%Y\\\"))\\n\\n\\tdaily_count = daily_uploads.count()\\n\\tmonthly_count = monthly_uploads.count()\\n\\tyearly_count = yearly_uploads.count()\\n\\t# breakpoint()\\n\\n\\tif 'doc_name' in request.GET:\\n\\t\\tpdf_list = user_docs.filter(name__icontains=request.GET['doc_name'])\\n\\telif 'month' in request.GET:\\n\\t\\tpdf_list = user_docs.filter(created_at__month=request.GET['month'])\\n\\t\\t# breakpoint()\\n\\telif 'year' in request.GET:\\n\\t\\tpdf_list = user_docs.filter(created_at__year=request.GET['year'])\\n\\telif 'from' in request.GET and 'to' in request.GET:\\n\\t\\t# breakpoint()\\n\\t\\tpdf_list = user_docs.filter(created_at__range=[request.GET['from'],request.GET['to']])\\n\\n\\telse:\\n\\t\\tpdf_list = user_docs\\n\\tcontext = {'daily_count': daily_count, 'monthly_count': monthly_count, 'yearly_count': yearly_count, 'pdf_list':pdf_list}\\n\\n\\treturn render(request, 'document_manager/report.html', context)\",\n \"def test_context_data_no_taggedtraits(self):\\n TaggedTrait.objects.all().delete()\\n response = self.client.get(self.get_url(self.study.pk))\\n context = response.context\\n self.assertIn('study', context)\\n self.assertEqual(context['study'], self.study)\\n self.assertIn('tag_counts', context)\\n self.assertEqual(len(context['tag_counts']), 0)\\n # The button linking to this view shouldn't be present because study.get_non_archived_traits_tagged_count is 0.\\n self.assertNotContains(response, self.get_url(self.study.pk))\",\n \"def view_graph(request, bullet_id):\\n bullet = Bullet.objects.filter(id=bullet_id).prefetch_related('results__velocity')\\n velocitylist = []\\n chargelist = []\\n moalist = []\\n for b in bullet: \\n for result in b.results.all():\\n chargelist.append(result.charge)\\n moalist.append(result.moa)\\n total_avg = 0 \\n for velocity in result.velocity.all():\\n total_avg += velocity.velocity\\n total_avg /= 3\\n velocitylist.append(total_avg) \\n \\n context = {'bullet': bullet, 'velocitylist': velocitylist, 'chargelist':chargelist, 'moalist':moalist} \\n return render(request, 'graph.html', context)\",\n \"def getTMVAResults(self):\\n return self.ResultsTMVA\",\n \"def monthly_report(request, site, year, month):\\n siteObj = get_object_or_404(Site, pk=site)\\n yearly = YearlyStatistics.objects.filter(siteId=siteObj).filter(year=year)\\n monthly = MonthlyStatistics.objects.filter(siteId=siteObj).filter(year=year).filter(month=month)\\n daily = DailyStatistics.objects.filter(siteId=siteObj).filter(year=year).filter(month=month)\\n dailyManual = RawManualData.objects.filter(siteId=siteObj).filter(year=year).filter(month=month)\\n\\n if siteObj and yearly and monthly and daily and (siteObj.isPublic and\\n siteObj.owner.is_active or siteObj.owner == request.user):\\n climate = {'temp': Climate().TEMP_DISTRIBUTION_LIMITS, 'wind': Climate().WIND_DIRECTION_LIMITS,\\n 'rh': Climate().RH_DISTRIBUTION_LIMITS}\\n datasetNum = []\\n a = MonthlyReport(site, year, month, monthly, yearly, daily, dailyManual)\\n\\n significants = {}\\n for code in Weather.WEATHER_CODE:\\n key = code[1]\\n value = monthly[0].significants.get(code[0], 0)\\n significants[key] = value\\n\\n return render(request, 'climate/monthly_view.html', {'site': siteObj, 'num': datasetNum, 'year': yearly,\\n 'month': month, 'report': a, 'climate': climate,\\n 'significants': significants})\\n else:\\n return render(request, 'climate/main.html', {})\",\n \"def sponsored_bar():\\n articles = []\\n articles.extend([c.child\\n for c in Content.objects\\n .filter(tags__text='Sponsored First')\\n .annotate(recent=Max('issue__issue_date'))\\n .order_by('-recent')[:1]])\\n articles.extend([c.child\\n for c in Content.objects\\n .filter(tags__text='Sponsored Second')\\n .annotate(recent=Max('issue__issue_date'))\\n .order_by('-recent')[:1]])\\n articles.extend([c.child\\n for c in Content.objects\\n .filter(tags__text='Sponsored Third')\\n .annotate(recent=Max('issue__issue_date'))\\n .order_by('-recent')[:1]])\\n articles.extend([c.child\\n for c in Content.objects\\n .filter(tags__text='Sponsored Fourth')\\n .annotate(recent=Max('issue__issue_date'))\\n .order_by('-recent')[:1]])\\n articles.extend([c.child\\n for c in Content.objects\\n .filter(tags__text='Sponsored Fifth')\\n .annotate(recent=Max('issue__issue_date'))\\n .order_by('-recent')[:1]])\\n exclude = ['Sponsored First', 'Sponsored Second', 'Sponsored Third',\\n 'Sponsored Fourth', 'Sponsored Fifth']\\n articles.extend([c.child\\n for c in Content.objects\\n .filter(tags__text='Sponsored Article')\\n .exclude(tags__text__in=exclude)\\n .annotate(recent=Max('issue__issue_date'))\\n .order_by('-recent')[:5 - len(articles)]])\\n \\\"\\\"\\\"\\n Enable this code (and disable the above) for sponsored articles returning\\n by date (without the tag preferences)\\n\\n articles.extend([c.child\\n for c in Content.objects\\n .filter(tags__text='Sponsored Article')\\n .annotate(recent=Max('issue__issue_date'))\\n .order_by('-recent')[:5 - len(articles)]])\\n \\\"\\\"\\\"\\n\\n return {'articles': articles}\",\n \"def show(request, pk, ck):\\n\\n project_container = get_object_or_404(ProjectContainer, id=pk)\\n coding = get_object_or_404(CodingProject, id=ck)\\n\\n user = get_user(request)\\n coder = Person.objects.using('datatracker').get(id=coding.coder)\\n if project_container.code_request is None:\\n mentor = coder\\n else:\\n mentor = Person.objects.using('datatracker').get(id=project_container.code_request.mentor)\\n\\n # According to model areas and working groups should come from documents\\n tags = []\\n keys = []\\n areas = []\\n if project_container.docs:\\n keys = filter(None, project_container.docs.split(';'))\\n docs = list(DocAlias.objects.using('datatracker').filter(name__in=keys).values_list('name', 'document__group__name',\\n 'document__group__parent__name'))\\n for name, gname, gparentname in docs:\\n if gparentname:\\n if gparentname not in areas:\\n areas.append(gparentname) # use acronym?\\n else:\\n areas.append(gname)\\n tags += coding.tags.all()\\n\\n if not areas:\\n areas = [constants.STRING_NONE]\\n if not tags:\\n tags = [constants.STRING_NONE]\\n\\n return render_page(request, constants.TEMPLATE_MATCHES_SHOW, {\\n 'projectcontainer': project_container,\\n 'coding': coding,\\n 'areas': areas,\\n 'tags': tags,\\n 'docs': docs,\\n 'coder': coder,\\n 'mentor': mentor,\\n 'owner': user,\\n 'list_template': constants.TEMPLATE_MATCHES_LIST\\n })\",\n \"def allBroken(request, page=1):\\n objects = im.Issue.objects.filter(resolved_state__isnull=True)\\n args = utils.generatePageList(request, objects, page)\\n args['issues'] = args['objects']\\n issues_list = {'Issues on Unusable Machines':[]}\\n for issue in args['issues']:\\n iss_id = issue.item.item_id\\n machine = mac.Item.objects.get(item_id=iss_id)\\n\\n if machine.unusable:\\n issues_list['Issues on Unusable Machines'].append(issue)\\n\\n args['object_list'] = issues_list.items() \\n args['no_results'] = args['page'].object_list.count() < 1\\n return render_to_response(\\\"grouped_issue_list.html\\\", args,\\n context_instance=RequestContext(request))\",\n \"def run(self, date):\\n\\n out = self.nanoutput()\\n\\n beh = date.glm()\\n expl = beh.explained()\\n for cellgroup in expl:\\n out['devexp_%s' % cellgroup] = expl[cellgroup]\\n\\n return out\",\n \"def minority_aggregation_as_json(request):\\n msa_target_lma_sum = 0\\n msa_target_mma_sum = 0\\n msa_target_hma_sum = 0\\n\\n msa_peer_lma_sum = 0\\n msa_peer_mma_sum = 0\\n msa_peer_hma_sum = 0\\n\\n\\n msa_stats = {}\\n\\n lar_data = loan_originations_as_json(request)\\n lender = get_object_or_404(Institution, pk=request.GET.get('lender'))\\n metro = get_object_or_404(Geo, geo_type=Geo.METRO_TYPE, geoid=request.GET.get('metro'))\\n peer_request = HttpRequest()\\n peer_request.GET['lender'] = lender.institution_id\\n peer_request.GET['metro']= metro.geoid\\n peer_request.GET['peers'] = 'true'\\n peer_lar_data = loan_originations_as_json(peer_request)\\n\\n msa_counties = Geo.objects.filter(geo_type=Geo.COUNTY_TYPE, cbsa=metro.cbsa, year=metro.year)\\n county_stats = {}\\n for county in msa_counties:\\n county_tracts = Geo.objects.filter(geo_type=Geo.TRACT_TYPE, state=county.state, county=county.county, year=metro.year)\\n minority_area_stats = get_minority_area_stats(lar_data, peer_lar_data, county_tracts)\\n county_stats[county.geoid] = assemble_stats(*minority_area_stats)\\n county_stats[county.geoid]['name'] = county.name\\n #tally target msa counts\\n msa_target_lma_sum += county_stats[county.geoid]['lma']\\n msa_target_mma_sum += county_stats[county.geoid]['mma']\\n msa_target_hma_sum += county_stats[county.geoid]['hma']\\n #tally peer msa counts\\n msa_peer_lma_sum += county_stats[county.geoid]['peer_lma']\\n msa_peer_mma_sum += county_stats[county.geoid]['peer_mma']\\n msa_peer_hma_sum += county_stats[county.geoid]['peer_hma']\\n #msa\\n msa_minority_area_stats = (msa_target_lma_sum, msa_target_mma_sum, msa_target_hma_sum, msa_peer_lma_sum, msa_peer_mma_sum, msa_peer_hma_sum)\\n msa_stats = assemble_stats(*msa_minority_area_stats)\\n \\n return {\\n 'msa': msa_stats,\\n 'counties': county_stats,\\n }\",\n \"def get_summary(self, **kwargs):\\n authorized_args = [\\n 'begin', 'end', 'tenant_id', 'service', 'groupby', 'all_tenants']\\n if kwargs.get('groupby', None):\\n kwargs['groupby'] = ','.join(kwargs['groupby'])\\n url = self.get_url('summary', kwargs, authorized_args)\\n return self.api_client.get(url).json()\",\n \"def metadata_reporter(self):\\n logging.info('Creating summary report')\\n header = '{}\\\\n'.format(','.join(self.headers))\\n # Create a string to store all the results\\n data = str()\\n for sample in self.metadata:\\n # Add the value of the appropriate attribute to the results string\\n data += GenObject.returnattr(sample, 'name')\\n # SampleName\\n data += GenObject.returnattr(sample.run, 'SamplePlate')\\n # Genus\\n data += GenObject.returnattr(sample.general, 'closestrefseqgenus')\\n # SamplePurity\\n data += GenObject.returnattr(sample.confindr, 'num_contaminated_snvs')\\n # N50\\n n50 = GenObject.returnattr(sample.quast, 'N50',\\n number=True)\\n if n50 != '-,':\\n data += n50\\n else:\\n data += '0,'\\n # NumContigs\\n data += GenObject.returnattr(sample.quast, 'num_contigs',\\n number=True)\\n # TotalLength\\n data += GenObject.returnattr(sample.quast, 'Total_length',\\n number=True)\\n # MeanInsertSize\\n data += GenObject.returnattr(sample.quast, 'mean_insert',\\n number=True)\\n # InsertSizeSTD\\n data += GenObject.returnattr(sample.quast, 'std_insert',\\n number=True)\\n # AverageCoverageDepth\\n data += GenObject.returnattr(sample.qualimap, 'MeanCoveragedata',\\n number=True)\\n # CoverageDepthSTD\\n data += GenObject.returnattr(sample.qualimap, 'StdCoveragedata',\\n number=True)\\n # PercentGC\\n data += GenObject.returnattr(sample.quast, 'GC',\\n number=True)\\n # MASH_ReferenceGenome\\n data += GenObject.returnattr(sample.mash, 'closestrefseq')\\n # MASH_NumMatchingHashes\\n data += GenObject.returnattr(sample.mash, 'nummatches')\\n # 16S_result\\n data += GenObject.returnattr(sample.sixteens_full, 'sixteens_match')\\n # 16S PercentID\\n data += GenObject.returnattr(sample.sixteens_full, 'percent_id')\\n # CoreGenesPresent\\n data += GenObject.returnattr(sample.gdcs, 'coreresults')\\n # rMLST_Result\\n try:\\n # If the number of matches to the closest reference profile is 53, return the profile number\\n if sample.rmlst.matches == 53:\\n if type(sample.rmlst.sequencetype) is list:\\n rmlst_seq_type = ';'.join(sorted(sample.rmlst.sequencetype)).rstrip(';') + ','\\n else:\\n rmlst_seq_type = GenObject.returnattr(sample.rmlst, 'sequencetype')\\n rmlst_seq_type = rmlst_seq_type if rmlst_seq_type != 'ND,' else 'new,'\\n data += rmlst_seq_type\\n else:\\n # Otherwise the profile is set to new\\n data += 'new,'\\n except AttributeError:\\n data += 'new,'\\n # MLST_Result\\n try:\\n if sample.mlst.matches == 7:\\n if type(sample.mlst.sequencetype) is list:\\n mlst_seq_type = ';'.join(sorted(sample.mlst.sequencetype)).rstrip(';') + ','\\n else:\\n mlst_seq_type = GenObject.returnattr(sample.mlst, 'sequencetype')\\n mlst_seq_type = mlst_seq_type if mlst_seq_type != 'ND,' else 'new,'\\n data += mlst_seq_type\\n else:\\n data += 'new,'\\n except AttributeError:\\n data += 'new,'\\n # MLST_gene_X_alleles\\n try:\\n # Create a set of all the genes present in the results (gene name split from allele)\\n gene_set = {gene.split('_')[0] for gene in sample.mlst.combined_metadata_results}\\n for gene in sorted(gene_set):\\n allele_list = list()\\n # Determine all the alleles that are present for each gene\\n for allele in sample.mlst.combined_metadata_results:\\n if gene in allele:\\n allele_list.append(allele.replace(' ', '_'))\\n # If there is more than one allele in the sample, add both to the string separated by a ';'\\n if len(allele_list) > 1:\\n data += '{},'.format(';'.join(allele_list))\\n # Otherwise add the only allele\\n else:\\n data += allele_list[0] + ','\\n # If there are fewer than seven matching alleles, add a ND for each missing result\\n if len(gene_set) < 7:\\n data += (7 - len(gene_set)) * 'ND,'\\n except AttributeError:\\n # data += '-,-,-,-,-,-,-,'\\n data += 'ND,ND,ND,ND,ND,ND,ND,'\\n # E_coli_Serotype\\n try:\\n # If no O-type was found, set the output to be O-untypeable\\n if ';'.join(sample.ectyper.o_type) == '-':\\n otype = 'O-untypeable'\\n else:\\n otype = sample.ectyper.o_type\\n # Same as above for the H-type\\n if ';'.join(sample.ectyper.h_type) == '-':\\n htype = 'H-untypeable'\\n\\n else:\\n htype = sample.ectyper.h_type\\n serotype = '{otype}:{htype},'.format(otype=otype,\\n htype=htype)\\n # Add the serotype to the data string unless neither O-type not H-type were found; add ND instead\\n data += serotype if serotype != 'O-untypeable:H-untypeable,' else 'ND,'\\n except AttributeError:\\n data += 'ND,'\\n # SISTR_serovar_antigen\\n data += GenObject.returnattr(sample.sistr, 'serovar_antigen').rstrip(';')\\n # SISTR_serovar_cgMLST\\n data += GenObject.returnattr(sample.sistr, 'serovar_cgmlst')\\n # SISTR_serogroup\\n data += GenObject.returnattr(sample.sistr, 'serogroup')\\n # SISTR_h1\\n data += GenObject.returnattr(sample.sistr, 'h1').rstrip(';')\\n # SISTR_h2\\n data += GenObject.returnattr(sample.sistr, 'h2').rstrip(';')\\n # SISTR_serovar\\n data += GenObject.returnattr(sample.sistr, 'serovar')\\n # GeneSeekr_Profile\\n try:\\n if sample.genesippr.report_output:\\n data += ';'.join(sample.genesippr.report_output) + ','\\n else:\\n data += 'ND,'\\n except AttributeError:\\n data += 'ND,'\\n # Vtyper_Profile\\n data += GenObject.returnattr(sample.verotoxin, 'verotoxin_subtypes_set')\\n # AMR_Profile and resistant/sensitive status\\n if sample.resfinder_assembled.pipelineresults:\\n # Profile\\n for resistance, resistance_set in sorted(sample.resfinder_assembled.pipelineresults.items()):\\n data += '{res}({r_set});'.format(res=resistance.replace(',', ';'),\\n r_set=';'.join(sorted(list(resistance_set))))\\n data += ','\\n # Resistant/Sensitive\\n data += 'Resistant,'\\n else:\\n # Profile\\n data += 'ND,'\\n # Resistant/Sensitive\\n data += 'Sensitive,'\\n # Plasmid Result'\\n if sample.mobrecon.pipelineresults:\\n for plasmid, details in sorted(sample.mobrecon.pipelineresults.items()):\\n data += '{plasmid}({details});'.format(plasmid=plasmid,\\n details=details)\\n data += ','\\n else:\\n data += 'ND,'\\n # TotalPredictedGenes\\n data += GenObject.returnattr(sample.prodigal, 'predictedgenestotal',\\n number=True)\\n # PredictedGenesOver3000bp\\n data += GenObject.returnattr(sample.prodigal, 'predictedgenesover3000bp',\\n number=True)\\n # PredictedGenesOver1000bp\\n data += GenObject.returnattr(sample.prodigal, 'predictedgenesover1000bp',\\n number=True)\\n # PredictedGenesOver500bp\\n data += GenObject.returnattr(sample.prodigal, 'predictedgenesover500bp',\\n number=True)\\n # PredictedGenesUnder500bp\\n data += GenObject.returnattr(sample.prodigal, 'predictedgenesunder500bp',\\n number=True)\\n # AssemblyDate\\n data += datetime.now().strftime('%Y-%m-%d') + ','\\n # PipelineVersion\\n data += self.commit + ','\\n # Name of the database used in the analyses\\n data += os.path.split(self.reffilepath)[-1] + ','\\n # Database download date\\n data += self.download_date\\n # Append a new line to the end of the results for this sample\\n data += '\\\\n'\\n # Replace any NA values with ND\\n cleandata = data.replace('NA', 'ND')\\n with open(os.path.join(self.reportpath, 'combinedMetadata.csv'), 'w') as metadatareport:\\n metadatareport.write(header)\\n metadatareport.write(cleandata)\",\n \"def features(request):\\n # Order features by amount of upvotes\\n features_list = Feature.objects.all().order_by('-upvotes')\\n \\n # Pagination for features\\n page = request.GET.get('page', 1)\\n paginator = Paginator(features_list, 10)\\n try:\\n features = paginator.page(page)\\n except PageNotAnInteger:\\n features = paginator.page(1)\\n except EmptyPage:\\n features = paginator.page(paginator.num_pages)\\n \\n # Display graphs\\n chart_total_feature = FeaturesTotalChart() \\n chart_feature_daily = FeaturesDailyStatus()\\n chart_feature_weekly = FeaturesWeeklyStatus()\\n chart_feature_monthly = FeaturesMonthlyStatus()\\n \\n return render(request, \\\"features.html\\\", {\\n \\\"features\\\": features,\\n 'chart_total_feature': chart_total_feature,\\n 'chart_feature_daily': chart_feature_daily,\\n 'chart_feature_weekly': chart_feature_weekly,\\n 'chart_feature_monthly': chart_feature_monthly\\n })\",\n \"def GatherPageData(self, mr):\\n # TODO(jrobbins): Allow deep-linking into this page.\\n canned_query_views = []\\n if mr.project_id:\\n with mr.profiler.Phase('getting canned queries'):\\n canned_queries = self.services.features.GetCannedQueriesByProjectID(\\n mr.cnxn, mr.project_id)\\n canned_query_views = [\\n savedqueries_helpers.SavedQueryView(sq, idx + 1, None, None)\\n for idx, sq in enumerate(canned_queries)]\\n\\n saved_query_views = []\\n if mr.auth.user_id and self.services.features:\\n with mr.profiler.Phase('getting saved queries'):\\n saved_queries = self.services.features.GetSavedQueriesByUserID(\\n mr.cnxn, mr.me_user_id)\\n saved_query_views = [\\n savedqueries_helpers.SavedQueryView(sq, idx + 1, None, None)\\n for idx, sq in enumerate(saved_queries)\\n if (mr.project_id in sq.executes_in_project_ids or\\n not mr.project_id)]\\n\\n return {\\n 'issue_tab_mode': 'issueAdvSearch',\\n 'page_perms': self.MakePagePerms(mr, None, permissions.CREATE_ISSUE),\\n 'canned_queries': canned_query_views,\\n 'saved_queries': saved_query_views,\\n }\",\n \"def tag_view(request, tag_id, error='', message=''):\\n tag = Tag.objects.get(id=tag_id)\\n return index(request=request, error=error, message=message, tag=tag, tag_id=tag_id)\",\n \"def job_metrics(job_classad):\\n counters = []\\n\\n try:\\n user_name = job_classad.get(\\\"Owner\\\",\\\"unknown\\\")\\n except:\\n user_name = \\\"unknown\\\"\\n try:\\n groups = re.findall(r'(?:group_)?(\\\\w+)',job_classad.get(\\\"AccountingGroup\\\",\\\"group_unknown\\\"))\\n exp_name = groups[0]\\n subgroups = []\\n if len(groups) > 1:\\n # sometimes each user has an accounting group, we don't want those\\n if groups[-1] == user_name:\\n subgroups = groups[1:len(groups)-1]\\n else:\\n subgroups = groups[1:]\\n except:\\n exp_name = \\\"unknown\\\"\\n subgroups = []\\n\\n if job_classad[\\\"JobUniverse\\\"] == 7:\\n counters = [\\\".dag.totals\\\"]\\n elif job_classad[\\\"JobStatus\\\"] == 1:\\n counters = [\\\".idle.totals\\\"]\\n if \\\"DESIRED_usage_model\\\" in job_classad:\\n models = set(job_classad[\\\"DESIRED_usage_model\\\"].split(\\\",\\\"))\\n if \\\"DESIRED_Sites\\\" in job_classad:\\n sites = job_classad[\\\"DESIRED_Sites\\\"].split(\\\",\\\")\\n for s in sites:\\n counters.append(\\\".idle.sites.\\\"+s)\\n #if \\\"Fermigrid\\\" not in sites:\\n # models.discard(\\\"DEDICATED\\\")\\n # models.discard(\\\"OPPORTUNISTIC\\\")\\n models_sorted = list(models)\\n if len(models_sorted) == 0:\\n models_sorted = [\\\"impossible\\\"]\\n else:\\n models_sorted.sort()\\n counters.append(\\\".idle.usage_models.\\\" + \\\"_\\\".join(models_sorted))\\n else:\\n counters.append(\\\".idle.usage_models.unknown\\\")\\n elif job_classad[\\\"JobStatus\\\"] == 2:\\n counters = [\\\".running.totals\\\"]\\n if \\\"MATCH_GLIDEIN_Site\\\" in job_classad:\\n site = job_classad[\\\"MATCH_GLIDEIN_Site\\\"]\\n if site == \\\"FNAL\\\" and \\\"MATCH_EXP_JOBGLIDEIN_ResourceName\\\" in job_classad:\\n site = job_classad[\\\"MATCH_EXP_JOBGLIDEIN_ResourceName\\\"]\\n counters.append(\\\".running.sites.\\\" + site)\\n else:\\n counters.append(\\\".running.sites.unknown\\\")\\n elif job_classad[\\\"JobStatus\\\"] == 5:\\n counters = [\\\".held.totals\\\"]\\n else:\\n counters = [\\\".unknown.totals\\\"]\\n\\n metrics = []\\n for counter in counters:\\n metrics.append(\\\"totals\\\"+counter)\\n metrics.append(\\\"experiments.\\\"+exp_name+\\\".totals\\\"+counter)\\n metrics.append(\\\"experiments.\\\"+exp_name+\\\".users.\\\"+user_name+counter)\\n if len(subgroups) > 0:\\n metrics.append(\\\"experiments.\\\"+exp_name+\\\".subgroups.\\\"+\\\".\\\".join(subgroups)+'.'+counter)\\n metrics.append(\\\"users.\\\"+user_name+counter)\\n return metrics\",\n \"def get_results_from_aggregation_sources(self, context):\",\n \"def test_returns_all_studies_with_unreviewed_tagged_traits(self):\\n tag = TagFactory.create()\\n tagged_traits = []\\n for study in self.studies:\\n tmp = TaggedTraitFactory.create(trait__source_dataset__source_study_version__study=study, tag=tag)\\n tagged_traits.append(tmp)\\n get_data = {'q': ''}\\n response = self.client.get(self.get_url(), get_data)\\n pks = get_autocomplete_view_ids(response)\\n self.assertEqual(sorted([study.pk for study in self.studies]), sorted(pks))\",\n \"def getMetaByTags(tags):\\n\\n if type(tags) is str:\\n tag = tags\\n else:\\n tag = tags[0]\\n\\n record = getRecordsByTags(tag)\\n\\n if record is not None:\\n geodata_meta = parseMeta(record)\\n return geodata_meta\\n else:\\n error = \\\"could not get record for tags from gnos\\\"\\n print(error)\\n return None\",\n \"def getMetaByTags(tags):\\n\\n if type(tags) is str:\\n tag = tags\\n else:\\n tag = tags[0]\\n\\n record = getRecordsByTags(tag)\\n\\n if record is not None:\\n geodata_meta = parseMeta(record)\\n return geodata_meta\\n else:\\n error = \\\"could not get record for tags from gnos\\\"\\n print(error)\\n return None\",\n \"def assets_by_bom(request):\\n # If this is a POST request then process the Form data\\n asset_by_bom = None\\n asset_by_range = None\\n warehouse = None\\n bom = None\\n if request.method == 'POST':\\n # Create a form instance and populate it\\n # with data from the request(binding):\\n form = AssetReportForm(request.POST)\\n # Check if the form is valid:\\n if form.is_valid():\\n bom = form.cleaned_data['bom']\\n st_date = form.cleaned_data['st_date']\\n ed_date = form.cleaned_data['ed_date']\\n if bom:\\n bom_obj = BillOfMaterial.objects.get(bom_name=bom)\\n asset_by_bom = list(\\n Asset.objects.values(\\n \\\"bom_name\\\",\\n \\\"product_name__product_name\\\",\\n )\\n .filter(bom_name=bom_obj)\\n .order_by('bom_name')\\n .annotate(count=Count(\\\"bom_name\\\"))\\n )\\n else:\\n asset_by_range = list(\\n Asset.objects.filter(\\n timestamp__gte=st_date,\\n timestamp__lte=ed_date,\\n ).values(\\n \\\"bom_name__bom_name\\\",\\n \\\"product_name__product_name\\\",\\n )\\n .order_by('bom_name')\\n .annotate(count=Count(\\\"bom_name\\\"))\\n )\\n else:\\n form = AssetReportForm()\\n context = {\\n 'form': form,\\n 'bom': bom,\\n 'asset_by_bom': asset_by_bom,\\n 'asset_by_range': asset_by_range\\n }\\n # print(context)\\n\\n return render(request, 'inventory/assets_by_bom.html', context)\",\n \"def get_queryset(self, request):\\n qs = super().get_queryset(request)\\n qs = qs.annotate(models.Count('work', distinct=True))\\n qs = qs.annotate(models.Count('recording', distinct=True))\\n return qs\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.665116","0.59123224","0.55685633","0.55224574","0.5493389","0.5459506","0.54581726","0.5311828","0.530699","0.52737117","0.5272512","0.527097","0.52511895","0.5246451","0.5195243","0.5169995","0.51681095","0.5164607","0.51173073","0.50936943","0.5049706","0.50459784","0.501436","0.49599352","0.49533662","0.49336955","0.4921607","0.49154752","0.4891702","0.48755807","0.48584738","0.4855949","0.4837439","0.48258528","0.48200122","0.48146266","0.48134014","0.48103663","0.48068666","0.48030096","0.47982442","0.47950166","0.47906798","0.47799936","0.4772128","0.477","0.47692156","0.47649902","0.47484833","0.47436312","0.4743606","0.4737141","0.47306943","0.4729637","0.4727296","0.47262493","0.471882","0.47166815","0.4702906","0.4702062","0.47001827","0.46989614","0.4696435","0.46950695","0.46916917","0.4689082","0.4682941","0.46798062","0.4677835","0.46770108","0.46763358","0.4675971","0.4668225","0.46575108","0.46522185","0.46504238","0.46417296","0.4638848","0.46360174","0.46337608","0.46331316","0.46327856","0.46217778","0.4618322","0.46181992","0.46134895","0.46110076","0.46051565","0.46040732","0.46021813","0.46000323","0.45992783","0.4598556","0.45906055","0.45903686","0.45857415","0.45852372","0.45852372","0.4582652","0.45807454"],"string":"[\n \"0.665116\",\n \"0.59123224\",\n \"0.55685633\",\n \"0.55224574\",\n \"0.5493389\",\n \"0.5459506\",\n \"0.54581726\",\n \"0.5311828\",\n \"0.530699\",\n \"0.52737117\",\n \"0.5272512\",\n \"0.527097\",\n \"0.52511895\",\n \"0.5246451\",\n \"0.5195243\",\n \"0.5169995\",\n \"0.51681095\",\n \"0.5164607\",\n \"0.51173073\",\n \"0.50936943\",\n \"0.5049706\",\n \"0.50459784\",\n \"0.501436\",\n \"0.49599352\",\n \"0.49533662\",\n \"0.49336955\",\n \"0.4921607\",\n \"0.49154752\",\n \"0.4891702\",\n \"0.48755807\",\n \"0.48584738\",\n \"0.4855949\",\n \"0.4837439\",\n \"0.48258528\",\n \"0.48200122\",\n \"0.48146266\",\n \"0.48134014\",\n \"0.48103663\",\n \"0.48068666\",\n \"0.48030096\",\n \"0.47982442\",\n \"0.47950166\",\n \"0.47906798\",\n \"0.47799936\",\n \"0.4772128\",\n \"0.477\",\n \"0.47692156\",\n \"0.47649902\",\n \"0.47484833\",\n \"0.47436312\",\n \"0.4743606\",\n \"0.4737141\",\n \"0.47306943\",\n \"0.4729637\",\n \"0.4727296\",\n \"0.47262493\",\n \"0.471882\",\n \"0.47166815\",\n \"0.4702906\",\n \"0.4702062\",\n \"0.47001827\",\n \"0.46989614\",\n \"0.4696435\",\n \"0.46950695\",\n \"0.46916917\",\n \"0.4689082\",\n \"0.4682941\",\n \"0.46798062\",\n \"0.4677835\",\n \"0.46770108\",\n \"0.46763358\",\n \"0.4675971\",\n \"0.4668225\",\n \"0.46575108\",\n \"0.46522185\",\n \"0.46504238\",\n \"0.46417296\",\n \"0.4638848\",\n \"0.46360174\",\n \"0.46337608\",\n \"0.46331316\",\n \"0.46327856\",\n \"0.46217778\",\n \"0.4618322\",\n \"0.46181992\",\n \"0.46134895\",\n \"0.46110076\",\n \"0.46051565\",\n \"0.46040732\",\n \"0.46021813\",\n \"0.46000323\",\n \"0.45992783\",\n \"0.4598556\",\n \"0.45906055\",\n \"0.45903686\",\n \"0.45857415\",\n \"0.45852372\",\n \"0.45852372\",\n \"0.4582652\",\n \"0.45807454\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7262409"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":11260,"cells":{"query":{"kind":"string","value":"This view returns return the usecases of pubmed reports"},"document":{"kind":"string","value":"def pubmed_reports(request):\n\n json_resp = {}\n json_resp['usecase'] = []\n reps = Report.objects.all()\n for r in reps:\n\n if r.id_report.startswith('PUBMED_') and not str(r.name_id) in json_resp['usecase']:\n json_resp['usecase'].append(str(r.name_id))\n return JsonResponse(json_resp)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def pubmed_missing_auto(request):\n\n usecases = UseCase.objects.all()\n json_resp = {}\n json_resp['annotated'] = 0\n json_resp['tot'] = 0\n json_resp['usecase'] = []\n languages = ['English','english']\n for el in usecases:\n use = el.name\n json_resp[use] = {}\n batches = []\n batches.append('all')\n if use.lower() in ['colon','lung','uterine cervix']:\n report = Report.objects.filter(name=el,language__in=languages,institute = 'PUBMED')\n for el in report:\n if el.batch not in batches:\n batches.append(el.batch)\n count_rep = report.count()\n\n if count_rep > 0:\n json_resp['usecase'].append(str(use))\n json_resp['tot'] = json_resp['tot'] + count_rep\n with connection.cursor() as cursor:\n cursor.execute(\n \"SELECT COUNT(*) FROM report AS r INNER JOIN ground_truth_log_file AS g ON g.id_report = r.id_report AND r.language = g.language WHERE r.name = %s AND g.username = %s AND gt_type=%s AND institute = %s and r.language in %s;\",\n [str(use),'Robot_user','labels','PUBMED',tuple(languages)]) # We could consider any of the gt_type\n groundTruths = cursor.fetchone()[0]\n\n json_resp['annotated'] = json_resp['annotated'] + groundTruths\n\n for batch in batches:\n\n json_resp[use][batch] = {}\n if batch == 'all' or batch is None:\n report = Report.objects.filter(name=use,language__in=languages, institute='PUBMED')\n count_rep = report.count()\n json_resp[use][batch]['tot'] = count_rep\n with connection.cursor() as cursor:\n cursor.execute(\n \"SELECT COUNT(*) FROM report AS r INNER JOIN ground_truth_log_file AS g ON g.id_report = r.id_report AND r.language = g.language WHERE r.name = %s AND g.username = %s AND gt_type=%s AND institute = %s and r.language in %s;\",\n [str(use),'Robot_user','labels','PUBMED',tuple(languages)]) # We could consider any of the gt_type\n groundTruths = cursor.fetchone()[0]\n\n if groundTruths is None:\n json_resp[use][batch]['annotated'] = 0\n else:\n json_resp[use][batch]['annotated'] = groundTruths\n else:\n report = Report.objects.filter(name=use,language__in=languages, institute='PUBMED',batch = batch)\n count_rep = report.count()\n json_resp[use][batch]['tot'] = count_rep\n with connection.cursor() as cursor:\n cursor.execute(\n \"SELECT COUNT(*) FROM report AS r INNER JOIN ground_truth_log_file AS g ON g.id_report = r.id_report AND r.language = g.language WHERE r.name = %s AND g.username = %s AND gt_type=%s AND institute = %s and batch = %s and r.language in %s;\",\n [str(use), 'Robot_user', 'labels', 'PUBMED',batch,tuple(languages)]) # We could consider any of the gt_type\n groundTruths = cursor.fetchone()[0]\n\n if groundTruths is None:\n json_resp[use][batch]['annotated'] = 0\n else:\n json_resp[use][batch]['annotated'] = groundTruths\n # print('risposta',json_resp)\n return JsonResponse(json_resp)","def usecases(request):\n\n context = {\n\n }\n\n return render(request, 'hydraviewer/usecases.html', context)","def summaryView(request):\n\n alert_errors = []\n alert_infos = []\n alert_filters = []\n\n runs = get_runs_from_request_filters(\n request, alert_errors, alert_infos, alert_filters\n )\n\n summary = SummaryReport(runs)\n\n context = {\n \"refs\": summary.reference_runs(),\n \"runs\": summary.runs_checked_per_type(),\n \"tk_maps\": summary.tracker_maps_per_type(),\n \"certified_runs\": summary.certified_runs_per_type(),\n \"sums\": summary.sum_of_quantities_per_type(),\n \"alert_errors\": alert_errors,\n \"alert_infos\": alert_infos,\n \"alert_filters\": alert_filters,\n }\n\n return render(request, \"certhelper/summary.html\", context)","def check_PUBMED_reports(request):\n\n json_resp = {}\n json_resp['count'] = 0\n pubmed_arts = Report.objects.filter(institute = 'PUBMED')\n for el in pubmed_arts:\n if el.id_report.startswith('PUBMED'):\n json_resp['count'] += 1\n return JsonResponse(json_resp,safe=False)","def medtag_reports(request):\n\n json_resp = {}\n json_resp['usecase'] = []\n reps = Report.objects.all()\n for r in reps:\n if not r.id_report.startswith('PUBMED_') and not str(r.name_id) in json_resp['usecase']:\n json_resp['usecase'].append(str(r.name_id))\n return JsonResponse(json_resp)","def check_medtag_reports(request):\n\n json_resp = {}\n json_resp['count'] = 0\n medtag_arts = Report.objects.all().exclude(institute = 'PUBMED')\n # for el in pubmed_arts:\n # if el.id_report.startswith('PUBMED'):\n json_resp['count'] = medtag_arts.count()\n return JsonResponse(json_resp,safe=False)","def summary_list(request):\r\n qs = Summary.objects.all()\r\n context = {'summary_records': qs}\r\n return render(request, 'weather/summary_list.html', context)","def training_report_view(request, application_slug):\n return training_report(request, application_slug, attach=False)","def survivor_reports(request):\n\n if request.method == 'GET':\n data = {}\n total_survivors = infected = non_infected = water = food = medication = ammunition = pointslost = 0\n for i in Survivor.objects.all():\n total_survivors += 1\n if i.infected is False:\n non_infected += 1\n water += i.water\n food += i.food\n medication += i.medication\n ammunition += i.ammunition\n if i.infected is True:\n infected += 1\n pointslost += (4 * i.water)\n pointslost += (3 * i.food)\n pointslost += (2 * i.medication)\n pointslost += (1 * i.ammunition)\n\n if total_survivors != 0:\n data['Percentage of infected survivors'] = str(round((infected/total_survivors), 2) * 100) + '%'\n data['Percentage of non-infected survivors'] = str(round((non_infected/total_survivors), 2) * 100) + '%'\n data['Average amount of water by survivor'] = round(water/non_infected,1)\n data['Average amount of food by survivor'] = round(food/non_infected,1)\n data['Average amount of medication by survivor'] = round(medication/non_infected,1)\n data['Average amount of ammunition by survivor'] = round(ammunition/non_infected,1)\n data['Points lost because of infected survivor'] = pointslost\n else:\n data['Percentage of infected survivors'] = '0.0%'\n data['Percentage of non-infected survivors'] = '0.0%'\n data['Average amount of water by survivor'] = 0\n data['Average amount of food by survivor'] = 0\n data['Average amount of medication by survivor'] = 0\n data['Average amount of ammunition by survivor'] = 0\n data['Points lost because of infected survivor'] = 0\n return Response(data, status=status.HTTP_200_OK)","def generate_report():\n\n # Fetch the top 3 most viewed articles and number of views and print them\n articles_query = get_articles_query()\n popular_articles = execute_query(articles_query)\n print_top_articles(popular_articles)\n\n # Fetch the most popular authors and print them\n authors_query = get_authors_query()\n popular_authors = execute_query(authors_query)\n print_authors(popular_authors)\n\n # Print the days when there were more than 1% errors in HTTP requests\n errors_query = get_errorData_query()\n error_data = execute_query(errors_query)\n print_error_data(error_data)","def report_missing_auto(request):\n\n usecases = UseCase.objects.all()\n json_resp = {}\n languages = ['english', 'English']\n for el in usecases:\n use = el.name\n batches = []\n batches.append('all')\n if use.lower() in ['colon','lung','uterine cervix']:\n report = Report.objects.filter(name=el,language__in=languages).exclude(institute = 'PUBMED')\n count_rep = report.count()\n for rp in report:\n if rp.batch not in batches:\n batches.append(rp.batch)\n # print(el)\n # print(count_rep)\n\n if count_rep > 0:\n json_resp[use] = {}\n for batch in batches:\n batch = str(batch)\n json_resp[use][batch] = {}\n if batch == 'all':\n json_resp[use][batch]['tot'] = count_rep\n with connection.cursor() as cursor:\n cursor.execute(\n \"SELECT COUNT(*) FROM report AS r INNER JOIN ground_truth_log_file AS g ON g.id_report = r.id_report AND r.language = g.language WHERE r.name = %s AND g.username = %s AND gt_type=%s and institute != %s and r.language in %s;\",\n [str(use),'Robot_user','labels','PUBMED',tuple(languages)]) # We could consider any of the gt_type\n groundTruths = cursor.fetchone()[0]\n if groundTruths is None:\n json_resp[use][batch]['annotated'] = 0\n else:\n json_resp[use][batch]['annotated'] = groundTruths\n else:\n report_count = Report.objects.filter(name=el,batch = batch,language__in=languages).exclude(institute = 'PUBMED').count()\n json_resp[use][batch]['tot'] = report_count\n with connection.cursor() as cursor:\n cursor.execute(\n \"SELECT COUNT(*) FROM report AS r INNER JOIN ground_truth_log_file AS g ON g.id_report = r.id_report AND r.language = g.language WHERE r.name = %s AND g.username = %s AND gt_type=%s and institute != %s and batch = %s and r.language in %s;\",\n [str(use), 'Robot_user', 'labels', 'PUBMED',batch, tuple(languages)]) # We could consider any of the gt_type\n groundTruths = cursor.fetchone()[0]\n if groundTruths is None:\n json_resp[use][batch]['annotated'] = 0\n else:\n json_resp[use][batch]['annotated'] = groundTruths\n # print(json_resp)\n return JsonResponse(json_resp)","def analysis_view(request):\n return render(request, \"tracker/analysis.html\")","def get_reports(request):\n\n inst = request.GET.get('institute',None)\n use = request.GET.get('usec',None)\n print(use)\n lang = request.GET.get('lang',None)\n batch = request.GET.get('batch',None)\n all = request.GET.get('all',None)\n actual_report = request.GET.get('actual_report',None)\n if all == 'all':\n # All the reports are returned independently of the usecase, the language or institute.\n use_obj = UseCase.objects.get(name = use)\n reps = Report.objects.filter(institute = inst,name = use_obj,language = lang).values('id_report','report_json','language')\n json_resp = {}\n json_resp['report'] = []\n\n for report in reps:\n json_rep = {}\n json_rep['id_report'] = report['id_report']\n json_rep['language'] = report['language']\n json_rep['report_json'] = report['report_json']\n json_resp['report'].append(json_rep)\n return JsonResponse(json_resp)\n\n if(inst != None and use != None and lang != None and batch != None):\n\n \"\"\" It is used in the options modal: if the reuqired combination of institute, language and usecase has 0 reports\n associated, a message is returned. In this case this view returns the number of reports associated to a specific \n configuration required \"\"\"\n\n rep = Report.objects.filter(institute = inst, name = use, language = lang, batch = batch)\n json_count = {'count':len(rep)}\n return JsonResponse(json_count)\n\n usecase = request.session.get('usecase',None)\n mode1 = request.session.get('mode',None)\n mode = NameSpace.objects.get(ns_id=mode1)\n language = request.session.get('language',None)\n institute = request.session.get('institute',None)\n username = request.session['username']\n batch = request.session['batch']\n token = request.GET.get('configure',None) # This parameter is set when\n\n jsonError = {'error':'something wrong with params!'}\n if usecase is not None and language is not None and institute is not None and batch is not None:\n # Get the reports associated to the usecase, language and institute of the SESSION\n reports1 = Report.objects.filter(name = usecase, language = language, institute = institute,batch=batch)\n if mode1 == 'Robot':\n # gts_r = GroundTruthLogFile.objects.filter(language = language,ns_id=mode).values('id_report')\n # gts_r1 = GroundTruthLogFile.objects.filter(language=language, ns_id=mode).order_by(\n # 'id_report').distinct('id_report').values('id_report')\n # ids1 = [el['id_report'] for el in gts_r1]\n # print(len(ids1))\n gts_r1 = GroundTruthLogFile.objects.filter(id_report__in = reports1,language = language,ns_id=mode).order_by('id_report').distinct('id_report').values('id_report')\n ids = [el['id_report'] for el in gts_r1]\n # print(len(ids))\n # print(ids == ids1)\n # for el in gts_r1:\n # # if el['id_report'] not in ids and Report.objects.filter(language = language, id_report = el['id_report'], batch = batch).exists():\n # ids.append(el['id_report'])\n\n reports1 = Report.objects.filter(id_report__in=ids,name = usecase, language = language, institute = institute,batch = batch)\n\n json_resp = {}\n json_resp['report'] = []\n if reports1.exists():\n reports = reports1.values('id_report','report_json','language')\n for report in reports:\n json_rep = {}\n json_rep['id_report'] = report['id_report']\n json_rep['language'] = report['language']\n json_rep['report_json'] = report['report_json']\n json_resp['report'].append(json_rep)\n\n json_resp['report'].sort(key=lambda json: json['id_report'], reverse=False) # Reports are sorted by ID\n # json_resp['report'].sort(key=lambda json: json['report_json']['report_id'], reverse=False) # Reports are sorted by ID\n json_resp['index'] = 0\n\n if token is not None:\n # Get the last ground truth given the session parameters.\n gt = get_last_groundtruth(username, usecase, language, institute,mode,batch)\n else:\n # Get the last ground truth of the user.\n gt = get_last_groundtruth(username,None, None, None,mode,batch)\n\n if gt is not None:\n # The index is updated and it characterizes the first report of the list shown to the user.\n id_report = gt['id_report']\n use = gt['use_case']\n lang = gt['language']\n institute = gt['institute']\n report_json = Report.objects.get(id_report = id_report, name = use, language = lang, institute = institute)\n rep_json = report_json.report_json\n index = json_resp['report'].index({'id_report':id_report,'language':lang,'report_json':rep_json})\n json_resp['index'] = int(index)\n if actual_report is not None:\n index = json_resp['report'].index(actual_report)\n json_resp['index'] = int(index)\n\n return JsonResponse(json_resp)\n else:\n return JsonResponse(jsonError,status=500)","def report(request):\n\tuser_docs = Document.objects.filter(user=User.objects.get(username=request.user.username))\n\t# breakpoint()\n\tdaily_uploads = user_docs.filter(created_at__day=timezone.now().strftime(\"%d\"))\n\tmonthly_uploads = user_docs.filter(created_at__month=timezone.now().strftime(\"%m\"))\n\tyearly_uploads = user_docs.filter(created_at__year=timezone.now().strftime(\"%Y\"))\n\n\tdaily_count = daily_uploads.count()\n\tmonthly_count = monthly_uploads.count()\n\tyearly_count = yearly_uploads.count()\n\t# breakpoint()\n\n\tif 'doc_name' in request.GET:\n\t\tpdf_list = user_docs.filter(name__icontains=request.GET['doc_name'])\n\telif 'month' in request.GET:\n\t\tpdf_list = user_docs.filter(created_at__month=request.GET['month'])\n\t\t# breakpoint()\n\telif 'year' in request.GET:\n\t\tpdf_list = user_docs.filter(created_at__year=request.GET['year'])\n\telif 'from' in request.GET and 'to' in request.GET:\n\t\t# breakpoint()\n\t\tpdf_list = user_docs.filter(created_at__range=[request.GET['from'],request.GET['to']])\n\n\telse:\n\t\tpdf_list = user_docs\n\tcontext = {'daily_count': daily_count, 'monthly_count': monthly_count, 'yearly_count': yearly_count, 'pdf_list':pdf_list}\n\n\treturn render(request, 'document_manager/report.html', context)","def admin_evaluate_reports(request):\n if not validate_request(request): return redirect(reverse(URL_FORBIDDEN))\n \n admin = auth.get_user(request)\n \n if request.method == \"GET\":\n all_thesis = [] # list of dict\n \n for panelmember in PanelMember.objects.filter(Q(status = 'F') | Q(status = 'Z')).filter(feedback_at = 'A'):\n thesis = panelmember.thesis\n dict = {}\n dict['title'] = thesis.title\n dict['student_full_name'] = thesis.student.first_name + \" \" + thesis.student.last_name\n dict['report'] = panelmember.feedback_with_referee_details\n dict['student_username'] = thesis.student.user.username\n dict['id'] = thesis.id\n dict['referee_name'] = panelmember.referee.user.first_name + ' ' + panelmember.referee.user.last_name\n dict['referee_id'] = panelmember.referee.id\n all_thesis.append(dict)\n \n return render(request, 'app/admin/view_finalReports.html', {\n 'title':'Final Reports',\n 'layout_data' : get_layout_data(request),\n 'all_thesis' : all_thesis\n })\n elif request.method == \"POST\":\n form = PanelMember2Form(request.POST, request.FILES)\n \n\n thesis = int(request.POST['thesis'])\n referee = int(request.POST['referee'])\n \n if form.is_valid() and validate_pdf(request.FILES['feedback_without_referee_details']):\n referee = Referee.objects.get(id = referee)\n thesis = Thesis.objects.get(id = thesis)\n panelmember = PanelMember.objects.get(thesis = thesis,referee = referee)\n panelmember.feedback_at = 'G'\n \n time = str(datetime.datetime.now())\n timestamp = ''\n for i in time:\n if not (i == ':' or i == '-'):\n timestamp += i\n request.FILES['feedback_without_referee_details'].name = \"Evaluation_Report_\"+thesis.student.user.username+\"_\"+timestamp+\".pdf\"\n \n panelmember.feedback_without_referee_details = request.FILES['feedback_without_referee_details']\n panelmember.save()\n\n total_feedbacks = PanelMember.objects.filter(thesis = thesis, feedback_at = 'G').count()\n if total_feedbacks == thesis.indian_referees_required + thesis.foreign_referees_required:\n _update_student_status(thesis, STATUS_ID_THESIS_FEEDBACKS_RECEIVED) \n\n # send notification to all guide\n send_notification_to_all_guides(admin, thesis, \"A feedback report has been sent of student \" + thesis.student.first_name + \" \" + thesis.student.last_name)\n # email\n subject = \"[Feed Back reports] of the Thesis titled\" + thesis.title\n content = \"<br>Dear Sir/Madam,</br><br></br><br></br>\"+\"A feedback report has been sent of student \" + thesis.student.first_name + \" \" + thesis.student.last_name +'. Please Check the PhD Portal for more details.'+\"<br></br><br></br>Regards,<br></br>PhDPortal.\"\n \n email = []\n\n for thesisGuide in ThesisGuide.objects.filter(thesis = thesis):\n receiver = Faculty.objects.get(user = thesisGuide.guide.user)\n email.append(receiver.email)\n\n send_email_task.delay(email, subject, content)\n return redirect(reverse(admin_evaluate_reports))\n else:\n return redirect(reverse(URL_BAD_REQUEST))\n else:\n return redirect(reverse(URL_BAD_REQUEST))","def data_set_characterization(request):\n admin_approved = Phenotype.objects.filter(phenotypestatus__status='A')\n user_submitted = Phenotype.objects.filter(phenotypestatus__status='U')\n sample_files = SampleFile.objects.all()\n\n admin_approved_count = len(admin_approved)\n user_submitted_count = len(user_submitted)\n\n traits_by_chrom = SNP.objects.filter(Q(snpstatus__status='A') | Q(snpstatus__status='U')).values('chromosome_num').annotate(num_traits=Count('chromosome_num'))\n\n context = {'user_submitted_count':user_submitted_count,\n 'admin_approved_count':admin_approved_count,\n 'sample_files':sample_files,\n 'traits_by_chrom':traits_by_chrom}\n\n return render(request, 'SNP_Feature_View/data_set_characterization.html', context)","def list(self, request, scope=None):\n\n qs = self.get_queryset()\n if scope == 'summary':\n total = qs.aggregate(total=Sum('total_value'))['total'] or 0.0\n return Response({'total_investment': total}, status=200)\n else:\n serializer = self.get_serializer(qs, many=True)\n return Response(serializer.data, status=status.HTTP_200_OK)","def referee_evaluate_synopsis(request):\n if not validate_request(request): return redirect(reverse(URL_FORBIDDEN))\n \n user = auth.get_user(request)\n referee = Referee.objects.get(user = user)\n \n if request.method == \"GET\":\n all_thesis = [] # list of dict\n \n for panelMember in PanelMember.objects.filter(referee = referee).filter(status = 'S'):\n thesis = panelMember.thesis\n dict = {}\n dict['title'] = thesis.title\n dict['student_full_name'] = thesis.student.first_name + \" \" + thesis.student.last_name\n dict['synopsis'] = thesis.synopsis\n dict['student_username'] = thesis.student.user.username\n dict['id'] = thesis.id\n \n all_thesis.append(dict)\n \n return render(request, 'app/referee/evaluate_synopsis.html', {\n 'title':'Unevaluated PhD Synopsis',\n 'layout_data' : get_layout_data(request),\n 'all_thesis' : all_thesis\n })\n else:\n return redirect(reverse(URL_BAD_REQUEST))","def get_stats_array_per_usecase(request):\n\n mode = request.GET.get('mode',None)\n usern = request.GET.get('member',request.session['username'])\n username = User.objects.get(username=usern, ns_id=mode)\n language = request.GET.get('language',request.session['language'])\n institute = request.GET.get('institute',request.session['institute'])\n batch = request.GET.get('batch',request.session['batch'])\n json_dict = {}\n js = {}\n js['original'] = {}\n js['percent'] = {}\n json_dict['medtag'] = get_array_per_usecase(username,mode,language,institute,batch)\n json_dict['pubmed'] = get_array_per_usecase_PUBMED(username,mode,language,institute,batch)\n\n\n # print(json_dict)\n return JsonResponse(json_dict)","def Reports(request):\n assert isinstance(request, HttpRequest)\n iscapable =False\n if request.user.username in get_librarians():\n iscapable=True;\n #getting books per each department\n booksperdepart={}\n borrowedperStudent={}\n ordersplacedbylibrairans={}\n \n books=get_valid_Books()\n invent=get_Inv()\n for k,v in books.items():\n if v.dpt_id.name not in booksperdepart.keys():\n booksperdepart[v.dpt_id.name]=v.invt.qty\n else:\n li =booksperdepart[v.dpt_id.name]\n li+=v.invt.qty\n booksperdepart[v.dpt_id.name]=li\n libmem =get_libmems()\n borrowed=get_Borrowed()\n for k,v in borrowed.items():\n composite=v.cwid.cwid.stu_name+\" - \"+v.cwid.cwid.stu_id\n if composite not in borrowedperStudent.keys():\n borrowedperStudent[composite]=1\n else:\n li =borrowedperStudent[composite]\n li+=1\n borrowedperStudent[composite]=li\n librianorders=get_LibrarianOrders()\n for k,v in librianorders.items():\n composite=v.lb_id.name+\" - \"+v.lb_id_id\n if composite not in ordersplacedbylibrairans.keys():\n ordersplacedbylibrairans[composite]=[list([v.i_id.i_id.title,v.qty,v.i_id.i_id.dpt_id.name,v.status])]\n else:\n li =ordersplacedbylibrairans[composite]\n li.append(list([v.i_id.i_id.title,v.qty,v.i_id.i_id.dpt_id.name,v.status]))\n ordersplacedbylibrairans[composite]=li\n\n \n\n\n \n\n return render(\n request,\n 'app/reports.html',\n {\n 'title':'Reports Page',\n 'perdptbks':list(zip(booksperdepart.keys(),booksperdepart.values())),\n 'peruserbks':list(zip(borrowedperStudent.keys(),borrowedperStudent.values())),\n 'perlibrarian':list(zip(ordersplacedbylibrairans.keys(),ordersplacedbylibrairans.values())),\n 'iscapable':iscapable,\n 'year':datetime.now().year,\n }\n )","def get_test_report(request, **kwargs): \n\t\n #Fetching the details of the selected event\n test_list = sidecar.events.test_report(project_id=kwargs['test_id'])\n report_list = []\n\t\n #Creating the list for the report\n for tests in test_list._logs:\n\tjson_test = json.loads(tests['data'])\n\ttests['success'] = json_test['success'] \n\ttests['time'] = json_test['time']\n\ttests['test_cases'] = json_test['test_cases']\n\treport_list.append(tests)\n\n #Making the context and sending to template\n context = {\n \"page_title\": _(\"Test Results\"),\n \"tests\": report_list\n }\n return render(request, 'rally_dashboard/events/test_detail.html', context)","def public_workouts(request):\n practices = Practice.objects.all()\n individuals = Individual.objects.all()\n workouts = Workout.objects.all()\n c = Context({\n 'workout_list': workouts,\n 'practice_list': practices,\n 'individuals_list': individuals,\n 'title': 'Community Workouts',\n })\n return render_to_response(\"workouts/list.html\", c,\n context_instance=RequestContext(request))","def summary(request):\n\n # Generate counts of some of the main objects\n num_courses = models.Course.objects.all().count()\n num_quizzes = models.Quiz.objects.all().count()\n num_questions = models.Question.objects.count()\n num_students = models.User.objects.count()\n num_visits = request.session.get('num_visits', 1)\n request.session['num_visits'] = num_visits + 1\n\n context = {\n 'num_courses': num_courses,\n 'num_quizzes': num_quizzes,\n 'num_questions': num_questions,\n 'num_students': num_students,\n 'num_visits': num_visits,\n }\n\n return Response(context)","def analysis():\n\n response_all_doctors_and_appointments = requests.post(server_url + 'doctor/all_doctors_and_all_appointments')\n doctors_and_appointments = response_all_doctors_and_appointments.json()\n\n return render_template('clerks/analysis.html', doctors_and_appointments=doctors_and_appointments)","def pyp_reports(request):\n student_id = int(get_from_matchdict('id', request.matchdict))\n\n pdf = get_from_matchdict('pdf', request.matchdict)\n check = request.params.get('check')\n if check and check.lower() == 'true':\n check = True\n else:\n check = False\n\n internal_check = request.params.get('internal_check')\n\n mb_user = request.session.get('mb_user', None)\n if not mb_user:\n # FIXME: Need to re-do it\n pass\n # api_token = request.params.get('api_token')\n # if not api_token or api_token != gns.config.managebac.api_token:\n # return HTTPForbidden()\n elif mb_user.type.startswith('Advisor') or mb_user.type == 'Account Admins':\n # let them in\n pass\n else:\n return HTTPForbidden()\n\n term_id = gns.config.managebac.current_term_id\n with DBSession() as session:\n try:\n rep_statement = session.query(PrimaryReport).\\\n options(joinedload('course')).\\\n filter(\n PrimaryReport.term_id == term_id,\n PrimaryReport.student_id == student_id,\n # PrimaryReport.homeroom_comment!=''\n )\n stu_statement = session.query(Students).filter_by(id=student_id)\n student = stu_statement.one()\n report = rep_statement.one()\n gns.tutorial(\"Got the target student\",edit=(stu_statement, '.sql'))\n gns.tutorial(\"Got Primary report with course information\", edit=(rep_statement, '.sql'))\n except NoResultFound:\n if pdf:\n # raw_input('no report entry for this student: {} with term_id {}'.format(student_id, term_id))\n raise HTTPNotFound()\n else:\n raise HTTPFound(location=request.route_url(\"student_pyp_report_no\", id=student_id))\n except MultipleResultsFound:\n print(\"Issue with database!\")\n raise HTTPInternalServerError(\"Issue with database!\")\n\n title = u\"IGB International School (June 2016): Student Report for {} {}\".format(student.first_name, student.last_name)\n\n # This bit is the only manual info that isn't on managebac\n uoi_table = {\n -1: {\n # ey sem 1\n 1: dict(title=\"Who We Are\", central_idea=\"Playing and learning together enables us to come to new understandings.\"),\n 2: dict(title=\"Sharing The Planet\", central_idea=\"Our lives are interconnected with living things.\"),\n # ey sem 2\n 3: dict(title=\"How the World Works\", central_idea=\"Water is all around us and has many uses.\"),\n 4: dict(title=\"How We Express Ourselves\", central_idea=\"Stories inform, provoke us and provide enjoyment.\"),\n },\n 0: {\n # kg sem 1\n 1: dict(title=\"Who We Are\", central_idea=\"We are part of a community who work, learn, and play together\"),\n 2: dict(title=\"How We Organise Ourselves\", central_idea=\"Communities create systems to fullfill a need.\"),\n 3: dict(title=\"Where We Are in Place and Time\", central_idea=\"Shelters look different and serve a purpose.\"),\n\n # kg sem 2\n 4: dict(title=\"Sharing the Planet\", central_idea=\"People's choices and actions impact the environment and their community.\"),\n 5: dict(title=\"How the World Works\", central_idea=\"Our body and man made resources help protect us from the natural environment.\"),\n 6: dict(title=\"How We Express Ourselves\", central_idea=\"An audience can be engaged through performance.\")\n },\n 1: {\n # gr1 sem 1\n 1: dict(title=\"How we organize ourselves\", central_idea=\"Humans use tools and strategies to understand and organise their environment.\"),\n 2: dict(title=\"Who We Are\", central_idea=\"Games provide us with opportunities to develop an understanding of ourselves and others.\"),\n 3: dict(title=\"How We Express Ourselves\", central_idea=\"Celebrations are an opportunity to reflect and appreciate cultures and beliefs.\"),\n # gr1 sem 2\n 4: dict(title=\"How the World Works\", central_idea=\"Machines make a difference to the way we live our lives.\"),\n 5: dict(title=\"Sharing the Planet\", central_idea=\"Water is essential to life and is a limited resource to many.\"),\n 6: dict(title=\"Where We Are in Place and Time\", central_idea=\"Clocks are a universal measurement tool of time that have had an impact in the past and the present.\"),\n },\n 2: {\n # gr2 sem 1\n 1: dict(title=\"Who We Are\", central_idea=\"With rights come responsibilities.\"),\n 2: dict(title=\"How We Express Ourselves\", central_idea=\"Cultures tell stories in different ways and for different reasons.\"),\n 3: dict(title=\"How We Organize Ourselves\", central_idea=\"Number system provide a common language we can use to make sense of the world.\"),\n # gr2 sem 2\n 4: dict(title=\"Sharing The Planet\", central_idea=\"Plants sustain life on earth and we have a responsible role to play\"),\n 5: dict(title=\"Where we are in Place and Time\", central_idea=\"Influence can change people and their environment.\"),\n 6: dict(title=\"How the World Works\", central_idea=\"Forces are a vital part of our survival.\"),\n },\n 3: {\n # gr3 sem 1\n 1: dict(title=\"How We Organise Ourselves\", central_idea=\"Communication connects people.\"),\n 2: dict(title=\"Sharing the Planet\", central_idea=\"People can conserve the world's resources through responsible behaviours\"),\n 3: dict(title=\"Where We are in Place and Time\", central_idea=\"Innovations from past civilizations have an influence on the present\"),\n # gr3 sem 2\n 4: dict(title=\"How the World Works\", central_idea=\"Safe structures are designed and built for purpose and consider the environment and materials.\"),\n 5: dict(title=\"Who We Are\", central_idea=\"Communication connects people and communities.\"),\n 6: dict(title=\"How We Express Ourselves\", central_idea=\"Nature can inspire people to express their creativity.\"),\n },\n 4: {\n # gr4 sem 1\n 1: dict(title=\"How We Express Ourselves\", central_idea=\"Media influences how we think and the choices we make.\"),\n 2: dict(title=\"Sharing the Planet\", central_idea=\"Organisms rely on one another to balance ecosystems.\"),\n 3: dict(title=\"How we Organise Ourselves\", central_idea=\"Societies establish systems for trade and commerce to meet needs and wants.\"),\n # gr4 sem 2\n 4: dict(title=\"Where We Are in Place and Time\", central_idea=\"The quest for understanding has led to exploration and discovery.\"),\n 5: dict(title=\"How The World Works\", central_idea=\"Earth has formed over time and is still changing.\"),\n 6: dict(title=\"Who We Are\", central_idea=\"People's beliefs influence their actions.\"),\n },\n 5: {\n # gr5 sem 1\n 1: dict(title=\"How we Organise Ourselves\", central_idea=\"All societies have rules and reasons for these rules.\"),\n 2: dict(title=\"Where We Are in Place and Time\", central_idea=\"Malaysia's cultural diversity has been shaped by its history.\"),\n 3: dict(title=\"How the World Works\", central_idea=\"Changes to matter can be of a chemical and/or physical nature.\"),\n # gr5 sem 2\n 4: dict(title=\"Sharing The Planet\", central_idea=\"The choices we make during moments of conflict affect our relationships\"),\n 5: dict(title=\"How We Express Ourselves: Exhibition\", central_idea=\"Artists seek to evoke an emotional response from their audience.\"),\n 6: dict(title=\"Who We Are\", central_idea=\"External and internal factors cause changes in our lives\"),\n },\n }\n\n chinese_teachers = {\n 10792613: [11203970, 10836999, 10912649, 10863230, 11544715, 11707916, 11609996, 11707918, 11708046, 10912651, 11707928, 11274137, 11707932, 11707934, 11204000, 11204641, 11204001, 11708067, 11270692, 11707940, 11204385, 11563304, 11204008, 11153068, 11573550, 11707952, 10882225, 11204017, 11707957, 10834618, 10866874, 11080380, 10893375, 11707840, 11190340, 10834630, 11611847, 10834633, 10834636, 11693517, 11707984, 11203923, 11707859, 10834645, 10834648, 10834649, 10834651, 11707870, 11182305, 11203938, 11200870, 10973671, 11707882, 11708014, 11203950, 11203952, 11708018, 11203954, 10882162, 11633398, 11707900, 11538429, 11124222, 11135103, 11737995, 11621139, 11707870, 10882159], # xiaopiong\n 11256632: [11204609, 10836994, 11707907, 11135108, 10836999, 11135112, 10837001, 11203979, 10865037, 11707924, 11621141, 11203988, 11204377, 11173915, 10913691, 11204637, 10856823, 11204383, 11204640, 11707939, 11204392, 11614634, 11364525, 10882226, 11204660, 11190071, 10834616, 10834617, 11464377, 10866873, 10866876, 10834621, 10834622, 10866877, 10856636, 11578945, 11611841, 10893379, 10834628, 10834625, 11611847, 10834635, 10834640, 10834642, 10834643, 11930324, 11707860, 11203926, 11707990, 11426392, 11502297, 11578839, 11707869, 11708005, 10834661, 11203946, 11324785, 11124210, 10863222, 11124215, 10856824, 11203961, 10856826, 11124219, 11204605, 11707902, 10986488], # nancy\n }\n\n students_chinese_teachers = {}\n\n for teacher_id, student_ids in chinese_teachers.items():\n with DBSession() as session:\n teacher = session.query(Teachers).filter_by(id=teacher_id).one()\n for this_student in student_ids:\n students_chinese_teachers[this_student] = teacher\n\n bahasa_teachers = {\n 10872708: [10908165, 10856828],\n }\n students_bahasa_teachers = {}\n for teacher_id, student_ids in bahasa_teachers.items():\n with DBSession() as session:\n teacher = session.query(Teachers).filter_by(id=teacher_id).one()\n for this_student in student_ids:\n students_bahasa_teachers[this_student] = teacher\n\n if 'Grade' in report.course.name or 'Kindergarten' in report.course.name:\n which_folder = 'grades'\n template = 'frontend:elem_reports/templates/student_pyp_report.pt'\n\n with DBSession() as session:\n try:\n rep_statement = session.query(PrimaryReport).\\\n options(joinedload('course')).\\\n options(joinedload('sections')).\\\n options(joinedload('sections.learning_outcomes')).\\\n options(joinedload('sections.teachers')).\\\n options(joinedload('sections.strands')).\\\n options(joinedload('teacher')).\\\n filter(\n PrimaryReport.term_id == term_id,\n PrimaryReport.student_id == student_id\n )\n att_statement = session.query(Absences).filter_by(term_id=term_id, student_id=student_id)\n\n attendance = att_statement.one()\n report = rep_statement.one()\n\n gns.tutorial(\"Got K-5 report info with joined information\", edit=(rep_statement, '.sql'), banner=True)\n except NoResultFound:\n if pdf:\n # raw_input(\"No K-5 report entry\")\n raise HTTPNotFound()\n else:\n raise HTTPFound(location=request.route_url(\"student_pyp_report_no\", id=student_id))\n\n subject_rank = {\n 'language': 0,\n 'mathematics': 1,\n 'unit of inquiry 1': 2,\n 'unit of inquiry 2': 3,\n 'unit of inquiry 3': 4,\n 'unit of inquiry 4': 4.1,\n 'unit of inquiry 5': 4.2,\n 'unit of inquiry 6': 4.3,\n 'art': 5,\n 'music': 6,\n 'physical education': 7,\n 'bahasa melayu': 8,\n 'chinese': 9,\n 'host nation': 10,\n 'self-management': 10000\n }\n report.sections = sorted([section for section in report.sections if subject_rank.get(section.name.lower(), 10001) < 10000], key=lambda x: subject_rank.get(x.name.lower(), 1000))\n report.sections = [section for section in report.sections if section.comment]\n\n # Only output sections that have any data in them\n # Comment out during development\n # report.sections = [section for section in report.sections if section.comment]\n\n if 'Kindergarten' in report.course.grade:\n grade_norm = 0\n else:\n grade_norm = int(re.sub(\"[^0-9]\", \"\", report.course.grade))\n\n rotate_list = [0, 1, 2, 5, 9]\n pagination_list = [0, 1, 4, 7, 10]\n\n for section in report.sections:\n section.rank = subject_rank.get(section.name.lower())\n report.sections = [s for s in report.sections if s.rank not in [4.1, 4.2, 4.3]] # skip\n\n gns.tutorial(\"Formatting each subject area in this order: {}\".format(\", \".join([r.name for r in report.sections])), banner=True)\n for section in report.sections:\n # Substitute the correct Chinese teachers based on manual info above\n # Do first so all subsequent operations take place properly\n if section.rank == 9 and student.id in students_chinese_teachers:\n section.teachers = [students_chinese_teachers.get(student.id)]\n\n if section.rank == 8 and student.id in students_bahasa_teachers:\n # Host Nations? and Bahasa mixed up maybe?\n section.teachers = [students_bahasa_teachers.get(student.id)]\n\n section.append_uoi_table = section.rank == 4\n section.display_rotated = section.rank in rotate_list\n\n if section.rank in [2]:\n section.organization_header = 'Units of Inquiry'\n section.name_after = \"\"\n elif section.rank in [3, 4]:\n section.organization_header = 'skip'\n section.name_after = \"\"\n else:\n section.organization_header = section.name + ' (' + \" & \".join([s.first_name + ' ' + s.last_name for s in section.teachers]) + ')'\n section.name_after = \"\"\n\n # Set the unit title if it needs to be\n if section.rank in [2, 3, 4, 4.1, 4.2, 4.3]:\n which_uoi = int(re.sub(\"[^0-9]\", \"\", section.name))\n section.name = uoi_table.get(grade_norm)[which_uoi]['title']\n\n # Determine pagination\n if section.rank in pagination_list: # TODO What about more than two inquiry units?\n section.pagination = True\n else:\n section.pagination = False\n\n section.learning_outcomes = sorted(section.learning_outcomes, key=lambda x: x.which)\n\n # Standardize the headings\n if section.rank in [2, 3, 4, 4.1, 4.2, 4.3]:\n section.name = section.name.title()\n section.name_after = uoi_table.get(grade_norm)[which_uoi]['central_idea']\n\n en_dash = u'\\u2013'\n for outcome in section.learning_outcomes:\n\n if section.rank in [2, 3, 4]:\n # Unit of inquiry\n outcome.heading = \"\"\n\n elif section.rank not in [0, 1]:\n outcome.heading = \"\" # blank\n\n else:\n # If it's a subject that we care to keep the data, standardize the format:\n outcome.heading = outcome.heading.replace(en_dash, '-')\n match = re.match('(.*)-', outcome.heading)\n if match:\n outcome.heading = match.group(1).strip()\n\n # Evaluates and adds data to items\n old_heading = None\n for outcome in section.learning_outcomes:\n\n if outcome.heading != old_heading:\n # Mark that indicates we need to evaluate\n\n if section.rank in [0, 1]:\n # Determine the effort assigned by the teacher for this\n effort = [s.selection for s in section.strands if s.label_titled.startswith(outcome.heading)]\n effort = effort[0] if len(effort) == 1 else (effort[0] if len(set(effort)) == 1 else \"<?>\")\n else:\n effort = [s.selection for s in section.strands if s.selection]\n effort = effort[0] if len(set(effort)) == 1 else str(effort)\n outcome.effort = {'G': \"Good\", 'N': \"Needs Improvement\", 'O': \"Outstanding\"}.get(effort, None)\n\n if not outcome.effort and internal_check:\n # Raise a problem here\n raise ReportIncomplete('something') # FIXME: There is no report incomplete exception\n\n old_heading = outcome.heading\n\n if not outcome.selection and internal_check:\n raise ReportIncomplete('something')\n gns.tutorial(\"Completed formatting of {} section\".format(section.name))\n\n report.sections = [s for s in report.sections if s.rank not in [4.1, 4.2, 4.3]] # skip\n\n elif 'Early' in report.course.name:\n which_folder = 'early_years'\n template = 'frontend:elem_reports/templates/student_pyp_ey_report.pt'\n\n # 1/2: semeseter\n # 0/1: early years\n\n ey_report_indicators = {\n 1: {\n 0: [\n {'name': 'Listening & Speaking', 'content': 'Learners show an understanding of the value of speaking and listening to communicate. They are using language to name their environment, to get to know each other, to initiate and explore relationships, to question and inquire.'},\n {'name': 'Viewing & Presenting', 'content': 'Learners show an understanding that the world around them is full of visual language that conveys meaning. They are able to interpret and respond to visual texts. They are extending and using visual language in more purposeful ways.'},\n {'name': 'Reading & Writing', 'content': 'Learners show an understanding that print represents the real or the imagined world. They have a concept of a \"book\", and an awareness of some of its structural elements. They use visual cues to recall sounds and the words they are \"reading\" to construct meaning.'},\n ],\n 1: [\n {'name': 'Number', 'content': 'Learners will understand that numbers are used for many different purposes in the real world. They will develop an understanding of one-to-one correspondence, be able to count and use number words and numerals to represent quantities.'},\n {'name': 'Shape and Space', 'content': 'Learners will develop an understanding that shapes have characteristics that can be described and compared.'},\n {'name': 'Pattern', 'content': 'Learners will develop an understanding that patterns and sequences occur in everyday situations. They will be able to identify and extend patterns in various ways.'},\n {'name': 'Measurement', 'content': 'Learners will develop an understanding of how measurement involves the comparison of objects and ordering.They will be able to identify and compare attributes of real objects.'},\n {'name': 'Data', 'content': 'Learners will develop an understanding of how the collection and organization of information helps to make sense of the world. They will sort and label objects by attributes and discuss information represented in graphs including pictographs and tally marks.'}\n ]\n },\n 2: {\n 0: [\n {'name': 'Listening & Speaking', 'content': 'Learners will show an understanding of the value of speaking and listening to communicate. They will use language to name their environment, to get to know each other, to initiate and explore relationships, to question and inquire.'},\n {'name': 'Viewing & Presenting', 'content': 'Learners will show an understanding that the world around them is full of visual language that conveys meaning. They will interpret and respond to visual texts. They will be extending and using visual language in more purposeful ways.'},\n {'name': 'Reading & Writing', 'content': 'Learners will show an understanding that print represents the real or the imagined world. They will develop the concept of a “book”, and an awareness of some of its structural elements. They will use visual cues to recall sounds and the words they are “reading” to construct meaning.'},\n ],\n 1: [\n {'name': 'Number', 'content': 'Learners will understand that numbers are used for many different purposes in the real world. They will develop an understanding of one-to-one correspondence, be able to count and use number words and numerals to represent quantities.'},\n {'name': 'Shape and Space', 'content': 'Learners will understand and use common language to describe paths, regions and boundaries of their immediate environment.'},\n {'name': 'Pattern', 'content': 'Learners will understand that patterns and sequences occur in everyday situations. They will be able to identify, describe, extend and create patterns in various ways.'},\n {'name': 'Measurement', 'content': 'Learners will develop an understanding of how measurement involves the comparison of objects and the ordering and sequencing of events. They will be able to identify, compare and describe attributes of real objects as well as describe and sequence familiar events in their daily routine.'},\n {'name': 'Data', 'content': 'Learners will develop an understanding of how the collection and organization of information helps to make sense of the world. They will sort and label objects by attributes and discuss information represented in graphs including pictographs and tally marks. The learners will discuss chance in daily events.'},\n ],\n },\n }\n with DBSession() as session:\n try: \n report = session.query(PrimaryReport).\\\n options(joinedload('course')).\\\n options(joinedload('sections')).\\\n options(joinedload('sections.learning_outcomes')).\\\n options(joinedload('sections.teachers')).\\\n options(joinedload('teacher')).\\\n filter(\n PrimaryReport.term_id == term_id,\n PrimaryReport.student_id == student_id,\n ).one()\n student = session.query(Students).filter_by(id=student_id).one()\n attendance = session.query(Absences).filter_by(term_id=term_id, student_id=student_id).one()\n except NoResultFound:\n if pdf:\n raise HTTPNotFound()\n else:\n raise HTTPFound(location=request.route_url(\"student_pyp_report_no\", id=student_id))\n\n subject_rank = {\n 'self-management': -1,\n 'language': 0,\n 'mathematics': 1,\n 'unit of inquiry 1': 2,\n 'unit of inquiry 2': 3,\n 'unit of inquiry 3': 4,\n 'unit of inquiry 4': 4.1,\n 'unit of inquiry 5': 4.2,\n 'unit of inquiry 6': 4.3,\n 'art': 5,\n 'music': 6,\n 'physical education': 7,\n 'bahasa melayu': 8,\n 'chinese': 9,\n 'host nation': 10\n }\n\n report.sections = sorted([section for section in report.sections if subject_rank.get(section.name.lower()) < 10000], key=lambda x: subject_rank.get(x.name.lower(), 1000))\n # report.sections = report_sections\n # Filter out the un-needed units of inquiry\n # report.sections = [s for s in report.sections if s.rank <= 1 or (s.rank >= 4 and s.rank not in [4,4.1])]\n\n\n # Only output sections that have any data in them\n # Comment out during development\n # report.sections = [section for section in report.sections if section.comment and subject_rank.get(section.name.lower()) not in [2, 3]]\n\n grade_norm = -1\n\n pagination_list = [0, 3, 7, 10]\n\n for section in report.sections:\n\n section.rank = subject_rank.get(section.name.lower())\n\n if section.rank == -1:\n # blurb for self-management\n section.blurb = \"<i><p>Within the PYP, the approaches to learning skill of self management encompasses the development of gross and fine motor skills, spatial awareness, safety, healthy lifestyles, codes of behaviour and informed choices. </p><p>In an Early Years context these are reflected through the play based approach to teaching and learning. Reporting about self management in Early Years focuses on the whole child, stressing the importance of developing independence, social and emotional skills such as making relationships, managing feelings and behaviour, self confidence and self awareness. In addition the development of physical skills (moving and handling, health and self care) are highlighted as well. </p></i>\"\n else:\n section.blurb = \"\"\n\n if section.rank in [0, 1]: # Could be Lanugage & Maths, set up the report indicators\n ey = int('Early Years 1' in report.course.name) + 1\n section.report_indicators = ey_report_indicators[ey][section.rank] # change this to 2 later\n else:\n section.report_indicators = None\n\n # Substitute the correct Chinese teachers based on manual info above\n if section.rank == 9 and student.id in students_chinese_teachers:\n section.teachers = [students_chinese_teachers.get(student.id)]\n\n if section.rank in [999999]: # Turn this off\n section.organization_header = \"Units of Inquiry\"\n section.name_after = \"\"\n elif section.rank in [4, 4.1]:\n section.organization_header = 'skip'\n section.name_after = \"\"\n else:\n section.organization_header = None\n section.name_after = ' (' + \" & \".join([s.first_name + ' ' + s.last_name for s in section.teachers]) + ')'\n\n if section.rank in [2, 3, 4, 4.1, 4.2,4.3,4.4]:\n which_uoi = int(re.sub(\"[^0-9]\", \"\", section.name))\n section.name = uoi_table.get(grade_norm)[which_uoi]['title']\n section.name_after = \"\"\n\n # Determine pagination\n if section.rank in pagination_list: #TODO What about more than two inquiry units?\n section.pagination = True\n else:\n section.pagination = False\n\n if section.rank in [2, 3, 4, 4.1, 4.2,4.3,4.4]:\n section.name = section.name.title() \n section.name_after = uoi_table.get(grade_norm)[which_uoi]['central_idea']\n\n section.learning_outcomes = sorted(section.learning_outcomes, key=lambda x: x.which)\n\n # ey sections\n report.sections = [s for s in report.sections if s.rank not in [4, 4.1]]\n\n\n options={\n 'quiet': '',\n 'disable-javascript': '',\n 'encoding': 'utf-8',\n 'header-html': 'http://igbisportal.vagrant:6543/header-html',\n 'header-spacing': '5',\n\n\n 'footer-html': 'http://igbisportal.vagrant:6543/footer-html?student_id={}'.format(student.id),\n\n 'print-media-type': '',\n\n 'margin-left': '3mm',\n 'margin-right': '3mm',\n 'margin-bottom': '10mm'\n }\n\n\n if check:\n stu = student.first_nickname_last_studentid\n message = []\n for s in report.sections:\n if not s.teachers:\n message.append(\"No teacher assigned in {}\".format(s.name))\n #raise HTTPNotFound(\"##No teacher assigned for {} in {}##\".format(stu, s.name))\n if not s.comment:\n teachers = \",\".join([t.username_handle for t in s.teachers])\n message.append('{} missing {} comment'.format(teachers, s.name))\n #raise HTTPNotFound('##{} missing {} comment for {}##'.format(teachers, s.name, stu))\n\n if s.learning_outcomes and not 'Early' in report.course.name:\n\n if s.overall_comment == 'N/A':\n for o in s.learning_outcomes:\n if hasattr(o, 'effort') and not o.effort:\n teachers = \",\".join([t.username_handle for t in s.teachers])\n message.append('{} did not enter {} effort for {}'.format(teachers, o.heading, s.name))\n # raise HTTPNotFound()\n if not o.selection:\n teachers = \",\".join([t.username_handle for t in s.teachers])\n message.append('{} did not enter {} indication for {}'.format(teachers, o.heading, s.name))\n # raise HTTPNotFound('##{} did not enter indication for {} in {}##'.format(teachers, s.name, stu))\n\n elif s.overall_comment == '':\n teachers = \",\".join([t.username_handle for t in s.teachers])\n message.append('{} did not enter effort for single subject {}'.format(teachers, s.name)) \n\n if message:\n raise HTTPNotFound('##\\n({}) {}:\\n\\t{}##'.format(student.grade, student.first_nickname_last_studentid, \"\\n\\t\".join(message)))\n\n raise HTTPFound()\n\n with DBSession() as session:\n try:\n record = session.query(db.table.PrimaryReportLastUpdated).filter(db.table.PrimaryReportLastUpdated.student_id == student.id).one()\n last_updated = record.timestamp\n last_updated_date = last_updated.strftime(gns.config.reports.last_updated_format)\n except NoResultFound:\n last_updated_date = '<Unknown>'\n except MultipleResultsFound:\n last_updated_date = '<Internal DB Error: Multiple results found>'\n\n if pdf:\n result = render(template,\n dict(\n title=title,\n report=report,\n student=student,\n attendance=attendance,\n pdf=True,\n download_url=\"\",\n link_to_mb=\"\",\n last_updated=\"\",\n ),\n request=request)\n import pdfkit # import here because installation on server is hard\n\n prefix_file_name = '{}/pdf-downloads/{}/{}-Grade{}-{}-[{}]-'.format(\n gns.config.paths.home,\n which_folder,\n '55048',\n grade_norm,\n student.first_name + '-' + student.last_name,\n student.student_id\n )\n\n full_file = '{}({}).pdf'.format(prefix_file_name, last_updated_date)\n\n for _file in glob.glob(\"{}.*\".format(prefix_file_name)):\n # Remove any old stuff still lingering in there\n if _file != full_file:\n os.remove(_file)\n\n path = '{}/pdf-downloads/{}/{}-Grade{}-{}-[{}]-({}).pdf'.format(\n gns.config.paths.home,\n which_folder,\n '55048',\n grade_norm,\n student.first_name + '-' + student.last_name,\n student.student_id,\n last_updated_date\n )\n\n gns.tutorial(\"Sending to pdfkit, also saving to {path}\".format(path=path), edit=(result, '.pretty'), banner=True)\n try:\n pdffile = pdfkit.from_string(result, path, options=options) # render as HTML and return as a string\n except OSError as err:\n return HTTPInternalServerError(\"Problem with file? {}\".format(err))\n\n pdffile # not used\n if pdf.lower() == \"download\":\n content_type = \"application/octet-stream\"\n\n response = FileResponse(path, request=request, content_type=content_type)\n response.content_disposition = u\"attachment; filename={}.pdf\".format(title)\n return response\n\n else:\n content_type = \"application/pdf\"\n response = FileResponse(path, request=request, content_type=content_type, charset='utf-8')\n return response\n\n else:\n # Check when it was last updated\n\n if gns.tutorial_on:\n import pkg_resources\n package, filename = template.split(\":\")\n abspath = pkg_resources.resource_filename(*template.split(\":\"))\n from chameleon import PageTemplateFile\n template_file = PageTemplateFile(abspath)\n gns.tutorial(\"Loaded the template\", edit=(template_file.read(), '.html'), banner=True)\n result = render(template,\n dict(\n title=title,\n report=report,\n student=student,\n attendance=attendance,\n pdf=False,\n download_url=\"/students/{}/pyp_report/download/\".format(student.id),\n link_to_mb=\"https://igbis.managebac.com/classes/{}/pyp-gradebook/tasks/term_grades?student={}&term={}\".format(report.course.id, student.id, gns.config.managebac.current_term_id),\n last_updated=last_updated_date,\n ),\n request=request\n )\n response = Response(result)\n return response","def referee_evaluate_thesis(request):\n\n if not validate_request(request): return redirect(reverse(URL_FORBIDDEN))\n\n \n user = auth.get_user(request)\n referee = Referee.objects.get(user = user)\n \n if request.method == \"GET\":\n all_thesis = [] # list of dict\n \n for panelMember in PanelMember.objects.filter(referee = referee).filter(status = 'A'):\n thesis = panelMember.thesis\n dict = {}\n dict['title'] = thesis.title\n\n dict['student_full_name'] = thesis.student.first_name + ' ' + thesis.student.last_name\n dict['synopsis'] = thesis.synopsis\n dict['thesis'] = thesis.thesis\n dict['keywords'] = []\n\n if panelMember.answer_for_questions == True:\n if thesis.thesis_modifications == \"NULL\" or thesis.thesis_modifications == \"\":\n dict['thesis_modifications'] = None\n else:\n dict['thesis_modifications'] = thesis.thesis_modifications\n else:\n dict['thesis_modifications'] = None\n\n\n for keys in ThesisKeyword.objects.filter(thesis = thesis):\n dict['keywords'].append((IEEEKeyword.objects.get(id = keys.keyword.id)).keyword)\n \n dict['student_username'] = thesis.student.user.username\n dict['id'] = thesis.id\n \n all_thesis.append(dict)\n return render(\n request,\n 'app/referee/evaluate_thesis.html',\n {\n 'title':'Evaluate Thesis',\n 'layout_data' : get_layout_data(request),\n 'all_thesis' : all_thesis\n }\n )\n else:\n return redirect(reverse(URL_BAD_REQUEST))","def achieve_viewall(request):\n is_loggedin, username = get_session_variables(request)\n contrib_list = []\n article_list = []\n gsoc_list = []\n speaker_list = []\n intern_list = []\n contest_participant_list = []\n icpc_participants_regional_list = []\n icpc_participants_final_list = []\n\n contrib_list_all = Contribution.objects.all()\n contrib_list = Contribution.objects.all()[:5]\n article_list = Article.objects.all()[:5]\n gsoc_list = Gsoc.objects.all()[:5]\n speaker_list = Speaker.objects.all()[:5]\n intern_list = Intern.objects.all()[:5]\n contest_list = Contest_won.objects.all()[:5]\n\n \n contrib_org = {}\n if contrib_list_all:\n for contrib in contrib_list_all:\n if contrib.org_name not in contrib_org.keys():\n contrib_org[contrib.org_name] = 0\n\n for contrib in contrib_list:\n contrib_org[contrib.org_name] += 1\n\n if contest_list:\t\n contest_participant_list = []\n\tfor contest_won_obj in contest_list:\t\n\t c_id = contest_won_obj.contest_id\n\t c_p_objs = Contest_won_participant.objects.filter(contest_id = c_id)\n\t contest_participant_list.extend(c_p_objs)\n \n icpc_list_regionals = ACM_ICPC_detail.objects.filter(level='regional').order_by('ranking')[:2]\n if icpc_list_regionals:\n for icpc_obj in icpc_list_regionals:\n team = icpc_obj.team_name\n member1 = [icpc_obj.participant1_name, \\\n get_username_from_email(icpc_obj.participant1_email)]\n\n member2 = [icpc_obj.participant2_name, \\\n get_username_from_email(icpc_obj.participant2_email)]\n\n member3 = [icpc_obj.participant3_name, \\\n get_username_from_email(icpc_obj.participant3_email)]\n\n icpc_participant_list = [icpc_obj, member1,member2,member3]\n icpc_participants_regional_list.append(icpc_participant_list)\n\n icpc_list_finals = ACM_ICPC_detail.objects.filter(level='finals').order_by('ranking')[:2]\n if icpc_list_finals:\n for icpc_obj in icpc_list_finals:\n team = icpc_obj.team_name\n member1 = [icpc_obj.participant1_name, \\\n get_username_from_email(icpc_obj.participant1_email)]\n\n member2 = [icpc_obj.participant2_name, \\\n get_username_from_email(icpc_obj.participant2_email)]\n\n member3 = [icpc_obj.participant3_name, \\\n get_username_from_email(icpc_obj.participant3_email)]\n\n icpc_participant_list = [icpc_obj, member1,member2,member3]\n icpc_participants_final_list.append(icpc_participant_list)\n\n return render_to_response('achievement/achievement_viewall.html',\\\n\t\t{'username':username, \\\n 'is_loggedin':is_loggedin, \\\n 'contrib_list':contrib_list, \\\n 'contrib_org':contrib_org,\\\n 'article_list':article_list, \\\n 'gsoc_list':gsoc_list, \\\n 'speaker_list':speaker_list, \\\n 'intern_list':intern_list, \\\n 'contest_list':contest_list, \\\n 'contest_participant_list':contest_participant_list, \\\n 'icpc_participants_final_list':icpc_participants_final_list, \\\n 'icpc_participants_regional_list':icpc_participants_regional_list}, \\\n RequestContext(request))","def display_report(request, **kwargs):\n\n #Getting the report of the tests \n try:\n outputStr = sidecar.events.test_logs(project_id=kwargs['project_id'])\n outputStr = outputStr.results\n except Exception, e:\n outputStr = \"Updating the logs...\"\n \n #Making the output\n context = {\n \"page_title\": _(\"Test Report\"),\n \"test_report\": outputStr\n }\n return render(request, 'rally_dashboard/events/view_report.html', context)","def performance_analysis(request):\n context = {}\n\n return render(request, 'classroom_main/performance_analysis.html', context)","def get_data(request):\n\n json_resp = {}\n # reports = Report.objects.filter(name = UseCase.objects.get(name=request.session['usecase']),institute = request.session['institute'],language = request.session['language'])\n\n json_resp['reports'] = []\n institute = request.GET.get('institute',request.session['institute'])\n usecase = request.GET.get('usecase',request.session['usecase'])\n print(usecase)\n language = request.GET.get('language',request.session['language'])\n ns_human = NameSpace.objects.get(ns_id='Human')\n ns_robot = NameSpace.objects.get(ns_id='Robot')\n user_robot = User.objects.get(username='Robot_user', ns_id=ns_robot)\n # usec = UseCase.objects.get(name = usecase)\n # reports = Report.objects.filter(name = usec,institute = institute, language = language).values('id_report')\n # gt_report = GroundTruthLogFile.objects.filter(language = language).exclude(username = user_robot,id_report__in=reports).order_by('id_report').distinct('id_report')\n cursor = connection.cursor()\n cursor.execute(\"SELECT r.id_report,r.language,r.report_json,r.name,r.institute,r.batch,COUNT(*) FROM report AS r INNER JOIN ground_truth_log_file AS g ON g.id_report = r.id_report AND r.language = g.language WHERE r.name = %s AND r.institute = %s AND r.language = %s AND g.username != %s GROUP BY (r.id_report,r.language,r.report_json,r.name,r.institute,r.batch)\",[usecase,institute,language,'Robot_user'])\n gt_report_ids = []\n indice = 0\n st = time.time()\n for el in cursor.fetchall():\n\n # report = Report.objects.get(language = language, id_report = el.id_report_id)\n gt_report_ids.append(el[0])\n # print(str(indice))\n indice +=1\n # report = Report.objects.get(id_report=el.id_report, language=el.language)\n # language = report.language\n\n # user_robot = User.objects.get(username = 'Robot_user',ns_id = ns_robot)\n gt_human = 1\n gt_robot = 0\n\n rep = json.loads(el[2])\n new_rep = {}\n for key in rep.keys():\n nkey = key+ '_0'\n new_rep[nkey] = rep[key]\n\n total = el[6]\n\n new_rep['usecase'] = usecase\n new_rep['id_report_not_hashed'] = rep.get('report_id',el[0])\n new_rep['id_report'] = el[0]\n new_rep['institute'] = institute\n new_rep['language'] = language\n new_rep['batch'] = el[5]\n\n json_resp['reports'].append({'total':total, 'report':new_rep,'id_report':el[0], 'language':language})\n\n usec = UseCase.objects.get(name = usecase)\n reports = Report.objects.filter(institute = institute,language = language,name = usec).exclude(id_report__in=gt_report_ids)\n # print(reports.count())\n indice = 0\n st = time.time()\n for el in reports:\n report = el\n # print(str(indice))\n indice += 1\n # report = Report.objects.get(id_report=el.id_report, language=el.language)\n # language = report.language\n\n # user_robot = User.objects.get(username = 'Robot_user',ns_id = ns_robot)\n gt_human = 0\n gt_robot = 0\n\n rep = report.report_json\n new_rep = {}\n for key in rep.keys():\n nkey = key + '_0'\n new_rep[nkey] = rep[key]\n\n total = gt_human + gt_robot\n\n new_rep['usecase'] = report.name_id\n new_rep['id_report_not_hashed'] = rep.get('report_id', report.id_report)\n new_rep['id_report'] = report.id_report\n new_rep['institute'] = report.institute\n new_rep['language'] = report.language\n new_rep['batch'] = report.batch\n\n json_resp['reports'].append(\n {'total': total, 'report': new_rep, 'id_report': report.id_report, 'language': report.language})\n # print('elaboro1',str(end1-st1))\n tot = time.time()\n print('totale',str(tot-st))\n\n return JsonResponse(json_resp,safe=False)","def covid19(request):\n\tip = get_ip(request, right_most_proxy=True)\n\tIpAddressInformation.objects.create(ip_address=ip)\n\n\tcontextres =[]\n\t#build elasticsearch query to search data\n\tquery={\"query\": {\n\t\t\"bool\": {\n\t\t\t\"must\": [\n\t\t\t\t{\"match\": {\"Associated with COVID-19\": \"Yes\"}},\n\t\t\t\t{\"match\": {\"UniprotKb entry status\": \"Yes\"}}\n\t\t\t]\n\t\t}\n\t}\n\t}\n\t#generate random file name to store search result in json format\n\tnameFIle=names.get_first_name()\n\tjsonfilename=nameFIle+'_basic_search_covid19.json'\n\tjsonfilepath=os.path.join(settings.BASE_DIR, 'resultFile', 'jsonData','resultJson', 'basicsearch', 'results', jsonfilename)\n\tjsonfileoutput= open(jsonfilepath,'w')\n\tjfinaldata=[]\n\tes.indices.refresh(index=\"mrmassaydb-index\")\n\t#elasticsearch will search data\n\tres=helpers.scan(client=es,scroll='2m',index=\"mrmassaydb-index\", doc_type=\"mrmassaydb-type\",query=query,request_timeout=30)\n\tjfinaldata=[]\n\tpepSeqList=[]\n\tproteinList=[]\n\t#if data is valid based on uniprotkb release then it will display\n\tfor i in res:\n\t\tjdic=i['_source']\n\t\tjdic={str(tkey):force_text(tvalue) for tkey,tvalue in jdic.items()}\n\t\tif jdic[\"UniprotKb entry status\"] ==\"Yes\" and jdic['UniProtKB Accession'] !='502':\n\t\t\tjdic[\"PPI\"] =\"View\"\n\t\t\tjdic[\"sel\"] =\"\"\n\t\t\tjdic[\"Drug Bank\"]=jdic[\"Drug Bank\"].replace('\\\\','')\n\t\t\tjdic[\"Drug Bank\"]=jdic[\"Drug Bank\"].replace('<br>','|')\n\t\t\tjdic[\"SRMAtlas URL\"]=jdic[\"SRMAtlas URL\"].replace('\\\\','')\n\t\t\tjdic[\"Passel URL\"]=jdic[\"Passel URL\"].replace('\\\\','')\n\t\t\tjdic[\"CPTAC URL\"]=jdic[\"CPTAC URL\"].replace('\\\\','')\n\t\t\tjdic[\"Panoramaweb URL\"]=jdic[\"Panoramaweb URL\"].replace('\\\\','')\n\t\t\tjdic[\"PeptideTracker URL\"]=jdic[\"PeptideTracker URL\"].replace('\\\\','')\n\t\t\t#if jdic[\"Pathway Name\"].lower() !='na':\n\t\t\t#\tjdic[\"Pathway Name\"]=re.sub(r\"(\\w)([A-Z])\",r\"\\1|\\2\",jdic[\"Pathway Name\"])\n\t\t\tjdic[\"Mean Concentration\"] =jdic[\"Mean Concentration\"].replace('fmol/','fmol/µ')\n\t\t\tjdic[\"Concentration\"] =jdic[\"Concentration\"].replace('fmol/','fmol/µ')\n\t\t\tif str(jdic[\"Associated with COVID-19\"]).strip().upper() =='YES':\n\t\t\t\tpepSeqList.append(jdic[\"Peptide Sequence\"].strip())\n\t\t\t\tproteinList.append(jdic[\"UniProtKB Accession\"].strip().split('-')[0])\n\t\t\tjfinaldata.append(jdic)\n\tes.indices.refresh(index=\"mrmassaydb-index\")\n\t#checking any result generated by database\n\tfoundHits=len(jfinaldata)\n\t#storing only 10000 rows in json format\n\tjson.dump(jfinaldata[:10000],jsonfileoutput)\n\tjsonfileoutput.close()\n\t# if result found then do other job\n\tif foundHits >0:\n\t\tstatsummary=summaryStatcal(jfinaldata) # sent data to this funcation for generating stat\n\t\tpathwaychart=statsummary['pathwaychart']\n\t\tpathwaychart=[i[:2] for i in pathwaychart]\n\t\tspecieslist=statsummary['specieslist']\n\t\ttotallist=statsummary['total']\n\t\tsubcell=statsummary['subcell']\n\t\tgodic=statsummary['godic']\n\t\tjvennprot=statsummary['jevennstat'][0]\n\t\tjvennpep=statsummary['jevennstat'][1]\n\t\tmrmdatabase=statsummary['jevennstat'][2]\n\t\tsortedgodic=OrderedDict(sorted(godic.items(), key=lambda t: t[1])) # sorting GO data\n\t\tupdatedgodic=dict(list(sortedgodic.items()))\n\t\tpepseqdataseries=ast.literal_eval(json.dumps(statsummary['pepseqdataseries'])) #dumping data into json format\n\t\tprodataseries=statsummary['prodataseries']\n\t\tunqisostat=statsummary['unqisostat']\n\t\tjsonfilepathStat=os.path.join(settings.BASE_DIR, 'resultFile', 'jsonData','resultJson', 'basicsearch', 'statsummary', jsonfilename) #storing stat result in json format\n\t\tjsonfileoutputStat= open(jsonfilepathStat,'w')\n\t\tjson.dump(statsummary,jsonfileoutputStat)\n\t\tjsonfileoutputStat.close()\n\t\turlname=\"'/resultFile/jsonData/resultJson/basicsearch/results/\"+jsonfilename+\"'\"\n\n\t\tcontextindex={\n\t\t\t\"filename\":urlname,\"colname\":json.dumps(colname),'foundHits':foundHits,\n\t\t\t'pathwaychart':pathwaychart[:11],'specieslist':specieslist,\n\t\t\t'totallist':totallist,'subcell':subcell,\n\t\t\t'updatedgodic':updatedgodic,'pepseqdataseries':pepseqdataseries,\n\t\t\t'prodataseries':prodataseries,'unqisostat':unqisostat,\n\t\t\t'uniquePepSeq':len(set(pepSeqList)),'uniqueProtein':len(set(proteinList)),\n\t\t\t'jvennprot':json.dumps(jvennprot),'jvennpep':json.dumps(jvennpep),'jvennmrmdb':json.dumps(mrmdatabase)\n\t\t\t}\n\t\treturn render(request,'covid19.html',contextindex)\n\telse:\n\t\treturn render(request,'covid19.html',{'foundHits':foundHits})","def reports_public(self):\r\n return reports.ReportsPublic(self)","def show_campaigns(request, utm_campaign, **kwargs):\n \n err_msg = ''\n try:\n err_msg = str(kwargs['kwargs']['err_msg'])\n except:\n pass\n \n test_type_override = ''\n try:\n test_type_override = MySQLdb._mysql.escape_string(request.POST['test_type_override'])\n \n if test_type_override == 'Banner':\n test_type_var = FDH._TESTTYPE_BANNER_\n elif test_type_override == 'Landing Page':\n test_type_var = FDH._TESTTYPE_LP_\n elif test_type_override == 'Banner and LP':\n test_type_var = FDH._TESTTYPE_BANNER_LP_\n \n except:\n test_type_var = ''\n pass\n \n try:\n \"\"\" Find the earliest and latest page views for a given campaign \"\"\"\n lptl = DL.LandingPageTableLoader()\n ccrml = DL.CiviCRMLoader()\n \n start_time = ccrml.get_earliest_donation(utm_campaign)\n end_time = ccrml.get_latest_donation(utm_campaign)\n \n one_step = lptl.is_one_step(start_time, end_time, utm_campaign) \n \n if not(one_step): \n start_time = lptl.get_earliest_campaign_view(utm_campaign)\n end_time = lptl.get_latest_campaign_view(utm_campaign) \n\n interval = 1\n \n \"\"\" Create reporting object to retrieve campaign data and write plots to image repo on disk \"\"\"\n ir = DR.IntervalReporting(was_run=False, use_labels=False, font_size=20, plot_type='line', query_type='campaign', file_path=projSet.__web_home__ + 'campaigns/static/images/')\n \n \"\"\" Produce analysis on the campaign view data \"\"\" \n ir.run(start_time, end_time, interval, 'views', utm_campaign, {}, one_step=one_step)\n \n \"\"\" \n ESTIMATE THE START AND END TIME OF THE CAMPAIGN\n ===============================================\n \n Search for the first instance when more than 10 views are observed over a sampling period\n \"\"\"\n \n col_names = ir._data_loader_.get_column_names()\n \n views_index = col_names.index('views')\n ts_index = col_names.index('ts')\n \n row_list = list(ir._data_loader_._results_) # copy the query results\n for row in row_list:\n if row[views_index] > 100:\n start_time_est = row[ts_index]\n break\n row_list.reverse()\n for row in row_list:\n if row[views_index] > 100:\n end_time_est = row[ts_index]\n break\n \n \n \"\"\"\n BUILD THE VISUALIZATION FOR THE TEST VIEWS OF THIS CAMAPAIGN\n ============================================================ \n \"\"\"\n \n \"\"\" Read the test name \"\"\"\n ttl = DL.TestTableLoader()\n row = ttl.get_test_row(utm_campaign)\n test_name = ttl.get_test_field(row ,'test_name')\n \n \"\"\" Regenerate the data using the estimated start and end times \"\"\"\n ir = DR.IntervalReporting(was_run=False, use_labels=False, font_size=20, plot_type='line', query_type='campaign', file_path=projSet.__web_home__ + 'campaigns/static/images/')\n ir.run(start_time_est, end_time_est, interval, 'views', utm_campaign, {}, one_step=one_step)\n \n \"\"\" Determine the type of test (if not overridden) and retrieve the artifacts \"\"\"\n test_type, artifact_name_list = FDH.get_test_type(utm_campaign, start_time, end_time, DL.CampaignReportingLoader(query_type=''), test_type_var)\n \n return render_to_response('campaigns/show_campaigns.html', {'utm_campaign' : utm_campaign, 'test_name' : test_name, 'start_time' : start_time_est, 'end_time' : end_time_est, 'one_step' : one_step, \\\n 'artifacts' : artifact_name_list, 'test_type' : test_type, 'err_msg' : err_msg}, context_instance=RequestContext(request)) \n\n except Exception as inst:\n \n logging.error('Failed to correctly produce campaign diagnostics.')\n logging.error(type(inst))\n logging.error(inst.args)\n logging.error(inst)\n \n \"\"\" Return to the index page with an error \"\"\"\n err_msg = 'There is insufficient data to analyze this campaign: %s. Check to see if the <a href=\"/LML/\">impressions have been loaded</a>. <br><br>ERROR:<br><br>%s' % (utm_campaign, inst.__str__())\n \n return index(request, kwargs={'err_msg' : err_msg})","def test_summary_data(self):\n self.driver.get('http://psl-outbreak.herokuapp.com/report')\n self.driver.find_element_by_id('summary_find_out').click()","def getReport(request):\n\n\t#parameters needed for different REST API's\n\tparams = {\n\t\t'rid':-1,\n\t\t'year':-1,\n\t\t'con_num':-1,\n\t\t'assign_num':-1,\n\t\t'item_num':-1,\n\t\t'wtype': -1,\n\t\t'payno': -1,\n\t\t'snap': 0, #default is 0 for snapshots (for now)\n\t\t'issue_date': -1,\n\t}\n\n\t#loop over the parameters and set them if they appear in the api url\n\tfor p in params:\n\t\tif p in request.GET:\n\t\t\tparams[p] = request.GET[p]\n\n\n\t#get the request session and load data\n\ts = requests.Session()\n\tif not isinstance(rgen.ReportGenerator.get_url(params), dict):\n\t\tresponse = s.get(rgen.ReportGenerator.get_url(params))\n\n\t\t#set the iterator and the content\n\t\tit = json.loads(response.content)\n\t\tcontent = json.loads(response.content)\n\t\t\n\t\t#while a next page exists, parse the api\n\t\tpageNum = 1\n\t\twhile \"next\" in it:\n\t\t\tresponse = s.get(rgen.ReportGenerator.get_url(params) + '?page=' + str(pageNum))\n\t\t\tit = json.loads(response.content)\n\t\t\tcontent[\"items\"].extend(it[\"items\"])\n\t\t\tpageNum += 1\n\n\telse:\n\t\t#if the url is a list\n\t\tcontent = {}\n\t\tfor part in rgen.ReportGenerator.get_url(params):\n\t\t\tresponse = s.get(rgen.ReportGenerator.get_url(params)[part])\n\t\t\tit = json.loads(response.content)\n\t\t\t#content = {\"part1\":{\"items\":[]}, \"part2\":{\"items\":[]}, \"part3\":{\"items\":[]}}\n\t\t\t\n\t\t\tcontent[part] = {}\n\t\t\tcontent[part][\"items\"] = []\n\t\t\tcontent[part][\"items\"].extend(it[\"items\"])\n\n\t\t\tpageNum = 1\n\t\t\twhile \"next\" in it:\n\t\t\t\tresponse = s.get(rgen.ReportGenerator.get_url(params)[part] + '?page=' + str(pageNum))\n\t\t\t\tit = json.loads(response.content)\n\t\t\t\tcontent[part][\"items\"].extend(it[\"items\"])\n\t\t\t\tpageNum += 1\n\t\n\t#set the file object to be returned as a download\n\tfile = HttpResponse(rgen.ReportGenerator.formExcel(content, params), content_type='application/vnd.openxmlformats-officedocument.spreadsheetml.sheet')\n\tif params[\"rid\"] == '70':\n\t\tfile['Content-Disposition'] = 'attachment; filename=' + rgen.r_dict[params[\"rid\"]][1] + ' No.' + params['issue_date'] + '.xlsx'\n\telse:\n\t\tfile['Content-Disposition'] = 'attachment; filename=' + rgen.r_dict[params[\"rid\"]][1] + '.xlsx'\n\ts.close()\n\treturn file","def test_get_vulnerability_occurrences_summary(self):\n pass","def test_summary_report(self):\n self.driver.get('http://psl-outbreak.herokuapp.com/report')\n self.driver.find_element_by_id('summary_report_cases').click()","def summary_page() :\r\n logger.debug(\"\")\r\n model = session_info.get_user_model(session)\r\n return render_template( \"summary_page.html\" , model=model ,\r\n stat_types=param_stats.StatTypes )","def get_queryset(self, request):\n qs = super().get_queryset(request)\n qs = qs.prefetch_related('writerinwork_set')\n qs = qs.prefetch_related('writers')\n qs = qs.prefetch_related('library_release__library')\n qs = qs.annotate(models.Count('cwr_exports', distinct=True))\n qs = qs.annotate(models.Count('recordings', distinct=True))\n return qs","def report(request):\n return render_to_response('application/report.html',\n {'search_suggestions': _search_suggestions(),\n },\n context_instance=RequestContext(request));","def index(request):\n\n # Generate counts of some of the main objects\n num_para = Paragraph.objects.all().count()\n num_source = Source.objects.all().count()\n\n context = {\n 'num_para': num_para,\n 'num_source': num_source,\n 'paragraph_list': queryset[:10],\n }\n\n return render(request, 'index.html', context=context)","def get_uses_missing_exa(request):\n\n use_to_ret = {}\n use_to_ret['labels_present'] = []\n use_to_ret['concepts_present'] = []\n use_to_ret['labels_missing'] = []\n use_to_ret['concepts_missing'] = []\n uses = ['colon','uterine cervix','lung']\n for el in uses:\n usecase = UseCase.objects.get(name=el)\n presence = True\n if Report.objects.filter(name = usecase).count() > 0:\n if not AnnotationLabel.objects.filter(name = usecase, annotation_mode = 'Manual and Automatic').exists():\n use_to_ret['labels_missing'].append(el)\n else:\n use_to_ret['labels_present'].append(el)\n\n cursor = connection.cursor()\n cursor.execute(\"SELECT c.annotation_mode FROM concept AS c INNER JOIN concept_has_uc AS hc ON c.concept_url = hc.concept_url WHERE hc.name = %s\",[str(el)])\n ans = cursor.fetchall()\n for concept in ans:\n if concept[0] != 'Manual and Automatic':\n presence = False\n break\n if len(ans) > 0:\n if presence == False:\n use_to_ret['concepts_missing'].append(el)\n else:\n use_to_ret['concepts_present'].append(el)\n else:\n use_to_ret['concepts_missing'].append(el)\n\n return JsonResponse(use_to_ret)","def get_designs(self):","def getOverviewSummary(report_type=choices.INCOMING):\n return {\n \"domain_cnt\" : Report.objects.filter(\n report_type=report_type).values(\"domain\").distinct().count(),\n \"report_cnt\" : Report.objects.filter(\n report_type=report_type).count(),\n \"message_cnt\" : Record.objects.filter(\n report__report_type=report_type).aggregate(\n cnt=Sum(\"count\"))[\"cnt\"],\n\n # Query per result aggregated message count for dkim, spf and\n # disposition and transform result number to display name\n \"dkim\" : [\n {\n \"cnt\": res[\"cnt\"],\n \"label\": dict(choices.DMARC_RESULT).get(res[\"dkim\"])\n } for res in Record.objects.filter(\n report__report_type=report_type).values(\n \"dkim\").annotate(cnt=Sum(\"count\"))\n ],\n\n \"spf\" : [\n {\n \"cnt\": res[\"cnt\"],\n \"label\": dict(choices.DMARC_RESULT).get(res[\"spf\"])\n } for res in Record.objects.filter(\n report__report_type=report_type).values(\n \"spf\").annotate(cnt=Sum(\"count\"))\n ],\n \"disposition\" : [\n {\n \"cnt\": res[\"cnt\"],\n \"label\": dict(choices.DISPOSITION_TYPE).get(\n res[\"disposition\"])\n } for res in Record.objects.filter(\n report__report_type=report_type).values(\n \"disposition\").annotate(cnt=Sum(\"count\"))\n ],\n }","def print_quick_report():\r\n print('function not yet written')\r\n # print a summary of the report as a structured pandas dataframe\r\n #Summary will include only date title and sentiment\r","def get(self, request):\n issue_list = Issue.objects.order_by('-created_at')\n open_issues = Issue.objects.filter(status='O').count()\n closed_issues = Issue.objects.filter(status='C').count()\n \n return render(request, 'issue.html', {\n 'issue_list': issue_list,\n 'open_issues': open_issues,\n 'closed_issues': closed_issues,\n })","def get(self, request, format=None):\n param_report = self.request.query_params.get('report', None)\n start_date = self.request.query_params.get('start_date', '')\n end_date = self.request.query_params.get('end_date', '')\n detailed_report = self.request.query_params.get('detailed_report', 'false')\n\n if start_date == '':\n start_date = datetime.date.today().strftime(\"%Y-%m-%d 16:00:00\")\n else:\n start_date = start_date.replace(\"T\", \" \", 1)\n if end_date == '':\n end_date = datetime.date.today().strftime(\"%Y-%m-%d 16:00:00\")\n else:\n end_date = end_date.replace(\"T\", \" \", 1)\n\n if param_report is None or param_report == \"\":\n return Response(\"No report specified\", status=status.HTTP_400_BAD_REQUEST)\n\n table_html = None\n table_title = None\n table_subtitle = \"\"\"%s - %s\"\"\" % (start_date, end_date)\n\n # if param_report == \"police_division_summary_report\":\n # table_html = get_police_division_summary()\n # table_title = \"Police Division Summary Report\"\n\n if param_report == \"category_wise_summary_report\":\n table_html = get_category_summary(start_date, end_date, detailed_report)\n table_title = \"No. of Incidents by Category\"\n\n elif param_report == \"mode_wise_summary_report\":\n table_html = get_mode_summary(start_date, end_date, detailed_report)\n table_title = \"No. of Incidents by Mode\"\n\n elif param_report == \"district_wise_summary_report\":\n table_html = get_district_summary(start_date, end_date, detailed_report)\n table_title = \"No. of Incidents by District\"\n\n elif param_report == \"severity_wise_summary_report\":\n table_html = get_severity_summary(start_date, end_date, detailed_report)\n table_title = \"No. of Incidents by Severity\"\n\n elif param_report == \"subcategory_wise_summary_report\":\n table_html = get_subcategory_summary(start_date, end_date, detailed_report)\n table_title = \"No. of Incidents by Subcategory\"\n\n elif param_report == \"status_wise_summary_report\":\n table_html = get_status_summary(start_date, end_date, detailed_report)\n table_title = \"No. of Incidents by Status\"\n\n if table_html is None:\n return Response(\"Report not found\", status=status.HTTP_400_BAD_REQUEST)\n\n table_html = apply_style(\n table_html.replace(\".0\", \"\", -1).replace(\"(Total No. of Incidents)\",\n \"<strong>(Total No. of Incidents)</strong>\", 1).replace(\n \"(Unassigned)\", \"<strong>(Unassigned)</strong>\", 1), table_title, table_subtitle)\n response = HttpResponse(content_type='application/pdf')\n response['Content-Disposition'] = 'attachment; filename=\"Report.pdf\"'\n pisa.CreatePDF(table_html, dest=response)\n\n return response","def index(request):\n\n # Generate counts of the main objects\n\n num_sales=Prodsale.objects.all().count()\n\n # Render the HTML template index.html with the data in the context variable\n\n return render(\n request, 'index.html',\n context={'num_sales':num_sales},\n )","def annual_report_view(self, document_id: str) -> Tuple[Any, Any]:\n self.__header_update_token()\n link = EFD_ENDPOINT_REPORT.format('annual', document_id)\n self.__ensure_fetching_rate_limit()\n response = self.session.get(link)\n soup = BeautifulSoup(response.text, features='html.parser')\n return soup.find('h1').parent, soup.findAll('section', {'class': 'card mb-2'})","def view_all(request, index_call=False):\n closed = request.GET.get('closed', '')\n if closed in ('0', 'false'):\n closed = False\n elif closed in ('1', 'true'):\n closed = True\n elif index_call:\n # for index we display only open issues by default\n closed = False\n else:\n closed = None\n\n nav_parameters = {}\n if closed is not None:\n nav_parameters['closed'] = int(closed)\n\n query = models.Issue.query(\n models.Issue.private == False).order(-models.Issue.modified)\n if closed is not None:\n # return only opened or closed issues\n query = query.filter(models.Issue.closed == closed)\n\n return _paginate_issues(reverse(view_all),\n request,\n query,\n 'all.html',\n extra_nav_parameters=nav_parameters,\n extra_template_params=dict(closed=closed))","def _compute_results(self):\n self.ensure_one()\n Result = self.env['pabi.common.supplier.payment.report.view']\n dom = [('invoice_id.source_document_type', 'in',\n ['advance', 'expense']),\n ('expense_id.pay_to', '!=', 'supplier')]\n if self.user_ids:\n dom += [('voucher_id.validate_user_id', 'in', self.user_ids.ids)]\n if self.source_document_type:\n dom += [('invoice_id.source_document_type', '=',\n self.source_document_type)]\n if self.fiscalyear_start_id:\n dom += [('voucher_id.date', '>=',\n self.fiscalyear_start_id.date_start)]\n if self.fiscalyear_end_id:\n dom += [('voucher_id.date', '<=',\n self.fiscalyear_end_id.date_stop)]\n if self.period_start_id:\n dom += [('voucher_id.date', '>=',\n self.period_start_id.date_start)]\n if self.period_end_id:\n dom += [('voucher_id.date', '<=',\n self.period_end_id.date_stop)]\n if self.date_start:\n dom += [('voucher_id.date', '>=', self.date_start)]\n if self.date_end:\n dom += [('voucher_id.date', '<=', self.date_end)]\n self.results = Result.search(\n dom, order=\"fiscalyear,voucher_number,invoice_number\")","def get_user_activity_report(request, ids, index, value, start_date, end_date, stats): \n\n domain = request.user.selected_domain\n if not ids: raise Exception(\"The requested form was not found\")\n \n _report = Report(\"CHW Group Total Activity Report\")\n _report.generating_url = request.path\n metadata = Metadata.objects.filter(timestart__gte=start_date)\n # the query below is used if you want to query by submission time (instead of form completion time)\n #metadata = Metadata.objects.filter(attachment__submission__submit_time__gte=start_date)\n \n # since we are working at a granularity of 'days', we want to make sure include \n # complete days in our queries, so we round up\n timespan = get_timespan(start_date, end_date)\n delta = timedelta(days=timespan.days+1)\n metadata = metadata.filter(timeend__lt=start_date+delta)\n # the query below is used if you want to query by submission time (instead of form completion time)\n #metadata = metadata.filter(attachment__submission__submit_time__lte=end_date)\n \n dataset = DataSet( unicode(value[0]) + \" per \" + unicode(index) )\n dataset.indices = unicode(index)\n dataset.params = request.GET\n\n # when 'organization' is properly populated, we can start using that\n # member_list = utils.get_members(organization)\n # for now, just use domain\n member_list = [r.chw_username for r in ReporterProfile.objects.filter(domain=domain).order_by(\"chw_username\")]\n\n # get a sum of all forms\n visits_per_member = Values( \"visits\" )\n for member in member_list:\n visits_per_member.append( (member, metadata.filter(username=member).count()) )\n visits_per_member.run_stats(stats)\n visits_per_member.sort(key=operator.itemgetter(1), reverse=True) \n dataset.valuesets.append( visits_per_member )\n \n # this report only requires the first form. you can imagine other reports doing \n # this iteration: for id in ids:\n form_per_member = Values( unicode(value[0]) )\n form_metadata = metadata.filter(formdefmodel=ids[0])\n for member in member_list:\n # values are tuples of dates and counts\n form_per_member.append( (member, form_metadata.filter(username=member).count()) )\n form_per_member.run_stats(stats)\n form_per_member.sort(key=operator.itemgetter(1), reverse=True) \n dataset.valuesets.append( form_per_member )\n \n _report.datasets.append(dataset)\n return _report","def summary(request):\n data = {'Yes':[], 'No':[], 'labels':{}}\n reasons = zip(list(range(RestartReason.objects.count())), \n RestartReason.objects.order_by('reason_id'))\n for i, reason in reasons:\n iv_attempts = __iv_attempts(reason.reason_id)\n data['Yes'].append(iv_attempts[True])\n data['No'].append(iv_attempts[False])\n data['labels'][i] = reason.reason_id\n return HttpResponse(simplejson.dumps(data), \n mimetype='application/javascript')","def view_insurances(request):\n insurance = Insurance.objects.filter(medical_information=request.user.userprofile.medicalinformation)\n return render(request, 'view_insurances.html', {'insurances': insurance})","def test_display_report():\n mr.initialize_donors()\n report = mr.text_report()\n\n print(report)\n for name in mr.mailroom.database.keys():\n assert name in report\n assert f'{mr.mailroom.database[name].total_donations:,.2f}' in report\n assert f'{mr.mailroom.database[name].average_donation:,.2f}' in report","def display_reports(self, layout): # pylint: disable=arguments-differ","def public_market_summaries(self):\n return self.get('markets/summaries')","def invoices_report_filter(request):\n qry = DBSession.query(Invoice)\n supplierlist = []\n for supplier in qry:\n if supplier.SupplierID:\n entry = {'Name': supplier.Order.Supplier.Name,\n 'ID': supplier.SupplierID}\n if entry not in supplierlist:\n supplierlist.append(entry)\n\n projectlist = []\n for project in qry:\n if project.ProjectID:\n entry = {'Name': project.Order.Project.Name,\n 'ID': project.ProjectID}\n if entry not in projectlist:\n projectlist.append(entry)\n\n paymentdatelist = []\n for paymentdate in qry:\n if paymentdate.PaymentDate:\n entry = paymentdate.PaymentDate.strftime(\"%d %B %Y\")\n if entry not in paymentdatelist:\n paymentdatelist.append(entry)\n\n return {'projects': sorted(projectlist, key=lambda k: k['Name'].upper()),\n 'suppliers': sorted(supplierlist, key=lambda k: k['Name'].upper()),\n 'paymentdates': sorted(paymentdatelist),\n 'paymentdates_exist': paymentdatelist != [],\n 'statuses': ['Draft', 'Due', 'Paid']}","def showcase(request):\n\n showcases = Showcase.objects.all\n context = {\n 'showcases': showcases,\n }\n\n return render(request, 'showcase/showcase.html', context)","def view_all(request, index_call=False):\n closed = request.GET.get('closed', '')\n if closed in ('0', 'false'):\n closed = False\n elif closed in ('1', 'true'):\n closed = True\n elif index_call:\n # for index we display only open issues by default\n closed = False\n else:\n closed = None\n\n nav_parameters = {}\n if closed is not None:\n nav_parameters['closed'] = int(closed)\n\n # This uses eventual consistency and cannot be made strongly consistent.\n query = models.Issue.query(\n models.Issue.private == False).order(-models.Issue.modified)\n if closed is not None:\n # return only opened or closed issues\n query = query.filter(models.Issue.closed == closed)\n\n return _paginate_issues(reverse(view_all),\n request,\n query,\n 'all.html',\n extra_nav_parameters=nav_parameters,\n extra_template_params=dict(closed=closed))","def sa_summary_pdf(sa_id):\n pass","def index(request):\n fund_logs = FundProcessLog.objects.all()\n activity_logs = []\n for log in fund_logs:\n activity_logs.append({'activity_date':log.created_on,\n 'description': '%s fund codes subsituted' % log.substitutions})\n logging.error(\"Number of activity logs:%s\" % len(activity_logs))\n utilities = [{'name':'csv',\n 'label':'Expand Fund codes to values',\n 'description':'''Takes order records in CSV format, replaces Fund codes with\n expanded Fund numeric values'''}]\n return direct_to_template(request,\n 'vendors/iii/index.html',\n {'activity_log':activity_logs,\n 'utilities':utilities})","def inscription_summary(request, pk):\n candidat = get_object_or_404(Candidate, pk=pk)\n buff = io.BytesIO()\n pdf = InscriptionSummaryPDF(buff)\n pdf.produce(candidat)\n filename = slugify('{0}_{1}'.format(candidat.last_name, candidat.first_name)) + '.pdf'\n buff.seek(0)\n return FileResponse(buff, as_attachment=True, filename=filename)","def get_PUBMED_batch_list(request):\n\n json_resp = {}\n usecase = request.GET.get('usecase')\n # print(usecase)\n use_obj = UseCase.objects.get(name=usecase)\n json_resp['batch_list'] = []\n batch = Report.objects.filter(name=use_obj,institute = 'PUBMED').values('batch')\n for el in batch:\n if el['batch'] not in json_resp['batch_list']:\n json_resp['batch_list'].append( el['batch'])\n # print(json_resp['batch_list'])\n json_resp['batch_list'] = sorted(json_resp['batch_list'])\n # print(json_resp)\n return JsonResponse(json_resp)","def getSiteWideReports(context):\n\n return getReports(context, category=\"Site Wide\")","def detail(request):\r\n\r\n\tdataset = request.GET.get('dataset', '')\r\n\tdatatype = request.GET.get('datatype', 'RNA')\r\n\ttissue = request.GET.get('tissue', 'brain')\r\n\tcategory = request.GET.get('category', 'region')\r\n\tgroup = request.GET.get('group', 'PFC')\r\n\tcomparison = request.GET.get('comparison', 'AD-vs-Control')\r\n\tfeature_symbols_in_interest = request.GET.get('features', '').split(' ')\r\n\tcollection_name = \"%s_%s_%s-%s_%s\" % (datatype,\r\n\t\t\t\t\t\t\t\t\t\t\ttissue,\r\n\t\t\t\t\t\t\t\t\t\t\tcategory,\r\n\t\t\t\t\t\t\t\t\t\t\tgroup,\r\n\t\t\t\t\t\t\t\t\t\t\tcomparison)\r\n\t\"\"\"\r\n\t\tWe should split POST[\"featureInput\"] here\r\n\t\"\"\"\r\n\t# import pdb; pdb.set_trace();\r\n\t# feature_symbols_in_interest = split_feature_input_to_list(request.POST[\"featureInput\"])\r\n\r\n\tway_to_choose_probe = request.GET.get('way_to_choose_probe', 'fold change')\r\n\r\n\tall_datasets = test_stat_client.get_all_datasets(collection_name)\r\n\r\n\ttest_statistics = list(test_stat_client.get_all_for_this_category(collection_name))\r\n\r\n\tdisease_state_list = test_stat_client.get_disease_state_list(collection_name)\r\n\r\n\ttest_statistics = pd.DataFrame(test_statistics)\r\n\r\n\t# Filter 1 - dataset accession & features in interest\r\n\tfilt_ind = (test_statistics['dataset_accession'] == dataset) & (test_statistics['symb'].isin(feature_symbols_in_interest))\r\n\ttest_stat_df = test_statistics[filt_ind]\r\n\r\n\t# Filter 2 - remove duplicates\r\n\t\"\"\"\r\n\t\tHere we provide options for user to choose how to select a probe when \r\n\t\tmultiple probes are corresponding to one feature\r\n\t\"\"\"\r\n\r\n\tif way_to_choose_probe == \"fold change\":\r\n\t\ttest_stat_df = filtered_duplicate_by(test_stat_df, by='fc', group_index=['symb'])\r\n\r\n\telif way_to_choose_probe == \"limma p value\" : \r\n\t\ttest_stat_df = filtered_duplicate_by(test_stat_df, by='lp', group_index=['symb'])\r\n\r\n\telif way_to_choose_probe == \"t test p value\" :\r\n\t\ttest_stat_df = filtered_duplicate_by(test_stat_df, by='tp', group_index=['symb'])\r\n\r\n\t# Split dataframe for stat table display and graph display\r\n\tstat_table = test_stat_df.drop(['eval', 'dsl'], axis=1)\r\n\tstat_graph_exprs = test_stat_df[['symb', 'eval']]\r\n\tstat_graph_ds = disease_state_list[dataset]\r\n\r\n\t# import pdb; pdb.set_trace()\r\n\tstat_table['entrez_gene_id'] = stat_table.apply(from_symbol_to_entrez_gene_id, axis=1)\t\t\t\r\n\tstat_table['string_id'] = from_single_symbol_to_string_id(stat_table['symb'])\r\n\r\n\tds_1_count = sum(stat_graph_ds)\r\n\tds_0_count = len(stat_graph_ds) - sum(stat_graph_ds)\r\n\r\n\tstat_graph_ds_1 = [True if x == 1 else False for x in stat_graph_ds]\r\n\tstat_graph_ds_0 = [True if x == 0 else False for x in stat_graph_ds]\r\n\t# stat_graph_ds_0 = stat_graph_ds == 0\r\n\r\n\theatmap_feature_count = test_stat_df.shape[0]\r\n\theatmap_sample_count = len(stat_graph_ds)\r\n\theatmap_df_row_count = heatmap_sample_count * heatmap_feature_count\r\n\r\n\t\r\n\t# import pdb;pdb.set_trace\r\n\t# Generate a expression table (row as feature)\r\n\texpression_table = pd.DataFrame(list(stat_graph_exprs['eval']))\r\n\r\n\t\r\n\t# import pdb;pdb.set_trace();\r\n\t# Transpose table before sorting by disease state\r\n\texpression_table = pd.DataFrame.transpose(expression_table)\r\n\r\n\t# Get new expression table sorted by disease state\r\n\texpression_table = expression_table[stat_graph_ds_1].append(expression_table[stat_graph_ds_0], ignore_index=True)\r\n\r\n\t### Normalize row expression\r\n\texpression_table_normalized = normalize_heatmap_row_expression(expression_table)\r\n\t\r\n\t# Get minimum and maximum value of expression\r\n\texprs_min = np.nanmin(expression_table_normalized.values)\r\n\texprs_max = np.nanmax(expression_table_normalized.values)\r\n\r\n\r\n\theatmap_dataset_df = pd.DataFrame({\r\n\t\t\t'0' : sorted(range(0, heatmap_sample_count) * heatmap_feature_count), # sample_x\r\n\t\t\t'1' : range(0, heatmap_feature_count) * heatmap_sample_count,\t# feature_y\r\n\t\t\t'2' : [val for row in expression_table_normalized.values.tolist() for val in row] #expression_z\r\n\t\t})\r\n\r\n\t# Remove NANs in heatmap data series\r\n\tnot_nan_index = np.invert(np.isnan(heatmap_dataset_df['2']))\r\n\theatmap_dataset_df = heatmap_dataset_df[not_nan_index]\r\n\t# Prepare one dimentional scatter plot\r\n\r\n\t# Final output\r\n\t# Scatter plot\r\n\tstate_1_data_series = generate_scatterplot_series(range(0, ds_1_count), 0, expression_table)\r\n\tstate_0_data_series = generate_scatterplot_series(range(ds_1_count, ds_1_count+ds_0_count), 1, expression_table)\t\r\n\tstate_1_name = \"AD\"\r\n\tstate_0_name = \"Control\"\r\n\t# Heatmap\r\n\theatmap_feature_list = [x.encode('utf-8') for x in list(stat_graph_exprs['symb'])]\r\n\theatmap_sample_ds_list = ['AD'] * ds_1_count + ['Control'] * ds_0_count\r\n\theatmap_datasets = heatmap_dataset_df.values.tolist()\r\n\theatmap_extremes = [exprs_min, exprs_max]\r\n\t# Statistic table\r\n\tstat_table_output = stat_table.to_dict(outtype='records')\r\n\r\n\treturn render(request, 'feature_stat_detail.html',\r\n\t\t\t\t{\r\n\t\t\t\t\t'dataset_name' : dataset,\r\n\t\t\t\t\t'test_stat' : stat_table_output,\r\n\t\t\t\t\t'feature_list' : heatmap_feature_list,\r\n\t\t\t\t\t'sample_state_list' : heatmap_sample_ds_list,\r\n\t\t\t\t\t'heatmap_datasets' : heatmap_datasets,\r\n\t\t\t\t\t'heatmap_extremes' : heatmap_extremes,\r\n\t\t\t\t\t'state_1_data_series' : state_1_data_series,\r\n\t\t\t\t\t'state_0_data_series' : state_0_data_series,\r\n\t\t\t\t\t'state_1_name' : state_1_name,\r\n\t\t\t\t\t'state_0_name' : state_0_name,\r\n\t\t\t\t})","def get_fields(request):\n\n json_resp = {}\n json_resp['fields'] = []\n json_resp['fields_to_ann'] = []\n all = request.GET.get('all',None)\n workpath = os.path.dirname(os.path.abspath(__file__)) # Returns the Path your .py file is in\n auto_request = request.GET.get('ns_id', None)\n report = request.GET.get('report', None)\n # print(request.session['report_type'])\n if report is not None or all == 'all':\n if report is not None:\n if report.startswith('PUBMED_'):\n json_resp['fields'] = ['volume','authors','year','journal']\n json_resp['fields_to_ann'] = ['title','abstract']\n else:\n json_resp = get_fields_from_json()\n if all == 'all':\n # All the possible fields for every usecase (MANUAL CONFIGURATION)\n json_resp = get_fields_from_json()\n if Report.objects.filter(institute = 'PUBMED').exists():\n json_resp['all_fields'].extend(['title','abstract','volume','journal','year','authors']) #aggiungo pubmed solo in coda!\n else:\n if request.session['report_type'] == 'pubmed':\n json_resp['fields'] = ['volume','authors','year','journal']\n json_resp['fields_to_ann'] = ['title','abstract']\n else:\n # Fileds related exclusively to a usecase\n json_resp = get_fields_from_json_configuration(request.session['usecase'],request.session['institute'],request.session['language'])\n if request.session['mode'] == 'Robot' or auto_request == 'Robot':\n with open(os.path.join(workpath, './automatic_annotation/auto_fields/auto_fields.json')) as out:\n data = json.load(out)\n json_resp['fields_to_ann'] = data['extract_fields'][request.session['usecase']]\n for el in json_resp['fields_to_ann']:\n if el in json_resp['fields']:\n json_resp['fields'].remove(el)\n # print('FIELDS', json_resp)\n return JsonResponse(json_resp)","def get_presence_robot_user(request):\n\n id_report = request.GET.get('id_report', None)\n language = request.GET.get('language', None)\n use = request.GET.get('usecase', None)\n rep = request.GET.get('report_type', None)\n json_resp = {'auto_annotation_count': 0}\n cursor = connection.cursor()\n\n reports_list = None\n if request.method == 'POST':\n request_body_json = json.loads(request.body)\n reports_list = request_body_json['reports']\n\n if id_report is not None and language is not None:\n\n usecase = Report.objects.get(id_report=id_report, language=language)\n use = usecase.name_id\n cursor.execute(\n \"SELECT COUNT(*) FROM ground_truth_log_file as g INNER JOIN report AS r ON r.id_report = g.id_report AND r.language = g.language WHERE g.username = %s and r.name = %s\",\n ['Robot_user', str(use)])\n ans = cursor.fetchone()[0]\n json_resp = {'auto_annotation_count': (ans)}\n\n elif use is not None and rep is not None:\n # print(rep)\n if rep == 'reports':\n cursor.execute(\n \"SELECT COUNT(*) FROM ground_truth_log_file as g INNER JOIN report AS r ON r.id_report = g.id_report AND r.language = g.language WHERE g.ns_id = %s AND g.username = %s and r.name = %s and r.institute != %s\",\n ['Robot', 'Robot_user', str(use), 'PUBMED'])\n\n elif rep == 'pubmed':\n cursor.execute(\n \"SELECT COUNT(*) FROM ground_truth_log_file as g INNER JOIN report AS r ON r.id_report = g.id_report AND r.language = g.language WHERE g.username = %s and r.name = %s and r.institute = %s\",\n ['Robot_user', str(use), 'PUBMED'])\n\n ans = cursor.fetchone()[0]\n if ans > 0:\n json_resp = {'auto_annotation_count': ans}\n # print(json_resp)\n elif reports_list is not None:\n report_list = json.loads(reports_list)\n # print(report_list)\n usecase_list = []\n for rep in report_list:\n\n if rep['usecase'] not in usecase_list:\n usecase_list.append(rep['usecase'])\n for u in usecase_list:\n cursor = connection.cursor()\n cursor.execute(\n \"SELECT COUNT(*) FROM ground_truth_log_file as g INNER JOIN report AS r ON r.id_report = g.id_report AND r.language = g.language WHERE g.username = %s and r.name = %s\",\n ['Robot_user', str(u)])\n ans = cursor.fetchone()[0]\n if ans > 0:\n json_resp = {'auto_annotation_count': ans}\n else:\n json_resp = {'auto_annotation_count': 0}\n\n elif use is None and reports_list is None and id_report is None and language is None:\n robot = NameSpace.objects.get(ns_id='Robot')\n gt = GroundTruthLogFile.objects.filter(ns_id=robot)\n json_resp = {'auto_annotation_count': gt.count()}\n\n print(json_resp)\n return JsonResponse(json_resp)","def testcases(self, request, *args, **kwargs):\n response = self.retrieve(request, *args, **kwargs)\n response.data = response.data['testcases']\n return response","def acceptedPapersIndustrial(request):\n # Test Comment\n assert isinstance(request, HttpRequest)\n return render(\n request,\n 'acceptedIndustrial.html',\n context_instance=RequestContext(request, {})\n )","def data():\n return render_template(\n 'data.html',\n title='World Happiness Report',\n year=datetime.now().year,\n message='Main Data Model'\n )","def get(self, request, *args, **kwargs):\n # ProcessedAliquots.objects.all().values('SampleType', 'Volume', 'VolumeUnit', 'PostProcessingStatus')\n samples = ProcessedAliquots.objects.all().values('SampleType').distinct()\n dist_sample = []\n for sample in samples:\n dist_sample += list(sample.values())\n volumes = ProcessedAliquots.objects.all().values('Volume').distinct()\n dist_volume = []\n for volume in volumes:\n dist_volume += list(volume.values())\n Units = ProcessedAliquots.objects.all().values('VolumeUnit').distinct()\n dist_unit = []\n for unit in Units:\n dist_unit += list(unit.values())\n status = ProcessedAliquots.objects.all().values(\n 'PostProcessingStatus').distinct()\n dist_status = []\n for stat in status:\n dist_status += list(stat.values())\n context = {'samples': dist_sample, 'volumes': dist_volume,\n 'units': dist_unit, 'status': dist_status}\n return render(request, self.template_name, context)","def test_publication_view(self):\n \n test_response = self.client.get('/papers/14-3-3-proteins-a-number-of-functions-for-a-numbered-protein/')\n self.assertEqual(test_response.status_code, 200)\n self.assertTrue('publication' in test_response.context) \n self.assertTemplateUsed(test_response, 'paper-detail.html')\n self.assertTemplateUsed(test_response, 'base.html') \n self.assertTemplateUsed(test_response, 'disqus_snippet.html') \n self.assertTemplateUsed(test_response, 'paper_sharing_widgets.html')\n self.assertTemplateUsed(test_response, 'altmetric_snippet.html') \n self.assertEqual(test_response.context['publication'].pk, 1)\n self.assertEqual(test_response.context['publication'].title, u'14-3-3 proteins: a number of functions for a numbered protein.')","def generate_report(df, start_date, end_date):\n # Remove any transactions that had to do with collecting or returning security\n security_df = df[(df[CATEGORY] == 'Security') | (df[CATEGORY] == 'Security-Income')]\n df = df[(df[CATEGORY] != 'Security')]\n\n # Exclude the data for everything except our quarter\n period_data = df.loc[start_date:end_date] # Note: NOT using extended quarter range\n rental_income = period_data[period_data[CATEGORY] == 'Rent']\n utilities = period_data[(period_data[CATEGORY] == 'Utilities')]\n other_income = period_data[(period_data['Transaction Type'] == 'credit') & (period_data[CATEGORY] != 'Rent')]\n expenses = period_data[(period_data['Transaction Type'] == 'debit')]\n unpaid_util_overages = float(0)\n\n # print(rental_income)\n # print(other_income)\n # print(expenses)\n \n html_config.initialize()\n print(html_config.HTML_OPEN)\n\n print('<H1>Income and Expense Report for %s-%s:' % (start_date, end_date), '</H1><p>')\n\n # List all unit specific rents and expenses for the quarter\n for UNIT in sorted(rental_income['Unit'].unique()):\n # Show rental income info\n temp_df = rental_income[rental_income['Unit'] == UNIT]\n print('<br><H2>Total rent for Unit ', UNIT, ': ${:,.2f}'.format(temp_df['Amount'].sum()), '</H2>')\n print(temp_df[['Description', 'Amount']].to_html())\n \n if not SKIP_UTIL_ANALYSIS:\n # Show utilities payments and calculate any overage due\n temp_df = utilities[(utilities['Unit'] == UNIT) & (utilities['Transaction Type'] == 'debit')]\n print('<br><H2>Utilities Expenses for Unit', UNIT, ': ${:,.2f}'.format(temp_df['Amount'].sum()), '</h2>')\n overage = temp_df.assign(Overage=lambda x: x.Amount - limit_df.loc[UNIT].Amount)\n # Disable warning when setting negative overage values to zero\n pd.set_option('mode.chained_assignment', None)\n overage.Overage[overage.Overage < 0] = 0\n pd.set_option('mode.chained_assignment', 'warn')\n print(overage[['Description', 'Amount', 'Overage']].to_html())\n # Show if utilties costs exceeded allotted amount\n if (overage['Overage'].sum() > 0):\n print('<h3>Utilities overage for unit', UNIT, ': ${:,.2f}'.format(overage['Overage'].sum()), '</h3>')\n unpaid_util_overages += overage['Overage'].sum()\n # Show any untilities that were collected \n overage_collected = utilities[(utilities['Unit'] == UNIT) & (utilities['Transaction Type'] == 'credit')]\n if not overage_collected.empty:\n print('<br><H2>Util overages collected for Unit ', UNIT, ': ${:,.2f}'.format(overage_collected['Amount'].sum()), '</H2>')\n print(overage_collected[['Description', 'Amount', CATEGORY]].to_html())\n print('<h3>Net Utils overage for unit', UNIT, 'for period: ${:,.2f}'.format(overage['Overage'].sum() - overage_collected['Amount'].sum()), '</h3>')\n \n\n \n # Generate unit specific Utility usage reports\n if GEN_TENANT_UTIL_REPORTS and OUTPUT_DIRECTORY:\n TENANT_FILE = '%s/122-Spring-St-%s-%s-Unit-%s-utils.html' % (OUTPUT_DIRECTORY, start_date, end_date, UNIT)\n TENANT_REPORTS.append(TENANT_FILE)\n sys.stdout = open(TENANT_FILE, 'w')\n print(html_config.HTML_OPEN)\n\n print('<H1>Unit', UNIT, '</H1>')\n print('<br><H2>Utilities Expenses for: %s-%s' % (start_date, end_date))\n print('<br>Utilites included in rent: ${:,.2f}'.format(limit_df.loc[UNIT].Amount))\n print('</H2>')\n print(overage[['Description', 'Amount', 'Overage']].to_html())\n # Show if any utilties overage may be due\n if (overage['Overage'].sum() > 0):\n print('<h3>Utilities overage for unit', UNIT, ': ${:,.2f}'.format(overage['Overage'].sum()), '</h3>')\n if not overage_collected.empty:\n print('<br><H2>Util overages collected for Unit ', UNIT, ': ${:,.2f}'.format(overage_collected['Amount'].sum()), '</H2>')\n print(overage_collected[['Description', 'Amount', CATEGORY]].to_html())\n print('<h3>Net Utils overage for unit', UNIT, 'for period: ${:,.2f}'.format(overage['Overage'].sum() - overage_collected['Amount'].sum()), '</h3>')\n\n print(html_config.HTML_CLOSE)\n\n # Restore stdout to the main report file\n sys.stdout = open(REPORT_FILE, 'a')\n \n # Show other unit specific transactions\n if SKIP_UTIL_ANALYSIS:\n unit_exp = expenses[(expenses['Unit'] == UNIT)]\n unit_income = other_income[other_income['Unit'] == UNIT]\n else:\n unit_exp = expenses[(expenses['Unit'] == UNIT) & (expenses[CATEGORY] != 'Utilities')]\n unit_income = other_income[(other_income['Unit'] == UNIT) & (other_income[CATEGORY] != 'Utilities')]\n \n if not unit_exp.empty:\n print('<br><H2>Other Unit specific expenses for: ', UNIT, ': ${:,.2f}'.format(unit_exp['Amount'].sum()), '</h2>')\n print(unit_exp[['Description', 'Amount', 'Unit', CATEGORY]].to_html())\n print('<p>')\n \n # Show any other unit specific credit\n other_income = other_income[other_income['Unit'] == UNIT]\n if not other_income.empty:\n print('<br><H2>Expense offsets for Unit ', UNIT, ': ${:,.2f}'.format(other_income['Amount'].sum()), '</H2>')\n print(other_income[['Description', 'Amount', CATEGORY]].to_html())\n \n # Add a line between units\n print('<hr>')\n \n # List the shared income and expenses for the quarter\n temp_df = other_income[other_income['Unit'].isnull()]\n if not temp_df.empty:\n print ('<br><H2>Non unit specific income: ${:,.2f}'.format(temp_df['Amount'].sum()), '</h2>')\n print(temp_df[['Description', 'Amount', CATEGORY]].to_html())\n gen_expenses = expenses[expenses['Unit'].isnull()]\n if not gen_expenses.empty:\n print ('<br><H1>Non unit specific expenses</h1>')\n # Get the list of expense categories and generate summary for each\n for category in sorted(gen_expenses[CATEGORY].unique()):\n temp_df = gen_expenses[(gen_expenses[CATEGORY] == category)]\n print ('<br><H2>'+ category +': ${:,.2f}'.format(temp_df['Amount'].sum()), '</h2>')\n print(temp_df[['Description', 'Amount', CATEGORY]].to_html())\n \n # If there were any security transactions in the period give a security report\n if not security_df.loc[start_date:end_date].empty:\n temp_df = security_df.loc[start_date:end_date] \n print('<hr><H2>Security related transactions:</H2>')\n print(temp_df[['Description', 'Amount', 'Transaction Type', 'Unit']].to_html())\n for UNIT in sorted(rental_income['Unit'].unique()):\n unit_df = security_df[security_df['Unit'] == UNIT]\n collected = unit_df[(unit_df['Transaction Type'] == 'credit')]['Amount'].sum()\n returned = unit_df[(unit_df['Transaction Type'] == 'debit')]['Amount'].sum()\n print('<center><H4>Current Liability on Unit '+str(UNIT)+': ${:,.2f}'.format(collected-returned), '</H4></center>')\n \n # # Summarize the periods income and expenses -- old way to be discarded...\n # print('<br><H3>Total income for period: ${:,.2f}'.format(rental_income['Amount'].sum()), '</H3>')\n # print('<H3>Total expense for period: ${:,.2f}'.format(expenses['Amount'].sum() - other_income['Amount'].sum()), '</H3>')\n # print('<H3>Profit for period: ${:,.2f}'.format(rental_income['Amount'].sum() + other_income['Amount'].sum() -expenses['Amount'].sum()), '</H3>')\n # Summarize the periods income and expenses\n print('<br><H3>Total income for period: ${:,.2f}'.format(rental_income['Amount'].sum()))\n print('<br><H3>Total expense for period: ${:,.2f}'.format(expenses['Amount'].sum() - other_income['Amount'].sum()), '</H3>')\n print('<H3>Profit for period: ${:,.2f}'.format(rental_income['Amount'].sum() + other_income['Amount'].sum() -expenses['Amount'].sum()), '</H3>')\n# print('</H3>')\n \n print(html_config.HTML_CLOSE)\n sys.stdout.flush()","def check_auto_presence_for_configuration(request):\n\n report_type = request.GET.get('report_type',None)\n usecase = request.GET.get('usecase',None)\n language = request.GET.get('language',None)\n institute = request.GET.get('institute',None)\n batch = request.GET.get('batch',None)\n languages = ['English','english']\n # print('BATCH',str(batch))\n use = UseCase.objects.get(name=usecase)\n json_resp = {}\n mode = NameSpace.objects.get(ns_id = 'Robot')\n user = User.objects.get(ns_id = mode, username='Robot_user')\n\n if report_type == 'pubmed':\n cursor = connection.cursor()\n cursor.execute(\"SELECT COUNT(*) FROM ground_truth_log_file AS g INNER JOIN report AS r ON r.id_report = g.id_report AND r.language = g.language WHERE g.ns_id = %s AND g.username = %s AND r.institute=%s AND r.language in %s AND r.name = %s AND r.batch = %s\",['Robot','Robot_user','PUBMED',tuple(languages),str(usecase),int(batch)])\n reports = cursor.fetchone()[0]\n json_resp['count'] = reports\n\n elif report_type == 'reports':\n cursor = connection.cursor()\n cursor.execute(\n \"SELECT COUNT(*) FROM ground_truth_log_file AS g INNER JOIN report AS r ON r.id_report = g.id_report AND r.language = g.language WHERE g.ns_id = %s AND g.username = %s AND r.institute!=%s AND r.institute = %s AND r.language = %s AND r.name = %s AND r.batch = %s\",\n ['Robot', 'Robot_user', 'PUBMED',str(institute),str(language),str(usecase),int(batch)])\n reports = cursor.fetchone()[0]\n json_resp['count'] = reports\n print(json_resp)\n return JsonResponse(json_resp)","def GetSummaryReports(self, parameters):\n # [GET] https://assets.falcon.crowdstrike.com/support/api/swagger.html#/falconx-sandbox/GetSummaryReports\n FULL_URL = self.base_url+'/falconx/entities/report-summaries/v1'\n HEADERS = self.headers\n PARAMS = parameters\n result = self.Result()\n try:\n response = requests.request(\"GET\", FULL_URL, params=PARAMS, headers=HEADERS, verify=False)\n returned = result(response.status_code, response.headers, response.json())\n except Exception as e:\n returned = result(500, {}, str(e))\n\n return returned","def forecast(request):\n\n all_documents = list()\n all_documents.extend(AE.objects.all())\n all_documents.extend(NI.objects.all())\n all_documents.extend(PT5.objects.all())\n all_documents.extend(PTM.objects.all())\n all_documents.extend(PTHOY.objects.all())\n all_documents.extend(PTRD.objects.all())\n all_documents.extend(PTT.objects.all())\n all_documents.extend(DP10.objects.all())\n all_documents.extend(PTTN.objects.all())\n all_documents.extend(EGT.objects.all())\n all_documents.extend(ACT.objects.all())\n\n notifications = request.user.notifications.unread()[:5]\n paginator = Paginator(all_documents, 10)\n page = request.GET.get('page')\n \n context = { \n 'documents_issues': None,\n 'notifications':notifications,\n 'form':forms.InputFileForm(),\n }\n\n if paginator.count > 0:\n context['documents_issues'] = paginator.get_page(page)\n\n return render(request, 'forecast.html', context)","def overview(request):\n LOGGER.info('Rendering WMT16 HIT overview for user \"{0}\".'.format(\n request.user.username or \"Anonymous\"))\n \n # Re-initialise random number generator.\n seed(None)\n \n # Collect available language pairs for the current user.\n language_codes = set([x[0] for x in LANGUAGE_PAIR_CHOICES])\n language_pairs = request.user.groups.filter(name__in=language_codes)\n \n # Collect available annotation projects for the current user.\n annotation_projects = request.user.project_set.all()\n \n hit_data = []\n total = [0, 0, 0]\n\n for language_pair in language_pairs:\n for annotation_project in annotation_projects:\n hit = _compute_next_task_for_user(request.user, annotation_project, language_pair)\n user_status = HIT.compute_status_for_user(request.user, annotation_project, language_pair)\n for i in range(3):\n total[i] = total[i] + user_status[i]\n \n if hit:\n # Convert status seconds back into datetime.time instances.\n for i in range(2):\n user_status[i+1] = seconds_to_timedelta(int(user_status[i+1]))\n \n hit_data.append(\n (hit.get_language_pair_display(), hit.get_absolute_url(),\n hit.hit_id, user_status, annotation_project)\n )\n \n # Convert total seconds back into datetime.timedelta instances.\n total[1] = seconds_to_timedelta(int(total[2]) / float(int(total[0]) or 1))\n \n # Remove microseconds to get a nicer timedelta rendering in templates.\n total[1] = total[1] - timedelta(microseconds=total[1].microseconds)\n \n total[2] = seconds_to_timedelta(int(total[2]))\n \n groups = _identify_groups_for_user(request.user)\n group = None\n if len(groups) > 1:\n LOGGER.debug(u'User \"{0}\" assigned to multiple annotation groups: {1}'.format(\n request.user.username or u'Anonymous',\n u', '.join([x.name for x in groups]))\n )\n group = groups[0]\n \n if group is not None:\n group_name = group.name\n group_status = HIT.compute_status_for_group(group)\n for i in range(2):\n group_status[i+1] = seconds_to_timedelta(int(group_status[i+1]))\n \n else:\n group_status = None\n group_name = None\n \n LOGGER.debug(u'\\n\\nHIT data for user \"{0}\":\\n\\n{1}\\n'.format(\n request.user.username or \"Anonymous\",\n u'\\n'.join([u'{0}\\t{1}\\t{2}\\t{3}'.format(*x) for x in hit_data])))\n\n # Compute admin URL for super users.\n admin_url = None\n if request.user.is_superuser:\n admin_url = reverse('admin:index')\n \n dictionary = {\n 'active_page': \"OVERVIEW\",\n 'hit_data': hit_data,\n 'total': total,\n 'group_name': group_name,\n 'group_status': group_status,\n 'admin_url': admin_url,\n 'title': 'WMT16 Dashboard',\n 'annotation_groups': [x.name for x in groups],\n }\n dictionary.update(BASE_CONTEXT)\n \n LOGGER.info(dictionary.values())\n \n return render(request, 'wmt16/overview.html', dictionary)","def survey_index(request):\n if request.user.is_authenticated:\n latest_survey_list = Survey.objects.filter(\n author=request.user,\n ).order_by('-pub_date')\n my_surveys = Survey.objects.filter(author=request.user).order_by('title')\n return render(request,\n 'skip_logic/survey_index.html',\n {'latest_survey_list': latest_survey_list, 'my_surveys': my_surveys})\n\n latest_survey_list = Survey.objects.filter(\n is_public=True,\n pub_date__lte=timezone.now(),\n ).order_by('title')\n\n return render(request,\n 'skip_logic/survey_index_public.html',\n {'latest_survey_list': latest_survey_list})","def index(request):\n\n # Generate counts of some of the main objects\n num_books = Book.objects.all().count()\n num_instances = BookInstance.objects.all().count()\n\n # Available books (status = 'a')\n num_instances_available = BookInstance.objects.filter(status__exact='a').count()\n\n # The 'all()' is implied by default.\n num_authors = Author.objects.count()\n\n # Number of visits to this view, as counted in the session variable.\n num_visits = request.session.get('num_visits', 0)\n request.session['num_visits'] = num_visits + 1\n\n \"\"\"\n Challenge\n Modify the view to generate counts for genres and books that contain a particular word (case insensitive), and \n pass the results to the context. You accomplish this in a similar way to creating and using num_books and \n num_instances_available. Then update the index template to include these variables.\n \"\"\"\n num_fantasy_genres = Genre.objects.filter(name__icontains='fantasy').count()\n num_thrones_books = Book.objects.filter(title__icontains='thrones').count()\n\n context = {\n 'num_books': num_books,\n 'num_instances': num_instances,\n 'num_instances_available': num_instances_available,\n 'num_authors': num_authors,\n 'num_fantasy_genres': num_fantasy_genres,\n 'num_thrones_books': num_thrones_books,\n 'num_visits': num_visits,\n }\n\n # Render the HTML template index.html with the data in the context variable\n return render(request, 'index.html', context=context)","def get_queryset(self, request):\n qs = super().get_queryset(request)\n qs = qs.annotate(\n libraryrelease__count=models.Count(\n 'release', distinct=True,\n filter=models.Q(release__cd_identifier__isnull=False)))\n qs = qs.annotate(\n commercialrelease__count=models.Count(\n 'release', distinct=True,\n filter=models.Q(release__cd_identifier__isnull=True)))\n qs = qs.annotate(models.Count('recording', distinct=True))\n return qs","def summary():\r\n\r\n average_age, counted = _find_average_age()\r\n male, female = _find_male_female_percentage()\r\n headings = [\"Total Number of Patients\", \"Average Age\",\r\n \"Patients Involved In Average Age\", \"Percentage of Male\",\r\n \"Percentage of Female\"]\r\n data = [len(resources), average_age, counted, male, female]\r\n return render_template(\"summary.html\", headings=headings, data=data)","def check_presence_exa_conc_lab(request):\n\n # reports = request.GET.get('reports',None)\n rep = request.GET.get('id_report',None)\n language = request.GET.get('language',None)\n usecase = request.GET.get('usecase',None)\n reports = None\n if request.method == 'POST':\n request_body_json = json.loads(request.body)\n reports = request_body_json['reports']\n if rep is not None and language is not None:\n report = Report.objects.get(id_report = rep,language = language)\n usecase = report.name_id\n # print(usecase)\n json_resp = {}\n if usecase in ['colon','uterine cervix','lung']:\n bool = check_exa_lab_conc_only(usecase)\n print('bool',bool)\n else:\n bool = [False,False]\n json_resp['labels'] = bool[0]\n json_resp['concepts'] = bool[1]\n elif usecase is not None:\n json_resp = {}\n json_resp['labels'] = False\n json_resp['concepts'] = False\n\n # labels = []\n # concepts = []\n json_resp = {}\n if usecase in ['colon','uterine cervix','lung']:\n bool = check_exa_lab_conc_only(usecase)\n else:\n bool = [False,False]\n json_resp['labels'] = bool[0]\n json_resp['concepts'] = bool[1]\n # labels.append(bool[0])\n # concepts.append(bool[1])\n # if False in labels:\n # json_resp['labels'] = False\n # else:\n # json_resp['labels'] = True\n #\n # if False in concepts:\n # json_resp['concepts'] = False\n # else:\n # json_resp['concepts'] = True\n elif reports is not None:\n report_list = json.loads(reports)\n json_resp = {}\n json_resp['labels'] = False\n json_resp['concepts'] = False\n usecases = []\n for rep in report_list:\n # rep = json.loads(rep)\n if rep['usecase'] not in usecases:\n usecases.append(rep['usecase'])\n labels = []\n concepts = []\n for u in usecases:\n # print(u)\n json_resp = {}\n if u in ['colon', 'uterine cervix', 'lung']:\n bool = check_exa_lab_conc_only(u)\n else:\n bool = [False, False]\n\n labels.append(bool[0])\n concepts.append(bool[1])\n if False in labels:\n json_resp['labels'] = False\n else:\n json_resp['labels'] = True\n\n if False in concepts:\n json_resp['concepts'] = False\n else:\n json_resp['concepts'] = True\n\n else:\n json_resp={'error':'a usecase is needed'}\n\n print(json_resp)\n return JsonResponse(json_resp)","def buildReports(self):\n pass","def index(request):\n # Generate counts of some of the main objects\n num_books=Book.objects.all().count()\n num_instances=BookInstance.objects.all().count()\n # Available books (status = 'a')\n num_instances_available=BookInstance.objects.filter(status__exact='a').count()\n \n # Render the HTML template index.html with the data in the context variable\n num_authors=Author.objects.count() # The 'all()' is implied by default.\n \n # Number of visits to this view, as counted in the session variable.\n num_visits=request.session.get('num_visits', 0)\n request.session['num_visits'] = num_visits+1\n \n # Render the HTML template index.html with the data in the context variable.\n return render(\n request,\n 'index.html',\n context={'num_books':num_books,'num_instances':num_instances,'num_instances_available':num_instances_available,'num_authors':num_authors,\n 'num_visits':num_visits}, # num_visits appended\n )","def download_sum_findings(self, ir_id, ir_ver):\r\n ir_details = get_interpretation_request_list(interpretation_request_id=ir_id, version=ir_ver)\r\n # Check only one record is returned\r\n if len(ir_details) == 1:\r\n # Check that there's only one summary of findings report\r\n if len(ir_details[0][\"clinical_reports\"]) == 1:\r\n # Download the report from CIP API\r\n session = AuthenticatedCIPAPISession()\r\n response = session.get(ir_details[0][\"clinical_reports\"][0]['url'])\r\n # Check response code is 200\r\n if response.status_code == 200:\r\n # Store the returned html for use by other methods\r\n self.html = response.text\r\n else: \r\n sys.exit(f'ERROR: Unable to download summary of findings. Response code {response.status_code}')\r\n else:\r\n sys.exit(f'ERROR: Number of clinical reports found {len(ir_details[0][\"clinical_reports\"])}')\r\n else:\r\n sys.exit(f'ERROR: Number of interpretation requests found {len(ir_details)}')","def reports(request, template_name='reporting/reports.html'):\n object_list = reporting.all_reports()\n # storing current user position in session\n request.session['enter_url'] = reverse('reports')\n return direct_to_template(request, template_name, {\n # HACK: object list rendered incorrectly in templates somehow\n 'object_list': [(slug, unicode(r.verbose_name)) for slug, r in object_list],\n })","def test_list_all_dx(self):\n self.client.login(username='temporary', password='temporary')\n response = self.client.get(reverse('dx_summary_list_filter'), follow=True)\n self.assertEqual(response.status_code, 200)\n responses_text = u'There are 5 studies in this list.'\n self.assertContains(response, responses_text)","def home(request):\n\n result = {}\n \n result['surveys'] = []\n result['review_requests'] = []\n\n u = request.user\n\n # get surveys\n surveys = Survey.objects.all()\n for s in surveys:\n status, created = eval(\"%s.objects.get_or_create(survey=s, user=u)\"%s.model_name)\n if created:\n status.uuid_token = uuid.uuid4()\n status.save()\n if not status.completed:\n result['surveys'].append(s.summary())\n \n my_products = TransactionLineItem.objects.filter(transaction__party=u).values('product')\n # Find review requests related to product I have purchased, that I haven't reviewed \n reqs = ReviewRequest.objects.exclude(requester=u).filter(product__in=my_products).exclude(replies__reviewer=u)\n \n for r in reqs:\n result['review_requests'].append(r.get_json(me=u))\n\n # TODO: Group purchase requests\n\n # get other people's feeds, filter by friends if in social group\n feeds = Feed.objects.exclude(actor=u)\n result['feeds'] = [ f.get_json(me=u) for f in feeds ]\n\n return JSONHttpResponse(result)","def get_report(client_id, client_secret):\n month = pick_month()\n next_month = month + timedelta(days=28)\n while next_month.month == month.month:\n next_month += timedelta(days=1)\n last_day = next_month - timedelta(days=1)\n\n pr_data = get_pr_data(repos.keys(), client_id, client_secret)\n user_data = get_user_data(pr_data, client_id, client_secret)\n pr_counts_text, pr_counts = get_pr_counts(pr_data, month, last_day)\n pr_details, total, new_users, new_prs = get_pr_details(\n pr_data, user_data, month, last_day, pr_counts)\n\n params = {\n 'month': month.strftime('%B'),\n 'next_month': next_month.strftime('%B'),\n 'month_id_suffix': month.strftime('%b-%y').lower(),\n 'year': month.year,\n 'total': total,\n 'pr_counts_text': pr_counts_text,\n 'pr_details': pr_details,\n 'prs_from_new_users': new_prs,\n 'new_users': new_users\n }\n\n return template % params","def get(self, request, *args, **kwargs):\n filters = eval(request.GET.get('filters'))\n settings = eval(request.GET.get('settings'))\n date_from = filters['DF'][0]\n date_to = filters['DT'][0]\n receipt_barcode = ReceiptRecords.objects.all().values_list('Barcode')\n visits = ManifestRecords.objects.filter(\n Barcode__in=receipt_barcode).values('Visit').distinct()\n dist_visits = []\n for visit in visits:\n dist_visits += list(visit.values())\n sites = ManifestRecords.objects.filter(\n Barcode__in=receipt_barcode).values('Site').distinct()\n dist_sites = []\n for site in sites:\n dist_sites += list(site.values())\n rooms = ManifestRecords.objects.filter(\n Barcode__in=receipt_barcode).values('Room').distinct()\n dist_rooms = []\n for room in rooms:\n dist_rooms += list(room.values())\n dist_state = ['ACTIVE', 'UNABLE_TO_DRAW', 'UNABLE_TO_PROCESS',\n 'PROCESSED_ON_TIME', 'PROCESSED_NOT_ON_TIME']\n context = {'sites': dist_sites,\n 'visits': dist_visits, 'rooms': dist_rooms, 'filters': filters, 'settings': settings, 'states': dist_state, 'from': date_from, 'to': date_to}\n return render(request, self.template_name, context)","def create_vuln_report():","def get_sna_results(request):\n dataset_type = request.POST.get('dataset')\n G, rels = SNAVizualizationManager.construct_nx_graph(request, dataset_type)\n result_dict = SNAVizualizationManager.get_SNA_metrics(G)\n print(result_dict)\n \n result_dict = {'sna_html':gen_SNA_html(result_dict)}\n \n return HttpResponse(\n json.dumps({\"status\": \"success\", 'result_dict':result_dict}),\n content_type=\"application/json\"\n )","def issueListing(self, v, i):\n #list of URLS within the issue\n# links = []\n issURL = self.link(vol = v, iss = i )\n html=urlopen(issURL)\n soup=BeautifulSoup(html,'html.parser')\n URLs = [] #Empty list\n \n# titles = soup.find_all('h5', class_=\"title\")\n# authors = soup.find_all('h6', class_=\"authors\")\n# pubs = soup.find_all('h6', class_=\"pub-info\")\n# for t, a, p in zip(titles, authors, pubs):\n blocks = soup.find_all('div', class_=\"article panel article-result\")\n for b in blocks:\n# print(b)\n titletag = b.find('h5', class_=\"title\")\n title = titletag.get_text()\n #Extract abstract url from title head\n aURL = titletag.find('a', href = True)['href']\n alink = 'https://journals.aps.org' + aURL\n #Print out the scraped information\n print(title)\n print(alink)\n #Extract research area and topic keywords\n kwlist = b.find('ul', class_=\"inline-list subjects\")\n #If the list tag exists\n if kwlist:\n lis = kwlist.find_all('li')\n kws = [li.get_text() for li in lis] \n print(kws)\n #Add utf-8 encode\n# print(kws.encode('utf-8')) \n print('----------------------------------------------------------------') \n #Collect URLs in the issue\n URLs.append('https://journals.aps.org' + aURL)\n return URLs","def viewexperiments(request):\r\n # Obtain the context from the HTTP request.\r\n\r\n context_instance = RequestContext(request)\r\n\r\n try:\r\n user = _validate_and_get_geniuser(request)\r\n except LoggedInButFailedGetGeniUserError:\r\n return _show_failed_get_geniuser_page(request)\r\n\r\n\r\n page_top_errors = []\r\n username = user.username\r\n ret = [] #returning list\r\n user_experiments = Experiment.objects.filter(geni_user=user)\r\n for experiment in reversed(user_experiments):\r\n #reversed so the oldest experiment is the last we show.\r\n experiment_sensors = []\r\n name_list = []\r\n experiment_sensors.extend(list(Battery.objects.filter(experiment_id=experiment)))\r\n experiment_sensors.extend(list(Bluetooth.objects.filter(experiment_id=experiment)))\r\n experiment_sensors.extend(list(Cellular.objects.filter(experiment_id=experiment)))\r\n experiment_sensors.extend(list(Settings.objects.filter(experiment_id=experiment)))\r\n experiment_sensors.extend(list(ConcretSensor.objects.filter(experiment_id=experiment)))\r\n experiment_sensors.extend(list(Location.objects.filter(experiment_id=experiment)))\r\n experiment_sensors.extend(list(Signal_strengths.objects.filter(experiment_id=experiment)))\r\n experiment_sensors.extend(list(Wifi.objects.filter(experiment_id=experiment)))\r\n\r\n for sensor in experiment_sensors:\r\n name_list.append(sensor.show_name())\r\n\r\n if name_list == []:\r\n name_list = \"None\"\r\n\r\n ret.append([experiment.expe_name,name_list,experiment.id])\r\n \r\n \r\n \r\n return render(request, 'control/viewexperiments.html', {'username' : username, \r\n 'page_top_errors' : page_top_errors, 'ret':ret})","def capacity_reports():\n return render_template('capacity.html')","def index(request):\n try:\n meeting = Meeting.objects.latest('when')\n num_rsvped = Person.objects.filter(ynm='Y').count()\n\n except (KeyError, Meeting.DoesNotExist, Person.DoesNotExist):\n raise Http404\n\n return render(request,'chipy/chipy.html',{'meeting':meeting,'num_rsvped':num_rsvped})","def view_result_main(request):\n\n\tcontext_dict = {\n\t\t'title': 'All Results Main',\n\t}\n\treturn render(request, \"viewResultMain.html\", context_dict)","def report_data(request):\n if request.method == \"GET\":\n params = request.query_params\n async_result = None\n all_providers = False\n provider_uuid = params.get(\"provider_uuid\")\n provider_type = params.get(\"provider_type\")\n schema_name = params.get(\"schema\")\n start_date = params.get(\"start_date\")\n end_date = params.get(\"end_date\")\n queue_name = params.get(\"queue\") or PRIORITY_QUEUE\n if provider_uuid is None and provider_type is None:\n errmsg = \"provider_uuid or provider_type must be supplied as a parameter.\"\n return Response({\"Error\": errmsg}, status=status.HTTP_400_BAD_REQUEST)\n if queue_name not in QUEUE_LIST:\n errmsg = f\"'queue' must be one of {QUEUE_LIST}.\"\n return Response({\"Error\": errmsg}, status=status.HTTP_400_BAD_REQUEST)\n\n if provider_uuid == \"*\":\n all_providers = True\n elif provider_uuid:\n with ProviderDBAccessor(provider_uuid) as provider_accessor:\n provider = provider_accessor.get_type()\n else:\n provider = provider_type\n\n if start_date is None:\n errmsg = \"start_date is a required parameter.\"\n return Response({\"Error\": errmsg}, status=status.HTTP_400_BAD_REQUEST)\n\n if not all_providers:\n if schema_name is None:\n errmsg = \"schema is a required parameter.\"\n return Response({\"Error\": errmsg}, status=status.HTTP_400_BAD_REQUEST)\n\n if provider is None:\n errmsg = \"Unable to determine provider type.\"\n return Response({\"Error\": errmsg}, status=status.HTTP_400_BAD_REQUEST)\n\n if provider_type and provider_type != provider:\n errmsg = \"provider_uuid and provider_type have mismatched provider types.\"\n return Response({\"Error\": errmsg}, status=status.HTTP_400_BAD_REQUEST)\n\n async_result = update_summary_tables.s(\n schema_name, provider, provider_uuid, start_date, end_date, queue_name=queue_name\n ).apply_async(queue=queue_name or PRIORITY_QUEUE)\n else:\n async_result = update_all_summary_tables.delay(start_date, end_date)\n return Response({REPORT_DATA_KEY: str(async_result)})\n\n if request.method == \"DELETE\":\n params = request.query_params\n\n schema_name = params.get(\"schema\")\n provider = params.get(\"provider\")\n provider_uuid = params.get(\"provider_uuid\")\n simulate = params.get(\"simulate\")\n\n if schema_name is None:\n errmsg = \"schema is a required parameter.\"\n return Response({\"Error\": errmsg}, status=status.HTTP_400_BAD_REQUEST)\n\n if provider is None:\n errmsg = \"provider is a required parameter.\"\n return Response({\"Error\": errmsg}, status=status.HTTP_400_BAD_REQUEST)\n\n if provider_uuid is None:\n errmsg = \"provider_uuid is a required parameter.\"\n return Response({\"Error\": errmsg}, status=status.HTTP_400_BAD_REQUEST)\n\n if simulate is not None and simulate.lower() not in (\"true\", \"false\"):\n errmsg = \"simulate must be a boolean.\"\n return Response({\"Error\": errmsg}, status=status.HTTP_400_BAD_REQUEST)\n\n if simulate is not None and simulate.lower() == \"true\":\n simulate = True\n else:\n simulate = False\n\n LOG.info(\"Calling remove_expired_data async task.\")\n\n async_result = remove_expired_data.delay(schema_name, provider, simulate, provider_uuid)\n\n return Response({\"Report Data Task ID\": str(async_result)})","def get_queryset(self, request):\n qs = super().get_queryset(request)\n qs = qs.annotate(\n work__count=models.Count('release__works', distinct=True))\n qs = qs.annotate(\n release__count=models.Count('release__id', distinct=True))\n return qs"],"string":"[\n \"def pubmed_missing_auto(request):\\n\\n usecases = UseCase.objects.all()\\n json_resp = {}\\n json_resp['annotated'] = 0\\n json_resp['tot'] = 0\\n json_resp['usecase'] = []\\n languages = ['English','english']\\n for el in usecases:\\n use = el.name\\n json_resp[use] = {}\\n batches = []\\n batches.append('all')\\n if use.lower() in ['colon','lung','uterine cervix']:\\n report = Report.objects.filter(name=el,language__in=languages,institute = 'PUBMED')\\n for el in report:\\n if el.batch not in batches:\\n batches.append(el.batch)\\n count_rep = report.count()\\n\\n if count_rep > 0:\\n json_resp['usecase'].append(str(use))\\n json_resp['tot'] = json_resp['tot'] + count_rep\\n with connection.cursor() as cursor:\\n cursor.execute(\\n \\\"SELECT COUNT(*) FROM report AS r INNER JOIN ground_truth_log_file AS g ON g.id_report = r.id_report AND r.language = g.language WHERE r.name = %s AND g.username = %s AND gt_type=%s AND institute = %s and r.language in %s;\\\",\\n [str(use),'Robot_user','labels','PUBMED',tuple(languages)]) # We could consider any of the gt_type\\n groundTruths = cursor.fetchone()[0]\\n\\n json_resp['annotated'] = json_resp['annotated'] + groundTruths\\n\\n for batch in batches:\\n\\n json_resp[use][batch] = {}\\n if batch == 'all' or batch is None:\\n report = Report.objects.filter(name=use,language__in=languages, institute='PUBMED')\\n count_rep = report.count()\\n json_resp[use][batch]['tot'] = count_rep\\n with connection.cursor() as cursor:\\n cursor.execute(\\n \\\"SELECT COUNT(*) FROM report AS r INNER JOIN ground_truth_log_file AS g ON g.id_report = r.id_report AND r.language = g.language WHERE r.name = %s AND g.username = %s AND gt_type=%s AND institute = %s and r.language in %s;\\\",\\n [str(use),'Robot_user','labels','PUBMED',tuple(languages)]) # We could consider any of the gt_type\\n groundTruths = cursor.fetchone()[0]\\n\\n if groundTruths is None:\\n json_resp[use][batch]['annotated'] = 0\\n else:\\n json_resp[use][batch]['annotated'] = groundTruths\\n else:\\n report = Report.objects.filter(name=use,language__in=languages, institute='PUBMED',batch = batch)\\n count_rep = report.count()\\n json_resp[use][batch]['tot'] = count_rep\\n with connection.cursor() as cursor:\\n cursor.execute(\\n \\\"SELECT COUNT(*) FROM report AS r INNER JOIN ground_truth_log_file AS g ON g.id_report = r.id_report AND r.language = g.language WHERE r.name = %s AND g.username = %s AND gt_type=%s AND institute = %s and batch = %s and r.language in %s;\\\",\\n [str(use), 'Robot_user', 'labels', 'PUBMED',batch,tuple(languages)]) # We could consider any of the gt_type\\n groundTruths = cursor.fetchone()[0]\\n\\n if groundTruths is None:\\n json_resp[use][batch]['annotated'] = 0\\n else:\\n json_resp[use][batch]['annotated'] = groundTruths\\n # print('risposta',json_resp)\\n return JsonResponse(json_resp)\",\n \"def usecases(request):\\n\\n context = {\\n\\n }\\n\\n return render(request, 'hydraviewer/usecases.html', context)\",\n \"def summaryView(request):\\n\\n alert_errors = []\\n alert_infos = []\\n alert_filters = []\\n\\n runs = get_runs_from_request_filters(\\n request, alert_errors, alert_infos, alert_filters\\n )\\n\\n summary = SummaryReport(runs)\\n\\n context = {\\n \\\"refs\\\": summary.reference_runs(),\\n \\\"runs\\\": summary.runs_checked_per_type(),\\n \\\"tk_maps\\\": summary.tracker_maps_per_type(),\\n \\\"certified_runs\\\": summary.certified_runs_per_type(),\\n \\\"sums\\\": summary.sum_of_quantities_per_type(),\\n \\\"alert_errors\\\": alert_errors,\\n \\\"alert_infos\\\": alert_infos,\\n \\\"alert_filters\\\": alert_filters,\\n }\\n\\n return render(request, \\\"certhelper/summary.html\\\", context)\",\n \"def check_PUBMED_reports(request):\\n\\n json_resp = {}\\n json_resp['count'] = 0\\n pubmed_arts = Report.objects.filter(institute = 'PUBMED')\\n for el in pubmed_arts:\\n if el.id_report.startswith('PUBMED'):\\n json_resp['count'] += 1\\n return JsonResponse(json_resp,safe=False)\",\n \"def medtag_reports(request):\\n\\n json_resp = {}\\n json_resp['usecase'] = []\\n reps = Report.objects.all()\\n for r in reps:\\n if not r.id_report.startswith('PUBMED_') and not str(r.name_id) in json_resp['usecase']:\\n json_resp['usecase'].append(str(r.name_id))\\n return JsonResponse(json_resp)\",\n \"def check_medtag_reports(request):\\n\\n json_resp = {}\\n json_resp['count'] = 0\\n medtag_arts = Report.objects.all().exclude(institute = 'PUBMED')\\n # for el in pubmed_arts:\\n # if el.id_report.startswith('PUBMED'):\\n json_resp['count'] = medtag_arts.count()\\n return JsonResponse(json_resp,safe=False)\",\n \"def summary_list(request):\\r\\n qs = Summary.objects.all()\\r\\n context = {'summary_records': qs}\\r\\n return render(request, 'weather/summary_list.html', context)\",\n \"def training_report_view(request, application_slug):\\n return training_report(request, application_slug, attach=False)\",\n \"def survivor_reports(request):\\n\\n if request.method == 'GET':\\n data = {}\\n total_survivors = infected = non_infected = water = food = medication = ammunition = pointslost = 0\\n for i in Survivor.objects.all():\\n total_survivors += 1\\n if i.infected is False:\\n non_infected += 1\\n water += i.water\\n food += i.food\\n medication += i.medication\\n ammunition += i.ammunition\\n if i.infected is True:\\n infected += 1\\n pointslost += (4 * i.water)\\n pointslost += (3 * i.food)\\n pointslost += (2 * i.medication)\\n pointslost += (1 * i.ammunition)\\n\\n if total_survivors != 0:\\n data['Percentage of infected survivors'] = str(round((infected/total_survivors), 2) * 100) + '%'\\n data['Percentage of non-infected survivors'] = str(round((non_infected/total_survivors), 2) * 100) + '%'\\n data['Average amount of water by survivor'] = round(water/non_infected,1)\\n data['Average amount of food by survivor'] = round(food/non_infected,1)\\n data['Average amount of medication by survivor'] = round(medication/non_infected,1)\\n data['Average amount of ammunition by survivor'] = round(ammunition/non_infected,1)\\n data['Points lost because of infected survivor'] = pointslost\\n else:\\n data['Percentage of infected survivors'] = '0.0%'\\n data['Percentage of non-infected survivors'] = '0.0%'\\n data['Average amount of water by survivor'] = 0\\n data['Average amount of food by survivor'] = 0\\n data['Average amount of medication by survivor'] = 0\\n data['Average amount of ammunition by survivor'] = 0\\n data['Points lost because of infected survivor'] = 0\\n return Response(data, status=status.HTTP_200_OK)\",\n \"def generate_report():\\n\\n # Fetch the top 3 most viewed articles and number of views and print them\\n articles_query = get_articles_query()\\n popular_articles = execute_query(articles_query)\\n print_top_articles(popular_articles)\\n\\n # Fetch the most popular authors and print them\\n authors_query = get_authors_query()\\n popular_authors = execute_query(authors_query)\\n print_authors(popular_authors)\\n\\n # Print the days when there were more than 1% errors in HTTP requests\\n errors_query = get_errorData_query()\\n error_data = execute_query(errors_query)\\n print_error_data(error_data)\",\n \"def report_missing_auto(request):\\n\\n usecases = UseCase.objects.all()\\n json_resp = {}\\n languages = ['english', 'English']\\n for el in usecases:\\n use = el.name\\n batches = []\\n batches.append('all')\\n if use.lower() in ['colon','lung','uterine cervix']:\\n report = Report.objects.filter(name=el,language__in=languages).exclude(institute = 'PUBMED')\\n count_rep = report.count()\\n for rp in report:\\n if rp.batch not in batches:\\n batches.append(rp.batch)\\n # print(el)\\n # print(count_rep)\\n\\n if count_rep > 0:\\n json_resp[use] = {}\\n for batch in batches:\\n batch = str(batch)\\n json_resp[use][batch] = {}\\n if batch == 'all':\\n json_resp[use][batch]['tot'] = count_rep\\n with connection.cursor() as cursor:\\n cursor.execute(\\n \\\"SELECT COUNT(*) FROM report AS r INNER JOIN ground_truth_log_file AS g ON g.id_report = r.id_report AND r.language = g.language WHERE r.name = %s AND g.username = %s AND gt_type=%s and institute != %s and r.language in %s;\\\",\\n [str(use),'Robot_user','labels','PUBMED',tuple(languages)]) # We could consider any of the gt_type\\n groundTruths = cursor.fetchone()[0]\\n if groundTruths is None:\\n json_resp[use][batch]['annotated'] = 0\\n else:\\n json_resp[use][batch]['annotated'] = groundTruths\\n else:\\n report_count = Report.objects.filter(name=el,batch = batch,language__in=languages).exclude(institute = 'PUBMED').count()\\n json_resp[use][batch]['tot'] = report_count\\n with connection.cursor() as cursor:\\n cursor.execute(\\n \\\"SELECT COUNT(*) FROM report AS r INNER JOIN ground_truth_log_file AS g ON g.id_report = r.id_report AND r.language = g.language WHERE r.name = %s AND g.username = %s AND gt_type=%s and institute != %s and batch = %s and r.language in %s;\\\",\\n [str(use), 'Robot_user', 'labels', 'PUBMED',batch, tuple(languages)]) # We could consider any of the gt_type\\n groundTruths = cursor.fetchone()[0]\\n if groundTruths is None:\\n json_resp[use][batch]['annotated'] = 0\\n else:\\n json_resp[use][batch]['annotated'] = groundTruths\\n # print(json_resp)\\n return JsonResponse(json_resp)\",\n \"def analysis_view(request):\\n return render(request, \\\"tracker/analysis.html\\\")\",\n \"def get_reports(request):\\n\\n inst = request.GET.get('institute',None)\\n use = request.GET.get('usec',None)\\n print(use)\\n lang = request.GET.get('lang',None)\\n batch = request.GET.get('batch',None)\\n all = request.GET.get('all',None)\\n actual_report = request.GET.get('actual_report',None)\\n if all == 'all':\\n # All the reports are returned independently of the usecase, the language or institute.\\n use_obj = UseCase.objects.get(name = use)\\n reps = Report.objects.filter(institute = inst,name = use_obj,language = lang).values('id_report','report_json','language')\\n json_resp = {}\\n json_resp['report'] = []\\n\\n for report in reps:\\n json_rep = {}\\n json_rep['id_report'] = report['id_report']\\n json_rep['language'] = report['language']\\n json_rep['report_json'] = report['report_json']\\n json_resp['report'].append(json_rep)\\n return JsonResponse(json_resp)\\n\\n if(inst != None and use != None and lang != None and batch != None):\\n\\n \\\"\\\"\\\" It is used in the options modal: if the reuqired combination of institute, language and usecase has 0 reports\\n associated, a message is returned. In this case this view returns the number of reports associated to a specific \\n configuration required \\\"\\\"\\\"\\n\\n rep = Report.objects.filter(institute = inst, name = use, language = lang, batch = batch)\\n json_count = {'count':len(rep)}\\n return JsonResponse(json_count)\\n\\n usecase = request.session.get('usecase',None)\\n mode1 = request.session.get('mode',None)\\n mode = NameSpace.objects.get(ns_id=mode1)\\n language = request.session.get('language',None)\\n institute = request.session.get('institute',None)\\n username = request.session['username']\\n batch = request.session['batch']\\n token = request.GET.get('configure',None) # This parameter is set when\\n\\n jsonError = {'error':'something wrong with params!'}\\n if usecase is not None and language is not None and institute is not None and batch is not None:\\n # Get the reports associated to the usecase, language and institute of the SESSION\\n reports1 = Report.objects.filter(name = usecase, language = language, institute = institute,batch=batch)\\n if mode1 == 'Robot':\\n # gts_r = GroundTruthLogFile.objects.filter(language = language,ns_id=mode).values('id_report')\\n # gts_r1 = GroundTruthLogFile.objects.filter(language=language, ns_id=mode).order_by(\\n # 'id_report').distinct('id_report').values('id_report')\\n # ids1 = [el['id_report'] for el in gts_r1]\\n # print(len(ids1))\\n gts_r1 = GroundTruthLogFile.objects.filter(id_report__in = reports1,language = language,ns_id=mode).order_by('id_report').distinct('id_report').values('id_report')\\n ids = [el['id_report'] for el in gts_r1]\\n # print(len(ids))\\n # print(ids == ids1)\\n # for el in gts_r1:\\n # # if el['id_report'] not in ids and Report.objects.filter(language = language, id_report = el['id_report'], batch = batch).exists():\\n # ids.append(el['id_report'])\\n\\n reports1 = Report.objects.filter(id_report__in=ids,name = usecase, language = language, institute = institute,batch = batch)\\n\\n json_resp = {}\\n json_resp['report'] = []\\n if reports1.exists():\\n reports = reports1.values('id_report','report_json','language')\\n for report in reports:\\n json_rep = {}\\n json_rep['id_report'] = report['id_report']\\n json_rep['language'] = report['language']\\n json_rep['report_json'] = report['report_json']\\n json_resp['report'].append(json_rep)\\n\\n json_resp['report'].sort(key=lambda json: json['id_report'], reverse=False) # Reports are sorted by ID\\n # json_resp['report'].sort(key=lambda json: json['report_json']['report_id'], reverse=False) # Reports are sorted by ID\\n json_resp['index'] = 0\\n\\n if token is not None:\\n # Get the last ground truth given the session parameters.\\n gt = get_last_groundtruth(username, usecase, language, institute,mode,batch)\\n else:\\n # Get the last ground truth of the user.\\n gt = get_last_groundtruth(username,None, None, None,mode,batch)\\n\\n if gt is not None:\\n # The index is updated and it characterizes the first report of the list shown to the user.\\n id_report = gt['id_report']\\n use = gt['use_case']\\n lang = gt['language']\\n institute = gt['institute']\\n report_json = Report.objects.get(id_report = id_report, name = use, language = lang, institute = institute)\\n rep_json = report_json.report_json\\n index = json_resp['report'].index({'id_report':id_report,'language':lang,'report_json':rep_json})\\n json_resp['index'] = int(index)\\n if actual_report is not None:\\n index = json_resp['report'].index(actual_report)\\n json_resp['index'] = int(index)\\n\\n return JsonResponse(json_resp)\\n else:\\n return JsonResponse(jsonError,status=500)\",\n \"def report(request):\\n\\tuser_docs = Document.objects.filter(user=User.objects.get(username=request.user.username))\\n\\t# breakpoint()\\n\\tdaily_uploads = user_docs.filter(created_at__day=timezone.now().strftime(\\\"%d\\\"))\\n\\tmonthly_uploads = user_docs.filter(created_at__month=timezone.now().strftime(\\\"%m\\\"))\\n\\tyearly_uploads = user_docs.filter(created_at__year=timezone.now().strftime(\\\"%Y\\\"))\\n\\n\\tdaily_count = daily_uploads.count()\\n\\tmonthly_count = monthly_uploads.count()\\n\\tyearly_count = yearly_uploads.count()\\n\\t# breakpoint()\\n\\n\\tif 'doc_name' in request.GET:\\n\\t\\tpdf_list = user_docs.filter(name__icontains=request.GET['doc_name'])\\n\\telif 'month' in request.GET:\\n\\t\\tpdf_list = user_docs.filter(created_at__month=request.GET['month'])\\n\\t\\t# breakpoint()\\n\\telif 'year' in request.GET:\\n\\t\\tpdf_list = user_docs.filter(created_at__year=request.GET['year'])\\n\\telif 'from' in request.GET and 'to' in request.GET:\\n\\t\\t# breakpoint()\\n\\t\\tpdf_list = user_docs.filter(created_at__range=[request.GET['from'],request.GET['to']])\\n\\n\\telse:\\n\\t\\tpdf_list = user_docs\\n\\tcontext = {'daily_count': daily_count, 'monthly_count': monthly_count, 'yearly_count': yearly_count, 'pdf_list':pdf_list}\\n\\n\\treturn render(request, 'document_manager/report.html', context)\",\n \"def admin_evaluate_reports(request):\\n if not validate_request(request): return redirect(reverse(URL_FORBIDDEN))\\n \\n admin = auth.get_user(request)\\n \\n if request.method == \\\"GET\\\":\\n all_thesis = [] # list of dict\\n \\n for panelmember in PanelMember.objects.filter(Q(status = 'F') | Q(status = 'Z')).filter(feedback_at = 'A'):\\n thesis = panelmember.thesis\\n dict = {}\\n dict['title'] = thesis.title\\n dict['student_full_name'] = thesis.student.first_name + \\\" \\\" + thesis.student.last_name\\n dict['report'] = panelmember.feedback_with_referee_details\\n dict['student_username'] = thesis.student.user.username\\n dict['id'] = thesis.id\\n dict['referee_name'] = panelmember.referee.user.first_name + ' ' + panelmember.referee.user.last_name\\n dict['referee_id'] = panelmember.referee.id\\n all_thesis.append(dict)\\n \\n return render(request, 'app/admin/view_finalReports.html', {\\n 'title':'Final Reports',\\n 'layout_data' : get_layout_data(request),\\n 'all_thesis' : all_thesis\\n })\\n elif request.method == \\\"POST\\\":\\n form = PanelMember2Form(request.POST, request.FILES)\\n \\n\\n thesis = int(request.POST['thesis'])\\n referee = int(request.POST['referee'])\\n \\n if form.is_valid() and validate_pdf(request.FILES['feedback_without_referee_details']):\\n referee = Referee.objects.get(id = referee)\\n thesis = Thesis.objects.get(id = thesis)\\n panelmember = PanelMember.objects.get(thesis = thesis,referee = referee)\\n panelmember.feedback_at = 'G'\\n \\n time = str(datetime.datetime.now())\\n timestamp = ''\\n for i in time:\\n if not (i == ':' or i == '-'):\\n timestamp += i\\n request.FILES['feedback_without_referee_details'].name = \\\"Evaluation_Report_\\\"+thesis.student.user.username+\\\"_\\\"+timestamp+\\\".pdf\\\"\\n \\n panelmember.feedback_without_referee_details = request.FILES['feedback_without_referee_details']\\n panelmember.save()\\n\\n total_feedbacks = PanelMember.objects.filter(thesis = thesis, feedback_at = 'G').count()\\n if total_feedbacks == thesis.indian_referees_required + thesis.foreign_referees_required:\\n _update_student_status(thesis, STATUS_ID_THESIS_FEEDBACKS_RECEIVED) \\n\\n # send notification to all guide\\n send_notification_to_all_guides(admin, thesis, \\\"A feedback report has been sent of student \\\" + thesis.student.first_name + \\\" \\\" + thesis.student.last_name)\\n # email\\n subject = \\\"[Feed Back reports] of the Thesis titled\\\" + thesis.title\\n content = \\\"<br>Dear Sir/Madam,</br><br></br><br></br>\\\"+\\\"A feedback report has been sent of student \\\" + thesis.student.first_name + \\\" \\\" + thesis.student.last_name +'. Please Check the PhD Portal for more details.'+\\\"<br></br><br></br>Regards,<br></br>PhDPortal.\\\"\\n \\n email = []\\n\\n for thesisGuide in ThesisGuide.objects.filter(thesis = thesis):\\n receiver = Faculty.objects.get(user = thesisGuide.guide.user)\\n email.append(receiver.email)\\n\\n send_email_task.delay(email, subject, content)\\n return redirect(reverse(admin_evaluate_reports))\\n else:\\n return redirect(reverse(URL_BAD_REQUEST))\\n else:\\n return redirect(reverse(URL_BAD_REQUEST))\",\n \"def data_set_characterization(request):\\n admin_approved = Phenotype.objects.filter(phenotypestatus__status='A')\\n user_submitted = Phenotype.objects.filter(phenotypestatus__status='U')\\n sample_files = SampleFile.objects.all()\\n\\n admin_approved_count = len(admin_approved)\\n user_submitted_count = len(user_submitted)\\n\\n traits_by_chrom = SNP.objects.filter(Q(snpstatus__status='A') | Q(snpstatus__status='U')).values('chromosome_num').annotate(num_traits=Count('chromosome_num'))\\n\\n context = {'user_submitted_count':user_submitted_count,\\n 'admin_approved_count':admin_approved_count,\\n 'sample_files':sample_files,\\n 'traits_by_chrom':traits_by_chrom}\\n\\n return render(request, 'SNP_Feature_View/data_set_characterization.html', context)\",\n \"def list(self, request, scope=None):\\n\\n qs = self.get_queryset()\\n if scope == 'summary':\\n total = qs.aggregate(total=Sum('total_value'))['total'] or 0.0\\n return Response({'total_investment': total}, status=200)\\n else:\\n serializer = self.get_serializer(qs, many=True)\\n return Response(serializer.data, status=status.HTTP_200_OK)\",\n \"def referee_evaluate_synopsis(request):\\n if not validate_request(request): return redirect(reverse(URL_FORBIDDEN))\\n \\n user = auth.get_user(request)\\n referee = Referee.objects.get(user = user)\\n \\n if request.method == \\\"GET\\\":\\n all_thesis = [] # list of dict\\n \\n for panelMember in PanelMember.objects.filter(referee = referee).filter(status = 'S'):\\n thesis = panelMember.thesis\\n dict = {}\\n dict['title'] = thesis.title\\n dict['student_full_name'] = thesis.student.first_name + \\\" \\\" + thesis.student.last_name\\n dict['synopsis'] = thesis.synopsis\\n dict['student_username'] = thesis.student.user.username\\n dict['id'] = thesis.id\\n \\n all_thesis.append(dict)\\n \\n return render(request, 'app/referee/evaluate_synopsis.html', {\\n 'title':'Unevaluated PhD Synopsis',\\n 'layout_data' : get_layout_data(request),\\n 'all_thesis' : all_thesis\\n })\\n else:\\n return redirect(reverse(URL_BAD_REQUEST))\",\n \"def get_stats_array_per_usecase(request):\\n\\n mode = request.GET.get('mode',None)\\n usern = request.GET.get('member',request.session['username'])\\n username = User.objects.get(username=usern, ns_id=mode)\\n language = request.GET.get('language',request.session['language'])\\n institute = request.GET.get('institute',request.session['institute'])\\n batch = request.GET.get('batch',request.session['batch'])\\n json_dict = {}\\n js = {}\\n js['original'] = {}\\n js['percent'] = {}\\n json_dict['medtag'] = get_array_per_usecase(username,mode,language,institute,batch)\\n json_dict['pubmed'] = get_array_per_usecase_PUBMED(username,mode,language,institute,batch)\\n\\n\\n # print(json_dict)\\n return JsonResponse(json_dict)\",\n \"def Reports(request):\\n assert isinstance(request, HttpRequest)\\n iscapable =False\\n if request.user.username in get_librarians():\\n iscapable=True;\\n #getting books per each department\\n booksperdepart={}\\n borrowedperStudent={}\\n ordersplacedbylibrairans={}\\n \\n books=get_valid_Books()\\n invent=get_Inv()\\n for k,v in books.items():\\n if v.dpt_id.name not in booksperdepart.keys():\\n booksperdepart[v.dpt_id.name]=v.invt.qty\\n else:\\n li =booksperdepart[v.dpt_id.name]\\n li+=v.invt.qty\\n booksperdepart[v.dpt_id.name]=li\\n libmem =get_libmems()\\n borrowed=get_Borrowed()\\n for k,v in borrowed.items():\\n composite=v.cwid.cwid.stu_name+\\\" - \\\"+v.cwid.cwid.stu_id\\n if composite not in borrowedperStudent.keys():\\n borrowedperStudent[composite]=1\\n else:\\n li =borrowedperStudent[composite]\\n li+=1\\n borrowedperStudent[composite]=li\\n librianorders=get_LibrarianOrders()\\n for k,v in librianorders.items():\\n composite=v.lb_id.name+\\\" - \\\"+v.lb_id_id\\n if composite not in ordersplacedbylibrairans.keys():\\n ordersplacedbylibrairans[composite]=[list([v.i_id.i_id.title,v.qty,v.i_id.i_id.dpt_id.name,v.status])]\\n else:\\n li =ordersplacedbylibrairans[composite]\\n li.append(list([v.i_id.i_id.title,v.qty,v.i_id.i_id.dpt_id.name,v.status]))\\n ordersplacedbylibrairans[composite]=li\\n\\n \\n\\n\\n \\n\\n return render(\\n request,\\n 'app/reports.html',\\n {\\n 'title':'Reports Page',\\n 'perdptbks':list(zip(booksperdepart.keys(),booksperdepart.values())),\\n 'peruserbks':list(zip(borrowedperStudent.keys(),borrowedperStudent.values())),\\n 'perlibrarian':list(zip(ordersplacedbylibrairans.keys(),ordersplacedbylibrairans.values())),\\n 'iscapable':iscapable,\\n 'year':datetime.now().year,\\n }\\n )\",\n \"def get_test_report(request, **kwargs): \\n\\t\\n #Fetching the details of the selected event\\n test_list = sidecar.events.test_report(project_id=kwargs['test_id'])\\n report_list = []\\n\\t\\n #Creating the list for the report\\n for tests in test_list._logs:\\n\\tjson_test = json.loads(tests['data'])\\n\\ttests['success'] = json_test['success'] \\n\\ttests['time'] = json_test['time']\\n\\ttests['test_cases'] = json_test['test_cases']\\n\\treport_list.append(tests)\\n\\n #Making the context and sending to template\\n context = {\\n \\\"page_title\\\": _(\\\"Test Results\\\"),\\n \\\"tests\\\": report_list\\n }\\n return render(request, 'rally_dashboard/events/test_detail.html', context)\",\n \"def public_workouts(request):\\n practices = Practice.objects.all()\\n individuals = Individual.objects.all()\\n workouts = Workout.objects.all()\\n c = Context({\\n 'workout_list': workouts,\\n 'practice_list': practices,\\n 'individuals_list': individuals,\\n 'title': 'Community Workouts',\\n })\\n return render_to_response(\\\"workouts/list.html\\\", c,\\n context_instance=RequestContext(request))\",\n \"def summary(request):\\n\\n # Generate counts of some of the main objects\\n num_courses = models.Course.objects.all().count()\\n num_quizzes = models.Quiz.objects.all().count()\\n num_questions = models.Question.objects.count()\\n num_students = models.User.objects.count()\\n num_visits = request.session.get('num_visits', 1)\\n request.session['num_visits'] = num_visits + 1\\n\\n context = {\\n 'num_courses': num_courses,\\n 'num_quizzes': num_quizzes,\\n 'num_questions': num_questions,\\n 'num_students': num_students,\\n 'num_visits': num_visits,\\n }\\n\\n return Response(context)\",\n \"def analysis():\\n\\n response_all_doctors_and_appointments = requests.post(server_url + 'doctor/all_doctors_and_all_appointments')\\n doctors_and_appointments = response_all_doctors_and_appointments.json()\\n\\n return render_template('clerks/analysis.html', doctors_and_appointments=doctors_and_appointments)\",\n \"def pyp_reports(request):\\n student_id = int(get_from_matchdict('id', request.matchdict))\\n\\n pdf = get_from_matchdict('pdf', request.matchdict)\\n check = request.params.get('check')\\n if check and check.lower() == 'true':\\n check = True\\n else:\\n check = False\\n\\n internal_check = request.params.get('internal_check')\\n\\n mb_user = request.session.get('mb_user', None)\\n if not mb_user:\\n # FIXME: Need to re-do it\\n pass\\n # api_token = request.params.get('api_token')\\n # if not api_token or api_token != gns.config.managebac.api_token:\\n # return HTTPForbidden()\\n elif mb_user.type.startswith('Advisor') or mb_user.type == 'Account Admins':\\n # let them in\\n pass\\n else:\\n return HTTPForbidden()\\n\\n term_id = gns.config.managebac.current_term_id\\n with DBSession() as session:\\n try:\\n rep_statement = session.query(PrimaryReport).\\\\\\n options(joinedload('course')).\\\\\\n filter(\\n PrimaryReport.term_id == term_id,\\n PrimaryReport.student_id == student_id,\\n # PrimaryReport.homeroom_comment!=''\\n )\\n stu_statement = session.query(Students).filter_by(id=student_id)\\n student = stu_statement.one()\\n report = rep_statement.one()\\n gns.tutorial(\\\"Got the target student\\\",edit=(stu_statement, '.sql'))\\n gns.tutorial(\\\"Got Primary report with course information\\\", edit=(rep_statement, '.sql'))\\n except NoResultFound:\\n if pdf:\\n # raw_input('no report entry for this student: {} with term_id {}'.format(student_id, term_id))\\n raise HTTPNotFound()\\n else:\\n raise HTTPFound(location=request.route_url(\\\"student_pyp_report_no\\\", id=student_id))\\n except MultipleResultsFound:\\n print(\\\"Issue with database!\\\")\\n raise HTTPInternalServerError(\\\"Issue with database!\\\")\\n\\n title = u\\\"IGB International School (June 2016): Student Report for {} {}\\\".format(student.first_name, student.last_name)\\n\\n # This bit is the only manual info that isn't on managebac\\n uoi_table = {\\n -1: {\\n # ey sem 1\\n 1: dict(title=\\\"Who We Are\\\", central_idea=\\\"Playing and learning together enables us to come to new understandings.\\\"),\\n 2: dict(title=\\\"Sharing The Planet\\\", central_idea=\\\"Our lives are interconnected with living things.\\\"),\\n # ey sem 2\\n 3: dict(title=\\\"How the World Works\\\", central_idea=\\\"Water is all around us and has many uses.\\\"),\\n 4: dict(title=\\\"How We Express Ourselves\\\", central_idea=\\\"Stories inform, provoke us and provide enjoyment.\\\"),\\n },\\n 0: {\\n # kg sem 1\\n 1: dict(title=\\\"Who We Are\\\", central_idea=\\\"We are part of a community who work, learn, and play together\\\"),\\n 2: dict(title=\\\"How We Organise Ourselves\\\", central_idea=\\\"Communities create systems to fullfill a need.\\\"),\\n 3: dict(title=\\\"Where We Are in Place and Time\\\", central_idea=\\\"Shelters look different and serve a purpose.\\\"),\\n\\n # kg sem 2\\n 4: dict(title=\\\"Sharing the Planet\\\", central_idea=\\\"People's choices and actions impact the environment and their community.\\\"),\\n 5: dict(title=\\\"How the World Works\\\", central_idea=\\\"Our body and man made resources help protect us from the natural environment.\\\"),\\n 6: dict(title=\\\"How We Express Ourselves\\\", central_idea=\\\"An audience can be engaged through performance.\\\")\\n },\\n 1: {\\n # gr1 sem 1\\n 1: dict(title=\\\"How we organize ourselves\\\", central_idea=\\\"Humans use tools and strategies to understand and organise their environment.\\\"),\\n 2: dict(title=\\\"Who We Are\\\", central_idea=\\\"Games provide us with opportunities to develop an understanding of ourselves and others.\\\"),\\n 3: dict(title=\\\"How We Express Ourselves\\\", central_idea=\\\"Celebrations are an opportunity to reflect and appreciate cultures and beliefs.\\\"),\\n # gr1 sem 2\\n 4: dict(title=\\\"How the World Works\\\", central_idea=\\\"Machines make a difference to the way we live our lives.\\\"),\\n 5: dict(title=\\\"Sharing the Planet\\\", central_idea=\\\"Water is essential to life and is a limited resource to many.\\\"),\\n 6: dict(title=\\\"Where We Are in Place and Time\\\", central_idea=\\\"Clocks are a universal measurement tool of time that have had an impact in the past and the present.\\\"),\\n },\\n 2: {\\n # gr2 sem 1\\n 1: dict(title=\\\"Who We Are\\\", central_idea=\\\"With rights come responsibilities.\\\"),\\n 2: dict(title=\\\"How We Express Ourselves\\\", central_idea=\\\"Cultures tell stories in different ways and for different reasons.\\\"),\\n 3: dict(title=\\\"How We Organize Ourselves\\\", central_idea=\\\"Number system provide a common language we can use to make sense of the world.\\\"),\\n # gr2 sem 2\\n 4: dict(title=\\\"Sharing The Planet\\\", central_idea=\\\"Plants sustain life on earth and we have a responsible role to play\\\"),\\n 5: dict(title=\\\"Where we are in Place and Time\\\", central_idea=\\\"Influence can change people and their environment.\\\"),\\n 6: dict(title=\\\"How the World Works\\\", central_idea=\\\"Forces are a vital part of our survival.\\\"),\\n },\\n 3: {\\n # gr3 sem 1\\n 1: dict(title=\\\"How We Organise Ourselves\\\", central_idea=\\\"Communication connects people.\\\"),\\n 2: dict(title=\\\"Sharing the Planet\\\", central_idea=\\\"People can conserve the world's resources through responsible behaviours\\\"),\\n 3: dict(title=\\\"Where We are in Place and Time\\\", central_idea=\\\"Innovations from past civilizations have an influence on the present\\\"),\\n # gr3 sem 2\\n 4: dict(title=\\\"How the World Works\\\", central_idea=\\\"Safe structures are designed and built for purpose and consider the environment and materials.\\\"),\\n 5: dict(title=\\\"Who We Are\\\", central_idea=\\\"Communication connects people and communities.\\\"),\\n 6: dict(title=\\\"How We Express Ourselves\\\", central_idea=\\\"Nature can inspire people to express their creativity.\\\"),\\n },\\n 4: {\\n # gr4 sem 1\\n 1: dict(title=\\\"How We Express Ourselves\\\", central_idea=\\\"Media influences how we think and the choices we make.\\\"),\\n 2: dict(title=\\\"Sharing the Planet\\\", central_idea=\\\"Organisms rely on one another to balance ecosystems.\\\"),\\n 3: dict(title=\\\"How we Organise Ourselves\\\", central_idea=\\\"Societies establish systems for trade and commerce to meet needs and wants.\\\"),\\n # gr4 sem 2\\n 4: dict(title=\\\"Where We Are in Place and Time\\\", central_idea=\\\"The quest for understanding has led to exploration and discovery.\\\"),\\n 5: dict(title=\\\"How The World Works\\\", central_idea=\\\"Earth has formed over time and is still changing.\\\"),\\n 6: dict(title=\\\"Who We Are\\\", central_idea=\\\"People's beliefs influence their actions.\\\"),\\n },\\n 5: {\\n # gr5 sem 1\\n 1: dict(title=\\\"How we Organise Ourselves\\\", central_idea=\\\"All societies have rules and reasons for these rules.\\\"),\\n 2: dict(title=\\\"Where We Are in Place and Time\\\", central_idea=\\\"Malaysia's cultural diversity has been shaped by its history.\\\"),\\n 3: dict(title=\\\"How the World Works\\\", central_idea=\\\"Changes to matter can be of a chemical and/or physical nature.\\\"),\\n # gr5 sem 2\\n 4: dict(title=\\\"Sharing The Planet\\\", central_idea=\\\"The choices we make during moments of conflict affect our relationships\\\"),\\n 5: dict(title=\\\"How We Express Ourselves: Exhibition\\\", central_idea=\\\"Artists seek to evoke an emotional response from their audience.\\\"),\\n 6: dict(title=\\\"Who We Are\\\", central_idea=\\\"External and internal factors cause changes in our lives\\\"),\\n },\\n }\\n\\n chinese_teachers = {\\n 10792613: [11203970, 10836999, 10912649, 10863230, 11544715, 11707916, 11609996, 11707918, 11708046, 10912651, 11707928, 11274137, 11707932, 11707934, 11204000, 11204641, 11204001, 11708067, 11270692, 11707940, 11204385, 11563304, 11204008, 11153068, 11573550, 11707952, 10882225, 11204017, 11707957, 10834618, 10866874, 11080380, 10893375, 11707840, 11190340, 10834630, 11611847, 10834633, 10834636, 11693517, 11707984, 11203923, 11707859, 10834645, 10834648, 10834649, 10834651, 11707870, 11182305, 11203938, 11200870, 10973671, 11707882, 11708014, 11203950, 11203952, 11708018, 11203954, 10882162, 11633398, 11707900, 11538429, 11124222, 11135103, 11737995, 11621139, 11707870, 10882159], # xiaopiong\\n 11256632: [11204609, 10836994, 11707907, 11135108, 10836999, 11135112, 10837001, 11203979, 10865037, 11707924, 11621141, 11203988, 11204377, 11173915, 10913691, 11204637, 10856823, 11204383, 11204640, 11707939, 11204392, 11614634, 11364525, 10882226, 11204660, 11190071, 10834616, 10834617, 11464377, 10866873, 10866876, 10834621, 10834622, 10866877, 10856636, 11578945, 11611841, 10893379, 10834628, 10834625, 11611847, 10834635, 10834640, 10834642, 10834643, 11930324, 11707860, 11203926, 11707990, 11426392, 11502297, 11578839, 11707869, 11708005, 10834661, 11203946, 11324785, 11124210, 10863222, 11124215, 10856824, 11203961, 10856826, 11124219, 11204605, 11707902, 10986488], # nancy\\n }\\n\\n students_chinese_teachers = {}\\n\\n for teacher_id, student_ids in chinese_teachers.items():\\n with DBSession() as session:\\n teacher = session.query(Teachers).filter_by(id=teacher_id).one()\\n for this_student in student_ids:\\n students_chinese_teachers[this_student] = teacher\\n\\n bahasa_teachers = {\\n 10872708: [10908165, 10856828],\\n }\\n students_bahasa_teachers = {}\\n for teacher_id, student_ids in bahasa_teachers.items():\\n with DBSession() as session:\\n teacher = session.query(Teachers).filter_by(id=teacher_id).one()\\n for this_student in student_ids:\\n students_bahasa_teachers[this_student] = teacher\\n\\n if 'Grade' in report.course.name or 'Kindergarten' in report.course.name:\\n which_folder = 'grades'\\n template = 'frontend:elem_reports/templates/student_pyp_report.pt'\\n\\n with DBSession() as session:\\n try:\\n rep_statement = session.query(PrimaryReport).\\\\\\n options(joinedload('course')).\\\\\\n options(joinedload('sections')).\\\\\\n options(joinedload('sections.learning_outcomes')).\\\\\\n options(joinedload('sections.teachers')).\\\\\\n options(joinedload('sections.strands')).\\\\\\n options(joinedload('teacher')).\\\\\\n filter(\\n PrimaryReport.term_id == term_id,\\n PrimaryReport.student_id == student_id\\n )\\n att_statement = session.query(Absences).filter_by(term_id=term_id, student_id=student_id)\\n\\n attendance = att_statement.one()\\n report = rep_statement.one()\\n\\n gns.tutorial(\\\"Got K-5 report info with joined information\\\", edit=(rep_statement, '.sql'), banner=True)\\n except NoResultFound:\\n if pdf:\\n # raw_input(\\\"No K-5 report entry\\\")\\n raise HTTPNotFound()\\n else:\\n raise HTTPFound(location=request.route_url(\\\"student_pyp_report_no\\\", id=student_id))\\n\\n subject_rank = {\\n 'language': 0,\\n 'mathematics': 1,\\n 'unit of inquiry 1': 2,\\n 'unit of inquiry 2': 3,\\n 'unit of inquiry 3': 4,\\n 'unit of inquiry 4': 4.1,\\n 'unit of inquiry 5': 4.2,\\n 'unit of inquiry 6': 4.3,\\n 'art': 5,\\n 'music': 6,\\n 'physical education': 7,\\n 'bahasa melayu': 8,\\n 'chinese': 9,\\n 'host nation': 10,\\n 'self-management': 10000\\n }\\n report.sections = sorted([section for section in report.sections if subject_rank.get(section.name.lower(), 10001) < 10000], key=lambda x: subject_rank.get(x.name.lower(), 1000))\\n report.sections = [section for section in report.sections if section.comment]\\n\\n # Only output sections that have any data in them\\n # Comment out during development\\n # report.sections = [section for section in report.sections if section.comment]\\n\\n if 'Kindergarten' in report.course.grade:\\n grade_norm = 0\\n else:\\n grade_norm = int(re.sub(\\\"[^0-9]\\\", \\\"\\\", report.course.grade))\\n\\n rotate_list = [0, 1, 2, 5, 9]\\n pagination_list = [0, 1, 4, 7, 10]\\n\\n for section in report.sections:\\n section.rank = subject_rank.get(section.name.lower())\\n report.sections = [s for s in report.sections if s.rank not in [4.1, 4.2, 4.3]] # skip\\n\\n gns.tutorial(\\\"Formatting each subject area in this order: {}\\\".format(\\\", \\\".join([r.name for r in report.sections])), banner=True)\\n for section in report.sections:\\n # Substitute the correct Chinese teachers based on manual info above\\n # Do first so all subsequent operations take place properly\\n if section.rank == 9 and student.id in students_chinese_teachers:\\n section.teachers = [students_chinese_teachers.get(student.id)]\\n\\n if section.rank == 8 and student.id in students_bahasa_teachers:\\n # Host Nations? and Bahasa mixed up maybe?\\n section.teachers = [students_bahasa_teachers.get(student.id)]\\n\\n section.append_uoi_table = section.rank == 4\\n section.display_rotated = section.rank in rotate_list\\n\\n if section.rank in [2]:\\n section.organization_header = 'Units of Inquiry'\\n section.name_after = \\\"\\\"\\n elif section.rank in [3, 4]:\\n section.organization_header = 'skip'\\n section.name_after = \\\"\\\"\\n else:\\n section.organization_header = section.name + ' (' + \\\" & \\\".join([s.first_name + ' ' + s.last_name for s in section.teachers]) + ')'\\n section.name_after = \\\"\\\"\\n\\n # Set the unit title if it needs to be\\n if section.rank in [2, 3, 4, 4.1, 4.2, 4.3]:\\n which_uoi = int(re.sub(\\\"[^0-9]\\\", \\\"\\\", section.name))\\n section.name = uoi_table.get(grade_norm)[which_uoi]['title']\\n\\n # Determine pagination\\n if section.rank in pagination_list: # TODO What about more than two inquiry units?\\n section.pagination = True\\n else:\\n section.pagination = False\\n\\n section.learning_outcomes = sorted(section.learning_outcomes, key=lambda x: x.which)\\n\\n # Standardize the headings\\n if section.rank in [2, 3, 4, 4.1, 4.2, 4.3]:\\n section.name = section.name.title()\\n section.name_after = uoi_table.get(grade_norm)[which_uoi]['central_idea']\\n\\n en_dash = u'\\\\u2013'\\n for outcome in section.learning_outcomes:\\n\\n if section.rank in [2, 3, 4]:\\n # Unit of inquiry\\n outcome.heading = \\\"\\\"\\n\\n elif section.rank not in [0, 1]:\\n outcome.heading = \\\"\\\" # blank\\n\\n else:\\n # If it's a subject that we care to keep the data, standardize the format:\\n outcome.heading = outcome.heading.replace(en_dash, '-')\\n match = re.match('(.*)-', outcome.heading)\\n if match:\\n outcome.heading = match.group(1).strip()\\n\\n # Evaluates and adds data to items\\n old_heading = None\\n for outcome in section.learning_outcomes:\\n\\n if outcome.heading != old_heading:\\n # Mark that indicates we need to evaluate\\n\\n if section.rank in [0, 1]:\\n # Determine the effort assigned by the teacher for this\\n effort = [s.selection for s in section.strands if s.label_titled.startswith(outcome.heading)]\\n effort = effort[0] if len(effort) == 1 else (effort[0] if len(set(effort)) == 1 else \\\"<?>\\\")\\n else:\\n effort = [s.selection for s in section.strands if s.selection]\\n effort = effort[0] if len(set(effort)) == 1 else str(effort)\\n outcome.effort = {'G': \\\"Good\\\", 'N': \\\"Needs Improvement\\\", 'O': \\\"Outstanding\\\"}.get(effort, None)\\n\\n if not outcome.effort and internal_check:\\n # Raise a problem here\\n raise ReportIncomplete('something') # FIXME: There is no report incomplete exception\\n\\n old_heading = outcome.heading\\n\\n if not outcome.selection and internal_check:\\n raise ReportIncomplete('something')\\n gns.tutorial(\\\"Completed formatting of {} section\\\".format(section.name))\\n\\n report.sections = [s for s in report.sections if s.rank not in [4.1, 4.2, 4.3]] # skip\\n\\n elif 'Early' in report.course.name:\\n which_folder = 'early_years'\\n template = 'frontend:elem_reports/templates/student_pyp_ey_report.pt'\\n\\n # 1/2: semeseter\\n # 0/1: early years\\n\\n ey_report_indicators = {\\n 1: {\\n 0: [\\n {'name': 'Listening & Speaking', 'content': 'Learners show an understanding of the value of speaking and listening to communicate. They are using language to name their environment, to get to know each other, to initiate and explore relationships, to question and inquire.'},\\n {'name': 'Viewing & Presenting', 'content': 'Learners show an understanding that the world around them is full of visual language that conveys meaning. They are able to interpret and respond to visual texts. They are extending and using visual language in more purposeful ways.'},\\n {'name': 'Reading & Writing', 'content': 'Learners show an understanding that print represents the real or the imagined world. They have a concept of a \\\"book\\\", and an awareness of some of its structural elements. They use visual cues to recall sounds and the words they are \\\"reading\\\" to construct meaning.'},\\n ],\\n 1: [\\n {'name': 'Number', 'content': 'Learners will understand that numbers are used for many different purposes in the real world. They will develop an understanding of one-to-one correspondence, be able to count and use number words and numerals to represent quantities.'},\\n {'name': 'Shape and Space', 'content': 'Learners will develop an understanding that shapes have characteristics that can be described and compared.'},\\n {'name': 'Pattern', 'content': 'Learners will develop an understanding that patterns and sequences occur in everyday situations. They will be able to identify and extend patterns in various ways.'},\\n {'name': 'Measurement', 'content': 'Learners will develop an understanding of how measurement involves the comparison of objects and ordering.They will be able to identify and compare attributes of real objects.'},\\n {'name': 'Data', 'content': 'Learners will develop an understanding of how the collection and organization of information helps to make sense of the world. They will sort and label objects by attributes and discuss information represented in graphs including pictographs and tally marks.'}\\n ]\\n },\\n 2: {\\n 0: [\\n {'name': 'Listening & Speaking', 'content': 'Learners will show an understanding of the value of speaking and listening to communicate. They will use language to name their environment, to get to know each other, to initiate and explore relationships, to question and inquire.'},\\n {'name': 'Viewing & Presenting', 'content': 'Learners will show an understanding that the world around them is full of visual language that conveys meaning. They will interpret and respond to visual texts. They will be extending and using visual language in more purposeful ways.'},\\n {'name': 'Reading & Writing', 'content': 'Learners will show an understanding that print represents the real or the imagined world. They will develop the concept of a “book”, and an awareness of some of its structural elements. They will use visual cues to recall sounds and the words they are “reading” to construct meaning.'},\\n ],\\n 1: [\\n {'name': 'Number', 'content': 'Learners will understand that numbers are used for many different purposes in the real world. They will develop an understanding of one-to-one correspondence, be able to count and use number words and numerals to represent quantities.'},\\n {'name': 'Shape and Space', 'content': 'Learners will understand and use common language to describe paths, regions and boundaries of their immediate environment.'},\\n {'name': 'Pattern', 'content': 'Learners will understand that patterns and sequences occur in everyday situations. They will be able to identify, describe, extend and create patterns in various ways.'},\\n {'name': 'Measurement', 'content': 'Learners will develop an understanding of how measurement involves the comparison of objects and the ordering and sequencing of events. They will be able to identify, compare and describe attributes of real objects as well as describe and sequence familiar events in their daily routine.'},\\n {'name': 'Data', 'content': 'Learners will develop an understanding of how the collection and organization of information helps to make sense of the world. They will sort and label objects by attributes and discuss information represented in graphs including pictographs and tally marks. The learners will discuss chance in daily events.'},\\n ],\\n },\\n }\\n with DBSession() as session:\\n try: \\n report = session.query(PrimaryReport).\\\\\\n options(joinedload('course')).\\\\\\n options(joinedload('sections')).\\\\\\n options(joinedload('sections.learning_outcomes')).\\\\\\n options(joinedload('sections.teachers')).\\\\\\n options(joinedload('teacher')).\\\\\\n filter(\\n PrimaryReport.term_id == term_id,\\n PrimaryReport.student_id == student_id,\\n ).one()\\n student = session.query(Students).filter_by(id=student_id).one()\\n attendance = session.query(Absences).filter_by(term_id=term_id, student_id=student_id).one()\\n except NoResultFound:\\n if pdf:\\n raise HTTPNotFound()\\n else:\\n raise HTTPFound(location=request.route_url(\\\"student_pyp_report_no\\\", id=student_id))\\n\\n subject_rank = {\\n 'self-management': -1,\\n 'language': 0,\\n 'mathematics': 1,\\n 'unit of inquiry 1': 2,\\n 'unit of inquiry 2': 3,\\n 'unit of inquiry 3': 4,\\n 'unit of inquiry 4': 4.1,\\n 'unit of inquiry 5': 4.2,\\n 'unit of inquiry 6': 4.3,\\n 'art': 5,\\n 'music': 6,\\n 'physical education': 7,\\n 'bahasa melayu': 8,\\n 'chinese': 9,\\n 'host nation': 10\\n }\\n\\n report.sections = sorted([section for section in report.sections if subject_rank.get(section.name.lower()) < 10000], key=lambda x: subject_rank.get(x.name.lower(), 1000))\\n # report.sections = report_sections\\n # Filter out the un-needed units of inquiry\\n # report.sections = [s for s in report.sections if s.rank <= 1 or (s.rank >= 4 and s.rank not in [4,4.1])]\\n\\n\\n # Only output sections that have any data in them\\n # Comment out during development\\n # report.sections = [section for section in report.sections if section.comment and subject_rank.get(section.name.lower()) not in [2, 3]]\\n\\n grade_norm = -1\\n\\n pagination_list = [0, 3, 7, 10]\\n\\n for section in report.sections:\\n\\n section.rank = subject_rank.get(section.name.lower())\\n\\n if section.rank == -1:\\n # blurb for self-management\\n section.blurb = \\\"<i><p>Within the PYP, the approaches to learning skill of self management encompasses the development of gross and fine motor skills, spatial awareness, safety, healthy lifestyles, codes of behaviour and informed choices. </p><p>In an Early Years context these are reflected through the play based approach to teaching and learning. Reporting about self management in Early Years focuses on the whole child, stressing the importance of developing independence, social and emotional skills such as making relationships, managing feelings and behaviour, self confidence and self awareness. In addition the development of physical skills (moving and handling, health and self care) are highlighted as well. </p></i>\\\"\\n else:\\n section.blurb = \\\"\\\"\\n\\n if section.rank in [0, 1]: # Could be Lanugage & Maths, set up the report indicators\\n ey = int('Early Years 1' in report.course.name) + 1\\n section.report_indicators = ey_report_indicators[ey][section.rank] # change this to 2 later\\n else:\\n section.report_indicators = None\\n\\n # Substitute the correct Chinese teachers based on manual info above\\n if section.rank == 9 and student.id in students_chinese_teachers:\\n section.teachers = [students_chinese_teachers.get(student.id)]\\n\\n if section.rank in [999999]: # Turn this off\\n section.organization_header = \\\"Units of Inquiry\\\"\\n section.name_after = \\\"\\\"\\n elif section.rank in [4, 4.1]:\\n section.organization_header = 'skip'\\n section.name_after = \\\"\\\"\\n else:\\n section.organization_header = None\\n section.name_after = ' (' + \\\" & \\\".join([s.first_name + ' ' + s.last_name for s in section.teachers]) + ')'\\n\\n if section.rank in [2, 3, 4, 4.1, 4.2,4.3,4.4]:\\n which_uoi = int(re.sub(\\\"[^0-9]\\\", \\\"\\\", section.name))\\n section.name = uoi_table.get(grade_norm)[which_uoi]['title']\\n section.name_after = \\\"\\\"\\n\\n # Determine pagination\\n if section.rank in pagination_list: #TODO What about more than two inquiry units?\\n section.pagination = True\\n else:\\n section.pagination = False\\n\\n if section.rank in [2, 3, 4, 4.1, 4.2,4.3,4.4]:\\n section.name = section.name.title() \\n section.name_after = uoi_table.get(grade_norm)[which_uoi]['central_idea']\\n\\n section.learning_outcomes = sorted(section.learning_outcomes, key=lambda x: x.which)\\n\\n # ey sections\\n report.sections = [s for s in report.sections if s.rank not in [4, 4.1]]\\n\\n\\n options={\\n 'quiet': '',\\n 'disable-javascript': '',\\n 'encoding': 'utf-8',\\n 'header-html': 'http://igbisportal.vagrant:6543/header-html',\\n 'header-spacing': '5',\\n\\n\\n 'footer-html': 'http://igbisportal.vagrant:6543/footer-html?student_id={}'.format(student.id),\\n\\n 'print-media-type': '',\\n\\n 'margin-left': '3mm',\\n 'margin-right': '3mm',\\n 'margin-bottom': '10mm'\\n }\\n\\n\\n if check:\\n stu = student.first_nickname_last_studentid\\n message = []\\n for s in report.sections:\\n if not s.teachers:\\n message.append(\\\"No teacher assigned in {}\\\".format(s.name))\\n #raise HTTPNotFound(\\\"##No teacher assigned for {} in {}##\\\".format(stu, s.name))\\n if not s.comment:\\n teachers = \\\",\\\".join([t.username_handle for t in s.teachers])\\n message.append('{} missing {} comment'.format(teachers, s.name))\\n #raise HTTPNotFound('##{} missing {} comment for {}##'.format(teachers, s.name, stu))\\n\\n if s.learning_outcomes and not 'Early' in report.course.name:\\n\\n if s.overall_comment == 'N/A':\\n for o in s.learning_outcomes:\\n if hasattr(o, 'effort') and not o.effort:\\n teachers = \\\",\\\".join([t.username_handle for t in s.teachers])\\n message.append('{} did not enter {} effort for {}'.format(teachers, o.heading, s.name))\\n # raise HTTPNotFound()\\n if not o.selection:\\n teachers = \\\",\\\".join([t.username_handle for t in s.teachers])\\n message.append('{} did not enter {} indication for {}'.format(teachers, o.heading, s.name))\\n # raise HTTPNotFound('##{} did not enter indication for {} in {}##'.format(teachers, s.name, stu))\\n\\n elif s.overall_comment == '':\\n teachers = \\\",\\\".join([t.username_handle for t in s.teachers])\\n message.append('{} did not enter effort for single subject {}'.format(teachers, s.name)) \\n\\n if message:\\n raise HTTPNotFound('##\\\\n({}) {}:\\\\n\\\\t{}##'.format(student.grade, student.first_nickname_last_studentid, \\\"\\\\n\\\\t\\\".join(message)))\\n\\n raise HTTPFound()\\n\\n with DBSession() as session:\\n try:\\n record = session.query(db.table.PrimaryReportLastUpdated).filter(db.table.PrimaryReportLastUpdated.student_id == student.id).one()\\n last_updated = record.timestamp\\n last_updated_date = last_updated.strftime(gns.config.reports.last_updated_format)\\n except NoResultFound:\\n last_updated_date = '<Unknown>'\\n except MultipleResultsFound:\\n last_updated_date = '<Internal DB Error: Multiple results found>'\\n\\n if pdf:\\n result = render(template,\\n dict(\\n title=title,\\n report=report,\\n student=student,\\n attendance=attendance,\\n pdf=True,\\n download_url=\\\"\\\",\\n link_to_mb=\\\"\\\",\\n last_updated=\\\"\\\",\\n ),\\n request=request)\\n import pdfkit # import here because installation on server is hard\\n\\n prefix_file_name = '{}/pdf-downloads/{}/{}-Grade{}-{}-[{}]-'.format(\\n gns.config.paths.home,\\n which_folder,\\n '55048',\\n grade_norm,\\n student.first_name + '-' + student.last_name,\\n student.student_id\\n )\\n\\n full_file = '{}({}).pdf'.format(prefix_file_name, last_updated_date)\\n\\n for _file in glob.glob(\\\"{}.*\\\".format(prefix_file_name)):\\n # Remove any old stuff still lingering in there\\n if _file != full_file:\\n os.remove(_file)\\n\\n path = '{}/pdf-downloads/{}/{}-Grade{}-{}-[{}]-({}).pdf'.format(\\n gns.config.paths.home,\\n which_folder,\\n '55048',\\n grade_norm,\\n student.first_name + '-' + student.last_name,\\n student.student_id,\\n last_updated_date\\n )\\n\\n gns.tutorial(\\\"Sending to pdfkit, also saving to {path}\\\".format(path=path), edit=(result, '.pretty'), banner=True)\\n try:\\n pdffile = pdfkit.from_string(result, path, options=options) # render as HTML and return as a string\\n except OSError as err:\\n return HTTPInternalServerError(\\\"Problem with file? {}\\\".format(err))\\n\\n pdffile # not used\\n if pdf.lower() == \\\"download\\\":\\n content_type = \\\"application/octet-stream\\\"\\n\\n response = FileResponse(path, request=request, content_type=content_type)\\n response.content_disposition = u\\\"attachment; filename={}.pdf\\\".format(title)\\n return response\\n\\n else:\\n content_type = \\\"application/pdf\\\"\\n response = FileResponse(path, request=request, content_type=content_type, charset='utf-8')\\n return response\\n\\n else:\\n # Check when it was last updated\\n\\n if gns.tutorial_on:\\n import pkg_resources\\n package, filename = template.split(\\\":\\\")\\n abspath = pkg_resources.resource_filename(*template.split(\\\":\\\"))\\n from chameleon import PageTemplateFile\\n template_file = PageTemplateFile(abspath)\\n gns.tutorial(\\\"Loaded the template\\\", edit=(template_file.read(), '.html'), banner=True)\\n result = render(template,\\n dict(\\n title=title,\\n report=report,\\n student=student,\\n attendance=attendance,\\n pdf=False,\\n download_url=\\\"/students/{}/pyp_report/download/\\\".format(student.id),\\n link_to_mb=\\\"https://igbis.managebac.com/classes/{}/pyp-gradebook/tasks/term_grades?student={}&term={}\\\".format(report.course.id, student.id, gns.config.managebac.current_term_id),\\n last_updated=last_updated_date,\\n ),\\n request=request\\n )\\n response = Response(result)\\n return response\",\n \"def referee_evaluate_thesis(request):\\n\\n if not validate_request(request): return redirect(reverse(URL_FORBIDDEN))\\n\\n \\n user = auth.get_user(request)\\n referee = Referee.objects.get(user = user)\\n \\n if request.method == \\\"GET\\\":\\n all_thesis = [] # list of dict\\n \\n for panelMember in PanelMember.objects.filter(referee = referee).filter(status = 'A'):\\n thesis = panelMember.thesis\\n dict = {}\\n dict['title'] = thesis.title\\n\\n dict['student_full_name'] = thesis.student.first_name + ' ' + thesis.student.last_name\\n dict['synopsis'] = thesis.synopsis\\n dict['thesis'] = thesis.thesis\\n dict['keywords'] = []\\n\\n if panelMember.answer_for_questions == True:\\n if thesis.thesis_modifications == \\\"NULL\\\" or thesis.thesis_modifications == \\\"\\\":\\n dict['thesis_modifications'] = None\\n else:\\n dict['thesis_modifications'] = thesis.thesis_modifications\\n else:\\n dict['thesis_modifications'] = None\\n\\n\\n for keys in ThesisKeyword.objects.filter(thesis = thesis):\\n dict['keywords'].append((IEEEKeyword.objects.get(id = keys.keyword.id)).keyword)\\n \\n dict['student_username'] = thesis.student.user.username\\n dict['id'] = thesis.id\\n \\n all_thesis.append(dict)\\n return render(\\n request,\\n 'app/referee/evaluate_thesis.html',\\n {\\n 'title':'Evaluate Thesis',\\n 'layout_data' : get_layout_data(request),\\n 'all_thesis' : all_thesis\\n }\\n )\\n else:\\n return redirect(reverse(URL_BAD_REQUEST))\",\n \"def achieve_viewall(request):\\n is_loggedin, username = get_session_variables(request)\\n contrib_list = []\\n article_list = []\\n gsoc_list = []\\n speaker_list = []\\n intern_list = []\\n contest_participant_list = []\\n icpc_participants_regional_list = []\\n icpc_participants_final_list = []\\n\\n contrib_list_all = Contribution.objects.all()\\n contrib_list = Contribution.objects.all()[:5]\\n article_list = Article.objects.all()[:5]\\n gsoc_list = Gsoc.objects.all()[:5]\\n speaker_list = Speaker.objects.all()[:5]\\n intern_list = Intern.objects.all()[:5]\\n contest_list = Contest_won.objects.all()[:5]\\n\\n \\n contrib_org = {}\\n if contrib_list_all:\\n for contrib in contrib_list_all:\\n if contrib.org_name not in contrib_org.keys():\\n contrib_org[contrib.org_name] = 0\\n\\n for contrib in contrib_list:\\n contrib_org[contrib.org_name] += 1\\n\\n if contest_list:\\t\\n contest_participant_list = []\\n\\tfor contest_won_obj in contest_list:\\t\\n\\t c_id = contest_won_obj.contest_id\\n\\t c_p_objs = Contest_won_participant.objects.filter(contest_id = c_id)\\n\\t contest_participant_list.extend(c_p_objs)\\n \\n icpc_list_regionals = ACM_ICPC_detail.objects.filter(level='regional').order_by('ranking')[:2]\\n if icpc_list_regionals:\\n for icpc_obj in icpc_list_regionals:\\n team = icpc_obj.team_name\\n member1 = [icpc_obj.participant1_name, \\\\\\n get_username_from_email(icpc_obj.participant1_email)]\\n\\n member2 = [icpc_obj.participant2_name, \\\\\\n get_username_from_email(icpc_obj.participant2_email)]\\n\\n member3 = [icpc_obj.participant3_name, \\\\\\n get_username_from_email(icpc_obj.participant3_email)]\\n\\n icpc_participant_list = [icpc_obj, member1,member2,member3]\\n icpc_participants_regional_list.append(icpc_participant_list)\\n\\n icpc_list_finals = ACM_ICPC_detail.objects.filter(level='finals').order_by('ranking')[:2]\\n if icpc_list_finals:\\n for icpc_obj in icpc_list_finals:\\n team = icpc_obj.team_name\\n member1 = [icpc_obj.participant1_name, \\\\\\n get_username_from_email(icpc_obj.participant1_email)]\\n\\n member2 = [icpc_obj.participant2_name, \\\\\\n get_username_from_email(icpc_obj.participant2_email)]\\n\\n member3 = [icpc_obj.participant3_name, \\\\\\n get_username_from_email(icpc_obj.participant3_email)]\\n\\n icpc_participant_list = [icpc_obj, member1,member2,member3]\\n icpc_participants_final_list.append(icpc_participant_list)\\n\\n return render_to_response('achievement/achievement_viewall.html',\\\\\\n\\t\\t{'username':username, \\\\\\n 'is_loggedin':is_loggedin, \\\\\\n 'contrib_list':contrib_list, \\\\\\n 'contrib_org':contrib_org,\\\\\\n 'article_list':article_list, \\\\\\n 'gsoc_list':gsoc_list, \\\\\\n 'speaker_list':speaker_list, \\\\\\n 'intern_list':intern_list, \\\\\\n 'contest_list':contest_list, \\\\\\n 'contest_participant_list':contest_participant_list, \\\\\\n 'icpc_participants_final_list':icpc_participants_final_list, \\\\\\n 'icpc_participants_regional_list':icpc_participants_regional_list}, \\\\\\n RequestContext(request))\",\n \"def display_report(request, **kwargs):\\n\\n #Getting the report of the tests \\n try:\\n outputStr = sidecar.events.test_logs(project_id=kwargs['project_id'])\\n outputStr = outputStr.results\\n except Exception, e:\\n outputStr = \\\"Updating the logs...\\\"\\n \\n #Making the output\\n context = {\\n \\\"page_title\\\": _(\\\"Test Report\\\"),\\n \\\"test_report\\\": outputStr\\n }\\n return render(request, 'rally_dashboard/events/view_report.html', context)\",\n \"def performance_analysis(request):\\n context = {}\\n\\n return render(request, 'classroom_main/performance_analysis.html', context)\",\n \"def get_data(request):\\n\\n json_resp = {}\\n # reports = Report.objects.filter(name = UseCase.objects.get(name=request.session['usecase']),institute = request.session['institute'],language = request.session['language'])\\n\\n json_resp['reports'] = []\\n institute = request.GET.get('institute',request.session['institute'])\\n usecase = request.GET.get('usecase',request.session['usecase'])\\n print(usecase)\\n language = request.GET.get('language',request.session['language'])\\n ns_human = NameSpace.objects.get(ns_id='Human')\\n ns_robot = NameSpace.objects.get(ns_id='Robot')\\n user_robot = User.objects.get(username='Robot_user', ns_id=ns_robot)\\n # usec = UseCase.objects.get(name = usecase)\\n # reports = Report.objects.filter(name = usec,institute = institute, language = language).values('id_report')\\n # gt_report = GroundTruthLogFile.objects.filter(language = language).exclude(username = user_robot,id_report__in=reports).order_by('id_report').distinct('id_report')\\n cursor = connection.cursor()\\n cursor.execute(\\\"SELECT r.id_report,r.language,r.report_json,r.name,r.institute,r.batch,COUNT(*) FROM report AS r INNER JOIN ground_truth_log_file AS g ON g.id_report = r.id_report AND r.language = g.language WHERE r.name = %s AND r.institute = %s AND r.language = %s AND g.username != %s GROUP BY (r.id_report,r.language,r.report_json,r.name,r.institute,r.batch)\\\",[usecase,institute,language,'Robot_user'])\\n gt_report_ids = []\\n indice = 0\\n st = time.time()\\n for el in cursor.fetchall():\\n\\n # report = Report.objects.get(language = language, id_report = el.id_report_id)\\n gt_report_ids.append(el[0])\\n # print(str(indice))\\n indice +=1\\n # report = Report.objects.get(id_report=el.id_report, language=el.language)\\n # language = report.language\\n\\n # user_robot = User.objects.get(username = 'Robot_user',ns_id = ns_robot)\\n gt_human = 1\\n gt_robot = 0\\n\\n rep = json.loads(el[2])\\n new_rep = {}\\n for key in rep.keys():\\n nkey = key+ '_0'\\n new_rep[nkey] = rep[key]\\n\\n total = el[6]\\n\\n new_rep['usecase'] = usecase\\n new_rep['id_report_not_hashed'] = rep.get('report_id',el[0])\\n new_rep['id_report'] = el[0]\\n new_rep['institute'] = institute\\n new_rep['language'] = language\\n new_rep['batch'] = el[5]\\n\\n json_resp['reports'].append({'total':total, 'report':new_rep,'id_report':el[0], 'language':language})\\n\\n usec = UseCase.objects.get(name = usecase)\\n reports = Report.objects.filter(institute = institute,language = language,name = usec).exclude(id_report__in=gt_report_ids)\\n # print(reports.count())\\n indice = 0\\n st = time.time()\\n for el in reports:\\n report = el\\n # print(str(indice))\\n indice += 1\\n # report = Report.objects.get(id_report=el.id_report, language=el.language)\\n # language = report.language\\n\\n # user_robot = User.objects.get(username = 'Robot_user',ns_id = ns_robot)\\n gt_human = 0\\n gt_robot = 0\\n\\n rep = report.report_json\\n new_rep = {}\\n for key in rep.keys():\\n nkey = key + '_0'\\n new_rep[nkey] = rep[key]\\n\\n total = gt_human + gt_robot\\n\\n new_rep['usecase'] = report.name_id\\n new_rep['id_report_not_hashed'] = rep.get('report_id', report.id_report)\\n new_rep['id_report'] = report.id_report\\n new_rep['institute'] = report.institute\\n new_rep['language'] = report.language\\n new_rep['batch'] = report.batch\\n\\n json_resp['reports'].append(\\n {'total': total, 'report': new_rep, 'id_report': report.id_report, 'language': report.language})\\n # print('elaboro1',str(end1-st1))\\n tot = time.time()\\n print('totale',str(tot-st))\\n\\n return JsonResponse(json_resp,safe=False)\",\n \"def covid19(request):\\n\\tip = get_ip(request, right_most_proxy=True)\\n\\tIpAddressInformation.objects.create(ip_address=ip)\\n\\n\\tcontextres =[]\\n\\t#build elasticsearch query to search data\\n\\tquery={\\\"query\\\": {\\n\\t\\t\\\"bool\\\": {\\n\\t\\t\\t\\\"must\\\": [\\n\\t\\t\\t\\t{\\\"match\\\": {\\\"Associated with COVID-19\\\": \\\"Yes\\\"}},\\n\\t\\t\\t\\t{\\\"match\\\": {\\\"UniprotKb entry status\\\": \\\"Yes\\\"}}\\n\\t\\t\\t]\\n\\t\\t}\\n\\t}\\n\\t}\\n\\t#generate random file name to store search result in json format\\n\\tnameFIle=names.get_first_name()\\n\\tjsonfilename=nameFIle+'_basic_search_covid19.json'\\n\\tjsonfilepath=os.path.join(settings.BASE_DIR, 'resultFile', 'jsonData','resultJson', 'basicsearch', 'results', jsonfilename)\\n\\tjsonfileoutput= open(jsonfilepath,'w')\\n\\tjfinaldata=[]\\n\\tes.indices.refresh(index=\\\"mrmassaydb-index\\\")\\n\\t#elasticsearch will search data\\n\\tres=helpers.scan(client=es,scroll='2m',index=\\\"mrmassaydb-index\\\", doc_type=\\\"mrmassaydb-type\\\",query=query,request_timeout=30)\\n\\tjfinaldata=[]\\n\\tpepSeqList=[]\\n\\tproteinList=[]\\n\\t#if data is valid based on uniprotkb release then it will display\\n\\tfor i in res:\\n\\t\\tjdic=i['_source']\\n\\t\\tjdic={str(tkey):force_text(tvalue) for tkey,tvalue in jdic.items()}\\n\\t\\tif jdic[\\\"UniprotKb entry status\\\"] ==\\\"Yes\\\" and jdic['UniProtKB Accession'] !='502':\\n\\t\\t\\tjdic[\\\"PPI\\\"] =\\\"View\\\"\\n\\t\\t\\tjdic[\\\"sel\\\"] =\\\"\\\"\\n\\t\\t\\tjdic[\\\"Drug Bank\\\"]=jdic[\\\"Drug Bank\\\"].replace('\\\\\\\\','')\\n\\t\\t\\tjdic[\\\"Drug Bank\\\"]=jdic[\\\"Drug Bank\\\"].replace('<br>','|')\\n\\t\\t\\tjdic[\\\"SRMAtlas URL\\\"]=jdic[\\\"SRMAtlas URL\\\"].replace('\\\\\\\\','')\\n\\t\\t\\tjdic[\\\"Passel URL\\\"]=jdic[\\\"Passel URL\\\"].replace('\\\\\\\\','')\\n\\t\\t\\tjdic[\\\"CPTAC URL\\\"]=jdic[\\\"CPTAC URL\\\"].replace('\\\\\\\\','')\\n\\t\\t\\tjdic[\\\"Panoramaweb URL\\\"]=jdic[\\\"Panoramaweb URL\\\"].replace('\\\\\\\\','')\\n\\t\\t\\tjdic[\\\"PeptideTracker URL\\\"]=jdic[\\\"PeptideTracker URL\\\"].replace('\\\\\\\\','')\\n\\t\\t\\t#if jdic[\\\"Pathway Name\\\"].lower() !='na':\\n\\t\\t\\t#\\tjdic[\\\"Pathway Name\\\"]=re.sub(r\\\"(\\\\w)([A-Z])\\\",r\\\"\\\\1|\\\\2\\\",jdic[\\\"Pathway Name\\\"])\\n\\t\\t\\tjdic[\\\"Mean Concentration\\\"] =jdic[\\\"Mean Concentration\\\"].replace('fmol/','fmol/µ')\\n\\t\\t\\tjdic[\\\"Concentration\\\"] =jdic[\\\"Concentration\\\"].replace('fmol/','fmol/µ')\\n\\t\\t\\tif str(jdic[\\\"Associated with COVID-19\\\"]).strip().upper() =='YES':\\n\\t\\t\\t\\tpepSeqList.append(jdic[\\\"Peptide Sequence\\\"].strip())\\n\\t\\t\\t\\tproteinList.append(jdic[\\\"UniProtKB Accession\\\"].strip().split('-')[0])\\n\\t\\t\\tjfinaldata.append(jdic)\\n\\tes.indices.refresh(index=\\\"mrmassaydb-index\\\")\\n\\t#checking any result generated by database\\n\\tfoundHits=len(jfinaldata)\\n\\t#storing only 10000 rows in json format\\n\\tjson.dump(jfinaldata[:10000],jsonfileoutput)\\n\\tjsonfileoutput.close()\\n\\t# if result found then do other job\\n\\tif foundHits >0:\\n\\t\\tstatsummary=summaryStatcal(jfinaldata) # sent data to this funcation for generating stat\\n\\t\\tpathwaychart=statsummary['pathwaychart']\\n\\t\\tpathwaychart=[i[:2] for i in pathwaychart]\\n\\t\\tspecieslist=statsummary['specieslist']\\n\\t\\ttotallist=statsummary['total']\\n\\t\\tsubcell=statsummary['subcell']\\n\\t\\tgodic=statsummary['godic']\\n\\t\\tjvennprot=statsummary['jevennstat'][0]\\n\\t\\tjvennpep=statsummary['jevennstat'][1]\\n\\t\\tmrmdatabase=statsummary['jevennstat'][2]\\n\\t\\tsortedgodic=OrderedDict(sorted(godic.items(), key=lambda t: t[1])) # sorting GO data\\n\\t\\tupdatedgodic=dict(list(sortedgodic.items()))\\n\\t\\tpepseqdataseries=ast.literal_eval(json.dumps(statsummary['pepseqdataseries'])) #dumping data into json format\\n\\t\\tprodataseries=statsummary['prodataseries']\\n\\t\\tunqisostat=statsummary['unqisostat']\\n\\t\\tjsonfilepathStat=os.path.join(settings.BASE_DIR, 'resultFile', 'jsonData','resultJson', 'basicsearch', 'statsummary', jsonfilename) #storing stat result in json format\\n\\t\\tjsonfileoutputStat= open(jsonfilepathStat,'w')\\n\\t\\tjson.dump(statsummary,jsonfileoutputStat)\\n\\t\\tjsonfileoutputStat.close()\\n\\t\\turlname=\\\"'/resultFile/jsonData/resultJson/basicsearch/results/\\\"+jsonfilename+\\\"'\\\"\\n\\n\\t\\tcontextindex={\\n\\t\\t\\t\\\"filename\\\":urlname,\\\"colname\\\":json.dumps(colname),'foundHits':foundHits,\\n\\t\\t\\t'pathwaychart':pathwaychart[:11],'specieslist':specieslist,\\n\\t\\t\\t'totallist':totallist,'subcell':subcell,\\n\\t\\t\\t'updatedgodic':updatedgodic,'pepseqdataseries':pepseqdataseries,\\n\\t\\t\\t'prodataseries':prodataseries,'unqisostat':unqisostat,\\n\\t\\t\\t'uniquePepSeq':len(set(pepSeqList)),'uniqueProtein':len(set(proteinList)),\\n\\t\\t\\t'jvennprot':json.dumps(jvennprot),'jvennpep':json.dumps(jvennpep),'jvennmrmdb':json.dumps(mrmdatabase)\\n\\t\\t\\t}\\n\\t\\treturn render(request,'covid19.html',contextindex)\\n\\telse:\\n\\t\\treturn render(request,'covid19.html',{'foundHits':foundHits})\",\n \"def reports_public(self):\\r\\n return reports.ReportsPublic(self)\",\n \"def show_campaigns(request, utm_campaign, **kwargs):\\n \\n err_msg = ''\\n try:\\n err_msg = str(kwargs['kwargs']['err_msg'])\\n except:\\n pass\\n \\n test_type_override = ''\\n try:\\n test_type_override = MySQLdb._mysql.escape_string(request.POST['test_type_override'])\\n \\n if test_type_override == 'Banner':\\n test_type_var = FDH._TESTTYPE_BANNER_\\n elif test_type_override == 'Landing Page':\\n test_type_var = FDH._TESTTYPE_LP_\\n elif test_type_override == 'Banner and LP':\\n test_type_var = FDH._TESTTYPE_BANNER_LP_\\n \\n except:\\n test_type_var = ''\\n pass\\n \\n try:\\n \\\"\\\"\\\" Find the earliest and latest page views for a given campaign \\\"\\\"\\\"\\n lptl = DL.LandingPageTableLoader()\\n ccrml = DL.CiviCRMLoader()\\n \\n start_time = ccrml.get_earliest_donation(utm_campaign)\\n end_time = ccrml.get_latest_donation(utm_campaign)\\n \\n one_step = lptl.is_one_step(start_time, end_time, utm_campaign) \\n \\n if not(one_step): \\n start_time = lptl.get_earliest_campaign_view(utm_campaign)\\n end_time = lptl.get_latest_campaign_view(utm_campaign) \\n\\n interval = 1\\n \\n \\\"\\\"\\\" Create reporting object to retrieve campaign data and write plots to image repo on disk \\\"\\\"\\\"\\n ir = DR.IntervalReporting(was_run=False, use_labels=False, font_size=20, plot_type='line', query_type='campaign', file_path=projSet.__web_home__ + 'campaigns/static/images/')\\n \\n \\\"\\\"\\\" Produce analysis on the campaign view data \\\"\\\"\\\" \\n ir.run(start_time, end_time, interval, 'views', utm_campaign, {}, one_step=one_step)\\n \\n \\\"\\\"\\\" \\n ESTIMATE THE START AND END TIME OF THE CAMPAIGN\\n ===============================================\\n \\n Search for the first instance when more than 10 views are observed over a sampling period\\n \\\"\\\"\\\"\\n \\n col_names = ir._data_loader_.get_column_names()\\n \\n views_index = col_names.index('views')\\n ts_index = col_names.index('ts')\\n \\n row_list = list(ir._data_loader_._results_) # copy the query results\\n for row in row_list:\\n if row[views_index] > 100:\\n start_time_est = row[ts_index]\\n break\\n row_list.reverse()\\n for row in row_list:\\n if row[views_index] > 100:\\n end_time_est = row[ts_index]\\n break\\n \\n \\n \\\"\\\"\\\"\\n BUILD THE VISUALIZATION FOR THE TEST VIEWS OF THIS CAMAPAIGN\\n ============================================================ \\n \\\"\\\"\\\"\\n \\n \\\"\\\"\\\" Read the test name \\\"\\\"\\\"\\n ttl = DL.TestTableLoader()\\n row = ttl.get_test_row(utm_campaign)\\n test_name = ttl.get_test_field(row ,'test_name')\\n \\n \\\"\\\"\\\" Regenerate the data using the estimated start and end times \\\"\\\"\\\"\\n ir = DR.IntervalReporting(was_run=False, use_labels=False, font_size=20, plot_type='line', query_type='campaign', file_path=projSet.__web_home__ + 'campaigns/static/images/')\\n ir.run(start_time_est, end_time_est, interval, 'views', utm_campaign, {}, one_step=one_step)\\n \\n \\\"\\\"\\\" Determine the type of test (if not overridden) and retrieve the artifacts \\\"\\\"\\\"\\n test_type, artifact_name_list = FDH.get_test_type(utm_campaign, start_time, end_time, DL.CampaignReportingLoader(query_type=''), test_type_var)\\n \\n return render_to_response('campaigns/show_campaigns.html', {'utm_campaign' : utm_campaign, 'test_name' : test_name, 'start_time' : start_time_est, 'end_time' : end_time_est, 'one_step' : one_step, \\\\\\n 'artifacts' : artifact_name_list, 'test_type' : test_type, 'err_msg' : err_msg}, context_instance=RequestContext(request)) \\n\\n except Exception as inst:\\n \\n logging.error('Failed to correctly produce campaign diagnostics.')\\n logging.error(type(inst))\\n logging.error(inst.args)\\n logging.error(inst)\\n \\n \\\"\\\"\\\" Return to the index page with an error \\\"\\\"\\\"\\n err_msg = 'There is insufficient data to analyze this campaign: %s. Check to see if the <a href=\\\"/LML/\\\">impressions have been loaded</a>. <br><br>ERROR:<br><br>%s' % (utm_campaign, inst.__str__())\\n \\n return index(request, kwargs={'err_msg' : err_msg})\",\n \"def test_summary_data(self):\\n self.driver.get('http://psl-outbreak.herokuapp.com/report')\\n self.driver.find_element_by_id('summary_find_out').click()\",\n \"def getReport(request):\\n\\n\\t#parameters needed for different REST API's\\n\\tparams = {\\n\\t\\t'rid':-1,\\n\\t\\t'year':-1,\\n\\t\\t'con_num':-1,\\n\\t\\t'assign_num':-1,\\n\\t\\t'item_num':-1,\\n\\t\\t'wtype': -1,\\n\\t\\t'payno': -1,\\n\\t\\t'snap': 0, #default is 0 for snapshots (for now)\\n\\t\\t'issue_date': -1,\\n\\t}\\n\\n\\t#loop over the parameters and set them if they appear in the api url\\n\\tfor p in params:\\n\\t\\tif p in request.GET:\\n\\t\\t\\tparams[p] = request.GET[p]\\n\\n\\n\\t#get the request session and load data\\n\\ts = requests.Session()\\n\\tif not isinstance(rgen.ReportGenerator.get_url(params), dict):\\n\\t\\tresponse = s.get(rgen.ReportGenerator.get_url(params))\\n\\n\\t\\t#set the iterator and the content\\n\\t\\tit = json.loads(response.content)\\n\\t\\tcontent = json.loads(response.content)\\n\\t\\t\\n\\t\\t#while a next page exists, parse the api\\n\\t\\tpageNum = 1\\n\\t\\twhile \\\"next\\\" in it:\\n\\t\\t\\tresponse = s.get(rgen.ReportGenerator.get_url(params) + '?page=' + str(pageNum))\\n\\t\\t\\tit = json.loads(response.content)\\n\\t\\t\\tcontent[\\\"items\\\"].extend(it[\\\"items\\\"])\\n\\t\\t\\tpageNum += 1\\n\\n\\telse:\\n\\t\\t#if the url is a list\\n\\t\\tcontent = {}\\n\\t\\tfor part in rgen.ReportGenerator.get_url(params):\\n\\t\\t\\tresponse = s.get(rgen.ReportGenerator.get_url(params)[part])\\n\\t\\t\\tit = json.loads(response.content)\\n\\t\\t\\t#content = {\\\"part1\\\":{\\\"items\\\":[]}, \\\"part2\\\":{\\\"items\\\":[]}, \\\"part3\\\":{\\\"items\\\":[]}}\\n\\t\\t\\t\\n\\t\\t\\tcontent[part] = {}\\n\\t\\t\\tcontent[part][\\\"items\\\"] = []\\n\\t\\t\\tcontent[part][\\\"items\\\"].extend(it[\\\"items\\\"])\\n\\n\\t\\t\\tpageNum = 1\\n\\t\\t\\twhile \\\"next\\\" in it:\\n\\t\\t\\t\\tresponse = s.get(rgen.ReportGenerator.get_url(params)[part] + '?page=' + str(pageNum))\\n\\t\\t\\t\\tit = json.loads(response.content)\\n\\t\\t\\t\\tcontent[part][\\\"items\\\"].extend(it[\\\"items\\\"])\\n\\t\\t\\t\\tpageNum += 1\\n\\t\\n\\t#set the file object to be returned as a download\\n\\tfile = HttpResponse(rgen.ReportGenerator.formExcel(content, params), content_type='application/vnd.openxmlformats-officedocument.spreadsheetml.sheet')\\n\\tif params[\\\"rid\\\"] == '70':\\n\\t\\tfile['Content-Disposition'] = 'attachment; filename=' + rgen.r_dict[params[\\\"rid\\\"]][1] + ' No.' + params['issue_date'] + '.xlsx'\\n\\telse:\\n\\t\\tfile['Content-Disposition'] = 'attachment; filename=' + rgen.r_dict[params[\\\"rid\\\"]][1] + '.xlsx'\\n\\ts.close()\\n\\treturn file\",\n \"def test_get_vulnerability_occurrences_summary(self):\\n pass\",\n \"def test_summary_report(self):\\n self.driver.get('http://psl-outbreak.herokuapp.com/report')\\n self.driver.find_element_by_id('summary_report_cases').click()\",\n \"def summary_page() :\\r\\n logger.debug(\\\"\\\")\\r\\n model = session_info.get_user_model(session)\\r\\n return render_template( \\\"summary_page.html\\\" , model=model ,\\r\\n stat_types=param_stats.StatTypes )\",\n \"def get_queryset(self, request):\\n qs = super().get_queryset(request)\\n qs = qs.prefetch_related('writerinwork_set')\\n qs = qs.prefetch_related('writers')\\n qs = qs.prefetch_related('library_release__library')\\n qs = qs.annotate(models.Count('cwr_exports', distinct=True))\\n qs = qs.annotate(models.Count('recordings', distinct=True))\\n return qs\",\n \"def report(request):\\n return render_to_response('application/report.html',\\n {'search_suggestions': _search_suggestions(),\\n },\\n context_instance=RequestContext(request));\",\n \"def index(request):\\n\\n # Generate counts of some of the main objects\\n num_para = Paragraph.objects.all().count()\\n num_source = Source.objects.all().count()\\n\\n context = {\\n 'num_para': num_para,\\n 'num_source': num_source,\\n 'paragraph_list': queryset[:10],\\n }\\n\\n return render(request, 'index.html', context=context)\",\n \"def get_uses_missing_exa(request):\\n\\n use_to_ret = {}\\n use_to_ret['labels_present'] = []\\n use_to_ret['concepts_present'] = []\\n use_to_ret['labels_missing'] = []\\n use_to_ret['concepts_missing'] = []\\n uses = ['colon','uterine cervix','lung']\\n for el in uses:\\n usecase = UseCase.objects.get(name=el)\\n presence = True\\n if Report.objects.filter(name = usecase).count() > 0:\\n if not AnnotationLabel.objects.filter(name = usecase, annotation_mode = 'Manual and Automatic').exists():\\n use_to_ret['labels_missing'].append(el)\\n else:\\n use_to_ret['labels_present'].append(el)\\n\\n cursor = connection.cursor()\\n cursor.execute(\\\"SELECT c.annotation_mode FROM concept AS c INNER JOIN concept_has_uc AS hc ON c.concept_url = hc.concept_url WHERE hc.name = %s\\\",[str(el)])\\n ans = cursor.fetchall()\\n for concept in ans:\\n if concept[0] != 'Manual and Automatic':\\n presence = False\\n break\\n if len(ans) > 0:\\n if presence == False:\\n use_to_ret['concepts_missing'].append(el)\\n else:\\n use_to_ret['concepts_present'].append(el)\\n else:\\n use_to_ret['concepts_missing'].append(el)\\n\\n return JsonResponse(use_to_ret)\",\n \"def get_designs(self):\",\n \"def getOverviewSummary(report_type=choices.INCOMING):\\n return {\\n \\\"domain_cnt\\\" : Report.objects.filter(\\n report_type=report_type).values(\\\"domain\\\").distinct().count(),\\n \\\"report_cnt\\\" : Report.objects.filter(\\n report_type=report_type).count(),\\n \\\"message_cnt\\\" : Record.objects.filter(\\n report__report_type=report_type).aggregate(\\n cnt=Sum(\\\"count\\\"))[\\\"cnt\\\"],\\n\\n # Query per result aggregated message count for dkim, spf and\\n # disposition and transform result number to display name\\n \\\"dkim\\\" : [\\n {\\n \\\"cnt\\\": res[\\\"cnt\\\"],\\n \\\"label\\\": dict(choices.DMARC_RESULT).get(res[\\\"dkim\\\"])\\n } for res in Record.objects.filter(\\n report__report_type=report_type).values(\\n \\\"dkim\\\").annotate(cnt=Sum(\\\"count\\\"))\\n ],\\n\\n \\\"spf\\\" : [\\n {\\n \\\"cnt\\\": res[\\\"cnt\\\"],\\n \\\"label\\\": dict(choices.DMARC_RESULT).get(res[\\\"spf\\\"])\\n } for res in Record.objects.filter(\\n report__report_type=report_type).values(\\n \\\"spf\\\").annotate(cnt=Sum(\\\"count\\\"))\\n ],\\n \\\"disposition\\\" : [\\n {\\n \\\"cnt\\\": res[\\\"cnt\\\"],\\n \\\"label\\\": dict(choices.DISPOSITION_TYPE).get(\\n res[\\\"disposition\\\"])\\n } for res in Record.objects.filter(\\n report__report_type=report_type).values(\\n \\\"disposition\\\").annotate(cnt=Sum(\\\"count\\\"))\\n ],\\n }\",\n \"def print_quick_report():\\r\\n print('function not yet written')\\r\\n # print a summary of the report as a structured pandas dataframe\\r\\n #Summary will include only date title and sentiment\\r\",\n \"def get(self, request):\\n issue_list = Issue.objects.order_by('-created_at')\\n open_issues = Issue.objects.filter(status='O').count()\\n closed_issues = Issue.objects.filter(status='C').count()\\n \\n return render(request, 'issue.html', {\\n 'issue_list': issue_list,\\n 'open_issues': open_issues,\\n 'closed_issues': closed_issues,\\n })\",\n \"def get(self, request, format=None):\\n param_report = self.request.query_params.get('report', None)\\n start_date = self.request.query_params.get('start_date', '')\\n end_date = self.request.query_params.get('end_date', '')\\n detailed_report = self.request.query_params.get('detailed_report', 'false')\\n\\n if start_date == '':\\n start_date = datetime.date.today().strftime(\\\"%Y-%m-%d 16:00:00\\\")\\n else:\\n start_date = start_date.replace(\\\"T\\\", \\\" \\\", 1)\\n if end_date == '':\\n end_date = datetime.date.today().strftime(\\\"%Y-%m-%d 16:00:00\\\")\\n else:\\n end_date = end_date.replace(\\\"T\\\", \\\" \\\", 1)\\n\\n if param_report is None or param_report == \\\"\\\":\\n return Response(\\\"No report specified\\\", status=status.HTTP_400_BAD_REQUEST)\\n\\n table_html = None\\n table_title = None\\n table_subtitle = \\\"\\\"\\\"%s - %s\\\"\\\"\\\" % (start_date, end_date)\\n\\n # if param_report == \\\"police_division_summary_report\\\":\\n # table_html = get_police_division_summary()\\n # table_title = \\\"Police Division Summary Report\\\"\\n\\n if param_report == \\\"category_wise_summary_report\\\":\\n table_html = get_category_summary(start_date, end_date, detailed_report)\\n table_title = \\\"No. of Incidents by Category\\\"\\n\\n elif param_report == \\\"mode_wise_summary_report\\\":\\n table_html = get_mode_summary(start_date, end_date, detailed_report)\\n table_title = \\\"No. of Incidents by Mode\\\"\\n\\n elif param_report == \\\"district_wise_summary_report\\\":\\n table_html = get_district_summary(start_date, end_date, detailed_report)\\n table_title = \\\"No. of Incidents by District\\\"\\n\\n elif param_report == \\\"severity_wise_summary_report\\\":\\n table_html = get_severity_summary(start_date, end_date, detailed_report)\\n table_title = \\\"No. of Incidents by Severity\\\"\\n\\n elif param_report == \\\"subcategory_wise_summary_report\\\":\\n table_html = get_subcategory_summary(start_date, end_date, detailed_report)\\n table_title = \\\"No. of Incidents by Subcategory\\\"\\n\\n elif param_report == \\\"status_wise_summary_report\\\":\\n table_html = get_status_summary(start_date, end_date, detailed_report)\\n table_title = \\\"No. of Incidents by Status\\\"\\n\\n if table_html is None:\\n return Response(\\\"Report not found\\\", status=status.HTTP_400_BAD_REQUEST)\\n\\n table_html = apply_style(\\n table_html.replace(\\\".0\\\", \\\"\\\", -1).replace(\\\"(Total No. of Incidents)\\\",\\n \\\"<strong>(Total No. of Incidents)</strong>\\\", 1).replace(\\n \\\"(Unassigned)\\\", \\\"<strong>(Unassigned)</strong>\\\", 1), table_title, table_subtitle)\\n response = HttpResponse(content_type='application/pdf')\\n response['Content-Disposition'] = 'attachment; filename=\\\"Report.pdf\\\"'\\n pisa.CreatePDF(table_html, dest=response)\\n\\n return response\",\n \"def index(request):\\n\\n # Generate counts of the main objects\\n\\n num_sales=Prodsale.objects.all().count()\\n\\n # Render the HTML template index.html with the data in the context variable\\n\\n return render(\\n request, 'index.html',\\n context={'num_sales':num_sales},\\n )\",\n \"def annual_report_view(self, document_id: str) -> Tuple[Any, Any]:\\n self.__header_update_token()\\n link = EFD_ENDPOINT_REPORT.format('annual', document_id)\\n self.__ensure_fetching_rate_limit()\\n response = self.session.get(link)\\n soup = BeautifulSoup(response.text, features='html.parser')\\n return soup.find('h1').parent, soup.findAll('section', {'class': 'card mb-2'})\",\n \"def view_all(request, index_call=False):\\n closed = request.GET.get('closed', '')\\n if closed in ('0', 'false'):\\n closed = False\\n elif closed in ('1', 'true'):\\n closed = True\\n elif index_call:\\n # for index we display only open issues by default\\n closed = False\\n else:\\n closed = None\\n\\n nav_parameters = {}\\n if closed is not None:\\n nav_parameters['closed'] = int(closed)\\n\\n query = models.Issue.query(\\n models.Issue.private == False).order(-models.Issue.modified)\\n if closed is not None:\\n # return only opened or closed issues\\n query = query.filter(models.Issue.closed == closed)\\n\\n return _paginate_issues(reverse(view_all),\\n request,\\n query,\\n 'all.html',\\n extra_nav_parameters=nav_parameters,\\n extra_template_params=dict(closed=closed))\",\n \"def _compute_results(self):\\n self.ensure_one()\\n Result = self.env['pabi.common.supplier.payment.report.view']\\n dom = [('invoice_id.source_document_type', 'in',\\n ['advance', 'expense']),\\n ('expense_id.pay_to', '!=', 'supplier')]\\n if self.user_ids:\\n dom += [('voucher_id.validate_user_id', 'in', self.user_ids.ids)]\\n if self.source_document_type:\\n dom += [('invoice_id.source_document_type', '=',\\n self.source_document_type)]\\n if self.fiscalyear_start_id:\\n dom += [('voucher_id.date', '>=',\\n self.fiscalyear_start_id.date_start)]\\n if self.fiscalyear_end_id:\\n dom += [('voucher_id.date', '<=',\\n self.fiscalyear_end_id.date_stop)]\\n if self.period_start_id:\\n dom += [('voucher_id.date', '>=',\\n self.period_start_id.date_start)]\\n if self.period_end_id:\\n dom += [('voucher_id.date', '<=',\\n self.period_end_id.date_stop)]\\n if self.date_start:\\n dom += [('voucher_id.date', '>=', self.date_start)]\\n if self.date_end:\\n dom += [('voucher_id.date', '<=', self.date_end)]\\n self.results = Result.search(\\n dom, order=\\\"fiscalyear,voucher_number,invoice_number\\\")\",\n \"def get_user_activity_report(request, ids, index, value, start_date, end_date, stats): \\n\\n domain = request.user.selected_domain\\n if not ids: raise Exception(\\\"The requested form was not found\\\")\\n \\n _report = Report(\\\"CHW Group Total Activity Report\\\")\\n _report.generating_url = request.path\\n metadata = Metadata.objects.filter(timestart__gte=start_date)\\n # the query below is used if you want to query by submission time (instead of form completion time)\\n #metadata = Metadata.objects.filter(attachment__submission__submit_time__gte=start_date)\\n \\n # since we are working at a granularity of 'days', we want to make sure include \\n # complete days in our queries, so we round up\\n timespan = get_timespan(start_date, end_date)\\n delta = timedelta(days=timespan.days+1)\\n metadata = metadata.filter(timeend__lt=start_date+delta)\\n # the query below is used if you want to query by submission time (instead of form completion time)\\n #metadata = metadata.filter(attachment__submission__submit_time__lte=end_date)\\n \\n dataset = DataSet( unicode(value[0]) + \\\" per \\\" + unicode(index) )\\n dataset.indices = unicode(index)\\n dataset.params = request.GET\\n\\n # when 'organization' is properly populated, we can start using that\\n # member_list = utils.get_members(organization)\\n # for now, just use domain\\n member_list = [r.chw_username for r in ReporterProfile.objects.filter(domain=domain).order_by(\\\"chw_username\\\")]\\n\\n # get a sum of all forms\\n visits_per_member = Values( \\\"visits\\\" )\\n for member in member_list:\\n visits_per_member.append( (member, metadata.filter(username=member).count()) )\\n visits_per_member.run_stats(stats)\\n visits_per_member.sort(key=operator.itemgetter(1), reverse=True) \\n dataset.valuesets.append( visits_per_member )\\n \\n # this report only requires the first form. you can imagine other reports doing \\n # this iteration: for id in ids:\\n form_per_member = Values( unicode(value[0]) )\\n form_metadata = metadata.filter(formdefmodel=ids[0])\\n for member in member_list:\\n # values are tuples of dates and counts\\n form_per_member.append( (member, form_metadata.filter(username=member).count()) )\\n form_per_member.run_stats(stats)\\n form_per_member.sort(key=operator.itemgetter(1), reverse=True) \\n dataset.valuesets.append( form_per_member )\\n \\n _report.datasets.append(dataset)\\n return _report\",\n \"def summary(request):\\n data = {'Yes':[], 'No':[], 'labels':{}}\\n reasons = zip(list(range(RestartReason.objects.count())), \\n RestartReason.objects.order_by('reason_id'))\\n for i, reason in reasons:\\n iv_attempts = __iv_attempts(reason.reason_id)\\n data['Yes'].append(iv_attempts[True])\\n data['No'].append(iv_attempts[False])\\n data['labels'][i] = reason.reason_id\\n return HttpResponse(simplejson.dumps(data), \\n mimetype='application/javascript')\",\n \"def view_insurances(request):\\n insurance = Insurance.objects.filter(medical_information=request.user.userprofile.medicalinformation)\\n return render(request, 'view_insurances.html', {'insurances': insurance})\",\n \"def test_display_report():\\n mr.initialize_donors()\\n report = mr.text_report()\\n\\n print(report)\\n for name in mr.mailroom.database.keys():\\n assert name in report\\n assert f'{mr.mailroom.database[name].total_donations:,.2f}' in report\\n assert f'{mr.mailroom.database[name].average_donation:,.2f}' in report\",\n \"def display_reports(self, layout): # pylint: disable=arguments-differ\",\n \"def public_market_summaries(self):\\n return self.get('markets/summaries')\",\n \"def invoices_report_filter(request):\\n qry = DBSession.query(Invoice)\\n supplierlist = []\\n for supplier in qry:\\n if supplier.SupplierID:\\n entry = {'Name': supplier.Order.Supplier.Name,\\n 'ID': supplier.SupplierID}\\n if entry not in supplierlist:\\n supplierlist.append(entry)\\n\\n projectlist = []\\n for project in qry:\\n if project.ProjectID:\\n entry = {'Name': project.Order.Project.Name,\\n 'ID': project.ProjectID}\\n if entry not in projectlist:\\n projectlist.append(entry)\\n\\n paymentdatelist = []\\n for paymentdate in qry:\\n if paymentdate.PaymentDate:\\n entry = paymentdate.PaymentDate.strftime(\\\"%d %B %Y\\\")\\n if entry not in paymentdatelist:\\n paymentdatelist.append(entry)\\n\\n return {'projects': sorted(projectlist, key=lambda k: k['Name'].upper()),\\n 'suppliers': sorted(supplierlist, key=lambda k: k['Name'].upper()),\\n 'paymentdates': sorted(paymentdatelist),\\n 'paymentdates_exist': paymentdatelist != [],\\n 'statuses': ['Draft', 'Due', 'Paid']}\",\n \"def showcase(request):\\n\\n showcases = Showcase.objects.all\\n context = {\\n 'showcases': showcases,\\n }\\n\\n return render(request, 'showcase/showcase.html', context)\",\n \"def view_all(request, index_call=False):\\n closed = request.GET.get('closed', '')\\n if closed in ('0', 'false'):\\n closed = False\\n elif closed in ('1', 'true'):\\n closed = True\\n elif index_call:\\n # for index we display only open issues by default\\n closed = False\\n else:\\n closed = None\\n\\n nav_parameters = {}\\n if closed is not None:\\n nav_parameters['closed'] = int(closed)\\n\\n # This uses eventual consistency and cannot be made strongly consistent.\\n query = models.Issue.query(\\n models.Issue.private == False).order(-models.Issue.modified)\\n if closed is not None:\\n # return only opened or closed issues\\n query = query.filter(models.Issue.closed == closed)\\n\\n return _paginate_issues(reverse(view_all),\\n request,\\n query,\\n 'all.html',\\n extra_nav_parameters=nav_parameters,\\n extra_template_params=dict(closed=closed))\",\n \"def sa_summary_pdf(sa_id):\\n pass\",\n \"def index(request):\\n fund_logs = FundProcessLog.objects.all()\\n activity_logs = []\\n for log in fund_logs:\\n activity_logs.append({'activity_date':log.created_on,\\n 'description': '%s fund codes subsituted' % log.substitutions})\\n logging.error(\\\"Number of activity logs:%s\\\" % len(activity_logs))\\n utilities = [{'name':'csv',\\n 'label':'Expand Fund codes to values',\\n 'description':'''Takes order records in CSV format, replaces Fund codes with\\n expanded Fund numeric values'''}]\\n return direct_to_template(request,\\n 'vendors/iii/index.html',\\n {'activity_log':activity_logs,\\n 'utilities':utilities})\",\n \"def inscription_summary(request, pk):\\n candidat = get_object_or_404(Candidate, pk=pk)\\n buff = io.BytesIO()\\n pdf = InscriptionSummaryPDF(buff)\\n pdf.produce(candidat)\\n filename = slugify('{0}_{1}'.format(candidat.last_name, candidat.first_name)) + '.pdf'\\n buff.seek(0)\\n return FileResponse(buff, as_attachment=True, filename=filename)\",\n \"def get_PUBMED_batch_list(request):\\n\\n json_resp = {}\\n usecase = request.GET.get('usecase')\\n # print(usecase)\\n use_obj = UseCase.objects.get(name=usecase)\\n json_resp['batch_list'] = []\\n batch = Report.objects.filter(name=use_obj,institute = 'PUBMED').values('batch')\\n for el in batch:\\n if el['batch'] not in json_resp['batch_list']:\\n json_resp['batch_list'].append( el['batch'])\\n # print(json_resp['batch_list'])\\n json_resp['batch_list'] = sorted(json_resp['batch_list'])\\n # print(json_resp)\\n return JsonResponse(json_resp)\",\n \"def getSiteWideReports(context):\\n\\n return getReports(context, category=\\\"Site Wide\\\")\",\n \"def detail(request):\\r\\n\\r\\n\\tdataset = request.GET.get('dataset', '')\\r\\n\\tdatatype = request.GET.get('datatype', 'RNA')\\r\\n\\ttissue = request.GET.get('tissue', 'brain')\\r\\n\\tcategory = request.GET.get('category', 'region')\\r\\n\\tgroup = request.GET.get('group', 'PFC')\\r\\n\\tcomparison = request.GET.get('comparison', 'AD-vs-Control')\\r\\n\\tfeature_symbols_in_interest = request.GET.get('features', '').split(' ')\\r\\n\\tcollection_name = \\\"%s_%s_%s-%s_%s\\\" % (datatype,\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\ttissue,\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tcategory,\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tgroup,\\r\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tcomparison)\\r\\n\\t\\\"\\\"\\\"\\r\\n\\t\\tWe should split POST[\\\"featureInput\\\"] here\\r\\n\\t\\\"\\\"\\\"\\r\\n\\t# import pdb; pdb.set_trace();\\r\\n\\t# feature_symbols_in_interest = split_feature_input_to_list(request.POST[\\\"featureInput\\\"])\\r\\n\\r\\n\\tway_to_choose_probe = request.GET.get('way_to_choose_probe', 'fold change')\\r\\n\\r\\n\\tall_datasets = test_stat_client.get_all_datasets(collection_name)\\r\\n\\r\\n\\ttest_statistics = list(test_stat_client.get_all_for_this_category(collection_name))\\r\\n\\r\\n\\tdisease_state_list = test_stat_client.get_disease_state_list(collection_name)\\r\\n\\r\\n\\ttest_statistics = pd.DataFrame(test_statistics)\\r\\n\\r\\n\\t# Filter 1 - dataset accession & features in interest\\r\\n\\tfilt_ind = (test_statistics['dataset_accession'] == dataset) & (test_statistics['symb'].isin(feature_symbols_in_interest))\\r\\n\\ttest_stat_df = test_statistics[filt_ind]\\r\\n\\r\\n\\t# Filter 2 - remove duplicates\\r\\n\\t\\\"\\\"\\\"\\r\\n\\t\\tHere we provide options for user to choose how to select a probe when \\r\\n\\t\\tmultiple probes are corresponding to one feature\\r\\n\\t\\\"\\\"\\\"\\r\\n\\r\\n\\tif way_to_choose_probe == \\\"fold change\\\":\\r\\n\\t\\ttest_stat_df = filtered_duplicate_by(test_stat_df, by='fc', group_index=['symb'])\\r\\n\\r\\n\\telif way_to_choose_probe == \\\"limma p value\\\" : \\r\\n\\t\\ttest_stat_df = filtered_duplicate_by(test_stat_df, by='lp', group_index=['symb'])\\r\\n\\r\\n\\telif way_to_choose_probe == \\\"t test p value\\\" :\\r\\n\\t\\ttest_stat_df = filtered_duplicate_by(test_stat_df, by='tp', group_index=['symb'])\\r\\n\\r\\n\\t# Split dataframe for stat table display and graph display\\r\\n\\tstat_table = test_stat_df.drop(['eval', 'dsl'], axis=1)\\r\\n\\tstat_graph_exprs = test_stat_df[['symb', 'eval']]\\r\\n\\tstat_graph_ds = disease_state_list[dataset]\\r\\n\\r\\n\\t# import pdb; pdb.set_trace()\\r\\n\\tstat_table['entrez_gene_id'] = stat_table.apply(from_symbol_to_entrez_gene_id, axis=1)\\t\\t\\t\\r\\n\\tstat_table['string_id'] = from_single_symbol_to_string_id(stat_table['symb'])\\r\\n\\r\\n\\tds_1_count = sum(stat_graph_ds)\\r\\n\\tds_0_count = len(stat_graph_ds) - sum(stat_graph_ds)\\r\\n\\r\\n\\tstat_graph_ds_1 = [True if x == 1 else False for x in stat_graph_ds]\\r\\n\\tstat_graph_ds_0 = [True if x == 0 else False for x in stat_graph_ds]\\r\\n\\t# stat_graph_ds_0 = stat_graph_ds == 0\\r\\n\\r\\n\\theatmap_feature_count = test_stat_df.shape[0]\\r\\n\\theatmap_sample_count = len(stat_graph_ds)\\r\\n\\theatmap_df_row_count = heatmap_sample_count * heatmap_feature_count\\r\\n\\r\\n\\t\\r\\n\\t# import pdb;pdb.set_trace\\r\\n\\t# Generate a expression table (row as feature)\\r\\n\\texpression_table = pd.DataFrame(list(stat_graph_exprs['eval']))\\r\\n\\r\\n\\t\\r\\n\\t# import pdb;pdb.set_trace();\\r\\n\\t# Transpose table before sorting by disease state\\r\\n\\texpression_table = pd.DataFrame.transpose(expression_table)\\r\\n\\r\\n\\t# Get new expression table sorted by disease state\\r\\n\\texpression_table = expression_table[stat_graph_ds_1].append(expression_table[stat_graph_ds_0], ignore_index=True)\\r\\n\\r\\n\\t### Normalize row expression\\r\\n\\texpression_table_normalized = normalize_heatmap_row_expression(expression_table)\\r\\n\\t\\r\\n\\t# Get minimum and maximum value of expression\\r\\n\\texprs_min = np.nanmin(expression_table_normalized.values)\\r\\n\\texprs_max = np.nanmax(expression_table_normalized.values)\\r\\n\\r\\n\\r\\n\\theatmap_dataset_df = pd.DataFrame({\\r\\n\\t\\t\\t'0' : sorted(range(0, heatmap_sample_count) * heatmap_feature_count), # sample_x\\r\\n\\t\\t\\t'1' : range(0, heatmap_feature_count) * heatmap_sample_count,\\t# feature_y\\r\\n\\t\\t\\t'2' : [val for row in expression_table_normalized.values.tolist() for val in row] #expression_z\\r\\n\\t\\t})\\r\\n\\r\\n\\t# Remove NANs in heatmap data series\\r\\n\\tnot_nan_index = np.invert(np.isnan(heatmap_dataset_df['2']))\\r\\n\\theatmap_dataset_df = heatmap_dataset_df[not_nan_index]\\r\\n\\t# Prepare one dimentional scatter plot\\r\\n\\r\\n\\t# Final output\\r\\n\\t# Scatter plot\\r\\n\\tstate_1_data_series = generate_scatterplot_series(range(0, ds_1_count), 0, expression_table)\\r\\n\\tstate_0_data_series = generate_scatterplot_series(range(ds_1_count, ds_1_count+ds_0_count), 1, expression_table)\\t\\r\\n\\tstate_1_name = \\\"AD\\\"\\r\\n\\tstate_0_name = \\\"Control\\\"\\r\\n\\t# Heatmap\\r\\n\\theatmap_feature_list = [x.encode('utf-8') for x in list(stat_graph_exprs['symb'])]\\r\\n\\theatmap_sample_ds_list = ['AD'] * ds_1_count + ['Control'] * ds_0_count\\r\\n\\theatmap_datasets = heatmap_dataset_df.values.tolist()\\r\\n\\theatmap_extremes = [exprs_min, exprs_max]\\r\\n\\t# Statistic table\\r\\n\\tstat_table_output = stat_table.to_dict(outtype='records')\\r\\n\\r\\n\\treturn render(request, 'feature_stat_detail.html',\\r\\n\\t\\t\\t\\t{\\r\\n\\t\\t\\t\\t\\t'dataset_name' : dataset,\\r\\n\\t\\t\\t\\t\\t'test_stat' : stat_table_output,\\r\\n\\t\\t\\t\\t\\t'feature_list' : heatmap_feature_list,\\r\\n\\t\\t\\t\\t\\t'sample_state_list' : heatmap_sample_ds_list,\\r\\n\\t\\t\\t\\t\\t'heatmap_datasets' : heatmap_datasets,\\r\\n\\t\\t\\t\\t\\t'heatmap_extremes' : heatmap_extremes,\\r\\n\\t\\t\\t\\t\\t'state_1_data_series' : state_1_data_series,\\r\\n\\t\\t\\t\\t\\t'state_0_data_series' : state_0_data_series,\\r\\n\\t\\t\\t\\t\\t'state_1_name' : state_1_name,\\r\\n\\t\\t\\t\\t\\t'state_0_name' : state_0_name,\\r\\n\\t\\t\\t\\t})\",\n \"def get_fields(request):\\n\\n json_resp = {}\\n json_resp['fields'] = []\\n json_resp['fields_to_ann'] = []\\n all = request.GET.get('all',None)\\n workpath = os.path.dirname(os.path.abspath(__file__)) # Returns the Path your .py file is in\\n auto_request = request.GET.get('ns_id', None)\\n report = request.GET.get('report', None)\\n # print(request.session['report_type'])\\n if report is not None or all == 'all':\\n if report is not None:\\n if report.startswith('PUBMED_'):\\n json_resp['fields'] = ['volume','authors','year','journal']\\n json_resp['fields_to_ann'] = ['title','abstract']\\n else:\\n json_resp = get_fields_from_json()\\n if all == 'all':\\n # All the possible fields for every usecase (MANUAL CONFIGURATION)\\n json_resp = get_fields_from_json()\\n if Report.objects.filter(institute = 'PUBMED').exists():\\n json_resp['all_fields'].extend(['title','abstract','volume','journal','year','authors']) #aggiungo pubmed solo in coda!\\n else:\\n if request.session['report_type'] == 'pubmed':\\n json_resp['fields'] = ['volume','authors','year','journal']\\n json_resp['fields_to_ann'] = ['title','abstract']\\n else:\\n # Fileds related exclusively to a usecase\\n json_resp = get_fields_from_json_configuration(request.session['usecase'],request.session['institute'],request.session['language'])\\n if request.session['mode'] == 'Robot' or auto_request == 'Robot':\\n with open(os.path.join(workpath, './automatic_annotation/auto_fields/auto_fields.json')) as out:\\n data = json.load(out)\\n json_resp['fields_to_ann'] = data['extract_fields'][request.session['usecase']]\\n for el in json_resp['fields_to_ann']:\\n if el in json_resp['fields']:\\n json_resp['fields'].remove(el)\\n # print('FIELDS', json_resp)\\n return JsonResponse(json_resp)\",\n \"def get_presence_robot_user(request):\\n\\n id_report = request.GET.get('id_report', None)\\n language = request.GET.get('language', None)\\n use = request.GET.get('usecase', None)\\n rep = request.GET.get('report_type', None)\\n json_resp = {'auto_annotation_count': 0}\\n cursor = connection.cursor()\\n\\n reports_list = None\\n if request.method == 'POST':\\n request_body_json = json.loads(request.body)\\n reports_list = request_body_json['reports']\\n\\n if id_report is not None and language is not None:\\n\\n usecase = Report.objects.get(id_report=id_report, language=language)\\n use = usecase.name_id\\n cursor.execute(\\n \\\"SELECT COUNT(*) FROM ground_truth_log_file as g INNER JOIN report AS r ON r.id_report = g.id_report AND r.language = g.language WHERE g.username = %s and r.name = %s\\\",\\n ['Robot_user', str(use)])\\n ans = cursor.fetchone()[0]\\n json_resp = {'auto_annotation_count': (ans)}\\n\\n elif use is not None and rep is not None:\\n # print(rep)\\n if rep == 'reports':\\n cursor.execute(\\n \\\"SELECT COUNT(*) FROM ground_truth_log_file as g INNER JOIN report AS r ON r.id_report = g.id_report AND r.language = g.language WHERE g.ns_id = %s AND g.username = %s and r.name = %s and r.institute != %s\\\",\\n ['Robot', 'Robot_user', str(use), 'PUBMED'])\\n\\n elif rep == 'pubmed':\\n cursor.execute(\\n \\\"SELECT COUNT(*) FROM ground_truth_log_file as g INNER JOIN report AS r ON r.id_report = g.id_report AND r.language = g.language WHERE g.username = %s and r.name = %s and r.institute = %s\\\",\\n ['Robot_user', str(use), 'PUBMED'])\\n\\n ans = cursor.fetchone()[0]\\n if ans > 0:\\n json_resp = {'auto_annotation_count': ans}\\n # print(json_resp)\\n elif reports_list is not None:\\n report_list = json.loads(reports_list)\\n # print(report_list)\\n usecase_list = []\\n for rep in report_list:\\n\\n if rep['usecase'] not in usecase_list:\\n usecase_list.append(rep['usecase'])\\n for u in usecase_list:\\n cursor = connection.cursor()\\n cursor.execute(\\n \\\"SELECT COUNT(*) FROM ground_truth_log_file as g INNER JOIN report AS r ON r.id_report = g.id_report AND r.language = g.language WHERE g.username = %s and r.name = %s\\\",\\n ['Robot_user', str(u)])\\n ans = cursor.fetchone()[0]\\n if ans > 0:\\n json_resp = {'auto_annotation_count': ans}\\n else:\\n json_resp = {'auto_annotation_count': 0}\\n\\n elif use is None and reports_list is None and id_report is None and language is None:\\n robot = NameSpace.objects.get(ns_id='Robot')\\n gt = GroundTruthLogFile.objects.filter(ns_id=robot)\\n json_resp = {'auto_annotation_count': gt.count()}\\n\\n print(json_resp)\\n return JsonResponse(json_resp)\",\n \"def testcases(self, request, *args, **kwargs):\\n response = self.retrieve(request, *args, **kwargs)\\n response.data = response.data['testcases']\\n return response\",\n \"def acceptedPapersIndustrial(request):\\n # Test Comment\\n assert isinstance(request, HttpRequest)\\n return render(\\n request,\\n 'acceptedIndustrial.html',\\n context_instance=RequestContext(request, {})\\n )\",\n \"def data():\\n return render_template(\\n 'data.html',\\n title='World Happiness Report',\\n year=datetime.now().year,\\n message='Main Data Model'\\n )\",\n \"def get(self, request, *args, **kwargs):\\n # ProcessedAliquots.objects.all().values('SampleType', 'Volume', 'VolumeUnit', 'PostProcessingStatus')\\n samples = ProcessedAliquots.objects.all().values('SampleType').distinct()\\n dist_sample = []\\n for sample in samples:\\n dist_sample += list(sample.values())\\n volumes = ProcessedAliquots.objects.all().values('Volume').distinct()\\n dist_volume = []\\n for volume in volumes:\\n dist_volume += list(volume.values())\\n Units = ProcessedAliquots.objects.all().values('VolumeUnit').distinct()\\n dist_unit = []\\n for unit in Units:\\n dist_unit += list(unit.values())\\n status = ProcessedAliquots.objects.all().values(\\n 'PostProcessingStatus').distinct()\\n dist_status = []\\n for stat in status:\\n dist_status += list(stat.values())\\n context = {'samples': dist_sample, 'volumes': dist_volume,\\n 'units': dist_unit, 'status': dist_status}\\n return render(request, self.template_name, context)\",\n \"def test_publication_view(self):\\n \\n test_response = self.client.get('/papers/14-3-3-proteins-a-number-of-functions-for-a-numbered-protein/')\\n self.assertEqual(test_response.status_code, 200)\\n self.assertTrue('publication' in test_response.context) \\n self.assertTemplateUsed(test_response, 'paper-detail.html')\\n self.assertTemplateUsed(test_response, 'base.html') \\n self.assertTemplateUsed(test_response, 'disqus_snippet.html') \\n self.assertTemplateUsed(test_response, 'paper_sharing_widgets.html')\\n self.assertTemplateUsed(test_response, 'altmetric_snippet.html') \\n self.assertEqual(test_response.context['publication'].pk, 1)\\n self.assertEqual(test_response.context['publication'].title, u'14-3-3 proteins: a number of functions for a numbered protein.')\",\n \"def generate_report(df, start_date, end_date):\\n # Remove any transactions that had to do with collecting or returning security\\n security_df = df[(df[CATEGORY] == 'Security') | (df[CATEGORY] == 'Security-Income')]\\n df = df[(df[CATEGORY] != 'Security')]\\n\\n # Exclude the data for everything except our quarter\\n period_data = df.loc[start_date:end_date] # Note: NOT using extended quarter range\\n rental_income = period_data[period_data[CATEGORY] == 'Rent']\\n utilities = period_data[(period_data[CATEGORY] == 'Utilities')]\\n other_income = period_data[(period_data['Transaction Type'] == 'credit') & (period_data[CATEGORY] != 'Rent')]\\n expenses = period_data[(period_data['Transaction Type'] == 'debit')]\\n unpaid_util_overages = float(0)\\n\\n # print(rental_income)\\n # print(other_income)\\n # print(expenses)\\n \\n html_config.initialize()\\n print(html_config.HTML_OPEN)\\n\\n print('<H1>Income and Expense Report for %s-%s:' % (start_date, end_date), '</H1><p>')\\n\\n # List all unit specific rents and expenses for the quarter\\n for UNIT in sorted(rental_income['Unit'].unique()):\\n # Show rental income info\\n temp_df = rental_income[rental_income['Unit'] == UNIT]\\n print('<br><H2>Total rent for Unit ', UNIT, ': ${:,.2f}'.format(temp_df['Amount'].sum()), '</H2>')\\n print(temp_df[['Description', 'Amount']].to_html())\\n \\n if not SKIP_UTIL_ANALYSIS:\\n # Show utilities payments and calculate any overage due\\n temp_df = utilities[(utilities['Unit'] == UNIT) & (utilities['Transaction Type'] == 'debit')]\\n print('<br><H2>Utilities Expenses for Unit', UNIT, ': ${:,.2f}'.format(temp_df['Amount'].sum()), '</h2>')\\n overage = temp_df.assign(Overage=lambda x: x.Amount - limit_df.loc[UNIT].Amount)\\n # Disable warning when setting negative overage values to zero\\n pd.set_option('mode.chained_assignment', None)\\n overage.Overage[overage.Overage < 0] = 0\\n pd.set_option('mode.chained_assignment', 'warn')\\n print(overage[['Description', 'Amount', 'Overage']].to_html())\\n # Show if utilties costs exceeded allotted amount\\n if (overage['Overage'].sum() > 0):\\n print('<h3>Utilities overage for unit', UNIT, ': ${:,.2f}'.format(overage['Overage'].sum()), '</h3>')\\n unpaid_util_overages += overage['Overage'].sum()\\n # Show any untilities that were collected \\n overage_collected = utilities[(utilities['Unit'] == UNIT) & (utilities['Transaction Type'] == 'credit')]\\n if not overage_collected.empty:\\n print('<br><H2>Util overages collected for Unit ', UNIT, ': ${:,.2f}'.format(overage_collected['Amount'].sum()), '</H2>')\\n print(overage_collected[['Description', 'Amount', CATEGORY]].to_html())\\n print('<h3>Net Utils overage for unit', UNIT, 'for period: ${:,.2f}'.format(overage['Overage'].sum() - overage_collected['Amount'].sum()), '</h3>')\\n \\n\\n \\n # Generate unit specific Utility usage reports\\n if GEN_TENANT_UTIL_REPORTS and OUTPUT_DIRECTORY:\\n TENANT_FILE = '%s/122-Spring-St-%s-%s-Unit-%s-utils.html' % (OUTPUT_DIRECTORY, start_date, end_date, UNIT)\\n TENANT_REPORTS.append(TENANT_FILE)\\n sys.stdout = open(TENANT_FILE, 'w')\\n print(html_config.HTML_OPEN)\\n\\n print('<H1>Unit', UNIT, '</H1>')\\n print('<br><H2>Utilities Expenses for: %s-%s' % (start_date, end_date))\\n print('<br>Utilites included in rent: ${:,.2f}'.format(limit_df.loc[UNIT].Amount))\\n print('</H2>')\\n print(overage[['Description', 'Amount', 'Overage']].to_html())\\n # Show if any utilties overage may be due\\n if (overage['Overage'].sum() > 0):\\n print('<h3>Utilities overage for unit', UNIT, ': ${:,.2f}'.format(overage['Overage'].sum()), '</h3>')\\n if not overage_collected.empty:\\n print('<br><H2>Util overages collected for Unit ', UNIT, ': ${:,.2f}'.format(overage_collected['Amount'].sum()), '</H2>')\\n print(overage_collected[['Description', 'Amount', CATEGORY]].to_html())\\n print('<h3>Net Utils overage for unit', UNIT, 'for period: ${:,.2f}'.format(overage['Overage'].sum() - overage_collected['Amount'].sum()), '</h3>')\\n\\n print(html_config.HTML_CLOSE)\\n\\n # Restore stdout to the main report file\\n sys.stdout = open(REPORT_FILE, 'a')\\n \\n # Show other unit specific transactions\\n if SKIP_UTIL_ANALYSIS:\\n unit_exp = expenses[(expenses['Unit'] == UNIT)]\\n unit_income = other_income[other_income['Unit'] == UNIT]\\n else:\\n unit_exp = expenses[(expenses['Unit'] == UNIT) & (expenses[CATEGORY] != 'Utilities')]\\n unit_income = other_income[(other_income['Unit'] == UNIT) & (other_income[CATEGORY] != 'Utilities')]\\n \\n if not unit_exp.empty:\\n print('<br><H2>Other Unit specific expenses for: ', UNIT, ': ${:,.2f}'.format(unit_exp['Amount'].sum()), '</h2>')\\n print(unit_exp[['Description', 'Amount', 'Unit', CATEGORY]].to_html())\\n print('<p>')\\n \\n # Show any other unit specific credit\\n other_income = other_income[other_income['Unit'] == UNIT]\\n if not other_income.empty:\\n print('<br><H2>Expense offsets for Unit ', UNIT, ': ${:,.2f}'.format(other_income['Amount'].sum()), '</H2>')\\n print(other_income[['Description', 'Amount', CATEGORY]].to_html())\\n \\n # Add a line between units\\n print('<hr>')\\n \\n # List the shared income and expenses for the quarter\\n temp_df = other_income[other_income['Unit'].isnull()]\\n if not temp_df.empty:\\n print ('<br><H2>Non unit specific income: ${:,.2f}'.format(temp_df['Amount'].sum()), '</h2>')\\n print(temp_df[['Description', 'Amount', CATEGORY]].to_html())\\n gen_expenses = expenses[expenses['Unit'].isnull()]\\n if not gen_expenses.empty:\\n print ('<br><H1>Non unit specific expenses</h1>')\\n # Get the list of expense categories and generate summary for each\\n for category in sorted(gen_expenses[CATEGORY].unique()):\\n temp_df = gen_expenses[(gen_expenses[CATEGORY] == category)]\\n print ('<br><H2>'+ category +': ${:,.2f}'.format(temp_df['Amount'].sum()), '</h2>')\\n print(temp_df[['Description', 'Amount', CATEGORY]].to_html())\\n \\n # If there were any security transactions in the period give a security report\\n if not security_df.loc[start_date:end_date].empty:\\n temp_df = security_df.loc[start_date:end_date] \\n print('<hr><H2>Security related transactions:</H2>')\\n print(temp_df[['Description', 'Amount', 'Transaction Type', 'Unit']].to_html())\\n for UNIT in sorted(rental_income['Unit'].unique()):\\n unit_df = security_df[security_df['Unit'] == UNIT]\\n collected = unit_df[(unit_df['Transaction Type'] == 'credit')]['Amount'].sum()\\n returned = unit_df[(unit_df['Transaction Type'] == 'debit')]['Amount'].sum()\\n print('<center><H4>Current Liability on Unit '+str(UNIT)+': ${:,.2f}'.format(collected-returned), '</H4></center>')\\n \\n # # Summarize the periods income and expenses -- old way to be discarded...\\n # print('<br><H3>Total income for period: ${:,.2f}'.format(rental_income['Amount'].sum()), '</H3>')\\n # print('<H3>Total expense for period: ${:,.2f}'.format(expenses['Amount'].sum() - other_income['Amount'].sum()), '</H3>')\\n # print('<H3>Profit for period: ${:,.2f}'.format(rental_income['Amount'].sum() + other_income['Amount'].sum() -expenses['Amount'].sum()), '</H3>')\\n # Summarize the periods income and expenses\\n print('<br><H3>Total income for period: ${:,.2f}'.format(rental_income['Amount'].sum()))\\n print('<br><H3>Total expense for period: ${:,.2f}'.format(expenses['Amount'].sum() - other_income['Amount'].sum()), '</H3>')\\n print('<H3>Profit for period: ${:,.2f}'.format(rental_income['Amount'].sum() + other_income['Amount'].sum() -expenses['Amount'].sum()), '</H3>')\\n# print('</H3>')\\n \\n print(html_config.HTML_CLOSE)\\n sys.stdout.flush()\",\n \"def check_auto_presence_for_configuration(request):\\n\\n report_type = request.GET.get('report_type',None)\\n usecase = request.GET.get('usecase',None)\\n language = request.GET.get('language',None)\\n institute = request.GET.get('institute',None)\\n batch = request.GET.get('batch',None)\\n languages = ['English','english']\\n # print('BATCH',str(batch))\\n use = UseCase.objects.get(name=usecase)\\n json_resp = {}\\n mode = NameSpace.objects.get(ns_id = 'Robot')\\n user = User.objects.get(ns_id = mode, username='Robot_user')\\n\\n if report_type == 'pubmed':\\n cursor = connection.cursor()\\n cursor.execute(\\\"SELECT COUNT(*) FROM ground_truth_log_file AS g INNER JOIN report AS r ON r.id_report = g.id_report AND r.language = g.language WHERE g.ns_id = %s AND g.username = %s AND r.institute=%s AND r.language in %s AND r.name = %s AND r.batch = %s\\\",['Robot','Robot_user','PUBMED',tuple(languages),str(usecase),int(batch)])\\n reports = cursor.fetchone()[0]\\n json_resp['count'] = reports\\n\\n elif report_type == 'reports':\\n cursor = connection.cursor()\\n cursor.execute(\\n \\\"SELECT COUNT(*) FROM ground_truth_log_file AS g INNER JOIN report AS r ON r.id_report = g.id_report AND r.language = g.language WHERE g.ns_id = %s AND g.username = %s AND r.institute!=%s AND r.institute = %s AND r.language = %s AND r.name = %s AND r.batch = %s\\\",\\n ['Robot', 'Robot_user', 'PUBMED',str(institute),str(language),str(usecase),int(batch)])\\n reports = cursor.fetchone()[0]\\n json_resp['count'] = reports\\n print(json_resp)\\n return JsonResponse(json_resp)\",\n \"def GetSummaryReports(self, parameters):\\n # [GET] https://assets.falcon.crowdstrike.com/support/api/swagger.html#/falconx-sandbox/GetSummaryReports\\n FULL_URL = self.base_url+'/falconx/entities/report-summaries/v1'\\n HEADERS = self.headers\\n PARAMS = parameters\\n result = self.Result()\\n try:\\n response = requests.request(\\\"GET\\\", FULL_URL, params=PARAMS, headers=HEADERS, verify=False)\\n returned = result(response.status_code, response.headers, response.json())\\n except Exception as e:\\n returned = result(500, {}, str(e))\\n\\n return returned\",\n \"def forecast(request):\\n\\n all_documents = list()\\n all_documents.extend(AE.objects.all())\\n all_documents.extend(NI.objects.all())\\n all_documents.extend(PT5.objects.all())\\n all_documents.extend(PTM.objects.all())\\n all_documents.extend(PTHOY.objects.all())\\n all_documents.extend(PTRD.objects.all())\\n all_documents.extend(PTT.objects.all())\\n all_documents.extend(DP10.objects.all())\\n all_documents.extend(PTTN.objects.all())\\n all_documents.extend(EGT.objects.all())\\n all_documents.extend(ACT.objects.all())\\n\\n notifications = request.user.notifications.unread()[:5]\\n paginator = Paginator(all_documents, 10)\\n page = request.GET.get('page')\\n \\n context = { \\n 'documents_issues': None,\\n 'notifications':notifications,\\n 'form':forms.InputFileForm(),\\n }\\n\\n if paginator.count > 0:\\n context['documents_issues'] = paginator.get_page(page)\\n\\n return render(request, 'forecast.html', context)\",\n \"def overview(request):\\n LOGGER.info('Rendering WMT16 HIT overview for user \\\"{0}\\\".'.format(\\n request.user.username or \\\"Anonymous\\\"))\\n \\n # Re-initialise random number generator.\\n seed(None)\\n \\n # Collect available language pairs for the current user.\\n language_codes = set([x[0] for x in LANGUAGE_PAIR_CHOICES])\\n language_pairs = request.user.groups.filter(name__in=language_codes)\\n \\n # Collect available annotation projects for the current user.\\n annotation_projects = request.user.project_set.all()\\n \\n hit_data = []\\n total = [0, 0, 0]\\n\\n for language_pair in language_pairs:\\n for annotation_project in annotation_projects:\\n hit = _compute_next_task_for_user(request.user, annotation_project, language_pair)\\n user_status = HIT.compute_status_for_user(request.user, annotation_project, language_pair)\\n for i in range(3):\\n total[i] = total[i] + user_status[i]\\n \\n if hit:\\n # Convert status seconds back into datetime.time instances.\\n for i in range(2):\\n user_status[i+1] = seconds_to_timedelta(int(user_status[i+1]))\\n \\n hit_data.append(\\n (hit.get_language_pair_display(), hit.get_absolute_url(),\\n hit.hit_id, user_status, annotation_project)\\n )\\n \\n # Convert total seconds back into datetime.timedelta instances.\\n total[1] = seconds_to_timedelta(int(total[2]) / float(int(total[0]) or 1))\\n \\n # Remove microseconds to get a nicer timedelta rendering in templates.\\n total[1] = total[1] - timedelta(microseconds=total[1].microseconds)\\n \\n total[2] = seconds_to_timedelta(int(total[2]))\\n \\n groups = _identify_groups_for_user(request.user)\\n group = None\\n if len(groups) > 1:\\n LOGGER.debug(u'User \\\"{0}\\\" assigned to multiple annotation groups: {1}'.format(\\n request.user.username or u'Anonymous',\\n u', '.join([x.name for x in groups]))\\n )\\n group = groups[0]\\n \\n if group is not None:\\n group_name = group.name\\n group_status = HIT.compute_status_for_group(group)\\n for i in range(2):\\n group_status[i+1] = seconds_to_timedelta(int(group_status[i+1]))\\n \\n else:\\n group_status = None\\n group_name = None\\n \\n LOGGER.debug(u'\\\\n\\\\nHIT data for user \\\"{0}\\\":\\\\n\\\\n{1}\\\\n'.format(\\n request.user.username or \\\"Anonymous\\\",\\n u'\\\\n'.join([u'{0}\\\\t{1}\\\\t{2}\\\\t{3}'.format(*x) for x in hit_data])))\\n\\n # Compute admin URL for super users.\\n admin_url = None\\n if request.user.is_superuser:\\n admin_url = reverse('admin:index')\\n \\n dictionary = {\\n 'active_page': \\\"OVERVIEW\\\",\\n 'hit_data': hit_data,\\n 'total': total,\\n 'group_name': group_name,\\n 'group_status': group_status,\\n 'admin_url': admin_url,\\n 'title': 'WMT16 Dashboard',\\n 'annotation_groups': [x.name for x in groups],\\n }\\n dictionary.update(BASE_CONTEXT)\\n \\n LOGGER.info(dictionary.values())\\n \\n return render(request, 'wmt16/overview.html', dictionary)\",\n \"def survey_index(request):\\n if request.user.is_authenticated:\\n latest_survey_list = Survey.objects.filter(\\n author=request.user,\\n ).order_by('-pub_date')\\n my_surveys = Survey.objects.filter(author=request.user).order_by('title')\\n return render(request,\\n 'skip_logic/survey_index.html',\\n {'latest_survey_list': latest_survey_list, 'my_surveys': my_surveys})\\n\\n latest_survey_list = Survey.objects.filter(\\n is_public=True,\\n pub_date__lte=timezone.now(),\\n ).order_by('title')\\n\\n return render(request,\\n 'skip_logic/survey_index_public.html',\\n {'latest_survey_list': latest_survey_list})\",\n \"def index(request):\\n\\n # Generate counts of some of the main objects\\n num_books = Book.objects.all().count()\\n num_instances = BookInstance.objects.all().count()\\n\\n # Available books (status = 'a')\\n num_instances_available = BookInstance.objects.filter(status__exact='a').count()\\n\\n # The 'all()' is implied by default.\\n num_authors = Author.objects.count()\\n\\n # Number of visits to this view, as counted in the session variable.\\n num_visits = request.session.get('num_visits', 0)\\n request.session['num_visits'] = num_visits + 1\\n\\n \\\"\\\"\\\"\\n Challenge\\n Modify the view to generate counts for genres and books that contain a particular word (case insensitive), and \\n pass the results to the context. You accomplish this in a similar way to creating and using num_books and \\n num_instances_available. Then update the index template to include these variables.\\n \\\"\\\"\\\"\\n num_fantasy_genres = Genre.objects.filter(name__icontains='fantasy').count()\\n num_thrones_books = Book.objects.filter(title__icontains='thrones').count()\\n\\n context = {\\n 'num_books': num_books,\\n 'num_instances': num_instances,\\n 'num_instances_available': num_instances_available,\\n 'num_authors': num_authors,\\n 'num_fantasy_genres': num_fantasy_genres,\\n 'num_thrones_books': num_thrones_books,\\n 'num_visits': num_visits,\\n }\\n\\n # Render the HTML template index.html with the data in the context variable\\n return render(request, 'index.html', context=context)\",\n \"def get_queryset(self, request):\\n qs = super().get_queryset(request)\\n qs = qs.annotate(\\n libraryrelease__count=models.Count(\\n 'release', distinct=True,\\n filter=models.Q(release__cd_identifier__isnull=False)))\\n qs = qs.annotate(\\n commercialrelease__count=models.Count(\\n 'release', distinct=True,\\n filter=models.Q(release__cd_identifier__isnull=True)))\\n qs = qs.annotate(models.Count('recording', distinct=True))\\n return qs\",\n \"def summary():\\r\\n\\r\\n average_age, counted = _find_average_age()\\r\\n male, female = _find_male_female_percentage()\\r\\n headings = [\\\"Total Number of Patients\\\", \\\"Average Age\\\",\\r\\n \\\"Patients Involved In Average Age\\\", \\\"Percentage of Male\\\",\\r\\n \\\"Percentage of Female\\\"]\\r\\n data = [len(resources), average_age, counted, male, female]\\r\\n return render_template(\\\"summary.html\\\", headings=headings, data=data)\",\n \"def check_presence_exa_conc_lab(request):\\n\\n # reports = request.GET.get('reports',None)\\n rep = request.GET.get('id_report',None)\\n language = request.GET.get('language',None)\\n usecase = request.GET.get('usecase',None)\\n reports = None\\n if request.method == 'POST':\\n request_body_json = json.loads(request.body)\\n reports = request_body_json['reports']\\n if rep is not None and language is not None:\\n report = Report.objects.get(id_report = rep,language = language)\\n usecase = report.name_id\\n # print(usecase)\\n json_resp = {}\\n if usecase in ['colon','uterine cervix','lung']:\\n bool = check_exa_lab_conc_only(usecase)\\n print('bool',bool)\\n else:\\n bool = [False,False]\\n json_resp['labels'] = bool[0]\\n json_resp['concepts'] = bool[1]\\n elif usecase is not None:\\n json_resp = {}\\n json_resp['labels'] = False\\n json_resp['concepts'] = False\\n\\n # labels = []\\n # concepts = []\\n json_resp = {}\\n if usecase in ['colon','uterine cervix','lung']:\\n bool = check_exa_lab_conc_only(usecase)\\n else:\\n bool = [False,False]\\n json_resp['labels'] = bool[0]\\n json_resp['concepts'] = bool[1]\\n # labels.append(bool[0])\\n # concepts.append(bool[1])\\n # if False in labels:\\n # json_resp['labels'] = False\\n # else:\\n # json_resp['labels'] = True\\n #\\n # if False in concepts:\\n # json_resp['concepts'] = False\\n # else:\\n # json_resp['concepts'] = True\\n elif reports is not None:\\n report_list = json.loads(reports)\\n json_resp = {}\\n json_resp['labels'] = False\\n json_resp['concepts'] = False\\n usecases = []\\n for rep in report_list:\\n # rep = json.loads(rep)\\n if rep['usecase'] not in usecases:\\n usecases.append(rep['usecase'])\\n labels = []\\n concepts = []\\n for u in usecases:\\n # print(u)\\n json_resp = {}\\n if u in ['colon', 'uterine cervix', 'lung']:\\n bool = check_exa_lab_conc_only(u)\\n else:\\n bool = [False, False]\\n\\n labels.append(bool[0])\\n concepts.append(bool[1])\\n if False in labels:\\n json_resp['labels'] = False\\n else:\\n json_resp['labels'] = True\\n\\n if False in concepts:\\n json_resp['concepts'] = False\\n else:\\n json_resp['concepts'] = True\\n\\n else:\\n json_resp={'error':'a usecase is needed'}\\n\\n print(json_resp)\\n return JsonResponse(json_resp)\",\n \"def buildReports(self):\\n pass\",\n \"def index(request):\\n # Generate counts of some of the main objects\\n num_books=Book.objects.all().count()\\n num_instances=BookInstance.objects.all().count()\\n # Available books (status = 'a')\\n num_instances_available=BookInstance.objects.filter(status__exact='a').count()\\n \\n # Render the HTML template index.html with the data in the context variable\\n num_authors=Author.objects.count() # The 'all()' is implied by default.\\n \\n # Number of visits to this view, as counted in the session variable.\\n num_visits=request.session.get('num_visits', 0)\\n request.session['num_visits'] = num_visits+1\\n \\n # Render the HTML template index.html with the data in the context variable.\\n return render(\\n request,\\n 'index.html',\\n context={'num_books':num_books,'num_instances':num_instances,'num_instances_available':num_instances_available,'num_authors':num_authors,\\n 'num_visits':num_visits}, # num_visits appended\\n )\",\n \"def download_sum_findings(self, ir_id, ir_ver):\\r\\n ir_details = get_interpretation_request_list(interpretation_request_id=ir_id, version=ir_ver)\\r\\n # Check only one record is returned\\r\\n if len(ir_details) == 1:\\r\\n # Check that there's only one summary of findings report\\r\\n if len(ir_details[0][\\\"clinical_reports\\\"]) == 1:\\r\\n # Download the report from CIP API\\r\\n session = AuthenticatedCIPAPISession()\\r\\n response = session.get(ir_details[0][\\\"clinical_reports\\\"][0]['url'])\\r\\n # Check response code is 200\\r\\n if response.status_code == 200:\\r\\n # Store the returned html for use by other methods\\r\\n self.html = response.text\\r\\n else: \\r\\n sys.exit(f'ERROR: Unable to download summary of findings. Response code {response.status_code}')\\r\\n else:\\r\\n sys.exit(f'ERROR: Number of clinical reports found {len(ir_details[0][\\\"clinical_reports\\\"])}')\\r\\n else:\\r\\n sys.exit(f'ERROR: Number of interpretation requests found {len(ir_details)}')\",\n \"def reports(request, template_name='reporting/reports.html'):\\n object_list = reporting.all_reports()\\n # storing current user position in session\\n request.session['enter_url'] = reverse('reports')\\n return direct_to_template(request, template_name, {\\n # HACK: object list rendered incorrectly in templates somehow\\n 'object_list': [(slug, unicode(r.verbose_name)) for slug, r in object_list],\\n })\",\n \"def test_list_all_dx(self):\\n self.client.login(username='temporary', password='temporary')\\n response = self.client.get(reverse('dx_summary_list_filter'), follow=True)\\n self.assertEqual(response.status_code, 200)\\n responses_text = u'There are 5 studies in this list.'\\n self.assertContains(response, responses_text)\",\n \"def home(request):\\n\\n result = {}\\n \\n result['surveys'] = []\\n result['review_requests'] = []\\n\\n u = request.user\\n\\n # get surveys\\n surveys = Survey.objects.all()\\n for s in surveys:\\n status, created = eval(\\\"%s.objects.get_or_create(survey=s, user=u)\\\"%s.model_name)\\n if created:\\n status.uuid_token = uuid.uuid4()\\n status.save()\\n if not status.completed:\\n result['surveys'].append(s.summary())\\n \\n my_products = TransactionLineItem.objects.filter(transaction__party=u).values('product')\\n # Find review requests related to product I have purchased, that I haven't reviewed \\n reqs = ReviewRequest.objects.exclude(requester=u).filter(product__in=my_products).exclude(replies__reviewer=u)\\n \\n for r in reqs:\\n result['review_requests'].append(r.get_json(me=u))\\n\\n # TODO: Group purchase requests\\n\\n # get other people's feeds, filter by friends if in social group\\n feeds = Feed.objects.exclude(actor=u)\\n result['feeds'] = [ f.get_json(me=u) for f in feeds ]\\n\\n return JSONHttpResponse(result)\",\n \"def get_report(client_id, client_secret):\\n month = pick_month()\\n next_month = month + timedelta(days=28)\\n while next_month.month == month.month:\\n next_month += timedelta(days=1)\\n last_day = next_month - timedelta(days=1)\\n\\n pr_data = get_pr_data(repos.keys(), client_id, client_secret)\\n user_data = get_user_data(pr_data, client_id, client_secret)\\n pr_counts_text, pr_counts = get_pr_counts(pr_data, month, last_day)\\n pr_details, total, new_users, new_prs = get_pr_details(\\n pr_data, user_data, month, last_day, pr_counts)\\n\\n params = {\\n 'month': month.strftime('%B'),\\n 'next_month': next_month.strftime('%B'),\\n 'month_id_suffix': month.strftime('%b-%y').lower(),\\n 'year': month.year,\\n 'total': total,\\n 'pr_counts_text': pr_counts_text,\\n 'pr_details': pr_details,\\n 'prs_from_new_users': new_prs,\\n 'new_users': new_users\\n }\\n\\n return template % params\",\n \"def get(self, request, *args, **kwargs):\\n filters = eval(request.GET.get('filters'))\\n settings = eval(request.GET.get('settings'))\\n date_from = filters['DF'][0]\\n date_to = filters['DT'][0]\\n receipt_barcode = ReceiptRecords.objects.all().values_list('Barcode')\\n visits = ManifestRecords.objects.filter(\\n Barcode__in=receipt_barcode).values('Visit').distinct()\\n dist_visits = []\\n for visit in visits:\\n dist_visits += list(visit.values())\\n sites = ManifestRecords.objects.filter(\\n Barcode__in=receipt_barcode).values('Site').distinct()\\n dist_sites = []\\n for site in sites:\\n dist_sites += list(site.values())\\n rooms = ManifestRecords.objects.filter(\\n Barcode__in=receipt_barcode).values('Room').distinct()\\n dist_rooms = []\\n for room in rooms:\\n dist_rooms += list(room.values())\\n dist_state = ['ACTIVE', 'UNABLE_TO_DRAW', 'UNABLE_TO_PROCESS',\\n 'PROCESSED_ON_TIME', 'PROCESSED_NOT_ON_TIME']\\n context = {'sites': dist_sites,\\n 'visits': dist_visits, 'rooms': dist_rooms, 'filters': filters, 'settings': settings, 'states': dist_state, 'from': date_from, 'to': date_to}\\n return render(request, self.template_name, context)\",\n \"def create_vuln_report():\",\n \"def get_sna_results(request):\\n dataset_type = request.POST.get('dataset')\\n G, rels = SNAVizualizationManager.construct_nx_graph(request, dataset_type)\\n result_dict = SNAVizualizationManager.get_SNA_metrics(G)\\n print(result_dict)\\n \\n result_dict = {'sna_html':gen_SNA_html(result_dict)}\\n \\n return HttpResponse(\\n json.dumps({\\\"status\\\": \\\"success\\\", 'result_dict':result_dict}),\\n content_type=\\\"application/json\\\"\\n )\",\n \"def issueListing(self, v, i):\\n #list of URLS within the issue\\n# links = []\\n issURL = self.link(vol = v, iss = i )\\n html=urlopen(issURL)\\n soup=BeautifulSoup(html,'html.parser')\\n URLs = [] #Empty list\\n \\n# titles = soup.find_all('h5', class_=\\\"title\\\")\\n# authors = soup.find_all('h6', class_=\\\"authors\\\")\\n# pubs = soup.find_all('h6', class_=\\\"pub-info\\\")\\n# for t, a, p in zip(titles, authors, pubs):\\n blocks = soup.find_all('div', class_=\\\"article panel article-result\\\")\\n for b in blocks:\\n# print(b)\\n titletag = b.find('h5', class_=\\\"title\\\")\\n title = titletag.get_text()\\n #Extract abstract url from title head\\n aURL = titletag.find('a', href = True)['href']\\n alink = 'https://journals.aps.org' + aURL\\n #Print out the scraped information\\n print(title)\\n print(alink)\\n #Extract research area and topic keywords\\n kwlist = b.find('ul', class_=\\\"inline-list subjects\\\")\\n #If the list tag exists\\n if kwlist:\\n lis = kwlist.find_all('li')\\n kws = [li.get_text() for li in lis] \\n print(kws)\\n #Add utf-8 encode\\n# print(kws.encode('utf-8')) \\n print('----------------------------------------------------------------') \\n #Collect URLs in the issue\\n URLs.append('https://journals.aps.org' + aURL)\\n return URLs\",\n \"def viewexperiments(request):\\r\\n # Obtain the context from the HTTP request.\\r\\n\\r\\n context_instance = RequestContext(request)\\r\\n\\r\\n try:\\r\\n user = _validate_and_get_geniuser(request)\\r\\n except LoggedInButFailedGetGeniUserError:\\r\\n return _show_failed_get_geniuser_page(request)\\r\\n\\r\\n\\r\\n page_top_errors = []\\r\\n username = user.username\\r\\n ret = [] #returning list\\r\\n user_experiments = Experiment.objects.filter(geni_user=user)\\r\\n for experiment in reversed(user_experiments):\\r\\n #reversed so the oldest experiment is the last we show.\\r\\n experiment_sensors = []\\r\\n name_list = []\\r\\n experiment_sensors.extend(list(Battery.objects.filter(experiment_id=experiment)))\\r\\n experiment_sensors.extend(list(Bluetooth.objects.filter(experiment_id=experiment)))\\r\\n experiment_sensors.extend(list(Cellular.objects.filter(experiment_id=experiment)))\\r\\n experiment_sensors.extend(list(Settings.objects.filter(experiment_id=experiment)))\\r\\n experiment_sensors.extend(list(ConcretSensor.objects.filter(experiment_id=experiment)))\\r\\n experiment_sensors.extend(list(Location.objects.filter(experiment_id=experiment)))\\r\\n experiment_sensors.extend(list(Signal_strengths.objects.filter(experiment_id=experiment)))\\r\\n experiment_sensors.extend(list(Wifi.objects.filter(experiment_id=experiment)))\\r\\n\\r\\n for sensor in experiment_sensors:\\r\\n name_list.append(sensor.show_name())\\r\\n\\r\\n if name_list == []:\\r\\n name_list = \\\"None\\\"\\r\\n\\r\\n ret.append([experiment.expe_name,name_list,experiment.id])\\r\\n \\r\\n \\r\\n \\r\\n return render(request, 'control/viewexperiments.html', {'username' : username, \\r\\n 'page_top_errors' : page_top_errors, 'ret':ret})\",\n \"def capacity_reports():\\n return render_template('capacity.html')\",\n \"def index(request):\\n try:\\n meeting = Meeting.objects.latest('when')\\n num_rsvped = Person.objects.filter(ynm='Y').count()\\n\\n except (KeyError, Meeting.DoesNotExist, Person.DoesNotExist):\\n raise Http404\\n\\n return render(request,'chipy/chipy.html',{'meeting':meeting,'num_rsvped':num_rsvped})\",\n \"def view_result_main(request):\\n\\n\\tcontext_dict = {\\n\\t\\t'title': 'All Results Main',\\n\\t}\\n\\treturn render(request, \\\"viewResultMain.html\\\", context_dict)\",\n \"def report_data(request):\\n if request.method == \\\"GET\\\":\\n params = request.query_params\\n async_result = None\\n all_providers = False\\n provider_uuid = params.get(\\\"provider_uuid\\\")\\n provider_type = params.get(\\\"provider_type\\\")\\n schema_name = params.get(\\\"schema\\\")\\n start_date = params.get(\\\"start_date\\\")\\n end_date = params.get(\\\"end_date\\\")\\n queue_name = params.get(\\\"queue\\\") or PRIORITY_QUEUE\\n if provider_uuid is None and provider_type is None:\\n errmsg = \\\"provider_uuid or provider_type must be supplied as a parameter.\\\"\\n return Response({\\\"Error\\\": errmsg}, status=status.HTTP_400_BAD_REQUEST)\\n if queue_name not in QUEUE_LIST:\\n errmsg = f\\\"'queue' must be one of {QUEUE_LIST}.\\\"\\n return Response({\\\"Error\\\": errmsg}, status=status.HTTP_400_BAD_REQUEST)\\n\\n if provider_uuid == \\\"*\\\":\\n all_providers = True\\n elif provider_uuid:\\n with ProviderDBAccessor(provider_uuid) as provider_accessor:\\n provider = provider_accessor.get_type()\\n else:\\n provider = provider_type\\n\\n if start_date is None:\\n errmsg = \\\"start_date is a required parameter.\\\"\\n return Response({\\\"Error\\\": errmsg}, status=status.HTTP_400_BAD_REQUEST)\\n\\n if not all_providers:\\n if schema_name is None:\\n errmsg = \\\"schema is a required parameter.\\\"\\n return Response({\\\"Error\\\": errmsg}, status=status.HTTP_400_BAD_REQUEST)\\n\\n if provider is None:\\n errmsg = \\\"Unable to determine provider type.\\\"\\n return Response({\\\"Error\\\": errmsg}, status=status.HTTP_400_BAD_REQUEST)\\n\\n if provider_type and provider_type != provider:\\n errmsg = \\\"provider_uuid and provider_type have mismatched provider types.\\\"\\n return Response({\\\"Error\\\": errmsg}, status=status.HTTP_400_BAD_REQUEST)\\n\\n async_result = update_summary_tables.s(\\n schema_name, provider, provider_uuid, start_date, end_date, queue_name=queue_name\\n ).apply_async(queue=queue_name or PRIORITY_QUEUE)\\n else:\\n async_result = update_all_summary_tables.delay(start_date, end_date)\\n return Response({REPORT_DATA_KEY: str(async_result)})\\n\\n if request.method == \\\"DELETE\\\":\\n params = request.query_params\\n\\n schema_name = params.get(\\\"schema\\\")\\n provider = params.get(\\\"provider\\\")\\n provider_uuid = params.get(\\\"provider_uuid\\\")\\n simulate = params.get(\\\"simulate\\\")\\n\\n if schema_name is None:\\n errmsg = \\\"schema is a required parameter.\\\"\\n return Response({\\\"Error\\\": errmsg}, status=status.HTTP_400_BAD_REQUEST)\\n\\n if provider is None:\\n errmsg = \\\"provider is a required parameter.\\\"\\n return Response({\\\"Error\\\": errmsg}, status=status.HTTP_400_BAD_REQUEST)\\n\\n if provider_uuid is None:\\n errmsg = \\\"provider_uuid is a required parameter.\\\"\\n return Response({\\\"Error\\\": errmsg}, status=status.HTTP_400_BAD_REQUEST)\\n\\n if simulate is not None and simulate.lower() not in (\\\"true\\\", \\\"false\\\"):\\n errmsg = \\\"simulate must be a boolean.\\\"\\n return Response({\\\"Error\\\": errmsg}, status=status.HTTP_400_BAD_REQUEST)\\n\\n if simulate is not None and simulate.lower() == \\\"true\\\":\\n simulate = True\\n else:\\n simulate = False\\n\\n LOG.info(\\\"Calling remove_expired_data async task.\\\")\\n\\n async_result = remove_expired_data.delay(schema_name, provider, simulate, provider_uuid)\\n\\n return Response({\\\"Report Data Task ID\\\": str(async_result)})\",\n \"def get_queryset(self, request):\\n qs = super().get_queryset(request)\\n qs = qs.annotate(\\n work__count=models.Count('release__works', distinct=True))\\n qs = qs.annotate(\\n release__count=models.Count('release__id', distinct=True))\\n return qs\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6889685","0.67816097","0.6578456","0.6462005","0.6354941","0.60960084","0.6039205","0.5924308","0.59200716","0.58842385","0.58725315","0.58610183","0.58609855","0.5834699","0.5742797","0.5734736","0.572304","0.56792724","0.5666692","0.56550825","0.5648603","0.56460035","0.5644065","0.5629163","0.56243056","0.5606261","0.56049025","0.5569737","0.55219495","0.5519488","0.5494011","0.5485957","0.5467629","0.5464228","0.54636484","0.546034","0.5436154","0.5435001","0.54142916","0.5406578","0.540404","0.53988075","0.5386622","0.53731805","0.5369394","0.53670096","0.53665185","0.5362102","0.53530294","0.53513086","0.53443485","0.53423357","0.5322597","0.53198874","0.53064597","0.52855784","0.5284223","0.5281559","0.52764547","0.5270223","0.52595294","0.5258418","0.5244907","0.5237464","0.52265286","0.5218293","0.5207844","0.5191761","0.5190661","0.5190551","0.518191","0.5165491","0.5163872","0.5154588","0.51507866","0.5147096","0.51461685","0.51393276","0.5129988","0.5128624","0.51250875","0.51228195","0.51173806","0.5115775","0.511364","0.5112763","0.5112037","0.5107258","0.51065886","0.510457","0.5102402","0.5099065","0.50985837","0.509382","0.5086369","0.50766563","0.50761765","0.50744355","0.50736874","0.5055985"],"string":"[\n \"0.6889685\",\n \"0.67816097\",\n \"0.6578456\",\n \"0.6462005\",\n \"0.6354941\",\n \"0.60960084\",\n \"0.6039205\",\n \"0.5924308\",\n \"0.59200716\",\n \"0.58842385\",\n \"0.58725315\",\n \"0.58610183\",\n \"0.58609855\",\n \"0.5834699\",\n \"0.5742797\",\n \"0.5734736\",\n \"0.572304\",\n \"0.56792724\",\n \"0.5666692\",\n \"0.56550825\",\n \"0.5648603\",\n \"0.56460035\",\n \"0.5644065\",\n \"0.5629163\",\n \"0.56243056\",\n \"0.5606261\",\n \"0.56049025\",\n \"0.5569737\",\n \"0.55219495\",\n \"0.5519488\",\n \"0.5494011\",\n \"0.5485957\",\n \"0.5467629\",\n \"0.5464228\",\n \"0.54636484\",\n \"0.546034\",\n \"0.5436154\",\n \"0.5435001\",\n \"0.54142916\",\n \"0.5406578\",\n \"0.540404\",\n \"0.53988075\",\n \"0.5386622\",\n \"0.53731805\",\n \"0.5369394\",\n \"0.53670096\",\n \"0.53665185\",\n \"0.5362102\",\n \"0.53530294\",\n \"0.53513086\",\n \"0.53443485\",\n \"0.53423357\",\n \"0.5322597\",\n \"0.53198874\",\n \"0.53064597\",\n \"0.52855784\",\n \"0.5284223\",\n \"0.5281559\",\n \"0.52764547\",\n \"0.5270223\",\n \"0.52595294\",\n \"0.5258418\",\n \"0.5244907\",\n \"0.5237464\",\n \"0.52265286\",\n \"0.5218293\",\n \"0.5207844\",\n \"0.5191761\",\n \"0.5190661\",\n \"0.5190551\",\n \"0.518191\",\n \"0.5165491\",\n \"0.5163872\",\n \"0.5154588\",\n \"0.51507866\",\n \"0.5147096\",\n \"0.51461685\",\n \"0.51393276\",\n \"0.5129988\",\n \"0.5128624\",\n \"0.51250875\",\n \"0.51228195\",\n \"0.51173806\",\n \"0.5115775\",\n \"0.511364\",\n \"0.5112763\",\n \"0.5112037\",\n \"0.5107258\",\n \"0.51065886\",\n \"0.510457\",\n \"0.5102402\",\n \"0.5099065\",\n \"0.50985837\",\n \"0.509382\",\n \"0.5086369\",\n \"0.50766563\",\n \"0.50761765\",\n \"0.50744355\",\n \"0.50736874\",\n \"0.5055985\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7125116"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":11261,"cells":{"query":{"kind":"string","value":"This view checks if a user has an associated gt for a report"},"document":{"kind":"string","value":"def check_gt_existence(request):\n\n\n action = request.GET.get('action',None)\n id_report = request.GET.get('id_report',None)\n language = request.GET.get('language',None)\n username = request.session['username']\n mode = request.session['mode']\n cursor = connection.cursor()\n cursor.execute(\"SELECT COUNT(*) FROM ground_truth_log_file where ns_id = %s and username = %s and language = %s and id_report = %s and gt_type = %s\",[mode,username,language,id_report,action])\n count = cursor.fetchone()[0]\n json_resp = {'count':count}\n return JsonResponse(json_resp)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def reported_by(self, user):\n return Report.objects.filter(recipe=self, chef=user).exists()","def has_ta(self, user, allow_superusers=True):\n return (user.is_superuser and allow_superusers) or len(self.tas.filter(id=user.id)) > 0","def _can_download_report(user):\r\n try:\r\n access_group = Group.objects.get(name=settings.PAYMENT_REPORT_GENERATOR_GROUP)\r\n except Group.DoesNotExist:\r\n return False\r\n return access_group in user.groups.all()","def can_be_viewed_by(self,user):\n return True","def test_user_is_study_tagger_true(self):\n response = self.client.get(self.get_url(self.trait.pk))\n context = response.context\n self.assertTrue(context['user_is_study_tagger'])","def not_complete(request):\n print(\"not_complete method in tutor_helper.py\")\n if user_auth(request):\n user = User.objects.get(email=request.user.email)\n print(\"\\t\", user)\n current_user = UserInformation.objects.get(user=user)\n if current_user.current_main_set is None:\n return False\n if current_user.completed_sets is not None:\n if current_user.current_main_set not in current_user.completed_sets.all():\n print(\"not complete\")\n print(current_user.current_main_set)\n return True\n else:\n if current_user.completed_sets is None:\n return True\n return False","def test_func(self):\n taxonomy = self.get_object()\n return self.request.user == taxonomy.author","def test_func(self):\n taxonomy = self.get_object()\n return self.request.user == taxonomy.author","def is_expert(self, user):\r\n return user.is_authenticated() and len(self.expert_set.filter(user=user))>0","def allow_view_by(self, user2_compare):\n # user2_compare is superuser\n if user2_compare.is_superuser:\n return True\n\n # this user is user2_compare self\n if user2_compare == self.user:\n return True\n\n # user2_compare is creator or owner of this user\n if (self.creator and self.creator == user2_compare) or \\\n (self.owner and self.owner == user2_compare):\n if self.status:\n return True\n\n # user2_compare can search users and has view perm\n if user2_compare.profile.allow_search_users():\n if user2_compare.has_perm('profiles.view_profile', self):\n return True\n\n # False for everythin else\n return False","def by_user(self, user):\n key = 'TIER_%i' % user.id\n current = threadlocals.get_thread_variable(key)\n\n if current is None:\n groups = user.groups.all()\n if groups:\n q = self.all()\n for group in groups:\n q = q.filter(group=group)\n if q.count() > 0:\n current = list(q)\n\n if current is None:\n current = \"no\"\n\n threadlocals.set_thread_variable(key, current)\n\n if current == \"no\":\n raise PricingTier.DoesNotExist\n\n return current","def is_examiner(self, user_obj):\n return self.examiners.filter(pk=user_obj.pk).count() > 0","def can_view(self, user):\r\n return True","def test_func(self):\n taxonomy = self.get_taxonomy()\n return self.request.user == taxonomy.author","def get_user_ground_truth(request):\n\n user = request.GET.get('user',None)\n action = request.GET.get('action',None)\n mode = request.GET.get('mode',None)\n report = request.GET.get('report',None)\n language = request.GET.get('language',request.session['language'])\n mode_obj = NameSpace.objects.get(ns_id=mode)\n report = Report.objects.get(id_report = report, language = language)\n gt = get_user_gt(user,mode_obj,report,language,action)\n return JsonResponse(gt)","def get_viewable(self, user):\n return True","def _has_data(cls):\n return User.objects.count() > 0","def ref_user_flag(self):\n try:\n ref = User.objects.get(\n associated_emails__email__iexact=self.reference_email,\n associated_emails__is_verified=True)\n return True\n except ObjectDoesNotExist:\n return False","def get_viewable(self, user):\n if user.get('role') in ('admin', 'manager', 'engineer'):\n return True\n return user['name'] == self.doc.get('customer')","def test_func(self):\n return (Student.objects.filter(user=self.request.user).exists())","def is_visible_to(self, user):\n return True","def has_access(self, user):\n if user.is_superuser:\n return True\n return self.user_objects(user).filter(id=self.id).exists()","def belongs_to_user(self) -> bool:\n return flask.g.user is not None and flask.g.user.id == getattr(\n self, 'user_id', False\n )","def isRelated(self):\n return len(self.user_storage.all()) > 0","def user_is_examiner(userobj):\n from .assignment_group import AssignmentGroup\n return AssignmentGroup.published_where_is_examiner(userobj).exists()","def compareUserMetrics(user_metrics, achievement_threshold):\n if user_metrics.works_visited < achievement_threshold.works_visited:\n return False\n elif user_metrics.works_found < achievement_threshold.works_found:\n return False\n return True","def current_user_has_access(self):\n return self.user_has_access(users.get_current_user())","def test_user_is_study_tagger_false(self):\n response = self.client.get(self.get_url(self.trait.pk))\n context = response.context\n self.assertFalse(context['user_is_study_tagger'])","def can_retrieve(self, user):\n return user.has_perm('agenda.can_see')","def test_get_all_reports_made_by_a_user(self):\n client = APIClient()\n response = client.get(reverse(\n 'authentication:user-article-reports'),**self.header_user2)\n self.assertEqual(response.status_code, status.HTTP_200_OK)","def test_func(self):\n member_to_view = self.get_object()\n is_self = self.request.user.rfid == member_to_view.rfid\n view_others = self.request.user.has_permission(\"core.view_member\")\n return view_others or is_self","def get_is_por_holder(self, obj):\n user = self.context['request'].user\n if not user.is_authenticated:\n return False\n # pylint: disable=no-member\n profile = UserProfile.objects.get(user=user)\n if obj in profile.get_council_privileges():\n return True\n return False","def has_permission(self, request, view):\n return request.user.group != 'patient'","def is_evaluador(user):\n return user.groups.filter(name='Evaluadores').exists()","def test_ReportingPeriodDetailView_current_employee_set_false(self):\n response = self.app.get(\n reverse(\n 'reports:ReportingPeriodDetailView',\n kwargs={'reporting_period': '2015-01-01'},\n )\n )\n self.assertEqual(\n len(response.html.find_all('tr', {'class': 'user'})), 2\n )","def get_presence_robot_user(request):\n\n id_report = request.GET.get('id_report', None)\n language = request.GET.get('language', None)\n use = request.GET.get('usecase', None)\n rep = request.GET.get('report_type', None)\n json_resp = {'auto_annotation_count': 0}\n cursor = connection.cursor()\n\n reports_list = None\n if request.method == 'POST':\n request_body_json = json.loads(request.body)\n reports_list = request_body_json['reports']\n\n if id_report is not None and language is not None:\n\n usecase = Report.objects.get(id_report=id_report, language=language)\n use = usecase.name_id\n cursor.execute(\n \"SELECT COUNT(*) FROM ground_truth_log_file as g INNER JOIN report AS r ON r.id_report = g.id_report AND r.language = g.language WHERE g.username = %s and r.name = %s\",\n ['Robot_user', str(use)])\n ans = cursor.fetchone()[0]\n json_resp = {'auto_annotation_count': (ans)}\n\n elif use is not None and rep is not None:\n # print(rep)\n if rep == 'reports':\n cursor.execute(\n \"SELECT COUNT(*) FROM ground_truth_log_file as g INNER JOIN report AS r ON r.id_report = g.id_report AND r.language = g.language WHERE g.ns_id = %s AND g.username = %s and r.name = %s and r.institute != %s\",\n ['Robot', 'Robot_user', str(use), 'PUBMED'])\n\n elif rep == 'pubmed':\n cursor.execute(\n \"SELECT COUNT(*) FROM ground_truth_log_file as g INNER JOIN report AS r ON r.id_report = g.id_report AND r.language = g.language WHERE g.username = %s and r.name = %s and r.institute = %s\",\n ['Robot_user', str(use), 'PUBMED'])\n\n ans = cursor.fetchone()[0]\n if ans > 0:\n json_resp = {'auto_annotation_count': ans}\n # print(json_resp)\n elif reports_list is not None:\n report_list = json.loads(reports_list)\n # print(report_list)\n usecase_list = []\n for rep in report_list:\n\n if rep['usecase'] not in usecase_list:\n usecase_list.append(rep['usecase'])\n for u in usecase_list:\n cursor = connection.cursor()\n cursor.execute(\n \"SELECT COUNT(*) FROM ground_truth_log_file as g INNER JOIN report AS r ON r.id_report = g.id_report AND r.language = g.language WHERE g.username = %s and r.name = %s\",\n ['Robot_user', str(u)])\n ans = cursor.fetchone()[0]\n if ans > 0:\n json_resp = {'auto_annotation_count': ans}\n else:\n json_resp = {'auto_annotation_count': 0}\n\n elif use is None and reports_list is None and id_report is None and language is None:\n robot = NameSpace.objects.get(ns_id='Robot')\n gt = GroundTruthLogFile.objects.filter(ns_id=robot)\n json_resp = {'auto_annotation_count': gt.count()}\n\n print(json_resp)\n return JsonResponse(json_resp)","def is_viewed(self):\n return self.has_label(VIEWED_LABEL)","def has_organizer(self, user):\n return self.main_organizer == user or self.team.filter(id=user.id).exists()","def view(self, user, notificationrequested, *args):\n if user.is_anonymous or user.is_client:\n return False\n\n if user.is_administrator:\n return True\n\n if user.is_manager:\n return False\n\n if user.is_advisor and user.group == notificationrequested.group:\n return True\n\n return self.admin_permission(user, notificationrequested, *args)","def has_report(self):\n return self.report is not None","def has_user(self, user): # pylint: disable=unused-argument\r\n return False","def is_liked_by(self, user):\n return user.liked_articles.filter(pk=self.pk).exists()","def has_voted(self, user, ip_address):\r\n if user.is_anonymous():\r\n return len(self.usedip_set.filter(address=ip_address))>0\r\n else:\r\n return self.is_expert(user) and self.expert_set.filter(user=user)[0].voted","def has_voted(self, user):\n return user.choice_set.filter(vote=self).exists()","def has_object_permission(self, request, view, obj):\n # Users authentified via LTI are identified by a TokenUser with the\n # resource_link_id as user ID.\n if str(self.get_resource_id(obj)) == request.user.id:\n return True\n\n return False","def get_is_interested(self, obj):\n # pylint: disable=no-member\n user = self.context['request'].user\n if not user.is_authenticated:\n return None\n profile = UserProfile.objects.get(user=user)\n return profile in obj.interested_users.all()","def user_auth_inst(request):\n if request.user.is_authenticated:\n user = User.objects.get(email=request.user.email)\n if UserInformation.objects.filter(user=user).exists():\n inst = UserInformation.objects.get(user=user)\n if(inst.user_instructor):\n return True\n return False","def personal(request):\n \n visits = Visit.objects.filter(user=request.user)\n \n \n return render(request, \"personal.html\", {'visits':visits})","def is_examiner(self, user_obj):\n warnings.warn(\"deprecated\", DeprecationWarning)\n return self.examiners.filter(user__id=user_obj.pk).count() > 0","def has_view_permissions(self, obj):\n queryset = self.model.objects.filter(pk=obj.pk)\n if hasattr(queryset, 'has_view_permissions'):\n return queryset.has_view_permissions( PyFormsMiddleware.user() )\n else:\n return True","def user_has_access(self, user):\n if self.visibility == self.PUBLIC:\n return True\n elif self.visibility == self.PRIVATE and self.created_by == user:\n return True\n elif self.visibility in (self.ORG_ONLY, self.ORG_ONLY_NO_EXTERNAL):\n if user.external and self.visibility == self.ORG_ONLY_NO_EXTERNAL:\n return False\n elif self.organization.memberships.filter(user=user).count() >= 1:\n return True\n return False","def get_user_has_liked(self, instance):\n request = self.context.get(\"request\")\n return instance.likes.filter(pk=request.user.pk).exists()","def get_user_has_liked(self, instance):\n request = self.context.get(\"request\")\n return instance.likes.filter(pk=request.user.pk).exists()","def view(self, user, action, *args):\n if user.is_anonymous or user.is_client:\n return False\n\n if user.is_administrator:\n return True\n\n if user.is_manager:\n return False\n\n # TODO check groups in request maybe ? dunno\n if user.is_advisor:\n return True\n\n return self.admin_permission(user, action, *args)","def user_has_permission(self, id: int, user: User) -> bool:\n return self.get_queryset().filter(pk=id).filter_for_user(user).exists()","def show_stats(self):\n if checkPermission('genweb.webmaster', self.context):\n return True","def has_object_permission(self, request, view, user):\n return user == request.user or request.user.is_superuser","def test_get_all_accessible_by_hash_as_user_returns_user_template(self):\n mock_request = create_mock_request(user=self.user1)\n templates = template_api.get_all_accessible_by_hash(\n self.fixture.user1_template.hash, request=mock_request\n )\n self.assertTrue(self.fixture.user1_template in list(templates))\n self.assertTrue(self.fixture.user2_template not in list(templates))\n self.assertTrue(self.fixture.global_template not in list(templates))","def has_permission(self, request, view):\n usuario = request.user\n return str(usuario.grupo) == \"Vendedor\"","def has_object_permission(self, request, view, obj):\n return request.user.id == obj.user_id","def IsAvailable():\n return settings.user.ui.Get('opportunities_showTemp', False)","def has_object_permission(self, request, view, obj):\n if Contributor.objects.filter(project=obj, user=request.user):\n return True\n else:\n return False","def bt_user_only(view):\r\n @google_login_required\r\n def wrapped(request, *args, **kwargs):\r\n uea = request.META['USER_EMAIL'];\r\n q = UserAccount.all().filter('email =',uea );\r\n if q.count() > 0 or users.is_current_user_admin():\r\n return view(request, *args, **kwargs)\r\n context = RequestContext(request);\r\n return rtr( 'access_limited.html', context,None )\r\n return wraps(view)(wrapped)","def can_access(user, page):\n page_groups = PageViewGroup.objects.filter(page=page)\n if user.is_anonymous():\n return page_groups.count() == 0\n else:\n groups = page_groups.filter(group__in=user.groups.all())\n return page_groups.count() == 0 or groups.count() > 0","def test_func(self):\n answer = self.get_object()\n return True if self.request.user == answer.author or self.request.user.is_superuser else False","def get_in_users(self, obj):\n try:\n user = self.context.get('request').user\n except Exception:\n # raise serializers.ValidationError('Could not access request.user')\n return False\n if user in obj.users.all():\n return True\n else:\n return False","def has_object_permission(self, request, view, obj):\n return request.user == obj","def has_permission(self, request, view):\n user = request.user\n try:\n user.user_client\n return True\n except Exception:\n return False","def user_has_access(self, user):\n if not user: return False\n query = db.Query(TaskListMember)\n query.filter('task_list =', self)\n query.filter('user =', user)\n return query.get()","def has_object_permission(self, request, view, obj):\n\n return request.user == obj","def has_permission(self, request):\n return request.user.is_active \\\n and request.user.groups.filter(name='users').count()","def test_func(self):\n return self.request.user.is_active # any active user","def has_student(self, user, allow_superusers=True):\n return (user.is_superuser and allow_superusers) or len(self.students.filter(id=user.id)) > 0","def can_view(self, user):\n if self.applicant == user:\n return True\n elif user.has_perm('funding.view_all_applications'):\n # Fundihg commitee\n return True\n elif user.has_perm('funding.make_application_decisions'):\n # Fundihg manager - should have the view permissions, but just in case\n return True\n return False","def test_func(self):\n if not self.request.user.is_authenticated:\n return False\n if self.request.user.is_staff:\n return True\n return self.get_user() == self.request.user","def get_statistic_for_user(self, attr):\n all_payments = Payment.objects.payments(user=self).exclude(project__isnull=True)\n user_impact = 0\n for payment in all_payments:\n project = payment.project\n if project:\n user_financial_contribution = payment.amount\n project_funding_total = (int)(project.funding_goal)\n project_impact = getattr(project.statistics, attr)\n user_impact_for_project = project_impact * user_financial_contribution * 1.0 / project_funding_total\n user_impact += user_impact_for_project\n return user_impact","def has_user(self, user, allow_superusers=True):\n return self.has_student(user, allow_superusers) or self.has_ta(user, False) or self.has_instructor(user, False)","def can_be_viewed_by(self,user):\n\n # check whether everyone is allowed to view this. Anymous user is the only member of group\n # 'everyone' for which permissions can be set\n anonymousUser = get_anonymous_user()\n\n if anonymousUser.has_perm(\"view_ComicSiteModel\",self):\n return True\n else:\n # if not everyone has access, check whether given user has permissions\n return user.has_perm(\"view_ComicSiteModel\",self)","def user_auth(request):\n if request.user.is_authenticated:\n user = User.objects.get(email=request.user.email)\n if UserInformation.objects.filter(user=user).exists():\n return True\n return False","def test_user_with_no_explorations_has_no_impact(self):\n user_a_id = self._sign_up_user(\n self.USER_A_EMAIL, self.USER_A_USERNAME)\n self._run_computation()\n user_stats_model = user_models.UserStatsModel.get(\n user_a_id, strict=False)\n self.assertIsNone(user_stats_model)","def test_func(self):\n post = self.get_object()\n\n return self.request.user == post.author","def test_func(self):\n post = self.get_object()\n\n return self.request.user == post.author","def shared_by(self, user):\n return Shares.objects.filter(recipe=self, chef=user).exists()","def test_func(self):\r\n \r\n comment = self.get_object()\r\n return self.request.user == comment.name","def can_be_accessed(self, user):\n if self.shared_with_everyone:\n return True\n\n if self.user == user or self.users_allowed.filter(pk=user.pk).exists():\n return True\n\n for group in self.groups_allowed.all():\n if user.groups.filter(pk=group.pk).exists():\n return True\n\n return False","def user_can_access(self, user):\n return user.profile.certificates().filter(id=self.id).exists()","def liked_by(self, user):\n return Likes.objects.filter(recipe=self, chef=user).exists()","def is_userAS(self, obj):\n # Some other places simply check for owner=None.\n return UserAS.objects.filter(as_ptr=obj).exists()","def test_func(self):\n return self.request.user.has_permission(\"core.view_staffer\")","def allocated_tickets(self, user=None):\n query = self.raffleticket_set.filter(raffle_prize=self)\n if user:\n query = query.filter(user=user)\n \n return query.count()","def view_agreements(request):\n user = request.user\n signed = DUASignature.objects.filter(user=user).order_by('-sign_datetime')\n\n return render(request, 'user/view_agreements.html', {'user': user,\n 'signed': signed})","def last_viewed(cls, user1, user2):\n f = UserFlag.objects.filter(sender=user1, receiver=user2, flag_type=5)\n if f:\n return f[0].created\n else:\n return None","def test_func(self):\r\n post = self.get_object()\r\n if self.request.user == post.author:\r\n return True\r\n return False","def test_func(self):\r\n post = self.get_object()\r\n if self.request.user == post.author:\r\n return True\r\n return False","def test_func(self):\r\n post = self.get_object()\r\n if self.request.user == post.author:\r\n return True\r\n return False","def has_object_permission(self, request, view, account):\n if request.user.is_authenticated():\n if request.user.is_staff:\n return True\n return account.username == request.user.username\n return False","def test_func(self):\n post = self.get_object()\n if self.request.user == post.author:\n return True\n return False","def test_get_all_accessible_by_hash_as_user_does_not_return_other_user_template(\n self,\n ):\n mock_request = create_mock_request(user=self.user1)\n templates = template_api.get_all_accessible_by_hash(\n self.fixture.user2_template.hash, request=mock_request\n )\n self.assertTrue(templates.count() == 0)","def test_all_user_active(self):\n procedure = Procedure.objects.first()\n students = Student.objects.filter(promotion=procedure.promotion)\n for student in students:\n self.assertEqual(student.user.is_active, True)","def review_abuse(request):\n site = get_current_site(request)\n\n # Exclude reports where:\n # - the user is not active\n # - the accused is the logged in user\n # - the accusor is the logged in user\n undecided_reports = (AbuseReport.objects.filter(decision_datetime=None)\n .exclude(logged_against__is_active=False)\n .exclude(logged_against=request.user)\n .exclude(logged_by=request.user)\n .select_related('logged_against', 'logged_by'))\n\n reported_users = set([report.logged_against\n for report in undecided_reports])\n\n warnings = AbuseReport.objects.filter(\n logged_against__in=reported_users,\n moderator_decision='WARN'\n ).select_related('logged_against', 'logged_by', 'moderator')\n\n form = ModerateAbuseForm()\n\n for report in undecided_reports:\n accused_user = report.logged_against\n report.prior_warnings = [warning for warning in warnings\n if warning.logged_against == accused_user]\n\n if request.POST:\n\n abuse_report = get_object_or_404(AbuseReport,\n id=request.POST['report_id'])\n form = ModerateAbuseForm(request.POST)\n\n if form.is_valid():\n decision = form.cleaned_data['decision']\n comments = form.cleaned_data['comments']\n moderator = request.user\n user = abuse_report.logged_against\n\n abuse_report.moderator = moderator\n abuse_report.moderator_decision = decision\n abuse_report.moderator_comment = comments\n abuse_report.decision_datetime = timezone.now()\n abuse_report.save()\n\n logged_by = abuse_report.logged_by\n logged_against = abuse_report.logged_against\n\n def send_email_to_reporting_user(subject, template):\n \"\"\"\n Wrapper function for sending email to the user who has made\n the abuse report.\n \"\"\"\n send_connect_email(subject=subject,\n template=template,\n recipient=logged_by,\n logged_against=logged_against,\n site=site,\n comments=comments)\n\n def send_email_to_offending_user(subject, template):\n \"\"\"\n Wrapper function for sending email to the user who the abuse\n report has been made against.\n \"\"\"\n send_connect_email(subject=subject,\n template=template,\n recipient=logged_against,\n logged_against=logged_against,\n site=site,\n comments=comments)\n\n if decision == 'DISMISS':\n msg_type = ModerationLogMsg.DISMISSAL\n confirmation_message = _(\"The report against {} \"\n \"has been dismissed.\".format(\n user.get_full_name().title()))\n\n # Send email to the user who made the report\n subject = _('Your {} Abuse Report has been dismissed'.format(\n site.name))\n template = 'moderation/emails/abuse_report_dismissed.html'\n send_email_to_reporting_user(subject, template)\n\n elif decision == 'WARN':\n msg_type = ModerationLogMsg.WARNING\n confirmation_message = _(\n \"{} has been issued a formal warning.\".format(\n user.get_full_name().title()))\n\n # send email to the user who made the report\n subject = _(\n '{} has been issued a formal warning from {}'.format(\n logged_against.get_full_name(), site.name))\n template = (\n 'moderation/emails/abuse_report_warn_other_user.html'\n )\n send_email_to_reporting_user(subject, template)\n\n # send email to the user the report is logged against\n subject = _('A formal warning from {}'.format(site.name))\n template = 'moderation/emails/abuse_report_warn_this_user.html'\n send_email_to_offending_user(subject, template)\n\n if decision == 'BAN':\n msg_type = ModerationLogMsg.BANNING\n confirmation_message = _(\"{} has been banned from {}.\".format(\n user.get_full_name().title(), site.name))\n\n # send email to the user who made the report\n subject = _('{} has been banned from {}'.format(\n logged_against.get_full_name(),\n site.name))\n template = 'moderation/emails/abuse_report_ban_other_user.html'\n send_email_to_reporting_user(subject, template)\n\n # send email to the user the report is logged against\n subject = _(('Your {} account has been terminated').format(\n site.name))\n template = 'moderation/emails/abuse_report_ban_this_user.html'\n send_email_to_offending_user(subject, template)\n\n # deactivate account\n user.is_active = False\n user.save()\n\n # Log moderation event\n log_moderator_event(msg_type=msg_type,\n user=user,\n moderator=moderator,\n comment=comments)\n\n messages.success(request, confirmation_message)\n return redirect('moderation:review-abuse')\n\n context = {\n 'reports': undecided_reports,\n 'form': form,\n }\n\n return render(request, 'moderation/review_abuse.html', context)"],"string":"[\n \"def reported_by(self, user):\\n return Report.objects.filter(recipe=self, chef=user).exists()\",\n \"def has_ta(self, user, allow_superusers=True):\\n return (user.is_superuser and allow_superusers) or len(self.tas.filter(id=user.id)) > 0\",\n \"def _can_download_report(user):\\r\\n try:\\r\\n access_group = Group.objects.get(name=settings.PAYMENT_REPORT_GENERATOR_GROUP)\\r\\n except Group.DoesNotExist:\\r\\n return False\\r\\n return access_group in user.groups.all()\",\n \"def can_be_viewed_by(self,user):\\n return True\",\n \"def test_user_is_study_tagger_true(self):\\n response = self.client.get(self.get_url(self.trait.pk))\\n context = response.context\\n self.assertTrue(context['user_is_study_tagger'])\",\n \"def not_complete(request):\\n print(\\\"not_complete method in tutor_helper.py\\\")\\n if user_auth(request):\\n user = User.objects.get(email=request.user.email)\\n print(\\\"\\\\t\\\", user)\\n current_user = UserInformation.objects.get(user=user)\\n if current_user.current_main_set is None:\\n return False\\n if current_user.completed_sets is not None:\\n if current_user.current_main_set not in current_user.completed_sets.all():\\n print(\\\"not complete\\\")\\n print(current_user.current_main_set)\\n return True\\n else:\\n if current_user.completed_sets is None:\\n return True\\n return False\",\n \"def test_func(self):\\n taxonomy = self.get_object()\\n return self.request.user == taxonomy.author\",\n \"def test_func(self):\\n taxonomy = self.get_object()\\n return self.request.user == taxonomy.author\",\n \"def is_expert(self, user):\\r\\n return user.is_authenticated() and len(self.expert_set.filter(user=user))>0\",\n \"def allow_view_by(self, user2_compare):\\n # user2_compare is superuser\\n if user2_compare.is_superuser:\\n return True\\n\\n # this user is user2_compare self\\n if user2_compare == self.user:\\n return True\\n\\n # user2_compare is creator or owner of this user\\n if (self.creator and self.creator == user2_compare) or \\\\\\n (self.owner and self.owner == user2_compare):\\n if self.status:\\n return True\\n\\n # user2_compare can search users and has view perm\\n if user2_compare.profile.allow_search_users():\\n if user2_compare.has_perm('profiles.view_profile', self):\\n return True\\n\\n # False for everythin else\\n return False\",\n \"def by_user(self, user):\\n key = 'TIER_%i' % user.id\\n current = threadlocals.get_thread_variable(key)\\n\\n if current is None:\\n groups = user.groups.all()\\n if groups:\\n q = self.all()\\n for group in groups:\\n q = q.filter(group=group)\\n if q.count() > 0:\\n current = list(q)\\n\\n if current is None:\\n current = \\\"no\\\"\\n\\n threadlocals.set_thread_variable(key, current)\\n\\n if current == \\\"no\\\":\\n raise PricingTier.DoesNotExist\\n\\n return current\",\n \"def is_examiner(self, user_obj):\\n return self.examiners.filter(pk=user_obj.pk).count() > 0\",\n \"def can_view(self, user):\\r\\n return True\",\n \"def test_func(self):\\n taxonomy = self.get_taxonomy()\\n return self.request.user == taxonomy.author\",\n \"def get_user_ground_truth(request):\\n\\n user = request.GET.get('user',None)\\n action = request.GET.get('action',None)\\n mode = request.GET.get('mode',None)\\n report = request.GET.get('report',None)\\n language = request.GET.get('language',request.session['language'])\\n mode_obj = NameSpace.objects.get(ns_id=mode)\\n report = Report.objects.get(id_report = report, language = language)\\n gt = get_user_gt(user,mode_obj,report,language,action)\\n return JsonResponse(gt)\",\n \"def get_viewable(self, user):\\n return True\",\n \"def _has_data(cls):\\n return User.objects.count() > 0\",\n \"def ref_user_flag(self):\\n try:\\n ref = User.objects.get(\\n associated_emails__email__iexact=self.reference_email,\\n associated_emails__is_verified=True)\\n return True\\n except ObjectDoesNotExist:\\n return False\",\n \"def get_viewable(self, user):\\n if user.get('role') in ('admin', 'manager', 'engineer'):\\n return True\\n return user['name'] == self.doc.get('customer')\",\n \"def test_func(self):\\n return (Student.objects.filter(user=self.request.user).exists())\",\n \"def is_visible_to(self, user):\\n return True\",\n \"def has_access(self, user):\\n if user.is_superuser:\\n return True\\n return self.user_objects(user).filter(id=self.id).exists()\",\n \"def belongs_to_user(self) -> bool:\\n return flask.g.user is not None and flask.g.user.id == getattr(\\n self, 'user_id', False\\n )\",\n \"def isRelated(self):\\n return len(self.user_storage.all()) > 0\",\n \"def user_is_examiner(userobj):\\n from .assignment_group import AssignmentGroup\\n return AssignmentGroup.published_where_is_examiner(userobj).exists()\",\n \"def compareUserMetrics(user_metrics, achievement_threshold):\\n if user_metrics.works_visited < achievement_threshold.works_visited:\\n return False\\n elif user_metrics.works_found < achievement_threshold.works_found:\\n return False\\n return True\",\n \"def current_user_has_access(self):\\n return self.user_has_access(users.get_current_user())\",\n \"def test_user_is_study_tagger_false(self):\\n response = self.client.get(self.get_url(self.trait.pk))\\n context = response.context\\n self.assertFalse(context['user_is_study_tagger'])\",\n \"def can_retrieve(self, user):\\n return user.has_perm('agenda.can_see')\",\n \"def test_get_all_reports_made_by_a_user(self):\\n client = APIClient()\\n response = client.get(reverse(\\n 'authentication:user-article-reports'),**self.header_user2)\\n self.assertEqual(response.status_code, status.HTTP_200_OK)\",\n \"def test_func(self):\\n member_to_view = self.get_object()\\n is_self = self.request.user.rfid == member_to_view.rfid\\n view_others = self.request.user.has_permission(\\\"core.view_member\\\")\\n return view_others or is_self\",\n \"def get_is_por_holder(self, obj):\\n user = self.context['request'].user\\n if not user.is_authenticated:\\n return False\\n # pylint: disable=no-member\\n profile = UserProfile.objects.get(user=user)\\n if obj in profile.get_council_privileges():\\n return True\\n return False\",\n \"def has_permission(self, request, view):\\n return request.user.group != 'patient'\",\n \"def is_evaluador(user):\\n return user.groups.filter(name='Evaluadores').exists()\",\n \"def test_ReportingPeriodDetailView_current_employee_set_false(self):\\n response = self.app.get(\\n reverse(\\n 'reports:ReportingPeriodDetailView',\\n kwargs={'reporting_period': '2015-01-01'},\\n )\\n )\\n self.assertEqual(\\n len(response.html.find_all('tr', {'class': 'user'})), 2\\n )\",\n \"def get_presence_robot_user(request):\\n\\n id_report = request.GET.get('id_report', None)\\n language = request.GET.get('language', None)\\n use = request.GET.get('usecase', None)\\n rep = request.GET.get('report_type', None)\\n json_resp = {'auto_annotation_count': 0}\\n cursor = connection.cursor()\\n\\n reports_list = None\\n if request.method == 'POST':\\n request_body_json = json.loads(request.body)\\n reports_list = request_body_json['reports']\\n\\n if id_report is not None and language is not None:\\n\\n usecase = Report.objects.get(id_report=id_report, language=language)\\n use = usecase.name_id\\n cursor.execute(\\n \\\"SELECT COUNT(*) FROM ground_truth_log_file as g INNER JOIN report AS r ON r.id_report = g.id_report AND r.language = g.language WHERE g.username = %s and r.name = %s\\\",\\n ['Robot_user', str(use)])\\n ans = cursor.fetchone()[0]\\n json_resp = {'auto_annotation_count': (ans)}\\n\\n elif use is not None and rep is not None:\\n # print(rep)\\n if rep == 'reports':\\n cursor.execute(\\n \\\"SELECT COUNT(*) FROM ground_truth_log_file as g INNER JOIN report AS r ON r.id_report = g.id_report AND r.language = g.language WHERE g.ns_id = %s AND g.username = %s and r.name = %s and r.institute != %s\\\",\\n ['Robot', 'Robot_user', str(use), 'PUBMED'])\\n\\n elif rep == 'pubmed':\\n cursor.execute(\\n \\\"SELECT COUNT(*) FROM ground_truth_log_file as g INNER JOIN report AS r ON r.id_report = g.id_report AND r.language = g.language WHERE g.username = %s and r.name = %s and r.institute = %s\\\",\\n ['Robot_user', str(use), 'PUBMED'])\\n\\n ans = cursor.fetchone()[0]\\n if ans > 0:\\n json_resp = {'auto_annotation_count': ans}\\n # print(json_resp)\\n elif reports_list is not None:\\n report_list = json.loads(reports_list)\\n # print(report_list)\\n usecase_list = []\\n for rep in report_list:\\n\\n if rep['usecase'] not in usecase_list:\\n usecase_list.append(rep['usecase'])\\n for u in usecase_list:\\n cursor = connection.cursor()\\n cursor.execute(\\n \\\"SELECT COUNT(*) FROM ground_truth_log_file as g INNER JOIN report AS r ON r.id_report = g.id_report AND r.language = g.language WHERE g.username = %s and r.name = %s\\\",\\n ['Robot_user', str(u)])\\n ans = cursor.fetchone()[0]\\n if ans > 0:\\n json_resp = {'auto_annotation_count': ans}\\n else:\\n json_resp = {'auto_annotation_count': 0}\\n\\n elif use is None and reports_list is None and id_report is None and language is None:\\n robot = NameSpace.objects.get(ns_id='Robot')\\n gt = GroundTruthLogFile.objects.filter(ns_id=robot)\\n json_resp = {'auto_annotation_count': gt.count()}\\n\\n print(json_resp)\\n return JsonResponse(json_resp)\",\n \"def is_viewed(self):\\n return self.has_label(VIEWED_LABEL)\",\n \"def has_organizer(self, user):\\n return self.main_organizer == user or self.team.filter(id=user.id).exists()\",\n \"def view(self, user, notificationrequested, *args):\\n if user.is_anonymous or user.is_client:\\n return False\\n\\n if user.is_administrator:\\n return True\\n\\n if user.is_manager:\\n return False\\n\\n if user.is_advisor and user.group == notificationrequested.group:\\n return True\\n\\n return self.admin_permission(user, notificationrequested, *args)\",\n \"def has_report(self):\\n return self.report is not None\",\n \"def has_user(self, user): # pylint: disable=unused-argument\\r\\n return False\",\n \"def is_liked_by(self, user):\\n return user.liked_articles.filter(pk=self.pk).exists()\",\n \"def has_voted(self, user, ip_address):\\r\\n if user.is_anonymous():\\r\\n return len(self.usedip_set.filter(address=ip_address))>0\\r\\n else:\\r\\n return self.is_expert(user) and self.expert_set.filter(user=user)[0].voted\",\n \"def has_voted(self, user):\\n return user.choice_set.filter(vote=self).exists()\",\n \"def has_object_permission(self, request, view, obj):\\n # Users authentified via LTI are identified by a TokenUser with the\\n # resource_link_id as user ID.\\n if str(self.get_resource_id(obj)) == request.user.id:\\n return True\\n\\n return False\",\n \"def get_is_interested(self, obj):\\n # pylint: disable=no-member\\n user = self.context['request'].user\\n if not user.is_authenticated:\\n return None\\n profile = UserProfile.objects.get(user=user)\\n return profile in obj.interested_users.all()\",\n \"def user_auth_inst(request):\\n if request.user.is_authenticated:\\n user = User.objects.get(email=request.user.email)\\n if UserInformation.objects.filter(user=user).exists():\\n inst = UserInformation.objects.get(user=user)\\n if(inst.user_instructor):\\n return True\\n return False\",\n \"def personal(request):\\n \\n visits = Visit.objects.filter(user=request.user)\\n \\n \\n return render(request, \\\"personal.html\\\", {'visits':visits})\",\n \"def is_examiner(self, user_obj):\\n warnings.warn(\\\"deprecated\\\", DeprecationWarning)\\n return self.examiners.filter(user__id=user_obj.pk).count() > 0\",\n \"def has_view_permissions(self, obj):\\n queryset = self.model.objects.filter(pk=obj.pk)\\n if hasattr(queryset, 'has_view_permissions'):\\n return queryset.has_view_permissions( PyFormsMiddleware.user() )\\n else:\\n return True\",\n \"def user_has_access(self, user):\\n if self.visibility == self.PUBLIC:\\n return True\\n elif self.visibility == self.PRIVATE and self.created_by == user:\\n return True\\n elif self.visibility in (self.ORG_ONLY, self.ORG_ONLY_NO_EXTERNAL):\\n if user.external and self.visibility == self.ORG_ONLY_NO_EXTERNAL:\\n return False\\n elif self.organization.memberships.filter(user=user).count() >= 1:\\n return True\\n return False\",\n \"def get_user_has_liked(self, instance):\\n request = self.context.get(\\\"request\\\")\\n return instance.likes.filter(pk=request.user.pk).exists()\",\n \"def get_user_has_liked(self, instance):\\n request = self.context.get(\\\"request\\\")\\n return instance.likes.filter(pk=request.user.pk).exists()\",\n \"def view(self, user, action, *args):\\n if user.is_anonymous or user.is_client:\\n return False\\n\\n if user.is_administrator:\\n return True\\n\\n if user.is_manager:\\n return False\\n\\n # TODO check groups in request maybe ? dunno\\n if user.is_advisor:\\n return True\\n\\n return self.admin_permission(user, action, *args)\",\n \"def user_has_permission(self, id: int, user: User) -> bool:\\n return self.get_queryset().filter(pk=id).filter_for_user(user).exists()\",\n \"def show_stats(self):\\n if checkPermission('genweb.webmaster', self.context):\\n return True\",\n \"def has_object_permission(self, request, view, user):\\n return user == request.user or request.user.is_superuser\",\n \"def test_get_all_accessible_by_hash_as_user_returns_user_template(self):\\n mock_request = create_mock_request(user=self.user1)\\n templates = template_api.get_all_accessible_by_hash(\\n self.fixture.user1_template.hash, request=mock_request\\n )\\n self.assertTrue(self.fixture.user1_template in list(templates))\\n self.assertTrue(self.fixture.user2_template not in list(templates))\\n self.assertTrue(self.fixture.global_template not in list(templates))\",\n \"def has_permission(self, request, view):\\n usuario = request.user\\n return str(usuario.grupo) == \\\"Vendedor\\\"\",\n \"def has_object_permission(self, request, view, obj):\\n return request.user.id == obj.user_id\",\n \"def IsAvailable():\\n return settings.user.ui.Get('opportunities_showTemp', False)\",\n \"def has_object_permission(self, request, view, obj):\\n if Contributor.objects.filter(project=obj, user=request.user):\\n return True\\n else:\\n return False\",\n \"def bt_user_only(view):\\r\\n @google_login_required\\r\\n def wrapped(request, *args, **kwargs):\\r\\n uea = request.META['USER_EMAIL'];\\r\\n q = UserAccount.all().filter('email =',uea );\\r\\n if q.count() > 0 or users.is_current_user_admin():\\r\\n return view(request, *args, **kwargs)\\r\\n context = RequestContext(request);\\r\\n return rtr( 'access_limited.html', context,None )\\r\\n return wraps(view)(wrapped)\",\n \"def can_access(user, page):\\n page_groups = PageViewGroup.objects.filter(page=page)\\n if user.is_anonymous():\\n return page_groups.count() == 0\\n else:\\n groups = page_groups.filter(group__in=user.groups.all())\\n return page_groups.count() == 0 or groups.count() > 0\",\n \"def test_func(self):\\n answer = self.get_object()\\n return True if self.request.user == answer.author or self.request.user.is_superuser else False\",\n \"def get_in_users(self, obj):\\n try:\\n user = self.context.get('request').user\\n except Exception:\\n # raise serializers.ValidationError('Could not access request.user')\\n return False\\n if user in obj.users.all():\\n return True\\n else:\\n return False\",\n \"def has_object_permission(self, request, view, obj):\\n return request.user == obj\",\n \"def has_permission(self, request, view):\\n user = request.user\\n try:\\n user.user_client\\n return True\\n except Exception:\\n return False\",\n \"def user_has_access(self, user):\\n if not user: return False\\n query = db.Query(TaskListMember)\\n query.filter('task_list =', self)\\n query.filter('user =', user)\\n return query.get()\",\n \"def has_object_permission(self, request, view, obj):\\n\\n return request.user == obj\",\n \"def has_permission(self, request):\\n return request.user.is_active \\\\\\n and request.user.groups.filter(name='users').count()\",\n \"def test_func(self):\\n return self.request.user.is_active # any active user\",\n \"def has_student(self, user, allow_superusers=True):\\n return (user.is_superuser and allow_superusers) or len(self.students.filter(id=user.id)) > 0\",\n \"def can_view(self, user):\\n if self.applicant == user:\\n return True\\n elif user.has_perm('funding.view_all_applications'):\\n # Fundihg commitee\\n return True\\n elif user.has_perm('funding.make_application_decisions'):\\n # Fundihg manager - should have the view permissions, but just in case\\n return True\\n return False\",\n \"def test_func(self):\\n if not self.request.user.is_authenticated:\\n return False\\n if self.request.user.is_staff:\\n return True\\n return self.get_user() == self.request.user\",\n \"def get_statistic_for_user(self, attr):\\n all_payments = Payment.objects.payments(user=self).exclude(project__isnull=True)\\n user_impact = 0\\n for payment in all_payments:\\n project = payment.project\\n if project:\\n user_financial_contribution = payment.amount\\n project_funding_total = (int)(project.funding_goal)\\n project_impact = getattr(project.statistics, attr)\\n user_impact_for_project = project_impact * user_financial_contribution * 1.0 / project_funding_total\\n user_impact += user_impact_for_project\\n return user_impact\",\n \"def has_user(self, user, allow_superusers=True):\\n return self.has_student(user, allow_superusers) or self.has_ta(user, False) or self.has_instructor(user, False)\",\n \"def can_be_viewed_by(self,user):\\n\\n # check whether everyone is allowed to view this. Anymous user is the only member of group\\n # 'everyone' for which permissions can be set\\n anonymousUser = get_anonymous_user()\\n\\n if anonymousUser.has_perm(\\\"view_ComicSiteModel\\\",self):\\n return True\\n else:\\n # if not everyone has access, check whether given user has permissions\\n return user.has_perm(\\\"view_ComicSiteModel\\\",self)\",\n \"def user_auth(request):\\n if request.user.is_authenticated:\\n user = User.objects.get(email=request.user.email)\\n if UserInformation.objects.filter(user=user).exists():\\n return True\\n return False\",\n \"def test_user_with_no_explorations_has_no_impact(self):\\n user_a_id = self._sign_up_user(\\n self.USER_A_EMAIL, self.USER_A_USERNAME)\\n self._run_computation()\\n user_stats_model = user_models.UserStatsModel.get(\\n user_a_id, strict=False)\\n self.assertIsNone(user_stats_model)\",\n \"def test_func(self):\\n post = self.get_object()\\n\\n return self.request.user == post.author\",\n \"def test_func(self):\\n post = self.get_object()\\n\\n return self.request.user == post.author\",\n \"def shared_by(self, user):\\n return Shares.objects.filter(recipe=self, chef=user).exists()\",\n \"def test_func(self):\\r\\n \\r\\n comment = self.get_object()\\r\\n return self.request.user == comment.name\",\n \"def can_be_accessed(self, user):\\n if self.shared_with_everyone:\\n return True\\n\\n if self.user == user or self.users_allowed.filter(pk=user.pk).exists():\\n return True\\n\\n for group in self.groups_allowed.all():\\n if user.groups.filter(pk=group.pk).exists():\\n return True\\n\\n return False\",\n \"def user_can_access(self, user):\\n return user.profile.certificates().filter(id=self.id).exists()\",\n \"def liked_by(self, user):\\n return Likes.objects.filter(recipe=self, chef=user).exists()\",\n \"def is_userAS(self, obj):\\n # Some other places simply check for owner=None.\\n return UserAS.objects.filter(as_ptr=obj).exists()\",\n \"def test_func(self):\\n return self.request.user.has_permission(\\\"core.view_staffer\\\")\",\n \"def allocated_tickets(self, user=None):\\n query = self.raffleticket_set.filter(raffle_prize=self)\\n if user:\\n query = query.filter(user=user)\\n \\n return query.count()\",\n \"def view_agreements(request):\\n user = request.user\\n signed = DUASignature.objects.filter(user=user).order_by('-sign_datetime')\\n\\n return render(request, 'user/view_agreements.html', {'user': user,\\n 'signed': signed})\",\n \"def last_viewed(cls, user1, user2):\\n f = UserFlag.objects.filter(sender=user1, receiver=user2, flag_type=5)\\n if f:\\n return f[0].created\\n else:\\n return None\",\n \"def test_func(self):\\r\\n post = self.get_object()\\r\\n if self.request.user == post.author:\\r\\n return True\\r\\n return False\",\n \"def test_func(self):\\r\\n post = self.get_object()\\r\\n if self.request.user == post.author:\\r\\n return True\\r\\n return False\",\n \"def test_func(self):\\r\\n post = self.get_object()\\r\\n if self.request.user == post.author:\\r\\n return True\\r\\n return False\",\n \"def has_object_permission(self, request, view, account):\\n if request.user.is_authenticated():\\n if request.user.is_staff:\\n return True\\n return account.username == request.user.username\\n return False\",\n \"def test_func(self):\\n post = self.get_object()\\n if self.request.user == post.author:\\n return True\\n return False\",\n \"def test_get_all_accessible_by_hash_as_user_does_not_return_other_user_template(\\n self,\\n ):\\n mock_request = create_mock_request(user=self.user1)\\n templates = template_api.get_all_accessible_by_hash(\\n self.fixture.user2_template.hash, request=mock_request\\n )\\n self.assertTrue(templates.count() == 0)\",\n \"def test_all_user_active(self):\\n procedure = Procedure.objects.first()\\n students = Student.objects.filter(promotion=procedure.promotion)\\n for student in students:\\n self.assertEqual(student.user.is_active, True)\",\n \"def review_abuse(request):\\n site = get_current_site(request)\\n\\n # Exclude reports where:\\n # - the user is not active\\n # - the accused is the logged in user\\n # - the accusor is the logged in user\\n undecided_reports = (AbuseReport.objects.filter(decision_datetime=None)\\n .exclude(logged_against__is_active=False)\\n .exclude(logged_against=request.user)\\n .exclude(logged_by=request.user)\\n .select_related('logged_against', 'logged_by'))\\n\\n reported_users = set([report.logged_against\\n for report in undecided_reports])\\n\\n warnings = AbuseReport.objects.filter(\\n logged_against__in=reported_users,\\n moderator_decision='WARN'\\n ).select_related('logged_against', 'logged_by', 'moderator')\\n\\n form = ModerateAbuseForm()\\n\\n for report in undecided_reports:\\n accused_user = report.logged_against\\n report.prior_warnings = [warning for warning in warnings\\n if warning.logged_against == accused_user]\\n\\n if request.POST:\\n\\n abuse_report = get_object_or_404(AbuseReport,\\n id=request.POST['report_id'])\\n form = ModerateAbuseForm(request.POST)\\n\\n if form.is_valid():\\n decision = form.cleaned_data['decision']\\n comments = form.cleaned_data['comments']\\n moderator = request.user\\n user = abuse_report.logged_against\\n\\n abuse_report.moderator = moderator\\n abuse_report.moderator_decision = decision\\n abuse_report.moderator_comment = comments\\n abuse_report.decision_datetime = timezone.now()\\n abuse_report.save()\\n\\n logged_by = abuse_report.logged_by\\n logged_against = abuse_report.logged_against\\n\\n def send_email_to_reporting_user(subject, template):\\n \\\"\\\"\\\"\\n Wrapper function for sending email to the user who has made\\n the abuse report.\\n \\\"\\\"\\\"\\n send_connect_email(subject=subject,\\n template=template,\\n recipient=logged_by,\\n logged_against=logged_against,\\n site=site,\\n comments=comments)\\n\\n def send_email_to_offending_user(subject, template):\\n \\\"\\\"\\\"\\n Wrapper function for sending email to the user who the abuse\\n report has been made against.\\n \\\"\\\"\\\"\\n send_connect_email(subject=subject,\\n template=template,\\n recipient=logged_against,\\n logged_against=logged_against,\\n site=site,\\n comments=comments)\\n\\n if decision == 'DISMISS':\\n msg_type = ModerationLogMsg.DISMISSAL\\n confirmation_message = _(\\\"The report against {} \\\"\\n \\\"has been dismissed.\\\".format(\\n user.get_full_name().title()))\\n\\n # Send email to the user who made the report\\n subject = _('Your {} Abuse Report has been dismissed'.format(\\n site.name))\\n template = 'moderation/emails/abuse_report_dismissed.html'\\n send_email_to_reporting_user(subject, template)\\n\\n elif decision == 'WARN':\\n msg_type = ModerationLogMsg.WARNING\\n confirmation_message = _(\\n \\\"{} has been issued a formal warning.\\\".format(\\n user.get_full_name().title()))\\n\\n # send email to the user who made the report\\n subject = _(\\n '{} has been issued a formal warning from {}'.format(\\n logged_against.get_full_name(), site.name))\\n template = (\\n 'moderation/emails/abuse_report_warn_other_user.html'\\n )\\n send_email_to_reporting_user(subject, template)\\n\\n # send email to the user the report is logged against\\n subject = _('A formal warning from {}'.format(site.name))\\n template = 'moderation/emails/abuse_report_warn_this_user.html'\\n send_email_to_offending_user(subject, template)\\n\\n if decision == 'BAN':\\n msg_type = ModerationLogMsg.BANNING\\n confirmation_message = _(\\\"{} has been banned from {}.\\\".format(\\n user.get_full_name().title(), site.name))\\n\\n # send email to the user who made the report\\n subject = _('{} has been banned from {}'.format(\\n logged_against.get_full_name(),\\n site.name))\\n template = 'moderation/emails/abuse_report_ban_other_user.html'\\n send_email_to_reporting_user(subject, template)\\n\\n # send email to the user the report is logged against\\n subject = _(('Your {} account has been terminated').format(\\n site.name))\\n template = 'moderation/emails/abuse_report_ban_this_user.html'\\n send_email_to_offending_user(subject, template)\\n\\n # deactivate account\\n user.is_active = False\\n user.save()\\n\\n # Log moderation event\\n log_moderator_event(msg_type=msg_type,\\n user=user,\\n moderator=moderator,\\n comment=comments)\\n\\n messages.success(request, confirmation_message)\\n return redirect('moderation:review-abuse')\\n\\n context = {\\n 'reports': undecided_reports,\\n 'form': form,\\n }\\n\\n return render(request, 'moderation/review_abuse.html', context)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6260666","0.6116303","0.5899749","0.5702829","0.5593343","0.5565848","0.5547417","0.5547417","0.5541943","0.5512871","0.55068207","0.5493356","0.54900455","0.5473812","0.5417657","0.53915375","0.5383512","0.53717935","0.5370812","0.53642553","0.5324591","0.5321725","0.53190655","0.53122014","0.5308337","0.52796865","0.52503365","0.522769","0.5182951","0.51618445","0.5160635","0.5137894","0.51377785","0.5122533","0.51057243","0.50995046","0.5091771","0.50862926","0.50837326","0.50831944","0.5080736","0.5064996","0.506217","0.50621206","0.50524664","0.5052381","0.5048315","0.5033964","0.50321215","0.5029318","0.5023696","0.5023659","0.5023659","0.5014515","0.50132465","0.50132054","0.5008873","0.50036883","0.5002136","0.49989176","0.49964896","0.49926376","0.4992529","0.49861762","0.49803448","0.49795443","0.49589455","0.49544743","0.49507073","0.49414966","0.4933446","0.4929802","0.49098584","0.49076426","0.4906417","0.4902957","0.49001762","0.4899431","0.48838037","0.48669368","0.48606202","0.48606202","0.48605248","0.48574015","0.48487803","0.48475587","0.48262218","0.48252118","0.48170185","0.48167673","0.4815842","0.48141596","0.48134777","0.48134777","0.48134777","0.48128083","0.48115054","0.48089647","0.48042345","0.480035"],"string":"[\n \"0.6260666\",\n \"0.6116303\",\n \"0.5899749\",\n \"0.5702829\",\n \"0.5593343\",\n \"0.5565848\",\n \"0.5547417\",\n \"0.5547417\",\n \"0.5541943\",\n \"0.5512871\",\n \"0.55068207\",\n \"0.5493356\",\n \"0.54900455\",\n \"0.5473812\",\n \"0.5417657\",\n \"0.53915375\",\n \"0.5383512\",\n \"0.53717935\",\n \"0.5370812\",\n \"0.53642553\",\n \"0.5324591\",\n \"0.5321725\",\n \"0.53190655\",\n \"0.53122014\",\n \"0.5308337\",\n \"0.52796865\",\n \"0.52503365\",\n \"0.522769\",\n \"0.5182951\",\n \"0.51618445\",\n \"0.5160635\",\n \"0.5137894\",\n \"0.51377785\",\n \"0.5122533\",\n \"0.51057243\",\n \"0.50995046\",\n \"0.5091771\",\n \"0.50862926\",\n \"0.50837326\",\n \"0.50831944\",\n \"0.5080736\",\n \"0.5064996\",\n \"0.506217\",\n \"0.50621206\",\n \"0.50524664\",\n \"0.5052381\",\n \"0.5048315\",\n \"0.5033964\",\n \"0.50321215\",\n \"0.5029318\",\n \"0.5023696\",\n \"0.5023659\",\n \"0.5023659\",\n \"0.5014515\",\n \"0.50132465\",\n \"0.50132054\",\n \"0.5008873\",\n \"0.50036883\",\n \"0.5002136\",\n \"0.49989176\",\n \"0.49964896\",\n \"0.49926376\",\n \"0.4992529\",\n \"0.49861762\",\n \"0.49803448\",\n \"0.49795443\",\n \"0.49589455\",\n \"0.49544743\",\n \"0.49507073\",\n \"0.49414966\",\n \"0.4933446\",\n \"0.4929802\",\n \"0.49098584\",\n \"0.49076426\",\n \"0.4906417\",\n \"0.4902957\",\n \"0.49001762\",\n \"0.4899431\",\n \"0.48838037\",\n \"0.48669368\",\n \"0.48606202\",\n \"0.48606202\",\n \"0.48605248\",\n \"0.48574015\",\n \"0.48487803\",\n \"0.48475587\",\n \"0.48262218\",\n \"0.48252118\",\n \"0.48170185\",\n \"0.48167673\",\n \"0.4815842\",\n \"0.48141596\",\n \"0.48134777\",\n \"0.48134777\",\n \"0.48134777\",\n \"0.48128083\",\n \"0.48115054\",\n \"0.48089647\",\n \"0.48042345\",\n \"0.480035\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5986029"},"document_rank":{"kind":"string","value":"2"}}},{"rowIdx":11262,"cells":{"query":{"kind":"string","value":"Returns the fuzz target of |benchmark|"},"document":{"kind":"string","value":"def get_fuzz_target(benchmark):\n # Do this because of OSS-Fuzz-on-demand.\n # TODO(metzman): Use classes to mock a benchmark config for\n # OSS_FUZZ_ON_DEMAND.\n return benchmark_config.get_config(benchmark).get(\n 'fuzz_target', environment.get('FUZZ_TARGET'))"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_fuzzer_benchmark_key(fuzzer: str, benchmark: str):\n return fuzzer + ' ' + benchmark","def get_benchmark(self, benchmark):\n\t\tif not isinstance(benchmark, str) and not callable(benchmark): return benchmark\n\t\telif benchmark in self.classes:\treturn self.classes[benchmark]()\n\t\traise TypeError('Passed benchmark is not defined!')","def findBenchFromDevice(self, device):\n return device.bench","def fuzz():\n if FUZZ:\n time.sleep(random.random())","def get_fuzzer(self, *, catalog: Optional[str] = None) -> BaseFuzzer:\n return self.get_fuzzer_cls(catalog=catalog)()","def benchmark_result(self):\n return self._benchmark_id","def main():\r\n _evaluative_test(5)\r\n _fuzz_test(1)\r\n _fuzz_test(1, 512)\r\n _fuzz_test(1, 1512)\r\n _fuzz_test(1000)\r\n _fuzz_test(1000, 512)\r\n _fuzz_test(1000, 4077)","def oss_fuzz_benchmark(fs):\n benchmark_config_contents = yaml.dump(OSS_FUZZ_BENCHMARK_CONFIG)\n benchmark_config_file = os.path.join(utils.ROOT_DIR, 'benchmarks',\n OSS_FUZZ_BENCHMARK_NAME,\n 'benchmark.yaml')\n fs.create_file(benchmark_config_file, contents=benchmark_config_contents)\n return OSS_FUZZ_BENCHMARK_NAME","def findBenchFromInstrument(self, instrument):\n return instrument.bench","def get_benchmark(client):\n r = client.get(config.API_PATH() + '/benchmarks')\n benchmarks = json.loads(r.data)\n return benchmarks['benchmarks'][0]['id']","def get_fan_target(self):\n return self.__fan_target","def fuzz(self):\n self.total_mutant_index = 0\n self.total_num_mutations = self.num_mutations()\n\n self._main_fuzz_loop(self._iterate_protocol())","def umbrae(self, benchmark: np.ndarray = None):\n return self.mbrae(benchmark) / (1 - self.mbrae(benchmark))","def get_fuzzy_match(object, answer, threshold=80):\n answer_phrase = generate_ngrams(answer)\n if answer_phrase:\n best_match = [fuzz.ratio(object, phr) for phr in answer_phrase]\n if np.max(best_match)>threshold:\n return np.max(best_match), answer_phrase[np.argmax(best_match)]\n else:\n return 0,''\n else:\n return 0, ''","def get_builder_image_url(benchmark, fuzzer, docker_registry):\n return f'{docker_registry}/builders/{fuzzer}/{benchmark}'","def benchmark(self):\n logger.info(self.benchmark.__doc__)\n return self.run(self.benchmark_profile())","def fuzz(binary, sample, verbose,loop_limit, prog):\n # PLACEHOLDER\n # Check magic bytes / struct of sample input -> make best guess for input format\n # This should be moved into mutation logic -> this is a shortcut for midpoint\n sample_processed = SampleParser(sample)\n \n try:\n # data: that one plaintext file\n # ASCII text: plaintext\n # JSON data: json\n # CSV text: csv\n # HTML document, ASCII text: xml2\n mutations = { # walmart switch statement\n 'JSON' : lambda sample_processed:JsonMutator(sample_processed.data, min=2, max=10),\n 'CSV': lambda sample_processed:CsvMutator(sample_processed.csv(), min=2, max=10),\n 'JFIF': lambda sample_processed:JpgMutator(sample_processed.jpg(), min=2, max=10),\n 'XML': lambda sample_processed:XmlMutator(sample_processed.xml(), min=2, max=10),\n 'HTML document, ASCII text': lambda sample_processed:XmlMutator(sample_processed.xml(), min=2, max=10)\n }[sample_processed.guess](sample_processed)\n except KeyError as e:\n print('Unmatched data type: {}, defaulting to generic mutator'.format(e))\n mutations = Mutator(sample_processed.data)\n # need a default: ascii\n except Exception as e:\n print(\"mutator fucked up: {}\".format(e))\n \n print('Running fuzzer with a {} second limit...'.format(TIME_LIMIT))\n\n # nevermind\n strategy = mutations.complex_mutate\n \n # Loop for whole timelimit \n # In future - try multiple strategies in time limit\n cov = float(0)\n last = 0\n while(1):\n prog.iterations += 1 \n if (prog.iterations - last >= loop_limit):\n prog.reinit_breakpoints\n cov = float(0)\n mutations.reset()\n\n # in future, call parent method -> give me a mutation.. \n current_input = strategy()\n\n # Spawn process - should be stopped after exec. \n pid, status = prog.spawn_process(stdout=False)\n prog.getregs()\n # Now that the process has been spawned, we can populate the breakpoints\n prog.populate_breakpoints()\n if verbose:\n print(current_input)\n print(\"coverage: {}, this run: {}\".format(prog.coverage(), cov))\n print(\"pid {}\".format(pid))\n #prog.breakpoint_status()\n\n # Start the process proper \n prog.cont()\n prog.send(current_input) \n\n # simulate EOF \n prog.close_input() \n # why in the everloving fuck does RESIZING A TERMINAL alter the behaviour of waitpid ????????\n # sigwinch. thats why. \n \n if prog.coverage() > cov:\n cov = prog.coverage()\n mutations.add_pop(current_input)\n last = prog.iterations\n # Wait for something to happen. \n while(1):\n # sigsegv doesn't count as a termination signal.\n # since it gets caught by ptrace (only sigkill goes through ptrace) \n # WSTOPSIG == 11 == SIGSEGV -> segfault\n\n \n\n pid, status = prog.wait()\n if(os.WIFSTOPPED(status) and (os.WSTOPSIG(status) == signal.SIGSEGV)):\n # Placeholder -> Need to create file with crash input and integrate \n # fuzzing engine. \n\n # Update stats\n prog.getregs()\n prog.crash_eips.append(prog.registers.eip) \n #if verbose:\n # print(\"Input crashed program with signal: {}\".format(os.WSTOPSIG(status)))\n\n with open(\"bad.txt\", \"ab+\") as f:\n # write the byte string\n # since most formats have newlines in them\n f.write(str(current_input).encode(\"unicode-escape\") + b\"\\n\")\n break\n # we have hit one of our basic block breakpoints\n elif(os.WIFSTOPPED(status) and (os.WSTOPSIG(status) == signal.SIGTRAP)):\n # we need to decrement eip, replace the breakpoint with its saved value\n prog.restore_current_bp() \n\n elif(os.WIFEXITED(status)):\n break\n\n #prog.step()\n prog.cont()","def GuessTargets(self, target_name):\n return difflib.get_close_matches(target_name, self.GetTargets(), 10, 0.4)","def calculate_appropriate_target(self):\n pass","def calculate_appropriate_target(self):\n pass","def fuzz(text):\r\n\r\n return ' '.join([fuzz_word(word) for word in text.split()])","def defuzzification(self):\n self.price = fuzz.defuzz(self.psf,self.fuzzy_output, 'som')","def get_perf(target, output, mask):\n\n\toutput = np.stack(output, axis=0)\n\tmk = mask*np.reshape(target[:,:,0] == 0, (par['num_time_steps'], par['batch_size']))\n\n\ttarget = np.argmax(target, axis = 2)\n\toutput = np.argmax(output, axis = 2)\n\n\treturn np.sum(np.float32(target == output)*np.squeeze(mk))/np.sum(mk)","def benchmark_selection(self):\n return self._benchmark_selection","def run_libfuzzer_engine(tool_name, target_name, arguments, testcase_path,\n output_path, timeout):\n arguments = list(arguments)\n if environment.is_trusted_host():\n from clusterfuzz._internal.bot.untrusted_runner import tasks_host\n\n # TODO(ochang): Remove hardcode.\n return tasks_host.process_testcase('libFuzzer', tool_name, target_name,\n arguments, testcase_path, output_path,\n timeout)\n\n target_path = engine_common.find_fuzzer_path(\n environment.get_value('BUILD_DIR'), target_name)\n if not target_path:\n return engine.ReproduceResult([], 0, 0, '')\n\n engine_impl = libfuzzer_engine.Engine()\n if tool_name == 'minimize':\n func = engine_impl.minimize_testcase\n else:\n assert tool_name == 'cleanse'\n func = engine_impl.cleanse\n\n return func(target_path, arguments, testcase_path, output_path, timeout)","def get_type(benchmark):\n # TODO(metzman): Use classes to mock a benchmark config for\n # OSS_FUZZ_ON_DEMAND.\n default_value = os.getenv('EXPERIMENT_TYPE', BenchmarkType.CODE.value)\n return benchmark_config.get_config(benchmark).get('type', default_value)","def double_fuzz(self, original,fl):\n result = self.r.choice(self.mutators)(self, original,fl)\n return self.r.choice(self.mutators)(self, result,fl)","def _fuzz_current_case(self, path):\n target = self.targets[0]\n\n self.pause() # only pauses conditionally\n\n message_path = \"->\".join([self.nodes[e.dst].name for e in path])\n\n if self.fuzz_node.mutant.name:\n primitive_under_test = self.fuzz_node.mutant.name\n else:\n primitive_under_test = 'no-name'\n\n test_case_name = \"{0}.{1}.{2}\".format(message_path, primitive_under_test, self.fuzz_node.mutant_index)\n\n self._fuzz_data_logger.open_test_case(\"{0}: {1}\".format(self.total_mutant_index, test_case_name),\n name=test_case_name, index=self.total_mutant_index)\n\n self._fuzz_data_logger.log_info(\n \"Type: %s. Default value: %s. Case %d of %d overall.\" % (\n type(self.fuzz_node.mutant).__name__,\n repr(self.fuzz_node.mutant.original_value),\n self.total_mutant_index,\n self.total_num_mutations))\n\n if target.procmon:\n self._fuzz_data_logger.open_test_step('Calling procmon pre_send()')\n target.procmon.pre_send(self.total_mutant_index)\n\n if target.netmon:\n self._fuzz_data_logger.open_test_step('Calling netmon pre_send()')\n target.netmon.pre_send(self.total_mutant_index)\n\n target.open()\n\n self.pre_send(target)\n\n try:\n for e in path[:-1]:\n node = self.nodes[e.dst]\n callback_data = self._callback_current_node(node=node, edge=e)\n self._fuzz_data_logger.open_test_step(\"Transmit Prep Node '{0}'\".format(node.name))\n self.transmit_normal(target, node, e, callback_data=callback_data)\n\n callback_data = self._callback_current_node(node=self.fuzz_node, edge=path[-1])\n except sex.BoofuzzTargetConnectionReset:\n # TODO: Switch _ignore_connection_reset for _ignore_transmission_error, or provide retry mechanism\n if self._ignore_connection_reset:\n self._fuzz_data_logger.log_info(\"Target connection reset.\")\n else:\n self._fuzz_data_logger.log_fail(\"Target connection reset.\")\n except sex.BoofuzzTargetConnectionAborted as e:\n # TODO: Switch _ignore_connection_aborted for _ignore_transmission_error, or provide retry mechanism\n if self._ignore_connection_aborted:\n self._fuzz_data_logger.log_info(\"Target connection lost (socket error: {0} {1}): You may have a \"\n \"network issue, or an issue with firewalls or anti-virus. Try \"\n \"disabling your firewall.\"\n .format(e.socket_errno, e.socket_errmsg))\n else:\n self._fuzz_data_logger.log_fail(\"Target connection lost (socket error: {0} {1}): You may have a \"\n \"network issue, or an issue with firewalls or anti-virus. Try \"\n \"disabling your firewall.\"\n .format(e.socket_errno, e.socket_errmsg))\n\n self._fuzz_data_logger.open_test_step(\"Fuzzing Node '{0}'\".format(self.fuzz_node.name))\n self.transmit_fuzz(target, self.fuzz_node, path[-1], callback_data=callback_data)\n target.close()\n\n if not self._check_for_passively_detected_failures(target=target):\n self._fuzz_data_logger.open_test_step(\"Calling post_send function:\")\n try:\n self.post_send(target=target, fuzz_data_logger=self._fuzz_data_logger, session=self, sock=target)\n except sex.BoofuzzTargetConnectionReset:\n self._fuzz_data_logger.log_fail(\n \"Target connection reset -- considered a failure case when triggered from post_send\")\n except sex.BoofuzzTargetConnectionAborted as e:\n self._fuzz_data_logger.log_info(\"Target connection lost (socket error: {0} {1}): You may have a \"\n \"network issue, or an issue with firewalls or anti-virus. Try \"\n \"disabling your firewall.\"\n .format(e.socket_errno, e.socket_errmsg))\n pass\n except sex.BoofuzzTargetConnectionFailedError:\n self._fuzz_data_logger.log_fail(\n \"Cannot connect to target; target presumed down.\"\n \" Note: Normally a failure should be detected, and the target reset.\"\n \" This error may mean you have no restart method configured, or your error\"\n \" detection is not working.\")\n except Exception:\n self._fuzz_data_logger.log_fail(\n \"Custom post_send method raised uncaught Exception.\" + traceback.format_exc())\n finally:\n target.close()\n self._check_procmon_failures(target=target)\n\n self._fuzz_data_logger.open_test_step(\"Sleep between tests.\")\n self._fuzz_data_logger.log_info(\"sleeping for %f seconds\" % self.sleep_time)\n time.sleep(self.sleep_time)\n\n self._process_failures(target=target)\n\n self._stop_netmon(target=target)\n\n self.export_file()","def test_make_benchmark_single_ll():\n benchmark = llvm.make_benchmark(INVALID_IR_PATH)\n assert str(benchmark.uri).startswith(\"benchmark://user-v0/\")\n assert benchmark.uri.scheme == \"benchmark\"\n assert benchmark.uri.dataset == \"user-v0\"","def reference_benchmark_result(self):\n return self._reference_id","def get_random_target():\n return get_random_colour(ColourTargetColBG)","def test_correct_fuzzer_build(self):\n test_fuzzer_dir = os.path.join(TEST_FILES_PATH, 'out')\n self.assertTrue(cifuzz.check_fuzzer_build(test_fuzzer_dir))","def run(self, runner):\n # record the bad input\n mutated = self.fuzz()\n print(f\"Trying {mutated}\")\n result = runner.run(mutated)[0]\n\n if result.returncode < 0:\n with open(\"bad.txt\", 'a') as f: \n f.write(mutated)\n f.write(\"\\n\")\n return runner.run(mutated)","def _get_target(self, prediction: Tensor) -> Tensor:\n if self.model_config.mode == ModelMode.binary_classification:\n # TODO: Allow customization of the thresholds used below.\n if self.model_config.return_type.value == 'raw':\n return (prediction > 0).long().view(-1)\n if self.model_config.return_type.value == 'probs':\n return (prediction > 0.5).long().view(-1)\n assert False\n\n if self.model_config.mode == ModelMode.multiclass_classification:\n return prediction.argmax(dim=-1)\n\n return prediction","def run_benchmark(curl, benchmark, test_config = TestConfig()):\n\n warmup_runs = benchmark.warmup_runs\n benchmark_runs = benchmark.benchmark_runs\n message = '' #Message is name of benchmark... print it?\n\n if (warmup_runs <= 0):\n raise Exception(\"Invalid number of warmup runs, must be > 0 :\" + warmup_runs)\n if (benchmark_runs <= 0):\n raise Exception(\"Invalid number of benchmark runs, must be > 0 :\" + benchmark_runs)\n\n #Initialize variables to store output\n output = BenchmarkResult()\n output.name = benchmark.name\n output.group = benchmark.group\n metricnames = list(benchmark.metrics)\n metricvalues = [METRICS[name] for name in metricnames] # Metric variable for curl, to avoid hash lookup for every metric name\n results = [list() for x in xrange(0, len(metricnames))] # Initialize arrays to store results for each metric\n\n curl.setopt(pycurl.WRITEFUNCTION, lambda x: None) #Do not store actual response body at all.\n\n #Benchmark warm-up to allow for caching, JIT compiling, on client\n logging.info('Warmup: ' + message + ' started')\n for x in xrange(0, warmup_runs):\n if benchmark.method == u'POST' or benchmark.method == u'PUT':\n curl.setopt(curl.READFUNCTION, StringIO.StringIO(benchmark.body).read)\n curl.perform()\n logging.info('Warmup: ' + message + ' finished')\n\n logging.info('Benchmark: ' + message + ' starting')\n\n for x in xrange(0, benchmark_runs): # Run the actual benchmarks\n if benchmark.method == u'POST' or benchmark.method == u'PUT':\n curl.setopt(curl.READFUNCTION, StringIO.StringIO(benchmark.body).read)\n\n try: # Run the curl call, if it errors, then add to failure counts for benchmark\n curl.perform()\n except Exception:\n output.failures = output.failures + 1\n continue # Skip metrics collection\n\n # Get all metrics values for this run, and store to metric lists\n for i in xrange(0, len(metricnames)):\n results[i].append( curl.getinfo(metricvalues[i]) )\n\n logging.info('Benchmark: ' + message + ' ending')\n\n temp_results = dict()\n for i in xrange(0, len(metricnames)):\n temp_results[metricnames[i]] = results[i]\n output.results = temp_results\n\n curl.close()\n return analyze_benchmark_results(output, benchmark)","def run_fuzz(\n rng: ast_generator.RngState,\n ast_generator_options: ast_generator.AstGeneratorOptions,\n calls_per_sample: int,\n save_temps: bool,\n sample_count: int,\n codegen: bool,\n simulate: bool = False,\n return_samples: bool = False) -> Optional[Tuple[sample.Sample, ...]]:\n samples = []\n for i in range(sample_count):\n smp = ast_generator.generate_sample(\n ast_generator_options, calls_per_sample,\n sample.SampleOptions(\n convert_to_ir=True,\n optimize_ir=True,\n codegen=codegen,\n simulate=simulate), rng)\n\n if return_samples:\n samples.append(smp)\n\n termcolor.cprint('=== Sample {}'.format(i), color='yellow')\n print(smp.input_text)\n\n sample_dir = tempfile.mkdtemp('run_fuzz_')\n run_sample(smp, sample_dir)\n if not save_temps:\n shutil.rmtree(sample_dir)\n\n if return_samples:\n return tuple(samples)","def get_target_per_score(self):\n pass","def worker():\n global counter\n\n fuzz()\n counter += 1\n fuzz()\n print(\"the count is %d\" % counter, end=\"\")\n fuzz()\n print(\"-------------------------\", end=\"\")\n fuzz()","def get_benchmark_file_path(cls, results_dir):\n return os.path.join(results_dir, \"benchmarks.json\")","def get_fuzzer_filestore_path(benchmark_df, fuzzer):\n fuzzer_df = benchmark_df[benchmark_df.fuzzer == fuzzer]\n filestore_path = fuzzer_df.experiment_filestore.unique()[0]\n exp_name = fuzzer_df.experiment.unique()[0]\n return posixpath.join(filestore_path, exp_name)","def _run_libfuzzer_tool(tool_name,\n testcase,\n testcase_file_path,\n timeout,\n expected_crash_state,\n set_dedup_flags=False):\n memory_tool_options_var = environment.get_current_memory_tool_var()\n saved_memory_tool_options = environment.get_value(memory_tool_options_var)\n\n def _set_dedup_flags():\n \"\"\"Allow libFuzzer to do its own crash comparison during minimization.\"\"\"\n memory_tool_options = environment.get_memory_tool_options(\n memory_tool_options_var, default_value={})\n\n memory_tool_options['symbolize'] = 1\n memory_tool_options['dedup_token_length'] = 3\n\n environment.set_memory_tool_options(memory_tool_options_var,\n memory_tool_options)\n\n def _unset_dedup_flags():\n \"\"\"Reset memory tool options.\"\"\"\n # This is needed so that when we re-run, we can symbolize ourselves\n # (ignoring inline frames).\n if saved_memory_tool_options is not None:\n environment.set_value(memory_tool_options_var, saved_memory_tool_options)\n\n output_file_path = get_temporary_file_name(testcase_file_path)\n\n arguments = data_handler.get_arguments(testcase).split()\n fuzzer_display = data_handler.get_fuzzer_display(testcase)\n\n if set_dedup_flags:\n _set_dedup_flags()\n\n try:\n result = run_libfuzzer_engine(tool_name, fuzzer_display.target, arguments,\n testcase_file_path, output_file_path, timeout)\n except TimeoutError:\n logs.log_warn('LibFuzzer timed out.')\n return None, None\n\n if set_dedup_flags:\n _unset_dedup_flags()\n\n if not os.path.exists(output_file_path):\n logs.log_warn('LibFuzzer %s run failed.' % tool_name, output=result.output)\n return None, None\n\n # Ensure that the crash parameters match. It's possible that we will\n # minimize/cleanse to an unrelated bug, such as a timeout.\n crash_result = _run_libfuzzer_testcase(testcase, output_file_path)\n state = crash_result.get_symbolized_data()\n security_flag = crash_result.is_security_issue()\n if (security_flag != testcase.security_flag or\n state.crash_state != expected_crash_state):\n logs.log_warn('Ignoring unrelated crash.\\n'\n 'State: %s (expected %s)\\n'\n 'Security: %s (expected %s)\\n'\n 'Output: %s\\n' %\n (state.crash_state, expected_crash_state, security_flag,\n testcase.security_flag, state.crash_stacktrace))\n return None, None\n\n with open(output_file_path, 'rb') as file_handle:\n minimized_keys = blobs.write_blob(file_handle)\n\n testcase.minimized_keys = minimized_keys\n testcase.put()\n\n return output_file_path, crash_result","def retrieve( self, benchmark, extraLabel='' ):\n if benchmark.reference is ReferenceBenchmark.SP:\n idx = np.argmax( self.sps )\n else:\n # Get reference for operation:\n if benchmark.reference is ReferenceBenchmark.Pd:\n ref = self.pds\n elif benchmark.reference is ReferenceBenchmark.Pf:\n ref = self.pfs\n delta = ref - benchmark.refVal\n idx = np.argmin( np.abs( delta ) )\n return PerformancePoint( name=extraLabel + benchmark.name\n , sp=self.sps[ idx ]\n , pd=self.pds[ idx ]\n , pf=self.pfs[idx]\n , thres=self.thresholds[idx]\n )","def need_target(self):\n\t\t# if we search for color targets, we are using cpu and potentially throwing the copter off the real target\n\t\t# can make this decision more complex if needded\n\t\tdelta_t = time() - self.t_last_seen\n\t\ttime_to_see = delta_t > .6 # arbitrary time threshold over which we should probably look for color targets\n\t\t\n\t\treturn time_to_see","def benchmark_profile(self):\n cb_bin = os.path.join(bin_path, 'compilebench')\n desc = \"benchmark\"\n test_name = \"compilebench_{0}\".format(to_safe_name(desc))\n test = TestProfile(\n name=test_name,\n desc=desc,\n test_path=self.test_path,\n bin_path=bin_path,\n command=\"{0} -D {1} -i 10 --makej\".format(cb_bin, self.test_path))\n\n return test","def __call__(self):\n return random.choice(self.fakers)","def compare_results(self, benchmark_result, reference_benchmark_result):\n pass","def mrae(self, benchmark: np.ndarray = None):\n return float(np.mean(np.abs(self._relative_error(benchmark))))","def target(self, example):\n return example[self.target_index]","def time_to_target_training(self) -> str:\r\n # TODO: Figure out how to implement this.\r\n self._logger.debug(log_message_formatter(\r\n \"get\", f\"{self}\", \"time_to_target_training\"))\r\n return self._training_modes[0]","def target(self) -> pulumi.Input[float]:\n return pulumi.get(self, \"target\")","def target(self) -> pulumi.Input[float]:\n return pulumi.get(self, \"target\")","def target(self) -> np.ndarray:\n return self._dist['target']","def run_fuzztest(test_one_input,\n custom_setup=None,\n setup_kwargs=None,\n expected_output=None,\n timeout=10,\n args=None,\n enabled_hooks=None):\n pipe = os.pipe()\n\n pid = os.fork()\n if pid == 0:\n _fuzztest_child(test_one_input, custom_setup or atheris.Setup,\n setup_kwargs, pipe, args, enabled_hooks)\n\n os.close(pipe[1])\n _set_nonblocking(pipe[0])\n\n stdout = b\"\"\n start_time = time.time()\n while True:\n data = b\"\"\n try:\n data = os.read(pipe[0], 1024)\n except BlockingIOError:\n pass\n\n sys.stderr.buffer.write(data)\n stdout += data\n\n if data:\n continue\n\n wpid = os.waitpid(pid, os.WNOHANG)\n\n if wpid == (0, 0):\n # Process not done yet\n if time.time() > start_time + timeout:\n os.kill(pid, signal.SIGTERM)\n raise TimeoutError(\"Fuzz target failed to exit within expected time.\")\n time.sleep(0.1)\n continue\n\n # Process done, get any remaining output.\n with os.fdopen(pipe[0], \"rb\") as f:\n data = f.read()\n sys.stderr.buffer.write(data)\n stdout += data\n break\n\n if expected_output:\n if expected_output not in stdout:\n raise AssertionError(\"Fuzz target did not produce the expected output \"\n f\"{expected_output}; actually got:\\n{stdout}\")","def fuzz_single_case(self, mutant_index):\n self.total_mutant_index = 0\n self.total_num_mutations = 1\n\n self._main_fuzz_loop(self._iterate_single_case_by_index(mutant_index))","def get_answer(self, t):\n if self.pause_time < self.time < self.q_duration:\n return self.correct[self.indices[self.data_index]]\n else:\n return np.zeros(self.n_classes)","def fuzzDevice(maxCommands=1000):\n global nFailures\n global nCommands\n global nConnects\n dev = BFieldControllerInterface(addr)\n for _ in range(random.randrange(0,maxCommands+1)):\n if dev.comTest(randomString()) is not True:\n nFailures += 1\n nCommands += 1\n dev.close()\n nConnects += 1","def get_benchmark_specification(benchmark = 'FSI1'):\n if benchmark == 'FSI1':\n rho_s = Constant(1e03)\n nu_s = Constant(0.4)\n mu_s = Constant(5e05)\n rho_f = Constant(1e03)\n nu_f = Constant(1e-03)\n U = 0.2\n T_end = 60.0\n result = \"results-FSI1/\"\n elif benchmark == 'FSI2':\n rho_s = Constant(1e04)\n nu_s = Constant(0.4)\n mu_s = Constant(5e05)\n rho_f = Constant(1e03)\n nu_f = Constant(1e-03)\n U = 1.0\n T_end = 15.0\n result = \"results-FSI2/\"\t\t\n elif benchmark == 'FSI3':\n rho_s = Constant(1e03)\n nu_s = Constant(0.4)\n mu_s = Constant(2e06)\n rho_f = Constant(1e03)\n nu_f = Constant(1e-03)\n U = 2.0\n T_end = 20.0\n result = \"results-FSI3/\"\t\t\n else:\n raise ValueError('\"{}\" is a wrong name for problem specification.'.format(benchmark))\n v_max = Constant(1.5*U) # mean velocity to maximum velocity \n # (we have parabolic profile)\n E_s = Constant(2*mu_s*(1+nu_s))\n lambda_s = Constant((nu_s*E_s)/((1+nu_s)*(1-2*nu_s)))\n mu_f = Constant(nu_f*rho_f)\n return v_max, lambda_s, mu_s, rho_s, mu_f, rho_f, T_end, result","def fuzzify(s, u):\n f_s = s @ u.T\n m_s = np_max(f_s, axis=0)\n m_s = np.maximum(m_s, 0, m_s)\n return m_s","def magabs(self):\n if not self.has_target():\n raise AttributeError(\"No target defined, I can't get the distance\")\n return self.mag - 5*( np.log10(self.target.distmpc*1.e6) - 1)","def test_get_benchmark_methods_filter(self):\n config = mock.Mock()\n config.workspace = 'workspace'\n config.benchmark_method_patterns = ['new_foo.BenchmarkClass.filter:bench.*']\n benchmark_runner = benchmark.BenchmarkRunner(config)\n\n mock_benchmark_class = mock.Mock()\n mock_benchmark_class.benchmark_method_1 = 'foo'\n\n mock_module = mock.Mock()\n sys.modules['new_foo'] = mock_module\n mock_module.BenchmarkClass.return_value = mock_benchmark_class\n\n methods = benchmark_runner._get_benchmark_methods()\n\n self.assertEqual(1, len(methods))\n self.assertEqual('new_foo.BenchmarkClass.benchmark_method_1', methods[0])","def Run(benchmark_spec):\n _UpdateBenchmarkSpecWithFlags(benchmark_spec)\n vm = benchmark_spec.vms[0]\n if benchmark_spec.tpus:\n # For MLPerf 1.0, the benchmake code of different hardware are different.\n if (benchmark_spec.tpu_groups['train'].GetAcceleratorType() == 'v3-32' or\n benchmark_spec.tpu_groups['train'].GetAcceleratorType() == 'v3-128' or\n benchmark_spec.tpu_groups['train'].GetAcceleratorType() == 'v3-256' or\n benchmark_spec.tpu_groups['train'].GetAcceleratorType() == 'v3-512' or\n benchmark_spec.tpu_groups['train'].GetAcceleratorType() == 'v3-1024' or\n benchmark_spec.tpu_groups['train'].GetAcceleratorType() == 'v3-2048'):\n run_path = (\n '$HOME/training_results_{version}/Google/benchmarks/{model}/tpu-{tpus}'\n .format(\n version=VERSION.value,\n model=benchmark_spec.benchmark,\n tpus=benchmark_spec.tpu_groups['train'].GetAcceleratorType()))\n code_path = (\n '$HOME/training_results_{version}/Google/benchmarks/{model}/implementations/tpu-{tpus}-{model}'\n .format(\n version=VERSION.value,\n model=benchmark_spec.benchmark,\n tpus=benchmark_spec.tpu_groups['train'].GetAcceleratorType()))\n\n if MASK in benchmark_spec.benchmark:\n model = 'mask_rcnn'\n elif GNMT in benchmark_spec.benchmark:\n model = 'nmt'\n else:\n model = benchmark_spec.benchmark\n\n mlperf_benchmark_cmd = (\n 'cd {code_path} && '\n 'export PYTHONPATH=$(pwd):$(pwd)/{model} && '\n 'cd {model} && '\n '{run_path}/run_and_time.sh'.format(\n code_path=code_path,\n model=model,\n run_path=run_path))\n\n if SSD in benchmark_spec.benchmark:\n mlperf_benchmark_cmd = (\n 'export '\n 'MLP_GCS_RESNET_CHECKPOINT={checkpoint}'\n ' && {cmd}'.format(\n checkpoint=FLAGS.mlperf_gcs_resnet_checkpoint,\n cmd=mlperf_benchmark_cmd))\n else:\n raise ValueError(\n 'MLPerf configurations do not support the hardware in PKB. PKB may '\n 'need to be updated if this is a new TPU type.')\n\n else:\n run_sub_paths = {RESNET: 'resnet/implementations/mxnet',\n TRANSFORMER: 'transformer/implementations/pytorch',\n MINIGO: 'minigo/implementations/tensorflow',\n MASK: 'maskrcnn/implementations/pytorch',\n GNMT: 'gnmt/implementations/pytorch',\n SSD: 'ssd/implementations/pytorch',\n BERT: 'bert/implementations/pytorch',}\n benchmark_path = f'$HOME/training_results_{VERSION.value}/NVIDIA/benchmarks'\n run_path = posixpath.join(benchmark_path,\n run_sub_paths[benchmark_spec.benchmark])\n env = {\n 'DGXSYSTEM': DGXSYSTEM,\n 'NEXP': 1,\n 'PULL': 0,\n 'LOGDIR': f'/tmp/{benchmark_spec.benchmark}',\n }\n envs = {\n RESNET: {},\n TRANSFORMER: {'DATADIR': '/data/wmt/utf8'},\n MINIGO: {'CONT': 'mlperf-nvidia:minigo'},\n MASK: {},\n GNMT: {'DATADIR': '/data/gnmt'},\n SSD: {'DATADIR': '/data'},\n BERT: {}\n }\n env.update(envs[benchmark_spec.benchmark])\n\n run_script = posixpath.join(run_path, 'run_with_docker.sh')\n vm_util.ReplaceText(vm, 'SYSLOGGING=1', 'SYSLOGGING=0', run_script)\n vm_util.ReplaceText(vm, 'docker exec -it', 'docker exec -t', run_script)\n if benchmark_spec.benchmark == RESNET:\n vm_util.ReplaceText(vm, r'mpirun.*run_and_time\\.sh',\n r'.\\/run_and_time.sh', run_script)\n\n env = ' '.join(f'{key}={value}' for key, value in env.items())\n if nvidia_driver.CheckNvidiaGpuExists(vm):\n env = f'{tensorflow.GetEnvironmentVars(vm)} {env}'\n\n mlperf_benchmark_cmd = (\n f'chmod 755 {run_script} && '\n f'cd {run_path} && '\n f'{env} {run_script}')\n\n samples = []\n metadata = _CreateMetadataDict(benchmark_spec)\n stdout, _ = vm.RobustRemoteCommand(mlperf_benchmark_cmd)\n if NONE in FLAGS.mlperf_profiler:\n samples.extend(\n MakeSamplesFromOutput(\n metadata,\n stdout,\n use_tpu=bool(benchmark_spec.tpus),\n model=benchmark_spec.benchmark))\n return samples","def matches(self, target):\n return fnmatch(str(target).lower(), self._pattern.lower())","def get_target(self, ):\n return self.get_parameter('target')","def mdrae(self, benchmark: np.ndarray = None) -> float:\n return float(np.median(np.abs(self._relative_error(benchmark))))","def guessLabel(self, sample):\n a = sample\n for layer in self.layers[1:]:\n a = layer.fastForward(a)\n return np.argmax(a)","def _fuzzy_match(\n self, target: str, scorer=None, score_cutoff=85, limit=1\n ) -> pd.Series:\n\n scorer = scorer or self.default_scorer()\n\n # result = pd.Series(name = target)\n extracted: list = process.extractBests(\n target, self.operator, scorer=scorer, limit=limit, score_cutoff=score_cutoff\n )\n df = pd.DataFrame.from_records(\n extracted, columns=[\"name\", \"index_score\", \"fuzzy_score\"]\n )\n return df","def _fuzzy_match(\n self, target: str, scorer=None, score_cutoff=85, limit=1\n ) -> pd.Series:\n\n scorer = scorer or self.default_scorer()\n\n # result = pd.Series(name = target)\n extracted: list = process.extractBests(\n target, self.operator, scorer=scorer, limit=limit, score_cutoff=score_cutoff\n )\n df = pd.DataFrame.from_records(\n extracted, columns=[\"name\", \"index_score\", \"fuzzy_score\"]\n )\n return df","def _fuzzy_match(\n self, target: str, scorer=None, score_cutoff=85, limit=1\n ) -> pd.Series:\n\n scorer = scorer or self.default_scorer()\n\n # result = pd.Series(name = target)\n extracted: list = process.extractBests(\n target, self.operator, scorer=scorer, limit=limit, score_cutoff=score_cutoff\n )\n df = pd.DataFrame.from_records(\n extracted, columns=[\"name\", \"index_score\", \"fuzzy_score\"]\n )\n return df","def extract_answer(self, answer_obj):\n if self.mode == 'test-dev' or self.mode == 'test':\n return -1\n answer_list = [answer_obj[i]['answer'] for i in range(10)]\n counter = Counter(answer_list)\n\n return counter.most_common(1)[0][0]","def defuzz(self, x, mfx, method:str='centroid', **kwargs) -> float:\n\n defuzz = DefuzzStore.get(method)()\n defuzz_value = defuzz.defuzz(x=x, mfx=mfx, method=method, params=kwargs)\n\n return defuzz_value","def get_latest_benchmark():\n\n benchmark_paths = glob.glob(\"./.benchmarks/*/*.json\")\n dates = [\n \"\".join(_b.split(\"/\")[-1].split(\"_\")[2:4]) for _b in benchmark_paths\n ]\n benchmarks = {date: value for date, value in zip(dates, benchmark_paths)}\n\n dates.sort()\n latest = dates[-1]\n benchmark_latest = benchmarks[latest]\n\n return benchmark_latest","def test_not_a_valid_fuzzer(self):\n self.assertFalse(cifuzz.check_fuzzer_build(TEST_FILES_PATH))","def Fuzz(self, str):\n exploded = [c for c in str]\n for count in range(self.count):\n size = random.randint(self.minimum, self.maximum)\n position = random.randint(0, len(str) - size)\n for iIter in range(size):\n exploded[position + iIter] = self.character\n return ''.join(exploded)","def get_fuzz_ratio(first_word, second_word):\n return fuzz.ratio(first_word, second_word), first_word, second_word","def get_target_speed(self, transform: pylot.utils.Transform,\n min_distance: float) -> float:\n wp_index = self._get_index(transform, min_distance)\n return self.target_speeds[wp_index]","def findTarget(self, initial_call):\n if self.vision.hasTarget():\n self.next_state(\"driveToTarget\")\n else:\n self.chassis.setOutput(self.SEARCH_SPEED, -self.SEARCH_SPEED)","def nearest_test_pulse(self):","def benchmark(self, df, target, test_spec=0.2):\n # Fit transformers on all data\n self.logger.info(\"Featurizing and cleaning {} samples from the entire\"\n \" dataframe.\".format(df.shape[0]))\n df = self.autofeaturizer.fit_transform(df, target)\n df = self.cleaner.fit_transform(df, target)\n\n # Split data for steps where combined transform could otherwise over-fit\n # or leak data from validation set into training set.\n if isinstance(test_spec, Iterable):\n traindf = df.iloc[~np.asarray(test_spec)]\n testdf = df.iloc[np.asarray(test_spec)]\n else:\n testdf, traindf = np.split(df.sample(frac=1),\n [int(test_spec * len(df))])\n self.logger.info(\"Dataframe split into training and testing fractions\"\n \" having {} and {} samples.\".format(traindf.shape[0],\n testdf.shape[0]))\n\n # Use transformers on separate training and testing dfs\n self.logger.info(\"Performing feature reduction and model selection on \"\n \"the {}-sample training set.\".format(traindf.shape[0]))\n traindf = self.reducer.fit_transform(traindf, target)\n self.post_fit_df = traindf\n self.learner.fit(traindf, target)\n\n if isinstance(test_spec, Iterable) or test_spec != 0:\n self.logger.info(\n \"Using pipe fitted on training data to predict target {} on \"\n \"{}-sample validation dataset\".format(target, testdf.shape[0]))\n testdf = self.reducer.transform(testdf, target)\n testdf = self.learner.predict(testdf, target)\n return testdf\n else:\n self.logger.warning(\"Validation fraction set to zero. Using \"\n \"cross-validation-only benchmarking...\")\n traindf = self.learner.predict(traindf, target)\n return traindf","def transmit_fuzz(self, sock, node, edge, callback_data):\n if callback_data:\n data = callback_data\n else:\n data = node.render()\n\n try: # send\n self.targets[0].send(data)\n self.last_send = data\n except sex.BoofuzzTargetConnectionReset:\n if self._ignore_connection_issues_when_sending_fuzz_data:\n self._fuzz_data_logger.log_info(\"Target connection reset.\")\n else:\n self._fuzz_data_logger.log_fail(\"Target connection reset.\")\n except sex.BoofuzzTargetConnectionAborted as e:\n if self._ignore_connection_issues_when_sending_fuzz_data:\n self._fuzz_data_logger.log_info(\"Target connection lost (socket error: {0} {1}): You may have a \"\n \"network issue, or an issue with firewalls or anti-virus. Try \"\n \"disabling your firewall.\"\n .format(e.socket_errno, e.socket_errmsg))\n else:\n self._fuzz_data_logger.log_fail(\"Target connection lost (socket error: {0} {1}): You may have a \"\n \"network issue, or an issue with firewalls or anti-virus. Try \"\n \"disabling your firewall.\"\n .format(e.socket_errno, e.socket_errmsg))\n\n try: # recv\n if self._receive_data_after_fuzz:\n self.last_recv = self.targets[0].recv(10000) # TODO: Remove magic number (10000)\n except sex.BoofuzzTargetConnectionReset:\n if self._check_data_received_each_request:\n self._fuzz_data_logger.log_fail(\"Target connection reset.\")\n else:\n self._fuzz_data_logger.log_info(\"Target connection reset.\")\n except sex.BoofuzzTargetConnectionAborted as e:\n if self._check_data_received_each_request:\n self._fuzz_data_logger.log_fail(\"Target connection lost (socket error: {0} {1}): You may have a \"\n \"network issue, or an issue with firewalls or anti-virus. Try \"\n \"disabling your firewall.\"\n .format(e.socket_errno, e.socket_errmsg))\n else:\n self._fuzz_data_logger.log_info(\"Target connection lost (socket error: {0} {1}): You may have a \"\n \"network issue, or an issue with firewalls or anti-virus. Try \"\n \"disabling your firewall.\"\n .format(e.socket_errno, e.socket_errmsg))\n pass","def get_perf_tester_name(self):\n original_bot_name = self.bisect_config.get('original_bot_name', '')\n recipe_tester_name = self.bisect_config.get('recipe_tester_name')\n if recipe_tester_name:\n return recipe_tester_name\n elif 'win' in original_bot_name: # pragma: no cover\n return 'win64_nv_tester'\n else: # pragma: no cover\n # Reasonable fallback.\n return 'linux_perf_tester'","def _target(self, data):\n index = self._get_index(data)\n value = data[index]\n return index, abs(value - data.mean())","def get_label(prob_label, target):\n return target if random.random() <= prob_label else 1 - target","def fuzz(self, variable, fz, type=None):\n # Check to see if the attribute exists, if so get the value\n if not hasattr(self, variable):\n raise ValueError(\"You tried to fuzz a parameter that does not exit\")\n var = getattr(self, variable)\n # if variable is an angle, treat it properly as \n # float(ephem.EllipticalBody().inc) gives the angle in radians\n if variable in ['inc', 'om', 'Om']:\n var = float(var)*180.0/math.pi\n # set fuzzer to percent\n fz = fz/100.0 if (fz > 1.0 and type is None) else fz\n var = (var*(1.0 + fz*(2.0*random()-1.0)) if type is None else\n (var + (2.0*random()-1.0)*fz))\n setattr(self, variable, var)","def c_test_weighting_bias(self, function):\r\n return general_weighting_bias(self.verbosity, function)","def c_test_weighting_bias(self, function):\r\n return general_weighting_bias(self.verbosity, function)","def c_test_weighting_bias(self, function):\r\n return general_weighting_bias(self.verbosity, function)","def get_runner_image_url(experiment, benchmark, fuzzer, docker_registry):\n tag = 'latest' if environment.get('LOCAL_EXPERIMENT') else experiment\n return f'{docker_registry}/runners/{fuzzer}/{benchmark}:{tag}'","def recall(output, target):\r\n with torch.no_grad():\r\n\r\n pred_mask = output > 0.5\r\n\r\n indx = (target == 1).nonzero(as_tuple = True)\r\n pred_ = pred_mask[indx]\r\n\r\n sum_val = pred_.cpu().numpy().sum()\r\n num_item = pred_.size(0)\r\n\r\n return float(sum_val), num_item","def __benchmark(self, clf):\n print('=' * 80)\n print('Training: ')\n print(clf)\n train_start = time()\n clf.fit(self.X_train, self.Y_train)\n train_time = time() - train_start\n print(\"The training time was: %0.3fs\" % train_time)\n\n test_start = time()\n pred = clf.predict(self.X_test)\n test_time = time() - test_start\n print(\"The test time was: %0.3fs\" % test_time)\n\n score = metrics.accuracy_score(self.Y_test, pred)\n print(\"accuracy: %0.3f\" % score)\n\n return score","def target(self) -> Optional[int]:\n return pulumi.get(self, \"target\")","def get_best_pred_finger(self,f):\n return min(self.best_finger_pred[f],\\\n key=lambda kn:dist_ident(kn.ident,self.get_finger_pred_loc(f)))","def back_test(self, turnover_frequency):","def pick(self, target: int) -> int:\n\t\tans = None\n cnt = 0\n for i, x in enumerate(self.nums): \n if x == target: \n cnt += 1\n if randint(1, cnt) == cnt: ans = i # prob 1/cnt\n return ans","def _process_failures(self, target):\n crash_synopses = self._fuzz_data_logger.failed_test_cases.get(self._fuzz_data_logger.all_test_cases[-1], [])\n if len(crash_synopses) > 0:\n self._fuzz_data_logger.open_test_step(\"Failure summary\")\n\n # retrieve the primitive that caused the crash and increment it's individual crash count.\n self.crashing_primitives[self.fuzz_node.mutant] = self.crashing_primitives.get(self.fuzz_node.mutant, 0) + 1\n self.crashing_primitives[self.fuzz_node] = self.crashing_primitives.get(self.fuzz_node, 0) + 1\n\n # print crash synopsis\n if len(crash_synopses) > 1:\n # Prepend a header if > 1 failure report, so that they are visible from the main web page\n synopsis = \"({0} reports) {1}\".format(len(crash_synopses), \"\\n\".join(crash_synopses))\n else:\n synopsis = \"\\n\".join(crash_synopses)\n self.procmon_results[self.total_mutant_index] = crash_synopses\n self._fuzz_data_logger.log_info(synopsis)\n\n if self.fuzz_node.mutant is not None and \\\n self.crashing_primitives[self.fuzz_node] >= self._crash_threshold_node:\n skipped = self.fuzz_node.num_mutations() - self.fuzz_node.mutant_index\n self._skip_current_node_after_current_test_case = True\n self._fuzz_data_logger.open_test_step(\n \"Crash threshold reached for this request, exhausting {0} mutants.\".format(skipped))\n self.total_mutant_index += skipped\n self.fuzz_node.mutant_index += skipped\n elif self.fuzz_node.mutant is not None and \\\n self.crashing_primitives[self.fuzz_node.mutant] >= self._crash_threshold_element:\n if not isinstance(self.fuzz_node.mutant, primitives.Group)\\\n and not isinstance(self.fuzz_node.mutant, blocks.Repeat):\n skipped = self.fuzz_node.mutant.num_mutations() - self.fuzz_node.mutant.mutant_index\n self._skip_current_element_after_current_test_case = True\n self._fuzz_data_logger.open_test_step(\n \"Crash threshold reached for this element, exhausting {0} mutants.\".format(skipped))\n self.total_mutant_index += skipped\n self.fuzz_node.mutant_index += skipped\n\n self.restart_target(target)\n return True\n else:\n return False","def compute_test_best(self, x, metric, target):\n index = self(x, metric)\n if metric.maximising:\n test = index > target\n else:\n test = index < target\n\n return index, test","def test_get_benchmark_methods_exact_match(self):\n config = mock.Mock()\n config.workspace = 'workspace'\n config.benchmark_method_patterns = [\n 'new_foo.BenchmarkClass.benchmark_method_1',\n 'new_foo.BenchmarkClass.benchmark_method_2']\n benchmark_runner = benchmark.BenchmarkRunner(config)\n\n methods = benchmark_runner._get_benchmark_methods()\n self.assertEqual(['new_foo.BenchmarkClass.benchmark_method_1',\n 'new_foo.BenchmarkClass.benchmark_method_2'], methods)","def getFuzzyValue(arrPredict, arrReal, frameSize=(0,0), nSize=1, banList=[0]):\r\n\r\n\r\n # Store the result both as a number and as an image\r\n fuzzyArr = numpy.zeros( (arrPredict.shape[0]-frameSize[0], arrPredict.shape[1]-frameSize[1]), dtype='float')\r\n fuzzyScore = 0.0\r\n\r\n for row in range(frameSize[0], arrPredict.shape[0]-frameSize[0]):\r\n for col in range(frameSize[1], arrPredict.shape[1]-frameSize[1]):\r\n actVal = arrReal[row, col] # We are looking for this value in the neighborhood\r\n\r\n # Don't compare values which should not be compared\r\n if actVal in banList:\r\n fuzzyArr[row, col] = 2\r\n continue\r\n\r\n fuzzyVal = 0.0\r\n distWeight = 0.0\r\n shortestDist = 999 # use infinity constant instead?\r\n\r\n # Search the neighborhood\r\n for r in range(-nSize, nSize+1):\r\n for c in range(-nSize, nSize+1):\r\n dist = (r**2 + c**2)**(1/2.0)\r\n try:\r\n foundVal = arrPredict[row+r, col+c]\r\n except:\r\n continue\r\n if foundVal in banList:\r\n continue\r\n\r\n if foundVal==actVal and dist < shortestDist:\r\n # Store the shortest distance at which we found the value\r\n distWeight = 1 - ( float(dist)/(nSize+1) )\r\n shortestDist = dist\r\n\r\n fuzzyVal = distWeight\r\n\r\n fuzzyArr[row, col] = fuzzyVal\r\n fuzzyScore += fuzzyVal\r\n return fuzzyArr, fuzzyScore","def benchmark(self, **kwargs):\n num_iterations = kwargs.get(\"benchmark_iterations\")\n\n start_time = time.time()\n\n # store how far off we are\n deviations = []\n\n for _ in xrange(num_iterations):\n kwargs[\"roll\"] = decimal.Decimal(random.uniform(\n self.MIN_BENCHMARK_ROLL, self.MAX_BENCHMARK_ROLL))\n kwargs[\"pitch\"] = decimal.Decimal(random.uniform(\n self.MIN_BENCHMARK_PITCH, self.MAX_BENCHMARK_PITCH))\n\n _, deviation = self.find_closest_trajectory(**kwargs)\n deviations.append(deviation)\n\n # calculate results from the benchmarking\n total_time = time.time() - start_time\n average_time = total_time / num_iterations\n average_deviation = sum(deviations) / len(deviations)\n\n print \"AVERAGE TIME: %s AVERAGE DEVIATION: %s\" \\\n % (average_time, average_deviation)","def _fuzz(self):\r\n\r\n # we can calculate which bytes to swap based on the number of tries\r\n # we've made on this seedfile\r\n a = self.sf.tries\r\n b = a + 1\r\n\r\n if b >= len(self.input):\r\n raise FuzzerExhaustedError('Iteration exceeds seed file length')\r\n\r\n logger.debug('%s - swap bytes %d <-> %d', self.sf.basename, a, b)\r\n self.input[b], self.input[a] = self.input[a], self.input[b]\r\n self.output = self.input","def measure_distance(actor: Entity, target: Entity) -> int:\n dst = int(actor is not target)\n return dst"],"string":"[\n \"def get_fuzzer_benchmark_key(fuzzer: str, benchmark: str):\\n return fuzzer + ' ' + benchmark\",\n \"def get_benchmark(self, benchmark):\\n\\t\\tif not isinstance(benchmark, str) and not callable(benchmark): return benchmark\\n\\t\\telif benchmark in self.classes:\\treturn self.classes[benchmark]()\\n\\t\\traise TypeError('Passed benchmark is not defined!')\",\n \"def findBenchFromDevice(self, device):\\n return device.bench\",\n \"def fuzz():\\n if FUZZ:\\n time.sleep(random.random())\",\n \"def get_fuzzer(self, *, catalog: Optional[str] = None) -> BaseFuzzer:\\n return self.get_fuzzer_cls(catalog=catalog)()\",\n \"def benchmark_result(self):\\n return self._benchmark_id\",\n \"def main():\\r\\n _evaluative_test(5)\\r\\n _fuzz_test(1)\\r\\n _fuzz_test(1, 512)\\r\\n _fuzz_test(1, 1512)\\r\\n _fuzz_test(1000)\\r\\n _fuzz_test(1000, 512)\\r\\n _fuzz_test(1000, 4077)\",\n \"def oss_fuzz_benchmark(fs):\\n benchmark_config_contents = yaml.dump(OSS_FUZZ_BENCHMARK_CONFIG)\\n benchmark_config_file = os.path.join(utils.ROOT_DIR, 'benchmarks',\\n OSS_FUZZ_BENCHMARK_NAME,\\n 'benchmark.yaml')\\n fs.create_file(benchmark_config_file, contents=benchmark_config_contents)\\n return OSS_FUZZ_BENCHMARK_NAME\",\n \"def findBenchFromInstrument(self, instrument):\\n return instrument.bench\",\n \"def get_benchmark(client):\\n r = client.get(config.API_PATH() + '/benchmarks')\\n benchmarks = json.loads(r.data)\\n return benchmarks['benchmarks'][0]['id']\",\n \"def get_fan_target(self):\\n return self.__fan_target\",\n \"def fuzz(self):\\n self.total_mutant_index = 0\\n self.total_num_mutations = self.num_mutations()\\n\\n self._main_fuzz_loop(self._iterate_protocol())\",\n \"def umbrae(self, benchmark: np.ndarray = None):\\n return self.mbrae(benchmark) / (1 - self.mbrae(benchmark))\",\n \"def get_fuzzy_match(object, answer, threshold=80):\\n answer_phrase = generate_ngrams(answer)\\n if answer_phrase:\\n best_match = [fuzz.ratio(object, phr) for phr in answer_phrase]\\n if np.max(best_match)>threshold:\\n return np.max(best_match), answer_phrase[np.argmax(best_match)]\\n else:\\n return 0,''\\n else:\\n return 0, ''\",\n \"def get_builder_image_url(benchmark, fuzzer, docker_registry):\\n return f'{docker_registry}/builders/{fuzzer}/{benchmark}'\",\n \"def benchmark(self):\\n logger.info(self.benchmark.__doc__)\\n return self.run(self.benchmark_profile())\",\n \"def fuzz(binary, sample, verbose,loop_limit, prog):\\n # PLACEHOLDER\\n # Check magic bytes / struct of sample input -> make best guess for input format\\n # This should be moved into mutation logic -> this is a shortcut for midpoint\\n sample_processed = SampleParser(sample)\\n \\n try:\\n # data: that one plaintext file\\n # ASCII text: plaintext\\n # JSON data: json\\n # CSV text: csv\\n # HTML document, ASCII text: xml2\\n mutations = { # walmart switch statement\\n 'JSON' : lambda sample_processed:JsonMutator(sample_processed.data, min=2, max=10),\\n 'CSV': lambda sample_processed:CsvMutator(sample_processed.csv(), min=2, max=10),\\n 'JFIF': lambda sample_processed:JpgMutator(sample_processed.jpg(), min=2, max=10),\\n 'XML': lambda sample_processed:XmlMutator(sample_processed.xml(), min=2, max=10),\\n 'HTML document, ASCII text': lambda sample_processed:XmlMutator(sample_processed.xml(), min=2, max=10)\\n }[sample_processed.guess](sample_processed)\\n except KeyError as e:\\n print('Unmatched data type: {}, defaulting to generic mutator'.format(e))\\n mutations = Mutator(sample_processed.data)\\n # need a default: ascii\\n except Exception as e:\\n print(\\\"mutator fucked up: {}\\\".format(e))\\n \\n print('Running fuzzer with a {} second limit...'.format(TIME_LIMIT))\\n\\n # nevermind\\n strategy = mutations.complex_mutate\\n \\n # Loop for whole timelimit \\n # In future - try multiple strategies in time limit\\n cov = float(0)\\n last = 0\\n while(1):\\n prog.iterations += 1 \\n if (prog.iterations - last >= loop_limit):\\n prog.reinit_breakpoints\\n cov = float(0)\\n mutations.reset()\\n\\n # in future, call parent method -> give me a mutation.. \\n current_input = strategy()\\n\\n # Spawn process - should be stopped after exec. \\n pid, status = prog.spawn_process(stdout=False)\\n prog.getregs()\\n # Now that the process has been spawned, we can populate the breakpoints\\n prog.populate_breakpoints()\\n if verbose:\\n print(current_input)\\n print(\\\"coverage: {}, this run: {}\\\".format(prog.coverage(), cov))\\n print(\\\"pid {}\\\".format(pid))\\n #prog.breakpoint_status()\\n\\n # Start the process proper \\n prog.cont()\\n prog.send(current_input) \\n\\n # simulate EOF \\n prog.close_input() \\n # why in the everloving fuck does RESIZING A TERMINAL alter the behaviour of waitpid ????????\\n # sigwinch. thats why. \\n \\n if prog.coverage() > cov:\\n cov = prog.coverage()\\n mutations.add_pop(current_input)\\n last = prog.iterations\\n # Wait for something to happen. \\n while(1):\\n # sigsegv doesn't count as a termination signal.\\n # since it gets caught by ptrace (only sigkill goes through ptrace) \\n # WSTOPSIG == 11 == SIGSEGV -> segfault\\n\\n \\n\\n pid, status = prog.wait()\\n if(os.WIFSTOPPED(status) and (os.WSTOPSIG(status) == signal.SIGSEGV)):\\n # Placeholder -> Need to create file with crash input and integrate \\n # fuzzing engine. \\n\\n # Update stats\\n prog.getregs()\\n prog.crash_eips.append(prog.registers.eip) \\n #if verbose:\\n # print(\\\"Input crashed program with signal: {}\\\".format(os.WSTOPSIG(status)))\\n\\n with open(\\\"bad.txt\\\", \\\"ab+\\\") as f:\\n # write the byte string\\n # since most formats have newlines in them\\n f.write(str(current_input).encode(\\\"unicode-escape\\\") + b\\\"\\\\n\\\")\\n break\\n # we have hit one of our basic block breakpoints\\n elif(os.WIFSTOPPED(status) and (os.WSTOPSIG(status) == signal.SIGTRAP)):\\n # we need to decrement eip, replace the breakpoint with its saved value\\n prog.restore_current_bp() \\n\\n elif(os.WIFEXITED(status)):\\n break\\n\\n #prog.step()\\n prog.cont()\",\n \"def GuessTargets(self, target_name):\\n return difflib.get_close_matches(target_name, self.GetTargets(), 10, 0.4)\",\n \"def calculate_appropriate_target(self):\\n pass\",\n \"def calculate_appropriate_target(self):\\n pass\",\n \"def fuzz(text):\\r\\n\\r\\n return ' '.join([fuzz_word(word) for word in text.split()])\",\n \"def defuzzification(self):\\n self.price = fuzz.defuzz(self.psf,self.fuzzy_output, 'som')\",\n \"def get_perf(target, output, mask):\\n\\n\\toutput = np.stack(output, axis=0)\\n\\tmk = mask*np.reshape(target[:,:,0] == 0, (par['num_time_steps'], par['batch_size']))\\n\\n\\ttarget = np.argmax(target, axis = 2)\\n\\toutput = np.argmax(output, axis = 2)\\n\\n\\treturn np.sum(np.float32(target == output)*np.squeeze(mk))/np.sum(mk)\",\n \"def benchmark_selection(self):\\n return self._benchmark_selection\",\n \"def run_libfuzzer_engine(tool_name, target_name, arguments, testcase_path,\\n output_path, timeout):\\n arguments = list(arguments)\\n if environment.is_trusted_host():\\n from clusterfuzz._internal.bot.untrusted_runner import tasks_host\\n\\n # TODO(ochang): Remove hardcode.\\n return tasks_host.process_testcase('libFuzzer', tool_name, target_name,\\n arguments, testcase_path, output_path,\\n timeout)\\n\\n target_path = engine_common.find_fuzzer_path(\\n environment.get_value('BUILD_DIR'), target_name)\\n if not target_path:\\n return engine.ReproduceResult([], 0, 0, '')\\n\\n engine_impl = libfuzzer_engine.Engine()\\n if tool_name == 'minimize':\\n func = engine_impl.minimize_testcase\\n else:\\n assert tool_name == 'cleanse'\\n func = engine_impl.cleanse\\n\\n return func(target_path, arguments, testcase_path, output_path, timeout)\",\n \"def get_type(benchmark):\\n # TODO(metzman): Use classes to mock a benchmark config for\\n # OSS_FUZZ_ON_DEMAND.\\n default_value = os.getenv('EXPERIMENT_TYPE', BenchmarkType.CODE.value)\\n return benchmark_config.get_config(benchmark).get('type', default_value)\",\n \"def double_fuzz(self, original,fl):\\n result = self.r.choice(self.mutators)(self, original,fl)\\n return self.r.choice(self.mutators)(self, result,fl)\",\n \"def _fuzz_current_case(self, path):\\n target = self.targets[0]\\n\\n self.pause() # only pauses conditionally\\n\\n message_path = \\\"->\\\".join([self.nodes[e.dst].name for e in path])\\n\\n if self.fuzz_node.mutant.name:\\n primitive_under_test = self.fuzz_node.mutant.name\\n else:\\n primitive_under_test = 'no-name'\\n\\n test_case_name = \\\"{0}.{1}.{2}\\\".format(message_path, primitive_under_test, self.fuzz_node.mutant_index)\\n\\n self._fuzz_data_logger.open_test_case(\\\"{0}: {1}\\\".format(self.total_mutant_index, test_case_name),\\n name=test_case_name, index=self.total_mutant_index)\\n\\n self._fuzz_data_logger.log_info(\\n \\\"Type: %s. Default value: %s. Case %d of %d overall.\\\" % (\\n type(self.fuzz_node.mutant).__name__,\\n repr(self.fuzz_node.mutant.original_value),\\n self.total_mutant_index,\\n self.total_num_mutations))\\n\\n if target.procmon:\\n self._fuzz_data_logger.open_test_step('Calling procmon pre_send()')\\n target.procmon.pre_send(self.total_mutant_index)\\n\\n if target.netmon:\\n self._fuzz_data_logger.open_test_step('Calling netmon pre_send()')\\n target.netmon.pre_send(self.total_mutant_index)\\n\\n target.open()\\n\\n self.pre_send(target)\\n\\n try:\\n for e in path[:-1]:\\n node = self.nodes[e.dst]\\n callback_data = self._callback_current_node(node=node, edge=e)\\n self._fuzz_data_logger.open_test_step(\\\"Transmit Prep Node '{0}'\\\".format(node.name))\\n self.transmit_normal(target, node, e, callback_data=callback_data)\\n\\n callback_data = self._callback_current_node(node=self.fuzz_node, edge=path[-1])\\n except sex.BoofuzzTargetConnectionReset:\\n # TODO: Switch _ignore_connection_reset for _ignore_transmission_error, or provide retry mechanism\\n if self._ignore_connection_reset:\\n self._fuzz_data_logger.log_info(\\\"Target connection reset.\\\")\\n else:\\n self._fuzz_data_logger.log_fail(\\\"Target connection reset.\\\")\\n except sex.BoofuzzTargetConnectionAborted as e:\\n # TODO: Switch _ignore_connection_aborted for _ignore_transmission_error, or provide retry mechanism\\n if self._ignore_connection_aborted:\\n self._fuzz_data_logger.log_info(\\\"Target connection lost (socket error: {0} {1}): You may have a \\\"\\n \\\"network issue, or an issue with firewalls or anti-virus. Try \\\"\\n \\\"disabling your firewall.\\\"\\n .format(e.socket_errno, e.socket_errmsg))\\n else:\\n self._fuzz_data_logger.log_fail(\\\"Target connection lost (socket error: {0} {1}): You may have a \\\"\\n \\\"network issue, or an issue with firewalls or anti-virus. Try \\\"\\n \\\"disabling your firewall.\\\"\\n .format(e.socket_errno, e.socket_errmsg))\\n\\n self._fuzz_data_logger.open_test_step(\\\"Fuzzing Node '{0}'\\\".format(self.fuzz_node.name))\\n self.transmit_fuzz(target, self.fuzz_node, path[-1], callback_data=callback_data)\\n target.close()\\n\\n if not self._check_for_passively_detected_failures(target=target):\\n self._fuzz_data_logger.open_test_step(\\\"Calling post_send function:\\\")\\n try:\\n self.post_send(target=target, fuzz_data_logger=self._fuzz_data_logger, session=self, sock=target)\\n except sex.BoofuzzTargetConnectionReset:\\n self._fuzz_data_logger.log_fail(\\n \\\"Target connection reset -- considered a failure case when triggered from post_send\\\")\\n except sex.BoofuzzTargetConnectionAborted as e:\\n self._fuzz_data_logger.log_info(\\\"Target connection lost (socket error: {0} {1}): You may have a \\\"\\n \\\"network issue, or an issue with firewalls or anti-virus. Try \\\"\\n \\\"disabling your firewall.\\\"\\n .format(e.socket_errno, e.socket_errmsg))\\n pass\\n except sex.BoofuzzTargetConnectionFailedError:\\n self._fuzz_data_logger.log_fail(\\n \\\"Cannot connect to target; target presumed down.\\\"\\n \\\" Note: Normally a failure should be detected, and the target reset.\\\"\\n \\\" This error may mean you have no restart method configured, or your error\\\"\\n \\\" detection is not working.\\\")\\n except Exception:\\n self._fuzz_data_logger.log_fail(\\n \\\"Custom post_send method raised uncaught Exception.\\\" + traceback.format_exc())\\n finally:\\n target.close()\\n self._check_procmon_failures(target=target)\\n\\n self._fuzz_data_logger.open_test_step(\\\"Sleep between tests.\\\")\\n self._fuzz_data_logger.log_info(\\\"sleeping for %f seconds\\\" % self.sleep_time)\\n time.sleep(self.sleep_time)\\n\\n self._process_failures(target=target)\\n\\n self._stop_netmon(target=target)\\n\\n self.export_file()\",\n \"def test_make_benchmark_single_ll():\\n benchmark = llvm.make_benchmark(INVALID_IR_PATH)\\n assert str(benchmark.uri).startswith(\\\"benchmark://user-v0/\\\")\\n assert benchmark.uri.scheme == \\\"benchmark\\\"\\n assert benchmark.uri.dataset == \\\"user-v0\\\"\",\n \"def reference_benchmark_result(self):\\n return self._reference_id\",\n \"def get_random_target():\\n return get_random_colour(ColourTargetColBG)\",\n \"def test_correct_fuzzer_build(self):\\n test_fuzzer_dir = os.path.join(TEST_FILES_PATH, 'out')\\n self.assertTrue(cifuzz.check_fuzzer_build(test_fuzzer_dir))\",\n \"def run(self, runner):\\n # record the bad input\\n mutated = self.fuzz()\\n print(f\\\"Trying {mutated}\\\")\\n result = runner.run(mutated)[0]\\n\\n if result.returncode < 0:\\n with open(\\\"bad.txt\\\", 'a') as f: \\n f.write(mutated)\\n f.write(\\\"\\\\n\\\")\\n return runner.run(mutated)\",\n \"def _get_target(self, prediction: Tensor) -> Tensor:\\n if self.model_config.mode == ModelMode.binary_classification:\\n # TODO: Allow customization of the thresholds used below.\\n if self.model_config.return_type.value == 'raw':\\n return (prediction > 0).long().view(-1)\\n if self.model_config.return_type.value == 'probs':\\n return (prediction > 0.5).long().view(-1)\\n assert False\\n\\n if self.model_config.mode == ModelMode.multiclass_classification:\\n return prediction.argmax(dim=-1)\\n\\n return prediction\",\n \"def run_benchmark(curl, benchmark, test_config = TestConfig()):\\n\\n warmup_runs = benchmark.warmup_runs\\n benchmark_runs = benchmark.benchmark_runs\\n message = '' #Message is name of benchmark... print it?\\n\\n if (warmup_runs <= 0):\\n raise Exception(\\\"Invalid number of warmup runs, must be > 0 :\\\" + warmup_runs)\\n if (benchmark_runs <= 0):\\n raise Exception(\\\"Invalid number of benchmark runs, must be > 0 :\\\" + benchmark_runs)\\n\\n #Initialize variables to store output\\n output = BenchmarkResult()\\n output.name = benchmark.name\\n output.group = benchmark.group\\n metricnames = list(benchmark.metrics)\\n metricvalues = [METRICS[name] for name in metricnames] # Metric variable for curl, to avoid hash lookup for every metric name\\n results = [list() for x in xrange(0, len(metricnames))] # Initialize arrays to store results for each metric\\n\\n curl.setopt(pycurl.WRITEFUNCTION, lambda x: None) #Do not store actual response body at all.\\n\\n #Benchmark warm-up to allow for caching, JIT compiling, on client\\n logging.info('Warmup: ' + message + ' started')\\n for x in xrange(0, warmup_runs):\\n if benchmark.method == u'POST' or benchmark.method == u'PUT':\\n curl.setopt(curl.READFUNCTION, StringIO.StringIO(benchmark.body).read)\\n curl.perform()\\n logging.info('Warmup: ' + message + ' finished')\\n\\n logging.info('Benchmark: ' + message + ' starting')\\n\\n for x in xrange(0, benchmark_runs): # Run the actual benchmarks\\n if benchmark.method == u'POST' or benchmark.method == u'PUT':\\n curl.setopt(curl.READFUNCTION, StringIO.StringIO(benchmark.body).read)\\n\\n try: # Run the curl call, if it errors, then add to failure counts for benchmark\\n curl.perform()\\n except Exception:\\n output.failures = output.failures + 1\\n continue # Skip metrics collection\\n\\n # Get all metrics values for this run, and store to metric lists\\n for i in xrange(0, len(metricnames)):\\n results[i].append( curl.getinfo(metricvalues[i]) )\\n\\n logging.info('Benchmark: ' + message + ' ending')\\n\\n temp_results = dict()\\n for i in xrange(0, len(metricnames)):\\n temp_results[metricnames[i]] = results[i]\\n output.results = temp_results\\n\\n curl.close()\\n return analyze_benchmark_results(output, benchmark)\",\n \"def run_fuzz(\\n rng: ast_generator.RngState,\\n ast_generator_options: ast_generator.AstGeneratorOptions,\\n calls_per_sample: int,\\n save_temps: bool,\\n sample_count: int,\\n codegen: bool,\\n simulate: bool = False,\\n return_samples: bool = False) -> Optional[Tuple[sample.Sample, ...]]:\\n samples = []\\n for i in range(sample_count):\\n smp = ast_generator.generate_sample(\\n ast_generator_options, calls_per_sample,\\n sample.SampleOptions(\\n convert_to_ir=True,\\n optimize_ir=True,\\n codegen=codegen,\\n simulate=simulate), rng)\\n\\n if return_samples:\\n samples.append(smp)\\n\\n termcolor.cprint('=== Sample {}'.format(i), color='yellow')\\n print(smp.input_text)\\n\\n sample_dir = tempfile.mkdtemp('run_fuzz_')\\n run_sample(smp, sample_dir)\\n if not save_temps:\\n shutil.rmtree(sample_dir)\\n\\n if return_samples:\\n return tuple(samples)\",\n \"def get_target_per_score(self):\\n pass\",\n \"def worker():\\n global counter\\n\\n fuzz()\\n counter += 1\\n fuzz()\\n print(\\\"the count is %d\\\" % counter, end=\\\"\\\")\\n fuzz()\\n print(\\\"-------------------------\\\", end=\\\"\\\")\\n fuzz()\",\n \"def get_benchmark_file_path(cls, results_dir):\\n return os.path.join(results_dir, \\\"benchmarks.json\\\")\",\n \"def get_fuzzer_filestore_path(benchmark_df, fuzzer):\\n fuzzer_df = benchmark_df[benchmark_df.fuzzer == fuzzer]\\n filestore_path = fuzzer_df.experiment_filestore.unique()[0]\\n exp_name = fuzzer_df.experiment.unique()[0]\\n return posixpath.join(filestore_path, exp_name)\",\n \"def _run_libfuzzer_tool(tool_name,\\n testcase,\\n testcase_file_path,\\n timeout,\\n expected_crash_state,\\n set_dedup_flags=False):\\n memory_tool_options_var = environment.get_current_memory_tool_var()\\n saved_memory_tool_options = environment.get_value(memory_tool_options_var)\\n\\n def _set_dedup_flags():\\n \\\"\\\"\\\"Allow libFuzzer to do its own crash comparison during minimization.\\\"\\\"\\\"\\n memory_tool_options = environment.get_memory_tool_options(\\n memory_tool_options_var, default_value={})\\n\\n memory_tool_options['symbolize'] = 1\\n memory_tool_options['dedup_token_length'] = 3\\n\\n environment.set_memory_tool_options(memory_tool_options_var,\\n memory_tool_options)\\n\\n def _unset_dedup_flags():\\n \\\"\\\"\\\"Reset memory tool options.\\\"\\\"\\\"\\n # This is needed so that when we re-run, we can symbolize ourselves\\n # (ignoring inline frames).\\n if saved_memory_tool_options is not None:\\n environment.set_value(memory_tool_options_var, saved_memory_tool_options)\\n\\n output_file_path = get_temporary_file_name(testcase_file_path)\\n\\n arguments = data_handler.get_arguments(testcase).split()\\n fuzzer_display = data_handler.get_fuzzer_display(testcase)\\n\\n if set_dedup_flags:\\n _set_dedup_flags()\\n\\n try:\\n result = run_libfuzzer_engine(tool_name, fuzzer_display.target, arguments,\\n testcase_file_path, output_file_path, timeout)\\n except TimeoutError:\\n logs.log_warn('LibFuzzer timed out.')\\n return None, None\\n\\n if set_dedup_flags:\\n _unset_dedup_flags()\\n\\n if not os.path.exists(output_file_path):\\n logs.log_warn('LibFuzzer %s run failed.' % tool_name, output=result.output)\\n return None, None\\n\\n # Ensure that the crash parameters match. It's possible that we will\\n # minimize/cleanse to an unrelated bug, such as a timeout.\\n crash_result = _run_libfuzzer_testcase(testcase, output_file_path)\\n state = crash_result.get_symbolized_data()\\n security_flag = crash_result.is_security_issue()\\n if (security_flag != testcase.security_flag or\\n state.crash_state != expected_crash_state):\\n logs.log_warn('Ignoring unrelated crash.\\\\n'\\n 'State: %s (expected %s)\\\\n'\\n 'Security: %s (expected %s)\\\\n'\\n 'Output: %s\\\\n' %\\n (state.crash_state, expected_crash_state, security_flag,\\n testcase.security_flag, state.crash_stacktrace))\\n return None, None\\n\\n with open(output_file_path, 'rb') as file_handle:\\n minimized_keys = blobs.write_blob(file_handle)\\n\\n testcase.minimized_keys = minimized_keys\\n testcase.put()\\n\\n return output_file_path, crash_result\",\n \"def retrieve( self, benchmark, extraLabel='' ):\\n if benchmark.reference is ReferenceBenchmark.SP:\\n idx = np.argmax( self.sps )\\n else:\\n # Get reference for operation:\\n if benchmark.reference is ReferenceBenchmark.Pd:\\n ref = self.pds\\n elif benchmark.reference is ReferenceBenchmark.Pf:\\n ref = self.pfs\\n delta = ref - benchmark.refVal\\n idx = np.argmin( np.abs( delta ) )\\n return PerformancePoint( name=extraLabel + benchmark.name\\n , sp=self.sps[ idx ]\\n , pd=self.pds[ idx ]\\n , pf=self.pfs[idx]\\n , thres=self.thresholds[idx]\\n )\",\n \"def need_target(self):\\n\\t\\t# if we search for color targets, we are using cpu and potentially throwing the copter off the real target\\n\\t\\t# can make this decision more complex if needded\\n\\t\\tdelta_t = time() - self.t_last_seen\\n\\t\\ttime_to_see = delta_t > .6 # arbitrary time threshold over which we should probably look for color targets\\n\\t\\t\\n\\t\\treturn time_to_see\",\n \"def benchmark_profile(self):\\n cb_bin = os.path.join(bin_path, 'compilebench')\\n desc = \\\"benchmark\\\"\\n test_name = \\\"compilebench_{0}\\\".format(to_safe_name(desc))\\n test = TestProfile(\\n name=test_name,\\n desc=desc,\\n test_path=self.test_path,\\n bin_path=bin_path,\\n command=\\\"{0} -D {1} -i 10 --makej\\\".format(cb_bin, self.test_path))\\n\\n return test\",\n \"def __call__(self):\\n return random.choice(self.fakers)\",\n \"def compare_results(self, benchmark_result, reference_benchmark_result):\\n pass\",\n \"def mrae(self, benchmark: np.ndarray = None):\\n return float(np.mean(np.abs(self._relative_error(benchmark))))\",\n \"def target(self, example):\\n return example[self.target_index]\",\n \"def time_to_target_training(self) -> str:\\r\\n # TODO: Figure out how to implement this.\\r\\n self._logger.debug(log_message_formatter(\\r\\n \\\"get\\\", f\\\"{self}\\\", \\\"time_to_target_training\\\"))\\r\\n return self._training_modes[0]\",\n \"def target(self) -> pulumi.Input[float]:\\n return pulumi.get(self, \\\"target\\\")\",\n \"def target(self) -> pulumi.Input[float]:\\n return pulumi.get(self, \\\"target\\\")\",\n \"def target(self) -> np.ndarray:\\n return self._dist['target']\",\n \"def run_fuzztest(test_one_input,\\n custom_setup=None,\\n setup_kwargs=None,\\n expected_output=None,\\n timeout=10,\\n args=None,\\n enabled_hooks=None):\\n pipe = os.pipe()\\n\\n pid = os.fork()\\n if pid == 0:\\n _fuzztest_child(test_one_input, custom_setup or atheris.Setup,\\n setup_kwargs, pipe, args, enabled_hooks)\\n\\n os.close(pipe[1])\\n _set_nonblocking(pipe[0])\\n\\n stdout = b\\\"\\\"\\n start_time = time.time()\\n while True:\\n data = b\\\"\\\"\\n try:\\n data = os.read(pipe[0], 1024)\\n except BlockingIOError:\\n pass\\n\\n sys.stderr.buffer.write(data)\\n stdout += data\\n\\n if data:\\n continue\\n\\n wpid = os.waitpid(pid, os.WNOHANG)\\n\\n if wpid == (0, 0):\\n # Process not done yet\\n if time.time() > start_time + timeout:\\n os.kill(pid, signal.SIGTERM)\\n raise TimeoutError(\\\"Fuzz target failed to exit within expected time.\\\")\\n time.sleep(0.1)\\n continue\\n\\n # Process done, get any remaining output.\\n with os.fdopen(pipe[0], \\\"rb\\\") as f:\\n data = f.read()\\n sys.stderr.buffer.write(data)\\n stdout += data\\n break\\n\\n if expected_output:\\n if expected_output not in stdout:\\n raise AssertionError(\\\"Fuzz target did not produce the expected output \\\"\\n f\\\"{expected_output}; actually got:\\\\n{stdout}\\\")\",\n \"def fuzz_single_case(self, mutant_index):\\n self.total_mutant_index = 0\\n self.total_num_mutations = 1\\n\\n self._main_fuzz_loop(self._iterate_single_case_by_index(mutant_index))\",\n \"def get_answer(self, t):\\n if self.pause_time < self.time < self.q_duration:\\n return self.correct[self.indices[self.data_index]]\\n else:\\n return np.zeros(self.n_classes)\",\n \"def fuzzDevice(maxCommands=1000):\\n global nFailures\\n global nCommands\\n global nConnects\\n dev = BFieldControllerInterface(addr)\\n for _ in range(random.randrange(0,maxCommands+1)):\\n if dev.comTest(randomString()) is not True:\\n nFailures += 1\\n nCommands += 1\\n dev.close()\\n nConnects += 1\",\n \"def get_benchmark_specification(benchmark = 'FSI1'):\\n if benchmark == 'FSI1':\\n rho_s = Constant(1e03)\\n nu_s = Constant(0.4)\\n mu_s = Constant(5e05)\\n rho_f = Constant(1e03)\\n nu_f = Constant(1e-03)\\n U = 0.2\\n T_end = 60.0\\n result = \\\"results-FSI1/\\\"\\n elif benchmark == 'FSI2':\\n rho_s = Constant(1e04)\\n nu_s = Constant(0.4)\\n mu_s = Constant(5e05)\\n rho_f = Constant(1e03)\\n nu_f = Constant(1e-03)\\n U = 1.0\\n T_end = 15.0\\n result = \\\"results-FSI2/\\\"\\t\\t\\n elif benchmark == 'FSI3':\\n rho_s = Constant(1e03)\\n nu_s = Constant(0.4)\\n mu_s = Constant(2e06)\\n rho_f = Constant(1e03)\\n nu_f = Constant(1e-03)\\n U = 2.0\\n T_end = 20.0\\n result = \\\"results-FSI3/\\\"\\t\\t\\n else:\\n raise ValueError('\\\"{}\\\" is a wrong name for problem specification.'.format(benchmark))\\n v_max = Constant(1.5*U) # mean velocity to maximum velocity \\n # (we have parabolic profile)\\n E_s = Constant(2*mu_s*(1+nu_s))\\n lambda_s = Constant((nu_s*E_s)/((1+nu_s)*(1-2*nu_s)))\\n mu_f = Constant(nu_f*rho_f)\\n return v_max, lambda_s, mu_s, rho_s, mu_f, rho_f, T_end, result\",\n \"def fuzzify(s, u):\\n f_s = s @ u.T\\n m_s = np_max(f_s, axis=0)\\n m_s = np.maximum(m_s, 0, m_s)\\n return m_s\",\n \"def magabs(self):\\n if not self.has_target():\\n raise AttributeError(\\\"No target defined, I can't get the distance\\\")\\n return self.mag - 5*( np.log10(self.target.distmpc*1.e6) - 1)\",\n \"def test_get_benchmark_methods_filter(self):\\n config = mock.Mock()\\n config.workspace = 'workspace'\\n config.benchmark_method_patterns = ['new_foo.BenchmarkClass.filter:bench.*']\\n benchmark_runner = benchmark.BenchmarkRunner(config)\\n\\n mock_benchmark_class = mock.Mock()\\n mock_benchmark_class.benchmark_method_1 = 'foo'\\n\\n mock_module = mock.Mock()\\n sys.modules['new_foo'] = mock_module\\n mock_module.BenchmarkClass.return_value = mock_benchmark_class\\n\\n methods = benchmark_runner._get_benchmark_methods()\\n\\n self.assertEqual(1, len(methods))\\n self.assertEqual('new_foo.BenchmarkClass.benchmark_method_1', methods[0])\",\n \"def Run(benchmark_spec):\\n _UpdateBenchmarkSpecWithFlags(benchmark_spec)\\n vm = benchmark_spec.vms[0]\\n if benchmark_spec.tpus:\\n # For MLPerf 1.0, the benchmake code of different hardware are different.\\n if (benchmark_spec.tpu_groups['train'].GetAcceleratorType() == 'v3-32' or\\n benchmark_spec.tpu_groups['train'].GetAcceleratorType() == 'v3-128' or\\n benchmark_spec.tpu_groups['train'].GetAcceleratorType() == 'v3-256' or\\n benchmark_spec.tpu_groups['train'].GetAcceleratorType() == 'v3-512' or\\n benchmark_spec.tpu_groups['train'].GetAcceleratorType() == 'v3-1024' or\\n benchmark_spec.tpu_groups['train'].GetAcceleratorType() == 'v3-2048'):\\n run_path = (\\n '$HOME/training_results_{version}/Google/benchmarks/{model}/tpu-{tpus}'\\n .format(\\n version=VERSION.value,\\n model=benchmark_spec.benchmark,\\n tpus=benchmark_spec.tpu_groups['train'].GetAcceleratorType()))\\n code_path = (\\n '$HOME/training_results_{version}/Google/benchmarks/{model}/implementations/tpu-{tpus}-{model}'\\n .format(\\n version=VERSION.value,\\n model=benchmark_spec.benchmark,\\n tpus=benchmark_spec.tpu_groups['train'].GetAcceleratorType()))\\n\\n if MASK in benchmark_spec.benchmark:\\n model = 'mask_rcnn'\\n elif GNMT in benchmark_spec.benchmark:\\n model = 'nmt'\\n else:\\n model = benchmark_spec.benchmark\\n\\n mlperf_benchmark_cmd = (\\n 'cd {code_path} && '\\n 'export PYTHONPATH=$(pwd):$(pwd)/{model} && '\\n 'cd {model} && '\\n '{run_path}/run_and_time.sh'.format(\\n code_path=code_path,\\n model=model,\\n run_path=run_path))\\n\\n if SSD in benchmark_spec.benchmark:\\n mlperf_benchmark_cmd = (\\n 'export '\\n 'MLP_GCS_RESNET_CHECKPOINT={checkpoint}'\\n ' && {cmd}'.format(\\n checkpoint=FLAGS.mlperf_gcs_resnet_checkpoint,\\n cmd=mlperf_benchmark_cmd))\\n else:\\n raise ValueError(\\n 'MLPerf configurations do not support the hardware in PKB. PKB may '\\n 'need to be updated if this is a new TPU type.')\\n\\n else:\\n run_sub_paths = {RESNET: 'resnet/implementations/mxnet',\\n TRANSFORMER: 'transformer/implementations/pytorch',\\n MINIGO: 'minigo/implementations/tensorflow',\\n MASK: 'maskrcnn/implementations/pytorch',\\n GNMT: 'gnmt/implementations/pytorch',\\n SSD: 'ssd/implementations/pytorch',\\n BERT: 'bert/implementations/pytorch',}\\n benchmark_path = f'$HOME/training_results_{VERSION.value}/NVIDIA/benchmarks'\\n run_path = posixpath.join(benchmark_path,\\n run_sub_paths[benchmark_spec.benchmark])\\n env = {\\n 'DGXSYSTEM': DGXSYSTEM,\\n 'NEXP': 1,\\n 'PULL': 0,\\n 'LOGDIR': f'/tmp/{benchmark_spec.benchmark}',\\n }\\n envs = {\\n RESNET: {},\\n TRANSFORMER: {'DATADIR': '/data/wmt/utf8'},\\n MINIGO: {'CONT': 'mlperf-nvidia:minigo'},\\n MASK: {},\\n GNMT: {'DATADIR': '/data/gnmt'},\\n SSD: {'DATADIR': '/data'},\\n BERT: {}\\n }\\n env.update(envs[benchmark_spec.benchmark])\\n\\n run_script = posixpath.join(run_path, 'run_with_docker.sh')\\n vm_util.ReplaceText(vm, 'SYSLOGGING=1', 'SYSLOGGING=0', run_script)\\n vm_util.ReplaceText(vm, 'docker exec -it', 'docker exec -t', run_script)\\n if benchmark_spec.benchmark == RESNET:\\n vm_util.ReplaceText(vm, r'mpirun.*run_and_time\\\\.sh',\\n r'.\\\\/run_and_time.sh', run_script)\\n\\n env = ' '.join(f'{key}={value}' for key, value in env.items())\\n if nvidia_driver.CheckNvidiaGpuExists(vm):\\n env = f'{tensorflow.GetEnvironmentVars(vm)} {env}'\\n\\n mlperf_benchmark_cmd = (\\n f'chmod 755 {run_script} && '\\n f'cd {run_path} && '\\n f'{env} {run_script}')\\n\\n samples = []\\n metadata = _CreateMetadataDict(benchmark_spec)\\n stdout, _ = vm.RobustRemoteCommand(mlperf_benchmark_cmd)\\n if NONE in FLAGS.mlperf_profiler:\\n samples.extend(\\n MakeSamplesFromOutput(\\n metadata,\\n stdout,\\n use_tpu=bool(benchmark_spec.tpus),\\n model=benchmark_spec.benchmark))\\n return samples\",\n \"def matches(self, target):\\n return fnmatch(str(target).lower(), self._pattern.lower())\",\n \"def get_target(self, ):\\n return self.get_parameter('target')\",\n \"def mdrae(self, benchmark: np.ndarray = None) -> float:\\n return float(np.median(np.abs(self._relative_error(benchmark))))\",\n \"def guessLabel(self, sample):\\n a = sample\\n for layer in self.layers[1:]:\\n a = layer.fastForward(a)\\n return np.argmax(a)\",\n \"def _fuzzy_match(\\n self, target: str, scorer=None, score_cutoff=85, limit=1\\n ) -> pd.Series:\\n\\n scorer = scorer or self.default_scorer()\\n\\n # result = pd.Series(name = target)\\n extracted: list = process.extractBests(\\n target, self.operator, scorer=scorer, limit=limit, score_cutoff=score_cutoff\\n )\\n df = pd.DataFrame.from_records(\\n extracted, columns=[\\\"name\\\", \\\"index_score\\\", \\\"fuzzy_score\\\"]\\n )\\n return df\",\n \"def _fuzzy_match(\\n self, target: str, scorer=None, score_cutoff=85, limit=1\\n ) -> pd.Series:\\n\\n scorer = scorer or self.default_scorer()\\n\\n # result = pd.Series(name = target)\\n extracted: list = process.extractBests(\\n target, self.operator, scorer=scorer, limit=limit, score_cutoff=score_cutoff\\n )\\n df = pd.DataFrame.from_records(\\n extracted, columns=[\\\"name\\\", \\\"index_score\\\", \\\"fuzzy_score\\\"]\\n )\\n return df\",\n \"def _fuzzy_match(\\n self, target: str, scorer=None, score_cutoff=85, limit=1\\n ) -> pd.Series:\\n\\n scorer = scorer or self.default_scorer()\\n\\n # result = pd.Series(name = target)\\n extracted: list = process.extractBests(\\n target, self.operator, scorer=scorer, limit=limit, score_cutoff=score_cutoff\\n )\\n df = pd.DataFrame.from_records(\\n extracted, columns=[\\\"name\\\", \\\"index_score\\\", \\\"fuzzy_score\\\"]\\n )\\n return df\",\n \"def extract_answer(self, answer_obj):\\n if self.mode == 'test-dev' or self.mode == 'test':\\n return -1\\n answer_list = [answer_obj[i]['answer'] for i in range(10)]\\n counter = Counter(answer_list)\\n\\n return counter.most_common(1)[0][0]\",\n \"def defuzz(self, x, mfx, method:str='centroid', **kwargs) -> float:\\n\\n defuzz = DefuzzStore.get(method)()\\n defuzz_value = defuzz.defuzz(x=x, mfx=mfx, method=method, params=kwargs)\\n\\n return defuzz_value\",\n \"def get_latest_benchmark():\\n\\n benchmark_paths = glob.glob(\\\"./.benchmarks/*/*.json\\\")\\n dates = [\\n \\\"\\\".join(_b.split(\\\"/\\\")[-1].split(\\\"_\\\")[2:4]) for _b in benchmark_paths\\n ]\\n benchmarks = {date: value for date, value in zip(dates, benchmark_paths)}\\n\\n dates.sort()\\n latest = dates[-1]\\n benchmark_latest = benchmarks[latest]\\n\\n return benchmark_latest\",\n \"def test_not_a_valid_fuzzer(self):\\n self.assertFalse(cifuzz.check_fuzzer_build(TEST_FILES_PATH))\",\n \"def Fuzz(self, str):\\n exploded = [c for c in str]\\n for count in range(self.count):\\n size = random.randint(self.minimum, self.maximum)\\n position = random.randint(0, len(str) - size)\\n for iIter in range(size):\\n exploded[position + iIter] = self.character\\n return ''.join(exploded)\",\n \"def get_fuzz_ratio(first_word, second_word):\\n return fuzz.ratio(first_word, second_word), first_word, second_word\",\n \"def get_target_speed(self, transform: pylot.utils.Transform,\\n min_distance: float) -> float:\\n wp_index = self._get_index(transform, min_distance)\\n return self.target_speeds[wp_index]\",\n \"def findTarget(self, initial_call):\\n if self.vision.hasTarget():\\n self.next_state(\\\"driveToTarget\\\")\\n else:\\n self.chassis.setOutput(self.SEARCH_SPEED, -self.SEARCH_SPEED)\",\n \"def nearest_test_pulse(self):\",\n \"def benchmark(self, df, target, test_spec=0.2):\\n # Fit transformers on all data\\n self.logger.info(\\\"Featurizing and cleaning {} samples from the entire\\\"\\n \\\" dataframe.\\\".format(df.shape[0]))\\n df = self.autofeaturizer.fit_transform(df, target)\\n df = self.cleaner.fit_transform(df, target)\\n\\n # Split data for steps where combined transform could otherwise over-fit\\n # or leak data from validation set into training set.\\n if isinstance(test_spec, Iterable):\\n traindf = df.iloc[~np.asarray(test_spec)]\\n testdf = df.iloc[np.asarray(test_spec)]\\n else:\\n testdf, traindf = np.split(df.sample(frac=1),\\n [int(test_spec * len(df))])\\n self.logger.info(\\\"Dataframe split into training and testing fractions\\\"\\n \\\" having {} and {} samples.\\\".format(traindf.shape[0],\\n testdf.shape[0]))\\n\\n # Use transformers on separate training and testing dfs\\n self.logger.info(\\\"Performing feature reduction and model selection on \\\"\\n \\\"the {}-sample training set.\\\".format(traindf.shape[0]))\\n traindf = self.reducer.fit_transform(traindf, target)\\n self.post_fit_df = traindf\\n self.learner.fit(traindf, target)\\n\\n if isinstance(test_spec, Iterable) or test_spec != 0:\\n self.logger.info(\\n \\\"Using pipe fitted on training data to predict target {} on \\\"\\n \\\"{}-sample validation dataset\\\".format(target, testdf.shape[0]))\\n testdf = self.reducer.transform(testdf, target)\\n testdf = self.learner.predict(testdf, target)\\n return testdf\\n else:\\n self.logger.warning(\\\"Validation fraction set to zero. Using \\\"\\n \\\"cross-validation-only benchmarking...\\\")\\n traindf = self.learner.predict(traindf, target)\\n return traindf\",\n \"def transmit_fuzz(self, sock, node, edge, callback_data):\\n if callback_data:\\n data = callback_data\\n else:\\n data = node.render()\\n\\n try: # send\\n self.targets[0].send(data)\\n self.last_send = data\\n except sex.BoofuzzTargetConnectionReset:\\n if self._ignore_connection_issues_when_sending_fuzz_data:\\n self._fuzz_data_logger.log_info(\\\"Target connection reset.\\\")\\n else:\\n self._fuzz_data_logger.log_fail(\\\"Target connection reset.\\\")\\n except sex.BoofuzzTargetConnectionAborted as e:\\n if self._ignore_connection_issues_when_sending_fuzz_data:\\n self._fuzz_data_logger.log_info(\\\"Target connection lost (socket error: {0} {1}): You may have a \\\"\\n \\\"network issue, or an issue with firewalls or anti-virus. Try \\\"\\n \\\"disabling your firewall.\\\"\\n .format(e.socket_errno, e.socket_errmsg))\\n else:\\n self._fuzz_data_logger.log_fail(\\\"Target connection lost (socket error: {0} {1}): You may have a \\\"\\n \\\"network issue, or an issue with firewalls or anti-virus. Try \\\"\\n \\\"disabling your firewall.\\\"\\n .format(e.socket_errno, e.socket_errmsg))\\n\\n try: # recv\\n if self._receive_data_after_fuzz:\\n self.last_recv = self.targets[0].recv(10000) # TODO: Remove magic number (10000)\\n except sex.BoofuzzTargetConnectionReset:\\n if self._check_data_received_each_request:\\n self._fuzz_data_logger.log_fail(\\\"Target connection reset.\\\")\\n else:\\n self._fuzz_data_logger.log_info(\\\"Target connection reset.\\\")\\n except sex.BoofuzzTargetConnectionAborted as e:\\n if self._check_data_received_each_request:\\n self._fuzz_data_logger.log_fail(\\\"Target connection lost (socket error: {0} {1}): You may have a \\\"\\n \\\"network issue, or an issue with firewalls or anti-virus. Try \\\"\\n \\\"disabling your firewall.\\\"\\n .format(e.socket_errno, e.socket_errmsg))\\n else:\\n self._fuzz_data_logger.log_info(\\\"Target connection lost (socket error: {0} {1}): You may have a \\\"\\n \\\"network issue, or an issue with firewalls or anti-virus. Try \\\"\\n \\\"disabling your firewall.\\\"\\n .format(e.socket_errno, e.socket_errmsg))\\n pass\",\n \"def get_perf_tester_name(self):\\n original_bot_name = self.bisect_config.get('original_bot_name', '')\\n recipe_tester_name = self.bisect_config.get('recipe_tester_name')\\n if recipe_tester_name:\\n return recipe_tester_name\\n elif 'win' in original_bot_name: # pragma: no cover\\n return 'win64_nv_tester'\\n else: # pragma: no cover\\n # Reasonable fallback.\\n return 'linux_perf_tester'\",\n \"def _target(self, data):\\n index = self._get_index(data)\\n value = data[index]\\n return index, abs(value - data.mean())\",\n \"def get_label(prob_label, target):\\n return target if random.random() <= prob_label else 1 - target\",\n \"def fuzz(self, variable, fz, type=None):\\n # Check to see if the attribute exists, if so get the value\\n if not hasattr(self, variable):\\n raise ValueError(\\\"You tried to fuzz a parameter that does not exit\\\")\\n var = getattr(self, variable)\\n # if variable is an angle, treat it properly as \\n # float(ephem.EllipticalBody().inc) gives the angle in radians\\n if variable in ['inc', 'om', 'Om']:\\n var = float(var)*180.0/math.pi\\n # set fuzzer to percent\\n fz = fz/100.0 if (fz > 1.0 and type is None) else fz\\n var = (var*(1.0 + fz*(2.0*random()-1.0)) if type is None else\\n (var + (2.0*random()-1.0)*fz))\\n setattr(self, variable, var)\",\n \"def c_test_weighting_bias(self, function):\\r\\n return general_weighting_bias(self.verbosity, function)\",\n \"def c_test_weighting_bias(self, function):\\r\\n return general_weighting_bias(self.verbosity, function)\",\n \"def c_test_weighting_bias(self, function):\\r\\n return general_weighting_bias(self.verbosity, function)\",\n \"def get_runner_image_url(experiment, benchmark, fuzzer, docker_registry):\\n tag = 'latest' if environment.get('LOCAL_EXPERIMENT') else experiment\\n return f'{docker_registry}/runners/{fuzzer}/{benchmark}:{tag}'\",\n \"def recall(output, target):\\r\\n with torch.no_grad():\\r\\n\\r\\n pred_mask = output > 0.5\\r\\n\\r\\n indx = (target == 1).nonzero(as_tuple = True)\\r\\n pred_ = pred_mask[indx]\\r\\n\\r\\n sum_val = pred_.cpu().numpy().sum()\\r\\n num_item = pred_.size(0)\\r\\n\\r\\n return float(sum_val), num_item\",\n \"def __benchmark(self, clf):\\n print('=' * 80)\\n print('Training: ')\\n print(clf)\\n train_start = time()\\n clf.fit(self.X_train, self.Y_train)\\n train_time = time() - train_start\\n print(\\\"The training time was: %0.3fs\\\" % train_time)\\n\\n test_start = time()\\n pred = clf.predict(self.X_test)\\n test_time = time() - test_start\\n print(\\\"The test time was: %0.3fs\\\" % test_time)\\n\\n score = metrics.accuracy_score(self.Y_test, pred)\\n print(\\\"accuracy: %0.3f\\\" % score)\\n\\n return score\",\n \"def target(self) -> Optional[int]:\\n return pulumi.get(self, \\\"target\\\")\",\n \"def get_best_pred_finger(self,f):\\n return min(self.best_finger_pred[f],\\\\\\n key=lambda kn:dist_ident(kn.ident,self.get_finger_pred_loc(f)))\",\n \"def back_test(self, turnover_frequency):\",\n \"def pick(self, target: int) -> int:\\n\\t\\tans = None\\n cnt = 0\\n for i, x in enumerate(self.nums): \\n if x == target: \\n cnt += 1\\n if randint(1, cnt) == cnt: ans = i # prob 1/cnt\\n return ans\",\n \"def _process_failures(self, target):\\n crash_synopses = self._fuzz_data_logger.failed_test_cases.get(self._fuzz_data_logger.all_test_cases[-1], [])\\n if len(crash_synopses) > 0:\\n self._fuzz_data_logger.open_test_step(\\\"Failure summary\\\")\\n\\n # retrieve the primitive that caused the crash and increment it's individual crash count.\\n self.crashing_primitives[self.fuzz_node.mutant] = self.crashing_primitives.get(self.fuzz_node.mutant, 0) + 1\\n self.crashing_primitives[self.fuzz_node] = self.crashing_primitives.get(self.fuzz_node, 0) + 1\\n\\n # print crash synopsis\\n if len(crash_synopses) > 1:\\n # Prepend a header if > 1 failure report, so that they are visible from the main web page\\n synopsis = \\\"({0} reports) {1}\\\".format(len(crash_synopses), \\\"\\\\n\\\".join(crash_synopses))\\n else:\\n synopsis = \\\"\\\\n\\\".join(crash_synopses)\\n self.procmon_results[self.total_mutant_index] = crash_synopses\\n self._fuzz_data_logger.log_info(synopsis)\\n\\n if self.fuzz_node.mutant is not None and \\\\\\n self.crashing_primitives[self.fuzz_node] >= self._crash_threshold_node:\\n skipped = self.fuzz_node.num_mutations() - self.fuzz_node.mutant_index\\n self._skip_current_node_after_current_test_case = True\\n self._fuzz_data_logger.open_test_step(\\n \\\"Crash threshold reached for this request, exhausting {0} mutants.\\\".format(skipped))\\n self.total_mutant_index += skipped\\n self.fuzz_node.mutant_index += skipped\\n elif self.fuzz_node.mutant is not None and \\\\\\n self.crashing_primitives[self.fuzz_node.mutant] >= self._crash_threshold_element:\\n if not isinstance(self.fuzz_node.mutant, primitives.Group)\\\\\\n and not isinstance(self.fuzz_node.mutant, blocks.Repeat):\\n skipped = self.fuzz_node.mutant.num_mutations() - self.fuzz_node.mutant.mutant_index\\n self._skip_current_element_after_current_test_case = True\\n self._fuzz_data_logger.open_test_step(\\n \\\"Crash threshold reached for this element, exhausting {0} mutants.\\\".format(skipped))\\n self.total_mutant_index += skipped\\n self.fuzz_node.mutant_index += skipped\\n\\n self.restart_target(target)\\n return True\\n else:\\n return False\",\n \"def compute_test_best(self, x, metric, target):\\n index = self(x, metric)\\n if metric.maximising:\\n test = index > target\\n else:\\n test = index < target\\n\\n return index, test\",\n \"def test_get_benchmark_methods_exact_match(self):\\n config = mock.Mock()\\n config.workspace = 'workspace'\\n config.benchmark_method_patterns = [\\n 'new_foo.BenchmarkClass.benchmark_method_1',\\n 'new_foo.BenchmarkClass.benchmark_method_2']\\n benchmark_runner = benchmark.BenchmarkRunner(config)\\n\\n methods = benchmark_runner._get_benchmark_methods()\\n self.assertEqual(['new_foo.BenchmarkClass.benchmark_method_1',\\n 'new_foo.BenchmarkClass.benchmark_method_2'], methods)\",\n \"def getFuzzyValue(arrPredict, arrReal, frameSize=(0,0), nSize=1, banList=[0]):\\r\\n\\r\\n\\r\\n # Store the result both as a number and as an image\\r\\n fuzzyArr = numpy.zeros( (arrPredict.shape[0]-frameSize[0], arrPredict.shape[1]-frameSize[1]), dtype='float')\\r\\n fuzzyScore = 0.0\\r\\n\\r\\n for row in range(frameSize[0], arrPredict.shape[0]-frameSize[0]):\\r\\n for col in range(frameSize[1], arrPredict.shape[1]-frameSize[1]):\\r\\n actVal = arrReal[row, col] # We are looking for this value in the neighborhood\\r\\n\\r\\n # Don't compare values which should not be compared\\r\\n if actVal in banList:\\r\\n fuzzyArr[row, col] = 2\\r\\n continue\\r\\n\\r\\n fuzzyVal = 0.0\\r\\n distWeight = 0.0\\r\\n shortestDist = 999 # use infinity constant instead?\\r\\n\\r\\n # Search the neighborhood\\r\\n for r in range(-nSize, nSize+1):\\r\\n for c in range(-nSize, nSize+1):\\r\\n dist = (r**2 + c**2)**(1/2.0)\\r\\n try:\\r\\n foundVal = arrPredict[row+r, col+c]\\r\\n except:\\r\\n continue\\r\\n if foundVal in banList:\\r\\n continue\\r\\n\\r\\n if foundVal==actVal and dist < shortestDist:\\r\\n # Store the shortest distance at which we found the value\\r\\n distWeight = 1 - ( float(dist)/(nSize+1) )\\r\\n shortestDist = dist\\r\\n\\r\\n fuzzyVal = distWeight\\r\\n\\r\\n fuzzyArr[row, col] = fuzzyVal\\r\\n fuzzyScore += fuzzyVal\\r\\n return fuzzyArr, fuzzyScore\",\n \"def benchmark(self, **kwargs):\\n num_iterations = kwargs.get(\\\"benchmark_iterations\\\")\\n\\n start_time = time.time()\\n\\n # store how far off we are\\n deviations = []\\n\\n for _ in xrange(num_iterations):\\n kwargs[\\\"roll\\\"] = decimal.Decimal(random.uniform(\\n self.MIN_BENCHMARK_ROLL, self.MAX_BENCHMARK_ROLL))\\n kwargs[\\\"pitch\\\"] = decimal.Decimal(random.uniform(\\n self.MIN_BENCHMARK_PITCH, self.MAX_BENCHMARK_PITCH))\\n\\n _, deviation = self.find_closest_trajectory(**kwargs)\\n deviations.append(deviation)\\n\\n # calculate results from the benchmarking\\n total_time = time.time() - start_time\\n average_time = total_time / num_iterations\\n average_deviation = sum(deviations) / len(deviations)\\n\\n print \\\"AVERAGE TIME: %s AVERAGE DEVIATION: %s\\\" \\\\\\n % (average_time, average_deviation)\",\n \"def _fuzz(self):\\r\\n\\r\\n # we can calculate which bytes to swap based on the number of tries\\r\\n # we've made on this seedfile\\r\\n a = self.sf.tries\\r\\n b = a + 1\\r\\n\\r\\n if b >= len(self.input):\\r\\n raise FuzzerExhaustedError('Iteration exceeds seed file length')\\r\\n\\r\\n logger.debug('%s - swap bytes %d <-> %d', self.sf.basename, a, b)\\r\\n self.input[b], self.input[a] = self.input[a], self.input[b]\\r\\n self.output = self.input\",\n \"def measure_distance(actor: Entity, target: Entity) -> int:\\n dst = int(actor is not target)\\n return dst\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6541956","0.62079185","0.57205427","0.5588023","0.55567586","0.543854","0.54338294","0.53948295","0.5363798","0.532333","0.5268365","0.5229397","0.5206228","0.51935446","0.51308554","0.5129264","0.5088721","0.5079886","0.5032553","0.5032553","0.50242513","0.5016661","0.50026","0.4977756","0.49376056","0.49229285","0.4918181","0.49130705","0.49082592","0.49063617","0.4886911","0.48710904","0.48594603","0.48520964","0.4841289","0.48346108","0.48166642","0.48110655","0.47710505","0.47618502","0.4741036","0.47290957","0.47209996","0.47099316","0.46912712","0.46833506","0.46734783","0.46689105","0.46628144","0.46530813","0.46530813","0.4633234","0.46312708","0.4622278","0.4621001","0.46181634","0.461267","0.46033612","0.45946413","0.4582021","0.45796028","0.45767763","0.4573378","0.45728064","0.45727965","0.45647871","0.45647871","0.45647871","0.45639944","0.45580465","0.45499587","0.45466584","0.45456636","0.45303333","0.451777","0.45126393","0.4502744","0.4499458","0.4493877","0.44919133","0.44897923","0.44883072","0.44872746","0.4469095","0.4469095","0.4469095","0.4466126","0.44549286","0.44535354","0.44512928","0.4448739","0.44470683","0.44390866","0.44369417","0.44294626","0.4421242","0.4418104","0.4413867","0.44115946","0.44084743"],"string":"[\n \"0.6541956\",\n \"0.62079185\",\n \"0.57205427\",\n \"0.5588023\",\n \"0.55567586\",\n \"0.543854\",\n \"0.54338294\",\n \"0.53948295\",\n \"0.5363798\",\n \"0.532333\",\n \"0.5268365\",\n \"0.5229397\",\n \"0.5206228\",\n \"0.51935446\",\n \"0.51308554\",\n \"0.5129264\",\n \"0.5088721\",\n \"0.5079886\",\n \"0.5032553\",\n \"0.5032553\",\n \"0.50242513\",\n \"0.5016661\",\n \"0.50026\",\n \"0.4977756\",\n \"0.49376056\",\n \"0.49229285\",\n \"0.4918181\",\n \"0.49130705\",\n \"0.49082592\",\n \"0.49063617\",\n \"0.4886911\",\n \"0.48710904\",\n \"0.48594603\",\n \"0.48520964\",\n \"0.4841289\",\n \"0.48346108\",\n \"0.48166642\",\n \"0.48110655\",\n \"0.47710505\",\n \"0.47618502\",\n \"0.4741036\",\n \"0.47290957\",\n \"0.47209996\",\n \"0.47099316\",\n \"0.46912712\",\n \"0.46833506\",\n \"0.46734783\",\n \"0.46689105\",\n \"0.46628144\",\n \"0.46530813\",\n \"0.46530813\",\n \"0.4633234\",\n \"0.46312708\",\n \"0.4622278\",\n \"0.4621001\",\n \"0.46181634\",\n \"0.461267\",\n \"0.46033612\",\n \"0.45946413\",\n \"0.4582021\",\n \"0.45796028\",\n \"0.45767763\",\n \"0.4573378\",\n \"0.45728064\",\n \"0.45727965\",\n \"0.45647871\",\n \"0.45647871\",\n \"0.45647871\",\n \"0.45639944\",\n \"0.45580465\",\n \"0.45499587\",\n \"0.45466584\",\n \"0.45456636\",\n \"0.45303333\",\n \"0.451777\",\n \"0.45126393\",\n \"0.4502744\",\n \"0.4499458\",\n \"0.4493877\",\n \"0.44919133\",\n \"0.44897923\",\n \"0.44883072\",\n \"0.44872746\",\n \"0.4469095\",\n \"0.4469095\",\n \"0.4469095\",\n \"0.4466126\",\n \"0.44549286\",\n \"0.44535354\",\n \"0.44512928\",\n \"0.4448739\",\n \"0.44470683\",\n \"0.44390866\",\n \"0.44369417\",\n \"0.44294626\",\n \"0.4421242\",\n \"0.4418104\",\n \"0.4413867\",\n \"0.44115946\",\n \"0.44084743\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8522614"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":11263,"cells":{"query":{"kind":"string","value":"Returns the project of |benchmark|"},"document":{"kind":"string","value":"def get_project(benchmark):\n return benchmark_config.get_config(benchmark)['project']"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def benchmark_result(self):\n return self._benchmark_id","def benchmark_selection(self):\n return self._benchmark_selection","def get_benchmark(client):\n r = client.get(config.API_PATH() + '/benchmarks')\n benchmarks = json.loads(r.data)\n return benchmarks['benchmarks'][0]['id']","def benchmark(self):\n logger.info(self.benchmark.__doc__)\n return self.run(self.benchmark_profile())","def benchmark_profile(self):\n cb_bin = os.path.join(bin_path, 'compilebench')\n desc = \"benchmark\"\n test_name = \"compilebench_{0}\".format(to_safe_name(desc))\n test = TestProfile(\n name=test_name,\n desc=desc,\n test_path=self.test_path,\n bin_path=bin_path,\n command=\"{0} -D {1} -i 10 --makej\".format(cb_bin, self.test_path))\n\n return test","def get_benchmark(self, benchmark):\n\t\tif not isinstance(benchmark, str) and not callable(benchmark): return benchmark\n\t\telif benchmark in self.classes:\treturn self.classes[benchmark]()\n\t\traise TypeError('Passed benchmark is not defined!')","def findBenchFromInstrument(self, instrument):\n return instrument.bench","def retrieve( self, benchmark, extraLabel='' ):\n if benchmark.reference is ReferenceBenchmark.SP:\n idx = np.argmax( self.sps )\n else:\n # Get reference for operation:\n if benchmark.reference is ReferenceBenchmark.Pd:\n ref = self.pds\n elif benchmark.reference is ReferenceBenchmark.Pf:\n ref = self.pfs\n delta = ref - benchmark.refVal\n idx = np.argmin( np.abs( delta ) )\n return PerformancePoint( name=extraLabel + benchmark.name\n , sp=self.sps[ idx ]\n , pd=self.pds[ idx ]\n , pf=self.pfs[idx]\n , thres=self.thresholds[idx]\n )","def findBenchFromDevice(self, device):\n return device.bench","def reference_benchmark_result(self):\n return self._reference_id","def umbrae(self, benchmark: np.ndarray = None):\n return self.mbrae(benchmark) / (1 - self.mbrae(benchmark))","def get_project(self, i):\r\n return self.__projects[i]","def project(self, v):\n return v","def project(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"project\")","def project(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"project\")","def project(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"project\")","def project(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"project\")","def project(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"project\")","def project(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"project\")","def project(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"project\")","def project(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"project\")","def project(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"project\")","def project(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"project\")","def project(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"project\")","def project(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"project\")","def project(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"project\")","def _get_gcd_project():\n return os.getenv(environment_vars.GCD_DATASET)","def project():","def project():","def project():","def get_benchmark_specification(benchmark = 'FSI1'):\n if benchmark == 'FSI1':\n rho_s = Constant(1e03)\n nu_s = Constant(0.4)\n mu_s = Constant(5e05)\n rho_f = Constant(1e03)\n nu_f = Constant(1e-03)\n U = 0.2\n T_end = 60.0\n result = \"results-FSI1/\"\n elif benchmark == 'FSI2':\n rho_s = Constant(1e04)\n nu_s = Constant(0.4)\n mu_s = Constant(5e05)\n rho_f = Constant(1e03)\n nu_f = Constant(1e-03)\n U = 1.0\n T_end = 15.0\n result = \"results-FSI2/\"\t\t\n elif benchmark == 'FSI3':\n rho_s = Constant(1e03)\n nu_s = Constant(0.4)\n mu_s = Constant(2e06)\n rho_f = Constant(1e03)\n nu_f = Constant(1e-03)\n U = 2.0\n T_end = 20.0\n result = \"results-FSI3/\"\t\t\n else:\n raise ValueError('\"{}\" is a wrong name for problem specification.'.format(benchmark))\n v_max = Constant(1.5*U) # mean velocity to maximum velocity \n # (we have parabolic profile)\n E_s = Constant(2*mu_s*(1+nu_s))\n lambda_s = Constant((nu_s*E_s)/((1+nu_s)*(1-2*nu_s)))\n mu_f = Constant(nu_f*rho_f)\n return v_max, lambda_s, mu_s, rho_s, mu_f, rho_f, T_end, result","def merge(self, projects):\n benchmarks = set()\n for project in projects:\n for runspec in project:\n for classresult in runspec:\n for instresult in classresult.instresults:\n instresult.instance.maxRuns = max(instresult.instance.maxRuns, len(instresult.runs))\n benchmarks.add(runspec.benchmark)\n return BenchmarkMerge(benchmarks)","def get_latest_benchmark():\n\n benchmark_paths = glob.glob(\"./.benchmarks/*/*.json\")\n dates = [\n \"\".join(_b.split(\"/\")[-1].split(\"_\")[2:4]) for _b in benchmark_paths\n ]\n benchmarks = {date: value for date, value in zip(dates, benchmark_paths)}\n\n dates.sort()\n latest = dates[-1]\n benchmark_latest = benchmarks[latest]\n\n return benchmark_latest","def project(self) -> 'prjmod.Level':\n return self.lineage.project","def get_current_project():\n return get_from_session(KEY_PROJECT)","def create_benchmark(self, benchmark):\n self.crd_client.create_namespaced_custom_object(\n group=\"ripsaw.cloudbulldozer.io\",\n version=\"v1alpha1\",\n namespace=benchmark[\"metadata\"][\"namespace\"],\n plural=\"benchmarks\",\n body=benchmark,\n )","def project(self):\n return self._project","def project(self):\n return self._project","def project(self):\n return self._project","def project(self):\n return self._project","def project(self):\n return self._project","def get_fuzz_target(benchmark):\n # Do this because of OSS-Fuzz-on-demand.\n # TODO(metzman): Use classes to mock a benchmark config for\n # OSS_FUZZ_ON_DEMAND.\n return benchmark_config.get_config(benchmark).get(\n 'fuzz_target', environment.get('FUZZ_TARGET'))","def getProject(self):\r\n return self.project","def project(self) -> str:\n return self.proto.project","def get_benchmark_metadata(self, name, namespace=\"benchmark-operator\"):\n benchmark = self.get_benchmark(name, namespace)\n return {\n \"name\": benchmark[\"metadata\"][\"name\"],\n \"namespace\": benchmark[\"metadata\"][\"namespace\"],\n \"uuid\": benchmark.get(\"status\", {}).get(\"uuid\", \"Not Assigned Yet\"),\n \"suuid\": benchmark.get(\"status\", {}).get(\"suuid\", \"Not Assigned Yet\"),\n \"status\": benchmark.get(\"status\", {}).get(\"state\", \"\"),\n }","def get_type(benchmark):\n # TODO(metzman): Use classes to mock a benchmark config for\n # OSS_FUZZ_ON_DEMAND.\n default_value = os.getenv('EXPERIMENT_TYPE', BenchmarkType.CODE.value)\n return benchmark_config.get_config(benchmark).get('type', default_value)","def get_project(self):\n raise NotImplementedError(\"get_project is not implemented\")","def get_project(arn=None):\n pass","def get_current_project(self):\n\n try:\n command = self._oc_command([\"project\", \"-q\"])\n output = run_cmd(command, return_output=True)\n except subprocess.CalledProcessError as ex:\n raise ConuException(\"Failed to obtain current project name : %s\" % ex)\n\n try:\n return output.rstrip() # remove '\\n'\n except IndexError:\n raise ConuException(\"Failed to obtain project name\")","def project(self, feature):\n return feature","def getProject(self):\n\t\treturn self.__project","def getBuild(number):","def getBuild(number):","def Run(benchmark_spec):\n vms = benchmark_spec.vms\n master_vm = vms[0]\n run_command = 'cd %s && %s ./%s' % (hpcg.HPCG_DIR,\n _GetEnvironmentVars(benchmark_spec),\n RUN_SCRIPT)\n output, _ = master_vm.RobustRemoteCommand(run_command)\n return _MakeSamplesFromOutput(benchmark_spec, output)","def get_coverage_benchmarks():\n return [\n benchmark for benchmark in get_all_benchmarks()\n if get_type(benchmark) == BenchmarkType.CODE.value\n ]","def get_benchmark_file_path(cls, results_dir):\n return os.path.join(results_dir, \"benchmarks.json\")","def project(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"project\")","def test_get_project(self):\n pass","def compare_results(self, benchmark_result, reference_benchmark_result):\n pass","def project_presets(project):\n\n return None","def getProjectName():","def project_by_name(self,project_name=''):\n logger.debug(f'project_by_name project_name={project_name}')\n return self.get('{}/groups/byName/{}'.format(ApiVersion.CM1.value,project_name))","def project(self):\n\n return self._project","def getProject(self):\n return self.project","def evaluate_benchmarks(self):\n\n # iterate over replicates\n results = {}\n for replicate_id, replicate in self.replicates:\n\n # evaluate benchmark for current replicate\n bmark = SimulationBenchmark(replicate.copy(),\n graph=self.graphs[replicate_id],\n **self.params)\n\n # store results\n results[replicate_id] = dict(\n\n labels_MAE=bmark.scores['labels'].MAE,\n level_only_MAE=bmark.scores['level_only'].MAE,\n spatial_only_MAE=bmark.scores['spatial_only'].MAE,\n community_MAE=bmark.scores['labels_comm'].MAE,\n\n labels_PCT=bmark.scores['labels'].percent_correct,\n level_only_PCT=bmark.scores['level_only'].percent_correct,\n spatial_only_PCT=bmark.scores['spatial_only'].percent_correct,\n community_PCT=bmark.scores['labels_comm'].percent_correct)\n\n # compile dataframe\n results = pd.DataFrame.from_dict(results, orient='index')\n results.index.set_names(self.multiindex, inplace=True)\n\n return results","def project(self) -> str:\n return pulumi.get(self, \"project\")","def project(self) -> str:\n return pulumi.get(self, \"project\")","def project(self) -> str:\n return pulumi.get(self, \"project\")","def project(self) -> str:\n return pulumi.get(self, \"project\")","def project(self) -> str:\n return pulumi.get(self, \"project\")","def project(self) -> str:\n return pulumi.get(self, \"project\")","def Run(benchmark_spec):\n _UpdateBenchmarkSpecWithFlags(benchmark_spec)\n vm = benchmark_spec.vms[0]\n if benchmark_spec.tpus:\n # For MLPerf 1.0, the benchmake code of different hardware are different.\n if (benchmark_spec.tpu_groups['train'].GetAcceleratorType() == 'v3-32' or\n benchmark_spec.tpu_groups['train'].GetAcceleratorType() == 'v3-128' or\n benchmark_spec.tpu_groups['train'].GetAcceleratorType() == 'v3-256' or\n benchmark_spec.tpu_groups['train'].GetAcceleratorType() == 'v3-512' or\n benchmark_spec.tpu_groups['train'].GetAcceleratorType() == 'v3-1024' or\n benchmark_spec.tpu_groups['train'].GetAcceleratorType() == 'v3-2048'):\n run_path = (\n '$HOME/training_results_{version}/Google/benchmarks/{model}/tpu-{tpus}'\n .format(\n version=VERSION.value,\n model=benchmark_spec.benchmark,\n tpus=benchmark_spec.tpu_groups['train'].GetAcceleratorType()))\n code_path = (\n '$HOME/training_results_{version}/Google/benchmarks/{model}/implementations/tpu-{tpus}-{model}'\n .format(\n version=VERSION.value,\n model=benchmark_spec.benchmark,\n tpus=benchmark_spec.tpu_groups['train'].GetAcceleratorType()))\n\n if MASK in benchmark_spec.benchmark:\n model = 'mask_rcnn'\n elif GNMT in benchmark_spec.benchmark:\n model = 'nmt'\n else:\n model = benchmark_spec.benchmark\n\n mlperf_benchmark_cmd = (\n 'cd {code_path} && '\n 'export PYTHONPATH=$(pwd):$(pwd)/{model} && '\n 'cd {model} && '\n '{run_path}/run_and_time.sh'.format(\n code_path=code_path,\n model=model,\n run_path=run_path))\n\n if SSD in benchmark_spec.benchmark:\n mlperf_benchmark_cmd = (\n 'export '\n 'MLP_GCS_RESNET_CHECKPOINT={checkpoint}'\n ' && {cmd}'.format(\n checkpoint=FLAGS.mlperf_gcs_resnet_checkpoint,\n cmd=mlperf_benchmark_cmd))\n else:\n raise ValueError(\n 'MLPerf configurations do not support the hardware in PKB. PKB may '\n 'need to be updated if this is a new TPU type.')\n\n else:\n run_sub_paths = {RESNET: 'resnet/implementations/mxnet',\n TRANSFORMER: 'transformer/implementations/pytorch',\n MINIGO: 'minigo/implementations/tensorflow',\n MASK: 'maskrcnn/implementations/pytorch',\n GNMT: 'gnmt/implementations/pytorch',\n SSD: 'ssd/implementations/pytorch',\n BERT: 'bert/implementations/pytorch',}\n benchmark_path = f'$HOME/training_results_{VERSION.value}/NVIDIA/benchmarks'\n run_path = posixpath.join(benchmark_path,\n run_sub_paths[benchmark_spec.benchmark])\n env = {\n 'DGXSYSTEM': DGXSYSTEM,\n 'NEXP': 1,\n 'PULL': 0,\n 'LOGDIR': f'/tmp/{benchmark_spec.benchmark}',\n }\n envs = {\n RESNET: {},\n TRANSFORMER: {'DATADIR': '/data/wmt/utf8'},\n MINIGO: {'CONT': 'mlperf-nvidia:minigo'},\n MASK: {},\n GNMT: {'DATADIR': '/data/gnmt'},\n SSD: {'DATADIR': '/data'},\n BERT: {}\n }\n env.update(envs[benchmark_spec.benchmark])\n\n run_script = posixpath.join(run_path, 'run_with_docker.sh')\n vm_util.ReplaceText(vm, 'SYSLOGGING=1', 'SYSLOGGING=0', run_script)\n vm_util.ReplaceText(vm, 'docker exec -it', 'docker exec -t', run_script)\n if benchmark_spec.benchmark == RESNET:\n vm_util.ReplaceText(vm, r'mpirun.*run_and_time\\.sh',\n r'.\\/run_and_time.sh', run_script)\n\n env = ' '.join(f'{key}={value}' for key, value in env.items())\n if nvidia_driver.CheckNvidiaGpuExists(vm):\n env = f'{tensorflow.GetEnvironmentVars(vm)} {env}'\n\n mlperf_benchmark_cmd = (\n f'chmod 755 {run_script} && '\n f'cd {run_path} && '\n f'{env} {run_script}')\n\n samples = []\n metadata = _CreateMetadataDict(benchmark_spec)\n stdout, _ = vm.RobustRemoteCommand(mlperf_benchmark_cmd)\n if NONE in FLAGS.mlperf_profiler:\n samples.extend(\n MakeSamplesFromOutput(\n metadata,\n stdout,\n use_tpu=bool(benchmark_spec.tpus),\n model=benchmark_spec.benchmark))\n return samples","def main(project_path='../benchmark_projects/JSON/JSON.und'):\n # project_path = '../benchmark_projects/ganttproject/biz.ganttproject.core/biz.ganttproject.core.und'\n db = understand.open(project_path)\n # entities = db.ents('Java Class')\n cmd_ = 'und export -dependencies class csv {0} {1}'.format('mdg/MDG.csv', project_path)\n os.system('cmd /c \"{0}\"'.format(cmd_))\n\n modulo = Modularity(graph_path=r'mdg/MDG.csv', db=db)\n q = modulo.compute_modularity_newman_leicht()\n print(q)\n return q","def project(self) -> aws_cdk.aws_codebuild.IProject:\n return jsii.get(self, \"project\")","def proj(self):\n return self._proj","def get_projects(self, *criterion):\n from wkcdd.models.helpers import get_project_list\n return get_project_list([self.id], *criterion)","def projects(self):\n sql = \"\"\"SELECT project\n FROM barcodes.sample\n LEFT JOIN barcodes.project_sample_sets USING (sample_set_id)\n LEFT JOIN barcodes.project USING (project_id)\n WHERE sample_id = %s\n UNION\n SELECT project\n FROM barcodes.project_samples\n LEFT JOIN barcodes.project USING (project_id)\n WHERE sample_id = %s\n \"\"\"\n with pm.sql.TRN:\n pm.sql.TRN.add(sql, [self.id, self.id])\n projects = pm.sql.TRN.execute_fetchflatten()\n return None if not projects else projects","def project_grp():\n pass","def getBuild():","def project_name(self):\n pass","def project(self):\n return read_small_file(self.homeDirectory + \"/.project\")","def test_create_benchmark(self, benchmark_config, benchmark_config_py):\n with OrionState() as cfg:\n bm1 = get_or_create_benchmark(\n cfg.storage,\n **benchmark_config_py,\n )\n bm1.close()\n\n bm2 = get_or_create_benchmark(cfg.storage, \"bm00001\")\n bm2.close()\n\n assert bm1.configuration == benchmark_config\n\n assert bm1.configuration == bm2.configuration","def get_project(name):\n tx = cypher_transaction()\n query = \"\"\"MATCH (n:project) WHERE n.name={project_name} RETURN n\"\"\"\n tx.append(query, parameters={'project_name': name})\n result = tx.commit()\n\n # Returns a result of the form [[\n # Record(\n # columns=('n',),\n # values=(Node('http://localhost:7474/db/data/node/233'),)\n # )\n # ]]\n return _first(result)[0].values[0]","def execute_benchmark(path, budget=None, threads=None):\n benchmark_path = runtime.binary_path(path)\n cmd = [benchmark_path, \"--benchmark_format=json\"]\n if budget is not None:\n cmd += [\"-b\", str(budget)]\n if threads is not None:\n cmd += [\"-t\", str(threads)]\n res = process.execute(cmd)\n return json.loads(res.std_out)","def test_benchmark1(capsys):\n student_1 = Student('114007245','Mario Castillo',2018, [\"INST 201\",\"INST 326\"])\n student_2 = Student('117006012', 'Joe Rogan', 2018, [\"MATH 115\",\"PSYC 100\"])\n student_3 = Student(\"117008490\", \"Kanye West\", 2018, [\"MATH 120\",\"STAT 003\"])\n student_4 = Student('118009044', \"Elon Musk\", 2018, [\"PSYC 100\",\"MATH 003\"])\n \n student_1.benchmark_I()\n outerr = capsys.readouterr()\n out = outerr.out\n assert out == ('You have not completed the Benchmark I requirements.\\n'\n 'You have not taken MATH 115 or higher.\\n'\n 'You have not taken PSYC 100.\\n')\n\n student_2.benchmark_I()\n outerr = capsys.readouterr()\n out = outerr.out\n assert out == (f'You have completed all of your Benchmark I courses! '\n f'Congratulations, {student_2.student_name}!\\n')\n\n student_3.benchmark_I()\n outerr = capsys.readouterr()\n out = outerr.out \n assert out == ('You have not completed the Benchmark I requirements.\\n'\n 'You have not taken PSYC 100.\\n')\n\n student_4.benchmark_I()\n outerr = capsys.readouterr()\n out = outerr.out\n assert out == ('You have not completed the Benchmark I requirements.\\n'\n 'You have not taken MATH 115 or higher.\\n')","def getProjectData(self) -> ghidra.framework.model.ProjectData:\n ...","def project(self) -> str:\n return self._db_data.project","def project(self, X):","def project(self, X):","def project(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"project\")","def project(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"project\")","def project(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"project\")","def project(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"project\")","def project(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"project\")","def project(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"project\")","def project(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"project\")","def project(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"project\")","def project(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"project\")","def project(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"project\")","def project(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"project\")"],"string":"[\n \"def benchmark_result(self):\\n return self._benchmark_id\",\n \"def benchmark_selection(self):\\n return self._benchmark_selection\",\n \"def get_benchmark(client):\\n r = client.get(config.API_PATH() + '/benchmarks')\\n benchmarks = json.loads(r.data)\\n return benchmarks['benchmarks'][0]['id']\",\n \"def benchmark(self):\\n logger.info(self.benchmark.__doc__)\\n return self.run(self.benchmark_profile())\",\n \"def benchmark_profile(self):\\n cb_bin = os.path.join(bin_path, 'compilebench')\\n desc = \\\"benchmark\\\"\\n test_name = \\\"compilebench_{0}\\\".format(to_safe_name(desc))\\n test = TestProfile(\\n name=test_name,\\n desc=desc,\\n test_path=self.test_path,\\n bin_path=bin_path,\\n command=\\\"{0} -D {1} -i 10 --makej\\\".format(cb_bin, self.test_path))\\n\\n return test\",\n \"def get_benchmark(self, benchmark):\\n\\t\\tif not isinstance(benchmark, str) and not callable(benchmark): return benchmark\\n\\t\\telif benchmark in self.classes:\\treturn self.classes[benchmark]()\\n\\t\\traise TypeError('Passed benchmark is not defined!')\",\n \"def findBenchFromInstrument(self, instrument):\\n return instrument.bench\",\n \"def retrieve( self, benchmark, extraLabel='' ):\\n if benchmark.reference is ReferenceBenchmark.SP:\\n idx = np.argmax( self.sps )\\n else:\\n # Get reference for operation:\\n if benchmark.reference is ReferenceBenchmark.Pd:\\n ref = self.pds\\n elif benchmark.reference is ReferenceBenchmark.Pf:\\n ref = self.pfs\\n delta = ref - benchmark.refVal\\n idx = np.argmin( np.abs( delta ) )\\n return PerformancePoint( name=extraLabel + benchmark.name\\n , sp=self.sps[ idx ]\\n , pd=self.pds[ idx ]\\n , pf=self.pfs[idx]\\n , thres=self.thresholds[idx]\\n )\",\n \"def findBenchFromDevice(self, device):\\n return device.bench\",\n \"def reference_benchmark_result(self):\\n return self._reference_id\",\n \"def umbrae(self, benchmark: np.ndarray = None):\\n return self.mbrae(benchmark) / (1 - self.mbrae(benchmark))\",\n \"def get_project(self, i):\\r\\n return self.__projects[i]\",\n \"def project(self, v):\\n return v\",\n \"def project(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"project\\\")\",\n \"def project(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"project\\\")\",\n \"def project(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"project\\\")\",\n \"def project(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"project\\\")\",\n \"def project(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"project\\\")\",\n \"def project(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"project\\\")\",\n \"def project(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"project\\\")\",\n \"def project(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"project\\\")\",\n \"def project(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"project\\\")\",\n \"def project(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"project\\\")\",\n \"def project(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"project\\\")\",\n \"def project(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"project\\\")\",\n \"def project(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"project\\\")\",\n \"def _get_gcd_project():\\n return os.getenv(environment_vars.GCD_DATASET)\",\n \"def project():\",\n \"def project():\",\n \"def project():\",\n \"def get_benchmark_specification(benchmark = 'FSI1'):\\n if benchmark == 'FSI1':\\n rho_s = Constant(1e03)\\n nu_s = Constant(0.4)\\n mu_s = Constant(5e05)\\n rho_f = Constant(1e03)\\n nu_f = Constant(1e-03)\\n U = 0.2\\n T_end = 60.0\\n result = \\\"results-FSI1/\\\"\\n elif benchmark == 'FSI2':\\n rho_s = Constant(1e04)\\n nu_s = Constant(0.4)\\n mu_s = Constant(5e05)\\n rho_f = Constant(1e03)\\n nu_f = Constant(1e-03)\\n U = 1.0\\n T_end = 15.0\\n result = \\\"results-FSI2/\\\"\\t\\t\\n elif benchmark == 'FSI3':\\n rho_s = Constant(1e03)\\n nu_s = Constant(0.4)\\n mu_s = Constant(2e06)\\n rho_f = Constant(1e03)\\n nu_f = Constant(1e-03)\\n U = 2.0\\n T_end = 20.0\\n result = \\\"results-FSI3/\\\"\\t\\t\\n else:\\n raise ValueError('\\\"{}\\\" is a wrong name for problem specification.'.format(benchmark))\\n v_max = Constant(1.5*U) # mean velocity to maximum velocity \\n # (we have parabolic profile)\\n E_s = Constant(2*mu_s*(1+nu_s))\\n lambda_s = Constant((nu_s*E_s)/((1+nu_s)*(1-2*nu_s)))\\n mu_f = Constant(nu_f*rho_f)\\n return v_max, lambda_s, mu_s, rho_s, mu_f, rho_f, T_end, result\",\n \"def merge(self, projects):\\n benchmarks = set()\\n for project in projects:\\n for runspec in project:\\n for classresult in runspec:\\n for instresult in classresult.instresults:\\n instresult.instance.maxRuns = max(instresult.instance.maxRuns, len(instresult.runs))\\n benchmarks.add(runspec.benchmark)\\n return BenchmarkMerge(benchmarks)\",\n \"def get_latest_benchmark():\\n\\n benchmark_paths = glob.glob(\\\"./.benchmarks/*/*.json\\\")\\n dates = [\\n \\\"\\\".join(_b.split(\\\"/\\\")[-1].split(\\\"_\\\")[2:4]) for _b in benchmark_paths\\n ]\\n benchmarks = {date: value for date, value in zip(dates, benchmark_paths)}\\n\\n dates.sort()\\n latest = dates[-1]\\n benchmark_latest = benchmarks[latest]\\n\\n return benchmark_latest\",\n \"def project(self) -> 'prjmod.Level':\\n return self.lineage.project\",\n \"def get_current_project():\\n return get_from_session(KEY_PROJECT)\",\n \"def create_benchmark(self, benchmark):\\n self.crd_client.create_namespaced_custom_object(\\n group=\\\"ripsaw.cloudbulldozer.io\\\",\\n version=\\\"v1alpha1\\\",\\n namespace=benchmark[\\\"metadata\\\"][\\\"namespace\\\"],\\n plural=\\\"benchmarks\\\",\\n body=benchmark,\\n )\",\n \"def project(self):\\n return self._project\",\n \"def project(self):\\n return self._project\",\n \"def project(self):\\n return self._project\",\n \"def project(self):\\n return self._project\",\n \"def project(self):\\n return self._project\",\n \"def get_fuzz_target(benchmark):\\n # Do this because of OSS-Fuzz-on-demand.\\n # TODO(metzman): Use classes to mock a benchmark config for\\n # OSS_FUZZ_ON_DEMAND.\\n return benchmark_config.get_config(benchmark).get(\\n 'fuzz_target', environment.get('FUZZ_TARGET'))\",\n \"def getProject(self):\\r\\n return self.project\",\n \"def project(self) -> str:\\n return self.proto.project\",\n \"def get_benchmark_metadata(self, name, namespace=\\\"benchmark-operator\\\"):\\n benchmark = self.get_benchmark(name, namespace)\\n return {\\n \\\"name\\\": benchmark[\\\"metadata\\\"][\\\"name\\\"],\\n \\\"namespace\\\": benchmark[\\\"metadata\\\"][\\\"namespace\\\"],\\n \\\"uuid\\\": benchmark.get(\\\"status\\\", {}).get(\\\"uuid\\\", \\\"Not Assigned Yet\\\"),\\n \\\"suuid\\\": benchmark.get(\\\"status\\\", {}).get(\\\"suuid\\\", \\\"Not Assigned Yet\\\"),\\n \\\"status\\\": benchmark.get(\\\"status\\\", {}).get(\\\"state\\\", \\\"\\\"),\\n }\",\n \"def get_type(benchmark):\\n # TODO(metzman): Use classes to mock a benchmark config for\\n # OSS_FUZZ_ON_DEMAND.\\n default_value = os.getenv('EXPERIMENT_TYPE', BenchmarkType.CODE.value)\\n return benchmark_config.get_config(benchmark).get('type', default_value)\",\n \"def get_project(self):\\n raise NotImplementedError(\\\"get_project is not implemented\\\")\",\n \"def get_project(arn=None):\\n pass\",\n \"def get_current_project(self):\\n\\n try:\\n command = self._oc_command([\\\"project\\\", \\\"-q\\\"])\\n output = run_cmd(command, return_output=True)\\n except subprocess.CalledProcessError as ex:\\n raise ConuException(\\\"Failed to obtain current project name : %s\\\" % ex)\\n\\n try:\\n return output.rstrip() # remove '\\\\n'\\n except IndexError:\\n raise ConuException(\\\"Failed to obtain project name\\\")\",\n \"def project(self, feature):\\n return feature\",\n \"def getProject(self):\\n\\t\\treturn self.__project\",\n \"def getBuild(number):\",\n \"def getBuild(number):\",\n \"def Run(benchmark_spec):\\n vms = benchmark_spec.vms\\n master_vm = vms[0]\\n run_command = 'cd %s && %s ./%s' % (hpcg.HPCG_DIR,\\n _GetEnvironmentVars(benchmark_spec),\\n RUN_SCRIPT)\\n output, _ = master_vm.RobustRemoteCommand(run_command)\\n return _MakeSamplesFromOutput(benchmark_spec, output)\",\n \"def get_coverage_benchmarks():\\n return [\\n benchmark for benchmark in get_all_benchmarks()\\n if get_type(benchmark) == BenchmarkType.CODE.value\\n ]\",\n \"def get_benchmark_file_path(cls, results_dir):\\n return os.path.join(results_dir, \\\"benchmarks.json\\\")\",\n \"def project(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"project\\\")\",\n \"def test_get_project(self):\\n pass\",\n \"def compare_results(self, benchmark_result, reference_benchmark_result):\\n pass\",\n \"def project_presets(project):\\n\\n return None\",\n \"def getProjectName():\",\n \"def project_by_name(self,project_name=''):\\n logger.debug(f'project_by_name project_name={project_name}')\\n return self.get('{}/groups/byName/{}'.format(ApiVersion.CM1.value,project_name))\",\n \"def project(self):\\n\\n return self._project\",\n \"def getProject(self):\\n return self.project\",\n \"def evaluate_benchmarks(self):\\n\\n # iterate over replicates\\n results = {}\\n for replicate_id, replicate in self.replicates:\\n\\n # evaluate benchmark for current replicate\\n bmark = SimulationBenchmark(replicate.copy(),\\n graph=self.graphs[replicate_id],\\n **self.params)\\n\\n # store results\\n results[replicate_id] = dict(\\n\\n labels_MAE=bmark.scores['labels'].MAE,\\n level_only_MAE=bmark.scores['level_only'].MAE,\\n spatial_only_MAE=bmark.scores['spatial_only'].MAE,\\n community_MAE=bmark.scores['labels_comm'].MAE,\\n\\n labels_PCT=bmark.scores['labels'].percent_correct,\\n level_only_PCT=bmark.scores['level_only'].percent_correct,\\n spatial_only_PCT=bmark.scores['spatial_only'].percent_correct,\\n community_PCT=bmark.scores['labels_comm'].percent_correct)\\n\\n # compile dataframe\\n results = pd.DataFrame.from_dict(results, orient='index')\\n results.index.set_names(self.multiindex, inplace=True)\\n\\n return results\",\n \"def project(self) -> str:\\n return pulumi.get(self, \\\"project\\\")\",\n \"def project(self) -> str:\\n return pulumi.get(self, \\\"project\\\")\",\n \"def project(self) -> str:\\n return pulumi.get(self, \\\"project\\\")\",\n \"def project(self) -> str:\\n return pulumi.get(self, \\\"project\\\")\",\n \"def project(self) -> str:\\n return pulumi.get(self, \\\"project\\\")\",\n \"def project(self) -> str:\\n return pulumi.get(self, \\\"project\\\")\",\n \"def Run(benchmark_spec):\\n _UpdateBenchmarkSpecWithFlags(benchmark_spec)\\n vm = benchmark_spec.vms[0]\\n if benchmark_spec.tpus:\\n # For MLPerf 1.0, the benchmake code of different hardware are different.\\n if (benchmark_spec.tpu_groups['train'].GetAcceleratorType() == 'v3-32' or\\n benchmark_spec.tpu_groups['train'].GetAcceleratorType() == 'v3-128' or\\n benchmark_spec.tpu_groups['train'].GetAcceleratorType() == 'v3-256' or\\n benchmark_spec.tpu_groups['train'].GetAcceleratorType() == 'v3-512' or\\n benchmark_spec.tpu_groups['train'].GetAcceleratorType() == 'v3-1024' or\\n benchmark_spec.tpu_groups['train'].GetAcceleratorType() == 'v3-2048'):\\n run_path = (\\n '$HOME/training_results_{version}/Google/benchmarks/{model}/tpu-{tpus}'\\n .format(\\n version=VERSION.value,\\n model=benchmark_spec.benchmark,\\n tpus=benchmark_spec.tpu_groups['train'].GetAcceleratorType()))\\n code_path = (\\n '$HOME/training_results_{version}/Google/benchmarks/{model}/implementations/tpu-{tpus}-{model}'\\n .format(\\n version=VERSION.value,\\n model=benchmark_spec.benchmark,\\n tpus=benchmark_spec.tpu_groups['train'].GetAcceleratorType()))\\n\\n if MASK in benchmark_spec.benchmark:\\n model = 'mask_rcnn'\\n elif GNMT in benchmark_spec.benchmark:\\n model = 'nmt'\\n else:\\n model = benchmark_spec.benchmark\\n\\n mlperf_benchmark_cmd = (\\n 'cd {code_path} && '\\n 'export PYTHONPATH=$(pwd):$(pwd)/{model} && '\\n 'cd {model} && '\\n '{run_path}/run_and_time.sh'.format(\\n code_path=code_path,\\n model=model,\\n run_path=run_path))\\n\\n if SSD in benchmark_spec.benchmark:\\n mlperf_benchmark_cmd = (\\n 'export '\\n 'MLP_GCS_RESNET_CHECKPOINT={checkpoint}'\\n ' && {cmd}'.format(\\n checkpoint=FLAGS.mlperf_gcs_resnet_checkpoint,\\n cmd=mlperf_benchmark_cmd))\\n else:\\n raise ValueError(\\n 'MLPerf configurations do not support the hardware in PKB. PKB may '\\n 'need to be updated if this is a new TPU type.')\\n\\n else:\\n run_sub_paths = {RESNET: 'resnet/implementations/mxnet',\\n TRANSFORMER: 'transformer/implementations/pytorch',\\n MINIGO: 'minigo/implementations/tensorflow',\\n MASK: 'maskrcnn/implementations/pytorch',\\n GNMT: 'gnmt/implementations/pytorch',\\n SSD: 'ssd/implementations/pytorch',\\n BERT: 'bert/implementations/pytorch',}\\n benchmark_path = f'$HOME/training_results_{VERSION.value}/NVIDIA/benchmarks'\\n run_path = posixpath.join(benchmark_path,\\n run_sub_paths[benchmark_spec.benchmark])\\n env = {\\n 'DGXSYSTEM': DGXSYSTEM,\\n 'NEXP': 1,\\n 'PULL': 0,\\n 'LOGDIR': f'/tmp/{benchmark_spec.benchmark}',\\n }\\n envs = {\\n RESNET: {},\\n TRANSFORMER: {'DATADIR': '/data/wmt/utf8'},\\n MINIGO: {'CONT': 'mlperf-nvidia:minigo'},\\n MASK: {},\\n GNMT: {'DATADIR': '/data/gnmt'},\\n SSD: {'DATADIR': '/data'},\\n BERT: {}\\n }\\n env.update(envs[benchmark_spec.benchmark])\\n\\n run_script = posixpath.join(run_path, 'run_with_docker.sh')\\n vm_util.ReplaceText(vm, 'SYSLOGGING=1', 'SYSLOGGING=0', run_script)\\n vm_util.ReplaceText(vm, 'docker exec -it', 'docker exec -t', run_script)\\n if benchmark_spec.benchmark == RESNET:\\n vm_util.ReplaceText(vm, r'mpirun.*run_and_time\\\\.sh',\\n r'.\\\\/run_and_time.sh', run_script)\\n\\n env = ' '.join(f'{key}={value}' for key, value in env.items())\\n if nvidia_driver.CheckNvidiaGpuExists(vm):\\n env = f'{tensorflow.GetEnvironmentVars(vm)} {env}'\\n\\n mlperf_benchmark_cmd = (\\n f'chmod 755 {run_script} && '\\n f'cd {run_path} && '\\n f'{env} {run_script}')\\n\\n samples = []\\n metadata = _CreateMetadataDict(benchmark_spec)\\n stdout, _ = vm.RobustRemoteCommand(mlperf_benchmark_cmd)\\n if NONE in FLAGS.mlperf_profiler:\\n samples.extend(\\n MakeSamplesFromOutput(\\n metadata,\\n stdout,\\n use_tpu=bool(benchmark_spec.tpus),\\n model=benchmark_spec.benchmark))\\n return samples\",\n \"def main(project_path='../benchmark_projects/JSON/JSON.und'):\\n # project_path = '../benchmark_projects/ganttproject/biz.ganttproject.core/biz.ganttproject.core.und'\\n db = understand.open(project_path)\\n # entities = db.ents('Java Class')\\n cmd_ = 'und export -dependencies class csv {0} {1}'.format('mdg/MDG.csv', project_path)\\n os.system('cmd /c \\\"{0}\\\"'.format(cmd_))\\n\\n modulo = Modularity(graph_path=r'mdg/MDG.csv', db=db)\\n q = modulo.compute_modularity_newman_leicht()\\n print(q)\\n return q\",\n \"def project(self) -> aws_cdk.aws_codebuild.IProject:\\n return jsii.get(self, \\\"project\\\")\",\n \"def proj(self):\\n return self._proj\",\n \"def get_projects(self, *criterion):\\n from wkcdd.models.helpers import get_project_list\\n return get_project_list([self.id], *criterion)\",\n \"def projects(self):\\n sql = \\\"\\\"\\\"SELECT project\\n FROM barcodes.sample\\n LEFT JOIN barcodes.project_sample_sets USING (sample_set_id)\\n LEFT JOIN barcodes.project USING (project_id)\\n WHERE sample_id = %s\\n UNION\\n SELECT project\\n FROM barcodes.project_samples\\n LEFT JOIN barcodes.project USING (project_id)\\n WHERE sample_id = %s\\n \\\"\\\"\\\"\\n with pm.sql.TRN:\\n pm.sql.TRN.add(sql, [self.id, self.id])\\n projects = pm.sql.TRN.execute_fetchflatten()\\n return None if not projects else projects\",\n \"def project_grp():\\n pass\",\n \"def getBuild():\",\n \"def project_name(self):\\n pass\",\n \"def project(self):\\n return read_small_file(self.homeDirectory + \\\"/.project\\\")\",\n \"def test_create_benchmark(self, benchmark_config, benchmark_config_py):\\n with OrionState() as cfg:\\n bm1 = get_or_create_benchmark(\\n cfg.storage,\\n **benchmark_config_py,\\n )\\n bm1.close()\\n\\n bm2 = get_or_create_benchmark(cfg.storage, \\\"bm00001\\\")\\n bm2.close()\\n\\n assert bm1.configuration == benchmark_config\\n\\n assert bm1.configuration == bm2.configuration\",\n \"def get_project(name):\\n tx = cypher_transaction()\\n query = \\\"\\\"\\\"MATCH (n:project) WHERE n.name={project_name} RETURN n\\\"\\\"\\\"\\n tx.append(query, parameters={'project_name': name})\\n result = tx.commit()\\n\\n # Returns a result of the form [[\\n # Record(\\n # columns=('n',),\\n # values=(Node('http://localhost:7474/db/data/node/233'),)\\n # )\\n # ]]\\n return _first(result)[0].values[0]\",\n \"def execute_benchmark(path, budget=None, threads=None):\\n benchmark_path = runtime.binary_path(path)\\n cmd = [benchmark_path, \\\"--benchmark_format=json\\\"]\\n if budget is not None:\\n cmd += [\\\"-b\\\", str(budget)]\\n if threads is not None:\\n cmd += [\\\"-t\\\", str(threads)]\\n res = process.execute(cmd)\\n return json.loads(res.std_out)\",\n \"def test_benchmark1(capsys):\\n student_1 = Student('114007245','Mario Castillo',2018, [\\\"INST 201\\\",\\\"INST 326\\\"])\\n student_2 = Student('117006012', 'Joe Rogan', 2018, [\\\"MATH 115\\\",\\\"PSYC 100\\\"])\\n student_3 = Student(\\\"117008490\\\", \\\"Kanye West\\\", 2018, [\\\"MATH 120\\\",\\\"STAT 003\\\"])\\n student_4 = Student('118009044', \\\"Elon Musk\\\", 2018, [\\\"PSYC 100\\\",\\\"MATH 003\\\"])\\n \\n student_1.benchmark_I()\\n outerr = capsys.readouterr()\\n out = outerr.out\\n assert out == ('You have not completed the Benchmark I requirements.\\\\n'\\n 'You have not taken MATH 115 or higher.\\\\n'\\n 'You have not taken PSYC 100.\\\\n')\\n\\n student_2.benchmark_I()\\n outerr = capsys.readouterr()\\n out = outerr.out\\n assert out == (f'You have completed all of your Benchmark I courses! '\\n f'Congratulations, {student_2.student_name}!\\\\n')\\n\\n student_3.benchmark_I()\\n outerr = capsys.readouterr()\\n out = outerr.out \\n assert out == ('You have not completed the Benchmark I requirements.\\\\n'\\n 'You have not taken PSYC 100.\\\\n')\\n\\n student_4.benchmark_I()\\n outerr = capsys.readouterr()\\n out = outerr.out\\n assert out == ('You have not completed the Benchmark I requirements.\\\\n'\\n 'You have not taken MATH 115 or higher.\\\\n')\",\n \"def getProjectData(self) -> ghidra.framework.model.ProjectData:\\n ...\",\n \"def project(self) -> str:\\n return self._db_data.project\",\n \"def project(self, X):\",\n \"def project(self, X):\",\n \"def project(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"project\\\")\",\n \"def project(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"project\\\")\",\n \"def project(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"project\\\")\",\n \"def project(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"project\\\")\",\n \"def project(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"project\\\")\",\n \"def project(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"project\\\")\",\n \"def project(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"project\\\")\",\n \"def project(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"project\\\")\",\n \"def project(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"project\\\")\",\n \"def project(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"project\\\")\",\n \"def project(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"project\\\")\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6512932","0.6404195","0.634007","0.62146837","0.6029168","0.59504235","0.5943799","0.5894067","0.581235","0.5640167","0.54331803","0.53937525","0.53754103","0.53711575","0.53711575","0.53711575","0.53711575","0.53711575","0.53711575","0.53711575","0.53711575","0.53711575","0.53711575","0.53711575","0.53711575","0.53711575","0.5320741","0.5305155","0.5305155","0.5305155","0.53033775","0.5297577","0.52931887","0.52452403","0.5132755","0.5130759","0.5092196","0.5092196","0.5092196","0.5092196","0.5092196","0.508232","0.5073821","0.50604963","0.50588703","0.5052553","0.5048933","0.50448763","0.50368816","0.5030383","0.5027121","0.4997465","0.4997465","0.49965158","0.49903548","0.49891016","0.49710017","0.49649626","0.49611804","0.49565667","0.4952913","0.49448943","0.49187967","0.49122328","0.4908808","0.49042892","0.49042892","0.49042892","0.49042892","0.49042892","0.49042892","0.49036622","0.49026316","0.48919082","0.48643726","0.48628592","0.48589215","0.484993","0.4843229","0.48411027","0.4840207","0.48394996","0.48349616","0.48255458","0.48185518","0.4818225","0.48155686","0.48060077","0.48060077","0.48058197","0.48058197","0.48058197","0.48058197","0.48058197","0.48058197","0.48058197","0.48058197","0.48058197","0.48058197","0.48058197"],"string":"[\n \"0.6512932\",\n \"0.6404195\",\n \"0.634007\",\n \"0.62146837\",\n \"0.6029168\",\n \"0.59504235\",\n \"0.5943799\",\n \"0.5894067\",\n \"0.581235\",\n \"0.5640167\",\n \"0.54331803\",\n \"0.53937525\",\n \"0.53754103\",\n \"0.53711575\",\n \"0.53711575\",\n \"0.53711575\",\n \"0.53711575\",\n \"0.53711575\",\n \"0.53711575\",\n \"0.53711575\",\n \"0.53711575\",\n \"0.53711575\",\n \"0.53711575\",\n \"0.53711575\",\n \"0.53711575\",\n \"0.53711575\",\n \"0.5320741\",\n \"0.5305155\",\n \"0.5305155\",\n \"0.5305155\",\n \"0.53033775\",\n \"0.5297577\",\n \"0.52931887\",\n \"0.52452403\",\n \"0.5132755\",\n \"0.5130759\",\n \"0.5092196\",\n \"0.5092196\",\n \"0.5092196\",\n \"0.5092196\",\n \"0.5092196\",\n \"0.508232\",\n \"0.5073821\",\n \"0.50604963\",\n \"0.50588703\",\n \"0.5052553\",\n \"0.5048933\",\n \"0.50448763\",\n \"0.50368816\",\n \"0.5030383\",\n \"0.5027121\",\n \"0.4997465\",\n \"0.4997465\",\n \"0.49965158\",\n \"0.49903548\",\n \"0.49891016\",\n \"0.49710017\",\n \"0.49649626\",\n \"0.49611804\",\n \"0.49565667\",\n \"0.4952913\",\n \"0.49448943\",\n \"0.49187967\",\n \"0.49122328\",\n \"0.4908808\",\n \"0.49042892\",\n \"0.49042892\",\n \"0.49042892\",\n \"0.49042892\",\n \"0.49042892\",\n \"0.49042892\",\n \"0.49036622\",\n \"0.49026316\",\n \"0.48919082\",\n \"0.48643726\",\n \"0.48628592\",\n \"0.48589215\",\n \"0.484993\",\n \"0.4843229\",\n \"0.48411027\",\n \"0.4840207\",\n \"0.48394996\",\n \"0.48349616\",\n \"0.48255458\",\n \"0.48185518\",\n \"0.4818225\",\n \"0.48155686\",\n \"0.48060077\",\n \"0.48060077\",\n \"0.48058197\",\n \"0.48058197\",\n \"0.48058197\",\n \"0.48058197\",\n \"0.48058197\",\n \"0.48058197\",\n \"0.48058197\",\n \"0.48058197\",\n \"0.48058197\",\n \"0.48058197\",\n \"0.48058197\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.81019706"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":11264,"cells":{"query":{"kind":"string","value":"Returns the type of |benchmark|"},"document":{"kind":"string","value":"def get_type(benchmark):\n # TODO(metzman): Use classes to mock a benchmark config for\n # OSS_FUZZ_ON_DEMAND.\n default_value = os.getenv('EXPERIMENT_TYPE', BenchmarkType.CODE.value)\n return benchmark_config.get_config(benchmark).get('type', default_value)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_benchmark(self, benchmark):\n\t\tif not isinstance(benchmark, str) and not callable(benchmark): return benchmark\n\t\telif benchmark in self.classes:\treturn self.classes[benchmark]()\n\t\traise TypeError('Passed benchmark is not defined!')","def validate_type(benchmark):\n benchmark_type = get_type(benchmark)\n if benchmark_type not in BENCHMARK_TYPE_STRS:\n logs.error('%s has an invalid benchmark type %s, must be one of %s',\n benchmark, benchmark_type, BENCHMARK_TYPE_STRS)\n return False\n return True","def benchmark_result(self):\n return self._benchmark_id","def get_benchmark(client):\n r = client.get(config.API_PATH() + '/benchmarks')\n benchmarks = json.loads(r.data)\n return benchmarks['benchmarks'][0]['id']","def get_testbench_name(self, tb_type: str) -> str:\n return f'{self._meas_name}_TB_{tb_type}'","def get_perfcount_type(self):\n return self._perfcount_type","def get_testbench_specs(self, tb_type: str) -> Dict[str, Any]:\n return self._specs['testbenches'][tb_type]","def _getStatisticType(self, statistic):\n\n instructions = simplejson.loads(statistic.instructions_json)\n return instructions['type']","def __get_dataset_type(dataset):\n op_type = None\n if isinstance(dataset, de.ShuffleDataset):\n op_type = OpName.SHUFFLE\n elif isinstance(dataset, de.MindDataset):\n op_type = OpName.MINDRECORD\n elif isinstance(dataset, de.BatchDataset):\n op_type = OpName.BATCH\n elif isinstance(dataset, de.SyncWaitDataset):\n op_type = OpName.BARRIER\n elif isinstance(dataset, de.ZipDataset):\n op_type = OpName.ZIP\n elif isinstance(dataset, de.ConcatDataset):\n op_type = OpName.CONCAT\n elif isinstance(dataset, de.MapDataset):\n op_type = OpName.MAP\n elif isinstance(dataset, de.FilterDataset):\n op_type = OpName.FILTER\n elif isinstance(dataset, de.RepeatDataset):\n op_type = OpName.REPEAT\n elif isinstance(dataset, de.SkipDataset):\n op_type = OpName.SKIP\n elif isinstance(dataset, de.TakeDataset):\n op_type = OpName.TAKE\n elif isinstance(dataset, de.ImageFolderDatasetV2):\n op_type = OpName.IMAGEFOLDER\n elif isinstance(dataset, de.GeneratorDataset):\n op_type = OpName.GENERATOR\n elif isinstance(dataset, de.TransferDataset):\n op_type = OpName.DEVICEQUEUE\n elif isinstance(dataset, de.RenameDataset):\n op_type = OpName.RENAME\n elif isinstance(dataset, de.TFRecordDataset):\n op_type = OpName.TFREADER\n elif isinstance(dataset, de.ProjectDataset):\n op_type = OpName.PROJECT\n elif isinstance(dataset, de.MnistDataset):\n op_type = OpName.MNIST\n elif isinstance(dataset, de.ManifestDataset):\n op_type = OpName.MANIFEST\n elif isinstance(dataset, de.VOCDataset):\n op_type = OpName.VOC\n elif isinstance(dataset, de.Cifar10Dataset):\n op_type = OpName.CIFAR10\n elif isinstance(dataset, de.Cifar100Dataset):\n op_type = OpName.CIFAR100\n elif isinstance(dataset, de.CelebADataset):\n op_type = OpName.CELEBA\n elif isinstance(dataset, de.RandomDataset):\n op_type = OpName.RANDOMDATA\n elif isinstance(dataset, de.TextFileDataset):\n op_type = OpName.TEXTFILE\n else:\n raise ValueError(\"Unsupported DatasetOp\")\n\n return op_type","def benchmark(self):\n logger.info(self.benchmark.__doc__)\n return self.run(self.benchmark_profile())","def unit_type(self) -> str:","def type(name):","def benchmark_selection(self):\n return self._benchmark_selection","def type_():\n pass","def type(self):\n return self._instrument_type","def get_benchmark_specification(benchmark = 'FSI1'):\n if benchmark == 'FSI1':\n rho_s = Constant(1e03)\n nu_s = Constant(0.4)\n mu_s = Constant(5e05)\n rho_f = Constant(1e03)\n nu_f = Constant(1e-03)\n U = 0.2\n T_end = 60.0\n result = \"results-FSI1/\"\n elif benchmark == 'FSI2':\n rho_s = Constant(1e04)\n nu_s = Constant(0.4)\n mu_s = Constant(5e05)\n rho_f = Constant(1e03)\n nu_f = Constant(1e-03)\n U = 1.0\n T_end = 15.0\n result = \"results-FSI2/\"\t\t\n elif benchmark == 'FSI3':\n rho_s = Constant(1e03)\n nu_s = Constant(0.4)\n mu_s = Constant(2e06)\n rho_f = Constant(1e03)\n nu_f = Constant(1e-03)\n U = 2.0\n T_end = 20.0\n result = \"results-FSI3/\"\t\t\n else:\n raise ValueError('\"{}\" is a wrong name for problem specification.'.format(benchmark))\n v_max = Constant(1.5*U) # mean velocity to maximum velocity \n # (we have parabolic profile)\n E_s = Constant(2*mu_s*(1+nu_s))\n lambda_s = Constant((nu_s*E_s)/((1+nu_s)*(1-2*nu_s)))\n mu_f = Constant(nu_f*rho_f)\n return v_max, lambda_s, mu_s, rho_s, mu_f, rho_f, T_end, result","def test_type(self):\n return self._test_type","def get_test_type(self):\n return self.test_type","def type(self):\n # easy enough\n return self._dataset._pyre_id.type","def compare_performance(self):\n\n if self.label_type == \"categorical\":\n self._eval_classifier()\n\n elif self.label_type == \"numerical\":\n self._eval_regressor()\n\n return self.performance_comparison","def criterion_type(self) -> str:\n return pulumi.get(self, \"criterion_type\")","def criterion_type(self) -> str:\n return pulumi.get(self, \"criterion_type\")","def sample_type(self) -> str:\n return self._sample_type","def _type(self) -> str:\n ...","def benchmark_profile(self):\n cb_bin = os.path.join(bin_path, 'compilebench')\n desc = \"benchmark\"\n test_name = \"compilebench_{0}\".format(to_safe_name(desc))\n test = TestProfile(\n name=test_name,\n desc=desc,\n test_path=self.test_path,\n bin_path=bin_path,\n command=\"{0} -D {1} -i 10 --makej\".format(cb_bin, self.test_path))\n\n return test","def type(cls):","def create_benchmark(self, benchmark):\n self.crd_client.create_namespaced_custom_object(\n group=\"ripsaw.cloudbulldozer.io\",\n version=\"v1alpha1\",\n namespace=benchmark[\"metadata\"][\"namespace\"],\n plural=\"benchmarks\",\n body=benchmark,\n )","def get_engine_type(self):","def _get_measure_class(measure_number, year=config.get('calculation.measures_year')):\n return MEASURE_NUMBER_TO_CLASS[year][measure_number]['measure_type']","def TMVAType(name):\n from ROOT import TMVA\n name = name.lower()\n if name.find('bdt') >= 0:\n return TMVA.Types.kBDT\n elif name.find('llh') >= 0:\n return TMVA.Types.kLikelihood\n else:\n raise ValueError('Unsupported TMVA classifier type')","def test_cpuload_get_kind(self):\n\n assert_equal(self.test_cpuload.get_kind(), 'mpcpuload')","def test_make_benchmark_single_ll():\n benchmark = llvm.make_benchmark(INVALID_IR_PATH)\n assert str(benchmark.uri).startswith(\"benchmark://user-v0/\")\n assert benchmark.uri.scheme == \"benchmark\"\n assert benchmark.uri.dataset == \"user-v0\"","def instrument_type(self):\n \n raise NotImplementedError()","def type(self) -> global___Type:","def type(self):\n pass","def type(self):\n pass","def type(self):\n pass","def t(self):\n\t\treturn self.type","def counter_type(self) -> str:\n return self._counter_type","def findBenchFromDevice(self, device):\n return device.bench","def get_type(self):\n if self.data[\"is_script\"]:\n return self.data[\"script\"]\n elif self.data[\"is_qt\"]:\n return \"qt\"\n else:\n return \"normal\"","def _type(self, item):\n return self.cv.type(item)","def type(self):\n\t\treturn self.type_","def type(self) -> MetricType:\n return self._type","def _type(self):\n return self._id[1]","def getType (self) :\n if self.buffer == None :\n self.buffer = self.fetch ()\n return self.buffer.classifi7","def _get_time(self, state: State) -> int:\n benchmark_time = {\n 'resnet': state.timestamp.epoch.value,\n 'bert': state.timestamp.sample.value,\n }\n return benchmark_time[self.benchmark]","def memtype(self):\n # easy enough\n return self._memtype","def type(self) -> str:\n return self.type_","def run_type(self):\n return self._run_type","def get_type(self) -> str:\n raise NotImplementedError","def get_type(self) -> str:\n raise NotImplementedError","def get_fuzz_target(benchmark):\n # Do this because of OSS-Fuzz-on-demand.\n # TODO(metzman): Use classes to mock a benchmark config for\n # OSS_FUZZ_ON_DEMAND.\n return benchmark_config.get_config(benchmark).get(\n 'fuzz_target', environment.get('FUZZ_TARGET'))","def _instantiate_benchmark_class(self, output_dir):\n module_import_path, class_name = self.config.test_class_str.rsplit('.', 1)\n module = importlib.import_module(module_import_path)\n class_ = getattr(module, class_name)\n\n instance = class_(output_dir=output_dir)\n instance.oss_report_object = benchmark_result.BenchmarkResult()\n return instance","def type_(self):\n return self._type","def type(self) -> str:\n return self._type","def type(self) -> str:\n return self._type","def type(self) -> str:\n return self._type","def build_benchmark(base_url, node):\n node = lowercase_keys(flatten_dictionaries(node)) # Make it usable\n\n benchmark = Benchmark()\n\n # Read & set basic test parameters\n benchmark = build_test(base_url, node, benchmark)\n\n # Complex parsing because of list/dictionary/singleton legal cases\n for key, value in node.items():\n if key == u'warmup_runs':\n benchmark.warmup_runs = int(value)\n elif key == u'benchmark_runs':\n benchmark.benchmark_runs = int(value)\n elif key == u'output_format':\n format = value.lower()\n if format in OUTPUT_FORMATS:\n benchmark.output_format = format\n else:\n raise Exception('Invalid benchmark output format: ' + format)\n elif key == u'output_file':\n if not isinstance(value, basestring):\n raise Exception(\"Invalid output file format\")\n benchmark.output_file = value\n elif key == u'metrics':\n if isinstance(value, unicode) or isinstance(value,str):\n # Single value\n benchmark.add_metric(unicode(value, 'UTF-8'))\n elif isinstance(value, list) or isinstance(value, set):\n # List of single values or list of {metric:aggregate, ...}\n for metric in value:\n if isinstance(metric, dict):\n for metricname, aggregate in metric.items():\n if not isinstance(metricname, basestring):\n raise Exception(\"Invalid metric input: non-string metric name\")\n if not isinstance(aggregate, basestring):\n raise Exception(\"Invalid aggregate input: non-string aggregate name\")\n # TODO unicode-safe this\n benchmark.add_metric(unicode(metricname,'UTF-8'), unicode(aggregate,'UTF-8'))\n\n elif isinstance(metric, unicode) or isinstance(metric, str):\n benchmark.add_metric(unicode(metric,'UTF-8'))\n elif isinstance(value, dict):\n # Dictionary of metric-aggregate pairs\n for metricname, aggregate in value.items():\n if not isinstance(metricname, basestring):\n raise Exception(\"Invalid metric input: non-string metric name\")\n if not isinstance(aggregate, basestring):\n raise Exception(\"Invalid aggregate input: non-string aggregate name\")\n benchmark.add_metric(unicode(metricname,'UTF-8'), unicode(aggregate,'UTF-8'))\n else:\n raise Exception(\"Invalid benchmark metric datatype: \"+str(value))\n\n return benchmark","def get_benchmark_requirements(cls):\n pass","def findBenchFromInstrument(self, instrument):\n return instrument.bench","def type(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"type\")","def type(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"type\")","def type(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"type\")","def type(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"type\")","def type(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"type\")","def type(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"type\")","def type(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"type\")","def type(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"type\")","def type(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"type\")","def type(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"type\")","def type(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"type\")","def type(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"type\")","def type(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"type\")","def type(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"type\")","def type(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"type\")","def type(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"type\")","def type(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"type\")","def type(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"type\")","def type(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"type\")","def type(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"type\")","def type(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"type\")","def type(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"type\")","def type(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"type\")","def type(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"type\")","def type(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"type\")","def _compression_module_type_to_attr_name(compression_module_type: CompressionModuleType):\n if compression_module_type == CompressionModuleType.FUNCTION_QUANTIZER:\n return \"function_quantizers\"\n if compression_module_type == CompressionModuleType.ACTIVATION_QUANTIZER:\n return \"activation_quantizers\"\n raise RuntimeError(\"Unknown extra module type\")","def type(self):\r\n return self.__class__.__name__","def get_comparison_type(self):\n return self.comparison_type.get_type()","def get_coverage_benchmarks():\n return [\n benchmark for benchmark in get_all_benchmarks()\n if get_type(benchmark) == BenchmarkType.CODE.value\n ]","def type(self):\r\n return self.__type","def type(self):\n return self._device.type_name","def get_type(self) -> str:\n # Note: this name conflicts with existing python builtins\n return self[\"Type\"]","def _determine_memtype(description):\n if description.startswith('.'):\n t = description.split()[0]\n if t in [ \".data\", \".bss\", \".rodata\" ]:\n return MemoryType.StaticData\n elif t == \".text\":\n return MemoryType.Code\n else:\n return MemoryType.OtherStatic\n elif description.startswith('['):\n if description == \"[stack]\":\n return MemoryType.Stack\n elif description in [ \"[vdso]\", \"[vsyscall]\", \"[vvar]\" ]:\n return MemoryType.LinuxSpecial\n elif description == \"[heap]\":\n return MemoryType.General\n else:\n return MemoryType.Unknown\n elif description.startswith(\"load\"):\n return MemoryType.General\n elif \"libc\" in description:\n # C library is known to have some odd-named sections\n return MemoryType.OtherStatic\n elif description == \"text_env\":\n return MemoryType.OtherStatic\n else:\n return MemoryType.Unknown","def _get_type(self):\n return self.__type","def _get_type(self):\n return self.__type","def _get_type(self):\n return self.__type","def _get_type(self):\n return self.__type","def _get_type(self):\n return self.__type","def _get_type(self):\n return self.__type"],"string":"[\n \"def get_benchmark(self, benchmark):\\n\\t\\tif not isinstance(benchmark, str) and not callable(benchmark): return benchmark\\n\\t\\telif benchmark in self.classes:\\treturn self.classes[benchmark]()\\n\\t\\traise TypeError('Passed benchmark is not defined!')\",\n \"def validate_type(benchmark):\\n benchmark_type = get_type(benchmark)\\n if benchmark_type not in BENCHMARK_TYPE_STRS:\\n logs.error('%s has an invalid benchmark type %s, must be one of %s',\\n benchmark, benchmark_type, BENCHMARK_TYPE_STRS)\\n return False\\n return True\",\n \"def benchmark_result(self):\\n return self._benchmark_id\",\n \"def get_benchmark(client):\\n r = client.get(config.API_PATH() + '/benchmarks')\\n benchmarks = json.loads(r.data)\\n return benchmarks['benchmarks'][0]['id']\",\n \"def get_testbench_name(self, tb_type: str) -> str:\\n return f'{self._meas_name}_TB_{tb_type}'\",\n \"def get_perfcount_type(self):\\n return self._perfcount_type\",\n \"def get_testbench_specs(self, tb_type: str) -> Dict[str, Any]:\\n return self._specs['testbenches'][tb_type]\",\n \"def _getStatisticType(self, statistic):\\n\\n instructions = simplejson.loads(statistic.instructions_json)\\n return instructions['type']\",\n \"def __get_dataset_type(dataset):\\n op_type = None\\n if isinstance(dataset, de.ShuffleDataset):\\n op_type = OpName.SHUFFLE\\n elif isinstance(dataset, de.MindDataset):\\n op_type = OpName.MINDRECORD\\n elif isinstance(dataset, de.BatchDataset):\\n op_type = OpName.BATCH\\n elif isinstance(dataset, de.SyncWaitDataset):\\n op_type = OpName.BARRIER\\n elif isinstance(dataset, de.ZipDataset):\\n op_type = OpName.ZIP\\n elif isinstance(dataset, de.ConcatDataset):\\n op_type = OpName.CONCAT\\n elif isinstance(dataset, de.MapDataset):\\n op_type = OpName.MAP\\n elif isinstance(dataset, de.FilterDataset):\\n op_type = OpName.FILTER\\n elif isinstance(dataset, de.RepeatDataset):\\n op_type = OpName.REPEAT\\n elif isinstance(dataset, de.SkipDataset):\\n op_type = OpName.SKIP\\n elif isinstance(dataset, de.TakeDataset):\\n op_type = OpName.TAKE\\n elif isinstance(dataset, de.ImageFolderDatasetV2):\\n op_type = OpName.IMAGEFOLDER\\n elif isinstance(dataset, de.GeneratorDataset):\\n op_type = OpName.GENERATOR\\n elif isinstance(dataset, de.TransferDataset):\\n op_type = OpName.DEVICEQUEUE\\n elif isinstance(dataset, de.RenameDataset):\\n op_type = OpName.RENAME\\n elif isinstance(dataset, de.TFRecordDataset):\\n op_type = OpName.TFREADER\\n elif isinstance(dataset, de.ProjectDataset):\\n op_type = OpName.PROJECT\\n elif isinstance(dataset, de.MnistDataset):\\n op_type = OpName.MNIST\\n elif isinstance(dataset, de.ManifestDataset):\\n op_type = OpName.MANIFEST\\n elif isinstance(dataset, de.VOCDataset):\\n op_type = OpName.VOC\\n elif isinstance(dataset, de.Cifar10Dataset):\\n op_type = OpName.CIFAR10\\n elif isinstance(dataset, de.Cifar100Dataset):\\n op_type = OpName.CIFAR100\\n elif isinstance(dataset, de.CelebADataset):\\n op_type = OpName.CELEBA\\n elif isinstance(dataset, de.RandomDataset):\\n op_type = OpName.RANDOMDATA\\n elif isinstance(dataset, de.TextFileDataset):\\n op_type = OpName.TEXTFILE\\n else:\\n raise ValueError(\\\"Unsupported DatasetOp\\\")\\n\\n return op_type\",\n \"def benchmark(self):\\n logger.info(self.benchmark.__doc__)\\n return self.run(self.benchmark_profile())\",\n \"def unit_type(self) -> str:\",\n \"def type(name):\",\n \"def benchmark_selection(self):\\n return self._benchmark_selection\",\n \"def type_():\\n pass\",\n \"def type(self):\\n return self._instrument_type\",\n \"def get_benchmark_specification(benchmark = 'FSI1'):\\n if benchmark == 'FSI1':\\n rho_s = Constant(1e03)\\n nu_s = Constant(0.4)\\n mu_s = Constant(5e05)\\n rho_f = Constant(1e03)\\n nu_f = Constant(1e-03)\\n U = 0.2\\n T_end = 60.0\\n result = \\\"results-FSI1/\\\"\\n elif benchmark == 'FSI2':\\n rho_s = Constant(1e04)\\n nu_s = Constant(0.4)\\n mu_s = Constant(5e05)\\n rho_f = Constant(1e03)\\n nu_f = Constant(1e-03)\\n U = 1.0\\n T_end = 15.0\\n result = \\\"results-FSI2/\\\"\\t\\t\\n elif benchmark == 'FSI3':\\n rho_s = Constant(1e03)\\n nu_s = Constant(0.4)\\n mu_s = Constant(2e06)\\n rho_f = Constant(1e03)\\n nu_f = Constant(1e-03)\\n U = 2.0\\n T_end = 20.0\\n result = \\\"results-FSI3/\\\"\\t\\t\\n else:\\n raise ValueError('\\\"{}\\\" is a wrong name for problem specification.'.format(benchmark))\\n v_max = Constant(1.5*U) # mean velocity to maximum velocity \\n # (we have parabolic profile)\\n E_s = Constant(2*mu_s*(1+nu_s))\\n lambda_s = Constant((nu_s*E_s)/((1+nu_s)*(1-2*nu_s)))\\n mu_f = Constant(nu_f*rho_f)\\n return v_max, lambda_s, mu_s, rho_s, mu_f, rho_f, T_end, result\",\n \"def test_type(self):\\n return self._test_type\",\n \"def get_test_type(self):\\n return self.test_type\",\n \"def type(self):\\n # easy enough\\n return self._dataset._pyre_id.type\",\n \"def compare_performance(self):\\n\\n if self.label_type == \\\"categorical\\\":\\n self._eval_classifier()\\n\\n elif self.label_type == \\\"numerical\\\":\\n self._eval_regressor()\\n\\n return self.performance_comparison\",\n \"def criterion_type(self) -> str:\\n return pulumi.get(self, \\\"criterion_type\\\")\",\n \"def criterion_type(self) -> str:\\n return pulumi.get(self, \\\"criterion_type\\\")\",\n \"def sample_type(self) -> str:\\n return self._sample_type\",\n \"def _type(self) -> str:\\n ...\",\n \"def benchmark_profile(self):\\n cb_bin = os.path.join(bin_path, 'compilebench')\\n desc = \\\"benchmark\\\"\\n test_name = \\\"compilebench_{0}\\\".format(to_safe_name(desc))\\n test = TestProfile(\\n name=test_name,\\n desc=desc,\\n test_path=self.test_path,\\n bin_path=bin_path,\\n command=\\\"{0} -D {1} -i 10 --makej\\\".format(cb_bin, self.test_path))\\n\\n return test\",\n \"def type(cls):\",\n \"def create_benchmark(self, benchmark):\\n self.crd_client.create_namespaced_custom_object(\\n group=\\\"ripsaw.cloudbulldozer.io\\\",\\n version=\\\"v1alpha1\\\",\\n namespace=benchmark[\\\"metadata\\\"][\\\"namespace\\\"],\\n plural=\\\"benchmarks\\\",\\n body=benchmark,\\n )\",\n \"def get_engine_type(self):\",\n \"def _get_measure_class(measure_number, year=config.get('calculation.measures_year')):\\n return MEASURE_NUMBER_TO_CLASS[year][measure_number]['measure_type']\",\n \"def TMVAType(name):\\n from ROOT import TMVA\\n name = name.lower()\\n if name.find('bdt') >= 0:\\n return TMVA.Types.kBDT\\n elif name.find('llh') >= 0:\\n return TMVA.Types.kLikelihood\\n else:\\n raise ValueError('Unsupported TMVA classifier type')\",\n \"def test_cpuload_get_kind(self):\\n\\n assert_equal(self.test_cpuload.get_kind(), 'mpcpuload')\",\n \"def test_make_benchmark_single_ll():\\n benchmark = llvm.make_benchmark(INVALID_IR_PATH)\\n assert str(benchmark.uri).startswith(\\\"benchmark://user-v0/\\\")\\n assert benchmark.uri.scheme == \\\"benchmark\\\"\\n assert benchmark.uri.dataset == \\\"user-v0\\\"\",\n \"def instrument_type(self):\\n \\n raise NotImplementedError()\",\n \"def type(self) -> global___Type:\",\n \"def type(self):\\n pass\",\n \"def type(self):\\n pass\",\n \"def type(self):\\n pass\",\n \"def t(self):\\n\\t\\treturn self.type\",\n \"def counter_type(self) -> str:\\n return self._counter_type\",\n \"def findBenchFromDevice(self, device):\\n return device.bench\",\n \"def get_type(self):\\n if self.data[\\\"is_script\\\"]:\\n return self.data[\\\"script\\\"]\\n elif self.data[\\\"is_qt\\\"]:\\n return \\\"qt\\\"\\n else:\\n return \\\"normal\\\"\",\n \"def _type(self, item):\\n return self.cv.type(item)\",\n \"def type(self):\\n\\t\\treturn self.type_\",\n \"def type(self) -> MetricType:\\n return self._type\",\n \"def _type(self):\\n return self._id[1]\",\n \"def getType (self) :\\n if self.buffer == None :\\n self.buffer = self.fetch ()\\n return self.buffer.classifi7\",\n \"def _get_time(self, state: State) -> int:\\n benchmark_time = {\\n 'resnet': state.timestamp.epoch.value,\\n 'bert': state.timestamp.sample.value,\\n }\\n return benchmark_time[self.benchmark]\",\n \"def memtype(self):\\n # easy enough\\n return self._memtype\",\n \"def type(self) -> str:\\n return self.type_\",\n \"def run_type(self):\\n return self._run_type\",\n \"def get_type(self) -> str:\\n raise NotImplementedError\",\n \"def get_type(self) -> str:\\n raise NotImplementedError\",\n \"def get_fuzz_target(benchmark):\\n # Do this because of OSS-Fuzz-on-demand.\\n # TODO(metzman): Use classes to mock a benchmark config for\\n # OSS_FUZZ_ON_DEMAND.\\n return benchmark_config.get_config(benchmark).get(\\n 'fuzz_target', environment.get('FUZZ_TARGET'))\",\n \"def _instantiate_benchmark_class(self, output_dir):\\n module_import_path, class_name = self.config.test_class_str.rsplit('.', 1)\\n module = importlib.import_module(module_import_path)\\n class_ = getattr(module, class_name)\\n\\n instance = class_(output_dir=output_dir)\\n instance.oss_report_object = benchmark_result.BenchmarkResult()\\n return instance\",\n \"def type_(self):\\n return self._type\",\n \"def type(self) -> str:\\n return self._type\",\n \"def type(self) -> str:\\n return self._type\",\n \"def type(self) -> str:\\n return self._type\",\n \"def build_benchmark(base_url, node):\\n node = lowercase_keys(flatten_dictionaries(node)) # Make it usable\\n\\n benchmark = Benchmark()\\n\\n # Read & set basic test parameters\\n benchmark = build_test(base_url, node, benchmark)\\n\\n # Complex parsing because of list/dictionary/singleton legal cases\\n for key, value in node.items():\\n if key == u'warmup_runs':\\n benchmark.warmup_runs = int(value)\\n elif key == u'benchmark_runs':\\n benchmark.benchmark_runs = int(value)\\n elif key == u'output_format':\\n format = value.lower()\\n if format in OUTPUT_FORMATS:\\n benchmark.output_format = format\\n else:\\n raise Exception('Invalid benchmark output format: ' + format)\\n elif key == u'output_file':\\n if not isinstance(value, basestring):\\n raise Exception(\\\"Invalid output file format\\\")\\n benchmark.output_file = value\\n elif key == u'metrics':\\n if isinstance(value, unicode) or isinstance(value,str):\\n # Single value\\n benchmark.add_metric(unicode(value, 'UTF-8'))\\n elif isinstance(value, list) or isinstance(value, set):\\n # List of single values or list of {metric:aggregate, ...}\\n for metric in value:\\n if isinstance(metric, dict):\\n for metricname, aggregate in metric.items():\\n if not isinstance(metricname, basestring):\\n raise Exception(\\\"Invalid metric input: non-string metric name\\\")\\n if not isinstance(aggregate, basestring):\\n raise Exception(\\\"Invalid aggregate input: non-string aggregate name\\\")\\n # TODO unicode-safe this\\n benchmark.add_metric(unicode(metricname,'UTF-8'), unicode(aggregate,'UTF-8'))\\n\\n elif isinstance(metric, unicode) or isinstance(metric, str):\\n benchmark.add_metric(unicode(metric,'UTF-8'))\\n elif isinstance(value, dict):\\n # Dictionary of metric-aggregate pairs\\n for metricname, aggregate in value.items():\\n if not isinstance(metricname, basestring):\\n raise Exception(\\\"Invalid metric input: non-string metric name\\\")\\n if not isinstance(aggregate, basestring):\\n raise Exception(\\\"Invalid aggregate input: non-string aggregate name\\\")\\n benchmark.add_metric(unicode(metricname,'UTF-8'), unicode(aggregate,'UTF-8'))\\n else:\\n raise Exception(\\\"Invalid benchmark metric datatype: \\\"+str(value))\\n\\n return benchmark\",\n \"def get_benchmark_requirements(cls):\\n pass\",\n \"def findBenchFromInstrument(self, instrument):\\n return instrument.bench\",\n \"def type(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"type\\\")\",\n \"def type(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"type\\\")\",\n \"def type(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"type\\\")\",\n \"def type(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"type\\\")\",\n \"def type(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"type\\\")\",\n \"def type(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"type\\\")\",\n \"def type(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"type\\\")\",\n \"def type(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"type\\\")\",\n \"def type(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"type\\\")\",\n \"def type(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"type\\\")\",\n \"def type(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"type\\\")\",\n \"def type(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"type\\\")\",\n \"def type(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"type\\\")\",\n \"def type(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"type\\\")\",\n \"def type(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"type\\\")\",\n \"def type(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"type\\\")\",\n \"def type(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"type\\\")\",\n \"def type(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"type\\\")\",\n \"def type(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"type\\\")\",\n \"def type(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"type\\\")\",\n \"def type(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"type\\\")\",\n \"def type(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"type\\\")\",\n \"def type(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"type\\\")\",\n \"def type(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"type\\\")\",\n \"def type(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"type\\\")\",\n \"def _compression_module_type_to_attr_name(compression_module_type: CompressionModuleType):\\n if compression_module_type == CompressionModuleType.FUNCTION_QUANTIZER:\\n return \\\"function_quantizers\\\"\\n if compression_module_type == CompressionModuleType.ACTIVATION_QUANTIZER:\\n return \\\"activation_quantizers\\\"\\n raise RuntimeError(\\\"Unknown extra module type\\\")\",\n \"def type(self):\\r\\n return self.__class__.__name__\",\n \"def get_comparison_type(self):\\n return self.comparison_type.get_type()\",\n \"def get_coverage_benchmarks():\\n return [\\n benchmark for benchmark in get_all_benchmarks()\\n if get_type(benchmark) == BenchmarkType.CODE.value\\n ]\",\n \"def type(self):\\r\\n return self.__type\",\n \"def type(self):\\n return self._device.type_name\",\n \"def get_type(self) -> str:\\n # Note: this name conflicts with existing python builtins\\n return self[\\\"Type\\\"]\",\n \"def _determine_memtype(description):\\n if description.startswith('.'):\\n t = description.split()[0]\\n if t in [ \\\".data\\\", \\\".bss\\\", \\\".rodata\\\" ]:\\n return MemoryType.StaticData\\n elif t == \\\".text\\\":\\n return MemoryType.Code\\n else:\\n return MemoryType.OtherStatic\\n elif description.startswith('['):\\n if description == \\\"[stack]\\\":\\n return MemoryType.Stack\\n elif description in [ \\\"[vdso]\\\", \\\"[vsyscall]\\\", \\\"[vvar]\\\" ]:\\n return MemoryType.LinuxSpecial\\n elif description == \\\"[heap]\\\":\\n return MemoryType.General\\n else:\\n return MemoryType.Unknown\\n elif description.startswith(\\\"load\\\"):\\n return MemoryType.General\\n elif \\\"libc\\\" in description:\\n # C library is known to have some odd-named sections\\n return MemoryType.OtherStatic\\n elif description == \\\"text_env\\\":\\n return MemoryType.OtherStatic\\n else:\\n return MemoryType.Unknown\",\n \"def _get_type(self):\\n return self.__type\",\n \"def _get_type(self):\\n return self.__type\",\n \"def _get_type(self):\\n return self.__type\",\n \"def _get_type(self):\\n return self.__type\",\n \"def _get_type(self):\\n return self.__type\",\n \"def _get_type(self):\\n return self.__type\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7427512","0.6534945","0.6310375","0.63042027","0.6122598","0.6042788","0.58986205","0.58470327","0.58307236","0.58241946","0.5813828","0.5782032","0.5728832","0.570958","0.5697104","0.56663436","0.56439966","0.5572599","0.5552849","0.5550577","0.5532445","0.5532445","0.55131143","0.5511789","0.5510949","0.54829085","0.5481124","0.5477772","0.54583263","0.54505855","0.54501605","0.54226995","0.5411142","0.53856766","0.5381162","0.5381162","0.5381162","0.53696525","0.5365409","0.53600454","0.53543913","0.5349989","0.5338076","0.53273976","0.53215194","0.5319051","0.5316295","0.528302","0.5279988","0.52492654","0.5241658","0.5241658","0.52409434","0.5231819","0.5221764","0.521937","0.521937","0.521937","0.5212023","0.52027357","0.5198802","0.5198055","0.5198055","0.5198055","0.5198055","0.5198055","0.5198055","0.5198055","0.5198055","0.5198055","0.5198055","0.5198055","0.5198055","0.5198055","0.5198055","0.5198055","0.5198055","0.5198055","0.5198055","0.5198055","0.5198055","0.5198055","0.5198055","0.5198055","0.5198055","0.5198055","0.51968783","0.51945895","0.5192525","0.5174939","0.51710784","0.51670784","0.5160068","0.5154711","0.5149377","0.5149377","0.5149377","0.5149377","0.5149377","0.5149377"],"string":"[\n \"0.7427512\",\n \"0.6534945\",\n \"0.6310375\",\n \"0.63042027\",\n \"0.6122598\",\n \"0.6042788\",\n \"0.58986205\",\n \"0.58470327\",\n \"0.58307236\",\n \"0.58241946\",\n \"0.5813828\",\n \"0.5782032\",\n \"0.5728832\",\n \"0.570958\",\n \"0.5697104\",\n \"0.56663436\",\n \"0.56439966\",\n \"0.5572599\",\n \"0.5552849\",\n \"0.5550577\",\n \"0.5532445\",\n \"0.5532445\",\n \"0.55131143\",\n \"0.5511789\",\n \"0.5510949\",\n \"0.54829085\",\n \"0.5481124\",\n \"0.5477772\",\n \"0.54583263\",\n \"0.54505855\",\n \"0.54501605\",\n \"0.54226995\",\n \"0.5411142\",\n \"0.53856766\",\n \"0.5381162\",\n \"0.5381162\",\n \"0.5381162\",\n \"0.53696525\",\n \"0.5365409\",\n \"0.53600454\",\n \"0.53543913\",\n \"0.5349989\",\n \"0.5338076\",\n \"0.53273976\",\n \"0.53215194\",\n \"0.5319051\",\n \"0.5316295\",\n \"0.528302\",\n \"0.5279988\",\n \"0.52492654\",\n \"0.5241658\",\n \"0.5241658\",\n \"0.52409434\",\n \"0.5231819\",\n \"0.5221764\",\n \"0.521937\",\n \"0.521937\",\n \"0.521937\",\n \"0.5212023\",\n \"0.52027357\",\n \"0.5198802\",\n \"0.5198055\",\n \"0.5198055\",\n \"0.5198055\",\n \"0.5198055\",\n \"0.5198055\",\n \"0.5198055\",\n \"0.5198055\",\n \"0.5198055\",\n \"0.5198055\",\n \"0.5198055\",\n \"0.5198055\",\n \"0.5198055\",\n \"0.5198055\",\n \"0.5198055\",\n \"0.5198055\",\n \"0.5198055\",\n \"0.5198055\",\n \"0.5198055\",\n \"0.5198055\",\n \"0.5198055\",\n \"0.5198055\",\n \"0.5198055\",\n \"0.5198055\",\n \"0.5198055\",\n \"0.5198055\",\n \"0.51968783\",\n \"0.51945895\",\n \"0.5192525\",\n \"0.5174939\",\n \"0.51710784\",\n \"0.51670784\",\n \"0.5160068\",\n \"0.5154711\",\n \"0.5149377\",\n \"0.5149377\",\n \"0.5149377\",\n \"0.5149377\",\n \"0.5149377\",\n \"0.5149377\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8139171"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":11265,"cells":{"query":{"kind":"string","value":"Get the URL of the docker runner image for fuzzing the benchmark with fuzzer."},"document":{"kind":"string","value":"def get_runner_image_url(experiment, benchmark, fuzzer, docker_registry):\n tag = 'latest' if environment.get('LOCAL_EXPERIMENT') else experiment\n return f'{docker_registry}/runners/{fuzzer}/{benchmark}:{tag}'"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_builder_image_url(benchmark, fuzzer, docker_registry):\n return f'{docker_registry}/builders/{fuzzer}/{benchmark}'","def _to_dockerfile_url(image):\n path = \"/\".join((image.platform, image.release, image.architecture, \"Dockerfile\"))\n return git.get_github_blob_url(path, ref=f\"v{image.version}\")","def get_fuzz_target(benchmark):\n # Do this because of OSS-Fuzz-on-demand.\n # TODO(metzman): Use classes to mock a benchmark config for\n # OSS_FUZZ_ON_DEMAND.\n return benchmark_config.get_config(benchmark).get(\n 'fuzz_target', environment.get('FUZZ_TARGET'))","def get_image_name():\n try:\n return os.environ['AIRFLOW_IMAGE']\n except KeyError:\n raise Exception(\"Please provide docker image name to pytest using environment variable AIRFLOW_IMAGE\")","def getBuildbotURL():","def get_rule_container_image_uri(name, region):\n if name is not None and name.startswith(\"DetailedProfilerProcessingJobConfig\"):\n # should have the format like \"123456789012.dkr.ecr.us-west-2.amazonaws.com/detailed-profiler-processing:latest\"\n return image_uris.retrieve(detailed_framework_name, region)\n\n return image_uris.retrieve(framework_name, region)","def docker_runner_factory(image):\n\n mapping = {\n 'gunicorn': GunicornDockerRunner,\n 'redis': RedisDockerRunner,\n 'consul': ConsulDockerRunner,\n 'postgres': PostgresDockerRunner,\n 'registrator': RegistratorDockerRunner,\n 'solr': SolrDockerRunner\n }\n\n for key in mapping:\n if key in image:\n return mapping[key]\n\n return DockerRunner","def get_buildbot_url():\n return \"http://10.45.4.98:8001/\"","def _ensure_image(testkit_path, branch_name, artifacts_path):\n # Construct Docker image name from branch name\n image_name = \"runner:%s\" % branch_name\n image_path = os.path.join(testkit_path, \"runner_image\")\n docker.build_and_tag(image_name, image_path, log_path=artifacts_path)\n\n return image_name","def _get_dockerfiles_for_test() -> str:\n project_root_dir = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))\n dockerfiles_dir = os.path.join(project_root_dir, \"dockerfiles\")\n if sys.version_info[0:2] == (3, 6):\n return os.path.join(dockerfiles_dir, \"centos7.Dockerfile\")\n elif sys.version_info[0:2] == (3, 9):\n return os.path.join(dockerfiles_dir, \"rocky8.Dockerfile\")\n else:\n raise Exception(\n \"Running the tests with INMANTA_TEST_INFRA_SETUP=true is only supported using a python3.6 or python3.9 venv\"\n )","def get_image_registry_url(self, image_name):\n c = self._oc_command([\"get\", \"is\", image_name,\n \"--output=jsonpath=\\'{ .status.dockerImageRepository }\\'\"])\n try:\n internal_registry_name = run_cmd(c, return_output=True)\n except subprocess.CalledProcessError as ex:\n raise ConuException(\"oc get is failed: %s\" % ex)\n\n logger.info(\"Image registry url: %s\", internal_registry_name)\n\n return internal_registry_name.replace(\"'\", \"\").replace('\"', '')","def _get_image(runtime):\n return \"{}:{}\".format(LambdaContainer._IMAGE_REPO_NAME, runtime)","def _dockerfile(self):\n return self.config.get('docker', {}).get('dockerfile', 'Dockerfile')","def get_image_url():","def dockerfile(self) -> pulumi.Output[Optional[str]]:\n return pulumi.get(self, \"dockerfile\")","def get_flower_url(self):\n return \"http://{0}:{1}\".format(self.get_head_node_ip(), self.FLOWER_PORT)","def _GetBuildBotUrl(builder_host, builder_port):\n if (builder_host == BISECT_BUILDER_HOST and\n builder_port == BISECT_BUILDER_PORT):\n return TRY_SERVER_URL\n else:\n return 'http://%s:%s' % (builder_host, builder_port)","def dockerfile(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"dockerfile\")","def get_base_docker_image(docker_file):\n with open(docker_file) as f:\n from_line = next(\n line for line in f.read().split(\"\\n\") if line.startswith(\"FROM\")\n )\n _from, base_image = from_line.split()\n return base_image","def url(self):\r\n return \"{}/container/{}\".format(BASE_URL, self.unit_locator)","def ping_docker():\n with Docker('unittest-36', image='python:3.6') as tun:\n return tun.call(python_version)[:2]","def docker_image_name(self):\n raise NotImplementedError","def dockerfile_dir(self):\n return self._dockerfile_dir","def docker_worker():","def http_service(docker_ip: Any, docker_services: Any) -> Any:\n # `port_for` takes a container port and returns the corresponding host port\n port = docker_services.port_for(\"fdk-baseregistries-publisher\", HOST_PORT)\n url = \"http://{}:{}\".format(docker_ip, port)\n docker_services.wait_until_responsive(\n timeout=30.0, pause=0.1, check=lambda: is_responsive(url)\n )\n return url","def url(self, name):\n return '%s/%s' % (self.container_url, name)","def docker_image_tag(self, app):\n return f\"briefcase/{app.bundle}.{app.app_name.lower()}:{app.target_vendor}-{app.target_codename}\"","def build_view_run_url(contest_id: int, run_id: int) -> str:\n parse_url = urllib.parse.urlparse(config.EJUDGE_PROXY_BASE_URL)\n new_path = str(pathlib.PurePosixPath(parse_url.path, f'c{contest_id}', f'r{run_id}'))\n parse_url = parse_url._replace(path=new_path)\n return parse_url.geturl()","def pyfunc_serve_from_docker_image(image_name, host_port, extra_args=None):\n env = dict(os.environ)\n env.update(LC_ALL=\"en_US.UTF-8\", LANG=\"en_US.UTF-8\")\n scoring_cmd = [\"docker\", \"run\", \"-p\", \"%s:8080\" % host_port, image_name]\n if extra_args is not None:\n scoring_cmd += extra_args\n return _start_scoring_proc(cmd=scoring_cmd, env=env)","def build_nighthawk_benchmark_image_from_source(manager: source_manager.SourceManager) -> None:\n # TODO: Inject the builder object into this method\n builder = nighthawk_builder.NightHawkBuilder(manager)\n builder.build_nighthawk_benchmark_image()","def discovery_url(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"discovery_url\")","def build_container(client):\n client.images.build(path=os.path.join(os.path.abspath(\"\"), \"docker\"), tag=\"scrape_light\")","def build_docker(c):\n tag = c.run('git describe', hide=True)\n docker_img = f'{docker_repo}:{tag.stdout.strip()}'\n c.run(f'docker build -t {docker_img} .')","def get_url() -> str:\n parser = ArgumentParser()\n\n parser.add_argument('--url',\n type=str,\n help='Url to download log file')\n\n args = parser.parse_args()\n url = args.url\n return url","def get_fuzzer_benchmark_key(fuzzer: str, benchmark: str):\n return fuzzer + ' ' + benchmark","def _is_docker(self) -> bool:\n from hubble.executor.helper import is_valid_docker_uri\n\n uses = getattr(self.args, 'uses', '')\n return is_valid_docker_uri(uses)","def docker_compose_project_name():\n return \"pytest{}\".format(os.getpid())","def service_url(self):\n return \"http://127.0.0.1:%d/wd/hub\"%self.port","def pyfunc_build_image(model_uri, extra_args=None):\n name = uuid.uuid4().hex\n cmd = [\"mlflow\", \"models\", \"build-docker\", \"-m\", model_uri, \"-n\", name]\n if extra_args:\n cmd += extra_args\n p = subprocess.Popen(cmd,)\n assert p.wait() == 0, \"Failed to build docker image to serve model from %s\" % model_uri\n return name","def image_url(self) -> str:\n return pulumi.get(self, \"image_url\")","def test_docker_build(rule_runner: RuleRunner) -> None:\n rule_runner.write_files(\n {\n \"src/BUILD\": \"docker_image(name='test-image', image_tags=['1.0'])\",\n \"src/Dockerfile\": \"FROM python:3.8\",\n }\n )\n target = rule_runner.get_target(Address(\"src\", target_name=\"test-image\"))\n result = run_docker(rule_runner, target)\n assert len(result.artifacts) == 1\n assert len(result.artifacts[0].extra_log_lines) == 2\n assert \"Built docker image: test-image:1.0\" == result.artifacts[0].extra_log_lines[0]\n assert \"Docker image ID:\" in result.artifacts[0].extra_log_lines[1]\n assert \"<unknown>\" not in result.artifacts[0].extra_log_lines[1]","def url(self) -> str:\n return self.DATASET_URLS[self.name]","def testDownloadDockerFile(self):\n expected_dockerfile = (\n '# Pseudo Dockerfile\\n'\n f'# Generated by de.py ({de_version})\\n\\n'\n 'COPY file:201f8f1849e89d53be9f6aa76937f5e209d745abfd15a8552fcf2ba45ab267f9'\n ' in / \\n'\n 'CMD [\"/hello\"]')\n\n with tempfile.TemporaryDirectory() as tmp_dir:\n self.dl_object._output_directory = tmp_dir\n self.dl_object.DownloadPseudoDockerfile()\n with open(os.path.join(tmp_dir, 'Dockerfile'), encoding='utf-8') as f:\n self.assertEqual(expected_dockerfile, f.read())","def test_url():\n return TEST_DATABASE_URL","def runner_path():\n git_base = os.popen('git rev-parse --show-toplevel').read().strip()\n return os.path.join(git_base, RUNNER_SCRIPT_BASENAME)","def discovery_url(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"discovery_url\")","def find_pipeline_runner():\n # First check if pipeline_runner is specified as an environment variable\n runner_fpath = os.environ.get('SPROKIT_PIPELINE_RUNNER', None)\n if runner_fpath is not None:\n return runner_fpath\n\n # If not, then search for the binary in the current dir and the PATH\n fnames = ['pipeline_runner']\n if sys.platform.startswith('win32'):\n fnames.insert(0, 'pipeline_runner.exe')\n\n search_paths = ['.']\n search_paths = os.environ.get('PATH', '').split(os.pathsep)\n\n for fname in fnames:\n for dpath in search_paths:\n fpath = join(dpath, fname)\n if os.path.isfile(fpath):\n return fpath","def get_fuzzer_filestore_path(benchmark_df, fuzzer):\n fuzzer_df = benchmark_df[benchmark_df.fuzzer == fuzzer]\n filestore_path = fuzzer_df.experiment_filestore.unique()[0]\n exp_name = fuzzer_df.experiment.unique()[0]\n return posixpath.join(filestore_path, exp_name)","def get_vidurl(self):\n if self.assets is None:\n self.get_assets()\n \n df = self.assets\n des = df.loc[(df['container']==self.container) & (df['display_name']==self.resolution), 'url']\n if des.shape[0] == 1:\n self.vidurl = des.iloc[0].replace('.bin',f'.{self.container}')\n return self.vidurl","def test_docker_image_hint(mock_tools):\n # Mock the return values for Docker verification\n mock_tools.subprocess.check_output.side_effect = [\n VALID_DOCKER_VERSION,\n VALID_DOCKER_INFO,\n VALID_BUILDX_VERSION,\n VALID_USER_MAPPING_IMAGE_CACHE,\n ]\n\n Docker.verify(mock_tools, image_tag=\"myimage:tagtorulethemall\")\n\n mock_tools.subprocess.run.assert_has_calls(\n [\n call(\n [\n \"docker\",\n \"run\",\n \"--rm\",\n \"--volume\",\n f\"{Path.cwd() / 'build'}:/host_write_test:z\",\n \"myimage:tagtorulethemall\",\n \"touch\",\n PurePosixPath(\"/host_write_test/container_write_test\"),\n ],\n check=True,\n ),\n call(\n [\n \"docker\",\n \"run\",\n \"--rm\",\n \"--volume\",\n f\"{Path.cwd() / 'build'}:/host_write_test:z\",\n \"myimage:tagtorulethemall\",\n \"rm\",\n \"-f\",\n PurePosixPath(\"/host_write_test/container_write_test\"),\n ],\n check=True,\n ),\n ]\n )","def get_url(self):\n if self.url is None: # pragma: no cover\n raise ImproperlyConfigured(\n \"{0} is missing a url to test. Define {0}.url \"\n \"or override {0}.get_url().\".format(self.__class__.__name__)\n )\n return self.url","def _build_url(self):\n url = BASE_URL.format(self._host, self._port)\n _LOGGER.debug(\"TOON fetch URL: %s\", url)\n return url","def docker_ip():\n\n # When talking to the Docker daemon via a UNIX socket, route all TCP\n # traffic to docker containers via the TCP loopback interface.\n docker_host = os.environ.get('DOCKER_HOST', '').strip()\n if not docker_host:\n return '127.0.0.1'\n\n match = re.match('^tcp://(.+?):\\d+$', docker_host)\n if not match:\n raise ValueError(\n 'Invalid value for DOCKER_HOST: \"%s\".' % (docker_host,)\n )\n return match.group(1)","def image_url(self) -> str:\n return self._image_url","def base_url(name):\n script_name = 'setup_base_url'\n script_data = nexus_groovy.setup_base_url\n\n ret = {'name': name,\n 'changes': {},\n 'result': True,\n 'comment': '\"{0}\" script run to set base_url: {1}'.format(script_name, name)}\n\n script_args = {'base_url': name}\n\n results = _script_processor(script_name, script_data, script_args, ret)\n return results","def get_scraper_url(self):\r\n \r\n return self.reformat_scraper_url()","def pull_image(image):\n\n subprocess.check_call(['docker', 'pull', image])","def container_image_name(registry, component_name, version):\n if version is None:\n image = component_name + ':dev'\n else:\n image = '%s/%s:%s' % (registry, component_name, version)\n\n return image","def get_thumbnail_url():","def get_url_for_min_resolution(self, min_height, min_width, image):","def get_url(self, cfg, option, default):\n if cfg.has_option('kattis', option):\n return cfg.get('kattis', option)\n else:\n return 'https://%s/%s' % (cfg.get('kattis', 'hostname'), default)","def instance_url(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"instance_url\")","def instance_url(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"instance_url\")","def instance_url(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"instance_url\")","def instance_url(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"instance_url\")","def instance_url(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"instance_url\")","def instance_url(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"instance_url\")","def instance_url(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"instance_url\")","def instance_url(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"instance_url\")","def get_random_proxy():\n url=requests.get(proxypool_url).text.strip()\n #logger.info(\"now url is\",url)\n return url","def test_baseurl(matrix):\n matrix.charm_config[\"enable-tls\"] = False\n result = matrix.get_public_baseurl()\n assert result == \"http://mock.fqdn:8008\"\n matrix.charm_config[\"enable-tls\"] = False\n matrix.external_port = 80\n result = matrix.get_public_baseurl()\n assert result == \"http://mock.fqdn\"\n matrix.charm_config[\"enable-tls\"] = True\n result = matrix.get_public_baseurl()\n assert result == \"https://mock.fqdn\"","def docker_client():\n return docker.from_env()","def get_image_url(self):\n return self.get_extract_image_urls(is_first=True)","def apk_url(self):","def instance_url(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"instance_url\")","def get_geckodriver_url(version: str) -> str:\n if _ARCHITECTURE == \"other\": # or platform BSD\n return f\"https://github.com/mozilla/geckodriver/archive/{version}.{_COMPRESSION}\"\n else:\n return f\"https://github.com/mozilla/geckodriver/releases/download/{version}\" \\\n f\"/geckodriver-{version}-{_PLATFORM}{_ARCHITECTURE}.{_COMPRESSION}\"","def runbook_url(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"runbook_url\")","def runbook_url(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"runbook_url\")","def build_url(cls, config, namespace, name):\n return \"hxxp://mock.repo.url/\" + namespace + \"/\" + name + \".git\"","def docker_run(c, port=8181):\n cli_tasks.docker_run.run(c, port)","def replay_url(self):\n if (\n self.cluster is None\n or self.steam_id is None\n or self.replay_salt is None\n ):\n return None\n else:\n return \"http://replay{0}.valve.net/570/{1}_{2}.dem.bz2\".format(\n self.cluster, self.steam_id, self.replay_salt\n )","def test_docker_install_url(host_os):\n assert host_os in Docker.DOCKER_INSTALL_URL","def _get_url(self):\n return 'http://{}:{}'.format(self.host, self.port)","def specs_url(self):\n\t\tscheme = 'http' if '5000' in self.base_url else 'https'\n\t\treturn url_for(self.endpoint('specs'), _external=True, _scheme=scheme)","def __discovery_url(self):\n port = self.sensor.options.agent_port\n if port == 0:\n port = AGENT_DEFAULT_PORT\n\n return \"http://%s:%s/%s\" % (self.host, port, AGENT_DISCOVERY_PATH)","def get_mock_image():\n zf = zipfile.ZipFile(os.path.join(script_dir,\n 'resources/test_spei.zip'))\n\n return zf.read('test_spei.tif')","def imageutil(self) -> imageutil.ImageUtil:\n return imageutil.ImageUtil(self.data.dockerfile_images, self.conf)","def app_logo_url():\n return \"https://raw.githubusercontent.com/aiidalab/aiidalab-hello-world/master/img/logo.png\"","def apk_url(self):\n if not self._apk_url:\n self._apk_url = utils.get_str_or_return_val(\n self.get_apk_url,\n version=self.version,\n soup=self.soup,\n )\n\n return self._apk_url","def _get_base_url(self):\n template = config.baseurl_template\n # get distro name and arch\n base_url = template.format(\n host=config.gitbuilder_host,\n proj=self.project,\n pkg_type=self.pkg_type,\n arch=self.arch,\n dist=self.distro,\n flavor=self.flavor,\n uri=self.uri_reference,\n )\n return base_url","def get_image_uri(self):\n return \"data:image/png;base64,\" + \\\n self.browser.get_screenshot_as_base64()","def query_repo_url_from_buildername(buildername):\n repo_name = query_repo_name_from_buildername(buildername)\n return buildapi.query_repo_url(repo_name)","def cache_path(self):\n benchmark_name, image_set_name, _ = self.name.rsplit(\"_\", 2)\n cache_path = os.path.join(self._cache_path,'{}_{}_cache'.format(benchmark_name, image_set_name))\n if not os.path.exists(cache_path):\n os.mkdir(cache_path)\n return cache_path","def create_image_builder_streaming_url(Name=None, Validity=None):\n pass","def registry_url(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"registry_url\")","def test_get_url_base_returns_url_base(self):\n # Arrange / Act\n return_value = BlobDownloader(\n f\"{settings.SERVER_URI}/987653456789\"\n ).get_url_base()\n # Assert\n self.assertEqual(return_value, SERVER_URI)","def main(url):\n print(f\"Running main with URL = {url}...\")\n imagehits(downloaddata(url))","def _get_base_url(self):\n return 'https://'+self.get_address_and_port_string()","def runbook_url(self) -> pulumi.Output[Optional[str]]:\n return pulumi.get(self, \"runbook_url\")","def _get_image_from_artifact(self):\n try:\n image = self.info.artifacts[\"image\"][\"file\"]\n repository = self.info.artifacts[\"image\"].get(\"repository\")\n except (AttributeError, KeyError):\n parent_artifacts = self.info.parent.get(\"artifacts\", {})\n image = parent_artifacts[\"image\"][\"file\"]\n repository = parent_artifacts.get(\"image\", {}).get(\"repository\")\n\n # Default to DockerHub\n if is_docker_hub(repository):\n return image\n\n # Otherwise add full path according to repository\n image = add_repo_url(image, repository, self.info.repositories)\n return image"],"string":"[\n \"def get_builder_image_url(benchmark, fuzzer, docker_registry):\\n return f'{docker_registry}/builders/{fuzzer}/{benchmark}'\",\n \"def _to_dockerfile_url(image):\\n path = \\\"/\\\".join((image.platform, image.release, image.architecture, \\\"Dockerfile\\\"))\\n return git.get_github_blob_url(path, ref=f\\\"v{image.version}\\\")\",\n \"def get_fuzz_target(benchmark):\\n # Do this because of OSS-Fuzz-on-demand.\\n # TODO(metzman): Use classes to mock a benchmark config for\\n # OSS_FUZZ_ON_DEMAND.\\n return benchmark_config.get_config(benchmark).get(\\n 'fuzz_target', environment.get('FUZZ_TARGET'))\",\n \"def get_image_name():\\n try:\\n return os.environ['AIRFLOW_IMAGE']\\n except KeyError:\\n raise Exception(\\\"Please provide docker image name to pytest using environment variable AIRFLOW_IMAGE\\\")\",\n \"def getBuildbotURL():\",\n \"def get_rule_container_image_uri(name, region):\\n if name is not None and name.startswith(\\\"DetailedProfilerProcessingJobConfig\\\"):\\n # should have the format like \\\"123456789012.dkr.ecr.us-west-2.amazonaws.com/detailed-profiler-processing:latest\\\"\\n return image_uris.retrieve(detailed_framework_name, region)\\n\\n return image_uris.retrieve(framework_name, region)\",\n \"def docker_runner_factory(image):\\n\\n mapping = {\\n 'gunicorn': GunicornDockerRunner,\\n 'redis': RedisDockerRunner,\\n 'consul': ConsulDockerRunner,\\n 'postgres': PostgresDockerRunner,\\n 'registrator': RegistratorDockerRunner,\\n 'solr': SolrDockerRunner\\n }\\n\\n for key in mapping:\\n if key in image:\\n return mapping[key]\\n\\n return DockerRunner\",\n \"def get_buildbot_url():\\n return \\\"http://10.45.4.98:8001/\\\"\",\n \"def _ensure_image(testkit_path, branch_name, artifacts_path):\\n # Construct Docker image name from branch name\\n image_name = \\\"runner:%s\\\" % branch_name\\n image_path = os.path.join(testkit_path, \\\"runner_image\\\")\\n docker.build_and_tag(image_name, image_path, log_path=artifacts_path)\\n\\n return image_name\",\n \"def _get_dockerfiles_for_test() -> str:\\n project_root_dir = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))\\n dockerfiles_dir = os.path.join(project_root_dir, \\\"dockerfiles\\\")\\n if sys.version_info[0:2] == (3, 6):\\n return os.path.join(dockerfiles_dir, \\\"centos7.Dockerfile\\\")\\n elif sys.version_info[0:2] == (3, 9):\\n return os.path.join(dockerfiles_dir, \\\"rocky8.Dockerfile\\\")\\n else:\\n raise Exception(\\n \\\"Running the tests with INMANTA_TEST_INFRA_SETUP=true is only supported using a python3.6 or python3.9 venv\\\"\\n )\",\n \"def get_image_registry_url(self, image_name):\\n c = self._oc_command([\\\"get\\\", \\\"is\\\", image_name,\\n \\\"--output=jsonpath=\\\\'{ .status.dockerImageRepository }\\\\'\\\"])\\n try:\\n internal_registry_name = run_cmd(c, return_output=True)\\n except subprocess.CalledProcessError as ex:\\n raise ConuException(\\\"oc get is failed: %s\\\" % ex)\\n\\n logger.info(\\\"Image registry url: %s\\\", internal_registry_name)\\n\\n return internal_registry_name.replace(\\\"'\\\", \\\"\\\").replace('\\\"', '')\",\n \"def _get_image(runtime):\\n return \\\"{}:{}\\\".format(LambdaContainer._IMAGE_REPO_NAME, runtime)\",\n \"def _dockerfile(self):\\n return self.config.get('docker', {}).get('dockerfile', 'Dockerfile')\",\n \"def get_image_url():\",\n \"def dockerfile(self) -> pulumi.Output[Optional[str]]:\\n return pulumi.get(self, \\\"dockerfile\\\")\",\n \"def get_flower_url(self):\\n return \\\"http://{0}:{1}\\\".format(self.get_head_node_ip(), self.FLOWER_PORT)\",\n \"def _GetBuildBotUrl(builder_host, builder_port):\\n if (builder_host == BISECT_BUILDER_HOST and\\n builder_port == BISECT_BUILDER_PORT):\\n return TRY_SERVER_URL\\n else:\\n return 'http://%s:%s' % (builder_host, builder_port)\",\n \"def dockerfile(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"dockerfile\\\")\",\n \"def get_base_docker_image(docker_file):\\n with open(docker_file) as f:\\n from_line = next(\\n line for line in f.read().split(\\\"\\\\n\\\") if line.startswith(\\\"FROM\\\")\\n )\\n _from, base_image = from_line.split()\\n return base_image\",\n \"def url(self):\\r\\n return \\\"{}/container/{}\\\".format(BASE_URL, self.unit_locator)\",\n \"def ping_docker():\\n with Docker('unittest-36', image='python:3.6') as tun:\\n return tun.call(python_version)[:2]\",\n \"def docker_image_name(self):\\n raise NotImplementedError\",\n \"def dockerfile_dir(self):\\n return self._dockerfile_dir\",\n \"def docker_worker():\",\n \"def http_service(docker_ip: Any, docker_services: Any) -> Any:\\n # `port_for` takes a container port and returns the corresponding host port\\n port = docker_services.port_for(\\\"fdk-baseregistries-publisher\\\", HOST_PORT)\\n url = \\\"http://{}:{}\\\".format(docker_ip, port)\\n docker_services.wait_until_responsive(\\n timeout=30.0, pause=0.1, check=lambda: is_responsive(url)\\n )\\n return url\",\n \"def url(self, name):\\n return '%s/%s' % (self.container_url, name)\",\n \"def docker_image_tag(self, app):\\n return f\\\"briefcase/{app.bundle}.{app.app_name.lower()}:{app.target_vendor}-{app.target_codename}\\\"\",\n \"def build_view_run_url(contest_id: int, run_id: int) -> str:\\n parse_url = urllib.parse.urlparse(config.EJUDGE_PROXY_BASE_URL)\\n new_path = str(pathlib.PurePosixPath(parse_url.path, f'c{contest_id}', f'r{run_id}'))\\n parse_url = parse_url._replace(path=new_path)\\n return parse_url.geturl()\",\n \"def pyfunc_serve_from_docker_image(image_name, host_port, extra_args=None):\\n env = dict(os.environ)\\n env.update(LC_ALL=\\\"en_US.UTF-8\\\", LANG=\\\"en_US.UTF-8\\\")\\n scoring_cmd = [\\\"docker\\\", \\\"run\\\", \\\"-p\\\", \\\"%s:8080\\\" % host_port, image_name]\\n if extra_args is not None:\\n scoring_cmd += extra_args\\n return _start_scoring_proc(cmd=scoring_cmd, env=env)\",\n \"def build_nighthawk_benchmark_image_from_source(manager: source_manager.SourceManager) -> None:\\n # TODO: Inject the builder object into this method\\n builder = nighthawk_builder.NightHawkBuilder(manager)\\n builder.build_nighthawk_benchmark_image()\",\n \"def discovery_url(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"discovery_url\\\")\",\n \"def build_container(client):\\n client.images.build(path=os.path.join(os.path.abspath(\\\"\\\"), \\\"docker\\\"), tag=\\\"scrape_light\\\")\",\n \"def build_docker(c):\\n tag = c.run('git describe', hide=True)\\n docker_img = f'{docker_repo}:{tag.stdout.strip()}'\\n c.run(f'docker build -t {docker_img} .')\",\n \"def get_url() -> str:\\n parser = ArgumentParser()\\n\\n parser.add_argument('--url',\\n type=str,\\n help='Url to download log file')\\n\\n args = parser.parse_args()\\n url = args.url\\n return url\",\n \"def get_fuzzer_benchmark_key(fuzzer: str, benchmark: str):\\n return fuzzer + ' ' + benchmark\",\n \"def _is_docker(self) -> bool:\\n from hubble.executor.helper import is_valid_docker_uri\\n\\n uses = getattr(self.args, 'uses', '')\\n return is_valid_docker_uri(uses)\",\n \"def docker_compose_project_name():\\n return \\\"pytest{}\\\".format(os.getpid())\",\n \"def service_url(self):\\n return \\\"http://127.0.0.1:%d/wd/hub\\\"%self.port\",\n \"def pyfunc_build_image(model_uri, extra_args=None):\\n name = uuid.uuid4().hex\\n cmd = [\\\"mlflow\\\", \\\"models\\\", \\\"build-docker\\\", \\\"-m\\\", model_uri, \\\"-n\\\", name]\\n if extra_args:\\n cmd += extra_args\\n p = subprocess.Popen(cmd,)\\n assert p.wait() == 0, \\\"Failed to build docker image to serve model from %s\\\" % model_uri\\n return name\",\n \"def image_url(self) -> str:\\n return pulumi.get(self, \\\"image_url\\\")\",\n \"def test_docker_build(rule_runner: RuleRunner) -> None:\\n rule_runner.write_files(\\n {\\n \\\"src/BUILD\\\": \\\"docker_image(name='test-image', image_tags=['1.0'])\\\",\\n \\\"src/Dockerfile\\\": \\\"FROM python:3.8\\\",\\n }\\n )\\n target = rule_runner.get_target(Address(\\\"src\\\", target_name=\\\"test-image\\\"))\\n result = run_docker(rule_runner, target)\\n assert len(result.artifacts) == 1\\n assert len(result.artifacts[0].extra_log_lines) == 2\\n assert \\\"Built docker image: test-image:1.0\\\" == result.artifacts[0].extra_log_lines[0]\\n assert \\\"Docker image ID:\\\" in result.artifacts[0].extra_log_lines[1]\\n assert \\\"<unknown>\\\" not in result.artifacts[0].extra_log_lines[1]\",\n \"def url(self) -> str:\\n return self.DATASET_URLS[self.name]\",\n \"def testDownloadDockerFile(self):\\n expected_dockerfile = (\\n '# Pseudo Dockerfile\\\\n'\\n f'# Generated by de.py ({de_version})\\\\n\\\\n'\\n 'COPY file:201f8f1849e89d53be9f6aa76937f5e209d745abfd15a8552fcf2ba45ab267f9'\\n ' in / \\\\n'\\n 'CMD [\\\"/hello\\\"]')\\n\\n with tempfile.TemporaryDirectory() as tmp_dir:\\n self.dl_object._output_directory = tmp_dir\\n self.dl_object.DownloadPseudoDockerfile()\\n with open(os.path.join(tmp_dir, 'Dockerfile'), encoding='utf-8') as f:\\n self.assertEqual(expected_dockerfile, f.read())\",\n \"def test_url():\\n return TEST_DATABASE_URL\",\n \"def runner_path():\\n git_base = os.popen('git rev-parse --show-toplevel').read().strip()\\n return os.path.join(git_base, RUNNER_SCRIPT_BASENAME)\",\n \"def discovery_url(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"discovery_url\\\")\",\n \"def find_pipeline_runner():\\n # First check if pipeline_runner is specified as an environment variable\\n runner_fpath = os.environ.get('SPROKIT_PIPELINE_RUNNER', None)\\n if runner_fpath is not None:\\n return runner_fpath\\n\\n # If not, then search for the binary in the current dir and the PATH\\n fnames = ['pipeline_runner']\\n if sys.platform.startswith('win32'):\\n fnames.insert(0, 'pipeline_runner.exe')\\n\\n search_paths = ['.']\\n search_paths = os.environ.get('PATH', '').split(os.pathsep)\\n\\n for fname in fnames:\\n for dpath in search_paths:\\n fpath = join(dpath, fname)\\n if os.path.isfile(fpath):\\n return fpath\",\n \"def get_fuzzer_filestore_path(benchmark_df, fuzzer):\\n fuzzer_df = benchmark_df[benchmark_df.fuzzer == fuzzer]\\n filestore_path = fuzzer_df.experiment_filestore.unique()[0]\\n exp_name = fuzzer_df.experiment.unique()[0]\\n return posixpath.join(filestore_path, exp_name)\",\n \"def get_vidurl(self):\\n if self.assets is None:\\n self.get_assets()\\n \\n df = self.assets\\n des = df.loc[(df['container']==self.container) & (df['display_name']==self.resolution), 'url']\\n if des.shape[0] == 1:\\n self.vidurl = des.iloc[0].replace('.bin',f'.{self.container}')\\n return self.vidurl\",\n \"def test_docker_image_hint(mock_tools):\\n # Mock the return values for Docker verification\\n mock_tools.subprocess.check_output.side_effect = [\\n VALID_DOCKER_VERSION,\\n VALID_DOCKER_INFO,\\n VALID_BUILDX_VERSION,\\n VALID_USER_MAPPING_IMAGE_CACHE,\\n ]\\n\\n Docker.verify(mock_tools, image_tag=\\\"myimage:tagtorulethemall\\\")\\n\\n mock_tools.subprocess.run.assert_has_calls(\\n [\\n call(\\n [\\n \\\"docker\\\",\\n \\\"run\\\",\\n \\\"--rm\\\",\\n \\\"--volume\\\",\\n f\\\"{Path.cwd() / 'build'}:/host_write_test:z\\\",\\n \\\"myimage:tagtorulethemall\\\",\\n \\\"touch\\\",\\n PurePosixPath(\\\"/host_write_test/container_write_test\\\"),\\n ],\\n check=True,\\n ),\\n call(\\n [\\n \\\"docker\\\",\\n \\\"run\\\",\\n \\\"--rm\\\",\\n \\\"--volume\\\",\\n f\\\"{Path.cwd() / 'build'}:/host_write_test:z\\\",\\n \\\"myimage:tagtorulethemall\\\",\\n \\\"rm\\\",\\n \\\"-f\\\",\\n PurePosixPath(\\\"/host_write_test/container_write_test\\\"),\\n ],\\n check=True,\\n ),\\n ]\\n )\",\n \"def get_url(self):\\n if self.url is None: # pragma: no cover\\n raise ImproperlyConfigured(\\n \\\"{0} is missing a url to test. Define {0}.url \\\"\\n \\\"or override {0}.get_url().\\\".format(self.__class__.__name__)\\n )\\n return self.url\",\n \"def _build_url(self):\\n url = BASE_URL.format(self._host, self._port)\\n _LOGGER.debug(\\\"TOON fetch URL: %s\\\", url)\\n return url\",\n \"def docker_ip():\\n\\n # When talking to the Docker daemon via a UNIX socket, route all TCP\\n # traffic to docker containers via the TCP loopback interface.\\n docker_host = os.environ.get('DOCKER_HOST', '').strip()\\n if not docker_host:\\n return '127.0.0.1'\\n\\n match = re.match('^tcp://(.+?):\\\\d+$', docker_host)\\n if not match:\\n raise ValueError(\\n 'Invalid value for DOCKER_HOST: \\\"%s\\\".' % (docker_host,)\\n )\\n return match.group(1)\",\n \"def image_url(self) -> str:\\n return self._image_url\",\n \"def base_url(name):\\n script_name = 'setup_base_url'\\n script_data = nexus_groovy.setup_base_url\\n\\n ret = {'name': name,\\n 'changes': {},\\n 'result': True,\\n 'comment': '\\\"{0}\\\" script run to set base_url: {1}'.format(script_name, name)}\\n\\n script_args = {'base_url': name}\\n\\n results = _script_processor(script_name, script_data, script_args, ret)\\n return results\",\n \"def get_scraper_url(self):\\r\\n \\r\\n return self.reformat_scraper_url()\",\n \"def pull_image(image):\\n\\n subprocess.check_call(['docker', 'pull', image])\",\n \"def container_image_name(registry, component_name, version):\\n if version is None:\\n image = component_name + ':dev'\\n else:\\n image = '%s/%s:%s' % (registry, component_name, version)\\n\\n return image\",\n \"def get_thumbnail_url():\",\n \"def get_url_for_min_resolution(self, min_height, min_width, image):\",\n \"def get_url(self, cfg, option, default):\\n if cfg.has_option('kattis', option):\\n return cfg.get('kattis', option)\\n else:\\n return 'https://%s/%s' % (cfg.get('kattis', 'hostname'), default)\",\n \"def instance_url(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"instance_url\\\")\",\n \"def instance_url(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"instance_url\\\")\",\n \"def instance_url(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"instance_url\\\")\",\n \"def instance_url(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"instance_url\\\")\",\n \"def instance_url(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"instance_url\\\")\",\n \"def instance_url(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"instance_url\\\")\",\n \"def instance_url(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"instance_url\\\")\",\n \"def instance_url(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"instance_url\\\")\",\n \"def get_random_proxy():\\n url=requests.get(proxypool_url).text.strip()\\n #logger.info(\\\"now url is\\\",url)\\n return url\",\n \"def test_baseurl(matrix):\\n matrix.charm_config[\\\"enable-tls\\\"] = False\\n result = matrix.get_public_baseurl()\\n assert result == \\\"http://mock.fqdn:8008\\\"\\n matrix.charm_config[\\\"enable-tls\\\"] = False\\n matrix.external_port = 80\\n result = matrix.get_public_baseurl()\\n assert result == \\\"http://mock.fqdn\\\"\\n matrix.charm_config[\\\"enable-tls\\\"] = True\\n result = matrix.get_public_baseurl()\\n assert result == \\\"https://mock.fqdn\\\"\",\n \"def docker_client():\\n return docker.from_env()\",\n \"def get_image_url(self):\\n return self.get_extract_image_urls(is_first=True)\",\n \"def apk_url(self):\",\n \"def instance_url(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"instance_url\\\")\",\n \"def get_geckodriver_url(version: str) -> str:\\n if _ARCHITECTURE == \\\"other\\\": # or platform BSD\\n return f\\\"https://github.com/mozilla/geckodriver/archive/{version}.{_COMPRESSION}\\\"\\n else:\\n return f\\\"https://github.com/mozilla/geckodriver/releases/download/{version}\\\" \\\\\\n f\\\"/geckodriver-{version}-{_PLATFORM}{_ARCHITECTURE}.{_COMPRESSION}\\\"\",\n \"def runbook_url(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"runbook_url\\\")\",\n \"def runbook_url(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"runbook_url\\\")\",\n \"def build_url(cls, config, namespace, name):\\n return \\\"hxxp://mock.repo.url/\\\" + namespace + \\\"/\\\" + name + \\\".git\\\"\",\n \"def docker_run(c, port=8181):\\n cli_tasks.docker_run.run(c, port)\",\n \"def replay_url(self):\\n if (\\n self.cluster is None\\n or self.steam_id is None\\n or self.replay_salt is None\\n ):\\n return None\\n else:\\n return \\\"http://replay{0}.valve.net/570/{1}_{2}.dem.bz2\\\".format(\\n self.cluster, self.steam_id, self.replay_salt\\n )\",\n \"def test_docker_install_url(host_os):\\n assert host_os in Docker.DOCKER_INSTALL_URL\",\n \"def _get_url(self):\\n return 'http://{}:{}'.format(self.host, self.port)\",\n \"def specs_url(self):\\n\\t\\tscheme = 'http' if '5000' in self.base_url else 'https'\\n\\t\\treturn url_for(self.endpoint('specs'), _external=True, _scheme=scheme)\",\n \"def __discovery_url(self):\\n port = self.sensor.options.agent_port\\n if port == 0:\\n port = AGENT_DEFAULT_PORT\\n\\n return \\\"http://%s:%s/%s\\\" % (self.host, port, AGENT_DISCOVERY_PATH)\",\n \"def get_mock_image():\\n zf = zipfile.ZipFile(os.path.join(script_dir,\\n 'resources/test_spei.zip'))\\n\\n return zf.read('test_spei.tif')\",\n \"def imageutil(self) -> imageutil.ImageUtil:\\n return imageutil.ImageUtil(self.data.dockerfile_images, self.conf)\",\n \"def app_logo_url():\\n return \\\"https://raw.githubusercontent.com/aiidalab/aiidalab-hello-world/master/img/logo.png\\\"\",\n \"def apk_url(self):\\n if not self._apk_url:\\n self._apk_url = utils.get_str_or_return_val(\\n self.get_apk_url,\\n version=self.version,\\n soup=self.soup,\\n )\\n\\n return self._apk_url\",\n \"def _get_base_url(self):\\n template = config.baseurl_template\\n # get distro name and arch\\n base_url = template.format(\\n host=config.gitbuilder_host,\\n proj=self.project,\\n pkg_type=self.pkg_type,\\n arch=self.arch,\\n dist=self.distro,\\n flavor=self.flavor,\\n uri=self.uri_reference,\\n )\\n return base_url\",\n \"def get_image_uri(self):\\n return \\\"data:image/png;base64,\\\" + \\\\\\n self.browser.get_screenshot_as_base64()\",\n \"def query_repo_url_from_buildername(buildername):\\n repo_name = query_repo_name_from_buildername(buildername)\\n return buildapi.query_repo_url(repo_name)\",\n \"def cache_path(self):\\n benchmark_name, image_set_name, _ = self.name.rsplit(\\\"_\\\", 2)\\n cache_path = os.path.join(self._cache_path,'{}_{}_cache'.format(benchmark_name, image_set_name))\\n if not os.path.exists(cache_path):\\n os.mkdir(cache_path)\\n return cache_path\",\n \"def create_image_builder_streaming_url(Name=None, Validity=None):\\n pass\",\n \"def registry_url(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"registry_url\\\")\",\n \"def test_get_url_base_returns_url_base(self):\\n # Arrange / Act\\n return_value = BlobDownloader(\\n f\\\"{settings.SERVER_URI}/987653456789\\\"\\n ).get_url_base()\\n # Assert\\n self.assertEqual(return_value, SERVER_URI)\",\n \"def main(url):\\n print(f\\\"Running main with URL = {url}...\\\")\\n imagehits(downloaddata(url))\",\n \"def _get_base_url(self):\\n return 'https://'+self.get_address_and_port_string()\",\n \"def runbook_url(self) -> pulumi.Output[Optional[str]]:\\n return pulumi.get(self, \\\"runbook_url\\\")\",\n \"def _get_image_from_artifact(self):\\n try:\\n image = self.info.artifacts[\\\"image\\\"][\\\"file\\\"]\\n repository = self.info.artifacts[\\\"image\\\"].get(\\\"repository\\\")\\n except (AttributeError, KeyError):\\n parent_artifacts = self.info.parent.get(\\\"artifacts\\\", {})\\n image = parent_artifacts[\\\"image\\\"][\\\"file\\\"]\\n repository = parent_artifacts.get(\\\"image\\\", {}).get(\\\"repository\\\")\\n\\n # Default to DockerHub\\n if is_docker_hub(repository):\\n return image\\n\\n # Otherwise add full path according to repository\\n image = add_repo_url(image, repository, self.info.repositories)\\n return image\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8196786","0.6458271","0.5928085","0.5869728","0.5578148","0.55031866","0.5449406","0.54289633","0.5422988","0.53856117","0.5382283","0.534175","0.5297771","0.5248548","0.5238496","0.5210609","0.5198708","0.519582","0.5189414","0.512294","0.51160717","0.51065534","0.5106042","0.50968045","0.5094282","0.50328517","0.49583447","0.49534687","0.4936412","0.4935533","0.49158934","0.491544","0.49130666","0.4905355","0.4902714","0.4899548","0.4897373","0.4889498","0.4871102","0.48648155","0.48644462","0.48575428","0.48423368","0.48371693","0.48198703","0.48183423","0.48166347","0.48016977","0.48012853","0.47964352","0.47907397","0.47867155","0.478653","0.47674018","0.47578055","0.47547626","0.47534654","0.47519457","0.47517288","0.47475448","0.47419304","0.4737827","0.4737827","0.4737827","0.4737827","0.4737827","0.4737827","0.4737827","0.4737827","0.47306156","0.47300443","0.47261837","0.47194332","0.4715677","0.4714293","0.47048104","0.46995977","0.46995977","0.46975783","0.46857","0.46855912","0.46846798","0.46784714","0.46716857","0.4668503","0.4667948","0.4660873","0.46602002","0.4659407","0.46527997","0.4648029","0.4642262","0.4641997","0.46407202","0.4639976","0.46371055","0.46354714","0.4633796","0.4631012","0.46275404"],"string":"[\n \"0.8196786\",\n \"0.6458271\",\n \"0.5928085\",\n \"0.5869728\",\n \"0.5578148\",\n \"0.55031866\",\n \"0.5449406\",\n \"0.54289633\",\n \"0.5422988\",\n \"0.53856117\",\n \"0.5382283\",\n \"0.534175\",\n \"0.5297771\",\n \"0.5248548\",\n \"0.5238496\",\n \"0.5210609\",\n \"0.5198708\",\n \"0.519582\",\n \"0.5189414\",\n \"0.512294\",\n \"0.51160717\",\n \"0.51065534\",\n \"0.5106042\",\n \"0.50968045\",\n \"0.5094282\",\n \"0.50328517\",\n \"0.49583447\",\n \"0.49534687\",\n \"0.4936412\",\n \"0.4935533\",\n \"0.49158934\",\n \"0.491544\",\n \"0.49130666\",\n \"0.4905355\",\n \"0.4902714\",\n \"0.4899548\",\n \"0.4897373\",\n \"0.4889498\",\n \"0.4871102\",\n \"0.48648155\",\n \"0.48644462\",\n \"0.48575428\",\n \"0.48423368\",\n \"0.48371693\",\n \"0.48198703\",\n \"0.48183423\",\n \"0.48166347\",\n \"0.48016977\",\n \"0.48012853\",\n \"0.47964352\",\n \"0.47907397\",\n \"0.47867155\",\n \"0.478653\",\n \"0.47674018\",\n \"0.47578055\",\n \"0.47547626\",\n \"0.47534654\",\n \"0.47519457\",\n \"0.47517288\",\n \"0.47475448\",\n \"0.47419304\",\n \"0.4737827\",\n \"0.4737827\",\n \"0.4737827\",\n \"0.4737827\",\n \"0.4737827\",\n \"0.4737827\",\n \"0.4737827\",\n \"0.4737827\",\n \"0.47306156\",\n \"0.47300443\",\n \"0.47261837\",\n \"0.47194332\",\n \"0.4715677\",\n \"0.4714293\",\n \"0.47048104\",\n \"0.46995977\",\n \"0.46995977\",\n \"0.46975783\",\n \"0.46857\",\n \"0.46855912\",\n \"0.46846798\",\n \"0.46784714\",\n \"0.46716857\",\n \"0.4668503\",\n \"0.4667948\",\n \"0.4660873\",\n \"0.46602002\",\n \"0.4659407\",\n \"0.46527997\",\n \"0.4648029\",\n \"0.4642262\",\n \"0.4641997\",\n \"0.46407202\",\n \"0.4639976\",\n \"0.46371055\",\n \"0.46354714\",\n \"0.4633796\",\n \"0.4631012\",\n \"0.46275404\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.87715006"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":11266,"cells":{"query":{"kind":"string","value":"Get the URL of the docker builder image for fuzzing the benchmark with fuzzer."},"document":{"kind":"string","value":"def get_builder_image_url(benchmark, fuzzer, docker_registry):\n return f'{docker_registry}/builders/{fuzzer}/{benchmark}'"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_runner_image_url(experiment, benchmark, fuzzer, docker_registry):\n tag = 'latest' if environment.get('LOCAL_EXPERIMENT') else experiment\n return f'{docker_registry}/runners/{fuzzer}/{benchmark}:{tag}'","def _to_dockerfile_url(image):\n path = \"/\".join((image.platform, image.release, image.architecture, \"Dockerfile\"))\n return git.get_github_blob_url(path, ref=f\"v{image.version}\")","def getBuildbotURL():","def _GetBuildBotUrl(builder_host, builder_port):\n if (builder_host == BISECT_BUILDER_HOST and\n builder_port == BISECT_BUILDER_PORT):\n return TRY_SERVER_URL\n else:\n return 'http://%s:%s' % (builder_host, builder_port)","def get_buildbot_url():\n return \"http://10.45.4.98:8001/\"","def get_fuzz_target(benchmark):\n # Do this because of OSS-Fuzz-on-demand.\n # TODO(metzman): Use classes to mock a benchmark config for\n # OSS_FUZZ_ON_DEMAND.\n return benchmark_config.get_config(benchmark).get(\n 'fuzz_target', environment.get('FUZZ_TARGET'))","def get_image_url():","def query_repo_url_from_buildername(buildername):\n repo_name = query_repo_name_from_buildername(buildername)\n return buildapi.query_repo_url(repo_name)","def get_image_name():\n try:\n return os.environ['AIRFLOW_IMAGE']\n except KeyError:\n raise Exception(\"Please provide docker image name to pytest using environment variable AIRFLOW_IMAGE\")","def get_image_registry_url(self, image_name):\n c = self._oc_command([\"get\", \"is\", image_name,\n \"--output=jsonpath=\\'{ .status.dockerImageRepository }\\'\"])\n try:\n internal_registry_name = run_cmd(c, return_output=True)\n except subprocess.CalledProcessError as ex:\n raise ConuException(\"oc get is failed: %s\" % ex)\n\n logger.info(\"Image registry url: %s\", internal_registry_name)\n\n return internal_registry_name.replace(\"'\", \"\").replace('\"', '')","def _dockerfile(self):\n return self.config.get('docker', {}).get('dockerfile', 'Dockerfile')","def build_container(client):\n client.images.build(path=os.path.join(os.path.abspath(\"\"), \"docker\"), tag=\"scrape_light\")","def _get_image(runtime):\n return \"{}:{}\".format(LambdaContainer._IMAGE_REPO_NAME, runtime)","def http_service(docker_ip: Any, docker_services: Any) -> Any:\n # `port_for` takes a container port and returns the corresponding host port\n port = docker_services.port_for(\"fdk-baseregistries-publisher\", HOST_PORT)\n url = \"http://{}:{}\".format(docker_ip, port)\n docker_services.wait_until_responsive(\n timeout=30.0, pause=0.1, check=lambda: is_responsive(url)\n )\n return url","def create_image_builder_streaming_url(Name=None, Validity=None):\n pass","def url(self):\r\n return \"{}/container/{}\".format(BASE_URL, self.unit_locator)","def _ensure_image(testkit_path, branch_name, artifacts_path):\n # Construct Docker image name from branch name\n image_name = \"runner:%s\" % branch_name\n image_path = os.path.join(testkit_path, \"runner_image\")\n docker.build_and_tag(image_name, image_path, log_path=artifacts_path)\n\n return image_name","def build_docker(c):\n tag = c.run('git describe', hide=True)\n docker_img = f'{docker_repo}:{tag.stdout.strip()}'\n c.run(f'docker build -t {docker_img} .')","def get_base_docker_image(docker_file):\n with open(docker_file) as f:\n from_line = next(\n line for line in f.read().split(\"\\n\") if line.startswith(\"FROM\")\n )\n _from, base_image = from_line.split()\n return base_image","def build_nighthawk_benchmark_image_from_source(manager: source_manager.SourceManager) -> None:\n # TODO: Inject the builder object into this method\n builder = nighthawk_builder.NightHawkBuilder(manager)\n builder.build_nighthawk_benchmark_image()","def docker_image_tag(self, app):\n return f\"briefcase/{app.bundle}.{app.app_name.lower()}:{app.target_vendor}-{app.target_codename}\"","def _get_base_url(self):\n template = config.baseurl_template\n # get distro name and arch\n base_url = template.format(\n host=config.gitbuilder_host,\n proj=self.project,\n pkg_type=self.pkg_type,\n arch=self.arch,\n dist=self.distro,\n flavor=self.flavor,\n uri=self.uri_reference,\n )\n return base_url","def dockerfile(self) -> pulumi.Output[Optional[str]]:\n return pulumi.get(self, \"dockerfile\")","def url(self, name):\n return '%s/%s' % (self.container_url, name)","def get_rule_container_image_uri(name, region):\n if name is not None and name.startswith(\"DetailedProfilerProcessingJobConfig\"):\n # should have the format like \"123456789012.dkr.ecr.us-west-2.amazonaws.com/detailed-profiler-processing:latest\"\n return image_uris.retrieve(detailed_framework_name, region)\n\n return image_uris.retrieve(framework_name, region)","def get_builder(self, request):\n base_url = request.application_url\n return images_view.ViewBuilderV10(base_url)","def _build_url(self):\n url = BASE_URL.format(self._host, self._port)\n _LOGGER.debug(\"TOON fetch URL: %s\", url)\n return url","def pyfunc_build_image(model_uri, extra_args=None):\n name = uuid.uuid4().hex\n cmd = [\"mlflow\", \"models\", \"build-docker\", \"-m\", model_uri, \"-n\", name]\n if extra_args:\n cmd += extra_args\n p = subprocess.Popen(cmd,)\n assert p.wait() == 0, \"Failed to build docker image to serve model from %s\" % model_uri\n return name","def dockerfile(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"dockerfile\")","def build_url(cls, config, namespace, name):\n return \"hxxp://mock.repo.url/\" + namespace + \"/\" + name + \".git\"","def container_image_name(registry, component_name, version):\n if version is None:\n image = component_name + ':dev'\n else:\n image = '%s/%s:%s' % (registry, component_name, version)\n\n return image","def image_url(self) -> str:\n return pulumi.get(self, \"image_url\")","def get_short_url_base():","def get_flower_url(self):\n return \"http://{0}:{1}\".format(self.get_head_node_ip(), self.FLOWER_PORT)","def _get_dockerfiles_for_test() -> str:\n project_root_dir = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))\n dockerfiles_dir = os.path.join(project_root_dir, \"dockerfiles\")\n if sys.version_info[0:2] == (3, 6):\n return os.path.join(dockerfiles_dir, \"centos7.Dockerfile\")\n elif sys.version_info[0:2] == (3, 9):\n return os.path.join(dockerfiles_dir, \"rocky8.Dockerfile\")\n else:\n raise Exception(\n \"Running the tests with INMANTA_TEST_INFRA_SETUP=true is only supported using a python3.6 or python3.9 venv\"\n )","def url_for(**options):\n\n url_parts = get_url_parts(**options)\n image_hash = hashlib.md5(b(options[\"image_url\"])).hexdigest()\n url_parts.append(image_hash)\n\n return \"/\".join(url_parts)","def dockerfile_dir(self):\n return self._dockerfile_dir","def apk_url(self):","def get_url_for_min_resolution(self, min_height, min_width, image):","def app_logo_url():\n return \"https://raw.githubusercontent.com/aiidalab/aiidalab-hello-world/master/img/logo.png\"","def get_thumbnail_url():","def test_client_build_url():\n eq_(\"{0}/{1}\".format(client.BASE_URL, \"v1/charges/\"), client.build_url(\"v1/charges/\"))","def test_baseurl(matrix):\n matrix.charm_config[\"enable-tls\"] = False\n result = matrix.get_public_baseurl()\n assert result == \"http://mock.fqdn:8008\"\n matrix.charm_config[\"enable-tls\"] = False\n matrix.external_port = 80\n result = matrix.get_public_baseurl()\n assert result == \"http://mock.fqdn\"\n matrix.charm_config[\"enable-tls\"] = True\n result = matrix.get_public_baseurl()\n assert result == \"https://mock.fqdn\"","def image_url(self) -> str:\n return self._image_url","def docker_image_name(self):\n raise NotImplementedError","def get_url_image(self, obj):\n return settings.SERVER_HOST + obj.image.url","def getReferenceImageUrl(self, name):\n bucket = self.productSearch.bucket\n blobName = self._getReferenceImageBlobName(name)\n return bucket.blob(blobName).public_url","def get_builder(self, req):\n base_url = req.application_url\n project_id = getattr(req.environ['nova.context'], 'project_id', '')\n return images_view.ViewBuilderV11(base_url, project_id)","def discovery_url(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"discovery_url\")","def _get_base_url(self):\n return 'https://'+self.get_address_and_port_string()","def get_api_url() -> str:\n\n\tsite = pywikibot.Site()\n\turl = site.protocol() + \"://\" + site.hostname() + site.apipath()\n\treturn url","def specs_url(self):\n\t\tscheme = 'http' if '5000' in self.base_url else 'https'\n\t\treturn url_for(self.endpoint('specs'), _external=True, _scheme=scheme)","def get_kojiroot_base_url(build_info, arch=\"x86_64\"):\n base_url = \"{kojiroot}/{package_name}/{ver}/{rel}/{arch}/\".format(\n kojiroot=config.kojiroot_url,\n package_name=build_info[\"package_name\"],\n ver=build_info[\"version\"],\n rel=build_info[\"release\"],\n arch=arch,\n )\n return base_url","def build_url(self, host, target, params=None):\n return \"https://%s%s\" % (host, self.build_path(target, params))","def PLACEHOLDER(width=100, height=100, HTTP=\"\", seperator='/'):\n return f\"{HTTP}://{CDN_IMG.PLACEHOLDER_SERVICE}/{width}{seperator}{height}\"","def get_url(self, cfg, option, default):\n if cfg.has_option('kattis', option):\n return cfg.get('kattis', option)\n else:\n return 'https://%s/%s' % (cfg.get('kattis', 'hostname'), default)","def get_geckodriver_url(version: str) -> str:\n if _ARCHITECTURE == \"other\": # or platform BSD\n return f\"https://github.com/mozilla/geckodriver/archive/{version}.{_COMPRESSION}\"\n else:\n return f\"https://github.com/mozilla/geckodriver/releases/download/{version}\" \\\n f\"/geckodriver-{version}-{_PLATFORM}{_ARCHITECTURE}.{_COMPRESSION}\"","def get_api_url() -> str:\n\n site = pywikibot.Site()\n url = site.protocol() + \"://\" + site.hostname() + site.apipath()\n return url","def test_force_with_local_image(self, mock_docker_environment):\n build_image_if_needed(TEST_IMAGE_NAME, force=True)\n mock_docker_environment.images.build.assert_called_with(**self.default_call_kwargs)","def image_url(self):\n context = aq_inner(self.context)\n obj_url = context.absolute_url()\n if hasattr(context, 'getField'):\n field = self.context.getField('image')\n if not field:\n field = context.getField(IMAGE_FIELD_NAME)\n\n if field and field.get_size(context) > 0:\n return u'%s/%s_%s' % (obj_url, field.getName(), 'thumb')\n\n return u\"%s/isaw_logo.png\" % self.portal.absolute_url()","def discovery_url(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"discovery_url\")","def test_url():\n return TEST_DATABASE_URL","def test_get_url_base_returns_url_base(self):\n # Arrange / Act\n return_value = BlobDownloader(\n f\"{settings.SERVER_URI}/987653456789\"\n ).get_url_base()\n # Assert\n self.assertEqual(return_value, SERVER_URI)","def docker_client():\n return docker.from_env()","def get_contribs_api_base_url(request_url=None, deployment=\"contribs\"):\n if is_localhost() and SETTINGS.API_EXTERNAL_ENDPOINT:\n return f\"https://{deployment}-api.materialsproject.org\"\n\n if has_request_context() and (not request_url):\n request_url = request.url\n\n return parse_request_url(request_url, f\"{deployment}-api\")","def get_image_uri(self):\n return \"data:image/png;base64,\" + \\\n self.browser.get_screenshot_as_base64()","def get_image_url(self, image_url):\n if image_url:\n return '{0}?source={1}'.format(self.config['links']['imageProxy'], image_url)\n else:\n return None","def apk_url(self):\n if not self._apk_url:\n self._apk_url = utils.get_str_or_return_val(\n self.get_apk_url,\n version=self.version,\n soup=self.soup,\n )\n\n return self._apk_url","def __discovery_url(self):\n port = self.sensor.options.agent_port\n if port == 0:\n port = AGENT_DEFAULT_PORT\n\n return \"http://%s:%s/%s\" % (self.host, port, AGENT_DISCOVERY_PATH)","def get_url_image(self, obj):\n return settings.IMAGE_HOST + obj.image.url","def get_url_image(self, obj):\n return settings.IMAGE_HOST + obj.image.url","def get_url_image(self, obj):\n return settings.IMAGE_HOST + obj.image.url","def get_image_url(self, size=None):\n return images.get_serving_url(self.image_blob_key, size=size)","def get_image_url(self, image, hostname):\n\n if image.image_type == 'local':\n return \"http://\" + hostname + \"/\" + settings.IMAGES_URL + \"/\" + image.deployment.campaign.short_name + \"/\" + image.deployment.short_name + \"/\" + image.image_name\n elif image.image_type == 'envirocoms':\n return \"http://\" + hostname + \"/ecoms_proxy?image=\" + image.image_path","def ping_docker():\n with Docker('unittest-36', image='python:3.6') as tun:\n return tun.call(python_version)[:2]","def _get_url(self):\n return 'http://{}:{}'.format(self.host, self.port)","def service_url(self):\n return \"http://127.0.0.1:%d/wd/hub\"%self.port","def build_url(self, endpoint_url: str) -> str:\n return self.base_url + endpoint_url % self.instance_id","def get_url():\n key = _get_key()\n return key.generate_url(300)","def _generate_url(self, **kwargs):\n path = self.url_path.format(**kwargs)\n return self.poolbot.generate_url(path)","def cache_path(self):\n benchmark_name, image_set_name, _ = self.name.rsplit(\"_\", 2)\n cache_path = os.path.join(self._cache_path,'{}_{}_cache'.format(benchmark_name, image_set_name))\n if not os.path.exists(cache_path):\n os.mkdir(cache_path)\n return cache_path","def build_envoy_docker_image(manager: source_manager.SourceManager, commit_hash: str) -> None:\n builder = envoy_builder.EnvoyBuilder(manager)\n source_repo = manager.get_source_repository(proto_source.SourceRepository.SRCID_ENVOY)\n source_repo.commit_hash = commit_hash\n builder.build_envoy_image_from_source()","def url(vmanage_host,vmanage_port,api):\r\n \"\"\" function to get the url provide api endpoint \"\"\"\r\n \r\n return f\"https://{vmanage_host}:{vmanage_port}{api}\"","def build(config, version_tag):\n config_dict = get_config(config)\n image_name = config_dict['IMAGE'].split(':')[0]\n image = '{}:{}'.format(image_name, version_tag)\n base_image = config_dict['BASE_IMAGE']\n\n cmd = 'docker build -t {image} --build-arg base_image={base_image} .'.format(image=image,\n base_image=base_image)\n with cd(env.project_dir):\n run(cmd)\n return image","def docker_runner_factory(image):\n\n mapping = {\n 'gunicorn': GunicornDockerRunner,\n 'redis': RedisDockerRunner,\n 'consul': ConsulDockerRunner,\n 'postgres': PostgresDockerRunner,\n 'registrator': RegistratorDockerRunner,\n 'solr': SolrDockerRunner\n }\n\n for key in mapping:\n if key in image:\n return mapping[key]\n\n return DockerRunner","def _get_image_from_artifact(self):\n try:\n image = self.info.artifacts[\"image\"][\"file\"]\n repository = self.info.artifacts[\"image\"].get(\"repository\")\n except (AttributeError, KeyError):\n parent_artifacts = self.info.parent.get(\"artifacts\", {})\n image = parent_artifacts[\"image\"][\"file\"]\n repository = parent_artifacts.get(\"image\", {}).get(\"repository\")\n\n # Default to DockerHub\n if is_docker_hub(repository):\n return image\n\n # Otherwise add full path according to repository\n image = add_repo_url(image, repository, self.info.repositories)\n return image","def build_test_version_of_container_image(\n image_type: ImageType,\n image_builder_cls: Type[ContainerisedAgentBuilder],\n architecture: CpuArch,\n result_image_name: str,\n ready_image_oci_tarball: pl.Path = None,\n install_additional_test_libs: bool = True,\n):\n\n image_builder = image_builder_cls()\n\n registry_container_name = \"agent_image_e2e_test_registry\"\n\n delete_container(container_name=registry_container_name)\n\n # Create temporary local registry to push production image there.\n subprocess.run(\n [\n \"docker\",\n \"run\",\n \"-d\",\n \"--rm\",\n \"-p=5000:5000\",\n f\"--name={registry_container_name}\",\n \"registry:2\",\n ],\n check=True,\n )\n try:\n all_image_tags = image_builder.generate_final_registry_tags(\n image_type=image_type,\n registry=\"localhost:5000\",\n name_prefix=\"user\",\n tags=[\"prod\"],\n )\n\n # Publish image to the local registry\n image_builder.publish(\n image_type=image_type,\n tags=all_image_tags,\n existing_oci_layout_tarball=ready_image_oci_tarball,\n no_verify_tls=True,\n )\n\n prod_image_tag = all_image_tags[0]\n if install_additional_test_libs:\n\n # Build agent image requirements, because it also includes requirements (like coverage) for testing.\n requirement_libs_dir = image_builder.build_requirement_libs(\n architecture=architecture,\n )\n\n # Build testable image.\n buildx_build(\n dockerfile_path=_PARENT_DIR / \"Dockerfile\",\n context_path=_PARENT_DIR,\n architectures=[architecture],\n build_contexts={\n \"prod_image\": f\"docker-image://{prod_image_tag}\",\n \"requirement_libs\": str(requirement_libs_dir),\n },\n output=DockerImageBuildOutput(\n name=result_image_name,\n ),\n )\n else:\n subprocess.run(\n [\n \"docker\",\n \"pull\",\n prod_image_tag,\n ],\n check=True,\n )\n\n subprocess.run(\n [\n \"docker\",\n \"tag\",\n prod_image_tag,\n result_image_name,\n ],\n check=True,\n )\n finally:\n delete_container(container_name=registry_container_name)\n\n return result_image_name","async def get_gw(test_cli) -> str:\n gw_resp = await test_cli.get('/api/v6/gateway')\n gw_json = await gw_resp.json\n return gw_json['url']","def test_build_image_default_context(self, mock_docker_environment):\n build_image(\"Dockerfile.test\", TEST_IMAGE_NAME, context=None)\n mock_docker_environment.images.build.assert_called_with(\n dockerfile=\"Dockerfile.test\", path=pwd(), tag=TEST_IMAGE_NAME\n )","async def _api_url(self) -> URL:\n return await self._gitlab_api_url(\"\")","def test_force_with_local_and_registry_image(self, mock_docker_environment):\n build_image_if_needed(TEST_IMAGE_NAME, force=True)\n mock_docker_environment.images.build.assert_called_with(**self.default_call_kwargs)","def still_image_url(self) -> str:\n\t\treturn 'grab.jpg?oid={0}'.format(self._oid)","def get_url(self):\n if self.url is None: # pragma: no cover\n raise ImproperlyConfigured(\n \"{0} is missing a url to test. Define {0}.url \"\n \"or override {0}.get_url().\".format(self.__class__.__name__)\n )\n return self.url","def get_base_url(self):\n try:\n return self.get_metadata()['api_endpoint']\n except requests.exceptions.RequestException:\n raise","def _base_url(self):\n # URL Protocol\n proto = 'https' if self._ssl else 'http'\n\n # Device port number\n if self._port is None:\n port = 8080 if self._ssl else 8008\n else:\n port = self._port\n \n return f'{proto}://{self._address}:{port}/api/v1'","def get_scraper_url(self):\r\n \r\n return self.reformat_scraper_url()","def build_url(self, config, query):\n if(not os.environ['FLICKR_API_KEY']):\n raise ValueError('Environement variable \"FLICKR_API_KEY\" is empty')\n \n current_provider = [provider for provider in config['providers'] if provider['name'] == self.provider_name][0]\n current_provider['query']['text'] = str(query)\n current_provider['query']['api_key'] = os.environ['FLICKR_API_KEY']\n\n query_strings = helper.build_query_strings(current_provider['query'])\n\n return current_provider['base_url'] + query_strings","def get_url(name, details):\n opsys, machine = get_platform()\n _platform = f\"{opsys}_{machine}\"\n\n try:\n version = details[\"version\"]\n except KeyError:\n raise KeyError(f\"version must be specified for plugin {name}\")\n\n # set the file name, allow it to be overridden with key \"filename\"\n default_file_name = f\"terraform-provider-{name}_{version}_{_platform}.zip\"\n file_name = details.get(\"filename\", default_file_name)\n\n # set the base url, allow it to be overridden with key \"baseURL\"\n default_base_url = (\n f\"https://releases.hashicorp.com/terraform-provider-{name}/{version}\"\n )\n base_uri = details.get(\"baseURL\", default_base_url).rstrip(\"/\")\n\n return f\"{base_uri}/{file_name}\"","def underlying_url(self):\n return 'http://{}:{}'.format(names.azure_url(self.dns_name), self.port)","def get_builder_bot_for_this_platform(self):\n if self.api.builder_bot: # pragma: no cover\n return self.api.builder_bot\n\n # TODO(prasadv): Refactor this code to remove hard coded values.\n bot_name = self.get_perf_tester_name()\n if 'win' in bot_name:\n if any(b in bot_name for b in ['x64', 'gpu']):\n return 'winx64_bisect_builder'\n return 'win_perf_bisect_builder'\n\n if 'android' in bot_name:\n if 'nexus9' in bot_name:\n return 'android_arm64_perf_bisect_builder'\n return 'android_perf_bisect_builder'\n\n if 'mac' in bot_name:\n return 'mac_perf_bisect_builder'\n\n return 'linux_perf_bisect_builder'"],"string":"[\n \"def get_runner_image_url(experiment, benchmark, fuzzer, docker_registry):\\n tag = 'latest' if environment.get('LOCAL_EXPERIMENT') else experiment\\n return f'{docker_registry}/runners/{fuzzer}/{benchmark}:{tag}'\",\n \"def _to_dockerfile_url(image):\\n path = \\\"/\\\".join((image.platform, image.release, image.architecture, \\\"Dockerfile\\\"))\\n return git.get_github_blob_url(path, ref=f\\\"v{image.version}\\\")\",\n \"def getBuildbotURL():\",\n \"def _GetBuildBotUrl(builder_host, builder_port):\\n if (builder_host == BISECT_BUILDER_HOST and\\n builder_port == BISECT_BUILDER_PORT):\\n return TRY_SERVER_URL\\n else:\\n return 'http://%s:%s' % (builder_host, builder_port)\",\n \"def get_buildbot_url():\\n return \\\"http://10.45.4.98:8001/\\\"\",\n \"def get_fuzz_target(benchmark):\\n # Do this because of OSS-Fuzz-on-demand.\\n # TODO(metzman): Use classes to mock a benchmark config for\\n # OSS_FUZZ_ON_DEMAND.\\n return benchmark_config.get_config(benchmark).get(\\n 'fuzz_target', environment.get('FUZZ_TARGET'))\",\n \"def get_image_url():\",\n \"def query_repo_url_from_buildername(buildername):\\n repo_name = query_repo_name_from_buildername(buildername)\\n return buildapi.query_repo_url(repo_name)\",\n \"def get_image_name():\\n try:\\n return os.environ['AIRFLOW_IMAGE']\\n except KeyError:\\n raise Exception(\\\"Please provide docker image name to pytest using environment variable AIRFLOW_IMAGE\\\")\",\n \"def get_image_registry_url(self, image_name):\\n c = self._oc_command([\\\"get\\\", \\\"is\\\", image_name,\\n \\\"--output=jsonpath=\\\\'{ .status.dockerImageRepository }\\\\'\\\"])\\n try:\\n internal_registry_name = run_cmd(c, return_output=True)\\n except subprocess.CalledProcessError as ex:\\n raise ConuException(\\\"oc get is failed: %s\\\" % ex)\\n\\n logger.info(\\\"Image registry url: %s\\\", internal_registry_name)\\n\\n return internal_registry_name.replace(\\\"'\\\", \\\"\\\").replace('\\\"', '')\",\n \"def _dockerfile(self):\\n return self.config.get('docker', {}).get('dockerfile', 'Dockerfile')\",\n \"def build_container(client):\\n client.images.build(path=os.path.join(os.path.abspath(\\\"\\\"), \\\"docker\\\"), tag=\\\"scrape_light\\\")\",\n \"def _get_image(runtime):\\n return \\\"{}:{}\\\".format(LambdaContainer._IMAGE_REPO_NAME, runtime)\",\n \"def http_service(docker_ip: Any, docker_services: Any) -> Any:\\n # `port_for` takes a container port and returns the corresponding host port\\n port = docker_services.port_for(\\\"fdk-baseregistries-publisher\\\", HOST_PORT)\\n url = \\\"http://{}:{}\\\".format(docker_ip, port)\\n docker_services.wait_until_responsive(\\n timeout=30.0, pause=0.1, check=lambda: is_responsive(url)\\n )\\n return url\",\n \"def create_image_builder_streaming_url(Name=None, Validity=None):\\n pass\",\n \"def url(self):\\r\\n return \\\"{}/container/{}\\\".format(BASE_URL, self.unit_locator)\",\n \"def _ensure_image(testkit_path, branch_name, artifacts_path):\\n # Construct Docker image name from branch name\\n image_name = \\\"runner:%s\\\" % branch_name\\n image_path = os.path.join(testkit_path, \\\"runner_image\\\")\\n docker.build_and_tag(image_name, image_path, log_path=artifacts_path)\\n\\n return image_name\",\n \"def build_docker(c):\\n tag = c.run('git describe', hide=True)\\n docker_img = f'{docker_repo}:{tag.stdout.strip()}'\\n c.run(f'docker build -t {docker_img} .')\",\n \"def get_base_docker_image(docker_file):\\n with open(docker_file) as f:\\n from_line = next(\\n line for line in f.read().split(\\\"\\\\n\\\") if line.startswith(\\\"FROM\\\")\\n )\\n _from, base_image = from_line.split()\\n return base_image\",\n \"def build_nighthawk_benchmark_image_from_source(manager: source_manager.SourceManager) -> None:\\n # TODO: Inject the builder object into this method\\n builder = nighthawk_builder.NightHawkBuilder(manager)\\n builder.build_nighthawk_benchmark_image()\",\n \"def docker_image_tag(self, app):\\n return f\\\"briefcase/{app.bundle}.{app.app_name.lower()}:{app.target_vendor}-{app.target_codename}\\\"\",\n \"def _get_base_url(self):\\n template = config.baseurl_template\\n # get distro name and arch\\n base_url = template.format(\\n host=config.gitbuilder_host,\\n proj=self.project,\\n pkg_type=self.pkg_type,\\n arch=self.arch,\\n dist=self.distro,\\n flavor=self.flavor,\\n uri=self.uri_reference,\\n )\\n return base_url\",\n \"def dockerfile(self) -> pulumi.Output[Optional[str]]:\\n return pulumi.get(self, \\\"dockerfile\\\")\",\n \"def url(self, name):\\n return '%s/%s' % (self.container_url, name)\",\n \"def get_rule_container_image_uri(name, region):\\n if name is not None and name.startswith(\\\"DetailedProfilerProcessingJobConfig\\\"):\\n # should have the format like \\\"123456789012.dkr.ecr.us-west-2.amazonaws.com/detailed-profiler-processing:latest\\\"\\n return image_uris.retrieve(detailed_framework_name, region)\\n\\n return image_uris.retrieve(framework_name, region)\",\n \"def get_builder(self, request):\\n base_url = request.application_url\\n return images_view.ViewBuilderV10(base_url)\",\n \"def _build_url(self):\\n url = BASE_URL.format(self._host, self._port)\\n _LOGGER.debug(\\\"TOON fetch URL: %s\\\", url)\\n return url\",\n \"def pyfunc_build_image(model_uri, extra_args=None):\\n name = uuid.uuid4().hex\\n cmd = [\\\"mlflow\\\", \\\"models\\\", \\\"build-docker\\\", \\\"-m\\\", model_uri, \\\"-n\\\", name]\\n if extra_args:\\n cmd += extra_args\\n p = subprocess.Popen(cmd,)\\n assert p.wait() == 0, \\\"Failed to build docker image to serve model from %s\\\" % model_uri\\n return name\",\n \"def dockerfile(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"dockerfile\\\")\",\n \"def build_url(cls, config, namespace, name):\\n return \\\"hxxp://mock.repo.url/\\\" + namespace + \\\"/\\\" + name + \\\".git\\\"\",\n \"def container_image_name(registry, component_name, version):\\n if version is None:\\n image = component_name + ':dev'\\n else:\\n image = '%s/%s:%s' % (registry, component_name, version)\\n\\n return image\",\n \"def image_url(self) -> str:\\n return pulumi.get(self, \\\"image_url\\\")\",\n \"def get_short_url_base():\",\n \"def get_flower_url(self):\\n return \\\"http://{0}:{1}\\\".format(self.get_head_node_ip(), self.FLOWER_PORT)\",\n \"def _get_dockerfiles_for_test() -> str:\\n project_root_dir = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))\\n dockerfiles_dir = os.path.join(project_root_dir, \\\"dockerfiles\\\")\\n if sys.version_info[0:2] == (3, 6):\\n return os.path.join(dockerfiles_dir, \\\"centos7.Dockerfile\\\")\\n elif sys.version_info[0:2] == (3, 9):\\n return os.path.join(dockerfiles_dir, \\\"rocky8.Dockerfile\\\")\\n else:\\n raise Exception(\\n \\\"Running the tests with INMANTA_TEST_INFRA_SETUP=true is only supported using a python3.6 or python3.9 venv\\\"\\n )\",\n \"def url_for(**options):\\n\\n url_parts = get_url_parts(**options)\\n image_hash = hashlib.md5(b(options[\\\"image_url\\\"])).hexdigest()\\n url_parts.append(image_hash)\\n\\n return \\\"/\\\".join(url_parts)\",\n \"def dockerfile_dir(self):\\n return self._dockerfile_dir\",\n \"def apk_url(self):\",\n \"def get_url_for_min_resolution(self, min_height, min_width, image):\",\n \"def app_logo_url():\\n return \\\"https://raw.githubusercontent.com/aiidalab/aiidalab-hello-world/master/img/logo.png\\\"\",\n \"def get_thumbnail_url():\",\n \"def test_client_build_url():\\n eq_(\\\"{0}/{1}\\\".format(client.BASE_URL, \\\"v1/charges/\\\"), client.build_url(\\\"v1/charges/\\\"))\",\n \"def test_baseurl(matrix):\\n matrix.charm_config[\\\"enable-tls\\\"] = False\\n result = matrix.get_public_baseurl()\\n assert result == \\\"http://mock.fqdn:8008\\\"\\n matrix.charm_config[\\\"enable-tls\\\"] = False\\n matrix.external_port = 80\\n result = matrix.get_public_baseurl()\\n assert result == \\\"http://mock.fqdn\\\"\\n matrix.charm_config[\\\"enable-tls\\\"] = True\\n result = matrix.get_public_baseurl()\\n assert result == \\\"https://mock.fqdn\\\"\",\n \"def image_url(self) -> str:\\n return self._image_url\",\n \"def docker_image_name(self):\\n raise NotImplementedError\",\n \"def get_url_image(self, obj):\\n return settings.SERVER_HOST + obj.image.url\",\n \"def getReferenceImageUrl(self, name):\\n bucket = self.productSearch.bucket\\n blobName = self._getReferenceImageBlobName(name)\\n return bucket.blob(blobName).public_url\",\n \"def get_builder(self, req):\\n base_url = req.application_url\\n project_id = getattr(req.environ['nova.context'], 'project_id', '')\\n return images_view.ViewBuilderV11(base_url, project_id)\",\n \"def discovery_url(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"discovery_url\\\")\",\n \"def _get_base_url(self):\\n return 'https://'+self.get_address_and_port_string()\",\n \"def get_api_url() -> str:\\n\\n\\tsite = pywikibot.Site()\\n\\turl = site.protocol() + \\\"://\\\" + site.hostname() + site.apipath()\\n\\treturn url\",\n \"def specs_url(self):\\n\\t\\tscheme = 'http' if '5000' in self.base_url else 'https'\\n\\t\\treturn url_for(self.endpoint('specs'), _external=True, _scheme=scheme)\",\n \"def get_kojiroot_base_url(build_info, arch=\\\"x86_64\\\"):\\n base_url = \\\"{kojiroot}/{package_name}/{ver}/{rel}/{arch}/\\\".format(\\n kojiroot=config.kojiroot_url,\\n package_name=build_info[\\\"package_name\\\"],\\n ver=build_info[\\\"version\\\"],\\n rel=build_info[\\\"release\\\"],\\n arch=arch,\\n )\\n return base_url\",\n \"def build_url(self, host, target, params=None):\\n return \\\"https://%s%s\\\" % (host, self.build_path(target, params))\",\n \"def PLACEHOLDER(width=100, height=100, HTTP=\\\"\\\", seperator='/'):\\n return f\\\"{HTTP}://{CDN_IMG.PLACEHOLDER_SERVICE}/{width}{seperator}{height}\\\"\",\n \"def get_url(self, cfg, option, default):\\n if cfg.has_option('kattis', option):\\n return cfg.get('kattis', option)\\n else:\\n return 'https://%s/%s' % (cfg.get('kattis', 'hostname'), default)\",\n \"def get_geckodriver_url(version: str) -> str:\\n if _ARCHITECTURE == \\\"other\\\": # or platform BSD\\n return f\\\"https://github.com/mozilla/geckodriver/archive/{version}.{_COMPRESSION}\\\"\\n else:\\n return f\\\"https://github.com/mozilla/geckodriver/releases/download/{version}\\\" \\\\\\n f\\\"/geckodriver-{version}-{_PLATFORM}{_ARCHITECTURE}.{_COMPRESSION}\\\"\",\n \"def get_api_url() -> str:\\n\\n site = pywikibot.Site()\\n url = site.protocol() + \\\"://\\\" + site.hostname() + site.apipath()\\n return url\",\n \"def test_force_with_local_image(self, mock_docker_environment):\\n build_image_if_needed(TEST_IMAGE_NAME, force=True)\\n mock_docker_environment.images.build.assert_called_with(**self.default_call_kwargs)\",\n \"def image_url(self):\\n context = aq_inner(self.context)\\n obj_url = context.absolute_url()\\n if hasattr(context, 'getField'):\\n field = self.context.getField('image')\\n if not field:\\n field = context.getField(IMAGE_FIELD_NAME)\\n\\n if field and field.get_size(context) > 0:\\n return u'%s/%s_%s' % (obj_url, field.getName(), 'thumb')\\n\\n return u\\\"%s/isaw_logo.png\\\" % self.portal.absolute_url()\",\n \"def discovery_url(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"discovery_url\\\")\",\n \"def test_url():\\n return TEST_DATABASE_URL\",\n \"def test_get_url_base_returns_url_base(self):\\n # Arrange / Act\\n return_value = BlobDownloader(\\n f\\\"{settings.SERVER_URI}/987653456789\\\"\\n ).get_url_base()\\n # Assert\\n self.assertEqual(return_value, SERVER_URI)\",\n \"def docker_client():\\n return docker.from_env()\",\n \"def get_contribs_api_base_url(request_url=None, deployment=\\\"contribs\\\"):\\n if is_localhost() and SETTINGS.API_EXTERNAL_ENDPOINT:\\n return f\\\"https://{deployment}-api.materialsproject.org\\\"\\n\\n if has_request_context() and (not request_url):\\n request_url = request.url\\n\\n return parse_request_url(request_url, f\\\"{deployment}-api\\\")\",\n \"def get_image_uri(self):\\n return \\\"data:image/png;base64,\\\" + \\\\\\n self.browser.get_screenshot_as_base64()\",\n \"def get_image_url(self, image_url):\\n if image_url:\\n return '{0}?source={1}'.format(self.config['links']['imageProxy'], image_url)\\n else:\\n return None\",\n \"def apk_url(self):\\n if not self._apk_url:\\n self._apk_url = utils.get_str_or_return_val(\\n self.get_apk_url,\\n version=self.version,\\n soup=self.soup,\\n )\\n\\n return self._apk_url\",\n \"def __discovery_url(self):\\n port = self.sensor.options.agent_port\\n if port == 0:\\n port = AGENT_DEFAULT_PORT\\n\\n return \\\"http://%s:%s/%s\\\" % (self.host, port, AGENT_DISCOVERY_PATH)\",\n \"def get_url_image(self, obj):\\n return settings.IMAGE_HOST + obj.image.url\",\n \"def get_url_image(self, obj):\\n return settings.IMAGE_HOST + obj.image.url\",\n \"def get_url_image(self, obj):\\n return settings.IMAGE_HOST + obj.image.url\",\n \"def get_image_url(self, size=None):\\n return images.get_serving_url(self.image_blob_key, size=size)\",\n \"def get_image_url(self, image, hostname):\\n\\n if image.image_type == 'local':\\n return \\\"http://\\\" + hostname + \\\"/\\\" + settings.IMAGES_URL + \\\"/\\\" + image.deployment.campaign.short_name + \\\"/\\\" + image.deployment.short_name + \\\"/\\\" + image.image_name\\n elif image.image_type == 'envirocoms':\\n return \\\"http://\\\" + hostname + \\\"/ecoms_proxy?image=\\\" + image.image_path\",\n \"def ping_docker():\\n with Docker('unittest-36', image='python:3.6') as tun:\\n return tun.call(python_version)[:2]\",\n \"def _get_url(self):\\n return 'http://{}:{}'.format(self.host, self.port)\",\n \"def service_url(self):\\n return \\\"http://127.0.0.1:%d/wd/hub\\\"%self.port\",\n \"def build_url(self, endpoint_url: str) -> str:\\n return self.base_url + endpoint_url % self.instance_id\",\n \"def get_url():\\n key = _get_key()\\n return key.generate_url(300)\",\n \"def _generate_url(self, **kwargs):\\n path = self.url_path.format(**kwargs)\\n return self.poolbot.generate_url(path)\",\n \"def cache_path(self):\\n benchmark_name, image_set_name, _ = self.name.rsplit(\\\"_\\\", 2)\\n cache_path = os.path.join(self._cache_path,'{}_{}_cache'.format(benchmark_name, image_set_name))\\n if not os.path.exists(cache_path):\\n os.mkdir(cache_path)\\n return cache_path\",\n \"def build_envoy_docker_image(manager: source_manager.SourceManager, commit_hash: str) -> None:\\n builder = envoy_builder.EnvoyBuilder(manager)\\n source_repo = manager.get_source_repository(proto_source.SourceRepository.SRCID_ENVOY)\\n source_repo.commit_hash = commit_hash\\n builder.build_envoy_image_from_source()\",\n \"def url(vmanage_host,vmanage_port,api):\\r\\n \\\"\\\"\\\" function to get the url provide api endpoint \\\"\\\"\\\"\\r\\n \\r\\n return f\\\"https://{vmanage_host}:{vmanage_port}{api}\\\"\",\n \"def build(config, version_tag):\\n config_dict = get_config(config)\\n image_name = config_dict['IMAGE'].split(':')[0]\\n image = '{}:{}'.format(image_name, version_tag)\\n base_image = config_dict['BASE_IMAGE']\\n\\n cmd = 'docker build -t {image} --build-arg base_image={base_image} .'.format(image=image,\\n base_image=base_image)\\n with cd(env.project_dir):\\n run(cmd)\\n return image\",\n \"def docker_runner_factory(image):\\n\\n mapping = {\\n 'gunicorn': GunicornDockerRunner,\\n 'redis': RedisDockerRunner,\\n 'consul': ConsulDockerRunner,\\n 'postgres': PostgresDockerRunner,\\n 'registrator': RegistratorDockerRunner,\\n 'solr': SolrDockerRunner\\n }\\n\\n for key in mapping:\\n if key in image:\\n return mapping[key]\\n\\n return DockerRunner\",\n \"def _get_image_from_artifact(self):\\n try:\\n image = self.info.artifacts[\\\"image\\\"][\\\"file\\\"]\\n repository = self.info.artifacts[\\\"image\\\"].get(\\\"repository\\\")\\n except (AttributeError, KeyError):\\n parent_artifacts = self.info.parent.get(\\\"artifacts\\\", {})\\n image = parent_artifacts[\\\"image\\\"][\\\"file\\\"]\\n repository = parent_artifacts.get(\\\"image\\\", {}).get(\\\"repository\\\")\\n\\n # Default to DockerHub\\n if is_docker_hub(repository):\\n return image\\n\\n # Otherwise add full path according to repository\\n image = add_repo_url(image, repository, self.info.repositories)\\n return image\",\n \"def build_test_version_of_container_image(\\n image_type: ImageType,\\n image_builder_cls: Type[ContainerisedAgentBuilder],\\n architecture: CpuArch,\\n result_image_name: str,\\n ready_image_oci_tarball: pl.Path = None,\\n install_additional_test_libs: bool = True,\\n):\\n\\n image_builder = image_builder_cls()\\n\\n registry_container_name = \\\"agent_image_e2e_test_registry\\\"\\n\\n delete_container(container_name=registry_container_name)\\n\\n # Create temporary local registry to push production image there.\\n subprocess.run(\\n [\\n \\\"docker\\\",\\n \\\"run\\\",\\n \\\"-d\\\",\\n \\\"--rm\\\",\\n \\\"-p=5000:5000\\\",\\n f\\\"--name={registry_container_name}\\\",\\n \\\"registry:2\\\",\\n ],\\n check=True,\\n )\\n try:\\n all_image_tags = image_builder.generate_final_registry_tags(\\n image_type=image_type,\\n registry=\\\"localhost:5000\\\",\\n name_prefix=\\\"user\\\",\\n tags=[\\\"prod\\\"],\\n )\\n\\n # Publish image to the local registry\\n image_builder.publish(\\n image_type=image_type,\\n tags=all_image_tags,\\n existing_oci_layout_tarball=ready_image_oci_tarball,\\n no_verify_tls=True,\\n )\\n\\n prod_image_tag = all_image_tags[0]\\n if install_additional_test_libs:\\n\\n # Build agent image requirements, because it also includes requirements (like coverage) for testing.\\n requirement_libs_dir = image_builder.build_requirement_libs(\\n architecture=architecture,\\n )\\n\\n # Build testable image.\\n buildx_build(\\n dockerfile_path=_PARENT_DIR / \\\"Dockerfile\\\",\\n context_path=_PARENT_DIR,\\n architectures=[architecture],\\n build_contexts={\\n \\\"prod_image\\\": f\\\"docker-image://{prod_image_tag}\\\",\\n \\\"requirement_libs\\\": str(requirement_libs_dir),\\n },\\n output=DockerImageBuildOutput(\\n name=result_image_name,\\n ),\\n )\\n else:\\n subprocess.run(\\n [\\n \\\"docker\\\",\\n \\\"pull\\\",\\n prod_image_tag,\\n ],\\n check=True,\\n )\\n\\n subprocess.run(\\n [\\n \\\"docker\\\",\\n \\\"tag\\\",\\n prod_image_tag,\\n result_image_name,\\n ],\\n check=True,\\n )\\n finally:\\n delete_container(container_name=registry_container_name)\\n\\n return result_image_name\",\n \"async def get_gw(test_cli) -> str:\\n gw_resp = await test_cli.get('/api/v6/gateway')\\n gw_json = await gw_resp.json\\n return gw_json['url']\",\n \"def test_build_image_default_context(self, mock_docker_environment):\\n build_image(\\\"Dockerfile.test\\\", TEST_IMAGE_NAME, context=None)\\n mock_docker_environment.images.build.assert_called_with(\\n dockerfile=\\\"Dockerfile.test\\\", path=pwd(), tag=TEST_IMAGE_NAME\\n )\",\n \"async def _api_url(self) -> URL:\\n return await self._gitlab_api_url(\\\"\\\")\",\n \"def test_force_with_local_and_registry_image(self, mock_docker_environment):\\n build_image_if_needed(TEST_IMAGE_NAME, force=True)\\n mock_docker_environment.images.build.assert_called_with(**self.default_call_kwargs)\",\n \"def still_image_url(self) -> str:\\n\\t\\treturn 'grab.jpg?oid={0}'.format(self._oid)\",\n \"def get_url(self):\\n if self.url is None: # pragma: no cover\\n raise ImproperlyConfigured(\\n \\\"{0} is missing a url to test. Define {0}.url \\\"\\n \\\"or override {0}.get_url().\\\".format(self.__class__.__name__)\\n )\\n return self.url\",\n \"def get_base_url(self):\\n try:\\n return self.get_metadata()['api_endpoint']\\n except requests.exceptions.RequestException:\\n raise\",\n \"def _base_url(self):\\n # URL Protocol\\n proto = 'https' if self._ssl else 'http'\\n\\n # Device port number\\n if self._port is None:\\n port = 8080 if self._ssl else 8008\\n else:\\n port = self._port\\n \\n return f'{proto}://{self._address}:{port}/api/v1'\",\n \"def get_scraper_url(self):\\r\\n \\r\\n return self.reformat_scraper_url()\",\n \"def build_url(self, config, query):\\n if(not os.environ['FLICKR_API_KEY']):\\n raise ValueError('Environement variable \\\"FLICKR_API_KEY\\\" is empty')\\n \\n current_provider = [provider for provider in config['providers'] if provider['name'] == self.provider_name][0]\\n current_provider['query']['text'] = str(query)\\n current_provider['query']['api_key'] = os.environ['FLICKR_API_KEY']\\n\\n query_strings = helper.build_query_strings(current_provider['query'])\\n\\n return current_provider['base_url'] + query_strings\",\n \"def get_url(name, details):\\n opsys, machine = get_platform()\\n _platform = f\\\"{opsys}_{machine}\\\"\\n\\n try:\\n version = details[\\\"version\\\"]\\n except KeyError:\\n raise KeyError(f\\\"version must be specified for plugin {name}\\\")\\n\\n # set the file name, allow it to be overridden with key \\\"filename\\\"\\n default_file_name = f\\\"terraform-provider-{name}_{version}_{_platform}.zip\\\"\\n file_name = details.get(\\\"filename\\\", default_file_name)\\n\\n # set the base url, allow it to be overridden with key \\\"baseURL\\\"\\n default_base_url = (\\n f\\\"https://releases.hashicorp.com/terraform-provider-{name}/{version}\\\"\\n )\\n base_uri = details.get(\\\"baseURL\\\", default_base_url).rstrip(\\\"/\\\")\\n\\n return f\\\"{base_uri}/{file_name}\\\"\",\n \"def underlying_url(self):\\n return 'http://{}:{}'.format(names.azure_url(self.dns_name), self.port)\",\n \"def get_builder_bot_for_this_platform(self):\\n if self.api.builder_bot: # pragma: no cover\\n return self.api.builder_bot\\n\\n # TODO(prasadv): Refactor this code to remove hard coded values.\\n bot_name = self.get_perf_tester_name()\\n if 'win' in bot_name:\\n if any(b in bot_name for b in ['x64', 'gpu']):\\n return 'winx64_bisect_builder'\\n return 'win_perf_bisect_builder'\\n\\n if 'android' in bot_name:\\n if 'nexus9' in bot_name:\\n return 'android_arm64_perf_bisect_builder'\\n return 'android_perf_bisect_builder'\\n\\n if 'mac' in bot_name:\\n return 'mac_perf_bisect_builder'\\n\\n return 'linux_perf_bisect_builder'\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.800936","0.68247736","0.62919927","0.6119331","0.60827637","0.5836558","0.5680232","0.5606805","0.5593281","0.55321765","0.5527971","0.55201703","0.5511697","0.5464959","0.5450358","0.5436414","0.5423743","0.5383551","0.53793126","0.5351387","0.53473455","0.5292844","0.5277089","0.5235646","0.5235194","0.52274054","0.5220325","0.52090615","0.51965827","0.5178419","0.5160508","0.51500416","0.5137805","0.5116511","0.5109225","0.5098648","0.5076821","0.5072366","0.50641006","0.50587076","0.50555384","0.50536114","0.5051039","0.50356394","0.5023805","0.4997169","0.49968097","0.4995038","0.4994835","0.4971616","0.49706283","0.49656278","0.49602076","0.49513945","0.4949838","0.49480158","0.49370387","0.49362025","0.49319613","0.49105152","0.4901107","0.4891908","0.48914334","0.48911646","0.48887464","0.48864025","0.48841745","0.48838955","0.48804948","0.48792538","0.48792538","0.48792538","0.48738793","0.48732415","0.48704064","0.48614562","0.4859663","0.48586038","0.4852415","0.48497722","0.48471242","0.4845583","0.48407534","0.483798","0.48327804","0.4831186","0.4829767","0.4828287","0.4826269","0.482372","0.48212284","0.48206958","0.48202538","0.4817333","0.48047742","0.4804208","0.47985688","0.47955978","0.47936556","0.47936404"],"string":"[\n \"0.800936\",\n \"0.68247736\",\n \"0.62919927\",\n \"0.6119331\",\n \"0.60827637\",\n \"0.5836558\",\n \"0.5680232\",\n \"0.5606805\",\n \"0.5593281\",\n \"0.55321765\",\n \"0.5527971\",\n \"0.55201703\",\n \"0.5511697\",\n \"0.5464959\",\n \"0.5450358\",\n \"0.5436414\",\n \"0.5423743\",\n \"0.5383551\",\n \"0.53793126\",\n \"0.5351387\",\n \"0.53473455\",\n \"0.5292844\",\n \"0.5277089\",\n \"0.5235646\",\n \"0.5235194\",\n \"0.52274054\",\n \"0.5220325\",\n \"0.52090615\",\n \"0.51965827\",\n \"0.5178419\",\n \"0.5160508\",\n \"0.51500416\",\n \"0.5137805\",\n \"0.5116511\",\n \"0.5109225\",\n \"0.5098648\",\n \"0.5076821\",\n \"0.5072366\",\n \"0.50641006\",\n \"0.50587076\",\n \"0.50555384\",\n \"0.50536114\",\n \"0.5051039\",\n \"0.50356394\",\n \"0.5023805\",\n \"0.4997169\",\n \"0.49968097\",\n \"0.4995038\",\n \"0.4994835\",\n \"0.4971616\",\n \"0.49706283\",\n \"0.49656278\",\n \"0.49602076\",\n \"0.49513945\",\n \"0.4949838\",\n \"0.49480158\",\n \"0.49370387\",\n \"0.49362025\",\n \"0.49319613\",\n \"0.49105152\",\n \"0.4901107\",\n \"0.4891908\",\n \"0.48914334\",\n \"0.48911646\",\n \"0.48887464\",\n \"0.48864025\",\n \"0.48841745\",\n \"0.48838955\",\n \"0.48804948\",\n \"0.48792538\",\n \"0.48792538\",\n \"0.48792538\",\n \"0.48738793\",\n \"0.48732415\",\n \"0.48704064\",\n \"0.48614562\",\n \"0.4859663\",\n \"0.48586038\",\n \"0.4852415\",\n \"0.48497722\",\n \"0.48471242\",\n \"0.4845583\",\n \"0.48407534\",\n \"0.483798\",\n \"0.48327804\",\n \"0.4831186\",\n \"0.4829767\",\n \"0.4828287\",\n \"0.4826269\",\n \"0.482372\",\n \"0.48212284\",\n \"0.48206958\",\n \"0.48202538\",\n \"0.4817333\",\n \"0.48047742\",\n \"0.4804208\",\n \"0.47985688\",\n \"0.47955978\",\n \"0.47936556\",\n \"0.47936404\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8987414"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":11267,"cells":{"query":{"kind":"string","value":"Returns True if |benchmark| is a valid fuzzbench benchmark name."},"document":{"kind":"string","value":"def validate_name(benchmark):\n if VALID_BENCHMARK_REGEX.match(benchmark) is None:\n logs.error('%s does not conform to %s pattern.', benchmark,\n VALID_BENCHMARK_REGEX.pattern)\n return False\n return True"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def validate(benchmark):\n if not validate_name(benchmark):\n return False\n\n if benchmark not in get_all_benchmarks():\n logs.error('%s must have a benchmark.yaml.', benchmark)\n return False\n\n # Validate config file can be parsed.\n try:\n get_fuzz_target(benchmark)\n except yaml.parser.ParserError:\n logs.error('%s must have a valid benchmark.yaml file. Failed to parse.',\n benchmark)\n return False\n except KeyError:\n logs.error('%s\\'s benchmark.yaml does not define \"fuzz_target\".',\n benchmark)\n return False\n\n # Validate type.\n return validate_type(benchmark)","def validate_type(benchmark):\n benchmark_type = get_type(benchmark)\n if benchmark_type not in BENCHMARK_TYPE_STRS:\n logs.error('%s has an invalid benchmark type %s, must be one of %s',\n benchmark, benchmark_type, BENCHMARK_TYPE_STRS)\n return False\n return True","def _is_valid_keyspace_name(self, keyspace_name):\n if keyspace_name == None or not keyspace_name:\n return False\n return re.match(r\"^[a-z_]*[^-]$\", keyspace_name)","def valid_routine_name(routine):\n\treturn re.match('^[a-z_]([a-z0-9_]*)', routine) is not None","def validName(configsetname):\n for c in configsetname:\n if not c in string.letters+string.digits+\"$_-\":\n return False\n return configsetname != \"\"","def is_dev_name_valid(self):\n return self._name_re.match(self.dev_name) is not None","def is_valid(name):\n return bool(name)","def validname(name):\r\n return len(name)>0 and (\r\n Context.__invalid_character.search(name) is None)","def get_benchmark(self, benchmark):\n\t\tif not isinstance(benchmark, str) and not callable(benchmark): return benchmark\n\t\telif benchmark in self.classes:\treturn self.classes[benchmark]()\n\t\traise TypeError('Passed benchmark is not defined!')","def isValidDataTypeName(name: unicode) -> bool:\n ...","def verify_name(name):\n try:\n if name.index(' '):\n return False\n except ValueError:\n return True","def is_bank_name_valid(name_to_check: str):\n def is_name_short_enough():\n return True if len(name_to_check) <= 12 else False\n\n def is_name_only_letter():\n return True if name_to_check.isalpha() else False\n\n return True if is_name_short_enough() and is_name_only_letter() else False","def is_valid_name(self):\n\n if self.whitelist_name == '':\n return True\n\n if len(self.whitelist_name) >= 64:\n LOGGER.debug('invalid name %s; must be less than 64 bytes',\n self.whitelist_name)\n return False\n\n return True","def is_builtin_name(name):\r\n if name.startswith('__') and name.endswith('__'):\r\n return ALL_LOWER_CASE_RE.match(name[2:-2]) is not None\r\n return False","def _is_fan(compressor: Compressor) -> bool:\n name = compressor.name.lower()\n return 'fan' in name or 'crtf' in name","def get_fuzzer_benchmark_key(fuzzer: str, benchmark: str):\n return fuzzer + ' ' + benchmark","def _validate_name(name):\r\n\tif HOST_NAME != name and len(name) > 0 and ZOOM_PHRASES[0] not in name and name not in WAITING_ROOM:\r\n\t\treturn True\r\n\treturn False","def name_valid(name):\n return name.isalpha()","def is_mbid(mbid):\n try:\n mbid = uuid.UUID(mbid)\n good = True\n except ValueError as e:\n good = False\n except AttributeError:\n good = False\n\n return good","def verify_name(name):\n if name and not name.isspace(): # if it's not empty/NULL and it's not whitespace\n return True\n else:\n return False","def _check_if_valid_dataset_name(dataset_name: str) -> str:\n if not re.match(r\"^[A-Za-z0-9_]+$\", dataset_name):\n raise ExecutionEngineError(\n f\"dataset_name: {dataset_name} is not valid, because it contains non-alphanumeric and _ characters.\"\n f\"Please check your configuration.\"\n )\n\n if len(dataset_name) >= MAX_TABLE_NAME_LENGTH:\n # starting from the end, so that we always get the index and sub_index\n new_dataset_name = dataset_name[-MAX_TABLE_NAME_LENGTH:]\n logger.info(\n f\"dataset_name: '{dataset_name}' was truncated to '{new_dataset_name}' to keep within length limits.\"\n )\n dataset_name = new_dataset_name\n\n while not re.match(r\"^[A-Za-z]+$\", dataset_name[0]):\n dataset_name = dataset_name[1:]\n\n return dataset_name","def create_benchmark(self, benchmark):\n self.crd_client.create_namespaced_custom_object(\n group=\"ripsaw.cloudbulldozer.io\",\n version=\"v1alpha1\",\n namespace=benchmark[\"metadata\"][\"namespace\"],\n plural=\"benchmarks\",\n body=benchmark,\n )","def is_valid_business_name(self):\n return self.business_name.lower() not in INVALID_BUSINESS_NAME","def has_name(self):\n return self.unpack_word(0x2) != 0","def is_valid_cname(common_name: str) -> bool:\n return True if Band.band_range(common_name) else False","def full_name(self, test_name: str) -> bool:\n if not self._full_test_name_patterns:\n return True\n\n if self._compiled_test_name_pattern is None:\n self._compiled_test_name_pattern = re.compile('|'.join(self._full_test_name_patterns))\n\n return self._compiled_test_name_pattern.match(test_name)","def IsValidFacename(*args, **kwargs):\n return _gdi_.FontEnumerator_IsValidFacename(*args, **kwargs)","def is_basic_name(name):\n if name is None:\n raise AdasDBError(\"Invalid name '%s'.\" % name)\n return name.find(PATH_SEPARATOR)","def _check_name(self, symbol):\n if symbol.type == self.scanner.NAME:\n return True\n else:\n return False","def get_fuzz_target(benchmark):\n # Do this because of OSS-Fuzz-on-demand.\n # TODO(metzman): Use classes to mock a benchmark config for\n # OSS_FUZZ_ON_DEMAND.\n return benchmark_config.get_config(benchmark).get(\n 'fuzz_target', environment.get('FUZZ_TARGET'))","def names_are_equal(filesystem_name, fixture_name):\n if filesystem_safe(filesystem_name) == fixture_name:\n return True\n return False","def legal_name(name, is_param_name=False):\n if name.startswith('_'):\n return False\n\n if name in ('self',):\n return False\n\n if keyword.iskeyword(name):\n return False\n\n regex = r'^[a-zA-Z][a-zA-Z0-9_]*$' if is_param_name else (\n r'^[a-zA-Z][.\\w-]*$')\n return bool(re.match(regex, name))","def validate_team_name(name):\n if not re.match('^[A-Za-z0-9_]*$', name):\n print('INVALID NAME. LETTERS, NUMBERS AND UNDERSCORES ONLY')\n return False\n elif len(name) > 10:\n print('INVALID NAME. 10 CHARACTERS MAX')\n return False\n elif len(name) == 0:\n print('INVALID NAME. NOT LONG ENOUGH')\n else:\n return True","def IsValidHotlistName(s):\n return (RE_HOTLIST_NAME.match(s) and\n len(s) <= framework_constants.MAX_HOTLIST_NAME_LENGTH)","def FontEnumerator_IsValidFacename(*args, **kwargs):\n return _gdi_.FontEnumerator_IsValidFacename(*args, **kwargs)","def validName(varname):\r\n if (len(varname[0])>32):\r\n return False\r\n if not(varname[0][0].isalpha()):\r\n return False \r\n for ch in varname[0][1:]:\r\n if not(ch.isalpha() or ch.isdigit() or ch=='_'):\r\n return False\r\n \r\n return True","def validate_name(name:str) -> bool:\r\n return name.isalpha() and name.count(\" \") == 0 and len(name) >= 2","def is_result_appropriate(self, benchmark_result):\n return True","def _is_valid_adapter(self, adapter_name):\n\n valid_adapters = self.valid_adapters\n\n result = False\n\n if adapter_name in valid_adapters:\n\n result = True\n\n return result","def is_valid_collection_name(collection_name):\n\n collection_name = to_text(collection_name)\n\n return bool(re.match(AnsibleCollectionRef.VALID_COLLECTION_NAME_RE, collection_name))","def is_valid_attribute_name(self, name):\n try:\n self.validate_attribute_name(name)\n return True\n except etal.LabelsSchemaError:\n return False","def is_cpp(benchmark):\n return get_language(benchmark) == 'c++'","def _check_is_name_valid(self, name):\n if name in self.forbidden_names or name.endswith(\n self.forbidden_extensions) or self.__check_is_match_regex(name):\n return False\n return True","def check_name(name, allow_services=False):","def check_banned(deck_format, card_name):\n if card_name in consts.BANNINGS[deck_format]:\n return True\n return False","def is_simple_name(s):\n\n assert utils.is_string_type(s)\n assert len(s) > 0\n\n def valid_first(c):\n return c.isalpha() or c == \"_\"\n def valid_later(c):\n return c.isalnum() or c == \"_\"\n return valid_first(s[0]) and all(valid_later(c) for c in s)","def invalid_name(name):\n if any(not item.isalpha() for item in str(name)):\n return True\n return False","def __isValidColor(self, name):\n try:\n if self.__isHexString(name) and len(name) in [3, 6, 9, 12]:\n return True\n return QColor.isValidColor(name)\n except AttributeError:\n if name.startswith(\"#\"):\n if len(name) not in [4, 7, 10, 13]:\n return False\n hexCheckStr = name[1:]\n return self.__isHexString(hexCheckStr)\n else:\n if self.__isHexString(name) and len(name) in [3, 6, 9, 12]:\n return True\n return name in QColor.colorNames()","def valid_hook(hook_file, hook_name):\n filename = os.path.basename(hook_file)\n basename = os.path.splitext(filename)[0]\n\n matching_hook = basename == hook_name\n supported_hook = basename in _HOOKS\n backup_file = filename.endswith('~')\n\n return matching_hook and supported_hook and not backup_file","def is_valid_flag(flag):\n return False if re.match(\"^FAUST_[A-Za-z0-9/\\\\+]{32}$\", flag) is None else True","def test_create_with_only_name(self):\n with OrionState() as cfg:\n name = \"bm00001\"\n with pytest.raises(NoConfigurationError) as exc:\n get_or_create_benchmark(cfg.storage, name).close()\n\n assert f\"Benchmark {name} does not exist in DB\" in str(exc.value)","def is_valid_channel_name(channel):\n if not is_channel_name(channel):\n return False\n\n test_section = channel[1:]\n\n if not MIN_CHANNEL_NAME_LEN < len(channel) < MAX_CHANNEL_NAME_LEN:\n return False\n\n valid_symbols = '#\\\\|^`[]{}_'\n valid_chars = string.ascii_letters + string.digits + valid_symbols\n\n for char in channel:\n if char not in valid_chars:\n return False","def is_shortcut_name(name: str) -> bool:\n return name.count(config.name_separator) == 1","def _check_key_name(cls, name):\n return (isinstance(name, basestring) and\n re.match('^[A-Za-z][A-Za-z0-9_]*$', name) and\n not hasattr(cls, name))","def test_is_valid_color_name(self):\n self.assertTrue(is_valid_color_name('black'))\n self.assertTrue(is_valid_color_name('red'))\n self.assertFalse(is_valid_color_name('#aabb11'))\n self.assertFalse(is_valid_color_name('bl(ack'))","def validate_names(name):\n return isinstance(name, str) and not re.search(r'[\\s]', name)","def _is_valid_varname(self, name):\n if name in RESERVED or re.match(r'^str[0-9]+$', name): return False\n return True if VALID_NAME_RE.match(name) else False","def delete_benchmark(self, name, namespace=\"benchmark-operator\"):\n logger.info(f\"Deleting benchmark {name} in namespace {namespace}\")\n self.crd_client.delete_namespaced_custom_object(\n group=\"ripsaw.cloudbulldozer.io\",\n version=\"v1alpha1\",\n namespace=namespace,\n plural=\"benchmarks\",\n name=name,\n )\n logger.info(f\"Deleted benchmark {name} in namespace {namespace}\")","def get_type(benchmark):\n # TODO(metzman): Use classes to mock a benchmark config for\n # OSS_FUZZ_ON_DEMAND.\n default_value = os.getenv('EXPERIMENT_TYPE', BenchmarkType.CODE.value)\n return benchmark_config.get_config(benchmark).get('type', default_value)","def is_valid(passwd: str) -> bool:\n return (\n re.search(r'abc|bcd|cde|def|efg|fgh|ghi|hij|jkl|klm|lmn|mno|nop|opq|pqr|qrs|rst|stu|tuv|uvw|vwx|wxy|xyz', passwd) is not None and\n all([c not in passwd for c in 'iol']) and\n re.search(r'([a-z])\\1.*([a-z])\\2', passwd) is not None\n )","def check_name(self, name: str):\n if name[0] == \"/\" or self.check_end_streaming(name):\n return True\n else:\n return False","def checkValidClusterName(self):\n p = re.compile('^[a-zA-Z0-9][a-zA-Z0-9_.\\-]*[a-zA-Z0-9]$')\n if len(self.clusterName) < 2 or len(self.clusterName) > 64 or not p.match(self.clusterName):\n raise RuntimeError(\"Invalid cluster name (%s).\"\n \" It must be between 2 and 64 characters and contain only alpha-numeric\"\n \" characters, hyphens, underscores, and periods. It must start and\"\n \" end only with alpha-numeric characters.\" % self.clusterName)","def _is_valid_varname(self, name):\n if name in RESERVED or re.match(r'^str([0-9]+|L)$', name): return False\n return True if VALID_NAME_RE.match(name) else False","def is_valid_string_name(src):\n\n return src[0].isupper()","def isValidTest(self):\n if not self.hasError():\n return False\n distance = dameraulevenshtein(self.word, self.error) \n if(distance > 1):\n return False\n regex = '.*[^a-zA-Z].*'\n if re.match(regex, self.word) or re.match(regex, self.error):\n return False\n return True","def isValidPart(name):\n\tfor n in name_forms:\n\t\tif re.match(n, name.lower()) is not None:\n\t\t\treturn True\n\treturn False","def check_valid_key_name(name):\n if type(name) not in [str]:\n return False\n bad_chars = [\"*\", \".\", \"&&&&\"]\n for k in bad_chars:\n if k in name:\n return False\n return True","def validate_name(name):\n name = name.strip()\n m = re.search('^[a-zA-Z0-9 ]{3,30}$', name)\n if m is None:\n return False\n else:\n return True","def IsValid(self, file_name):\n \n for suffix in self.file_list:\n if file_name.endswith(suffix):\n return True\n return False","def is_named(ucs):\n try:\n return bool(unicodedata.name(ucs))\n except ValueError:\n return False","def validate_name(name: str) -> None:\n\n # Disallow empty.\n if not name:\n raise CleanError('Feature set name cannot be empty.')\n\n # Require starting with a letter.\n if not name[0].isalpha():\n raise CleanError(\n f\"Invalid feature set name '{name}'\"\n ' - names must start with a letter.'\n )\n\n # Require only letters, numbers, and underscores.\n if not name.replace('_', '').isalnum():\n raise CleanError(\n f\"Invalid feature set name '{name}'\"\n ' - only letters, numbers, and underscores are allowed.'\n )\n\n # Require all lowercase.\n if not name.islower():\n raise CleanError(\n f\"Invalid feature set name '{name}'\"\n ' - only lowercase letters are allowed.'\n )\n\n # Disallow leading, trailing, or consecutive underscores.\n # (these will result in a '' in the split results which evals to False)\n if not all(name.split('_')):\n raise CleanError(\n f\"Invalid feature set name '{name}'\"\n ' - leading, trailing, and consecutive underscores are'\n ' not allowed.'\n )","def validate_name(self, username: str) -> bool:\n\t\treturn not self.registry.name_taken(username)","def has_ascii_name(self):\n return self.unpack_word(0x10) & 1 == 1","def _is_file_valid(name: str) -> bool:\n return not name.startswith(\".\")","def has_name(self, name: str):\n name_bytes = str_to_bytes_pad(name, MAX_NAME_LEN)\n res = self.dev.apdu_exchange(0x0e, name_bytes)\n assert len(res) == 1\n assert res[0] in (0, 1)\n return bool(res[0])","def _check_token(self, token):\n token = token.lower()\n check = re.sub(r'((^|\\')([a-z]+))+$', '', token)\n if check == '':\n return True\n return False","def is_absolute_name(name):\n if name is None:\n raise AdasDBError(\"Invalid name '%s'.\" % name)\n return name.startswith(PATH_SEPARATOR)","def chkdbname(dbname):\n ret = re.match('[a-zA-Z][a-zA-Z0-9]{0,43}', dbname) is not None and dbname != 'postgres'\n if not ret:\n warn(\"Invalid dbname: {0}\".format(safestr(dbname)))\n return ret","def is_valid_nick_name(nick):\n if not MIN_NICK_LEN < len(nick) < MAX_NICK_LEN:\n return False\n\n valid_symbols = '\\\\|^`[]()_'\n valid_chars = string.ascii_letters + string.digits + valid_symbols","def is__name__equals__main__(cmp: ast.Compare) -> bool:\n return isinstance(cmp.left, ast.Name) \\\n and cmp.left.id == '__name__' \\\n and len(cmp.ops) == 1 \\\n and isinstance(cmp.ops[0], ast.Eq) \\\n and len(cmp.comparators) == 1 \\\n and _is_str_constant(cmp.comparators[0], '__main__')","def is_reserved_with_trailing_underscore(name):\r\n if name.endswith('_') and not name.endswith('__'):\r\n return is_reserved_name(name[:-1])\r\n return False","def _assert_valid_name(name, container):\n container.file.name_validation(container.directory, name)","def is_suffix(suffix: str, word: str):\n return word.endswith(suffix)","def is_valid_matrix_server_name(string: str) -> bool:\n\n try:\n host, port = parse_server_name(string)\n except ValueError:\n return False\n\n valid_ipv4_addr = isIPAddress(host)\n valid_ipv6_literal = (\n host[0] == \"[\" and host[-1] == \"]\" and isIPv6Address(host[1:-1])\n )\n\n return valid_ipv4_addr or valid_ipv6_literal or is_valid_hostname(host)","def valid_builder(buildername):\n builders = query_builders()\n if buildername in builders:\n LOG.debug(\"Buildername %s is valid.\" % buildername)\n return True\n else:\n LOG.warning(\"Buildername %s is *NOT* valid.\" % buildername)\n LOG.info(\"Check the file we just created builders.txt for \"\n \"a list of valid builders.\")\n with open(path_to_file('builders.txt'), \"wb\") as fd:\n for b in sorted(builders):\n fd.write(b + \"\\n\")\n\n return False","def oss_fuzz_benchmark(fs):\n benchmark_config_contents = yaml.dump(OSS_FUZZ_BENCHMARK_CONFIG)\n benchmark_config_file = os.path.join(utils.ROOT_DIR, 'benchmarks',\n OSS_FUZZ_BENCHMARK_NAME,\n 'benchmark.yaml')\n fs.create_file(benchmark_config_file, contents=benchmark_config_contents)\n return OSS_FUZZ_BENCHMARK_NAME","def is_valid_package_name(package_name):\n return (\n package_name and\n bool(PACKAGE_NAME_RE.match(package_name)) and\n all(c.replace('.', '') != '' for c in package_name.split('/')))","def get_testbench_name(self, tb_type: str) -> str:\n return f'{self._meas_name}_TB_{tb_type}'","def is_data_by_filename(fname):\n return \"Run2017\" in fname","def check_suffix(custom_str: str) -> bool:\r\n\r\n if custom_str.startswith(\"-\"):\r\n return True\r\n if len(custom_str) < 4:\r\n custom_str = custom_str.lower()\r\n for c in ASCII_LOWER:\r\n if c in custom_str:\r\n return True\r\n return False","def check_dog_name(dog):\n if not isinstance(dog.name, str):\n raise NotStringError(\"Dog name entered is not a string\")","def check_filename(basename):\n return len(basename) <= MAXIMUM_FILENAME_LENGTH","def cfcheck_from_name(varname, vardata):\n data = VARIABLES[varname]\n if \"cell_methods\" in data:\n check_valid(\n vardata, \"cell_methods\", parse_cell_methods(data[\"cell_methods\"]) + \"*\"\n )\n if \"standard_name\" in data:\n check_valid(vardata, \"standard_name\", data[\"standard_name\"])","def is_real_name(name):\n return name.strip(\"<> \") in names_set","def is_real_name(name):\n return name.strip(\"<> \") in names_set","def is_valid_mac(address):\n m = \"[0-9a-f]{2}([-:])[0-9a-f]{2}(\\\\1[0-9a-f]{2}){4}$\"\n if isinstance(address, six.string_types) and re.match(m, address.lower()):\n return True\n return False","def has_appname(appname):\n return appname in Registry.monomers","def validateName(name):\r\n if not name:\r\n raise IllegalName('Name can not be an empty string.')\r\n\r\n m = _NAME_RE.match(name)\r\n\r\n if m is None or m.group(0) != name:\r\n raise IllegalName('Name has to start with a letter followed by an '\r\n 'arbitrary number of alphanumeric characters or '\r\n 'underscores.')","def check_for_token(token):\n try:\n decode_token(token)\n return True\n except:\n return False","def _pattern_is_simple(pattern):\n return bool(re.match('[\\\\w_]+$', tostring(pattern)))"],"string":"[\n \"def validate(benchmark):\\n if not validate_name(benchmark):\\n return False\\n\\n if benchmark not in get_all_benchmarks():\\n logs.error('%s must have a benchmark.yaml.', benchmark)\\n return False\\n\\n # Validate config file can be parsed.\\n try:\\n get_fuzz_target(benchmark)\\n except yaml.parser.ParserError:\\n logs.error('%s must have a valid benchmark.yaml file. Failed to parse.',\\n benchmark)\\n return False\\n except KeyError:\\n logs.error('%s\\\\'s benchmark.yaml does not define \\\"fuzz_target\\\".',\\n benchmark)\\n return False\\n\\n # Validate type.\\n return validate_type(benchmark)\",\n \"def validate_type(benchmark):\\n benchmark_type = get_type(benchmark)\\n if benchmark_type not in BENCHMARK_TYPE_STRS:\\n logs.error('%s has an invalid benchmark type %s, must be one of %s',\\n benchmark, benchmark_type, BENCHMARK_TYPE_STRS)\\n return False\\n return True\",\n \"def _is_valid_keyspace_name(self, keyspace_name):\\n if keyspace_name == None or not keyspace_name:\\n return False\\n return re.match(r\\\"^[a-z_]*[^-]$\\\", keyspace_name)\",\n \"def valid_routine_name(routine):\\n\\treturn re.match('^[a-z_]([a-z0-9_]*)', routine) is not None\",\n \"def validName(configsetname):\\n for c in configsetname:\\n if not c in string.letters+string.digits+\\\"$_-\\\":\\n return False\\n return configsetname != \\\"\\\"\",\n \"def is_dev_name_valid(self):\\n return self._name_re.match(self.dev_name) is not None\",\n \"def is_valid(name):\\n return bool(name)\",\n \"def validname(name):\\r\\n return len(name)>0 and (\\r\\n Context.__invalid_character.search(name) is None)\",\n \"def get_benchmark(self, benchmark):\\n\\t\\tif not isinstance(benchmark, str) and not callable(benchmark): return benchmark\\n\\t\\telif benchmark in self.classes:\\treturn self.classes[benchmark]()\\n\\t\\traise TypeError('Passed benchmark is not defined!')\",\n \"def isValidDataTypeName(name: unicode) -> bool:\\n ...\",\n \"def verify_name(name):\\n try:\\n if name.index(' '):\\n return False\\n except ValueError:\\n return True\",\n \"def is_bank_name_valid(name_to_check: str):\\n def is_name_short_enough():\\n return True if len(name_to_check) <= 12 else False\\n\\n def is_name_only_letter():\\n return True if name_to_check.isalpha() else False\\n\\n return True if is_name_short_enough() and is_name_only_letter() else False\",\n \"def is_valid_name(self):\\n\\n if self.whitelist_name == '':\\n return True\\n\\n if len(self.whitelist_name) >= 64:\\n LOGGER.debug('invalid name %s; must be less than 64 bytes',\\n self.whitelist_name)\\n return False\\n\\n return True\",\n \"def is_builtin_name(name):\\r\\n if name.startswith('__') and name.endswith('__'):\\r\\n return ALL_LOWER_CASE_RE.match(name[2:-2]) is not None\\r\\n return False\",\n \"def _is_fan(compressor: Compressor) -> bool:\\n name = compressor.name.lower()\\n return 'fan' in name or 'crtf' in name\",\n \"def get_fuzzer_benchmark_key(fuzzer: str, benchmark: str):\\n return fuzzer + ' ' + benchmark\",\n \"def _validate_name(name):\\r\\n\\tif HOST_NAME != name and len(name) > 0 and ZOOM_PHRASES[0] not in name and name not in WAITING_ROOM:\\r\\n\\t\\treturn True\\r\\n\\treturn False\",\n \"def name_valid(name):\\n return name.isalpha()\",\n \"def is_mbid(mbid):\\n try:\\n mbid = uuid.UUID(mbid)\\n good = True\\n except ValueError as e:\\n good = False\\n except AttributeError:\\n good = False\\n\\n return good\",\n \"def verify_name(name):\\n if name and not name.isspace(): # if it's not empty/NULL and it's not whitespace\\n return True\\n else:\\n return False\",\n \"def _check_if_valid_dataset_name(dataset_name: str) -> str:\\n if not re.match(r\\\"^[A-Za-z0-9_]+$\\\", dataset_name):\\n raise ExecutionEngineError(\\n f\\\"dataset_name: {dataset_name} is not valid, because it contains non-alphanumeric and _ characters.\\\"\\n f\\\"Please check your configuration.\\\"\\n )\\n\\n if len(dataset_name) >= MAX_TABLE_NAME_LENGTH:\\n # starting from the end, so that we always get the index and sub_index\\n new_dataset_name = dataset_name[-MAX_TABLE_NAME_LENGTH:]\\n logger.info(\\n f\\\"dataset_name: '{dataset_name}' was truncated to '{new_dataset_name}' to keep within length limits.\\\"\\n )\\n dataset_name = new_dataset_name\\n\\n while not re.match(r\\\"^[A-Za-z]+$\\\", dataset_name[0]):\\n dataset_name = dataset_name[1:]\\n\\n return dataset_name\",\n \"def create_benchmark(self, benchmark):\\n self.crd_client.create_namespaced_custom_object(\\n group=\\\"ripsaw.cloudbulldozer.io\\\",\\n version=\\\"v1alpha1\\\",\\n namespace=benchmark[\\\"metadata\\\"][\\\"namespace\\\"],\\n plural=\\\"benchmarks\\\",\\n body=benchmark,\\n )\",\n \"def is_valid_business_name(self):\\n return self.business_name.lower() not in INVALID_BUSINESS_NAME\",\n \"def has_name(self):\\n return self.unpack_word(0x2) != 0\",\n \"def is_valid_cname(common_name: str) -> bool:\\n return True if Band.band_range(common_name) else False\",\n \"def full_name(self, test_name: str) -> bool:\\n if not self._full_test_name_patterns:\\n return True\\n\\n if self._compiled_test_name_pattern is None:\\n self._compiled_test_name_pattern = re.compile('|'.join(self._full_test_name_patterns))\\n\\n return self._compiled_test_name_pattern.match(test_name)\",\n \"def IsValidFacename(*args, **kwargs):\\n return _gdi_.FontEnumerator_IsValidFacename(*args, **kwargs)\",\n \"def is_basic_name(name):\\n if name is None:\\n raise AdasDBError(\\\"Invalid name '%s'.\\\" % name)\\n return name.find(PATH_SEPARATOR)\",\n \"def _check_name(self, symbol):\\n if symbol.type == self.scanner.NAME:\\n return True\\n else:\\n return False\",\n \"def get_fuzz_target(benchmark):\\n # Do this because of OSS-Fuzz-on-demand.\\n # TODO(metzman): Use classes to mock a benchmark config for\\n # OSS_FUZZ_ON_DEMAND.\\n return benchmark_config.get_config(benchmark).get(\\n 'fuzz_target', environment.get('FUZZ_TARGET'))\",\n \"def names_are_equal(filesystem_name, fixture_name):\\n if filesystem_safe(filesystem_name) == fixture_name:\\n return True\\n return False\",\n \"def legal_name(name, is_param_name=False):\\n if name.startswith('_'):\\n return False\\n\\n if name in ('self',):\\n return False\\n\\n if keyword.iskeyword(name):\\n return False\\n\\n regex = r'^[a-zA-Z][a-zA-Z0-9_]*$' if is_param_name else (\\n r'^[a-zA-Z][.\\\\w-]*$')\\n return bool(re.match(regex, name))\",\n \"def validate_team_name(name):\\n if not re.match('^[A-Za-z0-9_]*$', name):\\n print('INVALID NAME. LETTERS, NUMBERS AND UNDERSCORES ONLY')\\n return False\\n elif len(name) > 10:\\n print('INVALID NAME. 10 CHARACTERS MAX')\\n return False\\n elif len(name) == 0:\\n print('INVALID NAME. NOT LONG ENOUGH')\\n else:\\n return True\",\n \"def IsValidHotlistName(s):\\n return (RE_HOTLIST_NAME.match(s) and\\n len(s) <= framework_constants.MAX_HOTLIST_NAME_LENGTH)\",\n \"def FontEnumerator_IsValidFacename(*args, **kwargs):\\n return _gdi_.FontEnumerator_IsValidFacename(*args, **kwargs)\",\n \"def validName(varname):\\r\\n if (len(varname[0])>32):\\r\\n return False\\r\\n if not(varname[0][0].isalpha()):\\r\\n return False \\r\\n for ch in varname[0][1:]:\\r\\n if not(ch.isalpha() or ch.isdigit() or ch=='_'):\\r\\n return False\\r\\n \\r\\n return True\",\n \"def validate_name(name:str) -> bool:\\r\\n return name.isalpha() and name.count(\\\" \\\") == 0 and len(name) >= 2\",\n \"def is_result_appropriate(self, benchmark_result):\\n return True\",\n \"def _is_valid_adapter(self, adapter_name):\\n\\n valid_adapters = self.valid_adapters\\n\\n result = False\\n\\n if adapter_name in valid_adapters:\\n\\n result = True\\n\\n return result\",\n \"def is_valid_collection_name(collection_name):\\n\\n collection_name = to_text(collection_name)\\n\\n return bool(re.match(AnsibleCollectionRef.VALID_COLLECTION_NAME_RE, collection_name))\",\n \"def is_valid_attribute_name(self, name):\\n try:\\n self.validate_attribute_name(name)\\n return True\\n except etal.LabelsSchemaError:\\n return False\",\n \"def is_cpp(benchmark):\\n return get_language(benchmark) == 'c++'\",\n \"def _check_is_name_valid(self, name):\\n if name in self.forbidden_names or name.endswith(\\n self.forbidden_extensions) or self.__check_is_match_regex(name):\\n return False\\n return True\",\n \"def check_name(name, allow_services=False):\",\n \"def check_banned(deck_format, card_name):\\n if card_name in consts.BANNINGS[deck_format]:\\n return True\\n return False\",\n \"def is_simple_name(s):\\n\\n assert utils.is_string_type(s)\\n assert len(s) > 0\\n\\n def valid_first(c):\\n return c.isalpha() or c == \\\"_\\\"\\n def valid_later(c):\\n return c.isalnum() or c == \\\"_\\\"\\n return valid_first(s[0]) and all(valid_later(c) for c in s)\",\n \"def invalid_name(name):\\n if any(not item.isalpha() for item in str(name)):\\n return True\\n return False\",\n \"def __isValidColor(self, name):\\n try:\\n if self.__isHexString(name) and len(name) in [3, 6, 9, 12]:\\n return True\\n return QColor.isValidColor(name)\\n except AttributeError:\\n if name.startswith(\\\"#\\\"):\\n if len(name) not in [4, 7, 10, 13]:\\n return False\\n hexCheckStr = name[1:]\\n return self.__isHexString(hexCheckStr)\\n else:\\n if self.__isHexString(name) and len(name) in [3, 6, 9, 12]:\\n return True\\n return name in QColor.colorNames()\",\n \"def valid_hook(hook_file, hook_name):\\n filename = os.path.basename(hook_file)\\n basename = os.path.splitext(filename)[0]\\n\\n matching_hook = basename == hook_name\\n supported_hook = basename in _HOOKS\\n backup_file = filename.endswith('~')\\n\\n return matching_hook and supported_hook and not backup_file\",\n \"def is_valid_flag(flag):\\n return False if re.match(\\\"^FAUST_[A-Za-z0-9/\\\\\\\\+]{32}$\\\", flag) is None else True\",\n \"def test_create_with_only_name(self):\\n with OrionState() as cfg:\\n name = \\\"bm00001\\\"\\n with pytest.raises(NoConfigurationError) as exc:\\n get_or_create_benchmark(cfg.storage, name).close()\\n\\n assert f\\\"Benchmark {name} does not exist in DB\\\" in str(exc.value)\",\n \"def is_valid_channel_name(channel):\\n if not is_channel_name(channel):\\n return False\\n\\n test_section = channel[1:]\\n\\n if not MIN_CHANNEL_NAME_LEN < len(channel) < MAX_CHANNEL_NAME_LEN:\\n return False\\n\\n valid_symbols = '#\\\\\\\\|^`[]{}_'\\n valid_chars = string.ascii_letters + string.digits + valid_symbols\\n\\n for char in channel:\\n if char not in valid_chars:\\n return False\",\n \"def is_shortcut_name(name: str) -> bool:\\n return name.count(config.name_separator) == 1\",\n \"def _check_key_name(cls, name):\\n return (isinstance(name, basestring) and\\n re.match('^[A-Za-z][A-Za-z0-9_]*$', name) and\\n not hasattr(cls, name))\",\n \"def test_is_valid_color_name(self):\\n self.assertTrue(is_valid_color_name('black'))\\n self.assertTrue(is_valid_color_name('red'))\\n self.assertFalse(is_valid_color_name('#aabb11'))\\n self.assertFalse(is_valid_color_name('bl(ack'))\",\n \"def validate_names(name):\\n return isinstance(name, str) and not re.search(r'[\\\\s]', name)\",\n \"def _is_valid_varname(self, name):\\n if name in RESERVED or re.match(r'^str[0-9]+$', name): return False\\n return True if VALID_NAME_RE.match(name) else False\",\n \"def delete_benchmark(self, name, namespace=\\\"benchmark-operator\\\"):\\n logger.info(f\\\"Deleting benchmark {name} in namespace {namespace}\\\")\\n self.crd_client.delete_namespaced_custom_object(\\n group=\\\"ripsaw.cloudbulldozer.io\\\",\\n version=\\\"v1alpha1\\\",\\n namespace=namespace,\\n plural=\\\"benchmarks\\\",\\n name=name,\\n )\\n logger.info(f\\\"Deleted benchmark {name} in namespace {namespace}\\\")\",\n \"def get_type(benchmark):\\n # TODO(metzman): Use classes to mock a benchmark config for\\n # OSS_FUZZ_ON_DEMAND.\\n default_value = os.getenv('EXPERIMENT_TYPE', BenchmarkType.CODE.value)\\n return benchmark_config.get_config(benchmark).get('type', default_value)\",\n \"def is_valid(passwd: str) -> bool:\\n return (\\n re.search(r'abc|bcd|cde|def|efg|fgh|ghi|hij|jkl|klm|lmn|mno|nop|opq|pqr|qrs|rst|stu|tuv|uvw|vwx|wxy|xyz', passwd) is not None and\\n all([c not in passwd for c in 'iol']) and\\n re.search(r'([a-z])\\\\1.*([a-z])\\\\2', passwd) is not None\\n )\",\n \"def check_name(self, name: str):\\n if name[0] == \\\"/\\\" or self.check_end_streaming(name):\\n return True\\n else:\\n return False\",\n \"def checkValidClusterName(self):\\n p = re.compile('^[a-zA-Z0-9][a-zA-Z0-9_.\\\\-]*[a-zA-Z0-9]$')\\n if len(self.clusterName) < 2 or len(self.clusterName) > 64 or not p.match(self.clusterName):\\n raise RuntimeError(\\\"Invalid cluster name (%s).\\\"\\n \\\" It must be between 2 and 64 characters and contain only alpha-numeric\\\"\\n \\\" characters, hyphens, underscores, and periods. It must start and\\\"\\n \\\" end only with alpha-numeric characters.\\\" % self.clusterName)\",\n \"def _is_valid_varname(self, name):\\n if name in RESERVED or re.match(r'^str([0-9]+|L)$', name): return False\\n return True if VALID_NAME_RE.match(name) else False\",\n \"def is_valid_string_name(src):\\n\\n return src[0].isupper()\",\n \"def isValidTest(self):\\n if not self.hasError():\\n return False\\n distance = dameraulevenshtein(self.word, self.error) \\n if(distance > 1):\\n return False\\n regex = '.*[^a-zA-Z].*'\\n if re.match(regex, self.word) or re.match(regex, self.error):\\n return False\\n return True\",\n \"def isValidPart(name):\\n\\tfor n in name_forms:\\n\\t\\tif re.match(n, name.lower()) is not None:\\n\\t\\t\\treturn True\\n\\treturn False\",\n \"def check_valid_key_name(name):\\n if type(name) not in [str]:\\n return False\\n bad_chars = [\\\"*\\\", \\\".\\\", \\\"&&&&\\\"]\\n for k in bad_chars:\\n if k in name:\\n return False\\n return True\",\n \"def validate_name(name):\\n name = name.strip()\\n m = re.search('^[a-zA-Z0-9 ]{3,30}$', name)\\n if m is None:\\n return False\\n else:\\n return True\",\n \"def IsValid(self, file_name):\\n \\n for suffix in self.file_list:\\n if file_name.endswith(suffix):\\n return True\\n return False\",\n \"def is_named(ucs):\\n try:\\n return bool(unicodedata.name(ucs))\\n except ValueError:\\n return False\",\n \"def validate_name(name: str) -> None:\\n\\n # Disallow empty.\\n if not name:\\n raise CleanError('Feature set name cannot be empty.')\\n\\n # Require starting with a letter.\\n if not name[0].isalpha():\\n raise CleanError(\\n f\\\"Invalid feature set name '{name}'\\\"\\n ' - names must start with a letter.'\\n )\\n\\n # Require only letters, numbers, and underscores.\\n if not name.replace('_', '').isalnum():\\n raise CleanError(\\n f\\\"Invalid feature set name '{name}'\\\"\\n ' - only letters, numbers, and underscores are allowed.'\\n )\\n\\n # Require all lowercase.\\n if not name.islower():\\n raise CleanError(\\n f\\\"Invalid feature set name '{name}'\\\"\\n ' - only lowercase letters are allowed.'\\n )\\n\\n # Disallow leading, trailing, or consecutive underscores.\\n # (these will result in a '' in the split results which evals to False)\\n if not all(name.split('_')):\\n raise CleanError(\\n f\\\"Invalid feature set name '{name}'\\\"\\n ' - leading, trailing, and consecutive underscores are'\\n ' not allowed.'\\n )\",\n \"def validate_name(self, username: str) -> bool:\\n\\t\\treturn not self.registry.name_taken(username)\",\n \"def has_ascii_name(self):\\n return self.unpack_word(0x10) & 1 == 1\",\n \"def _is_file_valid(name: str) -> bool:\\n return not name.startswith(\\\".\\\")\",\n \"def has_name(self, name: str):\\n name_bytes = str_to_bytes_pad(name, MAX_NAME_LEN)\\n res = self.dev.apdu_exchange(0x0e, name_bytes)\\n assert len(res) == 1\\n assert res[0] in (0, 1)\\n return bool(res[0])\",\n \"def _check_token(self, token):\\n token = token.lower()\\n check = re.sub(r'((^|\\\\')([a-z]+))+$', '', token)\\n if check == '':\\n return True\\n return False\",\n \"def is_absolute_name(name):\\n if name is None:\\n raise AdasDBError(\\\"Invalid name '%s'.\\\" % name)\\n return name.startswith(PATH_SEPARATOR)\",\n \"def chkdbname(dbname):\\n ret = re.match('[a-zA-Z][a-zA-Z0-9]{0,43}', dbname) is not None and dbname != 'postgres'\\n if not ret:\\n warn(\\\"Invalid dbname: {0}\\\".format(safestr(dbname)))\\n return ret\",\n \"def is_valid_nick_name(nick):\\n if not MIN_NICK_LEN < len(nick) < MAX_NICK_LEN:\\n return False\\n\\n valid_symbols = '\\\\\\\\|^`[]()_'\\n valid_chars = string.ascii_letters + string.digits + valid_symbols\",\n \"def is__name__equals__main__(cmp: ast.Compare) -> bool:\\n return isinstance(cmp.left, ast.Name) \\\\\\n and cmp.left.id == '__name__' \\\\\\n and len(cmp.ops) == 1 \\\\\\n and isinstance(cmp.ops[0], ast.Eq) \\\\\\n and len(cmp.comparators) == 1 \\\\\\n and _is_str_constant(cmp.comparators[0], '__main__')\",\n \"def is_reserved_with_trailing_underscore(name):\\r\\n if name.endswith('_') and not name.endswith('__'):\\r\\n return is_reserved_name(name[:-1])\\r\\n return False\",\n \"def _assert_valid_name(name, container):\\n container.file.name_validation(container.directory, name)\",\n \"def is_suffix(suffix: str, word: str):\\n return word.endswith(suffix)\",\n \"def is_valid_matrix_server_name(string: str) -> bool:\\n\\n try:\\n host, port = parse_server_name(string)\\n except ValueError:\\n return False\\n\\n valid_ipv4_addr = isIPAddress(host)\\n valid_ipv6_literal = (\\n host[0] == \\\"[\\\" and host[-1] == \\\"]\\\" and isIPv6Address(host[1:-1])\\n )\\n\\n return valid_ipv4_addr or valid_ipv6_literal or is_valid_hostname(host)\",\n \"def valid_builder(buildername):\\n builders = query_builders()\\n if buildername in builders:\\n LOG.debug(\\\"Buildername %s is valid.\\\" % buildername)\\n return True\\n else:\\n LOG.warning(\\\"Buildername %s is *NOT* valid.\\\" % buildername)\\n LOG.info(\\\"Check the file we just created builders.txt for \\\"\\n \\\"a list of valid builders.\\\")\\n with open(path_to_file('builders.txt'), \\\"wb\\\") as fd:\\n for b in sorted(builders):\\n fd.write(b + \\\"\\\\n\\\")\\n\\n return False\",\n \"def oss_fuzz_benchmark(fs):\\n benchmark_config_contents = yaml.dump(OSS_FUZZ_BENCHMARK_CONFIG)\\n benchmark_config_file = os.path.join(utils.ROOT_DIR, 'benchmarks',\\n OSS_FUZZ_BENCHMARK_NAME,\\n 'benchmark.yaml')\\n fs.create_file(benchmark_config_file, contents=benchmark_config_contents)\\n return OSS_FUZZ_BENCHMARK_NAME\",\n \"def is_valid_package_name(package_name):\\n return (\\n package_name and\\n bool(PACKAGE_NAME_RE.match(package_name)) and\\n all(c.replace('.', '') != '' for c in package_name.split('/')))\",\n \"def get_testbench_name(self, tb_type: str) -> str:\\n return f'{self._meas_name}_TB_{tb_type}'\",\n \"def is_data_by_filename(fname):\\n return \\\"Run2017\\\" in fname\",\n \"def check_suffix(custom_str: str) -> bool:\\r\\n\\r\\n if custom_str.startswith(\\\"-\\\"):\\r\\n return True\\r\\n if len(custom_str) < 4:\\r\\n custom_str = custom_str.lower()\\r\\n for c in ASCII_LOWER:\\r\\n if c in custom_str:\\r\\n return True\\r\\n return False\",\n \"def check_dog_name(dog):\\n if not isinstance(dog.name, str):\\n raise NotStringError(\\\"Dog name entered is not a string\\\")\",\n \"def check_filename(basename):\\n return len(basename) <= MAXIMUM_FILENAME_LENGTH\",\n \"def cfcheck_from_name(varname, vardata):\\n data = VARIABLES[varname]\\n if \\\"cell_methods\\\" in data:\\n check_valid(\\n vardata, \\\"cell_methods\\\", parse_cell_methods(data[\\\"cell_methods\\\"]) + \\\"*\\\"\\n )\\n if \\\"standard_name\\\" in data:\\n check_valid(vardata, \\\"standard_name\\\", data[\\\"standard_name\\\"])\",\n \"def is_real_name(name):\\n return name.strip(\\\"<> \\\") in names_set\",\n \"def is_real_name(name):\\n return name.strip(\\\"<> \\\") in names_set\",\n \"def is_valid_mac(address):\\n m = \\\"[0-9a-f]{2}([-:])[0-9a-f]{2}(\\\\\\\\1[0-9a-f]{2}){4}$\\\"\\n if isinstance(address, six.string_types) and re.match(m, address.lower()):\\n return True\\n return False\",\n \"def has_appname(appname):\\n return appname in Registry.monomers\",\n \"def validateName(name):\\r\\n if not name:\\r\\n raise IllegalName('Name can not be an empty string.')\\r\\n\\r\\n m = _NAME_RE.match(name)\\r\\n\\r\\n if m is None or m.group(0) != name:\\r\\n raise IllegalName('Name has to start with a letter followed by an '\\r\\n 'arbitrary number of alphanumeric characters or '\\r\\n 'underscores.')\",\n \"def check_for_token(token):\\n try:\\n decode_token(token)\\n return True\\n except:\\n return False\",\n \"def _pattern_is_simple(pattern):\\n return bool(re.match('[\\\\\\\\w_]+$', tostring(pattern)))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.75245595","0.70732296","0.5810576","0.5674972","0.5586452","0.55509335","0.55345166","0.5492754","0.54216146","0.5396599","0.533939","0.52890986","0.52809453","0.5276064","0.5262049","0.5236118","0.5226639","0.51745903","0.51519513","0.51472944","0.50536746","0.50375664","0.5028737","0.5014385","0.49864894","0.49848303","0.49750835","0.49659556","0.49601093","0.49598923","0.49561048","0.49484056","0.4939743","0.49249965","0.4915993","0.49021798","0.48830473","0.48647225","0.48343983","0.48301688","0.48132458","0.4779493","0.47779435","0.47766635","0.47748142","0.4762608","0.47574773","0.47383147","0.47343922","0.47317296","0.47282133","0.47256237","0.472344","0.4710279","0.4706986","0.4697322","0.46908733","0.46859723","0.46831945","0.46713862","0.46526778","0.46491063","0.46471432","0.464561","0.4642485","0.46416268","0.4626064","0.46249184","0.4616252","0.46085182","0.4598539","0.45983577","0.45911288","0.45868447","0.45801184","0.45784223","0.45713052","0.45647633","0.45616165","0.4558327","0.4556391","0.4554088","0.45502117","0.4545277","0.45442095","0.45441407","0.45424974","0.45386556","0.45338443","0.4523813","0.4523658","0.45179358","0.45167813","0.45133796","0.45133796","0.45094863","0.4508718","0.4508526","0.45053884","0.44907779"],"string":"[\n \"0.75245595\",\n \"0.70732296\",\n \"0.5810576\",\n \"0.5674972\",\n \"0.5586452\",\n \"0.55509335\",\n \"0.55345166\",\n \"0.5492754\",\n \"0.54216146\",\n \"0.5396599\",\n \"0.533939\",\n \"0.52890986\",\n \"0.52809453\",\n \"0.5276064\",\n \"0.5262049\",\n \"0.5236118\",\n \"0.5226639\",\n \"0.51745903\",\n \"0.51519513\",\n \"0.51472944\",\n \"0.50536746\",\n \"0.50375664\",\n \"0.5028737\",\n \"0.5014385\",\n \"0.49864894\",\n \"0.49848303\",\n \"0.49750835\",\n \"0.49659556\",\n \"0.49601093\",\n \"0.49598923\",\n \"0.49561048\",\n \"0.49484056\",\n \"0.4939743\",\n \"0.49249965\",\n \"0.4915993\",\n \"0.49021798\",\n \"0.48830473\",\n \"0.48647225\",\n \"0.48343983\",\n \"0.48301688\",\n \"0.48132458\",\n \"0.4779493\",\n \"0.47779435\",\n \"0.47766635\",\n \"0.47748142\",\n \"0.4762608\",\n \"0.47574773\",\n \"0.47383147\",\n \"0.47343922\",\n \"0.47317296\",\n \"0.47282133\",\n \"0.47256237\",\n \"0.472344\",\n \"0.4710279\",\n \"0.4706986\",\n \"0.4697322\",\n \"0.46908733\",\n \"0.46859723\",\n \"0.46831945\",\n \"0.46713862\",\n \"0.46526778\",\n \"0.46491063\",\n \"0.46471432\",\n \"0.464561\",\n \"0.4642485\",\n \"0.46416268\",\n \"0.4626064\",\n \"0.46249184\",\n \"0.4616252\",\n \"0.46085182\",\n \"0.4598539\",\n \"0.45983577\",\n \"0.45911288\",\n \"0.45868447\",\n \"0.45801184\",\n \"0.45784223\",\n \"0.45713052\",\n \"0.45647633\",\n \"0.45616165\",\n \"0.4558327\",\n \"0.4556391\",\n \"0.4554088\",\n \"0.45502117\",\n \"0.4545277\",\n \"0.45442095\",\n \"0.45441407\",\n \"0.45424974\",\n \"0.45386556\",\n \"0.45338443\",\n \"0.4523813\",\n \"0.4523658\",\n \"0.45179358\",\n \"0.45167813\",\n \"0.45133796\",\n \"0.45133796\",\n \"0.45094863\",\n \"0.4508718\",\n \"0.4508526\",\n \"0.45053884\",\n \"0.44907779\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.816597"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":11268,"cells":{"query":{"kind":"string","value":"Returns True if |benchmark| has a valid type."},"document":{"kind":"string","value":"def validate_type(benchmark):\n benchmark_type = get_type(benchmark)\n if benchmark_type not in BENCHMARK_TYPE_STRS:\n logs.error('%s has an invalid benchmark type %s, must be one of %s',\n benchmark, benchmark_type, BENCHMARK_TYPE_STRS)\n return False\n return True"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def validate(benchmark):\n if not validate_name(benchmark):\n return False\n\n if benchmark not in get_all_benchmarks():\n logs.error('%s must have a benchmark.yaml.', benchmark)\n return False\n\n # Validate config file can be parsed.\n try:\n get_fuzz_target(benchmark)\n except yaml.parser.ParserError:\n logs.error('%s must have a valid benchmark.yaml file. Failed to parse.',\n benchmark)\n return False\n except KeyError:\n logs.error('%s\\'s benchmark.yaml does not define \"fuzz_target\".',\n benchmark)\n return False\n\n # Validate type.\n return validate_type(benchmark)","def is_valid_type(type):\n return type in type_to_adapter","def is_valid_type(self, question_type):\n\t\treturn question_type in self.valid_types","def is_valid_type(self, attr: Optional[str] = None) -> bool:\n try:\n self.validate_type(attr)\n except TypeError:\n return False\n return True","def check_type(self):\n return True","def validate_name(benchmark):\n if VALID_BENCHMARK_REGEX.match(benchmark) is None:\n logs.error('%s does not conform to %s pattern.', benchmark,\n VALID_BENCHMARK_REGEX.pattern)\n return False\n return True","def is_type(self, type_name):\n\n return type_name in self._symtab","def verify_type(self, obj):\n return isinstance(obj, self.type_)","def _is_run_type(cls, object_):\n # Do a string comparison instead of using isinstance() to avoid needing\n # to import lyse or other modules with these classes.\n return (type(object_).__name__ in cls._RUN_TYPES)","def test_has_exactly_type():\r\n\r\n return has_exactly_type(1, int) and not has_exactly_type(True, int) and has_exactly_type(True, bool)","def _is_valid_type(_type: Type[Any]) -> bool:\n\n if _type in _TYPE_MAP:\n return True\n\n if not inspect.isclass(_type):\n return False\n\n return issubclass(_type, Table)","def get_type(benchmark):\n # TODO(metzman): Use classes to mock a benchmark config for\n # OSS_FUZZ_ON_DEMAND.\n default_value = os.getenv('EXPERIMENT_TYPE', BenchmarkType.CODE.value)\n return benchmark_config.get_config(benchmark).get('type', default_value)","def is_supported_type(self) -> bool:\n t = self.type.strip()\n return t in self.SUPPORTED_LABELS or t.lower() in self.SUPPORTED_LABELS","def check_frame_type(self, ftype, fitstbl, exprng=None):\n good_exp = framematch.check_frame_exptime(fitstbl['exptime'], exprng)\n if ftype in ['science', 'standard']:\n return good_exp & (np.logical_not(np.char.startswith(np.char.lower(fitstbl['target']), 'arclamp'))) & \\\n (np.char.lower(fitstbl['target']) != 'spectralflat') & \\\n (np.char.lower(fitstbl['target']) != 'bias')\n if ftype in ['arc', 'tilt']:\n return good_exp & (np.char.startswith(np.char.lower(fitstbl['target']), 'arclamp'))\n if ftype in ['pixelflat', 'trace', 'illumflat']:\n return good_exp & (np.char.lower(fitstbl['target']) == 'spectralflat')\n if ftype == 'bias':\n return good_exp & (np.char.lower(fitstbl['target']) == 'bias')\n\n msgs.warn('Cannot determine if frames are of type {0}.'.format(ftype))\n return np.zeros(len(fitstbl), dtype=bool)","def _type_check(data):\n if data.__class__.__name__ != \"Matrix3\":\n return False\n return True","def CheckType(self, *args, **kwargs):\n pass","def is_basic_type(self, objtype):\n if not hasattr(objtype, '_type_'):\n # could be python types\n return objtype in [int, long, float, bool]\n return self.is_basic_ctype(objtype)","def is_result_appropriate(self, benchmark_result):\n return True","def is_valid(self, attribute: Attribute) -> bool:\n return self.get_data_type() == attribute.type","def isquantized(val):\n # try: 1.1 * val ... except: might be more general, but could be expensive.\n retval = True\n if isinstance(val, (float, complex)):\n retval = False\n elif hasattr(val, 'issubdtype'):\n if numpy.issubdtype(val.dtype, float) or numpy.issubdtype(val.dtype, complex):\n retval = False\n return retval","def check_frame_type(self, ftype, fitstbl, exprng=None):\n good_exp = framematch.check_frame_exptime(fitstbl['exptime'], exprng)\n if ftype in ['science', 'standard']:\n return good_exp & (fitstbl['idname'] == 'OBJECT')\n if ftype == 'bias':\n return good_exp & (fitstbl['idname'] == 'BIAS')\n if ftype in ['pixelflat', 'trace', 'illumflat']:\n return good_exp & (fitstbl['idname'] == 'FLAT,LAMP')\n if ftype in ['pinhole', 'dark']:\n # Don't type pinhole or dark frames\n return np.zeros(len(fitstbl), dtype=bool)\n if ftype in ['arc','tilt']:\n return good_exp & (fitstbl['idname'] == 'WAVE,LAMP')\n msgs.warn('Cannot determine if frames are of type {0}.'.format(ftype))\n return np.zeros(len(fitstbl), dtype=bool)","def _valid_typable_object(ui_object, platform=Platform.ANDROID):\n if platform == Platform.ANDROID:\n return ui_object.obj_type in _TYPABLE_OBJECT_DESC.keys()\n else:\n assert False, 'Wrong Platform'","def checkType(self, value):\n pass","def check_frame_type(self, ftype, fitstbl, exprng=None):\n good_exp = framematch.check_frame_exptime(fitstbl['exptime'], exprng)\n # TODO: Allow for 'sky' frame type, for now include sky in\n # 'science' category\n if ftype == 'science':\n return good_exp & (fitstbl['idname'] == 'Object')\n if ftype == 'standard':\n return good_exp & (fitstbl['idname'] == 'Object')\n if ftype == 'bias':\n return good_exp & (fitstbl['idname'] == 'Bias')\n if ftype == 'dark':\n return good_exp & (fitstbl['idname'] == 'Dark')\n if ftype in ['pixelflat', 'trace']:\n # Flats and trace frames are typed together\n return good_exp & (fitstbl['idname'] == 'IntFlat')\n if ftype in ['arc', 'tilt']:\n # Arc and tilt frames are typed together\n return good_exp & (fitstbl['idname'] == 'Line')\n\n msgs.warn('Cannot determine if frames are of type {0}.'.format(ftype))\n return np.zeros(len(fitstbl), dtype=bool)","def is_valid_silktype(type_name, permit_array=False):\n if not type_name.replace(\"_\", \"x\").isalnum():\n return False\n\n if not type_name[0].isupper():\n return False\n\n if len(type_name) > 1 and type_name == type_name.upper():\n return False\n\n if permit_array:\n array_depth = 0\n while type_name.endswith(\"Array\"):\n type_name = type_name[:-len(\"Array\")]\n array_depth += 1\n\n if array_depth > max_array_depth:\n return False\n\n elif type_name.endswith(\"Array\"):\n return False\n\n if type_name in reserved_types:\n return False\n\n for ending in reserved_endings:\n if type_name.endswith(ending):\n return False\n return True","def check_frame_type(self, ftype, fitstbl, exprng=None):\n good_exp = framematch.check_frame_exptime(fitstbl['exptime'], exprng)\n if ftype in ['science','standard']:\n return good_exp & (fitstbl['target'] != 'ArcLamp_Xe') \\\n & (fitstbl['target'] != 'ArcLamp_HgAr') \\\n & (fitstbl['target'] != 'ArcLamp_Ne') \\\n & (fitstbl['target'] != 'SpectralFlat') \\\n & (fitstbl['target'] != 'BIAS')\n if ftype in ['arc', 'tilt']:\n return good_exp & ((fitstbl['target'] == 'ArcLamp_Xe') \\\n | (fitstbl['target'] == 'ArcLamp_HgAr') \\\n | (fitstbl['target'] == 'ArcLamp_Ne'))\n if ftype in ['pixelflat', 'trace', 'illumflat']:\n return good_exp & (fitstbl['target'] == 'SpectralFlat')\n if ftype == 'bias':\n return good_exp & (fitstbl['target'] == 'BIAS')\n\n msgs.warn('Cannot determine if frames are of type {0}.'.format(ftype))\n return np.zeros(len(fitstbl), dtype=bool)","def is_type(self, typ):\n return typ == self.__class__.__name__","def _is_typevar(typeval: Type) -> bool:\n return isinstance(typeval, TypeVar) # type: ignore","def check_frame_type(self, ftype, fitstbl, exprng=None):\n good_exp = framematch.check_frame_exptime(fitstbl['exptime'], exprng)\n if ftype in ['pinhole', 'bias']:\n # No pinhole or bias frames\n return np.zeros(len(fitstbl), dtype=bool)\n if ftype in ['pixelflat', 'trace', 'illumflat']:\n return good_exp & (fitstbl['idname'] == 'flat')\n if ftype == 'standard':\n return good_exp & (fitstbl['idname'] == 'object')\n if ftype == 'science':\n return good_exp & (fitstbl['idname'] == 'object')\n if ftype in ['arc', 'tilt']:\n return good_exp & (fitstbl['idname'] == 'object')\n if ftype == 'dark':\n return good_exp & (fitstbl['idname'] == 'dark')\n msgs.warn('Cannot determine if frames are of type {0}.'.format(ftype))\n return np.zeros(len(fitstbl), dtype=bool)","def test_expected_type(val, exp_type):\n\n if not isinstance(val, exp_type):\n return False","def check_frame_type(self, ftype, fitstbl, exprng=None):\n good_exp = framematch.check_frame_exptime(fitstbl['exptime'], exprng)\n if ftype in ['pinhole', 'bias']:\n # No pinhole or bias frames\n return np.zeros(len(fitstbl), dtype=bool)\n if ftype in ['pixelflat', 'trace']:\n return good_exp & (fitstbl['idname'] == 'PixFlat')\n if ftype == 'standard':\n return good_exp & (fitstbl['idname'] == 'Telluric')\n if ftype == 'science':\n return good_exp & (fitstbl['idname'] == 'Science')\n if ftype in ['arc', 'tilt']:\n return good_exp & (fitstbl['idname'] == 'Science')\n msgs.warn('Cannot determine if frames are of type {0}.'.format(ftype))\n return np.zeros(len(fitstbl), dtype=bool)","def is_cpp(benchmark):\n return get_language(benchmark) == 'c++'","def validatePredefinedType(self, type: int) -> bool:\n ...","def check_frame_type(self, ftype, fitstbl, exprng=None):\n good_exp = framematch.check_frame_exptime(fitstbl['exptime'], exprng)\n if ftype in ['pinhole', 'bias']:\n # No pinhole or bias frames\n return np.zeros(len(fitstbl), dtype=bool)\n if ftype in ['pixelflat', 'trace']:\n return good_exp & (fitstbl['idname'] == 'PixFlat')\n if ftype == 'standard':\n return good_exp & (fitstbl['idname'] == 'Telluric')\n if ftype == 'science':\n return good_exp & (fitstbl['idname'] == 'Science')\n if ftype in ['arc', 'tilt']:\n return good_exp & (fitstbl['idname'] == 'Arc')\n msgs.warn('Cannot determine if frames are of type {0}.'.format(ftype))\n return np.zeros(len(fitstbl), dtype=bool)","def _isinstance(cls, x):\n return isinstance(x, cls.PYTHON_TYPE_CHECK)","def has_exactly_type(obj, tpe):\r\n return type(obj) == tpe","def is_basic_ctype(self, objtype):\n if objtype in [self.c_char_p, self.c_void_p, self.CString]:\n return False\n # DOC: if <ctypes.c_uint> is not in self.__basic_types, its probably\n # because you are using the wrong ctypes Proxy instance\n return objtype in self.__basic_types","def _isinstancetype(an_obj):\n if an_obj is None: return False\n if not PY3K:\n return isinstance(an_obj, types.InstanceType)\n typstr = str(type(an_obj))\n # the following logic works, as PyRAF users expect, in both v2 and v3\n return typstr==\"<type 'instance'>\" or \\\n (typstr.startswith(\"<class '\") and ('.' in typstr))","def validates_classification_type(self,\n classification_type: ClassificationType) -> bool:\n return classification_type == ClassificationType.AMPLIFICATION","def check_input_type(var, type_name):\n\n type_options = [\"int\", \"float\", \"Date\", \"Region\"]\n if type_name == type_options[0]:\n if int(var):\n return True\n else:\n return False\n elif type_name == type_options[1]:\n if float(var):\n return True\n else:\n return False\n elif type_name == type_options[2]:\n if datetime.date.fromisoformat(var):\n return True\n else:\n return False\n elif type_name == type_options[3]:\n valid_regions = [\"NW\", \"SW\", \"MN\", \"MS\", \"NE\", \"SE\"]\n is_valid = False\n for region in valid_regions:\n if var == region:\n is_valid = True\n return is_valid\n else:\n Exception(\"This type doesn't exist in the checker!\")","def _has_numeric_or_bool(self) -> bool:\n dtypes: Set[str] = set(self._data.keys())\n return 'i' in dtypes or 'f' in dtypes or 'b' in dtypes","def validate(self,value):\r\n return type(value) is self.datatype","def _check(self, token_type):\n if self._is_at_end():\n return False\n\n return self._peek().token_type == token_type","def _is_valid_unit(unit: str, unit_type: str) -> bool:\n if unit_type == LENGTH:\n return unit in LENGTH_UNITS\n if unit_type == ACCUMULATED_PRECIPITATION:\n return unit in LENGTH_UNITS\n if unit_type == WIND_SPEED:\n return unit in WIND_SPEED_UNITS\n if unit_type == TEMPERATURE:\n return unit in TEMPERATURE_UNITS\n if unit_type == MASS:\n return unit in MASS_UNITS\n if unit_type == VOLUME:\n return unit in VOLUME_UNITS\n if unit_type == PRESSURE:\n return unit in PRESSURE_UNITS\n return False","def type_valid(self):\n return contain_in_list_equal(self._type_or_ref, PARAM_RES_TYPES)","def test_types(self):\n \n self.assertIsInstance(self.tx_data_in, numpy.ndarray)\n self.assertIsInstance(self.circuit_simulation, bool)\n self.assertIsInstance(self.bypass, bool)\n \n pass","def __datatype_check(self, record_attribute, attribute_schema):\n if 'INT' in attribute_schema[TYPE_KEY].upper():\n if record_attribute.isdigit():\n return True\n elif attribute_schema[TYPE_KEY].upper() in DECIMAL_TYPES:\n if record_attribute.isdecimal():\n return True\n elif 'CHAR' in attribute_schema[TYPE_KEY].upper() \\\n or 'TEXT' in attribute_schema[TYPE_KEY].upper():\n if type(record_attribute) is str:\n return True\n else:\n IS_VALID_FILE = False\n return False","def is_pyxb_d1_type_name(pyxb_obj, expected_pyxb_type_name):\n try:\n return pyxb_get_type_name(pyxb_obj) == expected_pyxb_type_name\n except AttributeError:\n return False","def is_valid(self) -> bool:\n from redun.scheduler import get_current_scheduler\n\n if self.type_name != self.__handle__.class_name:\n # Handle class_name might be out of date from deserialization.\n return False\n\n scheduler = get_current_scheduler()\n assert scheduler\n return scheduler.backend.is_valid_handle(self)","def _is_valid(self, *args, **kwargs):\n fn = args[0]\n if not fn.endswith(self._suffix):\n return False\n try:\n with h5py.File(fn, \"r\") as f:\n if \"arbor_type\" not in f.attrs:\n return False\n if f.attrs[\"arbor_type\"].astype(str) != \"YTreeArbor\":\n return False\n except BaseException:\n return False\n return True","def _valid_input_type(self, input_type):\n # pylint: disable=W0613, R0201\n return True","def isMeasureTypeMatch(self):\n return self._measure_type_match","def is_Type(tp):\n if isinstance(tp, type):\n return True\n try:\n typing._type_check(tp, '')\n return True\n except TypeError:\n return False","def is_builtin_dataclass(_cls: Type[Any]) -> bool:\n import dataclasses\n\n return not hasattr(_cls, '__processed__') and dataclasses.is_dataclass(_cls)","def valid_type(self, data, errors):\n\t\terrors.append(\"{}: valid_type() must be implemented for SchemaBase derived classes.\".format(self.__class__.__name__))\n\t\treturn False","def isLegal(self):\n counter = 0\n for t in self.types:\n if t > 0:\n counter = counter + 1\n if counter < 4:\n return True\n else:\n return False","def isValidDataTypeName(name: unicode) -> bool:\n ...","def is_valid_case_type(case_type):\n return bool(_case_type_regex.match(case_type or ''))","def _type_check_double(self, data):\n if type(data) not in self._VALID_TYPES:\n return False\n return True","def isUnitKind(*args):\n return _libsbml.Unit_isUnitKind(*args)","def is_type(obj: Any) -> bool:\n return type(obj).__name__ == \"type\"","def _is_valid(self, value):\n\n # Entities have an istypeof method that can perform more sophisticated\n # type checking.\n if hasattr(self._type, \"istypeof\"):\n return self._type.istypeof(value)\n else:\n return isinstance(value, self._type)","def is_type(value):\n if isinstance(value, type):\n return issubclass(value, Type)\n return isinstance(value, Type)","def _check(self, class_):\r\n\r\n if isinstance(class_, (types.FunctionType, types.LambdaType,\r\n types.ClassType, types.InstanceType)):\r\n return False\r\n if not hasattr(class_, '__dict__'):\r\n if not hasattr(class_, '__slots__'):\r\n return False\r\n return True","def is_address_of_type(self, address, *memtypes):\n memrange = self.get_range(address)\n return memrange is not None and memrange.memtype in memtypes","def _check_type(self):\n check_type = DESCRIPTOR_VALUE.get(self.descriptor.type)\n if check_type is None:\n raise ValueError(\"Unknown metric descriptor type\")\n for ts in self.time_series:\n if not ts.check_points_type(check_type):\n raise ValueError(\"Invalid point value type\")","def check_proc_type(image_proc_type):\n\n if isinstance(image_proc_type, str):\n return True\n else:\n logging.warning('Image processing type is not a string')\n print('Please choose only one processing technique.')\n raise ValueError('Please choose only one processing technique.')","def has_numeric_type(obj: _std_typing.Any) -> bool:\n return (not has_vector_type(obj)) and (not has_string_type(obj))","def _isnotsuite(test):\n try:\n iter(test)\n except TypeError:\n return True\n return False","def _is_mechanism_spec(spec):\n if inspect.isclass(spec) and issubclass(spec, Mechanism):\n return True\n if isinstance(spec, Mechanism):\n return True\n return False","def is_builtin_type(tp):\n return hasattr(__builtins__, tp.__name__) and tp is getattr(__builtins__, tp.__name__)","def is_of_type(cls, value) -> bool:\n # UTF8 = 'utf-8'\n # UTF16 = 'utf-16'\n # UTF32 = 'utf-32'\n # ASCII = 'ascii'\n # BINARY = 'binary'\n # OCTAL = 'octal'\n # HEXADECIMAL = 'hexadecimal'\n # CP1252 = 'cp1252'\n # WINDOWS1252 = 'windows-1252'\n # UNICODEESCAPE = 'unicode-escape'\n\n v = None\n if cls == cls.UTF8 or cls == cls.UTF16 or cls == cls.UTF32 or cls == cls.UNICODEESCAPE:\n try:\n v = bytes(value)\n except:\n return False\n\n if cls == cls.ASCII:\n try:\n v = ascii(value)\n except:\n return False\n\n if cls == cls.BINARY:\n try:\n v = bin(value)\n except:\n return False\n\n if cls == cls.OCTAL:\n try:\n v = oct(value)\n except:\n return False\n\n if cls == cls.HEXADECIMAL:\n try:\n v = hex(value)\n except:\n return False\n\n if cls == cls.WINDOWS1252 or cls == cls.CP1252:\n try:\n v = str(value)\n except:\n return False\n return True","def isdatatype(object):\n return isinstance(object, (str, int, bool, float, type(None)))","def _is_valid(self, *args, **kwargs):\n fn = args[0]\n if not fn.endswith(\".h5\"):\n return False\n try:\n with h5py.File(fn, \"r\") as f:\n if \"arbor_type\" not in f.attrs:\n return False\n if f.attrs[\"arbor_type\"].astype(str) != \"ArborArbor\":\n return False\n except BaseException:\n return False\n return True","def _is_valid_pt(content_type: str) -> bool:\n content_type = content_type.strip()\n return content_type in SPECIFICATION_PRIMITIVE_TYPES","def _check_value_type(self, value):\n if value is not None and self.value_type is not None:\n valid = isinstance(value, self.value_type)\n if not valid:\n return False\n return True","def _valid_typable_object_with_name(ui_object, platform=Platform.ANDROID):\n if platform == Platform.ANDROID:\n return (ui_object.obj_type in _TYPABLE_OBJECT_DESC.keys() and\n _valid_object_with_name(ui_object))\n else:\n assert False, 'Wrong Platform'","def is_my_type(type_str):\n raise NotImplementedError()","def _can_cast_to(self, value, cast_type):\n try:\n _ = cast_type(value)\n return True\n except ValueError:\n return False","def is_of_type(cmd):\r\n raise NotImplementedError()","def check_type(filename):\n try:\n im = Image.read(filename)\n except SanperaError:\n return False\n else:\n return im.original_format in [b'JPEG', b'PNG', b'GIF']","def test_data_type(self):\n self.assertTrue(self.tester.data_type(), \"18S\")","def is_valid_license_type(self):\n clean = self.license_type.lower().replace('-', ' ')\n return clean not in INVALID_LICENSE_TYPE","def check_if_quantized(model: Any) -> bool:\n nodes = model.get_ops()\n for op in nodes:\n if \"FakeQuantize\" == op.get_type_name():\n return True\n return False","def check_file_type(fname):\n ext = path.splitext(fname)[1]\n return ext in allowed_extensions","def has_value(cls, value):\n return bool(isinstance(value, numbers.Number) or isinstance(value, time) or \\\n isinstance(value, datetime) or value)","def _valid_output_type(self, output_type):\n # pylint: disable=W0613, R0201\n return True","def _check_validdtypeoutput(self, symbol):\n if symbol.type == self.scanner.KEYWORD and \\\n symbol.id in self.validdtypeoutputs:\n return True\n else:\n return False","def is_type(self, *seg_type: str) -> bool:\n return self.class_is_type(*seg_type)","def _is_valid_ct(content_type: str) -> bool:\n content_type = content_type.strip()\n return _is_valid_regex(CT_CONTENT_TYPE_REGEX_PATTERN, content_type)","def check_type(self, environment: Environment) -> Type:\n return self.typt_type","def NeedsArray(self, type_):\n return self._NameComponents(type_) in self._array_types","def _is_mutation_type(data):\n try:\n QuiverMutationType(data)\n return True\n except Exception:\n return False","def has_acceptable_type(self, value):\n if not value:\n return False\n if super().has_acceptable_type(value):\n return True\n # Hmmm ok maybe we're running under IPython:\n try:\n import IPython\n return isinstance(value, IPython.kernel.zmq.iostream.OutStream)\n except ImportError:\n return False","def has_type(self, item_type):\n raise NotImplementedError()","def canStandardize(self, datasetType):\n\n return hasattr(self, 'std_' + datasetType)","def _valid_types(arguments, types):\n for arg in arguments:\n if type(arg) not in types:\n return False\n return True","def _valid_types(arguments, types):\n for arg in arguments:\n if type(arg) not in types:\n return False\n return True","def _is_simple_type(cls):\n return all([\n AnnotationWrapper(anno).is_simple_in_opt_and_not_opt\n for anno in cls._used_annotations()\n ])","def is_valid(self):\n try:\n self.validate()\n return True\n except (TypeError, ValueError) as e:\n return False"],"string":"[\n \"def validate(benchmark):\\n if not validate_name(benchmark):\\n return False\\n\\n if benchmark not in get_all_benchmarks():\\n logs.error('%s must have a benchmark.yaml.', benchmark)\\n return False\\n\\n # Validate config file can be parsed.\\n try:\\n get_fuzz_target(benchmark)\\n except yaml.parser.ParserError:\\n logs.error('%s must have a valid benchmark.yaml file. Failed to parse.',\\n benchmark)\\n return False\\n except KeyError:\\n logs.error('%s\\\\'s benchmark.yaml does not define \\\"fuzz_target\\\".',\\n benchmark)\\n return False\\n\\n # Validate type.\\n return validate_type(benchmark)\",\n \"def is_valid_type(type):\\n return type in type_to_adapter\",\n \"def is_valid_type(self, question_type):\\n\\t\\treturn question_type in self.valid_types\",\n \"def is_valid_type(self, attr: Optional[str] = None) -> bool:\\n try:\\n self.validate_type(attr)\\n except TypeError:\\n return False\\n return True\",\n \"def check_type(self):\\n return True\",\n \"def validate_name(benchmark):\\n if VALID_BENCHMARK_REGEX.match(benchmark) is None:\\n logs.error('%s does not conform to %s pattern.', benchmark,\\n VALID_BENCHMARK_REGEX.pattern)\\n return False\\n return True\",\n \"def is_type(self, type_name):\\n\\n return type_name in self._symtab\",\n \"def verify_type(self, obj):\\n return isinstance(obj, self.type_)\",\n \"def _is_run_type(cls, object_):\\n # Do a string comparison instead of using isinstance() to avoid needing\\n # to import lyse or other modules with these classes.\\n return (type(object_).__name__ in cls._RUN_TYPES)\",\n \"def test_has_exactly_type():\\r\\n\\r\\n return has_exactly_type(1, int) and not has_exactly_type(True, int) and has_exactly_type(True, bool)\",\n \"def _is_valid_type(_type: Type[Any]) -> bool:\\n\\n if _type in _TYPE_MAP:\\n return True\\n\\n if not inspect.isclass(_type):\\n return False\\n\\n return issubclass(_type, Table)\",\n \"def get_type(benchmark):\\n # TODO(metzman): Use classes to mock a benchmark config for\\n # OSS_FUZZ_ON_DEMAND.\\n default_value = os.getenv('EXPERIMENT_TYPE', BenchmarkType.CODE.value)\\n return benchmark_config.get_config(benchmark).get('type', default_value)\",\n \"def is_supported_type(self) -> bool:\\n t = self.type.strip()\\n return t in self.SUPPORTED_LABELS or t.lower() in self.SUPPORTED_LABELS\",\n \"def check_frame_type(self, ftype, fitstbl, exprng=None):\\n good_exp = framematch.check_frame_exptime(fitstbl['exptime'], exprng)\\n if ftype in ['science', 'standard']:\\n return good_exp & (np.logical_not(np.char.startswith(np.char.lower(fitstbl['target']), 'arclamp'))) & \\\\\\n (np.char.lower(fitstbl['target']) != 'spectralflat') & \\\\\\n (np.char.lower(fitstbl['target']) != 'bias')\\n if ftype in ['arc', 'tilt']:\\n return good_exp & (np.char.startswith(np.char.lower(fitstbl['target']), 'arclamp'))\\n if ftype in ['pixelflat', 'trace', 'illumflat']:\\n return good_exp & (np.char.lower(fitstbl['target']) == 'spectralflat')\\n if ftype == 'bias':\\n return good_exp & (np.char.lower(fitstbl['target']) == 'bias')\\n\\n msgs.warn('Cannot determine if frames are of type {0}.'.format(ftype))\\n return np.zeros(len(fitstbl), dtype=bool)\",\n \"def _type_check(data):\\n if data.__class__.__name__ != \\\"Matrix3\\\":\\n return False\\n return True\",\n \"def CheckType(self, *args, **kwargs):\\n pass\",\n \"def is_basic_type(self, objtype):\\n if not hasattr(objtype, '_type_'):\\n # could be python types\\n return objtype in [int, long, float, bool]\\n return self.is_basic_ctype(objtype)\",\n \"def is_result_appropriate(self, benchmark_result):\\n return True\",\n \"def is_valid(self, attribute: Attribute) -> bool:\\n return self.get_data_type() == attribute.type\",\n \"def isquantized(val):\\n # try: 1.1 * val ... except: might be more general, but could be expensive.\\n retval = True\\n if isinstance(val, (float, complex)):\\n retval = False\\n elif hasattr(val, 'issubdtype'):\\n if numpy.issubdtype(val.dtype, float) or numpy.issubdtype(val.dtype, complex):\\n retval = False\\n return retval\",\n \"def check_frame_type(self, ftype, fitstbl, exprng=None):\\n good_exp = framematch.check_frame_exptime(fitstbl['exptime'], exprng)\\n if ftype in ['science', 'standard']:\\n return good_exp & (fitstbl['idname'] == 'OBJECT')\\n if ftype == 'bias':\\n return good_exp & (fitstbl['idname'] == 'BIAS')\\n if ftype in ['pixelflat', 'trace', 'illumflat']:\\n return good_exp & (fitstbl['idname'] == 'FLAT,LAMP')\\n if ftype in ['pinhole', 'dark']:\\n # Don't type pinhole or dark frames\\n return np.zeros(len(fitstbl), dtype=bool)\\n if ftype in ['arc','tilt']:\\n return good_exp & (fitstbl['idname'] == 'WAVE,LAMP')\\n msgs.warn('Cannot determine if frames are of type {0}.'.format(ftype))\\n return np.zeros(len(fitstbl), dtype=bool)\",\n \"def _valid_typable_object(ui_object, platform=Platform.ANDROID):\\n if platform == Platform.ANDROID:\\n return ui_object.obj_type in _TYPABLE_OBJECT_DESC.keys()\\n else:\\n assert False, 'Wrong Platform'\",\n \"def checkType(self, value):\\n pass\",\n \"def check_frame_type(self, ftype, fitstbl, exprng=None):\\n good_exp = framematch.check_frame_exptime(fitstbl['exptime'], exprng)\\n # TODO: Allow for 'sky' frame type, for now include sky in\\n # 'science' category\\n if ftype == 'science':\\n return good_exp & (fitstbl['idname'] == 'Object')\\n if ftype == 'standard':\\n return good_exp & (fitstbl['idname'] == 'Object')\\n if ftype == 'bias':\\n return good_exp & (fitstbl['idname'] == 'Bias')\\n if ftype == 'dark':\\n return good_exp & (fitstbl['idname'] == 'Dark')\\n if ftype in ['pixelflat', 'trace']:\\n # Flats and trace frames are typed together\\n return good_exp & (fitstbl['idname'] == 'IntFlat')\\n if ftype in ['arc', 'tilt']:\\n # Arc and tilt frames are typed together\\n return good_exp & (fitstbl['idname'] == 'Line')\\n\\n msgs.warn('Cannot determine if frames are of type {0}.'.format(ftype))\\n return np.zeros(len(fitstbl), dtype=bool)\",\n \"def is_valid_silktype(type_name, permit_array=False):\\n if not type_name.replace(\\\"_\\\", \\\"x\\\").isalnum():\\n return False\\n\\n if not type_name[0].isupper():\\n return False\\n\\n if len(type_name) > 1 and type_name == type_name.upper():\\n return False\\n\\n if permit_array:\\n array_depth = 0\\n while type_name.endswith(\\\"Array\\\"):\\n type_name = type_name[:-len(\\\"Array\\\")]\\n array_depth += 1\\n\\n if array_depth > max_array_depth:\\n return False\\n\\n elif type_name.endswith(\\\"Array\\\"):\\n return False\\n\\n if type_name in reserved_types:\\n return False\\n\\n for ending in reserved_endings:\\n if type_name.endswith(ending):\\n return False\\n return True\",\n \"def check_frame_type(self, ftype, fitstbl, exprng=None):\\n good_exp = framematch.check_frame_exptime(fitstbl['exptime'], exprng)\\n if ftype in ['science','standard']:\\n return good_exp & (fitstbl['target'] != 'ArcLamp_Xe') \\\\\\n & (fitstbl['target'] != 'ArcLamp_HgAr') \\\\\\n & (fitstbl['target'] != 'ArcLamp_Ne') \\\\\\n & (fitstbl['target'] != 'SpectralFlat') \\\\\\n & (fitstbl['target'] != 'BIAS')\\n if ftype in ['arc', 'tilt']:\\n return good_exp & ((fitstbl['target'] == 'ArcLamp_Xe') \\\\\\n | (fitstbl['target'] == 'ArcLamp_HgAr') \\\\\\n | (fitstbl['target'] == 'ArcLamp_Ne'))\\n if ftype in ['pixelflat', 'trace', 'illumflat']:\\n return good_exp & (fitstbl['target'] == 'SpectralFlat')\\n if ftype == 'bias':\\n return good_exp & (fitstbl['target'] == 'BIAS')\\n\\n msgs.warn('Cannot determine if frames are of type {0}.'.format(ftype))\\n return np.zeros(len(fitstbl), dtype=bool)\",\n \"def is_type(self, typ):\\n return typ == self.__class__.__name__\",\n \"def _is_typevar(typeval: Type) -> bool:\\n return isinstance(typeval, TypeVar) # type: ignore\",\n \"def check_frame_type(self, ftype, fitstbl, exprng=None):\\n good_exp = framematch.check_frame_exptime(fitstbl['exptime'], exprng)\\n if ftype in ['pinhole', 'bias']:\\n # No pinhole or bias frames\\n return np.zeros(len(fitstbl), dtype=bool)\\n if ftype in ['pixelflat', 'trace', 'illumflat']:\\n return good_exp & (fitstbl['idname'] == 'flat')\\n if ftype == 'standard':\\n return good_exp & (fitstbl['idname'] == 'object')\\n if ftype == 'science':\\n return good_exp & (fitstbl['idname'] == 'object')\\n if ftype in ['arc', 'tilt']:\\n return good_exp & (fitstbl['idname'] == 'object')\\n if ftype == 'dark':\\n return good_exp & (fitstbl['idname'] == 'dark')\\n msgs.warn('Cannot determine if frames are of type {0}.'.format(ftype))\\n return np.zeros(len(fitstbl), dtype=bool)\",\n \"def test_expected_type(val, exp_type):\\n\\n if not isinstance(val, exp_type):\\n return False\",\n \"def check_frame_type(self, ftype, fitstbl, exprng=None):\\n good_exp = framematch.check_frame_exptime(fitstbl['exptime'], exprng)\\n if ftype in ['pinhole', 'bias']:\\n # No pinhole or bias frames\\n return np.zeros(len(fitstbl), dtype=bool)\\n if ftype in ['pixelflat', 'trace']:\\n return good_exp & (fitstbl['idname'] == 'PixFlat')\\n if ftype == 'standard':\\n return good_exp & (fitstbl['idname'] == 'Telluric')\\n if ftype == 'science':\\n return good_exp & (fitstbl['idname'] == 'Science')\\n if ftype in ['arc', 'tilt']:\\n return good_exp & (fitstbl['idname'] == 'Science')\\n msgs.warn('Cannot determine if frames are of type {0}.'.format(ftype))\\n return np.zeros(len(fitstbl), dtype=bool)\",\n \"def is_cpp(benchmark):\\n return get_language(benchmark) == 'c++'\",\n \"def validatePredefinedType(self, type: int) -> bool:\\n ...\",\n \"def check_frame_type(self, ftype, fitstbl, exprng=None):\\n good_exp = framematch.check_frame_exptime(fitstbl['exptime'], exprng)\\n if ftype in ['pinhole', 'bias']:\\n # No pinhole or bias frames\\n return np.zeros(len(fitstbl), dtype=bool)\\n if ftype in ['pixelflat', 'trace']:\\n return good_exp & (fitstbl['idname'] == 'PixFlat')\\n if ftype == 'standard':\\n return good_exp & (fitstbl['idname'] == 'Telluric')\\n if ftype == 'science':\\n return good_exp & (fitstbl['idname'] == 'Science')\\n if ftype in ['arc', 'tilt']:\\n return good_exp & (fitstbl['idname'] == 'Arc')\\n msgs.warn('Cannot determine if frames are of type {0}.'.format(ftype))\\n return np.zeros(len(fitstbl), dtype=bool)\",\n \"def _isinstance(cls, x):\\n return isinstance(x, cls.PYTHON_TYPE_CHECK)\",\n \"def has_exactly_type(obj, tpe):\\r\\n return type(obj) == tpe\",\n \"def is_basic_ctype(self, objtype):\\n if objtype in [self.c_char_p, self.c_void_p, self.CString]:\\n return False\\n # DOC: if <ctypes.c_uint> is not in self.__basic_types, its probably\\n # because you are using the wrong ctypes Proxy instance\\n return objtype in self.__basic_types\",\n \"def _isinstancetype(an_obj):\\n if an_obj is None: return False\\n if not PY3K:\\n return isinstance(an_obj, types.InstanceType)\\n typstr = str(type(an_obj))\\n # the following logic works, as PyRAF users expect, in both v2 and v3\\n return typstr==\\\"<type 'instance'>\\\" or \\\\\\n (typstr.startswith(\\\"<class '\\\") and ('.' in typstr))\",\n \"def validates_classification_type(self,\\n classification_type: ClassificationType) -> bool:\\n return classification_type == ClassificationType.AMPLIFICATION\",\n \"def check_input_type(var, type_name):\\n\\n type_options = [\\\"int\\\", \\\"float\\\", \\\"Date\\\", \\\"Region\\\"]\\n if type_name == type_options[0]:\\n if int(var):\\n return True\\n else:\\n return False\\n elif type_name == type_options[1]:\\n if float(var):\\n return True\\n else:\\n return False\\n elif type_name == type_options[2]:\\n if datetime.date.fromisoformat(var):\\n return True\\n else:\\n return False\\n elif type_name == type_options[3]:\\n valid_regions = [\\\"NW\\\", \\\"SW\\\", \\\"MN\\\", \\\"MS\\\", \\\"NE\\\", \\\"SE\\\"]\\n is_valid = False\\n for region in valid_regions:\\n if var == region:\\n is_valid = True\\n return is_valid\\n else:\\n Exception(\\\"This type doesn't exist in the checker!\\\")\",\n \"def _has_numeric_or_bool(self) -> bool:\\n dtypes: Set[str] = set(self._data.keys())\\n return 'i' in dtypes or 'f' in dtypes or 'b' in dtypes\",\n \"def validate(self,value):\\r\\n return type(value) is self.datatype\",\n \"def _check(self, token_type):\\n if self._is_at_end():\\n return False\\n\\n return self._peek().token_type == token_type\",\n \"def _is_valid_unit(unit: str, unit_type: str) -> bool:\\n if unit_type == LENGTH:\\n return unit in LENGTH_UNITS\\n if unit_type == ACCUMULATED_PRECIPITATION:\\n return unit in LENGTH_UNITS\\n if unit_type == WIND_SPEED:\\n return unit in WIND_SPEED_UNITS\\n if unit_type == TEMPERATURE:\\n return unit in TEMPERATURE_UNITS\\n if unit_type == MASS:\\n return unit in MASS_UNITS\\n if unit_type == VOLUME:\\n return unit in VOLUME_UNITS\\n if unit_type == PRESSURE:\\n return unit in PRESSURE_UNITS\\n return False\",\n \"def type_valid(self):\\n return contain_in_list_equal(self._type_or_ref, PARAM_RES_TYPES)\",\n \"def test_types(self):\\n \\n self.assertIsInstance(self.tx_data_in, numpy.ndarray)\\n self.assertIsInstance(self.circuit_simulation, bool)\\n self.assertIsInstance(self.bypass, bool)\\n \\n pass\",\n \"def __datatype_check(self, record_attribute, attribute_schema):\\n if 'INT' in attribute_schema[TYPE_KEY].upper():\\n if record_attribute.isdigit():\\n return True\\n elif attribute_schema[TYPE_KEY].upper() in DECIMAL_TYPES:\\n if record_attribute.isdecimal():\\n return True\\n elif 'CHAR' in attribute_schema[TYPE_KEY].upper() \\\\\\n or 'TEXT' in attribute_schema[TYPE_KEY].upper():\\n if type(record_attribute) is str:\\n return True\\n else:\\n IS_VALID_FILE = False\\n return False\",\n \"def is_pyxb_d1_type_name(pyxb_obj, expected_pyxb_type_name):\\n try:\\n return pyxb_get_type_name(pyxb_obj) == expected_pyxb_type_name\\n except AttributeError:\\n return False\",\n \"def is_valid(self) -> bool:\\n from redun.scheduler import get_current_scheduler\\n\\n if self.type_name != self.__handle__.class_name:\\n # Handle class_name might be out of date from deserialization.\\n return False\\n\\n scheduler = get_current_scheduler()\\n assert scheduler\\n return scheduler.backend.is_valid_handle(self)\",\n \"def _is_valid(self, *args, **kwargs):\\n fn = args[0]\\n if not fn.endswith(self._suffix):\\n return False\\n try:\\n with h5py.File(fn, \\\"r\\\") as f:\\n if \\\"arbor_type\\\" not in f.attrs:\\n return False\\n if f.attrs[\\\"arbor_type\\\"].astype(str) != \\\"YTreeArbor\\\":\\n return False\\n except BaseException:\\n return False\\n return True\",\n \"def _valid_input_type(self, input_type):\\n # pylint: disable=W0613, R0201\\n return True\",\n \"def isMeasureTypeMatch(self):\\n return self._measure_type_match\",\n \"def is_Type(tp):\\n if isinstance(tp, type):\\n return True\\n try:\\n typing._type_check(tp, '')\\n return True\\n except TypeError:\\n return False\",\n \"def is_builtin_dataclass(_cls: Type[Any]) -> bool:\\n import dataclasses\\n\\n return not hasattr(_cls, '__processed__') and dataclasses.is_dataclass(_cls)\",\n \"def valid_type(self, data, errors):\\n\\t\\terrors.append(\\\"{}: valid_type() must be implemented for SchemaBase derived classes.\\\".format(self.__class__.__name__))\\n\\t\\treturn False\",\n \"def isLegal(self):\\n counter = 0\\n for t in self.types:\\n if t > 0:\\n counter = counter + 1\\n if counter < 4:\\n return True\\n else:\\n return False\",\n \"def isValidDataTypeName(name: unicode) -> bool:\\n ...\",\n \"def is_valid_case_type(case_type):\\n return bool(_case_type_regex.match(case_type or ''))\",\n \"def _type_check_double(self, data):\\n if type(data) not in self._VALID_TYPES:\\n return False\\n return True\",\n \"def isUnitKind(*args):\\n return _libsbml.Unit_isUnitKind(*args)\",\n \"def is_type(obj: Any) -> bool:\\n return type(obj).__name__ == \\\"type\\\"\",\n \"def _is_valid(self, value):\\n\\n # Entities have an istypeof method that can perform more sophisticated\\n # type checking.\\n if hasattr(self._type, \\\"istypeof\\\"):\\n return self._type.istypeof(value)\\n else:\\n return isinstance(value, self._type)\",\n \"def is_type(value):\\n if isinstance(value, type):\\n return issubclass(value, Type)\\n return isinstance(value, Type)\",\n \"def _check(self, class_):\\r\\n\\r\\n if isinstance(class_, (types.FunctionType, types.LambdaType,\\r\\n types.ClassType, types.InstanceType)):\\r\\n return False\\r\\n if not hasattr(class_, '__dict__'):\\r\\n if not hasattr(class_, '__slots__'):\\r\\n return False\\r\\n return True\",\n \"def is_address_of_type(self, address, *memtypes):\\n memrange = self.get_range(address)\\n return memrange is not None and memrange.memtype in memtypes\",\n \"def _check_type(self):\\n check_type = DESCRIPTOR_VALUE.get(self.descriptor.type)\\n if check_type is None:\\n raise ValueError(\\\"Unknown metric descriptor type\\\")\\n for ts in self.time_series:\\n if not ts.check_points_type(check_type):\\n raise ValueError(\\\"Invalid point value type\\\")\",\n \"def check_proc_type(image_proc_type):\\n\\n if isinstance(image_proc_type, str):\\n return True\\n else:\\n logging.warning('Image processing type is not a string')\\n print('Please choose only one processing technique.')\\n raise ValueError('Please choose only one processing technique.')\",\n \"def has_numeric_type(obj: _std_typing.Any) -> bool:\\n return (not has_vector_type(obj)) and (not has_string_type(obj))\",\n \"def _isnotsuite(test):\\n try:\\n iter(test)\\n except TypeError:\\n return True\\n return False\",\n \"def _is_mechanism_spec(spec):\\n if inspect.isclass(spec) and issubclass(spec, Mechanism):\\n return True\\n if isinstance(spec, Mechanism):\\n return True\\n return False\",\n \"def is_builtin_type(tp):\\n return hasattr(__builtins__, tp.__name__) and tp is getattr(__builtins__, tp.__name__)\",\n \"def is_of_type(cls, value) -> bool:\\n # UTF8 = 'utf-8'\\n # UTF16 = 'utf-16'\\n # UTF32 = 'utf-32'\\n # ASCII = 'ascii'\\n # BINARY = 'binary'\\n # OCTAL = 'octal'\\n # HEXADECIMAL = 'hexadecimal'\\n # CP1252 = 'cp1252'\\n # WINDOWS1252 = 'windows-1252'\\n # UNICODEESCAPE = 'unicode-escape'\\n\\n v = None\\n if cls == cls.UTF8 or cls == cls.UTF16 or cls == cls.UTF32 or cls == cls.UNICODEESCAPE:\\n try:\\n v = bytes(value)\\n except:\\n return False\\n\\n if cls == cls.ASCII:\\n try:\\n v = ascii(value)\\n except:\\n return False\\n\\n if cls == cls.BINARY:\\n try:\\n v = bin(value)\\n except:\\n return False\\n\\n if cls == cls.OCTAL:\\n try:\\n v = oct(value)\\n except:\\n return False\\n\\n if cls == cls.HEXADECIMAL:\\n try:\\n v = hex(value)\\n except:\\n return False\\n\\n if cls == cls.WINDOWS1252 or cls == cls.CP1252:\\n try:\\n v = str(value)\\n except:\\n return False\\n return True\",\n \"def isdatatype(object):\\n return isinstance(object, (str, int, bool, float, type(None)))\",\n \"def _is_valid(self, *args, **kwargs):\\n fn = args[0]\\n if not fn.endswith(\\\".h5\\\"):\\n return False\\n try:\\n with h5py.File(fn, \\\"r\\\") as f:\\n if \\\"arbor_type\\\" not in f.attrs:\\n return False\\n if f.attrs[\\\"arbor_type\\\"].astype(str) != \\\"ArborArbor\\\":\\n return False\\n except BaseException:\\n return False\\n return True\",\n \"def _is_valid_pt(content_type: str) -> bool:\\n content_type = content_type.strip()\\n return content_type in SPECIFICATION_PRIMITIVE_TYPES\",\n \"def _check_value_type(self, value):\\n if value is not None and self.value_type is not None:\\n valid = isinstance(value, self.value_type)\\n if not valid:\\n return False\\n return True\",\n \"def _valid_typable_object_with_name(ui_object, platform=Platform.ANDROID):\\n if platform == Platform.ANDROID:\\n return (ui_object.obj_type in _TYPABLE_OBJECT_DESC.keys() and\\n _valid_object_with_name(ui_object))\\n else:\\n assert False, 'Wrong Platform'\",\n \"def is_my_type(type_str):\\n raise NotImplementedError()\",\n \"def _can_cast_to(self, value, cast_type):\\n try:\\n _ = cast_type(value)\\n return True\\n except ValueError:\\n return False\",\n \"def is_of_type(cmd):\\r\\n raise NotImplementedError()\",\n \"def check_type(filename):\\n try:\\n im = Image.read(filename)\\n except SanperaError:\\n return False\\n else:\\n return im.original_format in [b'JPEG', b'PNG', b'GIF']\",\n \"def test_data_type(self):\\n self.assertTrue(self.tester.data_type(), \\\"18S\\\")\",\n \"def is_valid_license_type(self):\\n clean = self.license_type.lower().replace('-', ' ')\\n return clean not in INVALID_LICENSE_TYPE\",\n \"def check_if_quantized(model: Any) -> bool:\\n nodes = model.get_ops()\\n for op in nodes:\\n if \\\"FakeQuantize\\\" == op.get_type_name():\\n return True\\n return False\",\n \"def check_file_type(fname):\\n ext = path.splitext(fname)[1]\\n return ext in allowed_extensions\",\n \"def has_value(cls, value):\\n return bool(isinstance(value, numbers.Number) or isinstance(value, time) or \\\\\\n isinstance(value, datetime) or value)\",\n \"def _valid_output_type(self, output_type):\\n # pylint: disable=W0613, R0201\\n return True\",\n \"def _check_validdtypeoutput(self, symbol):\\n if symbol.type == self.scanner.KEYWORD and \\\\\\n symbol.id in self.validdtypeoutputs:\\n return True\\n else:\\n return False\",\n \"def is_type(self, *seg_type: str) -> bool:\\n return self.class_is_type(*seg_type)\",\n \"def _is_valid_ct(content_type: str) -> bool:\\n content_type = content_type.strip()\\n return _is_valid_regex(CT_CONTENT_TYPE_REGEX_PATTERN, content_type)\",\n \"def check_type(self, environment: Environment) -> Type:\\n return self.typt_type\",\n \"def NeedsArray(self, type_):\\n return self._NameComponents(type_) in self._array_types\",\n \"def _is_mutation_type(data):\\n try:\\n QuiverMutationType(data)\\n return True\\n except Exception:\\n return False\",\n \"def has_acceptable_type(self, value):\\n if not value:\\n return False\\n if super().has_acceptable_type(value):\\n return True\\n # Hmmm ok maybe we're running under IPython:\\n try:\\n import IPython\\n return isinstance(value, IPython.kernel.zmq.iostream.OutStream)\\n except ImportError:\\n return False\",\n \"def has_type(self, item_type):\\n raise NotImplementedError()\",\n \"def canStandardize(self, datasetType):\\n\\n return hasattr(self, 'std_' + datasetType)\",\n \"def _valid_types(arguments, types):\\n for arg in arguments:\\n if type(arg) not in types:\\n return False\\n return True\",\n \"def _valid_types(arguments, types):\\n for arg in arguments:\\n if type(arg) not in types:\\n return False\\n return True\",\n \"def _is_simple_type(cls):\\n return all([\\n AnnotationWrapper(anno).is_simple_in_opt_and_not_opt\\n for anno in cls._used_annotations()\\n ])\",\n \"def is_valid(self):\\n try:\\n self.validate()\\n return True\\n except (TypeError, ValueError) as e:\\n return False\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6986698","0.6440675","0.6303851","0.62154716","0.6090882","0.608178","0.6064316","0.6046675","0.5970051","0.5952641","0.58843154","0.58391774","0.5755268","0.57513607","0.5732901","0.57144207","0.56996477","0.56742626","0.55769104","0.5570763","0.55672246","0.5563812","0.5538002","0.5537267","0.55359256","0.55262315","0.55261034","0.549746","0.54852664","0.54820013","0.5464897","0.546331","0.5462606","0.54512084","0.54394567","0.54392","0.54376733","0.5436749","0.5430331","0.5430227","0.5414889","0.5404854","0.53959656","0.53924465","0.5389543","0.5381824","0.5353596","0.53400016","0.5331209","0.53285956","0.5325077","0.5317701","0.5310453","0.5306554","0.53053343","0.5299282","0.5288763","0.52857494","0.52827495","0.5276957","0.52757156","0.5274571","0.52676976","0.5267504","0.52673024","0.52626395","0.5256301","0.525491","0.52465355","0.5243517","0.52424955","0.52396333","0.5235186","0.52339566","0.52319664","0.5223859","0.52218145","0.5213398","0.5208817","0.5208165","0.52072054","0.52062654","0.520056","0.52004325","0.51904464","0.51892436","0.51881456","0.5177063","0.5174925","0.5173077","0.5168505","0.5166244","0.51645905","0.5164481","0.5163435","0.51597655","0.51571524","0.51571524","0.5152073","0.5150184"],"string":"[\n \"0.6986698\",\n \"0.6440675\",\n \"0.6303851\",\n \"0.62154716\",\n \"0.6090882\",\n \"0.608178\",\n \"0.6064316\",\n \"0.6046675\",\n \"0.5970051\",\n \"0.5952641\",\n \"0.58843154\",\n \"0.58391774\",\n \"0.5755268\",\n \"0.57513607\",\n \"0.5732901\",\n \"0.57144207\",\n \"0.56996477\",\n \"0.56742626\",\n \"0.55769104\",\n \"0.5570763\",\n \"0.55672246\",\n \"0.5563812\",\n \"0.5538002\",\n \"0.5537267\",\n \"0.55359256\",\n \"0.55262315\",\n \"0.55261034\",\n \"0.549746\",\n \"0.54852664\",\n \"0.54820013\",\n \"0.5464897\",\n \"0.546331\",\n \"0.5462606\",\n \"0.54512084\",\n \"0.54394567\",\n \"0.54392\",\n \"0.54376733\",\n \"0.5436749\",\n \"0.5430331\",\n \"0.5430227\",\n \"0.5414889\",\n \"0.5404854\",\n \"0.53959656\",\n \"0.53924465\",\n \"0.5389543\",\n \"0.5381824\",\n \"0.5353596\",\n \"0.53400016\",\n \"0.5331209\",\n \"0.53285956\",\n \"0.5325077\",\n \"0.5317701\",\n \"0.5310453\",\n \"0.5306554\",\n \"0.53053343\",\n \"0.5299282\",\n \"0.5288763\",\n \"0.52857494\",\n \"0.52827495\",\n \"0.5276957\",\n \"0.52757156\",\n \"0.5274571\",\n \"0.52676976\",\n \"0.5267504\",\n \"0.52673024\",\n \"0.52626395\",\n \"0.5256301\",\n \"0.525491\",\n \"0.52465355\",\n \"0.5243517\",\n \"0.52424955\",\n \"0.52396333\",\n \"0.5235186\",\n \"0.52339566\",\n \"0.52319664\",\n \"0.5223859\",\n \"0.52218145\",\n \"0.5213398\",\n \"0.5208817\",\n \"0.5208165\",\n \"0.52072054\",\n \"0.52062654\",\n \"0.520056\",\n \"0.52004325\",\n \"0.51904464\",\n \"0.51892436\",\n \"0.51881456\",\n \"0.5177063\",\n \"0.5174925\",\n \"0.5173077\",\n \"0.5168505\",\n \"0.5166244\",\n \"0.51645905\",\n \"0.5164481\",\n \"0.5163435\",\n \"0.51597655\",\n \"0.51571524\",\n \"0.51571524\",\n \"0.5152073\",\n \"0.5150184\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.837678"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":11269,"cells":{"query":{"kind":"string","value":"Returns True if |benchmark| is a valid fuzzbench benchmark."},"document":{"kind":"string","value":"def validate(benchmark):\n if not validate_name(benchmark):\n return False\n\n if benchmark not in get_all_benchmarks():\n logs.error('%s must have a benchmark.yaml.', benchmark)\n return False\n\n # Validate config file can be parsed.\n try:\n get_fuzz_target(benchmark)\n except yaml.parser.ParserError:\n logs.error('%s must have a valid benchmark.yaml file. Failed to parse.',\n benchmark)\n return False\n except KeyError:\n logs.error('%s\\'s benchmark.yaml does not define \"fuzz_target\".',\n benchmark)\n return False\n\n # Validate type.\n return validate_type(benchmark)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def validate_type(benchmark):\n benchmark_type = get_type(benchmark)\n if benchmark_type not in BENCHMARK_TYPE_STRS:\n logs.error('%s has an invalid benchmark type %s, must be one of %s',\n benchmark, benchmark_type, BENCHMARK_TYPE_STRS)\n return False\n return True","def validate_name(benchmark):\n if VALID_BENCHMARK_REGEX.match(benchmark) is None:\n logs.error('%s does not conform to %s pattern.', benchmark,\n VALID_BENCHMARK_REGEX.pattern)\n return False\n return True","def is_result_appropriate(self, benchmark_result):\n return True","def _is_fan(compressor: Compressor) -> bool:\n name = compressor.name.lower()\n return 'fan' in name or 'crtf' in name","def test_not_a_valid_fuzzer(self):\n self.assertFalse(cifuzz.check_fuzzer_build(TEST_FILES_PATH))","def check_benchmark_result(result, expectation):\n for storage_cfg, caches in result['cache_data'].items():\n for cache, percent_recorded in caches.items():\n if ((percent_recorded['min'] < expectation['min'])\n or (percent_recorded['avg'] < expectation['avg'])\n or (percent_recorded['max'] < expectation['max'])):\n return False\n return True","def get_benchmark(self, benchmark):\n\t\tif not isinstance(benchmark, str) and not callable(benchmark): return benchmark\n\t\telif benchmark in self.classes:\treturn self.classes[benchmark]()\n\t\traise TypeError('Passed benchmark is not defined!')","def validate(self, benchmarks):\n class_code = self.setup_src\n instance_creation = '\\ninstance = {}'.format(self.stmt)\n for i, benchmark in enumerate(benchmarks):\n if not benchmark.result_validation:\n break\n\n validation_code = class_code + instance_creation + '\\nvalidation_result = ' + benchmark.stmt\n validation_scope = {}\n exec(validation_code, validation_scope)\n # Store the result in the first function in the group.\n if i == 0:\n compare_against_function = benchmarks[0].callable.__name__\n compare_against_result = validation_scope['validation_result']\n logging.info('PyPerform: Validating group \"{b.group}\" against method '\n '\"{b.classname}.{b.callable.__name__}\"'.format(b=benchmarks[0]))\n else:\n if compare_against_result == validation_scope['validation_result']:\n logging.info('PyPerform: Validating {b.classname}.{b.callable.__name__}......PASSED!'\n .format(b=benchmark))\n else:\n error = 'Results of functions {0} and {1} are not equivalent.\\n{0}:\\t {2}\\n{1}:\\t{3}'\n raise ValidationError(error.format(compare_against_function, benchmark.callable.__name__,\n compare_against_result, validation_scope['validation_result']))","def get_fuzz_target(benchmark):\n # Do this because of OSS-Fuzz-on-demand.\n # TODO(metzman): Use classes to mock a benchmark config for\n # OSS_FUZZ_ON_DEMAND.\n return benchmark_config.get_config(benchmark).get(\n 'fuzz_target', environment.get('FUZZ_TARGET'))","def isValidTest(self):\n if not self.hasError():\n return False\n distance = dameraulevenshtein(self.word, self.error) \n if(distance > 1):\n return False\n regex = '.*[^a-zA-Z].*'\n if re.match(regex, self.word) or re.match(regex, self.error):\n return False\n return True","def is_mbid(mbid):\n try:\n mbid = uuid.UUID(mbid)\n good = True\n except ValueError as e:\n good = False\n except AttributeError:\n good = False\n\n return good","def _is_valid_adapter(self, adapter_name):\n\n valid_adapters = self.valid_adapters\n\n result = False\n\n if adapter_name in valid_adapters:\n\n result = True\n\n return result","def test_BenchmarkSuite_integration_test(\n benchmark_suite: typing.Callable, tempdir: pathlib.Path\n):\n with benchmark_suite() as bs:\n bs.ForceOpenCLEnvironment(cldrive_env.OclgrindOpenCLEnvironment())\n observer = MockBenchmarkObserver(stop_after=1)\n\n # `stop_after` raises BenchmarkInterrupt.\n try:\n bs.Run([observer])\n assert False\n except gpgpu.BenchmarkInterrupt:\n pass\n\n assert len(observer.logs) == 1\n assert observer.logs[0].benchmark_name in bs.benchmarks","def __isFastener(f):\n\n if type(f) != Fastener:\n raise TypeError(\"FastnerGroups may contain only Fasteners\")\n else:\n return True","def assert_models_equal(self, benchmark1, benchmark2):\n if (not isinstance(benchmark1, ate.BenchmarkATE) or\n not isinstance(benchmark2, ate.BenchmarkATE)):\n self.fail('object was not a BenchmarkATE')\n self.assertEqual(benchmark1.identifier, benchmark2.identifier)\n self.assertEqual(benchmark1.offset, benchmark2.offset)\n self.assertEqual(benchmark1.max_difference, benchmark2.max_difference)\n self.assertEqual(benchmark1.scale, benchmark2.scale)","def is_valid(passwd: str) -> bool:\n return (\n re.search(r'abc|bcd|cde|def|efg|fgh|ghi|hij|jkl|klm|lmn|mno|nop|opq|pqr|qrs|rst|stu|tuv|uvw|vwx|wxy|xyz', passwd) is not None and\n all([c not in passwd for c in 'iol']) and\n re.search(r'([a-z])\\1.*([a-z])\\2', passwd) is not None\n )","def create_benchmark(self, benchmark):\n self.crd_client.create_namespaced_custom_object(\n group=\"ripsaw.cloudbulldozer.io\",\n version=\"v1alpha1\",\n namespace=benchmark[\"metadata\"][\"namespace\"],\n plural=\"benchmarks\",\n body=benchmark,\n )","def is_valid(self, user_specific_config: Any, factor: str) -> bool:","def check(self, password):\n\n if len(password) < self.min_length:\n return False\n\n digits = len(findall(r\"\\d\", password))\n if digits < self.min_digits:\n return False\n\n special_chars = sum(v for k, v in Counter(password).items() if k in punctuation)\n if special_chars < self.min_special:\n return False\n\n alpha_chars = sum(v for k, v in Counter(password).items() if k in ascii_letters)\n if alpha_chars < self.min_alpha:\n return False\n\n upper_chars = sum(\n v for k, v in Counter(password).items() if k in ascii_uppercase\n )\n if upper_chars < self.min_upper:\n return False\n\n lower_chars = sum(\n v for k, v in Counter(password).items() if k in ascii_lowercase\n )\n if lower_chars < self.min_lower:\n return False\n\n if self.check_breaches and check_password(password):\n return False\n\n if self.func and not self.func(password):\n return False\n\n return True","def is_cpp(benchmark):\n return get_language(benchmark) == 'c++'","def _quick_and_dirty_glyph_is_empty(font, glyph_name):\n if 'glyf' in font:\n glyph = font['glyf'][glyph_name]\n if not glyph.isComposite():\n if glyph.numberOfContours == 0:\n return True\n return False\n elif 'CFF2' in font:\n top_dict = font['CFF2'].cff.topDictIndex[0]\n else:\n top_dict = font['CFF '].cff.topDictIndex[0]\n char_strings = top_dict.CharStrings\n char_string = char_strings[glyph_name]\n if len(char_string.bytecode) <= 1:\n return True\n return False","def assert_models_equal(self, benchmark1, benchmark2):\n if (not isinstance(benchmark1, detection_comp.FeatureDetectionComparison) or\n not isinstance(benchmark2, detection_comp.FeatureDetectionComparison)):\n self.fail('object was not a FeatureDetectionComparison')\n self.assertEqual(benchmark1.identifier, benchmark2.identifier)\n self.assertEqual(benchmark1._acceptable_radius, benchmark2._acceptable_radius)","def is_valid(name):\n return bool(name)","def is_valid(self):\n # check if cpf isn't in invalid_cpfs list\n if self.cpf in self.invalid_cpfs: return False\n\n # get first nine digits to calculate two verification digits\n cpf = self.cpf[:9]\n # while cpf isn't complete (this runs two loops)\n while len(cpf) < 11:\n\n # run trought numbers multiplying number (v) by weight (len(cpf)+1-i)\n # and then get sum rest of division by 11 as integer\n r = int(sum([math.floor((len(cpf)+1-i_v[0])*i_v[1]) for i_v in enumerate(cpf)]) % 11)\n\n # if digit is smaller than 2, turns 0\n if r < 2:\n f = 0\n else:\n f = 11 -r\n\n # append to cpf list\n cpf.append(f)\n\n # if created number is same as original number, cpf is valid\n return bool(cpf == self.cpf)","def is_system_ready_for_benchmarking():\n\n # check if scaling_governor is set to 'performance' for all cpu cores\n cpu_governors = glob.glob('/sys/devices/system/cpu/cpu*/cpufreq/scaling_governor')\n if not cpu_governors:\n logger.error('no scaling_governor found. Do you run on a Linux System?')\n return False\n for governor in sorted(cpu_governors):\n with open(governor, 'r') as f:\n line = f.read().splitlines()[0]\n logger.debug('%s is set to \\\"%s\\\"', governor, line)\n if line != 'performance':\n logger.warning('please set all scaling_governor to \\\"performance\\\" (using \"sudo ./ondemand.sh start\")')\n return False\n\n return True","def assert_passes_fuzz(self, feature_spec, tries=1000):\n feature_spec = make_feature(feature_spec)\n for i in range(tries):\n data_point = generate.generate(feature_spec.input_schema)\n try:\n feature = feature_spec(data_point)\n except Exception as e:\n self.fail(\"Error evaluating; input=%r error=%r\" %\n (data_point, e))\n try:\n feature_spec.output_schema.validate(feature)\n except schema.SchemaError:\n self.fail(\"Invalid output schema; input=%r output=%r\" %\n (data_point, feature))","def run_benchmark(curl, benchmark, test_config = TestConfig()):\n\n warmup_runs = benchmark.warmup_runs\n benchmark_runs = benchmark.benchmark_runs\n message = '' #Message is name of benchmark... print it?\n\n if (warmup_runs <= 0):\n raise Exception(\"Invalid number of warmup runs, must be > 0 :\" + warmup_runs)\n if (benchmark_runs <= 0):\n raise Exception(\"Invalid number of benchmark runs, must be > 0 :\" + benchmark_runs)\n\n #Initialize variables to store output\n output = BenchmarkResult()\n output.name = benchmark.name\n output.group = benchmark.group\n metricnames = list(benchmark.metrics)\n metricvalues = [METRICS[name] for name in metricnames] # Metric variable for curl, to avoid hash lookup for every metric name\n results = [list() for x in xrange(0, len(metricnames))] # Initialize arrays to store results for each metric\n\n curl.setopt(pycurl.WRITEFUNCTION, lambda x: None) #Do not store actual response body at all.\n\n #Benchmark warm-up to allow for caching, JIT compiling, on client\n logging.info('Warmup: ' + message + ' started')\n for x in xrange(0, warmup_runs):\n if benchmark.method == u'POST' or benchmark.method == u'PUT':\n curl.setopt(curl.READFUNCTION, StringIO.StringIO(benchmark.body).read)\n curl.perform()\n logging.info('Warmup: ' + message + ' finished')\n\n logging.info('Benchmark: ' + message + ' starting')\n\n for x in xrange(0, benchmark_runs): # Run the actual benchmarks\n if benchmark.method == u'POST' or benchmark.method == u'PUT':\n curl.setopt(curl.READFUNCTION, StringIO.StringIO(benchmark.body).read)\n\n try: # Run the curl call, if it errors, then add to failure counts for benchmark\n curl.perform()\n except Exception:\n output.failures = output.failures + 1\n continue # Skip metrics collection\n\n # Get all metrics values for this run, and store to metric lists\n for i in xrange(0, len(metricnames)):\n results[i].append( curl.getinfo(metricvalues[i]) )\n\n logging.info('Benchmark: ' + message + ' ending')\n\n temp_results = dict()\n for i in xrange(0, len(metricnames)):\n temp_results[metricnames[i]] = results[i]\n output.results = temp_results\n\n curl.close()\n return analyze_benchmark_results(output, benchmark)","def CheckPrerequisites(benchmark_config):\n dpb_service_type = benchmark_config.dpb_service.service_type\n if dpb_service_type not in SUPPORTED_DPB_BACKENDS:\n raise errors.Config.InvalidValue('Invalid backend for distcp. Not in:{}'.\n format(str(SUPPORTED_DPB_BACKENDS)))","def assert_models_equal(self, benchmark1, benchmark2):\n if (not isinstance(benchmark1, detection_comp.FeatureDetectionComparisonResult) or\n not isinstance(benchmark2, detection_comp.FeatureDetectionComparisonResult)):\n self.fail('object was not a FeatureDetectionComparisonResult')\n self.assertEqual(benchmark1.identifier, benchmark2.identifier)\n self.assertEqual(benchmark1.success, benchmark2.success)\n self.assertEqual(benchmark1.benchmark, benchmark2.benchmark)\n self.assertEqual(benchmark1.trial_result, benchmark2.trial_result)\n self.assertEqual(benchmark1.reference_trial_result, benchmark2.reference_trial_result)\n self.assertEqual(benchmark1._feature_changes, benchmark2._feature_changes)\n self.assertEqual(benchmark1._changes_id, benchmark2._changes_id)","def fuzz():\n if FUZZ:\n time.sleep(random.random())","def isValid(self):\n return self.isOutOfDate() == False \\\n and self.isQCValid() == True \\\n and self.getDisposeUntilNextCalibrationTest() == False \\\n and self.isValidationInProgress() == False \\\n and self.isCalibrationInProgress() == False","def sanity_check(self):\n res = True\n res = res and self.detected\n res = res and np.sum(self.diffs) < 30000 # experimental value\n return res","def is_valid_mbart(self) -> bool:\r\n if self.normalize_before and self.add_final_layer_norm and self.scale_embedding:\r\n return True\r\n if self.normalize_before or self.add_final_layer_norm or self.scale_embedding:\r\n logger.info(\"This configuration is a mixture of MBART and BART settings\")\r\n return False","def isQCValid(self):\n for last in self.getLatestReferenceAnalyses():\n rr = last.aq_parent.getResultsRangeDict()\n uid = last.getServiceUID()\n if uid not in rr:\n # This should never happen.\n # All QC Samples must have specs for its own AS\n continue\n\n specs = rr[uid];\n try:\n smin = float(specs.get('min', 0))\n smax = float(specs.get('max', 0))\n error = float(specs.get('error', 0))\n target = float(specs.get('result', 0))\n result = float(last.getResult())\n error_amount = ((target / 100) * error) if target > 0 else 0\n upper = smax + error_amount\n lower = smin - error_amount\n if result < lower or result > upper:\n return False\n except:\n # This should never happen.\n # All Reference Analysis Results and QC Samples specs\n # must be floatable\n continue\n\n return True","def is_decay_limits(limitfile):\n tokens = os.path.splitext(os.path.basename(limitfile))[0].split('_')\n if tokens[3] in ['point', 'dmap', 'dradial']:\n return True\n return tokens[2] in ['point', 'dmap', 'dradial']","def is_valid(values, dataset):\r\n # Only includes negative screens.\r\n if values[SCREEN_TYPE_COL] != \"negative selection\":\r\n STATS[NOT_NEG_SCREEN] += 1\r\n return False\r\n # Targets must have the correct length.\r\n if int(values[dataset.end_idx]) - int(values[dataset.start_idx]) !=\\\r\n consts.TARGET_LEN:\r\n STATS[WRONG_END_MINUS_START] += 1\r\n return False\r\n\r\n target = dataset.get_target(values)\r\n # Targets must have an NGG PAM sequence.\r\n if not target.endswith(\"GG\"):\r\n STATS[BAD_PAM] += 1\r\n return False\r\n # Another safety measure against targets with the wrong length.\r\n if len(target) != consts.TARGET_LEN:\r\n STATS[TARGET_BAD_LEN] += 1\r\n return False\r\n return True","def Run(benchmark_spec):\n _UpdateBenchmarkSpecWithFlags(benchmark_spec)\n vm = benchmark_spec.vms[0]\n if benchmark_spec.tpus:\n # For MLPerf 1.0, the benchmake code of different hardware are different.\n if (benchmark_spec.tpu_groups['train'].GetAcceleratorType() == 'v3-32' or\n benchmark_spec.tpu_groups['train'].GetAcceleratorType() == 'v3-128' or\n benchmark_spec.tpu_groups['train'].GetAcceleratorType() == 'v3-256' or\n benchmark_spec.tpu_groups['train'].GetAcceleratorType() == 'v3-512' or\n benchmark_spec.tpu_groups['train'].GetAcceleratorType() == 'v3-1024' or\n benchmark_spec.tpu_groups['train'].GetAcceleratorType() == 'v3-2048'):\n run_path = (\n '$HOME/training_results_{version}/Google/benchmarks/{model}/tpu-{tpus}'\n .format(\n version=VERSION.value,\n model=benchmark_spec.benchmark,\n tpus=benchmark_spec.tpu_groups['train'].GetAcceleratorType()))\n code_path = (\n '$HOME/training_results_{version}/Google/benchmarks/{model}/implementations/tpu-{tpus}-{model}'\n .format(\n version=VERSION.value,\n model=benchmark_spec.benchmark,\n tpus=benchmark_spec.tpu_groups['train'].GetAcceleratorType()))\n\n if MASK in benchmark_spec.benchmark:\n model = 'mask_rcnn'\n elif GNMT in benchmark_spec.benchmark:\n model = 'nmt'\n else:\n model = benchmark_spec.benchmark\n\n mlperf_benchmark_cmd = (\n 'cd {code_path} && '\n 'export PYTHONPATH=$(pwd):$(pwd)/{model} && '\n 'cd {model} && '\n '{run_path}/run_and_time.sh'.format(\n code_path=code_path,\n model=model,\n run_path=run_path))\n\n if SSD in benchmark_spec.benchmark:\n mlperf_benchmark_cmd = (\n 'export '\n 'MLP_GCS_RESNET_CHECKPOINT={checkpoint}'\n ' && {cmd}'.format(\n checkpoint=FLAGS.mlperf_gcs_resnet_checkpoint,\n cmd=mlperf_benchmark_cmd))\n else:\n raise ValueError(\n 'MLPerf configurations do not support the hardware in PKB. PKB may '\n 'need to be updated if this is a new TPU type.')\n\n else:\n run_sub_paths = {RESNET: 'resnet/implementations/mxnet',\n TRANSFORMER: 'transformer/implementations/pytorch',\n MINIGO: 'minigo/implementations/tensorflow',\n MASK: 'maskrcnn/implementations/pytorch',\n GNMT: 'gnmt/implementations/pytorch',\n SSD: 'ssd/implementations/pytorch',\n BERT: 'bert/implementations/pytorch',}\n benchmark_path = f'$HOME/training_results_{VERSION.value}/NVIDIA/benchmarks'\n run_path = posixpath.join(benchmark_path,\n run_sub_paths[benchmark_spec.benchmark])\n env = {\n 'DGXSYSTEM': DGXSYSTEM,\n 'NEXP': 1,\n 'PULL': 0,\n 'LOGDIR': f'/tmp/{benchmark_spec.benchmark}',\n }\n envs = {\n RESNET: {},\n TRANSFORMER: {'DATADIR': '/data/wmt/utf8'},\n MINIGO: {'CONT': 'mlperf-nvidia:minigo'},\n MASK: {},\n GNMT: {'DATADIR': '/data/gnmt'},\n SSD: {'DATADIR': '/data'},\n BERT: {}\n }\n env.update(envs[benchmark_spec.benchmark])\n\n run_script = posixpath.join(run_path, 'run_with_docker.sh')\n vm_util.ReplaceText(vm, 'SYSLOGGING=1', 'SYSLOGGING=0', run_script)\n vm_util.ReplaceText(vm, 'docker exec -it', 'docker exec -t', run_script)\n if benchmark_spec.benchmark == RESNET:\n vm_util.ReplaceText(vm, r'mpirun.*run_and_time\\.sh',\n r'.\\/run_and_time.sh', run_script)\n\n env = ' '.join(f'{key}={value}' for key, value in env.items())\n if nvidia_driver.CheckNvidiaGpuExists(vm):\n env = f'{tensorflow.GetEnvironmentVars(vm)} {env}'\n\n mlperf_benchmark_cmd = (\n f'chmod 755 {run_script} && '\n f'cd {run_path} && '\n f'{env} {run_script}')\n\n samples = []\n metadata = _CreateMetadataDict(benchmark_spec)\n stdout, _ = vm.RobustRemoteCommand(mlperf_benchmark_cmd)\n if NONE in FLAGS.mlperf_profiler:\n samples.extend(\n MakeSamplesFromOutput(\n metadata,\n stdout,\n use_tpu=bool(benchmark_spec.tpus),\n model=benchmark_spec.benchmark))\n return samples","def test_not_a_valid_fuzz_path(self):\n self.assertFalse(cifuzz.check_fuzzer_build('not/a/valid/path'))","def is_valid(self):\n if self.answer_type == 'F':\n return bool(self.text)\n\n if not self.answers.count():\n return False\n if not self.answers.filter(correct=True).count():\n return False\n return True","def is_good_bin(self, bin_num: int, min_amp: float = 0.25, min_corr: float = 0.10) -> bool:\n # Verify Amplitude data exist and all is good for the bin\n if self.IsAmplitude and not self.Amplitude.is_good_bin(bin_num, min_amp):\n return False\n # Verify Correlation data exist and all is good for the bin\n if self.IsCorrelation and not self.Correlation.is_good_bin(bin_num, min_corr):\n return False\n\n # Verify Earth Velocity data exist and all is good for the bin\n if self.IsEarthVelocity and not self.EarthVelocity.is_good_bin(bin_num):\n return False\n\n return True","def _IsApplicable(self, manifest):\n check_list = [(self.tool, manifest.tool), (self.board, manifest.board)]\n\n return all(fnmatch(text, pattern) for text, pattern in check_list)","def get_type(benchmark):\n # TODO(metzman): Use classes to mock a benchmark config for\n # OSS_FUZZ_ON_DEMAND.\n default_value = os.getenv('EXPERIMENT_TYPE', BenchmarkType.CODE.value)\n return benchmark_config.get_config(benchmark).get('type', default_value)","def check_for_token(token):\n try:\n decode_token(token)\n return True\n except:\n return False","def check_frame_type(self, ftype, fitstbl, exprng=None):\n good_exp = framematch.check_frame_exptime(fitstbl['exptime'], exprng)\n if ftype in ['science', 'standard']:\n return good_exp & (np.logical_not(np.char.startswith(np.char.lower(fitstbl['target']), 'arclamp'))) & \\\n (np.char.lower(fitstbl['target']) != 'spectralflat') & \\\n (np.char.lower(fitstbl['target']) != 'bias')\n if ftype in ['arc', 'tilt']:\n return good_exp & (np.char.startswith(np.char.lower(fitstbl['target']), 'arclamp'))\n if ftype in ['pixelflat', 'trace', 'illumflat']:\n return good_exp & (np.char.lower(fitstbl['target']) == 'spectralflat')\n if ftype == 'bias':\n return good_exp & (np.char.lower(fitstbl['target']) == 'bias')\n\n msgs.warn('Cannot determine if frames are of type {0}.'.format(ftype))\n return np.zeros(len(fitstbl), dtype=bool)","def _is_valid_keyspace_name(self, keyspace_name):\n if keyspace_name == None or not keyspace_name:\n return False\n return re.match(r\"^[a-z_]*[^-]$\", keyspace_name)","def should_evaluate(self, epoch: int) -> bool:\n return False","def has_shuffle(self):\r\n return hasattr(self, '_has_shuffle')","def valid(f):\n try:\n return not re.search(r'\\b0[0-9]', f) and eval(f) is True\n # \"\\b\" is a word boundary\n \"\"\"We need to exclude numbers starting with zero,\n as these are interpretted as base8 (octal). This in\n turn could cause interpretation errors, and exceptions\n (for example 09 is not octal and will throw and exception)\"\"\"\n except (ArithmeticError, SyntaxError):\n return False","def check_banned(deck_format, card_name):\n if card_name in consts.BANNINGS[deck_format]:\n return True\n return False","def is_valid_calendar(self, calendar):\n\n return calendar in self.available_calendars() or calendar == \"\"","def is_valid_sample_rate(rate):\n # type: (Any) -> bool\n\n # both booleans and NaN are instances of Real, so a) checking for Real\n # checks for the possibility of a boolean also, and b) we have to check\n # separately for NaN and Decimal does not derive from Real so need to check that too\n if not isinstance(rate, (Real, Decimal)) or math.isnan(rate):\n logger.warning(\n \"[Tracing] Given sample rate is invalid. Sample rate must be a boolean or a number between 0 and 1. Got {rate} of type {type}.\".format(\n rate=rate, type=type(rate)\n )\n )\n return False\n\n # in case rate is a boolean, it will get cast to 1 if it's True and 0 if it's False\n rate = float(rate)\n if rate < 0 or rate > 1:\n logger.warning(\n \"[Tracing] Given sample rate is invalid. Sample rate must be between 0 and 1. Got {rate}.\".format(\n rate=rate\n )\n )\n return False\n\n return True","def is_valid_type(self, question_type):\n\t\treturn question_type in self.valid_types","def test_correct_fuzzer_build(self):\n test_fuzzer_dir = os.path.join(TEST_FILES_PATH, 'out')\n self.assertTrue(cifuzz.check_fuzzer_build(test_fuzzer_dir))","def _check_procmon_failures(self, target):\n if target.procmon:\n self._fuzz_data_logger.open_test_step(\"Contact process monitor\")\n self._fuzz_data_logger.log_check(\"procmon.post_send()\")\n if target.procmon.post_send():\n self._fuzz_data_logger.log_pass(\"No crash detected.\")\n else:\n self._fuzz_data_logger.log_fail(\n \"procmon detected crash on test case #{0}: {1}\".format(self.total_mutant_index,\n target.procmon.get_crash_synopsis()))\n return True\n return False","def valid(self, nt_header):\n try:\n return (self.AddressOfFunctions < nt_header.OptionalHeader.SizeOfImage and\n self.AddressOfNameOrdinals < nt_header.OptionalHeader.SizeOfImage and\n self.AddressOfNames < nt_header.OptionalHeader.SizeOfImage and\n self.NumberOfFunctions < 0x7FFF and\n self.NumberOfNames < 0x7FFF)\n except obj.InvalidOffsetError:\n return False","def check_tbe_support(json_desc):\n if \"buffer_stitch\" in json_desc:\n logger.info(\"TBE not supports buffer stitch\")\n return False\n\n if \"parallel_fusion\" in json_desc:\n logger.info(\"TBE not supports parallel fusion\")\n return False\n\n if not json_desc.get(\"input_desc\"):\n logger.info(\"TBE not supports empty inputs\")\n return False\n\n for op in json_desc[\"op_desc\"]:\n op_name = op[\"name\"]\n if not get_op_reg_info(op_name, \"func\", False):\n logger.info(\"TBE op not registered: {}\".format(op_name))\n return False\n return True","def valid_builder(buildername):\n builders = query_builders()\n if buildername in builders:\n LOG.debug(\"Buildername %s is valid.\" % buildername)\n return True\n else:\n LOG.warning(\"Buildername %s is *NOT* valid.\" % buildername)\n LOG.info(\"Check the file we just created builders.txt for \"\n \"a list of valid builders.\")\n with open(path_to_file('builders.txt'), \"wb\") as fd:\n for b in sorted(builders):\n fd.write(b + \"\\n\")\n\n return False","def valid(f):\r\n try:\r\n return not re.search(r'\\b0[0-9]', f) and eval(f) is True\r\n except ArithmeticError:\r\n return False","def mnemonic_is_valid(mnemonic: str, wordlist=WORDLIST):\n try:\n mnemonic_to_bytes(mnemonic, wordlist=wordlist)\n return True\n except Exception as e:\n return False","def analyze_benchmark_results(benchmark_result, benchmark):\n\n output = BenchmarkResult()\n output.name = benchmark_result.name\n output.group = benchmark_result.group\n output.failures = benchmark_result.failures\n\n # Copy raw metric arrays over where necessary\n raw_results = benchmark_result.results\n temp = dict()\n for metric in benchmark.raw_metrics:\n temp[metric] = raw_results[metric]\n output.results = temp\n\n # Compute aggregates for each metric, and add tuples to aggregate results\n aggregate_results = list()\n for metricname, aggregate_list in benchmark.aggregated_metrics.iteritems():\n numbers = raw_results[metricname]\n for aggregate_name in aggregate_list:\n if numbers: # Only compute aggregates if numbers exist\n aggregate_function = AGGREGATES[aggregate_name]\n aggregate_results.append( (metricname, aggregate_name, aggregate_function(numbers)) )\n else:\n aggregate_results.append( (metricname, aggregate_name, None) )\n\n output.aggregates = aggregate_results\n return output","def is_valid_flag(flag):\n return False if re.match(\"^FAUST_[A-Za-z0-9/\\\\+]{32}$\", flag) is None else True","def is_dev_name_valid(self):\n return self._name_re.match(self.dev_name) is not None","def checkDeviceCanRunTestWithMask(self, device, testMask):\n return testMask == \"*\" or device.name.startswith(testMask)","def is_busy(self):\n threads = len(self.executor._threads)\n if threads == 0:\n return False\n\n capacity = self.executor._work_queue.qsize() / float(threads)\n if capacity > 2:\n return True\n elif capacity < 1:\n return False\n else:\n return capacity > (random.random() + 1)","def is_complex(num):\n try:\n complex(num)\n except Exception:\n return False\n return True","def isValidZ(self, z):\n if (z <= self.getZMin()):\n return False\n if (z >= self.getZMax()):\n return False\n else:\n return True","def check_valid(model, dataset):\n if hasattr(model.model, 'n_classes_'):\n if (nc := model.model.n_classes_) != dataset.cfg.n_classes:\n warnings.warn(\n f'Not all classes present in train data. '\n f'Skipping run.')\n return False\n return True","def isquantized(val):\n # try: 1.1 * val ... except: might be more general, but could be expensive.\n retval = True\n if isinstance(val, (float, complex)):\n retval = False\n elif hasattr(val, 'issubdtype'):\n if numpy.issubdtype(val.dtype, float) or numpy.issubdtype(val.dtype, complex):\n retval = False\n return retval","def __isValidColor(self, name):\n try:\n if self.__isHexString(name) and len(name) in [3, 6, 9, 12]:\n return True\n return QColor.isValidColor(name)\n except AttributeError:\n if name.startswith(\"#\"):\n if len(name) not in [4, 7, 10, 13]:\n return False\n hexCheckStr = name[1:]\n return self.__isHexString(hexCheckStr)\n else:\n if self.__isHexString(name) and len(name) in [3, 6, 9, 12]:\n return True\n return name in QColor.colorNames()","def valid(f):\n try:\n return not re.search(r'\\b0[0-9]', f) and eval(f) is True\n except ArithmeticError:\n return False","def valid(self) -> bool:\n pass","def _UpdateBenchmarkSpecWithFlags(benchmark_spec):\n benchmark_spec.imagenet_data_dir = FLAGS.imagenet_data_dir\n benchmark_spec.benchmark = FLAGS.mlperf_benchmark\n benchmark_spec.wmt_data_dir = FLAGS.wmt_data_dir\n benchmark_spec.coco_data_dir = FLAGS.coco_data_dir\n benchmark_spec.gnmt_data_dir = FLAGS.gnmt_data_dir\n benchmark_spec.bert_data_dir = FLAGS.bert_data_dir\n benchmark_spec.gcp_service_account = FLAGS.gcp_service_account","def check_blacklist(auth_token):\n token = BlacklistToken.query.filter_by(token=str(auth_token)).first()\n if token:\n return True\n\n return False","def testValid(self):\n validate = timing_util.ValidateMeasurementsFlag\n self.assertIs(validate([]), True)\n self.assertIs(validate(['none']), True)\n self.assertIs(validate(['end_to_end_runtime']), True)\n self.assertIs(validate(['runtimes']), True)\n self.assertIs(validate(['timestamps']), True)\n self.assertIs(validate(['end_to_end_runtime', 'runtimes']), True)\n self.assertIs(validate(['end_to_end_runtime', 'timestamps']), True)\n self.assertIs(validate(['runtimes', 'timestamps']), True)\n self.assertIs(\n validate(['end_to_end_runtime', 'runtimes', 'timestamps']), True)","def is_valid(self, attr: Optional[str] = None) -> bool:\n try:\n self.validate(attr)\n except (TypeError, ValueError):\n return False\n return True","def is_valid_cluster(self, cluster):\n return (cluster - 1) < self.max_clusters","def test_cli_classic_fizzbuzz_fail(self):\n r = self._runner.invoke(app, [CLASSIC_FIZZBUZZ_COMMAND, 'wwe'])\n self.assertNotEqual(r.exit_code, 0)","def verify(self):\n if not self.verify_succ_pred_fingers():\n return False\n\n return True","def _isnotsuite(test):\n try:\n iter(test)\n except TypeError:\n return True\n return False","def test_BenchmarkSuite_invalid_path_access(benchmark_suite: typing.Callable):\n bs = benchmark_suite()\n with test.Raises(TypeError):\n _ = bs.path","def _is_stack_cannery_check_bb(self, xbb):\n return self._is_stack_cannery_check_bb_x86(xbb)","def is_valid(self):\n try:\n self.validate()\n return True\n except (TypeError, ValueError) as e:\n return False","def is_valid_type(self, attr: Optional[str] = None) -> bool:\n try:\n self.validate_type(attr)\n except TypeError:\n return False\n return True","def is_dc(test_cases,foo):\r\n \r\n was_true=False\r\n was_false=False\r\n if len(test_cases)==1:\r\n return False\r\n else: \r\n for test_case in test_cases:\r\n if foo(*test_case) == True:\r\n was_true=True\r\n if foo(*test_case) == False:\r\n was_false=True\r\n return was_true and was_false","def _is_validation(video_id):\n hasher = md5()\n hasher.update(bytes(video_id, 'utf-8'))\n first = hasher.hexdigest()[0]\n return first in ['0', '1']","def is_unit(self, tolerance=1e-14):\n return abs(1.0 - self._sum_of_squares()) < tolerance # if _sum_of_squares is 1, norm is 1. This saves a call to sqrt()","def assert_data_correct(self) -> bool:\n corr_char = self.assert_data_characters_correct()\n print(\"Character data correct?\", corr_char)\n corr_font = FontImages().assert_data_correct()\n print(\"Font data correct?\", corr_font)\n corr_frag = self.assert_data_fragments_correct()\n print(\"Fragment data correct?\", corr_frag)\n corr_train_aug = self.assert_train_augmented()\n print(\"Train data augmented?\", corr_train_aug)\n truth_agree = corr_char and corr_font and corr_frag\n return True if truth_agree else False","def test_get_benchmark_methods_exact_match(self):\n config = mock.Mock()\n config.workspace = 'workspace'\n config.benchmark_method_patterns = [\n 'new_foo.BenchmarkClass.benchmark_method_1',\n 'new_foo.BenchmarkClass.benchmark_method_2']\n benchmark_runner = benchmark.BenchmarkRunner(config)\n\n methods = benchmark_runner._get_benchmark_methods()\n self.assertEqual(['new_foo.BenchmarkClass.benchmark_method_1',\n 'new_foo.BenchmarkClass.benchmark_method_2'], methods)","def should_evaluate(self, epoch: int) -> bool:\n raise NotImplementedError","def isValidDataTypeName(name: unicode) -> bool:\n ...","def isValidFeatureWriter(klass):\n if not isclass(klass):\n logger.error(\"%r is not a class\", klass)\n return False\n if not hasattr(klass, \"tableTag\"):\n logger.error(\"%r does not have required 'tableTag' attribute\", klass)\n return False\n if not hasattr(klass, \"write\"):\n logger.error(\"%r does not have a required 'write' method\", klass)\n return False\n if getfullargspec(klass.write).args != getfullargspec(BaseFeatureWriter.write).args:\n logger.error(\"%r 'write' method has incorrect signature\", klass)\n return False\n return True","def valid(formula):\r\n\r\n try:\r\n return not re.search(r'\\b0[0-9]', formula) and eval((formula) is True\r\n #except ArithmeticError:\r\n #return False\r\n except:\r\n return False","def oss_fuzz_benchmark(fs):\n benchmark_config_contents = yaml.dump(OSS_FUZZ_BENCHMARK_CONFIG)\n benchmark_config_file = os.path.join(utils.ROOT_DIR, 'benchmarks',\n OSS_FUZZ_BENCHMARK_NAME,\n 'benchmark.yaml')\n fs.create_file(benchmark_config_file, contents=benchmark_config_contents)\n return OSS_FUZZ_BENCHMARK_NAME","def isgood(self):\n\t\tanswer = True\n\t\t\n\t\tif self.mes_flux <= 0.0:\n\t\t\tanswer = False\n\n\t\treturn answer","def is_bank_name_valid(name_to_check: str):\n def is_name_short_enough():\n return True if len(name_to_check) <= 12 else False\n\n def is_name_only_letter():\n return True if name_to_check.isalpha() else False\n\n return True if is_name_short_enough() and is_name_only_letter() else False","def check_maccs(maccs):\n\n\t# print number of features\n\tprint (\"Number of features =\", len(maccs[0]))\n\n\t# Check if size of all fingerprints is 167\n\tcount = 0\n\tfor fp in maccs:\n\t\tif len(fp) != 167:\n\t\t\tcount += 1\n\n\tif count == 0:\n\t\tprint (\"All instances have length 167.\")\n\telse:\n\t\tprint (\"Data not uniform. Check lengths for instances.\")\n\t\treturn False\n\n\treturn True","def check_mag(mag_values):\n\t# logger.debug(\"Returned mag array: \" + str(mag_values))\n\t# if no magnitudes were found\n\tif mag_values is None:\n\t\tlogger.info(\"Magnitude: None found\")\n\t\treturn False\n\tmag = mag_values[0]\n\tmag_err = mag_values[1]\n\t#mag_err_too_large = mag_values[2]\n\tmag_err_too_large = (mag_err > MAX_MAG_ERR)\n\tlogger.info(\"Magnitude: \" + str(mag))\n\tlogger.info(\"Magnitude error: \" + str(mag_err))\n\n\t# more negative mags are brighter, so we want values less than\n\t# our minimum brightness magnitude\t\n\tif mag > MIN_MAG or mag_err_too_large:\n\t\treturn False\n\telse:\n\t\treturn True","def check_norm_state(modules, train_state):\n for mod in modules:\n if isinstance(mod, _BatchNorm):\n if mod.training != train_state:\n return False\n return True","def _is_valid(self, *args, **kwargs):\n fn = args[0]\n if not fn.endswith(self._suffix):\n return False\n try:\n with h5py.File(fn, \"r\") as f:\n if \"arbor_type\" not in f.attrs:\n return False\n if f.attrs[\"arbor_type\"].astype(str) != \"YTreeArbor\":\n return False\n except BaseException:\n return False\n return True","def valid(f):\n try: \n return not re.search(r'\\b0[0-9]', f) and eval(f) is True\n except ArithmeticError:\n return False"],"string":"[\n \"def validate_type(benchmark):\\n benchmark_type = get_type(benchmark)\\n if benchmark_type not in BENCHMARK_TYPE_STRS:\\n logs.error('%s has an invalid benchmark type %s, must be one of %s',\\n benchmark, benchmark_type, BENCHMARK_TYPE_STRS)\\n return False\\n return True\",\n \"def validate_name(benchmark):\\n if VALID_BENCHMARK_REGEX.match(benchmark) is None:\\n logs.error('%s does not conform to %s pattern.', benchmark,\\n VALID_BENCHMARK_REGEX.pattern)\\n return False\\n return True\",\n \"def is_result_appropriate(self, benchmark_result):\\n return True\",\n \"def _is_fan(compressor: Compressor) -> bool:\\n name = compressor.name.lower()\\n return 'fan' in name or 'crtf' in name\",\n \"def test_not_a_valid_fuzzer(self):\\n self.assertFalse(cifuzz.check_fuzzer_build(TEST_FILES_PATH))\",\n \"def check_benchmark_result(result, expectation):\\n for storage_cfg, caches in result['cache_data'].items():\\n for cache, percent_recorded in caches.items():\\n if ((percent_recorded['min'] < expectation['min'])\\n or (percent_recorded['avg'] < expectation['avg'])\\n or (percent_recorded['max'] < expectation['max'])):\\n return False\\n return True\",\n \"def get_benchmark(self, benchmark):\\n\\t\\tif not isinstance(benchmark, str) and not callable(benchmark): return benchmark\\n\\t\\telif benchmark in self.classes:\\treturn self.classes[benchmark]()\\n\\t\\traise TypeError('Passed benchmark is not defined!')\",\n \"def validate(self, benchmarks):\\n class_code = self.setup_src\\n instance_creation = '\\\\ninstance = {}'.format(self.stmt)\\n for i, benchmark in enumerate(benchmarks):\\n if not benchmark.result_validation:\\n break\\n\\n validation_code = class_code + instance_creation + '\\\\nvalidation_result = ' + benchmark.stmt\\n validation_scope = {}\\n exec(validation_code, validation_scope)\\n # Store the result in the first function in the group.\\n if i == 0:\\n compare_against_function = benchmarks[0].callable.__name__\\n compare_against_result = validation_scope['validation_result']\\n logging.info('PyPerform: Validating group \\\"{b.group}\\\" against method '\\n '\\\"{b.classname}.{b.callable.__name__}\\\"'.format(b=benchmarks[0]))\\n else:\\n if compare_against_result == validation_scope['validation_result']:\\n logging.info('PyPerform: Validating {b.classname}.{b.callable.__name__}......PASSED!'\\n .format(b=benchmark))\\n else:\\n error = 'Results of functions {0} and {1} are not equivalent.\\\\n{0}:\\\\t {2}\\\\n{1}:\\\\t{3}'\\n raise ValidationError(error.format(compare_against_function, benchmark.callable.__name__,\\n compare_against_result, validation_scope['validation_result']))\",\n \"def get_fuzz_target(benchmark):\\n # Do this because of OSS-Fuzz-on-demand.\\n # TODO(metzman): Use classes to mock a benchmark config for\\n # OSS_FUZZ_ON_DEMAND.\\n return benchmark_config.get_config(benchmark).get(\\n 'fuzz_target', environment.get('FUZZ_TARGET'))\",\n \"def isValidTest(self):\\n if not self.hasError():\\n return False\\n distance = dameraulevenshtein(self.word, self.error) \\n if(distance > 1):\\n return False\\n regex = '.*[^a-zA-Z].*'\\n if re.match(regex, self.word) or re.match(regex, self.error):\\n return False\\n return True\",\n \"def is_mbid(mbid):\\n try:\\n mbid = uuid.UUID(mbid)\\n good = True\\n except ValueError as e:\\n good = False\\n except AttributeError:\\n good = False\\n\\n return good\",\n \"def _is_valid_adapter(self, adapter_name):\\n\\n valid_adapters = self.valid_adapters\\n\\n result = False\\n\\n if adapter_name in valid_adapters:\\n\\n result = True\\n\\n return result\",\n \"def test_BenchmarkSuite_integration_test(\\n benchmark_suite: typing.Callable, tempdir: pathlib.Path\\n):\\n with benchmark_suite() as bs:\\n bs.ForceOpenCLEnvironment(cldrive_env.OclgrindOpenCLEnvironment())\\n observer = MockBenchmarkObserver(stop_after=1)\\n\\n # `stop_after` raises BenchmarkInterrupt.\\n try:\\n bs.Run([observer])\\n assert False\\n except gpgpu.BenchmarkInterrupt:\\n pass\\n\\n assert len(observer.logs) == 1\\n assert observer.logs[0].benchmark_name in bs.benchmarks\",\n \"def __isFastener(f):\\n\\n if type(f) != Fastener:\\n raise TypeError(\\\"FastnerGroups may contain only Fasteners\\\")\\n else:\\n return True\",\n \"def assert_models_equal(self, benchmark1, benchmark2):\\n if (not isinstance(benchmark1, ate.BenchmarkATE) or\\n not isinstance(benchmark2, ate.BenchmarkATE)):\\n self.fail('object was not a BenchmarkATE')\\n self.assertEqual(benchmark1.identifier, benchmark2.identifier)\\n self.assertEqual(benchmark1.offset, benchmark2.offset)\\n self.assertEqual(benchmark1.max_difference, benchmark2.max_difference)\\n self.assertEqual(benchmark1.scale, benchmark2.scale)\",\n \"def is_valid(passwd: str) -> bool:\\n return (\\n re.search(r'abc|bcd|cde|def|efg|fgh|ghi|hij|jkl|klm|lmn|mno|nop|opq|pqr|qrs|rst|stu|tuv|uvw|vwx|wxy|xyz', passwd) is not None and\\n all([c not in passwd for c in 'iol']) and\\n re.search(r'([a-z])\\\\1.*([a-z])\\\\2', passwd) is not None\\n )\",\n \"def create_benchmark(self, benchmark):\\n self.crd_client.create_namespaced_custom_object(\\n group=\\\"ripsaw.cloudbulldozer.io\\\",\\n version=\\\"v1alpha1\\\",\\n namespace=benchmark[\\\"metadata\\\"][\\\"namespace\\\"],\\n plural=\\\"benchmarks\\\",\\n body=benchmark,\\n )\",\n \"def is_valid(self, user_specific_config: Any, factor: str) -> bool:\",\n \"def check(self, password):\\n\\n if len(password) < self.min_length:\\n return False\\n\\n digits = len(findall(r\\\"\\\\d\\\", password))\\n if digits < self.min_digits:\\n return False\\n\\n special_chars = sum(v for k, v in Counter(password).items() if k in punctuation)\\n if special_chars < self.min_special:\\n return False\\n\\n alpha_chars = sum(v for k, v in Counter(password).items() if k in ascii_letters)\\n if alpha_chars < self.min_alpha:\\n return False\\n\\n upper_chars = sum(\\n v for k, v in Counter(password).items() if k in ascii_uppercase\\n )\\n if upper_chars < self.min_upper:\\n return False\\n\\n lower_chars = sum(\\n v for k, v in Counter(password).items() if k in ascii_lowercase\\n )\\n if lower_chars < self.min_lower:\\n return False\\n\\n if self.check_breaches and check_password(password):\\n return False\\n\\n if self.func and not self.func(password):\\n return False\\n\\n return True\",\n \"def is_cpp(benchmark):\\n return get_language(benchmark) == 'c++'\",\n \"def _quick_and_dirty_glyph_is_empty(font, glyph_name):\\n if 'glyf' in font:\\n glyph = font['glyf'][glyph_name]\\n if not glyph.isComposite():\\n if glyph.numberOfContours == 0:\\n return True\\n return False\\n elif 'CFF2' in font:\\n top_dict = font['CFF2'].cff.topDictIndex[0]\\n else:\\n top_dict = font['CFF '].cff.topDictIndex[0]\\n char_strings = top_dict.CharStrings\\n char_string = char_strings[glyph_name]\\n if len(char_string.bytecode) <= 1:\\n return True\\n return False\",\n \"def assert_models_equal(self, benchmark1, benchmark2):\\n if (not isinstance(benchmark1, detection_comp.FeatureDetectionComparison) or\\n not isinstance(benchmark2, detection_comp.FeatureDetectionComparison)):\\n self.fail('object was not a FeatureDetectionComparison')\\n self.assertEqual(benchmark1.identifier, benchmark2.identifier)\\n self.assertEqual(benchmark1._acceptable_radius, benchmark2._acceptable_radius)\",\n \"def is_valid(name):\\n return bool(name)\",\n \"def is_valid(self):\\n # check if cpf isn't in invalid_cpfs list\\n if self.cpf in self.invalid_cpfs: return False\\n\\n # get first nine digits to calculate two verification digits\\n cpf = self.cpf[:9]\\n # while cpf isn't complete (this runs two loops)\\n while len(cpf) < 11:\\n\\n # run trought numbers multiplying number (v) by weight (len(cpf)+1-i)\\n # and then get sum rest of division by 11 as integer\\n r = int(sum([math.floor((len(cpf)+1-i_v[0])*i_v[1]) for i_v in enumerate(cpf)]) % 11)\\n\\n # if digit is smaller than 2, turns 0\\n if r < 2:\\n f = 0\\n else:\\n f = 11 -r\\n\\n # append to cpf list\\n cpf.append(f)\\n\\n # if created number is same as original number, cpf is valid\\n return bool(cpf == self.cpf)\",\n \"def is_system_ready_for_benchmarking():\\n\\n # check if scaling_governor is set to 'performance' for all cpu cores\\n cpu_governors = glob.glob('/sys/devices/system/cpu/cpu*/cpufreq/scaling_governor')\\n if not cpu_governors:\\n logger.error('no scaling_governor found. Do you run on a Linux System?')\\n return False\\n for governor in sorted(cpu_governors):\\n with open(governor, 'r') as f:\\n line = f.read().splitlines()[0]\\n logger.debug('%s is set to \\\\\\\"%s\\\\\\\"', governor, line)\\n if line != 'performance':\\n logger.warning('please set all scaling_governor to \\\\\\\"performance\\\\\\\" (using \\\"sudo ./ondemand.sh start\\\")')\\n return False\\n\\n return True\",\n \"def assert_passes_fuzz(self, feature_spec, tries=1000):\\n feature_spec = make_feature(feature_spec)\\n for i in range(tries):\\n data_point = generate.generate(feature_spec.input_schema)\\n try:\\n feature = feature_spec(data_point)\\n except Exception as e:\\n self.fail(\\\"Error evaluating; input=%r error=%r\\\" %\\n (data_point, e))\\n try:\\n feature_spec.output_schema.validate(feature)\\n except schema.SchemaError:\\n self.fail(\\\"Invalid output schema; input=%r output=%r\\\" %\\n (data_point, feature))\",\n \"def run_benchmark(curl, benchmark, test_config = TestConfig()):\\n\\n warmup_runs = benchmark.warmup_runs\\n benchmark_runs = benchmark.benchmark_runs\\n message = '' #Message is name of benchmark... print it?\\n\\n if (warmup_runs <= 0):\\n raise Exception(\\\"Invalid number of warmup runs, must be > 0 :\\\" + warmup_runs)\\n if (benchmark_runs <= 0):\\n raise Exception(\\\"Invalid number of benchmark runs, must be > 0 :\\\" + benchmark_runs)\\n\\n #Initialize variables to store output\\n output = BenchmarkResult()\\n output.name = benchmark.name\\n output.group = benchmark.group\\n metricnames = list(benchmark.metrics)\\n metricvalues = [METRICS[name] for name in metricnames] # Metric variable for curl, to avoid hash lookup for every metric name\\n results = [list() for x in xrange(0, len(metricnames))] # Initialize arrays to store results for each metric\\n\\n curl.setopt(pycurl.WRITEFUNCTION, lambda x: None) #Do not store actual response body at all.\\n\\n #Benchmark warm-up to allow for caching, JIT compiling, on client\\n logging.info('Warmup: ' + message + ' started')\\n for x in xrange(0, warmup_runs):\\n if benchmark.method == u'POST' or benchmark.method == u'PUT':\\n curl.setopt(curl.READFUNCTION, StringIO.StringIO(benchmark.body).read)\\n curl.perform()\\n logging.info('Warmup: ' + message + ' finished')\\n\\n logging.info('Benchmark: ' + message + ' starting')\\n\\n for x in xrange(0, benchmark_runs): # Run the actual benchmarks\\n if benchmark.method == u'POST' or benchmark.method == u'PUT':\\n curl.setopt(curl.READFUNCTION, StringIO.StringIO(benchmark.body).read)\\n\\n try: # Run the curl call, if it errors, then add to failure counts for benchmark\\n curl.perform()\\n except Exception:\\n output.failures = output.failures + 1\\n continue # Skip metrics collection\\n\\n # Get all metrics values for this run, and store to metric lists\\n for i in xrange(0, len(metricnames)):\\n results[i].append( curl.getinfo(metricvalues[i]) )\\n\\n logging.info('Benchmark: ' + message + ' ending')\\n\\n temp_results = dict()\\n for i in xrange(0, len(metricnames)):\\n temp_results[metricnames[i]] = results[i]\\n output.results = temp_results\\n\\n curl.close()\\n return analyze_benchmark_results(output, benchmark)\",\n \"def CheckPrerequisites(benchmark_config):\\n dpb_service_type = benchmark_config.dpb_service.service_type\\n if dpb_service_type not in SUPPORTED_DPB_BACKENDS:\\n raise errors.Config.InvalidValue('Invalid backend for distcp. Not in:{}'.\\n format(str(SUPPORTED_DPB_BACKENDS)))\",\n \"def assert_models_equal(self, benchmark1, benchmark2):\\n if (not isinstance(benchmark1, detection_comp.FeatureDetectionComparisonResult) or\\n not isinstance(benchmark2, detection_comp.FeatureDetectionComparisonResult)):\\n self.fail('object was not a FeatureDetectionComparisonResult')\\n self.assertEqual(benchmark1.identifier, benchmark2.identifier)\\n self.assertEqual(benchmark1.success, benchmark2.success)\\n self.assertEqual(benchmark1.benchmark, benchmark2.benchmark)\\n self.assertEqual(benchmark1.trial_result, benchmark2.trial_result)\\n self.assertEqual(benchmark1.reference_trial_result, benchmark2.reference_trial_result)\\n self.assertEqual(benchmark1._feature_changes, benchmark2._feature_changes)\\n self.assertEqual(benchmark1._changes_id, benchmark2._changes_id)\",\n \"def fuzz():\\n if FUZZ:\\n time.sleep(random.random())\",\n \"def isValid(self):\\n return self.isOutOfDate() == False \\\\\\n and self.isQCValid() == True \\\\\\n and self.getDisposeUntilNextCalibrationTest() == False \\\\\\n and self.isValidationInProgress() == False \\\\\\n and self.isCalibrationInProgress() == False\",\n \"def sanity_check(self):\\n res = True\\n res = res and self.detected\\n res = res and np.sum(self.diffs) < 30000 # experimental value\\n return res\",\n \"def is_valid_mbart(self) -> bool:\\r\\n if self.normalize_before and self.add_final_layer_norm and self.scale_embedding:\\r\\n return True\\r\\n if self.normalize_before or self.add_final_layer_norm or self.scale_embedding:\\r\\n logger.info(\\\"This configuration is a mixture of MBART and BART settings\\\")\\r\\n return False\",\n \"def isQCValid(self):\\n for last in self.getLatestReferenceAnalyses():\\n rr = last.aq_parent.getResultsRangeDict()\\n uid = last.getServiceUID()\\n if uid not in rr:\\n # This should never happen.\\n # All QC Samples must have specs for its own AS\\n continue\\n\\n specs = rr[uid];\\n try:\\n smin = float(specs.get('min', 0))\\n smax = float(specs.get('max', 0))\\n error = float(specs.get('error', 0))\\n target = float(specs.get('result', 0))\\n result = float(last.getResult())\\n error_amount = ((target / 100) * error) if target > 0 else 0\\n upper = smax + error_amount\\n lower = smin - error_amount\\n if result < lower or result > upper:\\n return False\\n except:\\n # This should never happen.\\n # All Reference Analysis Results and QC Samples specs\\n # must be floatable\\n continue\\n\\n return True\",\n \"def is_decay_limits(limitfile):\\n tokens = os.path.splitext(os.path.basename(limitfile))[0].split('_')\\n if tokens[3] in ['point', 'dmap', 'dradial']:\\n return True\\n return tokens[2] in ['point', 'dmap', 'dradial']\",\n \"def is_valid(values, dataset):\\r\\n # Only includes negative screens.\\r\\n if values[SCREEN_TYPE_COL] != \\\"negative selection\\\":\\r\\n STATS[NOT_NEG_SCREEN] += 1\\r\\n return False\\r\\n # Targets must have the correct length.\\r\\n if int(values[dataset.end_idx]) - int(values[dataset.start_idx]) !=\\\\\\r\\n consts.TARGET_LEN:\\r\\n STATS[WRONG_END_MINUS_START] += 1\\r\\n return False\\r\\n\\r\\n target = dataset.get_target(values)\\r\\n # Targets must have an NGG PAM sequence.\\r\\n if not target.endswith(\\\"GG\\\"):\\r\\n STATS[BAD_PAM] += 1\\r\\n return False\\r\\n # Another safety measure against targets with the wrong length.\\r\\n if len(target) != consts.TARGET_LEN:\\r\\n STATS[TARGET_BAD_LEN] += 1\\r\\n return False\\r\\n return True\",\n \"def Run(benchmark_spec):\\n _UpdateBenchmarkSpecWithFlags(benchmark_spec)\\n vm = benchmark_spec.vms[0]\\n if benchmark_spec.tpus:\\n # For MLPerf 1.0, the benchmake code of different hardware are different.\\n if (benchmark_spec.tpu_groups['train'].GetAcceleratorType() == 'v3-32' or\\n benchmark_spec.tpu_groups['train'].GetAcceleratorType() == 'v3-128' or\\n benchmark_spec.tpu_groups['train'].GetAcceleratorType() == 'v3-256' or\\n benchmark_spec.tpu_groups['train'].GetAcceleratorType() == 'v3-512' or\\n benchmark_spec.tpu_groups['train'].GetAcceleratorType() == 'v3-1024' or\\n benchmark_spec.tpu_groups['train'].GetAcceleratorType() == 'v3-2048'):\\n run_path = (\\n '$HOME/training_results_{version}/Google/benchmarks/{model}/tpu-{tpus}'\\n .format(\\n version=VERSION.value,\\n model=benchmark_spec.benchmark,\\n tpus=benchmark_spec.tpu_groups['train'].GetAcceleratorType()))\\n code_path = (\\n '$HOME/training_results_{version}/Google/benchmarks/{model}/implementations/tpu-{tpus}-{model}'\\n .format(\\n version=VERSION.value,\\n model=benchmark_spec.benchmark,\\n tpus=benchmark_spec.tpu_groups['train'].GetAcceleratorType()))\\n\\n if MASK in benchmark_spec.benchmark:\\n model = 'mask_rcnn'\\n elif GNMT in benchmark_spec.benchmark:\\n model = 'nmt'\\n else:\\n model = benchmark_spec.benchmark\\n\\n mlperf_benchmark_cmd = (\\n 'cd {code_path} && '\\n 'export PYTHONPATH=$(pwd):$(pwd)/{model} && '\\n 'cd {model} && '\\n '{run_path}/run_and_time.sh'.format(\\n code_path=code_path,\\n model=model,\\n run_path=run_path))\\n\\n if SSD in benchmark_spec.benchmark:\\n mlperf_benchmark_cmd = (\\n 'export '\\n 'MLP_GCS_RESNET_CHECKPOINT={checkpoint}'\\n ' && {cmd}'.format(\\n checkpoint=FLAGS.mlperf_gcs_resnet_checkpoint,\\n cmd=mlperf_benchmark_cmd))\\n else:\\n raise ValueError(\\n 'MLPerf configurations do not support the hardware in PKB. PKB may '\\n 'need to be updated if this is a new TPU type.')\\n\\n else:\\n run_sub_paths = {RESNET: 'resnet/implementations/mxnet',\\n TRANSFORMER: 'transformer/implementations/pytorch',\\n MINIGO: 'minigo/implementations/tensorflow',\\n MASK: 'maskrcnn/implementations/pytorch',\\n GNMT: 'gnmt/implementations/pytorch',\\n SSD: 'ssd/implementations/pytorch',\\n BERT: 'bert/implementations/pytorch',}\\n benchmark_path = f'$HOME/training_results_{VERSION.value}/NVIDIA/benchmarks'\\n run_path = posixpath.join(benchmark_path,\\n run_sub_paths[benchmark_spec.benchmark])\\n env = {\\n 'DGXSYSTEM': DGXSYSTEM,\\n 'NEXP': 1,\\n 'PULL': 0,\\n 'LOGDIR': f'/tmp/{benchmark_spec.benchmark}',\\n }\\n envs = {\\n RESNET: {},\\n TRANSFORMER: {'DATADIR': '/data/wmt/utf8'},\\n MINIGO: {'CONT': 'mlperf-nvidia:minigo'},\\n MASK: {},\\n GNMT: {'DATADIR': '/data/gnmt'},\\n SSD: {'DATADIR': '/data'},\\n BERT: {}\\n }\\n env.update(envs[benchmark_spec.benchmark])\\n\\n run_script = posixpath.join(run_path, 'run_with_docker.sh')\\n vm_util.ReplaceText(vm, 'SYSLOGGING=1', 'SYSLOGGING=0', run_script)\\n vm_util.ReplaceText(vm, 'docker exec -it', 'docker exec -t', run_script)\\n if benchmark_spec.benchmark == RESNET:\\n vm_util.ReplaceText(vm, r'mpirun.*run_and_time\\\\.sh',\\n r'.\\\\/run_and_time.sh', run_script)\\n\\n env = ' '.join(f'{key}={value}' for key, value in env.items())\\n if nvidia_driver.CheckNvidiaGpuExists(vm):\\n env = f'{tensorflow.GetEnvironmentVars(vm)} {env}'\\n\\n mlperf_benchmark_cmd = (\\n f'chmod 755 {run_script} && '\\n f'cd {run_path} && '\\n f'{env} {run_script}')\\n\\n samples = []\\n metadata = _CreateMetadataDict(benchmark_spec)\\n stdout, _ = vm.RobustRemoteCommand(mlperf_benchmark_cmd)\\n if NONE in FLAGS.mlperf_profiler:\\n samples.extend(\\n MakeSamplesFromOutput(\\n metadata,\\n stdout,\\n use_tpu=bool(benchmark_spec.tpus),\\n model=benchmark_spec.benchmark))\\n return samples\",\n \"def test_not_a_valid_fuzz_path(self):\\n self.assertFalse(cifuzz.check_fuzzer_build('not/a/valid/path'))\",\n \"def is_valid(self):\\n if self.answer_type == 'F':\\n return bool(self.text)\\n\\n if not self.answers.count():\\n return False\\n if not self.answers.filter(correct=True).count():\\n return False\\n return True\",\n \"def is_good_bin(self, bin_num: int, min_amp: float = 0.25, min_corr: float = 0.10) -> bool:\\n # Verify Amplitude data exist and all is good for the bin\\n if self.IsAmplitude and not self.Amplitude.is_good_bin(bin_num, min_amp):\\n return False\\n # Verify Correlation data exist and all is good for the bin\\n if self.IsCorrelation and not self.Correlation.is_good_bin(bin_num, min_corr):\\n return False\\n\\n # Verify Earth Velocity data exist and all is good for the bin\\n if self.IsEarthVelocity and not self.EarthVelocity.is_good_bin(bin_num):\\n return False\\n\\n return True\",\n \"def _IsApplicable(self, manifest):\\n check_list = [(self.tool, manifest.tool), (self.board, manifest.board)]\\n\\n return all(fnmatch(text, pattern) for text, pattern in check_list)\",\n \"def get_type(benchmark):\\n # TODO(metzman): Use classes to mock a benchmark config for\\n # OSS_FUZZ_ON_DEMAND.\\n default_value = os.getenv('EXPERIMENT_TYPE', BenchmarkType.CODE.value)\\n return benchmark_config.get_config(benchmark).get('type', default_value)\",\n \"def check_for_token(token):\\n try:\\n decode_token(token)\\n return True\\n except:\\n return False\",\n \"def check_frame_type(self, ftype, fitstbl, exprng=None):\\n good_exp = framematch.check_frame_exptime(fitstbl['exptime'], exprng)\\n if ftype in ['science', 'standard']:\\n return good_exp & (np.logical_not(np.char.startswith(np.char.lower(fitstbl['target']), 'arclamp'))) & \\\\\\n (np.char.lower(fitstbl['target']) != 'spectralflat') & \\\\\\n (np.char.lower(fitstbl['target']) != 'bias')\\n if ftype in ['arc', 'tilt']:\\n return good_exp & (np.char.startswith(np.char.lower(fitstbl['target']), 'arclamp'))\\n if ftype in ['pixelflat', 'trace', 'illumflat']:\\n return good_exp & (np.char.lower(fitstbl['target']) == 'spectralflat')\\n if ftype == 'bias':\\n return good_exp & (np.char.lower(fitstbl['target']) == 'bias')\\n\\n msgs.warn('Cannot determine if frames are of type {0}.'.format(ftype))\\n return np.zeros(len(fitstbl), dtype=bool)\",\n \"def _is_valid_keyspace_name(self, keyspace_name):\\n if keyspace_name == None or not keyspace_name:\\n return False\\n return re.match(r\\\"^[a-z_]*[^-]$\\\", keyspace_name)\",\n \"def should_evaluate(self, epoch: int) -> bool:\\n return False\",\n \"def has_shuffle(self):\\r\\n return hasattr(self, '_has_shuffle')\",\n \"def valid(f):\\n try:\\n return not re.search(r'\\\\b0[0-9]', f) and eval(f) is True\\n # \\\"\\\\b\\\" is a word boundary\\n \\\"\\\"\\\"We need to exclude numbers starting with zero,\\n as these are interpretted as base8 (octal). This in\\n turn could cause interpretation errors, and exceptions\\n (for example 09 is not octal and will throw and exception)\\\"\\\"\\\"\\n except (ArithmeticError, SyntaxError):\\n return False\",\n \"def check_banned(deck_format, card_name):\\n if card_name in consts.BANNINGS[deck_format]:\\n return True\\n return False\",\n \"def is_valid_calendar(self, calendar):\\n\\n return calendar in self.available_calendars() or calendar == \\\"\\\"\",\n \"def is_valid_sample_rate(rate):\\n # type: (Any) -> bool\\n\\n # both booleans and NaN are instances of Real, so a) checking for Real\\n # checks for the possibility of a boolean also, and b) we have to check\\n # separately for NaN and Decimal does not derive from Real so need to check that too\\n if not isinstance(rate, (Real, Decimal)) or math.isnan(rate):\\n logger.warning(\\n \\\"[Tracing] Given sample rate is invalid. Sample rate must be a boolean or a number between 0 and 1. Got {rate} of type {type}.\\\".format(\\n rate=rate, type=type(rate)\\n )\\n )\\n return False\\n\\n # in case rate is a boolean, it will get cast to 1 if it's True and 0 if it's False\\n rate = float(rate)\\n if rate < 0 or rate > 1:\\n logger.warning(\\n \\\"[Tracing] Given sample rate is invalid. Sample rate must be between 0 and 1. Got {rate}.\\\".format(\\n rate=rate\\n )\\n )\\n return False\\n\\n return True\",\n \"def is_valid_type(self, question_type):\\n\\t\\treturn question_type in self.valid_types\",\n \"def test_correct_fuzzer_build(self):\\n test_fuzzer_dir = os.path.join(TEST_FILES_PATH, 'out')\\n self.assertTrue(cifuzz.check_fuzzer_build(test_fuzzer_dir))\",\n \"def _check_procmon_failures(self, target):\\n if target.procmon:\\n self._fuzz_data_logger.open_test_step(\\\"Contact process monitor\\\")\\n self._fuzz_data_logger.log_check(\\\"procmon.post_send()\\\")\\n if target.procmon.post_send():\\n self._fuzz_data_logger.log_pass(\\\"No crash detected.\\\")\\n else:\\n self._fuzz_data_logger.log_fail(\\n \\\"procmon detected crash on test case #{0}: {1}\\\".format(self.total_mutant_index,\\n target.procmon.get_crash_synopsis()))\\n return True\\n return False\",\n \"def valid(self, nt_header):\\n try:\\n return (self.AddressOfFunctions < nt_header.OptionalHeader.SizeOfImage and\\n self.AddressOfNameOrdinals < nt_header.OptionalHeader.SizeOfImage and\\n self.AddressOfNames < nt_header.OptionalHeader.SizeOfImage and\\n self.NumberOfFunctions < 0x7FFF and\\n self.NumberOfNames < 0x7FFF)\\n except obj.InvalidOffsetError:\\n return False\",\n \"def check_tbe_support(json_desc):\\n if \\\"buffer_stitch\\\" in json_desc:\\n logger.info(\\\"TBE not supports buffer stitch\\\")\\n return False\\n\\n if \\\"parallel_fusion\\\" in json_desc:\\n logger.info(\\\"TBE not supports parallel fusion\\\")\\n return False\\n\\n if not json_desc.get(\\\"input_desc\\\"):\\n logger.info(\\\"TBE not supports empty inputs\\\")\\n return False\\n\\n for op in json_desc[\\\"op_desc\\\"]:\\n op_name = op[\\\"name\\\"]\\n if not get_op_reg_info(op_name, \\\"func\\\", False):\\n logger.info(\\\"TBE op not registered: {}\\\".format(op_name))\\n return False\\n return True\",\n \"def valid_builder(buildername):\\n builders = query_builders()\\n if buildername in builders:\\n LOG.debug(\\\"Buildername %s is valid.\\\" % buildername)\\n return True\\n else:\\n LOG.warning(\\\"Buildername %s is *NOT* valid.\\\" % buildername)\\n LOG.info(\\\"Check the file we just created builders.txt for \\\"\\n \\\"a list of valid builders.\\\")\\n with open(path_to_file('builders.txt'), \\\"wb\\\") as fd:\\n for b in sorted(builders):\\n fd.write(b + \\\"\\\\n\\\")\\n\\n return False\",\n \"def valid(f):\\r\\n try:\\r\\n return not re.search(r'\\\\b0[0-9]', f) and eval(f) is True\\r\\n except ArithmeticError:\\r\\n return False\",\n \"def mnemonic_is_valid(mnemonic: str, wordlist=WORDLIST):\\n try:\\n mnemonic_to_bytes(mnemonic, wordlist=wordlist)\\n return True\\n except Exception as e:\\n return False\",\n \"def analyze_benchmark_results(benchmark_result, benchmark):\\n\\n output = BenchmarkResult()\\n output.name = benchmark_result.name\\n output.group = benchmark_result.group\\n output.failures = benchmark_result.failures\\n\\n # Copy raw metric arrays over where necessary\\n raw_results = benchmark_result.results\\n temp = dict()\\n for metric in benchmark.raw_metrics:\\n temp[metric] = raw_results[metric]\\n output.results = temp\\n\\n # Compute aggregates for each metric, and add tuples to aggregate results\\n aggregate_results = list()\\n for metricname, aggregate_list in benchmark.aggregated_metrics.iteritems():\\n numbers = raw_results[metricname]\\n for aggregate_name in aggregate_list:\\n if numbers: # Only compute aggregates if numbers exist\\n aggregate_function = AGGREGATES[aggregate_name]\\n aggregate_results.append( (metricname, aggregate_name, aggregate_function(numbers)) )\\n else:\\n aggregate_results.append( (metricname, aggregate_name, None) )\\n\\n output.aggregates = aggregate_results\\n return output\",\n \"def is_valid_flag(flag):\\n return False if re.match(\\\"^FAUST_[A-Za-z0-9/\\\\\\\\+]{32}$\\\", flag) is None else True\",\n \"def is_dev_name_valid(self):\\n return self._name_re.match(self.dev_name) is not None\",\n \"def checkDeviceCanRunTestWithMask(self, device, testMask):\\n return testMask == \\\"*\\\" or device.name.startswith(testMask)\",\n \"def is_busy(self):\\n threads = len(self.executor._threads)\\n if threads == 0:\\n return False\\n\\n capacity = self.executor._work_queue.qsize() / float(threads)\\n if capacity > 2:\\n return True\\n elif capacity < 1:\\n return False\\n else:\\n return capacity > (random.random() + 1)\",\n \"def is_complex(num):\\n try:\\n complex(num)\\n except Exception:\\n return False\\n return True\",\n \"def isValidZ(self, z):\\n if (z <= self.getZMin()):\\n return False\\n if (z >= self.getZMax()):\\n return False\\n else:\\n return True\",\n \"def check_valid(model, dataset):\\n if hasattr(model.model, 'n_classes_'):\\n if (nc := model.model.n_classes_) != dataset.cfg.n_classes:\\n warnings.warn(\\n f'Not all classes present in train data. '\\n f'Skipping run.')\\n return False\\n return True\",\n \"def isquantized(val):\\n # try: 1.1 * val ... except: might be more general, but could be expensive.\\n retval = True\\n if isinstance(val, (float, complex)):\\n retval = False\\n elif hasattr(val, 'issubdtype'):\\n if numpy.issubdtype(val.dtype, float) or numpy.issubdtype(val.dtype, complex):\\n retval = False\\n return retval\",\n \"def __isValidColor(self, name):\\n try:\\n if self.__isHexString(name) and len(name) in [3, 6, 9, 12]:\\n return True\\n return QColor.isValidColor(name)\\n except AttributeError:\\n if name.startswith(\\\"#\\\"):\\n if len(name) not in [4, 7, 10, 13]:\\n return False\\n hexCheckStr = name[1:]\\n return self.__isHexString(hexCheckStr)\\n else:\\n if self.__isHexString(name) and len(name) in [3, 6, 9, 12]:\\n return True\\n return name in QColor.colorNames()\",\n \"def valid(f):\\n try:\\n return not re.search(r'\\\\b0[0-9]', f) and eval(f) is True\\n except ArithmeticError:\\n return False\",\n \"def valid(self) -> bool:\\n pass\",\n \"def _UpdateBenchmarkSpecWithFlags(benchmark_spec):\\n benchmark_spec.imagenet_data_dir = FLAGS.imagenet_data_dir\\n benchmark_spec.benchmark = FLAGS.mlperf_benchmark\\n benchmark_spec.wmt_data_dir = FLAGS.wmt_data_dir\\n benchmark_spec.coco_data_dir = FLAGS.coco_data_dir\\n benchmark_spec.gnmt_data_dir = FLAGS.gnmt_data_dir\\n benchmark_spec.bert_data_dir = FLAGS.bert_data_dir\\n benchmark_spec.gcp_service_account = FLAGS.gcp_service_account\",\n \"def check_blacklist(auth_token):\\n token = BlacklistToken.query.filter_by(token=str(auth_token)).first()\\n if token:\\n return True\\n\\n return False\",\n \"def testValid(self):\\n validate = timing_util.ValidateMeasurementsFlag\\n self.assertIs(validate([]), True)\\n self.assertIs(validate(['none']), True)\\n self.assertIs(validate(['end_to_end_runtime']), True)\\n self.assertIs(validate(['runtimes']), True)\\n self.assertIs(validate(['timestamps']), True)\\n self.assertIs(validate(['end_to_end_runtime', 'runtimes']), True)\\n self.assertIs(validate(['end_to_end_runtime', 'timestamps']), True)\\n self.assertIs(validate(['runtimes', 'timestamps']), True)\\n self.assertIs(\\n validate(['end_to_end_runtime', 'runtimes', 'timestamps']), True)\",\n \"def is_valid(self, attr: Optional[str] = None) -> bool:\\n try:\\n self.validate(attr)\\n except (TypeError, ValueError):\\n return False\\n return True\",\n \"def is_valid_cluster(self, cluster):\\n return (cluster - 1) < self.max_clusters\",\n \"def test_cli_classic_fizzbuzz_fail(self):\\n r = self._runner.invoke(app, [CLASSIC_FIZZBUZZ_COMMAND, 'wwe'])\\n self.assertNotEqual(r.exit_code, 0)\",\n \"def verify(self):\\n if not self.verify_succ_pred_fingers():\\n return False\\n\\n return True\",\n \"def _isnotsuite(test):\\n try:\\n iter(test)\\n except TypeError:\\n return True\\n return False\",\n \"def test_BenchmarkSuite_invalid_path_access(benchmark_suite: typing.Callable):\\n bs = benchmark_suite()\\n with test.Raises(TypeError):\\n _ = bs.path\",\n \"def _is_stack_cannery_check_bb(self, xbb):\\n return self._is_stack_cannery_check_bb_x86(xbb)\",\n \"def is_valid(self):\\n try:\\n self.validate()\\n return True\\n except (TypeError, ValueError) as e:\\n return False\",\n \"def is_valid_type(self, attr: Optional[str] = None) -> bool:\\n try:\\n self.validate_type(attr)\\n except TypeError:\\n return False\\n return True\",\n \"def is_dc(test_cases,foo):\\r\\n \\r\\n was_true=False\\r\\n was_false=False\\r\\n if len(test_cases)==1:\\r\\n return False\\r\\n else: \\r\\n for test_case in test_cases:\\r\\n if foo(*test_case) == True:\\r\\n was_true=True\\r\\n if foo(*test_case) == False:\\r\\n was_false=True\\r\\n return was_true and was_false\",\n \"def _is_validation(video_id):\\n hasher = md5()\\n hasher.update(bytes(video_id, 'utf-8'))\\n first = hasher.hexdigest()[0]\\n return first in ['0', '1']\",\n \"def is_unit(self, tolerance=1e-14):\\n return abs(1.0 - self._sum_of_squares()) < tolerance # if _sum_of_squares is 1, norm is 1. This saves a call to sqrt()\",\n \"def assert_data_correct(self) -> bool:\\n corr_char = self.assert_data_characters_correct()\\n print(\\\"Character data correct?\\\", corr_char)\\n corr_font = FontImages().assert_data_correct()\\n print(\\\"Font data correct?\\\", corr_font)\\n corr_frag = self.assert_data_fragments_correct()\\n print(\\\"Fragment data correct?\\\", corr_frag)\\n corr_train_aug = self.assert_train_augmented()\\n print(\\\"Train data augmented?\\\", corr_train_aug)\\n truth_agree = corr_char and corr_font and corr_frag\\n return True if truth_agree else False\",\n \"def test_get_benchmark_methods_exact_match(self):\\n config = mock.Mock()\\n config.workspace = 'workspace'\\n config.benchmark_method_patterns = [\\n 'new_foo.BenchmarkClass.benchmark_method_1',\\n 'new_foo.BenchmarkClass.benchmark_method_2']\\n benchmark_runner = benchmark.BenchmarkRunner(config)\\n\\n methods = benchmark_runner._get_benchmark_methods()\\n self.assertEqual(['new_foo.BenchmarkClass.benchmark_method_1',\\n 'new_foo.BenchmarkClass.benchmark_method_2'], methods)\",\n \"def should_evaluate(self, epoch: int) -> bool:\\n raise NotImplementedError\",\n \"def isValidDataTypeName(name: unicode) -> bool:\\n ...\",\n \"def isValidFeatureWriter(klass):\\n if not isclass(klass):\\n logger.error(\\\"%r is not a class\\\", klass)\\n return False\\n if not hasattr(klass, \\\"tableTag\\\"):\\n logger.error(\\\"%r does not have required 'tableTag' attribute\\\", klass)\\n return False\\n if not hasattr(klass, \\\"write\\\"):\\n logger.error(\\\"%r does not have a required 'write' method\\\", klass)\\n return False\\n if getfullargspec(klass.write).args != getfullargspec(BaseFeatureWriter.write).args:\\n logger.error(\\\"%r 'write' method has incorrect signature\\\", klass)\\n return False\\n return True\",\n \"def valid(formula):\\r\\n\\r\\n try:\\r\\n return not re.search(r'\\\\b0[0-9]', formula) and eval((formula) is True\\r\\n #except ArithmeticError:\\r\\n #return False\\r\\n except:\\r\\n return False\",\n \"def oss_fuzz_benchmark(fs):\\n benchmark_config_contents = yaml.dump(OSS_FUZZ_BENCHMARK_CONFIG)\\n benchmark_config_file = os.path.join(utils.ROOT_DIR, 'benchmarks',\\n OSS_FUZZ_BENCHMARK_NAME,\\n 'benchmark.yaml')\\n fs.create_file(benchmark_config_file, contents=benchmark_config_contents)\\n return OSS_FUZZ_BENCHMARK_NAME\",\n \"def isgood(self):\\n\\t\\tanswer = True\\n\\t\\t\\n\\t\\tif self.mes_flux <= 0.0:\\n\\t\\t\\tanswer = False\\n\\n\\t\\treturn answer\",\n \"def is_bank_name_valid(name_to_check: str):\\n def is_name_short_enough():\\n return True if len(name_to_check) <= 12 else False\\n\\n def is_name_only_letter():\\n return True if name_to_check.isalpha() else False\\n\\n return True if is_name_short_enough() and is_name_only_letter() else False\",\n \"def check_maccs(maccs):\\n\\n\\t# print number of features\\n\\tprint (\\\"Number of features =\\\", len(maccs[0]))\\n\\n\\t# Check if size of all fingerprints is 167\\n\\tcount = 0\\n\\tfor fp in maccs:\\n\\t\\tif len(fp) != 167:\\n\\t\\t\\tcount += 1\\n\\n\\tif count == 0:\\n\\t\\tprint (\\\"All instances have length 167.\\\")\\n\\telse:\\n\\t\\tprint (\\\"Data not uniform. Check lengths for instances.\\\")\\n\\t\\treturn False\\n\\n\\treturn True\",\n \"def check_mag(mag_values):\\n\\t# logger.debug(\\\"Returned mag array: \\\" + str(mag_values))\\n\\t# if no magnitudes were found\\n\\tif mag_values is None:\\n\\t\\tlogger.info(\\\"Magnitude: None found\\\")\\n\\t\\treturn False\\n\\tmag = mag_values[0]\\n\\tmag_err = mag_values[1]\\n\\t#mag_err_too_large = mag_values[2]\\n\\tmag_err_too_large = (mag_err > MAX_MAG_ERR)\\n\\tlogger.info(\\\"Magnitude: \\\" + str(mag))\\n\\tlogger.info(\\\"Magnitude error: \\\" + str(mag_err))\\n\\n\\t# more negative mags are brighter, so we want values less than\\n\\t# our minimum brightness magnitude\\t\\n\\tif mag > MIN_MAG or mag_err_too_large:\\n\\t\\treturn False\\n\\telse:\\n\\t\\treturn True\",\n \"def check_norm_state(modules, train_state):\\n for mod in modules:\\n if isinstance(mod, _BatchNorm):\\n if mod.training != train_state:\\n return False\\n return True\",\n \"def _is_valid(self, *args, **kwargs):\\n fn = args[0]\\n if not fn.endswith(self._suffix):\\n return False\\n try:\\n with h5py.File(fn, \\\"r\\\") as f:\\n if \\\"arbor_type\\\" not in f.attrs:\\n return False\\n if f.attrs[\\\"arbor_type\\\"].astype(str) != \\\"YTreeArbor\\\":\\n return False\\n except BaseException:\\n return False\\n return True\",\n \"def valid(f):\\n try: \\n return not re.search(r'\\\\b0[0-9]', f) and eval(f) is True\\n except ArithmeticError:\\n return False\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.69998956","0.67811495","0.5615518","0.5273485","0.5138776","0.5041119","0.48480907","0.48040372","0.47753853","0.47583094","0.4682528","0.4670917","0.466327","0.4620052","0.45891234","0.45834735","0.45678422","0.4562975","0.45465788","0.45328742","0.45173","0.45090833","0.44996414","0.44785774","0.4448101","0.44395432","0.4427622","0.44208667","0.44115433","0.44073173","0.44008687","0.43955684","0.43851525","0.43659323","0.4364715","0.43606436","0.4356708","0.43525204","0.43521678","0.43374488","0.43350413","0.43339145","0.43278003","0.43206963","0.43151143","0.43033448","0.4299818","0.42977095","0.42883596","0.42848456","0.42814928","0.42765996","0.42693585","0.42682067","0.42600867","0.42453873","0.42321292","0.4227911","0.4218069","0.42147723","0.42143962","0.4210121","0.4208199","0.42053372","0.4203563","0.42014596","0.41984653","0.41958192","0.4193473","0.41910204","0.41909248","0.41907","0.4189252","0.41891426","0.4184212","0.41832742","0.41831782","0.4183143","0.41808653","0.41731644","0.41706806","0.4167038","0.41664776","0.41633844","0.41599545","0.41569123","0.41565034","0.41528946","0.4151195","0.41469994","0.41465175","0.4145885","0.4144273","0.41418666","0.41403407","0.4140286","0.41379923","0.41357905","0.41356826","0.41276014"],"string":"[\n \"0.69998956\",\n \"0.67811495\",\n \"0.5615518\",\n \"0.5273485\",\n \"0.5138776\",\n \"0.5041119\",\n \"0.48480907\",\n \"0.48040372\",\n \"0.47753853\",\n \"0.47583094\",\n \"0.4682528\",\n \"0.4670917\",\n \"0.466327\",\n \"0.4620052\",\n \"0.45891234\",\n \"0.45834735\",\n \"0.45678422\",\n \"0.4562975\",\n \"0.45465788\",\n \"0.45328742\",\n \"0.45173\",\n \"0.45090833\",\n \"0.44996414\",\n \"0.44785774\",\n \"0.4448101\",\n \"0.44395432\",\n \"0.4427622\",\n \"0.44208667\",\n \"0.44115433\",\n \"0.44073173\",\n \"0.44008687\",\n \"0.43955684\",\n \"0.43851525\",\n \"0.43659323\",\n \"0.4364715\",\n \"0.43606436\",\n \"0.4356708\",\n \"0.43525204\",\n \"0.43521678\",\n \"0.43374488\",\n \"0.43350413\",\n \"0.43339145\",\n \"0.43278003\",\n \"0.43206963\",\n \"0.43151143\",\n \"0.43033448\",\n \"0.4299818\",\n \"0.42977095\",\n \"0.42883596\",\n \"0.42848456\",\n \"0.42814928\",\n \"0.42765996\",\n \"0.42693585\",\n \"0.42682067\",\n \"0.42600867\",\n \"0.42453873\",\n \"0.42321292\",\n \"0.4227911\",\n \"0.4218069\",\n \"0.42147723\",\n \"0.42143962\",\n \"0.4210121\",\n \"0.4208199\",\n \"0.42053372\",\n \"0.4203563\",\n \"0.42014596\",\n \"0.41984653\",\n \"0.41958192\",\n \"0.4193473\",\n \"0.41910204\",\n \"0.41909248\",\n \"0.41907\",\n \"0.4189252\",\n \"0.41891426\",\n \"0.4184212\",\n \"0.41832742\",\n \"0.41831782\",\n \"0.4183143\",\n \"0.41808653\",\n \"0.41731644\",\n \"0.41706806\",\n \"0.4167038\",\n \"0.41664776\",\n \"0.41633844\",\n \"0.41599545\",\n \"0.41569123\",\n \"0.41565034\",\n \"0.41528946\",\n \"0.4151195\",\n \"0.41469994\",\n \"0.41465175\",\n \"0.4145885\",\n \"0.4144273\",\n \"0.41418666\",\n \"0.41403407\",\n \"0.4140286\",\n \"0.41379923\",\n \"0.41357905\",\n \"0.41356826\",\n \"0.41276014\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7685098"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":11270,"cells":{"query":{"kind":"string","value":"Returns the list of all benchmarks."},"document":{"kind":"string","value":"def get_all_benchmarks():\n all_benchmarks = []\n for benchmark in os.listdir(BENCHMARKS_DIR):\n benchmark_path = os.path.join(BENCHMARKS_DIR, benchmark)\n if os.path.isfile(os.path.join(benchmark_path, 'benchmark.yaml')):\n all_benchmarks.append(benchmark)\n return sorted(all_benchmarks)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def run_all(self):\n runs = []\n for run in self.benchmarks:\n run.start()\n run.wait()\n runs.append(run.metadata)\n return runs","def list(self, classes):\n\n def add(benchmarks, parts, flags, exclude):\n if (\n flags[\"language\"] != \"C++\"\n and flags[\"language\"] != \"Java\"\n and \"--drop-caches=true\" not in parts\n ):\n parts.append(\"--drop-caches=true\")\n command = \" \".join(parts)\n if command not in exclude:\n benchmarks.append({\"command\": command, \"flags\": flags})\n\n benchmarks = []\n for name, benchmark in classes.items():\n if name.startswith(\"example\"):\n continue\n\n instance, parts = benchmark(), [name]\n\n exclude = getattr(benchmark, \"exclude\", [])\n if \"source\" in getattr(benchmark, \"arguments\", []):\n parts.append(\"ALL\")\n\n iterations = getattr(instance, \"iterations\", 3)\n parts.append(f\"--iterations={iterations}\")\n\n if instance.cases:\n parts.append(\"--all=true\")\n\n flags = getattr(instance, \"flags\", {})\n\n if getattr(instance, \"r_only\", False):\n flags[\"language\"] = \"R\"\n add(benchmarks, parts, flags, exclude)\n else:\n if \"language\" not in flags:\n flags[\"language\"] = \"Python\"\n add(benchmarks, parts, flags, exclude)\n\n if hasattr(instance, \"r_name\"):\n flags_ = flags.copy()\n flags_[\"language\"] = \"R\"\n parts.append(\"--language=R\")\n add(benchmarks, parts, flags_, exclude)\n\n return sorted(benchmarks, key=lambda k: k[\"command\"])","def get(self, request, format=None):\n benchmarkmodels = BenchmarkModel.objects.all()\n serializer = BenchmarkModelListSerializer(benchmarkmodels, many=True)\n return Response(serializer.data)","def test_list_benchmarks(client):\n # The benchmark listing contains one element (independently of whether the\n # user is logged in or not).\n r = client.get(config.API_PATH() + '/workflows')\n assert r.status_code == 200\n doc = r.json\n assert len(doc[labels.WORKFLOW_LIST]) == 1\n # Create user and the request header that contains the API key for the\n # logged in user.\n _, token = create_user(client, '0000')\n headers = {HEADER_TOKEN: token}\n r = client.get(config.API_PATH() + '/workflows', headers=headers)\n assert r.status_code == 200\n doc = r.json\n assert len(doc[labels.WORKFLOW_LIST]) == 1","def get_coverage_benchmarks():\n return [\n benchmark for benchmark in get_all_benchmarks()\n if get_type(benchmark) == BenchmarkType.CODE.value\n ]","def delete_all_benchmarks(self, namespace=\"benchmark-operator\"):\n all_benchmarks = self.crd_client.list_namespaced_custom_object(\n group=\"ripsaw.cloudbulldozer.io\", version=\"v1alpha1\", namespace=namespace, plural=\"benchmarks\"\n )\n\n _ = [\n self.delete_benchmark(benchmark[\"metadata\"][\"name\"], namespace)\n for benchmark in all_benchmarks.get(\"items\", [])\n ]","def list_runtimes(self, workbench):\n pass","def benchmark(self):\n logger.info(self.benchmark.__doc__)\n return self.run(self.benchmark_profile())","def _parse_suite(\n self, results: dict, extra_tags: dict = None\n ) -> List[BenchmarkResult]:\n # all results share a batch id\n batch_id = uuid.uuid4().hex\n\n parsed_results = []\n for result in results[\"benchmarks\"]:\n result_parsed = self._parse_benchmark(\n result=GoogleBenchmark(**result),\n batch_id=batch_id,\n extra_tags=extra_tags,\n )\n parsed_results.append(result_parsed)\n\n return parsed_results","def get_benchmark_requirements(cls):\n pass","def __init__(self, conf, benchmarks, regex=None):\n self._conf = conf\n self._benchmark_dir = conf.benchmark_dir\n\n if not regex:\n regex = []\n if isinstance(regex, str):\n regex = [regex]\n\n self._all_benchmarks = {}\n self._benchmark_selection = {}\n for benchmark in benchmarks:\n self._all_benchmarks[benchmark['name']] = benchmark\n if benchmark['params']:\n self._benchmark_selection[benchmark['name']] = []\n for idx, param_set in enumerate(\n itertools.product(*benchmark['params'])):\n name = f\"{benchmark['name']}({', '.join(param_set)})\"\n if not regex or any(re.search(reg, name) for reg in regex):\n self[benchmark['name']] = benchmark\n self._benchmark_selection[benchmark['name']].append(idx)\n else:\n self._benchmark_selection[benchmark['name']] = None\n if not regex or any(re.search(reg, benchmark['name']) for reg in regex):\n self[benchmark['name']] = benchmark","def get_all_reporters():\r\n for ep in iter_entry_points('attest.reporters'):\r\n yield ep.name","def get_testbench_specs(self, tb_type: str) -> Dict[str, Any]:\n return self._specs['testbenches'][tb_type]","def get_methods(cls):\n # get all the methods that have the _benchmark_this flag\n for method in (getattr(cls, m) for m in dir(cls)):\n if hasattr(method, \"_benchmark_this\"):\n yield method","def get_list(cls, suite_model) -> list:\n suites = []\n for s in cls.SUITES:\n s[\"tests\"] = cls._get_tests(s)\n s[\"approxRunTime\"] = cls._get_average_run_time(suite_model)\n suites.append(s)\n return suites","def test_get_benchmark_methods_filter(self):\n config = mock.Mock()\n config.workspace = 'workspace'\n config.benchmark_method_patterns = ['new_foo.BenchmarkClass.filter:bench.*']\n benchmark_runner = benchmark.BenchmarkRunner(config)\n\n mock_benchmark_class = mock.Mock()\n mock_benchmark_class.benchmark_method_1 = 'foo'\n\n mock_module = mock.Mock()\n sys.modules['new_foo'] = mock_module\n mock_module.BenchmarkClass.return_value = mock_benchmark_class\n\n methods = benchmark_runner._get_benchmark_methods()\n\n self.assertEqual(1, len(methods))\n self.assertEqual('new_foo.BenchmarkClass.benchmark_method_1', methods[0])","def get_bug_benchmarks():\n return [\n benchmark for benchmark in get_all_benchmarks()\n if get_type(benchmark) == BenchmarkType.BUG.value\n ]","def get_benchmark(client):\n r = client.get(config.API_PATH() + '/benchmarks')\n benchmarks = json.loads(r.data)\n return benchmarks['benchmarks'][0]['id']","def test_get_benchmark_methods_exact_match(self):\n config = mock.Mock()\n config.workspace = 'workspace'\n config.benchmark_method_patterns = [\n 'new_foo.BenchmarkClass.benchmark_method_1',\n 'new_foo.BenchmarkClass.benchmark_method_2']\n benchmark_runner = benchmark.BenchmarkRunner(config)\n\n methods = benchmark_runner._get_benchmark_methods()\n self.assertEqual(['new_foo.BenchmarkClass.benchmark_method_1',\n 'new_foo.BenchmarkClass.benchmark_method_2'], methods)","def _generate_benchmark_variants(benchmark_spec):\n variants = []\n # Cold start.\n variants.append({\n 'name': benchmark_spec['name'] + ' (cold start)',\n 'app': benchmark_spec['app'],\n 'duration': benchmark_spec['duration'],\n 'measurements': benchmark_spec['measurements'],\n 'shell-args': benchmark_spec.get('shell-args',\n []) + _COLD_START_SHELL_ARGS})\n # Warm start.\n variants.append({\n 'name': benchmark_spec['name'] + ' (warm start)',\n 'app': benchmark_spec['app'],\n 'duration': benchmark_spec['duration'],\n 'measurements': benchmark_spec['measurements'],\n 'shell-args': benchmark_spec.get('shell-args', [])})\n return variants","def get_all_stats():\n\n return get_component(CachingPackage.COMPONENT_NAME).get_all_stats()","def _run():\n subprocess.check_call(\n [\n \"tools/bazel\",\n \"build\",\n \"-c\",\n \"opt\",\n \"test/core/memory_usage/memory_usage_test\",\n ]\n )\n ret = {}\n for name, benchmark_args in _BENCHMARKS.items():\n for scenario, extra_args in _SCENARIOS.items():\n # TODO(chenancy) Remove when minstack is implemented for channel\n if name == \"channel\" and scenario == \"minstack\":\n continue\n try:\n output = subprocess.check_output(\n [\n \"bazel-bin/test/core/memory_usage/memory_usage_test\",\n ]\n + benchmark_args\n + extra_args\n )\n except subprocess.CalledProcessError as e:\n print(\"Error running benchmark:\", e)\n continue\n for line in output.splitlines():\n for key, (pattern, conversion) in _INTERESTING.items():\n m = re.match(pattern, line)\n if m:\n ret[scenario + \": \" + key] = conversion(m.group(1))\n return ret","def measure_all(backend) -> Schedule:\n # backend is V2.\n if isinstance(backend, BackendV2):\n qubits = list(range(backend.num_qubits))\n else:\n qubits = list(range(backend.configuration().n_qubits))\n return measure(qubits=qubits, backend=backend)","def generate_figures():\r\n # create results directory if necessary\r\n try:\r\n makedirs(\"results\")\r\n except OSError as e:\r\n if e.errno != errno.EEXIST:\r\n raise\r\n \r\n for b in benchmarks:\r\n generate_figure(model[b], b)","def register_benchmarks(directory=None):\n dirs = places_to_look() if directory is None else [directory]\n for directory in dirs:\n with os.scandir(directory) as scan:\n for entry in scan:\n filename = entry.name\n if (\n filename.startswith(\".\")\n or not entry.is_file()\n or not filename.endswith(\".py\")\n ):\n continue\n if (\n filename.startswith(\"benchmark\")\n or filename.endswith(\"benchmark.py\")\n or filename.endswith(\"benchmarks.py\")\n ):\n import_path(f\"{directory}/{filename}\")","def count_benchmarks():\n return len(setup_storage().fetch_benchmark({}))","def suite_list(self):\n return ','.join([s.__unicode__() for s in self.suites.all()])","def suite_list(self):\n return ','.join([s.__unicode__() for s in self.suites.all()])","def ballot_get_all_contests():\r\n all_contests = []\r\n result = ballot_list_contests(limit=100000000).get('result')\r\n if result:\r\n contests = batch('ballot_get_contest_by_id', [[r] for r in result]).get('result')\r\n if contests:\r\n all_contests = contests\r\n\r\n return all_contests","def Run(benchmark_spec: bm_spec.BenchmarkSpec) -> List[sample.Sample]:\n discovery_duration = benchmark_spec.data_discovery_service.DiscoverData()\n return [\n sample.Sample('data_discovery_duration', discovery_duration, 'seconds',\n benchmark_spec.data_discovery_service.GetMetadata())]","def list_ciphers():\n global AVAILABLE_CIPHERS\n print(\"[!] Available ciphers: \")\n for i in range(len(AVAILABLE_CIPHERS)):\n print(\" ----> %s.%s\"%(i+1, AVAILABLE_CIPHERS[i]))\n exit()","def all_experiments():\n elo_explain_experiments()\n alpha_beta_experiments()\n mtcs_experiments()","def run_benchmark():\n import argparse\n parser = argparse.ArgumentParser(description='Benchmark alchemically modified system against unmodified system.')\n parser.add_argument('--platform', dest='platform_name', action='store', default=None, help='platform name to benchmark (default: None)')\n options = parser.parse_args()\n\n from sams.tests import testsystems\n for testsystem_name in ['AblImatinibExplicitAlchemical']:\n cls = getattr(testsystems, testsystem_name)\n testsystem = cls()\n factory_args = { 'ligand_atoms' : testsystem.alchemical_atoms, 'receptor_atoms' : range(0,4266) }\n benchmark(testsystem.system, testsystem.positions, platform_name=options.platform_name, nsteps=5000, timestep=1.0*unit.femtoseconds, factory_args=factory_args)","def benchmark_selection(self):\n return self._benchmark_selection","def _disc_benchmarks(cls, conf, repo, environments, commit_hashes, check):\n root = conf.benchmark_dir\n\n cls.check_tree(root)\n\n if len(environments) == 0:\n raise util.UserError(\"No available environments\")\n\n # Try several different commits:\n #\n # - First of commit_hashes provided\n # - Tips of branches from configuration file\n # - Rest of the commit_hashes\n #\n\n def iter_hashes():\n for h in commit_hashes[:1]:\n yield h\n for branch in conf.branches:\n try:\n yield repo.get_hash_from_name(branch)\n except NoSuchNameError:\n continue\n for h in commit_hashes[1:]:\n yield h\n\n def iter_unique(iter):\n seen = set()\n for item in iter:\n if item not in seen:\n seen.add(item)\n yield item\n\n try_hashes = iter_unique(iter_hashes())\n\n log.info(\"Discovering benchmarks\")\n with log.indent():\n last_err = None\n for env, commit_hash in itertools.product(environments, try_hashes):\n env.create()\n\n if last_err is not None:\n log.warning(\"Failed: trying different commit/environment\")\n\n result_dir = tempfile.mkdtemp()\n try:\n env.install_project(conf, repo, commit_hash)\n\n env_vars = dict(os.environ)\n env_vars.update(env.env_vars)\n\n result_file = os.path.join(result_dir, 'result.json')\n env.run(\n [runner.BENCHMARK_RUN_SCRIPT, 'discover',\n os.path.abspath(root),\n os.path.abspath(result_file)],\n cwd=result_dir,\n env=env_vars,\n dots=False)\n\n try:\n with open(result_file, 'r') as fp:\n benchmarks = json.load(fp)\n except (IOError, ValueError):\n log.error(\"Invalid discovery output\")\n raise util.UserError()\n\n break\n except (util.UserError, util.ProcessError) as err:\n last_err = err\n continue\n except KeyboardInterrupt:\n raise util.UserError(\"Interrupted.\")\n finally:\n util.long_path_rmtree(result_dir)\n else:\n raise util.UserError(\"Failed to build the project and import the benchmark suite.\")\n\n if check:\n log.info(\"Checking benchmarks\")\n with log.indent():\n result_dir = tempfile.mkdtemp()\n try:\n out, err, retcode = env.run(\n [runner.BENCHMARK_RUN_SCRIPT, 'check',\n os.path.abspath(root)],\n cwd=result_dir,\n dots=False,\n env=env_vars,\n valid_return_codes=None,\n return_stderr=True,\n redirect_stderr=True)\n finally:\n util.long_path_rmtree(result_dir)\n\n out = out.strip()\n if retcode == 0:\n if out:\n log.info(out)\n log.info(\"No problems found.\")\n else:\n if out:\n log.error(out)\n raise util.UserError(\"Benchmark suite check failed.\")\n\n return benchmarks","def _transform_results(self) -> List[BenchmarkResult]:\n with open(self.result_file, \"r\") as f:\n raw_results = json.load(f)\n\n parsed_results = []\n for suite in raw_results[\"suites\"]:\n parsed_results += self._parse_suite(\n results=suite,\n extra_tags={\"suite\": suite[\"name\"], \"source\": \"cpp-micro\"},\n )\n\n return parsed_results","def save(self):\n path = self.get_benchmark_file_path(self._conf.results_dir)\n util.write_json(path, self._all_benchmarks, self.api_version)","def get_all_casks(self):","def on_get(self, req: Request, resp: Response):\n benchmarks = self.storage.fetch_benchmark({})\n\n response = build_benchmarks_response(benchmarks)\n resp.text = json.dumps(response)","def __benchmark__(cls):\n results = []\n used_argsets = []\n for args in cls.arguments:\n used_argsets.append(args)\n # for each given argument\n for method in cls.get_methods():\n # append an empty list for the results with this argument\n method_results = []\n # for each repetition\n for n in xrange(cls.repetitions):\n # append the results to the list for this argument set\n trial_results = cls._trial(method, args)\n method_results.append(trial_results)\n # append a Result to `results`.\n results.append(Result(method, args, method_results))\n return results, used_argsets","def benchmark(self):\n nsites = []\n for m in self.methods:\n for name, structure in self.test_structures.items():\n cns = []\n if self.unique_sites:\n es = SpacegroupAnalyzer(structure).get_symmetrized_structure().equivalent_sites\n sites = [structure.index(x[0]) for x in es]\n else:\n sites = range(len(structure))\n\n for key, val in self.hi.items():\n if name == key:\n for j in sites:\n if isinstance(m, NearNeighbors):\n tmpcn = m.get_cn_dict(structure, j, self.use_weights)\n else:\n tmpcn = m.compute(structure, j)\n if tmpcn == \"null\":\n continue\n if self.nround:\n self._roundcns(tmpcn, self.nround)\n cns.append((structure[j].species_string, tmpcn))\n if self.cation_anion:\n for mat, cat in self.cations.items():\n if (name == mat) and cat:\n cns = self._popel(cns, cat)\n elif self.anion_cation:\n for mat, an in self.anions.items():\n if name == mat:\n cns = self._popel(cns, an)\n m._cns[name] = cns\n nsites.append(len(cns))\n self.nsites = max(nsites)","def exclude_non_cpp(benchmarks):\n return [benchmark for benchmark in benchmarks if is_cpp(benchmark)]","def find_benchmark_directories(self):\n for (benchmark, producer), result in self.results.items():\n pattern = benchmark + '_' + producer + '*'\n files = find_directory(pattern, self.root_folder)\n if files:\n # add just the latest one\n sorted_files = sorted(files)\n result.directory = sorted_files[-1]\n else:\n print('No benchmark directories found in ' + self.root_folder)","def main(benchmark, size=None, backend=None, repetitions=None, burnin=1, device=\"cpu\"):\n try:\n bm_module, bm_identifier = get_benchmark_module(benchmark)\n except ImportError as e:\n click.echo(f\"Error while loading benchmark {benchmark}: {e!s}\", err=True)\n raise click.Abort()\n\n available_backends = set(bm_module.__implementations__)\n\n if len(backend) == 0:\n backend = available_backends.copy()\n else:\n backend = set(backend)\n\n unsupported_backends = [b for b in backend if b not in available_backends]\n\n for b in unsupported_backends:\n click.echo(\n f'Backend \"{b}\" is not supported by chosen benchmark (skipping)', err=True\n )\n backend.remove(b)\n\n for b in backend.copy():\n try:\n with setup_functions[b](device=device) as bmod:\n click.echo(f\"Using {b} version {bmod.__version__}\")\n except BackendNotSupported as e:\n click.echo(\n f'Setup for backend \"{b}\" failed (skipping), reason: {e!s}', err=True\n )\n backend.remove(b)\n\n try:\n check_backend_conflicts(backend, device)\n except BackendConflict as exc:\n click.echo(f\"Backend conflict: {exc!s}\", err=True)\n raise click.Abort()\n\n runs = sorted(itertools.product(backend, size))\n\n if len(runs) == 0:\n click.echo(\"Nothing to do\")\n return\n\n timings = {run: [] for run in runs}\n\n if repetitions is None:\n click.echo(\"Estimating repetitions...\")\n repetitions = {}\n\n for b, s in runs:\n # use end-to-end runtime for repetition estimation\n def run_func():\n run = bm_module.get_callable(b, s, device=device)\n with setup_functions[b](device=device):\n run()\n\n repetitions[(b, s)] = estimate_repetitions(run_func)\n else:\n repetitions = {(b, s): repetitions for b, s in runs}\n\n all_runs = list(\n itertools.chain.from_iterable(\n [run] * (repetitions[run] + burnin) for run in runs\n )\n )\n random.shuffle(all_runs)\n\n results = {}\n checked = {r: False for r in runs}\n\n pbar = click.progressbar(\n label=f\"Running {len(all_runs)} benchmarks...\", length=len(runs)\n )\n\n try:\n with pbar:\n for (b, size) in all_runs:\n with setup_functions[b](device=device):\n run = bm_module.get_callable(b, size, device=device)\n with Timer() as t:\n res = run()\n\n # YOWO (you only warn once)\n if not checked[(b, size)]:\n if size in results:\n is_consistent = check_consistency(\n results[size], convert_to_numpy(res, b, device)\n )\n if not is_consistent:\n click.echo(\n f\"\\nWarning: inconsistent results for size {size}\",\n err=True,\n )\n else:\n results[size] = convert_to_numpy(res, b, device)\n checked[(b, size)] = True\n\n timings[(b, size)].append(t.elapsed)\n pbar.update(1.0 / (repetitions[(b, size)] + burnin))\n\n # push pbar to 100%\n pbar.update(1.0)\n\n for run in runs:\n assert len(timings[run]) == repetitions[run] + burnin\n\n finally:\n stats = compute_statistics(timings)\n click.echo(format_output(stats, bm_identifier, device=device))","def list_builders(self) -> List[str]:\n return sorted(_iter_builder_names(self._ns2data_dir))","def speed_list(self) -> list:\n wink_supported_speeds = self.wink.fan_speeds()\n supported_speeds = []\n if SPEED_AUTO in wink_supported_speeds:\n supported_speeds.append(SPEED_AUTO)\n if SPEED_LOWEST in wink_supported_speeds:\n supported_speeds.append(SPEED_LOWEST)\n if SPEED_LOW in wink_supported_speeds:\n supported_speeds.append(SPEED_LOW)\n if SPEED_MEDIUM in wink_supported_speeds:\n supported_speeds.append(SPEED_MEDIUM)\n if SPEED_HIGH in wink_supported_speeds:\n supported_speeds.append(SPEED_HIGH)\n return supported_speeds","async def getAll():\n return [cluster.export() for cluster in clusters.get_all()]","def evaluate_benchmarks(self):\n\n # iterate over replicates\n results = {}\n for replicate_id, replicate in self.replicates:\n\n # evaluate benchmark for current replicate\n bmark = SimulationBenchmark(replicate.copy(),\n graph=self.graphs[replicate_id],\n **self.params)\n\n # store results\n results[replicate_id] = dict(\n\n labels_MAE=bmark.scores['labels'].MAE,\n level_only_MAE=bmark.scores['level_only'].MAE,\n spatial_only_MAE=bmark.scores['spatial_only'].MAE,\n community_MAE=bmark.scores['labels_comm'].MAE,\n\n labels_PCT=bmark.scores['labels'].percent_correct,\n level_only_PCT=bmark.scores['level_only'].percent_correct,\n spatial_only_PCT=bmark.scores['spatial_only'].percent_correct,\n community_PCT=bmark.scores['labels_comm'].percent_correct)\n\n # compile dataframe\n results = pd.DataFrame.from_dict(results, orient='index')\n results.index.set_names(self.multiindex, inplace=True)\n\n return results","def print_scenario(benchmarks, name):\n # Remember, the first entry in the times array is an empty object.\n\n benchmarks = sorted(benchmarks, key = benchmark_sort_key)\n for benchmark in benchmarks:\n block_times = benchmark[\"times\"][2][\"block_times\"] # list of floats\n it = iter(block_times)\n for start in it:\n end = next(it)\n\n print(\"%s , %.3f \" % (benchmark[\"label\"], float(end) - float(start)))","def get_all_adapters(self):\n pass","def __init__(self, benchmarks):\n self.benchmarks = benchmarks\n instNum = 0\n classNum = 0\n for benchclass in self:\n benchclass.line = classNum\n benchclass.instStart = instNum\n for instance in benchclass: \n instance.line = instNum\n instNum += max(instance.maxRuns, 1)\n benchclass.instEnd = instNum - 1\n classNum += 1","def _parameterize_simple_benchmarks():\n parameterized_tuples = []\n for index, simple_benchmark in enumerate(\n all_benchmarks.all_benchmarks(modules=[simple_benchmarks])):\n # The index ensures all test cases have distinct names, even if multiple\n # benchmarks have the same name.\n test_case_name = '{index}_{name}'.format(index=index,\n name=simple_benchmark.name)\n parameterized_tuples.append((test_case_name, simple_benchmark))\n return parameterized_tuples","def Run(benchmark_spec):\n vms = benchmark_spec.vms\n results = []\n\n logging.info('Iperf Results:')\n\n # Send traffic in both directions\n for sending_vm, receiving_vm in vms, reversed(vms):\n # Send using external IP addresses\n if vm_util.ShouldRunOnExternalIpAddress():\n results.append(_RunIperf(sending_vm,\n receiving_vm,\n receiving_vm.ip_address,\n 'external'))\n\n # Send using internal IP addresses\n if vm_util.ShouldRunOnInternalIpAddress(sending_vm,\n receiving_vm):\n results.append(_RunIperf(sending_vm,\n receiving_vm,\n receiving_vm.internal_ip,\n 'internal'))\n\n return results","def time_binaries():\n print()\n print(\"=== BENCHMARKING ===\")\n with tempfile.NamedTemporaryFile(\n prefix=\"riff-benchmark\", suffix=\".gitlog\"\n ) as testdata:\n subprocess.check_call(\n [\"git\", \"log\", \"--color=always\", \"-p\", \"master\"], stdout=testdata\n )\n\n binaries = sorted(glob.glob(os.path.join(BINDIR, \"*\")), key=natural_keys)\n\n # Do riff-current last: https://stackoverflow.com/a/20320940/473672\n binaries.sort(key=lambda s: s.endswith(\"riff-current\"))\n\n for binary in binaries:\n print_timings(binary, testdata.name)\n print_timings(\"/bin/cat\", testdata.name)","def bench(participants=participants, benchmarks=benchmarks, bench_time=10.0):\n\n mcs = [p.factory() for p in participants]\n means = [[] for p in participants]\n stddevs = [[] for p in participants]\n\n # Have each lifter do one benchmark each\n for benchmark_name, f, args, kwargs in benchmarks:\n logger.info('%s', benchmark_name)\n\n for i, (participant, mc) in enumerate(zip(participants, mcs)):\n sw = Stopwatch()\n\n while sw.total() < bench_time:\n with sw.timing():\n f(mc, *args, **kwargs)\n\n means[i].append(sw.mean())\n stddevs[i].append(sw.stddev())\n\n logger.info(u'%s: %s', participant.name, sw)\n\n return means, stddevs","def __iter__(self):\n for benchclass in sorted(self.classes.values()):\n yield benchclass","def list_suites(arn=None, nextToken=None):\n pass","def list(args):\n experiments = sorted(os.listdir('./litmus'))\n print_color(\"Available Litmus Chaos Experiments:\\n\\n\")\n if (f\"{args.platform}\" == \"GKE\"):\n i = 1\n for experiment_file in experiments:\n print_color(f\"\\t{i}. {experiment_file.replace('.yaml', '')}\")\n i += 1\n\n if (f\"{args.platform}\" == \"kind\"):\n kind_supported = [\"pod-delete\",\"container-kill\",\"node-cpu-hog\",\"node-memory-hog\"]\n i = 0\n for i in range(0, len(kind_supported)):\n print_color(f\"\\t{i+1}. {kind_supported[i]}\")\n i += 1\n\n if (f\"{args.platform}\" == \"EKS\"):\n i = 1\n for experiment_file in experiments:\n print_color(f\"\\t{i}. {experiment_file.replace('.yaml', '')}\")\n i += 1","def get_all(self):\r\n return [{ 'label': x[0], 'timing': x[1], 'is_tool': x[0] in self._tool_labels}\r\n for x in sorted(self._timings_by_path.items(), key=lambda x: x[1], reverse=True)]","def benchmark():\n print defer.Deferred.__module__\n for func, args, iter in benchmarkFuncs:\n print func.__name__, args, timeit(func, iter, *args)","def output_benchmark_results(output_dir, ts_agent_list=None, ga_agent=None, title=None, auto_open=True):\n if (ts_agent_list is None or not all(ts_agent.benchmark for ts_agent in ts_agent_list)) \\\n and (ga_agent is None or not ga_agent.benchmark):\n raise UserWarning(\"agent arguments were None or were not ran in benchmark mode.\")\n\n if title is None:\n title = \"Benchmark Run {}\".format(datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M\"))\n\n output_dir = Path(output_dir)\n\n if not output_dir.exists():\n output_dir.mkdir(parents=True)\n\n def compute_stats(lst):\n return {\n 'min': round(min(lst)),\n 'median': round(statistics.median(lst)),\n 'max': round(max(lst)),\n 'std': round(statistics.stdev(lst)) if len(lst) > 1 else 0,\n 'var': round(statistics.variance(lst)) if len(lst) > 1 else 0,\n 'mean': round(statistics.mean(lst))\n }\n\n # tabu search results\n if ts_agent_list is not None and all(ts_agent.benchmark for ts_agent in ts_agent_list):\n _create_ts_plots(ts_agent_list, output_dir)\n ts_result_makespans = []\n ts_initial_makespans = []\n ts_iterations = []\n for ts_agent in ts_agent_list:\n ts_result_makespans.append(ts_agent.best_solution.makespan)\n ts_initial_makespans.append(ts_agent.initial_solution.makespan)\n ts_iterations.append(ts_agent.benchmark_iterations)\n\n ts_result_makespans_stats = compute_stats(ts_result_makespans)\n ts_initial_makespans_stats = compute_stats(ts_initial_makespans)\n ts_iterations_stats = compute_stats(ts_iterations)\n\n else:\n ts_result_makespans_stats = None\n ts_initial_makespans_stats = None\n ts_iterations_stats = None\n\n # genetic algorithm results\n if ga_agent is not None and ga_agent.benchmark:\n _create_ga_plots(ga_agent, output_dir)\n ga_initial_makespans = [sol.makespan for sol in ga_agent.initial_population]\n ga_result_makespans = [sol.makespan for sol in ga_agent.result_population]\n\n ga_initial_makespans_stats = compute_stats(ga_initial_makespans)\n ga_result_makespans_stats = compute_stats(ga_result_makespans)\n\n else:\n ga_initial_makespans_stats = None\n ga_result_makespans_stats = None\n\n # render template\n template = template_env.get_template(benchmark_template)\n rendered_template = template.render(\n title=title,\n ts_agent_list=ts_agent_list,\n ts_initial_makespans_stats=ts_initial_makespans_stats,\n ts_result_makespans_stats=ts_result_makespans_stats,\n iterations_per_ts_agent_stats=ts_iterations_stats,\n output_directory=output_dir.resolve(),\n ga_agent=ga_agent,\n ga_initial_makespans_stats=ga_initial_makespans_stats,\n ga_result_makespans_stats=ga_result_makespans_stats,\n )\n\n # create index.html\n with open(output_dir / 'index.html', 'w') as output_file:\n output_file.write(rendered_template)\n\n if auto_open:\n webbrowser.open(f'file://{output_dir.resolve()}/index.html')","def tests_generator(self):\n cb_bin = os.path.join(bin_path, 'compilebench')\n cmd_list = [\n (\"Initial Create/Compile/Read Compiled Tree\", \"{0} -D {1} -i 10 --makej -s {2}\"),\n ]\n\n tests = []\n for idx, (desc, cmd) in enumerate(cmd_list):\n test_name = \"compile_bench_{0}_{1}\".format(idx + 1, to_safe_name(desc))\n test = TestProfile(\n name=test_name,\n desc=desc,\n test_path=self.test_path,\n bin_path=bin_path,\n command=cmd.format(cb_bin, self.test_path, bin_path))\n tests.append(test)\n return tests","def get_named_suites():\n\n # Skip \"with_server\" and \"no_server\" because they do not define any test files to run.\n executor_only = set([\"with_server\", \"no_server\"])\n suite_names = [suite for suite in resmokeconfig.NAMED_SUITES if suite not in executor_only]\n suite_names.sort()\n return suite_names","def list_all():\n if os.path.exists(DATA_DIR):\n return os.listdir(DATA_DIR)\n return []","def merge(self, projects):\n benchmarks = set()\n for project in projects:\n for runspec in project:\n for classresult in runspec:\n for instresult in classresult.instresults:\n instresult.instance.maxRuns = max(instresult.instance.maxRuns, len(instresult.runs))\n benchmarks.add(runspec.benchmark)\n return BenchmarkMerge(benchmarks)","def getTestSets():\n return list(_testsetdict.keys())","def getAllBuilders(self):\n names = self.status.getBuilderNames(categories=self.categories)\n builders = [self.status.getBuilder(n) for n in names]\n return builders","def filter_out(self, skip):\n benchmarks = super(Benchmarks, self).__new__(self.__class__)\n benchmarks._conf = self._conf\n benchmarks._benchmark_dir = self._benchmark_dir\n benchmarks._all_benchmarks = self._all_benchmarks\n\n selected_idx = {}\n\n for name, benchmark in self.items():\n if name not in skip:\n benchmarks[name] = benchmark\n if name in self._benchmark_selection:\n selected_idx[name] = self._benchmark_selection[name]\n\n benchmarks._benchmark_selection = selected_idx\n\n return benchmarks","def get_tests():\n # tests = ['test_build_gaussian_pyramid_random', 'test_build_gaussian_pyramid_static', 'test_build_laplacian_pyramid_random', 'test_build_laplacian_pyramid_static', 'test_laplacian_to_image', 'test_render_pyramid_random', 'test_render_pyramid_static']\n # return [tester.TestEx3(method) for method in tests]\n return [tester.TestEx3(method) for method in dir(tester.TestEx3) if method.startswith('test')]","def fetch_markets_tickers(self):\r\n return self.__public_request('GET', '/api/v1/tickers')","def get_available_datasets():\n files = [file for file in glob.glob(os.path.join(MODULE_ROOT, \"datasets/*.json\"))]\n datasets = []\n for file in files:\n with open(file, \"r\") as f:\n dataset_info = json.load(f)\n datasets.append(dataset_info)\n return datasets","def list(self):\n print \"\\nAvailable Test Cases\"\n print \"====================\"\n for case in self.cases:\n print case.__name__","def list_datasets():\n return METADATA.keys()","def __iter__(self):\n for benchinst in sorted(self.instances.values()):\n yield benchinst","def suite():\n\tts = unittest.TestSuite()\n\tfor test_module in __all__:\n\t\tm = importlib.import_module(\"pyroclast.test.\" + test_module)\n\t\tfor n in dir(m):\n\t\t\tc = getattr(m, n)\n\t\t\tif is_test_case(c):\n\t\t\t\ts = unittest.TestLoader().loadTestsFromTestCase(c)\n\t\t\t\tts.addTests(s)\n\treturn ts","def load(cls, conf, regex=None):\n try:\n path = cls.get_benchmark_file_path(conf.results_dir)\n if not os.path.isfile(path):\n raise util.UserError(f\"Benchmark list file {path} missing!\")\n d = util.load_json(path, api_version=cls.api_version)\n benchmarks = d.values()\n return cls(conf, benchmarks, regex=regex)\n except util.UserError as err:\n if \"asv update\" in str(err):\n # Don't give conflicting instructions\n raise\n raise util.UserError(\"{}\\nUse `asv run --bench just-discover` to \"\n \"regenerate benchmarks.json\".format(str(err)))","def get_all_tools(project_dir: Union[str, os.PathLike]):\n return [\n get_tool(tool_name=name, project_dir=project_dir)\n for name in (Judy.name, Jumble.name, Major.name, Pit.name)\n ]","def get_all(self):\r\n ret = []\r\n for cache_name, stat in self.stats_per_cache.items():\r\n ret.append({\r\n 'cache_name': cache_name,\r\n 'num_hits': len(stat.hit_targets),\r\n 'num_misses': len(stat.miss_targets),\r\n 'hits': stat.hit_targets,\r\n 'misses': stat.miss_targets\r\n })\r\n return ret","def get_all_metrics(self):\n up_time = self.uptime()\n down_time = self.downtime()\n customer_sla = self.sla()\n objective = self.slo()\n indicator = self.sli()\n avail_percentage = self.availability()\n mt_bf = self.mtbf(up_time)\n mt_tr = self.mttr(down_time)\n list_results = [up_time,down_time,customer_sla,objective,indicator,avail_percentage,mt_bf,mt_tr]\n return list_results","def get_performance_test_cases(test_suite):\n return get_cases(test_suite, r'test_perf_')","def _get_all(cls) -> Iterator[\"PermutationStatistic\"]:\n yield from (cls(name, func) for name, func in PermutationStatistic._STATISTICS)","def allPresets():\n\n return [__cleanPresetTreeName(k) for k in __preset_lookup.iterkeys()]","def run_benchmark(curl, benchmark, test_config = TestConfig()):\n\n warmup_runs = benchmark.warmup_runs\n benchmark_runs = benchmark.benchmark_runs\n message = '' #Message is name of benchmark... print it?\n\n if (warmup_runs <= 0):\n raise Exception(\"Invalid number of warmup runs, must be > 0 :\" + warmup_runs)\n if (benchmark_runs <= 0):\n raise Exception(\"Invalid number of benchmark runs, must be > 0 :\" + benchmark_runs)\n\n #Initialize variables to store output\n output = BenchmarkResult()\n output.name = benchmark.name\n output.group = benchmark.group\n metricnames = list(benchmark.metrics)\n metricvalues = [METRICS[name] for name in metricnames] # Metric variable for curl, to avoid hash lookup for every metric name\n results = [list() for x in xrange(0, len(metricnames))] # Initialize arrays to store results for each metric\n\n curl.setopt(pycurl.WRITEFUNCTION, lambda x: None) #Do not store actual response body at all.\n\n #Benchmark warm-up to allow for caching, JIT compiling, on client\n logging.info('Warmup: ' + message + ' started')\n for x in xrange(0, warmup_runs):\n if benchmark.method == u'POST' or benchmark.method == u'PUT':\n curl.setopt(curl.READFUNCTION, StringIO.StringIO(benchmark.body).read)\n curl.perform()\n logging.info('Warmup: ' + message + ' finished')\n\n logging.info('Benchmark: ' + message + ' starting')\n\n for x in xrange(0, benchmark_runs): # Run the actual benchmarks\n if benchmark.method == u'POST' or benchmark.method == u'PUT':\n curl.setopt(curl.READFUNCTION, StringIO.StringIO(benchmark.body).read)\n\n try: # Run the curl call, if it errors, then add to failure counts for benchmark\n curl.perform()\n except Exception:\n output.failures = output.failures + 1\n continue # Skip metrics collection\n\n # Get all metrics values for this run, and store to metric lists\n for i in xrange(0, len(metricnames)):\n results[i].append( curl.getinfo(metricvalues[i]) )\n\n logging.info('Benchmark: ' + message + ' ending')\n\n temp_results = dict()\n for i in xrange(0, len(metricnames)):\n temp_results[metricnames[i]] = results[i]\n output.results = temp_results\n\n curl.close()\n return analyze_benchmark_results(output, benchmark)","def list_charts():\n charts_root = Path(R\".\\charm\\data\\charts\")\n charts = list(charts_root.rglob(\"*.chart\"))\n return charts","def main(args):\n\n # Compose the model list\n modellist = []\n if args['model']:\n modellist.append(bmark.ModelInfo(args['model'], os.getcwd(), args['classname']))\n\n # Load the benchmark settings\n benchmark = None\n benchmark = bmark.load_benchmark(args['benchmark'])\n corresponding_data = False\n if 'corresponding_data' in benchmark:\n corresponding_data = benchmark['corresponding_data']\n\n # Only extend if not cached\n cache_df = None\n if not args['cache']:\n modellist.extend(benchmark['models'])\n else:\n cache_df = pd.read_csv(args['cache'])\n\n # Extract comparator settings from benchmark description\n eval_comparator = comparator.EqualityComparator()\n if 'comparator' in benchmark:\n if benchmark['comparator'] == 'nvc':\n eval_comparator = comparator.NVCComparator()\n\n # Run the model evaluation\n is_silent = (args['output'] in ['html', 'server'])\n eva = None\n if benchmark['type'] == 'adaption':\n eva = evaluator.AdaptionEvaluator(\n modellist,\n eval_comparator,\n benchmark['data.test'],\n train_datafile=benchmark['data.train'],\n train_data_person=benchmark['data.train_person'],\n silent=is_silent,\n corresponding_data=corresponding_data,\n domain_encoders=benchmark['domain_encoders'],\n cache_df=cache_df\n )\n elif benchmark['type'] == 'coverage':\n # Check for benchmark validity\n if benchmark['data.train'] or benchmark['data.train_person']:\n print('WARNING: Ignoring specified training and train_person data ' \\\n + 'for coverage evaluation...')\n\n eva = evaluator.CoverageEvaluator(\n modellist,\n eval_comparator,\n benchmark['data.test'],\n train_datafile=benchmark['data.train'],\n train_data_person=benchmark['data.train_person'],\n silent=is_silent,\n corresponding_data=corresponding_data,\n domain_encoders=benchmark['domain_encoders'],\n cache_df=cache_df\n )\n else:\n raise ValueError('Unknown benchmark type: {}'.format(benchmark['type']))\n\n with silence_stdout(is_silent):\n res_df = eva.evaluate()\n\n if 'save' in args:\n res_df.to_csv(args['save'], index=False)\n\n # Run the metric visualizer\n htmlcrtr = html_creator.HTMLCreator([\n viz_plot.AccuracyVisualizer(),\n viz_plot.BoxplotVisualizer(),\n viz_plot.TableVisualizer()\n ])\n\n # Prepare the benchmark output information and visualize the evaluation results\n benchmark_info = {\n 'name': os.path.basename(args['benchmark']),\n 'data.train': os.path.basename(\n benchmark['data.train']) if benchmark['data.train'] else '',\n 'data.train_person': os.path.basename(\n benchmark['data.train_person']) if benchmark['data.train_person'] else '',\n 'data.test': os.path.basename(benchmark['data.test']),\n 'type': benchmark['type'],\n 'corresponding_data': benchmark['corresponding_data'],\n 'domains': list(res_df['domain'].unique()),\n 'response_types': list(res_df['response_type'].unique()),\n }\n\n if args['output'] == 'browser':\n html = htmlcrtr.to_html(res_df, benchmark_info, embedded=False)\n server.load_in_default_browser(html.encode('utf8'))\n elif args['output'] == 'server':\n html = htmlcrtr.to_html(res_df, benchmark_info, embedded=True)\n sys.stdout.buffer.write(html.encode('utf-8'))\n elif args['output'] == 'html':\n html = htmlcrtr.to_html(res_df, benchmark_info, embedded=False)\n print(html)","def get_latest_benchmark():\n\n benchmark_paths = glob.glob(\"./.benchmarks/*/*.json\")\n dates = [\n \"\".join(_b.split(\"/\")[-1].split(\"_\")[2:4]) for _b in benchmark_paths\n ]\n benchmarks = {date: value for date, value in zip(dates, benchmark_paths)}\n\n dates.sort()\n latest = dates[-1]\n benchmark_latest = benchmarks[latest]\n\n return benchmark_latest","def load_results(self):\n self.find_benchmark_directories()\n for (benchmark, producer), result in self.results.items():\n print('Reading results for ' + benchmark + ' ' + producer)\n if not result.directory:\n print('No results found for ' + benchmark + ' ' + producer)\n else:\n print('Generating report for: ' + result.directory)\n report = Report(result.directory)\n result.reports = report.generate()","def _get_all_spectra(self):\n pass","def get_all_labs():\n return Lab.query.all()","def available_datasets(self) -> List[str]:\n return sorted(self.__by_name.keys())","def tickers(self):\n self._refresh_tickers()\n return self._tickers","def get_results_for_all_classifiers(\n self, _sorted=True, reverse=True\n ) -> List[MachineLearningExperimentResults]:\n results = [\n self.get_results(classifier_name)\n for classifier_name in self.classifiers.keys()\n ]\n\n if _sorted:\n return sorted(results, reverse=reverse)\n\n return results","def speed_list(self) -> list:\n return self._speed_list","def performances(self) -> list[Performance]:\n return [\n Performance(\n training_time=Metric(p[\"training\"][\"duration\"], 0),\n latency=Metric(self.static_metrics[\"latency\"], 0),\n num_model_parameters=Metric(\n self.static_metrics[\"num_model_parameters\"], 0\n ),\n num_gradient_updates=Metric(\n p[\"training\"][\"num_gradient_updates\"], 0\n ),\n **{\n k: Metric(p[\"testing\"][k], 0)\n for k in [\"mase\", \"smape\", \"nrmse\", \"nd\", \"ncrps\"]\n },\n )\n for p in self.metrics\n ]","def specs_to_load(self):\n return self._create_list_for(\"autoload\")","def test_BenchmarkSuite_integration_test(\n benchmark_suite: typing.Callable, tempdir: pathlib.Path\n):\n with benchmark_suite() as bs:\n bs.ForceOpenCLEnvironment(cldrive_env.OclgrindOpenCLEnvironment())\n observer = MockBenchmarkObserver(stop_after=1)\n\n # `stop_after` raises BenchmarkInterrupt.\n try:\n bs.Run([observer])\n assert False\n except gpgpu.BenchmarkInterrupt:\n pass\n\n assert len(observer.logs) == 1\n assert observer.logs[0].benchmark_name in bs.benchmarks","def comports(include_links=False):\n return list(iterate_comports())","def list_backends(_):\n backends = [b.__name__ for b in available_backends()]\n print('\\n'.join(backends))","def load_tests_list(cls, to):\n tests = [unittest.TestLoader().loadTestsFromModule(to)]\n\n return tests","def get_all_providers() -> list[str]:\n return list(ALL_PROVIDERS)"],"string":"[\n \"def run_all(self):\\n runs = []\\n for run in self.benchmarks:\\n run.start()\\n run.wait()\\n runs.append(run.metadata)\\n return runs\",\n \"def list(self, classes):\\n\\n def add(benchmarks, parts, flags, exclude):\\n if (\\n flags[\\\"language\\\"] != \\\"C++\\\"\\n and flags[\\\"language\\\"] != \\\"Java\\\"\\n and \\\"--drop-caches=true\\\" not in parts\\n ):\\n parts.append(\\\"--drop-caches=true\\\")\\n command = \\\" \\\".join(parts)\\n if command not in exclude:\\n benchmarks.append({\\\"command\\\": command, \\\"flags\\\": flags})\\n\\n benchmarks = []\\n for name, benchmark in classes.items():\\n if name.startswith(\\\"example\\\"):\\n continue\\n\\n instance, parts = benchmark(), [name]\\n\\n exclude = getattr(benchmark, \\\"exclude\\\", [])\\n if \\\"source\\\" in getattr(benchmark, \\\"arguments\\\", []):\\n parts.append(\\\"ALL\\\")\\n\\n iterations = getattr(instance, \\\"iterations\\\", 3)\\n parts.append(f\\\"--iterations={iterations}\\\")\\n\\n if instance.cases:\\n parts.append(\\\"--all=true\\\")\\n\\n flags = getattr(instance, \\\"flags\\\", {})\\n\\n if getattr(instance, \\\"r_only\\\", False):\\n flags[\\\"language\\\"] = \\\"R\\\"\\n add(benchmarks, parts, flags, exclude)\\n else:\\n if \\\"language\\\" not in flags:\\n flags[\\\"language\\\"] = \\\"Python\\\"\\n add(benchmarks, parts, flags, exclude)\\n\\n if hasattr(instance, \\\"r_name\\\"):\\n flags_ = flags.copy()\\n flags_[\\\"language\\\"] = \\\"R\\\"\\n parts.append(\\\"--language=R\\\")\\n add(benchmarks, parts, flags_, exclude)\\n\\n return sorted(benchmarks, key=lambda k: k[\\\"command\\\"])\",\n \"def get(self, request, format=None):\\n benchmarkmodels = BenchmarkModel.objects.all()\\n serializer = BenchmarkModelListSerializer(benchmarkmodels, many=True)\\n return Response(serializer.data)\",\n \"def test_list_benchmarks(client):\\n # The benchmark listing contains one element (independently of whether the\\n # user is logged in or not).\\n r = client.get(config.API_PATH() + '/workflows')\\n assert r.status_code == 200\\n doc = r.json\\n assert len(doc[labels.WORKFLOW_LIST]) == 1\\n # Create user and the request header that contains the API key for the\\n # logged in user.\\n _, token = create_user(client, '0000')\\n headers = {HEADER_TOKEN: token}\\n r = client.get(config.API_PATH() + '/workflows', headers=headers)\\n assert r.status_code == 200\\n doc = r.json\\n assert len(doc[labels.WORKFLOW_LIST]) == 1\",\n \"def get_coverage_benchmarks():\\n return [\\n benchmark for benchmark in get_all_benchmarks()\\n if get_type(benchmark) == BenchmarkType.CODE.value\\n ]\",\n \"def delete_all_benchmarks(self, namespace=\\\"benchmark-operator\\\"):\\n all_benchmarks = self.crd_client.list_namespaced_custom_object(\\n group=\\\"ripsaw.cloudbulldozer.io\\\", version=\\\"v1alpha1\\\", namespace=namespace, plural=\\\"benchmarks\\\"\\n )\\n\\n _ = [\\n self.delete_benchmark(benchmark[\\\"metadata\\\"][\\\"name\\\"], namespace)\\n for benchmark in all_benchmarks.get(\\\"items\\\", [])\\n ]\",\n \"def list_runtimes(self, workbench):\\n pass\",\n \"def benchmark(self):\\n logger.info(self.benchmark.__doc__)\\n return self.run(self.benchmark_profile())\",\n \"def _parse_suite(\\n self, results: dict, extra_tags: dict = None\\n ) -> List[BenchmarkResult]:\\n # all results share a batch id\\n batch_id = uuid.uuid4().hex\\n\\n parsed_results = []\\n for result in results[\\\"benchmarks\\\"]:\\n result_parsed = self._parse_benchmark(\\n result=GoogleBenchmark(**result),\\n batch_id=batch_id,\\n extra_tags=extra_tags,\\n )\\n parsed_results.append(result_parsed)\\n\\n return parsed_results\",\n \"def get_benchmark_requirements(cls):\\n pass\",\n \"def __init__(self, conf, benchmarks, regex=None):\\n self._conf = conf\\n self._benchmark_dir = conf.benchmark_dir\\n\\n if not regex:\\n regex = []\\n if isinstance(regex, str):\\n regex = [regex]\\n\\n self._all_benchmarks = {}\\n self._benchmark_selection = {}\\n for benchmark in benchmarks:\\n self._all_benchmarks[benchmark['name']] = benchmark\\n if benchmark['params']:\\n self._benchmark_selection[benchmark['name']] = []\\n for idx, param_set in enumerate(\\n itertools.product(*benchmark['params'])):\\n name = f\\\"{benchmark['name']}({', '.join(param_set)})\\\"\\n if not regex or any(re.search(reg, name) for reg in regex):\\n self[benchmark['name']] = benchmark\\n self._benchmark_selection[benchmark['name']].append(idx)\\n else:\\n self._benchmark_selection[benchmark['name']] = None\\n if not regex or any(re.search(reg, benchmark['name']) for reg in regex):\\n self[benchmark['name']] = benchmark\",\n \"def get_all_reporters():\\r\\n for ep in iter_entry_points('attest.reporters'):\\r\\n yield ep.name\",\n \"def get_testbench_specs(self, tb_type: str) -> Dict[str, Any]:\\n return self._specs['testbenches'][tb_type]\",\n \"def get_methods(cls):\\n # get all the methods that have the _benchmark_this flag\\n for method in (getattr(cls, m) for m in dir(cls)):\\n if hasattr(method, \\\"_benchmark_this\\\"):\\n yield method\",\n \"def get_list(cls, suite_model) -> list:\\n suites = []\\n for s in cls.SUITES:\\n s[\\\"tests\\\"] = cls._get_tests(s)\\n s[\\\"approxRunTime\\\"] = cls._get_average_run_time(suite_model)\\n suites.append(s)\\n return suites\",\n \"def test_get_benchmark_methods_filter(self):\\n config = mock.Mock()\\n config.workspace = 'workspace'\\n config.benchmark_method_patterns = ['new_foo.BenchmarkClass.filter:bench.*']\\n benchmark_runner = benchmark.BenchmarkRunner(config)\\n\\n mock_benchmark_class = mock.Mock()\\n mock_benchmark_class.benchmark_method_1 = 'foo'\\n\\n mock_module = mock.Mock()\\n sys.modules['new_foo'] = mock_module\\n mock_module.BenchmarkClass.return_value = mock_benchmark_class\\n\\n methods = benchmark_runner._get_benchmark_methods()\\n\\n self.assertEqual(1, len(methods))\\n self.assertEqual('new_foo.BenchmarkClass.benchmark_method_1', methods[0])\",\n \"def get_bug_benchmarks():\\n return [\\n benchmark for benchmark in get_all_benchmarks()\\n if get_type(benchmark) == BenchmarkType.BUG.value\\n ]\",\n \"def get_benchmark(client):\\n r = client.get(config.API_PATH() + '/benchmarks')\\n benchmarks = json.loads(r.data)\\n return benchmarks['benchmarks'][0]['id']\",\n \"def test_get_benchmark_methods_exact_match(self):\\n config = mock.Mock()\\n config.workspace = 'workspace'\\n config.benchmark_method_patterns = [\\n 'new_foo.BenchmarkClass.benchmark_method_1',\\n 'new_foo.BenchmarkClass.benchmark_method_2']\\n benchmark_runner = benchmark.BenchmarkRunner(config)\\n\\n methods = benchmark_runner._get_benchmark_methods()\\n self.assertEqual(['new_foo.BenchmarkClass.benchmark_method_1',\\n 'new_foo.BenchmarkClass.benchmark_method_2'], methods)\",\n \"def _generate_benchmark_variants(benchmark_spec):\\n variants = []\\n # Cold start.\\n variants.append({\\n 'name': benchmark_spec['name'] + ' (cold start)',\\n 'app': benchmark_spec['app'],\\n 'duration': benchmark_spec['duration'],\\n 'measurements': benchmark_spec['measurements'],\\n 'shell-args': benchmark_spec.get('shell-args',\\n []) + _COLD_START_SHELL_ARGS})\\n # Warm start.\\n variants.append({\\n 'name': benchmark_spec['name'] + ' (warm start)',\\n 'app': benchmark_spec['app'],\\n 'duration': benchmark_spec['duration'],\\n 'measurements': benchmark_spec['measurements'],\\n 'shell-args': benchmark_spec.get('shell-args', [])})\\n return variants\",\n \"def get_all_stats():\\n\\n return get_component(CachingPackage.COMPONENT_NAME).get_all_stats()\",\n \"def _run():\\n subprocess.check_call(\\n [\\n \\\"tools/bazel\\\",\\n \\\"build\\\",\\n \\\"-c\\\",\\n \\\"opt\\\",\\n \\\"test/core/memory_usage/memory_usage_test\\\",\\n ]\\n )\\n ret = {}\\n for name, benchmark_args in _BENCHMARKS.items():\\n for scenario, extra_args in _SCENARIOS.items():\\n # TODO(chenancy) Remove when minstack is implemented for channel\\n if name == \\\"channel\\\" and scenario == \\\"minstack\\\":\\n continue\\n try:\\n output = subprocess.check_output(\\n [\\n \\\"bazel-bin/test/core/memory_usage/memory_usage_test\\\",\\n ]\\n + benchmark_args\\n + extra_args\\n )\\n except subprocess.CalledProcessError as e:\\n print(\\\"Error running benchmark:\\\", e)\\n continue\\n for line in output.splitlines():\\n for key, (pattern, conversion) in _INTERESTING.items():\\n m = re.match(pattern, line)\\n if m:\\n ret[scenario + \\\": \\\" + key] = conversion(m.group(1))\\n return ret\",\n \"def measure_all(backend) -> Schedule:\\n # backend is V2.\\n if isinstance(backend, BackendV2):\\n qubits = list(range(backend.num_qubits))\\n else:\\n qubits = list(range(backend.configuration().n_qubits))\\n return measure(qubits=qubits, backend=backend)\",\n \"def generate_figures():\\r\\n # create results directory if necessary\\r\\n try:\\r\\n makedirs(\\\"results\\\")\\r\\n except OSError as e:\\r\\n if e.errno != errno.EEXIST:\\r\\n raise\\r\\n \\r\\n for b in benchmarks:\\r\\n generate_figure(model[b], b)\",\n \"def register_benchmarks(directory=None):\\n dirs = places_to_look() if directory is None else [directory]\\n for directory in dirs:\\n with os.scandir(directory) as scan:\\n for entry in scan:\\n filename = entry.name\\n if (\\n filename.startswith(\\\".\\\")\\n or not entry.is_file()\\n or not filename.endswith(\\\".py\\\")\\n ):\\n continue\\n if (\\n filename.startswith(\\\"benchmark\\\")\\n or filename.endswith(\\\"benchmark.py\\\")\\n or filename.endswith(\\\"benchmarks.py\\\")\\n ):\\n import_path(f\\\"{directory}/{filename}\\\")\",\n \"def count_benchmarks():\\n return len(setup_storage().fetch_benchmark({}))\",\n \"def suite_list(self):\\n return ','.join([s.__unicode__() for s in self.suites.all()])\",\n \"def suite_list(self):\\n return ','.join([s.__unicode__() for s in self.suites.all()])\",\n \"def ballot_get_all_contests():\\r\\n all_contests = []\\r\\n result = ballot_list_contests(limit=100000000).get('result')\\r\\n if result:\\r\\n contests = batch('ballot_get_contest_by_id', [[r] for r in result]).get('result')\\r\\n if contests:\\r\\n all_contests = contests\\r\\n\\r\\n return all_contests\",\n \"def Run(benchmark_spec: bm_spec.BenchmarkSpec) -> List[sample.Sample]:\\n discovery_duration = benchmark_spec.data_discovery_service.DiscoverData()\\n return [\\n sample.Sample('data_discovery_duration', discovery_duration, 'seconds',\\n benchmark_spec.data_discovery_service.GetMetadata())]\",\n \"def list_ciphers():\\n global AVAILABLE_CIPHERS\\n print(\\\"[!] Available ciphers: \\\")\\n for i in range(len(AVAILABLE_CIPHERS)):\\n print(\\\" ----> %s.%s\\\"%(i+1, AVAILABLE_CIPHERS[i]))\\n exit()\",\n \"def all_experiments():\\n elo_explain_experiments()\\n alpha_beta_experiments()\\n mtcs_experiments()\",\n \"def run_benchmark():\\n import argparse\\n parser = argparse.ArgumentParser(description='Benchmark alchemically modified system against unmodified system.')\\n parser.add_argument('--platform', dest='platform_name', action='store', default=None, help='platform name to benchmark (default: None)')\\n options = parser.parse_args()\\n\\n from sams.tests import testsystems\\n for testsystem_name in ['AblImatinibExplicitAlchemical']:\\n cls = getattr(testsystems, testsystem_name)\\n testsystem = cls()\\n factory_args = { 'ligand_atoms' : testsystem.alchemical_atoms, 'receptor_atoms' : range(0,4266) }\\n benchmark(testsystem.system, testsystem.positions, platform_name=options.platform_name, nsteps=5000, timestep=1.0*unit.femtoseconds, factory_args=factory_args)\",\n \"def benchmark_selection(self):\\n return self._benchmark_selection\",\n \"def _disc_benchmarks(cls, conf, repo, environments, commit_hashes, check):\\n root = conf.benchmark_dir\\n\\n cls.check_tree(root)\\n\\n if len(environments) == 0:\\n raise util.UserError(\\\"No available environments\\\")\\n\\n # Try several different commits:\\n #\\n # - First of commit_hashes provided\\n # - Tips of branches from configuration file\\n # - Rest of the commit_hashes\\n #\\n\\n def iter_hashes():\\n for h in commit_hashes[:1]:\\n yield h\\n for branch in conf.branches:\\n try:\\n yield repo.get_hash_from_name(branch)\\n except NoSuchNameError:\\n continue\\n for h in commit_hashes[1:]:\\n yield h\\n\\n def iter_unique(iter):\\n seen = set()\\n for item in iter:\\n if item not in seen:\\n seen.add(item)\\n yield item\\n\\n try_hashes = iter_unique(iter_hashes())\\n\\n log.info(\\\"Discovering benchmarks\\\")\\n with log.indent():\\n last_err = None\\n for env, commit_hash in itertools.product(environments, try_hashes):\\n env.create()\\n\\n if last_err is not None:\\n log.warning(\\\"Failed: trying different commit/environment\\\")\\n\\n result_dir = tempfile.mkdtemp()\\n try:\\n env.install_project(conf, repo, commit_hash)\\n\\n env_vars = dict(os.environ)\\n env_vars.update(env.env_vars)\\n\\n result_file = os.path.join(result_dir, 'result.json')\\n env.run(\\n [runner.BENCHMARK_RUN_SCRIPT, 'discover',\\n os.path.abspath(root),\\n os.path.abspath(result_file)],\\n cwd=result_dir,\\n env=env_vars,\\n dots=False)\\n\\n try:\\n with open(result_file, 'r') as fp:\\n benchmarks = json.load(fp)\\n except (IOError, ValueError):\\n log.error(\\\"Invalid discovery output\\\")\\n raise util.UserError()\\n\\n break\\n except (util.UserError, util.ProcessError) as err:\\n last_err = err\\n continue\\n except KeyboardInterrupt:\\n raise util.UserError(\\\"Interrupted.\\\")\\n finally:\\n util.long_path_rmtree(result_dir)\\n else:\\n raise util.UserError(\\\"Failed to build the project and import the benchmark suite.\\\")\\n\\n if check:\\n log.info(\\\"Checking benchmarks\\\")\\n with log.indent():\\n result_dir = tempfile.mkdtemp()\\n try:\\n out, err, retcode = env.run(\\n [runner.BENCHMARK_RUN_SCRIPT, 'check',\\n os.path.abspath(root)],\\n cwd=result_dir,\\n dots=False,\\n env=env_vars,\\n valid_return_codes=None,\\n return_stderr=True,\\n redirect_stderr=True)\\n finally:\\n util.long_path_rmtree(result_dir)\\n\\n out = out.strip()\\n if retcode == 0:\\n if out:\\n log.info(out)\\n log.info(\\\"No problems found.\\\")\\n else:\\n if out:\\n log.error(out)\\n raise util.UserError(\\\"Benchmark suite check failed.\\\")\\n\\n return benchmarks\",\n \"def _transform_results(self) -> List[BenchmarkResult]:\\n with open(self.result_file, \\\"r\\\") as f:\\n raw_results = json.load(f)\\n\\n parsed_results = []\\n for suite in raw_results[\\\"suites\\\"]:\\n parsed_results += self._parse_suite(\\n results=suite,\\n extra_tags={\\\"suite\\\": suite[\\\"name\\\"], \\\"source\\\": \\\"cpp-micro\\\"},\\n )\\n\\n return parsed_results\",\n \"def save(self):\\n path = self.get_benchmark_file_path(self._conf.results_dir)\\n util.write_json(path, self._all_benchmarks, self.api_version)\",\n \"def get_all_casks(self):\",\n \"def on_get(self, req: Request, resp: Response):\\n benchmarks = self.storage.fetch_benchmark({})\\n\\n response = build_benchmarks_response(benchmarks)\\n resp.text = json.dumps(response)\",\n \"def __benchmark__(cls):\\n results = []\\n used_argsets = []\\n for args in cls.arguments:\\n used_argsets.append(args)\\n # for each given argument\\n for method in cls.get_methods():\\n # append an empty list for the results with this argument\\n method_results = []\\n # for each repetition\\n for n in xrange(cls.repetitions):\\n # append the results to the list for this argument set\\n trial_results = cls._trial(method, args)\\n method_results.append(trial_results)\\n # append a Result to `results`.\\n results.append(Result(method, args, method_results))\\n return results, used_argsets\",\n \"def benchmark(self):\\n nsites = []\\n for m in self.methods:\\n for name, structure in self.test_structures.items():\\n cns = []\\n if self.unique_sites:\\n es = SpacegroupAnalyzer(structure).get_symmetrized_structure().equivalent_sites\\n sites = [structure.index(x[0]) for x in es]\\n else:\\n sites = range(len(structure))\\n\\n for key, val in self.hi.items():\\n if name == key:\\n for j in sites:\\n if isinstance(m, NearNeighbors):\\n tmpcn = m.get_cn_dict(structure, j, self.use_weights)\\n else:\\n tmpcn = m.compute(structure, j)\\n if tmpcn == \\\"null\\\":\\n continue\\n if self.nround:\\n self._roundcns(tmpcn, self.nround)\\n cns.append((structure[j].species_string, tmpcn))\\n if self.cation_anion:\\n for mat, cat in self.cations.items():\\n if (name == mat) and cat:\\n cns = self._popel(cns, cat)\\n elif self.anion_cation:\\n for mat, an in self.anions.items():\\n if name == mat:\\n cns = self._popel(cns, an)\\n m._cns[name] = cns\\n nsites.append(len(cns))\\n self.nsites = max(nsites)\",\n \"def exclude_non_cpp(benchmarks):\\n return [benchmark for benchmark in benchmarks if is_cpp(benchmark)]\",\n \"def find_benchmark_directories(self):\\n for (benchmark, producer), result in self.results.items():\\n pattern = benchmark + '_' + producer + '*'\\n files = find_directory(pattern, self.root_folder)\\n if files:\\n # add just the latest one\\n sorted_files = sorted(files)\\n result.directory = sorted_files[-1]\\n else:\\n print('No benchmark directories found in ' + self.root_folder)\",\n \"def main(benchmark, size=None, backend=None, repetitions=None, burnin=1, device=\\\"cpu\\\"):\\n try:\\n bm_module, bm_identifier = get_benchmark_module(benchmark)\\n except ImportError as e:\\n click.echo(f\\\"Error while loading benchmark {benchmark}: {e!s}\\\", err=True)\\n raise click.Abort()\\n\\n available_backends = set(bm_module.__implementations__)\\n\\n if len(backend) == 0:\\n backend = available_backends.copy()\\n else:\\n backend = set(backend)\\n\\n unsupported_backends = [b for b in backend if b not in available_backends]\\n\\n for b in unsupported_backends:\\n click.echo(\\n f'Backend \\\"{b}\\\" is not supported by chosen benchmark (skipping)', err=True\\n )\\n backend.remove(b)\\n\\n for b in backend.copy():\\n try:\\n with setup_functions[b](device=device) as bmod:\\n click.echo(f\\\"Using {b} version {bmod.__version__}\\\")\\n except BackendNotSupported as e:\\n click.echo(\\n f'Setup for backend \\\"{b}\\\" failed (skipping), reason: {e!s}', err=True\\n )\\n backend.remove(b)\\n\\n try:\\n check_backend_conflicts(backend, device)\\n except BackendConflict as exc:\\n click.echo(f\\\"Backend conflict: {exc!s}\\\", err=True)\\n raise click.Abort()\\n\\n runs = sorted(itertools.product(backend, size))\\n\\n if len(runs) == 0:\\n click.echo(\\\"Nothing to do\\\")\\n return\\n\\n timings = {run: [] for run in runs}\\n\\n if repetitions is None:\\n click.echo(\\\"Estimating repetitions...\\\")\\n repetitions = {}\\n\\n for b, s in runs:\\n # use end-to-end runtime for repetition estimation\\n def run_func():\\n run = bm_module.get_callable(b, s, device=device)\\n with setup_functions[b](device=device):\\n run()\\n\\n repetitions[(b, s)] = estimate_repetitions(run_func)\\n else:\\n repetitions = {(b, s): repetitions for b, s in runs}\\n\\n all_runs = list(\\n itertools.chain.from_iterable(\\n [run] * (repetitions[run] + burnin) for run in runs\\n )\\n )\\n random.shuffle(all_runs)\\n\\n results = {}\\n checked = {r: False for r in runs}\\n\\n pbar = click.progressbar(\\n label=f\\\"Running {len(all_runs)} benchmarks...\\\", length=len(runs)\\n )\\n\\n try:\\n with pbar:\\n for (b, size) in all_runs:\\n with setup_functions[b](device=device):\\n run = bm_module.get_callable(b, size, device=device)\\n with Timer() as t:\\n res = run()\\n\\n # YOWO (you only warn once)\\n if not checked[(b, size)]:\\n if size in results:\\n is_consistent = check_consistency(\\n results[size], convert_to_numpy(res, b, device)\\n )\\n if not is_consistent:\\n click.echo(\\n f\\\"\\\\nWarning: inconsistent results for size {size}\\\",\\n err=True,\\n )\\n else:\\n results[size] = convert_to_numpy(res, b, device)\\n checked[(b, size)] = True\\n\\n timings[(b, size)].append(t.elapsed)\\n pbar.update(1.0 / (repetitions[(b, size)] + burnin))\\n\\n # push pbar to 100%\\n pbar.update(1.0)\\n\\n for run in runs:\\n assert len(timings[run]) == repetitions[run] + burnin\\n\\n finally:\\n stats = compute_statistics(timings)\\n click.echo(format_output(stats, bm_identifier, device=device))\",\n \"def list_builders(self) -> List[str]:\\n return sorted(_iter_builder_names(self._ns2data_dir))\",\n \"def speed_list(self) -> list:\\n wink_supported_speeds = self.wink.fan_speeds()\\n supported_speeds = []\\n if SPEED_AUTO in wink_supported_speeds:\\n supported_speeds.append(SPEED_AUTO)\\n if SPEED_LOWEST in wink_supported_speeds:\\n supported_speeds.append(SPEED_LOWEST)\\n if SPEED_LOW in wink_supported_speeds:\\n supported_speeds.append(SPEED_LOW)\\n if SPEED_MEDIUM in wink_supported_speeds:\\n supported_speeds.append(SPEED_MEDIUM)\\n if SPEED_HIGH in wink_supported_speeds:\\n supported_speeds.append(SPEED_HIGH)\\n return supported_speeds\",\n \"async def getAll():\\n return [cluster.export() for cluster in clusters.get_all()]\",\n \"def evaluate_benchmarks(self):\\n\\n # iterate over replicates\\n results = {}\\n for replicate_id, replicate in self.replicates:\\n\\n # evaluate benchmark for current replicate\\n bmark = SimulationBenchmark(replicate.copy(),\\n graph=self.graphs[replicate_id],\\n **self.params)\\n\\n # store results\\n results[replicate_id] = dict(\\n\\n labels_MAE=bmark.scores['labels'].MAE,\\n level_only_MAE=bmark.scores['level_only'].MAE,\\n spatial_only_MAE=bmark.scores['spatial_only'].MAE,\\n community_MAE=bmark.scores['labels_comm'].MAE,\\n\\n labels_PCT=bmark.scores['labels'].percent_correct,\\n level_only_PCT=bmark.scores['level_only'].percent_correct,\\n spatial_only_PCT=bmark.scores['spatial_only'].percent_correct,\\n community_PCT=bmark.scores['labels_comm'].percent_correct)\\n\\n # compile dataframe\\n results = pd.DataFrame.from_dict(results, orient='index')\\n results.index.set_names(self.multiindex, inplace=True)\\n\\n return results\",\n \"def print_scenario(benchmarks, name):\\n # Remember, the first entry in the times array is an empty object.\\n\\n benchmarks = sorted(benchmarks, key = benchmark_sort_key)\\n for benchmark in benchmarks:\\n block_times = benchmark[\\\"times\\\"][2][\\\"block_times\\\"] # list of floats\\n it = iter(block_times)\\n for start in it:\\n end = next(it)\\n\\n print(\\\"%s , %.3f \\\" % (benchmark[\\\"label\\\"], float(end) - float(start)))\",\n \"def get_all_adapters(self):\\n pass\",\n \"def __init__(self, benchmarks):\\n self.benchmarks = benchmarks\\n instNum = 0\\n classNum = 0\\n for benchclass in self:\\n benchclass.line = classNum\\n benchclass.instStart = instNum\\n for instance in benchclass: \\n instance.line = instNum\\n instNum += max(instance.maxRuns, 1)\\n benchclass.instEnd = instNum - 1\\n classNum += 1\",\n \"def _parameterize_simple_benchmarks():\\n parameterized_tuples = []\\n for index, simple_benchmark in enumerate(\\n all_benchmarks.all_benchmarks(modules=[simple_benchmarks])):\\n # The index ensures all test cases have distinct names, even if multiple\\n # benchmarks have the same name.\\n test_case_name = '{index}_{name}'.format(index=index,\\n name=simple_benchmark.name)\\n parameterized_tuples.append((test_case_name, simple_benchmark))\\n return parameterized_tuples\",\n \"def Run(benchmark_spec):\\n vms = benchmark_spec.vms\\n results = []\\n\\n logging.info('Iperf Results:')\\n\\n # Send traffic in both directions\\n for sending_vm, receiving_vm in vms, reversed(vms):\\n # Send using external IP addresses\\n if vm_util.ShouldRunOnExternalIpAddress():\\n results.append(_RunIperf(sending_vm,\\n receiving_vm,\\n receiving_vm.ip_address,\\n 'external'))\\n\\n # Send using internal IP addresses\\n if vm_util.ShouldRunOnInternalIpAddress(sending_vm,\\n receiving_vm):\\n results.append(_RunIperf(sending_vm,\\n receiving_vm,\\n receiving_vm.internal_ip,\\n 'internal'))\\n\\n return results\",\n \"def time_binaries():\\n print()\\n print(\\\"=== BENCHMARKING ===\\\")\\n with tempfile.NamedTemporaryFile(\\n prefix=\\\"riff-benchmark\\\", suffix=\\\".gitlog\\\"\\n ) as testdata:\\n subprocess.check_call(\\n [\\\"git\\\", \\\"log\\\", \\\"--color=always\\\", \\\"-p\\\", \\\"master\\\"], stdout=testdata\\n )\\n\\n binaries = sorted(glob.glob(os.path.join(BINDIR, \\\"*\\\")), key=natural_keys)\\n\\n # Do riff-current last: https://stackoverflow.com/a/20320940/473672\\n binaries.sort(key=lambda s: s.endswith(\\\"riff-current\\\"))\\n\\n for binary in binaries:\\n print_timings(binary, testdata.name)\\n print_timings(\\\"/bin/cat\\\", testdata.name)\",\n \"def bench(participants=participants, benchmarks=benchmarks, bench_time=10.0):\\n\\n mcs = [p.factory() for p in participants]\\n means = [[] for p in participants]\\n stddevs = [[] for p in participants]\\n\\n # Have each lifter do one benchmark each\\n for benchmark_name, f, args, kwargs in benchmarks:\\n logger.info('%s', benchmark_name)\\n\\n for i, (participant, mc) in enumerate(zip(participants, mcs)):\\n sw = Stopwatch()\\n\\n while sw.total() < bench_time:\\n with sw.timing():\\n f(mc, *args, **kwargs)\\n\\n means[i].append(sw.mean())\\n stddevs[i].append(sw.stddev())\\n\\n logger.info(u'%s: %s', participant.name, sw)\\n\\n return means, stddevs\",\n \"def __iter__(self):\\n for benchclass in sorted(self.classes.values()):\\n yield benchclass\",\n \"def list_suites(arn=None, nextToken=None):\\n pass\",\n \"def list(args):\\n experiments = sorted(os.listdir('./litmus'))\\n print_color(\\\"Available Litmus Chaos Experiments:\\\\n\\\\n\\\")\\n if (f\\\"{args.platform}\\\" == \\\"GKE\\\"):\\n i = 1\\n for experiment_file in experiments:\\n print_color(f\\\"\\\\t{i}. {experiment_file.replace('.yaml', '')}\\\")\\n i += 1\\n\\n if (f\\\"{args.platform}\\\" == \\\"kind\\\"):\\n kind_supported = [\\\"pod-delete\\\",\\\"container-kill\\\",\\\"node-cpu-hog\\\",\\\"node-memory-hog\\\"]\\n i = 0\\n for i in range(0, len(kind_supported)):\\n print_color(f\\\"\\\\t{i+1}. {kind_supported[i]}\\\")\\n i += 1\\n\\n if (f\\\"{args.platform}\\\" == \\\"EKS\\\"):\\n i = 1\\n for experiment_file in experiments:\\n print_color(f\\\"\\\\t{i}. {experiment_file.replace('.yaml', '')}\\\")\\n i += 1\",\n \"def get_all(self):\\r\\n return [{ 'label': x[0], 'timing': x[1], 'is_tool': x[0] in self._tool_labels}\\r\\n for x in sorted(self._timings_by_path.items(), key=lambda x: x[1], reverse=True)]\",\n \"def benchmark():\\n print defer.Deferred.__module__\\n for func, args, iter in benchmarkFuncs:\\n print func.__name__, args, timeit(func, iter, *args)\",\n \"def output_benchmark_results(output_dir, ts_agent_list=None, ga_agent=None, title=None, auto_open=True):\\n if (ts_agent_list is None or not all(ts_agent.benchmark for ts_agent in ts_agent_list)) \\\\\\n and (ga_agent is None or not ga_agent.benchmark):\\n raise UserWarning(\\\"agent arguments were None or were not ran in benchmark mode.\\\")\\n\\n if title is None:\\n title = \\\"Benchmark Run {}\\\".format(datetime.datetime.now().strftime(\\\"%Y-%m-%d %H:%M\\\"))\\n\\n output_dir = Path(output_dir)\\n\\n if not output_dir.exists():\\n output_dir.mkdir(parents=True)\\n\\n def compute_stats(lst):\\n return {\\n 'min': round(min(lst)),\\n 'median': round(statistics.median(lst)),\\n 'max': round(max(lst)),\\n 'std': round(statistics.stdev(lst)) if len(lst) > 1 else 0,\\n 'var': round(statistics.variance(lst)) if len(lst) > 1 else 0,\\n 'mean': round(statistics.mean(lst))\\n }\\n\\n # tabu search results\\n if ts_agent_list is not None and all(ts_agent.benchmark for ts_agent in ts_agent_list):\\n _create_ts_plots(ts_agent_list, output_dir)\\n ts_result_makespans = []\\n ts_initial_makespans = []\\n ts_iterations = []\\n for ts_agent in ts_agent_list:\\n ts_result_makespans.append(ts_agent.best_solution.makespan)\\n ts_initial_makespans.append(ts_agent.initial_solution.makespan)\\n ts_iterations.append(ts_agent.benchmark_iterations)\\n\\n ts_result_makespans_stats = compute_stats(ts_result_makespans)\\n ts_initial_makespans_stats = compute_stats(ts_initial_makespans)\\n ts_iterations_stats = compute_stats(ts_iterations)\\n\\n else:\\n ts_result_makespans_stats = None\\n ts_initial_makespans_stats = None\\n ts_iterations_stats = None\\n\\n # genetic algorithm results\\n if ga_agent is not None and ga_agent.benchmark:\\n _create_ga_plots(ga_agent, output_dir)\\n ga_initial_makespans = [sol.makespan for sol in ga_agent.initial_population]\\n ga_result_makespans = [sol.makespan for sol in ga_agent.result_population]\\n\\n ga_initial_makespans_stats = compute_stats(ga_initial_makespans)\\n ga_result_makespans_stats = compute_stats(ga_result_makespans)\\n\\n else:\\n ga_initial_makespans_stats = None\\n ga_result_makespans_stats = None\\n\\n # render template\\n template = template_env.get_template(benchmark_template)\\n rendered_template = template.render(\\n title=title,\\n ts_agent_list=ts_agent_list,\\n ts_initial_makespans_stats=ts_initial_makespans_stats,\\n ts_result_makespans_stats=ts_result_makespans_stats,\\n iterations_per_ts_agent_stats=ts_iterations_stats,\\n output_directory=output_dir.resolve(),\\n ga_agent=ga_agent,\\n ga_initial_makespans_stats=ga_initial_makespans_stats,\\n ga_result_makespans_stats=ga_result_makespans_stats,\\n )\\n\\n # create index.html\\n with open(output_dir / 'index.html', 'w') as output_file:\\n output_file.write(rendered_template)\\n\\n if auto_open:\\n webbrowser.open(f'file://{output_dir.resolve()}/index.html')\",\n \"def tests_generator(self):\\n cb_bin = os.path.join(bin_path, 'compilebench')\\n cmd_list = [\\n (\\\"Initial Create/Compile/Read Compiled Tree\\\", \\\"{0} -D {1} -i 10 --makej -s {2}\\\"),\\n ]\\n\\n tests = []\\n for idx, (desc, cmd) in enumerate(cmd_list):\\n test_name = \\\"compile_bench_{0}_{1}\\\".format(idx + 1, to_safe_name(desc))\\n test = TestProfile(\\n name=test_name,\\n desc=desc,\\n test_path=self.test_path,\\n bin_path=bin_path,\\n command=cmd.format(cb_bin, self.test_path, bin_path))\\n tests.append(test)\\n return tests\",\n \"def get_named_suites():\\n\\n # Skip \\\"with_server\\\" and \\\"no_server\\\" because they do not define any test files to run.\\n executor_only = set([\\\"with_server\\\", \\\"no_server\\\"])\\n suite_names = [suite for suite in resmokeconfig.NAMED_SUITES if suite not in executor_only]\\n suite_names.sort()\\n return suite_names\",\n \"def list_all():\\n if os.path.exists(DATA_DIR):\\n return os.listdir(DATA_DIR)\\n return []\",\n \"def merge(self, projects):\\n benchmarks = set()\\n for project in projects:\\n for runspec in project:\\n for classresult in runspec:\\n for instresult in classresult.instresults:\\n instresult.instance.maxRuns = max(instresult.instance.maxRuns, len(instresult.runs))\\n benchmarks.add(runspec.benchmark)\\n return BenchmarkMerge(benchmarks)\",\n \"def getTestSets():\\n return list(_testsetdict.keys())\",\n \"def getAllBuilders(self):\\n names = self.status.getBuilderNames(categories=self.categories)\\n builders = [self.status.getBuilder(n) for n in names]\\n return builders\",\n \"def filter_out(self, skip):\\n benchmarks = super(Benchmarks, self).__new__(self.__class__)\\n benchmarks._conf = self._conf\\n benchmarks._benchmark_dir = self._benchmark_dir\\n benchmarks._all_benchmarks = self._all_benchmarks\\n\\n selected_idx = {}\\n\\n for name, benchmark in self.items():\\n if name not in skip:\\n benchmarks[name] = benchmark\\n if name in self._benchmark_selection:\\n selected_idx[name] = self._benchmark_selection[name]\\n\\n benchmarks._benchmark_selection = selected_idx\\n\\n return benchmarks\",\n \"def get_tests():\\n # tests = ['test_build_gaussian_pyramid_random', 'test_build_gaussian_pyramid_static', 'test_build_laplacian_pyramid_random', 'test_build_laplacian_pyramid_static', 'test_laplacian_to_image', 'test_render_pyramid_random', 'test_render_pyramid_static']\\n # return [tester.TestEx3(method) for method in tests]\\n return [tester.TestEx3(method) for method in dir(tester.TestEx3) if method.startswith('test')]\",\n \"def fetch_markets_tickers(self):\\r\\n return self.__public_request('GET', '/api/v1/tickers')\",\n \"def get_available_datasets():\\n files = [file for file in glob.glob(os.path.join(MODULE_ROOT, \\\"datasets/*.json\\\"))]\\n datasets = []\\n for file in files:\\n with open(file, \\\"r\\\") as f:\\n dataset_info = json.load(f)\\n datasets.append(dataset_info)\\n return datasets\",\n \"def list(self):\\n print \\\"\\\\nAvailable Test Cases\\\"\\n print \\\"====================\\\"\\n for case in self.cases:\\n print case.__name__\",\n \"def list_datasets():\\n return METADATA.keys()\",\n \"def __iter__(self):\\n for benchinst in sorted(self.instances.values()):\\n yield benchinst\",\n \"def suite():\\n\\tts = unittest.TestSuite()\\n\\tfor test_module in __all__:\\n\\t\\tm = importlib.import_module(\\\"pyroclast.test.\\\" + test_module)\\n\\t\\tfor n in dir(m):\\n\\t\\t\\tc = getattr(m, n)\\n\\t\\t\\tif is_test_case(c):\\n\\t\\t\\t\\ts = unittest.TestLoader().loadTestsFromTestCase(c)\\n\\t\\t\\t\\tts.addTests(s)\\n\\treturn ts\",\n \"def load(cls, conf, regex=None):\\n try:\\n path = cls.get_benchmark_file_path(conf.results_dir)\\n if not os.path.isfile(path):\\n raise util.UserError(f\\\"Benchmark list file {path} missing!\\\")\\n d = util.load_json(path, api_version=cls.api_version)\\n benchmarks = d.values()\\n return cls(conf, benchmarks, regex=regex)\\n except util.UserError as err:\\n if \\\"asv update\\\" in str(err):\\n # Don't give conflicting instructions\\n raise\\n raise util.UserError(\\\"{}\\\\nUse `asv run --bench just-discover` to \\\"\\n \\\"regenerate benchmarks.json\\\".format(str(err)))\",\n \"def get_all_tools(project_dir: Union[str, os.PathLike]):\\n return [\\n get_tool(tool_name=name, project_dir=project_dir)\\n for name in (Judy.name, Jumble.name, Major.name, Pit.name)\\n ]\",\n \"def get_all(self):\\r\\n ret = []\\r\\n for cache_name, stat in self.stats_per_cache.items():\\r\\n ret.append({\\r\\n 'cache_name': cache_name,\\r\\n 'num_hits': len(stat.hit_targets),\\r\\n 'num_misses': len(stat.miss_targets),\\r\\n 'hits': stat.hit_targets,\\r\\n 'misses': stat.miss_targets\\r\\n })\\r\\n return ret\",\n \"def get_all_metrics(self):\\n up_time = self.uptime()\\n down_time = self.downtime()\\n customer_sla = self.sla()\\n objective = self.slo()\\n indicator = self.sli()\\n avail_percentage = self.availability()\\n mt_bf = self.mtbf(up_time)\\n mt_tr = self.mttr(down_time)\\n list_results = [up_time,down_time,customer_sla,objective,indicator,avail_percentage,mt_bf,mt_tr]\\n return list_results\",\n \"def get_performance_test_cases(test_suite):\\n return get_cases(test_suite, r'test_perf_')\",\n \"def _get_all(cls) -> Iterator[\\\"PermutationStatistic\\\"]:\\n yield from (cls(name, func) for name, func in PermutationStatistic._STATISTICS)\",\n \"def allPresets():\\n\\n return [__cleanPresetTreeName(k) for k in __preset_lookup.iterkeys()]\",\n \"def run_benchmark(curl, benchmark, test_config = TestConfig()):\\n\\n warmup_runs = benchmark.warmup_runs\\n benchmark_runs = benchmark.benchmark_runs\\n message = '' #Message is name of benchmark... print it?\\n\\n if (warmup_runs <= 0):\\n raise Exception(\\\"Invalid number of warmup runs, must be > 0 :\\\" + warmup_runs)\\n if (benchmark_runs <= 0):\\n raise Exception(\\\"Invalid number of benchmark runs, must be > 0 :\\\" + benchmark_runs)\\n\\n #Initialize variables to store output\\n output = BenchmarkResult()\\n output.name = benchmark.name\\n output.group = benchmark.group\\n metricnames = list(benchmark.metrics)\\n metricvalues = [METRICS[name] for name in metricnames] # Metric variable for curl, to avoid hash lookup for every metric name\\n results = [list() for x in xrange(0, len(metricnames))] # Initialize arrays to store results for each metric\\n\\n curl.setopt(pycurl.WRITEFUNCTION, lambda x: None) #Do not store actual response body at all.\\n\\n #Benchmark warm-up to allow for caching, JIT compiling, on client\\n logging.info('Warmup: ' + message + ' started')\\n for x in xrange(0, warmup_runs):\\n if benchmark.method == u'POST' or benchmark.method == u'PUT':\\n curl.setopt(curl.READFUNCTION, StringIO.StringIO(benchmark.body).read)\\n curl.perform()\\n logging.info('Warmup: ' + message + ' finished')\\n\\n logging.info('Benchmark: ' + message + ' starting')\\n\\n for x in xrange(0, benchmark_runs): # Run the actual benchmarks\\n if benchmark.method == u'POST' or benchmark.method == u'PUT':\\n curl.setopt(curl.READFUNCTION, StringIO.StringIO(benchmark.body).read)\\n\\n try: # Run the curl call, if it errors, then add to failure counts for benchmark\\n curl.perform()\\n except Exception:\\n output.failures = output.failures + 1\\n continue # Skip metrics collection\\n\\n # Get all metrics values for this run, and store to metric lists\\n for i in xrange(0, len(metricnames)):\\n results[i].append( curl.getinfo(metricvalues[i]) )\\n\\n logging.info('Benchmark: ' + message + ' ending')\\n\\n temp_results = dict()\\n for i in xrange(0, len(metricnames)):\\n temp_results[metricnames[i]] = results[i]\\n output.results = temp_results\\n\\n curl.close()\\n return analyze_benchmark_results(output, benchmark)\",\n \"def list_charts():\\n charts_root = Path(R\\\".\\\\charm\\\\data\\\\charts\\\")\\n charts = list(charts_root.rglob(\\\"*.chart\\\"))\\n return charts\",\n \"def main(args):\\n\\n # Compose the model list\\n modellist = []\\n if args['model']:\\n modellist.append(bmark.ModelInfo(args['model'], os.getcwd(), args['classname']))\\n\\n # Load the benchmark settings\\n benchmark = None\\n benchmark = bmark.load_benchmark(args['benchmark'])\\n corresponding_data = False\\n if 'corresponding_data' in benchmark:\\n corresponding_data = benchmark['corresponding_data']\\n\\n # Only extend if not cached\\n cache_df = None\\n if not args['cache']:\\n modellist.extend(benchmark['models'])\\n else:\\n cache_df = pd.read_csv(args['cache'])\\n\\n # Extract comparator settings from benchmark description\\n eval_comparator = comparator.EqualityComparator()\\n if 'comparator' in benchmark:\\n if benchmark['comparator'] == 'nvc':\\n eval_comparator = comparator.NVCComparator()\\n\\n # Run the model evaluation\\n is_silent = (args['output'] in ['html', 'server'])\\n eva = None\\n if benchmark['type'] == 'adaption':\\n eva = evaluator.AdaptionEvaluator(\\n modellist,\\n eval_comparator,\\n benchmark['data.test'],\\n train_datafile=benchmark['data.train'],\\n train_data_person=benchmark['data.train_person'],\\n silent=is_silent,\\n corresponding_data=corresponding_data,\\n domain_encoders=benchmark['domain_encoders'],\\n cache_df=cache_df\\n )\\n elif benchmark['type'] == 'coverage':\\n # Check for benchmark validity\\n if benchmark['data.train'] or benchmark['data.train_person']:\\n print('WARNING: Ignoring specified training and train_person data ' \\\\\\n + 'for coverage evaluation...')\\n\\n eva = evaluator.CoverageEvaluator(\\n modellist,\\n eval_comparator,\\n benchmark['data.test'],\\n train_datafile=benchmark['data.train'],\\n train_data_person=benchmark['data.train_person'],\\n silent=is_silent,\\n corresponding_data=corresponding_data,\\n domain_encoders=benchmark['domain_encoders'],\\n cache_df=cache_df\\n )\\n else:\\n raise ValueError('Unknown benchmark type: {}'.format(benchmark['type']))\\n\\n with silence_stdout(is_silent):\\n res_df = eva.evaluate()\\n\\n if 'save' in args:\\n res_df.to_csv(args['save'], index=False)\\n\\n # Run the metric visualizer\\n htmlcrtr = html_creator.HTMLCreator([\\n viz_plot.AccuracyVisualizer(),\\n viz_plot.BoxplotVisualizer(),\\n viz_plot.TableVisualizer()\\n ])\\n\\n # Prepare the benchmark output information and visualize the evaluation results\\n benchmark_info = {\\n 'name': os.path.basename(args['benchmark']),\\n 'data.train': os.path.basename(\\n benchmark['data.train']) if benchmark['data.train'] else '',\\n 'data.train_person': os.path.basename(\\n benchmark['data.train_person']) if benchmark['data.train_person'] else '',\\n 'data.test': os.path.basename(benchmark['data.test']),\\n 'type': benchmark['type'],\\n 'corresponding_data': benchmark['corresponding_data'],\\n 'domains': list(res_df['domain'].unique()),\\n 'response_types': list(res_df['response_type'].unique()),\\n }\\n\\n if args['output'] == 'browser':\\n html = htmlcrtr.to_html(res_df, benchmark_info, embedded=False)\\n server.load_in_default_browser(html.encode('utf8'))\\n elif args['output'] == 'server':\\n html = htmlcrtr.to_html(res_df, benchmark_info, embedded=True)\\n sys.stdout.buffer.write(html.encode('utf-8'))\\n elif args['output'] == 'html':\\n html = htmlcrtr.to_html(res_df, benchmark_info, embedded=False)\\n print(html)\",\n \"def get_latest_benchmark():\\n\\n benchmark_paths = glob.glob(\\\"./.benchmarks/*/*.json\\\")\\n dates = [\\n \\\"\\\".join(_b.split(\\\"/\\\")[-1].split(\\\"_\\\")[2:4]) for _b in benchmark_paths\\n ]\\n benchmarks = {date: value for date, value in zip(dates, benchmark_paths)}\\n\\n dates.sort()\\n latest = dates[-1]\\n benchmark_latest = benchmarks[latest]\\n\\n return benchmark_latest\",\n \"def load_results(self):\\n self.find_benchmark_directories()\\n for (benchmark, producer), result in self.results.items():\\n print('Reading results for ' + benchmark + ' ' + producer)\\n if not result.directory:\\n print('No results found for ' + benchmark + ' ' + producer)\\n else:\\n print('Generating report for: ' + result.directory)\\n report = Report(result.directory)\\n result.reports = report.generate()\",\n \"def _get_all_spectra(self):\\n pass\",\n \"def get_all_labs():\\n return Lab.query.all()\",\n \"def available_datasets(self) -> List[str]:\\n return sorted(self.__by_name.keys())\",\n \"def tickers(self):\\n self._refresh_tickers()\\n return self._tickers\",\n \"def get_results_for_all_classifiers(\\n self, _sorted=True, reverse=True\\n ) -> List[MachineLearningExperimentResults]:\\n results = [\\n self.get_results(classifier_name)\\n for classifier_name in self.classifiers.keys()\\n ]\\n\\n if _sorted:\\n return sorted(results, reverse=reverse)\\n\\n return results\",\n \"def speed_list(self) -> list:\\n return self._speed_list\",\n \"def performances(self) -> list[Performance]:\\n return [\\n Performance(\\n training_time=Metric(p[\\\"training\\\"][\\\"duration\\\"], 0),\\n latency=Metric(self.static_metrics[\\\"latency\\\"], 0),\\n num_model_parameters=Metric(\\n self.static_metrics[\\\"num_model_parameters\\\"], 0\\n ),\\n num_gradient_updates=Metric(\\n p[\\\"training\\\"][\\\"num_gradient_updates\\\"], 0\\n ),\\n **{\\n k: Metric(p[\\\"testing\\\"][k], 0)\\n for k in [\\\"mase\\\", \\\"smape\\\", \\\"nrmse\\\", \\\"nd\\\", \\\"ncrps\\\"]\\n },\\n )\\n for p in self.metrics\\n ]\",\n \"def specs_to_load(self):\\n return self._create_list_for(\\\"autoload\\\")\",\n \"def test_BenchmarkSuite_integration_test(\\n benchmark_suite: typing.Callable, tempdir: pathlib.Path\\n):\\n with benchmark_suite() as bs:\\n bs.ForceOpenCLEnvironment(cldrive_env.OclgrindOpenCLEnvironment())\\n observer = MockBenchmarkObserver(stop_after=1)\\n\\n # `stop_after` raises BenchmarkInterrupt.\\n try:\\n bs.Run([observer])\\n assert False\\n except gpgpu.BenchmarkInterrupt:\\n pass\\n\\n assert len(observer.logs) == 1\\n assert observer.logs[0].benchmark_name in bs.benchmarks\",\n \"def comports(include_links=False):\\n return list(iterate_comports())\",\n \"def list_backends(_):\\n backends = [b.__name__ for b in available_backends()]\\n print('\\\\n'.join(backends))\",\n \"def load_tests_list(cls, to):\\n tests = [unittest.TestLoader().loadTestsFromModule(to)]\\n\\n return tests\",\n \"def get_all_providers() -> list[str]:\\n return list(ALL_PROVIDERS)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.71444166","0.6889727","0.64351124","0.6397667","0.6397363","0.62924254","0.6077235","0.59082776","0.58880574","0.57301986","0.56475395","0.5644938","0.5587757","0.55655915","0.5550465","0.5548563","0.5522972","0.5509917","0.5496706","0.54954356","0.54953796","0.5484505","0.5480505","0.545044","0.5436377","0.54351074","0.54257315","0.54257315","0.5412383","0.5411625","0.5408039","0.5405774","0.53961045","0.53715646","0.53677404","0.535261","0.5348025","0.5326152","0.5303296","0.5294458","0.5293706","0.5291889","0.5276541","0.52707255","0.526585","0.52477276","0.523655","0.5230019","0.522776","0.52229893","0.5211965","0.5200476","0.5199274","0.5196385","0.5190058","0.51898926","0.5174058","0.51627904","0.5162259","0.5153719","0.5152957","0.5137045","0.5130175","0.51258516","0.5115113","0.5114199","0.51141423","0.51061493","0.51041955","0.5095654","0.50880116","0.5081963","0.50722337","0.50711894","0.50552106","0.5052158","0.5045811","0.50455666","0.5034576","0.50338197","0.5021934","0.5012089","0.5009351","0.5004567","0.4995621","0.49954215","0.49895498","0.49845266","0.49765134","0.49691135","0.4964481","0.49594527","0.4940974","0.49348202","0.49344555","0.4928335","0.4918447","0.49152508","0.49096146","0.49080607"],"string":"[\n \"0.71444166\",\n \"0.6889727\",\n \"0.64351124\",\n \"0.6397667\",\n \"0.6397363\",\n \"0.62924254\",\n \"0.6077235\",\n \"0.59082776\",\n \"0.58880574\",\n \"0.57301986\",\n \"0.56475395\",\n \"0.5644938\",\n \"0.5587757\",\n \"0.55655915\",\n \"0.5550465\",\n \"0.5548563\",\n \"0.5522972\",\n \"0.5509917\",\n \"0.5496706\",\n \"0.54954356\",\n \"0.54953796\",\n \"0.5484505\",\n \"0.5480505\",\n \"0.545044\",\n \"0.5436377\",\n \"0.54351074\",\n \"0.54257315\",\n \"0.54257315\",\n \"0.5412383\",\n \"0.5411625\",\n \"0.5408039\",\n \"0.5405774\",\n \"0.53961045\",\n \"0.53715646\",\n \"0.53677404\",\n \"0.535261\",\n \"0.5348025\",\n \"0.5326152\",\n \"0.5303296\",\n \"0.5294458\",\n \"0.5293706\",\n \"0.5291889\",\n \"0.5276541\",\n \"0.52707255\",\n \"0.526585\",\n \"0.52477276\",\n \"0.523655\",\n \"0.5230019\",\n \"0.522776\",\n \"0.52229893\",\n \"0.5211965\",\n \"0.5200476\",\n \"0.5199274\",\n \"0.5196385\",\n \"0.5190058\",\n \"0.51898926\",\n \"0.5174058\",\n \"0.51627904\",\n \"0.5162259\",\n \"0.5153719\",\n \"0.5152957\",\n \"0.5137045\",\n \"0.5130175\",\n \"0.51258516\",\n \"0.5115113\",\n \"0.5114199\",\n \"0.51141423\",\n \"0.51061493\",\n \"0.51041955\",\n \"0.5095654\",\n \"0.50880116\",\n \"0.5081963\",\n \"0.50722337\",\n \"0.50711894\",\n \"0.50552106\",\n \"0.5052158\",\n \"0.5045811\",\n \"0.50455666\",\n \"0.5034576\",\n \"0.50338197\",\n \"0.5021934\",\n \"0.5012089\",\n \"0.5009351\",\n \"0.5004567\",\n \"0.4995621\",\n \"0.49954215\",\n \"0.49895498\",\n \"0.49845266\",\n \"0.49765134\",\n \"0.49691135\",\n \"0.4964481\",\n \"0.49594527\",\n \"0.4940974\",\n \"0.49348202\",\n \"0.49344555\",\n \"0.4928335\",\n \"0.4918447\",\n \"0.49152508\",\n \"0.49096146\",\n \"0.49080607\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.79568917"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":11271,"cells":{"query":{"kind":"string","value":"Returns the list of all coverage benchmarks."},"document":{"kind":"string","value":"def get_coverage_benchmarks():\n return [\n benchmark for benchmark in get_all_benchmarks()\n if get_type(benchmark) == BenchmarkType.CODE.value\n ]"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_all_benchmarks():\n all_benchmarks = []\n for benchmark in os.listdir(BENCHMARKS_DIR):\n benchmark_path = os.path.join(BENCHMARKS_DIR, benchmark)\n if os.path.isfile(os.path.join(benchmark_path, 'benchmark.yaml')):\n all_benchmarks.append(benchmark)\n return sorted(all_benchmarks)","def run_all(self):\n runs = []\n for run in self.benchmarks:\n run.start()\n run.wait()\n runs.append(run.metadata)\n return runs","def list(self, classes):\n\n def add(benchmarks, parts, flags, exclude):\n if (\n flags[\"language\"] != \"C++\"\n and flags[\"language\"] != \"Java\"\n and \"--drop-caches=true\" not in parts\n ):\n parts.append(\"--drop-caches=true\")\n command = \" \".join(parts)\n if command not in exclude:\n benchmarks.append({\"command\": command, \"flags\": flags})\n\n benchmarks = []\n for name, benchmark in classes.items():\n if name.startswith(\"example\"):\n continue\n\n instance, parts = benchmark(), [name]\n\n exclude = getattr(benchmark, \"exclude\", [])\n if \"source\" in getattr(benchmark, \"arguments\", []):\n parts.append(\"ALL\")\n\n iterations = getattr(instance, \"iterations\", 3)\n parts.append(f\"--iterations={iterations}\")\n\n if instance.cases:\n parts.append(\"--all=true\")\n\n flags = getattr(instance, \"flags\", {})\n\n if getattr(instance, \"r_only\", False):\n flags[\"language\"] = \"R\"\n add(benchmarks, parts, flags, exclude)\n else:\n if \"language\" not in flags:\n flags[\"language\"] = \"Python\"\n add(benchmarks, parts, flags, exclude)\n\n if hasattr(instance, \"r_name\"):\n flags_ = flags.copy()\n flags_[\"language\"] = \"R\"\n parts.append(\"--language=R\")\n add(benchmarks, parts, flags_, exclude)\n\n return sorted(benchmarks, key=lambda k: k[\"command\"])","def coverage_files() -> Iterable[str]:\n packs_path = CONTENT_PATH / \"Packs\"\n for cov_path in packs_path.glob(\"*/Integrations/*/.coverage\"):\n yield str(cov_path)\n for cov_path in packs_path.glob(\"*/Scripts/*/.coverage\"):\n yield str(cov_path)","def benchmark(self):\n nsites = []\n for m in self.methods:\n for name, structure in self.test_structures.items():\n cns = []\n if self.unique_sites:\n es = SpacegroupAnalyzer(structure).get_symmetrized_structure().equivalent_sites\n sites = [structure.index(x[0]) for x in es]\n else:\n sites = range(len(structure))\n\n for key, val in self.hi.items():\n if name == key:\n for j in sites:\n if isinstance(m, NearNeighbors):\n tmpcn = m.get_cn_dict(structure, j, self.use_weights)\n else:\n tmpcn = m.compute(structure, j)\n if tmpcn == \"null\":\n continue\n if self.nround:\n self._roundcns(tmpcn, self.nround)\n cns.append((structure[j].species_string, tmpcn))\n if self.cation_anion:\n for mat, cat in self.cations.items():\n if (name == mat) and cat:\n cns = self._popel(cns, cat)\n elif self.anion_cation:\n for mat, an in self.anions.items():\n if name == mat:\n cns = self._popel(cns, an)\n m._cns[name] = cns\n nsites.append(len(cns))\n self.nsites = max(nsites)","def tests_generator(self):\n cb_bin = os.path.join(bin_path, 'compilebench')\n cmd_list = [\n (\"Initial Create/Compile/Read Compiled Tree\", \"{0} -D {1} -i 10 --makej -s {2}\"),\n ]\n\n tests = []\n for idx, (desc, cmd) in enumerate(cmd_list):\n test_name = \"compile_bench_{0}_{1}\".format(idx + 1, to_safe_name(desc))\n test = TestProfile(\n name=test_name,\n desc=desc,\n test_path=self.test_path,\n bin_path=bin_path,\n command=cmd.format(cb_bin, self.test_path, bin_path))\n tests.append(test)\n return tests","def get_bug_benchmarks():\n return [\n benchmark for benchmark in get_all_benchmarks()\n if get_type(benchmark) == BenchmarkType.BUG.value\n ]","def test_multiple_coverages(self):\n\n params_82 = {\n 'ReQuEsT': \"DescribeCoverage\",\n 'SeRvIcE': \"WCS\",\n \"BOGUS\": \"SSS\",\n 'Version': \"1.0.0\",\n \"COVERAGE\": \",\".join(self.names)\n }\n response = self.query_server(params_82)\n soup = BeautifulSoup(response.text, 'xml')\n self.assertTrue(\n len(soup.find_all('CoverageOffering')) == len(self.names),\n msg=\"If multiple valid coverages are submitted with a DescribeCoverage request, all requested coverages should be returned.\"\n )\n for elem in soup.find_all('CoverageOffering'):\n self.assertTrue(\n elem.find('name').text in self.names,\n msg=\"All requested coverages should be returned in a DescribeCoverage request.\")","def benchmark(self):\n logger.info(self.benchmark.__doc__)\n return self.run(self.benchmark_profile())","def cov():\n cov = coverage.coverage(branch=True, include='project/*')\n cov.start()\n tests = unittest.TestLoader().discover('tests')\n unittest.TextTestRunner(verbosity=2).run(tests)\n cov.stop()\n cov.save()\n print('Coverage Summary:')\n cov.report()\n basedir = os.path.abspath(os.path.dirname(__file__))\n covdir = os.path.join(basedir, 'tmp/coverage')\n cov.html_report(directory=covdir)\n print('HTML version: file://%s/index.html' % covdir)\n cov.erase()","def RunUnitTests():\n if FLAGS.test_targets:\n tests = FLAGS.test_targets\n else:\n tests = shell_interfaces.GetStdout(\n 'bazel query kind(\"cc_test\", ...)').split()\n\n # Run coverage, joining all data into one file.\n subprocess.check_call(['bazel', 'coverage', '--instrument_test_targets',\n '--experimental_cc_coverage',\n '--combined_report=lcov',\n ('--coverage_report_generator=@bazel_tools//tools/tes'\n 't/CoverageOutputGenerator/java/com/google/devtools/'\n 'coverageoutputgenerator:Main')] + tests)","def _disc_benchmarks(cls, conf, repo, environments, commit_hashes, check):\n root = conf.benchmark_dir\n\n cls.check_tree(root)\n\n if len(environments) == 0:\n raise util.UserError(\"No available environments\")\n\n # Try several different commits:\n #\n # - First of commit_hashes provided\n # - Tips of branches from configuration file\n # - Rest of the commit_hashes\n #\n\n def iter_hashes():\n for h in commit_hashes[:1]:\n yield h\n for branch in conf.branches:\n try:\n yield repo.get_hash_from_name(branch)\n except NoSuchNameError:\n continue\n for h in commit_hashes[1:]:\n yield h\n\n def iter_unique(iter):\n seen = set()\n for item in iter:\n if item not in seen:\n seen.add(item)\n yield item\n\n try_hashes = iter_unique(iter_hashes())\n\n log.info(\"Discovering benchmarks\")\n with log.indent():\n last_err = None\n for env, commit_hash in itertools.product(environments, try_hashes):\n env.create()\n\n if last_err is not None:\n log.warning(\"Failed: trying different commit/environment\")\n\n result_dir = tempfile.mkdtemp()\n try:\n env.install_project(conf, repo, commit_hash)\n\n env_vars = dict(os.environ)\n env_vars.update(env.env_vars)\n\n result_file = os.path.join(result_dir, 'result.json')\n env.run(\n [runner.BENCHMARK_RUN_SCRIPT, 'discover',\n os.path.abspath(root),\n os.path.abspath(result_file)],\n cwd=result_dir,\n env=env_vars,\n dots=False)\n\n try:\n with open(result_file, 'r') as fp:\n benchmarks = json.load(fp)\n except (IOError, ValueError):\n log.error(\"Invalid discovery output\")\n raise util.UserError()\n\n break\n except (util.UserError, util.ProcessError) as err:\n last_err = err\n continue\n except KeyboardInterrupt:\n raise util.UserError(\"Interrupted.\")\n finally:\n util.long_path_rmtree(result_dir)\n else:\n raise util.UserError(\"Failed to build the project and import the benchmark suite.\")\n\n if check:\n log.info(\"Checking benchmarks\")\n with log.indent():\n result_dir = tempfile.mkdtemp()\n try:\n out, err, retcode = env.run(\n [runner.BENCHMARK_RUN_SCRIPT, 'check',\n os.path.abspath(root)],\n cwd=result_dir,\n dots=False,\n env=env_vars,\n valid_return_codes=None,\n return_stderr=True,\n redirect_stderr=True)\n finally:\n util.long_path_rmtree(result_dir)\n\n out = out.strip()\n if retcode == 0:\n if out:\n log.info(out)\n log.info(\"No problems found.\")\n else:\n if out:\n log.error(out)\n raise util.UserError(\"Benchmark suite check failed.\")\n\n return benchmarks","def cov():\n cov = coverage.coverage(\n branch=True,\n include='project/*',\n omit=\"*/__init__.py\"\n )\n cov.start()\n tests = unittest.TestLoader().discover('tests')\n unittest.TextTestRunner(verbosity=2).run(tests)\n cov.stop()\n cov.save()\n print 'Coverage Summary:'\n cov.report()\n basedir = os.path.abspath(os.path.dirname(__file__))\n covdir = os.path.join(basedir, 'tmp/coverage')\n cov.html_report(directory=covdir)\n print('HTML version: file://%s/index.html' % covdir)\n cov.erase()","def delete_all_benchmarks(self, namespace=\"benchmark-operator\"):\n all_benchmarks = self.crd_client.list_namespaced_custom_object(\n group=\"ripsaw.cloudbulldozer.io\", version=\"v1alpha1\", namespace=namespace, plural=\"benchmarks\"\n )\n\n _ = [\n self.delete_benchmark(benchmark[\"metadata\"][\"name\"], namespace)\n for benchmark in all_benchmarks.get(\"items\", [])\n ]","def task_coverage():\n return {\n 'actions': ['py.test --cov nikola --cov-report term-missing tests/'],\n 'verbosity': 2,\n }","def list(self):\n print \"\\nAvailable Test Cases\"\n print \"====================\"\n for case in self.cases:\n print case.__name__","def gather_tests(self):\n rosie_tests_dir = os.path.join(cp_tests_dir(),\n \"circuitpython\",\n \"rosie_tests\")\n test_files = []\n for test in os.scandir(rosie_tests_dir):\n # TODO: implement exclusions by board\n if test.path.endswith(\".py\"):\n test_files.append(TestObject(test.path))\n\n return test_files","def coverage(session):\n session.install(\"coverage[toml]\", \"codecov\")\n session.run(\"coverage\", \"xml\", \"--fail-under=0\")\n session.run(\"codecov\", *session.posargs)","def test_list_benchmarks(client):\n # The benchmark listing contains one element (independently of whether the\n # user is logged in or not).\n r = client.get(config.API_PATH() + '/workflows')\n assert r.status_code == 200\n doc = r.json\n assert len(doc[labels.WORKFLOW_LIST]) == 1\n # Create user and the request header that contains the API key for the\n # logged in user.\n _, token = create_user(client, '0000')\n headers = {HEADER_TOKEN: token}\n r = client.get(config.API_PATH() + '/workflows', headers=headers)\n assert r.status_code == 200\n doc = r.json\n assert len(doc[labels.WORKFLOW_LIST]) == 1","def generate_coverage_reports(project, output_dir=None):\n outdir = output_dir or os.path.join(project.sdk_directory, \"coverage-results\")\n sdir = project.path\n # Make sure output dir exists and is empty:\n qisys.sh.rm(outdir)\n qisys.sh.mkdir(outdir, recursive=True)\n formats = {\"xml\": [\"--xml\"],\n \"html\": [\"--html\", \"--html-details\"]}\n for fmt, opts in formats.items():\n base_report = os.path.join(outdir, project.name + \".\" + fmt)\n cmd = [\"gcovr\",\n \"--root\", sdir,\n \"--exclude\", \".*test.*\",\n \"--exclude\", \".*external.*\",\n \"--exclude\", \".*example.*\"] + opts + \\\n [\"--output\", base_report]\n qisys.command.call(cmd, cwd=sdir, quiet=True)\n ui.info(ui.green, \"*\", ui.reset, \"Generated\", fmt.upper(),\n \"coverage report in\", ui.reset, ui.bold, base_report)","def get_methods(cls):\n # get all the methods that have the _benchmark_this flag\n for method in (getattr(cls, m) for m in dir(cls)):\n if hasattr(method, \"_benchmark_this\"):\n yield method","def evaluate_benchmarks(self):\n\n # iterate over replicates\n results = {}\n for replicate_id, replicate in self.replicates:\n\n # evaluate benchmark for current replicate\n bmark = SimulationBenchmark(replicate.copy(),\n graph=self.graphs[replicate_id],\n **self.params)\n\n # store results\n results[replicate_id] = dict(\n\n labels_MAE=bmark.scores['labels'].MAE,\n level_only_MAE=bmark.scores['level_only'].MAE,\n spatial_only_MAE=bmark.scores['spatial_only'].MAE,\n community_MAE=bmark.scores['labels_comm'].MAE,\n\n labels_PCT=bmark.scores['labels'].percent_correct,\n level_only_PCT=bmark.scores['level_only'].percent_correct,\n spatial_only_PCT=bmark.scores['spatial_only'].percent_correct,\n community_PCT=bmark.scores['labels_comm'].percent_correct)\n\n # compile dataframe\n results = pd.DataFrame.from_dict(results, orient='index')\n results.index.set_names(self.multiindex, inplace=True)\n\n return results","def test_get_benchmark_methods_filter(self):\n config = mock.Mock()\n config.workspace = 'workspace'\n config.benchmark_method_patterns = ['new_foo.BenchmarkClass.filter:bench.*']\n benchmark_runner = benchmark.BenchmarkRunner(config)\n\n mock_benchmark_class = mock.Mock()\n mock_benchmark_class.benchmark_method_1 = 'foo'\n\n mock_module = mock.Mock()\n sys.modules['new_foo'] = mock_module\n mock_module.BenchmarkClass.return_value = mock_benchmark_class\n\n methods = benchmark_runner._get_benchmark_methods()\n\n self.assertEqual(1, len(methods))\n self.assertEqual('new_foo.BenchmarkClass.benchmark_method_1', methods[0])","def get_benchmark_requirements(cls):\n pass","def exclude_non_cpp(benchmarks):\n return [benchmark for benchmark in benchmarks if is_cpp(benchmark)]","def get_all_stats():\n\n return get_component(CachingPackage.COMPONENT_NAME).get_all_stats()","def run_coverage(session):\n set_environment_variables(PYBAMM_ENV, session=session)\n session.run_always(\"pip\", \"install\", \"coverage\")\n session.run_always(\"pip\", \"install\", \"-e\", \".[all]\")\n if sys.platform != \"win32\":\n session.run_always(\"pip\", \"install\", \"-e\", \".[odes]\")\n session.run_always(\"pip\", \"install\", \"-e\", \".[jax]\")\n session.run(\"coverage\", \"run\", \"--rcfile=.coveragerc\", \"run-tests.py\", \"--nosub\")\n session.run(\"coverage\", \"combine\")\n session.run(\"coverage\", \"xml\")","def get(self, request, format=None):\n benchmarkmodels = BenchmarkModel.objects.all()\n serializer = BenchmarkModelListSerializer(benchmarkmodels, many=True)\n return Response(serializer.data)","def get_list(cls, suite_model) -> list:\n suites = []\n for s in cls.SUITES:\n s[\"tests\"] = cls._get_tests(s)\n s[\"approxRunTime\"] = cls._get_average_run_time(suite_model)\n suites.append(s)\n return suites","def test_get_benchmark_methods_exact_match(self):\n config = mock.Mock()\n config.workspace = 'workspace'\n config.benchmark_method_patterns = [\n 'new_foo.BenchmarkClass.benchmark_method_1',\n 'new_foo.BenchmarkClass.benchmark_method_2']\n benchmark_runner = benchmark.BenchmarkRunner(config)\n\n methods = benchmark_runner._get_benchmark_methods()\n self.assertEqual(['new_foo.BenchmarkClass.benchmark_method_1',\n 'new_foo.BenchmarkClass.benchmark_method_2'], methods)","def get_tests():\n # tests = ['test_build_gaussian_pyramid_random', 'test_build_gaussian_pyramid_static', 'test_build_laplacian_pyramid_random', 'test_build_laplacian_pyramid_static', 'test_laplacian_to_image', 'test_render_pyramid_random', 'test_render_pyramid_static']\n # return [tester.TestEx3(method) for method in tests]\n return [tester.TestEx3(method) for method in dir(tester.TestEx3) if method.startswith('test')]","def cov():\n tests = unittest.TestLoader().discover('tests')\n result = unittest.TextTestRunner(verbosity=1).run(tests)\n if result.wasSuccessful():\n COV.stop()\n COV.save()\n print('Coverage Summary:')\n COV.report()\n basedir = os.path.abspath(os.path.dirname(__file__))\n covdir = os.path.join(basedir, 'tmp/coverage')\n COV.html_report(directory=covdir)\n print('HTML version: file://%s/index.html' % covdir)\n COV.erase()\n return 0\n return 1","def get_currencies(self) -> list:\n return self.client.currencies.get_all()","def get_tests(self):\n subtests = itertools.chain(*(s.get_tests() for s in self.suites.values()))\n tt = [t for t in itertools.chain(self.tests,subtests)]\n return tt","def cov():\n tests = unittest.TestLoader().discover('project/tests')\n result = unittest.TextTestRunner(verbosity=2).run(tests)\n if result.wasSuccessful():\n COV.stop()\n COV.save()\n print('Coverage Summary:')\n COV.report()\n basedir = os.path.abspath(os.path.dirname(__file__))\n covdir = os.path.join(basedir, 'tmp/coverage')\n COV.html_report(directory=covdir)\n print('HTML version: file://%s/index.html' % covdir)\n COV.erase()\n return 0\n return 1","def _run():\n subprocess.check_call(\n [\n \"tools/bazel\",\n \"build\",\n \"-c\",\n \"opt\",\n \"test/core/memory_usage/memory_usage_test\",\n ]\n )\n ret = {}\n for name, benchmark_args in _BENCHMARKS.items():\n for scenario, extra_args in _SCENARIOS.items():\n # TODO(chenancy) Remove when minstack is implemented for channel\n if name == \"channel\" and scenario == \"minstack\":\n continue\n try:\n output = subprocess.check_output(\n [\n \"bazel-bin/test/core/memory_usage/memory_usage_test\",\n ]\n + benchmark_args\n + extra_args\n )\n except subprocess.CalledProcessError as e:\n print(\"Error running benchmark:\", e)\n continue\n for line in output.splitlines():\n for key, (pattern, conversion) in _INTERESTING.items():\n m = re.match(pattern, line)\n if m:\n ret[scenario + \": \" + key] = conversion(m.group(1))\n return ret","def cov():\n tests = unittest.TestLoader().discover('project/tests')\n result = unittest.TextTestRunner(verbosity=2).run(tests)\n if result.wasSuccessful():\n COV.stop()\n COV.save()\n print('Coverage Summary:')\n COV.report()\n COV.html_report()\n COV.erase()\n return 0\n return 1","def GetLcovDirectories():\n dirs = set()\n for dirname, _, files in os.walk(os.path.join(makani.HOME, 'bazel-bin')):\n for f in files:\n if f.endswith('.gcda'):\n dir_parts = dirname.split(os.sep)\n for i, p in enumerate(dir_parts):\n if p == '_objs':\n dirs.add(os.sep.join(dir_parts[:i+2]))\n\n return sorted(dirs)","def coverage(ctx):\n ctx.run(\"coverage run --source {PROJECT_NAME} -m pytest\".format(PROJECT_NAME=PROJECT_NAME))\n ctx.run(\"coverage report -m\")\n ctx.run(\"coverage html\")","def get_csv(self):\n all_csvs = [each for each in listdir(self.cur_dir) if each.endswith('.csv')]\n return all_csvs","def __init__(self, conf, benchmarks, regex=None):\n self._conf = conf\n self._benchmark_dir = conf.benchmark_dir\n\n if not regex:\n regex = []\n if isinstance(regex, str):\n regex = [regex]\n\n self._all_benchmarks = {}\n self._benchmark_selection = {}\n for benchmark in benchmarks:\n self._all_benchmarks[benchmark['name']] = benchmark\n if benchmark['params']:\n self._benchmark_selection[benchmark['name']] = []\n for idx, param_set in enumerate(\n itertools.product(*benchmark['params'])):\n name = f\"{benchmark['name']}({', '.join(param_set)})\"\n if not regex or any(re.search(reg, name) for reg in regex):\n self[benchmark['name']] = benchmark\n self._benchmark_selection[benchmark['name']].append(idx)\n else:\n self._benchmark_selection[benchmark['name']] = None\n if not regex or any(re.search(reg, benchmark['name']) for reg in regex):\n self[benchmark['name']] = benchmark","def get_all_casks(self):","def collect_cases(self, suite=False):\n cases = unittest.TestSuite()\n\n if suite:\n test_suites = []\n for file in os.listdir('.'):\n if self.suite_path in file:\n if os.path.isdir(file):\n test_suites.append(file)\n\n for test_suite in test_suites:\n self._collect_cases(cases, top_dir=test_suite)\n else:\n self._collect_cases(cases, top_dir=None)\n return cases","def coverage(session) -> None:\n session.install(\".[test]\", \"pytest-cov\")\n session.run(\n \"pytest\", \"-n\", \"auto\", \"--cov=./\", \"--cov-report=xml\", *session.posargs\n )","def get_all(self):\r\n ret = []\r\n for cache_name, stat in self.stats_per_cache.items():\r\n ret.append({\r\n 'cache_name': cache_name,\r\n 'num_hits': len(stat.hit_targets),\r\n 'num_misses': len(stat.miss_targets),\r\n 'hits': stat.hit_targets,\r\n 'misses': stat.miss_targets\r\n })\r\n return ret","def getTestSets():\n return list(_testsetdict.keys())","def suite():\n\tts = unittest.TestSuite()\n\tfor test_module in __all__:\n\t\tm = importlib.import_module(\"pyroclast.test.\" + test_module)\n\t\tfor n in dir(m):\n\t\t\tc = getattr(m, n)\n\t\t\tif is_test_case(c):\n\t\t\t\ts = unittest.TestLoader().loadTestsFromTestCase(c)\n\t\t\t\tts.addTests(s)\n\treturn ts","def time_binaries():\n print()\n print(\"=== BENCHMARKING ===\")\n with tempfile.NamedTemporaryFile(\n prefix=\"riff-benchmark\", suffix=\".gitlog\"\n ) as testdata:\n subprocess.check_call(\n [\"git\", \"log\", \"--color=always\", \"-p\", \"master\"], stdout=testdata\n )\n\n binaries = sorted(glob.glob(os.path.join(BINDIR, \"*\")), key=natural_keys)\n\n # Do riff-current last: https://stackoverflow.com/a/20320940/473672\n binaries.sort(key=lambda s: s.endswith(\"riff-current\"))\n\n for binary in binaries:\n print_timings(binary, testdata.name)\n print_timings(\"/bin/cat\", testdata.name)","def output_benchmark_results(output_dir, ts_agent_list=None, ga_agent=None, title=None, auto_open=True):\n if (ts_agent_list is None or not all(ts_agent.benchmark for ts_agent in ts_agent_list)) \\\n and (ga_agent is None or not ga_agent.benchmark):\n raise UserWarning(\"agent arguments were None or were not ran in benchmark mode.\")\n\n if title is None:\n title = \"Benchmark Run {}\".format(datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M\"))\n\n output_dir = Path(output_dir)\n\n if not output_dir.exists():\n output_dir.mkdir(parents=True)\n\n def compute_stats(lst):\n return {\n 'min': round(min(lst)),\n 'median': round(statistics.median(lst)),\n 'max': round(max(lst)),\n 'std': round(statistics.stdev(lst)) if len(lst) > 1 else 0,\n 'var': round(statistics.variance(lst)) if len(lst) > 1 else 0,\n 'mean': round(statistics.mean(lst))\n }\n\n # tabu search results\n if ts_agent_list is not None and all(ts_agent.benchmark for ts_agent in ts_agent_list):\n _create_ts_plots(ts_agent_list, output_dir)\n ts_result_makespans = []\n ts_initial_makespans = []\n ts_iterations = []\n for ts_agent in ts_agent_list:\n ts_result_makespans.append(ts_agent.best_solution.makespan)\n ts_initial_makespans.append(ts_agent.initial_solution.makespan)\n ts_iterations.append(ts_agent.benchmark_iterations)\n\n ts_result_makespans_stats = compute_stats(ts_result_makespans)\n ts_initial_makespans_stats = compute_stats(ts_initial_makespans)\n ts_iterations_stats = compute_stats(ts_iterations)\n\n else:\n ts_result_makespans_stats = None\n ts_initial_makespans_stats = None\n ts_iterations_stats = None\n\n # genetic algorithm results\n if ga_agent is not None and ga_agent.benchmark:\n _create_ga_plots(ga_agent, output_dir)\n ga_initial_makespans = [sol.makespan for sol in ga_agent.initial_population]\n ga_result_makespans = [sol.makespan for sol in ga_agent.result_population]\n\n ga_initial_makespans_stats = compute_stats(ga_initial_makespans)\n ga_result_makespans_stats = compute_stats(ga_result_makespans)\n\n else:\n ga_initial_makespans_stats = None\n ga_result_makespans_stats = None\n\n # render template\n template = template_env.get_template(benchmark_template)\n rendered_template = template.render(\n title=title,\n ts_agent_list=ts_agent_list,\n ts_initial_makespans_stats=ts_initial_makespans_stats,\n ts_result_makespans_stats=ts_result_makespans_stats,\n iterations_per_ts_agent_stats=ts_iterations_stats,\n output_directory=output_dir.resolve(),\n ga_agent=ga_agent,\n ga_initial_makespans_stats=ga_initial_makespans_stats,\n ga_result_makespans_stats=ga_result_makespans_stats,\n )\n\n # create index.html\n with open(output_dir / 'index.html', 'w') as output_file:\n output_file.write(rendered_template)\n\n if auto_open:\n webbrowser.open(f'file://{output_dir.resolve()}/index.html')","def sync_all_teams_coverage():\n teams = Team.objects.all()\n\n for team in teams:\n identifier = team.identifier\n\n sync_team_coverage.apply_async(args=(identifier, ))\n sync_team_cluster_stats.apply_async(args=(identifier, ))\n sync_team_advisory_stats.apply_async(args=(identifier, ))","def get_performance_test_cases(test_suite):\n return get_cases(test_suite, r'test_perf_')","def get(self) -> list:\n return self.__cogs","def get_benchmark_cov_dict(coverage_dict, benchmark):\n benchmark_cov_dict = {}\n for key_pair, covered_regions in coverage_dict.items():\n current_fuzzer, current_benchmark = key_pair.split()\n if current_benchmark == benchmark:\n covered_regions_in_set = set()\n for region in covered_regions:\n covered_regions_in_set.add(tuple(region))\n benchmark_cov_dict[current_fuzzer] = covered_regions_in_set\n return benchmark_cov_dict","def generate_figures():\r\n # create results directory if necessary\r\n try:\r\n makedirs(\"results\")\r\n except OSError as e:\r\n if e.errno != errno.EEXIST:\r\n raise\r\n \r\n for b in benchmarks:\r\n generate_figure(model[b], b)","def commands_all():\n lint()\n complexity()\n coverage()","def get_project_test_coverage(self) -> None:\n print_statistics = {}\n total_number_columns = 0\n number_columns_without_tests = 0\n\n for model_name in self.dbt_tests.keys():\n columns = self.dbt_tests[model_name]\n\n model_number_columns = 0\n model_columns_without_tests = 0\n\n for column in columns:\n total_number_columns += 1\n model_number_columns += 1\n\n if len(column[\"tests\"]) == 0:\n number_columns_without_tests += 1\n model_columns_without_tests += 1\n\n print_statistics[model_name] = self.calculate_coverage_percentage(\n misses=model_columns_without_tests, total=model_number_columns\n )\n\n print_statistics[\"\"] = \"\"\n print_statistics[\"Total\"] = self.calculate_coverage_percentage(\n misses=number_columns_without_tests, total=total_number_columns\n )\n\n self.create_table(\n title=\"Test Coverage\",\n columns=[\"Model Name\", r\"% coverage\"],\n data=print_statistics,\n )","async def getAll():\n return [cluster.export() for cluster in clusters.get_all()]","def collectTests(self, global_ctx):\n pass","def benchmark_profile(self):\n cb_bin = os.path.join(bin_path, 'compilebench')\n desc = \"benchmark\"\n test_name = \"compilebench_{0}\".format(to_safe_name(desc))\n test = TestProfile(\n name=test_name,\n desc=desc,\n test_path=self.test_path,\n bin_path=bin_path,\n command=\"{0} -D {1} -i 10 --makej\".format(cb_bin, self.test_path))\n\n return test","def get_testbench_specs(self, tb_type: str) -> Dict[str, Any]:\n return self._specs['testbenches'][tb_type]","def __iter__(self):\n for benchclass in sorted(self.classes.values()):\n yield benchclass","def getShortCircuitTests(self):\n return self._ShortCircuitTests","def get_all_reporters():\r\n for ep in iter_entry_points('attest.reporters'):\r\n yield ep.name","def test_cases(self) -> list[str]:\n cases = []\n for t in self._test_cases:\n if t not in cases:\n cases.append(t)\n return cases","def __init__(self, benchmarks):\n self.benchmarks = benchmarks\n instNum = 0\n classNum = 0\n for benchclass in self:\n benchclass.line = classNum\n benchclass.instStart = instNum\n for instance in benchclass: \n instance.line = instNum\n instNum += max(instance.maxRuns, 1)\n benchclass.instEnd = instNum - 1\n classNum += 1","def alltests(opts):\n \n print \"API Root: %s\" % options.apiroot\n print \"Token: %s\" % options.token\n print \"Output dir: %s\" % options.output\n print \"Running %d%% of tests\" % options.percent\n print\n \n # need to use DEPT-001, not ID#\n coursehistory_tests = [\n # basic tests:\n \"cis-120\", \"math-114\", \"engl-101\", \"econ-001\",\n # miscellaneously somewhat problematic:\n \"engl-016\", \"law-205\", \"hpr-612\", \"rels-414\", \"nurs-322\",\n \"writ-030\", \"be-310\", \"psci-010\", \"psci-136\",\n # crosslistings:\n \"engl-135\", \"writ-135\", \"fnar-264\", \"cogs-001\", \"russ-048\", \"hist-048\",\n # no reviews?:\n \"afam-271\", \"ames-071\", \"slav-532\", \"afam-285\", \"prtg-213\", \"slav-533\",\n # errors:\n \"99999\", \"moo\",\n ]\n\n instructor_tests = [\n # basic tests:\n \"403\", \"631\", \"1883\", \"2217-FERNANDO-C--PEREIRA\", \"1602-BENJAMIN-PIERCE\",\n # crosslistings:\n \"1034-LYLE-H-UNGAR\", \"2709-DAVID-P--COMBERG\",\n # miscellaneously somewhat problematic:\n \"1040-DAVID-FOX\", \"4268-BART-GERARD-C-DE-JONGHE\",\n # the instructors w/ the most sections\n \"1883\", \"1619\", \"2869\", \"942\", \"1644\", \"541\", \"767\", \"434\",\n # concerned citizens:\n \"1759-MAX-C--CAVITCH\", \"2824-TIMOTHY-CORRIGAN\",\n \"1763-EMILY-R-STEINER\", \"1624-VALERIE-ROSS\",\n # no reviews?:\n \"416-LUDO-ROCHER\", \"715-ELIZABETH-ANN-POLLARD\", \"1094-MARIA-A-COWLES\",\n \"1500-ANDREW-GALLIA\", \"1888-RUSSELL-DILEO\",\n \"1450-SORMANE-PEREIRA-GOMES\", \"2188-HUI-YI-CHEN\", \"1165-DOMENIC-VITIELLO\",\n \"2359-CLAUDIA-CANCINO\", \"2737-SHEN-WANG\", \"3229-BERLE-WHITBY\",\n # errors:\n \"99999\", \"moo\",\n ]\n\n dept_tests = [\n #fast\n \"CSE\", \"LAW\", \"ANAT\", \"KORN\", \"LATN\", \"COGS\", \"MSCI\", \"GAS\",\n #medium\n \"CIS\", \"MATH\", \"FNAR\", \"ACCT\", \"FNCE\", \"BE\", \"MUSC\", \"OPIM\",\n #slow\n #\"SPAN\", \"NURS\", \"ENGL\",\n #error\n \"EROR\"]\n\n index_tests = [\"\", \"instructors\", \"coursehistories\", \"depts\",\n \"semesters\", \"semesters/2010c\"]\n\n course_tests = [] # filled in by coursehistory_tests\n\n for t in fraclist(index_tests, options.percent):\n test(opts, t)\n \n for t in fraclist(coursehistory_tests, options.percent):\n obj = test(opts, \"coursehistories/%s\" % t)\n test(opts, \"coursehistories/%s/reviews\" % t)\n\n # now \"find\" some courses\n course_tests.append(\"2010c-%s\" % t)\n try:\n courseid = sorted(obj[\"result\"][\"courses\"])[0][\"id\"]\n course_tests.append(courseid)\n except (TypeError, KeyError, IndexError):\n pass\n \n for t in course_tests: # don't fraclist an autogenerated list\n # Some of the autogenerated courses don't exist, so ignore errors.\n root_success = test(opts, \"courses/%s\" % t, lderror_ok=True)\n if root_success:\n # Course exists, don't expect errors.\n test(opts, \"courses/%s/reviews\" % t)\n test(opts, \"courses/%s/coursehistories/\" % t)\n test(opts, \"courses/%s/sections\" % t)\n \n if test(opts, \"courses/%s/sections/001\" % t, lderror_ok=True):\n test(opts, \"courses/%s/sections/001/reviews\" % t) \n if '-' in str(t): # if we have a yyyys-dept-num test\n test(opts, \"sections/%s-001\" % t)\n # not tested: sections/001/reviews/instructor-id\n test(opts, \"courses/%s/sections/401\" % t, lderror_ok=True)\n \n for t in fraclist(instructor_tests, options.percent):\n test(opts, \"instructors/%s\" % t)\n test(opts, \"instructors/%s/sections\" % t)\n test(opts, \"instructors/%s/reviews\" % t)\n \n for t in fraclist(dept_tests, options.percent):\n test(opts, \"depts/%s\" % t)\n test(opts, \"depts/%s/reviews\" % t)\n test(opts, \"semesters/2010c/%s\" % t)","def generate_report():\n if os.path.isdir(\"build/coverage\"):\n shutil.rmtree(\"build/coverage\")\n commands = '''\nscons -uij32 --optimization=coverage controller/cplusplus_test\nlcov --base-directory build/coverage --directory build/coverage -c -o build/coverage/controller_test.info\ngenhtml -o build/coverage/controller/test_coverage -t test --num-spaces 4 build/coverage/controller_test.info\n'''\n for cmd in commands.splitlines():\n cmd_args = cmd.split()\n if (len(cmd_args) == 0):\n continue\n cmd = cmd_args[0]\n cmd_path = find_executable(cmd)\n if not cmd_path:\n continue\n pid = os.fork()\n if pid == 0:\n # Avoid stdout buffering by execing command into child process.\n os.execv(cmd_path, cmd_args)\n os.waitpid(pid, 0)","def benchmark_selection(self):\n return self._benchmark_selection","def register_benchmarks(directory=None):\n dirs = places_to_look() if directory is None else [directory]\n for directory in dirs:\n with os.scandir(directory) as scan:\n for entry in scan:\n filename = entry.name\n if (\n filename.startswith(\".\")\n or not entry.is_file()\n or not filename.endswith(\".py\")\n ):\n continue\n if (\n filename.startswith(\"benchmark\")\n or filename.endswith(\"benchmark.py\")\n or filename.endswith(\"benchmarks.py\")\n ):\n import_path(f\"{directory}/{filename}\")","def getBuildSets():","def generate_cobertura_xml(self, coverage_data):\n\n dom_impl = minidom.getDOMImplementation()\n doctype = dom_impl.createDocumentType(\"coverage\", None,\n \"http://cobertura.sourceforge.net/xml/coverage-03.dtd\")\n document = dom_impl.createDocument(None, \"coverage\", doctype)\n root = document.documentElement\n summary = coverage_data['summary']\n self._attrs(root, {\n 'branch-rate': self._percent(summary['branches-total'],\n summary['branches-covered']),\n 'branches-covered': str(summary['branches-covered']),\n 'branches-valid': str(summary['branches-total']),\n 'complexity': '0',\n 'line-rate': self._percent(summary['lines-total'],\n summary['lines-covered']),\n 'lines-valid': str(summary['lines-total']),\n 'timestamp': coverage_data['timestamp'],\n 'version': '1.9'\n })\n\n sources = self._el(document, 'sources', {})\n source = self._el(document, 'source', {})\n source.appendChild(document.createTextNode(self.base_dir))\n sources.appendChild(source)\n\n root.appendChild(sources)\n\n packages_el = self._el(document, 'packages', {})\n\n packages = coverage_data['packages']\n for package_name, package_data in list(packages.items()):\n package_el = self._el(document, 'package', {\n 'line-rate': package_data['line-rate'],\n 'branch-rate': package_data['branch-rate'],\n 'name': package_name\n })\n classes_el = self._el(document, 'classes', {})\n for class_name, class_data in list(package_data['classes'].items()):\n class_el = self._el(document, 'class', {\n 'branch-rate': self._percent(class_data['branches-total'],\n class_data['branches-covered']),\n 'complexity': '0',\n 'filename': class_name,\n 'line-rate': self._percent(class_data['lines-total'],\n class_data['lines-covered']),\n 'name': class_data['name']\n })\n\n # Process methods\n methods_el = self._el(document, 'methods', {})\n for method_name, hits in list(class_data['methods'].items()):\n method_el = self._el(document, 'method', {\n 'name': method_name,\n 'signature' : '',\n 'hits': hits\n })\n methods_el.appendChild(method_el)\n\n # Process lines\n lines_el = self._el(document, 'lines', {})\n lines = list(class_data['lines'].keys())\n lines.sort()\n for line_number in lines:\n line_el = self._el(document, 'line', {\n 'branch': class_data['lines'][line_number]['branch'],\n 'hits': str(class_data['lines'][line_number]['hits']),\n 'number': str(line_number)\n })\n if class_data['lines'][line_number]['branch'] == 'true':\n total = int(class_data['lines'][line_number]['branches-total'])\n covered = int(class_data['lines'][line_number]['branches-covered'])\n percentage = int((covered * 100.0) / total)\n line_el.setAttribute('condition-coverage',\n '{0}% ({1}/{2})'.format(\n percentage, covered, total))\n lines_el.appendChild(line_el)\n\n class_el.appendChild(methods_el)\n class_el.appendChild(lines_el)\n classes_el.appendChild(class_el)\n package_el.appendChild(classes_el)\n packages_el.appendChild(package_el)\n root.appendChild(packages_el)\n\n return document.toprettyxml()","def list(self, request):\n urls = {\n 'msg': 'Must use bulk_by_sample to get SCCmec Coverages',\n }\n\n return Response(urls)","def getCoverage(self, measurementsPerCell=None):\n\t\tcoverage = set()\n\n\t\tif measurementsPerCell is None:\n\t\t\tmeasurementsPerCell = self._binCapacity\n\n\t\tfor mBin in self._measurementBins.values():\n\n\t\t\t# Only take as many measurements as are available\n\t\t\tbound = min(measurementsPerCell, len(mBin))\n\t\t\tfor m in mBin[:bound]:\n\t\t\t\tcoverage.add(m.id)\n\n\t\treturn coverage","def count_benchmarks():\n return len(setup_storage().fetch_benchmark({}))","def get_benchmark(client):\n r = client.get(config.API_PATH() + '/benchmarks')\n benchmarks = json.loads(r.data)\n return benchmarks['benchmarks'][0]['id']","def filter_out(self, skip):\n benchmarks = super(Benchmarks, self).__new__(self.__class__)\n benchmarks._conf = self._conf\n benchmarks._benchmark_dir = self._benchmark_dir\n benchmarks._all_benchmarks = self._all_benchmarks\n\n selected_idx = {}\n\n for name, benchmark in self.items():\n if name not in skip:\n benchmarks[name] = benchmark\n if name in self._benchmark_selection:\n selected_idx[name] = self._benchmark_selection[name]\n\n benchmarks._benchmark_selection = selected_idx\n\n return benchmarks","def get_coverage(self):\n if len(self) == 1:\n return self.subacqs[0].get_coverage()\n return np.array([self.subacqs[i].get_coverage() for i in range(len(self))])","def get_cases(test_suite, test_name_regex):\n cases = []\n for test_case_name in dir(test_suite):\n test_case = getattr(test_suite, test_case_name)\n if callable(test_case) and re.match(test_name_regex, test_case_name):\n cases.append(test_case_name)\n\n return cases","def save(self):\n path = self.get_benchmark_file_path(self._conf.results_dir)\n util.write_json(path, self._all_benchmarks, self.api_version)","def get_covid_stats_for_all_states():\n url = \"https://corona.lmao.ninja/v2/states/\"\n response = requests.get(url)\n data = response.json()\n\n states = []\n\n for state in data:\n stateName = state[\"state\"]\n\n cases = state[\"cases\"]\n todaysCases = state[\"todayCases\"]\n deaths = state[\"deaths\"]\n todayDeaths = state[\"todayDeaths\"]\n recovered = state[\"recovered\"]\n activeCases = state[\"active\"]\n tests = state[\"tests\"]\n casesPerMil = state[\"casesPerOneMillion\"]\n deathsPerMil = state[\"deathsPerOneMillion\"]\n testsPerMil = state[\"testsPerOneMillion\"]\n\n population = state[\"population\"]\n\n if population != 0:\n millionsOfPop = population / 1000000.0\n activeCasesPerMil = activeCases / millionsOfPop\n color = get_color(activeCasesPerMil)\n states.append(\n StateStats(\n stateName,\n cases,\n todaysCases,\n activeCases,\n casesPerMil,\n deaths,\n todayDeaths,\n deathsPerMil,\n recovered,\n tests,\n testsPerMil,\n color,\n )\n )\n else:\n states.append(\n StateStats(\n stateName,\n cases,\n todaysCases,\n activeCases,\n casesPerMil,\n deaths,\n todayDeaths,\n deathsPerMil,\n recovered,\n tests,\n testsPerMil,\n )\n )\n\n return states","def list_test_cases(program):\n\n return list(INFO[program].test_cases)","def ballot_get_all_contests():\r\n all_contests = []\r\n result = ballot_list_contests(limit=100000000).get('result')\r\n if result:\r\n contests = batch('ballot_get_contest_by_id', [[r] for r in result]).get('result')\r\n if contests:\r\n all_contests = contests\r\n\r\n return all_contests","def comports(include_links=False):\n return list(iterate_comports())","def Run(benchmark_spec: bm_spec.BenchmarkSpec) -> List[sample.Sample]:\n discovery_duration = benchmark_spec.data_discovery_service.DiscoverData()\n return [\n sample.Sample('data_discovery_duration', discovery_duration, 'seconds',\n benchmark_spec.data_discovery_service.GetMetadata())]","def get_codecoolers(cls):\n return cls.codecoolers","def tests(c):\n results = [test(c, i) for i, test_path in enumerate(TEST_PATHS)]\n print('\\n\\n\\n############## SUMMARY ##############')\n for i, test_path in enumerate(TEST_PATHS):\n print(i, test_path, 'PASSED' if result[i] == 0 else 'FAILED')","def find_coverage_report(folder, exclude=None, filter_out='.*conda.*'):\r\n regexp = re.compile('data_file=([0-9a-zA-Z_]+)')\r\n regcov = re.compile(\r\n '<h1>Coveragereport:<spanclass=.?pc_cov.?>([0-9]+)%</span>')\r\n regout = re.compile(filter_out) if filter_out else None\r\n covs = {}\r\n subfold = os.listdir(folder)\r\n for sub in subfold:\r\n if exclude is not None and sub in exclude:\r\n continue\r\n full = os.path.join(folder, sub)\r\n keep = []\r\n nn = None\r\n cov = None\r\n for it in explore_folder_iterfile(full):\r\n name = os.path.split(it)[-1]\r\n dt = os.stat(full).st_mtime\r\n if name == 'index.html':\r\n with open(it, 'r') as f:\r\n htd = f.read().replace('\\n', '').replace('\\r', '').replace(' ', '')\r\n cont = regcov.findall(htd)\r\n if len(cont) > 0:\r\n cov = cont[0]\r\n if name == 'covlog.txt':\r\n with open(it, 'r') as f:\r\n logd = f.read()\r\n cont = regexp.findall(logd)\r\n if len(cont) > 0:\r\n nn = cont[0]\r\n if name == '.coverage':\r\n keep.append((dt, it))\r\n if len(keep) == 0:\r\n continue\r\n mx = max(keep)\r\n if regout is not None and regout.search(nn):\r\n continue\r\n covs[sub] = (mx[-1], nn, cov)\r\n return covs","def coverage(context):\n context.run(\" \".join([\n \"python -m pytest\",\n \"--cov=%s\" % PACKAGE_NAME,\n \"--cov-report html\",\n \"--cov-branch\",\n \"--cov-fail-under=75\"\n ]))","def covwatch(context):\n context.run(\" \".join([\n \"python -m pytest\",\n \"--cov=%s\" % PACKAGE_NAME,\n \"--cov-branch\",\n \"--cov-fail-under=75\"\n ]))","def getAllBuilders(self):\n names = self.status.getBuilderNames(categories=self.categories)\n builders = [self.status.getBuilder(n) for n in names]\n return builders","def get_all_connected_zi_instruments():\n from qcodes.station import Station\n if Station.default is not None:\n all_inst = Station.default.components\n else:\n from pycqed.instrument_drivers.instrument import Instrument\n all_inst = Instrument._all_instruments\n return [inst for inst in all_inst.values()\n if inst.get_idn().get('vendor', '') in\n ['ZurichInstruments', 'Zurich Instruments']]","def _parse_suite(\n self, results: dict, extra_tags: dict = None\n ) -> List[BenchmarkResult]:\n # all results share a batch id\n batch_id = uuid.uuid4().hex\n\n parsed_results = []\n for result in results[\"benchmarks\"]:\n result_parsed = self._parse_benchmark(\n result=GoogleBenchmark(**result),\n batch_id=batch_id,\n extra_tags=extra_tags,\n )\n parsed_results.append(result_parsed)\n\n return parsed_results","def clist() -> None:\n files = os.listdir(os.path.join(os.getcwd(), \"apis\"))\n files.remove(\"__init__.py\")\n files = [f.replace(\".py\", \"\") for f in files]\n print(files)","def cov(test_class):\n if test_class == 'all':\n tests = unittest.TestLoader().discover('project/tests')\n else:\n # note, test module must be imported above, doing lazily for now\n test_module = globals()[test_class]\n tests = unittest.TestLoader().loadTestsFromTestCase(test_module)\n result = unittest.TextTestRunner(verbosity=2).run(tests)\n if result.wasSuccessful():\n COV.stop()\n COV.save()\n print('Coverage Summary:')\n COV.report()\n basedir = os.path.abspath(os.path.dirname(__file__))\n covdir = os.path.join(basedir, 'tmp/coverage')\n COV.html_report(directory=covdir)\n print('HTML version: file://%s/index.html' % covdir)\n COV.erase()\n return 0\n return 1","def get_backend_coverage():\n\n onnx_coverage = {}\n experimental_op = set()\n for handler in BackendHandler.__subclasses__():\n handler.check_cls()\n\n versions = handler.get_versions()\n domain = handler.DOMAIN\n if getattr(handler, \"EXPERIMENTAL\", False):\n experimental_op.add(handler.ONNX_OP)\n _update_coverage(onnx_coverage, domain, handler.ONNX_OP, versions)\n return onnx_coverage, experimental_op","def __benchmark__(cls):\n results = []\n used_argsets = []\n for args in cls.arguments:\n used_argsets.append(args)\n # for each given argument\n for method in cls.get_methods():\n # append an empty list for the results with this argument\n method_results = []\n # for each repetition\n for n in xrange(cls.repetitions):\n # append the results to the list for this argument set\n trial_results = cls._trial(method, args)\n method_results.append(trial_results)\n # append a Result to `results`.\n results.append(Result(method, args, method_results))\n return results, used_argsets","def get_benchmark_aggregated_cov_df(coverage_dict, benchmark):\n dict_to_transform = {'fuzzer': [], 'aggregated_edges_covered': []}\n for key_pair, covered_regions in coverage_dict.items():\n current_fuzzer, current_benchmark = key_pair.split()\n if current_benchmark == benchmark:\n dict_to_transform['fuzzer'].append(current_fuzzer)\n dict_to_transform['aggregated_edges_covered'].append(\n len(covered_regions))\n return pd.DataFrame(dict_to_transform)","def list_ciphers():\n global AVAILABLE_CIPHERS\n print(\"[!] Available ciphers: \")\n for i in range(len(AVAILABLE_CIPHERS)):\n print(\" ----> %s.%s\"%(i+1, AVAILABLE_CIPHERS[i]))\n exit()","def print_scenario(benchmarks, name):\n # Remember, the first entry in the times array is an empty object.\n\n benchmarks = sorted(benchmarks, key = benchmark_sort_key)\n for benchmark in benchmarks:\n block_times = benchmark[\"times\"][2][\"block_times\"] # list of floats\n it = iter(block_times)\n for start in it:\n end = next(it)\n\n print(\"%s , %.3f \" % (benchmark[\"label\"], float(end) - float(start)))","def _transform_results(self) -> List[BenchmarkResult]:\n with open(self.result_file, \"r\") as f:\n raw_results = json.load(f)\n\n parsed_results = []\n for suite in raw_results[\"suites\"]:\n parsed_results += self._parse_suite(\n results=suite,\n extra_tags={\"suite\": suite[\"name\"], \"source\": \"cpp-micro\"},\n )\n\n return parsed_results"],"string":"[\n \"def get_all_benchmarks():\\n all_benchmarks = []\\n for benchmark in os.listdir(BENCHMARKS_DIR):\\n benchmark_path = os.path.join(BENCHMARKS_DIR, benchmark)\\n if os.path.isfile(os.path.join(benchmark_path, 'benchmark.yaml')):\\n all_benchmarks.append(benchmark)\\n return sorted(all_benchmarks)\",\n \"def run_all(self):\\n runs = []\\n for run in self.benchmarks:\\n run.start()\\n run.wait()\\n runs.append(run.metadata)\\n return runs\",\n \"def list(self, classes):\\n\\n def add(benchmarks, parts, flags, exclude):\\n if (\\n flags[\\\"language\\\"] != \\\"C++\\\"\\n and flags[\\\"language\\\"] != \\\"Java\\\"\\n and \\\"--drop-caches=true\\\" not in parts\\n ):\\n parts.append(\\\"--drop-caches=true\\\")\\n command = \\\" \\\".join(parts)\\n if command not in exclude:\\n benchmarks.append({\\\"command\\\": command, \\\"flags\\\": flags})\\n\\n benchmarks = []\\n for name, benchmark in classes.items():\\n if name.startswith(\\\"example\\\"):\\n continue\\n\\n instance, parts = benchmark(), [name]\\n\\n exclude = getattr(benchmark, \\\"exclude\\\", [])\\n if \\\"source\\\" in getattr(benchmark, \\\"arguments\\\", []):\\n parts.append(\\\"ALL\\\")\\n\\n iterations = getattr(instance, \\\"iterations\\\", 3)\\n parts.append(f\\\"--iterations={iterations}\\\")\\n\\n if instance.cases:\\n parts.append(\\\"--all=true\\\")\\n\\n flags = getattr(instance, \\\"flags\\\", {})\\n\\n if getattr(instance, \\\"r_only\\\", False):\\n flags[\\\"language\\\"] = \\\"R\\\"\\n add(benchmarks, parts, flags, exclude)\\n else:\\n if \\\"language\\\" not in flags:\\n flags[\\\"language\\\"] = \\\"Python\\\"\\n add(benchmarks, parts, flags, exclude)\\n\\n if hasattr(instance, \\\"r_name\\\"):\\n flags_ = flags.copy()\\n flags_[\\\"language\\\"] = \\\"R\\\"\\n parts.append(\\\"--language=R\\\")\\n add(benchmarks, parts, flags_, exclude)\\n\\n return sorted(benchmarks, key=lambda k: k[\\\"command\\\"])\",\n \"def coverage_files() -> Iterable[str]:\\n packs_path = CONTENT_PATH / \\\"Packs\\\"\\n for cov_path in packs_path.glob(\\\"*/Integrations/*/.coverage\\\"):\\n yield str(cov_path)\\n for cov_path in packs_path.glob(\\\"*/Scripts/*/.coverage\\\"):\\n yield str(cov_path)\",\n \"def benchmark(self):\\n nsites = []\\n for m in self.methods:\\n for name, structure in self.test_structures.items():\\n cns = []\\n if self.unique_sites:\\n es = SpacegroupAnalyzer(structure).get_symmetrized_structure().equivalent_sites\\n sites = [structure.index(x[0]) for x in es]\\n else:\\n sites = range(len(structure))\\n\\n for key, val in self.hi.items():\\n if name == key:\\n for j in sites:\\n if isinstance(m, NearNeighbors):\\n tmpcn = m.get_cn_dict(structure, j, self.use_weights)\\n else:\\n tmpcn = m.compute(structure, j)\\n if tmpcn == \\\"null\\\":\\n continue\\n if self.nround:\\n self._roundcns(tmpcn, self.nround)\\n cns.append((structure[j].species_string, tmpcn))\\n if self.cation_anion:\\n for mat, cat in self.cations.items():\\n if (name == mat) and cat:\\n cns = self._popel(cns, cat)\\n elif self.anion_cation:\\n for mat, an in self.anions.items():\\n if name == mat:\\n cns = self._popel(cns, an)\\n m._cns[name] = cns\\n nsites.append(len(cns))\\n self.nsites = max(nsites)\",\n \"def tests_generator(self):\\n cb_bin = os.path.join(bin_path, 'compilebench')\\n cmd_list = [\\n (\\\"Initial Create/Compile/Read Compiled Tree\\\", \\\"{0} -D {1} -i 10 --makej -s {2}\\\"),\\n ]\\n\\n tests = []\\n for idx, (desc, cmd) in enumerate(cmd_list):\\n test_name = \\\"compile_bench_{0}_{1}\\\".format(idx + 1, to_safe_name(desc))\\n test = TestProfile(\\n name=test_name,\\n desc=desc,\\n test_path=self.test_path,\\n bin_path=bin_path,\\n command=cmd.format(cb_bin, self.test_path, bin_path))\\n tests.append(test)\\n return tests\",\n \"def get_bug_benchmarks():\\n return [\\n benchmark for benchmark in get_all_benchmarks()\\n if get_type(benchmark) == BenchmarkType.BUG.value\\n ]\",\n \"def test_multiple_coverages(self):\\n\\n params_82 = {\\n 'ReQuEsT': \\\"DescribeCoverage\\\",\\n 'SeRvIcE': \\\"WCS\\\",\\n \\\"BOGUS\\\": \\\"SSS\\\",\\n 'Version': \\\"1.0.0\\\",\\n \\\"COVERAGE\\\": \\\",\\\".join(self.names)\\n }\\n response = self.query_server(params_82)\\n soup = BeautifulSoup(response.text, 'xml')\\n self.assertTrue(\\n len(soup.find_all('CoverageOffering')) == len(self.names),\\n msg=\\\"If multiple valid coverages are submitted with a DescribeCoverage request, all requested coverages should be returned.\\\"\\n )\\n for elem in soup.find_all('CoverageOffering'):\\n self.assertTrue(\\n elem.find('name').text in self.names,\\n msg=\\\"All requested coverages should be returned in a DescribeCoverage request.\\\")\",\n \"def benchmark(self):\\n logger.info(self.benchmark.__doc__)\\n return self.run(self.benchmark_profile())\",\n \"def cov():\\n cov = coverage.coverage(branch=True, include='project/*')\\n cov.start()\\n tests = unittest.TestLoader().discover('tests')\\n unittest.TextTestRunner(verbosity=2).run(tests)\\n cov.stop()\\n cov.save()\\n print('Coverage Summary:')\\n cov.report()\\n basedir = os.path.abspath(os.path.dirname(__file__))\\n covdir = os.path.join(basedir, 'tmp/coverage')\\n cov.html_report(directory=covdir)\\n print('HTML version: file://%s/index.html' % covdir)\\n cov.erase()\",\n \"def RunUnitTests():\\n if FLAGS.test_targets:\\n tests = FLAGS.test_targets\\n else:\\n tests = shell_interfaces.GetStdout(\\n 'bazel query kind(\\\"cc_test\\\", ...)').split()\\n\\n # Run coverage, joining all data into one file.\\n subprocess.check_call(['bazel', 'coverage', '--instrument_test_targets',\\n '--experimental_cc_coverage',\\n '--combined_report=lcov',\\n ('--coverage_report_generator=@bazel_tools//tools/tes'\\n 't/CoverageOutputGenerator/java/com/google/devtools/'\\n 'coverageoutputgenerator:Main')] + tests)\",\n \"def _disc_benchmarks(cls, conf, repo, environments, commit_hashes, check):\\n root = conf.benchmark_dir\\n\\n cls.check_tree(root)\\n\\n if len(environments) == 0:\\n raise util.UserError(\\\"No available environments\\\")\\n\\n # Try several different commits:\\n #\\n # - First of commit_hashes provided\\n # - Tips of branches from configuration file\\n # - Rest of the commit_hashes\\n #\\n\\n def iter_hashes():\\n for h in commit_hashes[:1]:\\n yield h\\n for branch in conf.branches:\\n try:\\n yield repo.get_hash_from_name(branch)\\n except NoSuchNameError:\\n continue\\n for h in commit_hashes[1:]:\\n yield h\\n\\n def iter_unique(iter):\\n seen = set()\\n for item in iter:\\n if item not in seen:\\n seen.add(item)\\n yield item\\n\\n try_hashes = iter_unique(iter_hashes())\\n\\n log.info(\\\"Discovering benchmarks\\\")\\n with log.indent():\\n last_err = None\\n for env, commit_hash in itertools.product(environments, try_hashes):\\n env.create()\\n\\n if last_err is not None:\\n log.warning(\\\"Failed: trying different commit/environment\\\")\\n\\n result_dir = tempfile.mkdtemp()\\n try:\\n env.install_project(conf, repo, commit_hash)\\n\\n env_vars = dict(os.environ)\\n env_vars.update(env.env_vars)\\n\\n result_file = os.path.join(result_dir, 'result.json')\\n env.run(\\n [runner.BENCHMARK_RUN_SCRIPT, 'discover',\\n os.path.abspath(root),\\n os.path.abspath(result_file)],\\n cwd=result_dir,\\n env=env_vars,\\n dots=False)\\n\\n try:\\n with open(result_file, 'r') as fp:\\n benchmarks = json.load(fp)\\n except (IOError, ValueError):\\n log.error(\\\"Invalid discovery output\\\")\\n raise util.UserError()\\n\\n break\\n except (util.UserError, util.ProcessError) as err:\\n last_err = err\\n continue\\n except KeyboardInterrupt:\\n raise util.UserError(\\\"Interrupted.\\\")\\n finally:\\n util.long_path_rmtree(result_dir)\\n else:\\n raise util.UserError(\\\"Failed to build the project and import the benchmark suite.\\\")\\n\\n if check:\\n log.info(\\\"Checking benchmarks\\\")\\n with log.indent():\\n result_dir = tempfile.mkdtemp()\\n try:\\n out, err, retcode = env.run(\\n [runner.BENCHMARK_RUN_SCRIPT, 'check',\\n os.path.abspath(root)],\\n cwd=result_dir,\\n dots=False,\\n env=env_vars,\\n valid_return_codes=None,\\n return_stderr=True,\\n redirect_stderr=True)\\n finally:\\n util.long_path_rmtree(result_dir)\\n\\n out = out.strip()\\n if retcode == 0:\\n if out:\\n log.info(out)\\n log.info(\\\"No problems found.\\\")\\n else:\\n if out:\\n log.error(out)\\n raise util.UserError(\\\"Benchmark suite check failed.\\\")\\n\\n return benchmarks\",\n \"def cov():\\n cov = coverage.coverage(\\n branch=True,\\n include='project/*',\\n omit=\\\"*/__init__.py\\\"\\n )\\n cov.start()\\n tests = unittest.TestLoader().discover('tests')\\n unittest.TextTestRunner(verbosity=2).run(tests)\\n cov.stop()\\n cov.save()\\n print 'Coverage Summary:'\\n cov.report()\\n basedir = os.path.abspath(os.path.dirname(__file__))\\n covdir = os.path.join(basedir, 'tmp/coverage')\\n cov.html_report(directory=covdir)\\n print('HTML version: file://%s/index.html' % covdir)\\n cov.erase()\",\n \"def delete_all_benchmarks(self, namespace=\\\"benchmark-operator\\\"):\\n all_benchmarks = self.crd_client.list_namespaced_custom_object(\\n group=\\\"ripsaw.cloudbulldozer.io\\\", version=\\\"v1alpha1\\\", namespace=namespace, plural=\\\"benchmarks\\\"\\n )\\n\\n _ = [\\n self.delete_benchmark(benchmark[\\\"metadata\\\"][\\\"name\\\"], namespace)\\n for benchmark in all_benchmarks.get(\\\"items\\\", [])\\n ]\",\n \"def task_coverage():\\n return {\\n 'actions': ['py.test --cov nikola --cov-report term-missing tests/'],\\n 'verbosity': 2,\\n }\",\n \"def list(self):\\n print \\\"\\\\nAvailable Test Cases\\\"\\n print \\\"====================\\\"\\n for case in self.cases:\\n print case.__name__\",\n \"def gather_tests(self):\\n rosie_tests_dir = os.path.join(cp_tests_dir(),\\n \\\"circuitpython\\\",\\n \\\"rosie_tests\\\")\\n test_files = []\\n for test in os.scandir(rosie_tests_dir):\\n # TODO: implement exclusions by board\\n if test.path.endswith(\\\".py\\\"):\\n test_files.append(TestObject(test.path))\\n\\n return test_files\",\n \"def coverage(session):\\n session.install(\\\"coverage[toml]\\\", \\\"codecov\\\")\\n session.run(\\\"coverage\\\", \\\"xml\\\", \\\"--fail-under=0\\\")\\n session.run(\\\"codecov\\\", *session.posargs)\",\n \"def test_list_benchmarks(client):\\n # The benchmark listing contains one element (independently of whether the\\n # user is logged in or not).\\n r = client.get(config.API_PATH() + '/workflows')\\n assert r.status_code == 200\\n doc = r.json\\n assert len(doc[labels.WORKFLOW_LIST]) == 1\\n # Create user and the request header that contains the API key for the\\n # logged in user.\\n _, token = create_user(client, '0000')\\n headers = {HEADER_TOKEN: token}\\n r = client.get(config.API_PATH() + '/workflows', headers=headers)\\n assert r.status_code == 200\\n doc = r.json\\n assert len(doc[labels.WORKFLOW_LIST]) == 1\",\n \"def generate_coverage_reports(project, output_dir=None):\\n outdir = output_dir or os.path.join(project.sdk_directory, \\\"coverage-results\\\")\\n sdir = project.path\\n # Make sure output dir exists and is empty:\\n qisys.sh.rm(outdir)\\n qisys.sh.mkdir(outdir, recursive=True)\\n formats = {\\\"xml\\\": [\\\"--xml\\\"],\\n \\\"html\\\": [\\\"--html\\\", \\\"--html-details\\\"]}\\n for fmt, opts in formats.items():\\n base_report = os.path.join(outdir, project.name + \\\".\\\" + fmt)\\n cmd = [\\\"gcovr\\\",\\n \\\"--root\\\", sdir,\\n \\\"--exclude\\\", \\\".*test.*\\\",\\n \\\"--exclude\\\", \\\".*external.*\\\",\\n \\\"--exclude\\\", \\\".*example.*\\\"] + opts + \\\\\\n [\\\"--output\\\", base_report]\\n qisys.command.call(cmd, cwd=sdir, quiet=True)\\n ui.info(ui.green, \\\"*\\\", ui.reset, \\\"Generated\\\", fmt.upper(),\\n \\\"coverage report in\\\", ui.reset, ui.bold, base_report)\",\n \"def get_methods(cls):\\n # get all the methods that have the _benchmark_this flag\\n for method in (getattr(cls, m) for m in dir(cls)):\\n if hasattr(method, \\\"_benchmark_this\\\"):\\n yield method\",\n \"def evaluate_benchmarks(self):\\n\\n # iterate over replicates\\n results = {}\\n for replicate_id, replicate in self.replicates:\\n\\n # evaluate benchmark for current replicate\\n bmark = SimulationBenchmark(replicate.copy(),\\n graph=self.graphs[replicate_id],\\n **self.params)\\n\\n # store results\\n results[replicate_id] = dict(\\n\\n labels_MAE=bmark.scores['labels'].MAE,\\n level_only_MAE=bmark.scores['level_only'].MAE,\\n spatial_only_MAE=bmark.scores['spatial_only'].MAE,\\n community_MAE=bmark.scores['labels_comm'].MAE,\\n\\n labels_PCT=bmark.scores['labels'].percent_correct,\\n level_only_PCT=bmark.scores['level_only'].percent_correct,\\n spatial_only_PCT=bmark.scores['spatial_only'].percent_correct,\\n community_PCT=bmark.scores['labels_comm'].percent_correct)\\n\\n # compile dataframe\\n results = pd.DataFrame.from_dict(results, orient='index')\\n results.index.set_names(self.multiindex, inplace=True)\\n\\n return results\",\n \"def test_get_benchmark_methods_filter(self):\\n config = mock.Mock()\\n config.workspace = 'workspace'\\n config.benchmark_method_patterns = ['new_foo.BenchmarkClass.filter:bench.*']\\n benchmark_runner = benchmark.BenchmarkRunner(config)\\n\\n mock_benchmark_class = mock.Mock()\\n mock_benchmark_class.benchmark_method_1 = 'foo'\\n\\n mock_module = mock.Mock()\\n sys.modules['new_foo'] = mock_module\\n mock_module.BenchmarkClass.return_value = mock_benchmark_class\\n\\n methods = benchmark_runner._get_benchmark_methods()\\n\\n self.assertEqual(1, len(methods))\\n self.assertEqual('new_foo.BenchmarkClass.benchmark_method_1', methods[0])\",\n \"def get_benchmark_requirements(cls):\\n pass\",\n \"def exclude_non_cpp(benchmarks):\\n return [benchmark for benchmark in benchmarks if is_cpp(benchmark)]\",\n \"def get_all_stats():\\n\\n return get_component(CachingPackage.COMPONENT_NAME).get_all_stats()\",\n \"def run_coverage(session):\\n set_environment_variables(PYBAMM_ENV, session=session)\\n session.run_always(\\\"pip\\\", \\\"install\\\", \\\"coverage\\\")\\n session.run_always(\\\"pip\\\", \\\"install\\\", \\\"-e\\\", \\\".[all]\\\")\\n if sys.platform != \\\"win32\\\":\\n session.run_always(\\\"pip\\\", \\\"install\\\", \\\"-e\\\", \\\".[odes]\\\")\\n session.run_always(\\\"pip\\\", \\\"install\\\", \\\"-e\\\", \\\".[jax]\\\")\\n session.run(\\\"coverage\\\", \\\"run\\\", \\\"--rcfile=.coveragerc\\\", \\\"run-tests.py\\\", \\\"--nosub\\\")\\n session.run(\\\"coverage\\\", \\\"combine\\\")\\n session.run(\\\"coverage\\\", \\\"xml\\\")\",\n \"def get(self, request, format=None):\\n benchmarkmodels = BenchmarkModel.objects.all()\\n serializer = BenchmarkModelListSerializer(benchmarkmodels, many=True)\\n return Response(serializer.data)\",\n \"def get_list(cls, suite_model) -> list:\\n suites = []\\n for s in cls.SUITES:\\n s[\\\"tests\\\"] = cls._get_tests(s)\\n s[\\\"approxRunTime\\\"] = cls._get_average_run_time(suite_model)\\n suites.append(s)\\n return suites\",\n \"def test_get_benchmark_methods_exact_match(self):\\n config = mock.Mock()\\n config.workspace = 'workspace'\\n config.benchmark_method_patterns = [\\n 'new_foo.BenchmarkClass.benchmark_method_1',\\n 'new_foo.BenchmarkClass.benchmark_method_2']\\n benchmark_runner = benchmark.BenchmarkRunner(config)\\n\\n methods = benchmark_runner._get_benchmark_methods()\\n self.assertEqual(['new_foo.BenchmarkClass.benchmark_method_1',\\n 'new_foo.BenchmarkClass.benchmark_method_2'], methods)\",\n \"def get_tests():\\n # tests = ['test_build_gaussian_pyramid_random', 'test_build_gaussian_pyramid_static', 'test_build_laplacian_pyramid_random', 'test_build_laplacian_pyramid_static', 'test_laplacian_to_image', 'test_render_pyramid_random', 'test_render_pyramid_static']\\n # return [tester.TestEx3(method) for method in tests]\\n return [tester.TestEx3(method) for method in dir(tester.TestEx3) if method.startswith('test')]\",\n \"def cov():\\n tests = unittest.TestLoader().discover('tests')\\n result = unittest.TextTestRunner(verbosity=1).run(tests)\\n if result.wasSuccessful():\\n COV.stop()\\n COV.save()\\n print('Coverage Summary:')\\n COV.report()\\n basedir = os.path.abspath(os.path.dirname(__file__))\\n covdir = os.path.join(basedir, 'tmp/coverage')\\n COV.html_report(directory=covdir)\\n print('HTML version: file://%s/index.html' % covdir)\\n COV.erase()\\n return 0\\n return 1\",\n \"def get_currencies(self) -> list:\\n return self.client.currencies.get_all()\",\n \"def get_tests(self):\\n subtests = itertools.chain(*(s.get_tests() for s in self.suites.values()))\\n tt = [t for t in itertools.chain(self.tests,subtests)]\\n return tt\",\n \"def cov():\\n tests = unittest.TestLoader().discover('project/tests')\\n result = unittest.TextTestRunner(verbosity=2).run(tests)\\n if result.wasSuccessful():\\n COV.stop()\\n COV.save()\\n print('Coverage Summary:')\\n COV.report()\\n basedir = os.path.abspath(os.path.dirname(__file__))\\n covdir = os.path.join(basedir, 'tmp/coverage')\\n COV.html_report(directory=covdir)\\n print('HTML version: file://%s/index.html' % covdir)\\n COV.erase()\\n return 0\\n return 1\",\n \"def _run():\\n subprocess.check_call(\\n [\\n \\\"tools/bazel\\\",\\n \\\"build\\\",\\n \\\"-c\\\",\\n \\\"opt\\\",\\n \\\"test/core/memory_usage/memory_usage_test\\\",\\n ]\\n )\\n ret = {}\\n for name, benchmark_args in _BENCHMARKS.items():\\n for scenario, extra_args in _SCENARIOS.items():\\n # TODO(chenancy) Remove when minstack is implemented for channel\\n if name == \\\"channel\\\" and scenario == \\\"minstack\\\":\\n continue\\n try:\\n output = subprocess.check_output(\\n [\\n \\\"bazel-bin/test/core/memory_usage/memory_usage_test\\\",\\n ]\\n + benchmark_args\\n + extra_args\\n )\\n except subprocess.CalledProcessError as e:\\n print(\\\"Error running benchmark:\\\", e)\\n continue\\n for line in output.splitlines():\\n for key, (pattern, conversion) in _INTERESTING.items():\\n m = re.match(pattern, line)\\n if m:\\n ret[scenario + \\\": \\\" + key] = conversion(m.group(1))\\n return ret\",\n \"def cov():\\n tests = unittest.TestLoader().discover('project/tests')\\n result = unittest.TextTestRunner(verbosity=2).run(tests)\\n if result.wasSuccessful():\\n COV.stop()\\n COV.save()\\n print('Coverage Summary:')\\n COV.report()\\n COV.html_report()\\n COV.erase()\\n return 0\\n return 1\",\n \"def GetLcovDirectories():\\n dirs = set()\\n for dirname, _, files in os.walk(os.path.join(makani.HOME, 'bazel-bin')):\\n for f in files:\\n if f.endswith('.gcda'):\\n dir_parts = dirname.split(os.sep)\\n for i, p in enumerate(dir_parts):\\n if p == '_objs':\\n dirs.add(os.sep.join(dir_parts[:i+2]))\\n\\n return sorted(dirs)\",\n \"def coverage(ctx):\\n ctx.run(\\\"coverage run --source {PROJECT_NAME} -m pytest\\\".format(PROJECT_NAME=PROJECT_NAME))\\n ctx.run(\\\"coverage report -m\\\")\\n ctx.run(\\\"coverage html\\\")\",\n \"def get_csv(self):\\n all_csvs = [each for each in listdir(self.cur_dir) if each.endswith('.csv')]\\n return all_csvs\",\n \"def __init__(self, conf, benchmarks, regex=None):\\n self._conf = conf\\n self._benchmark_dir = conf.benchmark_dir\\n\\n if not regex:\\n regex = []\\n if isinstance(regex, str):\\n regex = [regex]\\n\\n self._all_benchmarks = {}\\n self._benchmark_selection = {}\\n for benchmark in benchmarks:\\n self._all_benchmarks[benchmark['name']] = benchmark\\n if benchmark['params']:\\n self._benchmark_selection[benchmark['name']] = []\\n for idx, param_set in enumerate(\\n itertools.product(*benchmark['params'])):\\n name = f\\\"{benchmark['name']}({', '.join(param_set)})\\\"\\n if not regex or any(re.search(reg, name) for reg in regex):\\n self[benchmark['name']] = benchmark\\n self._benchmark_selection[benchmark['name']].append(idx)\\n else:\\n self._benchmark_selection[benchmark['name']] = None\\n if not regex or any(re.search(reg, benchmark['name']) for reg in regex):\\n self[benchmark['name']] = benchmark\",\n \"def get_all_casks(self):\",\n \"def collect_cases(self, suite=False):\\n cases = unittest.TestSuite()\\n\\n if suite:\\n test_suites = []\\n for file in os.listdir('.'):\\n if self.suite_path in file:\\n if os.path.isdir(file):\\n test_suites.append(file)\\n\\n for test_suite in test_suites:\\n self._collect_cases(cases, top_dir=test_suite)\\n else:\\n self._collect_cases(cases, top_dir=None)\\n return cases\",\n \"def coverage(session) -> None:\\n session.install(\\\".[test]\\\", \\\"pytest-cov\\\")\\n session.run(\\n \\\"pytest\\\", \\\"-n\\\", \\\"auto\\\", \\\"--cov=./\\\", \\\"--cov-report=xml\\\", *session.posargs\\n )\",\n \"def get_all(self):\\r\\n ret = []\\r\\n for cache_name, stat in self.stats_per_cache.items():\\r\\n ret.append({\\r\\n 'cache_name': cache_name,\\r\\n 'num_hits': len(stat.hit_targets),\\r\\n 'num_misses': len(stat.miss_targets),\\r\\n 'hits': stat.hit_targets,\\r\\n 'misses': stat.miss_targets\\r\\n })\\r\\n return ret\",\n \"def getTestSets():\\n return list(_testsetdict.keys())\",\n \"def suite():\\n\\tts = unittest.TestSuite()\\n\\tfor test_module in __all__:\\n\\t\\tm = importlib.import_module(\\\"pyroclast.test.\\\" + test_module)\\n\\t\\tfor n in dir(m):\\n\\t\\t\\tc = getattr(m, n)\\n\\t\\t\\tif is_test_case(c):\\n\\t\\t\\t\\ts = unittest.TestLoader().loadTestsFromTestCase(c)\\n\\t\\t\\t\\tts.addTests(s)\\n\\treturn ts\",\n \"def time_binaries():\\n print()\\n print(\\\"=== BENCHMARKING ===\\\")\\n with tempfile.NamedTemporaryFile(\\n prefix=\\\"riff-benchmark\\\", suffix=\\\".gitlog\\\"\\n ) as testdata:\\n subprocess.check_call(\\n [\\\"git\\\", \\\"log\\\", \\\"--color=always\\\", \\\"-p\\\", \\\"master\\\"], stdout=testdata\\n )\\n\\n binaries = sorted(glob.glob(os.path.join(BINDIR, \\\"*\\\")), key=natural_keys)\\n\\n # Do riff-current last: https://stackoverflow.com/a/20320940/473672\\n binaries.sort(key=lambda s: s.endswith(\\\"riff-current\\\"))\\n\\n for binary in binaries:\\n print_timings(binary, testdata.name)\\n print_timings(\\\"/bin/cat\\\", testdata.name)\",\n \"def output_benchmark_results(output_dir, ts_agent_list=None, ga_agent=None, title=None, auto_open=True):\\n if (ts_agent_list is None or not all(ts_agent.benchmark for ts_agent in ts_agent_list)) \\\\\\n and (ga_agent is None or not ga_agent.benchmark):\\n raise UserWarning(\\\"agent arguments were None or were not ran in benchmark mode.\\\")\\n\\n if title is None:\\n title = \\\"Benchmark Run {}\\\".format(datetime.datetime.now().strftime(\\\"%Y-%m-%d %H:%M\\\"))\\n\\n output_dir = Path(output_dir)\\n\\n if not output_dir.exists():\\n output_dir.mkdir(parents=True)\\n\\n def compute_stats(lst):\\n return {\\n 'min': round(min(lst)),\\n 'median': round(statistics.median(lst)),\\n 'max': round(max(lst)),\\n 'std': round(statistics.stdev(lst)) if len(lst) > 1 else 0,\\n 'var': round(statistics.variance(lst)) if len(lst) > 1 else 0,\\n 'mean': round(statistics.mean(lst))\\n }\\n\\n # tabu search results\\n if ts_agent_list is not None and all(ts_agent.benchmark for ts_agent in ts_agent_list):\\n _create_ts_plots(ts_agent_list, output_dir)\\n ts_result_makespans = []\\n ts_initial_makespans = []\\n ts_iterations = []\\n for ts_agent in ts_agent_list:\\n ts_result_makespans.append(ts_agent.best_solution.makespan)\\n ts_initial_makespans.append(ts_agent.initial_solution.makespan)\\n ts_iterations.append(ts_agent.benchmark_iterations)\\n\\n ts_result_makespans_stats = compute_stats(ts_result_makespans)\\n ts_initial_makespans_stats = compute_stats(ts_initial_makespans)\\n ts_iterations_stats = compute_stats(ts_iterations)\\n\\n else:\\n ts_result_makespans_stats = None\\n ts_initial_makespans_stats = None\\n ts_iterations_stats = None\\n\\n # genetic algorithm results\\n if ga_agent is not None and ga_agent.benchmark:\\n _create_ga_plots(ga_agent, output_dir)\\n ga_initial_makespans = [sol.makespan for sol in ga_agent.initial_population]\\n ga_result_makespans = [sol.makespan for sol in ga_agent.result_population]\\n\\n ga_initial_makespans_stats = compute_stats(ga_initial_makespans)\\n ga_result_makespans_stats = compute_stats(ga_result_makespans)\\n\\n else:\\n ga_initial_makespans_stats = None\\n ga_result_makespans_stats = None\\n\\n # render template\\n template = template_env.get_template(benchmark_template)\\n rendered_template = template.render(\\n title=title,\\n ts_agent_list=ts_agent_list,\\n ts_initial_makespans_stats=ts_initial_makespans_stats,\\n ts_result_makespans_stats=ts_result_makespans_stats,\\n iterations_per_ts_agent_stats=ts_iterations_stats,\\n output_directory=output_dir.resolve(),\\n ga_agent=ga_agent,\\n ga_initial_makespans_stats=ga_initial_makespans_stats,\\n ga_result_makespans_stats=ga_result_makespans_stats,\\n )\\n\\n # create index.html\\n with open(output_dir / 'index.html', 'w') as output_file:\\n output_file.write(rendered_template)\\n\\n if auto_open:\\n webbrowser.open(f'file://{output_dir.resolve()}/index.html')\",\n \"def sync_all_teams_coverage():\\n teams = Team.objects.all()\\n\\n for team in teams:\\n identifier = team.identifier\\n\\n sync_team_coverage.apply_async(args=(identifier, ))\\n sync_team_cluster_stats.apply_async(args=(identifier, ))\\n sync_team_advisory_stats.apply_async(args=(identifier, ))\",\n \"def get_performance_test_cases(test_suite):\\n return get_cases(test_suite, r'test_perf_')\",\n \"def get(self) -> list:\\n return self.__cogs\",\n \"def get_benchmark_cov_dict(coverage_dict, benchmark):\\n benchmark_cov_dict = {}\\n for key_pair, covered_regions in coverage_dict.items():\\n current_fuzzer, current_benchmark = key_pair.split()\\n if current_benchmark == benchmark:\\n covered_regions_in_set = set()\\n for region in covered_regions:\\n covered_regions_in_set.add(tuple(region))\\n benchmark_cov_dict[current_fuzzer] = covered_regions_in_set\\n return benchmark_cov_dict\",\n \"def generate_figures():\\r\\n # create results directory if necessary\\r\\n try:\\r\\n makedirs(\\\"results\\\")\\r\\n except OSError as e:\\r\\n if e.errno != errno.EEXIST:\\r\\n raise\\r\\n \\r\\n for b in benchmarks:\\r\\n generate_figure(model[b], b)\",\n \"def commands_all():\\n lint()\\n complexity()\\n coverage()\",\n \"def get_project_test_coverage(self) -> None:\\n print_statistics = {}\\n total_number_columns = 0\\n number_columns_without_tests = 0\\n\\n for model_name in self.dbt_tests.keys():\\n columns = self.dbt_tests[model_name]\\n\\n model_number_columns = 0\\n model_columns_without_tests = 0\\n\\n for column in columns:\\n total_number_columns += 1\\n model_number_columns += 1\\n\\n if len(column[\\\"tests\\\"]) == 0:\\n number_columns_without_tests += 1\\n model_columns_without_tests += 1\\n\\n print_statistics[model_name] = self.calculate_coverage_percentage(\\n misses=model_columns_without_tests, total=model_number_columns\\n )\\n\\n print_statistics[\\\"\\\"] = \\\"\\\"\\n print_statistics[\\\"Total\\\"] = self.calculate_coverage_percentage(\\n misses=number_columns_without_tests, total=total_number_columns\\n )\\n\\n self.create_table(\\n title=\\\"Test Coverage\\\",\\n columns=[\\\"Model Name\\\", r\\\"% coverage\\\"],\\n data=print_statistics,\\n )\",\n \"async def getAll():\\n return [cluster.export() for cluster in clusters.get_all()]\",\n \"def collectTests(self, global_ctx):\\n pass\",\n \"def benchmark_profile(self):\\n cb_bin = os.path.join(bin_path, 'compilebench')\\n desc = \\\"benchmark\\\"\\n test_name = \\\"compilebench_{0}\\\".format(to_safe_name(desc))\\n test = TestProfile(\\n name=test_name,\\n desc=desc,\\n test_path=self.test_path,\\n bin_path=bin_path,\\n command=\\\"{0} -D {1} -i 10 --makej\\\".format(cb_bin, self.test_path))\\n\\n return test\",\n \"def get_testbench_specs(self, tb_type: str) -> Dict[str, Any]:\\n return self._specs['testbenches'][tb_type]\",\n \"def __iter__(self):\\n for benchclass in sorted(self.classes.values()):\\n yield benchclass\",\n \"def getShortCircuitTests(self):\\n return self._ShortCircuitTests\",\n \"def get_all_reporters():\\r\\n for ep in iter_entry_points('attest.reporters'):\\r\\n yield ep.name\",\n \"def test_cases(self) -> list[str]:\\n cases = []\\n for t in self._test_cases:\\n if t not in cases:\\n cases.append(t)\\n return cases\",\n \"def __init__(self, benchmarks):\\n self.benchmarks = benchmarks\\n instNum = 0\\n classNum = 0\\n for benchclass in self:\\n benchclass.line = classNum\\n benchclass.instStart = instNum\\n for instance in benchclass: \\n instance.line = instNum\\n instNum += max(instance.maxRuns, 1)\\n benchclass.instEnd = instNum - 1\\n classNum += 1\",\n \"def alltests(opts):\\n \\n print \\\"API Root: %s\\\" % options.apiroot\\n print \\\"Token: %s\\\" % options.token\\n print \\\"Output dir: %s\\\" % options.output\\n print \\\"Running %d%% of tests\\\" % options.percent\\n print\\n \\n # need to use DEPT-001, not ID#\\n coursehistory_tests = [\\n # basic tests:\\n \\\"cis-120\\\", \\\"math-114\\\", \\\"engl-101\\\", \\\"econ-001\\\",\\n # miscellaneously somewhat problematic:\\n \\\"engl-016\\\", \\\"law-205\\\", \\\"hpr-612\\\", \\\"rels-414\\\", \\\"nurs-322\\\",\\n \\\"writ-030\\\", \\\"be-310\\\", \\\"psci-010\\\", \\\"psci-136\\\",\\n # crosslistings:\\n \\\"engl-135\\\", \\\"writ-135\\\", \\\"fnar-264\\\", \\\"cogs-001\\\", \\\"russ-048\\\", \\\"hist-048\\\",\\n # no reviews?:\\n \\\"afam-271\\\", \\\"ames-071\\\", \\\"slav-532\\\", \\\"afam-285\\\", \\\"prtg-213\\\", \\\"slav-533\\\",\\n # errors:\\n \\\"99999\\\", \\\"moo\\\",\\n ]\\n\\n instructor_tests = [\\n # basic tests:\\n \\\"403\\\", \\\"631\\\", \\\"1883\\\", \\\"2217-FERNANDO-C--PEREIRA\\\", \\\"1602-BENJAMIN-PIERCE\\\",\\n # crosslistings:\\n \\\"1034-LYLE-H-UNGAR\\\", \\\"2709-DAVID-P--COMBERG\\\",\\n # miscellaneously somewhat problematic:\\n \\\"1040-DAVID-FOX\\\", \\\"4268-BART-GERARD-C-DE-JONGHE\\\",\\n # the instructors w/ the most sections\\n \\\"1883\\\", \\\"1619\\\", \\\"2869\\\", \\\"942\\\", \\\"1644\\\", \\\"541\\\", \\\"767\\\", \\\"434\\\",\\n # concerned citizens:\\n \\\"1759-MAX-C--CAVITCH\\\", \\\"2824-TIMOTHY-CORRIGAN\\\",\\n \\\"1763-EMILY-R-STEINER\\\", \\\"1624-VALERIE-ROSS\\\",\\n # no reviews?:\\n \\\"416-LUDO-ROCHER\\\", \\\"715-ELIZABETH-ANN-POLLARD\\\", \\\"1094-MARIA-A-COWLES\\\",\\n \\\"1500-ANDREW-GALLIA\\\", \\\"1888-RUSSELL-DILEO\\\",\\n \\\"1450-SORMANE-PEREIRA-GOMES\\\", \\\"2188-HUI-YI-CHEN\\\", \\\"1165-DOMENIC-VITIELLO\\\",\\n \\\"2359-CLAUDIA-CANCINO\\\", \\\"2737-SHEN-WANG\\\", \\\"3229-BERLE-WHITBY\\\",\\n # errors:\\n \\\"99999\\\", \\\"moo\\\",\\n ]\\n\\n dept_tests = [\\n #fast\\n \\\"CSE\\\", \\\"LAW\\\", \\\"ANAT\\\", \\\"KORN\\\", \\\"LATN\\\", \\\"COGS\\\", \\\"MSCI\\\", \\\"GAS\\\",\\n #medium\\n \\\"CIS\\\", \\\"MATH\\\", \\\"FNAR\\\", \\\"ACCT\\\", \\\"FNCE\\\", \\\"BE\\\", \\\"MUSC\\\", \\\"OPIM\\\",\\n #slow\\n #\\\"SPAN\\\", \\\"NURS\\\", \\\"ENGL\\\",\\n #error\\n \\\"EROR\\\"]\\n\\n index_tests = [\\\"\\\", \\\"instructors\\\", \\\"coursehistories\\\", \\\"depts\\\",\\n \\\"semesters\\\", \\\"semesters/2010c\\\"]\\n\\n course_tests = [] # filled in by coursehistory_tests\\n\\n for t in fraclist(index_tests, options.percent):\\n test(opts, t)\\n \\n for t in fraclist(coursehistory_tests, options.percent):\\n obj = test(opts, \\\"coursehistories/%s\\\" % t)\\n test(opts, \\\"coursehistories/%s/reviews\\\" % t)\\n\\n # now \\\"find\\\" some courses\\n course_tests.append(\\\"2010c-%s\\\" % t)\\n try:\\n courseid = sorted(obj[\\\"result\\\"][\\\"courses\\\"])[0][\\\"id\\\"]\\n course_tests.append(courseid)\\n except (TypeError, KeyError, IndexError):\\n pass\\n \\n for t in course_tests: # don't fraclist an autogenerated list\\n # Some of the autogenerated courses don't exist, so ignore errors.\\n root_success = test(opts, \\\"courses/%s\\\" % t, lderror_ok=True)\\n if root_success:\\n # Course exists, don't expect errors.\\n test(opts, \\\"courses/%s/reviews\\\" % t)\\n test(opts, \\\"courses/%s/coursehistories/\\\" % t)\\n test(opts, \\\"courses/%s/sections\\\" % t)\\n \\n if test(opts, \\\"courses/%s/sections/001\\\" % t, lderror_ok=True):\\n test(opts, \\\"courses/%s/sections/001/reviews\\\" % t) \\n if '-' in str(t): # if we have a yyyys-dept-num test\\n test(opts, \\\"sections/%s-001\\\" % t)\\n # not tested: sections/001/reviews/instructor-id\\n test(opts, \\\"courses/%s/sections/401\\\" % t, lderror_ok=True)\\n \\n for t in fraclist(instructor_tests, options.percent):\\n test(opts, \\\"instructors/%s\\\" % t)\\n test(opts, \\\"instructors/%s/sections\\\" % t)\\n test(opts, \\\"instructors/%s/reviews\\\" % t)\\n \\n for t in fraclist(dept_tests, options.percent):\\n test(opts, \\\"depts/%s\\\" % t)\\n test(opts, \\\"depts/%s/reviews\\\" % t)\\n test(opts, \\\"semesters/2010c/%s\\\" % t)\",\n \"def generate_report():\\n if os.path.isdir(\\\"build/coverage\\\"):\\n shutil.rmtree(\\\"build/coverage\\\")\\n commands = '''\\nscons -uij32 --optimization=coverage controller/cplusplus_test\\nlcov --base-directory build/coverage --directory build/coverage -c -o build/coverage/controller_test.info\\ngenhtml -o build/coverage/controller/test_coverage -t test --num-spaces 4 build/coverage/controller_test.info\\n'''\\n for cmd in commands.splitlines():\\n cmd_args = cmd.split()\\n if (len(cmd_args) == 0):\\n continue\\n cmd = cmd_args[0]\\n cmd_path = find_executable(cmd)\\n if not cmd_path:\\n continue\\n pid = os.fork()\\n if pid == 0:\\n # Avoid stdout buffering by execing command into child process.\\n os.execv(cmd_path, cmd_args)\\n os.waitpid(pid, 0)\",\n \"def benchmark_selection(self):\\n return self._benchmark_selection\",\n \"def register_benchmarks(directory=None):\\n dirs = places_to_look() if directory is None else [directory]\\n for directory in dirs:\\n with os.scandir(directory) as scan:\\n for entry in scan:\\n filename = entry.name\\n if (\\n filename.startswith(\\\".\\\")\\n or not entry.is_file()\\n or not filename.endswith(\\\".py\\\")\\n ):\\n continue\\n if (\\n filename.startswith(\\\"benchmark\\\")\\n or filename.endswith(\\\"benchmark.py\\\")\\n or filename.endswith(\\\"benchmarks.py\\\")\\n ):\\n import_path(f\\\"{directory}/{filename}\\\")\",\n \"def getBuildSets():\",\n \"def generate_cobertura_xml(self, coverage_data):\\n\\n dom_impl = minidom.getDOMImplementation()\\n doctype = dom_impl.createDocumentType(\\\"coverage\\\", None,\\n \\\"http://cobertura.sourceforge.net/xml/coverage-03.dtd\\\")\\n document = dom_impl.createDocument(None, \\\"coverage\\\", doctype)\\n root = document.documentElement\\n summary = coverage_data['summary']\\n self._attrs(root, {\\n 'branch-rate': self._percent(summary['branches-total'],\\n summary['branches-covered']),\\n 'branches-covered': str(summary['branches-covered']),\\n 'branches-valid': str(summary['branches-total']),\\n 'complexity': '0',\\n 'line-rate': self._percent(summary['lines-total'],\\n summary['lines-covered']),\\n 'lines-valid': str(summary['lines-total']),\\n 'timestamp': coverage_data['timestamp'],\\n 'version': '1.9'\\n })\\n\\n sources = self._el(document, 'sources', {})\\n source = self._el(document, 'source', {})\\n source.appendChild(document.createTextNode(self.base_dir))\\n sources.appendChild(source)\\n\\n root.appendChild(sources)\\n\\n packages_el = self._el(document, 'packages', {})\\n\\n packages = coverage_data['packages']\\n for package_name, package_data in list(packages.items()):\\n package_el = self._el(document, 'package', {\\n 'line-rate': package_data['line-rate'],\\n 'branch-rate': package_data['branch-rate'],\\n 'name': package_name\\n })\\n classes_el = self._el(document, 'classes', {})\\n for class_name, class_data in list(package_data['classes'].items()):\\n class_el = self._el(document, 'class', {\\n 'branch-rate': self._percent(class_data['branches-total'],\\n class_data['branches-covered']),\\n 'complexity': '0',\\n 'filename': class_name,\\n 'line-rate': self._percent(class_data['lines-total'],\\n class_data['lines-covered']),\\n 'name': class_data['name']\\n })\\n\\n # Process methods\\n methods_el = self._el(document, 'methods', {})\\n for method_name, hits in list(class_data['methods'].items()):\\n method_el = self._el(document, 'method', {\\n 'name': method_name,\\n 'signature' : '',\\n 'hits': hits\\n })\\n methods_el.appendChild(method_el)\\n\\n # Process lines\\n lines_el = self._el(document, 'lines', {})\\n lines = list(class_data['lines'].keys())\\n lines.sort()\\n for line_number in lines:\\n line_el = self._el(document, 'line', {\\n 'branch': class_data['lines'][line_number]['branch'],\\n 'hits': str(class_data['lines'][line_number]['hits']),\\n 'number': str(line_number)\\n })\\n if class_data['lines'][line_number]['branch'] == 'true':\\n total = int(class_data['lines'][line_number]['branches-total'])\\n covered = int(class_data['lines'][line_number]['branches-covered'])\\n percentage = int((covered * 100.0) / total)\\n line_el.setAttribute('condition-coverage',\\n '{0}% ({1}/{2})'.format(\\n percentage, covered, total))\\n lines_el.appendChild(line_el)\\n\\n class_el.appendChild(methods_el)\\n class_el.appendChild(lines_el)\\n classes_el.appendChild(class_el)\\n package_el.appendChild(classes_el)\\n packages_el.appendChild(package_el)\\n root.appendChild(packages_el)\\n\\n return document.toprettyxml()\",\n \"def list(self, request):\\n urls = {\\n 'msg': 'Must use bulk_by_sample to get SCCmec Coverages',\\n }\\n\\n return Response(urls)\",\n \"def getCoverage(self, measurementsPerCell=None):\\n\\t\\tcoverage = set()\\n\\n\\t\\tif measurementsPerCell is None:\\n\\t\\t\\tmeasurementsPerCell = self._binCapacity\\n\\n\\t\\tfor mBin in self._measurementBins.values():\\n\\n\\t\\t\\t# Only take as many measurements as are available\\n\\t\\t\\tbound = min(measurementsPerCell, len(mBin))\\n\\t\\t\\tfor m in mBin[:bound]:\\n\\t\\t\\t\\tcoverage.add(m.id)\\n\\n\\t\\treturn coverage\",\n \"def count_benchmarks():\\n return len(setup_storage().fetch_benchmark({}))\",\n \"def get_benchmark(client):\\n r = client.get(config.API_PATH() + '/benchmarks')\\n benchmarks = json.loads(r.data)\\n return benchmarks['benchmarks'][0]['id']\",\n \"def filter_out(self, skip):\\n benchmarks = super(Benchmarks, self).__new__(self.__class__)\\n benchmarks._conf = self._conf\\n benchmarks._benchmark_dir = self._benchmark_dir\\n benchmarks._all_benchmarks = self._all_benchmarks\\n\\n selected_idx = {}\\n\\n for name, benchmark in self.items():\\n if name not in skip:\\n benchmarks[name] = benchmark\\n if name in self._benchmark_selection:\\n selected_idx[name] = self._benchmark_selection[name]\\n\\n benchmarks._benchmark_selection = selected_idx\\n\\n return benchmarks\",\n \"def get_coverage(self):\\n if len(self) == 1:\\n return self.subacqs[0].get_coverage()\\n return np.array([self.subacqs[i].get_coverage() for i in range(len(self))])\",\n \"def get_cases(test_suite, test_name_regex):\\n cases = []\\n for test_case_name in dir(test_suite):\\n test_case = getattr(test_suite, test_case_name)\\n if callable(test_case) and re.match(test_name_regex, test_case_name):\\n cases.append(test_case_name)\\n\\n return cases\",\n \"def save(self):\\n path = self.get_benchmark_file_path(self._conf.results_dir)\\n util.write_json(path, self._all_benchmarks, self.api_version)\",\n \"def get_covid_stats_for_all_states():\\n url = \\\"https://corona.lmao.ninja/v2/states/\\\"\\n response = requests.get(url)\\n data = response.json()\\n\\n states = []\\n\\n for state in data:\\n stateName = state[\\\"state\\\"]\\n\\n cases = state[\\\"cases\\\"]\\n todaysCases = state[\\\"todayCases\\\"]\\n deaths = state[\\\"deaths\\\"]\\n todayDeaths = state[\\\"todayDeaths\\\"]\\n recovered = state[\\\"recovered\\\"]\\n activeCases = state[\\\"active\\\"]\\n tests = state[\\\"tests\\\"]\\n casesPerMil = state[\\\"casesPerOneMillion\\\"]\\n deathsPerMil = state[\\\"deathsPerOneMillion\\\"]\\n testsPerMil = state[\\\"testsPerOneMillion\\\"]\\n\\n population = state[\\\"population\\\"]\\n\\n if population != 0:\\n millionsOfPop = population / 1000000.0\\n activeCasesPerMil = activeCases / millionsOfPop\\n color = get_color(activeCasesPerMil)\\n states.append(\\n StateStats(\\n stateName,\\n cases,\\n todaysCases,\\n activeCases,\\n casesPerMil,\\n deaths,\\n todayDeaths,\\n deathsPerMil,\\n recovered,\\n tests,\\n testsPerMil,\\n color,\\n )\\n )\\n else:\\n states.append(\\n StateStats(\\n stateName,\\n cases,\\n todaysCases,\\n activeCases,\\n casesPerMil,\\n deaths,\\n todayDeaths,\\n deathsPerMil,\\n recovered,\\n tests,\\n testsPerMil,\\n )\\n )\\n\\n return states\",\n \"def list_test_cases(program):\\n\\n return list(INFO[program].test_cases)\",\n \"def ballot_get_all_contests():\\r\\n all_contests = []\\r\\n result = ballot_list_contests(limit=100000000).get('result')\\r\\n if result:\\r\\n contests = batch('ballot_get_contest_by_id', [[r] for r in result]).get('result')\\r\\n if contests:\\r\\n all_contests = contests\\r\\n\\r\\n return all_contests\",\n \"def comports(include_links=False):\\n return list(iterate_comports())\",\n \"def Run(benchmark_spec: bm_spec.BenchmarkSpec) -> List[sample.Sample]:\\n discovery_duration = benchmark_spec.data_discovery_service.DiscoverData()\\n return [\\n sample.Sample('data_discovery_duration', discovery_duration, 'seconds',\\n benchmark_spec.data_discovery_service.GetMetadata())]\",\n \"def get_codecoolers(cls):\\n return cls.codecoolers\",\n \"def tests(c):\\n results = [test(c, i) for i, test_path in enumerate(TEST_PATHS)]\\n print('\\\\n\\\\n\\\\n############## SUMMARY ##############')\\n for i, test_path in enumerate(TEST_PATHS):\\n print(i, test_path, 'PASSED' if result[i] == 0 else 'FAILED')\",\n \"def find_coverage_report(folder, exclude=None, filter_out='.*conda.*'):\\r\\n regexp = re.compile('data_file=([0-9a-zA-Z_]+)')\\r\\n regcov = re.compile(\\r\\n '<h1>Coveragereport:<spanclass=.?pc_cov.?>([0-9]+)%</span>')\\r\\n regout = re.compile(filter_out) if filter_out else None\\r\\n covs = {}\\r\\n subfold = os.listdir(folder)\\r\\n for sub in subfold:\\r\\n if exclude is not None and sub in exclude:\\r\\n continue\\r\\n full = os.path.join(folder, sub)\\r\\n keep = []\\r\\n nn = None\\r\\n cov = None\\r\\n for it in explore_folder_iterfile(full):\\r\\n name = os.path.split(it)[-1]\\r\\n dt = os.stat(full).st_mtime\\r\\n if name == 'index.html':\\r\\n with open(it, 'r') as f:\\r\\n htd = f.read().replace('\\\\n', '').replace('\\\\r', '').replace(' ', '')\\r\\n cont = regcov.findall(htd)\\r\\n if len(cont) > 0:\\r\\n cov = cont[0]\\r\\n if name == 'covlog.txt':\\r\\n with open(it, 'r') as f:\\r\\n logd = f.read()\\r\\n cont = regexp.findall(logd)\\r\\n if len(cont) > 0:\\r\\n nn = cont[0]\\r\\n if name == '.coverage':\\r\\n keep.append((dt, it))\\r\\n if len(keep) == 0:\\r\\n continue\\r\\n mx = max(keep)\\r\\n if regout is not None and regout.search(nn):\\r\\n continue\\r\\n covs[sub] = (mx[-1], nn, cov)\\r\\n return covs\",\n \"def coverage(context):\\n context.run(\\\" \\\".join([\\n \\\"python -m pytest\\\",\\n \\\"--cov=%s\\\" % PACKAGE_NAME,\\n \\\"--cov-report html\\\",\\n \\\"--cov-branch\\\",\\n \\\"--cov-fail-under=75\\\"\\n ]))\",\n \"def covwatch(context):\\n context.run(\\\" \\\".join([\\n \\\"python -m pytest\\\",\\n \\\"--cov=%s\\\" % PACKAGE_NAME,\\n \\\"--cov-branch\\\",\\n \\\"--cov-fail-under=75\\\"\\n ]))\",\n \"def getAllBuilders(self):\\n names = self.status.getBuilderNames(categories=self.categories)\\n builders = [self.status.getBuilder(n) for n in names]\\n return builders\",\n \"def get_all_connected_zi_instruments():\\n from qcodes.station import Station\\n if Station.default is not None:\\n all_inst = Station.default.components\\n else:\\n from pycqed.instrument_drivers.instrument import Instrument\\n all_inst = Instrument._all_instruments\\n return [inst for inst in all_inst.values()\\n if inst.get_idn().get('vendor', '') in\\n ['ZurichInstruments', 'Zurich Instruments']]\",\n \"def _parse_suite(\\n self, results: dict, extra_tags: dict = None\\n ) -> List[BenchmarkResult]:\\n # all results share a batch id\\n batch_id = uuid.uuid4().hex\\n\\n parsed_results = []\\n for result in results[\\\"benchmarks\\\"]:\\n result_parsed = self._parse_benchmark(\\n result=GoogleBenchmark(**result),\\n batch_id=batch_id,\\n extra_tags=extra_tags,\\n )\\n parsed_results.append(result_parsed)\\n\\n return parsed_results\",\n \"def clist() -> None:\\n files = os.listdir(os.path.join(os.getcwd(), \\\"apis\\\"))\\n files.remove(\\\"__init__.py\\\")\\n files = [f.replace(\\\".py\\\", \\\"\\\") for f in files]\\n print(files)\",\n \"def cov(test_class):\\n if test_class == 'all':\\n tests = unittest.TestLoader().discover('project/tests')\\n else:\\n # note, test module must be imported above, doing lazily for now\\n test_module = globals()[test_class]\\n tests = unittest.TestLoader().loadTestsFromTestCase(test_module)\\n result = unittest.TextTestRunner(verbosity=2).run(tests)\\n if result.wasSuccessful():\\n COV.stop()\\n COV.save()\\n print('Coverage Summary:')\\n COV.report()\\n basedir = os.path.abspath(os.path.dirname(__file__))\\n covdir = os.path.join(basedir, 'tmp/coverage')\\n COV.html_report(directory=covdir)\\n print('HTML version: file://%s/index.html' % covdir)\\n COV.erase()\\n return 0\\n return 1\",\n \"def get_backend_coverage():\\n\\n onnx_coverage = {}\\n experimental_op = set()\\n for handler in BackendHandler.__subclasses__():\\n handler.check_cls()\\n\\n versions = handler.get_versions()\\n domain = handler.DOMAIN\\n if getattr(handler, \\\"EXPERIMENTAL\\\", False):\\n experimental_op.add(handler.ONNX_OP)\\n _update_coverage(onnx_coverage, domain, handler.ONNX_OP, versions)\\n return onnx_coverage, experimental_op\",\n \"def __benchmark__(cls):\\n results = []\\n used_argsets = []\\n for args in cls.arguments:\\n used_argsets.append(args)\\n # for each given argument\\n for method in cls.get_methods():\\n # append an empty list for the results with this argument\\n method_results = []\\n # for each repetition\\n for n in xrange(cls.repetitions):\\n # append the results to the list for this argument set\\n trial_results = cls._trial(method, args)\\n method_results.append(trial_results)\\n # append a Result to `results`.\\n results.append(Result(method, args, method_results))\\n return results, used_argsets\",\n \"def get_benchmark_aggregated_cov_df(coverage_dict, benchmark):\\n dict_to_transform = {'fuzzer': [], 'aggregated_edges_covered': []}\\n for key_pair, covered_regions in coverage_dict.items():\\n current_fuzzer, current_benchmark = key_pair.split()\\n if current_benchmark == benchmark:\\n dict_to_transform['fuzzer'].append(current_fuzzer)\\n dict_to_transform['aggregated_edges_covered'].append(\\n len(covered_regions))\\n return pd.DataFrame(dict_to_transform)\",\n \"def list_ciphers():\\n global AVAILABLE_CIPHERS\\n print(\\\"[!] Available ciphers: \\\")\\n for i in range(len(AVAILABLE_CIPHERS)):\\n print(\\\" ----> %s.%s\\\"%(i+1, AVAILABLE_CIPHERS[i]))\\n exit()\",\n \"def print_scenario(benchmarks, name):\\n # Remember, the first entry in the times array is an empty object.\\n\\n benchmarks = sorted(benchmarks, key = benchmark_sort_key)\\n for benchmark in benchmarks:\\n block_times = benchmark[\\\"times\\\"][2][\\\"block_times\\\"] # list of floats\\n it = iter(block_times)\\n for start in it:\\n end = next(it)\\n\\n print(\\\"%s , %.3f \\\" % (benchmark[\\\"label\\\"], float(end) - float(start)))\",\n \"def _transform_results(self) -> List[BenchmarkResult]:\\n with open(self.result_file, \\\"r\\\") as f:\\n raw_results = json.load(f)\\n\\n parsed_results = []\\n for suite in raw_results[\\\"suites\\\"]:\\n parsed_results += self._parse_suite(\\n results=suite,\\n extra_tags={\\\"suite\\\": suite[\\\"name\\\"], \\\"source\\\": \\\"cpp-micro\\\"},\\n )\\n\\n return parsed_results\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6990162","0.6294294","0.61898667","0.57358605","0.5668825","0.5625119","0.5591114","0.54327077","0.5404844","0.5391268","0.53692985","0.5357925","0.5353722","0.5342524","0.53213197","0.5292404","0.52296937","0.52178067","0.5196261","0.51935357","0.5189432","0.51893944","0.515206","0.5151165","0.51314455","0.5113743","0.5110983","0.5110962","0.5110231","0.5107682","0.5103227","0.5090984","0.5086041","0.5073073","0.5049928","0.50429726","0.50334233","0.50247455","0.5016607","0.50142646","0.5001498","0.49991342","0.49967068","0.4995006","0.4994955","0.49842414","0.49816772","0.49761465","0.49755245","0.49746332","0.49725923","0.49721885","0.49607697","0.49312484","0.49295345","0.49264774","0.4924687","0.49179804","0.49162182","0.49132225","0.4904643","0.49034712","0.48973665","0.4881842","0.4880682","0.48708925","0.4870671","0.4869097","0.48617724","0.486099","0.486008","0.48538736","0.4851544","0.4840891","0.48353106","0.48335728","0.4833405","0.48300827","0.48256704","0.4804999","0.47993115","0.47974172","0.4791352","0.4790888","0.4789223","0.47848022","0.47818804","0.47808778","0.47732264","0.47722813","0.4766355","0.4766167","0.47591236","0.47513363","0.4751071","0.47505742","0.47497115","0.47456512","0.47414002","0.47412556"],"string":"[\n \"0.6990162\",\n \"0.6294294\",\n \"0.61898667\",\n \"0.57358605\",\n \"0.5668825\",\n \"0.5625119\",\n \"0.5591114\",\n \"0.54327077\",\n \"0.5404844\",\n \"0.5391268\",\n \"0.53692985\",\n \"0.5357925\",\n \"0.5353722\",\n \"0.5342524\",\n \"0.53213197\",\n \"0.5292404\",\n \"0.52296937\",\n \"0.52178067\",\n \"0.5196261\",\n \"0.51935357\",\n \"0.5189432\",\n \"0.51893944\",\n \"0.515206\",\n \"0.5151165\",\n \"0.51314455\",\n \"0.5113743\",\n \"0.5110983\",\n \"0.5110962\",\n \"0.5110231\",\n \"0.5107682\",\n \"0.5103227\",\n \"0.5090984\",\n \"0.5086041\",\n \"0.5073073\",\n \"0.5049928\",\n \"0.50429726\",\n \"0.50334233\",\n \"0.50247455\",\n \"0.5016607\",\n \"0.50142646\",\n \"0.5001498\",\n \"0.49991342\",\n \"0.49967068\",\n \"0.4995006\",\n \"0.4994955\",\n \"0.49842414\",\n \"0.49816772\",\n \"0.49761465\",\n \"0.49755245\",\n \"0.49746332\",\n \"0.49725923\",\n \"0.49721885\",\n \"0.49607697\",\n \"0.49312484\",\n \"0.49295345\",\n \"0.49264774\",\n \"0.4924687\",\n \"0.49179804\",\n \"0.49162182\",\n \"0.49132225\",\n \"0.4904643\",\n \"0.49034712\",\n \"0.48973665\",\n \"0.4881842\",\n \"0.4880682\",\n \"0.48708925\",\n \"0.4870671\",\n \"0.4869097\",\n \"0.48617724\",\n \"0.486099\",\n \"0.486008\",\n \"0.48538736\",\n \"0.4851544\",\n \"0.4840891\",\n \"0.48353106\",\n \"0.48335728\",\n \"0.4833405\",\n \"0.48300827\",\n \"0.48256704\",\n \"0.4804999\",\n \"0.47993115\",\n \"0.47974172\",\n \"0.4791352\",\n \"0.4790888\",\n \"0.4789223\",\n \"0.47848022\",\n \"0.47818804\",\n \"0.47808778\",\n \"0.47732264\",\n \"0.47722813\",\n \"0.4766355\",\n \"0.4766167\",\n \"0.47591236\",\n \"0.47513363\",\n \"0.4751071\",\n \"0.47505742\",\n \"0.47497115\",\n \"0.47456512\",\n \"0.47414002\",\n \"0.47412556\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.80346346"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":11272,"cells":{"query":{"kind":"string","value":"Returns the list of standard bug benchmarks."},"document":{"kind":"string","value":"def get_bug_benchmarks():\n return [\n benchmark for benchmark in get_all_benchmarks()\n if get_type(benchmark) == BenchmarkType.BUG.value\n ]"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_all_benchmarks():\n all_benchmarks = []\n for benchmark in os.listdir(BENCHMARKS_DIR):\n benchmark_path = os.path.join(BENCHMARKS_DIR, benchmark)\n if os.path.isfile(os.path.join(benchmark_path, 'benchmark.yaml')):\n all_benchmarks.append(benchmark)\n return sorted(all_benchmarks)","def get_coverage_benchmarks():\n return [\n benchmark for benchmark in get_all_benchmarks()\n if get_type(benchmark) == BenchmarkType.CODE.value\n ]","def run_all(self):\n runs = []\n for run in self.benchmarks:\n run.start()\n run.wait()\n runs.append(run.metadata)\n return runs","def get_testbench_specs(self, tb_type: str) -> Dict[str, Any]:\n return self._specs['testbenches'][tb_type]","def get_tests():\n # tests = ['test_build_gaussian_pyramid_random', 'test_build_gaussian_pyramid_static', 'test_build_laplacian_pyramid_random', 'test_build_laplacian_pyramid_static', 'test_laplacian_to_image', 'test_render_pyramid_random', 'test_render_pyramid_static']\n # return [tester.TestEx3(method) for method in tests]\n return [tester.TestEx3(method) for method in dir(tester.TestEx3) if method.startswith('test')]","def list(self, classes):\n\n def add(benchmarks, parts, flags, exclude):\n if (\n flags[\"language\"] != \"C++\"\n and flags[\"language\"] != \"Java\"\n and \"--drop-caches=true\" not in parts\n ):\n parts.append(\"--drop-caches=true\")\n command = \" \".join(parts)\n if command not in exclude:\n benchmarks.append({\"command\": command, \"flags\": flags})\n\n benchmarks = []\n for name, benchmark in classes.items():\n if name.startswith(\"example\"):\n continue\n\n instance, parts = benchmark(), [name]\n\n exclude = getattr(benchmark, \"exclude\", [])\n if \"source\" in getattr(benchmark, \"arguments\", []):\n parts.append(\"ALL\")\n\n iterations = getattr(instance, \"iterations\", 3)\n parts.append(f\"--iterations={iterations}\")\n\n if instance.cases:\n parts.append(\"--all=true\")\n\n flags = getattr(instance, \"flags\", {})\n\n if getattr(instance, \"r_only\", False):\n flags[\"language\"] = \"R\"\n add(benchmarks, parts, flags, exclude)\n else:\n if \"language\" not in flags:\n flags[\"language\"] = \"Python\"\n add(benchmarks, parts, flags, exclude)\n\n if hasattr(instance, \"r_name\"):\n flags_ = flags.copy()\n flags_[\"language\"] = \"R\"\n parts.append(\"--language=R\")\n add(benchmarks, parts, flags_, exclude)\n\n return sorted(benchmarks, key=lambda k: k[\"command\"])","def get_benchmark_specification(benchmark = 'FSI1'):\n if benchmark == 'FSI1':\n rho_s = Constant(1e03)\n nu_s = Constant(0.4)\n mu_s = Constant(5e05)\n rho_f = Constant(1e03)\n nu_f = Constant(1e-03)\n U = 0.2\n T_end = 60.0\n result = \"results-FSI1/\"\n elif benchmark == 'FSI2':\n rho_s = Constant(1e04)\n nu_s = Constant(0.4)\n mu_s = Constant(5e05)\n rho_f = Constant(1e03)\n nu_f = Constant(1e-03)\n U = 1.0\n T_end = 15.0\n result = \"results-FSI2/\"\t\t\n elif benchmark == 'FSI3':\n rho_s = Constant(1e03)\n nu_s = Constant(0.4)\n mu_s = Constant(2e06)\n rho_f = Constant(1e03)\n nu_f = Constant(1e-03)\n U = 2.0\n T_end = 20.0\n result = \"results-FSI3/\"\t\t\n else:\n raise ValueError('\"{}\" is a wrong name for problem specification.'.format(benchmark))\n v_max = Constant(1.5*U) # mean velocity to maximum velocity \n # (we have parabolic profile)\n E_s = Constant(2*mu_s*(1+nu_s))\n lambda_s = Constant((nu_s*E_s)/((1+nu_s)*(1-2*nu_s)))\n mu_f = Constant(nu_f*rho_f)\n return v_max, lambda_s, mu_s, rho_s, mu_f, rho_f, T_end, result","def get_performance_test_cases(test_suite):\n return get_cases(test_suite, r'test_perf_')","def exclude_non_cpp(benchmarks):\n return [benchmark for benchmark in benchmarks if is_cpp(benchmark)]","def run_benchmark():\n import argparse\n parser = argparse.ArgumentParser(description='Benchmark alchemically modified system against unmodified system.')\n parser.add_argument('--platform', dest='platform_name', action='store', default=None, help='platform name to benchmark (default: None)')\n options = parser.parse_args()\n\n from sams.tests import testsystems\n for testsystem_name in ['AblImatinibExplicitAlchemical']:\n cls = getattr(testsystems, testsystem_name)\n testsystem = cls()\n factory_args = { 'ligand_atoms' : testsystem.alchemical_atoms, 'receptor_atoms' : range(0,4266) }\n benchmark(testsystem.system, testsystem.positions, platform_name=options.platform_name, nsteps=5000, timestep=1.0*unit.femtoseconds, factory_args=factory_args)","def _parameterize_simple_benchmarks():\n parameterized_tuples = []\n for index, simple_benchmark in enumerate(\n all_benchmarks.all_benchmarks(modules=[simple_benchmarks])):\n # The index ensures all test cases have distinct names, even if multiple\n # benchmarks have the same name.\n test_case_name = '{index}_{name}'.format(index=index,\n name=simple_benchmark.name)\n parameterized_tuples.append((test_case_name, simple_benchmark))\n return parameterized_tuples","def print_scenario(benchmarks, name):\n # Remember, the first entry in the times array is an empty object.\n\n benchmarks = sorted(benchmarks, key = benchmark_sort_key)\n for benchmark in benchmarks:\n block_times = benchmark[\"times\"][2][\"block_times\"] # list of floats\n it = iter(block_times)\n for start in it:\n end = next(it)\n\n print(\"%s , %.3f \" % (benchmark[\"label\"], float(end) - float(start)))","def delete_all_benchmarks(self, namespace=\"benchmark-operator\"):\n all_benchmarks = self.crd_client.list_namespaced_custom_object(\n group=\"ripsaw.cloudbulldozer.io\", version=\"v1alpha1\", namespace=namespace, plural=\"benchmarks\"\n )\n\n _ = [\n self.delete_benchmark(benchmark[\"metadata\"][\"name\"], namespace)\n for benchmark in all_benchmarks.get(\"items\", [])\n ]","def _disc_benchmarks(cls, conf, repo, environments, commit_hashes, check):\n root = conf.benchmark_dir\n\n cls.check_tree(root)\n\n if len(environments) == 0:\n raise util.UserError(\"No available environments\")\n\n # Try several different commits:\n #\n # - First of commit_hashes provided\n # - Tips of branches from configuration file\n # - Rest of the commit_hashes\n #\n\n def iter_hashes():\n for h in commit_hashes[:1]:\n yield h\n for branch in conf.branches:\n try:\n yield repo.get_hash_from_name(branch)\n except NoSuchNameError:\n continue\n for h in commit_hashes[1:]:\n yield h\n\n def iter_unique(iter):\n seen = set()\n for item in iter:\n if item not in seen:\n seen.add(item)\n yield item\n\n try_hashes = iter_unique(iter_hashes())\n\n log.info(\"Discovering benchmarks\")\n with log.indent():\n last_err = None\n for env, commit_hash in itertools.product(environments, try_hashes):\n env.create()\n\n if last_err is not None:\n log.warning(\"Failed: trying different commit/environment\")\n\n result_dir = tempfile.mkdtemp()\n try:\n env.install_project(conf, repo, commit_hash)\n\n env_vars = dict(os.environ)\n env_vars.update(env.env_vars)\n\n result_file = os.path.join(result_dir, 'result.json')\n env.run(\n [runner.BENCHMARK_RUN_SCRIPT, 'discover',\n os.path.abspath(root),\n os.path.abspath(result_file)],\n cwd=result_dir,\n env=env_vars,\n dots=False)\n\n try:\n with open(result_file, 'r') as fp:\n benchmarks = json.load(fp)\n except (IOError, ValueError):\n log.error(\"Invalid discovery output\")\n raise util.UserError()\n\n break\n except (util.UserError, util.ProcessError) as err:\n last_err = err\n continue\n except KeyboardInterrupt:\n raise util.UserError(\"Interrupted.\")\n finally:\n util.long_path_rmtree(result_dir)\n else:\n raise util.UserError(\"Failed to build the project and import the benchmark suite.\")\n\n if check:\n log.info(\"Checking benchmarks\")\n with log.indent():\n result_dir = tempfile.mkdtemp()\n try:\n out, err, retcode = env.run(\n [runner.BENCHMARK_RUN_SCRIPT, 'check',\n os.path.abspath(root)],\n cwd=result_dir,\n dots=False,\n env=env_vars,\n valid_return_codes=None,\n return_stderr=True,\n redirect_stderr=True)\n finally:\n util.long_path_rmtree(result_dir)\n\n out = out.strip()\n if retcode == 0:\n if out:\n log.info(out)\n log.info(\"No problems found.\")\n else:\n if out:\n log.error(out)\n raise util.UserError(\"Benchmark suite check failed.\")\n\n return benchmarks","def get_functional_test_cases(test_suite):\n return get_cases(test_suite, r'test_(?!perf_)')","def main():\n parser = optparse.OptionParser()\n parser.add_option('--debug', action='store_true', default=False,\n help='run in debug mode')\n parser.add_option('-i', '--iteration', type=int, default=DEFAULT_ITERATION,\n metavar='NUM',\n help='set the number of iterations for each test (defualt:%d)' % \\\n DEFAULT_ITERATION)\n parser.add_option('-f', '--fstypes', default='ext2,ext3,ext4,btrfs,xfs',\n type='string', metavar='TYPES', help='set the file systems to test')\n parser.add_option('-n', '--num', default=10000, type=int, metavar='NUM',\n help='set the number of file created')\n parser.add_option('-N', '--numa', action='store_true', default=False,\n help='run NUMA test')\n parser.add_option('-S', '--scalability', action='store_true', default=False,\n help='run scalability test')\n global options\n options, args = parser.parse_args()\n\n benchutils.check_root_or_die()\n suffix = ''\n if options.numa:\n suffix = 'numa'\n else:\n suffix = 'scale'\n output_dir = benchutils.get_output_directory(suffix=suffix, timestamp=True)\n fstypes = options.fstypes.split(',')\n for fs in fstypes:\n if options.numa:\n run_tests(output_dir, fs)\n elif options.scalability:\n run_scalability_tests(output_dir, fs)","def _run():\n subprocess.check_call(\n [\n \"tools/bazel\",\n \"build\",\n \"-c\",\n \"opt\",\n \"test/core/memory_usage/memory_usage_test\",\n ]\n )\n ret = {}\n for name, benchmark_args in _BENCHMARKS.items():\n for scenario, extra_args in _SCENARIOS.items():\n # TODO(chenancy) Remove when minstack is implemented for channel\n if name == \"channel\" and scenario == \"minstack\":\n continue\n try:\n output = subprocess.check_output(\n [\n \"bazel-bin/test/core/memory_usage/memory_usage_test\",\n ]\n + benchmark_args\n + extra_args\n )\n except subprocess.CalledProcessError as e:\n print(\"Error running benchmark:\", e)\n continue\n for line in output.splitlines():\n for key, (pattern, conversion) in _INTERESTING.items():\n m = re.match(pattern, line)\n if m:\n ret[scenario + \": \" + key] = conversion(m.group(1))\n return ret","def main():\r\n algos = [merge_sort, quick_sort, heap_sort, radix_sort, bucket_sort_general]\r\n array_sizes = [5000, 10000, 15000, 20000, 50000, 75000, 100000, 150000]\r\n results = {algo.__name__: [] for algo in algos}\r\n for algo in algos:\r\n result = []\r\n for size in array_sizes:\r\n time = test(algo, size)\r\n result.append(time)\r\n results[algo.__name__] = result\r\n\r\n display_results(results, array_sizes)","def generate_benchmarks(self, num, graphs = []):\n if num != 0:\n try:\n print(\"{0} graphs left\".format(num), end='\\r')\n graphs.append(self.generate_lfr_benchmark())\n return self.generate_benchmarks(num-1, graphs)\n except nx.exception.ExceededMaxIterations:\n return self.generate_benchmarks(num, graphs)\n else:\n return graphs","def test():\n\t\treturn [\"vice.core.dataframe\",\n\t\t\t[\n\t\t\t\ttests.test(run = False),\n\t\t\t\t_builtin_dataframes.test(run = False)\n\t\t\t]\n\t\t]","def get_benchmark_requirements(cls):\n pass","def list_test_cases(program):\n\n return list(INFO[program].test_cases)","def test_list_benchmarks(client):\n # The benchmark listing contains one element (independently of whether the\n # user is logged in or not).\n r = client.get(config.API_PATH() + '/workflows')\n assert r.status_code == 200\n doc = r.json\n assert len(doc[labels.WORKFLOW_LIST]) == 1\n # Create user and the request header that contains the API key for the\n # logged in user.\n _, token = create_user(client, '0000')\n headers = {HEADER_TOKEN: token}\n r = client.get(config.API_PATH() + '/workflows', headers=headers)\n assert r.status_code == 200\n doc = r.json\n assert len(doc[labels.WORKFLOW_LIST]) == 1","def main(r_min=1, r_max=101):\n fizz_buzz_all = (\n fizz_buzz1(r_min, r_max),\n fizz_buzz2(r_min, r_max),\n fizz_buzz3(r_min, r_max)\n )\n for f in fizz_buzz_all:\n start = time.time()\n print(*f, sep='\\n')\n end = time.time()\n print(\"Execution time: \", end - start)","def manquant(suite):\n manque = []\n for i in range(1, 10):\n if i not in suite:\n manque.append(i)\n return manque","def list_runtimes(self, workbench):\n pass","def Cleanup(benchmark_spec):\n pass","def benchmark():\n print defer.Deferred.__module__\n for func, args, iter in benchmarkFuncs:\n print func.__name__, args, timeit(func, iter, *args)","def tests_generator(self):\n cb_bin = os.path.join(bin_path, 'compilebench')\n cmd_list = [\n (\"Initial Create/Compile/Read Compiled Tree\", \"{0} -D {1} -i 10 --makej -s {2}\"),\n ]\n\n tests = []\n for idx, (desc, cmd) in enumerate(cmd_list):\n test_name = \"compile_bench_{0}_{1}\".format(idx + 1, to_safe_name(desc))\n test = TestProfile(\n name=test_name,\n desc=desc,\n test_path=self.test_path,\n bin_path=bin_path,\n command=cmd.format(cb_bin, self.test_path, bin_path))\n tests.append(test)\n return tests","def assert_tests_stable(tests):\n return (stress_test(t) for t in tests)","def get_a_list_of_testset_names() -> str:\n message = 'The available test sets are:'\n for testset in sorted(DATASETS.keys(), reverse=True):\n message += '\\n%20s: %s' % (testset, DATASETS[testset].get('description', ''))\n return message","def _fcts():\n import numpy # pylint: disable=C0415\n from .cbenchmark_dot import vector_dot_product # pylint: disable=E0611,C0415\n from .cbenchmark_dot import vector_dot_product16 # pylint: disable=E0611,C0415\n from .cbenchmark_dot import vector_dot_product16_nofcall # pylint: disable=E0611,C0415\n from .cbenchmark_dot import vector_dot_product16_sse # pylint: disable=E0611,C0415\n\n def simple_dot(values):\n return numpy.dot(values, values)\n\n def c11_dot(vect):\n return vector_dot_product(vect, vect)\n\n def c11_dot16(vect):\n return vector_dot_product16(vect, vect)\n\n def c11_dot16_nofcall(vect):\n return vector_dot_product16_nofcall(vect, vect)\n\n def c11_dot16_sse(vect):\n return vector_dot_product16_sse(vect, vect)\n\n return [simple_dot, c11_dot, c11_dot16, c11_dot16_nofcall, c11_dot16_sse]","def list_feature_tests(self):\n\t\treturn self.test_names","def get_test_suite():\n # max for a and p\n MAX = 2**31 - 1 # INT32_MAX, max value for a and p\n sqrt_MAX = floor(sqrt(MAX)) # max for n\n \n # first test suite\n a_list = [0, 0, 0, 1, 1, 2, 7, 2, 1, 0, 0, 3, 1, 0, 0, 0, 1]\n p_list = [5, 3, 3, 0, 0, 0, 8, 1, 1, 0, 0, 0, 0, 1, 2, 0, 1]\n n_list = [7, 2, 2, 7, 3, 3, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 1]\n\n suite = get_one_suite(a_list, p_list, n_list, MAX, sqrt_MAX)\n yield suite\n \n # second test suite\n a_list = [3, 5, 23, 25, 100, 200, MAX, MAX-1, MAX]\n p_list = [10, 5, 23, 25, 100, 200, 1000, 100, 500]\n n_list = [23, 1, 0, 7, 1, 100, sqrt_MAX, 3, 23]\n \n suite = get_one_suite(a_list, p_list, n_list, MAX, sqrt_MAX)\n yield suite\n\n # third test suite\n a_list = []\n p_list = []\n n_list = []\n\n # keep a = 0\n for _ in range(10):\n a_list.append(0)\n p_list.append(random.randint(0, 5000))\n n_list.append(random.randint(0, sqrt_MAX))\n # keep p = 0\n for _ in range(10):\n a_list.append(random.randint(0, MAX))\n p_list.append(0)\n n_list.append(random.randint(0, sqrt_MAX))\n # keep n = 0\n for _ in range(10):\n a_list.append(random.randint(0, MAX))\n p_list.append(random.randint(0, 5000))\n n_list.append(0)\n # keep a = 0 and p = 0\n for _ in range(10):\n a_list.append(0)\n p_list.append(0)\n n_list.append(random.randint(0, sqrt_MAX))\n # keep all non-zero\n for _ in range(30):\n a_list.append(random.randint(0, MAX))\n p_list.append(random.randint(0, 5000))\n n_list.append(random.randint(0, sqrt_MAX))\n\n suite = get_one_suite(a_list, p_list, n_list, MAX, sqrt_MAX)\n yield suite","def alltests(opts):\n \n print \"API Root: %s\" % options.apiroot\n print \"Token: %s\" % options.token\n print \"Output dir: %s\" % options.output\n print \"Running %d%% of tests\" % options.percent\n print\n \n # need to use DEPT-001, not ID#\n coursehistory_tests = [\n # basic tests:\n \"cis-120\", \"math-114\", \"engl-101\", \"econ-001\",\n # miscellaneously somewhat problematic:\n \"engl-016\", \"law-205\", \"hpr-612\", \"rels-414\", \"nurs-322\",\n \"writ-030\", \"be-310\", \"psci-010\", \"psci-136\",\n # crosslistings:\n \"engl-135\", \"writ-135\", \"fnar-264\", \"cogs-001\", \"russ-048\", \"hist-048\",\n # no reviews?:\n \"afam-271\", \"ames-071\", \"slav-532\", \"afam-285\", \"prtg-213\", \"slav-533\",\n # errors:\n \"99999\", \"moo\",\n ]\n\n instructor_tests = [\n # basic tests:\n \"403\", \"631\", \"1883\", \"2217-FERNANDO-C--PEREIRA\", \"1602-BENJAMIN-PIERCE\",\n # crosslistings:\n \"1034-LYLE-H-UNGAR\", \"2709-DAVID-P--COMBERG\",\n # miscellaneously somewhat problematic:\n \"1040-DAVID-FOX\", \"4268-BART-GERARD-C-DE-JONGHE\",\n # the instructors w/ the most sections\n \"1883\", \"1619\", \"2869\", \"942\", \"1644\", \"541\", \"767\", \"434\",\n # concerned citizens:\n \"1759-MAX-C--CAVITCH\", \"2824-TIMOTHY-CORRIGAN\",\n \"1763-EMILY-R-STEINER\", \"1624-VALERIE-ROSS\",\n # no reviews?:\n \"416-LUDO-ROCHER\", \"715-ELIZABETH-ANN-POLLARD\", \"1094-MARIA-A-COWLES\",\n \"1500-ANDREW-GALLIA\", \"1888-RUSSELL-DILEO\",\n \"1450-SORMANE-PEREIRA-GOMES\", \"2188-HUI-YI-CHEN\", \"1165-DOMENIC-VITIELLO\",\n \"2359-CLAUDIA-CANCINO\", \"2737-SHEN-WANG\", \"3229-BERLE-WHITBY\",\n # errors:\n \"99999\", \"moo\",\n ]\n\n dept_tests = [\n #fast\n \"CSE\", \"LAW\", \"ANAT\", \"KORN\", \"LATN\", \"COGS\", \"MSCI\", \"GAS\",\n #medium\n \"CIS\", \"MATH\", \"FNAR\", \"ACCT\", \"FNCE\", \"BE\", \"MUSC\", \"OPIM\",\n #slow\n #\"SPAN\", \"NURS\", \"ENGL\",\n #error\n \"EROR\"]\n\n index_tests = [\"\", \"instructors\", \"coursehistories\", \"depts\",\n \"semesters\", \"semesters/2010c\"]\n\n course_tests = [] # filled in by coursehistory_tests\n\n for t in fraclist(index_tests, options.percent):\n test(opts, t)\n \n for t in fraclist(coursehistory_tests, options.percent):\n obj = test(opts, \"coursehistories/%s\" % t)\n test(opts, \"coursehistories/%s/reviews\" % t)\n\n # now \"find\" some courses\n course_tests.append(\"2010c-%s\" % t)\n try:\n courseid = sorted(obj[\"result\"][\"courses\"])[0][\"id\"]\n course_tests.append(courseid)\n except (TypeError, KeyError, IndexError):\n pass\n \n for t in course_tests: # don't fraclist an autogenerated list\n # Some of the autogenerated courses don't exist, so ignore errors.\n root_success = test(opts, \"courses/%s\" % t, lderror_ok=True)\n if root_success:\n # Course exists, don't expect errors.\n test(opts, \"courses/%s/reviews\" % t)\n test(opts, \"courses/%s/coursehistories/\" % t)\n test(opts, \"courses/%s/sections\" % t)\n \n if test(opts, \"courses/%s/sections/001\" % t, lderror_ok=True):\n test(opts, \"courses/%s/sections/001/reviews\" % t) \n if '-' in str(t): # if we have a yyyys-dept-num test\n test(opts, \"sections/%s-001\" % t)\n # not tested: sections/001/reviews/instructor-id\n test(opts, \"courses/%s/sections/401\" % t, lderror_ok=True)\n \n for t in fraclist(instructor_tests, options.percent):\n test(opts, \"instructors/%s\" % t)\n test(opts, \"instructors/%s/sections\" % t)\n test(opts, \"instructors/%s/reviews\" % t)\n \n for t in fraclist(dept_tests, options.percent):\n test(opts, \"depts/%s\" % t)\n test(opts, \"depts/%s/reviews\" % t)\n test(opts, \"semesters/2010c/%s\" % t)","def test_benchmark1(capsys):\n student_1 = Student('114007245','Mario Castillo',2018, [\"INST 201\",\"INST 326\"])\n student_2 = Student('117006012', 'Joe Rogan', 2018, [\"MATH 115\",\"PSYC 100\"])\n student_3 = Student(\"117008490\", \"Kanye West\", 2018, [\"MATH 120\",\"STAT 003\"])\n student_4 = Student('118009044', \"Elon Musk\", 2018, [\"PSYC 100\",\"MATH 003\"])\n \n student_1.benchmark_I()\n outerr = capsys.readouterr()\n out = outerr.out\n assert out == ('You have not completed the Benchmark I requirements.\\n'\n 'You have not taken MATH 115 or higher.\\n'\n 'You have not taken PSYC 100.\\n')\n\n student_2.benchmark_I()\n outerr = capsys.readouterr()\n out = outerr.out\n assert out == (f'You have completed all of your Benchmark I courses! '\n f'Congratulations, {student_2.student_name}!\\n')\n\n student_3.benchmark_I()\n outerr = capsys.readouterr()\n out = outerr.out \n assert out == ('You have not completed the Benchmark I requirements.\\n'\n 'You have not taken PSYC 100.\\n')\n\n student_4.benchmark_I()\n outerr = capsys.readouterr()\n out = outerr.out\n assert out == ('You have not completed the Benchmark I requirements.\\n'\n 'You have not taken MATH 115 or higher.\\n')","def regression_suites(self):\n return [c for c in self.suites.all() if TestSuite.TS_REGRESSION == c.xtype]","def regression_suites(self):\n return [c for c in self.suites.all() if TestSuite.TS_REGRESSION == c.xtype]","def workbench_scenarios():\n return [\n (\"SummaryXBlock\",\n \"\"\"<summary/>\n \"\"\"),\n (\"Multiple SummaryXBlock\",\n \"\"\"<vertical_demo>\n <summary/>\n <summary/>\n <summary/>\n </vertical_demo>\n \"\"\"),\n ]","def test_get_benchmark_methods_exact_match(self):\n config = mock.Mock()\n config.workspace = 'workspace'\n config.benchmark_method_patterns = [\n 'new_foo.BenchmarkClass.benchmark_method_1',\n 'new_foo.BenchmarkClass.benchmark_method_2']\n benchmark_runner = benchmark.BenchmarkRunner(config)\n\n methods = benchmark_runner._get_benchmark_methods()\n self.assertEqual(['new_foo.BenchmarkClass.benchmark_method_1',\n 'new_foo.BenchmarkClass.benchmark_method_2'], methods)","def run_benchmarks(urls, urlIndices, trial_number):\n path.append(os.path.join(CHROMIUM_SRC, 'tools/perf/'))\n benchmark_path = os.path.join(CHROMIUM_SRC, 'tools/perf/run_benchmark')\n output_path = 'temp'\n trial_key = 'trial{0}'.format(trial_number)\n\n cmd = ('sudo ' + benchmark_path + ' --profiler=trace telemetryBenchmarks.url{0}')\n for i in urlIndices:\n try:\n out, err, returncode = get_benchmark_result(cmd.format(i))\n timeout = False\n print 'successfully ran benchmark for url' + str(i)\n except TimeoutError:\n # Benchmark failed\n print 'Benchmark Timeout!'\n out = ''\n returncode = 1\n timeout = True\n\n failed = ['FAILED']\n if returncode != 0 or any(x in out for x in failed) or timeout:\n # If a benchmark fails, remove its corresponding wpr file, and act\n # as if it didn't exist\n # Remove from data/wpr_source\n print 'Benchmark {0} failed'.format(i)\n print 'return code is ' + str(returncode)\n print 'Out:'\n print out\n print 'Err:'\n print err\n urlName = 'url{0}_page_set_000.wpr'.format(i)\n urlpcName = 'url{0}_pc_page_set_000.wpr'.format(i)\n urlFilePath = os.path.join('data/wpr_source',urlName)\n urlpcFilePath = os.path.join('data/wpr_source',urlpcName)\n urlCmd = 'rm -f {0}'.format(urlFilePath)\n urlpcCmd = 'rm -f {0}'.format(urlpcFilePath)\n print 'Removing: {0}, {1}'.format(urlFilePath, urlpcFilePath)\n commands = [\n 'rm -f {0}'.format(urlFilePath),\n 'rm -f {0}'.format(urlpcFilePath)\n ]\n for cmdss in commands:\n p = Popen(cmdss, shell=True)\n p.wait()\n # Skip the rest of this url\n print \"Moving on!\"\n continue\n\n # Parse data\n tmp_path = 'temp/tmp_benchmark_result_json'\n with open(tmp_path, 'rb') as f:\n tmp_json = json.load(f)\n benchmark_results = tmp_json['values']\n commands = [\n 'rm -f ~/page_load_time/telemetry/temp/tmp_benchmark_result_json',\n ]\n for cmds in commands:\n p = Popen(cmds, shell=True)\n p.wait()\n\n output = {urls[i]: {'cold_times': {trial_key: benchmark_results}}}\n output_file = os.path.join(output_path, urlsafe_b64encode(urls[i]))\n output_file += '.' + str(trial_number)\n try:\n with open(output_file, 'w') as f:\n json.dump(output, f)\n except IOError:\n raise IOError('Unable to write to {0}'.format(output_file))\n\n\n ############### Now run for Perfect Cache file ################\n\n try:\n out, err, returncode = \\\n get_benchmark_result(cmd.format(str(i) + '_pc'))\n timeout = False\n print 'successfully ran benchmark for url' + str(i) + '_pc'\n except TimeoutError:\n # Benchmark failed\n print 'Benchmark Timeout!'\n out = ''\n returncode = 1\n timeout = True\n\n failed = ['FAILED']\n if returncode != 0 or any(x in out for x in failed) or timeout:\n # If a benchmark fails, remove its corresponding wpr file, and act\n # as if it didn't exist\n # Remove from data/wpr_source\n\n print 'Benchmark {0}_pc failed'.format(i)\n print 'Out:'\n print out\n print 'Err:'\n print err\n urlName = 'url{0}_page_set_000.wpr'.format(i)\n urlpcName = 'url{0}_pc_page_set_000.wpr'.format(i)\n urlFilePath = os.path.join('data/wpr_source',urlName)\n urlpcFilePath = os.path.join('data/wpr_source',urlpcName)\n urlCmd = 'rm -f {0}'.format(urlFilePath)\n urlpcCmd = 'rm -f {0}'.format(urlpcFilePath)\n print 'Removing: {0}, {1}'.format(urlFilePath, urlpcFilePath)\n commands = [\n 'rm -f {0}'.format(urlFilePath),\n 'rm -f {0}'.format(urlpcFilePath)\n ]\n for cmdss in commands:\n p = Popen(cmdss, shell=True)\n p.wait()\n # Skip the rest of this url\n print \"Moving on!\"\n continue\n\n # Parse data\n tmp_path = 'temp/tmp_benchmark_result_json'\n with open(tmp_path, 'rb') as f:\n tmp_json = json.load(f)\n benchmark_results = tmp_json['values']\n\n commands = [\n 'rm -f ~/page_load_time/telemetry/temp/tmp_benchmark_result_json',\n ]\n for cmds in commands:\n p = Popen(cmds, shell=True)\n p.wait()\n\n output = {urls[i]: {'cold_times': {trial_key: benchmark_results}}}\n output_file = os.path.join(output_path, urlsafe_b64encode(urls[i]))\n output_file += '.' + str(trial_number) + '.pc'\n try:\n with open(output_file, 'w') as f:\n json.dump(output, f)\n except IOError:\n raise IOError('Unable to write to {0}'.format(output_file))","def main():\n logging.basicConfig(level=\"INFO\")\n assert len(sys.argv) == 2, \"Exactly one positional argument (path to the raw dataset) is \"\\\n \"needed. \\n\\nE.g. `python sparsity_benchmark ~/bff_data/final_table`\"\n\n # Prepares data for the benchmark, may take a while\n data_parameters = DATA_PARAMETERS.copy()\n data_parameters[\"input_file\"] = sys.argv[1]\n data_parameters[\"preprocessed_file\"] = os.path.join(\n os.path.dirname(data_parameters[\"input_file\"]),\n \"preprocessed_dataset.pkl\"\n )\n data_preprocessor = preprocess_dataset(data_parameters=data_parameters)\n\n # Note: the features here should be in range [0, ~1.2], according to the original experiments.\n # 0 corresponds to no data, everything else is linearly scaled from dB units.\n features, _ = data_preprocessor.load_dataset()\n\n logging.info(\"Starting benchmarks\")\n noisy_features = benchmark_noise(\n features=features,\n data_parameters=data_parameters,\n experiment_parameters=EXPERIMENT_PARAMETERS\n )\n benchmark_binarization(\n noisy_features=noisy_features,\n data_parameters=data_parameters,\n experiment_parameters=EXPERIMENT_PARAMETERS\n )\n logging.info(\"Done\")","def freeze_includes() -> List[str]:\n import _pytest\n\n result = list(_iter_all_modules(_pytest))\n return result","def workbench_scenarios():\n return [\n (\"FeedbackXBlock\",\n \"\"\"<vertical_demo>\n <feedback/>\n </vertical_demo>\n \"\"\"),\n ]","def test_cases(self) -> list[str]:\n cases = []\n for t in self._test_cases:\n if t not in cases:\n cases.append(t)\n return cases","def alldemos():\n rundemo(24,fig=True)\n rundemo(30,fig=True)\n rundemo(31,fig=True)\n rundemo(33)\n rundemo(34)","def get_tests(self):\n subtests = itertools.chain(*(s.get_tests() for s in self.suites.values()))\n tt = [t for t in itertools.chain(self.tests,subtests)]\n return tt","def test_run():\n for symbol in ['AAPL', 'IBM']:\n print(\"Max close\")\n print(symbol, get_max_close(symbol))","def main():\n test_merge_quick_sort()\n test_compare()","def main():\n\t\n\tDeck = []\n\tfor suite in range(suites):\n for typecard in range(1, typecard+1):\n cards.append(typecard)","def test_checkers_integration():\n for project in AVAILABLE_CHECKERS.values():\n assert project.get_latest_version()\n assert project.get_latest_major_versions()\n assert project.get_latest_minor_versions()","def workbench_scenarios():\n return [\n (\"filethumbs\",\n \"\"\"\\\n <vertical_demo>\n <filethumbs/>\n <filethumbs/>\n <filethumbs/>\n </vertical_demo>\n \"\"\")\n ]","def testFilterBySuite(self):\n self.setUpPyfakefs()\n gpu_json = {\n 'AAAAA1 AUTOGENERATED FILE DO NOT EDIT': {},\n 'Android Tester': {\n 'isolated_scripts': [\n {\n 'args': [\n 'webgl_conformance',\n ],\n 'isolate_name': 'not_telemetry',\n },\n ],\n },\n 'Linux Tester': {\n 'isolated_scripts': [\n {\n 'args': [\n 'not_a_suite',\n ],\n 'isolate_name': 'telemetry_gpu_integration_test',\n },\n ],\n },\n 'Windows Tester': {\n 'isolated_scripts': [\n {\n 'args': [\n 'webgl_conformance',\n ],\n 'isolate_name': 'telemetry_gpu_integration_test',\n },\n ],\n },\n }\n\n self.CreateFile(os.path.join(builders.TESTING_BUILDBOT_DIR,\n 'chromium.json'),\n contents=json.dumps(gpu_json))\n\n gpu_builders = builders.GetCiBuilders('webgl_conformance')\n self.assertEqual(gpu_builders, set(['Windows Tester']))","def test_benchmark_xsorted_debug(benchmark_items_fixture):\n do_benchmark(benchmark_items_fixture, xsorted)","def list_ciphers():\n global AVAILABLE_CIPHERS\n print(\"[!] Available ciphers: \")\n for i in range(len(AVAILABLE_CIPHERS)):\n print(\" ----> %s.%s\"%(i+1, AVAILABLE_CIPHERS[i]))\n exit()","def test_get_benchmark_methods_filter(self):\n config = mock.Mock()\n config.workspace = 'workspace'\n config.benchmark_method_patterns = ['new_foo.BenchmarkClass.filter:bench.*']\n benchmark_runner = benchmark.BenchmarkRunner(config)\n\n mock_benchmark_class = mock.Mock()\n mock_benchmark_class.benchmark_method_1 = 'foo'\n\n mock_module = mock.Mock()\n sys.modules['new_foo'] = mock_module\n mock_module.BenchmarkClass.return_value = mock_benchmark_class\n\n methods = benchmark_runner._get_benchmark_methods()\n\n self.assertEqual(1, len(methods))\n self.assertEqual('new_foo.BenchmarkClass.benchmark_method_1', methods[0])","def benchmark():\n parser = argparse.ArgumentParser(\n \n description='pyrpipe diagnostic utility\\nGenerate benchmark report.',\n \n usage='''pyrpipe_diagnostic report [<args>] <logfile>\n \n ''') \n parser.add_argument('-o', help='out file \\ndefault: same as input logfile',action=\"store\")\n parser.add_argument('-e', help='report output type: [MD,PDF,HTML] \\ndefault: PDF',default='PDF',action=\"store\")\n parser.add_argument('-v',help='verbose',action=\"store_true\")\n parser.add_argument('-f',help='Filter by programs. Provide a comma-separated list e.g., prefetch,STAR,bowtie2 \\ndefault None')\n parser.add_argument('-t',help='Temporary directory. \\ndefault ./tmp',action=\"store\")\n parser.add_argument('logfile', help='The log file generated by pyrpipe',action=\"store\")\n args = parser.parse_args(sys.argv[2:])\n \n logFile=args.logfile\n envLog=reports.checkEnvLog(logFile) \n #parse args\n vFlag=args.v\n if vFlag:\n print(\"Generating benchmarks\")\n outFile=\"\"\n if args.o is None:\n outFile=pu.get_file_basename(args.logfile)\n else:\n outFile=args.o\n outFile+='.'+args.e\n \n filters=[]\n if args.f is not None:\n filters= args.f.split(',')\n #create temp dir\n tempDir=\"\"\n if args.t is not None:\n tempDir= args.t\n else:\n tempDir=os.path.join(os.getcwd(),\"tmp\")\n #create tmp dir\n if not pu.check_paths_exist(tempDir):\n pu.mkdir(tempDir)\n \n reports.generateBenchmarkReport(logFile,envLog,filters,tempDir,outFile=outFile,verbose=args.v)","def main():\r\n _evaluative_test(5)\r\n _fuzz_test(1)\r\n _fuzz_test(1, 512)\r\n _fuzz_test(1, 1512)\r\n _fuzz_test(1000)\r\n _fuzz_test(1000, 512)\r\n _fuzz_test(1000, 4077)","def all_experiments():\n elo_explain_experiments()\n alpha_beta_experiments()\n mtcs_experiments()","def _generate_benchmark_variants(benchmark_spec):\n variants = []\n # Cold start.\n variants.append({\n 'name': benchmark_spec['name'] + ' (cold start)',\n 'app': benchmark_spec['app'],\n 'duration': benchmark_spec['duration'],\n 'measurements': benchmark_spec['measurements'],\n 'shell-args': benchmark_spec.get('shell-args',\n []) + _COLD_START_SHELL_ARGS})\n # Warm start.\n variants.append({\n 'name': benchmark_spec['name'] + ' (warm start)',\n 'app': benchmark_spec['app'],\n 'duration': benchmark_spec['duration'],\n 'measurements': benchmark_spec['measurements'],\n 'shell-args': benchmark_spec.get('shell-args', [])})\n return variants","def _missing_symbol_to_skipped_tests(self):\n return {\n \"MathMLElement\": [\"mathml\"],\n \"GraphicsLayer\": [\"compositing\"],\n \"WebCoreHas3DRendering\": [\"animations/3d\", \"transforms/3d\"],\n \"WebGLShader\": [\"fast/canvas/webgl\", \"compositing/webgl\", \"http/tests/canvas/webgl\"],\n \"MHTMLArchive\": [\"mhtml\"],\n }","def usage():\n \n print '-b <bench> the bench to show.'\n print '-c <config> the config to show (GPU, 8888, 565, etc).'\n print '-d <dir> a directory containing bench_r<revision>_<scalar> files.'\n print '-e <file> file containing expected bench values/ranges.'\n print ' Will raise exception if actual bench values are out of range.'\n print ' See bench_expectations.txt for data format and examples.'\n print '-f <revision>[:<revision>] the revisions to use for fitting.'\n print ' Negative <revision> is taken as offset from most recent revision.'\n print '-i <time> the time to ignore (w, c, g, etc).'\n print ' The flag is ignored when -t is set; otherwise we plot all the'\n print ' times except the one specified here.'\n print '-l <title> title to use for the output graph'\n print '-m <representation> representation of bench value.'\n print ' See _ListAlgorithm class in bench_util.py.'\n print '-o <path> path to which to write output; writes to stdout if not specified'\n print '-r <revision>[:<revision>] the revisions to show.'\n print ' Negative <revision> is taken as offset from most recent revision.'\n print '-s <setting>[=<value>] a setting to show (alpha, scalar, etc).'\n print '-t <time> the time to show (w, c, g, etc).'\n print '-x <int> the desired width of the svg.'\n print '-y <int> the desired height of the svg.'\n print '--default-setting <setting>[=<value>] setting for those without.'","def get_tests():\n\tret = []\n\tfor walk_tuple in os.walk(webnotes.defs.modules_path):\n\t\tfor test_file in filter(lambda x: x.startswith('test') and x.endswith('.py'), walk_tuple[2]):\n\t\t\tdir_path = os.path.relpath(walk_tuple[0], webnotes.defs.modules_path)\n\t\t\tif dir_path=='.':\n\t\t\t\tret.append(test_file[:-3])\n\t\t\telse:\n\t\t\t\tret.append(dir_path.replace('/', '.') + '.' + test_file[:-3])\t\t\t\n\treturn ret","def list(self):\n print \"\\nAvailable Test Cases\"\n print \"====================\"\n for case in self.cases:\n print case.__name__","def time_binaries():\n print()\n print(\"=== BENCHMARKING ===\")\n with tempfile.NamedTemporaryFile(\n prefix=\"riff-benchmark\", suffix=\".gitlog\"\n ) as testdata:\n subprocess.check_call(\n [\"git\", \"log\", \"--color=always\", \"-p\", \"master\"], stdout=testdata\n )\n\n binaries = sorted(glob.glob(os.path.join(BINDIR, \"*\")), key=natural_keys)\n\n # Do riff-current last: https://stackoverflow.com/a/20320940/473672\n binaries.sort(key=lambda s: s.endswith(\"riff-current\"))\n\n for binary in binaries:\n print_timings(binary, testdata.name)\n print_timings(\"/bin/cat\", testdata.name)","def spec_tests():\n pass","def compare_hard_noprint():\n\n\n cores = [1, 2, 4, 8, 16]\n s_times = []\n\n for n_cores in cores:\n print('analyzing', '-'.join(['data/bench', 's', 'np', str(n_cores)]) + '.out')\n data = split_output_file('-'.join(['data/bench', 's', 'np', str(n_cores)]) + '.out')\n s_times.append(data['FOOTER']['total_time'])\n\n # speedup plot\n fig, axs = plt.subplots()\n\n axs.plot(cores, [s_times[0] / x / k for x, k in zip(s_times, cores)], label='sequential')\n axs.set_xticks(cores)\n axs.ticklabel_format(style='sci', scilimits=(-128, 128))\n\n axs.set_ylabel('Relative speedup')\n axs.set_xlabel('Number of cores')\n axs.set_ylim(0, 1.1)\n\n fig.set_size_inches(4, 2, forward=True)\n plt.tight_layout()\n\n plt.show()","def run_tests(tests):\n return [test(t) for t in tests]","def BenchArgs(data_file):\n return ['--timers', 'wcg', '--logFile', data_file]","def eg_basic():\n\n print(\"\", \"True\", stats.bootstrap([8, 7, 6, 2, 5, 8, 7, 3], [\n 8, 7, 6, 2, 5, 8, 7, 3]), utils.cliffsDelta([8, 7, 6, 2, 5, 8, 7, 3], [8, 7, 6, 2, 5, 8, 7, 3]), sep=\"\\t\")\n print(\"\", \"False\", stats.bootstrap([8, 7, 6, 2, 5, 8, 7, 3], [\n 9, 9, 7, 8, 10, 9, 6]), utils.cliffsDelta([8, 7, 6, 2, 5, 8, 7, 3], [9, 9, 7, 8, 10, 9, 6]), sep=\"\\t\")\n print(\"\", \"False\", stats.bootstrap([0.34, 0.49, 0.51, 0.6, .34, .49, .51, .6], [0.6, 0.7, 0.8, 0.9, .6, .7, .8, .9]), utils.cliffsDelta(\n [0.34, 0.49, 0.51, 0.6, .34, .49, .51, .6], [0.6, 0.7, 0.8, 0.9, .6, .7, .8, .9]), sep=\"\\t\")","def GetAllOptimizationsAvailable() -> typing.List[str]:\n # We must disable logging here - this function is invoked to set\n # OPTIMIZATION_PASSES variable below, before flags are parsed.\n proc = Exec([\"-help-list-hidden\"], log=False)\n lines = proc.stdout.split(\"\\n\")\n # Find the start of the list of optimizations.\n for i in range(len(lines)):\n if lines[i] == \" Optimizations available:\":\n break\n else:\n raise OptException\n # Find the end of the list of optimizations.\n for j in range(i + 1, len(lines)):\n if not lines[j].startswith(\" -\"):\n break\n else:\n raise OptException\n\n # Extract the list of optimizations.\n optimizations = [line[len(\" \") :].split()[0] for line in lines[i + 1 : j]]\n if len(optimizations) < 2:\n raise OptException\n\n return optimizations","def generate_figures():\r\n # create results directory if necessary\r\n try:\r\n makedirs(\"results\")\r\n except OSError as e:\r\n if e.errno != errno.EEXIST:\r\n raise\r\n \r\n for b in benchmarks:\r\n generate_figure(model[b], b)","def main():\n test_problem1a()\n test_problem1b()\n test_problem1c()","def stabilisers(self) -> List[PauliTerm]:\n zeros = np.zeros(self.n, dtype='int')\n x_stabilisers = (\n pauli_term_for_row(self.parity_check_c1[i, :], zeros)\n for i in range(self.r_1)\n )\n z_stabilisers = (\n pauli_term_for_row(zeros, self.parity_check_c2[i, :])\n for i in range(self.r_2)\n )\n return list(itertools.chain(x_stabilisers, z_stabilisers))","def workbench_scenarios():\n return [\n (\"QnetXBlock\",\n \"\"\"<qnet/>\n \"\"\"),\n (\"Multiple QnetXBlock\",\n \"\"\"<vertical_demo>\n <qnet/>\n <qnet/>\n <qnet/>\n </vertical_demo>\n \"\"\"),\n ]","def list_bugs(self,active_only=True):\n for bug in self.bugs(active_only=active_only):\n print(\"*******************\")\n name=bug[\"bug_name\"]\n for k in bug.keys():\n \n print(name+\"\\t\"+k+\"\\t\"+str(bug[k]).replace(\"\\n\",\" ;; \"))\n\n print(\"*******************\")","def main() -> None:\r\n # pylint: disable=W0601\r\n global ARGS, BROWSERS\r\n BROWSERS = {'chrome': Chrome, 'firefox': Firefox, 'ie': Ie,\r\n 'safari': Safari, 'opera': Opera, 'edge': Edge}\r\n # pylint: disable=C0103\r\n PARSER = argparse.ArgumentParser()\r\n PARSER.add_argument('-m', '--modules', help='Which modules to test. One or more of '\r\n '[%(choices)s]. Default is all.', nargs='+', type=str,\r\n choices=MODULES.keys(), metavar='', default=list(MODULES.keys()))\r\n PARSER.add_argument('-l', '--locales', help='Which locales to test. One or more of '\r\n '[%(choices)s]. Default is all.', nargs='+', type=str,\r\n choices=LANGS.keys(), metavar='', default=list(LANGS.keys()))\r\n PARSER.add_argument('-b', '--browsers', help='Which browser to use. One or more of '\r\n '[%(choices)s]. Default is %(default)s', nargs='+', default=['chrome'],\r\n choices=BROWSERS.keys(), metavar='')\r\n PARSER.add_argument('-w', '--wait', help='Wait this many seconds before deciding an element is '\r\n 'missing. Default is %(default)s', default=[20], type=int, nargs=1)\r\n ARGS = PARSER.parse_args()\r\n parseargs()\r\n os.makedirs(SCREENSHOT_DIR, exist_ok=True)\r\n\r\n try:\r\n full_languages_modules_run(modfilter=ARGS.modules,\r\n langfilter=ARGS.locales, brows=ARGS.browsers)\r\n except Exception: # Too general is the point, it's a Final Action. pylint: disable=W0703\r\n with open(RESULTS_FILE, mode='a', encoding='UTF-8') as log:\r\n log.write('\\n\"Well, something went wrong. A manual exit, hopefully:\"\\n\\n' + tidy_error())\r\n raise","def test_nothing_fits():\n freetimes, _ = free([], 9, 0, 17, 0, day_range, 600)\n fmt_freetime = output_format(freetimes)\n for i in fmt_freetime:\n print(i)\n assert fmt_freetime == []","def describe_tests(remit, sourcelist):\n print('* run tests with \"%s\"' % remit)\n print('* tests to run: ')\n for line in pretty_list(sourcelist, 7):\n print(' ' + line)\n print('* writers to test: ')\n for line in pretty_list(spec.TEST['writer'], 7):\n print(' ' + line)\n print('* options to test: ')\n for line in pretty_list(spec.TEST['pandoc_options'], 7):\n print(' ' + line)\n print('* blacklisted: ')\n for config in spec.BLACKLIST:\n command = make_command(remit=remit,\n source='TEST',\n writer=config['writer'],\n pandoc_options=config['pandoc_options'],\n extension=config['extension'])\n print(' ' + ' '.join(command))\n print(' (' + config['comment'] + ')')","def list(args):\n experiments = sorted(os.listdir('./litmus'))\n print_color(\"Available Litmus Chaos Experiments:\\n\\n\")\n if (f\"{args.platform}\" == \"GKE\"):\n i = 1\n for experiment_file in experiments:\n print_color(f\"\\t{i}. {experiment_file.replace('.yaml', '')}\")\n i += 1\n\n if (f\"{args.platform}\" == \"kind\"):\n kind_supported = [\"pod-delete\",\"container-kill\",\"node-cpu-hog\",\"node-memory-hog\"]\n i = 0\n for i in range(0, len(kind_supported)):\n print_color(f\"\\t{i+1}. {kind_supported[i]}\")\n i += 1\n\n if (f\"{args.platform}\" == \"EKS\"):\n i = 1\n for experiment_file in experiments:\n print_color(f\"\\t{i}. {experiment_file.replace('.yaml', '')}\")\n i += 1","def run_experiment():\n return [random.random() < 0.5 for _ in range(1000)]","def run_experiment():\n return [random.random() < 0.5 for _ in range(1000)]","def run_experiment():\n return [random.random() < 0.5 for _ in range(1000)]","def run_experiment():\n return [random.random() < 0.5 for _ in range(1000)]","def get_all_platform_tests(self):\n for testitem in self.get_tests(self.discover_tests()):\n if not testitem:\n continue\n prefix = \"tests.\" + self.platform + \".\"\n self.formatted_tests_set.append(\n prefix + self.format_into_test_path(testitem)\n )\n\n if self.denylist:\n try:\n with open(self.denylist, \"r\") as f:\n denylist = f.read().splitlines()\n except FileNotFoundError:\n denylist = []\n\n self.formatted_tests_set = [\n t for t in self.formatted_tests_set if t not in denylist\n ]\n\n return self.formatted_tests_set","def test_BenchmarkSuite_integration_test(\n benchmark_suite: typing.Callable, tempdir: pathlib.Path\n):\n with benchmark_suite() as bs:\n bs.ForceOpenCLEnvironment(cldrive_env.OclgrindOpenCLEnvironment())\n observer = MockBenchmarkObserver(stop_after=1)\n\n # `stop_after` raises BenchmarkInterrupt.\n try:\n bs.Run([observer])\n assert False\n except gpgpu.BenchmarkInterrupt:\n pass\n\n assert len(observer.logs) == 1\n assert observer.logs[0].benchmark_name in bs.benchmarks","def test():\n\t\treturn [\"vice.core.objects.tests\",\n\t\t\t[\n\t\t\t\tagb.test_agb_grid_constructor(),\n\t\t\t\tagb.test_agb_grid_destructor(),\n\t\t\t\tcallback_1arg.test_callback_1arg_constructor(),\n\t\t\t\tcallback_1arg.test_callback_1arg_destructor(),\n\t\t\t\tcallback_2arg.test_callback_2arg_constructor(),\n\t\t\t\tcallback_2arg.test_callback_2arg_destructor(),\n\t\t\t\tccsne.test_ccsne_yield_specs_constructor(),\n\t\t\t\tccsne.test_ccsne_yield_specs_destructor(),\n\t\t\t\tchannel.test_channel_constructor(),\n\t\t\t\tchannel.test_channel_destructor(),\n\t\t\t\telement.test_element_constructor(),\n\t\t\t\telement.test_element_destructor(),\n\t\t\t\tfromfile.test_fromfile_constructor(),\n\t\t\t\tfromfile.test_fromfile_destructor(),\n\t\t\t\thydrodiskstars.test_hydrodiskstars_constructor(),\n\t\t\t\thydrodiskstars.test_hydrodiskstars_destructor(),\n\t\t\t\timf.test_imf_constructor(),\n\t\t\t\timf.test_imf_destructor(),\n\t\t\t\tintegral.test_integral_constructor(),\n\t\t\t\tintegral.test_integral_destructor(),\n\t\t\t\tinterp_scheme_1d.test_interp_scheme_1d_constructor(),\n\t\t\t\tinterp_scheme_1d.test_interp_scheme_1d_destructor(),\n\t\t\t\tinterp_scheme_2d.test_interp_scheme_2d_constructor(),\n\t\t\t\tinterp_scheme_2d.test_interp_scheme_2d_destructor(),\n\t\t\t\tism.test_ism_constructor(),\n\t\t\t\tism.test_ism_destructor(),\n\t\t\t\tmdf.test_mdf_constructor(),\n\t\t\t\tmdf.test_mdf_destructor(),\n\t\t\t\tmigration.test_migration_constructor(),\n\t\t\t\tmigration.test_migration_destructor(),\n\t\t\t\tmultizone.test_multizone_constructor(),\n\t\t\t\tmultizone.test_multizone_destructor(),\n\t\t\t\tsinglezone.test_singlezone_constructor(),\n\t\t\t\tsinglezone.test_singlezone_destructor(),\n\t\t\t\tsneia.test_sneia_yield_specs_constructor(),\n\t\t\t\tsneia.test_sneia_yield_specs_destructor(),\n\t\t\t\tssp.test_ssp_constructor(),\n\t\t\t\tssp.test_ssp_destructor(),\n\t\t\t\ttracer.test_tracer_constructor(),\n\t\t\t\ttracer.test_tracer_destructor()\n\t\t\t]\n\t\t]","def workbench_scenarios():\n return [\n (\"Discussion XBlock\",\n \"\"\"<vertical_demo>\n <discussion-forum/>\n </vertical_demo>\n \"\"\"),\n ]","def PrintUnexpectedPasses(unexpected_passes, args):\n for builder, passes in unexpected_passes.iteritems():\n passed_all = {}\n passed_some = {}\n for suite, tests in passes.iteritems():\n for test, num_passes in tests.iteritems():\n if num_passes == args.num_samples:\n passed_all.setdefault(suite, []).append(test)\n else:\n passed_some.setdefault(suite, []).append((test, num_passes))\n\n # Alphabetize for readability.\n for tests in passed_all.values():\n tests.sort()\n for tests in passed_some.values():\n tests.sort()\n\n print '##### %s #####' % builder\n if passed_all:\n print '----- Tests that passed in all runs -----'\n for suite, tests in passed_all.iteritems():\n print '%s:' % suite\n for test in tests:\n print ' %s' % test\n print ''\n if passed_some:\n print '----- Tests that passed in some runs -----'\n for suite, tests in passed_some.iteritems():\n print '%s:' % suite\n for (test, num_passes) in tests:\n print ' %s: %d/%d' % (test, num_passes, args.num_samples)\n print '\\n\\n'","def suite():\n test_suite = unittest.TestSuite()\n test_suite.addTest(unittest.makeSuite(TestReversi))\n test_suite.addTest(unittest.makeSuite(TestGuessNumberGame))\n test_suite.addTest(unittest.makeSuite(TestConnectFourGame))\n test_suite.addTest(unittest.makeSuite(TestBuscamina))\n test_suite.addTest(unittest.makeSuite(TestGame))\n test_suite.addTest(unittest.makeSuite(TestDamaGame))\n test_suite.addTest(unittest.makeSuite(TestTateti))\n test_suite.addTest(unittest.makeSuite(TestGameBase))\n test_suite.addTest(unittest.makeSuite(TestFourNumber))\n test_suite.addTest(unittest.makeSuite(test_game_generala))\n test_suite.addTest(unittest.makeSuite(test_categories))\n test_suite.addTest(unittest.makeSuite(test_player))\n test_suite.addTest(unittest.makeSuite(test_throw_class))\n test_suite.addTest(unittest.makeSuite(test_throw_dice))\n test_suite.addTest(unittest.makeSuite(TestBets))\n test_suite.addTest(unittest.makeSuite(TestDeck))\n test_suite.addTest(unittest.makeSuite(TestBlackjackGame))\n test_suite.addTest(unittest.makeSuite(TestHands))\n test_suite.addTest(unittest.makeSuite(PokerTest))\n test_suite.addTest(unittest.makeSuite(PokerGameTest))\n test_suite.addTest(unittest.makeSuite(TestBattleship))\n test_suite.addTest(unittest.makeSuite(TestBoard))\n test_suite.addTest(craps_suite())\n test_suite.addTest(sudoku_suite())\n test_suite.addTest(roulette_suite())\n test_suite.addTest(dungeon_suite())\n test_suite.addTest(unittest.makeSuite(TestSenku))\n test_suite.addTest(unittest.makeSuite(TestAhorcado))\n test_suite.addTest(unittest.makeSuite(TestHanoiTower))\n return test_suite","def zio_test_help():\n print(\"zio-ut [TESTS]\")\n print(\"\")\n print(\"[TESTS]: list of tests to perform. It can be the name of a specific test, or the name of a module of tests\")\n print(\" In alternative, you che use the test code:\")\n print(\"Code test case\")\n print(\"- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - \")\n i = 0\n for t in test_list:\n print(str(i) + \" \" + t)\n i = i + 1","def cli(argv):\r\n argv.append(\"--exhaust-materials\")\r\n cltestbench.cli(argv)","def suite_list(self):\n return ','.join([s.__unicode__() for s in self.suites.all()])","def suite_list(self):\n return ','.join([s.__unicode__() for s in self.suites.all()])","def main():\n reportSample = CompatibilityReportSample()\n reportSample.run()","def _parse_suite(\n self, results: dict, extra_tags: dict = None\n ) -> List[BenchmarkResult]:\n # all results share a batch id\n batch_id = uuid.uuid4().hex\n\n parsed_results = []\n for result in results[\"benchmarks\"]:\n result_parsed = self._parse_benchmark(\n result=GoogleBenchmark(**result),\n batch_id=batch_id,\n extra_tags=extra_tags,\n )\n parsed_results.append(result_parsed)\n\n return parsed_results","def test_big_numbers(self):\n arr, result = [535555555555, 5500000000000000000000000,\n 10, 15, 35, 0, -2, -67, -55], []\n fizz_buzz(arr, result)\n self.assertEqual(result, ['fizz', 'fizz', 'fizz', 'fizzbuzz',\n 'fizz', 'fizzbuzz', -2, -67, 'fizz'])","def get_supported_browsers_suggestions():\n supported_browsers = [\n 'chrome',\n 'chrome-remote',\n 'chrome-headless',\n 'chrome-remote-headless',\n 'firefox',\n 'firefox-remote',\n 'ie',\n 'ie-remote'\n ]\n return supported_browsers","def main():\n \n try:\n opts, _ = getopt.getopt(sys.argv[1:]\n , \"b:c:d:e:f:i:l:m:o:r:s:t:x:y:\"\n , \"default-setting=\")\n except getopt.GetoptError, err:\n print str(err) \n usage()\n sys.exit(2)\n \n directory = None\n config_of_interest = None\n bench_of_interest = None\n time_of_interest = None\n time_to_ignore = None\n bench_expectations = {}\n rep = None # bench representation algorithm\n revision_range = '0:'\n regression_range = '0:'\n latest_revision = None\n requested_height = None\n requested_width = None\n title = 'Bench graph'\n settings = {}\n default_settings = {}\n\n def parse_range(range):\n \"\"\"Takes '<old>[:<new>]' as a string and returns (old, new).\n Any revision numbers that are dependent on the latest revision number\n will be filled in based on latest_revision.\n \"\"\"\n old, _, new = range.partition(\":\")\n old = int(old)\n if old < 0:\n old += latest_revision;\n if not new:\n new = latest_revision;\n new = int(new)\n if new < 0:\n new += latest_revision;\n return (old, new)\n\n def add_setting(settings, setting):\n \"\"\"Takes <key>[=<value>] adds {key:value} or {key:True} to settings.\"\"\"\n name, _, value = setting.partition('=')\n if not value:\n settings[name] = True\n else:\n settings[name] = value\n\n def read_expectations(expectations, filename):\n \"\"\"Reads expectations data from file and put in expectations dict.\"\"\"\n for expectation in open(filename).readlines():\n elements = expectation.strip().split(',')\n if not elements[0] or elements[0].startswith('#'):\n continue\n if len(elements) != 5:\n raise Exception(\"Invalid expectation line format: %s\" %\n expectation)\n bench_entry = elements[0] + ',' + elements[1]\n if bench_entry in expectations:\n raise Exception(\"Dup entries for bench expectation %s\" %\n bench_entry)\n # [<Bench_BmpConfig_TimeType>,<Platform-Alg>] -> (LB, UB)\n expectations[bench_entry] = (float(elements[-2]),\n float(elements[-1]))\n\n def check_expectations(lines, expectations, newest_revision, key_suffix):\n \"\"\"Check if there are benches in latest rev outside expected range.\"\"\"\n exceptions = []\n for line in lines:\n line_str = str(line)\n bench_platform_key = (line_str[ : line_str.find('_{')] + ',' +\n key_suffix)\n this_revision, this_bench_value = lines[line][-1]\n if (this_revision != newest_revision or\n bench_platform_key not in expectations):\n # Skip benches without value for latest revision.\n continue\n this_min, this_max = expectations[bench_platform_key]\n if this_bench_value < this_min or this_bench_value > this_max:\n exceptions.append('Bench %s value %s out of range [%s, %s].' %\n (bench_platform_key, this_bench_value, this_min, this_max))\n if exceptions:\n raise Exception('Bench values out of range:\\n' +\n '\\n'.join(exceptions))\n\n try:\n for option, value in opts:\n if option == \"-b\":\n bench_of_interest = value\n elif option == \"-c\":\n config_of_interest = value\n elif option == \"-d\":\n directory = value\n elif option == \"-e\":\n read_expectations(bench_expectations, value)\n elif option == \"-f\":\n regression_range = value\n elif option == \"-i\":\n time_to_ignore = value\n elif option == \"-l\":\n title = value\n elif option == \"-m\":\n rep = value\n elif option == \"-o\":\n redirect_stdout(value)\n elif option == \"-r\":\n revision_range = value\n elif option == \"-s\":\n add_setting(settings, value)\n elif option == \"-t\":\n time_of_interest = value\n elif option == \"-x\":\n requested_width = int(value)\n elif option == \"-y\":\n requested_height = int(value)\n elif option == \"--default-setting\":\n add_setting(default_settings, value)\n else:\n usage()\n assert False, \"unhandled option\"\n except ValueError:\n usage()\n sys.exit(2)\n\n if directory is None:\n usage()\n sys.exit(2)\n\n if time_of_interest:\n time_to_ignore = None\n\n # The title flag (-l) provided in buildbot slave is in the format\n # Bench_Performance_for_Skia_<platform>, and we want to extract <platform>\n # for use in platform_and_alg to track matching benches later. If title flag\n # is not in this format, there may be no matching benches in the file\n # provided by the expectation_file flag (-e).\n platform_and_alg = title\n if platform_and_alg.startswith(TITLE_PREAMBLE):\n platform_and_alg = (\n platform_and_alg[TITLE_PREAMBLE_LENGTH:] + '-' + rep)\n title += ' [representation: %s]' % rep\n\n latest_revision = get_latest_revision(directory)\n oldest_revision, newest_revision = parse_range(revision_range)\n oldest_regression, newest_regression = parse_range(regression_range)\n\n unfiltered_revision_data_points = parse_dir(directory\n , default_settings\n , oldest_revision\n , newest_revision\n , rep)\n\n # Filter out any data points that are utterly bogus... make sure to report\n # that we did so later!\n (allowed_revision_data_points, ignored_revision_data_points) = filter_data_points(\n unfiltered_revision_data_points)\n\n # Update oldest_revision and newest_revision based on the data we could find\n all_revision_numbers = allowed_revision_data_points.keys()\n oldest_revision = min(all_revision_numbers)\n newest_revision = max(all_revision_numbers)\n\n lines = create_lines(allowed_revision_data_points\n , settings\n , bench_of_interest\n , config_of_interest\n , time_of_interest\n , time_to_ignore)\n\n regressions = create_regressions(lines\n , oldest_regression\n , newest_regression)\n\n output_xhtml(lines, oldest_revision, newest_revision, ignored_revision_data_points,\n regressions, requested_width, requested_height, title)\n\n check_expectations(lines, bench_expectations, newest_revision,\n platform_and_alg)","def Cleanup(benchmark_spec):\n del benchmark_spec # unused"],"string":"[\n \"def get_all_benchmarks():\\n all_benchmarks = []\\n for benchmark in os.listdir(BENCHMARKS_DIR):\\n benchmark_path = os.path.join(BENCHMARKS_DIR, benchmark)\\n if os.path.isfile(os.path.join(benchmark_path, 'benchmark.yaml')):\\n all_benchmarks.append(benchmark)\\n return sorted(all_benchmarks)\",\n \"def get_coverage_benchmarks():\\n return [\\n benchmark for benchmark in get_all_benchmarks()\\n if get_type(benchmark) == BenchmarkType.CODE.value\\n ]\",\n \"def run_all(self):\\n runs = []\\n for run in self.benchmarks:\\n run.start()\\n run.wait()\\n runs.append(run.metadata)\\n return runs\",\n \"def get_testbench_specs(self, tb_type: str) -> Dict[str, Any]:\\n return self._specs['testbenches'][tb_type]\",\n \"def get_tests():\\n # tests = ['test_build_gaussian_pyramid_random', 'test_build_gaussian_pyramid_static', 'test_build_laplacian_pyramid_random', 'test_build_laplacian_pyramid_static', 'test_laplacian_to_image', 'test_render_pyramid_random', 'test_render_pyramid_static']\\n # return [tester.TestEx3(method) for method in tests]\\n return [tester.TestEx3(method) for method in dir(tester.TestEx3) if method.startswith('test')]\",\n \"def list(self, classes):\\n\\n def add(benchmarks, parts, flags, exclude):\\n if (\\n flags[\\\"language\\\"] != \\\"C++\\\"\\n and flags[\\\"language\\\"] != \\\"Java\\\"\\n and \\\"--drop-caches=true\\\" not in parts\\n ):\\n parts.append(\\\"--drop-caches=true\\\")\\n command = \\\" \\\".join(parts)\\n if command not in exclude:\\n benchmarks.append({\\\"command\\\": command, \\\"flags\\\": flags})\\n\\n benchmarks = []\\n for name, benchmark in classes.items():\\n if name.startswith(\\\"example\\\"):\\n continue\\n\\n instance, parts = benchmark(), [name]\\n\\n exclude = getattr(benchmark, \\\"exclude\\\", [])\\n if \\\"source\\\" in getattr(benchmark, \\\"arguments\\\", []):\\n parts.append(\\\"ALL\\\")\\n\\n iterations = getattr(instance, \\\"iterations\\\", 3)\\n parts.append(f\\\"--iterations={iterations}\\\")\\n\\n if instance.cases:\\n parts.append(\\\"--all=true\\\")\\n\\n flags = getattr(instance, \\\"flags\\\", {})\\n\\n if getattr(instance, \\\"r_only\\\", False):\\n flags[\\\"language\\\"] = \\\"R\\\"\\n add(benchmarks, parts, flags, exclude)\\n else:\\n if \\\"language\\\" not in flags:\\n flags[\\\"language\\\"] = \\\"Python\\\"\\n add(benchmarks, parts, flags, exclude)\\n\\n if hasattr(instance, \\\"r_name\\\"):\\n flags_ = flags.copy()\\n flags_[\\\"language\\\"] = \\\"R\\\"\\n parts.append(\\\"--language=R\\\")\\n add(benchmarks, parts, flags_, exclude)\\n\\n return sorted(benchmarks, key=lambda k: k[\\\"command\\\"])\",\n \"def get_benchmark_specification(benchmark = 'FSI1'):\\n if benchmark == 'FSI1':\\n rho_s = Constant(1e03)\\n nu_s = Constant(0.4)\\n mu_s = Constant(5e05)\\n rho_f = Constant(1e03)\\n nu_f = Constant(1e-03)\\n U = 0.2\\n T_end = 60.0\\n result = \\\"results-FSI1/\\\"\\n elif benchmark == 'FSI2':\\n rho_s = Constant(1e04)\\n nu_s = Constant(0.4)\\n mu_s = Constant(5e05)\\n rho_f = Constant(1e03)\\n nu_f = Constant(1e-03)\\n U = 1.0\\n T_end = 15.0\\n result = \\\"results-FSI2/\\\"\\t\\t\\n elif benchmark == 'FSI3':\\n rho_s = Constant(1e03)\\n nu_s = Constant(0.4)\\n mu_s = Constant(2e06)\\n rho_f = Constant(1e03)\\n nu_f = Constant(1e-03)\\n U = 2.0\\n T_end = 20.0\\n result = \\\"results-FSI3/\\\"\\t\\t\\n else:\\n raise ValueError('\\\"{}\\\" is a wrong name for problem specification.'.format(benchmark))\\n v_max = Constant(1.5*U) # mean velocity to maximum velocity \\n # (we have parabolic profile)\\n E_s = Constant(2*mu_s*(1+nu_s))\\n lambda_s = Constant((nu_s*E_s)/((1+nu_s)*(1-2*nu_s)))\\n mu_f = Constant(nu_f*rho_f)\\n return v_max, lambda_s, mu_s, rho_s, mu_f, rho_f, T_end, result\",\n \"def get_performance_test_cases(test_suite):\\n return get_cases(test_suite, r'test_perf_')\",\n \"def exclude_non_cpp(benchmarks):\\n return [benchmark for benchmark in benchmarks if is_cpp(benchmark)]\",\n \"def run_benchmark():\\n import argparse\\n parser = argparse.ArgumentParser(description='Benchmark alchemically modified system against unmodified system.')\\n parser.add_argument('--platform', dest='platform_name', action='store', default=None, help='platform name to benchmark (default: None)')\\n options = parser.parse_args()\\n\\n from sams.tests import testsystems\\n for testsystem_name in ['AblImatinibExplicitAlchemical']:\\n cls = getattr(testsystems, testsystem_name)\\n testsystem = cls()\\n factory_args = { 'ligand_atoms' : testsystem.alchemical_atoms, 'receptor_atoms' : range(0,4266) }\\n benchmark(testsystem.system, testsystem.positions, platform_name=options.platform_name, nsteps=5000, timestep=1.0*unit.femtoseconds, factory_args=factory_args)\",\n \"def _parameterize_simple_benchmarks():\\n parameterized_tuples = []\\n for index, simple_benchmark in enumerate(\\n all_benchmarks.all_benchmarks(modules=[simple_benchmarks])):\\n # The index ensures all test cases have distinct names, even if multiple\\n # benchmarks have the same name.\\n test_case_name = '{index}_{name}'.format(index=index,\\n name=simple_benchmark.name)\\n parameterized_tuples.append((test_case_name, simple_benchmark))\\n return parameterized_tuples\",\n \"def print_scenario(benchmarks, name):\\n # Remember, the first entry in the times array is an empty object.\\n\\n benchmarks = sorted(benchmarks, key = benchmark_sort_key)\\n for benchmark in benchmarks:\\n block_times = benchmark[\\\"times\\\"][2][\\\"block_times\\\"] # list of floats\\n it = iter(block_times)\\n for start in it:\\n end = next(it)\\n\\n print(\\\"%s , %.3f \\\" % (benchmark[\\\"label\\\"], float(end) - float(start)))\",\n \"def delete_all_benchmarks(self, namespace=\\\"benchmark-operator\\\"):\\n all_benchmarks = self.crd_client.list_namespaced_custom_object(\\n group=\\\"ripsaw.cloudbulldozer.io\\\", version=\\\"v1alpha1\\\", namespace=namespace, plural=\\\"benchmarks\\\"\\n )\\n\\n _ = [\\n self.delete_benchmark(benchmark[\\\"metadata\\\"][\\\"name\\\"], namespace)\\n for benchmark in all_benchmarks.get(\\\"items\\\", [])\\n ]\",\n \"def _disc_benchmarks(cls, conf, repo, environments, commit_hashes, check):\\n root = conf.benchmark_dir\\n\\n cls.check_tree(root)\\n\\n if len(environments) == 0:\\n raise util.UserError(\\\"No available environments\\\")\\n\\n # Try several different commits:\\n #\\n # - First of commit_hashes provided\\n # - Tips of branches from configuration file\\n # - Rest of the commit_hashes\\n #\\n\\n def iter_hashes():\\n for h in commit_hashes[:1]:\\n yield h\\n for branch in conf.branches:\\n try:\\n yield repo.get_hash_from_name(branch)\\n except NoSuchNameError:\\n continue\\n for h in commit_hashes[1:]:\\n yield h\\n\\n def iter_unique(iter):\\n seen = set()\\n for item in iter:\\n if item not in seen:\\n seen.add(item)\\n yield item\\n\\n try_hashes = iter_unique(iter_hashes())\\n\\n log.info(\\\"Discovering benchmarks\\\")\\n with log.indent():\\n last_err = None\\n for env, commit_hash in itertools.product(environments, try_hashes):\\n env.create()\\n\\n if last_err is not None:\\n log.warning(\\\"Failed: trying different commit/environment\\\")\\n\\n result_dir = tempfile.mkdtemp()\\n try:\\n env.install_project(conf, repo, commit_hash)\\n\\n env_vars = dict(os.environ)\\n env_vars.update(env.env_vars)\\n\\n result_file = os.path.join(result_dir, 'result.json')\\n env.run(\\n [runner.BENCHMARK_RUN_SCRIPT, 'discover',\\n os.path.abspath(root),\\n os.path.abspath(result_file)],\\n cwd=result_dir,\\n env=env_vars,\\n dots=False)\\n\\n try:\\n with open(result_file, 'r') as fp:\\n benchmarks = json.load(fp)\\n except (IOError, ValueError):\\n log.error(\\\"Invalid discovery output\\\")\\n raise util.UserError()\\n\\n break\\n except (util.UserError, util.ProcessError) as err:\\n last_err = err\\n continue\\n except KeyboardInterrupt:\\n raise util.UserError(\\\"Interrupted.\\\")\\n finally:\\n util.long_path_rmtree(result_dir)\\n else:\\n raise util.UserError(\\\"Failed to build the project and import the benchmark suite.\\\")\\n\\n if check:\\n log.info(\\\"Checking benchmarks\\\")\\n with log.indent():\\n result_dir = tempfile.mkdtemp()\\n try:\\n out, err, retcode = env.run(\\n [runner.BENCHMARK_RUN_SCRIPT, 'check',\\n os.path.abspath(root)],\\n cwd=result_dir,\\n dots=False,\\n env=env_vars,\\n valid_return_codes=None,\\n return_stderr=True,\\n redirect_stderr=True)\\n finally:\\n util.long_path_rmtree(result_dir)\\n\\n out = out.strip()\\n if retcode == 0:\\n if out:\\n log.info(out)\\n log.info(\\\"No problems found.\\\")\\n else:\\n if out:\\n log.error(out)\\n raise util.UserError(\\\"Benchmark suite check failed.\\\")\\n\\n return benchmarks\",\n \"def get_functional_test_cases(test_suite):\\n return get_cases(test_suite, r'test_(?!perf_)')\",\n \"def main():\\n parser = optparse.OptionParser()\\n parser.add_option('--debug', action='store_true', default=False,\\n help='run in debug mode')\\n parser.add_option('-i', '--iteration', type=int, default=DEFAULT_ITERATION,\\n metavar='NUM',\\n help='set the number of iterations for each test (defualt:%d)' % \\\\\\n DEFAULT_ITERATION)\\n parser.add_option('-f', '--fstypes', default='ext2,ext3,ext4,btrfs,xfs',\\n type='string', metavar='TYPES', help='set the file systems to test')\\n parser.add_option('-n', '--num', default=10000, type=int, metavar='NUM',\\n help='set the number of file created')\\n parser.add_option('-N', '--numa', action='store_true', default=False,\\n help='run NUMA test')\\n parser.add_option('-S', '--scalability', action='store_true', default=False,\\n help='run scalability test')\\n global options\\n options, args = parser.parse_args()\\n\\n benchutils.check_root_or_die()\\n suffix = ''\\n if options.numa:\\n suffix = 'numa'\\n else:\\n suffix = 'scale'\\n output_dir = benchutils.get_output_directory(suffix=suffix, timestamp=True)\\n fstypes = options.fstypes.split(',')\\n for fs in fstypes:\\n if options.numa:\\n run_tests(output_dir, fs)\\n elif options.scalability:\\n run_scalability_tests(output_dir, fs)\",\n \"def _run():\\n subprocess.check_call(\\n [\\n \\\"tools/bazel\\\",\\n \\\"build\\\",\\n \\\"-c\\\",\\n \\\"opt\\\",\\n \\\"test/core/memory_usage/memory_usage_test\\\",\\n ]\\n )\\n ret = {}\\n for name, benchmark_args in _BENCHMARKS.items():\\n for scenario, extra_args in _SCENARIOS.items():\\n # TODO(chenancy) Remove when minstack is implemented for channel\\n if name == \\\"channel\\\" and scenario == \\\"minstack\\\":\\n continue\\n try:\\n output = subprocess.check_output(\\n [\\n \\\"bazel-bin/test/core/memory_usage/memory_usage_test\\\",\\n ]\\n + benchmark_args\\n + extra_args\\n )\\n except subprocess.CalledProcessError as e:\\n print(\\\"Error running benchmark:\\\", e)\\n continue\\n for line in output.splitlines():\\n for key, (pattern, conversion) in _INTERESTING.items():\\n m = re.match(pattern, line)\\n if m:\\n ret[scenario + \\\": \\\" + key] = conversion(m.group(1))\\n return ret\",\n \"def main():\\r\\n algos = [merge_sort, quick_sort, heap_sort, radix_sort, bucket_sort_general]\\r\\n array_sizes = [5000, 10000, 15000, 20000, 50000, 75000, 100000, 150000]\\r\\n results = {algo.__name__: [] for algo in algos}\\r\\n for algo in algos:\\r\\n result = []\\r\\n for size in array_sizes:\\r\\n time = test(algo, size)\\r\\n result.append(time)\\r\\n results[algo.__name__] = result\\r\\n\\r\\n display_results(results, array_sizes)\",\n \"def generate_benchmarks(self, num, graphs = []):\\n if num != 0:\\n try:\\n print(\\\"{0} graphs left\\\".format(num), end='\\\\r')\\n graphs.append(self.generate_lfr_benchmark())\\n return self.generate_benchmarks(num-1, graphs)\\n except nx.exception.ExceededMaxIterations:\\n return self.generate_benchmarks(num, graphs)\\n else:\\n return graphs\",\n \"def test():\\n\\t\\treturn [\\\"vice.core.dataframe\\\",\\n\\t\\t\\t[\\n\\t\\t\\t\\ttests.test(run = False),\\n\\t\\t\\t\\t_builtin_dataframes.test(run = False)\\n\\t\\t\\t]\\n\\t\\t]\",\n \"def get_benchmark_requirements(cls):\\n pass\",\n \"def list_test_cases(program):\\n\\n return list(INFO[program].test_cases)\",\n \"def test_list_benchmarks(client):\\n # The benchmark listing contains one element (independently of whether the\\n # user is logged in or not).\\n r = client.get(config.API_PATH() + '/workflows')\\n assert r.status_code == 200\\n doc = r.json\\n assert len(doc[labels.WORKFLOW_LIST]) == 1\\n # Create user and the request header that contains the API key for the\\n # logged in user.\\n _, token = create_user(client, '0000')\\n headers = {HEADER_TOKEN: token}\\n r = client.get(config.API_PATH() + '/workflows', headers=headers)\\n assert r.status_code == 200\\n doc = r.json\\n assert len(doc[labels.WORKFLOW_LIST]) == 1\",\n \"def main(r_min=1, r_max=101):\\n fizz_buzz_all = (\\n fizz_buzz1(r_min, r_max),\\n fizz_buzz2(r_min, r_max),\\n fizz_buzz3(r_min, r_max)\\n )\\n for f in fizz_buzz_all:\\n start = time.time()\\n print(*f, sep='\\\\n')\\n end = time.time()\\n print(\\\"Execution time: \\\", end - start)\",\n \"def manquant(suite):\\n manque = []\\n for i in range(1, 10):\\n if i not in suite:\\n manque.append(i)\\n return manque\",\n \"def list_runtimes(self, workbench):\\n pass\",\n \"def Cleanup(benchmark_spec):\\n pass\",\n \"def benchmark():\\n print defer.Deferred.__module__\\n for func, args, iter in benchmarkFuncs:\\n print func.__name__, args, timeit(func, iter, *args)\",\n \"def tests_generator(self):\\n cb_bin = os.path.join(bin_path, 'compilebench')\\n cmd_list = [\\n (\\\"Initial Create/Compile/Read Compiled Tree\\\", \\\"{0} -D {1} -i 10 --makej -s {2}\\\"),\\n ]\\n\\n tests = []\\n for idx, (desc, cmd) in enumerate(cmd_list):\\n test_name = \\\"compile_bench_{0}_{1}\\\".format(idx + 1, to_safe_name(desc))\\n test = TestProfile(\\n name=test_name,\\n desc=desc,\\n test_path=self.test_path,\\n bin_path=bin_path,\\n command=cmd.format(cb_bin, self.test_path, bin_path))\\n tests.append(test)\\n return tests\",\n \"def assert_tests_stable(tests):\\n return (stress_test(t) for t in tests)\",\n \"def get_a_list_of_testset_names() -> str:\\n message = 'The available test sets are:'\\n for testset in sorted(DATASETS.keys(), reverse=True):\\n message += '\\\\n%20s: %s' % (testset, DATASETS[testset].get('description', ''))\\n return message\",\n \"def _fcts():\\n import numpy # pylint: disable=C0415\\n from .cbenchmark_dot import vector_dot_product # pylint: disable=E0611,C0415\\n from .cbenchmark_dot import vector_dot_product16 # pylint: disable=E0611,C0415\\n from .cbenchmark_dot import vector_dot_product16_nofcall # pylint: disable=E0611,C0415\\n from .cbenchmark_dot import vector_dot_product16_sse # pylint: disable=E0611,C0415\\n\\n def simple_dot(values):\\n return numpy.dot(values, values)\\n\\n def c11_dot(vect):\\n return vector_dot_product(vect, vect)\\n\\n def c11_dot16(vect):\\n return vector_dot_product16(vect, vect)\\n\\n def c11_dot16_nofcall(vect):\\n return vector_dot_product16_nofcall(vect, vect)\\n\\n def c11_dot16_sse(vect):\\n return vector_dot_product16_sse(vect, vect)\\n\\n return [simple_dot, c11_dot, c11_dot16, c11_dot16_nofcall, c11_dot16_sse]\",\n \"def list_feature_tests(self):\\n\\t\\treturn self.test_names\",\n \"def get_test_suite():\\n # max for a and p\\n MAX = 2**31 - 1 # INT32_MAX, max value for a and p\\n sqrt_MAX = floor(sqrt(MAX)) # max for n\\n \\n # first test suite\\n a_list = [0, 0, 0, 1, 1, 2, 7, 2, 1, 0, 0, 3, 1, 0, 0, 0, 1]\\n p_list = [5, 3, 3, 0, 0, 0, 8, 1, 1, 0, 0, 0, 0, 1, 2, 0, 1]\\n n_list = [7, 2, 2, 7, 3, 3, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 1]\\n\\n suite = get_one_suite(a_list, p_list, n_list, MAX, sqrt_MAX)\\n yield suite\\n \\n # second test suite\\n a_list = [3, 5, 23, 25, 100, 200, MAX, MAX-1, MAX]\\n p_list = [10, 5, 23, 25, 100, 200, 1000, 100, 500]\\n n_list = [23, 1, 0, 7, 1, 100, sqrt_MAX, 3, 23]\\n \\n suite = get_one_suite(a_list, p_list, n_list, MAX, sqrt_MAX)\\n yield suite\\n\\n # third test suite\\n a_list = []\\n p_list = []\\n n_list = []\\n\\n # keep a = 0\\n for _ in range(10):\\n a_list.append(0)\\n p_list.append(random.randint(0, 5000))\\n n_list.append(random.randint(0, sqrt_MAX))\\n # keep p = 0\\n for _ in range(10):\\n a_list.append(random.randint(0, MAX))\\n p_list.append(0)\\n n_list.append(random.randint(0, sqrt_MAX))\\n # keep n = 0\\n for _ in range(10):\\n a_list.append(random.randint(0, MAX))\\n p_list.append(random.randint(0, 5000))\\n n_list.append(0)\\n # keep a = 0 and p = 0\\n for _ in range(10):\\n a_list.append(0)\\n p_list.append(0)\\n n_list.append(random.randint(0, sqrt_MAX))\\n # keep all non-zero\\n for _ in range(30):\\n a_list.append(random.randint(0, MAX))\\n p_list.append(random.randint(0, 5000))\\n n_list.append(random.randint(0, sqrt_MAX))\\n\\n suite = get_one_suite(a_list, p_list, n_list, MAX, sqrt_MAX)\\n yield suite\",\n \"def alltests(opts):\\n \\n print \\\"API Root: %s\\\" % options.apiroot\\n print \\\"Token: %s\\\" % options.token\\n print \\\"Output dir: %s\\\" % options.output\\n print \\\"Running %d%% of tests\\\" % options.percent\\n print\\n \\n # need to use DEPT-001, not ID#\\n coursehistory_tests = [\\n # basic tests:\\n \\\"cis-120\\\", \\\"math-114\\\", \\\"engl-101\\\", \\\"econ-001\\\",\\n # miscellaneously somewhat problematic:\\n \\\"engl-016\\\", \\\"law-205\\\", \\\"hpr-612\\\", \\\"rels-414\\\", \\\"nurs-322\\\",\\n \\\"writ-030\\\", \\\"be-310\\\", \\\"psci-010\\\", \\\"psci-136\\\",\\n # crosslistings:\\n \\\"engl-135\\\", \\\"writ-135\\\", \\\"fnar-264\\\", \\\"cogs-001\\\", \\\"russ-048\\\", \\\"hist-048\\\",\\n # no reviews?:\\n \\\"afam-271\\\", \\\"ames-071\\\", \\\"slav-532\\\", \\\"afam-285\\\", \\\"prtg-213\\\", \\\"slav-533\\\",\\n # errors:\\n \\\"99999\\\", \\\"moo\\\",\\n ]\\n\\n instructor_tests = [\\n # basic tests:\\n \\\"403\\\", \\\"631\\\", \\\"1883\\\", \\\"2217-FERNANDO-C--PEREIRA\\\", \\\"1602-BENJAMIN-PIERCE\\\",\\n # crosslistings:\\n \\\"1034-LYLE-H-UNGAR\\\", \\\"2709-DAVID-P--COMBERG\\\",\\n # miscellaneously somewhat problematic:\\n \\\"1040-DAVID-FOX\\\", \\\"4268-BART-GERARD-C-DE-JONGHE\\\",\\n # the instructors w/ the most sections\\n \\\"1883\\\", \\\"1619\\\", \\\"2869\\\", \\\"942\\\", \\\"1644\\\", \\\"541\\\", \\\"767\\\", \\\"434\\\",\\n # concerned citizens:\\n \\\"1759-MAX-C--CAVITCH\\\", \\\"2824-TIMOTHY-CORRIGAN\\\",\\n \\\"1763-EMILY-R-STEINER\\\", \\\"1624-VALERIE-ROSS\\\",\\n # no reviews?:\\n \\\"416-LUDO-ROCHER\\\", \\\"715-ELIZABETH-ANN-POLLARD\\\", \\\"1094-MARIA-A-COWLES\\\",\\n \\\"1500-ANDREW-GALLIA\\\", \\\"1888-RUSSELL-DILEO\\\",\\n \\\"1450-SORMANE-PEREIRA-GOMES\\\", \\\"2188-HUI-YI-CHEN\\\", \\\"1165-DOMENIC-VITIELLO\\\",\\n \\\"2359-CLAUDIA-CANCINO\\\", \\\"2737-SHEN-WANG\\\", \\\"3229-BERLE-WHITBY\\\",\\n # errors:\\n \\\"99999\\\", \\\"moo\\\",\\n ]\\n\\n dept_tests = [\\n #fast\\n \\\"CSE\\\", \\\"LAW\\\", \\\"ANAT\\\", \\\"KORN\\\", \\\"LATN\\\", \\\"COGS\\\", \\\"MSCI\\\", \\\"GAS\\\",\\n #medium\\n \\\"CIS\\\", \\\"MATH\\\", \\\"FNAR\\\", \\\"ACCT\\\", \\\"FNCE\\\", \\\"BE\\\", \\\"MUSC\\\", \\\"OPIM\\\",\\n #slow\\n #\\\"SPAN\\\", \\\"NURS\\\", \\\"ENGL\\\",\\n #error\\n \\\"EROR\\\"]\\n\\n index_tests = [\\\"\\\", \\\"instructors\\\", \\\"coursehistories\\\", \\\"depts\\\",\\n \\\"semesters\\\", \\\"semesters/2010c\\\"]\\n\\n course_tests = [] # filled in by coursehistory_tests\\n\\n for t in fraclist(index_tests, options.percent):\\n test(opts, t)\\n \\n for t in fraclist(coursehistory_tests, options.percent):\\n obj = test(opts, \\\"coursehistories/%s\\\" % t)\\n test(opts, \\\"coursehistories/%s/reviews\\\" % t)\\n\\n # now \\\"find\\\" some courses\\n course_tests.append(\\\"2010c-%s\\\" % t)\\n try:\\n courseid = sorted(obj[\\\"result\\\"][\\\"courses\\\"])[0][\\\"id\\\"]\\n course_tests.append(courseid)\\n except (TypeError, KeyError, IndexError):\\n pass\\n \\n for t in course_tests: # don't fraclist an autogenerated list\\n # Some of the autogenerated courses don't exist, so ignore errors.\\n root_success = test(opts, \\\"courses/%s\\\" % t, lderror_ok=True)\\n if root_success:\\n # Course exists, don't expect errors.\\n test(opts, \\\"courses/%s/reviews\\\" % t)\\n test(opts, \\\"courses/%s/coursehistories/\\\" % t)\\n test(opts, \\\"courses/%s/sections\\\" % t)\\n \\n if test(opts, \\\"courses/%s/sections/001\\\" % t, lderror_ok=True):\\n test(opts, \\\"courses/%s/sections/001/reviews\\\" % t) \\n if '-' in str(t): # if we have a yyyys-dept-num test\\n test(opts, \\\"sections/%s-001\\\" % t)\\n # not tested: sections/001/reviews/instructor-id\\n test(opts, \\\"courses/%s/sections/401\\\" % t, lderror_ok=True)\\n \\n for t in fraclist(instructor_tests, options.percent):\\n test(opts, \\\"instructors/%s\\\" % t)\\n test(opts, \\\"instructors/%s/sections\\\" % t)\\n test(opts, \\\"instructors/%s/reviews\\\" % t)\\n \\n for t in fraclist(dept_tests, options.percent):\\n test(opts, \\\"depts/%s\\\" % t)\\n test(opts, \\\"depts/%s/reviews\\\" % t)\\n test(opts, \\\"semesters/2010c/%s\\\" % t)\",\n \"def test_benchmark1(capsys):\\n student_1 = Student('114007245','Mario Castillo',2018, [\\\"INST 201\\\",\\\"INST 326\\\"])\\n student_2 = Student('117006012', 'Joe Rogan', 2018, [\\\"MATH 115\\\",\\\"PSYC 100\\\"])\\n student_3 = Student(\\\"117008490\\\", \\\"Kanye West\\\", 2018, [\\\"MATH 120\\\",\\\"STAT 003\\\"])\\n student_4 = Student('118009044', \\\"Elon Musk\\\", 2018, [\\\"PSYC 100\\\",\\\"MATH 003\\\"])\\n \\n student_1.benchmark_I()\\n outerr = capsys.readouterr()\\n out = outerr.out\\n assert out == ('You have not completed the Benchmark I requirements.\\\\n'\\n 'You have not taken MATH 115 or higher.\\\\n'\\n 'You have not taken PSYC 100.\\\\n')\\n\\n student_2.benchmark_I()\\n outerr = capsys.readouterr()\\n out = outerr.out\\n assert out == (f'You have completed all of your Benchmark I courses! '\\n f'Congratulations, {student_2.student_name}!\\\\n')\\n\\n student_3.benchmark_I()\\n outerr = capsys.readouterr()\\n out = outerr.out \\n assert out == ('You have not completed the Benchmark I requirements.\\\\n'\\n 'You have not taken PSYC 100.\\\\n')\\n\\n student_4.benchmark_I()\\n outerr = capsys.readouterr()\\n out = outerr.out\\n assert out == ('You have not completed the Benchmark I requirements.\\\\n'\\n 'You have not taken MATH 115 or higher.\\\\n')\",\n \"def regression_suites(self):\\n return [c for c in self.suites.all() if TestSuite.TS_REGRESSION == c.xtype]\",\n \"def regression_suites(self):\\n return [c for c in self.suites.all() if TestSuite.TS_REGRESSION == c.xtype]\",\n \"def workbench_scenarios():\\n return [\\n (\\\"SummaryXBlock\\\",\\n \\\"\\\"\\\"<summary/>\\n \\\"\\\"\\\"),\\n (\\\"Multiple SummaryXBlock\\\",\\n \\\"\\\"\\\"<vertical_demo>\\n <summary/>\\n <summary/>\\n <summary/>\\n </vertical_demo>\\n \\\"\\\"\\\"),\\n ]\",\n \"def test_get_benchmark_methods_exact_match(self):\\n config = mock.Mock()\\n config.workspace = 'workspace'\\n config.benchmark_method_patterns = [\\n 'new_foo.BenchmarkClass.benchmark_method_1',\\n 'new_foo.BenchmarkClass.benchmark_method_2']\\n benchmark_runner = benchmark.BenchmarkRunner(config)\\n\\n methods = benchmark_runner._get_benchmark_methods()\\n self.assertEqual(['new_foo.BenchmarkClass.benchmark_method_1',\\n 'new_foo.BenchmarkClass.benchmark_method_2'], methods)\",\n \"def run_benchmarks(urls, urlIndices, trial_number):\\n path.append(os.path.join(CHROMIUM_SRC, 'tools/perf/'))\\n benchmark_path = os.path.join(CHROMIUM_SRC, 'tools/perf/run_benchmark')\\n output_path = 'temp'\\n trial_key = 'trial{0}'.format(trial_number)\\n\\n cmd = ('sudo ' + benchmark_path + ' --profiler=trace telemetryBenchmarks.url{0}')\\n for i in urlIndices:\\n try:\\n out, err, returncode = get_benchmark_result(cmd.format(i))\\n timeout = False\\n print 'successfully ran benchmark for url' + str(i)\\n except TimeoutError:\\n # Benchmark failed\\n print 'Benchmark Timeout!'\\n out = ''\\n returncode = 1\\n timeout = True\\n\\n failed = ['FAILED']\\n if returncode != 0 or any(x in out for x in failed) or timeout:\\n # If a benchmark fails, remove its corresponding wpr file, and act\\n # as if it didn't exist\\n # Remove from data/wpr_source\\n print 'Benchmark {0} failed'.format(i)\\n print 'return code is ' + str(returncode)\\n print 'Out:'\\n print out\\n print 'Err:'\\n print err\\n urlName = 'url{0}_page_set_000.wpr'.format(i)\\n urlpcName = 'url{0}_pc_page_set_000.wpr'.format(i)\\n urlFilePath = os.path.join('data/wpr_source',urlName)\\n urlpcFilePath = os.path.join('data/wpr_source',urlpcName)\\n urlCmd = 'rm -f {0}'.format(urlFilePath)\\n urlpcCmd = 'rm -f {0}'.format(urlpcFilePath)\\n print 'Removing: {0}, {1}'.format(urlFilePath, urlpcFilePath)\\n commands = [\\n 'rm -f {0}'.format(urlFilePath),\\n 'rm -f {0}'.format(urlpcFilePath)\\n ]\\n for cmdss in commands:\\n p = Popen(cmdss, shell=True)\\n p.wait()\\n # Skip the rest of this url\\n print \\\"Moving on!\\\"\\n continue\\n\\n # Parse data\\n tmp_path = 'temp/tmp_benchmark_result_json'\\n with open(tmp_path, 'rb') as f:\\n tmp_json = json.load(f)\\n benchmark_results = tmp_json['values']\\n commands = [\\n 'rm -f ~/page_load_time/telemetry/temp/tmp_benchmark_result_json',\\n ]\\n for cmds in commands:\\n p = Popen(cmds, shell=True)\\n p.wait()\\n\\n output = {urls[i]: {'cold_times': {trial_key: benchmark_results}}}\\n output_file = os.path.join(output_path, urlsafe_b64encode(urls[i]))\\n output_file += '.' + str(trial_number)\\n try:\\n with open(output_file, 'w') as f:\\n json.dump(output, f)\\n except IOError:\\n raise IOError('Unable to write to {0}'.format(output_file))\\n\\n\\n ############### Now run for Perfect Cache file ################\\n\\n try:\\n out, err, returncode = \\\\\\n get_benchmark_result(cmd.format(str(i) + '_pc'))\\n timeout = False\\n print 'successfully ran benchmark for url' + str(i) + '_pc'\\n except TimeoutError:\\n # Benchmark failed\\n print 'Benchmark Timeout!'\\n out = ''\\n returncode = 1\\n timeout = True\\n\\n failed = ['FAILED']\\n if returncode != 0 or any(x in out for x in failed) or timeout:\\n # If a benchmark fails, remove its corresponding wpr file, and act\\n # as if it didn't exist\\n # Remove from data/wpr_source\\n\\n print 'Benchmark {0}_pc failed'.format(i)\\n print 'Out:'\\n print out\\n print 'Err:'\\n print err\\n urlName = 'url{0}_page_set_000.wpr'.format(i)\\n urlpcName = 'url{0}_pc_page_set_000.wpr'.format(i)\\n urlFilePath = os.path.join('data/wpr_source',urlName)\\n urlpcFilePath = os.path.join('data/wpr_source',urlpcName)\\n urlCmd = 'rm -f {0}'.format(urlFilePath)\\n urlpcCmd = 'rm -f {0}'.format(urlpcFilePath)\\n print 'Removing: {0}, {1}'.format(urlFilePath, urlpcFilePath)\\n commands = [\\n 'rm -f {0}'.format(urlFilePath),\\n 'rm -f {0}'.format(urlpcFilePath)\\n ]\\n for cmdss in commands:\\n p = Popen(cmdss, shell=True)\\n p.wait()\\n # Skip the rest of this url\\n print \\\"Moving on!\\\"\\n continue\\n\\n # Parse data\\n tmp_path = 'temp/tmp_benchmark_result_json'\\n with open(tmp_path, 'rb') as f:\\n tmp_json = json.load(f)\\n benchmark_results = tmp_json['values']\\n\\n commands = [\\n 'rm -f ~/page_load_time/telemetry/temp/tmp_benchmark_result_json',\\n ]\\n for cmds in commands:\\n p = Popen(cmds, shell=True)\\n p.wait()\\n\\n output = {urls[i]: {'cold_times': {trial_key: benchmark_results}}}\\n output_file = os.path.join(output_path, urlsafe_b64encode(urls[i]))\\n output_file += '.' + str(trial_number) + '.pc'\\n try:\\n with open(output_file, 'w') as f:\\n json.dump(output, f)\\n except IOError:\\n raise IOError('Unable to write to {0}'.format(output_file))\",\n \"def main():\\n logging.basicConfig(level=\\\"INFO\\\")\\n assert len(sys.argv) == 2, \\\"Exactly one positional argument (path to the raw dataset) is \\\"\\\\\\n \\\"needed. \\\\n\\\\nE.g. `python sparsity_benchmark ~/bff_data/final_table`\\\"\\n\\n # Prepares data for the benchmark, may take a while\\n data_parameters = DATA_PARAMETERS.copy()\\n data_parameters[\\\"input_file\\\"] = sys.argv[1]\\n data_parameters[\\\"preprocessed_file\\\"] = os.path.join(\\n os.path.dirname(data_parameters[\\\"input_file\\\"]),\\n \\\"preprocessed_dataset.pkl\\\"\\n )\\n data_preprocessor = preprocess_dataset(data_parameters=data_parameters)\\n\\n # Note: the features here should be in range [0, ~1.2], according to the original experiments.\\n # 0 corresponds to no data, everything else is linearly scaled from dB units.\\n features, _ = data_preprocessor.load_dataset()\\n\\n logging.info(\\\"Starting benchmarks\\\")\\n noisy_features = benchmark_noise(\\n features=features,\\n data_parameters=data_parameters,\\n experiment_parameters=EXPERIMENT_PARAMETERS\\n )\\n benchmark_binarization(\\n noisy_features=noisy_features,\\n data_parameters=data_parameters,\\n experiment_parameters=EXPERIMENT_PARAMETERS\\n )\\n logging.info(\\\"Done\\\")\",\n \"def freeze_includes() -> List[str]:\\n import _pytest\\n\\n result = list(_iter_all_modules(_pytest))\\n return result\",\n \"def workbench_scenarios():\\n return [\\n (\\\"FeedbackXBlock\\\",\\n \\\"\\\"\\\"<vertical_demo>\\n <feedback/>\\n </vertical_demo>\\n \\\"\\\"\\\"),\\n ]\",\n \"def test_cases(self) -> list[str]:\\n cases = []\\n for t in self._test_cases:\\n if t not in cases:\\n cases.append(t)\\n return cases\",\n \"def alldemos():\\n rundemo(24,fig=True)\\n rundemo(30,fig=True)\\n rundemo(31,fig=True)\\n rundemo(33)\\n rundemo(34)\",\n \"def get_tests(self):\\n subtests = itertools.chain(*(s.get_tests() for s in self.suites.values()))\\n tt = [t for t in itertools.chain(self.tests,subtests)]\\n return tt\",\n \"def test_run():\\n for symbol in ['AAPL', 'IBM']:\\n print(\\\"Max close\\\")\\n print(symbol, get_max_close(symbol))\",\n \"def main():\\n test_merge_quick_sort()\\n test_compare()\",\n \"def main():\\n\\t\\n\\tDeck = []\\n\\tfor suite in range(suites):\\n for typecard in range(1, typecard+1):\\n cards.append(typecard)\",\n \"def test_checkers_integration():\\n for project in AVAILABLE_CHECKERS.values():\\n assert project.get_latest_version()\\n assert project.get_latest_major_versions()\\n assert project.get_latest_minor_versions()\",\n \"def workbench_scenarios():\\n return [\\n (\\\"filethumbs\\\",\\n \\\"\\\"\\\"\\\\\\n <vertical_demo>\\n <filethumbs/>\\n <filethumbs/>\\n <filethumbs/>\\n </vertical_demo>\\n \\\"\\\"\\\")\\n ]\",\n \"def testFilterBySuite(self):\\n self.setUpPyfakefs()\\n gpu_json = {\\n 'AAAAA1 AUTOGENERATED FILE DO NOT EDIT': {},\\n 'Android Tester': {\\n 'isolated_scripts': [\\n {\\n 'args': [\\n 'webgl_conformance',\\n ],\\n 'isolate_name': 'not_telemetry',\\n },\\n ],\\n },\\n 'Linux Tester': {\\n 'isolated_scripts': [\\n {\\n 'args': [\\n 'not_a_suite',\\n ],\\n 'isolate_name': 'telemetry_gpu_integration_test',\\n },\\n ],\\n },\\n 'Windows Tester': {\\n 'isolated_scripts': [\\n {\\n 'args': [\\n 'webgl_conformance',\\n ],\\n 'isolate_name': 'telemetry_gpu_integration_test',\\n },\\n ],\\n },\\n }\\n\\n self.CreateFile(os.path.join(builders.TESTING_BUILDBOT_DIR,\\n 'chromium.json'),\\n contents=json.dumps(gpu_json))\\n\\n gpu_builders = builders.GetCiBuilders('webgl_conformance')\\n self.assertEqual(gpu_builders, set(['Windows Tester']))\",\n \"def test_benchmark_xsorted_debug(benchmark_items_fixture):\\n do_benchmark(benchmark_items_fixture, xsorted)\",\n \"def list_ciphers():\\n global AVAILABLE_CIPHERS\\n print(\\\"[!] Available ciphers: \\\")\\n for i in range(len(AVAILABLE_CIPHERS)):\\n print(\\\" ----> %s.%s\\\"%(i+1, AVAILABLE_CIPHERS[i]))\\n exit()\",\n \"def test_get_benchmark_methods_filter(self):\\n config = mock.Mock()\\n config.workspace = 'workspace'\\n config.benchmark_method_patterns = ['new_foo.BenchmarkClass.filter:bench.*']\\n benchmark_runner = benchmark.BenchmarkRunner(config)\\n\\n mock_benchmark_class = mock.Mock()\\n mock_benchmark_class.benchmark_method_1 = 'foo'\\n\\n mock_module = mock.Mock()\\n sys.modules['new_foo'] = mock_module\\n mock_module.BenchmarkClass.return_value = mock_benchmark_class\\n\\n methods = benchmark_runner._get_benchmark_methods()\\n\\n self.assertEqual(1, len(methods))\\n self.assertEqual('new_foo.BenchmarkClass.benchmark_method_1', methods[0])\",\n \"def benchmark():\\n parser = argparse.ArgumentParser(\\n \\n description='pyrpipe diagnostic utility\\\\nGenerate benchmark report.',\\n \\n usage='''pyrpipe_diagnostic report [<args>] <logfile>\\n \\n ''') \\n parser.add_argument('-o', help='out file \\\\ndefault: same as input logfile',action=\\\"store\\\")\\n parser.add_argument('-e', help='report output type: [MD,PDF,HTML] \\\\ndefault: PDF',default='PDF',action=\\\"store\\\")\\n parser.add_argument('-v',help='verbose',action=\\\"store_true\\\")\\n parser.add_argument('-f',help='Filter by programs. Provide a comma-separated list e.g., prefetch,STAR,bowtie2 \\\\ndefault None')\\n parser.add_argument('-t',help='Temporary directory. \\\\ndefault ./tmp',action=\\\"store\\\")\\n parser.add_argument('logfile', help='The log file generated by pyrpipe',action=\\\"store\\\")\\n args = parser.parse_args(sys.argv[2:])\\n \\n logFile=args.logfile\\n envLog=reports.checkEnvLog(logFile) \\n #parse args\\n vFlag=args.v\\n if vFlag:\\n print(\\\"Generating benchmarks\\\")\\n outFile=\\\"\\\"\\n if args.o is None:\\n outFile=pu.get_file_basename(args.logfile)\\n else:\\n outFile=args.o\\n outFile+='.'+args.e\\n \\n filters=[]\\n if args.f is not None:\\n filters= args.f.split(',')\\n #create temp dir\\n tempDir=\\\"\\\"\\n if args.t is not None:\\n tempDir= args.t\\n else:\\n tempDir=os.path.join(os.getcwd(),\\\"tmp\\\")\\n #create tmp dir\\n if not pu.check_paths_exist(tempDir):\\n pu.mkdir(tempDir)\\n \\n reports.generateBenchmarkReport(logFile,envLog,filters,tempDir,outFile=outFile,verbose=args.v)\",\n \"def main():\\r\\n _evaluative_test(5)\\r\\n _fuzz_test(1)\\r\\n _fuzz_test(1, 512)\\r\\n _fuzz_test(1, 1512)\\r\\n _fuzz_test(1000)\\r\\n _fuzz_test(1000, 512)\\r\\n _fuzz_test(1000, 4077)\",\n \"def all_experiments():\\n elo_explain_experiments()\\n alpha_beta_experiments()\\n mtcs_experiments()\",\n \"def _generate_benchmark_variants(benchmark_spec):\\n variants = []\\n # Cold start.\\n variants.append({\\n 'name': benchmark_spec['name'] + ' (cold start)',\\n 'app': benchmark_spec['app'],\\n 'duration': benchmark_spec['duration'],\\n 'measurements': benchmark_spec['measurements'],\\n 'shell-args': benchmark_spec.get('shell-args',\\n []) + _COLD_START_SHELL_ARGS})\\n # Warm start.\\n variants.append({\\n 'name': benchmark_spec['name'] + ' (warm start)',\\n 'app': benchmark_spec['app'],\\n 'duration': benchmark_spec['duration'],\\n 'measurements': benchmark_spec['measurements'],\\n 'shell-args': benchmark_spec.get('shell-args', [])})\\n return variants\",\n \"def _missing_symbol_to_skipped_tests(self):\\n return {\\n \\\"MathMLElement\\\": [\\\"mathml\\\"],\\n \\\"GraphicsLayer\\\": [\\\"compositing\\\"],\\n \\\"WebCoreHas3DRendering\\\": [\\\"animations/3d\\\", \\\"transforms/3d\\\"],\\n \\\"WebGLShader\\\": [\\\"fast/canvas/webgl\\\", \\\"compositing/webgl\\\", \\\"http/tests/canvas/webgl\\\"],\\n \\\"MHTMLArchive\\\": [\\\"mhtml\\\"],\\n }\",\n \"def usage():\\n \\n print '-b <bench> the bench to show.'\\n print '-c <config> the config to show (GPU, 8888, 565, etc).'\\n print '-d <dir> a directory containing bench_r<revision>_<scalar> files.'\\n print '-e <file> file containing expected bench values/ranges.'\\n print ' Will raise exception if actual bench values are out of range.'\\n print ' See bench_expectations.txt for data format and examples.'\\n print '-f <revision>[:<revision>] the revisions to use for fitting.'\\n print ' Negative <revision> is taken as offset from most recent revision.'\\n print '-i <time> the time to ignore (w, c, g, etc).'\\n print ' The flag is ignored when -t is set; otherwise we plot all the'\\n print ' times except the one specified here.'\\n print '-l <title> title to use for the output graph'\\n print '-m <representation> representation of bench value.'\\n print ' See _ListAlgorithm class in bench_util.py.'\\n print '-o <path> path to which to write output; writes to stdout if not specified'\\n print '-r <revision>[:<revision>] the revisions to show.'\\n print ' Negative <revision> is taken as offset from most recent revision.'\\n print '-s <setting>[=<value>] a setting to show (alpha, scalar, etc).'\\n print '-t <time> the time to show (w, c, g, etc).'\\n print '-x <int> the desired width of the svg.'\\n print '-y <int> the desired height of the svg.'\\n print '--default-setting <setting>[=<value>] setting for those without.'\",\n \"def get_tests():\\n\\tret = []\\n\\tfor walk_tuple in os.walk(webnotes.defs.modules_path):\\n\\t\\tfor test_file in filter(lambda x: x.startswith('test') and x.endswith('.py'), walk_tuple[2]):\\n\\t\\t\\tdir_path = os.path.relpath(walk_tuple[0], webnotes.defs.modules_path)\\n\\t\\t\\tif dir_path=='.':\\n\\t\\t\\t\\tret.append(test_file[:-3])\\n\\t\\t\\telse:\\n\\t\\t\\t\\tret.append(dir_path.replace('/', '.') + '.' + test_file[:-3])\\t\\t\\t\\n\\treturn ret\",\n \"def list(self):\\n print \\\"\\\\nAvailable Test Cases\\\"\\n print \\\"====================\\\"\\n for case in self.cases:\\n print case.__name__\",\n \"def time_binaries():\\n print()\\n print(\\\"=== BENCHMARKING ===\\\")\\n with tempfile.NamedTemporaryFile(\\n prefix=\\\"riff-benchmark\\\", suffix=\\\".gitlog\\\"\\n ) as testdata:\\n subprocess.check_call(\\n [\\\"git\\\", \\\"log\\\", \\\"--color=always\\\", \\\"-p\\\", \\\"master\\\"], stdout=testdata\\n )\\n\\n binaries = sorted(glob.glob(os.path.join(BINDIR, \\\"*\\\")), key=natural_keys)\\n\\n # Do riff-current last: https://stackoverflow.com/a/20320940/473672\\n binaries.sort(key=lambda s: s.endswith(\\\"riff-current\\\"))\\n\\n for binary in binaries:\\n print_timings(binary, testdata.name)\\n print_timings(\\\"/bin/cat\\\", testdata.name)\",\n \"def spec_tests():\\n pass\",\n \"def compare_hard_noprint():\\n\\n\\n cores = [1, 2, 4, 8, 16]\\n s_times = []\\n\\n for n_cores in cores:\\n print('analyzing', '-'.join(['data/bench', 's', 'np', str(n_cores)]) + '.out')\\n data = split_output_file('-'.join(['data/bench', 's', 'np', str(n_cores)]) + '.out')\\n s_times.append(data['FOOTER']['total_time'])\\n\\n # speedup plot\\n fig, axs = plt.subplots()\\n\\n axs.plot(cores, [s_times[0] / x / k for x, k in zip(s_times, cores)], label='sequential')\\n axs.set_xticks(cores)\\n axs.ticklabel_format(style='sci', scilimits=(-128, 128))\\n\\n axs.set_ylabel('Relative speedup')\\n axs.set_xlabel('Number of cores')\\n axs.set_ylim(0, 1.1)\\n\\n fig.set_size_inches(4, 2, forward=True)\\n plt.tight_layout()\\n\\n plt.show()\",\n \"def run_tests(tests):\\n return [test(t) for t in tests]\",\n \"def BenchArgs(data_file):\\n return ['--timers', 'wcg', '--logFile', data_file]\",\n \"def eg_basic():\\n\\n print(\\\"\\\", \\\"True\\\", stats.bootstrap([8, 7, 6, 2, 5, 8, 7, 3], [\\n 8, 7, 6, 2, 5, 8, 7, 3]), utils.cliffsDelta([8, 7, 6, 2, 5, 8, 7, 3], [8, 7, 6, 2, 5, 8, 7, 3]), sep=\\\"\\\\t\\\")\\n print(\\\"\\\", \\\"False\\\", stats.bootstrap([8, 7, 6, 2, 5, 8, 7, 3], [\\n 9, 9, 7, 8, 10, 9, 6]), utils.cliffsDelta([8, 7, 6, 2, 5, 8, 7, 3], [9, 9, 7, 8, 10, 9, 6]), sep=\\\"\\\\t\\\")\\n print(\\\"\\\", \\\"False\\\", stats.bootstrap([0.34, 0.49, 0.51, 0.6, .34, .49, .51, .6], [0.6, 0.7, 0.8, 0.9, .6, .7, .8, .9]), utils.cliffsDelta(\\n [0.34, 0.49, 0.51, 0.6, .34, .49, .51, .6], [0.6, 0.7, 0.8, 0.9, .6, .7, .8, .9]), sep=\\\"\\\\t\\\")\",\n \"def GetAllOptimizationsAvailable() -> typing.List[str]:\\n # We must disable logging here - this function is invoked to set\\n # OPTIMIZATION_PASSES variable below, before flags are parsed.\\n proc = Exec([\\\"-help-list-hidden\\\"], log=False)\\n lines = proc.stdout.split(\\\"\\\\n\\\")\\n # Find the start of the list of optimizations.\\n for i in range(len(lines)):\\n if lines[i] == \\\" Optimizations available:\\\":\\n break\\n else:\\n raise OptException\\n # Find the end of the list of optimizations.\\n for j in range(i + 1, len(lines)):\\n if not lines[j].startswith(\\\" -\\\"):\\n break\\n else:\\n raise OptException\\n\\n # Extract the list of optimizations.\\n optimizations = [line[len(\\\" \\\") :].split()[0] for line in lines[i + 1 : j]]\\n if len(optimizations) < 2:\\n raise OptException\\n\\n return optimizations\",\n \"def generate_figures():\\r\\n # create results directory if necessary\\r\\n try:\\r\\n makedirs(\\\"results\\\")\\r\\n except OSError as e:\\r\\n if e.errno != errno.EEXIST:\\r\\n raise\\r\\n \\r\\n for b in benchmarks:\\r\\n generate_figure(model[b], b)\",\n \"def main():\\n test_problem1a()\\n test_problem1b()\\n test_problem1c()\",\n \"def stabilisers(self) -> List[PauliTerm]:\\n zeros = np.zeros(self.n, dtype='int')\\n x_stabilisers = (\\n pauli_term_for_row(self.parity_check_c1[i, :], zeros)\\n for i in range(self.r_1)\\n )\\n z_stabilisers = (\\n pauli_term_for_row(zeros, self.parity_check_c2[i, :])\\n for i in range(self.r_2)\\n )\\n return list(itertools.chain(x_stabilisers, z_stabilisers))\",\n \"def workbench_scenarios():\\n return [\\n (\\\"QnetXBlock\\\",\\n \\\"\\\"\\\"<qnet/>\\n \\\"\\\"\\\"),\\n (\\\"Multiple QnetXBlock\\\",\\n \\\"\\\"\\\"<vertical_demo>\\n <qnet/>\\n <qnet/>\\n <qnet/>\\n </vertical_demo>\\n \\\"\\\"\\\"),\\n ]\",\n \"def list_bugs(self,active_only=True):\\n for bug in self.bugs(active_only=active_only):\\n print(\\\"*******************\\\")\\n name=bug[\\\"bug_name\\\"]\\n for k in bug.keys():\\n \\n print(name+\\\"\\\\t\\\"+k+\\\"\\\\t\\\"+str(bug[k]).replace(\\\"\\\\n\\\",\\\" ;; \\\"))\\n\\n print(\\\"*******************\\\")\",\n \"def main() -> None:\\r\\n # pylint: disable=W0601\\r\\n global ARGS, BROWSERS\\r\\n BROWSERS = {'chrome': Chrome, 'firefox': Firefox, 'ie': Ie,\\r\\n 'safari': Safari, 'opera': Opera, 'edge': Edge}\\r\\n # pylint: disable=C0103\\r\\n PARSER = argparse.ArgumentParser()\\r\\n PARSER.add_argument('-m', '--modules', help='Which modules to test. One or more of '\\r\\n '[%(choices)s]. Default is all.', nargs='+', type=str,\\r\\n choices=MODULES.keys(), metavar='', default=list(MODULES.keys()))\\r\\n PARSER.add_argument('-l', '--locales', help='Which locales to test. One or more of '\\r\\n '[%(choices)s]. Default is all.', nargs='+', type=str,\\r\\n choices=LANGS.keys(), metavar='', default=list(LANGS.keys()))\\r\\n PARSER.add_argument('-b', '--browsers', help='Which browser to use. One or more of '\\r\\n '[%(choices)s]. Default is %(default)s', nargs='+', default=['chrome'],\\r\\n choices=BROWSERS.keys(), metavar='')\\r\\n PARSER.add_argument('-w', '--wait', help='Wait this many seconds before deciding an element is '\\r\\n 'missing. Default is %(default)s', default=[20], type=int, nargs=1)\\r\\n ARGS = PARSER.parse_args()\\r\\n parseargs()\\r\\n os.makedirs(SCREENSHOT_DIR, exist_ok=True)\\r\\n\\r\\n try:\\r\\n full_languages_modules_run(modfilter=ARGS.modules,\\r\\n langfilter=ARGS.locales, brows=ARGS.browsers)\\r\\n except Exception: # Too general is the point, it's a Final Action. pylint: disable=W0703\\r\\n with open(RESULTS_FILE, mode='a', encoding='UTF-8') as log:\\r\\n log.write('\\\\n\\\"Well, something went wrong. A manual exit, hopefully:\\\"\\\\n\\\\n' + tidy_error())\\r\\n raise\",\n \"def test_nothing_fits():\\n freetimes, _ = free([], 9, 0, 17, 0, day_range, 600)\\n fmt_freetime = output_format(freetimes)\\n for i in fmt_freetime:\\n print(i)\\n assert fmt_freetime == []\",\n \"def describe_tests(remit, sourcelist):\\n print('* run tests with \\\"%s\\\"' % remit)\\n print('* tests to run: ')\\n for line in pretty_list(sourcelist, 7):\\n print(' ' + line)\\n print('* writers to test: ')\\n for line in pretty_list(spec.TEST['writer'], 7):\\n print(' ' + line)\\n print('* options to test: ')\\n for line in pretty_list(spec.TEST['pandoc_options'], 7):\\n print(' ' + line)\\n print('* blacklisted: ')\\n for config in spec.BLACKLIST:\\n command = make_command(remit=remit,\\n source='TEST',\\n writer=config['writer'],\\n pandoc_options=config['pandoc_options'],\\n extension=config['extension'])\\n print(' ' + ' '.join(command))\\n print(' (' + config['comment'] + ')')\",\n \"def list(args):\\n experiments = sorted(os.listdir('./litmus'))\\n print_color(\\\"Available Litmus Chaos Experiments:\\\\n\\\\n\\\")\\n if (f\\\"{args.platform}\\\" == \\\"GKE\\\"):\\n i = 1\\n for experiment_file in experiments:\\n print_color(f\\\"\\\\t{i}. {experiment_file.replace('.yaml', '')}\\\")\\n i += 1\\n\\n if (f\\\"{args.platform}\\\" == \\\"kind\\\"):\\n kind_supported = [\\\"pod-delete\\\",\\\"container-kill\\\",\\\"node-cpu-hog\\\",\\\"node-memory-hog\\\"]\\n i = 0\\n for i in range(0, len(kind_supported)):\\n print_color(f\\\"\\\\t{i+1}. {kind_supported[i]}\\\")\\n i += 1\\n\\n if (f\\\"{args.platform}\\\" == \\\"EKS\\\"):\\n i = 1\\n for experiment_file in experiments:\\n print_color(f\\\"\\\\t{i}. {experiment_file.replace('.yaml', '')}\\\")\\n i += 1\",\n \"def run_experiment():\\n return [random.random() < 0.5 for _ in range(1000)]\",\n \"def run_experiment():\\n return [random.random() < 0.5 for _ in range(1000)]\",\n \"def run_experiment():\\n return [random.random() < 0.5 for _ in range(1000)]\",\n \"def run_experiment():\\n return [random.random() < 0.5 for _ in range(1000)]\",\n \"def get_all_platform_tests(self):\\n for testitem in self.get_tests(self.discover_tests()):\\n if not testitem:\\n continue\\n prefix = \\\"tests.\\\" + self.platform + \\\".\\\"\\n self.formatted_tests_set.append(\\n prefix + self.format_into_test_path(testitem)\\n )\\n\\n if self.denylist:\\n try:\\n with open(self.denylist, \\\"r\\\") as f:\\n denylist = f.read().splitlines()\\n except FileNotFoundError:\\n denylist = []\\n\\n self.formatted_tests_set = [\\n t for t in self.formatted_tests_set if t not in denylist\\n ]\\n\\n return self.formatted_tests_set\",\n \"def test_BenchmarkSuite_integration_test(\\n benchmark_suite: typing.Callable, tempdir: pathlib.Path\\n):\\n with benchmark_suite() as bs:\\n bs.ForceOpenCLEnvironment(cldrive_env.OclgrindOpenCLEnvironment())\\n observer = MockBenchmarkObserver(stop_after=1)\\n\\n # `stop_after` raises BenchmarkInterrupt.\\n try:\\n bs.Run([observer])\\n assert False\\n except gpgpu.BenchmarkInterrupt:\\n pass\\n\\n assert len(observer.logs) == 1\\n assert observer.logs[0].benchmark_name in bs.benchmarks\",\n \"def test():\\n\\t\\treturn [\\\"vice.core.objects.tests\\\",\\n\\t\\t\\t[\\n\\t\\t\\t\\tagb.test_agb_grid_constructor(),\\n\\t\\t\\t\\tagb.test_agb_grid_destructor(),\\n\\t\\t\\t\\tcallback_1arg.test_callback_1arg_constructor(),\\n\\t\\t\\t\\tcallback_1arg.test_callback_1arg_destructor(),\\n\\t\\t\\t\\tcallback_2arg.test_callback_2arg_constructor(),\\n\\t\\t\\t\\tcallback_2arg.test_callback_2arg_destructor(),\\n\\t\\t\\t\\tccsne.test_ccsne_yield_specs_constructor(),\\n\\t\\t\\t\\tccsne.test_ccsne_yield_specs_destructor(),\\n\\t\\t\\t\\tchannel.test_channel_constructor(),\\n\\t\\t\\t\\tchannel.test_channel_destructor(),\\n\\t\\t\\t\\telement.test_element_constructor(),\\n\\t\\t\\t\\telement.test_element_destructor(),\\n\\t\\t\\t\\tfromfile.test_fromfile_constructor(),\\n\\t\\t\\t\\tfromfile.test_fromfile_destructor(),\\n\\t\\t\\t\\thydrodiskstars.test_hydrodiskstars_constructor(),\\n\\t\\t\\t\\thydrodiskstars.test_hydrodiskstars_destructor(),\\n\\t\\t\\t\\timf.test_imf_constructor(),\\n\\t\\t\\t\\timf.test_imf_destructor(),\\n\\t\\t\\t\\tintegral.test_integral_constructor(),\\n\\t\\t\\t\\tintegral.test_integral_destructor(),\\n\\t\\t\\t\\tinterp_scheme_1d.test_interp_scheme_1d_constructor(),\\n\\t\\t\\t\\tinterp_scheme_1d.test_interp_scheme_1d_destructor(),\\n\\t\\t\\t\\tinterp_scheme_2d.test_interp_scheme_2d_constructor(),\\n\\t\\t\\t\\tinterp_scheme_2d.test_interp_scheme_2d_destructor(),\\n\\t\\t\\t\\tism.test_ism_constructor(),\\n\\t\\t\\t\\tism.test_ism_destructor(),\\n\\t\\t\\t\\tmdf.test_mdf_constructor(),\\n\\t\\t\\t\\tmdf.test_mdf_destructor(),\\n\\t\\t\\t\\tmigration.test_migration_constructor(),\\n\\t\\t\\t\\tmigration.test_migration_destructor(),\\n\\t\\t\\t\\tmultizone.test_multizone_constructor(),\\n\\t\\t\\t\\tmultizone.test_multizone_destructor(),\\n\\t\\t\\t\\tsinglezone.test_singlezone_constructor(),\\n\\t\\t\\t\\tsinglezone.test_singlezone_destructor(),\\n\\t\\t\\t\\tsneia.test_sneia_yield_specs_constructor(),\\n\\t\\t\\t\\tsneia.test_sneia_yield_specs_destructor(),\\n\\t\\t\\t\\tssp.test_ssp_constructor(),\\n\\t\\t\\t\\tssp.test_ssp_destructor(),\\n\\t\\t\\t\\ttracer.test_tracer_constructor(),\\n\\t\\t\\t\\ttracer.test_tracer_destructor()\\n\\t\\t\\t]\\n\\t\\t]\",\n \"def workbench_scenarios():\\n return [\\n (\\\"Discussion XBlock\\\",\\n \\\"\\\"\\\"<vertical_demo>\\n <discussion-forum/>\\n </vertical_demo>\\n \\\"\\\"\\\"),\\n ]\",\n \"def PrintUnexpectedPasses(unexpected_passes, args):\\n for builder, passes in unexpected_passes.iteritems():\\n passed_all = {}\\n passed_some = {}\\n for suite, tests in passes.iteritems():\\n for test, num_passes in tests.iteritems():\\n if num_passes == args.num_samples:\\n passed_all.setdefault(suite, []).append(test)\\n else:\\n passed_some.setdefault(suite, []).append((test, num_passes))\\n\\n # Alphabetize for readability.\\n for tests in passed_all.values():\\n tests.sort()\\n for tests in passed_some.values():\\n tests.sort()\\n\\n print '##### %s #####' % builder\\n if passed_all:\\n print '----- Tests that passed in all runs -----'\\n for suite, tests in passed_all.iteritems():\\n print '%s:' % suite\\n for test in tests:\\n print ' %s' % test\\n print ''\\n if passed_some:\\n print '----- Tests that passed in some runs -----'\\n for suite, tests in passed_some.iteritems():\\n print '%s:' % suite\\n for (test, num_passes) in tests:\\n print ' %s: %d/%d' % (test, num_passes, args.num_samples)\\n print '\\\\n\\\\n'\",\n \"def suite():\\n test_suite = unittest.TestSuite()\\n test_suite.addTest(unittest.makeSuite(TestReversi))\\n test_suite.addTest(unittest.makeSuite(TestGuessNumberGame))\\n test_suite.addTest(unittest.makeSuite(TestConnectFourGame))\\n test_suite.addTest(unittest.makeSuite(TestBuscamina))\\n test_suite.addTest(unittest.makeSuite(TestGame))\\n test_suite.addTest(unittest.makeSuite(TestDamaGame))\\n test_suite.addTest(unittest.makeSuite(TestTateti))\\n test_suite.addTest(unittest.makeSuite(TestGameBase))\\n test_suite.addTest(unittest.makeSuite(TestFourNumber))\\n test_suite.addTest(unittest.makeSuite(test_game_generala))\\n test_suite.addTest(unittest.makeSuite(test_categories))\\n test_suite.addTest(unittest.makeSuite(test_player))\\n test_suite.addTest(unittest.makeSuite(test_throw_class))\\n test_suite.addTest(unittest.makeSuite(test_throw_dice))\\n test_suite.addTest(unittest.makeSuite(TestBets))\\n test_suite.addTest(unittest.makeSuite(TestDeck))\\n test_suite.addTest(unittest.makeSuite(TestBlackjackGame))\\n test_suite.addTest(unittest.makeSuite(TestHands))\\n test_suite.addTest(unittest.makeSuite(PokerTest))\\n test_suite.addTest(unittest.makeSuite(PokerGameTest))\\n test_suite.addTest(unittest.makeSuite(TestBattleship))\\n test_suite.addTest(unittest.makeSuite(TestBoard))\\n test_suite.addTest(craps_suite())\\n test_suite.addTest(sudoku_suite())\\n test_suite.addTest(roulette_suite())\\n test_suite.addTest(dungeon_suite())\\n test_suite.addTest(unittest.makeSuite(TestSenku))\\n test_suite.addTest(unittest.makeSuite(TestAhorcado))\\n test_suite.addTest(unittest.makeSuite(TestHanoiTower))\\n return test_suite\",\n \"def zio_test_help():\\n print(\\\"zio-ut [TESTS]\\\")\\n print(\\\"\\\")\\n print(\\\"[TESTS]: list of tests to perform. It can be the name of a specific test, or the name of a module of tests\\\")\\n print(\\\" In alternative, you che use the test code:\\\")\\n print(\\\"Code test case\\\")\\n print(\\\"- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - \\\")\\n i = 0\\n for t in test_list:\\n print(str(i) + \\\" \\\" + t)\\n i = i + 1\",\n \"def cli(argv):\\r\\n argv.append(\\\"--exhaust-materials\\\")\\r\\n cltestbench.cli(argv)\",\n \"def suite_list(self):\\n return ','.join([s.__unicode__() for s in self.suites.all()])\",\n \"def suite_list(self):\\n return ','.join([s.__unicode__() for s in self.suites.all()])\",\n \"def main():\\n reportSample = CompatibilityReportSample()\\n reportSample.run()\",\n \"def _parse_suite(\\n self, results: dict, extra_tags: dict = None\\n ) -> List[BenchmarkResult]:\\n # all results share a batch id\\n batch_id = uuid.uuid4().hex\\n\\n parsed_results = []\\n for result in results[\\\"benchmarks\\\"]:\\n result_parsed = self._parse_benchmark(\\n result=GoogleBenchmark(**result),\\n batch_id=batch_id,\\n extra_tags=extra_tags,\\n )\\n parsed_results.append(result_parsed)\\n\\n return parsed_results\",\n \"def test_big_numbers(self):\\n arr, result = [535555555555, 5500000000000000000000000,\\n 10, 15, 35, 0, -2, -67, -55], []\\n fizz_buzz(arr, result)\\n self.assertEqual(result, ['fizz', 'fizz', 'fizz', 'fizzbuzz',\\n 'fizz', 'fizzbuzz', -2, -67, 'fizz'])\",\n \"def get_supported_browsers_suggestions():\\n supported_browsers = [\\n 'chrome',\\n 'chrome-remote',\\n 'chrome-headless',\\n 'chrome-remote-headless',\\n 'firefox',\\n 'firefox-remote',\\n 'ie',\\n 'ie-remote'\\n ]\\n return supported_browsers\",\n \"def main():\\n \\n try:\\n opts, _ = getopt.getopt(sys.argv[1:]\\n , \\\"b:c:d:e:f:i:l:m:o:r:s:t:x:y:\\\"\\n , \\\"default-setting=\\\")\\n except getopt.GetoptError, err:\\n print str(err) \\n usage()\\n sys.exit(2)\\n \\n directory = None\\n config_of_interest = None\\n bench_of_interest = None\\n time_of_interest = None\\n time_to_ignore = None\\n bench_expectations = {}\\n rep = None # bench representation algorithm\\n revision_range = '0:'\\n regression_range = '0:'\\n latest_revision = None\\n requested_height = None\\n requested_width = None\\n title = 'Bench graph'\\n settings = {}\\n default_settings = {}\\n\\n def parse_range(range):\\n \\\"\\\"\\\"Takes '<old>[:<new>]' as a string and returns (old, new).\\n Any revision numbers that are dependent on the latest revision number\\n will be filled in based on latest_revision.\\n \\\"\\\"\\\"\\n old, _, new = range.partition(\\\":\\\")\\n old = int(old)\\n if old < 0:\\n old += latest_revision;\\n if not new:\\n new = latest_revision;\\n new = int(new)\\n if new < 0:\\n new += latest_revision;\\n return (old, new)\\n\\n def add_setting(settings, setting):\\n \\\"\\\"\\\"Takes <key>[=<value>] adds {key:value} or {key:True} to settings.\\\"\\\"\\\"\\n name, _, value = setting.partition('=')\\n if not value:\\n settings[name] = True\\n else:\\n settings[name] = value\\n\\n def read_expectations(expectations, filename):\\n \\\"\\\"\\\"Reads expectations data from file and put in expectations dict.\\\"\\\"\\\"\\n for expectation in open(filename).readlines():\\n elements = expectation.strip().split(',')\\n if not elements[0] or elements[0].startswith('#'):\\n continue\\n if len(elements) != 5:\\n raise Exception(\\\"Invalid expectation line format: %s\\\" %\\n expectation)\\n bench_entry = elements[0] + ',' + elements[1]\\n if bench_entry in expectations:\\n raise Exception(\\\"Dup entries for bench expectation %s\\\" %\\n bench_entry)\\n # [<Bench_BmpConfig_TimeType>,<Platform-Alg>] -> (LB, UB)\\n expectations[bench_entry] = (float(elements[-2]),\\n float(elements[-1]))\\n\\n def check_expectations(lines, expectations, newest_revision, key_suffix):\\n \\\"\\\"\\\"Check if there are benches in latest rev outside expected range.\\\"\\\"\\\"\\n exceptions = []\\n for line in lines:\\n line_str = str(line)\\n bench_platform_key = (line_str[ : line_str.find('_{')] + ',' +\\n key_suffix)\\n this_revision, this_bench_value = lines[line][-1]\\n if (this_revision != newest_revision or\\n bench_platform_key not in expectations):\\n # Skip benches without value for latest revision.\\n continue\\n this_min, this_max = expectations[bench_platform_key]\\n if this_bench_value < this_min or this_bench_value > this_max:\\n exceptions.append('Bench %s value %s out of range [%s, %s].' %\\n (bench_platform_key, this_bench_value, this_min, this_max))\\n if exceptions:\\n raise Exception('Bench values out of range:\\\\n' +\\n '\\\\n'.join(exceptions))\\n\\n try:\\n for option, value in opts:\\n if option == \\\"-b\\\":\\n bench_of_interest = value\\n elif option == \\\"-c\\\":\\n config_of_interest = value\\n elif option == \\\"-d\\\":\\n directory = value\\n elif option == \\\"-e\\\":\\n read_expectations(bench_expectations, value)\\n elif option == \\\"-f\\\":\\n regression_range = value\\n elif option == \\\"-i\\\":\\n time_to_ignore = value\\n elif option == \\\"-l\\\":\\n title = value\\n elif option == \\\"-m\\\":\\n rep = value\\n elif option == \\\"-o\\\":\\n redirect_stdout(value)\\n elif option == \\\"-r\\\":\\n revision_range = value\\n elif option == \\\"-s\\\":\\n add_setting(settings, value)\\n elif option == \\\"-t\\\":\\n time_of_interest = value\\n elif option == \\\"-x\\\":\\n requested_width = int(value)\\n elif option == \\\"-y\\\":\\n requested_height = int(value)\\n elif option == \\\"--default-setting\\\":\\n add_setting(default_settings, value)\\n else:\\n usage()\\n assert False, \\\"unhandled option\\\"\\n except ValueError:\\n usage()\\n sys.exit(2)\\n\\n if directory is None:\\n usage()\\n sys.exit(2)\\n\\n if time_of_interest:\\n time_to_ignore = None\\n\\n # The title flag (-l) provided in buildbot slave is in the format\\n # Bench_Performance_for_Skia_<platform>, and we want to extract <platform>\\n # for use in platform_and_alg to track matching benches later. If title flag\\n # is not in this format, there may be no matching benches in the file\\n # provided by the expectation_file flag (-e).\\n platform_and_alg = title\\n if platform_and_alg.startswith(TITLE_PREAMBLE):\\n platform_and_alg = (\\n platform_and_alg[TITLE_PREAMBLE_LENGTH:] + '-' + rep)\\n title += ' [representation: %s]' % rep\\n\\n latest_revision = get_latest_revision(directory)\\n oldest_revision, newest_revision = parse_range(revision_range)\\n oldest_regression, newest_regression = parse_range(regression_range)\\n\\n unfiltered_revision_data_points = parse_dir(directory\\n , default_settings\\n , oldest_revision\\n , newest_revision\\n , rep)\\n\\n # Filter out any data points that are utterly bogus... make sure to report\\n # that we did so later!\\n (allowed_revision_data_points, ignored_revision_data_points) = filter_data_points(\\n unfiltered_revision_data_points)\\n\\n # Update oldest_revision and newest_revision based on the data we could find\\n all_revision_numbers = allowed_revision_data_points.keys()\\n oldest_revision = min(all_revision_numbers)\\n newest_revision = max(all_revision_numbers)\\n\\n lines = create_lines(allowed_revision_data_points\\n , settings\\n , bench_of_interest\\n , config_of_interest\\n , time_of_interest\\n , time_to_ignore)\\n\\n regressions = create_regressions(lines\\n , oldest_regression\\n , newest_regression)\\n\\n output_xhtml(lines, oldest_revision, newest_revision, ignored_revision_data_points,\\n regressions, requested_width, requested_height, title)\\n\\n check_expectations(lines, bench_expectations, newest_revision,\\n platform_and_alg)\",\n \"def Cleanup(benchmark_spec):\\n del benchmark_spec # unused\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6447797","0.58259064","0.5799858","0.5799062","0.5770896","0.5712318","0.5706508","0.5695553","0.5673421","0.5479634","0.54711425","0.5403768","0.5383399","0.5374102","0.5373461","0.5352589","0.5352316","0.5337508","0.5330979","0.53233963","0.5322599","0.5299671","0.52748275","0.5273337","0.52399105","0.5226855","0.5219952","0.51898926","0.51733243","0.51176226","0.5110044","0.5107015","0.5092679","0.50813466","0.5069045","0.5066334","0.50630605","0.50630605","0.5053972","0.50510466","0.50405324","0.5033372","0.50317234","0.5028117","0.5022218","0.49977168","0.4975656","0.49741948","0.49740478","0.49702445","0.49616948","0.49611992","0.4959045","0.49458486","0.49373993","0.49264842","0.4926344","0.49243325","0.49219468","0.49199373","0.49178553","0.49076417","0.49008682","0.48831585","0.4880045","0.48732087","0.48717368","0.48519558","0.48497388","0.48348695","0.4834297","0.4833585","0.483054","0.48138115","0.48101443","0.4808591","0.48069915","0.4806982","0.4806519","0.48029628","0.48000628","0.48000628","0.48000628","0.48000628","0.4799954","0.47947806","0.4791296","0.4790677","0.47901836","0.4788666","0.47873467","0.47855863","0.47826886","0.47826886","0.47762027","0.47755194","0.47746298","0.47737876","0.47645643","0.47633877"],"string":"[\n \"0.6447797\",\n \"0.58259064\",\n \"0.5799858\",\n \"0.5799062\",\n \"0.5770896\",\n \"0.5712318\",\n \"0.5706508\",\n \"0.5695553\",\n \"0.5673421\",\n \"0.5479634\",\n \"0.54711425\",\n \"0.5403768\",\n \"0.5383399\",\n \"0.5374102\",\n \"0.5373461\",\n \"0.5352589\",\n \"0.5352316\",\n \"0.5337508\",\n \"0.5330979\",\n \"0.53233963\",\n \"0.5322599\",\n \"0.5299671\",\n \"0.52748275\",\n \"0.5273337\",\n \"0.52399105\",\n \"0.5226855\",\n \"0.5219952\",\n \"0.51898926\",\n \"0.51733243\",\n \"0.51176226\",\n \"0.5110044\",\n \"0.5107015\",\n \"0.5092679\",\n \"0.50813466\",\n \"0.5069045\",\n \"0.5066334\",\n \"0.50630605\",\n \"0.50630605\",\n \"0.5053972\",\n \"0.50510466\",\n \"0.50405324\",\n \"0.5033372\",\n \"0.50317234\",\n \"0.5028117\",\n \"0.5022218\",\n \"0.49977168\",\n \"0.4975656\",\n \"0.49741948\",\n \"0.49740478\",\n \"0.49702445\",\n \"0.49616948\",\n \"0.49611992\",\n \"0.4959045\",\n \"0.49458486\",\n \"0.49373993\",\n \"0.49264842\",\n \"0.4926344\",\n \"0.49243325\",\n \"0.49219468\",\n \"0.49199373\",\n \"0.49178553\",\n \"0.49076417\",\n \"0.49008682\",\n \"0.48831585\",\n \"0.4880045\",\n \"0.48732087\",\n \"0.48717368\",\n \"0.48519558\",\n \"0.48497388\",\n \"0.48348695\",\n \"0.4834297\",\n \"0.4833585\",\n \"0.483054\",\n \"0.48138115\",\n \"0.48101443\",\n \"0.4808591\",\n \"0.48069915\",\n \"0.4806982\",\n \"0.4806519\",\n \"0.48029628\",\n \"0.48000628\",\n \"0.48000628\",\n \"0.48000628\",\n \"0.48000628\",\n \"0.4799954\",\n \"0.47947806\",\n \"0.4791296\",\n \"0.4790677\",\n \"0.47901836\",\n \"0.4788666\",\n \"0.47873467\",\n \"0.47855863\",\n \"0.47826886\",\n \"0.47826886\",\n \"0.47762027\",\n \"0.47755194\",\n \"0.47746298\",\n \"0.47737876\",\n \"0.47645643\",\n \"0.47633877\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8056906"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":11273,"cells":{"query":{"kind":"string","value":"Returns True if |benchmark| is written in C/C++."},"document":{"kind":"string","value":"def is_cpp(benchmark):\n return get_language(benchmark) == 'c++'"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _c_optimizations_required():\n pure_env = os.environ.get('PURE_PYTHON')\n require_c = pure_env == \"0\"\n return require_c","def _should_attempt_c_optimizations():\n if PYPY:\n return False\n\n if _c_optimizations_required():\n return True\n return not _c_optimizations_ignored()","def supports_refcounts(self):\n return sys.implementation.name == \"cpython\"","def _c_optimizations_ignored():\n pure_env = os.environ.get('PURE_PYTHON')\n return pure_env != \"0\" if pure_env is not None else PYPY","def test_rust_code_analysis_tokei_c() -> None:\n\n ret_value = compare(\n \"rust-code-analysis\",\n \"tokei\",\n [\"-g\", \"-f\"],\n [\"SLOC\", \"PLOC\", \"CLOC\", \"BLANK\"],\n \"C\",\n \"bubble_sort.c\",\n )\n\n assert ret_value == 0","def is_cxx_compiler():\n\n wrapper_command = os.path.basename(sys.argv[0])\n return re.match(r'(.+)c\\+\\+(.*)', wrapper_command)","def is_library(code):\n return 3000 <= code <= 3999","def test_py_compile_condition(self):\n self._test_py_compile('coin')","def Cpp_test():\n pass","def _c_optimizations_available(module_name):\n import importlib\n catch = () if _c_optimizations_required() else (ImportError,)\n try:\n return importlib.import_module('BTrees._' + module_name)\n except catch: # pragma: no cover\n return False","def _clang_at_least(compiler: 'Compiler', minver: str, apple_minver: T.Optional[str]) -> bool:\n if isinstance(compiler, (AppleClangCCompiler, AppleClangCPPCompiler)):\n if apple_minver is None:\n return False\n return version_compare(compiler.version, apple_minver)\n return version_compare(compiler.version, minver)","def has_flag(compiler, flagname):\n import tempfile\n with tempfile.NamedTemporaryFile('w', suffix='.cc') as f:\n f.write('int main (int argc, char **argv) { return 0; }')\n try:\n compiler.compile([f.name], extra_postargs=[flagname])\n except setuptools.distutils.errors.CompileError:\n return False\n return True","def CustomCFlagCheck(context, flag, append=True):\n context.Message(\"Checking if C compiler supports \" + flag + \" flag \")\n ccflags = context.env[\"CCFLAGS\"]\n context.env.Append(CCFLAGS=flag)\n result = context.TryCompile(\"int main(int argc, char **argv) { return 0; }\", \".c\")\n context.Result(result)\n if not append or not result:\n context.env.Replace(CCFLAGS=ccflags)\n return result","def _supported_compilers_available():\n return _supported_gcc_available()[0] and supported_nvcc_available()[0]","def has_flag(compiler, flagname):\n import tempfile\n with tempfile.NamedTemporaryFile('w', suffix='.cpp') as f:\n f.write('int main (int argc, char **argv) { return 0; }')\n try:\n compiler.compile([f.name], extra_postargs=[flagname])\n except setuptools.distutils.errors.CompileError:\n return False\n return True","def has_flag(compiler, flagname):\n import tempfile\n\n with tempfile.NamedTemporaryFile(\"w\", suffix=\".cpp\") as f:\n f.write(\"int main (int argc, char **argv) { return 0; }\")\n try:\n compiler.compile([f.name], extra_postargs=[flagname])\n except setuptools.distutils.errors.CompileError:\n return False\n return True","def test_clang_cxx(self):\n self.assertEqual(\n self.ndk.clang_cxx,\n f\"/opt/android/android-ndk/toolchains/llvm/prebuilt/{self.ndk.host_tag}/bin/clang++\",\n )","def has_flag(compiler, flagname):\n with tempfile.TemporaryDirectory() as tmpdir:\n fname = join(tmpdir, \"test.cpp\")\n with open(fname, \"w\") as fp:\n fp.write(\"int main (int argc, char **argv) { return 0; }\")\n try:\n compiler.compile([fname], output_dir=tmpdir, extra_postargs=[flagname])\n except setuptools.distutils.errors.CompileError:\n return False\n return True","def test_make_benchmark_single_ll():\n benchmark = llvm.make_benchmark(INVALID_IR_PATH)\n assert str(benchmark.uri).startswith(\"benchmark://user-v0/\")\n assert benchmark.uri.scheme == \"benchmark\"\n assert benchmark.uri.dataset == \"user-v0\"","def exclude_non_cpp(benchmarks):\n return [benchmark for benchmark in benchmarks if is_cpp(benchmark)]","def supports_c_code(self, inputs):\r\n\r\n # If we don't even have the right method, we certainly\r\n # don't support the C code\r\n # (This is the test that used to be implemented by\r\n # local_gpu_sum)\r\n pattern = (''.join(str(i) for i in self.reduce_mask))\r\n if not hasattr(self, 'c_code_reduce_%s' % pattern):\r\n return False\r\n\r\n # Now that this is a general reduction op, we might\r\n # have a method for a pattern, but that pattern\r\n # might not be implemented for the current scalar op.\r\n # To detect this more complicated situation, we\r\n # make fake arguments to c_code, try to run them,\r\n # and see if NotImplementedError gets raised.\r\n\r\n node = self.make_node(*inputs)\r\n\r\n name = 'fake_name'\r\n\r\n inp = ['fake_input_name_%d' % i for i in xrange(len(inputs))]\r\n out = ['fake_output_name_%d' % i for i in xrange(len(node.outputs))]\r\n\r\n sub = {'fail': 'fake failure code'}\r\n\r\n try:\r\n self.c_code(node, name, inp, out, sub)\r\n self.c_support_code_apply(node, name)\r\n except NotImplementedError:\r\n return False\r\n return True","def supports_c_code(self, inputs):\r\n\r\n # If we don't even have the right method, we certainly\r\n # don't support the C code\r\n # (This is the test that used to be implemented by\r\n # local_gpu_sum)\r\n pattern = (''.join(str(i) for i in self.reduce_mask))\r\n if not hasattr(self, 'c_code_reduce_%s' % pattern):\r\n return False\r\n\r\n # Now that this is a general reduction op, we might\r\n # have a method for a pattern, but that pattern\r\n # might not be implemented for the current scalar op.\r\n # To detect this more complicated situation, we\r\n # make fake arguments to c_code, try to run them,\r\n # and see if NotImplementedError gets raised.\r\n\r\n node = self.make_node(*inputs)\r\n\r\n name = 'fake_name'\r\n\r\n inp = ['fake_input_name_%d' % i for i in xrange(len(inputs))]\r\n out = ['fake_output_name_%d' % i for i in xrange(len(node.outputs))]\r\n\r\n sub = {'fail': 'fake failure code'}\r\n\r\n try:\r\n self.c_code(node, name, inp, out, sub)\r\n self.c_support_code_apply(node, name)\r\n except NotImplementedError:\r\n return False\r\n return True","def has_flag(compiler, flagname):\n import tempfile\n import os\n with tempfile.NamedTemporaryFile('w', suffix='.cpp', delete=False) as f:\n f.write('int main (int argc, char **argv) { return 0; }')\n fname = f.name\n try:\n compiler.compile([fname], extra_postargs=[flagname])\n except setuptools.distutils.errors.CompileError:\n return False\n finally:\n try:\n os.remove(fname)\n except OSError:\n pass\n return True","def test_make_benchmark_from_command_line_mixed_source_and_object_files(\n env: LlvmEnv, retcode: int\n):\n with temporary_working_directory():\n with open(\"a.c\", \"w\") as f:\n f.write(\n \"\"\"\n#include \"b.h\"\n\nint A() {\n return B();\n}\n\nint main() {\n return A();\n}\n\"\"\"\n )\n\n with open(\"b.c\", \"w\") as f:\n f.write(f\"int B() {{ return {retcode}; }}\")\n\n with open(\"b.h\", \"w\") as f:\n f.write(\"int B();\")\n\n # Compile b.c to object file:\n subprocess.check_call([str(llvm_paths.clang_path()), \"b.c\", \"-c\"], timeout=60)\n assert (Path(\"b.o\")).is_file()\n\n bm = env.make_benchmark_from_command_line([\"gcc\", \"a.c\", \"b.o\", \"-o\", \"foo\"])\n env.reset(benchmark=bm)\n\n bm.compile(env)\n assert Path(\"foo\").is_file()\n\n p = subprocess.Popen([\"./foo\"])\n p.communicate(timeout=60)\n assert p.returncode == retcode","def check_java(interface):\n\n interface.info(__(\"I'm compiling a short test program, to see if you have a working JDK on your system.\"))\n\n if not run_slow(interface, plat.javac, plat.path(\"buildlib/CheckJDK8.java\"), use_path=True):\n interface.fail(__(\"I was unable to use javac to compile a test file. If you haven't installed the Java Development Kit yet, please download it from:\\n\\nhttp://www.oracle.com/technetwork/java/javase/downloads/jdk8-downloads-2133151.html\\n\\nThe JDK is different from the JRE, so it's possible you have Java without having the JDK. Without a working JDK, I can't continue.\"))\n\n if not run_slow(interface, plat.java, \"-classpath\", plat.path(\"buildlib\"), \"CheckJDK8\", use_path=True):\n interface.fail(__(\"The version of Java on your computer does not appear to be JDK 8, which is the only version supported by the Android SDK. If you need to install JDK 8, you can download it from:\\n\\nhttp://www.oracle.com/technetwork/java/javase/downloads/jdk8-downloads-2133151.html\\n\\nYou can also set the JAVA_HOME environment variable to use a different version of Java.\"))\n\n interface.success(__(\"The JDK is present and working. Good!\"))","def py_versiontest(c):\n pass","def CustomCompileCheck(context, message, source, extension=\".cc\"):\n context.Message(message)\n\n env = context.env\n if env.GetOption(\"clean\") or env.GetOption(\"help\") or env.GetOption(\"no_exec\"):\n result = True\n else:\n result = context.TryCompile(source, extension)\n\n context.Result(result)\n\n return result","def check_cc(self):\n # simply generates a C program containing a couple of calls\n # to MPI routines and checks if the compilation and execution\n # are succesful\n print 'Checking if cc works...',\n sys.stdout.flush()\n # generate\n writefile('tmpc.c',\"\"\"\n #include <stdio.h>\n int main(int argc, char **argv){\n int iam;\n fprintf(stdout, \\\"success\\\" );fflush(stdout);\n return 0;\n }\\n\"\"\")\n\n # compile\n ccomm = self.config.cc+\" \"+self.config.ccflags+\" \"+self.config.ldflags_c+\" -o tmpc \"+os.path.join(os.getcwd(),\"tmpc.c\")\n (output, error, retz) = runShellCommand(ccomm)\n\n if retz:\n print '\\n\\nCOMMON: C compiler not working! aborting...'\n print 'stderr:\\n','*'*40,'\\n',error,'\\n','*'*40\n sys.exit()\n\n # run\n comm = './tmpc'\n (output, error, retz) = runShellCommand(comm)\n if retz:\n print '\\n\\nCOMMON: cc not working! aborting...'\n print 'error is:\\n','*'*40,'\\n',error,'\\n','*'*40\n sys.exit()\n\n # cleanup\n killfiles(['tmpc.c','tmpc'])\n print 'yes'\n return 0;","def numba_check():\n numba = importlib.util.find_spec(\"numba\")\n return numba is not None","def is_ctu_capable():\n\n context = package_context.get_context()\n ctu_func_map_cmd = context.ctu_func_map_cmd\n try:\n version = subprocess.check_output([ctu_func_map_cmd, '-version'])\n except (subprocess.CalledProcessError, OSError):\n version = 'ERROR'\n return version != 'ERROR'","def isLibSBMLCompiledWith(*args):\n return _libsbml.isLibSBMLCompiledWith(*args)","def is_system_ready_for_benchmarking():\n\n # check if scaling_governor is set to 'performance' for all cpu cores\n cpu_governors = glob.glob('/sys/devices/system/cpu/cpu*/cpufreq/scaling_governor')\n if not cpu_governors:\n logger.error('no scaling_governor found. Do you run on a Linux System?')\n return False\n for governor in sorted(cpu_governors):\n with open(governor, 'r') as f:\n line = f.read().splitlines()[0]\n logger.debug('%s is set to \\\"%s\\\"', governor, line)\n if line != 'performance':\n logger.warning('please set all scaling_governor to \\\"performance\\\" (using \"sudo ./ondemand.sh start\")')\n return False\n\n return True","def has_cached_cfunc(*args):\n return _ida_hexrays.has_cached_cfunc(*args)","def is_cpp(filename: Path) -> bool:\n from fnmatch import fnmatch\n\n return any(fnmatch(os.path.basename(filename), p) for p in CPP_PATTERNS)","def test_rust_code_analysis_tokei_Rust() -> None:\n\n ret_value = compare(\n \"rust-code-analysis\",\n \"tokei\",\n [\"-g\", \"-f\"],\n [\"SLOC\", \"PLOC\", \"CLOC\", \"BLANK\"],\n \"Rust\",\n \"bubble_sort.rs\",\n )\n\n assert ret_value == 0","def test_c(self):\n self.failIf(cgs.speed_of_light/mks.speed_of_light!=100)","def test_unix(args):\n total = 0\n passed = 0\n\n print('============= Testing for Correctness (Unix) =============')\n for filename in os.listdir(args.testdir):\n if not filename.endswith('.c'):\n continue\n\n print( '{}:'.format(os.path.splitext(filename)[0]), end=' ')\n total += 1\n\n testfile = os.path.join(args.testdir, filename)\n valid, diff = test_output(args.glcpp, testfile, testfile + '.expected')\n if valid:\n passed += 1\n print('PASS')\n else:\n print('FAIL')\n for l in diff:\n print(l, file=sys.stderr)\n\n print('{}/{}'.format(passed, total), 'tests returned correct results')\n return total == passed","def test_clang(self):\n self.assertEqual(\n self.ndk.clang,\n f\"/opt/android/android-ndk/toolchains/llvm/prebuilt/{self.ndk.host_tag}/bin/clang\",\n )","def test_is_poor_style(self):\n self.assertEqual(1, uut.is_poor_style(\"test/with_leak.cc\"))\n self.assertEqual(0, uut.is_poor_style(\"test/without_leak.cc\"))","def have_cdc() -> bool:","def HasPython(self):\n oldind = copy.copy(self.ind)\n self.ind = 0\n mybool = self.FindNextEnv()\n self.ind = oldind#reset ind\n return bool(mybool)","def gcc_llvm():\r\n if gcc_llvm.is_llvm is None:\r\n pass\r\n p = None\r\n try:\r\n p_out = output_subprocess_Popen(['g++', '--version'])\r\n output = p_out[0] + p_out[1]\r\n except OSError:\r\n # Typically means g++ cannot be found.\r\n # So it is not an llvm compiler.\r\n\r\n # Normally this should not happen as we should not try to\r\n # compile when g++ is not available. If this happen, it\r\n # will crash later so supposing it is not llvm is \"safe\".\r\n output = b('')\r\n del p\r\n gcc_llvm.is_llvm = b(\"llvm\") in output\r\n return gcc_llvm.is_llvm","def can_compile(src):\n src = src if src.endswith(\"\\n\") else src + \"\\n\"\n src = transform_command(src, show_diff=False)\n src = src.lstrip()\n try:\n XSH.execer.compile(src, mode=\"single\", glbs=None, locs=XSH.ctx)\n rtn = True\n except SyntaxError:\n rtn = False\n except Exception:\n rtn = True\n return rtn","def CustomCppFlagCheck(context, flag, append=True):\n context.Message(\"Checking if C++ compiler supports \" + flag + \" flag \")\n cxxflags = context.env[\"CXXFLAGS\"]\n context.env.Append(CXXFLAGS=flag)\n result = context.TryCompile(\"int main(int argc, char **argv) { return 0; }\", \".cc\")\n context.Result(result)\n if not append or not result:\n context.env.Replace(CXXFLAGS=cxxflags)\n return result","def detectExtensions(builder):\n print (\"Checking if C extensions can be compiled, don't be alarmed if \"\n \"a few compile errors are printed.\")\n\n if not builder._compile_helper(\"#define X 1\\n\"):\n print \"Compiler not found, skipping C extensions.\"\n return []\n\n # Extension modules to build.\n exts = [\n Extension(\"twisted.spread.cBanana\",\n [\"twisted/spread/cBanana.c\"],\n define_macros=builder.define_macros),\n ]\n\n # urllib.unquote accelerator\n exts.append( Extension(\"twisted.protocols._c_urlarg\",\n [\"twisted/protocols/_c_urlarg.c\"],\n define_macros=builder.define_macros) )\n\n if sys.platform == 'darwin':\n exts.append(\n Extension(\"twisted.internet.cfsupport\",\n [\"twisted/internet/cfsupport/cfsupport.c\"],\n extra_compile_args=['-w'],\n extra_link_args=['-framework','CoreFoundation',\n '-framework','CoreServices',\n '-framework','Carbon'],\n define_macros=builder.define_macros))\n\n if sys.platform == 'win32':\n exts.append( Extension(\"twisted.internet.iocpreactor._iocp\",\n [\"twisted/internet/iocpreactor/_iocp.c\"],\n libraries=[\"ws2_32\", \"mswsock\"],\n define_macros=builder.define_macros))\n\n return exts","def is_result_appropriate(self, benchmark_result):\n return True","def checkCUDAisAvailable():\n # some possible lib names \n libnames = ('libcuda.so', 'libcuda.dylib', 'cuda.dll')\n libsOk = True\n for libname in libnames:\n try:\n cuda = ctypes.CDLL(libname)\n except OSError:\n continue\n else:\n break\n else:\n libsOk = False\n return libsOk","def test_make_benchmark_from_command_line_multiple_input_sources(\n env: LlvmEnv, retcode: int\n):\n with temporary_working_directory() as cwd:\n with open(\"a.c\", \"w\") as f:\n f.write(\"int main() { return B(); }\")\n\n with open(\"b.c\", \"w\") as f:\n f.write(f\"int B() {{ return {retcode}; }}\")\n\n bm = env.make_benchmark_from_command_line([\"gcc\", \"a.c\", \"b.c\", \"-o\", \"foo\"])\n assert not (cwd / \"foo\").is_file()\n\n env.reset(benchmark=bm)\n assert \"main()\" in env.ir\n\n bm.compile(env)\n assert (cwd / \"foo\").is_file()\n\n p = subprocess.Popen([\"./foo\"])\n p.communicate(timeout=60)\n assert p.returncode == retcode","def is_c(c_instr, code):\n out_instr='111' #start output instruction. 111 acccccc ddd jjj\n\n #to get dest, split at =\n if '=' in c_instr: #has destination\n comp_dest = c_instr.split('=',1) #split at =\n dest_key = comp_dest.pop(0) #get destination command. put before = in dest_key\n c_instr = comp_dest.pop(0) #get rest of c instruction. put after = in c_instr\n dest = code.get_dest(dest_key) #assign destination binary to dest\n else: #no destination\n #c instruction is unchanged\n dest = '000' #destination is null\n\n #to get jump, split at ';'\n if ';' in c_instr:\n split_line = c_instr.split(';', 1) #split at jump command\n # send jump command to function and concatenate is_jump output to out instruction\n jump = code.get_jump(split_line[1]) #get jump command\n c_instr = split_line[0] #line without jump command\n else: #jump is null\n #c instruction is unchanged\n jump = '000'\n\n comp = code.get_comp(c_instr) #get a cccc cc bits from computation dictionary\n return out_instr +comp+dest+jump #concatenate parts of binary equiv and return","def validate_type(benchmark):\n benchmark_type = get_type(benchmark)\n if benchmark_type not in BENCHMARK_TYPE_STRS:\n logs.error('%s has an invalid benchmark type %s, must be one of %s',\n benchmark, benchmark_type, BENCHMARK_TYPE_STRS)\n return False\n return True","def _is_fan(compressor: Compressor) -> bool:\n name = compressor.name.lower()\n return 'fan' in name or 'crtf' in name","def is_returnning_weak(c_type: str, jtype: str):\n return c_type in LONG_ISH_WEAKREF_SET and jtype == 'jlong'","def test_has_mac_accelerate(self):\n\t\t\n\t\texpected_has_accelerate = False\n\t\t\n\t\timport platform\n\t\timport numpy.distutils.system_info as sysinfo\n\n\t\tmac_arch = platform.machine()\n\t\tif mac_arch == 'arm64':\n\t\t\tinfo = sysinfo.get_info('accelerate')\n\t\t\tif info is not None and len(info)>0:\n\t\t\t for x in info['extra_link_args']:\n\t\t\t if 'Accelerate' in x:\n\t\t\t expected_has_accelerate = True\n\t\t\t \n\t\tactual_has_accelerate = RMT_Util.has_mac_accelerate()\n\t\tself.assertEqual(expected_has_accelerate, actual_has_accelerate)\n\t\treturn","def is_jetson():\n # TODO: check x86_64 explicitly instead in case somebody tries\n # to run this in mips or ppc or something\n # TODO: do further checking if aarch64 to determine whether in fact\n # a tegra system, and if so, which one\n return True if platform.processor() == 'aarch64' else False","def test_c(self):\n source = io.StringIO(\"int x(int a) { return a + 1 ; }\")\n arch = get_current_arch()\n obj = cc(source, arch, debug=True)\n m = load_obj(obj)\n y = m.x(101)\n self.assertEqual(102, y)","def _check_cachable(self, cres):\n cannot_cache = None\n if self._is_closure:\n cannot_cache = \"as it uses outer variables in a closure\"\n elif cres.lifted:\n cannot_cache = \"as it uses lifted loops\"\n elif cres.has_dynamic_globals:\n cannot_cache = \"as it uses dynamic globals (such as ctypes pointers)\"\n if cannot_cache:\n msg = ('Cannot cache compiled function \"%s\" %s'\n % (self._funcname, cannot_cache))\n warnings.warn_explicit(msg, NumbaWarning,\n self._source_path, self._lineno)\n return False\n return True","def test_py_compile_basic(self):\n self._test_py_compile('basic')","def python_compatible():\n result = False\n req_ver = vers.convert('3.9.5')\n pythonver = vers.convert('{major}.{minor}.{micro}'.format(major=sys.version_info.major,\n minor=sys.version_info.minor,\n micro=sys.version_info.micro))\n\n result = pythonver >= req_ver\n\n return result","def isOnNao():\n szCpuInfo = \"/proc/cpuinfo\";\n if not os.path.exists( szCpuInfo ): # already done by the getFileContents\n return False;\n szAllFile = getFileContents( szCpuInfo, bQuiet = True );\n if( szAllFile.find( \"Geode\" ) == -1 and szAllFile.find( \"Intel(R) Atom(TM)\" ) == -1 ):\n return False;\n return True;","def test01c(self):\n a = np.arange(1e5)\n cnames = bcolz.blosc_compressor_list()\n if common.verbose:\n print(\"Checking compressors:\", cnames)\n # print \"\\nsize b uncompressed-->\", a.size * a.dtype.itemsize\n for cname in cnames:\n with bcolz.defaults_ctx(bcolz.cparams(clevel=9, cname=cname)):\n self.assertTrue(bcolz.defaults.cparams['cname'] == cname)\n b = bcolz.carray(a, rootdir=self.rootdir)\n # print \"size b compressed -->\", b.cbytes, \"with '%s'\"%cname\n self.assertTrue(sys.getsizeof(b) < b.nbytes,\n \"carray does not seem to compress at all\")\n assert_array_equal(a, b[:], \"Arrays are not equal\")\n # Remove the array on disk before trying with the next one\n if self.disk:\n common.remove_tree(self.rootdir)","def test_lua(args: argparse.Namespace) -> bool:\n\n if not os.path.isfile(os.path.join(c.BUILD_DIR, LUA_ARCHIVE)):\n with pb.local.cwd(c.BUILD_DIR):\n download_archive(LUA_URL, LUA_ARCHIVE)\n if not os.path.isdir(LUA_SRC):\n with pb.local.cwd(c.BUILD_DIR):\n invoke_quietly(TAR, \"xf\", LUA_ARCHIVE)\n\n # unconditionally compile lua since we don't know if\n # cc_db was generated from the environment we're in.\n build_dir = os.path.join(LUA_SRC, \"build\")\n rmtree(build_dir, ignore_errors=True)\n os.mkdir(build_dir)\n with pb.local.cwd(build_dir), pb.local.env(CC=\"clang\"):\n invoke(CMAKE['-DCMAKE_EXPORT_COMPILE_COMMANDS=1', LUA_SRC])\n invoke(MAKE[JOBS])\n\n cc_db_file = os.path.join(LUA_SRC, \"build\", c.CC_DB_JSON)\n if not os.path.isfile(cc_db_file):\n die(\"missing \" + cc_db_file, errno.ENOENT)\n\n return transpile(cc_db_file)","def run_test(skip_run):\n # First off, we need the 'official' test data downloaded and unpacked\n # if necessary.\n if ref_test_data.install_and_check_test_files() != 0:\n return 1\n if install_inputs() != 0:\n return 1\n\n if not skip_run:\n sys.stdout.write(\"Executing CCC code...\\n\")\n ret = run.main()\n if ret != 0:\n return 1\n\n test_result_dir = os.path.join(ref_test_data.test_data_dir, \"result\")\n sys.stdout.write(\"Comparing CCC results with 'official' results...\\n\")\n ret = compare_results.main([sys.argv[0], \"result\", test_result_dir])\n if ret != 0:\n return 1","def get_language(benchmark):\n config = benchmark_config.get_config(benchmark)\n return config.get('language', 'c++')","def ignore_builtin_verification():\n return not current_space().skip_builtin_verification","def testSC(self):\n\n obt_np = compression.decByteOffet_numpy(compression.compByteOffet_numpy(self.ds))\n self.assertEqual(abs(self.ds - obt_np).max(), 0.0, \"numpy algo\")\n obt_cy = compression.decByteOffet_cython(compression.compByteOffet_numpy(self.ds))\n self.assertEqual(abs(self.ds - obt_cy).max(), 0.0, \"cython algo\")\n obt_cy2 = compression.decByteOffet_cython(compression.compByteOffet_numpy(self.ds), self.ds.size)\n self.assertEqual(abs(self.ds - obt_cy2).max(), 0.0, \"cython algo_orig\")\n obt_we = compression.decByteOffet_weave(compression.compByteOffet_numpy(self.ds), self.ds.size)\n self.assertEqual(abs(self.ds - obt_we).max(), 0.0, \"weave algo\")","def conditional_guard(src, dst):\n int64_count = 0\n float64_count = 0\n float16_count = 0\n if src in int64_types or dst in int64_types:\n int64_count = 1\n if src in float64_types or dst in float64_types:\n float64_count = 1\n if src in float16_types or dst in float16_types:\n float16_count = 1\n if float16_count > 0:\n print(\"#ifdef cl_khr_fp16\")\n if float64_count > 0:\n #In embedded profile, if cl_khr_fp64 is supported cles_khr_int64 has to be\n print(\"#ifdef cl_khr_fp64\")\n return 1 + float16_count\n elif int64_count > 0:\n print(\"#if defined cles_khr_int64 || !defined(__EMBEDDED_PROFILE__)\")\n return 1 + float16_count\n return float16_count","def check_cachable(self, cres):\n cannot_cache = None\n if any(not x.can_cache for x in cres.lifted):\n cannot_cache = \"as it uses lifted code\"\n elif cres.library.has_dynamic_globals:\n cannot_cache = (\"as it uses dynamic globals \"\n \"(such as ctypes pointers and large global arrays)\")\n if cannot_cache:\n msg = ('Cannot cache compiled function \"%s\" %s'\n % (cres.fndesc.qualname.split('.')[-1], cannot_cache))\n warnings.warn_explicit(msg, NumbaWarning,\n self._locator._py_file, self._lineno)\n return False\n return True","def passed(self):\n\n compile = self.compile_successful\n if self.compileTest or not compile: return compile\n\n compare = not self.doComparison or self.compare_successful\n analysis = self.analysisRoutine == \"\" or self.analysis_successful\n return compare and analysis","def test01(self):\n a = np.arange(2e5)\n b = bcolz.carray(a, rootdir=self.rootdir)\n # print \"size b uncompressed-->\", b.nbytes\n # print \"size b compressed -->\", b.cbytes\n self.assertTrue(sys.getsizeof(b) < b.nbytes,\n \"carray does not seem to compress at all\")","def is_scala(self):\r\n return self.has_label('scala')","def is_basic_ctype(self, objtype):\n if objtype in [self.c_char_p, self.c_void_p, self.CString]:\n return False\n # DOC: if <ctypes.c_uint> is not in self.__basic_types, its probably\n # because you are using the wrong ctypes Proxy instance\n return objtype in self.__basic_types","def isCfile(path:str) -> bool:\n if not isexist(path):\n return False\n \n name, ext = path.split(\".\")\n \n return ext == \"c\"","def is_clml_runtime_enabled():\n check_enabled = tvm.get_global_func(\"relay.op.is_clml_runtime_enabled\", True)\n if check_enabled:\n return check_enabled()\n return False","def has_flag(compiler, flag, ext=None):\n return try_compile(compiler, flags=[flag], ext=ext)","def has_cooling_system(bpr):\n\n if bpr.hvac['type_cs'] in {'T1', 'T2', 'T3'}:\n return True\n elif bpr.hvac['type_cs'] in {'T0'}:\n return False\n else:\n raise","def is_statistics_capable():\n context = package_context.get_context()\n\n analyzer = \"clangsa\"\n enabled_analyzers = [analyzer]\n cfg_handlers = analyzer_types.build_config_handlers({},\n context,\n enabled_analyzers)\n\n clangsa_cfg = cfg_handlers[analyzer]\n analyzer = analyzer_types.supported_analyzers[analyzer](clangsa_cfg,\n None)\n\n check_env = analyzer_env.get_check_env(context.path_env_extra,\n context.ld_lib_path_extra)\n\n checkers = analyzer.get_analyzer_checkers(clangsa_cfg, check_env)\n\n stat_checkers_pattern = re.compile(r'.+statisticscollector.+')\n\n for checker_name, _ in checkers:\n if stat_checkers_pattern.match(checker_name):\n return True\n\n return False","def IsClangWinBuild(build_dir, target):\n if not IsWindows():\n return False\n\n gn_file = os.path.join(build_dir, target, 'args.gn')\n if not os.path.isfile(gn_file):\n print 'WARNING: Unable to find the args.gn file.'\n return False\n # Matches e.g. \"gn_arg = value\"\n gn_arg_re = re.compile(r'^(?P<flag>[^= ]+)\\s*=\\s*(?P<value>[^ \\n]+)$')\n for line in open(gn_file):\n m = gn_arg_re.match(line)\n if m and m.group('flag') == 'is_clang':\n return m.group('value') == 'true'\n return False","def exists(_env):\n detector = DetectCompiler()\n if detector['icx'] is None:\n return False\n return True","def test02(self):\n a = np.arange(111)\n b = bcolz.carray(a)\n # print \"size b uncompressed-->\", b.nbytes\n # print \"size b compressed -->\", b.cbytes\n self.assertTrue(sys.getsizeof(b) > b.nbytes,\n \"carray compress too much??\")","def use_cg(self):\n return self.algo == \"cg\"","def test_script_is_callable(self):\n # This is really more a smoke test of this script than anything else.\n maker = Compilers(test_utils.MockMachines(\"mymachine\", \"SomeOS\"), version=2.0)\n test_xml = test_utils._wrap_config_compilers_xml(\n \"<compiler><SUPPORTS_CXX>FALSE</SUPPORTS_CXX></compiler>\"\n )\n test_utils.get_macros(maker, test_xml, \"Makefile\")","def is_jvm(self):\r\n return self.has_label('jvm')","def parse_is_self_cc(b):\n return f'{int(bool(b)):>{IS_SELF_CC_SIZE}}'","def check(self, runtime):\n return True","def evaluate_c(self, profiling_iterations=None):\n\n # get the C test function from it's .so (compiles if necessary)\n lib_path = Operator._make_generic_c(self.op_c_generic, self.op_name).encode('utf-8')\n fcn_name = (self.op_name + '_generic_cpp').encode('utf-8')\n\n self._define_eval_params(lib_path, fcn_name)\n\n if profiling_iterations is None:\n iters = 1\n else:\n if not isinstance(profiling_iterations, int) or profiling_iterations < 1:\n raise ValueError('Profiling iterations must be a positive int, but received: ' +\n str(profiling_iterations))\n iters = profiling_iterations\n\n eval_times_ms = np.empty(iters, dtype=np.float64)\n eval_times_ms[:] = np.nan\n\n num_inputs = len(self._input_types)\n num_outputs = len(self.output_types)\n\n if self._test_c_op is None:\n testlib_path = os.path.join(cache_directory, 'libtestcop.so.'+version)\n try:\n libtest = ctypes.cdll.LoadLibrary(testlib_path)\n except OSError:\n Operator._check_proto()\n this_file_path = os.path.abspath(__file__)\n this_directory = os.path.split(this_file_path)[0]\n\n # build the test framework library\n cc_path = os.path.join(this_directory, 'testcop.cc')\n\n try:\n subprocess.check_output(['g++', '-fPIC', '-Wall', '-shared',\n '-std=c++11', '-Ofast', '-Wextra',\n '-I'+this_directory,\n '-I'+cache_directory,\n '-o', testlib_path, cc_path],\n stderr=subprocess.STDOUT,\n universal_newlines=True)\n except subprocess.CalledProcessError as exception:\n tf.logging.log(tf.logging.ERROR, 'g++ error: ' + exception.output)\n raise\n\n libtest = ctypes.cdll.LoadLibrary(testlib_path)\n\n self._test_c_op = libtest.testCOperator\n self._test_c_op.restype = ctypes.c_int16\n self._test_c_op.argtypes = \\\n [ctypes.c_char_p, ctypes.c_char_p,\n ndpointer(dtype=_TensorParam, flags=\"C_CONTIGUOUS\"), ctypes.c_size_t,\n ndpointer(dtype=_TensorParam, flags=\"C_CONTIGUOUS\"), ctypes.c_size_t,\n ndpointer(dtype=ctypes.c_double, flags=\"C_CONTIGUOUS\"), ctypes.c_size_t]\n\n # run the operator\n err = self._test_c_op(lib_path, fcn_name,\n self._input_params, ctypes.c_size_t(num_inputs),\n self._output_params, ctypes.c_size_t(num_outputs),\n eval_times_ms,\n ctypes.c_size_t(iters))\n\n if err != 0 or np.isnan(eval_times_ms).any():\n tf.logging.log(tf.logging.ERROR, 'Test C operator failed for Op ' + self.__class__.__name__)\n raise ValueError('Test C operator failed for Op ' + self.__class__.__name__)\n\n if profiling_iterations is None:\n return Operator._unwrap_single(self._output_buffers)\n else:\n return Operator._unwrap_single(self._output_buffers), eval_times_ms","def _preprocess(self):\n if not super()._preprocess():\n return False\n\n # Format the arguments\n self._args.operation = self._args.operation.lower()\n\n # Check the arguments and generate the commands\n op = self._args.operation\n if op not in self.__operations:\n self._result.set_return_code(ReturnCode.INVALID_ARGUMENT)\n logger.error(\n 'Unsupported operation of NCCL test - benchmark: {}, operation: {}, expected: {}.'.format(\n self._name, op, ' '.join(list(self.__operations.keys()))\n )\n )\n return False\n else:\n self._bin_name = self.__operations[op]\n if not self._set_binary_path():\n return False\n\n command = os.path.join(self._args.bin_dir, self._bin_name)\n command += ' -b {} -e {} -f {} -g {} -c {} -n {} -w {}'.format(\n self._args.minbytes, self._args.maxbytes, str(self._args.stepfactor), str(self._args.ngpus),\n str(self._args.check), str(self._args.iters), str(self._args.warmup_iters)\n )\n self._commands.append(command)\n\n return True","def c_equals(a, b):\n alpha = library.PixelGetAlpha\n return bool(library.IsPixelWandSimilar(a, b, 0) and\n alpha(a) == alpha(b))","def _optimized(self):\n return False","def asan_supported(goroot: GoRoot) -> bool:\n if goroot.goos == \"linux\":\n return goroot.goarch in (\"arm64\", \"amd64\", \"riscv64\", \"ppc64le\")\n else:\n return False","def test(self):\n res = self._dll.JLINKARM_Test()\n return (res == 0)","def test_countdown_performance():\n profiler = cProfile.Profile()\n profiler.enable()\n countdown(\"0 0 0 0 0 0 1000\")\n profiler.disable()\n stats = profiler.getstats()\n tot_time = stats[0].totaltime\n assert tot_time < 3, \"Wow, your computer is really slow. Or is it my code?\"","def detect_compilers(self):\n # By users experience it is known which compiler flags are required\n # in some cases. This function tries to detect which compilers are used\n # and sets the flags accordingly\n\n print 'Detecting Fortran compiler...',\n if self.fc_is_intel():\n # The Intel FORTRAN compiler requires -nofor_main flag\n # for the linking and the -mp flag to maintain the\n # floating-point precision\n self.config.fcflags += ' -diag-disable vec -fltconsistency -fp_port'\n self.config.ldflags_c += ' ' # used to link\n self.config.ldflags_fc += ' '\n self.config.ld_fcmain = ' -nofor_main'\n self.config.noopt += ' -mp'\n self.testing = 0; # Cannot compile lintest with fc_main option\n print 'Intel'\n elif self.fc_is_gnu():\n print 'GNU'\n self.config.ld_fcmain = ''\n elif self.fc_is_xlf():\n self.config.fcflags += ' -qstrict -qthreaded'\n self.config.ld_fcmain = ''\n print 'IBM'\n elif self.fc_is_pgi():\n self.config.ldflags_c += ''\n self.config.ldflags_fc += ''\n self.config.ld_fcmain = ' -Mnomain'\n self.testing = 0; # Cannot compile lintest with fc_main option\n else:\n self.config.compiler = \"Unknown\"\n print 'unknown'\n\n print 'Detecting C compiler...',\n if self.cc_is_intel():\n self.config.compiler = \"Intel\"\n self.config.ccflags += ' -diag-disable vec'\n print 'Intel'\n elif self.cc_is_gnu():\n self.config.compiler = \"GNU\"\n print 'GNU'\n elif self.cc_is_xlc():\n self.config.compiler = \"XLC\"\n self.config.ccflags += ' -qstrict -qthreaded'\n print 'IBM'\n elif self.cc_is_pgi():\n self.config.compiler = \"PGI\"\n print 'PGI'\n else:\n print 'unknown'\n\n print 'Selected C compiler flags: '+self.config.ccflags\n print 'Selected Fortran compiler flags: '+self.config.fcflags\n print 'Selected loader flags (C main): '+self.config.ldflags_c\n print 'Selected loader flags (Fortran main): '+self.config.ldflags_fc\n return","def has_compiled(self, phase='predict'):\n return self._graph_executor.has_compiled(phase)","def can_trace_source(self, filename: str) -> bool:\n path = Path(filename)\n if not path.is_file():\n return False\n\n extension = path.suffix\n if extension in self.registered_compilers:\n compiler = self.registered_compilers[extension]\n if compiler.supports_source_tracing:\n return True\n\n # We are not able to get coverage for this file.\n return False","def cc():\n load_env_vars('dev')\n from tools.static_code_analysis import CyclomaticComplexity\n radon_cc = CyclomaticComplexity()\n score = radon_cc.run_test()\n radon_cc.create_badge(score)","def test_fastqc_notexec():\n try:\n obj = fastqc.FastQC(\"LICENSE\")\n except NotExecutableError:\n return True\n else:\n return False","def test07(self):\n a = np.arange(10000) > 5000\n b = bcolz.carray(a, chunklen=100)\n wt = a.nonzero()[0].tolist()[1020:2040]\n cwt = [i for i in b.wheretrue(skip=1020, limit=1020)]\n # print \"numpy ->\", a.nonzero()[0].tolist()[1020:2040]\n # print \"where ->\", [i for i in b.wheretrue(limit=1020,skip=1020)]\n self.assertTrue(wt == cwt, \"wheretrue() does not work correctly\")","def is_opencl_env_defined():\n variable_name = \"PYOPENCL_CTX\"\n if os.environ.get(variable_name):\n return True\n else:\n return False","def test02(self):\n a = np.arange(1, 1e5) < 0\n b = bcolz.carray(a)\n wt = a.nonzero()[0].tolist()\n cwt = [i for i in b.wheretrue()]\n # print \"numpy ->\", a.nonzero()[0].tolist()\n # print \"where ->\", [i for i in b.wheretrue()]\n self.assertTrue(wt == cwt, \"wheretrue() does not work correctly\")","def is_native(package_name: str) -> bool:\n result = False\n try:\n package = importlib.import_module(package_name)\n package_dir = os.path.dirname(package.__file__)\n for root, dirs, files in os.walk(package_dir):\n for filename in files:\n if filename.endswith('.so') or filename.endswith('.pyd'):\n return True\n result = False\n except ImportError:\n result = False\n except AttributeError:\n # No __file__, meaning it's built-in. Let's call it native.\n result = True\n except TypeError:\n # __file__ is there, but empty (os.path.dirname() returns TypeError). Let's call it native.\n result = True\n except ModuleNotFoundError:\n # This module is not installed; fail gracefully.\n result = False\n return result"],"string":"[\n \"def _c_optimizations_required():\\n pure_env = os.environ.get('PURE_PYTHON')\\n require_c = pure_env == \\\"0\\\"\\n return require_c\",\n \"def _should_attempt_c_optimizations():\\n if PYPY:\\n return False\\n\\n if _c_optimizations_required():\\n return True\\n return not _c_optimizations_ignored()\",\n \"def supports_refcounts(self):\\n return sys.implementation.name == \\\"cpython\\\"\",\n \"def _c_optimizations_ignored():\\n pure_env = os.environ.get('PURE_PYTHON')\\n return pure_env != \\\"0\\\" if pure_env is not None else PYPY\",\n \"def test_rust_code_analysis_tokei_c() -> None:\\n\\n ret_value = compare(\\n \\\"rust-code-analysis\\\",\\n \\\"tokei\\\",\\n [\\\"-g\\\", \\\"-f\\\"],\\n [\\\"SLOC\\\", \\\"PLOC\\\", \\\"CLOC\\\", \\\"BLANK\\\"],\\n \\\"C\\\",\\n \\\"bubble_sort.c\\\",\\n )\\n\\n assert ret_value == 0\",\n \"def is_cxx_compiler():\\n\\n wrapper_command = os.path.basename(sys.argv[0])\\n return re.match(r'(.+)c\\\\+\\\\+(.*)', wrapper_command)\",\n \"def is_library(code):\\n return 3000 <= code <= 3999\",\n \"def test_py_compile_condition(self):\\n self._test_py_compile('coin')\",\n \"def Cpp_test():\\n pass\",\n \"def _c_optimizations_available(module_name):\\n import importlib\\n catch = () if _c_optimizations_required() else (ImportError,)\\n try:\\n return importlib.import_module('BTrees._' + module_name)\\n except catch: # pragma: no cover\\n return False\",\n \"def _clang_at_least(compiler: 'Compiler', minver: str, apple_minver: T.Optional[str]) -> bool:\\n if isinstance(compiler, (AppleClangCCompiler, AppleClangCPPCompiler)):\\n if apple_minver is None:\\n return False\\n return version_compare(compiler.version, apple_minver)\\n return version_compare(compiler.version, minver)\",\n \"def has_flag(compiler, flagname):\\n import tempfile\\n with tempfile.NamedTemporaryFile('w', suffix='.cc') as f:\\n f.write('int main (int argc, char **argv) { return 0; }')\\n try:\\n compiler.compile([f.name], extra_postargs=[flagname])\\n except setuptools.distutils.errors.CompileError:\\n return False\\n return True\",\n \"def CustomCFlagCheck(context, flag, append=True):\\n context.Message(\\\"Checking if C compiler supports \\\" + flag + \\\" flag \\\")\\n ccflags = context.env[\\\"CCFLAGS\\\"]\\n context.env.Append(CCFLAGS=flag)\\n result = context.TryCompile(\\\"int main(int argc, char **argv) { return 0; }\\\", \\\".c\\\")\\n context.Result(result)\\n if not append or not result:\\n context.env.Replace(CCFLAGS=ccflags)\\n return result\",\n \"def _supported_compilers_available():\\n return _supported_gcc_available()[0] and supported_nvcc_available()[0]\",\n \"def has_flag(compiler, flagname):\\n import tempfile\\n with tempfile.NamedTemporaryFile('w', suffix='.cpp') as f:\\n f.write('int main (int argc, char **argv) { return 0; }')\\n try:\\n compiler.compile([f.name], extra_postargs=[flagname])\\n except setuptools.distutils.errors.CompileError:\\n return False\\n return True\",\n \"def has_flag(compiler, flagname):\\n import tempfile\\n\\n with tempfile.NamedTemporaryFile(\\\"w\\\", suffix=\\\".cpp\\\") as f:\\n f.write(\\\"int main (int argc, char **argv) { return 0; }\\\")\\n try:\\n compiler.compile([f.name], extra_postargs=[flagname])\\n except setuptools.distutils.errors.CompileError:\\n return False\\n return True\",\n \"def test_clang_cxx(self):\\n self.assertEqual(\\n self.ndk.clang_cxx,\\n f\\\"/opt/android/android-ndk/toolchains/llvm/prebuilt/{self.ndk.host_tag}/bin/clang++\\\",\\n )\",\n \"def has_flag(compiler, flagname):\\n with tempfile.TemporaryDirectory() as tmpdir:\\n fname = join(tmpdir, \\\"test.cpp\\\")\\n with open(fname, \\\"w\\\") as fp:\\n fp.write(\\\"int main (int argc, char **argv) { return 0; }\\\")\\n try:\\n compiler.compile([fname], output_dir=tmpdir, extra_postargs=[flagname])\\n except setuptools.distutils.errors.CompileError:\\n return False\\n return True\",\n \"def test_make_benchmark_single_ll():\\n benchmark = llvm.make_benchmark(INVALID_IR_PATH)\\n assert str(benchmark.uri).startswith(\\\"benchmark://user-v0/\\\")\\n assert benchmark.uri.scheme == \\\"benchmark\\\"\\n assert benchmark.uri.dataset == \\\"user-v0\\\"\",\n \"def exclude_non_cpp(benchmarks):\\n return [benchmark for benchmark in benchmarks if is_cpp(benchmark)]\",\n \"def supports_c_code(self, inputs):\\r\\n\\r\\n # If we don't even have the right method, we certainly\\r\\n # don't support the C code\\r\\n # (This is the test that used to be implemented by\\r\\n # local_gpu_sum)\\r\\n pattern = (''.join(str(i) for i in self.reduce_mask))\\r\\n if not hasattr(self, 'c_code_reduce_%s' % pattern):\\r\\n return False\\r\\n\\r\\n # Now that this is a general reduction op, we might\\r\\n # have a method for a pattern, but that pattern\\r\\n # might not be implemented for the current scalar op.\\r\\n # To detect this more complicated situation, we\\r\\n # make fake arguments to c_code, try to run them,\\r\\n # and see if NotImplementedError gets raised.\\r\\n\\r\\n node = self.make_node(*inputs)\\r\\n\\r\\n name = 'fake_name'\\r\\n\\r\\n inp = ['fake_input_name_%d' % i for i in xrange(len(inputs))]\\r\\n out = ['fake_output_name_%d' % i for i in xrange(len(node.outputs))]\\r\\n\\r\\n sub = {'fail': 'fake failure code'}\\r\\n\\r\\n try:\\r\\n self.c_code(node, name, inp, out, sub)\\r\\n self.c_support_code_apply(node, name)\\r\\n except NotImplementedError:\\r\\n return False\\r\\n return True\",\n \"def supports_c_code(self, inputs):\\r\\n\\r\\n # If we don't even have the right method, we certainly\\r\\n # don't support the C code\\r\\n # (This is the test that used to be implemented by\\r\\n # local_gpu_sum)\\r\\n pattern = (''.join(str(i) for i in self.reduce_mask))\\r\\n if not hasattr(self, 'c_code_reduce_%s' % pattern):\\r\\n return False\\r\\n\\r\\n # Now that this is a general reduction op, we might\\r\\n # have a method for a pattern, but that pattern\\r\\n # might not be implemented for the current scalar op.\\r\\n # To detect this more complicated situation, we\\r\\n # make fake arguments to c_code, try to run them,\\r\\n # and see if NotImplementedError gets raised.\\r\\n\\r\\n node = self.make_node(*inputs)\\r\\n\\r\\n name = 'fake_name'\\r\\n\\r\\n inp = ['fake_input_name_%d' % i for i in xrange(len(inputs))]\\r\\n out = ['fake_output_name_%d' % i for i in xrange(len(node.outputs))]\\r\\n\\r\\n sub = {'fail': 'fake failure code'}\\r\\n\\r\\n try:\\r\\n self.c_code(node, name, inp, out, sub)\\r\\n self.c_support_code_apply(node, name)\\r\\n except NotImplementedError:\\r\\n return False\\r\\n return True\",\n \"def has_flag(compiler, flagname):\\n import tempfile\\n import os\\n with tempfile.NamedTemporaryFile('w', suffix='.cpp', delete=False) as f:\\n f.write('int main (int argc, char **argv) { return 0; }')\\n fname = f.name\\n try:\\n compiler.compile([fname], extra_postargs=[flagname])\\n except setuptools.distutils.errors.CompileError:\\n return False\\n finally:\\n try:\\n os.remove(fname)\\n except OSError:\\n pass\\n return True\",\n \"def test_make_benchmark_from_command_line_mixed_source_and_object_files(\\n env: LlvmEnv, retcode: int\\n):\\n with temporary_working_directory():\\n with open(\\\"a.c\\\", \\\"w\\\") as f:\\n f.write(\\n \\\"\\\"\\\"\\n#include \\\"b.h\\\"\\n\\nint A() {\\n return B();\\n}\\n\\nint main() {\\n return A();\\n}\\n\\\"\\\"\\\"\\n )\\n\\n with open(\\\"b.c\\\", \\\"w\\\") as f:\\n f.write(f\\\"int B() {{ return {retcode}; }}\\\")\\n\\n with open(\\\"b.h\\\", \\\"w\\\") as f:\\n f.write(\\\"int B();\\\")\\n\\n # Compile b.c to object file:\\n subprocess.check_call([str(llvm_paths.clang_path()), \\\"b.c\\\", \\\"-c\\\"], timeout=60)\\n assert (Path(\\\"b.o\\\")).is_file()\\n\\n bm = env.make_benchmark_from_command_line([\\\"gcc\\\", \\\"a.c\\\", \\\"b.o\\\", \\\"-o\\\", \\\"foo\\\"])\\n env.reset(benchmark=bm)\\n\\n bm.compile(env)\\n assert Path(\\\"foo\\\").is_file()\\n\\n p = subprocess.Popen([\\\"./foo\\\"])\\n p.communicate(timeout=60)\\n assert p.returncode == retcode\",\n \"def check_java(interface):\\n\\n interface.info(__(\\\"I'm compiling a short test program, to see if you have a working JDK on your system.\\\"))\\n\\n if not run_slow(interface, plat.javac, plat.path(\\\"buildlib/CheckJDK8.java\\\"), use_path=True):\\n interface.fail(__(\\\"I was unable to use javac to compile a test file. If you haven't installed the Java Development Kit yet, please download it from:\\\\n\\\\nhttp://www.oracle.com/technetwork/java/javase/downloads/jdk8-downloads-2133151.html\\\\n\\\\nThe JDK is different from the JRE, so it's possible you have Java without having the JDK. Without a working JDK, I can't continue.\\\"))\\n\\n if not run_slow(interface, plat.java, \\\"-classpath\\\", plat.path(\\\"buildlib\\\"), \\\"CheckJDK8\\\", use_path=True):\\n interface.fail(__(\\\"The version of Java on your computer does not appear to be JDK 8, which is the only version supported by the Android SDK. If you need to install JDK 8, you can download it from:\\\\n\\\\nhttp://www.oracle.com/technetwork/java/javase/downloads/jdk8-downloads-2133151.html\\\\n\\\\nYou can also set the JAVA_HOME environment variable to use a different version of Java.\\\"))\\n\\n interface.success(__(\\\"The JDK is present and working. Good!\\\"))\",\n \"def py_versiontest(c):\\n pass\",\n \"def CustomCompileCheck(context, message, source, extension=\\\".cc\\\"):\\n context.Message(message)\\n\\n env = context.env\\n if env.GetOption(\\\"clean\\\") or env.GetOption(\\\"help\\\") or env.GetOption(\\\"no_exec\\\"):\\n result = True\\n else:\\n result = context.TryCompile(source, extension)\\n\\n context.Result(result)\\n\\n return result\",\n \"def check_cc(self):\\n # simply generates a C program containing a couple of calls\\n # to MPI routines and checks if the compilation and execution\\n # are succesful\\n print 'Checking if cc works...',\\n sys.stdout.flush()\\n # generate\\n writefile('tmpc.c',\\\"\\\"\\\"\\n #include <stdio.h>\\n int main(int argc, char **argv){\\n int iam;\\n fprintf(stdout, \\\\\\\"success\\\\\\\" );fflush(stdout);\\n return 0;\\n }\\\\n\\\"\\\"\\\")\\n\\n # compile\\n ccomm = self.config.cc+\\\" \\\"+self.config.ccflags+\\\" \\\"+self.config.ldflags_c+\\\" -o tmpc \\\"+os.path.join(os.getcwd(),\\\"tmpc.c\\\")\\n (output, error, retz) = runShellCommand(ccomm)\\n\\n if retz:\\n print '\\\\n\\\\nCOMMON: C compiler not working! aborting...'\\n print 'stderr:\\\\n','*'*40,'\\\\n',error,'\\\\n','*'*40\\n sys.exit()\\n\\n # run\\n comm = './tmpc'\\n (output, error, retz) = runShellCommand(comm)\\n if retz:\\n print '\\\\n\\\\nCOMMON: cc not working! aborting...'\\n print 'error is:\\\\n','*'*40,'\\\\n',error,'\\\\n','*'*40\\n sys.exit()\\n\\n # cleanup\\n killfiles(['tmpc.c','tmpc'])\\n print 'yes'\\n return 0;\",\n \"def numba_check():\\n numba = importlib.util.find_spec(\\\"numba\\\")\\n return numba is not None\",\n \"def is_ctu_capable():\\n\\n context = package_context.get_context()\\n ctu_func_map_cmd = context.ctu_func_map_cmd\\n try:\\n version = subprocess.check_output([ctu_func_map_cmd, '-version'])\\n except (subprocess.CalledProcessError, OSError):\\n version = 'ERROR'\\n return version != 'ERROR'\",\n \"def isLibSBMLCompiledWith(*args):\\n return _libsbml.isLibSBMLCompiledWith(*args)\",\n \"def is_system_ready_for_benchmarking():\\n\\n # check if scaling_governor is set to 'performance' for all cpu cores\\n cpu_governors = glob.glob('/sys/devices/system/cpu/cpu*/cpufreq/scaling_governor')\\n if not cpu_governors:\\n logger.error('no scaling_governor found. Do you run on a Linux System?')\\n return False\\n for governor in sorted(cpu_governors):\\n with open(governor, 'r') as f:\\n line = f.read().splitlines()[0]\\n logger.debug('%s is set to \\\\\\\"%s\\\\\\\"', governor, line)\\n if line != 'performance':\\n logger.warning('please set all scaling_governor to \\\\\\\"performance\\\\\\\" (using \\\"sudo ./ondemand.sh start\\\")')\\n return False\\n\\n return True\",\n \"def has_cached_cfunc(*args):\\n return _ida_hexrays.has_cached_cfunc(*args)\",\n \"def is_cpp(filename: Path) -> bool:\\n from fnmatch import fnmatch\\n\\n return any(fnmatch(os.path.basename(filename), p) for p in CPP_PATTERNS)\",\n \"def test_rust_code_analysis_tokei_Rust() -> None:\\n\\n ret_value = compare(\\n \\\"rust-code-analysis\\\",\\n \\\"tokei\\\",\\n [\\\"-g\\\", \\\"-f\\\"],\\n [\\\"SLOC\\\", \\\"PLOC\\\", \\\"CLOC\\\", \\\"BLANK\\\"],\\n \\\"Rust\\\",\\n \\\"bubble_sort.rs\\\",\\n )\\n\\n assert ret_value == 0\",\n \"def test_c(self):\\n self.failIf(cgs.speed_of_light/mks.speed_of_light!=100)\",\n \"def test_unix(args):\\n total = 0\\n passed = 0\\n\\n print('============= Testing for Correctness (Unix) =============')\\n for filename in os.listdir(args.testdir):\\n if not filename.endswith('.c'):\\n continue\\n\\n print( '{}:'.format(os.path.splitext(filename)[0]), end=' ')\\n total += 1\\n\\n testfile = os.path.join(args.testdir, filename)\\n valid, diff = test_output(args.glcpp, testfile, testfile + '.expected')\\n if valid:\\n passed += 1\\n print('PASS')\\n else:\\n print('FAIL')\\n for l in diff:\\n print(l, file=sys.stderr)\\n\\n print('{}/{}'.format(passed, total), 'tests returned correct results')\\n return total == passed\",\n \"def test_clang(self):\\n self.assertEqual(\\n self.ndk.clang,\\n f\\\"/opt/android/android-ndk/toolchains/llvm/prebuilt/{self.ndk.host_tag}/bin/clang\\\",\\n )\",\n \"def test_is_poor_style(self):\\n self.assertEqual(1, uut.is_poor_style(\\\"test/with_leak.cc\\\"))\\n self.assertEqual(0, uut.is_poor_style(\\\"test/without_leak.cc\\\"))\",\n \"def have_cdc() -> bool:\",\n \"def HasPython(self):\\n oldind = copy.copy(self.ind)\\n self.ind = 0\\n mybool = self.FindNextEnv()\\n self.ind = oldind#reset ind\\n return bool(mybool)\",\n \"def gcc_llvm():\\r\\n if gcc_llvm.is_llvm is None:\\r\\n pass\\r\\n p = None\\r\\n try:\\r\\n p_out = output_subprocess_Popen(['g++', '--version'])\\r\\n output = p_out[0] + p_out[1]\\r\\n except OSError:\\r\\n # Typically means g++ cannot be found.\\r\\n # So it is not an llvm compiler.\\r\\n\\r\\n # Normally this should not happen as we should not try to\\r\\n # compile when g++ is not available. If this happen, it\\r\\n # will crash later so supposing it is not llvm is \\\"safe\\\".\\r\\n output = b('')\\r\\n del p\\r\\n gcc_llvm.is_llvm = b(\\\"llvm\\\") in output\\r\\n return gcc_llvm.is_llvm\",\n \"def can_compile(src):\\n src = src if src.endswith(\\\"\\\\n\\\") else src + \\\"\\\\n\\\"\\n src = transform_command(src, show_diff=False)\\n src = src.lstrip()\\n try:\\n XSH.execer.compile(src, mode=\\\"single\\\", glbs=None, locs=XSH.ctx)\\n rtn = True\\n except SyntaxError:\\n rtn = False\\n except Exception:\\n rtn = True\\n return rtn\",\n \"def CustomCppFlagCheck(context, flag, append=True):\\n context.Message(\\\"Checking if C++ compiler supports \\\" + flag + \\\" flag \\\")\\n cxxflags = context.env[\\\"CXXFLAGS\\\"]\\n context.env.Append(CXXFLAGS=flag)\\n result = context.TryCompile(\\\"int main(int argc, char **argv) { return 0; }\\\", \\\".cc\\\")\\n context.Result(result)\\n if not append or not result:\\n context.env.Replace(CXXFLAGS=cxxflags)\\n return result\",\n \"def detectExtensions(builder):\\n print (\\\"Checking if C extensions can be compiled, don't be alarmed if \\\"\\n \\\"a few compile errors are printed.\\\")\\n\\n if not builder._compile_helper(\\\"#define X 1\\\\n\\\"):\\n print \\\"Compiler not found, skipping C extensions.\\\"\\n return []\\n\\n # Extension modules to build.\\n exts = [\\n Extension(\\\"twisted.spread.cBanana\\\",\\n [\\\"twisted/spread/cBanana.c\\\"],\\n define_macros=builder.define_macros),\\n ]\\n\\n # urllib.unquote accelerator\\n exts.append( Extension(\\\"twisted.protocols._c_urlarg\\\",\\n [\\\"twisted/protocols/_c_urlarg.c\\\"],\\n define_macros=builder.define_macros) )\\n\\n if sys.platform == 'darwin':\\n exts.append(\\n Extension(\\\"twisted.internet.cfsupport\\\",\\n [\\\"twisted/internet/cfsupport/cfsupport.c\\\"],\\n extra_compile_args=['-w'],\\n extra_link_args=['-framework','CoreFoundation',\\n '-framework','CoreServices',\\n '-framework','Carbon'],\\n define_macros=builder.define_macros))\\n\\n if sys.platform == 'win32':\\n exts.append( Extension(\\\"twisted.internet.iocpreactor._iocp\\\",\\n [\\\"twisted/internet/iocpreactor/_iocp.c\\\"],\\n libraries=[\\\"ws2_32\\\", \\\"mswsock\\\"],\\n define_macros=builder.define_macros))\\n\\n return exts\",\n \"def is_result_appropriate(self, benchmark_result):\\n return True\",\n \"def checkCUDAisAvailable():\\n # some possible lib names \\n libnames = ('libcuda.so', 'libcuda.dylib', 'cuda.dll')\\n libsOk = True\\n for libname in libnames:\\n try:\\n cuda = ctypes.CDLL(libname)\\n except OSError:\\n continue\\n else:\\n break\\n else:\\n libsOk = False\\n return libsOk\",\n \"def test_make_benchmark_from_command_line_multiple_input_sources(\\n env: LlvmEnv, retcode: int\\n):\\n with temporary_working_directory() as cwd:\\n with open(\\\"a.c\\\", \\\"w\\\") as f:\\n f.write(\\\"int main() { return B(); }\\\")\\n\\n with open(\\\"b.c\\\", \\\"w\\\") as f:\\n f.write(f\\\"int B() {{ return {retcode}; }}\\\")\\n\\n bm = env.make_benchmark_from_command_line([\\\"gcc\\\", \\\"a.c\\\", \\\"b.c\\\", \\\"-o\\\", \\\"foo\\\"])\\n assert not (cwd / \\\"foo\\\").is_file()\\n\\n env.reset(benchmark=bm)\\n assert \\\"main()\\\" in env.ir\\n\\n bm.compile(env)\\n assert (cwd / \\\"foo\\\").is_file()\\n\\n p = subprocess.Popen([\\\"./foo\\\"])\\n p.communicate(timeout=60)\\n assert p.returncode == retcode\",\n \"def is_c(c_instr, code):\\n out_instr='111' #start output instruction. 111 acccccc ddd jjj\\n\\n #to get dest, split at =\\n if '=' in c_instr: #has destination\\n comp_dest = c_instr.split('=',1) #split at =\\n dest_key = comp_dest.pop(0) #get destination command. put before = in dest_key\\n c_instr = comp_dest.pop(0) #get rest of c instruction. put after = in c_instr\\n dest = code.get_dest(dest_key) #assign destination binary to dest\\n else: #no destination\\n #c instruction is unchanged\\n dest = '000' #destination is null\\n\\n #to get jump, split at ';'\\n if ';' in c_instr:\\n split_line = c_instr.split(';', 1) #split at jump command\\n # send jump command to function and concatenate is_jump output to out instruction\\n jump = code.get_jump(split_line[1]) #get jump command\\n c_instr = split_line[0] #line without jump command\\n else: #jump is null\\n #c instruction is unchanged\\n jump = '000'\\n\\n comp = code.get_comp(c_instr) #get a cccc cc bits from computation dictionary\\n return out_instr +comp+dest+jump #concatenate parts of binary equiv and return\",\n \"def validate_type(benchmark):\\n benchmark_type = get_type(benchmark)\\n if benchmark_type not in BENCHMARK_TYPE_STRS:\\n logs.error('%s has an invalid benchmark type %s, must be one of %s',\\n benchmark, benchmark_type, BENCHMARK_TYPE_STRS)\\n return False\\n return True\",\n \"def _is_fan(compressor: Compressor) -> bool:\\n name = compressor.name.lower()\\n return 'fan' in name or 'crtf' in name\",\n \"def is_returnning_weak(c_type: str, jtype: str):\\n return c_type in LONG_ISH_WEAKREF_SET and jtype == 'jlong'\",\n \"def test_has_mac_accelerate(self):\\n\\t\\t\\n\\t\\texpected_has_accelerate = False\\n\\t\\t\\n\\t\\timport platform\\n\\t\\timport numpy.distutils.system_info as sysinfo\\n\\n\\t\\tmac_arch = platform.machine()\\n\\t\\tif mac_arch == 'arm64':\\n\\t\\t\\tinfo = sysinfo.get_info('accelerate')\\n\\t\\t\\tif info is not None and len(info)>0:\\n\\t\\t\\t for x in info['extra_link_args']:\\n\\t\\t\\t if 'Accelerate' in x:\\n\\t\\t\\t expected_has_accelerate = True\\n\\t\\t\\t \\n\\t\\tactual_has_accelerate = RMT_Util.has_mac_accelerate()\\n\\t\\tself.assertEqual(expected_has_accelerate, actual_has_accelerate)\\n\\t\\treturn\",\n \"def is_jetson():\\n # TODO: check x86_64 explicitly instead in case somebody tries\\n # to run this in mips or ppc or something\\n # TODO: do further checking if aarch64 to determine whether in fact\\n # a tegra system, and if so, which one\\n return True if platform.processor() == 'aarch64' else False\",\n \"def test_c(self):\\n source = io.StringIO(\\\"int x(int a) { return a + 1 ; }\\\")\\n arch = get_current_arch()\\n obj = cc(source, arch, debug=True)\\n m = load_obj(obj)\\n y = m.x(101)\\n self.assertEqual(102, y)\",\n \"def _check_cachable(self, cres):\\n cannot_cache = None\\n if self._is_closure:\\n cannot_cache = \\\"as it uses outer variables in a closure\\\"\\n elif cres.lifted:\\n cannot_cache = \\\"as it uses lifted loops\\\"\\n elif cres.has_dynamic_globals:\\n cannot_cache = \\\"as it uses dynamic globals (such as ctypes pointers)\\\"\\n if cannot_cache:\\n msg = ('Cannot cache compiled function \\\"%s\\\" %s'\\n % (self._funcname, cannot_cache))\\n warnings.warn_explicit(msg, NumbaWarning,\\n self._source_path, self._lineno)\\n return False\\n return True\",\n \"def test_py_compile_basic(self):\\n self._test_py_compile('basic')\",\n \"def python_compatible():\\n result = False\\n req_ver = vers.convert('3.9.5')\\n pythonver = vers.convert('{major}.{minor}.{micro}'.format(major=sys.version_info.major,\\n minor=sys.version_info.minor,\\n micro=sys.version_info.micro))\\n\\n result = pythonver >= req_ver\\n\\n return result\",\n \"def isOnNao():\\n szCpuInfo = \\\"/proc/cpuinfo\\\";\\n if not os.path.exists( szCpuInfo ): # already done by the getFileContents\\n return False;\\n szAllFile = getFileContents( szCpuInfo, bQuiet = True );\\n if( szAllFile.find( \\\"Geode\\\" ) == -1 and szAllFile.find( \\\"Intel(R) Atom(TM)\\\" ) == -1 ):\\n return False;\\n return True;\",\n \"def test01c(self):\\n a = np.arange(1e5)\\n cnames = bcolz.blosc_compressor_list()\\n if common.verbose:\\n print(\\\"Checking compressors:\\\", cnames)\\n # print \\\"\\\\nsize b uncompressed-->\\\", a.size * a.dtype.itemsize\\n for cname in cnames:\\n with bcolz.defaults_ctx(bcolz.cparams(clevel=9, cname=cname)):\\n self.assertTrue(bcolz.defaults.cparams['cname'] == cname)\\n b = bcolz.carray(a, rootdir=self.rootdir)\\n # print \\\"size b compressed -->\\\", b.cbytes, \\\"with '%s'\\\"%cname\\n self.assertTrue(sys.getsizeof(b) < b.nbytes,\\n \\\"carray does not seem to compress at all\\\")\\n assert_array_equal(a, b[:], \\\"Arrays are not equal\\\")\\n # Remove the array on disk before trying with the next one\\n if self.disk:\\n common.remove_tree(self.rootdir)\",\n \"def test_lua(args: argparse.Namespace) -> bool:\\n\\n if not os.path.isfile(os.path.join(c.BUILD_DIR, LUA_ARCHIVE)):\\n with pb.local.cwd(c.BUILD_DIR):\\n download_archive(LUA_URL, LUA_ARCHIVE)\\n if not os.path.isdir(LUA_SRC):\\n with pb.local.cwd(c.BUILD_DIR):\\n invoke_quietly(TAR, \\\"xf\\\", LUA_ARCHIVE)\\n\\n # unconditionally compile lua since we don't know if\\n # cc_db was generated from the environment we're in.\\n build_dir = os.path.join(LUA_SRC, \\\"build\\\")\\n rmtree(build_dir, ignore_errors=True)\\n os.mkdir(build_dir)\\n with pb.local.cwd(build_dir), pb.local.env(CC=\\\"clang\\\"):\\n invoke(CMAKE['-DCMAKE_EXPORT_COMPILE_COMMANDS=1', LUA_SRC])\\n invoke(MAKE[JOBS])\\n\\n cc_db_file = os.path.join(LUA_SRC, \\\"build\\\", c.CC_DB_JSON)\\n if not os.path.isfile(cc_db_file):\\n die(\\\"missing \\\" + cc_db_file, errno.ENOENT)\\n\\n return transpile(cc_db_file)\",\n \"def run_test(skip_run):\\n # First off, we need the 'official' test data downloaded and unpacked\\n # if necessary.\\n if ref_test_data.install_and_check_test_files() != 0:\\n return 1\\n if install_inputs() != 0:\\n return 1\\n\\n if not skip_run:\\n sys.stdout.write(\\\"Executing CCC code...\\\\n\\\")\\n ret = run.main()\\n if ret != 0:\\n return 1\\n\\n test_result_dir = os.path.join(ref_test_data.test_data_dir, \\\"result\\\")\\n sys.stdout.write(\\\"Comparing CCC results with 'official' results...\\\\n\\\")\\n ret = compare_results.main([sys.argv[0], \\\"result\\\", test_result_dir])\\n if ret != 0:\\n return 1\",\n \"def get_language(benchmark):\\n config = benchmark_config.get_config(benchmark)\\n return config.get('language', 'c++')\",\n \"def ignore_builtin_verification():\\n return not current_space().skip_builtin_verification\",\n \"def testSC(self):\\n\\n obt_np = compression.decByteOffet_numpy(compression.compByteOffet_numpy(self.ds))\\n self.assertEqual(abs(self.ds - obt_np).max(), 0.0, \\\"numpy algo\\\")\\n obt_cy = compression.decByteOffet_cython(compression.compByteOffet_numpy(self.ds))\\n self.assertEqual(abs(self.ds - obt_cy).max(), 0.0, \\\"cython algo\\\")\\n obt_cy2 = compression.decByteOffet_cython(compression.compByteOffet_numpy(self.ds), self.ds.size)\\n self.assertEqual(abs(self.ds - obt_cy2).max(), 0.0, \\\"cython algo_orig\\\")\\n obt_we = compression.decByteOffet_weave(compression.compByteOffet_numpy(self.ds), self.ds.size)\\n self.assertEqual(abs(self.ds - obt_we).max(), 0.0, \\\"weave algo\\\")\",\n \"def conditional_guard(src, dst):\\n int64_count = 0\\n float64_count = 0\\n float16_count = 0\\n if src in int64_types or dst in int64_types:\\n int64_count = 1\\n if src in float64_types or dst in float64_types:\\n float64_count = 1\\n if src in float16_types or dst in float16_types:\\n float16_count = 1\\n if float16_count > 0:\\n print(\\\"#ifdef cl_khr_fp16\\\")\\n if float64_count > 0:\\n #In embedded profile, if cl_khr_fp64 is supported cles_khr_int64 has to be\\n print(\\\"#ifdef cl_khr_fp64\\\")\\n return 1 + float16_count\\n elif int64_count > 0:\\n print(\\\"#if defined cles_khr_int64 || !defined(__EMBEDDED_PROFILE__)\\\")\\n return 1 + float16_count\\n return float16_count\",\n \"def check_cachable(self, cres):\\n cannot_cache = None\\n if any(not x.can_cache for x in cres.lifted):\\n cannot_cache = \\\"as it uses lifted code\\\"\\n elif cres.library.has_dynamic_globals:\\n cannot_cache = (\\\"as it uses dynamic globals \\\"\\n \\\"(such as ctypes pointers and large global arrays)\\\")\\n if cannot_cache:\\n msg = ('Cannot cache compiled function \\\"%s\\\" %s'\\n % (cres.fndesc.qualname.split('.')[-1], cannot_cache))\\n warnings.warn_explicit(msg, NumbaWarning,\\n self._locator._py_file, self._lineno)\\n return False\\n return True\",\n \"def passed(self):\\n\\n compile = self.compile_successful\\n if self.compileTest or not compile: return compile\\n\\n compare = not self.doComparison or self.compare_successful\\n analysis = self.analysisRoutine == \\\"\\\" or self.analysis_successful\\n return compare and analysis\",\n \"def test01(self):\\n a = np.arange(2e5)\\n b = bcolz.carray(a, rootdir=self.rootdir)\\n # print \\\"size b uncompressed-->\\\", b.nbytes\\n # print \\\"size b compressed -->\\\", b.cbytes\\n self.assertTrue(sys.getsizeof(b) < b.nbytes,\\n \\\"carray does not seem to compress at all\\\")\",\n \"def is_scala(self):\\r\\n return self.has_label('scala')\",\n \"def is_basic_ctype(self, objtype):\\n if objtype in [self.c_char_p, self.c_void_p, self.CString]:\\n return False\\n # DOC: if <ctypes.c_uint> is not in self.__basic_types, its probably\\n # because you are using the wrong ctypes Proxy instance\\n return objtype in self.__basic_types\",\n \"def isCfile(path:str) -> bool:\\n if not isexist(path):\\n return False\\n \\n name, ext = path.split(\\\".\\\")\\n \\n return ext == \\\"c\\\"\",\n \"def is_clml_runtime_enabled():\\n check_enabled = tvm.get_global_func(\\\"relay.op.is_clml_runtime_enabled\\\", True)\\n if check_enabled:\\n return check_enabled()\\n return False\",\n \"def has_flag(compiler, flag, ext=None):\\n return try_compile(compiler, flags=[flag], ext=ext)\",\n \"def has_cooling_system(bpr):\\n\\n if bpr.hvac['type_cs'] in {'T1', 'T2', 'T3'}:\\n return True\\n elif bpr.hvac['type_cs'] in {'T0'}:\\n return False\\n else:\\n raise\",\n \"def is_statistics_capable():\\n context = package_context.get_context()\\n\\n analyzer = \\\"clangsa\\\"\\n enabled_analyzers = [analyzer]\\n cfg_handlers = analyzer_types.build_config_handlers({},\\n context,\\n enabled_analyzers)\\n\\n clangsa_cfg = cfg_handlers[analyzer]\\n analyzer = analyzer_types.supported_analyzers[analyzer](clangsa_cfg,\\n None)\\n\\n check_env = analyzer_env.get_check_env(context.path_env_extra,\\n context.ld_lib_path_extra)\\n\\n checkers = analyzer.get_analyzer_checkers(clangsa_cfg, check_env)\\n\\n stat_checkers_pattern = re.compile(r'.+statisticscollector.+')\\n\\n for checker_name, _ in checkers:\\n if stat_checkers_pattern.match(checker_name):\\n return True\\n\\n return False\",\n \"def IsClangWinBuild(build_dir, target):\\n if not IsWindows():\\n return False\\n\\n gn_file = os.path.join(build_dir, target, 'args.gn')\\n if not os.path.isfile(gn_file):\\n print 'WARNING: Unable to find the args.gn file.'\\n return False\\n # Matches e.g. \\\"gn_arg = value\\\"\\n gn_arg_re = re.compile(r'^(?P<flag>[^= ]+)\\\\s*=\\\\s*(?P<value>[^ \\\\n]+)$')\\n for line in open(gn_file):\\n m = gn_arg_re.match(line)\\n if m and m.group('flag') == 'is_clang':\\n return m.group('value') == 'true'\\n return False\",\n \"def exists(_env):\\n detector = DetectCompiler()\\n if detector['icx'] is None:\\n return False\\n return True\",\n \"def test02(self):\\n a = np.arange(111)\\n b = bcolz.carray(a)\\n # print \\\"size b uncompressed-->\\\", b.nbytes\\n # print \\\"size b compressed -->\\\", b.cbytes\\n self.assertTrue(sys.getsizeof(b) > b.nbytes,\\n \\\"carray compress too much??\\\")\",\n \"def use_cg(self):\\n return self.algo == \\\"cg\\\"\",\n \"def test_script_is_callable(self):\\n # This is really more a smoke test of this script than anything else.\\n maker = Compilers(test_utils.MockMachines(\\\"mymachine\\\", \\\"SomeOS\\\"), version=2.0)\\n test_xml = test_utils._wrap_config_compilers_xml(\\n \\\"<compiler><SUPPORTS_CXX>FALSE</SUPPORTS_CXX></compiler>\\\"\\n )\\n test_utils.get_macros(maker, test_xml, \\\"Makefile\\\")\",\n \"def is_jvm(self):\\r\\n return self.has_label('jvm')\",\n \"def parse_is_self_cc(b):\\n return f'{int(bool(b)):>{IS_SELF_CC_SIZE}}'\",\n \"def check(self, runtime):\\n return True\",\n \"def evaluate_c(self, profiling_iterations=None):\\n\\n # get the C test function from it's .so (compiles if necessary)\\n lib_path = Operator._make_generic_c(self.op_c_generic, self.op_name).encode('utf-8')\\n fcn_name = (self.op_name + '_generic_cpp').encode('utf-8')\\n\\n self._define_eval_params(lib_path, fcn_name)\\n\\n if profiling_iterations is None:\\n iters = 1\\n else:\\n if not isinstance(profiling_iterations, int) or profiling_iterations < 1:\\n raise ValueError('Profiling iterations must be a positive int, but received: ' +\\n str(profiling_iterations))\\n iters = profiling_iterations\\n\\n eval_times_ms = np.empty(iters, dtype=np.float64)\\n eval_times_ms[:] = np.nan\\n\\n num_inputs = len(self._input_types)\\n num_outputs = len(self.output_types)\\n\\n if self._test_c_op is None:\\n testlib_path = os.path.join(cache_directory, 'libtestcop.so.'+version)\\n try:\\n libtest = ctypes.cdll.LoadLibrary(testlib_path)\\n except OSError:\\n Operator._check_proto()\\n this_file_path = os.path.abspath(__file__)\\n this_directory = os.path.split(this_file_path)[0]\\n\\n # build the test framework library\\n cc_path = os.path.join(this_directory, 'testcop.cc')\\n\\n try:\\n subprocess.check_output(['g++', '-fPIC', '-Wall', '-shared',\\n '-std=c++11', '-Ofast', '-Wextra',\\n '-I'+this_directory,\\n '-I'+cache_directory,\\n '-o', testlib_path, cc_path],\\n stderr=subprocess.STDOUT,\\n universal_newlines=True)\\n except subprocess.CalledProcessError as exception:\\n tf.logging.log(tf.logging.ERROR, 'g++ error: ' + exception.output)\\n raise\\n\\n libtest = ctypes.cdll.LoadLibrary(testlib_path)\\n\\n self._test_c_op = libtest.testCOperator\\n self._test_c_op.restype = ctypes.c_int16\\n self._test_c_op.argtypes = \\\\\\n [ctypes.c_char_p, ctypes.c_char_p,\\n ndpointer(dtype=_TensorParam, flags=\\\"C_CONTIGUOUS\\\"), ctypes.c_size_t,\\n ndpointer(dtype=_TensorParam, flags=\\\"C_CONTIGUOUS\\\"), ctypes.c_size_t,\\n ndpointer(dtype=ctypes.c_double, flags=\\\"C_CONTIGUOUS\\\"), ctypes.c_size_t]\\n\\n # run the operator\\n err = self._test_c_op(lib_path, fcn_name,\\n self._input_params, ctypes.c_size_t(num_inputs),\\n self._output_params, ctypes.c_size_t(num_outputs),\\n eval_times_ms,\\n ctypes.c_size_t(iters))\\n\\n if err != 0 or np.isnan(eval_times_ms).any():\\n tf.logging.log(tf.logging.ERROR, 'Test C operator failed for Op ' + self.__class__.__name__)\\n raise ValueError('Test C operator failed for Op ' + self.__class__.__name__)\\n\\n if profiling_iterations is None:\\n return Operator._unwrap_single(self._output_buffers)\\n else:\\n return Operator._unwrap_single(self._output_buffers), eval_times_ms\",\n \"def _preprocess(self):\\n if not super()._preprocess():\\n return False\\n\\n # Format the arguments\\n self._args.operation = self._args.operation.lower()\\n\\n # Check the arguments and generate the commands\\n op = self._args.operation\\n if op not in self.__operations:\\n self._result.set_return_code(ReturnCode.INVALID_ARGUMENT)\\n logger.error(\\n 'Unsupported operation of NCCL test - benchmark: {}, operation: {}, expected: {}.'.format(\\n self._name, op, ' '.join(list(self.__operations.keys()))\\n )\\n )\\n return False\\n else:\\n self._bin_name = self.__operations[op]\\n if not self._set_binary_path():\\n return False\\n\\n command = os.path.join(self._args.bin_dir, self._bin_name)\\n command += ' -b {} -e {} -f {} -g {} -c {} -n {} -w {}'.format(\\n self._args.minbytes, self._args.maxbytes, str(self._args.stepfactor), str(self._args.ngpus),\\n str(self._args.check), str(self._args.iters), str(self._args.warmup_iters)\\n )\\n self._commands.append(command)\\n\\n return True\",\n \"def c_equals(a, b):\\n alpha = library.PixelGetAlpha\\n return bool(library.IsPixelWandSimilar(a, b, 0) and\\n alpha(a) == alpha(b))\",\n \"def _optimized(self):\\n return False\",\n \"def asan_supported(goroot: GoRoot) -> bool:\\n if goroot.goos == \\\"linux\\\":\\n return goroot.goarch in (\\\"arm64\\\", \\\"amd64\\\", \\\"riscv64\\\", \\\"ppc64le\\\")\\n else:\\n return False\",\n \"def test(self):\\n res = self._dll.JLINKARM_Test()\\n return (res == 0)\",\n \"def test_countdown_performance():\\n profiler = cProfile.Profile()\\n profiler.enable()\\n countdown(\\\"0 0 0 0 0 0 1000\\\")\\n profiler.disable()\\n stats = profiler.getstats()\\n tot_time = stats[0].totaltime\\n assert tot_time < 3, \\\"Wow, your computer is really slow. Or is it my code?\\\"\",\n \"def detect_compilers(self):\\n # By users experience it is known which compiler flags are required\\n # in some cases. This function tries to detect which compilers are used\\n # and sets the flags accordingly\\n\\n print 'Detecting Fortran compiler...',\\n if self.fc_is_intel():\\n # The Intel FORTRAN compiler requires -nofor_main flag\\n # for the linking and the -mp flag to maintain the\\n # floating-point precision\\n self.config.fcflags += ' -diag-disable vec -fltconsistency -fp_port'\\n self.config.ldflags_c += ' ' # used to link\\n self.config.ldflags_fc += ' '\\n self.config.ld_fcmain = ' -nofor_main'\\n self.config.noopt += ' -mp'\\n self.testing = 0; # Cannot compile lintest with fc_main option\\n print 'Intel'\\n elif self.fc_is_gnu():\\n print 'GNU'\\n self.config.ld_fcmain = ''\\n elif self.fc_is_xlf():\\n self.config.fcflags += ' -qstrict -qthreaded'\\n self.config.ld_fcmain = ''\\n print 'IBM'\\n elif self.fc_is_pgi():\\n self.config.ldflags_c += ''\\n self.config.ldflags_fc += ''\\n self.config.ld_fcmain = ' -Mnomain'\\n self.testing = 0; # Cannot compile lintest with fc_main option\\n else:\\n self.config.compiler = \\\"Unknown\\\"\\n print 'unknown'\\n\\n print 'Detecting C compiler...',\\n if self.cc_is_intel():\\n self.config.compiler = \\\"Intel\\\"\\n self.config.ccflags += ' -diag-disable vec'\\n print 'Intel'\\n elif self.cc_is_gnu():\\n self.config.compiler = \\\"GNU\\\"\\n print 'GNU'\\n elif self.cc_is_xlc():\\n self.config.compiler = \\\"XLC\\\"\\n self.config.ccflags += ' -qstrict -qthreaded'\\n print 'IBM'\\n elif self.cc_is_pgi():\\n self.config.compiler = \\\"PGI\\\"\\n print 'PGI'\\n else:\\n print 'unknown'\\n\\n print 'Selected C compiler flags: '+self.config.ccflags\\n print 'Selected Fortran compiler flags: '+self.config.fcflags\\n print 'Selected loader flags (C main): '+self.config.ldflags_c\\n print 'Selected loader flags (Fortran main): '+self.config.ldflags_fc\\n return\",\n \"def has_compiled(self, phase='predict'):\\n return self._graph_executor.has_compiled(phase)\",\n \"def can_trace_source(self, filename: str) -> bool:\\n path = Path(filename)\\n if not path.is_file():\\n return False\\n\\n extension = path.suffix\\n if extension in self.registered_compilers:\\n compiler = self.registered_compilers[extension]\\n if compiler.supports_source_tracing:\\n return True\\n\\n # We are not able to get coverage for this file.\\n return False\",\n \"def cc():\\n load_env_vars('dev')\\n from tools.static_code_analysis import CyclomaticComplexity\\n radon_cc = CyclomaticComplexity()\\n score = radon_cc.run_test()\\n radon_cc.create_badge(score)\",\n \"def test_fastqc_notexec():\\n try:\\n obj = fastqc.FastQC(\\\"LICENSE\\\")\\n except NotExecutableError:\\n return True\\n else:\\n return False\",\n \"def test07(self):\\n a = np.arange(10000) > 5000\\n b = bcolz.carray(a, chunklen=100)\\n wt = a.nonzero()[0].tolist()[1020:2040]\\n cwt = [i for i in b.wheretrue(skip=1020, limit=1020)]\\n # print \\\"numpy ->\\\", a.nonzero()[0].tolist()[1020:2040]\\n # print \\\"where ->\\\", [i for i in b.wheretrue(limit=1020,skip=1020)]\\n self.assertTrue(wt == cwt, \\\"wheretrue() does not work correctly\\\")\",\n \"def is_opencl_env_defined():\\n variable_name = \\\"PYOPENCL_CTX\\\"\\n if os.environ.get(variable_name):\\n return True\\n else:\\n return False\",\n \"def test02(self):\\n a = np.arange(1, 1e5) < 0\\n b = bcolz.carray(a)\\n wt = a.nonzero()[0].tolist()\\n cwt = [i for i in b.wheretrue()]\\n # print \\\"numpy ->\\\", a.nonzero()[0].tolist()\\n # print \\\"where ->\\\", [i for i in b.wheretrue()]\\n self.assertTrue(wt == cwt, \\\"wheretrue() does not work correctly\\\")\",\n \"def is_native(package_name: str) -> bool:\\n result = False\\n try:\\n package = importlib.import_module(package_name)\\n package_dir = os.path.dirname(package.__file__)\\n for root, dirs, files in os.walk(package_dir):\\n for filename in files:\\n if filename.endswith('.so') or filename.endswith('.pyd'):\\n return True\\n result = False\\n except ImportError:\\n result = False\\n except AttributeError:\\n # No __file__, meaning it's built-in. Let's call it native.\\n result = True\\n except TypeError:\\n # __file__ is there, but empty (os.path.dirname() returns TypeError). Let's call it native.\\n result = True\\n except ModuleNotFoundError:\\n # This module is not installed; fail gracefully.\\n result = False\\n return result\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6263037","0.6229608","0.6097612","0.6020831","0.5982895","0.58478355","0.57693976","0.57392836","0.5669367","0.55203027","0.5460529","0.53889805","0.5354336","0.5303164","0.5300068","0.5299565","0.5283687","0.5242149","0.52138","0.5201266","0.5121601","0.5121601","0.51187706","0.5099935","0.5085485","0.507701","0.50553465","0.5052362","0.50408286","0.5039025","0.5032158","0.5022086","0.50190973","0.5010965","0.5007806","0.4994525","0.4977313","0.4975031","0.49731293","0.49723077","0.49652606","0.49534824","0.4921466","0.49206692","0.49175772","0.49119607","0.4902355","0.49010196","0.48896205","0.48712918","0.4869182","0.48571745","0.48512474","0.48501813","0.48499477","0.48484623","0.48468104","0.4843183","0.48378572","0.4836924","0.48253763","0.48129693","0.48118928","0.4809257","0.477433","0.4771258","0.47682923","0.47613233","0.47610697","0.47578508","0.4748941","0.47445115","0.47367626","0.4735034","0.47282583","0.47272027","0.47239828","0.47208932","0.4714044","0.47099927","0.4708855","0.46992412","0.46977186","0.46951288","0.46914375","0.46781677","0.4674626","0.46743137","0.46672204","0.4663165","0.46617353","0.4661248","0.4661149","0.46555224","0.46452078","0.4644533","0.4641248","0.46307915","0.46269056","0.46258396"],"string":"[\n \"0.6263037\",\n \"0.6229608\",\n \"0.6097612\",\n \"0.6020831\",\n \"0.5982895\",\n \"0.58478355\",\n \"0.57693976\",\n \"0.57392836\",\n \"0.5669367\",\n \"0.55203027\",\n \"0.5460529\",\n \"0.53889805\",\n \"0.5354336\",\n \"0.5303164\",\n \"0.5300068\",\n \"0.5299565\",\n \"0.5283687\",\n \"0.5242149\",\n \"0.52138\",\n \"0.5201266\",\n \"0.5121601\",\n \"0.5121601\",\n \"0.51187706\",\n \"0.5099935\",\n \"0.5085485\",\n \"0.507701\",\n \"0.50553465\",\n \"0.5052362\",\n \"0.50408286\",\n \"0.5039025\",\n \"0.5032158\",\n \"0.5022086\",\n \"0.50190973\",\n \"0.5010965\",\n \"0.5007806\",\n \"0.4994525\",\n \"0.4977313\",\n \"0.4975031\",\n \"0.49731293\",\n \"0.49723077\",\n \"0.49652606\",\n \"0.49534824\",\n \"0.4921466\",\n \"0.49206692\",\n \"0.49175772\",\n \"0.49119607\",\n \"0.4902355\",\n \"0.49010196\",\n \"0.48896205\",\n \"0.48712918\",\n \"0.4869182\",\n \"0.48571745\",\n \"0.48512474\",\n \"0.48501813\",\n \"0.48499477\",\n \"0.48484623\",\n \"0.48468104\",\n \"0.4843183\",\n \"0.48378572\",\n \"0.4836924\",\n \"0.48253763\",\n \"0.48129693\",\n \"0.48118928\",\n \"0.4809257\",\n \"0.477433\",\n \"0.4771258\",\n \"0.47682923\",\n \"0.47613233\",\n \"0.47610697\",\n \"0.47578508\",\n \"0.4748941\",\n \"0.47445115\",\n \"0.47367626\",\n \"0.4735034\",\n \"0.47282583\",\n \"0.47272027\",\n \"0.47239828\",\n \"0.47208932\",\n \"0.4714044\",\n \"0.47099927\",\n \"0.4708855\",\n \"0.46992412\",\n \"0.46977186\",\n \"0.46951288\",\n \"0.46914375\",\n \"0.46781677\",\n \"0.4674626\",\n \"0.46743137\",\n \"0.46672204\",\n \"0.4663165\",\n \"0.46617353\",\n \"0.4661248\",\n \"0.4661149\",\n \"0.46555224\",\n \"0.46452078\",\n \"0.4644533\",\n \"0.4641248\",\n \"0.46307915\",\n \"0.46269056\",\n \"0.46258396\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.82368374"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":11274,"cells":{"query":{"kind":"string","value":"Returns |benchmarks| with only benchmarks written in C/C++."},"document":{"kind":"string","value":"def exclude_non_cpp(benchmarks):\n return [benchmark for benchmark in benchmarks if is_cpp(benchmark)]"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_coverage_benchmarks():\n return [\n benchmark for benchmark in get_all_benchmarks()\n if get_type(benchmark) == BenchmarkType.CODE.value\n ]","def get_bug_benchmarks():\n return [\n benchmark for benchmark in get_all_benchmarks()\n if get_type(benchmark) == BenchmarkType.BUG.value\n ]","def get_all_benchmarks():\n all_benchmarks = []\n for benchmark in os.listdir(BENCHMARKS_DIR):\n benchmark_path = os.path.join(BENCHMARKS_DIR, benchmark)\n if os.path.isfile(os.path.join(benchmark_path, 'benchmark.yaml')):\n all_benchmarks.append(benchmark)\n return sorted(all_benchmarks)","def list(self, classes):\n\n def add(benchmarks, parts, flags, exclude):\n if (\n flags[\"language\"] != \"C++\"\n and flags[\"language\"] != \"Java\"\n and \"--drop-caches=true\" not in parts\n ):\n parts.append(\"--drop-caches=true\")\n command = \" \".join(parts)\n if command not in exclude:\n benchmarks.append({\"command\": command, \"flags\": flags})\n\n benchmarks = []\n for name, benchmark in classes.items():\n if name.startswith(\"example\"):\n continue\n\n instance, parts = benchmark(), [name]\n\n exclude = getattr(benchmark, \"exclude\", [])\n if \"source\" in getattr(benchmark, \"arguments\", []):\n parts.append(\"ALL\")\n\n iterations = getattr(instance, \"iterations\", 3)\n parts.append(f\"--iterations={iterations}\")\n\n if instance.cases:\n parts.append(\"--all=true\")\n\n flags = getattr(instance, \"flags\", {})\n\n if getattr(instance, \"r_only\", False):\n flags[\"language\"] = \"R\"\n add(benchmarks, parts, flags, exclude)\n else:\n if \"language\" not in flags:\n flags[\"language\"] = \"Python\"\n add(benchmarks, parts, flags, exclude)\n\n if hasattr(instance, \"r_name\"):\n flags_ = flags.copy()\n flags_[\"language\"] = \"R\"\n parts.append(\"--language=R\")\n add(benchmarks, parts, flags_, exclude)\n\n return sorted(benchmarks, key=lambda k: k[\"command\"])","def get_benchmark_requirements(cls):\n pass","def is_cpp(benchmark):\n return get_language(benchmark) == 'c++'","def count_benchmarks():\n return len(setup_storage().fetch_benchmark({}))","def _run():\n subprocess.check_call(\n [\n \"tools/bazel\",\n \"build\",\n \"-c\",\n \"opt\",\n \"test/core/memory_usage/memory_usage_test\",\n ]\n )\n ret = {}\n for name, benchmark_args in _BENCHMARKS.items():\n for scenario, extra_args in _SCENARIOS.items():\n # TODO(chenancy) Remove when minstack is implemented for channel\n if name == \"channel\" and scenario == \"minstack\":\n continue\n try:\n output = subprocess.check_output(\n [\n \"bazel-bin/test/core/memory_usage/memory_usage_test\",\n ]\n + benchmark_args\n + extra_args\n )\n except subprocess.CalledProcessError as e:\n print(\"Error running benchmark:\", e)\n continue\n for line in output.splitlines():\n for key, (pattern, conversion) in _INTERESTING.items():\n m = re.match(pattern, line)\n if m:\n ret[scenario + \": \" + key] = conversion(m.group(1))\n return ret","def filter_out(self, skip):\n benchmarks = super(Benchmarks, self).__new__(self.__class__)\n benchmarks._conf = self._conf\n benchmarks._benchmark_dir = self._benchmark_dir\n benchmarks._all_benchmarks = self._all_benchmarks\n\n selected_idx = {}\n\n for name, benchmark in self.items():\n if name not in skip:\n benchmarks[name] = benchmark\n if name in self._benchmark_selection:\n selected_idx[name] = self._benchmark_selection[name]\n\n benchmarks._benchmark_selection = selected_idx\n\n return benchmarks","def benchmark(self):\n logger.info(self.benchmark.__doc__)\n return self.run(self.benchmark_profile())","def test_rust_code_analysis_tokei_c() -> None:\n\n ret_value = compare(\n \"rust-code-analysis\",\n \"tokei\",\n [\"-g\", \"-f\"],\n [\"SLOC\", \"PLOC\", \"CLOC\", \"BLANK\"],\n \"C\",\n \"bubble_sort.c\",\n )\n\n assert ret_value == 0","def run_benchmark():\n import argparse\n parser = argparse.ArgumentParser(description='Benchmark alchemically modified system against unmodified system.')\n parser.add_argument('--platform', dest='platform_name', action='store', default=None, help='platform name to benchmark (default: None)')\n options = parser.parse_args()\n\n from sams.tests import testsystems\n for testsystem_name in ['AblImatinibExplicitAlchemical']:\n cls = getattr(testsystems, testsystem_name)\n testsystem = cls()\n factory_args = { 'ligand_atoms' : testsystem.alchemical_atoms, 'receptor_atoms' : range(0,4266) }\n benchmark(testsystem.system, testsystem.positions, platform_name=options.platform_name, nsteps=5000, timestep=1.0*unit.femtoseconds, factory_args=factory_args)","def time_binaries():\n print()\n print(\"=== BENCHMARKING ===\")\n with tempfile.NamedTemporaryFile(\n prefix=\"riff-benchmark\", suffix=\".gitlog\"\n ) as testdata:\n subprocess.check_call(\n [\"git\", \"log\", \"--color=always\", \"-p\", \"master\"], stdout=testdata\n )\n\n binaries = sorted(glob.glob(os.path.join(BINDIR, \"*\")), key=natural_keys)\n\n # Do riff-current last: https://stackoverflow.com/a/20320940/473672\n binaries.sort(key=lambda s: s.endswith(\"riff-current\"))\n\n for binary in binaries:\n print_timings(binary, testdata.name)\n print_timings(\"/bin/cat\", testdata.name)","def test_benchmark1(capsys):\n student_1 = Student('114007245','Mario Castillo',2018, [\"INST 201\",\"INST 326\"])\n student_2 = Student('117006012', 'Joe Rogan', 2018, [\"MATH 115\",\"PSYC 100\"])\n student_3 = Student(\"117008490\", \"Kanye West\", 2018, [\"MATH 120\",\"STAT 003\"])\n student_4 = Student('118009044', \"Elon Musk\", 2018, [\"PSYC 100\",\"MATH 003\"])\n \n student_1.benchmark_I()\n outerr = capsys.readouterr()\n out = outerr.out\n assert out == ('You have not completed the Benchmark I requirements.\\n'\n 'You have not taken MATH 115 or higher.\\n'\n 'You have not taken PSYC 100.\\n')\n\n student_2.benchmark_I()\n outerr = capsys.readouterr()\n out = outerr.out\n assert out == (f'You have completed all of your Benchmark I courses! '\n f'Congratulations, {student_2.student_name}!\\n')\n\n student_3.benchmark_I()\n outerr = capsys.readouterr()\n out = outerr.out \n assert out == ('You have not completed the Benchmark I requirements.\\n'\n 'You have not taken PSYC 100.\\n')\n\n student_4.benchmark_I()\n outerr = capsys.readouterr()\n out = outerr.out\n assert out == ('You have not completed the Benchmark I requirements.\\n'\n 'You have not taken MATH 115 or higher.\\n')","def Cleanup(benchmark_spec):\n pass","def delete_all_benchmarks(self, namespace=\"benchmark-operator\"):\n all_benchmarks = self.crd_client.list_namespaced_custom_object(\n group=\"ripsaw.cloudbulldozer.io\", version=\"v1alpha1\", namespace=namespace, plural=\"benchmarks\"\n )\n\n _ = [\n self.delete_benchmark(benchmark[\"metadata\"][\"name\"], namespace)\n for benchmark in all_benchmarks.get(\"items\", [])\n ]","def test_make_benchmark_from_command_line_multiple_input_sources(\n env: LlvmEnv, retcode: int\n):\n with temporary_working_directory() as cwd:\n with open(\"a.c\", \"w\") as f:\n f.write(\"int main() { return B(); }\")\n\n with open(\"b.c\", \"w\") as f:\n f.write(f\"int B() {{ return {retcode}; }}\")\n\n bm = env.make_benchmark_from_command_line([\"gcc\", \"a.c\", \"b.c\", \"-o\", \"foo\"])\n assert not (cwd / \"foo\").is_file()\n\n env.reset(benchmark=bm)\n assert \"main()\" in env.ir\n\n bm.compile(env)\n assert (cwd / \"foo\").is_file()\n\n p = subprocess.Popen([\"./foo\"])\n p.communicate(timeout=60)\n assert p.returncode == retcode","def benchmark_profile(self):\n cb_bin = os.path.join(bin_path, 'compilebench')\n desc = \"benchmark\"\n test_name = \"compilebench_{0}\".format(to_safe_name(desc))\n test = TestProfile(\n name=test_name,\n desc=desc,\n test_path=self.test_path,\n bin_path=bin_path,\n command=\"{0} -D {1} -i 10 --makej\".format(cb_bin, self.test_path))\n\n return test","def run_benchmarks(urls, urlIndices, trial_number):\n path.append(os.path.join(CHROMIUM_SRC, 'tools/perf/'))\n benchmark_path = os.path.join(CHROMIUM_SRC, 'tools/perf/run_benchmark')\n output_path = 'temp'\n trial_key = 'trial{0}'.format(trial_number)\n\n cmd = ('sudo ' + benchmark_path + ' --profiler=trace telemetryBenchmarks.url{0}')\n for i in urlIndices:\n try:\n out, err, returncode = get_benchmark_result(cmd.format(i))\n timeout = False\n print 'successfully ran benchmark for url' + str(i)\n except TimeoutError:\n # Benchmark failed\n print 'Benchmark Timeout!'\n out = ''\n returncode = 1\n timeout = True\n\n failed = ['FAILED']\n if returncode != 0 or any(x in out for x in failed) or timeout:\n # If a benchmark fails, remove its corresponding wpr file, and act\n # as if it didn't exist\n # Remove from data/wpr_source\n print 'Benchmark {0} failed'.format(i)\n print 'return code is ' + str(returncode)\n print 'Out:'\n print out\n print 'Err:'\n print err\n urlName = 'url{0}_page_set_000.wpr'.format(i)\n urlpcName = 'url{0}_pc_page_set_000.wpr'.format(i)\n urlFilePath = os.path.join('data/wpr_source',urlName)\n urlpcFilePath = os.path.join('data/wpr_source',urlpcName)\n urlCmd = 'rm -f {0}'.format(urlFilePath)\n urlpcCmd = 'rm -f {0}'.format(urlpcFilePath)\n print 'Removing: {0}, {1}'.format(urlFilePath, urlpcFilePath)\n commands = [\n 'rm -f {0}'.format(urlFilePath),\n 'rm -f {0}'.format(urlpcFilePath)\n ]\n for cmdss in commands:\n p = Popen(cmdss, shell=True)\n p.wait()\n # Skip the rest of this url\n print \"Moving on!\"\n continue\n\n # Parse data\n tmp_path = 'temp/tmp_benchmark_result_json'\n with open(tmp_path, 'rb') as f:\n tmp_json = json.load(f)\n benchmark_results = tmp_json['values']\n commands = [\n 'rm -f ~/page_load_time/telemetry/temp/tmp_benchmark_result_json',\n ]\n for cmds in commands:\n p = Popen(cmds, shell=True)\n p.wait()\n\n output = {urls[i]: {'cold_times': {trial_key: benchmark_results}}}\n output_file = os.path.join(output_path, urlsafe_b64encode(urls[i]))\n output_file += '.' + str(trial_number)\n try:\n with open(output_file, 'w') as f:\n json.dump(output, f)\n except IOError:\n raise IOError('Unable to write to {0}'.format(output_file))\n\n\n ############### Now run for Perfect Cache file ################\n\n try:\n out, err, returncode = \\\n get_benchmark_result(cmd.format(str(i) + '_pc'))\n timeout = False\n print 'successfully ran benchmark for url' + str(i) + '_pc'\n except TimeoutError:\n # Benchmark failed\n print 'Benchmark Timeout!'\n out = ''\n returncode = 1\n timeout = True\n\n failed = ['FAILED']\n if returncode != 0 or any(x in out for x in failed) or timeout:\n # If a benchmark fails, remove its corresponding wpr file, and act\n # as if it didn't exist\n # Remove from data/wpr_source\n\n print 'Benchmark {0}_pc failed'.format(i)\n print 'Out:'\n print out\n print 'Err:'\n print err\n urlName = 'url{0}_page_set_000.wpr'.format(i)\n urlpcName = 'url{0}_pc_page_set_000.wpr'.format(i)\n urlFilePath = os.path.join('data/wpr_source',urlName)\n urlpcFilePath = os.path.join('data/wpr_source',urlpcName)\n urlCmd = 'rm -f {0}'.format(urlFilePath)\n urlpcCmd = 'rm -f {0}'.format(urlpcFilePath)\n print 'Removing: {0}, {1}'.format(urlFilePath, urlpcFilePath)\n commands = [\n 'rm -f {0}'.format(urlFilePath),\n 'rm -f {0}'.format(urlpcFilePath)\n ]\n for cmdss in commands:\n p = Popen(cmdss, shell=True)\n p.wait()\n # Skip the rest of this url\n print \"Moving on!\"\n continue\n\n # Parse data\n tmp_path = 'temp/tmp_benchmark_result_json'\n with open(tmp_path, 'rb') as f:\n tmp_json = json.load(f)\n benchmark_results = tmp_json['values']\n\n commands = [\n 'rm -f ~/page_load_time/telemetry/temp/tmp_benchmark_result_json',\n ]\n for cmds in commands:\n p = Popen(cmds, shell=True)\n p.wait()\n\n output = {urls[i]: {'cold_times': {trial_key: benchmark_results}}}\n output_file = os.path.join(output_path, urlsafe_b64encode(urls[i]))\n output_file += '.' + str(trial_number) + '.pc'\n try:\n with open(output_file, 'w') as f:\n json.dump(output, f)\n except IOError:\n raise IOError('Unable to write to {0}'.format(output_file))","def _c_optimizations_ignored():\n pure_env = os.environ.get('PURE_PYTHON')\n return pure_env != \"0\" if pure_env is not None else PYPY","def _c_optimizations_required():\n pure_env = os.environ.get('PURE_PYTHON')\n require_c = pure_env == \"0\"\n return require_c","def benchmark(self):\n nsites = []\n for m in self.methods:\n for name, structure in self.test_structures.items():\n cns = []\n if self.unique_sites:\n es = SpacegroupAnalyzer(structure).get_symmetrized_structure().equivalent_sites\n sites = [structure.index(x[0]) for x in es]\n else:\n sites = range(len(structure))\n\n for key, val in self.hi.items():\n if name == key:\n for j in sites:\n if isinstance(m, NearNeighbors):\n tmpcn = m.get_cn_dict(structure, j, self.use_weights)\n else:\n tmpcn = m.compute(structure, j)\n if tmpcn == \"null\":\n continue\n if self.nround:\n self._roundcns(tmpcn, self.nround)\n cns.append((structure[j].species_string, tmpcn))\n if self.cation_anion:\n for mat, cat in self.cations.items():\n if (name == mat) and cat:\n cns = self._popel(cns, cat)\n elif self.anion_cation:\n for mat, an in self.anions.items():\n if name == mat:\n cns = self._popel(cns, an)\n m._cns[name] = cns\n nsites.append(len(cns))\n self.nsites = max(nsites)","def _disc_benchmarks(cls, conf, repo, environments, commit_hashes, check):\n root = conf.benchmark_dir\n\n cls.check_tree(root)\n\n if len(environments) == 0:\n raise util.UserError(\"No available environments\")\n\n # Try several different commits:\n #\n # - First of commit_hashes provided\n # - Tips of branches from configuration file\n # - Rest of the commit_hashes\n #\n\n def iter_hashes():\n for h in commit_hashes[:1]:\n yield h\n for branch in conf.branches:\n try:\n yield repo.get_hash_from_name(branch)\n except NoSuchNameError:\n continue\n for h in commit_hashes[1:]:\n yield h\n\n def iter_unique(iter):\n seen = set()\n for item in iter:\n if item not in seen:\n seen.add(item)\n yield item\n\n try_hashes = iter_unique(iter_hashes())\n\n log.info(\"Discovering benchmarks\")\n with log.indent():\n last_err = None\n for env, commit_hash in itertools.product(environments, try_hashes):\n env.create()\n\n if last_err is not None:\n log.warning(\"Failed: trying different commit/environment\")\n\n result_dir = tempfile.mkdtemp()\n try:\n env.install_project(conf, repo, commit_hash)\n\n env_vars = dict(os.environ)\n env_vars.update(env.env_vars)\n\n result_file = os.path.join(result_dir, 'result.json')\n env.run(\n [runner.BENCHMARK_RUN_SCRIPT, 'discover',\n os.path.abspath(root),\n os.path.abspath(result_file)],\n cwd=result_dir,\n env=env_vars,\n dots=False)\n\n try:\n with open(result_file, 'r') as fp:\n benchmarks = json.load(fp)\n except (IOError, ValueError):\n log.error(\"Invalid discovery output\")\n raise util.UserError()\n\n break\n except (util.UserError, util.ProcessError) as err:\n last_err = err\n continue\n except KeyboardInterrupt:\n raise util.UserError(\"Interrupted.\")\n finally:\n util.long_path_rmtree(result_dir)\n else:\n raise util.UserError(\"Failed to build the project and import the benchmark suite.\")\n\n if check:\n log.info(\"Checking benchmarks\")\n with log.indent():\n result_dir = tempfile.mkdtemp()\n try:\n out, err, retcode = env.run(\n [runner.BENCHMARK_RUN_SCRIPT, 'check',\n os.path.abspath(root)],\n cwd=result_dir,\n dots=False,\n env=env_vars,\n valid_return_codes=None,\n return_stderr=True,\n redirect_stderr=True)\n finally:\n util.long_path_rmtree(result_dir)\n\n out = out.strip()\n if retcode == 0:\n if out:\n log.info(out)\n log.info(\"No problems found.\")\n else:\n if out:\n log.error(out)\n raise util.UserError(\"Benchmark suite check failed.\")\n\n return benchmarks","def test_get_benchmark_methods_filter(self):\n config = mock.Mock()\n config.workspace = 'workspace'\n config.benchmark_method_patterns = ['new_foo.BenchmarkClass.filter:bench.*']\n benchmark_runner = benchmark.BenchmarkRunner(config)\n\n mock_benchmark_class = mock.Mock()\n mock_benchmark_class.benchmark_method_1 = 'foo'\n\n mock_module = mock.Mock()\n sys.modules['new_foo'] = mock_module\n mock_module.BenchmarkClass.return_value = mock_benchmark_class\n\n methods = benchmark_runner._get_benchmark_methods()\n\n self.assertEqual(1, len(methods))\n self.assertEqual('new_foo.BenchmarkClass.benchmark_method_1', methods[0])","def _run_benchmark(shell, shell_args, name, app, duration_seconds, measurements,\n verbose, android, save_traces):\n timeout = duration_seconds + _EXTRA_TIMEOUT\n benchmark_args = []\n benchmark_args.append('--app=' + app)\n benchmark_args.append('--duration=' + str(duration_seconds))\n\n output_file = None\n device_output_file = None\n if save_traces:\n output_file = 'benchmark-%s-%s.trace' % (name.replace(' ', '_'),\n time.strftime('%Y%m%d%H%M%S'))\n if android:\n device_output_file = os.path.join(shell.get_tmp_dir_path(), output_file)\n benchmark_args.append('--trace-output=' + device_output_file)\n else:\n benchmark_args.append('--trace-output=' + output_file)\n\n for measurement in measurements:\n benchmark_args.append(measurement)\n\n shell_args = list(shell_args)\n shell_args.append(_BENCHMARK_APP)\n shell_args.append('--force-offline-by-default')\n shell_args.append('--args-for=%s %s' % (_BENCHMARK_APP,\n ' '.join(benchmark_args)))\n\n if verbose:\n print 'shell arguments: ' + str(shell_args)\n return_code, output, did_time_out = shell.run_and_get_output(\n shell_args, timeout=timeout)\n\n if did_time_out:\n return False, 'timed out', output\n if return_code:\n return False, 'return code: ' + str(return_code), output\n\n # Pull the trace file even if some measurements are missing, as it can be\n # useful in debugging.\n if device_output_file:\n shell.pull_file(device_output_file, output_file, remove_original=True)\n\n return True, None, output","def get_performance_test_cases(test_suite):\n return get_cases(test_suite, r'test_perf_')","def main():\n test_folders = argv[1]\n benchmark_file = argv[2]\n output_path = argv[3]\n\n method = ['n', 'a', 'a']\n bigram = ['False', 'False', 'True']\n output_file = output_path + '/' + 'method_comparison_cosine_values.csv'\n\n with open(test_folders, 'r') as f:\n test_folders = f.read()\n\n test_folders = test_folders.splitlines()\n\n with open(benchmark_file, 'r') as f:\n benchmark_file = f.read()\n\n benchmark_file = benchmark_file.splitlines()\n\n # initialize big data frame\n frames = []\n\n for k in xrange(len(benchmark_file)):\n\n test = str(test_folders[k]).replace('\"', '')\n print \"Reading test files from folder:\"\n print test\n\n benchmark = str(benchmark_file[k]).replace('\"', '')\n print \"Reading benchmark form file:\"\n print benchmark\n\n # read file paths from test documents folder\n query = sorted([os.path.join(test, f) for f in os.listdir(test) if f.endswith('.txt')])\n\n # load benchmark text file\n with open(benchmark, \"r\", encoding=\"utf-8\", errors='ignore') as doc:\n raw = doc.read()\n\n # initialize dict of dicts for data frame\n method_csv = {}\n\n for j in xrange(len(method)):\n # extract features from benchmark\n dtm = ExtractFeatures(method[j], bigram[j])\n benchmark_name = benchmark_file[k].split('\\\\')[-1]\n benchmark_features = dtm.extract_features_from_text(raw, benchmark_name)\n\n # extract terms from each text document to create a vocabulary (keeping unique terms only)\n vocabulary = sorted(set(w[1] for w in benchmark_features))\n print \"{0} features produced.\".format(str(len(vocabulary)))\n\n benchmark_dtv = DTM(vocabulary, benchmark_name, benchmark_features)\n benchmark_dtv = benchmark_dtv.compute_dtv()\n\n # load test document features\n test_features = []\n for q in query:\n dtm1 = ExtractFeatures(method[j], bigram[j])\n test_features = test_features + dtm1.extract_features_from_file(q)\n\n documents = sorted(set([d for d, w in test_features]))\n print \"{0} test documents read.\".format(str(len(documents)))\n\n print \"Computing DTM...\"\n test_dtm = DTM(vocabulary, documents, test_features)\n test_dtm = test_dtm.compute_dtm()\n\n print \"Computing cosine values...\"\n dv = {}\n for i in xrange(len(documents)):\n d = 1 - spatial.distance.cosine(benchmark_dtv[benchmark_name], test_dtm[documents[i]])\n if isnan(d):\n d = 0\n dv[documents[i]] = d\n\n this_method = \"method=\" + method[j] + '_' + \"bigram=\" + bigram[j]\n method_csv[this_method] = pd.Series(dv)\n\n print \"Saving to data frame...\"\n df = pd.DataFrame(method_csv)\n test = test.split('\\\\')[-1]\n test = test.split('.')[0]\n df['test_group'] = test\n\n frames.append(df)\n\n result = pd.concat(frames)\n\n print \"Saving results to file: \", output_file\n result.to_csv(output_file)\n\n print 'Finished computing {0} data frames'.format(str(len(test_folders)))","def test_list_benchmarks(client):\n # The benchmark listing contains one element (independently of whether the\n # user is logged in or not).\n r = client.get(config.API_PATH() + '/workflows')\n assert r.status_code == 200\n doc = r.json\n assert len(doc[labels.WORKFLOW_LIST]) == 1\n # Create user and the request header that contains the API key for the\n # logged in user.\n _, token = create_user(client, '0000')\n headers = {HEADER_TOKEN: token}\n r = client.get(config.API_PATH() + '/workflows', headers=headers)\n assert r.status_code == 200\n doc = r.json\n assert len(doc[labels.WORKFLOW_LIST]) == 1","def get_benchmark_specification(benchmark = 'FSI1'):\n if benchmark == 'FSI1':\n rho_s = Constant(1e03)\n nu_s = Constant(0.4)\n mu_s = Constant(5e05)\n rho_f = Constant(1e03)\n nu_f = Constant(1e-03)\n U = 0.2\n T_end = 60.0\n result = \"results-FSI1/\"\n elif benchmark == 'FSI2':\n rho_s = Constant(1e04)\n nu_s = Constant(0.4)\n mu_s = Constant(5e05)\n rho_f = Constant(1e03)\n nu_f = Constant(1e-03)\n U = 1.0\n T_end = 15.0\n result = \"results-FSI2/\"\t\t\n elif benchmark == 'FSI3':\n rho_s = Constant(1e03)\n nu_s = Constant(0.4)\n mu_s = Constant(2e06)\n rho_f = Constant(1e03)\n nu_f = Constant(1e-03)\n U = 2.0\n T_end = 20.0\n result = \"results-FSI3/\"\t\t\n else:\n raise ValueError('\"{}\" is a wrong name for problem specification.'.format(benchmark))\n v_max = Constant(1.5*U) # mean velocity to maximum velocity \n # (we have parabolic profile)\n E_s = Constant(2*mu_s*(1+nu_s))\n lambda_s = Constant((nu_s*E_s)/((1+nu_s)*(1-2*nu_s)))\n mu_f = Constant(nu_f*rho_f)\n return v_max, lambda_s, mu_s, rho_s, mu_f, rho_f, T_end, result","def run_all(self):\n runs = []\n for run in self.benchmarks:\n run.start()\n run.wait()\n runs.append(run.metadata)\n return runs","def prepare_benchmark(trial_number, num_urls):\n telemetry_page_cycler_path = os.path.join(CHROMIUM_SRC,\n 'tools/perf/benchmarks/telemetryBenchmarks.py')\n\n # Dynamically writing python to a file\n class_template = (\"from measurements import page_cycler\\n\"\n \"import page_sets\\n\"\n \"from telemetry import benchmark\\n\\n\"\n \"class _PageCycler(benchmark.Benchmark):\\n\"\n \" options = {'pageset_repeat': 6}\\n\"\n \" @classmethod\\n\"\n \" def AddBenchmarkCommandLineArgs(cls, parser):\\n\"\n \" parser.add_option('--v8-object-stats',\\n\"\n \" action='store_true',\\n\"\n \" help='Enable detailed V8 object statistics.')\\n\"\n \" parser.add_option('--user-server-delay')\\n\"\n \" print 'IN BENCHMARK'\\n\"\n \" parser.add_option('--report-speed-index',\\n\"\n \" action='store_true',\\n\"\n \" help='Enable the speed index metric.')\\n\"\n \" parser.add_option('--cold-load-percent', type='int', \"\n \"default=50,\\n\"\n \" help='%d of page visits for which a cold load is \"\n \"forced')\\n\\n\"\n \" def CreatePageTest(self, options):\\n\"\n \" return page_cycler.PageCycler(\\n\"\n \" page_repeat = options.page_repeat,\\n\"\n \" pageset_repeat = options.pageset_repeat,\\n\"\n \" cold_load_percent = options.cold_load_percent,\\n\"\n \" record_v8_object_stats = options.v8_object_stats,\\n\"\n \" report_speed_index = options.report_speed_index)\\n\\n\")\n\n benchmark_template = (\"@benchmark.Enabled('android')\\n\"\n \"class PageCyclerUrl{0}(_PageCycler):\\n\"\n \" print 'Using page cycler'\\n\"\n \" page_set = page_sets.url{0}PageSet\\n\\n\")\n\n with open(telemetry_page_cycler_path, 'w') as f:\n f.write(class_template)\n for i in range(num_urls):\n f.write(benchmark_template.format(i))\n f.write(benchmark_template.format(str(i) + '_pc'))","def filter_benchmarks_for_category(\n self,\n category: str,\n available_drivers: Sequence[str],\n cpu_target_arch_filter: str,\n gpu_target_arch_filter: str,\n driver_filter: Optional[str] = None,\n mode_filter: Optional[str] = None,\n model_name_filter: Optional[str] = None) -> Sequence[BenchmarkCase]:\n\n category_dir = self.category_map.get(category)\n if category_dir is None:\n return []\n\n chosen_cases = []\n for benchmark_case in self.suite_map[category_dir]:\n driver = benchmark_case.driver[len(\"iree-\"):].lower()\n matched_driver = (driver in available_drivers) and (\n driver_filter is None or re.match(driver_filter, driver) is not None)\n target_arch = benchmark_case.target_arch.lower()\n matched_arch = (re.match(cpu_target_arch_filter,\n target_arch) is not None or\n re.match(gpu_target_arch_filter, target_arch) is not None)\n bench_mode = ','.join(benchmark_case.bench_mode)\n matched_mode = (mode_filter is None or\n re.match(mode_filter, bench_mode) is not None)\n\n # For backward compatibility, model_name_filter matches against the string:\n # <model name with tags>/<benchmark case name>\n model_and_case_name = f\"{benchmark_case.model_name_with_tags}/{os.path.basename(benchmark_case.benchmark_case_dir)}\"\n matched_model_name = (model_name_filter is None or re.match(\n model_name_filter, model_and_case_name) is not None)\n\n if (matched_driver and matched_arch and matched_model_name and\n matched_mode):\n chosen_cases.append(benchmark_case)\n\n return chosen_cases","def get_benchmark(client):\n r = client.get(config.API_PATH() + '/benchmarks')\n benchmarks = json.loads(r.data)\n return benchmarks['benchmarks'][0]['id']","def test_make_benchmark_single_ll():\n benchmark = llvm.make_benchmark(INVALID_IR_PATH)\n assert str(benchmark.uri).startswith(\"benchmark://user-v0/\")\n assert benchmark.uri.scheme == \"benchmark\"\n assert benchmark.uri.dataset == \"user-v0\"","def _should_attempt_c_optimizations():\n if PYPY:\n return False\n\n if _c_optimizations_required():\n return True\n return not _c_optimizations_ignored()","def workbench_scenarios():\n return [\n (\"UcCodemirrorXBlock\",\n \"\"\"<vertical_demo>\n <uc_codemirror/>\n <uc_codemirror/>\n <uc_codemirror/>\n </vertical_demo>\n \"\"\"),\n ]","def Run(benchmark_spec):\n _UpdateBenchmarkSpecWithFlags(benchmark_spec)\n vm = benchmark_spec.vms[0]\n if benchmark_spec.tpus:\n # For MLPerf 1.0, the benchmake code of different hardware are different.\n if (benchmark_spec.tpu_groups['train'].GetAcceleratorType() == 'v3-32' or\n benchmark_spec.tpu_groups['train'].GetAcceleratorType() == 'v3-128' or\n benchmark_spec.tpu_groups['train'].GetAcceleratorType() == 'v3-256' or\n benchmark_spec.tpu_groups['train'].GetAcceleratorType() == 'v3-512' or\n benchmark_spec.tpu_groups['train'].GetAcceleratorType() == 'v3-1024' or\n benchmark_spec.tpu_groups['train'].GetAcceleratorType() == 'v3-2048'):\n run_path = (\n '$HOME/training_results_{version}/Google/benchmarks/{model}/tpu-{tpus}'\n .format(\n version=VERSION.value,\n model=benchmark_spec.benchmark,\n tpus=benchmark_spec.tpu_groups['train'].GetAcceleratorType()))\n code_path = (\n '$HOME/training_results_{version}/Google/benchmarks/{model}/implementations/tpu-{tpus}-{model}'\n .format(\n version=VERSION.value,\n model=benchmark_spec.benchmark,\n tpus=benchmark_spec.tpu_groups['train'].GetAcceleratorType()))\n\n if MASK in benchmark_spec.benchmark:\n model = 'mask_rcnn'\n elif GNMT in benchmark_spec.benchmark:\n model = 'nmt'\n else:\n model = benchmark_spec.benchmark\n\n mlperf_benchmark_cmd = (\n 'cd {code_path} && '\n 'export PYTHONPATH=$(pwd):$(pwd)/{model} && '\n 'cd {model} && '\n '{run_path}/run_and_time.sh'.format(\n code_path=code_path,\n model=model,\n run_path=run_path))\n\n if SSD in benchmark_spec.benchmark:\n mlperf_benchmark_cmd = (\n 'export '\n 'MLP_GCS_RESNET_CHECKPOINT={checkpoint}'\n ' && {cmd}'.format(\n checkpoint=FLAGS.mlperf_gcs_resnet_checkpoint,\n cmd=mlperf_benchmark_cmd))\n else:\n raise ValueError(\n 'MLPerf configurations do not support the hardware in PKB. PKB may '\n 'need to be updated if this is a new TPU type.')\n\n else:\n run_sub_paths = {RESNET: 'resnet/implementations/mxnet',\n TRANSFORMER: 'transformer/implementations/pytorch',\n MINIGO: 'minigo/implementations/tensorflow',\n MASK: 'maskrcnn/implementations/pytorch',\n GNMT: 'gnmt/implementations/pytorch',\n SSD: 'ssd/implementations/pytorch',\n BERT: 'bert/implementations/pytorch',}\n benchmark_path = f'$HOME/training_results_{VERSION.value}/NVIDIA/benchmarks'\n run_path = posixpath.join(benchmark_path,\n run_sub_paths[benchmark_spec.benchmark])\n env = {\n 'DGXSYSTEM': DGXSYSTEM,\n 'NEXP': 1,\n 'PULL': 0,\n 'LOGDIR': f'/tmp/{benchmark_spec.benchmark}',\n }\n envs = {\n RESNET: {},\n TRANSFORMER: {'DATADIR': '/data/wmt/utf8'},\n MINIGO: {'CONT': 'mlperf-nvidia:minigo'},\n MASK: {},\n GNMT: {'DATADIR': '/data/gnmt'},\n SSD: {'DATADIR': '/data'},\n BERT: {}\n }\n env.update(envs[benchmark_spec.benchmark])\n\n run_script = posixpath.join(run_path, 'run_with_docker.sh')\n vm_util.ReplaceText(vm, 'SYSLOGGING=1', 'SYSLOGGING=0', run_script)\n vm_util.ReplaceText(vm, 'docker exec -it', 'docker exec -t', run_script)\n if benchmark_spec.benchmark == RESNET:\n vm_util.ReplaceText(vm, r'mpirun.*run_and_time\\.sh',\n r'.\\/run_and_time.sh', run_script)\n\n env = ' '.join(f'{key}={value}' for key, value in env.items())\n if nvidia_driver.CheckNvidiaGpuExists(vm):\n env = f'{tensorflow.GetEnvironmentVars(vm)} {env}'\n\n mlperf_benchmark_cmd = (\n f'chmod 755 {run_script} && '\n f'cd {run_path} && '\n f'{env} {run_script}')\n\n samples = []\n metadata = _CreateMetadataDict(benchmark_spec)\n stdout, _ = vm.RobustRemoteCommand(mlperf_benchmark_cmd)\n if NONE in FLAGS.mlperf_profiler:\n samples.extend(\n MakeSamplesFromOutput(\n metadata,\n stdout,\n use_tpu=bool(benchmark_spec.tpus),\n model=benchmark_spec.benchmark))\n return samples","def bench(participants=participants, benchmarks=benchmarks, bench_time=10.0):\n\n mcs = [p.factory() for p in participants]\n means = [[] for p in participants]\n stddevs = [[] for p in participants]\n\n # Have each lifter do one benchmark each\n for benchmark_name, f, args, kwargs in benchmarks:\n logger.info('%s', benchmark_name)\n\n for i, (participant, mc) in enumerate(zip(participants, mcs)):\n sw = Stopwatch()\n\n while sw.total() < bench_time:\n with sw.timing():\n f(mc, *args, **kwargs)\n\n means[i].append(sw.mean())\n stddevs[i].append(sw.stddev())\n\n logger.info(u'%s: %s', participant.name, sw)\n\n return means, stddevs","def main(args):\n\n # Compose the model list\n modellist = []\n if args['model']:\n modellist.append(bmark.ModelInfo(args['model'], os.getcwd(), args['classname']))\n\n # Load the benchmark settings\n benchmark = None\n benchmark = bmark.load_benchmark(args['benchmark'])\n corresponding_data = False\n if 'corresponding_data' in benchmark:\n corresponding_data = benchmark['corresponding_data']\n\n # Only extend if not cached\n cache_df = None\n if not args['cache']:\n modellist.extend(benchmark['models'])\n else:\n cache_df = pd.read_csv(args['cache'])\n\n # Extract comparator settings from benchmark description\n eval_comparator = comparator.EqualityComparator()\n if 'comparator' in benchmark:\n if benchmark['comparator'] == 'nvc':\n eval_comparator = comparator.NVCComparator()\n\n # Run the model evaluation\n is_silent = (args['output'] in ['html', 'server'])\n eva = None\n if benchmark['type'] == 'adaption':\n eva = evaluator.AdaptionEvaluator(\n modellist,\n eval_comparator,\n benchmark['data.test'],\n train_datafile=benchmark['data.train'],\n train_data_person=benchmark['data.train_person'],\n silent=is_silent,\n corresponding_data=corresponding_data,\n domain_encoders=benchmark['domain_encoders'],\n cache_df=cache_df\n )\n elif benchmark['type'] == 'coverage':\n # Check for benchmark validity\n if benchmark['data.train'] or benchmark['data.train_person']:\n print('WARNING: Ignoring specified training and train_person data ' \\\n + 'for coverage evaluation...')\n\n eva = evaluator.CoverageEvaluator(\n modellist,\n eval_comparator,\n benchmark['data.test'],\n train_datafile=benchmark['data.train'],\n train_data_person=benchmark['data.train_person'],\n silent=is_silent,\n corresponding_data=corresponding_data,\n domain_encoders=benchmark['domain_encoders'],\n cache_df=cache_df\n )\n else:\n raise ValueError('Unknown benchmark type: {}'.format(benchmark['type']))\n\n with silence_stdout(is_silent):\n res_df = eva.evaluate()\n\n if 'save' in args:\n res_df.to_csv(args['save'], index=False)\n\n # Run the metric visualizer\n htmlcrtr = html_creator.HTMLCreator([\n viz_plot.AccuracyVisualizer(),\n viz_plot.BoxplotVisualizer(),\n viz_plot.TableVisualizer()\n ])\n\n # Prepare the benchmark output information and visualize the evaluation results\n benchmark_info = {\n 'name': os.path.basename(args['benchmark']),\n 'data.train': os.path.basename(\n benchmark['data.train']) if benchmark['data.train'] else '',\n 'data.train_person': os.path.basename(\n benchmark['data.train_person']) if benchmark['data.train_person'] else '',\n 'data.test': os.path.basename(benchmark['data.test']),\n 'type': benchmark['type'],\n 'corresponding_data': benchmark['corresponding_data'],\n 'domains': list(res_df['domain'].unique()),\n 'response_types': list(res_df['response_type'].unique()),\n }\n\n if args['output'] == 'browser':\n html = htmlcrtr.to_html(res_df, benchmark_info, embedded=False)\n server.load_in_default_browser(html.encode('utf8'))\n elif args['output'] == 'server':\n html = htmlcrtr.to_html(res_df, benchmark_info, embedded=True)\n sys.stdout.buffer.write(html.encode('utf-8'))\n elif args['output'] == 'html':\n html = htmlcrtr.to_html(res_df, benchmark_info, embedded=False)\n print(html)","def test_get_benchmark_methods_exact_match(self):\n config = mock.Mock()\n config.workspace = 'workspace'\n config.benchmark_method_patterns = [\n 'new_foo.BenchmarkClass.benchmark_method_1',\n 'new_foo.BenchmarkClass.benchmark_method_2']\n benchmark_runner = benchmark.BenchmarkRunner(config)\n\n methods = benchmark_runner._get_benchmark_methods()\n self.assertEqual(['new_foo.BenchmarkClass.benchmark_method_1',\n 'new_foo.BenchmarkClass.benchmark_method_2'], methods)","def _fcts():\n import numpy # pylint: disable=C0415\n from .cbenchmark_dot import vector_dot_product # pylint: disable=E0611,C0415\n from .cbenchmark_dot import vector_dot_product16 # pylint: disable=E0611,C0415\n from .cbenchmark_dot import vector_dot_product16_nofcall # pylint: disable=E0611,C0415\n from .cbenchmark_dot import vector_dot_product16_sse # pylint: disable=E0611,C0415\n\n def simple_dot(values):\n return numpy.dot(values, values)\n\n def c11_dot(vect):\n return vector_dot_product(vect, vect)\n\n def c11_dot16(vect):\n return vector_dot_product16(vect, vect)\n\n def c11_dot16_nofcall(vect):\n return vector_dot_product16_nofcall(vect, vect)\n\n def c11_dot16_sse(vect):\n return vector_dot_product16_sse(vect, vect)\n\n return [simple_dot, c11_dot, c11_dot16, c11_dot16_nofcall, c11_dot16_sse]","def _generate_benchmark_variants(benchmark_spec):\n variants = []\n # Cold start.\n variants.append({\n 'name': benchmark_spec['name'] + ' (cold start)',\n 'app': benchmark_spec['app'],\n 'duration': benchmark_spec['duration'],\n 'measurements': benchmark_spec['measurements'],\n 'shell-args': benchmark_spec.get('shell-args',\n []) + _COLD_START_SHELL_ARGS})\n # Warm start.\n variants.append({\n 'name': benchmark_spec['name'] + ' (warm start)',\n 'app': benchmark_spec['app'],\n 'duration': benchmark_spec['duration'],\n 'measurements': benchmark_spec['measurements'],\n 'shell-args': benchmark_spec.get('shell-args', [])})\n return variants","def benchmark_selection(self):\n return self._benchmark_selection","def Cleanup(benchmark_spec):\n del benchmark_spec # unused","def Cleanup(benchmark_spec):\n del benchmark_spec # unused","def test_profile_memory():\n process = psutil.Process()\n\n num_strings, strings_length = int(1e5 / 2), 1000\n memory_usage_samples = collections.defaultdict(list)\n\n for function_under_test in (sorted, xsorted):\n\n strings = random_strings(length=strings_length, num=num_strings)\n thread = threading.Thread(target=do_benchmark, args=(strings, function_under_test))\n\n start = process.memory_info_ex().rss\n thread.start()\n start_time = time.clock()\n\n while thread.is_alive():\n thread.join(0.001)\n value = (process.memory_info_ex().rss - start) / 1e3\n point = time.clock() - start_time, value\n memory_usage_samples[function_under_test.__name__].append(point)\n\n export_memory_profile_chart(memory_usage_samples, num_strings, strings_length)\n\n # extract only the memory usage from the sorted dict for determining the peak usage for each function under test.\n values_only = (\n (sample[1] for sample in samples[1])\n for samples in sorted(memory_usage_samples.items())\n )\n peak_sorted, peak_xsorted = map(max, values_only)\n assert peak_sorted / peak_xsorted >= 15","def benchmark_program(timeout, algo, file) -> List[str]:\n base_name = os.path.basename(file)\n out_file = f\"out/lz/{base_name}.{algo}\"\n cmd = [\n \"externals/lzrr/build/compress.out\",\n \"--input_file\",\n file,\n \"--output_file\",\n out_file,\n \"--mode\",\n algo,\n ]\n print(\" \".join(cmd))\n num_factor = 0\n time_start = time.time()\n try:\n out = subprocess.check_output(cmd, shell=False, timeout=timeout)\n res_pre = b\"The number of factors : \"\n res_beg = out.find(res_pre) + len(res_pre)\n res_end = out.find(b\"\\n\", res_beg)\n num_factor = int(out[res_beg:res_end])\n status = \"complete\"\n except subprocess.TimeoutExpired:\n status = f\"timeout-{timeout}\"\n except Exception:\n status = \"error\"\n\n exp = LZExp(\n str(datetime.datetime.now()),\n status,\n algo,\n os.path.basename(file),\n len(open(file, \"rb\").read()),\n time.time() - time_start,\n num_factor,\n )\n\n expd = exp.__dict__\n return list(map(str, expd.values()))","def compare_hard_noprint():\n\n\n cores = [1, 2, 4, 8, 16]\n s_times = []\n\n for n_cores in cores:\n print('analyzing', '-'.join(['data/bench', 's', 'np', str(n_cores)]) + '.out')\n data = split_output_file('-'.join(['data/bench', 's', 'np', str(n_cores)]) + '.out')\n s_times.append(data['FOOTER']['total_time'])\n\n # speedup plot\n fig, axs = plt.subplots()\n\n axs.plot(cores, [s_times[0] / x / k for x, k in zip(s_times, cores)], label='sequential')\n axs.set_xticks(cores)\n axs.ticklabel_format(style='sci', scilimits=(-128, 128))\n\n axs.set_ylabel('Relative speedup')\n axs.set_xlabel('Number of cores')\n axs.set_ylim(0, 1.1)\n\n fig.set_size_inches(4, 2, forward=True)\n plt.tight_layout()\n\n plt.show()","def print_scenario(benchmarks, name):\n # Remember, the first entry in the times array is an empty object.\n\n benchmarks = sorted(benchmarks, key = benchmark_sort_key)\n for benchmark in benchmarks:\n block_times = benchmark[\"times\"][2][\"block_times\"] # list of floats\n it = iter(block_times)\n for start in it:\n end = next(it)\n\n print(\"%s , %.3f \" % (benchmark[\"label\"], float(end) - float(start)))","def test_measure_nondeterministic_without_sampling(self):\n shots = 2000\n circuits = ref_measure.measure_circuits_nondeterministic(allow_sampling=False)\n targets = ref_measure.measure_counts_nondeterministic(shots)\n job = execute(circuits, QasmSimulator(), shots=shots)\n result = job.result()\n self.is_completed(result)\n self.compare_counts(result, circuits, targets, delta=0.05 * shots)","def _parameterize_simple_benchmarks():\n parameterized_tuples = []\n for index, simple_benchmark in enumerate(\n all_benchmarks.all_benchmarks(modules=[simple_benchmarks])):\n # The index ensures all test cases have distinct names, even if multiple\n # benchmarks have the same name.\n test_case_name = '{index}_{name}'.format(index=index,\n name=simple_benchmark.name)\n parameterized_tuples.append((test_case_name, simple_benchmark))\n return parameterized_tuples","def benchmark():\n print defer.Deferred.__module__\n for func, args, iter in benchmarkFuncs:\n print func.__name__, args, timeit(func, iter, *args)","def get_testbench_specs(self, tb_type: str) -> Dict[str, Any]:\n return self._specs['testbenches'][tb_type]","def test01c(self):\n a = np.arange(1e5)\n cnames = bcolz.blosc_compressor_list()\n if common.verbose:\n print(\"Checking compressors:\", cnames)\n # print \"\\nsize b uncompressed-->\", a.size * a.dtype.itemsize\n for cname in cnames:\n with bcolz.defaults_ctx(bcolz.cparams(clevel=9, cname=cname)):\n self.assertTrue(bcolz.defaults.cparams['cname'] == cname)\n b = bcolz.carray(a, rootdir=self.rootdir)\n # print \"size b compressed -->\", b.cbytes, \"with '%s'\"%cname\n self.assertTrue(sys.getsizeof(b) < b.nbytes,\n \"carray does not seem to compress at all\")\n assert_array_equal(a, b[:], \"Arrays are not equal\")\n # Remove the array on disk before trying with the next one\n if self.disk:\n common.remove_tree(self.rootdir)","def RunBenchmark(path_to_apk, run_label):\n # `path_to_apk` is similar to `./out/59.0.3071.132_arm_MonochromeStable.apk`\n chrome_version = ChromeVersion(path_to_apk.split('/')[-1].split('_')[0])\n subprocess.call(['adb', 'install', '-r', '-d', path_to_apk])\n subprocess.call([os.path.join(utils.CHROMIUM_SRC, 'tools',\n 'perf', 'run_benchmark'),\n '--browser=android-system-chrome',\n '--pageset-repeat=1', # could remove this later\n '--results-label=%s' % str(chrome_version),\n # TODO(wangge):not sure if we should run in compatibility\n # mode even for the later version, probably add a check in\n # caller to determine if we should run it in compatibility\n # mode and add an argument `run_in_compatibility_mode` to\n # the `RunBenchmark` function\n '--compatibility-mode=no-field-trials',\n '--compatibility-mode=ignore-certificate-errors',\n '--compatibility-mode=legacy-command-line-path',\n '--compatibility-mode=gpu-benchmarking-fallbacks',\n '--story-filter=wikipedia', # could remove this\n # thinking of adding an argument to the tool to set this\n '--output-dir=%s' % os.path.join(\n utils.APP_ROOT, 'results', run_label,\n str(chrome_version.milestone)),\n # thinking of adding an argument to the tool to set this too\n 'system_health.memory_mobile'])","def add_default_runtimes(harness):\n\n if os.path.isfile('configs/env'):\n config_file = open('configs/env')\n for line in config_file:\n key, value = line.rstrip().split('=', 1)\n os.environ[key] = value\n\n def wllvm_make(workdir, make_env, **kwargs):\n if 'LLVM_COMPILER' not in os.environ:\n logger.error('enviroment variable \"LLVM_COMPILER\" is required to build with wllvm')\n return False\n\n return BenchmarkingHarness.default_make(workdir, make_env, **kwargs)\n\n def wllvm_executor(filepath, workdir, tool, **kwargs):\n with NamedTemporaryFile(delete=False) as tmp:\n bc_filename = os.path.splitext(os.path.basename(filepath))[0] + '.bc'\n bc_filepath = os.path.join(tmp, bc_filename)\n logger.debug('extract bitcode file to: \"%s\"', bc_filepath)\n\n\n with subprocess.Popen([os.path.expandvars(\"$WLLVM_DIR/extract-bc\"), filepath, '-o', bc_filepath]) as process:\n process.wait(timeout=30) # 30 Seconds should be way enough time to do the bitcode extraction\n\n assert os.path.isfile(bc_filepath)\n\n with subprocess.Popen([os.path.expandvars(tool), bc_filepath, '--output=json'], cwd=workdir, stdout=subprocess.PIPE) as process:\n stdout, _ = process.communicate(timeout=kwargs.get('timeout', 240))\n\n if process.returncode != 0:\n return None\n\n try:\n return json.loads(stdout)\n except json.JSONDecodeError:\n logger.error('invalid benchmark result: \\'%s\\'', stdout.decode('utf-8'))\n raise\n\n\n for config in glob.glob('configs/*.py'):\n exec(open(os.path.abspath(config)).read(), {'wllvm_make' : wllvm_make, 'wllvm_executor' : wllvm_executor, 'harness' : harness})","def testRunPerformanceTestsTelemetry_NoTestResults(self):\n tempdir = tempfile.mkdtemp()\n benchmarks = ['benchmark1', 'benchmark2']\n return_code, stdout = self.RunPerfScript(\n '../../testing/scripts/run_performance_tests.py '\n '../../tools/perf/testdata/fail_and_do_nothing '\n '--benchmarks=%s '\n '--browser=%s '\n '--isolated-script-test-output=%s' % (\n ','.join(benchmarks),\n self.options.browser_type,\n os.path.join(tempdir, 'output.json')\n ))\n self.assertNotEqual(return_code, 0)\n try:\n with open(os.path.join(tempdir, 'output.json')) as f:\n test_results = json.load(f)\n self.assertIsNotNone(\n test_results, 'json_test_results should be populated: ' + stdout)\n self.assertTrue(\n test_results['interrupted'],\n 'if the benchmark does not populate test results, then we should '\n 'populate it with a failure.')\n for benchmark in benchmarks:\n with open(os.path.join(tempdir, benchmark, 'test_results.json')) as f:\n test_results = json.load(f)\n self.assertIsNotNone(\n test_results, 'json_test_results should be populated: ' + stdout)\n self.assertTrue(\n test_results['interrupted'],\n 'if the benchmark does not populate test results, then we should '\n 'populate it with a failure.')\n except IOError as e:\n self.fail('json_test_results should be populated: ' + stdout + str(e))\n finally:\n shutil.rmtree(tempdir)","def test_measure_nondeterministic_multi_qubit_without_sampling(self):\n shots = 2000\n qobj = ref_measure.measure_circuits_qobj_nondeterministic(allow_sampling=False)\n qobj.config.shots = shots\n circuits = [experiment.header.name for experiment in qobj.experiments]\n targets = ref_measure.measure_counts_qobj_nondeterministic(shots)\n job = QasmSimulator().run(qobj)\n result = job.result()\n self.is_completed(result)\n self.compare_counts(result, circuits, targets, delta=0.05 * shots)","def ignore_pyc(root,names):\n return [name for name in names if name.endswith('pyc')]","def no_builtin_verification():\n current_space().skip_builtin_verification = True\n yield\n current_space().skip_builtin_verification = False","def bench():\n times = []\n blocks = np.round(np.logspace(3, 7, num=50))\n for b in blocks:\n times.append(timeit.timeit('cpu_vs_mem.inplace(block_size=%d)' % b,\n 'import cpu_vs_mem', number=1))\n print('Block size: %d Execution time: %.3f s' % (b, times[-1]))\n sys.stdout.flush()\n\n return blocks, times","def runtime_cflags(self, ctx: Context) -> Iterable[str]:\n if self.builtin_passes:\n return self.builtin_passes.runtime_cflags(ctx)\n return []","def list_runtimes(self, workbench):\n pass","def Run(benchmark_spec: bm_spec.BenchmarkSpec) -> List[sample.Sample]:\n discovery_duration = benchmark_spec.data_discovery_service.DiscoverData()\n return [\n sample.Sample('data_discovery_duration', discovery_duration, 'seconds',\n benchmark_spec.data_discovery_service.GetMetadata())]","def Run(benchmark_spec):\n vms = benchmark_spec.vms\n results = []\n\n logging.info('Iperf Results:')\n\n # Send traffic in both directions\n for sending_vm, receiving_vm in vms, reversed(vms):\n # Send using external IP addresses\n if vm_util.ShouldRunOnExternalIpAddress():\n results.append(_RunIperf(sending_vm,\n receiving_vm,\n receiving_vm.ip_address,\n 'external'))\n\n # Send using internal IP addresses\n if vm_util.ShouldRunOnInternalIpAddress(sending_vm,\n receiving_vm):\n results.append(_RunIperf(sending_vm,\n receiving_vm,\n receiving_vm.internal_ip,\n 'internal'))\n\n return results","def get_cpython_lines():\n cpython_url = 'https://github.com/python/cpython/blob/master/Python/pythonrun.c'\n soup = BeautifulSoup(requests.get(cpython_url).text, 'html.parser')\n cpython_soup = soup.find(\"div\",{\"itemprop\":\"text\", \"class\":\"Box-body p-0 blob-wrapper data type-c\"})\n tds = cpython_soup.find_all('td', {\"class\":\"blob-code blob-code-inner js-file-line\"})\n cpython_code_lines = []\n for td in tds:\n cpython_code_lines.append(\n ''.join([thing.text+\" \" for thing in td.find_all('span') if len(thing.text)>1]))\n return(cpython_code_lines)","def __init__(self, conf, benchmarks, regex=None):\n self._conf = conf\n self._benchmark_dir = conf.benchmark_dir\n\n if not regex:\n regex = []\n if isinstance(regex, str):\n regex = [regex]\n\n self._all_benchmarks = {}\n self._benchmark_selection = {}\n for benchmark in benchmarks:\n self._all_benchmarks[benchmark['name']] = benchmark\n if benchmark['params']:\n self._benchmark_selection[benchmark['name']] = []\n for idx, param_set in enumerate(\n itertools.product(*benchmark['params'])):\n name = f\"{benchmark['name']}({', '.join(param_set)})\"\n if not regex or any(re.search(reg, name) for reg in regex):\n self[benchmark['name']] = benchmark\n self._benchmark_selection[benchmark['name']].append(idx)\n else:\n self._benchmark_selection[benchmark['name']] = None\n if not regex or any(re.search(reg, benchmark['name']) for reg in regex):\n self[benchmark['name']] = benchmark","def compilation_options(self):\n #'-target','i386-pc-linux-gnu','-m32','-O2',\n opts = ['-Wno-implicit-function-declaration','-Wno-incompatible-library-redeclaration','-fno-vectorize',\n '-fno-slp-vectorize','-gline-tables-only','-Xclang','-disable-lifetime-markers','-Rpass=.*','-Rpass-missed=.*',\n '-Rpass-analysis=.*','-mllvm','-inline-threshold=15000','-Dassert=__VERIFIER_assert']\n if self._options.property.undefinedness():\n opts.append('-fsanitize=undefined')\n opts.append('-fno-sanitize=unsigned-integer-overflow')\n elif self._options.property.signedoverflow():\n opts.append('-fsanitize=signed-integer-overflow')\n opts.append('-fsanitize=shift')\n\n return opts","def benchmark(func):\n import time\n @wraps(func)\n def wrapper(*args, **kwargs):\n t = time.clock()\n res = func(*args, **kwargs)\n print(func.__name__, time.clock()-t)\n return res\n return wrapper","def benchmark(mh_func):\n fps = list(mltext())\n sim_errs = []\n dis_errs = []\n runtimes = []\n for abc in sliding_window(fps, 3, 2, fillvalue=None):\n abc = list(abc)\n if abc[-1] is None:\n continue\n texts = (load_text_file(t) for t in abc)\n n_texts = (text_normalize(t) for t in texts)\n features = [featurize(t) for t in n_texts]\n hf = [hashify_32(f) for f in features]\n sim_sim = jaccard(hf[0], hf[1])\n sim_dis = jaccard(hf[0], hf[2])\n\n start = time.time()\n mhashes = [mh_func(f) for f in features]\n end = time.time()\n runtimes.append((end - start) * 1000)\n mh_sim_sim = jaccard(mhashes[0], mhashes[1])\n mh_sim_dis = jaccard(mhashes[0], mhashes[2])\n sim_errs.append(abs(sim_sim - mh_sim_sim))\n dis_errs.append(abs(sim_dis - mh_sim_dis))\n print(\n f\"{mh_func.__name__}\\t\\t\\t\"\n f\"Rt: {mean(runtimes):.2f} - \"\n f\"Error Sim Mean {mean(sim_errs)} - Max {max(sim_errs)} Var {variance(sim_errs)}| \"\n f\"Error Dis Mean {mean(dis_errs)} - Max {max(dis_errs)} Var {variance(dis_errs)}\"\n )","def benchmark(func, inputs):\n t0 = time.clock()\n results = [func(x) for x in inputs]\n t1 = time.clock()\n average_time = (t1 - t0) / len(inputs)\n return average_time, results","def testFilterBySuite(self):\n self.setUpPyfakefs()\n gpu_json = {\n 'AAAAA1 AUTOGENERATED FILE DO NOT EDIT': {},\n 'Android Tester': {\n 'isolated_scripts': [\n {\n 'args': [\n 'webgl_conformance',\n ],\n 'isolate_name': 'not_telemetry',\n },\n ],\n },\n 'Linux Tester': {\n 'isolated_scripts': [\n {\n 'args': [\n 'not_a_suite',\n ],\n 'isolate_name': 'telemetry_gpu_integration_test',\n },\n ],\n },\n 'Windows Tester': {\n 'isolated_scripts': [\n {\n 'args': [\n 'webgl_conformance',\n ],\n 'isolate_name': 'telemetry_gpu_integration_test',\n },\n ],\n },\n }\n\n self.CreateFile(os.path.join(builders.TESTING_BUILDBOT_DIR,\n 'chromium.json'),\n contents=json.dumps(gpu_json))\n\n gpu_builders = builders.GetCiBuilders('webgl_conformance')\n self.assertEqual(gpu_builders, set(['Windows Tester']))","def workbench_scenarios():\n return [\n (\"Vr360XBlock\",\n \"\"\"<vr360/>\n \"\"\"),\n (\"Multiple Vr360XBlock\",\n \"\"\"<vertical_demo>\n <vr360/>\n <vr360/>\n <vr360/>\n </vertical_demo>\n \"\"\"),\n ]","def ignore_builtin_verification():\n return not current_space().skip_builtin_verification","def test01c(self):\n a = np.arange(1e5)\n filters = bcolz.filters.keys()\n if common.verbose:\n print(\"Checking filters:\", filters)\n # print(\"\\nsize b uncompressed-->\", a.size * a.dtype.itemsize)\n for filter_ in filters:\n with bcolz.defaults_ctx(bcolz.cparams(clevel=9, shuffle=filter_)):\n self.assertTrue(bcolz.defaults.cparams['shuffle'] == filter_)\n b = bcolz.carray(a, rootdir=self.rootdir)\n # print(\"size b compressed -->\", b.cbytes, \"with '%s'\" % filter_)\n if filter_ > 0:\n self.assertTrue(bcolz.defaults.cparams['shuffle'] == bcolz.SHUFFLE)\n self.assertTrue(sys.getsizeof(b) < b.nbytes,\n \"carray does not seem to compress at all\")\n assert_array_equal(a, b[:], \"Arrays are not equal\")\n # Remove the array on disk before trying with the next one\n if self.disk:\n common.remove_tree(self.rootdir)","def _missing_symbol_to_skipped_tests(self):\n return {\n \"MathMLElement\": [\"mathml\"],\n \"GraphicsLayer\": [\"compositing\"],\n \"WebCoreHas3DRendering\": [\"animations/3d\", \"transforms/3d\"],\n \"WebGLShader\": [\"fast/canvas/webgl\", \"compositing/webgl\", \"http/tests/canvas/webgl\"],\n \"MHTMLArchive\": [\"mhtml\"],\n }","def benchmark():\n parser = argparse.ArgumentParser(\n \n description='pyrpipe diagnostic utility\\nGenerate benchmark report.',\n \n usage='''pyrpipe_diagnostic report [<args>] <logfile>\n \n ''') \n parser.add_argument('-o', help='out file \\ndefault: same as input logfile',action=\"store\")\n parser.add_argument('-e', help='report output type: [MD,PDF,HTML] \\ndefault: PDF',default='PDF',action=\"store\")\n parser.add_argument('-v',help='verbose',action=\"store_true\")\n parser.add_argument('-f',help='Filter by programs. Provide a comma-separated list e.g., prefetch,STAR,bowtie2 \\ndefault None')\n parser.add_argument('-t',help='Temporary directory. \\ndefault ./tmp',action=\"store\")\n parser.add_argument('logfile', help='The log file generated by pyrpipe',action=\"store\")\n args = parser.parse_args(sys.argv[2:])\n \n logFile=args.logfile\n envLog=reports.checkEnvLog(logFile) \n #parse args\n vFlag=args.v\n if vFlag:\n print(\"Generating benchmarks\")\n outFile=\"\"\n if args.o is None:\n outFile=pu.get_file_basename(args.logfile)\n else:\n outFile=args.o\n outFile+='.'+args.e\n \n filters=[]\n if args.f is not None:\n filters= args.f.split(',')\n #create temp dir\n tempDir=\"\"\n if args.t is not None:\n tempDir= args.t\n else:\n tempDir=os.path.join(os.getcwd(),\"tmp\")\n #create tmp dir\n if not pu.check_paths_exist(tempDir):\n pu.mkdir(tempDir)\n \n reports.generateBenchmarkReport(logFile,envLog,filters,tempDir,outFile=outFile,verbose=args.v)","def benchmarks(tsp_algorithms, maps=Maps(30, 60)):\n for tsp in tsp_algorithms:\n time, results = benchmark(tsp, maps)\n lengths = [tour_length(r) for r in results]\n print(\"{:>25} |{:7.0f} ±{:4.0f} ({:5.0f} to {:5.0f}) |{:7.3f} secs/map | {} ⨉ {}-city maps\"\n .format(tsp.__name__, mean(lengths), stdev(lengths), min(lengths), max(lengths),\n time, len(maps), len(maps[0])))","def BenchArgs(data_file):\n return ['--timers', 'wcg', '--logFile', data_file]","def main():\n logging.basicConfig(level=\"INFO\")\n assert len(sys.argv) == 2, \"Exactly one positional argument (path to the raw dataset) is \"\\\n \"needed. \\n\\nE.g. `python sparsity_benchmark ~/bff_data/final_table`\"\n\n # Prepares data for the benchmark, may take a while\n data_parameters = DATA_PARAMETERS.copy()\n data_parameters[\"input_file\"] = sys.argv[1]\n data_parameters[\"preprocessed_file\"] = os.path.join(\n os.path.dirname(data_parameters[\"input_file\"]),\n \"preprocessed_dataset.pkl\"\n )\n data_preprocessor = preprocess_dataset(data_parameters=data_parameters)\n\n # Note: the features here should be in range [0, ~1.2], according to the original experiments.\n # 0 corresponds to no data, everything else is linearly scaled from dB units.\n features, _ = data_preprocessor.load_dataset()\n\n logging.info(\"Starting benchmarks\")\n noisy_features = benchmark_noise(\n features=features,\n data_parameters=data_parameters,\n experiment_parameters=EXPERIMENT_PARAMETERS\n )\n benchmark_binarization(\n noisy_features=noisy_features,\n data_parameters=data_parameters,\n experiment_parameters=EXPERIMENT_PARAMETERS\n )\n logging.info(\"Done\")","def test_create_benchmark(self, benchmark_config, benchmark_config_py):\n with OrionState() as cfg:\n bm1 = get_or_create_benchmark(\n cfg.storage,\n **benchmark_config_py,\n )\n bm1.close()\n\n bm2 = get_or_create_benchmark(cfg.storage, \"bm00001\")\n bm2.close()\n\n assert bm1.configuration == benchmark_config\n\n assert bm1.configuration == bm2.configuration","def test_optimizers(name: str) -> None:\n if any(x in name for x in [\"Chain\", \"SMAC\", \"BO\", \"AX\"]) and os.environ.get(\"CIRCLECI\", False):\n raise SkipTest(\"too slow for CircleCI!\")\n\n def doint(s): # Converting a string into an int.\n return 7 + sum([ord(c) * i for i, c in enumerate(s)])\n\n if doint(name) % 5 > 0:\n raise SkipTest(\"too many tests for CircleCI!\")\n if (\n sum([ord(c) for c in name]) % 4 > 0\n and name\n not in [\n \"DE\",\n \"CMA\",\n \"OnePlusOne\",\n \"Cobyla\",\n \"DiscreteLenglerOnePlusOne\",\n \"PSO\",\n ]\n or \"Tiny\" in name\n or \"Micro\" in name\n ) and os.environ.get(\"CIRCLECI\", False):\n raise SkipTest(\"Too expensive: we randomly skip 3/4 of these tests.\")\n if name in [\"CMAbounded\", \"NEWUOA\"]: # Not a general purpose optimization method.\n return\n if \"BO\" in name: # Bayesian Optimization is rarely good, let us save up time.\n return\n optimizer_cls = registry[name]\n if isinstance(optimizer_cls, base.ConfiguredOptimizer):\n assert any(\n hasattr(mod, name) for mod in (optlib, xpvariants)\n ) # make sure registration matches name in optlib/xpvariants\n assert (\n optimizer_cls.__class__(**optimizer_cls._config) == optimizer_cls\n ), \"Similar configuration are not equal\"\n # some classes of optimizer are eigher slow or not good with small budgets:\n nameparts = [\"Many\", \"Chain\", \"BO\", \"Discrete\", \"NLOPT\"] + [\"chain\"] # TODO remove chain when possible\n is_ngopt = inspect.isclass(optimizer_cls) and issubclass(optimizer_cls, NGOptBase) # type: ignore\n verify = (\n not optimizer_cls.one_shot\n and name not in SLOW\n and not any(x in name for x in nameparts)\n and not is_ngopt\n )\n budget = 300 if \"BO\" not in name and not is_ngopt else 4\n # the following context manager speeds up BO tests\n patched = partial(acq_max, n_warmup=10000, n_iter=2)\n with patch(\"bayes_opt.bayesian_optimization.acq_max\", patched):\n check_optimizer(optimizer_cls, budget=budget, verify_value=verify)","def Stop():\n\n if global_options.loglevel >= 1 and global_benchmark:\n t = time.time() - global_starting_time\n global_options.stdlog.write(\n \"######### Time spent in benchmarked functions #########\\n\")\n global_options.stdlog.write(\"# function\\tseconds\\tpercent\\n\")\n for key, value in global_benchmark.items():\n global_options.stdlog.write(\n \"# %s\\t%6i\\t%5.2f%%\\n\" % (key, value,\n (100.0 * float(value) / t)))\n global_options.stdlog.write(\n \"#######################################################\\n\")\n\n if global_options.loglevel >= 1:\n global_options.stdlog.write(getFooter() + \"\\n\")\n\n # close files\n if global_options.stdout != sys.stdout:\n global_options.stdout.close()\n # do not close log, otherwise error occurs in atext.py\n # if global_options.stdlog != sys.stdout:\n # global_options.stdlog.close()\n\n if global_options.stderr != sys.stderr:\n global_options.stderr.close()\n\n if global_options.timeit_file:\n\n outfile = open(global_options.timeit_file, \"a\")\n\n if global_options.timeit_header:\n outfile.write(\"\\t\".join(\n (\"name\", \"wall\", \"user\", \"sys\", \"cuser\", \"csys\",\n \"host\", \"system\", \"release\", \"machine\",\n \"start\", \"end\", \"path\", \"cmd\")) + \"\\n\")\n\n csystem, host, release, version, machine = map(str, os.uname())\n uusr, usys, c_usr, c_sys = map(lambda x: \"%5.2f\" % x, os.times()[:4])\n t_end = time.time()\n c_wall = \"%5.2f\" % (t_end - global_starting_time)\n\n if sys.argv[0] == \"run.py\":\n cmd = global_args[0]\n if len(global_args) > 1:\n cmd += \" '\" + \"' '\".join(global_args[1:]) + \"'\"\n else:\n cmd = sys.argv[0]\n\n result = \"\\t\".join((global_options.timeit_name,\n c_wall, uusr, usys, c_usr, c_sys,\n host, csystem, release, machine,\n time.asctime(time.localtime(global_starting_time)),\n time.asctime(time.localtime(t_end)),\n os.path.abspath(os.getcwd()),\n cmd)) + \"\\n\"\n\n outfile.write(result)\n outfile.close()","def PrepareBenchmark(benchmark_spec, vm=None):\n _UpdateBenchmarkSpecWithFlags(benchmark_spec)\n vm = vm or benchmark_spec.vms[0]\n\n has_gpu = nvidia_driver.CheckNvidiaGpuExists(vm)\n if has_gpu:\n vm.Install('cuda_toolkit')\n\n if (bool(benchmark_spec.tpus) and nvidia_driver.CheckNvidiaGpuExists(vm)):\n raise errors.Config.InvalidValue(\n 'Invalid configuration. GPUs and TPUs can not both present in the config.'\n )\n\n vm.RemoteCommand(\n f'if [ ! -d \"$HOME/training_results_{VERSION.value}\" ]; then '\n f' git clone https://github.com/mlcommons/training_results_{VERSION.value}.git ; '\n 'fi')\n vm.Install('pip3')\n if not HYPERTHREADS.value:\n if BERT in benchmark_spec.benchmark:\n vm_util.ReplaceText(\n vm, \"'bind_pyt'\", \"'bind_pyt' '--no_hyperthreads'\",\n f'training_results_{VERSION.value}/NVIDIA/benchmarks/bert/'\n 'implementations/pytorch/run_with_docker.sh')\n elif MASK in benchmark_spec.benchmark:\n vm_util.ReplaceText(\n vm, \"'bind_launch'\", \"'bind_launch' '--no_hyperthreads'\",\n f'training_results_{VERSION.value}/NVIDIA/benchmarks/maskrcnn/'\n 'implementations/pytorch/run_and_time.sh')\n elif RESNET in benchmark_spec.benchmark:\n vm_util.ReplaceText(\n vm, '--cpu=exclusive', '--cpu=exclusive,nosmt',\n f'training_results_{VERSION.value}/NVIDIA/benchmarks/resnet/'\n 'implementations/mxnet/run_and_time.sh')","def cli(argv):\r\n argv.append(\"--exhaust-materials\")\r\n cltestbench.cli(argv)","def parse_microbenchmark_data(artifact_processor_comparison):\n metadata = parse_standard_metadata()\n test_suite, test_name, metrics = parse_microbenchmark_comparison(\n artifact_processor_comparison)\n return metadata, test_suite, test_name, metrics","def workbench_scenarios():\n return [\n (\"Markdown\",\n \"\"\"<markdowna />\n \"\"\")\n ]","def _missing_feature_to_skipped_tests(self):\n # FIXME: This list matches WebKitWin and should be moved onto the Win port.\n return {\n \"Accelerated Compositing\": [\"compositing\"],\n \"3D Rendering\": [\"animations/3d\", \"transforms/3d\"],\n }","def test_make_benchmark_from_command_line_mixed_source_and_object_files(\n env: LlvmEnv, retcode: int\n):\n with temporary_working_directory():\n with open(\"a.c\", \"w\") as f:\n f.write(\n \"\"\"\n#include \"b.h\"\n\nint A() {\n return B();\n}\n\nint main() {\n return A();\n}\n\"\"\"\n )\n\n with open(\"b.c\", \"w\") as f:\n f.write(f\"int B() {{ return {retcode}; }}\")\n\n with open(\"b.h\", \"w\") as f:\n f.write(\"int B();\")\n\n # Compile b.c to object file:\n subprocess.check_call([str(llvm_paths.clang_path()), \"b.c\", \"-c\"], timeout=60)\n assert (Path(\"b.o\")).is_file()\n\n bm = env.make_benchmark_from_command_line([\"gcc\", \"a.c\", \"b.o\", \"-o\", \"foo\"])\n env.reset(benchmark=bm)\n\n bm.compile(env)\n assert Path(\"foo\").is_file()\n\n p = subprocess.Popen([\"./foo\"])\n p.communicate(timeout=60)\n assert p.returncode == retcode","def benchmark(nx, tstop):\n Lx = 10\n Ly = 10\n c = 1.0\n ny = nx\n\n # our use of weave requires string formulas:\n Is = StringFunction('exp(-pow(x-Lx/2.0,2)/2.0 -pow(y-Ly/2.0,2)/2.0)',\n independent_variables=('x','y'),\n Lx=Lx, Ly=Ly, globals=globals())\n fs = StringFunction('0.0', independent_variables=('x', 'y', 't'),\n globals=globals())\n BCs = StringFunction('0.0', independent_variables=('x', 'y', 't'),\n globals=globals())\n\n def action(u, xv, yv, t):\n #print t\n pass\n\n implementation = {}\n cpu = []\n for ic in 'f77', 'vec', 'scalar', 'weave':\n for bc in 'f77', 'vec', 'scalar', 'weave':\n for inner in 'f77', 'vec', 'scalar', 'weave':\n implementation['ic'] = ic\n implementation['inner'] = inner\n implementation['bc'] = bc\n # optimize StringFunction functions for the non-weave case:\n # implementation:\n if 'weave' in (ic, bc, inner) or 'f77' in (ic, bc, inner):\n I = Is; f = fs; BC = BCs\n else:\n I = Is.__call__; f = fs.__call__; BC = BCs.__call__\n\n t0 = time.clock()\n dt, cpu_ic, cpu_inner, cpu_bc = \\\n solver(I, f, c, BC, Lx, Ly, nx, ny, 0, tstop,\n user_action=None,\n implementation=implementation,\n verbose=False)\n t1 = time.clock()\n cpu_total = cpu_ic + cpu_inner + cpu_bc\n overhead = (t1-t0)-cpu_total\n cpu.append([implementation.copy(), cpu_total,\n cpu_ic, cpu_inner, cpu_bc, overhead])\n print t1-t0, implementation, 'overhead:', overhead\n # normalize CPU-times:\n cpu_min = min([abs(c) for i, c, c1, c2, c3, c4 in cpu])\n print '\\n\\nMinimum CPU time:', cpu_min\n print 'no of time steps:', int(tstop/dt)\n print 'interior/boundary ratio:', int(nx*ny*1.0/max(nx,ny))\n for impl, cpu, cpu_ic, cpu_inner, cpu_bc, overhead in cpu:\n # normalized-CPU ic inner bc overhead\n print \"%8.2f\" % (cpu/cpu_min),\n print \"%-10s %8.2f; \" % (impl['ic'], cpu_ic),\n print \"%-10s %8.2f; \" % (impl['inner'], cpu_inner),\n print \"%-10s %8.2f; \" % (impl['bc'], cpu_bc),\n print \"%d%%\" % (overhead/cpu*100)","def get_latest_benchmark():\n\n benchmark_paths = glob.glob(\"./.benchmarks/*/*.json\")\n dates = [\n \"\".join(_b.split(\"/\")[-1].split(\"_\")[2:4]) for _b in benchmark_paths\n ]\n benchmarks = {date: value for date, value in zip(dates, benchmark_paths)}\n\n dates.sort()\n latest = dates[-1]\n benchmark_latest = benchmarks[latest]\n\n return benchmark_latest","def test_countdown_performance():\n profiler = cProfile.Profile()\n profiler.enable()\n countdown(\"0 0 0 0 0 0 1000\")\n profiler.disable()\n stats = profiler.getstats()\n tot_time = stats[0].totaltime\n assert tot_time < 3, \"Wow, your computer is really slow. Or is it my code?\"","def test09(self):\n a = np.arange(10000) > 5000\n b = bcolz.carray(a, chunklen=100)\n b1 = b.wheretrue(skip=1020, limit=1020)\n b2 = b.wheretrue(skip=1030, limit=1020)\n a1 = a.nonzero()[0].tolist()[1020:2040]\n a2 = a.nonzero()[0].tolist()[1030:2050]\n # print \"result:\", [i for i in zip(b1, b2)]\n self.assertEqual([i for i in zip(a1, a2)], [i for i in zip(b1, b2)])","def workbench_scenarios():\n return [\n (\"SummaryXBlock\",\n \"\"\"<summary/>\n \"\"\"),\n (\"Multiple SummaryXBlock\",\n \"\"\"<vertical_demo>\n <summary/>\n <summary/>\n <summary/>\n </vertical_demo>\n \"\"\"),\n ]","def generate_rst_analysis(benchmarks, dbpath, outpath, gh_repo=None,\n checks=[ConsistentlyWorse(10, 0.01)]):\n with open(os.path.join(outpath, 'analysis.rst'), 'w') as f:\n print >> f, \"\"\"\nBenchmarks Performance Analysis\n===============================\n\"\"\"\n all_res = []\n for b in benchmarks:\n # basic analysis: find\n for check in checks:\n results = b.get_results(dbpath)\n res = check(results)\n if res:\n res['benchmark'] = \":ref:`%s`\" % b.get_rst_label()\n res['reference_date'] = res['reference'].name.strftime(\"%Y.%m.%d\")\n res['check'] = str(check)\n if res['latest_better'] is not None and res['earliest_notworse'] is not None:\n r1 = res['latest_better']['revision']\n r2 = res['earliest_notworse']['revision']\n # how many commits are in between\n ndiff = len(results[res['latest_better'].name:\n res['earliest_notworse'].name])-1\n diff = '%(r1)s...%(r2)s' % locals()\n diff_ = '(>=%(ndiff)d)%(diff)s' % locals() if ndiff > 1 else diff\n res['source_diff'] = \\\n ('`%(diff_)s <%(gh_repo)s/compare/%(diff)s>`__'\n if gh_repo else \"%(diff_)s\" ) % locals()\n else:\n res['source_diff'] = 'N/A'\n all_res.append(res)\n\n if res:\n # sort all by the slowdown_percent showing the slowest first\n all_res = sorted(all_res, key=lambda x:x['slowdown_percent'], reverse=True)\n print >> f, \"\"\"\n.. container:: benchmarks_analysis clear\n\n .. list-table::\n :header-rows: 1\n :stub-columns: 1\n :widths: 32 30 6 4 4 4 20\n\n * - Benchmark\n - Check\n - Slowdown %\n - Reference date\n - Reference timing\n - Target timing\n - Possible recent\"\"\"\n\n for res in all_res:\n print >> f, \"\"\"\\\n * - %(benchmark)s\n - %(check)s\n - %(slowdown_percent).1f\n - %(reference_date)s\n - %(reference_timing).2g\n - %(target_timing).2g\n - %(source_diff)s\"\"\" % res","def test00(self):\n a = np.arange(20)\n cnames = bcolz.blosc_compressor_list()\n if common.verbose:\n print(\"Checking compressors:\", cnames)\n # print \"\\nsize b uncompressed-->\", a.size * a.dtype.itemsize\n for cname in cnames:\n b = bcolz.carray(a, rootdir=self.rootdir,\n cparams=bcolz.cparams(clevel=9, cname=cname))\n # print \"size b compressed -->\", b.cbytes, \"with '%s'\"%cname\n self.assertTrue(sys.getsizeof(b) > b.nbytes,\n \"compression does not seem to have any overhead\")\n assert_array_equal(a, b[:], \"Arrays are not equal\")\n # Remove the array on disk before trying with the next one\n if self.disk:\n common.remove_tree(self.rootdir)","def benchmark(func):\n start = time.time()\n\n @functools.wraps(func)\n def wrapper(*args, **kwargs):\n rc = func(*args, **kwargs)\n print('Running time: {}'.format(time.time() - start))\n return rc\n return wrapper","def workbench_scenarios():\n return [\n (\"KVXBlock\",\n \"\"\"<vertical_demo>\n <kvxblock/>\n </vertical_demo>\n \"\"\"),\n ]","def execute_benchmark(path, budget=None, threads=None):\n benchmark_path = runtime.binary_path(path)\n cmd = [benchmark_path, \"--benchmark_format=json\"]\n if budget is not None:\n cmd += [\"-b\", str(budget)]\n if threads is not None:\n cmd += [\"-t\", str(threads)]\n res = process.execute(cmd)\n return json.loads(res.std_out)","def get_functional_test_cases(test_suite):\n return get_cases(test_suite, r'test_(?!perf_)')"],"string":"[\n \"def get_coverage_benchmarks():\\n return [\\n benchmark for benchmark in get_all_benchmarks()\\n if get_type(benchmark) == BenchmarkType.CODE.value\\n ]\",\n \"def get_bug_benchmarks():\\n return [\\n benchmark for benchmark in get_all_benchmarks()\\n if get_type(benchmark) == BenchmarkType.BUG.value\\n ]\",\n \"def get_all_benchmarks():\\n all_benchmarks = []\\n for benchmark in os.listdir(BENCHMARKS_DIR):\\n benchmark_path = os.path.join(BENCHMARKS_DIR, benchmark)\\n if os.path.isfile(os.path.join(benchmark_path, 'benchmark.yaml')):\\n all_benchmarks.append(benchmark)\\n return sorted(all_benchmarks)\",\n \"def list(self, classes):\\n\\n def add(benchmarks, parts, flags, exclude):\\n if (\\n flags[\\\"language\\\"] != \\\"C++\\\"\\n and flags[\\\"language\\\"] != \\\"Java\\\"\\n and \\\"--drop-caches=true\\\" not in parts\\n ):\\n parts.append(\\\"--drop-caches=true\\\")\\n command = \\\" \\\".join(parts)\\n if command not in exclude:\\n benchmarks.append({\\\"command\\\": command, \\\"flags\\\": flags})\\n\\n benchmarks = []\\n for name, benchmark in classes.items():\\n if name.startswith(\\\"example\\\"):\\n continue\\n\\n instance, parts = benchmark(), [name]\\n\\n exclude = getattr(benchmark, \\\"exclude\\\", [])\\n if \\\"source\\\" in getattr(benchmark, \\\"arguments\\\", []):\\n parts.append(\\\"ALL\\\")\\n\\n iterations = getattr(instance, \\\"iterations\\\", 3)\\n parts.append(f\\\"--iterations={iterations}\\\")\\n\\n if instance.cases:\\n parts.append(\\\"--all=true\\\")\\n\\n flags = getattr(instance, \\\"flags\\\", {})\\n\\n if getattr(instance, \\\"r_only\\\", False):\\n flags[\\\"language\\\"] = \\\"R\\\"\\n add(benchmarks, parts, flags, exclude)\\n else:\\n if \\\"language\\\" not in flags:\\n flags[\\\"language\\\"] = \\\"Python\\\"\\n add(benchmarks, parts, flags, exclude)\\n\\n if hasattr(instance, \\\"r_name\\\"):\\n flags_ = flags.copy()\\n flags_[\\\"language\\\"] = \\\"R\\\"\\n parts.append(\\\"--language=R\\\")\\n add(benchmarks, parts, flags_, exclude)\\n\\n return sorted(benchmarks, key=lambda k: k[\\\"command\\\"])\",\n \"def get_benchmark_requirements(cls):\\n pass\",\n \"def is_cpp(benchmark):\\n return get_language(benchmark) == 'c++'\",\n \"def count_benchmarks():\\n return len(setup_storage().fetch_benchmark({}))\",\n \"def _run():\\n subprocess.check_call(\\n [\\n \\\"tools/bazel\\\",\\n \\\"build\\\",\\n \\\"-c\\\",\\n \\\"opt\\\",\\n \\\"test/core/memory_usage/memory_usage_test\\\",\\n ]\\n )\\n ret = {}\\n for name, benchmark_args in _BENCHMARKS.items():\\n for scenario, extra_args in _SCENARIOS.items():\\n # TODO(chenancy) Remove when minstack is implemented for channel\\n if name == \\\"channel\\\" and scenario == \\\"minstack\\\":\\n continue\\n try:\\n output = subprocess.check_output(\\n [\\n \\\"bazel-bin/test/core/memory_usage/memory_usage_test\\\",\\n ]\\n + benchmark_args\\n + extra_args\\n )\\n except subprocess.CalledProcessError as e:\\n print(\\\"Error running benchmark:\\\", e)\\n continue\\n for line in output.splitlines():\\n for key, (pattern, conversion) in _INTERESTING.items():\\n m = re.match(pattern, line)\\n if m:\\n ret[scenario + \\\": \\\" + key] = conversion(m.group(1))\\n return ret\",\n \"def filter_out(self, skip):\\n benchmarks = super(Benchmarks, self).__new__(self.__class__)\\n benchmarks._conf = self._conf\\n benchmarks._benchmark_dir = self._benchmark_dir\\n benchmarks._all_benchmarks = self._all_benchmarks\\n\\n selected_idx = {}\\n\\n for name, benchmark in self.items():\\n if name not in skip:\\n benchmarks[name] = benchmark\\n if name in self._benchmark_selection:\\n selected_idx[name] = self._benchmark_selection[name]\\n\\n benchmarks._benchmark_selection = selected_idx\\n\\n return benchmarks\",\n \"def benchmark(self):\\n logger.info(self.benchmark.__doc__)\\n return self.run(self.benchmark_profile())\",\n \"def test_rust_code_analysis_tokei_c() -> None:\\n\\n ret_value = compare(\\n \\\"rust-code-analysis\\\",\\n \\\"tokei\\\",\\n [\\\"-g\\\", \\\"-f\\\"],\\n [\\\"SLOC\\\", \\\"PLOC\\\", \\\"CLOC\\\", \\\"BLANK\\\"],\\n \\\"C\\\",\\n \\\"bubble_sort.c\\\",\\n )\\n\\n assert ret_value == 0\",\n \"def run_benchmark():\\n import argparse\\n parser = argparse.ArgumentParser(description='Benchmark alchemically modified system against unmodified system.')\\n parser.add_argument('--platform', dest='platform_name', action='store', default=None, help='platform name to benchmark (default: None)')\\n options = parser.parse_args()\\n\\n from sams.tests import testsystems\\n for testsystem_name in ['AblImatinibExplicitAlchemical']:\\n cls = getattr(testsystems, testsystem_name)\\n testsystem = cls()\\n factory_args = { 'ligand_atoms' : testsystem.alchemical_atoms, 'receptor_atoms' : range(0,4266) }\\n benchmark(testsystem.system, testsystem.positions, platform_name=options.platform_name, nsteps=5000, timestep=1.0*unit.femtoseconds, factory_args=factory_args)\",\n \"def time_binaries():\\n print()\\n print(\\\"=== BENCHMARKING ===\\\")\\n with tempfile.NamedTemporaryFile(\\n prefix=\\\"riff-benchmark\\\", suffix=\\\".gitlog\\\"\\n ) as testdata:\\n subprocess.check_call(\\n [\\\"git\\\", \\\"log\\\", \\\"--color=always\\\", \\\"-p\\\", \\\"master\\\"], stdout=testdata\\n )\\n\\n binaries = sorted(glob.glob(os.path.join(BINDIR, \\\"*\\\")), key=natural_keys)\\n\\n # Do riff-current last: https://stackoverflow.com/a/20320940/473672\\n binaries.sort(key=lambda s: s.endswith(\\\"riff-current\\\"))\\n\\n for binary in binaries:\\n print_timings(binary, testdata.name)\\n print_timings(\\\"/bin/cat\\\", testdata.name)\",\n \"def test_benchmark1(capsys):\\n student_1 = Student('114007245','Mario Castillo',2018, [\\\"INST 201\\\",\\\"INST 326\\\"])\\n student_2 = Student('117006012', 'Joe Rogan', 2018, [\\\"MATH 115\\\",\\\"PSYC 100\\\"])\\n student_3 = Student(\\\"117008490\\\", \\\"Kanye West\\\", 2018, [\\\"MATH 120\\\",\\\"STAT 003\\\"])\\n student_4 = Student('118009044', \\\"Elon Musk\\\", 2018, [\\\"PSYC 100\\\",\\\"MATH 003\\\"])\\n \\n student_1.benchmark_I()\\n outerr = capsys.readouterr()\\n out = outerr.out\\n assert out == ('You have not completed the Benchmark I requirements.\\\\n'\\n 'You have not taken MATH 115 or higher.\\\\n'\\n 'You have not taken PSYC 100.\\\\n')\\n\\n student_2.benchmark_I()\\n outerr = capsys.readouterr()\\n out = outerr.out\\n assert out == (f'You have completed all of your Benchmark I courses! '\\n f'Congratulations, {student_2.student_name}!\\\\n')\\n\\n student_3.benchmark_I()\\n outerr = capsys.readouterr()\\n out = outerr.out \\n assert out == ('You have not completed the Benchmark I requirements.\\\\n'\\n 'You have not taken PSYC 100.\\\\n')\\n\\n student_4.benchmark_I()\\n outerr = capsys.readouterr()\\n out = outerr.out\\n assert out == ('You have not completed the Benchmark I requirements.\\\\n'\\n 'You have not taken MATH 115 or higher.\\\\n')\",\n \"def Cleanup(benchmark_spec):\\n pass\",\n \"def delete_all_benchmarks(self, namespace=\\\"benchmark-operator\\\"):\\n all_benchmarks = self.crd_client.list_namespaced_custom_object(\\n group=\\\"ripsaw.cloudbulldozer.io\\\", version=\\\"v1alpha1\\\", namespace=namespace, plural=\\\"benchmarks\\\"\\n )\\n\\n _ = [\\n self.delete_benchmark(benchmark[\\\"metadata\\\"][\\\"name\\\"], namespace)\\n for benchmark in all_benchmarks.get(\\\"items\\\", [])\\n ]\",\n \"def test_make_benchmark_from_command_line_multiple_input_sources(\\n env: LlvmEnv, retcode: int\\n):\\n with temporary_working_directory() as cwd:\\n with open(\\\"a.c\\\", \\\"w\\\") as f:\\n f.write(\\\"int main() { return B(); }\\\")\\n\\n with open(\\\"b.c\\\", \\\"w\\\") as f:\\n f.write(f\\\"int B() {{ return {retcode}; }}\\\")\\n\\n bm = env.make_benchmark_from_command_line([\\\"gcc\\\", \\\"a.c\\\", \\\"b.c\\\", \\\"-o\\\", \\\"foo\\\"])\\n assert not (cwd / \\\"foo\\\").is_file()\\n\\n env.reset(benchmark=bm)\\n assert \\\"main()\\\" in env.ir\\n\\n bm.compile(env)\\n assert (cwd / \\\"foo\\\").is_file()\\n\\n p = subprocess.Popen([\\\"./foo\\\"])\\n p.communicate(timeout=60)\\n assert p.returncode == retcode\",\n \"def benchmark_profile(self):\\n cb_bin = os.path.join(bin_path, 'compilebench')\\n desc = \\\"benchmark\\\"\\n test_name = \\\"compilebench_{0}\\\".format(to_safe_name(desc))\\n test = TestProfile(\\n name=test_name,\\n desc=desc,\\n test_path=self.test_path,\\n bin_path=bin_path,\\n command=\\\"{0} -D {1} -i 10 --makej\\\".format(cb_bin, self.test_path))\\n\\n return test\",\n \"def run_benchmarks(urls, urlIndices, trial_number):\\n path.append(os.path.join(CHROMIUM_SRC, 'tools/perf/'))\\n benchmark_path = os.path.join(CHROMIUM_SRC, 'tools/perf/run_benchmark')\\n output_path = 'temp'\\n trial_key = 'trial{0}'.format(trial_number)\\n\\n cmd = ('sudo ' + benchmark_path + ' --profiler=trace telemetryBenchmarks.url{0}')\\n for i in urlIndices:\\n try:\\n out, err, returncode = get_benchmark_result(cmd.format(i))\\n timeout = False\\n print 'successfully ran benchmark for url' + str(i)\\n except TimeoutError:\\n # Benchmark failed\\n print 'Benchmark Timeout!'\\n out = ''\\n returncode = 1\\n timeout = True\\n\\n failed = ['FAILED']\\n if returncode != 0 or any(x in out for x in failed) or timeout:\\n # If a benchmark fails, remove its corresponding wpr file, and act\\n # as if it didn't exist\\n # Remove from data/wpr_source\\n print 'Benchmark {0} failed'.format(i)\\n print 'return code is ' + str(returncode)\\n print 'Out:'\\n print out\\n print 'Err:'\\n print err\\n urlName = 'url{0}_page_set_000.wpr'.format(i)\\n urlpcName = 'url{0}_pc_page_set_000.wpr'.format(i)\\n urlFilePath = os.path.join('data/wpr_source',urlName)\\n urlpcFilePath = os.path.join('data/wpr_source',urlpcName)\\n urlCmd = 'rm -f {0}'.format(urlFilePath)\\n urlpcCmd = 'rm -f {0}'.format(urlpcFilePath)\\n print 'Removing: {0}, {1}'.format(urlFilePath, urlpcFilePath)\\n commands = [\\n 'rm -f {0}'.format(urlFilePath),\\n 'rm -f {0}'.format(urlpcFilePath)\\n ]\\n for cmdss in commands:\\n p = Popen(cmdss, shell=True)\\n p.wait()\\n # Skip the rest of this url\\n print \\\"Moving on!\\\"\\n continue\\n\\n # Parse data\\n tmp_path = 'temp/tmp_benchmark_result_json'\\n with open(tmp_path, 'rb') as f:\\n tmp_json = json.load(f)\\n benchmark_results = tmp_json['values']\\n commands = [\\n 'rm -f ~/page_load_time/telemetry/temp/tmp_benchmark_result_json',\\n ]\\n for cmds in commands:\\n p = Popen(cmds, shell=True)\\n p.wait()\\n\\n output = {urls[i]: {'cold_times': {trial_key: benchmark_results}}}\\n output_file = os.path.join(output_path, urlsafe_b64encode(urls[i]))\\n output_file += '.' + str(trial_number)\\n try:\\n with open(output_file, 'w') as f:\\n json.dump(output, f)\\n except IOError:\\n raise IOError('Unable to write to {0}'.format(output_file))\\n\\n\\n ############### Now run for Perfect Cache file ################\\n\\n try:\\n out, err, returncode = \\\\\\n get_benchmark_result(cmd.format(str(i) + '_pc'))\\n timeout = False\\n print 'successfully ran benchmark for url' + str(i) + '_pc'\\n except TimeoutError:\\n # Benchmark failed\\n print 'Benchmark Timeout!'\\n out = ''\\n returncode = 1\\n timeout = True\\n\\n failed = ['FAILED']\\n if returncode != 0 or any(x in out for x in failed) or timeout:\\n # If a benchmark fails, remove its corresponding wpr file, and act\\n # as if it didn't exist\\n # Remove from data/wpr_source\\n\\n print 'Benchmark {0}_pc failed'.format(i)\\n print 'Out:'\\n print out\\n print 'Err:'\\n print err\\n urlName = 'url{0}_page_set_000.wpr'.format(i)\\n urlpcName = 'url{0}_pc_page_set_000.wpr'.format(i)\\n urlFilePath = os.path.join('data/wpr_source',urlName)\\n urlpcFilePath = os.path.join('data/wpr_source',urlpcName)\\n urlCmd = 'rm -f {0}'.format(urlFilePath)\\n urlpcCmd = 'rm -f {0}'.format(urlpcFilePath)\\n print 'Removing: {0}, {1}'.format(urlFilePath, urlpcFilePath)\\n commands = [\\n 'rm -f {0}'.format(urlFilePath),\\n 'rm -f {0}'.format(urlpcFilePath)\\n ]\\n for cmdss in commands:\\n p = Popen(cmdss, shell=True)\\n p.wait()\\n # Skip the rest of this url\\n print \\\"Moving on!\\\"\\n continue\\n\\n # Parse data\\n tmp_path = 'temp/tmp_benchmark_result_json'\\n with open(tmp_path, 'rb') as f:\\n tmp_json = json.load(f)\\n benchmark_results = tmp_json['values']\\n\\n commands = [\\n 'rm -f ~/page_load_time/telemetry/temp/tmp_benchmark_result_json',\\n ]\\n for cmds in commands:\\n p = Popen(cmds, shell=True)\\n p.wait()\\n\\n output = {urls[i]: {'cold_times': {trial_key: benchmark_results}}}\\n output_file = os.path.join(output_path, urlsafe_b64encode(urls[i]))\\n output_file += '.' + str(trial_number) + '.pc'\\n try:\\n with open(output_file, 'w') as f:\\n json.dump(output, f)\\n except IOError:\\n raise IOError('Unable to write to {0}'.format(output_file))\",\n \"def _c_optimizations_ignored():\\n pure_env = os.environ.get('PURE_PYTHON')\\n return pure_env != \\\"0\\\" if pure_env is not None else PYPY\",\n \"def _c_optimizations_required():\\n pure_env = os.environ.get('PURE_PYTHON')\\n require_c = pure_env == \\\"0\\\"\\n return require_c\",\n \"def benchmark(self):\\n nsites = []\\n for m in self.methods:\\n for name, structure in self.test_structures.items():\\n cns = []\\n if self.unique_sites:\\n es = SpacegroupAnalyzer(structure).get_symmetrized_structure().equivalent_sites\\n sites = [structure.index(x[0]) for x in es]\\n else:\\n sites = range(len(structure))\\n\\n for key, val in self.hi.items():\\n if name == key:\\n for j in sites:\\n if isinstance(m, NearNeighbors):\\n tmpcn = m.get_cn_dict(structure, j, self.use_weights)\\n else:\\n tmpcn = m.compute(structure, j)\\n if tmpcn == \\\"null\\\":\\n continue\\n if self.nround:\\n self._roundcns(tmpcn, self.nround)\\n cns.append((structure[j].species_string, tmpcn))\\n if self.cation_anion:\\n for mat, cat in self.cations.items():\\n if (name == mat) and cat:\\n cns = self._popel(cns, cat)\\n elif self.anion_cation:\\n for mat, an in self.anions.items():\\n if name == mat:\\n cns = self._popel(cns, an)\\n m._cns[name] = cns\\n nsites.append(len(cns))\\n self.nsites = max(nsites)\",\n \"def _disc_benchmarks(cls, conf, repo, environments, commit_hashes, check):\\n root = conf.benchmark_dir\\n\\n cls.check_tree(root)\\n\\n if len(environments) == 0:\\n raise util.UserError(\\\"No available environments\\\")\\n\\n # Try several different commits:\\n #\\n # - First of commit_hashes provided\\n # - Tips of branches from configuration file\\n # - Rest of the commit_hashes\\n #\\n\\n def iter_hashes():\\n for h in commit_hashes[:1]:\\n yield h\\n for branch in conf.branches:\\n try:\\n yield repo.get_hash_from_name(branch)\\n except NoSuchNameError:\\n continue\\n for h in commit_hashes[1:]:\\n yield h\\n\\n def iter_unique(iter):\\n seen = set()\\n for item in iter:\\n if item not in seen:\\n seen.add(item)\\n yield item\\n\\n try_hashes = iter_unique(iter_hashes())\\n\\n log.info(\\\"Discovering benchmarks\\\")\\n with log.indent():\\n last_err = None\\n for env, commit_hash in itertools.product(environments, try_hashes):\\n env.create()\\n\\n if last_err is not None:\\n log.warning(\\\"Failed: trying different commit/environment\\\")\\n\\n result_dir = tempfile.mkdtemp()\\n try:\\n env.install_project(conf, repo, commit_hash)\\n\\n env_vars = dict(os.environ)\\n env_vars.update(env.env_vars)\\n\\n result_file = os.path.join(result_dir, 'result.json')\\n env.run(\\n [runner.BENCHMARK_RUN_SCRIPT, 'discover',\\n os.path.abspath(root),\\n os.path.abspath(result_file)],\\n cwd=result_dir,\\n env=env_vars,\\n dots=False)\\n\\n try:\\n with open(result_file, 'r') as fp:\\n benchmarks = json.load(fp)\\n except (IOError, ValueError):\\n log.error(\\\"Invalid discovery output\\\")\\n raise util.UserError()\\n\\n break\\n except (util.UserError, util.ProcessError) as err:\\n last_err = err\\n continue\\n except KeyboardInterrupt:\\n raise util.UserError(\\\"Interrupted.\\\")\\n finally:\\n util.long_path_rmtree(result_dir)\\n else:\\n raise util.UserError(\\\"Failed to build the project and import the benchmark suite.\\\")\\n\\n if check:\\n log.info(\\\"Checking benchmarks\\\")\\n with log.indent():\\n result_dir = tempfile.mkdtemp()\\n try:\\n out, err, retcode = env.run(\\n [runner.BENCHMARK_RUN_SCRIPT, 'check',\\n os.path.abspath(root)],\\n cwd=result_dir,\\n dots=False,\\n env=env_vars,\\n valid_return_codes=None,\\n return_stderr=True,\\n redirect_stderr=True)\\n finally:\\n util.long_path_rmtree(result_dir)\\n\\n out = out.strip()\\n if retcode == 0:\\n if out:\\n log.info(out)\\n log.info(\\\"No problems found.\\\")\\n else:\\n if out:\\n log.error(out)\\n raise util.UserError(\\\"Benchmark suite check failed.\\\")\\n\\n return benchmarks\",\n \"def test_get_benchmark_methods_filter(self):\\n config = mock.Mock()\\n config.workspace = 'workspace'\\n config.benchmark_method_patterns = ['new_foo.BenchmarkClass.filter:bench.*']\\n benchmark_runner = benchmark.BenchmarkRunner(config)\\n\\n mock_benchmark_class = mock.Mock()\\n mock_benchmark_class.benchmark_method_1 = 'foo'\\n\\n mock_module = mock.Mock()\\n sys.modules['new_foo'] = mock_module\\n mock_module.BenchmarkClass.return_value = mock_benchmark_class\\n\\n methods = benchmark_runner._get_benchmark_methods()\\n\\n self.assertEqual(1, len(methods))\\n self.assertEqual('new_foo.BenchmarkClass.benchmark_method_1', methods[0])\",\n \"def _run_benchmark(shell, shell_args, name, app, duration_seconds, measurements,\\n verbose, android, save_traces):\\n timeout = duration_seconds + _EXTRA_TIMEOUT\\n benchmark_args = []\\n benchmark_args.append('--app=' + app)\\n benchmark_args.append('--duration=' + str(duration_seconds))\\n\\n output_file = None\\n device_output_file = None\\n if save_traces:\\n output_file = 'benchmark-%s-%s.trace' % (name.replace(' ', '_'),\\n time.strftime('%Y%m%d%H%M%S'))\\n if android:\\n device_output_file = os.path.join(shell.get_tmp_dir_path(), output_file)\\n benchmark_args.append('--trace-output=' + device_output_file)\\n else:\\n benchmark_args.append('--trace-output=' + output_file)\\n\\n for measurement in measurements:\\n benchmark_args.append(measurement)\\n\\n shell_args = list(shell_args)\\n shell_args.append(_BENCHMARK_APP)\\n shell_args.append('--force-offline-by-default')\\n shell_args.append('--args-for=%s %s' % (_BENCHMARK_APP,\\n ' '.join(benchmark_args)))\\n\\n if verbose:\\n print 'shell arguments: ' + str(shell_args)\\n return_code, output, did_time_out = shell.run_and_get_output(\\n shell_args, timeout=timeout)\\n\\n if did_time_out:\\n return False, 'timed out', output\\n if return_code:\\n return False, 'return code: ' + str(return_code), output\\n\\n # Pull the trace file even if some measurements are missing, as it can be\\n # useful in debugging.\\n if device_output_file:\\n shell.pull_file(device_output_file, output_file, remove_original=True)\\n\\n return True, None, output\",\n \"def get_performance_test_cases(test_suite):\\n return get_cases(test_suite, r'test_perf_')\",\n \"def main():\\n test_folders = argv[1]\\n benchmark_file = argv[2]\\n output_path = argv[3]\\n\\n method = ['n', 'a', 'a']\\n bigram = ['False', 'False', 'True']\\n output_file = output_path + '/' + 'method_comparison_cosine_values.csv'\\n\\n with open(test_folders, 'r') as f:\\n test_folders = f.read()\\n\\n test_folders = test_folders.splitlines()\\n\\n with open(benchmark_file, 'r') as f:\\n benchmark_file = f.read()\\n\\n benchmark_file = benchmark_file.splitlines()\\n\\n # initialize big data frame\\n frames = []\\n\\n for k in xrange(len(benchmark_file)):\\n\\n test = str(test_folders[k]).replace('\\\"', '')\\n print \\\"Reading test files from folder:\\\"\\n print test\\n\\n benchmark = str(benchmark_file[k]).replace('\\\"', '')\\n print \\\"Reading benchmark form file:\\\"\\n print benchmark\\n\\n # read file paths from test documents folder\\n query = sorted([os.path.join(test, f) for f in os.listdir(test) if f.endswith('.txt')])\\n\\n # load benchmark text file\\n with open(benchmark, \\\"r\\\", encoding=\\\"utf-8\\\", errors='ignore') as doc:\\n raw = doc.read()\\n\\n # initialize dict of dicts for data frame\\n method_csv = {}\\n\\n for j in xrange(len(method)):\\n # extract features from benchmark\\n dtm = ExtractFeatures(method[j], bigram[j])\\n benchmark_name = benchmark_file[k].split('\\\\\\\\')[-1]\\n benchmark_features = dtm.extract_features_from_text(raw, benchmark_name)\\n\\n # extract terms from each text document to create a vocabulary (keeping unique terms only)\\n vocabulary = sorted(set(w[1] for w in benchmark_features))\\n print \\\"{0} features produced.\\\".format(str(len(vocabulary)))\\n\\n benchmark_dtv = DTM(vocabulary, benchmark_name, benchmark_features)\\n benchmark_dtv = benchmark_dtv.compute_dtv()\\n\\n # load test document features\\n test_features = []\\n for q in query:\\n dtm1 = ExtractFeatures(method[j], bigram[j])\\n test_features = test_features + dtm1.extract_features_from_file(q)\\n\\n documents = sorted(set([d for d, w in test_features]))\\n print \\\"{0} test documents read.\\\".format(str(len(documents)))\\n\\n print \\\"Computing DTM...\\\"\\n test_dtm = DTM(vocabulary, documents, test_features)\\n test_dtm = test_dtm.compute_dtm()\\n\\n print \\\"Computing cosine values...\\\"\\n dv = {}\\n for i in xrange(len(documents)):\\n d = 1 - spatial.distance.cosine(benchmark_dtv[benchmark_name], test_dtm[documents[i]])\\n if isnan(d):\\n d = 0\\n dv[documents[i]] = d\\n\\n this_method = \\\"method=\\\" + method[j] + '_' + \\\"bigram=\\\" + bigram[j]\\n method_csv[this_method] = pd.Series(dv)\\n\\n print \\\"Saving to data frame...\\\"\\n df = pd.DataFrame(method_csv)\\n test = test.split('\\\\\\\\')[-1]\\n test = test.split('.')[0]\\n df['test_group'] = test\\n\\n frames.append(df)\\n\\n result = pd.concat(frames)\\n\\n print \\\"Saving results to file: \\\", output_file\\n result.to_csv(output_file)\\n\\n print 'Finished computing {0} data frames'.format(str(len(test_folders)))\",\n \"def test_list_benchmarks(client):\\n # The benchmark listing contains one element (independently of whether the\\n # user is logged in or not).\\n r = client.get(config.API_PATH() + '/workflows')\\n assert r.status_code == 200\\n doc = r.json\\n assert len(doc[labels.WORKFLOW_LIST]) == 1\\n # Create user and the request header that contains the API key for the\\n # logged in user.\\n _, token = create_user(client, '0000')\\n headers = {HEADER_TOKEN: token}\\n r = client.get(config.API_PATH() + '/workflows', headers=headers)\\n assert r.status_code == 200\\n doc = r.json\\n assert len(doc[labels.WORKFLOW_LIST]) == 1\",\n \"def get_benchmark_specification(benchmark = 'FSI1'):\\n if benchmark == 'FSI1':\\n rho_s = Constant(1e03)\\n nu_s = Constant(0.4)\\n mu_s = Constant(5e05)\\n rho_f = Constant(1e03)\\n nu_f = Constant(1e-03)\\n U = 0.2\\n T_end = 60.0\\n result = \\\"results-FSI1/\\\"\\n elif benchmark == 'FSI2':\\n rho_s = Constant(1e04)\\n nu_s = Constant(0.4)\\n mu_s = Constant(5e05)\\n rho_f = Constant(1e03)\\n nu_f = Constant(1e-03)\\n U = 1.0\\n T_end = 15.0\\n result = \\\"results-FSI2/\\\"\\t\\t\\n elif benchmark == 'FSI3':\\n rho_s = Constant(1e03)\\n nu_s = Constant(0.4)\\n mu_s = Constant(2e06)\\n rho_f = Constant(1e03)\\n nu_f = Constant(1e-03)\\n U = 2.0\\n T_end = 20.0\\n result = \\\"results-FSI3/\\\"\\t\\t\\n else:\\n raise ValueError('\\\"{}\\\" is a wrong name for problem specification.'.format(benchmark))\\n v_max = Constant(1.5*U) # mean velocity to maximum velocity \\n # (we have parabolic profile)\\n E_s = Constant(2*mu_s*(1+nu_s))\\n lambda_s = Constant((nu_s*E_s)/((1+nu_s)*(1-2*nu_s)))\\n mu_f = Constant(nu_f*rho_f)\\n return v_max, lambda_s, mu_s, rho_s, mu_f, rho_f, T_end, result\",\n \"def run_all(self):\\n runs = []\\n for run in self.benchmarks:\\n run.start()\\n run.wait()\\n runs.append(run.metadata)\\n return runs\",\n \"def prepare_benchmark(trial_number, num_urls):\\n telemetry_page_cycler_path = os.path.join(CHROMIUM_SRC,\\n 'tools/perf/benchmarks/telemetryBenchmarks.py')\\n\\n # Dynamically writing python to a file\\n class_template = (\\\"from measurements import page_cycler\\\\n\\\"\\n \\\"import page_sets\\\\n\\\"\\n \\\"from telemetry import benchmark\\\\n\\\\n\\\"\\n \\\"class _PageCycler(benchmark.Benchmark):\\\\n\\\"\\n \\\" options = {'pageset_repeat': 6}\\\\n\\\"\\n \\\" @classmethod\\\\n\\\"\\n \\\" def AddBenchmarkCommandLineArgs(cls, parser):\\\\n\\\"\\n \\\" parser.add_option('--v8-object-stats',\\\\n\\\"\\n \\\" action='store_true',\\\\n\\\"\\n \\\" help='Enable detailed V8 object statistics.')\\\\n\\\"\\n \\\" parser.add_option('--user-server-delay')\\\\n\\\"\\n \\\" print 'IN BENCHMARK'\\\\n\\\"\\n \\\" parser.add_option('--report-speed-index',\\\\n\\\"\\n \\\" action='store_true',\\\\n\\\"\\n \\\" help='Enable the speed index metric.')\\\\n\\\"\\n \\\" parser.add_option('--cold-load-percent', type='int', \\\"\\n \\\"default=50,\\\\n\\\"\\n \\\" help='%d of page visits for which a cold load is \\\"\\n \\\"forced')\\\\n\\\\n\\\"\\n \\\" def CreatePageTest(self, options):\\\\n\\\"\\n \\\" return page_cycler.PageCycler(\\\\n\\\"\\n \\\" page_repeat = options.page_repeat,\\\\n\\\"\\n \\\" pageset_repeat = options.pageset_repeat,\\\\n\\\"\\n \\\" cold_load_percent = options.cold_load_percent,\\\\n\\\"\\n \\\" record_v8_object_stats = options.v8_object_stats,\\\\n\\\"\\n \\\" report_speed_index = options.report_speed_index)\\\\n\\\\n\\\")\\n\\n benchmark_template = (\\\"@benchmark.Enabled('android')\\\\n\\\"\\n \\\"class PageCyclerUrl{0}(_PageCycler):\\\\n\\\"\\n \\\" print 'Using page cycler'\\\\n\\\"\\n \\\" page_set = page_sets.url{0}PageSet\\\\n\\\\n\\\")\\n\\n with open(telemetry_page_cycler_path, 'w') as f:\\n f.write(class_template)\\n for i in range(num_urls):\\n f.write(benchmark_template.format(i))\\n f.write(benchmark_template.format(str(i) + '_pc'))\",\n \"def filter_benchmarks_for_category(\\n self,\\n category: str,\\n available_drivers: Sequence[str],\\n cpu_target_arch_filter: str,\\n gpu_target_arch_filter: str,\\n driver_filter: Optional[str] = None,\\n mode_filter: Optional[str] = None,\\n model_name_filter: Optional[str] = None) -> Sequence[BenchmarkCase]:\\n\\n category_dir = self.category_map.get(category)\\n if category_dir is None:\\n return []\\n\\n chosen_cases = []\\n for benchmark_case in self.suite_map[category_dir]:\\n driver = benchmark_case.driver[len(\\\"iree-\\\"):].lower()\\n matched_driver = (driver in available_drivers) and (\\n driver_filter is None or re.match(driver_filter, driver) is not None)\\n target_arch = benchmark_case.target_arch.lower()\\n matched_arch = (re.match(cpu_target_arch_filter,\\n target_arch) is not None or\\n re.match(gpu_target_arch_filter, target_arch) is not None)\\n bench_mode = ','.join(benchmark_case.bench_mode)\\n matched_mode = (mode_filter is None or\\n re.match(mode_filter, bench_mode) is not None)\\n\\n # For backward compatibility, model_name_filter matches against the string:\\n # <model name with tags>/<benchmark case name>\\n model_and_case_name = f\\\"{benchmark_case.model_name_with_tags}/{os.path.basename(benchmark_case.benchmark_case_dir)}\\\"\\n matched_model_name = (model_name_filter is None or re.match(\\n model_name_filter, model_and_case_name) is not None)\\n\\n if (matched_driver and matched_arch and matched_model_name and\\n matched_mode):\\n chosen_cases.append(benchmark_case)\\n\\n return chosen_cases\",\n \"def get_benchmark(client):\\n r = client.get(config.API_PATH() + '/benchmarks')\\n benchmarks = json.loads(r.data)\\n return benchmarks['benchmarks'][0]['id']\",\n \"def test_make_benchmark_single_ll():\\n benchmark = llvm.make_benchmark(INVALID_IR_PATH)\\n assert str(benchmark.uri).startswith(\\\"benchmark://user-v0/\\\")\\n assert benchmark.uri.scheme == \\\"benchmark\\\"\\n assert benchmark.uri.dataset == \\\"user-v0\\\"\",\n \"def _should_attempt_c_optimizations():\\n if PYPY:\\n return False\\n\\n if _c_optimizations_required():\\n return True\\n return not _c_optimizations_ignored()\",\n \"def workbench_scenarios():\\n return [\\n (\\\"UcCodemirrorXBlock\\\",\\n \\\"\\\"\\\"<vertical_demo>\\n <uc_codemirror/>\\n <uc_codemirror/>\\n <uc_codemirror/>\\n </vertical_demo>\\n \\\"\\\"\\\"),\\n ]\",\n \"def Run(benchmark_spec):\\n _UpdateBenchmarkSpecWithFlags(benchmark_spec)\\n vm = benchmark_spec.vms[0]\\n if benchmark_spec.tpus:\\n # For MLPerf 1.0, the benchmake code of different hardware are different.\\n if (benchmark_spec.tpu_groups['train'].GetAcceleratorType() == 'v3-32' or\\n benchmark_spec.tpu_groups['train'].GetAcceleratorType() == 'v3-128' or\\n benchmark_spec.tpu_groups['train'].GetAcceleratorType() == 'v3-256' or\\n benchmark_spec.tpu_groups['train'].GetAcceleratorType() == 'v3-512' or\\n benchmark_spec.tpu_groups['train'].GetAcceleratorType() == 'v3-1024' or\\n benchmark_spec.tpu_groups['train'].GetAcceleratorType() == 'v3-2048'):\\n run_path = (\\n '$HOME/training_results_{version}/Google/benchmarks/{model}/tpu-{tpus}'\\n .format(\\n version=VERSION.value,\\n model=benchmark_spec.benchmark,\\n tpus=benchmark_spec.tpu_groups['train'].GetAcceleratorType()))\\n code_path = (\\n '$HOME/training_results_{version}/Google/benchmarks/{model}/implementations/tpu-{tpus}-{model}'\\n .format(\\n version=VERSION.value,\\n model=benchmark_spec.benchmark,\\n tpus=benchmark_spec.tpu_groups['train'].GetAcceleratorType()))\\n\\n if MASK in benchmark_spec.benchmark:\\n model = 'mask_rcnn'\\n elif GNMT in benchmark_spec.benchmark:\\n model = 'nmt'\\n else:\\n model = benchmark_spec.benchmark\\n\\n mlperf_benchmark_cmd = (\\n 'cd {code_path} && '\\n 'export PYTHONPATH=$(pwd):$(pwd)/{model} && '\\n 'cd {model} && '\\n '{run_path}/run_and_time.sh'.format(\\n code_path=code_path,\\n model=model,\\n run_path=run_path))\\n\\n if SSD in benchmark_spec.benchmark:\\n mlperf_benchmark_cmd = (\\n 'export '\\n 'MLP_GCS_RESNET_CHECKPOINT={checkpoint}'\\n ' && {cmd}'.format(\\n checkpoint=FLAGS.mlperf_gcs_resnet_checkpoint,\\n cmd=mlperf_benchmark_cmd))\\n else:\\n raise ValueError(\\n 'MLPerf configurations do not support the hardware in PKB. PKB may '\\n 'need to be updated if this is a new TPU type.')\\n\\n else:\\n run_sub_paths = {RESNET: 'resnet/implementations/mxnet',\\n TRANSFORMER: 'transformer/implementations/pytorch',\\n MINIGO: 'minigo/implementations/tensorflow',\\n MASK: 'maskrcnn/implementations/pytorch',\\n GNMT: 'gnmt/implementations/pytorch',\\n SSD: 'ssd/implementations/pytorch',\\n BERT: 'bert/implementations/pytorch',}\\n benchmark_path = f'$HOME/training_results_{VERSION.value}/NVIDIA/benchmarks'\\n run_path = posixpath.join(benchmark_path,\\n run_sub_paths[benchmark_spec.benchmark])\\n env = {\\n 'DGXSYSTEM': DGXSYSTEM,\\n 'NEXP': 1,\\n 'PULL': 0,\\n 'LOGDIR': f'/tmp/{benchmark_spec.benchmark}',\\n }\\n envs = {\\n RESNET: {},\\n TRANSFORMER: {'DATADIR': '/data/wmt/utf8'},\\n MINIGO: {'CONT': 'mlperf-nvidia:minigo'},\\n MASK: {},\\n GNMT: {'DATADIR': '/data/gnmt'},\\n SSD: {'DATADIR': '/data'},\\n BERT: {}\\n }\\n env.update(envs[benchmark_spec.benchmark])\\n\\n run_script = posixpath.join(run_path, 'run_with_docker.sh')\\n vm_util.ReplaceText(vm, 'SYSLOGGING=1', 'SYSLOGGING=0', run_script)\\n vm_util.ReplaceText(vm, 'docker exec -it', 'docker exec -t', run_script)\\n if benchmark_spec.benchmark == RESNET:\\n vm_util.ReplaceText(vm, r'mpirun.*run_and_time\\\\.sh',\\n r'.\\\\/run_and_time.sh', run_script)\\n\\n env = ' '.join(f'{key}={value}' for key, value in env.items())\\n if nvidia_driver.CheckNvidiaGpuExists(vm):\\n env = f'{tensorflow.GetEnvironmentVars(vm)} {env}'\\n\\n mlperf_benchmark_cmd = (\\n f'chmod 755 {run_script} && '\\n f'cd {run_path} && '\\n f'{env} {run_script}')\\n\\n samples = []\\n metadata = _CreateMetadataDict(benchmark_spec)\\n stdout, _ = vm.RobustRemoteCommand(mlperf_benchmark_cmd)\\n if NONE in FLAGS.mlperf_profiler:\\n samples.extend(\\n MakeSamplesFromOutput(\\n metadata,\\n stdout,\\n use_tpu=bool(benchmark_spec.tpus),\\n model=benchmark_spec.benchmark))\\n return samples\",\n \"def bench(participants=participants, benchmarks=benchmarks, bench_time=10.0):\\n\\n mcs = [p.factory() for p in participants]\\n means = [[] for p in participants]\\n stddevs = [[] for p in participants]\\n\\n # Have each lifter do one benchmark each\\n for benchmark_name, f, args, kwargs in benchmarks:\\n logger.info('%s', benchmark_name)\\n\\n for i, (participant, mc) in enumerate(zip(participants, mcs)):\\n sw = Stopwatch()\\n\\n while sw.total() < bench_time:\\n with sw.timing():\\n f(mc, *args, **kwargs)\\n\\n means[i].append(sw.mean())\\n stddevs[i].append(sw.stddev())\\n\\n logger.info(u'%s: %s', participant.name, sw)\\n\\n return means, stddevs\",\n \"def main(args):\\n\\n # Compose the model list\\n modellist = []\\n if args['model']:\\n modellist.append(bmark.ModelInfo(args['model'], os.getcwd(), args['classname']))\\n\\n # Load the benchmark settings\\n benchmark = None\\n benchmark = bmark.load_benchmark(args['benchmark'])\\n corresponding_data = False\\n if 'corresponding_data' in benchmark:\\n corresponding_data = benchmark['corresponding_data']\\n\\n # Only extend if not cached\\n cache_df = None\\n if not args['cache']:\\n modellist.extend(benchmark['models'])\\n else:\\n cache_df = pd.read_csv(args['cache'])\\n\\n # Extract comparator settings from benchmark description\\n eval_comparator = comparator.EqualityComparator()\\n if 'comparator' in benchmark:\\n if benchmark['comparator'] == 'nvc':\\n eval_comparator = comparator.NVCComparator()\\n\\n # Run the model evaluation\\n is_silent = (args['output'] in ['html', 'server'])\\n eva = None\\n if benchmark['type'] == 'adaption':\\n eva = evaluator.AdaptionEvaluator(\\n modellist,\\n eval_comparator,\\n benchmark['data.test'],\\n train_datafile=benchmark['data.train'],\\n train_data_person=benchmark['data.train_person'],\\n silent=is_silent,\\n corresponding_data=corresponding_data,\\n domain_encoders=benchmark['domain_encoders'],\\n cache_df=cache_df\\n )\\n elif benchmark['type'] == 'coverage':\\n # Check for benchmark validity\\n if benchmark['data.train'] or benchmark['data.train_person']:\\n print('WARNING: Ignoring specified training and train_person data ' \\\\\\n + 'for coverage evaluation...')\\n\\n eva = evaluator.CoverageEvaluator(\\n modellist,\\n eval_comparator,\\n benchmark['data.test'],\\n train_datafile=benchmark['data.train'],\\n train_data_person=benchmark['data.train_person'],\\n silent=is_silent,\\n corresponding_data=corresponding_data,\\n domain_encoders=benchmark['domain_encoders'],\\n cache_df=cache_df\\n )\\n else:\\n raise ValueError('Unknown benchmark type: {}'.format(benchmark['type']))\\n\\n with silence_stdout(is_silent):\\n res_df = eva.evaluate()\\n\\n if 'save' in args:\\n res_df.to_csv(args['save'], index=False)\\n\\n # Run the metric visualizer\\n htmlcrtr = html_creator.HTMLCreator([\\n viz_plot.AccuracyVisualizer(),\\n viz_plot.BoxplotVisualizer(),\\n viz_plot.TableVisualizer()\\n ])\\n\\n # Prepare the benchmark output information and visualize the evaluation results\\n benchmark_info = {\\n 'name': os.path.basename(args['benchmark']),\\n 'data.train': os.path.basename(\\n benchmark['data.train']) if benchmark['data.train'] else '',\\n 'data.train_person': os.path.basename(\\n benchmark['data.train_person']) if benchmark['data.train_person'] else '',\\n 'data.test': os.path.basename(benchmark['data.test']),\\n 'type': benchmark['type'],\\n 'corresponding_data': benchmark['corresponding_data'],\\n 'domains': list(res_df['domain'].unique()),\\n 'response_types': list(res_df['response_type'].unique()),\\n }\\n\\n if args['output'] == 'browser':\\n html = htmlcrtr.to_html(res_df, benchmark_info, embedded=False)\\n server.load_in_default_browser(html.encode('utf8'))\\n elif args['output'] == 'server':\\n html = htmlcrtr.to_html(res_df, benchmark_info, embedded=True)\\n sys.stdout.buffer.write(html.encode('utf-8'))\\n elif args['output'] == 'html':\\n html = htmlcrtr.to_html(res_df, benchmark_info, embedded=False)\\n print(html)\",\n \"def test_get_benchmark_methods_exact_match(self):\\n config = mock.Mock()\\n config.workspace = 'workspace'\\n config.benchmark_method_patterns = [\\n 'new_foo.BenchmarkClass.benchmark_method_1',\\n 'new_foo.BenchmarkClass.benchmark_method_2']\\n benchmark_runner = benchmark.BenchmarkRunner(config)\\n\\n methods = benchmark_runner._get_benchmark_methods()\\n self.assertEqual(['new_foo.BenchmarkClass.benchmark_method_1',\\n 'new_foo.BenchmarkClass.benchmark_method_2'], methods)\",\n \"def _fcts():\\n import numpy # pylint: disable=C0415\\n from .cbenchmark_dot import vector_dot_product # pylint: disable=E0611,C0415\\n from .cbenchmark_dot import vector_dot_product16 # pylint: disable=E0611,C0415\\n from .cbenchmark_dot import vector_dot_product16_nofcall # pylint: disable=E0611,C0415\\n from .cbenchmark_dot import vector_dot_product16_sse # pylint: disable=E0611,C0415\\n\\n def simple_dot(values):\\n return numpy.dot(values, values)\\n\\n def c11_dot(vect):\\n return vector_dot_product(vect, vect)\\n\\n def c11_dot16(vect):\\n return vector_dot_product16(vect, vect)\\n\\n def c11_dot16_nofcall(vect):\\n return vector_dot_product16_nofcall(vect, vect)\\n\\n def c11_dot16_sse(vect):\\n return vector_dot_product16_sse(vect, vect)\\n\\n return [simple_dot, c11_dot, c11_dot16, c11_dot16_nofcall, c11_dot16_sse]\",\n \"def _generate_benchmark_variants(benchmark_spec):\\n variants = []\\n # Cold start.\\n variants.append({\\n 'name': benchmark_spec['name'] + ' (cold start)',\\n 'app': benchmark_spec['app'],\\n 'duration': benchmark_spec['duration'],\\n 'measurements': benchmark_spec['measurements'],\\n 'shell-args': benchmark_spec.get('shell-args',\\n []) + _COLD_START_SHELL_ARGS})\\n # Warm start.\\n variants.append({\\n 'name': benchmark_spec['name'] + ' (warm start)',\\n 'app': benchmark_spec['app'],\\n 'duration': benchmark_spec['duration'],\\n 'measurements': benchmark_spec['measurements'],\\n 'shell-args': benchmark_spec.get('shell-args', [])})\\n return variants\",\n \"def benchmark_selection(self):\\n return self._benchmark_selection\",\n \"def Cleanup(benchmark_spec):\\n del benchmark_spec # unused\",\n \"def Cleanup(benchmark_spec):\\n del benchmark_spec # unused\",\n \"def test_profile_memory():\\n process = psutil.Process()\\n\\n num_strings, strings_length = int(1e5 / 2), 1000\\n memory_usage_samples = collections.defaultdict(list)\\n\\n for function_under_test in (sorted, xsorted):\\n\\n strings = random_strings(length=strings_length, num=num_strings)\\n thread = threading.Thread(target=do_benchmark, args=(strings, function_under_test))\\n\\n start = process.memory_info_ex().rss\\n thread.start()\\n start_time = time.clock()\\n\\n while thread.is_alive():\\n thread.join(0.001)\\n value = (process.memory_info_ex().rss - start) / 1e3\\n point = time.clock() - start_time, value\\n memory_usage_samples[function_under_test.__name__].append(point)\\n\\n export_memory_profile_chart(memory_usage_samples, num_strings, strings_length)\\n\\n # extract only the memory usage from the sorted dict for determining the peak usage for each function under test.\\n values_only = (\\n (sample[1] for sample in samples[1])\\n for samples in sorted(memory_usage_samples.items())\\n )\\n peak_sorted, peak_xsorted = map(max, values_only)\\n assert peak_sorted / peak_xsorted >= 15\",\n \"def benchmark_program(timeout, algo, file) -> List[str]:\\n base_name = os.path.basename(file)\\n out_file = f\\\"out/lz/{base_name}.{algo}\\\"\\n cmd = [\\n \\\"externals/lzrr/build/compress.out\\\",\\n \\\"--input_file\\\",\\n file,\\n \\\"--output_file\\\",\\n out_file,\\n \\\"--mode\\\",\\n algo,\\n ]\\n print(\\\" \\\".join(cmd))\\n num_factor = 0\\n time_start = time.time()\\n try:\\n out = subprocess.check_output(cmd, shell=False, timeout=timeout)\\n res_pre = b\\\"The number of factors : \\\"\\n res_beg = out.find(res_pre) + len(res_pre)\\n res_end = out.find(b\\\"\\\\n\\\", res_beg)\\n num_factor = int(out[res_beg:res_end])\\n status = \\\"complete\\\"\\n except subprocess.TimeoutExpired:\\n status = f\\\"timeout-{timeout}\\\"\\n except Exception:\\n status = \\\"error\\\"\\n\\n exp = LZExp(\\n str(datetime.datetime.now()),\\n status,\\n algo,\\n os.path.basename(file),\\n len(open(file, \\\"rb\\\").read()),\\n time.time() - time_start,\\n num_factor,\\n )\\n\\n expd = exp.__dict__\\n return list(map(str, expd.values()))\",\n \"def compare_hard_noprint():\\n\\n\\n cores = [1, 2, 4, 8, 16]\\n s_times = []\\n\\n for n_cores in cores:\\n print('analyzing', '-'.join(['data/bench', 's', 'np', str(n_cores)]) + '.out')\\n data = split_output_file('-'.join(['data/bench', 's', 'np', str(n_cores)]) + '.out')\\n s_times.append(data['FOOTER']['total_time'])\\n\\n # speedup plot\\n fig, axs = plt.subplots()\\n\\n axs.plot(cores, [s_times[0] / x / k for x, k in zip(s_times, cores)], label='sequential')\\n axs.set_xticks(cores)\\n axs.ticklabel_format(style='sci', scilimits=(-128, 128))\\n\\n axs.set_ylabel('Relative speedup')\\n axs.set_xlabel('Number of cores')\\n axs.set_ylim(0, 1.1)\\n\\n fig.set_size_inches(4, 2, forward=True)\\n plt.tight_layout()\\n\\n plt.show()\",\n \"def print_scenario(benchmarks, name):\\n # Remember, the first entry in the times array is an empty object.\\n\\n benchmarks = sorted(benchmarks, key = benchmark_sort_key)\\n for benchmark in benchmarks:\\n block_times = benchmark[\\\"times\\\"][2][\\\"block_times\\\"] # list of floats\\n it = iter(block_times)\\n for start in it:\\n end = next(it)\\n\\n print(\\\"%s , %.3f \\\" % (benchmark[\\\"label\\\"], float(end) - float(start)))\",\n \"def test_measure_nondeterministic_without_sampling(self):\\n shots = 2000\\n circuits = ref_measure.measure_circuits_nondeterministic(allow_sampling=False)\\n targets = ref_measure.measure_counts_nondeterministic(shots)\\n job = execute(circuits, QasmSimulator(), shots=shots)\\n result = job.result()\\n self.is_completed(result)\\n self.compare_counts(result, circuits, targets, delta=0.05 * shots)\",\n \"def _parameterize_simple_benchmarks():\\n parameterized_tuples = []\\n for index, simple_benchmark in enumerate(\\n all_benchmarks.all_benchmarks(modules=[simple_benchmarks])):\\n # The index ensures all test cases have distinct names, even if multiple\\n # benchmarks have the same name.\\n test_case_name = '{index}_{name}'.format(index=index,\\n name=simple_benchmark.name)\\n parameterized_tuples.append((test_case_name, simple_benchmark))\\n return parameterized_tuples\",\n \"def benchmark():\\n print defer.Deferred.__module__\\n for func, args, iter in benchmarkFuncs:\\n print func.__name__, args, timeit(func, iter, *args)\",\n \"def get_testbench_specs(self, tb_type: str) -> Dict[str, Any]:\\n return self._specs['testbenches'][tb_type]\",\n \"def test01c(self):\\n a = np.arange(1e5)\\n cnames = bcolz.blosc_compressor_list()\\n if common.verbose:\\n print(\\\"Checking compressors:\\\", cnames)\\n # print \\\"\\\\nsize b uncompressed-->\\\", a.size * a.dtype.itemsize\\n for cname in cnames:\\n with bcolz.defaults_ctx(bcolz.cparams(clevel=9, cname=cname)):\\n self.assertTrue(bcolz.defaults.cparams['cname'] == cname)\\n b = bcolz.carray(a, rootdir=self.rootdir)\\n # print \\\"size b compressed -->\\\", b.cbytes, \\\"with '%s'\\\"%cname\\n self.assertTrue(sys.getsizeof(b) < b.nbytes,\\n \\\"carray does not seem to compress at all\\\")\\n assert_array_equal(a, b[:], \\\"Arrays are not equal\\\")\\n # Remove the array on disk before trying with the next one\\n if self.disk:\\n common.remove_tree(self.rootdir)\",\n \"def RunBenchmark(path_to_apk, run_label):\\n # `path_to_apk` is similar to `./out/59.0.3071.132_arm_MonochromeStable.apk`\\n chrome_version = ChromeVersion(path_to_apk.split('/')[-1].split('_')[0])\\n subprocess.call(['adb', 'install', '-r', '-d', path_to_apk])\\n subprocess.call([os.path.join(utils.CHROMIUM_SRC, 'tools',\\n 'perf', 'run_benchmark'),\\n '--browser=android-system-chrome',\\n '--pageset-repeat=1', # could remove this later\\n '--results-label=%s' % str(chrome_version),\\n # TODO(wangge):not sure if we should run in compatibility\\n # mode even for the later version, probably add a check in\\n # caller to determine if we should run it in compatibility\\n # mode and add an argument `run_in_compatibility_mode` to\\n # the `RunBenchmark` function\\n '--compatibility-mode=no-field-trials',\\n '--compatibility-mode=ignore-certificate-errors',\\n '--compatibility-mode=legacy-command-line-path',\\n '--compatibility-mode=gpu-benchmarking-fallbacks',\\n '--story-filter=wikipedia', # could remove this\\n # thinking of adding an argument to the tool to set this\\n '--output-dir=%s' % os.path.join(\\n utils.APP_ROOT, 'results', run_label,\\n str(chrome_version.milestone)),\\n # thinking of adding an argument to the tool to set this too\\n 'system_health.memory_mobile'])\",\n \"def add_default_runtimes(harness):\\n\\n if os.path.isfile('configs/env'):\\n config_file = open('configs/env')\\n for line in config_file:\\n key, value = line.rstrip().split('=', 1)\\n os.environ[key] = value\\n\\n def wllvm_make(workdir, make_env, **kwargs):\\n if 'LLVM_COMPILER' not in os.environ:\\n logger.error('enviroment variable \\\"LLVM_COMPILER\\\" is required to build with wllvm')\\n return False\\n\\n return BenchmarkingHarness.default_make(workdir, make_env, **kwargs)\\n\\n def wllvm_executor(filepath, workdir, tool, **kwargs):\\n with NamedTemporaryFile(delete=False) as tmp:\\n bc_filename = os.path.splitext(os.path.basename(filepath))[0] + '.bc'\\n bc_filepath = os.path.join(tmp, bc_filename)\\n logger.debug('extract bitcode file to: \\\"%s\\\"', bc_filepath)\\n\\n\\n with subprocess.Popen([os.path.expandvars(\\\"$WLLVM_DIR/extract-bc\\\"), filepath, '-o', bc_filepath]) as process:\\n process.wait(timeout=30) # 30 Seconds should be way enough time to do the bitcode extraction\\n\\n assert os.path.isfile(bc_filepath)\\n\\n with subprocess.Popen([os.path.expandvars(tool), bc_filepath, '--output=json'], cwd=workdir, stdout=subprocess.PIPE) as process:\\n stdout, _ = process.communicate(timeout=kwargs.get('timeout', 240))\\n\\n if process.returncode != 0:\\n return None\\n\\n try:\\n return json.loads(stdout)\\n except json.JSONDecodeError:\\n logger.error('invalid benchmark result: \\\\'%s\\\\'', stdout.decode('utf-8'))\\n raise\\n\\n\\n for config in glob.glob('configs/*.py'):\\n exec(open(os.path.abspath(config)).read(), {'wllvm_make' : wllvm_make, 'wllvm_executor' : wllvm_executor, 'harness' : harness})\",\n \"def testRunPerformanceTestsTelemetry_NoTestResults(self):\\n tempdir = tempfile.mkdtemp()\\n benchmarks = ['benchmark1', 'benchmark2']\\n return_code, stdout = self.RunPerfScript(\\n '../../testing/scripts/run_performance_tests.py '\\n '../../tools/perf/testdata/fail_and_do_nothing '\\n '--benchmarks=%s '\\n '--browser=%s '\\n '--isolated-script-test-output=%s' % (\\n ','.join(benchmarks),\\n self.options.browser_type,\\n os.path.join(tempdir, 'output.json')\\n ))\\n self.assertNotEqual(return_code, 0)\\n try:\\n with open(os.path.join(tempdir, 'output.json')) as f:\\n test_results = json.load(f)\\n self.assertIsNotNone(\\n test_results, 'json_test_results should be populated: ' + stdout)\\n self.assertTrue(\\n test_results['interrupted'],\\n 'if the benchmark does not populate test results, then we should '\\n 'populate it with a failure.')\\n for benchmark in benchmarks:\\n with open(os.path.join(tempdir, benchmark, 'test_results.json')) as f:\\n test_results = json.load(f)\\n self.assertIsNotNone(\\n test_results, 'json_test_results should be populated: ' + stdout)\\n self.assertTrue(\\n test_results['interrupted'],\\n 'if the benchmark does not populate test results, then we should '\\n 'populate it with a failure.')\\n except IOError as e:\\n self.fail('json_test_results should be populated: ' + stdout + str(e))\\n finally:\\n shutil.rmtree(tempdir)\",\n \"def test_measure_nondeterministic_multi_qubit_without_sampling(self):\\n shots = 2000\\n qobj = ref_measure.measure_circuits_qobj_nondeterministic(allow_sampling=False)\\n qobj.config.shots = shots\\n circuits = [experiment.header.name for experiment in qobj.experiments]\\n targets = ref_measure.measure_counts_qobj_nondeterministic(shots)\\n job = QasmSimulator().run(qobj)\\n result = job.result()\\n self.is_completed(result)\\n self.compare_counts(result, circuits, targets, delta=0.05 * shots)\",\n \"def ignore_pyc(root,names):\\n return [name for name in names if name.endswith('pyc')]\",\n \"def no_builtin_verification():\\n current_space().skip_builtin_verification = True\\n yield\\n current_space().skip_builtin_verification = False\",\n \"def bench():\\n times = []\\n blocks = np.round(np.logspace(3, 7, num=50))\\n for b in blocks:\\n times.append(timeit.timeit('cpu_vs_mem.inplace(block_size=%d)' % b,\\n 'import cpu_vs_mem', number=1))\\n print('Block size: %d Execution time: %.3f s' % (b, times[-1]))\\n sys.stdout.flush()\\n\\n return blocks, times\",\n \"def runtime_cflags(self, ctx: Context) -> Iterable[str]:\\n if self.builtin_passes:\\n return self.builtin_passes.runtime_cflags(ctx)\\n return []\",\n \"def list_runtimes(self, workbench):\\n pass\",\n \"def Run(benchmark_spec: bm_spec.BenchmarkSpec) -> List[sample.Sample]:\\n discovery_duration = benchmark_spec.data_discovery_service.DiscoverData()\\n return [\\n sample.Sample('data_discovery_duration', discovery_duration, 'seconds',\\n benchmark_spec.data_discovery_service.GetMetadata())]\",\n \"def Run(benchmark_spec):\\n vms = benchmark_spec.vms\\n results = []\\n\\n logging.info('Iperf Results:')\\n\\n # Send traffic in both directions\\n for sending_vm, receiving_vm in vms, reversed(vms):\\n # Send using external IP addresses\\n if vm_util.ShouldRunOnExternalIpAddress():\\n results.append(_RunIperf(sending_vm,\\n receiving_vm,\\n receiving_vm.ip_address,\\n 'external'))\\n\\n # Send using internal IP addresses\\n if vm_util.ShouldRunOnInternalIpAddress(sending_vm,\\n receiving_vm):\\n results.append(_RunIperf(sending_vm,\\n receiving_vm,\\n receiving_vm.internal_ip,\\n 'internal'))\\n\\n return results\",\n \"def get_cpython_lines():\\n cpython_url = 'https://github.com/python/cpython/blob/master/Python/pythonrun.c'\\n soup = BeautifulSoup(requests.get(cpython_url).text, 'html.parser')\\n cpython_soup = soup.find(\\\"div\\\",{\\\"itemprop\\\":\\\"text\\\", \\\"class\\\":\\\"Box-body p-0 blob-wrapper data type-c\\\"})\\n tds = cpython_soup.find_all('td', {\\\"class\\\":\\\"blob-code blob-code-inner js-file-line\\\"})\\n cpython_code_lines = []\\n for td in tds:\\n cpython_code_lines.append(\\n ''.join([thing.text+\\\" \\\" for thing in td.find_all('span') if len(thing.text)>1]))\\n return(cpython_code_lines)\",\n \"def __init__(self, conf, benchmarks, regex=None):\\n self._conf = conf\\n self._benchmark_dir = conf.benchmark_dir\\n\\n if not regex:\\n regex = []\\n if isinstance(regex, str):\\n regex = [regex]\\n\\n self._all_benchmarks = {}\\n self._benchmark_selection = {}\\n for benchmark in benchmarks:\\n self._all_benchmarks[benchmark['name']] = benchmark\\n if benchmark['params']:\\n self._benchmark_selection[benchmark['name']] = []\\n for idx, param_set in enumerate(\\n itertools.product(*benchmark['params'])):\\n name = f\\\"{benchmark['name']}({', '.join(param_set)})\\\"\\n if not regex or any(re.search(reg, name) for reg in regex):\\n self[benchmark['name']] = benchmark\\n self._benchmark_selection[benchmark['name']].append(idx)\\n else:\\n self._benchmark_selection[benchmark['name']] = None\\n if not regex or any(re.search(reg, benchmark['name']) for reg in regex):\\n self[benchmark['name']] = benchmark\",\n \"def compilation_options(self):\\n #'-target','i386-pc-linux-gnu','-m32','-O2',\\n opts = ['-Wno-implicit-function-declaration','-Wno-incompatible-library-redeclaration','-fno-vectorize',\\n '-fno-slp-vectorize','-gline-tables-only','-Xclang','-disable-lifetime-markers','-Rpass=.*','-Rpass-missed=.*',\\n '-Rpass-analysis=.*','-mllvm','-inline-threshold=15000','-Dassert=__VERIFIER_assert']\\n if self._options.property.undefinedness():\\n opts.append('-fsanitize=undefined')\\n opts.append('-fno-sanitize=unsigned-integer-overflow')\\n elif self._options.property.signedoverflow():\\n opts.append('-fsanitize=signed-integer-overflow')\\n opts.append('-fsanitize=shift')\\n\\n return opts\",\n \"def benchmark(func):\\n import time\\n @wraps(func)\\n def wrapper(*args, **kwargs):\\n t = time.clock()\\n res = func(*args, **kwargs)\\n print(func.__name__, time.clock()-t)\\n return res\\n return wrapper\",\n \"def benchmark(mh_func):\\n fps = list(mltext())\\n sim_errs = []\\n dis_errs = []\\n runtimes = []\\n for abc in sliding_window(fps, 3, 2, fillvalue=None):\\n abc = list(abc)\\n if abc[-1] is None:\\n continue\\n texts = (load_text_file(t) for t in abc)\\n n_texts = (text_normalize(t) for t in texts)\\n features = [featurize(t) for t in n_texts]\\n hf = [hashify_32(f) for f in features]\\n sim_sim = jaccard(hf[0], hf[1])\\n sim_dis = jaccard(hf[0], hf[2])\\n\\n start = time.time()\\n mhashes = [mh_func(f) for f in features]\\n end = time.time()\\n runtimes.append((end - start) * 1000)\\n mh_sim_sim = jaccard(mhashes[0], mhashes[1])\\n mh_sim_dis = jaccard(mhashes[0], mhashes[2])\\n sim_errs.append(abs(sim_sim - mh_sim_sim))\\n dis_errs.append(abs(sim_dis - mh_sim_dis))\\n print(\\n f\\\"{mh_func.__name__}\\\\t\\\\t\\\\t\\\"\\n f\\\"Rt: {mean(runtimes):.2f} - \\\"\\n f\\\"Error Sim Mean {mean(sim_errs)} - Max {max(sim_errs)} Var {variance(sim_errs)}| \\\"\\n f\\\"Error Dis Mean {mean(dis_errs)} - Max {max(dis_errs)} Var {variance(dis_errs)}\\\"\\n )\",\n \"def benchmark(func, inputs):\\n t0 = time.clock()\\n results = [func(x) for x in inputs]\\n t1 = time.clock()\\n average_time = (t1 - t0) / len(inputs)\\n return average_time, results\",\n \"def testFilterBySuite(self):\\n self.setUpPyfakefs()\\n gpu_json = {\\n 'AAAAA1 AUTOGENERATED FILE DO NOT EDIT': {},\\n 'Android Tester': {\\n 'isolated_scripts': [\\n {\\n 'args': [\\n 'webgl_conformance',\\n ],\\n 'isolate_name': 'not_telemetry',\\n },\\n ],\\n },\\n 'Linux Tester': {\\n 'isolated_scripts': [\\n {\\n 'args': [\\n 'not_a_suite',\\n ],\\n 'isolate_name': 'telemetry_gpu_integration_test',\\n },\\n ],\\n },\\n 'Windows Tester': {\\n 'isolated_scripts': [\\n {\\n 'args': [\\n 'webgl_conformance',\\n ],\\n 'isolate_name': 'telemetry_gpu_integration_test',\\n },\\n ],\\n },\\n }\\n\\n self.CreateFile(os.path.join(builders.TESTING_BUILDBOT_DIR,\\n 'chromium.json'),\\n contents=json.dumps(gpu_json))\\n\\n gpu_builders = builders.GetCiBuilders('webgl_conformance')\\n self.assertEqual(gpu_builders, set(['Windows Tester']))\",\n \"def workbench_scenarios():\\n return [\\n (\\\"Vr360XBlock\\\",\\n \\\"\\\"\\\"<vr360/>\\n \\\"\\\"\\\"),\\n (\\\"Multiple Vr360XBlock\\\",\\n \\\"\\\"\\\"<vertical_demo>\\n <vr360/>\\n <vr360/>\\n <vr360/>\\n </vertical_demo>\\n \\\"\\\"\\\"),\\n ]\",\n \"def ignore_builtin_verification():\\n return not current_space().skip_builtin_verification\",\n \"def test01c(self):\\n a = np.arange(1e5)\\n filters = bcolz.filters.keys()\\n if common.verbose:\\n print(\\\"Checking filters:\\\", filters)\\n # print(\\\"\\\\nsize b uncompressed-->\\\", a.size * a.dtype.itemsize)\\n for filter_ in filters:\\n with bcolz.defaults_ctx(bcolz.cparams(clevel=9, shuffle=filter_)):\\n self.assertTrue(bcolz.defaults.cparams['shuffle'] == filter_)\\n b = bcolz.carray(a, rootdir=self.rootdir)\\n # print(\\\"size b compressed -->\\\", b.cbytes, \\\"with '%s'\\\" % filter_)\\n if filter_ > 0:\\n self.assertTrue(bcolz.defaults.cparams['shuffle'] == bcolz.SHUFFLE)\\n self.assertTrue(sys.getsizeof(b) < b.nbytes,\\n \\\"carray does not seem to compress at all\\\")\\n assert_array_equal(a, b[:], \\\"Arrays are not equal\\\")\\n # Remove the array on disk before trying with the next one\\n if self.disk:\\n common.remove_tree(self.rootdir)\",\n \"def _missing_symbol_to_skipped_tests(self):\\n return {\\n \\\"MathMLElement\\\": [\\\"mathml\\\"],\\n \\\"GraphicsLayer\\\": [\\\"compositing\\\"],\\n \\\"WebCoreHas3DRendering\\\": [\\\"animations/3d\\\", \\\"transforms/3d\\\"],\\n \\\"WebGLShader\\\": [\\\"fast/canvas/webgl\\\", \\\"compositing/webgl\\\", \\\"http/tests/canvas/webgl\\\"],\\n \\\"MHTMLArchive\\\": [\\\"mhtml\\\"],\\n }\",\n \"def benchmark():\\n parser = argparse.ArgumentParser(\\n \\n description='pyrpipe diagnostic utility\\\\nGenerate benchmark report.',\\n \\n usage='''pyrpipe_diagnostic report [<args>] <logfile>\\n \\n ''') \\n parser.add_argument('-o', help='out file \\\\ndefault: same as input logfile',action=\\\"store\\\")\\n parser.add_argument('-e', help='report output type: [MD,PDF,HTML] \\\\ndefault: PDF',default='PDF',action=\\\"store\\\")\\n parser.add_argument('-v',help='verbose',action=\\\"store_true\\\")\\n parser.add_argument('-f',help='Filter by programs. Provide a comma-separated list e.g., prefetch,STAR,bowtie2 \\\\ndefault None')\\n parser.add_argument('-t',help='Temporary directory. \\\\ndefault ./tmp',action=\\\"store\\\")\\n parser.add_argument('logfile', help='The log file generated by pyrpipe',action=\\\"store\\\")\\n args = parser.parse_args(sys.argv[2:])\\n \\n logFile=args.logfile\\n envLog=reports.checkEnvLog(logFile) \\n #parse args\\n vFlag=args.v\\n if vFlag:\\n print(\\\"Generating benchmarks\\\")\\n outFile=\\\"\\\"\\n if args.o is None:\\n outFile=pu.get_file_basename(args.logfile)\\n else:\\n outFile=args.o\\n outFile+='.'+args.e\\n \\n filters=[]\\n if args.f is not None:\\n filters= args.f.split(',')\\n #create temp dir\\n tempDir=\\\"\\\"\\n if args.t is not None:\\n tempDir= args.t\\n else:\\n tempDir=os.path.join(os.getcwd(),\\\"tmp\\\")\\n #create tmp dir\\n if not pu.check_paths_exist(tempDir):\\n pu.mkdir(tempDir)\\n \\n reports.generateBenchmarkReport(logFile,envLog,filters,tempDir,outFile=outFile,verbose=args.v)\",\n \"def benchmarks(tsp_algorithms, maps=Maps(30, 60)):\\n for tsp in tsp_algorithms:\\n time, results = benchmark(tsp, maps)\\n lengths = [tour_length(r) for r in results]\\n print(\\\"{:>25} |{:7.0f} ±{:4.0f} ({:5.0f} to {:5.0f}) |{:7.3f} secs/map | {} ⨉ {}-city maps\\\"\\n .format(tsp.__name__, mean(lengths), stdev(lengths), min(lengths), max(lengths),\\n time, len(maps), len(maps[0])))\",\n \"def BenchArgs(data_file):\\n return ['--timers', 'wcg', '--logFile', data_file]\",\n \"def main():\\n logging.basicConfig(level=\\\"INFO\\\")\\n assert len(sys.argv) == 2, \\\"Exactly one positional argument (path to the raw dataset) is \\\"\\\\\\n \\\"needed. \\\\n\\\\nE.g. `python sparsity_benchmark ~/bff_data/final_table`\\\"\\n\\n # Prepares data for the benchmark, may take a while\\n data_parameters = DATA_PARAMETERS.copy()\\n data_parameters[\\\"input_file\\\"] = sys.argv[1]\\n data_parameters[\\\"preprocessed_file\\\"] = os.path.join(\\n os.path.dirname(data_parameters[\\\"input_file\\\"]),\\n \\\"preprocessed_dataset.pkl\\\"\\n )\\n data_preprocessor = preprocess_dataset(data_parameters=data_parameters)\\n\\n # Note: the features here should be in range [0, ~1.2], according to the original experiments.\\n # 0 corresponds to no data, everything else is linearly scaled from dB units.\\n features, _ = data_preprocessor.load_dataset()\\n\\n logging.info(\\\"Starting benchmarks\\\")\\n noisy_features = benchmark_noise(\\n features=features,\\n data_parameters=data_parameters,\\n experiment_parameters=EXPERIMENT_PARAMETERS\\n )\\n benchmark_binarization(\\n noisy_features=noisy_features,\\n data_parameters=data_parameters,\\n experiment_parameters=EXPERIMENT_PARAMETERS\\n )\\n logging.info(\\\"Done\\\")\",\n \"def test_create_benchmark(self, benchmark_config, benchmark_config_py):\\n with OrionState() as cfg:\\n bm1 = get_or_create_benchmark(\\n cfg.storage,\\n **benchmark_config_py,\\n )\\n bm1.close()\\n\\n bm2 = get_or_create_benchmark(cfg.storage, \\\"bm00001\\\")\\n bm2.close()\\n\\n assert bm1.configuration == benchmark_config\\n\\n assert bm1.configuration == bm2.configuration\",\n \"def test_optimizers(name: str) -> None:\\n if any(x in name for x in [\\\"Chain\\\", \\\"SMAC\\\", \\\"BO\\\", \\\"AX\\\"]) and os.environ.get(\\\"CIRCLECI\\\", False):\\n raise SkipTest(\\\"too slow for CircleCI!\\\")\\n\\n def doint(s): # Converting a string into an int.\\n return 7 + sum([ord(c) * i for i, c in enumerate(s)])\\n\\n if doint(name) % 5 > 0:\\n raise SkipTest(\\\"too many tests for CircleCI!\\\")\\n if (\\n sum([ord(c) for c in name]) % 4 > 0\\n and name\\n not in [\\n \\\"DE\\\",\\n \\\"CMA\\\",\\n \\\"OnePlusOne\\\",\\n \\\"Cobyla\\\",\\n \\\"DiscreteLenglerOnePlusOne\\\",\\n \\\"PSO\\\",\\n ]\\n or \\\"Tiny\\\" in name\\n or \\\"Micro\\\" in name\\n ) and os.environ.get(\\\"CIRCLECI\\\", False):\\n raise SkipTest(\\\"Too expensive: we randomly skip 3/4 of these tests.\\\")\\n if name in [\\\"CMAbounded\\\", \\\"NEWUOA\\\"]: # Not a general purpose optimization method.\\n return\\n if \\\"BO\\\" in name: # Bayesian Optimization is rarely good, let us save up time.\\n return\\n optimizer_cls = registry[name]\\n if isinstance(optimizer_cls, base.ConfiguredOptimizer):\\n assert any(\\n hasattr(mod, name) for mod in (optlib, xpvariants)\\n ) # make sure registration matches name in optlib/xpvariants\\n assert (\\n optimizer_cls.__class__(**optimizer_cls._config) == optimizer_cls\\n ), \\\"Similar configuration are not equal\\\"\\n # some classes of optimizer are eigher slow or not good with small budgets:\\n nameparts = [\\\"Many\\\", \\\"Chain\\\", \\\"BO\\\", \\\"Discrete\\\", \\\"NLOPT\\\"] + [\\\"chain\\\"] # TODO remove chain when possible\\n is_ngopt = inspect.isclass(optimizer_cls) and issubclass(optimizer_cls, NGOptBase) # type: ignore\\n verify = (\\n not optimizer_cls.one_shot\\n and name not in SLOW\\n and not any(x in name for x in nameparts)\\n and not is_ngopt\\n )\\n budget = 300 if \\\"BO\\\" not in name and not is_ngopt else 4\\n # the following context manager speeds up BO tests\\n patched = partial(acq_max, n_warmup=10000, n_iter=2)\\n with patch(\\\"bayes_opt.bayesian_optimization.acq_max\\\", patched):\\n check_optimizer(optimizer_cls, budget=budget, verify_value=verify)\",\n \"def Stop():\\n\\n if global_options.loglevel >= 1 and global_benchmark:\\n t = time.time() - global_starting_time\\n global_options.stdlog.write(\\n \\\"######### Time spent in benchmarked functions #########\\\\n\\\")\\n global_options.stdlog.write(\\\"# function\\\\tseconds\\\\tpercent\\\\n\\\")\\n for key, value in global_benchmark.items():\\n global_options.stdlog.write(\\n \\\"# %s\\\\t%6i\\\\t%5.2f%%\\\\n\\\" % (key, value,\\n (100.0 * float(value) / t)))\\n global_options.stdlog.write(\\n \\\"#######################################################\\\\n\\\")\\n\\n if global_options.loglevel >= 1:\\n global_options.stdlog.write(getFooter() + \\\"\\\\n\\\")\\n\\n # close files\\n if global_options.stdout != sys.stdout:\\n global_options.stdout.close()\\n # do not close log, otherwise error occurs in atext.py\\n # if global_options.stdlog != sys.stdout:\\n # global_options.stdlog.close()\\n\\n if global_options.stderr != sys.stderr:\\n global_options.stderr.close()\\n\\n if global_options.timeit_file:\\n\\n outfile = open(global_options.timeit_file, \\\"a\\\")\\n\\n if global_options.timeit_header:\\n outfile.write(\\\"\\\\t\\\".join(\\n (\\\"name\\\", \\\"wall\\\", \\\"user\\\", \\\"sys\\\", \\\"cuser\\\", \\\"csys\\\",\\n \\\"host\\\", \\\"system\\\", \\\"release\\\", \\\"machine\\\",\\n \\\"start\\\", \\\"end\\\", \\\"path\\\", \\\"cmd\\\")) + \\\"\\\\n\\\")\\n\\n csystem, host, release, version, machine = map(str, os.uname())\\n uusr, usys, c_usr, c_sys = map(lambda x: \\\"%5.2f\\\" % x, os.times()[:4])\\n t_end = time.time()\\n c_wall = \\\"%5.2f\\\" % (t_end - global_starting_time)\\n\\n if sys.argv[0] == \\\"run.py\\\":\\n cmd = global_args[0]\\n if len(global_args) > 1:\\n cmd += \\\" '\\\" + \\\"' '\\\".join(global_args[1:]) + \\\"'\\\"\\n else:\\n cmd = sys.argv[0]\\n\\n result = \\\"\\\\t\\\".join((global_options.timeit_name,\\n c_wall, uusr, usys, c_usr, c_sys,\\n host, csystem, release, machine,\\n time.asctime(time.localtime(global_starting_time)),\\n time.asctime(time.localtime(t_end)),\\n os.path.abspath(os.getcwd()),\\n cmd)) + \\\"\\\\n\\\"\\n\\n outfile.write(result)\\n outfile.close()\",\n \"def PrepareBenchmark(benchmark_spec, vm=None):\\n _UpdateBenchmarkSpecWithFlags(benchmark_spec)\\n vm = vm or benchmark_spec.vms[0]\\n\\n has_gpu = nvidia_driver.CheckNvidiaGpuExists(vm)\\n if has_gpu:\\n vm.Install('cuda_toolkit')\\n\\n if (bool(benchmark_spec.tpus) and nvidia_driver.CheckNvidiaGpuExists(vm)):\\n raise errors.Config.InvalidValue(\\n 'Invalid configuration. GPUs and TPUs can not both present in the config.'\\n )\\n\\n vm.RemoteCommand(\\n f'if [ ! -d \\\"$HOME/training_results_{VERSION.value}\\\" ]; then '\\n f' git clone https://github.com/mlcommons/training_results_{VERSION.value}.git ; '\\n 'fi')\\n vm.Install('pip3')\\n if not HYPERTHREADS.value:\\n if BERT in benchmark_spec.benchmark:\\n vm_util.ReplaceText(\\n vm, \\\"'bind_pyt'\\\", \\\"'bind_pyt' '--no_hyperthreads'\\\",\\n f'training_results_{VERSION.value}/NVIDIA/benchmarks/bert/'\\n 'implementations/pytorch/run_with_docker.sh')\\n elif MASK in benchmark_spec.benchmark:\\n vm_util.ReplaceText(\\n vm, \\\"'bind_launch'\\\", \\\"'bind_launch' '--no_hyperthreads'\\\",\\n f'training_results_{VERSION.value}/NVIDIA/benchmarks/maskrcnn/'\\n 'implementations/pytorch/run_and_time.sh')\\n elif RESNET in benchmark_spec.benchmark:\\n vm_util.ReplaceText(\\n vm, '--cpu=exclusive', '--cpu=exclusive,nosmt',\\n f'training_results_{VERSION.value}/NVIDIA/benchmarks/resnet/'\\n 'implementations/mxnet/run_and_time.sh')\",\n \"def cli(argv):\\r\\n argv.append(\\\"--exhaust-materials\\\")\\r\\n cltestbench.cli(argv)\",\n \"def parse_microbenchmark_data(artifact_processor_comparison):\\n metadata = parse_standard_metadata()\\n test_suite, test_name, metrics = parse_microbenchmark_comparison(\\n artifact_processor_comparison)\\n return metadata, test_suite, test_name, metrics\",\n \"def workbench_scenarios():\\n return [\\n (\\\"Markdown\\\",\\n \\\"\\\"\\\"<markdowna />\\n \\\"\\\"\\\")\\n ]\",\n \"def _missing_feature_to_skipped_tests(self):\\n # FIXME: This list matches WebKitWin and should be moved onto the Win port.\\n return {\\n \\\"Accelerated Compositing\\\": [\\\"compositing\\\"],\\n \\\"3D Rendering\\\": [\\\"animations/3d\\\", \\\"transforms/3d\\\"],\\n }\",\n \"def test_make_benchmark_from_command_line_mixed_source_and_object_files(\\n env: LlvmEnv, retcode: int\\n):\\n with temporary_working_directory():\\n with open(\\\"a.c\\\", \\\"w\\\") as f:\\n f.write(\\n \\\"\\\"\\\"\\n#include \\\"b.h\\\"\\n\\nint A() {\\n return B();\\n}\\n\\nint main() {\\n return A();\\n}\\n\\\"\\\"\\\"\\n )\\n\\n with open(\\\"b.c\\\", \\\"w\\\") as f:\\n f.write(f\\\"int B() {{ return {retcode}; }}\\\")\\n\\n with open(\\\"b.h\\\", \\\"w\\\") as f:\\n f.write(\\\"int B();\\\")\\n\\n # Compile b.c to object file:\\n subprocess.check_call([str(llvm_paths.clang_path()), \\\"b.c\\\", \\\"-c\\\"], timeout=60)\\n assert (Path(\\\"b.o\\\")).is_file()\\n\\n bm = env.make_benchmark_from_command_line([\\\"gcc\\\", \\\"a.c\\\", \\\"b.o\\\", \\\"-o\\\", \\\"foo\\\"])\\n env.reset(benchmark=bm)\\n\\n bm.compile(env)\\n assert Path(\\\"foo\\\").is_file()\\n\\n p = subprocess.Popen([\\\"./foo\\\"])\\n p.communicate(timeout=60)\\n assert p.returncode == retcode\",\n \"def benchmark(nx, tstop):\\n Lx = 10\\n Ly = 10\\n c = 1.0\\n ny = nx\\n\\n # our use of weave requires string formulas:\\n Is = StringFunction('exp(-pow(x-Lx/2.0,2)/2.0 -pow(y-Ly/2.0,2)/2.0)',\\n independent_variables=('x','y'),\\n Lx=Lx, Ly=Ly, globals=globals())\\n fs = StringFunction('0.0', independent_variables=('x', 'y', 't'),\\n globals=globals())\\n BCs = StringFunction('0.0', independent_variables=('x', 'y', 't'),\\n globals=globals())\\n\\n def action(u, xv, yv, t):\\n #print t\\n pass\\n\\n implementation = {}\\n cpu = []\\n for ic in 'f77', 'vec', 'scalar', 'weave':\\n for bc in 'f77', 'vec', 'scalar', 'weave':\\n for inner in 'f77', 'vec', 'scalar', 'weave':\\n implementation['ic'] = ic\\n implementation['inner'] = inner\\n implementation['bc'] = bc\\n # optimize StringFunction functions for the non-weave case:\\n # implementation:\\n if 'weave' in (ic, bc, inner) or 'f77' in (ic, bc, inner):\\n I = Is; f = fs; BC = BCs\\n else:\\n I = Is.__call__; f = fs.__call__; BC = BCs.__call__\\n\\n t0 = time.clock()\\n dt, cpu_ic, cpu_inner, cpu_bc = \\\\\\n solver(I, f, c, BC, Lx, Ly, nx, ny, 0, tstop,\\n user_action=None,\\n implementation=implementation,\\n verbose=False)\\n t1 = time.clock()\\n cpu_total = cpu_ic + cpu_inner + cpu_bc\\n overhead = (t1-t0)-cpu_total\\n cpu.append([implementation.copy(), cpu_total,\\n cpu_ic, cpu_inner, cpu_bc, overhead])\\n print t1-t0, implementation, 'overhead:', overhead\\n # normalize CPU-times:\\n cpu_min = min([abs(c) for i, c, c1, c2, c3, c4 in cpu])\\n print '\\\\n\\\\nMinimum CPU time:', cpu_min\\n print 'no of time steps:', int(tstop/dt)\\n print 'interior/boundary ratio:', int(nx*ny*1.0/max(nx,ny))\\n for impl, cpu, cpu_ic, cpu_inner, cpu_bc, overhead in cpu:\\n # normalized-CPU ic inner bc overhead\\n print \\\"%8.2f\\\" % (cpu/cpu_min),\\n print \\\"%-10s %8.2f; \\\" % (impl['ic'], cpu_ic),\\n print \\\"%-10s %8.2f; \\\" % (impl['inner'], cpu_inner),\\n print \\\"%-10s %8.2f; \\\" % (impl['bc'], cpu_bc),\\n print \\\"%d%%\\\" % (overhead/cpu*100)\",\n \"def get_latest_benchmark():\\n\\n benchmark_paths = glob.glob(\\\"./.benchmarks/*/*.json\\\")\\n dates = [\\n \\\"\\\".join(_b.split(\\\"/\\\")[-1].split(\\\"_\\\")[2:4]) for _b in benchmark_paths\\n ]\\n benchmarks = {date: value for date, value in zip(dates, benchmark_paths)}\\n\\n dates.sort()\\n latest = dates[-1]\\n benchmark_latest = benchmarks[latest]\\n\\n return benchmark_latest\",\n \"def test_countdown_performance():\\n profiler = cProfile.Profile()\\n profiler.enable()\\n countdown(\\\"0 0 0 0 0 0 1000\\\")\\n profiler.disable()\\n stats = profiler.getstats()\\n tot_time = stats[0].totaltime\\n assert tot_time < 3, \\\"Wow, your computer is really slow. Or is it my code?\\\"\",\n \"def test09(self):\\n a = np.arange(10000) > 5000\\n b = bcolz.carray(a, chunklen=100)\\n b1 = b.wheretrue(skip=1020, limit=1020)\\n b2 = b.wheretrue(skip=1030, limit=1020)\\n a1 = a.nonzero()[0].tolist()[1020:2040]\\n a2 = a.nonzero()[0].tolist()[1030:2050]\\n # print \\\"result:\\\", [i for i in zip(b1, b2)]\\n self.assertEqual([i for i in zip(a1, a2)], [i for i in zip(b1, b2)])\",\n \"def workbench_scenarios():\\n return [\\n (\\\"SummaryXBlock\\\",\\n \\\"\\\"\\\"<summary/>\\n \\\"\\\"\\\"),\\n (\\\"Multiple SummaryXBlock\\\",\\n \\\"\\\"\\\"<vertical_demo>\\n <summary/>\\n <summary/>\\n <summary/>\\n </vertical_demo>\\n \\\"\\\"\\\"),\\n ]\",\n \"def generate_rst_analysis(benchmarks, dbpath, outpath, gh_repo=None,\\n checks=[ConsistentlyWorse(10, 0.01)]):\\n with open(os.path.join(outpath, 'analysis.rst'), 'w') as f:\\n print >> f, \\\"\\\"\\\"\\nBenchmarks Performance Analysis\\n===============================\\n\\\"\\\"\\\"\\n all_res = []\\n for b in benchmarks:\\n # basic analysis: find\\n for check in checks:\\n results = b.get_results(dbpath)\\n res = check(results)\\n if res:\\n res['benchmark'] = \\\":ref:`%s`\\\" % b.get_rst_label()\\n res['reference_date'] = res['reference'].name.strftime(\\\"%Y.%m.%d\\\")\\n res['check'] = str(check)\\n if res['latest_better'] is not None and res['earliest_notworse'] is not None:\\n r1 = res['latest_better']['revision']\\n r2 = res['earliest_notworse']['revision']\\n # how many commits are in between\\n ndiff = len(results[res['latest_better'].name:\\n res['earliest_notworse'].name])-1\\n diff = '%(r1)s...%(r2)s' % locals()\\n diff_ = '(>=%(ndiff)d)%(diff)s' % locals() if ndiff > 1 else diff\\n res['source_diff'] = \\\\\\n ('`%(diff_)s <%(gh_repo)s/compare/%(diff)s>`__'\\n if gh_repo else \\\"%(diff_)s\\\" ) % locals()\\n else:\\n res['source_diff'] = 'N/A'\\n all_res.append(res)\\n\\n if res:\\n # sort all by the slowdown_percent showing the slowest first\\n all_res = sorted(all_res, key=lambda x:x['slowdown_percent'], reverse=True)\\n print >> f, \\\"\\\"\\\"\\n.. container:: benchmarks_analysis clear\\n\\n .. list-table::\\n :header-rows: 1\\n :stub-columns: 1\\n :widths: 32 30 6 4 4 4 20\\n\\n * - Benchmark\\n - Check\\n - Slowdown %\\n - Reference date\\n - Reference timing\\n - Target timing\\n - Possible recent\\\"\\\"\\\"\\n\\n for res in all_res:\\n print >> f, \\\"\\\"\\\"\\\\\\n * - %(benchmark)s\\n - %(check)s\\n - %(slowdown_percent).1f\\n - %(reference_date)s\\n - %(reference_timing).2g\\n - %(target_timing).2g\\n - %(source_diff)s\\\"\\\"\\\" % res\",\n \"def test00(self):\\n a = np.arange(20)\\n cnames = bcolz.blosc_compressor_list()\\n if common.verbose:\\n print(\\\"Checking compressors:\\\", cnames)\\n # print \\\"\\\\nsize b uncompressed-->\\\", a.size * a.dtype.itemsize\\n for cname in cnames:\\n b = bcolz.carray(a, rootdir=self.rootdir,\\n cparams=bcolz.cparams(clevel=9, cname=cname))\\n # print \\\"size b compressed -->\\\", b.cbytes, \\\"with '%s'\\\"%cname\\n self.assertTrue(sys.getsizeof(b) > b.nbytes,\\n \\\"compression does not seem to have any overhead\\\")\\n assert_array_equal(a, b[:], \\\"Arrays are not equal\\\")\\n # Remove the array on disk before trying with the next one\\n if self.disk:\\n common.remove_tree(self.rootdir)\",\n \"def benchmark(func):\\n start = time.time()\\n\\n @functools.wraps(func)\\n def wrapper(*args, **kwargs):\\n rc = func(*args, **kwargs)\\n print('Running time: {}'.format(time.time() - start))\\n return rc\\n return wrapper\",\n \"def workbench_scenarios():\\n return [\\n (\\\"KVXBlock\\\",\\n \\\"\\\"\\\"<vertical_demo>\\n <kvxblock/>\\n </vertical_demo>\\n \\\"\\\"\\\"),\\n ]\",\n \"def execute_benchmark(path, budget=None, threads=None):\\n benchmark_path = runtime.binary_path(path)\\n cmd = [benchmark_path, \\\"--benchmark_format=json\\\"]\\n if budget is not None:\\n cmd += [\\\"-b\\\", str(budget)]\\n if threads is not None:\\n cmd += [\\\"-t\\\", str(threads)]\\n res = process.execute(cmd)\\n return json.loads(res.std_out)\",\n \"def get_functional_test_cases(test_suite):\\n return get_cases(test_suite, r'test_(?!perf_)')\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.69631344","0.64020723","0.61482394","0.56974643","0.5492792","0.54700804","0.5441572","0.5409977","0.5309843","0.52634937","0.5233209","0.52182776","0.52005297","0.5169763","0.5148801","0.51456755","0.51412374","0.51203877","0.5105856","0.5091856","0.5067771","0.5053111","0.5039504","0.50090104","0.49922386","0.4979186","0.49667445","0.4966193","0.49571356","0.4929252","0.49290818","0.4896778","0.48925945","0.486683","0.4865787","0.48546195","0.48330063","0.48260778","0.48103616","0.48091775","0.479842","0.47874612","0.47872743","0.47753528","0.47753528","0.4766883","0.47658345","0.47398612","0.47114697","0.47074088","0.4700743","0.46945432","0.46923196","0.46842483","0.46795043","0.4673328","0.4670609","0.46626735","0.46557018","0.46477422","0.4632773","0.46276176","0.46268535","0.46167308","0.46081945","0.46064252","0.46057755","0.46043095","0.4604208","0.46026236","0.46008366","0.45927143","0.45859885","0.4583752","0.45684317","0.45614076","0.45561868","0.45552546","0.4552955","0.45518562","0.4550173","0.45484203","0.4547495","0.45416203","0.45393252","0.45312825","0.45297357","0.45234916","0.45186687","0.45138487","0.45133057","0.4510495","0.45096532","0.45019996","0.45008147","0.4499237","0.44854194","0.44804475","0.44734982","0.44688565"],"string":"[\n \"0.69631344\",\n \"0.64020723\",\n \"0.61482394\",\n \"0.56974643\",\n \"0.5492792\",\n \"0.54700804\",\n \"0.5441572\",\n \"0.5409977\",\n \"0.5309843\",\n \"0.52634937\",\n \"0.5233209\",\n \"0.52182776\",\n \"0.52005297\",\n \"0.5169763\",\n \"0.5148801\",\n \"0.51456755\",\n \"0.51412374\",\n \"0.51203877\",\n \"0.5105856\",\n \"0.5091856\",\n \"0.5067771\",\n \"0.5053111\",\n \"0.5039504\",\n \"0.50090104\",\n \"0.49922386\",\n \"0.4979186\",\n \"0.49667445\",\n \"0.4966193\",\n \"0.49571356\",\n \"0.4929252\",\n \"0.49290818\",\n \"0.4896778\",\n \"0.48925945\",\n \"0.486683\",\n \"0.4865787\",\n \"0.48546195\",\n \"0.48330063\",\n \"0.48260778\",\n \"0.48103616\",\n \"0.48091775\",\n \"0.479842\",\n \"0.47874612\",\n \"0.47872743\",\n \"0.47753528\",\n \"0.47753528\",\n \"0.4766883\",\n \"0.47658345\",\n \"0.47398612\",\n \"0.47114697\",\n \"0.47074088\",\n \"0.4700743\",\n \"0.46945432\",\n \"0.46923196\",\n \"0.46842483\",\n \"0.46795043\",\n \"0.4673328\",\n \"0.4670609\",\n \"0.46626735\",\n \"0.46557018\",\n \"0.46477422\",\n \"0.4632773\",\n \"0.46276176\",\n \"0.46268535\",\n \"0.46167308\",\n \"0.46081945\",\n \"0.46064252\",\n \"0.46057755\",\n \"0.46043095\",\n \"0.4604208\",\n \"0.46026236\",\n \"0.46008366\",\n \"0.45927143\",\n \"0.45859885\",\n \"0.4583752\",\n \"0.45684317\",\n \"0.45614076\",\n \"0.45561868\",\n \"0.45552546\",\n \"0.4552955\",\n \"0.45518562\",\n \"0.4550173\",\n \"0.45484203\",\n \"0.4547495\",\n \"0.45416203\",\n \"0.45393252\",\n \"0.45312825\",\n \"0.45297357\",\n \"0.45234916\",\n \"0.45186687\",\n \"0.45138487\",\n \"0.45133057\",\n \"0.4510495\",\n \"0.45096532\",\n \"0.45019996\",\n \"0.45008147\",\n \"0.4499237\",\n \"0.44854194\",\n \"0.44804475\",\n \"0.44734982\",\n \"0.44688565\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.801431"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":11275,"cells":{"query":{"kind":"string","value":"Returns the prorgamming language the benchmark was written in."},"document":{"kind":"string","value":"def get_language(benchmark):\n config = benchmark_config.get_config(benchmark)\n return config.get('language', 'c++')"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def programming_language(self) -> str:\n return self.random.choice(PROGRAMMING_LANGS)","def generation_language(self) -> pulumi.Output[Optional[str]]:\n return pulumi.get(self, \"generation_language\")","def code(self):\n return self.language()","def language(self):\n return \"QSS\"","def getWikiLanguageName():\r\n return \"wikidpad_mini_1_0\"","def get_language():\n disabled_modules = ['tokenizer', 'tagger', 'parser', 'textcat']\n nlp = spacy.load('en_core_web_md', disable=disabled_modules)\n # we are not interested in stop-words as most of them are\n # needed in the short sentence examples in relation definitions\n spacy_wmd_hook = wmd.WMD.SpacySimilarityHook(nlp, ignore_stops=False)\n nlp.add_pipe(spacy_wmd_hook, last=True)\n return nlp","def language(self, target):\n self._check_target(target)\n return target.language or self._default_language","def language(self, max_length=None, **kwargs):\n T = self.with_output()\n return T.language(max_length)","def language(self):\n # type: () -> string_types\n return self._language","def language(self):\n # type: () -> string_types\n return self._language","def audio_language(self):\n # type: () -> string_types\n return self._audio_language","def lang_genoeg(lengte):\n return","def language(self):\n portal_state = self.context.unrestrictedTraverse(\"@@plone_portal_state\")\n return aq_inner(self.context).Language() or portal_state.default_language()","def language():\r\n\r\n cursor.execute('SELECT name from languages order by RANDOM() limit 1;')\r\n return cursor.fetchone()[0]","def identifyLangage(script):\n\tlangage = \"undefined\"\n\tscriptNameInArray = script.split(\".\")\n\textension = scriptNameInArray[-1]\n\t\n\tif(extension == \"pl\"):\n\t\tlangage = \"perl\"\n\telif(extension == \"py\"):\n\t\tlangage = \"python\"\n\telif(extension == \"sh\"):\n\t\tlangage = \"bash\"\n\telse:\n\t\tlangage == \"not recognised\"\n\n\treturn langage","def language(self):\n if \"language\" in self._prop_dict:\n return self._prop_dict[\"language\"]\n else:\n return None","def get_full_language(self, language):\n if language:\n language = pycountry.languages.get(alpha_2=language)\n if language:\n language = language.name\n return language.title()","def language(self) -> str:\n if self.language_code in CODE_TO_LANGUAGE:\n return CODE_TO_LANGUAGE[self.language_code]\n\n return self.language_code","def get_language(self, text):\n try:\n post_lang = detect(text)\n except:\n post_lang = 'N/A'\n return post_lang","def language(self):\n hcell = self._get_hcell2()\n celltype = hcell[\"celltype\"]\n if celltype != \"code\":\n raise AttributeError\n return hcell.get(\"language\", \"python\")","def srclang(self):\n return self.__srclang","def language_code(self) -> str:\n return pulumi.get(self, \"language_code\")","def get_lang(self):\n return self.langs.lang","def get_language(mgroups):\n\n if mgroups:\n lang = mgroups[0].strip('[').strip(']')\n return lang.lower().strip()\n return None","def get_related_language(self) -> str:\n pass","def _getLang(self, language):\n if language == None:\n language = self.getDefaultLanguage()\n\n return language","def get_language(self) -> str:\n return self.language","def get_language(lang_code) -> str:\n langs = defaultdict(lambda: \"en\", {\"ru\": \"ru\"})\n return langs[lang_code.split(\"-\")[0]] if lang_code else \"en\"","def language(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"language\")","def get_locale():\n return \"he\"","def generation_language(self) -> Optional[pulumi.Input[Union[str, 'GenerationLanguage']]]:\n return pulumi.get(self, \"generation_language\")","def get_language(self):\n return self.lang","def get_lang(self):\n\n path = self.get_lang_path()\n for language in self.languages:\n if language in path:\n return language","def language(self) -> str:\n return self._language","def language(self) -> str:\n return self._language","def get_project_lang(self):\n return self.project_name_lang.currentText() # .replace(\"é\",\"e\").lower()","def get_language(self, word, lang=None):\n lang = lang or self.cfg.get('lang', 'en')\n # let's retrieve the word from configuration dict.\n try:\n return self.cfg['words_' + lang][word]\n except StandardError:\n return 'Do not know how to \"{}\" in \"{}\"'.format(word, lang)","def get_language(self, article):\r\n # we don't want to force the target laguage\r\n # so we use the article.meta_lang\r\n if self.config.use_meta_language == True:\r\n if article.meta_lang:\r\n return article.meta_lang[:2]\r\n return self.config.target_language","def language(self):\n lang = None\n if self.__dict__['TAG:language']:\n lang = self.__dict__['TAG:language']\n return lang","def get_language(self):\r\n return self.language","def language(self):\r\n return self._get('language', {})","def language( self, preferredWeaverClass=None ):\n if preferredWeaverClass:\n return preferredWeaverClass()\n self.logger.debug( \"Picking a weaver based on first chunk {!r}\".format(self.chunkSeq[0][:4]) )\n if self.chunkSeq[0].startswith('<'): \n return HTML()\n if self.chunkSeq[0].startswith('%') or self.chunkSeq[0].startswith('\\\\'): \n return LaTeX()\n return RST()","def lang(self):\n return self._lang","def is_cpp(benchmark):\n return get_language(benchmark) == 'c++'","def choose_language(self):\n\n current_dir = os.curdir\n path = os.path.join(current_dir, \"audio\")\n languages = os.listdir(path)\n language_num = randint(0, len(languages) - 1)\n return languages[language_num]","def _detect_language(self, text):\n\n ratios = self._calculate_languages_ratios(text)\n\n most_rated_language = max(ratios, key=ratios.get)\n\n return most_rated_language","def getLanguage(self):\n return self.getOrDefault(self.language)","def language(self):\n return self._language","def language(self):\n return self._language","def language(self):\n return self._language","def language(self):\n return self._language","def to_language(self):\n return self.language()","def get_lang(self):\n props = getToolByName(self.context,\n 'portal_properties')\n return props.site_properties.getProperty('default_language') or 'en'","def getTRSLanguage():\n try:\n return sys.argv[1]\n except IndexError as error:\n print(\"No language argument\\n\")\n sys.exit()","def subtitle_language(self):\n # type: () -> string_types\n return self._subtitle_language","def get_language_script(script):\n languages_scripts = {\n 'arab': ('ara', 'per'),\n 'cyrl': ('bel', 'chu', 'mac', 'rus', 'srp', 'ukr'),\n 'grek': ('grc', 'gre'),\n 'hani': ('chi', 'jpn'),\n 'hebr': ('heb', 'lad', 'yid'),\n 'jpan': ('jpn', ),\n 'kore': ('kor', ),\n 'zyyy': ('chi', )\n }\n if script in languages_scripts:\n languages = ([marc21.lang_from_008] +\n marc21.langs_from_041_a +\n marc21.langs_from_041_h)\n for lang in languages:\n if lang in languages_scripts[script]:\n return '-'.join([lang, script])\n error_print('WARNING LANGUAGE SCRIPTS:', marc21.bib_id,\n script, '008:', marc21.lang_from_008,\n '041$a:', marc21.langs_from_041_a,\n '041$h:', marc21.langs_from_041_h)\n return '-'.join(['und', script])","def get_exe_language(code_file):\n extension = code_file.split('.')[-1]\n if extension == 'py':\n return 'python'\n elif extension == 'm':\n return 'matlab'\n elif extension == 'sh':\n return 'bash'\n elif extension == 'rb':\n return 'ruby'\n else:\n print(\"Warning: file %s don't have any known extension \\\n(.py/.m/.sh/.rb)\" % code_file)\n return None","def assoc_language(self):\n # type: () -> string_types\n return self._assoc_language","def lemma(self) -> str:","def language_name(self, text: str) -> str:\n values = extract(text)\n input_fn = _to_func(([values], []))\n pos: int = next(self._classifier.predict_classes(input_fn=input_fn))\n\n LOGGER.debug(\"Predicted language position %s\", pos)\n return sorted(self.languages)[pos]","def get_language(self) -> str:\n return settings.LANGUAGE_CODE","def english_speaking(self) -> int:\n return self._english_speaking","def get_language(tokens, stopwords):\n\n languages_ratios = dict()\n\n words = [str(word).lower() for word in tokens]\n words_set = set(words)\n\n for language in stopwords.keys():\n common_elements = words_set.intersection(stopwords[language])\n\n languages_ratios[language] = len(common_elements) # language score\n\n return max(languages_ratios, key=languages_ratios.get)","def mostlikelycodeword(self):\n\n # Add your code here\n stoping_int = None # Replace\n best, first_index, last_index = None, None, None # Replace all three with an integer\n for i in range(stoping_int):\n for j in range(None, stoping_int): # Replace None. \n current = self.quality(None, None)\n if None > None # Replace both Nones\n best, first_index, last_index = current, i, j\n return self.preamble[None:None]","def language_code(self):\n return self._language_code","def detect_language(lang, kernel_source):\n if lang is None:\n if callable(kernel_source):\n raise TypeError(\"Please specify language when using a code generator function\")\n kernel_string = get_kernel_string(kernel_source)\n if \"__global__\" in kernel_string:\n lang = \"CUDA\"\n elif \"__kernel\" in kernel_string:\n lang = \"OpenCL\"\n else:\n lang = \"C\"\n return lang","def to_language(arg: str) -> Tuple[Union[str, None], str]: \n if (low:= arg.lower()) in LANGUAGES:\n return arg\n else:\n return LANGCODES.get(low, None)","def get_user_language() -> str:\n languages = {\n \"arabic\": \"arb\",\n \"chinese\": \"cmn-CN\",\n \"danish\": \"da-DK\",\n \"english\": \"en-GB\",\n \"french\": \"fr-FR\",\n \"german\": \"de-DE\",\n \"portuguese\": \"pl-PT\",\n \"spanish\": \"es-ES\"\n }\n textlang = input(\"What language do you want to hear?\")\n try:\n return languages[textlang.lower()]\n except KeyError as e:\n print(\"Enter a valid language.\")\n sys.exit(1)","def language_framework(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"language_framework\")","def get_language(self):\n return self.language if self.language is not None else get_language()","def get_language_name(self):\n return self.language_name","def algorithm(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"algorithm\")","def description(self, lang=0):\n if not lang in self.localized_strings: return \"\"\n return self.localized_strings[lang]","def get_language():\n try:\n from leaves.middleware import request_context\n return request_context.language\n except:\n return get_site().preferences.default_language","def get_proper_language():\n lang = config['summernote'].get('lang')\n\n if not lang:\n return config['lang_matches'].get(get_language(), 'en-US')\n\n return lang","def generate_language(self, properties):\n\n drink_property = self.find_drink_property(properties)\n\n if drink_property:\n return self.generate_sentence(drink_property)\n else:\n return \"\"","def detect_language(text, LANGUAGES):\n lang = None\n word_count = 0\n our_test = []\n \n for language in LANGUAGES:\n \n result = get_word_count(text, language['common_words'])\n print(result)\n #import pdb; pdb.set_trace()\n if result > word_count:\n lang = language['name']\n word_count = result\n \n return lang","def describeWikiLanguage(ver, app):\r\n\r\n return ((\"wikidpad_mini_1_0\", u\"WikidPad Mini 1.0\", parserFactory,\r\n True, languageHelperFactory, True),)","def languages():\n return \", \".join(sorted(\"{}: '{}'\".format(gTTS.LANGUAGES[k], k) for k in gTTS.LANGUAGES))","def Problem9():\n return 'MSS'","def test_parser_infers_language(self):\n filename = \"main.c\"\n language = self.parser.infer_language(filename)\n assert_equal(\"C\", language)","def test_langid_benchmark(basic_multilingual):\n examples = [\n {\"text\": \"contingentiam in naturalibus causis.\", \"label\": \"la\"},\n {\"text\": \"I jak opowiadał nieżyjący już pan Czesław\", \"label\": \"pl\"},\n {\"text\": \"Sonera gilt seit längerem als Übernahmekandidat\", \"label\": \"de\"},\n {\"text\": \"与银类似，汞也可以与空气中的硫化氢反应。\", \"label\": \"zh-hans\"},\n {\"text\": \"contradictionem implicat.\", \"label\": \"la\"},\n {\"text\": \"Bis zu Prozent gingen die Offerten etwa im\", \"label\": \"de\"},\n {\"text\": \"inneren Sicherheit vorgeschlagene Ausweitung der\", \"label\": \"de\"},\n {\"text\": \"Multimedia-PDA mit Mini-Tastatur\", \"label\": \"de\"},\n {\"text\": \"Ponášalo sa to na rovnicu o dvoch neznámych.\", \"label\": \"sk\"},\n {\"text\": \"이처럼 앞으로 심판의 그 날에 다시 올 메시아가 예수 그리스도이며 , 그는 모든 인류의\", \"label\": \"ko\"},\n {\"text\": \"Die Arbeitsgruppe bedauert , dass der weit über\", \"label\": \"de\"},\n {\"text\": \"И только раз довелось поговорить с ним не вполне\", \"label\": \"ru\"},\n {\"text\": \"de a-l lovi cu piciorul și conștiința că era\", \"label\": \"ro\"},\n {\"text\": \"relación coas pretensións do demandante e que, nos\", \"label\": \"gl\"},\n {\"text\": \"med petdeset in sedemdeset\", \"label\": \"sl\"},\n {\"text\": \"Catalunya; el Consell Comarcal del Vallès Oriental\", \"label\": \"ca\"},\n {\"text\": \"kunnen worden.\", \"label\": \"nl\"},\n {\"text\": \"Witkin je ve většině ohledů zcela jiný.\", \"label\": \"cs\"},\n {\"text\": \"lernen, so zu agieren, dass sie positive oder auch\", \"label\": \"de\"},\n {\"text\": \"olurmuş...\", \"label\": \"tr\"},\n {\"text\": \"sarcasmo de Altman, desde as «peruas» que discutem\", \"label\": \"pt\"},\n {\"text\": \"خلاف فوجداری مقدمہ درج کرے۔\", \"label\": \"ur\"},\n {\"text\": \"Norddal kommune :\", \"label\": \"no\"},\n {\"text\": \"dem Windows-.-Zeitalter , soll in diesem Jahr\", \"label\": \"de\"},\n {\"text\": \"przeklętych ucieleśniają mit poety-cygana,\", \"label\": \"pl\"},\n {\"text\": \"We do not believe the suspect has ties to this\", \"label\": \"en\"},\n {\"text\": \"groziņu pīšanu.\", \"label\": \"lv\"},\n {\"text\": \"Senior Vice-President David M. Thomas möchte\", \"label\": \"de\"},\n {\"text\": \"neomylně vybral nějakou knihu a začetl se.\", \"label\": \"cs\"},\n {\"text\": \"Statt dessen darf beispielsweise der Browser des\", \"label\": \"de\"},\n {\"text\": \"outubro, alcançando R $ bilhões em .\", \"label\": \"pt\"},\n {\"text\": \"(Porte, ), as it does other disciplines\", \"label\": \"en\"},\n {\"text\": \"uskupení se mylně domnívaly, že podporu\", \"label\": \"cs\"},\n {\"text\": \"Übernahme von Next Ende an dem System herum , das\", \"label\": \"de\"},\n {\"text\": \"No podemos decir a la Hacienda que los alemanes\", \"label\": \"es\"},\n {\"text\": \"и рѣста еи братья\", \"label\": \"orv\"},\n {\"text\": \"الذي اتخذ قرارا بتجميد اعلان الدولة الفلسطينية\", \"label\": \"ar\"},\n {\"text\": \"uurides Rootsi sõjaarhiivist toodud . sajandi\", \"label\": \"et\"},\n {\"text\": \"selskapets penger til å pusse opp sin enebolig på\", \"label\": \"no\"},\n {\"text\": \"средней полосе и севернее в Ярославской,\", \"label\": \"ru\"},\n {\"text\": \"il-massa żejda fil-ġemgħat u superġemgħat ta'\", \"label\": \"mt\"},\n {\"text\": \"The Global Beauties on internetilehekülg, mida\", \"label\": \"et\"},\n {\"text\": \"이스라엘 인들은 하나님이 그 큰 팔을 펴 이집트 인들을 치는 것을 보고 하나님을 두려워하며\", \"label\": \"ko\"},\n {\"text\": \"Snad ještě dodejme jeden ekonomický argument.\", \"label\": \"cs\"},\n {\"text\": \"Spalio d. vykusiame pirmajame rinkimų ture\", \"label\": \"lt\"},\n {\"text\": \"und schlechter Journalismus ein gutes Geschäft .\", \"label\": \"de\"},\n {\"text\": \"Du sodiečiai sėdi ant potvynio apsemtų namų stogo.\", \"label\": \"lt\"},\n {\"text\": \"цей є автентичним.\", \"label\": \"uk\"},\n {\"text\": \"Și îndegrabă fu cu îngerul mulțime de șireaguri\", \"label\": \"ro\"},\n {\"text\": \"sobra personal cualificado.\", \"label\": \"es\"},\n {\"text\": \"Tako se u Njemačkoj dvije trećine liječnika služe\", \"label\": \"hr\"},\n {\"text\": \"Dual-Athlon-Chipsatz noch in diesem Jahr\", \"label\": \"de\"},\n {\"text\": \"यहां तक कि चीन के चीफ ऑफ जनरल स्टाफ भी भारत का\", \"label\": \"hi\"},\n {\"text\": \"Li forestier du mont avale\", \"label\": \"fro\"},\n {\"text\": \"Netzwerken für Privatanwender zu bewundern .\", \"label\": \"de\"},\n {\"text\": \"만해는 승적을 가진 중이 결혼할 수 없다는 불교의 계율을 시대에 맞지 않는 것으로 보았다\", \"label\": \"ko\"},\n {\"text\": \"balance and weight distribution but not really for\", \"label\": \"en\"},\n {\"text\": \"og så e # tente vi opp den om morgonen å sfyrte\", \"label\": \"nn\"},\n {\"text\": \"변화는 의심의 여지가 없는 것이지만 반면에 진화는 논쟁의 씨앗이다 .\", \"label\": \"ko\"},\n {\"text\": \"puteare fac aceastea.\", \"label\": \"ro\"},\n {\"text\": \"Waitt seine Führungsmannschaft nicht dem\", \"label\": \"de\"},\n {\"text\": \"juhtimisega, tulid sealt.\", \"label\": \"et\"},\n {\"text\": \"Veränderungen .\", \"label\": \"de\"},\n {\"text\": \"banda en el Bayer Leverkusen de la Bundesliga de\", \"label\": \"es\"},\n {\"text\": \"В туже зиму посла всеволодъ сн҃а своѥго ст҃ослава\", \"label\": \"orv\"},\n {\"text\": \"пославъ приведе я мастеры ѿ грекъ\", \"label\": \"orv\"},\n {\"text\": \"En un nou escenari difícil d'imaginar fa poques\", \"label\": \"ca\"},\n {\"text\": \"καὶ γὰρ τινὲς αὐτοὺς εὐεργεσίαι εἶχον ἐκ Κροίσου\", \"label\": \"grc\"},\n {\"text\": \"직접적인 관련이 있다 .\", \"label\": \"ko\"},\n {\"text\": \"가까운 듯하면서도 멀다 .\", \"label\": \"ko\"},\n {\"text\": \"Er bietet ein ähnliches Leistungsniveau und\", \"label\": \"de\"},\n {\"text\": \"民都洛水牛是獨居的，並不會以群族聚居。\", \"label\": \"zh-hant\"},\n {\"text\": \"την τρομοκρατία.\", \"label\": \"el\"},\n {\"text\": \"hurbiltzen diren neurrian.\", \"label\": \"eu\"},\n {\"text\": \"Ah dimenticavo, ma tutta sta caciara per fare un\", \"label\": \"it\"},\n {\"text\": \"На первом этапе (-) прошла так называемая\", \"label\": \"ru\"},\n {\"text\": \"of games are on the market.\", \"label\": \"en\"},\n {\"text\": \"находится Мост дружбы, соединяющий узбекский и\", \"label\": \"ru\"},\n {\"text\": \"lessié je voldroie que li saint fussent aporté\", \"label\": \"fro\"},\n {\"text\": \"Дошла очередь и до Гималаев.\", \"label\": \"ru\"},\n {\"text\": \"vzácným suknem táhly pouští, si jednou chtěl do\", \"label\": \"cs\"},\n {\"text\": \"E no terceiro tipo sitúa a familias (%), nos que a\", \"label\": \"gl\"},\n {\"text\": \"وجابت دوريات امريكية وعراقية شوارع المدينة، فيما\", \"label\": \"ar\"},\n {\"text\": \"Jeg har bodd her i år .\", \"label\": \"no\"},\n {\"text\": \"Pohrozil, že odbory zostří postoj, pokud se\", \"label\": \"cs\"},\n {\"text\": \"tinham conseguido.\", \"label\": \"pt\"},\n {\"text\": \"Nicht-Erkrankten einen Anfangsverdacht für einen\", \"label\": \"de\"},\n {\"text\": \"permanece em aberto.\", \"label\": \"pt\"},\n {\"text\": \"questi possono promettere rendimenti fino a un\", \"label\": \"it\"},\n {\"text\": \"Tema juurutatud kahevedurisüsteemita oleksid\", \"label\": \"et\"},\n {\"text\": \"Поведение внешне простой игрушки оказалось\", \"label\": \"ru\"},\n {\"text\": \"Bundesländern war vom Börsenverein des Deutschen\", \"label\": \"de\"},\n {\"text\": \"acció, 'a mesura que avanci l'estiu, amb l'augment\", \"label\": \"ca\"},\n {\"text\": \"Dove trovare queste risorse? Jay Naidoo, ministro\", \"label\": \"it\"},\n {\"text\": \"essas gordurinhas.\", \"label\": \"pt\"},\n {\"text\": \"Im zweiten Schritt sollen im übernächsten Jahr\", \"label\": \"de\"},\n {\"text\": \"allveelaeva pole enam vaja, kuna külm sõda on läbi\", \"label\": \"et\"},\n {\"text\": \"उपद्रवी दुकानों को लूटने के साथ ही उनमें आग लगा\", \"label\": \"hi\"},\n {\"text\": \"@user nella sfortuna sei fortunata ..\", \"label\": \"it\"},\n {\"text\": \"математических школ в виде грозовых туч.\", \"label\": \"ru\"},\n {\"text\": \"No cambiaremos nunca nuestra forma de jugar por un\", \"label\": \"es\"},\n {\"text\": \"dla tej klasy ani wymogów minimalnych, z wyjątkiem\", \"label\": \"pl\"},\n {\"text\": \"en todo el mundo, mientras que en España consiguió\", \"label\": \"es\"},\n {\"text\": \"политики считать надежное обеспечение военной\", \"label\": \"ru\"},\n {\"text\": \"gogoratzen du, genio alemana delakoaren\", \"label\": \"eu\"},\n {\"text\": \"Бычий глаз.\", \"label\": \"ru\"},\n {\"text\": \"Opeření se v pravidelných obdobích obnovuje\", \"label\": \"cs\"},\n {\"text\": \"I no és només la seva, es tracta d'una resposta\", \"label\": \"ca\"},\n {\"text\": \"오경을 가르쳤다 .\", \"label\": \"ko\"},\n {\"text\": \"Nach der so genannten Start-up-Periode vergibt die\", \"label\": \"de\"},\n {\"text\": \"Saulista huomasi jo lapsena , että hänellä on\", \"label\": \"fi\"},\n {\"text\": \"Министерство культуры сочло нецелесообразным, и\", \"label\": \"ru\"},\n {\"text\": \"znepřátelené tábory v Tádžikistánu předseda\", \"label\": \"cs\"},\n {\"text\": \"καὶ ἦν ὁ λαὸς προσδοκῶν τὸν Ζαχαρίαν καὶ ἐθαύμαζον\", \"label\": \"grc\"},\n {\"text\": \"Вечером, в продукте, этот же человек говорил о\", \"label\": \"ru\"},\n {\"text\": \"lugar á formación de xuizos máis complexos.\", \"label\": \"gl\"},\n {\"text\": \"cheaper, in the end?\", \"label\": \"en\"},\n {\"text\": \"الوزارة في شأن صفقات بيع الشركات العامة التي تم\", \"label\": \"ar\"},\n {\"text\": \"tärkeintä elämässäni .\", \"label\": \"fi\"},\n {\"text\": \"Виконання Мінських угод було заблоковано Росією та\", \"label\": \"uk\"},\n {\"text\": \"Aby szybko rozpoznać żołnierzy desantu, należy\", \"label\": \"pl\"},\n {\"text\": \"Bankengeschäfte liegen vorn , sagte Strothmann .\", \"label\": \"de\"},\n {\"text\": \"продолжение работы.\", \"label\": \"ru\"},\n {\"text\": \"Metro AG plant Online-Offensive\", \"label\": \"de\"},\n {\"text\": \"nu vor veni, și să vor osîndi, aceia nu pot porni\", \"label\": \"ro\"},\n {\"text\": \"Ich denke , es geht in Wirklichkeit darum , NT bei\", \"label\": \"de\"},\n {\"text\": \"de turism care încasează contravaloarea\", \"label\": \"ro\"},\n {\"text\": \"Aurkaria itotzea da helburua, baloia lapurtu eta\", \"label\": \"eu\"},\n {\"text\": \"com a centre de formació en Tecnologies de la\", \"label\": \"ca\"},\n {\"text\": \"oportet igitur quod omne agens in agendo intendat\", \"label\": \"la\"},\n {\"text\": \"Jerzego Andrzejewskiego, oparty na chińskich\", \"label\": \"pl\"},\n {\"text\": \"sau một vài câu chuyện xã giao không dính dáng tới\", \"label\": \"vi\"},\n {\"text\": \"что экономическому прорыву жесткий авторитарный\", \"label\": \"ru\"},\n {\"text\": \"DRAM-Preisen scheinen DSPs ein\", \"label\": \"de\"},\n {\"text\": \"Jos dajan nubbái: Mana!\", \"label\": \"sme\"},\n {\"text\": \"toți carii ascultară de el să răsipiră.\", \"label\": \"ro\"},\n {\"text\": \"odpowiedzialności, które w systemie własności\", \"label\": \"pl\"},\n {\"text\": \"Dvomesečno potovanje do Mollenda v Peruju je\", \"label\": \"sl\"},\n {\"text\": \"d'entre les agències internacionals.\", \"label\": \"ca\"},\n {\"text\": \"Fahrzeugzugangssysteme gefertigt und an viele\", \"label\": \"de\"},\n {\"text\": \"in an answer to the sharers' petition in Cuthbert\", \"label\": \"en\"},\n {\"text\": \"Europa-Domain per Verordnung zu regeln .\", \"label\": \"de\"},\n {\"text\": \"#Balotelli. Su ebay prezzi stracciati per Silvio\", \"label\": \"it\"},\n {\"text\": \"Ne na košickém trávníku, ale už včera v letadle se\", \"label\": \"cs\"},\n {\"text\": \"zaměstnanosti a investičních strategií.\", \"label\": \"cs\"},\n {\"text\": \"Tatínku, udělej den\", \"label\": \"cs\"},\n {\"text\": \"frecuencia con Mary.\", \"label\": \"es\"},\n {\"text\": \"Свеаборге.\", \"label\": \"ru\"},\n {\"text\": \"opatření slovenské strany o certifikaci nejvíce\", \"label\": \"cs\"},\n {\"text\": \"En todas me decían: 'Espera que hagamos un estudio\", \"label\": \"es\"},\n {\"text\": \"Die Demonstration sollte nach Darstellung der\", \"label\": \"de\"},\n {\"text\": \"Ci vorrà un assoluto rigore se dietro i disavanzi\", \"label\": \"it\"},\n {\"text\": \"Tatínku, víš, že Honzovi odešla maminka?\", \"label\": \"cs\"},\n {\"text\": \"Die Anzahl der Rechner wuchs um % auf und die\", \"label\": \"de\"},\n {\"text\": \"האמריקאית על אדמת סעודיה עלולה לסבך את ישראל, אין\", \"label\": \"he\"},\n {\"text\": \"Volán Egyesülés, a Közlekedési Főfelügyelet is.\", \"label\": \"hu\"},\n {\"text\": \"Schejbala, který stejnou hru s velkým úspěchem\", \"label\": \"cs\"},\n {\"text\": \"depends on the data type of the field.\", \"label\": \"en\"},\n {\"text\": \"Umsatzwarnung zu Wochenbeginn zeitweise auf ein\", \"label\": \"de\"},\n {\"text\": \"niin heti nukun .\", \"label\": \"fi\"},\n {\"text\": \"Mobilfunkunternehmen gegen die Anwendung der so\", \"label\": \"de\"},\n {\"text\": \"sapessi le intenzioni del governo Monti e dell'UE\", \"label\": \"it\"},\n {\"text\": \"Di chi è figlia Martine Aubry?\", \"label\": \"it\"},\n {\"text\": \"avec le reste du monde.\", \"label\": \"fr\"},\n {\"text\": \"Այդ մաքոքը ինքնին նոր չէ, աշխարհը արդեն մի քանի\", \"label\": \"hy\"},\n {\"text\": \"și în cazul destrămării cenaclului.\", \"label\": \"ro\"},\n {\"text\": \"befriedigen kann , und ohne die auftretenden\", \"label\": \"de\"},\n {\"text\": \"Κύκνον τ̓ ἐξεναρεῖν καὶ ἀπὸ κλυτὰ τεύχεα δῦσαι.\", \"label\": \"grc\"},\n {\"text\": \"færdiguddannede.\", \"label\": \"da\"},\n {\"text\": \"Schmidt war Sohn eines Rittergutsbesitzers.\", \"label\": \"de\"},\n {\"text\": \"и вдаша попадь ѡпрати\", \"label\": \"orv\"},\n {\"text\": \"cine nu știe învățătură”.\", \"label\": \"ro\"},\n {\"text\": \"détacha et cette dernière tenta de tuer le jeune\", \"label\": \"fr\"},\n {\"text\": \"Der har saka også ei lengre forhistorie.\", \"label\": \"nn\"},\n {\"text\": \"Pieprz roztłuc w moździerzu, dodać do pasty,\", \"label\": \"pl\"},\n {\"text\": \"Лежа за гребнем оврага, как за бруствером, Ушаков\", \"label\": \"ru\"},\n {\"text\": \"gesucht habe, vielen Dank nochmals!\", \"label\": \"de\"},\n {\"text\": \"инструментальных сталей, повышения\", \"label\": \"ru\"},\n {\"text\": \"im Halbfinale Patrick Smith und im Finale dann\", \"label\": \"de\"},\n {\"text\": \"البنوك التريث في منح تسهيلات جديدة لمنتجي حديد\", \"label\": \"ar\"},\n {\"text\": \"una bolsa ventral, la cual se encuentra debajo de\", \"label\": \"es\"},\n {\"text\": \"za SETimes.\", \"label\": \"sr\"},\n {\"text\": \"de Irak, a un piloto italiano que había violado el\", \"label\": \"es\"},\n {\"text\": \"Er könne sich nicht erklären , wie die Zeitung auf\", \"label\": \"de\"},\n {\"text\": \"Прохорова.\", \"label\": \"ru\"},\n {\"text\": \"la democrazia perde sulla tecnocrazia? #\", \"label\": \"it\"},\n {\"text\": \"entre ambas instituciones, confirmó al medio que\", \"label\": \"es\"},\n {\"text\": \"Austlandet, vart det funne om lag førti\", \"label\": \"nn\"},\n {\"text\": \"уровнями власти.\", \"label\": \"ru\"},\n {\"text\": \"Dá tedy primáři úplatek, a často ne malý.\", \"label\": \"cs\"},\n {\"text\": \"brillantes del acto, al llevar a cabo en el\", \"label\": \"es\"},\n {\"text\": \"eee druga zadeva je majhen priročen gre kamorkoli\", \"label\": \"sl\"},\n {\"text\": \"Das ATX-Board paßt in herkömmliche PC-ATX-Gehäuse\", \"label\": \"de\"},\n {\"text\": \"Za vodné bylo v prvním pololetí zaplaceno v ČR\", \"label\": \"cs\"},\n {\"text\": \"Даже на полсантиметра.\", \"label\": \"ru\"},\n {\"text\": \"com la del primer tinent d'alcalde en funcions,\", \"label\": \"ca\"},\n {\"text\": \"кількох оповідань в цілості — щось на зразок того\", \"label\": \"uk\"},\n {\"text\": \"sed ad divitias congregandas, vel superfluum\", \"label\": \"la\"},\n {\"text\": \"Norma Talmadge, spela mot Valentino i en version\", \"label\": \"sv\"},\n {\"text\": \"Dlatego chciał się jej oświadczyć w niezwykłym\", \"label\": \"pl\"},\n {\"text\": \"будут выступать на одинаковых снарядах.\", \"label\": \"ru\"},\n {\"text\": \"Orang-orang terbunuh di sana.\", \"label\": \"id\"},\n {\"text\": \"لدى رايت شقيق اسمه أوسكار, وهو يعمل كرسام للكتب\", \"label\": \"ar\"},\n {\"text\": \"Wirklichkeit verlagerten und kaum noch\", \"label\": \"de\"},\n {\"text\": \"как перемешивают костяшки перед игрой в домино, и\", \"label\": \"ru\"},\n {\"text\": \"В средине дня, когда солнце светило в нашу\", \"label\": \"ru\"},\n {\"text\": \"d'aventure aux rôles de jeune romantique avec une\", \"label\": \"fr\"},\n {\"text\": \"My teď hledáme organizace, jež by s námi chtěly\", \"label\": \"cs\"},\n {\"text\": \"Urteilsfähigkeit einbüßen , wenn ich eigene\", \"label\": \"de\"},\n {\"text\": \"sua appartenenza anche a voci diverse da quella in\", \"label\": \"it\"},\n {\"text\": \"Aufträge dieses Jahr verdoppeln werden .\", \"label\": \"de\"},\n {\"text\": \"M.E.: Miała szanse mnie odnaleźć, gdyby naprawdę\", \"label\": \"pl\"},\n {\"text\": \"secundum contactum virtutis, cum careat dimensiva\", \"label\": \"la\"},\n {\"text\": \"ezinbestekoa dela esan zuen.\", \"label\": \"eu\"},\n {\"text\": \"Anek hurbiltzeko eskatzen zion besaulkitik, eta\", \"label\": \"eu\"},\n {\"text\": \"perfectius alio videat, quamvis uterque videat\", \"label\": \"la\"},\n {\"text\": \"Die Strecke war anspruchsvoll und führte unter\", \"label\": \"de\"},\n {\"text\": \"саморазоблачительным уроком, западные СМИ не\", \"label\": \"ru\"},\n {\"text\": \"han representerer radikal islamisme .\", \"label\": \"no\"},\n {\"text\": \"Què s'hi respira pel que fa a la reforma del\", \"label\": \"ca\"},\n {\"text\": \"previsto para também ser desconstruido.\", \"label\": \"pt\"},\n {\"text\": \"Ὠκεανοῦ βαθυκόλποις ἄνθεά τ̓ αἰνυμένην, ῥόδα καὶ\", \"label\": \"grc\"},\n {\"text\": \"para jovens de a anos nos Cieps.\", \"label\": \"pt\"},\n {\"text\": \"संघर्ष को अंजाम तक पहुंचाने का ऐलान किया है ।\", \"label\": \"hi\"},\n {\"text\": \"objeví i u nás.\", \"label\": \"cs\"},\n {\"text\": \"kvitteringer.\", \"label\": \"da\"},\n {\"text\": \"This report is no exception.\", \"label\": \"en\"},\n {\"text\": \"Разлепват доносниците до избирателните списъци\", \"label\": \"bg\"},\n {\"text\": \"anderem ihre Bewegungsfreiheit in den USA\", \"label\": \"de\"},\n {\"text\": \"Ñu tegoon ca kaw gor ña ay njotti bopp yu kenn\", \"label\": \"wo\"},\n {\"text\": \"Struktur kann beispielsweise der Schwerpunkt mehr\", \"label\": \"de\"},\n {\"text\": \"% la velocidad permitida, la sanción es muy grave.\", \"label\": \"es\"},\n {\"text\": \"Teles-Einstieg in ADSL-Markt\", \"label\": \"de\"},\n {\"text\": \"ettekäändeks liiga suure osamaksu.\", \"label\": \"et\"},\n {\"text\": \"als Indiz für die geänderte Marktpolitik des\", \"label\": \"de\"},\n {\"text\": \"quod quidem aperte consequitur ponentes\", \"label\": \"la\"},\n {\"text\": \"de negociación para el próximo de junio.\", \"label\": \"es\"},\n {\"text\": \"Tyto důmyslné dekorace doznaly v poslední době\", \"label\": \"cs\"},\n {\"text\": \"največjega uspeha doslej.\", \"label\": \"sl\"},\n {\"text\": \"Paul Allen je jedan od suosnivača Interval\", \"label\": \"hr\"},\n {\"text\": \"Federal (Seac / DF) eo Sindicato das Empresas de\", \"label\": \"pt\"},\n {\"text\": \"Quartal mit . Mark gegenüber dem gleichen Quartal\", \"label\": \"de\"},\n {\"text\": \"otros clubes y del Barça B saldrán varios\", \"label\": \"es\"},\n {\"text\": \"Jaskula (Pol.) -\", \"label\": \"cs\"},\n {\"text\": \"umožnily říci, že je možné přejít k mnohem\", \"label\": \"cs\"},\n {\"text\": \"اعلن الجنرال تومي فرانكس قائد القوات الامريكية\", \"label\": \"ar\"},\n {\"text\": \"Telekom-Chef Ron Sommer und der Vorstandssprecher\", \"label\": \"de\"},\n {\"text\": \"My, jako průmyslový a finanční holding, můžeme\", \"label\": \"cs\"},\n {\"text\": \"voorlichting onder andere betrekking kan hebben:\", \"label\": \"nl\"},\n {\"text\": \"Hinrichtung geistig Behinderter applaudiert oder\", \"label\": \"de\"},\n {\"text\": \"wie beispielsweise Anzahl erzielte Klicks ,\", \"label\": \"de\"},\n {\"text\": \"Intel-PC-SDRAM-Spezifikation in der Version . (\", \"label\": \"de\"},\n {\"text\": \"plângere în termen de zile de la comunicarea\", \"label\": \"ro\"},\n {\"text\": \"и Испания ще изгубят втория си комисар в ЕК.\", \"label\": \"bg\"},\n {\"text\": \"इसके चलते इस आदिवासी जनजाति का क्षरण हो रहा है ।\", \"label\": \"hi\"},\n {\"text\": \"aunque se mostró contrario a establecer un\", \"label\": \"es\"},\n {\"text\": \"des letzten Jahres von auf Millionen Euro .\", \"label\": \"de\"},\n {\"text\": \"Ankara se također poziva da u cijelosti ratificira\", \"label\": \"hr\"},\n {\"text\": \"herunterlädt .\", \"label\": \"de\"},\n {\"text\": \"стрессовую ситуацию для организма, каковой\", \"label\": \"ru\"},\n {\"text\": \"Státního shromáždění (parlamentu).\", \"label\": \"cs\"},\n {\"text\": \"diskutieren , ob und wie dieser Dienst weiterhin\", \"label\": \"de\"},\n {\"text\": \"Verbindungen zu FPÖ-nahen Polizisten gepflegt und\", \"label\": \"de\"},\n {\"text\": \"Pražského volebního lídra ovšem nevybírá Miloš\", \"label\": \"cs\"},\n {\"text\": \"Nach einem Bericht der Washington Post bleibt das\", \"label\": \"de\"},\n {\"text\": \"للوضع آنذاك، لكني في قرارة نفسي كنت سعيداً لما\", \"label\": \"ar\"},\n {\"text\": \"не желаят запазването на статуквото.\", \"label\": \"bg\"},\n {\"text\": \"Offenburg gewesen .\", \"label\": \"de\"},\n {\"text\": \"ἐὰν ὑμῖν εἴπω οὐ μὴ πιστεύσητε\", \"label\": \"grc\"},\n {\"text\": \"all'odiato compagno di squadra Prost, il quale\", \"label\": \"it\"},\n {\"text\": \"historischen Gänselieselbrunnens.\", \"label\": \"de\"},\n {\"text\": \"למידע מלווייני הריגול האמריקאיים העוקבים אחר\", \"label\": \"he\"},\n {\"text\": \"οὐδὲν ἄρα διαφέρεις Ἀμάσιος τοῦ Ἠλείου, ὃν\", \"label\": \"grc\"},\n {\"text\": \"movementos migratorios.\", \"label\": \"gl\"},\n {\"text\": \"Handy und ein Spracherkennungsprogramm sämtliche\", \"label\": \"de\"},\n {\"text\": \"Kümne aasta jooksul on Eestisse ohjeldamatult\", \"label\": \"et\"},\n {\"text\": \"H.G. Bücknera.\", \"label\": \"pl\"},\n {\"text\": \"protiv krijumčarenja, ili pak traženju ukidanja\", \"label\": \"hr\"},\n {\"text\": \"Topware-Anteile mehrere Millionen Mark gefordert\", \"label\": \"de\"},\n {\"text\": \"Maar de mensen die nu over Van Dijk bij FC Twente\", \"label\": \"nl\"},\n {\"text\": \"poidan experimentar as percepcións do interesado,\", \"label\": \"gl\"},\n {\"text\": \"Miał przecież w kieszeni nóż.\", \"label\": \"pl\"},\n {\"text\": \"Avšak žádná z nich nepronikla za hranice přímé\", \"label\": \"cs\"},\n {\"text\": \"esim. helpottamalla luottoja muiden\", \"label\": \"fi\"},\n {\"text\": \"Podle předběžných výsledků zvítězila v\", \"label\": \"cs\"},\n {\"text\": \"Nicht nur das Web-Frontend , auch die\", \"label\": \"de\"},\n {\"text\": \"Regierungsinstitutionen oder Universitäten bei\", \"label\": \"de\"},\n {\"text\": \"Խուլեն Լոպետեգիին, պատճառաբանելով, որ վերջինս\", \"label\": \"hy\"},\n {\"text\": \"Афганистана, где в последние дни идут ожесточенные\", \"label\": \"ru\"},\n {\"text\": \"лѧхове же не идоша\", \"label\": \"orv\"},\n {\"text\": \"Mit Hilfe von IBMs Chip-Management-Systemen sollen\", \"label\": \"de\"},\n {\"text\": \", als Manager zu Telefonica zu wechseln .\", \"label\": \"de\"},\n {\"text\": \"którym zajmuje się człowiek, zmienia go i pozwala\", \"label\": \"pl\"},\n {\"text\": \"činí kyperských liber, to je asi USD.\", \"label\": \"cs\"},\n {\"text\": \"Studienplätze getauscht werden .\", \"label\": \"de\"},\n {\"text\": \"учёных, орнитологов признают вид.\", \"label\": \"ru\"},\n {\"text\": \"acordare a concediilor prevăzute de legislațiile\", \"label\": \"ro\"},\n {\"text\": \"at større innsats for fornybar, berekraftig energi\", \"label\": \"nn\"},\n {\"text\": \"Politiet veit ikkje kor mange personar som deltok\", \"label\": \"nn\"},\n {\"text\": \"offentligheten av unge , sinte menn som har\", \"label\": \"no\"},\n {\"text\": \"însuși în jurul lapunei, care încet DISPARE în\", \"label\": \"ro\"},\n {\"text\": \"O motivo da decisão é evitar uma sobrecarga ainda\", \"label\": \"pt\"},\n {\"text\": \"El Apostolado de la prensa contribuye en modo\", \"label\": \"es\"},\n {\"text\": \"Teltow ( Kreis Teltow-Fläming ) ist Schmitt einer\", \"label\": \"de\"},\n {\"text\": \"grozījumus un iesniegt tos Apvienoto Nāciju\", \"label\": \"lv\"},\n {\"text\": \"Gestalt einer deutschen Nationalmannschaft als\", \"label\": \"de\"},\n {\"text\": \"D überholt zu haben , konterte am heutigen Montag\", \"label\": \"de\"},\n {\"text\": \"Softwarehersteller Oracle hat im dritten Quartal\", \"label\": \"de\"},\n {\"text\": \"Během nich se ekonomické podmínky mohou radikálně\", \"label\": \"cs\"},\n {\"text\": \"Dziki kot w górach zeskakuje z kamienia.\", \"label\": \"pl\"},\n {\"text\": \"Ačkoliv ligový nováček prohrál, opět potvrdil, že\", \"label\": \"cs\"},\n {\"text\": \"des Tages , Portraits internationaler Stars sowie\", \"label\": \"de\"},\n {\"text\": \"Communicator bekannt wurde .\", \"label\": \"de\"},\n {\"text\": \"τῷ δ’ ἄρα καὶ αὐτῷ ἡ γυνή ἐπίτεξ ἐοῦσα πᾶσαν\", \"label\": \"grc\"},\n {\"text\": \"Triadú tenia, mentre redactava 'Dies de memòria',\", \"label\": \"ca\"},\n {\"text\": \"دستهجمعی در درخشندگی ماه سیمگون زمزمه ستاینده و\", \"label\": \"fa\"},\n {\"text\": \"Книгу, наполненную мелочной заботой об одежде,\", \"label\": \"ru\"},\n {\"text\": \"putares canem leporem persequi.\", \"label\": \"la\"},\n {\"text\": \"В дальнейшем эта яркость слегка померкла, но в\", \"label\": \"ru\"},\n {\"text\": \"offizielles Verfahren gegen die Telekom\", \"label\": \"de\"},\n {\"text\": \"podrían haber sido habitantes de la Península\", \"label\": \"es\"},\n {\"text\": \"Grundlage für dieses Verfahren sind spezielle\", \"label\": \"de\"},\n {\"text\": \"Rechtsausschuß vorgelegten Entwurf der Richtlinie\", \"label\": \"de\"},\n {\"text\": \"Im so genannten Portalgeschäft sei das Unternehmen\", \"label\": \"de\"},\n {\"text\": \"ⲏ ⲉⲓϣⲁⲛϥⲓ ⲛⲉⲓⲇⲱⲗⲟⲛ ⲉⲧϩⲙⲡⲉⲕⲏⲓ ⲙⲏ ⲉⲓⲛⲁϣϩⲱⲡ ⲟⲛ ⲙⲡⲣⲏ\", \"label\": \"cop\"},\n {\"text\": \"juego podían matar a cualquier herbívoro, pero\", \"label\": \"es\"},\n {\"text\": \"Nach Angaben von Axent nutzen Unternehmen aus der\", \"label\": \"de\"},\n {\"text\": \"hrdiny Havlovy Zahradní slavnosti (premiéra ) se\", \"label\": \"cs\"},\n {\"text\": \"Een zin van heb ik jou daar\", \"label\": \"nl\"},\n {\"text\": \"hat sein Hirn an der CeBIT-Kasse vergessen .\", \"label\": \"de\"},\n {\"text\": \"καὶ τοὺς ἐκπλαγέντας οὐκ ἔχειν ἔτι ἐλεγχομένους\", \"label\": \"grc\"},\n {\"text\": \"nachgewiesenen langfristigen Kosten , sowie den im\", \"label\": \"de\"},\n {\"text\": \"jučer nakon četiri dana putovanja u Helsinki.\", \"label\": \"hr\"},\n {\"text\": \"pašto paslaugos teikėjas gali susitarti su\", \"label\": \"lt\"},\n {\"text\": \"В результате, эти золотые кадры переходят из одной\", \"label\": \"ru\"},\n {\"text\": \"द फाइव-ईयर एंगेजमेंट में अभिनय किया जिसमें जैसन\", \"label\": \"hi\"},\n {\"text\": \"výpis o počtu akcií.\", \"label\": \"cs\"},\n {\"text\": \"Enfin, elles arrivent à un pavillon chinois\", \"label\": \"fr\"},\n {\"text\": \"Tentu saja, tren yang berhubungandengan\", \"label\": \"id\"},\n {\"text\": \"Arbeidarpartiet og SV har sikra seg fleirtal mot\", \"label\": \"nn\"},\n {\"text\": \"eles: 'Tudo isso está errado' , disse um\", \"label\": \"pt\"},\n {\"text\": \"The islands are in their own time zone, minutes\", \"label\": \"en\"},\n {\"text\": \"Auswahl debütierte er am .\", \"label\": \"de\"},\n {\"text\": \"Bu komisyonlar, arazilerini satın almak için\", \"label\": \"tr\"},\n {\"text\": \"Geschütze gegen Redmond aufgefahren .\", \"label\": \"de\"},\n {\"text\": \"Time scything the hours, but at the top, over the\", \"label\": \"en\"},\n {\"text\": \"Di musim semi , berharap mengadaptasi Tintin untuk\", \"label\": \"id\"},\n {\"text\": \"крупнейшей геополитической катастрофой XX века.\", \"label\": \"ru\"},\n {\"text\": \"Rajojen avaaminen ei suju ongelmitta .\", \"label\": \"fi\"},\n {\"text\": \"непроницаемым, как для СССР.\", \"label\": \"ru\"},\n {\"text\": \"Ma non mancano le polemiche.\", \"label\": \"it\"},\n {\"text\": \"Internet als Ort politischer Diskussion und auch\", \"label\": \"de\"},\n {\"text\": \"incomplets.\", \"label\": \"ca\"},\n {\"text\": \"Su padre luchó al lado de Luis Moya, primer Jefe\", \"label\": \"es\"},\n {\"text\": \"informazione.\", \"label\": \"it\"},\n {\"text\": \"Primacom bietet für Telekom-Kabelnetz\", \"label\": \"de\"},\n {\"text\": \"Oświadczenie prezydencji w imieniu Unii\", \"label\": \"pl\"},\n {\"text\": \"foran rattet i familiens gamle Baleno hvis døra på\", \"label\": \"no\"},\n {\"text\": \"[speaker:laughter]\", \"label\": \"sl\"},\n {\"text\": \"Dog med langt mindre utstyr med seg.\", \"label\": \"nn\"},\n {\"text\": \"dass es nicht schon mit der anfänglichen\", \"label\": \"de\"},\n {\"text\": \"इस पर दोनों पक्षों में नोकझोंक शुरू हो गई ।\", \"label\": \"hi\"},\n {\"text\": \"کے ترجمان منیش تیواری اور دگ وجئے سنگھ نے بھی یہ\", \"label\": \"ur\"},\n {\"text\": \"dell'Assemblea Costituente che posseggono i\", \"label\": \"it\"},\n {\"text\": \"и аште вьси съблазнѧтъ сѧ нъ не азъ\", \"label\": \"cu\"},\n {\"text\": \"In Irvine hat auch das Logistikunternehmen Atlas\", \"label\": \"de\"},\n {\"text\": \"законодательных норм, принимаемых существующей\", \"label\": \"ru\"},\n {\"text\": \"Κροίσῳ προτείνων τὰς χεῖρας ἐπικατασφάξαι μιν\", \"label\": \"grc\"},\n {\"text\": \"МИНУСЫ: ИНФЛЯЦИЯ И КРИЗИС В ЖИВОТНОВОДСТВЕ.\", \"label\": \"ru\"},\n {\"text\": \"unterschiedlicher Meinung .\", \"label\": \"de\"},\n {\"text\": \"Jospa joku ystävällinen sielu auttaisi kassieni\", \"label\": \"fi\"},\n {\"text\": \"Añadió que, en el futuro se harán otros\", \"label\": \"es\"},\n {\"text\": \"Sessiz tonlama hem Fince, hem de Kuzey Sami\", \"label\": \"tr\"},\n {\"text\": \"nicht ihnen gehört und sie nicht alles , was sie\", \"label\": \"de\"},\n {\"text\": \"Etelästä Kuivajärveen laskee Tammelan Liesjärvestä\", \"label\": \"fi\"},\n {\"text\": \"ICANNs Vorsitzender Vint Cerf warb mit dem Hinweis\", \"label\": \"de\"},\n {\"text\": \"Norsk politikk frå til kan dermed, i\", \"label\": \"nn\"},\n {\"text\": \"Głosowało posłów.\", \"label\": \"pl\"},\n {\"text\": \"Danny Jones -- smithjones@ev.net\", \"label\": \"en\"},\n {\"text\": \"sebeuvědomění moderní civilizace sehrála lučavka\", \"label\": \"cs\"},\n {\"text\": \"относительно спокойный сон: тому гарантия\", \"label\": \"ru\"},\n {\"text\": \"A halte voiz prist li pedra a crïer\", \"label\": \"fro\"},\n {\"text\": \"آنها امیدوارند این واکسن بهزودی در دسترس بیماران\", \"label\": \"fa\"},\n {\"text\": \"vlastní důstojnou vousatou tváří.\", \"label\": \"cs\"},\n {\"text\": \"ora aprire la strada a nuove cause e alimentare il\", \"label\": \"it\"},\n {\"text\": \"Die Zahl der Vielleser nahm von auf Prozent zu ,\", \"label\": \"de\"},\n {\"text\": \"Finanzvorstand von Hotline-Dienstleister InfoGenie\", \"label\": \"de\"},\n {\"text\": \"entwickeln .\", \"label\": \"de\"},\n {\"text\": \"incolumità pubblica.\", \"label\": \"it\"},\n {\"text\": \"lehtija televisiomainonta\", \"label\": \"fi\"},\n {\"text\": \"joistakin kohdista eri mieltä.\", \"label\": \"fi\"},\n {\"text\": \"Hlavně anglická nezávislá scéna, Dead Can Dance,\", \"label\": \"cs\"},\n {\"text\": \"pásmech od do bodů bodové stupnice.\", \"label\": \"cs\"},\n {\"text\": \"Zu Beginn des Ersten Weltkrieges zählte das\", \"label\": \"de\"},\n {\"text\": \"Així van sorgir, damunt els antics cementiris,\", \"label\": \"ca\"},\n {\"text\": \"In manchem Gedicht der spätern Alten, wie zum\", \"label\": \"de\"},\n {\"text\": \"gaweihaida jah insandida in þana fairƕu jus qiþiþ\", \"label\": \"got\"},\n {\"text\": \"Beides sollte gelöscht werden!\", \"label\": \"de\"},\n {\"text\": \"modifiqués la seva petició inicial de anys de\", \"label\": \"ca\"},\n {\"text\": \"В день открытия симпозиума состоялась закладка\", \"label\": \"ru\"},\n {\"text\": \"tõestatud.\", \"label\": \"et\"},\n {\"text\": \"ἵππῳ πίπτει αὐτοῦ ταύτῃ\", \"label\": \"grc\"},\n {\"text\": \"bisher nie enttäuscht!\", \"label\": \"de\"},\n {\"text\": \"De bohte ollu tuollárat ja suttolaččat ja\", \"label\": \"sme\"},\n {\"text\": \"Klarsignal från röstlängdsläsaren, tre tryck i\", \"label\": \"sv\"},\n {\"text\": \"Tvůrcem nového termínu je Joseph Fisher.\", \"label\": \"cs\"},\n {\"text\": \"Nie miałem czasu na reakcję twierdzi Norbert,\", \"label\": \"pl\"},\n {\"text\": \"potentia Schöpfer.\", \"label\": \"de\"},\n {\"text\": \"Un poquito caro, pero vale mucho la pena;\", \"label\": \"es\"},\n {\"text\": \"οὔ τε γὰρ ἴφθιμοι Λύκιοι Δαναῶν ἐδύναντο τεῖχος\", \"label\": \"grc\"},\n {\"text\": \"vajec, sladového výtažku a některých vitamínových\", \"label\": \"cs\"},\n {\"text\": \"Настоящие герои, те, чьи истории потом\", \"label\": \"ru\"},\n {\"text\": \"praesumptio:\", \"label\": \"la\"},\n {\"text\": \"Olin justkui nende vastutusel.\", \"label\": \"et\"},\n {\"text\": \"Jokainen keinahdus tuo lähemmäksi hetkeä jolloin\", \"label\": \"fi\"},\n {\"text\": \"ekonomicky výhodných způsobů odvodnění těžkých,\", \"label\": \"cs\"},\n {\"text\": \"Poprvé ve své historii dokázala v kvalifikaci pro\", \"label\": \"cs\"},\n {\"text\": \"zpracovatelského a spotřebního průmyslu bude nutné\", \"label\": \"cs\"},\n {\"text\": \"Windows CE zu integrieren .\", \"label\": \"de\"},\n {\"text\": \"Armangué, a través d'un decret, ordenés l'aturada\", \"label\": \"ca\"},\n {\"text\": \"to, co nás Evropany spojuje, než to, co nás od\", \"label\": \"cs\"},\n {\"text\": \"ergänzt durch einen gesetzlich verankertes\", \"label\": \"de\"},\n {\"text\": \"Насчитал, что с начала года всего три дня были\", \"label\": \"ru\"},\n {\"text\": \"Borisovu tražeći od njega da prihvati njenu\", \"label\": \"sr\"},\n {\"text\": \"la presenza di ben veleni diversi: . chili di\", \"label\": \"it\"},\n {\"text\": \"καὶ τῶν ἐκλεκτῶν ἀγγέλων ἵνα ταῦτα φυλάξῃς χωρὶς\", \"label\": \"grc\"},\n {\"text\": \"pretraživale obližnju bolnicu i stambene zgrade u\", \"label\": \"hr\"},\n {\"text\": \"An rund Katzen habe Wolf seine Spiele getestet ,\", \"label\": \"de\"},\n {\"text\": \"investigating since March.\", \"label\": \"en\"},\n {\"text\": \"Tonböden (Mullböden).\", \"label\": \"de\"},\n {\"text\": \"Stálý dopisovatel LN v SRN Bedřich Utitz\", \"label\": \"cs\"},\n {\"text\": \"červnu předložené smlouvy.\", \"label\": \"cs\"},\n {\"text\": \"πνεύματι ᾧ ἐλάλει\", \"label\": \"grc\"},\n {\"text\": \".%의 신장세를 보였다.\", \"label\": \"ko\"},\n {\"text\": \"Foae verde, foi de nuc, Prin pădure, prin colnic,\", \"label\": \"ro\"},\n {\"text\": \"διαπέμψας ἄλλους ἄλλῃ τοὺς μὲν ἐς Δελφοὺς ἰέναι\", \"label\": \"grc\"},\n {\"text\": \"المسلمين أو أي تيار سياسي طالما عمل ذلك التيار في\", \"label\": \"ar\"},\n {\"text\": \"As informações são da Dow Jones.\", \"label\": \"pt\"},\n {\"text\": \"Milliarde DM ausgestattet sein .\", \"label\": \"de\"},\n {\"text\": \"De utgår fortfarande från att kvinnans jämlikhet\", \"label\": \"sv\"},\n {\"text\": \"Sneeuw maakte in Davos bij de voorbereiding een\", \"label\": \"nl\"},\n {\"text\": \"De ahí que en este mercado puedan negociarse\", \"label\": \"es\"},\n {\"text\": \"intenzívnějšímu sbírání a studiu.\", \"label\": \"cs\"},\n {\"text\": \"और औसकर ४.० पैकेज का प्रयोग किया गया है ।\", \"label\": \"hi\"},\n {\"text\": \"Adipati Kuningan karena Kuningan menjadi bagian\", \"label\": \"id\"},\n {\"text\": \"Svako je bar jednom poželeo da mašine prosto umeju\", \"label\": \"sr\"},\n {\"text\": \"Im vergangenen Jahr haben die Regierungen einen\", \"label\": \"de\"},\n {\"text\": \"durat motus, aliquid fit et non est;\", \"label\": \"la\"},\n {\"text\": \"Dominować będą piosenki do tekstów Edwarda\", \"label\": \"pl\"},\n {\"text\": \"beantwortet .\", \"label\": \"de\"},\n {\"text\": \"О гуманитариях было кому рассказывать, а вот за\", \"label\": \"ru\"},\n {\"text\": \"Helsingin kaupunki riitautti vuokrasopimuksen\", \"label\": \"fi\"},\n {\"text\": \"chợt tan biến.\", \"label\": \"vi\"},\n {\"text\": \"avtomobil ločuje od drugih.\", \"label\": \"sl\"},\n {\"text\": \"Congress has proven itself ineffective as a body.\", \"label\": \"en\"},\n {\"text\": \"मैक्सिको ने इस तरह का शो इस समय आयोजित करने का\", \"label\": \"hi\"},\n {\"text\": \"No minimum order amount.\", \"label\": \"en\"},\n {\"text\": \"Convertassa .\", \"label\": \"fi\"},\n {\"text\": \"Как это можно сделать?\", \"label\": \"ru\"},\n {\"text\": \"tha mi creidsinn gu robh iad ceart cho saor shuas\", \"label\": \"gd\"},\n {\"text\": \"실제 일제는 이런 만해의 논리를 묵살하고 한반도를 침략한 다음 , 이어 만주를 침략하고\", \"label\": \"ko\"},\n {\"text\": \"Da un semplice richiamo all'ordine fino a grandi\", \"label\": \"it\"},\n {\"text\": \"pozoruhodný nejen po umělecké stránce, jež\", \"label\": \"cs\"},\n {\"text\": \"La comida y el servicio aprueban.\", \"label\": \"es\"},\n {\"text\": \"again, connected not with each other but to the\", \"label\": \"en\"},\n {\"text\": \"Protokol výslovně stanoví, že nikdo nemůže být\", \"label\": \"cs\"},\n {\"text\": \"ఒక విషయం అడగాలని ఉంది .\", \"label\": \"te\"},\n {\"text\": \"Безгранично почитая дирекцию, ловя на лету каждое\", \"label\": \"ru\"},\n {\"text\": \"rovnoběžných růstových vrstev, zůstávají krychlové\", \"label\": \"cs\"},\n {\"text\": \"प्रवेश और पूर्व प्रधानमंत्री लाल बहादुर शास्त्री\", \"label\": \"hi\"},\n {\"text\": \"Bronzen medaille in de Europese marathon.\", \"label\": \"nl\"},\n {\"text\": \"- gadu vecumā viņi to nesaprot.\", \"label\": \"lv\"},\n {\"text\": \"Realizó sus estudios primarios en la Escuela Julia\", \"label\": \"es\"},\n {\"text\": \"cuartos de final, su clasificación para la final a\", \"label\": \"es\"},\n {\"text\": \"Sem si pro něho přiletí americký raketoplán, na\", \"label\": \"cs\"},\n {\"text\": \"Way to go!\", \"label\": \"en\"},\n {\"text\": \"gehört der neuen SPD-Führung unter Parteichef\", \"label\": \"de\"},\n {\"text\": \"Somit simuliert der Player mit einer GByte-Platte\", \"label\": \"de\"},\n {\"text\": \"Berufung auf kommissionsnahe Kreise , die bereits\", \"label\": \"de\"},\n {\"text\": \"Dist Clarïen\", \"label\": \"fro\"},\n {\"text\": \"Schon nach den Gerüchten , die Telekom wolle den\", \"label\": \"de\"},\n {\"text\": \"Software von NetObjects ist nach Angaben des\", \"label\": \"de\"},\n {\"text\": \"si enim per legem iustitia ergo Christus gratis\", \"label\": \"la\"},\n {\"text\": \"ducerent in ipsam magis quam in corpus christi,\", \"label\": \"la\"},\n {\"text\": \"Neustar-Melbourne-IT-Partnerschaft NeuLevel .\", \"label\": \"de\"},\n {\"text\": \"forderte dagegen seine drastische Verschärfung.\", \"label\": \"de\"},\n {\"text\": \"pemmican på hundrede forskellige måder.\", \"label\": \"da\"},\n {\"text\": \"Lehån, själv matematiklärare, visar hur den nya\", \"label\": \"sv\"},\n {\"text\": \"I highly recommend his shop.\", \"label\": \"en\"},\n {\"text\": \"verità, giovani fedeli prostratevi #amen\", \"label\": \"it\"},\n {\"text\": \"उत्तर प्रदेश के अध्यक्ष पद से हटाए गए विनय कटियार\", \"label\": \"hi\"},\n {\"text\": \"() روزی مےں کشادگی ہوتی ہے۔\", \"label\": \"ur\"},\n {\"text\": \"Prozessorgeschäft profitieren kann , stellen\", \"label\": \"de\"},\n {\"text\": \"školy začalo počítat pytle s moukou a zjistilo, že\", \"label\": \"cs\"},\n {\"text\": \"प्रभावशाली पर गैर सरकारी लोगों के घरों में भी\", \"label\": \"hi\"},\n {\"text\": \"geschichtslos , oder eine Farce , wie sich\", \"label\": \"de\"},\n {\"text\": \"Ústrednými mocnosťami v marci však spôsobilo, že\", \"label\": \"sk\"},\n {\"text\": \"التسليح بدون مبرر، واستمرار الأضرار الناجمة عن فرض\", \"label\": \"ar\"},\n {\"text\": \"Například Pedagogická fakulta Univerzity Karlovy\", \"label\": \"cs\"},\n {\"text\": \"nostris ut eriperet nos de praesenti saeculo\", \"label\": \"la\"}]\n \n docs = [Document([], text=example[\"text\"]) for example in examples]\n gold_labels = [example[\"label\"] for example in examples]\n basic_multilingual(docs)\n accuracy = sum([(doc.lang == label) for doc,label in zip(docs,gold_labels)])/len(docs)\n assert accuracy >= 0.98","def Language(self, default=None):\n return self.data.get('language', default)","def language_version(language):\n return _PREFERRED_VERSIONS[language]","def default_language(self):\n return self._default_language","def get_locale():\n if (session.get(\"language\") is not None):\n return session.get('language')['charcode']\n return request.accept_languages.best_match(app.config['LANGUAGES'].keys())","def get_weather_language(self):\n return self.bot_data_file[\"weather\"][\"default_language\"]","def get_default_language():\n return getattr(thread_locals, 'DEFAULT_LANGUAGE',\n settings.DEFAULT_LANGUAGE)","def lemma(self):\n if self.metadata.get('COMLEX'):\n return self.metadata['COMLEX'][0].features['ORTH'][0][1:-1]\n elif self.label in ['NNP', 'NNPS']:\n return self.text\n else:\n return self.text.lower()","def text_language(text):\n hebrew = 0\n english = 0\n for char in text:\n if char in \"אבגדהוזחטיכךלמםנסעפףצץקרשת\":\n hebrew += 1\n elif char.lower() in \"abcdefghijklmnopqrstuvwxyz\":\n english += 1\n return {True: \"hebrew\", False: \"english\"}[hebrew > english]","def genlangs(self):\r\n raise NotImplementedError","def get_language(fn):\n # FIXME - this expects the fn to be '.../XX/LC_MESSAGES/messages.po'\n return fn.split(os.sep)[-3]","def language_version(self) -> pulumi.Output[Optional[str]]:\n return pulumi.get(self, \"language_version\")","def gpwDefaultLanguage(self):\n parent = self.getFolderWhenPortalFactory()\n if hasattr(parent, 'getRawLanguage') and parent.getRawLanguage():\n return parent.getRawLanguage()\n tool = getToolByName(self, 'portal_languages', None)\n if tool is not None:\n return tool.getDefaultLanguage()\n return config.LANGUAGE_DEFAULT","def get_lang_code(lang_code):\r\n if lang_code not in constants.SUPPORTED_LANG_CODES_ANALYZERS:\r\n return constants.FALLBACK_LANG_CODE\r\n return lang_code","def get_default_language():\n utility = queryUtility(ILanguageAvailability)\n if utility is not None:\n return utility.getDefaultLanguage()\n return DEFAULT_LANGUAGE","def celex_pron_loc(language, lemma):\n pron = 5\n if language == \"german\" or language== \"dutch\": pron = pron -2 #german one less\n if lemma == \"wordform\": pron += 1\n return pron","def algorithm(self) -> str:\n return pulumi.get(self, \"algorithm\")","def algorithm(self) -> str:\n return pulumi.get(self, \"algorithm\")","def wikiLanguages():\n return languages"],"string":"[\n \"def programming_language(self) -> str:\\n return self.random.choice(PROGRAMMING_LANGS)\",\n \"def generation_language(self) -> pulumi.Output[Optional[str]]:\\n return pulumi.get(self, \\\"generation_language\\\")\",\n \"def code(self):\\n return self.language()\",\n \"def language(self):\\n return \\\"QSS\\\"\",\n \"def getWikiLanguageName():\\r\\n return \\\"wikidpad_mini_1_0\\\"\",\n \"def get_language():\\n disabled_modules = ['tokenizer', 'tagger', 'parser', 'textcat']\\n nlp = spacy.load('en_core_web_md', disable=disabled_modules)\\n # we are not interested in stop-words as most of them are\\n # needed in the short sentence examples in relation definitions\\n spacy_wmd_hook = wmd.WMD.SpacySimilarityHook(nlp, ignore_stops=False)\\n nlp.add_pipe(spacy_wmd_hook, last=True)\\n return nlp\",\n \"def language(self, target):\\n self._check_target(target)\\n return target.language or self._default_language\",\n \"def language(self, max_length=None, **kwargs):\\n T = self.with_output()\\n return T.language(max_length)\",\n \"def language(self):\\n # type: () -> string_types\\n return self._language\",\n \"def language(self):\\n # type: () -> string_types\\n return self._language\",\n \"def audio_language(self):\\n # type: () -> string_types\\n return self._audio_language\",\n \"def lang_genoeg(lengte):\\n return\",\n \"def language(self):\\n portal_state = self.context.unrestrictedTraverse(\\\"@@plone_portal_state\\\")\\n return aq_inner(self.context).Language() or portal_state.default_language()\",\n \"def language():\\r\\n\\r\\n cursor.execute('SELECT name from languages order by RANDOM() limit 1;')\\r\\n return cursor.fetchone()[0]\",\n \"def identifyLangage(script):\\n\\tlangage = \\\"undefined\\\"\\n\\tscriptNameInArray = script.split(\\\".\\\")\\n\\textension = scriptNameInArray[-1]\\n\\t\\n\\tif(extension == \\\"pl\\\"):\\n\\t\\tlangage = \\\"perl\\\"\\n\\telif(extension == \\\"py\\\"):\\n\\t\\tlangage = \\\"python\\\"\\n\\telif(extension == \\\"sh\\\"):\\n\\t\\tlangage = \\\"bash\\\"\\n\\telse:\\n\\t\\tlangage == \\\"not recognised\\\"\\n\\n\\treturn langage\",\n \"def language(self):\\n if \\\"language\\\" in self._prop_dict:\\n return self._prop_dict[\\\"language\\\"]\\n else:\\n return None\",\n \"def get_full_language(self, language):\\n if language:\\n language = pycountry.languages.get(alpha_2=language)\\n if language:\\n language = language.name\\n return language.title()\",\n \"def language(self) -> str:\\n if self.language_code in CODE_TO_LANGUAGE:\\n return CODE_TO_LANGUAGE[self.language_code]\\n\\n return self.language_code\",\n \"def get_language(self, text):\\n try:\\n post_lang = detect(text)\\n except:\\n post_lang = 'N/A'\\n return post_lang\",\n \"def language(self):\\n hcell = self._get_hcell2()\\n celltype = hcell[\\\"celltype\\\"]\\n if celltype != \\\"code\\\":\\n raise AttributeError\\n return hcell.get(\\\"language\\\", \\\"python\\\")\",\n \"def srclang(self):\\n return self.__srclang\",\n \"def language_code(self) -> str:\\n return pulumi.get(self, \\\"language_code\\\")\",\n \"def get_lang(self):\\n return self.langs.lang\",\n \"def get_language(mgroups):\\n\\n if mgroups:\\n lang = mgroups[0].strip('[').strip(']')\\n return lang.lower().strip()\\n return None\",\n \"def get_related_language(self) -> str:\\n pass\",\n \"def _getLang(self, language):\\n if language == None:\\n language = self.getDefaultLanguage()\\n\\n return language\",\n \"def get_language(self) -> str:\\n return self.language\",\n \"def get_language(lang_code) -> str:\\n langs = defaultdict(lambda: \\\"en\\\", {\\\"ru\\\": \\\"ru\\\"})\\n return langs[lang_code.split(\\\"-\\\")[0]] if lang_code else \\\"en\\\"\",\n \"def language(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"language\\\")\",\n \"def get_locale():\\n return \\\"he\\\"\",\n \"def generation_language(self) -> Optional[pulumi.Input[Union[str, 'GenerationLanguage']]]:\\n return pulumi.get(self, \\\"generation_language\\\")\",\n \"def get_language(self):\\n return self.lang\",\n \"def get_lang(self):\\n\\n path = self.get_lang_path()\\n for language in self.languages:\\n if language in path:\\n return language\",\n \"def language(self) -> str:\\n return self._language\",\n \"def language(self) -> str:\\n return self._language\",\n \"def get_project_lang(self):\\n return self.project_name_lang.currentText() # .replace(\\\"é\\\",\\\"e\\\").lower()\",\n \"def get_language(self, word, lang=None):\\n lang = lang or self.cfg.get('lang', 'en')\\n # let's retrieve the word from configuration dict.\\n try:\\n return self.cfg['words_' + lang][word]\\n except StandardError:\\n return 'Do not know how to \\\"{}\\\" in \\\"{}\\\"'.format(word, lang)\",\n \"def get_language(self, article):\\r\\n # we don't want to force the target laguage\\r\\n # so we use the article.meta_lang\\r\\n if self.config.use_meta_language == True:\\r\\n if article.meta_lang:\\r\\n return article.meta_lang[:2]\\r\\n return self.config.target_language\",\n \"def language(self):\\n lang = None\\n if self.__dict__['TAG:language']:\\n lang = self.__dict__['TAG:language']\\n return lang\",\n \"def get_language(self):\\r\\n return self.language\",\n \"def language(self):\\r\\n return self._get('language', {})\",\n \"def language( self, preferredWeaverClass=None ):\\n if preferredWeaverClass:\\n return preferredWeaverClass()\\n self.logger.debug( \\\"Picking a weaver based on first chunk {!r}\\\".format(self.chunkSeq[0][:4]) )\\n if self.chunkSeq[0].startswith('<'): \\n return HTML()\\n if self.chunkSeq[0].startswith('%') or self.chunkSeq[0].startswith('\\\\\\\\'): \\n return LaTeX()\\n return RST()\",\n \"def lang(self):\\n return self._lang\",\n \"def is_cpp(benchmark):\\n return get_language(benchmark) == 'c++'\",\n \"def choose_language(self):\\n\\n current_dir = os.curdir\\n path = os.path.join(current_dir, \\\"audio\\\")\\n languages = os.listdir(path)\\n language_num = randint(0, len(languages) - 1)\\n return languages[language_num]\",\n \"def _detect_language(self, text):\\n\\n ratios = self._calculate_languages_ratios(text)\\n\\n most_rated_language = max(ratios, key=ratios.get)\\n\\n return most_rated_language\",\n \"def getLanguage(self):\\n return self.getOrDefault(self.language)\",\n \"def language(self):\\n return self._language\",\n \"def language(self):\\n return self._language\",\n \"def language(self):\\n return self._language\",\n \"def language(self):\\n return self._language\",\n \"def to_language(self):\\n return self.language()\",\n \"def get_lang(self):\\n props = getToolByName(self.context,\\n 'portal_properties')\\n return props.site_properties.getProperty('default_language') or 'en'\",\n \"def getTRSLanguage():\\n try:\\n return sys.argv[1]\\n except IndexError as error:\\n print(\\\"No language argument\\\\n\\\")\\n sys.exit()\",\n \"def subtitle_language(self):\\n # type: () -> string_types\\n return self._subtitle_language\",\n \"def get_language_script(script):\\n languages_scripts = {\\n 'arab': ('ara', 'per'),\\n 'cyrl': ('bel', 'chu', 'mac', 'rus', 'srp', 'ukr'),\\n 'grek': ('grc', 'gre'),\\n 'hani': ('chi', 'jpn'),\\n 'hebr': ('heb', 'lad', 'yid'),\\n 'jpan': ('jpn', ),\\n 'kore': ('kor', ),\\n 'zyyy': ('chi', )\\n }\\n if script in languages_scripts:\\n languages = ([marc21.lang_from_008] +\\n marc21.langs_from_041_a +\\n marc21.langs_from_041_h)\\n for lang in languages:\\n if lang in languages_scripts[script]:\\n return '-'.join([lang, script])\\n error_print('WARNING LANGUAGE SCRIPTS:', marc21.bib_id,\\n script, '008:', marc21.lang_from_008,\\n '041$a:', marc21.langs_from_041_a,\\n '041$h:', marc21.langs_from_041_h)\\n return '-'.join(['und', script])\",\n \"def get_exe_language(code_file):\\n extension = code_file.split('.')[-1]\\n if extension == 'py':\\n return 'python'\\n elif extension == 'm':\\n return 'matlab'\\n elif extension == 'sh':\\n return 'bash'\\n elif extension == 'rb':\\n return 'ruby'\\n else:\\n print(\\\"Warning: file %s don't have any known extension \\\\\\n(.py/.m/.sh/.rb)\\\" % code_file)\\n return None\",\n \"def assoc_language(self):\\n # type: () -> string_types\\n return self._assoc_language\",\n \"def lemma(self) -> str:\",\n \"def language_name(self, text: str) -> str:\\n values = extract(text)\\n input_fn = _to_func(([values], []))\\n pos: int = next(self._classifier.predict_classes(input_fn=input_fn))\\n\\n LOGGER.debug(\\\"Predicted language position %s\\\", pos)\\n return sorted(self.languages)[pos]\",\n \"def get_language(self) -> str:\\n return settings.LANGUAGE_CODE\",\n \"def english_speaking(self) -> int:\\n return self._english_speaking\",\n \"def get_language(tokens, stopwords):\\n\\n languages_ratios = dict()\\n\\n words = [str(word).lower() for word in tokens]\\n words_set = set(words)\\n\\n for language in stopwords.keys():\\n common_elements = words_set.intersection(stopwords[language])\\n\\n languages_ratios[language] = len(common_elements) # language score\\n\\n return max(languages_ratios, key=languages_ratios.get)\",\n \"def mostlikelycodeword(self):\\n\\n # Add your code here\\n stoping_int = None # Replace\\n best, first_index, last_index = None, None, None # Replace all three with an integer\\n for i in range(stoping_int):\\n for j in range(None, stoping_int): # Replace None. \\n current = self.quality(None, None)\\n if None > None # Replace both Nones\\n best, first_index, last_index = current, i, j\\n return self.preamble[None:None]\",\n \"def language_code(self):\\n return self._language_code\",\n \"def detect_language(lang, kernel_source):\\n if lang is None:\\n if callable(kernel_source):\\n raise TypeError(\\\"Please specify language when using a code generator function\\\")\\n kernel_string = get_kernel_string(kernel_source)\\n if \\\"__global__\\\" in kernel_string:\\n lang = \\\"CUDA\\\"\\n elif \\\"__kernel\\\" in kernel_string:\\n lang = \\\"OpenCL\\\"\\n else:\\n lang = \\\"C\\\"\\n return lang\",\n \"def to_language(arg: str) -> Tuple[Union[str, None], str]: \\n if (low:= arg.lower()) in LANGUAGES:\\n return arg\\n else:\\n return LANGCODES.get(low, None)\",\n \"def get_user_language() -> str:\\n languages = {\\n \\\"arabic\\\": \\\"arb\\\",\\n \\\"chinese\\\": \\\"cmn-CN\\\",\\n \\\"danish\\\": \\\"da-DK\\\",\\n \\\"english\\\": \\\"en-GB\\\",\\n \\\"french\\\": \\\"fr-FR\\\",\\n \\\"german\\\": \\\"de-DE\\\",\\n \\\"portuguese\\\": \\\"pl-PT\\\",\\n \\\"spanish\\\": \\\"es-ES\\\"\\n }\\n textlang = input(\\\"What language do you want to hear?\\\")\\n try:\\n return languages[textlang.lower()]\\n except KeyError as e:\\n print(\\\"Enter a valid language.\\\")\\n sys.exit(1)\",\n \"def language_framework(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"language_framework\\\")\",\n \"def get_language(self):\\n return self.language if self.language is not None else get_language()\",\n \"def get_language_name(self):\\n return self.language_name\",\n \"def algorithm(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"algorithm\\\")\",\n \"def description(self, lang=0):\\n if not lang in self.localized_strings: return \\\"\\\"\\n return self.localized_strings[lang]\",\n \"def get_language():\\n try:\\n from leaves.middleware import request_context\\n return request_context.language\\n except:\\n return get_site().preferences.default_language\",\n \"def get_proper_language():\\n lang = config['summernote'].get('lang')\\n\\n if not lang:\\n return config['lang_matches'].get(get_language(), 'en-US')\\n\\n return lang\",\n \"def generate_language(self, properties):\\n\\n drink_property = self.find_drink_property(properties)\\n\\n if drink_property:\\n return self.generate_sentence(drink_property)\\n else:\\n return \\\"\\\"\",\n \"def detect_language(text, LANGUAGES):\\n lang = None\\n word_count = 0\\n our_test = []\\n \\n for language in LANGUAGES:\\n \\n result = get_word_count(text, language['common_words'])\\n print(result)\\n #import pdb; pdb.set_trace()\\n if result > word_count:\\n lang = language['name']\\n word_count = result\\n \\n return lang\",\n \"def describeWikiLanguage(ver, app):\\r\\n\\r\\n return ((\\\"wikidpad_mini_1_0\\\", u\\\"WikidPad Mini 1.0\\\", parserFactory,\\r\\n True, languageHelperFactory, True),)\",\n \"def languages():\\n return \\\", \\\".join(sorted(\\\"{}: '{}'\\\".format(gTTS.LANGUAGES[k], k) for k in gTTS.LANGUAGES))\",\n \"def Problem9():\\n return 'MSS'\",\n \"def test_parser_infers_language(self):\\n filename = \\\"main.c\\\"\\n language = self.parser.infer_language(filename)\\n assert_equal(\\\"C\\\", language)\",\n \"def test_langid_benchmark(basic_multilingual):\\n examples = [\\n {\\\"text\\\": \\\"contingentiam in naturalibus causis.\\\", \\\"label\\\": \\\"la\\\"},\\n {\\\"text\\\": \\\"I jak opowiadał nieżyjący już pan Czesław\\\", \\\"label\\\": \\\"pl\\\"},\\n {\\\"text\\\": \\\"Sonera gilt seit längerem als Übernahmekandidat\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"与银类似，汞也可以与空气中的硫化氢反应。\\\", \\\"label\\\": \\\"zh-hans\\\"},\\n {\\\"text\\\": \\\"contradictionem implicat.\\\", \\\"label\\\": \\\"la\\\"},\\n {\\\"text\\\": \\\"Bis zu Prozent gingen die Offerten etwa im\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"inneren Sicherheit vorgeschlagene Ausweitung der\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"Multimedia-PDA mit Mini-Tastatur\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"Ponášalo sa to na rovnicu o dvoch neznámych.\\\", \\\"label\\\": \\\"sk\\\"},\\n {\\\"text\\\": \\\"이처럼 앞으로 심판의 그 날에 다시 올 메시아가 예수 그리스도이며 , 그는 모든 인류의\\\", \\\"label\\\": \\\"ko\\\"},\\n {\\\"text\\\": \\\"Die Arbeitsgruppe bedauert , dass der weit über\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"И только раз довелось поговорить с ним не вполне\\\", \\\"label\\\": \\\"ru\\\"},\\n {\\\"text\\\": \\\"de a-l lovi cu piciorul și conștiința că era\\\", \\\"label\\\": \\\"ro\\\"},\\n {\\\"text\\\": \\\"relación coas pretensións do demandante e que, nos\\\", \\\"label\\\": \\\"gl\\\"},\\n {\\\"text\\\": \\\"med petdeset in sedemdeset\\\", \\\"label\\\": \\\"sl\\\"},\\n {\\\"text\\\": \\\"Catalunya; el Consell Comarcal del Vallès Oriental\\\", \\\"label\\\": \\\"ca\\\"},\\n {\\\"text\\\": \\\"kunnen worden.\\\", \\\"label\\\": \\\"nl\\\"},\\n {\\\"text\\\": \\\"Witkin je ve většině ohledů zcela jiný.\\\", \\\"label\\\": \\\"cs\\\"},\\n {\\\"text\\\": \\\"lernen, so zu agieren, dass sie positive oder auch\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"olurmuş...\\\", \\\"label\\\": \\\"tr\\\"},\\n {\\\"text\\\": \\\"sarcasmo de Altman, desde as «peruas» que discutem\\\", \\\"label\\\": \\\"pt\\\"},\\n {\\\"text\\\": \\\"خلاف فوجداری مقدمہ درج کرے۔\\\", \\\"label\\\": \\\"ur\\\"},\\n {\\\"text\\\": \\\"Norddal kommune :\\\", \\\"label\\\": \\\"no\\\"},\\n {\\\"text\\\": \\\"dem Windows-.-Zeitalter , soll in diesem Jahr\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"przeklętych ucieleśniają mit poety-cygana,\\\", \\\"label\\\": \\\"pl\\\"},\\n {\\\"text\\\": \\\"We do not believe the suspect has ties to this\\\", \\\"label\\\": \\\"en\\\"},\\n {\\\"text\\\": \\\"groziņu pīšanu.\\\", \\\"label\\\": \\\"lv\\\"},\\n {\\\"text\\\": \\\"Senior Vice-President David M. Thomas möchte\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"neomylně vybral nějakou knihu a začetl se.\\\", \\\"label\\\": \\\"cs\\\"},\\n {\\\"text\\\": \\\"Statt dessen darf beispielsweise der Browser des\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"outubro, alcançando R $ bilhões em .\\\", \\\"label\\\": \\\"pt\\\"},\\n {\\\"text\\\": \\\"(Porte, ), as it does other disciplines\\\", \\\"label\\\": \\\"en\\\"},\\n {\\\"text\\\": \\\"uskupení se mylně domnívaly, že podporu\\\", \\\"label\\\": \\\"cs\\\"},\\n {\\\"text\\\": \\\"Übernahme von Next Ende an dem System herum , das\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"No podemos decir a la Hacienda que los alemanes\\\", \\\"label\\\": \\\"es\\\"},\\n {\\\"text\\\": \\\"и рѣста еи братья\\\", \\\"label\\\": \\\"orv\\\"},\\n {\\\"text\\\": \\\"الذي اتخذ قرارا بتجميد اعلان الدولة الفلسطينية\\\", \\\"label\\\": \\\"ar\\\"},\\n {\\\"text\\\": \\\"uurides Rootsi sõjaarhiivist toodud . sajandi\\\", \\\"label\\\": \\\"et\\\"},\\n {\\\"text\\\": \\\"selskapets penger til å pusse opp sin enebolig på\\\", \\\"label\\\": \\\"no\\\"},\\n {\\\"text\\\": \\\"средней полосе и севернее в Ярославской,\\\", \\\"label\\\": \\\"ru\\\"},\\n {\\\"text\\\": \\\"il-massa żejda fil-ġemgħat u superġemgħat ta'\\\", \\\"label\\\": \\\"mt\\\"},\\n {\\\"text\\\": \\\"The Global Beauties on internetilehekülg, mida\\\", \\\"label\\\": \\\"et\\\"},\\n {\\\"text\\\": \\\"이스라엘 인들은 하나님이 그 큰 팔을 펴 이집트 인들을 치는 것을 보고 하나님을 두려워하며\\\", \\\"label\\\": \\\"ko\\\"},\\n {\\\"text\\\": \\\"Snad ještě dodejme jeden ekonomický argument.\\\", \\\"label\\\": \\\"cs\\\"},\\n {\\\"text\\\": \\\"Spalio d. vykusiame pirmajame rinkimų ture\\\", \\\"label\\\": \\\"lt\\\"},\\n {\\\"text\\\": \\\"und schlechter Journalismus ein gutes Geschäft .\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"Du sodiečiai sėdi ant potvynio apsemtų namų stogo.\\\", \\\"label\\\": \\\"lt\\\"},\\n {\\\"text\\\": \\\"цей є автентичним.\\\", \\\"label\\\": \\\"uk\\\"},\\n {\\\"text\\\": \\\"Și îndegrabă fu cu îngerul mulțime de șireaguri\\\", \\\"label\\\": \\\"ro\\\"},\\n {\\\"text\\\": \\\"sobra personal cualificado.\\\", \\\"label\\\": \\\"es\\\"},\\n {\\\"text\\\": \\\"Tako se u Njemačkoj dvije trećine liječnika služe\\\", \\\"label\\\": \\\"hr\\\"},\\n {\\\"text\\\": \\\"Dual-Athlon-Chipsatz noch in diesem Jahr\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"यहां तक कि चीन के चीफ ऑफ जनरल स्टाफ भी भारत का\\\", \\\"label\\\": \\\"hi\\\"},\\n {\\\"text\\\": \\\"Li forestier du mont avale\\\", \\\"label\\\": \\\"fro\\\"},\\n {\\\"text\\\": \\\"Netzwerken für Privatanwender zu bewundern .\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"만해는 승적을 가진 중이 결혼할 수 없다는 불교의 계율을 시대에 맞지 않는 것으로 보았다\\\", \\\"label\\\": \\\"ko\\\"},\\n {\\\"text\\\": \\\"balance and weight distribution but not really for\\\", \\\"label\\\": \\\"en\\\"},\\n {\\\"text\\\": \\\"og så e # tente vi opp den om morgonen å sfyrte\\\", \\\"label\\\": \\\"nn\\\"},\\n {\\\"text\\\": \\\"변화는 의심의 여지가 없는 것이지만 반면에 진화는 논쟁의 씨앗이다 .\\\", \\\"label\\\": \\\"ko\\\"},\\n {\\\"text\\\": \\\"puteare fac aceastea.\\\", \\\"label\\\": \\\"ro\\\"},\\n {\\\"text\\\": \\\"Waitt seine Führungsmannschaft nicht dem\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"juhtimisega, tulid sealt.\\\", \\\"label\\\": \\\"et\\\"},\\n {\\\"text\\\": \\\"Veränderungen .\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"banda en el Bayer Leverkusen de la Bundesliga de\\\", \\\"label\\\": \\\"es\\\"},\\n {\\\"text\\\": \\\"В туже зиму посла всеволодъ сн҃а своѥго ст҃ослава\\\", \\\"label\\\": \\\"orv\\\"},\\n {\\\"text\\\": \\\"пославъ приведе я мастеры ѿ грекъ\\\", \\\"label\\\": \\\"orv\\\"},\\n {\\\"text\\\": \\\"En un nou escenari difícil d'imaginar fa poques\\\", \\\"label\\\": \\\"ca\\\"},\\n {\\\"text\\\": \\\"καὶ γὰρ τινὲς αὐτοὺς εὐεργεσίαι εἶχον ἐκ Κροίσου\\\", \\\"label\\\": \\\"grc\\\"},\\n {\\\"text\\\": \\\"직접적인 관련이 있다 .\\\", \\\"label\\\": \\\"ko\\\"},\\n {\\\"text\\\": \\\"가까운 듯하면서도 멀다 .\\\", \\\"label\\\": \\\"ko\\\"},\\n {\\\"text\\\": \\\"Er bietet ein ähnliches Leistungsniveau und\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"民都洛水牛是獨居的，並不會以群族聚居。\\\", \\\"label\\\": \\\"zh-hant\\\"},\\n {\\\"text\\\": \\\"την τρομοκρατία.\\\", \\\"label\\\": \\\"el\\\"},\\n {\\\"text\\\": \\\"hurbiltzen diren neurrian.\\\", \\\"label\\\": \\\"eu\\\"},\\n {\\\"text\\\": \\\"Ah dimenticavo, ma tutta sta caciara per fare un\\\", \\\"label\\\": \\\"it\\\"},\\n {\\\"text\\\": \\\"На первом этапе (-) прошла так называемая\\\", \\\"label\\\": \\\"ru\\\"},\\n {\\\"text\\\": \\\"of games are on the market.\\\", \\\"label\\\": \\\"en\\\"},\\n {\\\"text\\\": \\\"находится Мост дружбы, соединяющий узбекский и\\\", \\\"label\\\": \\\"ru\\\"},\\n {\\\"text\\\": \\\"lessié je voldroie que li saint fussent aporté\\\", \\\"label\\\": \\\"fro\\\"},\\n {\\\"text\\\": \\\"Дошла очередь и до Гималаев.\\\", \\\"label\\\": \\\"ru\\\"},\\n {\\\"text\\\": \\\"vzácným suknem táhly pouští, si jednou chtěl do\\\", \\\"label\\\": \\\"cs\\\"},\\n {\\\"text\\\": \\\"E no terceiro tipo sitúa a familias (%), nos que a\\\", \\\"label\\\": \\\"gl\\\"},\\n {\\\"text\\\": \\\"وجابت دوريات امريكية وعراقية شوارع المدينة، فيما\\\", \\\"label\\\": \\\"ar\\\"},\\n {\\\"text\\\": \\\"Jeg har bodd her i år .\\\", \\\"label\\\": \\\"no\\\"},\\n {\\\"text\\\": \\\"Pohrozil, že odbory zostří postoj, pokud se\\\", \\\"label\\\": \\\"cs\\\"},\\n {\\\"text\\\": \\\"tinham conseguido.\\\", \\\"label\\\": \\\"pt\\\"},\\n {\\\"text\\\": \\\"Nicht-Erkrankten einen Anfangsverdacht für einen\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"permanece em aberto.\\\", \\\"label\\\": \\\"pt\\\"},\\n {\\\"text\\\": \\\"questi possono promettere rendimenti fino a un\\\", \\\"label\\\": \\\"it\\\"},\\n {\\\"text\\\": \\\"Tema juurutatud kahevedurisüsteemita oleksid\\\", \\\"label\\\": \\\"et\\\"},\\n {\\\"text\\\": \\\"Поведение внешне простой игрушки оказалось\\\", \\\"label\\\": \\\"ru\\\"},\\n {\\\"text\\\": \\\"Bundesländern war vom Börsenverein des Deutschen\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"acció, 'a mesura que avanci l'estiu, amb l'augment\\\", \\\"label\\\": \\\"ca\\\"},\\n {\\\"text\\\": \\\"Dove trovare queste risorse? Jay Naidoo, ministro\\\", \\\"label\\\": \\\"it\\\"},\\n {\\\"text\\\": \\\"essas gordurinhas.\\\", \\\"label\\\": \\\"pt\\\"},\\n {\\\"text\\\": \\\"Im zweiten Schritt sollen im übernächsten Jahr\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"allveelaeva pole enam vaja, kuna külm sõda on läbi\\\", \\\"label\\\": \\\"et\\\"},\\n {\\\"text\\\": \\\"उपद्रवी दुकानों को लूटने के साथ ही उनमें आग लगा\\\", \\\"label\\\": \\\"hi\\\"},\\n {\\\"text\\\": \\\"@user nella sfortuna sei fortunata ..\\\", \\\"label\\\": \\\"it\\\"},\\n {\\\"text\\\": \\\"математических школ в виде грозовых туч.\\\", \\\"label\\\": \\\"ru\\\"},\\n {\\\"text\\\": \\\"No cambiaremos nunca nuestra forma de jugar por un\\\", \\\"label\\\": \\\"es\\\"},\\n {\\\"text\\\": \\\"dla tej klasy ani wymogów minimalnych, z wyjątkiem\\\", \\\"label\\\": \\\"pl\\\"},\\n {\\\"text\\\": \\\"en todo el mundo, mientras que en España consiguió\\\", \\\"label\\\": \\\"es\\\"},\\n {\\\"text\\\": \\\"политики считать надежное обеспечение военной\\\", \\\"label\\\": \\\"ru\\\"},\\n {\\\"text\\\": \\\"gogoratzen du, genio alemana delakoaren\\\", \\\"label\\\": \\\"eu\\\"},\\n {\\\"text\\\": \\\"Бычий глаз.\\\", \\\"label\\\": \\\"ru\\\"},\\n {\\\"text\\\": \\\"Opeření se v pravidelných obdobích obnovuje\\\", \\\"label\\\": \\\"cs\\\"},\\n {\\\"text\\\": \\\"I no és només la seva, es tracta d'una resposta\\\", \\\"label\\\": \\\"ca\\\"},\\n {\\\"text\\\": \\\"오경을 가르쳤다 .\\\", \\\"label\\\": \\\"ko\\\"},\\n {\\\"text\\\": \\\"Nach der so genannten Start-up-Periode vergibt die\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"Saulista huomasi jo lapsena , että hänellä on\\\", \\\"label\\\": \\\"fi\\\"},\\n {\\\"text\\\": \\\"Министерство культуры сочло нецелесообразным, и\\\", \\\"label\\\": \\\"ru\\\"},\\n {\\\"text\\\": \\\"znepřátelené tábory v Tádžikistánu předseda\\\", \\\"label\\\": \\\"cs\\\"},\\n {\\\"text\\\": \\\"καὶ ἦν ὁ λαὸς προσδοκῶν τὸν Ζαχαρίαν καὶ ἐθαύμαζον\\\", \\\"label\\\": \\\"grc\\\"},\\n {\\\"text\\\": \\\"Вечером, в продукте, этот же человек говорил о\\\", \\\"label\\\": \\\"ru\\\"},\\n {\\\"text\\\": \\\"lugar á formación de xuizos máis complexos.\\\", \\\"label\\\": \\\"gl\\\"},\\n {\\\"text\\\": \\\"cheaper, in the end?\\\", \\\"label\\\": \\\"en\\\"},\\n {\\\"text\\\": \\\"الوزارة في شأن صفقات بيع الشركات العامة التي تم\\\", \\\"label\\\": \\\"ar\\\"},\\n {\\\"text\\\": \\\"tärkeintä elämässäni .\\\", \\\"label\\\": \\\"fi\\\"},\\n {\\\"text\\\": \\\"Виконання Мінських угод було заблоковано Росією та\\\", \\\"label\\\": \\\"uk\\\"},\\n {\\\"text\\\": \\\"Aby szybko rozpoznać żołnierzy desantu, należy\\\", \\\"label\\\": \\\"pl\\\"},\\n {\\\"text\\\": \\\"Bankengeschäfte liegen vorn , sagte Strothmann .\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"продолжение работы.\\\", \\\"label\\\": \\\"ru\\\"},\\n {\\\"text\\\": \\\"Metro AG plant Online-Offensive\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"nu vor veni, și să vor osîndi, aceia nu pot porni\\\", \\\"label\\\": \\\"ro\\\"},\\n {\\\"text\\\": \\\"Ich denke , es geht in Wirklichkeit darum , NT bei\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"de turism care încasează contravaloarea\\\", \\\"label\\\": \\\"ro\\\"},\\n {\\\"text\\\": \\\"Aurkaria itotzea da helburua, baloia lapurtu eta\\\", \\\"label\\\": \\\"eu\\\"},\\n {\\\"text\\\": \\\"com a centre de formació en Tecnologies de la\\\", \\\"label\\\": \\\"ca\\\"},\\n {\\\"text\\\": \\\"oportet igitur quod omne agens in agendo intendat\\\", \\\"label\\\": \\\"la\\\"},\\n {\\\"text\\\": \\\"Jerzego Andrzejewskiego, oparty na chińskich\\\", \\\"label\\\": \\\"pl\\\"},\\n {\\\"text\\\": \\\"sau một vài câu chuyện xã giao không dính dáng tới\\\", \\\"label\\\": \\\"vi\\\"},\\n {\\\"text\\\": \\\"что экономическому прорыву жесткий авторитарный\\\", \\\"label\\\": \\\"ru\\\"},\\n {\\\"text\\\": \\\"DRAM-Preisen scheinen DSPs ein\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"Jos dajan nubbái: Mana!\\\", \\\"label\\\": \\\"sme\\\"},\\n {\\\"text\\\": \\\"toți carii ascultară de el să răsipiră.\\\", \\\"label\\\": \\\"ro\\\"},\\n {\\\"text\\\": \\\"odpowiedzialności, które w systemie własności\\\", \\\"label\\\": \\\"pl\\\"},\\n {\\\"text\\\": \\\"Dvomesečno potovanje do Mollenda v Peruju je\\\", \\\"label\\\": \\\"sl\\\"},\\n {\\\"text\\\": \\\"d'entre les agències internacionals.\\\", \\\"label\\\": \\\"ca\\\"},\\n {\\\"text\\\": \\\"Fahrzeugzugangssysteme gefertigt und an viele\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"in an answer to the sharers' petition in Cuthbert\\\", \\\"label\\\": \\\"en\\\"},\\n {\\\"text\\\": \\\"Europa-Domain per Verordnung zu regeln .\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"#Balotelli. Su ebay prezzi stracciati per Silvio\\\", \\\"label\\\": \\\"it\\\"},\\n {\\\"text\\\": \\\"Ne na košickém trávníku, ale už včera v letadle se\\\", \\\"label\\\": \\\"cs\\\"},\\n {\\\"text\\\": \\\"zaměstnanosti a investičních strategií.\\\", \\\"label\\\": \\\"cs\\\"},\\n {\\\"text\\\": \\\"Tatínku, udělej den\\\", \\\"label\\\": \\\"cs\\\"},\\n {\\\"text\\\": \\\"frecuencia con Mary.\\\", \\\"label\\\": \\\"es\\\"},\\n {\\\"text\\\": \\\"Свеаборге.\\\", \\\"label\\\": \\\"ru\\\"},\\n {\\\"text\\\": \\\"opatření slovenské strany o certifikaci nejvíce\\\", \\\"label\\\": \\\"cs\\\"},\\n {\\\"text\\\": \\\"En todas me decían: 'Espera que hagamos un estudio\\\", \\\"label\\\": \\\"es\\\"},\\n {\\\"text\\\": \\\"Die Demonstration sollte nach Darstellung der\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"Ci vorrà un assoluto rigore se dietro i disavanzi\\\", \\\"label\\\": \\\"it\\\"},\\n {\\\"text\\\": \\\"Tatínku, víš, že Honzovi odešla maminka?\\\", \\\"label\\\": \\\"cs\\\"},\\n {\\\"text\\\": \\\"Die Anzahl der Rechner wuchs um % auf und die\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"האמריקאית על אדמת סעודיה עלולה לסבך את ישראל, אין\\\", \\\"label\\\": \\\"he\\\"},\\n {\\\"text\\\": \\\"Volán Egyesülés, a Közlekedési Főfelügyelet is.\\\", \\\"label\\\": \\\"hu\\\"},\\n {\\\"text\\\": \\\"Schejbala, který stejnou hru s velkým úspěchem\\\", \\\"label\\\": \\\"cs\\\"},\\n {\\\"text\\\": \\\"depends on the data type of the field.\\\", \\\"label\\\": \\\"en\\\"},\\n {\\\"text\\\": \\\"Umsatzwarnung zu Wochenbeginn zeitweise auf ein\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"niin heti nukun .\\\", \\\"label\\\": \\\"fi\\\"},\\n {\\\"text\\\": \\\"Mobilfunkunternehmen gegen die Anwendung der so\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"sapessi le intenzioni del governo Monti e dell'UE\\\", \\\"label\\\": \\\"it\\\"},\\n {\\\"text\\\": \\\"Di chi è figlia Martine Aubry?\\\", \\\"label\\\": \\\"it\\\"},\\n {\\\"text\\\": \\\"avec le reste du monde.\\\", \\\"label\\\": \\\"fr\\\"},\\n {\\\"text\\\": \\\"Այդ մաքոքը ինքնին նոր չէ, աշխարհը արդեն մի քանի\\\", \\\"label\\\": \\\"hy\\\"},\\n {\\\"text\\\": \\\"și în cazul destrămării cenaclului.\\\", \\\"label\\\": \\\"ro\\\"},\\n {\\\"text\\\": \\\"befriedigen kann , und ohne die auftretenden\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"Κύκνον τ̓ ἐξεναρεῖν καὶ ἀπὸ κλυτὰ τεύχεα δῦσαι.\\\", \\\"label\\\": \\\"grc\\\"},\\n {\\\"text\\\": \\\"færdiguddannede.\\\", \\\"label\\\": \\\"da\\\"},\\n {\\\"text\\\": \\\"Schmidt war Sohn eines Rittergutsbesitzers.\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"и вдаша попадь ѡпрати\\\", \\\"label\\\": \\\"orv\\\"},\\n {\\\"text\\\": \\\"cine nu știe învățătură”.\\\", \\\"label\\\": \\\"ro\\\"},\\n {\\\"text\\\": \\\"détacha et cette dernière tenta de tuer le jeune\\\", \\\"label\\\": \\\"fr\\\"},\\n {\\\"text\\\": \\\"Der har saka også ei lengre forhistorie.\\\", \\\"label\\\": \\\"nn\\\"},\\n {\\\"text\\\": \\\"Pieprz roztłuc w moździerzu, dodać do pasty,\\\", \\\"label\\\": \\\"pl\\\"},\\n {\\\"text\\\": \\\"Лежа за гребнем оврага, как за бруствером, Ушаков\\\", \\\"label\\\": \\\"ru\\\"},\\n {\\\"text\\\": \\\"gesucht habe, vielen Dank nochmals!\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"инструментальных сталей, повышения\\\", \\\"label\\\": \\\"ru\\\"},\\n {\\\"text\\\": \\\"im Halbfinale Patrick Smith und im Finale dann\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"البنوك التريث في منح تسهيلات جديدة لمنتجي حديد\\\", \\\"label\\\": \\\"ar\\\"},\\n {\\\"text\\\": \\\"una bolsa ventral, la cual se encuentra debajo de\\\", \\\"label\\\": \\\"es\\\"},\\n {\\\"text\\\": \\\"za SETimes.\\\", \\\"label\\\": \\\"sr\\\"},\\n {\\\"text\\\": \\\"de Irak, a un piloto italiano que había violado el\\\", \\\"label\\\": \\\"es\\\"},\\n {\\\"text\\\": \\\"Er könne sich nicht erklären , wie die Zeitung auf\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"Прохорова.\\\", \\\"label\\\": \\\"ru\\\"},\\n {\\\"text\\\": \\\"la democrazia perde sulla tecnocrazia? #\\\", \\\"label\\\": \\\"it\\\"},\\n {\\\"text\\\": \\\"entre ambas instituciones, confirmó al medio que\\\", \\\"label\\\": \\\"es\\\"},\\n {\\\"text\\\": \\\"Austlandet, vart det funne om lag førti\\\", \\\"label\\\": \\\"nn\\\"},\\n {\\\"text\\\": \\\"уровнями власти.\\\", \\\"label\\\": \\\"ru\\\"},\\n {\\\"text\\\": \\\"Dá tedy primáři úplatek, a často ne malý.\\\", \\\"label\\\": \\\"cs\\\"},\\n {\\\"text\\\": \\\"brillantes del acto, al llevar a cabo en el\\\", \\\"label\\\": \\\"es\\\"},\\n {\\\"text\\\": \\\"eee druga zadeva je majhen priročen gre kamorkoli\\\", \\\"label\\\": \\\"sl\\\"},\\n {\\\"text\\\": \\\"Das ATX-Board paßt in herkömmliche PC-ATX-Gehäuse\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"Za vodné bylo v prvním pololetí zaplaceno v ČR\\\", \\\"label\\\": \\\"cs\\\"},\\n {\\\"text\\\": \\\"Даже на полсантиметра.\\\", \\\"label\\\": \\\"ru\\\"},\\n {\\\"text\\\": \\\"com la del primer tinent d'alcalde en funcions,\\\", \\\"label\\\": \\\"ca\\\"},\\n {\\\"text\\\": \\\"кількох оповідань в цілості — щось на зразок того\\\", \\\"label\\\": \\\"uk\\\"},\\n {\\\"text\\\": \\\"sed ad divitias congregandas, vel superfluum\\\", \\\"label\\\": \\\"la\\\"},\\n {\\\"text\\\": \\\"Norma Talmadge, spela mot Valentino i en version\\\", \\\"label\\\": \\\"sv\\\"},\\n {\\\"text\\\": \\\"Dlatego chciał się jej oświadczyć w niezwykłym\\\", \\\"label\\\": \\\"pl\\\"},\\n {\\\"text\\\": \\\"будут выступать на одинаковых снарядах.\\\", \\\"label\\\": \\\"ru\\\"},\\n {\\\"text\\\": \\\"Orang-orang terbunuh di sana.\\\", \\\"label\\\": \\\"id\\\"},\\n {\\\"text\\\": \\\"لدى رايت شقيق اسمه أوسكار, وهو يعمل كرسام للكتب\\\", \\\"label\\\": \\\"ar\\\"},\\n {\\\"text\\\": \\\"Wirklichkeit verlagerten und kaum noch\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"как перемешивают костяшки перед игрой в домино, и\\\", \\\"label\\\": \\\"ru\\\"},\\n {\\\"text\\\": \\\"В средине дня, когда солнце светило в нашу\\\", \\\"label\\\": \\\"ru\\\"},\\n {\\\"text\\\": \\\"d'aventure aux rôles de jeune romantique avec une\\\", \\\"label\\\": \\\"fr\\\"},\\n {\\\"text\\\": \\\"My teď hledáme organizace, jež by s námi chtěly\\\", \\\"label\\\": \\\"cs\\\"},\\n {\\\"text\\\": \\\"Urteilsfähigkeit einbüßen , wenn ich eigene\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"sua appartenenza anche a voci diverse da quella in\\\", \\\"label\\\": \\\"it\\\"},\\n {\\\"text\\\": \\\"Aufträge dieses Jahr verdoppeln werden .\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"M.E.: Miała szanse mnie odnaleźć, gdyby naprawdę\\\", \\\"label\\\": \\\"pl\\\"},\\n {\\\"text\\\": \\\"secundum contactum virtutis, cum careat dimensiva\\\", \\\"label\\\": \\\"la\\\"},\\n {\\\"text\\\": \\\"ezinbestekoa dela esan zuen.\\\", \\\"label\\\": \\\"eu\\\"},\\n {\\\"text\\\": \\\"Anek hurbiltzeko eskatzen zion besaulkitik, eta\\\", \\\"label\\\": \\\"eu\\\"},\\n {\\\"text\\\": \\\"perfectius alio videat, quamvis uterque videat\\\", \\\"label\\\": \\\"la\\\"},\\n {\\\"text\\\": \\\"Die Strecke war anspruchsvoll und führte unter\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"саморазоблачительным уроком, западные СМИ не\\\", \\\"label\\\": \\\"ru\\\"},\\n {\\\"text\\\": \\\"han representerer radikal islamisme .\\\", \\\"label\\\": \\\"no\\\"},\\n {\\\"text\\\": \\\"Què s'hi respira pel que fa a la reforma del\\\", \\\"label\\\": \\\"ca\\\"},\\n {\\\"text\\\": \\\"previsto para também ser desconstruido.\\\", \\\"label\\\": \\\"pt\\\"},\\n {\\\"text\\\": \\\"Ὠκεανοῦ βαθυκόλποις ἄνθεά τ̓ αἰνυμένην, ῥόδα καὶ\\\", \\\"label\\\": \\\"grc\\\"},\\n {\\\"text\\\": \\\"para jovens de a anos nos Cieps.\\\", \\\"label\\\": \\\"pt\\\"},\\n {\\\"text\\\": \\\"संघर्ष को अंजाम तक पहुंचाने का ऐलान किया है ।\\\", \\\"label\\\": \\\"hi\\\"},\\n {\\\"text\\\": \\\"objeví i u nás.\\\", \\\"label\\\": \\\"cs\\\"},\\n {\\\"text\\\": \\\"kvitteringer.\\\", \\\"label\\\": \\\"da\\\"},\\n {\\\"text\\\": \\\"This report is no exception.\\\", \\\"label\\\": \\\"en\\\"},\\n {\\\"text\\\": \\\"Разлепват доносниците до избирателните списъци\\\", \\\"label\\\": \\\"bg\\\"},\\n {\\\"text\\\": \\\"anderem ihre Bewegungsfreiheit in den USA\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"Ñu tegoon ca kaw gor ña ay njotti bopp yu kenn\\\", \\\"label\\\": \\\"wo\\\"},\\n {\\\"text\\\": \\\"Struktur kann beispielsweise der Schwerpunkt mehr\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"% la velocidad permitida, la sanción es muy grave.\\\", \\\"label\\\": \\\"es\\\"},\\n {\\\"text\\\": \\\"Teles-Einstieg in ADSL-Markt\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"ettekäändeks liiga suure osamaksu.\\\", \\\"label\\\": \\\"et\\\"},\\n {\\\"text\\\": \\\"als Indiz für die geänderte Marktpolitik des\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"quod quidem aperte consequitur ponentes\\\", \\\"label\\\": \\\"la\\\"},\\n {\\\"text\\\": \\\"de negociación para el próximo de junio.\\\", \\\"label\\\": \\\"es\\\"},\\n {\\\"text\\\": \\\"Tyto důmyslné dekorace doznaly v poslední době\\\", \\\"label\\\": \\\"cs\\\"},\\n {\\\"text\\\": \\\"največjega uspeha doslej.\\\", \\\"label\\\": \\\"sl\\\"},\\n {\\\"text\\\": \\\"Paul Allen je jedan od suosnivača Interval\\\", \\\"label\\\": \\\"hr\\\"},\\n {\\\"text\\\": \\\"Federal (Seac / DF) eo Sindicato das Empresas de\\\", \\\"label\\\": \\\"pt\\\"},\\n {\\\"text\\\": \\\"Quartal mit . Mark gegenüber dem gleichen Quartal\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"otros clubes y del Barça B saldrán varios\\\", \\\"label\\\": \\\"es\\\"},\\n {\\\"text\\\": \\\"Jaskula (Pol.) -\\\", \\\"label\\\": \\\"cs\\\"},\\n {\\\"text\\\": \\\"umožnily říci, že je možné přejít k mnohem\\\", \\\"label\\\": \\\"cs\\\"},\\n {\\\"text\\\": \\\"اعلن الجنرال تومي فرانكس قائد القوات الامريكية\\\", \\\"label\\\": \\\"ar\\\"},\\n {\\\"text\\\": \\\"Telekom-Chef Ron Sommer und der Vorstandssprecher\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"My, jako průmyslový a finanční holding, můžeme\\\", \\\"label\\\": \\\"cs\\\"},\\n {\\\"text\\\": \\\"voorlichting onder andere betrekking kan hebben:\\\", \\\"label\\\": \\\"nl\\\"},\\n {\\\"text\\\": \\\"Hinrichtung geistig Behinderter applaudiert oder\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"wie beispielsweise Anzahl erzielte Klicks ,\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"Intel-PC-SDRAM-Spezifikation in der Version . (\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"plângere în termen de zile de la comunicarea\\\", \\\"label\\\": \\\"ro\\\"},\\n {\\\"text\\\": \\\"и Испания ще изгубят втория си комисар в ЕК.\\\", \\\"label\\\": \\\"bg\\\"},\\n {\\\"text\\\": \\\"इसके चलते इस आदिवासी जनजाति का क्षरण हो रहा है ।\\\", \\\"label\\\": \\\"hi\\\"},\\n {\\\"text\\\": \\\"aunque se mostró contrario a establecer un\\\", \\\"label\\\": \\\"es\\\"},\\n {\\\"text\\\": \\\"des letzten Jahres von auf Millionen Euro .\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"Ankara se također poziva da u cijelosti ratificira\\\", \\\"label\\\": \\\"hr\\\"},\\n {\\\"text\\\": \\\"herunterlädt .\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"стрессовую ситуацию для организма, каковой\\\", \\\"label\\\": \\\"ru\\\"},\\n {\\\"text\\\": \\\"Státního shromáždění (parlamentu).\\\", \\\"label\\\": \\\"cs\\\"},\\n {\\\"text\\\": \\\"diskutieren , ob und wie dieser Dienst weiterhin\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"Verbindungen zu FPÖ-nahen Polizisten gepflegt und\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"Pražského volebního lídra ovšem nevybírá Miloš\\\", \\\"label\\\": \\\"cs\\\"},\\n {\\\"text\\\": \\\"Nach einem Bericht der Washington Post bleibt das\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"للوضع آنذاك، لكني في قرارة نفسي كنت سعيداً لما\\\", \\\"label\\\": \\\"ar\\\"},\\n {\\\"text\\\": \\\"не желаят запазването на статуквото.\\\", \\\"label\\\": \\\"bg\\\"},\\n {\\\"text\\\": \\\"Offenburg gewesen .\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"ἐὰν ὑμῖν εἴπω οὐ μὴ πιστεύσητε\\\", \\\"label\\\": \\\"grc\\\"},\\n {\\\"text\\\": \\\"all'odiato compagno di squadra Prost, il quale\\\", \\\"label\\\": \\\"it\\\"},\\n {\\\"text\\\": \\\"historischen Gänselieselbrunnens.\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"למידע מלווייני הריגול האמריקאיים העוקבים אחר\\\", \\\"label\\\": \\\"he\\\"},\\n {\\\"text\\\": \\\"οὐδὲν ἄρα διαφέρεις Ἀμάσιος τοῦ Ἠλείου, ὃν\\\", \\\"label\\\": \\\"grc\\\"},\\n {\\\"text\\\": \\\"movementos migratorios.\\\", \\\"label\\\": \\\"gl\\\"},\\n {\\\"text\\\": \\\"Handy und ein Spracherkennungsprogramm sämtliche\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"Kümne aasta jooksul on Eestisse ohjeldamatult\\\", \\\"label\\\": \\\"et\\\"},\\n {\\\"text\\\": \\\"H.G. Bücknera.\\\", \\\"label\\\": \\\"pl\\\"},\\n {\\\"text\\\": \\\"protiv krijumčarenja, ili pak traženju ukidanja\\\", \\\"label\\\": \\\"hr\\\"},\\n {\\\"text\\\": \\\"Topware-Anteile mehrere Millionen Mark gefordert\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"Maar de mensen die nu over Van Dijk bij FC Twente\\\", \\\"label\\\": \\\"nl\\\"},\\n {\\\"text\\\": \\\"poidan experimentar as percepcións do interesado,\\\", \\\"label\\\": \\\"gl\\\"},\\n {\\\"text\\\": \\\"Miał przecież w kieszeni nóż.\\\", \\\"label\\\": \\\"pl\\\"},\\n {\\\"text\\\": \\\"Avšak žádná z nich nepronikla za hranice přímé\\\", \\\"label\\\": \\\"cs\\\"},\\n {\\\"text\\\": \\\"esim. helpottamalla luottoja muiden\\\", \\\"label\\\": \\\"fi\\\"},\\n {\\\"text\\\": \\\"Podle předběžných výsledků zvítězila v\\\", \\\"label\\\": \\\"cs\\\"},\\n {\\\"text\\\": \\\"Nicht nur das Web-Frontend , auch die\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"Regierungsinstitutionen oder Universitäten bei\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"Խուլեն Լոպետեգիին, պատճառաբանելով, որ վերջինս\\\", \\\"label\\\": \\\"hy\\\"},\\n {\\\"text\\\": \\\"Афганистана, где в последние дни идут ожесточенные\\\", \\\"label\\\": \\\"ru\\\"},\\n {\\\"text\\\": \\\"лѧхове же не идоша\\\", \\\"label\\\": \\\"orv\\\"},\\n {\\\"text\\\": \\\"Mit Hilfe von IBMs Chip-Management-Systemen sollen\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\", als Manager zu Telefonica zu wechseln .\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"którym zajmuje się człowiek, zmienia go i pozwala\\\", \\\"label\\\": \\\"pl\\\"},\\n {\\\"text\\\": \\\"činí kyperských liber, to je asi USD.\\\", \\\"label\\\": \\\"cs\\\"},\\n {\\\"text\\\": \\\"Studienplätze getauscht werden .\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"учёных, орнитологов признают вид.\\\", \\\"label\\\": \\\"ru\\\"},\\n {\\\"text\\\": \\\"acordare a concediilor prevăzute de legislațiile\\\", \\\"label\\\": \\\"ro\\\"},\\n {\\\"text\\\": \\\"at større innsats for fornybar, berekraftig energi\\\", \\\"label\\\": \\\"nn\\\"},\\n {\\\"text\\\": \\\"Politiet veit ikkje kor mange personar som deltok\\\", \\\"label\\\": \\\"nn\\\"},\\n {\\\"text\\\": \\\"offentligheten av unge , sinte menn som har\\\", \\\"label\\\": \\\"no\\\"},\\n {\\\"text\\\": \\\"însuși în jurul lapunei, care încet DISPARE în\\\", \\\"label\\\": \\\"ro\\\"},\\n {\\\"text\\\": \\\"O motivo da decisão é evitar uma sobrecarga ainda\\\", \\\"label\\\": \\\"pt\\\"},\\n {\\\"text\\\": \\\"El Apostolado de la prensa contribuye en modo\\\", \\\"label\\\": \\\"es\\\"},\\n {\\\"text\\\": \\\"Teltow ( Kreis Teltow-Fläming ) ist Schmitt einer\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"grozījumus un iesniegt tos Apvienoto Nāciju\\\", \\\"label\\\": \\\"lv\\\"},\\n {\\\"text\\\": \\\"Gestalt einer deutschen Nationalmannschaft als\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"D überholt zu haben , konterte am heutigen Montag\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"Softwarehersteller Oracle hat im dritten Quartal\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"Během nich se ekonomické podmínky mohou radikálně\\\", \\\"label\\\": \\\"cs\\\"},\\n {\\\"text\\\": \\\"Dziki kot w górach zeskakuje z kamienia.\\\", \\\"label\\\": \\\"pl\\\"},\\n {\\\"text\\\": \\\"Ačkoliv ligový nováček prohrál, opět potvrdil, že\\\", \\\"label\\\": \\\"cs\\\"},\\n {\\\"text\\\": \\\"des Tages , Portraits internationaler Stars sowie\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"Communicator bekannt wurde .\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"τῷ δ’ ἄρα καὶ αὐτῷ ἡ γυνή ἐπίτεξ ἐοῦσα πᾶσαν\\\", \\\"label\\\": \\\"grc\\\"},\\n {\\\"text\\\": \\\"Triadú tenia, mentre redactava 'Dies de memòria',\\\", \\\"label\\\": \\\"ca\\\"},\\n {\\\"text\\\": \\\"دستهجمعی در درخشندگی ماه سیمگون زمزمه ستاینده و\\\", \\\"label\\\": \\\"fa\\\"},\\n {\\\"text\\\": \\\"Книгу, наполненную мелочной заботой об одежде,\\\", \\\"label\\\": \\\"ru\\\"},\\n {\\\"text\\\": \\\"putares canem leporem persequi.\\\", \\\"label\\\": \\\"la\\\"},\\n {\\\"text\\\": \\\"В дальнейшем эта яркость слегка померкла, но в\\\", \\\"label\\\": \\\"ru\\\"},\\n {\\\"text\\\": \\\"offizielles Verfahren gegen die Telekom\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"podrían haber sido habitantes de la Península\\\", \\\"label\\\": \\\"es\\\"},\\n {\\\"text\\\": \\\"Grundlage für dieses Verfahren sind spezielle\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"Rechtsausschuß vorgelegten Entwurf der Richtlinie\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"Im so genannten Portalgeschäft sei das Unternehmen\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"ⲏ ⲉⲓϣⲁⲛϥⲓ ⲛⲉⲓⲇⲱⲗⲟⲛ ⲉⲧϩⲙⲡⲉⲕⲏⲓ ⲙⲏ ⲉⲓⲛⲁϣϩⲱⲡ ⲟⲛ ⲙⲡⲣⲏ\\\", \\\"label\\\": \\\"cop\\\"},\\n {\\\"text\\\": \\\"juego podían matar a cualquier herbívoro, pero\\\", \\\"label\\\": \\\"es\\\"},\\n {\\\"text\\\": \\\"Nach Angaben von Axent nutzen Unternehmen aus der\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"hrdiny Havlovy Zahradní slavnosti (premiéra ) se\\\", \\\"label\\\": \\\"cs\\\"},\\n {\\\"text\\\": \\\"Een zin van heb ik jou daar\\\", \\\"label\\\": \\\"nl\\\"},\\n {\\\"text\\\": \\\"hat sein Hirn an der CeBIT-Kasse vergessen .\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"καὶ τοὺς ἐκπλαγέντας οὐκ ἔχειν ἔτι ἐλεγχομένους\\\", \\\"label\\\": \\\"grc\\\"},\\n {\\\"text\\\": \\\"nachgewiesenen langfristigen Kosten , sowie den im\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"jučer nakon četiri dana putovanja u Helsinki.\\\", \\\"label\\\": \\\"hr\\\"},\\n {\\\"text\\\": \\\"pašto paslaugos teikėjas gali susitarti su\\\", \\\"label\\\": \\\"lt\\\"},\\n {\\\"text\\\": \\\"В результате, эти золотые кадры переходят из одной\\\", \\\"label\\\": \\\"ru\\\"},\\n {\\\"text\\\": \\\"द फाइव-ईयर एंगेजमेंट में अभिनय किया जिसमें जैसन\\\", \\\"label\\\": \\\"hi\\\"},\\n {\\\"text\\\": \\\"výpis o počtu akcií.\\\", \\\"label\\\": \\\"cs\\\"},\\n {\\\"text\\\": \\\"Enfin, elles arrivent à un pavillon chinois\\\", \\\"label\\\": \\\"fr\\\"},\\n {\\\"text\\\": \\\"Tentu saja, tren yang berhubungandengan\\\", \\\"label\\\": \\\"id\\\"},\\n {\\\"text\\\": \\\"Arbeidarpartiet og SV har sikra seg fleirtal mot\\\", \\\"label\\\": \\\"nn\\\"},\\n {\\\"text\\\": \\\"eles: 'Tudo isso está errado' , disse um\\\", \\\"label\\\": \\\"pt\\\"},\\n {\\\"text\\\": \\\"The islands are in their own time zone, minutes\\\", \\\"label\\\": \\\"en\\\"},\\n {\\\"text\\\": \\\"Auswahl debütierte er am .\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"Bu komisyonlar, arazilerini satın almak için\\\", \\\"label\\\": \\\"tr\\\"},\\n {\\\"text\\\": \\\"Geschütze gegen Redmond aufgefahren .\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"Time scything the hours, but at the top, over the\\\", \\\"label\\\": \\\"en\\\"},\\n {\\\"text\\\": \\\"Di musim semi , berharap mengadaptasi Tintin untuk\\\", \\\"label\\\": \\\"id\\\"},\\n {\\\"text\\\": \\\"крупнейшей геополитической катастрофой XX века.\\\", \\\"label\\\": \\\"ru\\\"},\\n {\\\"text\\\": \\\"Rajojen avaaminen ei suju ongelmitta .\\\", \\\"label\\\": \\\"fi\\\"},\\n {\\\"text\\\": \\\"непроницаемым, как для СССР.\\\", \\\"label\\\": \\\"ru\\\"},\\n {\\\"text\\\": \\\"Ma non mancano le polemiche.\\\", \\\"label\\\": \\\"it\\\"},\\n {\\\"text\\\": \\\"Internet als Ort politischer Diskussion und auch\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"incomplets.\\\", \\\"label\\\": \\\"ca\\\"},\\n {\\\"text\\\": \\\"Su padre luchó al lado de Luis Moya, primer Jefe\\\", \\\"label\\\": \\\"es\\\"},\\n {\\\"text\\\": \\\"informazione.\\\", \\\"label\\\": \\\"it\\\"},\\n {\\\"text\\\": \\\"Primacom bietet für Telekom-Kabelnetz\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"Oświadczenie prezydencji w imieniu Unii\\\", \\\"label\\\": \\\"pl\\\"},\\n {\\\"text\\\": \\\"foran rattet i familiens gamle Baleno hvis døra på\\\", \\\"label\\\": \\\"no\\\"},\\n {\\\"text\\\": \\\"[speaker:laughter]\\\", \\\"label\\\": \\\"sl\\\"},\\n {\\\"text\\\": \\\"Dog med langt mindre utstyr med seg.\\\", \\\"label\\\": \\\"nn\\\"},\\n {\\\"text\\\": \\\"dass es nicht schon mit der anfänglichen\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"इस पर दोनों पक्षों में नोकझोंक शुरू हो गई ।\\\", \\\"label\\\": \\\"hi\\\"},\\n {\\\"text\\\": \\\"کے ترجمان منیش تیواری اور دگ وجئے سنگھ نے بھی یہ\\\", \\\"label\\\": \\\"ur\\\"},\\n {\\\"text\\\": \\\"dell'Assemblea Costituente che posseggono i\\\", \\\"label\\\": \\\"it\\\"},\\n {\\\"text\\\": \\\"и аште вьси съблазнѧтъ сѧ нъ не азъ\\\", \\\"label\\\": \\\"cu\\\"},\\n {\\\"text\\\": \\\"In Irvine hat auch das Logistikunternehmen Atlas\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"законодательных норм, принимаемых существующей\\\", \\\"label\\\": \\\"ru\\\"},\\n {\\\"text\\\": \\\"Κροίσῳ προτείνων τὰς χεῖρας ἐπικατασφάξαι μιν\\\", \\\"label\\\": \\\"grc\\\"},\\n {\\\"text\\\": \\\"МИНУСЫ: ИНФЛЯЦИЯ И КРИЗИС В ЖИВОТНОВОДСТВЕ.\\\", \\\"label\\\": \\\"ru\\\"},\\n {\\\"text\\\": \\\"unterschiedlicher Meinung .\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"Jospa joku ystävällinen sielu auttaisi kassieni\\\", \\\"label\\\": \\\"fi\\\"},\\n {\\\"text\\\": \\\"Añadió que, en el futuro se harán otros\\\", \\\"label\\\": \\\"es\\\"},\\n {\\\"text\\\": \\\"Sessiz tonlama hem Fince, hem de Kuzey Sami\\\", \\\"label\\\": \\\"tr\\\"},\\n {\\\"text\\\": \\\"nicht ihnen gehört und sie nicht alles , was sie\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"Etelästä Kuivajärveen laskee Tammelan Liesjärvestä\\\", \\\"label\\\": \\\"fi\\\"},\\n {\\\"text\\\": \\\"ICANNs Vorsitzender Vint Cerf warb mit dem Hinweis\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"Norsk politikk frå til kan dermed, i\\\", \\\"label\\\": \\\"nn\\\"},\\n {\\\"text\\\": \\\"Głosowało posłów.\\\", \\\"label\\\": \\\"pl\\\"},\\n {\\\"text\\\": \\\"Danny Jones -- smithjones@ev.net\\\", \\\"label\\\": \\\"en\\\"},\\n {\\\"text\\\": \\\"sebeuvědomění moderní civilizace sehrála lučavka\\\", \\\"label\\\": \\\"cs\\\"},\\n {\\\"text\\\": \\\"относительно спокойный сон: тому гарантия\\\", \\\"label\\\": \\\"ru\\\"},\\n {\\\"text\\\": \\\"A halte voiz prist li pedra a crïer\\\", \\\"label\\\": \\\"fro\\\"},\\n {\\\"text\\\": \\\"آنها امیدوارند این واکسن بهزودی در دسترس بیماران\\\", \\\"label\\\": \\\"fa\\\"},\\n {\\\"text\\\": \\\"vlastní důstojnou vousatou tváří.\\\", \\\"label\\\": \\\"cs\\\"},\\n {\\\"text\\\": \\\"ora aprire la strada a nuove cause e alimentare il\\\", \\\"label\\\": \\\"it\\\"},\\n {\\\"text\\\": \\\"Die Zahl der Vielleser nahm von auf Prozent zu ,\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"Finanzvorstand von Hotline-Dienstleister InfoGenie\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"entwickeln .\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"incolumità pubblica.\\\", \\\"label\\\": \\\"it\\\"},\\n {\\\"text\\\": \\\"lehtija televisiomainonta\\\", \\\"label\\\": \\\"fi\\\"},\\n {\\\"text\\\": \\\"joistakin kohdista eri mieltä.\\\", \\\"label\\\": \\\"fi\\\"},\\n {\\\"text\\\": \\\"Hlavně anglická nezávislá scéna, Dead Can Dance,\\\", \\\"label\\\": \\\"cs\\\"},\\n {\\\"text\\\": \\\"pásmech od do bodů bodové stupnice.\\\", \\\"label\\\": \\\"cs\\\"},\\n {\\\"text\\\": \\\"Zu Beginn des Ersten Weltkrieges zählte das\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"Així van sorgir, damunt els antics cementiris,\\\", \\\"label\\\": \\\"ca\\\"},\\n {\\\"text\\\": \\\"In manchem Gedicht der spätern Alten, wie zum\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"gaweihaida jah insandida in þana fairƕu jus qiþiþ\\\", \\\"label\\\": \\\"got\\\"},\\n {\\\"text\\\": \\\"Beides sollte gelöscht werden!\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"modifiqués la seva petició inicial de anys de\\\", \\\"label\\\": \\\"ca\\\"},\\n {\\\"text\\\": \\\"В день открытия симпозиума состоялась закладка\\\", \\\"label\\\": \\\"ru\\\"},\\n {\\\"text\\\": \\\"tõestatud.\\\", \\\"label\\\": \\\"et\\\"},\\n {\\\"text\\\": \\\"ἵππῳ πίπτει αὐτοῦ ταύτῃ\\\", \\\"label\\\": \\\"grc\\\"},\\n {\\\"text\\\": \\\"bisher nie enttäuscht!\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"De bohte ollu tuollárat ja suttolaččat ja\\\", \\\"label\\\": \\\"sme\\\"},\\n {\\\"text\\\": \\\"Klarsignal från röstlängdsläsaren, tre tryck i\\\", \\\"label\\\": \\\"sv\\\"},\\n {\\\"text\\\": \\\"Tvůrcem nového termínu je Joseph Fisher.\\\", \\\"label\\\": \\\"cs\\\"},\\n {\\\"text\\\": \\\"Nie miałem czasu na reakcję twierdzi Norbert,\\\", \\\"label\\\": \\\"pl\\\"},\\n {\\\"text\\\": \\\"potentia Schöpfer.\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"Un poquito caro, pero vale mucho la pena;\\\", \\\"label\\\": \\\"es\\\"},\\n {\\\"text\\\": \\\"οὔ τε γὰρ ἴφθιμοι Λύκιοι Δαναῶν ἐδύναντο τεῖχος\\\", \\\"label\\\": \\\"grc\\\"},\\n {\\\"text\\\": \\\"vajec, sladového výtažku a některých vitamínových\\\", \\\"label\\\": \\\"cs\\\"},\\n {\\\"text\\\": \\\"Настоящие герои, те, чьи истории потом\\\", \\\"label\\\": \\\"ru\\\"},\\n {\\\"text\\\": \\\"praesumptio:\\\", \\\"label\\\": \\\"la\\\"},\\n {\\\"text\\\": \\\"Olin justkui nende vastutusel.\\\", \\\"label\\\": \\\"et\\\"},\\n {\\\"text\\\": \\\"Jokainen keinahdus tuo lähemmäksi hetkeä jolloin\\\", \\\"label\\\": \\\"fi\\\"},\\n {\\\"text\\\": \\\"ekonomicky výhodných způsobů odvodnění těžkých,\\\", \\\"label\\\": \\\"cs\\\"},\\n {\\\"text\\\": \\\"Poprvé ve své historii dokázala v kvalifikaci pro\\\", \\\"label\\\": \\\"cs\\\"},\\n {\\\"text\\\": \\\"zpracovatelského a spotřebního průmyslu bude nutné\\\", \\\"label\\\": \\\"cs\\\"},\\n {\\\"text\\\": \\\"Windows CE zu integrieren .\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"Armangué, a través d'un decret, ordenés l'aturada\\\", \\\"label\\\": \\\"ca\\\"},\\n {\\\"text\\\": \\\"to, co nás Evropany spojuje, než to, co nás od\\\", \\\"label\\\": \\\"cs\\\"},\\n {\\\"text\\\": \\\"ergänzt durch einen gesetzlich verankertes\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"Насчитал, что с начала года всего три дня были\\\", \\\"label\\\": \\\"ru\\\"},\\n {\\\"text\\\": \\\"Borisovu tražeći od njega da prihvati njenu\\\", \\\"label\\\": \\\"sr\\\"},\\n {\\\"text\\\": \\\"la presenza di ben veleni diversi: . chili di\\\", \\\"label\\\": \\\"it\\\"},\\n {\\\"text\\\": \\\"καὶ τῶν ἐκλεκτῶν ἀγγέλων ἵνα ταῦτα φυλάξῃς χωρὶς\\\", \\\"label\\\": \\\"grc\\\"},\\n {\\\"text\\\": \\\"pretraživale obližnju bolnicu i stambene zgrade u\\\", \\\"label\\\": \\\"hr\\\"},\\n {\\\"text\\\": \\\"An rund Katzen habe Wolf seine Spiele getestet ,\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"investigating since March.\\\", \\\"label\\\": \\\"en\\\"},\\n {\\\"text\\\": \\\"Tonböden (Mullböden).\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"Stálý dopisovatel LN v SRN Bedřich Utitz\\\", \\\"label\\\": \\\"cs\\\"},\\n {\\\"text\\\": \\\"červnu předložené smlouvy.\\\", \\\"label\\\": \\\"cs\\\"},\\n {\\\"text\\\": \\\"πνεύματι ᾧ ἐλάλει\\\", \\\"label\\\": \\\"grc\\\"},\\n {\\\"text\\\": \\\".%의 신장세를 보였다.\\\", \\\"label\\\": \\\"ko\\\"},\\n {\\\"text\\\": \\\"Foae verde, foi de nuc, Prin pădure, prin colnic,\\\", \\\"label\\\": \\\"ro\\\"},\\n {\\\"text\\\": \\\"διαπέμψας ἄλλους ἄλλῃ τοὺς μὲν ἐς Δελφοὺς ἰέναι\\\", \\\"label\\\": \\\"grc\\\"},\\n {\\\"text\\\": \\\"المسلمين أو أي تيار سياسي طالما عمل ذلك التيار في\\\", \\\"label\\\": \\\"ar\\\"},\\n {\\\"text\\\": \\\"As informações são da Dow Jones.\\\", \\\"label\\\": \\\"pt\\\"},\\n {\\\"text\\\": \\\"Milliarde DM ausgestattet sein .\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"De utgår fortfarande från att kvinnans jämlikhet\\\", \\\"label\\\": \\\"sv\\\"},\\n {\\\"text\\\": \\\"Sneeuw maakte in Davos bij de voorbereiding een\\\", \\\"label\\\": \\\"nl\\\"},\\n {\\\"text\\\": \\\"De ahí que en este mercado puedan negociarse\\\", \\\"label\\\": \\\"es\\\"},\\n {\\\"text\\\": \\\"intenzívnějšímu sbírání a studiu.\\\", \\\"label\\\": \\\"cs\\\"},\\n {\\\"text\\\": \\\"और औसकर ४.० पैकेज का प्रयोग किया गया है ।\\\", \\\"label\\\": \\\"hi\\\"},\\n {\\\"text\\\": \\\"Adipati Kuningan karena Kuningan menjadi bagian\\\", \\\"label\\\": \\\"id\\\"},\\n {\\\"text\\\": \\\"Svako je bar jednom poželeo da mašine prosto umeju\\\", \\\"label\\\": \\\"sr\\\"},\\n {\\\"text\\\": \\\"Im vergangenen Jahr haben die Regierungen einen\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"durat motus, aliquid fit et non est;\\\", \\\"label\\\": \\\"la\\\"},\\n {\\\"text\\\": \\\"Dominować będą piosenki do tekstów Edwarda\\\", \\\"label\\\": \\\"pl\\\"},\\n {\\\"text\\\": \\\"beantwortet .\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"О гуманитариях было кому рассказывать, а вот за\\\", \\\"label\\\": \\\"ru\\\"},\\n {\\\"text\\\": \\\"Helsingin kaupunki riitautti vuokrasopimuksen\\\", \\\"label\\\": \\\"fi\\\"},\\n {\\\"text\\\": \\\"chợt tan biến.\\\", \\\"label\\\": \\\"vi\\\"},\\n {\\\"text\\\": \\\"avtomobil ločuje od drugih.\\\", \\\"label\\\": \\\"sl\\\"},\\n {\\\"text\\\": \\\"Congress has proven itself ineffective as a body.\\\", \\\"label\\\": \\\"en\\\"},\\n {\\\"text\\\": \\\"मैक्सिको ने इस तरह का शो इस समय आयोजित करने का\\\", \\\"label\\\": \\\"hi\\\"},\\n {\\\"text\\\": \\\"No minimum order amount.\\\", \\\"label\\\": \\\"en\\\"},\\n {\\\"text\\\": \\\"Convertassa .\\\", \\\"label\\\": \\\"fi\\\"},\\n {\\\"text\\\": \\\"Как это можно сделать?\\\", \\\"label\\\": \\\"ru\\\"},\\n {\\\"text\\\": \\\"tha mi creidsinn gu robh iad ceart cho saor shuas\\\", \\\"label\\\": \\\"gd\\\"},\\n {\\\"text\\\": \\\"실제 일제는 이런 만해의 논리를 묵살하고 한반도를 침략한 다음 , 이어 만주를 침략하고\\\", \\\"label\\\": \\\"ko\\\"},\\n {\\\"text\\\": \\\"Da un semplice richiamo all'ordine fino a grandi\\\", \\\"label\\\": \\\"it\\\"},\\n {\\\"text\\\": \\\"pozoruhodný nejen po umělecké stránce, jež\\\", \\\"label\\\": \\\"cs\\\"},\\n {\\\"text\\\": \\\"La comida y el servicio aprueban.\\\", \\\"label\\\": \\\"es\\\"},\\n {\\\"text\\\": \\\"again, connected not with each other but to the\\\", \\\"label\\\": \\\"en\\\"},\\n {\\\"text\\\": \\\"Protokol výslovně stanoví, že nikdo nemůže být\\\", \\\"label\\\": \\\"cs\\\"},\\n {\\\"text\\\": \\\"ఒక విషయం అడగాలని ఉంది .\\\", \\\"label\\\": \\\"te\\\"},\\n {\\\"text\\\": \\\"Безгранично почитая дирекцию, ловя на лету каждое\\\", \\\"label\\\": \\\"ru\\\"},\\n {\\\"text\\\": \\\"rovnoběžných růstových vrstev, zůstávají krychlové\\\", \\\"label\\\": \\\"cs\\\"},\\n {\\\"text\\\": \\\"प्रवेश और पूर्व प्रधानमंत्री लाल बहादुर शास्त्री\\\", \\\"label\\\": \\\"hi\\\"},\\n {\\\"text\\\": \\\"Bronzen medaille in de Europese marathon.\\\", \\\"label\\\": \\\"nl\\\"},\\n {\\\"text\\\": \\\"- gadu vecumā viņi to nesaprot.\\\", \\\"label\\\": \\\"lv\\\"},\\n {\\\"text\\\": \\\"Realizó sus estudios primarios en la Escuela Julia\\\", \\\"label\\\": \\\"es\\\"},\\n {\\\"text\\\": \\\"cuartos de final, su clasificación para la final a\\\", \\\"label\\\": \\\"es\\\"},\\n {\\\"text\\\": \\\"Sem si pro něho přiletí americký raketoplán, na\\\", \\\"label\\\": \\\"cs\\\"},\\n {\\\"text\\\": \\\"Way to go!\\\", \\\"label\\\": \\\"en\\\"},\\n {\\\"text\\\": \\\"gehört der neuen SPD-Führung unter Parteichef\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"Somit simuliert der Player mit einer GByte-Platte\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"Berufung auf kommissionsnahe Kreise , die bereits\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"Dist Clarïen\\\", \\\"label\\\": \\\"fro\\\"},\\n {\\\"text\\\": \\\"Schon nach den Gerüchten , die Telekom wolle den\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"Software von NetObjects ist nach Angaben des\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"si enim per legem iustitia ergo Christus gratis\\\", \\\"label\\\": \\\"la\\\"},\\n {\\\"text\\\": \\\"ducerent in ipsam magis quam in corpus christi,\\\", \\\"label\\\": \\\"la\\\"},\\n {\\\"text\\\": \\\"Neustar-Melbourne-IT-Partnerschaft NeuLevel .\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"forderte dagegen seine drastische Verschärfung.\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"pemmican på hundrede forskellige måder.\\\", \\\"label\\\": \\\"da\\\"},\\n {\\\"text\\\": \\\"Lehån, själv matematiklärare, visar hur den nya\\\", \\\"label\\\": \\\"sv\\\"},\\n {\\\"text\\\": \\\"I highly recommend his shop.\\\", \\\"label\\\": \\\"en\\\"},\\n {\\\"text\\\": \\\"verità, giovani fedeli prostratevi #amen\\\", \\\"label\\\": \\\"it\\\"},\\n {\\\"text\\\": \\\"उत्तर प्रदेश के अध्यक्ष पद से हटाए गए विनय कटियार\\\", \\\"label\\\": \\\"hi\\\"},\\n {\\\"text\\\": \\\"() روزی مےں کشادگی ہوتی ہے۔\\\", \\\"label\\\": \\\"ur\\\"},\\n {\\\"text\\\": \\\"Prozessorgeschäft profitieren kann , stellen\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"školy začalo počítat pytle s moukou a zjistilo, že\\\", \\\"label\\\": \\\"cs\\\"},\\n {\\\"text\\\": \\\"प्रभावशाली पर गैर सरकारी लोगों के घरों में भी\\\", \\\"label\\\": \\\"hi\\\"},\\n {\\\"text\\\": \\\"geschichtslos , oder eine Farce , wie sich\\\", \\\"label\\\": \\\"de\\\"},\\n {\\\"text\\\": \\\"Ústrednými mocnosťami v marci však spôsobilo, že\\\", \\\"label\\\": \\\"sk\\\"},\\n {\\\"text\\\": \\\"التسليح بدون مبرر، واستمرار الأضرار الناجمة عن فرض\\\", \\\"label\\\": \\\"ar\\\"},\\n {\\\"text\\\": \\\"Například Pedagogická fakulta Univerzity Karlovy\\\", \\\"label\\\": \\\"cs\\\"},\\n {\\\"text\\\": \\\"nostris ut eriperet nos de praesenti saeculo\\\", \\\"label\\\": \\\"la\\\"}]\\n \\n docs = [Document([], text=example[\\\"text\\\"]) for example in examples]\\n gold_labels = [example[\\\"label\\\"] for example in examples]\\n basic_multilingual(docs)\\n accuracy = sum([(doc.lang == label) for doc,label in zip(docs,gold_labels)])/len(docs)\\n assert accuracy >= 0.98\",\n \"def Language(self, default=None):\\n return self.data.get('language', default)\",\n \"def language_version(language):\\n return _PREFERRED_VERSIONS[language]\",\n \"def default_language(self):\\n return self._default_language\",\n \"def get_locale():\\n if (session.get(\\\"language\\\") is not None):\\n return session.get('language')['charcode']\\n return request.accept_languages.best_match(app.config['LANGUAGES'].keys())\",\n \"def get_weather_language(self):\\n return self.bot_data_file[\\\"weather\\\"][\\\"default_language\\\"]\",\n \"def get_default_language():\\n return getattr(thread_locals, 'DEFAULT_LANGUAGE',\\n settings.DEFAULT_LANGUAGE)\",\n \"def lemma(self):\\n if self.metadata.get('COMLEX'):\\n return self.metadata['COMLEX'][0].features['ORTH'][0][1:-1]\\n elif self.label in ['NNP', 'NNPS']:\\n return self.text\\n else:\\n return self.text.lower()\",\n \"def text_language(text):\\n hebrew = 0\\n english = 0\\n for char in text:\\n if char in \\\"אבגדהוזחטיכךלמםנסעפףצץקרשת\\\":\\n hebrew += 1\\n elif char.lower() in \\\"abcdefghijklmnopqrstuvwxyz\\\":\\n english += 1\\n return {True: \\\"hebrew\\\", False: \\\"english\\\"}[hebrew > english]\",\n \"def genlangs(self):\\r\\n raise NotImplementedError\",\n \"def get_language(fn):\\n # FIXME - this expects the fn to be '.../XX/LC_MESSAGES/messages.po'\\n return fn.split(os.sep)[-3]\",\n \"def language_version(self) -> pulumi.Output[Optional[str]]:\\n return pulumi.get(self, \\\"language_version\\\")\",\n \"def gpwDefaultLanguage(self):\\n parent = self.getFolderWhenPortalFactory()\\n if hasattr(parent, 'getRawLanguage') and parent.getRawLanguage():\\n return parent.getRawLanguage()\\n tool = getToolByName(self, 'portal_languages', None)\\n if tool is not None:\\n return tool.getDefaultLanguage()\\n return config.LANGUAGE_DEFAULT\",\n \"def get_lang_code(lang_code):\\r\\n if lang_code not in constants.SUPPORTED_LANG_CODES_ANALYZERS:\\r\\n return constants.FALLBACK_LANG_CODE\\r\\n return lang_code\",\n \"def get_default_language():\\n utility = queryUtility(ILanguageAvailability)\\n if utility is not None:\\n return utility.getDefaultLanguage()\\n return DEFAULT_LANGUAGE\",\n \"def celex_pron_loc(language, lemma):\\n pron = 5\\n if language == \\\"german\\\" or language== \\\"dutch\\\": pron = pron -2 #german one less\\n if lemma == \\\"wordform\\\": pron += 1\\n return pron\",\n \"def algorithm(self) -> str:\\n return pulumi.get(self, \\\"algorithm\\\")\",\n \"def algorithm(self) -> str:\\n return pulumi.get(self, \\\"algorithm\\\")\",\n \"def wikiLanguages():\\n return languages\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.72861236","0.65404236","0.6198768","0.61458135","0.6069835","0.6027784","0.59970075","0.5983806","0.5961204","0.5961204","0.5915103","0.5895517","0.5860599","0.58391285","0.58170784","0.57956773","0.5790107","0.57584816","0.57295835","0.572077","0.57125485","0.56796414","0.56795347","0.56661993","0.56655854","0.5632322","0.5631046","0.5628728","0.5628638","0.5610644","0.5600505","0.5585515","0.55600506","0.5559067","0.5559067","0.55542755","0.5538187","0.55233735","0.5518321","0.55174714","0.55059","0.5495459","0.5491285","0.54865223","0.54821527","0.5476861","0.54606324","0.54510903","0.54510903","0.54510903","0.54510903","0.54425704","0.54421425","0.54413724","0.54314005","0.540212","0.540171","0.53943694","0.5382318","0.53708","0.5368969","0.53389174","0.5333884","0.532905","0.53125477","0.5290375","0.52886784","0.5269391","0.5265038","0.5262813","0.5250049","0.5235847","0.5233703","0.52319044","0.5229028","0.52209127","0.5214811","0.52069","0.5204257","0.5195555","0.51937187","0.5189394","0.51740307","0.5160921","0.51582634","0.5140691","0.51286083","0.5128368","0.51270264","0.5124792","0.51227295","0.5118389","0.51019675","0.5091653","0.5091296","0.50862175","0.5079842","0.50763005","0.50763005","0.50754696"],"string":"[\n \"0.72861236\",\n \"0.65404236\",\n \"0.6198768\",\n \"0.61458135\",\n \"0.6069835\",\n \"0.6027784\",\n \"0.59970075\",\n \"0.5983806\",\n \"0.5961204\",\n \"0.5961204\",\n \"0.5915103\",\n \"0.5895517\",\n \"0.5860599\",\n \"0.58391285\",\n \"0.58170784\",\n \"0.57956773\",\n \"0.5790107\",\n \"0.57584816\",\n \"0.57295835\",\n \"0.572077\",\n \"0.57125485\",\n \"0.56796414\",\n \"0.56795347\",\n \"0.56661993\",\n \"0.56655854\",\n \"0.5632322\",\n \"0.5631046\",\n \"0.5628728\",\n \"0.5628638\",\n \"0.5610644\",\n \"0.5600505\",\n \"0.5585515\",\n \"0.55600506\",\n \"0.5559067\",\n \"0.5559067\",\n \"0.55542755\",\n \"0.5538187\",\n \"0.55233735\",\n \"0.5518321\",\n \"0.55174714\",\n \"0.55059\",\n \"0.5495459\",\n \"0.5491285\",\n \"0.54865223\",\n \"0.54821527\",\n \"0.5476861\",\n \"0.54606324\",\n \"0.54510903\",\n \"0.54510903\",\n \"0.54510903\",\n \"0.54510903\",\n \"0.54425704\",\n \"0.54421425\",\n \"0.54413724\",\n \"0.54314005\",\n \"0.540212\",\n \"0.540171\",\n \"0.53943694\",\n \"0.5382318\",\n \"0.53708\",\n \"0.5368969\",\n \"0.53389174\",\n \"0.5333884\",\n \"0.532905\",\n \"0.53125477\",\n \"0.5290375\",\n \"0.52886784\",\n \"0.5269391\",\n \"0.5265038\",\n \"0.5262813\",\n \"0.5250049\",\n \"0.5235847\",\n \"0.5233703\",\n \"0.52319044\",\n \"0.5229028\",\n \"0.52209127\",\n \"0.5214811\",\n \"0.52069\",\n \"0.5204257\",\n \"0.5195555\",\n \"0.51937187\",\n \"0.5189394\",\n \"0.51740307\",\n \"0.5160921\",\n \"0.51582634\",\n \"0.5140691\",\n \"0.51286083\",\n \"0.5128368\",\n \"0.51270264\",\n \"0.5124792\",\n \"0.51227295\",\n \"0.5118389\",\n \"0.51019675\",\n \"0.5091653\",\n \"0.5091296\",\n \"0.50862175\",\n \"0.5079842\",\n \"0.50763005\",\n \"0.50763005\",\n \"0.50754696\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.76412416"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":11276,"cells":{"query":{"kind":"string","value":"Remove any nonwhite listed chars from a string"},"document":{"kind":"string","value":"def _filter_string(cls, string, extra_chars=\"\"):\n char_white_list = ascii_letters + digits + extra_chars\n return \"\".join([char for char in string if char in char_white_list])"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _removeWhitespaces(self, s):\n return s.translate({ord(c): None for c in string.whitespace})","def filter(string):\n # remove all unwanted characters\n return regex2.sub(' ', string)","def stripword( s ) :\n return re.sub( '[\\W\\d]', '', s )","def string_cleanup(s, garbage=\":,-()&\"):\n s_new = ''\n for x in s:\n if x not in garbage:\n s_new += x\n\n return s_new","def non_letter_removal(text):\n return re.sub('[^a-zA-Z]', ' ', text)","def remove_nonalpha(text):\n text = ''.join(c for c in text if c.isalpha() or c == ' ')\n return re.sub(\" +\", \" \", text)","def remove_string_special_characters(s):\n stripped = re.sub('[^\\w\\s]', '', s)\n stripped = re.sub('_', '', stripped)\n stripped = re.sub('\\s+', ' ', stripped)\n stripped = stripped.strip()\n\n return stripped","def clean_str_sst(string):\n string = re.sub(r\"[^A-Za-z0-9(),!?\\'\\`]\", \" \", string) \n string = re.sub(r\"\\s{2,}\", \" \", string) \n return string.strip().lower()","def _cleanse(text):\n return ''.join([character for character in text\n if character.isalnum()]).lower()","def preprocess(string):\n return regex.sub('', string)","def strip(str):\n return ''.join([char for char in str if char.isalpha()])","def replace_any_non_letter_or_number_character(text):\n text = text.strip()\n text = re.sub('[^A-Za-z0-9 ]+', '', text)\n return text","def clean_str_sst(string):\n string = re.sub(r\"[^A-Za-z0-9(),!?\\'\\`]\", \" \", string)\n string = re.sub(r\"\\s{2,}\", \" \", string)\n return string.strip().lower()","def clean(s):\n punctuations = \"-,.?!;:\\n\\t()[]\\\"-\"\n return s.translate(None, string.punctuation).lower()","def clean_string(s):\n c = re.sub(r'\\s+', ' ', re.sub(r'[^A-Za-z0-9 .:]', '', s))\n return c","def cleanString(self, s):\r\n s = s.lower()\r\n for x in s: \r\n if x in punctuation:\r\n s = s.replace(x, '')\r\n return s","def _strip_remove_non_alpha(stringSentence):\n # type: (str) -> str\n\n _stripped = stringSentence.strip()\n _words = _stripped.split(' ')\n _realWords = [word for word in _words if word.isalpha()]\n\n return ' '.join(_realWords)","def clean_str_sst(string):\n string = re.sub(r\"[^A-Za-z0-9(),!?\\'\\`]\", \" \", string)\n string = re.sub(r\"\\s{2,}\", \" \", string)\n return string.strip().lower()","def get_strip_string(self, i_str):\n return ''.join(e for e in i_str if e.isalnum())","def remove_non_alphabetic_text(text):\n return RegexFilters.replace_non_alphabetic_text(text, \"\")","def sanitize_str(s):\n # throw away unkown characters\n return [c for c in s if c in letters]","def remove_special_chars(s):\n stripped = re.sub('[^\\w\\s]', ' ', s)\n stripped = re.sub('_', ' ', stripped)\n\n # Make all whitespaces only one space\n stripped = re.sub('\\s+', ' ', stripped)\n\n stripped = stripped.strip()\n\n return stripped","def scrub(input_string):\n return ''.join(k for k in input_string if k.isalnum())","def clean_string(in_str):\n # Remove extra whitespaces\n in_str = ' '.join(in_str.split())\n # Remove whitespaces before punctuation\n in_str = re.sub(r'\\s([?.!\"](?:\\s|$))', r'\\1', in_str)\n\n return in_str","def _strip(s, chars):\n # leading characters\n while len(s) > 0 and s[0] in chars:\n s = s[1:]\n # trailing characters\n while len(s) > 0 and s[-1] in chars:\n s = s[:-1]\n return s","def delete_whitespaces(str):\n global legal_white_spaces\n\n try:\n str_copy = str\n for i in legal_white_spaces:\n str_copy = str_copy.replace(i, '')\n return str_copy\n except Exception as e:\n print(e)\n return None","def remove_non_ascii(text):\n return re.sub(r'[^\\x00-\\x7F]', ' ', text)","def _clean(s):\n return re.sub(r'\\s+', ' ', s.strip())","def super_clean_str(string):\n return ''.join(x for x in string if x.isalnum()).lower()","def remove_special(s):\n return ansi_escape_chars.sub('', s)","def clean_str(string):\n # Remove punctuation\n string = re.sub(r\"[^\\u4e00-\\u9fff]\", \" \", string)\n string = re.sub(r\"\\s{2,}\", \" \", string)\n return string.strip()","def remove_special_characters_from_text(text) -> str:\n return re.sub(r'[^\\w\\s]', '', text.strip())","def remove_single_chars(text: str) -> str:\n return ' '.join(word for word in text.split()\n if len(word) > 1)","def _StripWS(s):\r\n return re.sub('\\s+', '', s)","def _StripWS(s):\r\n return re.sub('\\s+', '', s)","def strip_string(input):\n return input.lower().replace(\" \", \"\")","def ignore_whitespace(a):\n WHITE_MAP = dict.fromkeys(ord(c) for c in string.whitespace)\n return a.translate(WHITE_MAP)","def to_clean_str(s: str) -> str:\n return re.sub(\"[^a-zA-Z0-9]\", \"\", s).lower()","def _only_letters(s):\r\n\treturn _regex_non_letters.sub('', s)","def str_clean(string):\n\timport re\n\treturn re.sub(ur'[\\W_]+', u'', string, flags=re.UNICODE).lower()","def CLEAN(text):\n return _control_char_re.sub('', text)","def strip_other_charcter():\n pass","def clean_word( s ):\n words = ''\n for i in s:\n if i.isalpha():\n words += i\n else:\n words = words\n return words","def removeNonAsciiFromText(self, text):\n\t\treturn ''.join([i if ord(i) < 128 else '' for i in text])","def remove_single_char(text):\n return \" \".join([word for word in text.split() if not len(word)==1])","def basic_cleaning2(string):\n\n string = string.lower()\n string = re.sub('[0-9\\(\\)\\!\\^\\%\\$\\'\\\"\\.;,-\\?\\{\\}\\[\\]\\\\/]', ' ', string)\n string = re.sub(' +', ' ', string)\n return string","def fixString(string):\n string = re.sub(r\"[^A-Z-]\", \"\", string)\n string = string.strip(\"\\n\")\n return string","def _strip_text(text):\n text = re.sub(r'[ ,?:]|%s', \"\", text.lower())\n for chr in \"-%\":\n new_text = text.replace(chr, \"\")\n if new_text:\n text = new_text\n return text.lower()","def strip(self, str_text):\n punct_chars = [' ', '.', ',', '!', '?', '&', '\"', \"'\", '-', ':']\n str_text = [i for i in str_text if i not in punct_chars]\n str_text = ''.join(str_text)\n return str_text","def fixString(string):\n string = re.sub(r\"[^A-Z-]\", \"\", string)\n return string","def remove_special_characters(string_list):","def stripspecialchars(input_str):\n\n import unicodedata\n\n nfkd_form = unicodedata.normalize('NFKD', unicode(input_str))\n\n return ''.join([c for c in nfkd_form if not unicodedata.combining(c)])","def remove_non_alpha(self,text):\n \n removelist=\"-\\.\\/\\?\\@\"\n re_alpha_numeric1=r\"[^0-9a-zA-Z\"+removelist+\" ]\"\n clean_text=re.sub(re_alpha_numeric1,'',text)\n clean_text=clean_text.replace('/',' ')\n clean_text=re.sub(' +', ' ', clean_text)\n return clean_text","def strip_not_alnum_char(str):\n\n i = 0\n # While we don't find a character or a digit,\n # that means it's a special char (logical!)\n if str:\n while not str[i].isalnum() and i < len(str) - 1:\n i += 1\n if i != len(str) - 2:\n str = str[i:]\n return str","def rem_whitespace(string):\n unwanted_chars = punctuation + whitespace\n\n pat_l = [r'[' + unwanted_chars + ']',\n r'\\s+',\n r' ',\n r' \\\\',\n r' \\ '\n ]\n\n for p in pat_l:\n rx = re.compile(p)\n string = re.sub(rx, ' ', string)\n\n return string.strip()","def retain_alpha_nums(s):\n return re.sub(r'[^a-zA-Z0-9]', ' ', s)","def clean_word(word):\n return \"\".join([c for c in word.lower() if ord(c) < 128])","def clean_training_text(txt):\n return re.sub('[^A-Za-z0-9]+', ' ', str(txt)).strip()","def removeNonAscii(s):\r\n return \"\".join([i for i in s if ord(i)>31])","def clean_unnecessary_characters(self, tweet):\n tweet = tweet.lstrip(\"\\\"\").rstrip(\"\\\"\")\n tweet = re.sub(self.compiledAlphanumericRegex, ' ', tweet)\n tweet = tweet.replace('_', ' ')\n return tweet","def remove_inner_word_characters(text):\n return RegexFilters.replace_inner_word_characters(text, \"\")","def clean_white_spaces(string):\n try:\n # in case it is in byte value\n string = string.decode('utf-8')\n except:\n pass\n\n res = ''\n words = string.split()\n for word in words:\n res = res + str(word)\n return res","def stripchars(string):\n return ''.join(re.split('\\t+|\\n+', string))","def remove_special_characters(text):\n soup = BeautifulSoup(text, \"html.parser\")\n review = soup.get_text()\n review = r\"[^a-zA-z0-9\\s]\"\n review = re.sub(review, \"\", text)\n return review.lower()","def clean_non_word_chars(tokens):\n toks = []\n for token in tokens:\n t = re.sub(r'\\W', \"\", token)\n if len(t) > 1:\n toks.append(t)\n\n return toks","def clean(sent):\n p1 = re.compile('\\W')\n p2 = re.compile('\\s+')\n sent = re.sub(r\"http\\S+\", \"\", sent)\n sent = ReplaceThreeOrMore(sent)\n sent = remove_unicode_diac(sent)\n sent = sent.replace('_', ' ')\n sent = re.sub(r'[A-Za-z0-9]', r'', sent)\n sent = re.sub(p1, ' ', sent)\n sent = re.sub(p2, ' ', sent)\n return sent","def _clean_text(self, text):\n output = []\n for char in text:\n cp = ord(char)\n if cp == 0 or cp == 65533 or _is_control(char):\n continue\n if _is_whitespace(char):\n output.append(' ')\n else:\n output.append(char)\n return ''.join(output)","def cleaning(string):\n\n if type(string) == float or type(string) == int:\n return string\n res = ''\n if string != string:\n return string\n string = string.replace(\"\\\\r\", \"\")\n string = string.replace(\"\\\\n\", \"\")\n string = string.replace(\"\\\\b\", \"\")\n string = string.replace(\"\\\\t\", \"\")\n for i in string:\n if i.isalpha():\n res = res + i\n return res.lower()","def clean_str_vn(string):\n string = re.sub(r\"[~`@#$%^&*-+]\", \" \", string)\n def sharp(str):\n b = re.sub('\\s[A-Za-z]\\s\\.', ' .', ' '+str)\n while (b.find('. . ')>=0): b = re.sub(r'\\.\\s\\.\\s', '. ', b)\n b = re.sub(r'\\s\\.\\s', ' # ', b)\n return b\n string = sharp(string)\n string = re.sub(r\" : \", \":\", string)\n string = re.sub(r\",\", \" , \", string)\n string = re.sub(r\"!\", \" ! \", string)\n string = re.sub(r\"\\(\", \" \\( \", string)\n string = re.sub(r\"\\)\", \" \\) \", string)\n string = re.sub(r\"\\?\", \"\", string)\n string = re.sub(r\"\\s{2,}\", \" \", string)\n return string.strip().lower()","def clean_text(text):\n text = text.lower()\n all_letters = \"abcdefghijklmnopqrstuvwxyz\"\n text_to_keep = \"\"\n for char in text:\n if char in all_letters:\n text_to_keep += char\n return text_to_keep","def _clean_text(self, text):\n output = []\n for char in text:\n cp = ord(char)\n if cp == 0 or cp == 0xFFFD or _is_control(char):\n continue # pragma: no cover\n if _is_whitespace(char):\n output.append(\" \")\n else:\n output.append(char)\n return \"\".join(output)","def safe(s):\n return (\"\".join(c for c in s if c.isalnum() or c == '_' or c == '-').rstrip())","def remove_punctations_fun(self): \n self.doc = re.sub('[^a-zA-Z0-9]', ' ', self.doc)","def _clean(self, string):\n return re.sub('\\s+', ' ', string).strip()","def scrub_string(str):\n return \"\".join(ch for ch in str if ch.isalpha() or ch.isdigit()).lower()","def removeChars(inStr, chars):\n newStr = inStr\n for char in chars:\n newStr = newStr.replace(char, \"\")\n return newStr","def removeSpecialChars(self) -> None:\n self.text = re.sub('[^a-zA-z0-9\\n\\.\\s]', '', self.text)","def remove_specials(sentence):\n sentence = sentence.replace('-', ' ')\n sentence = re.sub(r'[^\\w\\s]', '', sentence)\n return sentence","def clean_data(s):\n s = s.strip()\n s = s.lower()\n return s","def remove_special_chars(text):\n \n text = re.sub(' +', ' ', re.sub('[^A-Za-z ]+', ' ', text).strip())\n return text","def clean_word(word: str) -> str:\n\n cleaned_word = ''\n for char in word.lower():\n if char.isalnum():\n cleaned_word = cleaned_word + char\n return cleaned_word","def _clean_text(self, text):\n output = []\n for char in text:\n cp = ord(char)\n if cp == 0 or cp == 0xfffd or _is_control(char):\n continue\n if _is_whitespace(char):\n output.append(\" \")\n else:\n output.append(char)\n return \"\".join(output)","def clean_text_from_nonbasic_characters(text):\n text = re.sub(r\"([^\\u0000-\\u007F])\", \" \", text)\n text = replace_newline_with_space(text).strip()\n text = text.replace(\"_\", \"\")\n text = clean_text_from_multiple_consecutive_whitespaces(text)\n return text","def clean_text_from_nonbasic_characters(text):\n text = re.sub(r\"([^\\u0000-\\u007F])\", \" \", text)\n text = replace_newline_with_space(text).strip()\n text = text.replace(\"_\", \"\")\n text = clean_text_from_multiple_consecutive_whitespaces(text)\n return text","def remove_characters(tokens):\n pattern = re.compile('[{}]'.format(re.escape(string.punctuation)))\n no_char_tokens = filter(None, [pattern.sub('', token) for token in tokens])\n return no_char_tokens","def remove_chars(old_str, chars):\n new_string = old_str\n for char in chars:\n new_string = new_string.replace(char, '')\n \n return new_string","def preprocess(self, s):\n stripped = re.sub(\"[^\\w\\s]\", \"\", s)\n stripped = re.sub(\"_\", \"\", stripped)\n\n stripped = re.sub(\"\\s+\", \" \", stripped)\n\n stripped = stripped.strip()\n\n return stripped.lower()","def _remove_whitespaces(self, text: str) -> str:\n return \" \".join(re.sub(\"\\xa0\", \" \", str(text)).split())","def _replace_non_alnum(self):\n no_punct = [x if x.isalnum() else ' ' for x in self._phrase.lower()]\n return ''.join(no_punct) # Convert an array of char to string","def _remove_custom_chars(self, text: str) -> str:\n patterns = \"|\".join([x for x in self.custom_chars])\n return re.sub(patterns, \"\", str(text), flags=re.IGNORECASE)","def sanitize(string):\n retval = string.lower()\n retval = re.sub(r\"[^\\w\\s]\", '', retval)\n retval = re.sub(r\"\\s+\", '_', retval)\n return retval","def cleanword(s):\n word = \"\"\n\n for letter in s:\n if letter not in string.punctuation:\n word += letter\n\n return word","def clean_str(string):\n\tstring = re.sub(r\"\\\"+\", \" \", string)\n\tstring = re.sub(r\"\\'+\", \" \", string)\n\tstring = re.sub(r\",+\", \",\", string)\n\tstring = re.sub(r\"\\.+\", \".\", string)\n\tstring = re.sub(r\"\\?+\", \"?\", string)\n\tstring = re.sub(r\"!+\", \"!\", string)\n\tstring = re.sub(r\",\", \" , \", string)\n\tstring = re.sub(r\"!\", \" ! \", string)\n\tstring = re.sub(r\"\\(\", \" ( \", string)\n\tstring = re.sub(r\"\\)\", \" ) \", string)\n\tstring = re.sub(r\"\\?\", \" ? \", string)\n\tstring = re.sub(r\"\\s{2,}\", \" \", string)\n\treturn string.strip().lower()","def clean_word(self, word):\n return self.filter_pattern.sub(u'', word.lower())","def clean_text(text):\n output = []\n for char in text:\n cp = ord(char)\n if cp == 0 or cp == 0xfffd or is_control(char):\n continue\n if is_whitespace(char):\n output.append(\" \")\n else:\n output.append(char)\n return \"\".join(output)","def Clean(s):\n for c in BAD_CHARACTERS:\n s = s.replace(c, '_')\n return s","def clean_str(data, remove=''):\n return data.translate(None, remove)","def strip_non_unicode(value):\n UNICODE_PATTERN = r'[^\\x00-\\x7F]+'\n try:\n value = re.sub(UNICODE_PATTERN, '', value)\n return value.strip()\n except Exception:\n return value","def remove_emoji_punc(text):\n \n allchars = [str for str in text]\n emoji_list = [c for c in allchars if c in emoji.UNICODE_EMOJI]\n clean_text = ' '.join([str for str in text.split() if not any(i in str for i in emoji_list)])\n\n \n s1 = clean_text.replace(u'’', u\"\").replace(\"'\",\"\")\n s1 = re.sub(r'[^a-z0-9 ]+', ' ', s1)\n \n return \" \".join(s1.split())"],"string":"[\n \"def _removeWhitespaces(self, s):\\n return s.translate({ord(c): None for c in string.whitespace})\",\n \"def filter(string):\\n # remove all unwanted characters\\n return regex2.sub(' ', string)\",\n \"def stripword( s ) :\\n return re.sub( '[\\\\W\\\\d]', '', s )\",\n \"def string_cleanup(s, garbage=\\\":,-()&\\\"):\\n s_new = ''\\n for x in s:\\n if x not in garbage:\\n s_new += x\\n\\n return s_new\",\n \"def non_letter_removal(text):\\n return re.sub('[^a-zA-Z]', ' ', text)\",\n \"def remove_nonalpha(text):\\n text = ''.join(c for c in text if c.isalpha() or c == ' ')\\n return re.sub(\\\" +\\\", \\\" \\\", text)\",\n \"def remove_string_special_characters(s):\\n stripped = re.sub('[^\\\\w\\\\s]', '', s)\\n stripped = re.sub('_', '', stripped)\\n stripped = re.sub('\\\\s+', ' ', stripped)\\n stripped = stripped.strip()\\n\\n return stripped\",\n \"def clean_str_sst(string):\\n string = re.sub(r\\\"[^A-Za-z0-9(),!?\\\\'\\\\`]\\\", \\\" \\\", string) \\n string = re.sub(r\\\"\\\\s{2,}\\\", \\\" \\\", string) \\n return string.strip().lower()\",\n \"def _cleanse(text):\\n return ''.join([character for character in text\\n if character.isalnum()]).lower()\",\n \"def preprocess(string):\\n return regex.sub('', string)\",\n \"def strip(str):\\n return ''.join([char for char in str if char.isalpha()])\",\n \"def replace_any_non_letter_or_number_character(text):\\n text = text.strip()\\n text = re.sub('[^A-Za-z0-9 ]+', '', text)\\n return text\",\n \"def clean_str_sst(string):\\n string = re.sub(r\\\"[^A-Za-z0-9(),!?\\\\'\\\\`]\\\", \\\" \\\", string)\\n string = re.sub(r\\\"\\\\s{2,}\\\", \\\" \\\", string)\\n return string.strip().lower()\",\n \"def clean(s):\\n punctuations = \\\"-,.?!;:\\\\n\\\\t()[]\\\\\\\"-\\\"\\n return s.translate(None, string.punctuation).lower()\",\n \"def clean_string(s):\\n c = re.sub(r'\\\\s+', ' ', re.sub(r'[^A-Za-z0-9 .:]', '', s))\\n return c\",\n \"def cleanString(self, s):\\r\\n s = s.lower()\\r\\n for x in s: \\r\\n if x in punctuation:\\r\\n s = s.replace(x, '')\\r\\n return s\",\n \"def _strip_remove_non_alpha(stringSentence):\\n # type: (str) -> str\\n\\n _stripped = stringSentence.strip()\\n _words = _stripped.split(' ')\\n _realWords = [word for word in _words if word.isalpha()]\\n\\n return ' '.join(_realWords)\",\n \"def clean_str_sst(string):\\n string = re.sub(r\\\"[^A-Za-z0-9(),!?\\\\'\\\\`]\\\", \\\" \\\", string)\\n string = re.sub(r\\\"\\\\s{2,}\\\", \\\" \\\", string)\\n return string.strip().lower()\",\n \"def get_strip_string(self, i_str):\\n return ''.join(e for e in i_str if e.isalnum())\",\n \"def remove_non_alphabetic_text(text):\\n return RegexFilters.replace_non_alphabetic_text(text, \\\"\\\")\",\n \"def sanitize_str(s):\\n # throw away unkown characters\\n return [c for c in s if c in letters]\",\n \"def remove_special_chars(s):\\n stripped = re.sub('[^\\\\w\\\\s]', ' ', s)\\n stripped = re.sub('_', ' ', stripped)\\n\\n # Make all whitespaces only one space\\n stripped = re.sub('\\\\s+', ' ', stripped)\\n\\n stripped = stripped.strip()\\n\\n return stripped\",\n \"def scrub(input_string):\\n return ''.join(k for k in input_string if k.isalnum())\",\n \"def clean_string(in_str):\\n # Remove extra whitespaces\\n in_str = ' '.join(in_str.split())\\n # Remove whitespaces before punctuation\\n in_str = re.sub(r'\\\\s([?.!\\\"](?:\\\\s|$))', r'\\\\1', in_str)\\n\\n return in_str\",\n \"def _strip(s, chars):\\n # leading characters\\n while len(s) > 0 and s[0] in chars:\\n s = s[1:]\\n # trailing characters\\n while len(s) > 0 and s[-1] in chars:\\n s = s[:-1]\\n return s\",\n \"def delete_whitespaces(str):\\n global legal_white_spaces\\n\\n try:\\n str_copy = str\\n for i in legal_white_spaces:\\n str_copy = str_copy.replace(i, '')\\n return str_copy\\n except Exception as e:\\n print(e)\\n return None\",\n \"def remove_non_ascii(text):\\n return re.sub(r'[^\\\\x00-\\\\x7F]', ' ', text)\",\n \"def _clean(s):\\n return re.sub(r'\\\\s+', ' ', s.strip())\",\n \"def super_clean_str(string):\\n return ''.join(x for x in string if x.isalnum()).lower()\",\n \"def remove_special(s):\\n return ansi_escape_chars.sub('', s)\",\n \"def clean_str(string):\\n # Remove punctuation\\n string = re.sub(r\\\"[^\\\\u4e00-\\\\u9fff]\\\", \\\" \\\", string)\\n string = re.sub(r\\\"\\\\s{2,}\\\", \\\" \\\", string)\\n return string.strip()\",\n \"def remove_special_characters_from_text(text) -> str:\\n return re.sub(r'[^\\\\w\\\\s]', '', text.strip())\",\n \"def remove_single_chars(text: str) -> str:\\n return ' '.join(word for word in text.split()\\n if len(word) > 1)\",\n \"def _StripWS(s):\\r\\n return re.sub('\\\\s+', '', s)\",\n \"def _StripWS(s):\\r\\n return re.sub('\\\\s+', '', s)\",\n \"def strip_string(input):\\n return input.lower().replace(\\\" \\\", \\\"\\\")\",\n \"def ignore_whitespace(a):\\n WHITE_MAP = dict.fromkeys(ord(c) for c in string.whitespace)\\n return a.translate(WHITE_MAP)\",\n \"def to_clean_str(s: str) -> str:\\n return re.sub(\\\"[^a-zA-Z0-9]\\\", \\\"\\\", s).lower()\",\n \"def _only_letters(s):\\r\\n\\treturn _regex_non_letters.sub('', s)\",\n \"def str_clean(string):\\n\\timport re\\n\\treturn re.sub(ur'[\\\\W_]+', u'', string, flags=re.UNICODE).lower()\",\n \"def CLEAN(text):\\n return _control_char_re.sub('', text)\",\n \"def strip_other_charcter():\\n pass\",\n \"def clean_word( s ):\\n words = ''\\n for i in s:\\n if i.isalpha():\\n words += i\\n else:\\n words = words\\n return words\",\n \"def removeNonAsciiFromText(self, text):\\n\\t\\treturn ''.join([i if ord(i) < 128 else '' for i in text])\",\n \"def remove_single_char(text):\\n return \\\" \\\".join([word for word in text.split() if not len(word)==1])\",\n \"def basic_cleaning2(string):\\n\\n string = string.lower()\\n string = re.sub('[0-9\\\\(\\\\)\\\\!\\\\^\\\\%\\\\$\\\\'\\\\\\\"\\\\.;,-\\\\?\\\\{\\\\}\\\\[\\\\]\\\\\\\\/]', ' ', string)\\n string = re.sub(' +', ' ', string)\\n return string\",\n \"def fixString(string):\\n string = re.sub(r\\\"[^A-Z-]\\\", \\\"\\\", string)\\n string = string.strip(\\\"\\\\n\\\")\\n return string\",\n \"def _strip_text(text):\\n text = re.sub(r'[ ,?:]|%s', \\\"\\\", text.lower())\\n for chr in \\\"-%\\\":\\n new_text = text.replace(chr, \\\"\\\")\\n if new_text:\\n text = new_text\\n return text.lower()\",\n \"def strip(self, str_text):\\n punct_chars = [' ', '.', ',', '!', '?', '&', '\\\"', \\\"'\\\", '-', ':']\\n str_text = [i for i in str_text if i not in punct_chars]\\n str_text = ''.join(str_text)\\n return str_text\",\n \"def fixString(string):\\n string = re.sub(r\\\"[^A-Z-]\\\", \\\"\\\", string)\\n return string\",\n \"def remove_special_characters(string_list):\",\n \"def stripspecialchars(input_str):\\n\\n import unicodedata\\n\\n nfkd_form = unicodedata.normalize('NFKD', unicode(input_str))\\n\\n return ''.join([c for c in nfkd_form if not unicodedata.combining(c)])\",\n \"def remove_non_alpha(self,text):\\n \\n removelist=\\\"-\\\\.\\\\/\\\\?\\\\@\\\"\\n re_alpha_numeric1=r\\\"[^0-9a-zA-Z\\\"+removelist+\\\" ]\\\"\\n clean_text=re.sub(re_alpha_numeric1,'',text)\\n clean_text=clean_text.replace('/',' ')\\n clean_text=re.sub(' +', ' ', clean_text)\\n return clean_text\",\n \"def strip_not_alnum_char(str):\\n\\n i = 0\\n # While we don't find a character or a digit,\\n # that means it's a special char (logical!)\\n if str:\\n while not str[i].isalnum() and i < len(str) - 1:\\n i += 1\\n if i != len(str) - 2:\\n str = str[i:]\\n return str\",\n \"def rem_whitespace(string):\\n unwanted_chars = punctuation + whitespace\\n\\n pat_l = [r'[' + unwanted_chars + ']',\\n r'\\\\s+',\\n r' ',\\n r' \\\\\\\\',\\n r' \\\\ '\\n ]\\n\\n for p in pat_l:\\n rx = re.compile(p)\\n string = re.sub(rx, ' ', string)\\n\\n return string.strip()\",\n \"def retain_alpha_nums(s):\\n return re.sub(r'[^a-zA-Z0-9]', ' ', s)\",\n \"def clean_word(word):\\n return \\\"\\\".join([c for c in word.lower() if ord(c) < 128])\",\n \"def clean_training_text(txt):\\n return re.sub('[^A-Za-z0-9]+', ' ', str(txt)).strip()\",\n \"def removeNonAscii(s):\\r\\n return \\\"\\\".join([i for i in s if ord(i)>31])\",\n \"def clean_unnecessary_characters(self, tweet):\\n tweet = tweet.lstrip(\\\"\\\\\\\"\\\").rstrip(\\\"\\\\\\\"\\\")\\n tweet = re.sub(self.compiledAlphanumericRegex, ' ', tweet)\\n tweet = tweet.replace('_', ' ')\\n return tweet\",\n \"def remove_inner_word_characters(text):\\n return RegexFilters.replace_inner_word_characters(text, \\\"\\\")\",\n \"def clean_white_spaces(string):\\n try:\\n # in case it is in byte value\\n string = string.decode('utf-8')\\n except:\\n pass\\n\\n res = ''\\n words = string.split()\\n for word in words:\\n res = res + str(word)\\n return res\",\n \"def stripchars(string):\\n return ''.join(re.split('\\\\t+|\\\\n+', string))\",\n \"def remove_special_characters(text):\\n soup = BeautifulSoup(text, \\\"html.parser\\\")\\n review = soup.get_text()\\n review = r\\\"[^a-zA-z0-9\\\\s]\\\"\\n review = re.sub(review, \\\"\\\", text)\\n return review.lower()\",\n \"def clean_non_word_chars(tokens):\\n toks = []\\n for token in tokens:\\n t = re.sub(r'\\\\W', \\\"\\\", token)\\n if len(t) > 1:\\n toks.append(t)\\n\\n return toks\",\n \"def clean(sent):\\n p1 = re.compile('\\\\W')\\n p2 = re.compile('\\\\s+')\\n sent = re.sub(r\\\"http\\\\S+\\\", \\\"\\\", sent)\\n sent = ReplaceThreeOrMore(sent)\\n sent = remove_unicode_diac(sent)\\n sent = sent.replace('_', ' ')\\n sent = re.sub(r'[A-Za-z0-9]', r'', sent)\\n sent = re.sub(p1, ' ', sent)\\n sent = re.sub(p2, ' ', sent)\\n return sent\",\n \"def _clean_text(self, text):\\n output = []\\n for char in text:\\n cp = ord(char)\\n if cp == 0 or cp == 65533 or _is_control(char):\\n continue\\n if _is_whitespace(char):\\n output.append(' ')\\n else:\\n output.append(char)\\n return ''.join(output)\",\n \"def cleaning(string):\\n\\n if type(string) == float or type(string) == int:\\n return string\\n res = ''\\n if string != string:\\n return string\\n string = string.replace(\\\"\\\\\\\\r\\\", \\\"\\\")\\n string = string.replace(\\\"\\\\\\\\n\\\", \\\"\\\")\\n string = string.replace(\\\"\\\\\\\\b\\\", \\\"\\\")\\n string = string.replace(\\\"\\\\\\\\t\\\", \\\"\\\")\\n for i in string:\\n if i.isalpha():\\n res = res + i\\n return res.lower()\",\n \"def clean_str_vn(string):\\n string = re.sub(r\\\"[~`@#$%^&*-+]\\\", \\\" \\\", string)\\n def sharp(str):\\n b = re.sub('\\\\s[A-Za-z]\\\\s\\\\.', ' .', ' '+str)\\n while (b.find('. . ')>=0): b = re.sub(r'\\\\.\\\\s\\\\.\\\\s', '. ', b)\\n b = re.sub(r'\\\\s\\\\.\\\\s', ' # ', b)\\n return b\\n string = sharp(string)\\n string = re.sub(r\\\" : \\\", \\\":\\\", string)\\n string = re.sub(r\\\",\\\", \\\" , \\\", string)\\n string = re.sub(r\\\"!\\\", \\\" ! \\\", string)\\n string = re.sub(r\\\"\\\\(\\\", \\\" \\\\( \\\", string)\\n string = re.sub(r\\\"\\\\)\\\", \\\" \\\\) \\\", string)\\n string = re.sub(r\\\"\\\\?\\\", \\\"\\\", string)\\n string = re.sub(r\\\"\\\\s{2,}\\\", \\\" \\\", string)\\n return string.strip().lower()\",\n \"def clean_text(text):\\n text = text.lower()\\n all_letters = \\\"abcdefghijklmnopqrstuvwxyz\\\"\\n text_to_keep = \\\"\\\"\\n for char in text:\\n if char in all_letters:\\n text_to_keep += char\\n return text_to_keep\",\n \"def _clean_text(self, text):\\n output = []\\n for char in text:\\n cp = ord(char)\\n if cp == 0 or cp == 0xFFFD or _is_control(char):\\n continue # pragma: no cover\\n if _is_whitespace(char):\\n output.append(\\\" \\\")\\n else:\\n output.append(char)\\n return \\\"\\\".join(output)\",\n \"def safe(s):\\n return (\\\"\\\".join(c for c in s if c.isalnum() or c == '_' or c == '-').rstrip())\",\n \"def remove_punctations_fun(self): \\n self.doc = re.sub('[^a-zA-Z0-9]', ' ', self.doc)\",\n \"def _clean(self, string):\\n return re.sub('\\\\s+', ' ', string).strip()\",\n \"def scrub_string(str):\\n return \\\"\\\".join(ch for ch in str if ch.isalpha() or ch.isdigit()).lower()\",\n \"def removeChars(inStr, chars):\\n newStr = inStr\\n for char in chars:\\n newStr = newStr.replace(char, \\\"\\\")\\n return newStr\",\n \"def removeSpecialChars(self) -> None:\\n self.text = re.sub('[^a-zA-z0-9\\\\n\\\\.\\\\s]', '', self.text)\",\n \"def remove_specials(sentence):\\n sentence = sentence.replace('-', ' ')\\n sentence = re.sub(r'[^\\\\w\\\\s]', '', sentence)\\n return sentence\",\n \"def clean_data(s):\\n s = s.strip()\\n s = s.lower()\\n return s\",\n \"def remove_special_chars(text):\\n \\n text = re.sub(' +', ' ', re.sub('[^A-Za-z ]+', ' ', text).strip())\\n return text\",\n \"def clean_word(word: str) -> str:\\n\\n cleaned_word = ''\\n for char in word.lower():\\n if char.isalnum():\\n cleaned_word = cleaned_word + char\\n return cleaned_word\",\n \"def _clean_text(self, text):\\n output = []\\n for char in text:\\n cp = ord(char)\\n if cp == 0 or cp == 0xfffd or _is_control(char):\\n continue\\n if _is_whitespace(char):\\n output.append(\\\" \\\")\\n else:\\n output.append(char)\\n return \\\"\\\".join(output)\",\n \"def clean_text_from_nonbasic_characters(text):\\n text = re.sub(r\\\"([^\\\\u0000-\\\\u007F])\\\", \\\" \\\", text)\\n text = replace_newline_with_space(text).strip()\\n text = text.replace(\\\"_\\\", \\\"\\\")\\n text = clean_text_from_multiple_consecutive_whitespaces(text)\\n return text\",\n \"def clean_text_from_nonbasic_characters(text):\\n text = re.sub(r\\\"([^\\\\u0000-\\\\u007F])\\\", \\\" \\\", text)\\n text = replace_newline_with_space(text).strip()\\n text = text.replace(\\\"_\\\", \\\"\\\")\\n text = clean_text_from_multiple_consecutive_whitespaces(text)\\n return text\",\n \"def remove_characters(tokens):\\n pattern = re.compile('[{}]'.format(re.escape(string.punctuation)))\\n no_char_tokens = filter(None, [pattern.sub('', token) for token in tokens])\\n return no_char_tokens\",\n \"def remove_chars(old_str, chars):\\n new_string = old_str\\n for char in chars:\\n new_string = new_string.replace(char, '')\\n \\n return new_string\",\n \"def preprocess(self, s):\\n stripped = re.sub(\\\"[^\\\\w\\\\s]\\\", \\\"\\\", s)\\n stripped = re.sub(\\\"_\\\", \\\"\\\", stripped)\\n\\n stripped = re.sub(\\\"\\\\s+\\\", \\\" \\\", stripped)\\n\\n stripped = stripped.strip()\\n\\n return stripped.lower()\",\n \"def _remove_whitespaces(self, text: str) -> str:\\n return \\\" \\\".join(re.sub(\\\"\\\\xa0\\\", \\\" \\\", str(text)).split())\",\n \"def _replace_non_alnum(self):\\n no_punct = [x if x.isalnum() else ' ' for x in self._phrase.lower()]\\n return ''.join(no_punct) # Convert an array of char to string\",\n \"def _remove_custom_chars(self, text: str) -> str:\\n patterns = \\\"|\\\".join([x for x in self.custom_chars])\\n return re.sub(patterns, \\\"\\\", str(text), flags=re.IGNORECASE)\",\n \"def sanitize(string):\\n retval = string.lower()\\n retval = re.sub(r\\\"[^\\\\w\\\\s]\\\", '', retval)\\n retval = re.sub(r\\\"\\\\s+\\\", '_', retval)\\n return retval\",\n \"def cleanword(s):\\n word = \\\"\\\"\\n\\n for letter in s:\\n if letter not in string.punctuation:\\n word += letter\\n\\n return word\",\n \"def clean_str(string):\\n\\tstring = re.sub(r\\\"\\\\\\\"+\\\", \\\" \\\", string)\\n\\tstring = re.sub(r\\\"\\\\'+\\\", \\\" \\\", string)\\n\\tstring = re.sub(r\\\",+\\\", \\\",\\\", string)\\n\\tstring = re.sub(r\\\"\\\\.+\\\", \\\".\\\", string)\\n\\tstring = re.sub(r\\\"\\\\?+\\\", \\\"?\\\", string)\\n\\tstring = re.sub(r\\\"!+\\\", \\\"!\\\", string)\\n\\tstring = re.sub(r\\\",\\\", \\\" , \\\", string)\\n\\tstring = re.sub(r\\\"!\\\", \\\" ! \\\", string)\\n\\tstring = re.sub(r\\\"\\\\(\\\", \\\" ( \\\", string)\\n\\tstring = re.sub(r\\\"\\\\)\\\", \\\" ) \\\", string)\\n\\tstring = re.sub(r\\\"\\\\?\\\", \\\" ? \\\", string)\\n\\tstring = re.sub(r\\\"\\\\s{2,}\\\", \\\" \\\", string)\\n\\treturn string.strip().lower()\",\n \"def clean_word(self, word):\\n return self.filter_pattern.sub(u'', word.lower())\",\n \"def clean_text(text):\\n output = []\\n for char in text:\\n cp = ord(char)\\n if cp == 0 or cp == 0xfffd or is_control(char):\\n continue\\n if is_whitespace(char):\\n output.append(\\\" \\\")\\n else:\\n output.append(char)\\n return \\\"\\\".join(output)\",\n \"def Clean(s):\\n for c in BAD_CHARACTERS:\\n s = s.replace(c, '_')\\n return s\",\n \"def clean_str(data, remove=''):\\n return data.translate(None, remove)\",\n \"def strip_non_unicode(value):\\n UNICODE_PATTERN = r'[^\\\\x00-\\\\x7F]+'\\n try:\\n value = re.sub(UNICODE_PATTERN, '', value)\\n return value.strip()\\n except Exception:\\n return value\",\n \"def remove_emoji_punc(text):\\n \\n allchars = [str for str in text]\\n emoji_list = [c for c in allchars if c in emoji.UNICODE_EMOJI]\\n clean_text = ' '.join([str for str in text.split() if not any(i in str for i in emoji_list)])\\n\\n \\n s1 = clean_text.replace(u'’', u\\\"\\\").replace(\\\"'\\\",\\\"\\\")\\n s1 = re.sub(r'[^a-z0-9 ]+', ' ', s1)\\n \\n return \\\" \\\".join(s1.split())\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7945294","0.7738483","0.7600506","0.75452673","0.74735063","0.7469556","0.7400172","0.73478335","0.73437715","0.73410106","0.7331665","0.7326543","0.7289203","0.72830564","0.728172","0.727238","0.7259143","0.7258027","0.72176147","0.7203688","0.72031343","0.716749","0.715169","0.7150154","0.71395516","0.7123855","0.7090636","0.7082491","0.7080159","0.70695657","0.7068492","0.7045027","0.70289963","0.70247275","0.70247275","0.7015857","0.69885683","0.6975385","0.69709265","0.69707274","0.6961627","0.69612503","0.69578344","0.6953509","0.6947568","0.69397646","0.6927352","0.69229156","0.69208795","0.69115824","0.68926007","0.6891271","0.6884189","0.6882894","0.6881586","0.688006","0.6876059","0.6875756","0.6875354","0.68745995","0.6870511","0.6845917","0.68360114","0.6835539","0.68305475","0.6827506","0.6826174","0.6823423","0.68152744","0.6798154","0.6795719","0.67901367","0.67872876","0.6786575","0.6785015","0.6781475","0.67806506","0.67775434","0.67767215","0.6775401","0.677323","0.6763524","0.6757732","0.6757732","0.67530376","0.67507786","0.6727709","0.6726219","0.6724953","0.67143553","0.6709004","0.6696241","0.66946495","0.6686085","0.66778624","0.66750365","0.6663551","0.6655125","0.6652601"],"string":"[\n \"0.7945294\",\n \"0.7738483\",\n \"0.7600506\",\n \"0.75452673\",\n \"0.74735063\",\n \"0.7469556\",\n \"0.7400172\",\n \"0.73478335\",\n \"0.73437715\",\n \"0.73410106\",\n \"0.7331665\",\n \"0.7326543\",\n \"0.7289203\",\n \"0.72830564\",\n \"0.728172\",\n \"0.727238\",\n \"0.7259143\",\n \"0.7258027\",\n \"0.72176147\",\n \"0.7203688\",\n \"0.72031343\",\n \"0.716749\",\n \"0.715169\",\n \"0.7150154\",\n \"0.71395516\",\n \"0.7123855\",\n \"0.7090636\",\n \"0.7082491\",\n \"0.7080159\",\n \"0.70695657\",\n \"0.7068492\",\n \"0.7045027\",\n \"0.70289963\",\n \"0.70247275\",\n \"0.70247275\",\n \"0.7015857\",\n \"0.69885683\",\n \"0.6975385\",\n \"0.69709265\",\n \"0.69707274\",\n \"0.6961627\",\n \"0.69612503\",\n \"0.69578344\",\n \"0.6953509\",\n \"0.6947568\",\n \"0.69397646\",\n \"0.6927352\",\n \"0.69229156\",\n \"0.69208795\",\n \"0.69115824\",\n \"0.68926007\",\n \"0.6891271\",\n \"0.6884189\",\n \"0.6882894\",\n \"0.6881586\",\n \"0.688006\",\n \"0.6876059\",\n \"0.6875756\",\n \"0.6875354\",\n \"0.68745995\",\n \"0.6870511\",\n \"0.6845917\",\n \"0.68360114\",\n \"0.6835539\",\n \"0.68305475\",\n \"0.6827506\",\n \"0.6826174\",\n \"0.6823423\",\n \"0.68152744\",\n \"0.6798154\",\n \"0.6795719\",\n \"0.67901367\",\n \"0.67872876\",\n \"0.6786575\",\n \"0.6785015\",\n \"0.6781475\",\n \"0.67806506\",\n \"0.67775434\",\n \"0.67767215\",\n \"0.6775401\",\n \"0.677323\",\n \"0.6763524\",\n \"0.6757732\",\n \"0.6757732\",\n \"0.67530376\",\n \"0.67507786\",\n \"0.6727709\",\n \"0.6726219\",\n \"0.6724953\",\n \"0.67143553\",\n \"0.6709004\",\n \"0.6696241\",\n \"0.66946495\",\n \"0.6686085\",\n \"0.66778624\",\n \"0.66750365\",\n \"0.6663551\",\n \"0.6655125\",\n \"0.6652601\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7396111"},"document_rank":{"kind":"string","value":"7"}}},{"rowIdx":11277,"cells":{"query":{"kind":"string","value":"Return the object whose id is _id"},"document":{"kind":"string","value":"def by_id(cls, _id):\n return dbsession.query(cls).filter_by(id=_id).first()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_object(id):","def get_object(self, id_):\n return self._objects.get(id_, None)","def at(cls, _id):\n return cls.where(cls.primarykey == _id)","def get(self, _id):","def get_object(self, id=None):\n if id is None and self.kwargs.get('field') == 'id':\n id = self.kwargs.get('constraint')\n self.object = self.get_model_obj().objects.get(pk=id)\n return self.object","def get_object(self, object_id):\r\n model = self.model\r\n object_id = model._meta.pk.to_python(object_id)\r\n return self.queryset().get(pk=object_id)","def get_by_id (self, id):\n clsfor_obj = self.classfor()\n clsfor_obj.from_id (id)\n return clsfor_obj\n return\n for name, obj in inspect.getmembers (clsfor_obj):\n if isinstance (obj, RField):\n key = clsfor_obj.prepare_key (name, int(id))","def get_object(self):\n pk = self.kwargs.get('id')\n return get_object_or_404(Book, pk=pk)","def find_by_id(self, id_):\n return self.by_id.get(id_)","def get_by_id(cls, id):\n return cls.query().get(id)","def find_by_id(cls, iid: int):\n return cls.query.filter_by(id=iid).first()","def get(self, id):\n return self.__model__.query.get(id)","def get_id(self):\n return self[\"_id\"]","def get_by_id(cls, id):\n e = api.get([key.Key(cls.__name__, id)])\n if e:\n return cls.from_entity(e[0])\n raise ObjectDoesNotExist","def get_object(self):\n queryset = self.get_queryset() # acquire queryset\n for key in self.lookup_args:\n if self.kwargs.get(key):\n id = self.kwargs[key]\n try:\n instance = queryset.get(id=id) # acquire current instance\n return instance \n except models.ObjectDoesNotExist:\n raise Http404('NO object found.')\n \n raise Http404('No object found.')","def get_one_by_id(self, object, id):\n self.lock.acquire()\n result = self.__Session.query(object).get(id)\n self.lock.release()\n return result","def get(cls, id):\n\n return cls.query.get(id)","def get(cls, id):\n\n return cls.query.get(id)","def get_object(self, ObjectClass, id):\n try:\n object = ObjectClass.objects.get(id=id)\n except (ObjectClass.DoesNotExist, ObjectClass.MultipleObjectsReturned):\n object = None\n return object","def fetch_obj(type, id, error=404, new_id=False):\n if id is None:\n abort(error)\n obj_q = Session.query(type)\n obj = obj_q.get(int(id))\n #else:\n # obj = obj_q.filter(type.ID==int(id)).first()\n\n if obj is None:\n abort(error)\n return obj","def get_object(self, id, **args):\n return self.request(\"{0}/{1}\".format(self.version, id), args)","def get(self, query_data=None, id_obj=None):\n if id_obj:\n return self.collection.find_one({'_id': id_obj})\n return self.collection.find_one(query_data)","def find(cls, id=None):\n return cls.query.filter_by(id=id).one_or_none()","def by_id(cls, id):\n try:\n return DBSession.query(cls).filter(cls.id == id).one()\n except (NoResultFound, MultipleResultsFound):\n return None","def by_id(self, id):\n\n query = self.session.query(self.model_class)\n query = query.filter(self.model_class.id == id)\n return query.first()","async def get_one(self, pk):\n\n return await self._expand(await self.db.get_one(pk=pk))","def get_object_by_id(self, object_list, object_id):\n obj = None\n for i in object_list:\n if i.get_id() == object_id:\n obj = i\n break\n return obj","def find_by_id(cls, object_id):\n try:\n return mongo_db[cls.__collection__].find_one({\"_id\": ObjectId(object_id)})\n except InvalidId:\n # TODO: Log the exception\n print('Invalid bson id: {}'.format(object_id))\n return None","def _object_get(self, pk):\n try:\n return self.model.objects.get(pk=pk)\n except self.model.DoesNotExist:\n raise DoesNotExist(self.model.__name__.lower(), primary_key=pk)","def get_object(self, id, **args):\n return self.request(id, args)","def get_by_id(self, id):\n return Entry.all().filter('entry_id = ', id).get()","def id(self):\n return self.get('id')","def get(self, cls, id):\n pass","def getByID(self, pid):\r\n i = self.pids.index(pid)\r\n return self.getByInd(i)","def _get_id(self):\n return self.__id","def _get_id(self):\n return self.__id","def _get_id(self):\n return self.__id","def _get_id(self):\n return self.__id","def _get_id(self):\n return self.__id","def _get_id(self):\n return self.__id","def _get_id(self):\n return self.__id","def _get_id(self):\n return self.__id","def _get_id(self):\n return self.__id","def _get_id(self):\n return self.__id","def _get_id(self):\n return self.__id","def _get_id(self):\n return self.__id","def _get_id(self):\n return self.__id","def _get_id(self):\n return self.__id","def _get_id(self):\n return self.__id","def _get_id(self):\n return self.__id","def _get_id(self):\n return self.__id","def _get_id(self):\n return self.__id","def _get_id(self):\n return self.__id","def _get_id(self):\n return self.__id","def _get_id(self):\n return self.__id","def _get_id(self):\n return self.__id","def _get_id(self):\n return self.__id","def _get_id(self):\n return self.__id","def _get_id(self):\n return self.__id","def _get_id(self):\n return self.__id","def _get_id(self):\n return self.__id","def _get_id(self):\n return self.__id","def _get_id(self):\n return self.__id","def _get_id(self):\n return self.__id","def _get_id(self):\n return self.__id","def _get_id(self):\n return self.__id","def _get_id(self):\n return self.__id","def _get_id(self):\n return self.__id","def _get_id(self):\n return self.__id","def _get_id(self):\n return self.__id","def get_by_id(cls, id):\n return db.session.query(cls).get(id)","def getid(obj):\n try:\n return obj.id\n except AttributeError:\n return obj","def get_object(self):\n queryset = self.get_queryset()\n\n model = self.get_model()\n obj = queryset.get(get_primary_keys(model, self.kwargs))\n\n if not obj:\n raise Http404('No %s matches the given query.' % model.__name__)\n\n return obj","def get_by_id(self, id: int):\n\n\t\traise NotImplemented","def get_by_id(cls, item_id):\n return db_session.query(cls).filter(cls.id == item_id).first()","def get(self, id):\n return Entry.query.filter(Entry.id == id).one()","def get_object(self, id):\n try:\n return Post.objects.get(id=id)\n except Post.DoesNotExist:\n raise Http404","def id(self):\n return self.getattr('id')","def get_by_id(cls, id):\n try:\n return cls.objects.get(id=id)\n except(IntegrityError, OperationalError):\n return None","def get_or_404(self, id):\n return self.__model__.query.get_or_404(id)","def get(self, _id):\n try:\n doc = self._db[_id]\n # For speed testing\n del self._db[_id]\n except KeyError:\n return None\n else:\n return self._parse_doc(doc)","def find_object_by_id(stix_objects, obj_id):\n ret_obj = None\n for obj in stix_objects:\n if obj[\"id\"] == obj_id:\n ret_obj = obj\n break\n return ret_obj","def __getitem__(self,id):\n \n # make sure id is an integer\n try:\n if not isinstance(id,IntType):\n id=atoi(id)\n except ValueError:\n raise KeyError, id\n \n # make sure it's in our list of children\n if not self.ids.has_key(id):\n raise KeyError, id\n \n # return the posting\n return self.data[id].__of__(self)","def obj_get(self, request=None, **kwargs):\n return Document(self.get_collection(request).find_one({\n \"_id\": ObjectId(kwargs.get(\"pk\"))\n }))","async def find_by_id(self, _id: int) -> Record:\n conn: Connection\n async with self.db_pool.acquire() as conn:\n return await conn.fetchrow(\n f\"SELECT * FROM {self.table_name} WHERE {self.primary_key}=$1\",\n _id,\n )","def id(obj):\n return obj","def get(self, id):\n return {'id': id}","def object_for(objectid):","def getid(obj):\n\n try:\n return obj.id\n except AttributeError:\n return obj","def id(self):\n return self[\"id\"]","def find(self, id):\n return self._select_one('''\n select\n *\n from\n {table}\n where\n {primary_key} = %s\n '''.format(table=self.__class__._table,\n primary_key=self.__class__._primary_key), [id])","def find_by_id(cls, _id):\n user = cls.query.filter_by(id=_id).first()\n return user","def get(cls, db, id):\n doc = cls.collection(db).find_one(filter={ '_id': ObjectId(id) })\n return Todo(**doc)","def get_record_by_id(self, id_: str) -> Union[DBModelInstance, NoReturn]:\n return self._get(id_)","def get_object(self, id):\n try:\n return Comment.objects.get(id=id)\n except Comment.DoesNotExist:\n raise Http404","def _get_id(self):\n return self.id","def __index__(self):\n return self.id","def id(self):\n\t\treturn self.__id"],"string":"[\n \"def get_object(id):\",\n \"def get_object(self, id_):\\n return self._objects.get(id_, None)\",\n \"def at(cls, _id):\\n return cls.where(cls.primarykey == _id)\",\n \"def get(self, _id):\",\n \"def get_object(self, id=None):\\n if id is None and self.kwargs.get('field') == 'id':\\n id = self.kwargs.get('constraint')\\n self.object = self.get_model_obj().objects.get(pk=id)\\n return self.object\",\n \"def get_object(self, object_id):\\r\\n model = self.model\\r\\n object_id = model._meta.pk.to_python(object_id)\\r\\n return self.queryset().get(pk=object_id)\",\n \"def get_by_id (self, id):\\n clsfor_obj = self.classfor()\\n clsfor_obj.from_id (id)\\n return clsfor_obj\\n return\\n for name, obj in inspect.getmembers (clsfor_obj):\\n if isinstance (obj, RField):\\n key = clsfor_obj.prepare_key (name, int(id))\",\n \"def get_object(self):\\n pk = self.kwargs.get('id')\\n return get_object_or_404(Book, pk=pk)\",\n \"def find_by_id(self, id_):\\n return self.by_id.get(id_)\",\n \"def get_by_id(cls, id):\\n return cls.query().get(id)\",\n \"def find_by_id(cls, iid: int):\\n return cls.query.filter_by(id=iid).first()\",\n \"def get(self, id):\\n return self.__model__.query.get(id)\",\n \"def get_id(self):\\n return self[\\\"_id\\\"]\",\n \"def get_by_id(cls, id):\\n e = api.get([key.Key(cls.__name__, id)])\\n if e:\\n return cls.from_entity(e[0])\\n raise ObjectDoesNotExist\",\n \"def get_object(self):\\n queryset = self.get_queryset() # acquire queryset\\n for key in self.lookup_args:\\n if self.kwargs.get(key):\\n id = self.kwargs[key]\\n try:\\n instance = queryset.get(id=id) # acquire current instance\\n return instance \\n except models.ObjectDoesNotExist:\\n raise Http404('NO object found.')\\n \\n raise Http404('No object found.')\",\n \"def get_one_by_id(self, object, id):\\n self.lock.acquire()\\n result = self.__Session.query(object).get(id)\\n self.lock.release()\\n return result\",\n \"def get(cls, id):\\n\\n return cls.query.get(id)\",\n \"def get(cls, id):\\n\\n return cls.query.get(id)\",\n \"def get_object(self, ObjectClass, id):\\n try:\\n object = ObjectClass.objects.get(id=id)\\n except (ObjectClass.DoesNotExist, ObjectClass.MultipleObjectsReturned):\\n object = None\\n return object\",\n \"def fetch_obj(type, id, error=404, new_id=False):\\n if id is None:\\n abort(error)\\n obj_q = Session.query(type)\\n obj = obj_q.get(int(id))\\n #else:\\n # obj = obj_q.filter(type.ID==int(id)).first()\\n\\n if obj is None:\\n abort(error)\\n return obj\",\n \"def get_object(self, id, **args):\\n return self.request(\\\"{0}/{1}\\\".format(self.version, id), args)\",\n \"def get(self, query_data=None, id_obj=None):\\n if id_obj:\\n return self.collection.find_one({'_id': id_obj})\\n return self.collection.find_one(query_data)\",\n \"def find(cls, id=None):\\n return cls.query.filter_by(id=id).one_or_none()\",\n \"def by_id(cls, id):\\n try:\\n return DBSession.query(cls).filter(cls.id == id).one()\\n except (NoResultFound, MultipleResultsFound):\\n return None\",\n \"def by_id(self, id):\\n\\n query = self.session.query(self.model_class)\\n query = query.filter(self.model_class.id == id)\\n return query.first()\",\n \"async def get_one(self, pk):\\n\\n return await self._expand(await self.db.get_one(pk=pk))\",\n \"def get_object_by_id(self, object_list, object_id):\\n obj = None\\n for i in object_list:\\n if i.get_id() == object_id:\\n obj = i\\n break\\n return obj\",\n \"def find_by_id(cls, object_id):\\n try:\\n return mongo_db[cls.__collection__].find_one({\\\"_id\\\": ObjectId(object_id)})\\n except InvalidId:\\n # TODO: Log the exception\\n print('Invalid bson id: {}'.format(object_id))\\n return None\",\n \"def _object_get(self, pk):\\n try:\\n return self.model.objects.get(pk=pk)\\n except self.model.DoesNotExist:\\n raise DoesNotExist(self.model.__name__.lower(), primary_key=pk)\",\n \"def get_object(self, id, **args):\\n return self.request(id, args)\",\n \"def get_by_id(self, id):\\n return Entry.all().filter('entry_id = ', id).get()\",\n \"def id(self):\\n return self.get('id')\",\n \"def get(self, cls, id):\\n pass\",\n \"def getByID(self, pid):\\r\\n i = self.pids.index(pid)\\r\\n return self.getByInd(i)\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def get_by_id(cls, id):\\n return db.session.query(cls).get(id)\",\n \"def getid(obj):\\n try:\\n return obj.id\\n except AttributeError:\\n return obj\",\n \"def get_object(self):\\n queryset = self.get_queryset()\\n\\n model = self.get_model()\\n obj = queryset.get(get_primary_keys(model, self.kwargs))\\n\\n if not obj:\\n raise Http404('No %s matches the given query.' % model.__name__)\\n\\n return obj\",\n \"def get_by_id(self, id: int):\\n\\n\\t\\traise NotImplemented\",\n \"def get_by_id(cls, item_id):\\n return db_session.query(cls).filter(cls.id == item_id).first()\",\n \"def get(self, id):\\n return Entry.query.filter(Entry.id == id).one()\",\n \"def get_object(self, id):\\n try:\\n return Post.objects.get(id=id)\\n except Post.DoesNotExist:\\n raise Http404\",\n \"def id(self):\\n return self.getattr('id')\",\n \"def get_by_id(cls, id):\\n try:\\n return cls.objects.get(id=id)\\n except(IntegrityError, OperationalError):\\n return None\",\n \"def get_or_404(self, id):\\n return self.__model__.query.get_or_404(id)\",\n \"def get(self, _id):\\n try:\\n doc = self._db[_id]\\n # For speed testing\\n del self._db[_id]\\n except KeyError:\\n return None\\n else:\\n return self._parse_doc(doc)\",\n \"def find_object_by_id(stix_objects, obj_id):\\n ret_obj = None\\n for obj in stix_objects:\\n if obj[\\\"id\\\"] == obj_id:\\n ret_obj = obj\\n break\\n return ret_obj\",\n \"def __getitem__(self,id):\\n \\n # make sure id is an integer\\n try:\\n if not isinstance(id,IntType):\\n id=atoi(id)\\n except ValueError:\\n raise KeyError, id\\n \\n # make sure it's in our list of children\\n if not self.ids.has_key(id):\\n raise KeyError, id\\n \\n # return the posting\\n return self.data[id].__of__(self)\",\n \"def obj_get(self, request=None, **kwargs):\\n return Document(self.get_collection(request).find_one({\\n \\\"_id\\\": ObjectId(kwargs.get(\\\"pk\\\"))\\n }))\",\n \"async def find_by_id(self, _id: int) -> Record:\\n conn: Connection\\n async with self.db_pool.acquire() as conn:\\n return await conn.fetchrow(\\n f\\\"SELECT * FROM {self.table_name} WHERE {self.primary_key}=$1\\\",\\n _id,\\n )\",\n \"def id(obj):\\n return obj\",\n \"def get(self, id):\\n return {'id': id}\",\n \"def object_for(objectid):\",\n \"def getid(obj):\\n\\n try:\\n return obj.id\\n except AttributeError:\\n return obj\",\n \"def id(self):\\n return self[\\\"id\\\"]\",\n \"def find(self, id):\\n return self._select_one('''\\n select\\n *\\n from\\n {table}\\n where\\n {primary_key} = %s\\n '''.format(table=self.__class__._table,\\n primary_key=self.__class__._primary_key), [id])\",\n \"def find_by_id(cls, _id):\\n user = cls.query.filter_by(id=_id).first()\\n return user\",\n \"def get(cls, db, id):\\n doc = cls.collection(db).find_one(filter={ '_id': ObjectId(id) })\\n return Todo(**doc)\",\n \"def get_record_by_id(self, id_: str) -> Union[DBModelInstance, NoReturn]:\\n return self._get(id_)\",\n \"def get_object(self, id):\\n try:\\n return Comment.objects.get(id=id)\\n except Comment.DoesNotExist:\\n raise Http404\",\n \"def _get_id(self):\\n return self.id\",\n \"def __index__(self):\\n return self.id\",\n \"def id(self):\\n\\t\\treturn self.__id\"\n]","isConversational":false},"negative_scores":{"kind":"list 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_uuid"},"document":{"kind":"string","value":"def by_uuid(cls, _uuid):\n return dbsession.query(cls).filter_by(uuid=_uuid).first()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_by_uuid(self, uuid):\n return self.get(uuid=uuid)","def by_uuid(cls, uuid):\n return dbsession.query(cls).filter_by(uuid=uuid).first()","def find(cls, uuid):\n entries = cls.objects.filter(uuid=uuid)\n if not entries:\n return None\n else:\n return entries.first()","def get_by_uuid(self, table, uuid, **kwargs):\n if uuid is None:\n raise UndefinedUuidOrName(table)\n return self.query_one(table, WHERE={'uuid': uuid}, **kwargs)","def get_by_uuid(self, uuid):\n\n result_filter = [r for r in self.content if r.uuid() == uuid]\n\n try:\n return result_filter[0]\n except IndexError:\n return None","def get_article(uuid):\n return Article.get(Article.uuid == uuid)","def get_relateddata(uuid):\n return RelatedData.get(RelatedData.uuid == uuid)","def by_id(cls: t.Type[Article], uuid: str, db: Session) -> t.Optional[Article]:\n q = db.query(cls)\n q = q.filter(cls.id == uuid)\n return q.one_or_none()","def get_uuid(self, obj):\n return IUUID(obj, None)","def get_uuid(self, obj):\n return IUUID(obj, None)","def get_object(self, queryset=None):\n return get_object_or_404(\n models.Device, uuid=self.kwargs[\"uuid\"], user=self.request.user\n )","def get_object(self, queryset=None):\n return get_object_or_404(\n models.Device, uuid=self.kwargs[\"uuid\"], user=self.request.user\n )","def get_primitive_instance_by_uuid(context, instance_uuid):\n instance = db.instance_get_by_uuid(context, instance_uuid)\n return jsonutils.to_primitive(instance)","def get_object(id):","def get_by_uuid(self, uuid, datastore=None):\n storage = self.storage(datastore)\n model = storage.get_by_uuid(uuid)\n # unless forcing ES datastore, check write storage if not found in read\n # if datastore == 'database' and storage is self.read:\n # Old is above - See C4-30\n # if not specifically specifying datastore=elasticsearch, always fall back to DB\n if not datastore == 'elasticsearch':\n if model is None:\n return self.write.get_by_uuid(uuid)\n return model","def get_object_by_uuid_or_404(model, uuid_pk):\n if isinstance(uuid_pk, str):\n try:\n uuid.UUID(uuid_pk)\n except Exception as e:\n raise Http404(str(e))\n return get_object_or_404(model, pk=uuid_pk)","def __getitem__(self, uuid: UUID) -> Mutant:\n return self.__mutants[uuid]","def _get_from_datastore( uuid ):\n return db.Query(Stats).filter('uuid =', uuid).get()","def find_object_by_uuid(remote, uuid):\n cmd = mmapi.StoredCommands()\n cmd_key = cmd.AppendSceneCommand_FindObjectByUUID(uuid)\n remote.runCommand(cmd)\n result_val = mmapi.any_result()\n bFound = cmd.GetSceneCommandResult_FindObjectByUUID(cmd_key, result_val)\n return (bFound, result_val.i)","def get_uuid():\n animal_uuid = request.args.get(\"uuid\", default=1, type=str)\n return jsonify(rd.get(animal_uuid))","def uuid(self):\n if self._uuid is not None:\n return self._uuid\n if not self.exists:\n return None\n self.retr_uuid()\n return self._uuid","def get_record(uuid):\n\n return jsonify(record=collection[uuid].dict)","def ex_get_node_by_uuid(self, uuid):\n domain = self._get_domain_for_uuid(uuid=uuid)\n node = self._to_node(domain=domain)\n return node","def get_object(self):\n url_kwarg = self.lookup_url_kwarg or self.lookup_field\n lookup = self.kwargs.get(url_kwarg, None)\n # try converting to UUID, call parent to lookup by UUID if successful\n try:\n lookup = UUID(lookup)\n self.lookup_url_kwarg = url_kwarg\n self.lookup_field = 'uuid'\n except ValueError:\n pass\n return super(StudyInternalsFilterMixin, self).get_object()","def get(self, uuid):\n\n\t\treturn self._get(\"/tag/%s\" % base.getid(uuid), \"tag\")","def get_uuid(self, obj):\n if \"opensim\" in obj.properties:\n if \"uuid\" in obj.properties[\"opensim\"]:\n return obj.properties['opensim']['uuid']","def uuid(self) -> str:\n return self.obj.uuid","def get_visitor(uuid):\n log.debug('Getting visitor by uuid(%s)', uuid)\n try:\n visitor = models.Visitor.objects.get(uuid=uuid)\n except Exception:\n visitor = models.Visitor()\n visitor.uuid = uuid\n visitor.save()\n return visitor","def get(self, uuid):\n return self.__get_object(super(Pipelines, self).get(uuid))","def uuid(self) -> UUIDFilter:\n return self.__uuid","def get_by_name_or_uuid(self, table, uuid_or_name, **kwargs):\n if uuid_or_name is None:\n raise UndefinedUuidOrName(table)\n\n key = 'uuid' if check_valid_uuid(uuid_or_name) else 'name'\n return self.query_one(table, WHERE={key: uuid_or_name}, **kwargs)","def get_node(self, uuid, clean=True):\n if clean:\n uuid = ProcessNode.strip_uuid(uuid)\n return self._get_tree_queryset().get(uuid_full__startswith=uuid)","def _get_rec_uuid(self, uuid, context=None):\n if context is not None:\n moduuid = context.get('moduuid')\n if moduuid:\n return get_uuid(moduuid, uuid)\n return uuid","def find_with_uuid(self, groupid, objects, section):\n if groupid in self._total[section]:\n # we get the objects by name to avoid memory corruption issues,\n # but we're not checking if the names change!\n return objects[self._total[section][groupid]]\n else:\n for obj in objects:\n obj_uuid = self.get_uuid(obj)\n if obj_uuid:\n #self._total[section][obj_uuid] = obj.name\n if obj_uuid == groupid:\n return obj","def uuid(self):\n return self._uuid","def uuid(self):\n return self._uuid","def ztest_get_from_uuid(self):\n \n queue = NMSQueue()\n \n item = NMSQueueItem(5,\"data %s\" % (1))\n \n item.set_uuid()\n \n print(\"item = %s\\n\" %(item))\n \n queue.put(item)\n \n newitem = queue.get_item(item.uuid)\n \n print(\"new item = %s\\n\" % (newitem) )","def uuid(self):\n return self.__uuid","def uuid(self):\n return self.__uuid","def test_api_object_uuid(self, api_object):\n assert isinstance(api_object.uuid_, uuid.UUID)","def get_employee_by_uuid(uuid):\n employee = db.session.query(Employee).filter_by(uuid=uuid).first()\n if employee is None:\n raise ValueError('Invalid employee uuid')\n return employee","def test_get_user_by_uuiduser_uuid_get(self):\n pass","def get_flavor_by_uuid(cls, flavor_uuid):\n return cls.dbdriver.get_flavor_by_uuid(flavor_uuid)","def get_device_by_uuid(cls, device_id):\n return cls.dbdriver.get_device_by_uuid(device_id)","def get_token_by_uuid(uuid, owner=None):\n try:\n query = AppSpecificAuthToken.select().where(\n AppSpecificAuthToken.uuid == uuid,\n (\n (AppSpecificAuthToken.expiration > datetime.now())\n | (AppSpecificAuthToken.expiration >> None)\n ),\n )\n if owner is not None:\n query = query.where(AppSpecificAuthToken.user == owner)\n\n return query.get()\n except AppSpecificAuthToken.DoesNotExist:\n return None","def get(self, uuid=None):\n\n if not uuid:\n genres = db.session.query(Genre).all()\n return self.genre_schema.dump(genres, many=True), 200\n\n genre = db.session.query(Genre).filter_by(id=uuid).first()\n if not genre:\n return {\"Error\": \"Object was not found\"}, 404\n\n return self.genre_schema.dump(genre), 200","def getUser(self, user_uuid):\n if user_uuid in self.users.keys():\n return self.users[user_uuid]\n else:\n return None","def uuid(self, type, val):\n picker = lambda x: x.get('uuid', x)\n return self._get((type, val), picker)","def _getObjectByUid(self):\n # check for unique id handler tool\n if not PRODUCTS_CMFUID_INSTALLED:\n return\n uidtool = queryUtility(IUniqueIdHandler)\n if uidtool is None:\n return\n\n # check for remote uid info on object\n uid = getattr(aq_base(self), 'remote_uid', None)\n if uid is None:\n return\n\n return uidtool.queryObject(uid, None)","def get_one(self, uuid: str) -> t.Optional[KeyValueEntity]: # dead: disable\n if uuid not in self._data:\n return None\n\n return KeyValueEntity(uuid=uuid, val=self._data.get(uuid, None))","def iroko_uuid_fetcher(record_uuid, data):\n # pid_field = current_app.config['PIDSTORE_RECID_FIELD']\n pid_field = 'id'\n return FetchedPID(\n provider=providers.IrokoUUIDProvider,\n pid_type=providers.IrokoUUIDProvider.pid_type,\n pid_value=str(data[pid_field]),\n )","def uuid(self):\n\n return self._get_field(\"uuid\")","def get_objectID(self):\n return self.resource.uuid","def get_by_uuid_direct(self, uuid, item_type, default=None):\n if self.read is not None:\n # must pass registry for access to settings\n return self.read.get_by_uuid_direct(uuid, item_type)\n\n return self.write.get_by_uuid_direct(uuid, item_type, default)","def find_uuids_linked_to_item(self, uuid):\n if self.read is not None:\n return self.read.find_uuids_linked_to_item(uuid)\n\n return self.write.find_uuids_linked_to_item(uuid)","def getid(obj):\n\n # Try to return the object's UUID first, if we have a UUID.\n try:\n if obj.uuid:\n return obj.uuid\n except AttributeError:\n pass\n try:\n return obj.id\n except AttributeError:\n return obj","def _uuid(self):\n u = self.__uuid\n if u is None:\n u = str(uuid.uuid1())\n self._set_uuid(u)\n return u","def uuid(self): # type: ignore\n return self._uuid","def get_one(self, uuid):\n if self._from_chassis:\n raise exception.OperationNotPermitted\n\n rpc_node = objects.Node.get_by_uuid(pecan.request.context, uuid)\n return Node.convert_with_links(rpc_node)","def find_characteristic(self, uuid):\n for char in self.list_characteristics():\n if char.uuid == uuid:\n return char\n return None","def getMember(unique_name):","def getMember(unique_name):","def is_uuid(my_object):\n try:\n my_uuid = uuid.UUID(my_object, version=4)\n except ValueError:\n return False\n return str(my_uuid) == my_object","async def uuid(self) -> str:\n if not hasattr(self, \"_uuid\"):\n result = await self.app.sparql.query(\"\"\"\n SELECT DISTINCT ?o\n WHERE {\n <{{uri}}> <http://mu.semte.ch/vocabularies/core/uuid> ?o .\n }\n \"\"\", uri=self.uri)\n self._uuid = result['results']['bindings'][0]['o']['value']\n return self._uuid","def get_descriptor(self, uuid):","def test_specified_uuid(self):\n movie = Movie.objects.get(title='The Return of the King')\n self.assertEqual(str(movie.id), '01234567-0123-0123-0123-0123456789ab')","def find_descriptor(self, uuid):\n for desc in self.list_descriptors():\n if desc.uuid == uuid:\n return desc\n return None","def get_queryset(self):\n return models.Device.objects.filter(uuid=self.kwargs[\"uuid\"])","def find_with_uuid(self, groupid, objects, section):\n if groupid in self._total[section]:\n # we get the objects by name to avoid memory corruption issues,\n # but we're not checking if the names change!\n return self.wrap_object(objects[self._total[section][groupid]],\n section)\n else:\n for obj in objects:\n obj_uuid = self.get_uuid(obj)\n if obj_uuid:\n self._total[section][obj_uuid] = obj.name\n if obj_uuid == groupid:\n return self.wrap_object(obj, section)","def uuid(self):\n\n return self.__uuid","def uuid(self):\n raise NotImplementedError","def uuid(self):\n raise NotImplementedError","def uuid(self):\n raise NotImplementedError","def get_device_by_uuid(self, device_id: str):\n return get_device_by_uuid(self.api_key, device_id)","def get_current_user(uuid='1000211211101'):\n logger.info(\"User Detail get\", data={'uuid': str(uuid)})\n return uuid","def uuid(self) -> str:\n return self.__uuid","def get_uuid(controller_url,uuid_url):\n\n # setting up header\n h_api_version = \"@@{API_VERSION}@@\"\n h_encoding = \"@@{ENCODING}@@\"\n h_content = \"@@{CONTENT}@@\"\n h_sessionid = \"@@{SESSION_ID}@@\"\n h_csrftoken = \"@@{CSRF_TOKEN}@@\"\n \n\n headers = {\n 'cookie': \"sessionid=\" + h_sessionid +\"; csrftoken=\" + h_csrftoken,\n 'X-Avi-Version': h_api_version,\n 'Accept-Encoding': h_encoding,\n 'Content-type': h_content\n }\n \n # endregion\n\t# going through the uuid_url to get the uuid\n for var_name, url in uuid_url.items():\n for endpoint, object_name in url.items():\n endpoint_url = controller_url + endpoint + object_name\n #print(\"{}={}\".format(var_name, object_name))\n print \"Enpoint url =\", endpoint_url\n response = urlreq(endpoint_url, verb='GET', headers=headers, verify=False)\n \n # deal with the result/response\n if response.ok:\n print \"Request was successfully\"\n result = json.loads(response.content)\n print \"{}={}\".format(var_name,result['results'][0]['uuid'])\n \n else:\n print(\"Request failed\")\n print(\"Headers: {}\".format(headers))\n print('Status code: {}'.format(response.status_code))\n print('Response: {}'.format(response.text))\n exit(1)\n \n # endregion","def get_object(self, id_):\n return self._objects.get(id_, None)","def get_member_by_raw_uuid(raw_uuid: bytes, what: str, addressbook, mid: str):\n\n try:\n text_uuid = str(uuid.UUID(bytes=raw_uuid))\n except ValueError as e:\n logger.warn(f\"Failed to parse {what} UUID for message {mid}: {str(e)}\")\n return None\n\n recipient = addressbook.get_recipient_by_uuid(text_uuid)\n member_name = recipient.name if recipient else text_uuid\n return member_name","def get_by_id(self,\n uuid: str,\n additional_properties: List[str]=None,\n with_vector: bool=False\n ) -> Optional[dict]:\n\n return self.get(\n uuid=uuid,\n additional_properties=additional_properties,\n with_vector=with_vector\n )","def uuid(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"uuid\")","def uuid(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"uuid\")","def uuid(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"uuid\")","def find_instance(cls, identifier):\r\n for instance in cls.all:\r\n if instance.identifier == identifier:\r\n return instance\r\n return None","def getUserUuid(userId):\n return searchForUser(userId)['uuid']","def uuid(self) -> str:\n return self._uuid","def uuid(self) -> str:\n return self._uuid","def uuid(self) -> str:\n return self._uuid","def retrieve_by_uuid(self, uuid, target_path=os.path.curdir, use_symlinks=False):\n count = self.retrieve(\"uuid == @uuid\", {\"uuid\": uuid}, target_path, use_symlinks)\n assert(count <= 1)\n\n if count == 0:\n raise Error(\"product with uuid '%s' not found\" % uuid)\n\n return count","def get_objectID(self):\n return self.collection.uuid","def info_by_uuid(self, uuid):\n return self.rpc.call(MsfRpcMethod.ModuleResults, [uuid])","def object_for(objectid):","def getUUID(part):\n global quickList\n if len(quickList) == 0:\n fillQuickList()\n return quickList[part]","def UUID(self, default=None):\n return self.data.get('uuid', default)","def test_uuid_retreival_uuid(self):\n movie = Movie.objects.get(id=uuid.UUID(\n '01234567-0123-0123-0123-0123456789ab',\n ))\n self.assertEqual(movie.title, 'The Return of the King')","def get_uuid(self):\n\t\treturn call_sdk_function('PrlUsrInfo_GetUuid', self.handle)","def get_event(self, uuid):\n return Event.deserialize(self._get_single('events', {'uuid': uuid}))","def check_uuid(self, obj, groupid):\n if self.get_uuid(obj) == groupid:\n return True","def search_uuid(owner, title):\n db = choose_database(\"datadb\")\n condition = \"owner LIKE \\'%\" + owner + \"%\\' AND title=\\'\" + title + \"\\'\"\n result = select_columns(db, 'games', 'game_uuid', condition=condition)\n if len(result) > 0:\n return result[0][0]\n else:\n return None"],"string":"[\n \"def get_by_uuid(self, uuid):\\n return self.get(uuid=uuid)\",\n \"def by_uuid(cls, uuid):\\n return dbsession.query(cls).filter_by(uuid=uuid).first()\",\n \"def find(cls, uuid):\\n entries = cls.objects.filter(uuid=uuid)\\n if not entries:\\n return None\\n else:\\n return entries.first()\",\n \"def get_by_uuid(self, table, uuid, **kwargs):\\n if uuid is None:\\n raise UndefinedUuidOrName(table)\\n return self.query_one(table, WHERE={'uuid': uuid}, **kwargs)\",\n \"def get_by_uuid(self, uuid):\\n\\n result_filter = [r for r in self.content if r.uuid() == uuid]\\n\\n try:\\n return result_filter[0]\\n except IndexError:\\n return None\",\n \"def get_article(uuid):\\n return Article.get(Article.uuid == uuid)\",\n \"def get_relateddata(uuid):\\n return RelatedData.get(RelatedData.uuid == uuid)\",\n \"def by_id(cls: t.Type[Article], uuid: str, db: Session) -> t.Optional[Article]:\\n q = db.query(cls)\\n q = q.filter(cls.id == uuid)\\n return q.one_or_none()\",\n \"def get_uuid(self, obj):\\n return IUUID(obj, None)\",\n \"def get_uuid(self, obj):\\n return IUUID(obj, None)\",\n \"def get_object(self, queryset=None):\\n return get_object_or_404(\\n models.Device, uuid=self.kwargs[\\\"uuid\\\"], user=self.request.user\\n )\",\n \"def get_object(self, queryset=None):\\n return get_object_or_404(\\n models.Device, uuid=self.kwargs[\\\"uuid\\\"], user=self.request.user\\n )\",\n \"def get_primitive_instance_by_uuid(context, instance_uuid):\\n instance = db.instance_get_by_uuid(context, instance_uuid)\\n return jsonutils.to_primitive(instance)\",\n \"def get_object(id):\",\n \"def get_by_uuid(self, uuid, datastore=None):\\n storage = self.storage(datastore)\\n model = storage.get_by_uuid(uuid)\\n # unless forcing ES datastore, check write storage if not found in read\\n # if datastore == 'database' and storage is self.read:\\n # Old is above - See C4-30\\n # if not specifically specifying datastore=elasticsearch, always fall back to DB\\n if not datastore == 'elasticsearch':\\n if model is None:\\n return self.write.get_by_uuid(uuid)\\n return model\",\n \"def get_object_by_uuid_or_404(model, uuid_pk):\\n if isinstance(uuid_pk, str):\\n try:\\n uuid.UUID(uuid_pk)\\n except Exception as e:\\n raise Http404(str(e))\\n return get_object_or_404(model, pk=uuid_pk)\",\n \"def __getitem__(self, uuid: UUID) -> Mutant:\\n return self.__mutants[uuid]\",\n \"def _get_from_datastore( uuid ):\\n return db.Query(Stats).filter('uuid =', uuid).get()\",\n \"def find_object_by_uuid(remote, uuid):\\n cmd = mmapi.StoredCommands()\\n cmd_key = cmd.AppendSceneCommand_FindObjectByUUID(uuid)\\n remote.runCommand(cmd)\\n result_val = mmapi.any_result()\\n bFound = cmd.GetSceneCommandResult_FindObjectByUUID(cmd_key, result_val)\\n return (bFound, result_val.i)\",\n \"def get_uuid():\\n animal_uuid = request.args.get(\\\"uuid\\\", default=1, type=str)\\n return jsonify(rd.get(animal_uuid))\",\n \"def uuid(self):\\n if self._uuid is not None:\\n return self._uuid\\n if not self.exists:\\n return None\\n self.retr_uuid()\\n return self._uuid\",\n \"def get_record(uuid):\\n\\n return jsonify(record=collection[uuid].dict)\",\n \"def ex_get_node_by_uuid(self, uuid):\\n domain = self._get_domain_for_uuid(uuid=uuid)\\n node = self._to_node(domain=domain)\\n return node\",\n \"def get_object(self):\\n url_kwarg = self.lookup_url_kwarg or self.lookup_field\\n lookup = self.kwargs.get(url_kwarg, None)\\n # try converting to UUID, call parent to lookup by UUID if successful\\n try:\\n lookup = UUID(lookup)\\n self.lookup_url_kwarg = url_kwarg\\n self.lookup_field = 'uuid'\\n except ValueError:\\n pass\\n return super(StudyInternalsFilterMixin, self).get_object()\",\n \"def get(self, uuid):\\n\\n\\t\\treturn self._get(\\\"/tag/%s\\\" % base.getid(uuid), \\\"tag\\\")\",\n \"def get_uuid(self, obj):\\n if \\\"opensim\\\" in obj.properties:\\n if \\\"uuid\\\" in obj.properties[\\\"opensim\\\"]:\\n return obj.properties['opensim']['uuid']\",\n \"def uuid(self) -> str:\\n return self.obj.uuid\",\n \"def get_visitor(uuid):\\n log.debug('Getting visitor by uuid(%s)', uuid)\\n try:\\n visitor = models.Visitor.objects.get(uuid=uuid)\\n except Exception:\\n visitor = models.Visitor()\\n visitor.uuid = uuid\\n visitor.save()\\n return visitor\",\n \"def get(self, uuid):\\n return self.__get_object(super(Pipelines, self).get(uuid))\",\n \"def uuid(self) -> UUIDFilter:\\n return self.__uuid\",\n \"def get_by_name_or_uuid(self, table, uuid_or_name, **kwargs):\\n if uuid_or_name is None:\\n raise UndefinedUuidOrName(table)\\n\\n key = 'uuid' if check_valid_uuid(uuid_or_name) else 'name'\\n return self.query_one(table, WHERE={key: uuid_or_name}, **kwargs)\",\n \"def get_node(self, uuid, clean=True):\\n if clean:\\n uuid = ProcessNode.strip_uuid(uuid)\\n return self._get_tree_queryset().get(uuid_full__startswith=uuid)\",\n \"def _get_rec_uuid(self, uuid, context=None):\\n if context is not None:\\n moduuid = context.get('moduuid')\\n if moduuid:\\n return get_uuid(moduuid, uuid)\\n return uuid\",\n \"def find_with_uuid(self, groupid, objects, section):\\n if groupid in self._total[section]:\\n # we get the objects by name to avoid memory corruption issues,\\n # but we're not checking if the names change!\\n return objects[self._total[section][groupid]]\\n else:\\n for obj in objects:\\n obj_uuid = self.get_uuid(obj)\\n if obj_uuid:\\n #self._total[section][obj_uuid] = obj.name\\n if obj_uuid == groupid:\\n return obj\",\n \"def uuid(self):\\n return self._uuid\",\n \"def uuid(self):\\n return self._uuid\",\n \"def ztest_get_from_uuid(self):\\n \\n queue = NMSQueue()\\n \\n item = NMSQueueItem(5,\\\"data %s\\\" % (1))\\n \\n item.set_uuid()\\n \\n print(\\\"item = %s\\\\n\\\" %(item))\\n \\n queue.put(item)\\n \\n newitem = queue.get_item(item.uuid)\\n \\n print(\\\"new item = %s\\\\n\\\" % (newitem) )\",\n \"def uuid(self):\\n return self.__uuid\",\n \"def uuid(self):\\n return self.__uuid\",\n \"def test_api_object_uuid(self, api_object):\\n assert isinstance(api_object.uuid_, uuid.UUID)\",\n \"def get_employee_by_uuid(uuid):\\n employee = db.session.query(Employee).filter_by(uuid=uuid).first()\\n if employee is None:\\n raise ValueError('Invalid employee uuid')\\n return employee\",\n \"def test_get_user_by_uuiduser_uuid_get(self):\\n pass\",\n \"def get_flavor_by_uuid(cls, flavor_uuid):\\n return cls.dbdriver.get_flavor_by_uuid(flavor_uuid)\",\n \"def get_device_by_uuid(cls, device_id):\\n return cls.dbdriver.get_device_by_uuid(device_id)\",\n \"def get_token_by_uuid(uuid, owner=None):\\n try:\\n query = AppSpecificAuthToken.select().where(\\n AppSpecificAuthToken.uuid == uuid,\\n (\\n (AppSpecificAuthToken.expiration > datetime.now())\\n | (AppSpecificAuthToken.expiration >> None)\\n ),\\n )\\n if owner is not None:\\n query = query.where(AppSpecificAuthToken.user == owner)\\n\\n return query.get()\\n except AppSpecificAuthToken.DoesNotExist:\\n return None\",\n \"def get(self, uuid=None):\\n\\n if not uuid:\\n genres = db.session.query(Genre).all()\\n return self.genre_schema.dump(genres, many=True), 200\\n\\n genre = db.session.query(Genre).filter_by(id=uuid).first()\\n if not genre:\\n return {\\\"Error\\\": \\\"Object was not found\\\"}, 404\\n\\n return self.genre_schema.dump(genre), 200\",\n \"def getUser(self, user_uuid):\\n if user_uuid in self.users.keys():\\n return self.users[user_uuid]\\n else:\\n return None\",\n \"def uuid(self, type, val):\\n picker = lambda x: x.get('uuid', x)\\n return self._get((type, val), picker)\",\n \"def _getObjectByUid(self):\\n # check for unique id handler tool\\n if not PRODUCTS_CMFUID_INSTALLED:\\n return\\n uidtool = queryUtility(IUniqueIdHandler)\\n if uidtool is None:\\n return\\n\\n # check for remote uid info on object\\n uid = getattr(aq_base(self), 'remote_uid', None)\\n if uid is None:\\n return\\n\\n return uidtool.queryObject(uid, None)\",\n \"def get_one(self, uuid: str) -> t.Optional[KeyValueEntity]: # dead: disable\\n if uuid not in self._data:\\n return None\\n\\n return KeyValueEntity(uuid=uuid, val=self._data.get(uuid, None))\",\n \"def iroko_uuid_fetcher(record_uuid, data):\\n # pid_field = current_app.config['PIDSTORE_RECID_FIELD']\\n pid_field = 'id'\\n return FetchedPID(\\n provider=providers.IrokoUUIDProvider,\\n pid_type=providers.IrokoUUIDProvider.pid_type,\\n pid_value=str(data[pid_field]),\\n )\",\n \"def uuid(self):\\n\\n return self._get_field(\\\"uuid\\\")\",\n \"def get_objectID(self):\\n return self.resource.uuid\",\n \"def get_by_uuid_direct(self, uuid, item_type, default=None):\\n if self.read is not None:\\n # must pass registry for access to settings\\n return self.read.get_by_uuid_direct(uuid, item_type)\\n\\n return self.write.get_by_uuid_direct(uuid, item_type, default)\",\n \"def find_uuids_linked_to_item(self, uuid):\\n if self.read is not None:\\n return self.read.find_uuids_linked_to_item(uuid)\\n\\n return self.write.find_uuids_linked_to_item(uuid)\",\n \"def getid(obj):\\n\\n # Try to return the object's UUID first, if we have a UUID.\\n try:\\n if obj.uuid:\\n return obj.uuid\\n except AttributeError:\\n pass\\n try:\\n return obj.id\\n except AttributeError:\\n return obj\",\n \"def _uuid(self):\\n u = self.__uuid\\n if u is None:\\n u = str(uuid.uuid1())\\n self._set_uuid(u)\\n return u\",\n \"def uuid(self): # type: ignore\\n return self._uuid\",\n \"def get_one(self, uuid):\\n if self._from_chassis:\\n raise exception.OperationNotPermitted\\n\\n rpc_node = objects.Node.get_by_uuid(pecan.request.context, uuid)\\n return Node.convert_with_links(rpc_node)\",\n \"def find_characteristic(self, uuid):\\n for char in self.list_characteristics():\\n if char.uuid == uuid:\\n return char\\n return None\",\n \"def getMember(unique_name):\",\n \"def getMember(unique_name):\",\n \"def is_uuid(my_object):\\n try:\\n my_uuid = uuid.UUID(my_object, version=4)\\n except ValueError:\\n return False\\n return str(my_uuid) == my_object\",\n \"async def uuid(self) -> str:\\n if not hasattr(self, \\\"_uuid\\\"):\\n result = await self.app.sparql.query(\\\"\\\"\\\"\\n SELECT DISTINCT ?o\\n WHERE {\\n <{{uri}}> <http://mu.semte.ch/vocabularies/core/uuid> ?o .\\n }\\n \\\"\\\"\\\", uri=self.uri)\\n self._uuid = result['results']['bindings'][0]['o']['value']\\n return self._uuid\",\n \"def get_descriptor(self, uuid):\",\n \"def test_specified_uuid(self):\\n movie = Movie.objects.get(title='The Return of the King')\\n self.assertEqual(str(movie.id), '01234567-0123-0123-0123-0123456789ab')\",\n \"def find_descriptor(self, uuid):\\n for desc in self.list_descriptors():\\n if desc.uuid == uuid:\\n return desc\\n return None\",\n \"def get_queryset(self):\\n return models.Device.objects.filter(uuid=self.kwargs[\\\"uuid\\\"])\",\n \"def find_with_uuid(self, groupid, objects, section):\\n if groupid in self._total[section]:\\n # we get the objects by name to avoid memory corruption issues,\\n # but we're not checking if the names change!\\n return self.wrap_object(objects[self._total[section][groupid]],\\n section)\\n else:\\n for obj in objects:\\n obj_uuid = self.get_uuid(obj)\\n if obj_uuid:\\n self._total[section][obj_uuid] = obj.name\\n if obj_uuid == groupid:\\n return self.wrap_object(obj, section)\",\n \"def uuid(self):\\n\\n return self.__uuid\",\n \"def uuid(self):\\n raise NotImplementedError\",\n \"def uuid(self):\\n raise NotImplementedError\",\n \"def uuid(self):\\n raise NotImplementedError\",\n \"def get_device_by_uuid(self, device_id: str):\\n return get_device_by_uuid(self.api_key, device_id)\",\n \"def get_current_user(uuid='1000211211101'):\\n logger.info(\\\"User Detail get\\\", data={'uuid': str(uuid)})\\n return uuid\",\n \"def uuid(self) -> str:\\n return self.__uuid\",\n \"def get_uuid(controller_url,uuid_url):\\n\\n # setting up header\\n h_api_version = \\\"@@{API_VERSION}@@\\\"\\n h_encoding = \\\"@@{ENCODING}@@\\\"\\n h_content = \\\"@@{CONTENT}@@\\\"\\n h_sessionid = \\\"@@{SESSION_ID}@@\\\"\\n h_csrftoken = \\\"@@{CSRF_TOKEN}@@\\\"\\n \\n\\n headers = {\\n 'cookie': \\\"sessionid=\\\" + h_sessionid +\\\"; csrftoken=\\\" + h_csrftoken,\\n 'X-Avi-Version': h_api_version,\\n 'Accept-Encoding': h_encoding,\\n 'Content-type': h_content\\n }\\n \\n # endregion\\n\\t# going through the uuid_url to get the uuid\\n for var_name, url in uuid_url.items():\\n for endpoint, object_name in url.items():\\n endpoint_url = controller_url + endpoint + object_name\\n #print(\\\"{}={}\\\".format(var_name, object_name))\\n print \\\"Enpoint url =\\\", endpoint_url\\n response = urlreq(endpoint_url, verb='GET', headers=headers, verify=False)\\n \\n # deal with the result/response\\n if response.ok:\\n print \\\"Request was successfully\\\"\\n result = json.loads(response.content)\\n print \\\"{}={}\\\".format(var_name,result['results'][0]['uuid'])\\n \\n else:\\n print(\\\"Request failed\\\")\\n print(\\\"Headers: {}\\\".format(headers))\\n print('Status code: {}'.format(response.status_code))\\n print('Response: {}'.format(response.text))\\n exit(1)\\n \\n # endregion\",\n \"def get_object(self, id_):\\n return self._objects.get(id_, None)\",\n \"def get_member_by_raw_uuid(raw_uuid: bytes, what: str, addressbook, mid: str):\\n\\n try:\\n text_uuid = str(uuid.UUID(bytes=raw_uuid))\\n except ValueError as e:\\n logger.warn(f\\\"Failed to parse {what} UUID for message {mid}: {str(e)}\\\")\\n return None\\n\\n recipient = addressbook.get_recipient_by_uuid(text_uuid)\\n member_name = recipient.name if recipient else text_uuid\\n return member_name\",\n \"def get_by_id(self,\\n uuid: str,\\n additional_properties: List[str]=None,\\n with_vector: bool=False\\n ) -> Optional[dict]:\\n\\n return self.get(\\n uuid=uuid,\\n additional_properties=additional_properties,\\n with_vector=with_vector\\n )\",\n \"def uuid(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"uuid\\\")\",\n \"def uuid(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"uuid\\\")\",\n \"def uuid(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"uuid\\\")\",\n \"def find_instance(cls, identifier):\\r\\n for instance in cls.all:\\r\\n if instance.identifier == identifier:\\r\\n return instance\\r\\n return None\",\n \"def getUserUuid(userId):\\n return searchForUser(userId)['uuid']\",\n \"def uuid(self) -> str:\\n return self._uuid\",\n \"def uuid(self) -> str:\\n return self._uuid\",\n \"def uuid(self) -> str:\\n return self._uuid\",\n \"def retrieve_by_uuid(self, uuid, target_path=os.path.curdir, use_symlinks=False):\\n count = self.retrieve(\\\"uuid == @uuid\\\", {\\\"uuid\\\": uuid}, target_path, use_symlinks)\\n assert(count <= 1)\\n\\n if count == 0:\\n raise Error(\\\"product with uuid '%s' not found\\\" % uuid)\\n\\n return count\",\n \"def get_objectID(self):\\n return self.collection.uuid\",\n \"def info_by_uuid(self, uuid):\\n return self.rpc.call(MsfRpcMethod.ModuleResults, [uuid])\",\n \"def object_for(objectid):\",\n \"def getUUID(part):\\n global quickList\\n if len(quickList) == 0:\\n fillQuickList()\\n return quickList[part]\",\n \"def UUID(self, default=None):\\n return self.data.get('uuid', default)\",\n \"def test_uuid_retreival_uuid(self):\\n movie = Movie.objects.get(id=uuid.UUID(\\n '01234567-0123-0123-0123-0123456789ab',\\n ))\\n self.assertEqual(movie.title, 'The Return of the King')\",\n \"def get_uuid(self):\\n\\t\\treturn call_sdk_function('PrlUsrInfo_GetUuid', self.handle)\",\n \"def get_event(self, uuid):\\n return Event.deserialize(self._get_single('events', {'uuid': uuid}))\",\n \"def check_uuid(self, obj, groupid):\\n if self.get_uuid(obj) == groupid:\\n return True\",\n \"def search_uuid(owner, title):\\n db = choose_database(\\\"datadb\\\")\\n condition = \\\"owner LIKE \\\\'%\\\" + owner + \\\"%\\\\' AND title=\\\\'\\\" + title + \\\"\\\\'\\\"\\n result = select_columns(db, 'games', 'game_uuid', condition=condition)\\n if len(result) > 0:\\n return result[0][0]\\n else:\\n return None\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.82511705","0.7933739","0.7587574","0.74948627","0.74105513","0.705008","0.69275194","0.6883316","0.68186414","0.68186414","0.6762099","0.6762099","0.6734712","0.67203295","0.6711133","0.66830343","0.6674249","0.6659611","0.6656396","0.6648054","0.66044545","0.6532576","0.6510357","0.6470805","0.64533585","0.6441048","0.64401025","0.6423502","0.6420565","0.6420393","0.6397662","0.6384669","0.63785446","0.63505465","0.63495713","0.63495713","0.63265866","0.6315927","0.6315927","0.6313101","0.6307058","0.6262787","0.62042475","0.6203617","0.6172196","0.615418","0.61503506","0.613799","0.61320704","0.61240304","0.6114257","0.610825","0.6098493","0.6084266","0.6072246","0.6043654","0.60431474","0.60288525","0.6027144","0.60208565","0.6016958","0.6016958","0.6005298","0.5998651","0.59928674","0.59835","0.5980604","0.59774196","0.5975841","0.5944415","0.5942025","0.5942025","0.5942025","0.5941427","0.5921958","0.591828","0.5902298","0.58776236","0.58697045","0.58633363","0.585677","0.585677","0.585677","0.5855184","0.58544594","0.58469796","0.58469796","0.58469796","0.5839125","0.58313805","0.58040696","0.5787676","0.57873243","0.57846516","0.5782293","0.57727104","0.5772423","0.5769442","0.5759372"],"string":"[\n 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\"0.81269974\",\n \"0.80951434\",\n \"0.7999025\",\n \"0.79542536\",\n \"0.79282635\",\n \"0.7537779\",\n \"0.73879355\",\n \"0.73344094\",\n \"0.7329022\",\n \"0.7317708\",\n \"0.7281263\",\n \"0.7255151\",\n \"0.7209064\",\n \"0.7190735\",\n \"0.71191245\",\n \"0.7043156\",\n \"0.7034967\",\n \"0.703377\",\n \"0.703377\",\n \"0.69024086\",\n \"0.683505\",\n \"0.6825827\",\n \"0.6804044\",\n \"0.6804044\",\n \"0.6804044\",\n \"0.6804044\",\n \"0.6804044\",\n \"0.6804044\",\n \"0.6804044\",\n \"0.6804044\",\n \"0.6804044\",\n \"0.6804044\",\n \"0.6804044\",\n \"0.6804044\",\n \"0.6804044\",\n \"0.6804044\",\n \"0.6804044\",\n \"0.6804044\",\n \"0.6804044\",\n \"0.6804044\",\n \"0.6804044\",\n \"0.6804044\",\n \"0.6804044\",\n \"0.6804044\",\n \"0.6804044\",\n \"0.6804044\",\n \"0.6804044\",\n \"0.6804044\",\n \"0.6804044\",\n \"0.6804044\",\n \"0.6804044\",\n \"0.6804044\",\n \"0.6804044\",\n \"0.6804044\",\n \"0.6804044\",\n \"0.6804044\",\n \"0.6804044\",\n \"0.6804044\",\n \"0.6804044\",\n \"0.6804044\",\n \"0.6804044\",\n \"0.6804044\",\n \"0.6804044\",\n \"0.6804044\",\n \"0.6804044\",\n \"0.6804044\",\n \"0.6804044\",\n \"0.6804044\",\n \"0.6804044\",\n \"0.6804044\",\n \"0.6804044\",\n \"0.6804044\",\n \"0.6804044\",\n \"0.6804044\",\n \"0.6804044\",\n \"0.6804044\",\n \"0.6804044\",\n \"0.6804044\",\n \"0.6804044\",\n \"0.6804044\",\n \"0.6804044\",\n \"0.6804044\",\n \"0.6804044\",\n \"0.6804044\",\n \"0.6804044\",\n \"0.6804044\",\n \"0.6804044\",\n \"0.6804044\",\n \"0.6804044\",\n \"0.6804044\",\n \"0.6804044\",\n \"0.6804044\",\n \"0.6804044\",\n \"0.6804044\",\n \"0.6804044\",\n \"0.6804044\",\n \"0.6804044\",\n \"0.6804044\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.72039217"},"document_rank":{"kind":"string","value":"15"}}},{"rowIdx":11280,"cells":{"query":{"kind":"string","value":"Remove any nonwhite listed chars from a string"},"document":{"kind":"string","value":"def _filter_string(cls, string, extra_chars=\"\"):\n char_white_list = ascii_letters + digits + extra_chars\n return \"\".join([char for char in string if char in char_white_list])"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _removeWhitespaces(self, s):\n return s.translate({ord(c): None for c in string.whitespace})","def filter(string):\n # remove all unwanted characters\n return regex2.sub(' ', string)","def stripword( s ) :\n return re.sub( '[\\W\\d]', '', s )","def string_cleanup(s, garbage=\":,-()&\"):\n s_new = ''\n for x in s:\n if x not in garbage:\n s_new += x\n\n return s_new","def non_letter_removal(text):\n return re.sub('[^a-zA-Z]', ' ', text)","def remove_nonalpha(text):\n text = ''.join(c for c in text if c.isalpha() or c == ' ')\n return re.sub(\" +\", \" \", text)","def remove_string_special_characters(s):\n stripped = re.sub('[^\\w\\s]', '', s)\n stripped = re.sub('_', '', stripped)\n stripped = re.sub('\\s+', ' ', stripped)\n stripped = stripped.strip()\n\n return stripped","def clean_str_sst(string):\n string = re.sub(r\"[^A-Za-z0-9(),!?\\'\\`]\", \" \", string) \n string = re.sub(r\"\\s{2,}\", \" \", string) \n return string.strip().lower()","def _cleanse(text):\n return ''.join([character for character in text\n if character.isalnum()]).lower()","def preprocess(string):\n return regex.sub('', string)","def strip(str):\n return ''.join([char for char in str if char.isalpha()])","def replace_any_non_letter_or_number_character(text):\n text = text.strip()\n text = re.sub('[^A-Za-z0-9 ]+', '', text)\n return text","def clean_str_sst(string):\n string = re.sub(r\"[^A-Za-z0-9(),!?\\'\\`]\", \" \", string)\n string = re.sub(r\"\\s{2,}\", \" \", string)\n return string.strip().lower()","def clean(s):\n punctuations = \"-,.?!;:\\n\\t()[]\\\"-\"\n return s.translate(None, string.punctuation).lower()","def clean_string(s):\n c = re.sub(r'\\s+', ' ', re.sub(r'[^A-Za-z0-9 .:]', '', s))\n return c","def cleanString(self, s):\r\n s = s.lower()\r\n for x in s: \r\n if x in punctuation:\r\n s = s.replace(x, '')\r\n return s","def _strip_remove_non_alpha(stringSentence):\n # type: (str) -> str\n\n _stripped = stringSentence.strip()\n _words = _stripped.split(' ')\n _realWords = [word for word in _words if word.isalpha()]\n\n return ' '.join(_realWords)","def clean_str_sst(string):\n string = re.sub(r\"[^A-Za-z0-9(),!?\\'\\`]\", \" \", string)\n string = re.sub(r\"\\s{2,}\", \" \", string)\n return string.strip().lower()","def get_strip_string(self, i_str):\n return ''.join(e for e in i_str if e.isalnum())","def remove_non_alphabetic_text(text):\n return RegexFilters.replace_non_alphabetic_text(text, \"\")","def sanitize_str(s):\n # throw away unkown characters\n return [c for c in s if c in letters]","def remove_special_chars(s):\n stripped = re.sub('[^\\w\\s]', ' ', s)\n stripped = re.sub('_', ' ', stripped)\n\n # Make all whitespaces only one space\n stripped = re.sub('\\s+', ' ', stripped)\n\n stripped = stripped.strip()\n\n return stripped","def scrub(input_string):\n return ''.join(k for k in input_string if k.isalnum())","def clean_string(in_str):\n # Remove extra whitespaces\n in_str = ' '.join(in_str.split())\n # Remove whitespaces before punctuation\n in_str = re.sub(r'\\s([?.!\"](?:\\s|$))', r'\\1', in_str)\n\n return in_str","def _strip(s, chars):\n # leading characters\n while len(s) > 0 and s[0] in chars:\n s = s[1:]\n # trailing characters\n while len(s) > 0 and s[-1] in chars:\n s = s[:-1]\n return s","def delete_whitespaces(str):\n global legal_white_spaces\n\n try:\n str_copy = str\n for i in legal_white_spaces:\n str_copy = str_copy.replace(i, '')\n return str_copy\n except Exception as e:\n print(e)\n return None","def remove_non_ascii(text):\n return re.sub(r'[^\\x00-\\x7F]', ' ', text)","def _clean(s):\n return re.sub(r'\\s+', ' ', s.strip())","def super_clean_str(string):\n return ''.join(x for x in string if x.isalnum()).lower()","def remove_special(s):\n return ansi_escape_chars.sub('', s)","def clean_str(string):\n # Remove punctuation\n string = re.sub(r\"[^\\u4e00-\\u9fff]\", \" \", string)\n string = re.sub(r\"\\s{2,}\", \" \", string)\n return string.strip()","def remove_special_characters_from_text(text) -> str:\n return re.sub(r'[^\\w\\s]', '', text.strip())","def remove_single_chars(text: str) -> str:\n return ' '.join(word for word in text.split()\n if len(word) > 1)","def _StripWS(s):\r\n return re.sub('\\s+', '', s)","def _StripWS(s):\r\n return re.sub('\\s+', '', s)","def strip_string(input):\n return input.lower().replace(\" \", \"\")","def ignore_whitespace(a):\n WHITE_MAP = dict.fromkeys(ord(c) for c in string.whitespace)\n return a.translate(WHITE_MAP)","def to_clean_str(s: str) -> str:\n return re.sub(\"[^a-zA-Z0-9]\", \"\", s).lower()","def _only_letters(s):\r\n\treturn _regex_non_letters.sub('', s)","def str_clean(string):\n\timport re\n\treturn re.sub(ur'[\\W_]+', u'', string, flags=re.UNICODE).lower()","def CLEAN(text):\n return _control_char_re.sub('', text)","def strip_other_charcter():\n pass","def clean_word( s ):\n words = ''\n for i in s:\n if i.isalpha():\n words += i\n else:\n words = words\n return words","def removeNonAsciiFromText(self, text):\n\t\treturn ''.join([i if ord(i) < 128 else '' for i in text])","def remove_single_char(text):\n return \" \".join([word for word in text.split() if not len(word)==1])","def basic_cleaning2(string):\n\n string = string.lower()\n string = re.sub('[0-9\\(\\)\\!\\^\\%\\$\\'\\\"\\.;,-\\?\\{\\}\\[\\]\\\\/]', ' ', string)\n string = re.sub(' +', ' ', string)\n return string","def fixString(string):\n string = re.sub(r\"[^A-Z-]\", \"\", string)\n string = string.strip(\"\\n\")\n return string","def _strip_text(text):\n text = re.sub(r'[ ,?:]|%s', \"\", text.lower())\n for chr in \"-%\":\n new_text = text.replace(chr, \"\")\n if new_text:\n text = new_text\n return text.lower()","def strip(self, str_text):\n punct_chars = [' ', '.', ',', '!', '?', '&', '\"', \"'\", '-', ':']\n str_text = [i for i in str_text if i not in punct_chars]\n str_text = ''.join(str_text)\n return str_text","def fixString(string):\n string = re.sub(r\"[^A-Z-]\", \"\", string)\n return string","def remove_special_characters(string_list):","def stripspecialchars(input_str):\n\n import unicodedata\n\n nfkd_form = unicodedata.normalize('NFKD', unicode(input_str))\n\n return ''.join([c for c in nfkd_form if not unicodedata.combining(c)])","def remove_non_alpha(self,text):\n \n removelist=\"-\\.\\/\\?\\@\"\n re_alpha_numeric1=r\"[^0-9a-zA-Z\"+removelist+\" ]\"\n clean_text=re.sub(re_alpha_numeric1,'',text)\n clean_text=clean_text.replace('/',' ')\n clean_text=re.sub(' +', ' ', clean_text)\n return clean_text","def strip_not_alnum_char(str):\n\n i = 0\n # While we don't find a character or a digit,\n # that means it's a special char (logical!)\n if str:\n while not str[i].isalnum() and i < len(str) - 1:\n i += 1\n if i != len(str) - 2:\n str = str[i:]\n return str","def rem_whitespace(string):\n unwanted_chars = punctuation + whitespace\n\n pat_l = [r'[' + unwanted_chars + ']',\n r'\\s+',\n r' ',\n r' \\\\',\n r' \\ '\n ]\n\n for p in pat_l:\n rx = re.compile(p)\n string = re.sub(rx, ' ', string)\n\n return string.strip()","def retain_alpha_nums(s):\n return re.sub(r'[^a-zA-Z0-9]', ' ', s)","def clean_word(word):\n return \"\".join([c for c in word.lower() if ord(c) < 128])","def clean_training_text(txt):\n return re.sub('[^A-Za-z0-9]+', ' ', str(txt)).strip()","def removeNonAscii(s):\r\n return \"\".join([i for i in s if ord(i)>31])","def clean_unnecessary_characters(self, tweet):\n tweet = tweet.lstrip(\"\\\"\").rstrip(\"\\\"\")\n tweet = re.sub(self.compiledAlphanumericRegex, ' ', tweet)\n tweet = tweet.replace('_', ' ')\n return tweet","def remove_inner_word_characters(text):\n return RegexFilters.replace_inner_word_characters(text, \"\")","def clean_white_spaces(string):\n try:\n # in case it is in byte value\n string = string.decode('utf-8')\n except:\n pass\n\n res = ''\n words = string.split()\n for word in words:\n res = res + str(word)\n return res","def stripchars(string):\n return ''.join(re.split('\\t+|\\n+', string))","def remove_special_characters(text):\n soup = BeautifulSoup(text, \"html.parser\")\n review = soup.get_text()\n review = r\"[^a-zA-z0-9\\s]\"\n review = re.sub(review, \"\", text)\n return review.lower()","def clean_non_word_chars(tokens):\n toks = []\n for token in tokens:\n t = re.sub(r'\\W', \"\", token)\n if len(t) > 1:\n toks.append(t)\n\n return toks","def clean(sent):\n p1 = re.compile('\\W')\n p2 = re.compile('\\s+')\n sent = re.sub(r\"http\\S+\", \"\", sent)\n sent = ReplaceThreeOrMore(sent)\n sent = remove_unicode_diac(sent)\n sent = sent.replace('_', ' ')\n sent = re.sub(r'[A-Za-z0-9]', r'', sent)\n sent = re.sub(p1, ' ', sent)\n sent = re.sub(p2, ' ', sent)\n return sent","def _clean_text(self, text):\n output = []\n for char in text:\n cp = ord(char)\n if cp == 0 or cp == 65533 or _is_control(char):\n continue\n if _is_whitespace(char):\n output.append(' ')\n else:\n output.append(char)\n return ''.join(output)","def cleaning(string):\n\n if type(string) == float or type(string) == int:\n return string\n res = ''\n if string != string:\n return string\n string = string.replace(\"\\\\r\", \"\")\n string = string.replace(\"\\\\n\", \"\")\n string = string.replace(\"\\\\b\", \"\")\n string = string.replace(\"\\\\t\", \"\")\n for i in string:\n if i.isalpha():\n res = res + i\n return res.lower()","def clean_str_vn(string):\n string = re.sub(r\"[~`@#$%^&*-+]\", \" \", string)\n def sharp(str):\n b = re.sub('\\s[A-Za-z]\\s\\.', ' .', ' '+str)\n while (b.find('. . ')>=0): b = re.sub(r'\\.\\s\\.\\s', '. ', b)\n b = re.sub(r'\\s\\.\\s', ' # ', b)\n return b\n string = sharp(string)\n string = re.sub(r\" : \", \":\", string)\n string = re.sub(r\",\", \" , \", string)\n string = re.sub(r\"!\", \" ! \", string)\n string = re.sub(r\"\\(\", \" \\( \", string)\n string = re.sub(r\"\\)\", \" \\) \", string)\n string = re.sub(r\"\\?\", \"\", string)\n string = re.sub(r\"\\s{2,}\", \" \", string)\n return string.strip().lower()","def clean_text(text):\n text = text.lower()\n all_letters = \"abcdefghijklmnopqrstuvwxyz\"\n text_to_keep = \"\"\n for char in text:\n if char in all_letters:\n text_to_keep += char\n return text_to_keep","def _clean_text(self, text):\n output = []\n for char in text:\n cp = ord(char)\n if cp == 0 or cp == 0xFFFD or _is_control(char):\n continue # pragma: no cover\n if _is_whitespace(char):\n output.append(\" \")\n else:\n output.append(char)\n return \"\".join(output)","def safe(s):\n return (\"\".join(c for c in s if c.isalnum() or c == '_' or c == '-').rstrip())","def remove_punctations_fun(self): \n self.doc = re.sub('[^a-zA-Z0-9]', ' ', self.doc)","def _clean(self, string):\n return re.sub('\\s+', ' ', string).strip()","def scrub_string(str):\n return \"\".join(ch for ch in str if ch.isalpha() or ch.isdigit()).lower()","def removeChars(inStr, chars):\n newStr = inStr\n for char in chars:\n newStr = newStr.replace(char, \"\")\n return newStr","def removeSpecialChars(self) -> None:\n self.text = re.sub('[^a-zA-z0-9\\n\\.\\s]', '', self.text)","def remove_specials(sentence):\n sentence = sentence.replace('-', ' ')\n sentence = re.sub(r'[^\\w\\s]', '', sentence)\n return sentence","def clean_data(s):\n s = s.strip()\n s = s.lower()\n return s","def remove_special_chars(text):\n \n text = re.sub(' +', ' ', re.sub('[^A-Za-z ]+', ' ', text).strip())\n return text","def clean_word(word: str) -> str:\n\n cleaned_word = ''\n for char in word.lower():\n if char.isalnum():\n cleaned_word = cleaned_word + char\n return cleaned_word","def _clean_text(self, text):\n output = []\n for char in text:\n cp = ord(char)\n if cp == 0 or cp == 0xfffd or _is_control(char):\n continue\n if _is_whitespace(char):\n output.append(\" \")\n else:\n output.append(char)\n return \"\".join(output)","def clean_text_from_nonbasic_characters(text):\n text = re.sub(r\"([^\\u0000-\\u007F])\", \" \", text)\n text = replace_newline_with_space(text).strip()\n text = text.replace(\"_\", \"\")\n text = clean_text_from_multiple_consecutive_whitespaces(text)\n return text","def clean_text_from_nonbasic_characters(text):\n text = re.sub(r\"([^\\u0000-\\u007F])\", \" \", text)\n text = replace_newline_with_space(text).strip()\n text = text.replace(\"_\", \"\")\n text = clean_text_from_multiple_consecutive_whitespaces(text)\n return text","def remove_characters(tokens):\n pattern = re.compile('[{}]'.format(re.escape(string.punctuation)))\n no_char_tokens = filter(None, [pattern.sub('', token) for token in tokens])\n return no_char_tokens","def remove_chars(old_str, chars):\n new_string = old_str\n for char in chars:\n new_string = new_string.replace(char, '')\n \n return new_string","def preprocess(self, s):\n stripped = re.sub(\"[^\\w\\s]\", \"\", s)\n stripped = re.sub(\"_\", \"\", stripped)\n\n stripped = re.sub(\"\\s+\", \" \", stripped)\n\n stripped = stripped.strip()\n\n return stripped.lower()","def _remove_whitespaces(self, text: str) -> str:\n return \" \".join(re.sub(\"\\xa0\", \" \", str(text)).split())","def _replace_non_alnum(self):\n no_punct = [x if x.isalnum() else ' ' for x in self._phrase.lower()]\n return ''.join(no_punct) # Convert an array of char to string","def _remove_custom_chars(self, text: str) -> str:\n patterns = \"|\".join([x for x in self.custom_chars])\n return re.sub(patterns, \"\", str(text), flags=re.IGNORECASE)","def sanitize(string):\n retval = string.lower()\n retval = re.sub(r\"[^\\w\\s]\", '', retval)\n retval = re.sub(r\"\\s+\", '_', retval)\n return retval","def cleanword(s):\n word = \"\"\n\n for letter in s:\n if letter not in string.punctuation:\n word += letter\n\n return word","def clean_str(string):\n\tstring = re.sub(r\"\\\"+\", \" \", string)\n\tstring = re.sub(r\"\\'+\", \" \", string)\n\tstring = re.sub(r\",+\", \",\", string)\n\tstring = re.sub(r\"\\.+\", \".\", string)\n\tstring = re.sub(r\"\\?+\", \"?\", string)\n\tstring = re.sub(r\"!+\", \"!\", string)\n\tstring = re.sub(r\",\", \" , \", string)\n\tstring = re.sub(r\"!\", \" ! \", string)\n\tstring = re.sub(r\"\\(\", \" ( \", string)\n\tstring = re.sub(r\"\\)\", \" ) \", string)\n\tstring = re.sub(r\"\\?\", \" ? \", string)\n\tstring = re.sub(r\"\\s{2,}\", \" \", string)\n\treturn string.strip().lower()","def clean_word(self, word):\n return self.filter_pattern.sub(u'', word.lower())","def clean_text(text):\n output = []\n for char in text:\n cp = ord(char)\n if cp == 0 or cp == 0xfffd or is_control(char):\n continue\n if is_whitespace(char):\n output.append(\" \")\n else:\n output.append(char)\n return \"\".join(output)","def Clean(s):\n for c in BAD_CHARACTERS:\n s = s.replace(c, '_')\n return s","def clean_str(data, remove=''):\n return data.translate(None, remove)","def strip_non_unicode(value):\n UNICODE_PATTERN = r'[^\\x00-\\x7F]+'\n try:\n value = re.sub(UNICODE_PATTERN, '', value)\n return value.strip()\n except Exception:\n return value","def remove_emoji_punc(text):\n \n allchars = [str for str in text]\n emoji_list = [c for c in allchars if c in emoji.UNICODE_EMOJI]\n clean_text = ' '.join([str for str in text.split() if not any(i in str for i in emoji_list)])\n\n \n s1 = clean_text.replace(u'’', u\"\").replace(\"'\",\"\")\n s1 = re.sub(r'[^a-z0-9 ]+', ' ', s1)\n \n return \" \".join(s1.split())"],"string":"[\n \"def _removeWhitespaces(self, s):\\n return s.translate({ord(c): None for c in string.whitespace})\",\n \"def filter(string):\\n # remove all unwanted characters\\n return regex2.sub(' ', string)\",\n \"def stripword( s ) :\\n return re.sub( '[\\\\W\\\\d]', '', s )\",\n \"def string_cleanup(s, garbage=\\\":,-()&\\\"):\\n s_new = ''\\n for x in s:\\n if x not in garbage:\\n s_new += x\\n\\n return s_new\",\n \"def non_letter_removal(text):\\n return re.sub('[^a-zA-Z]', ' ', text)\",\n \"def remove_nonalpha(text):\\n text = ''.join(c for c in text if c.isalpha() or c == ' ')\\n return re.sub(\\\" +\\\", \\\" \\\", text)\",\n \"def remove_string_special_characters(s):\\n stripped = re.sub('[^\\\\w\\\\s]', '', s)\\n stripped = re.sub('_', '', stripped)\\n stripped = re.sub('\\\\s+', ' ', stripped)\\n stripped = stripped.strip()\\n\\n return stripped\",\n \"def clean_str_sst(string):\\n string = re.sub(r\\\"[^A-Za-z0-9(),!?\\\\'\\\\`]\\\", \\\" \\\", string) \\n string = re.sub(r\\\"\\\\s{2,}\\\", \\\" \\\", string) \\n return string.strip().lower()\",\n \"def _cleanse(text):\\n return ''.join([character for character in text\\n if character.isalnum()]).lower()\",\n \"def preprocess(string):\\n return regex.sub('', string)\",\n \"def strip(str):\\n return ''.join([char for char in str if char.isalpha()])\",\n \"def replace_any_non_letter_or_number_character(text):\\n text = text.strip()\\n text = re.sub('[^A-Za-z0-9 ]+', '', text)\\n return text\",\n \"def clean_str_sst(string):\\n string = re.sub(r\\\"[^A-Za-z0-9(),!?\\\\'\\\\`]\\\", \\\" \\\", string)\\n string = re.sub(r\\\"\\\\s{2,}\\\", \\\" \\\", string)\\n return string.strip().lower()\",\n \"def clean(s):\\n punctuations = \\\"-,.?!;:\\\\n\\\\t()[]\\\\\\\"-\\\"\\n return s.translate(None, string.punctuation).lower()\",\n \"def clean_string(s):\\n c = re.sub(r'\\\\s+', ' ', re.sub(r'[^A-Za-z0-9 .:]', '', s))\\n return c\",\n \"def cleanString(self, s):\\r\\n s = s.lower()\\r\\n for x in s: \\r\\n if x in punctuation:\\r\\n s = s.replace(x, '')\\r\\n return s\",\n \"def _strip_remove_non_alpha(stringSentence):\\n # type: (str) -> str\\n\\n _stripped = stringSentence.strip()\\n _words = _stripped.split(' ')\\n _realWords = [word for word in _words if word.isalpha()]\\n\\n return ' '.join(_realWords)\",\n \"def clean_str_sst(string):\\n string = re.sub(r\\\"[^A-Za-z0-9(),!?\\\\'\\\\`]\\\", \\\" \\\", string)\\n string = re.sub(r\\\"\\\\s{2,}\\\", \\\" \\\", string)\\n return string.strip().lower()\",\n \"def get_strip_string(self, i_str):\\n return ''.join(e for e in i_str if e.isalnum())\",\n \"def remove_non_alphabetic_text(text):\\n return RegexFilters.replace_non_alphabetic_text(text, \\\"\\\")\",\n \"def sanitize_str(s):\\n # throw away unkown characters\\n return [c for c in s if c in letters]\",\n \"def remove_special_chars(s):\\n stripped = re.sub('[^\\\\w\\\\s]', ' ', s)\\n stripped = re.sub('_', ' ', stripped)\\n\\n # Make all whitespaces only one space\\n stripped = re.sub('\\\\s+', ' ', stripped)\\n\\n stripped = stripped.strip()\\n\\n return stripped\",\n \"def scrub(input_string):\\n return ''.join(k for k in input_string if k.isalnum())\",\n \"def clean_string(in_str):\\n # Remove extra whitespaces\\n in_str = ' '.join(in_str.split())\\n # Remove whitespaces before punctuation\\n in_str = re.sub(r'\\\\s([?.!\\\"](?:\\\\s|$))', r'\\\\1', in_str)\\n\\n return in_str\",\n \"def _strip(s, chars):\\n # leading characters\\n while len(s) > 0 and s[0] in chars:\\n s = s[1:]\\n # trailing characters\\n while len(s) > 0 and s[-1] in chars:\\n s = s[:-1]\\n return s\",\n \"def delete_whitespaces(str):\\n global legal_white_spaces\\n\\n try:\\n str_copy = str\\n for i in legal_white_spaces:\\n str_copy = str_copy.replace(i, '')\\n return str_copy\\n except Exception as e:\\n print(e)\\n return None\",\n \"def remove_non_ascii(text):\\n return re.sub(r'[^\\\\x00-\\\\x7F]', ' ', text)\",\n \"def _clean(s):\\n return re.sub(r'\\\\s+', ' ', s.strip())\",\n \"def super_clean_str(string):\\n return ''.join(x for x in string if x.isalnum()).lower()\",\n \"def remove_special(s):\\n return ansi_escape_chars.sub('', s)\",\n \"def clean_str(string):\\n # Remove punctuation\\n string = re.sub(r\\\"[^\\\\u4e00-\\\\u9fff]\\\", \\\" \\\", string)\\n string = re.sub(r\\\"\\\\s{2,}\\\", \\\" \\\", string)\\n return string.strip()\",\n \"def remove_special_characters_from_text(text) -> str:\\n return re.sub(r'[^\\\\w\\\\s]', '', text.strip())\",\n \"def remove_single_chars(text: str) -> str:\\n return ' '.join(word for word in text.split()\\n if len(word) > 1)\",\n \"def _StripWS(s):\\r\\n return re.sub('\\\\s+', '', s)\",\n \"def _StripWS(s):\\r\\n return re.sub('\\\\s+', '', s)\",\n \"def strip_string(input):\\n return input.lower().replace(\\\" \\\", \\\"\\\")\",\n \"def ignore_whitespace(a):\\n WHITE_MAP = dict.fromkeys(ord(c) for c in string.whitespace)\\n return a.translate(WHITE_MAP)\",\n \"def to_clean_str(s: str) -> str:\\n return re.sub(\\\"[^a-zA-Z0-9]\\\", \\\"\\\", s).lower()\",\n \"def _only_letters(s):\\r\\n\\treturn _regex_non_letters.sub('', s)\",\n \"def str_clean(string):\\n\\timport re\\n\\treturn re.sub(ur'[\\\\W_]+', u'', string, flags=re.UNICODE).lower()\",\n \"def CLEAN(text):\\n return _control_char_re.sub('', text)\",\n \"def strip_other_charcter():\\n pass\",\n \"def clean_word( s ):\\n words = ''\\n for i in s:\\n if i.isalpha():\\n words += i\\n else:\\n words = words\\n return words\",\n \"def removeNonAsciiFromText(self, text):\\n\\t\\treturn ''.join([i if ord(i) < 128 else '' for i in text])\",\n \"def remove_single_char(text):\\n return \\\" \\\".join([word for word in text.split() if not len(word)==1])\",\n \"def basic_cleaning2(string):\\n\\n string = string.lower()\\n string = re.sub('[0-9\\\\(\\\\)\\\\!\\\\^\\\\%\\\\$\\\\'\\\\\\\"\\\\.;,-\\\\?\\\\{\\\\}\\\\[\\\\]\\\\\\\\/]', ' ', string)\\n string = re.sub(' +', ' ', string)\\n return string\",\n \"def fixString(string):\\n string = re.sub(r\\\"[^A-Z-]\\\", \\\"\\\", string)\\n string = string.strip(\\\"\\\\n\\\")\\n return string\",\n \"def _strip_text(text):\\n text = re.sub(r'[ ,?:]|%s', \\\"\\\", text.lower())\\n for chr in \\\"-%\\\":\\n new_text = text.replace(chr, \\\"\\\")\\n if new_text:\\n text = new_text\\n return text.lower()\",\n \"def strip(self, str_text):\\n punct_chars = [' ', '.', ',', '!', '?', '&', '\\\"', \\\"'\\\", '-', ':']\\n str_text = [i for i in str_text if i not in punct_chars]\\n str_text = ''.join(str_text)\\n return str_text\",\n \"def fixString(string):\\n string = re.sub(r\\\"[^A-Z-]\\\", \\\"\\\", string)\\n return string\",\n \"def remove_special_characters(string_list):\",\n \"def stripspecialchars(input_str):\\n\\n import unicodedata\\n\\n nfkd_form = unicodedata.normalize('NFKD', unicode(input_str))\\n\\n return ''.join([c for c in nfkd_form if not unicodedata.combining(c)])\",\n \"def remove_non_alpha(self,text):\\n \\n removelist=\\\"-\\\\.\\\\/\\\\?\\\\@\\\"\\n re_alpha_numeric1=r\\\"[^0-9a-zA-Z\\\"+removelist+\\\" ]\\\"\\n clean_text=re.sub(re_alpha_numeric1,'',text)\\n clean_text=clean_text.replace('/',' ')\\n clean_text=re.sub(' +', ' ', clean_text)\\n return clean_text\",\n \"def strip_not_alnum_char(str):\\n\\n i = 0\\n # While we don't find a character or a digit,\\n # that means it's a special char (logical!)\\n if str:\\n while not str[i].isalnum() and i < len(str) - 1:\\n i += 1\\n if i != len(str) - 2:\\n str = str[i:]\\n return str\",\n \"def rem_whitespace(string):\\n unwanted_chars = punctuation + whitespace\\n\\n pat_l = [r'[' + unwanted_chars + ']',\\n r'\\\\s+',\\n r' ',\\n r' \\\\\\\\',\\n r' \\\\ '\\n ]\\n\\n for p in pat_l:\\n rx = re.compile(p)\\n string = re.sub(rx, ' ', string)\\n\\n return string.strip()\",\n \"def retain_alpha_nums(s):\\n return re.sub(r'[^a-zA-Z0-9]', ' ', s)\",\n \"def clean_word(word):\\n return \\\"\\\".join([c for c in word.lower() if ord(c) < 128])\",\n \"def clean_training_text(txt):\\n return re.sub('[^A-Za-z0-9]+', ' ', str(txt)).strip()\",\n \"def removeNonAscii(s):\\r\\n return \\\"\\\".join([i for i in s if ord(i)>31])\",\n \"def clean_unnecessary_characters(self, tweet):\\n tweet = tweet.lstrip(\\\"\\\\\\\"\\\").rstrip(\\\"\\\\\\\"\\\")\\n tweet = re.sub(self.compiledAlphanumericRegex, ' ', tweet)\\n tweet = tweet.replace('_', ' ')\\n return tweet\",\n \"def remove_inner_word_characters(text):\\n return RegexFilters.replace_inner_word_characters(text, \\\"\\\")\",\n \"def clean_white_spaces(string):\\n try:\\n # in case it is in byte value\\n string = string.decode('utf-8')\\n except:\\n pass\\n\\n res = ''\\n words = string.split()\\n for word in words:\\n res = res + str(word)\\n return res\",\n \"def stripchars(string):\\n return ''.join(re.split('\\\\t+|\\\\n+', string))\",\n \"def remove_special_characters(text):\\n soup = BeautifulSoup(text, \\\"html.parser\\\")\\n review = soup.get_text()\\n review = r\\\"[^a-zA-z0-9\\\\s]\\\"\\n review = re.sub(review, \\\"\\\", text)\\n return review.lower()\",\n \"def clean_non_word_chars(tokens):\\n toks = []\\n for token in tokens:\\n t = re.sub(r'\\\\W', \\\"\\\", token)\\n if len(t) > 1:\\n toks.append(t)\\n\\n return toks\",\n \"def clean(sent):\\n p1 = re.compile('\\\\W')\\n p2 = re.compile('\\\\s+')\\n sent = re.sub(r\\\"http\\\\S+\\\", \\\"\\\", sent)\\n sent = ReplaceThreeOrMore(sent)\\n sent = remove_unicode_diac(sent)\\n sent = sent.replace('_', ' ')\\n sent = re.sub(r'[A-Za-z0-9]', r'', sent)\\n sent = re.sub(p1, ' ', sent)\\n sent = re.sub(p2, ' ', sent)\\n return sent\",\n \"def _clean_text(self, text):\\n output = []\\n for char in text:\\n cp = ord(char)\\n if cp == 0 or cp == 65533 or _is_control(char):\\n continue\\n if _is_whitespace(char):\\n output.append(' ')\\n else:\\n output.append(char)\\n return ''.join(output)\",\n \"def cleaning(string):\\n\\n if type(string) == float or type(string) == int:\\n return string\\n res = ''\\n if string != string:\\n return string\\n string = string.replace(\\\"\\\\\\\\r\\\", \\\"\\\")\\n string = string.replace(\\\"\\\\\\\\n\\\", \\\"\\\")\\n string = string.replace(\\\"\\\\\\\\b\\\", \\\"\\\")\\n string = string.replace(\\\"\\\\\\\\t\\\", \\\"\\\")\\n for i in string:\\n if i.isalpha():\\n res = res + i\\n return res.lower()\",\n \"def clean_str_vn(string):\\n string = re.sub(r\\\"[~`@#$%^&*-+]\\\", \\\" \\\", string)\\n def sharp(str):\\n b = re.sub('\\\\s[A-Za-z]\\\\s\\\\.', ' .', ' '+str)\\n while (b.find('. . ')>=0): b = re.sub(r'\\\\.\\\\s\\\\.\\\\s', '. ', b)\\n b = re.sub(r'\\\\s\\\\.\\\\s', ' # ', b)\\n return b\\n string = sharp(string)\\n string = re.sub(r\\\" : \\\", \\\":\\\", string)\\n string = re.sub(r\\\",\\\", \\\" , \\\", string)\\n string = re.sub(r\\\"!\\\", \\\" ! \\\", string)\\n string = re.sub(r\\\"\\\\(\\\", \\\" \\\\( \\\", string)\\n string = re.sub(r\\\"\\\\)\\\", \\\" \\\\) \\\", string)\\n string = re.sub(r\\\"\\\\?\\\", \\\"\\\", string)\\n string = re.sub(r\\\"\\\\s{2,}\\\", \\\" \\\", string)\\n return string.strip().lower()\",\n \"def clean_text(text):\\n text = text.lower()\\n all_letters = \\\"abcdefghijklmnopqrstuvwxyz\\\"\\n text_to_keep = \\\"\\\"\\n for char in text:\\n if char in all_letters:\\n text_to_keep += char\\n return text_to_keep\",\n \"def _clean_text(self, text):\\n output = []\\n for char in text:\\n cp = ord(char)\\n if cp == 0 or cp == 0xFFFD or _is_control(char):\\n continue # pragma: no cover\\n if _is_whitespace(char):\\n output.append(\\\" \\\")\\n else:\\n output.append(char)\\n return \\\"\\\".join(output)\",\n \"def safe(s):\\n return (\\\"\\\".join(c for c in s if c.isalnum() or c == '_' or c == '-').rstrip())\",\n \"def remove_punctations_fun(self): \\n self.doc = re.sub('[^a-zA-Z0-9]', ' ', self.doc)\",\n \"def _clean(self, string):\\n return re.sub('\\\\s+', ' ', string).strip()\",\n \"def scrub_string(str):\\n return \\\"\\\".join(ch for ch in str if ch.isalpha() or ch.isdigit()).lower()\",\n \"def removeChars(inStr, chars):\\n newStr = inStr\\n for char in chars:\\n newStr = newStr.replace(char, \\\"\\\")\\n return newStr\",\n \"def removeSpecialChars(self) -> None:\\n self.text = re.sub('[^a-zA-z0-9\\\\n\\\\.\\\\s]', '', self.text)\",\n \"def remove_specials(sentence):\\n sentence = sentence.replace('-', ' ')\\n sentence = re.sub(r'[^\\\\w\\\\s]', '', sentence)\\n return sentence\",\n \"def clean_data(s):\\n s = s.strip()\\n s = s.lower()\\n return s\",\n \"def remove_special_chars(text):\\n \\n text = re.sub(' +', ' ', re.sub('[^A-Za-z ]+', ' ', text).strip())\\n return text\",\n \"def clean_word(word: str) -> str:\\n\\n cleaned_word = ''\\n for char in word.lower():\\n if char.isalnum():\\n cleaned_word = cleaned_word + char\\n return cleaned_word\",\n \"def _clean_text(self, text):\\n output = []\\n for char in text:\\n cp = ord(char)\\n if cp == 0 or cp == 0xfffd or _is_control(char):\\n continue\\n if _is_whitespace(char):\\n output.append(\\\" \\\")\\n else:\\n output.append(char)\\n return \\\"\\\".join(output)\",\n \"def clean_text_from_nonbasic_characters(text):\\n text = re.sub(r\\\"([^\\\\u0000-\\\\u007F])\\\", \\\" \\\", text)\\n text = replace_newline_with_space(text).strip()\\n text = text.replace(\\\"_\\\", \\\"\\\")\\n text = clean_text_from_multiple_consecutive_whitespaces(text)\\n return text\",\n \"def clean_text_from_nonbasic_characters(text):\\n text = re.sub(r\\\"([^\\\\u0000-\\\\u007F])\\\", \\\" \\\", text)\\n text = replace_newline_with_space(text).strip()\\n text = text.replace(\\\"_\\\", \\\"\\\")\\n text = clean_text_from_multiple_consecutive_whitespaces(text)\\n return text\",\n \"def remove_characters(tokens):\\n pattern = re.compile('[{}]'.format(re.escape(string.punctuation)))\\n no_char_tokens = filter(None, [pattern.sub('', token) for token in tokens])\\n return no_char_tokens\",\n \"def remove_chars(old_str, chars):\\n new_string = old_str\\n for char in chars:\\n new_string = new_string.replace(char, '')\\n \\n return new_string\",\n \"def preprocess(self, s):\\n stripped = re.sub(\\\"[^\\\\w\\\\s]\\\", \\\"\\\", s)\\n stripped = re.sub(\\\"_\\\", \\\"\\\", stripped)\\n\\n stripped = re.sub(\\\"\\\\s+\\\", \\\" \\\", stripped)\\n\\n stripped = stripped.strip()\\n\\n return stripped.lower()\",\n \"def _remove_whitespaces(self, text: str) -> str:\\n return \\\" \\\".join(re.sub(\\\"\\\\xa0\\\", \\\" \\\", str(text)).split())\",\n \"def _replace_non_alnum(self):\\n no_punct = [x if x.isalnum() else ' ' for x in self._phrase.lower()]\\n return ''.join(no_punct) # Convert an array of char to string\",\n \"def _remove_custom_chars(self, text: str) -> str:\\n patterns = \\\"|\\\".join([x for x in self.custom_chars])\\n return re.sub(patterns, \\\"\\\", str(text), flags=re.IGNORECASE)\",\n \"def sanitize(string):\\n retval = string.lower()\\n retval = re.sub(r\\\"[^\\\\w\\\\s]\\\", '', retval)\\n retval = re.sub(r\\\"\\\\s+\\\", '_', retval)\\n return retval\",\n \"def cleanword(s):\\n word = \\\"\\\"\\n\\n for letter in s:\\n if letter not in string.punctuation:\\n word += letter\\n\\n return word\",\n \"def clean_str(string):\\n\\tstring = re.sub(r\\\"\\\\\\\"+\\\", \\\" \\\", string)\\n\\tstring = re.sub(r\\\"\\\\'+\\\", \\\" \\\", string)\\n\\tstring = re.sub(r\\\",+\\\", \\\",\\\", string)\\n\\tstring = re.sub(r\\\"\\\\.+\\\", \\\".\\\", string)\\n\\tstring = re.sub(r\\\"\\\\?+\\\", \\\"?\\\", string)\\n\\tstring = re.sub(r\\\"!+\\\", \\\"!\\\", string)\\n\\tstring = re.sub(r\\\",\\\", \\\" , \\\", string)\\n\\tstring = re.sub(r\\\"!\\\", \\\" ! \\\", string)\\n\\tstring = re.sub(r\\\"\\\\(\\\", \\\" ( \\\", string)\\n\\tstring = re.sub(r\\\"\\\\)\\\", \\\" ) \\\", string)\\n\\tstring = re.sub(r\\\"\\\\?\\\", \\\" ? \\\", string)\\n\\tstring = re.sub(r\\\"\\\\s{2,}\\\", \\\" \\\", string)\\n\\treturn string.strip().lower()\",\n \"def clean_word(self, word):\\n return self.filter_pattern.sub(u'', word.lower())\",\n \"def clean_text(text):\\n output = []\\n for char in text:\\n cp = ord(char)\\n if cp == 0 or cp == 0xfffd or is_control(char):\\n continue\\n if is_whitespace(char):\\n output.append(\\\" \\\")\\n else:\\n output.append(char)\\n return \\\"\\\".join(output)\",\n \"def Clean(s):\\n for c in BAD_CHARACTERS:\\n s = s.replace(c, '_')\\n return s\",\n \"def clean_str(data, remove=''):\\n return data.translate(None, remove)\",\n \"def strip_non_unicode(value):\\n UNICODE_PATTERN = r'[^\\\\x00-\\\\x7F]+'\\n try:\\n value = re.sub(UNICODE_PATTERN, '', value)\\n return value.strip()\\n except Exception:\\n return value\",\n \"def remove_emoji_punc(text):\\n \\n allchars = [str for str in text]\\n emoji_list = [c for c in allchars if c in emoji.UNICODE_EMOJI]\\n clean_text = ' '.join([str for str in text.split() if not any(i in str for i in emoji_list)])\\n\\n \\n s1 = clean_text.replace(u'’', u\\\"\\\").replace(\\\"'\\\",\\\"\\\")\\n s1 = re.sub(r'[^a-z0-9 ]+', ' ', s1)\\n \\n return \\\" \\\".join(s1.split())\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7945294","0.7738483","0.7600506","0.75452673","0.74735063","0.7469556","0.7400172","0.73478335","0.73437715","0.73410106","0.7331665","0.7326543","0.7289203","0.72830564","0.728172","0.727238","0.7259143","0.7258027","0.72176147","0.7203688","0.72031343","0.716749","0.715169","0.7150154","0.71395516","0.7123855","0.7090636","0.7082491","0.7080159","0.70695657","0.7068492","0.7045027","0.70289963","0.70247275","0.70247275","0.7015857","0.69885683","0.6975385","0.69709265","0.69707274","0.6961627","0.69612503","0.69578344","0.6953509","0.6947568","0.69397646","0.6927352","0.69229156","0.69208795","0.69115824","0.68926007","0.6891271","0.6884189","0.6882894","0.6881586","0.688006","0.6876059","0.6875756","0.6875354","0.68745995","0.6870511","0.6845917","0.68360114","0.6835539","0.68305475","0.6827506","0.6826174","0.6823423","0.68152744","0.6798154","0.6795719","0.67901367","0.67872876","0.6786575","0.6785015","0.6781475","0.67806506","0.67775434","0.67767215","0.6775401","0.677323","0.6763524","0.6757732","0.6757732","0.67530376","0.67507786","0.6727709","0.6726219","0.6724953","0.67143553","0.6709004","0.6696241","0.66946495","0.6686085","0.66778624","0.66750365","0.6663551","0.6655125","0.6652601"],"string":"[\n \"0.7945294\",\n \"0.7738483\",\n \"0.7600506\",\n \"0.75452673\",\n \"0.74735063\",\n \"0.7469556\",\n \"0.7400172\",\n \"0.73478335\",\n \"0.73437715\",\n \"0.73410106\",\n \"0.7331665\",\n \"0.7326543\",\n \"0.7289203\",\n \"0.72830564\",\n \"0.728172\",\n \"0.727238\",\n \"0.7259143\",\n \"0.7258027\",\n \"0.72176147\",\n \"0.7203688\",\n \"0.72031343\",\n \"0.716749\",\n \"0.715169\",\n \"0.7150154\",\n \"0.71395516\",\n \"0.7123855\",\n \"0.7090636\",\n \"0.7082491\",\n \"0.7080159\",\n \"0.70695657\",\n \"0.7068492\",\n \"0.7045027\",\n \"0.70289963\",\n \"0.70247275\",\n \"0.70247275\",\n \"0.7015857\",\n \"0.69885683\",\n \"0.6975385\",\n \"0.69709265\",\n \"0.69707274\",\n \"0.6961627\",\n \"0.69612503\",\n \"0.69578344\",\n \"0.6953509\",\n \"0.6947568\",\n \"0.69397646\",\n \"0.6927352\",\n \"0.69229156\",\n \"0.69208795\",\n \"0.69115824\",\n \"0.68926007\",\n \"0.6891271\",\n \"0.6884189\",\n \"0.6882894\",\n \"0.6881586\",\n \"0.688006\",\n \"0.6876059\",\n \"0.6875756\",\n \"0.6875354\",\n \"0.68745995\",\n \"0.6870511\",\n \"0.6845917\",\n \"0.68360114\",\n \"0.6835539\",\n \"0.68305475\",\n \"0.6827506\",\n \"0.6826174\",\n \"0.6823423\",\n \"0.68152744\",\n \"0.6798154\",\n \"0.6795719\",\n \"0.67901367\",\n \"0.67872876\",\n \"0.6786575\",\n \"0.6785015\",\n \"0.6781475\",\n \"0.67806506\",\n \"0.67775434\",\n \"0.67767215\",\n \"0.6775401\",\n \"0.677323\",\n \"0.6763524\",\n \"0.6757732\",\n \"0.6757732\",\n \"0.67530376\",\n \"0.67507786\",\n \"0.6727709\",\n \"0.6726219\",\n \"0.6724953\",\n \"0.67143553\",\n \"0.6709004\",\n \"0.6696241\",\n \"0.66946495\",\n \"0.6686085\",\n \"0.66778624\",\n \"0.66750365\",\n \"0.6663551\",\n \"0.6655125\",\n \"0.6652601\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7396111"},"document_rank":{"kind":"string","value":"8"}}},{"rowIdx":11281,"cells":{"query":{"kind":"string","value":"Returns list of IDs of tags for specified model name by (code, name) pair"},"document":{"kind":"string","value":"def get_tag_ids(self, cr, uid, model, code=None, name=None, context=None):\n assert bool(code) or bool(name), \"code or name must not be None! (code=%s;name=%s)\" % (code, name)\n tag_domain = [('model_id.model', '=', model)]\n if code is not None:\n tag_domain.append(('code', '=', code))\n if name is not None:\n tag_domain.append(('name', '=', name))\n return self.search(cr, uid, tag_domain, context=context)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _getTagIDs(self):\n paths = self._criteria.get('paths')\n if paths:\n store = getMainStore()\n return list(store.find(Tag.id, Tag.path.is_in(paths)))","def get_tag_ids(tag_names):\n\ttag_names = tuple(tag_names)\n\twith postgres, postgres.cursor() as cur:\n\t\tcur.execute(\"select id from mustard.tags where english_name in %s\", (tag_names,))\n\t\treturn [row[0] for row in cur]","def findTaggedServiceIds(self, name):\n pass;","def find_usefull_tags(tags, tagmodel, tag_count_vect):\n\n final_tags = []\n for tag in tags:\n if tag == None:\n continue\n else:\n tagpd = pd.Series(tag)\n tag_feature = tag_count_vect.transform(tagpd)\n result = tagmodel.predict(tag_feature)\n\n result = result.tolist() \n result = str(result)\n if result == '[1]':\n final_tags.append(tag)\n final_tags = list(dict.fromkeys(final_tags))\n return(final_tags)","def get_tags_list(*args, **kwargs):\n return Tag.objects.active()","def get_tags_list(*args, **kwargs):\n return Tag.objects.active()","def dataset_tags(connection):\n assert connection\n query = \"\"\"select * from tags()\"\"\"\n result = sqlio.read_sql_query(query, connection)\n return [item.strip() for item in result['name']], [tag_id.strip() for tag_id in result['tag_id']]","def tags():","def _model_tags(kwargs, key):\r\n if key not in kwargs:\r\n return []\r\n\r\n instance = kwargs[key]\r\n tags = [\r\n u'{}.{}:{}'.format(key, attr, getattr(instance, attr))\r\n for attr in getattr(instance, 'MODEL_TAGS', [])\r\n ]\r\n tags.append(u'model_class:{}'.format(instance.__class__.__name__))\r\n return tags","def get_tags(request):\n as_list = request.params.get('as_list')\n if as_list:\n return [\n tag.name\n for tag in Tag.query.all()\n ]\n else:\n return [\n {\n 'name': tag.name,\n 'id': tag.id\n }\n for tag in Tag.query.all()\n ]","def get_tags(self):\r\n\r\n\r\n\r\n #using database\r\n\r\n if self.using_database:\r\n aprint('GET TAGS')\r\n value_tuple = (notebookname,)\r\n db_cursor.execute(\"SELECT tag\"\r\n +\" FROM tags_to_keys\"\r\n +\" WHERE notebook=?;\",\r\n value_tuple)\r\n fetched = db_cursor.fetchall()\r\n if fetched:\r\n return {tag[0] for tag in fetched}\r\n\r\n return set()\r\n\r\n #using shelf\r\n\r\n return self.tag_dict.keys()","def get_tagname(tags, tagid):\n for tag in tags:\n if tag['id'] == tagid:\n return tag['name']","def tags(self) -> list[str]:\n _args: list[Arg] = []\n _ctx = self._select(\"tags\", _args)\n return _ctx.execute_sync(list[str])","def get_tag_names(language_code: str, tag_field: dict):\n tag_names = {}\n tag_names_default = _read_translation_file(\"en\", \"tag_names\")\n tag_names_lang = _read_translation_file(language_code, \"tag_names\")\n\n if tag_field:\n for tag in tag_field[\"tags\"]:\n if tag in tag_names_lang:\n tag_names[tag] = tag_names_lang[tag]\n else:\n tag_names[tag] = tag_names_default[tag]\n\n return tag_names","def prepare_tags(self, obj):\n return [tag.name for tag in obj.tags.all()]","def tag_mapping(sentences):\n tags = [[word[-1] for word in s] for s in sentences]\n dico = create_dico(tags)\n tag_to_id, id_to_tag = create_mapping(dico)\n print(\"Found %i unique named entity tags\" % len(dico))\n return dico, tag_to_id, id_to_tag","def _tags(self):\n retval = []\n for of in self.tagnames:\n retval.append([of, self.get_datatype(of), self.get(of)])\n return retval","def get_tag(tag_name, tag_list):\n for i in range(len(tag_list)):\n if tag_name == str(tag_list[i]):\n return tag_list[i]","def get_photo_tags(self, photo_id):\n\n query_string = '''\n select photo_tag.tag_name from photo\n join photo_tag on(photo_tag.photo_id=photo.photo_id)\n where photo.photo_id={}\n '''.format(photo_id)\n\n # so an array of tags would be ok\n tag_data = self.db.get_query_as_list(query_string)\n for tag in tag_data:\n # print(self.decode_tag(tag['tag_name']))\n\n tag['human_readable_tag'] = self.decode_tag(tag['tag_name'])\n\n # print(tag_data)\n\n return tag_data","def tag_mapping(sentences):\n tags = [[char[-1] for char in s] for s in sentences]\n dico = create_dico(tags)\n tag_to_id, id_to_tag = create_mapping(dico)\n print(\"Found %i unique named entity tags\" % len(dico))\n return dico, tag_to_id, id_to_tag","def get_tag_list(tags):\r\n from tagging.models import Tag\r\n if isinstance(tags, Tag):\r\n return [tags]\r\n elif isinstance(tags, QuerySet) and tags.model is Tag:\r\n return tags\r\n elif isinstance(tags, types.StringTypes):\r\n return Tag.objects.filter(name__in=parse_tag_input(tags))\r\n elif isinstance(tags, (types.ListType, types.TupleType)):\r\n if len(tags) == 0:\r\n return tags\r\n contents = set()\r\n for item in tags:\r\n if isinstance(item, types.StringTypes):\r\n contents.add('string')\r\n elif isinstance(item, Tag):\r\n contents.add('tag')\r\n elif isinstance(item, (types.IntType, types.LongType)):\r\n contents.add('int')\r\n if len(contents) == 1:\r\n if 'string' in contents:\r\n return Tag.objects.filter(name__in=[force_unicode(tag) \\\r\n for tag in tags])\r\n elif 'tag' in contents:\r\n return tags\r\n elif 'int' in contents:\r\n return Tag.objects.filter(id__in=tags)\r\n else:\r\n raise ValueError(_('If a list or tuple of tags is provided, they must all be tag names, Tag objects or Tag ids.'))\r\n else:\r\n raise ValueError(_('The tag input given was invalid.'))","def getTags(number=None):","def get_all_tagged(self,tag_name):\n return self.tag2elements[tag_name]","def get_all_id_and_tags(self):\n return self.database.select(self.tname,\n [self.primary_key, 'tags'])","def get_ids(self) -> List[str]:","def tag_mapping(data_path, data_type):\n with open(data_path+data_type+\"_labels.txt\", \"r\") as file1:\n tags = [line.split(\" \")[:-1] for line in file1.readlines()]\n dico = create_dico(tags)\n dico[model.START_TAG] = -1\n dico[model.STOP_TAG] = -2\n tag_to_id, id_to_tag = create_mapping(dico)\n print(\"Found %i unique named entity tags\" % len(dico))\n return dico, tag_to_id, id_to_tag","def get_tags(self, tags):\n tag_list = []\n for tag in tags:\n tag_list.append(tag[\"name\"])\n return tag_list","def get_post_tags(postid, posttags, tags):\n _tags = []\n _nametags = []\n for item in posttags:\n if item['post_id'] == postid:\n _tags.append(item['tag_id'])\n for tag in _tags:\n nametag = get_tagname(tags, tag)\n _nametags.append(nametag)\n return _nametags","def list_tags():\r\n tags = Tag.query.order_by(Tag.name).all()\r\n return render_template('tags.html', tags=tags)","def get_keys_for_tag(self,tag):\r\n\r\n #using database\r\n if self.using_database:\r\n value_tuple = (notebookname, tag,)\r\n db_cursor.execute(\"SELECT keyword\"\r\n +\" FROM tags_to_keys\"\r\n +\" WHERE notebook=?\"\r\n +\" AND tag=?;\",\r\n value_tuple)\r\n fetched = db_cursor.fetchall()\r\n if fetched:\r\n return {tag[0] for tag in fetched}\r\n\r\n return set()\r\n #using shelf\r\n if self.using_shelf:\r\n if self.tag_dict_contains(tag):\r\n return self.tag_dict[tag]\r\n return set()","def tag_names(self):\r\n return [tag.name for tag in self.get_tags()]","def name_get(self):\n result = []\n for product in self:\n if product.code and product.name:\n name = '[%s] %s'%(product.code, product.name)\n else: \n name = product.name\n \n result.append((product.id, name))\n \n return result","def get_ids(voc_path):\n ids = []\n print(\"voc\")\n\n files_images = glob.iglob(os.path.join(voc_path, \"*.JPEG\"))\n for x in files_images:\n name = os.path.splitext(os.path.basename(x))[0]\n ids.append(name)\n print(\"names: \", ids)\n return ids","def model_ids(self, protocol=None, groups=None):\n return [model.id for model in self.models(protocol, groups)]","def get_tag_choices(language_code: str = \"en\"):\n tag_choices = []\n tag_names_default = _read_translation_file(\"en\", \"tag_names\")\n tag_names_lang = _read_translation_file(language_code, \"tag_names\")\n\n for tag_key, tag_value in tag_names_default.items():\n lang_keys = tag_names_lang.keys()\n if tag_key in lang_keys:\n tag_tuple = (tag_key, tag_names_lang[tag_key])\n else:\n tag_tuple = (tag_key, tag_value)\n\n tag_choices.append(tag_tuple)\n\n tag_choices.sort(key=lambda a: a[1])\n\n TAG_CHOICES = tuple(tag_choices)\n\n return TAG_CHOICES","def tag_ids(self, convthread_id=None):\n if None == convthread_id:\n return [tag[0] for tag in self.dfs[\"tags\"][[\"tag_id\"]].values]\n else :\n df = self.dfs[\"convthreads_with_tags\"]\n tag_records = df[df.convthread_id == convthread_id]\n return tag_records[\"tag_id\"].values","def tag_strings(self):\n return [tag.tag_text for tag in self.tags.all()]","def list_tags(self, entry_name):\n return self.__datacatalog.list_tags(parent=entry_name)","def list_all_tags(self,obs):","def _get_ids_from_label(self, label):\r\n keys = self.list_keys()\r\n results = []\r\n for key in keys:\r\n if key['label'] == label:\r\n results.append(key['id'])\r\n return results","def tag_list(request):\r\n rdict = request.matchdict\r\n username = rdict.get(\"username\", None)\r\n if username:\r\n username = username.lower()\r\n\r\n tags_found = TagMgr.find(username=username)\r\n\r\n return {\r\n 'tag_list': tags_found,\r\n 'tag_count': len(tags_found),\r\n 'username': username,\r\n }","def get_asg_tag(tags, tag_name):\n result = {}\n for tag in tags:\n for key, val in tag.items():\n if val == tag_name:\n result = tag\n return result","def tags(self, tagmap=None):\n\n tags = self.find(\n 'guidle:classifications/'\n 'guidle:classification[@type=\"PRIMARY\"]/'\n 'guidle:tag'\n )\n tags = [tag.get('subcategoryName') or tag.get('name') for tag in tags]\n tags = set([tag for tag in tags if tag])\n if tagmap:\n return (\n {tagmap[tag] for tag in tags if tag in tagmap},\n tags - tagmap.keys()\n )\n return tags, set()","def list_tags():\n\n tags = Tag.query.all()\n return render_template('tags/list_tags.html', tags=tags)","def _list_vlans_by_name(self, name):\r\n results = self.list_vlans(name=name, mask='id')\r\n return [result['id'] for result in results]","def tag_cmd(context, json, name):\n store: Store = context.obj[\"store\"]\n LOG.info(\"Fetch tags\")\n tag_objs = store.get_tags()\n template = schema.TagSchema()\n result = []\n for tag_obj in tag_objs:\n if name and (tag_obj.name not in name):\n continue\n LOG.debug(\"Use tag %s\", tag_obj.name)\n result.append(template.dump(tag_obj))\n if not result:\n LOG.info(\"Could not find any of the specified tags [%s]\", \", \".join(name))\n return\n if json:\n click.echo(jsonlib.dumps(result))\n return\n console = Console()\n console.print(get_tags_table(result))","def tag_list(self, obj): # pylint: disable=no-self-use\n return u\", \".join(o.name for o in obj.tags.all())","def queryModelIDs(self):\n jobID = self.getJobID()\n modelCounterPairs = _clientJobsDB().modelsGetUpdateCounters(jobID)\n modelIDs = tuple(x[0] for x in modelCounterPairs)\n\n return modelIDs","def getNoteTagNames(self, authenticationToken, guid):\r\n pass","def tag_list(self, obj):\n logger.debug('Called Tag_list in admin: %s', self)\n return u\", \".join(o.name for o in obj.tags.all())","def create_tags(apps, _):\n type_model = apps.get_model(\"projects\", \"Type\")\n tag_model = apps.get_model(\"projects\", \"Tag\")\n\n # Tags which are a 1:1 migration\n global education_tag\n global covid_tag\n global innovative_tag\n global other_tag\n education_tag = tag_model(\n name=\"Computing Education\",\n description=\"Seeding inclusive computing education for the next generation \"\n \"and all computer-science training\",\n )\n covid_tag = tag_model(\n name=\"COVID\",\n description=\"Related to COVID-19\",\n )\n innovative_tag = tag_model(\n name=\"Innovative Application\", description=\"Applications for domain sciences\"\n )\n other_tag = tag_model(\n name=\"Other\",\n description=\"My project research area doesn’t fit in any of \"\n \"the predefined categories\",\n )\n\n tags = [\n education_tag,\n covid_tag,\n innovative_tag,\n other_tag,\n tag_model(\n name=\"Computer Architecture\",\n description=\"Designing computer systems optimized for high performance, \"\n \"energy efficiency, and scalability\",\n ),\n tag_model(\n name=\"Data Science\",\n description=\"Developing algorithms for managing and analyzing data at scale\",\n ),\n tag_model(\n name=\"Database Systems\",\n description=\"Designing systems for managing and storing data at scale\",\n ),\n tag_model(\n name=\"Human Computer Interaction\",\n description=\"Exploring the interfaces between people and technologies\",\n ),\n tag_model(\n name=\"AI and Machine Learning\",\n description=\"Foundations and applications of computer algorithms making \"\n \"data-centric models, predictions, and decisions\",\n ),\n tag_model(\n name=\"Networking\",\n description=\"Analysis, design, implementation, and use of local, \"\n \"wide-area, and mobile networks that link computers together\",\n ),\n tag_model(\n name=\"Programming Languages\",\n description=\"Devising new and better ways of programming the computers\",\n ),\n tag_model(\n name=\"Robotics\",\n description=\"Design, construction, operation, and use of robots\",\n ),\n tag_model(\n name=\"Scientific and High-Performance Computing\",\n description=\"Scientific discovery at the frontiers of computational \"\n \"performance, intelligence, and scale\",\n ),\n tag_model(\n name=\"Security and Privacy\",\n description=\"Understanding and defending against emerging threats in our \"\n \"increasingly computational world\",\n ),\n tag_model(\n name=\"Software Engineering\",\n description=\"Design, development, testing, and maintenance of \"\n \"software applications\",\n ),\n tag_model(\n name=\"Distributed Systems\",\n description=\"Harness the power of multiple computational units\",\n ),\n tag_model(\n name=\"Operating Systems\",\n description=\"Analysis, design, and implementation of operating systems\",\n ),\n tag_model(\n name=\"Storage Systems\",\n description=\"Capturing, managing, securing, and prioritizing data\",\n ),\n tag_model(\n name=\"Cloud Computing\",\n description=\"Delivering computing services over the Internet to offer \"\n \"faster innovation, flexible resources, and economies of scale\",\n ),\n tag_model(\n name=\"Edge Computing\",\n description=\"Bring applications closer to data sources such as IoT \"\n \"devices or local edge servers\",\n ),\n tag_model(\n name=\"Vision and Graphics\",\n description=\"Creating and analyzing data from the visual world, \"\n \"and visually understanding complex data\",\n ),\n tag_model(\n name=\"Theory of Computation\",\n description=\"Mathematical foundations of computation, including \"\n \"algorithm design, complexity and logic\",\n ),\n tag_model(\n name=\"Daypass\",\n description=\"Daypass project\",\n expose=False,\n ),\n ]\n\n tag_model.objects.bulk_create(tags)\n\n if type_model.objects.count() == 0:\n return\n covid_type = type_model.objects.get(name=\"COVID\")\n research_type = type_model.objects.get(name=\"CS Research\")\n education_type = type_model.objects.get(name=\"Education\")\n innovative_type = type_model.objects.get(name=\"Innovative Application\")\n\n # Gather the old tags. We have to remove the type model from the project model\n # to add the projects to the new tag model,\n # So all we do is collect them here, and then move them later.\n global old_covid_projects\n global old_research_projects\n global old_education_projects\n global old_innovative_projects\n old_covid_projects = list(covid_type.project_type.all())\n old_research_projects = list(research_type.project_type.all())\n old_education_projects = list(education_type.project_type.all())\n old_innovative_projects = list(innovative_type.project_type.all())","def getTag(self, inputs, tag):\n result = {}\n for into in inputs:\n for i in into:\n if i in self.sim.agents:\n agentTags = self.sim.agents[i].access[\"tags\"]\n if tag in agentTags:\n result[i] = agentTags[tag]\n return result","def get_tags(self, tag_name: str):\n return self.soup.find_all(tag_name)","def tags(self, uuid):\n return self._backend.tags(uuid)","def tags(self) -> List[str]:\n return self._db_data.tags","def intf_TAGQUERY(E):\n if not inc.TXT_or_LST_of_TXTs(E.The,1):\n print(\"Input Error: tag?\")\n print(intf_TAGQUERY.__doc__)\n return # Without doing much of anything.\n mytags= E.The.StackPop().val\n if type(mytags)==type(list()):\n #mytags= map(lambda x:x.val, mytags) # Should now be a list of TXTs.\n mytags= [x.val for x in mytags] # Should now be a list of TXTs.\n else:\n mytags= [ mytags ] # Also a (1 item) list of ints.\n qualifying_ents= list()\n for myeid in MMEL.El.keys():\n alltagshere= True # Assume they're here until one is not found.\n for mytag in mytags:\n #print(\"Searching entity #%d for tag ''%s''\" % (myeid,mytag))\n if not MMEL.El[myeid].has_tag(mytag):\n alltagshere= False\n break\n if alltagshere:\n qualifying_ents.append( objectifier.StackOB_VAL(myeid) )\n E.The.StackPush( objectifier.StackOB_LST(qualifying_ents) )","def getTagList(self):\n if not self.proxy:\n self.proxy = self.session.service(\"ALBehaviorManager\")\n return self.proxy.getTagList()","def getIDs():","def get_tags():\n\treturn jsonify(tags=[i.serialise for i in Tag.query.all()])","def get(self, label_sn):\n tags = self.list()\n return [\n tag\n for tag\n in tags\n if str(label_sn) in tag.get('args', {}).values()\n ]","def list_tags(filename):\n storeapps = APP.config[\"storage\"]\n filename = filename.encode(\"utf-8\")\n\n try:\n application = list(nativeapps.io.ls(storeapps, r\".*\" + filename + \"$\"))[0]\n meta_path = os.path.join(os.path.dirname(application), \"metadata.json\")\n metadata = json.loads(nativeapps.io.readfile(meta_path))\n tags = metadata.get(\"tags\", [])\n return flask.jsonify(tags)\n except IndexError:\n return \"Unknown application: %s\" % (application), 404","def tags_2_vec(tags, w2v_model=None):\n if len(tags) == 0 or len([tag for tag in tags if tag in w2v_model]) == 0:\n return np.zeros(200)\n else:\n output = np.sum([w2v_model[tag] for tag in tags if tag in w2v_model], axis=0)\n return output / np.linalg.norm(output)","def get_tags(self, tags, filename):\n return self.get_tags_batch(tags, [filename])[0]","def get_entities(tags):\n pass","def test_get_device_tags_by_id(self):\n pass","def get_tag_index(self) -> List[str]:\n path = os.path.join(self.directory_path, \"__tags.json\")\n if not os.path.exists(path):\n return list()\n try:\n with open(path) as f:\n return json.load(f)\n except json.decoder.JSONDecodeError:\n print(f\"Could not get tag index. Check file: {path}\")","def name_key(tags):\r\n if 'name' not in tags:\r\n return None\r\n \r\n if not tags['name']:\r\n return None\r\n \r\n return (tags['name'], )","def get_tag_indices(tags):\n tag_counts = count_tags(tags);\n tag_names = tag_counts.keys()\n return {name : index for (index, name) in enumerate(tag_names)}","def get_by_tag(cls, tag):\n out = []\n \n tags = Tag.expand_implied_by([tag])\n \n for t in tags:\n results = cls.objects.filter(owner=tag.owner, tags=t)\n \n for b in results:\n if b not in out:\n out.append(b)\n \n return out","def list(self):\n\n\t\treturn self._list(\"/tag\", \"tag\")","def tag(self,name):\n return self._tags.get(name,None)","def files_of(mage_id):\n\tcursor.execute('select tag.id, tag.name from tag, tagging as is_file_tagging, tagging where tag.id = tagging.child_id and tag.id = is_file_tagging.child_id and is_file_tagging.parent_id = ? and tagging.parent_id = ?', (settings['file'],mage_id))\n\treturn cursor.fetchall()","def getTagList(tags):\n tags = tags[1:len(tags)-1]\n return tags.split('><')","def get_repository_tags(repository_name):\n tags_query = \"SELECT * FROM release_tag where repository=?\"\n return dbutils.execute_query(tags_query, (repository_name,), DATABASE_FILE)","def get_uuids_from_tag(dictionary, tag):\n return dictionary[tag]","def _get_tags(tag_string):\n print \"inside get_tags, string is: \", tag_string\n tag_objects_list = []\n # remove all whitespaces\n tag_string_cleaned = tag_string.replace(\" \", \"\")\n tokens = tag_string_cleaned.split(',')\n for tok in tokens:\n try:\n tag_object = Tag.objects.get(tag_name=tok)\n except Tag.DoesNotExist:\n tag_object = Tag(tag_name=tok)\n tag_object.save()\n if tag_object not in tag_objects_list:\n tag_objects_list.append(tag_object)\n return tag_objects_list","def get_pred_ids(predictions):\n le_classes = ['Emotet', 'Mirai', 'Zeus'] \n malwares_dict = {'Emotet': 1, 'Mirai': 2, 'Zeus': 3}\n predicted_ids = []\n \n for idx in predictions:\n pred_name = le_classes[idx]\n pred_id = malwares_dict[pred_name]\n predicted_ids.append(pred_id)\n \n return predicted_ids","def _list_input_regressor_code(self, model_code):\n regressor_code = [\n code for code in model_code.ravel() if (code != 0) and (str(code)[0] != \"1\")\n ]\n return np.asarray(regressor_code)","def tags(self) -> Mapping[str, str]:\n return pulumi.get(self, \"tags\")","def tags(self) -> Mapping[str, str]:\n return pulumi.get(self, \"tags\")","def tags(self) -> Mapping[str, str]:\n return pulumi.get(self, \"tags\")","def tags(self) -> Mapping[str, str]:\n return pulumi.get(self, \"tags\")","def list(self):\n return self._post(\n request='list',\n uri=ApiUri.TAGS.value,\n ).get('tags')","def get_tags(self) -> Dict:\n return self.orthanc.get_instance_tags(self.identifier)","def getGUIName( self, modelName ):\r\n # For comaptibility with older AToM3 models\r\n try:\r\n exec 'import ' + modelName\r\n return [modelName]\r\n except:\r\n pass\r\n \r\n # This is the most common case (must do second for compatibility)\r\n try:\r\n exec 'import ' + modelName + '_META'\r\n return [modelName + '_META']\r\n except:\r\n pass\r\n \r\n # Ugh, crapola!\r\n print 'AToM3 was unable to find the buttons model (META file) for ' \\\r\n + modelName\r\n return []","def GetRoomIndex( data) :\n labels = ['etage', 'roomID']\n columns = data.columns\n\n result =[ l for l in labels if l in columns ]\n if not result : raise RuntimeError( 'No tagging possible for a room' )\n return result","def getTags(self,):\n\t\treturn self.tags;","def named_entities(self) -> List[str]:","def tags(self) -> Sequence[str]:\n return pulumi.get(self, \"tags\")","def get(self, currency, entity):\n check_inputs(currency=currency, entity=entity)\n tags = entitiesDAO.list_entity_tags(currency, entity)\n return tags","def get_tags_list(url, auth_token, repo_name):\n response, _ = get_response(url + '/v2/' + repo_name + '/tags/list',\n auth_token)\n result = response.get('tags', [])\n return result","def getNames():\n imgs = Image.objects.raw({})\n ans = []\n for img in imgs:\n ans.append(img.name)\n ans.sort()\n return ans","def build_taglist(tags):\n taglist = []\n for tag in tags:\n taglist.append(tag['value'].lower())\n return taglist","def get_pipeline_custom_tags(module_name, args, tags):\n try:\n _imports = __import__(module_name, fromlist=[\"get_pipeline_custom_tags\"])\n kwargs = convert_struct(args)\n return _imports.get_pipeline_custom_tags(\n tags, kwargs[\"region\"], kwargs[\"sagemaker_project_arn\"]\n )\n except Exception as e:\n print(f\"Error getting project tags: {e}\")\n return tags","def test_get_device_tags_by_id1(self):\n pass","def tags(catalog,lista,tag):\n final=lt.newList(datastructure='ARRAY_LIST')\n i=it.newIterator(lista)\n while it.hasNext(i):\n vid=it.next(i)\n if tag in vid['tags']:\n lt.addLast(final,vid)\n return final","def get_tag_index(self, m):\n return self.tags.index(m)","def get_tag_names(**kwargs):\n\n if kwargs:\n if 'key' and 'values' in kwargs:\n names = Tags.get_filtered_names_in(kwargs['key'], kwargs['values'])\n\n else:\n names = Tags.get_filtered_names(**kwargs)\n\n else:\n names = Tags.get_names()\n\n return names","def _identify_combos(model, combo_tags):\n \n # Identify which load combinations to evaluate\n if combo_tags is None:\n combo_list = model.LoadCombos.values()\n else:\n combo_list = []\n for combo in model.LoadCombos.values():\n if any(tag in combo.combo_tags for tag in combo_tags):\n combo_list.append(combo)\n \n return combo_list","def search_tag(input) :\n j = _jpdb()\n _input = _process_search_input(input)\n if not _input : return None\n f = j.base_format\n q = Query().select(f.tags, f.tags.id, f.tags.name)\n q.where().equal(f.tags.name, _input)\n tag_data = j.executeQuery(q)\n\n if tag_data:\n tag_id, tag_name = tag_data[0]\n examples = _create_examples(j.list_word_by_tag, tag_name)\n return SelectorResult('tag', tag_id, tag_name, *examples)"],"string":"[\n \"def _getTagIDs(self):\\n paths = self._criteria.get('paths')\\n if paths:\\n store = getMainStore()\\n return list(store.find(Tag.id, Tag.path.is_in(paths)))\",\n \"def get_tag_ids(tag_names):\\n\\ttag_names = tuple(tag_names)\\n\\twith postgres, postgres.cursor() as cur:\\n\\t\\tcur.execute(\\\"select id from mustard.tags where english_name in %s\\\", (tag_names,))\\n\\t\\treturn [row[0] for row in cur]\",\n \"def findTaggedServiceIds(self, name):\\n pass;\",\n \"def find_usefull_tags(tags, tagmodel, tag_count_vect):\\n\\n final_tags = []\\n for tag in tags:\\n if tag == None:\\n continue\\n else:\\n tagpd = pd.Series(tag)\\n tag_feature = tag_count_vect.transform(tagpd)\\n result = tagmodel.predict(tag_feature)\\n\\n result = result.tolist() \\n result = str(result)\\n if result == '[1]':\\n final_tags.append(tag)\\n final_tags = list(dict.fromkeys(final_tags))\\n return(final_tags)\",\n \"def get_tags_list(*args, **kwargs):\\n return Tag.objects.active()\",\n \"def get_tags_list(*args, **kwargs):\\n return Tag.objects.active()\",\n \"def dataset_tags(connection):\\n assert connection\\n query = \\\"\\\"\\\"select * from tags()\\\"\\\"\\\"\\n result = sqlio.read_sql_query(query, connection)\\n return [item.strip() for item in result['name']], [tag_id.strip() for tag_id in result['tag_id']]\",\n \"def tags():\",\n \"def _model_tags(kwargs, key):\\r\\n if key not in kwargs:\\r\\n return []\\r\\n\\r\\n instance = kwargs[key]\\r\\n tags = [\\r\\n u'{}.{}:{}'.format(key, attr, getattr(instance, attr))\\r\\n for attr in getattr(instance, 'MODEL_TAGS', [])\\r\\n ]\\r\\n tags.append(u'model_class:{}'.format(instance.__class__.__name__))\\r\\n return tags\",\n \"def get_tags(request):\\n as_list = request.params.get('as_list')\\n if as_list:\\n return [\\n tag.name\\n for tag in Tag.query.all()\\n ]\\n else:\\n return [\\n {\\n 'name': tag.name,\\n 'id': tag.id\\n }\\n for tag in Tag.query.all()\\n ]\",\n \"def get_tags(self):\\r\\n\\r\\n\\r\\n\\r\\n #using database\\r\\n\\r\\n if self.using_database:\\r\\n aprint('GET TAGS')\\r\\n value_tuple = (notebookname,)\\r\\n db_cursor.execute(\\\"SELECT tag\\\"\\r\\n +\\\" FROM tags_to_keys\\\"\\r\\n +\\\" WHERE notebook=?;\\\",\\r\\n value_tuple)\\r\\n fetched = db_cursor.fetchall()\\r\\n if fetched:\\r\\n return {tag[0] for tag in fetched}\\r\\n\\r\\n return set()\\r\\n\\r\\n #using shelf\\r\\n\\r\\n return self.tag_dict.keys()\",\n \"def get_tagname(tags, tagid):\\n for tag in tags:\\n if tag['id'] == tagid:\\n return tag['name']\",\n \"def tags(self) -> list[str]:\\n _args: list[Arg] = []\\n _ctx = self._select(\\\"tags\\\", _args)\\n return _ctx.execute_sync(list[str])\",\n \"def get_tag_names(language_code: str, tag_field: dict):\\n tag_names = {}\\n tag_names_default = _read_translation_file(\\\"en\\\", \\\"tag_names\\\")\\n tag_names_lang = _read_translation_file(language_code, \\\"tag_names\\\")\\n\\n if tag_field:\\n for tag in tag_field[\\\"tags\\\"]:\\n if tag in tag_names_lang:\\n tag_names[tag] = tag_names_lang[tag]\\n else:\\n tag_names[tag] = tag_names_default[tag]\\n\\n return tag_names\",\n \"def prepare_tags(self, obj):\\n return [tag.name for tag in obj.tags.all()]\",\n \"def tag_mapping(sentences):\\n tags = [[word[-1] for word in s] for s in sentences]\\n dico = create_dico(tags)\\n tag_to_id, id_to_tag = create_mapping(dico)\\n print(\\\"Found %i unique named entity tags\\\" % len(dico))\\n return dico, tag_to_id, id_to_tag\",\n \"def _tags(self):\\n retval = []\\n for of in self.tagnames:\\n retval.append([of, self.get_datatype(of), self.get(of)])\\n return retval\",\n \"def get_tag(tag_name, tag_list):\\n for i in range(len(tag_list)):\\n if tag_name == str(tag_list[i]):\\n return tag_list[i]\",\n \"def get_photo_tags(self, photo_id):\\n\\n query_string = '''\\n select photo_tag.tag_name from photo\\n join photo_tag on(photo_tag.photo_id=photo.photo_id)\\n where photo.photo_id={}\\n '''.format(photo_id)\\n\\n # so an array of tags would be ok\\n tag_data = self.db.get_query_as_list(query_string)\\n for tag in tag_data:\\n # print(self.decode_tag(tag['tag_name']))\\n\\n tag['human_readable_tag'] = self.decode_tag(tag['tag_name'])\\n\\n # print(tag_data)\\n\\n return tag_data\",\n \"def tag_mapping(sentences):\\n tags = [[char[-1] for char in s] for s in sentences]\\n dico = create_dico(tags)\\n tag_to_id, id_to_tag = create_mapping(dico)\\n print(\\\"Found %i unique named entity tags\\\" % len(dico))\\n return dico, tag_to_id, id_to_tag\",\n \"def get_tag_list(tags):\\r\\n from tagging.models import Tag\\r\\n if isinstance(tags, Tag):\\r\\n return [tags]\\r\\n elif isinstance(tags, QuerySet) and tags.model is Tag:\\r\\n return tags\\r\\n elif isinstance(tags, types.StringTypes):\\r\\n return Tag.objects.filter(name__in=parse_tag_input(tags))\\r\\n elif isinstance(tags, (types.ListType, types.TupleType)):\\r\\n if len(tags) == 0:\\r\\n return tags\\r\\n contents = set()\\r\\n for item in tags:\\r\\n if isinstance(item, types.StringTypes):\\r\\n contents.add('string')\\r\\n elif isinstance(item, Tag):\\r\\n contents.add('tag')\\r\\n elif isinstance(item, (types.IntType, types.LongType)):\\r\\n contents.add('int')\\r\\n if len(contents) == 1:\\r\\n if 'string' in contents:\\r\\n return Tag.objects.filter(name__in=[force_unicode(tag) \\\\\\r\\n for tag in tags])\\r\\n elif 'tag' in contents:\\r\\n return tags\\r\\n elif 'int' in contents:\\r\\n return Tag.objects.filter(id__in=tags)\\r\\n else:\\r\\n raise ValueError(_('If a list or tuple of tags is provided, they must all be tag names, Tag objects or Tag ids.'))\\r\\n else:\\r\\n raise ValueError(_('The tag input given was invalid.'))\",\n \"def getTags(number=None):\",\n \"def get_all_tagged(self,tag_name):\\n return self.tag2elements[tag_name]\",\n \"def get_all_id_and_tags(self):\\n return self.database.select(self.tname,\\n [self.primary_key, 'tags'])\",\n \"def get_ids(self) -> List[str]:\",\n \"def tag_mapping(data_path, data_type):\\n with open(data_path+data_type+\\\"_labels.txt\\\", \\\"r\\\") as file1:\\n tags = [line.split(\\\" \\\")[:-1] for line in file1.readlines()]\\n dico = create_dico(tags)\\n dico[model.START_TAG] = -1\\n dico[model.STOP_TAG] = -2\\n tag_to_id, id_to_tag = create_mapping(dico)\\n print(\\\"Found %i unique named entity tags\\\" % len(dico))\\n return dico, tag_to_id, id_to_tag\",\n \"def get_tags(self, tags):\\n tag_list = []\\n for tag in tags:\\n tag_list.append(tag[\\\"name\\\"])\\n return tag_list\",\n \"def get_post_tags(postid, posttags, tags):\\n _tags = []\\n _nametags = []\\n for item in posttags:\\n if item['post_id'] == postid:\\n _tags.append(item['tag_id'])\\n for tag in _tags:\\n nametag = get_tagname(tags, tag)\\n _nametags.append(nametag)\\n return _nametags\",\n \"def list_tags():\\r\\n tags = Tag.query.order_by(Tag.name).all()\\r\\n return render_template('tags.html', tags=tags)\",\n \"def get_keys_for_tag(self,tag):\\r\\n\\r\\n #using database\\r\\n if self.using_database:\\r\\n value_tuple = (notebookname, tag,)\\r\\n db_cursor.execute(\\\"SELECT keyword\\\"\\r\\n +\\\" FROM tags_to_keys\\\"\\r\\n +\\\" WHERE notebook=?\\\"\\r\\n +\\\" AND tag=?;\\\",\\r\\n value_tuple)\\r\\n fetched = db_cursor.fetchall()\\r\\n if fetched:\\r\\n return {tag[0] for tag in fetched}\\r\\n\\r\\n return set()\\r\\n #using shelf\\r\\n if self.using_shelf:\\r\\n if self.tag_dict_contains(tag):\\r\\n return self.tag_dict[tag]\\r\\n return set()\",\n \"def tag_names(self):\\r\\n return [tag.name for tag in self.get_tags()]\",\n \"def name_get(self):\\n result = []\\n for product in self:\\n if product.code and product.name:\\n name = '[%s] %s'%(product.code, product.name)\\n else: \\n name = product.name\\n \\n result.append((product.id, name))\\n \\n return result\",\n \"def get_ids(voc_path):\\n ids = []\\n print(\\\"voc\\\")\\n\\n files_images = glob.iglob(os.path.join(voc_path, \\\"*.JPEG\\\"))\\n for x in files_images:\\n name = os.path.splitext(os.path.basename(x))[0]\\n ids.append(name)\\n print(\\\"names: \\\", ids)\\n return ids\",\n \"def model_ids(self, protocol=None, groups=None):\\n return [model.id for model in self.models(protocol, groups)]\",\n \"def get_tag_choices(language_code: str = \\\"en\\\"):\\n tag_choices = []\\n tag_names_default = _read_translation_file(\\\"en\\\", \\\"tag_names\\\")\\n tag_names_lang = _read_translation_file(language_code, \\\"tag_names\\\")\\n\\n for tag_key, tag_value in tag_names_default.items():\\n lang_keys = tag_names_lang.keys()\\n if tag_key in lang_keys:\\n tag_tuple = (tag_key, tag_names_lang[tag_key])\\n else:\\n tag_tuple = (tag_key, tag_value)\\n\\n tag_choices.append(tag_tuple)\\n\\n tag_choices.sort(key=lambda a: a[1])\\n\\n TAG_CHOICES = tuple(tag_choices)\\n\\n return TAG_CHOICES\",\n \"def tag_ids(self, convthread_id=None):\\n if None == convthread_id:\\n return [tag[0] for tag in self.dfs[\\\"tags\\\"][[\\\"tag_id\\\"]].values]\\n else :\\n df = self.dfs[\\\"convthreads_with_tags\\\"]\\n tag_records = df[df.convthread_id == convthread_id]\\n return tag_records[\\\"tag_id\\\"].values\",\n \"def tag_strings(self):\\n return [tag.tag_text for tag in self.tags.all()]\",\n \"def list_tags(self, entry_name):\\n return self.__datacatalog.list_tags(parent=entry_name)\",\n \"def list_all_tags(self,obs):\",\n \"def _get_ids_from_label(self, label):\\r\\n keys = self.list_keys()\\r\\n results = []\\r\\n for key in keys:\\r\\n if key['label'] == label:\\r\\n results.append(key['id'])\\r\\n return results\",\n \"def tag_list(request):\\r\\n rdict = request.matchdict\\r\\n username = rdict.get(\\\"username\\\", None)\\r\\n if username:\\r\\n username = username.lower()\\r\\n\\r\\n tags_found = TagMgr.find(username=username)\\r\\n\\r\\n return {\\r\\n 'tag_list': tags_found,\\r\\n 'tag_count': len(tags_found),\\r\\n 'username': username,\\r\\n }\",\n \"def get_asg_tag(tags, tag_name):\\n result = {}\\n for tag in tags:\\n for key, val in tag.items():\\n if val == tag_name:\\n result = tag\\n return result\",\n \"def tags(self, tagmap=None):\\n\\n tags = self.find(\\n 'guidle:classifications/'\\n 'guidle:classification[@type=\\\"PRIMARY\\\"]/'\\n 'guidle:tag'\\n )\\n tags = [tag.get('subcategoryName') or tag.get('name') for tag in tags]\\n tags = set([tag for tag in tags if tag])\\n if tagmap:\\n return (\\n {tagmap[tag] for tag in tags if tag in tagmap},\\n tags - tagmap.keys()\\n )\\n return tags, set()\",\n \"def list_tags():\\n\\n tags = Tag.query.all()\\n return render_template('tags/list_tags.html', tags=tags)\",\n \"def _list_vlans_by_name(self, name):\\r\\n results = self.list_vlans(name=name, mask='id')\\r\\n return [result['id'] for result in results]\",\n \"def tag_cmd(context, json, name):\\n store: Store = context.obj[\\\"store\\\"]\\n LOG.info(\\\"Fetch tags\\\")\\n tag_objs = store.get_tags()\\n template = schema.TagSchema()\\n result = []\\n for tag_obj in tag_objs:\\n if name and (tag_obj.name not in name):\\n continue\\n LOG.debug(\\\"Use tag %s\\\", tag_obj.name)\\n result.append(template.dump(tag_obj))\\n if not result:\\n LOG.info(\\\"Could not find any of the specified tags [%s]\\\", \\\", \\\".join(name))\\n return\\n if json:\\n click.echo(jsonlib.dumps(result))\\n return\\n console = Console()\\n console.print(get_tags_table(result))\",\n \"def tag_list(self, obj): # pylint: disable=no-self-use\\n return u\\\", \\\".join(o.name for o in obj.tags.all())\",\n \"def queryModelIDs(self):\\n jobID = self.getJobID()\\n modelCounterPairs = _clientJobsDB().modelsGetUpdateCounters(jobID)\\n modelIDs = tuple(x[0] for x in modelCounterPairs)\\n\\n return modelIDs\",\n \"def getNoteTagNames(self, authenticationToken, guid):\\r\\n pass\",\n \"def tag_list(self, obj):\\n logger.debug('Called Tag_list in admin: %s', self)\\n return u\\\", \\\".join(o.name for o in obj.tags.all())\",\n \"def create_tags(apps, _):\\n type_model = apps.get_model(\\\"projects\\\", \\\"Type\\\")\\n tag_model = apps.get_model(\\\"projects\\\", \\\"Tag\\\")\\n\\n # Tags which are a 1:1 migration\\n global education_tag\\n global covid_tag\\n global innovative_tag\\n global other_tag\\n education_tag = tag_model(\\n name=\\\"Computing Education\\\",\\n description=\\\"Seeding inclusive computing education for the next generation \\\"\\n \\\"and all computer-science training\\\",\\n )\\n covid_tag = tag_model(\\n name=\\\"COVID\\\",\\n description=\\\"Related to COVID-19\\\",\\n )\\n innovative_tag = tag_model(\\n name=\\\"Innovative Application\\\", description=\\\"Applications for domain sciences\\\"\\n )\\n other_tag = tag_model(\\n name=\\\"Other\\\",\\n description=\\\"My project research area doesn’t fit in any of \\\"\\n \\\"the predefined categories\\\",\\n )\\n\\n tags = [\\n education_tag,\\n covid_tag,\\n innovative_tag,\\n other_tag,\\n tag_model(\\n name=\\\"Computer Architecture\\\",\\n description=\\\"Designing computer systems optimized for high performance, \\\"\\n \\\"energy efficiency, and scalability\\\",\\n ),\\n tag_model(\\n name=\\\"Data Science\\\",\\n description=\\\"Developing algorithms for managing and analyzing data at scale\\\",\\n ),\\n tag_model(\\n name=\\\"Database Systems\\\",\\n description=\\\"Designing systems for managing and storing data at scale\\\",\\n ),\\n tag_model(\\n name=\\\"Human Computer Interaction\\\",\\n description=\\\"Exploring the interfaces between people and technologies\\\",\\n ),\\n tag_model(\\n name=\\\"AI and Machine Learning\\\",\\n description=\\\"Foundations and applications of computer algorithms making \\\"\\n \\\"data-centric models, predictions, and decisions\\\",\\n ),\\n tag_model(\\n name=\\\"Networking\\\",\\n description=\\\"Analysis, design, implementation, and use of local, \\\"\\n \\\"wide-area, and mobile networks that link computers together\\\",\\n ),\\n tag_model(\\n name=\\\"Programming Languages\\\",\\n description=\\\"Devising new and better ways of programming the computers\\\",\\n ),\\n tag_model(\\n name=\\\"Robotics\\\",\\n description=\\\"Design, construction, operation, and use of robots\\\",\\n ),\\n tag_model(\\n name=\\\"Scientific and High-Performance Computing\\\",\\n description=\\\"Scientific discovery at the frontiers of computational \\\"\\n \\\"performance, intelligence, and scale\\\",\\n ),\\n tag_model(\\n name=\\\"Security and Privacy\\\",\\n description=\\\"Understanding and defending against emerging threats in our \\\"\\n \\\"increasingly computational world\\\",\\n ),\\n tag_model(\\n name=\\\"Software Engineering\\\",\\n description=\\\"Design, development, testing, and maintenance of \\\"\\n \\\"software applications\\\",\\n ),\\n tag_model(\\n name=\\\"Distributed Systems\\\",\\n description=\\\"Harness the power of multiple computational units\\\",\\n ),\\n tag_model(\\n name=\\\"Operating Systems\\\",\\n description=\\\"Analysis, design, and implementation of operating systems\\\",\\n ),\\n tag_model(\\n name=\\\"Storage Systems\\\",\\n description=\\\"Capturing, managing, securing, and prioritizing data\\\",\\n ),\\n tag_model(\\n name=\\\"Cloud Computing\\\",\\n description=\\\"Delivering computing services over the Internet to offer \\\"\\n \\\"faster innovation, flexible resources, and economies of scale\\\",\\n ),\\n tag_model(\\n name=\\\"Edge Computing\\\",\\n description=\\\"Bring applications closer to data sources such as IoT \\\"\\n \\\"devices or local edge servers\\\",\\n ),\\n tag_model(\\n name=\\\"Vision and Graphics\\\",\\n description=\\\"Creating and analyzing data from the visual world, \\\"\\n \\\"and visually understanding complex data\\\",\\n ),\\n tag_model(\\n name=\\\"Theory of Computation\\\",\\n description=\\\"Mathematical foundations of computation, including \\\"\\n \\\"algorithm design, complexity and logic\\\",\\n ),\\n tag_model(\\n name=\\\"Daypass\\\",\\n description=\\\"Daypass project\\\",\\n expose=False,\\n ),\\n ]\\n\\n tag_model.objects.bulk_create(tags)\\n\\n if type_model.objects.count() == 0:\\n return\\n covid_type = type_model.objects.get(name=\\\"COVID\\\")\\n research_type = type_model.objects.get(name=\\\"CS Research\\\")\\n education_type = type_model.objects.get(name=\\\"Education\\\")\\n innovative_type = type_model.objects.get(name=\\\"Innovative Application\\\")\\n\\n # Gather the old tags. We have to remove the type model from the project model\\n # to add the projects to the new tag model,\\n # So all we do is collect them here, and then move them later.\\n global old_covid_projects\\n global old_research_projects\\n global old_education_projects\\n global old_innovative_projects\\n old_covid_projects = list(covid_type.project_type.all())\\n old_research_projects = list(research_type.project_type.all())\\n old_education_projects = list(education_type.project_type.all())\\n old_innovative_projects = list(innovative_type.project_type.all())\",\n \"def getTag(self, inputs, tag):\\n result = {}\\n for into in inputs:\\n for i in into:\\n if i in self.sim.agents:\\n agentTags = self.sim.agents[i].access[\\\"tags\\\"]\\n if tag in agentTags:\\n result[i] = agentTags[tag]\\n return result\",\n \"def get_tags(self, tag_name: str):\\n return self.soup.find_all(tag_name)\",\n \"def tags(self, uuid):\\n return self._backend.tags(uuid)\",\n \"def tags(self) -> List[str]:\\n return self._db_data.tags\",\n \"def intf_TAGQUERY(E):\\n if not inc.TXT_or_LST_of_TXTs(E.The,1):\\n print(\\\"Input Error: tag?\\\")\\n print(intf_TAGQUERY.__doc__)\\n return # Without doing much of anything.\\n mytags= E.The.StackPop().val\\n if type(mytags)==type(list()):\\n #mytags= map(lambda x:x.val, mytags) # Should now be a list of TXTs.\\n mytags= [x.val for x in mytags] # Should now be a list of TXTs.\\n else:\\n mytags= [ mytags ] # Also a (1 item) list of ints.\\n qualifying_ents= list()\\n for myeid in MMEL.El.keys():\\n alltagshere= True # Assume they're here until one is not found.\\n for mytag in mytags:\\n #print(\\\"Searching entity #%d for tag ''%s''\\\" % (myeid,mytag))\\n if not MMEL.El[myeid].has_tag(mytag):\\n alltagshere= False\\n break\\n if alltagshere:\\n qualifying_ents.append( objectifier.StackOB_VAL(myeid) )\\n E.The.StackPush( objectifier.StackOB_LST(qualifying_ents) )\",\n \"def getTagList(self):\\n if not self.proxy:\\n self.proxy = self.session.service(\\\"ALBehaviorManager\\\")\\n return self.proxy.getTagList()\",\n \"def getIDs():\",\n \"def get_tags():\\n\\treturn jsonify(tags=[i.serialise for i in Tag.query.all()])\",\n \"def get(self, label_sn):\\n tags = self.list()\\n return [\\n tag\\n for tag\\n in tags\\n if str(label_sn) in tag.get('args', {}).values()\\n ]\",\n \"def list_tags(filename):\\n storeapps = APP.config[\\\"storage\\\"]\\n filename = filename.encode(\\\"utf-8\\\")\\n\\n try:\\n application = list(nativeapps.io.ls(storeapps, r\\\".*\\\" + filename + \\\"$\\\"))[0]\\n meta_path = os.path.join(os.path.dirname(application), \\\"metadata.json\\\")\\n metadata = json.loads(nativeapps.io.readfile(meta_path))\\n tags = metadata.get(\\\"tags\\\", [])\\n return flask.jsonify(tags)\\n except IndexError:\\n return \\\"Unknown application: %s\\\" % (application), 404\",\n \"def tags_2_vec(tags, w2v_model=None):\\n if len(tags) == 0 or len([tag for tag in tags if tag in w2v_model]) == 0:\\n return np.zeros(200)\\n else:\\n output = np.sum([w2v_model[tag] for tag in tags if tag in w2v_model], axis=0)\\n return output / np.linalg.norm(output)\",\n \"def get_tags(self, tags, filename):\\n return self.get_tags_batch(tags, [filename])[0]\",\n \"def get_entities(tags):\\n pass\",\n \"def test_get_device_tags_by_id(self):\\n pass\",\n \"def get_tag_index(self) -> List[str]:\\n path = os.path.join(self.directory_path, \\\"__tags.json\\\")\\n if not os.path.exists(path):\\n return list()\\n try:\\n with open(path) as f:\\n return json.load(f)\\n except json.decoder.JSONDecodeError:\\n print(f\\\"Could not get tag index. Check file: {path}\\\")\",\n \"def name_key(tags):\\r\\n if 'name' not in tags:\\r\\n return None\\r\\n \\r\\n if not tags['name']:\\r\\n return None\\r\\n \\r\\n return (tags['name'], )\",\n \"def get_tag_indices(tags):\\n tag_counts = count_tags(tags);\\n tag_names = tag_counts.keys()\\n return {name : index for (index, name) in enumerate(tag_names)}\",\n \"def get_by_tag(cls, tag):\\n out = []\\n \\n tags = Tag.expand_implied_by([tag])\\n \\n for t in tags:\\n results = cls.objects.filter(owner=tag.owner, tags=t)\\n \\n for b in results:\\n if b not in out:\\n out.append(b)\\n \\n return out\",\n \"def list(self):\\n\\n\\t\\treturn self._list(\\\"/tag\\\", \\\"tag\\\")\",\n \"def tag(self,name):\\n return self._tags.get(name,None)\",\n \"def files_of(mage_id):\\n\\tcursor.execute('select tag.id, tag.name from tag, tagging as is_file_tagging, tagging where tag.id = tagging.child_id and tag.id = is_file_tagging.child_id and is_file_tagging.parent_id = ? and tagging.parent_id = ?', (settings['file'],mage_id))\\n\\treturn cursor.fetchall()\",\n \"def getTagList(tags):\\n tags = tags[1:len(tags)-1]\\n return tags.split('><')\",\n \"def get_repository_tags(repository_name):\\n tags_query = \\\"SELECT * FROM release_tag where repository=?\\\"\\n return dbutils.execute_query(tags_query, (repository_name,), DATABASE_FILE)\",\n \"def get_uuids_from_tag(dictionary, tag):\\n return dictionary[tag]\",\n \"def _get_tags(tag_string):\\n print \\\"inside get_tags, string is: \\\", tag_string\\n tag_objects_list = []\\n # remove all whitespaces\\n tag_string_cleaned = tag_string.replace(\\\" \\\", \\\"\\\")\\n tokens = tag_string_cleaned.split(',')\\n for tok in tokens:\\n try:\\n tag_object = Tag.objects.get(tag_name=tok)\\n except Tag.DoesNotExist:\\n tag_object = Tag(tag_name=tok)\\n tag_object.save()\\n if tag_object not in tag_objects_list:\\n tag_objects_list.append(tag_object)\\n return tag_objects_list\",\n \"def get_pred_ids(predictions):\\n le_classes = ['Emotet', 'Mirai', 'Zeus'] \\n malwares_dict = {'Emotet': 1, 'Mirai': 2, 'Zeus': 3}\\n predicted_ids = []\\n \\n for idx in predictions:\\n pred_name = le_classes[idx]\\n pred_id = malwares_dict[pred_name]\\n predicted_ids.append(pred_id)\\n \\n return predicted_ids\",\n \"def _list_input_regressor_code(self, model_code):\\n regressor_code = [\\n code for code in model_code.ravel() if (code != 0) and (str(code)[0] != \\\"1\\\")\\n ]\\n return np.asarray(regressor_code)\",\n \"def tags(self) -> Mapping[str, str]:\\n return pulumi.get(self, \\\"tags\\\")\",\n \"def tags(self) -> Mapping[str, str]:\\n return pulumi.get(self, \\\"tags\\\")\",\n \"def tags(self) -> Mapping[str, str]:\\n return pulumi.get(self, \\\"tags\\\")\",\n \"def tags(self) -> Mapping[str, str]:\\n return pulumi.get(self, \\\"tags\\\")\",\n \"def list(self):\\n return self._post(\\n request='list',\\n uri=ApiUri.TAGS.value,\\n ).get('tags')\",\n \"def get_tags(self) -> Dict:\\n return self.orthanc.get_instance_tags(self.identifier)\",\n \"def getGUIName( self, modelName ):\\r\\n # For comaptibility with older AToM3 models\\r\\n try:\\r\\n exec 'import ' + modelName\\r\\n return [modelName]\\r\\n except:\\r\\n pass\\r\\n \\r\\n # This is the most common case (must do second for compatibility)\\r\\n try:\\r\\n exec 'import ' + modelName + '_META'\\r\\n return [modelName + '_META']\\r\\n except:\\r\\n pass\\r\\n \\r\\n # Ugh, crapola!\\r\\n print 'AToM3 was unable to find the buttons model (META file) for ' \\\\\\r\\n + modelName\\r\\n return []\",\n \"def GetRoomIndex( data) :\\n labels = ['etage', 'roomID']\\n columns = data.columns\\n\\n result =[ l for l in labels if l in columns ]\\n if not result : raise RuntimeError( 'No tagging possible for a room' )\\n return result\",\n \"def getTags(self,):\\n\\t\\treturn self.tags;\",\n \"def named_entities(self) -> List[str]:\",\n \"def tags(self) -> Sequence[str]:\\n return pulumi.get(self, \\\"tags\\\")\",\n \"def get(self, currency, entity):\\n check_inputs(currency=currency, entity=entity)\\n tags = entitiesDAO.list_entity_tags(currency, entity)\\n return tags\",\n \"def get_tags_list(url, auth_token, repo_name):\\n response, _ = get_response(url + '/v2/' + repo_name + '/tags/list',\\n auth_token)\\n result = response.get('tags', [])\\n return result\",\n \"def getNames():\\n imgs = Image.objects.raw({})\\n ans = []\\n for img in imgs:\\n ans.append(img.name)\\n ans.sort()\\n return ans\",\n \"def build_taglist(tags):\\n taglist = []\\n for tag in tags:\\n taglist.append(tag['value'].lower())\\n return taglist\",\n \"def get_pipeline_custom_tags(module_name, args, tags):\\n try:\\n _imports = __import__(module_name, fromlist=[\\\"get_pipeline_custom_tags\\\"])\\n kwargs = convert_struct(args)\\n return _imports.get_pipeline_custom_tags(\\n tags, kwargs[\\\"region\\\"], kwargs[\\\"sagemaker_project_arn\\\"]\\n )\\n except Exception as e:\\n print(f\\\"Error getting project tags: {e}\\\")\\n return tags\",\n \"def test_get_device_tags_by_id1(self):\\n pass\",\n \"def tags(catalog,lista,tag):\\n final=lt.newList(datastructure='ARRAY_LIST')\\n i=it.newIterator(lista)\\n while it.hasNext(i):\\n vid=it.next(i)\\n if tag in vid['tags']:\\n lt.addLast(final,vid)\\n return final\",\n \"def get_tag_index(self, m):\\n return self.tags.index(m)\",\n \"def get_tag_names(**kwargs):\\n\\n if kwargs:\\n if 'key' and 'values' in kwargs:\\n names = Tags.get_filtered_names_in(kwargs['key'], kwargs['values'])\\n\\n else:\\n names = Tags.get_filtered_names(**kwargs)\\n\\n else:\\n names = Tags.get_names()\\n\\n return names\",\n \"def _identify_combos(model, combo_tags):\\n \\n # Identify which load combinations to evaluate\\n if combo_tags is None:\\n combo_list = model.LoadCombos.values()\\n else:\\n combo_list = []\\n for combo in model.LoadCombos.values():\\n if any(tag in combo.combo_tags for tag in combo_tags):\\n combo_list.append(combo)\\n \\n return combo_list\",\n \"def search_tag(input) :\\n j = _jpdb()\\n _input = _process_search_input(input)\\n if not _input : return None\\n f = j.base_format\\n q = Query().select(f.tags, f.tags.id, f.tags.name)\\n q.where().equal(f.tags.name, _input)\\n tag_data = j.executeQuery(q)\\n\\n if tag_data:\\n tag_id, tag_name = tag_data[0]\\n examples = _create_examples(j.list_word_by_tag, tag_name)\\n return SelectorResult('tag', tag_id, tag_name, *examples)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6331528","0.61098045","0.5848875","0.5817225","0.57898426","0.57898426","0.5781574","0.5777682","0.5743095","0.56093746","0.5590287","0.5589514","0.5496177","0.54333645","0.5430249","0.5416349","0.54159564","0.54110205","0.54062647","0.53955483","0.5384277","0.5354498","0.5345137","0.5321173","0.52960914","0.5277989","0.52756363","0.52575725","0.52567166","0.5253493","0.5244506","0.5190703","0.51751304","0.5166054","0.51568747","0.51563424","0.5156255","0.5152272","0.51462865","0.514072","0.5131407","0.5128324","0.5125156","0.5124858","0.51123494","0.51036453","0.5092565","0.5055536","0.50555164","0.5053872","0.505184","0.5032343","0.5022518","0.5001503","0.49860382","0.49841383","0.49807766","0.49802262","0.49774468","0.4968575","0.4960144","0.49471357","0.49448293","0.4937635","0.49367094","0.49363938","0.4927887","0.49261189","0.49157655","0.4914443","0.49038416","0.4903773","0.48995444","0.4892915","0.48818952","0.48810515","0.4880608","0.48791167","0.4877434","0.4877434","0.4877434","0.4877434","0.48773128","0.48736066","0.48669755","0.4864704","0.486442","0.48602372","0.48558828","0.48551607","0.48482633","0.48444462","0.48415294","0.4840084","0.48380822","0.48289302","0.4827454","0.48242763","0.48174205","0.4812581"],"string":"[\n \"0.6331528\",\n \"0.61098045\",\n \"0.5848875\",\n \"0.5817225\",\n \"0.57898426\",\n \"0.57898426\",\n \"0.5781574\",\n \"0.5777682\",\n \"0.5743095\",\n \"0.56093746\",\n \"0.5590287\",\n \"0.5589514\",\n \"0.5496177\",\n \"0.54333645\",\n \"0.5430249\",\n \"0.5416349\",\n \"0.54159564\",\n \"0.54110205\",\n \"0.54062647\",\n \"0.53955483\",\n \"0.5384277\",\n \"0.5354498\",\n \"0.5345137\",\n \"0.5321173\",\n \"0.52960914\",\n \"0.5277989\",\n \"0.52756363\",\n \"0.52575725\",\n \"0.52567166\",\n \"0.5253493\",\n \"0.5244506\",\n \"0.5190703\",\n \"0.51751304\",\n \"0.5166054\",\n \"0.51568747\",\n \"0.51563424\",\n \"0.5156255\",\n \"0.5152272\",\n \"0.51462865\",\n \"0.514072\",\n \"0.5131407\",\n \"0.5128324\",\n \"0.5125156\",\n \"0.5124858\",\n \"0.51123494\",\n \"0.51036453\",\n \"0.5092565\",\n \"0.5055536\",\n \"0.50555164\",\n \"0.5053872\",\n \"0.505184\",\n \"0.5032343\",\n \"0.5022518\",\n \"0.5001503\",\n \"0.49860382\",\n \"0.49841383\",\n \"0.49807766\",\n \"0.49802262\",\n \"0.49774468\",\n \"0.4968575\",\n \"0.4960144\",\n \"0.49471357\",\n \"0.49448293\",\n \"0.4937635\",\n \"0.49367094\",\n \"0.49363938\",\n \"0.4927887\",\n \"0.49261189\",\n \"0.49157655\",\n \"0.4914443\",\n \"0.49038416\",\n \"0.4903773\",\n \"0.48995444\",\n \"0.4892915\",\n \"0.48818952\",\n \"0.48810515\",\n \"0.4880608\",\n \"0.48791167\",\n \"0.4877434\",\n \"0.4877434\",\n \"0.4877434\",\n \"0.4877434\",\n \"0.48773128\",\n \"0.48736066\",\n \"0.48669755\",\n \"0.4864704\",\n \"0.486442\",\n \"0.48602372\",\n \"0.48558828\",\n \"0.48551607\",\n \"0.48482633\",\n \"0.48444462\",\n \"0.48415294\",\n \"0.4840084\",\n \"0.48380822\",\n \"0.48289302\",\n \"0.4827454\",\n \"0.48242763\",\n \"0.48174205\",\n \"0.4812581\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8124625"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":11282,"cells":{"query":{"kind":"string","value":"Adds tag new tag to object."},"document":{"kind":"string","value":"def add_tag(self, cr, uid, ids, code=None, name=None, create=False, context=None):\n tag_obj = self.pool.get('res.tag')\n tag_ids = tag_obj.get_tag_ids(cr, uid, self._name, code=code, name=name, context=context)\n if not tag_ids and create:\n model_id = self.pool.get('res.tag.model').search(cr, uid, [('model', '=', self._name)])[0]\n tag_ids = [tag_obj.create(cr, uid, {'name': name, 'code': code, 'model_id': model_id}, context=context)]\n\n if tag_ids:\n self.write(cr, uid, ids, {'tag_ids': [(4, tid) for tid in tag_ids]}, context=context)\n\n return bool(tag_ids)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def add(self, tag):\n self.tags[tag.name] = tag","def add_tag(self, tag):\n self.tags.append(tag)","def add_tag(self, obj, tag_name):\r\n tag_names = parse_tag_input(tag_name)\r\n if not len(tag_names):\r\n raise AttributeError(_('No tags were given: \"%s\".') % tag_name)\r\n if len(tag_names) > 1:\r\n raise AttributeError(_('Multiple tags were given: \"%s\".') % tag_name)\r\n tag_name = tag_names[0]\r\n if settings.FORCE_LOWERCASE_TAGS:\r\n tag_name = tag_name.lower()\r\n tag, created = self.get_or_create(name=tag_name)\r\n ctype = ContentType.objects.get_for_model(obj)\r\n TaggedItem._default_manager.get_or_create(\r\n tag=tag, content_type=ctype, object_id=obj.pk)","def add_tag(self, tag):\n cp = self.copy()\n cp.tags.add(tag)\n return cp","def add_tag(self, tag):\n if tag not in self._tag:\n self._tag.append(tag)\n\n return self","def add_tag(self, tag: str) -> None:\n tags = self.get_tag_index()\n tags.append(tag)\n self.write_tag_index(list(set(tags)))","def _add_tag(self, tag_name):\n tag = TagInfo()\n tag._name = tag_name\n self._tags.append(tag)\n return tag","def add_tag(self, tag, attributes, extent):\n self.tags.append((tag, attributes, extent))","def setTag(self, tag, btag_obj):\n\n # Search for the tag\n if (len(self._tagmap) > 0):\n i = bisect.bisect_left(self._tags, tag)\n # Tag exists -> store new value\n if i != len(self._tagmap) and self._tagmap[i][0] == tag:\n self._data[self._tagmap[i][1]] = (tag, btag_obj)\n return\n self._tagmap.append((tag, len(self._data)))\n self._tags.append(tag)\n if len(self._tagmap) > 1:\n self._tagmap.sort(key=lambda x: x[0])\n for i in range(len(self._tagmap)):\n self._tags[i] = self._tagmap[i][0]\n self._data.append((tag, btag_obj))","def tag(self, tag):\n self.tag = tag","def add_tag(self, key, value=''):\r\n status = self.connection.create_tags([self.id], {key : value})\r\n if self.tags is None:\r\n self.tags = TagSet()\r\n self.tags[key] = value","def add_tag(self, session, tag):\n self._tag(session.put, key=tag, session=session)","def tag(self, tag):\n \n if isinstance(tag, six.integer_types):\n try:\n tag = Tag.objects.get(pk=tag, owner=self.owner)\n except Tag.DoesNotExist:\n #Handle this better?\n return\n \n if isinstance(tag, six.string_types):\n tname = tag\n try:\n tag = Tag(owner=self.owner, name=tag)\n tag.save()\n except IntegrityError:\n tag = Tag.objects.get(slug=makeslug(tname), owner=self.owner)\n \n tag.save() # If this isn't here there are crashes for some reason\n self.tags.add(tag)","def AddTag(self, tag):\n\n if not self.persistant:\n return\n\n self.db.ExecuteSql('insert into tags(tag, track_id) values(\"%s\", %d);'\n %(tag, self.persistant['id']))\n self.db.ExecuteSql('commit;')","def append(self, tag):\r\n self.insert(len(self.contents), tag)","def tag(self, tag):\n\n self._tag = tag","def tag(self, tag):\n\n self._tag = tag","def tag(self, tag):\n\n self._tag = tag","def tag(self, tag):\n\n self._tag = tag","def set_tag(self, tag):\n self.update(tag=tag)","def testAddTag(self):\n project = self.session.create_project()\n\n project.add_tag(\"test\")\n self.assertEqual(project.tags, [\"test\"], \"Can add a tag to a project.\")\n\n json_str = project.to_json()\n doc = json.loads(json_str)\n\n self.assertEqual(doc['meta']['tags'], [\"test\"],\n \"JSON representation had correct tags after add_tag().\")\n\n # Try adding the same tag yet again, shouldn't get a duplicate\n with self.assertRaises(ValueError):\n project.add_tag(\"test\")\n\n json_str = project.to_json()\n doc2 = json.loads(json_str)\n\n self.assertEqual(doc2['meta']['tags'], [\"test\"],\n \"JSON document did not end up with duplicate tags.\")","def put_tag(self, key, tag):\n self._entries[key] = tag","def add_tag(self, dataset: \"Dataset\", tag: \"DatasetTag\"):\n raise NotImplementedError","def test_add_tag(self):\n fc = self.read_feature(region='Adriatic_Sea')\n\n fc.tag(tags=['tag1', 'tag2', 'Mediterranean_Basin'])\n assert (fc.features[0]['properties']['tags'] ==\n 'Adriatic_Sea;Mediterranean_Basin;tag1;tag2')\n\n self.check_feature(fc.features[0])","def add_new_tag():\n\n return render_template('create-tag.html')","def add_tag(self, tag):\n\n # directional relation: tag is the blank of everything in the list\n self.relations[tag] = {\n \"overlord\": [],\n \"hegemon\": [], # for tributary\n \"tributary\": [],\n \"vassal\": [],\n \"guaranteeing\": [],\n \"guarantor\": [],\n \"alliance\": [],\n \"senior\": [],\n \"junior\": [],\n \"marriage\": []\n }","def add_tag(*, tag='tag!'):\n def _apply_on(f):\n setattr(f, 'my_tag', tag)\n return f\n return _apply_on","def create(self, name, tag):\n\n\t\turl_json = urllib.urlencode({\"name\": name, \"tag\": tag})\n\t\treturn self._create(\"/tag?json_hash=%s\" % url_json, \"tag\")","def GachaCraftNodeExcelAddTag_(builder, Tag_):\n return AddTag_(builder, Tag_)","def set_tag(self, val):\n self.__tag__ = val","def add_tag(tag):\n check_call(['git', 'tag', tag])","def update_tag(tag):\n remove_tag(tag)\n add_tag(tag)","def add_tag(self, tag_name, name = 'tag'):\r\n if self._only_whitespace.match(tag_name):\r\n # Don't allow an empty tag\r\n return\r\n\r\n try:\r\n tag = Tag._by_name(tag_name)\r\n except NotFound:\r\n tag = Tag._new(tag_name)\r\n tag._commit()\r\n\r\n # See if link already has this tag\r\n tags = LinkTag._fast_query(tup(self), tup(tag), name=name)\r\n link_tag = tags[(self, tag, name)]\r\n if not link_tag:\r\n link_tag = LinkTag(self, tag, name=name)\r\n link_tag._commit()\r\n\r\n return link_tag","def createTag(self, authenticationToken, tag):\r\n pass","def tags_add(self, item, tags):\n self._createTagAction(item, \"tags_add\", tags)","async def slashtag_add(\n self,\n ctx: commands.Context,\n tag_name: TagName(check_global=False),\n *,\n tagscript: TagScriptConverter,\n ):\n await self.create_slash_tag(ctx, tag_name, tagscript, is_global=False)","async def addtags(self, ctx, tag, *, data):\r\n\t\tTag = self.settings.ServerConfig(ctx.guild.id, 'Tags')\r\n\t\tif not tag in Tag:\r\n\t\t\tTag[tag] = self.Conf.Tags\r\n\t\t\tawait ctx.send('Added Tag: {}'.format(tag))\r\n\t\telse:\r\n\t\t\tawait ctx.send('Edited Tag: '.format(tag))\r\n\r\n\t\tnowgmt = time.strftime(\"%H:%M:%S, %d/%m/%Y\", time.gmtime())\r\n\t\t\r\n\t\tTag[tag]['user'] = ctx.author.id\r\n\t\tTag[tag]['data'] = data\r\n\t\tTag[tag]['time'] = nowgmt\r\n\t\tself.settings.ServerConfig(ctx.guild.id, 'Tags', Tag)","def add_tag():\n \n return render_template('tags/add_tag.html')","def set_tag(self, t) -> None:\n self.tag = t","def addItemTag(self, item, tag):\r\n if self.inItemTagTransaction:\r\n # XXX: what if item's parent is not a feed?\r\n if not tag in self.addTagBacklog:\r\n self.addTagBacklog[tag] = [] \r\n self.addTagBacklog[tag].append({'i': item.id, 's': item.parent.id})\r\n return \"OK\"\r\n else:\r\n return self._modifyItemTag(item.id, 'a', tag)","def add_tag_data(tag):\n\n add_tag = Tag(tag=tag)\n db.session.add(add_tag)\n try:\n db.session.commit()\n except (Exception, exc.SQLAlchemyError, exc.InvalidRequestError, exc.IntegrityError) as e:\n print(tag + '\\n' + str(e))","def add_tag(request):\n if request.method != 'POST':\n return HttpResponseRedirect(reverse('wainz.views.composite'))\n else:\n img_id = request.POST['id']\n try:\n img = Image.objects.get(pk=img_id)\n except:\n return HttpResponseRedirect(reverse('wainz.views.composite'))\n tag_val = request.POST['tag']\n try:\n tag = tag_utils.TagsFromText(tag_val)[0]\n added = True\n img.tags.add(tag)\n img.save()\n except:\n added = False\n resp = rest.rest_success(request, img_id)\n respJson = json.loads(resp.content)\n respJson['added'] = added\n resp.content = json.dumps(respJson)\n return resp","def push_tag(self, tag):\n _tag_entity('task', self.task_id, tag)","def tag(self, tag_name):\r\n return Tag(self, tag_name)","def AddNodeWithTag(tree, nodepath, nodetype, tag):\n tree.addNode(nodepath, usage=nodetype)\n tempnode = tree.getNode(nodepath)\n tempnode.addTag(tag)","def create_tag(self, entry_name, tag):\n return self.__datacatalog.create_tag(parent=entry_name, tag=tag)","def add_tag(self, val: str):\n if isinstance(val, str):\n self._tags.append(val)\n if isinstance(val, list):\n for value in val.items():\n self._tags.append(value)","def add_tag(self, transaction, media_handle, tag_handle):\n media = self.dbstate.db.get_object_from_handle(media_handle)\n media.add_tag(tag_handle)\n self.dbstate.db.commit_media_object(media, transaction)","def add_tag(session, tag_name, user_id=None, username='system_user'):\n session = validate_session(session)\n date_created=datetime.now()\n try:\n add_tag = TagInfo(tag_name, date_created, user_id)\n session.add(add_tag)\n session.commit()\n return(True, \"Tag %s added\" % (tag_name), add_tag)\n except Exception as e:\n session.rollback()\n return(False, \"Tag %s failed to add\" % (tag_name))","def add_tag(*, tag='tag!', f=DECORATED):\n setattr(f, 'my_tag', tag)\n return f","def _handle_new_tag(self, tag_name):\n log.debug(\"Handling new tag: %s\", tag_name)\n tag, created = Tag.get_or_create(name=tag_name)\n if tag not in self.doc.tags:\n self.listbox.body.insert(-1, MenuButton(tag_name, None))\n tag.documents.add(self.doc)\n self.new_tag.set_edit_text(\"\")","def tag_add(self, remote_path, corpus_id, tag, storage_id=None):\n client, remote_path = self._get_storage(remote_path, storage_id=storage_id)\n return client.tag_add(corpus_id, tag)","def _add_tags(self):\n\n if self.version != 'live':\n return\n\n tags = [t.strip() for t in self.tags_text.split(',')]\n tags = list(set(tags))\n\n for tag_name in tags:\n tag_slug = slugify(tag_name)\n if tag_slug:\n try:\n tag = Tag.objects.get(blog=self.blog, slug=tag_slug)\n except Tag.DoesNotExist:\n tag = Tag( blog = self.blog,\n name = tag_name,\n slug = tag_slug)\n\n tag.increment()\n tag.save()\n\n self.tags.add(tag)","def add_tag(self, transaction, citation_handle, tag_handle):\n citation = self.dbstate.db.get_citation_from_handle(citation_handle)\n citation.add_tag(tag_handle)\n self.dbstate.db.commit_citation(citation, transaction)","def tag(self, text):\n\t\tpass","def add_tag (self,tag,key):\r\n\r\n #with shelf\r\n\r\n if self.using_shelf:\r\n\r\n if tag in self.tag_dict:\r\n\r\n self.tag_dict[tag].add(key)\r\n\r\n else:\r\n\r\n self.tag_dict[tag] = {key}\r\n\r\n #with database\r\n\r\n if self.using_database:\r\n\r\n value_tuple = (notebookname, tag, key,)\r\n db_cursor.execute(\"INSERT OR REPLACE \"\r\n +\"INTO tags_to_keys \"\r\n +\"(notebook, tag, keyword) \"\r\n +\"VALUES (?,?,?);\",value_tuple)","def make_new_tag():\n return render_template('tags/new_tag.html')","def create_tag(self, session, tags):\n self._tag(session.put, tags=tags, session=session)","def push_tag(self, tagname, attrs=[]):\n tag = '<%s%s>' % (tagname, string_for_attrs(attrs))\n self.str += '\\n' + indent(tag, self.indent_level())\n self._tagstack.append(tagname)","def add_tagging(self, task_instance):","def append_tags(self, tags):\n\n tags = H.to_list(tags)\n # self._tags.update(tags)\n self.tags.update(tags)","def add_genre(self, tag: Genre):\n raise NotImplementedError","def AddEventTag(self, event_tag):\n self._RaiseIfNotWritable()\n\n # TODO: refactor to use AddOrUpdateAttributeContainer\n self._storage_file.AddEventTag(event_tag)\n\n self._UpdateEventLabelsSessionCounter(event_tag)","def add(self, k_spec, tag):\n self.k_spec_dict[tag] = k_spec","def tag_element(self,tag_name,element):\n self.__add_to_dictlist(self.element2tags,tag_name,element)\n self.__add_to_dictlist(self.tag2elements,element,tag_name)","def test_add_tagitem(self):\n record = self.good_record()\n record['tag'] = self.tag\n # Create a fake record in mongo\n id = self.images.insert(record)\n\n status = self.m.add_tag(id, self.system, 'testtag')\n self.assertTrue(status)\n rec = self.images.find_one({'_id': id})\n self.assertIsNotNone(rec)\n self.assertIn(self.tag, rec['tag'])\n self.assertIn('testtag', rec['tag'])","def add_new_tag():\n\n name = request.form.get('name')\n\n new_tag = Tag(name=name)\n db.session.add(new_tag)\n db.session.commit()\n\n return redirect(f'/tags')","def tag(request, tag_name):\n raise NotImplementedError","def add_tag(self, *,\n id: str,\n tag: str,\n tag_type: str = 'default',\n resource_type: ResourceType = ResourceType.Table) -> None:\n LOGGER.info(f'New tag {tag} for id {id} with type {tag_type} and resource type {resource_type.name}')\n\n resource_table = f'{resource_type.name.lower()}_tag'\n resource_model = self._get_model_from_table_name(resource_table)\n if not resource_model:\n raise NotImplementedError(f'The resource type {resource_type.name} is not defined!')\n\n resource_key = f'{resource_type.name.lower()}_rk'\n\n tag_record = RDSTag(rk=tag, tag_type=tag_type)\n resource_tag_record = resource_model(tag_rk=tag)\n resource_tag_record.__setattr__(resource_key, id)\n try:\n with self.client.create_session() as session:\n session.merge(tag_record)\n session.merge(resource_tag_record)\n session.commit()\n except Exception as e:\n LOGGER.exception(f'Failed to add tag {tag} for {id}')\n raise e","def AddEventTag(self, event_tag):\n self._RaiseIfNotWritable()\n\n event_identifier = event_tag.GetEventIdentifier()\n if not isinstance(\n event_identifier, identifiers.SerializedStreamIdentifier):\n raise IOError('Unsupported event identifier type: {0:s}'.format(\n type(event_identifier)))\n\n event_tag.event_stream_number = event_identifier.stream_number\n event_tag.event_entry_index = event_identifier.entry_index\n\n # Check if the event has already been tagged on a previous occasion,\n # we need to append the event tag any existing event tag.\n stored_event_tag = self._GetEventTagByIdentifier(event_identifier)\n if stored_event_tag:\n event_tag.AddComment(stored_event_tag.comment)\n event_tag.AddLabels(stored_event_tag.labels)\n\n self._AddAttributeContainer('event_tag', event_tag)\n\n self._UpdateEventTagIndex(event_tag)","def setAddTags(self,value):\n self.PDFreactorConfiguration.in1[\"addTags\"] = value","def __init__(self, tag):\n self.tag = tag","def add_tags(event):\n\n add_tags_from_presets()","def trackEmbedding(self, tag):\n self.embeddedTags.append(tag)","def tag_card(self, card, tag: str):\n list = self.tags[tag]\n list.append(card.multiverse_id)\n self.db.tag_card_add(tag, card.multiverse_id)","def add_node(self, node_data):\n self.__rtags.append(True)\n self.__nodedata.append(data)\n self.__ltags.append(True)","def for_tag(self, tag):\n if not isinstance(tag, str):\n raise TypeError('Tag must be a string')\n\n self.token['requiredTag'] = tag\n\n return self","def add_tag(e, driver):\n tag = random_tag(8)\n e.find_element_by_class_name('add-tag').click()\n \n driver.find_element_by_class_name('tag_input')\\\n .send_keys(tag)\n driver.find_element_by_class_name('tag_input')\\\n .send_keys(Keys.ENTER)\n # driver.find_elements_by_class_name('save-tag').click()\n return tag","def add_tag(self, tag):\n for task in self._tasks:\n task.add_tag(tag)\n\n return self","def add_tag(filename, tag_name):\n storeapps = APP.config[\"storage\"]\n filename = filename.encode(\"utf-8\")\n print filename\n\n try:\n application = list(nativeapps.io.ls(storeapps, r\".*\" + filename + \"$\"))[0]\n meta_path = os.path.join(os.path.dirname(application), \"metadata.json\")\n metadata = json.loads(nativeapps.io.readfile(meta_path))\n tags = set(metadata.get(\"tags\", []))\n tags.add(tag_name)\n metadata[\"tags\"] = list(tags)\n nativeapps.io.writefile(meta_path, json.dumps(metadata))\n except IndexError:\n return \"Unknown application: %s\" % (application), 404\n\n return \"added\", 200","def handle_starttag(self, tag, attrs):\n if tag == \"a\":\n curr_tag = Tag(tag)\n for attribute in attrs:\n curr_tag.add_attribute(attribute[0], attribute[1])\n self.current_tags.append(curr_tag)\n elif tag == \"img\":\n curr_tag = Tag(tag)\n for attribute in attrs:\n curr_tag.add_attribute(attribute[0], attribute[1])\n self.images.append(curr_tag)","def set_tag(self, key, value):\n return self","def _set_tag(self, v, load=False):\n if hasattr(v, \"_utype\"):\n v = v._utype(v)\n try:\n t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['-2147483648..2147483647']}, int_size=32), is_leaf=True, yang_name=\"tag\", rest_name=\"tag\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-bgp-operational', defining_module='brocade-bgp-operational', yang_type='int32', is_config=False)\n except (TypeError, ValueError):\n raise ValueError({\n 'error-string': \"\"\"tag must be of a type compatible with int32\"\"\",\n 'defined-type': \"int32\",\n 'generated-type': \"\"\"YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['-2147483648..2147483647']}, int_size=32), is_leaf=True, yang_name=\"tag\", rest_name=\"tag\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-bgp-operational', defining_module='brocade-bgp-operational', yang_type='int32', is_config=False)\"\"\",\n })\n\n self.__tag = t\n if hasattr(self, '_set'):\n self._set()","def handle_add_new_tag():\n tag = Tag(name=request.form['name'])\n\n db.session.add(tag)\n db.session.commit()\n\n return redirect('/tags')","def add_new_tag(self, body, **kwargs):\n kwargs['_return_http_data_only'] = True\n if kwargs.get('callback'):\n return self.add_new_tag_with_http_info(body, **kwargs)\n else:\n (data) = self.add_new_tag_with_http_info(body, **kwargs)\n return data","def add_tags(self, tags):\n\n if isinstance(tags, string_types):\n message = \"tags should be a list or None, got tags={}\".format(tags)\n raise TypeError(message)\n\n self.tags = self.tags.union(tags)","def addTag(id = 0):\n\tinsertQueries.addTag(request.form)\n\tresults = queries.package(id)\n\treturn render_template('package.html', package=results)","def add_tag():\n\n tag_name = request.form[\"name\"]\n\n if not tag_name:\n flash(\"Please enter tag name\")\n return redirect(\"/tags/new\")\n\n tag = Tag(name=tag_name)\n db.session.add(tag)\n db.session.commit()\n\n return redirect(\"/tags\")","def insert_tag(self, tagname, attrs=[], text=\"\",\n autoclose=False, newline=False):\n if autoclose:\n self.str += '\\n<%s%s%s>' % (tagname, string_for_attrs(attrs),\n ' /' if self.is_xml else '')\n else:\n self.push_tag(tagname, attrs)\n if text:\n if newline:\n self.insert_text('\\n' + indent(text, self.indent_level()))\n else:\n self.insert_text(text)\n self.pop_tag(newline=newline)","def create_or_get_tag(self, tag_name: str, *args, **kwargs):\n\n tag_data = api.create_or_get_tag(\n tag_name,\n *args,\n api_key=self.__creds.api_key_v2, \n **kwargs)\n return en.Tag(tag_data)","def add_tag(convo_ID, tag_ID):\n # Make API request\n url = \"https://api2.frontapp.com/conversations/\" + convo_ID + \"/tags\"\n payload = json.dumps({\"tag_ids\": [tag_ID]})\n headers = {\"Authorization\": BEARER_TOKEN, \"Content-Type\": \"application/json\"}\n requests.request(\"POST\", url, headers=headers, data=payload)","def updateTag(self, authenticationToken, tag):\r\n pass","def set_tag(self, xaf, name, value, add_latest=True):\n counter_str_value = str(self._get_counter_tag_value(xaf))\n tag_name = self.__get_tag_name(name, counter_str_value)\n self.__set_tag(xaf, tag_name, value)\n if add_latest:\n self._set_tag_latest(xaf, name, value)","def update_tag(self, tag):\n resp = self.put(_u.build_uri(\"tag\", domain=self.domain),\n data={'tag': tag})\n return utils.handle_response(resp)","def create_tag_with_entry(title):\n tag = Tag.objects.create(title=title)\n tag.save()\n tag.entry.add(1)\n return tag","def add(self, obj):\n raise NotImplementedError","def add_tag_meta(self, new_tag_meta: TagMeta) -> ProjectMeta:\n return self.add_tag_metas([new_tag_meta])","def _add(object, name, value):\n self.__added__.append(name)\n setattr(object, name, value)","def create_a_tag(self, tag_id, contact_id):\n data = {\"contactTag\":{\"contact\":str(contact_id),\"tag\":str(tag_id)}}\n\n return self.client._post(\"/contactTags\", json=data)","def add_tags_to_resource(ResourceId=None, Tags=None):\n pass","def setTag(self, tag):\n\t\tself.config.TAG = tag"],"string":"[\n \"def add(self, tag):\\n self.tags[tag.name] = tag\",\n \"def add_tag(self, tag):\\n self.tags.append(tag)\",\n \"def add_tag(self, obj, tag_name):\\r\\n tag_names = parse_tag_input(tag_name)\\r\\n if not len(tag_names):\\r\\n raise AttributeError(_('No tags were given: \\\"%s\\\".') % tag_name)\\r\\n if len(tag_names) > 1:\\r\\n raise AttributeError(_('Multiple tags were given: \\\"%s\\\".') % tag_name)\\r\\n tag_name = tag_names[0]\\r\\n if settings.FORCE_LOWERCASE_TAGS:\\r\\n tag_name = tag_name.lower()\\r\\n tag, created = self.get_or_create(name=tag_name)\\r\\n ctype = ContentType.objects.get_for_model(obj)\\r\\n TaggedItem._default_manager.get_or_create(\\r\\n tag=tag, content_type=ctype, object_id=obj.pk)\",\n \"def add_tag(self, tag):\\n cp = self.copy()\\n cp.tags.add(tag)\\n return cp\",\n \"def add_tag(self, tag):\\n if tag not in self._tag:\\n self._tag.append(tag)\\n\\n return self\",\n \"def add_tag(self, tag: str) -> None:\\n tags = self.get_tag_index()\\n tags.append(tag)\\n self.write_tag_index(list(set(tags)))\",\n \"def _add_tag(self, tag_name):\\n tag = TagInfo()\\n tag._name = tag_name\\n self._tags.append(tag)\\n return tag\",\n \"def add_tag(self, tag, attributes, extent):\\n self.tags.append((tag, attributes, extent))\",\n \"def setTag(self, tag, btag_obj):\\n\\n # Search for the tag\\n if (len(self._tagmap) > 0):\\n i = bisect.bisect_left(self._tags, tag)\\n # Tag exists -> store new value\\n if i != len(self._tagmap) and self._tagmap[i][0] == tag:\\n self._data[self._tagmap[i][1]] = (tag, btag_obj)\\n return\\n self._tagmap.append((tag, len(self._data)))\\n self._tags.append(tag)\\n if len(self._tagmap) > 1:\\n self._tagmap.sort(key=lambda x: x[0])\\n for i in range(len(self._tagmap)):\\n self._tags[i] = self._tagmap[i][0]\\n self._data.append((tag, btag_obj))\",\n \"def tag(self, tag):\\n self.tag = tag\",\n \"def add_tag(self, key, value=''):\\r\\n status = self.connection.create_tags([self.id], {key : value})\\r\\n if self.tags is None:\\r\\n self.tags = TagSet()\\r\\n self.tags[key] = value\",\n \"def add_tag(self, session, tag):\\n self._tag(session.put, key=tag, session=session)\",\n \"def tag(self, tag):\\n \\n if isinstance(tag, six.integer_types):\\n try:\\n tag = Tag.objects.get(pk=tag, owner=self.owner)\\n except Tag.DoesNotExist:\\n #Handle this better?\\n return\\n \\n if isinstance(tag, six.string_types):\\n tname = tag\\n try:\\n tag = Tag(owner=self.owner, name=tag)\\n tag.save()\\n except IntegrityError:\\n tag = Tag.objects.get(slug=makeslug(tname), owner=self.owner)\\n \\n tag.save() # If this isn't here there are crashes for some reason\\n self.tags.add(tag)\",\n \"def AddTag(self, tag):\\n\\n if not self.persistant:\\n return\\n\\n self.db.ExecuteSql('insert into tags(tag, track_id) values(\\\"%s\\\", %d);'\\n %(tag, self.persistant['id']))\\n self.db.ExecuteSql('commit;')\",\n \"def append(self, tag):\\r\\n self.insert(len(self.contents), tag)\",\n \"def tag(self, tag):\\n\\n self._tag = tag\",\n \"def tag(self, tag):\\n\\n self._tag = tag\",\n \"def tag(self, tag):\\n\\n self._tag = tag\",\n \"def tag(self, tag):\\n\\n self._tag = tag\",\n \"def set_tag(self, tag):\\n self.update(tag=tag)\",\n \"def testAddTag(self):\\n project = self.session.create_project()\\n\\n project.add_tag(\\\"test\\\")\\n self.assertEqual(project.tags, [\\\"test\\\"], \\\"Can add a tag to a project.\\\")\\n\\n json_str = project.to_json()\\n doc = json.loads(json_str)\\n\\n self.assertEqual(doc['meta']['tags'], [\\\"test\\\"],\\n \\\"JSON representation had correct tags after add_tag().\\\")\\n\\n # Try adding the same tag yet again, shouldn't get a duplicate\\n with self.assertRaises(ValueError):\\n project.add_tag(\\\"test\\\")\\n\\n json_str = project.to_json()\\n doc2 = json.loads(json_str)\\n\\n self.assertEqual(doc2['meta']['tags'], [\\\"test\\\"],\\n \\\"JSON document did not end up with duplicate tags.\\\")\",\n \"def put_tag(self, key, tag):\\n self._entries[key] = tag\",\n \"def add_tag(self, dataset: \\\"Dataset\\\", tag: \\\"DatasetTag\\\"):\\n raise NotImplementedError\",\n \"def test_add_tag(self):\\n fc = self.read_feature(region='Adriatic_Sea')\\n\\n fc.tag(tags=['tag1', 'tag2', 'Mediterranean_Basin'])\\n assert (fc.features[0]['properties']['tags'] ==\\n 'Adriatic_Sea;Mediterranean_Basin;tag1;tag2')\\n\\n self.check_feature(fc.features[0])\",\n \"def add_new_tag():\\n\\n return render_template('create-tag.html')\",\n \"def add_tag(self, tag):\\n\\n # directional relation: tag is the blank of everything in the list\\n self.relations[tag] = {\\n \\\"overlord\\\": [],\\n \\\"hegemon\\\": [], # for tributary\\n \\\"tributary\\\": [],\\n \\\"vassal\\\": [],\\n \\\"guaranteeing\\\": [],\\n \\\"guarantor\\\": [],\\n \\\"alliance\\\": [],\\n \\\"senior\\\": [],\\n \\\"junior\\\": [],\\n \\\"marriage\\\": []\\n }\",\n \"def add_tag(*, tag='tag!'):\\n def _apply_on(f):\\n setattr(f, 'my_tag', tag)\\n return f\\n return _apply_on\",\n \"def create(self, name, tag):\\n\\n\\t\\turl_json = urllib.urlencode({\\\"name\\\": name, \\\"tag\\\": tag})\\n\\t\\treturn self._create(\\\"/tag?json_hash=%s\\\" % url_json, \\\"tag\\\")\",\n \"def GachaCraftNodeExcelAddTag_(builder, Tag_):\\n return AddTag_(builder, Tag_)\",\n \"def set_tag(self, val):\\n self.__tag__ = val\",\n \"def add_tag(tag):\\n check_call(['git', 'tag', tag])\",\n \"def update_tag(tag):\\n remove_tag(tag)\\n add_tag(tag)\",\n \"def add_tag(self, tag_name, name = 'tag'):\\r\\n if self._only_whitespace.match(tag_name):\\r\\n # Don't allow an empty tag\\r\\n return\\r\\n\\r\\n try:\\r\\n tag = Tag._by_name(tag_name)\\r\\n except NotFound:\\r\\n tag = Tag._new(tag_name)\\r\\n tag._commit()\\r\\n\\r\\n # See if link already has this tag\\r\\n tags = LinkTag._fast_query(tup(self), tup(tag), name=name)\\r\\n link_tag = tags[(self, tag, name)]\\r\\n if not link_tag:\\r\\n link_tag = LinkTag(self, tag, name=name)\\r\\n link_tag._commit()\\r\\n\\r\\n return link_tag\",\n \"def createTag(self, authenticationToken, tag):\\r\\n pass\",\n \"def tags_add(self, item, tags):\\n self._createTagAction(item, \\\"tags_add\\\", tags)\",\n \"async def slashtag_add(\\n self,\\n ctx: commands.Context,\\n tag_name: TagName(check_global=False),\\n *,\\n tagscript: TagScriptConverter,\\n ):\\n await self.create_slash_tag(ctx, tag_name, tagscript, is_global=False)\",\n \"async def addtags(self, ctx, tag, *, data):\\r\\n\\t\\tTag = self.settings.ServerConfig(ctx.guild.id, 'Tags')\\r\\n\\t\\tif not tag in Tag:\\r\\n\\t\\t\\tTag[tag] = self.Conf.Tags\\r\\n\\t\\t\\tawait ctx.send('Added Tag: {}'.format(tag))\\r\\n\\t\\telse:\\r\\n\\t\\t\\tawait ctx.send('Edited Tag: '.format(tag))\\r\\n\\r\\n\\t\\tnowgmt = time.strftime(\\\"%H:%M:%S, %d/%m/%Y\\\", time.gmtime())\\r\\n\\t\\t\\r\\n\\t\\tTag[tag]['user'] = ctx.author.id\\r\\n\\t\\tTag[tag]['data'] = data\\r\\n\\t\\tTag[tag]['time'] = nowgmt\\r\\n\\t\\tself.settings.ServerConfig(ctx.guild.id, 'Tags', Tag)\",\n \"def add_tag():\\n \\n return render_template('tags/add_tag.html')\",\n \"def set_tag(self, t) -> None:\\n self.tag = t\",\n \"def addItemTag(self, item, tag):\\r\\n if self.inItemTagTransaction:\\r\\n # XXX: what if item's parent is not a feed?\\r\\n if not tag in self.addTagBacklog:\\r\\n self.addTagBacklog[tag] = [] \\r\\n self.addTagBacklog[tag].append({'i': item.id, 's': item.parent.id})\\r\\n return \\\"OK\\\"\\r\\n else:\\r\\n return self._modifyItemTag(item.id, 'a', tag)\",\n \"def add_tag_data(tag):\\n\\n add_tag = Tag(tag=tag)\\n db.session.add(add_tag)\\n try:\\n db.session.commit()\\n except (Exception, exc.SQLAlchemyError, exc.InvalidRequestError, exc.IntegrityError) as e:\\n print(tag + '\\\\n' + str(e))\",\n \"def add_tag(request):\\n if request.method != 'POST':\\n return HttpResponseRedirect(reverse('wainz.views.composite'))\\n else:\\n img_id = request.POST['id']\\n try:\\n img = Image.objects.get(pk=img_id)\\n except:\\n return HttpResponseRedirect(reverse('wainz.views.composite'))\\n tag_val = request.POST['tag']\\n try:\\n tag = tag_utils.TagsFromText(tag_val)[0]\\n added = True\\n img.tags.add(tag)\\n img.save()\\n except:\\n added = False\\n resp = rest.rest_success(request, img_id)\\n respJson = json.loads(resp.content)\\n respJson['added'] = added\\n resp.content = json.dumps(respJson)\\n return resp\",\n \"def push_tag(self, tag):\\n _tag_entity('task', self.task_id, tag)\",\n \"def tag(self, tag_name):\\r\\n return Tag(self, tag_name)\",\n \"def AddNodeWithTag(tree, nodepath, nodetype, tag):\\n tree.addNode(nodepath, usage=nodetype)\\n tempnode = tree.getNode(nodepath)\\n tempnode.addTag(tag)\",\n \"def create_tag(self, entry_name, tag):\\n return self.__datacatalog.create_tag(parent=entry_name, tag=tag)\",\n \"def add_tag(self, val: str):\\n if isinstance(val, str):\\n self._tags.append(val)\\n if isinstance(val, list):\\n for value in val.items():\\n self._tags.append(value)\",\n \"def add_tag(self, transaction, media_handle, tag_handle):\\n media = self.dbstate.db.get_object_from_handle(media_handle)\\n media.add_tag(tag_handle)\\n self.dbstate.db.commit_media_object(media, transaction)\",\n \"def add_tag(session, tag_name, user_id=None, username='system_user'):\\n session = validate_session(session)\\n date_created=datetime.now()\\n try:\\n add_tag = TagInfo(tag_name, date_created, user_id)\\n session.add(add_tag)\\n session.commit()\\n return(True, \\\"Tag %s added\\\" % (tag_name), add_tag)\\n except Exception as e:\\n session.rollback()\\n return(False, \\\"Tag %s failed to add\\\" % (tag_name))\",\n \"def add_tag(*, tag='tag!', f=DECORATED):\\n setattr(f, 'my_tag', tag)\\n return f\",\n \"def _handle_new_tag(self, tag_name):\\n log.debug(\\\"Handling new tag: %s\\\", tag_name)\\n tag, created = Tag.get_or_create(name=tag_name)\\n if tag not in self.doc.tags:\\n self.listbox.body.insert(-1, MenuButton(tag_name, None))\\n tag.documents.add(self.doc)\\n self.new_tag.set_edit_text(\\\"\\\")\",\n \"def tag_add(self, remote_path, corpus_id, tag, storage_id=None):\\n client, remote_path = self._get_storage(remote_path, storage_id=storage_id)\\n return client.tag_add(corpus_id, tag)\",\n \"def _add_tags(self):\\n\\n if self.version != 'live':\\n return\\n\\n tags = [t.strip() for t in self.tags_text.split(',')]\\n tags = list(set(tags))\\n\\n for tag_name in tags:\\n tag_slug = slugify(tag_name)\\n if tag_slug:\\n try:\\n tag = Tag.objects.get(blog=self.blog, slug=tag_slug)\\n except Tag.DoesNotExist:\\n tag = Tag( blog = self.blog,\\n name = tag_name,\\n slug = tag_slug)\\n\\n tag.increment()\\n tag.save()\\n\\n self.tags.add(tag)\",\n \"def add_tag(self, transaction, citation_handle, tag_handle):\\n citation = self.dbstate.db.get_citation_from_handle(citation_handle)\\n citation.add_tag(tag_handle)\\n self.dbstate.db.commit_citation(citation, transaction)\",\n \"def tag(self, text):\\n\\t\\tpass\",\n \"def add_tag (self,tag,key):\\r\\n\\r\\n #with shelf\\r\\n\\r\\n if self.using_shelf:\\r\\n\\r\\n if tag in self.tag_dict:\\r\\n\\r\\n self.tag_dict[tag].add(key)\\r\\n\\r\\n else:\\r\\n\\r\\n self.tag_dict[tag] = {key}\\r\\n\\r\\n #with database\\r\\n\\r\\n if self.using_database:\\r\\n\\r\\n value_tuple = (notebookname, tag, key,)\\r\\n db_cursor.execute(\\\"INSERT OR REPLACE \\\"\\r\\n +\\\"INTO tags_to_keys \\\"\\r\\n +\\\"(notebook, tag, keyword) \\\"\\r\\n +\\\"VALUES (?,?,?);\\\",value_tuple)\",\n \"def make_new_tag():\\n return render_template('tags/new_tag.html')\",\n \"def create_tag(self, session, tags):\\n self._tag(session.put, tags=tags, session=session)\",\n \"def push_tag(self, tagname, attrs=[]):\\n tag = '<%s%s>' % (tagname, string_for_attrs(attrs))\\n self.str += '\\\\n' + indent(tag, self.indent_level())\\n self._tagstack.append(tagname)\",\n \"def add_tagging(self, task_instance):\",\n \"def append_tags(self, tags):\\n\\n tags = H.to_list(tags)\\n # self._tags.update(tags)\\n self.tags.update(tags)\",\n \"def add_genre(self, tag: Genre):\\n raise NotImplementedError\",\n \"def AddEventTag(self, event_tag):\\n self._RaiseIfNotWritable()\\n\\n # TODO: refactor to use AddOrUpdateAttributeContainer\\n self._storage_file.AddEventTag(event_tag)\\n\\n self._UpdateEventLabelsSessionCounter(event_tag)\",\n \"def add(self, k_spec, tag):\\n self.k_spec_dict[tag] = k_spec\",\n \"def tag_element(self,tag_name,element):\\n self.__add_to_dictlist(self.element2tags,tag_name,element)\\n self.__add_to_dictlist(self.tag2elements,element,tag_name)\",\n \"def test_add_tagitem(self):\\n record = self.good_record()\\n record['tag'] = self.tag\\n # Create a fake record in mongo\\n id = self.images.insert(record)\\n\\n status = self.m.add_tag(id, self.system, 'testtag')\\n self.assertTrue(status)\\n rec = self.images.find_one({'_id': id})\\n self.assertIsNotNone(rec)\\n self.assertIn(self.tag, rec['tag'])\\n self.assertIn('testtag', rec['tag'])\",\n \"def add_new_tag():\\n\\n name = request.form.get('name')\\n\\n new_tag = Tag(name=name)\\n db.session.add(new_tag)\\n db.session.commit()\\n\\n return redirect(f'/tags')\",\n \"def tag(request, tag_name):\\n raise NotImplementedError\",\n \"def add_tag(self, *,\\n id: str,\\n tag: str,\\n tag_type: str = 'default',\\n resource_type: ResourceType = ResourceType.Table) -> None:\\n LOGGER.info(f'New tag {tag} for id {id} with type {tag_type} and resource type {resource_type.name}')\\n\\n resource_table = f'{resource_type.name.lower()}_tag'\\n resource_model = self._get_model_from_table_name(resource_table)\\n if not resource_model:\\n raise NotImplementedError(f'The resource type {resource_type.name} is not defined!')\\n\\n resource_key = f'{resource_type.name.lower()}_rk'\\n\\n tag_record = RDSTag(rk=tag, tag_type=tag_type)\\n resource_tag_record = resource_model(tag_rk=tag)\\n resource_tag_record.__setattr__(resource_key, id)\\n try:\\n with self.client.create_session() as session:\\n session.merge(tag_record)\\n session.merge(resource_tag_record)\\n session.commit()\\n except Exception as e:\\n LOGGER.exception(f'Failed to add tag {tag} for {id}')\\n raise e\",\n \"def AddEventTag(self, event_tag):\\n self._RaiseIfNotWritable()\\n\\n event_identifier = event_tag.GetEventIdentifier()\\n if not isinstance(\\n event_identifier, identifiers.SerializedStreamIdentifier):\\n raise IOError('Unsupported event identifier type: {0:s}'.format(\\n type(event_identifier)))\\n\\n event_tag.event_stream_number = event_identifier.stream_number\\n event_tag.event_entry_index = event_identifier.entry_index\\n\\n # Check if the event has already been tagged on a previous occasion,\\n # we need to append the event tag any existing event tag.\\n stored_event_tag = self._GetEventTagByIdentifier(event_identifier)\\n if stored_event_tag:\\n event_tag.AddComment(stored_event_tag.comment)\\n event_tag.AddLabels(stored_event_tag.labels)\\n\\n self._AddAttributeContainer('event_tag', event_tag)\\n\\n self._UpdateEventTagIndex(event_tag)\",\n \"def setAddTags(self,value):\\n self.PDFreactorConfiguration.in1[\\\"addTags\\\"] = value\",\n \"def __init__(self, tag):\\n self.tag = tag\",\n \"def add_tags(event):\\n\\n add_tags_from_presets()\",\n \"def trackEmbedding(self, tag):\\n self.embeddedTags.append(tag)\",\n \"def tag_card(self, card, tag: str):\\n list = self.tags[tag]\\n list.append(card.multiverse_id)\\n self.db.tag_card_add(tag, card.multiverse_id)\",\n \"def add_node(self, node_data):\\n self.__rtags.append(True)\\n self.__nodedata.append(data)\\n self.__ltags.append(True)\",\n \"def for_tag(self, tag):\\n if not isinstance(tag, str):\\n raise TypeError('Tag must be a string')\\n\\n self.token['requiredTag'] = tag\\n\\n return self\",\n \"def add_tag(e, driver):\\n tag = random_tag(8)\\n e.find_element_by_class_name('add-tag').click()\\n \\n driver.find_element_by_class_name('tag_input')\\\\\\n .send_keys(tag)\\n driver.find_element_by_class_name('tag_input')\\\\\\n .send_keys(Keys.ENTER)\\n # driver.find_elements_by_class_name('save-tag').click()\\n return tag\",\n \"def add_tag(self, tag):\\n for task in self._tasks:\\n task.add_tag(tag)\\n\\n return self\",\n \"def add_tag(filename, tag_name):\\n storeapps = APP.config[\\\"storage\\\"]\\n filename = filename.encode(\\\"utf-8\\\")\\n print filename\\n\\n try:\\n application = list(nativeapps.io.ls(storeapps, r\\\".*\\\" + filename + \\\"$\\\"))[0]\\n meta_path = os.path.join(os.path.dirname(application), \\\"metadata.json\\\")\\n metadata = json.loads(nativeapps.io.readfile(meta_path))\\n tags = set(metadata.get(\\\"tags\\\", []))\\n tags.add(tag_name)\\n metadata[\\\"tags\\\"] = list(tags)\\n nativeapps.io.writefile(meta_path, json.dumps(metadata))\\n except IndexError:\\n return \\\"Unknown application: %s\\\" % (application), 404\\n\\n return \\\"added\\\", 200\",\n \"def handle_starttag(self, tag, attrs):\\n if tag == \\\"a\\\":\\n curr_tag = Tag(tag)\\n for attribute in attrs:\\n curr_tag.add_attribute(attribute[0], attribute[1])\\n self.current_tags.append(curr_tag)\\n elif tag == \\\"img\\\":\\n curr_tag = Tag(tag)\\n for attribute in attrs:\\n curr_tag.add_attribute(attribute[0], attribute[1])\\n self.images.append(curr_tag)\",\n \"def set_tag(self, key, value):\\n return self\",\n \"def _set_tag(self, v, load=False):\\n if hasattr(v, \\\"_utype\\\"):\\n v = v._utype(v)\\n try:\\n t = YANGDynClass(v,base=RestrictedClassType(base_type=long, restriction_dict={'range': ['-2147483648..2147483647']}, int_size=32), is_leaf=True, yang_name=\\\"tag\\\", rest_name=\\\"tag\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-bgp-operational', defining_module='brocade-bgp-operational', yang_type='int32', is_config=False)\\n except (TypeError, ValueError):\\n raise ValueError({\\n 'error-string': \\\"\\\"\\\"tag must be of a type compatible with int32\\\"\\\"\\\",\\n 'defined-type': \\\"int32\\\",\\n 'generated-type': \\\"\\\"\\\"YANGDynClass(base=RestrictedClassType(base_type=long, restriction_dict={'range': ['-2147483648..2147483647']}, int_size=32), is_leaf=True, yang_name=\\\"tag\\\", rest_name=\\\"tag\\\", parent=self, path_helper=self._path_helper, extmethods=self._extmethods, register_paths=True, namespace='urn:brocade.com:mgmt:brocade-bgp-operational', defining_module='brocade-bgp-operational', yang_type='int32', is_config=False)\\\"\\\"\\\",\\n })\\n\\n self.__tag = t\\n if hasattr(self, '_set'):\\n self._set()\",\n \"def handle_add_new_tag():\\n tag = Tag(name=request.form['name'])\\n\\n db.session.add(tag)\\n db.session.commit()\\n\\n return redirect('/tags')\",\n \"def add_new_tag(self, body, **kwargs):\\n kwargs['_return_http_data_only'] = True\\n if kwargs.get('callback'):\\n return self.add_new_tag_with_http_info(body, **kwargs)\\n else:\\n (data) = self.add_new_tag_with_http_info(body, **kwargs)\\n return data\",\n \"def add_tags(self, tags):\\n\\n if isinstance(tags, string_types):\\n message = \\\"tags should be a list or None, got tags={}\\\".format(tags)\\n raise TypeError(message)\\n\\n self.tags = self.tags.union(tags)\",\n \"def addTag(id = 0):\\n\\tinsertQueries.addTag(request.form)\\n\\tresults = queries.package(id)\\n\\treturn render_template('package.html', package=results)\",\n \"def add_tag():\\n\\n tag_name = request.form[\\\"name\\\"]\\n\\n if not tag_name:\\n flash(\\\"Please enter tag name\\\")\\n return redirect(\\\"/tags/new\\\")\\n\\n tag = Tag(name=tag_name)\\n db.session.add(tag)\\n db.session.commit()\\n\\n return redirect(\\\"/tags\\\")\",\n \"def insert_tag(self, tagname, attrs=[], text=\\\"\\\",\\n autoclose=False, newline=False):\\n if autoclose:\\n self.str += '\\\\n<%s%s%s>' % (tagname, string_for_attrs(attrs),\\n ' /' if self.is_xml else '')\\n else:\\n self.push_tag(tagname, attrs)\\n if text:\\n if newline:\\n self.insert_text('\\\\n' + indent(text, self.indent_level()))\\n else:\\n self.insert_text(text)\\n self.pop_tag(newline=newline)\",\n \"def create_or_get_tag(self, tag_name: str, *args, **kwargs):\\n\\n tag_data = api.create_or_get_tag(\\n tag_name,\\n *args,\\n api_key=self.__creds.api_key_v2, \\n **kwargs)\\n return en.Tag(tag_data)\",\n \"def add_tag(convo_ID, tag_ID):\\n # Make API request\\n url = \\\"https://api2.frontapp.com/conversations/\\\" + convo_ID + \\\"/tags\\\"\\n payload = json.dumps({\\\"tag_ids\\\": [tag_ID]})\\n headers = {\\\"Authorization\\\": BEARER_TOKEN, \\\"Content-Type\\\": \\\"application/json\\\"}\\n requests.request(\\\"POST\\\", url, headers=headers, data=payload)\",\n \"def updateTag(self, authenticationToken, tag):\\r\\n pass\",\n \"def set_tag(self, xaf, name, value, add_latest=True):\\n counter_str_value = str(self._get_counter_tag_value(xaf))\\n tag_name = self.__get_tag_name(name, counter_str_value)\\n self.__set_tag(xaf, tag_name, value)\\n if add_latest:\\n self._set_tag_latest(xaf, name, value)\",\n \"def update_tag(self, tag):\\n resp = self.put(_u.build_uri(\\\"tag\\\", domain=self.domain),\\n data={'tag': tag})\\n return utils.handle_response(resp)\",\n \"def create_tag_with_entry(title):\\n tag = Tag.objects.create(title=title)\\n tag.save()\\n tag.entry.add(1)\\n return tag\",\n \"def add(self, obj):\\n raise NotImplementedError\",\n \"def add_tag_meta(self, new_tag_meta: TagMeta) -> ProjectMeta:\\n return self.add_tag_metas([new_tag_meta])\",\n \"def _add(object, name, value):\\n self.__added__.append(name)\\n setattr(object, name, value)\",\n \"def create_a_tag(self, tag_id, contact_id):\\n data = {\\\"contactTag\\\":{\\\"contact\\\":str(contact_id),\\\"tag\\\":str(tag_id)}}\\n\\n return self.client._post(\\\"/contactTags\\\", json=data)\",\n \"def add_tags_to_resource(ResourceId=None, Tags=None):\\n pass\",\n \"def setTag(self, tag):\\n\\t\\tself.config.TAG = tag\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8477626","0.83942044","0.7990679","0.79390234","0.77830034","0.7674143","0.7644332","0.761934","0.72644126","0.7179257","0.7171568","0.71568304","0.70963436","0.70908105","0.6981176","0.69674325","0.69674325","0.69674325","0.69674325","0.68249136","0.6822304","0.67993975","0.6798608","0.6729537","0.67142737","0.67108727","0.66678905","0.6629171","0.662506","0.6624074","0.66177917","0.66054016","0.66031903","0.6597485","0.6587438","0.6563493","0.6559178","0.6551275","0.6533099","0.6528926","0.65250933","0.65208864","0.65183085","0.6505376","0.64971745","0.64751554","0.6432787","0.6418398","0.6384972","0.6376041","0.63695925","0.6321445","0.6307368","0.6295858","0.62437654","0.6234011","0.6219095","0.62175846","0.62165666","0.6178402","0.61773175","0.6165396","0.61619025","0.61492944","0.61410457","0.6130532","0.61246353","0.6124046","0.61111206","0.6106288","0.6096927","0.6096541","0.6092846","0.6084088","0.60758215","0.6057679","0.60463643","0.6039883","0.6017449","0.6017355","0.60169506","0.6010824","0.59998286","0.59974706","0.59945726","0.5973445","0.5970084","0.59487355","0.594719","0.59466124","0.59427804","0.59271616","0.5922512","0.5917673","0.59133947","0.5897641","0.58788925","0.58775526","0.58751637","0.5868985","0.58662874"],"string":"[\n \"0.8477626\",\n \"0.83942044\",\n \"0.7990679\",\n \"0.79390234\",\n \"0.77830034\",\n \"0.7674143\",\n \"0.7644332\",\n \"0.761934\",\n \"0.72644126\",\n \"0.7179257\",\n \"0.7171568\",\n \"0.71568304\",\n \"0.70963436\",\n \"0.70908105\",\n \"0.6981176\",\n \"0.69674325\",\n \"0.69674325\",\n \"0.69674325\",\n \"0.69674325\",\n \"0.68249136\",\n \"0.6822304\",\n \"0.67993975\",\n \"0.6798608\",\n \"0.6729537\",\n \"0.67142737\",\n \"0.67108727\",\n \"0.66678905\",\n \"0.6629171\",\n \"0.662506\",\n \"0.6624074\",\n \"0.66177917\",\n \"0.66054016\",\n \"0.66031903\",\n \"0.6597485\",\n \"0.6587438\",\n \"0.6563493\",\n \"0.6559178\",\n \"0.6551275\",\n \"0.6533099\",\n \"0.6528926\",\n \"0.65250933\",\n \"0.65208864\",\n \"0.65183085\",\n \"0.6505376\",\n \"0.64971745\",\n \"0.64751554\",\n \"0.6432787\",\n \"0.6418398\",\n \"0.6384972\",\n \"0.6376041\",\n \"0.63695925\",\n \"0.6321445\",\n \"0.6307368\",\n \"0.6295858\",\n \"0.62437654\",\n \"0.6234011\",\n \"0.6219095\",\n \"0.62175846\",\n \"0.62165666\",\n \"0.6178402\",\n \"0.61773175\",\n \"0.6165396\",\n \"0.61619025\",\n \"0.61492944\",\n \"0.61410457\",\n \"0.6130532\",\n \"0.61246353\",\n \"0.6124046\",\n \"0.61111206\",\n \"0.6106288\",\n \"0.6096927\",\n \"0.6096541\",\n \"0.6092846\",\n \"0.6084088\",\n \"0.60758215\",\n \"0.6057679\",\n \"0.60463643\",\n \"0.6039883\",\n \"0.6017449\",\n \"0.6017355\",\n \"0.60169506\",\n \"0.6010824\",\n \"0.59998286\",\n \"0.59974706\",\n \"0.59945726\",\n \"0.5973445\",\n \"0.5970084\",\n \"0.59487355\",\n \"0.594719\",\n \"0.59466124\",\n \"0.59427804\",\n \"0.59271616\",\n \"0.5922512\",\n \"0.5917673\",\n \"0.59133947\",\n \"0.5897641\",\n \"0.58788925\",\n \"0.58775526\",\n \"0.58751637\",\n \"0.5868985\",\n \"0.58662874\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":11283,"cells":{"query":{"kind":"string","value":"Removes tags specified by code/name from specified cargoes"},"document":{"kind":"string","value":"def remove_tag(self, cr, uid, ids, code=None, name=None, context=None):\n tag_obj = self.pool.get('res.tag')\n tag_ids = tag_obj.get_tag_ids(cr, uid, self._name, code=code, name=name, context=context)\n\n if tag_ids:\n self.write(cr, uid, ids, {'tag_ids': [(3, tid) for tid in tag_ids]}, context=context)\n\n return bool(tag_ids)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def remove_tag(args):","def remove_tags(self, tags):\n\n tags = H.to_list(tags)\n # self._tags.difference_update(tags)\n self.tags.difference_update(tags)","def remove_tags(html, *tags):\n to_remove = []\n for t in tags:\n to_remove.extend(list(_make_tag_patterns(t)))\n return remove(html, *to_remove)","def remove_node_by_tagname(nodes: List, tagname: str):\n\n for remove in [node for node in nodes if node.tagname == tagname]:\n nodes.remove(remove)","def ancestor_remove_tags(self, tags):\n\n for ancestor in self.lineage():\n ancestor.remove_tags(tags)","def remove_tags(self, tags):\n cp = self.copy()\n cp.tags = cp.tags - set(tags)\n return cp","def remove_tags(self, tags):\n for tag in tags:\n self.remove_tag(tag)\n\n return self","def test_remove_tag(self):\n fc = self.read_feature(region='Adriatic_Sea')\n\n fc.tag(tags=['Mediterranean_Basin', 'tag1'], remove=True)\n assert (fc.features[0]['properties']['tags'] == 'Adriatic_Sea')\n\n self.check_feature(fc.features[0])","def remove(name):","def delTags(self):\r\n for tag in self.tags:\r\n self.canvasCirkt.delete(tag)\r\n self.canvasCirkt.update()","def RemoveTags(obj):\n tags = obj.GetTags() # Get tags\n for t in tags: # Iterate through tags\n t.Remove() # Remove tag","def remove_tags_from_resource(ResourceId=None, TagKeys=None):\n pass","def _drop_tags(target, *regexps):\n\n for tagname in list(target.keys()):\n for _ in (x for x in regexps if re.search(x, tagname)):\n try:\n del target[tagname]\n logger.debug('Drop tag {0}[{1}]'.format(\n type(target).__name__, tagname))\n except KeyError:\n pass\n break","def deep_remove_tags(self, tags):\n\n self.remove_tags(tags)\n for child in self.all_children():\n child.remove_tags(tags)","def delete_tags(self, entry, tags, tag_template_name):\n persisted_tags = self.list_tags(entry.name)\n\n # Fetch GRPCIterator.\n persisted_tags = [tag for tag in persisted_tags]\n\n for persisted_tag in persisted_tags:\n logging.info('Processing Tag from Template: %s ...',\n persisted_tag.template)\n tag_to_delete = None\n\n if tag_template_name in persisted_tag.template:\n tag_to_delete = persisted_tag\n for tag in tags:\n if tag.template == persisted_tag.template and \\\n tag.column == persisted_tag.column:\n tag_to_delete = None\n break\n\n if tag_to_delete:\n self.delete_tag(tag_to_delete)\n logging.info('Tag deleted: %s', tag_to_delete.name)\n else:\n logging.info('Tag is up-to-date: %s', persisted_tag.name)","def remove_tag(self, rules):\n for rule in rules:\n [s.extract() for s in self.soup.find_all(limit=1, **rule)]","async def delete_tags(tags: List[str], map_name: str, conn: Connection, channel: TextChannel) -> None:\n for tag in tags:\n insert_sql = \"\"\"delete from tags where \n map_id in (select map_id from maps where map_path=?) and \n tag_name=? \"\"\"\n select(conn, insert_sql, (map_name, tag))\n await channel.send(f\"Removed tags `{' '.join(tags)}` from map {map_name}\")","async def removetags(self, ctx, tag=None):\r\n\t\tTag = self.settings.ServerConfig(ctx.guild.id, 'Tags')\r\n\t\tif not tag in Tag:\r\n\t\t\treturn await ctx.send('Can\\'t find Tag: '.format(tag))\t\r\n\r\n\t\tdel Tag[tag]\r\n\t\tself.settings.ServerConfig(ctx.guild.id, 'Tags', Tag)\r\n\r\n\t\tawait ctx.send('Removed Tag: '.format(tag))","def remove_tags_and_contents(html, *tags):\n # remove *tags and the stuff between them. tag is \"table\"\n for tag in tags:\n start, end = _make_tag_patterns(tag)\n reobj = re.compile(r\"%s.*?%s\" % (start, end), re.IGNORECASE|re.DOTALL)\n html = reobj.sub(\" \", html)\n return html","def remove_tag(self, tag):\n if tag in self.tags:\n index = self.tags.index(tag)\n self.tags[index:index + 1] = []\n self.stop_times[index:index + 1] = []","def clean(self):\n tags = self.get_tags()\n for tag in tags:\n image_name = self.build_image_name(tag)\n try:\n self.client.images.remove(image_name, force=True)\n except Exception as ex:\n print('Cannot remove {}: {}'.format(tag, str(ex)))","def clean_nodes_no_names(tag, data):\n\tif not isinstance(tag, tuple):\n\t\tfor each in data:\n\t\t\tif each['k'] != [] and each['v'] != []:\n\t\t\t\tif tag in each['k'] and 'name' not in each['k']:\n\t\t\t\t\teach['removed'] = 'true'\n\t\t\t\t\ttagValueData = dict(zip(each['k'], each['v']))\n\t\t\t\t\tif tagValueData.get('amenity') == 'atm':\n\t\t\t\t\t\teach['removed'] = 'false'\n\t\t\tyield each\n\telse:\n\t\tfor each in data:\n\t\t\tif each['k'] != [] and each['v'] != []:\n\t\t\t\tif tag[0] in each['k'] and tag[1] in each['v'] and 'name' not in each['k']:\n\t\t\t\t\teach['removed'] = 'true'\n\t\t\tyield each","def remove_tag(request, ck, tag_name):\n\n refresh_template = request.session[constants.ACTUAL_TEMPLATE]\n\n tags = request.session[constants.ADD_TAGS]\n tag = next(el for el in tags if el.name == tag_name)\n\n if ck != \"0\":\n coding = get_object_or_404(CodingProject, id=ck)\n\n # TODO: Review this \n us = get_user(request)\n user = us\n\n # Coding must have been created by the current user and\n if coding.coder != user.id:\n raise Http404\n\n if coding.tags.filter(name=tag_name):\n cache_list = request.session[constants.REM_TAGS]\n cache_list.append(tag)\n\n tags.remove(tag)\n request.session[constants.ADD_TAGS] = tags\n\n # TODO: Centralize this?\n return HttpResponseRedirect(refresh_template)","def remove_tags_recursive(self, tags2remove: List[str]) -> None:\n self.tags = [x for x in self.tags if x not in tags2remove]\n for data in self._child_data.values():\n data.remove_tags_recursive(tags2remove)","def remove(self, value):\n tags = self.__all_tags()\n if value in tags:\n tags.remove(value)\n self.__post_changes(tags)","def remove_tag(self, key, value=None):\r\n if value:\r\n tags = {key : value}\r\n else:\r\n tags = [key]\r\n status = self.connection.delete_tags([self.id], tags)\r\n if key in self.tags:\r\n del self.tags[key]","def delete_tags(configurationIds=None, tags=None):\n pass","def tags_remove(self, item, tags):\n self._createTagAction(item, \"tags_remove\", tags)","def remove_tags(self, tags):\n for task in self._tasks:\n task.remove_tags(tags)\n\n return self","def remove_tags(self, rules):\n for rule in rules:\n [s.extract() for s in self.soup.find_all(**rule)]","def removeParameter(cTag, name): #@NoSelf","def remove():","def delete_tag(filename, tag_name):\n storeapps = APP.config[\"storage\"]\n filename = filename.encode(\"utf-8\")\n\n try:\n application = list(nativeapps.io.ls(storeapps, r\".*\" + filename + \"$\"))[0]\n meta_path = os.path.join(os.path.dirname(application), \"metadata.json\")\n metadata = json.loads(nativeapps.io.readfile(meta_path))\n tags = metadata.get(\"tags\", [])\n if tag_name in tags:\n tags.remove(tag_name)\n metadata[\"tags\"] = tags\n nativeapps.io.writefile(meta_path, json.dumps(metadata))\n except IndexError:\n return \"Unknown application: %s\" % (application), 404\n\n return \"removed\", 200","def remove_cells(self, tag):\n tagged_cells = self.get_cells(tag)\n if tagged_cells:\n print(f\"- removing cells tagged {tag} from {self.filename}\")\n self.content.cells = filter(lambda cell: cell not in tagged_cells, self.content.cells)","def remove_many_descriptors(self, uuids):","def test_remove_tags(self):\n p = self.load_policy({\n 'name': 'test-azure-tag',\n 'resource': 'azure.resourcegroup',\n 'filters': [\n {'type': 'value',\n 'key': 'name',\n 'op': 'eq',\n 'value_type': 'normalize',\n 'value': 'test_vm'}\n ],\n 'actions': [\n {'type': 'tag',\n 'tags': {'pre-existing-1': 'to-keep', 'pre-existing-2': 'to-keep',\n 'added-1': 'to-delete', 'added-2': 'to-delete'}},\n ],\n })\n p.run()\n\n # verify initial tag set\n s = Session()\n client = s.client('azure.mgmt.resource.ResourceManagementClient')\n rg = [rg for rg in client.resource_groups.list() if rg.name == 'test_vm'][0]\n self.assertEqual(rg.tags,\n {'pre-existing-1': 'to-keep', 'pre-existing-2': 'to-keep',\n 'added-1': 'to-delete', 'added-2': 'to-delete'})\n\n p = self.load_policy({\n 'name': 'test-azure-remove-tag',\n 'resource': 'azure.resourcegroup',\n 'filters': [\n {'type': 'value',\n 'key': 'name',\n 'op': 'eq',\n 'value_type': 'normalize',\n 'value': 'test_vm'}\n ],\n 'actions': [\n {'type': 'untag',\n 'tags': ['added-1', 'added-2']}\n ],\n })\n p.run()\n\n # verify tags removed and pre-existing tags not removed\n rg = [rg for rg in client.resource_groups.list() if rg.name == 'test_vm'][0]\n self.assertEqual(rg.tags,\n {'pre-existing-1': 'to-keep', 'pre-existing-2': 'to-keep'})","def destroyContainer(tag): #@NoSelf","async def remove(self, ctx, name: str):\n if self.config.hexists(\"config:tags:global\", name):\n if not checks.sudo_check(ctx.message):\n await ctx.send(\"Only {} can remove global tags.\".format(self.bot.owner))\n else:\n self.config.hdel(\"config:tags:global\", name)\n\n elif self.config.hexists(\"chan:{}:tags\".format(ctx.message.channel.id), name):\n self.config.hdel(\"chan:{}:tags\".format(ctx.message.channel.id), name)\n # Don't allow ability to remove global tags\n\n elif self.config.hexists(\"guild:{}:tags\".format(ctx.message.guild.id), name):\n self.config.hdel(\"guild:{}:tags\".format(ctx.message.guild.id, name))\n\n await ctx.send(\"Tag {} removed.\".format(name))","def delete_tag(self,tag):\r\n\r\n # with shelf\r\n if self.using_shelf:\r\n del self.tag_dict[tag]","def cleanup(self):\n for residue in self.debumper.biomolecule.residues:\n if not isinstance(residue, aa.Amino):\n continue\n if residue.name == \"GLH\" or \"GLH\" in residue.patches:\n if residue.has_atom(\"HE1\") and residue.has_atom(\"HE2\"):\n residue.remove_atom(\"HE1\")\n elif residue.name == \"ASH\" or \"ASH\" in residue.patches:\n if residue.has_atom(\"HD1\") and residue.has_atom(\"HD2\"):\n residue.remove_atom(\"HD1\")","def completely_remove_tag(self, owner_userid, tag_name):\n\t\tself.log.debug(\"completely_remove_tag()\")\n\t\towner_userid = validation.cast_integer(owner_userid, 'owner_userid')\n\n\t\td = self.app.db.runOperation(\"SELECT zoto_remove_tag_from_all_user_images(%s, %s)\",\n\t\t\t\t (owner_userid, tag_name))\n\t\td.addCallback(lambda _: (0, \"success\"))\n\t\td.addErrback(lambda _: (-1, _.getErrorMessage()))\n\t\treturn d","def __delitem__(self, name):\n tag = self._find(name)\n if tag is not None:\n self.meta.remove(tag)\n else:\n raise KeyError(name)","def remove_unwanted_tags(soup: bs4.BeautifulSoup):\n for tag in soup.find_all(['script', 'style']):\n tag.decompose()","def removeTags(self, words):\n\t\treturn re.sub(r'<.*?>', '', words)","def remove_tag(tag):\n check_call(['git', 'tag', '-d', tag])","def remove_html_tags(self, text, tags):\n\t\tcheck_if_any_type(text, [str, str])\n\n\t\tfor tag in tags:\n\t\t\tcheck_if_any_type(tag, [str, str])\n\t\t\ttext = re.compile('<\\/?%s\\/?>' % tag, re.U).sub('', text)\n\t\treturn text","def tags_clear(self, item, tags):\n self._createTagAction(item, \"tags_clear\", tags)","def strip_tags(name):\n pattern = r'[\\s]*\\[[^\\]]*\\][\\s]*'\n name = re.sub(pattern, '', name)\n pattern = r'[\\s]*\\([^\\)]*\\)[\\s]*'\n name = re.sub(pattern, '', name)\n return name","def remove(self, name):\n for var in self.inputs:\n if var.name == name:\n self.inputs.remove(var)\n return\n for var in self.outputs:\n if var.name == name:\n self.outputs.remove(var)\n return","def untag_element(self,tag_name,element):\n pass","def cli(env, dry_run):\n\n tag_manager = TagManager(env.client)\n empty_tags = tag_manager.get_unattached_tags()\n\n for tag in empty_tags:\n if dry_run:\n click.secho(f\"(Dry Run) Removing {tag.get('name')}\", fg='yellow')\n else:\n result = tag_manager.delete_tag(tag.get('name'))\n color = 'green' if result else 'red'\n click.secho(f\"Removing {tag.get('name')}\", fg=color)","def tags():","def remove_from_xml_tree(the_config, tag):\n for elt in the_config.iterdescendants():\n if tag in elt.tag:\n elt.getparent().remove(elt)","def remove_city(g, city_name):\n code = g.convert[city_name]\n \n for key in g.city_dict:\n \n old_flights_in = g.city_dict[key].get_flights_in()\n new_flights_in = []\n for flight in old_flights_in:\n if(flight[0] != code):\n new_flights_in.append(flight)\n \n old_flights_out = g.city_dict[key].get_flights_out()\n new_flights_out = []\n for flight in old_flights_out:\n if(flight[0] != code):\n new_flights_out.append(flight)\n \n g.city_dict[key].set_flights_in(new_flights_in)\n g.city_dict[key].set_flights_out(new_flights_out)\n \n del g.city_dict[code]\n del g.convert[city_name]\n \n return g","def delete_tag(tag):\n tag.destroy()","def removeEmbedded(self, tag):\n self.embeddedTags = self.embeddedTags[:-1]","def remove_tag(self, dataset: \"Dataset\", tag: \"DatasetTag\"):\n raise NotImplementedError","def delete_all(self):\n for tag in self._segments['APP1'].get_tag_list():\n try:\n self.__delattr__(tag)\n except AttributeError:\n warnings.warn(\"could not delete tag \" + tag, RuntimeWarning)","def remove_Tags(self,text):\n cleaned_text = re.sub('<[^<]+?>', '', text)","def untag(self, uuid, tags=None):\n if isinstance(tags, basestring):\n tags = [tags]\n\n self._backend.untag(uuid, tags)","def remove(self, egg):","def remove_asset(self, name):\n if name in self.assets:\n del self.assets[name]","def strip_tags(self, rules):\n tags = list()\n for rule in rules:\n for tag in self.soup.find_all(**rule):\n tag.replace_with_children()\n tags.append(tag.name)\n return tags","def removeNode(cTag, nTag): #@NoSelf","def clean(vendor):\n remove_all(\n path\n for path in vendor.glob('*')\n if path.basename() != 'vendored.txt'\n )","def remove_tag(self, tag: str) -> None:\n tags = self.get_tag_index()\n tags.remove(tag)\n self.write_tag_index(list(set(tags)))","def remove_mix(self, name: str) -> None:\n self.remove(name)","def delete_unused_tags( self, owner ):\n self.filter(\n owner = owner,\n documents__isnull = True).delete()","def remove_book(name):\n global BOOKS\n BOOKS = [book for book in BOOKS if book['name'] != name]","def delete_terms(self, *terms):\n result = self.sequence\n for term in ANCOVA(*terms).sequence:\n result.remove(term)\n return ANCOVA(*result)","def remove_region_names(self):\r\n for region in self.gt_seq_region:\r\n region.attributes.name = ''","def strip_tags(self, *a, **kw):\n return strip_tags(self._widget_cget(text_s), *a, **kw)","def remove(self, name: str) -> None:\n del self.components[name]","def remove(self, tag_name: str, category: ty.Optional[str] = None):\n tags = self.__holder.db_tags.filter(lambda t: t.name == tag_name)\n if category is not None:\n tags = tags.filter(category=category)\n\n tag = tags.first()\n if tag:\n self.__holder.db_tags.remove(tag)","def purge_categories(app, env, docname):\n if not hasattr(env, \"categories\"):\n return # nothing to do\n\n categories = env.categories\n try:\n name, version = algorithm_name_and_version(docname)\n except RuntimeError: # not an algorithm page\n return\n\n deadref = PageRef(name, docname)\n for category in categories.itervalues():\n pages = category.pages\n if deadref in pages:\n pages.remove(deadref)","def set_tags(self, tags):\r\n current_tags = set(self.tag_names())\r\n updated_tags = set(tags)\r\n removed_tags = current_tags.difference(updated_tags)\r\n new_tags = updated_tags.difference(current_tags)\r\n \r\n for tag in new_tags:\r\n self.add_tag(tag)\r\n \r\n for tag in removed_tags:\r\n self.remove_tag(tag)","def remove_tag(convo_ID, tag_ID):\n # Make API request\n url = \"https://api2.frontapp.com/conversations/\" + convo_ID + \"/tags\"\n payload = json.dumps({\"tag_ids\": [tag_ID]})\n headers = {\"Authorization\": BEARER_TOKEN, \"Content-Type\": \"application/json\"}\n requests.request(\"DELETE\", url, headers=headers, data=payload)","def eliminate(sv, nam):\r\n del sv.Object[nam] # from sv.Object dictionary\r\n sv.Object_list.remove(nam)","def remove_low_locality_tags(locality, tags_list):\n LOCALITY_THRESHOLD = 1 # Locality of 'newyorkcity' is 0.057\n tags_to_remove = []\n for tag in tags_list:\n if tag not in locality:\n tags_to_remove.append(tag)\n else:\n locality_score = locality[tag]\n if locality_score[0] < LOCALITY_THRESHOLD:\n tags_to_remove.append(tag)\n for tag in tags_to_remove:\n tags_list.remove(tag)","def remove(self, *args):\n pass","def untag(tagged_sentence):\n return [w for (w, t) in tagged_sentence]","def tags_remove(self, item_id, tags, **params):\n\n if isinstance(tags, basestring):\n tags = tags.split(',')\n\n self.queue('tags_remove', item_id=item_id, tags=tags, **params)","def untag(tagged_sentence):\n return [w for w, _ in tagged_sentence]","def remove_tag(self, tag):\n cp = self.copy()\n cp.tags.remove(tag)\n return cp","def remove_custom(self, custom: List[Dict[str, any]]) -> None:\n for entry in custom:\n phrase = as_phrase_object(entry, ngram_size=self.ngram_size, skip_size=self.skip_size)\n for custom_property in phrase.metadata:\n del self.custom[phrase.phrase_string][custom_property]","def remove(self, comp):\n\t\tif isinstance(comp, Component):\n\t\t\tif comp in self._contents:\n\t\t\t\tcomp.container = None\n\t\telse:\n\t\t\tfor item in comp:\n\t\t\t\tself.remove(item)","def remove_by_name(self, values, name):\n for d in values:\n if d[\"name\"] == name:\n values.remove(d)\n return","def remove(self, *names):\n for name in names:\n self._storage.pop(name, None)","async def slashtag_remove(self, ctx: commands.Context, *, tag: GuildTagConverter):\n await ctx.send(await tag.delete())","def untagAll(self, authenticationToken, guid):\r\n pass","def remove(self, *args):\n return _libsbml.ListOfCompartments_remove(self, *args)","def remove(self, name):\n raise NotImplementedError","def replace_tags(self, photo_id, tag_list):\n # get all the tags attached to the photo\n current_tags = self.db.make_query(\n '''\n select * from photo_tag where photo_id = {}\n '''.format(photo_id)\n )\n\n print(current_tags)\n\n # remove the current tags\n self.db.make_query(\n '''\n delete from photo_tag where photo_id = {}\n '''.format(photo_id)\n )\n\n for tag in tag_list:\n # add tags in the tag_list\n self.db.make_query(\n '''\n insert into photo_tag (photo_id, tag_name)\n values ({}, \"{}\")\n '''.format(photo_id, tag)\n )\n\n self.update_photo_count(tag)","def discard_key_from_tag(self,tag,key):\r\n\r\n # with shelf\r\n if self.using_shelf:\r\n\r\n self.tag_dict[tag].discard(key)\r\n\r\n\r\n #with database\r\n if self.using_database:\r\n value_tuple = (notebookname,tag,key,)\r\n db_cursor.execute(\"DELETE FROM tags_to_keys\"\r\n +\" WHERE notebook=? AND tag=?\"\r\n +\" AND keyword=?;\",\r\n value_tuple)","def update_tags(self, tags, **kwargs):\n request = RequestMiddleware.get_request()\n is_admin = request.user and request.user.is_admin\n # Keep all tags that start with pf: because they are reserved.\n preserved = [tag for tag in self.tags if tag.startswith('pf:')]\n if is_admin:\n remove = [tag[1:] for tag in tags if tag.startswith('-pf:')]\n preserved = [tag for tag in preserved if tag not in remove]\n\n # Filter out new tags that are invalid or reserved.\n accepted = [tag for tag in tags\n if TAG_REGEX_COMPILED.match(tag)\n and (is_admin or not tag.startswith('pf:'))]\n # Limit the number of tags per entity.\n if len(accepted + preserved) > settings.MAX_TAGS_PER_ENTITY:\n accepted = accepted[:settings.MAX_TAGS_PER_ENTITY - len(preserved)]\n self.tags = list(set(accepted + preserved))","def replace_tags(codes, new_codes, tag_dict):\n\n # replace tags in code\n for _dict in new_codes:\n for key, value in _dict.items():\n _replace_tags(codes, key, value, tag_dict, 0)\n\n # `codes` is updated inplace, no return","def remove_tag(self, tag):\n for task in self._tasks:\n task.remove_tag(tag)\n\n return self","def remove_cat(self, path: Path):\n if not self.active:\n return\n if path is None:\n return\n for i, coord in enumerate(path.path):\n self.cat[coord[1]][coord[0]].remove((path.identifier, i))","def delete_taggit_tags(apps, schema_editor):\n TaggitTag = apps.get_model('taggit', 'Tag')\n TaggitTag.objects.all().delete()","def remove_collection_names(account, dataset, tags):\n\n token = get_access_token()\n\n if dataset.startswith(\"N:dataset:\"):\n selected_dataset_id = dataset\n else:\n selected_dataset_id = get_dataset_id(token, dataset)\n\n if not has_edit_permissions(token, selected_dataset_id):\n abort(403, \"You do not have permission to edit this dataset.\")\n\n for tagid in tags:\n r = requests.delete(f\"{PENNSIEVE_URL}/datasets/{str(selected_dataset_id)}/collections/{str(tagid)}\", headers=create_request_headers(token))\n r.raise_for_status()\n\n return dict({\"collection\": \"Collection removed\"})"],"string":"[\n \"def remove_tag(args):\",\n \"def remove_tags(self, tags):\\n\\n tags = H.to_list(tags)\\n # self._tags.difference_update(tags)\\n self.tags.difference_update(tags)\",\n \"def remove_tags(html, *tags):\\n to_remove = []\\n for t in tags:\\n to_remove.extend(list(_make_tag_patterns(t)))\\n return remove(html, *to_remove)\",\n \"def remove_node_by_tagname(nodes: List, tagname: str):\\n\\n for remove in [node for node in nodes if node.tagname == tagname]:\\n nodes.remove(remove)\",\n \"def ancestor_remove_tags(self, tags):\\n\\n for ancestor in self.lineage():\\n ancestor.remove_tags(tags)\",\n \"def remove_tags(self, tags):\\n cp = self.copy()\\n cp.tags = cp.tags - set(tags)\\n return cp\",\n \"def remove_tags(self, tags):\\n for tag in tags:\\n self.remove_tag(tag)\\n\\n return self\",\n \"def test_remove_tag(self):\\n fc = self.read_feature(region='Adriatic_Sea')\\n\\n fc.tag(tags=['Mediterranean_Basin', 'tag1'], remove=True)\\n assert (fc.features[0]['properties']['tags'] == 'Adriatic_Sea')\\n\\n self.check_feature(fc.features[0])\",\n \"def remove(name):\",\n \"def delTags(self):\\r\\n for tag in self.tags:\\r\\n self.canvasCirkt.delete(tag)\\r\\n self.canvasCirkt.update()\",\n \"def RemoveTags(obj):\\n tags = obj.GetTags() # Get tags\\n for t in tags: # Iterate through tags\\n t.Remove() # Remove tag\",\n \"def remove_tags_from_resource(ResourceId=None, TagKeys=None):\\n pass\",\n \"def _drop_tags(target, *regexps):\\n\\n for tagname in list(target.keys()):\\n for _ in (x for x in regexps if re.search(x, tagname)):\\n try:\\n del target[tagname]\\n logger.debug('Drop tag {0}[{1}]'.format(\\n type(target).__name__, tagname))\\n except KeyError:\\n pass\\n break\",\n \"def deep_remove_tags(self, tags):\\n\\n self.remove_tags(tags)\\n for child in self.all_children():\\n child.remove_tags(tags)\",\n \"def delete_tags(self, entry, tags, tag_template_name):\\n persisted_tags = self.list_tags(entry.name)\\n\\n # Fetch GRPCIterator.\\n persisted_tags = [tag for tag in persisted_tags]\\n\\n for persisted_tag in persisted_tags:\\n logging.info('Processing Tag from Template: %s ...',\\n persisted_tag.template)\\n tag_to_delete = None\\n\\n if tag_template_name in persisted_tag.template:\\n tag_to_delete = persisted_tag\\n for tag in tags:\\n if tag.template == persisted_tag.template and \\\\\\n tag.column == persisted_tag.column:\\n tag_to_delete = None\\n break\\n\\n if tag_to_delete:\\n self.delete_tag(tag_to_delete)\\n logging.info('Tag deleted: %s', tag_to_delete.name)\\n else:\\n logging.info('Tag is up-to-date: %s', persisted_tag.name)\",\n \"def remove_tag(self, rules):\\n for rule in rules:\\n [s.extract() for s in self.soup.find_all(limit=1, **rule)]\",\n \"async def delete_tags(tags: List[str], map_name: str, conn: Connection, channel: TextChannel) -> None:\\n for tag in tags:\\n insert_sql = \\\"\\\"\\\"delete from tags where \\n map_id in (select map_id from maps where map_path=?) and \\n tag_name=? \\\"\\\"\\\"\\n select(conn, insert_sql, (map_name, tag))\\n await channel.send(f\\\"Removed tags `{' '.join(tags)}` from map {map_name}\\\")\",\n \"async def removetags(self, ctx, tag=None):\\r\\n\\t\\tTag = self.settings.ServerConfig(ctx.guild.id, 'Tags')\\r\\n\\t\\tif not tag in Tag:\\r\\n\\t\\t\\treturn await ctx.send('Can\\\\'t find Tag: '.format(tag))\\t\\r\\n\\r\\n\\t\\tdel Tag[tag]\\r\\n\\t\\tself.settings.ServerConfig(ctx.guild.id, 'Tags', Tag)\\r\\n\\r\\n\\t\\tawait ctx.send('Removed Tag: '.format(tag))\",\n \"def remove_tags_and_contents(html, *tags):\\n # remove *tags and the stuff between them. tag is \\\"table\\\"\\n for tag in tags:\\n start, end = _make_tag_patterns(tag)\\n reobj = re.compile(r\\\"%s.*?%s\\\" % (start, end), re.IGNORECASE|re.DOTALL)\\n html = reobj.sub(\\\" \\\", html)\\n return html\",\n \"def remove_tag(self, tag):\\n if tag in self.tags:\\n index = self.tags.index(tag)\\n self.tags[index:index + 1] = []\\n self.stop_times[index:index + 1] = []\",\n \"def clean(self):\\n tags = self.get_tags()\\n for tag in tags:\\n image_name = self.build_image_name(tag)\\n try:\\n self.client.images.remove(image_name, force=True)\\n except Exception as ex:\\n print('Cannot remove {}: {}'.format(tag, str(ex)))\",\n \"def clean_nodes_no_names(tag, data):\\n\\tif not isinstance(tag, tuple):\\n\\t\\tfor each in data:\\n\\t\\t\\tif each['k'] != [] and each['v'] != []:\\n\\t\\t\\t\\tif tag in each['k'] and 'name' not in each['k']:\\n\\t\\t\\t\\t\\teach['removed'] = 'true'\\n\\t\\t\\t\\t\\ttagValueData = dict(zip(each['k'], each['v']))\\n\\t\\t\\t\\t\\tif tagValueData.get('amenity') == 'atm':\\n\\t\\t\\t\\t\\t\\teach['removed'] = 'false'\\n\\t\\t\\tyield each\\n\\telse:\\n\\t\\tfor each in data:\\n\\t\\t\\tif each['k'] != [] and each['v'] != []:\\n\\t\\t\\t\\tif tag[0] in each['k'] and tag[1] in each['v'] and 'name' not in each['k']:\\n\\t\\t\\t\\t\\teach['removed'] = 'true'\\n\\t\\t\\tyield each\",\n \"def remove_tag(request, ck, tag_name):\\n\\n refresh_template = request.session[constants.ACTUAL_TEMPLATE]\\n\\n tags = request.session[constants.ADD_TAGS]\\n tag = next(el for el in tags if el.name == tag_name)\\n\\n if ck != \\\"0\\\":\\n coding = get_object_or_404(CodingProject, id=ck)\\n\\n # TODO: Review this \\n us = get_user(request)\\n user = us\\n\\n # Coding must have been created by the current user and\\n if coding.coder != user.id:\\n raise Http404\\n\\n if coding.tags.filter(name=tag_name):\\n cache_list = request.session[constants.REM_TAGS]\\n cache_list.append(tag)\\n\\n tags.remove(tag)\\n request.session[constants.ADD_TAGS] = tags\\n\\n # TODO: Centralize this?\\n return HttpResponseRedirect(refresh_template)\",\n \"def remove_tags_recursive(self, tags2remove: List[str]) -> None:\\n self.tags = [x for x in self.tags if x not in tags2remove]\\n for data in self._child_data.values():\\n data.remove_tags_recursive(tags2remove)\",\n \"def remove(self, value):\\n tags = self.__all_tags()\\n if value in tags:\\n tags.remove(value)\\n self.__post_changes(tags)\",\n \"def remove_tag(self, key, value=None):\\r\\n if value:\\r\\n tags = {key : value}\\r\\n else:\\r\\n tags = [key]\\r\\n status = self.connection.delete_tags([self.id], tags)\\r\\n if key in self.tags:\\r\\n del self.tags[key]\",\n \"def delete_tags(configurationIds=None, tags=None):\\n pass\",\n \"def tags_remove(self, item, tags):\\n self._createTagAction(item, \\\"tags_remove\\\", tags)\",\n \"def remove_tags(self, tags):\\n for task in self._tasks:\\n task.remove_tags(tags)\\n\\n return self\",\n \"def remove_tags(self, rules):\\n for rule in rules:\\n [s.extract() for s in self.soup.find_all(**rule)]\",\n \"def removeParameter(cTag, name): #@NoSelf\",\n \"def remove():\",\n \"def delete_tag(filename, tag_name):\\n storeapps = APP.config[\\\"storage\\\"]\\n filename = filename.encode(\\\"utf-8\\\")\\n\\n try:\\n application = list(nativeapps.io.ls(storeapps, r\\\".*\\\" + filename + \\\"$\\\"))[0]\\n meta_path = os.path.join(os.path.dirname(application), \\\"metadata.json\\\")\\n metadata = json.loads(nativeapps.io.readfile(meta_path))\\n tags = metadata.get(\\\"tags\\\", [])\\n if tag_name in tags:\\n tags.remove(tag_name)\\n metadata[\\\"tags\\\"] = tags\\n nativeapps.io.writefile(meta_path, json.dumps(metadata))\\n except IndexError:\\n return \\\"Unknown application: %s\\\" % (application), 404\\n\\n return \\\"removed\\\", 200\",\n \"def remove_cells(self, tag):\\n tagged_cells = self.get_cells(tag)\\n if tagged_cells:\\n print(f\\\"- removing cells tagged {tag} from {self.filename}\\\")\\n self.content.cells = filter(lambda cell: cell not in tagged_cells, self.content.cells)\",\n \"def remove_many_descriptors(self, uuids):\",\n \"def test_remove_tags(self):\\n p = self.load_policy({\\n 'name': 'test-azure-tag',\\n 'resource': 'azure.resourcegroup',\\n 'filters': [\\n {'type': 'value',\\n 'key': 'name',\\n 'op': 'eq',\\n 'value_type': 'normalize',\\n 'value': 'test_vm'}\\n ],\\n 'actions': [\\n {'type': 'tag',\\n 'tags': {'pre-existing-1': 'to-keep', 'pre-existing-2': 'to-keep',\\n 'added-1': 'to-delete', 'added-2': 'to-delete'}},\\n ],\\n })\\n p.run()\\n\\n # verify initial tag set\\n s = Session()\\n client = s.client('azure.mgmt.resource.ResourceManagementClient')\\n rg = [rg for rg in client.resource_groups.list() if rg.name == 'test_vm'][0]\\n self.assertEqual(rg.tags,\\n {'pre-existing-1': 'to-keep', 'pre-existing-2': 'to-keep',\\n 'added-1': 'to-delete', 'added-2': 'to-delete'})\\n\\n p = self.load_policy({\\n 'name': 'test-azure-remove-tag',\\n 'resource': 'azure.resourcegroup',\\n 'filters': [\\n {'type': 'value',\\n 'key': 'name',\\n 'op': 'eq',\\n 'value_type': 'normalize',\\n 'value': 'test_vm'}\\n ],\\n 'actions': [\\n {'type': 'untag',\\n 'tags': ['added-1', 'added-2']}\\n ],\\n })\\n p.run()\\n\\n # verify tags removed and pre-existing tags not removed\\n rg = [rg for rg in client.resource_groups.list() if rg.name == 'test_vm'][0]\\n self.assertEqual(rg.tags,\\n {'pre-existing-1': 'to-keep', 'pre-existing-2': 'to-keep'})\",\n \"def destroyContainer(tag): #@NoSelf\",\n \"async def remove(self, ctx, name: str):\\n if self.config.hexists(\\\"config:tags:global\\\", name):\\n if not checks.sudo_check(ctx.message):\\n await ctx.send(\\\"Only {} can remove global tags.\\\".format(self.bot.owner))\\n else:\\n self.config.hdel(\\\"config:tags:global\\\", name)\\n\\n elif self.config.hexists(\\\"chan:{}:tags\\\".format(ctx.message.channel.id), name):\\n self.config.hdel(\\\"chan:{}:tags\\\".format(ctx.message.channel.id), name)\\n # Don't allow ability to remove global tags\\n\\n elif self.config.hexists(\\\"guild:{}:tags\\\".format(ctx.message.guild.id), name):\\n self.config.hdel(\\\"guild:{}:tags\\\".format(ctx.message.guild.id, name))\\n\\n await ctx.send(\\\"Tag {} removed.\\\".format(name))\",\n \"def delete_tag(self,tag):\\r\\n\\r\\n # with shelf\\r\\n if self.using_shelf:\\r\\n del self.tag_dict[tag]\",\n \"def cleanup(self):\\n for residue in self.debumper.biomolecule.residues:\\n if not isinstance(residue, aa.Amino):\\n continue\\n if residue.name == \\\"GLH\\\" or \\\"GLH\\\" in residue.patches:\\n if residue.has_atom(\\\"HE1\\\") and residue.has_atom(\\\"HE2\\\"):\\n residue.remove_atom(\\\"HE1\\\")\\n elif residue.name == \\\"ASH\\\" or \\\"ASH\\\" in residue.patches:\\n if residue.has_atom(\\\"HD1\\\") and residue.has_atom(\\\"HD2\\\"):\\n residue.remove_atom(\\\"HD1\\\")\",\n \"def completely_remove_tag(self, owner_userid, tag_name):\\n\\t\\tself.log.debug(\\\"completely_remove_tag()\\\")\\n\\t\\towner_userid = validation.cast_integer(owner_userid, 'owner_userid')\\n\\n\\t\\td = self.app.db.runOperation(\\\"SELECT zoto_remove_tag_from_all_user_images(%s, %s)\\\",\\n\\t\\t\\t\\t (owner_userid, tag_name))\\n\\t\\td.addCallback(lambda _: (0, \\\"success\\\"))\\n\\t\\td.addErrback(lambda _: (-1, _.getErrorMessage()))\\n\\t\\treturn d\",\n \"def __delitem__(self, name):\\n tag = self._find(name)\\n if tag is not None:\\n self.meta.remove(tag)\\n else:\\n raise KeyError(name)\",\n \"def remove_unwanted_tags(soup: bs4.BeautifulSoup):\\n for tag in soup.find_all(['script', 'style']):\\n tag.decompose()\",\n \"def removeTags(self, words):\\n\\t\\treturn re.sub(r'<.*?>', '', words)\",\n \"def remove_tag(tag):\\n check_call(['git', 'tag', '-d', tag])\",\n \"def remove_html_tags(self, text, tags):\\n\\t\\tcheck_if_any_type(text, [str, str])\\n\\n\\t\\tfor tag in tags:\\n\\t\\t\\tcheck_if_any_type(tag, [str, str])\\n\\t\\t\\ttext = re.compile('<\\\\/?%s\\\\/?>' % tag, re.U).sub('', text)\\n\\t\\treturn text\",\n \"def tags_clear(self, item, tags):\\n self._createTagAction(item, \\\"tags_clear\\\", tags)\",\n \"def strip_tags(name):\\n pattern = r'[\\\\s]*\\\\[[^\\\\]]*\\\\][\\\\s]*'\\n name = re.sub(pattern, '', name)\\n pattern = r'[\\\\s]*\\\\([^\\\\)]*\\\\)[\\\\s]*'\\n name = re.sub(pattern, '', name)\\n return name\",\n \"def remove(self, name):\\n for var in self.inputs:\\n if var.name == name:\\n self.inputs.remove(var)\\n return\\n for var in self.outputs:\\n if var.name == name:\\n self.outputs.remove(var)\\n return\",\n \"def untag_element(self,tag_name,element):\\n pass\",\n \"def cli(env, dry_run):\\n\\n tag_manager = TagManager(env.client)\\n empty_tags = tag_manager.get_unattached_tags()\\n\\n for tag in empty_tags:\\n if dry_run:\\n click.secho(f\\\"(Dry Run) Removing {tag.get('name')}\\\", fg='yellow')\\n else:\\n result = tag_manager.delete_tag(tag.get('name'))\\n color = 'green' if result else 'red'\\n click.secho(f\\\"Removing {tag.get('name')}\\\", fg=color)\",\n \"def tags():\",\n \"def remove_from_xml_tree(the_config, tag):\\n for elt in the_config.iterdescendants():\\n if tag in elt.tag:\\n elt.getparent().remove(elt)\",\n \"def remove_city(g, city_name):\\n code = g.convert[city_name]\\n \\n for key in g.city_dict:\\n \\n old_flights_in = g.city_dict[key].get_flights_in()\\n new_flights_in = []\\n for flight in old_flights_in:\\n if(flight[0] != code):\\n new_flights_in.append(flight)\\n \\n old_flights_out = g.city_dict[key].get_flights_out()\\n new_flights_out = []\\n for flight in old_flights_out:\\n if(flight[0] != code):\\n new_flights_out.append(flight)\\n \\n g.city_dict[key].set_flights_in(new_flights_in)\\n g.city_dict[key].set_flights_out(new_flights_out)\\n \\n del g.city_dict[code]\\n del g.convert[city_name]\\n \\n return g\",\n \"def delete_tag(tag):\\n tag.destroy()\",\n \"def removeEmbedded(self, tag):\\n self.embeddedTags = self.embeddedTags[:-1]\",\n \"def remove_tag(self, dataset: \\\"Dataset\\\", tag: \\\"DatasetTag\\\"):\\n raise NotImplementedError\",\n \"def delete_all(self):\\n for tag in self._segments['APP1'].get_tag_list():\\n try:\\n self.__delattr__(tag)\\n except AttributeError:\\n warnings.warn(\\\"could not delete tag \\\" + tag, RuntimeWarning)\",\n \"def remove_Tags(self,text):\\n cleaned_text = re.sub('<[^<]+?>', '', text)\",\n \"def untag(self, uuid, tags=None):\\n if isinstance(tags, basestring):\\n tags = [tags]\\n\\n self._backend.untag(uuid, tags)\",\n \"def remove(self, egg):\",\n \"def remove_asset(self, name):\\n if name in self.assets:\\n del self.assets[name]\",\n \"def strip_tags(self, rules):\\n tags = list()\\n for rule in rules:\\n for tag in self.soup.find_all(**rule):\\n tag.replace_with_children()\\n tags.append(tag.name)\\n return tags\",\n \"def removeNode(cTag, nTag): #@NoSelf\",\n \"def clean(vendor):\\n remove_all(\\n path\\n for path in vendor.glob('*')\\n if path.basename() != 'vendored.txt'\\n )\",\n \"def remove_tag(self, tag: str) -> None:\\n tags = self.get_tag_index()\\n tags.remove(tag)\\n self.write_tag_index(list(set(tags)))\",\n \"def remove_mix(self, name: str) -> None:\\n self.remove(name)\",\n \"def delete_unused_tags( self, owner ):\\n self.filter(\\n owner = owner,\\n documents__isnull = True).delete()\",\n \"def remove_book(name):\\n global BOOKS\\n BOOKS = [book for book in BOOKS if book['name'] != name]\",\n \"def delete_terms(self, *terms):\\n result = self.sequence\\n for term in ANCOVA(*terms).sequence:\\n result.remove(term)\\n return ANCOVA(*result)\",\n \"def remove_region_names(self):\\r\\n for region in self.gt_seq_region:\\r\\n region.attributes.name = ''\",\n \"def strip_tags(self, *a, **kw):\\n return strip_tags(self._widget_cget(text_s), *a, **kw)\",\n \"def remove(self, name: str) -> None:\\n del self.components[name]\",\n \"def remove(self, tag_name: str, category: ty.Optional[str] = None):\\n tags = self.__holder.db_tags.filter(lambda t: t.name == tag_name)\\n if category is not None:\\n tags = tags.filter(category=category)\\n\\n tag = tags.first()\\n if tag:\\n self.__holder.db_tags.remove(tag)\",\n \"def purge_categories(app, env, docname):\\n if not hasattr(env, \\\"categories\\\"):\\n return # nothing to do\\n\\n categories = env.categories\\n try:\\n name, version = algorithm_name_and_version(docname)\\n except RuntimeError: # not an algorithm page\\n return\\n\\n deadref = PageRef(name, docname)\\n for category in categories.itervalues():\\n pages = category.pages\\n if deadref in pages:\\n pages.remove(deadref)\",\n \"def set_tags(self, tags):\\r\\n current_tags = set(self.tag_names())\\r\\n updated_tags = set(tags)\\r\\n removed_tags = current_tags.difference(updated_tags)\\r\\n new_tags = updated_tags.difference(current_tags)\\r\\n \\r\\n for tag in new_tags:\\r\\n self.add_tag(tag)\\r\\n \\r\\n for tag in removed_tags:\\r\\n self.remove_tag(tag)\",\n \"def remove_tag(convo_ID, tag_ID):\\n # Make API request\\n url = \\\"https://api2.frontapp.com/conversations/\\\" + convo_ID + \\\"/tags\\\"\\n payload = json.dumps({\\\"tag_ids\\\": [tag_ID]})\\n headers = {\\\"Authorization\\\": BEARER_TOKEN, \\\"Content-Type\\\": \\\"application/json\\\"}\\n requests.request(\\\"DELETE\\\", url, headers=headers, data=payload)\",\n \"def eliminate(sv, nam):\\r\\n del sv.Object[nam] # from sv.Object dictionary\\r\\n sv.Object_list.remove(nam)\",\n \"def remove_low_locality_tags(locality, tags_list):\\n LOCALITY_THRESHOLD = 1 # Locality of 'newyorkcity' is 0.057\\n tags_to_remove = []\\n for tag in tags_list:\\n if tag not in locality:\\n tags_to_remove.append(tag)\\n else:\\n locality_score = locality[tag]\\n if locality_score[0] < LOCALITY_THRESHOLD:\\n tags_to_remove.append(tag)\\n for tag in tags_to_remove:\\n tags_list.remove(tag)\",\n \"def remove(self, *args):\\n pass\",\n \"def untag(tagged_sentence):\\n return [w for (w, t) in tagged_sentence]\",\n \"def tags_remove(self, item_id, tags, **params):\\n\\n if isinstance(tags, basestring):\\n tags = tags.split(',')\\n\\n self.queue('tags_remove', item_id=item_id, tags=tags, **params)\",\n \"def untag(tagged_sentence):\\n return [w for w, _ in tagged_sentence]\",\n \"def remove_tag(self, tag):\\n cp = self.copy()\\n cp.tags.remove(tag)\\n return cp\",\n \"def remove_custom(self, custom: List[Dict[str, any]]) -> None:\\n for entry in custom:\\n phrase = as_phrase_object(entry, ngram_size=self.ngram_size, skip_size=self.skip_size)\\n for custom_property in phrase.metadata:\\n del self.custom[phrase.phrase_string][custom_property]\",\n \"def remove(self, comp):\\n\\t\\tif isinstance(comp, Component):\\n\\t\\t\\tif comp in self._contents:\\n\\t\\t\\t\\tcomp.container = None\\n\\t\\telse:\\n\\t\\t\\tfor item in comp:\\n\\t\\t\\t\\tself.remove(item)\",\n \"def remove_by_name(self, values, name):\\n for d in values:\\n if d[\\\"name\\\"] == name:\\n values.remove(d)\\n return\",\n \"def remove(self, *names):\\n for name in names:\\n self._storage.pop(name, None)\",\n \"async def slashtag_remove(self, ctx: commands.Context, *, tag: GuildTagConverter):\\n await ctx.send(await tag.delete())\",\n \"def untagAll(self, authenticationToken, guid):\\r\\n pass\",\n \"def remove(self, *args):\\n return _libsbml.ListOfCompartments_remove(self, *args)\",\n \"def remove(self, name):\\n raise NotImplementedError\",\n \"def replace_tags(self, photo_id, tag_list):\\n # get all the tags attached to the photo\\n current_tags = self.db.make_query(\\n '''\\n select * from photo_tag where photo_id = {}\\n '''.format(photo_id)\\n )\\n\\n print(current_tags)\\n\\n # remove the current tags\\n self.db.make_query(\\n '''\\n delete from photo_tag where photo_id = {}\\n '''.format(photo_id)\\n )\\n\\n for tag in tag_list:\\n # add tags in the tag_list\\n self.db.make_query(\\n '''\\n insert into photo_tag (photo_id, tag_name)\\n values ({}, \\\"{}\\\")\\n '''.format(photo_id, tag)\\n )\\n\\n self.update_photo_count(tag)\",\n \"def discard_key_from_tag(self,tag,key):\\r\\n\\r\\n # with shelf\\r\\n if self.using_shelf:\\r\\n\\r\\n self.tag_dict[tag].discard(key)\\r\\n\\r\\n\\r\\n #with database\\r\\n if self.using_database:\\r\\n value_tuple = (notebookname,tag,key,)\\r\\n db_cursor.execute(\\\"DELETE FROM tags_to_keys\\\"\\r\\n +\\\" WHERE notebook=? AND tag=?\\\"\\r\\n +\\\" AND keyword=?;\\\",\\r\\n value_tuple)\",\n \"def update_tags(self, tags, **kwargs):\\n request = RequestMiddleware.get_request()\\n is_admin = request.user and request.user.is_admin\\n # Keep all tags that start with pf: because they are reserved.\\n preserved = [tag for tag in self.tags if tag.startswith('pf:')]\\n if is_admin:\\n remove = [tag[1:] for tag in tags if tag.startswith('-pf:')]\\n preserved = [tag for tag in preserved if tag not in remove]\\n\\n # Filter out new tags that are invalid or reserved.\\n accepted = [tag for tag in tags\\n if TAG_REGEX_COMPILED.match(tag)\\n and (is_admin or not tag.startswith('pf:'))]\\n # Limit the number of tags per entity.\\n if len(accepted + preserved) > settings.MAX_TAGS_PER_ENTITY:\\n accepted = accepted[:settings.MAX_TAGS_PER_ENTITY - len(preserved)]\\n self.tags = list(set(accepted + preserved))\",\n \"def replace_tags(codes, new_codes, tag_dict):\\n\\n # replace tags in code\\n for _dict in new_codes:\\n for key, value in _dict.items():\\n _replace_tags(codes, key, value, tag_dict, 0)\\n\\n # `codes` is updated inplace, no return\",\n \"def remove_tag(self, tag):\\n for task in self._tasks:\\n task.remove_tag(tag)\\n\\n return self\",\n \"def remove_cat(self, path: Path):\\n if not self.active:\\n return\\n if path is None:\\n return\\n for i, coord in enumerate(path.path):\\n self.cat[coord[1]][coord[0]].remove((path.identifier, i))\",\n \"def delete_taggit_tags(apps, schema_editor):\\n TaggitTag = apps.get_model('taggit', 'Tag')\\n TaggitTag.objects.all().delete()\",\n \"def remove_collection_names(account, dataset, tags):\\n\\n token = get_access_token()\\n\\n if dataset.startswith(\\\"N:dataset:\\\"):\\n selected_dataset_id = dataset\\n else:\\n selected_dataset_id = get_dataset_id(token, dataset)\\n\\n if not has_edit_permissions(token, selected_dataset_id):\\n abort(403, \\\"You do not have permission to edit this dataset.\\\")\\n\\n for tagid in tags:\\n r = requests.delete(f\\\"{PENNSIEVE_URL}/datasets/{str(selected_dataset_id)}/collections/{str(tagid)}\\\", headers=create_request_headers(token))\\n r.raise_for_status()\\n\\n return dict({\\\"collection\\\": \\\"Collection removed\\\"})\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7223684","0.65725476","0.6513158","0.64437586","0.64056754","0.6315268","0.6279433","0.6213618","0.6207464","0.6185791","0.6162285","0.60926586","0.6090244","0.6027659","0.5954956","0.5875241","0.58474445","0.58383733","0.5830504","0.5779694","0.5775148","0.57689977","0.57571906","0.5725873","0.57060766","0.5683741","0.56742954","0.56450576","0.5636017","0.56353724","0.56174654","0.5601211","0.5588073","0.5539659","0.5529954","0.552094","0.5516801","0.5477217","0.5448852","0.54373527","0.5430872","0.54285204","0.5421333","0.54208493","0.5420536","0.53638715","0.5363654","0.5359111","0.5337057","0.5329828","0.53158224","0.53094286","0.5309017","0.53069204","0.5294087","0.5292131","0.52900654","0.528396","0.5274175","0.52737194","0.5273695","0.52618665","0.52452624","0.5244405","0.5241955","0.5237571","0.5233061","0.5232744","0.5232607","0.5222649","0.52195704","0.52133083","0.520614","0.51959264","0.5174965","0.5170706","0.51597095","0.51455307","0.5142786","0.5139046","0.513827","0.5132655","0.5127526","0.5120556","0.51152295","0.51130915","0.51120895","0.5109818","0.51027644","0.5101269","0.50953776","0.5088165","0.5083195","0.50825745","0.5081917","0.50816375","0.5076519","0.5072768","0.50723255","0.5066526"],"string":"[\n \"0.7223684\",\n \"0.65725476\",\n \"0.6513158\",\n \"0.64437586\",\n \"0.64056754\",\n \"0.6315268\",\n \"0.6279433\",\n \"0.6213618\",\n \"0.6207464\",\n \"0.6185791\",\n \"0.6162285\",\n \"0.60926586\",\n \"0.6090244\",\n \"0.6027659\",\n \"0.5954956\",\n \"0.5875241\",\n \"0.58474445\",\n \"0.58383733\",\n \"0.5830504\",\n \"0.5779694\",\n \"0.5775148\",\n \"0.57689977\",\n \"0.57571906\",\n \"0.5725873\",\n \"0.57060766\",\n \"0.5683741\",\n \"0.56742954\",\n \"0.56450576\",\n \"0.5636017\",\n \"0.56353724\",\n \"0.56174654\",\n \"0.5601211\",\n \"0.5588073\",\n \"0.5539659\",\n \"0.5529954\",\n \"0.552094\",\n \"0.5516801\",\n \"0.5477217\",\n \"0.5448852\",\n \"0.54373527\",\n \"0.5430872\",\n \"0.54285204\",\n \"0.5421333\",\n \"0.54208493\",\n \"0.5420536\",\n \"0.53638715\",\n \"0.5363654\",\n \"0.5359111\",\n \"0.5337057\",\n \"0.5329828\",\n \"0.53158224\",\n \"0.53094286\",\n \"0.5309017\",\n \"0.53069204\",\n \"0.5294087\",\n \"0.5292131\",\n \"0.52900654\",\n \"0.528396\",\n \"0.5274175\",\n \"0.52737194\",\n \"0.5273695\",\n \"0.52618665\",\n \"0.52452624\",\n \"0.5244405\",\n \"0.5241955\",\n \"0.5237571\",\n \"0.5233061\",\n \"0.5232744\",\n \"0.5232607\",\n \"0.5222649\",\n \"0.52195704\",\n \"0.52133083\",\n \"0.520614\",\n \"0.51959264\",\n \"0.5174965\",\n \"0.5170706\",\n \"0.51597095\",\n \"0.51455307\",\n \"0.5142786\",\n \"0.5139046\",\n \"0.513827\",\n \"0.5132655\",\n \"0.5127526\",\n \"0.5120556\",\n \"0.51152295\",\n \"0.51130915\",\n \"0.51120895\",\n \"0.5109818\",\n \"0.51027644\",\n \"0.5101269\",\n \"0.50953776\",\n \"0.5088165\",\n \"0.5083195\",\n \"0.50825745\",\n \"0.5081917\",\n \"0.50816375\",\n \"0.5076519\",\n \"0.5072768\",\n \"0.50723255\",\n \"0.5066526\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.56744355"},"document_rank":{"kind":"string","value":"26"}}},{"rowIdx":11284,"cells":{"query":{"kind":"string","value":"Checks if all of supplied objects have tag with specified code and/or name Return True if all object ids has specified tags"},"document":{"kind":"string","value":"def check_tag(self, cr, uid, ids, code=None, name=None, context=None):\n assert bool(code is None) or bool(name is None), \"code or name must not be None\"\n tag_domain = [('id', 'in', ids)]\n if code is not None:\n tag_domain.append(('tag_ids.code', '=', code))\n if name is not None:\n tag_domain.append(('tag_ids.name', '=', name))\n\n count = self.search(cr, uid, tag_domain, count=1)\n return bool(count == len(ids))"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def check_for_tag(tags, tagged_events):\n found_tags = set()\n tags_set = set(tags)\n for tag in tags:\n for tag_event in tagged_events:\n if tag in tag_event[1][\"tag\"][\"labels\"]:\n found_tags.add(tag)\n not_found = tags_set - found_tags\n tag_status = {}\n for tag in found_tags:\n tag_status[tag] = True\n for tag in not_found:\n tag_status[tag] = False\n return tag_status","def is_tagged(self,tag_name,element):\n return (tag_name in self.tag2elements.keys()) and (element in self.tag2elements[tag_name])","def match(self, name, tags):\n return name.lower() in tags","def supported(self, tags):\n # type: (List[Any]) -> bool\n return not self.file_tags.isdisjoint(tags)","def id_in_list(obj_list, sb_object):\n if __debug__:\n print(\"Checking if sb_object in list...\")\n for sb_objects in obj_list:\n if sb_object.ID == sb_objects.ID:\n if __debug__:\n print(\"Object in list.\")\n return True\n if __debug__:\n print(\"Object not in list\")\n return False","def match(self, name, tags):\n S, tags = self.get_compiled(name, tags)\n return bool(S & tags)","def match(self, name, tags):\n or_exprs, tags = self.get_compiled(name, tags)\n \n # or_exprs = [{'a'}, {'c'}, {'d', 'a'}, {'d', 'e'}]\n return any(and_expr <= tags for and_expr in or_exprs)","def has_all(self, tag, indexes):\n\n return all(self.has(tag, index) for index in indexes)","def IsTagExists(self, ResourceId, TagName):\n\n try:\n if self.Service == 'ec2':\n response = self.DescribeTags(ResourceId)\n if TagName in list(map(lambda x: x['Key'], [x for x in response['Tags']])):\n return True\n elif self.Service == 's3':\n response = self.GetBucketTagging(ResourceId)\n if TagName in list(map(lambda x: x['Key'], [x for x in response['TagSet']])):\n return True\n elif self.Service == 'lambda':\n response = self.ListTags(ResourceId)\n if TagName in [x for x in response['Tags']]:\n return True\n elif self.Service == 'logs':\n response = self.ListTagsLogGroup(ResourceId)\n if TagName in [x for x in response['tags']]:\n return True\n elif self.Service == 'rds':\n response = self.ListTagsForResource(ResourceId)\n if TagName in list(map(lambda x: x['Key'], [x for x in response['TagList']])):\n return True\n elif self.Service == 'es':\n response = self.ListTags(ResourceId)\n if TagName in list(map(lambda x: x['Key'], [x for x in response['TagList']])):\n return True\n elif self.Service == 'emr':\n response = self.DescribeCluster(ResourceId)\n if TagName in list(map(lambda x: x['Key'], [Tag for Tag in response['Cluster']['Tags']])):\n return True\n elif self.Service == 'dynamodb':\n response = self.ListTagsOfResource(ResourceId)\n if TagName in list(map(lambda x: x['Key'], [x for x in response['Tags']])):\n return True\n elif self.Service == 'firehose':\n response = self.ListTagsForDeliveryStream(ResourceId)\n if TagName in list(map(lambda x: x['Key'], [x for x in response['Tags']])):\n return True\n elif self.Service == 'glacier':\n response = self.ListTagsForVault(ResourceId)\n if TagName in [x for x in response['Tags']]:\n return True\n elif self.Service == 'kms':\n response = self.ListResourceTags(ResourceId)\n if TagName in list(map(lambda x: x['TagKey'], [x for x in response['Tags']])):\n return True\n elif self.Service == 'apigateway':\n print('No api to list tags')\n return False\n elif self.Service == 'kinesis':\n response = self.ListTagsForStream(ResourceId)\n if TagName in list(map(lambda x: x['Key'], [x for x in response['Tags']])):\n return True\n elif self.Service == 'cloudtrail':\n response = self.ListTags(ResourceId)\n TagsList = map(lambda RTL: RTL['TagsList'], [RTL for RTL in response['ResourceTagList']])\n for Tags in TagsList:\n for Tag in Tags:\n if Tag['Key'] == 'Channel':\n return True\n elif self.Service == 'sqs':\n response = self.ListTags(ResourceId)\n if TagName in [x for x in response['Tags']]:\n return True\n elif self.Service == 'secretsmanager':\n response = self.DescribeSecret(ResourceId)\n if TagName in list(map(lambda x: x['Key'], [x for x in response['Tags']])):\n return True\n elif self.Service == 'cloudfront':\n response = self.ListTagsForResource(ResourceId)\n if TagName in list(map(lambda x: x['Key'], [x for x in response['Tags']])):\n return True\n elif self.Service == 'efs':\n response = self.DescribeTags(ResourceId)\n if TagName in list(map(lambda x: x['Key'], [x for x in response['Tags']])):\n return True\n elif self.Service == 'sagemaker':\n response = self.ListTags(ResourceId)\n if TagName in list(map(lambda x: x['Key'], [x for x in response['Tags']])):\n return True\n elif self.Service == 'redshift':\n response = self.DescribeTags(ResourceId)\n if TagName in list(map(lambda x: x['Key'], [x for x in response['Tags']])):\n return True\n elif self.Service == 'elasticache':\n response = self.ListTagsForResource(ResourceId)\n if TagName in list(map(lambda x: x['Key'], [x for x in response['TagList']])):\n return True\n elif self.Service == 'workspaces':\n response = self.DescribeTags(ResourceId)\n if TagName in list(map(lambda x: x['Key'], [x for x in response['Tags']])):\n return True\n elif self.Service == 'ds':\n response = self.ListTagsForResource(ResourceId)\n if TagName in list(map(lambda x: x['Key'], [x for x in response['Tags']])):\n return True\n elif self.Service == 'dax':\n response = self.ListTags(ResourceId)\n if TagName in list(map(lambda x: x['Key'], [x for x in response['Tags']])):\n return True\n elif self.Service == 'route53':\n response = self.ListTagsForResource(ResourceId)\n if TagName in list(map(lambda x: x['Key'], [x for x in response['Tags']])):\n return True\n elif self.Service == 'directconnect':\n response = self.DescribeTags(ResourceId)\n if TagName in list(map(lambda x: x['Key'], [x for x in response['Tags']])):\n return True\n elif self.Service == 'datapipeline':\n response = self.DescribePipelines(ResourceId)\n Tags = list(map(lambda x: x['tags'], [tags for tags in response['pipelineDescriptionList']]))\n for i in Tags:\n for j in i:\n if j['key'] == 'Channel':\n return True\n else:\n raise TagNotSupportedError(self.Service)\n except Exception as e:\n raise e\n\n return False","def objExists(*args, **kwargs)->bool:\n pass","def check_for_tags(self, data_in):\n # possible header tags\n tags = ['NODE:', 'PORT:', 'STARTOFFSET:', 'ENDOFFSET:']\n\n # check for tags\n for tag in tags:\n if data_in.find(tag) != -1:\n print \"Found tag %s in data file\" % tag\n return False\n\n return True","def ok_tags(tags: dict) -> bool:\n\n if not tags:\n return True\n depth = 0\n queue = [(i, depth+1) for i in tags.values() if isinstance(i, dict)]\n max_depth = 0\n while queue and max_depth < 2:\n sub, depth = queue.pop()\n max_depth = max(max_depth, depth)\n queue = queue + [(i, depth+1) for i in sub.values() if isinstance(i, dict)]\n\n return max_depth < 2 and all(isinstance(k, str) and isinstance(tags[k], str) for k in tags)","def __contains__(self, obj):\n if isinstance(obj, self):\n query = self.where(**obj.data).select()\n result = query.execute()\n if result.count:\n return True\n return False","def check_inner_tag_availability(**kwargs):\n\n inner = kwargs.pop('inner')\n tag = kwargs.pop('tag')\n used_inners = [item.inner for item in [\n *MLPAv4.objects.filter(tag=tag),\n *MLPAv6.objects.filter(tag=tag),\n *BilateralPeer.objects.filter(tag=tag),\n *Monitorv4.objects.filter(tag=tag)]]\n check_inner = inner in used_inners\n if check_inner:\n return False\n else:\n return True","def tag_exists(tag, directory=None):\n return tag in get_tags(directory)","def __contains__(self, obj):\n\n if isinstance(obj, str):\n return obj in FileStorage.__objects\n return key(type(obj), obj.id) in FileStorage.__objects","def has_tag(self, tag):\n return tag in self.tags","def has_tag(self, tag):\n return tag in self.tags","def tag_dict_contains (self,\r\n tag):\r\n\r\n\r\n\r\n if self.using_database:\r\n aprint('TAGDICT CONTAINS')\r\n value_tuple = (notebookname, tag,)\r\n db_cursor.execute(\"SELECT rowid \"\r\n +\"FROM tags_to_keys\"\r\n +\" WHERE notebook=?\"\r\n +\" AND tag=?;\",\r\n value_tuple)\r\n try:\r\n return db_cursor.fetchone()[0] # MIGHT BE PROBLEMATIC\r\n except:\r\n return False\r\n\r\n return str(tag) in self.tag_dict","def containsAll(self, *args):\n pass","def hasattrs(obj, names):\n return all(hasattr(obj, attr) for attr in names)","def hasattrs(obj, names):\n return all(hasattr(obj, attr) for attr in names)","def exists(cls, ko):\n if isinstance(ko, BagDocument):\n return ko._key in cls._dbag\n else:\n return ko in cls._dbag","def label_intersects_tags(label, tags):\n for tag in tags:\n if tag in label:\n return True\n return False","def has_tag(lst, tag):\n if not isinstance(lst, list):\n lst = [lst]\n for l in lst:\n if l.tag == tag:\n return True\n else:\n return False","def assert_tags_present(payload, tags_to_find):\n tags_found = []\n for tag_dict in payload['data']['tags']:\n tags_found.append(tag_dict.get('text'))\n for tag in tags_to_find:\n assert tag in tags_found\n return","def contains(self, *args):\n pass","def validate(self, obj):\n if 'tags' in obj and not isinstance(obj['tags'], list):\n raise aomi.exceptions.Validation('tags must be a list')\n\n if self.present:\n check_obj(self.required_fields, self.name(), obj)","def tag_key_exists(self, key):\n return key in self.map","def tag_all_the_things(self, netbox_handler):\n\n for object_type in NetBoxObject.__subclasses__():\n\n for this_object in self.get_all_items(object_type):\n\n # if object was found in source\n if this_object.source is not None:\n this_object.add_tags([netbox_handler.primary_tag, this_object.source.source_tag])\n\n # if object was orphaned remove tag again\n if netbox_handler.orphaned_tag in this_object.get_tags():\n this_object.remove_tags(netbox_handler.orphaned_tag)\n\n # if object was tagged by this program in previous runs but is not present\n # anymore then add the orphaned tag except it originated from a disabled source\n else:\n if bool(set(this_object.get_tags()).intersection(self.source_tags_of_disabled_sources)) is True:\n log.debug2(f\"Object '{this_object.get_display_name()}' was added \"\n f\"from a currently disabled source. Skipping orphaned tagging.\")\n continue\n\n if getattr(this_object, \"prune\", False) is True:\n if netbox_handler.primary_tag in this_object.get_tags():\n this_object.add_tags(netbox_handler.orphaned_tag)\n\n # or just remove primary tag if pruning is disabled\n else:\n if netbox_handler.primary_tag in this_object.get_tags():\n this_object.remove_tags(netbox_handler.primary_tag)\n if netbox_handler.orphaned_tag in this_object.get_tags():\n this_object.remove_tags(netbox_handler.orphaned_tag)","def __contains__(self, obj):\n return obj in self.actors","def is_taggable(t: str) -> bool:\n return t in taggable_resource_types","def items_should_contain_array_of_tags(context):\n items = context.response.json()['items']\n for item in items:\n assert 'tags' in item\n assert isinstance(item['tags'], list)\n assert len(item['tags']) > 0","def __contains__(self, items):\n if type(items) != list:\n raise PJFInvalidType(items, list)\n ret = 0\n for item in items:\n for key in self.__dict__:\n if isinstance(self.__dict__[key], JsonFactory):\n ret += item in self.__dict__[key]\n elif item == key:\n ret += 1\n return len(items) == ret","def contains(item, obj):\n return obj.__contains__(item)","def contains(self, *args):\n return _libsbml.IdList_contains(self, *args)","def _is_desired_tag(self, tag):\n if self._tags is None:\n return True\n\n if self._ignore_namespace:\n for desired_tag in self._tags:\n if tag.localname == desired_tag.localname:\n return True\n else:\n for desired_tag in self._tags:\n if tag == desired_tag:\n return True\n\n return False","def testGetAboutTagValuesWithObjectIDs(self):\n objectID1 = uuid4()\n objectID2 = uuid4()\n value = self.store.add(AboutTagValue(objectID1, u'foo'))\n self.store.add(AboutTagValue(objectID2, u'bar'))\n self.assertEqual(value, getAboutTagValues(objectIDs=[objectID1]).one())","async def exists(self, tag_name):\n try:\n if await self.get_id(tag_name):\n return True\n except RtbDoesntExists:\n return False","def has_tags_in_content(self):\n\t\treturn self.get_content() and re_tag.search(self.get_content())","def has_tags(self):\n return bool(self.tags)","def contains(self, object_id):\n return libplasma.contains(self.conn, object_id)","def contains(self, *__args): # real signature unknown; restored from __doc__ with multiple overloads\r\n return False","def contains(self, *__args): # real signature unknown; restored from __doc__ with multiple overloads\r\n return False","def exists(self, obj):\n return False","def test_tags(question):\n assert \"tags\" in question[\"instance\"]\n tags = set(question[\"instance\"][\"tags\"])\n # there should be at least one tag\n assert len(tags) >= 1\n # each tags should be in VALID_TAGS\n assert len(tags - VALID_TAGS) == 0\n # there should be exactly one category-defining tag\n assert len(tags.intersection(CATEGORY_TAGS)) == 1","def has(self, *args):\n return _ida_frame.xreflist_t_has(self, *args)","def contains_all(self, *items):\n return all(item in self for item in items)","def get_tags(self, obj):\n if QuestionTag.objects.filter(question=obj).exists():\n id_tags = QuestionTag.objects.filter(question=obj).values('tag__id')\n tags_obj = Tag.objects.filter(id__in=id_tags)\n return TagSerializer(tags_obj, many=True).data\n else:\n return \"No tags\"","def has_obstacle(lst):\n for x in lst:\n if x.tag is not None and x.tag == OBSTACLE:\n return True\n else:\n return False","def checkTags(instance_id, tagSet, nt_id):\n tags = []\n missingOwnersItem = \"\"\n missingPatchesItem = \"\"\n patch_name = \"\"\n isPatchTag = False\n isExpiration = False\n isCreatorID = False\n isOwnerMailTag = False\n isOwnerTeamTag = False\n for tag in tagSet:\n if tag['Key'] == 'Expiration':\n isExpiration = True\n\n if tag['Key'] == 'Creator ID':\n isCreatorID = True\n\n if tag['Key'] == 'Patch Group':\n isPatchTag = checkPatchValidity(tag['Value'])\n\n if tag['Key'] == 'Owning_Mail':\n isOwnerMailTag = True\n \n if tag['Key'] == 'Owning_Team':\n isOwnerTeamTag = True\n\n if not isExpiration:\n tags.append(\n { \n 'Key': 'Expiration',\n 'Value': (datetime.date.today() + datetime.timedelta(days = 30)).strftime('%Y-%m-%d')\n })\n if not isCreatorID:\n tags.append(\n {\n 'Key': 'Creator ID',\n 'Value': nt_id\n })\n\n if not isOwnerTeamTag or not isOwnerMailTag:\n missingOwnersItem = instance_id\n\n if not isPatchTag:\n print(\"invalid patch tags!\")\n missingPatchesItem = instance_id\n\n patch_name = createPatchTag(tags, instance_id, nt_id)\n tags.append({ 'Patch Group' : patch_name })\n\n print(\"Attaching tags: \" + str(tags) + \" to instance \" + instance_id)\n if MODE == 'enforce':\n attachInstanceTags(instance_id, tags)\n \n if MODE == \"enforce\" and patch_name != \"Not yet populated\" and isOwnerMailTag and isOwnerTeamTag:\n return True\n \n return False, missingOwnersItem, missingPatchesItem","def __contains__(self, obj):\n pass","def contains(self, x: object):\n return x in self.items","def get_intersection_by_model(self, queryset_or_model, tags):\r\n tags = get_tag_list(tags)\r\n tag_count = len(tags)\r\n queryset, model = get_queryset_and_model(queryset_or_model)\r\n\r\n if not tag_count:\r\n return model._default_manager.none()\r\n\r\n model_table = qn(model._meta.db_table)\r\n # This query selects the ids of all objects which have all the\r\n # given tags.\r\n query = \"\"\"\r\n SELECT %(model_pk)s\r\n FROM %(model)s, %(tagged_item)s\r\n WHERE %(tagged_item)s.content_type_id = %(content_type_id)s\r\n AND %(tagged_item)s.tag_id IN (%(tag_id_placeholders)s)\r\n AND %(model_pk)s = %(tagged_item)s.object_id\r\n GROUP BY %(model_pk)s\r\n HAVING COUNT(%(model_pk)s) = %(tag_count)s\"\"\" % {\r\n 'model_pk': '%s.%s' % (model_table, qn(model._meta.pk.column)),\r\n 'model': model_table,\r\n 'tagged_item': qn(self.model._meta.db_table),\r\n 'content_type_id': ContentType.objects.get_for_model(model).pk,\r\n 'tag_id_placeholders': ','.join(['%s'] * tag_count),\r\n 'tag_count': tag_count,\r\n }\r\n\r\n cursor = connection.cursor()\r\n cursor.execute(query, [tag.pk for tag in tags])\r\n object_ids = [row[0] for row in cursor.fetchall()]\r\n if len(object_ids) > 0:\r\n return queryset.filter(pk__in=object_ids)\r\n else:\r\n return model._default_manager.none()","def testGetTagValuesWithTagIDsAndObjectIDs(self):\n objectID1 = uuid4()\n objectID2 = uuid4()\n user = createUser(u'username', u'password', u'User',\n u'user@example.com')\n user.namespaceID = createNamespace(user, user.username, None).id\n tag = createTag(user, user.namespace, u'name')\n value = self.store.add(TagValue(user.id, tag.id, objectID1, None))\n self.store.add(TagValue(user.id, tag.id, objectID2, 42))\n self.assertEqual(value, getTagValues([(objectID1, tag.id)]).one())","def SelectObjects( object_ids):\n id = rhutil.coerceguid(object_ids, False)\n if id: object_ids = [id]\n rc = 0\n for id in object_ids:\n if SelectObject(id)==True: rc += 1\n return rc","def check_all_tag_photo_counts(self):\n data = self.db.get_query_as_list(\n '''\n select * from tag\n '''\n )\n\n for tag in data:\n print()\n print(tag)\n # query for the number of photos using the tag\n # compare it to the number in the photos column\n # update if necessary\n query_count = self.db.get_query_as_list(\n '''\n select count(tag_name)\n from photo_tag\n where tag_name = \"{}\"\n '''.format(tag['tag_name'])\n )\n\n if query_count[0]['count(tag_name)'] == tag['photos']:\n print('OK', 'actual photos number with tag',\n query_count[0]['count(tag_name)'], 'in photos column', tag['photos'])\n else:\n print('MISSMATCH IN PHOTOS AND PHOTOS WITH TAG\\n', 'actual photos number with tag',\n query_count[0]['count(tag_name)'], 'in photos column', tag['photos'])\n\n tag_name = tag['tag_name']\n count = query_count[0]['count(tag_name)']\n break\n\n print('\\nDONE NO PROBLEMS!')","def HasEventTags(self):\n for name in self._GetStreamNames():\n if name.startswith('event_tag_data.'):\n return True\n\n return False","def __contains__(self, name):\n return name in set(self)","def match(self, name, tags):\n name, tags = self.get_compiled(name, tags)\n \n def index_of_letter(l):\n return ord(l) - ord('a')\n \n true_val, false_val = name\n \n if true_val:\n return index_of_letter(true_val) in tags\n else:\n return index_of_letter(false_val) not in tags","def is_matching_product_with_tags(product, lat, lng, radius, tags):\n return vincenty(\n (lat, lng),\n (product.shop.lat, product.shop.lng)\n ).meters <= radius and any(tag in product.shop.tags for tag in tags)","def get_intersection_by_model(self, queryset_or_model, tags):\n tags = self.tag_model.get_tag_list(tags)\n tag_count = len(tags)\n queryset, model = get_queryset_and_model(queryset_or_model)\n\n if not tag_count:\n return model._default_manager.none()\n\n model_table = qn(model._meta.db_table)\n # This query selects the ids of all objects which have all the\n # given tags.\n query = \"\"\"\n SELECT %(model_pk)s\n FROM %(model)s, %(tagged_item)s\n WHERE %(tagged_item)s.content_type_id = %(content_type_id)s\n AND %(tagged_item)s.tag_id IN (%(tag_id_placeholders)s)\n AND %(model_pk)s = %(tagged_item)s.object_id\n GROUP BY %(model_pk)s\n HAVING COUNT(%(model_pk)s) = %(tag_count)s\"\"\" % {\n 'model_pk': '%s.%s' % (model_table, qn(model._meta.pk.column)),\n 'model': model_table,\n 'tagged_item': qn(self.model._meta.db_table),\n 'content_type_id': ContentType.objects.get_for_model(model).pk,\n 'tag_id_placeholders': ','.join(['%s'] * tag_count),\n 'tag_count': tag_count,\n }\n\n cursor = connection.cursor()\n cursor.execute(query, [tag.pk for tag in tags])\n object_ids = [row[0] for row in cursor.fetchall()]\n if len(object_ids) > 0:\n return queryset.filter(pk__in=object_ids)\n else:\n return model._default_manager.none()","def __contains__(self, val):\n return val in self.ids or super().__contains__(val)","def tags():","def has(self, tag_name: str, category: ty.Optional[str] = None) -> bool:\n tags = self.__holder.db_tags.filter(lambda t: t.name == tag_name)\n if category is not None:\n tags = tags.filter(category=category)\n\n return len(tags) >= 1","def check_photo_tag(self, tag_name):\n data = self.db.make_query(\n '''select * from photo_tag where tag_name = \"{}\" '''\n .format(tag_name))\n\n if len(data) > 0:\n return True\n return False","def hastag(obj, key):\n key = TAG_PREFIX + key\n if not isinstance(obj, unittest.TestCase):\n return hasattr(obj, key)\n tc_method = getattr(obj, obj._testMethodName)\n return hasattr(tc_method, key) or hasattr(obj, key)","def hasUnoInterfaces( oObject, *cInterfaces ):\n for cInterface in cInterfaces:\n if not hasUnoInterface( oObject, cInterface ):\n return False\n return True","def hasUnoInterfaces( oObject, *cInterfaces ):\n for cInterface in cInterfaces:\n if not hasUnoInterface( oObject, cInterface ):\n return False\n return True","def get_objects(self) -> Response:\n tags = [tag for tag in request.args.get(\"tags\", \"\").split(\",\") if tag]\n # filter types\n types = [type_ for type_ in request.args.get(\"types\", \"\").split(\",\") if type_]\n\n try:\n tagged_objects = TagDAO.get_tagged_objects_for_tags(tags, types)\n result = [\n self.object_entity_response_schema.dump(tagged_object)\n for tagged_object in tagged_objects\n ]\n return self.response(200, result=result)\n except TagInvalidError as ex:\n return self.response_422(message=ex.normalized_messages())\n except TagCreateFailedError as ex:\n logger.error(\n \"Error creating model %s: %s\",\n self.__class__.__name__,\n str(ex),\n exc_info=True,\n )\n return self.response_422(message=str(ex))","def update_tags(self, obj, tags):\n content_type = ContentType.objects.get_for_model(obj)\n current_tags = list(self.filter(items__content_type__pk=content_type.pk,\n items__object_id=obj.pk))\n updated_tags = self.model.get_tag_list(tags)\n \n # Remove tags which no longer apply\n tags_for_removal = [tag for tag in current_tags \\\n if tag not in updated_tags]\n if len(tags_for_removal):\n self.intermediary_table_model._default_manager.filter(content_type__pk=content_type.pk,\n object_id=obj.pk,\n tag__in=tags_for_removal).delete()\n # Add new tags\n for tag in updated_tags:\n if tag not in current_tags:\n self.intermediary_table_model._default_manager.create(tag=tag, content_object=obj)","def check_objects_exists():\n objects = s3.list_objects(Bucket=BUCKET, Prefix=TRAIN_SET_PATH)\n if objects:\n return objects\n return False","def add_tags_to_photo(self, photo_id, tag_list):\n print('\\nHello from add_tags_to_photo, the tag list is: ', tag_list)\n\n # for each tag\n # check if the tag is in the database already\n # if it is not then add it to the tag table\n for tag in tag_list:\n\n # will return None if the tag is not in the tag table\n # tag_name is the column name\n data = self.db.get_row('tag', 'tag_name', tag)\n\n print('data is', data)\n\n if data is None:\n\n print('\\nthat value {} is not in the db\\n'.format(tag))\n\n self.db.make_query(\n '''\n insert into tag (tag_name, user_id, photos)\n values (\"{}\", \"{}\", {})\n '''.format(\n tag,\n '28035310@N00',\n self.get_photo_count_by_tag(tag)\n )\n )\n\n print('\\nshould be added now...\\n')\n\n if self.db.get_row('tag', 'tag_name', tag):\n print('\\nadded tag, ', tag, '\\n')\n\n # UNIQUE constraint can cause problems here\n # so catch any exceptions\n try:\n # The tag is now in the database.\n self.db.make_query(\n '''\n insert into photo_tag (photo_id, tag_name)\n values ({}, \"{}\")\n '''.format(photo_id, tag)\n )\n except Exception as e:\n print('Problem adding tag to photo_tag ', e)\n\n data = self.db.make_query(\n '''\n select * from photo_tag where photo_id = {}\n '''.format(photo_id)\n )\n\n tags_in_data = []\n if len(data) > 0:\n for tag in data:\n tags_in_data.append(tag[1])\n\n print(tags_in_data)\n for tag in tag_list:\n if tag not in tags_in_data:\n return False\n else:\n self.update_photo_count(tag)\n\n return True","def test_in_list(self):\n\n # get available ids\n ids = list(DQ(\"(b.id) Book b\").tuples())\n ids = [id[0] for id in ids]\n\n # take just three of them\n c = {\"ids\": ids[:3]}\n dq = DQ(\"(b.id, b.name) Book{b.id in '$(ids)'} b\")\n r = list(dq.context(c).dicts())\n\n # make sure we got three of them\n self.assertEqual(len(r), 3)","def find_entities(self, area=EVERYWHERE, tags=frozenset()):\n\t\t#entities are added into binary box tree by their binary box cover[s] for the corresponding tags\n\t\t#when searching build a set of all possibly affected entities\n\t\t#then do a real test on the hitboxes\n\t\tif isinstance(tags, str):\n\t\t\ttags = {tags} #M# could use frozenset instead\n\t\tfor entity in self.entities:\n\t\t\tif tags.issubset(entity[\"tags\"]):\n\t\t\t\tif area.collides_with(entity.HITBOX + entity[\"position\"]):\n\t\t\t\t\tyield entity","def __contains__(self, obj):\r\n return assert_(obj in self.obj, '%r not in %r' % (obj, self.obj))","def is_tag(tag_name, user_path, current_user) -> bool:\n user = current_user[0]\n tag_list = os.listdir((user_path + '\\\\' + user).encode('unicode_escape'))\n temp = list(map(bytes.decode, tag_list))\n if tag_name in temp:\n return True\n else:\n return False","def is_tag_available(self, tag):\n return tag in self.available_tags","def validBookObject(bookObject):\n return (\"name\" in bookObject and\n \"price\" in bookObject and\n \"isbn\" in bookObject)","def verify_objects(directory: str, /, data: dict) -> bool:\n\n if type(data) is not dict:\n raise TypeError(\"Data parameter must be a dictionary.\")\n\n if \"files\" not in data or \"folders\" not in data or \"extensions\" not in data:\n raise TypeError(\n \"The dictionary does not contain one of the following keys: 'files', 'folders', 'extensions'.\"\n )\n\n objects = listdir(directory)\n\n for folder in data[\"folders\"]:\n if folder in objects:\n return True\n\n for file in data[\"files\"]:\n if file in objects:\n return True\n\n for o in objects:\n ext = splitext(o)[-1]\n if ext and ext in data[\"extensions\"]:\n return True\n\n return False","def __contains__(self, rq):\n return rq in self._data","def intersects(self, *__args): # real signature unknown; restored from __doc__ with multiple overloads\r\n return False","def isTaggedList(exp, tag):\n return isPair(exp) and isinstance(car(exp), Symbol) and (car(exp).name == tag.name)","def _canProcessTags(self, grammar, pos_tags):\n badTags = []\n for tag in pos_tags:\n if tag not in grammar.tags:\n badTags.append(tag)\n logger.debug(\"Grammar can't handle tag:\" + tag)\n if badTags:\n return False\n else:\n return True","def is_tagged(self, instance_id, tag_name):\n tag_value = self.get_tag_for_instance(instance_id, tag_name)\n if tag_value is not None and tag_value == 'true':\n return True\n else:\n return False","def has_hashtag(self, tag_list, **kwargs):\n lowlist = [tag.lower() for tag in tag_list]\n alllower = ('case_sensitive' in kwargs and not kwargs['case_sensitive'])\n for ht in self.original.entities['hashtags']:\n lowht = ht['text'].lower()\n if alllower and lowht in lowlist or '#' + lowht in lowlist:\n return True\n if ht['text'] in tag_list or '#' + ht['text'] in tag_list:\n return True\n return False","def do_tagged_objects(parser, token):\r\n bits = token.contents.split()\r\n if len(bits) != 6:\r\n raise TemplateSyntaxError(_('%s tag requires exactly five arguments') % bits[0])\r\n if bits[2] != 'in':\r\n raise TemplateSyntaxError(_(\"second argument to %s tag must be 'in'\") % bits[0])\r\n if bits[4] != 'as':\r\n raise TemplateSyntaxError(_(\"fourth argument to %s tag must be 'as'\") % bits[0])\r\n return TaggedObjectsNode(bits[1], bits[3], bits[5])","def has(self, tag_name: str) -> bool:\n return hasattr(self, tag_name)","def on_object(self, image, objects):\n for obj in objects:\n if self.is_object_recognition_appropriate(obj.name):\n self.say(\"I see a {}\".format(obj.name))","def test_page_tags(self):\n page, page_2 = self.get_pages()\n page_tags = models.PageTags.objects.create(extended_object=page)\n page_tags.tags.add(*self.tag_strings)\n\n self.assertTrue(page_has_tag(page, slugify(self.tag_strings[0])))\n self.assertTrue(page_has_tag(page, Tag.objects.get(slug=slugify(self.tag_strings[0]))))\n self.assertEqual(set(self.tag_strings), {tag.name for tag in get_page_tags(page)})\n\n self.assertFalse(page_has_tag(page_2, slugify(self.tag_strings[0])))\n self.assertEqual(set(), {tag.name for tag in get_page_tags(page_2)})","def __contains__(self, item):\n return item in self.attrs","def dict_has_items(obj, items):\n has_items = False\n if isinstance(obj, basestring):\n obj = json.loads(obj)\n for item in items:\n for lookup_key, lookup_val in item.iteritems():\n if lookup_key in obj and obj[lookup_key] == lookup_val:\n has_items = True\n else:\n return False\n return has_items","def list_all_tags(self,obs):","def __contains__(self, name):\n return name in self.__resources","def _match_all(self, obj, criteria):\n return all(getattr(obj, key_, None) == value_ for\n key_, value_ in criteria.items())","def applies(cls, obj):\n return type(obj) in cls.types","def has_taxa(self, **kwargs):\n if \"taxa\" not in kwargs and \"oids\" not in kwargs and \"labels\" not in kwargs:\n raise TypeError(\"Need to specify `taxa`, `oids` or `labels` list.\")\n taxa = set(kwargs.get(\"taxa\", []))\n oids = set(kwargs.get(\"oids\", []))\n labels = set(kwargs.get(\"labels\", []))\n taxon_oids = set([t.oid for t in self])\n taxon_labels = set([t.label for t in self])\n return taxa.issubset(self) \\\n and oids.issubset(taxon_oids) \\\n and labels.issubset(taxon_labels)","def hasname(self, tag: str) -> bool:\n for key in self.formal_names:\n if key in tag.lower():\n return True\n\n # Exit case if key -> value not in mapping \n return False","def isinstance_blender_object(self, b_obj):\n # lame and slow, but functional\n return b_obj in Blender.Object.Get()","def _verify_tags(self):\n for tag in self.tags:\n if tag.lower() in VASP_TAG_LIST:\n continue\n else:\n print((\"Warning: unknown INCAR tag '\" + tag + \"' with value '\" + str(self.tags[tag]) + \"'\"))"],"string":"[\n \"def check_for_tag(tags, tagged_events):\\n found_tags = set()\\n tags_set = set(tags)\\n for tag in tags:\\n for tag_event in tagged_events:\\n if tag in tag_event[1][\\\"tag\\\"][\\\"labels\\\"]:\\n found_tags.add(tag)\\n not_found = tags_set - found_tags\\n tag_status = {}\\n for tag in found_tags:\\n tag_status[tag] = True\\n for tag in not_found:\\n tag_status[tag] = False\\n return tag_status\",\n \"def is_tagged(self,tag_name,element):\\n return (tag_name in self.tag2elements.keys()) and (element in self.tag2elements[tag_name])\",\n \"def match(self, name, tags):\\n return name.lower() in tags\",\n \"def supported(self, tags):\\n # type: (List[Any]) -> bool\\n return not self.file_tags.isdisjoint(tags)\",\n \"def id_in_list(obj_list, sb_object):\\n if __debug__:\\n print(\\\"Checking if sb_object in list...\\\")\\n for sb_objects in obj_list:\\n if sb_object.ID == sb_objects.ID:\\n if __debug__:\\n print(\\\"Object in list.\\\")\\n return True\\n if __debug__:\\n print(\\\"Object not in list\\\")\\n return False\",\n \"def match(self, name, tags):\\n S, tags = self.get_compiled(name, tags)\\n return bool(S & tags)\",\n \"def match(self, name, tags):\\n or_exprs, tags = self.get_compiled(name, tags)\\n \\n # or_exprs = [{'a'}, {'c'}, {'d', 'a'}, {'d', 'e'}]\\n return any(and_expr <= tags for and_expr in or_exprs)\",\n \"def has_all(self, tag, indexes):\\n\\n return all(self.has(tag, index) for index in indexes)\",\n \"def IsTagExists(self, ResourceId, TagName):\\n\\n try:\\n if self.Service == 'ec2':\\n response = self.DescribeTags(ResourceId)\\n if TagName in list(map(lambda x: x['Key'], [x for x in response['Tags']])):\\n return True\\n elif self.Service == 's3':\\n response = self.GetBucketTagging(ResourceId)\\n if TagName in list(map(lambda x: x['Key'], [x for x in response['TagSet']])):\\n return True\\n elif self.Service == 'lambda':\\n response = self.ListTags(ResourceId)\\n if TagName in [x for x in response['Tags']]:\\n return True\\n elif self.Service == 'logs':\\n response = self.ListTagsLogGroup(ResourceId)\\n if TagName in [x for x in response['tags']]:\\n return True\\n elif self.Service == 'rds':\\n response = self.ListTagsForResource(ResourceId)\\n if TagName in list(map(lambda x: x['Key'], [x for x in response['TagList']])):\\n return True\\n elif self.Service == 'es':\\n response = self.ListTags(ResourceId)\\n if TagName in list(map(lambda x: x['Key'], [x for x in response['TagList']])):\\n return True\\n elif self.Service == 'emr':\\n response = self.DescribeCluster(ResourceId)\\n if TagName in list(map(lambda x: x['Key'], [Tag for Tag in response['Cluster']['Tags']])):\\n return True\\n elif self.Service == 'dynamodb':\\n response = self.ListTagsOfResource(ResourceId)\\n if TagName in list(map(lambda x: x['Key'], [x for x in response['Tags']])):\\n return True\\n elif self.Service == 'firehose':\\n response = self.ListTagsForDeliveryStream(ResourceId)\\n if TagName in list(map(lambda x: x['Key'], [x for x in response['Tags']])):\\n return True\\n elif self.Service == 'glacier':\\n response = self.ListTagsForVault(ResourceId)\\n if TagName in [x for x in response['Tags']]:\\n return True\\n elif self.Service == 'kms':\\n response = self.ListResourceTags(ResourceId)\\n if TagName in list(map(lambda x: x['TagKey'], [x for x in response['Tags']])):\\n return True\\n elif self.Service == 'apigateway':\\n print('No api to list tags')\\n return False\\n elif self.Service == 'kinesis':\\n response = self.ListTagsForStream(ResourceId)\\n if TagName in list(map(lambda x: x['Key'], [x for x in response['Tags']])):\\n return True\\n elif self.Service == 'cloudtrail':\\n response = self.ListTags(ResourceId)\\n TagsList = map(lambda RTL: RTL['TagsList'], [RTL for RTL in response['ResourceTagList']])\\n for Tags in TagsList:\\n for Tag in Tags:\\n if Tag['Key'] == 'Channel':\\n return True\\n elif self.Service == 'sqs':\\n response = self.ListTags(ResourceId)\\n if TagName in [x for x in response['Tags']]:\\n return True\\n elif self.Service == 'secretsmanager':\\n response = self.DescribeSecret(ResourceId)\\n if TagName in list(map(lambda x: x['Key'], [x for x in response['Tags']])):\\n return True\\n elif self.Service == 'cloudfront':\\n response = self.ListTagsForResource(ResourceId)\\n if TagName in list(map(lambda x: x['Key'], [x for x in response['Tags']])):\\n return True\\n elif self.Service == 'efs':\\n response = self.DescribeTags(ResourceId)\\n if TagName in list(map(lambda x: x['Key'], [x for x in response['Tags']])):\\n return True\\n elif self.Service == 'sagemaker':\\n response = self.ListTags(ResourceId)\\n if TagName in list(map(lambda x: x['Key'], [x for x in response['Tags']])):\\n return True\\n elif self.Service == 'redshift':\\n response = self.DescribeTags(ResourceId)\\n if TagName in list(map(lambda x: x['Key'], [x for x in response['Tags']])):\\n return True\\n elif self.Service == 'elasticache':\\n response = self.ListTagsForResource(ResourceId)\\n if TagName in list(map(lambda x: x['Key'], [x for x in response['TagList']])):\\n return True\\n elif self.Service == 'workspaces':\\n response = self.DescribeTags(ResourceId)\\n if TagName in list(map(lambda x: x['Key'], [x for x in response['Tags']])):\\n return True\\n elif self.Service == 'ds':\\n response = self.ListTagsForResource(ResourceId)\\n if TagName in list(map(lambda x: x['Key'], [x for x in response['Tags']])):\\n return True\\n elif self.Service == 'dax':\\n response = self.ListTags(ResourceId)\\n if TagName in list(map(lambda x: x['Key'], [x for x in response['Tags']])):\\n return True\\n elif self.Service == 'route53':\\n response = self.ListTagsForResource(ResourceId)\\n if TagName in list(map(lambda x: x['Key'], [x for x in response['Tags']])):\\n return True\\n elif self.Service == 'directconnect':\\n response = self.DescribeTags(ResourceId)\\n if TagName in list(map(lambda x: x['Key'], [x for x in response['Tags']])):\\n return True\\n elif self.Service == 'datapipeline':\\n response = self.DescribePipelines(ResourceId)\\n Tags = list(map(lambda x: x['tags'], [tags for tags in response['pipelineDescriptionList']]))\\n for i in Tags:\\n for j in i:\\n if j['key'] == 'Channel':\\n return True\\n else:\\n raise TagNotSupportedError(self.Service)\\n except Exception as e:\\n raise e\\n\\n return False\",\n \"def objExists(*args, **kwargs)->bool:\\n pass\",\n \"def check_for_tags(self, data_in):\\n # possible header tags\\n tags = ['NODE:', 'PORT:', 'STARTOFFSET:', 'ENDOFFSET:']\\n\\n # check for tags\\n for tag in tags:\\n if data_in.find(tag) != -1:\\n print \\\"Found tag %s in data file\\\" % tag\\n return False\\n\\n return True\",\n \"def ok_tags(tags: dict) -> bool:\\n\\n if not tags:\\n return True\\n depth = 0\\n queue = [(i, depth+1) for i in tags.values() if isinstance(i, dict)]\\n max_depth = 0\\n while queue and max_depth < 2:\\n sub, depth = queue.pop()\\n max_depth = max(max_depth, depth)\\n queue = queue + [(i, depth+1) for i in sub.values() if isinstance(i, dict)]\\n\\n return max_depth < 2 and all(isinstance(k, str) and isinstance(tags[k], str) for k in tags)\",\n \"def __contains__(self, obj):\\n if isinstance(obj, self):\\n query = self.where(**obj.data).select()\\n result = query.execute()\\n if result.count:\\n return True\\n return False\",\n \"def check_inner_tag_availability(**kwargs):\\n\\n inner = kwargs.pop('inner')\\n tag = kwargs.pop('tag')\\n used_inners = [item.inner for item in [\\n *MLPAv4.objects.filter(tag=tag),\\n *MLPAv6.objects.filter(tag=tag),\\n *BilateralPeer.objects.filter(tag=tag),\\n *Monitorv4.objects.filter(tag=tag)]]\\n check_inner = inner in used_inners\\n if check_inner:\\n return False\\n else:\\n return True\",\n \"def tag_exists(tag, directory=None):\\n return tag in get_tags(directory)\",\n \"def __contains__(self, obj):\\n\\n if isinstance(obj, str):\\n return obj in FileStorage.__objects\\n return key(type(obj), obj.id) in FileStorage.__objects\",\n \"def has_tag(self, tag):\\n return tag in self.tags\",\n \"def has_tag(self, tag):\\n return tag in self.tags\",\n \"def tag_dict_contains (self,\\r\\n tag):\\r\\n\\r\\n\\r\\n\\r\\n if self.using_database:\\r\\n aprint('TAGDICT CONTAINS')\\r\\n value_tuple = (notebookname, tag,)\\r\\n db_cursor.execute(\\\"SELECT rowid \\\"\\r\\n +\\\"FROM tags_to_keys\\\"\\r\\n +\\\" WHERE notebook=?\\\"\\r\\n +\\\" AND tag=?;\\\",\\r\\n value_tuple)\\r\\n try:\\r\\n return db_cursor.fetchone()[0] # MIGHT BE PROBLEMATIC\\r\\n except:\\r\\n return False\\r\\n\\r\\n return str(tag) in self.tag_dict\",\n \"def containsAll(self, *args):\\n pass\",\n \"def hasattrs(obj, names):\\n return all(hasattr(obj, attr) for attr in names)\",\n \"def hasattrs(obj, names):\\n return all(hasattr(obj, attr) for attr in names)\",\n \"def exists(cls, ko):\\n if isinstance(ko, BagDocument):\\n return ko._key in cls._dbag\\n else:\\n return ko in cls._dbag\",\n \"def label_intersects_tags(label, tags):\\n for tag in tags:\\n if tag in label:\\n return True\\n return False\",\n \"def has_tag(lst, tag):\\n if not isinstance(lst, list):\\n lst = [lst]\\n for l in lst:\\n if l.tag == tag:\\n return True\\n else:\\n return False\",\n \"def assert_tags_present(payload, tags_to_find):\\n tags_found = []\\n for tag_dict in payload['data']['tags']:\\n tags_found.append(tag_dict.get('text'))\\n for tag in tags_to_find:\\n assert tag in tags_found\\n return\",\n \"def contains(self, *args):\\n pass\",\n \"def validate(self, obj):\\n if 'tags' in obj and not isinstance(obj['tags'], list):\\n raise aomi.exceptions.Validation('tags must be a list')\\n\\n if self.present:\\n check_obj(self.required_fields, self.name(), obj)\",\n \"def tag_key_exists(self, key):\\n return key in self.map\",\n \"def tag_all_the_things(self, netbox_handler):\\n\\n for object_type in NetBoxObject.__subclasses__():\\n\\n for this_object in self.get_all_items(object_type):\\n\\n # if object was found in source\\n if this_object.source is not None:\\n this_object.add_tags([netbox_handler.primary_tag, this_object.source.source_tag])\\n\\n # if object was orphaned remove tag again\\n if netbox_handler.orphaned_tag in this_object.get_tags():\\n this_object.remove_tags(netbox_handler.orphaned_tag)\\n\\n # if object was tagged by this program in previous runs but is not present\\n # anymore then add the orphaned tag except it originated from a disabled source\\n else:\\n if bool(set(this_object.get_tags()).intersection(self.source_tags_of_disabled_sources)) is True:\\n log.debug2(f\\\"Object '{this_object.get_display_name()}' was added \\\"\\n f\\\"from a currently disabled source. Skipping orphaned tagging.\\\")\\n continue\\n\\n if getattr(this_object, \\\"prune\\\", False) is True:\\n if netbox_handler.primary_tag in this_object.get_tags():\\n this_object.add_tags(netbox_handler.orphaned_tag)\\n\\n # or just remove primary tag if pruning is disabled\\n else:\\n if netbox_handler.primary_tag in this_object.get_tags():\\n this_object.remove_tags(netbox_handler.primary_tag)\\n if netbox_handler.orphaned_tag in this_object.get_tags():\\n this_object.remove_tags(netbox_handler.orphaned_tag)\",\n \"def __contains__(self, obj):\\n return obj in self.actors\",\n \"def is_taggable(t: str) -> bool:\\n return t in taggable_resource_types\",\n \"def items_should_contain_array_of_tags(context):\\n items = context.response.json()['items']\\n for item in items:\\n assert 'tags' in item\\n assert isinstance(item['tags'], list)\\n assert len(item['tags']) > 0\",\n \"def __contains__(self, items):\\n if type(items) != list:\\n raise PJFInvalidType(items, list)\\n ret = 0\\n for item in items:\\n for key in self.__dict__:\\n if isinstance(self.__dict__[key], JsonFactory):\\n ret += item in self.__dict__[key]\\n elif item == key:\\n ret += 1\\n return len(items) == ret\",\n \"def contains(item, obj):\\n return obj.__contains__(item)\",\n \"def contains(self, *args):\\n return _libsbml.IdList_contains(self, *args)\",\n \"def _is_desired_tag(self, tag):\\n if self._tags is None:\\n return True\\n\\n if self._ignore_namespace:\\n for desired_tag in self._tags:\\n if tag.localname == desired_tag.localname:\\n return True\\n else:\\n for desired_tag in self._tags:\\n if tag == desired_tag:\\n return True\\n\\n return False\",\n \"def testGetAboutTagValuesWithObjectIDs(self):\\n objectID1 = uuid4()\\n objectID2 = uuid4()\\n value = self.store.add(AboutTagValue(objectID1, u'foo'))\\n self.store.add(AboutTagValue(objectID2, u'bar'))\\n self.assertEqual(value, getAboutTagValues(objectIDs=[objectID1]).one())\",\n \"async def exists(self, tag_name):\\n try:\\n if await self.get_id(tag_name):\\n return True\\n except RtbDoesntExists:\\n return False\",\n \"def has_tags_in_content(self):\\n\\t\\treturn self.get_content() and re_tag.search(self.get_content())\",\n \"def has_tags(self):\\n return bool(self.tags)\",\n \"def contains(self, object_id):\\n return libplasma.contains(self.conn, object_id)\",\n \"def contains(self, *__args): # real signature unknown; restored from __doc__ with multiple overloads\\r\\n return False\",\n \"def contains(self, *__args): # real signature unknown; restored from __doc__ with multiple overloads\\r\\n return False\",\n \"def exists(self, obj):\\n return False\",\n \"def test_tags(question):\\n assert \\\"tags\\\" in question[\\\"instance\\\"]\\n tags = set(question[\\\"instance\\\"][\\\"tags\\\"])\\n # there should be at least one tag\\n assert len(tags) >= 1\\n # each tags should be in VALID_TAGS\\n assert len(tags - VALID_TAGS) == 0\\n # there should be exactly one category-defining tag\\n assert len(tags.intersection(CATEGORY_TAGS)) == 1\",\n \"def has(self, *args):\\n return _ida_frame.xreflist_t_has(self, *args)\",\n \"def contains_all(self, *items):\\n return all(item in self for item in items)\",\n \"def get_tags(self, obj):\\n if QuestionTag.objects.filter(question=obj).exists():\\n id_tags = QuestionTag.objects.filter(question=obj).values('tag__id')\\n tags_obj = Tag.objects.filter(id__in=id_tags)\\n return TagSerializer(tags_obj, many=True).data\\n else:\\n return \\\"No tags\\\"\",\n \"def has_obstacle(lst):\\n for x in lst:\\n if x.tag is not None and x.tag == OBSTACLE:\\n return True\\n else:\\n return False\",\n \"def checkTags(instance_id, tagSet, nt_id):\\n tags = []\\n missingOwnersItem = \\\"\\\"\\n missingPatchesItem = \\\"\\\"\\n patch_name = \\\"\\\"\\n isPatchTag = False\\n isExpiration = False\\n isCreatorID = False\\n isOwnerMailTag = False\\n isOwnerTeamTag = False\\n for tag in tagSet:\\n if tag['Key'] == 'Expiration':\\n isExpiration = True\\n\\n if tag['Key'] == 'Creator ID':\\n isCreatorID = True\\n\\n if tag['Key'] == 'Patch Group':\\n isPatchTag = checkPatchValidity(tag['Value'])\\n\\n if tag['Key'] == 'Owning_Mail':\\n isOwnerMailTag = True\\n \\n if tag['Key'] == 'Owning_Team':\\n isOwnerTeamTag = True\\n\\n if not isExpiration:\\n tags.append(\\n { \\n 'Key': 'Expiration',\\n 'Value': (datetime.date.today() + datetime.timedelta(days = 30)).strftime('%Y-%m-%d')\\n })\\n if not isCreatorID:\\n tags.append(\\n {\\n 'Key': 'Creator ID',\\n 'Value': nt_id\\n })\\n\\n if not isOwnerTeamTag or not isOwnerMailTag:\\n missingOwnersItem = instance_id\\n\\n if not isPatchTag:\\n print(\\\"invalid patch tags!\\\")\\n missingPatchesItem = instance_id\\n\\n patch_name = createPatchTag(tags, instance_id, nt_id)\\n tags.append({ 'Patch Group' : patch_name })\\n\\n print(\\\"Attaching tags: \\\" + str(tags) + \\\" to instance \\\" + instance_id)\\n if MODE == 'enforce':\\n attachInstanceTags(instance_id, tags)\\n \\n if MODE == \\\"enforce\\\" and patch_name != \\\"Not yet populated\\\" and isOwnerMailTag and isOwnerTeamTag:\\n return True\\n \\n return False, missingOwnersItem, missingPatchesItem\",\n \"def __contains__(self, obj):\\n pass\",\n \"def contains(self, x: object):\\n return x in self.items\",\n \"def get_intersection_by_model(self, queryset_or_model, tags):\\r\\n tags = get_tag_list(tags)\\r\\n tag_count = len(tags)\\r\\n queryset, model = get_queryset_and_model(queryset_or_model)\\r\\n\\r\\n if not tag_count:\\r\\n return model._default_manager.none()\\r\\n\\r\\n model_table = qn(model._meta.db_table)\\r\\n # This query selects the ids of all objects which have all the\\r\\n # given tags.\\r\\n query = \\\"\\\"\\\"\\r\\n SELECT %(model_pk)s\\r\\n FROM %(model)s, %(tagged_item)s\\r\\n WHERE %(tagged_item)s.content_type_id = %(content_type_id)s\\r\\n AND %(tagged_item)s.tag_id IN (%(tag_id_placeholders)s)\\r\\n AND %(model_pk)s = %(tagged_item)s.object_id\\r\\n GROUP BY %(model_pk)s\\r\\n HAVING COUNT(%(model_pk)s) = %(tag_count)s\\\"\\\"\\\" % {\\r\\n 'model_pk': '%s.%s' % (model_table, qn(model._meta.pk.column)),\\r\\n 'model': model_table,\\r\\n 'tagged_item': qn(self.model._meta.db_table),\\r\\n 'content_type_id': ContentType.objects.get_for_model(model).pk,\\r\\n 'tag_id_placeholders': ','.join(['%s'] * tag_count),\\r\\n 'tag_count': tag_count,\\r\\n }\\r\\n\\r\\n cursor = connection.cursor()\\r\\n cursor.execute(query, [tag.pk for tag in tags])\\r\\n object_ids = [row[0] for row in cursor.fetchall()]\\r\\n if len(object_ids) > 0:\\r\\n return queryset.filter(pk__in=object_ids)\\r\\n else:\\r\\n return model._default_manager.none()\",\n \"def testGetTagValuesWithTagIDsAndObjectIDs(self):\\n objectID1 = uuid4()\\n objectID2 = uuid4()\\n user = createUser(u'username', u'password', u'User',\\n u'user@example.com')\\n user.namespaceID = createNamespace(user, user.username, None).id\\n tag = createTag(user, user.namespace, u'name')\\n value = self.store.add(TagValue(user.id, tag.id, objectID1, None))\\n self.store.add(TagValue(user.id, tag.id, objectID2, 42))\\n self.assertEqual(value, getTagValues([(objectID1, tag.id)]).one())\",\n \"def SelectObjects( object_ids):\\n id = rhutil.coerceguid(object_ids, False)\\n if id: object_ids = [id]\\n rc = 0\\n for id in object_ids:\\n if SelectObject(id)==True: rc += 1\\n return rc\",\n \"def check_all_tag_photo_counts(self):\\n data = self.db.get_query_as_list(\\n '''\\n select * from tag\\n '''\\n )\\n\\n for tag in data:\\n print()\\n print(tag)\\n # query for the number of photos using the tag\\n # compare it to the number in the photos column\\n # update if necessary\\n query_count = self.db.get_query_as_list(\\n '''\\n select count(tag_name)\\n from photo_tag\\n where tag_name = \\\"{}\\\"\\n '''.format(tag['tag_name'])\\n )\\n\\n if query_count[0]['count(tag_name)'] == tag['photos']:\\n print('OK', 'actual photos number with tag',\\n query_count[0]['count(tag_name)'], 'in photos column', tag['photos'])\\n else:\\n print('MISSMATCH IN PHOTOS AND PHOTOS WITH TAG\\\\n', 'actual photos number with tag',\\n query_count[0]['count(tag_name)'], 'in photos column', tag['photos'])\\n\\n tag_name = tag['tag_name']\\n count = query_count[0]['count(tag_name)']\\n break\\n\\n print('\\\\nDONE NO PROBLEMS!')\",\n \"def HasEventTags(self):\\n for name in self._GetStreamNames():\\n if name.startswith('event_tag_data.'):\\n return True\\n\\n return False\",\n \"def __contains__(self, name):\\n return name in set(self)\",\n \"def match(self, name, tags):\\n name, tags = self.get_compiled(name, tags)\\n \\n def index_of_letter(l):\\n return ord(l) - ord('a')\\n \\n true_val, false_val = name\\n \\n if true_val:\\n return index_of_letter(true_val) in tags\\n else:\\n return index_of_letter(false_val) not in tags\",\n \"def is_matching_product_with_tags(product, lat, lng, radius, tags):\\n return vincenty(\\n (lat, lng),\\n (product.shop.lat, product.shop.lng)\\n ).meters <= radius and any(tag in product.shop.tags for tag in tags)\",\n \"def get_intersection_by_model(self, queryset_or_model, tags):\\n tags = self.tag_model.get_tag_list(tags)\\n tag_count = len(tags)\\n queryset, model = get_queryset_and_model(queryset_or_model)\\n\\n if not tag_count:\\n return model._default_manager.none()\\n\\n model_table = qn(model._meta.db_table)\\n # This query selects the ids of all objects which have all the\\n # given tags.\\n query = \\\"\\\"\\\"\\n SELECT %(model_pk)s\\n FROM %(model)s, %(tagged_item)s\\n WHERE %(tagged_item)s.content_type_id = %(content_type_id)s\\n AND %(tagged_item)s.tag_id IN (%(tag_id_placeholders)s)\\n AND %(model_pk)s = %(tagged_item)s.object_id\\n GROUP BY %(model_pk)s\\n HAVING COUNT(%(model_pk)s) = %(tag_count)s\\\"\\\"\\\" % {\\n 'model_pk': '%s.%s' % (model_table, qn(model._meta.pk.column)),\\n 'model': model_table,\\n 'tagged_item': qn(self.model._meta.db_table),\\n 'content_type_id': ContentType.objects.get_for_model(model).pk,\\n 'tag_id_placeholders': ','.join(['%s'] * tag_count),\\n 'tag_count': tag_count,\\n }\\n\\n cursor = connection.cursor()\\n cursor.execute(query, [tag.pk for tag in tags])\\n object_ids = [row[0] for row in cursor.fetchall()]\\n if len(object_ids) > 0:\\n return queryset.filter(pk__in=object_ids)\\n else:\\n return model._default_manager.none()\",\n \"def __contains__(self, val):\\n return val in self.ids or super().__contains__(val)\",\n \"def tags():\",\n \"def has(self, tag_name: str, category: ty.Optional[str] = None) -> bool:\\n tags = self.__holder.db_tags.filter(lambda t: t.name == tag_name)\\n if category is not None:\\n tags = tags.filter(category=category)\\n\\n return len(tags) >= 1\",\n \"def check_photo_tag(self, tag_name):\\n data = self.db.make_query(\\n '''select * from photo_tag where tag_name = \\\"{}\\\" '''\\n .format(tag_name))\\n\\n if len(data) > 0:\\n return True\\n return False\",\n \"def hastag(obj, key):\\n key = TAG_PREFIX + key\\n if not isinstance(obj, unittest.TestCase):\\n return hasattr(obj, key)\\n tc_method = getattr(obj, obj._testMethodName)\\n return hasattr(tc_method, key) or hasattr(obj, key)\",\n \"def hasUnoInterfaces( oObject, *cInterfaces ):\\n for cInterface in cInterfaces:\\n if not hasUnoInterface( oObject, cInterface ):\\n return False\\n return True\",\n \"def hasUnoInterfaces( oObject, *cInterfaces ):\\n for cInterface in cInterfaces:\\n if not hasUnoInterface( oObject, cInterface ):\\n return False\\n return True\",\n \"def get_objects(self) -> Response:\\n tags = [tag for tag in request.args.get(\\\"tags\\\", \\\"\\\").split(\\\",\\\") if tag]\\n # filter types\\n types = [type_ for type_ in request.args.get(\\\"types\\\", \\\"\\\").split(\\\",\\\") if type_]\\n\\n try:\\n tagged_objects = TagDAO.get_tagged_objects_for_tags(tags, types)\\n result = [\\n self.object_entity_response_schema.dump(tagged_object)\\n for tagged_object in tagged_objects\\n ]\\n return self.response(200, result=result)\\n except TagInvalidError as ex:\\n return self.response_422(message=ex.normalized_messages())\\n except TagCreateFailedError as ex:\\n logger.error(\\n \\\"Error creating model %s: %s\\\",\\n self.__class__.__name__,\\n str(ex),\\n exc_info=True,\\n )\\n return self.response_422(message=str(ex))\",\n \"def update_tags(self, obj, tags):\\n content_type = ContentType.objects.get_for_model(obj)\\n current_tags = list(self.filter(items__content_type__pk=content_type.pk,\\n items__object_id=obj.pk))\\n updated_tags = self.model.get_tag_list(tags)\\n \\n # Remove tags which no longer apply\\n tags_for_removal = [tag for tag in current_tags \\\\\\n if tag not in updated_tags]\\n if len(tags_for_removal):\\n self.intermediary_table_model._default_manager.filter(content_type__pk=content_type.pk,\\n object_id=obj.pk,\\n tag__in=tags_for_removal).delete()\\n # Add new tags\\n for tag in updated_tags:\\n if tag not in current_tags:\\n self.intermediary_table_model._default_manager.create(tag=tag, content_object=obj)\",\n \"def check_objects_exists():\\n objects = s3.list_objects(Bucket=BUCKET, Prefix=TRAIN_SET_PATH)\\n if objects:\\n return objects\\n return False\",\n \"def add_tags_to_photo(self, photo_id, tag_list):\\n print('\\\\nHello from add_tags_to_photo, the tag list is: ', tag_list)\\n\\n # for each tag\\n # check if the tag is in the database already\\n # if it is not then add it to the tag table\\n for tag in tag_list:\\n\\n # will return None if the tag is not in the tag table\\n # tag_name is the column name\\n data = self.db.get_row('tag', 'tag_name', tag)\\n\\n print('data is', data)\\n\\n if data is None:\\n\\n print('\\\\nthat value {} is not in the db\\\\n'.format(tag))\\n\\n self.db.make_query(\\n '''\\n insert into tag (tag_name, user_id, photos)\\n values (\\\"{}\\\", \\\"{}\\\", {})\\n '''.format(\\n tag,\\n '28035310@N00',\\n self.get_photo_count_by_tag(tag)\\n )\\n )\\n\\n print('\\\\nshould be added now...\\\\n')\\n\\n if self.db.get_row('tag', 'tag_name', tag):\\n print('\\\\nadded tag, ', tag, '\\\\n')\\n\\n # UNIQUE constraint can cause problems here\\n # so catch any exceptions\\n try:\\n # The tag is now in the database.\\n self.db.make_query(\\n '''\\n insert into photo_tag (photo_id, tag_name)\\n values ({}, \\\"{}\\\")\\n '''.format(photo_id, tag)\\n )\\n except Exception as e:\\n print('Problem adding tag to photo_tag ', e)\\n\\n data = self.db.make_query(\\n '''\\n select * from photo_tag where photo_id = {}\\n '''.format(photo_id)\\n )\\n\\n tags_in_data = []\\n if len(data) > 0:\\n for tag in data:\\n tags_in_data.append(tag[1])\\n\\n print(tags_in_data)\\n for tag in tag_list:\\n if tag not in tags_in_data:\\n return False\\n else:\\n self.update_photo_count(tag)\\n\\n return True\",\n \"def test_in_list(self):\\n\\n # get available ids\\n ids = list(DQ(\\\"(b.id) Book b\\\").tuples())\\n ids = [id[0] for id in ids]\\n\\n # take just three of them\\n c = {\\\"ids\\\": ids[:3]}\\n dq = DQ(\\\"(b.id, b.name) Book{b.id in '$(ids)'} b\\\")\\n r = list(dq.context(c).dicts())\\n\\n # make sure we got three of them\\n self.assertEqual(len(r), 3)\",\n \"def find_entities(self, area=EVERYWHERE, tags=frozenset()):\\n\\t\\t#entities are added into binary box tree by their binary box cover[s] for the corresponding tags\\n\\t\\t#when searching build a set of all possibly affected entities\\n\\t\\t#then do a real test on the hitboxes\\n\\t\\tif isinstance(tags, str):\\n\\t\\t\\ttags = {tags} #M# could use frozenset instead\\n\\t\\tfor entity in self.entities:\\n\\t\\t\\tif tags.issubset(entity[\\\"tags\\\"]):\\n\\t\\t\\t\\tif area.collides_with(entity.HITBOX + entity[\\\"position\\\"]):\\n\\t\\t\\t\\t\\tyield entity\",\n \"def __contains__(self, obj):\\r\\n return assert_(obj in self.obj, '%r not in %r' % (obj, self.obj))\",\n \"def is_tag(tag_name, user_path, current_user) -> bool:\\n user = current_user[0]\\n tag_list = os.listdir((user_path + '\\\\\\\\' + user).encode('unicode_escape'))\\n temp = list(map(bytes.decode, tag_list))\\n if tag_name in temp:\\n return True\\n else:\\n return False\",\n \"def is_tag_available(self, tag):\\n return tag in self.available_tags\",\n \"def validBookObject(bookObject):\\n return (\\\"name\\\" in bookObject and\\n \\\"price\\\" in bookObject and\\n \\\"isbn\\\" in bookObject)\",\n \"def verify_objects(directory: str, /, data: dict) -> bool:\\n\\n if type(data) is not dict:\\n raise TypeError(\\\"Data parameter must be a dictionary.\\\")\\n\\n if \\\"files\\\" not in data or \\\"folders\\\" not in data or \\\"extensions\\\" not in data:\\n raise TypeError(\\n \\\"The dictionary does not contain one of the following keys: 'files', 'folders', 'extensions'.\\\"\\n )\\n\\n objects = listdir(directory)\\n\\n for folder in data[\\\"folders\\\"]:\\n if folder in objects:\\n return True\\n\\n for file in data[\\\"files\\\"]:\\n if file in objects:\\n return True\\n\\n for o in objects:\\n ext = splitext(o)[-1]\\n if ext and ext in data[\\\"extensions\\\"]:\\n return True\\n\\n return False\",\n \"def __contains__(self, rq):\\n return rq in self._data\",\n \"def intersects(self, *__args): # real signature unknown; restored from __doc__ with multiple overloads\\r\\n return False\",\n \"def isTaggedList(exp, tag):\\n return isPair(exp) and isinstance(car(exp), Symbol) and (car(exp).name == tag.name)\",\n \"def _canProcessTags(self, grammar, pos_tags):\\n badTags = []\\n for tag in pos_tags:\\n if tag not in grammar.tags:\\n badTags.append(tag)\\n logger.debug(\\\"Grammar can't handle tag:\\\" + tag)\\n if badTags:\\n return False\\n else:\\n return True\",\n \"def is_tagged(self, instance_id, tag_name):\\n tag_value = self.get_tag_for_instance(instance_id, tag_name)\\n if tag_value is not None and tag_value == 'true':\\n return True\\n else:\\n return False\",\n \"def has_hashtag(self, tag_list, **kwargs):\\n lowlist = [tag.lower() for tag in tag_list]\\n alllower = ('case_sensitive' in kwargs and not kwargs['case_sensitive'])\\n for ht in self.original.entities['hashtags']:\\n lowht = ht['text'].lower()\\n if alllower and lowht in lowlist or '#' + lowht in lowlist:\\n return True\\n if ht['text'] in tag_list or '#' + ht['text'] in tag_list:\\n return True\\n return False\",\n \"def do_tagged_objects(parser, token):\\r\\n bits = token.contents.split()\\r\\n if len(bits) != 6:\\r\\n raise TemplateSyntaxError(_('%s tag requires exactly five arguments') % bits[0])\\r\\n if bits[2] != 'in':\\r\\n raise TemplateSyntaxError(_(\\\"second argument to %s tag must be 'in'\\\") % bits[0])\\r\\n if bits[4] != 'as':\\r\\n raise TemplateSyntaxError(_(\\\"fourth argument to %s tag must be 'as'\\\") % bits[0])\\r\\n return TaggedObjectsNode(bits[1], bits[3], bits[5])\",\n \"def has(self, tag_name: str) -> bool:\\n return hasattr(self, tag_name)\",\n \"def on_object(self, image, objects):\\n for obj in objects:\\n if self.is_object_recognition_appropriate(obj.name):\\n self.say(\\\"I see a {}\\\".format(obj.name))\",\n \"def test_page_tags(self):\\n page, page_2 = self.get_pages()\\n page_tags = models.PageTags.objects.create(extended_object=page)\\n page_tags.tags.add(*self.tag_strings)\\n\\n self.assertTrue(page_has_tag(page, slugify(self.tag_strings[0])))\\n self.assertTrue(page_has_tag(page, Tag.objects.get(slug=slugify(self.tag_strings[0]))))\\n self.assertEqual(set(self.tag_strings), {tag.name for tag in get_page_tags(page)})\\n\\n self.assertFalse(page_has_tag(page_2, slugify(self.tag_strings[0])))\\n self.assertEqual(set(), {tag.name for tag in get_page_tags(page_2)})\",\n \"def __contains__(self, item):\\n return item in self.attrs\",\n \"def dict_has_items(obj, items):\\n has_items = False\\n if isinstance(obj, basestring):\\n obj = json.loads(obj)\\n for item in items:\\n for lookup_key, lookup_val in item.iteritems():\\n if lookup_key in obj and obj[lookup_key] == lookup_val:\\n has_items = True\\n else:\\n return False\\n return has_items\",\n \"def list_all_tags(self,obs):\",\n \"def __contains__(self, name):\\n return name in self.__resources\",\n \"def _match_all(self, obj, criteria):\\n return all(getattr(obj, key_, None) == value_ for\\n key_, value_ in criteria.items())\",\n \"def applies(cls, obj):\\n return type(obj) in cls.types\",\n \"def has_taxa(self, **kwargs):\\n if \\\"taxa\\\" not in kwargs and \\\"oids\\\" not in kwargs and \\\"labels\\\" not in kwargs:\\n raise TypeError(\\\"Need to specify `taxa`, `oids` or `labels` list.\\\")\\n taxa = set(kwargs.get(\\\"taxa\\\", []))\\n oids = set(kwargs.get(\\\"oids\\\", []))\\n labels = set(kwargs.get(\\\"labels\\\", []))\\n taxon_oids = set([t.oid for t in self])\\n taxon_labels = set([t.label for t in self])\\n return taxa.issubset(self) \\\\\\n and oids.issubset(taxon_oids) \\\\\\n and labels.issubset(taxon_labels)\",\n \"def hasname(self, tag: str) -> bool:\\n for key in self.formal_names:\\n if key in tag.lower():\\n return True\\n\\n # Exit case if key -> value not in mapping \\n return False\",\n \"def isinstance_blender_object(self, b_obj):\\n # lame and slow, but functional\\n return b_obj in Blender.Object.Get()\",\n \"def _verify_tags(self):\\n for tag in self.tags:\\n if tag.lower() in VASP_TAG_LIST:\\n continue\\n else:\\n print((\\\"Warning: unknown INCAR tag '\\\" + tag + \\\"' with value '\\\" + str(self.tags[tag]) + \\\"'\\\"))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.61141014","0.6103309","0.5986072","0.5873774","0.5779968","0.57688856","0.57678956","0.576175","0.5726771","0.57225233","0.57074004","0.563474","0.5622186","0.5621437","0.5620893","0.56001145","0.55933976","0.55933976","0.5574528","0.5566219","0.5503636","0.5503636","0.5503575","0.5502966","0.548435","0.546435","0.54636055","0.5444265","0.54361796","0.5434005","0.5427121","0.541199","0.5409278","0.53972656","0.53664947","0.53603286","0.5339045","0.53375685","0.5312451","0.53123534","0.53070575","0.52995837","0.5291501","0.5291501","0.5286985","0.5282","0.5265533","0.5265522","0.5262464","0.52531993","0.5247718","0.5243928","0.52303576","0.5224133","0.5223819","0.5222344","0.5219634","0.5210632","0.5192914","0.5176666","0.51747453","0.5170886","0.5168739","0.5166297","0.5164474","0.5160258","0.5156839","0.5148247","0.5148247","0.5147503","0.5136073","0.5132992","0.51262754","0.5110735","0.51083446","0.5083534","0.5083044","0.50780207","0.507643","0.50713545","0.5066205","0.50602996","0.5054295","0.5046022","0.5045993","0.5044138","0.50419414","0.5035897","0.5023805","0.5019999","0.50183797","0.50148064","0.5014273","0.5013312","0.50124866","0.5011188","0.5006607","0.50060755","0.49985194","0.49982485"],"string":"[\n \"0.61141014\",\n \"0.6103309\",\n \"0.5986072\",\n \"0.5873774\",\n \"0.5779968\",\n \"0.57688856\",\n \"0.57678956\",\n \"0.576175\",\n \"0.5726771\",\n \"0.57225233\",\n \"0.57074004\",\n \"0.563474\",\n \"0.5622186\",\n \"0.5621437\",\n \"0.5620893\",\n \"0.56001145\",\n \"0.55933976\",\n \"0.55933976\",\n \"0.5574528\",\n \"0.5566219\",\n \"0.5503636\",\n \"0.5503636\",\n \"0.5503575\",\n \"0.5502966\",\n \"0.548435\",\n \"0.546435\",\n \"0.54636055\",\n \"0.5444265\",\n \"0.54361796\",\n \"0.5434005\",\n \"0.5427121\",\n \"0.541199\",\n \"0.5409278\",\n \"0.53972656\",\n \"0.53664947\",\n \"0.53603286\",\n \"0.5339045\",\n \"0.53375685\",\n \"0.5312451\",\n \"0.53123534\",\n \"0.53070575\",\n \"0.52995837\",\n \"0.5291501\",\n \"0.5291501\",\n \"0.5286985\",\n \"0.5282\",\n \"0.5265533\",\n \"0.5265522\",\n \"0.5262464\",\n \"0.52531993\",\n \"0.5247718\",\n \"0.5243928\",\n \"0.52303576\",\n \"0.5224133\",\n \"0.5223819\",\n \"0.5222344\",\n \"0.5219634\",\n \"0.5210632\",\n \"0.5192914\",\n \"0.5176666\",\n \"0.51747453\",\n \"0.5170886\",\n \"0.5168739\",\n \"0.5166297\",\n \"0.5164474\",\n \"0.5160258\",\n \"0.5156839\",\n \"0.5148247\",\n \"0.5148247\",\n \"0.5147503\",\n \"0.5136073\",\n \"0.5132992\",\n \"0.51262754\",\n \"0.5110735\",\n \"0.51083446\",\n \"0.5083534\",\n \"0.5083044\",\n \"0.50780207\",\n \"0.507643\",\n \"0.50713545\",\n \"0.5066205\",\n \"0.50602996\",\n \"0.5054295\",\n \"0.5046022\",\n \"0.5045993\",\n \"0.5044138\",\n \"0.50419414\",\n \"0.5035897\",\n \"0.5023805\",\n \"0.5019999\",\n \"0.50183797\",\n \"0.50148064\",\n \"0.5014273\",\n \"0.5013312\",\n \"0.50124866\",\n \"0.5011188\",\n \"0.5006607\",\n \"0.50060755\",\n \"0.49985194\",\n \"0.49982485\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.67218775"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":11285,"cells":{"query":{"kind":"string","value":"A simple slug generator. Slugs are pure ASCII lowercase strings that can be used in URLs an other places where a name has to be machinesafe."},"document":{"kind":"string","value":"def slugify(value: Any, sep: str = \"-\") -> Optional[str]:\n text = stringify(value)\n if text is None:\n return None\n text = text.replace(sep, WS)\n # run this first because it'll give better results on special\n # characters.\n text = category_replace(text, SLUG_CATEGORIES)\n text = latinize_text(text, ascii=True)\n if text is None:\n return None\n text = text.lower()\n text = \"\".join([c for c in text if c in VALID_CHARS])\n text = collapse_spaces(text)\n if text is None or len(text) == 0:\n return None\n return text.replace(WS, sep)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def generate_slug(self):\n return slugify(self.name)","def generate_slug(name):\n # Slugify the title and check\n slug = secure_filename(name).lower().replace('_', '-')\n slug_n = slug\n n = 1\n\n # Append an increasing number to the slug until we find an available url\n while not slug_available(slug_n):\n n += 1\n slug_n = slug + '-' + str(n)\n\n return slug_n","def smart_slugify(name, min_length=3):\n url_slug = slugify(name)\n if len(url_slug) >= min_length:\n return url_slug\n # try converting to latin characters:\n for lang in ('ru', 'ka', 'hy', 'el', 'bg', 'uk'):\n trans_url_slug = slugify(translit(name, lang, reversed=True))\n if len(trans_url_slug) >= min_length:\n return trans_url_slug\n # if the url is random lets make it slightly longer\n min_length = 6\n return url_slug + ''.join([random.choice(string.ascii_lowercase) for _ in range(min_length - len(url_slug))])","def create_slug(val):\n slug = slugify(unidecode(val))\n for s in settings.DJCAT_SLUG_RESERVED:\n slug = slug.replace(s, '')\n return slug","def slugify(s):\n slug = s.lower() # force to lower case\n slug = re.sub('[^a-z0-9 -]', '', slug) # remove invalid chars\n slug = re.sub(r'\\s+', '-', slug) # collapse whitespace and replace by -\n slug = re.sub('-+', '-', slug) # collapse dashes\n if not slug:\n slug = 'untitled'\n return slug","def slugify(name):\n return name.lower().strip().replace(' ', '-').replace('+', '_')","def slug(value):\n return slugify(value)","def slugify(value):\n import unicodedata\n import re\n # value = unicodedata.normalize('NFKD', value).encode('ascii', 'ignore')\n value = str(re.sub('[^\\w\\s-]', '', value).strip().lower())\n\n value = str(re.sub('[-\\s]+', '-', value))\n rstr = r\"[\\/\\\\\\:\\*\\?\\\"\\<\\>\\|]\" # '/ \\ : * ? \" < > |'\n new_title = re.sub(rstr, \"_\", value) # 替换为下划线\n if new_title is None or len(new_title) == 0:\n random_title = get_random_string(5)\n print('文件名称:{}非法，将生成随机名称:{}'.format(new_title, random_title))\n return random_title\n return new_title\n # return value","def generate_slug(string: str) -> str:\n string = string.replace(\" \", \"-\").lower()\n # Remove any non letters, numbers, and non-dashes\n return re.sub(r\"[^a-zA-Z0-9\\-\\+]+\", \"\", string)","def camel_to_slug(self, name):\n name = re.sub(r'([a-z])([A-Z])', r'\\1 \\2', name).title().replace(\" \", \"\").replace(\"_\", \"\")\n s1 = re.sub('(.)([A-Z][a-z]+)', r'\\1_\\2', name)\n slug = re.sub('([a-z0-9])([A-Z])', r'\\1_\\2', s1).lower()\n return slug","def unique_slugify(text: str, slugs_in_use: list):\n slug = slugify(text)\n if not slug:\n raise ValueError(\"Unable to slugify input\")\n if slug in slugs_in_use:\n i = 2\n slug = f\"{slug}-{i}\"\n while slug in slugs_in_use:\n i += 1\n slug = f\"{slug[:-2]}-{i}\"\n return slug","def generate_slug(self):\n slug = slugify(self.title)\n new_slug = slug\n s = 1\n while Article.objects.filter(slug=new_slug).exists():\n \"\"\"increase value of slug by one\"\"\"\n new_slug = f'{slug}-{s}'\n s += 1\n return new_slug","def format_slug(text):\n allowed_chars = (\n \"abcdefghijklmnopqrstuvxyzABCDEFGHIJKLMNOPQRSTUVWXYZ\" # Alphabet\n \"01234567890\" # Numbers\n \"_-\" # Symbols\n )\n # Replace seperators with dash\n seperators = [\" \", \",\", \".\"]\n for sep in seperators:\n text = text.replace(sep, \"-\")\n # Strip unacceptable characters\n text = \"\".join([c for c in text if c in allowed_chars])\n # Enforce max length\n return truncate(text, max_len=50).lower()","def test_slug():\n slugify = fields.SlugField().adapt\n\n for input, expect in [\n ('01 HDR test', '01-hdr-test'),\n ('--&*$#(8$jjsdsd77-----test phrase12 123--', '8jjsdsd77-test-phrase12-123'),\n ('1234', '1234'),\n ('abcdEFG', 'abcdefg'),\n ]:\n assert slugify(input) == expect","def deslugify(_slug):\n return string.capwords(_slug.replace('-', ' '))","def create_slug(self):\n slug = slugify(self.title)\n new_slug = slug\n n = 1\n while Article.objects.filter(slug=new_slug).exists():\n new_slug = '{}-{}'.format(slug, n)\n n += 1\n\n return new_slug","def _get_slug(str):\n return str.replace(\" \", \"_\")","def sluggify(cls, name: str) -> str:\n return \"-\".join([word for word in cls.split_on_non_word.split(name.strip().lower()) if word])","def slug(self):\n slug = slugify(self.text)\n return \"%s/%s\" % (self.uid, slug[:36]) # max 24 characters","def slugify(s):\n from future.builtins import str\n return django_slugify(unidecode(str(s)))","def slugify(value):\n return '_' + value.replace(' ', '-').lower()","def namify(text):\n return slugify(text).replace('-','_')","def unique_slug(s, model, num_chars=50):\n slug = slugify(s)\n slug = slug[:num_chars].strip('-')\n while True:\n dup = model.objects.filter(slug=slug)\n if not dup:\n return slug\n\n slug = slug[:39] + '-' + random_string(10)","def set_slug(self):\n if not self.slug:\n self.slug = slugify(self.name)[:50]","def parse_to_slug(words, maxlen=24):\n\tslug = ''\n\tmaxlen = min(maxlen, len(words))\n\tfor c in words:\n\t\tif len(slug) == maxlen:\n\t\t\tbreak\n\t\tc = ord(c)\n\t\tif c == 0x27:\n\t\t\tcontinue\n\t\telif c >= 0x30 and c <= 0x39:\n\t\t\tslug += chr(c)\n\t\telif c >= 0x41 and c <= 0x5a:\n\t\t\tslug += chr(c + 0x20)\n\t\telif c >= 0x61 and c <= 0x7a:\n\t\t\tslug += chr(c)\n\t\telif len(slug) and slug[-1] != '-':\n\t\t\tslug += '-'\n\tif len(slug) and slug[-1] == '-':\n\t\tslug = slug[:-1]\n\treturn slug","def to_slug(value, incoming=None, errors=\"strict\"):\r\n value = safe_decode(value, incoming, errors)\r\n # NOTE(aababilov): no need to use safe_(encode|decode) here:\r\n # encodings are always \"ascii\", error handling is always \"ignore\"\r\n # and types are always known (first: unicode; second: str)\r\n value = unicodedata.normalize(\"NFKD\", value).encode(\r\n \"ascii\", \"ignore\").decode(\"ascii\")\r\n value = SLUGIFY_STRIP_RE.sub(\"\", value).strip().lower()\r\n return SLUGIFY_HYPHENATE_RE.sub(\"-\", value)","def format_slug(text: str) -> str:\n allowed_chars = (\n string.ascii_lowercase # Alphabet\n + string.digits # Numbers\n + \"_-\" # Symbols\n )\n # Convert to lowercase\n text = text.lower()\n # Replace separators with dash\n text = text.translate({ord(sep): ord('-') for sep in \" ,.\"})\n # Strip unacceptable characters\n text = ''.join(c for c in text if c in allowed_chars)\n # Enforce max length\n return truncate(text, max_len=50)","def slugify(text):\n concatenated = re.sub('\\s+', '-', text.lower())\n return re.sub('[^A-Za-z0-9_-]', '', concatenated)","def slugified(text):\n return re.sub(\"-+\", \"-\", re.sub(r\"\\W\", \"-\", text.lower()))","def generate_student_slug(name: str) -> str:\n\n slug = slugify(name)\n if not Student.objects.filter(slug=slug).exists():\n return slug\n\n # Duplicate found, add integer to end until it's unique\n counter = 1\n while True:\n modified_slug = f\"{slug}-{counter}\"\n if not Student.objects.filter(slug=modified_slug).exists():\n return modified_slug\n counter += 1","def int_to_slug(i: int) -> str:\n byt = str(i).encode('utf-8')\n slug_bytes = base64.urlsafe_b64encode(byt)\n return slug_bytes.decode('utf-8')","def _slug(self, value):\n if isinstance(value, basestring):\n try:\n converted = value\n except Exception, exception:\n logger.error(exception)\n raise\n elif isinstance(value, str):\n try:\n converted = unicode(value, \"utf-8\")\n except Exception, exception:\n logger.error(exception)\n raise\n elif isinstance(value, (int, long, float)):\n self.assertNotIsInstance(value, basestring)\n try:\n converted = str(value)\n converted = unicode(converted)\n except Exception, exception:\n logger.error(exception)\n raise\n else:\n self.assertNotIsInstance(value, basestring)\n try:\n converted = unicode(value)\n except Exception, exception:\n logger.error(exception)\n raise\n output = converted.lower().strip().replace(\" \", \"-\")\n output = re.sub(r'[^a-z0-9]+', '-', output).strip('-')\n output = re.sub(r'[-]+', '-', output)\n output = re.sub(r\"[^\\w-]\", \"\", output)\n if isinstance(output, basestring):\n number_of_spaces = output.count(\" \")\n if number_of_spaces == 0:\n return output\n else:\n return False","def test_get_slug(self):\n\n test_slug = slugify(self.book.name.replace(\"ı\", \"i\"))\n self.assertEqual(test_slug, self.book.slug)","def __calculate_slug(self):\n\n return slugify.slugify(self.metadata['title'])","def slugify(s):\n s = reg_nonchar.sub('-', s).lower()\n s = reg_dashes.sub('-', s)\n s = reg_outer_dashes.sub('', s)\n return s","def slugify(value):\n value = re.sub('[^\\w\\s-]', '', value).strip()\n return re.sub('[-\\s]+', '-', value)","def slugify(text: str) -> str:\n return text.strip().replace(', ', '-').replace(' ', '_').lower()","def slugify(s):\n\treturn re.sub('[!@#$%^&*()\\\\\\\\/:.\"\"]+', '', s).replace(' ', '-').replace('--', '-').lower()","def _safe_build_slug(build_slug, limit, hash_length=6):\n build_slug_hash = hashlib.sha256(build_slug.encode(\"utf-8\")).hexdigest()\n safe_chars = set(string.ascii_letters + string.digits)\n\n def escape(s):\n return escapism.escape(s, safe=safe_chars, escape_char=\"-\")\n\n build_slug = escape(build_slug)\n return \"{name}-{hash}\".format(\n name=build_slug[: limit - hash_length - 1],\n hash=build_slug_hash[:hash_length],\n ).lower()","def slugify(value: str) -> str:\n value = re.sub(r\"[^\\w\\s-]\", \"\", value.lower())\n return re.sub(r\"[-\\s]+\", \"-\", value).strip(\"-_\")","def slugify(value):\n #import unicodedata\n #value = unicodedata.normalize('NFKD', value).encode('ascii', 'ignore')\n value = unicode(re.sub('[^\\w\\s-]', '', value).strip().lower())\n value = re.sub('[-\\s]+', '-', value)\n return value","def _make_slug(\n self, instance: BaseModel,\n field: Optional[str] = None,\n fields: Optional[List[str]] = None) -> str:\n if field is not None:\n text = str(getattr(instance, field))\n if text is None or text == '':\n raise ValueError('There was no string to make a slug from')\n elif fields is not None:\n parts = [str(getattr(instance, field)) for field in fields]\n text = '-'.join(parts)\n if text is None or text.replace('-', '') == '':\n raise ValueError('There was no string to make a slug from')\n\n concrete_slug = slugify(text, to_lower=True)\n slug = concrete_slug\n counter = 1\n while self.q.filter_by(slug=slug).first() is not None:\n slug = '{}-{}'.format(concrete_slug, counter)\n counter += 1\n return slug","def unique_slug_generator(instance, new_slug=None):\n if new_slug is not None:\n slug = new_slug\n else:\n slug = slugify(instance.username)\n\n Klass = instance.__class__\n qs_exists = Klass.objects.filter(slug = slug).exists()\n if qs_exists:\n new_slug = \"{slug}-{randstr}\".format(\n slug = slug,\n randstr = random_string_generator(size = 4)\n )\n return unique_slug_generator(instance, new_slug = new_slug)\n return slug","def unique_slug_generator(instance, new_slug=None):\n if new_slug is not None:\n slug = new_slug\n else:\n slug = slugify(instance.title)\n\n Klass = instance.__class__\n qs_exists = Klass.objects.filter(slug = slug).exists()\n if qs_exists:\n new_slug = \"{slug}-{randstr}\".format(\n slug = slug,\n randstr = random_string_generator(size = 4)\n )\n return unique_slug_generator(instance, new_slug = new_slug)\n return slug","def unique_slug(manager, slug_field, slug):\n max_length = manager.model._meta.get_field(slug_field).max_length\n slug = slug[:max_length]\n i = 0\n while True:\n if i > 0:\n if i > 1:\n slug = slug.rsplit(\"-\", 1)[0]\n # We need to keep the slug length under the slug fields max length. We need to\n # account for the length that is added by adding a random integer and `-`.\n slug = \"%s-%s\" % (slug[:max_length - len(str(i)) - 1], i)\n if not manager.filter(**{slug_field: slug}):\n break\n i += 1\n return slug","def slugify(s: str) -> str:\n\n return re.sub(re_forbidden, ' ', s.strip()).strip().replace(' ', '-')","def unique_slug_generator(instance, new_slug=None):\n if new_slug is not None:\n slug = new_slug\n else:\n slug = slugify(instance.title)\n\n Klass = instance.__class__\n qs_exists = Klass.objects.filter(slug=slug).exists()\n if qs_exists:\n new_slug = \"{slug}-{randstr}\".format(\n slug=slug,\n randstr=random_string_generator(size=4)\n )\n return unique_slug_generator(instance, new_slug=new_slug)\n return slug","def encoded_name(self):\n return slugify(str(self).lower())","def slugify(value):\n value = unicode(str(value))\n value = unicodedata.normalize('NFKD', value).encode('ascii', 'ignore').decode('ascii')\n value = re.sub(r'[^\\w\\s-]', '', value).strip().lower()\n return re.sub(r'[-\\s]+', '-', value)","def _slugify(value):\r\n try:\r\n import unicodedata\r\n value = unicodedata.normalize('NFKD', value)\r\n except ImportError:\r\n pass\r\n value = value.encode('ascii', 'ignore').decode()\r\n value = _slugify_strip_re.sub('', value).strip().lower()\r\n return _slugify_hyphenate_re.sub('-', value)","def create_slug_from_tag_name(sender, instance, *args, **kwargs):\n if not instance.slug:\n instance.slug = slugify(instance.name)","def _slugify(value):\n value = unicodedata.normalize('NFKD', value).encode('ascii', 'ignore')\n value = re.sub(r'[^\\w\\s-]', '', value.decode('utf-8', 'ignore'))\n value = value.strip().lower()\n value = re.sub(r'[-\\s]+', '-', value)\n return value","def slugify(self, space='-'):\n #strtitle = unicode(self.title)\n strtitle = self.title\n import unicodedata\n import re\n strtitle = unicodedata.normalize('NFKD', strtitle).encode('ascii', 'ignore')\n strtitle = unicode(re.sub('[^\\w\\s-]', '', strtitle).strip().lower())\n return re.sub('[-\\s]+', space, strtitle)","def set_unique_slug(self):\n set_unique_slug(self, based_on_field='name')","def slugify(s):\n s = s.lower()\n\n for c in [' ', '-', '.', '/']:\n s = s.replace(c, '_')\n\n s = re.sub('\\W', '', s)\n s = s.replace('_', ' ')\n s = re.sub('\\s+', ' ', s)\n s = s.strip()\n s = s.replace(' ', '-')\n\n return s","def slugify(value):\n value = unicode(value)\n value = unicodedata.normalize('NFKD', value).encode('ascii', 'ignore').decode('ascii')\n value = re.sub('[^\\w\\s-]', '', value).strip().lower()\n return re.sub('[-\\s]+', '-', value)","def slugify(value):\n import unicodedata\n value = unicode(value)\n value = unicodedata.normalize('NFKD', value).encode('ascii', 'ignore')\n value = unicode(re.sub('[^\\w\\s-]', '', value).strip().lower())\n value = unicode(re.sub('[-\\s]+', '-', value))\n return value","def test_slugify(self):\n self.assertEqual(self.cat_short.slug, 'short-blog-name')\n regex = r'[@:%._\\+~#=]+'\n self.assertNotRegex(self.cat_short.slug, regex)\n self.assertNotRegex(self.cat_long.slug, regex)","def get_slug(self, headline):\n exclude = set(string.punctuation)\n s = ''.join(ch for ch in headline if ch not in exclude)\n return s.lower().replace(\" \", \"-\")","def test_unique_slug(self):\n # While unique_slug lives in the storybase app, I test it here\n # because this is the app where the relevent models live\n from storybase.utils import unique_slugify\n story = create_story(title=\"Test Story\", summary=\"Test Summary\",\n byline=\"Test Byline\")\n self.assertEqual(story.slug, \"test-story\")\n story2 = create_story(title=\"Test Story\", summary=\"Test Summary 2\",\n byline=\"Test Byline 2\", slug=\"non-colliding-slug\")\n self.assertEqual(story2.slug, \"non-colliding-slug\")\n unique_slugify(story2, story2.title)\n self.assertEqual(story2.slug, \"test-story-2\")","def _slugify(value):\n import unicodedata\n if not isinstance(value, unicode):\n value = unicode(value)\n value = unicodedata.normalize('NFKD', value).encode('ascii', 'ignore')\n value = unicode(_slugify_strip_re.sub('', value).strip().lower())\n return _slugify_hyphenate_re.sub('-', value)","def _make_url_friendly(title):\n # RFC 3986 section 2.3 says: letters, numbers, and -_.~ are unreserved\n return re.sub('[^-_.~a-zA-Z0-9]', '-', title)","def slugify(value):\n # import unicodedata\n # value = unicodedata.normalize('NFKD', value).encode('ascii', 'ignore')\n # value = unicode(re.sub('[^\\w\\s-]', '', value).strip().lower())\n # value = unicode(re.sub('[-\\s]+', '-', value))\n value = re.sub('[^\\w\\s-]', '', value).strip().lower()\n value = re.sub('[-\\s]+', '-', value)\n return value","def get_unique_slug(instance):\n slug = slugify(instance.title)\n extension = 1\n ModelClass = instance.__class__\n\n while ModelClass.objects.filter(slug=slug).exists():\n slug = '{}-{}'.format(slug, extension)\n extension += 1\n\n return slug","def slugify(value):\n value = unicodedata.normalize('NFKD', value).encode('ascii', 'ignore')\n value = str(re.sub('[^\\w\\s-]', '', str(value)).strip().lower())\n return re.sub('[-\\s]+', '-', str(value))","def test_auto_slug(self):\n title = ('Transportation Challenges Limit Education Choices for '\n 'Denver Parents')\n story = Story()\n story.save()\n story_translation = StoryTranslation(title=title, story=story)\n story_translation.save()\n self.assertEqual(story.slug, slugify(title))","def test_auto_slug(self):\n title = ('Transportation Challenges Limit Education Choices for '\n 'Denver Parents')\n story = Story()\n story.save()\n story_translation = StoryTranslation(title=title, story=story)\n story_translation.save()\n self.assertEqual(story.slug, slugify(title))","def slugify(value):\n if type(value) == unicode:\n import unicodedata\n value = unicodedata.normalize('NFKD', value).encode('ascii', 'ignore')\n value = unicode(re.sub('[^\\w\\s-]', '', value).strip().lower())\n return re.sub('[-\\s]+', '-', value)","def test_slugify(self):\n self.assertEqual(self.art_short.slug, 'short-article-title')\n regex = r'[@:%._\\+~#=]+'\n self.assertNotRegex(self.art_short.slug, regex)\n self.assertNotRegex(self.art_long.slug, regex)","def slug(self) -> str:\n return pulumi.get(self, \"slug\")","def to_slug(dtype,name,tag=None):\n FreezableAPI.validate_freezable_name(dtype)\n FreezableAPI.validate_freezable_name(name)\n slug = f\"{FreezableAPI._slug_prefix}.{dtype}.{name}\"\n if tag:\n FreezableAPI.validate_tagname(tag)\n slug = f\"{slug}:{tag}\"\n return slug","def test_slug(self):\n m = Machine.objects.get(id=1)\n self.assertEqual(m.slug, \"linac-name-01\")","def create_slug_from_category_name(sender, instance, *args, **kwargs):\n if not instance.slug:\n instance.slug = slugify(instance.name)","def slugify(filename):\n filename = os.path.relpath(filename, \"\") # The relative path from current folder to the yaml file.\n # Remove \"experiments\" in filename.\n if filename.startswith(\"experiments/\"):\n filename = filename[len(\"experiments/\"):]\n return os.path.splitext(filename)[0].lower().replace(\"/\", \"_\").replace(\".\", \"_\")","def slugify2(value):\n try:\n value = unicodedata.normalize('NFC', value)\n value = downcode(value)\n value = unicodedata.normalize('NFD', value).encode('ascii', 'ignore')\n value = unicode(re.sub('[^\\w\\s-]', '', value).strip().lower())\n return safestring.mark_safe(re.sub('[-\\s]+', '-', value))\n except:\n if settings.TEMPLATE_DEBUG:\n raise\n else:\n return u''","def slugify(value):\n import unicodedata\n import re\n from django.utils.safestring import mark_safe\n value = unicodedata.normalize('NFKD', value).encode('ascii', 'ignore')\n value = unicode(re.sub('[^\\w\\s-]', '', value).strip().lower())\n return mark_safe(re.sub('[-\\s]+', '-', value))","def slugify(value, allow_unicode=False):\n file, ext = splitext(value)\n value = str(file)\n if allow_unicode:\n value = unicodedata.normalize('NFKC', value)\n else:\n value = unicodedata.normalize('NFKD', value).encode('ascii', 'ignore').decode('ascii')\n value = re.sub(r'[^\\w\\s-]', '', value).strip().lower()\n return re.sub(r'[-\\s]+', '-', value) + ext","def slugify(_string):\n valid_chars = frozenset(\n \"-_.{}{}\".format(string.ascii_letters, string.digits))\n return ''.join(c if c in valid_chars else '_' for c in _string)","def slugify(value, delimiter='-'):\n words = slug_pattern.split(value)\n return delimiter.join(filter(truth, words)).lower()","def xslugify(value):\n value = unicodedata.normalize('NFKD', value).encode('ascii', 'ignore').decode('ascii')\n value = re.sub('[^\\w\\s/-]', '', value).strip().lower()\n return mark_safe(re.sub('[-\\s/]+', '-', value))","def unique_slugify(klass: type, title: str, instance=None):\n origin_slug = slugify(title)\n unique_slug = origin_slug\n numb = 1\n\n if instance is not None:\n while klass.objects.filter(slug=unique_slug).exclude(pk=instance.pk).exists():\n unique_slug = \"%s-%d\" % (origin_slug, numb)\n numb += 1\n else:\n while klass.objects.filter(slug=unique_slug).exists():\n unique_slug = \"%s-%d\" % (origin_slug, numb)\n numb += 1\n\n return unique_slug","def create_id(s: str, sep: str = '/') -> str:\n if sep and sep in s:\n return sep.join([slugify(part) for part in s.split(sep) if part])\n return slugify(s)","def create_order_slug():\n dt = datetime.now()\n part1 = '{0:s}{1:02d}'.format(str(dt.year)[-2:], dt.month)\n part2 = ''.join(random.choice(string.ascii_uppercase + string.digits) for _ in range(3))\n part3 = ''.join(random.choice(string.ascii_uppercase + string.digits) for _ in range(3))\n return '-'.join((part1, part2, part3))","def slug(label: str, level: str) -> str:\n\n if level not in [\"none\", \"simple\", \"full\"]:\n raise ValueError(f\"Unknown level of slugging `{level}`.\")\n\n logging.debug(\"Slugging label `%s` with level `%s`.\", label, level)\n\n # This implementation of the different levels of slugging seems a\n # bit cumbersome at first, but makes it easy for us to explore alternatives\n if level in [\"simple\", \"full\"]:\n label = unidecode(label)\n if level in [\"full\"]:\n label = label.lower()\n if level in [\"simple\"]:\n label = \"\".join(\n [\n char\n for char in label\n if char in string.ascii_letters + string.digits + \"-_\"\n ]\n )\n if level in [\"full\"]:\n label = \"\".join([char for char in label if char in string.ascii_letters])\n if level in [\"simple\", \"full\"]:\n label = re.sub(r\"\\s+\", \"_\", label.strip())\n label = label.replace(\"'\", \"__\")\n\n logging.debug(\"Label slugged to `%s`.\", label)\n\n return label","def generate_unique_slug(klass, field, instance=None):\n origin_slug = slugify(field)\n unique_slug = origin_slug\n numb = 1\n if instance is not None:\n while klass.objects.filter(slug=unique_slug).exclude(id=instance.id).exists():\n unique_slug = '%s-%d' % (origin_slug, numb)\n numb += 1\n else:\n while klass.objects.filter(slug=unique_slug).exists():\n unique_slug = '%s-%d' % (origin_slug, numb)\n numb += 1\n return unique_slug","def get_unique_slug(self):\n slug = slugify(self.team_name)\n unique_slug = slug\n extension = 1\n while TeamMod.objects.filter(slug=unique_slug).exists():\n unique_slug = '{}-{}'.format(slug, extension)\n extension += 1\n return unique_slug","def generate_clean_url(self):\n\n\t\tspaces_replaced = self.title.replace(' ', '-')\n\t\tpattern = re.compile('[^a-zA-Z0-9-]+')\n\t\tstripped = pattern.sub('', spaces_replaced)\n\t\tself.cleanurl = '-'.join([str(self.pid), stripped.lower()])","def set_url_slug(self, slug):\n slug = slug[-7:]\n if len(slug) == 7 and \\\n slug[0] == \"p\" and \\\n all(x in ascii_letters + digits for x in slug):\n self.slug = slug\n self.url = \"https://rpaste.com/\" + slug\n self.api_url = \"https://rpaste.com/api/paste/view/\" + slug\n return\n raise Exception(\"Invalid slug : \" + url)","def slugify(value):\n import unicodedata\n try:\n unicode_type = unicode\n except NameError:\n unicode_type = str\n if not isinstance(value, unicode_type):\n value = unicode_type(value)\n value = unicodedata.normalize('NFKD', value).encode('ascii', 'ignore').decode('ascii')\n value = unicode_type(_slugify_strip_re.sub('', value).strip())\n return _slugify_hyphenate_re.sub('_', value)","def slug(self) -> str:\n return self._slug","def test_get_slug_same_name_condition(self):\n\n book2 = Book.objects.create(\n category=self.category,\n language=self.language,\n level=self.level,\n name=\"Kitap denemesi\",\n description=\n \"Kitap açıklama denemesi aynı isimli kitap slug testi için\",\n author=self.author)\n test_slug = slugify(book2.name.replace(\"ı\", \"i\")) + \"-1\"\n self.assertEqual(test_slug, book2.slug)","def name_generator():\n prefix_list = [\n \"admiring\", \"adoring\", \"affectionate\", \"agitated\", \"amazing\", \"angry\",\n \"awesome\", \"beautiful\", \"blissful\", \"bold\", \"boring\", \"brave\", \"busy\",\n \"charming\", \"clever\", \"cool\", \"compassionate\", \"competent\",\n \"confident\", \"crazy\", \"dazzling\", \"determined\", \"distracted\",\n \"ecstatic\", \"elegant\", \"eloquent\", \"epic\", \"exciting\", \"festive\",\n \"flamboyant\", \"focused\", \"friendly\", \"frosty\", \"funny\", \"gifted\",\n \"goofy\", \"gracious\", \"great\", \"stoic\", \"strange\", \"suspicious\",\n \"tender\", \"thirsty\", \"trusting\", \"unruffled\", \"upbeat\",\t\"vibrant\",\n \"vigilant\", \"vigorous\", \"wizardly\", \"wonderful\", \"youthful\", \"zealous\",\n \"dreamy\", \"eager\", \"sweet\", \"zen\"\n ]\n\n postfix_list = [\n \"deepak\", \"vrushal\", \"abhishek\", \"kausthubh\", \"sandeep\", \"praveen\",\n \"shailesh\", \"sachine\", \"chetan\", \"smita\", \"digvijay\", \"kedar\",\n \"prayas\", \"mandar\", \"ashish\", \"arvind\"\n ]\n\n name = prefix_list[randint(0, len(prefix_list)-1)]+\"-\"+postfix_list[randint(0, len(postfix_list)-1)]+\"-\"+str(randint(100, 999))\n return name","def nice():\n rawBytes = uuid.uuid4().bytes\n rawBytes =bytes(chr((rawBytes[0]) & 0x7f),'ascii')+rawBytes[1:] # Ensure slug starts with [A-Za-f]\n return base64.urlsafe_b64encode(rawBytes)[:-2] # Drop '==' padding","def create_slug_from_post_title(sender, instance, *args, **kwargs):\n if not instance.slug:\n instance.slug = slugify(instance.title)","def normalize_hospital_name(name):\n normalized_name = name.lower()\n\n stopword_list = stopwords.words('english')\n filtered_words = [word for word in wordpunct_tokenize(normalized_name) if word not in stopword_list]\n\n slug = slugify(' '.join(filtered_words))\n\n return slug","def slugify(value, substitutions=()):\r\n # TODO Maybe steal again from current Django 1.5dev\r\n value = Markup(value).striptags()\r\n # value must be unicode per se\r\n import unicodedata\r\n from unidecode import unidecode\r\n # unidecode returns str in Py2 and 3, so in Py2 we have to make\r\n # it unicode again\r\n value = unidecode(value)\r\n if isinstance(value, six.binary_type):\r\n value = value.decode('ascii')\r\n # still unicode\r\n value = unicodedata.normalize('NFKD', value).lower()\r\n for src, dst in substitutions:\r\n value = value.replace(src.lower(), dst.lower())\r\n value = re.sub('[^\\w\\s-]', '', value).strip()\r\n value = re.sub('[-\\s]+', '-', value)\r\n # we want only ASCII chars\r\n value = value.encode('ascii', 'ignore')\r\n # but Pelican should generally use only unicode\r\n return value.decode('ascii')","def slugify(value, allow_unicode=False):\n value = str(value)\n if allow_unicode:\n value = unicodedata.normalize('NFKC', value)\n else:\n value = unicodedata.normalize('NFKD', value).encode('ascii', 'ignore').decode('ascii')\n value = re.sub(r'[^\\w\\s-]', '', value).strip().lower()\n return re.sub(r'[-\\s]+', '-', value)","def slugify(value, allow_unicode=False):\n value = str(value)\n if allow_unicode:\n value = unicodedata.normalize('NFKC', value)\n else:\n value = unicodedata.normalize('NFKD', value).encode('ascii', 'ignore').decode('ascii')\n value = re.sub(r'[^\\w\\s-]', '', value).strip().lower()\n return re.sub(r'[-\\s]+', '-', value)","def str_to_filename(input_string: str):\n return slugify(input_string)","def unique_slug(model, slug, instance=None, reserved_slugs=[]):\n orig, apdx = split_slug_on_appendix(slug)\n for x in itertools.count(1):\n if instance:\n if not model.objects.filter(slug=slug).exclude(pk=instance.pk).exists() and slug not in reserved_slugs:\n break\n else:\n if not model.objects.filter(slug=slug).exists() and slug not in reserved_slugs:\n break\n slug = '{}{}{}'.format(orig, settings.DJCAT_SLUG_UNIQNUMBER_DELIMITER, x)\n return slug","def slugify(text, delim=u\"-\"):\n result = []\n for word in _punct_re.split(text.lower()):\n result.extend(unidecode(word).split())\n result = unicode(delim.join(result))\n return \"untitled\" if not result else result"],"string":"[\n \"def generate_slug(self):\\n return slugify(self.name)\",\n \"def generate_slug(name):\\n # Slugify the title and check\\n slug = secure_filename(name).lower().replace('_', '-')\\n slug_n = slug\\n n = 1\\n\\n # Append an increasing number to the slug until we find an available url\\n while not slug_available(slug_n):\\n n += 1\\n slug_n = slug + '-' + str(n)\\n\\n return slug_n\",\n \"def smart_slugify(name, min_length=3):\\n url_slug = slugify(name)\\n if len(url_slug) >= min_length:\\n return url_slug\\n # try converting to latin characters:\\n for lang in ('ru', 'ka', 'hy', 'el', 'bg', 'uk'):\\n trans_url_slug = slugify(translit(name, lang, reversed=True))\\n if len(trans_url_slug) >= min_length:\\n return trans_url_slug\\n # if the url is random lets make it slightly longer\\n min_length = 6\\n return url_slug + ''.join([random.choice(string.ascii_lowercase) for _ in range(min_length - len(url_slug))])\",\n \"def create_slug(val):\\n slug = slugify(unidecode(val))\\n for s in settings.DJCAT_SLUG_RESERVED:\\n slug = slug.replace(s, '')\\n return slug\",\n \"def slugify(s):\\n slug = s.lower() # force to lower case\\n slug = re.sub('[^a-z0-9 -]', '', slug) # remove invalid chars\\n slug = re.sub(r'\\\\s+', '-', slug) # collapse whitespace and replace by -\\n slug = re.sub('-+', '-', slug) # collapse dashes\\n if not slug:\\n slug = 'untitled'\\n return slug\",\n \"def slugify(name):\\n return name.lower().strip().replace(' ', '-').replace('+', '_')\",\n \"def slug(value):\\n return slugify(value)\",\n \"def slugify(value):\\n import unicodedata\\n import re\\n # value = unicodedata.normalize('NFKD', value).encode('ascii', 'ignore')\\n value = str(re.sub('[^\\\\w\\\\s-]', '', value).strip().lower())\\n\\n value = str(re.sub('[-\\\\s]+', '-', value))\\n rstr = r\\\"[\\\\/\\\\\\\\\\\\:\\\\*\\\\?\\\\\\\"\\\\<\\\\>\\\\|]\\\" # '/ \\\\ : * ? \\\" < > |'\\n new_title = re.sub(rstr, \\\"_\\\", value) # 替换为下划线\\n if new_title is None or len(new_title) == 0:\\n random_title = get_random_string(5)\\n print('文件名称:{}非法，将生成随机名称:{}'.format(new_title, random_title))\\n return random_title\\n return new_title\\n # return value\",\n \"def generate_slug(string: str) -> str:\\n string = string.replace(\\\" \\\", \\\"-\\\").lower()\\n # Remove any non letters, numbers, and non-dashes\\n return re.sub(r\\\"[^a-zA-Z0-9\\\\-\\\\+]+\\\", \\\"\\\", string)\",\n \"def camel_to_slug(self, name):\\n name = re.sub(r'([a-z])([A-Z])', r'\\\\1 \\\\2', name).title().replace(\\\" \\\", \\\"\\\").replace(\\\"_\\\", \\\"\\\")\\n s1 = re.sub('(.)([A-Z][a-z]+)', r'\\\\1_\\\\2', name)\\n slug = re.sub('([a-z0-9])([A-Z])', r'\\\\1_\\\\2', s1).lower()\\n return slug\",\n \"def unique_slugify(text: str, slugs_in_use: list):\\n slug = slugify(text)\\n if not slug:\\n raise ValueError(\\\"Unable to slugify input\\\")\\n if slug in slugs_in_use:\\n i = 2\\n slug = f\\\"{slug}-{i}\\\"\\n while slug in slugs_in_use:\\n i += 1\\n slug = f\\\"{slug[:-2]}-{i}\\\"\\n return slug\",\n \"def generate_slug(self):\\n slug = slugify(self.title)\\n new_slug = slug\\n s = 1\\n while Article.objects.filter(slug=new_slug).exists():\\n \\\"\\\"\\\"increase value of slug by one\\\"\\\"\\\"\\n new_slug = f'{slug}-{s}'\\n s += 1\\n return new_slug\",\n \"def format_slug(text):\\n allowed_chars = (\\n \\\"abcdefghijklmnopqrstuvxyzABCDEFGHIJKLMNOPQRSTUVWXYZ\\\" # Alphabet\\n \\\"01234567890\\\" # Numbers\\n \\\"_-\\\" # Symbols\\n )\\n # Replace seperators with dash\\n seperators = [\\\" \\\", \\\",\\\", \\\".\\\"]\\n for sep in seperators:\\n text = text.replace(sep, \\\"-\\\")\\n # Strip unacceptable characters\\n text = \\\"\\\".join([c for c in text if c in allowed_chars])\\n # Enforce max length\\n return truncate(text, max_len=50).lower()\",\n \"def test_slug():\\n slugify = fields.SlugField().adapt\\n\\n for input, expect in [\\n ('01 HDR test', '01-hdr-test'),\\n ('--&*$#(8$jjsdsd77-----test phrase12 123--', '8jjsdsd77-test-phrase12-123'),\\n ('1234', '1234'),\\n ('abcdEFG', 'abcdefg'),\\n ]:\\n assert slugify(input) == expect\",\n \"def deslugify(_slug):\\n return string.capwords(_slug.replace('-', ' '))\",\n \"def create_slug(self):\\n slug = slugify(self.title)\\n new_slug = slug\\n n = 1\\n while Article.objects.filter(slug=new_slug).exists():\\n new_slug = '{}-{}'.format(slug, n)\\n n += 1\\n\\n return new_slug\",\n \"def _get_slug(str):\\n return str.replace(\\\" \\\", \\\"_\\\")\",\n \"def sluggify(cls, name: str) -> str:\\n return \\\"-\\\".join([word for word in cls.split_on_non_word.split(name.strip().lower()) if word])\",\n \"def slug(self):\\n slug = slugify(self.text)\\n return \\\"%s/%s\\\" % (self.uid, slug[:36]) # max 24 characters\",\n \"def slugify(s):\\n from future.builtins import str\\n return django_slugify(unidecode(str(s)))\",\n \"def slugify(value):\\n return '_' + value.replace(' ', '-').lower()\",\n \"def namify(text):\\n return slugify(text).replace('-','_')\",\n \"def unique_slug(s, model, num_chars=50):\\n slug = slugify(s)\\n slug = slug[:num_chars].strip('-')\\n while True:\\n dup = model.objects.filter(slug=slug)\\n if not dup:\\n return slug\\n\\n slug = slug[:39] + '-' + random_string(10)\",\n \"def set_slug(self):\\n if not self.slug:\\n self.slug = slugify(self.name)[:50]\",\n \"def parse_to_slug(words, maxlen=24):\\n\\tslug = ''\\n\\tmaxlen = min(maxlen, len(words))\\n\\tfor c in words:\\n\\t\\tif len(slug) == maxlen:\\n\\t\\t\\tbreak\\n\\t\\tc = ord(c)\\n\\t\\tif c == 0x27:\\n\\t\\t\\tcontinue\\n\\t\\telif c >= 0x30 and c <= 0x39:\\n\\t\\t\\tslug += chr(c)\\n\\t\\telif c >= 0x41 and c <= 0x5a:\\n\\t\\t\\tslug += chr(c + 0x20)\\n\\t\\telif c >= 0x61 and c <= 0x7a:\\n\\t\\t\\tslug += chr(c)\\n\\t\\telif len(slug) and slug[-1] != '-':\\n\\t\\t\\tslug += '-'\\n\\tif len(slug) and slug[-1] == '-':\\n\\t\\tslug = slug[:-1]\\n\\treturn slug\",\n \"def to_slug(value, incoming=None, errors=\\\"strict\\\"):\\r\\n value = safe_decode(value, incoming, errors)\\r\\n # NOTE(aababilov): no need to use safe_(encode|decode) here:\\r\\n # encodings are always \\\"ascii\\\", error handling is always \\\"ignore\\\"\\r\\n # and types are always known (first: unicode; second: str)\\r\\n value = unicodedata.normalize(\\\"NFKD\\\", value).encode(\\r\\n \\\"ascii\\\", \\\"ignore\\\").decode(\\\"ascii\\\")\\r\\n value = SLUGIFY_STRIP_RE.sub(\\\"\\\", value).strip().lower()\\r\\n return SLUGIFY_HYPHENATE_RE.sub(\\\"-\\\", value)\",\n \"def format_slug(text: str) -> str:\\n allowed_chars = (\\n string.ascii_lowercase # Alphabet\\n + string.digits # Numbers\\n + \\\"_-\\\" # Symbols\\n )\\n # Convert to lowercase\\n text = text.lower()\\n # Replace separators with dash\\n text = text.translate({ord(sep): ord('-') for sep in \\\" ,.\\\"})\\n # Strip unacceptable characters\\n text = ''.join(c for c in text if c in allowed_chars)\\n # Enforce max length\\n return truncate(text, max_len=50)\",\n \"def slugify(text):\\n concatenated = re.sub('\\\\s+', '-', text.lower())\\n return re.sub('[^A-Za-z0-9_-]', '', concatenated)\",\n \"def slugified(text):\\n return re.sub(\\\"-+\\\", \\\"-\\\", re.sub(r\\\"\\\\W\\\", \\\"-\\\", text.lower()))\",\n \"def generate_student_slug(name: str) -> str:\\n\\n slug = slugify(name)\\n if not Student.objects.filter(slug=slug).exists():\\n return slug\\n\\n # Duplicate found, add integer to end until it's unique\\n counter = 1\\n while True:\\n modified_slug = f\\\"{slug}-{counter}\\\"\\n if not Student.objects.filter(slug=modified_slug).exists():\\n return modified_slug\\n counter += 1\",\n \"def int_to_slug(i: int) -> str:\\n byt = str(i).encode('utf-8')\\n slug_bytes = base64.urlsafe_b64encode(byt)\\n return slug_bytes.decode('utf-8')\",\n \"def _slug(self, value):\\n if isinstance(value, basestring):\\n try:\\n converted = value\\n except Exception, exception:\\n logger.error(exception)\\n raise\\n elif isinstance(value, str):\\n try:\\n converted = unicode(value, \\\"utf-8\\\")\\n except Exception, exception:\\n logger.error(exception)\\n raise\\n elif isinstance(value, (int, long, float)):\\n self.assertNotIsInstance(value, basestring)\\n try:\\n converted = str(value)\\n converted = unicode(converted)\\n except Exception, exception:\\n logger.error(exception)\\n raise\\n else:\\n self.assertNotIsInstance(value, basestring)\\n try:\\n converted = unicode(value)\\n except Exception, exception:\\n logger.error(exception)\\n raise\\n output = converted.lower().strip().replace(\\\" \\\", \\\"-\\\")\\n output = re.sub(r'[^a-z0-9]+', '-', output).strip('-')\\n output = re.sub(r'[-]+', '-', output)\\n output = re.sub(r\\\"[^\\\\w-]\\\", \\\"\\\", output)\\n if isinstance(output, basestring):\\n number_of_spaces = output.count(\\\" \\\")\\n if number_of_spaces == 0:\\n return output\\n else:\\n return False\",\n \"def test_get_slug(self):\\n\\n test_slug = slugify(self.book.name.replace(\\\"ı\\\", \\\"i\\\"))\\n self.assertEqual(test_slug, self.book.slug)\",\n \"def __calculate_slug(self):\\n\\n return slugify.slugify(self.metadata['title'])\",\n \"def slugify(s):\\n s = reg_nonchar.sub('-', s).lower()\\n s = reg_dashes.sub('-', s)\\n s = reg_outer_dashes.sub('', s)\\n return s\",\n \"def slugify(value):\\n value = re.sub('[^\\\\w\\\\s-]', '', value).strip()\\n return re.sub('[-\\\\s]+', '-', value)\",\n \"def slugify(text: str) -> str:\\n return text.strip().replace(', ', '-').replace(' ', '_').lower()\",\n \"def slugify(s):\\n\\treturn re.sub('[!@#$%^&*()\\\\\\\\\\\\\\\\/:.\\\"\\\"]+', '', s).replace(' ', '-').replace('--', '-').lower()\",\n \"def _safe_build_slug(build_slug, limit, hash_length=6):\\n build_slug_hash = hashlib.sha256(build_slug.encode(\\\"utf-8\\\")).hexdigest()\\n safe_chars = set(string.ascii_letters + string.digits)\\n\\n def escape(s):\\n return escapism.escape(s, safe=safe_chars, escape_char=\\\"-\\\")\\n\\n build_slug = escape(build_slug)\\n return \\\"{name}-{hash}\\\".format(\\n name=build_slug[: limit - hash_length - 1],\\n hash=build_slug_hash[:hash_length],\\n ).lower()\",\n \"def slugify(value: str) -> str:\\n value = re.sub(r\\\"[^\\\\w\\\\s-]\\\", \\\"\\\", value.lower())\\n return re.sub(r\\\"[-\\\\s]+\\\", \\\"-\\\", value).strip(\\\"-_\\\")\",\n \"def slugify(value):\\n #import unicodedata\\n #value = unicodedata.normalize('NFKD', value).encode('ascii', 'ignore')\\n value = unicode(re.sub('[^\\\\w\\\\s-]', '', value).strip().lower())\\n value = re.sub('[-\\\\s]+', '-', value)\\n return value\",\n \"def _make_slug(\\n self, instance: BaseModel,\\n field: Optional[str] = None,\\n fields: Optional[List[str]] = None) -> str:\\n if field is not None:\\n text = str(getattr(instance, field))\\n if text is None or text == '':\\n raise ValueError('There was no string to make a slug from')\\n elif fields is not None:\\n parts = [str(getattr(instance, field)) for field in fields]\\n text = '-'.join(parts)\\n if text is None or text.replace('-', '') == '':\\n raise ValueError('There was no string to make a slug from')\\n\\n concrete_slug = slugify(text, to_lower=True)\\n slug = concrete_slug\\n counter = 1\\n while self.q.filter_by(slug=slug).first() is not None:\\n slug = '{}-{}'.format(concrete_slug, counter)\\n counter += 1\\n return slug\",\n \"def unique_slug_generator(instance, new_slug=None):\\n if new_slug is not None:\\n slug = new_slug\\n else:\\n slug = slugify(instance.username)\\n\\n Klass = instance.__class__\\n qs_exists = Klass.objects.filter(slug = slug).exists()\\n if qs_exists:\\n new_slug = \\\"{slug}-{randstr}\\\".format(\\n slug = slug,\\n randstr = random_string_generator(size = 4)\\n )\\n return unique_slug_generator(instance, new_slug = new_slug)\\n return slug\",\n \"def unique_slug_generator(instance, new_slug=None):\\n if new_slug is not None:\\n slug = new_slug\\n else:\\n slug = slugify(instance.title)\\n\\n Klass = instance.__class__\\n qs_exists = Klass.objects.filter(slug = slug).exists()\\n if qs_exists:\\n new_slug = \\\"{slug}-{randstr}\\\".format(\\n slug = slug,\\n randstr = random_string_generator(size = 4)\\n )\\n return unique_slug_generator(instance, new_slug = new_slug)\\n return slug\",\n \"def unique_slug(manager, slug_field, slug):\\n max_length = manager.model._meta.get_field(slug_field).max_length\\n slug = slug[:max_length]\\n i = 0\\n while True:\\n if i > 0:\\n if i > 1:\\n slug = slug.rsplit(\\\"-\\\", 1)[0]\\n # We need to keep the slug length under the slug fields max length. We need to\\n # account for the length that is added by adding a random integer and `-`.\\n slug = \\\"%s-%s\\\" % (slug[:max_length - len(str(i)) - 1], i)\\n if not manager.filter(**{slug_field: slug}):\\n break\\n i += 1\\n return slug\",\n \"def slugify(s: str) -> str:\\n\\n return re.sub(re_forbidden, ' ', s.strip()).strip().replace(' ', '-')\",\n \"def unique_slug_generator(instance, new_slug=None):\\n if new_slug is not None:\\n slug = new_slug\\n else:\\n slug = slugify(instance.title)\\n\\n Klass = instance.__class__\\n qs_exists = Klass.objects.filter(slug=slug).exists()\\n if qs_exists:\\n new_slug = \\\"{slug}-{randstr}\\\".format(\\n slug=slug,\\n randstr=random_string_generator(size=4)\\n )\\n return unique_slug_generator(instance, new_slug=new_slug)\\n return slug\",\n \"def encoded_name(self):\\n return slugify(str(self).lower())\",\n \"def slugify(value):\\n value = unicode(str(value))\\n value = unicodedata.normalize('NFKD', value).encode('ascii', 'ignore').decode('ascii')\\n value = re.sub(r'[^\\\\w\\\\s-]', '', value).strip().lower()\\n return re.sub(r'[-\\\\s]+', '-', value)\",\n \"def _slugify(value):\\r\\n try:\\r\\n import unicodedata\\r\\n value = unicodedata.normalize('NFKD', value)\\r\\n except ImportError:\\r\\n pass\\r\\n value = value.encode('ascii', 'ignore').decode()\\r\\n value = _slugify_strip_re.sub('', value).strip().lower()\\r\\n return _slugify_hyphenate_re.sub('-', value)\",\n \"def create_slug_from_tag_name(sender, instance, *args, **kwargs):\\n if not instance.slug:\\n instance.slug = slugify(instance.name)\",\n \"def _slugify(value):\\n value = unicodedata.normalize('NFKD', value).encode('ascii', 'ignore')\\n value = re.sub(r'[^\\\\w\\\\s-]', '', value.decode('utf-8', 'ignore'))\\n value = value.strip().lower()\\n value = re.sub(r'[-\\\\s]+', '-', value)\\n return value\",\n \"def slugify(self, space='-'):\\n #strtitle = unicode(self.title)\\n strtitle = self.title\\n import unicodedata\\n import re\\n strtitle = unicodedata.normalize('NFKD', strtitle).encode('ascii', 'ignore')\\n strtitle = unicode(re.sub('[^\\\\w\\\\s-]', '', strtitle).strip().lower())\\n return re.sub('[-\\\\s]+', space, strtitle)\",\n \"def set_unique_slug(self):\\n set_unique_slug(self, based_on_field='name')\",\n \"def slugify(s):\\n s = s.lower()\\n\\n for c in [' ', '-', '.', '/']:\\n s = s.replace(c, '_')\\n\\n s = re.sub('\\\\W', '', s)\\n s = s.replace('_', ' ')\\n s = re.sub('\\\\s+', ' ', s)\\n s = s.strip()\\n s = s.replace(' ', '-')\\n\\n return s\",\n \"def slugify(value):\\n value = unicode(value)\\n value = unicodedata.normalize('NFKD', value).encode('ascii', 'ignore').decode('ascii')\\n value = re.sub('[^\\\\w\\\\s-]', '', value).strip().lower()\\n return re.sub('[-\\\\s]+', '-', value)\",\n \"def slugify(value):\\n import unicodedata\\n value = unicode(value)\\n value = unicodedata.normalize('NFKD', value).encode('ascii', 'ignore')\\n value = unicode(re.sub('[^\\\\w\\\\s-]', '', value).strip().lower())\\n value = unicode(re.sub('[-\\\\s]+', '-', value))\\n return value\",\n \"def test_slugify(self):\\n self.assertEqual(self.cat_short.slug, 'short-blog-name')\\n regex = r'[@:%._\\\\+~#=]+'\\n self.assertNotRegex(self.cat_short.slug, regex)\\n self.assertNotRegex(self.cat_long.slug, regex)\",\n \"def get_slug(self, headline):\\n exclude = set(string.punctuation)\\n s = ''.join(ch for ch in headline if ch not in exclude)\\n return s.lower().replace(\\\" \\\", \\\"-\\\")\",\n \"def test_unique_slug(self):\\n # While unique_slug lives in the storybase app, I test it here\\n # because this is the app where the relevent models live\\n from storybase.utils import unique_slugify\\n story = create_story(title=\\\"Test Story\\\", summary=\\\"Test Summary\\\",\\n byline=\\\"Test Byline\\\")\\n self.assertEqual(story.slug, \\\"test-story\\\")\\n story2 = create_story(title=\\\"Test Story\\\", summary=\\\"Test Summary 2\\\",\\n byline=\\\"Test Byline 2\\\", slug=\\\"non-colliding-slug\\\")\\n self.assertEqual(story2.slug, \\\"non-colliding-slug\\\")\\n unique_slugify(story2, story2.title)\\n self.assertEqual(story2.slug, \\\"test-story-2\\\")\",\n \"def _slugify(value):\\n import unicodedata\\n if not isinstance(value, unicode):\\n value = unicode(value)\\n value = unicodedata.normalize('NFKD', value).encode('ascii', 'ignore')\\n value = unicode(_slugify_strip_re.sub('', value).strip().lower())\\n return _slugify_hyphenate_re.sub('-', value)\",\n \"def _make_url_friendly(title):\\n # RFC 3986 section 2.3 says: letters, numbers, and -_.~ are unreserved\\n return re.sub('[^-_.~a-zA-Z0-9]', '-', title)\",\n \"def slugify(value):\\n # import unicodedata\\n # value = unicodedata.normalize('NFKD', value).encode('ascii', 'ignore')\\n # value = unicode(re.sub('[^\\\\w\\\\s-]', '', value).strip().lower())\\n # value = unicode(re.sub('[-\\\\s]+', '-', value))\\n value = re.sub('[^\\\\w\\\\s-]', '', value).strip().lower()\\n value = re.sub('[-\\\\s]+', '-', value)\\n return value\",\n \"def get_unique_slug(instance):\\n slug = slugify(instance.title)\\n extension = 1\\n ModelClass = instance.__class__\\n\\n while ModelClass.objects.filter(slug=slug).exists():\\n slug = '{}-{}'.format(slug, extension)\\n extension += 1\\n\\n return slug\",\n \"def slugify(value):\\n value = unicodedata.normalize('NFKD', value).encode('ascii', 'ignore')\\n value = str(re.sub('[^\\\\w\\\\s-]', '', str(value)).strip().lower())\\n return re.sub('[-\\\\s]+', '-', str(value))\",\n \"def test_auto_slug(self):\\n title = ('Transportation Challenges Limit Education Choices for '\\n 'Denver Parents')\\n story = Story()\\n story.save()\\n story_translation = StoryTranslation(title=title, story=story)\\n story_translation.save()\\n self.assertEqual(story.slug, slugify(title))\",\n \"def test_auto_slug(self):\\n title = ('Transportation Challenges Limit Education Choices for '\\n 'Denver Parents')\\n story = Story()\\n story.save()\\n story_translation = StoryTranslation(title=title, story=story)\\n story_translation.save()\\n self.assertEqual(story.slug, slugify(title))\",\n \"def slugify(value):\\n if type(value) == unicode:\\n import unicodedata\\n value = unicodedata.normalize('NFKD', value).encode('ascii', 'ignore')\\n value = unicode(re.sub('[^\\\\w\\\\s-]', '', value).strip().lower())\\n return re.sub('[-\\\\s]+', '-', value)\",\n \"def test_slugify(self):\\n self.assertEqual(self.art_short.slug, 'short-article-title')\\n regex = r'[@:%._\\\\+~#=]+'\\n self.assertNotRegex(self.art_short.slug, regex)\\n self.assertNotRegex(self.art_long.slug, regex)\",\n \"def slug(self) -> str:\\n return pulumi.get(self, \\\"slug\\\")\",\n \"def to_slug(dtype,name,tag=None):\\n FreezableAPI.validate_freezable_name(dtype)\\n FreezableAPI.validate_freezable_name(name)\\n slug = f\\\"{FreezableAPI._slug_prefix}.{dtype}.{name}\\\"\\n if tag:\\n FreezableAPI.validate_tagname(tag)\\n slug = f\\\"{slug}:{tag}\\\"\\n return slug\",\n \"def test_slug(self):\\n m = Machine.objects.get(id=1)\\n self.assertEqual(m.slug, \\\"linac-name-01\\\")\",\n \"def create_slug_from_category_name(sender, instance, *args, **kwargs):\\n if not instance.slug:\\n instance.slug = slugify(instance.name)\",\n \"def slugify(filename):\\n filename = os.path.relpath(filename, \\\"\\\") # The relative path from current folder to the yaml file.\\n # Remove \\\"experiments\\\" in filename.\\n if filename.startswith(\\\"experiments/\\\"):\\n filename = filename[len(\\\"experiments/\\\"):]\\n return os.path.splitext(filename)[0].lower().replace(\\\"/\\\", \\\"_\\\").replace(\\\".\\\", \\\"_\\\")\",\n \"def slugify2(value):\\n try:\\n value = unicodedata.normalize('NFC', value)\\n value = downcode(value)\\n value = unicodedata.normalize('NFD', value).encode('ascii', 'ignore')\\n value = unicode(re.sub('[^\\\\w\\\\s-]', '', value).strip().lower())\\n return safestring.mark_safe(re.sub('[-\\\\s]+', '-', value))\\n except:\\n if settings.TEMPLATE_DEBUG:\\n raise\\n else:\\n return u''\",\n \"def slugify(value):\\n import unicodedata\\n import re\\n from django.utils.safestring import mark_safe\\n value = unicodedata.normalize('NFKD', value).encode('ascii', 'ignore')\\n value = unicode(re.sub('[^\\\\w\\\\s-]', '', value).strip().lower())\\n return mark_safe(re.sub('[-\\\\s]+', '-', value))\",\n \"def slugify(value, allow_unicode=False):\\n file, ext = splitext(value)\\n value = str(file)\\n if allow_unicode:\\n value = unicodedata.normalize('NFKC', value)\\n else:\\n value = unicodedata.normalize('NFKD', value).encode('ascii', 'ignore').decode('ascii')\\n value = re.sub(r'[^\\\\w\\\\s-]', '', value).strip().lower()\\n return re.sub(r'[-\\\\s]+', '-', value) + ext\",\n \"def slugify(_string):\\n valid_chars = frozenset(\\n \\\"-_.{}{}\\\".format(string.ascii_letters, string.digits))\\n return ''.join(c if c in valid_chars else '_' for c in _string)\",\n \"def slugify(value, delimiter='-'):\\n words = slug_pattern.split(value)\\n return delimiter.join(filter(truth, words)).lower()\",\n \"def xslugify(value):\\n value = unicodedata.normalize('NFKD', value).encode('ascii', 'ignore').decode('ascii')\\n value = re.sub('[^\\\\w\\\\s/-]', '', value).strip().lower()\\n return mark_safe(re.sub('[-\\\\s/]+', '-', value))\",\n \"def unique_slugify(klass: type, title: str, instance=None):\\n origin_slug = slugify(title)\\n unique_slug = origin_slug\\n numb = 1\\n\\n if instance is not None:\\n while klass.objects.filter(slug=unique_slug).exclude(pk=instance.pk).exists():\\n unique_slug = \\\"%s-%d\\\" % (origin_slug, numb)\\n numb += 1\\n else:\\n while klass.objects.filter(slug=unique_slug).exists():\\n unique_slug = \\\"%s-%d\\\" % (origin_slug, numb)\\n numb += 1\\n\\n return unique_slug\",\n \"def create_id(s: str, sep: str = '/') -> str:\\n if sep and sep in s:\\n return sep.join([slugify(part) for part in s.split(sep) if part])\\n return slugify(s)\",\n \"def create_order_slug():\\n dt = datetime.now()\\n part1 = '{0:s}{1:02d}'.format(str(dt.year)[-2:], dt.month)\\n part2 = ''.join(random.choice(string.ascii_uppercase + string.digits) for _ in range(3))\\n part3 = ''.join(random.choice(string.ascii_uppercase + string.digits) for _ in range(3))\\n return '-'.join((part1, part2, part3))\",\n \"def slug(label: str, level: str) -> str:\\n\\n if level not in [\\\"none\\\", \\\"simple\\\", \\\"full\\\"]:\\n raise ValueError(f\\\"Unknown level of slugging `{level}`.\\\")\\n\\n logging.debug(\\\"Slugging label `%s` with level `%s`.\\\", label, level)\\n\\n # This implementation of the different levels of slugging seems a\\n # bit cumbersome at first, but makes it easy for us to explore alternatives\\n if level in [\\\"simple\\\", \\\"full\\\"]:\\n label = unidecode(label)\\n if level in [\\\"full\\\"]:\\n label = label.lower()\\n if level in [\\\"simple\\\"]:\\n label = \\\"\\\".join(\\n [\\n char\\n for char in label\\n if char in string.ascii_letters + string.digits + \\\"-_\\\"\\n ]\\n )\\n if level in [\\\"full\\\"]:\\n label = \\\"\\\".join([char for char in label if char in string.ascii_letters])\\n if level in [\\\"simple\\\", \\\"full\\\"]:\\n label = re.sub(r\\\"\\\\s+\\\", \\\"_\\\", label.strip())\\n label = label.replace(\\\"'\\\", \\\"__\\\")\\n\\n logging.debug(\\\"Label slugged to `%s`.\\\", label)\\n\\n return label\",\n \"def generate_unique_slug(klass, field, instance=None):\\n origin_slug = slugify(field)\\n unique_slug = origin_slug\\n numb = 1\\n if instance is not None:\\n while klass.objects.filter(slug=unique_slug).exclude(id=instance.id).exists():\\n unique_slug = '%s-%d' % (origin_slug, numb)\\n numb += 1\\n else:\\n while klass.objects.filter(slug=unique_slug).exists():\\n unique_slug = '%s-%d' % (origin_slug, numb)\\n numb += 1\\n return unique_slug\",\n \"def get_unique_slug(self):\\n slug = slugify(self.team_name)\\n unique_slug = slug\\n extension = 1\\n while TeamMod.objects.filter(slug=unique_slug).exists():\\n unique_slug = '{}-{}'.format(slug, extension)\\n extension += 1\\n return unique_slug\",\n \"def generate_clean_url(self):\\n\\n\\t\\tspaces_replaced = self.title.replace(' ', '-')\\n\\t\\tpattern = re.compile('[^a-zA-Z0-9-]+')\\n\\t\\tstripped = pattern.sub('', spaces_replaced)\\n\\t\\tself.cleanurl = '-'.join([str(self.pid), stripped.lower()])\",\n \"def set_url_slug(self, slug):\\n slug = slug[-7:]\\n if len(slug) == 7 and \\\\\\n slug[0] == \\\"p\\\" and \\\\\\n all(x in ascii_letters + digits for x in slug):\\n self.slug = slug\\n self.url = \\\"https://rpaste.com/\\\" + slug\\n self.api_url = \\\"https://rpaste.com/api/paste/view/\\\" + slug\\n return\\n raise Exception(\\\"Invalid slug : \\\" + url)\",\n \"def slugify(value):\\n import unicodedata\\n try:\\n unicode_type = unicode\\n except NameError:\\n unicode_type = str\\n if not isinstance(value, unicode_type):\\n value = unicode_type(value)\\n value = unicodedata.normalize('NFKD', value).encode('ascii', 'ignore').decode('ascii')\\n value = unicode_type(_slugify_strip_re.sub('', value).strip())\\n return _slugify_hyphenate_re.sub('_', value)\",\n \"def slug(self) -> str:\\n return self._slug\",\n \"def test_get_slug_same_name_condition(self):\\n\\n book2 = Book.objects.create(\\n category=self.category,\\n language=self.language,\\n level=self.level,\\n name=\\\"Kitap denemesi\\\",\\n description=\\n \\\"Kitap açıklama denemesi aynı isimli kitap slug testi için\\\",\\n author=self.author)\\n test_slug = slugify(book2.name.replace(\\\"ı\\\", \\\"i\\\")) + \\\"-1\\\"\\n self.assertEqual(test_slug, book2.slug)\",\n \"def name_generator():\\n prefix_list = [\\n \\\"admiring\\\", \\\"adoring\\\", \\\"affectionate\\\", \\\"agitated\\\", \\\"amazing\\\", \\\"angry\\\",\\n \\\"awesome\\\", \\\"beautiful\\\", \\\"blissful\\\", \\\"bold\\\", \\\"boring\\\", \\\"brave\\\", \\\"busy\\\",\\n \\\"charming\\\", \\\"clever\\\", \\\"cool\\\", \\\"compassionate\\\", \\\"competent\\\",\\n \\\"confident\\\", \\\"crazy\\\", \\\"dazzling\\\", \\\"determined\\\", \\\"distracted\\\",\\n \\\"ecstatic\\\", \\\"elegant\\\", \\\"eloquent\\\", \\\"epic\\\", \\\"exciting\\\", \\\"festive\\\",\\n \\\"flamboyant\\\", \\\"focused\\\", \\\"friendly\\\", \\\"frosty\\\", \\\"funny\\\", \\\"gifted\\\",\\n \\\"goofy\\\", \\\"gracious\\\", \\\"great\\\", \\\"stoic\\\", \\\"strange\\\", \\\"suspicious\\\",\\n \\\"tender\\\", \\\"thirsty\\\", \\\"trusting\\\", \\\"unruffled\\\", \\\"upbeat\\\",\\t\\\"vibrant\\\",\\n \\\"vigilant\\\", \\\"vigorous\\\", \\\"wizardly\\\", \\\"wonderful\\\", \\\"youthful\\\", \\\"zealous\\\",\\n \\\"dreamy\\\", \\\"eager\\\", \\\"sweet\\\", \\\"zen\\\"\\n ]\\n\\n postfix_list = [\\n \\\"deepak\\\", \\\"vrushal\\\", \\\"abhishek\\\", \\\"kausthubh\\\", \\\"sandeep\\\", \\\"praveen\\\",\\n \\\"shailesh\\\", \\\"sachine\\\", \\\"chetan\\\", \\\"smita\\\", \\\"digvijay\\\", \\\"kedar\\\",\\n \\\"prayas\\\", \\\"mandar\\\", \\\"ashish\\\", \\\"arvind\\\"\\n ]\\n\\n name = prefix_list[randint(0, len(prefix_list)-1)]+\\\"-\\\"+postfix_list[randint(0, len(postfix_list)-1)]+\\\"-\\\"+str(randint(100, 999))\\n return name\",\n \"def nice():\\n rawBytes = uuid.uuid4().bytes\\n rawBytes =bytes(chr((rawBytes[0]) & 0x7f),'ascii')+rawBytes[1:] # Ensure slug starts with [A-Za-f]\\n return base64.urlsafe_b64encode(rawBytes)[:-2] # Drop '==' padding\",\n \"def create_slug_from_post_title(sender, instance, *args, **kwargs):\\n if not instance.slug:\\n instance.slug = slugify(instance.title)\",\n \"def normalize_hospital_name(name):\\n normalized_name = name.lower()\\n\\n stopword_list = stopwords.words('english')\\n filtered_words = [word for word in wordpunct_tokenize(normalized_name) if word not in stopword_list]\\n\\n slug = slugify(' '.join(filtered_words))\\n\\n return slug\",\n \"def slugify(value, substitutions=()):\\r\\n # TODO Maybe steal again from current Django 1.5dev\\r\\n value = Markup(value).striptags()\\r\\n # value must be unicode per se\\r\\n import unicodedata\\r\\n from unidecode import unidecode\\r\\n # unidecode returns str in Py2 and 3, so in Py2 we have to make\\r\\n # it unicode again\\r\\n value = unidecode(value)\\r\\n if isinstance(value, six.binary_type):\\r\\n value = value.decode('ascii')\\r\\n # still unicode\\r\\n value = unicodedata.normalize('NFKD', value).lower()\\r\\n for src, dst in substitutions:\\r\\n value = value.replace(src.lower(), dst.lower())\\r\\n value = re.sub('[^\\\\w\\\\s-]', '', value).strip()\\r\\n value = re.sub('[-\\\\s]+', '-', value)\\r\\n # we want only ASCII chars\\r\\n value = value.encode('ascii', 'ignore')\\r\\n # but Pelican should generally use only unicode\\r\\n return value.decode('ascii')\",\n \"def slugify(value, allow_unicode=False):\\n value = str(value)\\n if allow_unicode:\\n value = unicodedata.normalize('NFKC', value)\\n else:\\n value = unicodedata.normalize('NFKD', value).encode('ascii', 'ignore').decode('ascii')\\n value = re.sub(r'[^\\\\w\\\\s-]', '', value).strip().lower()\\n return re.sub(r'[-\\\\s]+', '-', value)\",\n \"def slugify(value, allow_unicode=False):\\n value = str(value)\\n if allow_unicode:\\n value = unicodedata.normalize('NFKC', value)\\n else:\\n value = unicodedata.normalize('NFKD', value).encode('ascii', 'ignore').decode('ascii')\\n value = re.sub(r'[^\\\\w\\\\s-]', '', value).strip().lower()\\n return re.sub(r'[-\\\\s]+', '-', value)\",\n \"def str_to_filename(input_string: str):\\n return slugify(input_string)\",\n \"def unique_slug(model, slug, instance=None, reserved_slugs=[]):\\n orig, apdx = split_slug_on_appendix(slug)\\n for x in itertools.count(1):\\n if instance:\\n if not model.objects.filter(slug=slug).exclude(pk=instance.pk).exists() and slug not in reserved_slugs:\\n break\\n else:\\n if not model.objects.filter(slug=slug).exists() and slug not in reserved_slugs:\\n break\\n slug = '{}{}{}'.format(orig, settings.DJCAT_SLUG_UNIQNUMBER_DELIMITER, x)\\n return slug\",\n \"def slugify(text, delim=u\\\"-\\\"):\\n result = []\\n for word in _punct_re.split(text.lower()):\\n result.extend(unidecode(word).split())\\n result = unicode(delim.join(result))\\n return \\\"untitled\\\" if not result else result\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7597692","0.7439659","0.7417883","0.7406391","0.73748773","0.7310329","0.72734207","0.72529685","0.717194","0.7162989","0.70815015","0.70509","0.70415765","0.7019472","0.69859445","0.6970225","0.694027","0.69378775","0.6929656","0.68890053","0.68677574","0.68338066","0.68159133","0.6815391","0.680756","0.6791909","0.67862606","0.66522783","0.66374063","0.66209036","0.6620657","0.6587946","0.65780777","0.6568829","0.6564647","0.6544506","0.6534755","0.65062964","0.650461","0.64780587","0.64555144","0.6431759","0.6417043","0.6404797","0.6377993","0.63744664","0.63626987","0.6348976","0.6345399","0.6337986","0.63375944","0.6329185","0.6320237","0.631638","0.6295493","0.62591094","0.6255364","0.6243311","0.62409765","0.6234679","0.6231961","0.62297434","0.6226785","0.6224587","0.6218655","0.62035936","0.62035936","0.62019306","0.6159284","0.6154251","0.615336","0.6146553","0.61380935","0.61023146","0.6100427","0.61001027","0.6084509","0.60563517","0.6052331","0.6023605","0.60112906","0.6006289","0.60043067","0.6004228","0.5992547","0.59862465","0.59370124","0.593523","0.591921","0.589142","0.58910835","0.58867985","0.5883556","0.5876892","0.58752865","0.5863249","0.5861263","0.5861263","0.58538485","0.58480716","0.5846642"],"string":"[\n \"0.7597692\",\n \"0.7439659\",\n \"0.7417883\",\n \"0.7406391\",\n \"0.73748773\",\n \"0.7310329\",\n \"0.72734207\",\n \"0.72529685\",\n \"0.717194\",\n \"0.7162989\",\n \"0.70815015\",\n \"0.70509\",\n \"0.70415765\",\n \"0.7019472\",\n \"0.69859445\",\n \"0.6970225\",\n \"0.694027\",\n \"0.69378775\",\n \"0.6929656\",\n \"0.68890053\",\n \"0.68677574\",\n \"0.68338066\",\n \"0.68159133\",\n \"0.6815391\",\n \"0.680756\",\n \"0.6791909\",\n \"0.67862606\",\n \"0.66522783\",\n \"0.66374063\",\n \"0.66209036\",\n \"0.6620657\",\n \"0.6587946\",\n \"0.65780777\",\n \"0.6568829\",\n \"0.6564647\",\n \"0.6544506\",\n \"0.6534755\",\n \"0.65062964\",\n \"0.650461\",\n \"0.64780587\",\n \"0.64555144\",\n \"0.6431759\",\n \"0.6417043\",\n \"0.6404797\",\n \"0.6377993\",\n \"0.63744664\",\n \"0.63626987\",\n \"0.6348976\",\n \"0.6345399\",\n \"0.6337986\",\n \"0.63375944\",\n \"0.6329185\",\n \"0.6320237\",\n \"0.631638\",\n \"0.6295493\",\n \"0.62591094\",\n \"0.6255364\",\n \"0.6243311\",\n \"0.62409765\",\n \"0.6234679\",\n \"0.6231961\",\n \"0.62297434\",\n \"0.6226785\",\n \"0.6224587\",\n \"0.6218655\",\n \"0.62035936\",\n \"0.62035936\",\n \"0.62019306\",\n \"0.6159284\",\n \"0.6154251\",\n \"0.615336\",\n \"0.6146553\",\n \"0.61380935\",\n \"0.61023146\",\n \"0.6100427\",\n \"0.61001027\",\n \"0.6084509\",\n \"0.60563517\",\n \"0.6052331\",\n \"0.6023605\",\n \"0.60112906\",\n \"0.6006289\",\n \"0.60043067\",\n \"0.6004228\",\n \"0.5992547\",\n \"0.59862465\",\n \"0.59370124\",\n \"0.593523\",\n \"0.591921\",\n \"0.589142\",\n \"0.58910835\",\n \"0.58867985\",\n \"0.5883556\",\n \"0.5876892\",\n \"0.58752865\",\n \"0.5863249\",\n \"0.5861263\",\n \"0.5861263\",\n \"0.58538485\",\n \"0.58480716\",\n \"0.5846642\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":11286,"cells":{"query":{"kind":"string","value":"(train / valid / test) are all using data from 20102017"},"document":{"kind":"string","value":"def split_dataset(valid_datetime):\n\tprint(\"all_frames: \", valid_datetime.shape[0])\n\tprint(\"valid_frames: \", np.sum(valid_datetime[:,1]))\n\tprint(\"no_valid_frames: \", np.sum(1-valid_datetime[:,1]))\n\n\tvalid_datetime_bool = (valid_datetime[:,1] == 1)\n\tvalid_datetime_idx = np.where(valid_datetime_bool)[0]\n\tvalid_datetime = valid_datetime[valid_datetime_bool][:,0]\n\n\ttrain_set = []\n\tvalid_set = []\n\ttest_set = []\n\t\n\tdate = set()\n\tfor dt in valid_datetime.astype(str):\n\t\tdate.add(dt[:8])\n\tdate = list(date)\n\tdate.sort()\n\n\tfor i in date:\n\t\tflag = np.random.randint(0,14)\n\t\tif flag < 11:\n\t\t\ttrain_set.append(i)\n\t\telif flag == 11:\n\t\t\tvalid_set.append(i)\n\t\telse:\n\t\t\ttest_set.append(i)\n\n\tprint(\"valid days: \", len(date))\n\tprint(\"train set days: \", len(train_set))\n\tprint(\"valid set days: \", len(valid_set))\n\tprint(\"test set days: \", len(test_set))\n\n\ttrain_set_data = []\n\tvalid_set_data = []\n\ttest_set_data = []\n\tfor dt,idx in zip(valid_datetime.astype(str), valid_datetime_idx):\n\t\tt_date = dt[:8]\n\t\tif t_date in train_set:\n\t\t\ttrain_set_data.append([dt,idx])\n\t\telif t_date in valid_set:\n\t\t\tvalid_set_data.append([dt,idx])\n\t\telse:\n\t\t\ttest_set_data.append([dt,idx])\n\n\tprint(\"train set samples: \", len(train_set_data))\n\tprint(\"valid set samples: \", len(valid_set_data))\n\tprint(\"test set samples: \", len(test_set_data))\t\n\n\t#assert(np.sum(valid_datetime[:,1]) == valid_datetime.shape[0])\n\treturn [train_set_data, valid_set_data, test_set_data]"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def test_data(self):\n\n return self.__valid_data, self.__valid_labels","def test_training(self):\n\t\tpass","def prep_data_fn(self, st_train_dt, end_train_dt, st_val_dt, end_val_dt, st_test_dt, end_test_dt):\n df = self.get_prep_data()\n train = df[(df['ft_data_dt'] >= st_train_dt) & (df['ft_data_dt'] <= end_train_dt)]\n val = df[(df['ft_data_dt'] >= st_val_dt) & (df['ft_data_dt'] <= end_val_dt)].sample(frac=0.4, random_state=2021)\n test = df[(df['ft_data_dt'] >= st_test_dt) & (df['ft_data_dt'] <= end_test_dt)]\n print(f'----train----')\n print(train[['ft_data_dt', 'target', 'idd']].groupby(['ft_data_dt', 'target']).agg(['count']))\n print(f'----validation----')\n print(val[['ft_data_dt', 'target', 'idd']].groupby(['ft_data_dt', 'target']).agg(['count']))\n print(f'----test----')\n print(test[['ft_data_dt', 'target', 'idd']].groupby(['ft_data_dt', 'target']).agg(['count']))\n self.set_train(train)\n self.set_validation(val)\n self.set_test(test)\n train_X = train[[c for c in train.columns if c not in ['idd', 'ft_data_dt', 'target']]]\n train_y = train['target']\n val_X = val[[c for c in train.columns if c not in ['idd', 'ft_data_dt', 'target']]]\n val_y = val['target']\n test_X = test[[c for c in train.columns if c not in ['idd', 'ft_data_dt', 'target']]]\n test_y = test['target']\n self.set_train_X(train_X)\n self.set_train_y(train_y)\n self.set_val_X(val_X)\n self.set_val_y(val_y)\n self.set_test_X(test_X)\n self.set_test_y(test_y)","def create_train_valid_set(self):\n\n if not self.eq_train:\n X_train_high_level, X_valid_high_level, X_train_low_level, X_valid_low_level, train_w, valid_w, y_train, y_valid = train_test_split(self.X_train_high_level, self.X_train_low_level, self.train_weights, self.y_train,\n train_size=0.7, test_size=0.3\n )\n else:\n X_train_high_level, X_valid_high_level, X_train_low_level, X_valid_low_level, train_w, valid_w, w_train_eq, w_valid_eq, y_train, y_valid = train_test_split(self.X_train_high_level, self.X_train_low_level,\n self.train_weights, self.train_weights_eq, self.y_train,\n train_size=0.7, test_size=0.3\n )\n self.train_weights_eq = w_train_eq\n\n #NOTE: might need to re-equalise weights in each folds as sumW_sig != sumW_bkg anymroe!\n self.train_weights = train_w\n self.valid_weights = valid_w #validation weights should never be equalised weights!\n\n print 'creating validation dataset'\n self.X_train_high_level = X_train_high_level\n self.X_train_low_level = self.join_objects(X_train_low_level)\n\n self.X_valid_high_level = X_valid_high_level\n self.X_valid_low_level = self.join_objects(X_valid_low_level)\n print 'finished creating validation dataset'\n\n self.y_train = y_train\n self.y_valid = y_valid","def get_training_and_testing_sets(data, Y):\r\n data = pd.concat([data, Y], axis=1)\r\n x,y=data.shape\r\n train_X_sub1=data[0:x//6]\r\n dev_X_sub1 = data[x//6:x//6 + x//12]\r\n test_X_sub1 = data[x//6 + x//12:x//3]\r\n\r\n train_X_sub2 = data[x//3:x//3+x//6]\r\n dev_X_sub2 = data[x//6 + x//3:x//3 + x//6 + x//12]\r\n test_X_sub2 = data[x//3 + x//6 + x//12:2*x//3]\r\n\r\n train_X_sub3 = data[2*x//3:(2*x//3) +x//6]\r\n dev_X_sub3 = data[x//6 + 2*x//3: (2*x//3) + x//6 + x//12]\r\n test_X_sub3 = data[2*x//3 + x//6 + x//12:x]\r\n\r\n train_X=train_X_sub1.append(train_X_sub2,ignore_index = True)\r\n train_X =train_X.append(train_X_sub3,ignore_index = True)\r\n dev_X= dev_X_sub1.append(dev_X_sub2,ignore_index = True)\r\n dev_X = dev_X.append(dev_X_sub3,ignore_index = True)\r\n test_X = test_X_sub1.append(test_X_sub2,ignore_index = True)\r\n test_X = test_X.append(test_X_sub3,ignore_index = True)\r\n\r\n\r\n train_X = util.shuffle(train_X)\r\n train_X = train_X.reset_index(drop=True)\r\n\r\n dev_X = util.shuffle(dev_X)\r\n dev_X = dev_X.reset_index(drop=True)\r\n\r\n test_X = util.shuffle(test_X)\r\n test_X = test_X.reset_index(drop=True)\r\n\r\n train_X_final=train_X\r\n dev_X_final = dev_X\r\n test_X_final = test_X\r\n x, y = train_X_final.shape\r\n train_X = train_X_final.iloc[:, 0:y - 1]\r\n train_Y = train_X_final.iloc[:, y - 1]\r\n\r\n x, y = test_X_final.shape\r\n test_X = test_X_final.iloc[:, 0:y - 1]\r\n test_Y = test_X_final.iloc[:, y - 1]\r\n\r\n x, y = dev_X_final.shape\r\n dev_X = dev_X_final.iloc[:, 0:y - 1]\r\n dev_Y = dev_X_final.iloc[:, y - 1]\r\n\r\n return train_X, train_Y, dev_X,dev_Y,test_X, test_Y","def Train_data():\n print (\"loading train data ...\")\n time_start = time.time()\n data_root = '/media/keziwen/86AA9651AA963E1D'\n with h5py.File(join(data_root, './data/train_real2.h5')) as f:\n data_real = f['train_real'][:]\n num, nt, ny, nx = data_real.shape\n data_real = np.transpose(data_real, (0, 1, 3, 2))\n with h5py.File(join(data_root, './data/train_imag2.h5')) as f:\n data_imag = f['train_imag'][:]\n num, nt, ny, nx = data_imag.shape\n data_imag = np.transpose(data_imag, (0, 1, 3, 2))\n data = data_real+1j*data_imag\n num_train = 15000\n num_validate = 2000\n train_data = data[0:num_train]\n validate_data = data[num_train:num_train+num_validate]\n\n train_data = np.random.permutation(train_data)\n\n time_end = time.time()\n print ('dataset has been created using {}s'.format(time_end-time_start))\n return train_data, validate_data","def engineer(train, valid):\n train = dummify_all_categorical(train)\n valid = dummify_all_categorical(valid)\n train, valid = utils.common_columns(train, valid) #keep only common features\n\n return train, valid","def train_and_test(self, data):\n\n np.random.shuffle(data)\n datalist = self.unpack_data(data)\n\n logger.info('[*] 75-25 partition of datasets ...')\n\n markline1 = math.floor(0.75*(len(datalist['features'])))\n markline2 = math.floor(0.75*len(datalist['labels']))\n\n train_features = datalist['features'][:(markline1)]\n test_features = datalist['features'][(markline1):]\n \n train_labels = datalist['labels'][:(markline2)]\n test_labels = datalist['labels'][(markline2):]\n\n logger.info('[*] Training started with 75% Dataset ...')\n\n self.knn_model.fit(train_features, train_labels)\n\n logger.info('[*] Testing started with 25% Dataset ...')\n print('\\n/---------------Accuracy----------------/') \n \n accuracy = self.knn_model.score(train_features, train_labels)\n print('Test set accuracy {:.2f} %'.format(accuracy*100))\n\n if accuracy < 0.40:\n logger.warning('[-.-!] Thanks for tryin\\' but this machine ain\\'t learning.')\n\n return True","def prepare_data(train_csv, test_csv):\n\n train = pd.read_csv(train_csv)\n test = pd.read_csv(test_csv)\n train = train[test.shape[0]:]\n valid = train[0:test.shape[0]]\n\n x_train = train.drop(columns=\"label\") / 255\n y_train = train.label\n x_valid = valid.drop(columns=\"label\") / 255\n y_valid = valid.label\n x_test = test.drop(columns=\"label\") / 255\n y_test = test.label\n\n y_train = tf.keras.utils.to_categorical(y_train)\n y_valid = tf.keras.utils.to_categorical(y_valid)\n y_test = tf.keras.utils.to_categorical(y_test)\n x_train = x_train.values\n x_valid = x_valid.values\n x_test = x_test.values\n\n return x_train, y_train, x_valid, y_valid, x_test, y_test","def __init__(self):\r\n \r\n #self.max_stocks = 100\r\n self.max_stocks = 200\r\n \"\"\" cv_factor determines what portion of stocks to put in cross validation set and what portion\r\n to leave in training set. cv_factor = 2 means every other stock goes into cross validation\r\n set. cv_factor = 3 means every third stock goes into cross validation set \"\"\"\r\n self.cv_factor = 2 \r\n \"\"\" future_day is how many training days in the future we train for. Setting future_day = 25\r\n means we are measuring how the stock does 25 days out \"\"\"\r\n self.future_day = 25\r\n \"\"\" The train_dates are the dates for training and cross validation\"\"\"\r\n self.train_dates = []\r\n first_train_date = dateutl.days_since_1900('2001-01-01')\r\n num_train_dates = 10\r\n train_date_increment = 60\r\n self.train_dates.append(first_train_date)\r\n for iday in range(1,num_train_dates):\r\n last_train_date = self.train_dates[iday-1]\r\n self.train_dates.append(last_train_date + train_date_increment)\r\n \"\"\"self.train_dates[1] -= 1 \"\"\"\r\n \r\n \"\"\" test_dates are the dates we are using for testing \"\"\"\r\n self.test_dates = []\r\n first_test_date = dateutl.days_since_1900('2010-01-01')\r\n num_test_dates = 10\r\n test_date_increment = 60\r\n self.test_dates.append(first_test_date) \r\n for iday in range(1,num_test_dates):\r\n last_test_date = self.test_dates[iday-1]\r\n self.test_dates.append(last_test_date + test_date_increment)\r\n \"\"\"self.test_dates[1] -= 1\r\n self.test_dates[3] += 1\r\n self.test_dates[4] += 3\r\n self.test_dates[5] += 4\r\n self.test_dates.append(dateutl.days_since_1900('2010-01-01'))\r\n self.test_dates.append(dateutl.days_since_1900('2010-03-01'))\r\n self.test_dates.append(dateutl.days_since_1900('2010-05-01'))\r\n self.test_dates.append(dateutl.days_since_1900('2010-07-01'))\r\n self.test_dates.append(dateutl.days_since_1900('2010-09-01'))\r\n self.test_dates.append(dateutl.days_since_1900('2010-11-01'))\"\"\"\r\n \"\"\"train_history_days and train_increment set how many historical days we use to\r\n train and the increment used. Setting train_history_days = 21 and train_increment = 5\r\n means we are using the values at days days 5, 10, 15 and 20 days before the reference day\r\n as input features \"\"\"\r\n self.train_days = 21\r\n self.train_increment = 5\r\n self.features = ['rsi','tsi','ppo','adx','dip14','dim14','cci', \\\r\n 'cmo','mfi','natr','roc','stoch','uo']\r\n \"\"\" output is just a boolean about calling the output function to write out \r\n appropriate X and y matricies. The default is False meaning do not write out\r\n matricies \"\"\"\r\n self.output = False","def data(self):\n (x_train, y_train), (_, _) = datasets.fashion_mnist.load_data()\n x_train = x_train.reshape((-1, 28, 28, 1))\n x_train, y_train = x_train.astype('float16') / 255.0, \\\n tf.keras.utils.to_categorical(y_train.astype('float16'), 10)\n (x_train, x_eval) = x_train[5000:], x_train[:5000]\n (y_train, y_eval) = y_train[5000:], y_train[:5000]\n train_data, eval_data = (x_train, y_train), (x_eval, y_eval)\n return train_data, eval_data","def run(train_file: str, val_file: str):\n logging.info(f'Training data from: {train_file}')\n train_df = pd.read_csv(train_file)\n logging.info(f'Validation data from: {val_file}')\n val_df = pd.read_csv(val_file)\n print(train_df.head(3))\n train_data = pd.DataFrame({\n 'QueryLength': train_df['Query'].str.len(),\n 'UnigramCount': train_df['Unigram Count'],\n 'Hamming': train_df['Hamming'],\n 'Relevant': train_df['Is Relevant']\n })\n val_data = pd.DataFrame({\n 'QueryLength': val_df['Query'].str.len(),\n 'UnigramCount': val_df['Unigram Count'],\n 'Hamming': val_df['Hamming'],\n 'Relevant': val_df['Is Relevant']\n })\n logging.info('Sample prepared data')\n print(train_data.head(3))\n logging.info('Summary of traininging data')\n print(train_data.info())\n logging.info('Summary of validation data')\n print(train_data.info())\n train_ds = to_dataset(train_data)\n val_ds = to_dataset(val_data)\n feature_columns = []\n headers = ['QueryLength', 'UnigramCount', 'Hamming']\n for header in headers:\n feature_columns.append(feature_column.numeric_column(header))\n feature_layer = tf.keras.layers.DenseFeatures(feature_columns)\n model = tf.keras.Sequential([\n feature_layer,\n layers.Dense(128, activation='relu'),\n layers.Dense(128, activation='relu'),\n layers.Dropout(.1),\n layers.Dense(1)\n ])\n model.compile(optimizer='adam',\n loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),\n metrics=['accuracy'])\n model.fit(train_ds, validation_data=val_ds, epochs=10)\n #y_pred = model.predict(train_ds)\n #cm = confusion_matrix(headers, y_pred)\n #print(cm)","def data_setup(self):\n # Make sure the dataset is download and put into the data folder\n training_data = pd.read_csv('./data/train.csv', sep=',', nrows=self.training_dataset_size)\n testing_data = pd.read_csv('./data/test.csv', sep=',' , nrows=self.training_dataset_size)\n question_list1 = training_data['question1']\n question_list2 = training_data['question2']\n is_duplicate = training_data['is_duplicate']\n # for will\n X = []\n Y = []\n for i in range(0, 1000):\n print(\"*\"*20, i ,\"*\"*20 )\n feature = self.call_feature_generator(question_list1[i],question_list2[i], self.feature_code )\n X.append(feature)\n Y.append(is_duplicate[i])\n print(feature)\n print(is_duplicate[i])\n print(question_list1[i])\n print(question_list2[i])\n\n # we train classifier\n\n classifer = self.call_classifier(X, Y, self.classifier_code)\n\n # testing\n testX = []\n testY = []\n\n for i in range(1001, 1500):\n print(\"-\"*20, i ,\"-\"*20 )\n feature = self.call_feature_generator(question_list1[i],question_list2[i], self.feature_code )\n testX.append(feature)\n testY.append(is_duplicate[i])\n\n X= np.array(testX).reshape(-1,1)\n\n calculate_y = classifer.predict(X)\n\n print(calculate_y)\n tp = 0.0\n fp = 0.0\n fn = 0.0\n\n for i in range(0, len(calculate_y)):\n if calculate_y[i] == testY[i]:\n print(\"Tp : \", testX[i], question_list1[i], question_list2[i], is_duplicate[i] )\n tp += 1.0\n else:\n if testY[i] == 1 and calculate_y[i] == 0:\n print(\"Fn : \", testX[i] , question_list1[i], question_list2[i], is_duplicate[i] )\n fn += 1.0\n else:\n print(\"Fp : \", testX[i], question_list1[i], question_list2[i], is_duplicate[i])\n fp += 1.0\n\n print(\"Tp: \", tp, \" Fp: \", fp, \" Fn: \", fn)\n print(\"Accuracy \", tp/( tp+fn), \"%\")\n\n result = precision_recall_fscore_support(testY, calculate_y)\n print (\"Precision: Class 1 - \", result[0][0], \"% and Class 0 - \", result[0][1], \"%\")\n print (\"Recall: Class 1 - \", result[1][0], \"% and Class 0 - \", result[1][1], \"%\")\n print (\"F-Score: Class 1 - \", result[2][0], \"% and Class 0 - \", result[2][1], \"%\")","def load_data(train=True):\n#test set\n start = time.clock()\n\n if train==False:\n dp = '/home/thomas/Desktop/OttoChallenge/test.csv'\n df = pd.read_csv(dp)\n X = df.values.astype(np.float32)[:,1:]\n return X\n#train set\n else:\n dp = '/home/thomas/Desktop/OttoChallenge/train.csv'\n df = pd.read_csv(dp)\n X = df[df.columns[:-1]].values.astype(np.float32)[:,1:]\n y = df.target\n y =y.apply(lambda X: int(X[-1])).values\n y = y.astype(np.int32)\n X, y = shuffle(X, y)\n #print(X.shape,y.shape)\n end = time.clock()\n print(end-start)\n\n return X,y","def generate_train_test(self):\n x, y = self.read_data()\n x_train, y_train, x_test, y_test = self.sample_data(x, y)\n self.train = (x_train, y_train)\n self.test = (x_test, y_test)","def train_test_dataset(self, train_rate=0.8):\n point_date = int(len(self.y) * train_rate)\n y_to_train = self.y[:point_date]\n y_to_val = self.y[point_date:]\n predict_date = len(self.y) - len(y_to_train) # the number of data points for the test set\n date_val = self.y.index[point_date]\n return y_to_train, y_to_val, predict_date, date_val","def test_valid_dataset():\n train = ((\"Lorem ipsum dolor sit amet\", 3, 4.5),\n (\"Sed ut perspiciatis unde\", 5, 5.5))\n val = ((\"ipsum quia dolor sit\", 10, 3.5),)\n test = ((\"Ut enim ad minima veniam\", 100, 35),)\n\n t = TabularDataset(train, val, test)\n\n assert len(t) == 4\n assert len(t.train) == 2\n assert len(t.val) == 1\n assert len(t.test) == 1\n\n def check(d, t):\n for i, tu in enumerate(d):\n v0, v1, v2 = tu\n assert t[i][0] == v0\n assert t[i][1] == v1\n assert t[i][2] == v2\n\n check(train, t.train)\n check(val, t.val)\n check(test, t.test)","def is_valid(self, dataset):\n pass","def train(self, training_data):\n pass","def get_training_data(self):\n\n # this actually never was a set\n # src_set = self.target['src'].values\n # dst_set = self.target['dst'].values\n\n # train_negative = self.get_negative_edges(src_set, dst_set, self.train_ind.shape[0]) # * self.K)\n # test_negative = self.get_negative_edges(src_set, dst_set, self.test_ind.shape[0])\n\n train_positive = self.target.iloc[self.train_edge_ind].values\n test_positive = self.target.iloc[self.test_edge_ind].values\n\n # # print(train_positive.shape, train_negative.shape, test_positive.shape, test_negative.shape)\n # print(f\"Working with {train_positive.shape[0]} positive and {train_negative.shape[0]} negative samples in the train set, {test_positive.shape[0]} and {test_negative.shape[0]} - in test set\")\n\n X_train = train_positive\n X_test = test_positive\n\n y_train = np.ones((self.train_edge_ind.shape[0],))\n y_test = np.ones((self.test_edge_ind.shape[0],))\n\n # X_train = np.vstack([\n # train_positive,\n # train_negative\n # ])\n\n # X_test = np.vstack([\n # test_positive,\n # test_negative\n # ])\n\n # y_train = np.concatenate([np.ones((self.train_ind.shape[0],)), np.zeros((self.train_ind.shape[0]),)]) # self.train_ind.shape[0]) * self.K\n # y_test = np.concatenate([np.ones((self.test_ind.shape[0],)), np.zeros((self.test_ind.shape[0],))])\n\n assert X_train.shape[0] == y_train.shape[0]\n assert X_test.shape[0] == y_test.shape[0]\n\n def shuffle(X, y):\n ind_shuffle = np.arange(0, X.shape[0])\n np.random.shuffle(ind_shuffle)\n return X[ind_shuffle], y[ind_shuffle]\n\n self.X_train, self.y_train = shuffle(X_train, y_train)\n self.X_test, self.y_test = shuffle(X_test, y_test)\n\n print(f\"Splitting into {self.X_train.shape[0]} train and {self.X_test.shape[0]} test samples\")\n\n # return X_train, X_test, y_train, y_test","def data_set_maker():\n\n # crate a folder in your code directory and name it: \"files\". put the .npy files iside that folder\n\n x_all = np.load(path + '/files/tinyX.npy', 'r') # reads the input file\n y_all = np.load(path + '/files/tinyY.npy', 'r') # reads the input file\n\n # split the data into 10% validation-set and 90% training set\n raw_train, raw_valid, y_train, y_valid = train_test_split(x_all, y_all, test_size=0.2, random_state=43)\n return raw_train, raw_valid, y_train, y_valid","def __test_and_train(self):\n f = open(\"all_data_info.csv\")\n reader = csv.DictReader(f, delimiter=\",\")\n data = []\n for line in reader:\n if line['artist_group'] == \"train_and_test\" and line[\"in_train\"] == \"False\":\n # the img's artist is in training set\n # but the img is in test set only\n data.append((line['artist'], line['new_filename']))\n\n return data","def run():\r\n \r\n LABEL = data.LabelField(use_vocab=True)\r\n TEXT = data.Field(sequential=True, tokenize=lambda x:x.split(), lower=True, fix_length=config.MAX_LENGTH)\r\n\r\n### 1/5\r\n dataset = data.TabularDataset(path=config.TRAIN_DATASET_FNAME, \r\n format='csv', \r\n fields=[('text', TEXT),('label', LABEL)], \r\n skip_header=True)\r\n # split the dataset, 8:2\r\n train_dataset, valid_dataset = dataset.split(split_ratio=[0.8,0.2], random_state=random.getstate())\r\n \r\n test_data = data.TabularDataset(path=config.TEST_DATASET_FNAME,\r\n format='csv', \r\n fields=[('text', TEXT),('label', LABEL)], \r\n skip_header=True)\r\n \r\n### 2\r\n# train_dataset = data.TabularDataset(path=config.TRAIN_DATASET_FNAME, \r\n# format='csv', \r\n# fields=[('text', TEXT),('label', LABEL)], \r\n# skip_header=True) \r\n# valid_dataset = data.TabularDataset(path=config.VAL_DATASET_FNAME, \r\n# format='csv', \r\n# fields=[('text', TEXT),('label', LABEL)], \r\n# skip_header=True) \r\n \r\n# test_data = data.TabularDataset(path=config.TEST_DATASET_FNAME,\r\n# format='csv', \r\n# fields=[('text', TEXT),('label', LABEL)], \r\n# skip_header=True)\r\n \r\n### 3/4\r\n# train_dataset = data.TabularDataset(path=config.TRAIN_DATASET_FNAME, \r\n# format='csv', \r\n# fields=[('text', TEXT),('label', LABEL)], \r\n# skip_header=True) \r\n \r\n# dataset = data.TabularDataset(path=config.TEST_DATASET_FNAME, \r\n# format='csv', \r\n# fields=[('text', TEXT),('label', LABEL)], \r\n# skip_header=True)\r\n# # split the dataset, 5:5\r\n# valid_dataset, test_data = dataset.split(split_ratio=[0.5,0.5], random_state=random.getstate())\r\n\r\n### 5\r\n\r\n\r\n\r\n # load embeddings\r\n vectors_data = load_vectors(config.EMBEDDING_FNAME)\r\n\r\n TEXT.build_vocab(train_dataset, vectors=vectors_data)\r\n LABEL.build_vocab(train_dataset)\r\n print ('vector size:',TEXT.vocab.vectors.size())\r\n embedding_pretrained_matrix = TEXT.vocab.vectors\r\n \r\n # create torch device\r\n print(\"To device...\")\r\n USE_CUDA = torch.cuda.is_available()\r\n device = torch.device(\"cuda\" if USE_CUDA else \"cpu\")\r\n\r\n train_it, valid_it = data.BucketIterator.splits((train_dataset, valid_dataset),\r\n batch_sizes=(config.TRAIN_BATCH_SIZE,config.VAL_BATCH_SIZE), \r\n device=device, \r\n sort_key=lambda x: len(x.text),\r\n sort_within_batch=False,\r\n shuffle=True,\r\n repeat=False)\r\n test_it = data.BucketIterator(test_data, \r\n batch_size=config.TEST_BATCH_SIZE, \r\n sort_key=lambda x: len(x.text), \r\n shuffle=False,\r\n device=device)\r\n \r\n \r\n # fetch model\r\n vocab_size = len(TEXT.vocab) # TEXT.vocab.vectors.size()\r\n# pretrained_vec = TEXT.vocab.vectors\r\n \r\n # selecte network \r\n x = import_module('networks.'+config.NETWORK)\r\n model = x.Model(vocab_size,embedding_pretrained=embedding_pretrained_matrix)\r\n \r\n # send model to device\r\n model.to(device)\r\n\r\n # initialize Adam optimizer\r\n optimizer = torch.optim.Adam(model.parameters(), lr=config.LEARNING_RATE)\r\n\r\n # if you have multiple GPUs, model model to DataParallel to use multiple GPUs\r\n if torch.cuda.device_count() > 1:\r\n model = nn.DataParallel(model)\r\n \r\n params_list = []\r\n # train and validate for all epochs\r\n for epoch in range(config.EPOCHS):\r\n epoch_start_time = time.time()\r\n\r\n ###----Train--------\r\n train_outputs, train_labels, train_loss = engine.train_fn(train_it, model, optimizer, device)\r\n train_outputs = torch.Tensor(train_outputs)\r\n _, train_predicted = torch.max(train_outputs, dim=1)\r\n train_parameters_dict = metrics_func.performance_evaluation_func(train_predicted,train_labels,epoch=str(epoch))\r\n # save train paremeters\r\n params_list.append(train_parameters_dict)\r\n train_f1 = train_parameters_dict['f1_score_macro']\r\n train_prec = train_parameters_dict['precision_macro']\r\n train_recall = train_parameters_dict['precision_macro']\r\n print('\\n')\r\n print(f\" Train Epoch: {epoch}, F1 = {train_f1},precision = {train_prec},recall = {train_recall}\")\r\n ###------------\r\n \r\n # validate\r\n val_outputs, val_labels, valid_loss = engine.evaluate_fn(valid_it, model, device)\r\n val_outputs = torch.Tensor(val_outputs)\r\n _, val_predicted = torch.max(val_outputs, dim=1) \r\n # calculate evaluation paremeters\r\n val_parameters_dict = metrics_func.performance_evaluation_func(val_predicted, val_labels, epoch=str(epoch),flag='val')\r\n # save evaluation paremeters\r\n params_list.append(val_parameters_dict)\r\n \r\n val_f1 = val_parameters_dict['f1_score_macro']\r\n val_prec = val_parameters_dict['precision_macro']\r\n val_recall = val_parameters_dict['recall_macro']\r\n print(f\"Val Epoch: {epoch},F1 = {val_f1},precision = {val_prec}, recall = {val_recall}\")\r\n \r\n ###-------Test-----------------------\r\n test_outputs, test_labels, test_loss = engine.evaluate_fn(test_it, model, device)\r\n test_outputs = torch.Tensor(test_outputs)\r\n _, test_predicted = torch.max(test_outputs, dim=1) \r\n # calculate evaluation paremeters\r\n test_parameters_dict = metrics_func.performance_evaluation_func(test_predicted, test_labels, epoch=str(epoch),flag='test')\r\n # save evaluation paremeters\r\n params_list.append(test_parameters_dict)\r\n \r\n test_f1 = test_parameters_dict['f1_score_macro']\r\n test_prec = test_parameters_dict['precision_macro']\r\n test_recall = test_parameters_dict['recall_macro']\r\n print(f\"test Epoch: {epoch},F1 = {test_f1},precision = {test_prec}, recall = {test_recall}\")\r\n \r\n lr_scheduler = LRScheduler(optimizer)\r\n lr_scheduler(valid_loss)\r\n \r\n \r\n # simple early stopping\r\n# val_f1 = float(val_f1)\r\n #f1 = (float(train_f1) + float(val_f1)) / 2\r\n val_loss = float(valid_loss)\r\n early_stopping(val_loss, model)\r\n if early_stopping.early_stop:\r\n print(\"Early stopping\")\r\n break\r\n # 获得 early stopping 时的模型参数\r\n# model.load_state_dict(torch.load('checkpoint.pt'))\r\n\r\n# save_model_func(model, epoch, path='outputs')\r\n \r\n metrics_func.save_parameters_txt(params_list)","def train(self, data):\n pass","def load_train_test_transactions(train_size=0.7):\n X, y = features_target_split()\n X_train, X_test, y_train, y_test = train_test_split(X,y,train_size=train_size, random_state=7)\n print('\\nTraining and testing data creation successful\\n')\n return X_train, X_test, y_train,y_test","def load_data():\n print(\"PARSING TRAIN\")\n ys_train, x_train, ids_train = load_pickle_data(\"ys_train\"), load_pickle_data(\"x_train\"), load_pickle_data(\n \"ids_train\")\n if ys_train is None or x_train is None or ids_train is None:\n ys_train, x_train, ids_train = load_csv_data(\"{}/train.csv\".format(DATA_DIR))\n dump_pickle_data(ys_train, \"ys_train\")\n dump_pickle_data(x_train, \"x_train\")\n dump_pickle_data(ids_train, \"ids_train\")\n\n print(\"PARSING TEST\")\n x_test, ids_test = load_pickle_data(\"x_test\"), load_pickle_data(\"ids_test\")\n if x_test is None or ids_test is None:\n _, x_test, ids_test = load_csv_data(\"{}/test.csv\".format(DATA_DIR))\n dump_pickle_data(x_test, \"x_test\")\n dump_pickle_data(ids_test, \"ids_test\")\n\n return ys_train, x_train, ids_train, x_test, ids_test","def prepare_train_validation(self) -> Tuple:\n Xt, Xv, Yt, Yv = self.dataset.train_test_split_representations()\n\n Xt = self.dataset.prepare_input_samples(Xt)\n Yt = self.dataset.prepare_output_samples(Yt)\n traindataset = tf.data.Dataset.from_tensor_slices((Xt, Yt))\n traindataset = traindataset.batch(\n self.batch_size,\n num_parallel_calls=tf.data.experimental.AUTOTUNE\n )\n\n Xv = self.dataset.prepare_input_samples(Xv)\n Yv = self.dataset.prepare_output_samples(Yv)\n validdataset = tf.data.Dataset.from_tensor_slices((Xv, Yv))\n validdataset = validdataset.batch(\n self.batch_size,\n num_parallel_calls=tf.data.experimental.AUTOTUNE\n )\n\n return traindataset, validdataset","def test(self, data):\n num = len(data['y'])\n self.reshape_mat(data)\n\n acc = 0\n order = np.random.permutation(num)\n for i in range(0, num):\n x, y = data['x'][order[i]], data['y'][order[i]]\n yhat = self.predict(self.w, x)\n if y == yhat:\n acc += 1\n accuracy = acc / float(num)\n print 'Test: acc = %f' % (accuracy * 100)","def get_training_and_validation_df():\n df = get_cleaned_processed_df()\n val_df = pd.DataFrame.from_csv(VALIDATION_DATA_PATH)\n y_train = df.pop(\"label\")\n y_val = val_df.pop(\"label\")\n\n df, val_df = complete_columns(df, val_df)\n df.fillna(0, inplace=True)\n val_df.fillna(0, inplace=True)\n df = fill_text_features(df)\n val_df = fill_text_features(val_df)\n\n df = drop_text_features(df)\n val_df = drop_text_features(val_df)\n return df.values, y_train, val_df.values, y_val","def main():\n # Read in trainingSet and testSet as a DataFrame\n trainingOriginal = pd.read_csv(\n filepath_or_buffer=\"~/Desktop/KNN Implementation/adult.train.5fold.csv\")\n testOriginal = pd.read_csv(filepath_or_buffer=\"~/Desktop/KNN Implementation/adult.test.csv\")\n\n # Select only the numeric data\n training = pd.DataFrame(trainingOriginal.select_dtypes(['number']))\n training = pd.concat([training.reset_index(drop=True),\n trainingOriginal['earns'].reset_index(drop=True)], axis=1)\n\n # Select only the numeric data\n test = pd.DataFrame(testOriginal.select_dtypes(['number']))\n test = pd.concat([test.reset_index(drop=True),\n testOriginal['earns'].reset_index(drop=True)], axis=1)\n\n # Normalize the columns for training and test\n # print training['age'].min()\n # print training['age'].max()\n # print training.head()\n\n # Run max-min normalization on numerical columns for testing and training data\n for i in range(6):\n training.iloc[:, i] = (training.iloc[:, i]- training.iloc[:, i].min())/(training.iloc[:, i].max() - training.iloc[:, i].min())\n test.iloc[:, i] = (test.iloc[:, i]- test.iloc[:, i].min())/(test.iloc[:, i].max() - test.iloc[:, i].min())\n\n # Convert the 'earns' column to boolean as follows\n training['earns'] = training['earns'] == '>50K'\n test['earns'] = test['earns'] == ' >50K'\n\n # Group the training set by the fold attribute as given by the dataset\n trainingForFinal = training\n training = training.groupby('fold')\n\n # Since we want to consider odd k-values from 1 to 39, construct a list with these values\n kList = []\n for i in range(40):\n if i % 2 == 1:\n kList.append(i)\n\n # Empty dictionary to hold performance of each k-values and its accuracy\n performance = {}\n\n # Compute the performance for each k-value\n for k in kList:\n performance = crossValidation(training, k, performance)\n\n # Sort the performance dictionary by its accuracy (value)\n performance = sorted(performance.items(), key=operator.itemgetter(1), reverse=True)\n\n # Open file to write results\n file = open('grid.results.txt', 'w')\n # Write the results to file\n file.write(\"K | Accuracy\\n\")\n for item in performance:\n if item[0] < 10:\n file.write(str(item[0]) + ' | ' + str(item[1]) + '\\n')\n else:\n file.write(str(item[0]) + ' | ' + str(item[1]) + '\\n')\n # Close file\n file.close()\n\n # The best K is the one at the top of the list after the sorting\n bestK = performance[0][0]\n\n print 'Running Test Set with K = ' + str(bestK)\n\n applyModel(test,trainingForFinal,bestK)","def train_models(self,member,train_dates,valid_dates):\n train_data, train_label = self.dldataeng.extract_training_data(member,\n train_dates,self.model_type)\n \n #valid_data, valid_label = self.dldataeng.extract_validation_data(member,valid_dates,self.model_type)\n valid_data, valid_label = [],[]\n \n if self.model_type == 'CNN':\n onehot_encoder = OneHotEncoder(sparse=False,categories='auto')\n encoded_label = onehot_encoder.fit_transform(train_label.reshape(-1, 1))\n self.train_CNN(member,train_data,encoded_label,valid_data,valid_label)\n\n elif 'UNET' in self.model_type:\n #train_label[train_label >= 50.] = 50. \n #log_train_label = np.log((train_label+1.0))\n self.train_UNET(member,train_data,train_label,valid_data,valid_label)\n \n return","def buildAndTrain(trainingData):\n\tname = trainingData.drop(['count', 'casual', 'registered'], axis=1).columns\n\ttarget = trainingData['count'].values\n\tfeature = trainingData.drop(['count', 'casual', 'registered'], axis=1).values\n\t# feature scaling\n\tfeature_scaled = preprocessing.scale(feature)\n\t# 0.5 cross validate\n\tcv = cross_validation.ShuffleSplit(len(feature_scaled), n_iter=5, test_size=0.2, random_state=0)\n\t# build model, then training and get accuracy of it\n\tprint('\\n---------岭回归结果--------\\n')\n\tfor train, test in cv:\n\t\tregLR = linear_model.Ridge().fit(feature_scaled[train], target[train])\n\t\tprint('train score:{0:.3f}, test score:{1:.3f}\\n'.format(\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tregLR.score(feature_scaled[train], target[train]),\n\t\t regLR.score(feature_scaled[test], target[test])))\n\tprint('\\n---------svm结果--------\\n')\n\tfor train, test in cv:\n\t\tregSvm = svm.SVR().fit(feature_scaled[train], target[train])\n\t\tprint('train score:{0:.3f}, test score:{1:.3f}\\n'.format(\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tregSvm.score(feature_scaled[train], target[train]),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tregSvm.score(feature_scaled[test], target[test])))\n\tprint('\\n---------随机森林结果--------\\n')\n\tfor train, test in cv:\n\t\tregRF = RandomForestRegressor(n_estimators=100).fit(feature_scaled[train], target[train])\n\t\tprint('train score:{0:.3f}, test score:{1:.3f}\\n'.format(\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tregRF.score(feature_scaled[train], target[train]),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tregRF.score(feature_scaled[test], target[test])))\n\t# reduce some low correction feature\n\tfeatureReduced = trainingData.drop(['count', 'casual', 'registered', 'holiday', 'workingday', 'day'], axis=1).values\n\tfeatureReduced_scaled = preprocessing.scale(featureReduced)\n\tprint('\\n---------减少特征维度以避免过拟合后的随机森林结果--------\\n')\n\tfor train, test in cv:\n\t\tregRFImpr = RandomForestRegressor(n_estimators=100).fit(featureReduced_scaled[train], target[train])\n\t\tprint('train score:{0:.3f}, test score:{1:.3f}\\n'.format(\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tregRFImpr.score(featureReduced_scaled[train], target[train]),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tregRFImpr.score(featureReduced_scaled[test], target[test])))\n\t# use grid search algorithm to improve random forest regression\n\tX_train, X_test, y_train, y_test = cross_validation.train_test_split(\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tfeature_scaled, target, test_size=0.2, random_state=0)\n\ttuned_parameters = [{'n_estimators': [10,100,500], 'max_depth': [2,3,4,5,6,7,8,9,10]}]\n\tscores = ['r2']\n\n\tfor score in scores:\n\t\tprint(score)\n\t\tclf = GridSearchCV(RandomForestRegressor(), tuned_parameters, cv=5, scoring=score)\n\t\tclf.fit(X_train, y_train)\n\t\tprint(clf.best_estimator_)\n\t\tprint('each parameter combination is ')\n\t\tfor params, mean_score, scores in clf.grid_scores_:\n\t\t\tprint('{0:.3f} (+/-{1:.03f}) for {2}'.format(mean_score, scores.std()/2, params))\n\n\tprint('--------最优参数下的随机森林结果--------')\n\tfor train, test in cv:\n\t\tregRFBest = RandomForestRegressor(n_estimators=100, max_depth=10).fit(feature_scaled[train], target[train])\n\t\tprint('train score:{0:.3f}, test score:{1:.3f}\\n'.format(\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tregRFBest.score(feature_scaled[train], target[train]),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tregRFBest.score(feature_scaled[test], target[test])))\n\treturn regRFBest, feature_scaled, target","def test_training(self, epochs, learning_rate):\n # Make sure the data exists\n make_data = MakeDataset(generated_images_per_image=1)\n make_data.make_dataset()\n\n # Run the entire training procedure\n trained_model_filepath = \"models/trained_model.pth\"\n training_statistics_filepath = \"data/processed/\"\n training_figures_filepath = \"reports/figures/\"\n\n dict = train_model(\n trained_model_filepath,\n training_statistics_filepath,\n training_figures_filepath,\n epochs=epochs,\n learning_rate=learning_rate,\n )\n\n # Check that the losses and accuracies have the right length\n assert len(dict[\"train_losses\"]) == epochs\n assert len(dict[\"train_accuracies\"]) == epochs\n assert len(dict[\"val_losses\"]) == epochs\n assert len(dict[\"val_accuracies\"]) == epochs\n\n # Test that a model has been saved and can be loaded\n assert os.path.isfile(trained_model_filepath)\n state_dict = torch.load(trained_model_filepath)\n\n # Check that the state dictionary was loaded\n assert hasattr(state_dict, \"values\")\n\n # Test that the learning curves have been saved\n assert os.path.isfile(\n os.path.join(training_figures_filepath, \"Training_Loss.pdf\")\n )\n assert os.path.isfile(\n os.path.join(training_figures_filepath, \"Training_Accuracy.pdf\")\n )\n\n # Test that losses and accuracies have been saved and can be loaded\n dict_path = os.path.join(training_statistics_filepath, \"train_val_dict.pickle\")\n assert os.path.isfile(dict_path)\n with open(dict_path, \"rb\") as f:\n dict_load = pickle.load(f)\n\n # Check that the dictionary read in has the same values as the training output\n assert dict[\"train_losses\"] == dict_load[\"train_losses\"]\n assert dict[\"train_accuracies\"] == dict_load[\"train_accuracies\"]\n assert dict[\"val_losses\"] == dict_load[\"val_losses\"]\n assert dict[\"val_accuracies\"] == dict_load[\"val_accuracies\"]","def get_valid_data(x_train, y_train, valid_ratio=0.1):\n\n assert x_train.shape[0] == y_train.shape[0]\n\n valid_shape = np.int(np.round(x_train.shape[0]*valid_ratio))\n x_valid = x_train[-valid_shape:]\n y_valid = y_train[-valid_shape:]\n\n x_train = x_train[:-valid_shape]\n y_train = y_train[:-valid_shape]\n\n return (x_train, y_train), (x_valid, y_valid)","def prepare_data(self):\n # Set up the path\n self.path_target_train = os.path.join(self.data_dir, self.train_path_file_target + \".pkl\")\n self.path_target_test = os.path.join(self.data_dir, self.test_path_file_target + \".pkl\")\n\n if not os.path.exists(self.path_target_train) or not os.path.exists(self.path_target_test):\n # Create vocabularies of the appropriate sizes.\n self.create_vocabulary(self.train_path_file)\n\n # Create token ids for the training data.\n input_train_path = self.train_path_file\n target_train_path = self.train_path_file_target\n train_input, train_input_length, train_labels = self.data_to_token_ids(input_train_path, target_train_path)\n\n # Create token ids for the validation data.\n input_test_path = self.test_path_file\n target_test_path = self.test_path_file_target\n test_input, test_input_length, _ = self.data_to_token_ids(input_test_path, target_test_path, train=False)\n\n # Collect data into a list\n training_data = [train_input, train_input_length, train_labels]\n test_data = [test_input, test_input_length]\n\n # Save all the data\n with open(self.path_target_train, 'wb') as f:\n pickle.dump(training_data,f)\n with open(self.path_target_test, 'wb') as f:\n pickle.dump(test_data, f)\n else:\n # Load data\n with open(self.path_target_train, 'rb') as f:\n training_data = pickle.load(f)\n with open(self.path_target_test, 'rb') as f:\n test_data = pickle.load(f)\n\n # Initialize vocabulary\n self.initialize_vocabulary()\n\n # Convert list into a numpy array - train data\n train_input = pd.DataFrame(training_data[0]).fillna(value=0).astype(int).values\n train_length_input = np.array(training_data[1], dtype=int)\n train_labels = np.array(training_data[2], dtype=int)\n\n # Convert list into a numpy array - test data\n test_input = pd.DataFrame(test_data[0]).fillna(value=0).astype(int).values\n test_length_input = pd.DataFrame(test_data[1]).fillna(value=0).astype(int).values\n\n # Printing maximum length\n print(\"Shape of the input training matrix {}\".format(str(train_input.shape)))\n print(\"Shape of the input test matrix {}\".format(str(test_input.shape)))\n\n # Copy the files\n self.copy_files()\n\n # Return output\n return train_input, train_length_input, train_labels, test_input, test_length_input","def test_train_model_attributes_good():\n\tdf = pd.read_csv(\"test/sample_features.csv\")\n\n\ty_train = df['price']\n\tX_train = df.loc[:, df.columns != 'price']\n\n\tparams = {'n_estimators': 5, 'random_state': 2}\n\trf_test = train(X_train, y_train, params)\n\n\t# test model attributes\n\tassert rf_test.get_params()['n_estimators'] == 5\n\tassert rf_test.get_params()['random_state'] == 2","def test_data_split_good():\n\tdf_true = pd.read_csv(\"test/sample_data.csv\")\n\tdf_test = df_true.copy()\n\ttarget = df_true['price']\n\tfeatures = df_true.loc[:, df_true.columns != 'price']\n\n\tX_train_true, X_test_true, y_train_true, y_test_true = train_test_split(features, target, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttest_size=0.4, random_state=0)\n\n\tsplit_data = {'test_size': 0.4, 'random_state': 0}\n\tX_train_test, X_test_test, y_train_test, y_test_test = data_split(df_test, 'price', split_data)\n\n\tassert X_train_true.equals(X_train_test)\n\tassert y_train_true.equals(y_train_test)\n\tassert X_test_true.equals(X_test_test)\n\tassert y_test_true.equals(y_test_test)","def load_data(trainfile, testfile):\n raw_train = pd.read_csv(trainfile, header=None)\n raw_test = pd.read_csv(testfile, header=None)\n train = raw_train.values\n test = raw_test.values\n train_features = train[0::, 1::]\n train_label = train[::, 0]\n test_features = test[0::, 1::]\n test_label = test[::, 0]\n train, cv , train_label, cv_label = train_test_split(train_features,train_label, test_size=0.33, random_state=42)\n return train, train_label, \\\n cv, cv_label, \\\n test_features, test_label","def test(self, dataset):\n test_accuracy = 0\n test_loss = 0\n num_examples_tested = 0\n # Put model into evaluation mode\n self.model.eval()\n for num, batch in enumerate(dataset.loader):\n xs, ys = batch\n batch_size = len(xs)\n num_examples_tested += batch_size\n iloss, iaccuracy = self.model(xs, ys)\n test_loss += iloss.cpu().data.numpy().item() * batch_size\n test_accuracy += iaccuracy.cpu().data.numpy().item() * batch_size\n test_accuracy = test_accuracy / num_examples_tested\n test_loss = test_loss / num_examples_tested\n # Return accuracy and loss for this model on the test set\n return test_accuracy, test_loss","def data():\n\n run_type = 'standardised'\n sr = 48000\n train_perc = 0.9\n\n if sr == 48000:\n time_dimension = 282\n if sr == 44100:\n time_dimension = 259\n\n x_train, y_train, x_test, y_test = essential.compile_dataset(run_type, sr)\n\n # reshape for CNN input\n x_train = np.array([x.reshape((128, time_dimension, 1)) for x in x_train])\n x_test = np.array([x.reshape((128, time_dimension, 1)) for x in x_test])\n\n # encoded \n encoder = LabelEncoder()\n encoder.fit(y_train)\n encoder.fit(y_test)\n y_train = encoder.transform(y_train)\n y_test = encoder.transform(y_test)\n\n return x_train, y_train, x_test, y_test","def learn(self, Xtrain, ytrain):","def existing_data(self):\n # Set the directory and file name\n data_summary_dir = op.join('../logs', self.name, 'data_summary')\n file_name = 'Train_Test_Summary_generative.csv'\n\n # Read the csv and obtain the train data list\n df = pd.read_csv(op.join(data_summary_dir, file_name))\n train_data = df['Train Data'].dropna().values.tolist()\n test_data = df['Test Data'].dropna().values.tolist()\n\n train_data_list, test_data_list = [], []\n for single_train in train_data:\n data_name = single_train.split('_')[0]\n if data_name == 'LTRC':\n series = single_train.split('_')[3] + '_' + single_train.split('_')[4]\n else:\n series = single_train.split('_')[3] + '_' + single_train.split('_')[4] + '_' + single_train.split('_')[5]\n full_data_name = single_train.split('_')[0] + '_' + single_train.split('_')[1] + '_' + single_train.split('_')[2] + '_' + series\n train_data_list.append(full_data_name)\n\n for single_test in test_data:\n data_name = single_test.split('_')[0]\n if data_name == 'LTRC':\n series = single_test.split('_')[3] + '_' + single_test.split('_')[4]\n else:\n series = single_test.split('_')[3] + '_' + single_test.split('_')[4] + '_' + single_test.split('_')[5]\n full_data_name = single_test.split('_')[0] + '_' + single_test.split('_')[1] + '_' + single_test.split('_')[2] + '_' + series\n test_data_list.append(full_data_name)\n\n # Obtain the label map and CT list and file names\n label_map_list = glob(op.join(self.save_root_dir, 'source_data_2', '*'))\n ct_list = glob(op.join(self.save_root_dir, 'target_data_2', '*'))\n\n label_map_files = [single_file.split('/')[-1] for single_file in label_map_list]\n ct_files = [single_file.split('/')[-1] for single_file in ct_list]\n label_map_files.sort(), ct_files.sort()\n\n # Initialize empty list\n existing_train_lm, existing_train_ct = [], []\n existing_test_lm, existing_test_ct = [], []\n\n for single_lm, single_ct in zip(label_map_files, ct_files):\n\n ct_data_name = single_ct.split('_')[0] + '_' + single_ct.split('_')[1] + '_' + single_ct.split('_')[2]\n lm_data_name = single_lm.split('_')[0] + '_' + single_lm.split('_')[1] + '_' + single_lm.split('_')[2]\n\n assert ct_data_name == lm_data_name, 'Data is not the same.'\n\n data_name = single_ct.split('_')[0]\n if data_name == 'LTRC':\n series = single_ct.split('_')[3] + '_' + single_ct.split('_')[4]\n else:\n series = single_ct.split('_')[3] + '_' + single_ct.split('_')[4] + '_' + single_ct.split('_')[5]\n full_data_name = single_ct.split('_')[0] + '_' + single_ct.split('_')[1] + '_' + single_ct.split('_')[2]\\\n + '_' + series\n\n if full_data_name in train_data_list:\n existing_train_lm.append(single_lm)\n existing_train_ct.append(single_ct)\n if full_data_name in test_data_list:\n existing_test_lm.append(single_lm)\n existing_test_ct.append(single_ct)\n existing_train_data = [existing_train_lm, existing_train_ct]\n existing_test_data = [existing_test_lm, existing_test_ct]\n return existing_train_data, existing_test_data","def _create_data():\n tf.logging.info(\"Create records..\")\n train, val, test = util.load_data(data_dir, FLAGS[\"is_aug\"])\n tf.logging.info(\"Dataset size: Train-{} Test-{} Val-{}\".format(len(train), len(test), len(val)))\n return train, val, test","def get_data(train_split=.7, test_split=.85):\n (X_train, y_train), (X_test, y_test) = mnist.load_data()\n\n X_train = X_train.reshape((len(X_train), 28, 28, 1))\n test_len = len(X_test)\n\n X_test = X_test.reshape((test_len, 28, 28, 1))\n\n # divide X values bei 255.0 since MNIST data set changed such that pixel values are in [0,255]\n X = np.concatenate((X_train, X_test)) / 255.0\n y = np.concatenate((y_train, y_test))\n\n indices = np.arange(X.shape[0])\n np.random.shuffle(indices)\n X = X[indices]\n y = y[indices]\n\n # define boundaries for train,validation and test set at .7 and .85 % of the MNIST data set\n x_len = len(X)\n boundaries = [int(x_len * 0.7), int(x_len*0.85)]\n\n [X_train, X_test, X_validate] = np.split(X, boundaries)\n\n [y_train, y_test, y_validate] = np.split(y, boundaries)\n\n # non-anomalies\n zero_indices = np.where(y_train == 0)\n zeros_train = X_train[zero_indices]\n # anomalies\n eights_indices = np.where(y_train == 8)\n eights_train = X_train[eights_indices]\n\n # one-hot encode target columns\n y_train = to_categorical(y_train)\n y_test = to_categorical(y_test)\n y_validate = to_categorical(y_validate)\n\n return (X_train, X_test, X_validate), (y_train, y_test, y_validate)","def load_data(input_file, test_size, random_seed ):\n ratings = np.genfromtxt(input_file, delimiter=',')\n #ratings = ratings[:200,:]\n train_masks = np.zeros_like(ratings)\n test_masks = np.zeros_like(ratings)\n pairs = list()\n for i in range(ratings.shape[0]):\n for j in range(ratings.shape[1]):\n pairs.append((i,j))\n train, test = train_test_split(pairs ,test_size = test_size, random_state= random_seed)\n for user,item in train:\n train_masks[user, item]= 1\n for user,book in test:\n test_masks[user, item]= 1 \n return ratings, train_masks, test_masks","def test(self):\n self.training = False","def load_data():\r\n from sklearn.feature_extraction.text import CountVectorizer\r\n\r\n # Load the data\r\n\r\n with open(\"clean_real.txt\", 'r') as RealNews:\r\n RealStrAr = RealNews.read().split('\\n')\r\n\r\n with open(\"clean_fake.txt\", 'r') as FakeNews:\r\n FakeStrAr = FakeNews.read().split('\\n')\r\n\r\n # Preprocess it using a vectorizer\r\n\r\n MyCoolVectorizer = CountVectorizer()\r\n X = MyCoolVectorizer.fit_transform(RealStrAr + FakeStrAr)\r\n\r\n RealLabels = np.ones((len(RealStrAr), 1)) # means real\r\n FakeLabels = np.zeros((len(FakeStrAr), 1)) # means fake\r\n AllLabels = np.append(RealLabels, FakeLabels, axis=0)\r\n\r\n FinalTensor = np.append(X.toarray(), AllLabels, axis=1)\r\n\r\n # Randomize it and split it\r\n\r\n np.random.shuffle(FinalTensor)\r\n\r\n # divide and multiply by 2 just to make sure it's even\r\n ROUGHLY70 = 2 * ((FinalTensor.shape[0] * 70 / 100) / 2)\r\n ROUGHLY15 = (FinalTensor.shape[0] - ROUGHLY70) / 2\r\n\r\n # TEST SET VALIDATION SET TRAINING SET DICTIONARY\r\n return (FinalTensor[:ROUGHLY15], FinalTensor[ROUGHLY15 : 2 * ROUGHLY15], FinalTensor[-ROUGHLY70:], MyCoolVectorizer.get_feature_names())","def fit_test(self):","def dataset_preparation():\r\n with open('../data/patterns_num.txt', 'r') as f:\r\n data = f.readlines()\r\n X, Y = [], []\r\n for line in data:\r\n x, y = line.split('\\t')\r\n if len(x) > 5 and x not in X: # better results are achieved excluding short query patterns\r\n X.append(x.replace(\"X\", \"\").replace(\"Y\", \"\").lower())\r\n Y.append(int(y.replace('\\n', '')))\r\n test_size = 0.2\r\n # print('Test size:', test_size, '\\nWrong classifications:\\n')\r\n\r\n X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=test_size, random_state=42, stratify=Y)\r\n return X_train, y_train, X_test, y_test","def _load_training_data(self):\n self._save_training_data()","def load_data_and_labels_another():\n\n y_train = []\n y_valid =[]\n y_test = []\n x_train =[]\n x_valid = []\n x_test = []\n index = []\n labels = {}\n topics = ['finance' , 'international', 'legal', 'social','tech','hemp']\n for idx, topic in enumerate(topics):\n folder_name = \"data/\" + topic\n all_files = os.listdir(folder_name)\n clean_news = []\n # read in files in each topic's folder\n for single_file in all_files:\n raw_data_file_name = os.path.join(folder_name, single_file)\n news = list(open(raw_data_file_name, mode = 'rb').readlines())\n clean_news = clean_news + news\n clean_news = [s.strip() for s in clean_news]\n clean_news = [clean_str(s) for s in clean_news]\n clean_news = [normalizing_str(s) for s in clean_news]\n clean_news = [extract_clean_words(s) for s in clean_news]\n clean_news = [s for s in clean_news if s is not None]\n length_of_news = len(clean_news)\n test_num = int(length_of_news * 0.3)\n valid_num = int(length_of_news * 0.3)\n train_num = length_of_news - test_num - valid_num\n clean_news = np.array(clean_news)\n\n \n \n #x_text = x_text + clean_news\n if topic == 'finance':\n y_topic = [[1,0,0,0,0,0] for _ in clean_news]\n elif topic == 'international':\n y_topic = [[0,1,0,0,0,0] for _ in clean_news]\n elif topic == 'legal':\n y_topic = [[0,0,1,0,0,0] for _ in clean_news]\n elif topic == 'social':\n y_topic = [[0,0,0,1,0,0] for _ in clean_news]\n elif topic == 'tech':\n y_topic = [[0,0,0,0,1,0] for _ in clean_news]\n elif topic == 'hemp':\n y_topic = [[0,0,0,0,0,1] for _ in clean_news]\n y_topic = np.array(y_topic)\n\n #randomly shuffle the data and divide them into train, valid and test\n np.random.seed(9)\n indices = np.random.permutation(clean_news.shape[0])\n training_idx = indices[:train_num]\n valid_idx = indices[train_num:train_num+valid_num]\n test_idx = indices[train_num+valid_num: ]\n # Testing: no record is missed.\n #tem = np.concatenate((training_idx, valid_idx, test_idx),axis = 0)\n #print(tem.sort()) \n train_piece_x = list(clean_news[training_idx])\n valid_piece_x = list(clean_news[valid_idx])\n test_piece_x = list(clean_news[test_idx])\n train_piece_y = list(y_topic[training_idx])\n valid_piece_y = list(y_topic[valid_idx])\n test_piece_y = list(y_topic[test_idx])\n y_train = y_train + train_piece_y\n y_valid = y_valid + valid_piece_y\n y_test = y_test + test_piece_y\n x_train = x_train + train_piece_x\n x_valid = x_valid + valid_piece_x\n x_test = x_test + test_piece_x\n\n # Store the data in data_pickle.\n y_train = np.array(y_train)\n y_valid = np.array(y_valid)\n y_test = np.array(y_test)\n file = open('data_pickle', 'wb')\n pickle.dump([x_train,x_valid,x_test,y_train,y_valid,y_test], file)\n file.close()\n print(\"-------------------------------------------------------\")\n print(\"*****Dumped Data_pickle*****\")","def __init__(self, calendar_data_path_str, sell_prices_data_path_str, sales_train_validation_data_path_str, train_test_date_split, dept_id = None):\n\n self.sales_train_validation_df = pd.read_csv(sales_train_validation_data_path_str)\n self.sales_train_validation_df.sort_values(\"id\", inplace = True)\n self.calendar_df = pd.read_csv(calendar_data_path_str)\n self.sell_prices_df = pd.read_csv(sell_prices_data_path_str)\n\n # Filter by dept_id if needed\n if dept_id is not None:\n self.sales_train_validation_df = self.sales_train_validation_df.loc[self.sales_train_validation_df[\"cat_id\"] == dept_id]\n\n # Generate a validation set if enable_validation is True\n train_cols_lst = [i for i in self.sales_train_validation_df.columns if not i.startswith(\"d_\")] + self.calendar_df[\"d\"].loc[self.calendar_df[\"date\"] <= \"2016-03-27\"].tolist()\n test_cols_lst = [i for i in self.sales_train_validation_df.columns if not i.startswith(\"d_\")] + self.calendar_df[\"d\"].loc[(self.calendar_df[\"date\"] > \"2016-03-27\") & (self.calendar_df[\"date\"] <= \"2016-04-24\")].tolist()\n self.train_df = self.sales_train_validation_df[train_cols_lst].reset_index(drop = True)\n self.valid_df = self.sales_train_validation_df[test_cols_lst].reset_index(drop = True)\n \n self.train_target_columns = [i for i in self.train_df.columns if i.startswith(\"d_\")]\n self.weight_columns = self.train_df.iloc[:, -28:].columns.tolist()\n self.train_df[\"all_id\"] = \"all\"\n self.valid_df[\"all_id\"] = \"all\"\n \n self.id_columns = [i for i in self.train_df.columns if not i.startswith(\"d_\")]\n self.valid_target_columns = [i for i in self.valid_df.columns if i.startswith(\"d_\")]\n\n self.group_ids = (\"all_id\", \"state_id\", \"store_id\", \"cat_id\", \"dept_id\", \"item_id\", \n [\"state_id\", \"cat_id\"], [\"state_id\", \"dept_id\"], [\"store_id\", \"cat_id\"], \n [\"store_id\", \"dept_id\"], [\"item_id\", \"state_id\"], [\"item_id\", \"store_id\"])\n\n self.train_series = self.trans_30490_to_42840(self.train_df, self.train_target_columns)\n self.valid_series = self.trans_30490_to_42840(self.valid_df, self.valid_target_columns)\n self.roll_mat_csr, self.roll_index = self.create_rollup_index()\n self.group_ids_items = self.generate_group_ids_items()\n\n self.S = self.get_s()\n self.W = self.get_w()\n self.SW = self.W / np.sqrt(self.S)\n\n self.weights = self.get_weight_df() # Equivalent to get_w()\n self.scale = self.get_scale() # Equivalent to get_s()\n\n #self.train_series = None\n #self.train_df = None\n gc.collect()","def load(train_file, test_file):\n print('\\nLoad the raw training and test set data...')\n y_train, tx_train, ids_train = load_csv_data(train_file)\n y_test, tx_test, ids_test = load_csv_data(test_file)\n print('\\n... finished.')\n return y_train, tx_train, ids_train, y_test, tx_test, ids_test","def test_02_train(self):\n today = date.today()\n log_file = os.path.join(LOG_DIR, \"{}-train-{}-{}.log\".format(LOG_PREFIX, today.year, today.month))\n \n ## update the log\n country = 'india'\n date_range = ('2017-11-29', '2019-05-24')\n metric = {'rmse':0.5}\n runtime = \"00:00:01\"\n model_version = 0.1\n model_version_note = \"test model\"\n \n update_train_log(country, date_range, metric, runtime,\n model_version, model_version_note ,test=True, prefix=LOG_PREFIX)\n\n df = pd.read_csv(log_file)\n logged_metric = [literal_eval(i) for i in df['metric'].copy()][-1]\n self.assertEqual(metric,logged_metric)","def test_data():\n batch_size = 10\n input_dim = 28\n test_data = np.random.rand(batch_size, input_dim)\n\n return test_data","def load_data():\r\n train = convert_corpus_to_lists(ConllCorpusReader('CoNLL-2003', 'train.txt', ['words', 'pos', 'ignore', 'chunk']))\r\n val = convert_corpus_to_lists(ConllCorpusReader('CoNLL-2003', 'valid.txt', ['words', 'pos', 'ignore', 'chunk'])) # testa will be our val set\r\n test = convert_corpus_to_lists(ConllCorpusReader('CoNLL-2003', 'test.txt', ['words', 'pos', 'ignore', 'chunk']))\r\n\r\n return train, val, test","def load_data(opt=\"mnist\"):\n if opt == \"mnist\":\n train, test = tf.keras.datasets.mnist.load_data()\n \n x_train, y_train = train\n x_test, y_test = test\n \n x_train = x_train.reshape(x_train.shape[0], 28 * 28)\n x_test = x_test.reshape(x_test.shape[0], 28 * 28)\n \n y_train = y_train.astype(np.int)\n y_test = y_test.astype(np.int)\n for i in range(len(y_train)):\n y_train[i] = 1 if y_train[i] % 2 == 0 else -1\n for i in range(len(y_test)):\n y_test[i] = 1 if y_test[i] % 2 == 0 else -1\n\n elif opt == \"covertype\":\n df = pd.read_csv(\"covtype.data\", header=None)\n x = df.iloc[:, 0:54].values\n y = df[54].values\n for i in range(len(y)):\n y[i] = 1 if y[i] % 2 == 0 else -1\n x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3)\n \n else:\n logging.error(\"Unknown dataset!!\")\n\n logging.info(\"train data shape: {}\".format(x_train.shape))\n logging.info(\"test data shape: {}\".format(x_test.shape))\n return (x_train, y_train), (x_test, y_test)","def check_data():\n check_docs(\"Training\")\n check_docs(\"dev\")\n check_docs(\"Test\")","def trainData(self, X, y, NeuralNet, epochs):","def train_test_data_df(train_data_file, test_data_file):\n dtype_dict = {\n \"age\": np.int32,\n \"education-num\": np.int32,\n \"capital-gain\": np.int32,\n \"capital-loss\": np.int32,\n \"hours-per-week\": np.int32\n }\n cols = [i for i in range(15) if i != 2]\n train_data = pd.read_csv(train_data_file, sep=\", \", header=0, dtype=dtype_dict, na_values=\"?\", usecols=cols)\n train_data = train_data.dropna(axis=0, how=\"any\")\n test_data = pd.read_csv(test_data_file, sep=\", \", header=0, dtype=dtype_dict, na_values=\"?\", usecols=cols)\n test_data = test_data.dropna(axis=0, how=\"any\")\n return train_data, test_data","def CrossCheck(dataloader):","def train(self, data, labels, validationData, validationLabels): # data x training, y_training\n # ## TODO: Your code here\n count = util.Counter()\n\n #print 'Labels of the data is:', labels #printing all the labels from which the frequency has to be found\n\n for x in labels: # for loop\n count[x] += 1\n\n #print count\n val = count.argMax()\n self.guess = val\n #print val","def train(self):\n pass","def train(self):\n pass","def train(self):\n pass","def train(self):\n pass","def train(self):\n pass","def load_data():\n df = pd.read_csv(\"../../Data/breast_cancer_data/data.csv\")\n\n cols = df.columns\n X = df[cols[2:-1]].to_numpy()\n y = df[cols[1]].to_numpy()\n y = (y=='M').astype(np.int) * 2 - 1\n\n train_X = X[:-150]\n train_y = y[:-150]\n\n test_X = X[-150:]\n test_y = y[-150:]\n\n return train_X, train_y, test_X, test_y","def validation_epoch(self):\n self.model.eval()\n\n # Compute for training set\n train_loss, train_acc = compute_loss_and_accuracy(\n self.dataloader_train, self.model, self.loss_criterion\n )\n self.TRAIN_ACC.append(train_acc)\n self.TRAIN_LOSS.append(train_loss)\n\n # Compute for validation set\n validation_loss, validation_acc = compute_loss_and_accuracy(\n self.dataloader_val, self.model, self.loss_criterion\n )\n self.VALIDATION_ACC.append(validation_acc)\n self.VALIDATION_LOSS.append(validation_loss)\n print(\"Current validation loss:\", validation_loss, \" Accuracy:\", validation_acc)\n # Compute for testing set\n test_loss, test_acc = compute_loss_and_accuracy(\n self.dataloader_test, self.model, self.loss_criterion\n )\n self.TEST_ACC.append(test_acc)\n self.TEST_LOSS.append(test_loss)\n\n self.model.train()","def parse_data():\n n_train, n_test = 15000, 4996\n n_features = 1355191\n\n print('- parsing train data')\n X_train = sp.lil_matrix((n_train, n_features))\n y_train = np.zeros(n_train)\n with open('/Users/kitazawa/data/news20.train') as f:\n lines = map(lambda l: l.rstrip().split(' '), f.readlines())\n for i, line in enumerate(lines):\n y_train[i] = int(line[0])\n\n for fv in line[1:]:\n f, v = fv.split(':')\n X_train[i, (int(f) - 1)] = float(v)\n print('-- density: %f' % (X_train.nnz / (n_train * n_features)))\n\n print('- parsing test data')\n X_test = sp.lil_matrix((n_test, n_features))\n y_test = np.zeros(n_test)\n with open('/Users/kitazawa/data/news20.test') as f:\n lines = map(lambda l: l.rstrip().split(' '), f.readlines())\n for i, line in enumerate(lines):\n y_test[i] = int(line[0])\n\n for fv in line[1:]:\n f, v = fv.split(':')\n X_test[i, (int(f) - 1)] = float(v)\n print('-- density: %f' % (X_test.nnz / (n_test * n_features)))\n\n return X_train, y_train, X_test, y_test","def prepare_data(self):\n try:\n self.train_dataset = self.datasets['train']\n self.val_dataset = self.datasets['val']\n try:\n self.test_dataset = self.datasets['test']\n except:\n pass\n except Exception as e:\n print('Data was not succesfully prepared:', e)","def load_data_simple_train_test(train_pos_data_path, train_neg_data_path, stopwords):\n\n\ttrain_files_pos = [join(train_pos_data_path, f) for f in listdir(train_pos_data_path) if isfile(join(train_pos_data_path, f))]\n\ttrain_files_neg = [join(train_neg_data_path, f) for f in listdir(train_neg_data_path) if isfile(join(train_neg_data_path, f))]\n\n\ttrain_files_pos.sort(key = lambda x: x.split(\"_\")[0].replace(\"cv\", \"\"))\n\ttrain_files_neg.sort(key = lambda x: x.split(\"_\")[0].replace(\"cv\", \"\"))\n\n\tprint(\"The count of all positive files is: \", len(train_files_pos))\n\tprint(\"The count of all negative files is: \", len(train_files_neg))\n\n\tall_pos_docs = collect_train_data(train_files_pos, stopwords)\n\tall_neg_docs = collect_train_data(train_files_neg, stopwords)\n\n\tpos_train = all_pos_docs[:900]\n\tneg_train = all_neg_docs[:900]\n\tpos_test = all_pos_docs[900:]\n\tneg_test = all_neg_docs[900:]\n\n\tprint(\"The size of the positive training set is: \", len(pos_train))\n\tprint(\"The size of the negative training set is: \", len(neg_train))\n\tprint(\"The size of the positive test set is: \", len(pos_test))\n\tprint(\"The size of the negative test set is: \", len(neg_test))\n\n\treturn pos_train, pos_test, neg_train, neg_test","def train(x_train, y_train, x_valid, y_valid, config):\n train_acc = []\n valid_acc = []\n train_loss = []\n valid_loss = []\n best_model = None\n NUM_EPOCH = config['epochs']\n EARLY_STOP = config['early_stop']\n EARLY_STOP_EPOCH = config['early_stop_epoch']\n BATCH_SIZE = config['batch_size']\n model = NeuralNetwork(config=config)\n loss = float('inf')\n best_loss = float('inf')\n best_accuracy = 0\n patience = 0\n\n\n\n for i in range (NUM_EPOCH):\n\n x_train, y_train = shuffle(x_train, y_train)\n x_train = np.asarray(x_train)\n y_train = np.asarray(y_train)\n\n for j in range (0, len(x_train), BATCH_SIZE):\n start = j\n end = j + BATCH_SIZE\n if (end > len(x_train)):\n end = len(x_train)\n\n x = x_train[start:end]\n y = y_train[start:end]\n\n model.forward(x, y) \n model.backward()\n\n train_epoch_loss = model.forward(x_train, y_train)\n \n train_predict = np.zeros_like(model.y)\n train_predict[np.arange(len(model.y)), model.y.argmax(1)] = 1\n\n train_accuracy = sum([1 if all(train_predict[i] == y_train[i]) else 0 for i in range(len(y_train))])/len(y_train)\n\n train_loss.append(train_epoch_loss)\n train_acc.append(train_accuracy)\n \n valid_epoch_loss = model.forward(x_valid, y_valid)\n valid_predict = np.zeros_like(model.y)\n valid_predict[np.arange(len(model.y)), model.y.argmax(1)] = 1\n\n valid_accuracy = sum([1 if all(valid_predict[i] == y_valid[i]) else 0 for i in range(len(y_valid))])/len(y_valid)\n\n valid_loss.append(valid_epoch_loss)\n valid_acc.append(valid_accuracy)\n\n\n print(\"Epoch:\", i, \"Train Accuracy|Loss:\", train_accuracy,\"| \", train_epoch_loss, \"~|~ Valid: \", valid_accuracy, \" | \", valid_epoch_loss)\n if EARLY_STOP:\n if valid_epoch_loss > best_loss and patience >= EARLY_STOP_EPOCH:\n return train_acc, valid_acc, train_loss, valid_loss, best_model\n elif valid_epoch_loss > best_loss and patience < EARLY_STOP_EPOCH:\n patience += 1\n else:\n patience = 0\n if valid_epoch_loss < best_loss:\n best_loss = valid_epoch_loss\n best_accuracy = valid_accuracy\n best_model = copy.deepcopy(model)\n\n loss = valid_epoch_loss\n\n \n best_model = model \n return train_acc, valid_acc, train_loss, valid_loss, best_model","def load_dataset_train():\n df_train = load_csv_file(\"31_train.csv\")\n df_train_target = load_csv_file(\"31_target_train.csv\")\n\n return df_train.values, df_train_target.values","def load_data():\n # Load data\n X_train = pd.read_csv(\n \"data/trimmed_merged_no_track_id_or_session_id.csv\"\n ) # load data from csv\n\n drop_columns = [\"mode\"] # drop unimportant columns\n X_train.drop(labels=drop_columns, axis=1, inplace=True)\n\n y_train = X_train[\"skip\"]\n X_train.drop(labels=\"skip\", axis=1, inplace=True) # ground truth from train_data\n\n # split data 60% train, 20% validation, 20% test\n X_train, X_val, y_train, y_val = train_test_split(\n X_train, y_train, test_size=0.2, random_state=1\n )\n X_train, X_test, y_train, y_test = train_test_split(\n X_train, y_train, test_size=0.25, random_state=1\n )\n\n print(\"X_train:\", len(X_train))\n print(\"y_train:\", len(y_train))\n print(\"X_val:\", len(X_val))\n print(\"y_val:\", len(y_val))\n print(\"X_test:\", len(X_test))\n print(\"y_test:\", len(y_test))\n\n return X_train, y_train, X_val, y_val, X_test, y_test","def test_data():\n global _MEAN # pylint: disable=global-statement\n _np.random.seed(1)\n view = _skdc10.view.OfficialImageClassificationTask()\n permutation = _np.random.permutation(range(10000))\n if _MEAN is None:\n _MEAN = view.train.x.reshape((50000 * 32 * 32, 3)).mean(axis=0)\n return ((view.test.x[:10000, :][permutation, :] - _MEAN).\n transpose((0, 3, 1, 2)).astype('float32'),\n view.test.y[:10000][permutation].reshape((10000, 1)).astype('float32'))","def prepareData(mdl, X_train, X_val, X_test, Y_train, Y_val, Y_test):\n\t\n\tX2_train = []\n\n\tX2_val = []\n\n\tX2_test = []\n\n\n\tYPs = mdl.predict(X_train)\n\n\tfor i in tqdm(range(len(X_train))):\n\n\t\tX2_train.append(np.array(YPs[i]))\n\n\n\n\tYPs = mdl.predict(X_val)\n\n\tfor i in tqdm(range(len(X_val))):\n\n\t\tX2_val.append(np.array(YPs[i]))\n\n\n\n\tYPs = mdl.predict(X_test)\n\n\tfor i in tqdm(range(len(X_test))):\n\n\t\tX2_test.append(np.array(YPs[i]))\n\n\n\tX2_train = np.array(X2_train)\n\n\tX2_val = np.array(X2_val)\n\n\tX2_test = np.array(X2_test)\n\n\treturn (X2_train, X2_val, X2_test)","def merge_data():\n\n\tconfig = Config()\n\tfilename_train, filename_test = \"../data/train.csv\", \"../data/test.csv\" \n\n # create datasets\n\ttrain, test = config.load_data(filename_train, filename_test, print_EDA=False)\n\n # 1. datetime features\n\t# diff between weekday and day?\n\t#weekday - Return the day of the week as an integer, where Monday is 0 and Sunday is 6.\n\t#day - Between 1 and the number of days in the given month of the given year.\n\ttrain['pickup_hour'] = train.pickup_datetime.dt.hour.astype('uint8')\n\ttrain['pickup_day'] = train.pickup_datetime.dt.day.astype('uint8')\n\ttrain['pickup_weekday'] = train.pickup_datetime.dt.weekday.astype('uint8')\n\ttrain['pickup_minute'] = train.pickup_datetime.dt.minute.astype('uint8')\n\ttrain['pickup_month'] = train.pickup_datetime.dt.month.astype('uint8')\n\ttrain['pickup_hour_weekofyear'] = train['pickup_datetime'].dt.weekofyear\n\ttrain['pickup_weekday_hour'] = train['pickup_weekday']*24 + train['pickup_hour']\n\n\ttest['pickup_hour'] = test.pickup_datetime.dt.hour.astype('uint8')\n\ttest['pickup_day'] = test.pickup_datetime.dt.day.astype('uint8')\n\ttest['pickup_weekday'] = test.pickup_datetime.dt.weekday.astype('uint8')\n\ttest['pickup_minute'] = test.pickup_datetime.dt.minute.astype('uint8')\n\ttest['pickup_month'] = test.pickup_datetime.dt.month.astype('uint8')\n\ttest['pickup_hour_weekofyear'] = test['pickup_datetime'].dt.weekofyear\n\ttest['pickup_weekday_hour'] = test['pickup_weekday']*24 + test['pickup_hour']\n\n\t# 2. Location features\n\tdef haversine(lon1, lat1, lon2, lat2):\n\t lon1, lat1, lon2, lat2 = map(np.radians, [lon1, lat1, lon2, lat2])\n\t dlon = lon2 - lon1\n\t dlat = lat2 - lat1\n\t a = np.sin(dlat/2.0)**2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlon/2.0)**2\n\t c = 2 * np.arcsin(np.sqrt(a))\n\t km = 6367 * c # AVG_EARTH_RADIUS=6367\n\t miles = km * 0.621371\n\t return miles\n\n\t# def dummy_manhattan_distance(lat1, lng1, lat2, lng2):\n\t# a = haversine_array(lat1, lng1, lat1, lng2)\n\t# b = haversine_array(lat1, lng1, lat2, lng1)\n\t# return a + b\n\n\t# def bearing_array(lat1, lng1, lat2, lng2):\n\t# AVG_EARTH_RADIUS = 6371 # in km\n\t# lng_delta_rad = np.radians(lng2 - lng1)\n\t# lat1, lng1, lat2, lng2 = map(np.radians, (lat1, lng1, lat2, lng2))\n\t# y = np.sin(lng_delta_rad) * np.cos(lat2)\n\t# x = np.cos(lat1) * np.sin(lat2) - np.sin(lat1) * np.cos(lat2) * np.cos(lng_delta_rad)\n\t# return np.degrees(np.arctan2(y, x))\n\n\ttrain['distance'] = haversine(train.pickup_longitude, train.pickup_latitude,\n\t train.dropoff_longitude, train.dropoff_latitude)\n\ttest['distance'] = haversine(test.pickup_longitude, test.pickup_latitude,\n\t test.dropoff_longitude, test.dropoff_latitude)\n\n\n\t# 3. Use outsource data\n\tweatherdata_filename = \"../data/outsource_data/weather_data_nyc_centralpark_2016.csv\"\n\tfastestroute_data_train = \"../data/outsource_data/fastest_train.csv\"\n\tfastestroute_data_test = \"../data/outsource_data/fastest_routes_test.csv\"\n\n\n\twd = pd.read_csv(weatherdata_filename, header=0)\n\twd['date'] = pd.to_datetime(wd.date, format=\"%d-%m-%Y\")\n\twd['pickup_day'] = wd['date'].dt.day\n\twd['snow fall'] = wd['snow fall'].replace('T', 0.05).astype(np.float32) \n\twd['precipitation'] = wd['precipitation'].replace('T', 0.05).astype(np.float32) \n\twd['snow depth'] = wd['snow depth'].replace('T', 0.05).astype(np.float32) \n\n\t# Merge training data with weather data on pickup_day\n\tprint(\"Merging training data with weather data ....\")\n\twd_train = pd.merge(train, wd, on='pickup_day')\n\twd_train = wd_train.drop(['date','maximum temperature','minimum temperature'],axis=1)\n\tgc.collect()\n\n\t# Merge wd_train with fastestroute_data\n\tfastest = pd.read_csv(fastestroute_data_train, header=0)\n\tprint(\"Merging Location data with weather and training data ....\")\n\twd_train_fastest = pd.merge(wd_train, fastest, on='id', how='outer')\n\n\tgc.collect()\n\n\n\tprint(\"===================== CHECK TRAINING DATA =====================\")\n\n\tprint(wd_train_fastest.head(2))\n\tprint(\"Semi-final training data shape is: {}\".format(wd_train_fastest.shape))\n\tprint(\"Training data columns: {}\".format(wd_train_fastest.columns))\n\n\n\t# Use the same outsource data with test set\n\t# merge outsource data with test data as well\n\tft_test_cols = [ 'id', 'starting_street','end_street','total_distance',\t'total_travel_time',\n\t\t\t\t\t'number_of_steps','street_for_each_step','distance_per_step','travel_time_per_step',\n\t 'step_maneuvers','step_direction',\t'step_location_list']\n\tfastest_test = pd.read_csv(fastestroute_data_test, names=ft_test_cols, header=0)\n\n\tprint(\"Merging test data with Location data ....\")\n\ttest = pd.merge(test, fastest_test, on='id', how='outer')\n\ttest = test.drop(['step_location_list','step_direction','step_maneuvers','travel_time_per_step','distance_per_step','street_for_each_step','number_of_steps','starting_street',\n\t 'end_street'], axis=1)\n\tprint(\"Merging test data with weather data ....\")\n\ttest = pd.merge(test, wd, on='pickup_day')\n\n\tprint(\"===================== CHECK TEST DATA =====================\")\n\n\tprint(test.head(2))\n\tprint(\"Semi-final test data shape is: {}\".format(test.shape))\n\tprint(\"Test data columns: {}\".format(test.columns))\n\n\n\n\t# 4. Do more data munging\n\tmask = ((wd_train_fastest.trip_duration > 60) & (wd_train_fastest.distance < 0.05))\n\twd_train_fastest = wd_train_fastest[~mask]\n\tmask = (wd_train_fastest.trip_duration < 60) \n\twd_train_fastest = wd_train_fastest[~mask]\n\tmask = wd_train_fastest.trip_duration > 79200\n\twd_train_fastest = wd_train_fastest[~mask]\n\tmask = wd_train_fastest.distance/(wd_train_fastest.trip_duration/3600) > 60\n\twd_train_fastest = wd_train_fastest[~mask]\n\twd_train_fastest.trip_duration = wd_train_fastest.trip_duration.astype(np.uint16)\n\twd_train_fastest = wd_train_fastest[wd_train_fastest.passenger_count > 0]\n\n\t# 5. Do some data maskig based on location to create jfk and lgo features\n\tjfk_lon = -73.778889\n\tjfk_lat = 40.639722\n\tlga_lon = -73.872611\n\tlga_lat = 40.77725\n\n\twd_train_fastest['jfk_pickup_dist'] = wd_train_fastest.apply(lambda row: haversine(jfk_lon, jfk_lat, row['pickup_longitude'],row['pickup_latitude']), axis=1)\n\twd_train_fastest['lga_pickup_dist'] = wd_train_fastest.apply(lambda row: haversine(lga_lon, lga_lat, row['pickup_longitude'],row['pickup_latitude']), axis=1)\n\twd_train_fastest['jfk_dropoff_dist'] = wd_train_fastest.apply(lambda row: haversine(jfk_lon, jfk_lat, row['dropoff_longitude'],row['dropoff_latitude']), axis=1)\n\twd_train_fastest['lga_dropoff_dist'] = wd_train_fastest.apply(lambda row: haversine(lga_lon, lga_lat, row['dropoff_longitude'],row['dropoff_latitude']), axis=1)\n\n\twd_train_fastest['jfk'] = ((wd_train_fastest['jfk_pickup_dist'] < 2) | (wd_train_fastest['jfk_dropoff_dist'] < 2))\n\twd_train_fastest['lga'] = ((wd_train_fastest['lga_pickup_dist'] < 2) | (wd_train_fastest['lga_dropoff_dist'] < 2))\n\twd_train_fastest = wd_train_fastest.drop(['jfk_pickup_dist','lga_pickup_dist','jfk_dropoff_dist','lga_dropoff_dist'],axis=1)\n\twd_train_fastest['workday'] = ((wd_train_fastest['pickup_hour'] > 8) & (wd_train_fastest['pickup_hour'] < 18))\n\n\n\tprint(\"===================== CHECK TRAINING DATA AGAIN =====================\")\n\n\tprint(wd_train_fastest.head(2))\n\tprint(\"Final training data shape is: {}\".format(wd_train_fastest.shape))\n\tprint(\"Training data columns: {}\".format(wd_train_fastest.columns))\n\n\n\treturn wd_train_fastest, test","def gen_data(n_samples=200):\r\n\tnp.random.seed(13)\r\n\tx = np.random.uniform(0,10,size=n_samples) #从一个均匀分布[0,10)中随机采样\r\n\tx.sort()\r\n\ty = ground_truth(x) + 0.75*np.random.normal(size=n_samples) #均值为0，标准差为1的正态分布\r\n\t#print(\"x, y: \", x[:10], y[:10])\r\n\ttrain_mask = np.random.randint(0,2,size=n_samples).astype(np.bool) #返回值为True or False\r\n\t#print(\"train_mask: \",train_mask[:10])\r\n\tx_train, y_train = x[train_mask, np.newaxis], y[train_mask] #如果train_mask是1，就将x,y作为训练集，np.newaxis表示将行向量转为列向量\r\n\t#print(\"x_train, y_train: \", x_train[:10], y_train[:10])\r\n\tx_test, y_test = x[~train_mask, np.newaxis], y[~train_mask] #train_mask为False，将x,y作为测试集\r\n\t#print(\"x_test, y_test: \", x_test[:10], y_test[:10])\r\n\treturn x_train, x_test, y_train, y_test","def test(self, training_set, original_test_set, imitation_test_set ):\n\n plt.figure()\n\n training_axis = np.arange(len(training_set))\n original_test_axis = np.arange(len(original_test_set))+len(training_axis)\n imitation_test_axis = np.arange(len(imitation_test_set))+len(training_axis)+len(original_test_set)\n\n training_scores = []\n original_test_scores = []\n imitation_test_scores = []\n\n for signature in training_set:\n vectorized_signature = signature.tolist()\n score = -1*self.model.score(vectorized_signature)\n training_scores.append(score)\n\n for signature in original_test_set:\n vectorized_signature = signature.tolist()\n score = -1*self.model.score(vectorized_signature)\n original_test_scores.append(score)\n\n for signature in imitation_test_set:\n vectorized_signature = signature.tolist()\n score = -1*self.model.score(vectorized_signature)\n imitation_test_scores.append(score)\n\n accuracy, threshold = self.evaluate(training_scores, original_test_scores, imitation_test_scores)\n\n xaxis = np.arange(len(imitation_test_set)+len(training_axis)+len(original_test_set))\n plt.plot( xaxis, threshold*np.ones(len(xaxis)), \"--\", label=\"Threshold\" )\n plt.scatter(training_axis,training_scores, label=\"Training data\")\n plt.scatter(original_test_axis, original_test_scores, c=\"g\", label=\"Original Test data\")\n plt.scatter(imitation_test_axis, imitation_test_scores, c=\"r\", label=\"Imitated Test data\")\n plt.legend(loc=\"best\")\n plt.title(f\"{self.user} data. Accuracy={accuracy} \")\n plt.ylabel(\"Score\")\n plt.xlabel(\"File\")\n plt.savefig(f\"{self.n_components}_{self.user}.png\")\n\n Model.accuracies.append(accuracy)","def load_data(mode='train'):\r\n if mode == 'train':\r\n\r\n x_train = scipy.io.loadmat('train_images_14bus.mat')['x_train']\r\n y_train = scipy.io.loadmat('train_labels_14bus.mat')['y_train']\r\n\r\n x_valid = scipy.io.loadmat('valid_images_14bus.mat')['x_valid']\r\n y_valid = scipy.io.loadmat('valid_labels_14bus.mat')['y_valid']\r\n print(x_valid.shape, y_valid.shape, type(x_valid), type(y_valid))\r\n\r\n x_train, _ = reformat(x_train, y_train)\r\n x_valid, _ = reformat(x_valid, y_valid)\r\n print(x_valid.shape, y_valid.shape, type(x_valid), type(y_valid))\r\n\r\n return x_train, y_train, x_valid, y_valid\r\n elif mode == 'test':\r\n x_test = scipy.io.loadmat('test_images_14bus.mat')['x_test']\r\n y_test = scipy.io.loadmat('test_labels_14bus.mat')['y_test']\r\n x_test, _ = reformat(x_test, y_test)\r\n return x_test, y_test","def model_data():\n x_train, y_train, x_val, y_val, x_test, y_test = read_data(\"src/tests/dataclassificationmodel/ferPlus_processed.pbz2\", False)\n return x_train, y_train, x_val, y_val, x_test, y_test","def load_data_clean(source='local'):\n\n train_bl, test_bl = load_data_raw(source)\n\n train = train_bl.copy()\n train = encode_dtypes(train)\n train = impute_missing(train)\n train = clean_data(train)\n\n test = test_bl.copy()\n test = encode_dtypes(test)\n test = impute_missing(test)\n test = clean_data(test)\n\n return train, test","def train_test_data(data):\n data = data.fillna(0)\n \"\"\"One hot encoding the rank\"\"\"\n processed_data = pd.get_dummies(data, columns=[\"rank\"])\n \"\"\"Normalizing the values , place all data within range on (0,1)\"\"\"\n processed_data[\"gre\"] = processed_data[\"gre\"]/800\n processed_data[\"gpa\"] = processed_data[\"gpa\"]/4\n \n \"\"\"split data\"\"\"\n X=np.array(processed_data)[:,1:].astype('float32')\n y = to_categorical(data[\"admit\"],num_classes=2)\n \n print('Shape of X ', X.shape)\n print('Shape of y ', y.shape)\n \n \"\"\" Split train/test data \"\"\"\n X_train, X_test = X[50:], X[:50]\n y_train, y_test = y[50:], y[:50]\n \n model=Sequential()\n model.add(Dense(128, activation='relu', input_dim=X.shape[1]))\n model.add(Dropout(0.2))\n model.add(Dense(64,activation='relu'))\n model.add(Dropout(0.1))\n model.add(Dense(2, activation='softmax'))\n \n model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=[\"accuracy\"])\n \n model.summary()\n \n model.fit(X_train, y_train, epochs=200, batch_size=100, verbose=0)\n \n score = model.evaluate(X_train, y_train)\n print('Training score ', score[1])\n score = model.evaluate(X_test, y_test)\n print('Testing score ', score[1])","def train(self):\n\t\tself.model.fit(self.training_data, self.training_labels)","def train_all(X_train_fuse, Y_train, X_dev_fuse, Y_dev, R_train, R_dev, hyperparams):","def validate_dataset(self):\n pass","def validate(model, dataloader):\n model.eval()\n device = model.device \n epoch_start = time.time() \n running_loss = 0.0\n running_accuracy = 0.0 \n all_prob, all_labels = [], []\n\n with torch.no_grad():\n for (batch_seqs, batch_seq_masks, batch_seq_segments, batch_labels) in dataloader:\n seqs = batch_seqs.to(device) \n masks = batch_seq_masks.to(device)\n segments = batch_seq_segments.to(device)\n labels = batch_labels.to(device)\n \n loss, logits, probabilities = model(seqs, masks, segments, labels)\n running_loss += loss.item()\n running_accuracy += correct_predictions(probabilities, labels)\n \n all_prob.extend(probabilities[:, 1].cpu().numpy())\n all_labels.extend(batch_labels)\n epoch_time = time.time() - epoch_start \n epoch_loss = running_loss / len(dataloader)\n epoch_accuracy = running_accuracy / (len(dataloader.dataset))\n # epoch_f1 = epoch_accuracy \n return epoch_time, epoch_loss, epoch_accuracy, roc_auc_score(all_labels, all_prob),","def partition_train_valid_test2(data, classes, others, ratio=(1,1,1), rng=np.random.RandomState(1000)):\n k=sum(ratio) # ratio must be a vector of integers\n ind=kfold_cross_validation(classes,k=k,shuffle=True,rng=rng)\n sequence=np.arange(len(classes))\n train_ind=np.array([],dtype=int)\n valid_ind=np.array([],dtype=int)\n test_ind=np.array([],dtype=int)\n count=0\n for ki in range(k):\n if count<ratio[0]:\n train_ind=np.append(train_ind,sequence[ind==ki])\n count=count+1\n continue\n if count>=ratio[0] and count <ratio[0]+ratio[1]:\n valid_ind=np.append(valid_ind,sequence[ind==ki])\n count=count+1\n continue\n if count>=ratio[0]+ratio[1] and ratio[2]>0:\n test_ind=np.append(test_ind,sequence[ind==ki])\n count=count+1\n continue\n train_set_x=data[train_ind]\n train_set_y=classes[train_ind]\n if others is not None:\n train_set_others=others[train_ind]\n else:\n train_set_others=None\n valid_set_x=data[valid_ind]\n valid_set_y=classes[valid_ind]\n if others is not None:\n valid_set_others=others[valid_ind]\n else:\n valid_set_others=None\n test_set_x=data[test_ind]\n test_set_y=classes[test_ind]\n if others is not None:\n test_set_others=others[test_ind]\n else:\n test_set_others=None\n \n return train_set_x,train_set_y,train_set_others,valid_set_x,valid_set_y,valid_set_others,test_set_x,test_set_y,test_set_others","def test_model(valid_features, valid_labels):\n\n #valid_features, valid_labels = load_preprocess_testset()\n loaded_graph = tf.Graph()\n \n with tf.Session(graph=loaded_graph) as sess:\n # Load model\n loader = tf.train.import_meta_graph(SAVE_MODEL_PATH + '.meta')\n loader.restore(sess, SAVE_MODEL_PATH)\n\n # Get Tensors from loaded model\n loaded_x = loaded_graph.get_tensor_by_name('x:0')\n loaded_y = loaded_graph.get_tensor_by_name('y:0')\n loaded_keep_prob = loaded_graph.get_tensor_by_name('keep_prob:0')\n loaded_logits = loaded_graph.get_tensor_by_name('logits:0')\n loaded_acc = loaded_graph.get_tensor_by_name('accuracy:0')\n \n\n acc = sess.run(\n loaded_acc,\n feed_dict={loaded_x: valid_features, loaded_y: valid_labels, loaded_keep_prob: 1.0})\n\n return acc","def load_data(train_path, test_path):\n\n train_data = pd.read_csv(train_path)\n test_data = pd.read_csv(test_path)\n\n print(\"number of training examples = \" + str(train_data.shape[0]))\n print(\"number of test examples = \" + str(test_data.shape[0]))\n print(\"train shape: \" + str(train_data.shape))\n print(\"test shape: \" + str(test_data.shape))\n\n return train_data, test_data","def load_data(train_path, test_path):\n\n train_data = pd.read_csv(train_path)\n test_data = pd.read_csv(test_path)\n\n print(\"number of training examples = \" + str(train_data.shape[0]))\n print(\"number of test examples = \" + str(test_data.shape[0]))\n print(\"train shape: \" + str(train_data.shape))\n print(\"test shape: \" + str(test_data.shape))\n\n return train_data, test_data","def get_training_data():\n \n X = pd.read_csv('../data/train_values.csv').set_index('sequence_id')\n y = pd.read_csv('../data/train_labels.csv').set_index('sequence_id')\n return X, y","def test_data_set_load(data_set):\n data = data_set()\n train_data, train_labels, test_data, test_labels = data.load_data()\n\n assert len(train_data) > 0\n assert len(test_data) > 0\n assert len(train_data) == len(train_labels)\n assert len(test_data) == len(test_labels)","def _split_train_tst(self):\n num_samples = self.Y.shape[0]\n mapper_file = self.checkpointer.get_mapper_file_location()\n if not self.checkpointer.is_mapper_checkpointed():\n print 'No mapper checkpoint found. Fresh loading in progress ...'\n # Now shuffle the data\n sample_id = range(num_samples)\n random.shuffle(sample_id)\n print 'Dumping the mapper shuffle for reuse.'\n Pickle.dump(sample_id, open(mapper_file, 'wb'))\n print 'Dump complete. Moving Forward...'\n else:\n print 'Mapper Checkpoint found... Reading from mapper dump'\n sample_id = Pickle.load(open(mapper_file, 'rb'))\n print 'Mapping unpickling complete.. Moving forward...'\n\n self.X_fwd = self.X_fwd[sample_id]\n self.X_bwd = self.X_bwd[sample_id]\n self.Y = self.Y[sample_id]\n # Now divide the data into test ans train set\n test_fraction = 0.01\n self.test_size = int(test_fraction * num_samples)\n self.train_size = num_samples - self.test_size\n # Forward review\n self.X_trn_fwd = self.X_fwd[0:self.train_size]\n self.X_tst_fwd = self.X_fwd[self.train_size:num_samples]\n # Backward review\n self.X_trn_bwd = self.X_bwd[0:self.train_size]\n self.X_tst_bwd = self.X_bwd[self.train_size:num_samples]\n # Summary\n self.Y_trn = self.Y[0:self.train_size]\n self.Y_tst = self.Y[self.train_size:num_samples]","def test_text_classifier_update_training_samples(self):\n pass","def training_data(loan_data):\n return loan_data[loan_data.status != \"LIVE\"]","def test_training(self):\n model = PoincareModel(self.data_large, burn_in=0, negative=3)\n old_vectors = np.copy(model.kv.syn0)\n model.train(epochs=2)\n self.assertFalse(np.allclose(old_vectors, model.kv.syn0))","def test_model(all_data):\n nll = 0\n xent = 0\n step = 0\n for data in jsb.batch_iterator(all_data, FLAGS.batch_size, FLAGS.sequence_length):\n feed = fill_feed(inputs, targets, data)\n\n batch_xent, batch_nll = sess.run([loss_op, nll_op],\n feed_dict=feed)\n\n xent += batch_xent\n nll += batch_nll\n step += 1\n return xent/step, nll/step","def train_test_official(self):\n return self.sub_set(self.idcs_train), self.sub_set(self.idcs_test)"],"string":"[\n \"def test_data(self):\\n\\n return self.__valid_data, self.__valid_labels\",\n \"def test_training(self):\\n\\t\\tpass\",\n \"def prep_data_fn(self, st_train_dt, end_train_dt, st_val_dt, end_val_dt, st_test_dt, end_test_dt):\\n df = self.get_prep_data()\\n train = df[(df['ft_data_dt'] >= st_train_dt) & (df['ft_data_dt'] <= end_train_dt)]\\n val = df[(df['ft_data_dt'] >= st_val_dt) & (df['ft_data_dt'] <= end_val_dt)].sample(frac=0.4, random_state=2021)\\n test = df[(df['ft_data_dt'] >= st_test_dt) & (df['ft_data_dt'] <= end_test_dt)]\\n print(f'----train----')\\n print(train[['ft_data_dt', 'target', 'idd']].groupby(['ft_data_dt', 'target']).agg(['count']))\\n print(f'----validation----')\\n print(val[['ft_data_dt', 'target', 'idd']].groupby(['ft_data_dt', 'target']).agg(['count']))\\n print(f'----test----')\\n print(test[['ft_data_dt', 'target', 'idd']].groupby(['ft_data_dt', 'target']).agg(['count']))\\n self.set_train(train)\\n self.set_validation(val)\\n self.set_test(test)\\n train_X = train[[c for c in train.columns if c not in ['idd', 'ft_data_dt', 'target']]]\\n train_y = train['target']\\n val_X = val[[c for c in train.columns if c not in ['idd', 'ft_data_dt', 'target']]]\\n val_y = val['target']\\n test_X = test[[c for c in train.columns if c not in ['idd', 'ft_data_dt', 'target']]]\\n test_y = test['target']\\n self.set_train_X(train_X)\\n self.set_train_y(train_y)\\n self.set_val_X(val_X)\\n self.set_val_y(val_y)\\n self.set_test_X(test_X)\\n self.set_test_y(test_y)\",\n \"def create_train_valid_set(self):\\n\\n if not self.eq_train:\\n X_train_high_level, X_valid_high_level, X_train_low_level, X_valid_low_level, train_w, valid_w, y_train, y_valid = train_test_split(self.X_train_high_level, self.X_train_low_level, self.train_weights, self.y_train,\\n train_size=0.7, test_size=0.3\\n )\\n else:\\n X_train_high_level, X_valid_high_level, X_train_low_level, X_valid_low_level, train_w, valid_w, w_train_eq, w_valid_eq, y_train, y_valid = train_test_split(self.X_train_high_level, self.X_train_low_level,\\n self.train_weights, self.train_weights_eq, self.y_train,\\n train_size=0.7, test_size=0.3\\n )\\n self.train_weights_eq = w_train_eq\\n\\n #NOTE: might need to re-equalise weights in each folds as sumW_sig != sumW_bkg anymroe!\\n self.train_weights = train_w\\n self.valid_weights = valid_w #validation weights should never be equalised weights!\\n\\n print 'creating validation dataset'\\n self.X_train_high_level = X_train_high_level\\n self.X_train_low_level = self.join_objects(X_train_low_level)\\n\\n self.X_valid_high_level = X_valid_high_level\\n self.X_valid_low_level = self.join_objects(X_valid_low_level)\\n print 'finished creating validation dataset'\\n\\n self.y_train = y_train\\n self.y_valid = y_valid\",\n \"def get_training_and_testing_sets(data, Y):\\r\\n data = pd.concat([data, Y], axis=1)\\r\\n x,y=data.shape\\r\\n train_X_sub1=data[0:x//6]\\r\\n dev_X_sub1 = data[x//6:x//6 + x//12]\\r\\n test_X_sub1 = data[x//6 + x//12:x//3]\\r\\n\\r\\n train_X_sub2 = data[x//3:x//3+x//6]\\r\\n dev_X_sub2 = data[x//6 + x//3:x//3 + x//6 + x//12]\\r\\n test_X_sub2 = data[x//3 + x//6 + x//12:2*x//3]\\r\\n\\r\\n train_X_sub3 = data[2*x//3:(2*x//3) +x//6]\\r\\n dev_X_sub3 = data[x//6 + 2*x//3: (2*x//3) + x//6 + x//12]\\r\\n test_X_sub3 = data[2*x//3 + x//6 + x//12:x]\\r\\n\\r\\n train_X=train_X_sub1.append(train_X_sub2,ignore_index = True)\\r\\n train_X =train_X.append(train_X_sub3,ignore_index = True)\\r\\n dev_X= dev_X_sub1.append(dev_X_sub2,ignore_index = True)\\r\\n dev_X = dev_X.append(dev_X_sub3,ignore_index = True)\\r\\n test_X = test_X_sub1.append(test_X_sub2,ignore_index = True)\\r\\n test_X = test_X.append(test_X_sub3,ignore_index = True)\\r\\n\\r\\n\\r\\n train_X = util.shuffle(train_X)\\r\\n train_X = train_X.reset_index(drop=True)\\r\\n\\r\\n dev_X = util.shuffle(dev_X)\\r\\n dev_X = dev_X.reset_index(drop=True)\\r\\n\\r\\n test_X = util.shuffle(test_X)\\r\\n test_X = test_X.reset_index(drop=True)\\r\\n\\r\\n train_X_final=train_X\\r\\n dev_X_final = dev_X\\r\\n test_X_final = test_X\\r\\n x, y = train_X_final.shape\\r\\n train_X = train_X_final.iloc[:, 0:y - 1]\\r\\n train_Y = train_X_final.iloc[:, y - 1]\\r\\n\\r\\n x, y = test_X_final.shape\\r\\n test_X = test_X_final.iloc[:, 0:y - 1]\\r\\n test_Y = test_X_final.iloc[:, y - 1]\\r\\n\\r\\n x, y = dev_X_final.shape\\r\\n dev_X = dev_X_final.iloc[:, 0:y - 1]\\r\\n dev_Y = dev_X_final.iloc[:, y - 1]\\r\\n\\r\\n return train_X, train_Y, dev_X,dev_Y,test_X, test_Y\",\n \"def Train_data():\\n print (\\\"loading train data ...\\\")\\n time_start = time.time()\\n data_root = '/media/keziwen/86AA9651AA963E1D'\\n with h5py.File(join(data_root, './data/train_real2.h5')) as f:\\n data_real = f['train_real'][:]\\n num, nt, ny, nx = data_real.shape\\n data_real = np.transpose(data_real, (0, 1, 3, 2))\\n with h5py.File(join(data_root, './data/train_imag2.h5')) as f:\\n data_imag = f['train_imag'][:]\\n num, nt, ny, nx = data_imag.shape\\n data_imag = np.transpose(data_imag, (0, 1, 3, 2))\\n data = data_real+1j*data_imag\\n num_train = 15000\\n num_validate = 2000\\n train_data = data[0:num_train]\\n validate_data = data[num_train:num_train+num_validate]\\n\\n train_data = np.random.permutation(train_data)\\n\\n time_end = time.time()\\n print ('dataset has been created using {}s'.format(time_end-time_start))\\n return train_data, validate_data\",\n \"def engineer(train, valid):\\n train = dummify_all_categorical(train)\\n valid = dummify_all_categorical(valid)\\n train, valid = utils.common_columns(train, valid) #keep only common features\\n\\n return train, valid\",\n \"def train_and_test(self, data):\\n\\n np.random.shuffle(data)\\n datalist = self.unpack_data(data)\\n\\n logger.info('[*] 75-25 partition of datasets ...')\\n\\n markline1 = math.floor(0.75*(len(datalist['features'])))\\n markline2 = math.floor(0.75*len(datalist['labels']))\\n\\n train_features = datalist['features'][:(markline1)]\\n test_features = datalist['features'][(markline1):]\\n \\n train_labels = datalist['labels'][:(markline2)]\\n test_labels = datalist['labels'][(markline2):]\\n\\n logger.info('[*] Training started with 75% Dataset ...')\\n\\n self.knn_model.fit(train_features, train_labels)\\n\\n logger.info('[*] Testing started with 25% Dataset ...')\\n print('\\\\n/---------------Accuracy----------------/') \\n \\n accuracy = self.knn_model.score(train_features, train_labels)\\n print('Test set accuracy {:.2f} %'.format(accuracy*100))\\n\\n if accuracy < 0.40:\\n logger.warning('[-.-!] Thanks for tryin\\\\' but this machine ain\\\\'t learning.')\\n\\n return True\",\n \"def prepare_data(train_csv, test_csv):\\n\\n train = pd.read_csv(train_csv)\\n test = pd.read_csv(test_csv)\\n train = train[test.shape[0]:]\\n valid = train[0:test.shape[0]]\\n\\n x_train = train.drop(columns=\\\"label\\\") / 255\\n y_train = train.label\\n x_valid = valid.drop(columns=\\\"label\\\") / 255\\n y_valid = valid.label\\n x_test = test.drop(columns=\\\"label\\\") / 255\\n y_test = test.label\\n\\n y_train = tf.keras.utils.to_categorical(y_train)\\n y_valid = tf.keras.utils.to_categorical(y_valid)\\n y_test = tf.keras.utils.to_categorical(y_test)\\n x_train = x_train.values\\n x_valid = x_valid.values\\n x_test = x_test.values\\n\\n return x_train, y_train, x_valid, y_valid, x_test, y_test\",\n \"def __init__(self):\\r\\n \\r\\n #self.max_stocks = 100\\r\\n self.max_stocks = 200\\r\\n \\\"\\\"\\\" cv_factor determines what portion of stocks to put in cross validation set and what portion\\r\\n to leave in training set. cv_factor = 2 means every other stock goes into cross validation\\r\\n set. cv_factor = 3 means every third stock goes into cross validation set \\\"\\\"\\\"\\r\\n self.cv_factor = 2 \\r\\n \\\"\\\"\\\" future_day is how many training days in the future we train for. Setting future_day = 25\\r\\n means we are measuring how the stock does 25 days out \\\"\\\"\\\"\\r\\n self.future_day = 25\\r\\n \\\"\\\"\\\" The train_dates are the dates for training and cross validation\\\"\\\"\\\"\\r\\n self.train_dates = []\\r\\n first_train_date = dateutl.days_since_1900('2001-01-01')\\r\\n num_train_dates = 10\\r\\n train_date_increment = 60\\r\\n self.train_dates.append(first_train_date)\\r\\n for iday in range(1,num_train_dates):\\r\\n last_train_date = self.train_dates[iday-1]\\r\\n self.train_dates.append(last_train_date + train_date_increment)\\r\\n \\\"\\\"\\\"self.train_dates[1] -= 1 \\\"\\\"\\\"\\r\\n \\r\\n \\\"\\\"\\\" test_dates are the dates we are using for testing \\\"\\\"\\\"\\r\\n self.test_dates = []\\r\\n first_test_date = dateutl.days_since_1900('2010-01-01')\\r\\n num_test_dates = 10\\r\\n test_date_increment = 60\\r\\n self.test_dates.append(first_test_date) \\r\\n for iday in range(1,num_test_dates):\\r\\n last_test_date = self.test_dates[iday-1]\\r\\n self.test_dates.append(last_test_date + test_date_increment)\\r\\n \\\"\\\"\\\"self.test_dates[1] -= 1\\r\\n self.test_dates[3] += 1\\r\\n self.test_dates[4] += 3\\r\\n self.test_dates[5] += 4\\r\\n self.test_dates.append(dateutl.days_since_1900('2010-01-01'))\\r\\n self.test_dates.append(dateutl.days_since_1900('2010-03-01'))\\r\\n self.test_dates.append(dateutl.days_since_1900('2010-05-01'))\\r\\n self.test_dates.append(dateutl.days_since_1900('2010-07-01'))\\r\\n self.test_dates.append(dateutl.days_since_1900('2010-09-01'))\\r\\n self.test_dates.append(dateutl.days_since_1900('2010-11-01'))\\\"\\\"\\\"\\r\\n \\\"\\\"\\\"train_history_days and train_increment set how many historical days we use to\\r\\n train and the increment used. Setting train_history_days = 21 and train_increment = 5\\r\\n means we are using the values at days days 5, 10, 15 and 20 days before the reference day\\r\\n as input features \\\"\\\"\\\"\\r\\n self.train_days = 21\\r\\n self.train_increment = 5\\r\\n self.features = ['rsi','tsi','ppo','adx','dip14','dim14','cci', \\\\\\r\\n 'cmo','mfi','natr','roc','stoch','uo']\\r\\n \\\"\\\"\\\" output is just a boolean about calling the output function to write out \\r\\n appropriate X and y matricies. The default is False meaning do not write out\\r\\n matricies \\\"\\\"\\\"\\r\\n self.output = False\",\n \"def data(self):\\n (x_train, y_train), (_, _) = datasets.fashion_mnist.load_data()\\n x_train = x_train.reshape((-1, 28, 28, 1))\\n x_train, y_train = x_train.astype('float16') / 255.0, \\\\\\n tf.keras.utils.to_categorical(y_train.astype('float16'), 10)\\n (x_train, x_eval) = x_train[5000:], x_train[:5000]\\n (y_train, y_eval) = y_train[5000:], y_train[:5000]\\n train_data, eval_data = (x_train, y_train), (x_eval, y_eval)\\n return train_data, eval_data\",\n \"def run(train_file: str, val_file: str):\\n logging.info(f'Training data from: {train_file}')\\n train_df = pd.read_csv(train_file)\\n logging.info(f'Validation data from: {val_file}')\\n val_df = pd.read_csv(val_file)\\n print(train_df.head(3))\\n train_data = pd.DataFrame({\\n 'QueryLength': train_df['Query'].str.len(),\\n 'UnigramCount': train_df['Unigram Count'],\\n 'Hamming': train_df['Hamming'],\\n 'Relevant': train_df['Is Relevant']\\n })\\n val_data = pd.DataFrame({\\n 'QueryLength': val_df['Query'].str.len(),\\n 'UnigramCount': val_df['Unigram Count'],\\n 'Hamming': val_df['Hamming'],\\n 'Relevant': val_df['Is Relevant']\\n })\\n logging.info('Sample prepared data')\\n print(train_data.head(3))\\n logging.info('Summary of traininging data')\\n print(train_data.info())\\n logging.info('Summary of validation data')\\n print(train_data.info())\\n train_ds = to_dataset(train_data)\\n val_ds = to_dataset(val_data)\\n feature_columns = []\\n headers = ['QueryLength', 'UnigramCount', 'Hamming']\\n for header in headers:\\n feature_columns.append(feature_column.numeric_column(header))\\n feature_layer = tf.keras.layers.DenseFeatures(feature_columns)\\n model = tf.keras.Sequential([\\n feature_layer,\\n layers.Dense(128, activation='relu'),\\n layers.Dense(128, activation='relu'),\\n layers.Dropout(.1),\\n layers.Dense(1)\\n ])\\n model.compile(optimizer='adam',\\n loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),\\n metrics=['accuracy'])\\n model.fit(train_ds, validation_data=val_ds, epochs=10)\\n #y_pred = model.predict(train_ds)\\n #cm = confusion_matrix(headers, y_pred)\\n #print(cm)\",\n \"def data_setup(self):\\n # Make sure the dataset is download and put into the data folder\\n training_data = pd.read_csv('./data/train.csv', sep=',', nrows=self.training_dataset_size)\\n testing_data = pd.read_csv('./data/test.csv', sep=',' , nrows=self.training_dataset_size)\\n question_list1 = training_data['question1']\\n question_list2 = training_data['question2']\\n is_duplicate = training_data['is_duplicate']\\n # for will\\n X = []\\n Y = []\\n for i in range(0, 1000):\\n print(\\\"*\\\"*20, i ,\\\"*\\\"*20 )\\n feature = self.call_feature_generator(question_list1[i],question_list2[i], self.feature_code )\\n X.append(feature)\\n Y.append(is_duplicate[i])\\n print(feature)\\n print(is_duplicate[i])\\n print(question_list1[i])\\n print(question_list2[i])\\n\\n # we train classifier\\n\\n classifer = self.call_classifier(X, Y, self.classifier_code)\\n\\n # testing\\n testX = []\\n testY = []\\n\\n for i in range(1001, 1500):\\n print(\\\"-\\\"*20, i ,\\\"-\\\"*20 )\\n feature = self.call_feature_generator(question_list1[i],question_list2[i], self.feature_code )\\n testX.append(feature)\\n testY.append(is_duplicate[i])\\n\\n X= np.array(testX).reshape(-1,1)\\n\\n calculate_y = classifer.predict(X)\\n\\n print(calculate_y)\\n tp = 0.0\\n fp = 0.0\\n fn = 0.0\\n\\n for i in range(0, len(calculate_y)):\\n if calculate_y[i] == testY[i]:\\n print(\\\"Tp : \\\", testX[i], question_list1[i], question_list2[i], is_duplicate[i] )\\n tp += 1.0\\n else:\\n if testY[i] == 1 and calculate_y[i] == 0:\\n print(\\\"Fn : \\\", testX[i] , question_list1[i], question_list2[i], is_duplicate[i] )\\n fn += 1.0\\n else:\\n print(\\\"Fp : \\\", testX[i], question_list1[i], question_list2[i], is_duplicate[i])\\n fp += 1.0\\n\\n print(\\\"Tp: \\\", tp, \\\" Fp: \\\", fp, \\\" Fn: \\\", fn)\\n print(\\\"Accuracy \\\", tp/( tp+fn), \\\"%\\\")\\n\\n result = precision_recall_fscore_support(testY, calculate_y)\\n print (\\\"Precision: Class 1 - \\\", result[0][0], \\\"% and Class 0 - \\\", result[0][1], \\\"%\\\")\\n print (\\\"Recall: Class 1 - \\\", result[1][0], \\\"% and Class 0 - \\\", result[1][1], \\\"%\\\")\\n print (\\\"F-Score: Class 1 - \\\", result[2][0], \\\"% and Class 0 - \\\", result[2][1], \\\"%\\\")\",\n \"def load_data(train=True):\\n#test set\\n start = time.clock()\\n\\n if train==False:\\n dp = '/home/thomas/Desktop/OttoChallenge/test.csv'\\n df = pd.read_csv(dp)\\n X = df.values.astype(np.float32)[:,1:]\\n return X\\n#train set\\n else:\\n dp = '/home/thomas/Desktop/OttoChallenge/train.csv'\\n df = pd.read_csv(dp)\\n X = df[df.columns[:-1]].values.astype(np.float32)[:,1:]\\n y = df.target\\n y =y.apply(lambda X: int(X[-1])).values\\n y = y.astype(np.int32)\\n X, y = shuffle(X, y)\\n #print(X.shape,y.shape)\\n end = time.clock()\\n print(end-start)\\n\\n return X,y\",\n \"def generate_train_test(self):\\n x, y = self.read_data()\\n x_train, y_train, x_test, y_test = self.sample_data(x, y)\\n self.train = (x_train, y_train)\\n self.test = (x_test, y_test)\",\n \"def train_test_dataset(self, train_rate=0.8):\\n point_date = int(len(self.y) * train_rate)\\n y_to_train = self.y[:point_date]\\n y_to_val = self.y[point_date:]\\n predict_date = len(self.y) - len(y_to_train) # the number of data points for the test set\\n date_val = self.y.index[point_date]\\n return y_to_train, y_to_val, predict_date, date_val\",\n \"def test_valid_dataset():\\n train = ((\\\"Lorem ipsum dolor sit amet\\\", 3, 4.5),\\n (\\\"Sed ut perspiciatis unde\\\", 5, 5.5))\\n val = ((\\\"ipsum quia dolor sit\\\", 10, 3.5),)\\n test = ((\\\"Ut enim ad minima veniam\\\", 100, 35),)\\n\\n t = TabularDataset(train, val, test)\\n\\n assert len(t) == 4\\n assert len(t.train) == 2\\n assert len(t.val) == 1\\n assert len(t.test) == 1\\n\\n def check(d, t):\\n for i, tu in enumerate(d):\\n v0, v1, v2 = tu\\n assert t[i][0] == v0\\n assert t[i][1] == v1\\n assert t[i][2] == v2\\n\\n check(train, t.train)\\n check(val, t.val)\\n check(test, t.test)\",\n \"def is_valid(self, dataset):\\n pass\",\n \"def train(self, training_data):\\n pass\",\n \"def get_training_data(self):\\n\\n # this actually never was a set\\n # src_set = self.target['src'].values\\n # dst_set = self.target['dst'].values\\n\\n # train_negative = self.get_negative_edges(src_set, dst_set, self.train_ind.shape[0]) # * self.K)\\n # test_negative = self.get_negative_edges(src_set, dst_set, self.test_ind.shape[0])\\n\\n train_positive = self.target.iloc[self.train_edge_ind].values\\n test_positive = self.target.iloc[self.test_edge_ind].values\\n\\n # # print(train_positive.shape, train_negative.shape, test_positive.shape, test_negative.shape)\\n # print(f\\\"Working with {train_positive.shape[0]} positive and {train_negative.shape[0]} negative samples in the train set, {test_positive.shape[0]} and {test_negative.shape[0]} - in test set\\\")\\n\\n X_train = train_positive\\n X_test = test_positive\\n\\n y_train = np.ones((self.train_edge_ind.shape[0],))\\n y_test = np.ones((self.test_edge_ind.shape[0],))\\n\\n # X_train = np.vstack([\\n # train_positive,\\n # train_negative\\n # ])\\n\\n # X_test = np.vstack([\\n # test_positive,\\n # test_negative\\n # ])\\n\\n # y_train = np.concatenate([np.ones((self.train_ind.shape[0],)), np.zeros((self.train_ind.shape[0]),)]) # self.train_ind.shape[0]) * self.K\\n # y_test = np.concatenate([np.ones((self.test_ind.shape[0],)), np.zeros((self.test_ind.shape[0],))])\\n\\n assert X_train.shape[0] == y_train.shape[0]\\n assert X_test.shape[0] == y_test.shape[0]\\n\\n def shuffle(X, y):\\n ind_shuffle = np.arange(0, X.shape[0])\\n np.random.shuffle(ind_shuffle)\\n return X[ind_shuffle], y[ind_shuffle]\\n\\n self.X_train, self.y_train = shuffle(X_train, y_train)\\n self.X_test, self.y_test = shuffle(X_test, y_test)\\n\\n print(f\\\"Splitting into {self.X_train.shape[0]} train and {self.X_test.shape[0]} test samples\\\")\\n\\n # return X_train, X_test, y_train, y_test\",\n \"def data_set_maker():\\n\\n # crate a folder in your code directory and name it: \\\"files\\\". put the .npy files iside that folder\\n\\n x_all = np.load(path + '/files/tinyX.npy', 'r') # reads the input file\\n y_all = np.load(path + '/files/tinyY.npy', 'r') # reads the input file\\n\\n # split the data into 10% validation-set and 90% training set\\n raw_train, raw_valid, y_train, y_valid = train_test_split(x_all, y_all, test_size=0.2, random_state=43)\\n return raw_train, raw_valid, y_train, y_valid\",\n \"def __test_and_train(self):\\n f = open(\\\"all_data_info.csv\\\")\\n reader = csv.DictReader(f, delimiter=\\\",\\\")\\n data = []\\n for line in reader:\\n if line['artist_group'] == \\\"train_and_test\\\" and line[\\\"in_train\\\"] == \\\"False\\\":\\n # the img's artist is in training set\\n # but the img is in test set only\\n data.append((line['artist'], line['new_filename']))\\n\\n return data\",\n \"def run():\\r\\n \\r\\n LABEL = data.LabelField(use_vocab=True)\\r\\n TEXT = data.Field(sequential=True, tokenize=lambda x:x.split(), lower=True, fix_length=config.MAX_LENGTH)\\r\\n\\r\\n### 1/5\\r\\n dataset = data.TabularDataset(path=config.TRAIN_DATASET_FNAME, \\r\\n format='csv', \\r\\n fields=[('text', TEXT),('label', LABEL)], \\r\\n skip_header=True)\\r\\n # split the dataset, 8:2\\r\\n train_dataset, valid_dataset = dataset.split(split_ratio=[0.8,0.2], random_state=random.getstate())\\r\\n \\r\\n test_data = data.TabularDataset(path=config.TEST_DATASET_FNAME,\\r\\n format='csv', \\r\\n fields=[('text', TEXT),('label', LABEL)], \\r\\n skip_header=True)\\r\\n \\r\\n### 2\\r\\n# train_dataset = data.TabularDataset(path=config.TRAIN_DATASET_FNAME, \\r\\n# format='csv', \\r\\n# fields=[('text', TEXT),('label', LABEL)], \\r\\n# skip_header=True) \\r\\n# valid_dataset = data.TabularDataset(path=config.VAL_DATASET_FNAME, \\r\\n# format='csv', \\r\\n# fields=[('text', TEXT),('label', LABEL)], \\r\\n# skip_header=True) \\r\\n \\r\\n# test_data = data.TabularDataset(path=config.TEST_DATASET_FNAME,\\r\\n# format='csv', \\r\\n# fields=[('text', TEXT),('label', LABEL)], \\r\\n# skip_header=True)\\r\\n \\r\\n### 3/4\\r\\n# train_dataset = data.TabularDataset(path=config.TRAIN_DATASET_FNAME, \\r\\n# format='csv', \\r\\n# fields=[('text', TEXT),('label', LABEL)], \\r\\n# skip_header=True) \\r\\n \\r\\n# dataset = data.TabularDataset(path=config.TEST_DATASET_FNAME, \\r\\n# format='csv', \\r\\n# fields=[('text', TEXT),('label', LABEL)], \\r\\n# skip_header=True)\\r\\n# # split the dataset, 5:5\\r\\n# valid_dataset, test_data = dataset.split(split_ratio=[0.5,0.5], random_state=random.getstate())\\r\\n\\r\\n### 5\\r\\n\\r\\n\\r\\n\\r\\n # load embeddings\\r\\n vectors_data = load_vectors(config.EMBEDDING_FNAME)\\r\\n\\r\\n TEXT.build_vocab(train_dataset, vectors=vectors_data)\\r\\n LABEL.build_vocab(train_dataset)\\r\\n print ('vector size:',TEXT.vocab.vectors.size())\\r\\n embedding_pretrained_matrix = TEXT.vocab.vectors\\r\\n \\r\\n # create torch device\\r\\n print(\\\"To device...\\\")\\r\\n USE_CUDA = torch.cuda.is_available()\\r\\n device = torch.device(\\\"cuda\\\" if USE_CUDA else \\\"cpu\\\")\\r\\n\\r\\n train_it, valid_it = data.BucketIterator.splits((train_dataset, valid_dataset),\\r\\n batch_sizes=(config.TRAIN_BATCH_SIZE,config.VAL_BATCH_SIZE), \\r\\n device=device, \\r\\n sort_key=lambda x: len(x.text),\\r\\n sort_within_batch=False,\\r\\n shuffle=True,\\r\\n repeat=False)\\r\\n test_it = data.BucketIterator(test_data, \\r\\n batch_size=config.TEST_BATCH_SIZE, \\r\\n sort_key=lambda x: len(x.text), \\r\\n shuffle=False,\\r\\n device=device)\\r\\n \\r\\n \\r\\n # fetch model\\r\\n vocab_size = len(TEXT.vocab) # TEXT.vocab.vectors.size()\\r\\n# pretrained_vec = TEXT.vocab.vectors\\r\\n \\r\\n # selecte network \\r\\n x = import_module('networks.'+config.NETWORK)\\r\\n model = x.Model(vocab_size,embedding_pretrained=embedding_pretrained_matrix)\\r\\n \\r\\n # send model to device\\r\\n model.to(device)\\r\\n\\r\\n # initialize Adam optimizer\\r\\n optimizer = torch.optim.Adam(model.parameters(), lr=config.LEARNING_RATE)\\r\\n\\r\\n # if you have multiple GPUs, model model to DataParallel to use multiple GPUs\\r\\n if torch.cuda.device_count() > 1:\\r\\n model = nn.DataParallel(model)\\r\\n \\r\\n params_list = []\\r\\n # train and validate for all epochs\\r\\n for epoch in range(config.EPOCHS):\\r\\n epoch_start_time = time.time()\\r\\n\\r\\n ###----Train--------\\r\\n train_outputs, train_labels, train_loss = engine.train_fn(train_it, model, optimizer, device)\\r\\n train_outputs = torch.Tensor(train_outputs)\\r\\n _, train_predicted = torch.max(train_outputs, dim=1)\\r\\n train_parameters_dict = metrics_func.performance_evaluation_func(train_predicted,train_labels,epoch=str(epoch))\\r\\n # save train paremeters\\r\\n params_list.append(train_parameters_dict)\\r\\n train_f1 = train_parameters_dict['f1_score_macro']\\r\\n train_prec = train_parameters_dict['precision_macro']\\r\\n train_recall = train_parameters_dict['precision_macro']\\r\\n print('\\\\n')\\r\\n print(f\\\" Train Epoch: {epoch}, F1 = {train_f1},precision = {train_prec},recall = {train_recall}\\\")\\r\\n ###------------\\r\\n \\r\\n # validate\\r\\n val_outputs, val_labels, valid_loss = engine.evaluate_fn(valid_it, model, device)\\r\\n val_outputs = torch.Tensor(val_outputs)\\r\\n _, val_predicted = torch.max(val_outputs, dim=1) \\r\\n # calculate evaluation paremeters\\r\\n val_parameters_dict = metrics_func.performance_evaluation_func(val_predicted, val_labels, epoch=str(epoch),flag='val')\\r\\n # save evaluation paremeters\\r\\n params_list.append(val_parameters_dict)\\r\\n \\r\\n val_f1 = val_parameters_dict['f1_score_macro']\\r\\n val_prec = val_parameters_dict['precision_macro']\\r\\n val_recall = val_parameters_dict['recall_macro']\\r\\n print(f\\\"Val Epoch: {epoch},F1 = {val_f1},precision = {val_prec}, recall = {val_recall}\\\")\\r\\n \\r\\n ###-------Test-----------------------\\r\\n test_outputs, test_labels, test_loss = engine.evaluate_fn(test_it, model, device)\\r\\n test_outputs = torch.Tensor(test_outputs)\\r\\n _, test_predicted = torch.max(test_outputs, dim=1) \\r\\n # calculate evaluation paremeters\\r\\n test_parameters_dict = metrics_func.performance_evaluation_func(test_predicted, test_labels, epoch=str(epoch),flag='test')\\r\\n # save evaluation paremeters\\r\\n params_list.append(test_parameters_dict)\\r\\n \\r\\n test_f1 = test_parameters_dict['f1_score_macro']\\r\\n test_prec = test_parameters_dict['precision_macro']\\r\\n test_recall = test_parameters_dict['recall_macro']\\r\\n print(f\\\"test Epoch: {epoch},F1 = {test_f1},precision = {test_prec}, recall = {test_recall}\\\")\\r\\n \\r\\n lr_scheduler = LRScheduler(optimizer)\\r\\n lr_scheduler(valid_loss)\\r\\n \\r\\n \\r\\n # simple early stopping\\r\\n# val_f1 = float(val_f1)\\r\\n #f1 = (float(train_f1) + float(val_f1)) / 2\\r\\n val_loss = float(valid_loss)\\r\\n early_stopping(val_loss, model)\\r\\n if early_stopping.early_stop:\\r\\n print(\\\"Early stopping\\\")\\r\\n break\\r\\n # 获得 early stopping 时的模型参数\\r\\n# model.load_state_dict(torch.load('checkpoint.pt'))\\r\\n\\r\\n# save_model_func(model, epoch, path='outputs')\\r\\n \\r\\n metrics_func.save_parameters_txt(params_list)\",\n \"def train(self, data):\\n pass\",\n \"def load_train_test_transactions(train_size=0.7):\\n X, y = features_target_split()\\n X_train, X_test, y_train, y_test = train_test_split(X,y,train_size=train_size, random_state=7)\\n print('\\\\nTraining and testing data creation successful\\\\n')\\n return X_train, X_test, y_train,y_test\",\n \"def load_data():\\n print(\\\"PARSING TRAIN\\\")\\n ys_train, x_train, ids_train = load_pickle_data(\\\"ys_train\\\"), load_pickle_data(\\\"x_train\\\"), load_pickle_data(\\n \\\"ids_train\\\")\\n if ys_train is None or x_train is None or ids_train is None:\\n ys_train, x_train, ids_train = load_csv_data(\\\"{}/train.csv\\\".format(DATA_DIR))\\n dump_pickle_data(ys_train, \\\"ys_train\\\")\\n dump_pickle_data(x_train, \\\"x_train\\\")\\n dump_pickle_data(ids_train, \\\"ids_train\\\")\\n\\n print(\\\"PARSING TEST\\\")\\n x_test, ids_test = load_pickle_data(\\\"x_test\\\"), load_pickle_data(\\\"ids_test\\\")\\n if x_test is None or ids_test is None:\\n _, x_test, ids_test = load_csv_data(\\\"{}/test.csv\\\".format(DATA_DIR))\\n dump_pickle_data(x_test, \\\"x_test\\\")\\n dump_pickle_data(ids_test, \\\"ids_test\\\")\\n\\n return ys_train, x_train, ids_train, x_test, ids_test\",\n \"def prepare_train_validation(self) -> Tuple:\\n Xt, Xv, Yt, Yv = self.dataset.train_test_split_representations()\\n\\n Xt = self.dataset.prepare_input_samples(Xt)\\n Yt = self.dataset.prepare_output_samples(Yt)\\n traindataset = tf.data.Dataset.from_tensor_slices((Xt, Yt))\\n traindataset = traindataset.batch(\\n self.batch_size,\\n num_parallel_calls=tf.data.experimental.AUTOTUNE\\n )\\n\\n Xv = self.dataset.prepare_input_samples(Xv)\\n Yv = self.dataset.prepare_output_samples(Yv)\\n validdataset = tf.data.Dataset.from_tensor_slices((Xv, Yv))\\n validdataset = validdataset.batch(\\n self.batch_size,\\n num_parallel_calls=tf.data.experimental.AUTOTUNE\\n )\\n\\n return traindataset, validdataset\",\n \"def test(self, data):\\n num = len(data['y'])\\n self.reshape_mat(data)\\n\\n acc = 0\\n order = np.random.permutation(num)\\n for i in range(0, num):\\n x, y = data['x'][order[i]], data['y'][order[i]]\\n yhat = self.predict(self.w, x)\\n if y == yhat:\\n acc += 1\\n accuracy = acc / float(num)\\n print 'Test: acc = %f' % (accuracy * 100)\",\n \"def get_training_and_validation_df():\\n df = get_cleaned_processed_df()\\n val_df = pd.DataFrame.from_csv(VALIDATION_DATA_PATH)\\n y_train = df.pop(\\\"label\\\")\\n y_val = val_df.pop(\\\"label\\\")\\n\\n df, val_df = complete_columns(df, val_df)\\n df.fillna(0, inplace=True)\\n val_df.fillna(0, inplace=True)\\n df = fill_text_features(df)\\n val_df = fill_text_features(val_df)\\n\\n df = drop_text_features(df)\\n val_df = drop_text_features(val_df)\\n return df.values, y_train, val_df.values, y_val\",\n \"def main():\\n # Read in trainingSet and testSet as a DataFrame\\n trainingOriginal = pd.read_csv(\\n filepath_or_buffer=\\\"~/Desktop/KNN Implementation/adult.train.5fold.csv\\\")\\n testOriginal = pd.read_csv(filepath_or_buffer=\\\"~/Desktop/KNN Implementation/adult.test.csv\\\")\\n\\n # Select only the numeric data\\n training = pd.DataFrame(trainingOriginal.select_dtypes(['number']))\\n training = pd.concat([training.reset_index(drop=True),\\n trainingOriginal['earns'].reset_index(drop=True)], axis=1)\\n\\n # Select only the numeric data\\n test = pd.DataFrame(testOriginal.select_dtypes(['number']))\\n test = pd.concat([test.reset_index(drop=True),\\n testOriginal['earns'].reset_index(drop=True)], axis=1)\\n\\n # Normalize the columns for training and test\\n # print training['age'].min()\\n # print training['age'].max()\\n # print training.head()\\n\\n # Run max-min normalization on numerical columns for testing and training data\\n for i in range(6):\\n training.iloc[:, i] = (training.iloc[:, i]- training.iloc[:, i].min())/(training.iloc[:, i].max() - training.iloc[:, i].min())\\n test.iloc[:, i] = (test.iloc[:, i]- test.iloc[:, i].min())/(test.iloc[:, i].max() - test.iloc[:, i].min())\\n\\n # Convert the 'earns' column to boolean as follows\\n training['earns'] = training['earns'] == '>50K'\\n test['earns'] = test['earns'] == ' >50K'\\n\\n # Group the training set by the fold attribute as given by the dataset\\n trainingForFinal = training\\n training = training.groupby('fold')\\n\\n # Since we want to consider odd k-values from 1 to 39, construct a list with these values\\n kList = []\\n for i in range(40):\\n if i % 2 == 1:\\n kList.append(i)\\n\\n # Empty dictionary to hold performance of each k-values and its accuracy\\n performance = {}\\n\\n # Compute the performance for each k-value\\n for k in kList:\\n performance = crossValidation(training, k, performance)\\n\\n # Sort the performance dictionary by its accuracy (value)\\n performance = sorted(performance.items(), key=operator.itemgetter(1), reverse=True)\\n\\n # Open file to write results\\n file = open('grid.results.txt', 'w')\\n # Write the results to file\\n file.write(\\\"K | Accuracy\\\\n\\\")\\n for item in performance:\\n if item[0] < 10:\\n file.write(str(item[0]) + ' | ' + str(item[1]) + '\\\\n')\\n else:\\n file.write(str(item[0]) + ' | ' + str(item[1]) + '\\\\n')\\n # Close file\\n file.close()\\n\\n # The best K is the one at the top of the list after the sorting\\n bestK = performance[0][0]\\n\\n print 'Running Test Set with K = ' + str(bestK)\\n\\n applyModel(test,trainingForFinal,bestK)\",\n \"def train_models(self,member,train_dates,valid_dates):\\n train_data, train_label = self.dldataeng.extract_training_data(member,\\n train_dates,self.model_type)\\n \\n #valid_data, valid_label = self.dldataeng.extract_validation_data(member,valid_dates,self.model_type)\\n valid_data, valid_label = [],[]\\n \\n if self.model_type == 'CNN':\\n onehot_encoder = OneHotEncoder(sparse=False,categories='auto')\\n encoded_label = onehot_encoder.fit_transform(train_label.reshape(-1, 1))\\n self.train_CNN(member,train_data,encoded_label,valid_data,valid_label)\\n\\n elif 'UNET' in self.model_type:\\n #train_label[train_label >= 50.] = 50. \\n #log_train_label = np.log((train_label+1.0))\\n self.train_UNET(member,train_data,train_label,valid_data,valid_label)\\n \\n return\",\n \"def buildAndTrain(trainingData):\\n\\tname = trainingData.drop(['count', 'casual', 'registered'], axis=1).columns\\n\\ttarget = trainingData['count'].values\\n\\tfeature = trainingData.drop(['count', 'casual', 'registered'], axis=1).values\\n\\t# feature scaling\\n\\tfeature_scaled = preprocessing.scale(feature)\\n\\t# 0.5 cross validate\\n\\tcv = cross_validation.ShuffleSplit(len(feature_scaled), n_iter=5, test_size=0.2, random_state=0)\\n\\t# build model, then training and get accuracy of it\\n\\tprint('\\\\n---------岭回归结果--------\\\\n')\\n\\tfor train, test in cv:\\n\\t\\tregLR = linear_model.Ridge().fit(feature_scaled[train], target[train])\\n\\t\\tprint('train score:{0:.3f}, test score:{1:.3f}\\\\n'.format(\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tregLR.score(feature_scaled[train], target[train]),\\n\\t\\t regLR.score(feature_scaled[test], target[test])))\\n\\tprint('\\\\n---------svm结果--------\\\\n')\\n\\tfor train, test in cv:\\n\\t\\tregSvm = svm.SVR().fit(feature_scaled[train], target[train])\\n\\t\\tprint('train score:{0:.3f}, test score:{1:.3f}\\\\n'.format(\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tregSvm.score(feature_scaled[train], target[train]),\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tregSvm.score(feature_scaled[test], target[test])))\\n\\tprint('\\\\n---------随机森林结果--------\\\\n')\\n\\tfor train, test in cv:\\n\\t\\tregRF = RandomForestRegressor(n_estimators=100).fit(feature_scaled[train], target[train])\\n\\t\\tprint('train score:{0:.3f}, test score:{1:.3f}\\\\n'.format(\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tregRF.score(feature_scaled[train], target[train]),\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tregRF.score(feature_scaled[test], target[test])))\\n\\t# reduce some low correction feature\\n\\tfeatureReduced = trainingData.drop(['count', 'casual', 'registered', 'holiday', 'workingday', 'day'], axis=1).values\\n\\tfeatureReduced_scaled = preprocessing.scale(featureReduced)\\n\\tprint('\\\\n---------减少特征维度以避免过拟合后的随机森林结果--------\\\\n')\\n\\tfor train, test in cv:\\n\\t\\tregRFImpr = RandomForestRegressor(n_estimators=100).fit(featureReduced_scaled[train], target[train])\\n\\t\\tprint('train score:{0:.3f}, test score:{1:.3f}\\\\n'.format(\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tregRFImpr.score(featureReduced_scaled[train], target[train]),\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tregRFImpr.score(featureReduced_scaled[test], target[test])))\\n\\t# use grid search algorithm to improve random forest regression\\n\\tX_train, X_test, y_train, y_test = cross_validation.train_test_split(\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tfeature_scaled, target, test_size=0.2, random_state=0)\\n\\ttuned_parameters = [{'n_estimators': [10,100,500], 'max_depth': [2,3,4,5,6,7,8,9,10]}]\\n\\tscores = ['r2']\\n\\n\\tfor score in scores:\\n\\t\\tprint(score)\\n\\t\\tclf = GridSearchCV(RandomForestRegressor(), tuned_parameters, cv=5, scoring=score)\\n\\t\\tclf.fit(X_train, y_train)\\n\\t\\tprint(clf.best_estimator_)\\n\\t\\tprint('each parameter combination is ')\\n\\t\\tfor params, mean_score, scores in clf.grid_scores_:\\n\\t\\t\\tprint('{0:.3f} (+/-{1:.03f}) for {2}'.format(mean_score, scores.std()/2, params))\\n\\n\\tprint('--------最优参数下的随机森林结果--------')\\n\\tfor train, test in cv:\\n\\t\\tregRFBest = RandomForestRegressor(n_estimators=100, max_depth=10).fit(feature_scaled[train], target[train])\\n\\t\\tprint('train score:{0:.3f}, test score:{1:.3f}\\\\n'.format(\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tregRFBest.score(feature_scaled[train], target[train]),\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tregRFBest.score(feature_scaled[test], target[test])))\\n\\treturn regRFBest, feature_scaled, target\",\n \"def test_training(self, epochs, learning_rate):\\n # Make sure the data exists\\n make_data = MakeDataset(generated_images_per_image=1)\\n make_data.make_dataset()\\n\\n # Run the entire training procedure\\n trained_model_filepath = \\\"models/trained_model.pth\\\"\\n training_statistics_filepath = \\\"data/processed/\\\"\\n training_figures_filepath = \\\"reports/figures/\\\"\\n\\n dict = train_model(\\n trained_model_filepath,\\n training_statistics_filepath,\\n training_figures_filepath,\\n epochs=epochs,\\n learning_rate=learning_rate,\\n )\\n\\n # Check that the losses and accuracies have the right length\\n assert len(dict[\\\"train_losses\\\"]) == epochs\\n assert len(dict[\\\"train_accuracies\\\"]) == epochs\\n assert len(dict[\\\"val_losses\\\"]) == epochs\\n assert len(dict[\\\"val_accuracies\\\"]) == epochs\\n\\n # Test that a model has been saved and can be loaded\\n assert os.path.isfile(trained_model_filepath)\\n state_dict = torch.load(trained_model_filepath)\\n\\n # Check that the state dictionary was loaded\\n assert hasattr(state_dict, \\\"values\\\")\\n\\n # Test that the learning curves have been saved\\n assert os.path.isfile(\\n os.path.join(training_figures_filepath, \\\"Training_Loss.pdf\\\")\\n )\\n assert os.path.isfile(\\n os.path.join(training_figures_filepath, \\\"Training_Accuracy.pdf\\\")\\n )\\n\\n # Test that losses and accuracies have been saved and can be loaded\\n dict_path = os.path.join(training_statistics_filepath, \\\"train_val_dict.pickle\\\")\\n assert os.path.isfile(dict_path)\\n with open(dict_path, \\\"rb\\\") as f:\\n dict_load = pickle.load(f)\\n\\n # Check that the dictionary read in has the same values as the training output\\n assert dict[\\\"train_losses\\\"] == dict_load[\\\"train_losses\\\"]\\n assert dict[\\\"train_accuracies\\\"] == dict_load[\\\"train_accuracies\\\"]\\n assert dict[\\\"val_losses\\\"] == dict_load[\\\"val_losses\\\"]\\n assert dict[\\\"val_accuracies\\\"] == dict_load[\\\"val_accuracies\\\"]\",\n \"def get_valid_data(x_train, y_train, valid_ratio=0.1):\\n\\n assert x_train.shape[0] == y_train.shape[0]\\n\\n valid_shape = np.int(np.round(x_train.shape[0]*valid_ratio))\\n x_valid = x_train[-valid_shape:]\\n y_valid = y_train[-valid_shape:]\\n\\n x_train = x_train[:-valid_shape]\\n y_train = y_train[:-valid_shape]\\n\\n return (x_train, y_train), (x_valid, y_valid)\",\n \"def prepare_data(self):\\n # Set up the path\\n self.path_target_train = os.path.join(self.data_dir, self.train_path_file_target + \\\".pkl\\\")\\n self.path_target_test = os.path.join(self.data_dir, self.test_path_file_target + \\\".pkl\\\")\\n\\n if not os.path.exists(self.path_target_train) or not os.path.exists(self.path_target_test):\\n # Create vocabularies of the appropriate sizes.\\n self.create_vocabulary(self.train_path_file)\\n\\n # Create token ids for the training data.\\n input_train_path = self.train_path_file\\n target_train_path = self.train_path_file_target\\n train_input, train_input_length, train_labels = self.data_to_token_ids(input_train_path, target_train_path)\\n\\n # Create token ids for the validation data.\\n input_test_path = self.test_path_file\\n target_test_path = self.test_path_file_target\\n test_input, test_input_length, _ = self.data_to_token_ids(input_test_path, target_test_path, train=False)\\n\\n # Collect data into a list\\n training_data = [train_input, train_input_length, train_labels]\\n test_data = [test_input, test_input_length]\\n\\n # Save all the data\\n with open(self.path_target_train, 'wb') as f:\\n pickle.dump(training_data,f)\\n with open(self.path_target_test, 'wb') as f:\\n pickle.dump(test_data, f)\\n else:\\n # Load data\\n with open(self.path_target_train, 'rb') as f:\\n training_data = pickle.load(f)\\n with open(self.path_target_test, 'rb') as f:\\n test_data = pickle.load(f)\\n\\n # Initialize vocabulary\\n self.initialize_vocabulary()\\n\\n # Convert list into a numpy array - train data\\n train_input = pd.DataFrame(training_data[0]).fillna(value=0).astype(int).values\\n train_length_input = np.array(training_data[1], dtype=int)\\n train_labels = np.array(training_data[2], dtype=int)\\n\\n # Convert list into a numpy array - test data\\n test_input = pd.DataFrame(test_data[0]).fillna(value=0).astype(int).values\\n test_length_input = pd.DataFrame(test_data[1]).fillna(value=0).astype(int).values\\n\\n # Printing maximum length\\n print(\\\"Shape of the input training matrix {}\\\".format(str(train_input.shape)))\\n print(\\\"Shape of the input test matrix {}\\\".format(str(test_input.shape)))\\n\\n # Copy the files\\n self.copy_files()\\n\\n # Return output\\n return train_input, train_length_input, train_labels, test_input, test_length_input\",\n \"def test_train_model_attributes_good():\\n\\tdf = pd.read_csv(\\\"test/sample_features.csv\\\")\\n\\n\\ty_train = df['price']\\n\\tX_train = df.loc[:, df.columns != 'price']\\n\\n\\tparams = {'n_estimators': 5, 'random_state': 2}\\n\\trf_test = train(X_train, y_train, params)\\n\\n\\t# test model attributes\\n\\tassert rf_test.get_params()['n_estimators'] == 5\\n\\tassert rf_test.get_params()['random_state'] == 2\",\n \"def test_data_split_good():\\n\\tdf_true = pd.read_csv(\\\"test/sample_data.csv\\\")\\n\\tdf_test = df_true.copy()\\n\\ttarget = df_true['price']\\n\\tfeatures = df_true.loc[:, df_true.columns != 'price']\\n\\n\\tX_train_true, X_test_true, y_train_true, y_test_true = train_test_split(features, target, \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\ttest_size=0.4, random_state=0)\\n\\n\\tsplit_data = {'test_size': 0.4, 'random_state': 0}\\n\\tX_train_test, X_test_test, y_train_test, y_test_test = data_split(df_test, 'price', split_data)\\n\\n\\tassert X_train_true.equals(X_train_test)\\n\\tassert y_train_true.equals(y_train_test)\\n\\tassert X_test_true.equals(X_test_test)\\n\\tassert y_test_true.equals(y_test_test)\",\n \"def load_data(trainfile, testfile):\\n raw_train = pd.read_csv(trainfile, header=None)\\n raw_test = pd.read_csv(testfile, header=None)\\n train = raw_train.values\\n test = raw_test.values\\n train_features = train[0::, 1::]\\n train_label = train[::, 0]\\n test_features = test[0::, 1::]\\n test_label = test[::, 0]\\n train, cv , train_label, cv_label = train_test_split(train_features,train_label, test_size=0.33, random_state=42)\\n return train, train_label, \\\\\\n cv, cv_label, \\\\\\n test_features, test_label\",\n \"def test(self, dataset):\\n test_accuracy = 0\\n test_loss = 0\\n num_examples_tested = 0\\n # Put model into evaluation mode\\n self.model.eval()\\n for num, batch in enumerate(dataset.loader):\\n xs, ys = batch\\n batch_size = len(xs)\\n num_examples_tested += batch_size\\n iloss, iaccuracy = self.model(xs, ys)\\n test_loss += iloss.cpu().data.numpy().item() * batch_size\\n test_accuracy += iaccuracy.cpu().data.numpy().item() * batch_size\\n test_accuracy = test_accuracy / num_examples_tested\\n test_loss = test_loss / num_examples_tested\\n # Return accuracy and loss for this model on the test set\\n return test_accuracy, test_loss\",\n \"def data():\\n\\n run_type = 'standardised'\\n sr = 48000\\n train_perc = 0.9\\n\\n if sr == 48000:\\n time_dimension = 282\\n if sr == 44100:\\n time_dimension = 259\\n\\n x_train, y_train, x_test, y_test = essential.compile_dataset(run_type, sr)\\n\\n # reshape for CNN input\\n x_train = np.array([x.reshape((128, time_dimension, 1)) for x in x_train])\\n x_test = np.array([x.reshape((128, time_dimension, 1)) for x in x_test])\\n\\n # encoded \\n encoder = LabelEncoder()\\n encoder.fit(y_train)\\n encoder.fit(y_test)\\n y_train = encoder.transform(y_train)\\n y_test = encoder.transform(y_test)\\n\\n return x_train, y_train, x_test, y_test\",\n \"def learn(self, Xtrain, ytrain):\",\n \"def existing_data(self):\\n # Set the directory and file name\\n data_summary_dir = op.join('../logs', self.name, 'data_summary')\\n file_name = 'Train_Test_Summary_generative.csv'\\n\\n # Read the csv and obtain the train data list\\n df = pd.read_csv(op.join(data_summary_dir, file_name))\\n train_data = df['Train Data'].dropna().values.tolist()\\n test_data = df['Test Data'].dropna().values.tolist()\\n\\n train_data_list, test_data_list = [], []\\n for single_train in train_data:\\n data_name = single_train.split('_')[0]\\n if data_name == 'LTRC':\\n series = single_train.split('_')[3] + '_' + single_train.split('_')[4]\\n else:\\n series = single_train.split('_')[3] + '_' + single_train.split('_')[4] + '_' + single_train.split('_')[5]\\n full_data_name = single_train.split('_')[0] + '_' + single_train.split('_')[1] + '_' + single_train.split('_')[2] + '_' + series\\n train_data_list.append(full_data_name)\\n\\n for single_test in test_data:\\n data_name = single_test.split('_')[0]\\n if data_name == 'LTRC':\\n series = single_test.split('_')[3] + '_' + single_test.split('_')[4]\\n else:\\n series = single_test.split('_')[3] + '_' + single_test.split('_')[4] + '_' + single_test.split('_')[5]\\n full_data_name = single_test.split('_')[0] + '_' + single_test.split('_')[1] + '_' + single_test.split('_')[2] + '_' + series\\n test_data_list.append(full_data_name)\\n\\n # Obtain the label map and CT list and file names\\n label_map_list = glob(op.join(self.save_root_dir, 'source_data_2', '*'))\\n ct_list = glob(op.join(self.save_root_dir, 'target_data_2', '*'))\\n\\n label_map_files = [single_file.split('/')[-1] for single_file in label_map_list]\\n ct_files = [single_file.split('/')[-1] for single_file in ct_list]\\n label_map_files.sort(), ct_files.sort()\\n\\n # Initialize empty list\\n existing_train_lm, existing_train_ct = [], []\\n existing_test_lm, existing_test_ct = [], []\\n\\n for single_lm, single_ct in zip(label_map_files, ct_files):\\n\\n ct_data_name = single_ct.split('_')[0] + '_' + single_ct.split('_')[1] + '_' + single_ct.split('_')[2]\\n lm_data_name = single_lm.split('_')[0] + '_' + single_lm.split('_')[1] + '_' + single_lm.split('_')[2]\\n\\n assert ct_data_name == lm_data_name, 'Data is not the same.'\\n\\n data_name = single_ct.split('_')[0]\\n if data_name == 'LTRC':\\n series = single_ct.split('_')[3] + '_' + single_ct.split('_')[4]\\n else:\\n series = single_ct.split('_')[3] + '_' + single_ct.split('_')[4] + '_' + single_ct.split('_')[5]\\n full_data_name = single_ct.split('_')[0] + '_' + single_ct.split('_')[1] + '_' + single_ct.split('_')[2]\\\\\\n + '_' + series\\n\\n if full_data_name in train_data_list:\\n existing_train_lm.append(single_lm)\\n existing_train_ct.append(single_ct)\\n if full_data_name in test_data_list:\\n existing_test_lm.append(single_lm)\\n existing_test_ct.append(single_ct)\\n existing_train_data = [existing_train_lm, existing_train_ct]\\n existing_test_data = [existing_test_lm, existing_test_ct]\\n return existing_train_data, existing_test_data\",\n \"def _create_data():\\n tf.logging.info(\\\"Create records..\\\")\\n train, val, test = util.load_data(data_dir, FLAGS[\\\"is_aug\\\"])\\n tf.logging.info(\\\"Dataset size: Train-{} Test-{} Val-{}\\\".format(len(train), len(test), len(val)))\\n return train, val, test\",\n \"def get_data(train_split=.7, test_split=.85):\\n (X_train, y_train), (X_test, y_test) = mnist.load_data()\\n\\n X_train = X_train.reshape((len(X_train), 28, 28, 1))\\n test_len = len(X_test)\\n\\n X_test = X_test.reshape((test_len, 28, 28, 1))\\n\\n # divide X values bei 255.0 since MNIST data set changed such that pixel values are in [0,255]\\n X = np.concatenate((X_train, X_test)) / 255.0\\n y = np.concatenate((y_train, y_test))\\n\\n indices = np.arange(X.shape[0])\\n np.random.shuffle(indices)\\n X = X[indices]\\n y = y[indices]\\n\\n # define boundaries for train,validation and test set at .7 and .85 % of the MNIST data set\\n x_len = len(X)\\n boundaries = [int(x_len * 0.7), int(x_len*0.85)]\\n\\n [X_train, X_test, X_validate] = np.split(X, boundaries)\\n\\n [y_train, y_test, y_validate] = np.split(y, boundaries)\\n\\n # non-anomalies\\n zero_indices = np.where(y_train == 0)\\n zeros_train = X_train[zero_indices]\\n # anomalies\\n eights_indices = np.where(y_train == 8)\\n eights_train = X_train[eights_indices]\\n\\n # one-hot encode target columns\\n y_train = to_categorical(y_train)\\n y_test = to_categorical(y_test)\\n y_validate = to_categorical(y_validate)\\n\\n return (X_train, X_test, X_validate), (y_train, y_test, y_validate)\",\n \"def load_data(input_file, test_size, random_seed ):\\n ratings = np.genfromtxt(input_file, delimiter=',')\\n #ratings = ratings[:200,:]\\n train_masks = np.zeros_like(ratings)\\n test_masks = np.zeros_like(ratings)\\n pairs = list()\\n for i in range(ratings.shape[0]):\\n for j in range(ratings.shape[1]):\\n pairs.append((i,j))\\n train, test = train_test_split(pairs ,test_size = test_size, random_state= random_seed)\\n for user,item in train:\\n train_masks[user, item]= 1\\n for user,book in test:\\n test_masks[user, item]= 1 \\n return ratings, train_masks, test_masks\",\n \"def test(self):\\n self.training = False\",\n \"def load_data():\\r\\n from sklearn.feature_extraction.text import CountVectorizer\\r\\n\\r\\n # Load the data\\r\\n\\r\\n with open(\\\"clean_real.txt\\\", 'r') as RealNews:\\r\\n RealStrAr = RealNews.read().split('\\\\n')\\r\\n\\r\\n with open(\\\"clean_fake.txt\\\", 'r') as FakeNews:\\r\\n FakeStrAr = FakeNews.read().split('\\\\n')\\r\\n\\r\\n # Preprocess it using a vectorizer\\r\\n\\r\\n MyCoolVectorizer = CountVectorizer()\\r\\n X = MyCoolVectorizer.fit_transform(RealStrAr + FakeStrAr)\\r\\n\\r\\n RealLabels = np.ones((len(RealStrAr), 1)) # means real\\r\\n FakeLabels = np.zeros((len(FakeStrAr), 1)) # means fake\\r\\n AllLabels = np.append(RealLabels, FakeLabels, axis=0)\\r\\n\\r\\n FinalTensor = np.append(X.toarray(), AllLabels, axis=1)\\r\\n\\r\\n # Randomize it and split it\\r\\n\\r\\n np.random.shuffle(FinalTensor)\\r\\n\\r\\n # divide and multiply by 2 just to make sure it's even\\r\\n ROUGHLY70 = 2 * ((FinalTensor.shape[0] * 70 / 100) / 2)\\r\\n ROUGHLY15 = (FinalTensor.shape[0] - ROUGHLY70) / 2\\r\\n\\r\\n # TEST SET VALIDATION SET TRAINING SET DICTIONARY\\r\\n return (FinalTensor[:ROUGHLY15], FinalTensor[ROUGHLY15 : 2 * ROUGHLY15], FinalTensor[-ROUGHLY70:], MyCoolVectorizer.get_feature_names())\",\n \"def fit_test(self):\",\n \"def dataset_preparation():\\r\\n with open('../data/patterns_num.txt', 'r') as f:\\r\\n data = f.readlines()\\r\\n X, Y = [], []\\r\\n for line in data:\\r\\n x, y = line.split('\\\\t')\\r\\n if len(x) > 5 and x not in X: # better results are achieved excluding short query patterns\\r\\n X.append(x.replace(\\\"X\\\", \\\"\\\").replace(\\\"Y\\\", \\\"\\\").lower())\\r\\n Y.append(int(y.replace('\\\\n', '')))\\r\\n test_size = 0.2\\r\\n # print('Test size:', test_size, '\\\\nWrong classifications:\\\\n')\\r\\n\\r\\n X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=test_size, random_state=42, stratify=Y)\\r\\n return X_train, y_train, X_test, y_test\",\n \"def _load_training_data(self):\\n self._save_training_data()\",\n \"def load_data_and_labels_another():\\n\\n y_train = []\\n y_valid =[]\\n y_test = []\\n x_train =[]\\n x_valid = []\\n x_test = []\\n index = []\\n labels = {}\\n topics = ['finance' , 'international', 'legal', 'social','tech','hemp']\\n for idx, topic in enumerate(topics):\\n folder_name = \\\"data/\\\" + topic\\n all_files = os.listdir(folder_name)\\n clean_news = []\\n # read in files in each topic's folder\\n for single_file in all_files:\\n raw_data_file_name = os.path.join(folder_name, single_file)\\n news = list(open(raw_data_file_name, mode = 'rb').readlines())\\n clean_news = clean_news + news\\n clean_news = [s.strip() for s in clean_news]\\n clean_news = [clean_str(s) for s in clean_news]\\n clean_news = [normalizing_str(s) for s in clean_news]\\n clean_news = [extract_clean_words(s) for s in clean_news]\\n clean_news = [s for s in clean_news if s is not None]\\n length_of_news = len(clean_news)\\n test_num = int(length_of_news * 0.3)\\n valid_num = int(length_of_news * 0.3)\\n train_num = length_of_news - test_num - valid_num\\n clean_news = np.array(clean_news)\\n\\n \\n \\n #x_text = x_text + clean_news\\n if topic == 'finance':\\n y_topic = [[1,0,0,0,0,0] for _ in clean_news]\\n elif topic == 'international':\\n y_topic = [[0,1,0,0,0,0] for _ in clean_news]\\n elif topic == 'legal':\\n y_topic = [[0,0,1,0,0,0] for _ in clean_news]\\n elif topic == 'social':\\n y_topic = [[0,0,0,1,0,0] for _ in clean_news]\\n elif topic == 'tech':\\n y_topic = [[0,0,0,0,1,0] for _ in clean_news]\\n elif topic == 'hemp':\\n y_topic = [[0,0,0,0,0,1] for _ in clean_news]\\n y_topic = np.array(y_topic)\\n\\n #randomly shuffle the data and divide them into train, valid and test\\n np.random.seed(9)\\n indices = np.random.permutation(clean_news.shape[0])\\n training_idx = indices[:train_num]\\n valid_idx = indices[train_num:train_num+valid_num]\\n test_idx = indices[train_num+valid_num: ]\\n # Testing: no record is missed.\\n #tem = np.concatenate((training_idx, valid_idx, test_idx),axis = 0)\\n #print(tem.sort()) \\n train_piece_x = list(clean_news[training_idx])\\n valid_piece_x = list(clean_news[valid_idx])\\n test_piece_x = list(clean_news[test_idx])\\n train_piece_y = list(y_topic[training_idx])\\n valid_piece_y = list(y_topic[valid_idx])\\n test_piece_y = list(y_topic[test_idx])\\n y_train = y_train + train_piece_y\\n y_valid = y_valid + valid_piece_y\\n y_test = y_test + test_piece_y\\n x_train = x_train + train_piece_x\\n x_valid = x_valid + valid_piece_x\\n x_test = x_test + test_piece_x\\n\\n # Store the data in data_pickle.\\n y_train = np.array(y_train)\\n y_valid = np.array(y_valid)\\n y_test = np.array(y_test)\\n file = open('data_pickle', 'wb')\\n pickle.dump([x_train,x_valid,x_test,y_train,y_valid,y_test], file)\\n file.close()\\n print(\\\"-------------------------------------------------------\\\")\\n print(\\\"*****Dumped Data_pickle*****\\\")\",\n \"def __init__(self, calendar_data_path_str, sell_prices_data_path_str, sales_train_validation_data_path_str, train_test_date_split, dept_id = None):\\n\\n self.sales_train_validation_df = pd.read_csv(sales_train_validation_data_path_str)\\n self.sales_train_validation_df.sort_values(\\\"id\\\", inplace = True)\\n self.calendar_df = pd.read_csv(calendar_data_path_str)\\n self.sell_prices_df = pd.read_csv(sell_prices_data_path_str)\\n\\n # Filter by dept_id if needed\\n if dept_id is not None:\\n self.sales_train_validation_df = self.sales_train_validation_df.loc[self.sales_train_validation_df[\\\"cat_id\\\"] == dept_id]\\n\\n # Generate a validation set if enable_validation is True\\n train_cols_lst = [i for i in self.sales_train_validation_df.columns if not i.startswith(\\\"d_\\\")] + self.calendar_df[\\\"d\\\"].loc[self.calendar_df[\\\"date\\\"] <= \\\"2016-03-27\\\"].tolist()\\n test_cols_lst = [i for i in self.sales_train_validation_df.columns if not i.startswith(\\\"d_\\\")] + self.calendar_df[\\\"d\\\"].loc[(self.calendar_df[\\\"date\\\"] > \\\"2016-03-27\\\") & (self.calendar_df[\\\"date\\\"] <= \\\"2016-04-24\\\")].tolist()\\n self.train_df = self.sales_train_validation_df[train_cols_lst].reset_index(drop = True)\\n self.valid_df = self.sales_train_validation_df[test_cols_lst].reset_index(drop = True)\\n \\n self.train_target_columns = [i for i in self.train_df.columns if i.startswith(\\\"d_\\\")]\\n self.weight_columns = self.train_df.iloc[:, -28:].columns.tolist()\\n self.train_df[\\\"all_id\\\"] = \\\"all\\\"\\n self.valid_df[\\\"all_id\\\"] = \\\"all\\\"\\n \\n self.id_columns = [i for i in self.train_df.columns if not i.startswith(\\\"d_\\\")]\\n self.valid_target_columns = [i for i in self.valid_df.columns if i.startswith(\\\"d_\\\")]\\n\\n self.group_ids = (\\\"all_id\\\", \\\"state_id\\\", \\\"store_id\\\", \\\"cat_id\\\", \\\"dept_id\\\", \\\"item_id\\\", \\n [\\\"state_id\\\", \\\"cat_id\\\"], [\\\"state_id\\\", \\\"dept_id\\\"], [\\\"store_id\\\", \\\"cat_id\\\"], \\n [\\\"store_id\\\", \\\"dept_id\\\"], [\\\"item_id\\\", \\\"state_id\\\"], [\\\"item_id\\\", \\\"store_id\\\"])\\n\\n self.train_series = self.trans_30490_to_42840(self.train_df, self.train_target_columns)\\n self.valid_series = self.trans_30490_to_42840(self.valid_df, self.valid_target_columns)\\n self.roll_mat_csr, self.roll_index = self.create_rollup_index()\\n self.group_ids_items = self.generate_group_ids_items()\\n\\n self.S = self.get_s()\\n self.W = self.get_w()\\n self.SW = self.W / np.sqrt(self.S)\\n\\n self.weights = self.get_weight_df() # Equivalent to get_w()\\n self.scale = self.get_scale() # Equivalent to get_s()\\n\\n #self.train_series = None\\n #self.train_df = None\\n gc.collect()\",\n \"def load(train_file, test_file):\\n print('\\\\nLoad the raw training and test set data...')\\n y_train, tx_train, ids_train = load_csv_data(train_file)\\n y_test, tx_test, ids_test = load_csv_data(test_file)\\n print('\\\\n... finished.')\\n return y_train, tx_train, ids_train, y_test, tx_test, ids_test\",\n \"def test_02_train(self):\\n today = date.today()\\n log_file = os.path.join(LOG_DIR, \\\"{}-train-{}-{}.log\\\".format(LOG_PREFIX, today.year, today.month))\\n \\n ## update the log\\n country = 'india'\\n date_range = ('2017-11-29', '2019-05-24')\\n metric = {'rmse':0.5}\\n runtime = \\\"00:00:01\\\"\\n model_version = 0.1\\n model_version_note = \\\"test model\\\"\\n \\n update_train_log(country, date_range, metric, runtime,\\n model_version, model_version_note ,test=True, prefix=LOG_PREFIX)\\n\\n df = pd.read_csv(log_file)\\n logged_metric = [literal_eval(i) for i in df['metric'].copy()][-1]\\n self.assertEqual(metric,logged_metric)\",\n \"def test_data():\\n batch_size = 10\\n input_dim = 28\\n test_data = np.random.rand(batch_size, input_dim)\\n\\n return test_data\",\n \"def load_data():\\r\\n train = convert_corpus_to_lists(ConllCorpusReader('CoNLL-2003', 'train.txt', ['words', 'pos', 'ignore', 'chunk']))\\r\\n val = convert_corpus_to_lists(ConllCorpusReader('CoNLL-2003', 'valid.txt', ['words', 'pos', 'ignore', 'chunk'])) # testa will be our val set\\r\\n test = convert_corpus_to_lists(ConllCorpusReader('CoNLL-2003', 'test.txt', ['words', 'pos', 'ignore', 'chunk']))\\r\\n\\r\\n return train, val, test\",\n \"def load_data(opt=\\\"mnist\\\"):\\n if opt == \\\"mnist\\\":\\n train, test = tf.keras.datasets.mnist.load_data()\\n \\n x_train, y_train = train\\n x_test, y_test = test\\n \\n x_train = x_train.reshape(x_train.shape[0], 28 * 28)\\n x_test = x_test.reshape(x_test.shape[0], 28 * 28)\\n \\n y_train = y_train.astype(np.int)\\n y_test = y_test.astype(np.int)\\n for i in range(len(y_train)):\\n y_train[i] = 1 if y_train[i] % 2 == 0 else -1\\n for i in range(len(y_test)):\\n y_test[i] = 1 if y_test[i] % 2 == 0 else -1\\n\\n elif opt == \\\"covertype\\\":\\n df = pd.read_csv(\\\"covtype.data\\\", header=None)\\n x = df.iloc[:, 0:54].values\\n y = df[54].values\\n for i in range(len(y)):\\n y[i] = 1 if y[i] % 2 == 0 else -1\\n x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3)\\n \\n else:\\n logging.error(\\\"Unknown dataset!!\\\")\\n\\n logging.info(\\\"train data shape: {}\\\".format(x_train.shape))\\n logging.info(\\\"test data shape: {}\\\".format(x_test.shape))\\n return (x_train, y_train), (x_test, y_test)\",\n \"def check_data():\\n check_docs(\\\"Training\\\")\\n check_docs(\\\"dev\\\")\\n check_docs(\\\"Test\\\")\",\n \"def trainData(self, X, y, NeuralNet, epochs):\",\n \"def train_test_data_df(train_data_file, test_data_file):\\n dtype_dict = {\\n \\\"age\\\": np.int32,\\n \\\"education-num\\\": np.int32,\\n \\\"capital-gain\\\": np.int32,\\n \\\"capital-loss\\\": np.int32,\\n \\\"hours-per-week\\\": np.int32\\n }\\n cols = [i for i in range(15) if i != 2]\\n train_data = pd.read_csv(train_data_file, sep=\\\", \\\", header=0, dtype=dtype_dict, na_values=\\\"?\\\", usecols=cols)\\n train_data = train_data.dropna(axis=0, how=\\\"any\\\")\\n test_data = pd.read_csv(test_data_file, sep=\\\", \\\", header=0, dtype=dtype_dict, na_values=\\\"?\\\", usecols=cols)\\n test_data = test_data.dropna(axis=0, how=\\\"any\\\")\\n return train_data, test_data\",\n \"def CrossCheck(dataloader):\",\n \"def train(self, data, labels, validationData, validationLabels): # data x training, y_training\\n # ## TODO: Your code here\\n count = util.Counter()\\n\\n #print 'Labels of the data is:', labels #printing all the labels from which the frequency has to be found\\n\\n for x in labels: # for loop\\n count[x] += 1\\n\\n #print count\\n val = count.argMax()\\n self.guess = val\\n #print val\",\n \"def train(self):\\n pass\",\n \"def train(self):\\n pass\",\n \"def train(self):\\n pass\",\n \"def train(self):\\n pass\",\n \"def train(self):\\n pass\",\n \"def load_data():\\n df = pd.read_csv(\\\"../../Data/breast_cancer_data/data.csv\\\")\\n\\n cols = df.columns\\n X = df[cols[2:-1]].to_numpy()\\n y = df[cols[1]].to_numpy()\\n y = (y=='M').astype(np.int) * 2 - 1\\n\\n train_X = X[:-150]\\n train_y = y[:-150]\\n\\n test_X = X[-150:]\\n test_y = y[-150:]\\n\\n return train_X, train_y, test_X, test_y\",\n \"def validation_epoch(self):\\n self.model.eval()\\n\\n # Compute for training set\\n train_loss, train_acc = compute_loss_and_accuracy(\\n self.dataloader_train, self.model, self.loss_criterion\\n )\\n self.TRAIN_ACC.append(train_acc)\\n self.TRAIN_LOSS.append(train_loss)\\n\\n # Compute for validation set\\n validation_loss, validation_acc = compute_loss_and_accuracy(\\n self.dataloader_val, self.model, self.loss_criterion\\n )\\n self.VALIDATION_ACC.append(validation_acc)\\n self.VALIDATION_LOSS.append(validation_loss)\\n print(\\\"Current validation loss:\\\", validation_loss, \\\" Accuracy:\\\", validation_acc)\\n # Compute for testing set\\n test_loss, test_acc = compute_loss_and_accuracy(\\n self.dataloader_test, self.model, self.loss_criterion\\n )\\n self.TEST_ACC.append(test_acc)\\n self.TEST_LOSS.append(test_loss)\\n\\n self.model.train()\",\n \"def parse_data():\\n n_train, n_test = 15000, 4996\\n n_features = 1355191\\n\\n print('- parsing train data')\\n X_train = sp.lil_matrix((n_train, n_features))\\n y_train = np.zeros(n_train)\\n with open('/Users/kitazawa/data/news20.train') as f:\\n lines = map(lambda l: l.rstrip().split(' '), f.readlines())\\n for i, line in enumerate(lines):\\n y_train[i] = int(line[0])\\n\\n for fv in line[1:]:\\n f, v = fv.split(':')\\n X_train[i, (int(f) - 1)] = float(v)\\n print('-- density: %f' % (X_train.nnz / (n_train * n_features)))\\n\\n print('- parsing test data')\\n X_test = sp.lil_matrix((n_test, n_features))\\n y_test = np.zeros(n_test)\\n with open('/Users/kitazawa/data/news20.test') as f:\\n lines = map(lambda l: l.rstrip().split(' '), f.readlines())\\n for i, line in enumerate(lines):\\n y_test[i] = int(line[0])\\n\\n for fv in line[1:]:\\n f, v = fv.split(':')\\n X_test[i, (int(f) - 1)] = float(v)\\n print('-- density: %f' % (X_test.nnz / (n_test * n_features)))\\n\\n return X_train, y_train, X_test, y_test\",\n \"def prepare_data(self):\\n try:\\n self.train_dataset = self.datasets['train']\\n self.val_dataset = self.datasets['val']\\n try:\\n self.test_dataset = self.datasets['test']\\n except:\\n pass\\n except Exception as e:\\n print('Data was not succesfully prepared:', e)\",\n \"def load_data_simple_train_test(train_pos_data_path, train_neg_data_path, stopwords):\\n\\n\\ttrain_files_pos = [join(train_pos_data_path, f) for f in listdir(train_pos_data_path) if isfile(join(train_pos_data_path, f))]\\n\\ttrain_files_neg = [join(train_neg_data_path, f) for f in listdir(train_neg_data_path) if isfile(join(train_neg_data_path, f))]\\n\\n\\ttrain_files_pos.sort(key = lambda x: x.split(\\\"_\\\")[0].replace(\\\"cv\\\", \\\"\\\"))\\n\\ttrain_files_neg.sort(key = lambda x: x.split(\\\"_\\\")[0].replace(\\\"cv\\\", \\\"\\\"))\\n\\n\\tprint(\\\"The count of all positive files is: \\\", len(train_files_pos))\\n\\tprint(\\\"The count of all negative files is: \\\", len(train_files_neg))\\n\\n\\tall_pos_docs = collect_train_data(train_files_pos, stopwords)\\n\\tall_neg_docs = collect_train_data(train_files_neg, stopwords)\\n\\n\\tpos_train = all_pos_docs[:900]\\n\\tneg_train = all_neg_docs[:900]\\n\\tpos_test = all_pos_docs[900:]\\n\\tneg_test = all_neg_docs[900:]\\n\\n\\tprint(\\\"The size of the positive training set is: \\\", len(pos_train))\\n\\tprint(\\\"The size of the negative training set is: \\\", len(neg_train))\\n\\tprint(\\\"The size of the positive test set is: \\\", len(pos_test))\\n\\tprint(\\\"The size of the negative test set is: \\\", len(neg_test))\\n\\n\\treturn pos_train, pos_test, neg_train, neg_test\",\n \"def train(x_train, y_train, x_valid, y_valid, config):\\n train_acc = []\\n valid_acc = []\\n train_loss = []\\n valid_loss = []\\n best_model = None\\n NUM_EPOCH = config['epochs']\\n EARLY_STOP = config['early_stop']\\n EARLY_STOP_EPOCH = config['early_stop_epoch']\\n BATCH_SIZE = config['batch_size']\\n model = NeuralNetwork(config=config)\\n loss = float('inf')\\n best_loss = float('inf')\\n best_accuracy = 0\\n patience = 0\\n\\n\\n\\n for i in range (NUM_EPOCH):\\n\\n x_train, y_train = shuffle(x_train, y_train)\\n x_train = np.asarray(x_train)\\n y_train = np.asarray(y_train)\\n\\n for j in range (0, len(x_train), BATCH_SIZE):\\n start = j\\n end = j + BATCH_SIZE\\n if (end > len(x_train)):\\n end = len(x_train)\\n\\n x = x_train[start:end]\\n y = y_train[start:end]\\n\\n model.forward(x, y) \\n model.backward()\\n\\n train_epoch_loss = model.forward(x_train, y_train)\\n \\n train_predict = np.zeros_like(model.y)\\n train_predict[np.arange(len(model.y)), model.y.argmax(1)] = 1\\n\\n train_accuracy = sum([1 if all(train_predict[i] == y_train[i]) else 0 for i in range(len(y_train))])/len(y_train)\\n\\n train_loss.append(train_epoch_loss)\\n train_acc.append(train_accuracy)\\n \\n valid_epoch_loss = model.forward(x_valid, y_valid)\\n valid_predict = np.zeros_like(model.y)\\n valid_predict[np.arange(len(model.y)), model.y.argmax(1)] = 1\\n\\n valid_accuracy = sum([1 if all(valid_predict[i] == y_valid[i]) else 0 for i in range(len(y_valid))])/len(y_valid)\\n\\n valid_loss.append(valid_epoch_loss)\\n valid_acc.append(valid_accuracy)\\n\\n\\n print(\\\"Epoch:\\\", i, \\\"Train Accuracy|Loss:\\\", train_accuracy,\\\"| \\\", train_epoch_loss, \\\"~|~ Valid: \\\", valid_accuracy, \\\" | \\\", valid_epoch_loss)\\n if EARLY_STOP:\\n if valid_epoch_loss > best_loss and patience >= EARLY_STOP_EPOCH:\\n return train_acc, valid_acc, train_loss, valid_loss, best_model\\n elif valid_epoch_loss > best_loss and patience < EARLY_STOP_EPOCH:\\n patience += 1\\n else:\\n patience = 0\\n if valid_epoch_loss < best_loss:\\n best_loss = valid_epoch_loss\\n best_accuracy = valid_accuracy\\n best_model = copy.deepcopy(model)\\n\\n loss = valid_epoch_loss\\n\\n \\n best_model = model \\n return train_acc, valid_acc, train_loss, valid_loss, best_model\",\n \"def load_dataset_train():\\n df_train = load_csv_file(\\\"31_train.csv\\\")\\n df_train_target = load_csv_file(\\\"31_target_train.csv\\\")\\n\\n return df_train.values, df_train_target.values\",\n \"def load_data():\\n # Load data\\n X_train = pd.read_csv(\\n \\\"data/trimmed_merged_no_track_id_or_session_id.csv\\\"\\n ) # load data from csv\\n\\n drop_columns = [\\\"mode\\\"] # drop unimportant columns\\n X_train.drop(labels=drop_columns, axis=1, inplace=True)\\n\\n y_train = X_train[\\\"skip\\\"]\\n X_train.drop(labels=\\\"skip\\\", axis=1, inplace=True) # ground truth from train_data\\n\\n # split data 60% train, 20% validation, 20% test\\n X_train, X_val, y_train, y_val = train_test_split(\\n X_train, y_train, test_size=0.2, random_state=1\\n )\\n X_train, X_test, y_train, y_test = train_test_split(\\n X_train, y_train, test_size=0.25, random_state=1\\n )\\n\\n print(\\\"X_train:\\\", len(X_train))\\n print(\\\"y_train:\\\", len(y_train))\\n print(\\\"X_val:\\\", len(X_val))\\n print(\\\"y_val:\\\", len(y_val))\\n print(\\\"X_test:\\\", len(X_test))\\n print(\\\"y_test:\\\", len(y_test))\\n\\n return X_train, y_train, X_val, y_val, X_test, y_test\",\n \"def test_data():\\n global _MEAN # pylint: disable=global-statement\\n _np.random.seed(1)\\n view = _skdc10.view.OfficialImageClassificationTask()\\n permutation = _np.random.permutation(range(10000))\\n if _MEAN is None:\\n _MEAN = view.train.x.reshape((50000 * 32 * 32, 3)).mean(axis=0)\\n return ((view.test.x[:10000, :][permutation, :] - _MEAN).\\n transpose((0, 3, 1, 2)).astype('float32'),\\n view.test.y[:10000][permutation].reshape((10000, 1)).astype('float32'))\",\n \"def prepareData(mdl, X_train, X_val, X_test, Y_train, Y_val, Y_test):\\n\\t\\n\\tX2_train = []\\n\\n\\tX2_val = []\\n\\n\\tX2_test = []\\n\\n\\n\\tYPs = mdl.predict(X_train)\\n\\n\\tfor i in tqdm(range(len(X_train))):\\n\\n\\t\\tX2_train.append(np.array(YPs[i]))\\n\\n\\n\\n\\tYPs = mdl.predict(X_val)\\n\\n\\tfor i in tqdm(range(len(X_val))):\\n\\n\\t\\tX2_val.append(np.array(YPs[i]))\\n\\n\\n\\n\\tYPs = mdl.predict(X_test)\\n\\n\\tfor i in tqdm(range(len(X_test))):\\n\\n\\t\\tX2_test.append(np.array(YPs[i]))\\n\\n\\n\\tX2_train = np.array(X2_train)\\n\\n\\tX2_val = np.array(X2_val)\\n\\n\\tX2_test = np.array(X2_test)\\n\\n\\treturn (X2_train, X2_val, X2_test)\",\n \"def merge_data():\\n\\n\\tconfig = Config()\\n\\tfilename_train, filename_test = \\\"../data/train.csv\\\", \\\"../data/test.csv\\\" \\n\\n # create datasets\\n\\ttrain, test = config.load_data(filename_train, filename_test, print_EDA=False)\\n\\n # 1. datetime features\\n\\t# diff between weekday and day?\\n\\t#weekday - Return the day of the week as an integer, where Monday is 0 and Sunday is 6.\\n\\t#day - Between 1 and the number of days in the given month of the given year.\\n\\ttrain['pickup_hour'] = train.pickup_datetime.dt.hour.astype('uint8')\\n\\ttrain['pickup_day'] = train.pickup_datetime.dt.day.astype('uint8')\\n\\ttrain['pickup_weekday'] = train.pickup_datetime.dt.weekday.astype('uint8')\\n\\ttrain['pickup_minute'] = train.pickup_datetime.dt.minute.astype('uint8')\\n\\ttrain['pickup_month'] = train.pickup_datetime.dt.month.astype('uint8')\\n\\ttrain['pickup_hour_weekofyear'] = train['pickup_datetime'].dt.weekofyear\\n\\ttrain['pickup_weekday_hour'] = train['pickup_weekday']*24 + train['pickup_hour']\\n\\n\\ttest['pickup_hour'] = test.pickup_datetime.dt.hour.astype('uint8')\\n\\ttest['pickup_day'] = test.pickup_datetime.dt.day.astype('uint8')\\n\\ttest['pickup_weekday'] = test.pickup_datetime.dt.weekday.astype('uint8')\\n\\ttest['pickup_minute'] = test.pickup_datetime.dt.minute.astype('uint8')\\n\\ttest['pickup_month'] = test.pickup_datetime.dt.month.astype('uint8')\\n\\ttest['pickup_hour_weekofyear'] = test['pickup_datetime'].dt.weekofyear\\n\\ttest['pickup_weekday_hour'] = test['pickup_weekday']*24 + test['pickup_hour']\\n\\n\\t# 2. Location features\\n\\tdef haversine(lon1, lat1, lon2, lat2):\\n\\t lon1, lat1, lon2, lat2 = map(np.radians, [lon1, lat1, lon2, lat2])\\n\\t dlon = lon2 - lon1\\n\\t dlat = lat2 - lat1\\n\\t a = np.sin(dlat/2.0)**2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlon/2.0)**2\\n\\t c = 2 * np.arcsin(np.sqrt(a))\\n\\t km = 6367 * c # AVG_EARTH_RADIUS=6367\\n\\t miles = km * 0.621371\\n\\t return miles\\n\\n\\t# def dummy_manhattan_distance(lat1, lng1, lat2, lng2):\\n\\t# a = haversine_array(lat1, lng1, lat1, lng2)\\n\\t# b = haversine_array(lat1, lng1, lat2, lng1)\\n\\t# return a + b\\n\\n\\t# def bearing_array(lat1, lng1, lat2, lng2):\\n\\t# AVG_EARTH_RADIUS = 6371 # in km\\n\\t# lng_delta_rad = np.radians(lng2 - lng1)\\n\\t# lat1, lng1, lat2, lng2 = map(np.radians, (lat1, lng1, lat2, lng2))\\n\\t# y = np.sin(lng_delta_rad) * np.cos(lat2)\\n\\t# x = np.cos(lat1) * np.sin(lat2) - np.sin(lat1) * np.cos(lat2) * np.cos(lng_delta_rad)\\n\\t# return np.degrees(np.arctan2(y, x))\\n\\n\\ttrain['distance'] = haversine(train.pickup_longitude, train.pickup_latitude,\\n\\t train.dropoff_longitude, train.dropoff_latitude)\\n\\ttest['distance'] = haversine(test.pickup_longitude, test.pickup_latitude,\\n\\t test.dropoff_longitude, test.dropoff_latitude)\\n\\n\\n\\t# 3. Use outsource data\\n\\tweatherdata_filename = \\\"../data/outsource_data/weather_data_nyc_centralpark_2016.csv\\\"\\n\\tfastestroute_data_train = \\\"../data/outsource_data/fastest_train.csv\\\"\\n\\tfastestroute_data_test = \\\"../data/outsource_data/fastest_routes_test.csv\\\"\\n\\n\\n\\twd = pd.read_csv(weatherdata_filename, header=0)\\n\\twd['date'] = pd.to_datetime(wd.date, format=\\\"%d-%m-%Y\\\")\\n\\twd['pickup_day'] = wd['date'].dt.day\\n\\twd['snow fall'] = wd['snow fall'].replace('T', 0.05).astype(np.float32) \\n\\twd['precipitation'] = wd['precipitation'].replace('T', 0.05).astype(np.float32) \\n\\twd['snow depth'] = wd['snow depth'].replace('T', 0.05).astype(np.float32) \\n\\n\\t# Merge training data with weather data on pickup_day\\n\\tprint(\\\"Merging training data with weather data ....\\\")\\n\\twd_train = pd.merge(train, wd, on='pickup_day')\\n\\twd_train = wd_train.drop(['date','maximum temperature','minimum temperature'],axis=1)\\n\\tgc.collect()\\n\\n\\t# Merge wd_train with fastestroute_data\\n\\tfastest = pd.read_csv(fastestroute_data_train, header=0)\\n\\tprint(\\\"Merging Location data with weather and training data ....\\\")\\n\\twd_train_fastest = pd.merge(wd_train, fastest, on='id', how='outer')\\n\\n\\tgc.collect()\\n\\n\\n\\tprint(\\\"===================== CHECK TRAINING DATA =====================\\\")\\n\\n\\tprint(wd_train_fastest.head(2))\\n\\tprint(\\\"Semi-final training data shape is: {}\\\".format(wd_train_fastest.shape))\\n\\tprint(\\\"Training data columns: {}\\\".format(wd_train_fastest.columns))\\n\\n\\n\\t# Use the same outsource data with test set\\n\\t# merge outsource data with test data as well\\n\\tft_test_cols = [ 'id', 'starting_street','end_street','total_distance',\\t'total_travel_time',\\n\\t\\t\\t\\t\\t'number_of_steps','street_for_each_step','distance_per_step','travel_time_per_step',\\n\\t 'step_maneuvers','step_direction',\\t'step_location_list']\\n\\tfastest_test = pd.read_csv(fastestroute_data_test, names=ft_test_cols, header=0)\\n\\n\\tprint(\\\"Merging test data with Location data ....\\\")\\n\\ttest = pd.merge(test, fastest_test, on='id', how='outer')\\n\\ttest = test.drop(['step_location_list','step_direction','step_maneuvers','travel_time_per_step','distance_per_step','street_for_each_step','number_of_steps','starting_street',\\n\\t 'end_street'], axis=1)\\n\\tprint(\\\"Merging test data with weather data ....\\\")\\n\\ttest = pd.merge(test, wd, on='pickup_day')\\n\\n\\tprint(\\\"===================== CHECK TEST DATA =====================\\\")\\n\\n\\tprint(test.head(2))\\n\\tprint(\\\"Semi-final test data shape is: {}\\\".format(test.shape))\\n\\tprint(\\\"Test data columns: {}\\\".format(test.columns))\\n\\n\\n\\n\\t# 4. Do more data munging\\n\\tmask = ((wd_train_fastest.trip_duration > 60) & (wd_train_fastest.distance < 0.05))\\n\\twd_train_fastest = wd_train_fastest[~mask]\\n\\tmask = (wd_train_fastest.trip_duration < 60) \\n\\twd_train_fastest = wd_train_fastest[~mask]\\n\\tmask = wd_train_fastest.trip_duration > 79200\\n\\twd_train_fastest = wd_train_fastest[~mask]\\n\\tmask = wd_train_fastest.distance/(wd_train_fastest.trip_duration/3600) > 60\\n\\twd_train_fastest = wd_train_fastest[~mask]\\n\\twd_train_fastest.trip_duration = wd_train_fastest.trip_duration.astype(np.uint16)\\n\\twd_train_fastest = wd_train_fastest[wd_train_fastest.passenger_count > 0]\\n\\n\\t# 5. Do some data maskig based on location to create jfk and lgo features\\n\\tjfk_lon = -73.778889\\n\\tjfk_lat = 40.639722\\n\\tlga_lon = -73.872611\\n\\tlga_lat = 40.77725\\n\\n\\twd_train_fastest['jfk_pickup_dist'] = wd_train_fastest.apply(lambda row: haversine(jfk_lon, jfk_lat, row['pickup_longitude'],row['pickup_latitude']), axis=1)\\n\\twd_train_fastest['lga_pickup_dist'] = wd_train_fastest.apply(lambda row: haversine(lga_lon, lga_lat, row['pickup_longitude'],row['pickup_latitude']), axis=1)\\n\\twd_train_fastest['jfk_dropoff_dist'] = wd_train_fastest.apply(lambda row: haversine(jfk_lon, jfk_lat, row['dropoff_longitude'],row['dropoff_latitude']), axis=1)\\n\\twd_train_fastest['lga_dropoff_dist'] = wd_train_fastest.apply(lambda row: haversine(lga_lon, lga_lat, row['dropoff_longitude'],row['dropoff_latitude']), axis=1)\\n\\n\\twd_train_fastest['jfk'] = ((wd_train_fastest['jfk_pickup_dist'] < 2) | (wd_train_fastest['jfk_dropoff_dist'] < 2))\\n\\twd_train_fastest['lga'] = ((wd_train_fastest['lga_pickup_dist'] < 2) | (wd_train_fastest['lga_dropoff_dist'] < 2))\\n\\twd_train_fastest = wd_train_fastest.drop(['jfk_pickup_dist','lga_pickup_dist','jfk_dropoff_dist','lga_dropoff_dist'],axis=1)\\n\\twd_train_fastest['workday'] = ((wd_train_fastest['pickup_hour'] > 8) & (wd_train_fastest['pickup_hour'] < 18))\\n\\n\\n\\tprint(\\\"===================== CHECK TRAINING DATA AGAIN =====================\\\")\\n\\n\\tprint(wd_train_fastest.head(2))\\n\\tprint(\\\"Final training data shape is: {}\\\".format(wd_train_fastest.shape))\\n\\tprint(\\\"Training data columns: {}\\\".format(wd_train_fastest.columns))\\n\\n\\n\\treturn wd_train_fastest, test\",\n \"def gen_data(n_samples=200):\\r\\n\\tnp.random.seed(13)\\r\\n\\tx = np.random.uniform(0,10,size=n_samples) #从一个均匀分布[0,10)中随机采样\\r\\n\\tx.sort()\\r\\n\\ty = ground_truth(x) + 0.75*np.random.normal(size=n_samples) #均值为0，标准差为1的正态分布\\r\\n\\t#print(\\\"x, y: \\\", x[:10], y[:10])\\r\\n\\ttrain_mask = np.random.randint(0,2,size=n_samples).astype(np.bool) #返回值为True or False\\r\\n\\t#print(\\\"train_mask: \\\",train_mask[:10])\\r\\n\\tx_train, y_train = x[train_mask, np.newaxis], y[train_mask] #如果train_mask是1，就将x,y作为训练集，np.newaxis表示将行向量转为列向量\\r\\n\\t#print(\\\"x_train, y_train: \\\", x_train[:10], y_train[:10])\\r\\n\\tx_test, y_test = x[~train_mask, np.newaxis], y[~train_mask] #train_mask为False，将x,y作为测试集\\r\\n\\t#print(\\\"x_test, y_test: \\\", x_test[:10], y_test[:10])\\r\\n\\treturn x_train, x_test, y_train, y_test\",\n \"def test(self, training_set, original_test_set, imitation_test_set ):\\n\\n plt.figure()\\n\\n training_axis = np.arange(len(training_set))\\n original_test_axis = np.arange(len(original_test_set))+len(training_axis)\\n imitation_test_axis = np.arange(len(imitation_test_set))+len(training_axis)+len(original_test_set)\\n\\n training_scores = []\\n original_test_scores = []\\n imitation_test_scores = []\\n\\n for signature in training_set:\\n vectorized_signature = signature.tolist()\\n score = -1*self.model.score(vectorized_signature)\\n training_scores.append(score)\\n\\n for signature in original_test_set:\\n vectorized_signature = signature.tolist()\\n score = -1*self.model.score(vectorized_signature)\\n original_test_scores.append(score)\\n\\n for signature in imitation_test_set:\\n vectorized_signature = signature.tolist()\\n score = -1*self.model.score(vectorized_signature)\\n imitation_test_scores.append(score)\\n\\n accuracy, threshold = self.evaluate(training_scores, original_test_scores, imitation_test_scores)\\n\\n xaxis = np.arange(len(imitation_test_set)+len(training_axis)+len(original_test_set))\\n plt.plot( xaxis, threshold*np.ones(len(xaxis)), \\\"--\\\", label=\\\"Threshold\\\" )\\n plt.scatter(training_axis,training_scores, label=\\\"Training data\\\")\\n plt.scatter(original_test_axis, original_test_scores, c=\\\"g\\\", label=\\\"Original Test data\\\")\\n plt.scatter(imitation_test_axis, imitation_test_scores, c=\\\"r\\\", label=\\\"Imitated Test data\\\")\\n plt.legend(loc=\\\"best\\\")\\n plt.title(f\\\"{self.user} data. Accuracy={accuracy} \\\")\\n plt.ylabel(\\\"Score\\\")\\n plt.xlabel(\\\"File\\\")\\n plt.savefig(f\\\"{self.n_components}_{self.user}.png\\\")\\n\\n Model.accuracies.append(accuracy)\",\n \"def load_data(mode='train'):\\r\\n if mode == 'train':\\r\\n\\r\\n x_train = scipy.io.loadmat('train_images_14bus.mat')['x_train']\\r\\n y_train = scipy.io.loadmat('train_labels_14bus.mat')['y_train']\\r\\n\\r\\n x_valid = scipy.io.loadmat('valid_images_14bus.mat')['x_valid']\\r\\n y_valid = scipy.io.loadmat('valid_labels_14bus.mat')['y_valid']\\r\\n print(x_valid.shape, y_valid.shape, type(x_valid), type(y_valid))\\r\\n\\r\\n x_train, _ = reformat(x_train, y_train)\\r\\n x_valid, _ = reformat(x_valid, y_valid)\\r\\n print(x_valid.shape, y_valid.shape, type(x_valid), type(y_valid))\\r\\n\\r\\n return x_train, y_train, x_valid, y_valid\\r\\n elif mode == 'test':\\r\\n x_test = scipy.io.loadmat('test_images_14bus.mat')['x_test']\\r\\n y_test = scipy.io.loadmat('test_labels_14bus.mat')['y_test']\\r\\n x_test, _ = reformat(x_test, y_test)\\r\\n return x_test, y_test\",\n \"def model_data():\\n x_train, y_train, x_val, y_val, x_test, y_test = read_data(\\\"src/tests/dataclassificationmodel/ferPlus_processed.pbz2\\\", False)\\n return x_train, y_train, x_val, y_val, x_test, y_test\",\n \"def load_data_clean(source='local'):\\n\\n train_bl, test_bl = load_data_raw(source)\\n\\n train = train_bl.copy()\\n train = encode_dtypes(train)\\n train = impute_missing(train)\\n train = clean_data(train)\\n\\n test = test_bl.copy()\\n test = encode_dtypes(test)\\n test = impute_missing(test)\\n test = clean_data(test)\\n\\n return train, test\",\n \"def train_test_data(data):\\n data = data.fillna(0)\\n \\\"\\\"\\\"One hot encoding the rank\\\"\\\"\\\"\\n processed_data = pd.get_dummies(data, columns=[\\\"rank\\\"])\\n \\\"\\\"\\\"Normalizing the values , place all data within range on (0,1)\\\"\\\"\\\"\\n processed_data[\\\"gre\\\"] = processed_data[\\\"gre\\\"]/800\\n processed_data[\\\"gpa\\\"] = processed_data[\\\"gpa\\\"]/4\\n \\n \\\"\\\"\\\"split data\\\"\\\"\\\"\\n X=np.array(processed_data)[:,1:].astype('float32')\\n y = to_categorical(data[\\\"admit\\\"],num_classes=2)\\n \\n print('Shape of X ', X.shape)\\n print('Shape of y ', y.shape)\\n \\n \\\"\\\"\\\" Split train/test data \\\"\\\"\\\"\\n X_train, X_test = X[50:], X[:50]\\n y_train, y_test = y[50:], y[:50]\\n \\n model=Sequential()\\n model.add(Dense(128, activation='relu', input_dim=X.shape[1]))\\n model.add(Dropout(0.2))\\n model.add(Dense(64,activation='relu'))\\n model.add(Dropout(0.1))\\n model.add(Dense(2, activation='softmax'))\\n \\n model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=[\\\"accuracy\\\"])\\n \\n model.summary()\\n \\n model.fit(X_train, y_train, epochs=200, batch_size=100, verbose=0)\\n \\n score = model.evaluate(X_train, y_train)\\n print('Training score ', score[1])\\n score = model.evaluate(X_test, y_test)\\n print('Testing score ', score[1])\",\n \"def train(self):\\n\\t\\tself.model.fit(self.training_data, self.training_labels)\",\n \"def train_all(X_train_fuse, Y_train, X_dev_fuse, Y_dev, R_train, R_dev, hyperparams):\",\n \"def validate_dataset(self):\\n pass\",\n \"def validate(model, dataloader):\\n model.eval()\\n device = model.device \\n epoch_start = time.time() \\n running_loss = 0.0\\n running_accuracy = 0.0 \\n all_prob, all_labels = [], []\\n\\n with torch.no_grad():\\n for (batch_seqs, batch_seq_masks, batch_seq_segments, batch_labels) in dataloader:\\n seqs = batch_seqs.to(device) \\n masks = batch_seq_masks.to(device)\\n segments = batch_seq_segments.to(device)\\n labels = batch_labels.to(device)\\n \\n loss, logits, probabilities = model(seqs, masks, segments, labels)\\n running_loss += loss.item()\\n running_accuracy += correct_predictions(probabilities, labels)\\n \\n all_prob.extend(probabilities[:, 1].cpu().numpy())\\n all_labels.extend(batch_labels)\\n epoch_time = time.time() - epoch_start \\n epoch_loss = running_loss / len(dataloader)\\n epoch_accuracy = running_accuracy / (len(dataloader.dataset))\\n # epoch_f1 = epoch_accuracy \\n return epoch_time, epoch_loss, epoch_accuracy, roc_auc_score(all_labels, all_prob),\",\n \"def partition_train_valid_test2(data, classes, others, ratio=(1,1,1), rng=np.random.RandomState(1000)):\\n k=sum(ratio) # ratio must be a vector of integers\\n ind=kfold_cross_validation(classes,k=k,shuffle=True,rng=rng)\\n sequence=np.arange(len(classes))\\n train_ind=np.array([],dtype=int)\\n valid_ind=np.array([],dtype=int)\\n test_ind=np.array([],dtype=int)\\n count=0\\n for ki in range(k):\\n if count<ratio[0]:\\n train_ind=np.append(train_ind,sequence[ind==ki])\\n count=count+1\\n continue\\n if count>=ratio[0] and count <ratio[0]+ratio[1]:\\n valid_ind=np.append(valid_ind,sequence[ind==ki])\\n count=count+1\\n continue\\n if count>=ratio[0]+ratio[1] and ratio[2]>0:\\n test_ind=np.append(test_ind,sequence[ind==ki])\\n count=count+1\\n continue\\n train_set_x=data[train_ind]\\n train_set_y=classes[train_ind]\\n if others is not None:\\n train_set_others=others[train_ind]\\n else:\\n train_set_others=None\\n valid_set_x=data[valid_ind]\\n valid_set_y=classes[valid_ind]\\n if others is not None:\\n valid_set_others=others[valid_ind]\\n else:\\n valid_set_others=None\\n test_set_x=data[test_ind]\\n test_set_y=classes[test_ind]\\n if others is not None:\\n test_set_others=others[test_ind]\\n else:\\n test_set_others=None\\n \\n return train_set_x,train_set_y,train_set_others,valid_set_x,valid_set_y,valid_set_others,test_set_x,test_set_y,test_set_others\",\n \"def test_model(valid_features, valid_labels):\\n\\n #valid_features, valid_labels = load_preprocess_testset()\\n loaded_graph = tf.Graph()\\n \\n with tf.Session(graph=loaded_graph) as sess:\\n # Load model\\n loader = tf.train.import_meta_graph(SAVE_MODEL_PATH + '.meta')\\n loader.restore(sess, SAVE_MODEL_PATH)\\n\\n # Get Tensors from loaded model\\n loaded_x = loaded_graph.get_tensor_by_name('x:0')\\n loaded_y = loaded_graph.get_tensor_by_name('y:0')\\n loaded_keep_prob = loaded_graph.get_tensor_by_name('keep_prob:0')\\n loaded_logits = loaded_graph.get_tensor_by_name('logits:0')\\n loaded_acc = loaded_graph.get_tensor_by_name('accuracy:0')\\n \\n\\n acc = sess.run(\\n loaded_acc,\\n feed_dict={loaded_x: valid_features, loaded_y: valid_labels, loaded_keep_prob: 1.0})\\n\\n return acc\",\n \"def load_data(train_path, test_path):\\n\\n train_data = pd.read_csv(train_path)\\n test_data = pd.read_csv(test_path)\\n\\n print(\\\"number of training examples = \\\" + str(train_data.shape[0]))\\n print(\\\"number of test examples = \\\" + str(test_data.shape[0]))\\n print(\\\"train shape: \\\" + str(train_data.shape))\\n print(\\\"test shape: \\\" + str(test_data.shape))\\n\\n return train_data, test_data\",\n \"def load_data(train_path, test_path):\\n\\n train_data = pd.read_csv(train_path)\\n test_data = pd.read_csv(test_path)\\n\\n print(\\\"number of training examples = \\\" + str(train_data.shape[0]))\\n print(\\\"number of test examples = \\\" + str(test_data.shape[0]))\\n print(\\\"train shape: \\\" + str(train_data.shape))\\n print(\\\"test shape: \\\" + str(test_data.shape))\\n\\n return train_data, test_data\",\n \"def get_training_data():\\n \\n X = pd.read_csv('../data/train_values.csv').set_index('sequence_id')\\n y = pd.read_csv('../data/train_labels.csv').set_index('sequence_id')\\n return X, y\",\n \"def test_data_set_load(data_set):\\n data = data_set()\\n train_data, train_labels, test_data, test_labels = data.load_data()\\n\\n assert len(train_data) > 0\\n assert len(test_data) > 0\\n assert len(train_data) == len(train_labels)\\n assert len(test_data) == len(test_labels)\",\n \"def _split_train_tst(self):\\n num_samples = self.Y.shape[0]\\n mapper_file = self.checkpointer.get_mapper_file_location()\\n if not self.checkpointer.is_mapper_checkpointed():\\n print 'No mapper checkpoint found. Fresh loading in progress ...'\\n # Now shuffle the data\\n sample_id = range(num_samples)\\n random.shuffle(sample_id)\\n print 'Dumping the mapper shuffle for reuse.'\\n Pickle.dump(sample_id, open(mapper_file, 'wb'))\\n print 'Dump complete. Moving Forward...'\\n else:\\n print 'Mapper Checkpoint found... Reading from mapper dump'\\n sample_id = Pickle.load(open(mapper_file, 'rb'))\\n print 'Mapping unpickling complete.. Moving forward...'\\n\\n self.X_fwd = self.X_fwd[sample_id]\\n self.X_bwd = self.X_bwd[sample_id]\\n self.Y = self.Y[sample_id]\\n # Now divide the data into test ans train set\\n test_fraction = 0.01\\n self.test_size = int(test_fraction * num_samples)\\n self.train_size = num_samples - self.test_size\\n # Forward review\\n self.X_trn_fwd = self.X_fwd[0:self.train_size]\\n self.X_tst_fwd = self.X_fwd[self.train_size:num_samples]\\n # Backward review\\n self.X_trn_bwd = self.X_bwd[0:self.train_size]\\n self.X_tst_bwd = self.X_bwd[self.train_size:num_samples]\\n # Summary\\n self.Y_trn = self.Y[0:self.train_size]\\n self.Y_tst = self.Y[self.train_size:num_samples]\",\n \"def test_text_classifier_update_training_samples(self):\\n pass\",\n \"def training_data(loan_data):\\n return loan_data[loan_data.status != \\\"LIVE\\\"]\",\n \"def test_training(self):\\n model = PoincareModel(self.data_large, burn_in=0, negative=3)\\n old_vectors = np.copy(model.kv.syn0)\\n model.train(epochs=2)\\n self.assertFalse(np.allclose(old_vectors, model.kv.syn0))\",\n \"def test_model(all_data):\\n nll = 0\\n xent = 0\\n step = 0\\n for data in jsb.batch_iterator(all_data, FLAGS.batch_size, FLAGS.sequence_length):\\n feed = fill_feed(inputs, targets, data)\\n\\n batch_xent, batch_nll = sess.run([loss_op, nll_op],\\n feed_dict=feed)\\n\\n xent += batch_xent\\n nll += batch_nll\\n step += 1\\n return xent/step, nll/step\",\n \"def train_test_official(self):\\n return self.sub_set(self.idcs_train), self.sub_set(self.idcs_test)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.70418376","0.6841699","0.68156826","0.67502785","0.6705622","0.66910946","0.66823065","0.6620052","0.66126984","0.6508516","0.64929533","0.6471718","0.644612","0.6441467","0.64393294","0.64252573","0.64106035","0.6407785","0.6401149","0.6393558","0.6393503","0.6381467","0.6375812","0.6366684","0.6359297","0.63475454","0.63185424","0.6316066","0.6315807","0.63011134","0.62841845","0.6280324","0.6266139","0.62601364","0.6259291","0.62485623","0.62446666","0.6236738","0.62362057","0.62239397","0.6222206","0.6216211","0.62160325","0.62158227","0.6214601","0.6212758","0.6206943","0.6201712","0.620151","0.61953574","0.61936826","0.6188172","0.6176142","0.61704934","0.6169392","0.61632246","0.61617446","0.61516935","0.6150566","0.614405","0.6136684","0.61341965","0.6133246","0.6133246","0.6133246","0.6133246","0.6133246","0.61317813","0.6121744","0.61201745","0.61162704","0.6112369","0.61063355","0.6091996","0.60917556","0.60908973","0.60907066","0.6085182","0.6085152","0.6080464","0.6077364","0.60735714","0.6070598","0.6069042","0.6066562","0.60637987","0.60602075","0.6058056","0.6056275","0.60532284","0.604661","0.604661","0.6044036","0.60407096","0.60372037","0.6037161","0.6036399","0.6034342","0.6030652","0.60287917"],"string":"[\n \"0.70418376\",\n \"0.6841699\",\n \"0.68156826\",\n \"0.67502785\",\n \"0.6705622\",\n \"0.66910946\",\n \"0.66823065\",\n \"0.6620052\",\n \"0.66126984\",\n \"0.6508516\",\n \"0.64929533\",\n \"0.6471718\",\n \"0.644612\",\n \"0.6441467\",\n \"0.64393294\",\n \"0.64252573\",\n \"0.64106035\",\n \"0.6407785\",\n \"0.6401149\",\n \"0.6393558\",\n \"0.6393503\",\n \"0.6381467\",\n \"0.6375812\",\n \"0.6366684\",\n \"0.6359297\",\n \"0.63475454\",\n \"0.63185424\",\n \"0.6316066\",\n \"0.6315807\",\n \"0.63011134\",\n \"0.62841845\",\n \"0.6280324\",\n \"0.6266139\",\n \"0.62601364\",\n \"0.6259291\",\n \"0.62485623\",\n \"0.62446666\",\n \"0.6236738\",\n \"0.62362057\",\n \"0.62239397\",\n \"0.6222206\",\n \"0.6216211\",\n \"0.62160325\",\n \"0.62158227\",\n \"0.6214601\",\n \"0.6212758\",\n \"0.6206943\",\n \"0.6201712\",\n \"0.620151\",\n \"0.61953574\",\n \"0.61936826\",\n \"0.6188172\",\n \"0.6176142\",\n \"0.61704934\",\n \"0.6169392\",\n \"0.61632246\",\n \"0.61617446\",\n \"0.61516935\",\n \"0.6150566\",\n \"0.614405\",\n \"0.6136684\",\n \"0.61341965\",\n \"0.6133246\",\n \"0.6133246\",\n \"0.6133246\",\n \"0.6133246\",\n \"0.6133246\",\n \"0.61317813\",\n \"0.6121744\",\n \"0.61201745\",\n \"0.61162704\",\n \"0.6112369\",\n \"0.61063355\",\n \"0.6091996\",\n \"0.60917556\",\n \"0.60908973\",\n \"0.60907066\",\n \"0.6085182\",\n \"0.6085152\",\n \"0.6080464\",\n \"0.6077364\",\n \"0.60735714\",\n \"0.6070598\",\n \"0.6069042\",\n \"0.6066562\",\n \"0.60637987\",\n \"0.60602075\",\n \"0.6058056\",\n \"0.6056275\",\n \"0.60532284\",\n \"0.604661\",\n \"0.604661\",\n \"0.6044036\",\n \"0.60407096\",\n \"0.60372037\",\n \"0.6037161\",\n \"0.6036399\",\n \"0.6034342\",\n \"0.6030652\",\n \"0.60287917\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6403365"},"document_rank":{"kind":"string","value":"18"}}},{"rowIdx":11287,"cells":{"query":{"kind":"string","value":"read train/valid/test datetime set"},"document":{"kind":"string","value":"def read_datetime_set(filename, seq_len):\n\tdate_set = []\n\twith open(os.path.join(info_path, \"squence_len_{}\".format(seq_len), filename), 'r') as f:\n\t\tfor line in f:\n\t\t\tnew_line = line.rstrip('\\n').split('\\t')\n\t\t\tdate_set.append([int(new_line[0]), int(new_line[1])])\n\treturn np.array(date_set)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def split_dataset(valid_datetime):\n\tprint(\"all_frames: \", valid_datetime.shape[0])\n\tprint(\"valid_frames: \", np.sum(valid_datetime[:,1]))\n\tprint(\"no_valid_frames: \", np.sum(1-valid_datetime[:,1]))\n\n\tvalid_datetime_bool = (valid_datetime[:,1] == 1)\n\tvalid_datetime_idx = np.where(valid_datetime_bool)[0]\n\tvalid_datetime = valid_datetime[valid_datetime_bool][:,0]\n\n\ttrain_set = []\n\tvalid_set = []\n\ttest_set = []\n\t\n\tdate = set()\n\tfor dt in valid_datetime.astype(str):\n\t\tdate.add(dt[:8])\n\tdate = list(date)\n\tdate.sort()\n\n\tfor i in date:\n\t\tflag = np.random.randint(0,14)\n\t\tif flag < 11:\n\t\t\ttrain_set.append(i)\n\t\telif flag == 11:\n\t\t\tvalid_set.append(i)\n\t\telse:\n\t\t\ttest_set.append(i)\n\n\tprint(\"valid days: \", len(date))\n\tprint(\"train set days: \", len(train_set))\n\tprint(\"valid set days: \", len(valid_set))\n\tprint(\"test set days: \", len(test_set))\n\n\ttrain_set_data = []\n\tvalid_set_data = []\n\ttest_set_data = []\n\tfor dt,idx in zip(valid_datetime.astype(str), valid_datetime_idx):\n\t\tt_date = dt[:8]\n\t\tif t_date in train_set:\n\t\t\ttrain_set_data.append([dt,idx])\n\t\telif t_date in valid_set:\n\t\t\tvalid_set_data.append([dt,idx])\n\t\telse:\n\t\t\ttest_set_data.append([dt,idx])\n\n\tprint(\"train set samples: \", len(train_set_data))\n\tprint(\"valid set samples: \", len(valid_set_data))\n\tprint(\"test set samples: \", len(test_set_data))\t\n\n\t#assert(np.sum(valid_datetime[:,1]) == valid_datetime.shape[0])\n\treturn [train_set_data, valid_set_data, test_set_data]","def _load_data(self, ti, tf):\n # return pre loaded\n try:\n if ti == self.ti_prev and tf == self.tf_prev:\n return self.fM, self.ys\n except AttributeError:\n pass\n\n # read from CSV file\n try:\n t = pd.to_datetime(pd.read_csv(self.featfile, index_col=0, parse_dates=['time'], usecols=['time'], infer_datetime_format=True).index.values)\n if (t[0] <= ti) and (t[-1] >= tf):\n self.ti_prev = ti\n self.tf_prev = tf\n fM,ys = self._extract_features(ti,tf)\n self.fM = fM\n self.ys = ys\n return fM,ys\n except FileNotFoundError:\n pass\n\n # range checking\n if tf > self.data.tf:\n raise ValueError(\"Model end date '{:s}' beyond data range '{:s}'\".format(tf, self.data.tf))\n if ti < self.data.ti:\n raise ValueError(\"Model start date '{:s}' predates data range '{:s}'\".format(ti, self.data.ti))\n \n # divide training period into years\n ts = [datetime(*[yr, 1, 1, 0, 0, 0]) for yr in list(range(ti.year+1, tf.year+1))]\n if ti - self.dtw < self.data.ti:\n ti = self.data.ti + self.dtw\n ts.insert(0,ti)\n ts.append(tf)\n\n for t0,t1 in zip(ts[:-1], ts[1:]):\n print('feature extraction {:s} to {:s}'.format(t0.strftime('%Y-%m-%d'), t1.strftime('%Y-%m-%d')))\n fM,ys = self._extract_features(ti,t1)\n\n self.ti_prev = ti\n self.tf_prev = tf\n self.fM = fM\n self.ys = ys\n return fM, ys","def load(train_file, test_file):\n print('\\nLoad the raw training and test set data...')\n y_train, tx_train, ids_train = load_csv_data(train_file)\n y_test, tx_test, ids_test = load_csv_data(test_file)\n print('\\n... finished.')\n return y_train, tx_train, ids_train, y_test, tx_test, ids_test","def read_ct_data(train_start, train_count, eval_start, eval_count):\n data = pd.read_csv('/opt/train.csv')\n\n # Dropping the id column\n data.drop(['ID_code'], axis=1, inplace=True)\n\n data = data.values\n return (data[train_start:train_start + train_count],\n data[eval_start:eval_start + eval_count])","def prep_data_fn(self, st_train_dt, end_train_dt, st_val_dt, end_val_dt, st_test_dt, end_test_dt):\n df = self.get_prep_data()\n train = df[(df['ft_data_dt'] >= st_train_dt) & (df['ft_data_dt'] <= end_train_dt)]\n val = df[(df['ft_data_dt'] >= st_val_dt) & (df['ft_data_dt'] <= end_val_dt)].sample(frac=0.4, random_state=2021)\n test = df[(df['ft_data_dt'] >= st_test_dt) & (df['ft_data_dt'] <= end_test_dt)]\n print(f'----train----')\n print(train[['ft_data_dt', 'target', 'idd']].groupby(['ft_data_dt', 'target']).agg(['count']))\n print(f'----validation----')\n print(val[['ft_data_dt', 'target', 'idd']].groupby(['ft_data_dt', 'target']).agg(['count']))\n print(f'----test----')\n print(test[['ft_data_dt', 'target', 'idd']].groupby(['ft_data_dt', 'target']).agg(['count']))\n self.set_train(train)\n self.set_validation(val)\n self.set_test(test)\n train_X = train[[c for c in train.columns if c not in ['idd', 'ft_data_dt', 'target']]]\n train_y = train['target']\n val_X = val[[c for c in train.columns if c not in ['idd', 'ft_data_dt', 'target']]]\n val_y = val['target']\n test_X = test[[c for c in train.columns if c not in ['idd', 'ft_data_dt', 'target']]]\n test_y = test['target']\n self.set_train_X(train_X)\n self.set_train_y(train_y)\n self.set_val_X(val_X)\n self.set_val_y(val_y)\n self.set_test_X(test_X)\n self.set_test_y(test_y)","def test_training_time(self):\n self.assertIsInstance(self.one_off_training.time, datetime.time)\n self.assertEqual(self.one_off_training.time, datetime.time(9, 0))","def train_test_dataset(self, train_rate=0.8):\n point_date = int(len(self.y) * train_rate)\n y_to_train = self.y[:point_date]\n y_to_val = self.y[point_date:]\n predict_date = len(self.y) - len(y_to_train) # the number of data points for the test set\n date_val = self.y.index[point_date]\n return y_to_train, y_to_val, predict_date, date_val","def test_02_train(self):\n today = date.today()\n log_file = os.path.join(LOG_DIR, \"{}-train-{}-{}.log\".format(LOG_PREFIX, today.year, today.month))\n \n ## update the log\n country = 'india'\n date_range = ('2017-11-29', '2019-05-24')\n metric = {'rmse':0.5}\n runtime = \"00:00:01\"\n model_version = 0.1\n model_version_note = \"test model\"\n \n update_train_log(country, date_range, metric, runtime,\n model_version, model_version_note ,test=True, prefix=LOG_PREFIX)\n\n df = pd.read_csv(log_file)\n logged_metric = [literal_eval(i) for i in df['metric'].copy()][-1]\n self.assertEqual(metric,logged_metric)","def load_data(train=True):\n#test set\n start = time.clock()\n\n if train==False:\n dp = '/home/thomas/Desktop/OttoChallenge/test.csv'\n df = pd.read_csv(dp)\n X = df.values.astype(np.float32)[:,1:]\n return X\n#train set\n else:\n dp = '/home/thomas/Desktop/OttoChallenge/train.csv'\n df = pd.read_csv(dp)\n X = df[df.columns[:-1]].values.astype(np.float32)[:,1:]\n y = df.target\n y =y.apply(lambda X: int(X[-1])).values\n y = y.astype(np.int32)\n X, y = shuffle(X, y)\n #print(X.shape,y.shape)\n end = time.clock()\n print(end-start)\n\n return X,y","def load_data(train_file, test_file):\n\n data = np.asarray(pd.read_csv(train_file, header=0))\n data_ts = np.asarray(pd.read_csv(test_file, header=0))\n\n x_tra = data[:, :-1]\n y_tra = data[:, -1]\n\n return x_tra, y_tra, data_ts","def load_trec(loc=DATA_DIR):\n train, test = [], []\n with open(os.path.join(loc, 'TREC', 'train_5500.label'), 'rb') as f:\n for line in f:\n train.append(line.strip())\n with open(os.path.join(loc, 'TREC', 'TREC_10.label'), 'rb') as f:\n for line in f:\n test.append(line.strip())\n return train, test","def read_timestamps(self, tasks):\n from reframe.core.deferrable import evaluate\n\n self.begin_stamps = []\n self.end_stamps = []\n for t in tasks:\n with open(evaluate(t.check.stdout), 'r') as f:\n self.begin_stamps.append(float(f.readline().strip()))\n self.end_stamps.append(float(f.readline().strip()))\n\n self.begin_stamps.sort()\n self.end_stamps.sort()","def load_data(trainfile, testfile):\n raw_train = pd.read_csv(trainfile, header=None)\n raw_test = pd.read_csv(testfile, header=None)\n train = raw_train.values\n test = raw_test.values\n train_features = train[0::, 1::]\n train_label = train[::, 0]\n test_features = test[0::, 1::]\n test_label = test[::, 0]\n train, cv , train_label, cv_label = train_test_split(train_features,train_label, test_size=0.33, random_state=42)\n return train, train_label, \\\n cv, cv_label, \\\n test_features, test_label","def test__load_training_set():\n classifier = classifier_module.Classifier(None)\n set = classifier._load_training_set('test')\n for i in range(0, 5):\n signal_list = set[i]\n assert signal_list[0].get_x() == 1.0 + i * 0.028\n assert signal_list[0].get_y() == 1.00 - i * i * 0.20 * 0.30\n\n assert signal_list[1].get_x() == 2.0 - i * 0.011\n assert signal_list[1].get_y() == 2.00 - i * 0.020","def test_read(self):\n df = dep.read_env(get_path('good_env.txt'))\n df2 = df[df['date'] == datetime.datetime(2010, 6, 5)]\n self.assertEqual(len(df2.index), 1)\n row = df2.iloc[0]\n self.assertEquals(row['runoff'], 86.3)","def test_one_off_training_date(self):\n self.assertIsInstance(self.one_off_training.date, datetime.date)\n self.assertEqual(\n self.one_off_training.date,\n datetime.date(2020, 6, 14)\n )","def test_validate_datetime(self):\n self.datatrue = pd.read_csv(pkg_resources.resource_filename(resource_package, 'tests/testing_data/report_2_counts.csv'))\n\n self.datafalse = pd.read_csv(pkg_resources.resource_filename(resource_package, 'tests/testing_data/random_date1.csv'))\n\n self.test1 = utils.validate_datetime(self.datatrue)\n\n self.test2 = utils.validate_datetime(self.datafalse)\n\n self.assertTrue(isinstance(self.test1, pd.DataFrame))\n\n self.assertTrue(np.dtype('datetime64[ns]') in self.test1.dtypes.tolist())\n\n self.assertFalse(np.dtype('datetime64[ns]') in self.test2.dtypes.tolist())","def get_data(t_idx):\n train_data = []\n train_label = []\n valid_data = []\n valid_label = []\n test_data = []\n test_label = []\n \n lines = []\n #f = open('/home/liwenzhe/myworkspace/is13/dataset/closingAdjLog.csv', 'rb')\n f = open('dataset/closingAdjLog.csv')\n reader = csv.reader(f)\n for row in reader:\n lines.append(row)\n lines.pop(0)\n \n n = len(lines)\n # use the first 80% for the training data, the next 10% for the validation data\n # and the last 10% for the test data. \n # TODO : create folds instead. \n for i in xrange(n-1, -1, -1):\n curr_line = lines[i]\n if i > n * 0.2:\n # add into training data\n if t_idx == 0:\n train_data.append(curr_line[t_idx+1:])\n else:\n train_data.append(curr_line[:t_idx-1] + curr_line[t_idx:])\n train_label.append(curr_line[t_idx]) \n elif i > n * 0.1:\n # add into validation data\n if t_idx == 0:\n valid_data.append(curr_line[t_idx+1:])\n else: \n valid_data.append(curr_line[:t_idx-1] + curr_line[t_idx:])\n valid_label.append(curr_line[t_idx]) \n else:\n # add into test data\n if t_idx == 0:\n test_data.append(curr_line[t_idx+1:])\n else:\n test_data.append(curr_line[:t_idx-1] + curr_line[t_idx:])\n test_label.append(curr_line[t_idx]) \n \n return [train_data, train_label, valid_data, valid_label, test_data, test_label]","def data_set_maker():\n\n # crate a folder in your code directory and name it: \"files\". put the .npy files iside that folder\n\n x_all = np.load(path + '/files/tinyX.npy', 'r') # reads the input file\n y_all = np.load(path + '/files/tinyY.npy', 'r') # reads the input file\n\n # split the data into 10% validation-set and 90% training set\n raw_train, raw_valid, y_train, y_valid = train_test_split(x_all, y_all, test_size=0.2, random_state=43)\n return raw_train, raw_valid, y_train, y_valid","def time_split_dataset(df, train_start_date, train_end_date, holdout_end_date, date_col):\n\n train_set = df.copy()[\n (df[date_col] >= train_start_date) & (df[date_col] <= train_end_date)]\n\n test_set = df.copy()[\n (df[date_col] > train_end_date) & (df[date_col] <= holdout_end_date)]\n\n return train_set, test_set","def _assess(self):\n # get eruptions\n with open(os.sep.join(getfile(currentframe()).split(os.sep)[:-2]+['data','eruptive_periods.txt']),'r') as fp:\n self.tes = [datetimeify(ln.rstrip()) for ln in fp.readlines()]\n # check if data file exists\n self.exists = os.path.isfile(self.file)\n if not self.exists:\n t0 = datetime(2011,1,1)\n t1 = datetime(2011,1,2)\n self.update(t0,t1)\n # check date of latest data in file\n self.df = pd.read_csv(self.file, index_col=0, parse_dates=[0,], infer_datetime_format=True)\n self.ti = self.df.index[0]\n self.tf = self.df.index[-1]","def test_sample_date(self):\r\n self.assertEqual(self.test_sample.date, '2018-08-02 22:32:23')","def check_dataset_dates(self):\n # TODO: graph traverse and date checking\n pass","def get_train_data():\n # train set\n train = pd.read_csv(\"train.csv\")\n\n return train","def get_datetime_diffs(self, train: NumpyOrPandas) -> Optional[LAMLTransformer]:\n base_dates, datetimes = self.get_cols_for_datetime(train)\n if len(datetimes) == 0 or len(base_dates) == 0:\n return\n\n dt_processing = SequentialTransformer(\n [\n ColumnsSelector(keys=list(set(datetimes + base_dates))),\n BaseDiff(base_names=base_dates, diff_names=datetimes),\n ]\n )\n return dt_processing","def load_raw_data(self, nrows=None, save_to_hdf=False):\n train = load_train(nrows=nrows, save_to_hdf=save_to_hdf)\n test = load_test(save_to_hdf=save_to_hdf)\n\n train.drop(self.drop_features, axis=1, inplace=True)\n test.drop(self.drop_features, axis=1, inplace=True)\n \n correct_dates(train)\n to_datetime(train, keep_dates=True)\n correct_dates(test)\n to_datetime(test, keep_dates=True)\n test.loc[test.listen_type == 0, 'listen_type'] = 1\n \n train['diff_days'] = (train.dt_listen - train.dt_media).dt.days\n test['diff_days'] = (test.dt_listen - test.dt_media).dt.days\n train.drop(['dt_listen', 'dt_media',], axis=1, inplace=True)\n test.drop(['dt_listen', 'dt_media',], axis=1, inplace=True)\n \n train.to_pickle('../input/train_clean.pkl')\n test.to_pickle('../input/test_clean.pkl')\n\n self.train = train\n self.test = test","def test_set_get_dt():\n data = io.create_sample_dataset()\n assert data.attrs[\"dt\"] == 1.0\n assert data.piv.dt == 1.0\n data.piv.set_dt(2.0)\n assert data.attrs[\"dt\"] == 2.0","def loadtrainData():\n train_x = []\n train_y = []\n fileIn = open(PATH + 'traindata_Subtask4.txt')\n for line in fileIn.readlines():\n lineArr = line.strip().split()\n train_x.append([float(lineArr[i]) for i in range(len(lineArr) - 1)])\n train_y.append(int(lineArr[-1]))\n return np.mat(train_x), np.mat(train_y).transpose()","def read_training(index_columns=None, both=False, weather=False):\n if weather:\n raw_X_train = pd.read_csv('data\\\\train_X.csv', parse_dates=['date'])\n raw_weather = pd.read_csv('data\\\\weather_data.csv', parse_dates=['date'])\n\n raw_X_train = ffill_nans(raw_X_train)\n raw_X_train = raw_X_train.merge(raw_weather, how='left', on=['date','hour'])\n raw_X_train = raw_X_train.set_index(index_columns)\n\n else:\n raw_X_train = pd.read_csv(\n 'data\\\\train_X.csv',\n parse_dates=['date'],\n index_col=index_columns)\n if both:\n raw_y_train = pd.read_csv(\n 'data\\\\train_y.csv',\n parse_dates=['date'],\n index_col=index_columns)\n\n return raw_X_train, raw_y_train\n \n return raw_X_train","def test_dates_and_Datetimes(self):\n if self.skip_tests:\n return\n recipe = (\n self.recipe()\n .dimensions(\"year_by_format\")\n .metrics(\"count\")\n .order_by(\"year_by_format\")\n )\n self.assertRecipeCSV(\n recipe,\n \"\"\"\n year_by_format,count,year_by_format_id\n 2005-01-01 00:00:00,1,2005-01-01 00:00:00\n 2013-01-01 00:00:00,1,2013-01-01 00:00:00\n \"\"\",\n )\n recipe = (\n self.recipe()\n .dimensions(\"year_by_format\")\n .metrics(\"count\")\n .order_by(\"-year_by_format\")\n )\n self.assertRecipeCSV(\n recipe,\n \"\"\"\n year_by_format,count,year_by_format_id\n 2013-01-01 00:00:00,1,2013-01-01 00:00:00\n 2005-01-01 00:00:00,1,2005-01-01 00:00:00\n \"\"\",\n )\n\n # Test a month() conversion\n recipe = (\n self.recipe()\n .dimensions(\"test_month\")\n .metrics(\"age\", \"count\")\n .order_by(\"-test_month\")\n )\n self.assertRecipeCSV(\n recipe,\n \"\"\"\n test_month,age,count,test_month_id\n 2015-05-01,10,1,2015-05-01\n 2015-01-01,5,1,2015-01-01\n \"\"\",\n )","def time_series_train_test_split_y(df, test_date_start):\n test_date_cutoff = datetime.strptime(test_date_start, '%Y-%m-%d')\n train_date_cutoff = test_date_cutoff - timedelta(1)\n \n y = df['AverageTemperature']\n y_train = y.loc[:train_date_cutoff]\n y_test = y.loc[test_date_cutoff:]\n \n return y, y_train, y_test","def load_data(path):\n train = pd.read_csv(os.path.join(path,'train.csv'))\n test = pd.read_csv(os.path.join(path,'test.csv'))\n \n return train, test","def load_train_test_transactions(train_size=0.7):\n X, y = features_target_split()\n X_train, X_test, y_train, y_test = train_test_split(X,y,train_size=train_size, random_state=7)\n print('\\nTraining and testing data creation successful\\n')\n return X_train, X_test, y_train,y_test","def getTrainTimes(t1,testTimes):\n trn=t1.copy(deep=True)\n for i,j in testTimes.iteritems():\n df0=trn[(i<=trn.index)&(trn.index<=j)].index # train starts within test\n df1=trn[(i<=trn)&(trn<=j)].index # train ends within test\n df2=trn[(trn.index<=i)&(j<=trn)].index # train envelops test\n trn=trn.drop(df0.union(df1).union(df2))\n return trn","def test_data_set_load(data_set):\n data = data_set()\n train_data, train_labels, test_data, test_labels = data.load_data()\n\n assert len(train_data) > 0\n assert len(test_data) > 0\n assert len(train_data) == len(train_labels)\n assert len(test_data) == len(test_labels)","def __init__(self):\r\n \r\n #self.max_stocks = 100\r\n self.max_stocks = 200\r\n \"\"\" cv_factor determines what portion of stocks to put in cross validation set and what portion\r\n to leave in training set. cv_factor = 2 means every other stock goes into cross validation\r\n set. cv_factor = 3 means every third stock goes into cross validation set \"\"\"\r\n self.cv_factor = 2 \r\n \"\"\" future_day is how many training days in the future we train for. Setting future_day = 25\r\n means we are measuring how the stock does 25 days out \"\"\"\r\n self.future_day = 25\r\n \"\"\" The train_dates are the dates for training and cross validation\"\"\"\r\n self.train_dates = []\r\n first_train_date = dateutl.days_since_1900('2001-01-01')\r\n num_train_dates = 10\r\n train_date_increment = 60\r\n self.train_dates.append(first_train_date)\r\n for iday in range(1,num_train_dates):\r\n last_train_date = self.train_dates[iday-1]\r\n self.train_dates.append(last_train_date + train_date_increment)\r\n \"\"\"self.train_dates[1] -= 1 \"\"\"\r\n \r\n \"\"\" test_dates are the dates we are using for testing \"\"\"\r\n self.test_dates = []\r\n first_test_date = dateutl.days_since_1900('2010-01-01')\r\n num_test_dates = 10\r\n test_date_increment = 60\r\n self.test_dates.append(first_test_date) \r\n for iday in range(1,num_test_dates):\r\n last_test_date = self.test_dates[iday-1]\r\n self.test_dates.append(last_test_date + test_date_increment)\r\n \"\"\"self.test_dates[1] -= 1\r\n self.test_dates[3] += 1\r\n self.test_dates[4] += 3\r\n self.test_dates[5] += 4\r\n self.test_dates.append(dateutl.days_since_1900('2010-01-01'))\r\n self.test_dates.append(dateutl.days_since_1900('2010-03-01'))\r\n self.test_dates.append(dateutl.days_since_1900('2010-05-01'))\r\n self.test_dates.append(dateutl.days_since_1900('2010-07-01'))\r\n self.test_dates.append(dateutl.days_since_1900('2010-09-01'))\r\n self.test_dates.append(dateutl.days_since_1900('2010-11-01'))\"\"\"\r\n \"\"\"train_history_days and train_increment set how many historical days we use to\r\n train and the increment used. Setting train_history_days = 21 and train_increment = 5\r\n means we are using the values at days days 5, 10, 15 and 20 days before the reference day\r\n as input features \"\"\"\r\n self.train_days = 21\r\n self.train_increment = 5\r\n self.features = ['rsi','tsi','ppo','adx','dip14','dim14','cci', \\\r\n 'cmo','mfi','natr','roc','stoch','uo']\r\n \"\"\" output is just a boolean about calling the output function to write out \r\n appropriate X and y matricies. The default is False meaning do not write out\r\n matricies \"\"\"\r\n self.output = False","def test_01_train(self):\n today = date.today()\n log_file = os.path.join(LOG_DIR, \"{}-train-{}-{}.log\".format(LOG_PREFIX, today.year, today.month))\n if os.path.exists(log_file):\n os.remove(log_file)\n \n ## update the log\n country = 'india'\n date_range = ('2017-11-29', '2019-05-24')\n metric = {'rmse':0.5}\n runtime = \"00:00:01\"\n model_version = 0.1\n model_version_note = \"test model\"\n \n update_train_log(country, date_range, metric, runtime,\n model_version, model_version_note, test=True, prefix=LOG_PREFIX)\n\n self.assertTrue(os.path.exists(log_file))","def test_dates(self):\n result = export.processExport(houseId=1,\n startDate = datetime.datetime(2013, 01, 06) #5 Days\n )\n\n self.assertEqual(result.shape, (1440, 2))\n self.assertEqual(result.index[0], datetime.datetime(2013, 01, 06))\n self.assertEqual(result.index[-1], datetime.datetime(2013, 01, 10, 23, 55))\n\n\n #Stop at 00:00 on the 5th\n result = export.processExport(houseId=1,\n endDate = datetime.datetime(2013, 01, 05, 23, 55) #5 Days\n )\n\n self.assertEqual(result.shape, (1440, 2))\n self.assertEqual(result.index[0], datetime.datetime(2013, 01, 01))\n self.assertEqual(result.index[-1], datetime.datetime(2013, 01, 05, 23, 55))","def Train_data():\n print (\"loading train data ...\")\n time_start = time.time()\n data_root = '/media/keziwen/86AA9651AA963E1D'\n with h5py.File(join(data_root, './data/train_real2.h5')) as f:\n data_real = f['train_real'][:]\n num, nt, ny, nx = data_real.shape\n data_real = np.transpose(data_real, (0, 1, 3, 2))\n with h5py.File(join(data_root, './data/train_imag2.h5')) as f:\n data_imag = f['train_imag'][:]\n num, nt, ny, nx = data_imag.shape\n data_imag = np.transpose(data_imag, (0, 1, 3, 2))\n data = data_real+1j*data_imag\n num_train = 15000\n num_validate = 2000\n train_data = data[0:num_train]\n validate_data = data[num_train:num_train+num_validate]\n\n train_data = np.random.permutation(train_data)\n\n time_end = time.time()\n print ('dataset has been created using {}s'.format(time_end-time_start))\n return train_data, validate_data","def train(self):\n acc_time = []\n data_test = self.val_data[0][0][0]\n labels_test = self.val_data[0][0][1]\n for i, train_batch in enumerate(self.dataset):\n \n writerDIM = SummaryWriter('runs/experiment_DIM'+str(i))\n data,labels, t = train_batch\n\n index_tr,index_cv,coreset = data_split(data.shape[0],777)\n\n # adding eventual replay patterns to the current batch\n if i == 0:\n ext_mem = [data[coreset], labels[coreset]]\n dataC = np.concatenate((data[index_tr], data[index_cv]),axis=0)\n labC = np.concatenate((labels[index_tr],labels[index_cv]),axis=0)\n else:\n dataP = ext_mem[0]\n labP = ext_mem[1]\n\n ext_mem = [\n np.concatenate((data[coreset], ext_mem[0])),\n np.concatenate((labels[coreset], ext_mem[1]))]\n if self.replay:\n dataC = np.concatenate((data[index_tr], data[index_cv],dataP),axis=0)\n labC = np.concatenate((labels[index_tr],labels[index_cv],labP),axis=0)\n else:\n dataC = np.concatenate((data[index_tr], data[index_cv]),axis=0)\n labC = np.concatenate((labels[index_tr],labels[index_cv]),axis=0)\n\n\n\n print(\"----------- batch {0} -------------\".format(i))\n print(\"Task Label: \", t)\n trC,cvC = data_split_Tr_CV(dataC.shape[0],777)\n\n train_set = LoadDataset(dataC,labC,transform=self.tr,indices=trC)\n val_set = LoadDataset(dataC,labC,transform=self.tr,indices=cvC)\n print('Training set: {0} \\nValidation Set {1}'.format(train_set.__len__(),val_set.__len__()))\n batch_size=32\n train_loader = DataLoader(train_set, batch_size=batch_size, shuffle=True)\n valid_loader = DataLoader(val_set, batch_size=batch_size, shuffle=False)\n dataloaders = {'train':train_loader,'val':valid_loader}\n \n ####### Set hyperparameters for the training\n if i ==0: \n prior = False\n ep=40\n dim_model = DIM_model(batch_s=32,num_classes =128,feature=True) \n dim_model.to(self.device)\n classifierM = _classifier(n_input=128,n_class=50,n_neurons=[256,256,128])\n classifierM = classifierM.to(self.device)\n writer = SummaryWriter('runs/experiment_C'+str(i))\n lr_new = 0.00001\n lrC=0.0001\n \n else:\n prior = True\n ep=6\n \n lr_new =0.000005\n lrC = 0.00005\n\n optimizer = torch.optim.Adam(dim_model.parameters(),lr=lr_new)\n scheduler = lr_scheduler.StepLR(optimizer,step_size=40,gamma=0.1) #there is also MultiStepLR\n\n tr_dict_enc = {'ep':ep,'writer':writerDIM,'best_loss':1e10,'t_board':True,\n 'gamma':.5,'beta':.5,'Prior_Flag':prior,'discriminator':classifierM} \n tr_dict_cl = {'ep':40,'writer':writer,'best_loss':1e10,'t_board':True,'gamma':1}#40\n\n if i==0 and self.load:\n print('Load DIM model weights first step')\n dim_model.load_state_dict(torch.load(self.path + 'weights/weightsDIM_T0.pt'))\n else:\n ############################## Train Encoder########################################\n dim_model,self.stats = trainEnc_MI(self.stats,dim_model, optimizer, scheduler,dataloaders,self.device,tr_dict_enc)\n ####################################################################################\n if i==0:\n torch.save(dim_model.state_dict(), self.path + 'weights/weightsDIM_T'+str(i)+'.pt')\n\n ####\n #Conversion of image into latent space representation for classifier training\n ####\n dim_model.requires_grad_(False)\n for phase in ['train','val']:\n dataF = None\n labF = None\n for inputs, labels in dataloaders[phase]:\n torch.cuda.empty_cache()\n if len(inputs.shape)==5:\n\n inputs = inputs[:,:,:,:,0].to(self.device)\n else:\n inputs = inputs.to(self.device)\n\n _,_,pred = dim_model(inputs)\n pred_l = pred.data.cpu().numpy()\n if dataF is None:\n dataF = pred_l\n labF = labels.data.cpu().numpy()\n else:\n dataF = np.concatenate((dataF,pred_l),axis=0)\n labF = np.concatenate((labF,labels.data.cpu().numpy()),axis=0)\n\n if phase == 'train':\n dataTr_f = dataF\n labTr_f = labF\n else:\n dataCv_f = dataF\n labCv_f = labF\n \n dim_model.requires_grad_(True)\n train_set = LoadFeat(dataTr_f,labTr_f)\n val_set = LoadFeat(dataCv_f,labCv_f)\n batch_size=32\n\n train_loader = DataLoader(train_set, batch_size=batch_size, shuffle=True)\n valid_loader = DataLoader(val_set, batch_size=batch_size, shuffle=False)\n dataloaderC = {'train':train_loader,'val':valid_loader}\n\n optimizerC = torch.optim.Adam(classifierM.parameters(),lr=lrC)\n schedulerC = lr_scheduler.StepLR(optimizerC,step_size=40,gamma=0.1)\n classifierM.requires_grad_(True)\n\n ############################## Train Classifier ########################################\n classifierM,self.stats = train_classifier(self.stats,classifierM, optimizerC, schedulerC,dataloaderC,self.device,tr_dict_cl) \n #################################### #################################### ##############\n\n torch.save(classifierM.state_dict(), self.path + 'weights/weightsC_T'+str(i)+'.pt')\n dim_model.eval()\n classifierM.eval()\n #### Cross_val Testing\n \n test_set = LoadDataset(data_test,labels_test,transform=self.trT)\n batch_size=32\n test_loader = DataLoader(test_set, batch_size=batch_size, shuffle=False)\n score= []\n\n for inputs, labels in test_loader:\n torch.cuda.empty_cache()\n inputs = inputs.to(self.device)\n labels = labels.to(self.device) \n _,_,ww =dim_model(inputs)\n pred = classifierM(ww)\n pred_l = pred.data.cpu().numpy()\n score.append(np.sum(np.argmax(pred_l,axis=1)==labels.data.cpu().numpy())/pred_l.shape[0])\n print('TEST PERFORMANCES:', np.asarray(score).mean())\n acc_time.append(np.asarray(score).mean())\n del test_set,test_loader\n \n self.dim_model = dim_model\n self.classifierM = classifierM\n acc_time = np.asarray(acc_time)\n return self.stats,acc_time","def test_time_dicts():\n dmd = DMD()\n dmd.fit(X=sample_data_1, Y=sample_data_2)\n expected_dict = {\"dt\": 1, \"t0\": 0, \"tend\": 13}\n np.testing.assert_equal(dmd.original_time, expected_dict)\n np.testing.assert_equal(dmd.dmd_time, expected_dict)","def test_model_time_series(self):\n job = DarshanIngestedJob(label=\"job\", file_details={})\n job.time_start = 0\n\n # With no file data, we should end up with empty time series\n series = job.model_time_series()\n self.assertEqual(len(series), 0)\n\n # Otherwise, there should be time-series created with the correct totals\n\n file1 = DarshanIngestedJobFile(name=\"file1\")\n file2 = DarshanIngestedJobFile(name=\"file2\")\n\n file1.bytes_read = 1024 * 99\n file2.bytes_read = 1024 * 100\n file1.read_time_start = 11.2\n file1.read_time_end = 13.2\n file2.read_time_start = 13.5\n file2.read_time_end = 15.0\n\n file1.bytes_written = 1024 * 101\n file2.bytes_written = 1024 * 102\n file1.write_time_start = 19.2\n file1.write_time_end = 21.3\n file2.write_time_start = 32\n file2.write_time_end = 35\n\n job.file_details = {\"file1\": file1, \"file2\": file2}\n\n series = job.model_time_series()\n self.assertIn('kb_read', series)\n self.assertIn('kb_write', series)\n reads = series['kb_read']\n writes = series['kb_write']\n\n self.assertEqual(reads.sum, 199)\n self.assertEqual(writes.sum, 203)\n self.assertEqual(len(reads.xvalues), 2)\n self.assertEqual(len(writes.xvalues), 2)\n self.assertEqual(len(reads.yvalues), 2)\n self.assertEqual(len(writes.yvalues), 2)\n self.assertEqual(set(reads.xvalues), {11.2, 13.5})\n self.assertEqual(set(writes.xvalues), {19.2, 32})\n self.assertEqual(set(reads.yvalues), {99.0, 100.0})\n self.assertEqual(set(writes.yvalues), {101, 102})","def get_ts_properties(self):\r\n filename = self.C.training_set[:-3] + \"csv\"\r\n self.ts_properties = util.load_ts_properties(csv_path=filename)","def train(self, trainfile):","def test_003_not_enough_datetimes() -> None:\n df = generate_test_data()\n df = df.head(2)\n skim(df)","def train_predict(train_file_arg):\n\n # Load command line arguments\n train_file = train_file_arg\n parameter_file = \"prophet/lstm/trainingConfig.json\"\n\n # Load training parameters\n params = json.loads(open(parameter_file).read())\n\n # Load time series dataset, and split it into train and test\n x_train, y_train, x_test, _, x_test_raw, _,\\\n last_window_raw, last_window, last_datetime_epoch = dataHelper.load_timeseries(train_file, params)\n\n # Build RNN (LSTM) model\n lstm_layer = [1, params[\"window_size\"], params[\"hidden_unit\"], 1]\n model = buildModel.rnn_lstm(lstm_layer, params)\n\n # Train RNN (LSTM) model with train set\n model.fit(\n x_train,\n y_train,\n batch_size=params[\"batch_size\"],\n epochs=params[\"epochs\"],\n validation_split=params[\"validation_split\"])\n\n # Check the model against test set\n predicted = buildModel.predict_next_timestamp(model, x_test)\n predicted_raw = []\n for i in range(len(x_test_raw)):\n predicted_raw.append((predicted[i] + 1) * x_test_raw[i][0])\n\n # Predict next time stamp\n next_timestamp = buildModel.predict_next_timestamp(model, last_window)\n next_timestamp_raw = (next_timestamp[0] + 1) * last_window_raw[0][0]\n print(\"The next time stamp forecasting is: {}\".format(next_timestamp_raw))\n\n # Add 5 minutes for a new timestamp of predictions\n last_datetime = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime(last_datetime_epoch))\n last_datetime = datetime.strptime(last_datetime, \"%Y-%m-%d %H:%M:%S\")\n new_datetime = last_datetime + timedelta(seconds=300)\n new_datetime_epoch = time.mktime(new_datetime.timetuple())\n new_datetime = new_datetime.strftime(\"%Y-%m-%d %H:%M:%S\")\n\n # Concatenate datetime and price forecast\n new_entry = \"coin_name,\" + str(new_datetime_epoch) + \",\" \\\n + str(next_timestamp_raw) + \",coin_supply\" + \",coin_mc\" + \"\\n\"\n\n # Write to CSV file of new prediction\n fd = open(train_file, \"a\")\n fd.write(new_entry)\n fd.close()\n\n # Return new prediction\n return [new_datetime, str(next_timestamp_raw)]","def merge_data():\n\n\tconfig = Config()\n\tfilename_train, filename_test = \"../data/train.csv\", \"../data/test.csv\" \n\n # create datasets\n\ttrain, test = config.load_data(filename_train, filename_test, print_EDA=False)\n\n # 1. datetime features\n\t# diff between weekday and day?\n\t#weekday - Return the day of the week as an integer, where Monday is 0 and Sunday is 6.\n\t#day - Between 1 and the number of days in the given month of the given year.\n\ttrain['pickup_hour'] = train.pickup_datetime.dt.hour.astype('uint8')\n\ttrain['pickup_day'] = train.pickup_datetime.dt.day.astype('uint8')\n\ttrain['pickup_weekday'] = train.pickup_datetime.dt.weekday.astype('uint8')\n\ttrain['pickup_minute'] = train.pickup_datetime.dt.minute.astype('uint8')\n\ttrain['pickup_month'] = train.pickup_datetime.dt.month.astype('uint8')\n\ttrain['pickup_hour_weekofyear'] = train['pickup_datetime'].dt.weekofyear\n\ttrain['pickup_weekday_hour'] = train['pickup_weekday']*24 + train['pickup_hour']\n\n\ttest['pickup_hour'] = test.pickup_datetime.dt.hour.astype('uint8')\n\ttest['pickup_day'] = test.pickup_datetime.dt.day.astype('uint8')\n\ttest['pickup_weekday'] = test.pickup_datetime.dt.weekday.astype('uint8')\n\ttest['pickup_minute'] = test.pickup_datetime.dt.minute.astype('uint8')\n\ttest['pickup_month'] = test.pickup_datetime.dt.month.astype('uint8')\n\ttest['pickup_hour_weekofyear'] = test['pickup_datetime'].dt.weekofyear\n\ttest['pickup_weekday_hour'] = test['pickup_weekday']*24 + test['pickup_hour']\n\n\t# 2. Location features\n\tdef haversine(lon1, lat1, lon2, lat2):\n\t lon1, lat1, lon2, lat2 = map(np.radians, [lon1, lat1, lon2, lat2])\n\t dlon = lon2 - lon1\n\t dlat = lat2 - lat1\n\t a = np.sin(dlat/2.0)**2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlon/2.0)**2\n\t c = 2 * np.arcsin(np.sqrt(a))\n\t km = 6367 * c # AVG_EARTH_RADIUS=6367\n\t miles = km * 0.621371\n\t return miles\n\n\t# def dummy_manhattan_distance(lat1, lng1, lat2, lng2):\n\t# a = haversine_array(lat1, lng1, lat1, lng2)\n\t# b = haversine_array(lat1, lng1, lat2, lng1)\n\t# return a + b\n\n\t# def bearing_array(lat1, lng1, lat2, lng2):\n\t# AVG_EARTH_RADIUS = 6371 # in km\n\t# lng_delta_rad = np.radians(lng2 - lng1)\n\t# lat1, lng1, lat2, lng2 = map(np.radians, (lat1, lng1, lat2, lng2))\n\t# y = np.sin(lng_delta_rad) * np.cos(lat2)\n\t# x = np.cos(lat1) * np.sin(lat2) - np.sin(lat1) * np.cos(lat2) * np.cos(lng_delta_rad)\n\t# return np.degrees(np.arctan2(y, x))\n\n\ttrain['distance'] = haversine(train.pickup_longitude, train.pickup_latitude,\n\t train.dropoff_longitude, train.dropoff_latitude)\n\ttest['distance'] = haversine(test.pickup_longitude, test.pickup_latitude,\n\t test.dropoff_longitude, test.dropoff_latitude)\n\n\n\t# 3. Use outsource data\n\tweatherdata_filename = \"../data/outsource_data/weather_data_nyc_centralpark_2016.csv\"\n\tfastestroute_data_train = \"../data/outsource_data/fastest_train.csv\"\n\tfastestroute_data_test = \"../data/outsource_data/fastest_routes_test.csv\"\n\n\n\twd = pd.read_csv(weatherdata_filename, header=0)\n\twd['date'] = pd.to_datetime(wd.date, format=\"%d-%m-%Y\")\n\twd['pickup_day'] = wd['date'].dt.day\n\twd['snow fall'] = wd['snow fall'].replace('T', 0.05).astype(np.float32) \n\twd['precipitation'] = wd['precipitation'].replace('T', 0.05).astype(np.float32) \n\twd['snow depth'] = wd['snow depth'].replace('T', 0.05).astype(np.float32) \n\n\t# Merge training data with weather data on pickup_day\n\tprint(\"Merging training data with weather data ....\")\n\twd_train = pd.merge(train, wd, on='pickup_day')\n\twd_train = wd_train.drop(['date','maximum temperature','minimum temperature'],axis=1)\n\tgc.collect()\n\n\t# Merge wd_train with fastestroute_data\n\tfastest = pd.read_csv(fastestroute_data_train, header=0)\n\tprint(\"Merging Location data with weather and training data ....\")\n\twd_train_fastest = pd.merge(wd_train, fastest, on='id', how='outer')\n\n\tgc.collect()\n\n\n\tprint(\"===================== CHECK TRAINING DATA =====================\")\n\n\tprint(wd_train_fastest.head(2))\n\tprint(\"Semi-final training data shape is: {}\".format(wd_train_fastest.shape))\n\tprint(\"Training data columns: {}\".format(wd_train_fastest.columns))\n\n\n\t# Use the same outsource data with test set\n\t# merge outsource data with test data as well\n\tft_test_cols = [ 'id', 'starting_street','end_street','total_distance',\t'total_travel_time',\n\t\t\t\t\t'number_of_steps','street_for_each_step','distance_per_step','travel_time_per_step',\n\t 'step_maneuvers','step_direction',\t'step_location_list']\n\tfastest_test = pd.read_csv(fastestroute_data_test, names=ft_test_cols, header=0)\n\n\tprint(\"Merging test data with Location data ....\")\n\ttest = pd.merge(test, fastest_test, on='id', how='outer')\n\ttest = test.drop(['step_location_list','step_direction','step_maneuvers','travel_time_per_step','distance_per_step','street_for_each_step','number_of_steps','starting_street',\n\t 'end_street'], axis=1)\n\tprint(\"Merging test data with weather data ....\")\n\ttest = pd.merge(test, wd, on='pickup_day')\n\n\tprint(\"===================== CHECK TEST DATA =====================\")\n\n\tprint(test.head(2))\n\tprint(\"Semi-final test data shape is: {}\".format(test.shape))\n\tprint(\"Test data columns: {}\".format(test.columns))\n\n\n\n\t# 4. Do more data munging\n\tmask = ((wd_train_fastest.trip_duration > 60) & (wd_train_fastest.distance < 0.05))\n\twd_train_fastest = wd_train_fastest[~mask]\n\tmask = (wd_train_fastest.trip_duration < 60) \n\twd_train_fastest = wd_train_fastest[~mask]\n\tmask = wd_train_fastest.trip_duration > 79200\n\twd_train_fastest = wd_train_fastest[~mask]\n\tmask = wd_train_fastest.distance/(wd_train_fastest.trip_duration/3600) > 60\n\twd_train_fastest = wd_train_fastest[~mask]\n\twd_train_fastest.trip_duration = wd_train_fastest.trip_duration.astype(np.uint16)\n\twd_train_fastest = wd_train_fastest[wd_train_fastest.passenger_count > 0]\n\n\t# 5. Do some data maskig based on location to create jfk and lgo features\n\tjfk_lon = -73.778889\n\tjfk_lat = 40.639722\n\tlga_lon = -73.872611\n\tlga_lat = 40.77725\n\n\twd_train_fastest['jfk_pickup_dist'] = wd_train_fastest.apply(lambda row: haversine(jfk_lon, jfk_lat, row['pickup_longitude'],row['pickup_latitude']), axis=1)\n\twd_train_fastest['lga_pickup_dist'] = wd_train_fastest.apply(lambda row: haversine(lga_lon, lga_lat, row['pickup_longitude'],row['pickup_latitude']), axis=1)\n\twd_train_fastest['jfk_dropoff_dist'] = wd_train_fastest.apply(lambda row: haversine(jfk_lon, jfk_lat, row['dropoff_longitude'],row['dropoff_latitude']), axis=1)\n\twd_train_fastest['lga_dropoff_dist'] = wd_train_fastest.apply(lambda row: haversine(lga_lon, lga_lat, row['dropoff_longitude'],row['dropoff_latitude']), axis=1)\n\n\twd_train_fastest['jfk'] = ((wd_train_fastest['jfk_pickup_dist'] < 2) | (wd_train_fastest['jfk_dropoff_dist'] < 2))\n\twd_train_fastest['lga'] = ((wd_train_fastest['lga_pickup_dist'] < 2) | (wd_train_fastest['lga_dropoff_dist'] < 2))\n\twd_train_fastest = wd_train_fastest.drop(['jfk_pickup_dist','lga_pickup_dist','jfk_dropoff_dist','lga_dropoff_dist'],axis=1)\n\twd_train_fastest['workday'] = ((wd_train_fastest['pickup_hour'] > 8) & (wd_train_fastest['pickup_hour'] < 18))\n\n\n\tprint(\"===================== CHECK TRAINING DATA AGAIN =====================\")\n\n\tprint(wd_train_fastest.head(2))\n\tprint(\"Final training data shape is: {}\".format(wd_train_fastest.shape))\n\tprint(\"Training data columns: {}\".format(wd_train_fastest.columns))\n\n\n\treturn wd_train_fastest, test","def load_dataset(path, test_or_train):\n senta_batch, sentb_batch, scores_batch = [], [], []\n with open(path, encoding='utf-8') as f:\n for i, line in enumerate(f):\n items = line.strip().split('\\t')\n if test_or_train == 'train':\n senta, sentb, score = items[-2], items[-1], float(items[-3])\n elif test_or_train in ['dev', 'test']:\n senta, sentb, score = items[-2], items[-1], float(items[-3])\n else:\n raise Exception(\"{} error\".format(test_or_train))\n senta_batch.append(senta)\n sentb_batch.append(sentb)\n scores_batch.append(score)\n return senta_batch, sentb_batch, scores_batch","def load_data(train_path, test_path):\n\n train_data = pd.read_csv(train_path)\n test_data = pd.read_csv(test_path)\n\n print(\"number of training examples = \" + str(train_data.shape[0]))\n print(\"number of test examples = \" + str(test_data.shape[0]))\n print(\"train shape: \" + str(train_data.shape))\n print(\"test shape: \" + str(test_data.shape))\n\n return train_data, test_data","def load_data(train_path, test_path):\n\n train_data = pd.read_csv(train_path)\n test_data = pd.read_csv(test_path)\n\n print(\"number of training examples = \" + str(train_data.shape[0]))\n print(\"number of test examples = \" + str(test_data.shape[0]))\n print(\"train shape: \" + str(train_data.shape))\n print(\"test shape: \" + str(test_data.shape))\n\n return train_data, test_data","def __test_and_train(self):\n f = open(\"all_data_info.csv\")\n reader = csv.DictReader(f, delimiter=\",\")\n data = []\n for line in reader:\n if line['artist_group'] == \"train_and_test\" and line[\"in_train\"] == \"False\":\n # the img's artist is in training set\n # but the img is in test set only\n data.append((line['artist'], line['new_filename']))\n\n return data","def test_sample_one_date(self):\r\n self.assertEqual(self.test_sample.date, datetime.datetime(2016, 2, 12, 7, 34, 26))","def test_convert_datetime():","def read(train_path, test_path, label_name):\n train_dataset = pd.read_csv(train_path)\n test_dataset = pd.read_csv(test_path)\n\n train_labels = train_dataset.pop(label_name)\n\n imputer = DataFrameImputer().fit(train_dataset)\n train_dataset = imputer.transform(train_dataset)\n test_dataset = imputer.transform(test_dataset)\n\n train_dataset = pd.get_dummies(train_dataset)\n test_dataset = pd.get_dummies(test_dataset)\n\n train_dataset = train_dataset.drop(train_dataset.columns.difference(test_dataset.columns), axis=1)\n test_dataset = test_dataset.drop(test_dataset.columns.difference(train_dataset.columns), axis=1)\n\n scaler = StandardScaler().fit(train_dataset)\n train_dataset = scaler.transform(train_dataset)\n test_dataset = scaler.transform(test_dataset)\n\n return train_dataset, train_labels, test_dataset","def get_data_set(train=True):\n\n # 1\n train_or_test = \"train\" if train == True else \"test\"\n data_path = os.path.join(data_dir, \"aclImdb\",train_or_test)\n\n # 2\n pos_glob_pattern = os.path.join(data_path, \"pos\", \"*.txt\")\n neg_glob_pattern = os.path.join(data_path, \"neg\", \"*.txt\")\n pos_file_path_seq = glob.glob(pos_glob_pattern)\n neg_file_path_seq = glob.glob(neg_glob_pattern)\n\n # 3\n pos_dataset = [text_to_one_line(path) for path in pos_file_path_seq]\n neg_dataset = [text_to_one_line(path) for path in neg_file_path_seq]\n x = pos_dataset + neg_dataset\n y = [1.0] * len(pos_dataset) + [0.0] * len(neg_dataset)\n\n return x, y","def set_training_data(self, *, inputs: Inputs, outputs: Outputs) -> None:\n\n # save copy of train data so we don't predict for each row in training\n self._output_columns = outputs.columns\n self._train_data = inputs.copy()\n\n # combine inputs and outputs for internal TimeSeries object\n self._ts_frame = inputs.append_columns(outputs)\n\n # Parse cols needed for ts object\n # TODO should only find cols to drop once!\n self._get_cols(self._ts_frame.metadata)\n\n # Mark time difference (between min and min + 1 timestamp)\n if self._grouping_column is None:\n self._max_train = max(self._ts_frame.iloc[:, self._timestamp_column])\n self._train_diff = int(\n np.diff(np.sort(self._ts_frame.iloc[:, self._timestamp_column]))[0]\n )\n else:\n g_col, t_col = (\n self._ts_frame.columns[self._grouping_column],\n self._ts_frame.columns[self._timestamp_column],\n )\n self._max_train = self._ts_frame.groupby(g_col)[t_col].agg(\"max\")\n # making simplifying assumption that difference is the same across all groups\n self._train_diff = int(\n self._ts_frame.groupby(g_col)[t_col]\n .apply(lambda x: np.diff(np.sort(x))[0])\n .iloc[0]\n )\n\n # assumption is that integer timestamps are days (treated this way by DeepAR objects)\n timestamp_semantic_types = self._ts_frame.metadata.query_column_field(\n self._timestamp_column, \"semantic_types\"\n )\n if \"http://schema.org/Integer\" in timestamp_semantic_types:\n self._integer_timestamps = True\n else:\n self._integer_timestamps = False\n\n # drop cols if multiple grouping columns\n if len(self._drop_cols) > 0:\n self._ts_frame = self._ts_frame.remove_columns(self._drop_cols)\n self._update_indices()\n\n # Create TimeSeries dataset object and learner\n self._create_data_object_and_learner(self.hyperparams[\"val_split\"])\n\n # mark that new training data has been set\n self._new_train_data = True","def get_training_data():\n \n X = pd.read_csv('../data/train_values.csv').set_index('sequence_id')\n y = pd.read_csv('../data/train_labels.csv').set_index('sequence_id')\n return X, y","def read_data(train_data_dir, test_data_dir):\r\n train_clients, train_data = read_dir(train_data_dir)\r\n test_clients, test_data = read_dir(test_data_dir)\r\n # 可能clients读入的顺序不一样\r\n assert train_clients.sort() == test_clients.sort()\r\n\r\n return train_clients, train_data, test_data","def load_data(loc='../data/SICK/'):\n trainA, trainB, testA, testB = [],[],[],[]\n trainS, testS = [],[]\n\n with open(loc + 'SICK_train.txt', 'rb') as f:\n for line in f:\n text = line.strip().split('\\t')\n trainA.append(text[1])\n trainB.append(text[2])\n trainS.append(text[3])\n with open(loc + 'SICK_test_annotated.txt', 'rb') as f:\n for line in f:\n text = line.strip().split('\\t')\n testA.append(text[1])\n testB.append(text[2])\n testS.append(text[3])\n\n trainS = [float(s) for s in trainS[1:]]\n testS = [float(s) for s in testS[1:]]\n\n return [trainA[1:], trainB[1:]], [testA[1:], testB[1:]], [trainS, testS]","def test_load_from_tsfile():\n data_path = MODULE + \"/data/UnitTest/UnitTest_TRAIN.ts\"\n # Test 1.1: load univariate equal length (UnitTest), should return 2D array and 1D\n # array, test first and last data\n # Test 1.2: Load a problem without y values (UnitTest), test first and last data.\n X, y = load_from_tsfile(data_path, return_data_type=\"np2D\")\n X2 = load_from_tsfile(data_path, return_y=False, return_data_type=\"np2D\")\n assert isinstance(X, np.ndarray) and isinstance(y, np.ndarray)\n assert X.ndim == 2 and X2.ndim == 2\n assert X.shape == (20, 24) and y.shape == (20,)\n assert X[0][0] == 573.0\n X2 = load_from_tsfile(data_path, return_y=False, return_data_type=\"numpy3D\")\n assert isinstance(X2, np.ndarray)\n assert X2.ndim == 3\n assert X2.shape == (20, 1, 24)\n assert X2[0][0][0] == 573.0\n\n # Test 2: load multivare equal length (BasicMotions), should return 3D array and 1D\n # array, test first and last data.\n data_path = MODULE + \"/data/BasicMotions/BasicMotions_TRAIN.ts\"\n X, y = load_from_tsfile(data_path, return_data_type=\"numpy3d\")\n assert isinstance(X, np.ndarray) and isinstance(y, np.ndarray)\n assert X.shape == (40, 6, 100) and y.shape == (40,)\n assert X[1][2][3] == -1.898794\n X, y = load_from_tsfile(data_path)\n assert isinstance(X, pd.DataFrame) and isinstance(y, np.ndarray)\n assert X.shape == (40, 6) and y.shape == (40,)\n assert isinstance(X.iloc[1, 2], pd.Series)\n assert X.iloc[1, 2].iloc[3] == -1.898794\n\n # Test 3.1: load univariate unequal length (PLAID), should return a one column\n # dataframe,\n data_path = MODULE + \"/data/PLAID/PLAID_TRAIN.ts\"\n X, y = load_from_tsfile(full_file_path_and_name=data_path)\n assert isinstance(X, pd.DataFrame) and isinstance(y, np.ndarray)\n assert X.shape == (537, 1) and y.shape == (537,)\n # Test 3.2: load multivariate unequal length (JapaneseVowels), should return a X\n # columns dataframe,\n data_path = MODULE + \"/data/JapaneseVowels/JapaneseVowels_TRAIN.ts\"\n X, y = load_from_tsfile(full_file_path_and_name=data_path)\n assert isinstance(X, pd.DataFrame) and isinstance(y, np.ndarray)\n assert X.shape == (270, 12) and y.shape == (270,)","def load_data(args):\n data_df = pd.read_csv(os.path.join(args.data_dir, 'driving_log.csv'))\n\n X = data_df[['center', 'left', 'right']].values\n y = data_df['steering'].values\n\n X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=args.test_size, random_state=0)\n\n return X_train, X_valid, y_train, y_valid","def parse_data():\n n_train, n_test = 15000, 4996\n n_features = 1355191\n\n print('- parsing train data')\n X_train = sp.lil_matrix((n_train, n_features))\n y_train = np.zeros(n_train)\n with open('/Users/kitazawa/data/news20.train') as f:\n lines = map(lambda l: l.rstrip().split(' '), f.readlines())\n for i, line in enumerate(lines):\n y_train[i] = int(line[0])\n\n for fv in line[1:]:\n f, v = fv.split(':')\n X_train[i, (int(f) - 1)] = float(v)\n print('-- density: %f' % (X_train.nnz / (n_train * n_features)))\n\n print('- parsing test data')\n X_test = sp.lil_matrix((n_test, n_features))\n y_test = np.zeros(n_test)\n with open('/Users/kitazawa/data/news20.test') as f:\n lines = map(lambda l: l.rstrip().split(' '), f.readlines())\n for i, line in enumerate(lines):\n y_test[i] = int(line[0])\n\n for fv in line[1:]:\n f, v = fv.split(':')\n X_test[i, (int(f) - 1)] = float(v)\n print('-- density: %f' % (X_test.nnz / (n_test * n_features)))\n\n return X_train, y_train, X_test, y_test","def load_data(conso_train_file='../input/conso_train.csv',\n meteo_train_file='../input/meteo_train.csv',\n meteo_test_file='../input/meteo_prev.csv'):\n # Loading the weather data\n train_meteo = pd.read_csv(meteo_train_file, sep=';')\n test_meteo = pd.read_csv(meteo_test_file, sep=';')\n\n # Marking the type and merge the train/test dataframes\n train_meteo['type'] = 'train'\n test_meteo['type'] = 'test'\n data_meteo = pd.concat([train_meteo, test_meteo])\n # Correct the weather data\n data_meteo = correct_weather_data(data_meteo)\n\n # Load the train consumption\n train_conso = pd.read_csv(conso_train_file, sep=';')\n # Correct the consumption data\n train_conso = correct_conso_data(train_conso)\n\n # Mark the type and merge the consumption/weather dataframes\n train_conso['type'] = 'train'\n data = pd.merge(train_conso, data_meteo,\n on=['date', 'type'], how='outer', sort=True)\n\n # Complete the test dates\n data = add_test_dates(data)\n # Correct the test data from the merging\n data = correct_test_data(data)\n\n return data","def islay(path):\n import pandas as pd\n path = os.path.expanduser(path)\n filename = 'islay.csv'\n if not os.path.exists(os.path.join(path, filename)):\n url = 'http://dustintran.com/data/r/boot/islay.csv'\n maybe_download_and_extract(path, url,\n save_file_name='islay.csv',\n resume=False)\n\n data = pd.read_csv(os.path.join(path, filename), index_col=0,\n parse_dates=True)\n x_train = data.values\n metadata = {'columns': data.columns}\n return x_train, metadata","def test_4_data_fetching_unix_time_and_insertion(self):\n d1 = date.today()\n dt1 = datetime(d1.year, d1.month, d1.day) + timedelta(hours=8)\n result, success = self.fitness.get_columns_given_range(dt1, dt1+timedelta(days=1))\n self.assertTrue(success)\n self.assertEqual(result[0]['Datetime'],self.unix_time)","def load_data():\r\n train = convert_corpus_to_lists(ConllCorpusReader('CoNLL-2003', 'train.txt', ['words', 'pos', 'ignore', 'chunk']))\r\n val = convert_corpus_to_lists(ConllCorpusReader('CoNLL-2003', 'valid.txt', ['words', 'pos', 'ignore', 'chunk'])) # testa will be our val set\r\n test = convert_corpus_to_lists(ConllCorpusReader('CoNLL-2003', 'test.txt', ['words', 'pos', 'ignore', 'chunk']))\r\n\r\n return train, val, test","def train(self, train_set=dt.treat_data(dt.load_data(\"data/train.csv\"))):\n if self.model is None:\n return\n my_callback = keras.callbacks.callbacks.EarlyStopping(monitor='loss', min_delta=0.0, patience=1000, verbose=2,\n mode='auto', baseline=None, restore_best_weights=False)\n\n train_data, train_labels = train_set\n history = self.model.fit(x=train_data, y=train_labels, epochs=100000, batch_size=45,\n callbacks=[my_callback], verbose=2, shuffle=True)\n self.model.save(\"titanic_\" + str(time.time()) + \".h5\")\n return history","def load_dustrak(filepath, tz):\n\n with open(filepath) as file:\n meta, values = re.split(\"\\n\\n\", file.read())\n\n metadata = {}\n for row in meta.splitlines():\n key, value = row.strip().split(\",\")\n metadata[key] = value\n\n data = pd.read_csv(io.StringIO(values))\n\n if data.columns[0] != \"Elapsed Time [s]\":\n raise ValueError(\"First column must be elapsed time in seconds\")\n\n start_time = \" \".join([metadata[\"Test Start Date\"], metadata[\"Test Start Time\"]])\n start_time = datetime.datetime.strptime(start_time, \"%m/%d/%Y %I:%M:%S %p\")\n\n local_timezone = pytz.timezone(tz)\n start_time = local_timezone.localize(start_time)\n start_time = start_time.astimezone(pytz.timezone(\"UTC\"))\n\n sample_interval_minutes = metadata[\"Test Interval [M:S]\"].split(\":\")[0]\n if sample_interval_minutes != \"0\":\n raise NotImplementedError(\"Minute sampling intervals not supported\")\n\n sample_offsets = np.array(data[\"Elapsed Time [s]\"], dtype=\"timedelta64[s]\")\n sample_times = pd.Timestamp(start_time) + pd.to_timedelta(sample_offsets)\n\n data[\"time\"] = sample_times\n data.sort_values(by=\"time\", inplace=True)\n\n return metadata, data","def load_tfile_data(self, path, start=None, stop=None, dss=28):\n filename = path+\".\"+str(int(self.name))\n datafile = open(filename,\"r\")\n labels = datafile.readline().strip().split()\n logger.debug(\"load_tfile_data: labels: %s\", labels)\n datafile.close()\n labels.insert(0,'DOY')\n labels.insert(0,'Year')\n logger.debug(\"load_tfile_data: new labels: %s\", labels)\n \n # colums are: Year DOY UTC Epoch Chan Tsys Int Az El Diode Level CryoTemp\n # i4 i4 S8 f8 S2 f4 f4 f4 f4 i4 f4 f4\n data = numpy.loadtxt(filename,skiprows=1,\n dtype = {'names': tuple(labels),\n 'formats': ('i4','i4','S8','f8',\n 'S2','f4','f4','f4','f4','i4','f4', 'f4')})\n return data","def load_data():\n df = pd.read_csv(\"../../Data/breast_cancer_data/data.csv\")\n\n cols = df.columns\n X = df[cols[2:-1]].to_numpy()\n y = df[cols[1]].to_numpy()\n y = (y=='M').astype(np.int) * 2 - 1\n\n train_X = X[:-150]\n train_y = y[:-150]\n\n test_X = X[-150:]\n test_y = y[-150:]\n\n return train_X, train_y, test_X, test_y","def readData(path_to_dataset, train_size=0.8, validation_size=0.2):\n data = pd.read_csv(os.path.join(path_to_dataset, 'training_set_rel3.tsv'), sep='\\t', encoding='ISO-8859-1')\n # Drop columns that has null value \n data = data.dropna(axis=1)\n # Only take 4 columns of data from the dataset: essay_id, essay_set, essay, domain1_score\n data = data[['essay_id', 'essay_set', 'essay', 'domain1_score']]\n # Perform 80:20 train-test split on the training data\n train_set, test_set = train_test_split(data, train_size=train_size, random_state=0)\n # Split the 80% training set further into 60:20\n training_set, validation_set = train_test_split(train_set, test_size=validation_size, random_state=0)\n return training_set, test_set, validation_set","def load_dataset_train():\n df_train = load_csv_file(\"31_train.csv\")\n df_train_target = load_csv_file(\"31_target_train.csv\")\n\n return df_train.values, df_train_target.values","def read_data(args):\n\n print(\"Start read_data\")\n t_tot = 0 # sum of times for the all dataset\n date_dirs = os.listdir(args.path_data_base)\n for n_iter, date_dir in enumerate(date_dirs):\n # get access to each sequence\n path1 = os.path.join(args.path_data_base, date_dir)\n if not os.path.isdir(path1):\n continue\n date_dirs2 = os.listdir(path1)\n\n for date_dir2 in date_dirs2:\n path2 = os.path.join(path1, date_dir2)\n if not os.path.isdir(path2):\n continue\n # read data\n oxts_files = sorted(glob.glob(os.path.join(path2, 'oxts', 'data', '*.txt')))\n oxts = KITTIDataset.load_oxts_packets_and_poses(oxts_files)\n\n \"\"\" Note on difference between ground truth and oxts solution:\n - orientation is the same\n - north and east axis are inverted\n - position are closed to but different\n => oxts solution is not loaded\n \"\"\"\n\n print(\"\\n Sequence name : \" + date_dir2)\n if len(oxts) < KITTIDataset.min_seq_dim: # sequence shorter than 30 s are rejected\n cprint(\"Dataset is too short ({:.2f} s)\".format(len(oxts) / 100), 'yellow')\n continue\n lat_oxts = np.zeros(len(oxts))\n lon_oxts = np.zeros(len(oxts))\n alt_oxts = np.zeros(len(oxts))\n roll_oxts = np.zeros(len(oxts))\n pitch_oxts = np.zeros(len(oxts))\n yaw_oxts = np.zeros(len(oxts))\n roll_gt = np.zeros(len(oxts))\n pitch_gt = np.zeros(len(oxts))\n yaw_gt = np.zeros(len(oxts))\n t = KITTIDataset.load_timestamps(path2)\n acc = np.zeros((len(oxts), 3))\n acc_bis = np.zeros((len(oxts), 3))\n gyro = np.zeros((len(oxts), 3))\n gyro_bis = np.zeros((len(oxts), 3))\n p_gt = np.zeros((len(oxts), 3))\n v_gt = np.zeros((len(oxts), 3))\n v_rob_gt = np.zeros((len(oxts), 3))\n\n k_max = len(oxts)\n for k in range(k_max):\n oxts_k = oxts[k]\n t[k] = 3600 * t[k].hour + 60 * t[k].minute + t[k].second + t[\n k].microsecond / 1e6\n lat_oxts[k] = oxts_k[0].lat\n lon_oxts[k] = oxts_k[0].lon\n alt_oxts[k] = oxts_k[0].alt\n acc[k, 0] = oxts_k[0].af\n acc[k, 1] = oxts_k[0].al\n acc[k, 2] = oxts_k[0].au\n acc_bis[k, 0] = oxts_k[0].ax\n acc_bis[k, 1] = oxts_k[0].ay\n acc_bis[k, 2] = oxts_k[0].az\n gyro[k, 0] = oxts_k[0].wf\n gyro[k, 1] = oxts_k[0].wl\n gyro[k, 2] = oxts_k[0].wu\n gyro_bis[k, 0] = oxts_k[0].wx\n gyro_bis[k, 1] = oxts_k[0].wy\n gyro_bis[k, 2] = oxts_k[0].wz\n roll_oxts[k] = oxts_k[0].roll\n pitch_oxts[k] = oxts_k[0].pitch\n yaw_oxts[k] = oxts_k[0].yaw\n v_gt[k, 0] = oxts_k[0].ve\n v_gt[k, 1] = oxts_k[0].vn\n v_gt[k, 2] = oxts_k[0].vu\n v_rob_gt[k, 0] = oxts_k[0].vf\n v_rob_gt[k, 1] = oxts_k[0].vl\n v_rob_gt[k, 2] = oxts_k[0].vu\n p_gt[k] = oxts_k[1][:3, 3]\n Rot_gt_k = oxts_k[1][:3, :3]\n roll_gt[k], pitch_gt[k], yaw_gt[k] = IEKF.to_rpy(Rot_gt_k)\n\n t0 = t[0]\n t = np.array(t) - t[0]\n # some data can have gps out\n if np.max(t[:-1] - t[1:]) > 0.1:\n cprint(date_dir2 + \" has time problem\", 'yellow')\n ang_gt = np.zeros((roll_gt.shape[0], 3))\n ang_gt[:, 0] = roll_gt\n ang_gt[:, 1] = pitch_gt\n ang_gt[:, 2] = yaw_gt\n\n p_oxts = lla2ned(lat_oxts, lon_oxts, alt_oxts, lat_oxts[0], lon_oxts[0],\n alt_oxts[0], latlon_unit='deg', alt_unit='m', model='wgs84')\n p_oxts[:, [0, 1]] = p_oxts[:, [1, 0]] # see note\n\n # take correct imu measurements\n u = np.concatenate((gyro_bis, acc_bis), -1)\n # convert from numpy\n t = torch.from_numpy(t)\n p_gt = torch.from_numpy(p_gt)\n v_gt = torch.from_numpy(v_gt)\n ang_gt = torch.from_numpy(ang_gt)\n u = torch.from_numpy(u)\n\n # convert to float\n t = t.float()\n u = u.float()\n p_gt = p_gt.float()\n ang_gt = ang_gt.float()\n v_gt = v_gt.float()\n\n mondict = {\n 't': t, 'p_gt': p_gt, 'ang_gt': ang_gt, 'v_gt': v_gt,\n 'u': u, 'name': date_dir2, 't0': t0\n }\n\n t_tot += t[-1] - t[0]\n KITTIDataset.dump(mondict, args.path_data_save, date_dir2)\n print(\"\\n Total dataset duration : {:.2f} s\".format(t_tot))","def load_data():\n directories=[\"./track1/\",\n \"./track1_recovery/\",\n \"./track2/\",\n \"./track1_reverse/\",\n \"./track2_reverse/\",#Additional data for model built on top of lenet.h5\n \"./track2_recovery/\",#Additions data for model built on top of lenet.h5\n ]\n lines=[]\n for directory in directories:\n with open(directory+\"driving_log.csv\") as csvfile:\n reader=csv.reader(csvfile)\n for line in reader:\n lines.append(line)\n train_samples, validation_samples = train_test_split(lines, test_size=0.2)\n return train_samples, validation_samples","def create_trainset(\n self,\n s=\"2016-01-01\",\n e=\"2018-12-31\",\n freq=\"D\",\n ) -> DateTensors:\n self.date_series = DatasetDateSeries(\n start=s, end=e, wsz=self.wsz, to_onehot=True\n ) # wsz same as W\n\n # ti window data to tensor\n ti = Tsr(self.date_series.ti_win)\n\n # tc window data to tensor\n N, W, Dtc = len(ti), self.date_series.wsz, 3\n tc = torch.randint(0, 2, (1, 1, Dtc)).repeat(N, W, 1) # shape: (N, W, Dtc)\n\n # kn window data to tensor\n kn = Tsr(self.date_series.kn_win)\n\n # create target data as `tg` (target)\n tg = self.dct_curve[self.model_type](ti).repeat(1, 1, self.Dout)\n\n ti, tc, kn, tg = self.to_device(ti, tc, kn, tg)\n trainset = DateTensors(\n ti=ti, tc=tc, kn=kn, tg=tg, device=self.device\n ) # ti/tc/kn.shape: (N, W, Dout), tg.shape = (N, 1, Dout)\n self.trainset = trainset\n return trainset","def load_data():\r\n train_dataset = h5py.File('train_catvnoncat.h5', \"r\") # Change the directory as per your system\r\n train_set_x_orig = np.array(train_dataset[\"train_set_x\"][:]) # your train set features\r\n train_set_y_orig = np.array(train_dataset[\"train_set_y\"][:]) # your train set labels\r\n\r\n test_dataset = h5py.File('test_catvnoncat.h5', \"r\") # Change the directory as per your system\r\n test_set_x_orig = np.array(test_dataset[\"test_set_x\"][:]) # your test set features\r\n test_set_y_orig = np.array(test_dataset[\"test_set_y\"][:]) # your test set labels\r\n\r\n classes = np.array(test_dataset[\"list_classes\"][:]) # the list of classes\r\n \r\n train_set_y_orig = train_set_y_orig.reshape((1, train_set_y_orig.shape[0]))\r\n test_set_y_orig = test_set_y_orig.reshape((1, test_set_y_orig.shape[0]))\r\n \r\n return train_set_x_orig, train_set_y_orig, test_set_x_orig, test_set_y_orig, classes","def load_data():\n print(\"PARSING TRAIN\")\n ys_train, x_train, ids_train = load_pickle_data(\"ys_train\"), load_pickle_data(\"x_train\"), load_pickle_data(\n \"ids_train\")\n if ys_train is None or x_train is None or ids_train is None:\n ys_train, x_train, ids_train = load_csv_data(\"{}/train.csv\".format(DATA_DIR))\n dump_pickle_data(ys_train, \"ys_train\")\n dump_pickle_data(x_train, \"x_train\")\n dump_pickle_data(ids_train, \"ids_train\")\n\n print(\"PARSING TEST\")\n x_test, ids_test = load_pickle_data(\"x_test\"), load_pickle_data(\"ids_test\")\n if x_test is None or ids_test is None:\n _, x_test, ids_test = load_csv_data(\"{}/test.csv\".format(DATA_DIR))\n dump_pickle_data(x_test, \"x_test\")\n dump_pickle_data(ids_test, \"ids_test\")\n\n return ys_train, x_train, ids_train, x_test, ids_test","def test_csv_and_tfrecord(self):\n\n # Test model training on CSV data\n data_dir = os.path.join(self.root_data_dir, \"csv\")\n feature_config_path = os.path.join(self.root_data_dir, \"configs\", self.feature_config_fname)\n\n csv_loss, csv_mrr = self.run_default_pipeline(\n data_dir=data_dir, data_format=\"csv\", feature_config_path=feature_config_path\n )\n\n # Check if the loss and accuracy on the test set is the same\n assert np.isclose(csv_loss, 0.56748, rtol=0.01)\n assert np.isclose(csv_mrr, 0.70396, rtol=0.01)\n\n # Test model training on TFRecord SequenceExample data\n data_dir = os.path.join(self.root_data_dir, \"tfrecord\")\n feature_config_path = os.path.join(self.root_data_dir, \"configs\", self.feature_config_fname)\n\n tfrecord_loss, tfrecord_mrr = self.run_default_pipeline(\n data_dir=data_dir, data_format=\"tfrecord\", feature_config_path=feature_config_path\n )\n\n # Check if the loss and accuracy on the test set is the same\n assert np.isclose(tfrecord_loss, 0.56748, rtol=0.01)\n assert np.isclose(tfrecord_mrr, 0.70396, rtol=0.01)\n\n # Compare CSV and TFRecord loss and accuracies\n assert np.isclose(tfrecord_loss, csv_loss, rtol=0.01)\n assert np.isclose(tfrecord_mrr, csv_mrr, rtol=0.01)","def load_data():\n t = time()\n print 'loading tweets, please wait...'\n trained_tweets = load_tweets('training_dataset')\n eval_tweets = load_tweets('evaluation_dataset')\n print 'Time taken {}'.format(time() - t)\n t = time()\n print 'loading w2v model, please wait...'\n model = w2v_load_model('GoogleNews-vectors-negative300.bin')\n print 'Time taken {}'.format(time() - t)\n return trained_tweets, eval_tweets, model","def readDAT(testname):\n\n dateparse = lambda x: pd.datetime.strptime(x, '%Y.%m.%d')\n \n df = pandas.read_csv(testname, names=['date','time','open', 'max', 'min', 'close', 'vol'])\n df['datetime'] = df['date'] + ' - ' + df['time']\n df['datetime'] = pandas.to_datetime(df['datetime'], format='%Y.%m.%d - %H:%M')\n return df","def data_import(path):\n train_path = os.path.join(path, \"train.csv\")\n test_path = os.path.join(path, \"test.csv\")\n df_train = pd.read_csv(train_path)\n df_test = pd.read_csv(test_path)\n return df_train, df_test","def test_device_readings_get_past_dates(self):\n request = self.client().get(f'/devices/{self.device_uuid}/readings/'\n f'?start={int(self.setup_time - 75)}'\n f'&end={int(self.setup_time + 25)}')\n\n self.assertEqual(len(request.json), 2)","def _get_setup_from_timestamps(self):\n self._parse_timestamps()\n IFOs = self.detectors.split(\",\")\n # at this point, it's definitely a comma-separated string\n tsfiles = self.timestamps.split(\",\")\n if len(IFOs) != len(tsfiles):\n raise ValueError(\n f\"Length of detectors=='{self.detectors}'\"\n f\" does not match that of timestamps=='{self.timestamps}'\"\n f\" ({len(IFOs)}!={len(tsfiles)})\"\n )\n tstart = []\n tend = []\n self.sftfilenames = [] # This refers to the MFD output!\n for X, IFO in enumerate(IFOs):\n tsX = np.genfromtxt(tsfiles[X], comments=\"%\")\n if tsX.ndim > 1:\n logger.warning(\n f\"Timestamps file {tsfiles[X]} has more than 1 column,\"\n \" we will ignore the rest.\"\n )\n tsX = tsX[:, 0]\n if not tsX[0].is_integer() or not tsX[-1].is_integer():\n logger.warning(\n \"Detected non-integer timestamps in timestamp file.\"\n \" We will floor start and end times to the nearest integer\"\n \" for the SFT name,\"\n \" and let lalpulsar_Makefakedata_v5 handle the rest.\"\n )\n\n this_start_time = int(tsX[0])\n this_end_time = int(tsX[-1]) + self.Tsft\n tstart.append(this_start_time)\n tend.append(this_end_time)\n self.sftfilenames.append(\n utils.get_official_sft_filename(\n IFO,\n len(tsX),\n self.Tsft,\n this_start_time,\n this_end_time - this_start_time,\n self.label,\n )\n )\n self.tstart = min(tstart)\n self.duration = max(tend) - self.tstart","def test_set_get_dt():\n data = io.create_sample_Dataset()\n assert data.attrs[\"delta_t\"] == 0.0\n\n data.piv.set_delta_t(2.0)\n assert data.attrs[\"delta_t\"] == 2.0","def test_read(self):\n dt = np.dtype('(3,)f8')\n dset = self.f.create_dataset('x',(10,),dtype=dt)\n self.assertEqual(dset.shape, (10,))\n self.assertEqual(dset.dtype, dt)\n\n # Full read\n out = dset[...]\n self.assertEqual(out.dtype, np.dtype('f8'))\n self.assertEqual(out.shape, (10,3))\n\n # Single element\n out = dset[0]\n self.assertEqual(out.dtype, np.dtype('f8'))\n self.assertEqual(out.shape, (3,))\n\n # Range\n out = dset[2:8:2]\n self.assertEqual(out.dtype, np.dtype('f8'))\n self.assertEqual(out.shape, (3,3))","def get_trainset(set_name=\"car\"):\n names_file = open(\"../../data/\"+set_str+\"/\"+set_name+\"_names.dat\", \"r\")\n data_file= open(\"../../data/\"+set_str+\"/\"+set_name+\"_data.dat\", \"r\")\n \n names_strs = names_file.readlines()\n v_index = 0\n a_index = 0\n for index,line in enumerate(names_strs):\n if line.find(\"| class values\") != -1:\n v_index = index\n elif line.find(\"| attributes\") != -1:\n a_index = index\n break\n class_values_strs = re.split(r\"\\W+\",names_strs[v_index+2].strip())\n attribute_namea_value_list = []\n for i in range(a_index, len(names_strs)):\n if re.search(r\"^\\w+:\\s+[\\w,\\s]+\\.\",names_strs[i]) != None:\n temp_strs = re.split(r\"\\W+\",names_strs[i][:-2])\n attribute_namea_value_list.append((temp_strs[0],temp_strs[1:]))\n# print class_values_strs\n# print attribute_namea_value_list\n train_set = []\n while True:\n temp_str = data_file.readline()[:-1]\n if temp_str == \"\":\n break\n values = temp_str.split(\",\")\n train_set.append((values[:-1], values[-1]))\n return class_values_strs,attribute_namea_value_list,train_set","def readFiles(trainFile, testFile):\r\n\r\n\t# Open both files and split into lines\r\n\twith open(trainFile) as f:\r\n\t\ttrainLines = f.read().splitlines()\r\n\r\n\twith open(testFile) as f:\r\n\t\ttestLines = f.read().splitlines()\r\n\r\n\t\t\r\n\t# Extract training data\r\n\tfor line in trainLines:\r\n\t\tline = line.split()\r\n\t\t\r\n\t\tid = line[0]\r\n\t\tclass_id = line[1]\r\n\t\twords = line[2:]\r\n\t\t\r\n\t\trow = [id, class_id, words]\r\n\t\t\r\n\t\ttrainingData.append(row)\r\n\t\t\r\n\t\t\r\n\t# Extract testing data\r\n\tfor line in testLines:\r\n\t\tline = line.split()\r\n\t\t\r\n\t\tid = line[0]\r\n\t\tclass_id = line[1]\r\n\t\twords = line[2:]\r\n\t\t\r\n\t\trow = [id, class_id, words]\r\n\t\t\r\n\t\ttestData.append(row)","def load(self):\n\n X_train, y_train, X_test, y_test, variable_types, name = _load_data(\n self.task_id)\n\n self.X_train = X_train\n self.y_train = y_train\n self.X_test = X_test\n self.y_test = y_test\n self.variable_types = variable_types\n self.name = name\n\n return self.X_train, self.y_train, self.X_test, self.y_test","def test_parse_date(self):\n d_expected = '2018-03-02 15:19:08'\n d_out = datetime.datetime.strptime(test_structures.example_erratum['errata']['rhba']['created_at'], '%Y-%m-%dT%H:%M:%SZ')\n self.assertEqual(str(d_out), d_expected)","def set_training_data(self):\n # Optional training data period\n # TODO: add training data period feature to training data query\n if not self.training_period == None:\n training_period_date = (datetime.datetime.utcnow() - timedelta(minutes=self.training_period)).strftime(\"%Y-%m-%d\")\n print(f\"Training data start date: {training_period_date}\")\n # Extract queried data from Athena\n #athena = athena_connect.Athena()\n #features_df = athena.pandas_read_athena(self.training_data_sql)\n with open('feature_sql.txt', 'w') as f:\n print(self.training_data_sql, file=f) \n features_df = pd.read_sql(self.training_data_sql, self.logic_db_engine())\n features_df.fillna(0, inplace=True)\n print(features_df.shape)\n features_df = features_df[max(self.feature_minutes_list):]\n print(features_df.shape)\n # Remove infinity string\n features_df.replace({'Infinity': 0}, inplace=True)\n # Convert all object fields to numeric except date fields\n object_col_list = features_df.columns[features_df.dtypes.eq('object')]\n object_col_list = [col for col in object_col_list if 'trade_date' not in col]\n features_df[object_col_list] = features_df[object_col_list].apply(pd.to_numeric, errors='coerce')\n self.training_df = features_df","def load_data(self):\n with open('data/fordTrain.csv') as f:\n data = csv.reader(f, delimiter=',')\n train = [x for i, x in enumerate(data) if i > 0] \n # Extract features and target variable separately\n trainx = [x[3:] for x in train]\n trainy = [x[2] for x in train]\n\n with open('data/fordTest.csv') as f:\n data = csv.reader(f, delimiter=',')\n testx = [x[3:] for i, x in enumerate(data) if i > 0] \n\n with open('data/Solution.csv') as f:\n data = csv.reader(f, delimiter=',')\n testy = [x[2] for i, x in enumerate(data) if i > 0] \n\n # Extract features and target variable, convert to numpy array\n trainx = np.asarray(trainx, dtype=np.float32)\n trainy = np.asarray(trainy, dtype=np.int8)\n testx = np.asarray(testx, dtype=np.float32)\n testy = np.asarray(testy, dtype=np.int8)\n\n # Return training and test sets\n trainSet = Dataset(trainx, trainy)\n testSet = Dataset(testx, testy)\n return trainSet, testSet","def test_parser():\n data = parse_csv(TEST_DATA)\n assert data['2020-01-03'] == ['recycle']\n assert data['2020-01-08'] == ['bio', 'trash']\n assert data['2021-01-09'] == ['christmas']","def get_cols_for_datetime(train: NumpyOrPandas) -> Tuple[List[str], List[str]]:\n base_dates = get_columns_by_role(train, \"Datetime\", base_date=True)\n datetimes = get_columns_by_role(train, \"Datetime\", base_date=False) + get_columns_by_role(\n train, \"Datetime\", base_date=True, base_feats=True\n )\n\n return base_dates, datetimes","def loadRawData():\n samples = []\n with open('Sample_data/driving_log.csv') as csvfile:\n reader = csv.reader(csvfile)\n for line in reader:\n # skip the first line, which is the title line\n if line[0] != 'center':\n samples.append(line)\n # for testing implementation only, commented for GPU training\n #if len(samples)>100:\n # break\n train_samples, validation_samples = train_test_split(samples, test_size=0.2)\n return train_samples, validation_samples","def load_data(data_path):\n headers = ['center', 'left', 'right','steering','throttle','brake','speed']\n data = pd.read_csv(data_path + '/driving_log.csv', names = headers)\n \n # Use SciKit not keras splitting so it randomly selects validation set\n d_train, d_valid = train_test_split(data, test_size=0.2, random_state=42)\n\n return d_train, d_valid","def test_dataset_from_file(train_dataset):\n dummy = \"justo. Praesent luctus. Curabitur egestas nunc sed libero. Proin sed\"\n assert train_dataset[0][0] == dummy\n assert train_dataset[0][1] == '6'","def read_driving_records(data_directory, test_fraction=0.05, valid_fraction=0.05):\n\n # read data from csv file\n data_directory = os.path.join(data_directory, 'driving_log.csv').replace('\\\\','/')\n data_col = ['center', 'left', 'right', 'steering', 'throttle', 'reverse', 'speed']\n data = pd.read_csv(data_directory, names= data_col)\n # smoothing the steering angle to avoid jettering in the output\n data['steering'] = uniform_filter1d(data['steering'], size=5)\n\n # train test valid split \n data_len = data.shape[0]\n train_len = int(data_len * (1 - valid_fraction - test_fraction))\n valid_len = int(data_len * valid_fraction)\n print(f'Length of train: {train_len}, valid: {valid_len}, test: {data_len - train_len - valid_len}')\n train_records, valid_records, test_records = np.split(data.sample(frac=1, random_state=42), [train_len, train_len + valid_len])\n\n return train_records, valid_records, test_records","def _create_data():\n tf.logging.info(\"Create records..\")\n train, val, test = util.load_data(data_dir, FLAGS[\"is_aug\"])\n tf.logging.info(\"Dataset size: Train-{} Test-{} Val-{}\".format(len(train), len(test), len(val)))\n return train, val, test","def get_train_data(path='.', is_train = True):\n\n try:\n with open('data/linky.pickle','rb') as file:\n Z = pickle.load(file)\n except FileNotFoundError:\n print('reading raw data')\n Z = pd.read_excel(\"data/linky.xlsx\")\n try:\n with open('data/linky.pickle','wb') as file:\n pickle.dump(Z, file)\n print('raw data pickled')\n except:\n print(\"Couldn't pickle raw file\")\n \n \n if is_train:\n # we are in train data\n start_row = 0\n end_row = int(Z.shape[0] * RATIO_TRAIN_TEST)\n else:\n start_row = int(Z.shape[0] * RATIO_TRAIN_TEST) \n end_row = Z.shape[0] \n \n Z = Z.iloc[start_row:end_row]\n if FAST_MODE:\n Z = Z.iloc[:3000]\n \n # print('get raw data, from row {} to row {}'.format(start_row, end_row))\n Y = Z.drop(columns = 'Id_client')\n Y = Y.fillna(0).values # hack to consider all columns and all rows (we can also drops rows or columns with NaN, or use an Imputer)\n return Z, Y","def run(self, df: pd.DataFrame, model: Any):\n print(f'Running cross validation with the following model:\\n{model}')\n\n df['timestamp'] = pd.to_datetime(df['timestamp'])\n\n date_1 = datetime.datetime(year=2016, month=1, day=1)\n date_2 = datetime.datetime(year=2016, month=4, day=1)\n date_3 = datetime.datetime(year=2016, month=7, day=1)\n date_4 = datetime.datetime(year=2016, month=10, day=1)\n date_5 = datetime.datetime(year=2017, month=1, day=1)\n\n summaries: List[FoldSummary] = []\n\n for train_start, train_end, test_start, test_end in [\n (date_1, date_2, date_2, date_3),\n # (date_1, date_3, date_3, date_4),\n # (date_1, date_4, date_4, date_5)\n ]:\n print('Calculating train and test datasets')\n train_df = df[(df['timestamp'] >= train_start) & (df['timestamp'] < train_end)]\n test_df = df[(df['timestamp'] >= test_start) & (df['timestamp'] < test_end)]\n\n columns = list(train_df.columns)\n columns.remove('timestamp')\n columns.remove('meter_reading')\n\n print(columns)\n\n train_data = train_df[columns]\n test_data = test_df[columns]\n\n print(f'Fitting the model on train dataset of size {len(train_data)}')\n model.fit(train_data, train_df['meter_reading'])\n print(f'Predicting for test dataset of size {len(test_data)}')\n predictions = model.predict(test_data)\n\n score = self._calculate_score(predictions, test_df['meter_reading'])\n print(f'Score: {score}')\n\n summaries.append(FoldSummary(\n train_start=train_start,\n train_end=train_end,\n test_start=test_start,\n test_end=test_end,\n score=score\n ))\n\n filename = f'../resources/runs/{time.time()}.txt'\n os.makedirs(os.path.dirname(filename), exist_ok=True)\n with open(filename, 'w+') as f:\n f.write(f'{model.__class__.__name__}\\n')\n f.write(f'{str(model.get_params())}\\n')\n for summary in summaries:\n f.write(f'Summary (\\n'\n f'\\ttrain start: {summary.train_start}\\n'\n f'\\ttrain end: {summary.train_end}\\n'\n f'\\ttest start: {summary.test_start}\\n'\n f'\\ttest end: {summary.test_end}\\n'\n f'\\tscore: {summary.score}\\n'\n f')\\n')\n\n print(summaries)\n\n return model"],"string":"[\n \"def split_dataset(valid_datetime):\\n\\tprint(\\\"all_frames: \\\", valid_datetime.shape[0])\\n\\tprint(\\\"valid_frames: \\\", np.sum(valid_datetime[:,1]))\\n\\tprint(\\\"no_valid_frames: \\\", np.sum(1-valid_datetime[:,1]))\\n\\n\\tvalid_datetime_bool = (valid_datetime[:,1] == 1)\\n\\tvalid_datetime_idx = np.where(valid_datetime_bool)[0]\\n\\tvalid_datetime = valid_datetime[valid_datetime_bool][:,0]\\n\\n\\ttrain_set = []\\n\\tvalid_set = []\\n\\ttest_set = []\\n\\t\\n\\tdate = set()\\n\\tfor dt in valid_datetime.astype(str):\\n\\t\\tdate.add(dt[:8])\\n\\tdate = list(date)\\n\\tdate.sort()\\n\\n\\tfor i in date:\\n\\t\\tflag = np.random.randint(0,14)\\n\\t\\tif flag < 11:\\n\\t\\t\\ttrain_set.append(i)\\n\\t\\telif flag == 11:\\n\\t\\t\\tvalid_set.append(i)\\n\\t\\telse:\\n\\t\\t\\ttest_set.append(i)\\n\\n\\tprint(\\\"valid days: \\\", len(date))\\n\\tprint(\\\"train set days: \\\", len(train_set))\\n\\tprint(\\\"valid set days: \\\", len(valid_set))\\n\\tprint(\\\"test set days: \\\", len(test_set))\\n\\n\\ttrain_set_data = []\\n\\tvalid_set_data = []\\n\\ttest_set_data = []\\n\\tfor dt,idx in zip(valid_datetime.astype(str), valid_datetime_idx):\\n\\t\\tt_date = dt[:8]\\n\\t\\tif t_date in train_set:\\n\\t\\t\\ttrain_set_data.append([dt,idx])\\n\\t\\telif t_date in valid_set:\\n\\t\\t\\tvalid_set_data.append([dt,idx])\\n\\t\\telse:\\n\\t\\t\\ttest_set_data.append([dt,idx])\\n\\n\\tprint(\\\"train set samples: \\\", len(train_set_data))\\n\\tprint(\\\"valid set samples: \\\", len(valid_set_data))\\n\\tprint(\\\"test set samples: \\\", len(test_set_data))\\t\\n\\n\\t#assert(np.sum(valid_datetime[:,1]) == valid_datetime.shape[0])\\n\\treturn [train_set_data, valid_set_data, test_set_data]\",\n \"def _load_data(self, ti, tf):\\n # return pre loaded\\n try:\\n if ti == self.ti_prev and tf == self.tf_prev:\\n return self.fM, self.ys\\n except AttributeError:\\n pass\\n\\n # read from CSV file\\n try:\\n t = pd.to_datetime(pd.read_csv(self.featfile, index_col=0, parse_dates=['time'], usecols=['time'], infer_datetime_format=True).index.values)\\n if (t[0] <= ti) and (t[-1] >= tf):\\n self.ti_prev = ti\\n self.tf_prev = tf\\n fM,ys = self._extract_features(ti,tf)\\n self.fM = fM\\n self.ys = ys\\n return fM,ys\\n except FileNotFoundError:\\n pass\\n\\n # range checking\\n if tf > self.data.tf:\\n raise ValueError(\\\"Model end date '{:s}' beyond data range '{:s}'\\\".format(tf, self.data.tf))\\n if ti < self.data.ti:\\n raise ValueError(\\\"Model start date '{:s}' predates data range '{:s}'\\\".format(ti, self.data.ti))\\n \\n # divide training period into years\\n ts = [datetime(*[yr, 1, 1, 0, 0, 0]) for yr in list(range(ti.year+1, tf.year+1))]\\n if ti - self.dtw < self.data.ti:\\n ti = self.data.ti + self.dtw\\n ts.insert(0,ti)\\n ts.append(tf)\\n\\n for t0,t1 in zip(ts[:-1], ts[1:]):\\n print('feature extraction {:s} to {:s}'.format(t0.strftime('%Y-%m-%d'), t1.strftime('%Y-%m-%d')))\\n fM,ys = self._extract_features(ti,t1)\\n\\n self.ti_prev = ti\\n self.tf_prev = tf\\n self.fM = fM\\n self.ys = ys\\n return fM, ys\",\n \"def load(train_file, test_file):\\n print('\\\\nLoad the raw training and test set data...')\\n y_train, tx_train, ids_train = load_csv_data(train_file)\\n y_test, tx_test, ids_test = load_csv_data(test_file)\\n print('\\\\n... finished.')\\n return y_train, tx_train, ids_train, y_test, tx_test, ids_test\",\n \"def read_ct_data(train_start, train_count, eval_start, eval_count):\\n data = pd.read_csv('/opt/train.csv')\\n\\n # Dropping the id column\\n data.drop(['ID_code'], axis=1, inplace=True)\\n\\n data = data.values\\n return (data[train_start:train_start + train_count],\\n data[eval_start:eval_start + eval_count])\",\n \"def prep_data_fn(self, st_train_dt, end_train_dt, st_val_dt, end_val_dt, st_test_dt, end_test_dt):\\n df = self.get_prep_data()\\n train = df[(df['ft_data_dt'] >= st_train_dt) & (df['ft_data_dt'] <= end_train_dt)]\\n val = df[(df['ft_data_dt'] >= st_val_dt) & (df['ft_data_dt'] <= end_val_dt)].sample(frac=0.4, random_state=2021)\\n test = df[(df['ft_data_dt'] >= st_test_dt) & (df['ft_data_dt'] <= end_test_dt)]\\n print(f'----train----')\\n print(train[['ft_data_dt', 'target', 'idd']].groupby(['ft_data_dt', 'target']).agg(['count']))\\n print(f'----validation----')\\n print(val[['ft_data_dt', 'target', 'idd']].groupby(['ft_data_dt', 'target']).agg(['count']))\\n print(f'----test----')\\n print(test[['ft_data_dt', 'target', 'idd']].groupby(['ft_data_dt', 'target']).agg(['count']))\\n self.set_train(train)\\n self.set_validation(val)\\n self.set_test(test)\\n train_X = train[[c for c in train.columns if c not in ['idd', 'ft_data_dt', 'target']]]\\n train_y = train['target']\\n val_X = val[[c for c in train.columns if c not in ['idd', 'ft_data_dt', 'target']]]\\n val_y = val['target']\\n test_X = test[[c for c in train.columns if c not in ['idd', 'ft_data_dt', 'target']]]\\n test_y = test['target']\\n self.set_train_X(train_X)\\n self.set_train_y(train_y)\\n self.set_val_X(val_X)\\n self.set_val_y(val_y)\\n self.set_test_X(test_X)\\n self.set_test_y(test_y)\",\n \"def test_training_time(self):\\n self.assertIsInstance(self.one_off_training.time, datetime.time)\\n self.assertEqual(self.one_off_training.time, datetime.time(9, 0))\",\n \"def train_test_dataset(self, train_rate=0.8):\\n point_date = int(len(self.y) * train_rate)\\n y_to_train = self.y[:point_date]\\n y_to_val = self.y[point_date:]\\n predict_date = len(self.y) - len(y_to_train) # the number of data points for the test set\\n date_val = self.y.index[point_date]\\n return y_to_train, y_to_val, predict_date, date_val\",\n \"def test_02_train(self):\\n today = date.today()\\n log_file = os.path.join(LOG_DIR, \\\"{}-train-{}-{}.log\\\".format(LOG_PREFIX, today.year, today.month))\\n \\n ## update the log\\n country = 'india'\\n date_range = ('2017-11-29', '2019-05-24')\\n metric = {'rmse':0.5}\\n runtime = \\\"00:00:01\\\"\\n model_version = 0.1\\n model_version_note = \\\"test model\\\"\\n \\n update_train_log(country, date_range, metric, runtime,\\n model_version, model_version_note ,test=True, prefix=LOG_PREFIX)\\n\\n df = pd.read_csv(log_file)\\n logged_metric = [literal_eval(i) for i in df['metric'].copy()][-1]\\n self.assertEqual(metric,logged_metric)\",\n \"def load_data(train=True):\\n#test set\\n start = time.clock()\\n\\n if train==False:\\n dp = '/home/thomas/Desktop/OttoChallenge/test.csv'\\n df = pd.read_csv(dp)\\n X = df.values.astype(np.float32)[:,1:]\\n return X\\n#train set\\n else:\\n dp = '/home/thomas/Desktop/OttoChallenge/train.csv'\\n df = pd.read_csv(dp)\\n X = df[df.columns[:-1]].values.astype(np.float32)[:,1:]\\n y = df.target\\n y =y.apply(lambda X: int(X[-1])).values\\n y = y.astype(np.int32)\\n X, y = shuffle(X, y)\\n #print(X.shape,y.shape)\\n end = time.clock()\\n print(end-start)\\n\\n return X,y\",\n \"def load_data(train_file, test_file):\\n\\n data = np.asarray(pd.read_csv(train_file, header=0))\\n data_ts = np.asarray(pd.read_csv(test_file, header=0))\\n\\n x_tra = data[:, :-1]\\n y_tra = data[:, -1]\\n\\n return x_tra, y_tra, data_ts\",\n \"def load_trec(loc=DATA_DIR):\\n train, test = [], []\\n with open(os.path.join(loc, 'TREC', 'train_5500.label'), 'rb') as f:\\n for line in f:\\n train.append(line.strip())\\n with open(os.path.join(loc, 'TREC', 'TREC_10.label'), 'rb') as f:\\n for line in f:\\n test.append(line.strip())\\n return train, test\",\n \"def read_timestamps(self, tasks):\\n from reframe.core.deferrable import evaluate\\n\\n self.begin_stamps = []\\n self.end_stamps = []\\n for t in tasks:\\n with open(evaluate(t.check.stdout), 'r') as f:\\n self.begin_stamps.append(float(f.readline().strip()))\\n self.end_stamps.append(float(f.readline().strip()))\\n\\n self.begin_stamps.sort()\\n self.end_stamps.sort()\",\n \"def load_data(trainfile, testfile):\\n raw_train = pd.read_csv(trainfile, header=None)\\n raw_test = pd.read_csv(testfile, header=None)\\n train = raw_train.values\\n test = raw_test.values\\n train_features = train[0::, 1::]\\n train_label = train[::, 0]\\n test_features = test[0::, 1::]\\n test_label = test[::, 0]\\n train, cv , train_label, cv_label = train_test_split(train_features,train_label, test_size=0.33, random_state=42)\\n return train, train_label, \\\\\\n cv, cv_label, \\\\\\n test_features, test_label\",\n \"def test__load_training_set():\\n classifier = classifier_module.Classifier(None)\\n set = classifier._load_training_set('test')\\n for i in range(0, 5):\\n signal_list = set[i]\\n assert signal_list[0].get_x() == 1.0 + i * 0.028\\n assert signal_list[0].get_y() == 1.00 - i * i * 0.20 * 0.30\\n\\n assert signal_list[1].get_x() == 2.0 - i * 0.011\\n assert signal_list[1].get_y() == 2.00 - i * 0.020\",\n \"def test_read(self):\\n df = dep.read_env(get_path('good_env.txt'))\\n df2 = df[df['date'] == datetime.datetime(2010, 6, 5)]\\n self.assertEqual(len(df2.index), 1)\\n row = df2.iloc[0]\\n self.assertEquals(row['runoff'], 86.3)\",\n \"def test_one_off_training_date(self):\\n self.assertIsInstance(self.one_off_training.date, datetime.date)\\n self.assertEqual(\\n self.one_off_training.date,\\n datetime.date(2020, 6, 14)\\n )\",\n \"def test_validate_datetime(self):\\n self.datatrue = pd.read_csv(pkg_resources.resource_filename(resource_package, 'tests/testing_data/report_2_counts.csv'))\\n\\n self.datafalse = pd.read_csv(pkg_resources.resource_filename(resource_package, 'tests/testing_data/random_date1.csv'))\\n\\n self.test1 = utils.validate_datetime(self.datatrue)\\n\\n self.test2 = utils.validate_datetime(self.datafalse)\\n\\n self.assertTrue(isinstance(self.test1, pd.DataFrame))\\n\\n self.assertTrue(np.dtype('datetime64[ns]') in self.test1.dtypes.tolist())\\n\\n self.assertFalse(np.dtype('datetime64[ns]') in self.test2.dtypes.tolist())\",\n \"def get_data(t_idx):\\n train_data = []\\n train_label = []\\n valid_data = []\\n valid_label = []\\n test_data = []\\n test_label = []\\n \\n lines = []\\n #f = open('/home/liwenzhe/myworkspace/is13/dataset/closingAdjLog.csv', 'rb')\\n f = open('dataset/closingAdjLog.csv')\\n reader = csv.reader(f)\\n for row in reader:\\n lines.append(row)\\n lines.pop(0)\\n \\n n = len(lines)\\n # use the first 80% for the training data, the next 10% for the validation data\\n # and the last 10% for the test data. \\n # TODO : create folds instead. \\n for i in xrange(n-1, -1, -1):\\n curr_line = lines[i]\\n if i > n * 0.2:\\n # add into training data\\n if t_idx == 0:\\n train_data.append(curr_line[t_idx+1:])\\n else:\\n train_data.append(curr_line[:t_idx-1] + curr_line[t_idx:])\\n train_label.append(curr_line[t_idx]) \\n elif i > n * 0.1:\\n # add into validation data\\n if t_idx == 0:\\n valid_data.append(curr_line[t_idx+1:])\\n else: \\n valid_data.append(curr_line[:t_idx-1] + curr_line[t_idx:])\\n valid_label.append(curr_line[t_idx]) \\n else:\\n # add into test data\\n if t_idx == 0:\\n test_data.append(curr_line[t_idx+1:])\\n else:\\n test_data.append(curr_line[:t_idx-1] + curr_line[t_idx:])\\n test_label.append(curr_line[t_idx]) \\n \\n return [train_data, train_label, valid_data, valid_label, test_data, test_label]\",\n \"def data_set_maker():\\n\\n # crate a folder in your code directory and name it: \\\"files\\\". put the .npy files iside that folder\\n\\n x_all = np.load(path + '/files/tinyX.npy', 'r') # reads the input file\\n y_all = np.load(path + '/files/tinyY.npy', 'r') # reads the input file\\n\\n # split the data into 10% validation-set and 90% training set\\n raw_train, raw_valid, y_train, y_valid = train_test_split(x_all, y_all, test_size=0.2, random_state=43)\\n return raw_train, raw_valid, y_train, y_valid\",\n \"def time_split_dataset(df, train_start_date, train_end_date, holdout_end_date, date_col):\\n\\n train_set = df.copy()[\\n (df[date_col] >= train_start_date) & (df[date_col] <= train_end_date)]\\n\\n test_set = df.copy()[\\n (df[date_col] > train_end_date) & (df[date_col] <= holdout_end_date)]\\n\\n return train_set, test_set\",\n \"def _assess(self):\\n # get eruptions\\n with open(os.sep.join(getfile(currentframe()).split(os.sep)[:-2]+['data','eruptive_periods.txt']),'r') as fp:\\n self.tes = [datetimeify(ln.rstrip()) for ln in fp.readlines()]\\n # check if data file exists\\n self.exists = os.path.isfile(self.file)\\n if not self.exists:\\n t0 = datetime(2011,1,1)\\n t1 = datetime(2011,1,2)\\n self.update(t0,t1)\\n # check date of latest data in file\\n self.df = pd.read_csv(self.file, index_col=0, parse_dates=[0,], infer_datetime_format=True)\\n self.ti = self.df.index[0]\\n self.tf = self.df.index[-1]\",\n \"def test_sample_date(self):\\r\\n self.assertEqual(self.test_sample.date, '2018-08-02 22:32:23')\",\n \"def check_dataset_dates(self):\\n # TODO: graph traverse and date checking\\n pass\",\n \"def get_train_data():\\n # train set\\n train = pd.read_csv(\\\"train.csv\\\")\\n\\n return train\",\n \"def get_datetime_diffs(self, train: NumpyOrPandas) -> Optional[LAMLTransformer]:\\n base_dates, datetimes = self.get_cols_for_datetime(train)\\n if len(datetimes) == 0 or len(base_dates) == 0:\\n return\\n\\n dt_processing = SequentialTransformer(\\n [\\n ColumnsSelector(keys=list(set(datetimes + base_dates))),\\n BaseDiff(base_names=base_dates, diff_names=datetimes),\\n ]\\n )\\n return dt_processing\",\n \"def load_raw_data(self, nrows=None, save_to_hdf=False):\\n train = load_train(nrows=nrows, save_to_hdf=save_to_hdf)\\n test = load_test(save_to_hdf=save_to_hdf)\\n\\n train.drop(self.drop_features, axis=1, inplace=True)\\n test.drop(self.drop_features, axis=1, inplace=True)\\n \\n correct_dates(train)\\n to_datetime(train, keep_dates=True)\\n correct_dates(test)\\n to_datetime(test, keep_dates=True)\\n test.loc[test.listen_type == 0, 'listen_type'] = 1\\n \\n train['diff_days'] = (train.dt_listen - train.dt_media).dt.days\\n test['diff_days'] = (test.dt_listen - test.dt_media).dt.days\\n train.drop(['dt_listen', 'dt_media',], axis=1, inplace=True)\\n test.drop(['dt_listen', 'dt_media',], axis=1, inplace=True)\\n \\n train.to_pickle('../input/train_clean.pkl')\\n test.to_pickle('../input/test_clean.pkl')\\n\\n self.train = train\\n self.test = test\",\n \"def test_set_get_dt():\\n data = io.create_sample_dataset()\\n assert data.attrs[\\\"dt\\\"] == 1.0\\n assert data.piv.dt == 1.0\\n data.piv.set_dt(2.0)\\n assert data.attrs[\\\"dt\\\"] == 2.0\",\n \"def loadtrainData():\\n train_x = []\\n train_y = []\\n fileIn = open(PATH + 'traindata_Subtask4.txt')\\n for line in fileIn.readlines():\\n lineArr = line.strip().split()\\n train_x.append([float(lineArr[i]) for i in range(len(lineArr) - 1)])\\n train_y.append(int(lineArr[-1]))\\n return np.mat(train_x), np.mat(train_y).transpose()\",\n \"def read_training(index_columns=None, both=False, weather=False):\\n if weather:\\n raw_X_train = pd.read_csv('data\\\\\\\\train_X.csv', parse_dates=['date'])\\n raw_weather = pd.read_csv('data\\\\\\\\weather_data.csv', parse_dates=['date'])\\n\\n raw_X_train = ffill_nans(raw_X_train)\\n raw_X_train = raw_X_train.merge(raw_weather, how='left', on=['date','hour'])\\n raw_X_train = raw_X_train.set_index(index_columns)\\n\\n else:\\n raw_X_train = pd.read_csv(\\n 'data\\\\\\\\train_X.csv',\\n parse_dates=['date'],\\n index_col=index_columns)\\n if both:\\n raw_y_train = pd.read_csv(\\n 'data\\\\\\\\train_y.csv',\\n parse_dates=['date'],\\n index_col=index_columns)\\n\\n return raw_X_train, raw_y_train\\n \\n return raw_X_train\",\n \"def test_dates_and_Datetimes(self):\\n if self.skip_tests:\\n return\\n recipe = (\\n self.recipe()\\n .dimensions(\\\"year_by_format\\\")\\n .metrics(\\\"count\\\")\\n .order_by(\\\"year_by_format\\\")\\n )\\n self.assertRecipeCSV(\\n recipe,\\n \\\"\\\"\\\"\\n year_by_format,count,year_by_format_id\\n 2005-01-01 00:00:00,1,2005-01-01 00:00:00\\n 2013-01-01 00:00:00,1,2013-01-01 00:00:00\\n \\\"\\\"\\\",\\n )\\n recipe = (\\n self.recipe()\\n .dimensions(\\\"year_by_format\\\")\\n .metrics(\\\"count\\\")\\n .order_by(\\\"-year_by_format\\\")\\n )\\n self.assertRecipeCSV(\\n recipe,\\n \\\"\\\"\\\"\\n year_by_format,count,year_by_format_id\\n 2013-01-01 00:00:00,1,2013-01-01 00:00:00\\n 2005-01-01 00:00:00,1,2005-01-01 00:00:00\\n \\\"\\\"\\\",\\n )\\n\\n # Test a month() conversion\\n recipe = (\\n self.recipe()\\n .dimensions(\\\"test_month\\\")\\n .metrics(\\\"age\\\", \\\"count\\\")\\n .order_by(\\\"-test_month\\\")\\n )\\n self.assertRecipeCSV(\\n recipe,\\n \\\"\\\"\\\"\\n test_month,age,count,test_month_id\\n 2015-05-01,10,1,2015-05-01\\n 2015-01-01,5,1,2015-01-01\\n \\\"\\\"\\\",\\n )\",\n \"def time_series_train_test_split_y(df, test_date_start):\\n test_date_cutoff = datetime.strptime(test_date_start, '%Y-%m-%d')\\n train_date_cutoff = test_date_cutoff - timedelta(1)\\n \\n y = df['AverageTemperature']\\n y_train = y.loc[:train_date_cutoff]\\n y_test = y.loc[test_date_cutoff:]\\n \\n return y, y_train, y_test\",\n \"def load_data(path):\\n train = pd.read_csv(os.path.join(path,'train.csv'))\\n test = pd.read_csv(os.path.join(path,'test.csv'))\\n \\n return train, test\",\n \"def load_train_test_transactions(train_size=0.7):\\n X, y = features_target_split()\\n X_train, X_test, y_train, y_test = train_test_split(X,y,train_size=train_size, random_state=7)\\n print('\\\\nTraining and testing data creation successful\\\\n')\\n return X_train, X_test, y_train,y_test\",\n \"def getTrainTimes(t1,testTimes):\\n trn=t1.copy(deep=True)\\n for i,j in testTimes.iteritems():\\n df0=trn[(i<=trn.index)&(trn.index<=j)].index # train starts within test\\n df1=trn[(i<=trn)&(trn<=j)].index # train ends within test\\n df2=trn[(trn.index<=i)&(j<=trn)].index # train envelops test\\n trn=trn.drop(df0.union(df1).union(df2))\\n return trn\",\n \"def test_data_set_load(data_set):\\n data = data_set()\\n train_data, train_labels, test_data, test_labels = data.load_data()\\n\\n assert len(train_data) > 0\\n assert len(test_data) > 0\\n assert len(train_data) == len(train_labels)\\n assert len(test_data) == len(test_labels)\",\n \"def __init__(self):\\r\\n \\r\\n #self.max_stocks = 100\\r\\n self.max_stocks = 200\\r\\n \\\"\\\"\\\" cv_factor determines what portion of stocks to put in cross validation set and what portion\\r\\n to leave in training set. cv_factor = 2 means every other stock goes into cross validation\\r\\n set. cv_factor = 3 means every third stock goes into cross validation set \\\"\\\"\\\"\\r\\n self.cv_factor = 2 \\r\\n \\\"\\\"\\\" future_day is how many training days in the future we train for. Setting future_day = 25\\r\\n means we are measuring how the stock does 25 days out \\\"\\\"\\\"\\r\\n self.future_day = 25\\r\\n \\\"\\\"\\\" The train_dates are the dates for training and cross validation\\\"\\\"\\\"\\r\\n self.train_dates = []\\r\\n first_train_date = dateutl.days_since_1900('2001-01-01')\\r\\n num_train_dates = 10\\r\\n train_date_increment = 60\\r\\n self.train_dates.append(first_train_date)\\r\\n for iday in range(1,num_train_dates):\\r\\n last_train_date = self.train_dates[iday-1]\\r\\n self.train_dates.append(last_train_date + train_date_increment)\\r\\n \\\"\\\"\\\"self.train_dates[1] -= 1 \\\"\\\"\\\"\\r\\n \\r\\n \\\"\\\"\\\" test_dates are the dates we are using for testing \\\"\\\"\\\"\\r\\n self.test_dates = []\\r\\n first_test_date = dateutl.days_since_1900('2010-01-01')\\r\\n num_test_dates = 10\\r\\n test_date_increment = 60\\r\\n self.test_dates.append(first_test_date) \\r\\n for iday in range(1,num_test_dates):\\r\\n last_test_date = self.test_dates[iday-1]\\r\\n self.test_dates.append(last_test_date + test_date_increment)\\r\\n \\\"\\\"\\\"self.test_dates[1] -= 1\\r\\n self.test_dates[3] += 1\\r\\n self.test_dates[4] += 3\\r\\n self.test_dates[5] += 4\\r\\n self.test_dates.append(dateutl.days_since_1900('2010-01-01'))\\r\\n self.test_dates.append(dateutl.days_since_1900('2010-03-01'))\\r\\n self.test_dates.append(dateutl.days_since_1900('2010-05-01'))\\r\\n self.test_dates.append(dateutl.days_since_1900('2010-07-01'))\\r\\n self.test_dates.append(dateutl.days_since_1900('2010-09-01'))\\r\\n self.test_dates.append(dateutl.days_since_1900('2010-11-01'))\\\"\\\"\\\"\\r\\n \\\"\\\"\\\"train_history_days and train_increment set how many historical days we use to\\r\\n train and the increment used. Setting train_history_days = 21 and train_increment = 5\\r\\n means we are using the values at days days 5, 10, 15 and 20 days before the reference day\\r\\n as input features \\\"\\\"\\\"\\r\\n self.train_days = 21\\r\\n self.train_increment = 5\\r\\n self.features = ['rsi','tsi','ppo','adx','dip14','dim14','cci', \\\\\\r\\n 'cmo','mfi','natr','roc','stoch','uo']\\r\\n \\\"\\\"\\\" output is just a boolean about calling the output function to write out \\r\\n appropriate X and y matricies. The default is False meaning do not write out\\r\\n matricies \\\"\\\"\\\"\\r\\n self.output = False\",\n \"def test_01_train(self):\\n today = date.today()\\n log_file = os.path.join(LOG_DIR, \\\"{}-train-{}-{}.log\\\".format(LOG_PREFIX, today.year, today.month))\\n if os.path.exists(log_file):\\n os.remove(log_file)\\n \\n ## update the log\\n country = 'india'\\n date_range = ('2017-11-29', '2019-05-24')\\n metric = {'rmse':0.5}\\n runtime = \\\"00:00:01\\\"\\n model_version = 0.1\\n model_version_note = \\\"test model\\\"\\n \\n update_train_log(country, date_range, metric, runtime,\\n model_version, model_version_note, test=True, prefix=LOG_PREFIX)\\n\\n self.assertTrue(os.path.exists(log_file))\",\n \"def test_dates(self):\\n result = export.processExport(houseId=1,\\n startDate = datetime.datetime(2013, 01, 06) #5 Days\\n )\\n\\n self.assertEqual(result.shape, (1440, 2))\\n self.assertEqual(result.index[0], datetime.datetime(2013, 01, 06))\\n self.assertEqual(result.index[-1], datetime.datetime(2013, 01, 10, 23, 55))\\n\\n\\n #Stop at 00:00 on the 5th\\n result = export.processExport(houseId=1,\\n endDate = datetime.datetime(2013, 01, 05, 23, 55) #5 Days\\n )\\n\\n self.assertEqual(result.shape, (1440, 2))\\n self.assertEqual(result.index[0], datetime.datetime(2013, 01, 01))\\n self.assertEqual(result.index[-1], datetime.datetime(2013, 01, 05, 23, 55))\",\n \"def Train_data():\\n print (\\\"loading train data ...\\\")\\n time_start = time.time()\\n data_root = '/media/keziwen/86AA9651AA963E1D'\\n with h5py.File(join(data_root, './data/train_real2.h5')) as f:\\n data_real = f['train_real'][:]\\n num, nt, ny, nx = data_real.shape\\n data_real = np.transpose(data_real, (0, 1, 3, 2))\\n with h5py.File(join(data_root, './data/train_imag2.h5')) as f:\\n data_imag = f['train_imag'][:]\\n num, nt, ny, nx = data_imag.shape\\n data_imag = np.transpose(data_imag, (0, 1, 3, 2))\\n data = data_real+1j*data_imag\\n num_train = 15000\\n num_validate = 2000\\n train_data = data[0:num_train]\\n validate_data = data[num_train:num_train+num_validate]\\n\\n train_data = np.random.permutation(train_data)\\n\\n time_end = time.time()\\n print ('dataset has been created using {}s'.format(time_end-time_start))\\n return train_data, validate_data\",\n \"def train(self):\\n acc_time = []\\n data_test = self.val_data[0][0][0]\\n labels_test = self.val_data[0][0][1]\\n for i, train_batch in enumerate(self.dataset):\\n \\n writerDIM = SummaryWriter('runs/experiment_DIM'+str(i))\\n data,labels, t = train_batch\\n\\n index_tr,index_cv,coreset = data_split(data.shape[0],777)\\n\\n # adding eventual replay patterns to the current batch\\n if i == 0:\\n ext_mem = [data[coreset], labels[coreset]]\\n dataC = np.concatenate((data[index_tr], data[index_cv]),axis=0)\\n labC = np.concatenate((labels[index_tr],labels[index_cv]),axis=0)\\n else:\\n dataP = ext_mem[0]\\n labP = ext_mem[1]\\n\\n ext_mem = [\\n np.concatenate((data[coreset], ext_mem[0])),\\n np.concatenate((labels[coreset], ext_mem[1]))]\\n if self.replay:\\n dataC = np.concatenate((data[index_tr], data[index_cv],dataP),axis=0)\\n labC = np.concatenate((labels[index_tr],labels[index_cv],labP),axis=0)\\n else:\\n dataC = np.concatenate((data[index_tr], data[index_cv]),axis=0)\\n labC = np.concatenate((labels[index_tr],labels[index_cv]),axis=0)\\n\\n\\n\\n print(\\\"----------- batch {0} -------------\\\".format(i))\\n print(\\\"Task Label: \\\", t)\\n trC,cvC = data_split_Tr_CV(dataC.shape[0],777)\\n\\n train_set = LoadDataset(dataC,labC,transform=self.tr,indices=trC)\\n val_set = LoadDataset(dataC,labC,transform=self.tr,indices=cvC)\\n print('Training set: {0} \\\\nValidation Set {1}'.format(train_set.__len__(),val_set.__len__()))\\n batch_size=32\\n train_loader = DataLoader(train_set, batch_size=batch_size, shuffle=True)\\n valid_loader = DataLoader(val_set, batch_size=batch_size, shuffle=False)\\n dataloaders = {'train':train_loader,'val':valid_loader}\\n \\n ####### Set hyperparameters for the training\\n if i ==0: \\n prior = False\\n ep=40\\n dim_model = DIM_model(batch_s=32,num_classes =128,feature=True) \\n dim_model.to(self.device)\\n classifierM = _classifier(n_input=128,n_class=50,n_neurons=[256,256,128])\\n classifierM = classifierM.to(self.device)\\n writer = SummaryWriter('runs/experiment_C'+str(i))\\n lr_new = 0.00001\\n lrC=0.0001\\n \\n else:\\n prior = True\\n ep=6\\n \\n lr_new =0.000005\\n lrC = 0.00005\\n\\n optimizer = torch.optim.Adam(dim_model.parameters(),lr=lr_new)\\n scheduler = lr_scheduler.StepLR(optimizer,step_size=40,gamma=0.1) #there is also MultiStepLR\\n\\n tr_dict_enc = {'ep':ep,'writer':writerDIM,'best_loss':1e10,'t_board':True,\\n 'gamma':.5,'beta':.5,'Prior_Flag':prior,'discriminator':classifierM} \\n tr_dict_cl = {'ep':40,'writer':writer,'best_loss':1e10,'t_board':True,'gamma':1}#40\\n\\n if i==0 and self.load:\\n print('Load DIM model weights first step')\\n dim_model.load_state_dict(torch.load(self.path + 'weights/weightsDIM_T0.pt'))\\n else:\\n ############################## Train Encoder########################################\\n dim_model,self.stats = trainEnc_MI(self.stats,dim_model, optimizer, scheduler,dataloaders,self.device,tr_dict_enc)\\n ####################################################################################\\n if i==0:\\n torch.save(dim_model.state_dict(), self.path + 'weights/weightsDIM_T'+str(i)+'.pt')\\n\\n ####\\n #Conversion of image into latent space representation for classifier training\\n ####\\n dim_model.requires_grad_(False)\\n for phase in ['train','val']:\\n dataF = None\\n labF = None\\n for inputs, labels in dataloaders[phase]:\\n torch.cuda.empty_cache()\\n if len(inputs.shape)==5:\\n\\n inputs = inputs[:,:,:,:,0].to(self.device)\\n else:\\n inputs = inputs.to(self.device)\\n\\n _,_,pred = dim_model(inputs)\\n pred_l = pred.data.cpu().numpy()\\n if dataF is None:\\n dataF = pred_l\\n labF = labels.data.cpu().numpy()\\n else:\\n dataF = np.concatenate((dataF,pred_l),axis=0)\\n labF = np.concatenate((labF,labels.data.cpu().numpy()),axis=0)\\n\\n if phase == 'train':\\n dataTr_f = dataF\\n labTr_f = labF\\n else:\\n dataCv_f = dataF\\n labCv_f = labF\\n \\n dim_model.requires_grad_(True)\\n train_set = LoadFeat(dataTr_f,labTr_f)\\n val_set = LoadFeat(dataCv_f,labCv_f)\\n batch_size=32\\n\\n train_loader = DataLoader(train_set, batch_size=batch_size, shuffle=True)\\n valid_loader = DataLoader(val_set, batch_size=batch_size, shuffle=False)\\n dataloaderC = {'train':train_loader,'val':valid_loader}\\n\\n optimizerC = torch.optim.Adam(classifierM.parameters(),lr=lrC)\\n schedulerC = lr_scheduler.StepLR(optimizerC,step_size=40,gamma=0.1)\\n classifierM.requires_grad_(True)\\n\\n ############################## Train Classifier ########################################\\n classifierM,self.stats = train_classifier(self.stats,classifierM, optimizerC, schedulerC,dataloaderC,self.device,tr_dict_cl) \\n #################################### #################################### ##############\\n\\n torch.save(classifierM.state_dict(), self.path + 'weights/weightsC_T'+str(i)+'.pt')\\n dim_model.eval()\\n classifierM.eval()\\n #### Cross_val Testing\\n \\n test_set = LoadDataset(data_test,labels_test,transform=self.trT)\\n batch_size=32\\n test_loader = DataLoader(test_set, batch_size=batch_size, shuffle=False)\\n score= []\\n\\n for inputs, labels in test_loader:\\n torch.cuda.empty_cache()\\n inputs = inputs.to(self.device)\\n labels = labels.to(self.device) \\n _,_,ww =dim_model(inputs)\\n pred = classifierM(ww)\\n pred_l = pred.data.cpu().numpy()\\n score.append(np.sum(np.argmax(pred_l,axis=1)==labels.data.cpu().numpy())/pred_l.shape[0])\\n print('TEST PERFORMANCES:', np.asarray(score).mean())\\n acc_time.append(np.asarray(score).mean())\\n del test_set,test_loader\\n \\n self.dim_model = dim_model\\n self.classifierM = classifierM\\n acc_time = np.asarray(acc_time)\\n return self.stats,acc_time\",\n \"def test_time_dicts():\\n dmd = DMD()\\n dmd.fit(X=sample_data_1, Y=sample_data_2)\\n expected_dict = {\\\"dt\\\": 1, \\\"t0\\\": 0, \\\"tend\\\": 13}\\n np.testing.assert_equal(dmd.original_time, expected_dict)\\n np.testing.assert_equal(dmd.dmd_time, expected_dict)\",\n \"def test_model_time_series(self):\\n job = DarshanIngestedJob(label=\\\"job\\\", file_details={})\\n job.time_start = 0\\n\\n # With no file data, we should end up with empty time series\\n series = job.model_time_series()\\n self.assertEqual(len(series), 0)\\n\\n # Otherwise, there should be time-series created with the correct totals\\n\\n file1 = DarshanIngestedJobFile(name=\\\"file1\\\")\\n file2 = DarshanIngestedJobFile(name=\\\"file2\\\")\\n\\n file1.bytes_read = 1024 * 99\\n file2.bytes_read = 1024 * 100\\n file1.read_time_start = 11.2\\n file1.read_time_end = 13.2\\n file2.read_time_start = 13.5\\n file2.read_time_end = 15.0\\n\\n file1.bytes_written = 1024 * 101\\n file2.bytes_written = 1024 * 102\\n file1.write_time_start = 19.2\\n file1.write_time_end = 21.3\\n file2.write_time_start = 32\\n file2.write_time_end = 35\\n\\n job.file_details = {\\\"file1\\\": file1, \\\"file2\\\": file2}\\n\\n series = job.model_time_series()\\n self.assertIn('kb_read', series)\\n self.assertIn('kb_write', series)\\n reads = series['kb_read']\\n writes = series['kb_write']\\n\\n self.assertEqual(reads.sum, 199)\\n self.assertEqual(writes.sum, 203)\\n self.assertEqual(len(reads.xvalues), 2)\\n self.assertEqual(len(writes.xvalues), 2)\\n self.assertEqual(len(reads.yvalues), 2)\\n self.assertEqual(len(writes.yvalues), 2)\\n self.assertEqual(set(reads.xvalues), {11.2, 13.5})\\n self.assertEqual(set(writes.xvalues), {19.2, 32})\\n self.assertEqual(set(reads.yvalues), {99.0, 100.0})\\n self.assertEqual(set(writes.yvalues), {101, 102})\",\n \"def get_ts_properties(self):\\r\\n filename = self.C.training_set[:-3] + \\\"csv\\\"\\r\\n self.ts_properties = util.load_ts_properties(csv_path=filename)\",\n \"def train(self, trainfile):\",\n \"def test_003_not_enough_datetimes() -> None:\\n df = generate_test_data()\\n df = df.head(2)\\n skim(df)\",\n \"def train_predict(train_file_arg):\\n\\n # Load command line arguments\\n train_file = train_file_arg\\n parameter_file = \\\"prophet/lstm/trainingConfig.json\\\"\\n\\n # Load training parameters\\n params = json.loads(open(parameter_file).read())\\n\\n # Load time series dataset, and split it into train and test\\n x_train, y_train, x_test, _, x_test_raw, _,\\\\\\n last_window_raw, last_window, last_datetime_epoch = dataHelper.load_timeseries(train_file, params)\\n\\n # Build RNN (LSTM) model\\n lstm_layer = [1, params[\\\"window_size\\\"], params[\\\"hidden_unit\\\"], 1]\\n model = buildModel.rnn_lstm(lstm_layer, params)\\n\\n # Train RNN (LSTM) model with train set\\n model.fit(\\n x_train,\\n y_train,\\n batch_size=params[\\\"batch_size\\\"],\\n epochs=params[\\\"epochs\\\"],\\n validation_split=params[\\\"validation_split\\\"])\\n\\n # Check the model against test set\\n predicted = buildModel.predict_next_timestamp(model, x_test)\\n predicted_raw = []\\n for i in range(len(x_test_raw)):\\n predicted_raw.append((predicted[i] + 1) * x_test_raw[i][0])\\n\\n # Predict next time stamp\\n next_timestamp = buildModel.predict_next_timestamp(model, last_window)\\n next_timestamp_raw = (next_timestamp[0] + 1) * last_window_raw[0][0]\\n print(\\\"The next time stamp forecasting is: {}\\\".format(next_timestamp_raw))\\n\\n # Add 5 minutes for a new timestamp of predictions\\n last_datetime = time.strftime(\\\"%Y-%m-%d %H:%M:%S\\\", time.localtime(last_datetime_epoch))\\n last_datetime = datetime.strptime(last_datetime, \\\"%Y-%m-%d %H:%M:%S\\\")\\n new_datetime = last_datetime + timedelta(seconds=300)\\n new_datetime_epoch = time.mktime(new_datetime.timetuple())\\n new_datetime = new_datetime.strftime(\\\"%Y-%m-%d %H:%M:%S\\\")\\n\\n # Concatenate datetime and price forecast\\n new_entry = \\\"coin_name,\\\" + str(new_datetime_epoch) + \\\",\\\" \\\\\\n + str(next_timestamp_raw) + \\\",coin_supply\\\" + \\\",coin_mc\\\" + \\\"\\\\n\\\"\\n\\n # Write to CSV file of new prediction\\n fd = open(train_file, \\\"a\\\")\\n fd.write(new_entry)\\n fd.close()\\n\\n # Return new prediction\\n return [new_datetime, str(next_timestamp_raw)]\",\n \"def merge_data():\\n\\n\\tconfig = Config()\\n\\tfilename_train, filename_test = \\\"../data/train.csv\\\", \\\"../data/test.csv\\\" \\n\\n # create datasets\\n\\ttrain, test = config.load_data(filename_train, filename_test, print_EDA=False)\\n\\n # 1. datetime features\\n\\t# diff between weekday and day?\\n\\t#weekday - Return the day of the week as an integer, where Monday is 0 and Sunday is 6.\\n\\t#day - Between 1 and the number of days in the given month of the given year.\\n\\ttrain['pickup_hour'] = train.pickup_datetime.dt.hour.astype('uint8')\\n\\ttrain['pickup_day'] = train.pickup_datetime.dt.day.astype('uint8')\\n\\ttrain['pickup_weekday'] = train.pickup_datetime.dt.weekday.astype('uint8')\\n\\ttrain['pickup_minute'] = train.pickup_datetime.dt.minute.astype('uint8')\\n\\ttrain['pickup_month'] = train.pickup_datetime.dt.month.astype('uint8')\\n\\ttrain['pickup_hour_weekofyear'] = train['pickup_datetime'].dt.weekofyear\\n\\ttrain['pickup_weekday_hour'] = train['pickup_weekday']*24 + train['pickup_hour']\\n\\n\\ttest['pickup_hour'] = test.pickup_datetime.dt.hour.astype('uint8')\\n\\ttest['pickup_day'] = test.pickup_datetime.dt.day.astype('uint8')\\n\\ttest['pickup_weekday'] = test.pickup_datetime.dt.weekday.astype('uint8')\\n\\ttest['pickup_minute'] = test.pickup_datetime.dt.minute.astype('uint8')\\n\\ttest['pickup_month'] = test.pickup_datetime.dt.month.astype('uint8')\\n\\ttest['pickup_hour_weekofyear'] = test['pickup_datetime'].dt.weekofyear\\n\\ttest['pickup_weekday_hour'] = test['pickup_weekday']*24 + test['pickup_hour']\\n\\n\\t# 2. Location features\\n\\tdef haversine(lon1, lat1, lon2, lat2):\\n\\t lon1, lat1, lon2, lat2 = map(np.radians, [lon1, lat1, lon2, lat2])\\n\\t dlon = lon2 - lon1\\n\\t dlat = lat2 - lat1\\n\\t a = np.sin(dlat/2.0)**2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlon/2.0)**2\\n\\t c = 2 * np.arcsin(np.sqrt(a))\\n\\t km = 6367 * c # AVG_EARTH_RADIUS=6367\\n\\t miles = km * 0.621371\\n\\t return miles\\n\\n\\t# def dummy_manhattan_distance(lat1, lng1, lat2, lng2):\\n\\t# a = haversine_array(lat1, lng1, lat1, lng2)\\n\\t# b = haversine_array(lat1, lng1, lat2, lng1)\\n\\t# return a + b\\n\\n\\t# def bearing_array(lat1, lng1, lat2, lng2):\\n\\t# AVG_EARTH_RADIUS = 6371 # in km\\n\\t# lng_delta_rad = np.radians(lng2 - lng1)\\n\\t# lat1, lng1, lat2, lng2 = map(np.radians, (lat1, lng1, lat2, lng2))\\n\\t# y = np.sin(lng_delta_rad) * np.cos(lat2)\\n\\t# x = np.cos(lat1) * np.sin(lat2) - np.sin(lat1) * np.cos(lat2) * np.cos(lng_delta_rad)\\n\\t# return np.degrees(np.arctan2(y, x))\\n\\n\\ttrain['distance'] = haversine(train.pickup_longitude, train.pickup_latitude,\\n\\t train.dropoff_longitude, train.dropoff_latitude)\\n\\ttest['distance'] = haversine(test.pickup_longitude, test.pickup_latitude,\\n\\t test.dropoff_longitude, test.dropoff_latitude)\\n\\n\\n\\t# 3. Use outsource data\\n\\tweatherdata_filename = \\\"../data/outsource_data/weather_data_nyc_centralpark_2016.csv\\\"\\n\\tfastestroute_data_train = \\\"../data/outsource_data/fastest_train.csv\\\"\\n\\tfastestroute_data_test = \\\"../data/outsource_data/fastest_routes_test.csv\\\"\\n\\n\\n\\twd = pd.read_csv(weatherdata_filename, header=0)\\n\\twd['date'] = pd.to_datetime(wd.date, format=\\\"%d-%m-%Y\\\")\\n\\twd['pickup_day'] = wd['date'].dt.day\\n\\twd['snow fall'] = wd['snow fall'].replace('T', 0.05).astype(np.float32) \\n\\twd['precipitation'] = wd['precipitation'].replace('T', 0.05).astype(np.float32) \\n\\twd['snow depth'] = wd['snow depth'].replace('T', 0.05).astype(np.float32) \\n\\n\\t# Merge training data with weather data on pickup_day\\n\\tprint(\\\"Merging training data with weather data ....\\\")\\n\\twd_train = pd.merge(train, wd, on='pickup_day')\\n\\twd_train = wd_train.drop(['date','maximum temperature','minimum temperature'],axis=1)\\n\\tgc.collect()\\n\\n\\t# Merge wd_train with fastestroute_data\\n\\tfastest = pd.read_csv(fastestroute_data_train, header=0)\\n\\tprint(\\\"Merging Location data with weather and training data ....\\\")\\n\\twd_train_fastest = pd.merge(wd_train, fastest, on='id', how='outer')\\n\\n\\tgc.collect()\\n\\n\\n\\tprint(\\\"===================== CHECK TRAINING DATA =====================\\\")\\n\\n\\tprint(wd_train_fastest.head(2))\\n\\tprint(\\\"Semi-final training data shape is: {}\\\".format(wd_train_fastest.shape))\\n\\tprint(\\\"Training data columns: {}\\\".format(wd_train_fastest.columns))\\n\\n\\n\\t# Use the same outsource data with test set\\n\\t# merge outsource data with test data as well\\n\\tft_test_cols = [ 'id', 'starting_street','end_street','total_distance',\\t'total_travel_time',\\n\\t\\t\\t\\t\\t'number_of_steps','street_for_each_step','distance_per_step','travel_time_per_step',\\n\\t 'step_maneuvers','step_direction',\\t'step_location_list']\\n\\tfastest_test = pd.read_csv(fastestroute_data_test, names=ft_test_cols, header=0)\\n\\n\\tprint(\\\"Merging test data with Location data ....\\\")\\n\\ttest = pd.merge(test, fastest_test, on='id', how='outer')\\n\\ttest = test.drop(['step_location_list','step_direction','step_maneuvers','travel_time_per_step','distance_per_step','street_for_each_step','number_of_steps','starting_street',\\n\\t 'end_street'], axis=1)\\n\\tprint(\\\"Merging test data with weather data ....\\\")\\n\\ttest = pd.merge(test, wd, on='pickup_day')\\n\\n\\tprint(\\\"===================== CHECK TEST DATA =====================\\\")\\n\\n\\tprint(test.head(2))\\n\\tprint(\\\"Semi-final test data shape is: {}\\\".format(test.shape))\\n\\tprint(\\\"Test data columns: {}\\\".format(test.columns))\\n\\n\\n\\n\\t# 4. Do more data munging\\n\\tmask = ((wd_train_fastest.trip_duration > 60) & (wd_train_fastest.distance < 0.05))\\n\\twd_train_fastest = wd_train_fastest[~mask]\\n\\tmask = (wd_train_fastest.trip_duration < 60) \\n\\twd_train_fastest = wd_train_fastest[~mask]\\n\\tmask = wd_train_fastest.trip_duration > 79200\\n\\twd_train_fastest = wd_train_fastest[~mask]\\n\\tmask = wd_train_fastest.distance/(wd_train_fastest.trip_duration/3600) > 60\\n\\twd_train_fastest = wd_train_fastest[~mask]\\n\\twd_train_fastest.trip_duration = wd_train_fastest.trip_duration.astype(np.uint16)\\n\\twd_train_fastest = wd_train_fastest[wd_train_fastest.passenger_count > 0]\\n\\n\\t# 5. Do some data maskig based on location to create jfk and lgo features\\n\\tjfk_lon = -73.778889\\n\\tjfk_lat = 40.639722\\n\\tlga_lon = -73.872611\\n\\tlga_lat = 40.77725\\n\\n\\twd_train_fastest['jfk_pickup_dist'] = wd_train_fastest.apply(lambda row: haversine(jfk_lon, jfk_lat, row['pickup_longitude'],row['pickup_latitude']), axis=1)\\n\\twd_train_fastest['lga_pickup_dist'] = wd_train_fastest.apply(lambda row: haversine(lga_lon, lga_lat, row['pickup_longitude'],row['pickup_latitude']), axis=1)\\n\\twd_train_fastest['jfk_dropoff_dist'] = wd_train_fastest.apply(lambda row: haversine(jfk_lon, jfk_lat, row['dropoff_longitude'],row['dropoff_latitude']), axis=1)\\n\\twd_train_fastest['lga_dropoff_dist'] = wd_train_fastest.apply(lambda row: haversine(lga_lon, lga_lat, row['dropoff_longitude'],row['dropoff_latitude']), axis=1)\\n\\n\\twd_train_fastest['jfk'] = ((wd_train_fastest['jfk_pickup_dist'] < 2) | (wd_train_fastest['jfk_dropoff_dist'] < 2))\\n\\twd_train_fastest['lga'] = ((wd_train_fastest['lga_pickup_dist'] < 2) | (wd_train_fastest['lga_dropoff_dist'] < 2))\\n\\twd_train_fastest = wd_train_fastest.drop(['jfk_pickup_dist','lga_pickup_dist','jfk_dropoff_dist','lga_dropoff_dist'],axis=1)\\n\\twd_train_fastest['workday'] = ((wd_train_fastest['pickup_hour'] > 8) & (wd_train_fastest['pickup_hour'] < 18))\\n\\n\\n\\tprint(\\\"===================== CHECK TRAINING DATA AGAIN =====================\\\")\\n\\n\\tprint(wd_train_fastest.head(2))\\n\\tprint(\\\"Final training data shape is: {}\\\".format(wd_train_fastest.shape))\\n\\tprint(\\\"Training data columns: {}\\\".format(wd_train_fastest.columns))\\n\\n\\n\\treturn wd_train_fastest, test\",\n \"def load_dataset(path, test_or_train):\\n senta_batch, sentb_batch, scores_batch = [], [], []\\n with open(path, encoding='utf-8') as f:\\n for i, line in enumerate(f):\\n items = line.strip().split('\\\\t')\\n if test_or_train == 'train':\\n senta, sentb, score = items[-2], items[-1], float(items[-3])\\n elif test_or_train in ['dev', 'test']:\\n senta, sentb, score = items[-2], items[-1], float(items[-3])\\n else:\\n raise Exception(\\\"{} error\\\".format(test_or_train))\\n senta_batch.append(senta)\\n sentb_batch.append(sentb)\\n scores_batch.append(score)\\n return senta_batch, sentb_batch, scores_batch\",\n \"def load_data(train_path, test_path):\\n\\n train_data = pd.read_csv(train_path)\\n test_data = pd.read_csv(test_path)\\n\\n print(\\\"number of training examples = \\\" + str(train_data.shape[0]))\\n print(\\\"number of test examples = \\\" + str(test_data.shape[0]))\\n print(\\\"train shape: \\\" + str(train_data.shape))\\n print(\\\"test shape: \\\" + str(test_data.shape))\\n\\n return train_data, test_data\",\n \"def load_data(train_path, test_path):\\n\\n train_data = pd.read_csv(train_path)\\n test_data = pd.read_csv(test_path)\\n\\n print(\\\"number of training examples = \\\" + str(train_data.shape[0]))\\n print(\\\"number of test examples = \\\" + str(test_data.shape[0]))\\n print(\\\"train shape: \\\" + str(train_data.shape))\\n print(\\\"test shape: \\\" + str(test_data.shape))\\n\\n return train_data, test_data\",\n \"def __test_and_train(self):\\n f = open(\\\"all_data_info.csv\\\")\\n reader = csv.DictReader(f, delimiter=\\\",\\\")\\n data = []\\n for line in reader:\\n if line['artist_group'] == \\\"train_and_test\\\" and line[\\\"in_train\\\"] == \\\"False\\\":\\n # the img's artist is in training set\\n # but the img is in test set only\\n data.append((line['artist'], line['new_filename']))\\n\\n return data\",\n \"def test_sample_one_date(self):\\r\\n self.assertEqual(self.test_sample.date, datetime.datetime(2016, 2, 12, 7, 34, 26))\",\n \"def test_convert_datetime():\",\n \"def read(train_path, test_path, label_name):\\n train_dataset = pd.read_csv(train_path)\\n test_dataset = pd.read_csv(test_path)\\n\\n train_labels = train_dataset.pop(label_name)\\n\\n imputer = DataFrameImputer().fit(train_dataset)\\n train_dataset = imputer.transform(train_dataset)\\n test_dataset = imputer.transform(test_dataset)\\n\\n train_dataset = pd.get_dummies(train_dataset)\\n test_dataset = pd.get_dummies(test_dataset)\\n\\n train_dataset = train_dataset.drop(train_dataset.columns.difference(test_dataset.columns), axis=1)\\n test_dataset = test_dataset.drop(test_dataset.columns.difference(train_dataset.columns), axis=1)\\n\\n scaler = StandardScaler().fit(train_dataset)\\n train_dataset = scaler.transform(train_dataset)\\n test_dataset = scaler.transform(test_dataset)\\n\\n return train_dataset, train_labels, test_dataset\",\n \"def get_data_set(train=True):\\n\\n # 1\\n train_or_test = \\\"train\\\" if train == True else \\\"test\\\"\\n data_path = os.path.join(data_dir, \\\"aclImdb\\\",train_or_test)\\n\\n # 2\\n pos_glob_pattern = os.path.join(data_path, \\\"pos\\\", \\\"*.txt\\\")\\n neg_glob_pattern = os.path.join(data_path, \\\"neg\\\", \\\"*.txt\\\")\\n pos_file_path_seq = glob.glob(pos_glob_pattern)\\n neg_file_path_seq = glob.glob(neg_glob_pattern)\\n\\n # 3\\n pos_dataset = [text_to_one_line(path) for path in pos_file_path_seq]\\n neg_dataset = [text_to_one_line(path) for path in neg_file_path_seq]\\n x = pos_dataset + neg_dataset\\n y = [1.0] * len(pos_dataset) + [0.0] * len(neg_dataset)\\n\\n return x, y\",\n \"def set_training_data(self, *, inputs: Inputs, outputs: Outputs) -> None:\\n\\n # save copy of train data so we don't predict for each row in training\\n self._output_columns = outputs.columns\\n self._train_data = inputs.copy()\\n\\n # combine inputs and outputs for internal TimeSeries object\\n self._ts_frame = inputs.append_columns(outputs)\\n\\n # Parse cols needed for ts object\\n # TODO should only find cols to drop once!\\n self._get_cols(self._ts_frame.metadata)\\n\\n # Mark time difference (between min and min + 1 timestamp)\\n if self._grouping_column is None:\\n self._max_train = max(self._ts_frame.iloc[:, self._timestamp_column])\\n self._train_diff = int(\\n np.diff(np.sort(self._ts_frame.iloc[:, self._timestamp_column]))[0]\\n )\\n else:\\n g_col, t_col = (\\n self._ts_frame.columns[self._grouping_column],\\n self._ts_frame.columns[self._timestamp_column],\\n )\\n self._max_train = self._ts_frame.groupby(g_col)[t_col].agg(\\\"max\\\")\\n # making simplifying assumption that difference is the same across all groups\\n self._train_diff = int(\\n self._ts_frame.groupby(g_col)[t_col]\\n .apply(lambda x: np.diff(np.sort(x))[0])\\n .iloc[0]\\n )\\n\\n # assumption is that integer timestamps are days (treated this way by DeepAR objects)\\n timestamp_semantic_types = self._ts_frame.metadata.query_column_field(\\n self._timestamp_column, \\\"semantic_types\\\"\\n )\\n if \\\"http://schema.org/Integer\\\" in timestamp_semantic_types:\\n self._integer_timestamps = True\\n else:\\n self._integer_timestamps = False\\n\\n # drop cols if multiple grouping columns\\n if len(self._drop_cols) > 0:\\n self._ts_frame = self._ts_frame.remove_columns(self._drop_cols)\\n self._update_indices()\\n\\n # Create TimeSeries dataset object and learner\\n self._create_data_object_and_learner(self.hyperparams[\\\"val_split\\\"])\\n\\n # mark that new training data has been set\\n self._new_train_data = True\",\n \"def get_training_data():\\n \\n X = pd.read_csv('../data/train_values.csv').set_index('sequence_id')\\n y = pd.read_csv('../data/train_labels.csv').set_index('sequence_id')\\n return X, y\",\n \"def read_data(train_data_dir, test_data_dir):\\r\\n train_clients, train_data = read_dir(train_data_dir)\\r\\n test_clients, test_data = read_dir(test_data_dir)\\r\\n # 可能clients读入的顺序不一样\\r\\n assert train_clients.sort() == test_clients.sort()\\r\\n\\r\\n return train_clients, train_data, test_data\",\n \"def load_data(loc='../data/SICK/'):\\n trainA, trainB, testA, testB = [],[],[],[]\\n trainS, testS = [],[]\\n\\n with open(loc + 'SICK_train.txt', 'rb') as f:\\n for line in f:\\n text = line.strip().split('\\\\t')\\n trainA.append(text[1])\\n trainB.append(text[2])\\n trainS.append(text[3])\\n with open(loc + 'SICK_test_annotated.txt', 'rb') as f:\\n for line in f:\\n text = line.strip().split('\\\\t')\\n testA.append(text[1])\\n testB.append(text[2])\\n testS.append(text[3])\\n\\n trainS = [float(s) for s in trainS[1:]]\\n testS = [float(s) for s in testS[1:]]\\n\\n return [trainA[1:], trainB[1:]], [testA[1:], testB[1:]], [trainS, testS]\",\n \"def test_load_from_tsfile():\\n data_path = MODULE + \\\"/data/UnitTest/UnitTest_TRAIN.ts\\\"\\n # Test 1.1: load univariate equal length (UnitTest), should return 2D array and 1D\\n # array, test first and last data\\n # Test 1.2: Load a problem without y values (UnitTest), test first and last data.\\n X, y = load_from_tsfile(data_path, return_data_type=\\\"np2D\\\")\\n X2 = load_from_tsfile(data_path, return_y=False, return_data_type=\\\"np2D\\\")\\n assert isinstance(X, np.ndarray) and isinstance(y, np.ndarray)\\n assert X.ndim == 2 and X2.ndim == 2\\n assert X.shape == (20, 24) and y.shape == (20,)\\n assert X[0][0] == 573.0\\n X2 = load_from_tsfile(data_path, return_y=False, return_data_type=\\\"numpy3D\\\")\\n assert isinstance(X2, np.ndarray)\\n assert X2.ndim == 3\\n assert X2.shape == (20, 1, 24)\\n assert X2[0][0][0] == 573.0\\n\\n # Test 2: load multivare equal length (BasicMotions), should return 3D array and 1D\\n # array, test first and last data.\\n data_path = MODULE + \\\"/data/BasicMotions/BasicMotions_TRAIN.ts\\\"\\n X, y = load_from_tsfile(data_path, return_data_type=\\\"numpy3d\\\")\\n assert isinstance(X, np.ndarray) and isinstance(y, np.ndarray)\\n assert X.shape == (40, 6, 100) and y.shape == (40,)\\n assert X[1][2][3] == -1.898794\\n X, y = load_from_tsfile(data_path)\\n assert isinstance(X, pd.DataFrame) and isinstance(y, np.ndarray)\\n assert X.shape == (40, 6) and y.shape == (40,)\\n assert isinstance(X.iloc[1, 2], pd.Series)\\n assert X.iloc[1, 2].iloc[3] == -1.898794\\n\\n # Test 3.1: load univariate unequal length (PLAID), should return a one column\\n # dataframe,\\n data_path = MODULE + \\\"/data/PLAID/PLAID_TRAIN.ts\\\"\\n X, y = load_from_tsfile(full_file_path_and_name=data_path)\\n assert isinstance(X, pd.DataFrame) and isinstance(y, np.ndarray)\\n assert X.shape == (537, 1) and y.shape == (537,)\\n # Test 3.2: load multivariate unequal length (JapaneseVowels), should return a X\\n # columns dataframe,\\n data_path = MODULE + \\\"/data/JapaneseVowels/JapaneseVowels_TRAIN.ts\\\"\\n X, y = load_from_tsfile(full_file_path_and_name=data_path)\\n assert isinstance(X, pd.DataFrame) and isinstance(y, np.ndarray)\\n assert X.shape == (270, 12) and y.shape == (270,)\",\n \"def load_data(args):\\n data_df = pd.read_csv(os.path.join(args.data_dir, 'driving_log.csv'))\\n\\n X = data_df[['center', 'left', 'right']].values\\n y = data_df['steering'].values\\n\\n X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=args.test_size, random_state=0)\\n\\n return X_train, X_valid, y_train, y_valid\",\n \"def parse_data():\\n n_train, n_test = 15000, 4996\\n n_features = 1355191\\n\\n print('- parsing train data')\\n X_train = sp.lil_matrix((n_train, n_features))\\n y_train = np.zeros(n_train)\\n with open('/Users/kitazawa/data/news20.train') as f:\\n lines = map(lambda l: l.rstrip().split(' '), f.readlines())\\n for i, line in enumerate(lines):\\n y_train[i] = int(line[0])\\n\\n for fv in line[1:]:\\n f, v = fv.split(':')\\n X_train[i, (int(f) - 1)] = float(v)\\n print('-- density: %f' % (X_train.nnz / (n_train * n_features)))\\n\\n print('- parsing test data')\\n X_test = sp.lil_matrix((n_test, n_features))\\n y_test = np.zeros(n_test)\\n with open('/Users/kitazawa/data/news20.test') as f:\\n lines = map(lambda l: l.rstrip().split(' '), f.readlines())\\n for i, line in enumerate(lines):\\n y_test[i] = int(line[0])\\n\\n for fv in line[1:]:\\n f, v = fv.split(':')\\n X_test[i, (int(f) - 1)] = float(v)\\n print('-- density: %f' % (X_test.nnz / (n_test * n_features)))\\n\\n return X_train, y_train, X_test, y_test\",\n \"def load_data(conso_train_file='../input/conso_train.csv',\\n meteo_train_file='../input/meteo_train.csv',\\n meteo_test_file='../input/meteo_prev.csv'):\\n # Loading the weather data\\n train_meteo = pd.read_csv(meteo_train_file, sep=';')\\n test_meteo = pd.read_csv(meteo_test_file, sep=';')\\n\\n # Marking the type and merge the train/test dataframes\\n train_meteo['type'] = 'train'\\n test_meteo['type'] = 'test'\\n data_meteo = pd.concat([train_meteo, test_meteo])\\n # Correct the weather data\\n data_meteo = correct_weather_data(data_meteo)\\n\\n # Load the train consumption\\n train_conso = pd.read_csv(conso_train_file, sep=';')\\n # Correct the consumption data\\n train_conso = correct_conso_data(train_conso)\\n\\n # Mark the type and merge the consumption/weather dataframes\\n train_conso['type'] = 'train'\\n data = pd.merge(train_conso, data_meteo,\\n on=['date', 'type'], how='outer', sort=True)\\n\\n # Complete the test dates\\n data = add_test_dates(data)\\n # Correct the test data from the merging\\n data = correct_test_data(data)\\n\\n return data\",\n \"def islay(path):\\n import pandas as pd\\n path = os.path.expanduser(path)\\n filename = 'islay.csv'\\n if not os.path.exists(os.path.join(path, filename)):\\n url = 'http://dustintran.com/data/r/boot/islay.csv'\\n maybe_download_and_extract(path, url,\\n save_file_name='islay.csv',\\n resume=False)\\n\\n data = pd.read_csv(os.path.join(path, filename), index_col=0,\\n parse_dates=True)\\n x_train = data.values\\n metadata = {'columns': data.columns}\\n return x_train, metadata\",\n \"def test_4_data_fetching_unix_time_and_insertion(self):\\n d1 = date.today()\\n dt1 = datetime(d1.year, d1.month, d1.day) + timedelta(hours=8)\\n result, success = self.fitness.get_columns_given_range(dt1, dt1+timedelta(days=1))\\n self.assertTrue(success)\\n self.assertEqual(result[0]['Datetime'],self.unix_time)\",\n \"def load_data():\\r\\n train = convert_corpus_to_lists(ConllCorpusReader('CoNLL-2003', 'train.txt', ['words', 'pos', 'ignore', 'chunk']))\\r\\n val = convert_corpus_to_lists(ConllCorpusReader('CoNLL-2003', 'valid.txt', ['words', 'pos', 'ignore', 'chunk'])) # testa will be our val set\\r\\n test = convert_corpus_to_lists(ConllCorpusReader('CoNLL-2003', 'test.txt', ['words', 'pos', 'ignore', 'chunk']))\\r\\n\\r\\n return train, val, test\",\n \"def train(self, train_set=dt.treat_data(dt.load_data(\\\"data/train.csv\\\"))):\\n if self.model is None:\\n return\\n my_callback = keras.callbacks.callbacks.EarlyStopping(monitor='loss', min_delta=0.0, patience=1000, verbose=2,\\n mode='auto', baseline=None, restore_best_weights=False)\\n\\n train_data, train_labels = train_set\\n history = self.model.fit(x=train_data, y=train_labels, epochs=100000, batch_size=45,\\n callbacks=[my_callback], verbose=2, shuffle=True)\\n self.model.save(\\\"titanic_\\\" + str(time.time()) + \\\".h5\\\")\\n return history\",\n \"def load_dustrak(filepath, tz):\\n\\n with open(filepath) as file:\\n meta, values = re.split(\\\"\\\\n\\\\n\\\", file.read())\\n\\n metadata = {}\\n for row in meta.splitlines():\\n key, value = row.strip().split(\\\",\\\")\\n metadata[key] = value\\n\\n data = pd.read_csv(io.StringIO(values))\\n\\n if data.columns[0] != \\\"Elapsed Time [s]\\\":\\n raise ValueError(\\\"First column must be elapsed time in seconds\\\")\\n\\n start_time = \\\" \\\".join([metadata[\\\"Test Start Date\\\"], metadata[\\\"Test Start Time\\\"]])\\n start_time = datetime.datetime.strptime(start_time, \\\"%m/%d/%Y %I:%M:%S %p\\\")\\n\\n local_timezone = pytz.timezone(tz)\\n start_time = local_timezone.localize(start_time)\\n start_time = start_time.astimezone(pytz.timezone(\\\"UTC\\\"))\\n\\n sample_interval_minutes = metadata[\\\"Test Interval [M:S]\\\"].split(\\\":\\\")[0]\\n if sample_interval_minutes != \\\"0\\\":\\n raise NotImplementedError(\\\"Minute sampling intervals not supported\\\")\\n\\n sample_offsets = np.array(data[\\\"Elapsed Time [s]\\\"], dtype=\\\"timedelta64[s]\\\")\\n sample_times = pd.Timestamp(start_time) + pd.to_timedelta(sample_offsets)\\n\\n data[\\\"time\\\"] = sample_times\\n data.sort_values(by=\\\"time\\\", inplace=True)\\n\\n return metadata, data\",\n \"def load_tfile_data(self, path, start=None, stop=None, dss=28):\\n filename = path+\\\".\\\"+str(int(self.name))\\n datafile = open(filename,\\\"r\\\")\\n labels = datafile.readline().strip().split()\\n logger.debug(\\\"load_tfile_data: labels: %s\\\", labels)\\n datafile.close()\\n labels.insert(0,'DOY')\\n labels.insert(0,'Year')\\n logger.debug(\\\"load_tfile_data: new labels: %s\\\", labels)\\n \\n # colums are: Year DOY UTC Epoch Chan Tsys Int Az El Diode Level CryoTemp\\n # i4 i4 S8 f8 S2 f4 f4 f4 f4 i4 f4 f4\\n data = numpy.loadtxt(filename,skiprows=1,\\n dtype = {'names': tuple(labels),\\n 'formats': ('i4','i4','S8','f8',\\n 'S2','f4','f4','f4','f4','i4','f4', 'f4')})\\n return data\",\n \"def load_data():\\n df = pd.read_csv(\\\"../../Data/breast_cancer_data/data.csv\\\")\\n\\n cols = df.columns\\n X = df[cols[2:-1]].to_numpy()\\n y = df[cols[1]].to_numpy()\\n y = (y=='M').astype(np.int) * 2 - 1\\n\\n train_X = X[:-150]\\n train_y = y[:-150]\\n\\n test_X = X[-150:]\\n test_y = y[-150:]\\n\\n return train_X, train_y, test_X, test_y\",\n \"def readData(path_to_dataset, train_size=0.8, validation_size=0.2):\\n data = pd.read_csv(os.path.join(path_to_dataset, 'training_set_rel3.tsv'), sep='\\\\t', encoding='ISO-8859-1')\\n # Drop columns that has null value \\n data = data.dropna(axis=1)\\n # Only take 4 columns of data from the dataset: essay_id, essay_set, essay, domain1_score\\n data = data[['essay_id', 'essay_set', 'essay', 'domain1_score']]\\n # Perform 80:20 train-test split on the training data\\n train_set, test_set = train_test_split(data, train_size=train_size, random_state=0)\\n # Split the 80% training set further into 60:20\\n training_set, validation_set = train_test_split(train_set, test_size=validation_size, random_state=0)\\n return training_set, test_set, validation_set\",\n \"def load_dataset_train():\\n df_train = load_csv_file(\\\"31_train.csv\\\")\\n df_train_target = load_csv_file(\\\"31_target_train.csv\\\")\\n\\n return df_train.values, df_train_target.values\",\n \"def read_data(args):\\n\\n print(\\\"Start read_data\\\")\\n t_tot = 0 # sum of times for the all dataset\\n date_dirs = os.listdir(args.path_data_base)\\n for n_iter, date_dir in enumerate(date_dirs):\\n # get access to each sequence\\n path1 = os.path.join(args.path_data_base, date_dir)\\n if not os.path.isdir(path1):\\n continue\\n date_dirs2 = os.listdir(path1)\\n\\n for date_dir2 in date_dirs2:\\n path2 = os.path.join(path1, date_dir2)\\n if not os.path.isdir(path2):\\n continue\\n # read data\\n oxts_files = sorted(glob.glob(os.path.join(path2, 'oxts', 'data', '*.txt')))\\n oxts = KITTIDataset.load_oxts_packets_and_poses(oxts_files)\\n\\n \\\"\\\"\\\" Note on difference between ground truth and oxts solution:\\n - orientation is the same\\n - north and east axis are inverted\\n - position are closed to but different\\n => oxts solution is not loaded\\n \\\"\\\"\\\"\\n\\n print(\\\"\\\\n Sequence name : \\\" + date_dir2)\\n if len(oxts) < KITTIDataset.min_seq_dim: # sequence shorter than 30 s are rejected\\n cprint(\\\"Dataset is too short ({:.2f} s)\\\".format(len(oxts) / 100), 'yellow')\\n continue\\n lat_oxts = np.zeros(len(oxts))\\n lon_oxts = np.zeros(len(oxts))\\n alt_oxts = np.zeros(len(oxts))\\n roll_oxts = np.zeros(len(oxts))\\n pitch_oxts = np.zeros(len(oxts))\\n yaw_oxts = np.zeros(len(oxts))\\n roll_gt = np.zeros(len(oxts))\\n pitch_gt = np.zeros(len(oxts))\\n yaw_gt = np.zeros(len(oxts))\\n t = KITTIDataset.load_timestamps(path2)\\n acc = np.zeros((len(oxts), 3))\\n acc_bis = np.zeros((len(oxts), 3))\\n gyro = np.zeros((len(oxts), 3))\\n gyro_bis = np.zeros((len(oxts), 3))\\n p_gt = np.zeros((len(oxts), 3))\\n v_gt = np.zeros((len(oxts), 3))\\n v_rob_gt = np.zeros((len(oxts), 3))\\n\\n k_max = len(oxts)\\n for k in range(k_max):\\n oxts_k = oxts[k]\\n t[k] = 3600 * t[k].hour + 60 * t[k].minute + t[k].second + t[\\n k].microsecond / 1e6\\n lat_oxts[k] = oxts_k[0].lat\\n lon_oxts[k] = oxts_k[0].lon\\n alt_oxts[k] = oxts_k[0].alt\\n acc[k, 0] = oxts_k[0].af\\n acc[k, 1] = oxts_k[0].al\\n acc[k, 2] = oxts_k[0].au\\n acc_bis[k, 0] = oxts_k[0].ax\\n acc_bis[k, 1] = oxts_k[0].ay\\n acc_bis[k, 2] = oxts_k[0].az\\n gyro[k, 0] = oxts_k[0].wf\\n gyro[k, 1] = oxts_k[0].wl\\n gyro[k, 2] = oxts_k[0].wu\\n gyro_bis[k, 0] = oxts_k[0].wx\\n gyro_bis[k, 1] = oxts_k[0].wy\\n gyro_bis[k, 2] = oxts_k[0].wz\\n roll_oxts[k] = oxts_k[0].roll\\n pitch_oxts[k] = oxts_k[0].pitch\\n yaw_oxts[k] = oxts_k[0].yaw\\n v_gt[k, 0] = oxts_k[0].ve\\n v_gt[k, 1] = oxts_k[0].vn\\n v_gt[k, 2] = oxts_k[0].vu\\n v_rob_gt[k, 0] = oxts_k[0].vf\\n v_rob_gt[k, 1] = oxts_k[0].vl\\n v_rob_gt[k, 2] = oxts_k[0].vu\\n p_gt[k] = oxts_k[1][:3, 3]\\n Rot_gt_k = oxts_k[1][:3, :3]\\n roll_gt[k], pitch_gt[k], yaw_gt[k] = IEKF.to_rpy(Rot_gt_k)\\n\\n t0 = t[0]\\n t = np.array(t) - t[0]\\n # some data can have gps out\\n if np.max(t[:-1] - t[1:]) > 0.1:\\n cprint(date_dir2 + \\\" has time problem\\\", 'yellow')\\n ang_gt = np.zeros((roll_gt.shape[0], 3))\\n ang_gt[:, 0] = roll_gt\\n ang_gt[:, 1] = pitch_gt\\n ang_gt[:, 2] = yaw_gt\\n\\n p_oxts = lla2ned(lat_oxts, lon_oxts, alt_oxts, lat_oxts[0], lon_oxts[0],\\n alt_oxts[0], latlon_unit='deg', alt_unit='m', model='wgs84')\\n p_oxts[:, [0, 1]] = p_oxts[:, [1, 0]] # see note\\n\\n # take correct imu measurements\\n u = np.concatenate((gyro_bis, acc_bis), -1)\\n # convert from numpy\\n t = torch.from_numpy(t)\\n p_gt = torch.from_numpy(p_gt)\\n v_gt = torch.from_numpy(v_gt)\\n ang_gt = torch.from_numpy(ang_gt)\\n u = torch.from_numpy(u)\\n\\n # convert to float\\n t = t.float()\\n u = u.float()\\n p_gt = p_gt.float()\\n ang_gt = ang_gt.float()\\n v_gt = v_gt.float()\\n\\n mondict = {\\n 't': t, 'p_gt': p_gt, 'ang_gt': ang_gt, 'v_gt': v_gt,\\n 'u': u, 'name': date_dir2, 't0': t0\\n }\\n\\n t_tot += t[-1] - t[0]\\n KITTIDataset.dump(mondict, args.path_data_save, date_dir2)\\n print(\\\"\\\\n Total dataset duration : {:.2f} s\\\".format(t_tot))\",\n \"def load_data():\\n directories=[\\\"./track1/\\\",\\n \\\"./track1_recovery/\\\",\\n \\\"./track2/\\\",\\n \\\"./track1_reverse/\\\",\\n \\\"./track2_reverse/\\\",#Additional data for model built on top of lenet.h5\\n \\\"./track2_recovery/\\\",#Additions data for model built on top of lenet.h5\\n ]\\n lines=[]\\n for directory in directories:\\n with open(directory+\\\"driving_log.csv\\\") as csvfile:\\n reader=csv.reader(csvfile)\\n for line in reader:\\n lines.append(line)\\n train_samples, validation_samples = train_test_split(lines, test_size=0.2)\\n return train_samples, validation_samples\",\n \"def create_trainset(\\n self,\\n s=\\\"2016-01-01\\\",\\n e=\\\"2018-12-31\\\",\\n freq=\\\"D\\\",\\n ) -> DateTensors:\\n self.date_series = DatasetDateSeries(\\n start=s, end=e, wsz=self.wsz, to_onehot=True\\n ) # wsz same as W\\n\\n # ti window data to tensor\\n ti = Tsr(self.date_series.ti_win)\\n\\n # tc window data to tensor\\n N, W, Dtc = len(ti), self.date_series.wsz, 3\\n tc = torch.randint(0, 2, (1, 1, Dtc)).repeat(N, W, 1) # shape: (N, W, Dtc)\\n\\n # kn window data to tensor\\n kn = Tsr(self.date_series.kn_win)\\n\\n # create target data as `tg` (target)\\n tg = self.dct_curve[self.model_type](ti).repeat(1, 1, self.Dout)\\n\\n ti, tc, kn, tg = self.to_device(ti, tc, kn, tg)\\n trainset = DateTensors(\\n ti=ti, tc=tc, kn=kn, tg=tg, device=self.device\\n ) # ti/tc/kn.shape: (N, W, Dout), tg.shape = (N, 1, Dout)\\n self.trainset = trainset\\n return trainset\",\n \"def load_data():\\r\\n train_dataset = h5py.File('train_catvnoncat.h5', \\\"r\\\") # Change the directory as per your system\\r\\n train_set_x_orig = np.array(train_dataset[\\\"train_set_x\\\"][:]) # your train set features\\r\\n train_set_y_orig = np.array(train_dataset[\\\"train_set_y\\\"][:]) # your train set labels\\r\\n\\r\\n test_dataset = h5py.File('test_catvnoncat.h5', \\\"r\\\") # Change the directory as per your system\\r\\n test_set_x_orig = np.array(test_dataset[\\\"test_set_x\\\"][:]) # your test set features\\r\\n test_set_y_orig = np.array(test_dataset[\\\"test_set_y\\\"][:]) # your test set labels\\r\\n\\r\\n classes = np.array(test_dataset[\\\"list_classes\\\"][:]) # the list of classes\\r\\n \\r\\n train_set_y_orig = train_set_y_orig.reshape((1, train_set_y_orig.shape[0]))\\r\\n test_set_y_orig = test_set_y_orig.reshape((1, test_set_y_orig.shape[0]))\\r\\n \\r\\n return train_set_x_orig, train_set_y_orig, test_set_x_orig, test_set_y_orig, classes\",\n \"def load_data():\\n print(\\\"PARSING TRAIN\\\")\\n ys_train, x_train, ids_train = load_pickle_data(\\\"ys_train\\\"), load_pickle_data(\\\"x_train\\\"), load_pickle_data(\\n \\\"ids_train\\\")\\n if ys_train is None or x_train is None or ids_train is None:\\n ys_train, x_train, ids_train = load_csv_data(\\\"{}/train.csv\\\".format(DATA_DIR))\\n dump_pickle_data(ys_train, \\\"ys_train\\\")\\n dump_pickle_data(x_train, \\\"x_train\\\")\\n dump_pickle_data(ids_train, \\\"ids_train\\\")\\n\\n print(\\\"PARSING TEST\\\")\\n x_test, ids_test = load_pickle_data(\\\"x_test\\\"), load_pickle_data(\\\"ids_test\\\")\\n if x_test is None or ids_test is None:\\n _, x_test, ids_test = load_csv_data(\\\"{}/test.csv\\\".format(DATA_DIR))\\n dump_pickle_data(x_test, \\\"x_test\\\")\\n dump_pickle_data(ids_test, \\\"ids_test\\\")\\n\\n return ys_train, x_train, ids_train, x_test, ids_test\",\n \"def test_csv_and_tfrecord(self):\\n\\n # Test model training on CSV data\\n data_dir = os.path.join(self.root_data_dir, \\\"csv\\\")\\n feature_config_path = os.path.join(self.root_data_dir, \\\"configs\\\", self.feature_config_fname)\\n\\n csv_loss, csv_mrr = self.run_default_pipeline(\\n data_dir=data_dir, data_format=\\\"csv\\\", feature_config_path=feature_config_path\\n )\\n\\n # Check if the loss and accuracy on the test set is the same\\n assert np.isclose(csv_loss, 0.56748, rtol=0.01)\\n assert np.isclose(csv_mrr, 0.70396, rtol=0.01)\\n\\n # Test model training on TFRecord SequenceExample data\\n data_dir = os.path.join(self.root_data_dir, \\\"tfrecord\\\")\\n feature_config_path = os.path.join(self.root_data_dir, \\\"configs\\\", self.feature_config_fname)\\n\\n tfrecord_loss, tfrecord_mrr = self.run_default_pipeline(\\n data_dir=data_dir, data_format=\\\"tfrecord\\\", feature_config_path=feature_config_path\\n )\\n\\n # Check if the loss and accuracy on the test set is the same\\n assert np.isclose(tfrecord_loss, 0.56748, rtol=0.01)\\n assert np.isclose(tfrecord_mrr, 0.70396, rtol=0.01)\\n\\n # Compare CSV and TFRecord loss and accuracies\\n assert np.isclose(tfrecord_loss, csv_loss, rtol=0.01)\\n assert np.isclose(tfrecord_mrr, csv_mrr, rtol=0.01)\",\n \"def load_data():\\n t = time()\\n print 'loading tweets, please wait...'\\n trained_tweets = load_tweets('training_dataset')\\n eval_tweets = load_tweets('evaluation_dataset')\\n print 'Time taken {}'.format(time() - t)\\n t = time()\\n print 'loading w2v model, please wait...'\\n model = w2v_load_model('GoogleNews-vectors-negative300.bin')\\n print 'Time taken {}'.format(time() - t)\\n return trained_tweets, eval_tweets, model\",\n \"def readDAT(testname):\\n\\n dateparse = lambda x: pd.datetime.strptime(x, '%Y.%m.%d')\\n \\n df = pandas.read_csv(testname, names=['date','time','open', 'max', 'min', 'close', 'vol'])\\n df['datetime'] = df['date'] + ' - ' + df['time']\\n df['datetime'] = pandas.to_datetime(df['datetime'], format='%Y.%m.%d - %H:%M')\\n return df\",\n \"def data_import(path):\\n train_path = os.path.join(path, \\\"train.csv\\\")\\n test_path = os.path.join(path, \\\"test.csv\\\")\\n df_train = pd.read_csv(train_path)\\n df_test = pd.read_csv(test_path)\\n return df_train, df_test\",\n \"def test_device_readings_get_past_dates(self):\\n request = self.client().get(f'/devices/{self.device_uuid}/readings/'\\n f'?start={int(self.setup_time - 75)}'\\n f'&end={int(self.setup_time + 25)}')\\n\\n self.assertEqual(len(request.json), 2)\",\n \"def _get_setup_from_timestamps(self):\\n self._parse_timestamps()\\n IFOs = self.detectors.split(\\\",\\\")\\n # at this point, it's definitely a comma-separated string\\n tsfiles = self.timestamps.split(\\\",\\\")\\n if len(IFOs) != len(tsfiles):\\n raise ValueError(\\n f\\\"Length of detectors=='{self.detectors}'\\\"\\n f\\\" does not match that of timestamps=='{self.timestamps}'\\\"\\n f\\\" ({len(IFOs)}!={len(tsfiles)})\\\"\\n )\\n tstart = []\\n tend = []\\n self.sftfilenames = [] # This refers to the MFD output!\\n for X, IFO in enumerate(IFOs):\\n tsX = np.genfromtxt(tsfiles[X], comments=\\\"%\\\")\\n if tsX.ndim > 1:\\n logger.warning(\\n f\\\"Timestamps file {tsfiles[X]} has more than 1 column,\\\"\\n \\\" we will ignore the rest.\\\"\\n )\\n tsX = tsX[:, 0]\\n if not tsX[0].is_integer() or not tsX[-1].is_integer():\\n logger.warning(\\n \\\"Detected non-integer timestamps in timestamp file.\\\"\\n \\\" We will floor start and end times to the nearest integer\\\"\\n \\\" for the SFT name,\\\"\\n \\\" and let lalpulsar_Makefakedata_v5 handle the rest.\\\"\\n )\\n\\n this_start_time = int(tsX[0])\\n this_end_time = int(tsX[-1]) + self.Tsft\\n tstart.append(this_start_time)\\n tend.append(this_end_time)\\n self.sftfilenames.append(\\n utils.get_official_sft_filename(\\n IFO,\\n len(tsX),\\n self.Tsft,\\n this_start_time,\\n this_end_time - this_start_time,\\n self.label,\\n )\\n )\\n self.tstart = min(tstart)\\n self.duration = max(tend) - self.tstart\",\n \"def test_set_get_dt():\\n data = io.create_sample_Dataset()\\n assert data.attrs[\\\"delta_t\\\"] == 0.0\\n\\n data.piv.set_delta_t(2.0)\\n assert data.attrs[\\\"delta_t\\\"] == 2.0\",\n \"def test_read(self):\\n dt = np.dtype('(3,)f8')\\n dset = self.f.create_dataset('x',(10,),dtype=dt)\\n self.assertEqual(dset.shape, (10,))\\n self.assertEqual(dset.dtype, dt)\\n\\n # Full read\\n out = dset[...]\\n self.assertEqual(out.dtype, np.dtype('f8'))\\n self.assertEqual(out.shape, (10,3))\\n\\n # Single element\\n out = dset[0]\\n self.assertEqual(out.dtype, np.dtype('f8'))\\n self.assertEqual(out.shape, (3,))\\n\\n # Range\\n out = dset[2:8:2]\\n self.assertEqual(out.dtype, np.dtype('f8'))\\n self.assertEqual(out.shape, (3,3))\",\n \"def get_trainset(set_name=\\\"car\\\"):\\n names_file = open(\\\"../../data/\\\"+set_str+\\\"/\\\"+set_name+\\\"_names.dat\\\", \\\"r\\\")\\n data_file= open(\\\"../../data/\\\"+set_str+\\\"/\\\"+set_name+\\\"_data.dat\\\", \\\"r\\\")\\n \\n names_strs = names_file.readlines()\\n v_index = 0\\n a_index = 0\\n for index,line in enumerate(names_strs):\\n if line.find(\\\"| class values\\\") != -1:\\n v_index = index\\n elif line.find(\\\"| attributes\\\") != -1:\\n a_index = index\\n break\\n class_values_strs = re.split(r\\\"\\\\W+\\\",names_strs[v_index+2].strip())\\n attribute_namea_value_list = []\\n for i in range(a_index, len(names_strs)):\\n if re.search(r\\\"^\\\\w+:\\\\s+[\\\\w,\\\\s]+\\\\.\\\",names_strs[i]) != None:\\n temp_strs = re.split(r\\\"\\\\W+\\\",names_strs[i][:-2])\\n attribute_namea_value_list.append((temp_strs[0],temp_strs[1:]))\\n# print class_values_strs\\n# print attribute_namea_value_list\\n train_set = []\\n while True:\\n temp_str = data_file.readline()[:-1]\\n if temp_str == \\\"\\\":\\n break\\n values = temp_str.split(\\\",\\\")\\n train_set.append((values[:-1], values[-1]))\\n return class_values_strs,attribute_namea_value_list,train_set\",\n \"def readFiles(trainFile, testFile):\\r\\n\\r\\n\\t# Open both files and split into lines\\r\\n\\twith open(trainFile) as f:\\r\\n\\t\\ttrainLines = f.read().splitlines()\\r\\n\\r\\n\\twith open(testFile) as f:\\r\\n\\t\\ttestLines = f.read().splitlines()\\r\\n\\r\\n\\t\\t\\r\\n\\t# Extract training data\\r\\n\\tfor line in trainLines:\\r\\n\\t\\tline = line.split()\\r\\n\\t\\t\\r\\n\\t\\tid = line[0]\\r\\n\\t\\tclass_id = line[1]\\r\\n\\t\\twords = line[2:]\\r\\n\\t\\t\\r\\n\\t\\trow = [id, class_id, words]\\r\\n\\t\\t\\r\\n\\t\\ttrainingData.append(row)\\r\\n\\t\\t\\r\\n\\t\\t\\r\\n\\t# Extract testing data\\r\\n\\tfor line in testLines:\\r\\n\\t\\tline = line.split()\\r\\n\\t\\t\\r\\n\\t\\tid = line[0]\\r\\n\\t\\tclass_id = line[1]\\r\\n\\t\\twords = line[2:]\\r\\n\\t\\t\\r\\n\\t\\trow = [id, class_id, words]\\r\\n\\t\\t\\r\\n\\t\\ttestData.append(row)\",\n \"def load(self):\\n\\n X_train, y_train, X_test, y_test, variable_types, name = _load_data(\\n self.task_id)\\n\\n self.X_train = X_train\\n self.y_train = y_train\\n self.X_test = X_test\\n self.y_test = y_test\\n self.variable_types = variable_types\\n self.name = name\\n\\n return self.X_train, self.y_train, self.X_test, self.y_test\",\n \"def test_parse_date(self):\\n d_expected = '2018-03-02 15:19:08'\\n d_out = datetime.datetime.strptime(test_structures.example_erratum['errata']['rhba']['created_at'], '%Y-%m-%dT%H:%M:%SZ')\\n self.assertEqual(str(d_out), d_expected)\",\n \"def set_training_data(self):\\n # Optional training data period\\n # TODO: add training data period feature to training data query\\n if not self.training_period == None:\\n training_period_date = (datetime.datetime.utcnow() - timedelta(minutes=self.training_period)).strftime(\\\"%Y-%m-%d\\\")\\n print(f\\\"Training data start date: {training_period_date}\\\")\\n # Extract queried data from Athena\\n #athena = athena_connect.Athena()\\n #features_df = athena.pandas_read_athena(self.training_data_sql)\\n with open('feature_sql.txt', 'w') as f:\\n print(self.training_data_sql, file=f) \\n features_df = pd.read_sql(self.training_data_sql, self.logic_db_engine())\\n features_df.fillna(0, inplace=True)\\n print(features_df.shape)\\n features_df = features_df[max(self.feature_minutes_list):]\\n print(features_df.shape)\\n # Remove infinity string\\n features_df.replace({'Infinity': 0}, inplace=True)\\n # Convert all object fields to numeric except date fields\\n object_col_list = features_df.columns[features_df.dtypes.eq('object')]\\n object_col_list = [col for col in object_col_list if 'trade_date' not in col]\\n features_df[object_col_list] = features_df[object_col_list].apply(pd.to_numeric, errors='coerce')\\n self.training_df = features_df\",\n \"def load_data(self):\\n with open('data/fordTrain.csv') as f:\\n data = csv.reader(f, delimiter=',')\\n train = [x for i, x in enumerate(data) if i > 0] \\n # Extract features and target variable separately\\n trainx = [x[3:] for x in train]\\n trainy = [x[2] for x in train]\\n\\n with open('data/fordTest.csv') as f:\\n data = csv.reader(f, delimiter=',')\\n testx = [x[3:] for i, x in enumerate(data) if i > 0] \\n\\n with open('data/Solution.csv') as f:\\n data = csv.reader(f, delimiter=',')\\n testy = [x[2] for i, x in enumerate(data) if i > 0] \\n\\n # Extract features and target variable, convert to numpy array\\n trainx = np.asarray(trainx, dtype=np.float32)\\n trainy = np.asarray(trainy, dtype=np.int8)\\n testx = np.asarray(testx, dtype=np.float32)\\n testy = np.asarray(testy, dtype=np.int8)\\n\\n # Return training and test sets\\n trainSet = Dataset(trainx, trainy)\\n testSet = Dataset(testx, testy)\\n return trainSet, testSet\",\n \"def test_parser():\\n data = parse_csv(TEST_DATA)\\n assert data['2020-01-03'] == ['recycle']\\n assert data['2020-01-08'] == ['bio', 'trash']\\n assert data['2021-01-09'] == ['christmas']\",\n \"def get_cols_for_datetime(train: NumpyOrPandas) -> Tuple[List[str], List[str]]:\\n base_dates = get_columns_by_role(train, \\\"Datetime\\\", base_date=True)\\n datetimes = get_columns_by_role(train, \\\"Datetime\\\", base_date=False) + get_columns_by_role(\\n train, \\\"Datetime\\\", base_date=True, base_feats=True\\n )\\n\\n return base_dates, datetimes\",\n \"def loadRawData():\\n samples = []\\n with open('Sample_data/driving_log.csv') as csvfile:\\n reader = csv.reader(csvfile)\\n for line in reader:\\n # skip the first line, which is the title line\\n if line[0] != 'center':\\n samples.append(line)\\n # for testing implementation only, commented for GPU training\\n #if len(samples)>100:\\n # break\\n train_samples, validation_samples = train_test_split(samples, test_size=0.2)\\n return train_samples, validation_samples\",\n \"def load_data(data_path):\\n headers = ['center', 'left', 'right','steering','throttle','brake','speed']\\n data = pd.read_csv(data_path + '/driving_log.csv', names = headers)\\n \\n # Use SciKit not keras splitting so it randomly selects validation set\\n d_train, d_valid = train_test_split(data, test_size=0.2, random_state=42)\\n\\n return d_train, d_valid\",\n \"def test_dataset_from_file(train_dataset):\\n dummy = \\\"justo. Praesent luctus. Curabitur egestas nunc sed libero. Proin sed\\\"\\n assert train_dataset[0][0] == dummy\\n assert train_dataset[0][1] == '6'\",\n \"def read_driving_records(data_directory, test_fraction=0.05, valid_fraction=0.05):\\n\\n # read data from csv file\\n data_directory = os.path.join(data_directory, 'driving_log.csv').replace('\\\\\\\\','/')\\n data_col = ['center', 'left', 'right', 'steering', 'throttle', 'reverse', 'speed']\\n data = pd.read_csv(data_directory, names= data_col)\\n # smoothing the steering angle to avoid jettering in the output\\n data['steering'] = uniform_filter1d(data['steering'], size=5)\\n\\n # train test valid split \\n data_len = data.shape[0]\\n train_len = int(data_len * (1 - valid_fraction - test_fraction))\\n valid_len = int(data_len * valid_fraction)\\n print(f'Length of train: {train_len}, valid: {valid_len}, test: {data_len - train_len - valid_len}')\\n train_records, valid_records, test_records = np.split(data.sample(frac=1, random_state=42), [train_len, train_len + valid_len])\\n\\n return train_records, valid_records, test_records\",\n \"def _create_data():\\n tf.logging.info(\\\"Create records..\\\")\\n train, val, test = util.load_data(data_dir, FLAGS[\\\"is_aug\\\"])\\n tf.logging.info(\\\"Dataset size: Train-{} Test-{} Val-{}\\\".format(len(train), len(test), len(val)))\\n return train, val, test\",\n \"def get_train_data(path='.', is_train = True):\\n\\n try:\\n with open('data/linky.pickle','rb') as file:\\n Z = pickle.load(file)\\n except FileNotFoundError:\\n print('reading raw data')\\n Z = pd.read_excel(\\\"data/linky.xlsx\\\")\\n try:\\n with open('data/linky.pickle','wb') as file:\\n pickle.dump(Z, file)\\n print('raw data pickled')\\n except:\\n print(\\\"Couldn't pickle raw file\\\")\\n \\n \\n if is_train:\\n # we are in train data\\n start_row = 0\\n end_row = int(Z.shape[0] * RATIO_TRAIN_TEST)\\n else:\\n start_row = int(Z.shape[0] * RATIO_TRAIN_TEST) \\n end_row = Z.shape[0] \\n \\n Z = Z.iloc[start_row:end_row]\\n if FAST_MODE:\\n Z = Z.iloc[:3000]\\n \\n # print('get raw data, from row {} to row {}'.format(start_row, end_row))\\n Y = Z.drop(columns = 'Id_client')\\n Y = Y.fillna(0).values # hack to consider all columns and all rows (we can also drops rows or columns with NaN, or use an Imputer)\\n return Z, Y\",\n \"def run(self, df: pd.DataFrame, model: Any):\\n print(f'Running cross validation with the following model:\\\\n{model}')\\n\\n df['timestamp'] = pd.to_datetime(df['timestamp'])\\n\\n date_1 = datetime.datetime(year=2016, month=1, day=1)\\n date_2 = datetime.datetime(year=2016, month=4, day=1)\\n date_3 = datetime.datetime(year=2016, month=7, day=1)\\n date_4 = datetime.datetime(year=2016, month=10, day=1)\\n date_5 = datetime.datetime(year=2017, month=1, day=1)\\n\\n summaries: List[FoldSummary] = []\\n\\n for train_start, train_end, test_start, test_end in [\\n (date_1, date_2, date_2, date_3),\\n # (date_1, date_3, date_3, date_4),\\n # (date_1, date_4, date_4, date_5)\\n ]:\\n print('Calculating train and test datasets')\\n train_df = df[(df['timestamp'] >= train_start) & (df['timestamp'] < train_end)]\\n test_df = df[(df['timestamp'] >= test_start) & (df['timestamp'] < test_end)]\\n\\n columns = list(train_df.columns)\\n columns.remove('timestamp')\\n columns.remove('meter_reading')\\n\\n print(columns)\\n\\n train_data = train_df[columns]\\n test_data = test_df[columns]\\n\\n print(f'Fitting the model on train dataset of size {len(train_data)}')\\n model.fit(train_data, train_df['meter_reading'])\\n print(f'Predicting for test dataset of size {len(test_data)}')\\n predictions = model.predict(test_data)\\n\\n score = self._calculate_score(predictions, test_df['meter_reading'])\\n print(f'Score: {score}')\\n\\n summaries.append(FoldSummary(\\n train_start=train_start,\\n train_end=train_end,\\n test_start=test_start,\\n test_end=test_end,\\n score=score\\n ))\\n\\n filename = f'../resources/runs/{time.time()}.txt'\\n os.makedirs(os.path.dirname(filename), exist_ok=True)\\n with open(filename, 'w+') as f:\\n f.write(f'{model.__class__.__name__}\\\\n')\\n f.write(f'{str(model.get_params())}\\\\n')\\n for summary in summaries:\\n f.write(f'Summary (\\\\n'\\n f'\\\\ttrain start: {summary.train_start}\\\\n'\\n f'\\\\ttrain end: {summary.train_end}\\\\n'\\n f'\\\\ttest start: {summary.test_start}\\\\n'\\n f'\\\\ttest end: {summary.test_end}\\\\n'\\n f'\\\\tscore: {summary.score}\\\\n'\\n f')\\\\n')\\n\\n print(summaries)\\n\\n return model\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.66455895","0.6168198","0.6038736","0.6012535","0.59450936","0.5928109","0.5890639","0.5885514","0.58625025","0.58129436","0.57628024","0.57618076","0.5719605","0.5701487","0.5698947","0.569637","0.5668824","0.5663031","0.565223","0.5649751","0.5647297","0.56287885","0.5611016","0.55934995","0.5580389","0.55778486","0.55660546","0.5559632","0.5541351","0.55338776","0.55335265","0.55306196","0.5507942","0.55012494","0.5495822","0.5489462","0.54817885","0.54678327","0.54570574","0.5440915","0.5432901","0.5430267","0.54218215","0.5409631","0.5407417","0.5400539","0.5399592","0.5399437","0.5390352","0.5390352","0.53834933","0.53780687","0.5377243","0.5375118","0.53708494","0.535342","0.53522176","0.5327522","0.53235954","0.53214014","0.53199387","0.5317168","0.5306073","0.52979463","0.5289864","0.5288918","0.5279934","0.5273946","0.5263055","0.52535605","0.5242032","0.5234639","0.52317685","0.5229709","0.52227634","0.52219754","0.5219227","0.5213402","0.5208051","0.52036315","0.5189009","0.5182119","0.5175606","0.51739424","0.51691645","0.5168221","0.51673406","0.5159329","0.51582444","0.5155696","0.51495695","0.5147127","0.51466936","0.51430887","0.51358473","0.5135195","0.51241505","0.5123406","0.5122072","0.5118494"],"string":"[\n \"0.66455895\",\n \"0.6168198\",\n \"0.6038736\",\n \"0.6012535\",\n \"0.59450936\",\n \"0.5928109\",\n \"0.5890639\",\n \"0.5885514\",\n \"0.58625025\",\n \"0.58129436\",\n \"0.57628024\",\n \"0.57618076\",\n \"0.5719605\",\n \"0.5701487\",\n \"0.5698947\",\n \"0.569637\",\n \"0.5668824\",\n \"0.5663031\",\n \"0.565223\",\n \"0.5649751\",\n \"0.5647297\",\n \"0.56287885\",\n \"0.5611016\",\n \"0.55934995\",\n \"0.5580389\",\n \"0.55778486\",\n \"0.55660546\",\n \"0.5559632\",\n \"0.5541351\",\n \"0.55338776\",\n \"0.55335265\",\n \"0.55306196\",\n \"0.5507942\",\n \"0.55012494\",\n \"0.5495822\",\n \"0.5489462\",\n \"0.54817885\",\n \"0.54678327\",\n \"0.54570574\",\n \"0.5440915\",\n \"0.5432901\",\n \"0.5430267\",\n \"0.54218215\",\n \"0.5409631\",\n \"0.5407417\",\n \"0.5400539\",\n \"0.5399592\",\n \"0.5399437\",\n \"0.5390352\",\n \"0.5390352\",\n \"0.53834933\",\n \"0.53780687\",\n \"0.5377243\",\n \"0.5375118\",\n \"0.53708494\",\n \"0.535342\",\n \"0.53522176\",\n \"0.5327522\",\n \"0.53235954\",\n \"0.53214014\",\n \"0.53199387\",\n \"0.5317168\",\n \"0.5306073\",\n \"0.52979463\",\n \"0.5289864\",\n \"0.5288918\",\n \"0.5279934\",\n \"0.5273946\",\n \"0.5263055\",\n \"0.52535605\",\n \"0.5242032\",\n \"0.5234639\",\n \"0.52317685\",\n \"0.5229709\",\n \"0.52227634\",\n \"0.52219754\",\n \"0.5219227\",\n \"0.5213402\",\n \"0.5208051\",\n \"0.52036315\",\n \"0.5189009\",\n \"0.5182119\",\n \"0.5175606\",\n \"0.51739424\",\n \"0.51691645\",\n \"0.5168221\",\n \"0.51673406\",\n \"0.5159329\",\n \"0.51582444\",\n \"0.5155696\",\n \"0.51495695\",\n \"0.5147127\",\n \"0.51466936\",\n \"0.51430887\",\n \"0.51358473\",\n \"0.5135195\",\n \"0.51241505\",\n \"0.5123406\",\n \"0.5122072\",\n \"0.5118494\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6003768"},"document_rank":{"kind":"string","value":"4"}}},{"rowIdx":11288,"cells":{"query":{"kind":"string","value":"Take the path to a raw json asset and convert it to target directory."},"document":{"kind":"string","value":"def processed_json_dir(path):\n return os.path.dirname(path.replace(RAW_ASSETS_PATH, ASSETS_PATH))"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def processed_json_path(path):\n return path.replace(RAW_ASSETS_PATH, ASSETS_PATH).replace('.json', '.bin')","def copy_json():\n sourcePath = 'contents/external/'\n targetPath = 'build/external/'\n for base,subdirs,files in os.walk(sourcePath):\n for file in files:\n orig = os.path.join(base, file)\n if os.path.isfile(orig) and file[-5:] == '.json':\n targetBase = os.path.join(targetPath, base[len(sourcePath):])\n dest = os.path.join(targetBase, file)\n puts(\"Checking diretory %s\" % targetBase)\n if not os.path.exists(targetBase):\n puts(yellow(\"Not found! Creating...\"))\n os.makedirs(targetBase)\n puts(\"Copying from %s to %s\" % (orig, dest))\n copyfile(orig, dest)","def processed_texture_path(path):\n return path.replace(RAW_ASSETS_PATH, ASSETS_PATH).replace('png', 'webp')","def get_source_file_name(json_name):\n assert json_name.endswith(JSON_EXT)\n (directory, base_name) = os.path.split(json_name)\n new_directory = os.path.relpath(directory, start=CACHE)\n new_directory = os.path.join(\"/\", new_directory)\n new_base_name = base_name[:-len(JSON_EXT)]\n i = new_base_name.rfind(\"-\")\n if i != -1:\n new_base_name = new_base_name[:i] + \".\" + new_base_name[i + 1:]\n result = os.path.join(new_directory, new_base_name)\n return result","def processed_to_raw_path(self, processed_path):\n # Extract useful information from <path>\n stage, hash_dir, cloud_id = \\\n osp.splitext(processed_path)[0].split('/')[-3:]\n\n # Remove the tiling in the cloud_id, if any\n base_cloud_id = self.id_to_base_id(cloud_id)\n\n # Read the raw cloud data\n raw_ext = osp.splitext(self.raw_file_names_3d[0])[1]\n raw_path = osp.join(self.raw_dir, base_cloud_id + raw_ext)\n\n return raw_path","def save_json_file(article_data, dir_path):\n current_date = str(datetime.now().date())\n output_path = os.path.join(dir_path, current_date)\n os.makedirs(output_path, exist_ok=True)\n filepath = os.path.join(output_path, article_data['source'] + '.json')\n if not os.path.isfile(filepath):\n with open(filepath, 'w') as fp:\n json_data = {\n 'results': {\n article_data['id']: article_data\n }\n }\n json.dump(json_data, fp)\n else:\n with open(filepath, 'r') as fp:\n json_data = json.load(fp)\n json_data['results'][article_data['id']] = article_data\n\n with open(filepath, 'w') as fp2:\n json.dump(json_data, fp2)","def prep_path(path):\n os.makedirs(os.path.join(path, 'processed'), exist_ok=True)\n os.makedirs(os.path.join(path, 'raw'), exist_ok=True)","def json_file():\r\n urlretrieve(URL, PATH)\r\n return PATH","def json_file():\r\n urlretrieve(URL, PATH)\r\n return PATH","def Sourceify(path):\n return path","def process_json(path):\n path = os.path.abspath(os.path.expanduser(path))\n try:\n with open(path) as f:\n return json.load(f, object_hook=ascii_encode_dict)\n except ValueError as e:\n logging.error(\"File: %s\\nInvalid JSON:\\n%s\", str(path), str(e))\n raise\n except IOError as io:\n logging.error(\"Provided json file path does not exist %s\", str(path))\n raise","def minify_json(self) -> None:\n print(f\"[FileManager: INFO] Minifing file {self.path}\")\n file_data = open(self.path, \"r\", encoding=\"utf-8\").read()\n json_data = json.loads(file_data) \n json_string = json.dumps(json_data, separators=(',', \":\")) \n path = str(self.path).replace(\".json\", \"\")\n new_path = \"{0}_min.json\".format(path)\n open(new_path, \"w+\", encoding=\"utf-8\").write(json_string)","def load_json(path):\n with open(normpath(path), 'r', encoding='utf-8') as file:\n return json.load(file)","def from_json(path: str):\n with open(path) as f:\n return json.load(f)","def setup_rawpath(job, raw_path):\n\n logging.info(f\"Destination is {raw_path}\")\n if not os.path.exists(raw_path):\n try:\n os.makedirs(raw_path)\n except OSError:\n err = f\"Couldn't create the base file path: {raw_path}. Probably a permissions error\"\n logging.error(err)\n else:\n logging.info(f\"{raw_path} exists. Adding timestamp.\")\n raw_path = os.path.join(str(job.config.RAW_PATH), f\"{job.title}_{job.stage}\")\n logging.info(f\"raw_path is {raw_path}\")\n try:\n os.makedirs(raw_path)\n except OSError:\n err = f\"Couldn't create the base file path: {raw_path}. Probably a permissions error\"\n raise OSError(err) from OSError\n return raw_path","def fullpath(data_folder, name):\n return os.path.join(data_folder, f\"{alias(name)}.json\")","def convert_tmpfile(src_file_name:str, dest_path:str):\n src_path = os.path.join(\n current_app.config['UPLOAD_FOLDER'],\n src_file_name\n )\n if not os.path.exists(src_path):\n abort(http.HTTPStatus.BAD_REQUEST, message='raw file not exist')\n pathlib.Path(os.path.dirname(dest_path)).mkdir(parents=True, exist_ok=True)\n shutil.move(src_path, dest_path)","def from_path(cls, path: str):\n with open(path) as f:\n return json.load(f)","def format_data(PATH, download=False):\n # iterate over files in directory\n for file_name in listdir(PATH):\n if file_name[0] == '.': # skip .DS_Store and other hidden files\n continue\n # open each JSON file\n with open(PATH+'/'+file_name) as json_file:\n data = json.load(json_file)\n\n # get image url and download it\n image_name = data['image']['original_filename']\n if download:\n url = data['image']['url']\n download_file_from_url(url, image_name, PATH)\n\n # rename json file to match image name\n hash_name = image_name[:-4]\n rename(PATH+'/'+file_name, PATH+'/'+hash_name+'.json')","def write(self, path):\n\n annotation = copy.deepcopy(self.annotation)\n\n for image_info in annotation['images']:\n image_info['file_name'] = os.path.relpath(image_info['file_name'],\n os.path.dirname(path))\n\n with open(path, 'w') as read_file:\n json.dump(annotation, read_file)","def load_json(self, unformatted_path: str):\n formatted_path = unformatted_path.format(\n experiment_folder=self.experiment_folder\n )\n if not os.path.isfile(formatted_path):\n self.dump_json(formatted_path, data={})\n with open(formatted_path, \"r\") as infile:\n json_data = json.load(infile)\n return json_data","def _save_file(json_response, path, filename):\n if path is not None:\n if path[-1] != \"/\":\n path = path+\"/\"\n filepath = os.path.join(path, filename)\n if not os.path.exists(path):\n os.makedirs(path)\n\n with open(filepath+'.json', 'w') as output_file:\n output_file.write(json_response.text)","def asset_forward(path):\n asset = get(f'http://localhost:5001/assets/{path}', timeout=15)\n return Response(\n asset.content,\n mimetype=asset.headers['Content-Type']\n )","def json_to_cache(new_json: Dict, file_name: str) -> None:\n\n json_path = os.path.join(CACHE_DIR, file_name)\n ensure_dir(json_path)\n with open(json_path, \"w\") as outfile:\n json.dump(new_json, outfile, ensure_ascii=False)","def get_output_json(self, case_path):\r\n if not os.path.exists(case_path):\r\n logging.ERROR('the path of source files does not exist')\r\n else:\r\n self.case_path = os.path.abspath(case_path)\r\n self.case_json = os.path.join(self.case_path, 'output.json')\r\n self.case_image = os.path.join(self.case_path, 'images')\r\n self.num_name = os.path.abspath(self.case_path).split(sep='\\\\')[-2]\r\n self.chi_name = IdToChinese[self.num_name]\r\n\r\n with io.open(self.case_json, 'r', encoding='utf-8') as f:\r\n json_data = json.load(f)\r\n self.audioResult = json_data['data']['audioResult']\r\n self.docs = self.audioResult['docs']\r\n self.classify_four_w= self.audioResult['4W']\r\n self.approval_information = self.audioResult['approval_information']\r\n return True","def load_json(path, name):\n if 'txt' not in name:\n name += '.json'\n with open(os.path.join(path, name), 'r') as json_file:\n return json.load(json_file)","def make_cached_json(file_name):\n result = None\n path = clean_path(file_name)\n working_directory = os.path.split(os.path.abspath(file_name))[0]\n file_type_switch = \"-s\" if is_sats_file(file_name) else \"-d\"\n command = []\n command.append(\"patsopt\")\n command.append(\"--jsonize-2\")\n command.append(file_type_switch)\n command.append(path)\n (stdout, _stderr, return_code) = run(\n working_directory,\n command,\n POSTIATS_ENCODING)\n if return_code == 0:\n cached_file_name = get_cached_file_name(path)\n cached_directory = os.path.split(cached_file_name)[0]\n if not os.path.exists(cached_directory):\n os.makedirs(cached_directory)\n output = open(cached_file_name, \"w\")\n output.write(stdout)\n output.close()\n result = json.loads(stdout)\n return result","def unformat_file(path: str, out_dir: str):\n\n data_dir = get_data_dir()\n path = Path(path)\n out_dir = Path(out_dir)\n if not path.exists() and out_dir.exists() and out_dir.is_dir():\n return\n\n if path.is_dir():\n path.mkdir(exist_ok=True)\n for filename in path.iterdir():\n unformat_file(filename, str(out_dir))\n\n else:\n dataobj = frontmatter.load(str(path))\n\n try:\n # get relative path of object in `data` dir\n datapath = path.parent.resolve().relative_to(data_dir)\n except ValueError:\n datapath = Path()\n\n # create subdir if doesn't exist\n (out_dir / datapath).mkdir(exist_ok=True)\n new_path = out_dir / datapath / f\"{dataobj.metadata['title']}.md\"\n with new_path.open(\"w\") as f:\n f.write(dataobj.content)\n\n current_app.logger.info(\n f\"Unformatted and moved {str(path)} to {str(new_path.resolve())}\"\n )\n path.unlink()","def calc_fullpath(data_folder, name):\n return os.path.join(data_folder, f\"{alias(name)}.json\")","def _path(name: str):\n return os.path.join(ASSET_PATH, name)","def _raw_read(self, filepath, dirpath=None):\n self.json_dict = super().read(filepath)\n return self.json_dict","def transform_file(input_file_path: str, output_file_path: str):\n\n # Open json\n with open(input_file_path, mode=\"r\") as in_file:\n input_data = json.load(in_file)\n\n # Transform and write\n with jsonlines.open(output_file_path, mode=\"w\", compact=True) as out_file:\n for item in input_data[\"items\"]:\n out_file.write(transform_item(item))","def __init_output_folder():\n try:\n os.makedirs(Result.__json_dir)\n except OSError as e:\n if e.errno != errno.EEXIST:\n raise e","def _localloadjson(path: str) -> JSONType:\n with open(path, encoding=\"utf-8\") as fh:\n return json.load(fh)","def test_convert(bed_path, zarr_tmp_path): # noqa\n convert(bed_path, zarr_tmp_path)\n assert zarr_tmp_path.is_dir()","def _copy_to_media(self, template_name, source=''):\n dirpath = os.path.join(self.cache_root, os.path.dirname(template_name))\n filename = os.path.basename(template_name)\n fullpath = os.path.join(dirpath, filename)\n\n if not os.path.isfile(fullpath) or settings.DEBUG:\n if not os.path.exists(dirpath):\n os.makedirs(dirpath)\n\n f = open(fullpath, 'w')\n f.write(source)\n f.close()\n\n return urljoin(self.cache_url, template_name), filename","def parse_json_from_path(self, infile_path):\r\n with open(infile_path, 'r') as infile:\r\n return self.parse_from_json(infile)","def _get_json(self, path):\n cur_dir = path_stroke_fix(path)\n path = f\"{cur_dir}config/config.json\"\n return json.load(open(path, 'r'))","def get_asset_path(name):\n return os.path.join(constants.ROOT_DIR, 'assets', name)","def load_json(path: Path) -> Any:\n with path.open() as f:\n return json.load(f)","def convert_service_banner_json(json):\n\n # open json file\n with open(json, \"r\") as j:\n json_string = j.read()\n\n return json_string","def assetsPath(self, pattern, source):\n return self.assets(pattern, AssetSource.create(source))","def make_image_path(raw_img, input_base, base_path):\n path = os.path.dirname(raw_img)\n relpath = os.path.relpath(path, input_base)\n if relpath == '.':\n dest_folder = base_path\n else:\n dest_folder = os.path.join(base_path, relpath)\n return os.path.normpath(dest_folder)\n # return dest_folder","def render_json(file_path):\n dir_path = os.path.dirname(file_path)\n\n # Aborting if file doesn't exist\n if not os.path.exists(file_path):\n print('File {} does not exist.'.format(file_path))\n return\n\n # Loading saved game\n file_content = open(file_path, 'r').read()\n saved_game = json.loads(file_content)\n\n # Rendering\n render_saved_game(saved_game, dir_path)","def dashboard_convert(source_path, build_path, format, layout):\n logging.info(\n \"Searching path `{}` for JSON dashboards to convert ...\".format(source_path)\n )\n set_logger()\n convert_dashboards(source_path, build_path, format, layout)","def _StageMetadata(json_metadata, storage_service, staged_file: str):\n # Write computed metadata to object storage.\n temp_run_dir = temp_dir.GetRunDirPath()\n local_file = os.path.join(temp_run_dir, os.path.basename(staged_file))\n with open(local_file, 'w') as f:\n json.dump(json_metadata, f)\n storage_service.Copy(local_file, staged_file)","def modify_jsonyaml_paths(jsonyaml_file):\n loader = schema_salad.ref_resolver.Loader(\n {\"location\": {\"@type\": \"@id\"}, \"path\": {\"@type\": \"@id\"}}\n )\n input_dict, _ = loader.resolve_ref(jsonyaml_file, checklinks=False)\n basedir = os.path.dirname(jsonyaml_file)\n\n def fixpaths(d):\n \"\"\"Make sure all paths have a URI scheme.\"\"\"\n if isinstance(d, dict):\n if \"path\" in d:\n if \":\" not in d[\"path\"]:\n local_path = os.path.normpath(\n os.path.join(os.getcwd(), basedir, d[\"path\"])\n )\n d[\"location\"] = pathname2url(local_path)\n else:\n d[\"location\"] = d[\"path\"]\n del d[\"path\"]\n\n visit(input_dict, fixpaths)\n return json.dumps(input_dict)","def _source_path_reader(self, src, encoding=\"utf-8\"):\n if src is None:\n return src\n if isinstance(src, dict) and \"content\" in src:\n with tempfile.NamedTemporaryFile(mode=\"w\", encoding=encoding, delete=False) as fp:\n fp.write(src[\"content\"])\n return fp.name\n elif isinstance(src, dict) and \"file\" in src:\n if os.path.exists(src[\"file\"]) is False:\n raise FileNotFound(src)\n return src[\"file\"]\n else:\n raise InvalidParameter(\"The parameter is invalid.\")","def make(filename):\r\n\r\n # Source file is csv file\r\n extension = \".csv\"\r\n if filename.endswith(extension):\r\n return JSONFromCSV(re.sub((extension + \"$\"), \"\", filename))\r\n\r\n return None","def create_file(output_json):\n folder = \"data/\"\n filename = datetime.now().strftime(\"%d-%m-%Y\") + \"-moisture-read.json\"\n filepath = folder+filename\n\n # Create Local folder\n try:\n os.mkdir(folder)\n except OSError:\n pass\n #print(\"Directory already created or a failure occured on directory (%s)\" % folder)\n\n # Create Empty Json file if it doesnt exists\n if(Path(filepath)).exists():\n pass\n else:\n try:\n f = open(filepath, \"a\")\n f.write('{\\n\"moisture_iot_project\":[]\\n}')\n f.close()\n except Exception as e:\n print(\"Failure occured creating the JSON file (%s)\" % e)\n\n # Open Json file to append current structure\n with open(filepath) as outfile:\n data = json.load(outfile)\n\n # Get list with all dictionaries\n temp = data['moisture_iot_project']\n\n # Append current structure\n temp.append(output_json)\n\n # Reorganize List values and re-write to JSON file\n data['moisture_iot_project'] = temp\n write_json(data, filepath)","def _get_json_file_content(file_dir_arg):\n result = None\n\n try:\n with open(file_dir_arg, 'r', encoding='UTF-8-SIG') as f:\n result_tmp = f\n result = load(result_tmp)\n except Exception as e:\n print(e.args)\n\n return result","def get_full_path(_path: str):\n if _path is None:\n return None\n\n with open(r\"bot\\data\\image_config.json\") as f:\n try:\n image_config = json.load(f)\n\n except json.decoder.JSONDecodeError as e:\n print(e)\n return None\n\n base_url = image_config.get(\"base_url\")\n poster_size = image_config.get(\"poster_sizes\")[-2]\n return f\"{base_url}{poster_size}{_path}\"","def test_save_json_not_existed_dir(temp_dir):\n data = json.dumps({\"k\": \"v\", \"list\": [1, 2, 3]})\n dist = os.path.join(temp_dir, 'not_existed')\n save_json(dist, data, step=1)\n\n assert os.path.exists(os.path.join(dist, \"json\", \"1.json\"))","def download_from_storage_client(self, **kwargs):\n json_parameter_file_path = kwargs.get(\"srs_path\", None)\n\n source_path = None\n\n if json_parameter_file_path is not None:\n # download from json file\n with open(str(json_parameter_file_path), 'r') as fp:\n json_model: LocalStorageMetadataDTO = deserialize_json_to_object(fp) # read in the json file\n fp.close()\n\n source_path = json_model.source_path\n\n elif 'source_path' in kwargs:\n source_path = kwargs.get('source_path')\n else:\n raise ValueError(\"Must provide the \\'source_path\\' parameter for local storage client to find the file!\")\n\n destination_path = kwargs.get('destination_path', None)\n decompression = kwargs.get('decompression', None)\n\n if not destination_path:\n destination = self.__prj_root_dir\n print(\"User did not specify a destination path for the Local storage client to store at,\"\n \" using default value {}\", self.__prj_root_dir)\n\n self.__check_dir(destination)\n\n if decompression:\n print(\"Decompression mode = True, decompressing archive file.\")\n decompress(source_path, destination)\n print(\"Decompressed file stored in: {}\", destination)\n\n else:\n print(\"Decompression mode = False, moving archive file.\")\n source_parent = str(Path(source_path).parents[0])\n if not (source_parent == destination):\n shutil.move(source_path, destination)\n print(\"Decompressed file stored in: {}\", destination)","def transcode(filePath: str) -> str:\n asset_uuid = uuid.uuid4()\n outPath = os.path.join(\"/tmp\", str(asset_uuid)+'.mp4')\n ffmpeg.input(filePath).output(outPath).run()\n return outPath","def _adjust_paths(asset_kwargs: Dict[str, Any], asset_dir: PathLike) -> Dict[str, Any]:\n def _adjust_path(p):\n if isinstance(p, str) and p.startswith(\"{asset_dir}/\"):\n return str(asset_dir / p[12:])\n elif isinstance(p, dict):\n return {key: _adjust_path(value) for key, value in p.items()}\n else:\n return p\n\n return {k: _adjust_path(v) for k, v in asset_kwargs.items()}","def get_asset_path(test):\n return DEVICE_ASSETS_PATH + os.path.basename(test)","def transcode(self, src: Path, dest: Path) -> None:\n pass","def clean_path(source):\n source.file_name = source.file_name.replace('\\\\','/')\n return source","def restructure_file(raw_file_name, destination_file, label_file):\n unique_words = answer_vocabulary(raw_file_name)\n answer_labels = convert2labels(unique_words, label_file)\n\n qa_list = json.load(open(raw_file_name))\n new_qa_list = list()\n\n for qa_dict in qa_list:\n answer = str(qa_dict[\"answer\"]).lower()\n first_word = answer.split(\" \")[0]\n\n new_qa_dict = {\n \"image_name\": qa_dict[\"image_name\"],\n \"question\": qa_dict[\"question\"],\n \"answer_label\": answer_labels[first_word]\n }\n\n new_qa_list.append(new_qa_dict)\n\n json.dump(new_qa_list, open(destination_file, \"w\"), indent=4)","def read_osu_file(path, convert=False, wav_name=\"wavfile.wav\", json_name=\"temp/temp_json_file.json\"):\n file_dir = os.path.dirname(os.path.abspath(path))\n\n # ask node.js to convert the .osu file to .json format\n result = run_command([\"node\", \"load_map.js\", \"jq\", path, json_name])\n if(len(result) > 1):\n print(result.decode(\"utf-8\"))\n raise Exception(\"Map Convert Failure\")\n\n with open(json_name, encoding=\"utf-8\") as map_json:\n map_dict = json.load(map_json)\n\n if convert:\n mp3_file = os.path.join(\n file_dir, map_dict[\"general\"][\"AudioFilename\"])\n # result = run_command([FFMPEG_PATH, \"-y\", \"-i\", mp3_file, wav_name]);\n # if(len(result) > 1):\n # print(result.decode(\"utf-8\"));\n # raise Exception(\"FFMPEG Failure\");\n\n # delete the temp json later\n # if json_name == \"temp/temp_json_file.json\":\n # os.remove(json_name);\n\n return map_dict, mp3_file","def test_write_to_json():\r\n tmp_dir = os.getcwd()\r\n json_content = '{ \"name\":\"John\", \"age\":30}'\r\n directory = os.path.join(tmp_dir, 'inputspec.json')\r\n write_to_json(directory, json_content) \r\n with open(directory) as json_file:\r\n data = json.load(json_file)\r\n json_string = json.dumps(data)\r\n if os.path.exists(directory):\r\n os.remove(directory)\r\n assert json_string.replace(' ', '') == json_content.replace(' ' , '')","def fromJSON(self, path='') -> dict:\n try:\n return(importJSON(path))\n except Exception as error:\n print(f\"Error: self.fromJSON({path}) -> {error}\")","def ingest_json_file(request):\n path = save_file(request) \n try:\n with open(path, encoding='utf-8') as f:\n data = json.loads(f.read())\n except Exception as e:\n log.error(log.exc(e))\n return None\n return data","def convert_raw(raw_path, dest, remote_base, host, timeout, user=None,\n msconvert_options=None, dryrun=False):\n ssh = partial(call_ssh, host=host, user=user, timeout=timeout)\n rsync_base = partial(rsync, timeout=timeout, extra_options=['-qr'])\n rsync_to = partial(rsync_base, dest_user=user, dest_host=host)\n rsync_from = partial(rsync_base, source_host=host, source_user=user)\n\n remote_dir, _ = ssh(['mktemp', '-dq', '-p', remote_base])\n try:\n try:\n remote_dir = remote_dir.decode().strip()\n except AttributeError:\n remote_dir = remote_dir.strip()\n remote_file = os.path.join(remote_dir, os.path.basename(raw_path))\n rsync_to(source=raw_path, dest=remote_dir)#changed this from remote_file to remote_dir so it works for folders (.d)\n\n remote_mzml = os.path.splitext(remote_file)[0] + '.mzML'\n if dryrun:\n ssh(['cp', remote_file, remote_mzml])\n else:\n raw_name = os.path.basename(raw_path)\n ssh(['msconvert', raw_name, '--outfile', remote_mzml], cwd=remote_dir)\n rsync_from(source=remote_mzml, dest=dest)\n finally:\n try:\n ssh([\"rm\", \"-rf\", remote_dir])\n except Exception:\n logging.exception(\"Could not remove remote dir.\")","def json_to_dictionary(directoryfile, jsonName):\n\n #We set the path where we'll get the title screen image\n directoryproject = os.path.dirname(__file__)\n filePath = os.path.join(directoryproject, directoryfile, jsonName)\n\n #We open the json file\n jsonFile = open(filePath)\n data = json.load(jsonFile)\n\n return data","def sanitize_json_files(unsanitized_json_file):\n with open(develop_baseline_dir + unsanitized_json_file) as f:\n lines = f.readlines()\n\n with open('sanitizedJson/sanitized_{}'.format(unsanitized_json_file), 'w') as w:\n w.writelines([item for item in lines[:-1]])","def download_hero_images(file_name, output_dir):\n with open(file_name,'r') as json_file:\n hero_json = json.load(json_file) \n try:\n os.mkdir(output_dir)\n except OSError:\n print (\"Failed to create directory %s\" % output_dir)\n return\n for hero in hero_json:\n print (\"Processing %s.\" % hero['name'])\n hero_dir = output_dir + '/' + str(hero['hero_id'])\n os.mkdir(hero_dir)\n urlr.urlretrieve(hero['img_url'], hero_dir + '/main.' + IMAGE_EXTENSION);\n urlr.urlretrieve(hero['portrait_img_url'], hero_dir + '/portrait.' + IMAGE_EXTENSION);\n\n for ability in hero['abilities']:\n ability_img = hero_dir + '/ability_' + str(ability['ability_id']) + '.' + IMAGE_EXTENSION\n urlr.urlretrieve(ability['img_url'], ability_img);","def main():\n # Create / clean output dir\n if os.path.isdir(OUT_DIR):\n shutil.rmtree(OUT_DIR)\n os.mkdir(OUT_DIR)\n\n # Write all assets to the directory\n for fname, bb in create_assets().items():\n filename = os.path.join(OUT_DIR, fname)\n dirname = os.path.dirname(filename)\n if not os.path.isdir(dirname):\n os.makedirs(dirname)\n with open(filename, \"wb\") as f:\n f.write(bb)","def post_asset_update(lock, course):\r\n upload_date = datetime(2013, 6, 1, 10, 30, tzinfo=UTC)\r\n asset_location = course.id.make_asset_key('asset', 'sample_static.txt')\r\n url = reverse_course_url('assets_handler', course.id, kwargs={'asset_key_string': unicode(asset_location)})\r\n\r\n resp = self.client.post(\r\n url,\r\n json.dumps(assets._get_asset_json(\"sample_static.txt\", upload_date, asset_location, None, lock)),\r\n \"application/json\"\r\n )\r\n self.assertEqual(resp.status_code, 201)\r\n return json.loads(resp.content)","def loadJSONFromPath(self, path):\n\t\tself.root = TreeNode(' ');\n\t\tcontent = codecs.open(path, 'r', 'utf-8').read()\n\t\tdata = json.loads(content)\n\t\tself.root = TreeNode()\n\t\tself.root.loadJSON(data)","def convertPath(srcpath, dstdir):\n bits = srcpath.split(\"/\")\n bits.pop(0)\n # Strip out leading 'unsigned' from paths like unsigned/update/win32/...\n if bits[0] == 'unsigned':\n bits.pop(0)\n return os.path.join(dstdir, *bits)","def mapillary_raw_sample():\n return \"tests/data/mapillary/sample/\"","def transform(img_path):\n\n img = self.loader(img_path)\n return self.transform(img)","def save_file(obj, file_path):\n with gzip.open(file_path, \"wb\") as fp:\n fp.write(json.dumps(obj).encode('utf-8'))","def JSONtoObject(fileName):\n # TODO: ensure file exists first!!\n \n with open(fileName) as json_data:\n d = json.load(json_data)\n \n return d\n #return json.loads(d, object_hook=_json_object_hook)","def get_or_create_sjson(item):\r\n user_filename = item.transcripts[item.transcript_language]\r\n user_subs_id = os.path.splitext(user_filename)[0]\r\n source_subs_id, result_subs_dict = user_subs_id, {1.0: user_subs_id}\r\n try:\r\n sjson_transcript = Transcript.asset(item.location, source_subs_id, item.transcript_language).data\r\n except (NotFoundError): # generating sjson from srt\r\n generate_sjson_for_all_speeds(item, user_filename, result_subs_dict, item.transcript_language)\r\n sjson_transcript = Transcript.asset(item.location, source_subs_id, item.transcript_language).data\r\n return sjson_transcript","def get_file_path(name: str) -> str:\n return os.path.join(DIR, 'scores', f'{name}.json')","def makeFileResource(path):\n\n path = unwrapStr(path)\n segments = [segment.encode(\"utf-8\") for segment in path.split(u'/')]\n if not path.startswith(u'/'):\n # Relative path.\n segments = os.getcwd().split('/') + segments\n log.log([\"fs\"], u\"makeFileResource.run/1: Relative path '%s'\" % path)\n return FileResource(segments)","def test_serve_built_files(self):\r\n self.mkbundle('file1', 'file2', output=\"out\").build()\r\n # I tried using the test client for this, but it would\r\n # need to be setup using StaticFilesHandler, which is\r\n # incompatible with the test client.\r\n from django_assets.finders import AssetsFinder\r\n assert AssetsFinder().find('out') == self.path(\"media/out\")","def _convert(self):\n\n json_data = xjson.loads(self.jfile_path)\n\n with io.open(self.yfile_path, 'w', encoding='utf8') as f:\n yaml.dump(json_data, f, default_flow_style=False, allow_unicode=True)","def test_json_to_file(self):\n\n self.parser.parse()\n self.parser.json_to_file(JSON_FILE)\n json_string = self.parser.to_json()\n\n with open(JSON_FILE) as f:\n json_file_string = f.read()\n \n self.assertEqual(json_string, json_file_string)","def test_get_pathless_raw_file_name_json(self):\n\n this_pathless_file_name = probsevere_io._get_pathless_raw_file_name(\n unix_time_sec=VALID_TIME_UNIX_SEC,\n file_extension=probsevere_io.JSON_FILE_EXTENSION)\n\n self.assertTrue(this_pathless_file_name == PATHLESS_JSON_FILE_NAME)","def json_to_txt():\n def correct_pathname(path):\n invalid_char = ['/', '\\t', ':']\n for char in invalid_char:\n path = path.replace(char, '')\n return path\n \n base = 'data_html'\n if not os.path.exists(base):\n os.mkdir(base)\n \n data = load_jt('data.json')\n \n os.chdir(base)\n for disease_name, v in data.iteritems():\n disease_name = correct_pathname(disease_name) # eliminate invalid character\n if not os.path.exists(disease_name):\n os.mkdir(disease_name)\n for category, content in v.iteritems():\n if not os.path.exists(os.path.join(disease_name, '%s.html' % category)):\n with open(os.path.join(disease_name, '%s.html' % category), 'wb') as f:\n f.write(content)","def test_load_path(parser):\n doc = parser.load(pathlib.Path('jsonexamples') / 'small' / 'demo.json')\n doc.at_pointer('/Image/Width')","def loadJson (self, path):\n\n # get all lines in json, concatenate then into a big string then parse it\n with open(path, \"r\") as file_content:\n all_lines = file_content.readlines()\n all_content_str = \"\".join(all_lines)\n json_dict = json.loads(all_content_str)\n self.tile_reprs = list(json_dict['tiles']['structural-tiles'].keys())\n\n # remove this empty char\n self.tile_reprs.remove(\"-\")","def data_path(path: str, createdir: bool = False) -> str:\n path_obj = Path(path)\n if not path_obj.is_absolute():\n if inside_project():\n path_obj = Path(project_data_dir(), path)\n else:\n path_obj = Path(\".scrapy\", path)\n if createdir and not path_obj.exists():\n path_obj.mkdir(parents=True)\n return str(path_obj)","def load_json(directory=None):\n \n if directory:\n chdir(directory)\n with open('memedPost_json_data.txt') as json_data:\n data = load(json_data)\n return data\n else:\n chdir(curdir)\n with open('memedPost_json_data.txt') as json_data:\n data = load(json_data)\n return data","def flatpath(cam):\n return os.path.join(BASEPATH, cam + \"_flats\")","def _get_data_file(self, data_path):\n\n return json.load(open(data_path))","def _files_from_json(self, file_json):\n self.compressed_file_json = zlib.compress(json.dumps(file_json).encode('utf-8'))\n self.compression_algorithm = 'gzip'\n self.compressed_content_hash = hashlib.sha256(self.compressed_file_json).hexdigest()","def fix_jsons_in(bids_dir: Path):\n\n print(\"Finalizing task json files.\")\n\n for path in bids_dir.rglob(\"func/*_task-*.json\"):\n append_to_json_file(key=\"TaskName\", value=task_name_of(path), path_to_json=path)\n\n print(\"Appending echo times to phase difference json files.\")\n\n for path in bids_dir.rglob(\"fmap/*_phasediff.json\"):\n magnitude1_path = the_path_that_matches(pattern=\"sub-*_magnitude1.json\", in_directory=path.parent)\n magnitude2_path = the_path_that_matches(pattern=\"sub-*_magnitude2.json\", in_directory=path.parent)\n echo_time1 = value_of_key_in_json_file(\"EchoTime\", magnitude1_path)\n echo_time2 = value_of_key_in_json_file(\"EchoTime\", magnitude2_path)\n append_to_json_file(key=\"EchoTime1\", value=echo_time1, path_to_json=path)\n append_to_json_file(key=\"EchoTime2\", value=echo_time2, path_to_json=path)\n\n print(\"Setting targets of fieldmap json files.\")\n\n for path in bids_dir.rglob(\"fmap/*.json\"):\n func_dir = path.parent.parent / \"func\"\n trimmed_func_paths = [\"func/\" + func_path.name for func_path in func_dir.glob(\"*.nii\")]\n append_to_json_file(key=\"IntendedFor\", value=trimmed_func_paths, path_to_json=path)","def write(self, target):\n mpath = path.join(self._working_dir, 'manifest.json')\n with open(mpath, 'w') as mani:\n json.dump(self.item, mani)\n\n directory = path.abspath(self._working_dir)\n with zipfile.ZipFile(target, 'w', allowZip64=True) as zip:\n for root, dirs, files in walk(directory):\n for f in files:\n abspath = path.join(root, f)\n relpath = path.relpath(abspath, directory)\n zip.write(abspath, relpath)\n return target","def save_json_to_destination_file(self):\n if self.source_file != self.output_file:\n click.secho(f'Saving output JSON file to {self.output_file}', fg='white')\n with open(self.output_file, 'w') as file:\n ujson.dump(self.data, file, indent=4, encode_html_chars=True, escape_forward_slashes=False,\n ensure_ascii=False)","def test_repo_to_json(self):\n if path.exists(\"report.csv\"):\n remove(\"report.csv\")\n\n with open(\"test/fixtures/results.json\", 'r') as data:\n data = json.load(data)\n client = CSVReport()\n client.process(data)\n\n self.assertTrue(path.exists(\"report.csv\"))","def test_find_raw_file_json(self):\n\n this_file_name = probsevere_io.find_raw_file(\n top_directory_name=TOP_DIRECTORY_NAME,\n unix_time_sec=VALID_TIME_UNIX_SEC,\n file_extension=probsevere_io.JSON_FILE_EXTENSION,\n raise_error_if_missing=False)\n\n self.assertTrue(this_file_name == JSON_FILE_NAME)","def prepare_src_folder(self, src_folder: str) -> None:","def load_path(path):\n\n p = PathWalker(path)\n listfiles = p.walk()\n\n full_json = dict()\n\n for file in listfiles:\n try:\n gz = GzipFile(filename=file, mode='rb')\n except Exception:\n raise Exception(\"cant open file\", str(file))\n cur_json = get_formatted_dict(json.load(gz))\n full_json.update(cur_json)\n\n return full_json","def fix_path(self):\n paths = self.data['path'].tolist()\n prefixes = [re.findall(r'[A-Z\\-0-9]+', path) for path in paths]\n prefix_good = [str(prefix[0]) + \".json\" for prefix in prefixes]\n self.data['path'] = prefix_good","def fake_full_path(self) -> PurePath:\n if self.category:\n # Giza wrote out yaml file artifacts under a directory. e.g. steps-foo.yaml becomes\n # steps/foo.rst\n return self.source_path.parent.joinpath(\n PurePath(self.category), self.output_filename\n )\n return self.source_path"],"string":"[\n \"def processed_json_path(path):\\n return path.replace(RAW_ASSETS_PATH, ASSETS_PATH).replace('.json', '.bin')\",\n \"def copy_json():\\n sourcePath = 'contents/external/'\\n targetPath = 'build/external/'\\n for base,subdirs,files in os.walk(sourcePath):\\n for file in files:\\n orig = os.path.join(base, file)\\n if os.path.isfile(orig) and file[-5:] == '.json':\\n targetBase = os.path.join(targetPath, base[len(sourcePath):])\\n dest = os.path.join(targetBase, file)\\n puts(\\\"Checking diretory %s\\\" % targetBase)\\n if not os.path.exists(targetBase):\\n puts(yellow(\\\"Not found! Creating...\\\"))\\n os.makedirs(targetBase)\\n puts(\\\"Copying from %s to %s\\\" % (orig, dest))\\n copyfile(orig, dest)\",\n \"def processed_texture_path(path):\\n return path.replace(RAW_ASSETS_PATH, ASSETS_PATH).replace('png', 'webp')\",\n \"def get_source_file_name(json_name):\\n assert json_name.endswith(JSON_EXT)\\n (directory, base_name) = os.path.split(json_name)\\n new_directory = os.path.relpath(directory, start=CACHE)\\n new_directory = os.path.join(\\\"/\\\", new_directory)\\n new_base_name = base_name[:-len(JSON_EXT)]\\n i = new_base_name.rfind(\\\"-\\\")\\n if i != -1:\\n new_base_name = new_base_name[:i] + \\\".\\\" + new_base_name[i + 1:]\\n result = os.path.join(new_directory, new_base_name)\\n return result\",\n \"def processed_to_raw_path(self, processed_path):\\n # Extract useful information from <path>\\n stage, hash_dir, cloud_id = \\\\\\n osp.splitext(processed_path)[0].split('/')[-3:]\\n\\n # Remove the tiling in the cloud_id, if any\\n base_cloud_id = self.id_to_base_id(cloud_id)\\n\\n # Read the raw cloud data\\n raw_ext = osp.splitext(self.raw_file_names_3d[0])[1]\\n raw_path = osp.join(self.raw_dir, base_cloud_id + raw_ext)\\n\\n return raw_path\",\n \"def save_json_file(article_data, dir_path):\\n current_date = str(datetime.now().date())\\n output_path = os.path.join(dir_path, current_date)\\n os.makedirs(output_path, exist_ok=True)\\n filepath = os.path.join(output_path, article_data['source'] + '.json')\\n if not os.path.isfile(filepath):\\n with open(filepath, 'w') as fp:\\n json_data = {\\n 'results': {\\n article_data['id']: article_data\\n }\\n }\\n json.dump(json_data, fp)\\n else:\\n with open(filepath, 'r') as fp:\\n json_data = json.load(fp)\\n json_data['results'][article_data['id']] = article_data\\n\\n with open(filepath, 'w') as fp2:\\n json.dump(json_data, fp2)\",\n \"def prep_path(path):\\n os.makedirs(os.path.join(path, 'processed'), exist_ok=True)\\n os.makedirs(os.path.join(path, 'raw'), exist_ok=True)\",\n \"def json_file():\\r\\n urlretrieve(URL, PATH)\\r\\n return PATH\",\n \"def json_file():\\r\\n urlretrieve(URL, PATH)\\r\\n return PATH\",\n \"def Sourceify(path):\\n return path\",\n \"def process_json(path):\\n path = os.path.abspath(os.path.expanduser(path))\\n try:\\n with open(path) as f:\\n return json.load(f, object_hook=ascii_encode_dict)\\n except ValueError as e:\\n logging.error(\\\"File: %s\\\\nInvalid JSON:\\\\n%s\\\", str(path), str(e))\\n raise\\n except IOError as io:\\n logging.error(\\\"Provided json file path does not exist %s\\\", str(path))\\n raise\",\n \"def minify_json(self) -> None:\\n print(f\\\"[FileManager: INFO] Minifing file {self.path}\\\")\\n file_data = open(self.path, \\\"r\\\", encoding=\\\"utf-8\\\").read()\\n json_data = json.loads(file_data) \\n json_string = json.dumps(json_data, separators=(',', \\\":\\\")) \\n path = str(self.path).replace(\\\".json\\\", \\\"\\\")\\n new_path = \\\"{0}_min.json\\\".format(path)\\n open(new_path, \\\"w+\\\", encoding=\\\"utf-8\\\").write(json_string)\",\n \"def load_json(path):\\n with open(normpath(path), 'r', encoding='utf-8') as file:\\n return json.load(file)\",\n \"def from_json(path: str):\\n with open(path) as f:\\n return json.load(f)\",\n \"def setup_rawpath(job, raw_path):\\n\\n logging.info(f\\\"Destination is {raw_path}\\\")\\n if not os.path.exists(raw_path):\\n try:\\n os.makedirs(raw_path)\\n except OSError:\\n err = f\\\"Couldn't create the base file path: {raw_path}. Probably a permissions error\\\"\\n logging.error(err)\\n else:\\n logging.info(f\\\"{raw_path} exists. Adding timestamp.\\\")\\n raw_path = os.path.join(str(job.config.RAW_PATH), f\\\"{job.title}_{job.stage}\\\")\\n logging.info(f\\\"raw_path is {raw_path}\\\")\\n try:\\n os.makedirs(raw_path)\\n except OSError:\\n err = f\\\"Couldn't create the base file path: {raw_path}. Probably a permissions error\\\"\\n raise OSError(err) from OSError\\n return raw_path\",\n \"def fullpath(data_folder, name):\\n return os.path.join(data_folder, f\\\"{alias(name)}.json\\\")\",\n \"def convert_tmpfile(src_file_name:str, dest_path:str):\\n src_path = os.path.join(\\n current_app.config['UPLOAD_FOLDER'],\\n src_file_name\\n )\\n if not os.path.exists(src_path):\\n abort(http.HTTPStatus.BAD_REQUEST, message='raw file not exist')\\n pathlib.Path(os.path.dirname(dest_path)).mkdir(parents=True, exist_ok=True)\\n shutil.move(src_path, dest_path)\",\n \"def from_path(cls, path: str):\\n with open(path) as f:\\n return json.load(f)\",\n \"def format_data(PATH, download=False):\\n # iterate over files in directory\\n for file_name in listdir(PATH):\\n if file_name[0] == '.': # skip .DS_Store and other hidden files\\n continue\\n # open each JSON file\\n with open(PATH+'/'+file_name) as json_file:\\n data = json.load(json_file)\\n\\n # get image url and download it\\n image_name = data['image']['original_filename']\\n if download:\\n url = data['image']['url']\\n download_file_from_url(url, image_name, PATH)\\n\\n # rename json file to match image name\\n hash_name = image_name[:-4]\\n rename(PATH+'/'+file_name, PATH+'/'+hash_name+'.json')\",\n \"def write(self, path):\\n\\n annotation = copy.deepcopy(self.annotation)\\n\\n for image_info in annotation['images']:\\n image_info['file_name'] = os.path.relpath(image_info['file_name'],\\n os.path.dirname(path))\\n\\n with open(path, 'w') as read_file:\\n json.dump(annotation, read_file)\",\n \"def load_json(self, unformatted_path: str):\\n formatted_path = unformatted_path.format(\\n experiment_folder=self.experiment_folder\\n )\\n if not os.path.isfile(formatted_path):\\n self.dump_json(formatted_path, data={})\\n with open(formatted_path, \\\"r\\\") as infile:\\n json_data = json.load(infile)\\n return json_data\",\n \"def _save_file(json_response, path, filename):\\n if path is not None:\\n if path[-1] != \\\"/\\\":\\n path = path+\\\"/\\\"\\n filepath = os.path.join(path, filename)\\n if not os.path.exists(path):\\n os.makedirs(path)\\n\\n with open(filepath+'.json', 'w') as output_file:\\n output_file.write(json_response.text)\",\n \"def asset_forward(path):\\n asset = get(f'http://localhost:5001/assets/{path}', timeout=15)\\n return Response(\\n asset.content,\\n mimetype=asset.headers['Content-Type']\\n )\",\n \"def json_to_cache(new_json: Dict, file_name: str) -> None:\\n\\n json_path = os.path.join(CACHE_DIR, file_name)\\n ensure_dir(json_path)\\n with open(json_path, \\\"w\\\") as outfile:\\n json.dump(new_json, outfile, ensure_ascii=False)\",\n \"def get_output_json(self, case_path):\\r\\n if not os.path.exists(case_path):\\r\\n logging.ERROR('the path of source files does not exist')\\r\\n else:\\r\\n self.case_path = os.path.abspath(case_path)\\r\\n self.case_json = os.path.join(self.case_path, 'output.json')\\r\\n self.case_image = os.path.join(self.case_path, 'images')\\r\\n self.num_name = os.path.abspath(self.case_path).split(sep='\\\\\\\\')[-2]\\r\\n self.chi_name = IdToChinese[self.num_name]\\r\\n\\r\\n with io.open(self.case_json, 'r', encoding='utf-8') as f:\\r\\n json_data = json.load(f)\\r\\n self.audioResult = json_data['data']['audioResult']\\r\\n self.docs = self.audioResult['docs']\\r\\n self.classify_four_w= self.audioResult['4W']\\r\\n self.approval_information = self.audioResult['approval_information']\\r\\n return True\",\n \"def load_json(path, name):\\n if 'txt' not in name:\\n name += '.json'\\n with open(os.path.join(path, name), 'r') as json_file:\\n return json.load(json_file)\",\n \"def make_cached_json(file_name):\\n result = None\\n path = clean_path(file_name)\\n working_directory = os.path.split(os.path.abspath(file_name))[0]\\n file_type_switch = \\\"-s\\\" if is_sats_file(file_name) else \\\"-d\\\"\\n command = []\\n command.append(\\\"patsopt\\\")\\n command.append(\\\"--jsonize-2\\\")\\n command.append(file_type_switch)\\n command.append(path)\\n (stdout, _stderr, return_code) = run(\\n working_directory,\\n command,\\n POSTIATS_ENCODING)\\n if return_code == 0:\\n cached_file_name = get_cached_file_name(path)\\n cached_directory = os.path.split(cached_file_name)[0]\\n if not os.path.exists(cached_directory):\\n os.makedirs(cached_directory)\\n output = open(cached_file_name, \\\"w\\\")\\n output.write(stdout)\\n output.close()\\n result = json.loads(stdout)\\n return result\",\n \"def unformat_file(path: str, out_dir: str):\\n\\n data_dir = get_data_dir()\\n path = Path(path)\\n out_dir = Path(out_dir)\\n if not path.exists() and out_dir.exists() and out_dir.is_dir():\\n return\\n\\n if path.is_dir():\\n path.mkdir(exist_ok=True)\\n for filename in path.iterdir():\\n unformat_file(filename, str(out_dir))\\n\\n else:\\n dataobj = frontmatter.load(str(path))\\n\\n try:\\n # get relative path of object in `data` dir\\n datapath = path.parent.resolve().relative_to(data_dir)\\n except ValueError:\\n datapath = Path()\\n\\n # create subdir if doesn't exist\\n (out_dir / datapath).mkdir(exist_ok=True)\\n new_path = out_dir / datapath / f\\\"{dataobj.metadata['title']}.md\\\"\\n with new_path.open(\\\"w\\\") as f:\\n f.write(dataobj.content)\\n\\n current_app.logger.info(\\n f\\\"Unformatted and moved {str(path)} to {str(new_path.resolve())}\\\"\\n )\\n path.unlink()\",\n \"def calc_fullpath(data_folder, name):\\n return os.path.join(data_folder, f\\\"{alias(name)}.json\\\")\",\n \"def _path(name: str):\\n return os.path.join(ASSET_PATH, name)\",\n \"def _raw_read(self, filepath, dirpath=None):\\n self.json_dict = super().read(filepath)\\n return self.json_dict\",\n \"def transform_file(input_file_path: str, output_file_path: str):\\n\\n # Open json\\n with open(input_file_path, mode=\\\"r\\\") as in_file:\\n input_data = json.load(in_file)\\n\\n # Transform and write\\n with jsonlines.open(output_file_path, mode=\\\"w\\\", compact=True) as out_file:\\n for item in input_data[\\\"items\\\"]:\\n out_file.write(transform_item(item))\",\n \"def __init_output_folder():\\n try:\\n os.makedirs(Result.__json_dir)\\n except OSError as e:\\n if e.errno != errno.EEXIST:\\n raise e\",\n \"def _localloadjson(path: str) -> JSONType:\\n with open(path, encoding=\\\"utf-8\\\") as fh:\\n return json.load(fh)\",\n \"def test_convert(bed_path, zarr_tmp_path): # noqa\\n convert(bed_path, zarr_tmp_path)\\n assert zarr_tmp_path.is_dir()\",\n \"def _copy_to_media(self, template_name, source=''):\\n dirpath = os.path.join(self.cache_root, os.path.dirname(template_name))\\n filename = os.path.basename(template_name)\\n fullpath = os.path.join(dirpath, filename)\\n\\n if not os.path.isfile(fullpath) or settings.DEBUG:\\n if not os.path.exists(dirpath):\\n os.makedirs(dirpath)\\n\\n f = open(fullpath, 'w')\\n f.write(source)\\n f.close()\\n\\n return urljoin(self.cache_url, template_name), filename\",\n \"def parse_json_from_path(self, infile_path):\\r\\n with open(infile_path, 'r') as infile:\\r\\n return self.parse_from_json(infile)\",\n \"def _get_json(self, path):\\n cur_dir = path_stroke_fix(path)\\n path = f\\\"{cur_dir}config/config.json\\\"\\n return json.load(open(path, 'r'))\",\n \"def get_asset_path(name):\\n return os.path.join(constants.ROOT_DIR, 'assets', name)\",\n \"def load_json(path: Path) -> Any:\\n with path.open() as f:\\n return json.load(f)\",\n \"def convert_service_banner_json(json):\\n\\n # open json file\\n with open(json, \\\"r\\\") as j:\\n json_string = j.read()\\n\\n return json_string\",\n \"def assetsPath(self, pattern, source):\\n return self.assets(pattern, AssetSource.create(source))\",\n \"def make_image_path(raw_img, input_base, base_path):\\n path = os.path.dirname(raw_img)\\n relpath = os.path.relpath(path, input_base)\\n if relpath == '.':\\n dest_folder = base_path\\n else:\\n dest_folder = os.path.join(base_path, relpath)\\n return os.path.normpath(dest_folder)\\n # return dest_folder\",\n \"def render_json(file_path):\\n dir_path = os.path.dirname(file_path)\\n\\n # Aborting if file doesn't exist\\n if not os.path.exists(file_path):\\n print('File {} does not exist.'.format(file_path))\\n return\\n\\n # Loading saved game\\n file_content = open(file_path, 'r').read()\\n saved_game = json.loads(file_content)\\n\\n # Rendering\\n render_saved_game(saved_game, dir_path)\",\n \"def dashboard_convert(source_path, build_path, format, layout):\\n logging.info(\\n \\\"Searching path `{}` for JSON dashboards to convert ...\\\".format(source_path)\\n )\\n set_logger()\\n convert_dashboards(source_path, build_path, format, layout)\",\n \"def _StageMetadata(json_metadata, storage_service, staged_file: str):\\n # Write computed metadata to object storage.\\n temp_run_dir = temp_dir.GetRunDirPath()\\n local_file = os.path.join(temp_run_dir, os.path.basename(staged_file))\\n with open(local_file, 'w') as f:\\n json.dump(json_metadata, f)\\n storage_service.Copy(local_file, staged_file)\",\n \"def modify_jsonyaml_paths(jsonyaml_file):\\n loader = schema_salad.ref_resolver.Loader(\\n {\\\"location\\\": {\\\"@type\\\": \\\"@id\\\"}, \\\"path\\\": {\\\"@type\\\": \\\"@id\\\"}}\\n )\\n input_dict, _ = loader.resolve_ref(jsonyaml_file, checklinks=False)\\n basedir = os.path.dirname(jsonyaml_file)\\n\\n def fixpaths(d):\\n \\\"\\\"\\\"Make sure all paths have a URI scheme.\\\"\\\"\\\"\\n if isinstance(d, dict):\\n if \\\"path\\\" in d:\\n if \\\":\\\" not in d[\\\"path\\\"]:\\n local_path = os.path.normpath(\\n os.path.join(os.getcwd(), basedir, d[\\\"path\\\"])\\n )\\n d[\\\"location\\\"] = pathname2url(local_path)\\n else:\\n d[\\\"location\\\"] = d[\\\"path\\\"]\\n del d[\\\"path\\\"]\\n\\n visit(input_dict, fixpaths)\\n return json.dumps(input_dict)\",\n \"def _source_path_reader(self, src, encoding=\\\"utf-8\\\"):\\n if src is None:\\n return src\\n if isinstance(src, dict) and \\\"content\\\" in src:\\n with tempfile.NamedTemporaryFile(mode=\\\"w\\\", encoding=encoding, delete=False) as fp:\\n fp.write(src[\\\"content\\\"])\\n return fp.name\\n elif isinstance(src, dict) and \\\"file\\\" in src:\\n if os.path.exists(src[\\\"file\\\"]) is False:\\n raise FileNotFound(src)\\n return src[\\\"file\\\"]\\n else:\\n raise InvalidParameter(\\\"The parameter is invalid.\\\")\",\n \"def make(filename):\\r\\n\\r\\n # Source file is csv file\\r\\n extension = \\\".csv\\\"\\r\\n if filename.endswith(extension):\\r\\n return JSONFromCSV(re.sub((extension + \\\"$\\\"), \\\"\\\", filename))\\r\\n\\r\\n return None\",\n \"def create_file(output_json):\\n folder = \\\"data/\\\"\\n filename = datetime.now().strftime(\\\"%d-%m-%Y\\\") + \\\"-moisture-read.json\\\"\\n filepath = folder+filename\\n\\n # Create Local folder\\n try:\\n os.mkdir(folder)\\n except OSError:\\n pass\\n #print(\\\"Directory already created or a failure occured on directory (%s)\\\" % folder)\\n\\n # Create Empty Json file if it doesnt exists\\n if(Path(filepath)).exists():\\n pass\\n else:\\n try:\\n f = open(filepath, \\\"a\\\")\\n f.write('{\\\\n\\\"moisture_iot_project\\\":[]\\\\n}')\\n f.close()\\n except Exception as e:\\n print(\\\"Failure occured creating the JSON file (%s)\\\" % e)\\n\\n # Open Json file to append current structure\\n with open(filepath) as outfile:\\n data = json.load(outfile)\\n\\n # Get list with all dictionaries\\n temp = data['moisture_iot_project']\\n\\n # Append current structure\\n temp.append(output_json)\\n\\n # Reorganize List values and re-write to JSON file\\n data['moisture_iot_project'] = temp\\n write_json(data, filepath)\",\n \"def _get_json_file_content(file_dir_arg):\\n result = None\\n\\n try:\\n with open(file_dir_arg, 'r', encoding='UTF-8-SIG') as f:\\n result_tmp = f\\n result = load(result_tmp)\\n except Exception as e:\\n print(e.args)\\n\\n return result\",\n \"def get_full_path(_path: str):\\n if _path is None:\\n return None\\n\\n with open(r\\\"bot\\\\data\\\\image_config.json\\\") as f:\\n try:\\n image_config = json.load(f)\\n\\n except json.decoder.JSONDecodeError as e:\\n print(e)\\n return None\\n\\n base_url = image_config.get(\\\"base_url\\\")\\n poster_size = image_config.get(\\\"poster_sizes\\\")[-2]\\n return f\\\"{base_url}{poster_size}{_path}\\\"\",\n \"def test_save_json_not_existed_dir(temp_dir):\\n data = json.dumps({\\\"k\\\": \\\"v\\\", \\\"list\\\": [1, 2, 3]})\\n dist = os.path.join(temp_dir, 'not_existed')\\n save_json(dist, data, step=1)\\n\\n assert os.path.exists(os.path.join(dist, \\\"json\\\", \\\"1.json\\\"))\",\n \"def download_from_storage_client(self, **kwargs):\\n json_parameter_file_path = kwargs.get(\\\"srs_path\\\", None)\\n\\n source_path = None\\n\\n if json_parameter_file_path is not None:\\n # download from json file\\n with open(str(json_parameter_file_path), 'r') as fp:\\n json_model: LocalStorageMetadataDTO = deserialize_json_to_object(fp) # read in the json file\\n fp.close()\\n\\n source_path = json_model.source_path\\n\\n elif 'source_path' in kwargs:\\n source_path = kwargs.get('source_path')\\n else:\\n raise ValueError(\\\"Must provide the \\\\'source_path\\\\' parameter for local storage client to find the file!\\\")\\n\\n destination_path = kwargs.get('destination_path', None)\\n decompression = kwargs.get('decompression', None)\\n\\n if not destination_path:\\n destination = self.__prj_root_dir\\n print(\\\"User did not specify a destination path for the Local storage client to store at,\\\"\\n \\\" using default value {}\\\", self.__prj_root_dir)\\n\\n self.__check_dir(destination)\\n\\n if decompression:\\n print(\\\"Decompression mode = True, decompressing archive file.\\\")\\n decompress(source_path, destination)\\n print(\\\"Decompressed file stored in: {}\\\", destination)\\n\\n else:\\n print(\\\"Decompression mode = False, moving archive file.\\\")\\n source_parent = str(Path(source_path).parents[0])\\n if not (source_parent == destination):\\n shutil.move(source_path, destination)\\n print(\\\"Decompressed file stored in: {}\\\", destination)\",\n \"def transcode(filePath: str) -> str:\\n asset_uuid = uuid.uuid4()\\n outPath = os.path.join(\\\"/tmp\\\", str(asset_uuid)+'.mp4')\\n ffmpeg.input(filePath).output(outPath).run()\\n return outPath\",\n \"def _adjust_paths(asset_kwargs: Dict[str, Any], asset_dir: PathLike) -> Dict[str, Any]:\\n def _adjust_path(p):\\n if isinstance(p, str) and p.startswith(\\\"{asset_dir}/\\\"):\\n return str(asset_dir / p[12:])\\n elif isinstance(p, dict):\\n return {key: _adjust_path(value) for key, value in p.items()}\\n else:\\n return p\\n\\n return {k: _adjust_path(v) for k, v in asset_kwargs.items()}\",\n \"def get_asset_path(test):\\n return DEVICE_ASSETS_PATH + os.path.basename(test)\",\n \"def transcode(self, src: Path, dest: Path) -> None:\\n pass\",\n \"def clean_path(source):\\n source.file_name = source.file_name.replace('\\\\\\\\','/')\\n return source\",\n \"def restructure_file(raw_file_name, destination_file, label_file):\\n unique_words = answer_vocabulary(raw_file_name)\\n answer_labels = convert2labels(unique_words, label_file)\\n\\n qa_list = json.load(open(raw_file_name))\\n new_qa_list = list()\\n\\n for qa_dict in qa_list:\\n answer = str(qa_dict[\\\"answer\\\"]).lower()\\n first_word = answer.split(\\\" \\\")[0]\\n\\n new_qa_dict = {\\n \\\"image_name\\\": qa_dict[\\\"image_name\\\"],\\n \\\"question\\\": qa_dict[\\\"question\\\"],\\n \\\"answer_label\\\": answer_labels[first_word]\\n }\\n\\n new_qa_list.append(new_qa_dict)\\n\\n json.dump(new_qa_list, open(destination_file, \\\"w\\\"), indent=4)\",\n \"def read_osu_file(path, convert=False, wav_name=\\\"wavfile.wav\\\", json_name=\\\"temp/temp_json_file.json\\\"):\\n file_dir = os.path.dirname(os.path.abspath(path))\\n\\n # ask node.js to convert the .osu file to .json format\\n result = run_command([\\\"node\\\", \\\"load_map.js\\\", \\\"jq\\\", path, json_name])\\n if(len(result) > 1):\\n print(result.decode(\\\"utf-8\\\"))\\n raise Exception(\\\"Map Convert Failure\\\")\\n\\n with open(json_name, encoding=\\\"utf-8\\\") as map_json:\\n map_dict = json.load(map_json)\\n\\n if convert:\\n mp3_file = os.path.join(\\n file_dir, map_dict[\\\"general\\\"][\\\"AudioFilename\\\"])\\n # result = run_command([FFMPEG_PATH, \\\"-y\\\", \\\"-i\\\", mp3_file, wav_name]);\\n # if(len(result) > 1):\\n # print(result.decode(\\\"utf-8\\\"));\\n # raise Exception(\\\"FFMPEG Failure\\\");\\n\\n # delete the temp json later\\n # if json_name == \\\"temp/temp_json_file.json\\\":\\n # os.remove(json_name);\\n\\n return map_dict, mp3_file\",\n \"def test_write_to_json():\\r\\n tmp_dir = os.getcwd()\\r\\n json_content = '{ \\\"name\\\":\\\"John\\\", \\\"age\\\":30}'\\r\\n directory = os.path.join(tmp_dir, 'inputspec.json')\\r\\n write_to_json(directory, json_content) \\r\\n with open(directory) as json_file:\\r\\n data = json.load(json_file)\\r\\n json_string = json.dumps(data)\\r\\n if os.path.exists(directory):\\r\\n os.remove(directory)\\r\\n assert json_string.replace(' ', '') == json_content.replace(' ' , '')\",\n \"def fromJSON(self, path='') -> dict:\\n try:\\n return(importJSON(path))\\n except Exception as error:\\n print(f\\\"Error: self.fromJSON({path}) -> {error}\\\")\",\n \"def ingest_json_file(request):\\n path = save_file(request) \\n try:\\n with open(path, encoding='utf-8') as f:\\n data = json.loads(f.read())\\n except Exception as e:\\n log.error(log.exc(e))\\n return None\\n return data\",\n \"def convert_raw(raw_path, dest, remote_base, host, timeout, user=None,\\n msconvert_options=None, dryrun=False):\\n ssh = partial(call_ssh, host=host, user=user, timeout=timeout)\\n rsync_base = partial(rsync, timeout=timeout, extra_options=['-qr'])\\n rsync_to = partial(rsync_base, dest_user=user, dest_host=host)\\n rsync_from = partial(rsync_base, source_host=host, source_user=user)\\n\\n remote_dir, _ = ssh(['mktemp', '-dq', '-p', remote_base])\\n try:\\n try:\\n remote_dir = remote_dir.decode().strip()\\n except AttributeError:\\n remote_dir = remote_dir.strip()\\n remote_file = os.path.join(remote_dir, os.path.basename(raw_path))\\n rsync_to(source=raw_path, dest=remote_dir)#changed this from remote_file to remote_dir so it works for folders (.d)\\n\\n remote_mzml = os.path.splitext(remote_file)[0] + '.mzML'\\n if dryrun:\\n ssh(['cp', remote_file, remote_mzml])\\n else:\\n raw_name = os.path.basename(raw_path)\\n ssh(['msconvert', raw_name, '--outfile', remote_mzml], cwd=remote_dir)\\n rsync_from(source=remote_mzml, dest=dest)\\n finally:\\n try:\\n ssh([\\\"rm\\\", \\\"-rf\\\", remote_dir])\\n except Exception:\\n logging.exception(\\\"Could not remove remote dir.\\\")\",\n \"def json_to_dictionary(directoryfile, jsonName):\\n\\n #We set the path where we'll get the title screen image\\n directoryproject = os.path.dirname(__file__)\\n filePath = os.path.join(directoryproject, directoryfile, jsonName)\\n\\n #We open the json file\\n jsonFile = open(filePath)\\n data = json.load(jsonFile)\\n\\n return data\",\n \"def sanitize_json_files(unsanitized_json_file):\\n with open(develop_baseline_dir + unsanitized_json_file) as f:\\n lines = f.readlines()\\n\\n with open('sanitizedJson/sanitized_{}'.format(unsanitized_json_file), 'w') as w:\\n w.writelines([item for item in lines[:-1]])\",\n \"def download_hero_images(file_name, output_dir):\\n with open(file_name,'r') as json_file:\\n hero_json = json.load(json_file) \\n try:\\n os.mkdir(output_dir)\\n except OSError:\\n print (\\\"Failed to create directory %s\\\" % output_dir)\\n return\\n for hero in hero_json:\\n print (\\\"Processing %s.\\\" % hero['name'])\\n hero_dir = output_dir + '/' + str(hero['hero_id'])\\n os.mkdir(hero_dir)\\n urlr.urlretrieve(hero['img_url'], hero_dir + '/main.' + IMAGE_EXTENSION);\\n urlr.urlretrieve(hero['portrait_img_url'], hero_dir + '/portrait.' + IMAGE_EXTENSION);\\n\\n for ability in hero['abilities']:\\n ability_img = hero_dir + '/ability_' + str(ability['ability_id']) + '.' + IMAGE_EXTENSION\\n urlr.urlretrieve(ability['img_url'], ability_img);\",\n \"def main():\\n # Create / clean output dir\\n if os.path.isdir(OUT_DIR):\\n shutil.rmtree(OUT_DIR)\\n os.mkdir(OUT_DIR)\\n\\n # Write all assets to the directory\\n for fname, bb in create_assets().items():\\n filename = os.path.join(OUT_DIR, fname)\\n dirname = os.path.dirname(filename)\\n if not os.path.isdir(dirname):\\n os.makedirs(dirname)\\n with open(filename, \\\"wb\\\") as f:\\n f.write(bb)\",\n \"def post_asset_update(lock, course):\\r\\n upload_date = datetime(2013, 6, 1, 10, 30, tzinfo=UTC)\\r\\n asset_location = course.id.make_asset_key('asset', 'sample_static.txt')\\r\\n url = reverse_course_url('assets_handler', course.id, kwargs={'asset_key_string': unicode(asset_location)})\\r\\n\\r\\n resp = self.client.post(\\r\\n url,\\r\\n json.dumps(assets._get_asset_json(\\\"sample_static.txt\\\", upload_date, asset_location, None, lock)),\\r\\n \\\"application/json\\\"\\r\\n )\\r\\n self.assertEqual(resp.status_code, 201)\\r\\n return json.loads(resp.content)\",\n \"def loadJSONFromPath(self, path):\\n\\t\\tself.root = TreeNode(' ');\\n\\t\\tcontent = codecs.open(path, 'r', 'utf-8').read()\\n\\t\\tdata = json.loads(content)\\n\\t\\tself.root = TreeNode()\\n\\t\\tself.root.loadJSON(data)\",\n \"def convertPath(srcpath, dstdir):\\n bits = srcpath.split(\\\"/\\\")\\n bits.pop(0)\\n # Strip out leading 'unsigned' from paths like unsigned/update/win32/...\\n if bits[0] == 'unsigned':\\n bits.pop(0)\\n return os.path.join(dstdir, *bits)\",\n \"def mapillary_raw_sample():\\n return \\\"tests/data/mapillary/sample/\\\"\",\n \"def transform(img_path):\\n\\n img = self.loader(img_path)\\n return self.transform(img)\",\n \"def save_file(obj, file_path):\\n with gzip.open(file_path, \\\"wb\\\") as fp:\\n fp.write(json.dumps(obj).encode('utf-8'))\",\n \"def JSONtoObject(fileName):\\n # TODO: ensure file exists first!!\\n \\n with open(fileName) as json_data:\\n d = json.load(json_data)\\n \\n return d\\n #return json.loads(d, object_hook=_json_object_hook)\",\n \"def get_or_create_sjson(item):\\r\\n user_filename = item.transcripts[item.transcript_language]\\r\\n user_subs_id = os.path.splitext(user_filename)[0]\\r\\n source_subs_id, result_subs_dict = user_subs_id, {1.0: user_subs_id}\\r\\n try:\\r\\n sjson_transcript = Transcript.asset(item.location, source_subs_id, item.transcript_language).data\\r\\n except (NotFoundError): # generating sjson from srt\\r\\n generate_sjson_for_all_speeds(item, user_filename, result_subs_dict, item.transcript_language)\\r\\n sjson_transcript = Transcript.asset(item.location, source_subs_id, item.transcript_language).data\\r\\n return sjson_transcript\",\n \"def get_file_path(name: str) -> str:\\n return os.path.join(DIR, 'scores', f'{name}.json')\",\n \"def makeFileResource(path):\\n\\n path = unwrapStr(path)\\n segments = [segment.encode(\\\"utf-8\\\") for segment in path.split(u'/')]\\n if not path.startswith(u'/'):\\n # Relative path.\\n segments = os.getcwd().split('/') + segments\\n log.log([\\\"fs\\\"], u\\\"makeFileResource.run/1: Relative path '%s'\\\" % path)\\n return FileResource(segments)\",\n \"def test_serve_built_files(self):\\r\\n self.mkbundle('file1', 'file2', output=\\\"out\\\").build()\\r\\n # I tried using the test client for this, but it would\\r\\n # need to be setup using StaticFilesHandler, which is\\r\\n # incompatible with the test client.\\r\\n from django_assets.finders import AssetsFinder\\r\\n assert AssetsFinder().find('out') == self.path(\\\"media/out\\\")\",\n \"def _convert(self):\\n\\n json_data = xjson.loads(self.jfile_path)\\n\\n with io.open(self.yfile_path, 'w', encoding='utf8') as f:\\n yaml.dump(json_data, f, default_flow_style=False, allow_unicode=True)\",\n \"def test_json_to_file(self):\\n\\n self.parser.parse()\\n self.parser.json_to_file(JSON_FILE)\\n json_string = self.parser.to_json()\\n\\n with open(JSON_FILE) as f:\\n json_file_string = f.read()\\n \\n self.assertEqual(json_string, json_file_string)\",\n \"def test_get_pathless_raw_file_name_json(self):\\n\\n this_pathless_file_name = probsevere_io._get_pathless_raw_file_name(\\n unix_time_sec=VALID_TIME_UNIX_SEC,\\n file_extension=probsevere_io.JSON_FILE_EXTENSION)\\n\\n self.assertTrue(this_pathless_file_name == PATHLESS_JSON_FILE_NAME)\",\n \"def json_to_txt():\\n def correct_pathname(path):\\n invalid_char = ['/', '\\\\t', ':']\\n for char in invalid_char:\\n path = path.replace(char, '')\\n return path\\n \\n base = 'data_html'\\n if not os.path.exists(base):\\n os.mkdir(base)\\n \\n data = load_jt('data.json')\\n \\n os.chdir(base)\\n for disease_name, v in data.iteritems():\\n disease_name = correct_pathname(disease_name) # eliminate invalid character\\n if not os.path.exists(disease_name):\\n os.mkdir(disease_name)\\n for category, content in v.iteritems():\\n if not os.path.exists(os.path.join(disease_name, '%s.html' % category)):\\n with open(os.path.join(disease_name, '%s.html' % category), 'wb') as f:\\n f.write(content)\",\n \"def test_load_path(parser):\\n doc = parser.load(pathlib.Path('jsonexamples') / 'small' / 'demo.json')\\n doc.at_pointer('/Image/Width')\",\n \"def loadJson (self, path):\\n\\n # get all lines in json, concatenate then into a big string then parse it\\n with open(path, \\\"r\\\") as file_content:\\n all_lines = file_content.readlines()\\n all_content_str = \\\"\\\".join(all_lines)\\n json_dict = json.loads(all_content_str)\\n self.tile_reprs = list(json_dict['tiles']['structural-tiles'].keys())\\n\\n # remove this empty char\\n self.tile_reprs.remove(\\\"-\\\")\",\n \"def data_path(path: str, createdir: bool = False) -> str:\\n path_obj = Path(path)\\n if not path_obj.is_absolute():\\n if inside_project():\\n path_obj = Path(project_data_dir(), path)\\n else:\\n path_obj = Path(\\\".scrapy\\\", path)\\n if createdir and not path_obj.exists():\\n path_obj.mkdir(parents=True)\\n return str(path_obj)\",\n \"def load_json(directory=None):\\n \\n if directory:\\n chdir(directory)\\n with open('memedPost_json_data.txt') as json_data:\\n data = load(json_data)\\n return data\\n else:\\n chdir(curdir)\\n with open('memedPost_json_data.txt') as json_data:\\n data = load(json_data)\\n return data\",\n \"def flatpath(cam):\\n return os.path.join(BASEPATH, cam + \\\"_flats\\\")\",\n \"def _get_data_file(self, data_path):\\n\\n return json.load(open(data_path))\",\n \"def _files_from_json(self, file_json):\\n self.compressed_file_json = zlib.compress(json.dumps(file_json).encode('utf-8'))\\n self.compression_algorithm = 'gzip'\\n self.compressed_content_hash = hashlib.sha256(self.compressed_file_json).hexdigest()\",\n \"def fix_jsons_in(bids_dir: Path):\\n\\n print(\\\"Finalizing task json files.\\\")\\n\\n for path in bids_dir.rglob(\\\"func/*_task-*.json\\\"):\\n append_to_json_file(key=\\\"TaskName\\\", value=task_name_of(path), path_to_json=path)\\n\\n print(\\\"Appending echo times to phase difference json files.\\\")\\n\\n for path in bids_dir.rglob(\\\"fmap/*_phasediff.json\\\"):\\n magnitude1_path = the_path_that_matches(pattern=\\\"sub-*_magnitude1.json\\\", in_directory=path.parent)\\n magnitude2_path = the_path_that_matches(pattern=\\\"sub-*_magnitude2.json\\\", in_directory=path.parent)\\n echo_time1 = value_of_key_in_json_file(\\\"EchoTime\\\", magnitude1_path)\\n echo_time2 = value_of_key_in_json_file(\\\"EchoTime\\\", magnitude2_path)\\n append_to_json_file(key=\\\"EchoTime1\\\", value=echo_time1, path_to_json=path)\\n append_to_json_file(key=\\\"EchoTime2\\\", value=echo_time2, path_to_json=path)\\n\\n print(\\\"Setting targets of fieldmap json files.\\\")\\n\\n for path in bids_dir.rglob(\\\"fmap/*.json\\\"):\\n func_dir = path.parent.parent / \\\"func\\\"\\n trimmed_func_paths = [\\\"func/\\\" + func_path.name for func_path in func_dir.glob(\\\"*.nii\\\")]\\n append_to_json_file(key=\\\"IntendedFor\\\", value=trimmed_func_paths, path_to_json=path)\",\n \"def write(self, target):\\n mpath = path.join(self._working_dir, 'manifest.json')\\n with open(mpath, 'w') as mani:\\n json.dump(self.item, mani)\\n\\n directory = path.abspath(self._working_dir)\\n with zipfile.ZipFile(target, 'w', allowZip64=True) as zip:\\n for root, dirs, files in walk(directory):\\n for f in files:\\n abspath = path.join(root, f)\\n relpath = path.relpath(abspath, directory)\\n zip.write(abspath, relpath)\\n return target\",\n \"def save_json_to_destination_file(self):\\n if self.source_file != self.output_file:\\n click.secho(f'Saving output JSON file to {self.output_file}', fg='white')\\n with open(self.output_file, 'w') as file:\\n ujson.dump(self.data, file, indent=4, encode_html_chars=True, escape_forward_slashes=False,\\n ensure_ascii=False)\",\n \"def test_repo_to_json(self):\\n if path.exists(\\\"report.csv\\\"):\\n remove(\\\"report.csv\\\")\\n\\n with open(\\\"test/fixtures/results.json\\\", 'r') as data:\\n data = json.load(data)\\n client = CSVReport()\\n client.process(data)\\n\\n self.assertTrue(path.exists(\\\"report.csv\\\"))\",\n \"def test_find_raw_file_json(self):\\n\\n this_file_name = probsevere_io.find_raw_file(\\n top_directory_name=TOP_DIRECTORY_NAME,\\n unix_time_sec=VALID_TIME_UNIX_SEC,\\n file_extension=probsevere_io.JSON_FILE_EXTENSION,\\n raise_error_if_missing=False)\\n\\n self.assertTrue(this_file_name == JSON_FILE_NAME)\",\n \"def prepare_src_folder(self, src_folder: str) -> None:\",\n \"def load_path(path):\\n\\n p = PathWalker(path)\\n listfiles = p.walk()\\n\\n full_json = dict()\\n\\n for file in listfiles:\\n try:\\n gz = GzipFile(filename=file, mode='rb')\\n except Exception:\\n raise Exception(\\\"cant open file\\\", str(file))\\n cur_json = get_formatted_dict(json.load(gz))\\n full_json.update(cur_json)\\n\\n return full_json\",\n \"def fix_path(self):\\n paths = self.data['path'].tolist()\\n prefixes = [re.findall(r'[A-Z\\\\-0-9]+', path) for path in paths]\\n prefix_good = [str(prefix[0]) + \\\".json\\\" for prefix in prefixes]\\n self.data['path'] = prefix_good\",\n \"def fake_full_path(self) -> PurePath:\\n if self.category:\\n # Giza wrote out yaml file artifacts under a directory. e.g. steps-foo.yaml becomes\\n # steps/foo.rst\\n return self.source_path.parent.joinpath(\\n PurePath(self.category), self.output_filename\\n )\\n return self.source_path\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7122769","0.6316052","0.5610777","0.54790395","0.52522516","0.522991","0.5213277","0.51795095","0.51795095","0.51695764","0.51168185","0.5074967","0.5063665","0.50393045","0.50152886","0.4990693","0.49906608","0.49886268","0.49885842","0.49729767","0.49597967","0.49533492","0.49173167","0.49170667","0.49080256","0.48977965","0.48974547","0.48953485","0.48904803","0.4885386","0.4873679","0.4842496","0.48390672","0.4837268","0.48163742","0.47946402","0.47889644","0.47618327","0.4752179","0.47440577","0.47340703","0.47327292","0.4728455","0.47228274","0.47196913","0.47177777","0.47157985","0.4714714","0.47130117","0.47075334","0.47060907","0.470389","0.46834752","0.46834674","0.4682296","0.46787563","0.46715066","0.46705052","0.46609434","0.4659425","0.46578762","0.46526158","0.4649639","0.46475223","0.46450812","0.46360964","0.46333334","0.46323922","0.46320814","0.46298093","0.4627462","0.46271977","0.4620768","0.46204692","0.4617809","0.4617587","0.4614771","0.45982313","0.45969394","0.4596461","0.45956618","0.45955172","0.45846084","0.45842135","0.45811275","0.45802164","0.45686564","0.45671555","0.45635116","0.45633802","0.45598763","0.45583728","0.45557246","0.45554253","0.45530006","0.45473623","0.45435685","0.45431313","0.45429516","0.45394897"],"string":"[\n \"0.7122769\",\n \"0.6316052\",\n \"0.5610777\",\n \"0.54790395\",\n \"0.52522516\",\n \"0.522991\",\n \"0.5213277\",\n \"0.51795095\",\n \"0.51795095\",\n \"0.51695764\",\n \"0.51168185\",\n \"0.5074967\",\n \"0.5063665\",\n \"0.50393045\",\n \"0.50152886\",\n \"0.4990693\",\n \"0.49906608\",\n \"0.49886268\",\n \"0.49885842\",\n \"0.49729767\",\n \"0.49597967\",\n \"0.49533492\",\n \"0.49173167\",\n \"0.49170667\",\n \"0.49080256\",\n \"0.48977965\",\n \"0.48974547\",\n \"0.48953485\",\n \"0.48904803\",\n \"0.4885386\",\n \"0.4873679\",\n \"0.4842496\",\n \"0.48390672\",\n \"0.4837268\",\n \"0.48163742\",\n \"0.47946402\",\n \"0.47889644\",\n \"0.47618327\",\n \"0.4752179\",\n \"0.47440577\",\n \"0.47340703\",\n \"0.47327292\",\n \"0.4728455\",\n \"0.47228274\",\n \"0.47196913\",\n \"0.47177777\",\n \"0.47157985\",\n \"0.4714714\",\n \"0.47130117\",\n \"0.47075334\",\n \"0.47060907\",\n \"0.470389\",\n \"0.46834752\",\n \"0.46834674\",\n \"0.4682296\",\n \"0.46787563\",\n \"0.46715066\",\n \"0.46705052\",\n \"0.46609434\",\n \"0.4659425\",\n \"0.46578762\",\n \"0.46526158\",\n \"0.4649639\",\n \"0.46475223\",\n \"0.46450812\",\n \"0.46360964\",\n \"0.46333334\",\n \"0.46323922\",\n \"0.46320814\",\n \"0.46298093\",\n \"0.4627462\",\n \"0.46271977\",\n \"0.4620768\",\n \"0.46204692\",\n \"0.4617809\",\n \"0.4617587\",\n \"0.4614771\",\n \"0.45982313\",\n \"0.45969394\",\n \"0.4596461\",\n \"0.45956618\",\n \"0.45955172\",\n \"0.45846084\",\n \"0.45842135\",\n \"0.45811275\",\n \"0.45802164\",\n \"0.45686564\",\n \"0.45671555\",\n \"0.45635116\",\n \"0.45633802\",\n \"0.45598763\",\n \"0.45583728\",\n \"0.45557246\",\n \"0.45554253\",\n \"0.45530006\",\n \"0.45473623\",\n \"0.45435685\",\n \"0.45431313\",\n \"0.45429516\",\n \"0.45394897\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6894641"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":11289,"cells":{"query":{"kind":"string","value":"Initializes this object's schema, input_files and output_path."},"document":{"kind":"string","value":"def __init__(self, schema, input_files, output_path):\n self.schema = schema\n self.input_files = input_files\n self.output_path = output_path"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def __init__(self):\n\n self.read_input_file()\n self.read_simulation_files()","def __init__(self):\n\t\tself.parsed = False\n\t\tdir_path = os.path.dirname(os.path.realpath(__file__))\n\t\tself.xsdfilename = os.path.join(dir_path, 'xml', 'schema.xsd')\n\t\tself.schema = 'schema.xsd'\n\t\tself.predictors = []\n\t\tself.predictors_types = []\n\t\tself.preprocessing_methods = []","def __init__(\n self,\n data_path: str,\n output_path: str\n ):\n\n self.data_path = data_path\n self.output_path = output_path","def _set_up(self):\n # Also recreates any attributes not in _io_attrs on load from input file.\n # See IOAble class docstring for more info.\n self._enforce_symmetry()\n self._sort_modes()\n self._create_idx()","def __init__(self, reader_schema, writer_schema=None, input_file=None):\n\n if writer_schema is None:\n writer_schema = reader_schema\n self._reader_schema = reader_schema\n self._writer_schema = writer_schema\n self._reader_schema_json = json.loads(str(self._reader_schema))\n self._writer_schema_json = json.loads(str(self._writer_schema))\n self._input_file = input_file\n self._set_avro_readers()","def __init__(self, inputmanager=None, outputpath=default.output_path):\n self.__inputmanager = inputmanager\n self.__outputpath = outputpath","def __init__(self, data=None, filename=None, schema=None):\n self.data = None\n self.schema = None\n self.filename = None\n if schema:\n self.load_schema(schema)\n if filename:\n self.load_file(filename)\n if data:\n self.load_data(data)","def init(self):\n\n self.checkDirectory(self.output_dir,\"output\")\n self.checkDirectory(self.working_dir,\"working\")","def __init__(self, input_directory, save_directory):\n self.input_directory = input_directory\n self.save_directory = save_directory\n self.__concatonate_files_controller()","def __init__(self, db_location, input_location = None,\n force_regenerate_input = False):\n \n if input_location == None:\n input_location = str(Path(db_location, 'rf_input_data'))\n \n # Check if at least gauge.parquet, refer_x0y0.parquet and radar_x0y0.parquet\n # are present\n valid = True\n if not os.path.exists(input_location):\n valid = False\n os.makedirs(input_location)\n files = glob.glob(str(Path(input_location, '*')))\n files = [os.path.basename(f) for f in files]\n if ('gauge.parquet' not in files or 'reference_x0y0.parquet' not in files\n or 'radar_x0y0.parquet' not in files):\n valid = False\n \n self.input_location = input_location\n self.db_location = db_location\n \n if not valid :\n logging.info('Could not find valid input data from the folder {:s}'.format(input_location))\n if force_regenerate_input or not valid:\n logging.info('The program will now compute this input data from the database, this takes quite some time')\n self.prepare_input()","def __init__(self):\n INSTALL_DIR = dirname(__file__)\n CONFIG_DIR = '/etc/Model2WADL/'\n logging.basicConfig(level=logging.ERROR)\n logging.config.fileConfig([join(CONFIG_DIR, 'logging.conf'), expanduser('~/.logging.conf'), 'logging.conf'])\n self.__log = logging.getLogger('thesis')\n\n self.__log.debug(\"Reading general configuration from Model2WADL.cfg\")\n self.__m2wConfig = ConfigParser.SafeConfigParser()\n self.__m2wConfig.read(\n [join(CONFIG_DIR, 'Physical2Virtual.cfg'), expanduser('~/.Physical2Virtual.cfg'), 'Physical2Virtual.cfg'])\n\n self.__baseURI = self.__m2wConfig.get(\"Config\", \"baseURI\")\n self.__basePackage = self.__m2wConfig.get(\"Config\", \"basePackage\")\n self.__schemaFile = self.__m2wConfig.get(\"Config\", \"schemaFile\")\n self.__model = None\n self.__input = None\n self.__output = None","def __init__(self):\n\n self.root_path = os.path.dirname(os.path.abspath(__file__))[:-5]\n self.config_path = os.path.join(self.root_path, \"files\\\\CONFIG.txt\")\n self.metrics_path = os.path.join(self.root_path, \"files\\\\metrics.txt\")\n\n self.setup_metrics_file()\n\n if self.check_configuration() is False:\n self.setup_configuration_file()","def __init__(self, schema_name, schema_path):\n self.__schema_name = schema_name\n self.__schema_path = schema_path\n\n self.__test_set_list = [] # Creates an empty list of test sets\n\n self.__validate_and_compile()","def __init__(self):\n\n self.current_path = os.getcwd()\n self.data_path = self.current_path + \"/data\"\n\n self.original_files = {}\n self.imitation_files = {}\n self.original_test_files = {}\n self.imitation_test_files = {}\n\n self.training_set = None\n self.original_test_set = None\n self.imitation_test_set = None\n\n self.accuracy = 0.\n self.threshold = 0.\n\n self.get_files()","def initialize_options(self):\n self.input_dir = getcwd()\n self.output_dir = path.join(getcwd(), 'dependency', 'static', 'apidocs')","def __init__(self, data, schema_file=None):\n if schema_file is not None:\n if not os.path.exists(schema_file):\n logging.warning(\n 'Configuration schema file could not be found. Please check the path and try again.'\n )\n raise Exception()\n\n self.schema_file = schema_file\n else:\n self.schema_file = None\n\n if not os.path.exists(data):\n logging.warning(\n 'Configuration data could not be found. Please check the path and try again.'\n )\n raise Exception()\n\n self.datafile = data\n\n self.data = None","def init(self) -> None:\n self.faithful = self.is_faithful()\n if not self.faithful:\n # We retrieve a list of each element into the directory_path\n files_into_directory = list_files_into_directory(self.directory_path)\n if files_into_directory.__len__() == 0:\n self.create_required_structure()\n self.faithful = True\n else:\n raise AttributeError(\"The directory seems improper but not empty\")\n self.class_names = self.find_class_names()\n self.training_image_names = self.find_training_image_names()\n self.extracted_features = self.find_features_already_extracted()\n if len(self.extracted_features) != 0:\n self.selected_features = set(self.extracted_features)\n self.generate_csv_dictionary()","def __init__(self, **kwargs):\n cls = self.__class__\n\n # Initialize all configurables and input arguments\n for arg in cls.configurables():\n try: # Read from class constructor\n setattr(self, arg, kwargs[arg])\n except KeyError:\n try: # Set from default value defined in class\n default_value = getattr(self, arg).kwargs[\"default\"]\n setattr(self, arg, default_value)\n except KeyError: # if nothing is provided, fallbakcs to None\n setattr(self, arg, None)\n\n self.input_arguments = None\n if cls.input_configurables():\n self.input_arguments = [\n getattr(self, arg) for arg in cls.input_configurables()\n ]\n\n self.json_config = cfg.JsonConfig(self.config)\n self.output_objects = []\n self.file = None","def __init__(self, output_dir):\n self.output_dir = os.path.abspath(output_dir)\n # Create the file if it doesn't already exist\n os.makedirs(self.output_dir, exist_ok=True)\n self.f = None\n self.data = None\n self.L = None","def __init__(self, input_files):\n\n self._input_files = input_files\n self._cached_workspace_names = {} # {ID: Name}","def __init__(self, synapse_df):\n self._file_df = synapse_df[synapse_df.type == \"file\"]\n self._folder_df = synapse_df[synapse_df.type == \"folder\"]\n self._init_file_types()\n self._identify_archive_folders()\n self._walk_files()","def setUp(self, path, structure_file, input_file):\n database.clean()\n self.path = path\n self.structure_file = path + structure_file\n self.input_file = path + input_file\n\n string_processor.project = Project()\n\n self.input_project = Project()\n self.input_project.document_files.append(\n DocumentFile(path=self.input_file))\n self.input_project.save()\n\n with open(self.structure_file) as f:\n self.json = json.load(f)\n\n self.xml = etree.parse(self.input_file)\n self.extractor = StructureExtractor(string_processor,\n self.structure_file)","def __init__(self,\n output_type=None,\n gzip_filehandle=None,\n gzip_filehandle_parent=None,\n schema=None):\n self.output_type = output_type\n self.gzip_filehandle = gzip_filehandle\n self.schema = schema\n self.gzip_filehandle_parent = gzip_filehandle_parent","def __init__(self, \n save_data_folder: str,\n reader:FileReader = None,\n input_file:str = None,\n *args, **kwargs):\n \n if reader:\n self.files, self.attr_names = reader.read_file(input_file, *args, **kwargs)\n \n self.save_data_folder = Path(save_data_folder)\n self.save_data_folder.mkdir(parents=True, exist_ok=True)\n BaseProcess.set_logger('generator.log')","def __init__(self, path, input_type='f'):\n if input_type == 'f':\n file = open(path, 'r')\n elif input_type == 's':\n file = path\n else:\n raise exceptions.BadInputError(f\"invalid input type {input_type}\")\n\n pdl = yaml.safe_load(file)\n\n self.type_checks = {\n 'typedef': self.validate_typedef,\n 'component': self.validate_component,\n 'graph': self.validate_graph,\n }\n\n self.imports = []\n if 'import' in pdl:\n self.imports = pdl['import']\n\n self.namespace = pdl['name']\n self.body = pdl['body']\n self.typedefs = {}\n self.components = []\n self.graphs = []\n self.validate()","def initialize(self):\n self.initialize_edges()\n self.initialize_prob()\n self.initialize_total_input_dict()\n\n self.initialize_fpmusigv_dict()","def __init__(self, targetDir, model):\n \n self.categoryFolder = targetDir\n self.model = model\n self.inputsFolder = os.path.join(targetDir, \"Inputs\")","def setup_class(cls):\n self = cls()\n self.remove_files_created_during_previous_runs()\n if not os.path.exists(self.plaintext_directory):\n os.makedirs(self.plaintext_directory)\n\n if not os.path.exists(self.training_path):\n os.makedirs(self.training_path)\n\n if not os.path.exists(self.heldout_path):\n os.makedirs(self.heldout_path)\n\n prepare_data(self.paths)","def __init__(self, input_filename='input.txt', output_filename='output.txt'):\n self._input = input_filename\n self._output = output_filename\n self._fin = open(self._input, 'r')\n self._fout = open(self._output, 'w')","def init(self):\n\t\tfrom splat_to_db import splat_to_db\n\t\tfrom visualize.clustering_test import clustering_test\n\t\tfrom codense.codense2db import codense2db\n\t\tself.splat_to_db_instance = splat_to_db()\n\t\tself.clustering_test_instance = clustering_test()\n\t\tself.codense2db_instance = codense2db()\n\t\t\n\t\tif not os.path.isdir(self.dir_files):\n\t\t\tos.makedirs(self.dir_files)\n\t\telse:\n\t\t\tsys.stderr.write(\"Warning, directory %s already exists.\\n\"%(self.dir_files))\n\t\tself.tmpinfname = os.path.join(self.dir_files, 'input')\n\t\tself.tmpoutfname = os.path.join(self.dir_files, 'output')\n\t\t\n\t\tself.crack_dict = {1: crack_by_modes(self.debug),\n\t\t\t2:crack_by_splat(self.debug)}\n\t\tself.argument1_dict = {1: self.clustering_test_instance,\n\t\t\t2: self.splat_to_db_instance}\n\t\t\n\t\t#two descending tables\n\t\tself.splat_table = '%ss'%self.table\n\t\tself.mcl_table = self.splat_table.replace('splat','mcl')\n\t\tif self.mcl_table == self.splat_table:\n\t\t\tsys.stderr.write(\"Error: new splat and mcl tables have the same name, %s\\n\"%self.splat_table)\n\t\t\tsys.exit(2)","def __init__(self):\n self.data_set_loc = conf.config_section_mapper(\"filePath\").get(\"data_set_loc\")\n self.data_extractor = DataExtractor(self.data_set_loc)","def __init__(self):\n self.config = configs.Configuration()\n self.log = logger.CustomLogger(__name__).get_logger()\n self.output_dir = self.config.getConfigValue('OUTPUT_DIR')\n self.s3_directory = self.config.getConfigValue('S3_FILE_PATH_TRANSFORM')","def initialize(self) -> None:\n self.model = load(self.path)","def __init__(self):\n self.parser_model_dir = None\n self.parser_options = {}\n self.reranker_model = None\n self.unified_model_dir = None","def __init__(self, **kwargs):\n super().__init__(**kwargs)\n\n if not all([hasattr(self, arg) for arg in PREPARE_ARGS]):\n # this should never happened due to object creation with parse_args in sumo/__init__.py\n raise AttributeError(\"Cannot create SumoPrepare object, missing constructor arguments\")\n\n # check positional arguments\n if not all([os.path.exists(fname) for fname in self.infiles]):\n raise FileNotFoundError(\"Input file not found\")\n\n self.ftypes = []\n for fname in self.infiles:\n suff = pathlib.Path(fname).suffix if pathlib.Path(fname).suffix not in ['.gz', '.bz2'] else ''.join(\n pathlib.Path(fname).suffixes[-2:])\n if suff not in SUPPORTED_EXT:\n raise ValueError(\"Unrecognized input file type '{}'\".format(suff))\n self.ftypes.append(suff if suff != \"\" else \".txt\")\n\n if not all([method in SIMILARITY_METHODS for method in self.method]):\n raise ValueError(\"Unrecognized similarity method\")\n\n self.plot_base = None\n if self.plot:\n basename = os.path.basename(self.plot)\n dirname = os.path.dirname(self.plot)\n self.plot_base = os.path.join(dirname, basename.split('.png')[0])\n\n self.logger = setup_logger(\"main\", self.log, self.logfile)","def __init__(self, inputs, resources, port, logger):\n self.inputs = inputs\n self.port = port\n self.logger = logger\n self.resources = resources\n self.fileserver_path = 'File-Server'\n self.blueprint = 'test-start-fileserver-vm-blueprint.yaml'\n self.blueprint_path = os.path.join(os.path.dirname(__file__),\n 'resources', self.blueprint)\n self.server_cmd = 'python -m SimpleHTTPServer {0}'.format(self.port)\n self.fs_base_url = None\n self.local_env = None\n self.processed_inputs = None\n self.fab_env_conf = {}","def __init__(self, output_file, table_model):\n pass","def __init__(self, source_path, output_path):\n self.storage_type = 's3' if 's3' in source_path else 'local'\n self.source_path = source_path\n self.output_path = output_path","def _initialize(self) -> None:\n p = self.params\n # We make self.input public so that users can access its methods like\n # IdsToStrings if needed.\n with py_utils.infeed_context_scope(\n infeed_host_index=p.infeed_host_index,\n num_infeed_hosts=p.num_infeed_hosts):\n self.input = p.input.Instantiate()\n\n if hasattr(self.input, 'datasource') and isinstance(\n self.input.datasource, datasource.TFDatasetSource):\n # For the special case when the input is implemented by a tf.data.Dataset,\n # use it directly. Otherwise roundtrip adaptions may result in returning\n # duplciate batches.\n self._get_next_fn = self.input.datasource.GetNext\n else:\n self._get_next_fn = tf.function(self._get_batch)\n self._num_batches_produced = 0","def __init__(self):\n self.model_description: Dict[str, Any] = get_model_description()\n self.model_name: str = self.model_description['name']\n self.model_version: str = self.model_description['version']\n\n # Make sure we do not have a trailing slash to muck up processing later.\n self.event_dir: Optional[str] = None\n self.zone_name: Optional[str] = None\n self.fault_time: Optional[str] = None\n\n self.example: Example = None\n self.validator: ExampleValidator = ExampleValidator()\n self.common_features_df: pd.DataFrame = None\n\n self.cavity_onnx_session: rt.InferenceSession = rt.InferenceSession(os.path.join(os.path.dirname(__file__),\n 'model_files',\n 'cavity_model.onnx'))\n self.fault_onnx_session: rt.InferenceSession = rt.InferenceSession(os.path.join(os.path.dirname(__file__),\n 'model_files',\n 'fault_model.onnx'))","def __init__(self, writer_schema, output_file=None, skip_validate=True):\n self._writer_schema = writer_schema\n self._writer_schema_json = json.loads(str(self._writer_schema))\n self._output_file = output_file\n self._set_avro_writers()","def __init__(self, output_path, input_path, config_dir, labels):\n self.output_path = output_path\n self.input_path = input_path\n self.config_dir = config_dir\n self.engine_name = labels['engine_name']\n self.algorithm_name = labels['algorithm_name']\n self.file_suffix = labels['file_suffix']\n # the graph type set as the name of the class\n self.graph_type = \"base\"","def __init__(__self__, *,\n content_type: pulumi.Input[Union[str, 'FileImportContentType']],\n import_file: pulumi.Input['FileMetadataArgs'],\n ingestion_mode: pulumi.Input[Union[str, 'IngestionMode']],\n resource_group_name: pulumi.Input[str],\n source: pulumi.Input[str],\n workspace_name: pulumi.Input[str],\n file_import_id: Optional[pulumi.Input[str]] = None):\n pulumi.set(__self__, \"content_type\", content_type)\n pulumi.set(__self__, \"import_file\", import_file)\n pulumi.set(__self__, \"ingestion_mode\", ingestion_mode)\n pulumi.set(__self__, \"resource_group_name\", resource_group_name)\n pulumi.set(__self__, \"source\", source)\n pulumi.set(__self__, \"workspace_name\", workspace_name)\n if file_import_id is not None:\n pulumi.set(__self__, \"file_import_id\", file_import_id)","def setup_structure(self):\n seekpath_result = seekpath_structure(self.inputs.structure)\n self.ctx.structure_initial_primitive = seekpath_result['primitive_structure']","def __init__(self):\n self.filepath = os.path.dirname(__file__)\n self.filepath = os.path.join(self.filepath, \"Datenbank\")\n self.filepath_render_database = os.path.join(self.filepath, self.render_database)\n self.filepath_object_database = os.path.join(self.filepath, self.object_database)\n self.filepath_output_database = os.path.join(self.filepath, self.output_database)\n self.create_database()","def __init__(self, root_path):\n self.root_path = root_path\n # Immutable data entries\n self.uid = None\n self.commit_sha = None\n self.timestamp = None\n self.filename = None\n self.comment = None\n # Mutable data entries\n self.train_data = {} # anything produced during training, epoch by epoch\n self.val_data = {} # results of intermediate tests during training\n self.test_data = {} # results of a test\n self.model_files = [] # saved model parameters\n self.custom_data = {} # whatever the user might like to save\n # Load everything from the data file\n if not self._create_flag:\n self.deserialize()","def __init__(self, args):\n\n self.directory = args.directory\n \n # generate string with only base data directory\n dirstring = os.path.basename(os.path.normpath(self.directory))\n self.string_directory = \"Data: {}\".format(dirstring) \n\n # get location of this file\n self.filepath = os.path.dirname(os.path.realpath(__file__))\n\n # initialize attributes to hold files and (inferEM, sample) dirs\n self.files = []\n self.betas = []\n self.compare_segs_dict = {}\n self.sample_dirs = {}\n self.prob_dirs = {}\n self.full_dataset = None\n self.full_dataset_tuple = None","def __init__(self, *args):\n _snap.Schema_swiginit(self, _snap.new_Schema(*args))","def initialize(self):\n try:\n os.makedirs('./output/' + self.output)\n except:\n pass\n with open(self.trajectory_file, 'w') as f:\n f.write('MODEL 0\\n')\n for atom in self.atoms:\n f.write(atom.to_pdb_line() + '\\n')\n f.write('TER\\n')\n potential_energy = 0\n kinetic_energy = 0\n for atom in self.atoms:\n potential_energy += atom.potential_energy\n kinetic_energy += atom.get_kinetic_energy()\n with open(self.kinetic_energy_file, 'w') as f:\n f.write('0,' + str(kinetic_energy) + '\\n')\n with open(self.potential_energy_file, 'w') as f:\n f.write('0,' + str(potential_energy) + '\\n')","def __init__(self, schema=None):\n self.schema = schema or {}","def __init__(self,\n config_file=None,\n input_path=None,\n output_format=None,\n input_format=None,\n context='',\n ):\n\n absolute_path = path.join(context, config_file)\n import_dir_path = path.dirname(absolute_path)\n import_filename = path.basename(absolute_path)\n\n root_configs = {\n 'imports': [\n import_filename\n ],\n 'parameters': {\n 'input': {},\n 'output': {}\n },\n }\n\n if input_path is not None:\n root_configs['parameters']['input']['path'] = input_path\n\n if input_format is not None:\n root_configs['parameters']['input']['format'] = input_format\n\n if output_format is not None:\n root_configs['parameters']['output']['format'] = output_format\n\n self.configs = Config(import_dir_path, configs=root_configs)\n\n try:\n self.input_dir_path = path.join(context, self.params('input', 'path'))\n except Exception as e:\n Logger.debug(e)\n Logger.error('inp_path_missing')\n\n try:\n self.configs.get('parameters', 'output', 'path')\n except Exception as e:\n # setting the default output folder\n # this could be generalized and encapsulated as a setter on the config class\n self.configs.configs['parameters']['output']['path'] = 'output'\n self.configs.config_paths['parameters']['output']['path'] = context\n\n reader = self.params('input', 'format')\n if not is_str(reader) and issubclass(reader, Reader):\n self.module_loader.set_reader(reader)\n else:\n try:\n self.module_loader.load_reader(reader)\n Logger.info('input_format_detected', self.params('input', 'format'))\n except KeyError as e:\n Logger.debug(e)\n Logger.error('inp_format_missing')\n except Exception as e:\n Logger.debug(e)\n Logger.error('bad_inp_format', self.params('input', 'format'), str(e))\n\n writer = self.params('output', 'format')\n if not is_str(writer) and issubclass(writer, Writer):\n self.module_loader.set_writer(writer)\n else:\n try:\n self.module_loader.load_writer(writer)\n Logger.info('output_format_detected', self.params('output', 'format'))\n except KeyError as e:\n Logger.debug(e)\n Logger.error('out_format_missing')\n except Exception as e:\n Logger.debug(e)\n Logger.error('bad_out_format', self.params('output', 'format'), str(e))","def __init__(__self__, *,\n creation_time: Optional[pulumi.Input['TimestampArgs']] = None,\n output: Optional[pulumi.Input['FileReferenceArgs']] = None,\n test_case: Optional[pulumi.Input['TestCaseReferenceArgs']] = None):\n if creation_time is not None:\n pulumi.set(__self__, \"creation_time\", creation_time)\n if output is not None:\n pulumi.set(__self__, \"output\", output)\n if test_case is not None:\n pulumi.set(__self__, \"test_case\", test_case)","def __init__(self, file_name: Optional[str] = None):\n self.entries = OrderedDict() # Dict[str, PathElement]\n self.file_name = file_name # input file for logging\n self.jsonf = None # json image of input file\n self.namespaces = Namespaces('http://hl7.org/fhir/StructureDefinition/')\n self.path_map = {} # map from path to name (Dict[str, str])","def _call_initialization(self,\r\n input_fp,\r\n output_dir,\r\n params,\r\n job_prefix,\r\n poll_directly,\r\n suppress_submit_jobs):\r\n pass","def __init__(self):\n self.params = {}\n self.counts = 0\n self.failed = 0\n self.path = os.path.join(os.getcwd(), OUTPUT_DIR)\n \n if not os.path.exists(self.path):\n os.makedirs(self.path)","def __init__(self):\n super().__init__()\n self.indices_dir = ''\n self.split_file = ''\n\n self.model = '' # string identifying the model\n self.experiment = '' # string to describe experiment\n self.maps = [data.ID_MAP_T1H2O, data.ID_MAP_FF, data.ID_MAP_B1] # the used maps\n self.patch_size = [1, 32, 32]\n\n # training configuration\n self.loss = 'mse' # string identifying the loss function (huber, mse or mae)\n self.learning_rate = 0.01 # the learning rate\n self.dropout_p = 0.2\n self.norm = 'bn' # none, bn\n\n # we use the mean absolute error as best model score\n self.best_model_score_is_positive = True\n self.best_model_score_name = 'mae'","def path_defs(self):\n if self.input_xml is not None:\n self.input_xml = os.path.abspath(os.path.expandvars(self.input_xml))\n if self.pointing_file is not None:\n self.pointing_file = os.path.abspath(os.path.expandvars(self.pointing_file))\n self.output_dir = os.path.abspath(os.path.expandvars(self.output_dir))\n self.simdata_output_dir = os.path.abspath(os.path.expandvars(self.simdata_output_dir))\n if self.table_file is not None:\n self.table_file = os.path.abspath(os.path.expandvars(self.table_file))\n\n ensure_dir_exists(self.output_dir)\n ensure_dir_exists(self.simdata_output_dir)\n\n if self.observation_list_file is not None:\n self.observation_list_file = os.path.abspath(os.path.expandvars(self.observation_list_file))","def __init__(self):\n self.read_input()\n self.update_binaries()","def __init__(self):\n self._names = []\n self._forwardFactories = []\n self._inputs = []\n self._inputFilters = {}\n self._outputFilters = []\n self._inputCheckers = []\n pass","def __init__(self, input_file=None, output_file=None, gt_file=None):\n\n self.input_file = input_file\n self.output_file = output_file\n self.gt_file = gt_file\n\n #print('Created object: ', musicxml_file)","def __init__(self, filename: str | Path, *args, **kwargs) -> None:\n super().__init__(filename, *args, **kwargs)\n self._non_metadata_keys = None\n self._score_key = None\n self._rt_key = None\n self._spectrum_rt_key = None\n self._qvalue_key = None\n self._pep_key = None\n\n self._source = self._infer_source()","def initialize(self):\n\n if not os.path.exists(CONFIG_Preprocess):\n with io.open(CONFIG_Preprocess, 'w') as fh:\n json.dump(DEFAULT_Preprocess, fh, sort_keys=True, indent=4, separators=(\",\", \": \"))\n return()\n\n # Load all options.\n with io.open(CONFIG_Preprocess, 'r') as fh:\n data = json.load(fh)\n \n for key, value in data.items():\n setattr(self, key, value)\n \n # Make sure freqs. for downsampling are in descending order.\n self.Fs_new.sort() # Now in ascending order\n self.Fs_new=self.Fs_new[::-1] # Now in descending order","def __init__(self, **kwargs):\n super(Converter, self).__init__()\n self._specfile = kwargs.get(\"specfile\", None)\n self._parsed = False\n self._columns = []\n self._offsets = []\n self._fixed_with_encoding = Encodings.default_fixedwidth_enc()\n self._included_header = False\n self._delimited_encoding = Encodings.default_delimited_enc()\n self.encoder_spec()","def __init__(self, data_file_paths, targets, transform=None):\n \n self.transform = transform\n self.data_file_paths = data_file_paths\n self.targets = targets\n return","def __init__(self, path: str = './') -> None:\n super(DataHandler, self).__init__()\n self.path = path\n self.dataset = None # type: str\n self._file_format = None # type: str\n self._max_file_count = 0","def __init__(self, input_filename, config):\n\t\tself.input_filename = input_filename\n\t\tself.db = mysql.connect(config['mysql_host'], config['mysql_user'], config['mysql_pass'], config['mysql_db'])\n\t\tself.cursor = self.db.cursor()\n\t\t\"\"\"These are foreign keys that link the reports to their metadata and policy information\"\"\"\n\t\tself.metadata_fk = \"\"\n\t\tself.policy_fk = \"\"","def __init__(self):\n self.out = None\n self.in_shape = None\n self.work_shape = None","def __init__(self, language, datasource, source_file, source_path,\n target_path, pipeline_config, shuffle_file):\n self.language = language\n self.datasource = datasource\n self.source_file = source_file\n self.source_path = source_path\n self.target_path = target_path\n self.location = target_path\n self.pipeline_config = pipeline_config\n self.shuffle_file = shuffle_file\n self.data_path = os.path.join(self.target_path, 'data')\n self.conf_path = os.path.join(self.target_path, 'config')\n self.file_list = os.path.join(self.conf_path, FNAME_FILELIST)\n if self.source_file is not None or self.source_path is not None:\n self._initialize_directory()","def __init__(self):\n self.model = {'mol':[], 'nmol':0}\n self.template = {} \n self.config = {}\n self.config['tfile'] = 'gau-template-bsse.gjf'\n self.config['xyzfile'] = 'model.xyz'\n self.config['jobfile'] = 'gau.gjf'\n self.config['job_prefix'] = self.config['jobfile'].split(\".\")[0]\n self.config['incr'] = 1\n \n self.rd_cmd_stream()\n return","def initialize(self, args):\n # You must parse model_config. JSON string is not parsed here\n self.model_config = json.loads(args['model_config'])\n print(\"model_config:\", self.model_config)\n\n self.input_names = []\n for input_config in self.model_config[\"input\"]:\n self.input_names.append(input_config[\"name\"])\n print(\"postprocess input names:\", self.input_names)\n\n self.output_names = []\n self.output_dtype = []\n for output_config in self.model_config[\"output\"]:\n self.output_names.append(output_config[\"name\"])\n dtype = pb_utils.triton_string_to_numpy(output_config[\"data_type\"])\n self.output_dtype.append(dtype)\n print(\"postprocess output names:\", self.output_names)\n self.postprocessor = fd.vision.ocr.DBDetectorPostprocessor()\n self.cls_preprocessor = fd.vision.ocr.ClassifierPreprocessor()\n self.rec_preprocessor = fd.vision.ocr.RecognizerPreprocessor()\n self.cls_threshold = 0.9","def setup(self):\n self.bqSession.update_mex('Initializing...')\n self.mex_parameter_parser(self.bqSession.mex.xmltree)\n self.output_file = None","def __init__(self, the_input_shape, num_classes, path_weights):\n self.the_input_shape = the_input_shape\n self.num_classes = num_classes\n self.path_weights = path_weights","def __init__(self, path: str) -> None:\n self.path = path\n\n self.path_token_idx_corpus = os.path.join(\n path, 'processed_corpus/token_idx_corpus.txt')\n self.path_token_terms_idxs = os.path.join(\n path, 'processed_corpus/token_terms_idxs.txt')\n self.path_tf_tokens = os.path.join(\n path, 'frequencies/tf_tokens.json')\n self.path_df_tokens = os.path.join(\n path, 'frequencies/df_tokens.json')\n self.path_tfidf_tokens = os.path.join(\n path, 'frequencies/tfidf_tokens.json')\n\n self.path_lemma_idx_corpus = os.path.join(\n path, 'processed_corpus/lemma_idx_corpus.txt')\n self.path_lemma_terms_idxs = os.path.join(\n path, 'processed_corpus/lemma_terms_idxs.txt')\n self.path_tf_lemmas = os.path.join(\n path, 'frequencies/tf_lemmas.json')\n self.path_df_lemmas = os.path.join(\n path, 'frequencies/df_lemmas.json')\n self.path_tfidf_lemmas = os.path.join(\n path, 'frequencies/tfidf_lemmas.json')\n\n self.path_dl = os.path.join(\n path, 'frequencies/dl.json')\n # self.path_token_contains = os.path.join(\n # path, 'processed_corpus/token_contains.json')\n # self.path_lemma_contains = os.path.join(\n # path, 'processed_corpus/lemma_contains.json')\n\n self._file_write_threshhold = 100\n # self._num_docs = 1000\n self._docs_processed = 0\n # super().__init__(path, path, max_docs)","def initialize(self):\n LOG.info(\"Initializing Model.\")\n self.model = self.convert(df=self.training_df)\n if self.bootstraps is not None:\n LOG.info(\"Bootstrapping Data.\")\n self.bootstrap_data()","def __init__(self):\n # Read configuration into dictionary\n self.directories = general.config_directories()\n self.config = general.read_yaml_files(self.directories)","def __init__(self, model_filename, sim_filename, include_paths = None):\n\n self.model_filename = model_filename\n self.sim_filename = sim_filename\n self.include_paths = include_paths\n \n self.simulation = None\n self.fit_input = None","def __init__(self, inputfolder, gpu_id=0):\n if gpu_id < 0:\n caffe.set_mode_cpu()\n else:\n caffe.set_mode_gpu()\n #caffe.set_device(gpu_id)\n inputfolder = realpath(inputfolder)\n self.protofile, self.caffemodel = self._findFiles(inputfolder)\n self.mean = [128.,128.,128.]","def __init__(self):\n super().__init__()\n self.data_set_loc = conf.config_section_mapper(\"filePath\").get(\"data_set_loc\")\n self.data_extractor = DataExtractor(self.data_set_loc)\n self.sim_act_diff_mov_tf = SimilarActorsFromDiffMovies()","def __init__(self):\n super().__init__()\n self.data_set_loc = conf.config_section_mapper(\"filePath\").get(\"data_set_loc\")\n self.data_extractor = DataExtractor(self.data_set_loc)\n self.sim_act_diff_mov_tf = SimilarActorsFromDiffMovies()","def setUp(cls):\n arkane = Arkane()\n cls.job_list = arkane.load_input_file(os.path.join(os.path.dirname(os.path.abspath(__file__)),\n 'data', 'Benzyl', 'input.py'))","def __init__(self, input_file_name, ast_root_node):\n\n self.input_file_name = input_file_name\n self.ast_root_node = ast_root_node\n self.object_file = ObjectFile(self.input_file_name)\n self.assembler = X64Assembler()","def __init__(self):\n super().__init__()\n self.index_dir = self.base_dir + \"user/\"\n self.index_schema = self.__get_index_schema()\n if not os.path.exists(self.index_dir):\n os.makedirs(self.index_dir)\n self.indexer = index.create_in(self.index_dir, self.index_schema) # creates the index\n else:\n self.indexer = index.open_dir(self.index_dir) # opens the index if it already exists","def __init__(__self__,\n resource_name: str,\n opts: Optional[pulumi.ResourceOptions] = None,\n content_type: Optional[pulumi.Input[Union[str, 'FileImportContentType']]] = None,\n file_import_id: Optional[pulumi.Input[str]] = None,\n import_file: Optional[pulumi.Input[pulumi.InputType['FileMetadataArgs']]] = None,\n ingestion_mode: Optional[pulumi.Input[Union[str, 'IngestionMode']]] = None,\n resource_group_name: Optional[pulumi.Input[str]] = None,\n source: Optional[pulumi.Input[str]] = None,\n workspace_name: Optional[pulumi.Input[str]] = None,\n __props__=None):\n ...","def _create_init(self):\n\n assert self._topology is not None, \\\n \"Topology must be given for a creation constructor\"\n\n # initialize the runs group\n runs_grp = self._h5.create_group(RUNS)\n\n # initialize the settings group\n settings_grp = self._h5.create_group(SETTINGS)\n\n # create the topology dataset\n self._h5.create_dataset(TOPOLOGY, data=self._topology)\n\n # sparse fields\n if self._sparse_fields is not None:\n\n # make a dataset for the sparse fields allowed. this requires\n # a 'special' datatype for variable length strings. This is\n # supported by HDF5 but not numpy.\n vlen_str_dt = h5py.special_dtype(vlen=str)\n\n # create the dataset with empty values for the length of the\n # sparse fields given\n sparse_fields_ds = settings_grp.create_dataset(SPARSE_FIELDS,\n (len(self._sparse_fields),),\n dtype=vlen_str_dt,\n maxshape=(None,))\n\n # set the flags\n for i, sparse_field in enumerate(self._sparse_fields):\n sparse_fields_ds[i] = sparse_field\n\n\n # field feature shapes and dtypes\n\n # initialize to the defaults, this gives values to\n # self._n_coords, and self.field_feature_dtypes, and\n # self.field_feature_shapes\n self._set_default_init_field_attributes(n_dims=self._n_dims)\n\n # save the number of dimensions and number of atoms in settings\n settings_grp.create_dataset(N_DIMS_STR, data=np.array(self._n_dims))\n settings_grp.create_dataset(N_ATOMS, data=np.array(self._n_coords))\n\n # the main rep atom idxs\n settings_grp.create_dataset(MAIN_REP_IDXS, data=self._main_rep_idxs, dtype=int)\n\n # alt_reps settings\n alt_reps_idxs_grp = settings_grp.create_group(ALT_REPS_IDXS)\n for alt_rep_name, idxs in self._alt_reps.items():\n alt_reps_idxs_grp.create_dataset(alt_rep_name, data=idxs, dtype=int)\n\n # if both feature shapes and dtypes were specified overwrite\n # (or initialize if not set by defaults) the defaults\n if (self._field_feature_shapes_kwarg is not None) and\\\n (self._field_feature_dtypes_kwarg is not None):\n\n self._field_feature_shapes.update(self._field_feature_shapes_kwarg)\n self._field_feature_dtypes.update(self._field_feature_dtypes_kwarg)\n\n # any sparse field with unspecified shape and dtype must be\n # set to None so that it will be set at runtime\n for sparse_field in self.sparse_fields:\n if (not sparse_field in self._field_feature_shapes) or \\\n (not sparse_field in self._field_feature_dtypes):\n self._field_feature_shapes[sparse_field] = None\n self._field_feature_dtypes[sparse_field] = None\n\n\n # save the field feature shapes and dtypes in the settings group\n shapes_grp = settings_grp.create_group(FIELD_FEATURE_SHAPES_STR)\n for field_path, field_shape in self._field_feature_shapes.items():\n if field_shape is None:\n # set it as a dimensionless array of NaN\n field_shape = np.array(np.nan)\n\n shapes_grp.create_dataset(field_path, data=field_shape)\n\n dtypes_grp = settings_grp.create_group(FIELD_FEATURE_DTYPES_STR)\n for field_path, field_dtype in self._field_feature_dtypes.items():\n if field_dtype is None:\n dt_str = NONE_STR\n else:\n # make a json string of the datatype that can be read\n # in again, we call np.dtype again because there is no\n # np.float.descr attribute\n dt_str = json.dumps(np.dtype(field_dtype).descr)\n\n dtypes_grp.create_dataset(field_path, data=dt_str)\n\n # initialize the units group\n unit_grp = self._h5.create_group(UNITS)\n\n # if units were not given set them all to None\n if self._units is None:\n self._units = {}\n for field_path in self._field_feature_shapes.keys():\n self._units[field_path] = None\n\n # set the units\n for field_path, unit_value in self._units.items():\n\n # ignore the field if not given\n if unit_value is None:\n continue\n\n unit_path = '{}/{}'.format(UNITS, field_path)\n\n unit_grp.create_dataset(unit_path, data=unit_value)\n\n\n # create the group for the run data records\n records_grp = settings_grp.create_group(RECORD_FIELDS)\n\n # create a dataset for the continuation run tuples\n # (continuation_run, base_run), where the first element\n # of the new run that is continuing the run in the second\n # position\n self._init_continuations()","def __init__(self):\n # load files\n self.init_bin_start_time = load_last_bin_time()\n self.prev_value_cache = load_prev_val_cache()\n self.init_job_vals_dict = load_job_init_vals()\n\n config = load_config()\n self.database_domain = config[ConfigFields.DATABASE_DOMAIN]\n self.metrics = config[ConfigFields.METRICS]\n self.bin_duration = config[ConfigFields.BIN_DURATION]\n\n \"to be overwritten when server time is found in a job\"\n self.current_time = int(time.time())\n self.bin_start_times = None # updated below\n self.final_bin_end_time = None # updated below\n self.update_bin_times() # updates","def __init__(self, run, configuration: dict):\n\n self.run = run\n self.raw_configuration = configuration\n self.input_definitions = self.run.analysis_version.input_definitions","def __init__(self):\n\n self.this_dir = os.path.dirname(os.path.abspath(__file__))\n self.db_path = self._discover_in_dir(self.this_dir)","def __init__(self, filenames, **kwargs):\n self.filenames = filenames\n self.meta = assign_default_kwargs(self, kwargs, self.defaults)\n # Grab logger\n self.log = get_logger(__name__)\n\n # Performance tracking\n self.errors = []\n self.uploaded = 0\n\n # Grab db using credentials\n self.log.info('Accessing Database {}'.format(self.db_name))\n engine, self.session = get_db(self.db_name, credentials=self.credentials)\n\n self.log.info('Preparing to upload {} files...'.format(len(filenames)))","def __init__(self):\n self.dataset_path = input('Enter the path to the root directory of your dataset:\\n')\n self.classes = [c.lower() for c in os.listdir(self.dataset_path)]\n self.year = str(datetime.datetime.now().year)\n self.kit_path = input(\"Enter the path ot your VOCdevkit directory:\\n\")\n self.annotation_path = self.kit_path + '/VOC' + self.year + '/Annotations'\n self.renamer = data_renamer.DataRenamer(self.dataset_path, self.year)\n self.data_splitter = data_splitter.DataSplitter(self.dataset_path, self.classes, self.year, self.kit_path)\n self.annotation_maker = annotation_maker.AnnotationMaker(self.dataset_path, self.kit_path, self.year,\n self.annotation_path)","def prepare(self):\n\t\tif len(os.listdir(self.storagedir)):\n\t\t\traise Exception(\"There are already files in\", self.storagedir + \".\", \"Delete manually!\")\n\t\t\n\t\tself.worddict = {\"__pad__\": 0, \"__oov__\": 1}\n\t\tself.classdict = {}\n\t\tself.raw_documents, self.tokenized_documents = {}, {}\n\t\tself.X, self.Y = {}, {}\n\n\t\tfor dataset in self.DATASETS_TMP:\n\t\t\tself.get_raw_data(dataset)\n\t\t\tself.delete_empty_documents(dataset)\n\t\t\tself.tokenize_documents(dataset)\n\n\t\tself.make_classdict()\n\t\tself.make_worddict()\n\t\tself.make_embeddings()\n\t\tself.reverse_dicts()\n\n\t\tfor dataset in self.DATASETS_TMP:\n\t\t\tself.make_X(dataset)\n\t\t\tself.shuffle_dataset(dataset)\n\n\t\tif not \"dev\" in self.X:\n\t\t\tself.split_dev()\t\t\n\t\tself.make_hybrid()\n\t\tself.store()","def __init__(self, the_input_shape, num_classes, path_weights, hyperparameters):\n self.the_input_shape = the_input_shape\n self.num_classes = num_classes\n self.path_weights = path_weights\n self.hyperparameters = hyperparameters","def __init__(\n self,\n dataset: Dataset,\n compiled_model_path: PathOrURI,\n modelframework: str,\n input_model_path: PathOrURI):\n self.modelframework = modelframework\n self.input_model_path = input_model_path\n self.outputtypes = [self.modelframework]\n super().__init__(dataset, compiled_model_path)","def __init__(self, base_path):\n super(FileSystemDatastore, self).__init__(base_path)","def __init__(self):\n self.args = self._prepare_args(locals())\n self.requires_full_dataset_in_memory = False","def __init__(self, doc_set):\n self.__doc_set = doc_set\n self.__preprocessor = InputPreprocessor(doc_set)","def __init__(self, filename, validate=True):\n pass","def _initialize_directory(self):\n self._generate_settings()\n if os.path.exists(self.target_path):\n sys.exit(\"WARNING: %s already exists, exiting\" % self.target_path)\n self._print_initialize_message()\n self._create_directories()\n self._create_general_config_file()\n self._create_default_pipeline_config_file()\n self._create_filelist()\n print","def __init__(self):\n _snap.TFile_swiginit(self, _snap.new_TFile())","def _generate_file_paths(self):\n for table_name in self.tables:\n logger.info(f\"Generating input and output paths for table '{table_name}'...\")\n self.input_paths[table_name] = os.path.join(self.pipeline['input_dir'], f'{table_name}.xml')\n logger.info(f\"Input path for table '{table_name}': {self.input_paths[table_name]}\")\n self.output_paths[table_name] = os.path.join(self.pipeline['output_dir'], f'{table_name}.jsonl')\n logger.info(f\"Output path for table '{table_name}': {self.output_paths[table_name]}\")\n logger.info(f\"Generated {len(self.input_paths)} input paths and {len(self.output_paths)} output paths.\")","def __init__(self):\n self.fileReader = FileReader()\n self.path = os.path.abspath(\"\")"],"string":"[\n \"def __init__(self):\\n\\n self.read_input_file()\\n self.read_simulation_files()\",\n \"def __init__(self):\\n\\t\\tself.parsed = False\\n\\t\\tdir_path = os.path.dirname(os.path.realpath(__file__))\\n\\t\\tself.xsdfilename = os.path.join(dir_path, 'xml', 'schema.xsd')\\n\\t\\tself.schema = 'schema.xsd'\\n\\t\\tself.predictors = []\\n\\t\\tself.predictors_types = []\\n\\t\\tself.preprocessing_methods = []\",\n \"def __init__(\\n self,\\n data_path: str,\\n output_path: str\\n ):\\n\\n self.data_path = data_path\\n self.output_path = output_path\",\n \"def _set_up(self):\\n # Also recreates any attributes not in _io_attrs on load from input file.\\n # See IOAble class docstring for more info.\\n self._enforce_symmetry()\\n self._sort_modes()\\n self._create_idx()\",\n \"def __init__(self, reader_schema, writer_schema=None, input_file=None):\\n\\n if writer_schema is None:\\n writer_schema = reader_schema\\n self._reader_schema = reader_schema\\n self._writer_schema = writer_schema\\n self._reader_schema_json = json.loads(str(self._reader_schema))\\n self._writer_schema_json = json.loads(str(self._writer_schema))\\n self._input_file = input_file\\n self._set_avro_readers()\",\n \"def __init__(self, inputmanager=None, outputpath=default.output_path):\\n self.__inputmanager = inputmanager\\n self.__outputpath = outputpath\",\n \"def __init__(self, data=None, filename=None, schema=None):\\n self.data = None\\n self.schema = None\\n self.filename = None\\n if schema:\\n self.load_schema(schema)\\n if filename:\\n self.load_file(filename)\\n if data:\\n self.load_data(data)\",\n \"def init(self):\\n\\n self.checkDirectory(self.output_dir,\\\"output\\\")\\n self.checkDirectory(self.working_dir,\\\"working\\\")\",\n \"def __init__(self, input_directory, save_directory):\\n self.input_directory = input_directory\\n self.save_directory = save_directory\\n self.__concatonate_files_controller()\",\n \"def __init__(self, db_location, input_location = None,\\n force_regenerate_input = False):\\n \\n if input_location == None:\\n input_location = str(Path(db_location, 'rf_input_data'))\\n \\n # Check if at least gauge.parquet, refer_x0y0.parquet and radar_x0y0.parquet\\n # are present\\n valid = True\\n if not os.path.exists(input_location):\\n valid = False\\n os.makedirs(input_location)\\n files = glob.glob(str(Path(input_location, '*')))\\n files = [os.path.basename(f) for f in files]\\n if ('gauge.parquet' not in files or 'reference_x0y0.parquet' not in files\\n or 'radar_x0y0.parquet' not in files):\\n valid = False\\n \\n self.input_location = input_location\\n self.db_location = db_location\\n \\n if not valid :\\n logging.info('Could not find valid input data from the folder {:s}'.format(input_location))\\n if force_regenerate_input or not valid:\\n logging.info('The program will now compute this input data from the database, this takes quite some time')\\n self.prepare_input()\",\n \"def __init__(self):\\n INSTALL_DIR = dirname(__file__)\\n CONFIG_DIR = '/etc/Model2WADL/'\\n logging.basicConfig(level=logging.ERROR)\\n logging.config.fileConfig([join(CONFIG_DIR, 'logging.conf'), expanduser('~/.logging.conf'), 'logging.conf'])\\n self.__log = logging.getLogger('thesis')\\n\\n self.__log.debug(\\\"Reading general configuration from Model2WADL.cfg\\\")\\n self.__m2wConfig = ConfigParser.SafeConfigParser()\\n self.__m2wConfig.read(\\n [join(CONFIG_DIR, 'Physical2Virtual.cfg'), expanduser('~/.Physical2Virtual.cfg'), 'Physical2Virtual.cfg'])\\n\\n self.__baseURI = self.__m2wConfig.get(\\\"Config\\\", \\\"baseURI\\\")\\n self.__basePackage = self.__m2wConfig.get(\\\"Config\\\", \\\"basePackage\\\")\\n self.__schemaFile = self.__m2wConfig.get(\\\"Config\\\", \\\"schemaFile\\\")\\n self.__model = None\\n self.__input = None\\n self.__output = None\",\n \"def __init__(self):\\n\\n self.root_path = os.path.dirname(os.path.abspath(__file__))[:-5]\\n self.config_path = os.path.join(self.root_path, \\\"files\\\\\\\\CONFIG.txt\\\")\\n self.metrics_path = os.path.join(self.root_path, \\\"files\\\\\\\\metrics.txt\\\")\\n\\n self.setup_metrics_file()\\n\\n if self.check_configuration() is False:\\n self.setup_configuration_file()\",\n \"def __init__(self, schema_name, schema_path):\\n self.__schema_name = schema_name\\n self.__schema_path = schema_path\\n\\n self.__test_set_list = [] # Creates an empty list of test sets\\n\\n self.__validate_and_compile()\",\n \"def __init__(self):\\n\\n self.current_path = os.getcwd()\\n self.data_path = self.current_path + \\\"/data\\\"\\n\\n self.original_files = {}\\n self.imitation_files = {}\\n self.original_test_files = {}\\n self.imitation_test_files = {}\\n\\n self.training_set = None\\n self.original_test_set = None\\n self.imitation_test_set = None\\n\\n self.accuracy = 0.\\n self.threshold = 0.\\n\\n self.get_files()\",\n \"def initialize_options(self):\\n self.input_dir = getcwd()\\n self.output_dir = path.join(getcwd(), 'dependency', 'static', 'apidocs')\",\n \"def __init__(self, data, schema_file=None):\\n if schema_file is not None:\\n if not os.path.exists(schema_file):\\n logging.warning(\\n 'Configuration schema file could not be found. Please check the path and try again.'\\n )\\n raise Exception()\\n\\n self.schema_file = schema_file\\n else:\\n self.schema_file = None\\n\\n if not os.path.exists(data):\\n logging.warning(\\n 'Configuration data could not be found. Please check the path and try again.'\\n )\\n raise Exception()\\n\\n self.datafile = data\\n\\n self.data = None\",\n \"def init(self) -> None:\\n self.faithful = self.is_faithful()\\n if not self.faithful:\\n # We retrieve a list of each element into the directory_path\\n files_into_directory = list_files_into_directory(self.directory_path)\\n if files_into_directory.__len__() == 0:\\n self.create_required_structure()\\n self.faithful = True\\n else:\\n raise AttributeError(\\\"The directory seems improper but not empty\\\")\\n self.class_names = self.find_class_names()\\n self.training_image_names = self.find_training_image_names()\\n self.extracted_features = self.find_features_already_extracted()\\n if len(self.extracted_features) != 0:\\n self.selected_features = set(self.extracted_features)\\n self.generate_csv_dictionary()\",\n \"def __init__(self, **kwargs):\\n cls = self.__class__\\n\\n # Initialize all configurables and input arguments\\n for arg in cls.configurables():\\n try: # Read from class constructor\\n setattr(self, arg, kwargs[arg])\\n except KeyError:\\n try: # Set from default value defined in class\\n default_value = getattr(self, arg).kwargs[\\\"default\\\"]\\n setattr(self, arg, default_value)\\n except KeyError: # if nothing is provided, fallbakcs to None\\n setattr(self, arg, None)\\n\\n self.input_arguments = None\\n if cls.input_configurables():\\n self.input_arguments = [\\n getattr(self, arg) for arg in cls.input_configurables()\\n ]\\n\\n self.json_config = cfg.JsonConfig(self.config)\\n self.output_objects = []\\n self.file = None\",\n \"def __init__(self, output_dir):\\n self.output_dir = os.path.abspath(output_dir)\\n # Create the file if it doesn't already exist\\n os.makedirs(self.output_dir, exist_ok=True)\\n self.f = None\\n self.data = None\\n self.L = None\",\n \"def __init__(self, input_files):\\n\\n self._input_files = input_files\\n self._cached_workspace_names = {} # {ID: Name}\",\n \"def __init__(self, synapse_df):\\n self._file_df = synapse_df[synapse_df.type == \\\"file\\\"]\\n self._folder_df = synapse_df[synapse_df.type == \\\"folder\\\"]\\n self._init_file_types()\\n self._identify_archive_folders()\\n self._walk_files()\",\n \"def setUp(self, path, structure_file, input_file):\\n database.clean()\\n self.path = path\\n self.structure_file = path + structure_file\\n self.input_file = path + input_file\\n\\n string_processor.project = Project()\\n\\n self.input_project = Project()\\n self.input_project.document_files.append(\\n DocumentFile(path=self.input_file))\\n self.input_project.save()\\n\\n with open(self.structure_file) as f:\\n self.json = json.load(f)\\n\\n self.xml = etree.parse(self.input_file)\\n self.extractor = StructureExtractor(string_processor,\\n self.structure_file)\",\n \"def __init__(self,\\n output_type=None,\\n gzip_filehandle=None,\\n gzip_filehandle_parent=None,\\n schema=None):\\n self.output_type = output_type\\n self.gzip_filehandle = gzip_filehandle\\n self.schema = schema\\n self.gzip_filehandle_parent = gzip_filehandle_parent\",\n \"def __init__(self, \\n save_data_folder: str,\\n reader:FileReader = None,\\n input_file:str = None,\\n *args, **kwargs):\\n \\n if reader:\\n self.files, self.attr_names = reader.read_file(input_file, *args, **kwargs)\\n \\n self.save_data_folder = Path(save_data_folder)\\n self.save_data_folder.mkdir(parents=True, exist_ok=True)\\n BaseProcess.set_logger('generator.log')\",\n \"def __init__(self, path, input_type='f'):\\n if input_type == 'f':\\n file = open(path, 'r')\\n elif input_type == 's':\\n file = path\\n else:\\n raise exceptions.BadInputError(f\\\"invalid input type {input_type}\\\")\\n\\n pdl = yaml.safe_load(file)\\n\\n self.type_checks = {\\n 'typedef': self.validate_typedef,\\n 'component': self.validate_component,\\n 'graph': self.validate_graph,\\n }\\n\\n self.imports = []\\n if 'import' in pdl:\\n self.imports = pdl['import']\\n\\n self.namespace = pdl['name']\\n self.body = pdl['body']\\n self.typedefs = {}\\n self.components = []\\n self.graphs = []\\n self.validate()\",\n \"def initialize(self):\\n self.initialize_edges()\\n self.initialize_prob()\\n self.initialize_total_input_dict()\\n\\n self.initialize_fpmusigv_dict()\",\n \"def __init__(self, targetDir, model):\\n \\n self.categoryFolder = targetDir\\n self.model = model\\n self.inputsFolder = os.path.join(targetDir, \\\"Inputs\\\")\",\n \"def setup_class(cls):\\n self = cls()\\n self.remove_files_created_during_previous_runs()\\n if not os.path.exists(self.plaintext_directory):\\n os.makedirs(self.plaintext_directory)\\n\\n if not os.path.exists(self.training_path):\\n os.makedirs(self.training_path)\\n\\n if not os.path.exists(self.heldout_path):\\n os.makedirs(self.heldout_path)\\n\\n prepare_data(self.paths)\",\n \"def __init__(self, input_filename='input.txt', output_filename='output.txt'):\\n self._input = input_filename\\n self._output = output_filename\\n self._fin = open(self._input, 'r')\\n self._fout = open(self._output, 'w')\",\n \"def init(self):\\n\\t\\tfrom splat_to_db import splat_to_db\\n\\t\\tfrom visualize.clustering_test import clustering_test\\n\\t\\tfrom codense.codense2db import codense2db\\n\\t\\tself.splat_to_db_instance = splat_to_db()\\n\\t\\tself.clustering_test_instance = clustering_test()\\n\\t\\tself.codense2db_instance = codense2db()\\n\\t\\t\\n\\t\\tif not os.path.isdir(self.dir_files):\\n\\t\\t\\tos.makedirs(self.dir_files)\\n\\t\\telse:\\n\\t\\t\\tsys.stderr.write(\\\"Warning, directory %s already exists.\\\\n\\\"%(self.dir_files))\\n\\t\\tself.tmpinfname = os.path.join(self.dir_files, 'input')\\n\\t\\tself.tmpoutfname = os.path.join(self.dir_files, 'output')\\n\\t\\t\\n\\t\\tself.crack_dict = {1: crack_by_modes(self.debug),\\n\\t\\t\\t2:crack_by_splat(self.debug)}\\n\\t\\tself.argument1_dict = {1: self.clustering_test_instance,\\n\\t\\t\\t2: self.splat_to_db_instance}\\n\\t\\t\\n\\t\\t#two descending tables\\n\\t\\tself.splat_table = '%ss'%self.table\\n\\t\\tself.mcl_table = self.splat_table.replace('splat','mcl')\\n\\t\\tif self.mcl_table == self.splat_table:\\n\\t\\t\\tsys.stderr.write(\\\"Error: new splat and mcl tables have the same name, %s\\\\n\\\"%self.splat_table)\\n\\t\\t\\tsys.exit(2)\",\n \"def __init__(self):\\n self.data_set_loc = conf.config_section_mapper(\\\"filePath\\\").get(\\\"data_set_loc\\\")\\n self.data_extractor = DataExtractor(self.data_set_loc)\",\n \"def __init__(self):\\n self.config = configs.Configuration()\\n self.log = logger.CustomLogger(__name__).get_logger()\\n self.output_dir = self.config.getConfigValue('OUTPUT_DIR')\\n self.s3_directory = self.config.getConfigValue('S3_FILE_PATH_TRANSFORM')\",\n \"def initialize(self) -> None:\\n self.model = load(self.path)\",\n \"def __init__(self):\\n self.parser_model_dir = None\\n self.parser_options = {}\\n self.reranker_model = None\\n self.unified_model_dir = None\",\n \"def __init__(self, **kwargs):\\n super().__init__(**kwargs)\\n\\n if not all([hasattr(self, arg) for arg in PREPARE_ARGS]):\\n # this should never happened due to object creation with parse_args in sumo/__init__.py\\n raise AttributeError(\\\"Cannot create SumoPrepare object, missing constructor arguments\\\")\\n\\n # check positional arguments\\n if not all([os.path.exists(fname) for fname in self.infiles]):\\n raise FileNotFoundError(\\\"Input file not found\\\")\\n\\n self.ftypes = []\\n for fname in self.infiles:\\n suff = pathlib.Path(fname).suffix if pathlib.Path(fname).suffix not in ['.gz', '.bz2'] else ''.join(\\n pathlib.Path(fname).suffixes[-2:])\\n if suff not in SUPPORTED_EXT:\\n raise ValueError(\\\"Unrecognized input file type '{}'\\\".format(suff))\\n self.ftypes.append(suff if suff != \\\"\\\" else \\\".txt\\\")\\n\\n if not all([method in SIMILARITY_METHODS for method in self.method]):\\n raise ValueError(\\\"Unrecognized similarity method\\\")\\n\\n self.plot_base = None\\n if self.plot:\\n basename = os.path.basename(self.plot)\\n dirname = os.path.dirname(self.plot)\\n self.plot_base = os.path.join(dirname, basename.split('.png')[0])\\n\\n self.logger = setup_logger(\\\"main\\\", self.log, self.logfile)\",\n \"def __init__(self, inputs, resources, port, logger):\\n self.inputs = inputs\\n self.port = port\\n self.logger = logger\\n self.resources = resources\\n self.fileserver_path = 'File-Server'\\n self.blueprint = 'test-start-fileserver-vm-blueprint.yaml'\\n self.blueprint_path = os.path.join(os.path.dirname(__file__),\\n 'resources', self.blueprint)\\n self.server_cmd = 'python -m SimpleHTTPServer {0}'.format(self.port)\\n self.fs_base_url = None\\n self.local_env = None\\n self.processed_inputs = None\\n self.fab_env_conf = {}\",\n \"def __init__(self, output_file, table_model):\\n pass\",\n \"def __init__(self, source_path, output_path):\\n self.storage_type = 's3' if 's3' in source_path else 'local'\\n self.source_path = source_path\\n self.output_path = output_path\",\n \"def _initialize(self) -> None:\\n p = self.params\\n # We make self.input public so that users can access its methods like\\n # IdsToStrings if needed.\\n with py_utils.infeed_context_scope(\\n infeed_host_index=p.infeed_host_index,\\n num_infeed_hosts=p.num_infeed_hosts):\\n self.input = p.input.Instantiate()\\n\\n if hasattr(self.input, 'datasource') and isinstance(\\n self.input.datasource, datasource.TFDatasetSource):\\n # For the special case when the input is implemented by a tf.data.Dataset,\\n # use it directly. Otherwise roundtrip adaptions may result in returning\\n # duplciate batches.\\n self._get_next_fn = self.input.datasource.GetNext\\n else:\\n self._get_next_fn = tf.function(self._get_batch)\\n self._num_batches_produced = 0\",\n \"def __init__(self):\\n self.model_description: Dict[str, Any] = get_model_description()\\n self.model_name: str = self.model_description['name']\\n self.model_version: str = self.model_description['version']\\n\\n # Make sure we do not have a trailing slash to muck up processing later.\\n self.event_dir: Optional[str] = None\\n self.zone_name: Optional[str] = None\\n self.fault_time: Optional[str] = None\\n\\n self.example: Example = None\\n self.validator: ExampleValidator = ExampleValidator()\\n self.common_features_df: pd.DataFrame = None\\n\\n self.cavity_onnx_session: rt.InferenceSession = rt.InferenceSession(os.path.join(os.path.dirname(__file__),\\n 'model_files',\\n 'cavity_model.onnx'))\\n self.fault_onnx_session: rt.InferenceSession = rt.InferenceSession(os.path.join(os.path.dirname(__file__),\\n 'model_files',\\n 'fault_model.onnx'))\",\n \"def __init__(self, writer_schema, output_file=None, skip_validate=True):\\n self._writer_schema = writer_schema\\n self._writer_schema_json = json.loads(str(self._writer_schema))\\n self._output_file = output_file\\n self._set_avro_writers()\",\n \"def __init__(self, output_path, input_path, config_dir, labels):\\n self.output_path = output_path\\n self.input_path = input_path\\n self.config_dir = config_dir\\n self.engine_name = labels['engine_name']\\n self.algorithm_name = labels['algorithm_name']\\n self.file_suffix = labels['file_suffix']\\n # the graph type set as the name of the class\\n self.graph_type = \\\"base\\\"\",\n \"def __init__(__self__, *,\\n content_type: pulumi.Input[Union[str, 'FileImportContentType']],\\n import_file: pulumi.Input['FileMetadataArgs'],\\n ingestion_mode: pulumi.Input[Union[str, 'IngestionMode']],\\n resource_group_name: pulumi.Input[str],\\n source: pulumi.Input[str],\\n workspace_name: pulumi.Input[str],\\n file_import_id: Optional[pulumi.Input[str]] = None):\\n pulumi.set(__self__, \\\"content_type\\\", content_type)\\n pulumi.set(__self__, \\\"import_file\\\", import_file)\\n pulumi.set(__self__, \\\"ingestion_mode\\\", ingestion_mode)\\n pulumi.set(__self__, \\\"resource_group_name\\\", resource_group_name)\\n pulumi.set(__self__, \\\"source\\\", source)\\n pulumi.set(__self__, \\\"workspace_name\\\", workspace_name)\\n if file_import_id is not None:\\n pulumi.set(__self__, \\\"file_import_id\\\", file_import_id)\",\n \"def setup_structure(self):\\n seekpath_result = seekpath_structure(self.inputs.structure)\\n self.ctx.structure_initial_primitive = seekpath_result['primitive_structure']\",\n \"def __init__(self):\\n self.filepath = os.path.dirname(__file__)\\n self.filepath = os.path.join(self.filepath, \\\"Datenbank\\\")\\n self.filepath_render_database = os.path.join(self.filepath, self.render_database)\\n self.filepath_object_database = os.path.join(self.filepath, self.object_database)\\n self.filepath_output_database = os.path.join(self.filepath, self.output_database)\\n self.create_database()\",\n \"def __init__(self, root_path):\\n self.root_path = root_path\\n # Immutable data entries\\n self.uid = None\\n self.commit_sha = None\\n self.timestamp = None\\n self.filename = None\\n self.comment = None\\n # Mutable data entries\\n self.train_data = {} # anything produced during training, epoch by epoch\\n self.val_data = {} # results of intermediate tests during training\\n self.test_data = {} # results of a test\\n self.model_files = [] # saved model parameters\\n self.custom_data = {} # whatever the user might like to save\\n # Load everything from the data file\\n if not self._create_flag:\\n self.deserialize()\",\n \"def __init__(self, args):\\n\\n self.directory = args.directory\\n \\n # generate string with only base data directory\\n dirstring = os.path.basename(os.path.normpath(self.directory))\\n self.string_directory = \\\"Data: {}\\\".format(dirstring) \\n\\n # get location of this file\\n self.filepath = os.path.dirname(os.path.realpath(__file__))\\n\\n # initialize attributes to hold files and (inferEM, sample) dirs\\n self.files = []\\n self.betas = []\\n self.compare_segs_dict = {}\\n self.sample_dirs = {}\\n self.prob_dirs = {}\\n self.full_dataset = None\\n self.full_dataset_tuple = None\",\n \"def __init__(self, *args):\\n _snap.Schema_swiginit(self, _snap.new_Schema(*args))\",\n \"def initialize(self):\\n try:\\n os.makedirs('./output/' + self.output)\\n except:\\n pass\\n with open(self.trajectory_file, 'w') as f:\\n f.write('MODEL 0\\\\n')\\n for atom in self.atoms:\\n f.write(atom.to_pdb_line() + '\\\\n')\\n f.write('TER\\\\n')\\n potential_energy = 0\\n kinetic_energy = 0\\n for atom in self.atoms:\\n potential_energy += atom.potential_energy\\n kinetic_energy += atom.get_kinetic_energy()\\n with open(self.kinetic_energy_file, 'w') as f:\\n f.write('0,' + str(kinetic_energy) + '\\\\n')\\n with open(self.potential_energy_file, 'w') as f:\\n f.write('0,' + str(potential_energy) + '\\\\n')\",\n \"def __init__(self, schema=None):\\n self.schema = schema or {}\",\n \"def __init__(self,\\n config_file=None,\\n input_path=None,\\n output_format=None,\\n input_format=None,\\n context='',\\n ):\\n\\n absolute_path = path.join(context, config_file)\\n import_dir_path = path.dirname(absolute_path)\\n import_filename = path.basename(absolute_path)\\n\\n root_configs = {\\n 'imports': [\\n import_filename\\n ],\\n 'parameters': {\\n 'input': {},\\n 'output': {}\\n },\\n }\\n\\n if input_path is not None:\\n root_configs['parameters']['input']['path'] = input_path\\n\\n if input_format is not None:\\n root_configs['parameters']['input']['format'] = input_format\\n\\n if output_format is not None:\\n root_configs['parameters']['output']['format'] = output_format\\n\\n self.configs = Config(import_dir_path, configs=root_configs)\\n\\n try:\\n self.input_dir_path = path.join(context, self.params('input', 'path'))\\n except Exception as e:\\n Logger.debug(e)\\n Logger.error('inp_path_missing')\\n\\n try:\\n self.configs.get('parameters', 'output', 'path')\\n except Exception as e:\\n # setting the default output folder\\n # this could be generalized and encapsulated as a setter on the config class\\n self.configs.configs['parameters']['output']['path'] = 'output'\\n self.configs.config_paths['parameters']['output']['path'] = context\\n\\n reader = self.params('input', 'format')\\n if not is_str(reader) and issubclass(reader, Reader):\\n self.module_loader.set_reader(reader)\\n else:\\n try:\\n self.module_loader.load_reader(reader)\\n Logger.info('input_format_detected', self.params('input', 'format'))\\n except KeyError as e:\\n Logger.debug(e)\\n Logger.error('inp_format_missing')\\n except Exception as e:\\n Logger.debug(e)\\n Logger.error('bad_inp_format', self.params('input', 'format'), str(e))\\n\\n writer = self.params('output', 'format')\\n if not is_str(writer) and issubclass(writer, Writer):\\n self.module_loader.set_writer(writer)\\n else:\\n try:\\n self.module_loader.load_writer(writer)\\n Logger.info('output_format_detected', self.params('output', 'format'))\\n except KeyError as e:\\n Logger.debug(e)\\n Logger.error('out_format_missing')\\n except Exception as e:\\n Logger.debug(e)\\n Logger.error('bad_out_format', self.params('output', 'format'), str(e))\",\n \"def __init__(__self__, *,\\n creation_time: Optional[pulumi.Input['TimestampArgs']] = None,\\n output: Optional[pulumi.Input['FileReferenceArgs']] = None,\\n test_case: Optional[pulumi.Input['TestCaseReferenceArgs']] = None):\\n if creation_time is not None:\\n pulumi.set(__self__, \\\"creation_time\\\", creation_time)\\n if output is not None:\\n pulumi.set(__self__, \\\"output\\\", output)\\n if test_case is not None:\\n pulumi.set(__self__, \\\"test_case\\\", test_case)\",\n \"def __init__(self, file_name: Optional[str] = None):\\n self.entries = OrderedDict() # Dict[str, PathElement]\\n self.file_name = file_name # input file for logging\\n self.jsonf = None # json image of input file\\n self.namespaces = Namespaces('http://hl7.org/fhir/StructureDefinition/')\\n self.path_map = {} # map from path to name (Dict[str, str])\",\n \"def _call_initialization(self,\\r\\n input_fp,\\r\\n output_dir,\\r\\n params,\\r\\n job_prefix,\\r\\n poll_directly,\\r\\n suppress_submit_jobs):\\r\\n pass\",\n \"def __init__(self):\\n self.params = {}\\n self.counts = 0\\n self.failed = 0\\n self.path = os.path.join(os.getcwd(), OUTPUT_DIR)\\n \\n if not os.path.exists(self.path):\\n os.makedirs(self.path)\",\n \"def __init__(self):\\n super().__init__()\\n self.indices_dir = ''\\n self.split_file = ''\\n\\n self.model = '' # string identifying the model\\n self.experiment = '' # string to describe experiment\\n self.maps = [data.ID_MAP_T1H2O, data.ID_MAP_FF, data.ID_MAP_B1] # the used maps\\n self.patch_size = [1, 32, 32]\\n\\n # training configuration\\n self.loss = 'mse' # string identifying the loss function (huber, mse or mae)\\n self.learning_rate = 0.01 # the learning rate\\n self.dropout_p = 0.2\\n self.norm = 'bn' # none, bn\\n\\n # we use the mean absolute error as best model score\\n self.best_model_score_is_positive = True\\n self.best_model_score_name = 'mae'\",\n \"def path_defs(self):\\n if self.input_xml is not None:\\n self.input_xml = os.path.abspath(os.path.expandvars(self.input_xml))\\n if self.pointing_file is not None:\\n self.pointing_file = os.path.abspath(os.path.expandvars(self.pointing_file))\\n self.output_dir = os.path.abspath(os.path.expandvars(self.output_dir))\\n self.simdata_output_dir = os.path.abspath(os.path.expandvars(self.simdata_output_dir))\\n if self.table_file is not None:\\n self.table_file = os.path.abspath(os.path.expandvars(self.table_file))\\n\\n ensure_dir_exists(self.output_dir)\\n ensure_dir_exists(self.simdata_output_dir)\\n\\n if self.observation_list_file is not None:\\n self.observation_list_file = os.path.abspath(os.path.expandvars(self.observation_list_file))\",\n \"def __init__(self):\\n self.read_input()\\n self.update_binaries()\",\n \"def __init__(self):\\n self._names = []\\n self._forwardFactories = []\\n self._inputs = []\\n self._inputFilters = {}\\n self._outputFilters = []\\n self._inputCheckers = []\\n pass\",\n \"def __init__(self, input_file=None, output_file=None, gt_file=None):\\n\\n self.input_file = input_file\\n self.output_file = output_file\\n self.gt_file = gt_file\\n\\n #print('Created object: ', musicxml_file)\",\n \"def __init__(self, filename: str | Path, *args, **kwargs) -> None:\\n super().__init__(filename, *args, **kwargs)\\n self._non_metadata_keys = None\\n self._score_key = None\\n self._rt_key = None\\n self._spectrum_rt_key = None\\n self._qvalue_key = None\\n self._pep_key = None\\n\\n self._source = self._infer_source()\",\n \"def initialize(self):\\n\\n if not os.path.exists(CONFIG_Preprocess):\\n with io.open(CONFIG_Preprocess, 'w') as fh:\\n json.dump(DEFAULT_Preprocess, fh, sort_keys=True, indent=4, separators=(\\\",\\\", \\\": \\\"))\\n return()\\n\\n # Load all options.\\n with io.open(CONFIG_Preprocess, 'r') as fh:\\n data = json.load(fh)\\n \\n for key, value in data.items():\\n setattr(self, key, value)\\n \\n # Make sure freqs. for downsampling are in descending order.\\n self.Fs_new.sort() # Now in ascending order\\n self.Fs_new=self.Fs_new[::-1] # Now in descending order\",\n \"def __init__(self, **kwargs):\\n super(Converter, self).__init__()\\n self._specfile = kwargs.get(\\\"specfile\\\", None)\\n self._parsed = False\\n self._columns = []\\n self._offsets = []\\n self._fixed_with_encoding = Encodings.default_fixedwidth_enc()\\n self._included_header = False\\n self._delimited_encoding = Encodings.default_delimited_enc()\\n self.encoder_spec()\",\n \"def __init__(self, data_file_paths, targets, transform=None):\\n \\n self.transform = transform\\n self.data_file_paths = data_file_paths\\n self.targets = targets\\n return\",\n \"def __init__(self, path: str = './') -> None:\\n super(DataHandler, self).__init__()\\n self.path = path\\n self.dataset = None # type: str\\n self._file_format = None # type: str\\n self._max_file_count = 0\",\n \"def __init__(self, input_filename, config):\\n\\t\\tself.input_filename = input_filename\\n\\t\\tself.db = mysql.connect(config['mysql_host'], config['mysql_user'], config['mysql_pass'], config['mysql_db'])\\n\\t\\tself.cursor = self.db.cursor()\\n\\t\\t\\\"\\\"\\\"These are foreign keys that link the reports to their metadata and policy information\\\"\\\"\\\"\\n\\t\\tself.metadata_fk = \\\"\\\"\\n\\t\\tself.policy_fk = \\\"\\\"\",\n \"def __init__(self):\\n self.out = None\\n self.in_shape = None\\n self.work_shape = None\",\n \"def __init__(self, language, datasource, source_file, source_path,\\n target_path, pipeline_config, shuffle_file):\\n self.language = language\\n self.datasource = datasource\\n self.source_file = source_file\\n self.source_path = source_path\\n self.target_path = target_path\\n self.location = target_path\\n self.pipeline_config = pipeline_config\\n self.shuffle_file = shuffle_file\\n self.data_path = os.path.join(self.target_path, 'data')\\n self.conf_path = os.path.join(self.target_path, 'config')\\n self.file_list = os.path.join(self.conf_path, FNAME_FILELIST)\\n if self.source_file is not None or self.source_path is not None:\\n self._initialize_directory()\",\n \"def __init__(self):\\n self.model = {'mol':[], 'nmol':0}\\n self.template = {} \\n self.config = {}\\n self.config['tfile'] = 'gau-template-bsse.gjf'\\n self.config['xyzfile'] = 'model.xyz'\\n self.config['jobfile'] = 'gau.gjf'\\n self.config['job_prefix'] = self.config['jobfile'].split(\\\".\\\")[0]\\n self.config['incr'] = 1\\n \\n self.rd_cmd_stream()\\n return\",\n \"def initialize(self, args):\\n # You must parse model_config. JSON string is not parsed here\\n self.model_config = json.loads(args['model_config'])\\n print(\\\"model_config:\\\", self.model_config)\\n\\n self.input_names = []\\n for input_config in self.model_config[\\\"input\\\"]:\\n self.input_names.append(input_config[\\\"name\\\"])\\n print(\\\"postprocess input names:\\\", self.input_names)\\n\\n self.output_names = []\\n self.output_dtype = []\\n for output_config in self.model_config[\\\"output\\\"]:\\n self.output_names.append(output_config[\\\"name\\\"])\\n dtype = pb_utils.triton_string_to_numpy(output_config[\\\"data_type\\\"])\\n self.output_dtype.append(dtype)\\n print(\\\"postprocess output names:\\\", self.output_names)\\n self.postprocessor = fd.vision.ocr.DBDetectorPostprocessor()\\n self.cls_preprocessor = fd.vision.ocr.ClassifierPreprocessor()\\n self.rec_preprocessor = fd.vision.ocr.RecognizerPreprocessor()\\n self.cls_threshold = 0.9\",\n \"def setup(self):\\n self.bqSession.update_mex('Initializing...')\\n self.mex_parameter_parser(self.bqSession.mex.xmltree)\\n self.output_file = None\",\n \"def __init__(self, the_input_shape, num_classes, path_weights):\\n self.the_input_shape = the_input_shape\\n self.num_classes = num_classes\\n self.path_weights = path_weights\",\n \"def __init__(self, path: str) -> None:\\n self.path = path\\n\\n self.path_token_idx_corpus = os.path.join(\\n path, 'processed_corpus/token_idx_corpus.txt')\\n self.path_token_terms_idxs = os.path.join(\\n path, 'processed_corpus/token_terms_idxs.txt')\\n self.path_tf_tokens = os.path.join(\\n path, 'frequencies/tf_tokens.json')\\n self.path_df_tokens = os.path.join(\\n path, 'frequencies/df_tokens.json')\\n self.path_tfidf_tokens = os.path.join(\\n path, 'frequencies/tfidf_tokens.json')\\n\\n self.path_lemma_idx_corpus = os.path.join(\\n path, 'processed_corpus/lemma_idx_corpus.txt')\\n self.path_lemma_terms_idxs = os.path.join(\\n path, 'processed_corpus/lemma_terms_idxs.txt')\\n self.path_tf_lemmas = os.path.join(\\n path, 'frequencies/tf_lemmas.json')\\n self.path_df_lemmas = os.path.join(\\n path, 'frequencies/df_lemmas.json')\\n self.path_tfidf_lemmas = os.path.join(\\n path, 'frequencies/tfidf_lemmas.json')\\n\\n self.path_dl = os.path.join(\\n path, 'frequencies/dl.json')\\n # self.path_token_contains = os.path.join(\\n # path, 'processed_corpus/token_contains.json')\\n # self.path_lemma_contains = os.path.join(\\n # path, 'processed_corpus/lemma_contains.json')\\n\\n self._file_write_threshhold = 100\\n # self._num_docs = 1000\\n self._docs_processed = 0\\n # super().__init__(path, path, max_docs)\",\n \"def initialize(self):\\n LOG.info(\\\"Initializing Model.\\\")\\n self.model = self.convert(df=self.training_df)\\n if self.bootstraps is not None:\\n LOG.info(\\\"Bootstrapping Data.\\\")\\n self.bootstrap_data()\",\n \"def __init__(self):\\n # Read configuration into dictionary\\n self.directories = general.config_directories()\\n self.config = general.read_yaml_files(self.directories)\",\n \"def __init__(self, model_filename, sim_filename, include_paths = None):\\n\\n self.model_filename = model_filename\\n self.sim_filename = sim_filename\\n self.include_paths = include_paths\\n \\n self.simulation = None\\n self.fit_input = None\",\n \"def __init__(self, inputfolder, gpu_id=0):\\n if gpu_id < 0:\\n caffe.set_mode_cpu()\\n else:\\n caffe.set_mode_gpu()\\n #caffe.set_device(gpu_id)\\n inputfolder = realpath(inputfolder)\\n self.protofile, self.caffemodel = self._findFiles(inputfolder)\\n self.mean = [128.,128.,128.]\",\n \"def __init__(self):\\n super().__init__()\\n self.data_set_loc = conf.config_section_mapper(\\\"filePath\\\").get(\\\"data_set_loc\\\")\\n self.data_extractor = DataExtractor(self.data_set_loc)\\n self.sim_act_diff_mov_tf = SimilarActorsFromDiffMovies()\",\n \"def __init__(self):\\n super().__init__()\\n self.data_set_loc = conf.config_section_mapper(\\\"filePath\\\").get(\\\"data_set_loc\\\")\\n self.data_extractor = DataExtractor(self.data_set_loc)\\n self.sim_act_diff_mov_tf = SimilarActorsFromDiffMovies()\",\n \"def setUp(cls):\\n arkane = Arkane()\\n cls.job_list = arkane.load_input_file(os.path.join(os.path.dirname(os.path.abspath(__file__)),\\n 'data', 'Benzyl', 'input.py'))\",\n \"def __init__(self, input_file_name, ast_root_node):\\n\\n self.input_file_name = input_file_name\\n self.ast_root_node = ast_root_node\\n self.object_file = ObjectFile(self.input_file_name)\\n self.assembler = X64Assembler()\",\n \"def __init__(self):\\n super().__init__()\\n self.index_dir = self.base_dir + \\\"user/\\\"\\n self.index_schema = self.__get_index_schema()\\n if not os.path.exists(self.index_dir):\\n os.makedirs(self.index_dir)\\n self.indexer = index.create_in(self.index_dir, self.index_schema) # creates the index\\n else:\\n self.indexer = index.open_dir(self.index_dir) # opens the index if it already exists\",\n \"def __init__(__self__,\\n resource_name: str,\\n opts: Optional[pulumi.ResourceOptions] = None,\\n content_type: Optional[pulumi.Input[Union[str, 'FileImportContentType']]] = None,\\n file_import_id: Optional[pulumi.Input[str]] = None,\\n import_file: Optional[pulumi.Input[pulumi.InputType['FileMetadataArgs']]] = None,\\n ingestion_mode: Optional[pulumi.Input[Union[str, 'IngestionMode']]] = None,\\n resource_group_name: Optional[pulumi.Input[str]] = None,\\n source: Optional[pulumi.Input[str]] = None,\\n workspace_name: Optional[pulumi.Input[str]] = None,\\n __props__=None):\\n ...\",\n \"def _create_init(self):\\n\\n assert self._topology is not None, \\\\\\n \\\"Topology must be given for a creation constructor\\\"\\n\\n # initialize the runs group\\n runs_grp = self._h5.create_group(RUNS)\\n\\n # initialize the settings group\\n settings_grp = self._h5.create_group(SETTINGS)\\n\\n # create the topology dataset\\n self._h5.create_dataset(TOPOLOGY, data=self._topology)\\n\\n # sparse fields\\n if self._sparse_fields is not None:\\n\\n # make a dataset for the sparse fields allowed. this requires\\n # a 'special' datatype for variable length strings. This is\\n # supported by HDF5 but not numpy.\\n vlen_str_dt = h5py.special_dtype(vlen=str)\\n\\n # create the dataset with empty values for the length of the\\n # sparse fields given\\n sparse_fields_ds = settings_grp.create_dataset(SPARSE_FIELDS,\\n (len(self._sparse_fields),),\\n dtype=vlen_str_dt,\\n maxshape=(None,))\\n\\n # set the flags\\n for i, sparse_field in enumerate(self._sparse_fields):\\n sparse_fields_ds[i] = sparse_field\\n\\n\\n # field feature shapes and dtypes\\n\\n # initialize to the defaults, this gives values to\\n # self._n_coords, and self.field_feature_dtypes, and\\n # self.field_feature_shapes\\n self._set_default_init_field_attributes(n_dims=self._n_dims)\\n\\n # save the number of dimensions and number of atoms in settings\\n settings_grp.create_dataset(N_DIMS_STR, data=np.array(self._n_dims))\\n settings_grp.create_dataset(N_ATOMS, data=np.array(self._n_coords))\\n\\n # the main rep atom idxs\\n settings_grp.create_dataset(MAIN_REP_IDXS, data=self._main_rep_idxs, dtype=int)\\n\\n # alt_reps settings\\n alt_reps_idxs_grp = settings_grp.create_group(ALT_REPS_IDXS)\\n for alt_rep_name, idxs in self._alt_reps.items():\\n alt_reps_idxs_grp.create_dataset(alt_rep_name, data=idxs, dtype=int)\\n\\n # if both feature shapes and dtypes were specified overwrite\\n # (or initialize if not set by defaults) the defaults\\n if (self._field_feature_shapes_kwarg is not None) and\\\\\\n (self._field_feature_dtypes_kwarg is not None):\\n\\n self._field_feature_shapes.update(self._field_feature_shapes_kwarg)\\n self._field_feature_dtypes.update(self._field_feature_dtypes_kwarg)\\n\\n # any sparse field with unspecified shape and dtype must be\\n # set to None so that it will be set at runtime\\n for sparse_field in self.sparse_fields:\\n if (not sparse_field in self._field_feature_shapes) or \\\\\\n (not sparse_field in self._field_feature_dtypes):\\n self._field_feature_shapes[sparse_field] = None\\n self._field_feature_dtypes[sparse_field] = None\\n\\n\\n # save the field feature shapes and dtypes in the settings group\\n shapes_grp = settings_grp.create_group(FIELD_FEATURE_SHAPES_STR)\\n for field_path, field_shape in self._field_feature_shapes.items():\\n if field_shape is None:\\n # set it as a dimensionless array of NaN\\n field_shape = np.array(np.nan)\\n\\n shapes_grp.create_dataset(field_path, data=field_shape)\\n\\n dtypes_grp = settings_grp.create_group(FIELD_FEATURE_DTYPES_STR)\\n for field_path, field_dtype in self._field_feature_dtypes.items():\\n if field_dtype is None:\\n dt_str = NONE_STR\\n else:\\n # make a json string of the datatype that can be read\\n # in again, we call np.dtype again because there is no\\n # np.float.descr attribute\\n dt_str = json.dumps(np.dtype(field_dtype).descr)\\n\\n dtypes_grp.create_dataset(field_path, data=dt_str)\\n\\n # initialize the units group\\n unit_grp = self._h5.create_group(UNITS)\\n\\n # if units were not given set them all to None\\n if self._units is None:\\n self._units = {}\\n for field_path in self._field_feature_shapes.keys():\\n self._units[field_path] = None\\n\\n # set the units\\n for field_path, unit_value in self._units.items():\\n\\n # ignore the field if not given\\n if unit_value is None:\\n continue\\n\\n unit_path = '{}/{}'.format(UNITS, field_path)\\n\\n unit_grp.create_dataset(unit_path, data=unit_value)\\n\\n\\n # create the group for the run data records\\n records_grp = settings_grp.create_group(RECORD_FIELDS)\\n\\n # create a dataset for the continuation run tuples\\n # (continuation_run, base_run), where the first element\\n # of the new run that is continuing the run in the second\\n # position\\n self._init_continuations()\",\n \"def __init__(self):\\n # load files\\n self.init_bin_start_time = load_last_bin_time()\\n self.prev_value_cache = load_prev_val_cache()\\n self.init_job_vals_dict = load_job_init_vals()\\n\\n config = load_config()\\n self.database_domain = config[ConfigFields.DATABASE_DOMAIN]\\n self.metrics = config[ConfigFields.METRICS]\\n self.bin_duration = config[ConfigFields.BIN_DURATION]\\n\\n \\\"to be overwritten when server time is found in a job\\\"\\n self.current_time = int(time.time())\\n self.bin_start_times = None # updated below\\n self.final_bin_end_time = None # updated below\\n self.update_bin_times() # updates\",\n \"def __init__(self, run, configuration: dict):\\n\\n self.run = run\\n self.raw_configuration = configuration\\n self.input_definitions = self.run.analysis_version.input_definitions\",\n \"def __init__(self):\\n\\n self.this_dir = os.path.dirname(os.path.abspath(__file__))\\n self.db_path = self._discover_in_dir(self.this_dir)\",\n \"def __init__(self, filenames, **kwargs):\\n self.filenames = filenames\\n self.meta = assign_default_kwargs(self, kwargs, self.defaults)\\n # Grab logger\\n self.log = get_logger(__name__)\\n\\n # Performance tracking\\n self.errors = []\\n self.uploaded = 0\\n\\n # Grab db using credentials\\n self.log.info('Accessing Database {}'.format(self.db_name))\\n engine, self.session = get_db(self.db_name, credentials=self.credentials)\\n\\n self.log.info('Preparing to upload {} files...'.format(len(filenames)))\",\n \"def __init__(self):\\n self.dataset_path = input('Enter the path to the root directory of your dataset:\\\\n')\\n self.classes = [c.lower() for c in os.listdir(self.dataset_path)]\\n self.year = str(datetime.datetime.now().year)\\n self.kit_path = input(\\\"Enter the path ot your VOCdevkit directory:\\\\n\\\")\\n self.annotation_path = self.kit_path + '/VOC' + self.year + '/Annotations'\\n self.renamer = data_renamer.DataRenamer(self.dataset_path, self.year)\\n self.data_splitter = data_splitter.DataSplitter(self.dataset_path, self.classes, self.year, self.kit_path)\\n self.annotation_maker = annotation_maker.AnnotationMaker(self.dataset_path, self.kit_path, self.year,\\n self.annotation_path)\",\n \"def prepare(self):\\n\\t\\tif len(os.listdir(self.storagedir)):\\n\\t\\t\\traise Exception(\\\"There are already files in\\\", self.storagedir + \\\".\\\", \\\"Delete manually!\\\")\\n\\t\\t\\n\\t\\tself.worddict = {\\\"__pad__\\\": 0, \\\"__oov__\\\": 1}\\n\\t\\tself.classdict = {}\\n\\t\\tself.raw_documents, self.tokenized_documents = {}, {}\\n\\t\\tself.X, self.Y = {}, {}\\n\\n\\t\\tfor dataset in self.DATASETS_TMP:\\n\\t\\t\\tself.get_raw_data(dataset)\\n\\t\\t\\tself.delete_empty_documents(dataset)\\n\\t\\t\\tself.tokenize_documents(dataset)\\n\\n\\t\\tself.make_classdict()\\n\\t\\tself.make_worddict()\\n\\t\\tself.make_embeddings()\\n\\t\\tself.reverse_dicts()\\n\\n\\t\\tfor dataset in self.DATASETS_TMP:\\n\\t\\t\\tself.make_X(dataset)\\n\\t\\t\\tself.shuffle_dataset(dataset)\\n\\n\\t\\tif not \\\"dev\\\" in self.X:\\n\\t\\t\\tself.split_dev()\\t\\t\\n\\t\\tself.make_hybrid()\\n\\t\\tself.store()\",\n \"def __init__(self, the_input_shape, num_classes, path_weights, hyperparameters):\\n self.the_input_shape = the_input_shape\\n self.num_classes = num_classes\\n self.path_weights = path_weights\\n self.hyperparameters = hyperparameters\",\n \"def __init__(\\n self,\\n dataset: Dataset,\\n compiled_model_path: PathOrURI,\\n modelframework: str,\\n input_model_path: PathOrURI):\\n self.modelframework = modelframework\\n self.input_model_path = input_model_path\\n self.outputtypes = [self.modelframework]\\n super().__init__(dataset, compiled_model_path)\",\n \"def __init__(self, base_path):\\n super(FileSystemDatastore, self).__init__(base_path)\",\n \"def __init__(self):\\n self.args = self._prepare_args(locals())\\n self.requires_full_dataset_in_memory = False\",\n \"def __init__(self, doc_set):\\n self.__doc_set = doc_set\\n self.__preprocessor = InputPreprocessor(doc_set)\",\n \"def __init__(self, filename, validate=True):\\n pass\",\n \"def _initialize_directory(self):\\n self._generate_settings()\\n if os.path.exists(self.target_path):\\n sys.exit(\\\"WARNING: %s already exists, exiting\\\" % self.target_path)\\n self._print_initialize_message()\\n self._create_directories()\\n self._create_general_config_file()\\n self._create_default_pipeline_config_file()\\n self._create_filelist()\\n print\",\n \"def __init__(self):\\n _snap.TFile_swiginit(self, _snap.new_TFile())\",\n \"def _generate_file_paths(self):\\n for table_name in self.tables:\\n logger.info(f\\\"Generating input and output paths for table '{table_name}'...\\\")\\n self.input_paths[table_name] = os.path.join(self.pipeline['input_dir'], f'{table_name}.xml')\\n logger.info(f\\\"Input path for table '{table_name}': {self.input_paths[table_name]}\\\")\\n self.output_paths[table_name] = os.path.join(self.pipeline['output_dir'], f'{table_name}.jsonl')\\n logger.info(f\\\"Output path for table '{table_name}': {self.output_paths[table_name]}\\\")\\n logger.info(f\\\"Generated {len(self.input_paths)} input paths and {len(self.output_paths)} output paths.\\\")\",\n \"def __init__(self):\\n self.fileReader = FileReader()\\n self.path = os.path.abspath(\\\"\\\")\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.69186145","0.6781864","0.6756163","0.6735031","0.6637486","0.6575436","0.6545899","0.65405905","0.6535554","0.65054566","0.6497379","0.6446711","0.6439875","0.64162254","0.6388665","0.63697845","0.63621044","0.63425386","0.6337463","0.6333185","0.6330976","0.6310273","0.6302738","0.62996733","0.62843364","0.62698","0.62557936","0.6251302","0.622112","0.6208984","0.6202893","0.618277","0.6177997","0.6167657","0.6162764","0.6149183","0.6136443","0.611831","0.611751","0.61104333","0.60988086","0.60986304","0.60820407","0.60795516","0.60669464","0.6058795","0.6050821","0.60489213","0.604664","0.6040224","0.6023607","0.6021475","0.60171914","0.60164464","0.60111207","0.60057825","0.5999322","0.5994272","0.5986613","0.5981885","0.59736884","0.5969059","0.5962824","0.5953018","0.59521276","0.59363824","0.591931","0.5917199","0.5913423","0.59132487","0.5912039","0.5909921","0.5906824","0.59059274","0.5903998","0.5901128","0.58810586","0.588051","0.588051","0.5864499","0.58569646","0.58558965","0.58540577","0.5852888","0.5844985","0.58435524","0.5840909","0.58358717","0.5827199","0.58209115","0.582002","0.58170646","0.5813364","0.58123577","0.5811916","0.58043444","0.5804241","0.5797706","0.5796187","0.57907575"],"string":"[\n \"0.69186145\",\n \"0.6781864\",\n \"0.6756163\",\n \"0.6735031\",\n \"0.6637486\",\n \"0.6575436\",\n \"0.6545899\",\n \"0.65405905\",\n \"0.6535554\",\n \"0.65054566\",\n \"0.6497379\",\n \"0.6446711\",\n \"0.6439875\",\n \"0.64162254\",\n \"0.6388665\",\n \"0.63697845\",\n \"0.63621044\",\n \"0.63425386\",\n \"0.6337463\",\n \"0.6333185\",\n \"0.6330976\",\n \"0.6310273\",\n \"0.6302738\",\n \"0.62996733\",\n \"0.62843364\",\n \"0.62698\",\n \"0.62557936\",\n \"0.6251302\",\n \"0.622112\",\n \"0.6208984\",\n \"0.6202893\",\n \"0.618277\",\n \"0.6177997\",\n \"0.6167657\",\n \"0.6162764\",\n \"0.6149183\",\n \"0.6136443\",\n \"0.611831\",\n \"0.611751\",\n \"0.61104333\",\n \"0.60988086\",\n \"0.60986304\",\n \"0.60820407\",\n \"0.60795516\",\n \"0.60669464\",\n \"0.6058795\",\n \"0.6050821\",\n \"0.60489213\",\n \"0.604664\",\n \"0.6040224\",\n \"0.6023607\",\n \"0.6021475\",\n \"0.60171914\",\n \"0.60164464\",\n \"0.60111207\",\n \"0.60057825\",\n \"0.5999322\",\n \"0.5994272\",\n \"0.5986613\",\n \"0.5981885\",\n \"0.59736884\",\n \"0.5969059\",\n \"0.5962824\",\n \"0.5953018\",\n \"0.59521276\",\n \"0.59363824\",\n \"0.591931\",\n \"0.5917199\",\n \"0.5913423\",\n \"0.59132487\",\n \"0.5912039\",\n \"0.5909921\",\n \"0.5906824\",\n \"0.59059274\",\n \"0.5903998\",\n \"0.5901128\",\n \"0.58810586\",\n \"0.588051\",\n \"0.588051\",\n \"0.5864499\",\n \"0.58569646\",\n \"0.58558965\",\n \"0.58540577\",\n \"0.5852888\",\n \"0.5844985\",\n \"0.58435524\",\n \"0.5840909\",\n \"0.58358717\",\n \"0.5827199\",\n \"0.58209115\",\n \"0.582002\",\n \"0.58170646\",\n \"0.5813364\",\n \"0.58123577\",\n \"0.5811916\",\n \"0.58043444\",\n \"0.5804241\",\n \"0.5797706\",\n \"0.5796187\",\n \"0.57907575\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8291974"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":11290,"cells":{"query":{"kind":"string","value":"Take the path to a raw png asset and convert it to target webp path."},"document":{"kind":"string","value":"def processed_texture_path(path):\n return path.replace(RAW_ASSETS_PATH, ASSETS_PATH).replace('png', 'webp')"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def img2webp(path):\n file, ext = os.path.splitext(path)\n image = Image.open(path).convert(\"RGBA\")\n image = ImageOps.expand(image, 75)\n image.save(file + \".webp\", \"WEBP\")\n os.remove(path)","def image_webp():\n data = resource(\"images/wolf_1.webp\")\n return Response(data, headers={\"Content-Type\": \"image/webp\"})","def convert_png_image_to_webp(png, out, quality=80):\n command = [CWEBP, '-q', str(quality), png, '-o', out]\n run_subprocess(command)","def generate_webp_textures():\n input_files = PNG_TEXTURES['input_files']\n output_files = PNG_TEXTURES['output_files']\n if not os.path.exists(TEXTURE_PATH):\n os.makedirs(TEXTURE_PATH)\n for png, out in zip(input_files, output_files):\n if needs_rebuild(png, out):\n convert_png_image_to_webp(png, out, WEBP_QUALITY)","def make_image(self, path):\n\t\treturn None","def _webp(self, tile: bytes) -> np.ndarray:\n decoded = np.rollaxis(imagecodecs.webp_decode(tile), 2, 0)\n return decoded","def test_write_lossless_rgb(tmp_path):\n\n temp_file = str(tmp_path / \"temp.webp\")\n # temp_file = \"temp.webp\"\n\n pil_image = hopper(\"RGBA\")\n\n mask = Image.new(\"RGBA\", (64, 64), (128, 128, 128, 128))\n # Add some partially transparent bits:\n pil_image.paste(mask, (0, 0), mask)\n\n pil_image.save(temp_file, lossless=True)\n\n with Image.open(temp_file) as image:\n image.load()\n\n assert image.mode == \"RGBA\"\n assert image.size == pil_image.size\n assert image.format == \"WEBP\"\n image.load()\n image.getdata()\n\n assert_image_equal(image, pil_image)","def handle_as_url(view: View, point: int, string: str, name: str):\n\n # Let's assume this url as input:\n # (https://upload.wikimedia.org/wikipedia/commons/8/84/Example.svg)\n\n # Download the image\n # FIXME: avoid nested try-except clauses\n try:\n try:\n f = urlopen(unquote(string)) # <==\n except:\n try:\n url_path = quote(string).replace(\"%3A\", ':', 1)\n f = urlopen(url_path)\n except:\n f = urlopen(string)\n # don't fill the console with stack-trace when there`s no connection !!\n except Exception as e:\n print(e)\n return\n\n # file needs conversion ?\n need_conversion = name.endswith(FORMAT_TO_CONVERT) # => True\n basename, ext = osp.splitext(name) # => (\"Example\", \".svg\")\n # create a temporary file\n tmp_file = osp.join(TEMP_DIR,\n \"tmp_image\" + (ext if need_conversion else \".png\")\n ) # => \"TEMP_DIR/tmp_image.svg\"\n\n # Save downloaded data in the temporary file\n content = f.read()\n with open(tmp_file, \"wb\") as dst:\n dst.write(content)\n\n # if the file needs conversion, convert it then read data from the resulting png\n if need_conversion:\n # keep the image's temporary file and name for later use\n conv_file = tmp_file # => \"TEMP_DIR/tmp_image.svg\"\n conv_name = name # => \"Example.svg\"\n\n # => \"TEMP_DIR/tmp_image.png\"\n png = osp.splitext(tmp_file)[0] + \".png\"\n\n # use the magick command of Imagemagick to convert the image to png\n magick(tmp_file, png)\n\n # set temp_file and name to the png file\n tmp_file = png # => \"TEMP_DIR/tmp_image.png\"\n name = basename + \".png\" # => \"Example.png\"\n\n # read data from the resulting png\n with open(tmp_file, \"rb\") as dst:\n content = dst.read()\n\n real_width, real_height, size = get_image_size(tmp_file)\n width, height = get_dimensions(view, tmp_file)\n encoded = str(base64.b64encode(content), \"utf-8\")\n size = str(size // 1024) + \"KB\" if size >= 1024 else str(size) + 'B'\n\n def on_navigate(href):\n\n if href == \"save\":\n if need_conversion:\n save(conv_file, conv_name, \"url\")\n else:\n save(tmp_file, name, \"url\")\n elif href == \"save_as\":\n if need_conversion:\n convert(conv_file, \"url\", conv_name)\n else:\n convert(tmp_file, \"url\", name)\n else:\n sublime.active_window().open_file(tmp_file)\n\n view.show_popup(\n TEMPLATE % (width, height, \"png\", encoded, real_width, real_height, size),\n sublime.HIDE_ON_MOUSE_MOVE_AWAY,\n point,\n *view.viewport_extent(),\n on_navigate=on_navigate\n )","def save_image(data, file_path):\n with open(file_path, 'wb'):\n prefix = 'data:image/webp;base64,'\n data = data[len(prefix):]\n byte_data = base64.b64decode(data)\n image_data = BytesIO(byte_data)\n img = Image.open(image_data)\n img.save(file_path)\n return True","def handle_as_url(view: sublime.View, point: int, string: str, name: str):\n\n # Let's assume this url as input:\n # (https://upload.wikimedia.org/wikipedia/commons/8/84/Example.svg)\n\n # Download the image\n # FIXME: avoid nested try-except clauses\n try:\n try:\n f = urlopen(unquote(string)) # <==\n except Exception:\n try:\n url_path = quote(string).replace(\"%3A\", ':', 1)\n f = urlopen(url_path)\n except Exception:\n f = urlopen(string)\n # don't fill the console with stack-trace when there`s no connection !!\n except Exception as e:\n print(e)\n return\n\n # file needs conversion ?\n need_conversion = name.endswith(formats_to_convert) # => True\n basename, ext = osp.splitext(name) # => (\"Example\", \".svg\")\n # create a temporary file\n temp_img = osp.join(TEMP_DIR, \"tmp_image\" + ext) # => \"TEMP_DIR/tmp_image.svg\"\n\n # Save downloaded data in the temporary file\n content = f.read()\n with open(temp_img, \"wb\") as img:\n img.write(content)\n\n # if the file needs conversion, convert it then read data from the resulting png\n if need_conversion:\n ext = \".png\"\n # keep the image's temporary file and name for later use\n conv_file = temp_img # => \"TEMP_DIR/tmp_image.svg\"\n\n # => \"TEMP_DIR/tmp_image.png\"\n temp_png = osp.splitext(temp_img)[0] + \".png\"\n\n # use the magick command of Imagemagick to convert the image to png\n magick(temp_img, temp_png)\n\n # read data from the resulting png\n with open(temp_png, \"rb\") as png:\n content = png.read()\n\n # set temp_file and name to the png file\n temp_img = temp_png # => \"TEMP_DIR/tmp_image.png\"\n\n width, height, real_width, real_height, size = get_data(view, temp_img)\n encoded = str(base64.b64encode(content), \"utf-8\")\n\n def on_navigate(href):\n\n if href == \"save\":\n if need_conversion:\n save(conv_file, name, \"url\")\n else:\n save(temp_img, name, \"url\")\n elif href == \"save_as\":\n if need_conversion:\n convert(conv_file, \"url\", name)\n else:\n convert(temp_img, \"url\", name)\n else:\n sublime.active_window().open_file(temp_img)\n\n view.show_popup(\n TEMPLATE % (width, height, ext, encoded, real_width, real_height,\n str(size // 1024) + \"KB\" if size >= 1024 else str(size) + 'B'),\n sublime.HIDE_ON_MOUSE_MOVE_AWAY,\n point,\n *view.viewport_extent(),\n on_navigate=on_navigate\n )","def clean_webp_textures():\n for webp in PNG_TEXTURES['output_files']:\n if os.path.isfile(webp):\n os.remove(webp)","def toPng(self):\n\t\tif self.isPng:\n\t\t\treturn self\n\t\telse:\n\t\t\treturn textureFile( self.path.replace( self.extension, '.png' ) )","def get_png_abs_path() -> pathlib.Path:\n return PathManager._ROOT.joinpath(\n PathManager._TILINGS_GUI, PathManager._RESOURCES, \"img\", \"png\"\n )","def _copy_to_media(self, template_name, source=''):\n dirpath = os.path.join(self.cache_root, os.path.dirname(template_name))\n filename = os.path.basename(template_name)\n fullpath = os.path.join(dirpath, filename)\n\n if not os.path.isfile(fullpath) or settings.DEBUG:\n if not os.path.exists(dirpath):\n os.makedirs(dirpath)\n\n f = open(fullpath, 'w')\n f.write(source)\n f.close()\n\n return urljoin(self.cache_url, template_name), filename","def test_write_unsupported_mode_PA(tmp_path):\n\n temp_file = str(tmp_path / \"temp.webp\")\n file_path = \"Tests/images/transparent.gif\"\n with Image.open(file_path) as im:\n im.save(temp_file)\n with Image.open(temp_file) as image:\n assert image.mode == \"RGBA\"\n assert image.size == (200, 150)\n assert image.format == \"WEBP\"\n\n image.load()\n image.getdata()\n with Image.open(file_path) as im:\n target = im.convert(\"RGBA\")\n\n assert_image_similar(image, target, 25.0)","def make_image_path(raw_img, input_base, base_path):\n path = os.path.dirname(raw_img)\n relpath = os.path.relpath(path, input_base)\n if relpath == '.':\n dest_folder = base_path\n else:\n dest_folder = os.path.join(base_path, relpath)\n return os.path.normpath(dest_folder)\n # return dest_folder","def build_image_path(self, src):\r\n o = urlparse(src)\r\n # we have a full url\r\n if o.hostname:\r\n return o.geturl()\r\n # we have a relative url\r\n return urljoin(self.target_url, src)","def copy_png(\n inpath: str, outpath: str, filter_chunks: Optional[Callable[[bytes], bool]] = None, verify_crc: bool = False\n) -> None:\n\n with open(inpath, \"rb\") as fr, open(outpath, \"xb\") as fw:\n copy_png_fp(fr, fw, filter_chunks=filter_chunks, verify_crc=verify_crc)","def absolute_asset_url(module, path):\n return absolute_uri(get_asset_url(module, path))","def get_image_qm(html_src, todir):\n #print url\n\n img_url, title = img_details(html_src)\n \n r = requests.get(img_url)\n with open(todir+title+'.jpg','wb') as f:\n f.write(r.content)","def test_write_rgba(tmp_path):\n\n temp_file = str(tmp_path / \"temp.webp\")\n\n pil_image = Image.new(\"RGBA\", (10, 10), (255, 0, 0, 20))\n pil_image.save(temp_file)\n\n if _webp.WebPDecoderBuggyAlpha():\n return\n\n with Image.open(temp_file) as image:\n image.load()\n\n assert image.mode == \"RGBA\"\n assert image.size == (10, 10)\n assert image.format == \"WEBP\"\n image.load()\n image.getdata()\n\n # Early versions of WebP are known to produce higher deviations:\n # deal with it\n if _webp.WebPDecoderVersion() <= 0x201:\n assert_image_similar(image, pil_image, 3.0)\n else:\n assert_image_similar(image, pil_image, 1.0)","def imgOutputPath(self, newpath):\n newimg = self.imagePath().replace(self.home, newpath)\n return newimg","def test_encode_webp():\n width = 51\n height = 26\n channels = 3\n bmp_file = os.path.join(\n os.path.dirname(os.path.abspath(__file__)), \"test_image\", \"lena.bmp\")\n with open(bmp_file, 'rb') as f:\n bmp_contents = f.read()\n image_v = tf.image.decode_bmp(bmp_contents)\n assert image_v.shape == [height, width, channels]\n bmp_encoded = image_io.encode_bmp(image_v)\n image_e = tf.image.decode_bmp(bmp_encoded)\n assert np.all(image_v.numpy() == image_e.numpy())","def get_image(url, path):\n r = requests.get(url, stream=True)\n if r.status_code == 200:\n with open(path, 'wb') as f:\n r.raw.decode_content = True\n shutil.copyfileobj(r.raw, f)\n print(\"[>] get\", url, \">>\", path)\n f.close()","def read_webp_image(h5_dataset):\n data = h5_dataset[:].tobytes()\n img_bytesio = BytesIO(data)\n pil_img = Image.open(img_bytesio, 'r')\n return pil_img","def custom_static_serve(*args, **keywords):\n response = serve(*args, **keywords)\n if keywords[\"path\"].endswith(\".webp\"):\n response.headers[\"Content-Type\"] = \"image/webp\"\n return response","def from_url(self) -> PngImagePlugin.PngImageFile:\n response = requests.get(self.url)\n img = Image.open(BytesIO(response.content))\n\n return img","def convert_to_high_res(url):\n return add_string_to_image_url(url, 'high-res')","def source(request, filepath_pseudos):\n filepath_pseudo = pathlib.Path(filepath_pseudos()) / 'Ar.upf'\n\n if request.param is str:\n return str(filepath_pseudo)\n\n if request.param is pathlib.Path:\n return filepath_pseudo\n\n return io.BytesIO(filepath_pseudo.read_bytes())","def __make_png(self, abspath_img_rgb):\n if not os.path.exists(DIR_PNG):\n os.makedirs(DIR_PNG)\n\n outsize = '{}%'.format(OUTSIZE_RGB)\n img_name_rgb = os.path.basename(abspath_img_rgb)\n suffix_extension_tif = Utils.get_suffix_tif(img_name_rgb)\n img_png = img_name_rgb.replace(suffix_extension_tif, '.png')\n path_img_png = os.path.join(DIR_PNG, img_png)\n\n command = \"gdal_translate -ot byte -of PNG -outsize {} {} \" \\\n \"-a_nodata 0 -q {} {}\".format(\n outsize, outsize, abspath_img_rgb, path_img_png\n )\n os.system(command)\n return os.path.join(DIR_PNG_TO_DB, img_png)","def processed_json_path(path):\n return path.replace(RAW_ASSETS_PATH, ASSETS_PATH).replace('.json', '.bin')","def b64str_to_img_file(src, saveto, urlsafe=False):\n file_base64 = src.encode('utf-8')\n\n if urlsafe:\n file_byte = base64.urlsafe_b64decode(file_base64)\n else:\n file_byte = base64.standard_b64encode(file_base64)\n\n OUT_FILE = open(saveto, 'wb')\n return OUT_FILE.write(file_byte)","def testConvertHtmlWithPngDataUrlToPdf(self):\n self._testBase(\"data/test_with_png_dataurl.html\")","def to_file_path(self, resourcePath: str) -> PurePath:\n rel = resourcePath.replace('res://', '')\n return self._root.joinpath(rel)","def convert_to_web(base_path, files):\n for i, f in enumerate(files):\n imagetype = get_imagetype_from_filename(f)\n cmd = ('rsync '\n '{base_path}/qc/phantom/{imagetype}/{f} '\n '{base_path}/website/assets/{output}'.format(\n base_path=base_path, imagetype=imagetype, \n f=f, output=f[9:]))\n os.system(cmd)","def make_image(self, path):\n\t\treturn self.ui.get_icon(path)","def paster_in_svg(self, src, elem):\n loger.info(\"start svg pasting\")\n with open(src) as f:\n tree = etree.parse(f)\n root = tree.getroot()\n element = tree.xpath('image')\n\n if element:\n # Replaces <gco_CharacterString> text\n for key, value in element[0].attrib.iteritems():\n if value == 'avatar':\n # element[0].attrib[key] = os.path.abspath(elem)\n element[0].attrib[key] = \"/home/kryvonis/PycharmProjects/Book_Creator/image_end/1.png\"\n # Save back to the XML file\n etree.ElementTree(root).write(src, pretty_print=True)\n loger.info('svg created - OK')","def CanvasToPngString(canvas):\n\n temp_file_name = \"temp_io.png\"\n canvas.Print(temp_file_name) \n image = open(temp_file_name, 'r')\n\n data_uri = image.read().encode(\"base64\")\n img_html_src = \"data:image/png;base64,%s\" % data_uri\n\n image.close()\n os.remove(temp_file_name)\n\n return img_html_src","def test_raw_static_check():\r\n path = '\"/static/foo.png?raw\"'\r\n assert_equals(path, replace_static_urls(path, DATA_DIRECTORY))\r\n\r\n text = 'text <tag a=\"/static/js/capa/protex/protex.nocache.js?raw\"/><div class=\"'\r\n assert_equals(path, replace_static_urls(path, text))","def handle_as_file(view: View, point: int, string: str):\n # \"screenshot.png\"\n\n name = osp.basename(string)\n file, folder = get_file(view, string, name)\n\n # if file doesn't exist, return\n if not osp.isfile(file):\n return\n\n # does the file need conversion ?\n need_conversion = file.endswith(FORMAT_TO_CONVERT)\n\n # if the file needs conversion, convert it and read data from the resulting png\n if need_conversion:\n # keep the image's file and name for later use\n conv_file = file\n conv_name = name\n\n # create a temporary file\n tmp_file = osp.join(TEMP_DIR, \"tmp_png.png\")\n name = osp.splitext(name)[0] + \".png\"\n\n # use the magick command of Imagemagick to convert the image to png\n magick(file, tmp_file)\n\n file = tmp_file\n\n with open(file, \"rb\") as f:\n encoded = str(base64.b64encode(f.read()), \"utf-8\")\n\n real_width, real_height, size = get_image_size(file)\n width, height = get_dimensions(view, file)\n size = str(size // 1024) + \"KB\" if size >= 1024 else str(size) + 'B'\n\n def on_navigate(href):\n\n if href == \"save\":\n if need_conversion:\n save(conv_file, conv_name, \"file\")\n else:\n save(file, name, \"file\", folder)\n elif href == \"save_as\":\n convert(conv_file if need_conversion else file, \"file\")\n else:\n sublime.active_window().open_file(file)\n\n view.show_popup(\n TEMPLATE % (width, height, \"png\", encoded, real_width,\n real_height, size),\n sublime.HIDE_ON_MOUSE_MOVE_AWAY,\n point,\n *view.viewport_extent(),\n on_navigate=on_navigate)","def create_compressed_file(self):\n\t\tself._compressed_file_name = 'c_' + self.file_name\n\t\tself._compressed_save_path = self.full_path.replace(self.file_name, self._compressed_file_name)\n\t\tself._is_png = 'png' in self.file_extension\n\t\tself._is_jpg = 'jpg' in self.file_extension\n\n\t\timage = Image.open(self.full_path)\n\n\t\tif self._is_png:\n\t\t\timage.save(self._compressed_save_path, quality=85, optimize=False, compress_level=9)\n\t\telif self._is_jpg:\n\t\t\timage.save(self._compressed_save_path, quality=85, progressive=False)\n\t\telse:\n\t\t\tprint('Non-recognized asset format!!')\n\t\t\texit()\n\n\t\tself._compressed_file_size = ufo.get_file_size_in_bytes(self._compressed_save_path)\n\n\n\t\ttransfer_path = self._compressed_save_path.replace('c_' + self.file_name, self.file_name).replace('/configuration_files/', '/quasar_site_django/')\n\t\tufo.copy_file_to_path(self._compressed_save_path, transfer_path)","def get_raw_path(self):\n\n return self.raw_img_path","def convert_gif(ctx):\n ctx.run(\n 'ffmpeg '\n '-i resources/demo.mkv -filter_complex \"[0:v] palettegen\" '\n 'resources/palette.png',\n pty=True\n )\n ctx.run(\n 'ffmpeg -i resources/demo.mkv '\n '-i resources/palette.png '\n '-filter_complex \"[0:v][1:v] paletteuse\" '\n 'resources/demo.gif',\n pty=True\n )","def get_full_path(_path: str):\n if _path is None:\n return None\n\n with open(r\"bot\\data\\image_config.json\") as f:\n try:\n image_config = json.load(f)\n\n except json.decoder.JSONDecodeError as e:\n print(e)\n return None\n\n base_url = image_config.get(\"base_url\")\n poster_size = image_config.get(\"poster_sizes\")[-2]\n return f\"{base_url}{poster_size}{_path}\"","def _path(name: str):\n return os.path.join(ASSET_PATH, name)","def download(self, url, path_to_dir):\n\n if not os.path.exists(path_to_dir):\n os.makedirs(path_to_dir)\n\n raw_data = self.__class__.get_raw_data(url)\n path_to_image = os.path.join(path_to_dir, url.split('/')[-1].split('?')[0])\n with open(path_to_image, 'wb') as f:\n self.__class__.copy_to(raw_data, f)\n\n return path_to_image","def outputPath():\n scenePath = bpy.data.filepath\n # If the scene hasn't been saved yet the path is empty.\n # Returning an empty path prompts the user for saving the scene.\n if not scenePath:\n return\n renderPath = os.path.join(os.path.dirname(scenePath), \"{}_thumbs\".format(NAME))\n return renderPath","def pil_loader(path):\n with open(path, 'rb') as f:\n with Image.open(f) as img:\n sqrWidth = np.ceil(np.sqrt(img.size[0]*img.size[1])).astype(int)\n return img.convert('L').resize((sqrWidth, sqrWidth))","def transcode(filePath: str) -> str:\n asset_uuid = uuid.uuid4()\n outPath = os.path.join(\"/tmp\", str(asset_uuid)+'.mp4')\n ffmpeg.input(filePath).output(outPath).run()\n return outPath","def get_image_url():","def convert_canvas_to_img_js():\n return None","def media_path_to_url(path):\n media_url = settings.MEDIA_URL\n if media_url.endswith('/'):\n media_url = media_url[:-1]\n return path.replace(settings.MEDIA_ROOT, media_url)","def make_image(self):\n\n if self.type == 'passthrough':\n return\n render_template(\n os.path.dirname(self.main_module),\n os.path.basename(self.main_module_path),\n self.language,\n self.requirements,\n self.whitelist,\n self.type,\n into=self.code_dir)\n self.build()","def assetsPath(self, pattern, source):\n return self.assets(pattern, AssetSource.create(source))","def get_file_url(path, config):\n file_url_regex = re.compile(config['file_url_regex'])\n new_path = re.sub(file_url_regex, config['file_url_base'], path)\n return new_path","def _url_to_image_file(output_path, response, file_name=None):\n if not response.status_code == 200:\n result = False\n elif response.headers[\"content-type\"].startswith(\"image/\"):\n if not file_name:\n file_name = _filename_from_url(response.url)\n _bin_body_to_file(join(output_path, file_name), response)\n result = True\n else:\n warn_msg = (\"URL is valid and address is reachable, but it doesn't lead to an image file.\"\n f\"MIME type is {response.headers['content-type']}.\")\n logger.warning(warn_msg)\n result = True\n return result","def create_website_image(fname, item):\n\n public_site_files_path = os.path.abspath(\n frappe.get_site_path('public', 'files'))\n\n # Create a symbolic link and a thumbnail for the website image\n path, ext = os.path.splitext(fname)\n web_fname = path + '_web' + ext\n thumb_fname = path + '_thumb' + ext\n\n # Full paths to original file, web image symlink and thumbnail\n file_fpath = os.path.join(public_site_files_path, fname)\n web_fpath = os.path.join(public_site_files_path, web_fname)\n thumb_fpath = os.path.join(public_site_files_path, thumb_fname)\n\n # URLs on website for web image symlink and thumbnail\n web_url = '/' + os.path.join('files', web_fname)\n thumb_url = '/' + os.path.join('files', thumb_fname)\n\n # Create the symbolic link and create the thumbnail\n try:\n os.symlink(file_fpath, web_fpath)\n except OSError:\n if os.path.islink(web_fpath):\n os.remove(web_fpath)\n files = frappe.get_all(\n 'File', filters={'file_url': web_url})\n for file in files:\n frappe.delete_doc('File', file['name'],\n ignore_permissions=True)\n os.symlink(file_fpath, web_fpath)\n else:\n raise\n resize_image(file_fpath, out=thumb_fpath, thumbnail=True)\n\n # Document for web image\n f = frappe.get_doc({\n \"doctype\": \"File\",\n \"file_url\": web_url,\n \"file_name\": web_fname,\n \"attached_to_doctype\": \"Item\",\n \"attached_to_name\": item,\n \"attached_to_field\": None,\n \"folder\": 'Home/Attachments',\n \"file_size\": os.path.getsize(web_fpath),\n \"is_private\": 0\n })\n try:\n f.insert(ignore_permissions=True)\n except frappe.FileAlreadyAttachedException:\n # If already attached, don't attach again\n pass\n\n return web_url, thumb_url","def _target_sprite_sheet_path(self) -> Path:","def JPGtoPNGConverter(source, dest):\n files = os.listdir(f\"./{source}\")\n if not os.path.exists(f\"./{dest}\"):os.makedirs(f\"./{dest}\")\n\n for file in files:\n if os.path.splitext(file)[-1] == \".jpg\":\n img = Image.open(f\"./{source}/{file}\")\n clean_text = os.path.splitext(file)[0]\n img.save(f\"./{dest}/{clean_text}.png\",\"png\")\n else:\n print(f\"Your filename: {file} is not in .JPG format !!\")\n return \"All files converted successfully :) \"","def translate(source):\n # ignore URLs and absolute file system paths as a failsafe\n if core.utils.is_absolute_url(source) or os.path.isabs(source):\n return source\n\n # process relative path\n # if REFINERY_DEPLOYMENT_PLATFORM = 'aws' and REFINERY_S3_USER_DATA\n # use settings.COGNITO_IDENTITY_POOL_ID\n if identity_id:\n source = \"s3://{}/{}/{}\".format(\n settings.UPLOAD_BUCKET, identity_id, source\n )\n elif base_path:\n source = os.path.join(base_path, source)\n elif username:\n source = os.path.join(\n settings.REFINERY_DATA_IMPORT_DIR, username, source)\n else:\n raise ValueError(\"Failed to translate relative source path: \"\n \"must provide either username or base_path\")\n return source","def download_pil_image(self, url):\r\n return Image.open(urlopen(url))","def drop_png_image(im, trg):\n\n\tif trg[-4:] != '.png':\n\t\tERROR('destination name must be png extension', 'drop_png_image', 1)\n\n\tif isinstance(trg, basestring):\n\t\tim['render_min'] = im['minimum']\n\t\tim['render_max'] = im['maximum']\n\t\tim.write_image(trg, 0)\n\telse:\n\t\tERROR('destination is not a file name', 'drop_png_image', 1)","def latex2img(expression, filename):\n webp = False\n\n extension = \"png\"\n\n # Preparing text strings\n server = \"http://latex.codecogs.com/\" + extension + \".download?\"\n fullname = filename + \".\" + extension\n size = \"%5Cdpi%7B100%7D%20\"\n\n # Quote expression引用表达式\n expression = quote(expression)\n url = server + size + expression\n\n # Download file from url and save to output_file:\n with urlopen(url) as response, open(fullname, 'wb') as output_file:\n data = response.read() # Un objeto \"bytes\"\n output_file.write(data) # Se escribe en disco\n\n if webp:\n img2webp(fullname)\n extension = \"webp\"\n\n return filename + \".\" + extension","def decode_file(source, palette):\n\n (CHRStart, CHRSize) = get_CHR_data_position(source)\n charRowCount = CHRSize // 256 # 16 characters/row\n\n img = Image.new(\"P\", (128, charRowCount * 8), 0)\n img.putpalette(itertools.chain.from_iterable(palette))\n\n source.seek(CHRStart)\n for (y, pixelRow) in enumerate(decode_pixel_rows(source, charRowCount)):\n for (x, value) in enumerate(pixelRow):\n img.putpixel((x, y), value)\n\n return img","def transform(img_path):\n\n img = self.loader(img_path)\n return self.transform(img)","def Sourceify(path):\n return path","def image_png():\n data = resource(\"images/pig_icon.png\")\n return Response(data, headers={\"Content-Type\": \"image/png\"})","def resourcePath(relative):\r\n try:\r\n # PyInstaller creates a temp folder and stores path in _MEIPASS\r\n base_path = sys._MEIPASS\r\n except Exception:\r\n base_path = os.path.abspath(os.path.join(os.path.dirname(sys.modules[__name__].__file__), 'assets'))\r\n\r\n return os.path.join(base_path, relative)","def save_file(self, input_path, output_path):\n try:\n im = Image.open(input_path)\n im.save(output_path)\n return output_path\n except Exception as e:\n return '!ERROR' + str(e)","def test_handle_new_picture():\n import os\n from .views.default import handle_new_picture\n here = os.path.abspath(os.path.dirname(__file__))\n path = os.path.join(here, 'scripts/img_questions/how.jpg')\n with open(path, 'rb') as ouput_file:\n new_picture = handle_new_picture(\"name\", ouput_file)\n assert isinstance(new_picture, bytes)","def propose_image_path():\n image_name = \"image_{}.png\".format(''.join(random.choice('abcdefghijklmnopqrstuvwxyz') for _ in range(8)))\n image_path = os.path.join(_IMAGE_BASE_PATH, image_name)\n image_url = '/images/' + image_name\n return image_path, image_url","def wx2PIL(self, bitmap):\n size = tuple(bitmap.GetSize())\n try:\n buf = size[0]*size[1]*3*\"\\x00\"\n bitmap.CopyToBuffer(buf)\n except:\n del buf\n buf = bitmap.ConvertToImage().GetData()\n return Image.frombuffer(\"RGB\", size, buf, \"raw\", \"RGB\", 0, 1)","async def inverter(self, ctx, bilde=None):\n\n async with ctx.channel.typing():\n\n if not await LBlend_utils.download_photo(ctx, link=bilde, max_file_size=8, meassurement_type='MB',\n filepath=f'./assets/temp/{ctx.author.id}_raw.png'):\n return\n\n raw_image = Image.open(f'./assets/temp/{ctx.author.id}_raw.png')\n image_edit = raw_image.convert('RGB')\n image_edit = ImageOps.invert(image_edit)\n image_edit.save(f'./assets/temp/{ctx.author.id}_edit.png')\n\n f = discord.File(f'./assets/temp/{ctx.author.id}_edit.png')\n embed = discord.Embed(color=ctx.me.color)\n embed.set_image(url=f'attachment://{ctx.author.id}_edit.png')\n await Defaults.set_footer(ctx, embed)\n await ctx.send(embed=embed, file=f)\n\n try:\n remove(f'./assets/temp/{ctx.author.id}_raw.png')\n remove(f'./assets/temp/{ctx.author.id}_edit.png')\n except:\n pass\n\n return","def get_asset_path(name):\n return os.path.join(constants.ROOT_DIR, 'assets', name)","def load_img(path: PurePath, convert_alpha: bool = True) -> Surface:\n if convert_alpha:\n return load(str(path)).convert_alpha()\n return load(str(path)).convert()","def prepare_bubble(path): \n img = load_ipl_as_array(path)\n img = gray_bg(img)\n return img","def decode(p):\n #assert p.endswith('.' + EXTENSION)\n p2 = os.path.basename(p).replace('baseline.png', '.png')\n p2p = os.path.join('/mnt/Volume0/test/clic2020-devkit/result/', p2) #add by me\n pp = os.path.join('/mnt/Volume0/test/clic2020-devkit/targets',p2)\n p2 = os.path.join('/mnt/Volume0/test/clic2020-devkit/inputs/', p2) #add by me\n p1 = pframe_dataset_shared.get_previous_frame_path(p2)\n #p1 = os.path.join('/mnt/Volume0/test/clic2020-devkit/test_data/inputs/', p1)\n #assert os.path.isfile(p1), (p2, p1, p, len(glob.glob('*.png')))\n b = Image.open(p).convert('L')\n f2_reconstructed = decoder(np.array(Image.open(p1)), b)\n Image.fromarray(f2_reconstructed).save(p2p)\n return f2_reconstructed, np.array(Image.open(pp))","def convertPath(srcpath, dstdir):\n bits = srcpath.split(\"/\")\n bits.pop(0)\n # Strip out leading 'unsigned' from paths like unsigned/update/win32/...\n if bits[0] == 'unsigned':\n bits.pop(0)\n return os.path.join(dstdir, *bits)","def getRoverImage(self):\n # Your code goes here, this code is just an example\n return 'mario.ppm'","def normalize_path(img_file):\n\n img_file = img_file.split('/')[-1]\n img_file = 'data/IMG/'+img_file.split('\\\\')[-1]\n return img_file","def test_projectedtexture(pngfile):\n tex = omf.ProjectedTexture()\n tex.image = pngfile\n assert tex.validate()","def resource_path(self,relative_path):\n try:\n # PyInstaller creates a temp folder and stores path in _MEIPASS\n base_path = sys._MEIPASS\n except Exception:\n base_path = os.path.abspath(\".\\\\Visual_Ressources\\\\\"+self.language+\"\\\\\") \n # \".\"\n # 'Content\\\\Back_End\\\\'\n return os.path.join(base_path, relative_path)","def copy_web_resources(output_dir):\n mypath = os.path.dirname(os.path.realpath(__file__))\n web_path = os.path.join(mypath, 'web')\n\n for (dirpath, dirnames, filenames) in os.walk(web_path):\n relpath = os.path.relpath(dirpath, web_path)\n tgtpath = os.path.join(output_dir, relpath)\n if not os.path.exists(tgtpath):\n os.makedirs(tgtpath)\n\n for f in [os.path.join(dirpath, filename) for filename in filenames]:\n shutil.copy(f, tgtpath)","def update_png_crusher():\n if os.path.exists(PNG_CRUSHER):\n return\n\n for path in glob.glob(os.path.join(libdot.BIN_DIR, '.png.crusher.*')):\n os.unlink(path)\n\n r = requests.get(PNG_CRUSHER_URL + '?format=TEXT')\n with open(PNG_CRUSHER, 'wb') as fp:\n fp.write(base64.b64decode(r.text))\n\n os.chmod(PNG_CRUSHER, 0o755)","def getPILFromPath(self, path):\n obj = self.getImageObject(path)\n return self.getPILFromObject(obj)","def get_asset_path(test):\n return DEVICE_ASSETS_PATH + os.path.basename(test)","def prepare_single_image(cls, path):\n return T.Compose(cls.base_transforms)(Image.open(path))","def img_url_tiny(self):\n url = '%s=s%s-c' % (self.img_url, self.TINY_SIZE_PX)\n if self.img_rot in Plaque.ALLOWED_ROTATIONS:\n url = \"%s-r%s\" % (url, self.img_rot)\n return url","def _serve_asset(self, path, gzipped_asset_bytes, request):\n mimetype = mimetypes.guess_type(path)[0] or 'application/octet-stream'\n return http_util.Respond(\n request, gzipped_asset_bytes, mimetype, content_encoding='gzip')","def fits_to_pl( in_image, out_image=None ):\n try:\n if out_image is None:\n input_ext = os.path.splitext( in_image )[1]\n out_image = in_image.replace( input_ext, '.pl' )\n \"\"\"\n Delete the old file, if existed\n \"\"\"\n if os.path.exists( out_image ):\n os.remove( out_image )\n \"\"\" imcopy \"\"\"\n iraf.imcopy( in_image, out_image )\n \"\"\" Return the path of the output \"\"\"\n return out_image\n except:\n print \"<Error>: \", sys.exc_info()[1]\n raise","def resource_path(name):\n return os.path.join(\n os.path.dirname(__file__), 'images', 'resource', name)","def save_file(output: Optional[Path], input_path: Path, image: Image.Image):\n if isinstance(output, Path) and str(output) != \"none\":\n if output.is_dir() and output.exists():\n image.save(output.joinpath(input_path.with_suffix(\".png\").name))\n elif output.suffix != \"\":\n if output.suffix != \".png\":\n warnings.warn(\n f\"Only export with .png extension is supported! Your {output.suffix}\"\n f\" extension will be ignored and replaced with .png!\"\n )\n image.save(output.with_suffix(\".png\"))\n else:\n raise ValueError(\"Wrong output path!\")\n elif output is None or str(output) == \"none\":\n image.save(\n input_path.with_name(\n input_path.stem.split(\".\")[0] + \"_bg_removed\"\n ).with_suffix(\".png\")\n )","def server_static_img(filename):\n return static_file(filename, root='static/img')","def uri_to_file(uri):\n if not uri:\n return\n\n header, data = uri.split(',')\n md5_hash = hashlib.md5(data).hexdigest()\n with open(\"static/images/%s.jpg\" % md5_hash, 'wb') as f:\n f.write(data.decode('base64'))\n return md5_hash","def image(self):\n # TODO: make sure this method works for png, gif, tiff\n if self.has_metadata:\n self.extract_metadata()\n tempdir_path = self.make_tempdir()\n tempfile_path = os.path.join(tempdir_path, self.filename)\n warnings.simplefilter('error', Image.DecompressionBombWarning)\n try: # Do image conversions\n img_in = Image.open(self.src_path)\n img_out = Image.frombytes(img_in.mode, img_in.size, img_in.tobytes())\n img_out.save(tempfile_path)\n self.src_path = tempfile_path\n except Exception as e: # Catch decompression bombs\n # TODO: change this from all Exceptions to specific DecompressionBombWarning\n self.add_error(e, \"Caught exception (possible decompression bomb?) while translating file {}.\".format(self.src_path))\n self.make_dangerous()\n self.add_file_string('Image file')\n self.set_property('processing_type', 'image')","def convert_pptx_to_rgb(input_file, output_file):\n with ZipFile(input_file) as input_pptx_file:\n # theme is defined in the presentation rels\n rel = PresentationRel(input_pptx_file)\n theme_name = f'ppt/{rel.theme}'\n theme = read_color_map(zip_file=input_pptx_file, file_name=theme_name)\n color_map = theme[1]\n\n new_file_data = []\n\n file_list = input_pptx_file.namelist()\n\n for file in file_list:\n with input_pptx_file.open(name=file) as current_file:\n file_contents = current_file.read()\n # only convert XML files of slides\n if re.match(r'ppt/slides/slide(\\d+).xml', file):\n new_contents = convert(content=file_contents, color_map=color_map)\n else:\n new_contents = file_contents\n new_file_data.append(\n {\n 'file': file,\n 'data': new_contents\n }\n )\n\n # create/write the new ZIP\n with ZipFile(output_file, 'w') as output_pptx_file:\n for data in new_file_data:\n output_pptx_file.writestr(data['file'], data['data'], ZIP_DEFLATED)","def _get_src_from_image(img, fallback_image_file):\n # If the image is None, then we can't process, so we should return the\n # path to the file itself\n if img is None:\n return fallback_image_file\n # Target format should be the same as the original image format, unless it's\n # a TIF/TIFF, which can't be displayed by most browsers; we convert these\n # to jpeg\n target_format = img.format\n if target_format.lower() in ['tif', 'tiff']:\n target_format = 'JPEG'\n # If we have an actual Image, great - put together the base64 image string\n try:\n bytesio = io.BytesIO()\n img.save(bytesio, target_format)\n byte_value = bytesio.getvalue()\n b64 = base64.b64encode(byte_value)\n return 'data:image/%s;base64,%s' % (target_format.lower(), b64)\n except IOError as exptn:\n print('IOError while saving image bytes: %s' % exptn)\n return fallback_image_file","def transcode(path, outpath):\n\n needs_transcode = determine_transcode(path)\n logger.info(f\"Transcoding {path} to {outpath}...\")\n\n cmd = [\n \"ffmpeg\", \"-y\",\n \"-i\", path,\n \"-an\",\n \"-metadata:s\", \"handler_name=tator\",\n \"-vcodec\", \"libx264\",\n \"-g\", \"25\",\n \"-preset\", \"fast\",\n \"-pix_fmt\", \"yuv420p\",\n \"-vf\", \"pad=ceil(iw/2)*2:ceil(ih/2)*2\",\n \"-movflags\",\n \"faststart+frag_keyframe+empty_moov+default_base_moof\",\n \"-tune\", \"fastdecode\",\n ]\n\n if needs_transcode[1]:\n #Resize to 720p\n cmd.extend([\"-vf\", \"scale=-2:720\"])\n\n cmd.append(outpath)\n logger.info('ffmpeg cmd = {}'.format(cmd))\n subprocess.run(cmd, check=True)\n logger.info(\"Transcoding finished!\")","def test_keep_rgb_values_when_transparent(tmp_path):\n\n image = hopper(\"RGB\")\n\n # create a copy of the image\n # with the left half transparent\n half_transparent_image = image.copy()\n new_alpha = Image.new(\"L\", (128, 128), 255)\n new_alpha.paste(0, (0, 0, 64, 128))\n half_transparent_image.putalpha(new_alpha)\n\n # save with transparent area preserved\n temp_file = str(tmp_path / \"temp.webp\")\n half_transparent_image.save(temp_file, exact=True, lossless=True)\n\n with Image.open(temp_file) as reloaded:\n assert reloaded.mode == \"RGBA\"\n assert reloaded.format == \"WEBP\"\n\n # even though it is lossless, if we don't use exact=True\n # in libwebp >= 0.5, the transparent area will be filled with black\n # (or something more conducive to compression)\n assert_image_equal(reloaded.convert(\"RGB\"), image)","def generate_image_path(playbook_name: str, custom_image_path: str):\n if custom_image_path:\n playbook_image_path = custom_image_path\n else:\n playbook_image_path = \"../doc_files/\" + playbook_name.replace(\" \", \"_\") + \".png\"\n\n return f\"\""],"string":"[\n \"def img2webp(path):\\n file, ext = os.path.splitext(path)\\n image = Image.open(path).convert(\\\"RGBA\\\")\\n image = ImageOps.expand(image, 75)\\n image.save(file + \\\".webp\\\", \\\"WEBP\\\")\\n os.remove(path)\",\n \"def image_webp():\\n data = resource(\\\"images/wolf_1.webp\\\")\\n return Response(data, headers={\\\"Content-Type\\\": \\\"image/webp\\\"})\",\n \"def convert_png_image_to_webp(png, out, quality=80):\\n command = [CWEBP, '-q', str(quality), png, '-o', out]\\n run_subprocess(command)\",\n \"def generate_webp_textures():\\n input_files = PNG_TEXTURES['input_files']\\n output_files = PNG_TEXTURES['output_files']\\n if not os.path.exists(TEXTURE_PATH):\\n os.makedirs(TEXTURE_PATH)\\n for png, out in zip(input_files, output_files):\\n if needs_rebuild(png, out):\\n convert_png_image_to_webp(png, out, WEBP_QUALITY)\",\n \"def make_image(self, path):\\n\\t\\treturn None\",\n \"def _webp(self, tile: bytes) -> np.ndarray:\\n decoded = np.rollaxis(imagecodecs.webp_decode(tile), 2, 0)\\n return decoded\",\n \"def test_write_lossless_rgb(tmp_path):\\n\\n temp_file = str(tmp_path / \\\"temp.webp\\\")\\n # temp_file = \\\"temp.webp\\\"\\n\\n pil_image = hopper(\\\"RGBA\\\")\\n\\n mask = Image.new(\\\"RGBA\\\", (64, 64), (128, 128, 128, 128))\\n # Add some partially transparent bits:\\n pil_image.paste(mask, (0, 0), mask)\\n\\n pil_image.save(temp_file, lossless=True)\\n\\n with Image.open(temp_file) as image:\\n image.load()\\n\\n assert image.mode == \\\"RGBA\\\"\\n assert image.size == pil_image.size\\n assert image.format == \\\"WEBP\\\"\\n image.load()\\n image.getdata()\\n\\n assert_image_equal(image, pil_image)\",\n \"def handle_as_url(view: View, point: int, string: str, name: str):\\n\\n # Let's assume this url as input:\\n # (https://upload.wikimedia.org/wikipedia/commons/8/84/Example.svg)\\n\\n # Download the image\\n # FIXME: avoid nested try-except clauses\\n try:\\n try:\\n f = urlopen(unquote(string)) # <==\\n except:\\n try:\\n url_path = quote(string).replace(\\\"%3A\\\", ':', 1)\\n f = urlopen(url_path)\\n except:\\n f = urlopen(string)\\n # don't fill the console with stack-trace when there`s no connection !!\\n except Exception as e:\\n print(e)\\n return\\n\\n # file needs conversion ?\\n need_conversion = name.endswith(FORMAT_TO_CONVERT) # => True\\n basename, ext = osp.splitext(name) # => (\\\"Example\\\", \\\".svg\\\")\\n # create a temporary file\\n tmp_file = osp.join(TEMP_DIR,\\n \\\"tmp_image\\\" + (ext if need_conversion else \\\".png\\\")\\n ) # => \\\"TEMP_DIR/tmp_image.svg\\\"\\n\\n # Save downloaded data in the temporary file\\n content = f.read()\\n with open(tmp_file, \\\"wb\\\") as dst:\\n dst.write(content)\\n\\n # if the file needs conversion, convert it then read data from the resulting png\\n if need_conversion:\\n # keep the image's temporary file and name for later use\\n conv_file = tmp_file # => \\\"TEMP_DIR/tmp_image.svg\\\"\\n conv_name = name # => \\\"Example.svg\\\"\\n\\n # => \\\"TEMP_DIR/tmp_image.png\\\"\\n png = osp.splitext(tmp_file)[0] + \\\".png\\\"\\n\\n # use the magick command of Imagemagick to convert the image to png\\n magick(tmp_file, png)\\n\\n # set temp_file and name to the png file\\n tmp_file = png # => \\\"TEMP_DIR/tmp_image.png\\\"\\n name = basename + \\\".png\\\" # => \\\"Example.png\\\"\\n\\n # read data from the resulting png\\n with open(tmp_file, \\\"rb\\\") as dst:\\n content = dst.read()\\n\\n real_width, real_height, size = get_image_size(tmp_file)\\n width, height = get_dimensions(view, tmp_file)\\n encoded = str(base64.b64encode(content), \\\"utf-8\\\")\\n size = str(size // 1024) + \\\"KB\\\" if size >= 1024 else str(size) + 'B'\\n\\n def on_navigate(href):\\n\\n if href == \\\"save\\\":\\n if need_conversion:\\n save(conv_file, conv_name, \\\"url\\\")\\n else:\\n save(tmp_file, name, \\\"url\\\")\\n elif href == \\\"save_as\\\":\\n if need_conversion:\\n convert(conv_file, \\\"url\\\", conv_name)\\n else:\\n convert(tmp_file, \\\"url\\\", name)\\n else:\\n sublime.active_window().open_file(tmp_file)\\n\\n view.show_popup(\\n TEMPLATE % (width, height, \\\"png\\\", encoded, real_width, real_height, size),\\n sublime.HIDE_ON_MOUSE_MOVE_AWAY,\\n point,\\n *view.viewport_extent(),\\n on_navigate=on_navigate\\n )\",\n \"def save_image(data, file_path):\\n with open(file_path, 'wb'):\\n prefix = 'data:image/webp;base64,'\\n data = data[len(prefix):]\\n byte_data = base64.b64decode(data)\\n image_data = BytesIO(byte_data)\\n img = Image.open(image_data)\\n img.save(file_path)\\n return True\",\n \"def handle_as_url(view: sublime.View, point: int, string: str, name: str):\\n\\n # Let's assume this url as input:\\n # (https://upload.wikimedia.org/wikipedia/commons/8/84/Example.svg)\\n\\n # Download the image\\n # FIXME: avoid nested try-except clauses\\n try:\\n try:\\n f = urlopen(unquote(string)) # <==\\n except Exception:\\n try:\\n url_path = quote(string).replace(\\\"%3A\\\", ':', 1)\\n f = urlopen(url_path)\\n except Exception:\\n f = urlopen(string)\\n # don't fill the console with stack-trace when there`s no connection !!\\n except Exception as e:\\n print(e)\\n return\\n\\n # file needs conversion ?\\n need_conversion = name.endswith(formats_to_convert) # => True\\n basename, ext = osp.splitext(name) # => (\\\"Example\\\", \\\".svg\\\")\\n # create a temporary file\\n temp_img = osp.join(TEMP_DIR, \\\"tmp_image\\\" + ext) # => \\\"TEMP_DIR/tmp_image.svg\\\"\\n\\n # Save downloaded data in the temporary file\\n content = f.read()\\n with open(temp_img, \\\"wb\\\") as img:\\n img.write(content)\\n\\n # if the file needs conversion, convert it then read data from the resulting png\\n if need_conversion:\\n ext = \\\".png\\\"\\n # keep the image's temporary file and name for later use\\n conv_file = temp_img # => \\\"TEMP_DIR/tmp_image.svg\\\"\\n\\n # => \\\"TEMP_DIR/tmp_image.png\\\"\\n temp_png = osp.splitext(temp_img)[0] + \\\".png\\\"\\n\\n # use the magick command of Imagemagick to convert the image to png\\n magick(temp_img, temp_png)\\n\\n # read data from the resulting png\\n with open(temp_png, \\\"rb\\\") as png:\\n content = png.read()\\n\\n # set temp_file and name to the png file\\n temp_img = temp_png # => \\\"TEMP_DIR/tmp_image.png\\\"\\n\\n width, height, real_width, real_height, size = get_data(view, temp_img)\\n encoded = str(base64.b64encode(content), \\\"utf-8\\\")\\n\\n def on_navigate(href):\\n\\n if href == \\\"save\\\":\\n if need_conversion:\\n save(conv_file, name, \\\"url\\\")\\n else:\\n save(temp_img, name, \\\"url\\\")\\n elif href == \\\"save_as\\\":\\n if need_conversion:\\n convert(conv_file, \\\"url\\\", name)\\n else:\\n convert(temp_img, \\\"url\\\", name)\\n else:\\n sublime.active_window().open_file(temp_img)\\n\\n view.show_popup(\\n TEMPLATE % (width, height, ext, encoded, real_width, real_height,\\n str(size // 1024) + \\\"KB\\\" if size >= 1024 else str(size) + 'B'),\\n sublime.HIDE_ON_MOUSE_MOVE_AWAY,\\n point,\\n *view.viewport_extent(),\\n on_navigate=on_navigate\\n )\",\n \"def clean_webp_textures():\\n for webp in PNG_TEXTURES['output_files']:\\n if os.path.isfile(webp):\\n os.remove(webp)\",\n \"def toPng(self):\\n\\t\\tif self.isPng:\\n\\t\\t\\treturn self\\n\\t\\telse:\\n\\t\\t\\treturn textureFile( self.path.replace( self.extension, '.png' ) )\",\n \"def get_png_abs_path() -> pathlib.Path:\\n return PathManager._ROOT.joinpath(\\n PathManager._TILINGS_GUI, PathManager._RESOURCES, \\\"img\\\", \\\"png\\\"\\n )\",\n \"def _copy_to_media(self, template_name, source=''):\\n dirpath = os.path.join(self.cache_root, os.path.dirname(template_name))\\n filename = os.path.basename(template_name)\\n fullpath = os.path.join(dirpath, filename)\\n\\n if not os.path.isfile(fullpath) or settings.DEBUG:\\n if not os.path.exists(dirpath):\\n os.makedirs(dirpath)\\n\\n f = open(fullpath, 'w')\\n f.write(source)\\n f.close()\\n\\n return urljoin(self.cache_url, template_name), filename\",\n \"def test_write_unsupported_mode_PA(tmp_path):\\n\\n temp_file = str(tmp_path / \\\"temp.webp\\\")\\n file_path = \\\"Tests/images/transparent.gif\\\"\\n with Image.open(file_path) as im:\\n im.save(temp_file)\\n with Image.open(temp_file) as image:\\n assert image.mode == \\\"RGBA\\\"\\n assert image.size == (200, 150)\\n assert image.format == \\\"WEBP\\\"\\n\\n image.load()\\n image.getdata()\\n with Image.open(file_path) as im:\\n target = im.convert(\\\"RGBA\\\")\\n\\n assert_image_similar(image, target, 25.0)\",\n \"def make_image_path(raw_img, input_base, base_path):\\n path = os.path.dirname(raw_img)\\n relpath = os.path.relpath(path, input_base)\\n if relpath == '.':\\n dest_folder = base_path\\n else:\\n dest_folder = os.path.join(base_path, relpath)\\n return os.path.normpath(dest_folder)\\n # return dest_folder\",\n \"def build_image_path(self, src):\\r\\n o = urlparse(src)\\r\\n # we have a full url\\r\\n if o.hostname:\\r\\n return o.geturl()\\r\\n # we have a relative url\\r\\n return urljoin(self.target_url, src)\",\n \"def copy_png(\\n inpath: str, outpath: str, filter_chunks: Optional[Callable[[bytes], bool]] = None, verify_crc: bool = False\\n) -> None:\\n\\n with open(inpath, \\\"rb\\\") as fr, open(outpath, \\\"xb\\\") as fw:\\n copy_png_fp(fr, fw, filter_chunks=filter_chunks, verify_crc=verify_crc)\",\n \"def absolute_asset_url(module, path):\\n return absolute_uri(get_asset_url(module, path))\",\n \"def get_image_qm(html_src, todir):\\n #print url\\n\\n img_url, title = img_details(html_src)\\n \\n r = requests.get(img_url)\\n with open(todir+title+'.jpg','wb') as f:\\n f.write(r.content)\",\n \"def test_write_rgba(tmp_path):\\n\\n temp_file = str(tmp_path / \\\"temp.webp\\\")\\n\\n pil_image = Image.new(\\\"RGBA\\\", (10, 10), (255, 0, 0, 20))\\n pil_image.save(temp_file)\\n\\n if _webp.WebPDecoderBuggyAlpha():\\n return\\n\\n with Image.open(temp_file) as image:\\n image.load()\\n\\n assert image.mode == \\\"RGBA\\\"\\n assert image.size == (10, 10)\\n assert image.format == \\\"WEBP\\\"\\n image.load()\\n image.getdata()\\n\\n # Early versions of WebP are known to produce higher deviations:\\n # deal with it\\n if _webp.WebPDecoderVersion() <= 0x201:\\n assert_image_similar(image, pil_image, 3.0)\\n else:\\n assert_image_similar(image, pil_image, 1.0)\",\n \"def imgOutputPath(self, newpath):\\n newimg = self.imagePath().replace(self.home, newpath)\\n return newimg\",\n \"def test_encode_webp():\\n width = 51\\n height = 26\\n channels = 3\\n bmp_file = os.path.join(\\n os.path.dirname(os.path.abspath(__file__)), \\\"test_image\\\", \\\"lena.bmp\\\")\\n with open(bmp_file, 'rb') as f:\\n bmp_contents = f.read()\\n image_v = tf.image.decode_bmp(bmp_contents)\\n assert image_v.shape == [height, width, channels]\\n bmp_encoded = image_io.encode_bmp(image_v)\\n image_e = tf.image.decode_bmp(bmp_encoded)\\n assert np.all(image_v.numpy() == image_e.numpy())\",\n \"def get_image(url, path):\\n r = requests.get(url, stream=True)\\n if r.status_code == 200:\\n with open(path, 'wb') as f:\\n r.raw.decode_content = True\\n shutil.copyfileobj(r.raw, f)\\n print(\\\"[>] get\\\", url, \\\">>\\\", path)\\n f.close()\",\n \"def read_webp_image(h5_dataset):\\n data = h5_dataset[:].tobytes()\\n img_bytesio = BytesIO(data)\\n pil_img = Image.open(img_bytesio, 'r')\\n return pil_img\",\n \"def custom_static_serve(*args, **keywords):\\n response = serve(*args, **keywords)\\n if keywords[\\\"path\\\"].endswith(\\\".webp\\\"):\\n response.headers[\\\"Content-Type\\\"] = \\\"image/webp\\\"\\n return response\",\n \"def from_url(self) -> PngImagePlugin.PngImageFile:\\n response = requests.get(self.url)\\n img = Image.open(BytesIO(response.content))\\n\\n return img\",\n \"def convert_to_high_res(url):\\n return add_string_to_image_url(url, 'high-res')\",\n \"def source(request, filepath_pseudos):\\n filepath_pseudo = pathlib.Path(filepath_pseudos()) / 'Ar.upf'\\n\\n if request.param is str:\\n return str(filepath_pseudo)\\n\\n if request.param is pathlib.Path:\\n return filepath_pseudo\\n\\n return io.BytesIO(filepath_pseudo.read_bytes())\",\n \"def __make_png(self, abspath_img_rgb):\\n if not os.path.exists(DIR_PNG):\\n os.makedirs(DIR_PNG)\\n\\n outsize = '{}%'.format(OUTSIZE_RGB)\\n img_name_rgb = os.path.basename(abspath_img_rgb)\\n suffix_extension_tif = Utils.get_suffix_tif(img_name_rgb)\\n img_png = img_name_rgb.replace(suffix_extension_tif, '.png')\\n path_img_png = os.path.join(DIR_PNG, img_png)\\n\\n command = \\\"gdal_translate -ot byte -of PNG -outsize {} {} \\\" \\\\\\n \\\"-a_nodata 0 -q {} {}\\\".format(\\n outsize, outsize, abspath_img_rgb, path_img_png\\n )\\n os.system(command)\\n return os.path.join(DIR_PNG_TO_DB, img_png)\",\n \"def processed_json_path(path):\\n return path.replace(RAW_ASSETS_PATH, ASSETS_PATH).replace('.json', '.bin')\",\n \"def b64str_to_img_file(src, saveto, urlsafe=False):\\n file_base64 = src.encode('utf-8')\\n\\n if urlsafe:\\n file_byte = base64.urlsafe_b64decode(file_base64)\\n else:\\n file_byte = base64.standard_b64encode(file_base64)\\n\\n OUT_FILE = open(saveto, 'wb')\\n return OUT_FILE.write(file_byte)\",\n \"def testConvertHtmlWithPngDataUrlToPdf(self):\\n self._testBase(\\\"data/test_with_png_dataurl.html\\\")\",\n \"def to_file_path(self, resourcePath: str) -> PurePath:\\n rel = resourcePath.replace('res://', '')\\n return self._root.joinpath(rel)\",\n \"def convert_to_web(base_path, files):\\n for i, f in enumerate(files):\\n imagetype = get_imagetype_from_filename(f)\\n cmd = ('rsync '\\n '{base_path}/qc/phantom/{imagetype}/{f} '\\n '{base_path}/website/assets/{output}'.format(\\n base_path=base_path, imagetype=imagetype, \\n f=f, output=f[9:]))\\n os.system(cmd)\",\n \"def make_image(self, path):\\n\\t\\treturn self.ui.get_icon(path)\",\n \"def paster_in_svg(self, src, elem):\\n loger.info(\\\"start svg pasting\\\")\\n with open(src) as f:\\n tree = etree.parse(f)\\n root = tree.getroot()\\n element = tree.xpath('image')\\n\\n if element:\\n # Replaces <gco_CharacterString> text\\n for key, value in element[0].attrib.iteritems():\\n if value == 'avatar':\\n # element[0].attrib[key] = os.path.abspath(elem)\\n element[0].attrib[key] = \\\"/home/kryvonis/PycharmProjects/Book_Creator/image_end/1.png\\\"\\n # Save back to the XML file\\n etree.ElementTree(root).write(src, pretty_print=True)\\n loger.info('svg created - OK')\",\n \"def CanvasToPngString(canvas):\\n\\n temp_file_name = \\\"temp_io.png\\\"\\n canvas.Print(temp_file_name) \\n image = open(temp_file_name, 'r')\\n\\n data_uri = image.read().encode(\\\"base64\\\")\\n img_html_src = \\\"data:image/png;base64,%s\\\" % data_uri\\n\\n image.close()\\n os.remove(temp_file_name)\\n\\n return img_html_src\",\n \"def test_raw_static_check():\\r\\n path = '\\\"/static/foo.png?raw\\\"'\\r\\n assert_equals(path, replace_static_urls(path, DATA_DIRECTORY))\\r\\n\\r\\n text = 'text <tag a=\\\"/static/js/capa/protex/protex.nocache.js?raw\\\"/><div class=\\\"'\\r\\n assert_equals(path, replace_static_urls(path, text))\",\n \"def handle_as_file(view: View, point: int, string: str):\\n # \\\"screenshot.png\\\"\\n\\n name = osp.basename(string)\\n file, folder = get_file(view, string, name)\\n\\n # if file doesn't exist, return\\n if not osp.isfile(file):\\n return\\n\\n # does the file need conversion ?\\n need_conversion = file.endswith(FORMAT_TO_CONVERT)\\n\\n # if the file needs conversion, convert it and read data from the resulting png\\n if need_conversion:\\n # keep the image's file and name for later use\\n conv_file = file\\n conv_name = name\\n\\n # create a temporary file\\n tmp_file = osp.join(TEMP_DIR, \\\"tmp_png.png\\\")\\n name = osp.splitext(name)[0] + \\\".png\\\"\\n\\n # use the magick command of Imagemagick to convert the image to png\\n magick(file, tmp_file)\\n\\n file = tmp_file\\n\\n with open(file, \\\"rb\\\") as f:\\n encoded = str(base64.b64encode(f.read()), \\\"utf-8\\\")\\n\\n real_width, real_height, size = get_image_size(file)\\n width, height = get_dimensions(view, file)\\n size = str(size // 1024) + \\\"KB\\\" if size >= 1024 else str(size) + 'B'\\n\\n def on_navigate(href):\\n\\n if href == \\\"save\\\":\\n if need_conversion:\\n save(conv_file, conv_name, \\\"file\\\")\\n else:\\n save(file, name, \\\"file\\\", folder)\\n elif href == \\\"save_as\\\":\\n convert(conv_file if need_conversion else file, \\\"file\\\")\\n else:\\n sublime.active_window().open_file(file)\\n\\n view.show_popup(\\n TEMPLATE % (width, height, \\\"png\\\", encoded, real_width,\\n real_height, size),\\n sublime.HIDE_ON_MOUSE_MOVE_AWAY,\\n point,\\n *view.viewport_extent(),\\n on_navigate=on_navigate)\",\n \"def create_compressed_file(self):\\n\\t\\tself._compressed_file_name = 'c_' + self.file_name\\n\\t\\tself._compressed_save_path = self.full_path.replace(self.file_name, self._compressed_file_name)\\n\\t\\tself._is_png = 'png' in self.file_extension\\n\\t\\tself._is_jpg = 'jpg' in self.file_extension\\n\\n\\t\\timage = Image.open(self.full_path)\\n\\n\\t\\tif self._is_png:\\n\\t\\t\\timage.save(self._compressed_save_path, quality=85, optimize=False, compress_level=9)\\n\\t\\telif self._is_jpg:\\n\\t\\t\\timage.save(self._compressed_save_path, quality=85, progressive=False)\\n\\t\\telse:\\n\\t\\t\\tprint('Non-recognized asset format!!')\\n\\t\\t\\texit()\\n\\n\\t\\tself._compressed_file_size = ufo.get_file_size_in_bytes(self._compressed_save_path)\\n\\n\\n\\t\\ttransfer_path = self._compressed_save_path.replace('c_' + self.file_name, self.file_name).replace('/configuration_files/', '/quasar_site_django/')\\n\\t\\tufo.copy_file_to_path(self._compressed_save_path, transfer_path)\",\n \"def get_raw_path(self):\\n\\n return self.raw_img_path\",\n \"def convert_gif(ctx):\\n ctx.run(\\n 'ffmpeg '\\n '-i resources/demo.mkv -filter_complex \\\"[0:v] palettegen\\\" '\\n 'resources/palette.png',\\n pty=True\\n )\\n ctx.run(\\n 'ffmpeg -i resources/demo.mkv '\\n '-i resources/palette.png '\\n '-filter_complex \\\"[0:v][1:v] paletteuse\\\" '\\n 'resources/demo.gif',\\n pty=True\\n )\",\n \"def get_full_path(_path: str):\\n if _path is None:\\n return None\\n\\n with open(r\\\"bot\\\\data\\\\image_config.json\\\") as f:\\n try:\\n image_config = json.load(f)\\n\\n except json.decoder.JSONDecodeError as e:\\n print(e)\\n return None\\n\\n base_url = image_config.get(\\\"base_url\\\")\\n poster_size = image_config.get(\\\"poster_sizes\\\")[-2]\\n return f\\\"{base_url}{poster_size}{_path}\\\"\",\n \"def _path(name: str):\\n return os.path.join(ASSET_PATH, name)\",\n \"def download(self, url, path_to_dir):\\n\\n if not os.path.exists(path_to_dir):\\n os.makedirs(path_to_dir)\\n\\n raw_data = self.__class__.get_raw_data(url)\\n path_to_image = os.path.join(path_to_dir, url.split('/')[-1].split('?')[0])\\n with open(path_to_image, 'wb') as f:\\n self.__class__.copy_to(raw_data, f)\\n\\n return path_to_image\",\n \"def outputPath():\\n scenePath = bpy.data.filepath\\n # If the scene hasn't been saved yet the path is empty.\\n # Returning an empty path prompts the user for saving the scene.\\n if not scenePath:\\n return\\n renderPath = os.path.join(os.path.dirname(scenePath), \\\"{}_thumbs\\\".format(NAME))\\n return renderPath\",\n \"def pil_loader(path):\\n with open(path, 'rb') as f:\\n with Image.open(f) as img:\\n sqrWidth = np.ceil(np.sqrt(img.size[0]*img.size[1])).astype(int)\\n return img.convert('L').resize((sqrWidth, sqrWidth))\",\n \"def transcode(filePath: str) -> str:\\n asset_uuid = uuid.uuid4()\\n outPath = os.path.join(\\\"/tmp\\\", str(asset_uuid)+'.mp4')\\n ffmpeg.input(filePath).output(outPath).run()\\n return outPath\",\n \"def get_image_url():\",\n \"def convert_canvas_to_img_js():\\n return None\",\n \"def media_path_to_url(path):\\n media_url = settings.MEDIA_URL\\n if media_url.endswith('/'):\\n media_url = media_url[:-1]\\n return path.replace(settings.MEDIA_ROOT, media_url)\",\n \"def make_image(self):\\n\\n if self.type == 'passthrough':\\n return\\n render_template(\\n os.path.dirname(self.main_module),\\n os.path.basename(self.main_module_path),\\n self.language,\\n self.requirements,\\n self.whitelist,\\n self.type,\\n into=self.code_dir)\\n self.build()\",\n \"def assetsPath(self, pattern, source):\\n return self.assets(pattern, AssetSource.create(source))\",\n \"def get_file_url(path, config):\\n file_url_regex = re.compile(config['file_url_regex'])\\n new_path = re.sub(file_url_regex, config['file_url_base'], path)\\n return new_path\",\n \"def _url_to_image_file(output_path, response, file_name=None):\\n if not response.status_code == 200:\\n result = False\\n elif response.headers[\\\"content-type\\\"].startswith(\\\"image/\\\"):\\n if not file_name:\\n file_name = _filename_from_url(response.url)\\n _bin_body_to_file(join(output_path, file_name), response)\\n result = True\\n else:\\n warn_msg = (\\\"URL is valid and address is reachable, but it doesn't lead to an image file.\\\"\\n f\\\"MIME type is {response.headers['content-type']}.\\\")\\n logger.warning(warn_msg)\\n result = True\\n return result\",\n \"def create_website_image(fname, item):\\n\\n public_site_files_path = os.path.abspath(\\n frappe.get_site_path('public', 'files'))\\n\\n # Create a symbolic link and a thumbnail for the website image\\n path, ext = os.path.splitext(fname)\\n web_fname = path + '_web' + ext\\n thumb_fname = path + '_thumb' + ext\\n\\n # Full paths to original file, web image symlink and thumbnail\\n file_fpath = os.path.join(public_site_files_path, fname)\\n web_fpath = os.path.join(public_site_files_path, web_fname)\\n thumb_fpath = os.path.join(public_site_files_path, thumb_fname)\\n\\n # URLs on website for web image symlink and thumbnail\\n web_url = '/' + os.path.join('files', web_fname)\\n thumb_url = '/' + os.path.join('files', thumb_fname)\\n\\n # Create the symbolic link and create the thumbnail\\n try:\\n os.symlink(file_fpath, web_fpath)\\n except OSError:\\n if os.path.islink(web_fpath):\\n os.remove(web_fpath)\\n files = frappe.get_all(\\n 'File', filters={'file_url': web_url})\\n for file in files:\\n frappe.delete_doc('File', file['name'],\\n ignore_permissions=True)\\n os.symlink(file_fpath, web_fpath)\\n else:\\n raise\\n resize_image(file_fpath, out=thumb_fpath, thumbnail=True)\\n\\n # Document for web image\\n f = frappe.get_doc({\\n \\\"doctype\\\": \\\"File\\\",\\n \\\"file_url\\\": web_url,\\n \\\"file_name\\\": web_fname,\\n \\\"attached_to_doctype\\\": \\\"Item\\\",\\n \\\"attached_to_name\\\": item,\\n \\\"attached_to_field\\\": None,\\n \\\"folder\\\": 'Home/Attachments',\\n \\\"file_size\\\": os.path.getsize(web_fpath),\\n \\\"is_private\\\": 0\\n })\\n try:\\n f.insert(ignore_permissions=True)\\n except frappe.FileAlreadyAttachedException:\\n # If already attached, don't attach again\\n pass\\n\\n return web_url, thumb_url\",\n \"def _target_sprite_sheet_path(self) -> Path:\",\n \"def JPGtoPNGConverter(source, dest):\\n files = os.listdir(f\\\"./{source}\\\")\\n if not os.path.exists(f\\\"./{dest}\\\"):os.makedirs(f\\\"./{dest}\\\")\\n\\n for file in files:\\n if os.path.splitext(file)[-1] == \\\".jpg\\\":\\n img = Image.open(f\\\"./{source}/{file}\\\")\\n clean_text = os.path.splitext(file)[0]\\n img.save(f\\\"./{dest}/{clean_text}.png\\\",\\\"png\\\")\\n else:\\n print(f\\\"Your filename: {file} is not in .JPG format !!\\\")\\n return \\\"All files converted successfully :) \\\"\",\n \"def translate(source):\\n # ignore URLs and absolute file system paths as a failsafe\\n if core.utils.is_absolute_url(source) or os.path.isabs(source):\\n return source\\n\\n # process relative path\\n # if REFINERY_DEPLOYMENT_PLATFORM = 'aws' and REFINERY_S3_USER_DATA\\n # use settings.COGNITO_IDENTITY_POOL_ID\\n if identity_id:\\n source = \\\"s3://{}/{}/{}\\\".format(\\n settings.UPLOAD_BUCKET, identity_id, source\\n )\\n elif base_path:\\n source = os.path.join(base_path, source)\\n elif username:\\n source = os.path.join(\\n settings.REFINERY_DATA_IMPORT_DIR, username, source)\\n else:\\n raise ValueError(\\\"Failed to translate relative source path: \\\"\\n \\\"must provide either username or base_path\\\")\\n return source\",\n \"def download_pil_image(self, url):\\r\\n return Image.open(urlopen(url))\",\n \"def drop_png_image(im, trg):\\n\\n\\tif trg[-4:] != '.png':\\n\\t\\tERROR('destination name must be png extension', 'drop_png_image', 1)\\n\\n\\tif isinstance(trg, basestring):\\n\\t\\tim['render_min'] = im['minimum']\\n\\t\\tim['render_max'] = im['maximum']\\n\\t\\tim.write_image(trg, 0)\\n\\telse:\\n\\t\\tERROR('destination is not a file name', 'drop_png_image', 1)\",\n \"def latex2img(expression, filename):\\n webp = False\\n\\n extension = \\\"png\\\"\\n\\n # Preparing text strings\\n server = \\\"http://latex.codecogs.com/\\\" + extension + \\\".download?\\\"\\n fullname = filename + \\\".\\\" + extension\\n size = \\\"%5Cdpi%7B100%7D%20\\\"\\n\\n # Quote expression引用表达式\\n expression = quote(expression)\\n url = server + size + expression\\n\\n # Download file from url and save to output_file:\\n with urlopen(url) as response, open(fullname, 'wb') as output_file:\\n data = response.read() # Un objeto \\\"bytes\\\"\\n output_file.write(data) # Se escribe en disco\\n\\n if webp:\\n img2webp(fullname)\\n extension = \\\"webp\\\"\\n\\n return filename + \\\".\\\" + extension\",\n \"def decode_file(source, palette):\\n\\n (CHRStart, CHRSize) = get_CHR_data_position(source)\\n charRowCount = CHRSize // 256 # 16 characters/row\\n\\n img = Image.new(\\\"P\\\", (128, charRowCount * 8), 0)\\n img.putpalette(itertools.chain.from_iterable(palette))\\n\\n source.seek(CHRStart)\\n for (y, pixelRow) in enumerate(decode_pixel_rows(source, charRowCount)):\\n for (x, value) in enumerate(pixelRow):\\n img.putpixel((x, y), value)\\n\\n return img\",\n \"def transform(img_path):\\n\\n img = self.loader(img_path)\\n return self.transform(img)\",\n \"def Sourceify(path):\\n return path\",\n \"def image_png():\\n data = resource(\\\"images/pig_icon.png\\\")\\n return Response(data, headers={\\\"Content-Type\\\": \\\"image/png\\\"})\",\n \"def resourcePath(relative):\\r\\n try:\\r\\n # PyInstaller creates a temp folder and stores path in _MEIPASS\\r\\n base_path = sys._MEIPASS\\r\\n except Exception:\\r\\n base_path = os.path.abspath(os.path.join(os.path.dirname(sys.modules[__name__].__file__), 'assets'))\\r\\n\\r\\n return os.path.join(base_path, relative)\",\n \"def save_file(self, input_path, output_path):\\n try:\\n im = Image.open(input_path)\\n im.save(output_path)\\n return output_path\\n except Exception as e:\\n return '!ERROR' + str(e)\",\n \"def test_handle_new_picture():\\n import os\\n from .views.default import handle_new_picture\\n here = os.path.abspath(os.path.dirname(__file__))\\n path = os.path.join(here, 'scripts/img_questions/how.jpg')\\n with open(path, 'rb') as ouput_file:\\n new_picture = handle_new_picture(\\\"name\\\", ouput_file)\\n assert isinstance(new_picture, bytes)\",\n \"def propose_image_path():\\n image_name = \\\"image_{}.png\\\".format(''.join(random.choice('abcdefghijklmnopqrstuvwxyz') for _ in range(8)))\\n image_path = os.path.join(_IMAGE_BASE_PATH, image_name)\\n image_url = '/images/' + image_name\\n return image_path, image_url\",\n \"def wx2PIL(self, bitmap):\\n size = tuple(bitmap.GetSize())\\n try:\\n buf = size[0]*size[1]*3*\\\"\\\\x00\\\"\\n bitmap.CopyToBuffer(buf)\\n except:\\n del buf\\n buf = bitmap.ConvertToImage().GetData()\\n return Image.frombuffer(\\\"RGB\\\", size, buf, \\\"raw\\\", \\\"RGB\\\", 0, 1)\",\n \"async def inverter(self, ctx, bilde=None):\\n\\n async with ctx.channel.typing():\\n\\n if not await LBlend_utils.download_photo(ctx, link=bilde, max_file_size=8, meassurement_type='MB',\\n filepath=f'./assets/temp/{ctx.author.id}_raw.png'):\\n return\\n\\n raw_image = Image.open(f'./assets/temp/{ctx.author.id}_raw.png')\\n image_edit = raw_image.convert('RGB')\\n image_edit = ImageOps.invert(image_edit)\\n image_edit.save(f'./assets/temp/{ctx.author.id}_edit.png')\\n\\n f = discord.File(f'./assets/temp/{ctx.author.id}_edit.png')\\n embed = discord.Embed(color=ctx.me.color)\\n embed.set_image(url=f'attachment://{ctx.author.id}_edit.png')\\n await Defaults.set_footer(ctx, embed)\\n await ctx.send(embed=embed, file=f)\\n\\n try:\\n remove(f'./assets/temp/{ctx.author.id}_raw.png')\\n remove(f'./assets/temp/{ctx.author.id}_edit.png')\\n except:\\n pass\\n\\n return\",\n \"def get_asset_path(name):\\n return os.path.join(constants.ROOT_DIR, 'assets', name)\",\n \"def load_img(path: PurePath, convert_alpha: bool = True) -> Surface:\\n if convert_alpha:\\n return load(str(path)).convert_alpha()\\n return load(str(path)).convert()\",\n \"def prepare_bubble(path): \\n img = load_ipl_as_array(path)\\n img = gray_bg(img)\\n return img\",\n \"def decode(p):\\n #assert p.endswith('.' + EXTENSION)\\n p2 = os.path.basename(p).replace('baseline.png', '.png')\\n p2p = os.path.join('/mnt/Volume0/test/clic2020-devkit/result/', p2) #add by me\\n pp = os.path.join('/mnt/Volume0/test/clic2020-devkit/targets',p2)\\n p2 = os.path.join('/mnt/Volume0/test/clic2020-devkit/inputs/', p2) #add by me\\n p1 = pframe_dataset_shared.get_previous_frame_path(p2)\\n #p1 = os.path.join('/mnt/Volume0/test/clic2020-devkit/test_data/inputs/', p1)\\n #assert os.path.isfile(p1), (p2, p1, p, len(glob.glob('*.png')))\\n b = Image.open(p).convert('L')\\n f2_reconstructed = decoder(np.array(Image.open(p1)), b)\\n Image.fromarray(f2_reconstructed).save(p2p)\\n return f2_reconstructed, np.array(Image.open(pp))\",\n \"def convertPath(srcpath, dstdir):\\n bits = srcpath.split(\\\"/\\\")\\n bits.pop(0)\\n # Strip out leading 'unsigned' from paths like unsigned/update/win32/...\\n if bits[0] == 'unsigned':\\n bits.pop(0)\\n return os.path.join(dstdir, *bits)\",\n \"def getRoverImage(self):\\n # Your code goes here, this code is just an example\\n return 'mario.ppm'\",\n \"def normalize_path(img_file):\\n\\n img_file = img_file.split('/')[-1]\\n img_file = 'data/IMG/'+img_file.split('\\\\\\\\')[-1]\\n return img_file\",\n \"def test_projectedtexture(pngfile):\\n tex = omf.ProjectedTexture()\\n tex.image = pngfile\\n assert tex.validate()\",\n \"def resource_path(self,relative_path):\\n try:\\n # PyInstaller creates a temp folder and stores path in _MEIPASS\\n base_path = sys._MEIPASS\\n except Exception:\\n base_path = os.path.abspath(\\\".\\\\\\\\Visual_Ressources\\\\\\\\\\\"+self.language+\\\"\\\\\\\\\\\") \\n # \\\".\\\"\\n # 'Content\\\\\\\\Back_End\\\\\\\\'\\n return os.path.join(base_path, relative_path)\",\n \"def copy_web_resources(output_dir):\\n mypath = os.path.dirname(os.path.realpath(__file__))\\n web_path = os.path.join(mypath, 'web')\\n\\n for (dirpath, dirnames, filenames) in os.walk(web_path):\\n relpath = os.path.relpath(dirpath, web_path)\\n tgtpath = os.path.join(output_dir, relpath)\\n if not os.path.exists(tgtpath):\\n os.makedirs(tgtpath)\\n\\n for f in [os.path.join(dirpath, filename) for filename in filenames]:\\n shutil.copy(f, tgtpath)\",\n \"def update_png_crusher():\\n if os.path.exists(PNG_CRUSHER):\\n return\\n\\n for path in glob.glob(os.path.join(libdot.BIN_DIR, '.png.crusher.*')):\\n os.unlink(path)\\n\\n r = requests.get(PNG_CRUSHER_URL + '?format=TEXT')\\n with open(PNG_CRUSHER, 'wb') as fp:\\n fp.write(base64.b64decode(r.text))\\n\\n os.chmod(PNG_CRUSHER, 0o755)\",\n \"def getPILFromPath(self, path):\\n obj = self.getImageObject(path)\\n return self.getPILFromObject(obj)\",\n \"def get_asset_path(test):\\n return DEVICE_ASSETS_PATH + os.path.basename(test)\",\n \"def prepare_single_image(cls, path):\\n return T.Compose(cls.base_transforms)(Image.open(path))\",\n \"def img_url_tiny(self):\\n url = '%s=s%s-c' % (self.img_url, self.TINY_SIZE_PX)\\n if self.img_rot in Plaque.ALLOWED_ROTATIONS:\\n url = \\\"%s-r%s\\\" % (url, self.img_rot)\\n return url\",\n \"def _serve_asset(self, path, gzipped_asset_bytes, request):\\n mimetype = mimetypes.guess_type(path)[0] or 'application/octet-stream'\\n return http_util.Respond(\\n request, gzipped_asset_bytes, mimetype, content_encoding='gzip')\",\n \"def fits_to_pl( in_image, out_image=None ):\\n try:\\n if out_image is None:\\n input_ext = os.path.splitext( in_image )[1]\\n out_image = in_image.replace( input_ext, '.pl' )\\n \\\"\\\"\\\"\\n Delete the old file, if existed\\n \\\"\\\"\\\"\\n if os.path.exists( out_image ):\\n os.remove( out_image )\\n \\\"\\\"\\\" imcopy \\\"\\\"\\\"\\n iraf.imcopy( in_image, out_image )\\n \\\"\\\"\\\" Return the path of the output \\\"\\\"\\\"\\n return out_image\\n except:\\n print \\\"<Error>: \\\", sys.exc_info()[1]\\n raise\",\n \"def resource_path(name):\\n return os.path.join(\\n os.path.dirname(__file__), 'images', 'resource', name)\",\n \"def save_file(output: Optional[Path], input_path: Path, image: Image.Image):\\n if isinstance(output, Path) and str(output) != \\\"none\\\":\\n if output.is_dir() and output.exists():\\n image.save(output.joinpath(input_path.with_suffix(\\\".png\\\").name))\\n elif output.suffix != \\\"\\\":\\n if output.suffix != \\\".png\\\":\\n warnings.warn(\\n f\\\"Only export with .png extension is supported! Your {output.suffix}\\\"\\n f\\\" extension will be ignored and replaced with .png!\\\"\\n )\\n image.save(output.with_suffix(\\\".png\\\"))\\n else:\\n raise ValueError(\\\"Wrong output path!\\\")\\n elif output is None or str(output) == \\\"none\\\":\\n image.save(\\n input_path.with_name(\\n input_path.stem.split(\\\".\\\")[0] + \\\"_bg_removed\\\"\\n ).with_suffix(\\\".png\\\")\\n )\",\n \"def server_static_img(filename):\\n return static_file(filename, root='static/img')\",\n \"def uri_to_file(uri):\\n if not uri:\\n return\\n\\n header, data = uri.split(',')\\n md5_hash = hashlib.md5(data).hexdigest()\\n with open(\\\"static/images/%s.jpg\\\" % md5_hash, 'wb') as f:\\n f.write(data.decode('base64'))\\n return md5_hash\",\n \"def image(self):\\n # TODO: make sure this method works for png, gif, tiff\\n if self.has_metadata:\\n self.extract_metadata()\\n tempdir_path = self.make_tempdir()\\n tempfile_path = os.path.join(tempdir_path, self.filename)\\n warnings.simplefilter('error', Image.DecompressionBombWarning)\\n try: # Do image conversions\\n img_in = Image.open(self.src_path)\\n img_out = Image.frombytes(img_in.mode, img_in.size, img_in.tobytes())\\n img_out.save(tempfile_path)\\n self.src_path = tempfile_path\\n except Exception as e: # Catch decompression bombs\\n # TODO: change this from all Exceptions to specific DecompressionBombWarning\\n self.add_error(e, \\\"Caught exception (possible decompression bomb?) while translating file {}.\\\".format(self.src_path))\\n self.make_dangerous()\\n self.add_file_string('Image file')\\n self.set_property('processing_type', 'image')\",\n \"def convert_pptx_to_rgb(input_file, output_file):\\n with ZipFile(input_file) as input_pptx_file:\\n # theme is defined in the presentation rels\\n rel = PresentationRel(input_pptx_file)\\n theme_name = f'ppt/{rel.theme}'\\n theme = read_color_map(zip_file=input_pptx_file, file_name=theme_name)\\n color_map = theme[1]\\n\\n new_file_data = []\\n\\n file_list = input_pptx_file.namelist()\\n\\n for file in file_list:\\n with input_pptx_file.open(name=file) as current_file:\\n file_contents = current_file.read()\\n # only convert XML files of slides\\n if re.match(r'ppt/slides/slide(\\\\d+).xml', file):\\n new_contents = convert(content=file_contents, color_map=color_map)\\n else:\\n new_contents = file_contents\\n new_file_data.append(\\n {\\n 'file': file,\\n 'data': new_contents\\n }\\n )\\n\\n # create/write the new ZIP\\n with ZipFile(output_file, 'w') as output_pptx_file:\\n for data in new_file_data:\\n output_pptx_file.writestr(data['file'], data['data'], ZIP_DEFLATED)\",\n \"def _get_src_from_image(img, fallback_image_file):\\n # If the image is None, then we can't process, so we should return the\\n # path to the file itself\\n if img is None:\\n return fallback_image_file\\n # Target format should be the same as the original image format, unless it's\\n # a TIF/TIFF, which can't be displayed by most browsers; we convert these\\n # to jpeg\\n target_format = img.format\\n if target_format.lower() in ['tif', 'tiff']:\\n target_format = 'JPEG'\\n # If we have an actual Image, great - put together the base64 image string\\n try:\\n bytesio = io.BytesIO()\\n img.save(bytesio, target_format)\\n byte_value = bytesio.getvalue()\\n b64 = base64.b64encode(byte_value)\\n return 'data:image/%s;base64,%s' % (target_format.lower(), b64)\\n except IOError as exptn:\\n print('IOError while saving image bytes: %s' % exptn)\\n return fallback_image_file\",\n \"def transcode(path, outpath):\\n\\n needs_transcode = determine_transcode(path)\\n logger.info(f\\\"Transcoding {path} to {outpath}...\\\")\\n\\n cmd = [\\n \\\"ffmpeg\\\", \\\"-y\\\",\\n \\\"-i\\\", path,\\n \\\"-an\\\",\\n \\\"-metadata:s\\\", \\\"handler_name=tator\\\",\\n \\\"-vcodec\\\", \\\"libx264\\\",\\n \\\"-g\\\", \\\"25\\\",\\n \\\"-preset\\\", \\\"fast\\\",\\n \\\"-pix_fmt\\\", \\\"yuv420p\\\",\\n \\\"-vf\\\", \\\"pad=ceil(iw/2)*2:ceil(ih/2)*2\\\",\\n \\\"-movflags\\\",\\n \\\"faststart+frag_keyframe+empty_moov+default_base_moof\\\",\\n \\\"-tune\\\", \\\"fastdecode\\\",\\n ]\\n\\n if needs_transcode[1]:\\n #Resize to 720p\\n cmd.extend([\\\"-vf\\\", \\\"scale=-2:720\\\"])\\n\\n cmd.append(outpath)\\n logger.info('ffmpeg cmd = {}'.format(cmd))\\n subprocess.run(cmd, check=True)\\n logger.info(\\\"Transcoding finished!\\\")\",\n \"def test_keep_rgb_values_when_transparent(tmp_path):\\n\\n image = hopper(\\\"RGB\\\")\\n\\n # create a copy of the image\\n # with the left half transparent\\n half_transparent_image = image.copy()\\n new_alpha = Image.new(\\\"L\\\", (128, 128), 255)\\n new_alpha.paste(0, (0, 0, 64, 128))\\n half_transparent_image.putalpha(new_alpha)\\n\\n # save with transparent area preserved\\n temp_file = str(tmp_path / \\\"temp.webp\\\")\\n half_transparent_image.save(temp_file, exact=True, lossless=True)\\n\\n with Image.open(temp_file) as reloaded:\\n assert reloaded.mode == \\\"RGBA\\\"\\n assert reloaded.format == \\\"WEBP\\\"\\n\\n # even though it is lossless, if we don't use exact=True\\n # in libwebp >= 0.5, the transparent area will be filled with black\\n # (or something more conducive to compression)\\n assert_image_equal(reloaded.convert(\\\"RGB\\\"), image)\",\n \"def generate_image_path(playbook_name: str, custom_image_path: str):\\n if custom_image_path:\\n playbook_image_path = custom_image_path\\n else:\\n playbook_image_path = \\\"../doc_files/\\\" + playbook_name.replace(\\\" \\\", \\\"_\\\") + \\\".png\\\"\\n\\n return f\\\"\\\"\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.66254675","0.6108834","0.59269124","0.57612014","0.54063636","0.5362226","0.5322664","0.53141046","0.5290474","0.5259612","0.520573","0.51810825","0.51549965","0.51190436","0.5110097","0.51075953","0.50395477","0.5022422","0.48821872","0.48676977","0.4838838","0.48383144","0.48332033","0.4826889","0.48095867","0.48065874","0.4792933","0.47814026","0.47511697","0.47389063","0.47190306","0.4716091","0.47151935","0.47058833","0.46798578","0.4679319","0.4655083","0.4650965","0.46442086","0.46384895","0.4636359","0.46138862","0.4612137","0.46118236","0.46053794","0.46049708","0.45964375","0.45920712","0.45821264","0.45817184","0.45703888","0.4563953","0.4563663","0.45608196","0.4555983","0.455178","0.45494223","0.45492715","0.45486775","0.4547559","0.45457053","0.4542117","0.45403823","0.4532879","0.45270854","0.45256156","0.45220548","0.45143616","0.4512514","0.45092243","0.45026153","0.44969028","0.44862068","0.4485771","0.44792667","0.44739336","0.4473851","0.4473475","0.44646817","0.44629303","0.4459021","0.44556805","0.44549936","0.4454337","0.44389454","0.44376156","0.44300818","0.44264203","0.4425759","0.44249204","0.44216415","0.44047195","0.43973452","0.43964785","0.4395852","0.4392699","0.4378071","0.43774202","0.43773723","0.437664"],"string":"[\n \"0.66254675\",\n \"0.6108834\",\n \"0.59269124\",\n \"0.57612014\",\n \"0.54063636\",\n \"0.5362226\",\n \"0.5322664\",\n \"0.53141046\",\n \"0.5290474\",\n \"0.5259612\",\n \"0.520573\",\n \"0.51810825\",\n \"0.51549965\",\n \"0.51190436\",\n \"0.5110097\",\n \"0.51075953\",\n \"0.50395477\",\n \"0.5022422\",\n \"0.48821872\",\n \"0.48676977\",\n \"0.4838838\",\n \"0.48383144\",\n \"0.48332033\",\n \"0.4826889\",\n \"0.48095867\",\n \"0.48065874\",\n \"0.4792933\",\n \"0.47814026\",\n \"0.47511697\",\n \"0.47389063\",\n \"0.47190306\",\n \"0.4716091\",\n \"0.47151935\",\n \"0.47058833\",\n \"0.46798578\",\n \"0.4679319\",\n \"0.4655083\",\n \"0.4650965\",\n \"0.46442086\",\n \"0.46384895\",\n \"0.4636359\",\n \"0.46138862\",\n \"0.4612137\",\n \"0.46118236\",\n \"0.46053794\",\n \"0.46049708\",\n \"0.45964375\",\n \"0.45920712\",\n \"0.45821264\",\n \"0.45817184\",\n \"0.45703888\",\n \"0.4563953\",\n \"0.4563663\",\n \"0.45608196\",\n \"0.4555983\",\n \"0.455178\",\n \"0.45494223\",\n \"0.45492715\",\n \"0.45486775\",\n \"0.4547559\",\n \"0.45457053\",\n \"0.4542117\",\n \"0.45403823\",\n \"0.4532879\",\n \"0.45270854\",\n \"0.45256156\",\n \"0.45220548\",\n \"0.45143616\",\n \"0.4512514\",\n \"0.45092243\",\n \"0.45026153\",\n \"0.44969028\",\n \"0.44862068\",\n \"0.4485771\",\n \"0.44792667\",\n \"0.44739336\",\n \"0.4473851\",\n \"0.4473475\",\n \"0.44646817\",\n \"0.44629303\",\n \"0.4459021\",\n \"0.44556805\",\n \"0.44549936\",\n \"0.4454337\",\n \"0.44389454\",\n \"0.44376156\",\n \"0.44300818\",\n \"0.44264203\",\n \"0.4425759\",\n \"0.44249204\",\n \"0.44216415\",\n \"0.44047195\",\n \"0.43973452\",\n \"0.43964785\",\n \"0.4395852\",\n \"0.4392699\",\n \"0.4378071\",\n \"0.43774202\",\n \"0.43773723\",\n \"0.437664\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.72864443"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":11291,"cells":{"query":{"kind":"string","value":"Searches for a file with named `name` in the given paths and returns it."},"document":{"kind":"string","value":"def find_executable(name, paths):\n for path in paths:\n full_path = os.path.join(path, name)\n if os.path.isfile(full_path):\n return full_path\n # If not found, just assume it's in the PATH.\n return name"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def find_in_paths(name, paths):\n for path in paths:\n file_ = osp.join(path, name)\n if osp.exists(file_) and not osp.isdir(file_):\n abs_file = osp.abspath(file_)\n if os.access(file_, os.X_OK):\n return abs_file","def search_file(file_name, search_path):\n if os.path.exists(search_path):\n filenames = os.listdir(search_path)\n for filename in filenames:\n if file_name in filenames:\n return os.path.join(search_path, file_name)\n return None","def find(name):\n\n if os.path.exists(name):\n return name\n\n path = os.path.dirname(__file__) or '.'\n filename = os.path.abspath(os.path.join(path,name))\n if os.path.exists(filename):\n return filename\n\n for d in os.listdir(path):\n fullpath = os.path.abspath(os.path.join(path,d))\n if os.path.isdir(fullpath):\n filename = os.path.abspath(os.path.join(fullpath,name))\n if os.path.exists(filename):\n return filename\n return None","def find_file_by_name(**kwargs):\n return AppServer.service.find_file_by_name(name=kwargs['name'])","def search_file_by_name(cls, name: str, root_path: str, return_path_only: bool = True,\n\t\t\t\t\t\t\treturn_all_files: bool = False,\n\t\t\t\t\t\t\tallow_partial_name: bool = False):\n\n\t\t# if allow_partial_name and return_path_only:\n\t\t# \timport warnings\n\t\t# \twarnings.warn(\n\t\t# \t\t'{}.{} - Due to \"allow_partial_name\" is set to True, \"return_path_only\" has been set to False '.format(\n\t\t# \t\t\t__name__, cls.search_file_by_name.__name__), RuntimeWarning)\n\t\tresult_list = []\n\t\tfor dirpath, dirnames, filenames in os.walk(root_path):\n\t\t\tfor filename in filenames:\n\t\t\t\tif (name == filename and (not allow_partial_name)) or (\n\t\t\t\t\t\t(filename.find(name) != -1) and allow_partial_name):\n\t\t\t\t\tif return_path_only and not allow_partial_name:\n\t\t\t\t\t\tabsolute_path = dirpath\n\t\t\t\t\telse:\n\t\t\t\t\t\tabsolute_path = os.path.join(dirpath, filename)\n\t\t\t\t\tif not return_all_files:\n\t\t\t\t\t\treturn absolute_path\n\t\t\t\t\telse:\n\t\t\t\t\t\tresult_list.append(absolute_path)\n\t\tif result_list:\n\t\t\treturn result_list\n\t\treturn None","def find_file(filename: str, paths: List[str] = None, extensions=None) -> str:\n\n extensions = conf.SEARCH_EXTENSIONS if not extensions else extensions\n paths = conf.SEARCH_DIRS if not paths else paths\n\n if '' not in extensions:\n extensions += ['']\n\n if os.path.isabs(filename):\n with open(filename, 'r'):\n return filename\n\n for ext in extensions:\n _fn = [f'{filename}{ext}']\n if '/' in filename:\n _fn = list(filename.split('/'))\n _fn[-1] = f'{_fn[-1]}{ext}'\n\n for p in paths:\n fpath = join(p, *_fn)\n try:\n with open(fpath, 'r'):\n return fpath\n except FileNotFoundError:\n continue\n\n raise FileNotFoundError(f'File \"{filename}\" could not be found in any of the given paths.')","def find(path, name):\n found = [] # list of found items\n\n def find_recursive(path, name, found): # recursive function to traverse tree and append found items\n path_abs = os.path.abspath(path) # get the absolute path name\n \n try:\n dir_items = os.listdir(path_abs)\n except:\n print(f\"ERROR: Directory {path} not found...\")\n sys.exit(1)\n\n dir_items = os.listdir(path_abs) # list all times in the currrent directory\n\n for item in dir_items: # iterate over the dir list\n full_path = os.path.join(path_abs, item) # get the full path name path + filename/directory\n if os.path.isdir(full_path): # if is a directory\n find_recursive(full_path, name, found) # make recursive call to function\n\n if os.path.isfile(full_path): # if a file\n if item == name: # check for name equality\n found.append(full_path) # if true: add to list\n\n find_recursive(path, name, found) # call recursive function\n\n return found","def get_file_by_name(mod_files: List[PatchedFile], filename: str):\n for mod in mod_files:\n if mod.path == filename:\n return mod\n return None","def find_in_path(filenames):\n # http://aspn.activestate.com/ASPN/Cookbook/Python/Recipe/52224\n from os.path import exists, join, abspath\n from os import pathsep, environ\n search_path = environ[\"PATH\"]\n paths = search_path.split(pathsep)\n for path in paths:\n for filename in filenames:\n if exists(join(path, filename)):\n return abspath(join(path, filename))","def search_on_path(filenames):\n # http://aspn.activestate.com/ASPN/Cookbook/Python/Recipe/52224\n\n from os.path import exists, join, abspath\n from os import pathsep, environ\n\n search_path = environ[\"PATH\"]\n\n paths = search_path.split(pathsep)\n for path in paths:\n for filename in filenames:\n if exists(join(path, filename)):\n return abspath(join(path, filename))","def search(cls, name, lookup=[]):\r\n if os.path.isfile(name): return name\r\n for spath in lookup:\r\n fname = os.path.join(spath, name)\r\n if os.path.isfile(fname):\r\n return fname\r\n for ext in cls.extensions:\r\n if os.path.isfile('%s.%s' % (fname, ext)):\r\n return '%s.%s' % (fname, ext)","def search_file(filename, search_path):\n file_path = None\n for path in search_path:\n if exists(join(path, filename)):\n file_path = path\n break\n if file_path:\n return abspath(join(file_path, filename))\n return None","def findPath(self, name=None, pattern=None, extension=None, ignorePatterns=None,\n path=None, paths=None):\n paths = self.findPaths(name, pattern, extension, ignorePatterns, path, paths)\n if paths: # If anything found, then answer the first one.\n return paths[0]\n return None","def _findfile(self, path):\n\n # Build list of possible local file paths\n if not self._isurl(path):\n # Valid local paths\n filelist = self._possible_names(path)\n # Paths in self._destpath\n filelist += self._possible_names(self.abspath(path))\n else:\n # Cached URLs in self._destpath\n filelist = self._possible_names(self.abspath(path))\n # Remote URLs\n filelist = filelist + self._possible_names(path)\n\n for name in filelist:\n if self.exists(name):\n if self._isurl(name):\n name = self._cache(name)\n return name\n return None","def find_file(filesystem, dirs, filename):\r\n for directory in dirs:\r\n filepath = path(directory) / filename\r\n if filesystem.exists(filepath):\r\n return filepath\r\n raise ResourceNotFoundError(u\"Could not find {0}\".format(filename))","def get_path(file_name):\n path_project = os.getcwd()\n list_files = os.listdir(path_project)\n # print (\"Getting File in your Current Directory\", path_project)\n __log(1, 'Gatering Files from current Directory.')\n\n # for i in list_files:\n if file_name in list_files:\n file_path = path_project + '\\\\' + file_name\n __log(1, 'Relevant File Found.')\n return file_path\n else:\n __log(2, 'File Not Found.')\n return -1\n # print (\"File Found\",file_path)","def search_file(filename, search_path, pathsep=os.pathsep):\n for path in string.split(search_path, pathsep):\n candidate = os.path.join(path, filename)\n if os.path.exists(candidate): return os.path.abspath(candidate)\n return None","def search_file(filename, search_path, pathsep=os.pathsep):\n for path in string.split(search_path, pathsep):\n candidate = os.path.join(path, filename)\n if os.path.exists(candidate): return os.path.abspath(candidate)\n return None","def search_image(name=None, path=['.']):\n name = strutils.decode(name)\n\n for image_dir in path:\n if not os.path.isdir(image_dir):\n continue\n image_dir = strutils.decode(image_dir)\n image_path = os.path.join(image_dir, name)\n if os.path.isfile(image_path):\n return strutils.encode(image_path)\n\n for image_path in list_all_image(image_dir):\n if not image_name_match(name, image_path):\n continue\n return strutils.encode(image_path)\n return None","def find_file(filename, std_dirs, paths):\n\n # Check the standard locations\n for dir in std_dirs:\n f = os.path.join(dir, filename)\n print('looking for', f)\n if os.path.exists(f):\n return []\n\n # Check the additional directories\n for dir in paths:\n f = os.path.join(dir, filename)\n print('looking for', f)\n if os.path.exists(f):\n return [dir]\n\n # Not found anywhere\n return None","def find_one(path, filenames):\n for _path in find_all(path, filenames):\n return _path\n return ''","def find_paths(file_name, app_name, search_folders=None):\n search_folders = search_folders or default_search_folders(app_name)\n possible_files = [os.path.join(folder, file_name) for folder in search_folders]\n return [path for path in possible_files if os.path.exists(path)]","def SearchPath(name):\n search_path = os.getenv('PATH', os.defpath).split(os.pathsep)\n for directory in search_path:\n if directory:\n path = os.path.join(directory, name)\n if os.path.isfile(path) and os.access(path, os.X_OK):\n return path\n return None","def find_file(file_name):\n if (pathlib.Path(file_name).resolve()):\n file_name = str(file_name)\n logging.info(f' found {file_name}.')\n return file_name\n else:\n logging.error(f' no file {file_name} found for processing.')\n sys.exit()","def find_file_in_folders(filename, folders):\n for folder in folders:\n folder = os.path.expanduser(folder)\n full_path = os.path.join(folder, filename)\n if os.path.exists(full_path):\n return full_path\n raise FileNotFoundError(\"Did not find file %s!\" % filename)","def find_file(filename):\n for i in list(_ctx.include_paths) + [ os.path.dirname(_ctx.filename) ]:\n full_path = os.path.join(i, filename)\n if os.path.exists(full_path):\n return full_path\n return filename # failure gets handled later on","def search_file(path, f):\n output = []\n for root, dirs, files in os.walk(path, topdown=True):\n for file in files:\n if file == f:\n path = os.path.join(root, file)\n output.append(path)\n\n return output","def find_file_by_name(file_name: str) -> Union[bool, dict]:\n page_token = None\n getting_files = True\n file_data = False\n\n while getting_files:\n if not page_token:\n response = drive_service().files().list(q=\"mimeType != 'application/vnd.google-apps.folder'\",\n spaces='drive').execute()\n else:\n response = drive_service().files().list(q=\"mimeType != 'application/vnd.google-apps.folder'\",\n spaces='drive',\n pageToken=page_token).execute()\n\n key_list = list(response.keys())\n if \"nextPageToken\" not in key_list:\n getting_files = False\n else:\n page_token = response[\"nextPageToken\"]\n\n folders = response['files']\n for folder in folders:\n if file_name == folder[\"name\"]:\n file_data = folder\n getting_files = False\n\n return file_data","def file_by_file_name(self, file_name):\n ls = self.files.filter_by(file_name=file_name)\n return ls[0] if 0 < len(ls) else None","def find_file(self, filename, pathlist = ['.']):\n if filename.startswith('http://') or filename.startswith('https://'):\n return (urlopen(filename), filename)\n for path in [''] + pathlist:\n filepath = abspath(path + '/' + filename)\n if isfile(filepath):\n f = open(filepath, 'r')\n return (f, filepath)\n raise FileNotFoundError(filename, pathlist)","def find_file(file_path=None, args=None, locations=DEFAULT_LOCATIONS,\n file_name='weewx.conf'):\n\n # Start by searching args (if available)\n if file_path is None and args:\n for i in range(len(args)):\n if not args[i].startswith('-'):\n file_path = args[i]\n del args[i]\n break\n\n if file_path is None:\n for directory in locations:\n # If this is a relative path, then prepend with the\n # directory this file is in:\n if not directory.startswith('/'):\n directory = os.path.join(os.path.dirname(__file__), directory)\n candidate = os.path.abspath(os.path.join(directory, file_name))\n if os.path.isfile(candidate):\n return candidate\n\n if file_path is None:\n raise IOError(\"Unable to find file '%s'. Tried directories %s\" %\n (file_name, locations))\n elif not os.path.isfile(file_path):\n raise IOError(\"%s is not a file\" % file_path)\n\n return file_path","def get_file(view: View, string: str, name: str):\n\n # if it's an absolute path get it\n if osp.isabs(string):\n return string, None\n\n # if search_mode: \"project\", search only in project\n elif SEARCH_MODE == \"project\":\n # Get base project folders\n base_folders = sublime.active_window().folders()\n # if \"recursive\": true, recursively search for the name\n if RECURSIVE:\n ch_rec = check_recursive(base_folders, name)\n if ch_rec:\n base_folder, root = ch_rec\n return osp.join(root, name), base_folder\n return \"\", None\n else:\n # search only in base folders for the relative path\n for base_folder in base_folders:\n file_name = osp.normpath(osp.join(base_folder, string))\n if osp.exists(file_name):\n return file_name, base_folder\n return \"\", None\n # if search_mode: \"file\" join the relative path to the file path\n else:\n return osp.normpath(osp.join(osp.dirname(view.file_name()), string)), None","def get_file(self, name):\n return self.files[name]","def get_file(view: sublime.View, string: str, name: str) -> 'Tuple[str, Optional[str]]':\n\n # if it's an absolute path get it\n if osp.isabs(string):\n return string, None\n\n # if search_mode: \"project\", search only in project\n elif Settings.search_mode == \"project\":\n # Get base project folders\n base_folders = sublime.active_window().folders()\n # if \"recursive\": true, recursively search for the name\n if Settings.recursive:\n ch_rec = check_recursive(base_folders, name)\n if ch_rec:\n base_folder, root = ch_rec\n return osp.join(root, name), base_folder\n return \"\", None\n else:\n # search only in base folders for the relative path\n for base_folder in base_folders:\n file_name = osp.normpath(osp.join(base_folder, string))\n if osp.exists(file_name):\n return file_name, base_folder\n return \"\", None\n # if search_mode: \"file\" join the relative path to the file path\n else:\n return osp.normpath(osp.join(osp.dirname(view.file_name()), string)), None","def get(name):\n\n filename = find(name)\n if filename == None:\n return name\n return open(filename).read()","def get_data_in_paths(dfile, paths):\n for pth in paths:\n for f in os.listdir(pth):\n if f == dfile:\n return os.path.abspath(os.path.join(pth, dfile))","def findInPath(fileName):\n\timport posixpath\n\tfor pathdir in ([\".\"]+sys.path):\n\t\tif posixpath.isfile(pathdir+\"/\"+fileName):\n\t\t\treturn pathdir+\"/\"+fileName","def find_file(folders, path):\n for name in path[:-1]:\n folders = [sub for folder in folders for sub in subdirs(folder, name)]\n return [\n os.path.join(folder, f)\n for folder in folders\n for f in os.listdir(folder)\n if f.lower() == path[-1]\n ]","def find_file_by_name(point,target):\n launcher_path = \"\"\n for (path, dir, files) in os.walk(point):\n for filename in files:\n # if target in filename:\n if filename == target:\n launcher_path = os.path.join(path, filename)\n return launcher_path","def find_file(self, name):\n if not self._namespace:\n raise NotInitializedError()\n dirs = []\n if self._namespace._config_dirs:\n for directory in self._namespace._config_dirs:\n dirs.append(_fixpath(directory))\n\n for cf in reversed(self.config_file):\n dirs.append(os.path.dirname(_fixpath(cf)))\n\n dirs.extend(_get_config_dirs(self.project))\n\n return _search_dirs(dirs, name)","def find_path(name, path=None, exact=False):\n if path is None:\n path = os.environ.get('PATH', os.defpath)\n if isinstance(path, str):\n dpaths = path.split(os.pathsep)\n else:\n dpaths = path\n candidates = (join(dpath, name) for dpath in dpaths)\n if exact:\n if WIN32: # nocover\n # on WIN32 allow ``name`` to omit the extension suffix by trying\n # to match with all possible \"valid\" suffixes specified by PATHEXT\n pathext = [''] + os.environ.get('PATHEXT', '').split(os.pathsep)\n candidates = (p + ext for p in candidates for ext in pathext)\n candidates = filter(exists, candidates)\n else:\n import glob\n candidates = it.chain.from_iterable(\n glob.glob(pattern) for pattern in candidates)\n\n for candidate in candidates:\n yield candidate","def _find_file(self, name, check_dir='c_files'):\n testdir = os.path.dirname(__file__)\n name = os.path.join(testdir, check_dir, name)\n return name","def retrieve(self, file_name):\n ret = os.path.join(self.path, file_name)\n temp = (self.path + file_name).find(self.path, 1, -1)\n if os.path.exists(ret):\n return os.path.join(self.path, file_name)\n elif temp != -1:\n if os.name == \"posix\":\n return os.getcwd() + '/' + file_name\n else:\n return os.getcwd() + '\\\\' + file_name\n else:\n return None","def file_by_name(self, context, params):\n\n token = util.get_access_token(context['headers'])\n response, code = OnedriveApi.search(token, params['name'])\n\n if code == 400:\n return response\n\n result = []\n\n for item in response['value']:\n if item.get('file'):\n item_data = self.get_item_data(item)\n result.append(item_data)\n\n return result","def SearchPath(name, path=None):\n path = path or os.environ['PATH']\n for dir in path.split(os.pathsep):\n binpath = os.path.join(dir, name)\n if os.path.exists(binpath):\n return os.path.abspath(binpath)\n return None","def _findfile(self,path,label):\n files=[];filenames=os.listdir(path)\n for name in filenames:\n if os.path.splitext(name)[0]==str(label):\n files.append(name)\n return files","def recursively_find_file(folder, file_name):\n # TODO: print a hint when not founding file_name","def _search_path_to_file(self, directory, binary_name):\n for root, dirs, files in os.walk(directory):\n if binary_name in files:\n return os.path.join(root, binary_name)\n raise micp_kernel.NoExecutableError","def find(cls, paths):\r\n pythons = []\r\n for path in paths:\r\n for fn in cls.expand_path(path):\r\n basefile = os.path.basename(fn)\r\n if any(matcher.match(basefile) is not None for matcher in cls.REGEXEN):\r\n try:\r\n pythons.append(cls.from_binary(fn))\r\n except Exception as e:\r\n TRACER.log('Could not identify %s: %s' % (fn, e))\r\n continue\r\n return pythons","def search_for_files(\n run_name: str,\n raw_extension: Optional[str] = None,\n cellpy_file_extension: Optional[str] = None,\n raw_file_dir: Union[OtherPath, pathlib.Path, str, None] = None,\n project_dir: Union[OtherPath, pathlib.Path, str, None] = None,\n cellpy_file_dir: Union[OtherPath, pathlib.Path, str, None] = None,\n prm_filename: Union[pathlib.Path, str, None] = None,\n file_name_format: Optional[str] = None,\n reg_exp: Optional[str] = None,\n sub_folders: Optional[bool] = True,\n file_list: Optional[List[str]] = None,\n with_prefix: Optional[bool] = True,\n pre_path: Union[OtherPath, pathlib.Path, str, None] = None,\n) -> Tuple[List[str], str]:\n\n # TODO: @jepe - use reg_exp\n # TODO: @jepe - allow for searching in cloud\n # TODO: @jepe - how to implement searching in db?\n # TODO: update prms and conf file to allow for setting if search should be done in\n # sub-folders, several folders, db, cloud etc\n # TODO: @jepe - find a way to implement automatic file_list creation in a top level func.\n logging.debug(f\"searching for {run_name}\")\n version = 0.4\n t0 = time.time()\n\n if reg_exp is None:\n reg_exp = prms.FileNames.reg_exp\n\n if raw_extension is None:\n raw_extension = prms.FileNames.raw_extension\n\n if raw_extension is None:\n raw_extension = prms.FileNames.raw_extension\n\n if cellpy_file_extension is None:\n cellpy_file_extension = prms.FileNames.cellpy_file_extension\n\n # backward compatibility check:\n if cellpy_file_extension.startswith(\".\"):\n warnings.warn(\n \"Deprecation warning: cellpy_file_extension should not include the '.'\"\n )\n cellpy_file_extension = cellpy_file_extension[1:]\n\n if raw_file_dir is None:\n raw_file_dir = prms.Paths.rawdatadir\n\n if file_name_format is None:\n file_name_format = prms.FileNames.file_name_format\n\n # 'dressing' the raw_file_dir in a list in case we want to\n # search in several folders (not implemented yet):\n if not isinstance(raw_file_dir, (list, tuple)):\n raw_file_dir = [OtherPath(raw_file_dir)]\n else:\n raw_file_dir = [OtherPath(d) for d in raw_file_dir]\n\n if project_dir is not None:\n raw_file_dir = [r / project_dir for r in raw_file_dir]\n\n if reg_exp:\n logging.warning(f\"Got reg_exp: {reg_exp}\")\n logging.warning(\"Sorry, but using reg exp is not implemented yet.\")\n\n if prm_filename is not None:\n logging.debug(\"Sorry, reading prm file is not implemented yet.\")\n\n if cellpy_file_dir is None:\n cellpy_file_dir = OtherPath(prms.Paths.cellpydatadir)\n else:\n cellpy_file_dir = OtherPath(cellpy_file_dir)\n\n if file_name_format is None and reg_exp is None:\n try:\n # To be implemented in version 0.5:\n file_name_format = prms.FileNames.file_name_format\n except AttributeError:\n file_name_format = \"YYYYMMDD_[name]EEE_CC_TT_RR\"\n\n if file_name_format.upper() == \"YYYYMMDD_[NAME]EEE_CC_TT_RR\":\n # backward compatibility check\n if raw_extension.startswith(\".\"):\n warnings.warn(\n \"Deprecation warning: raw_extension should not include the '.'\"\n )\n raw_extension = raw_extension[1:]\n warnings.warn(f\"Removing it -> {raw_extension}\")\n # TODO: give warning/error-message if run_name contains more than one file (due to duplicate in db)\n glob_text_raw = f\"{run_name}*.{raw_extension}\"\n else:\n glob_text_raw = file_name_format\n\n logging.debug(f\"searching for raw files in: {raw_file_dir}\")\n logging.debug(f\"searching for {run_name}\")\n logging.debug(f\"using glob {glob_text_raw}\")\n\n cellpy_file = f\"{run_name}.{cellpy_file_extension}\"\n cellpy_file = cellpy_file_dir / cellpy_file\n # Not sure if for example pandas can handle OtherPath objects:\n cellpy_file = cellpy_file.full_path\n\n logging.debug(f\"generated cellpy filename {cellpy_file}\")\n\n if file_list is not None:\n if len(raw_file_dir) > 1:\n logging.info(\"you provided several raw file directories\")\n logging.debug(\"searching within provided list of files\")\n\n if with_prefix:\n glob_text_raw_for_list = f\"*{glob_text_raw}\"\n else:\n glob_text_raw_for_list = glob_text_raw\n\n run_files = fnmatch.filter(file_list, glob_text_raw_for_list)\n\n if pre_path is not None:\n pre_path = OtherPath(pre_path)\n run_files = list(map(lambda x: pre_path / x, run_files))\n\n else:\n run_files = []\n for d in raw_file_dir:\n if d.is_external:\n logging.debug(\"external file\")\n if not d.is_dir():\n warnings.warn(\"your raw file directory cannot be accessed!\")\n # raise cellpy.exceptions.IOError(\"your raw file directory cannot be accessed!\")\n _run_files = []\n else:\n logging.debug(f\"checking in folder {d}\")\n logging.debug(f\"{sub_folders=}\")\n if sub_folders:\n _run_files = d.rglob(glob_text_raw)\n\n else:\n _run_files = d.glob(glob_text_raw)\n\n _run_files = [str(_f.resolve()) for _f in _run_files]\n _run_files.sort()\n run_files.extend(_run_files)\n\n return run_files, cellpy_file","def sysfind(cls, name, checker=None, paths=None):\n if isabs(name):\n p = local(name)\n if p.check(file=1):\n return p\n else:\n if paths is None:\n if iswin32:\n paths = os.environ[\"Path\"].split(\";\")\n if \"\" not in paths and \".\" not in paths:\n paths.append(\".\")\n try:\n systemroot = os.environ[\"SYSTEMROOT\"]\n except KeyError:\n pass\n else:\n paths = [\n path.replace(\"%SystemRoot%\", systemroot) for path in paths\n ]\n else:\n paths = os.environ[\"PATH\"].split(\":\")\n tryadd = []\n if iswin32:\n tryadd += os.environ[\"PATHEXT\"].split(os.pathsep)\n tryadd.append(\"\")\n\n for x in paths:\n for addext in tryadd:\n p = local(x).join(name, abs=True) + addext\n try:\n if p.check(file=1):\n if checker:\n if not checker(p):\n continue\n return p\n except error.EACCES:\n pass\n return None","def file_match(file_name, names):\n fname = basename(file_name)\n for name in names:\n if fnmatch(fname, name):\n return True\n return False","def module_path_finder(path, name, recursive=True):\n path_ = None\n paths = glob(os.path.join(path, '**', '*.py'), recursive=recursive)\n for path in paths:\n file_name = os.path.basename(path)\n if file_name == name + '.py':\n path_ = os.path.abspath(path)\n\n if path_ is None:\n raise FileNotFoundError(\"module %s not found\" % name)\n\n return path_","def search_files(filename, search_path, pathsep=os.pathsep):\n clidFiles = []\n for path in search_path.split(pathsep):\n candidate = os.path.join(path, filename)\n if os.path.exists(candidate): clidFiles.append(os.path.abspath(candidate))\n return clidFiles","def find_files_by_name_only(path, file_name, allowed_postfix=None):\n all_files = []\n for root, dirs, files in os.walk(path):\n all_files += files\n\n found_files = [f for f in all_files if fnmatch.fnmatch(f, '{}*'.format(file_name))]\n if allowed_postfix:\n found_files = [f for f in found_files if f.split('.').pop() in allowed_postfix]\n \n return found_files","def getNamedPath(name):\n def findNamed(path):\n pp = os.path.split(path)\n if pp[-1] == '':\n return None\n if pp[-1] != name:\n path = findNamed(pp[0])\n return path\n return findNamed(os.getcwd())","def find_program(ctx, names, paths=None, *, quieter=0):\n\n if paths is None:\n paths = os.environ['PATH'].split(os.pathsep)\n\n # If we're running on windows, we need to append '.exe' to the filenames\n # that we're searching for.\n if sys.platform == 'win32':\n new_names = []\n for name in names:\n if \\\n not name.endswith('.exe') or \\\n not name.endswith('.cmd') or \\\n not name.endswith('.bat'):\n new_names.append(name + '.exe')\n new_names.append(name + '.cmd')\n new_names.append(name + '.bat')\n new_names.append(name)\n names = new_names\n\n for name in names:\n ctx.logger.check('looking for program ' + name, verbose=quieter)\n\n filename = fbuild.path.Path(name)\n if filename.exists() and filename.isfile():\n ctx.logger.passed('ok %s' % filename, verbose=quieter)\n return fbuild.path.Path(name)\n else:\n for path in paths:\n filename = fbuild.path.Path(path, name)\n if filename.exists() and filename.isfile():\n ctx.logger.passed('ok %s' % filename, verbose=quieter)\n return fbuild.path.Path(filename)\n\n ctx.logger.failed(verbose=quieter)\n\n raise MissingProgram(names)","def find_by_name ( self, name, **kw ):\n try:\n return next ( self.find_all_by_name ( name, **kw ) )\n except StopIteration:\n return None","def get_filepath_filename(filepath, return_filenames):\n main_dir_path = ''\n names = []\n for dirpath, dirnames, filenames in os.walk(filepath):\n main_dir_path = dirpath\n names = filenames\n for name in names:\n _ = os.path.join(main_dir_path, name)\n return_filenames.append(_)","def filesearch(word=\"\"):\n logger.info('Starting filesearch')\n file = []\n for f in glob.glob(\"*\"):\n if word[0] == \".\":\n if f.endswith(word):\n file.append(f)\n\n elif word in f:\n file.append(f)\n #return file\n logger.debug(file)\n return file","def which(self, name):\n\n valid_names = [\n \"{0}.{1}\".format(name, ext).lower() if ext else \"{0}\".format(name).lower()\n for ext in KNOWN_EXTS\n ]\n finder = filter(operator.attrgetter(\"is_executable\"), self.children.values())\n name_getter = operator.attrgetter(\"path.name\")\n return next(\n (child for child in finder if name_getter(child).lower() in valid_names),\n None,\n )","def path_to_file(fname, dirs):\n for each in dirs:\n path = '/'.join([each, fname])\n if os.path.exists(path):\n return path\n return None","def find_by_name(name):\n return repository.find_by_name(name)","def get_files(filepath='./', name_contains=\"\", absolute_path=True, subdirectories=True):\n\n all_files = []\n\n\n for (dirpath, dirnames, filenames) in os.walk(filepath):\n \n # filenames specified\n if name_contains: \n all_files.extend(glob(os.path.join(dirpath,name_contains)))\n\n elif not name_contains:\n all_files.extend(filenames)\n \n # exclude subdirectories, break loop \n if not subdirectories:\n break\n # otherwise continue with loop, walking down the directory\n\n # get absolute path\n if absolute_path: \n all_files_absolute = [os.path.abspath(f) for f in all_files]\n return all_files_absolute\n\n else:\n return all_files","def find_program(name):\r\n # See MSDN for the REAL search order.\r\n base, ext = os.path.splitext(name)\r\n if ext:\r\n exts = [ext]\r\n else:\r\n exts = ['.bat', '.exe']\r\n for directory in os.environ['PATH'].split(os.pathsep):\r\n for e in exts:\r\n fname = os.path.join(directory, base + e)\r\n if os.path.exists(fname):\r\n return fname\r\n return None","def locate_file(fname):\n out = []\n if platform._is_win: # pragma: windows\n out += find_all(fname, None)\n else:\n for path in os.environ.get('PATH').split(os.pathsep):\n if path:\n out += find_all(fname, path)\n if not out:\n return False\n first = out[0]\n if len(out) > 1:\n # pprint.pprint(out)\n warnings.warn(\"More than one (%d) match to %s. Using first match (%s)\" % (\n len(out), fname, first))\n return first","def search_for_name(self, name):\n for p in self.books_all:\n if p['name'] == name:\n return p","def find_executable(*names: str,\n additional_roots: Iterable[Path] = (),\n additional_files: Iterable[Path] = ())-> Optional[Path]:\n for path in additional_files:\n if path_is_executable(path):\n return path\n\n for root in chain(additional_roots, os.get_exec_path()):\n root = Path(root)\n for name in names:\n path = root / name\n if path_is_executable(path):\n return path\n return None","def _find_file(config, startswith=False):\n remote_files = _get_remote_files(config)\n if startswith:\n remote_folders = {}\n for fname, (pid, _) in remote_files.items():\n remote_folders[os.path.dirname(fname)] = (pid, None)\n remote_files = remote_folders\n\n def glob_match(f1, f2):\n \"\"\"Check for wildcard glob style matches.\n \"\"\"\n if f1.find(\"*\") >= 0:\n if fnmatch.fnmatch(f2, \"*/%s\" % f1):\n return True\n\n def get_file(f):\n if _is_remote(f):\n f = _get_id_fname(f)[-1]\n # handle both bare lookups and project-prefixed\n if f.find(\":\") > 0:\n fproject, f = f.split(\":\")\n else:\n fproject = None\n # check for exact matches\n for project, folder in _remote_folders(config):\n if fproject is None or fproject == project:\n folder_f = os.path.join(folder, f)\n if folder_f in remote_files:\n pid, fid = remote_files[folder_f]\n return \"%s:%s/%s:%s\" % (KEY, fid, pid, folder_f)\n # find any files nested in sub folders or as globs\n out = []\n for project, folder in _remote_folders(config):\n for rfname, (pid, rid) in remote_files.items():\n if rfname.startswith(folder + \"/\") and (rfname.endswith(\"/\" + f) or glob_match(f, rfname)):\n out.append(\"%s:%s/%s:%s\" % (KEY, rid, pid, rfname))\n if len(out) == 1:\n return out[0]\n elif len(out) > 1:\n return out\n return get_file","def find_in_PATH(filename: str):\n if path.isfile(filename):\n return path.normpath(filename)\n\n os_paths = os.environ['PATH'].split(path.pathsep)\n for os_path in os_paths:\n fullpath_file = path.join(os_path, filename)\n if path.isfile(fullpath_file):\n return path.normpath(fullpath_file)\n raise FileNotFoundError(f'could not find {filename}')","def find_template_filename(self, template_name):\n\n def next_file():\n filename = self.path / template_name\n yield filename\n try:\n exts = self.default_file_extensions\n except AttributeError:\n return\n\n strfilename = str(filename)\n for ext in exts:\n yield Path(strfilename + ext)\n\n for filename in next_file():\n if filename.is_file():\n return filename","def _load(name, paths):\n for base_path in paths:\n parts = name.split('.')\n number_of_parts = len(parts)\n\n for folder_parts in range(number_of_parts):\n folder = os.path.join(base_path, *parts[:folder_parts])\n filename = '.'.join(parts[folder_parts:]) + '.json'\n json_path = os.path.join(folder, filename)\n\n if os.path.isfile(json_path):\n with open(json_path, 'r') as json_file:\n LOGGER.debug('Loading %s from %s', name, json_path)\n return json.load(json_file)","def find_executable(name):\n for dir in os.environ.get('PATH', '').split(os.pathsep):\n if not dir:\n continue\n fn = os.path.abspath(os.path.join(dir, name))\n if os.path.exists(fn):\n return os.path.abspath(fn)","def find_executable(name):\n if os.path.isabs(name):\n return name if os.path.isfile(name) else None\n else:\n if iswin32:\n paths = os.environ['Path'].split(';')\n if '' not in paths and '.' not in paths:\n paths.append('.')\n try:\n systemroot = os.environ['SYSTEMROOT']\n except KeyError:\n pass\n else:\n paths = [re.sub('%SystemRoot%', systemroot, path)\n for path in paths]\n else:\n paths = os.environ['PATH'].split(':')\n tryadd = []\n if iswin32:\n tryadd += os.environ['PATHEXT'].split(os.pathsep)\n tryadd.append(\"\")\n\n for x in paths:\n for addext in tryadd:\n p = os.path.join(x, name) + addext\n try:\n if os.path.isfile(p):\n return p\n except Exception:\n pass\n return None","def find_axfile(name, search_path=[u\".\", u\"$GT3AXISDIR\", u\"$GTOOLDIR/gt3\"]):\n axis_path = map(os.path.expandvars, search_path)\n axis_path = [a for a in axis_path if a.find('$') < 0]\n axis_path = [a for a in axis_path if os.path.exists(a)]\n\n if (opt_debug):\n print('*** axis_path:')\n print(axis_path)\n\n axfile = None\n for axdir in axis_path:\n # print(axdir)\n axfile = os.path.join(axdir, 'GTAXLOC.'+name)\n if (os.path.exists(axfile)):\n break\n\n # print(\"dbg:find_axfile:\",axfile)\n return axfile","def ffind(path, shellglobs=None, namefs=None, relative=True):\n if not os.access(path, os.R_OK):\n raise ScriptError(\"cannot access path: '%s'\" % path)\n\n fileList = [] # result list\n try:\n for dir, subdirs, files in os.walk(path):\n if shellglobs:\n matched = []\n for pattern in shellglobs:\n filterf = lambda s: fnmatch.fnmatchcase(s, pattern)\n matched.extend(list(filter(filterf, files)))\n fileList.extend(['%s%s%s' % (dir, os.sep, f) for f in matched])\n else:\n fileList.extend(['%s%s%s' % (dir, os.sep, f) for f in files])\n if not relative: fileList = list(map(os.path.abspath, fileList))\n if namefs:\n for ff in namefs: fileList = list(filter(ff, fileList))\n except Exception as e: raise ScriptError(str(e))\n return(fileList)","def get_result_by_name(self, name):\n found = None\n for result in self.results:\n if result.heading == name:\n found = result\n break\n return found","def get_path(self, path_name):\n if not path_name:\n return None\n for path in self._paths:\n if path._path == path_name:\n return path\n pass\n new_path = self.add_path(path_name)\n return new_path","def _find_config_file(self) -> str or None:\n import os\n\n for path in self.paths:\n path = os.path.expanduser(path)\n for extension in self.file_extensions:\n for file_name in self.file_names:\n file_path = os.path.join(path, \"{}.{}\".format(file_name, extension))\n if os.path.isfile(file_path):\n return file_path\n\n return None","def checkFilePath(self, filename, searchpath=[]):\n\t\tif filename is None:\n\t\t\treturn None\n\t\telif os.path.isfile(filename):\n\t\t\treturn filename\n\t\telse:\n\t\t\t# Append current dir to searchpath and try each in turn\n\t\t\tsearchpath.append(os.path.dirname(__file__))\n\t\t\t# print(searchpath)\n\t\t\tfor folder in searchpath:\n\t\t\t\tfilepath = os.path.join(folder, filename)\n\t\t\t\tif os.path.isfile(filepath):\n\t\t\t\t\treturn filepath\n\n\t\t# File not found\n\t\treturn None","def find_stub_files(name: str) -> List[str]:\n result = []\n for root, dirs, files in os.walk(name):\n for file in files:\n if file.endswith(\".pyi\"):\n if os.path.sep in root:\n sub_root = root.split(os.path.sep, 1)[-1]\n file = os.path.join(sub_root, file)\n result.append(file)\n return result","def find_file(research_structure, raise_on_all_missing=True):\n filenames = []\n paths_searched = []\n ## config file lookup resolution\n for enforce_file_existence, fun in research_structure:\n candidate = fun()\n if candidate is None:\n continue\n if not os.path.exists(candidate):\n if enforce_file_existence:\n raise ValueError(\"File %r does not exists.\" % candidate)\n else:\n paths_searched.append(candidate)\n else:\n return candidate\n if not filenames and raise_on_all_missing:\n raise ValueError(\"No config file was found in those paths: %s.\"\n % ', '.join(paths_searched))\n return","def getImageFinder(self, name: str) -> Optional[str]:\n trace = 'themes' in g.app.debug\n exists = g.os_path_exists\n getString = g.app.config.getString\n\n def dump(var: str, val: str) -> None:\n print(f\"{var:20}: {val}\")\n\n join = g.os_path_join\n #\n # \"Just works\" for --theme and theme .leo files *provided* that\n # theme .leo files actually contain these settings!\n #\n theme_name1 = getString('color-theme')\n theme_name2 = getString('theme-name')\n roots = [\n g.os_path_join(g.computeHomeDir(), '.leo'),\n g.computeLeoDir(),\n ]\n theme_subs = [\n \"themes/{theme}/Icons\",\n \"themes/{theme}\",\n \"Icons/{theme}\",\n ]\n # \".\" for icons referred to as Icons/blah/blah.png\n bare_subs = [\"Icons\", \".\"]\n paths = []\n for theme_name in (theme_name1, theme_name2):\n for root in roots:\n for sub in theme_subs:\n paths.append(join(root, sub.format(theme=theme_name)))\n for root in roots:\n for sub in bare_subs:\n paths.append(join(root, sub))\n table = [z for z in paths if exists(z)]\n for base_dir in table:\n path = join(base_dir, name)\n if exists(path):\n if trace:\n g.trace(f\"Found {name} in {base_dir}\")\n return path\n # if trace: g.trace(name, 'not in', base_dir)\n if trace:\n g.trace('not found:', name)\n return None","def read_files(path, file_name):\n\n if os.path.exists(\n r'{}\\{}_dynamic.csv'.format(path, file_name)) and os.path.exists(\n r'{}\\{}_static.csv'.format(path, file_name)) and os.path.exists(\n r'{}\\{}_ego.csv'.format(path, file_name)):\n with open(r'{}\\{}_dynamic.csv'.format(path, file_name)) as tmp_dynamic:\n dynamic_csv = pd.read_csv(tmp_dynamic)\n print('Dynamic csv file found')\n with open(r'{}\\{}_static.csv'.format(path, file_name)) as tmp_static:\n static_csv = pd.read_csv(tmp_static)\n print('Static csv file found')\n with open(r'{}\\{}_ego.csv'.format(path, file_name)) as tmp_ego:\n ego_csv = pd.read_csv(tmp_ego)\n print('Ego csv file found')\n return ego_csv, dynamic_csv, static_csv\n\n else:\n print('No available data')\n sys.exit(0)","def which(self, name):\n if not len(self):\n self.update()\n try:\n return [version for version in self if os.path.basename(version) == name][0]\n except IndexError:\n return None","def find_file(path, filename, max_depth=5):\n for root, dirs, files in os.walk(path):\n if filename in files:\n return os.path.join(root, filename)\n\n # Don't search past max_depth\n depth = root[len(path) + 1:].count(os.sep)\n if depth > max_depth:\n del dirs[:] # Clear dirs\n return None","def _find_named_files(self):\n for name, description in self.named_files.iteritems():\n name = name.format(job_name=self.job_name)\n f_path = '{}/{}'.format(self.rism3d_folder, name)\n if os.path.isfile(f_path):\n self.file_path_dic[description] = f_path\n else:\n self._not_found_error(f_path)","def find_by_name(self, name):\n return self.get(name)","def find(self, file, version):\n matches = []\n for root, dirnames, filenames in os.walk(self.full_doc_path(version)):\n for filename in fnmatch.filter(filenames, file):\n matches.append(os.path.join(root, filename))\n return matches","def compare(self, name):\n result = []\n for i in (os.listdir(self.path)):\n if i != name:\n comparison = filecmp.cmp(self.path.__add__(i), self.path.__add__(name), shallow=False)\n if comparison:\n result.append(i)\n return result","def find(self, name):\n path = self.directory.joinpath(name).with_suffix('.yaml')\n if path.is_file():\n return self.from_path(path)\n raise LookupError(\"Job {} does not exist\".format(repr(name)))","def find(self, name):\n if self.policies is None:\n self.load_file()\n line = self.policies.get(name, None)\n if line is None:\n return \"\"\n filename = os.path.basename(self.filename)\n return f\"{filename}:{line}\"","def search_file(pattern, filename):\n if not os.path.exists(filename):\n raise Exception(\"Can't open file for reading! \" + filename)\n\n fh = open(filename, \"r\")\n for line in fh:\n allmatch = re.findall(pattern, line)\n if allmatch:\n fh.close()\n return allmatch[0]\n\n fh.close()\n return None","def compfile(input_path, name=\"\", list_files=None):\n __string(input_path, \"%s path\" % name, True)\n\n if list_files is None:\n list_files = []\n elif not list_files:\n __ex(\"File list is empty (no files to compare with).\", True)\n else:\n for item in list_files:\n if not isinstance(item, list):\n __ex(\"Every list item must be a sub-list.\", True)\n if not len(item) == 2:\n __ex(\"Every sub-list must contain two items.\", True)\n\n input_path = os.path.abspath(input_path)\n for item in list_files:\n path_compare = os.path.abspath(str(item[0]))\n name_compare = str(item[1])\n if input_path == path_compare:\n __ex(\"The %s and the %s file path must not be identical.\" %\n (name, name_compare), False)\n if os.path.exists(input_path) and os.path.exists(path_compare):\n if filecmp.cmp(input_path, path_compare, 0):\n __ex(\"The %s and %s file content must not be identical.\" %\n (name, name_compare), False)","def get_files_for_path(file_ids_tree, path):\n entry = file_ids_tree\n parts = path.split('/')\n for part in parts:\n if part != '':\n try:\n entry = entry[part]\n except KeyError as e:\n files = entry[FILE_MARKER]\n for entry in files:\n if entry[0] == part:\n return entry\n raise e\n\n return entry","def locate_file(self, filename):\n return locate_file(filename, self.observatory)","def _find_files(directory, dirs_to_look_in, files_to_search_for, \n current_dir, see_files):\n full_name = True\n if see_files:\n full_name = False\n files_to_load = search_directory(directory, \n look_in=dirs_to_look_in,\n search_for=files_to_search_for,\n file_type='files',\n current_dir=current_dir,\n full_name=full_name)\n if not files_to_load:\n raise UserWarning('No files were found matching the search for %s'\\\n ' in the directory(s) %s%s' \\\n % (files_to_search_for, directory, \n dirs_to_look_in))\n return files_to_load","def which(name):\n # Inspired by https://twistedmatrix.com/trac/browser/tags/releases/\n # twisted-8.2.0/twisted/python/procutils.py\n # pylint: disable=W0141\n result = []\n path = os.environ.get('PATH', None)\n if path is None:\n return []\n for pdir in os.environ.get('PATH', '').split(os.pathsep):\n fname = os.path.join(pdir, name)\n if os.path.isfile(fname) and os.access(fname, os.X_OK):\n result.append(fname)\n return result[0] if result else None","def _find_by_name_pattern(search_directory, name_pattern, is_file=True):\n if not os.listdir(search_directory):\n return None\n # filter check list\n choose_list = []\n if is_file:\n choose_list += filter(lambda x: os.path.isfile(x), [os.path.join(search_directory, y) for y in os.listdir(search_directory)])\n else:\n choose_list += filter(lambda x: os.path.isdir(x), [os.path.join(search_directory, y) for y in os.listdir(search_directory)])\n # check\n for sub_name in choose_list:\n if re.match(name_pattern, os.path.basename(sub_name), re.IGNORECASE):\n return sub_name\n return None","def _locate_config_file(search_dirs):\n tried = []\n for base in search_dirs:\n for path in [os.path.join(base, CONFIG_NAME + suffix) for suffix in \".yml\", \".json\"]:\n log.debug(\"searching for config file: %s\", path)\n tried.append(path)\n if os.path.isfile(path):\n log.info(\"using config file: %s\", path)\n return path\n raise ConfigError(\"no config file found in: %s\" % \", \".join(tried))"],"string":"[\n \"def find_in_paths(name, paths):\\n for path in paths:\\n file_ = osp.join(path, name)\\n if osp.exists(file_) and not osp.isdir(file_):\\n abs_file = osp.abspath(file_)\\n if os.access(file_, os.X_OK):\\n return abs_file\",\n \"def search_file(file_name, search_path):\\n if os.path.exists(search_path):\\n filenames = os.listdir(search_path)\\n for filename in filenames:\\n if file_name in filenames:\\n return os.path.join(search_path, file_name)\\n return None\",\n \"def find(name):\\n\\n if os.path.exists(name):\\n return name\\n\\n path = os.path.dirname(__file__) or '.'\\n filename = os.path.abspath(os.path.join(path,name))\\n if os.path.exists(filename):\\n return filename\\n\\n for d in os.listdir(path):\\n fullpath = os.path.abspath(os.path.join(path,d))\\n if os.path.isdir(fullpath):\\n filename = os.path.abspath(os.path.join(fullpath,name))\\n if os.path.exists(filename):\\n return filename\\n return None\",\n \"def find_file_by_name(**kwargs):\\n return AppServer.service.find_file_by_name(name=kwargs['name'])\",\n \"def search_file_by_name(cls, name: str, root_path: str, return_path_only: bool = True,\\n\\t\\t\\t\\t\\t\\t\\treturn_all_files: bool = False,\\n\\t\\t\\t\\t\\t\\t\\tallow_partial_name: bool = False):\\n\\n\\t\\t# if allow_partial_name and return_path_only:\\n\\t\\t# \\timport warnings\\n\\t\\t# \\twarnings.warn(\\n\\t\\t# \\t\\t'{}.{} - Due to \\\"allow_partial_name\\\" is set to True, \\\"return_path_only\\\" has been set to False '.format(\\n\\t\\t# \\t\\t\\t__name__, cls.search_file_by_name.__name__), RuntimeWarning)\\n\\t\\tresult_list = []\\n\\t\\tfor dirpath, dirnames, filenames in os.walk(root_path):\\n\\t\\t\\tfor filename in filenames:\\n\\t\\t\\t\\tif (name == filename and (not allow_partial_name)) or (\\n\\t\\t\\t\\t\\t\\t(filename.find(name) != -1) and allow_partial_name):\\n\\t\\t\\t\\t\\tif return_path_only and not allow_partial_name:\\n\\t\\t\\t\\t\\t\\tabsolute_path = dirpath\\n\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\tabsolute_path = os.path.join(dirpath, filename)\\n\\t\\t\\t\\t\\tif not return_all_files:\\n\\t\\t\\t\\t\\t\\treturn absolute_path\\n\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\tresult_list.append(absolute_path)\\n\\t\\tif result_list:\\n\\t\\t\\treturn result_list\\n\\t\\treturn None\",\n \"def find_file(filename: str, paths: List[str] = None, extensions=None) -> str:\\n\\n extensions = conf.SEARCH_EXTENSIONS if not extensions else extensions\\n paths = conf.SEARCH_DIRS if not paths else paths\\n\\n if '' not in extensions:\\n extensions += ['']\\n\\n if os.path.isabs(filename):\\n with open(filename, 'r'):\\n return filename\\n\\n for ext in extensions:\\n _fn = [f'{filename}{ext}']\\n if '/' in filename:\\n _fn = list(filename.split('/'))\\n _fn[-1] = f'{_fn[-1]}{ext}'\\n\\n for p in paths:\\n fpath = join(p, *_fn)\\n try:\\n with open(fpath, 'r'):\\n return fpath\\n except FileNotFoundError:\\n continue\\n\\n raise FileNotFoundError(f'File \\\"{filename}\\\" could not be found in any of the given paths.')\",\n \"def find(path, name):\\n found = [] # list of found items\\n\\n def find_recursive(path, name, found): # recursive function to traverse tree and append found items\\n path_abs = os.path.abspath(path) # get the absolute path name\\n \\n try:\\n dir_items = os.listdir(path_abs)\\n except:\\n print(f\\\"ERROR: Directory {path} not found...\\\")\\n sys.exit(1)\\n\\n dir_items = os.listdir(path_abs) # list all times in the currrent directory\\n\\n for item in dir_items: # iterate over the dir list\\n full_path = os.path.join(path_abs, item) # get the full path name path + filename/directory\\n if os.path.isdir(full_path): # if is a directory\\n find_recursive(full_path, name, found) # make recursive call to function\\n\\n if os.path.isfile(full_path): # if a file\\n if item == name: # check for name equality\\n found.append(full_path) # if true: add to list\\n\\n find_recursive(path, name, found) # call recursive function\\n\\n return found\",\n \"def get_file_by_name(mod_files: List[PatchedFile], filename: str):\\n for mod in mod_files:\\n if mod.path == filename:\\n return mod\\n return None\",\n \"def find_in_path(filenames):\\n # http://aspn.activestate.com/ASPN/Cookbook/Python/Recipe/52224\\n from os.path import exists, join, abspath\\n from os import pathsep, environ\\n search_path = environ[\\\"PATH\\\"]\\n paths = search_path.split(pathsep)\\n for path in paths:\\n for filename in filenames:\\n if exists(join(path, filename)):\\n return abspath(join(path, filename))\",\n \"def search_on_path(filenames):\\n # http://aspn.activestate.com/ASPN/Cookbook/Python/Recipe/52224\\n\\n from os.path import exists, join, abspath\\n from os import pathsep, environ\\n\\n search_path = environ[\\\"PATH\\\"]\\n\\n paths = search_path.split(pathsep)\\n for path in paths:\\n for filename in filenames:\\n if exists(join(path, filename)):\\n return abspath(join(path, filename))\",\n \"def search(cls, name, lookup=[]):\\r\\n if os.path.isfile(name): return name\\r\\n for spath in lookup:\\r\\n fname = os.path.join(spath, name)\\r\\n if os.path.isfile(fname):\\r\\n return fname\\r\\n for ext in cls.extensions:\\r\\n if os.path.isfile('%s.%s' % (fname, ext)):\\r\\n return '%s.%s' % (fname, ext)\",\n \"def search_file(filename, search_path):\\n file_path = None\\n for path in search_path:\\n if exists(join(path, filename)):\\n file_path = path\\n break\\n if file_path:\\n return abspath(join(file_path, filename))\\n return None\",\n \"def findPath(self, name=None, pattern=None, extension=None, ignorePatterns=None,\\n path=None, paths=None):\\n paths = self.findPaths(name, pattern, extension, ignorePatterns, path, paths)\\n if paths: # If anything found, then answer the first one.\\n return paths[0]\\n return None\",\n \"def _findfile(self, path):\\n\\n # Build list of possible local file paths\\n if not self._isurl(path):\\n # Valid local paths\\n filelist = self._possible_names(path)\\n # Paths in self._destpath\\n filelist += self._possible_names(self.abspath(path))\\n else:\\n # Cached URLs in self._destpath\\n filelist = self._possible_names(self.abspath(path))\\n # Remote URLs\\n filelist = filelist + self._possible_names(path)\\n\\n for name in filelist:\\n if self.exists(name):\\n if self._isurl(name):\\n name = self._cache(name)\\n return name\\n return None\",\n \"def find_file(filesystem, dirs, filename):\\r\\n for directory in dirs:\\r\\n filepath = path(directory) / filename\\r\\n if filesystem.exists(filepath):\\r\\n return filepath\\r\\n raise ResourceNotFoundError(u\\\"Could not find {0}\\\".format(filename))\",\n \"def get_path(file_name):\\n path_project = os.getcwd()\\n list_files = os.listdir(path_project)\\n # print (\\\"Getting File in your Current Directory\\\", path_project)\\n __log(1, 'Gatering Files from current Directory.')\\n\\n # for i in list_files:\\n if file_name in list_files:\\n file_path = path_project + '\\\\\\\\' + file_name\\n __log(1, 'Relevant File Found.')\\n return file_path\\n else:\\n __log(2, 'File Not Found.')\\n return -1\\n # print (\\\"File Found\\\",file_path)\",\n \"def search_file(filename, search_path, pathsep=os.pathsep):\\n for path in string.split(search_path, pathsep):\\n candidate = os.path.join(path, filename)\\n if os.path.exists(candidate): return os.path.abspath(candidate)\\n return None\",\n \"def search_file(filename, search_path, pathsep=os.pathsep):\\n for path in string.split(search_path, pathsep):\\n candidate = os.path.join(path, filename)\\n if os.path.exists(candidate): return os.path.abspath(candidate)\\n return None\",\n \"def search_image(name=None, path=['.']):\\n name = strutils.decode(name)\\n\\n for image_dir in path:\\n if not os.path.isdir(image_dir):\\n continue\\n image_dir = strutils.decode(image_dir)\\n image_path = os.path.join(image_dir, name)\\n if os.path.isfile(image_path):\\n return strutils.encode(image_path)\\n\\n for image_path in list_all_image(image_dir):\\n if not image_name_match(name, image_path):\\n continue\\n return strutils.encode(image_path)\\n return None\",\n \"def find_file(filename, std_dirs, paths):\\n\\n # Check the standard locations\\n for dir in std_dirs:\\n f = os.path.join(dir, filename)\\n print('looking for', f)\\n if os.path.exists(f):\\n return []\\n\\n # Check the additional directories\\n for dir in paths:\\n f = os.path.join(dir, filename)\\n print('looking for', f)\\n if os.path.exists(f):\\n return [dir]\\n\\n # Not found anywhere\\n return None\",\n \"def find_one(path, filenames):\\n for _path in find_all(path, filenames):\\n return _path\\n return ''\",\n \"def find_paths(file_name, app_name, search_folders=None):\\n search_folders = search_folders or default_search_folders(app_name)\\n possible_files = [os.path.join(folder, file_name) for folder in search_folders]\\n return [path for path in possible_files if os.path.exists(path)]\",\n \"def SearchPath(name):\\n search_path = os.getenv('PATH', os.defpath).split(os.pathsep)\\n for directory in search_path:\\n if directory:\\n path = os.path.join(directory, name)\\n if os.path.isfile(path) and os.access(path, os.X_OK):\\n return path\\n return None\",\n \"def find_file(file_name):\\n if (pathlib.Path(file_name).resolve()):\\n file_name = str(file_name)\\n logging.info(f' found {file_name}.')\\n return file_name\\n else:\\n logging.error(f' no file {file_name} found for processing.')\\n sys.exit()\",\n \"def find_file_in_folders(filename, folders):\\n for folder in folders:\\n folder = os.path.expanduser(folder)\\n full_path = os.path.join(folder, filename)\\n if os.path.exists(full_path):\\n return full_path\\n raise FileNotFoundError(\\\"Did not find file %s!\\\" % filename)\",\n \"def find_file(filename):\\n for i in list(_ctx.include_paths) + [ os.path.dirname(_ctx.filename) ]:\\n full_path = os.path.join(i, filename)\\n if os.path.exists(full_path):\\n return full_path\\n return filename # failure gets handled later on\",\n \"def search_file(path, f):\\n output = []\\n for root, dirs, files in os.walk(path, topdown=True):\\n for file in files:\\n if file == f:\\n path = os.path.join(root, file)\\n output.append(path)\\n\\n return output\",\n \"def find_file_by_name(file_name: str) -> Union[bool, dict]:\\n page_token = None\\n getting_files = True\\n file_data = False\\n\\n while getting_files:\\n if not page_token:\\n response = drive_service().files().list(q=\\\"mimeType != 'application/vnd.google-apps.folder'\\\",\\n spaces='drive').execute()\\n else:\\n response = drive_service().files().list(q=\\\"mimeType != 'application/vnd.google-apps.folder'\\\",\\n spaces='drive',\\n pageToken=page_token).execute()\\n\\n key_list = list(response.keys())\\n if \\\"nextPageToken\\\" not in key_list:\\n getting_files = False\\n else:\\n page_token = response[\\\"nextPageToken\\\"]\\n\\n folders = response['files']\\n for folder in folders:\\n if file_name == folder[\\\"name\\\"]:\\n file_data = folder\\n getting_files = False\\n\\n return file_data\",\n \"def file_by_file_name(self, file_name):\\n ls = self.files.filter_by(file_name=file_name)\\n return ls[0] if 0 < len(ls) else None\",\n \"def find_file(self, filename, pathlist = ['.']):\\n if filename.startswith('http://') or filename.startswith('https://'):\\n return (urlopen(filename), filename)\\n for path in [''] + pathlist:\\n filepath = abspath(path + '/' + filename)\\n if isfile(filepath):\\n f = open(filepath, 'r')\\n return (f, filepath)\\n raise FileNotFoundError(filename, pathlist)\",\n \"def find_file(file_path=None, args=None, locations=DEFAULT_LOCATIONS,\\n file_name='weewx.conf'):\\n\\n # Start by searching args (if available)\\n if file_path is None and args:\\n for i in range(len(args)):\\n if not args[i].startswith('-'):\\n file_path = args[i]\\n del args[i]\\n break\\n\\n if file_path is None:\\n for directory in locations:\\n # If this is a relative path, then prepend with the\\n # directory this file is in:\\n if not directory.startswith('/'):\\n directory = os.path.join(os.path.dirname(__file__), directory)\\n candidate = os.path.abspath(os.path.join(directory, file_name))\\n if os.path.isfile(candidate):\\n return candidate\\n\\n if file_path is None:\\n raise IOError(\\\"Unable to find file '%s'. Tried directories %s\\\" %\\n (file_name, locations))\\n elif not os.path.isfile(file_path):\\n raise IOError(\\\"%s is not a file\\\" % file_path)\\n\\n return file_path\",\n \"def get_file(view: View, string: str, name: str):\\n\\n # if it's an absolute path get it\\n if osp.isabs(string):\\n return string, None\\n\\n # if search_mode: \\\"project\\\", search only in project\\n elif SEARCH_MODE == \\\"project\\\":\\n # Get base project folders\\n base_folders = sublime.active_window().folders()\\n # if \\\"recursive\\\": true, recursively search for the name\\n if RECURSIVE:\\n ch_rec = check_recursive(base_folders, name)\\n if ch_rec:\\n base_folder, root = ch_rec\\n return osp.join(root, name), base_folder\\n return \\\"\\\", None\\n else:\\n # search only in base folders for the relative path\\n for base_folder in base_folders:\\n file_name = osp.normpath(osp.join(base_folder, string))\\n if osp.exists(file_name):\\n return file_name, base_folder\\n return \\\"\\\", None\\n # if search_mode: \\\"file\\\" join the relative path to the file path\\n else:\\n return osp.normpath(osp.join(osp.dirname(view.file_name()), string)), None\",\n \"def get_file(self, name):\\n return self.files[name]\",\n \"def get_file(view: sublime.View, string: str, name: str) -> 'Tuple[str, Optional[str]]':\\n\\n # if it's an absolute path get it\\n if osp.isabs(string):\\n return string, None\\n\\n # if search_mode: \\\"project\\\", search only in project\\n elif Settings.search_mode == \\\"project\\\":\\n # Get base project folders\\n base_folders = sublime.active_window().folders()\\n # if \\\"recursive\\\": true, recursively search for the name\\n if Settings.recursive:\\n ch_rec = check_recursive(base_folders, name)\\n if ch_rec:\\n base_folder, root = ch_rec\\n return osp.join(root, name), base_folder\\n return \\\"\\\", None\\n else:\\n # search only in base folders for the relative path\\n for base_folder in base_folders:\\n file_name = osp.normpath(osp.join(base_folder, string))\\n if osp.exists(file_name):\\n return file_name, base_folder\\n return \\\"\\\", None\\n # if search_mode: \\\"file\\\" join the relative path to the file path\\n else:\\n return osp.normpath(osp.join(osp.dirname(view.file_name()), string)), None\",\n \"def get(name):\\n\\n filename = find(name)\\n if filename == None:\\n return name\\n return open(filename).read()\",\n \"def get_data_in_paths(dfile, paths):\\n for pth in paths:\\n for f in os.listdir(pth):\\n if f == dfile:\\n return os.path.abspath(os.path.join(pth, dfile))\",\n \"def findInPath(fileName):\\n\\timport posixpath\\n\\tfor pathdir in ([\\\".\\\"]+sys.path):\\n\\t\\tif posixpath.isfile(pathdir+\\\"/\\\"+fileName):\\n\\t\\t\\treturn pathdir+\\\"/\\\"+fileName\",\n \"def find_file(folders, path):\\n for name in path[:-1]:\\n folders = [sub for folder in folders for sub in subdirs(folder, name)]\\n return [\\n os.path.join(folder, f)\\n for folder in folders\\n for f in os.listdir(folder)\\n if f.lower() == path[-1]\\n ]\",\n \"def find_file_by_name(point,target):\\n launcher_path = \\\"\\\"\\n for (path, dir, files) in os.walk(point):\\n for filename in files:\\n # if target in filename:\\n if filename == target:\\n launcher_path = os.path.join(path, filename)\\n return launcher_path\",\n \"def find_file(self, name):\\n if not self._namespace:\\n raise NotInitializedError()\\n dirs = []\\n if self._namespace._config_dirs:\\n for directory in self._namespace._config_dirs:\\n dirs.append(_fixpath(directory))\\n\\n for cf in reversed(self.config_file):\\n dirs.append(os.path.dirname(_fixpath(cf)))\\n\\n dirs.extend(_get_config_dirs(self.project))\\n\\n return _search_dirs(dirs, name)\",\n \"def find_path(name, path=None, exact=False):\\n if path is None:\\n path = os.environ.get('PATH', os.defpath)\\n if isinstance(path, str):\\n dpaths = path.split(os.pathsep)\\n else:\\n dpaths = path\\n candidates = (join(dpath, name) for dpath in dpaths)\\n if exact:\\n if WIN32: # nocover\\n # on WIN32 allow ``name`` to omit the extension suffix by trying\\n # to match with all possible \\\"valid\\\" suffixes specified by PATHEXT\\n pathext = [''] + os.environ.get('PATHEXT', '').split(os.pathsep)\\n candidates = (p + ext for p in candidates for ext in pathext)\\n candidates = filter(exists, candidates)\\n else:\\n import glob\\n candidates = it.chain.from_iterable(\\n glob.glob(pattern) for pattern in candidates)\\n\\n for candidate in candidates:\\n yield candidate\",\n \"def _find_file(self, name, check_dir='c_files'):\\n testdir = os.path.dirname(__file__)\\n name = os.path.join(testdir, check_dir, name)\\n return name\",\n \"def retrieve(self, file_name):\\n ret = os.path.join(self.path, file_name)\\n temp = (self.path + file_name).find(self.path, 1, -1)\\n if os.path.exists(ret):\\n return os.path.join(self.path, file_name)\\n elif temp != -1:\\n if os.name == \\\"posix\\\":\\n return os.getcwd() + '/' + file_name\\n else:\\n return os.getcwd() + '\\\\\\\\' + file_name\\n else:\\n return None\",\n \"def file_by_name(self, context, params):\\n\\n token = util.get_access_token(context['headers'])\\n response, code = OnedriveApi.search(token, params['name'])\\n\\n if code == 400:\\n return response\\n\\n result = []\\n\\n for item in response['value']:\\n if item.get('file'):\\n item_data = self.get_item_data(item)\\n result.append(item_data)\\n\\n return result\",\n \"def SearchPath(name, path=None):\\n path = path or os.environ['PATH']\\n for dir in path.split(os.pathsep):\\n binpath = os.path.join(dir, name)\\n if os.path.exists(binpath):\\n return os.path.abspath(binpath)\\n return None\",\n \"def _findfile(self,path,label):\\n files=[];filenames=os.listdir(path)\\n for name in filenames:\\n if os.path.splitext(name)[0]==str(label):\\n files.append(name)\\n return files\",\n \"def recursively_find_file(folder, file_name):\\n # TODO: print a hint when not founding file_name\",\n \"def _search_path_to_file(self, directory, binary_name):\\n for root, dirs, files in os.walk(directory):\\n if binary_name in files:\\n return os.path.join(root, binary_name)\\n raise micp_kernel.NoExecutableError\",\n \"def find(cls, paths):\\r\\n pythons = []\\r\\n for path in paths:\\r\\n for fn in cls.expand_path(path):\\r\\n basefile = os.path.basename(fn)\\r\\n if any(matcher.match(basefile) is not None for matcher in cls.REGEXEN):\\r\\n try:\\r\\n pythons.append(cls.from_binary(fn))\\r\\n except Exception as e:\\r\\n TRACER.log('Could not identify %s: %s' % (fn, e))\\r\\n continue\\r\\n return pythons\",\n \"def search_for_files(\\n run_name: str,\\n raw_extension: Optional[str] = None,\\n cellpy_file_extension: Optional[str] = None,\\n raw_file_dir: Union[OtherPath, pathlib.Path, str, None] = None,\\n project_dir: Union[OtherPath, pathlib.Path, str, None] = None,\\n cellpy_file_dir: Union[OtherPath, pathlib.Path, str, None] = None,\\n prm_filename: Union[pathlib.Path, str, None] = None,\\n file_name_format: Optional[str] = None,\\n reg_exp: Optional[str] = None,\\n sub_folders: Optional[bool] = True,\\n file_list: Optional[List[str]] = None,\\n with_prefix: Optional[bool] = True,\\n pre_path: Union[OtherPath, pathlib.Path, str, None] = None,\\n) -> Tuple[List[str], str]:\\n\\n # TODO: @jepe - use reg_exp\\n # TODO: @jepe - allow for searching in cloud\\n # TODO: @jepe - how to implement searching in db?\\n # TODO: update prms and conf file to allow for setting if search should be done in\\n # sub-folders, several folders, db, cloud etc\\n # TODO: @jepe - find a way to implement automatic file_list creation in a top level func.\\n logging.debug(f\\\"searching for {run_name}\\\")\\n version = 0.4\\n t0 = time.time()\\n\\n if reg_exp is None:\\n reg_exp = prms.FileNames.reg_exp\\n\\n if raw_extension is None:\\n raw_extension = prms.FileNames.raw_extension\\n\\n if raw_extension is None:\\n raw_extension = prms.FileNames.raw_extension\\n\\n if cellpy_file_extension is None:\\n cellpy_file_extension = prms.FileNames.cellpy_file_extension\\n\\n # backward compatibility check:\\n if cellpy_file_extension.startswith(\\\".\\\"):\\n warnings.warn(\\n \\\"Deprecation warning: cellpy_file_extension should not include the '.'\\\"\\n )\\n cellpy_file_extension = cellpy_file_extension[1:]\\n\\n if raw_file_dir is None:\\n raw_file_dir = prms.Paths.rawdatadir\\n\\n if file_name_format is None:\\n file_name_format = prms.FileNames.file_name_format\\n\\n # 'dressing' the raw_file_dir in a list in case we want to\\n # search in several folders (not implemented yet):\\n if not isinstance(raw_file_dir, (list, tuple)):\\n raw_file_dir = [OtherPath(raw_file_dir)]\\n else:\\n raw_file_dir = [OtherPath(d) for d in raw_file_dir]\\n\\n if project_dir is not None:\\n raw_file_dir = [r / project_dir for r in raw_file_dir]\\n\\n if reg_exp:\\n logging.warning(f\\\"Got reg_exp: {reg_exp}\\\")\\n logging.warning(\\\"Sorry, but using reg exp is not implemented yet.\\\")\\n\\n if prm_filename is not None:\\n logging.debug(\\\"Sorry, reading prm file is not implemented yet.\\\")\\n\\n if cellpy_file_dir is None:\\n cellpy_file_dir = OtherPath(prms.Paths.cellpydatadir)\\n else:\\n cellpy_file_dir = OtherPath(cellpy_file_dir)\\n\\n if file_name_format is None and reg_exp is None:\\n try:\\n # To be implemented in version 0.5:\\n file_name_format = prms.FileNames.file_name_format\\n except AttributeError:\\n file_name_format = \\\"YYYYMMDD_[name]EEE_CC_TT_RR\\\"\\n\\n if file_name_format.upper() == \\\"YYYYMMDD_[NAME]EEE_CC_TT_RR\\\":\\n # backward compatibility check\\n if raw_extension.startswith(\\\".\\\"):\\n warnings.warn(\\n \\\"Deprecation warning: raw_extension should not include the '.'\\\"\\n )\\n raw_extension = raw_extension[1:]\\n warnings.warn(f\\\"Removing it -> {raw_extension}\\\")\\n # TODO: give warning/error-message if run_name contains more than one file (due to duplicate in db)\\n glob_text_raw = f\\\"{run_name}*.{raw_extension}\\\"\\n else:\\n glob_text_raw = file_name_format\\n\\n logging.debug(f\\\"searching for raw files in: {raw_file_dir}\\\")\\n logging.debug(f\\\"searching for {run_name}\\\")\\n logging.debug(f\\\"using glob {glob_text_raw}\\\")\\n\\n cellpy_file = f\\\"{run_name}.{cellpy_file_extension}\\\"\\n cellpy_file = cellpy_file_dir / cellpy_file\\n # Not sure if for example pandas can handle OtherPath objects:\\n cellpy_file = cellpy_file.full_path\\n\\n logging.debug(f\\\"generated cellpy filename {cellpy_file}\\\")\\n\\n if file_list is not None:\\n if len(raw_file_dir) > 1:\\n logging.info(\\\"you provided several raw file directories\\\")\\n logging.debug(\\\"searching within provided list of files\\\")\\n\\n if with_prefix:\\n glob_text_raw_for_list = f\\\"*{glob_text_raw}\\\"\\n else:\\n glob_text_raw_for_list = glob_text_raw\\n\\n run_files = fnmatch.filter(file_list, glob_text_raw_for_list)\\n\\n if pre_path is not None:\\n pre_path = OtherPath(pre_path)\\n run_files = list(map(lambda x: pre_path / x, run_files))\\n\\n else:\\n run_files = []\\n for d in raw_file_dir:\\n if d.is_external:\\n logging.debug(\\\"external file\\\")\\n if not d.is_dir():\\n warnings.warn(\\\"your raw file directory cannot be accessed!\\\")\\n # raise cellpy.exceptions.IOError(\\\"your raw file directory cannot be accessed!\\\")\\n _run_files = []\\n else:\\n logging.debug(f\\\"checking in folder {d}\\\")\\n logging.debug(f\\\"{sub_folders=}\\\")\\n if sub_folders:\\n _run_files = d.rglob(glob_text_raw)\\n\\n else:\\n _run_files = d.glob(glob_text_raw)\\n\\n _run_files = [str(_f.resolve()) for _f in _run_files]\\n _run_files.sort()\\n run_files.extend(_run_files)\\n\\n return run_files, cellpy_file\",\n \"def sysfind(cls, name, checker=None, paths=None):\\n if isabs(name):\\n p = local(name)\\n if p.check(file=1):\\n return p\\n else:\\n if paths is None:\\n if iswin32:\\n paths = os.environ[\\\"Path\\\"].split(\\\";\\\")\\n if \\\"\\\" not in paths and \\\".\\\" not in paths:\\n paths.append(\\\".\\\")\\n try:\\n systemroot = os.environ[\\\"SYSTEMROOT\\\"]\\n except KeyError:\\n pass\\n else:\\n paths = [\\n path.replace(\\\"%SystemRoot%\\\", systemroot) for path in paths\\n ]\\n else:\\n paths = os.environ[\\\"PATH\\\"].split(\\\":\\\")\\n tryadd = []\\n if iswin32:\\n tryadd += os.environ[\\\"PATHEXT\\\"].split(os.pathsep)\\n tryadd.append(\\\"\\\")\\n\\n for x in paths:\\n for addext in tryadd:\\n p = local(x).join(name, abs=True) + addext\\n try:\\n if p.check(file=1):\\n if checker:\\n if not checker(p):\\n continue\\n return p\\n except error.EACCES:\\n pass\\n return None\",\n \"def file_match(file_name, names):\\n fname = basename(file_name)\\n for name in names:\\n if fnmatch(fname, name):\\n return True\\n return False\",\n \"def module_path_finder(path, name, recursive=True):\\n path_ = None\\n paths = glob(os.path.join(path, '**', '*.py'), recursive=recursive)\\n for path in paths:\\n file_name = os.path.basename(path)\\n if file_name == name + '.py':\\n path_ = os.path.abspath(path)\\n\\n if path_ is None:\\n raise FileNotFoundError(\\\"module %s not found\\\" % name)\\n\\n return path_\",\n \"def search_files(filename, search_path, pathsep=os.pathsep):\\n clidFiles = []\\n for path in search_path.split(pathsep):\\n candidate = os.path.join(path, filename)\\n if os.path.exists(candidate): clidFiles.append(os.path.abspath(candidate))\\n return clidFiles\",\n \"def find_files_by_name_only(path, file_name, allowed_postfix=None):\\n all_files = []\\n for root, dirs, files in os.walk(path):\\n all_files += files\\n\\n found_files = [f for f in all_files if fnmatch.fnmatch(f, '{}*'.format(file_name))]\\n if allowed_postfix:\\n found_files = [f for f in found_files if f.split('.').pop() in allowed_postfix]\\n \\n return found_files\",\n \"def getNamedPath(name):\\n def findNamed(path):\\n pp = os.path.split(path)\\n if pp[-1] == '':\\n return None\\n if pp[-1] != name:\\n path = findNamed(pp[0])\\n return path\\n return findNamed(os.getcwd())\",\n \"def find_program(ctx, names, paths=None, *, quieter=0):\\n\\n if paths is None:\\n paths = os.environ['PATH'].split(os.pathsep)\\n\\n # If we're running on windows, we need to append '.exe' to the filenames\\n # that we're searching for.\\n if sys.platform == 'win32':\\n new_names = []\\n for name in names:\\n if \\\\\\n not name.endswith('.exe') or \\\\\\n not name.endswith('.cmd') or \\\\\\n not name.endswith('.bat'):\\n new_names.append(name + '.exe')\\n new_names.append(name + '.cmd')\\n new_names.append(name + '.bat')\\n new_names.append(name)\\n names = new_names\\n\\n for name in names:\\n ctx.logger.check('looking for program ' + name, verbose=quieter)\\n\\n filename = fbuild.path.Path(name)\\n if filename.exists() and filename.isfile():\\n ctx.logger.passed('ok %s' % filename, verbose=quieter)\\n return fbuild.path.Path(name)\\n else:\\n for path in paths:\\n filename = fbuild.path.Path(path, name)\\n if filename.exists() and filename.isfile():\\n ctx.logger.passed('ok %s' % filename, verbose=quieter)\\n return fbuild.path.Path(filename)\\n\\n ctx.logger.failed(verbose=quieter)\\n\\n raise MissingProgram(names)\",\n \"def find_by_name ( self, name, **kw ):\\n try:\\n return next ( self.find_all_by_name ( name, **kw ) )\\n except StopIteration:\\n return None\",\n \"def get_filepath_filename(filepath, return_filenames):\\n main_dir_path = ''\\n names = []\\n for dirpath, dirnames, filenames in os.walk(filepath):\\n main_dir_path = dirpath\\n names = filenames\\n for name in names:\\n _ = os.path.join(main_dir_path, name)\\n return_filenames.append(_)\",\n \"def filesearch(word=\\\"\\\"):\\n logger.info('Starting filesearch')\\n file = []\\n for f in glob.glob(\\\"*\\\"):\\n if word[0] == \\\".\\\":\\n if f.endswith(word):\\n file.append(f)\\n\\n elif word in f:\\n file.append(f)\\n #return file\\n logger.debug(file)\\n return file\",\n \"def which(self, name):\\n\\n valid_names = [\\n \\\"{0}.{1}\\\".format(name, ext).lower() if ext else \\\"{0}\\\".format(name).lower()\\n for ext in KNOWN_EXTS\\n ]\\n finder = filter(operator.attrgetter(\\\"is_executable\\\"), self.children.values())\\n name_getter = operator.attrgetter(\\\"path.name\\\")\\n return next(\\n (child for child in finder if name_getter(child).lower() in valid_names),\\n None,\\n )\",\n \"def path_to_file(fname, dirs):\\n for each in dirs:\\n path = '/'.join([each, fname])\\n if os.path.exists(path):\\n return path\\n return None\",\n \"def find_by_name(name):\\n return repository.find_by_name(name)\",\n \"def get_files(filepath='./', name_contains=\\\"\\\", absolute_path=True, subdirectories=True):\\n\\n all_files = []\\n\\n\\n for (dirpath, dirnames, filenames) in os.walk(filepath):\\n \\n # filenames specified\\n if name_contains: \\n all_files.extend(glob(os.path.join(dirpath,name_contains)))\\n\\n elif not name_contains:\\n all_files.extend(filenames)\\n \\n # exclude subdirectories, break loop \\n if not subdirectories:\\n break\\n # otherwise continue with loop, walking down the directory\\n\\n # get absolute path\\n if absolute_path: \\n all_files_absolute = [os.path.abspath(f) for f in all_files]\\n return all_files_absolute\\n\\n else:\\n return all_files\",\n \"def find_program(name):\\r\\n # See MSDN for the REAL search order.\\r\\n base, ext = os.path.splitext(name)\\r\\n if ext:\\r\\n exts = [ext]\\r\\n else:\\r\\n exts = ['.bat', '.exe']\\r\\n for directory in os.environ['PATH'].split(os.pathsep):\\r\\n for e in exts:\\r\\n fname = os.path.join(directory, base + e)\\r\\n if os.path.exists(fname):\\r\\n return fname\\r\\n return None\",\n \"def locate_file(fname):\\n out = []\\n if platform._is_win: # pragma: windows\\n out += find_all(fname, None)\\n else:\\n for path in os.environ.get('PATH').split(os.pathsep):\\n if path:\\n out += find_all(fname, path)\\n if not out:\\n return False\\n first = out[0]\\n if len(out) > 1:\\n # pprint.pprint(out)\\n warnings.warn(\\\"More than one (%d) match to %s. Using first match (%s)\\\" % (\\n len(out), fname, first))\\n return first\",\n \"def search_for_name(self, name):\\n for p in self.books_all:\\n if p['name'] == name:\\n return p\",\n \"def find_executable(*names: str,\\n additional_roots: Iterable[Path] = (),\\n additional_files: Iterable[Path] = ())-> Optional[Path]:\\n for path in additional_files:\\n if path_is_executable(path):\\n return path\\n\\n for root in chain(additional_roots, os.get_exec_path()):\\n root = Path(root)\\n for name in names:\\n path = root / name\\n if path_is_executable(path):\\n return path\\n return None\",\n \"def _find_file(config, startswith=False):\\n remote_files = _get_remote_files(config)\\n if startswith:\\n remote_folders = {}\\n for fname, (pid, _) in remote_files.items():\\n remote_folders[os.path.dirname(fname)] = (pid, None)\\n remote_files = remote_folders\\n\\n def glob_match(f1, f2):\\n \\\"\\\"\\\"Check for wildcard glob style matches.\\n \\\"\\\"\\\"\\n if f1.find(\\\"*\\\") >= 0:\\n if fnmatch.fnmatch(f2, \\\"*/%s\\\" % f1):\\n return True\\n\\n def get_file(f):\\n if _is_remote(f):\\n f = _get_id_fname(f)[-1]\\n # handle both bare lookups and project-prefixed\\n if f.find(\\\":\\\") > 0:\\n fproject, f = f.split(\\\":\\\")\\n else:\\n fproject = None\\n # check for exact matches\\n for project, folder in _remote_folders(config):\\n if fproject is None or fproject == project:\\n folder_f = os.path.join(folder, f)\\n if folder_f in remote_files:\\n pid, fid = remote_files[folder_f]\\n return \\\"%s:%s/%s:%s\\\" % (KEY, fid, pid, folder_f)\\n # find any files nested in sub folders or as globs\\n out = []\\n for project, folder in _remote_folders(config):\\n for rfname, (pid, rid) in remote_files.items():\\n if rfname.startswith(folder + \\\"/\\\") and (rfname.endswith(\\\"/\\\" + f) or glob_match(f, rfname)):\\n out.append(\\\"%s:%s/%s:%s\\\" % (KEY, rid, pid, rfname))\\n if len(out) == 1:\\n return out[0]\\n elif len(out) > 1:\\n return out\\n return get_file\",\n \"def find_in_PATH(filename: str):\\n if path.isfile(filename):\\n return path.normpath(filename)\\n\\n os_paths = os.environ['PATH'].split(path.pathsep)\\n for os_path in os_paths:\\n fullpath_file = path.join(os_path, filename)\\n if path.isfile(fullpath_file):\\n return path.normpath(fullpath_file)\\n raise FileNotFoundError(f'could not find {filename}')\",\n \"def find_template_filename(self, template_name):\\n\\n def next_file():\\n filename = self.path / template_name\\n yield filename\\n try:\\n exts = self.default_file_extensions\\n except AttributeError:\\n return\\n\\n strfilename = str(filename)\\n for ext in exts:\\n yield Path(strfilename + ext)\\n\\n for filename in next_file():\\n if filename.is_file():\\n return filename\",\n \"def _load(name, paths):\\n for base_path in paths:\\n parts = name.split('.')\\n number_of_parts = len(parts)\\n\\n for folder_parts in range(number_of_parts):\\n folder = os.path.join(base_path, *parts[:folder_parts])\\n filename = '.'.join(parts[folder_parts:]) + '.json'\\n json_path = os.path.join(folder, filename)\\n\\n if os.path.isfile(json_path):\\n with open(json_path, 'r') as json_file:\\n LOGGER.debug('Loading %s from %s', name, json_path)\\n return json.load(json_file)\",\n \"def find_executable(name):\\n for dir in os.environ.get('PATH', '').split(os.pathsep):\\n if not dir:\\n continue\\n fn = os.path.abspath(os.path.join(dir, name))\\n if os.path.exists(fn):\\n return os.path.abspath(fn)\",\n \"def find_executable(name):\\n if os.path.isabs(name):\\n return name if os.path.isfile(name) else None\\n else:\\n if iswin32:\\n paths = os.environ['Path'].split(';')\\n if '' not in paths and '.' not in paths:\\n paths.append('.')\\n try:\\n systemroot = os.environ['SYSTEMROOT']\\n except KeyError:\\n pass\\n else:\\n paths = [re.sub('%SystemRoot%', systemroot, path)\\n for path in paths]\\n else:\\n paths = os.environ['PATH'].split(':')\\n tryadd = []\\n if iswin32:\\n tryadd += os.environ['PATHEXT'].split(os.pathsep)\\n tryadd.append(\\\"\\\")\\n\\n for x in paths:\\n for addext in tryadd:\\n p = os.path.join(x, name) + addext\\n try:\\n if os.path.isfile(p):\\n return p\\n except Exception:\\n pass\\n return None\",\n \"def find_axfile(name, search_path=[u\\\".\\\", u\\\"$GT3AXISDIR\\\", u\\\"$GTOOLDIR/gt3\\\"]):\\n axis_path = map(os.path.expandvars, search_path)\\n axis_path = [a for a in axis_path if a.find('$') < 0]\\n axis_path = [a for a in axis_path if os.path.exists(a)]\\n\\n if (opt_debug):\\n print('*** axis_path:')\\n print(axis_path)\\n\\n axfile = None\\n for axdir in axis_path:\\n # print(axdir)\\n axfile = os.path.join(axdir, 'GTAXLOC.'+name)\\n if (os.path.exists(axfile)):\\n break\\n\\n # print(\\\"dbg:find_axfile:\\\",axfile)\\n return axfile\",\n \"def ffind(path, shellglobs=None, namefs=None, relative=True):\\n if not os.access(path, os.R_OK):\\n raise ScriptError(\\\"cannot access path: '%s'\\\" % path)\\n\\n fileList = [] # result list\\n try:\\n for dir, subdirs, files in os.walk(path):\\n if shellglobs:\\n matched = []\\n for pattern in shellglobs:\\n filterf = lambda s: fnmatch.fnmatchcase(s, pattern)\\n matched.extend(list(filter(filterf, files)))\\n fileList.extend(['%s%s%s' % (dir, os.sep, f) for f in matched])\\n else:\\n fileList.extend(['%s%s%s' % (dir, os.sep, f) for f in files])\\n if not relative: fileList = list(map(os.path.abspath, fileList))\\n if namefs:\\n for ff in namefs: fileList = list(filter(ff, fileList))\\n except Exception as e: raise ScriptError(str(e))\\n return(fileList)\",\n \"def get_result_by_name(self, name):\\n found = None\\n for result in self.results:\\n if result.heading == name:\\n found = result\\n break\\n return found\",\n \"def get_path(self, path_name):\\n if not path_name:\\n return None\\n for path in self._paths:\\n if path._path == path_name:\\n return path\\n pass\\n new_path = self.add_path(path_name)\\n return new_path\",\n \"def _find_config_file(self) -> str or None:\\n import os\\n\\n for path in self.paths:\\n path = os.path.expanduser(path)\\n for extension in self.file_extensions:\\n for file_name in self.file_names:\\n file_path = os.path.join(path, \\\"{}.{}\\\".format(file_name, extension))\\n if os.path.isfile(file_path):\\n return file_path\\n\\n return None\",\n \"def checkFilePath(self, filename, searchpath=[]):\\n\\t\\tif filename is None:\\n\\t\\t\\treturn None\\n\\t\\telif os.path.isfile(filename):\\n\\t\\t\\treturn filename\\n\\t\\telse:\\n\\t\\t\\t# Append current dir to searchpath and try each in turn\\n\\t\\t\\tsearchpath.append(os.path.dirname(__file__))\\n\\t\\t\\t# print(searchpath)\\n\\t\\t\\tfor folder in searchpath:\\n\\t\\t\\t\\tfilepath = os.path.join(folder, filename)\\n\\t\\t\\t\\tif os.path.isfile(filepath):\\n\\t\\t\\t\\t\\treturn filepath\\n\\n\\t\\t# File not found\\n\\t\\treturn None\",\n \"def find_stub_files(name: str) -> List[str]:\\n result = []\\n for root, dirs, files in os.walk(name):\\n for file in files:\\n if file.endswith(\\\".pyi\\\"):\\n if os.path.sep in root:\\n sub_root = root.split(os.path.sep, 1)[-1]\\n file = os.path.join(sub_root, file)\\n result.append(file)\\n return result\",\n \"def find_file(research_structure, raise_on_all_missing=True):\\n filenames = []\\n paths_searched = []\\n ## config file lookup resolution\\n for enforce_file_existence, fun in research_structure:\\n candidate = fun()\\n if candidate is None:\\n continue\\n if not os.path.exists(candidate):\\n if enforce_file_existence:\\n raise ValueError(\\\"File %r does not exists.\\\" % candidate)\\n else:\\n paths_searched.append(candidate)\\n else:\\n return candidate\\n if not filenames and raise_on_all_missing:\\n raise ValueError(\\\"No config file was found in those paths: %s.\\\"\\n % ', '.join(paths_searched))\\n return\",\n \"def getImageFinder(self, name: str) -> Optional[str]:\\n trace = 'themes' in g.app.debug\\n exists = g.os_path_exists\\n getString = g.app.config.getString\\n\\n def dump(var: str, val: str) -> None:\\n print(f\\\"{var:20}: {val}\\\")\\n\\n join = g.os_path_join\\n #\\n # \\\"Just works\\\" for --theme and theme .leo files *provided* that\\n # theme .leo files actually contain these settings!\\n #\\n theme_name1 = getString('color-theme')\\n theme_name2 = getString('theme-name')\\n roots = [\\n g.os_path_join(g.computeHomeDir(), '.leo'),\\n g.computeLeoDir(),\\n ]\\n theme_subs = [\\n \\\"themes/{theme}/Icons\\\",\\n \\\"themes/{theme}\\\",\\n \\\"Icons/{theme}\\\",\\n ]\\n # \\\".\\\" for icons referred to as Icons/blah/blah.png\\n bare_subs = [\\\"Icons\\\", \\\".\\\"]\\n paths = []\\n for theme_name in (theme_name1, theme_name2):\\n for root in roots:\\n for sub in theme_subs:\\n paths.append(join(root, sub.format(theme=theme_name)))\\n for root in roots:\\n for sub in bare_subs:\\n paths.append(join(root, sub))\\n table = [z for z in paths if exists(z)]\\n for base_dir in table:\\n path = join(base_dir, name)\\n if exists(path):\\n if trace:\\n g.trace(f\\\"Found {name} in {base_dir}\\\")\\n return path\\n # if trace: g.trace(name, 'not in', base_dir)\\n if trace:\\n g.trace('not found:', name)\\n return None\",\n \"def read_files(path, file_name):\\n\\n if os.path.exists(\\n r'{}\\\\{}_dynamic.csv'.format(path, file_name)) and os.path.exists(\\n r'{}\\\\{}_static.csv'.format(path, file_name)) and os.path.exists(\\n r'{}\\\\{}_ego.csv'.format(path, file_name)):\\n with open(r'{}\\\\{}_dynamic.csv'.format(path, file_name)) as tmp_dynamic:\\n dynamic_csv = pd.read_csv(tmp_dynamic)\\n print('Dynamic csv file found')\\n with open(r'{}\\\\{}_static.csv'.format(path, file_name)) as tmp_static:\\n static_csv = pd.read_csv(tmp_static)\\n print('Static csv file found')\\n with open(r'{}\\\\{}_ego.csv'.format(path, file_name)) as tmp_ego:\\n ego_csv = pd.read_csv(tmp_ego)\\n print('Ego csv file found')\\n return ego_csv, dynamic_csv, static_csv\\n\\n else:\\n print('No available data')\\n sys.exit(0)\",\n \"def which(self, name):\\n if not len(self):\\n self.update()\\n try:\\n return [version for version in self if os.path.basename(version) == name][0]\\n except IndexError:\\n return None\",\n \"def find_file(path, filename, max_depth=5):\\n for root, dirs, files in os.walk(path):\\n if filename in files:\\n return os.path.join(root, filename)\\n\\n # Don't search past max_depth\\n depth = root[len(path) + 1:].count(os.sep)\\n if depth > max_depth:\\n del dirs[:] # Clear dirs\\n return None\",\n \"def _find_named_files(self):\\n for name, description in self.named_files.iteritems():\\n name = name.format(job_name=self.job_name)\\n f_path = '{}/{}'.format(self.rism3d_folder, name)\\n if os.path.isfile(f_path):\\n self.file_path_dic[description] = f_path\\n else:\\n self._not_found_error(f_path)\",\n \"def find_by_name(self, name):\\n return self.get(name)\",\n \"def find(self, file, version):\\n matches = []\\n for root, dirnames, filenames in os.walk(self.full_doc_path(version)):\\n for filename in fnmatch.filter(filenames, file):\\n matches.append(os.path.join(root, filename))\\n return matches\",\n \"def compare(self, name):\\n result = []\\n for i in (os.listdir(self.path)):\\n if i != name:\\n comparison = filecmp.cmp(self.path.__add__(i), self.path.__add__(name), shallow=False)\\n if comparison:\\n result.append(i)\\n return result\",\n \"def find(self, name):\\n path = self.directory.joinpath(name).with_suffix('.yaml')\\n if path.is_file():\\n return self.from_path(path)\\n raise LookupError(\\\"Job {} does not exist\\\".format(repr(name)))\",\n \"def find(self, name):\\n if self.policies is None:\\n self.load_file()\\n line = self.policies.get(name, None)\\n if line is None:\\n return \\\"\\\"\\n filename = os.path.basename(self.filename)\\n return f\\\"{filename}:{line}\\\"\",\n \"def search_file(pattern, filename):\\n if not os.path.exists(filename):\\n raise Exception(\\\"Can't open file for reading! \\\" + filename)\\n\\n fh = open(filename, \\\"r\\\")\\n for line in fh:\\n allmatch = re.findall(pattern, line)\\n if allmatch:\\n fh.close()\\n return allmatch[0]\\n\\n fh.close()\\n return None\",\n \"def compfile(input_path, name=\\\"\\\", list_files=None):\\n __string(input_path, \\\"%s path\\\" % name, True)\\n\\n if list_files is None:\\n list_files = []\\n elif not list_files:\\n __ex(\\\"File list is empty (no files to compare with).\\\", True)\\n else:\\n for item in list_files:\\n if not isinstance(item, list):\\n __ex(\\\"Every list item must be a sub-list.\\\", True)\\n if not len(item) == 2:\\n __ex(\\\"Every sub-list must contain two items.\\\", True)\\n\\n input_path = os.path.abspath(input_path)\\n for item in list_files:\\n path_compare = os.path.abspath(str(item[0]))\\n name_compare = str(item[1])\\n if input_path == path_compare:\\n __ex(\\\"The %s and the %s file path must not be identical.\\\" %\\n (name, name_compare), False)\\n if os.path.exists(input_path) and os.path.exists(path_compare):\\n if filecmp.cmp(input_path, path_compare, 0):\\n __ex(\\\"The %s and %s file content must not be identical.\\\" %\\n (name, name_compare), False)\",\n \"def get_files_for_path(file_ids_tree, path):\\n entry = file_ids_tree\\n parts = path.split('/')\\n for part in parts:\\n if part != '':\\n try:\\n entry = entry[part]\\n except KeyError as e:\\n files = entry[FILE_MARKER]\\n for entry in files:\\n if entry[0] == part:\\n return entry\\n raise e\\n\\n return entry\",\n \"def locate_file(self, filename):\\n return locate_file(filename, self.observatory)\",\n \"def _find_files(directory, dirs_to_look_in, files_to_search_for, \\n current_dir, see_files):\\n full_name = True\\n if see_files:\\n full_name = False\\n files_to_load = search_directory(directory, \\n look_in=dirs_to_look_in,\\n search_for=files_to_search_for,\\n file_type='files',\\n current_dir=current_dir,\\n full_name=full_name)\\n if not files_to_load:\\n raise UserWarning('No files were found matching the search for %s'\\\\\\n ' in the directory(s) %s%s' \\\\\\n % (files_to_search_for, directory, \\n dirs_to_look_in))\\n return files_to_load\",\n \"def which(name):\\n # Inspired by https://twistedmatrix.com/trac/browser/tags/releases/\\n # twisted-8.2.0/twisted/python/procutils.py\\n # pylint: disable=W0141\\n result = []\\n path = os.environ.get('PATH', None)\\n if path is None:\\n return []\\n for pdir in os.environ.get('PATH', '').split(os.pathsep):\\n fname = os.path.join(pdir, name)\\n if os.path.isfile(fname) and os.access(fname, os.X_OK):\\n result.append(fname)\\n return result[0] if result else None\",\n \"def _find_by_name_pattern(search_directory, name_pattern, is_file=True):\\n if not os.listdir(search_directory):\\n return None\\n # filter check list\\n choose_list = []\\n if is_file:\\n choose_list += filter(lambda x: os.path.isfile(x), [os.path.join(search_directory, y) for y in os.listdir(search_directory)])\\n else:\\n choose_list += filter(lambda x: os.path.isdir(x), [os.path.join(search_directory, y) for y in os.listdir(search_directory)])\\n # check\\n for sub_name in choose_list:\\n if re.match(name_pattern, os.path.basename(sub_name), re.IGNORECASE):\\n return sub_name\\n return None\",\n \"def _locate_config_file(search_dirs):\\n tried = []\\n for base in search_dirs:\\n for path in [os.path.join(base, CONFIG_NAME + suffix) for suffix in \\\".yml\\\", \\\".json\\\"]:\\n log.debug(\\\"searching for config file: %s\\\", path)\\n tried.append(path)\\n if os.path.isfile(path):\\n log.info(\\\"using config file: %s\\\", path)\\n return path\\n raise ConfigError(\\\"no config file found in: %s\\\" % \\\", \\\".join(tried))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.80682737","0.72417116","0.706997","0.7050534","0.67950416","0.6720487","0.67091864","0.6678427","0.6671641","0.6649197","0.6628015","0.6592693","0.6471008","0.6440933","0.64034975","0.63482285","0.63181823","0.63181823","0.6291936","0.62775","0.62671673","0.6195756","0.61911416","0.6178068","0.616592","0.6092481","0.60881084","0.6045829","0.6011803","0.60059416","0.5943387","0.5913936","0.5910651","0.5887042","0.5872696","0.5859958","0.5845183","0.5837335","0.58185637","0.58081424","0.57691115","0.5762131","0.57485145","0.5739176","0.57225347","0.5695623","0.5664675","0.56596565","0.5652641","0.5634293","0.562739","0.56218183","0.5618498","0.5611197","0.560285","0.5596155","0.5555069","0.5541995","0.5540007","0.5531576","0.5519832","0.55177987","0.55156434","0.5508926","0.54924744","0.5483209","0.54783094","0.5477371","0.54748046","0.54691195","0.54570067","0.54559416","0.54557526","0.5451322","0.54448926","0.5439093","0.54365844","0.5433829","0.54332733","0.54095805","0.54000765","0.5386236","0.53861785","0.53847283","0.53816277","0.53781295","0.5375561","0.5372191","0.5371186","0.53591","0.5355633","0.5355105","0.53515214","0.53488195","0.53477114","0.5345579","0.53414404","0.5340259","0.53259766","0.5319714"],"string":"[\n \"0.80682737\",\n \"0.72417116\",\n \"0.706997\",\n \"0.7050534\",\n \"0.67950416\",\n \"0.6720487\",\n \"0.67091864\",\n \"0.6678427\",\n \"0.6671641\",\n \"0.6649197\",\n \"0.6628015\",\n \"0.6592693\",\n \"0.6471008\",\n \"0.6440933\",\n \"0.64034975\",\n \"0.63482285\",\n \"0.63181823\",\n \"0.63181823\",\n \"0.6291936\",\n \"0.62775\",\n \"0.62671673\",\n \"0.6195756\",\n \"0.61911416\",\n \"0.6178068\",\n \"0.616592\",\n \"0.6092481\",\n \"0.60881084\",\n \"0.6045829\",\n \"0.6011803\",\n \"0.60059416\",\n \"0.5943387\",\n \"0.5913936\",\n \"0.5910651\",\n \"0.5887042\",\n \"0.5872696\",\n \"0.5859958\",\n \"0.5845183\",\n \"0.5837335\",\n \"0.58185637\",\n \"0.58081424\",\n \"0.57691115\",\n \"0.5762131\",\n \"0.57485145\",\n \"0.5739176\",\n \"0.57225347\",\n \"0.5695623\",\n \"0.5664675\",\n \"0.56596565\",\n \"0.5652641\",\n \"0.5634293\",\n \"0.562739\",\n \"0.56218183\",\n \"0.5618498\",\n \"0.5611197\",\n \"0.560285\",\n \"0.5596155\",\n \"0.5555069\",\n \"0.5541995\",\n \"0.5540007\",\n \"0.5531576\",\n \"0.5519832\",\n \"0.55177987\",\n \"0.55156434\",\n \"0.5508926\",\n \"0.54924744\",\n \"0.5483209\",\n \"0.54783094\",\n \"0.5477371\",\n \"0.54748046\",\n \"0.54691195\",\n \"0.54570067\",\n \"0.54559416\",\n \"0.54557526\",\n \"0.5451322\",\n \"0.54448926\",\n \"0.5439093\",\n \"0.54365844\",\n \"0.5433829\",\n \"0.54332733\",\n \"0.54095805\",\n \"0.54000765\",\n \"0.5386236\",\n \"0.53861785\",\n \"0.53847283\",\n \"0.53816277\",\n \"0.53781295\",\n \"0.5375561\",\n \"0.5372191\",\n \"0.5371186\",\n \"0.53591\",\n \"0.5355633\",\n \"0.5355105\",\n \"0.53515214\",\n \"0.53488195\",\n \"0.53477114\",\n \"0.5345579\",\n \"0.53414404\",\n \"0.5340259\",\n \"0.53259766\",\n \"0.5319714\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.66123325"},"document_rank":{"kind":"string","value":"11"}}},{"rowIdx":11292,"cells":{"query":{"kind":"string","value":"Run the flatbuffer compiler on the given json file and schema."},"document":{"kind":"string","value":"def convert_json_to_flatbuffer_binary(json, schema, out_dir):\n command = [FLATC, '-o', out_dir, '-b', schema, json]\n run_subprocess(command)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def run(\n self,\n input_file=sys.stdin,\n output_file=sys.stdout,\n schema_map=None,\n ):\n schema_map, error_logs = self.deduce_schema(\n input_file, schema_map=schema_map\n )\n\n for error in error_logs:\n logging.info(\n f\"Problem on line {error['line_number']}: {error['msg']}\"\n )\n\n if self.debugging_map:\n json.dump(schema_map, output_file, indent=2)\n print(file=output_file)\n else:\n schema = self.flatten_schema(schema_map)\n json.dump(schema, output_file, indent=2)\n print(file=output_file)","def main(input_json, output_markdown):\n parser = Parser()\n output_md = parser.parse_schema(json.load(input_json))\n output_markdown.writelines(output_md)\n click.secho(\"✔ Successfully parsed schema!\", bold=True, fg=\"green\")","def main(schema_file, cxx=None, py=None):\n logging.info('Working on file: ' + schema_file)\n\n # validate file exists\n if not os.path.isfile(schema_file):\n logging.info('File not found')\n return -1\n\n # loading schema from file\n json_parser = JsonComment(json)\n with open(schema_file) as fd:\n schema = json_parser.load(fd)\n\n # validating the schema\n status, msg = validate_protocol_schema(schema)\n if not status:\n logging.info('Invalid Schema:' + msg)\n return -2\n logging.info('Schema is valid')\n\n # Build the protocol\n protocol = Protocol(schema)\n\n # build CXX\n if cxx is not None:\n if not os.path.isdir(cxx):\n logging.info('CXX Dest not found')\n return -1\n\n message.make_message_cxx(protocol, cxx)\n\n for r in protocol.endpoints:\n router.make_router_cxx(protocol, r, cxx)\n\n # build py\n if py is not None:\n if not os.path.isdir(py):\n logging.info('PY Dest not found')\n return -1\n\n message.make_message_py(protocol, py)\n\n for r in protocol.endpoints:\n router.make_router_py(protocol, r, py)\n\n return 0","def test_json():\n schemas = {\n 'schema-languages': 'bible/languages.json',\n 'schema-book-metadata': 'bible/book-metadata.json',\n 'schema-bible': 'bible/bible-*.json'\n }\n for schema_name, data_path_glob in schemas.items():\n schema_path = 'schemas/{}.json'.format(schema_name)\n with open(schema_path) as schema_file:\n schema = json.load(schema_file)\n data_paths = glob.iglob(data_path_glob)\n for data_path in data_paths:\n with open(data_path) as data_file:\n data = json.load(data_file)\n yield jsonschema.validate, data, schema","def generate_flatbuffer_binaries():\n for element in FLATBUFFERS_CONVERSION_DATA:\n schema = element.schema\n output_path = element.output_path\n if not os.path.exists(output_path):\n os.makedirs(output_path)\n for json in element.input_files:\n target = processed_json_path(json)\n if needs_rebuild(json, target) or needs_rebuild(schema, target):\n convert_json_to_flatbuffer_binary(\n json, schema, output_path)","def main(gtfs_file, input_json_file):\n\n with open(input_json_file) as jsonfile:\n input_json = json.load(jsonfile)\n\n gtfs_feed = mzgtfs.feed.Feed(filename=gtfs_file)\n\n for fare_id, rules_attributes in input_json.iteritems():\n add_fare_id(gtfs_feed, fare_id, rules_attributes)\n\n files = ['fare_attributes.txt', 'fare_rules.txt']\n gtfs_feed.write('fare_attributes.txt', gtfs_feed.fares())\n gtfs_feed.write('fare_rules.txt', gtfs_feed.fare_rules())\n\n gtfs_feed.make_zip('output.zip', files=files, clone=gtfs_file)\n shutil.move('output.zip', gtfs_file)\n\n util.delete_temp_files(files)","def main():\n\n json_str = sys.stdin.read()\n\n try:\n object_json = json.loads(json_str)\n\n except json.JSONDecodeError:\n print(\"Error with the JSON input. Please check your JSON file.\")\n sys.exit(1)\n\n final_object = flatten_json(object_json, prefix=\"\")\n object_json_output = json.dumps(final_object, indent=4)\n\n print(object_json_output)","def main(db_path, schema_json):\n create_db(db_path, schema_json)","def main():\n\n # Load arguments\n args = get_args()\n \n assert os.path.exists(args.csv), ' [ERR] File' + os.path.exists(args.csv) +'does not exist'\n\n print(args)\n try:\n dir_name = os.path.dirname(args.json)\n os.mkdir(dir_name)\n print(' [INFO] Creating', dir_name, 'directory')\n except:\n print(' [INFO] Directory', dir_name, 'already exists. Data will be replaced')\n pass\n\n if args.config:\n assert os.path.exists(args.config), ' [ERR] File' + os.path.exists(args.config) +'does not exist'\n dic_types = read_config(args.config)\n else:\n dic_types = {}\n \n # Create json\n create_json_from_csv(args.csv, args.delimiter, args.cols_delimiter, args.keep, dic_types, args.infer_types, args.max_docs, args.json, args.per_line)\n\n return 0","def main(filename):\n with open(filename) as json_file:\n data = json.load(json_file)\n\n course_dict = {}\n course_dict['course_id'] = str(os.path.split(filename.strip('/'))[-1])\n course_dict['blocks'] = build_course_map(data)\n\n filename = '%s' % course_dict['course_id']\n filepath = os.path.join('../input/', filename)\n\n with open(filepath, 'w') as outfile:\n json.dump(course_dict, outfile, indent=4)","def _load_schema(self, json_schema):\n # use jsonrefs to resolve all $refs in json\n data = jsonref.loads(json.dumps(json_schema))\n return self.__initialise_template(data)","def __json_schema_generator(file):\n try:\n data = json.load(file)\n metadata_set = set()\n try:\n for datum in data['meta']['view']['columns']:\n metadata_set.add(datum['name'])\n except Exception as e:\n metadata_set.clear()\n for datum in data:\n if isinstance(datum, str):\n metadata_set.add(datum)\n else:\n for datum_property in datum:\n metadata_set.add(str(datum_property))\n\n metadata_list = list(metadata_set)\n # assumes list of objects with sparsse data\n # OR\n # for data_property in data[0]:\n # metadata_list.append(data_property)\n # assumes list of objects and that first entry has full list of properties\n\n return SchemaGenerator.__build_schema(metadata_list)\n except Exception as e:\n logging.error('Failed to parse json file into schema: ' + str(e))\n raise FailedCreatingSchemaException(\"Failed to create schema from json file.\")","def run_json(filepath: str, strict: bool = False, debug: bool = False):\n with open(filepath) as jse:\n return _run_file_full(filepath, json.load(jse), strict=strict, debug=debug)","def _convert(self, fn, suffix='json', path='jsonschema', name=None,\n root_class_name=None, data_files=[], target_class=None):\n ie = JsonSchemaImportEngine()\n d = os.path.join(INPUT_DIR, path)\n schema = ie.load(os.path.join(d, f'{fn}.{suffix}'), name=name, format=suffix, root_class_name=root_class_name)\n model_path = os.path.join(OUTPUT_DIR, f'{fn}.yaml')\n write_schema(schema, model_path)\n roundtrip_path = os.path.join(OUTPUT_DIR, f'{fn}.roundtrip.json')\n with open(roundtrip_path, 'w') as stream:\n stream.write(JsonSchemaGenerator(model_path).serialize())\n python_path = os.path.join(OUTPUT_DIR, f'{fn}.py')\n with open(python_path, 'w') as stream:\n stream.write(PythonGenerator(model_path).serialize())\n compile_python(python_path)\n # TODO: test data_files\n return schema","def main():\n\n handler = PlanetSourceHandler()\n\n parser = xml.sax.make_parser()\n parser.setFeature(xml.sax.handler.feature_namespaces, 1)\n parser.setContentHandler(handler)\n parser.parse(sys.stdin)\n\n print simplejson.JSONEncoder(indent=True).encode(handler.sources)","def main():\n args = parse_arguments()\n\n with open(args.input_file) as infile:\n data = json.load(infile)\n if \"expected_format\" in data and \"compatible_fragment_ratio\" in data:\n send_message(save(\"strandedness\", data[\"expected_format\"]))\n send_message(\n save(\"fragment_ratio\", str(round(data[\"compatible_fragment_ratio\"], 2)))\n )\n else:\n send_message(error(\"Cannot parse library type information file.\"))","def main(args=None):\n if args is None:\n args = sys.argv[1:]\n\n usage_ = \"\"\"%prog [OPTIONS] JsonFile [MoreJsonFiles...]\nFormat/Beautify one or more JSON file(s).\"\"\"\n parser = OptionParser(usage=usage_, version=VERSION)\n parser.add_option(\"-i\", \"--indent\", dest=\"indent_size\",\n default=DEFAULT_INDENT_SIZE, type=\"int\",\n help=\"Indent size to use (default: %default).\")\n parser.add_option(\"-c\", \"--compact\", dest=\"compact\",\n action=\"store_true\", default=False,\n help=\"Use compact format (default: %default).\")\n parser.add_option(\"-n\", \"--dry-run\", dest=\"dry_run\",\n action=\"store_true\", default=False,\n help=\"Check only if JSON is well-formed (default: %default).\")\n options, filenames = parser.parse_args(args) #< pylint: disable=W0612\n if not filenames:\n parser.error(\"OOPS, no filenames provided.\")\n if options.compact:\n options.indent_size = None\n\n # -- STEP: Init logging subsystem.\n format_ = \"json.format: %(message)s\"\n logging.basicConfig(level=logging.WARN, format=format_)\n console = logging.getLogger(\"console\")\n\n # -- DOS-SHELL SUPPORT: Perform filename globbing w/ wildcards.\n skipped = 0\n filenames2 = []\n for filename in filenames:\n if \"*\" in filenames:\n files = glob.glob(filename)\n filenames2.extend(files)\n elif os.path.isdir(filename):\n # -- CONVENIENCE-SHORTCUT: Use DIR as shortcut for JSON files.\n files = glob.glob(os.path.join(filename, \"*.json\"))\n filenames2.extend(files)\n if not files:\n console.info(\"SKIP %s, no JSON files found in dir.\", filename)\n skipped += 1\n elif not os.path.exists(filename):\n console.warning(\"SKIP %s, file not found.\", filename)\n skipped += 1\n continue\n else:\n assert os.path.exists(filename)\n filenames2.append(filename)\n filenames = filenames2\n\n # -- NORMAL PROCESSING:\n errors = json_formatall(filenames, options.indent_size,\n dry_run=options.dry_run)\n console.error(\"Processed %d files (%d with errors, skipped=%d).\",\n len(filenames), errors, skipped)\n if not filenames:\n errors += 1\n return errors","def load_json_schema(filename):\n relative_path = join('../schema', filename)\n absolute_path = join(dirname(__file__), relative_path)\n\n base_path = dirname(absolute_path)\n base_uri = 'file://{}/'.format(base_path)\n\n with open(absolute_path) as schema_file:\n return jsonref.loads(\n schema_file.read(), base_uri=base_uri, jsonschema=True)","def main() -> None:\n # The first thing to do is get the lines of the PyFlex file we are given.\n parser = Parser(filename=sys.argv[1])\n parsed_data = parser.ParseFile()\n\n # Upon retrieving the Parsed Data, assign the parsed data to the\n # Symbol Table.\n SymbolTable.RULESET = parsed_data['ruleset']\n SymbolTable.INSTRUCTIONS = parsed_data['instructions']\n SymbolTable.CODE = parsed_data['code']\n # SymbolTable.PrintTable()\n\n # Using the Generator backend, we can build the generated script\n generator = Generator()\n generator.GenerateNewScript()\n\n autopep8.fix_file(filename=generator.file_main)\n\n print(\"Generated Script can be found in {}\".format(generator.file_main))","def _load_json_schema(filename):\n\n relative_path = join(\"schemas\", filename)\n absolute_path = join(dirname(__file__), relative_path)\n\n base_path = dirname(absolute_path)\n base_uri = 'file://{}/'.format(base_path)\n\n print(f\"base uri {base_uri}\")\n print(f\"base path {base_path}\")\n print(f\"relative_path {relative_path}\")\n print(f\"absolute_path {absolute_path}\")\n\n with open(absolute_path) as schema_file:\n return jsonref.loads(schema_file.read(), base_uri=base_uri, jsonschema=True)","def start(self, **kwargs):\n super(Json, self).start(**kwargs)\n flat = self.get_arg_value(\"json_flat\")\n\n self._first_row = True\n self.open_fd()\n begin = \"\" if flat else \"[\"\n self._fd.write(begin)","def process_jsonld_file(fname):\n with open(fname, 'r', encoding='utf-8') as fh:\n json_dict = json.load(fh)\n return process_jsonld(json_dict)","def main():\n widget = ParseGrypeJSON()\n logging.debug(f'argv {\",\".join(sys.argv)}')\n\n if len(sys.argv) > 1:\n widget.filename(sys.argv[1])\n\n sys.exit(widget.report())","def main():\n # if there are no command line args, run as normal with the input from stdin\n if len(sys.argv) == 1:\n json_string = \"\".join(sys.stdin)\n hierarchical_jsons = [load_json(json_string)]\n # if there are command line args, run using the first argument as a file path to a file with\n # correctly formatted test json values\n else:\n with open(sys.argv[1]) as file:\n hierarchical_jsons = json.load(file)\n\n flattened_jsons = []\n\n # for all the jsons given, in the non-test case there will only be one\n for hierarchical_json in hierarchical_jsons:\n # flatten the json object\n flat_json = flatten_json(hierarchical_json)\n # print the prettied json to stdout\n json_string = json.dumps(flat_json, indent=4)\n flattened_jsons.append(json_string)\n print(json_string)\n\n return flattened_jsons","def main():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"datafile\", help=\"Name of JSON file containing WP3-mapped metadata\")\n parser.add_argument(\"csvfile\", help=\"Corrected CSV file\")\n parser.add_argument(\"vcffile\", help=\"VCF file with samples\")\n args = parser.parse_args()\n\n samples = vcf_samples(args.vcffile)\n\n with open(args.datafile, 'r') as infile:\n data = json.load(infile)\n\n if not data:\n print(f\"Error reading data file {args.datafile}.\", file=sys.stderr)\n return\n\n with open(args.csvfile, newline='') as csvfile:\n reader = csv.DictReader(csvfile)\n for idx, row in enumerate(reader):\n data[idx][\"physiologicalMeasurements\"][\"anthropometry\"][\"weight\"] = [str(float(row['birth_weight_g'])/1000.), row['sbjt_weight_kg']]\n data[idx][\"physiologicalMeasurements\"][\"anthropometry\"][\"height\"] = [row['height_cm'], row['sbjt_length_cm']]\n data[idx][\"physiologicalMeasurements\"][\"circulationAndRespiration\"][\"bloodPressure\"] = [row['sbjt_blood_pressure_systolic']]\n if len(samples) > idx:\n data[idx][\"sample\"] = samples[idx]\n\n print(json.dumps(data))","def _load_json_schema(filename):\n\n relative_path = join('schemas', filename)\n absolute_path = join(dirname(__file__), relative_path)\n\n with open(absolute_path) as schema_file:\n return json.loads(schema_file.read())","def cli(ctx, path, **kwds):\n if not os.path.exists(path):\n io.error(\"Failed to tool test json file at %s\" % path)\n return 1\n\n test_data = StructuredData(path)\n handle_reports(ctx, test_data.structured_data, kwds)","def from_json(self, json_filename, dont_change=False, pure_json=False, binary_format=False):\n gc.disable()\n\n with open(json_filename, 'rb') as f:\n s = f.read()\n\n if not pure_json:\n # Please do not pass unicode to jsmin under py2.\n # Otherwise, it cannot use cStringIO and it bursts performance\n s = jsmin(s)\n elif self.VERBOSE:\n print('Assumed file %s is pure json - skip pre-processing' % json_filename)\n\n types = ujson.loads(s.decode(\"utf-8\"))\n if binary_format:\n dawg_checksum = types[0].get(ProductTypeDict.DAWG_CHECKSUM_ATTR)\n in_memory_dawg_checksum = TypeTerm.term_dict.dawg_checksum()\n if not dawg_checksum or in_memory_dawg_checksum != dawg_checksum:\n raise IOError('DAWG checksum does not correspond in memory version')\n types = types[1]\n\n if self.VERBOSE:\n file_rel_count = sum(map(len, types.values())) - (len(types) if not binary_format else 0)\n print(\"Parsing %d type tuples with %d relations from file %s\" % (len(types), file_rel_count, json_filename))\n\n if not dont_change:\n ProductType.reload()\n if self.VERBOSE:\n print(\"Global product types has been reloaded\")\n\n def parse_terms(terms_str):\n terms = [int(ts) if binary_format else ts.strip() for ts in terms_str.split(u'+')]\n if not binary_format:\n # For text format convert to TypeTerms\n terms = map(TypeTerm.make, terms)\n return terms\n\n type_tuples = defaultdict(list)\n pseudo_sqn = 1\n seen_rel = defaultdict(dict)\n for type_str, rel_str in types.viewitems():\n type_items = parse_terms(type_str)\n # All types are loaded from external sources or knowledge base are considered meaningful regardless of\n # other their characteristics\n if dont_change:\n p_type = ProductType(*type_items, meaningful=True, singleton=False)\n wrapper = EqWrapper(p_type)\n if wrapper in seen_rel:\n # Type has been added already by another relation\n p_type = wrapper.match\n else:\n p_type = ProductType.make_from_terms(type_items, meaningful=True)\n type_tuples[p_type] = []\n\n start_relations_index = 0\n if binary_format:\n type_tuples[p_type] = [to_str(pseudo_sqn)]\n pseudo_sqn += 1\n else:\n if isinstance(rel_str[0], int) or rel_str[0].isdigit():\n # Compatibility with old format: there was one plain integer and json parse it to int() itself\n sqns_in_self = rel_str[0]\n start_relations_index = 1\n else:\n sqns_in_self, _ = re.findall('^(\\d+)(?:/(\\d+))?$', rel_str[0])[0]\n if sqns_in_self and sqns_in_self.isdigit():\n start_relations_index = 1\n\n for i in range(int(sqns_in_self)):\n type_tuples[p_type].append(to_str(pseudo_sqn))\n pseudo_sqn += 1\n\n rel_type, rel_attr, is_soft, type_to_str = [None] * 4\n for r_str in rel_str[start_relations_index:]:\n if binary_format:\n r_match = re.findall('^([0-9+]+)(\\w)(.*)$', r_str)\n if r_match:\n type_to_str, rel_type, rel_attr = r_match[0]\n rel_type = ProductTypeDict.REL_MAPPING_BACKWARD[rel_type]\n is_soft = rel_type in (TYPE_TUPLE_RELATION_ALMOST, TYPE_TUPLE_RELATION_SIMILAR)\n else:\n r_match = re.findall('^(\\w+)(?:\\[([^\\]]+)\\])?(~)?\\s+(.*)$', r_str)\n if r_match:\n rel_type, rel_attr, is_soft, type_to_str = r_match[0]\n is_soft = is_soft == u'~'\n\n if rel_type and type_to_str:\n type_to_items = parse_terms(type_to_str)\n\n if dont_change:\n p_type_related = ProductType(*type_to_items, meaningful=True, singleton=False)\n wrapper = EqWrapper(p_type_related)\n if p_type in seen_rel and wrapper in seen_rel[p_type]:\n # Reverse to this relation has been already created. Use type instance from it\n p_type_related = wrapper.match\n else:\n p_type_related = ProductType.make_from_terms(type_to_items, meaningful=True)\n\n if rel_attr:\n try:\n rel_attr = literal_eval(rel_attr)\n except ValueError:\n pass # Just use string as is\n relation = ProductType.Relation(from_type=p_type, to_type=p_type_related, rel_type=rel_type,\n is_soft=is_soft, rel_attr=rel_attr)\n if p_type in seen_rel and p_type_related in seen_rel[p_type]:\n # Already processed back relation\n r_from = seen_rel[p_type][p_type_related]\n assert r_from, \"The same relation is detected more than two times\"\n p_type.make_relation(p_type_related, relation.rel_type, r_from.rel_type,\n relation.is_soft, relation.rel_attr, r_from.rel_attr)\n seen_rel[p_type][p_type_related] = None\n else:\n # Keep back relation until related type is met\n seen_rel[p_type_related][p_type] = relation\n elif self.VERBOSE:\n print(\"WARN: meet unparseable relation: %s\" % r_str)\n\n if self.VERBOSE:\n # Check remaining unresolved relations\n for p_type_related in seen_rel:\n for p_type in seen_rel[p_type_related]:\n if seen_rel[p_type_related][p_type] is not None:\n print(\"Detected hanged unresolved relation from type %s: %s\" % (\n p_type, to_str(seen_rel[p_type_related][p_type])))\n\n self._type_tuples.clear()\n self._type_tuples.update(type_tuples)\n self._type_tuples_on_change()\n\n gc.enable()\n if self.VERBOSE:\n rel_count = sum(map(len, (p_type.relations() for p_type in type_tuples)))\n print(\"Loaded %d type tuples with %d relations from json %s\" % (len(type_tuples), rel_count, json_filename))\n\n return type_tuples","def main():\n parser = argparse.ArgumentParser(description=\"Script for generating an index template out of a document\")\n parser.add_argument(\"INDEX_NAME\", help=\"Name of index\")\n parser.add_argument(\"--output_file\", help=\"File to write schema to\")\n args = parser.parse_args()\n\n output = generate_template(args.INDEX_NAME)\n if args.output_file:\n with open(args.output_file, \"w\") as file:\n json.dump(output, file, ensure_ascii=False, indent=4, sort_keys=True)\n else:\n print(json.dumps(output, ensure_ascii=False, indent=4, sort_keys=True))","def test_read_json_schema():\n json_schema = os.path.join(TEST_DATA_PATH, 'example_schema.json')\n schema_tree = schema.load_schema(json_schema, resolve_references=True)\n schema.check_schema(schema_tree)","def main(arguments):\n\n # The input file can be optionally encoded with gzip format:\n input_file = arguments.input_file[0]\n assert isinstance(input_file, str)\n if input_file.endswith(\".gz\"):\n _open = gzip.open\n else:\n _open = open\n with _open(input_file, \"rt\",\n encoding='utf-8') as fd:\n print(\"Loading JSON content into memory....\")\n raw = json.load(fd) # Parses all the input file.\n\n # Also the output file can be optionally encoded with gzip format:\n output_file = arguments.output_file[0]\n assert isinstance(output_file, str)\n uuid = 0\n if output_file.endswith(\".gz\"):\n _open = gzip.open\n else:\n _open = open\n with _open(output_file, \"wt\",\n encoding='utf-8') as fd:\n # for each element extracted from the input\n print(\"Generating distilled file\")\n for item in load_input(raw):\n uuid += 1 # generates incremental uuid from 1\n item['uuid'] = uuid\n fd.write(json.dumps(item,\n sort_keys=True))\n fd.write(\"\\n\") # one encoded document per line\n\n print(\"{} documents imported\".format(uuid))","def main(infile: List[str], prefix: str, verbose: bool, no_origin_filter: bool):\n common.init_logging(verbosity=int(verbose) + 1)\n _LOGGER.info(\"Running with arguments: %s.\", locals())\n\n file_id = -1\n with fileinput.input(files=infile, openhook=fileinput.hook_compressed) \\\n as json_lines:\n results = (json.loads(line) for line in json_lines)\n\n for file_id, graph in enumerate(\n extract_graphs(results, use_origin=not no_origin_filter)\n ):\n path = Path(f\"{prefix}{file_id:04d}.json\")\n if not path.is_file():\n path.write_text(graph.to_json())\n else:\n _LOGGER.info(\"Refusing to overwrite: %s\", path)\n _LOGGER.info(\"Script complete. Extracted %d dependency graphs.\", file_id+1)","def _load_json_schema(filename: str):\n relative_path = path.join('schemas', filename)\n absolute_path = path.join(path.dirname(__file__), relative_path)\n\n with open(absolute_path, 'r', encoding='utf-8') as schema_file:\n schema = json.loads(schema_file.read())\n\n return schema","def parseProgram(inputFile):\n print(\"Program\")\n parseStatements(inputFile)","def main(json_input, output_folder):\n with open(json_input, 'r') as f:\n data = json.loads(f.read())['data']\n\n with open(path.join(output_folder, 'qgeneration.context.txt'), 'w') as context_f, \\\n open(path.join(output_folder, 'qgeneration.context.nojson.txt'), 'w') as context_f_nojson, \\\n open(path.join(output_folder, 'qgeneration.gold.txt'), 'w') as question_f:\n for item in data:\n context_f.write(json.dumps(item['context']) + '\\n')\n context_f_nojson.write(item['context'][:1000] + '\\n')\n question_f.write(json.dumps(item['answer']) + '\\n')\n\n print('DONE')","def main():\n\n # Set up argument parser.\n parser = argparse.ArgumentParser(\n description='Removes duplicate key-value pairs from JSON files.')\n parser.add_argument('--suffix', default='',\n help='optional suffix for output files; '\n 'if empty, files will be changed in place')\n parser.add_argument('files', nargs='+', help='input files')\n args = parser.parse_args()\n\n # Iterate over files.\n for filename in args.files:\n # Read in json using Python libraries. This eliminates duplicates.\n print('Processing ' + filename + '...')\n try:\n with codecs.open(filename, 'r', 'utf-8') as infile:\n j = json.load(infile)\n except ValueError as e:\n print('Error reading ' + filename)\n raise InputError(filename, str(e))\n\n # Built up output strings as an array to make output of delimiters easier.\n output = []\n for key in j:\n if key != '@metadata':\n output.append('\\t\"' + key + '\": \"' +\n j[key].replace('\\n', '\\\\n') + '\"')\n\n # Output results.\n with codecs.open(filename + args.suffix, 'w', 'utf-8') as outfile:\n outfile.write('{\\n')\n outfile.write(',\\n'.join(output))\n outfile.write('\\n}\\n')","def _create_schema(self, cypher_file):\n if len(self.graph.nodes) > 0:\n msg = \"Cypher file specified but the graph is not empty. Aborting.\"\n raise ValueError(msg)\n cyp = open(cypher_file, 'r').read()\n self.graph.run(cyp)","def main():\n parse_file(sys.argv[1])","def cli(yamlfile, **args):\n print(LogicProgramGenerator(yamlfile, **args).serialize(**args))","def run(filename=\"input.json\", path=\".\", **args):\n\n logger = logging.getLogger(__name__)\n \n #read input file (need to add command line specification)\n logger.info(\"Begin processing input file: %s\" % filename)\n eos_dict, thermo_dict, output_file = read_input.extract_calc_data(filename, path, **args)\n eos_dict['jit'] = args['jit']\n\n if output_file:\n file_dict = {\"output_file\":output_file}\n else:\n file_dict = {\"output_file\": \"despasito_out.txt\"}\n\n logger.debug(\"EOS dict:\", eos_dict)\n logger.debug(\"Thermo dict:\", thermo_dict)\n logger.info(\"Finish processing input file: {}\".format(filename))\n \n eos = eos_mod(**eos_dict)\n \n # Run either parametrization or thermodynamic calculation\n if \"opt_params\" in list(thermo_dict.keys()):\n logger.info(\"Initializing parametrization procedure\")\n output_dict = fit(eos, thermo_dict)\n #output = fit(eos, thermo_dict)\n logger.info(\"Finished parametrization\")\n write_output.writeout_fit_dict(output_dict,eos,**file_dict)\n else:\n logger.info(\"Initializing thermodynamic calculation\")\n output_dict = thermo(eos, thermo_dict)\n logger.info(\"Finished thermodynamic calculation\")\n write_output.writeout_thermo_dict(output_dict,thermo_dict[\"calculation_type\"],**file_dict)","def generate_bq_schema(self, file_name, schema_file_name=None):\n if not schema_file_name:\n schema_file_name = f'{self.directory}/schema_temp.json'\n os.system(f\"generate-schema --keep_nulls < {file_name} > {schema_file_name}\")\n\n schema = open(schema_file_name, 'r').read()\n\n os.remove(schema_file_name)\n\n return json.loads(schema)","def test_test_body_with_file_schema(self):\n pass","def main():\n\n parser = argparse.ArgumentParser()\n\n parser.add_argument(\"api_base_url\", type=str,\n help=\"base url for all tests\")\n parser.add_argument(\"test_file_name\", type=str,\n help=\"name of file containing JSON array of tests\")\n parser.add_argument(\"-f\", \"--format\", default=\"json\", type=str,\n help=\"output format - must be either json or text\")\n\n args = parser.parse_args()\n\n try:\n\n run_tests_from_file(args.api_base_url, args.test_file_name, \n args.format)\n\n except KeyError as e:\n print(\"Required key '%s' not found. Check tests file.\" % str(e.args[0]))\n exit(1)\n\n except FileNotFoundError:\n print(\"Cannot open file '%s'. File not found.\" % args.test_file_name)\n exit(1)\n\n except ValueError:\n print(\"Cannot decode JSON from file '%s'.\" % args.test_file_name)\n exit(1)","def parse(fileobject, schema=None):\n if schema:\n # with validation\n parser = objectify.makeparser(schema=schema.schema, strip_cdata=False)\n return objectify.parse(fileobject, parser=parser)\n else:\n # without validation\n parser = objectify.makeparser(strip_cdata=False)\n return objectify.parse(fileobject, parser=parser)","def main():\n parser = argparse.ArgumentParser()\n parser.add_argument(\n '--config_file',\n dest='config_file',\n required=True,\n default=None,\n help='JSON config file.')\n args = parser.parse_args()\n\n config = Config(args.config_file)\n\n logger.info(\"Executing preprocessing pipeline...\")\n run_pipeline(config)\n logger.info(\"Done.\")","def decide_schema(self, json_data):\n pass","def decide_schema(self, json_data):\n pass","def main():\n parser = ArgumentParser()\n\n parser.add_argument(\"-C\", \"--clinvar\", dest=\"clinvar\",\n help=\"ClinVar VCF file\", metavar=\"CLINVAR\")\n parser.add_argument(\"-i\", \"--input\", dest=\"inputfile\",\n help=\"Input VCF file\", metavar=\"INPUT\")\n parser.add_argument(\"-F\", \"--output-format\", dest=\"format\",\n help=\"Output format (currently 'csv' or 'json')\",\n metavar=\"FORMAT\")\n parser.add_argument(\"-V\", \"--schema-version\", dest=\"schema_version\",\n help=\"Version to include report (JSON only)\",\n metavar=\"OUTVERSION\")\n parser.add_argument(\"-n\", \"--notes\", dest=\"notes\",\n help=\"Notes, as a JSON string, to include in report (JSON only)\",\n metavar=\"NOTES\")\n parser.add_argument(\"-g\", \"--genome-build\", dest=\"build\",\n help=\"Genome build to include in report (JSON only)\",\n metavar=\"GENOMEBUILD\")\n options = parser.parse_args()\n\n if sys.stdin.isatty():\n if options.inputfile:\n if options.inputfile.endswith('.vcf'):\n input_genome_file = open(options.inputfile)\n elif options.inputfile.endswith('.vcf.gz'):\n input_genome_file = gzip.open(options.inputfile)\n elif options.inputfile.endswith('.vcf.bz2'):\n input_genome_file = bz2.BZ2File(options.inputfile)\n else:\n raise IOError(\"Genome filename expected to end with ''.vcf',\" +\n \" '.vcf.gz', or '.vcf.bz2'.\")\n else:\n sys.stderr.write(\"Provide input VCF file\\n\")\n parser.print_help()\n sys.exit(1)\n else:\n input_genome_file = sys.stdin\n\n if options.clinvar:\n if options.clinvar.endswith('.vcf'):\n input_clinvar_file = open(options.clinvar)\n elif options.clinvar.endswith('.vcf.gz'):\n input_clinvar_file = gzip.open(options.clinvar)\n elif options.clinvar.endswith('.vcf.bz2'):\n input_clinvar_file = bz2.BZ2File(options.clinvar)\n else:\n raise IOError(\"ClinVar filename expected to end with ''.vcf',\" +\n \" '.vcf.gz', or '.vcf.bz2'.\")\n else:\n sys.stderr.write(\"Provide ClinVar VCF file\\n\")\n parser.print_help()\n sys.exit(1)\n\n output_format = \"csv\"\n if options.format:\n if options.format == \"csv\":\n output_format = \"csv\"\n elif options.format == \"json\":\n output_format = \"json\"\n\n if output_format == \"csv\":\n csv_out = csv.writer(sys.stdout)\n header = (\"Chromosome\", \"Position\", \"Name\", \"Significance\", \"Frequency\",\n \"Zygosity\", \"ACC URL\")\n csv_out.writerow(header)\n\n metadata = {}\n metadata[\"notes\"] = options.clinvar\n\n build = \"unknown\"\n if options.build:\n build = options.build\n metadata[\"genome_build\"] = build\n\n notes_json = {}\n if options.notes:\n notes_json[\"parameter\"] = options.notes\n try:\n notes_json = json.loads(options.notes)\n except:\n sys.stderr.write(\"Could not parse JSON notes field\\n\")\n\n json_report = {}\n json_report[\"schema_version\"] = options.schema_version\n json_report[\"notes\"] = notes_json\n json_report[\"metadata\"] = metadata\n json_report[\"variants\"] = []\n\n matching = match_to_clinvar(input_genome_file, input_clinvar_file)\n for var in matching:\n\n chrom = var[0]\n pos = var[1]\n ref_allele = var[2]\n alt_allele = var[3]\n name_acc = var[4]\n allele_freq = var[5]\n zygosity = var[6]\n\n for spec in name_acc:\n ele = {}\n ele[\"chrom\"] = REV_CHROM_INDEX[chrom]\n ele[\"pos\"] = pos\n ele[\"ref_allele\"] = ref_allele\n ele[\"alt_allele\"] = alt_allele\n ele[\"allele_freq\"] = allele_freq\n ele[\"zygosity\"] = zygosity\n\n url = \"http://www.ncbi.nlm.nih.gov/clinvar/\" + str(spec[0])\n name = spec[1]\n clnsig = spec[2]\n\n ele[\"acc_url\"] = url\n ele[\"name\"] = name\n ele[\"clinical_significance\"] = clnsig\n\n json_report[\"variants\"].append(ele)\n\n if output_format == \"csv\":\n data = (chrom, pos, name, clnsig, allele_freq, zygosity, url)\n csv_out.writerow(data)\n\n if output_format == \"json\":\n print json.dumps(json_report)","def main():\n arg_parser = argparse.ArgumentParser(\n description='Runs jdeps (dependency analysis tool) on a given JAR and '\n 'writes the resulting dependency graph into a JSON file.')\n required_arg_group = arg_parser.add_argument_group('required arguments')\n required_arg_group.add_argument(\n '-t',\n '--target',\n required=True,\n help='Path to the JAR file to run jdeps on.')\n required_arg_group.add_argument(\n '-o',\n '--output',\n required=True,\n help='Path to the file to write JSON output to. Will be created '\n 'if it does not yet exist and overwrite existing '\n 'content if it does.')\n arg_parser.add_argument('-j',\n '--jdeps-path',\n default=JDEPS_PATH,\n help='Path to the jdeps executable.')\n arguments = arg_parser.parse_args()\n\n print('Running jdeps and parsing output...')\n raw_jdeps_output = run_jdeps(arguments.jdeps_path, arguments.target)\n jdeps_parser = JavaClassJdepsParser()\n jdeps_parser.parse_raw_jdeps_output(raw_jdeps_output)\n\n class_graph = jdeps_parser.graph\n print(f'Parsed class-level dependency graph, '\n f'got {class_graph.num_nodes} nodes '\n f'and {class_graph.num_edges} edges.')\n\n package_graph = package_dependency.JavaPackageDependencyGraph(class_graph)\n print(f'Created package-level dependency graph, '\n f'got {package_graph.num_nodes} nodes '\n f'and {package_graph.num_edges} edges.')\n\n print(f'Dumping JSON representation to {arguments.output}.')\n serialization.dump_class_and_package_graphs_to_file(\n class_graph, package_graph, arguments.output)","def run(self, verbose=False):\n from utils import write_to_file # function to write json to file\n self.read_json()\n graph = self.parse_jsons()\n json = self.pipe_vl2vg(graph)\n return self.write_to_file(rawinput=json, filetype='json', output_path=self.output_path, engine_name=self.engine_name, algorithm_name=self.algorithm_name, suffix=self.file_suffix, verbose=verbose)","def main():\n parser = optparse.OptionParser()\n info = ('The input json-format file name. Such as: '\n '--dest_dir=/hangonman/android')\n parser.add_option('-d', '--dest_dir', action='store', dest='dest_dir',\n help=info)\n opts, _ = parser.parse_args()\n if not os.path.exists(opts.dest_dir):\n print 'Destination directory is not existed!'\n return 1\n latest_url = ('http://wrt-build.sh.intel.com/archive/'\n 'snapshots/dev-wrt-android-build/LATEST')\n file_name = 'xwalk_app_template.tar.gz'\n app_template_handler = GetXWalkAppTemplate(latest_url, file_name)\n try:\n app_template_handler.ExtractFile(opts.dest_dir)\n except tarfile.TarError:\n raise Exception('Error in the process of tar file.')\n return 0","def run():\n params = parseParams()\n args = []\n kwargs = {}\n if params.source != None:\n kwargs['source'] = os.path.abspath(params.source)\n else:\n kwargs['source'] = sys.stdin\n kwargs['output'] = os.path.abspath(params.output)\n kwargs['fmt'] = params.fmt\n kwargs['syntax'] = params.syntax\n return parse.parse(*args, **kwargs) # pylint: disable=W0142","def main():\n os.makedirs(PATH)\n fetch_data()\n convert_to_json(model_list, 'models.json', is_model=True)\n convert_to_json(backend_list, 'backends.json')\n convert_to_json(type_list, 'types.json')\n convert_to_json(featurizer_list, 'featurizers.json')","def stich_from_file(jsonname):\n with open(jsonname) as f:\n data = json.loads(f.read())\n stich(data)","def main():\n f_name = sys.argv[1]\n file_contents = open(f_name).read()\n C = CAST([], \"python\")\n C2 = C.from_json_str(file_contents)\n\n V = CASTToAGraphVisitor(C2)\n last_slash_idx = f_name.rfind(\"/\")\n file_ending_idx = f_name.rfind(\".\")\n pdf_file_name = f\"{f_name[last_slash_idx + 1 : file_ending_idx]}.pdf\"\n V.to_pdf(pdf_file_name)","def json_schema(schema_file=None, output=\"-\"):\n schemas = read_yaml(schema_file)\n dump_yaml(output, JsonSchemaConverterFromAccessSchema.convert_schemas(schemas))","def main():\n parser = specify_parser()\n args = parser.parse_args()\n\n mapping = init_data(args.datafile[0])\n loaded_data = read(args.input)\n\n mount(mapping, loaded_data)","def load_schema(filename):\n with open(filename) as f:\n schema = json.load(f)\n\n return schema","def readjamschema(schema):\n raise NotImplementedError(msg)","def main():\n # There are no args, but parse them just so help works\n args = docopt(__doc__)\n print(process_files_json(), end=\"\")\n return None","def from_json_file(cls, json_file:str):\n with open(json_file) as file:\n data = json.load(file)\n validate(data, schema)\n instance = cls.from_dict(data)\n return instance","def main():\n args = docopt(__doc__)\n meta = fetch_metadata(args['DIRECTORY'], **args)\n if args['--fasta']:\n meta.assembly.update({'file': args['--fasta']})\n taxdump = None\n dependencies = {}\n for field in FIELDS:\n if args[field['flag']]:\n for dep in field['depends']:\n if dep not in dependencies or not dependencies[dep]:\n dependencies[dep] = fetch_field(args['DIRECTORY'], dep, meta)\n if field['flag'] == '--hits':\n if not taxdump:\n taxdump = fetch_taxdump(args['--taxdump'])\n parents = field['module'].parent()\n parsed = field['module'].parse(\n args[field['flag']],\n **{key: args[key] for key in PARAMS},\n taxdump=taxdump,\n dependencies=dependencies,\n meta=meta)\n if not isinstance(parsed, list):\n parsed = [parsed]\n for data in parsed:\n if not args['--replace']:\n if has_field_warning(meta, data.field_id):\n continue\n for parent in data.parents:\n if 'range' in parent:\n parent_meta = meta.field_meta(parent['id'])\n if parent_meta and 'range' in parent_meta:\n parent['range'][0] = min(parent['range'][0], parent_meta['range'][0])\n parent['range'][1] = max(parent['range'][1], parent_meta['range'][1])\n meta.add_field(parents+data.parents, **data.meta)\n if isinstance(data, Identifier):\n meta.records = len(data.values)\n json_file = \"%s/%s.json\" % (args['DIRECTORY'], data.field_id)\n file_io.write_file(json_file, data.values_to_dict())\n dependencies[data.field_id] = data\n if 'identifiers' not in dependencies:\n dependencies['identifiers'] = fetch_field(args['DIRECTORY'], 'identifiers', meta)\n for string in args['--link']:\n link.add(string, meta, dependencies['identifiers'].values, args['--skip-link-test'])\n for string in args['--key']:\n key.add(string, meta)\n if args['--taxid']:\n if not taxdump:\n taxdump = fetch_taxdump(args['--taxdump'])\n taxid.add(args['--taxid'], taxdump, meta)\n file_io.write_file(\"%s/meta.json\" % args['DIRECTORY'], meta.to_dict())","def compile_from_input_json(\n input_json: Dict, silent: bool = True, allow_paths: Optional[str] = None\n) -> Dict:\n\n optimizer = input_json[\"settings\"][\"optimizer\"]\n input_json[\"settings\"].setdefault(\"evmVersion\", None)\n if input_json[\"settings\"][\"evmVersion\"] in EVM_EQUIVALENTS:\n input_json[\"settings\"][\"evmVersion\"] = EVM_EQUIVALENTS[input_json[\"settings\"][\"evmVersion\"]]\n\n if not silent:\n print(f\"Compiling contracts...\\n Solc version: {str(solcx.get_solc_version())}\")\n\n opt = f\"Enabled Runs: {optimizer['runs']}\" if optimizer[\"enabled\"] else \"Disabled\"\n print(f\" Optimizer: {opt}\")\n\n if input_json[\"settings\"][\"evmVersion\"]:\n print(f\" EVM Version: {input_json['settings']['evmVersion'].capitalize()}\")\n\n try:\n return solcx.compile_standard(input_json, allow_paths=allow_paths)\n except solcx.exceptions.SolcError as e:\n raise CompilerError(e, \"solc\")","def main():\n\n # Command Line Interface\n parse = command_line()\n args = parse.parse_args()\n if not os.path.isdir(args.directory):\n raise IOError\n\n # Abstract File Tree\n filetree = tree_walk(args.directory, args.replace, args.depth)\n jsontree = json.dumps(\n filetree,\n indent=4,\n sort_keys=True,\n separators=(', ', ': '),\n )\n\n # Pipe vs Redirection\n if sys.stdout.isatty():\n try: jsontree = highlight(\n jsontree,\n JsonLexer(),\n Terminal256Formatter(style='autumn'))\n except:\n pass\n\n print(jsontree)","def get_schema(path):\n with open(path, 'r') as f:\n return json.load(f)","def __validate_and_compile(self):\n self.__validate_file(self.__schema_path, \"RNG\")\n\n with open(self.__schema_path) as relax_file_handler:\n\n # Parse schema file\n relax_parsed = etree.parse(relax_file_handler)\n\n # Compile schema file\n self.__compiled = etree.RelaxNG(relax_parsed)","def run_analysis(args):\n import google.datalab.bigquery as bq\n if args.bigquery_table:\n table = bq.Table(args.bigquery_table)\n schema_list = table.schema._bq_schema\n else:\n schema_list = json.loads(\n file_io.read_file_to_string(args.schema_file).decode())\n table = bq.ExternalDataSource(\n source=args.input_file_pattern,\n schema=bq.Schema(schema_list))\n\n # Check the schema is supported.\n for col_schema in schema_list:\n col_type = col_schema['type'].lower()\n if col_type != 'string' and col_type != 'integer' and col_type != 'float':\n raise ValueError('Schema contains an unsupported type %s.' % col_type)\n\n run_numerical_analysis(table, schema_list, args)\n run_categorical_analysis(table, schema_list, args)\n\n # Save a copy of the schema to the output location.\n file_io.write_string_to_file(\n os.path.join(args.output_dir, SCHEMA_FILE),\n json.dumps(schema_list, indent=2, separators=(',', ': ')))","def _runParser(self):\n with open(self.var('filePath')) as f:\n return json.load(f)","def gen_review_data(fp: str) -> None:\n with open(fp, encoding='utf-8') as f:\n for line in f:\n data = json.loads(line)\n utils.preprocess_raw_json(data)\n doc = {\n \"_index\": \"review\",\n \"_source\": data\n }\n yield doc","def parse_from_json(self, infile):\r\n obj = json.load(infile)\r\n relations = Relations.from_json_obj(obj['relations'])\r\n stamps = Stamps.from_json_obj(obj['stamps'])\r\n apis = APIs.from_json_obj(obj['apis'])\r\n source_infos = SourceInfos.from_json_obj(obj['source infos'])\r\n compilations = Compilations.from_json_obj(obj['compilations'])\r\n compile_setup = Compilations.from_json_obj(obj['compile setup'])\r\n return ZincAnalysis(relations, stamps, apis, source_infos, compilations, compile_setup)","def handle_json_file(context: click.Context, param: click.Parameter, json_file: str) -> None:\n if not json_file or context.resilient_parsing:\n return\n try:\n with click.open_file(json_file) as f:\n info = json.load(f)\n pretty_echo(context.obj.client.custom_request(info))\n except json.JSONDecodeError:\n raise click.BadParameter(f'{json_file} is not a valid json file')\n except HttpError as e:\n pretty_echo(e.error_message)\n context.exit()","def compile_program(self, json_name: Path, info_name: Path) -> int:\n testutils.log.info(\"---------------------- Compile with p4c-bm2-ss ----------------------\")\n compilation_cmd = (\n f\"{self.options.rootdir}/build/p4c-bm2-ss --target bmv2 --arch v1model \"\n f\"--p4runtime-files {info_name} {self.options.p4_file} -o {json_name}\"\n )\n _, returncode = testutils.exec_process(compilation_cmd, timeout=30)\n if returncode != testutils.SUCCESS:\n testutils.log.error(\"Failed to compile the P4 program %s.\", self.options.p4_file)\n return returncode","def BqTableSchemaFileProcessor(file_arg):\n table_schema_type = GetApiMessage('TableSchema')\n schema_field_type = GetApiMessage('TableFieldSchema')\n\n try:\n schema_json = yaml.load(file_arg)\n schema_json = schema_json.get('schema', None)\n\n if not schema_json or not isinstance(schema_json, list):\n raise SchemaFileError(\n 'Error parsing schema file: no schema field list defined in file')\n\n all_fields = []\n for field in schema_json:\n new_field = schema_field_type(name=field['name'],\n type=field['type'],\n mode=field.get('mode', 'NULLABLE'))\n all_fields.append(new_field)\n\n return table_schema_type(fields=sorted(all_fields, key=lambda x: x.name))\n except yaml.YAMLParseError as ype:\n raise SchemaFileError('Error parsing schema file [{}]'.format(ype))\n except (AttributeError, KeyError) as e:\n raise SchemaFileError(\n 'Error parsing schema file, invalid field definition [{}]'.format(e))","def validate_json_schema(path, name, data, schema, full_schema=not is_extension):\n errors = 0\n\n # The standard repository has an example extension.\n if 'docs/examples/organizations/organizational_units/ocds_divisionCode_extension' in path:\n full_schema = False\n\n # Kingfisher Collect uses JSON Schema files to validate Scrapy items.\n code_repo = repo_name == 'kingfisher-collect'\n\n # Non-OCDS schema don't:\n # * pair \"enum\" and \"codelist\"\n # * disallow \"null\" in \"type\" of \"items\"\n # * UpperCamelCase definitions and lowerCamelCase properties\n # * allow \"null\" in the \"type\" of optional fields\n # * include \"id\" fields in objects within arrays\n # * require \"title\", \"description\" and \"type\" properties\n json_schema_exceptions = {\n 'json-schema-draft-4.json',\n 'meta-schema.json',\n 'meta-schema-patch.json',\n }\n ocds_schema_exceptions = {\n 'dereferenced-release-schema.json',\n # standard-maintenance-scripts\n 'codelist-schema.json',\n 'extension-schema.json',\n # extension_registry\n 'extensions-schema.json',\n 'extension_versions-schema.json',\n # spoonbill\n 'ocds-simplified-schema.json',\n }\n schema_exceptions = json_schema_exceptions | ocds_schema_exceptions\n\n validate_items_type_kwargs = {\n 'allow_invalid': {\n '/definitions/Amendment/properties/changes/items', # deprecated\n '/definitions/AmendmentUnversioned/properties/changes/items', # deprecated\n '/definitions/record/properties/releases/oneOf/0/items', # 1.1\n },\n }\n\n def validate_codelist_enum_allow_missing(codelist):\n return is_extension and codelist in external_codelists\n\n validate_codelist_enum_kwargs = {\n 'fallback': {\n '/definitions/Metric/properties/id': ['string'],\n '/definitions/Milestone/properties/code': ['string', 'null'],\n },\n 'allow_missing': validate_codelist_enum_allow_missing,\n }\n\n validate_letter_case_kwargs = {\n 'property_exceptions': {'former_value'}, # deprecated\n 'definition_exceptions': {'record'}, # 1.1\n }\n\n def validate_metadata_presence_allow_missing(pointer):\n return 'links' in pointer.split('/') or code_repo # ocds_pagination_extension\n\n validate_metadata_presence_kwargs = {\n 'allow_missing': validate_metadata_presence_allow_missing,\n }\n\n def validate_object_id_allow_missing(pointer):\n parts = pointer.split('/')\n return 'versionedRelease' in parts or parts[-1] in {\n 'changes', # deprecated\n 'records', # uses `ocid` not `id`\n '0', # linked releases\n }\n\n validate_object_id_kwargs = {\n 'allow_missing': validate_object_id_allow_missing,\n 'allow_optional': {\n # 2.0 fixes.\n # See https://github.com/open-contracting/standard/issues/650\n '/definitions/Amendment',\n '/definitions/Organization',\n '/definitions/OrganizationReference',\n '/definitions/RelatedProcess',\n },\n }\n if repo_name == 'infrastructure':\n validate_object_id_kwargs['allow_optional'].add('/definitions/Classification')\n\n validate_null_type_kwargs = {\n # OCDS allows null. OC4IDS disallows null.\n 'no_null': repo_name == 'infrastructure' or code_repo,\n 'allow_object_null': {\n '/definitions/Amendment/properties/changes/items/properties/former_value', # deprecated\n # See https://github.com/open-contracting/standard/pull/738#issuecomment-440727233\n '/definitions/Organization/properties/details',\n },\n 'allow_no_null': {\n '/definitions/Amendment/properties/changes/items/properties/property', # deprecated\n\n # Children of fields with omitWhenMerged.\n '/definitions/Link/properties/rel',\n '/definitions/Link/properties/href',\n\n # 2.0 fixes.\n # See https://github.com/open-contracting/standard/issues/650\n '/definitions/Organization/properties/id',\n '/definitions/OrganizationReference/properties/id',\n '/definitions/RelatedProcess/properties/id',\n },\n }\n\n validate_array_items_kwargs = {\n 'allow_invalid': {\n '/definitions/Amendment/properties/changes/items/properties/former_value', # deprecated\n '/definitions/Location/properties/geometry/properties/coordinates/items', # recursion\n },\n }\n\n validate_deep_properties_kwargs = {\n 'allow_deep': {\n '/definitions/Amendment/properties/changes/items', # deprecated\n },\n }\n if is_extension: # avoid repetition in extensions\n validate_deep_properties_kwargs['allow_deep'].add('/definitions/Item/properties/unit')\n\n validator = Draft4Validator(schema, format_checker=FormatChecker())\n\n errors += validate_schema(path, data, validator)\n if errors:\n warnings.warn(f'{path} is not valid JSON Schema ({errors} errors)')\n\n if name not in schema_exceptions:\n if 'versioned-release-validation-schema.json' in path:\n validate_items_type_kwargs['additional_valid_types'] = ['object']\n errors += validate_array_items(path, data, **validate_array_items_kwargs)\n errors += validate_items_type(path, data, **validate_items_type_kwargs)\n if not code_repo:\n errors += validate_codelist_enum(path, data, **validate_codelist_enum_kwargs)\n errors += validate_letter_case(path, data, **validate_letter_case_kwargs)\n errors += validate_merge_properties(path, data)\n\n # `full_schema` is set to not expect extensions to repeat information from core.\n if full_schema:\n exceptions_plus_versioned = schema_exceptions | {\n 'versioned-release-validation-schema.json',\n }\n\n exceptions_plus_versioned_and_packages = exceptions_plus_versioned | {\n 'project-package-schema.json',\n 'record-package-schema.json',\n 'release-package-schema.json',\n }\n\n if not code_repo:\n # Extensions aren't expected to repeat referenced `definitions`.\n errors += validate_ref(path, data)\n\n if name not in exceptions_plus_versioned:\n # Extensions aren't expected to repeat `title`, `description`, `type`.\n errors += validate_metadata_presence(path, data, **validate_metadata_presence_kwargs)\n if not code_repo:\n # Extensions aren't expected to repeat referenced `definitions`.\n errors += validate_object_id(path, jsonref.replace_refs(data), **validate_object_id_kwargs)\n\n if name not in exceptions_plus_versioned_and_packages:\n # Extensions aren't expected to repeat `required`. Packages don't have merge rules.\n errors += validate_null_type(path, data, **validate_null_type_kwargs)\n # Extensions aren't expected to repeat referenced codelist CSV files\n # TODO: This code assumes each schema uses all codelists. So, for now, skip package schema.\n errors += validate_schema_codelists_match(path, data, cwd, is_extension, is_profile, external_codelists)\n\n else:\n # Don't count these as errors.\n validate_deep_properties(path, data, **validate_deep_properties_kwargs)\n\n assert not errors, 'One or more JSON Schema files are invalid. See warnings below.'","def __init__(self, schema, input_files, output_path):\n self.schema = schema\n self.input_files = input_files\n self.output_path = output_path","def main(argv):\n ret = 0\n parser = utils.parser.createParser()\n\n opt = parser.parse_args(argv)\n\n try:\n # Stop execution if help flag is on\n if opt.help:\n raise ShowHelpException()\n\n # Read materials we need for later processing\n cfg = auxiliary.readConfig(opt.config)\n rawJson = auxiliary.readJson(opt.input[0])\n\n # List we are going to manipulate :)\n commands = rawJson['commands']\n\n # Pump commands and instruction for filtering, sorting etc\n rawJson['commands'] = pump(commands, cfg)\n\n # Write final result\n auxiliary.writeJSONFile(rawJson, opt.output[0])\n\n except ShowHelpException:\n parser.print_help()\n ret = 0\n except FilterException, e:\n ret = 1\n print('ERROR')\n print(e)\n\n return ret","def main(input_filepath, output_filepath):\n logger = logging.getLogger(__name__)\n logger.info('making final data set from raw data')\n\n conn = sqlite3.connect('../raw/td_V2.db')\n git_commits = pd.read_sql_query(\"SELECT * FROM GIT_COMMITS\",conn)\n szz_fault_inducing_commits = pd.read_sql_query(\"SELECT * FROM szz_fault_inducing_commits\",conn)\n refactoring_miner = pd.read_sql_query(\"SELECT * FROM refactoring_miner\",conn)\n refactoring_miner = refactoring_miner[refactoring_miner[\"COMMIT_HASH\"].isin(git_commits[\"COMMIT_HASH\"])]\n git_commits_changes = pd.read_sql_query(\"SELECT * FROM GIT_COMMITS_CHANGES\", conn)\n git_commits_changes = git_commits_changes[git_commits_changes[\"COMMIT_HASH\"].isin(refactoring_miner[\"COMMIT_HASH\"])]\n\n preprocess(git_commits, szz_fault_inducing_commits, refactoring_miner, git_commits_changes)","def schema_load(filename):\n print(uc.schema_load(filename))","def main():\n\n obj_lookup = interfaces_dir / \"FrameLib-obj-jlookup.json\"\n\n worker = jParseAndBuild()\n\n refpages = [x for x in refpages_dir.rglob(\"fl.*.xml\")]\n\n for ref in refpages:\n worker.extract_from_refpage(ref)\n\n write_json(obj_lookup, worker.j_master_dict)","def deduce_schema(self, input_data, *, schema_map=None):\n\n if self.input_format == 'csv':\n if self.csv_dialect:\n reader = csv.DictReader(input_data, dialect=self.csv_dialect)\n else:\n reader = csv.DictReader(input_data)\n elif self.input_format == 'json' or self.input_format is None:\n reader = json_reader(input_data)\n elif self.input_format == 'dict':\n reader = input_data\n else:\n raise Exception(f\"Unknown input_format '{self.input_format}'\")\n\n if schema_map is None:\n schema_map = OrderedDict()\n\n try:\n for json_object in reader:\n\n # Print a progress message periodically.\n self.line_number += 1\n if self.line_number % self.debugging_interval == 0:\n logging.info(f'Processing line {self.line_number}')\n\n # Deduce the schema from this given data record.\n if isinstance(json_object, dict):\n self.deduce_schema_for_record(\n json_object=json_object,\n schema_map=schema_map,\n )\n elif isinstance(json_object, Exception):\n self.log_error(\n f'Record could not be parsed: Exception: {json_object}'\n )\n if not self.ignore_invalid_lines:\n raise json_object\n else:\n self.log_error(\n 'Record should be a JSON Object '\n f'but was a {type(json_object)}'\n )\n if not self.ignore_invalid_lines:\n raise Exception(f'Record must be a JSON Object '\n f'but was a {type(json_object)}')\n finally:\n logging.info(f'Processed {self.line_number} lines')\n\n return schema_map, self.error_logs","def scheme(input, output, delimiter, encoding, verbose, format_in, zipfile, stype):\n if verbose:\n enableVerbose()\n options = {}\n options['delimiter'] = delimiter\n options['output'] = output\n options['encoding'] = encoding\n options['format_in'] = format_in\n options['zipfile'] = zipfile\n options['stype'] = stype\n acmd = Schemer()\n acmd.generate_scheme(input, options)\n pass","def _main():\n\n # setup paths\n json_model_path = osp.join(FLAGS.input_dir, FLAGS.json_model_fname)\n weights_path = osp.join(FLAGS.input_dir, FLAGS.weights_fname)\n save_path = osp.splitext(json_model_path)[0][:-6] + \"graph_w\" + str(weights_path.split(\"_\")[-1][:-3]) + \".pb\"\n print(\"Loading Model: \" + json_model_path)\n print(\"Loading Weights: \" + weights_path)\n\n # Set keras to test phase\n k.set_learning_phase(0)\n\n # Load json and weights, then compile model\n with open(json_model_path, 'r') as json_file:\n loaded_model_json = json_file.read()\n model = model_from_json(loaded_model_json)\n model.load_weights(weights_path)\n model.compile(loss='mse', optimizer='sgd')\n\n # Freeze graph\n frozen_graph = freeze_session(k.get_session(), output_names=[out.op.name for out in model.outputs])\n\n # Write graph to protobuf file\n tf.train.write_graph(frozen_graph, \"model\", save_path, as_text=False)\n print(\"Written Graph to: \" + save_path)","def handle(self, *args, **options):\n file = StringIO()\n call_command(\"generateschema\", stdout=file)\n file.seek(0)\n document = yaml.load(file, Loader=yaml.FullLoader)\n document.update({\n \"externalDocs\": {\n \"description\": \"Check us out on GitHub\",\n \"url\": \"https://github.com/ractf\",\n },\n \"info\": {\n \"title\": \"RACTF Core\",\n \"version\": os.popen(\"git rev-parse HEAD\").read().strip()[:8],\n \"description\": \"The API for RACTF.\",\n \"contact\": {\n \"name\": \"Support\",\n \"email\": \"support@reallyawesome.atlassian.net\",\n \"url\": \"https://reallyawesome.atlassian.net/servicedesk/customer/portals\",\n },\n \"x-logo\": {\n \"url\": \"https://www.ractf.co.uk/brand_assets/combined/wordmark_white.svg\",\n \"altText\": \"RACTF Logo\",\n },\n }\n })\n print(yaml.dump(document))","def _main(args):\n # model = keras.models.load_model(args.h5_file, custom_objects={'prelu': prelu})\n with tf.keras.utils.custom_object_scope({'prelu': prelu}):\n converter = tf.lite.TFLiteConverter.from_keras_model_file(args.h5_file)\n tflite_file = converter.convert()\n open(args.tflite_file, 'wb').write(tflite_file)\n print('='*30)\n print('tffile file save in {}.'.format(args.tflite_file))","def local(ctx):\n _do_codegen_user(\"demo\")\n _do_codegen_user(\"errors\")\n _do_codegen_user(\"mpi\")\n _do_codegen_user(\"omp\")\n _do_codegen_user(\"python\")\n\n # Do codegen for libfake\n for so in LIB_FAKE_FILES:\n _do_codegen_file(so)\n\n # Run the WAMR codegen required by the tests\n codegen(ctx, \"demo\", \"echo\", wamr=True)\n codegen(ctx, \"demo\", \"chain\", wamr=True)\n\n # Run the SGX codegen required by the tests\n codegen(ctx, \"demo\", \"hello\", wamr=True, sgx=True)\n codegen(ctx, \"demo\", \"chain_named_a\", wamr=True, sgx=True)\n codegen(ctx, \"demo\", \"chain_named_b\", wamr=True, sgx=True)\n codegen(ctx, \"demo\", \"chain_named_c\", wamr=True, sgx=True)","def run(argv=None):\n program_conf = get_program_conf()\n\n # Test usage of utils... do something with ddl\n print(get_ddl_list('dev')[2])\n\n # Parse args\n program_args = program_conf[PROGRAM_ARGS_CONF]\n pipeline_args = program_conf[PIPELINE_ARGS_CONF]\n pipeline_options = PipelineOptions(pipeline_args)\n pipeline_options.view_as(SetupOptions).save_main_session = True\n\n # Get schema path key for validation\n gcs_client = storage.Client()\n etl_config_dict = get_gcs_json_as_dict(program_args[ETL_CONFIG_PATH_CONF], gcs_client)\n schema_dict = get_gcs_json_as_dict(program_args[SCHEMA_PATH_KEY], gcs_client)\n input_file_path = program_args[INPUT_CONF]\n\n with beam.Pipeline(options=pipeline_options) as pipeline:\n validated_records = (\n pipeline \n | 'read' >> ReadFromText(input_file_path)\n | 'validate' >> beam.ParDo(ValidateRecord(schema_dict, input_file_path, etl_config_dict))\n | 'filter_data' >> beam.Filter(lambda x: x[1] and x[2] and x[3])\n | 'recover_data' >> beam.Map(lambda x: x[0])\n # | beam.Map(print)\n )\n if IS_VALID_FILE:\n validated_records | 'write_success' >> WriteToText(program_args[OUTPUT_CONF]) \n else:\n validated_records | 'write_reject' >> WriteToText(program_args[REJECT_CONF])","def read_json(self, filename, multi_line=False, schema=None):\n self.logger.info(\"# Reading a JSON file \" + filename)\n sql_context = SQLContext(self.spark)\n df = None\n if schema is None and multi_line == False:\n df = sql_context.read.json(filename)\n elif multi_line:\n df = sql_context.read.json(filename, multiLine=multi_line)\n elif schema is not None:\n df = sql_context.read.json(filename, schema=schema)\n return df","def main():\n args = docopt.docopt(__doc__, version=__version__)\n\n config_filename = args[\"<packer-json>\"]\n\n kms_map_string = \"\".join(sys.stdin.readlines())\n\n if not kms_map_string:\n eprint(\"The region to kms map must be passed via stdin.\")\n sys.exit(-1)\n kms_map = make_kms_map(kms_map_string)\n\n patch_config(config_filename, kms_map)\n\n sys.exit(0)","def validate_schema(self, schema):\n json_schema_path = os.path.join(_ROOT, 'data', 'schema.json')\n json_schema = load_json_or_yaml(json_schema_path)\n return validate(schema, json_schema)","def load_schema(name):\r\n\r\n data = pkgutil.get_data(__package__, \"schemas/{0}.json\".format(name))\r\n return json.loads(data.decode(\"utf-8\"))","def load(path_or_file, validate=True, strict=True, fmt='auto'):\n\n with _open(path_or_file, mode='r', fmt=fmt) as fdesc:\n jam = JAMS(**json.load(fdesc))\n\n if validate:\n jam.validate(strict=strict)\n\n return jam","def reconstitute():\n with open(TEXT_FPATH, 'w') as txt:\n for jfpath in json_fpaths():\n with open(jfpath) as f:\n jstruct = json.load(f)\n\n for recipe in jstruct.keys():\n _reconstitute_recipe(txt, jstruct[recipe])","def main():\n\n dbase = sqlite3.connect('database.db')\n with dbase:\n cursor = dbase.cursor()\n try:\n cursor.execute(\"DROP TABLE Graph;\")\n except sqlite3.OperationalError:\n pass\n\n try:\n cursor.execute(\"CREATE TABLE Graph (name TEXT, json TEXT);\")\n except sqlite3.OperationalError:\n pass\n\n for i, item in enumerate(SOME_JSONS):\n cursor.execute(\"insert into Graph (name, json) values\"\n \"(?, ?);\",\n ('example_' + str(i + 1), json.dumps(item)))","def read_json_schema(schema_file_path):\n with open(schema_file_path) as f:\n schema = json.load(f)\n return schema","def Compile(f, filename, gr, start_symbol, mode):\n\n tokens = tokenize.generate_tokens(f.readline)\n\n p = parse.Parser(gr, convert=py2st)\n parse_tree = driver.PushTokens(p, tokens, gr.symbol2number[start_symbol])\n\n tr = transformer.Transformer()\n as_tree = tr.transform(parse_tree)\n\n # NOTE: This currently does nothing!\n v = syntax.SyntaxErrorChecker()\n v.Dispatch(as_tree)\n\n s = symbols.SymbolVisitor()\n s.Dispatch(as_tree)\n\n graph = pyassem.FlowGraph() # Mutated by code generator\n if mode == \"single\":\n ctx = _ModuleContext(filename, s.scopes)\n # NOTE: the name of the Frame is a comment, not exposed to users.\n frame = pyassem.Frame(\"<interactive>\", filename) # mutated\n gen = pycodegen.InteractiveCodeGenerator(ctx, frame, graph)\n gen.set_lineno(as_tree)\n\n elif mode == \"exec\":\n # TODO: Does this need to be made more efficient?\n p1 = future.FutureParser()\n p2 = future.BadFutureParser()\n p1.Dispatch(as_tree)\n p2.Dispatch(as_tree)\n\n ctx = _ModuleContext(filename, s.scopes, futures=p1.get_features())\n frame = pyassem.Frame(\"<module>\", filename) # mutated\n\n gen = pycodegen.TopLevelCodeGenerator(ctx, frame, graph)\n\n elif mode == \"eval\":\n ctx = _ModuleContext(filename, s.scopes)\n frame = pyassem.Frame(\"<expression>\", filename) # mutated\n gen = pycodegen.TopLevelCodeGenerator(ctx, frame, graph)\n\n else:\n raise AssertionError('Invalid mode %r' % mode)\n\n # NOTE: There is no Start() or FindLocals() at the top level.\n gen.Dispatch(as_tree) # mutates graph\n gen.Finish()\n\n co = pyassem.MakeCodeObject(frame, graph)\n\n # TODO: Could call marshal.dump here?\n return co","def run(self, edit):\n logger.debug('Schema Hacker: open file')\n for sel in self.view.sel():\n schema, tbl = get_names(self.view, sel)\n schema = schema or 'public'\n folders = self.view.window().folders()\n script_path = ['misc', 'create_insert_function.pl']\n script = find_file(folders, script_path)[0]\n env = os.environ.copy()\n for key, val in (settings.get('env_var') or {}).items():\n env[key] = val\n fnc = subprocess.check_output(\n ['/usr/bin/perl', script, schema, tbl], env=env\n ).decode('utf8')\n file_name = fnc[\n fnc.index('CREATE OR REPLACE FUNCTION ') + 27 : fnc.index('(')\n ].lower() + '.sql'\n main_dir = os.path.dirname(os.path.dirname(script))\n fnc_path = os.path.join(main_dir, schema, 'FUNCTIONS', file_name)\n # Else: Create the file and open it\n if os.path.isfile(fnc_path):\n # Open existing file and put generated func in output panel\n view = self.view.window().open_file(fnc_path)\n panel = self.view.window().create_output_panel('schema_hacker')\n panel.run_command('append', {'characters': fnc, 'pos': 0})\n self.view.window().run_command(\n 'show_panel', {'panel': 'output.schema_hacker'})\n else: # Create function file and then open it\n with open(fnc_path, mode='w') as file:\n file.write(fnc)\n view = self.view.window().open_file(fnc_path)","def run_mapping(path: str):\n config = {\n 'extensions': ['.js', '.jsx', '.ts', '.tsx', '.vue'],\n 'exceptions': [\n 'react-app-env.d.ts',\n 'reportWebVitals.ts',\n 'setupTests.ts'\n ],\n 'patterns': [\n {\n 'type': 'startswith',\n 'query': 'import',\n 'on_match': {\n 'add_to': 'imports',\n 'extraction_regex': r'import (\\w+)',\n 'other_property_patterns': [\n {\n 'property_name': 'from_path',\n 'regex': r'from\\s*?(?:\"|\\')(.*)(?:\"|\\')'\n }\n ]\n }\n },\n {\n 'type': 'startswith',\n 'query': 'import',\n 'on_match': {\n 'add_to': 'imports',\n 'extraction_regex': r'(\\w+)',\n 'preprocessing_patterns': [\n '{(.*)}'\n ],\n 'other_property_patterns': [\n {\n 'property_name': 'from_path',\n 'regex': r'from\\s*?(?:\"|\\')(.*)(?:\"|\\')'\n }\n ]\n }\n },\n {\n 'type': 'regex',\n 'query': r'import\\s*?\\(',\n 'on_match': {\n 'add_to': 'imports',\n 'extraction_regex': r'/(\\w+)\\'',\n 'other_property_patterns': [\n {\n 'property_name': 'from_path',\n 'regex': r'import\\s*?\\(\\'(.*)\\'\\)'\n }\n ]\n }\n },\n {\n 'type': 'contains',\n 'query': '}',\n 'on_match': {\n 'add_to': 'blocks',\n 'extraction_regex': r'(\\w+)',\n 'preprocessing_patterns': [\n '{(.*)}'\n ],\n 'other_property_patterns': [\n {\n 'property_name': 'closed',\n 'value': True\n }\n ]\n }\n },\n {\n 'type': 'startswith',\n 'query': 'import {',\n 'on_match': {\n 'add_to': 'blocks',\n 'extraction_regex': r'(\\w+)',\n 'preprocessing_patterns': [\n '{(.*)}'\n ],\n 'other_property_patterns': [\n {\n 'property_name': 'closed',\n 'value': False\n }\n ]\n }\n }\n ],\n 'diagrams': [\n {\n 'name': 'dependency-diagram',\n 'mapping_scheme': {\n 'data_list_to_map': 'files_by_path',\n 'data_grouping_map': 'dirs_by_path',\n 'entity_types': [\n {\n 'name': 'file_name',\n 'type': 'class',\n 'match_by': {\n 'field': 'extension',\n 'value': ['jsx', 'tsx', 'vue']\n }\n },\n {\n 'name': 'file_name',\n 'type': 'entity',\n 'match_by': {\n 'field': 'extension',\n 'value': ['js', 'ts']\n }\n }\n ],\n 'entity_name': 'name',\n 'connect_by': 'imports',\n 'connect_path': 'from_path'\n\n }\n }\n ]\n }\n print('generating mapping diagram...')\n print()\n root = path[:-1] if path.endswith('/') else path\n\n # try:\n src_data = scan(root, config)\n draw(src_data, config)\n print()\n print('generation completed!')\n # except:\n # print()\n # print('error - mapping failed')","def load_json_obj(path: str) -> RAW_CFG:\n with fsspec.open(path) as json_file:\n return json.load(json_file)","def test_json_with_tabs() -> None:\n temp_schema_file = tempfile.NamedTemporaryFile(mode=\"w+\", delete=False, suffix=\".json\")\n created_filename = temp_schema_file.name\n with open(os.path.abspath(os.path.join(examples_dir, \"cases\", f\"basic.json\"))) as schema_fp:\n for line in schema_fp:\n temp_schema_file.write(line.replace(\" \", \"\\t\"))\n with tempfile.NamedTemporaryFile(mode=\"w+\") as temp_html_file:\n temp_schema_file.seek(0)\n generate_from_file_object(temp_schema_file, temp_html_file, True, False, False, True)\n temp_schema_file.close()\n os.remove(created_filename)\n temp_html_file.seek(0)\n soup = BeautifulSoup(temp_html_file.read(), \"html.parser\")\n tests.html_schema_doc_asserts.assert_basic_case(soup)","def main():\n\t\n\tfilename = optParse()\n\t\n\ttry:\n\t\tinput = loadFile(filename)\n\texcept IOError, (errno, msg):\n\t\tprint >>sys.stderr, msg\n\t\tsys.exit(-1)\n\t\n\toutput = parse(input)\n\tprint output"],"string":"[\n \"def run(\\n self,\\n input_file=sys.stdin,\\n output_file=sys.stdout,\\n schema_map=None,\\n ):\\n schema_map, error_logs = self.deduce_schema(\\n input_file, schema_map=schema_map\\n )\\n\\n for error in error_logs:\\n logging.info(\\n f\\\"Problem on line {error['line_number']}: {error['msg']}\\\"\\n )\\n\\n if self.debugging_map:\\n json.dump(schema_map, output_file, indent=2)\\n print(file=output_file)\\n else:\\n schema = self.flatten_schema(schema_map)\\n json.dump(schema, output_file, indent=2)\\n print(file=output_file)\",\n \"def main(input_json, output_markdown):\\n parser = Parser()\\n output_md = parser.parse_schema(json.load(input_json))\\n output_markdown.writelines(output_md)\\n click.secho(\\\"✔ Successfully parsed schema!\\\", bold=True, fg=\\\"green\\\")\",\n \"def main(schema_file, cxx=None, py=None):\\n logging.info('Working on file: ' + schema_file)\\n\\n # validate file exists\\n if not os.path.isfile(schema_file):\\n logging.info('File not found')\\n return -1\\n\\n # loading schema from file\\n json_parser = JsonComment(json)\\n with open(schema_file) as fd:\\n schema = json_parser.load(fd)\\n\\n # validating the schema\\n status, msg = validate_protocol_schema(schema)\\n if not status:\\n logging.info('Invalid Schema:' + msg)\\n return -2\\n logging.info('Schema is valid')\\n\\n # Build the protocol\\n protocol = Protocol(schema)\\n\\n # build CXX\\n if cxx is not None:\\n if not os.path.isdir(cxx):\\n logging.info('CXX Dest not found')\\n return -1\\n\\n message.make_message_cxx(protocol, cxx)\\n\\n for r in protocol.endpoints:\\n router.make_router_cxx(protocol, r, cxx)\\n\\n # build py\\n if py is not None:\\n if not os.path.isdir(py):\\n logging.info('PY Dest not found')\\n return -1\\n\\n message.make_message_py(protocol, py)\\n\\n for r in protocol.endpoints:\\n router.make_router_py(protocol, r, py)\\n\\n return 0\",\n \"def test_json():\\n schemas = {\\n 'schema-languages': 'bible/languages.json',\\n 'schema-book-metadata': 'bible/book-metadata.json',\\n 'schema-bible': 'bible/bible-*.json'\\n }\\n for schema_name, data_path_glob in schemas.items():\\n schema_path = 'schemas/{}.json'.format(schema_name)\\n with open(schema_path) as schema_file:\\n schema = json.load(schema_file)\\n data_paths = glob.iglob(data_path_glob)\\n for data_path in data_paths:\\n with open(data_path) as data_file:\\n data = json.load(data_file)\\n yield jsonschema.validate, data, schema\",\n \"def generate_flatbuffer_binaries():\\n for element in FLATBUFFERS_CONVERSION_DATA:\\n schema = element.schema\\n output_path = element.output_path\\n if not os.path.exists(output_path):\\n os.makedirs(output_path)\\n for json in element.input_files:\\n target = processed_json_path(json)\\n if needs_rebuild(json, target) or needs_rebuild(schema, target):\\n convert_json_to_flatbuffer_binary(\\n json, schema, output_path)\",\n \"def main(gtfs_file, input_json_file):\\n\\n with open(input_json_file) as jsonfile:\\n input_json = json.load(jsonfile)\\n\\n gtfs_feed = mzgtfs.feed.Feed(filename=gtfs_file)\\n\\n for fare_id, rules_attributes in input_json.iteritems():\\n add_fare_id(gtfs_feed, fare_id, rules_attributes)\\n\\n files = ['fare_attributes.txt', 'fare_rules.txt']\\n gtfs_feed.write('fare_attributes.txt', gtfs_feed.fares())\\n gtfs_feed.write('fare_rules.txt', gtfs_feed.fare_rules())\\n\\n gtfs_feed.make_zip('output.zip', files=files, clone=gtfs_file)\\n shutil.move('output.zip', gtfs_file)\\n\\n util.delete_temp_files(files)\",\n \"def main():\\n\\n json_str = sys.stdin.read()\\n\\n try:\\n object_json = json.loads(json_str)\\n\\n except json.JSONDecodeError:\\n print(\\\"Error with the JSON input. Please check your JSON file.\\\")\\n sys.exit(1)\\n\\n final_object = flatten_json(object_json, prefix=\\\"\\\")\\n object_json_output = json.dumps(final_object, indent=4)\\n\\n print(object_json_output)\",\n \"def main(db_path, schema_json):\\n create_db(db_path, schema_json)\",\n \"def main():\\n\\n # Load arguments\\n args = get_args()\\n \\n assert os.path.exists(args.csv), ' [ERR] File' + os.path.exists(args.csv) +'does not exist'\\n\\n print(args)\\n try:\\n dir_name = os.path.dirname(args.json)\\n os.mkdir(dir_name)\\n print(' [INFO] Creating', dir_name, 'directory')\\n except:\\n print(' [INFO] Directory', dir_name, 'already exists. Data will be replaced')\\n pass\\n\\n if args.config:\\n assert os.path.exists(args.config), ' [ERR] File' + os.path.exists(args.config) +'does not exist'\\n dic_types = read_config(args.config)\\n else:\\n dic_types = {}\\n \\n # Create json\\n create_json_from_csv(args.csv, args.delimiter, args.cols_delimiter, args.keep, dic_types, args.infer_types, args.max_docs, args.json, args.per_line)\\n\\n return 0\",\n \"def main(filename):\\n with open(filename) as json_file:\\n data = json.load(json_file)\\n\\n course_dict = {}\\n course_dict['course_id'] = str(os.path.split(filename.strip('/'))[-1])\\n course_dict['blocks'] = build_course_map(data)\\n\\n filename = '%s' % course_dict['course_id']\\n filepath = os.path.join('../input/', filename)\\n\\n with open(filepath, 'w') as outfile:\\n json.dump(course_dict, outfile, indent=4)\",\n \"def _load_schema(self, json_schema):\\n # use jsonrefs to resolve all $refs in json\\n data = jsonref.loads(json.dumps(json_schema))\\n return self.__initialise_template(data)\",\n \"def __json_schema_generator(file):\\n try:\\n data = json.load(file)\\n metadata_set = set()\\n try:\\n for datum in data['meta']['view']['columns']:\\n metadata_set.add(datum['name'])\\n except Exception as e:\\n metadata_set.clear()\\n for datum in data:\\n if isinstance(datum, str):\\n metadata_set.add(datum)\\n else:\\n for datum_property in datum:\\n metadata_set.add(str(datum_property))\\n\\n metadata_list = list(metadata_set)\\n # assumes list of objects with sparsse data\\n # OR\\n # for data_property in data[0]:\\n # metadata_list.append(data_property)\\n # assumes list of objects and that first entry has full list of properties\\n\\n return SchemaGenerator.__build_schema(metadata_list)\\n except Exception as e:\\n logging.error('Failed to parse json file into schema: ' + str(e))\\n raise FailedCreatingSchemaException(\\\"Failed to create schema from json file.\\\")\",\n \"def run_json(filepath: str, strict: bool = False, debug: bool = False):\\n with open(filepath) as jse:\\n return _run_file_full(filepath, json.load(jse), strict=strict, debug=debug)\",\n \"def _convert(self, fn, suffix='json', path='jsonschema', name=None,\\n root_class_name=None, data_files=[], target_class=None):\\n ie = JsonSchemaImportEngine()\\n d = os.path.join(INPUT_DIR, path)\\n schema = ie.load(os.path.join(d, f'{fn}.{suffix}'), name=name, format=suffix, root_class_name=root_class_name)\\n model_path = os.path.join(OUTPUT_DIR, f'{fn}.yaml')\\n write_schema(schema, model_path)\\n roundtrip_path = os.path.join(OUTPUT_DIR, f'{fn}.roundtrip.json')\\n with open(roundtrip_path, 'w') as stream:\\n stream.write(JsonSchemaGenerator(model_path).serialize())\\n python_path = os.path.join(OUTPUT_DIR, f'{fn}.py')\\n with open(python_path, 'w') as stream:\\n stream.write(PythonGenerator(model_path).serialize())\\n compile_python(python_path)\\n # TODO: test data_files\\n return schema\",\n \"def main():\\n\\n handler = PlanetSourceHandler()\\n\\n parser = xml.sax.make_parser()\\n parser.setFeature(xml.sax.handler.feature_namespaces, 1)\\n parser.setContentHandler(handler)\\n parser.parse(sys.stdin)\\n\\n print simplejson.JSONEncoder(indent=True).encode(handler.sources)\",\n \"def main():\\n args = parse_arguments()\\n\\n with open(args.input_file) as infile:\\n data = json.load(infile)\\n if \\\"expected_format\\\" in data and \\\"compatible_fragment_ratio\\\" in data:\\n send_message(save(\\\"strandedness\\\", data[\\\"expected_format\\\"]))\\n send_message(\\n save(\\\"fragment_ratio\\\", str(round(data[\\\"compatible_fragment_ratio\\\"], 2)))\\n )\\n else:\\n send_message(error(\\\"Cannot parse library type information file.\\\"))\",\n \"def main(args=None):\\n if args is None:\\n args = sys.argv[1:]\\n\\n usage_ = \\\"\\\"\\\"%prog [OPTIONS] JsonFile [MoreJsonFiles...]\\nFormat/Beautify one or more JSON file(s).\\\"\\\"\\\"\\n parser = OptionParser(usage=usage_, version=VERSION)\\n parser.add_option(\\\"-i\\\", \\\"--indent\\\", dest=\\\"indent_size\\\",\\n default=DEFAULT_INDENT_SIZE, type=\\\"int\\\",\\n help=\\\"Indent size to use (default: %default).\\\")\\n parser.add_option(\\\"-c\\\", \\\"--compact\\\", dest=\\\"compact\\\",\\n action=\\\"store_true\\\", default=False,\\n help=\\\"Use compact format (default: %default).\\\")\\n parser.add_option(\\\"-n\\\", \\\"--dry-run\\\", dest=\\\"dry_run\\\",\\n action=\\\"store_true\\\", default=False,\\n help=\\\"Check only if JSON is well-formed (default: %default).\\\")\\n options, filenames = parser.parse_args(args) #< pylint: disable=W0612\\n if not filenames:\\n parser.error(\\\"OOPS, no filenames provided.\\\")\\n if options.compact:\\n options.indent_size = None\\n\\n # -- STEP: Init logging subsystem.\\n format_ = \\\"json.format: %(message)s\\\"\\n logging.basicConfig(level=logging.WARN, format=format_)\\n console = logging.getLogger(\\\"console\\\")\\n\\n # -- DOS-SHELL SUPPORT: Perform filename globbing w/ wildcards.\\n skipped = 0\\n filenames2 = []\\n for filename in filenames:\\n if \\\"*\\\" in filenames:\\n files = glob.glob(filename)\\n filenames2.extend(files)\\n elif os.path.isdir(filename):\\n # -- CONVENIENCE-SHORTCUT: Use DIR as shortcut for JSON files.\\n files = glob.glob(os.path.join(filename, \\\"*.json\\\"))\\n filenames2.extend(files)\\n if not files:\\n console.info(\\\"SKIP %s, no JSON files found in dir.\\\", filename)\\n skipped += 1\\n elif not os.path.exists(filename):\\n console.warning(\\\"SKIP %s, file not found.\\\", filename)\\n skipped += 1\\n continue\\n else:\\n assert os.path.exists(filename)\\n filenames2.append(filename)\\n filenames = filenames2\\n\\n # -- NORMAL PROCESSING:\\n errors = json_formatall(filenames, options.indent_size,\\n dry_run=options.dry_run)\\n console.error(\\\"Processed %d files (%d with errors, skipped=%d).\\\",\\n len(filenames), errors, skipped)\\n if not filenames:\\n errors += 1\\n return errors\",\n \"def load_json_schema(filename):\\n relative_path = join('../schema', filename)\\n absolute_path = join(dirname(__file__), relative_path)\\n\\n base_path = dirname(absolute_path)\\n base_uri = 'file://{}/'.format(base_path)\\n\\n with open(absolute_path) as schema_file:\\n return jsonref.loads(\\n schema_file.read(), base_uri=base_uri, jsonschema=True)\",\n \"def main() -> None:\\n # The first thing to do is get the lines of the PyFlex file we are given.\\n parser = Parser(filename=sys.argv[1])\\n parsed_data = parser.ParseFile()\\n\\n # Upon retrieving the Parsed Data, assign the parsed data to the\\n # Symbol Table.\\n SymbolTable.RULESET = parsed_data['ruleset']\\n SymbolTable.INSTRUCTIONS = parsed_data['instructions']\\n SymbolTable.CODE = parsed_data['code']\\n # SymbolTable.PrintTable()\\n\\n # Using the Generator backend, we can build the generated script\\n generator = Generator()\\n generator.GenerateNewScript()\\n\\n autopep8.fix_file(filename=generator.file_main)\\n\\n print(\\\"Generated Script can be found in {}\\\".format(generator.file_main))\",\n \"def _load_json_schema(filename):\\n\\n relative_path = join(\\\"schemas\\\", filename)\\n absolute_path = join(dirname(__file__), relative_path)\\n\\n base_path = dirname(absolute_path)\\n base_uri = 'file://{}/'.format(base_path)\\n\\n print(f\\\"base uri {base_uri}\\\")\\n print(f\\\"base path {base_path}\\\")\\n print(f\\\"relative_path {relative_path}\\\")\\n print(f\\\"absolute_path {absolute_path}\\\")\\n\\n with open(absolute_path) as schema_file:\\n return jsonref.loads(schema_file.read(), base_uri=base_uri, jsonschema=True)\",\n \"def start(self, **kwargs):\\n super(Json, self).start(**kwargs)\\n flat = self.get_arg_value(\\\"json_flat\\\")\\n\\n self._first_row = True\\n self.open_fd()\\n begin = \\\"\\\" if flat else \\\"[\\\"\\n self._fd.write(begin)\",\n \"def process_jsonld_file(fname):\\n with open(fname, 'r', encoding='utf-8') as fh:\\n json_dict = json.load(fh)\\n return process_jsonld(json_dict)\",\n \"def main():\\n widget = ParseGrypeJSON()\\n logging.debug(f'argv {\\\",\\\".join(sys.argv)}')\\n\\n if len(sys.argv) > 1:\\n widget.filename(sys.argv[1])\\n\\n sys.exit(widget.report())\",\n \"def main():\\n # if there are no command line args, run as normal with the input from stdin\\n if len(sys.argv) == 1:\\n json_string = \\\"\\\".join(sys.stdin)\\n hierarchical_jsons = [load_json(json_string)]\\n # if there are command line args, run using the first argument as a file path to a file with\\n # correctly formatted test json values\\n else:\\n with open(sys.argv[1]) as file:\\n hierarchical_jsons = json.load(file)\\n\\n flattened_jsons = []\\n\\n # for all the jsons given, in the non-test case there will only be one\\n for hierarchical_json in hierarchical_jsons:\\n # flatten the json object\\n flat_json = flatten_json(hierarchical_json)\\n # print the prettied json to stdout\\n json_string = json.dumps(flat_json, indent=4)\\n flattened_jsons.append(json_string)\\n print(json_string)\\n\\n return flattened_jsons\",\n \"def main():\\n parser = argparse.ArgumentParser()\\n parser.add_argument(\\\"datafile\\\", help=\\\"Name of JSON file containing WP3-mapped metadata\\\")\\n parser.add_argument(\\\"csvfile\\\", help=\\\"Corrected CSV file\\\")\\n parser.add_argument(\\\"vcffile\\\", help=\\\"VCF file with samples\\\")\\n args = parser.parse_args()\\n\\n samples = vcf_samples(args.vcffile)\\n\\n with open(args.datafile, 'r') as infile:\\n data = json.load(infile)\\n\\n if not data:\\n print(f\\\"Error reading data file {args.datafile}.\\\", file=sys.stderr)\\n return\\n\\n with open(args.csvfile, newline='') as csvfile:\\n reader = csv.DictReader(csvfile)\\n for idx, row in enumerate(reader):\\n data[idx][\\\"physiologicalMeasurements\\\"][\\\"anthropometry\\\"][\\\"weight\\\"] = [str(float(row['birth_weight_g'])/1000.), row['sbjt_weight_kg']]\\n data[idx][\\\"physiologicalMeasurements\\\"][\\\"anthropometry\\\"][\\\"height\\\"] = [row['height_cm'], row['sbjt_length_cm']]\\n data[idx][\\\"physiologicalMeasurements\\\"][\\\"circulationAndRespiration\\\"][\\\"bloodPressure\\\"] = [row['sbjt_blood_pressure_systolic']]\\n if len(samples) > idx:\\n data[idx][\\\"sample\\\"] = samples[idx]\\n\\n print(json.dumps(data))\",\n \"def _load_json_schema(filename):\\n\\n relative_path = join('schemas', filename)\\n absolute_path = join(dirname(__file__), relative_path)\\n\\n with open(absolute_path) as schema_file:\\n return json.loads(schema_file.read())\",\n \"def cli(ctx, path, **kwds):\\n if not os.path.exists(path):\\n io.error(\\\"Failed to tool test json file at %s\\\" % path)\\n return 1\\n\\n test_data = StructuredData(path)\\n handle_reports(ctx, test_data.structured_data, kwds)\",\n \"def from_json(self, json_filename, dont_change=False, pure_json=False, binary_format=False):\\n gc.disable()\\n\\n with open(json_filename, 'rb') as f:\\n s = f.read()\\n\\n if not pure_json:\\n # Please do not pass unicode to jsmin under py2.\\n # Otherwise, it cannot use cStringIO and it bursts performance\\n s = jsmin(s)\\n elif self.VERBOSE:\\n print('Assumed file %s is pure json - skip pre-processing' % json_filename)\\n\\n types = ujson.loads(s.decode(\\\"utf-8\\\"))\\n if binary_format:\\n dawg_checksum = types[0].get(ProductTypeDict.DAWG_CHECKSUM_ATTR)\\n in_memory_dawg_checksum = TypeTerm.term_dict.dawg_checksum()\\n if not dawg_checksum or in_memory_dawg_checksum != dawg_checksum:\\n raise IOError('DAWG checksum does not correspond in memory version')\\n types = types[1]\\n\\n if self.VERBOSE:\\n file_rel_count = sum(map(len, types.values())) - (len(types) if not binary_format else 0)\\n print(\\\"Parsing %d type tuples with %d relations from file %s\\\" % (len(types), file_rel_count, json_filename))\\n\\n if not dont_change:\\n ProductType.reload()\\n if self.VERBOSE:\\n print(\\\"Global product types has been reloaded\\\")\\n\\n def parse_terms(terms_str):\\n terms = [int(ts) if binary_format else ts.strip() for ts in terms_str.split(u'+')]\\n if not binary_format:\\n # For text format convert to TypeTerms\\n terms = map(TypeTerm.make, terms)\\n return terms\\n\\n type_tuples = defaultdict(list)\\n pseudo_sqn = 1\\n seen_rel = defaultdict(dict)\\n for type_str, rel_str in types.viewitems():\\n type_items = parse_terms(type_str)\\n # All types are loaded from external sources or knowledge base are considered meaningful regardless of\\n # other their characteristics\\n if dont_change:\\n p_type = ProductType(*type_items, meaningful=True, singleton=False)\\n wrapper = EqWrapper(p_type)\\n if wrapper in seen_rel:\\n # Type has been added already by another relation\\n p_type = wrapper.match\\n else:\\n p_type = ProductType.make_from_terms(type_items, meaningful=True)\\n type_tuples[p_type] = []\\n\\n start_relations_index = 0\\n if binary_format:\\n type_tuples[p_type] = [to_str(pseudo_sqn)]\\n pseudo_sqn += 1\\n else:\\n if isinstance(rel_str[0], int) or rel_str[0].isdigit():\\n # Compatibility with old format: there was one plain integer and json parse it to int() itself\\n sqns_in_self = rel_str[0]\\n start_relations_index = 1\\n else:\\n sqns_in_self, _ = re.findall('^(\\\\d+)(?:/(\\\\d+))?$', rel_str[0])[0]\\n if sqns_in_self and sqns_in_self.isdigit():\\n start_relations_index = 1\\n\\n for i in range(int(sqns_in_self)):\\n type_tuples[p_type].append(to_str(pseudo_sqn))\\n pseudo_sqn += 1\\n\\n rel_type, rel_attr, is_soft, type_to_str = [None] * 4\\n for r_str in rel_str[start_relations_index:]:\\n if binary_format:\\n r_match = re.findall('^([0-9+]+)(\\\\w)(.*)$', r_str)\\n if r_match:\\n type_to_str, rel_type, rel_attr = r_match[0]\\n rel_type = ProductTypeDict.REL_MAPPING_BACKWARD[rel_type]\\n is_soft = rel_type in (TYPE_TUPLE_RELATION_ALMOST, TYPE_TUPLE_RELATION_SIMILAR)\\n else:\\n r_match = re.findall('^(\\\\w+)(?:\\\\[([^\\\\]]+)\\\\])?(~)?\\\\s+(.*)$', r_str)\\n if r_match:\\n rel_type, rel_attr, is_soft, type_to_str = r_match[0]\\n is_soft = is_soft == u'~'\\n\\n if rel_type and type_to_str:\\n type_to_items = parse_terms(type_to_str)\\n\\n if dont_change:\\n p_type_related = ProductType(*type_to_items, meaningful=True, singleton=False)\\n wrapper = EqWrapper(p_type_related)\\n if p_type in seen_rel and wrapper in seen_rel[p_type]:\\n # Reverse to this relation has been already created. Use type instance from it\\n p_type_related = wrapper.match\\n else:\\n p_type_related = ProductType.make_from_terms(type_to_items, meaningful=True)\\n\\n if rel_attr:\\n try:\\n rel_attr = literal_eval(rel_attr)\\n except ValueError:\\n pass # Just use string as is\\n relation = ProductType.Relation(from_type=p_type, to_type=p_type_related, rel_type=rel_type,\\n is_soft=is_soft, rel_attr=rel_attr)\\n if p_type in seen_rel and p_type_related in seen_rel[p_type]:\\n # Already processed back relation\\n r_from = seen_rel[p_type][p_type_related]\\n assert r_from, \\\"The same relation is detected more than two times\\\"\\n p_type.make_relation(p_type_related, relation.rel_type, r_from.rel_type,\\n relation.is_soft, relation.rel_attr, r_from.rel_attr)\\n seen_rel[p_type][p_type_related] = None\\n else:\\n # Keep back relation until related type is met\\n seen_rel[p_type_related][p_type] = relation\\n elif self.VERBOSE:\\n print(\\\"WARN: meet unparseable relation: %s\\\" % r_str)\\n\\n if self.VERBOSE:\\n # Check remaining unresolved relations\\n for p_type_related in seen_rel:\\n for p_type in seen_rel[p_type_related]:\\n if seen_rel[p_type_related][p_type] is not None:\\n print(\\\"Detected hanged unresolved relation from type %s: %s\\\" % (\\n p_type, to_str(seen_rel[p_type_related][p_type])))\\n\\n self._type_tuples.clear()\\n self._type_tuples.update(type_tuples)\\n self._type_tuples_on_change()\\n\\n gc.enable()\\n if self.VERBOSE:\\n rel_count = sum(map(len, (p_type.relations() for p_type in type_tuples)))\\n print(\\\"Loaded %d type tuples with %d relations from json %s\\\" % (len(type_tuples), rel_count, json_filename))\\n\\n return type_tuples\",\n \"def main():\\n parser = argparse.ArgumentParser(description=\\\"Script for generating an index template out of a document\\\")\\n parser.add_argument(\\\"INDEX_NAME\\\", help=\\\"Name of index\\\")\\n parser.add_argument(\\\"--output_file\\\", help=\\\"File to write schema to\\\")\\n args = parser.parse_args()\\n\\n output = generate_template(args.INDEX_NAME)\\n if args.output_file:\\n with open(args.output_file, \\\"w\\\") as file:\\n json.dump(output, file, ensure_ascii=False, indent=4, sort_keys=True)\\n else:\\n print(json.dumps(output, ensure_ascii=False, indent=4, sort_keys=True))\",\n \"def test_read_json_schema():\\n json_schema = os.path.join(TEST_DATA_PATH, 'example_schema.json')\\n schema_tree = schema.load_schema(json_schema, resolve_references=True)\\n schema.check_schema(schema_tree)\",\n \"def main(arguments):\\n\\n # The input file can be optionally encoded with gzip format:\\n input_file = arguments.input_file[0]\\n assert isinstance(input_file, str)\\n if input_file.endswith(\\\".gz\\\"):\\n _open = gzip.open\\n else:\\n _open = open\\n with _open(input_file, \\\"rt\\\",\\n encoding='utf-8') as fd:\\n print(\\\"Loading JSON content into memory....\\\")\\n raw = json.load(fd) # Parses all the input file.\\n\\n # Also the output file can be optionally encoded with gzip format:\\n output_file = arguments.output_file[0]\\n assert isinstance(output_file, str)\\n uuid = 0\\n if output_file.endswith(\\\".gz\\\"):\\n _open = gzip.open\\n else:\\n _open = open\\n with _open(output_file, \\\"wt\\\",\\n encoding='utf-8') as fd:\\n # for each element extracted from the input\\n print(\\\"Generating distilled file\\\")\\n for item in load_input(raw):\\n uuid += 1 # generates incremental uuid from 1\\n item['uuid'] = uuid\\n fd.write(json.dumps(item,\\n sort_keys=True))\\n fd.write(\\\"\\\\n\\\") # one encoded document per line\\n\\n print(\\\"{} documents imported\\\".format(uuid))\",\n \"def main(infile: List[str], prefix: str, verbose: bool, no_origin_filter: bool):\\n common.init_logging(verbosity=int(verbose) + 1)\\n _LOGGER.info(\\\"Running with arguments: %s.\\\", locals())\\n\\n file_id = -1\\n with fileinput.input(files=infile, openhook=fileinput.hook_compressed) \\\\\\n as json_lines:\\n results = (json.loads(line) for line in json_lines)\\n\\n for file_id, graph in enumerate(\\n extract_graphs(results, use_origin=not no_origin_filter)\\n ):\\n path = Path(f\\\"{prefix}{file_id:04d}.json\\\")\\n if not path.is_file():\\n path.write_text(graph.to_json())\\n else:\\n _LOGGER.info(\\\"Refusing to overwrite: %s\\\", path)\\n _LOGGER.info(\\\"Script complete. Extracted %d dependency graphs.\\\", file_id+1)\",\n \"def _load_json_schema(filename: str):\\n relative_path = path.join('schemas', filename)\\n absolute_path = path.join(path.dirname(__file__), relative_path)\\n\\n with open(absolute_path, 'r', encoding='utf-8') as schema_file:\\n schema = json.loads(schema_file.read())\\n\\n return schema\",\n \"def parseProgram(inputFile):\\n print(\\\"Program\\\")\\n parseStatements(inputFile)\",\n \"def main(json_input, output_folder):\\n with open(json_input, 'r') as f:\\n data = json.loads(f.read())['data']\\n\\n with open(path.join(output_folder, 'qgeneration.context.txt'), 'w') as context_f, \\\\\\n open(path.join(output_folder, 'qgeneration.context.nojson.txt'), 'w') as context_f_nojson, \\\\\\n open(path.join(output_folder, 'qgeneration.gold.txt'), 'w') as question_f:\\n for item in data:\\n context_f.write(json.dumps(item['context']) + '\\\\n')\\n context_f_nojson.write(item['context'][:1000] + '\\\\n')\\n question_f.write(json.dumps(item['answer']) + '\\\\n')\\n\\n print('DONE')\",\n \"def main():\\n\\n # Set up argument parser.\\n parser = argparse.ArgumentParser(\\n description='Removes duplicate key-value pairs from JSON files.')\\n parser.add_argument('--suffix', default='',\\n help='optional suffix for output files; '\\n 'if empty, files will be changed in place')\\n parser.add_argument('files', nargs='+', help='input files')\\n args = parser.parse_args()\\n\\n # Iterate over files.\\n for filename in args.files:\\n # Read in json using Python libraries. This eliminates duplicates.\\n print('Processing ' + filename + '...')\\n try:\\n with codecs.open(filename, 'r', 'utf-8') as infile:\\n j = json.load(infile)\\n except ValueError as e:\\n print('Error reading ' + filename)\\n raise InputError(filename, str(e))\\n\\n # Built up output strings as an array to make output of delimiters easier.\\n output = []\\n for key in j:\\n if key != '@metadata':\\n output.append('\\\\t\\\"' + key + '\\\": \\\"' +\\n j[key].replace('\\\\n', '\\\\\\\\n') + '\\\"')\\n\\n # Output results.\\n with codecs.open(filename + args.suffix, 'w', 'utf-8') as outfile:\\n outfile.write('{\\\\n')\\n outfile.write(',\\\\n'.join(output))\\n outfile.write('\\\\n}\\\\n')\",\n \"def _create_schema(self, cypher_file):\\n if len(self.graph.nodes) > 0:\\n msg = \\\"Cypher file specified but the graph is not empty. Aborting.\\\"\\n raise ValueError(msg)\\n cyp = open(cypher_file, 'r').read()\\n self.graph.run(cyp)\",\n \"def main():\\n parse_file(sys.argv[1])\",\n \"def cli(yamlfile, **args):\\n print(LogicProgramGenerator(yamlfile, **args).serialize(**args))\",\n \"def run(filename=\\\"input.json\\\", path=\\\".\\\", **args):\\n\\n logger = logging.getLogger(__name__)\\n \\n #read input file (need to add command line specification)\\n logger.info(\\\"Begin processing input file: %s\\\" % filename)\\n eos_dict, thermo_dict, output_file = read_input.extract_calc_data(filename, path, **args)\\n eos_dict['jit'] = args['jit']\\n\\n if output_file:\\n file_dict = {\\\"output_file\\\":output_file}\\n else:\\n file_dict = {\\\"output_file\\\": \\\"despasito_out.txt\\\"}\\n\\n logger.debug(\\\"EOS dict:\\\", eos_dict)\\n logger.debug(\\\"Thermo dict:\\\", thermo_dict)\\n logger.info(\\\"Finish processing input file: {}\\\".format(filename))\\n \\n eos = eos_mod(**eos_dict)\\n \\n # Run either parametrization or thermodynamic calculation\\n if \\\"opt_params\\\" in list(thermo_dict.keys()):\\n logger.info(\\\"Initializing parametrization procedure\\\")\\n output_dict = fit(eos, thermo_dict)\\n #output = fit(eos, thermo_dict)\\n logger.info(\\\"Finished parametrization\\\")\\n write_output.writeout_fit_dict(output_dict,eos,**file_dict)\\n else:\\n logger.info(\\\"Initializing thermodynamic calculation\\\")\\n output_dict = thermo(eos, thermo_dict)\\n logger.info(\\\"Finished thermodynamic calculation\\\")\\n write_output.writeout_thermo_dict(output_dict,thermo_dict[\\\"calculation_type\\\"],**file_dict)\",\n \"def generate_bq_schema(self, file_name, schema_file_name=None):\\n if not schema_file_name:\\n schema_file_name = f'{self.directory}/schema_temp.json'\\n os.system(f\\\"generate-schema --keep_nulls < {file_name} > {schema_file_name}\\\")\\n\\n schema = open(schema_file_name, 'r').read()\\n\\n os.remove(schema_file_name)\\n\\n return json.loads(schema)\",\n \"def test_test_body_with_file_schema(self):\\n pass\",\n \"def main():\\n\\n parser = argparse.ArgumentParser()\\n\\n parser.add_argument(\\\"api_base_url\\\", type=str,\\n help=\\\"base url for all tests\\\")\\n parser.add_argument(\\\"test_file_name\\\", type=str,\\n help=\\\"name of file containing JSON array of tests\\\")\\n parser.add_argument(\\\"-f\\\", \\\"--format\\\", default=\\\"json\\\", type=str,\\n help=\\\"output format - must be either json or text\\\")\\n\\n args = parser.parse_args()\\n\\n try:\\n\\n run_tests_from_file(args.api_base_url, args.test_file_name, \\n args.format)\\n\\n except KeyError as e:\\n print(\\\"Required key '%s' not found. Check tests file.\\\" % str(e.args[0]))\\n exit(1)\\n\\n except FileNotFoundError:\\n print(\\\"Cannot open file '%s'. File not found.\\\" % args.test_file_name)\\n exit(1)\\n\\n except ValueError:\\n print(\\\"Cannot decode JSON from file '%s'.\\\" % args.test_file_name)\\n exit(1)\",\n \"def parse(fileobject, schema=None):\\n if schema:\\n # with validation\\n parser = objectify.makeparser(schema=schema.schema, strip_cdata=False)\\n return objectify.parse(fileobject, parser=parser)\\n else:\\n # without validation\\n parser = objectify.makeparser(strip_cdata=False)\\n return objectify.parse(fileobject, parser=parser)\",\n \"def main():\\n parser = argparse.ArgumentParser()\\n parser.add_argument(\\n '--config_file',\\n dest='config_file',\\n required=True,\\n default=None,\\n help='JSON config file.')\\n args = parser.parse_args()\\n\\n config = Config(args.config_file)\\n\\n logger.info(\\\"Executing preprocessing pipeline...\\\")\\n run_pipeline(config)\\n logger.info(\\\"Done.\\\")\",\n \"def decide_schema(self, json_data):\\n pass\",\n \"def decide_schema(self, json_data):\\n pass\",\n \"def main():\\n parser = ArgumentParser()\\n\\n parser.add_argument(\\\"-C\\\", \\\"--clinvar\\\", dest=\\\"clinvar\\\",\\n help=\\\"ClinVar VCF file\\\", metavar=\\\"CLINVAR\\\")\\n parser.add_argument(\\\"-i\\\", \\\"--input\\\", dest=\\\"inputfile\\\",\\n help=\\\"Input VCF file\\\", metavar=\\\"INPUT\\\")\\n parser.add_argument(\\\"-F\\\", \\\"--output-format\\\", dest=\\\"format\\\",\\n help=\\\"Output format (currently 'csv' or 'json')\\\",\\n metavar=\\\"FORMAT\\\")\\n parser.add_argument(\\\"-V\\\", \\\"--schema-version\\\", dest=\\\"schema_version\\\",\\n help=\\\"Version to include report (JSON only)\\\",\\n metavar=\\\"OUTVERSION\\\")\\n parser.add_argument(\\\"-n\\\", \\\"--notes\\\", dest=\\\"notes\\\",\\n help=\\\"Notes, as a JSON string, to include in report (JSON only)\\\",\\n metavar=\\\"NOTES\\\")\\n parser.add_argument(\\\"-g\\\", \\\"--genome-build\\\", dest=\\\"build\\\",\\n help=\\\"Genome build to include in report (JSON only)\\\",\\n metavar=\\\"GENOMEBUILD\\\")\\n options = parser.parse_args()\\n\\n if sys.stdin.isatty():\\n if options.inputfile:\\n if options.inputfile.endswith('.vcf'):\\n input_genome_file = open(options.inputfile)\\n elif options.inputfile.endswith('.vcf.gz'):\\n input_genome_file = gzip.open(options.inputfile)\\n elif options.inputfile.endswith('.vcf.bz2'):\\n input_genome_file = bz2.BZ2File(options.inputfile)\\n else:\\n raise IOError(\\\"Genome filename expected to end with ''.vcf',\\\" +\\n \\\" '.vcf.gz', or '.vcf.bz2'.\\\")\\n else:\\n sys.stderr.write(\\\"Provide input VCF file\\\\n\\\")\\n parser.print_help()\\n sys.exit(1)\\n else:\\n input_genome_file = sys.stdin\\n\\n if options.clinvar:\\n if options.clinvar.endswith('.vcf'):\\n input_clinvar_file = open(options.clinvar)\\n elif options.clinvar.endswith('.vcf.gz'):\\n input_clinvar_file = gzip.open(options.clinvar)\\n elif options.clinvar.endswith('.vcf.bz2'):\\n input_clinvar_file = bz2.BZ2File(options.clinvar)\\n else:\\n raise IOError(\\\"ClinVar filename expected to end with ''.vcf',\\\" +\\n \\\" '.vcf.gz', or '.vcf.bz2'.\\\")\\n else:\\n sys.stderr.write(\\\"Provide ClinVar VCF file\\\\n\\\")\\n parser.print_help()\\n sys.exit(1)\\n\\n output_format = \\\"csv\\\"\\n if options.format:\\n if options.format == \\\"csv\\\":\\n output_format = \\\"csv\\\"\\n elif options.format == \\\"json\\\":\\n output_format = \\\"json\\\"\\n\\n if output_format == \\\"csv\\\":\\n csv_out = csv.writer(sys.stdout)\\n header = (\\\"Chromosome\\\", \\\"Position\\\", \\\"Name\\\", \\\"Significance\\\", \\\"Frequency\\\",\\n \\\"Zygosity\\\", \\\"ACC URL\\\")\\n csv_out.writerow(header)\\n\\n metadata = {}\\n metadata[\\\"notes\\\"] = options.clinvar\\n\\n build = \\\"unknown\\\"\\n if options.build:\\n build = options.build\\n metadata[\\\"genome_build\\\"] = build\\n\\n notes_json = {}\\n if options.notes:\\n notes_json[\\\"parameter\\\"] = options.notes\\n try:\\n notes_json = json.loads(options.notes)\\n except:\\n sys.stderr.write(\\\"Could not parse JSON notes field\\\\n\\\")\\n\\n json_report = {}\\n json_report[\\\"schema_version\\\"] = options.schema_version\\n json_report[\\\"notes\\\"] = notes_json\\n json_report[\\\"metadata\\\"] = metadata\\n json_report[\\\"variants\\\"] = []\\n\\n matching = match_to_clinvar(input_genome_file, input_clinvar_file)\\n for var in matching:\\n\\n chrom = var[0]\\n pos = var[1]\\n ref_allele = var[2]\\n alt_allele = var[3]\\n name_acc = var[4]\\n allele_freq = var[5]\\n zygosity = var[6]\\n\\n for spec in name_acc:\\n ele = {}\\n ele[\\\"chrom\\\"] = REV_CHROM_INDEX[chrom]\\n ele[\\\"pos\\\"] = pos\\n ele[\\\"ref_allele\\\"] = ref_allele\\n ele[\\\"alt_allele\\\"] = alt_allele\\n ele[\\\"allele_freq\\\"] = allele_freq\\n ele[\\\"zygosity\\\"] = zygosity\\n\\n url = \\\"http://www.ncbi.nlm.nih.gov/clinvar/\\\" + str(spec[0])\\n name = spec[1]\\n clnsig = spec[2]\\n\\n ele[\\\"acc_url\\\"] = url\\n ele[\\\"name\\\"] = name\\n ele[\\\"clinical_significance\\\"] = clnsig\\n\\n json_report[\\\"variants\\\"].append(ele)\\n\\n if output_format == \\\"csv\\\":\\n data = (chrom, pos, name, clnsig, allele_freq, zygosity, url)\\n csv_out.writerow(data)\\n\\n if output_format == \\\"json\\\":\\n print json.dumps(json_report)\",\n \"def main():\\n arg_parser = argparse.ArgumentParser(\\n description='Runs jdeps (dependency analysis tool) on a given JAR and '\\n 'writes the resulting dependency graph into a JSON file.')\\n required_arg_group = arg_parser.add_argument_group('required arguments')\\n required_arg_group.add_argument(\\n '-t',\\n '--target',\\n required=True,\\n help='Path to the JAR file to run jdeps on.')\\n required_arg_group.add_argument(\\n '-o',\\n '--output',\\n required=True,\\n help='Path to the file to write JSON output to. Will be created '\\n 'if it does not yet exist and overwrite existing '\\n 'content if it does.')\\n arg_parser.add_argument('-j',\\n '--jdeps-path',\\n default=JDEPS_PATH,\\n help='Path to the jdeps executable.')\\n arguments = arg_parser.parse_args()\\n\\n print('Running jdeps and parsing output...')\\n raw_jdeps_output = run_jdeps(arguments.jdeps_path, arguments.target)\\n jdeps_parser = JavaClassJdepsParser()\\n jdeps_parser.parse_raw_jdeps_output(raw_jdeps_output)\\n\\n class_graph = jdeps_parser.graph\\n print(f'Parsed class-level dependency graph, '\\n f'got {class_graph.num_nodes} nodes '\\n f'and {class_graph.num_edges} edges.')\\n\\n package_graph = package_dependency.JavaPackageDependencyGraph(class_graph)\\n print(f'Created package-level dependency graph, '\\n f'got {package_graph.num_nodes} nodes '\\n f'and {package_graph.num_edges} edges.')\\n\\n print(f'Dumping JSON representation to {arguments.output}.')\\n serialization.dump_class_and_package_graphs_to_file(\\n class_graph, package_graph, arguments.output)\",\n \"def run(self, verbose=False):\\n from utils import write_to_file # function to write json to file\\n self.read_json()\\n graph = self.parse_jsons()\\n json = self.pipe_vl2vg(graph)\\n return self.write_to_file(rawinput=json, filetype='json', output_path=self.output_path, engine_name=self.engine_name, algorithm_name=self.algorithm_name, suffix=self.file_suffix, verbose=verbose)\",\n \"def main():\\n parser = optparse.OptionParser()\\n info = ('The input json-format file name. Such as: '\\n '--dest_dir=/hangonman/android')\\n parser.add_option('-d', '--dest_dir', action='store', dest='dest_dir',\\n help=info)\\n opts, _ = parser.parse_args()\\n if not os.path.exists(opts.dest_dir):\\n print 'Destination directory is not existed!'\\n return 1\\n latest_url = ('http://wrt-build.sh.intel.com/archive/'\\n 'snapshots/dev-wrt-android-build/LATEST')\\n file_name = 'xwalk_app_template.tar.gz'\\n app_template_handler = GetXWalkAppTemplate(latest_url, file_name)\\n try:\\n app_template_handler.ExtractFile(opts.dest_dir)\\n except tarfile.TarError:\\n raise Exception('Error in the process of tar file.')\\n return 0\",\n \"def run():\\n params = parseParams()\\n args = []\\n kwargs = {}\\n if params.source != None:\\n kwargs['source'] = os.path.abspath(params.source)\\n else:\\n kwargs['source'] = sys.stdin\\n kwargs['output'] = os.path.abspath(params.output)\\n kwargs['fmt'] = params.fmt\\n kwargs['syntax'] = params.syntax\\n return parse.parse(*args, **kwargs) # pylint: disable=W0142\",\n \"def main():\\n os.makedirs(PATH)\\n fetch_data()\\n convert_to_json(model_list, 'models.json', is_model=True)\\n convert_to_json(backend_list, 'backends.json')\\n convert_to_json(type_list, 'types.json')\\n convert_to_json(featurizer_list, 'featurizers.json')\",\n \"def stich_from_file(jsonname):\\n with open(jsonname) as f:\\n data = json.loads(f.read())\\n stich(data)\",\n \"def main():\\n f_name = sys.argv[1]\\n file_contents = open(f_name).read()\\n C = CAST([], \\\"python\\\")\\n C2 = C.from_json_str(file_contents)\\n\\n V = CASTToAGraphVisitor(C2)\\n last_slash_idx = f_name.rfind(\\\"/\\\")\\n file_ending_idx = f_name.rfind(\\\".\\\")\\n pdf_file_name = f\\\"{f_name[last_slash_idx + 1 : file_ending_idx]}.pdf\\\"\\n V.to_pdf(pdf_file_name)\",\n \"def json_schema(schema_file=None, output=\\\"-\\\"):\\n schemas = read_yaml(schema_file)\\n dump_yaml(output, JsonSchemaConverterFromAccessSchema.convert_schemas(schemas))\",\n \"def main():\\n parser = specify_parser()\\n args = parser.parse_args()\\n\\n mapping = init_data(args.datafile[0])\\n loaded_data = read(args.input)\\n\\n mount(mapping, loaded_data)\",\n \"def load_schema(filename):\\n with open(filename) as f:\\n schema = json.load(f)\\n\\n return schema\",\n \"def readjamschema(schema):\\n raise NotImplementedError(msg)\",\n \"def main():\\n # There are no args, but parse them just so help works\\n args = docopt(__doc__)\\n print(process_files_json(), end=\\\"\\\")\\n return None\",\n \"def from_json_file(cls, json_file:str):\\n with open(json_file) as file:\\n data = json.load(file)\\n validate(data, schema)\\n instance = cls.from_dict(data)\\n return instance\",\n \"def main():\\n args = docopt(__doc__)\\n meta = fetch_metadata(args['DIRECTORY'], **args)\\n if args['--fasta']:\\n meta.assembly.update({'file': args['--fasta']})\\n taxdump = None\\n dependencies = {}\\n for field in FIELDS:\\n if args[field['flag']]:\\n for dep in field['depends']:\\n if dep not in dependencies or not dependencies[dep]:\\n dependencies[dep] = fetch_field(args['DIRECTORY'], dep, meta)\\n if field['flag'] == '--hits':\\n if not taxdump:\\n taxdump = fetch_taxdump(args['--taxdump'])\\n parents = field['module'].parent()\\n parsed = field['module'].parse(\\n args[field['flag']],\\n **{key: args[key] for key in PARAMS},\\n taxdump=taxdump,\\n dependencies=dependencies,\\n meta=meta)\\n if not isinstance(parsed, list):\\n parsed = [parsed]\\n for data in parsed:\\n if not args['--replace']:\\n if has_field_warning(meta, data.field_id):\\n continue\\n for parent in data.parents:\\n if 'range' in parent:\\n parent_meta = meta.field_meta(parent['id'])\\n if parent_meta and 'range' in parent_meta:\\n parent['range'][0] = min(parent['range'][0], parent_meta['range'][0])\\n parent['range'][1] = max(parent['range'][1], parent_meta['range'][1])\\n meta.add_field(parents+data.parents, **data.meta)\\n if isinstance(data, Identifier):\\n meta.records = len(data.values)\\n json_file = \\\"%s/%s.json\\\" % (args['DIRECTORY'], data.field_id)\\n file_io.write_file(json_file, data.values_to_dict())\\n dependencies[data.field_id] = data\\n if 'identifiers' not in dependencies:\\n dependencies['identifiers'] = fetch_field(args['DIRECTORY'], 'identifiers', meta)\\n for string in args['--link']:\\n link.add(string, meta, dependencies['identifiers'].values, args['--skip-link-test'])\\n for string in args['--key']:\\n key.add(string, meta)\\n if args['--taxid']:\\n if not taxdump:\\n taxdump = fetch_taxdump(args['--taxdump'])\\n taxid.add(args['--taxid'], taxdump, meta)\\n file_io.write_file(\\\"%s/meta.json\\\" % args['DIRECTORY'], meta.to_dict())\",\n \"def compile_from_input_json(\\n input_json: Dict, silent: bool = True, allow_paths: Optional[str] = None\\n) -> Dict:\\n\\n optimizer = input_json[\\\"settings\\\"][\\\"optimizer\\\"]\\n input_json[\\\"settings\\\"].setdefault(\\\"evmVersion\\\", None)\\n if input_json[\\\"settings\\\"][\\\"evmVersion\\\"] in EVM_EQUIVALENTS:\\n input_json[\\\"settings\\\"][\\\"evmVersion\\\"] = EVM_EQUIVALENTS[input_json[\\\"settings\\\"][\\\"evmVersion\\\"]]\\n\\n if not silent:\\n print(f\\\"Compiling contracts...\\\\n Solc version: {str(solcx.get_solc_version())}\\\")\\n\\n opt = f\\\"Enabled Runs: {optimizer['runs']}\\\" if optimizer[\\\"enabled\\\"] else \\\"Disabled\\\"\\n print(f\\\" Optimizer: {opt}\\\")\\n\\n if input_json[\\\"settings\\\"][\\\"evmVersion\\\"]:\\n print(f\\\" EVM Version: {input_json['settings']['evmVersion'].capitalize()}\\\")\\n\\n try:\\n return solcx.compile_standard(input_json, allow_paths=allow_paths)\\n except solcx.exceptions.SolcError as e:\\n raise CompilerError(e, \\\"solc\\\")\",\n \"def main():\\n\\n # Command Line Interface\\n parse = command_line()\\n args = parse.parse_args()\\n if not os.path.isdir(args.directory):\\n raise IOError\\n\\n # Abstract File Tree\\n filetree = tree_walk(args.directory, args.replace, args.depth)\\n jsontree = json.dumps(\\n filetree,\\n indent=4,\\n sort_keys=True,\\n separators=(', ', ': '),\\n )\\n\\n # Pipe vs Redirection\\n if sys.stdout.isatty():\\n try: jsontree = highlight(\\n jsontree,\\n JsonLexer(),\\n Terminal256Formatter(style='autumn'))\\n except:\\n pass\\n\\n print(jsontree)\",\n \"def get_schema(path):\\n with open(path, 'r') as f:\\n return json.load(f)\",\n \"def __validate_and_compile(self):\\n self.__validate_file(self.__schema_path, \\\"RNG\\\")\\n\\n with open(self.__schema_path) as relax_file_handler:\\n\\n # Parse schema file\\n relax_parsed = etree.parse(relax_file_handler)\\n\\n # Compile schema file\\n self.__compiled = etree.RelaxNG(relax_parsed)\",\n \"def run_analysis(args):\\n import google.datalab.bigquery as bq\\n if args.bigquery_table:\\n table = bq.Table(args.bigquery_table)\\n schema_list = table.schema._bq_schema\\n else:\\n schema_list = json.loads(\\n file_io.read_file_to_string(args.schema_file).decode())\\n table = bq.ExternalDataSource(\\n source=args.input_file_pattern,\\n schema=bq.Schema(schema_list))\\n\\n # Check the schema is supported.\\n for col_schema in schema_list:\\n col_type = col_schema['type'].lower()\\n if col_type != 'string' and col_type != 'integer' and col_type != 'float':\\n raise ValueError('Schema contains an unsupported type %s.' % col_type)\\n\\n run_numerical_analysis(table, schema_list, args)\\n run_categorical_analysis(table, schema_list, args)\\n\\n # Save a copy of the schema to the output location.\\n file_io.write_string_to_file(\\n os.path.join(args.output_dir, SCHEMA_FILE),\\n json.dumps(schema_list, indent=2, separators=(',', ': ')))\",\n \"def _runParser(self):\\n with open(self.var('filePath')) as f:\\n return json.load(f)\",\n \"def gen_review_data(fp: str) -> None:\\n with open(fp, encoding='utf-8') as f:\\n for line in f:\\n data = json.loads(line)\\n utils.preprocess_raw_json(data)\\n doc = {\\n \\\"_index\\\": \\\"review\\\",\\n \\\"_source\\\": data\\n }\\n yield doc\",\n \"def parse_from_json(self, infile):\\r\\n obj = json.load(infile)\\r\\n relations = Relations.from_json_obj(obj['relations'])\\r\\n stamps = Stamps.from_json_obj(obj['stamps'])\\r\\n apis = APIs.from_json_obj(obj['apis'])\\r\\n source_infos = SourceInfos.from_json_obj(obj['source infos'])\\r\\n compilations = Compilations.from_json_obj(obj['compilations'])\\r\\n compile_setup = Compilations.from_json_obj(obj['compile setup'])\\r\\n return ZincAnalysis(relations, stamps, apis, source_infos, compilations, compile_setup)\",\n \"def handle_json_file(context: click.Context, param: click.Parameter, json_file: str) -> None:\\n if not json_file or context.resilient_parsing:\\n return\\n try:\\n with click.open_file(json_file) as f:\\n info = json.load(f)\\n pretty_echo(context.obj.client.custom_request(info))\\n except json.JSONDecodeError:\\n raise click.BadParameter(f'{json_file} is not a valid json file')\\n except HttpError as e:\\n pretty_echo(e.error_message)\\n context.exit()\",\n \"def compile_program(self, json_name: Path, info_name: Path) -> int:\\n testutils.log.info(\\\"---------------------- Compile with p4c-bm2-ss ----------------------\\\")\\n compilation_cmd = (\\n f\\\"{self.options.rootdir}/build/p4c-bm2-ss --target bmv2 --arch v1model \\\"\\n f\\\"--p4runtime-files {info_name} {self.options.p4_file} -o {json_name}\\\"\\n )\\n _, returncode = testutils.exec_process(compilation_cmd, timeout=30)\\n if returncode != testutils.SUCCESS:\\n testutils.log.error(\\\"Failed to compile the P4 program %s.\\\", self.options.p4_file)\\n return returncode\",\n \"def BqTableSchemaFileProcessor(file_arg):\\n table_schema_type = GetApiMessage('TableSchema')\\n schema_field_type = GetApiMessage('TableFieldSchema')\\n\\n try:\\n schema_json = yaml.load(file_arg)\\n schema_json = schema_json.get('schema', None)\\n\\n if not schema_json or not isinstance(schema_json, list):\\n raise SchemaFileError(\\n 'Error parsing schema file: no schema field list defined in file')\\n\\n all_fields = []\\n for field in schema_json:\\n new_field = schema_field_type(name=field['name'],\\n type=field['type'],\\n mode=field.get('mode', 'NULLABLE'))\\n all_fields.append(new_field)\\n\\n return table_schema_type(fields=sorted(all_fields, key=lambda x: x.name))\\n except yaml.YAMLParseError as ype:\\n raise SchemaFileError('Error parsing schema file [{}]'.format(ype))\\n except (AttributeError, KeyError) as e:\\n raise SchemaFileError(\\n 'Error parsing schema file, invalid field definition [{}]'.format(e))\",\n \"def validate_json_schema(path, name, data, schema, full_schema=not is_extension):\\n errors = 0\\n\\n # The standard repository has an example extension.\\n if 'docs/examples/organizations/organizational_units/ocds_divisionCode_extension' in path:\\n full_schema = False\\n\\n # Kingfisher Collect uses JSON Schema files to validate Scrapy items.\\n code_repo = repo_name == 'kingfisher-collect'\\n\\n # Non-OCDS schema don't:\\n # * pair \\\"enum\\\" and \\\"codelist\\\"\\n # * disallow \\\"null\\\" in \\\"type\\\" of \\\"items\\\"\\n # * UpperCamelCase definitions and lowerCamelCase properties\\n # * allow \\\"null\\\" in the \\\"type\\\" of optional fields\\n # * include \\\"id\\\" fields in objects within arrays\\n # * require \\\"title\\\", \\\"description\\\" and \\\"type\\\" properties\\n json_schema_exceptions = {\\n 'json-schema-draft-4.json',\\n 'meta-schema.json',\\n 'meta-schema-patch.json',\\n }\\n ocds_schema_exceptions = {\\n 'dereferenced-release-schema.json',\\n # standard-maintenance-scripts\\n 'codelist-schema.json',\\n 'extension-schema.json',\\n # extension_registry\\n 'extensions-schema.json',\\n 'extension_versions-schema.json',\\n # spoonbill\\n 'ocds-simplified-schema.json',\\n }\\n schema_exceptions = json_schema_exceptions | ocds_schema_exceptions\\n\\n validate_items_type_kwargs = {\\n 'allow_invalid': {\\n '/definitions/Amendment/properties/changes/items', # deprecated\\n '/definitions/AmendmentUnversioned/properties/changes/items', # deprecated\\n '/definitions/record/properties/releases/oneOf/0/items', # 1.1\\n },\\n }\\n\\n def validate_codelist_enum_allow_missing(codelist):\\n return is_extension and codelist in external_codelists\\n\\n validate_codelist_enum_kwargs = {\\n 'fallback': {\\n '/definitions/Metric/properties/id': ['string'],\\n '/definitions/Milestone/properties/code': ['string', 'null'],\\n },\\n 'allow_missing': validate_codelist_enum_allow_missing,\\n }\\n\\n validate_letter_case_kwargs = {\\n 'property_exceptions': {'former_value'}, # deprecated\\n 'definition_exceptions': {'record'}, # 1.1\\n }\\n\\n def validate_metadata_presence_allow_missing(pointer):\\n return 'links' in pointer.split('/') or code_repo # ocds_pagination_extension\\n\\n validate_metadata_presence_kwargs = {\\n 'allow_missing': validate_metadata_presence_allow_missing,\\n }\\n\\n def validate_object_id_allow_missing(pointer):\\n parts = pointer.split('/')\\n return 'versionedRelease' in parts or parts[-1] in {\\n 'changes', # deprecated\\n 'records', # uses `ocid` not `id`\\n '0', # linked releases\\n }\\n\\n validate_object_id_kwargs = {\\n 'allow_missing': validate_object_id_allow_missing,\\n 'allow_optional': {\\n # 2.0 fixes.\\n # See https://github.com/open-contracting/standard/issues/650\\n '/definitions/Amendment',\\n '/definitions/Organization',\\n '/definitions/OrganizationReference',\\n '/definitions/RelatedProcess',\\n },\\n }\\n if repo_name == 'infrastructure':\\n validate_object_id_kwargs['allow_optional'].add('/definitions/Classification')\\n\\n validate_null_type_kwargs = {\\n # OCDS allows null. OC4IDS disallows null.\\n 'no_null': repo_name == 'infrastructure' or code_repo,\\n 'allow_object_null': {\\n '/definitions/Amendment/properties/changes/items/properties/former_value', # deprecated\\n # See https://github.com/open-contracting/standard/pull/738#issuecomment-440727233\\n '/definitions/Organization/properties/details',\\n },\\n 'allow_no_null': {\\n '/definitions/Amendment/properties/changes/items/properties/property', # deprecated\\n\\n # Children of fields with omitWhenMerged.\\n '/definitions/Link/properties/rel',\\n '/definitions/Link/properties/href',\\n\\n # 2.0 fixes.\\n # See https://github.com/open-contracting/standard/issues/650\\n '/definitions/Organization/properties/id',\\n '/definitions/OrganizationReference/properties/id',\\n '/definitions/RelatedProcess/properties/id',\\n },\\n }\\n\\n validate_array_items_kwargs = {\\n 'allow_invalid': {\\n '/definitions/Amendment/properties/changes/items/properties/former_value', # deprecated\\n '/definitions/Location/properties/geometry/properties/coordinates/items', # recursion\\n },\\n }\\n\\n validate_deep_properties_kwargs = {\\n 'allow_deep': {\\n '/definitions/Amendment/properties/changes/items', # deprecated\\n },\\n }\\n if is_extension: # avoid repetition in extensions\\n validate_deep_properties_kwargs['allow_deep'].add('/definitions/Item/properties/unit')\\n\\n validator = Draft4Validator(schema, format_checker=FormatChecker())\\n\\n errors += validate_schema(path, data, validator)\\n if errors:\\n warnings.warn(f'{path} is not valid JSON Schema ({errors} errors)')\\n\\n if name not in schema_exceptions:\\n if 'versioned-release-validation-schema.json' in path:\\n validate_items_type_kwargs['additional_valid_types'] = ['object']\\n errors += validate_array_items(path, data, **validate_array_items_kwargs)\\n errors += validate_items_type(path, data, **validate_items_type_kwargs)\\n if not code_repo:\\n errors += validate_codelist_enum(path, data, **validate_codelist_enum_kwargs)\\n errors += validate_letter_case(path, data, **validate_letter_case_kwargs)\\n errors += validate_merge_properties(path, data)\\n\\n # `full_schema` is set to not expect extensions to repeat information from core.\\n if full_schema:\\n exceptions_plus_versioned = schema_exceptions | {\\n 'versioned-release-validation-schema.json',\\n }\\n\\n exceptions_plus_versioned_and_packages = exceptions_plus_versioned | {\\n 'project-package-schema.json',\\n 'record-package-schema.json',\\n 'release-package-schema.json',\\n }\\n\\n if not code_repo:\\n # Extensions aren't expected to repeat referenced `definitions`.\\n errors += validate_ref(path, data)\\n\\n if name not in exceptions_plus_versioned:\\n # Extensions aren't expected to repeat `title`, `description`, `type`.\\n errors += validate_metadata_presence(path, data, **validate_metadata_presence_kwargs)\\n if not code_repo:\\n # Extensions aren't expected to repeat referenced `definitions`.\\n errors += validate_object_id(path, jsonref.replace_refs(data), **validate_object_id_kwargs)\\n\\n if name not in exceptions_plus_versioned_and_packages:\\n # Extensions aren't expected to repeat `required`. Packages don't have merge rules.\\n errors += validate_null_type(path, data, **validate_null_type_kwargs)\\n # Extensions aren't expected to repeat referenced codelist CSV files\\n # TODO: This code assumes each schema uses all codelists. So, for now, skip package schema.\\n errors += validate_schema_codelists_match(path, data, cwd, is_extension, is_profile, external_codelists)\\n\\n else:\\n # Don't count these as errors.\\n validate_deep_properties(path, data, **validate_deep_properties_kwargs)\\n\\n assert not errors, 'One or more JSON Schema files are invalid. See warnings below.'\",\n \"def __init__(self, schema, input_files, output_path):\\n self.schema = schema\\n self.input_files = input_files\\n self.output_path = output_path\",\n \"def main(argv):\\n ret = 0\\n parser = utils.parser.createParser()\\n\\n opt = parser.parse_args(argv)\\n\\n try:\\n # Stop execution if help flag is on\\n if opt.help:\\n raise ShowHelpException()\\n\\n # Read materials we need for later processing\\n cfg = auxiliary.readConfig(opt.config)\\n rawJson = auxiliary.readJson(opt.input[0])\\n\\n # List we are going to manipulate :)\\n commands = rawJson['commands']\\n\\n # Pump commands and instruction for filtering, sorting etc\\n rawJson['commands'] = pump(commands, cfg)\\n\\n # Write final result\\n auxiliary.writeJSONFile(rawJson, opt.output[0])\\n\\n except ShowHelpException:\\n parser.print_help()\\n ret = 0\\n except FilterException, e:\\n ret = 1\\n print('ERROR')\\n print(e)\\n\\n return ret\",\n \"def main(input_filepath, output_filepath):\\n logger = logging.getLogger(__name__)\\n logger.info('making final data set from raw data')\\n\\n conn = sqlite3.connect('../raw/td_V2.db')\\n git_commits = pd.read_sql_query(\\\"SELECT * FROM GIT_COMMITS\\\",conn)\\n szz_fault_inducing_commits = pd.read_sql_query(\\\"SELECT * FROM szz_fault_inducing_commits\\\",conn)\\n refactoring_miner = pd.read_sql_query(\\\"SELECT * FROM refactoring_miner\\\",conn)\\n refactoring_miner = refactoring_miner[refactoring_miner[\\\"COMMIT_HASH\\\"].isin(git_commits[\\\"COMMIT_HASH\\\"])]\\n git_commits_changes = pd.read_sql_query(\\\"SELECT * FROM GIT_COMMITS_CHANGES\\\", conn)\\n git_commits_changes = git_commits_changes[git_commits_changes[\\\"COMMIT_HASH\\\"].isin(refactoring_miner[\\\"COMMIT_HASH\\\"])]\\n\\n preprocess(git_commits, szz_fault_inducing_commits, refactoring_miner, git_commits_changes)\",\n \"def schema_load(filename):\\n print(uc.schema_load(filename))\",\n \"def main():\\n\\n obj_lookup = interfaces_dir / \\\"FrameLib-obj-jlookup.json\\\"\\n\\n worker = jParseAndBuild()\\n\\n refpages = [x for x in refpages_dir.rglob(\\\"fl.*.xml\\\")]\\n\\n for ref in refpages:\\n worker.extract_from_refpage(ref)\\n\\n write_json(obj_lookup, worker.j_master_dict)\",\n \"def deduce_schema(self, input_data, *, schema_map=None):\\n\\n if self.input_format == 'csv':\\n if self.csv_dialect:\\n reader = csv.DictReader(input_data, dialect=self.csv_dialect)\\n else:\\n reader = csv.DictReader(input_data)\\n elif self.input_format == 'json' or self.input_format is None:\\n reader = json_reader(input_data)\\n elif self.input_format == 'dict':\\n reader = input_data\\n else:\\n raise Exception(f\\\"Unknown input_format '{self.input_format}'\\\")\\n\\n if schema_map is None:\\n schema_map = OrderedDict()\\n\\n try:\\n for json_object in reader:\\n\\n # Print a progress message periodically.\\n self.line_number += 1\\n if self.line_number % self.debugging_interval == 0:\\n logging.info(f'Processing line {self.line_number}')\\n\\n # Deduce the schema from this given data record.\\n if isinstance(json_object, dict):\\n self.deduce_schema_for_record(\\n json_object=json_object,\\n schema_map=schema_map,\\n )\\n elif isinstance(json_object, Exception):\\n self.log_error(\\n f'Record could not be parsed: Exception: {json_object}'\\n )\\n if not self.ignore_invalid_lines:\\n raise json_object\\n else:\\n self.log_error(\\n 'Record should be a JSON Object '\\n f'but was a {type(json_object)}'\\n )\\n if not self.ignore_invalid_lines:\\n raise Exception(f'Record must be a JSON Object '\\n f'but was a {type(json_object)}')\\n finally:\\n logging.info(f'Processed {self.line_number} lines')\\n\\n return schema_map, self.error_logs\",\n \"def scheme(input, output, delimiter, encoding, verbose, format_in, zipfile, stype):\\n if verbose:\\n enableVerbose()\\n options = {}\\n options['delimiter'] = delimiter\\n options['output'] = output\\n options['encoding'] = encoding\\n options['format_in'] = format_in\\n options['zipfile'] = zipfile\\n options['stype'] = stype\\n acmd = Schemer()\\n acmd.generate_scheme(input, options)\\n pass\",\n \"def _main():\\n\\n # setup paths\\n json_model_path = osp.join(FLAGS.input_dir, FLAGS.json_model_fname)\\n weights_path = osp.join(FLAGS.input_dir, FLAGS.weights_fname)\\n save_path = osp.splitext(json_model_path)[0][:-6] + \\\"graph_w\\\" + str(weights_path.split(\\\"_\\\")[-1][:-3]) + \\\".pb\\\"\\n print(\\\"Loading Model: \\\" + json_model_path)\\n print(\\\"Loading Weights: \\\" + weights_path)\\n\\n # Set keras to test phase\\n k.set_learning_phase(0)\\n\\n # Load json and weights, then compile model\\n with open(json_model_path, 'r') as json_file:\\n loaded_model_json = json_file.read()\\n model = model_from_json(loaded_model_json)\\n model.load_weights(weights_path)\\n model.compile(loss='mse', optimizer='sgd')\\n\\n # Freeze graph\\n frozen_graph = freeze_session(k.get_session(), output_names=[out.op.name for out in model.outputs])\\n\\n # Write graph to protobuf file\\n tf.train.write_graph(frozen_graph, \\\"model\\\", save_path, as_text=False)\\n print(\\\"Written Graph to: \\\" + save_path)\",\n \"def handle(self, *args, **options):\\n file = StringIO()\\n call_command(\\\"generateschema\\\", stdout=file)\\n file.seek(0)\\n document = yaml.load(file, Loader=yaml.FullLoader)\\n document.update({\\n \\\"externalDocs\\\": {\\n \\\"description\\\": \\\"Check us out on GitHub\\\",\\n \\\"url\\\": \\\"https://github.com/ractf\\\",\\n },\\n \\\"info\\\": {\\n \\\"title\\\": \\\"RACTF Core\\\",\\n \\\"version\\\": os.popen(\\\"git rev-parse HEAD\\\").read().strip()[:8],\\n \\\"description\\\": \\\"The API for RACTF.\\\",\\n \\\"contact\\\": {\\n \\\"name\\\": \\\"Support\\\",\\n \\\"email\\\": \\\"support@reallyawesome.atlassian.net\\\",\\n \\\"url\\\": \\\"https://reallyawesome.atlassian.net/servicedesk/customer/portals\\\",\\n },\\n \\\"x-logo\\\": {\\n \\\"url\\\": \\\"https://www.ractf.co.uk/brand_assets/combined/wordmark_white.svg\\\",\\n \\\"altText\\\": \\\"RACTF Logo\\\",\\n },\\n }\\n })\\n print(yaml.dump(document))\",\n \"def _main(args):\\n # model = keras.models.load_model(args.h5_file, custom_objects={'prelu': prelu})\\n with tf.keras.utils.custom_object_scope({'prelu': prelu}):\\n converter = tf.lite.TFLiteConverter.from_keras_model_file(args.h5_file)\\n tflite_file = converter.convert()\\n open(args.tflite_file, 'wb').write(tflite_file)\\n print('='*30)\\n print('tffile file save in {}.'.format(args.tflite_file))\",\n \"def local(ctx):\\n _do_codegen_user(\\\"demo\\\")\\n _do_codegen_user(\\\"errors\\\")\\n _do_codegen_user(\\\"mpi\\\")\\n _do_codegen_user(\\\"omp\\\")\\n _do_codegen_user(\\\"python\\\")\\n\\n # Do codegen for libfake\\n for so in LIB_FAKE_FILES:\\n _do_codegen_file(so)\\n\\n # Run the WAMR codegen required by the tests\\n codegen(ctx, \\\"demo\\\", \\\"echo\\\", wamr=True)\\n codegen(ctx, \\\"demo\\\", \\\"chain\\\", wamr=True)\\n\\n # Run the SGX codegen required by the tests\\n codegen(ctx, \\\"demo\\\", \\\"hello\\\", wamr=True, sgx=True)\\n codegen(ctx, \\\"demo\\\", \\\"chain_named_a\\\", wamr=True, sgx=True)\\n codegen(ctx, \\\"demo\\\", \\\"chain_named_b\\\", wamr=True, sgx=True)\\n codegen(ctx, \\\"demo\\\", \\\"chain_named_c\\\", wamr=True, sgx=True)\",\n \"def run(argv=None):\\n program_conf = get_program_conf()\\n\\n # Test usage of utils... do something with ddl\\n print(get_ddl_list('dev')[2])\\n\\n # Parse args\\n program_args = program_conf[PROGRAM_ARGS_CONF]\\n pipeline_args = program_conf[PIPELINE_ARGS_CONF]\\n pipeline_options = PipelineOptions(pipeline_args)\\n pipeline_options.view_as(SetupOptions).save_main_session = True\\n\\n # Get schema path key for validation\\n gcs_client = storage.Client()\\n etl_config_dict = get_gcs_json_as_dict(program_args[ETL_CONFIG_PATH_CONF], gcs_client)\\n schema_dict = get_gcs_json_as_dict(program_args[SCHEMA_PATH_KEY], gcs_client)\\n input_file_path = program_args[INPUT_CONF]\\n\\n with beam.Pipeline(options=pipeline_options) as pipeline:\\n validated_records = (\\n pipeline \\n | 'read' >> ReadFromText(input_file_path)\\n | 'validate' >> beam.ParDo(ValidateRecord(schema_dict, input_file_path, etl_config_dict))\\n | 'filter_data' >> beam.Filter(lambda x: x[1] and x[2] and x[3])\\n | 'recover_data' >> beam.Map(lambda x: x[0])\\n # | beam.Map(print)\\n )\\n if IS_VALID_FILE:\\n validated_records | 'write_success' >> WriteToText(program_args[OUTPUT_CONF]) \\n else:\\n validated_records | 'write_reject' >> WriteToText(program_args[REJECT_CONF])\",\n \"def read_json(self, filename, multi_line=False, schema=None):\\n self.logger.info(\\\"# Reading a JSON file \\\" + filename)\\n sql_context = SQLContext(self.spark)\\n df = None\\n if schema is None and multi_line == False:\\n df = sql_context.read.json(filename)\\n elif multi_line:\\n df = sql_context.read.json(filename, multiLine=multi_line)\\n elif schema is not None:\\n df = sql_context.read.json(filename, schema=schema)\\n return df\",\n \"def main():\\n args = docopt.docopt(__doc__, version=__version__)\\n\\n config_filename = args[\\\"<packer-json>\\\"]\\n\\n kms_map_string = \\\"\\\".join(sys.stdin.readlines())\\n\\n if not kms_map_string:\\n eprint(\\\"The region to kms map must be passed via stdin.\\\")\\n sys.exit(-1)\\n kms_map = make_kms_map(kms_map_string)\\n\\n patch_config(config_filename, kms_map)\\n\\n sys.exit(0)\",\n \"def validate_schema(self, schema):\\n json_schema_path = os.path.join(_ROOT, 'data', 'schema.json')\\n json_schema = load_json_or_yaml(json_schema_path)\\n return validate(schema, json_schema)\",\n \"def load_schema(name):\\r\\n\\r\\n data = pkgutil.get_data(__package__, \\\"schemas/{0}.json\\\".format(name))\\r\\n return json.loads(data.decode(\\\"utf-8\\\"))\",\n \"def load(path_or_file, validate=True, strict=True, fmt='auto'):\\n\\n with _open(path_or_file, mode='r', fmt=fmt) as fdesc:\\n jam = JAMS(**json.load(fdesc))\\n\\n if validate:\\n jam.validate(strict=strict)\\n\\n return jam\",\n \"def reconstitute():\\n with open(TEXT_FPATH, 'w') as txt:\\n for jfpath in json_fpaths():\\n with open(jfpath) as f:\\n jstruct = json.load(f)\\n\\n for recipe in jstruct.keys():\\n _reconstitute_recipe(txt, jstruct[recipe])\",\n \"def main():\\n\\n dbase = sqlite3.connect('database.db')\\n with dbase:\\n cursor = dbase.cursor()\\n try:\\n cursor.execute(\\\"DROP TABLE Graph;\\\")\\n except sqlite3.OperationalError:\\n pass\\n\\n try:\\n cursor.execute(\\\"CREATE TABLE Graph (name TEXT, json TEXT);\\\")\\n except sqlite3.OperationalError:\\n pass\\n\\n for i, item in enumerate(SOME_JSONS):\\n cursor.execute(\\\"insert into Graph (name, json) values\\\"\\n \\\"(?, ?);\\\",\\n ('example_' + str(i + 1), json.dumps(item)))\",\n \"def read_json_schema(schema_file_path):\\n with open(schema_file_path) as f:\\n schema = json.load(f)\\n return schema\",\n \"def Compile(f, filename, gr, start_symbol, mode):\\n\\n tokens = tokenize.generate_tokens(f.readline)\\n\\n p = parse.Parser(gr, convert=py2st)\\n parse_tree = driver.PushTokens(p, tokens, gr.symbol2number[start_symbol])\\n\\n tr = transformer.Transformer()\\n as_tree = tr.transform(parse_tree)\\n\\n # NOTE: This currently does nothing!\\n v = syntax.SyntaxErrorChecker()\\n v.Dispatch(as_tree)\\n\\n s = symbols.SymbolVisitor()\\n s.Dispatch(as_tree)\\n\\n graph = pyassem.FlowGraph() # Mutated by code generator\\n if mode == \\\"single\\\":\\n ctx = _ModuleContext(filename, s.scopes)\\n # NOTE: the name of the Frame is a comment, not exposed to users.\\n frame = pyassem.Frame(\\\"<interactive>\\\", filename) # mutated\\n gen = pycodegen.InteractiveCodeGenerator(ctx, frame, graph)\\n gen.set_lineno(as_tree)\\n\\n elif mode == \\\"exec\\\":\\n # TODO: Does this need to be made more efficient?\\n p1 = future.FutureParser()\\n p2 = future.BadFutureParser()\\n p1.Dispatch(as_tree)\\n p2.Dispatch(as_tree)\\n\\n ctx = _ModuleContext(filename, s.scopes, futures=p1.get_features())\\n frame = pyassem.Frame(\\\"<module>\\\", filename) # mutated\\n\\n gen = pycodegen.TopLevelCodeGenerator(ctx, frame, graph)\\n\\n elif mode == \\\"eval\\\":\\n ctx = _ModuleContext(filename, s.scopes)\\n frame = pyassem.Frame(\\\"<expression>\\\", filename) # mutated\\n gen = pycodegen.TopLevelCodeGenerator(ctx, frame, graph)\\n\\n else:\\n raise AssertionError('Invalid mode %r' % mode)\\n\\n # NOTE: There is no Start() or FindLocals() at the top level.\\n gen.Dispatch(as_tree) # mutates graph\\n gen.Finish()\\n\\n co = pyassem.MakeCodeObject(frame, graph)\\n\\n # TODO: Could call marshal.dump here?\\n return co\",\n \"def run(self, edit):\\n logger.debug('Schema Hacker: open file')\\n for sel in self.view.sel():\\n schema, tbl = get_names(self.view, sel)\\n schema = schema or 'public'\\n folders = self.view.window().folders()\\n script_path = ['misc', 'create_insert_function.pl']\\n script = find_file(folders, script_path)[0]\\n env = os.environ.copy()\\n for key, val in (settings.get('env_var') or {}).items():\\n env[key] = val\\n fnc = subprocess.check_output(\\n ['/usr/bin/perl', script, schema, tbl], env=env\\n ).decode('utf8')\\n file_name = fnc[\\n fnc.index('CREATE OR REPLACE FUNCTION ') + 27 : fnc.index('(')\\n ].lower() + '.sql'\\n main_dir = os.path.dirname(os.path.dirname(script))\\n fnc_path = os.path.join(main_dir, schema, 'FUNCTIONS', file_name)\\n # Else: Create the file and open it\\n if os.path.isfile(fnc_path):\\n # Open existing file and put generated func in output panel\\n view = self.view.window().open_file(fnc_path)\\n panel = self.view.window().create_output_panel('schema_hacker')\\n panel.run_command('append', {'characters': fnc, 'pos': 0})\\n self.view.window().run_command(\\n 'show_panel', {'panel': 'output.schema_hacker'})\\n else: # Create function file and then open it\\n with open(fnc_path, mode='w') as file:\\n file.write(fnc)\\n view = self.view.window().open_file(fnc_path)\",\n \"def run_mapping(path: str):\\n config = {\\n 'extensions': ['.js', '.jsx', '.ts', '.tsx', '.vue'],\\n 'exceptions': [\\n 'react-app-env.d.ts',\\n 'reportWebVitals.ts',\\n 'setupTests.ts'\\n ],\\n 'patterns': [\\n {\\n 'type': 'startswith',\\n 'query': 'import',\\n 'on_match': {\\n 'add_to': 'imports',\\n 'extraction_regex': r'import (\\\\w+)',\\n 'other_property_patterns': [\\n {\\n 'property_name': 'from_path',\\n 'regex': r'from\\\\s*?(?:\\\"|\\\\')(.*)(?:\\\"|\\\\')'\\n }\\n ]\\n }\\n },\\n {\\n 'type': 'startswith',\\n 'query': 'import',\\n 'on_match': {\\n 'add_to': 'imports',\\n 'extraction_regex': r'(\\\\w+)',\\n 'preprocessing_patterns': [\\n '{(.*)}'\\n ],\\n 'other_property_patterns': [\\n {\\n 'property_name': 'from_path',\\n 'regex': r'from\\\\s*?(?:\\\"|\\\\')(.*)(?:\\\"|\\\\')'\\n }\\n ]\\n }\\n },\\n {\\n 'type': 'regex',\\n 'query': r'import\\\\s*?\\\\(',\\n 'on_match': {\\n 'add_to': 'imports',\\n 'extraction_regex': r'/(\\\\w+)\\\\'',\\n 'other_property_patterns': [\\n {\\n 'property_name': 'from_path',\\n 'regex': r'import\\\\s*?\\\\(\\\\'(.*)\\\\'\\\\)'\\n }\\n ]\\n }\\n },\\n {\\n 'type': 'contains',\\n 'query': '}',\\n 'on_match': {\\n 'add_to': 'blocks',\\n 'extraction_regex': r'(\\\\w+)',\\n 'preprocessing_patterns': [\\n '{(.*)}'\\n ],\\n 'other_property_patterns': [\\n {\\n 'property_name': 'closed',\\n 'value': True\\n }\\n ]\\n }\\n },\\n {\\n 'type': 'startswith',\\n 'query': 'import {',\\n 'on_match': {\\n 'add_to': 'blocks',\\n 'extraction_regex': r'(\\\\w+)',\\n 'preprocessing_patterns': [\\n '{(.*)}'\\n ],\\n 'other_property_patterns': [\\n {\\n 'property_name': 'closed',\\n 'value': False\\n }\\n ]\\n }\\n }\\n ],\\n 'diagrams': [\\n {\\n 'name': 'dependency-diagram',\\n 'mapping_scheme': {\\n 'data_list_to_map': 'files_by_path',\\n 'data_grouping_map': 'dirs_by_path',\\n 'entity_types': [\\n {\\n 'name': 'file_name',\\n 'type': 'class',\\n 'match_by': {\\n 'field': 'extension',\\n 'value': ['jsx', 'tsx', 'vue']\\n }\\n },\\n {\\n 'name': 'file_name',\\n 'type': 'entity',\\n 'match_by': {\\n 'field': 'extension',\\n 'value': ['js', 'ts']\\n }\\n }\\n ],\\n 'entity_name': 'name',\\n 'connect_by': 'imports',\\n 'connect_path': 'from_path'\\n\\n }\\n }\\n ]\\n }\\n print('generating mapping diagram...')\\n print()\\n root = path[:-1] if path.endswith('/') else path\\n\\n # try:\\n src_data = scan(root, config)\\n draw(src_data, config)\\n print()\\n print('generation completed!')\\n # except:\\n # print()\\n # print('error - mapping failed')\",\n \"def load_json_obj(path: str) -> RAW_CFG:\\n with fsspec.open(path) as json_file:\\n return json.load(json_file)\",\n \"def test_json_with_tabs() -> None:\\n temp_schema_file = tempfile.NamedTemporaryFile(mode=\\\"w+\\\", delete=False, suffix=\\\".json\\\")\\n created_filename = temp_schema_file.name\\n with open(os.path.abspath(os.path.join(examples_dir, \\\"cases\\\", f\\\"basic.json\\\"))) as schema_fp:\\n for line in schema_fp:\\n temp_schema_file.write(line.replace(\\\" \\\", \\\"\\\\t\\\"))\\n with tempfile.NamedTemporaryFile(mode=\\\"w+\\\") as temp_html_file:\\n temp_schema_file.seek(0)\\n generate_from_file_object(temp_schema_file, temp_html_file, True, False, False, True)\\n temp_schema_file.close()\\n os.remove(created_filename)\\n temp_html_file.seek(0)\\n soup = BeautifulSoup(temp_html_file.read(), \\\"html.parser\\\")\\n tests.html_schema_doc_asserts.assert_basic_case(soup)\",\n \"def main():\\n\\t\\n\\tfilename = optParse()\\n\\t\\n\\ttry:\\n\\t\\tinput = loadFile(filename)\\n\\texcept IOError, (errno, msg):\\n\\t\\tprint >>sys.stderr, msg\\n\\t\\tsys.exit(-1)\\n\\t\\n\\toutput = parse(input)\\n\\tprint output\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6357114","0.634119","0.6240263","0.6036583","0.598429","0.59565043","0.57677084","0.56092983","0.5584701","0.55134827","0.54945713","0.5492932","0.5417545","0.5400936","0.5390043","0.53854465","0.5339671","0.53241897","0.5318094","0.5313744","0.5285585","0.5254115","0.5212975","0.5172481","0.5163187","0.5134912","0.51278704","0.5100583","0.5064265","0.50622743","0.50622165","0.50464785","0.5044109","0.5042728","0.5032813","0.50309676","0.501816","0.5016315","0.50157744","0.5014234","0.49915084","0.49903724","0.4986295","0.49777922","0.49700978","0.49691817","0.49691817","0.49647385","0.49614307","0.49545872","0.4951529","0.4945093","0.49388313","0.4938105","0.4919579","0.49187213","0.4915003","0.4901376","0.4884948","0.48844165","0.48814648","0.4874382","0.48720518","0.4866399","0.48607358","0.4856371","0.48562512","0.48291388","0.48200154","0.4817116","0.4813529","0.4811005","0.47968027","0.4795954","0.47948012","0.479382","0.4786669","0.4783865","0.47830668","0.47825444","0.4775589","0.47445938","0.47432035","0.4741247","0.4731877","0.47210744","0.4720898","0.47205636","0.4719643","0.47128975","0.47126886","0.4711691","0.47055927","0.46898517","0.4681336","0.46792302","0.46771535","0.46722507","0.46659687","0.46645698"],"string":"[\n \"0.6357114\",\n \"0.634119\",\n \"0.6240263\",\n \"0.6036583\",\n \"0.598429\",\n \"0.59565043\",\n \"0.57677084\",\n \"0.56092983\",\n \"0.5584701\",\n \"0.55134827\",\n \"0.54945713\",\n \"0.5492932\",\n \"0.5417545\",\n \"0.5400936\",\n \"0.5390043\",\n \"0.53854465\",\n \"0.5339671\",\n \"0.53241897\",\n \"0.5318094\",\n \"0.5313744\",\n \"0.5285585\",\n \"0.5254115\",\n \"0.5212975\",\n \"0.5172481\",\n \"0.5163187\",\n \"0.5134912\",\n \"0.51278704\",\n \"0.5100583\",\n \"0.5064265\",\n \"0.50622743\",\n \"0.50622165\",\n \"0.50464785\",\n \"0.5044109\",\n \"0.5042728\",\n \"0.5032813\",\n \"0.50309676\",\n \"0.501816\",\n \"0.5016315\",\n \"0.50157744\",\n \"0.5014234\",\n \"0.49915084\",\n \"0.49903724\",\n \"0.4986295\",\n \"0.49777922\",\n \"0.49700978\",\n \"0.49691817\",\n \"0.49691817\",\n \"0.49647385\",\n \"0.49614307\",\n \"0.49545872\",\n \"0.4951529\",\n \"0.4945093\",\n \"0.49388313\",\n \"0.4938105\",\n \"0.4919579\",\n \"0.49187213\",\n \"0.4915003\",\n \"0.4901376\",\n \"0.4884948\",\n \"0.48844165\",\n \"0.48814648\",\n \"0.4874382\",\n \"0.48720518\",\n \"0.4866399\",\n \"0.48607358\",\n \"0.4856371\",\n \"0.48562512\",\n \"0.48291388\",\n \"0.48200154\",\n \"0.4817116\",\n \"0.4813529\",\n \"0.4811005\",\n \"0.47968027\",\n \"0.4795954\",\n \"0.47948012\",\n \"0.479382\",\n \"0.4786669\",\n \"0.4783865\",\n \"0.47830668\",\n \"0.47825444\",\n \"0.4775589\",\n \"0.47445938\",\n \"0.47432035\",\n \"0.4741247\",\n \"0.4731877\",\n \"0.47210744\",\n \"0.4720898\",\n \"0.47205636\",\n \"0.4719643\",\n \"0.47128975\",\n \"0.47126886\",\n \"0.4711691\",\n \"0.47055927\",\n \"0.46898517\",\n \"0.4681336\",\n \"0.46792302\",\n \"0.46771535\",\n \"0.46722507\",\n \"0.46659687\",\n \"0.46645698\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.69766474"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":11293,"cells":{"query":{"kind":"string","value":"Run the webp converter on the given png file."},"document":{"kind":"string","value":"def convert_png_image_to_webp(png, out, quality=80):\n command = [CWEBP, '-q', str(quality), png, '-o', out]\n run_subprocess(command)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def img2webp(path):\n file, ext = os.path.splitext(path)\n image = Image.open(path).convert(\"RGBA\")\n image = ImageOps.expand(image, 75)\n image.save(file + \".webp\", \"WEBP\")\n os.remove(path)","def generate_webp_textures():\n input_files = PNG_TEXTURES['input_files']\n output_files = PNG_TEXTURES['output_files']\n if not os.path.exists(TEXTURE_PATH):\n os.makedirs(TEXTURE_PATH)\n for png, out in zip(input_files, output_files):\n if needs_rebuild(png, out):\n convert_png_image_to_webp(png, out, WEBP_QUALITY)","def run_turtle_program(source):\n ast = parser.parse(source)\n\n t = turtle.Turtle()\n for stmt in ast.statement:\n do_statement(stmt, t)\n canvas = turtle.Screen().getcanvas()\n canvas.postscript(file='image.eps')\n img = Image.open('image.eps')\n img.save('image.png', 'png')\n turtle.Screen().bye()\n return 'image.png'","def test_basic_run_png(self):\n self.expect_datatore_lookup('SomeBlobKey', True)\n self.expect_open_image('SomeBlobKey', (1600, 1200), mime_type='PNG')\n self.expect_resize(blob_image._DEFAULT_SERVING_SIZE)\n self.expect_encode_image('SomeImageInPng',\n images_service_pb.OutputSettings.PNG)\n self.run_request('image/png', 'SomeImageInPng')","def handle_as_url(view: sublime.View, point: int, string: str, name: str):\n\n # Let's assume this url as input:\n # (https://upload.wikimedia.org/wikipedia/commons/8/84/Example.svg)\n\n # Download the image\n # FIXME: avoid nested try-except clauses\n try:\n try:\n f = urlopen(unquote(string)) # <==\n except Exception:\n try:\n url_path = quote(string).replace(\"%3A\", ':', 1)\n f = urlopen(url_path)\n except Exception:\n f = urlopen(string)\n # don't fill the console with stack-trace when there`s no connection !!\n except Exception as e:\n print(e)\n return\n\n # file needs conversion ?\n need_conversion = name.endswith(formats_to_convert) # => True\n basename, ext = osp.splitext(name) # => (\"Example\", \".svg\")\n # create a temporary file\n temp_img = osp.join(TEMP_DIR, \"tmp_image\" + ext) # => \"TEMP_DIR/tmp_image.svg\"\n\n # Save downloaded data in the temporary file\n content = f.read()\n with open(temp_img, \"wb\") as img:\n img.write(content)\n\n # if the file needs conversion, convert it then read data from the resulting png\n if need_conversion:\n ext = \".png\"\n # keep the image's temporary file and name for later use\n conv_file = temp_img # => \"TEMP_DIR/tmp_image.svg\"\n\n # => \"TEMP_DIR/tmp_image.png\"\n temp_png = osp.splitext(temp_img)[0] + \".png\"\n\n # use the magick command of Imagemagick to convert the image to png\n magick(temp_img, temp_png)\n\n # read data from the resulting png\n with open(temp_png, \"rb\") as png:\n content = png.read()\n\n # set temp_file and name to the png file\n temp_img = temp_png # => \"TEMP_DIR/tmp_image.png\"\n\n width, height, real_width, real_height, size = get_data(view, temp_img)\n encoded = str(base64.b64encode(content), \"utf-8\")\n\n def on_navigate(href):\n\n if href == \"save\":\n if need_conversion:\n save(conv_file, name, \"url\")\n else:\n save(temp_img, name, \"url\")\n elif href == \"save_as\":\n if need_conversion:\n convert(conv_file, \"url\", name)\n else:\n convert(temp_img, \"url\", name)\n else:\n sublime.active_window().open_file(temp_img)\n\n view.show_popup(\n TEMPLATE % (width, height, ext, encoded, real_width, real_height,\n str(size // 1024) + \"KB\" if size >= 1024 else str(size) + 'B'),\n sublime.HIDE_ON_MOUSE_MOVE_AWAY,\n point,\n *view.viewport_extent(),\n on_navigate=on_navigate\n )","def latex2img(expression, filename):\n webp = False\n\n extension = \"png\"\n\n # Preparing text strings\n server = \"http://latex.codecogs.com/\" + extension + \".download?\"\n fullname = filename + \".\" + extension\n size = \"%5Cdpi%7B100%7D%20\"\n\n # Quote expression引用表达式\n expression = quote(expression)\n url = server + size + expression\n\n # Download file from url and save to output_file:\n with urlopen(url) as response, open(fullname, 'wb') as output_file:\n data = response.read() # Un objeto \"bytes\"\n output_file.write(data) # Se escribe en disco\n\n if webp:\n img2webp(fullname)\n extension = \"webp\"\n\n return filename + \".\" + extension","def main():\n try:\n pixid = sys.argv[1]\n except IndexError:\n print('Usage: python pixget.py [pixid] (save_path)')\n exit(1)\n\n # get the path\n if len(sys.argv) > 2:\n path = sys.argv[2]\n else:\n path = '.'\n\n imgInfoPool = []\n if get_image_url(pixid, imgInfoPool):\n exit(1)\n download_image(path, imgInfoPool)","def image_webp():\n data = resource(\"images/wolf_1.webp\")\n return Response(data, headers={\"Content-Type\": \"image/webp\"})","def main():\n argvs = sys.argv\n argc = len(argvs)\n if argc == 1:\n print('usage: convert2png.py <path/to/*.ppm> ...')\n sys.exit(1)\n\n os.makedirs('result/convert2png', exist_ok=True)\n\n for i in range(1, argc):\n img = cv2.imread(argvs[i])\n\n # root, ext = os.path.splitext(argvs[i])\n # cv2.imwrite(root + '.png', img)\n\n root, ext = os.path.splitext(argvs[i])\n strImgName = root.split('/')[-1]\n cv2.imwrite('result/convert2png/' + strImgName + '.png', img)","def handle_as_file(view: View, point: int, string: str):\n # \"screenshot.png\"\n\n name = osp.basename(string)\n file, folder = get_file(view, string, name)\n\n # if file doesn't exist, return\n if not osp.isfile(file):\n return\n\n # does the file need conversion ?\n need_conversion = file.endswith(FORMAT_TO_CONVERT)\n\n # if the file needs conversion, convert it and read data from the resulting png\n if need_conversion:\n # keep the image's file and name for later use\n conv_file = file\n conv_name = name\n\n # create a temporary file\n tmp_file = osp.join(TEMP_DIR, \"tmp_png.png\")\n name = osp.splitext(name)[0] + \".png\"\n\n # use the magick command of Imagemagick to convert the image to png\n magick(file, tmp_file)\n\n file = tmp_file\n\n with open(file, \"rb\") as f:\n encoded = str(base64.b64encode(f.read()), \"utf-8\")\n\n real_width, real_height, size = get_image_size(file)\n width, height = get_dimensions(view, file)\n size = str(size // 1024) + \"KB\" if size >= 1024 else str(size) + 'B'\n\n def on_navigate(href):\n\n if href == \"save\":\n if need_conversion:\n save(conv_file, conv_name, \"file\")\n else:\n save(file, name, \"file\", folder)\n elif href == \"save_as\":\n convert(conv_file if need_conversion else file, \"file\")\n else:\n sublime.active_window().open_file(file)\n\n view.show_popup(\n TEMPLATE % (width, height, \"png\", encoded, real_width,\n real_height, size),\n sublime.HIDE_ON_MOUSE_MOVE_AWAY,\n point,\n *view.viewport_extent(),\n on_navigate=on_navigate)","def _webp(self, tile: bytes) -> np.ndarray:\n decoded = np.rollaxis(imagecodecs.webp_decode(tile), 2, 0)\n return decoded","def convert_gif(ctx):\n ctx.run(\n 'ffmpeg '\n '-i resources/demo.mkv -filter_complex \"[0:v] palettegen\" '\n 'resources/palette.png',\n pty=True\n )\n ctx.run(\n 'ffmpeg -i resources/demo.mkv '\n '-i resources/palette.png '\n '-filter_complex \"[0:v][1:v] paletteuse\" '\n 'resources/demo.gif',\n pty=True\n )","def handle_as_url(view: View, point: int, string: str, name: str):\n\n # Let's assume this url as input:\n # (https://upload.wikimedia.org/wikipedia/commons/8/84/Example.svg)\n\n # Download the image\n # FIXME: avoid nested try-except clauses\n try:\n try:\n f = urlopen(unquote(string)) # <==\n except:\n try:\n url_path = quote(string).replace(\"%3A\", ':', 1)\n f = urlopen(url_path)\n except:\n f = urlopen(string)\n # don't fill the console with stack-trace when there`s no connection !!\n except Exception as e:\n print(e)\n return\n\n # file needs conversion ?\n need_conversion = name.endswith(FORMAT_TO_CONVERT) # => True\n basename, ext = osp.splitext(name) # => (\"Example\", \".svg\")\n # create a temporary file\n tmp_file = osp.join(TEMP_DIR,\n \"tmp_image\" + (ext if need_conversion else \".png\")\n ) # => \"TEMP_DIR/tmp_image.svg\"\n\n # Save downloaded data in the temporary file\n content = f.read()\n with open(tmp_file, \"wb\") as dst:\n dst.write(content)\n\n # if the file needs conversion, convert it then read data from the resulting png\n if need_conversion:\n # keep the image's temporary file and name for later use\n conv_file = tmp_file # => \"TEMP_DIR/tmp_image.svg\"\n conv_name = name # => \"Example.svg\"\n\n # => \"TEMP_DIR/tmp_image.png\"\n png = osp.splitext(tmp_file)[0] + \".png\"\n\n # use the magick command of Imagemagick to convert the image to png\n magick(tmp_file, png)\n\n # set temp_file and name to the png file\n tmp_file = png # => \"TEMP_DIR/tmp_image.png\"\n name = basename + \".png\" # => \"Example.png\"\n\n # read data from the resulting png\n with open(tmp_file, \"rb\") as dst:\n content = dst.read()\n\n real_width, real_height, size = get_image_size(tmp_file)\n width, height = get_dimensions(view, tmp_file)\n encoded = str(base64.b64encode(content), \"utf-8\")\n size = str(size // 1024) + \"KB\" if size >= 1024 else str(size) + 'B'\n\n def on_navigate(href):\n\n if href == \"save\":\n if need_conversion:\n save(conv_file, conv_name, \"url\")\n else:\n save(tmp_file, name, \"url\")\n elif href == \"save_as\":\n if need_conversion:\n convert(conv_file, \"url\", conv_name)\n else:\n convert(tmp_file, \"url\", name)\n else:\n sublime.active_window().open_file(tmp_file)\n\n view.show_popup(\n TEMPLATE % (width, height, \"png\", encoded, real_width, real_height, size),\n sublime.HIDE_ON_MOUSE_MOVE_AWAY,\n point,\n *view.viewport_extent(),\n on_navigate=on_navigate\n )","def main():\n p = argparse.ArgumentParser(description='Convert images into unicode')\n p.add_argument('image', metavar='<path>', type=str,\n help='path to the file, use - for stdin')\n p.add_argument('--no-x256', action='store_false', dest='x256', default=True,\n help='prints with x256 unicode coloring')\n p.add_argument('--char-set', metavar='<name>', default='default',\n help='prints with character set (e.g. windows)')\n args = p.parse_args()\n print_image_as_unicode(args.image, char_set=CHAR_SETS[args.char_set],\n x256=args.x256)","def handle_as_file(view: sublime.View, point: int, string: str):\n\n name = osp.basename(string)\n file, folder = get_file(view, string, name)\n\n # if file doesn't exist, return\n if not osp.isfile(file):\n return\n\n # does the file need conversion ?\n need_conversion = file.endswith(formats_to_convert)\n ext = name.rsplit('.', 1)[1]\n\n # if the file needs conversion, convert it and read data from the resulting png\n if need_conversion:\n ext = \".png\"\n # keep the image's file and name for later use\n conv_file = file\n\n # create a temporary file\n temp_png = osp.join(TEMP_DIR, \"temp_png.png\")\n\n # use the magick command of Imagemagick to convert the image to png\n magick(file, temp_png)\n\n file = temp_png\n\n with open(file, \"rb\") as img:\n encoded = str(base64.b64encode(img.read()), \"utf-8\")\n\n width, height, real_width, real_height, size = get_data(view, file)\n\n def on_navigate(href):\n\n if href == \"save\":\n if need_conversion:\n save(conv_file, name, \"file\")\n else:\n save(file, name, \"file\", folder)\n elif href == \"save_as\":\n convert(conv_file if need_conversion else file, \"file\")\n else:\n sublime.active_window().open_file(file)\n\n view.show_popup(\n TEMPLATE % (width, height, ext, encoded, real_width, real_height,\n str(size // 1024) + \"KB\" if size >= 1024 else str(size) + 'B'),\n sublime.HIDE_ON_MOUSE_MOVE_AWAY,\n point,\n *view.viewport_extent(),\n on_navigate=on_navigate)","def run(self):\n generated_gif = self.generate()\n with open(self.out_filename, 'wb') as out_fd:\n out_fd.write(generated_gif)","def testConvertHtmlWithPngDataUrlToPdf(self):\n self._testBase(\"data/test_with_png_dataurl.html\")","def main():\n tmp_dir = xm.constants['dir_tmp']\n exr_f = join(tmp_dir, 'test.exr')\n exr = EXR(exr_f)\n exr.extract_normal(join(tmp_dir, 'test.png'), vis=True)","def start_pupil():\n # runs Pupil Capture from source\n path = os.path.abspath(\"../../pupil/pupil_src/capture/main.py\")\n return subprocess.call('python ' + shellformat(path), shell=True)\n \n # if running Pupil Capture using the app, comment the above code and uncomment below:\n '''\n path = os.path.abspath(\"../pupil_capture_0.4.1_mac.app\")\n return subprocess.call('open ' + shellformat(path), shell=True)\n '''","def main():\n folder = \"D:\\\\Noam10\\\\Documents\\\\Documents\\\\dither 2\"\n filename = \"kirigiri\"\n filetype = \".jpg\"\n input_file = folder + \"\\\\\" + filename + filetype\n for palette in paletteDict.keys():\n output_file = folder + \"\\\\\" + filename + \"(\" + palette + \").bmp\"\n Dither(input_file, output=output_file, palette=paletteDict[palette])\n print(output_file)","def main():\n test_image = load_image()\n\n pixelate_image(\n normalize_image(test_image)\n )\n pass","def main(url):\n print(f\"Running main with URL = {url}...\")\n imagehits(downloaddata(url))","def __main__() :\n try :\n poly = Polyhedre(sys.argv[1])\n \n name = sys.argv[2]\n \n createAllFiles(poly, name)\n\n createAllImageFiles(poly, name)\n \n except FileNotFoundError :\n print(\"Use an existing file\")","def create_png(dotfile=\"tree.dot\", pngfile=\"tree.png\"):\n dotfile = utils.get_abspath(dotfile)\n pngfile = utils.get_abspath(pngfile)\n cmd = create_cmd(dotfile, pngfile)\n subprocess.run(cmd, check=True)","def test_encode_webp():\n width = 51\n height = 26\n channels = 3\n bmp_file = os.path.join(\n os.path.dirname(os.path.abspath(__file__)), \"test_image\", \"lena.bmp\")\n with open(bmp_file, 'rb') as f:\n bmp_contents = f.read()\n image_v = tf.image.decode_bmp(bmp_contents)\n assert image_v.shape == [height, width, channels]\n bmp_encoded = image_io.encode_bmp(image_v)\n image_e = tf.image.decode_bmp(bmp_encoded)\n assert np.all(image_v.numpy() == image_e.numpy())","def new_func():\n dirname, _ = os.path.split(os.path.abspath(__file__))\n pngfile = os.path.sep.join([dirname, \"out.png\"])\n img = [\"110010010011\", \"101011010100\", \"110010110101\", \"100010010011\"]\n img = [[int(val) for val in value] for value in img]\n writer = png.Writer(len(img[0]), len(img), greyscale=True, bitdepth=16)\n with open(pngfile, \"wb\") as file:\n writer.write(file, img)\n try:\n func(pngfile)\n finally:\n os.remove(pngfile)","def process(image):\n pass","def on_pushButton_2_clicked(self):\n # TODO: not implemented yet\n try:\n str='str.png'\n process_pic.graphics ().process (str)\n self.click=\"process\"\n pixMap = QPixmap(\"temp.png\").scaled(self.label.width(),self.label.height())\n self.label.setPixmap(pixMap)\n except:\n button=QMessageBox.about(self, '注意', '应先向空白处导入图片后再进行处理')\n else:\n pass\n\n\n\n #os.popen('python process_pic.py')","def save_png(self, filename):\n post_script = self.canvas.postscript().encode()\n img = Image.open(io.BytesIO(post_script))\n img.save(filename, format=\"PNG\")","def convert_pdf_to_web(input_file, quality=90, resolution=150):\n input_file = Path(input_file)\n input_file.resolve(strict=True) # Raises FileNotFound\n output_file, no_change = get_output_file(input_file, 'WEB')\n if no_change:\n return output_file\n\n rgb_profile = Path(__file__).parent / 'sRGB.icc'\n if not rgb_profile.exists():\n msg = f'Color profile \"{rgb_profile.name}\" is missing'\n raise RuntimeError(msg)\n args = [\n GHOSTSCRIPT,\n '-q',\n '-dColorConversionStrategy=/DeviceRGB',\n '-dColorConversionStrategyForImages=/DeviceRGB',\n '-dBATCH',\n '-dNOPAUSE',\n '-sDEVICE=pdfwrite',\n '-dConvertCMYKImagesToRGB=true',\n '-dDownsampleColorImages=true',\n '-dDownsampleGrayImages=true',\n '-dDownsampleMonoImages=true',\n f'-sDefaultRGBProfile={rgb_profile}',\n f'-dJPEGQ={quality}',\n f'-dColorImageResolution={resolution}',\n f'-dGrayImageResolution={resolution}',\n f'-dMonoImageResolution={resolution}',\n '-o',\n output_file,\n input_file,\n ]\n subprocess.run(map(str, args))\n logger.debug(\n f'{input_file} ({input_file.stat().st_size}) -> '\n f'{output_file} ({output_file.stat().st_size})'\n )\n return output_file","def test_write_rgba(tmp_path):\n\n temp_file = str(tmp_path / \"temp.webp\")\n\n pil_image = Image.new(\"RGBA\", (10, 10), (255, 0, 0, 20))\n pil_image.save(temp_file)\n\n if _webp.WebPDecoderBuggyAlpha():\n return\n\n with Image.open(temp_file) as image:\n image.load()\n\n assert image.mode == \"RGBA\"\n assert image.size == (10, 10)\n assert image.format == \"WEBP\"\n image.load()\n image.getdata()\n\n # Early versions of WebP are known to produce higher deviations:\n # deal with it\n if _webp.WebPDecoderVersion() <= 0x201:\n assert_image_similar(image, pil_image, 3.0)\n else:\n assert_image_similar(image, pil_image, 1.0)","def convert_pptx_to_rgb(input_file, output_file):\n with ZipFile(input_file) as input_pptx_file:\n # theme is defined in the presentation rels\n rel = PresentationRel(input_pptx_file)\n theme_name = f'ppt/{rel.theme}'\n theme = read_color_map(zip_file=input_pptx_file, file_name=theme_name)\n color_map = theme[1]\n\n new_file_data = []\n\n file_list = input_pptx_file.namelist()\n\n for file in file_list:\n with input_pptx_file.open(name=file) as current_file:\n file_contents = current_file.read()\n # only convert XML files of slides\n if re.match(r'ppt/slides/slide(\\d+).xml', file):\n new_contents = convert(content=file_contents, color_map=color_map)\n else:\n new_contents = file_contents\n new_file_data.append(\n {\n 'file': file,\n 'data': new_contents\n }\n )\n\n # create/write the new ZIP\n with ZipFile(output_file, 'w') as output_pptx_file:\n for data in new_file_data:\n output_pptx_file.writestr(data['file'], data['data'], ZIP_DEFLATED)","def main():\n\n wallpaper_url = argv[1]\n if '://getwallpapers.com/' not in wallpaper_url:\n print(\"Invalid URL\")\n exit(0)\n else:\n soup = parse_link(wallpaper_url)\n folder_name = get_folder(soup)\n download(soup, folder_name)","def pnghack(filepath, width=2000, height=2000):\t#cmd.png() doesnt work with api\n cmd.set('ray_trace_frames', 1) # Frames are raytraced before saving an image.\n cmd.viewport(width, height) # Set resolution\n cmd.mpng(filepath, 1, 1) # Use batch png mode with 1 frame only\n cmd.mplay() # cmd.mpng needs the animation to 'run'","def main():\n convert(\"env_100000.mp4\", TargetFormat.GIF)","def main():\n\n import codecs\n\n file_path = '../sample_texts/hi-Deva.txt'\n with codecs.open(file_path, 'r', encoding='UTF-8') as input_file:\n sample_text = input_file.read()\n create_png(sample_text.strip(), 'hindi.png',\n family='Noto Sans Devanagari', language='hi', rtl=False)\n\n file_path = '../sample_texts/ar-Arab.txt'\n with codecs.open(file_path, 'r', encoding='UTF-8') as input_file:\n sample_text = input_file.read()\n create_png(sample_text.strip(), 'arabic.png',\n family='Noto Naskh Arabic', language='ar', rtl=True)\n\n file_path = '../sample_texts/mn-Mong.txt'\n with codecs.open(file_path, 'r', encoding='UTF-8') as input_file:\n sample_text = input_file.read()\n create_png(sample_text.strip(), 'mong.png',\n family='Noto Sans Mongolian', language='mn', vertical=True, rtl=False)","def run_pixel_evaluation(gt_file, run_file, task=2, subset=None):\n\n gt_task_2 = read_annotations_gt(gt_file, task=task, subset=subset)\n\n print(run_file)\n participant_task_2 = read_participant_run(run_file, task=task,\n subset=subset)\n\n pixel_annotations_gt = convert_pixel_images(gt_task_2)\n pixel_annotations_participant = convert_pixel_images(participant_task_2)\n\n return calculate_iou_metrics_run(pixel_annotations_gt,\n pixel_annotations_participant)","def main():\n\n imagepaths = (\"icons/facebook.png\", \"icons/github.png\", \"icons/linkedin.png\", \"icons/pinterest.png\", \"icons/twitter.png\", \"icons/youtube.png\")\n\n try:\n\n spritemaker.create_sprites(imagepaths, \"sprites.png\", \"spritestyles.css\")\n\n except IOError as e:\n\n print(e)","def main():\n\ttile_dimensions = (512, 512)\n\ttile_count = (5, 4) \n\timage = Image.new(\"RGB\", (tile_dimensions[0] * tile_count[0], tile_dimensions[1] * tile_count[1]), None)\n\n\ttry:\n\t\turl = sys.argv[1]\n\texcept:\n\t\tprint \"Please enter a Google Maps URL as a parameter.\"\n\t\tprint \"Usage: $ python get_pano.py https://www.google.com/maps/@34.1027387,-118.340471,3a,75y,32.1h,87.53t/data=!3m7!1e1!3m5!1soInaTCic7TsAAAQDMaZ31A!2e0!3e2!7i13312!8i6656\"\n\t\treturn\n\n\ttry:\n\t\tprint \"************************************\"\n\t\tprint \"Fetching images from Google Maps, this could take some time...\"\n\t\tregex = re.compile(r'panoid\\=([^&]*)', re.I)\n\t\tpano_id = regex.findall(urllib.unquote(url))[0]\n\t\tfor y in range(tile_count[1]):\n\t\t\tfor x in range(tile_count[0]):\n\t\t\t\timg_url = \"https://geo2.ggpht.com/cbk?cb_client=maps_sv.tactile&authuser=0&hl=en&panoid=\" + pano_id + \"&output=tile&x=\"+str(x)+\"&y=\"+str(y)+\"&zoom=3&nbt&fover=2\"\n\t\t\t\tresponse = urllib2.urlopen(img_url)\n\t\t\t\tfile_data = StringIO(response.read())\n\t\t\t\timage.paste(Image.open(file_data), (x * tile_dimensions[0], y * tile_dimensions[1]))\n\t\t\n\t\tfilename = \"pano-\" + pano_id + \".jpg\"\n\t\timage.save(filename)\n\t\tprint \"Success, image saved as \\033[96m\" + filename + \"\\033[00m\"\n\t\tprint \"************************************\"\n\n\n\texcept Exception as e:\n\t\tprint \"Sorry something broke.\"\n\t\tprint e","def run(filename):\n try:\n with open(filename) as f:\n interp.runcode(f.read())\n except IOError as e:\n self.perror(e)","def get_carbon_image(fName):\r\n\r\n print(f'On File {fName}')\r\n\r\n global driver\r\n with open(fName) as f:\r\n code = f.read()\r\n\r\n code = urllib.parse.quote_plus(code)\r\n url = CARBON.format(code=code)\r\n\r\n\r\n driver.get(url)\r\n\r\n driver.find_element_by_xpath(\"//button[contains(text(),'Export')]\").click()\r\n driver.find_element_by_xpath(\"//button[contains(text(),'Open')]\").click()\r\n sleep(5) # this might take a bit\r\n driver.save_screenshot(fName.strip('.py')+'.png')\r\n print(f\"{fName.strip('.py')+'.png'} saved.\")\r\n return True","def photo2web_process_hattenbach():\n\n os.chdir('/Volumes/SSD External/Hattenbach_v2')\n \n dir_base = os.getcwd()\n \n dir_p2w = '/Users/throop/photos/Trips/'\n \n dirs = sorted(glob.glob(os.path.join(dir_base, '*')))\n \n quality_out = '60'\n size_out = '2000x2000'\n \n for i,dir in enumerate(dirs):\n if os.path.isdir(dir):\n os.chdir(dir)\n dir_originals = os.path.join(dir, 'originals')\n dir_originals_fullres = os.path.join(dir, 'originals_fullres')\n\n# For HH files, copy the 'actual' originals into a 'fullres' folder, for safekeeping\n\n if not os.path.isdir(dir_originals_fullres):\n os.rename(dir_originals, dir_originals_fullres)\n os.mkdir(dir_originals)\n \n files = glob.glob(os.path.join(dir_originals_fullres, '*'))\n\n# Get a list of all the images\n\n# For each image, make a low-res, low-quality image. This is just because the scanned files\n# are huge and high-quality, and not useful for online. They are much larger than necessary. \n# So we use 'convert' to shrink them in size and quality, and put the output into 'originals' directory \n# for photo2web.\n\n for file in files:\n file_short = os.path.basename(file)\n file_in = os.path.join(dir_originals_fullres,file_short)\n file_out = os.path.join(dir_originals,file_short)\n if not os.path.isfile(file_out):\n cmd = (f'convert -resize {size_out} -quality {quality_out}' +\n f' {file_in}' +\n f' {file_out}')\n print(f'{cmd}')\n \n subprocess.run(['convert', '-resize', size_out, '-quality', quality_out,\n file_in,\n file_out])\n\n# Now, finally, go thru and do photo2web on all of them.\n \n print(f'\\nProcessing directory {i}/{len(dirs)} {dir}\\n')\n subprocess.run(['cp', '-r', os.path.join(dir_p2w, 'header.txt'), '.'])\n subprocess.run(['cp', '-r', os.path.join(dir_p2w, 'photos.css'), '.'])\n if not os.path.exists('captions.txt'):\n subprocess.run(['captions_photo2web']) \n subprocess.run(['photo2web_old'])\n subprocess.run(['photo2web'])","def main():\r\n original = SimpleImage(\"images/poppy.png\")\r\n original.show()\r\n # shrink function\r\n after_shrink = shrink('images/poppy.png')\r\n after_shrink.show()","def convert(fn):\n assert fn[-4:] == \".npy\", \"%s: File extension should match '.npy'\" % fn\n\n print \"Converting file '%s' to .png\" % fn\n numpy_img = np.load(fn)\n\n #Rescale to 0-255 and convert to uint8\n rescaled = (255.0 / numpy_img.max() * (numpy_img - numpy_img.min())).astype(np.uint8)\n\n im = Image.fromarray(rescaled)\n im.save(fn[:-4] + \".png\")","def convert_to_web(base_path, files):\n for i, f in enumerate(files):\n imagetype = get_imagetype_from_filename(f)\n cmd = ('rsync '\n '{base_path}/qc/phantom/{imagetype}/{f} '\n '{base_path}/website/assets/{output}'.format(\n base_path=base_path, imagetype=imagetype, \n f=f, output=f[9:]))\n os.system(cmd)","def handle_as_data_url(view: sublime.View, point: int, ext: str, encoded: str):\n\n need_conversion = False\n # TODO: is this the only case ?\n if ext == \"svg+xml\":\n ext = \"svg\"\n need_conversion = True\n\n # create a temporary file\n temp_img = osp.join(TEMP_DIR, \"tmp_data_image.\" + ext)\n basename = str(int(hashlib.sha1(encoded.encode('utf-8')).hexdigest(), 16) % (10 ** 8))\n name = basename + \".\" + ext\n\n # Save downloaded data in the temporary file\n try:\n img = open(temp_img, \"wb\")\n img.write(base64.b64decode(encoded))\n except Exception as e:\n print(e)\n return\n finally:\n img.close()\n\n if need_conversion:\n ext = \".png\"\n\n conv_file = temp_img\n\n temp_png = osp.splitext(temp_img)[0] + \".png\"\n\n magick(temp_img, temp_png)\n\n with open(temp_png, \"rb\") as png:\n encoded = str(base64.b64encode(png.read()), \"utf-8\")\n\n temp_img = temp_png\n\n def on_navigate(href):\n\n if href == \"save\":\n if need_conversion:\n save(conv_file, name, \"data_url\")\n else:\n save(temp_img, name, \"data_url\")\n elif href == \"save_as\":\n if need_conversion:\n convert(conv_file, \"dat_url\", name)\n else:\n convert(temp_img, \"data_url\", name)\n else:\n sublime.active_window().open_file(temp_img)\n\n width, height, real_width, real_height, size = get_data(view, temp_img)\n\n view.show_popup(\n TEMPLATE % (width, height, ext, encoded, real_width, real_height,\n str(size // 1024) + \"KB\" if size >= 1024 else str(size) + 'B'),\n sublime.HIDE_ON_MOUSE_MOVE_AWAY,\n point,\n *view.viewport_extent(),\n on_navigate=on_navigate\n )","def make_image():\n click.echo(\"make_image\")","def main():\n parser = argparse.ArgumentParser(\n description=\"A program to generate geometric wallpaper.\")\n\n parser.add_argument(\"-s\", \"--shape\",\n help=\"the name of shapes to generate\",\n type=str,\n choices=(\"hexagon\",\n \"square\",\n \"diamond\",\n \"half-diamond\"),\n default=\"hexagon\")\n\n parser.add_argument(\"-n\", \"--num-across\",\n help=\"the number of shapes across the canvas to create\",\n type=int,\n default=10)\n\n parser.add_argument(\"--size\",\n help=\"the size of the created image\",\n type=int,\n nargs=2,\n default=instagram_size)\n\n parser.add_argument(\"-o\", \"--outfile\",\n help=\"name of the created file\",\n type=str)\n\n args = parser.parse_args()\n\n # Create the image.\n im = Image.new('RGB', args.size)\n draw = ImageDraw.Draw(im)\n\n for shape in gw.shapes.cover_in_shapes(args.shape, im.size,\n args.num_across):\n draw.polygon(shape, fill=gw.colors.palette_chooser())\n\n # Save the image.\n if args.outfile:\n im.save(args.outfile)\n else:\n im.save(\"{}_{}_{}x{}.png\".format(args.shape, args.num_across,\n args.size[0], args.size[1]),\n \"PNG\")","def test_write_lossless_rgb(tmp_path):\n\n temp_file = str(tmp_path / \"temp.webp\")\n # temp_file = \"temp.webp\"\n\n pil_image = hopper(\"RGBA\")\n\n mask = Image.new(\"RGBA\", (64, 64), (128, 128, 128, 128))\n # Add some partially transparent bits:\n pil_image.paste(mask, (0, 0), mask)\n\n pil_image.save(temp_file, lossless=True)\n\n with Image.open(temp_file) as image:\n image.load()\n\n assert image.mode == \"RGBA\"\n assert image.size == pil_image.size\n assert image.format == \"WEBP\"\n image.load()\n image.getdata()\n\n assert_image_equal(image, pil_image)","def main(filename=\"./img/roulette.gif\", emblem=\"./img/cat.png\", fontname=\"disco\"):\n # Load resources\n logo = Image.open(emblem).resize((160, 160))\n font = ImageFont.truetype(f\"./img/font/{fontname}.ttf\", 48)\n\n # Run the animation\n colors = [COLOR_GREEN] + ([COLOR_RED, COLOR_BLACK] * 18)\n display_numbers = [str(x) for x in NUMBERS]\n frames, durations, ang = spinner.generate_animation(\n 90,\n 37,\n display_numbers,\n colors,\n logo,\n font,\n 416,\n 448,\n 4,\n emblem_func=render_emblem,\n )\n\n # Save the GIF\n frames[0].save(\n filename,\n format=\"GIF\",\n append_images=frames[1:],\n save_all=True,\n duration=durations,\n loop=0,\n )\n\n # Figure out the winning prize\n width = 360 / 37\n offset = 180 + (width / 2)\n print(NUMBERS[math.floor(((ang + offset) % 360) / width)])","def execute():\n args = parse()\n \n image = args.image\n \n # Switch on the options\n if args.test:\n unittest()\n elif args.grade:\n grade(image)\n elif args.encode:\n encode(image)\n else:\n launchgui(image)","def run(self):\n result = Image.run(self)\n reference = directives.uri(self.arguments[0])\n self.options[\"uri\"] = data_uri_from_file(reference)\n return result","def generate_image(encoded_url):\n url_to_fetch = urllib.parse.unquote_plus(encoded_url)\n # domain = os.environ.get('DOMAIN', 'https://casparwre.de')\n # if not url_to_fetch.startswith(domain):\n # app.logger.info(f'Not allowed to generate preview for this domain: {url_to_fetch}')\n # abort(405)\n app.logger.debug(f'Generating preview for {url_to_fetch}')\n driver.get(url_to_fetch)\n sleep(2)\n driver.set_window_size(1100, 600)\n\n screenshot_path = '/tmp/image.png'\n driver.save_screenshot(screenshot_path)\n output_size = (550,300)\n i = Image.open(screenshot_path)\n i.thumbnail(output_size)\n i.save(screenshot_path)\n # driver.quit()\n return send_file(screenshot_path, mimetype='image/png')","def run_plugin(request):\n img = plugin.run_plugins(request.matchdict['plugins'][:-1],\n request.matchdict['plugins'][-1],\n request,\n plot=True)\n \n response = Response(body=img, content_type='image/jpeg')\n\n return response","def save(self, filename):\n try:\n import PIL\n except ImportError:\n raise RuntimeError('Could not import PIL. PIL (pillow) is required to save fresnel images.')\n else:\n if self._output is None:\n self.render()\n image = PIL.Image.fromarray(self._output[:], mode='RGBA')\n image.save(filename)","def save_image(data, file_path):\n with open(file_path, 'wb'):\n prefix = 'data:image/webp;base64,'\n data = data[len(prefix):]\n byte_data = base64.b64decode(data)\n image_data = BytesIO(byte_data)\n img = Image.open(image_data)\n img.save(file_path)\n return True","def test_image(filename, x_size=def_x_size, y_size=def_y_size):\n # Create image and loop over all pixels\n im = Image.new(\"RGB\", (x_size, y_size))\n pixels = im.load()\n for i in range(x_size):\n for j in range(y_size):\n x = remap_interval(i, 0, x_size, -1, 1)\n y = remap_interval(j, 0, y_size, -1, 1)\n pixels[i, j] = (random.randint(0, 255), # Red channel\n random.randint(0, 255), # Green channel\n random.randint(0, 255)) # Blue channel\n im.save(filename)","def process(self, image):","def JPGtoPNGConverter(source, dest):\n files = os.listdir(f\"./{source}\")\n if not os.path.exists(f\"./{dest}\"):os.makedirs(f\"./{dest}\")\n\n for file in files:\n if os.path.splitext(file)[-1] == \".jpg\":\n img = Image.open(f\"./{source}/{file}\")\n clean_text = os.path.splitext(file)[0]\n img.save(f\"./{dest}/{clean_text}.png\",\"png\")\n else:\n print(f\"Your filename: {file} is not in .JPG format !!\")\n return \"All files converted successfully :) \"","def clean_webp_textures():\n for webp in PNG_TEXTURES['output_files']:\n if os.path.isfile(webp):\n os.remove(webp)","def toPng(self):\n\t\tif self.isPng:\n\t\t\treturn self\n\t\telse:\n\t\t\treturn textureFile( self.path.replace( self.extension, '.png' ) )","def main(args):\n parser = create_parser()\n\n if not args:\n parser.print_usage()\n sys.exit(1)\n\n parsed_args = parser.parse_args(args)\n\n img_urls = read_urls(parsed_args.logfile)\n\n if parsed_args.todir:\n download_images(img_urls, parsed_args.todir)\n else:\n print('\\n'.join(img_urls))","def main(args):\n parser = create_parser()\n\n if not args:\n parser.print_usage()\n sys.exit(1)\n\n parsed_args = parser.parse_args(args)\n\n img_urls = read_urls(parsed_args.logfile)\n\n if parsed_args.todir:\n download_images(img_urls, parsed_args.todir)\n else:\n print('\\n'.join(img_urls))","def convert_wikicommons_url_to_png_url(url: str, image_width: int = 1000) -> str:\n\n if image_width <= 0:\n raise ValueError(f'Image width has to be a positive number, passed {image_width}')\n\n match = re.match(r'https?://commons\\.wikimedia\\.org/wiki/(?:Special:FilePath/|File:)(.+)', url)\n\n if not match or url[-4:] != '.svg':\n raise ValueError(f'Invalid Wikimedia Commons URL passed: {url}')\n\n file_name = match.group(1)\n file_name = urllib.parse.unquote(file_name).replace(' ', '_')\n md5_hash = hashlib.md5(file_name.encode('utf-8')).hexdigest()\n\n return (f'https://upload.wikimedia.org/wikipedia/commons/thumb/'\n f'{md5_hash[0]}/{md5_hash[:2]}/{file_name}/{image_width}px-{file_name}.png')","def read_webp_image(h5_dataset):\n data = h5_dataset[:].tobytes()\n img_bytesio = BytesIO(data)\n pil_img = Image.open(img_bytesio, 'r')\n return pil_img","def main(args):\n parser = create_parser()\n\n if not args:\n parser.print_usage()\n sys.exit(1)\n\n parsed_args = parser.parse_args(args)\n\n img_urls = read_urls(parsed_args.logfile)\n if parsed_args.todir:\n download_images(img_urls, parsed_args.todir)\n else:\n print('\\n'.join(img_urls))","def update_png_crusher():\n if os.path.exists(PNG_CRUSHER):\n return\n\n for path in glob.glob(os.path.join(libdot.BIN_DIR, '.png.crusher.*')):\n os.unlink(path)\n\n r = requests.get(PNG_CRUSHER_URL + '?format=TEXT')\n with open(PNG_CRUSHER, 'wb') as fp:\n fp.write(base64.b64decode(r.text))\n\n os.chmod(PNG_CRUSHER, 0o755)","def preview_image(view: sublime.View, point: int):\n\n line = view.line(point)\n\n string = view.substr(line)\n # the offset of point relative to the start of the line\n offset_point = point - line.a\n\n # search for the match in the string that contains the point\n\n # ==================URL=====================\n for match in image_url_re.finditer(string):\n if match.start() <= offset_point <= match.end():\n string, protocol, name = match.group(0, 1, 2)\n # if the url doesn't start with http or https try adding it\n # \"www.gettyimages.fr/gi-resources/images/Embed/new/embed2.jpg\"\n if not protocol:\n string = \"http://\" + string\n # print(\"[Image Preview] URL:\", match.group(0, 1, 2))\n\n # don't block ST while handling the url\n return sublime.set_timeout_async(lambda: handle_as_url(view, point, string, name), 0)\n\n # =================DATA URL=================\n for match in IMAGE_DATA_URL_RE.finditer(string):\n if match.start() <= offset_point <= match.end():\n # print(\"[Image Preview] data URL:\", match.groups())\n return handle_as_data_url(view, point, *match.groups())\n\n # =================FILE=====================\n # find full and relative paths (e.g ./screenshot.png)\n for match in image_file_re.finditer(string):\n if match.start() <= offset_point <= match.end():\n # print(\"[Image Preview] file:\", match.group(0))\n return handle_as_file(view, point, match.group(0))\n\n # find file name (e.g screenshot.png)\n for match in image_file_name_re.finditer(string):\n if match.start() <= offset_point <= match.end():\n # print(\"[Image Preview] filename:\", match.group(0))\n return handle_as_file(view, point, match.group(0))","def testGrayscale(name = \"smokey.gif\"):\n image = Image(name)\n print(\"Close the image window to see the transformation\")\n image.draw()\n grayscale(image)\n image.draw()","def image(self, path):\n im = Image.open(path).convert(\"RGB\")\n # Convert the RGB image in printable image\n self._convert_and_print_image(im)","def bands_py_to_png(py_dir: str, png_dir: str):\n import glob\n\n py_pattern = \"bandsdiff_*.py\"\n print(f\"Searching {py_pattern} in {py_dir}, save png in {png_dir}\")\n\n globbed = glob.glob(f\"{py_dir}/{py_pattern}\")\n for filename in globbed:\n with open(filename, encoding=\"utf-8\") as handle:\n mplcode = \"\".join(handle.readlines())\n mplcode = mplcode.replace(\n \"fig = pl.figure()\", \"fig = pl.figure(figsize=(16,10))\"\n )\n png_filename = filename.removesuffix(\".py\") + \".png\"\n print(f\"{py_dir}/{filename} -> {png_dir}/{png_filename}\")\n mplcode = mplcode.replace(\n \"pl.show()\",\n f\"pl.savefig('{png_dir}/{png_filename}', bbox_inches='tight')\",\n )\n exec(mplcode) # pylint: disable=exec-used","def main():\n\n parser = argparse.ArgumentParser(description='codec_compare')\n parser.add_argument('path', metavar='DIR',\n help='path to images folder')\n args = parser.parse_args()\n classpath = args.path\n classname = classpath.split('/')[1]\n\n images = set(listdir_full_path(classpath))\n if len(images) <= 0:\n print \"\\033[91m[ERROR]\\033[0m\" + \" no source files in ./images.\"\n sys.exit(1)\n\n codeclist_full = set(['aom', 'deepcoder', 'deepcoder-lite', 'fuif', 'fvdo', 'hevc', 'kakadu', 'jpeg',\n 'pik', 'tat', 'xavs', 'xavs-fast', 'xavs-median', 'webp'])\n\n bpp_targets = set([0.06, 0.12, 0.25, 0.50, 0.75, 1.00, 1.50, 2.00])\n for image in images:\n width, height, depth = get_dimensions(image, classname)\n name, imgfmt = os.path.splitext(image)\n imgfmt = os.path.basename(image).split(\".\")[-1]\n derivative_images = []\n if classname[:6] == 'classB':\n derivative_images = create_derivatives(image, classname)\n else:\n derivative_images.append((image, imgfmt))\n\n for derivative_image, pix_fmt in derivative_images:\n json_dir = 'metrics'\n mkdir_p(json_dir)\n json_file = os.path.join(json_dir,\n os.path.splitext(os.path.basename(derivative_image))[0] + \".\" + pix_fmt + \".json\")\n # if os.path.isfile(json_file):\n # print \"\\033[92m[JSON OK]\\033[0m \" + json_file\n # continue\n main_dict = dict()\n derivative_image_metrics = dict()\n for codecname in codeclist_full:\n convertflag = 1\n caseflag = pix_fmt\n if (codecname == 'webp' or codecname == 'tat' or 'deepcoder' in codecname) and depth != '8':\n continue\n if 'xavs' in codecname and depth != '8' and depth != '10':\n continue\n if 'classE' in classname and ('tat' in codecname or 'xavs' in codecname or 'deepcoder' in codecname):\n continue\n if codecname == 'kakadu' and classname[:6] == 'classB':\n convertflag = 0\n caseflag = imgfmt\n bpp_target_metrics = dict()\n for bpp_target in bpp_targets:\n print(codecname)\n if codecname == 'aom' and classname[:6] == 'classB':\n # ('AERIAL2' in image or 'CATS' in image or 'XRAY' in image or 'GOLD' in image or 'TEXTURE1' in image):\n encoded_image_name = os.path.splitext(os.path.basename(derivative_image))[\n 0] + '_' + str(bpp_target) + '_' + imgfmt + '.' + 'av1'\n encoded_image = os.path.join('outputs', codecname, encoded_image_name)\n decoded_image = os.path.join('outputs', codecname, 'decoded', encoded_image_name + '.' + imgfmt)\n original_image = image\n elif codecname == 'kakadu' and classname[:6] == 'classB':\n encoded_image_name = os.path.splitext(os.path.basename(derivative_image))[\n 0] + '_' + str(bpp_target) + '_' + imgfmt + '.' + codecname\n encoded_image = os.path.join('outputs', codecname, encoded_image_name)\n decoded_image = os.path.join('outputs', codecname, 'decoded', encoded_image_name + '.' + imgfmt)\n original_image = image\n elif 'xavs' in codecname and classname[:6] == 'classB':\n encoded_image_name = os.path.splitext(os.path.basename(derivative_image))[\n 0] + '_' + str(bpp_target) + '_' + imgfmt + '.' + codecname\n encoded_image = os.path.join('outputs', codecname, encoded_image_name)\n decoded_image = os.path.join('outputs', codecname, 'decoded', encoded_image_name + '.' + imgfmt)\n original_image = image\n elif codecname == 'fvdo' and classname[:6] == 'classB':\n encoded_image_name = os.path.splitext(os.path.basename(derivative_image))[\n 0] + '_' + str(bpp_target) + '_pgm' + '.' + codecname\n encoded_image = os.path.join('outputs', codecname, encoded_image_name)\n decoded_image = os.path.join('outputs', codecname, 'decoded', encoded_image_name + '.pgm')\n original_image = image\n else:\n if codecname == 'fuif' and 'tif' in imgfmt:\n encoded_image_name = os.path.splitext(os.path.basename(derivative_image))[\n 0] + '.tif_' + str(bpp_target) + '_' + pix_fmt + '.' + codecname\n elif codecname == 'webp' or codecname == 'tat':\n encoded_image_name = os.path.splitext(os.path.basename(derivative_image))[\n 0] + '_' + str(bpp_target) + '_yuv420p.' + codecname\n else:\n encoded_image_name = os.path.splitext(os.path.basename(derivative_image))[\n 0] + '_' + str(bpp_target) + '_' + pix_fmt + '.' + codecname\n encoded_image = os.path.join('outputs', codecname, encoded_image_name)\n decoded_image_path = os.path.join('outputs', codecname, 'decoded')\n decoded_image = ''\n for decodedfile in os.listdir(decoded_image_path):\n encoderoot = '_'.join(os.path.splitext(os.path.basename(encoded_image_name))[0].split('_')[:-1])\n if encoderoot in decodedfile:\n if ('tat' in codecname or 'webp' in codecname) and os.path.splitext(os.path.basename(decodedfile))[1] == '.yuv':\n decoded_image = os.path.join('outputs', codecname, 'decoded', decodedfile)\n print(decoded_image)\n if ('tat' not in codecname or 'webp' not in codecname) and os.path.splitext(os.path.basename(decodedfile))[1] != '.yuv':\n decoded_image = os.path.join('outputs', codecname, 'decoded', decodedfile)\n if 'classE' not in classname and 'classB' not in classname and os.path.isfile(decoded_image):\n decoded_image = convert_decoded(decoded_image, width, height, depth, codecname)\n original_image = convert_decoded(derivative_image, width, height, depth, 'reference')\n else:\n original_image = derivative_image\n\n print('Reference:' + original_image)\n print('Encoded:' + encoded_image)\n print('Decoded:' + decoded_image)\n if (os.path.isfile(original_image) and os.path.isfile(decoded_image) and os.path.isfile(encoded_image)):\n if 'classE' in classname:\n metrics = compute_metrics_HDR(original_image, decoded_image, encoded_image, bpp_target,\n codecname, width, height, pix_fmt, depth)\n\n elif 'classB' in classname:\n metrics = compute_metrics(original_image, decoded_image, encoded_image, bpp_target, codecname,\n width, height, pix_fmt)\n else:\n metrics = compute_metrics_SDR(original_image, decoded_image, encoded_image, bpp_target,\n codecname, width,\n height, imgfmt, depth)\n measured_bpp = (os.path.getsize(encoded_image) * 1.024 * 8) / (float((int(width) * int(height))))\n bpp_target_metrics[measured_bpp] = metrics\n else:\n continue\n \n derivative_image_metrics[codecname] = bpp_target_metrics\n main_dict[derivative_image] = derivative_image_metrics\n\n mkdir_p(json_dir)\n with open(json_file, 'w') as f:\n f.write(json.dumps(main_dict, indent=2))","def image_png():\n data = resource(\"images/pig_icon.png\")\n return Response(data, headers={\"Content-Type\": \"image/png\"})","def main_convert(args):\n try:\n file_path = args.file_name # os.path.join(static_path, args.file_name)\n if args.slides:\n config_path = os.path.join(static_path, \"config\", \"slides_config.py\")\n output = subprocess.check_output(\n [\n \"jupyter\",\n \"nbconvert\",\n file_path,\n \"--to\",\n \"slides\",\n \"--CSSHTMLHeaderPreprocessor.style=colorful\",\n \"--reveal-prefix\",\n args.reveal_prefix,\n \"--config\",\n config_path,\n ],\n stderr=subprocess.STDOUT,\n ).decode(\"utf-8\")\n else:\n config_path = os.path.join(static_path, \"config\", \"nb_config.py\")\n output = subprocess.check_output(\n [\n \"jupyter\",\n \"nbconvert\",\n file_path,\n \"--to\",\n \"html\",\n \"--config\",\n config_path,\n ],\n stderr=subprocess.STDOUT,\n ).decode(\"utf-8\")\n print(output.rstrip())\n _name = get_out_name(args)\n # _name = output.split(\" \")[-1].rstrip()\n if args.c:\n with open(_name, \"r\") as f:\n clean_file = clean_html(f.read())\n with open(_name, \"w\") as f:\n f.write(clean_file)\n if args.bib_name is not None:\n add_ref(_name, args.bib_name, keep_label=args.l, slides=args.slides)\n else:\n with open(_name, \"r\") as f:\n clean_file = clean_html_refs(clean_file)\n with open(_name, \"w\") as f:\n f.write(clean_file)\n except IndexError:\n print(\"Provide the name of the notebook.\")","def find_flames(filename):\r\n image = SimpleImage(filename)\r\n # TODO: your code here\r\n return image","def testPosterize(name = \"smokey.gif\", triple = (0,0,0)):\n image = Image(name)\n print(\"Close the image window to see the transformation\")\n image.draw()\n posterize(image, triple)\n image.draw()","def spew_to_image(bitstream, img_name):\n pixel_width = int(IMAGE_FULL_X / NUM_BITS_X)\n pixel_height = int(IMAGE_FULL_Y / NUM_BITS_Y)\n\n bits_per_pixel = 1\n\n total_length = NUM_BITS_X * NUM_BITS_Y * bits_per_pixel\n\n if len(bitstream) != total_length:\n print(f'Bitstream is not correct length. Expected {total_length},', end='');\n print(f'but found {len(bitstream)}')\n exit(0)\n\n is_binary = all(ch in('01') for ch in bitstream)\n if not is_binary:\n print(f'Bitstream must be all 0s and 1s. {bitstream} has other chars')\n exit(0)\n\n img = Image.new('RGB', (IMAGE_FULL_X,IMAGE_FULL_Y), BLACK)\n draw = ImageDraw.Draw(img)\n\n for i in range(len(bitstream)):\n y_start = int(i/NUM_BITS_X) * pixel_height\n y_end = y_start + pixel_height\n\n x_start = (i % NUM_BITS_X) * pixel_width\n x_end = x_start + pixel_width\n\n color = 'white' if int( bitstream[i] )==1 else 'black'\n\n draw.rectangle( ((x_start,y_start),\n (x_end,y_end)), fill=color)\n # print(f'({x_start},{y_start}) to ({x_end},{y_end}) = {bitstream[i]}')\n img.save(img_name, 'PNG')","def testImageProcessing():\n Im_pix = getRGB( 'in.png' ) # read in the in.png image\n print \"The first two pixels of the first row are\",\n print Im_pix[0][0:2]\n # remember that Im_pix is a list (the image)\n # of lists (each row) of lists (each pixel is [R,G,B])\n New_pix = [ [ [255 - num for num in p] for p in row ] for row in Im_pix ]\n # now, save to the file 'out.png'\n saveRGB( New_pix, 'out.png' )","def decode_image(file_location=\"images/encoded_sample.png\"):\n encoded_image = Image.open(file_location)\n\n x_size = encoded_image.size[0]\n y_size = encoded_image.size[1]\n\n decoded_image = Image.new(\"RGB\", encoded_image.size)\n pixels = decoded_image.load()\n\n for x in range(x_size):\n for y in range(y_size):\n if lsb_of_red_pixel(encoded_image, x, y):\n pixels[x, y] = (255,255,255)\n else:\n pixels[x, y] = (0, 0, 0)\n\n #pixels[x, y] = [(0,0,0) if lsb_of_pixel(red_channel, x, y) else (1,1,1)]\n\n decoded_image.save(\"images/decoded_image.png\")\n decoded_image.show()","def run(self):\n result = Image.run(self)\n reference = directives.uri(self.arguments[0])\n self.options['uri'] = data_uri(reference)\n return result","def _add_png(self, pngfile):\n with open(pngfile, 'rb') as png:\n if png.read(8) != self.magic:\n raise ValueError(\"{} is not a PNG file\".format(pngfile))\n while True:\n chead = png.read(8)\n if len(chead) == 0:\n break\n clen, ctype = struct.unpack(\">L4s\", chead)\n cdata = png.read(clen)\n ccrc = png.read(4)\n utype = ctype.decode(\"ascii\")\n self._current_chunk = (chead[:4], ctype, cdata, ccrc)\n if ctype in self.mustmatch:\n ref = self._matchref.get(ctype)\n if ref is None:\n self._matchref[ctype] = cdata\n self._copy()\n else:\n if cdata != ref:\n raise ValueError(\"Chunk {} mismatch\".format(utype))\n met = (\"_first_\" if self._first else \"_next_\") + utype\n try:\n met = getattr(self, met)\n except AttributeError:\n pass\n else:\n met(cdata)\n self._first = False","def decode_image(file_location=\"images/encoded_sample.png\"):\n encoded_image = Image.open(file_location)\n red_channel = encoded_image.split()[0]\n\n\n x_size = encoded_image.size[0]\n y_size = encoded_image.size[1]\n\n\n decoded_image = Image.new(\"RGB\", encoded_image.size)\n pixels = decoded_image.load()\n for x in range(x_size):\n for y in range(y_size):\n red_pixel = red_channel.getpixel((x,y))\n binary = bin(red_pixel)\n\n lsb = int(binary[-1])\n if(lsb == 0):\n pixels[x,y] = (0,0,0)\n elif(lsb == 1):\n pixels[x,y] = (255,255,255)\n\n pass\n decoded_image.save(\"images/decoded_image.png\")","def from_url(self) -> PngImagePlugin.PngImageFile:\n response = requests.get(self.url)\n img = Image.open(BytesIO(response.content))\n\n return img","def plant_uml_create_png_and_return_image_url(plant_uml_txt):\n # import hashlib\n # dbg(hashlib.md5(plant_uml_txt.encode('utf-8')).hexdigest())\n\n # plant_uml_server = calc_plantuml_server_url()\n PLANTUML_URL_ON_INTERNET = \"http://www.plantuml.com/plantuml/uml\"\n plant_uml_server = PLANTUML_URL_ON_INTERNET\n log.info(\"plant_uml_server calculated to be %s\" % (plant_uml_server,))\n\n try:\n # response = requests.post(plant_uml_server, data={'text': plant_uml_txt})\n\n url = os.path.join(plant_uml_server, deflate_and_encode(plant_uml_txt))\n response = requests.get(url)\n\n except (ConnectionError, requests.exceptions.RequestException) as e:\n # log.exception(\"Trying to render using plantuml server %s str(e)\" % plant_uml_server)\n log.error(f\"Error trying to fetch initial html from plantuml server {plant_uml_server} {str(e)}\")\n return None, None\n\n if response.status_code == 200:\n # print(\"plant_uml_server responded with 200 ok\")\n log.info(\"plant_uml_server responded with 200 ok\")\n\n \"\"\"\n Need to find the fragment:\n <p id=\"diagram\">\n <img src=\"http://www.plantuml.com:80/plantuml/png/SyfFKj2rKt3CoKnELR1Io4ZDoSa70000\" alt=\"PlantUML diagram\" onload=\"doneLoading()\">\n </p>\n in the response.\n \"\"\"\n regex = r'.*<p id=\"diagram\".*\\s*<.*img src=\\\"(.*?)\\\"'\n image_url = re.findall(regex, response.text, re.MULTILINE)\n if image_url:\n image_url = image_url[\n 0\n ] # this is likely referencing localhost due to calc_plantuml_server_url() giving us a localhost\n # if PLANT_UML_LOCAL:\n # image_url = normalise_plantuml_url(\n # image_url\n # ) # substitute the real host we are on. doesn't really matter, cos always will dynamically repair in all list and update views - but need this repair for ztree1 non persistent debug view, so that can ipad in to e.g. http://192.168.0.3:8000/ztree1 whilst images being returned are refering to localhost which the ipad cannot reach (cos its a different machine)\n else:\n image_url = None\n return image_url, response\n else:\n log.error(\"plant_uml_server responded with %d ok\" % (response.status_code,))\n return None, response","def OnBuildPNGs(self, e):\n if (not defaults.use_tex):\n msg = \"LaTeX is disabled in the defaults.py file. To use this functionality, change the\"\n msg += \" use_tex option to True and restart the GUI.\"\n ShowMessage(msg, kind='warn')\n return\n question = \"Quantity code formulas are displayed using PNG images, which need to be generated.\"\n question += \"\\n\\n\\nImages should only be generated if they do not already exist or\"\n question += \" the quantity codes have changed, e.g., more custom outputs have been added.\"\n question += \"\\n\\n\\nThis can take ~60 sec, do you want to proceed?\"\n proceed = AskYesNo(question, title='Generate LaTeX Formula Images?')\n if (not proceed): return\n\n question = \"Choose a path where the images will be saved. The default value from defaults.py is shown.\"\n path = AskText(question, default=defaults.quantity_code_image_path, title=\"Where to store images?\")\n if (path is None): return\n defaults.quantity_code_image_path = path # user overrode this quantity, remember for later\n\n question = \"If image files already exist, do you want to overwrite them?\"\n overwrite = AskYesNo(question, title='Overwrite Existing Files?')\n\n # call render routine and display a progress bar\n Nq = len(self.mainparent.nmlpanel.output_quantities.quantities)\n offsets = list(self.mainparent.nmlpanel.output_quantities.offsets.keys())\n\n P = ProgressBar(Nq)\n P(0)\n for i,Q in enumerate(self.mainparent.nmlpanel.output_quantities.quantities):\n if (Q.name in offsets): continue\n render_tex(Q.code, Q.tex, defaults.quantity_code_image_path, overwrite=overwrite)\n\n P(i+1) # update progress bar","def main():\n\n parser = argparse.ArgumentParser(description=DESCRIPTION)\n parser.add_argument('-o', '--output', default='output.jpg',\n help=\"Filename to use for the output image\")\n parser.add_argument('INPUT_IMAGE', type=str,\n help=\"Filename for the input content image\")\n parser.add_argument('STYLE_IMAGE', type=str,\n help=\"Filename to for the input style image\")\n parser.add_argument('-s', '--style-weight', default=1e-2, type=float,\n help=\"Weight to apply for the loss associated with the style image. \"\n \"Making this bigger results in a final image more like the style image\")\n parser.add_argument('-c', '--content-weight', default=1e4, type=float,\n help=\"Weight to apply for the loss associated with the content image. \"\n \"Making this bigger results in a final image more like the content image\")\n parser.add_argument('-t', '--total-variation-loss', default=30, type=float,\n help=\"Weight for the total variation loss.\")\n\n args = parser.parse_args()\n\n print(args)","def test_image(filename, x_size=350, y_size=350):\n # Create image and loop over all pixels\n im = Image.new(\"RGB\", (x_size, y_size))\n pixels = im.load()\n for i in range(x_size):\n for j in range(y_size):\n x = remap_interval(i, 0, x_size, -1, 1)\n y = remap_interval(j, 0, y_size, -1, 1)\n pixels[i, j] = (random.randint(0, 255), # Red channel\n random.randint(0, 255), # Green channel\n random.randint(0, 255)) # Blue channel\n\n im.save(filename)","def test_image(filename, x_size=350, y_size=350):\n # Create image and loop over all pixels\n im = Image.new(\"RGB\", (x_size, y_size))\n pixels = im.load()\n for i in range(x_size):\n for j in range(y_size):\n x = remap_interval(i, 0, x_size, -1, 1)\n y = remap_interval(j, 0, y_size, -1, 1)\n pixels[i, j] = (random.randint(0, 255), # Red channel\n random.randint(0, 255), # Green channel\n random.randint(0, 255)) # Blue channel\n\n im.save(filename)","def test_image(filename, x_size=350, y_size=350):\n # Create image and loop over all pixels\n im = Image.new(\"RGB\", (x_size, y_size))\n pixels = im.load()\n for i in range(x_size):\n for j in range(y_size):\n x = remap_interval(i, 0, x_size, -1, 1)\n y = remap_interval(j, 0, y_size, -1, 1)\n pixels[i, j] = (random.randint(0, 255), # Red channel\n random.randint(0, 255), # Green channel\n random.randint(0, 255)) # Blue channel\n\n im.save(filename)","def main(url, localfile):\n ph.download_file(url, localfile)","def run_frame(self, ti, img):\n pass","def render_png(self, target=None, split_pages=False, resolution=300):\n if target is not None and split_pages:\n # get destination filename and extension\n filename, ext = os.path.splitext(target)\n for page_num, page in enumerate(self._document.pages):\n page_name = filename + f\"_pg_{page_num}\" + ext\n self._document.copy([page]).write_png(\n target=page_name, resolution=resolution\n )\n return None\n elif target is None:\n # return image bytes string if no target is specified\n png_bytes, png_width, png_height = self._document.write_png(\n target=target, resolution=resolution\n )\n return png_bytes\n else:\n return self._document.write_png(target=target, resolution=resolution)","def main():\n app = QApplication(sys.argv)\n window = URPlayground()\n window.setupUI()\n window.show()\n sys.exit(app.exec_())","async def processing(message: types.Message):\n\n if message.text == 'Bad quality, low time':\n image_size = 128\n elif message.text == 'Medium quality, medium time':\n image_size = 256\n else:\n image_size = 300\n\n await message.answer(text='Style transfering starts. '\n 'Wait a bit.',\n reply_markup=types.ReplyKeyboardRemove())\n transform('content.jpg', 'style.jpg', image_size)\n with open('result.jpg', 'rb') as file:\n await message.answer_photo(file, caption='Work is done!')","def test_image(filename, x_size=350, y_size=350):\n # Create image and loop over all pixels\n im = Image.new(\"RGB\", (x_size, y_size))\n pixels = im.load()\n for i in range(x_size):\n for j in range(y_size):\n x = remap(i, 0, x_size, -1, 1)\n y = remap(j, 0, y_size, -1, 1)\n pixels[i, j] = (random.randint(0, 255), # Red channel\n random.randint(0, 255), # Green channel\n random.randint(0, 255)) # Blue channel\n\n im.save(filename)","def test_read_rgba():\n\n # Generated with `cwebp transparent.png -o transparent.webp`\n file_path = \"Tests/images/transparent.webp\"\n with Image.open(file_path) as image:\n assert image.mode == \"RGBA\"\n assert image.size == (200, 150)\n assert image.format == \"WEBP\"\n image.load()\n image.getdata()\n\n image.tobytes()\n\n assert_image_similar_tofile(image, \"Tests/images/transparent.png\", 20.0)","def saveImage(turtle, filename):\n ts = turtle.getscreen()\n tc = ts.getcanvas()\n tc.postscript(file=filename)","def decode(p):\n #assert p.endswith('.' + EXTENSION)\n p2 = os.path.basename(p).replace('baseline.png', '.png')\n p2p = os.path.join('/mnt/Volume0/test/clic2020-devkit/result/', p2) #add by me\n pp = os.path.join('/mnt/Volume0/test/clic2020-devkit/targets',p2)\n p2 = os.path.join('/mnt/Volume0/test/clic2020-devkit/inputs/', p2) #add by me\n p1 = pframe_dataset_shared.get_previous_frame_path(p2)\n #p1 = os.path.join('/mnt/Volume0/test/clic2020-devkit/test_data/inputs/', p1)\n #assert os.path.isfile(p1), (p2, p1, p, len(glob.glob('*.png')))\n b = Image.open(p).convert('L')\n f2_reconstructed = decoder(np.array(Image.open(p1)), b)\n Image.fromarray(f2_reconstructed).save(p2p)\n return f2_reconstructed, np.array(Image.open(pp))","def main():\n original = SimpleImage('images/mt-rainier.jpg')\n original.show()\n reflected = make_reflected('images/mt-rainier.jpg')\n reflected.show()","def main() -> None:\n try:\n input_image_path = argv[1]\n output_image_path = argv[2]\n\n color_spray( input_image_path, output_image_path )\n except IndexError:\n RuntimeError('Usage: INPUT_GRAY_IMAGE_PATH OUTPUT_RGB_IMAGE_PATH')\n return None"],"string":"[\n \"def img2webp(path):\\n file, ext = os.path.splitext(path)\\n image = Image.open(path).convert(\\\"RGBA\\\")\\n image = ImageOps.expand(image, 75)\\n image.save(file + \\\".webp\\\", \\\"WEBP\\\")\\n os.remove(path)\",\n \"def generate_webp_textures():\\n input_files = PNG_TEXTURES['input_files']\\n output_files = PNG_TEXTURES['output_files']\\n if not os.path.exists(TEXTURE_PATH):\\n os.makedirs(TEXTURE_PATH)\\n for png, out in zip(input_files, output_files):\\n if needs_rebuild(png, out):\\n convert_png_image_to_webp(png, out, WEBP_QUALITY)\",\n \"def run_turtle_program(source):\\n ast = parser.parse(source)\\n\\n t = turtle.Turtle()\\n for stmt in ast.statement:\\n do_statement(stmt, t)\\n canvas = turtle.Screen().getcanvas()\\n canvas.postscript(file='image.eps')\\n img = Image.open('image.eps')\\n img.save('image.png', 'png')\\n turtle.Screen().bye()\\n return 'image.png'\",\n \"def test_basic_run_png(self):\\n self.expect_datatore_lookup('SomeBlobKey', True)\\n self.expect_open_image('SomeBlobKey', (1600, 1200), mime_type='PNG')\\n self.expect_resize(blob_image._DEFAULT_SERVING_SIZE)\\n self.expect_encode_image('SomeImageInPng',\\n images_service_pb.OutputSettings.PNG)\\n self.run_request('image/png', 'SomeImageInPng')\",\n \"def handle_as_url(view: sublime.View, point: int, string: str, name: str):\\n\\n # Let's assume this url as input:\\n # (https://upload.wikimedia.org/wikipedia/commons/8/84/Example.svg)\\n\\n # Download the image\\n # FIXME: avoid nested try-except clauses\\n try:\\n try:\\n f = urlopen(unquote(string)) # <==\\n except Exception:\\n try:\\n url_path = quote(string).replace(\\\"%3A\\\", ':', 1)\\n f = urlopen(url_path)\\n except Exception:\\n f = urlopen(string)\\n # don't fill the console with stack-trace when there`s no connection !!\\n except Exception as e:\\n print(e)\\n return\\n\\n # file needs conversion ?\\n need_conversion = name.endswith(formats_to_convert) # => True\\n basename, ext = osp.splitext(name) # => (\\\"Example\\\", \\\".svg\\\")\\n # create a temporary file\\n temp_img = osp.join(TEMP_DIR, \\\"tmp_image\\\" + ext) # => \\\"TEMP_DIR/tmp_image.svg\\\"\\n\\n # Save downloaded data in the temporary file\\n content = f.read()\\n with open(temp_img, \\\"wb\\\") as img:\\n img.write(content)\\n\\n # if the file needs conversion, convert it then read data from the resulting png\\n if need_conversion:\\n ext = \\\".png\\\"\\n # keep the image's temporary file and name for later use\\n conv_file = temp_img # => \\\"TEMP_DIR/tmp_image.svg\\\"\\n\\n # => \\\"TEMP_DIR/tmp_image.png\\\"\\n temp_png = osp.splitext(temp_img)[0] + \\\".png\\\"\\n\\n # use the magick command of Imagemagick to convert the image to png\\n magick(temp_img, temp_png)\\n\\n # read data from the resulting png\\n with open(temp_png, \\\"rb\\\") as png:\\n content = png.read()\\n\\n # set temp_file and name to the png file\\n temp_img = temp_png # => \\\"TEMP_DIR/tmp_image.png\\\"\\n\\n width, height, real_width, real_height, size = get_data(view, temp_img)\\n encoded = str(base64.b64encode(content), \\\"utf-8\\\")\\n\\n def on_navigate(href):\\n\\n if href == \\\"save\\\":\\n if need_conversion:\\n save(conv_file, name, \\\"url\\\")\\n else:\\n save(temp_img, name, \\\"url\\\")\\n elif href == \\\"save_as\\\":\\n if need_conversion:\\n convert(conv_file, \\\"url\\\", name)\\n else:\\n convert(temp_img, \\\"url\\\", name)\\n else:\\n sublime.active_window().open_file(temp_img)\\n\\n view.show_popup(\\n TEMPLATE % (width, height, ext, encoded, real_width, real_height,\\n str(size // 1024) + \\\"KB\\\" if size >= 1024 else str(size) + 'B'),\\n sublime.HIDE_ON_MOUSE_MOVE_AWAY,\\n point,\\n *view.viewport_extent(),\\n on_navigate=on_navigate\\n )\",\n \"def latex2img(expression, filename):\\n webp = False\\n\\n extension = \\\"png\\\"\\n\\n # Preparing text strings\\n server = \\\"http://latex.codecogs.com/\\\" + extension + \\\".download?\\\"\\n fullname = filename + \\\".\\\" + extension\\n size = \\\"%5Cdpi%7B100%7D%20\\\"\\n\\n # Quote expression引用表达式\\n expression = quote(expression)\\n url = server + size + expression\\n\\n # Download file from url and save to output_file:\\n with urlopen(url) as response, open(fullname, 'wb') as output_file:\\n data = response.read() # Un objeto \\\"bytes\\\"\\n output_file.write(data) # Se escribe en disco\\n\\n if webp:\\n img2webp(fullname)\\n extension = \\\"webp\\\"\\n\\n return filename + \\\".\\\" + extension\",\n \"def main():\\n try:\\n pixid = sys.argv[1]\\n except IndexError:\\n print('Usage: python pixget.py [pixid] (save_path)')\\n exit(1)\\n\\n # get the path\\n if len(sys.argv) > 2:\\n path = sys.argv[2]\\n else:\\n path = '.'\\n\\n imgInfoPool = []\\n if get_image_url(pixid, imgInfoPool):\\n exit(1)\\n download_image(path, imgInfoPool)\",\n \"def image_webp():\\n data = resource(\\\"images/wolf_1.webp\\\")\\n return Response(data, headers={\\\"Content-Type\\\": \\\"image/webp\\\"})\",\n \"def main():\\n argvs = sys.argv\\n argc = len(argvs)\\n if argc == 1:\\n print('usage: convert2png.py <path/to/*.ppm> ...')\\n sys.exit(1)\\n\\n os.makedirs('result/convert2png', exist_ok=True)\\n\\n for i in range(1, argc):\\n img = cv2.imread(argvs[i])\\n\\n # root, ext = os.path.splitext(argvs[i])\\n # cv2.imwrite(root + '.png', img)\\n\\n root, ext = os.path.splitext(argvs[i])\\n strImgName = root.split('/')[-1]\\n cv2.imwrite('result/convert2png/' + strImgName + '.png', img)\",\n \"def handle_as_file(view: View, point: int, string: str):\\n # \\\"screenshot.png\\\"\\n\\n name = osp.basename(string)\\n file, folder = get_file(view, string, name)\\n\\n # if file doesn't exist, return\\n if not osp.isfile(file):\\n return\\n\\n # does the file need conversion ?\\n need_conversion = file.endswith(FORMAT_TO_CONVERT)\\n\\n # if the file needs conversion, convert it and read data from the resulting png\\n if need_conversion:\\n # keep the image's file and name for later use\\n conv_file = file\\n conv_name = name\\n\\n # create a temporary file\\n tmp_file = osp.join(TEMP_DIR, \\\"tmp_png.png\\\")\\n name = osp.splitext(name)[0] + \\\".png\\\"\\n\\n # use the magick command of Imagemagick to convert the image to png\\n magick(file, tmp_file)\\n\\n file = tmp_file\\n\\n with open(file, \\\"rb\\\") as f:\\n encoded = str(base64.b64encode(f.read()), \\\"utf-8\\\")\\n\\n real_width, real_height, size = get_image_size(file)\\n width, height = get_dimensions(view, file)\\n size = str(size // 1024) + \\\"KB\\\" if size >= 1024 else str(size) + 'B'\\n\\n def on_navigate(href):\\n\\n if href == \\\"save\\\":\\n if need_conversion:\\n save(conv_file, conv_name, \\\"file\\\")\\n else:\\n save(file, name, \\\"file\\\", folder)\\n elif href == \\\"save_as\\\":\\n convert(conv_file if need_conversion else file, \\\"file\\\")\\n else:\\n sublime.active_window().open_file(file)\\n\\n view.show_popup(\\n TEMPLATE % (width, height, \\\"png\\\", encoded, real_width,\\n real_height, size),\\n sublime.HIDE_ON_MOUSE_MOVE_AWAY,\\n point,\\n *view.viewport_extent(),\\n on_navigate=on_navigate)\",\n \"def _webp(self, tile: bytes) -> np.ndarray:\\n decoded = np.rollaxis(imagecodecs.webp_decode(tile), 2, 0)\\n return decoded\",\n \"def convert_gif(ctx):\\n ctx.run(\\n 'ffmpeg '\\n '-i resources/demo.mkv -filter_complex \\\"[0:v] palettegen\\\" '\\n 'resources/palette.png',\\n pty=True\\n )\\n ctx.run(\\n 'ffmpeg -i resources/demo.mkv '\\n '-i resources/palette.png '\\n '-filter_complex \\\"[0:v][1:v] paletteuse\\\" '\\n 'resources/demo.gif',\\n pty=True\\n )\",\n \"def handle_as_url(view: View, point: int, string: str, name: str):\\n\\n # Let's assume this url as input:\\n # (https://upload.wikimedia.org/wikipedia/commons/8/84/Example.svg)\\n\\n # Download the image\\n # FIXME: avoid nested try-except clauses\\n try:\\n try:\\n f = urlopen(unquote(string)) # <==\\n except:\\n try:\\n url_path = quote(string).replace(\\\"%3A\\\", ':', 1)\\n f = urlopen(url_path)\\n except:\\n f = urlopen(string)\\n # don't fill the console with stack-trace when there`s no connection !!\\n except Exception as e:\\n print(e)\\n return\\n\\n # file needs conversion ?\\n need_conversion = name.endswith(FORMAT_TO_CONVERT) # => True\\n basename, ext = osp.splitext(name) # => (\\\"Example\\\", \\\".svg\\\")\\n # create a temporary file\\n tmp_file = osp.join(TEMP_DIR,\\n \\\"tmp_image\\\" + (ext if need_conversion else \\\".png\\\")\\n ) # => \\\"TEMP_DIR/tmp_image.svg\\\"\\n\\n # Save downloaded data in the temporary file\\n content = f.read()\\n with open(tmp_file, \\\"wb\\\") as dst:\\n dst.write(content)\\n\\n # if the file needs conversion, convert it then read data from the resulting png\\n if need_conversion:\\n # keep the image's temporary file and name for later use\\n conv_file = tmp_file # => \\\"TEMP_DIR/tmp_image.svg\\\"\\n conv_name = name # => \\\"Example.svg\\\"\\n\\n # => \\\"TEMP_DIR/tmp_image.png\\\"\\n png = osp.splitext(tmp_file)[0] + \\\".png\\\"\\n\\n # use the magick command of Imagemagick to convert the image to png\\n magick(tmp_file, png)\\n\\n # set temp_file and name to the png file\\n tmp_file = png # => \\\"TEMP_DIR/tmp_image.png\\\"\\n name = basename + \\\".png\\\" # => \\\"Example.png\\\"\\n\\n # read data from the resulting png\\n with open(tmp_file, \\\"rb\\\") as dst:\\n content = dst.read()\\n\\n real_width, real_height, size = get_image_size(tmp_file)\\n width, height = get_dimensions(view, tmp_file)\\n encoded = str(base64.b64encode(content), \\\"utf-8\\\")\\n size = str(size // 1024) + \\\"KB\\\" if size >= 1024 else str(size) + 'B'\\n\\n def on_navigate(href):\\n\\n if href == \\\"save\\\":\\n if need_conversion:\\n save(conv_file, conv_name, \\\"url\\\")\\n else:\\n save(tmp_file, name, \\\"url\\\")\\n elif href == \\\"save_as\\\":\\n if need_conversion:\\n convert(conv_file, \\\"url\\\", conv_name)\\n else:\\n convert(tmp_file, \\\"url\\\", name)\\n else:\\n sublime.active_window().open_file(tmp_file)\\n\\n view.show_popup(\\n TEMPLATE % (width, height, \\\"png\\\", encoded, real_width, real_height, size),\\n sublime.HIDE_ON_MOUSE_MOVE_AWAY,\\n point,\\n *view.viewport_extent(),\\n on_navigate=on_navigate\\n )\",\n \"def main():\\n p = argparse.ArgumentParser(description='Convert images into unicode')\\n p.add_argument('image', metavar='<path>', type=str,\\n help='path to the file, use - for stdin')\\n p.add_argument('--no-x256', action='store_false', dest='x256', default=True,\\n help='prints with x256 unicode coloring')\\n p.add_argument('--char-set', metavar='<name>', default='default',\\n help='prints with character set (e.g. windows)')\\n args = p.parse_args()\\n print_image_as_unicode(args.image, char_set=CHAR_SETS[args.char_set],\\n x256=args.x256)\",\n \"def handle_as_file(view: sublime.View, point: int, string: str):\\n\\n name = osp.basename(string)\\n file, folder = get_file(view, string, name)\\n\\n # if file doesn't exist, return\\n if not osp.isfile(file):\\n return\\n\\n # does the file need conversion ?\\n need_conversion = file.endswith(formats_to_convert)\\n ext = name.rsplit('.', 1)[1]\\n\\n # if the file needs conversion, convert it and read data from the resulting png\\n if need_conversion:\\n ext = \\\".png\\\"\\n # keep the image's file and name for later use\\n conv_file = file\\n\\n # create a temporary file\\n temp_png = osp.join(TEMP_DIR, \\\"temp_png.png\\\")\\n\\n # use the magick command of Imagemagick to convert the image to png\\n magick(file, temp_png)\\n\\n file = temp_png\\n\\n with open(file, \\\"rb\\\") as img:\\n encoded = str(base64.b64encode(img.read()), \\\"utf-8\\\")\\n\\n width, height, real_width, real_height, size = get_data(view, file)\\n\\n def on_navigate(href):\\n\\n if href == \\\"save\\\":\\n if need_conversion:\\n save(conv_file, name, \\\"file\\\")\\n else:\\n save(file, name, \\\"file\\\", folder)\\n elif href == \\\"save_as\\\":\\n convert(conv_file if need_conversion else file, \\\"file\\\")\\n else:\\n sublime.active_window().open_file(file)\\n\\n view.show_popup(\\n TEMPLATE % (width, height, ext, encoded, real_width, real_height,\\n str(size // 1024) + \\\"KB\\\" if size >= 1024 else str(size) + 'B'),\\n sublime.HIDE_ON_MOUSE_MOVE_AWAY,\\n point,\\n *view.viewport_extent(),\\n on_navigate=on_navigate)\",\n \"def run(self):\\n generated_gif = self.generate()\\n with open(self.out_filename, 'wb') as out_fd:\\n out_fd.write(generated_gif)\",\n \"def testConvertHtmlWithPngDataUrlToPdf(self):\\n self._testBase(\\\"data/test_with_png_dataurl.html\\\")\",\n \"def main():\\n tmp_dir = xm.constants['dir_tmp']\\n exr_f = join(tmp_dir, 'test.exr')\\n exr = EXR(exr_f)\\n exr.extract_normal(join(tmp_dir, 'test.png'), vis=True)\",\n \"def start_pupil():\\n # runs Pupil Capture from source\\n path = os.path.abspath(\\\"../../pupil/pupil_src/capture/main.py\\\")\\n return subprocess.call('python ' + shellformat(path), shell=True)\\n \\n # if running Pupil Capture using the app, comment the above code and uncomment below:\\n '''\\n path = os.path.abspath(\\\"../pupil_capture_0.4.1_mac.app\\\")\\n return subprocess.call('open ' + shellformat(path), shell=True)\\n '''\",\n \"def main():\\n folder = \\\"D:\\\\\\\\Noam10\\\\\\\\Documents\\\\\\\\Documents\\\\\\\\dither 2\\\"\\n filename = \\\"kirigiri\\\"\\n filetype = \\\".jpg\\\"\\n input_file = folder + \\\"\\\\\\\\\\\" + filename + filetype\\n for palette in paletteDict.keys():\\n output_file = folder + \\\"\\\\\\\\\\\" + filename + \\\"(\\\" + palette + \\\").bmp\\\"\\n Dither(input_file, output=output_file, palette=paletteDict[palette])\\n print(output_file)\",\n \"def main():\\n test_image = load_image()\\n\\n pixelate_image(\\n normalize_image(test_image)\\n )\\n pass\",\n \"def main(url):\\n print(f\\\"Running main with URL = {url}...\\\")\\n imagehits(downloaddata(url))\",\n \"def __main__() :\\n try :\\n poly = Polyhedre(sys.argv[1])\\n \\n name = sys.argv[2]\\n \\n createAllFiles(poly, name)\\n\\n createAllImageFiles(poly, name)\\n \\n except FileNotFoundError :\\n print(\\\"Use an existing file\\\")\",\n \"def create_png(dotfile=\\\"tree.dot\\\", pngfile=\\\"tree.png\\\"):\\n dotfile = utils.get_abspath(dotfile)\\n pngfile = utils.get_abspath(pngfile)\\n cmd = create_cmd(dotfile, pngfile)\\n subprocess.run(cmd, check=True)\",\n \"def test_encode_webp():\\n width = 51\\n height = 26\\n channels = 3\\n bmp_file = os.path.join(\\n os.path.dirname(os.path.abspath(__file__)), \\\"test_image\\\", \\\"lena.bmp\\\")\\n with open(bmp_file, 'rb') as f:\\n bmp_contents = f.read()\\n image_v = tf.image.decode_bmp(bmp_contents)\\n assert image_v.shape == [height, width, channels]\\n bmp_encoded = image_io.encode_bmp(image_v)\\n image_e = tf.image.decode_bmp(bmp_encoded)\\n assert np.all(image_v.numpy() == image_e.numpy())\",\n \"def new_func():\\n dirname, _ = os.path.split(os.path.abspath(__file__))\\n pngfile = os.path.sep.join([dirname, \\\"out.png\\\"])\\n img = [\\\"110010010011\\\", \\\"101011010100\\\", \\\"110010110101\\\", \\\"100010010011\\\"]\\n img = [[int(val) for val in value] for value in img]\\n writer = png.Writer(len(img[0]), len(img), greyscale=True, bitdepth=16)\\n with open(pngfile, \\\"wb\\\") as file:\\n writer.write(file, img)\\n try:\\n func(pngfile)\\n finally:\\n os.remove(pngfile)\",\n \"def process(image):\\n pass\",\n \"def on_pushButton_2_clicked(self):\\n # TODO: not implemented yet\\n try:\\n str='str.png'\\n process_pic.graphics ().process (str)\\n self.click=\\\"process\\\"\\n pixMap = QPixmap(\\\"temp.png\\\").scaled(self.label.width(),self.label.height())\\n self.label.setPixmap(pixMap)\\n except:\\n button=QMessageBox.about(self, '注意', '应先向空白处导入图片后再进行处理')\\n else:\\n pass\\n\\n\\n\\n #os.popen('python process_pic.py')\",\n \"def save_png(self, filename):\\n post_script = self.canvas.postscript().encode()\\n img = Image.open(io.BytesIO(post_script))\\n img.save(filename, format=\\\"PNG\\\")\",\n \"def convert_pdf_to_web(input_file, quality=90, resolution=150):\\n input_file = Path(input_file)\\n input_file.resolve(strict=True) # Raises FileNotFound\\n output_file, no_change = get_output_file(input_file, 'WEB')\\n if no_change:\\n return output_file\\n\\n rgb_profile = Path(__file__).parent / 'sRGB.icc'\\n if not rgb_profile.exists():\\n msg = f'Color profile \\\"{rgb_profile.name}\\\" is missing'\\n raise RuntimeError(msg)\\n args = [\\n GHOSTSCRIPT,\\n '-q',\\n '-dColorConversionStrategy=/DeviceRGB',\\n '-dColorConversionStrategyForImages=/DeviceRGB',\\n '-dBATCH',\\n '-dNOPAUSE',\\n '-sDEVICE=pdfwrite',\\n '-dConvertCMYKImagesToRGB=true',\\n '-dDownsampleColorImages=true',\\n '-dDownsampleGrayImages=true',\\n '-dDownsampleMonoImages=true',\\n f'-sDefaultRGBProfile={rgb_profile}',\\n f'-dJPEGQ={quality}',\\n f'-dColorImageResolution={resolution}',\\n f'-dGrayImageResolution={resolution}',\\n f'-dMonoImageResolution={resolution}',\\n '-o',\\n output_file,\\n input_file,\\n ]\\n subprocess.run(map(str, args))\\n logger.debug(\\n f'{input_file} ({input_file.stat().st_size}) -> '\\n f'{output_file} ({output_file.stat().st_size})'\\n )\\n return output_file\",\n \"def test_write_rgba(tmp_path):\\n\\n temp_file = str(tmp_path / \\\"temp.webp\\\")\\n\\n pil_image = Image.new(\\\"RGBA\\\", (10, 10), (255, 0, 0, 20))\\n pil_image.save(temp_file)\\n\\n if _webp.WebPDecoderBuggyAlpha():\\n return\\n\\n with Image.open(temp_file) as image:\\n image.load()\\n\\n assert image.mode == \\\"RGBA\\\"\\n assert image.size == (10, 10)\\n assert image.format == \\\"WEBP\\\"\\n image.load()\\n image.getdata()\\n\\n # Early versions of WebP are known to produce higher deviations:\\n # deal with it\\n if _webp.WebPDecoderVersion() <= 0x201:\\n assert_image_similar(image, pil_image, 3.0)\\n else:\\n assert_image_similar(image, pil_image, 1.0)\",\n \"def convert_pptx_to_rgb(input_file, output_file):\\n with ZipFile(input_file) as input_pptx_file:\\n # theme is defined in the presentation rels\\n rel = PresentationRel(input_pptx_file)\\n theme_name = f'ppt/{rel.theme}'\\n theme = read_color_map(zip_file=input_pptx_file, file_name=theme_name)\\n color_map = theme[1]\\n\\n new_file_data = []\\n\\n file_list = input_pptx_file.namelist()\\n\\n for file in file_list:\\n with input_pptx_file.open(name=file) as current_file:\\n file_contents = current_file.read()\\n # only convert XML files of slides\\n if re.match(r'ppt/slides/slide(\\\\d+).xml', file):\\n new_contents = convert(content=file_contents, color_map=color_map)\\n else:\\n new_contents = file_contents\\n new_file_data.append(\\n {\\n 'file': file,\\n 'data': new_contents\\n }\\n )\\n\\n # create/write the new ZIP\\n with ZipFile(output_file, 'w') as output_pptx_file:\\n for data in new_file_data:\\n output_pptx_file.writestr(data['file'], data['data'], ZIP_DEFLATED)\",\n \"def main():\\n\\n wallpaper_url = argv[1]\\n if '://getwallpapers.com/' not in wallpaper_url:\\n print(\\\"Invalid URL\\\")\\n exit(0)\\n else:\\n soup = parse_link(wallpaper_url)\\n folder_name = get_folder(soup)\\n download(soup, folder_name)\",\n \"def pnghack(filepath, width=2000, height=2000):\\t#cmd.png() doesnt work with api\\n cmd.set('ray_trace_frames', 1) # Frames are raytraced before saving an image.\\n cmd.viewport(width, height) # Set resolution\\n cmd.mpng(filepath, 1, 1) # Use batch png mode with 1 frame only\\n cmd.mplay() # cmd.mpng needs the animation to 'run'\",\n \"def main():\\n convert(\\\"env_100000.mp4\\\", TargetFormat.GIF)\",\n \"def main():\\n\\n import codecs\\n\\n file_path = '../sample_texts/hi-Deva.txt'\\n with codecs.open(file_path, 'r', encoding='UTF-8') as input_file:\\n sample_text = input_file.read()\\n create_png(sample_text.strip(), 'hindi.png',\\n family='Noto Sans Devanagari', language='hi', rtl=False)\\n\\n file_path = '../sample_texts/ar-Arab.txt'\\n with codecs.open(file_path, 'r', encoding='UTF-8') as input_file:\\n sample_text = input_file.read()\\n create_png(sample_text.strip(), 'arabic.png',\\n family='Noto Naskh Arabic', language='ar', rtl=True)\\n\\n file_path = '../sample_texts/mn-Mong.txt'\\n with codecs.open(file_path, 'r', encoding='UTF-8') as input_file:\\n sample_text = input_file.read()\\n create_png(sample_text.strip(), 'mong.png',\\n family='Noto Sans Mongolian', language='mn', vertical=True, rtl=False)\",\n \"def run_pixel_evaluation(gt_file, run_file, task=2, subset=None):\\n\\n gt_task_2 = read_annotations_gt(gt_file, task=task, subset=subset)\\n\\n print(run_file)\\n participant_task_2 = read_participant_run(run_file, task=task,\\n subset=subset)\\n\\n pixel_annotations_gt = convert_pixel_images(gt_task_2)\\n pixel_annotations_participant = convert_pixel_images(participant_task_2)\\n\\n return calculate_iou_metrics_run(pixel_annotations_gt,\\n pixel_annotations_participant)\",\n \"def main():\\n\\n imagepaths = (\\\"icons/facebook.png\\\", \\\"icons/github.png\\\", \\\"icons/linkedin.png\\\", \\\"icons/pinterest.png\\\", \\\"icons/twitter.png\\\", \\\"icons/youtube.png\\\")\\n\\n try:\\n\\n spritemaker.create_sprites(imagepaths, \\\"sprites.png\\\", \\\"spritestyles.css\\\")\\n\\n except IOError as e:\\n\\n print(e)\",\n \"def main():\\n\\ttile_dimensions = (512, 512)\\n\\ttile_count = (5, 4) \\n\\timage = Image.new(\\\"RGB\\\", (tile_dimensions[0] * tile_count[0], tile_dimensions[1] * tile_count[1]), None)\\n\\n\\ttry:\\n\\t\\turl = sys.argv[1]\\n\\texcept:\\n\\t\\tprint \\\"Please enter a Google Maps URL as a parameter.\\\"\\n\\t\\tprint \\\"Usage: $ python get_pano.py https://www.google.com/maps/@34.1027387,-118.340471,3a,75y,32.1h,87.53t/data=!3m7!1e1!3m5!1soInaTCic7TsAAAQDMaZ31A!2e0!3e2!7i13312!8i6656\\\"\\n\\t\\treturn\\n\\n\\ttry:\\n\\t\\tprint \\\"************************************\\\"\\n\\t\\tprint \\\"Fetching images from Google Maps, this could take some time...\\\"\\n\\t\\tregex = re.compile(r'panoid\\\\=([^&]*)', re.I)\\n\\t\\tpano_id = regex.findall(urllib.unquote(url))[0]\\n\\t\\tfor y in range(tile_count[1]):\\n\\t\\t\\tfor x in range(tile_count[0]):\\n\\t\\t\\t\\timg_url = \\\"https://geo2.ggpht.com/cbk?cb_client=maps_sv.tactile&authuser=0&hl=en&panoid=\\\" + pano_id + \\\"&output=tile&x=\\\"+str(x)+\\\"&y=\\\"+str(y)+\\\"&zoom=3&nbt&fover=2\\\"\\n\\t\\t\\t\\tresponse = urllib2.urlopen(img_url)\\n\\t\\t\\t\\tfile_data = StringIO(response.read())\\n\\t\\t\\t\\timage.paste(Image.open(file_data), (x * tile_dimensions[0], y * tile_dimensions[1]))\\n\\t\\t\\n\\t\\tfilename = \\\"pano-\\\" + pano_id + \\\".jpg\\\"\\n\\t\\timage.save(filename)\\n\\t\\tprint \\\"Success, image saved as \\\\033[96m\\\" + filename + \\\"\\\\033[00m\\\"\\n\\t\\tprint \\\"************************************\\\"\\n\\n\\n\\texcept Exception as e:\\n\\t\\tprint \\\"Sorry something broke.\\\"\\n\\t\\tprint e\",\n \"def run(filename):\\n try:\\n with open(filename) as f:\\n interp.runcode(f.read())\\n except IOError as e:\\n self.perror(e)\",\n \"def get_carbon_image(fName):\\r\\n\\r\\n print(f'On File {fName}')\\r\\n\\r\\n global driver\\r\\n with open(fName) as f:\\r\\n code = f.read()\\r\\n\\r\\n code = urllib.parse.quote_plus(code)\\r\\n url = CARBON.format(code=code)\\r\\n\\r\\n\\r\\n driver.get(url)\\r\\n\\r\\n driver.find_element_by_xpath(\\\"//button[contains(text(),'Export')]\\\").click()\\r\\n driver.find_element_by_xpath(\\\"//button[contains(text(),'Open')]\\\").click()\\r\\n sleep(5) # this might take a bit\\r\\n driver.save_screenshot(fName.strip('.py')+'.png')\\r\\n print(f\\\"{fName.strip('.py')+'.png'} saved.\\\")\\r\\n return True\",\n \"def photo2web_process_hattenbach():\\n\\n os.chdir('/Volumes/SSD External/Hattenbach_v2')\\n \\n dir_base = os.getcwd()\\n \\n dir_p2w = '/Users/throop/photos/Trips/'\\n \\n dirs = sorted(glob.glob(os.path.join(dir_base, '*')))\\n \\n quality_out = '60'\\n size_out = '2000x2000'\\n \\n for i,dir in enumerate(dirs):\\n if os.path.isdir(dir):\\n os.chdir(dir)\\n dir_originals = os.path.join(dir, 'originals')\\n dir_originals_fullres = os.path.join(dir, 'originals_fullres')\\n\\n# For HH files, copy the 'actual' originals into a 'fullres' folder, for safekeeping\\n\\n if not os.path.isdir(dir_originals_fullres):\\n os.rename(dir_originals, dir_originals_fullres)\\n os.mkdir(dir_originals)\\n \\n files = glob.glob(os.path.join(dir_originals_fullres, '*'))\\n\\n# Get a list of all the images\\n\\n# For each image, make a low-res, low-quality image. This is just because the scanned files\\n# are huge and high-quality, and not useful for online. They are much larger than necessary. \\n# So we use 'convert' to shrink them in size and quality, and put the output into 'originals' directory \\n# for photo2web.\\n\\n for file in files:\\n file_short = os.path.basename(file)\\n file_in = os.path.join(dir_originals_fullres,file_short)\\n file_out = os.path.join(dir_originals,file_short)\\n if not os.path.isfile(file_out):\\n cmd = (f'convert -resize {size_out} -quality {quality_out}' +\\n f' {file_in}' +\\n f' {file_out}')\\n print(f'{cmd}')\\n \\n subprocess.run(['convert', '-resize', size_out, '-quality', quality_out,\\n file_in,\\n file_out])\\n\\n# Now, finally, go thru and do photo2web on all of them.\\n \\n print(f'\\\\nProcessing directory {i}/{len(dirs)} {dir}\\\\n')\\n subprocess.run(['cp', '-r', os.path.join(dir_p2w, 'header.txt'), '.'])\\n subprocess.run(['cp', '-r', os.path.join(dir_p2w, 'photos.css'), '.'])\\n if not os.path.exists('captions.txt'):\\n subprocess.run(['captions_photo2web']) \\n subprocess.run(['photo2web_old'])\\n subprocess.run(['photo2web'])\",\n \"def main():\\r\\n original = SimpleImage(\\\"images/poppy.png\\\")\\r\\n original.show()\\r\\n # shrink function\\r\\n after_shrink = shrink('images/poppy.png')\\r\\n after_shrink.show()\",\n \"def convert(fn):\\n assert fn[-4:] == \\\".npy\\\", \\\"%s: File extension should match '.npy'\\\" % fn\\n\\n print \\\"Converting file '%s' to .png\\\" % fn\\n numpy_img = np.load(fn)\\n\\n #Rescale to 0-255 and convert to uint8\\n rescaled = (255.0 / numpy_img.max() * (numpy_img - numpy_img.min())).astype(np.uint8)\\n\\n im = Image.fromarray(rescaled)\\n im.save(fn[:-4] + \\\".png\\\")\",\n \"def convert_to_web(base_path, files):\\n for i, f in enumerate(files):\\n imagetype = get_imagetype_from_filename(f)\\n cmd = ('rsync '\\n '{base_path}/qc/phantom/{imagetype}/{f} '\\n '{base_path}/website/assets/{output}'.format(\\n base_path=base_path, imagetype=imagetype, \\n f=f, output=f[9:]))\\n os.system(cmd)\",\n \"def handle_as_data_url(view: sublime.View, point: int, ext: str, encoded: str):\\n\\n need_conversion = False\\n # TODO: is this the only case ?\\n if ext == \\\"svg+xml\\\":\\n ext = \\\"svg\\\"\\n need_conversion = True\\n\\n # create a temporary file\\n temp_img = osp.join(TEMP_DIR, \\\"tmp_data_image.\\\" + ext)\\n basename = str(int(hashlib.sha1(encoded.encode('utf-8')).hexdigest(), 16) % (10 ** 8))\\n name = basename + \\\".\\\" + ext\\n\\n # Save downloaded data in the temporary file\\n try:\\n img = open(temp_img, \\\"wb\\\")\\n img.write(base64.b64decode(encoded))\\n except Exception as e:\\n print(e)\\n return\\n finally:\\n img.close()\\n\\n if need_conversion:\\n ext = \\\".png\\\"\\n\\n conv_file = temp_img\\n\\n temp_png = osp.splitext(temp_img)[0] + \\\".png\\\"\\n\\n magick(temp_img, temp_png)\\n\\n with open(temp_png, \\\"rb\\\") as png:\\n encoded = str(base64.b64encode(png.read()), \\\"utf-8\\\")\\n\\n temp_img = temp_png\\n\\n def on_navigate(href):\\n\\n if href == \\\"save\\\":\\n if need_conversion:\\n save(conv_file, name, \\\"data_url\\\")\\n else:\\n save(temp_img, name, \\\"data_url\\\")\\n elif href == \\\"save_as\\\":\\n if need_conversion:\\n convert(conv_file, \\\"dat_url\\\", name)\\n else:\\n convert(temp_img, \\\"data_url\\\", name)\\n else:\\n sublime.active_window().open_file(temp_img)\\n\\n width, height, real_width, real_height, size = get_data(view, temp_img)\\n\\n view.show_popup(\\n TEMPLATE % (width, height, ext, encoded, real_width, real_height,\\n str(size // 1024) + \\\"KB\\\" if size >= 1024 else str(size) + 'B'),\\n sublime.HIDE_ON_MOUSE_MOVE_AWAY,\\n point,\\n *view.viewport_extent(),\\n on_navigate=on_navigate\\n )\",\n \"def make_image():\\n click.echo(\\\"make_image\\\")\",\n \"def main():\\n parser = argparse.ArgumentParser(\\n description=\\\"A program to generate geometric wallpaper.\\\")\\n\\n parser.add_argument(\\\"-s\\\", \\\"--shape\\\",\\n help=\\\"the name of shapes to generate\\\",\\n type=str,\\n choices=(\\\"hexagon\\\",\\n \\\"square\\\",\\n \\\"diamond\\\",\\n \\\"half-diamond\\\"),\\n default=\\\"hexagon\\\")\\n\\n parser.add_argument(\\\"-n\\\", \\\"--num-across\\\",\\n help=\\\"the number of shapes across the canvas to create\\\",\\n type=int,\\n default=10)\\n\\n parser.add_argument(\\\"--size\\\",\\n help=\\\"the size of the created image\\\",\\n type=int,\\n nargs=2,\\n default=instagram_size)\\n\\n parser.add_argument(\\\"-o\\\", \\\"--outfile\\\",\\n help=\\\"name of the created file\\\",\\n type=str)\\n\\n args = parser.parse_args()\\n\\n # Create the image.\\n im = Image.new('RGB', args.size)\\n draw = ImageDraw.Draw(im)\\n\\n for shape in gw.shapes.cover_in_shapes(args.shape, im.size,\\n args.num_across):\\n draw.polygon(shape, fill=gw.colors.palette_chooser())\\n\\n # Save the image.\\n if args.outfile:\\n im.save(args.outfile)\\n else:\\n im.save(\\\"{}_{}_{}x{}.png\\\".format(args.shape, args.num_across,\\n args.size[0], args.size[1]),\\n \\\"PNG\\\")\",\n \"def test_write_lossless_rgb(tmp_path):\\n\\n temp_file = str(tmp_path / \\\"temp.webp\\\")\\n # temp_file = \\\"temp.webp\\\"\\n\\n pil_image = hopper(\\\"RGBA\\\")\\n\\n mask = Image.new(\\\"RGBA\\\", (64, 64), (128, 128, 128, 128))\\n # Add some partially transparent bits:\\n pil_image.paste(mask, (0, 0), mask)\\n\\n pil_image.save(temp_file, lossless=True)\\n\\n with Image.open(temp_file) as image:\\n image.load()\\n\\n assert image.mode == \\\"RGBA\\\"\\n assert image.size == pil_image.size\\n assert image.format == \\\"WEBP\\\"\\n image.load()\\n image.getdata()\\n\\n assert_image_equal(image, pil_image)\",\n \"def main(filename=\\\"./img/roulette.gif\\\", emblem=\\\"./img/cat.png\\\", fontname=\\\"disco\\\"):\\n # Load resources\\n logo = Image.open(emblem).resize((160, 160))\\n font = ImageFont.truetype(f\\\"./img/font/{fontname}.ttf\\\", 48)\\n\\n # Run the animation\\n colors = [COLOR_GREEN] + ([COLOR_RED, COLOR_BLACK] * 18)\\n display_numbers = [str(x) for x in NUMBERS]\\n frames, durations, ang = spinner.generate_animation(\\n 90,\\n 37,\\n display_numbers,\\n colors,\\n logo,\\n font,\\n 416,\\n 448,\\n 4,\\n emblem_func=render_emblem,\\n )\\n\\n # Save the GIF\\n frames[0].save(\\n filename,\\n format=\\\"GIF\\\",\\n append_images=frames[1:],\\n save_all=True,\\n duration=durations,\\n loop=0,\\n )\\n\\n # Figure out the winning prize\\n width = 360 / 37\\n offset = 180 + (width / 2)\\n print(NUMBERS[math.floor(((ang + offset) % 360) / width)])\",\n \"def execute():\\n args = parse()\\n \\n image = args.image\\n \\n # Switch on the options\\n if args.test:\\n unittest()\\n elif args.grade:\\n grade(image)\\n elif args.encode:\\n encode(image)\\n else:\\n launchgui(image)\",\n \"def run(self):\\n result = Image.run(self)\\n reference = directives.uri(self.arguments[0])\\n self.options[\\\"uri\\\"] = data_uri_from_file(reference)\\n return result\",\n \"def generate_image(encoded_url):\\n url_to_fetch = urllib.parse.unquote_plus(encoded_url)\\n # domain = os.environ.get('DOMAIN', 'https://casparwre.de')\\n # if not url_to_fetch.startswith(domain):\\n # app.logger.info(f'Not allowed to generate preview for this domain: {url_to_fetch}')\\n # abort(405)\\n app.logger.debug(f'Generating preview for {url_to_fetch}')\\n driver.get(url_to_fetch)\\n sleep(2)\\n driver.set_window_size(1100, 600)\\n\\n screenshot_path = '/tmp/image.png'\\n driver.save_screenshot(screenshot_path)\\n output_size = (550,300)\\n i = Image.open(screenshot_path)\\n i.thumbnail(output_size)\\n i.save(screenshot_path)\\n # driver.quit()\\n return send_file(screenshot_path, mimetype='image/png')\",\n \"def run_plugin(request):\\n img = plugin.run_plugins(request.matchdict['plugins'][:-1],\\n request.matchdict['plugins'][-1],\\n request,\\n plot=True)\\n \\n response = Response(body=img, content_type='image/jpeg')\\n\\n return response\",\n \"def save(self, filename):\\n try:\\n import PIL\\n except ImportError:\\n raise RuntimeError('Could not import PIL. PIL (pillow) is required to save fresnel images.')\\n else:\\n if self._output is None:\\n self.render()\\n image = PIL.Image.fromarray(self._output[:], mode='RGBA')\\n image.save(filename)\",\n \"def save_image(data, file_path):\\n with open(file_path, 'wb'):\\n prefix = 'data:image/webp;base64,'\\n data = data[len(prefix):]\\n byte_data = base64.b64decode(data)\\n image_data = BytesIO(byte_data)\\n img = Image.open(image_data)\\n img.save(file_path)\\n return True\",\n \"def test_image(filename, x_size=def_x_size, y_size=def_y_size):\\n # Create image and loop over all pixels\\n im = Image.new(\\\"RGB\\\", (x_size, y_size))\\n pixels = im.load()\\n for i in range(x_size):\\n for j in range(y_size):\\n x = remap_interval(i, 0, x_size, -1, 1)\\n y = remap_interval(j, 0, y_size, -1, 1)\\n pixels[i, j] = (random.randint(0, 255), # Red channel\\n random.randint(0, 255), # Green channel\\n random.randint(0, 255)) # Blue channel\\n im.save(filename)\",\n \"def process(self, image):\",\n \"def JPGtoPNGConverter(source, dest):\\n files = os.listdir(f\\\"./{source}\\\")\\n if not os.path.exists(f\\\"./{dest}\\\"):os.makedirs(f\\\"./{dest}\\\")\\n\\n for file in files:\\n if os.path.splitext(file)[-1] == \\\".jpg\\\":\\n img = Image.open(f\\\"./{source}/{file}\\\")\\n clean_text = os.path.splitext(file)[0]\\n img.save(f\\\"./{dest}/{clean_text}.png\\\",\\\"png\\\")\\n else:\\n print(f\\\"Your filename: {file} is not in .JPG format !!\\\")\\n return \\\"All files converted successfully :) \\\"\",\n \"def clean_webp_textures():\\n for webp in PNG_TEXTURES['output_files']:\\n if os.path.isfile(webp):\\n os.remove(webp)\",\n \"def toPng(self):\\n\\t\\tif self.isPng:\\n\\t\\t\\treturn self\\n\\t\\telse:\\n\\t\\t\\treturn textureFile( self.path.replace( self.extension, '.png' ) )\",\n \"def main(args):\\n parser = create_parser()\\n\\n if not args:\\n parser.print_usage()\\n sys.exit(1)\\n\\n parsed_args = parser.parse_args(args)\\n\\n img_urls = read_urls(parsed_args.logfile)\\n\\n if parsed_args.todir:\\n download_images(img_urls, parsed_args.todir)\\n else:\\n print('\\\\n'.join(img_urls))\",\n \"def main(args):\\n parser = create_parser()\\n\\n if not args:\\n parser.print_usage()\\n sys.exit(1)\\n\\n parsed_args = parser.parse_args(args)\\n\\n img_urls = read_urls(parsed_args.logfile)\\n\\n if parsed_args.todir:\\n download_images(img_urls, parsed_args.todir)\\n else:\\n print('\\\\n'.join(img_urls))\",\n \"def convert_wikicommons_url_to_png_url(url: str, image_width: int = 1000) -> str:\\n\\n if image_width <= 0:\\n raise ValueError(f'Image width has to be a positive number, passed {image_width}')\\n\\n match = re.match(r'https?://commons\\\\.wikimedia\\\\.org/wiki/(?:Special:FilePath/|File:)(.+)', url)\\n\\n if not match or url[-4:] != '.svg':\\n raise ValueError(f'Invalid Wikimedia Commons URL passed: {url}')\\n\\n file_name = match.group(1)\\n file_name = urllib.parse.unquote(file_name).replace(' ', '_')\\n md5_hash = hashlib.md5(file_name.encode('utf-8')).hexdigest()\\n\\n return (f'https://upload.wikimedia.org/wikipedia/commons/thumb/'\\n f'{md5_hash[0]}/{md5_hash[:2]}/{file_name}/{image_width}px-{file_name}.png')\",\n \"def read_webp_image(h5_dataset):\\n data = h5_dataset[:].tobytes()\\n img_bytesio = BytesIO(data)\\n pil_img = Image.open(img_bytesio, 'r')\\n return pil_img\",\n \"def main(args):\\n parser = create_parser()\\n\\n if not args:\\n parser.print_usage()\\n sys.exit(1)\\n\\n parsed_args = parser.parse_args(args)\\n\\n img_urls = read_urls(parsed_args.logfile)\\n if parsed_args.todir:\\n download_images(img_urls, parsed_args.todir)\\n else:\\n print('\\\\n'.join(img_urls))\",\n \"def update_png_crusher():\\n if os.path.exists(PNG_CRUSHER):\\n return\\n\\n for path in glob.glob(os.path.join(libdot.BIN_DIR, '.png.crusher.*')):\\n os.unlink(path)\\n\\n r = requests.get(PNG_CRUSHER_URL + '?format=TEXT')\\n with open(PNG_CRUSHER, 'wb') as fp:\\n fp.write(base64.b64decode(r.text))\\n\\n os.chmod(PNG_CRUSHER, 0o755)\",\n \"def preview_image(view: sublime.View, point: int):\\n\\n line = view.line(point)\\n\\n string = view.substr(line)\\n # the offset of point relative to the start of the line\\n offset_point = point - line.a\\n\\n # search for the match in the string that contains the point\\n\\n # ==================URL=====================\\n for match in image_url_re.finditer(string):\\n if match.start() <= offset_point <= match.end():\\n string, protocol, name = match.group(0, 1, 2)\\n # if the url doesn't start with http or https try adding it\\n # \\\"www.gettyimages.fr/gi-resources/images/Embed/new/embed2.jpg\\\"\\n if not protocol:\\n string = \\\"http://\\\" + string\\n # print(\\\"[Image Preview] URL:\\\", match.group(0, 1, 2))\\n\\n # don't block ST while handling the url\\n return sublime.set_timeout_async(lambda: handle_as_url(view, point, string, name), 0)\\n\\n # =================DATA URL=================\\n for match in IMAGE_DATA_URL_RE.finditer(string):\\n if match.start() <= offset_point <= match.end():\\n # print(\\\"[Image Preview] data URL:\\\", match.groups())\\n return handle_as_data_url(view, point, *match.groups())\\n\\n # =================FILE=====================\\n # find full and relative paths (e.g ./screenshot.png)\\n for match in image_file_re.finditer(string):\\n if match.start() <= offset_point <= match.end():\\n # print(\\\"[Image Preview] file:\\\", match.group(0))\\n return handle_as_file(view, point, match.group(0))\\n\\n # find file name (e.g screenshot.png)\\n for match in image_file_name_re.finditer(string):\\n if match.start() <= offset_point <= match.end():\\n # print(\\\"[Image Preview] filename:\\\", match.group(0))\\n return handle_as_file(view, point, match.group(0))\",\n \"def testGrayscale(name = \\\"smokey.gif\\\"):\\n image = Image(name)\\n print(\\\"Close the image window to see the transformation\\\")\\n image.draw()\\n grayscale(image)\\n image.draw()\",\n \"def image(self, path):\\n im = Image.open(path).convert(\\\"RGB\\\")\\n # Convert the RGB image in printable image\\n self._convert_and_print_image(im)\",\n \"def bands_py_to_png(py_dir: str, png_dir: str):\\n import glob\\n\\n py_pattern = \\\"bandsdiff_*.py\\\"\\n print(f\\\"Searching {py_pattern} in {py_dir}, save png in {png_dir}\\\")\\n\\n globbed = glob.glob(f\\\"{py_dir}/{py_pattern}\\\")\\n for filename in globbed:\\n with open(filename, encoding=\\\"utf-8\\\") as handle:\\n mplcode = \\\"\\\".join(handle.readlines())\\n mplcode = mplcode.replace(\\n \\\"fig = pl.figure()\\\", \\\"fig = pl.figure(figsize=(16,10))\\\"\\n )\\n png_filename = filename.removesuffix(\\\".py\\\") + \\\".png\\\"\\n print(f\\\"{py_dir}/{filename} -> {png_dir}/{png_filename}\\\")\\n mplcode = mplcode.replace(\\n \\\"pl.show()\\\",\\n f\\\"pl.savefig('{png_dir}/{png_filename}', bbox_inches='tight')\\\",\\n )\\n exec(mplcode) # pylint: disable=exec-used\",\n \"def main():\\n\\n parser = argparse.ArgumentParser(description='codec_compare')\\n parser.add_argument('path', metavar='DIR',\\n help='path to images folder')\\n args = parser.parse_args()\\n classpath = args.path\\n classname = classpath.split('/')[1]\\n\\n images = set(listdir_full_path(classpath))\\n if len(images) <= 0:\\n print \\\"\\\\033[91m[ERROR]\\\\033[0m\\\" + \\\" no source files in ./images.\\\"\\n sys.exit(1)\\n\\n codeclist_full = set(['aom', 'deepcoder', 'deepcoder-lite', 'fuif', 'fvdo', 'hevc', 'kakadu', 'jpeg',\\n 'pik', 'tat', 'xavs', 'xavs-fast', 'xavs-median', 'webp'])\\n\\n bpp_targets = set([0.06, 0.12, 0.25, 0.50, 0.75, 1.00, 1.50, 2.00])\\n for image in images:\\n width, height, depth = get_dimensions(image, classname)\\n name, imgfmt = os.path.splitext(image)\\n imgfmt = os.path.basename(image).split(\\\".\\\")[-1]\\n derivative_images = []\\n if classname[:6] == 'classB':\\n derivative_images = create_derivatives(image, classname)\\n else:\\n derivative_images.append((image, imgfmt))\\n\\n for derivative_image, pix_fmt in derivative_images:\\n json_dir = 'metrics'\\n mkdir_p(json_dir)\\n json_file = os.path.join(json_dir,\\n os.path.splitext(os.path.basename(derivative_image))[0] + \\\".\\\" + pix_fmt + \\\".json\\\")\\n # if os.path.isfile(json_file):\\n # print \\\"\\\\033[92m[JSON OK]\\\\033[0m \\\" + json_file\\n # continue\\n main_dict = dict()\\n derivative_image_metrics = dict()\\n for codecname in codeclist_full:\\n convertflag = 1\\n caseflag = pix_fmt\\n if (codecname == 'webp' or codecname == 'tat' or 'deepcoder' in codecname) and depth != '8':\\n continue\\n if 'xavs' in codecname and depth != '8' and depth != '10':\\n continue\\n if 'classE' in classname and ('tat' in codecname or 'xavs' in codecname or 'deepcoder' in codecname):\\n continue\\n if codecname == 'kakadu' and classname[:6] == 'classB':\\n convertflag = 0\\n caseflag = imgfmt\\n bpp_target_metrics = dict()\\n for bpp_target in bpp_targets:\\n print(codecname)\\n if codecname == 'aom' and classname[:6] == 'classB':\\n # ('AERIAL2' in image or 'CATS' in image or 'XRAY' in image or 'GOLD' in image or 'TEXTURE1' in image):\\n encoded_image_name = os.path.splitext(os.path.basename(derivative_image))[\\n 0] + '_' + str(bpp_target) + '_' + imgfmt + '.' + 'av1'\\n encoded_image = os.path.join('outputs', codecname, encoded_image_name)\\n decoded_image = os.path.join('outputs', codecname, 'decoded', encoded_image_name + '.' + imgfmt)\\n original_image = image\\n elif codecname == 'kakadu' and classname[:6] == 'classB':\\n encoded_image_name = os.path.splitext(os.path.basename(derivative_image))[\\n 0] + '_' + str(bpp_target) + '_' + imgfmt + '.' + codecname\\n encoded_image = os.path.join('outputs', codecname, encoded_image_name)\\n decoded_image = os.path.join('outputs', codecname, 'decoded', encoded_image_name + '.' + imgfmt)\\n original_image = image\\n elif 'xavs' in codecname and classname[:6] == 'classB':\\n encoded_image_name = os.path.splitext(os.path.basename(derivative_image))[\\n 0] + '_' + str(bpp_target) + '_' + imgfmt + '.' + codecname\\n encoded_image = os.path.join('outputs', codecname, encoded_image_name)\\n decoded_image = os.path.join('outputs', codecname, 'decoded', encoded_image_name + '.' + imgfmt)\\n original_image = image\\n elif codecname == 'fvdo' and classname[:6] == 'classB':\\n encoded_image_name = os.path.splitext(os.path.basename(derivative_image))[\\n 0] + '_' + str(bpp_target) + '_pgm' + '.' + codecname\\n encoded_image = os.path.join('outputs', codecname, encoded_image_name)\\n decoded_image = os.path.join('outputs', codecname, 'decoded', encoded_image_name + '.pgm')\\n original_image = image\\n else:\\n if codecname == 'fuif' and 'tif' in imgfmt:\\n encoded_image_name = os.path.splitext(os.path.basename(derivative_image))[\\n 0] + '.tif_' + str(bpp_target) + '_' + pix_fmt + '.' + codecname\\n elif codecname == 'webp' or codecname == 'tat':\\n encoded_image_name = os.path.splitext(os.path.basename(derivative_image))[\\n 0] + '_' + str(bpp_target) + '_yuv420p.' + codecname\\n else:\\n encoded_image_name = os.path.splitext(os.path.basename(derivative_image))[\\n 0] + '_' + str(bpp_target) + '_' + pix_fmt + '.' + codecname\\n encoded_image = os.path.join('outputs', codecname, encoded_image_name)\\n decoded_image_path = os.path.join('outputs', codecname, 'decoded')\\n decoded_image = ''\\n for decodedfile in os.listdir(decoded_image_path):\\n encoderoot = '_'.join(os.path.splitext(os.path.basename(encoded_image_name))[0].split('_')[:-1])\\n if encoderoot in decodedfile:\\n if ('tat' in codecname or 'webp' in codecname) and os.path.splitext(os.path.basename(decodedfile))[1] == '.yuv':\\n decoded_image = os.path.join('outputs', codecname, 'decoded', decodedfile)\\n print(decoded_image)\\n if ('tat' not in codecname or 'webp' not in codecname) and os.path.splitext(os.path.basename(decodedfile))[1] != '.yuv':\\n decoded_image = os.path.join('outputs', codecname, 'decoded', decodedfile)\\n if 'classE' not in classname and 'classB' not in classname and os.path.isfile(decoded_image):\\n decoded_image = convert_decoded(decoded_image, width, height, depth, codecname)\\n original_image = convert_decoded(derivative_image, width, height, depth, 'reference')\\n else:\\n original_image = derivative_image\\n\\n print('Reference:' + original_image)\\n print('Encoded:' + encoded_image)\\n print('Decoded:' + decoded_image)\\n if (os.path.isfile(original_image) and os.path.isfile(decoded_image) and os.path.isfile(encoded_image)):\\n if 'classE' in classname:\\n metrics = compute_metrics_HDR(original_image, decoded_image, encoded_image, bpp_target,\\n codecname, width, height, pix_fmt, depth)\\n\\n elif 'classB' in classname:\\n metrics = compute_metrics(original_image, decoded_image, encoded_image, bpp_target, codecname,\\n width, height, pix_fmt)\\n else:\\n metrics = compute_metrics_SDR(original_image, decoded_image, encoded_image, bpp_target,\\n codecname, width,\\n height, imgfmt, depth)\\n measured_bpp = (os.path.getsize(encoded_image) * 1.024 * 8) / (float((int(width) * int(height))))\\n bpp_target_metrics[measured_bpp] = metrics\\n else:\\n continue\\n \\n derivative_image_metrics[codecname] = bpp_target_metrics\\n main_dict[derivative_image] = derivative_image_metrics\\n\\n mkdir_p(json_dir)\\n with open(json_file, 'w') as f:\\n f.write(json.dumps(main_dict, indent=2))\",\n \"def image_png():\\n data = resource(\\\"images/pig_icon.png\\\")\\n return Response(data, headers={\\\"Content-Type\\\": \\\"image/png\\\"})\",\n \"def main_convert(args):\\n try:\\n file_path = args.file_name # os.path.join(static_path, args.file_name)\\n if args.slides:\\n config_path = os.path.join(static_path, \\\"config\\\", \\\"slides_config.py\\\")\\n output = subprocess.check_output(\\n [\\n \\\"jupyter\\\",\\n \\\"nbconvert\\\",\\n file_path,\\n \\\"--to\\\",\\n \\\"slides\\\",\\n \\\"--CSSHTMLHeaderPreprocessor.style=colorful\\\",\\n \\\"--reveal-prefix\\\",\\n args.reveal_prefix,\\n \\\"--config\\\",\\n config_path,\\n ],\\n stderr=subprocess.STDOUT,\\n ).decode(\\\"utf-8\\\")\\n else:\\n config_path = os.path.join(static_path, \\\"config\\\", \\\"nb_config.py\\\")\\n output = subprocess.check_output(\\n [\\n \\\"jupyter\\\",\\n \\\"nbconvert\\\",\\n file_path,\\n \\\"--to\\\",\\n \\\"html\\\",\\n \\\"--config\\\",\\n config_path,\\n ],\\n stderr=subprocess.STDOUT,\\n ).decode(\\\"utf-8\\\")\\n print(output.rstrip())\\n _name = get_out_name(args)\\n # _name = output.split(\\\" \\\")[-1].rstrip()\\n if args.c:\\n with open(_name, \\\"r\\\") as f:\\n clean_file = clean_html(f.read())\\n with open(_name, \\\"w\\\") as f:\\n f.write(clean_file)\\n if args.bib_name is not None:\\n add_ref(_name, args.bib_name, keep_label=args.l, slides=args.slides)\\n else:\\n with open(_name, \\\"r\\\") as f:\\n clean_file = clean_html_refs(clean_file)\\n with open(_name, \\\"w\\\") as f:\\n f.write(clean_file)\\n except IndexError:\\n print(\\\"Provide the name of the notebook.\\\")\",\n \"def find_flames(filename):\\r\\n image = SimpleImage(filename)\\r\\n # TODO: your code here\\r\\n return image\",\n \"def testPosterize(name = \\\"smokey.gif\\\", triple = (0,0,0)):\\n image = Image(name)\\n print(\\\"Close the image window to see the transformation\\\")\\n image.draw()\\n posterize(image, triple)\\n image.draw()\",\n \"def spew_to_image(bitstream, img_name):\\n pixel_width = int(IMAGE_FULL_X / NUM_BITS_X)\\n pixel_height = int(IMAGE_FULL_Y / NUM_BITS_Y)\\n\\n bits_per_pixel = 1\\n\\n total_length = NUM_BITS_X * NUM_BITS_Y * bits_per_pixel\\n\\n if len(bitstream) != total_length:\\n print(f'Bitstream is not correct length. Expected {total_length},', end='');\\n print(f'but found {len(bitstream)}')\\n exit(0)\\n\\n is_binary = all(ch in('01') for ch in bitstream)\\n if not is_binary:\\n print(f'Bitstream must be all 0s and 1s. {bitstream} has other chars')\\n exit(0)\\n\\n img = Image.new('RGB', (IMAGE_FULL_X,IMAGE_FULL_Y), BLACK)\\n draw = ImageDraw.Draw(img)\\n\\n for i in range(len(bitstream)):\\n y_start = int(i/NUM_BITS_X) * pixel_height\\n y_end = y_start + pixel_height\\n\\n x_start = (i % NUM_BITS_X) * pixel_width\\n x_end = x_start + pixel_width\\n\\n color = 'white' if int( bitstream[i] )==1 else 'black'\\n\\n draw.rectangle( ((x_start,y_start),\\n (x_end,y_end)), fill=color)\\n # print(f'({x_start},{y_start}) to ({x_end},{y_end}) = {bitstream[i]}')\\n img.save(img_name, 'PNG')\",\n \"def testImageProcessing():\\n Im_pix = getRGB( 'in.png' ) # read in the in.png image\\n print \\\"The first two pixels of the first row are\\\",\\n print Im_pix[0][0:2]\\n # remember that Im_pix is a list (the image)\\n # of lists (each row) of lists (each pixel is [R,G,B])\\n New_pix = [ [ [255 - num for num in p] for p in row ] for row in Im_pix ]\\n # now, save to the file 'out.png'\\n saveRGB( New_pix, 'out.png' )\",\n \"def decode_image(file_location=\\\"images/encoded_sample.png\\\"):\\n encoded_image = Image.open(file_location)\\n\\n x_size = encoded_image.size[0]\\n y_size = encoded_image.size[1]\\n\\n decoded_image = Image.new(\\\"RGB\\\", encoded_image.size)\\n pixels = decoded_image.load()\\n\\n for x in range(x_size):\\n for y in range(y_size):\\n if lsb_of_red_pixel(encoded_image, x, y):\\n pixels[x, y] = (255,255,255)\\n else:\\n pixels[x, y] = (0, 0, 0)\\n\\n #pixels[x, y] = [(0,0,0) if lsb_of_pixel(red_channel, x, y) else (1,1,1)]\\n\\n decoded_image.save(\\\"images/decoded_image.png\\\")\\n decoded_image.show()\",\n \"def run(self):\\n result = Image.run(self)\\n reference = directives.uri(self.arguments[0])\\n self.options['uri'] = data_uri(reference)\\n return result\",\n \"def _add_png(self, pngfile):\\n with open(pngfile, 'rb') as png:\\n if png.read(8) != self.magic:\\n raise ValueError(\\\"{} is not a PNG file\\\".format(pngfile))\\n while True:\\n chead = png.read(8)\\n if len(chead) == 0:\\n break\\n clen, ctype = struct.unpack(\\\">L4s\\\", chead)\\n cdata = png.read(clen)\\n ccrc = png.read(4)\\n utype = ctype.decode(\\\"ascii\\\")\\n self._current_chunk = (chead[:4], ctype, cdata, ccrc)\\n if ctype in self.mustmatch:\\n ref = self._matchref.get(ctype)\\n if ref is None:\\n self._matchref[ctype] = cdata\\n self._copy()\\n else:\\n if cdata != ref:\\n raise ValueError(\\\"Chunk {} mismatch\\\".format(utype))\\n met = (\\\"_first_\\\" if self._first else \\\"_next_\\\") + utype\\n try:\\n met = getattr(self, met)\\n except AttributeError:\\n pass\\n else:\\n met(cdata)\\n self._first = False\",\n \"def decode_image(file_location=\\\"images/encoded_sample.png\\\"):\\n encoded_image = Image.open(file_location)\\n red_channel = encoded_image.split()[0]\\n\\n\\n x_size = encoded_image.size[0]\\n y_size = encoded_image.size[1]\\n\\n\\n decoded_image = Image.new(\\\"RGB\\\", encoded_image.size)\\n pixels = decoded_image.load()\\n for x in range(x_size):\\n for y in range(y_size):\\n red_pixel = red_channel.getpixel((x,y))\\n binary = bin(red_pixel)\\n\\n lsb = int(binary[-1])\\n if(lsb == 0):\\n pixels[x,y] = (0,0,0)\\n elif(lsb == 1):\\n pixels[x,y] = (255,255,255)\\n\\n pass\\n decoded_image.save(\\\"images/decoded_image.png\\\")\",\n \"def from_url(self) -> PngImagePlugin.PngImageFile:\\n response = requests.get(self.url)\\n img = Image.open(BytesIO(response.content))\\n\\n return img\",\n \"def plant_uml_create_png_and_return_image_url(plant_uml_txt):\\n # import hashlib\\n # dbg(hashlib.md5(plant_uml_txt.encode('utf-8')).hexdigest())\\n\\n # plant_uml_server = calc_plantuml_server_url()\\n PLANTUML_URL_ON_INTERNET = \\\"http://www.plantuml.com/plantuml/uml\\\"\\n plant_uml_server = PLANTUML_URL_ON_INTERNET\\n log.info(\\\"plant_uml_server calculated to be %s\\\" % (plant_uml_server,))\\n\\n try:\\n # response = requests.post(plant_uml_server, data={'text': plant_uml_txt})\\n\\n url = os.path.join(plant_uml_server, deflate_and_encode(plant_uml_txt))\\n response = requests.get(url)\\n\\n except (ConnectionError, requests.exceptions.RequestException) as e:\\n # log.exception(\\\"Trying to render using plantuml server %s str(e)\\\" % plant_uml_server)\\n log.error(f\\\"Error trying to fetch initial html from plantuml server {plant_uml_server} {str(e)}\\\")\\n return None, None\\n\\n if response.status_code == 200:\\n # print(\\\"plant_uml_server responded with 200 ok\\\")\\n log.info(\\\"plant_uml_server responded with 200 ok\\\")\\n\\n \\\"\\\"\\\"\\n Need to find the fragment:\\n <p id=\\\"diagram\\\">\\n <img src=\\\"http://www.plantuml.com:80/plantuml/png/SyfFKj2rKt3CoKnELR1Io4ZDoSa70000\\\" alt=\\\"PlantUML diagram\\\" onload=\\\"doneLoading()\\\">\\n </p>\\n in the response.\\n \\\"\\\"\\\"\\n regex = r'.*<p id=\\\"diagram\\\".*\\\\s*<.*img src=\\\\\\\"(.*?)\\\\\\\"'\\n image_url = re.findall(regex, response.text, re.MULTILINE)\\n if image_url:\\n image_url = image_url[\\n 0\\n ] # this is likely referencing localhost due to calc_plantuml_server_url() giving us a localhost\\n # if PLANT_UML_LOCAL:\\n # image_url = normalise_plantuml_url(\\n # image_url\\n # ) # substitute the real host we are on. doesn't really matter, cos always will dynamically repair in all list and update views - but need this repair for ztree1 non persistent debug view, so that can ipad in to e.g. http://192.168.0.3:8000/ztree1 whilst images being returned are refering to localhost which the ipad cannot reach (cos its a different machine)\\n else:\\n image_url = None\\n return image_url, response\\n else:\\n log.error(\\\"plant_uml_server responded with %d ok\\\" % (response.status_code,))\\n return None, response\",\n \"def OnBuildPNGs(self, e):\\n if (not defaults.use_tex):\\n msg = \\\"LaTeX is disabled in the defaults.py file. To use this functionality, change the\\\"\\n msg += \\\" use_tex option to True and restart the GUI.\\\"\\n ShowMessage(msg, kind='warn')\\n return\\n question = \\\"Quantity code formulas are displayed using PNG images, which need to be generated.\\\"\\n question += \\\"\\\\n\\\\n\\\\nImages should only be generated if they do not already exist or\\\"\\n question += \\\" the quantity codes have changed, e.g., more custom outputs have been added.\\\"\\n question += \\\"\\\\n\\\\n\\\\nThis can take ~60 sec, do you want to proceed?\\\"\\n proceed = AskYesNo(question, title='Generate LaTeX Formula Images?')\\n if (not proceed): return\\n\\n question = \\\"Choose a path where the images will be saved. The default value from defaults.py is shown.\\\"\\n path = AskText(question, default=defaults.quantity_code_image_path, title=\\\"Where to store images?\\\")\\n if (path is None): return\\n defaults.quantity_code_image_path = path # user overrode this quantity, remember for later\\n\\n question = \\\"If image files already exist, do you want to overwrite them?\\\"\\n overwrite = AskYesNo(question, title='Overwrite Existing Files?')\\n\\n # call render routine and display a progress bar\\n Nq = len(self.mainparent.nmlpanel.output_quantities.quantities)\\n offsets = list(self.mainparent.nmlpanel.output_quantities.offsets.keys())\\n\\n P = ProgressBar(Nq)\\n P(0)\\n for i,Q in enumerate(self.mainparent.nmlpanel.output_quantities.quantities):\\n if (Q.name in offsets): continue\\n render_tex(Q.code, Q.tex, defaults.quantity_code_image_path, overwrite=overwrite)\\n\\n P(i+1) # update progress bar\",\n \"def main():\\n\\n parser = argparse.ArgumentParser(description=DESCRIPTION)\\n parser.add_argument('-o', '--output', default='output.jpg',\\n help=\\\"Filename to use for the output image\\\")\\n parser.add_argument('INPUT_IMAGE', type=str,\\n help=\\\"Filename for the input content image\\\")\\n parser.add_argument('STYLE_IMAGE', type=str,\\n help=\\\"Filename to for the input style image\\\")\\n parser.add_argument('-s', '--style-weight', default=1e-2, type=float,\\n help=\\\"Weight to apply for the loss associated with the style image. \\\"\\n \\\"Making this bigger results in a final image more like the style image\\\")\\n parser.add_argument('-c', '--content-weight', default=1e4, type=float,\\n help=\\\"Weight to apply for the loss associated with the content image. \\\"\\n \\\"Making this bigger results in a final image more like the content image\\\")\\n parser.add_argument('-t', '--total-variation-loss', default=30, type=float,\\n help=\\\"Weight for the total variation loss.\\\")\\n\\n args = parser.parse_args()\\n\\n print(args)\",\n \"def test_image(filename, x_size=350, y_size=350):\\n # Create image and loop over all pixels\\n im = Image.new(\\\"RGB\\\", (x_size, y_size))\\n pixels = im.load()\\n for i in range(x_size):\\n for j in range(y_size):\\n x = remap_interval(i, 0, x_size, -1, 1)\\n y = remap_interval(j, 0, y_size, -1, 1)\\n pixels[i, j] = (random.randint(0, 255), # Red channel\\n random.randint(0, 255), # Green channel\\n random.randint(0, 255)) # Blue channel\\n\\n im.save(filename)\",\n \"def test_image(filename, x_size=350, y_size=350):\\n # Create image and loop over all pixels\\n im = Image.new(\\\"RGB\\\", (x_size, y_size))\\n pixels = im.load()\\n for i in range(x_size):\\n for j in range(y_size):\\n x = remap_interval(i, 0, x_size, -1, 1)\\n y = remap_interval(j, 0, y_size, -1, 1)\\n pixels[i, j] = (random.randint(0, 255), # Red channel\\n random.randint(0, 255), # Green channel\\n random.randint(0, 255)) # Blue channel\\n\\n im.save(filename)\",\n \"def test_image(filename, x_size=350, y_size=350):\\n # Create image and loop over all pixels\\n im = Image.new(\\\"RGB\\\", (x_size, y_size))\\n pixels = im.load()\\n for i in range(x_size):\\n for j in range(y_size):\\n x = remap_interval(i, 0, x_size, -1, 1)\\n y = remap_interval(j, 0, y_size, -1, 1)\\n pixels[i, j] = (random.randint(0, 255), # Red channel\\n random.randint(0, 255), # Green channel\\n random.randint(0, 255)) # Blue channel\\n\\n im.save(filename)\",\n \"def main(url, localfile):\\n ph.download_file(url, localfile)\",\n \"def run_frame(self, ti, img):\\n pass\",\n \"def render_png(self, target=None, split_pages=False, resolution=300):\\n if target is not None and split_pages:\\n # get destination filename and extension\\n filename, ext = os.path.splitext(target)\\n for page_num, page in enumerate(self._document.pages):\\n page_name = filename + f\\\"_pg_{page_num}\\\" + ext\\n self._document.copy([page]).write_png(\\n target=page_name, resolution=resolution\\n )\\n return None\\n elif target is None:\\n # return image bytes string if no target is specified\\n png_bytes, png_width, png_height = self._document.write_png(\\n target=target, resolution=resolution\\n )\\n return png_bytes\\n else:\\n return self._document.write_png(target=target, resolution=resolution)\",\n \"def main():\\n app = QApplication(sys.argv)\\n window = URPlayground()\\n window.setupUI()\\n window.show()\\n sys.exit(app.exec_())\",\n \"async def processing(message: types.Message):\\n\\n if message.text == 'Bad quality, low time':\\n image_size = 128\\n elif message.text == 'Medium quality, medium time':\\n image_size = 256\\n else:\\n image_size = 300\\n\\n await message.answer(text='Style transfering starts. '\\n 'Wait a bit.',\\n reply_markup=types.ReplyKeyboardRemove())\\n transform('content.jpg', 'style.jpg', image_size)\\n with open('result.jpg', 'rb') as file:\\n await message.answer_photo(file, caption='Work is done!')\",\n \"def test_image(filename, x_size=350, y_size=350):\\n # Create image and loop over all pixels\\n im = Image.new(\\\"RGB\\\", (x_size, y_size))\\n pixels = im.load()\\n for i in range(x_size):\\n for j in range(y_size):\\n x = remap(i, 0, x_size, -1, 1)\\n y = remap(j, 0, y_size, -1, 1)\\n pixels[i, j] = (random.randint(0, 255), # Red channel\\n random.randint(0, 255), # Green channel\\n random.randint(0, 255)) # Blue channel\\n\\n im.save(filename)\",\n \"def test_read_rgba():\\n\\n # Generated with `cwebp transparent.png -o transparent.webp`\\n file_path = \\\"Tests/images/transparent.webp\\\"\\n with Image.open(file_path) as image:\\n assert image.mode == \\\"RGBA\\\"\\n assert image.size == (200, 150)\\n assert image.format == \\\"WEBP\\\"\\n image.load()\\n image.getdata()\\n\\n image.tobytes()\\n\\n assert_image_similar_tofile(image, \\\"Tests/images/transparent.png\\\", 20.0)\",\n \"def saveImage(turtle, filename):\\n ts = turtle.getscreen()\\n tc = ts.getcanvas()\\n tc.postscript(file=filename)\",\n \"def decode(p):\\n #assert p.endswith('.' + EXTENSION)\\n p2 = os.path.basename(p).replace('baseline.png', '.png')\\n p2p = os.path.join('/mnt/Volume0/test/clic2020-devkit/result/', p2) #add by me\\n pp = os.path.join('/mnt/Volume0/test/clic2020-devkit/targets',p2)\\n p2 = os.path.join('/mnt/Volume0/test/clic2020-devkit/inputs/', p2) #add by me\\n p1 = pframe_dataset_shared.get_previous_frame_path(p2)\\n #p1 = os.path.join('/mnt/Volume0/test/clic2020-devkit/test_data/inputs/', p1)\\n #assert os.path.isfile(p1), (p2, p1, p, len(glob.glob('*.png')))\\n b = Image.open(p).convert('L')\\n f2_reconstructed = decoder(np.array(Image.open(p1)), b)\\n Image.fromarray(f2_reconstructed).save(p2p)\\n return f2_reconstructed, np.array(Image.open(pp))\",\n \"def main():\\n original = SimpleImage('images/mt-rainier.jpg')\\n original.show()\\n reflected = make_reflected('images/mt-rainier.jpg')\\n reflected.show()\",\n \"def main() -> None:\\n try:\\n input_image_path = argv[1]\\n output_image_path = argv[2]\\n\\n color_spray( input_image_path, output_image_path )\\n except IndexError:\\n RuntimeError('Usage: INPUT_GRAY_IMAGE_PATH OUTPUT_RGB_IMAGE_PATH')\\n return None\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.59895134","0.5819123","0.5774753","0.5767522","0.57351494","0.57216007","0.56728303","0.5661198","0.5626706","0.55680203","0.5460881","0.5396583","0.53937674","0.5302584","0.5294534","0.5288155","0.52613574","0.523901","0.51677066","0.5115962","0.51095086","0.5104897","0.50971484","0.50862503","0.501369","0.50127435","0.50103486","0.49888846","0.49851465","0.4984686","0.49790838","0.4978701","0.49712855","0.4967086","0.49670345","0.49278924","0.49124974","0.49085888","0.4904621","0.48868957","0.48858246","0.4871659","0.48713773","0.48488107","0.48347345","0.48335028","0.4822933","0.48200703","0.48080313","0.4804429","0.47909668","0.47902083","0.47865486","0.47714737","0.47679183","0.4762241","0.4756438","0.47486666","0.47267336","0.4725646","0.47226712","0.47209656","0.47209656","0.47143662","0.47089523","0.46998757","0.46994683","0.46970278","0.4696633","0.4694177","0.4682204","0.46814874","0.46606672","0.4659801","0.46590555","0.4654824","0.4648573","0.46337146","0.46329466","0.46319652","0.46295398","0.46225044","0.46193704","0.46156183","0.46140775","0.46099454","0.46084794","0.46084794","0.46084794","0.4607565","0.4607539","0.45835578","0.45816612","0.45792544","0.45720413","0.45711455","0.45702732","0.4568811","0.45613685","0.4559959"],"string":"[\n \"0.59895134\",\n \"0.5819123\",\n \"0.5774753\",\n \"0.5767522\",\n \"0.57351494\",\n \"0.57216007\",\n \"0.56728303\",\n \"0.5661198\",\n \"0.5626706\",\n \"0.55680203\",\n \"0.5460881\",\n \"0.5396583\",\n \"0.53937674\",\n \"0.5302584\",\n \"0.5294534\",\n \"0.5288155\",\n \"0.52613574\",\n \"0.523901\",\n \"0.51677066\",\n \"0.5115962\",\n \"0.51095086\",\n \"0.5104897\",\n \"0.50971484\",\n \"0.50862503\",\n \"0.501369\",\n \"0.50127435\",\n \"0.50103486\",\n \"0.49888846\",\n \"0.49851465\",\n \"0.4984686\",\n \"0.49790838\",\n \"0.4978701\",\n \"0.49712855\",\n \"0.4967086\",\n \"0.49670345\",\n \"0.49278924\",\n \"0.49124974\",\n \"0.49085888\",\n \"0.4904621\",\n \"0.48868957\",\n \"0.48858246\",\n \"0.4871659\",\n \"0.48713773\",\n \"0.48488107\",\n \"0.48347345\",\n \"0.48335028\",\n \"0.4822933\",\n \"0.48200703\",\n \"0.48080313\",\n \"0.4804429\",\n \"0.47909668\",\n \"0.47902083\",\n \"0.47865486\",\n \"0.47714737\",\n \"0.47679183\",\n \"0.4762241\",\n \"0.4756438\",\n \"0.47486666\",\n \"0.47267336\",\n \"0.4725646\",\n \"0.47226712\",\n \"0.47209656\",\n \"0.47209656\",\n \"0.47143662\",\n \"0.47089523\",\n \"0.46998757\",\n \"0.46994683\",\n \"0.46970278\",\n \"0.4696633\",\n \"0.4694177\",\n \"0.4682204\",\n \"0.46814874\",\n \"0.46606672\",\n \"0.4659801\",\n \"0.46590555\",\n \"0.4654824\",\n \"0.4648573\",\n \"0.46337146\",\n \"0.46329466\",\n \"0.46319652\",\n \"0.46295398\",\n \"0.46225044\",\n \"0.46193704\",\n \"0.46156183\",\n \"0.46140775\",\n \"0.46099454\",\n \"0.46084794\",\n \"0.46084794\",\n \"0.46084794\",\n \"0.4607565\",\n \"0.4607539\",\n \"0.45835578\",\n \"0.45816612\",\n \"0.45792544\",\n \"0.45720413\",\n \"0.45711455\",\n \"0.45702732\",\n \"0.4568811\",\n \"0.45613685\",\n \"0.4559959\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.67798376"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":11294,"cells":{"query":{"kind":"string","value":"Checks if the source file needs to be rebuilt."},"document":{"kind":"string","value":"def needs_rebuild(source, target):\n return not os.path.isfile(target) or (\n os.path.getmtime(source) > os.path.getmtime(target))"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def has_source_file( self ):\n return self._source_file is not None","def source_changed(source, cache):\n return os.path.getmtime(source)>os.path.getmtime(cache)","def should_run(self):\n # from IPython.html.tasks.py\n\n css_targets = [pjoin(static, 'css', 'style.min.css')]\n css_maps = [t + '.map' for t in css_targets]\n targets = css_targets + css_maps\n if not all(os.path.exists(t) for t in targets):\n # some generated files don't exist\n return True\n earliest_target = sorted(mtime(t) for t in targets)[0]\n\n # check if any .less files are newer than the generated targets\n for dirpath, dirnames, filenames in os.walk(static):\n for f in filenames:\n if f.endswith('.less'):\n path = pjoin(static, dirpath, f)\n timestamp = mtime(path)\n if timestamp > earliest_target:\n return True\n\n return False","def _check_compiled(self):\n\n if self._compiled is None:\n self.compile()","def _check_build(self, gppkg_file, gppkg_spec):\n return gppkg_file == gppkg_spec.get_filename()","def _need_generate(paths):\r\n if not os.path.exists(paths.generated_dir):\r\n return True\r\n\r\n if not os.path.exists(paths.index_file):\r\n return True\r\n\r\n # Use the index file to determine if regeneration is necessary\r\n with open(paths.index_file, 'r',newline='\\n') as index_file:\r\n indexed = [item for item in\r\n index_file.read().split('\\n') if len(item) != 0 and\r\n not item.startswith(\"#\")]\r\n return indexed != paths.resource_files","def _check_if_cff_file_needs_rewriting(self, content):\n logger.info(\"Checking if we can re-use injection config file...\")\n if os.path.isfile(self.config_file_name) is False:\n logger.info(\"...no config file {} found.\".format(self.config_file_name))\n return True\n else:\n logger.info(\n \"...OK: config file {} already exists.\".format(self.config_file_name)\n )\n\n with open(self.config_file_name, \"r\") as f:\n file_content = f.read()\n if file_content == content:\n logger.info(\n \"...OK: file contents match, no update of {} required.\".format(\n self.config_file_name\n )\n )\n return False\n else:\n logger.info(\n \"...file contents unmatched, updating {}.\".format(\n self.config_file_name\n )\n )\n return True","def has_source(self):\n return any(map(utils.assert_package_is_source, self.pkg_arguments))","def test_verify_changed_source_file(self):\n # This test was made to pass in fixing Bug #1354880\n self.backup(u\"full\", u\"testfiles/various_file_types\", options=[])\n\n # Edit source file\n with open('testfiles/various_file_types/executable', 'r+') as f:\n f.write('This changes a source file.')\n\n # Test verify for the file\n self.verify(u'testfiles/various_file_types/executable', file_to_verify=u'executable', options=[])","def checkfile(filename, source=None):\n if source:\n # Let's check some sums\n if os.path.exists(filename) and os.path.exists(source):\n src_sha = calchash(source)\n dest_sha = calchash(filename)\n if DRYRUN:\n print(\"{src} hash {src_sha}. {dest} hash {dest_sha}\".format(src=source, dest=filename, src_sha=src_sha.hexdigest(), dest_sha=dest_sha.hexdigest()))\n return src_sha.digest() == dest_sha.digest()\n else:\n return os.path.exists(filename)","def needs_rebuild(self) -> bool:\n old_hash = self._cache.get(\"config\", None)\n new_hash = utilities.hash_object_sha256(self._get_config_raw())\n self._cache[\"config\"] = new_hash\n\n if not old_hash:\n return False\n return old_hash != new_hash","def ShouldBuild(self, src_files, dst_files):\n if self.force:\n return True\n\n oldest = None\n for dst in dst_files:\n if not os.path.exists(dst):\n self.DebugMsg(\"Build because %s does not exist\" % dst)\n return True\n modified = os.path.getmtime(dst)\n if oldest == None or modified < oldest:\n old = dst\n oldest = modified\n\n for src in src_files:\n modified = os.path.getmtime(src)\n if modified > oldest:\n self.DebugMsg(\"Build because %s is newer than %s\" % (src, old))\n return True\n\n self.DebugMsg(\"%s are up to date\" % \", \".join(dst_files))\n return False","def checkBuildStatus(self):\n pass","def process(self, source_path: pathlib.Path) -> bool:","def _is_prebuilt(self, cfg, patch_idx, prefix=\"PREFIX\"):\n ext = None\n dir = None\n\n if (cfg.load_models_dir is None):\n return False\n\n # Get the unique lookup file path\n fpath = self._get_unique_lookup_filepath(patch_idx, cfg.load_models_dir, prefix, NNModel._M_FILE_EXT)\n\n # Check the existence of the file\n if not os.path.isfile(fpath):\n raise Exception('Model file does not exist: {0}'.format(fpath))\n\n return True","def check_one(filename):\n\n # The file may have been removed from the filesystem.\n # ===================================================\n\n if not isfile(filename):\n if filename in mtimes:\n sys.exit(1) # trigger restart\n else:\n # We haven't seen the file before. It has probably been loaded \n # from a zip (egg) archive.\n return\n\n\n # Or not, in which case, check the mod time.\n # ==========================================\n\n mtime = os.stat(filename).st_mtime\n if filename not in mtimes: # first time we've seen it\n mtimes[filename] = mtime\n if mtime > mtimes[filename]:\n sys.exit(1) # trigger restart","def check_cached_data_okay_to_use(self, cl_mfd):\n\n need_new = \"Will create new SFT file(s).\"\n\n logger.info(\"Checking if we can re-use existing SFT data file(s)...\")\n for sftfile in self.sftfilenames:\n if os.path.isfile(sftfile) is False:\n logger.info(\n \"...no SFT file matching '{}' found. {}\".format(sftfile, need_new)\n )\n return False\n logger.info(\"...OK: file(s) found matching '{}'.\".format(sftfile))\n\n if os.path.isfile(self.config_file_name):\n if np.any(\n [\n os.path.getmtime(sftfile) < os.path.getmtime(self.config_file_name)\n for sftfile in self.sftfilenames\n ]\n ):\n logger.info(\n (\n \"...the config file '{}' has been modified since\"\n \" creation of the SFT file(s) '{}'. {}\"\n ).format(self.config_file_name, self.sftfilepath, need_new)\n )\n return False\n else:\n logger.info(\n \"...OK: The config file '{}' is older than the SFT file(s)\"\n \" '{}'.\".format(self.config_file_name, self.sftfilepath)\n )\n # NOTE: at this point we assume it's safe to re-use, since\n # _check_if_cff_file_needs_rewriting()\n # should have already been called before\n elif \"injectionSources\" in cl_mfd:\n raise RuntimeError(\n \"Commandline requires file '{}' but it is missing.\".format(\n self.config_file_name\n )\n )\n\n logger.info(\"...checking new commandline against existing SFT header(s)...\")\n # here we check one SFT header from each SFT file,\n # assuming that any concatenated file has been sanely constructed with\n # matching CLs\n for sftfile in self.sftfilenames:\n catalog = lalpulsar.SFTdataFind(sftfile, None)\n cl_old = utils.get_commandline_from_SFTDescriptor(catalog.data[0])\n if len(cl_old) == 0:\n logger.info(\n \"......could not obtain comparison commandline from first SFT\"\n \" header in old file '{}'. {}\".format(sftfile, need_new)\n )\n return False\n if not utils.match_commandlines(cl_old, cl_mfd):\n logger.info(\n \"......commandlines unmatched for first SFT in old\"\n \" file '{}':\".format(sftfile)\n )\n logger.info(cl_old)\n logger.info(cl_mfd)\n logger.info(need_new)\n return False\n logger.info(\"......OK: Commandline matched with old SFT header(s).\")\n logger.info(\n \"...all data consistency checks passed: Looks like existing\"\n \" SFT data matches current options, will re-use it!\"\n )\n return True","def needs_update(self):\n return not self.config.same_as(self._stored_config,\n excluded=[\"cmake_generator\"])","def md5sum_check_if_build_is_needed(md5sum_cache_dir: Path, skip_provider_dependencies_check: bool) -> bool:\n build_needed = False\n modified_files, not_modified_files = calculate_md5_checksum_for_files(\n md5sum_cache_dir, update=False, skip_provider_dependencies_check=skip_provider_dependencies_check\n )\n if modified_files:\n get_console().print(\n f\"[warning]The following important files are modified in {AIRFLOW_SOURCES_ROOT} \"\n f\"since last time image was built: [/]\\n\\n\"\n )\n for file in modified_files:\n get_console().print(f\" * [info]{file}[/]\")\n get_console().print(\"\\n[warning]Likely CI image needs rebuild[/]\\n\")\n build_needed = True\n else:\n get_console().print(\n \"[info]Docker image build is not needed for CI build as no important files are changed! \"\n \"You can add --force-build to force it[/]\"\n )\n return build_needed","def already_processed(self):\n # If the flag file has been created by a previous run\n # or if any of the rules have already been re-ordered\n # then we shouldn't make any more changes and instead\n # the system needs to be rebooted.\n return self.syspaths.flag_exists","def should_build(target_platform, changed_files):\n return any(_should_file_trigger_build(target_platform, file) for file in changed_files)","def built_file_must_exist(self, name, type=None, **kw):\n return self.must_exist(self.built_file_path(name, type, **kw))","def test_check_source_3(self):\n self.eval_flags[\"check_host_typo\"] = False\n import_genome.check_source(self.src1, self.eval_flags,\n host_genus=\"Mycobacterium\")\n self.assertEqual(len(self.src1.evaluations), 1)","def needs_reinit(self):\n current_cmake_generator = self.config.get(\"cmake_generator\")\n stored_cmake_generator = self._stored_config.get(\"cmake_generator\")\n return ((current_cmake_generator != stored_cmake_generator) or\n not self.has_stored_config_file())","def test_check_source_1(self):\n import_genome.check_source(self.src1, self.eval_flags,\n host_genus=\"Mycobacterium\")\n self.assertEqual(len(self.src1.evaluations), 4)","def _source_filename_field_was_properly_initialized(self):\n if not Rule.sources_list_is_initialized:\n Rule.sources_list.append(self.source)\n Rule.sources_list_is_initialized = True\n # print(f\"if {self.source} not in {Rule.sources_list}\")\n if self.source not in Rule.sources_list:\n # print(f\"In rule: {self}\")\n # print(f\"Rule.sources_list = {Rule.sources_list}\")\n raise UninitializedSourceError(f\"{repr(self.source)} not initialized.\")\n if self.target not in Rule.sources_list:\n Rule.sources_list.append(self.target)\n return True","def test_check_source_2(self):\n self.eval_flags[\"check_id_typo\"] = False\n import_genome.check_source(self.src1, self.eval_flags,\n host_genus=\"Mycobacterium\")\n self.assertEqual(len(self.src1.evaluations), 3)","def _validate_sources(generated_sources, original_sources):\n\n generated_sources = list(set(generated_sources))\n original_sources = list(set(original_sources))\n not_existent_source = []\n for source in original_sources:\n if source not in generated_sources:\n not_existent_source.append(source)\n\n if not_existent_source:\n print('WARN: Some sources did exist in generated file')\n print(not_existent_source)\n return False\n\n return True","def check_source(source_name):\n source_ext = \".pyx\"\n if not HAS_CYTHON:\n source_name = source_name.replace(\".pyx.in\", \".c\")\n source_name = source_name.replace(\".pyx\", \".c\")\n source_ext = \".c\"\n if not os.path.exists(source_name):\n msg = (\n \"C source not found. You must have Cython installed to \"\n \"build if the C source files have not been generated.\"\n )\n raise IOError(msg)\n return source_name, source_ext","def test_check_source_9(self):\n self.src1._host_host_genus = \"Gordonia\"\n import_genome.check_source(self.src1, self.eval_flags,\n host_genus=\"Mycobacterium\")\n count = count_status(self.src1, \"error\", \"warning\")\n self.assertEqual(count, 1)","def is_outdated(compiler_suite: str, grammar_source: str) -> bool:\n try:\n _, grammar, _, _ = load_compiler_suite(compiler_suite)\n return grammar_changed(grammar(), grammar_source)\n except ValueError:\n return True","def update_package(self) -> bool:\n log.info(f\"- Update {self.package_path.name}\")\n log.trace(f\"{self.package_path.as_posix()}\")\n\n # check if the sources exist\n ok = self.are_package_sources_available()\n if not ok:\n log.debug(f\"{self.package_name}: skipping as one or more source stub folders are missing\")\n self.status[\"error\"] = \"Skipped, stub folder(s) missing\"\n shutil.rmtree(self.package_path.as_posix())\n self._publish = False # type: ignore\n return False\n try:\n self.update_package_files()\n self.update_included_stubs()\n self.check()\n except Exception as e: # pragma: no cover\n log.error(f\"{self.package_name}: {e}\")\n self.status[\"error\"] = str(e)\n return False\n return True","def is_valid(project: Project) -> bool:\n return bool(compileall.compile_dir(project.root, quiet=1))","def test_check_source_7(self):\n self.src1._organism_name = \"Trixie\"\n import_genome.check_source(self.src1, self.eval_flags,\n host_genus=\"Mycobacterium\")\n count = count_status(self.src1, \"error\", \"warning\")\n self.assertEqual(count, 1)","def pdb_file_valid(pdb_file_name, user_rand):\n dcd_file = \"media/files/\" + user_rand + '/' + \"scr_for_checks.dcd\"\n\n fix_not_needed = True\n try:\n scr_for_checks(pdb_file_name, user_rand)\n except Exception as e:\n # print(str(e))\n fix_not_needed = False\n finally:\n if os.path.exists(dcd_file):\n os.remove(dcd_file)\n\n if fix_not_needed:\n return True\n\n try:\n fix_pdb(pdb_file_name)\n scr_for_checks(pdb_file_name, user_rand)\n except Exception as e:\n print(str(e))\n return False\n\n return True","def test_verify_changed_source_file_adjust_mtime(self):\n\n # Get the atime and mtime of the file\n file_info = os.stat('testfiles/various_file_types/executable')\n\n # Set the atime and mtime of the file to the time that we collected, as on some systems\n # the times from a stat call don't match what a utime will set.\n os.utime('testfiles/various_file_types/executable', (file_info.st_atime, file_info.st_mtime))\n\n self.backup(u\"full\", u\"testfiles/various_file_types\", options=[])\n\n # Edit source file\n with open('testfiles/various_file_types/executable', 'r+') as f:\n f.write('This changes a source file.')\n\n # Set the atime and mtime for the file back to what it was prior to the edit\n os.utime('testfiles/various_file_types/executable', (file_info.st_atime, file_info.st_mtime))\n\n # Test verify for the file\n self.verify(u'testfiles/various_file_types/executable', file_to_verify=u'executable', options=[])","def has_resource_changed(self, resource):\n logger.debug(\"Checking for changes in %s\" % resource)\n self.load_template_if_needed()\n self.load_site_if_needed()\n\n target = File(self.site.config.deploy_root_path.child(\n resource.relative_deploy_path))\n if not target.exists or target.older_than(resource.source_file):\n logger.debug(\"Found changes in %s\" % resource)\n return True\n if resource.source_file.is_binary:\n logger.debug(\"No Changes found in %s\" % resource)\n return False\n if self.site.config.needs_refresh() or \\\n not target.has_changed_since(self.site.config.last_modified):\n logger.debug(\"Site configuration changed\")\n return True\n\n deps = self.get_dependencies(resource)\n if not deps or None in deps:\n logger.debug(\"No changes found in %s\" % resource)\n return False\n content = self.site.content.source_folder\n layout = Folder(self.site.sitepath).child_folder('layout')\n logger.debug(\"Checking for changes in dependents:%s\" % deps)\n for dep in deps:\n if not dep:\n return True\n source = File(content.child(dep))\n if not source.exists:\n source = File(layout.child(dep))\n if not source.exists:\n return True\n if target.older_than(source):\n return True\n logger.debug(\"No changes found in %s\" % resource)\n return False","def checkIfAllowedToModify(self):\n\n oldBytes = b''\n testFileName = self.MAPSTUDIO + self.inputFiles[0] + '.msb'\n\n with open(testFileName, 'rb') as oldf:\n oldBytes = oldf.read()\n\n # Try writing something to the file\n\n try:\n with open(testFileName, 'wb') as outf:\n outf.write(b'TESTINGIFICANWRITEINTOTHISFILE')\n except:\n return False\n\n # Because apparently for _some_ reason it doesn't throw an error sometimes(?) so we confirm if the file was actually modified\n\n newBytes = b''\n with open(testFileName, 'rb') as oldf:\n newBytes = oldf.read()\n\n if (oldBytes == newBytes):\n return False\n\n # Restore the file to normal\n\n with open(testFileName, 'wb') as outf:\n outf.write(oldBytes)\n\n oldBytes = b''\n newBytes = b''\n\n return True","def test_check_source_11(self):\n self.src1._organism_host_genus = \"Mycobacterio\"\n import_genome.check_source(self.src1, self.eval_flags,\n host_genus=\"Mycobacterium\")\n count = count_status(self.src1, \"error\")\n self.assertEqual(count, 0)","def check_structure_is_modified(self):\n if not self.structure_has_been_modified: \n print('NEED TO MODIFY STRUCTURE BEFORE PROCEEDING FURTHER!')\n sys.exit()","def isHandled(self, actionType, source):\n handledActions = {\"modified\", \"created\"}\n return actionType in handledActions\\\n and source.startswith(self.projectPath)\\\n and os.path.isfile(source)","def _check_version(self, project, targetdir):\r\n versionfile = os.path.join(targetdir, 'project.version')\r\n if (os.path.exists(versionfile)):\r\n file_ = open(versionfile, \"r\")\r\n projectname = file_.read().strip()\r\n file_.close()\r\n if (projectname == project.objectname):\r\n return True\r\n return False","def preliminary_file_check(self):\n\n if self.has_error():\n return False\n\n if not self.filepath:\n self.add_error(\"A file was specified!\")\n return False\n\n if not isfile(self.filepath):\n self.add_error(\"The file was not found: %s\" % basename(self.filepath))\n return False\n\n if getsize(self.filepath) < 1:\n self.add_error(\"The file is empty (no bytes): %s\" % basename(self.filepath))\n return False\n\n if self.file_ext in ['xls', 'xlsx']:\n self.is_excel = True\n\n return True","def check_modified(self) -> bool:\n return bool(self._modified)","def can_trace_source(self, filename: str) -> bool:\n path = Path(filename)\n if not path.is_file():\n return False\n\n extension = path.suffix\n if extension in self.registered_compilers:\n compiler = self.registered_compilers[extension]\n if compiler.supports_source_tracing:\n return True\n\n # We are not able to get coverage for this file.\n return False","def can_compile(src):\n src = src if src.endswith(\"\\n\") else src + \"\\n\"\n src = transform_command(src, show_diff=False)\n src = src.lstrip()\n try:\n XSH.execer.compile(src, mode=\"single\", glbs=None, locs=XSH.ctx)\n rtn = True\n except SyntaxError:\n rtn = False\n except Exception:\n rtn = True\n return rtn","def hasChanged(self):\n return ((self.mtime != getmtime(self.path)) or\n (self.size != os.path.getsize(self.path)) )","def copy_file_check(self):\n pass","def _check_orig(self):\n if self.is_dir():\n self._orig = False\n return\n\n parts = self._path.split('.')\n try:\n if parts[-1] == 'tgz':\n self._orig = True\n elif parts[-2] == 'tar':\n if (parts[-1] in Compressor.Opts or\n parts[-1] in Compressor.Aliases):\n self._orig = True\n except IndexError:\n self._orig = False","def built_file_must_match(self, name, contents, **kw):\n return self.must_match(self.built_file_path(name, **kw), contents)","def _assets_are_stale(self, sourcedirectory, cachedirectory):\n comparison = filecmp.dircmp(sourcedirectory, cachedirectory, [], [])\n if comparison.left_only or comparison.right_only:\n # We have files in one directory and not the other\n return True\n if comparison.diff_files:\n # Some of the files have changed\n return True\n\n return False","def check_files(self) -> None:\n notfound = False\n give_neuro_data_hint = False\n fullpaths = [f for f, _ in self.input_sources]\n if self.target_sources is not None:\n fullpaths.extend([f for f, _ in self.target_sources])\n for p in fullpaths:\n if not os.path.exists(p):\n print('{} not found.'.format(p))\n notfound = True\n if 'neuro_data_cdhw' in p:\n give_neuro_data_hint = True\n if give_neuro_data_hint:\n print('\\nIt looks like you are referencing the neuro_data_cdhw dataset.\\n'\n 'To install the neuro_data_xzy dataset to the default location, run:\\n'\n ' $ wget https://github.com/ELEKTRONN/elektronn.github.io/releases/download/neuro_data_cdhw/neuro_data_cdhw.zip\\n'\n ' $ unzip neuro_data_cdhw.zip -d ~/neuro_data_cdhw')\n if notfound:\n print('\\nPlease fetch the necessary dataset and/or '\n 'change the relevant file paths in the network config.')\n sys.stdout.flush()\n sys.exit(1)","def checkFileDirty(self, fn):\n for editor in self.editors:\n if Utilities.samepath(fn, editor.getFileName()):\n break\n else:\n return True\n \n res = self.checkDirty(editor)\n return res","def menu_check_source_with_pychecker(self, event=None):\n if self.app.children:\n self.app.childActive.check_source_with_pychecker()","def check_build(self, bld_num):\n # QQQ In future this should be replaced with a query to the\n # build database\n bld_dir = os.path.join(self.ver_dir, str(bld_num))\n for plat in self.plats.keys():\n if self.plats[plat]:\n # QQQ Assumes format of filename unique to couchbase-server\n files = glob.glob(\"{}/couchbase-server-enterprise?{}*{}*\".format(\n bld_dir, self.version, plat\n ))\n files = [x for x in files if not (x.endswith(\".md5\") or x.endswith(\".sha256\"))]\n if len(files) == 0:\n print (\"Platform {} is missing\".format(plat))\n return False\n return True","def dirty(self) -> bool:\n return len(self.detect_changed_files()) != 0","def __checkDestination(self):\n return os.path.exists(self.__targetPath)","def on_file(self) -> bool:\n\n return (\n self.env_var_helper.set_name(\"PYFUNCEBLE_DEBUG\").exists()\n or self.env_var_helper.set_name(\"DEBUG_PYFUNCEBLE\").exists()\n )","def is_incomplete(source, filename, symbol):\n try:\n code = self.compile(source, filename, symbol)\n except (OverflowError, SyntaxError, ValueError):\n return False\n if code is None:\n return True\n return False","def check_precondition(self, code_of_source: str) -> bool:\n sections = metamorph.get_sections(code_of_source)\n source_code_circuit = sections[\"CIRCUIT\"]\n instructions = metamorph.get_instructions(source_code_circuit)\n self.instruction_x_gate = [\n i for i in instructions if i[\"gate\"] == \"XGate\"]\n self.tot_n_x_gates = len(self.instruction_x_gate)\n return self.tot_n_x_gates > 0","def is_safe_to_upgrade():\n return not contains_locked_file(get_rlbot_directory())","def _checkIntegrity(self):\n return (\n os.path.isfile(os.path.join(self._root, 'processed/train.pkl'))\n and os.path.isfile(os.path.join(self._root, 'processed/test.pkl')))","def should_run(self):\n return self.pod.file_exists('/gulpfile.js')","def is_valid_test_file(test_file):\n return '.pyc' not in test_file and '__pycache__' not in test_file","def precheck(self):\n if (not dfs.exists(self.outputpath)):\n logger.debug(\"precheck(%s): outputpath %s does not exist, ready to run.\" \n % (self, self.outputpath))\n return 'ready'\n inTSs = [dfs.modtime(file) for file in self.inputpaths]\n outTS = dfs.modtime(self.outputpath)\n newer = reduce(lambda x,y: x or y, [(inTS>outTS) for inTS in inTSs])\n logger.debug(\"Input timestamps: %s\" % inTSs)\n logger.debug(\"Output timestamp: %s\" % outTS)\n if newer:\n logger.debug(\"At least one input file is newer than outputfile, ready to run.\")\n dfs.delete(self.outputpath)\n return 'ready'\n else:\n logger.debug(\"All input files are newer than outputfile, skipping.\")\n return 'skip'","def verifyBuilt(self):\n htmlDir = self.sphinxDir.sibling(\"doc\")\n self.assertTrue(htmlDir.isdir())\n doctreeDir = htmlDir.child(\"doctrees\")\n self.assertFalse(doctreeDir.exists())\n\n self.verifyFileExists(htmlDir, \"index.html\")\n self.verifyFileExists(htmlDir, \"genindex.html\")\n self.verifyFileExists(htmlDir, \"objects.inv\")\n self.verifyFileExists(htmlDir, \"search.html\")\n self.verifyFileExists(htmlDir, \"searchindex.js\")","def canReloadWithChange(self, externalFilePath):\n return False","def CustomCompileCheck(context, message, source, extension=\".cc\"):\n context.Message(message)\n\n env = context.env\n if env.GetOption(\"clean\") or env.GetOption(\"help\") or env.GetOption(\"no_exec\"):\n result = True\n else:\n result = context.TryCompile(source, extension)\n\n context.Result(result)\n\n return result","def check(self):\n mtime = self.get_mtime()\n if mtime == self.mtime:\n return False\n if mtime is None:\n log.info(\"Option file disappeared\")\n elif self.mtime is None:\n log.info(\"Option file loaded for first time\")\n else:\n log.info(\"Option file was modified {age} ago, passed time={passed}\".format(\n age=timedelta(seconds=time() - mtime),\n passed=timedelta(seconds=mtime - self.mtime)\n ))\n self.mtime = mtime\n self.values = self.read_content()\n return True","def is_source_prefix_mangled(self):\n return False","def has_file(self, doc):\n return len(doc.package.files) != 0","def valid(self):\n return (self.get(\"~#mtime\", 0) and\n self[\"~#mtime\"] == util.mtime(self[\"~filename\"]))","def is_untouched(self):\n return self.fname is None and not self.is_modified","def has_changed(self) -> bool:\n # TODO: Add in change logic here\n state = None\n if state != self._file_state:\n self._changed_flag = True\n self._file_state = state\n return self._changed_flag","def test_check_source_8(self):\n self.src1._organism_host_genus = \"Gordonia\"\n import_genome.check_source(self.src1, self.eval_flags,\n host_genus=\"Mycobacterium\")\n count = count_status(self.src1, \"error\", \"warning\")\n self.assertEqual(count, 1)","def check_consistency(trained_model, filename):\n # First, check that the model supports consistency checking (has _source_hash).\n if not hasattr(trained_model, '_source_hash'):\n return True # No check was done (so we assume it's all fine).\n trained_source_hash = trained_model._source_hash\n with open(filename, 'r') as ff:\n code = ff.read()\n m = hashlib.sha256()\n m.update(code.encode())\n true_source_hash = m.hexdigest()\n return trained_source_hash == true_source_hash","def needs_refreshing(filepath):\n today = datetime.date.today()\n year = today.year - 2000 # Obviously does not work prior to 2000\n if today.month <= 6:\n current_season = str(year - 1) + str(year)\n else:\n current_season = str(year) + str(year + 1)\n return (current_season in filepath and\n last_modified_date(filepath) != today)","def DoesModifiedExist(name):\n if os.path.exists(GetModifiedFilename(name)):\n return True\n else:\n return False","def autoBuildTick (self, event = None):\r\n for pathname, oldmtime in self.autobuildfiles.iteritems():\r\n newmtime = os.stat(pathname).st_mtime\r\n if newmtime != oldmtime:\r\n #print \"Auto rebuild triggered by: \", pathname\r\n self.autobuildfiles[pathname] = newmtime\r\n self.rebuild()\r\n break","def test_check_source_10(self):\n self.src1._lab_host_host_genus = \"Gordonia\"\n import_genome.check_source(self.src1, self.eval_flags,\n host_genus=\"Mycobacterium\")\n count = count_status(self.src1, \"error\", \"warning\")\n self.assertEqual(count, 1)","def check_build_exists(self):\n path = self.base_dir + \"/\" + self.app_name + \"/\" + \"build\"\n from django_swagger_utils.core.utils.check_path_exists import check_path_exists\n build_dir = check_path_exists(path)\n if build_dir:\n raise Exception(\"Build Directory Already Exist, please run update_specs_build\")","def check_dirty(args):\n man = load_manifest()\n any_dirty = False\n for (name, project) in man.projects.iteritems():\n repo = GitRepo(workdir_for_project(project))\n any_dirty = check_dirty_repo(repo) or any_dirty\n return any_dirty","def _source_is_valid_filename(self):\n filename = self.source\n if not filename_is_valid(filename):\n raise BadFilenameError(f\"{repr(filename)} must be a valid filename.\")\n return True","def invalidate_buildpath():\n raise NotImplementedError","def needs_update(self) -> bool:\n return False","def test_verify_compare_data_changed_source_file(self):\n self.backup(u\"full\", u\"testfiles/various_file_types\", options=[])\n\n # Edit source file\n with open('testfiles/various_file_types/executable', 'r+') as f:\n f.write('This changes a source file.')\n\n # Test verify for edited file fails with --compare-data\n try:\n self.verify(u'testfiles/various_file_types/executable', file_to_verify=u'executable',\n options=[u\"--compare-data\"])\n except CmdError as e:\n self.assertEqual(e.exit_status, 1, str(e))\n else:\n self.fail('Expected CmdError not thrown')","def isSource(self):\n return (len(self.parents()) == 0)","def built_file_must_not_match(self, name, contents, **kw):\n return self.must_not_match(self.built_file_path(name, **kw), contents)","def check_paths(self):\r\n\t\tself.check_line_edits_and_refresh_filestate()\r\n\t\t# paths\r\n\t\tsource_img_filename = self.source_img_entry.text().replace(\"\\\\\", \"/\")\r\n\t\tsink_dir_name = self.sink_dir_entry.text().replace(\"\\\\\", \"/\")\r\n\t\tsink_db_name_entry_text = self.sink_db_name_entry.text()\r\n\t\tdb_ext = \".db\" if not sink_db_name_entry_text.lower().endswith(\".db\") else \"\"\r\n\t\tsink_db_filename = os.path.join(sink_dir_name, sink_db_name_entry_text + db_ext).replace(\"\\\\\", \"/\")\r\n\t\tsource_db_filename = \"\"\r\n\r\n\t\t# check validity\r\n\t\tsource_img_filename_valid = self.filestate.is_valid(source_img_filename, SOURCE_IMG)\r\n\t\tsink_dir_name_valid = self.filestate.is_valid(sink_dir_name, SINK_DIR)\r\n\t\tsink_db_filename_valid = self.filestate.is_valid(sink_db_filename, SINK_DB)\r\n\t\tsource_db_filename_valid = True\r\n\r\n\t\tall_paths_valid = source_img_filename_valid and sink_dir_name_valid and sink_db_filename_valid\r\n\r\n\t\tif self.existing_case:\r\n\t\t\tsource_db_filename = self.source_db_entry.text()\r\n\t\t\tsource_db_filename_valid = self.filestate.is_valid(source_db_filename, SOURCE_DB)\r\n\t\t\tall_paths_valid = all_paths_valid and source_db_filename_valid\r\n\r\n\t\tif all_paths_valid:\r\n\t\t\tself.filestate.set_source_img_filename(source_img_filename)\r\n\t\t\tself.filestate.set_sink_dir_name(sink_dir_name)\r\n\t\t\tself.filestate.set_sink_db_filename(sink_db_filename)\r\n\t\t\tif self.existing_case:\r\n\t\t\t\tself.filestate.set_source_db_filename(source_db_filename)\r\n\t\t\tself.refresh_UI()\r\n\t\t\treturn True\r\n\r\n\t\t# in the case of invalidity\r\n\t\tif not source_img_filename_valid:\r\n\t\t\tif not self.filestate.source_img_file_exists:\r\n\t\t\t\tdisplay_warning_message(self, \"Provided source image file at does not exist.\")\r\n\t\t\telif not self.filestate.source_img_file_format_valid:\r\n\t\t\t\tdisplay_warning_message(self, \"Provided source image file type is invalid (must be .npy).\")\r\n\t\t\tself.filestate.set_source_img_filename(\"\")\r\n\t\tif not source_db_filename_valid: # only if existing case\r\n\t\t\tif not self.source_db_file_exists:\r\n\t\t\t\tdisplay_warning_message(self, \"Provided source database file does not exist.\")\r\n\t\t\telif not self.filestate.source_db_file_format_valid:\r\n\t\t\t\tdisplay_warning_message(self, \"Provided source database file type is invalid (must be .db)\")\r\n\t\t\tself.filestate.set_source_db_filename(\"\")\r\n\t\tif not sink_dir_name_valid:\r\n\t\t\tif not self.filestate.sink_dir_exists:\r\n\t\t\t\tdisplay_warning_message(self, \"Provided sink directory does not exist.\")\r\n\t\t\telif not self.sink_dir_format_valid:\r\n\t\t\t\tdisplay_warning_message(self, \"Provided sink directory format is invalid.\")\r\n\t\t\tself.filestate.set_sink_dir_name(\"\")\r\n\t\tif not sink_db_filename_valid:\r\n\t\t\tif sink_dir_name_valid and not self.filestate.sink_db_file_preexists and \\\r\n\t\t\t\t\tself.filestate.sink_db_file_format_valid and \\\r\n\t\t\t\t\tdisplay_yes_no_message(self, \"Create file at \" + sink_db_filename + \"?\"):\r\n\t\t\t\t# create file with read write permissions\r\n\t\t\t\t###########################################\r\n\t\t\t\ttry:\r\n\t\t\t\t\tsink_db_file = open(sink_db_filename, \"w+\")\r\n\t\t\t\t\tsink_db_file.close()\r\n\t\t\t\texcept IOError as error:\r\n\t\t\t\t\tdisplay_warning_message(self, \"Failed to create provided sink database file: \" + error)\r\n\t\t\t\t###########################################\r\n\t\t\t\t# set sink db filename\r\n\t\t\t\telse:\r\n\t\t\t\t\tself.filestate.set_sink_db_filename(sink_db_filename)\r\n\t\t\t\t\tself.refresh_UI()\r\n\t\t\t\t\treturn True\r\n\t\t\telif not self.filestate.sink_db_file_format_valid:\r\n\t\t\t\tdisplay_warning_message(self, \"Be sure to specify a name for the sink database.\")\r\n\t\t\tself.filestate.set_sink_db_filename(\"\")\r\n\r\n\t\t# print(\"paths invalid\")\r\n\t\tself.refresh_UI()\r\n\t\treturn False","def check(self):\n with working_dir(self.build_directory):\n self._if_ninja_target_execute(\"test\", parallel=False)","def built_file_must_not_exist(self, name, type=None, **kw):\n return self.must_not_exist(self.built_file_path(name, type, **kw))","def _ShouldCopyFile(self, path):\n\n # check for C runtime, if desired\n path = os.path.normcase(path)\n dirName, fileName = os.path.split(path)\n if fileName.startswith(\"msvcr\") and fileName.endswith(\".dll\"):\n self.msvcRuntimeDir = dirName\n return self.includeMSVCR\n\n # check the full path\n if path in self.binIncludes:\n return True\n if path in self.binExcludes:\n return False\n\n # check the file name by itself (with any included version numbers)\n if fileName in self.binIncludes:\n return True\n if fileName in self.binExcludes:\n return False\n\n # check the file name by itself (version numbers removed)\n name = self._RemoveVersionNumbers(fileName)\n if name in self.binIncludes:\n return True\n if name in self.binExcludes:\n return False\n\n # check the path for inclusion/exclusion\n for path in self.binPathIncludes:\n if dirName.startswith(path):\n return True\n for path in self.binPathExcludes:\n if dirName.startswith(path):\n return False\n\n return True","def is_source_prefix_mangled(self):\n return self._source_prefix_mangled","def should_clean_before_packaging(self) -> bool:\n return False","def is_outdated(self):\n\n if not self.is_done:\n return False\n elif not (self.input_files and self.output_files):\n return False\n\n return fileutils.modified_after(self.input_files, self.output_files)","def output_out_of_date(self):\n if not os.path.exists(self.output_file):\n logging.info(\"will generate, missing binding output file\")\n return True\n output_mtime = os.path.getmtime(self.output_file)\n if self._any_files_newer(self.header_files, output_mtime):\n logging.info(\"will generate, header files newer\")\n return True\n if self._any_files_newer(self.interface_files, output_mtime):\n logging.info(\"will generate, interface files newer\")\n return True\n if self._file_newer(self.input_file, output_mtime):\n logging.info(\"will generate, swig input file newer\")\n return True\n if self._file_newer(self.extensions_file, output_mtime):\n logging.info(\"will generate, swig extensions file newer\")\n return True\n if self._file_newer(self.wrapper_file, output_mtime):\n logging.info(\"will generate, swig wrapper file newer\")\n return True\n if self._file_newer(self.typemaps_file, output_mtime):\n logging.info(\"will generate, swig typemaps file newer\")\n return True\n if self._file_newer(self.safecast_file, output_mtime):\n logging.info(\"will generate, swig safecast file newer\")\n return True\n\n # If we made it here, nothing is newer than the output file.\n # Thus, the output file is not out of date.\n return False","def _check_guts_toc_mtime(attr, old, toc, last_build, pyc=0):\n for (nm, fnm, typ) in old:\n if mtime(fnm) > last_build:\n print \"building because %s changed\" % fnm\n return True\n elif pyc and mtime(fnm[:-1]) > last_build:\n print \"building because %s changed\" % fnm[:-1]\n return True\n return False","def needs_update(self, cache_key):\r\n return self._read_sha(cache_key) != cache_key.hash","def _check_file_not_used(self):\n module_files = set(self._get_module_files())\n referenced_files = set(self._get_manifest_referenced_files()).union(\n set(self._get_xml_referenced_files())\n )\n excluded_dirs = ['static', 'test', 'tests', 'migrations']\n no_referenced_files = [\n f for f in (module_files - referenced_files)\n if f.split(os.path.sep)[0] not in excluded_dirs\n ]\n self.msg_args = no_referenced_files\n return not no_referenced_files","def has_path_source(self) -> bool:\n\n return any(self.is_path_type(x) for x in self.parameters)"],"string":"[\n \"def has_source_file( self ):\\n return self._source_file is not None\",\n \"def source_changed(source, cache):\\n return os.path.getmtime(source)>os.path.getmtime(cache)\",\n \"def should_run(self):\\n # from IPython.html.tasks.py\\n\\n css_targets = [pjoin(static, 'css', 'style.min.css')]\\n css_maps = [t + '.map' for t in css_targets]\\n targets = css_targets + css_maps\\n if not all(os.path.exists(t) for t in targets):\\n # some generated files don't exist\\n return True\\n earliest_target = sorted(mtime(t) for t in targets)[0]\\n\\n # check if any .less files are newer than the generated targets\\n for dirpath, dirnames, filenames in os.walk(static):\\n for f in filenames:\\n if f.endswith('.less'):\\n path = pjoin(static, dirpath, f)\\n timestamp = mtime(path)\\n if timestamp > earliest_target:\\n return True\\n\\n return False\",\n \"def _check_compiled(self):\\n\\n if self._compiled is None:\\n self.compile()\",\n \"def _check_build(self, gppkg_file, gppkg_spec):\\n return gppkg_file == gppkg_spec.get_filename()\",\n \"def _need_generate(paths):\\r\\n if not os.path.exists(paths.generated_dir):\\r\\n return True\\r\\n\\r\\n if not os.path.exists(paths.index_file):\\r\\n return True\\r\\n\\r\\n # Use the index file to determine if regeneration is necessary\\r\\n with open(paths.index_file, 'r',newline='\\\\n') as index_file:\\r\\n indexed = [item for item in\\r\\n index_file.read().split('\\\\n') if len(item) != 0 and\\r\\n not item.startswith(\\\"#\\\")]\\r\\n return indexed != paths.resource_files\",\n \"def _check_if_cff_file_needs_rewriting(self, content):\\n logger.info(\\\"Checking if we can re-use injection config file...\\\")\\n if os.path.isfile(self.config_file_name) is False:\\n logger.info(\\\"...no config file {} found.\\\".format(self.config_file_name))\\n return True\\n else:\\n logger.info(\\n \\\"...OK: config file {} already exists.\\\".format(self.config_file_name)\\n )\\n\\n with open(self.config_file_name, \\\"r\\\") as f:\\n file_content = f.read()\\n if file_content == content:\\n logger.info(\\n \\\"...OK: file contents match, no update of {} required.\\\".format(\\n self.config_file_name\\n )\\n )\\n return False\\n else:\\n logger.info(\\n \\\"...file contents unmatched, updating {}.\\\".format(\\n self.config_file_name\\n )\\n )\\n return True\",\n \"def has_source(self):\\n return any(map(utils.assert_package_is_source, self.pkg_arguments))\",\n \"def test_verify_changed_source_file(self):\\n # This test was made to pass in fixing Bug #1354880\\n self.backup(u\\\"full\\\", u\\\"testfiles/various_file_types\\\", options=[])\\n\\n # Edit source file\\n with open('testfiles/various_file_types/executable', 'r+') as f:\\n f.write('This changes a source file.')\\n\\n # Test verify for the file\\n self.verify(u'testfiles/various_file_types/executable', file_to_verify=u'executable', options=[])\",\n \"def checkfile(filename, source=None):\\n if source:\\n # Let's check some sums\\n if os.path.exists(filename) and os.path.exists(source):\\n src_sha = calchash(source)\\n dest_sha = calchash(filename)\\n if DRYRUN:\\n print(\\\"{src} hash {src_sha}. {dest} hash {dest_sha}\\\".format(src=source, dest=filename, src_sha=src_sha.hexdigest(), dest_sha=dest_sha.hexdigest()))\\n return src_sha.digest() == dest_sha.digest()\\n else:\\n return os.path.exists(filename)\",\n \"def needs_rebuild(self) -> bool:\\n old_hash = self._cache.get(\\\"config\\\", None)\\n new_hash = utilities.hash_object_sha256(self._get_config_raw())\\n self._cache[\\\"config\\\"] = new_hash\\n\\n if not old_hash:\\n return False\\n return old_hash != new_hash\",\n \"def ShouldBuild(self, src_files, dst_files):\\n if self.force:\\n return True\\n\\n oldest = None\\n for dst in dst_files:\\n if not os.path.exists(dst):\\n self.DebugMsg(\\\"Build because %s does not exist\\\" % dst)\\n return True\\n modified = os.path.getmtime(dst)\\n if oldest == None or modified < oldest:\\n old = dst\\n oldest = modified\\n\\n for src in src_files:\\n modified = os.path.getmtime(src)\\n if modified > oldest:\\n self.DebugMsg(\\\"Build because %s is newer than %s\\\" % (src, old))\\n return True\\n\\n self.DebugMsg(\\\"%s are up to date\\\" % \\\", \\\".join(dst_files))\\n return False\",\n \"def checkBuildStatus(self):\\n pass\",\n \"def process(self, source_path: pathlib.Path) -> bool:\",\n \"def _is_prebuilt(self, cfg, patch_idx, prefix=\\\"PREFIX\\\"):\\n ext = None\\n dir = None\\n\\n if (cfg.load_models_dir is None):\\n return False\\n\\n # Get the unique lookup file path\\n fpath = self._get_unique_lookup_filepath(patch_idx, cfg.load_models_dir, prefix, NNModel._M_FILE_EXT)\\n\\n # Check the existence of the file\\n if not os.path.isfile(fpath):\\n raise Exception('Model file does not exist: {0}'.format(fpath))\\n\\n return True\",\n \"def check_one(filename):\\n\\n # The file may have been removed from the filesystem.\\n # ===================================================\\n\\n if not isfile(filename):\\n if filename in mtimes:\\n sys.exit(1) # trigger restart\\n else:\\n # We haven't seen the file before. It has probably been loaded \\n # from a zip (egg) archive.\\n return\\n\\n\\n # Or not, in which case, check the mod time.\\n # ==========================================\\n\\n mtime = os.stat(filename).st_mtime\\n if filename not in mtimes: # first time we've seen it\\n mtimes[filename] = mtime\\n if mtime > mtimes[filename]:\\n sys.exit(1) # trigger restart\",\n \"def check_cached_data_okay_to_use(self, cl_mfd):\\n\\n need_new = \\\"Will create new SFT file(s).\\\"\\n\\n logger.info(\\\"Checking if we can re-use existing SFT data file(s)...\\\")\\n for sftfile in self.sftfilenames:\\n if os.path.isfile(sftfile) is False:\\n logger.info(\\n \\\"...no SFT file matching '{}' found. {}\\\".format(sftfile, need_new)\\n )\\n return False\\n logger.info(\\\"...OK: file(s) found matching '{}'.\\\".format(sftfile))\\n\\n if os.path.isfile(self.config_file_name):\\n if np.any(\\n [\\n os.path.getmtime(sftfile) < os.path.getmtime(self.config_file_name)\\n for sftfile in self.sftfilenames\\n ]\\n ):\\n logger.info(\\n (\\n \\\"...the config file '{}' has been modified since\\\"\\n \\\" creation of the SFT file(s) '{}'. {}\\\"\\n ).format(self.config_file_name, self.sftfilepath, need_new)\\n )\\n return False\\n else:\\n logger.info(\\n \\\"...OK: The config file '{}' is older than the SFT file(s)\\\"\\n \\\" '{}'.\\\".format(self.config_file_name, self.sftfilepath)\\n )\\n # NOTE: at this point we assume it's safe to re-use, since\\n # _check_if_cff_file_needs_rewriting()\\n # should have already been called before\\n elif \\\"injectionSources\\\" in cl_mfd:\\n raise RuntimeError(\\n \\\"Commandline requires file '{}' but it is missing.\\\".format(\\n self.config_file_name\\n )\\n )\\n\\n logger.info(\\\"...checking new commandline against existing SFT header(s)...\\\")\\n # here we check one SFT header from each SFT file,\\n # assuming that any concatenated file has been sanely constructed with\\n # matching CLs\\n for sftfile in self.sftfilenames:\\n catalog = lalpulsar.SFTdataFind(sftfile, None)\\n cl_old = utils.get_commandline_from_SFTDescriptor(catalog.data[0])\\n if len(cl_old) == 0:\\n logger.info(\\n \\\"......could not obtain comparison commandline from first SFT\\\"\\n \\\" header in old file '{}'. {}\\\".format(sftfile, need_new)\\n )\\n return False\\n if not utils.match_commandlines(cl_old, cl_mfd):\\n logger.info(\\n \\\"......commandlines unmatched for first SFT in old\\\"\\n \\\" file '{}':\\\".format(sftfile)\\n )\\n logger.info(cl_old)\\n logger.info(cl_mfd)\\n logger.info(need_new)\\n return False\\n logger.info(\\\"......OK: Commandline matched with old SFT header(s).\\\")\\n logger.info(\\n \\\"...all data consistency checks passed: Looks like existing\\\"\\n \\\" SFT data matches current options, will re-use it!\\\"\\n )\\n return True\",\n \"def needs_update(self):\\n return not self.config.same_as(self._stored_config,\\n excluded=[\\\"cmake_generator\\\"])\",\n \"def md5sum_check_if_build_is_needed(md5sum_cache_dir: Path, skip_provider_dependencies_check: bool) -> bool:\\n build_needed = False\\n modified_files, not_modified_files = calculate_md5_checksum_for_files(\\n md5sum_cache_dir, update=False, skip_provider_dependencies_check=skip_provider_dependencies_check\\n )\\n if modified_files:\\n get_console().print(\\n f\\\"[warning]The following important files are modified in {AIRFLOW_SOURCES_ROOT} \\\"\\n f\\\"since last time image was built: [/]\\\\n\\\\n\\\"\\n )\\n for file in modified_files:\\n get_console().print(f\\\" * [info]{file}[/]\\\")\\n get_console().print(\\\"\\\\n[warning]Likely CI image needs rebuild[/]\\\\n\\\")\\n build_needed = True\\n else:\\n get_console().print(\\n \\\"[info]Docker image build is not needed for CI build as no important files are changed! \\\"\\n \\\"You can add --force-build to force it[/]\\\"\\n )\\n return build_needed\",\n \"def already_processed(self):\\n # If the flag file has been created by a previous run\\n # or if any of the rules have already been re-ordered\\n # then we shouldn't make any more changes and instead\\n # the system needs to be rebooted.\\n return self.syspaths.flag_exists\",\n \"def should_build(target_platform, changed_files):\\n return any(_should_file_trigger_build(target_platform, file) for file in changed_files)\",\n \"def built_file_must_exist(self, name, type=None, **kw):\\n return self.must_exist(self.built_file_path(name, type, **kw))\",\n \"def test_check_source_3(self):\\n self.eval_flags[\\\"check_host_typo\\\"] = False\\n import_genome.check_source(self.src1, self.eval_flags,\\n host_genus=\\\"Mycobacterium\\\")\\n self.assertEqual(len(self.src1.evaluations), 1)\",\n \"def needs_reinit(self):\\n current_cmake_generator = self.config.get(\\\"cmake_generator\\\")\\n stored_cmake_generator = self._stored_config.get(\\\"cmake_generator\\\")\\n return ((current_cmake_generator != stored_cmake_generator) or\\n not self.has_stored_config_file())\",\n \"def test_check_source_1(self):\\n import_genome.check_source(self.src1, self.eval_flags,\\n host_genus=\\\"Mycobacterium\\\")\\n self.assertEqual(len(self.src1.evaluations), 4)\",\n \"def _source_filename_field_was_properly_initialized(self):\\n if not Rule.sources_list_is_initialized:\\n Rule.sources_list.append(self.source)\\n Rule.sources_list_is_initialized = True\\n # print(f\\\"if {self.source} not in {Rule.sources_list}\\\")\\n if self.source not in Rule.sources_list:\\n # print(f\\\"In rule: {self}\\\")\\n # print(f\\\"Rule.sources_list = {Rule.sources_list}\\\")\\n raise UninitializedSourceError(f\\\"{repr(self.source)} not initialized.\\\")\\n if self.target not in Rule.sources_list:\\n Rule.sources_list.append(self.target)\\n return True\",\n \"def test_check_source_2(self):\\n self.eval_flags[\\\"check_id_typo\\\"] = False\\n import_genome.check_source(self.src1, self.eval_flags,\\n host_genus=\\\"Mycobacterium\\\")\\n self.assertEqual(len(self.src1.evaluations), 3)\",\n \"def _validate_sources(generated_sources, original_sources):\\n\\n generated_sources = list(set(generated_sources))\\n original_sources = list(set(original_sources))\\n not_existent_source = []\\n for source in original_sources:\\n if source not in generated_sources:\\n not_existent_source.append(source)\\n\\n if not_existent_source:\\n print('WARN: Some sources did exist in generated file')\\n print(not_existent_source)\\n return False\\n\\n return True\",\n \"def check_source(source_name):\\n source_ext = \\\".pyx\\\"\\n if not HAS_CYTHON:\\n source_name = source_name.replace(\\\".pyx.in\\\", \\\".c\\\")\\n source_name = source_name.replace(\\\".pyx\\\", \\\".c\\\")\\n source_ext = \\\".c\\\"\\n if not os.path.exists(source_name):\\n msg = (\\n \\\"C source not found. You must have Cython installed to \\\"\\n \\\"build if the C source files have not been generated.\\\"\\n )\\n raise IOError(msg)\\n return source_name, source_ext\",\n \"def test_check_source_9(self):\\n self.src1._host_host_genus = \\\"Gordonia\\\"\\n import_genome.check_source(self.src1, self.eval_flags,\\n host_genus=\\\"Mycobacterium\\\")\\n count = count_status(self.src1, \\\"error\\\", \\\"warning\\\")\\n self.assertEqual(count, 1)\",\n \"def is_outdated(compiler_suite: str, grammar_source: str) -> bool:\\n try:\\n _, grammar, _, _ = load_compiler_suite(compiler_suite)\\n return grammar_changed(grammar(), grammar_source)\\n except ValueError:\\n return True\",\n \"def update_package(self) -> bool:\\n log.info(f\\\"- Update {self.package_path.name}\\\")\\n log.trace(f\\\"{self.package_path.as_posix()}\\\")\\n\\n # check if the sources exist\\n ok = self.are_package_sources_available()\\n if not ok:\\n log.debug(f\\\"{self.package_name}: skipping as one or more source stub folders are missing\\\")\\n self.status[\\\"error\\\"] = \\\"Skipped, stub folder(s) missing\\\"\\n shutil.rmtree(self.package_path.as_posix())\\n self._publish = False # type: ignore\\n return False\\n try:\\n self.update_package_files()\\n self.update_included_stubs()\\n self.check()\\n except Exception as e: # pragma: no cover\\n log.error(f\\\"{self.package_name}: {e}\\\")\\n self.status[\\\"error\\\"] = str(e)\\n return False\\n return True\",\n \"def is_valid(project: Project) -> bool:\\n return bool(compileall.compile_dir(project.root, quiet=1))\",\n \"def test_check_source_7(self):\\n self.src1._organism_name = \\\"Trixie\\\"\\n import_genome.check_source(self.src1, self.eval_flags,\\n host_genus=\\\"Mycobacterium\\\")\\n count = count_status(self.src1, \\\"error\\\", \\\"warning\\\")\\n self.assertEqual(count, 1)\",\n \"def pdb_file_valid(pdb_file_name, user_rand):\\n dcd_file = \\\"media/files/\\\" + user_rand + '/' + \\\"scr_for_checks.dcd\\\"\\n\\n fix_not_needed = True\\n try:\\n scr_for_checks(pdb_file_name, user_rand)\\n except Exception as e:\\n # print(str(e))\\n fix_not_needed = False\\n finally:\\n if os.path.exists(dcd_file):\\n os.remove(dcd_file)\\n\\n if fix_not_needed:\\n return True\\n\\n try:\\n fix_pdb(pdb_file_name)\\n scr_for_checks(pdb_file_name, user_rand)\\n except Exception as e:\\n print(str(e))\\n return False\\n\\n return True\",\n \"def test_verify_changed_source_file_adjust_mtime(self):\\n\\n # Get the atime and mtime of the file\\n file_info = os.stat('testfiles/various_file_types/executable')\\n\\n # Set the atime and mtime of the file to the time that we collected, as on some systems\\n # the times from a stat call don't match what a utime will set.\\n os.utime('testfiles/various_file_types/executable', (file_info.st_atime, file_info.st_mtime))\\n\\n self.backup(u\\\"full\\\", u\\\"testfiles/various_file_types\\\", options=[])\\n\\n # Edit source file\\n with open('testfiles/various_file_types/executable', 'r+') as f:\\n f.write('This changes a source file.')\\n\\n # Set the atime and mtime for the file back to what it was prior to the edit\\n os.utime('testfiles/various_file_types/executable', (file_info.st_atime, file_info.st_mtime))\\n\\n # Test verify for the file\\n self.verify(u'testfiles/various_file_types/executable', file_to_verify=u'executable', options=[])\",\n \"def has_resource_changed(self, resource):\\n logger.debug(\\\"Checking for changes in %s\\\" % resource)\\n self.load_template_if_needed()\\n self.load_site_if_needed()\\n\\n target = File(self.site.config.deploy_root_path.child(\\n resource.relative_deploy_path))\\n if not target.exists or target.older_than(resource.source_file):\\n logger.debug(\\\"Found changes in %s\\\" % resource)\\n return True\\n if resource.source_file.is_binary:\\n logger.debug(\\\"No Changes found in %s\\\" % resource)\\n return False\\n if self.site.config.needs_refresh() or \\\\\\n not target.has_changed_since(self.site.config.last_modified):\\n logger.debug(\\\"Site configuration changed\\\")\\n return True\\n\\n deps = self.get_dependencies(resource)\\n if not deps or None in deps:\\n logger.debug(\\\"No changes found in %s\\\" % resource)\\n return False\\n content = self.site.content.source_folder\\n layout = Folder(self.site.sitepath).child_folder('layout')\\n logger.debug(\\\"Checking for changes in dependents:%s\\\" % deps)\\n for dep in deps:\\n if not dep:\\n return True\\n source = File(content.child(dep))\\n if not source.exists:\\n source = File(layout.child(dep))\\n if not source.exists:\\n return True\\n if target.older_than(source):\\n return True\\n logger.debug(\\\"No changes found in %s\\\" % resource)\\n return False\",\n \"def checkIfAllowedToModify(self):\\n\\n oldBytes = b''\\n testFileName = self.MAPSTUDIO + self.inputFiles[0] + '.msb'\\n\\n with open(testFileName, 'rb') as oldf:\\n oldBytes = oldf.read()\\n\\n # Try writing something to the file\\n\\n try:\\n with open(testFileName, 'wb') as outf:\\n outf.write(b'TESTINGIFICANWRITEINTOTHISFILE')\\n except:\\n return False\\n\\n # Because apparently for _some_ reason it doesn't throw an error sometimes(?) so we confirm if the file was actually modified\\n\\n newBytes = b''\\n with open(testFileName, 'rb') as oldf:\\n newBytes = oldf.read()\\n\\n if (oldBytes == newBytes):\\n return False\\n\\n # Restore the file to normal\\n\\n with open(testFileName, 'wb') as outf:\\n outf.write(oldBytes)\\n\\n oldBytes = b''\\n newBytes = b''\\n\\n return True\",\n \"def test_check_source_11(self):\\n self.src1._organism_host_genus = \\\"Mycobacterio\\\"\\n import_genome.check_source(self.src1, self.eval_flags,\\n host_genus=\\\"Mycobacterium\\\")\\n count = count_status(self.src1, \\\"error\\\")\\n self.assertEqual(count, 0)\",\n \"def check_structure_is_modified(self):\\n if not self.structure_has_been_modified: \\n print('NEED TO MODIFY STRUCTURE BEFORE PROCEEDING FURTHER!')\\n sys.exit()\",\n \"def isHandled(self, actionType, source):\\n handledActions = {\\\"modified\\\", \\\"created\\\"}\\n return actionType in handledActions\\\\\\n and source.startswith(self.projectPath)\\\\\\n and os.path.isfile(source)\",\n \"def _check_version(self, project, targetdir):\\r\\n versionfile = os.path.join(targetdir, 'project.version')\\r\\n if (os.path.exists(versionfile)):\\r\\n file_ = open(versionfile, \\\"r\\\")\\r\\n projectname = file_.read().strip()\\r\\n file_.close()\\r\\n if (projectname == project.objectname):\\r\\n return True\\r\\n return False\",\n \"def preliminary_file_check(self):\\n\\n if self.has_error():\\n return False\\n\\n if not self.filepath:\\n self.add_error(\\\"A file was specified!\\\")\\n return False\\n\\n if not isfile(self.filepath):\\n self.add_error(\\\"The file was not found: %s\\\" % basename(self.filepath))\\n return False\\n\\n if getsize(self.filepath) < 1:\\n self.add_error(\\\"The file is empty (no bytes): %s\\\" % basename(self.filepath))\\n return False\\n\\n if self.file_ext in ['xls', 'xlsx']:\\n self.is_excel = True\\n\\n return True\",\n \"def check_modified(self) -> bool:\\n return bool(self._modified)\",\n \"def can_trace_source(self, filename: str) -> bool:\\n path = Path(filename)\\n if not path.is_file():\\n return False\\n\\n extension = path.suffix\\n if extension in self.registered_compilers:\\n compiler = self.registered_compilers[extension]\\n if compiler.supports_source_tracing:\\n return True\\n\\n # We are not able to get coverage for this file.\\n return False\",\n \"def can_compile(src):\\n src = src if src.endswith(\\\"\\\\n\\\") else src + \\\"\\\\n\\\"\\n src = transform_command(src, show_diff=False)\\n src = src.lstrip()\\n try:\\n XSH.execer.compile(src, mode=\\\"single\\\", glbs=None, locs=XSH.ctx)\\n rtn = True\\n except SyntaxError:\\n rtn = False\\n except Exception:\\n rtn = True\\n return rtn\",\n \"def hasChanged(self):\\n return ((self.mtime != getmtime(self.path)) or\\n (self.size != os.path.getsize(self.path)) )\",\n \"def copy_file_check(self):\\n pass\",\n \"def _check_orig(self):\\n if self.is_dir():\\n self._orig = False\\n return\\n\\n parts = self._path.split('.')\\n try:\\n if parts[-1] == 'tgz':\\n self._orig = True\\n elif parts[-2] == 'tar':\\n if (parts[-1] in Compressor.Opts or\\n parts[-1] in Compressor.Aliases):\\n self._orig = True\\n except IndexError:\\n self._orig = False\",\n \"def built_file_must_match(self, name, contents, **kw):\\n return self.must_match(self.built_file_path(name, **kw), contents)\",\n \"def _assets_are_stale(self, sourcedirectory, cachedirectory):\\n comparison = filecmp.dircmp(sourcedirectory, cachedirectory, [], [])\\n if comparison.left_only or comparison.right_only:\\n # We have files in one directory and not the other\\n return True\\n if comparison.diff_files:\\n # Some of the files have changed\\n return True\\n\\n return False\",\n \"def check_files(self) -> None:\\n notfound = False\\n give_neuro_data_hint = False\\n fullpaths = [f for f, _ in self.input_sources]\\n if self.target_sources is not None:\\n fullpaths.extend([f for f, _ in self.target_sources])\\n for p in fullpaths:\\n if not os.path.exists(p):\\n print('{} not found.'.format(p))\\n notfound = True\\n if 'neuro_data_cdhw' in p:\\n give_neuro_data_hint = True\\n if give_neuro_data_hint:\\n print('\\\\nIt looks like you are referencing the neuro_data_cdhw dataset.\\\\n'\\n 'To install the neuro_data_xzy dataset to the default location, run:\\\\n'\\n ' $ wget https://github.com/ELEKTRONN/elektronn.github.io/releases/download/neuro_data_cdhw/neuro_data_cdhw.zip\\\\n'\\n ' $ unzip neuro_data_cdhw.zip -d ~/neuro_data_cdhw')\\n if notfound:\\n print('\\\\nPlease fetch the necessary dataset and/or '\\n 'change the relevant file paths in the network config.')\\n sys.stdout.flush()\\n sys.exit(1)\",\n \"def checkFileDirty(self, fn):\\n for editor in self.editors:\\n if Utilities.samepath(fn, editor.getFileName()):\\n break\\n else:\\n return True\\n \\n res = self.checkDirty(editor)\\n return res\",\n \"def menu_check_source_with_pychecker(self, event=None):\\n if self.app.children:\\n self.app.childActive.check_source_with_pychecker()\",\n \"def check_build(self, bld_num):\\n # QQQ In future this should be replaced with a query to the\\n # build database\\n bld_dir = os.path.join(self.ver_dir, str(bld_num))\\n for plat in self.plats.keys():\\n if self.plats[plat]:\\n # QQQ Assumes format of filename unique to couchbase-server\\n files = glob.glob(\\\"{}/couchbase-server-enterprise?{}*{}*\\\".format(\\n bld_dir, self.version, plat\\n ))\\n files = [x for x in files if not (x.endswith(\\\".md5\\\") or x.endswith(\\\".sha256\\\"))]\\n if len(files) == 0:\\n print (\\\"Platform {} is missing\\\".format(plat))\\n return False\\n return True\",\n \"def dirty(self) -> bool:\\n return len(self.detect_changed_files()) != 0\",\n \"def __checkDestination(self):\\n return os.path.exists(self.__targetPath)\",\n \"def on_file(self) -> bool:\\n\\n return (\\n self.env_var_helper.set_name(\\\"PYFUNCEBLE_DEBUG\\\").exists()\\n or self.env_var_helper.set_name(\\\"DEBUG_PYFUNCEBLE\\\").exists()\\n )\",\n \"def is_incomplete(source, filename, symbol):\\n try:\\n code = self.compile(source, filename, symbol)\\n except (OverflowError, SyntaxError, ValueError):\\n return False\\n if code is None:\\n return True\\n return False\",\n \"def check_precondition(self, code_of_source: str) -> bool:\\n sections = metamorph.get_sections(code_of_source)\\n source_code_circuit = sections[\\\"CIRCUIT\\\"]\\n instructions = metamorph.get_instructions(source_code_circuit)\\n self.instruction_x_gate = [\\n i for i in instructions if i[\\\"gate\\\"] == \\\"XGate\\\"]\\n self.tot_n_x_gates = len(self.instruction_x_gate)\\n return self.tot_n_x_gates > 0\",\n \"def is_safe_to_upgrade():\\n return not contains_locked_file(get_rlbot_directory())\",\n \"def _checkIntegrity(self):\\n return (\\n os.path.isfile(os.path.join(self._root, 'processed/train.pkl'))\\n and os.path.isfile(os.path.join(self._root, 'processed/test.pkl')))\",\n \"def should_run(self):\\n return self.pod.file_exists('/gulpfile.js')\",\n \"def is_valid_test_file(test_file):\\n return '.pyc' not in test_file and '__pycache__' not in test_file\",\n \"def precheck(self):\\n if (not dfs.exists(self.outputpath)):\\n logger.debug(\\\"precheck(%s): outputpath %s does not exist, ready to run.\\\" \\n % (self, self.outputpath))\\n return 'ready'\\n inTSs = [dfs.modtime(file) for file in self.inputpaths]\\n outTS = dfs.modtime(self.outputpath)\\n newer = reduce(lambda x,y: x or y, [(inTS>outTS) for inTS in inTSs])\\n logger.debug(\\\"Input timestamps: %s\\\" % inTSs)\\n logger.debug(\\\"Output timestamp: %s\\\" % outTS)\\n if newer:\\n logger.debug(\\\"At least one input file is newer than outputfile, ready to run.\\\")\\n dfs.delete(self.outputpath)\\n return 'ready'\\n else:\\n logger.debug(\\\"All input files are newer than outputfile, skipping.\\\")\\n return 'skip'\",\n \"def verifyBuilt(self):\\n htmlDir = self.sphinxDir.sibling(\\\"doc\\\")\\n self.assertTrue(htmlDir.isdir())\\n doctreeDir = htmlDir.child(\\\"doctrees\\\")\\n self.assertFalse(doctreeDir.exists())\\n\\n self.verifyFileExists(htmlDir, \\\"index.html\\\")\\n self.verifyFileExists(htmlDir, \\\"genindex.html\\\")\\n self.verifyFileExists(htmlDir, \\\"objects.inv\\\")\\n self.verifyFileExists(htmlDir, \\\"search.html\\\")\\n self.verifyFileExists(htmlDir, \\\"searchindex.js\\\")\",\n \"def canReloadWithChange(self, externalFilePath):\\n return False\",\n \"def CustomCompileCheck(context, message, source, extension=\\\".cc\\\"):\\n context.Message(message)\\n\\n env = context.env\\n if env.GetOption(\\\"clean\\\") or env.GetOption(\\\"help\\\") or env.GetOption(\\\"no_exec\\\"):\\n result = True\\n else:\\n result = context.TryCompile(source, extension)\\n\\n context.Result(result)\\n\\n return result\",\n \"def check(self):\\n mtime = self.get_mtime()\\n if mtime == self.mtime:\\n return False\\n if mtime is None:\\n log.info(\\\"Option file disappeared\\\")\\n elif self.mtime is None:\\n log.info(\\\"Option file loaded for first time\\\")\\n else:\\n log.info(\\\"Option file was modified {age} ago, passed time={passed}\\\".format(\\n age=timedelta(seconds=time() - mtime),\\n passed=timedelta(seconds=mtime - self.mtime)\\n ))\\n self.mtime = mtime\\n self.values = self.read_content()\\n return True\",\n \"def is_source_prefix_mangled(self):\\n return False\",\n \"def has_file(self, doc):\\n return len(doc.package.files) != 0\",\n \"def valid(self):\\n return (self.get(\\\"~#mtime\\\", 0) and\\n self[\\\"~#mtime\\\"] == util.mtime(self[\\\"~filename\\\"]))\",\n \"def is_untouched(self):\\n return self.fname is None and not self.is_modified\",\n \"def has_changed(self) -> bool:\\n # TODO: Add in change logic here\\n state = None\\n if state != self._file_state:\\n self._changed_flag = True\\n self._file_state = state\\n return self._changed_flag\",\n \"def test_check_source_8(self):\\n self.src1._organism_host_genus = \\\"Gordonia\\\"\\n import_genome.check_source(self.src1, self.eval_flags,\\n host_genus=\\\"Mycobacterium\\\")\\n count = count_status(self.src1, \\\"error\\\", \\\"warning\\\")\\n self.assertEqual(count, 1)\",\n \"def check_consistency(trained_model, filename):\\n # First, check that the model supports consistency checking (has _source_hash).\\n if not hasattr(trained_model, '_source_hash'):\\n return True # No check was done (so we assume it's all fine).\\n trained_source_hash = trained_model._source_hash\\n with open(filename, 'r') as ff:\\n code = ff.read()\\n m = hashlib.sha256()\\n m.update(code.encode())\\n true_source_hash = m.hexdigest()\\n return trained_source_hash == true_source_hash\",\n \"def needs_refreshing(filepath):\\n today = datetime.date.today()\\n year = today.year - 2000 # Obviously does not work prior to 2000\\n if today.month <= 6:\\n current_season = str(year - 1) + str(year)\\n else:\\n current_season = str(year) + str(year + 1)\\n return (current_season in filepath and\\n last_modified_date(filepath) != today)\",\n \"def DoesModifiedExist(name):\\n if os.path.exists(GetModifiedFilename(name)):\\n return True\\n else:\\n return False\",\n \"def autoBuildTick (self, event = None):\\r\\n for pathname, oldmtime in self.autobuildfiles.iteritems():\\r\\n newmtime = os.stat(pathname).st_mtime\\r\\n if newmtime != oldmtime:\\r\\n #print \\\"Auto rebuild triggered by: \\\", pathname\\r\\n self.autobuildfiles[pathname] = newmtime\\r\\n self.rebuild()\\r\\n break\",\n \"def test_check_source_10(self):\\n self.src1._lab_host_host_genus = \\\"Gordonia\\\"\\n import_genome.check_source(self.src1, self.eval_flags,\\n host_genus=\\\"Mycobacterium\\\")\\n count = count_status(self.src1, \\\"error\\\", \\\"warning\\\")\\n self.assertEqual(count, 1)\",\n \"def check_build_exists(self):\\n path = self.base_dir + \\\"/\\\" + self.app_name + \\\"/\\\" + \\\"build\\\"\\n from django_swagger_utils.core.utils.check_path_exists import check_path_exists\\n build_dir = check_path_exists(path)\\n if build_dir:\\n raise Exception(\\\"Build Directory Already Exist, please run update_specs_build\\\")\",\n \"def check_dirty(args):\\n man = load_manifest()\\n any_dirty = False\\n for (name, project) in man.projects.iteritems():\\n repo = GitRepo(workdir_for_project(project))\\n any_dirty = check_dirty_repo(repo) or any_dirty\\n return any_dirty\",\n \"def _source_is_valid_filename(self):\\n filename = self.source\\n if not filename_is_valid(filename):\\n raise BadFilenameError(f\\\"{repr(filename)} must be a valid filename.\\\")\\n return True\",\n \"def invalidate_buildpath():\\n raise NotImplementedError\",\n \"def needs_update(self) -> bool:\\n return False\",\n \"def test_verify_compare_data_changed_source_file(self):\\n self.backup(u\\\"full\\\", u\\\"testfiles/various_file_types\\\", options=[])\\n\\n # Edit source file\\n with open('testfiles/various_file_types/executable', 'r+') as f:\\n f.write('This changes a source file.')\\n\\n # Test verify for edited file fails with --compare-data\\n try:\\n self.verify(u'testfiles/various_file_types/executable', file_to_verify=u'executable',\\n options=[u\\\"--compare-data\\\"])\\n except CmdError as e:\\n self.assertEqual(e.exit_status, 1, str(e))\\n else:\\n self.fail('Expected CmdError not thrown')\",\n \"def isSource(self):\\n return (len(self.parents()) == 0)\",\n \"def built_file_must_not_match(self, name, contents, **kw):\\n return self.must_not_match(self.built_file_path(name, **kw), contents)\",\n \"def check_paths(self):\\r\\n\\t\\tself.check_line_edits_and_refresh_filestate()\\r\\n\\t\\t# paths\\r\\n\\t\\tsource_img_filename = self.source_img_entry.text().replace(\\\"\\\\\\\\\\\", \\\"/\\\")\\r\\n\\t\\tsink_dir_name = self.sink_dir_entry.text().replace(\\\"\\\\\\\\\\\", \\\"/\\\")\\r\\n\\t\\tsink_db_name_entry_text = self.sink_db_name_entry.text()\\r\\n\\t\\tdb_ext = \\\".db\\\" if not sink_db_name_entry_text.lower().endswith(\\\".db\\\") else \\\"\\\"\\r\\n\\t\\tsink_db_filename = os.path.join(sink_dir_name, sink_db_name_entry_text + db_ext).replace(\\\"\\\\\\\\\\\", \\\"/\\\")\\r\\n\\t\\tsource_db_filename = \\\"\\\"\\r\\n\\r\\n\\t\\t# check validity\\r\\n\\t\\tsource_img_filename_valid = self.filestate.is_valid(source_img_filename, SOURCE_IMG)\\r\\n\\t\\tsink_dir_name_valid = self.filestate.is_valid(sink_dir_name, SINK_DIR)\\r\\n\\t\\tsink_db_filename_valid = self.filestate.is_valid(sink_db_filename, SINK_DB)\\r\\n\\t\\tsource_db_filename_valid = True\\r\\n\\r\\n\\t\\tall_paths_valid = source_img_filename_valid and sink_dir_name_valid and sink_db_filename_valid\\r\\n\\r\\n\\t\\tif self.existing_case:\\r\\n\\t\\t\\tsource_db_filename = self.source_db_entry.text()\\r\\n\\t\\t\\tsource_db_filename_valid = self.filestate.is_valid(source_db_filename, SOURCE_DB)\\r\\n\\t\\t\\tall_paths_valid = all_paths_valid and source_db_filename_valid\\r\\n\\r\\n\\t\\tif all_paths_valid:\\r\\n\\t\\t\\tself.filestate.set_source_img_filename(source_img_filename)\\r\\n\\t\\t\\tself.filestate.set_sink_dir_name(sink_dir_name)\\r\\n\\t\\t\\tself.filestate.set_sink_db_filename(sink_db_filename)\\r\\n\\t\\t\\tif self.existing_case:\\r\\n\\t\\t\\t\\tself.filestate.set_source_db_filename(source_db_filename)\\r\\n\\t\\t\\tself.refresh_UI()\\r\\n\\t\\t\\treturn True\\r\\n\\r\\n\\t\\t# in the case of invalidity\\r\\n\\t\\tif not source_img_filename_valid:\\r\\n\\t\\t\\tif not self.filestate.source_img_file_exists:\\r\\n\\t\\t\\t\\tdisplay_warning_message(self, \\\"Provided source image file at does not exist.\\\")\\r\\n\\t\\t\\telif not self.filestate.source_img_file_format_valid:\\r\\n\\t\\t\\t\\tdisplay_warning_message(self, \\\"Provided source image file type is invalid (must be .npy).\\\")\\r\\n\\t\\t\\tself.filestate.set_source_img_filename(\\\"\\\")\\r\\n\\t\\tif not source_db_filename_valid: # only if existing case\\r\\n\\t\\t\\tif not self.source_db_file_exists:\\r\\n\\t\\t\\t\\tdisplay_warning_message(self, \\\"Provided source database file does not exist.\\\")\\r\\n\\t\\t\\telif not self.filestate.source_db_file_format_valid:\\r\\n\\t\\t\\t\\tdisplay_warning_message(self, \\\"Provided source database file type is invalid (must be .db)\\\")\\r\\n\\t\\t\\tself.filestate.set_source_db_filename(\\\"\\\")\\r\\n\\t\\tif not sink_dir_name_valid:\\r\\n\\t\\t\\tif not self.filestate.sink_dir_exists:\\r\\n\\t\\t\\t\\tdisplay_warning_message(self, \\\"Provided sink directory does not exist.\\\")\\r\\n\\t\\t\\telif not self.sink_dir_format_valid:\\r\\n\\t\\t\\t\\tdisplay_warning_message(self, \\\"Provided sink directory format is invalid.\\\")\\r\\n\\t\\t\\tself.filestate.set_sink_dir_name(\\\"\\\")\\r\\n\\t\\tif not sink_db_filename_valid:\\r\\n\\t\\t\\tif sink_dir_name_valid and not self.filestate.sink_db_file_preexists and \\\\\\r\\n\\t\\t\\t\\t\\tself.filestate.sink_db_file_format_valid and \\\\\\r\\n\\t\\t\\t\\t\\tdisplay_yes_no_message(self, \\\"Create file at \\\" + sink_db_filename + \\\"?\\\"):\\r\\n\\t\\t\\t\\t# create file with read write permissions\\r\\n\\t\\t\\t\\t###########################################\\r\\n\\t\\t\\t\\ttry:\\r\\n\\t\\t\\t\\t\\tsink_db_file = open(sink_db_filename, \\\"w+\\\")\\r\\n\\t\\t\\t\\t\\tsink_db_file.close()\\r\\n\\t\\t\\t\\texcept IOError as error:\\r\\n\\t\\t\\t\\t\\tdisplay_warning_message(self, \\\"Failed to create provided sink database file: \\\" + error)\\r\\n\\t\\t\\t\\t###########################################\\r\\n\\t\\t\\t\\t# set sink db filename\\r\\n\\t\\t\\t\\telse:\\r\\n\\t\\t\\t\\t\\tself.filestate.set_sink_db_filename(sink_db_filename)\\r\\n\\t\\t\\t\\t\\tself.refresh_UI()\\r\\n\\t\\t\\t\\t\\treturn True\\r\\n\\t\\t\\telif not self.filestate.sink_db_file_format_valid:\\r\\n\\t\\t\\t\\tdisplay_warning_message(self, \\\"Be sure to specify a name for the sink database.\\\")\\r\\n\\t\\t\\tself.filestate.set_sink_db_filename(\\\"\\\")\\r\\n\\r\\n\\t\\t# print(\\\"paths invalid\\\")\\r\\n\\t\\tself.refresh_UI()\\r\\n\\t\\treturn False\",\n \"def check(self):\\n with working_dir(self.build_directory):\\n self._if_ninja_target_execute(\\\"test\\\", parallel=False)\",\n \"def built_file_must_not_exist(self, name, type=None, **kw):\\n return self.must_not_exist(self.built_file_path(name, type, **kw))\",\n \"def _ShouldCopyFile(self, path):\\n\\n # check for C runtime, if desired\\n path = os.path.normcase(path)\\n dirName, fileName = os.path.split(path)\\n if fileName.startswith(\\\"msvcr\\\") and fileName.endswith(\\\".dll\\\"):\\n self.msvcRuntimeDir = dirName\\n return self.includeMSVCR\\n\\n # check the full path\\n if path in self.binIncludes:\\n return True\\n if path in self.binExcludes:\\n return False\\n\\n # check the file name by itself (with any included version numbers)\\n if fileName in self.binIncludes:\\n return True\\n if fileName in self.binExcludes:\\n return False\\n\\n # check the file name by itself (version numbers removed)\\n name = self._RemoveVersionNumbers(fileName)\\n if name in self.binIncludes:\\n return True\\n if name in self.binExcludes:\\n return False\\n\\n # check the path for inclusion/exclusion\\n for path in self.binPathIncludes:\\n if dirName.startswith(path):\\n return True\\n for path in self.binPathExcludes:\\n if dirName.startswith(path):\\n return False\\n\\n return True\",\n \"def is_source_prefix_mangled(self):\\n return self._source_prefix_mangled\",\n \"def should_clean_before_packaging(self) -> bool:\\n return False\",\n \"def is_outdated(self):\\n\\n if not self.is_done:\\n return False\\n elif not (self.input_files and self.output_files):\\n return False\\n\\n return fileutils.modified_after(self.input_files, self.output_files)\",\n \"def output_out_of_date(self):\\n if not os.path.exists(self.output_file):\\n logging.info(\\\"will generate, missing binding output file\\\")\\n return True\\n output_mtime = os.path.getmtime(self.output_file)\\n if self._any_files_newer(self.header_files, output_mtime):\\n logging.info(\\\"will generate, header files newer\\\")\\n return True\\n if self._any_files_newer(self.interface_files, output_mtime):\\n logging.info(\\\"will generate, interface files newer\\\")\\n return True\\n if self._file_newer(self.input_file, output_mtime):\\n logging.info(\\\"will generate, swig input file newer\\\")\\n return True\\n if self._file_newer(self.extensions_file, output_mtime):\\n logging.info(\\\"will generate, swig extensions file newer\\\")\\n return True\\n if self._file_newer(self.wrapper_file, output_mtime):\\n logging.info(\\\"will generate, swig wrapper file newer\\\")\\n return True\\n if self._file_newer(self.typemaps_file, output_mtime):\\n logging.info(\\\"will generate, swig typemaps file newer\\\")\\n return True\\n if self._file_newer(self.safecast_file, output_mtime):\\n logging.info(\\\"will generate, swig safecast file newer\\\")\\n return True\\n\\n # If we made it here, nothing is newer than the output file.\\n # Thus, the output file is not out of date.\\n return False\",\n \"def _check_guts_toc_mtime(attr, old, toc, last_build, pyc=0):\\n for (nm, fnm, typ) in old:\\n if mtime(fnm) > last_build:\\n print \\\"building because %s changed\\\" % fnm\\n return True\\n elif pyc and mtime(fnm[:-1]) > last_build:\\n print \\\"building because %s changed\\\" % fnm[:-1]\\n return True\\n return False\",\n \"def needs_update(self, cache_key):\\r\\n return self._read_sha(cache_key) != cache_key.hash\",\n \"def _check_file_not_used(self):\\n module_files = set(self._get_module_files())\\n referenced_files = set(self._get_manifest_referenced_files()).union(\\n set(self._get_xml_referenced_files())\\n )\\n excluded_dirs = ['static', 'test', 'tests', 'migrations']\\n no_referenced_files = [\\n f for f in (module_files - referenced_files)\\n if f.split(os.path.sep)[0] not in excluded_dirs\\n ]\\n self.msg_args = no_referenced_files\\n return not no_referenced_files\",\n \"def has_path_source(self) -> bool:\\n\\n return any(self.is_path_type(x) for x in self.parameters)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6899336","0.66479534","0.6410455","0.63760024","0.63628346","0.63059103","0.62350976","0.619009","0.61705863","0.615026","0.6144818","0.6059382","0.6036777","0.5976113","0.5958864","0.5946087","0.5920723","0.591227","0.5905643","0.58806807","0.58798784","0.58223265","0.58142906","0.58027095","0.5776974","0.5767098","0.57249373","0.5715421","0.5711625","0.5702834","0.5700682","0.5687046","0.56752735","0.56742156","0.56707025","0.5659626","0.5653463","0.5652387","0.56420857","0.56418043","0.5611331","0.56080866","0.5606509","0.56023747","0.5601759","0.56003463","0.5598104","0.5596759","0.5593968","0.5585812","0.557652","0.5576238","0.55608684","0.5547445","0.55458546","0.55301964","0.55124325","0.5508765","0.5508041","0.55053216","0.5497678","0.5480278","0.5473642","0.5469832","0.54678535","0.54656243","0.5463294","0.54603183","0.54585725","0.5455623","0.5445518","0.54443765","0.54424435","0.5438462","0.54376394","0.54371727","0.54303855","0.543035","0.5417246","0.5416573","0.5409663","0.5408774","0.53949714","0.539409","0.5391979","0.53865683","0.53863543","0.53839093","0.5377712","0.53776866","0.53759265","0.53676873","0.5354013","0.53473485","0.534439","0.5338572","0.5333624","0.5333226","0.53283924","0.53249085"],"string":"[\n \"0.6899336\",\n \"0.66479534\",\n \"0.6410455\",\n \"0.63760024\",\n \"0.63628346\",\n \"0.63059103\",\n \"0.62350976\",\n \"0.619009\",\n \"0.61705863\",\n \"0.615026\",\n \"0.6144818\",\n \"0.6059382\",\n \"0.6036777\",\n \"0.5976113\",\n \"0.5958864\",\n \"0.5946087\",\n \"0.5920723\",\n \"0.591227\",\n \"0.5905643\",\n \"0.58806807\",\n \"0.58798784\",\n \"0.58223265\",\n \"0.58142906\",\n \"0.58027095\",\n \"0.5776974\",\n \"0.5767098\",\n \"0.57249373\",\n \"0.5715421\",\n \"0.5711625\",\n \"0.5702834\",\n \"0.5700682\",\n \"0.5687046\",\n \"0.56752735\",\n \"0.56742156\",\n \"0.56707025\",\n \"0.5659626\",\n \"0.5653463\",\n \"0.5652387\",\n \"0.56420857\",\n \"0.56418043\",\n \"0.5611331\",\n \"0.56080866\",\n \"0.5606509\",\n \"0.56023747\",\n \"0.5601759\",\n \"0.56003463\",\n \"0.5598104\",\n \"0.5596759\",\n \"0.5593968\",\n \"0.5585812\",\n \"0.557652\",\n \"0.5576238\",\n \"0.55608684\",\n \"0.5547445\",\n \"0.55458546\",\n \"0.55301964\",\n \"0.55124325\",\n \"0.5508765\",\n \"0.5508041\",\n \"0.55053216\",\n \"0.5497678\",\n \"0.5480278\",\n \"0.5473642\",\n \"0.5469832\",\n \"0.54678535\",\n \"0.54656243\",\n \"0.5463294\",\n \"0.54603183\",\n \"0.54585725\",\n \"0.5455623\",\n \"0.5445518\",\n \"0.54443765\",\n \"0.54424435\",\n \"0.5438462\",\n \"0.54376394\",\n \"0.54371727\",\n \"0.54303855\",\n \"0.543035\",\n \"0.5417246\",\n \"0.5416573\",\n \"0.5409663\",\n \"0.5408774\",\n \"0.53949714\",\n \"0.539409\",\n \"0.5391979\",\n \"0.53865683\",\n \"0.53863543\",\n \"0.53839093\",\n \"0.5377712\",\n \"0.53776866\",\n \"0.53759265\",\n \"0.53676873\",\n \"0.5354013\",\n \"0.53473485\",\n \"0.534439\",\n \"0.5338572\",\n \"0.5333624\",\n \"0.5333226\",\n \"0.53283924\",\n \"0.53249085\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.78058094"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":11295,"cells":{"query":{"kind":"string","value":"Take the path to a raw json asset and convert it to target bin path."},"document":{"kind":"string","value":"def processed_json_path(path):\n return path.replace(RAW_ASSETS_PATH, ASSETS_PATH).replace('.json', '.bin')"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def processed_json_dir(path):\n return os.path.dirname(path.replace(RAW_ASSETS_PATH, ASSETS_PATH))","def processed_texture_path(path):\n return path.replace(RAW_ASSETS_PATH, ASSETS_PATH).replace('png', 'webp')","def copy_json():\n sourcePath = 'contents/external/'\n targetPath = 'build/external/'\n for base,subdirs,files in os.walk(sourcePath):\n for file in files:\n orig = os.path.join(base, file)\n if os.path.isfile(orig) and file[-5:] == '.json':\n targetBase = os.path.join(targetPath, base[len(sourcePath):])\n dest = os.path.join(targetBase, file)\n puts(\"Checking diretory %s\" % targetBase)\n if not os.path.exists(targetBase):\n puts(yellow(\"Not found! Creating...\"))\n os.makedirs(targetBase)\n puts(\"Copying from %s to %s\" % (orig, dest))\n copyfile(orig, dest)","def bin_path(self) -> Path:\n return self._root_path / \"stefan-on-software-api-client\" / \"bin\"","def processed_to_raw_path(self, processed_path):\n # Extract useful information from <path>\n stage, hash_dir, cloud_id = \\\n osp.splitext(processed_path)[0].split('/')[-3:]\n\n # Remove the tiling in the cloud_id, if any\n base_cloud_id = self.id_to_base_id(cloud_id)\n\n # Read the raw cloud data\n raw_ext = osp.splitext(self.raw_file_names_3d[0])[1]\n raw_path = osp.join(self.raw_dir, base_cloud_id + raw_ext)\n\n return raw_path","def Sourceify(path):\n return path","def get_source_file_name(json_name):\n assert json_name.endswith(JSON_EXT)\n (directory, base_name) = os.path.split(json_name)\n new_directory = os.path.relpath(directory, start=CACHE)\n new_directory = os.path.join(\"/\", new_directory)\n new_base_name = base_name[:-len(JSON_EXT)]\n i = new_base_name.rfind(\"-\")\n if i != -1:\n new_base_name = new_base_name[:i] + \".\" + new_base_name[i + 1:]\n result = os.path.join(new_directory, new_base_name)\n return result","def resourcePath(relative):\r\n try:\r\n # PyInstaller creates a temp folder and stores path in _MEIPASS\r\n base_path = sys._MEIPASS\r\n except Exception:\r\n base_path = os.path.abspath(os.path.join(os.path.dirname(sys.modules[__name__].__file__), 'assets'))\r\n\r\n return os.path.join(base_path, relative)","def Mapping2Bin(decode_cfg, jsonconfig, filename=\"\"):\n if isinstance(decode_cfg, str):\n decode_cfg = bytearray(decode_cfg)\n\n\n # get binary header data to use the correct version template from device\n _, version, _, setting = GetTemplateSetting(decode_cfg)\n\n # make empty binarray array\n _buffer = bytearray()\n # add data\n _buffer.extend(decode_cfg)\n\n if setting is not None:\n # iterate through restore data mapping\n for name in jsonconfig:\n # key must exist in both dict\n if name in setting:\n SetField(_buffer, name, setting[name], jsonconfig[name], addroffset=0, filename=filename)\n else:\n if name != 'header':\n exit(ExitCode.RESTORE_DATA_ERROR, \"Restore file '{}' contains obsolete name '{}', skipped\".format(filename, name), type_=LogType.WARNING, doexit=not args.ignorewarning)\n\n if 'cfg_crc' in setting:\n crc = GetSettingsCrc(_buffer)\n struct.pack_into(setting['cfg_crc'][0], _buffer, setting['cfg_crc'][1], crc)\n if 'cfg_crc32' in setting:\n crc32 = GetSettingsCrc32(_buffer)\n struct.pack_into(setting['cfg_crc32'][0], _buffer, setting['cfg_crc32'][1], crc32)\n return _buffer\n\n else:\n exit(ExitCode.UNSUPPORTED_VERSION,\"File '{}', Tasmota configuration version 0x{:x} not supported\".format(filename, version), type_=LogType.WARNING, doexit=not args.ignorewarning)\n\n return None","def get_target_binary():\n file_location = prompt_base(\"where is the file located?\")\n file_location = os.path.abspath(file_location)\n return file_location","def convert_json_to_flatbuffer_binary(json, schema, out_dir):\n command = [FLATC, '-o', out_dir, '-b', schema, json]\n run_subprocess(command)","def _path(name: str):\n return os.path.join(ASSET_PATH, name)","def _json_probe(srcfile):\n return json.loads(__run(srcfile))","def loadAsset(self, *args):\n\n asset = OL.loadAssemblyReference(self.name)\n return asset","def load_json(path):\n with open(normpath(path), 'r', encoding='utf-8') as file:\n return json.load(file)","def _get_json(self, path):\n cur_dir = path_stroke_fix(path)\n path = f\"{cur_dir}config/config.json\"\n return json.load(open(path, 'r'))","def _localloadjson(path: str) -> JSONType:\n with open(path, encoding=\"utf-8\") as fh:\n return json.load(fh)","def get_asset_path(test):\n return DEVICE_ASSETS_PATH + os.path.basename(test)","def normalizeNativePath(path: unicode) -> unicode:\n ...","def path(relative_path):\n try:\n # PyInstaller creates a temp folder and stores path in _MEIPASS\n base_path = sys._MEIPASS\n except Exception:\n base_path = os.path.abspath(\"./\")\n\n print(\"[RESOURCE]\", relative_path)\n rPath = os.path.join(base_path, relative_path)\n return rPath","def json_file():\r\n urlretrieve(URL, PATH)\r\n return PATH","def json_file():\r\n urlretrieve(URL, PATH)\r\n return PATH","def test_get_pathless_raw_file_name_json(self):\n\n this_pathless_file_name = probsevere_io._get_pathless_raw_file_name(\n unix_time_sec=VALID_TIME_UNIX_SEC,\n file_extension=probsevere_io.JSON_FILE_EXTENSION)\n\n self.assertTrue(this_pathless_file_name == PATHLESS_JSON_FILE_NAME)","def parse_bundle_for_file(fhir_bundle_path):\n\n with open(fhir_bundle_path, 'r', encoding='UTF-8') as f:\n bundle = bu.Bundle(json.load(f))\n return bundle","def get_asset_path(name):\n return os.path.join(constants.ROOT_DIR, 'assets', name)","def load_json_obj(path: str) -> RAW_CFG:\n with fsspec.open(path) as json_file:\n return json.load(json_file)","def bin(self, resource, id):\n self.cli.follow_redirects = False\n response, content = self.cli.request(self.url + '/' + resource + '/' + str(id) + '.bin', 'GET')\n self.cli.follow_redirects = True\n return self.cli.request(response['location'], 'GET')[1]","def setup_rawpath(job, raw_path):\n\n logging.info(f\"Destination is {raw_path}\")\n if not os.path.exists(raw_path):\n try:\n os.makedirs(raw_path)\n except OSError:\n err = f\"Couldn't create the base file path: {raw_path}. Probably a permissions error\"\n logging.error(err)\n else:\n logging.info(f\"{raw_path} exists. Adding timestamp.\")\n raw_path = os.path.join(str(job.config.RAW_PATH), f\"{job.title}_{job.stage}\")\n logging.info(f\"raw_path is {raw_path}\")\n try:\n os.makedirs(raw_path)\n except OSError:\n err = f\"Couldn't create the base file path: {raw_path}. Probably a permissions error\"\n raise OSError(err) from OSError\n return raw_path","def fixup_bin(url):\n f = open('build\\\\pop-nedry.bin', 'r+b')\n f.seek(0x1dd)\n f.write(url)\n f.close()","def dir_bin():\n return abspath('bin')","def process_json(path):\n path = os.path.abspath(os.path.expanduser(path))\n try:\n with open(path) as f:\n return json.load(f, object_hook=ascii_encode_dict)\n except ValueError as e:\n logging.error(\"File: %s\\nInvalid JSON:\\n%s\", str(path), str(e))\n raise\n except IOError as io:\n logging.error(\"Provided json file path does not exist %s\", str(path))\n raise","def test_load_path(parser):\n doc = parser.load(pathlib.Path('jsonexamples') / 'small' / 'demo.json')\n doc.at_pointer('/Image/Width')","def fullpath(data_folder, name):\n return os.path.join(data_folder, f\"{alias(name)}.json\")","def asset_path(bundle_key: str) -> str:\n asset_base_path = current_app.config.get('ASSET_BASE_PATH', '')\n asset_file = current_app.config.get('assets', {}).get(bundle_key)\n if not asset_file:\n raise LookupError(f\"Missing asset file for {bundle_key}.\")\n return os.path.join(asset_base_path, asset_file)","def from_path(cls, path: str):\n with open(path) as f:\n return json.load(f)","def import_bin(self, bin_file):\n self.__run_import_script(file=bin_file, is_bin=True)","def resourcePath(self,relative_path):\n base_path = getattr(sys, '_MEIPASS', os.path.dirname(os.path.dirname(os.path.abspath(__file__))))\n output = base_path + relative_path\n return output","def get_test_binary():\n return \"./Source/astcenc\"","def calc_fullpath(data_folder, name):\n return os.path.join(data_folder, f\"{alias(name)}.json\")","def load_json(path: Path) -> Any:\n with path.open() as f:\n return json.load(f)","def _get_json_file_content(file_dir_arg):\n result = None\n\n try:\n with open(file_dir_arg, 'r', encoding='UTF-8-SIG') as f:\n result_tmp = f\n result = load(result_tmp)\n except Exception as e:\n print(e.args)\n\n return result","def setDataPath(_path_data_bundle, _path_bin_data, preload=True, verbose=True):\n global path_bin_data\n global path_data_bundle\n path_data_bundle = _path_data_bundle\n path_bin_data = _path_bin_data\n if preload:\n loadExistent(verbose)","def from_json(path: str):\n with open(path) as f:\n return json.load(f)","def _raw_read(self, filepath, dirpath=None):\n self.json_dict = super().read(filepath)\n return self.json_dict","def test_find_raw_file_json(self):\n\n this_file_name = probsevere_io.find_raw_file(\n top_directory_name=TOP_DIRECTORY_NAME,\n unix_time_sec=VALID_TIME_UNIX_SEC,\n file_extension=probsevere_io.JSON_FILE_EXTENSION,\n raise_error_if_missing=False)\n\n self.assertTrue(this_file_name == JSON_FILE_NAME)","def assetsPath(self, pattern, source):\n return self.assets(pattern, AssetSource.create(source))","def binpath(self):\n return self.__bin","def resource_path(relative_path):\n try:\n # PyInstaller creates a temp folder and stores path in _MEIPASS \n base_path = sys._MEIPASS\n _BINARY_DIST = True\n #print sys._MEIPASS\n except Exception:\n base_path = os.path.abspath(\".\")\n\n return os.path.join(base_path, relative_path)","def asset_forward(path):\n asset = get(f'http://localhost:5001/assets/{path}', timeout=15)\n return Response(\n asset.content,\n mimetype=asset.headers['Content-Type']\n )","def resource_path(relative_path) :\n\n try :\n # PyInstaller creates a temp folder and stores path in _MEIPASS\n base_path = sys._MEIPASS\n except :\n base_path = os.path.abspath(\".\")\n return os.path.join(base_path, relative_path)","def _remoteloadjson(path: str) -> JSONType:\n return json.loads(request.urlopen(path).read())","def get_full_path(_path: str):\n if _path is None:\n return None\n\n with open(r\"bot\\data\\image_config.json\") as f:\n try:\n image_config = json.load(f)\n\n except json.decoder.JSONDecodeError as e:\n print(e)\n return None\n\n base_url = image_config.get(\"base_url\")\n poster_size = image_config.get(\"poster_sizes\")[-2]\n return f\"{base_url}{poster_size}{_path}\"","def get_script(blob):\n return get_script_class(blob.path)(source=blob.abspath)","def getAssetData(self, assetId):\n return self.get_json('/asset/%s/raw' % assetId)","def _json_txt_to_binary(self, json_text):\n message = self.message_class()\n try:\n message = json_format.Parse(json_text, message)\n binary = message.SerializeToString()\n except json_format.ParseError:\n # TODO log\n return None\n return binary","def convert_service_banner_json(json):\n\n # open json file\n with open(json, \"r\") as j:\n json_string = j.read()\n\n return json_string","def json_full_path(base_path, key):\n if base_path is None or base_path == \"\":\n return key\n else:\n return f'{base_path}.{key}'","def transcode(filePath: str) -> str:\n asset_uuid = uuid.uuid4()\n outPath = os.path.join(\"/tmp\", str(asset_uuid)+'.mp4')\n ffmpeg.input(filePath).output(outPath).run()\n return outPath","def modify_jsonyaml_paths(jsonyaml_file):\n loader = schema_salad.ref_resolver.Loader(\n {\"location\": {\"@type\": \"@id\"}, \"path\": {\"@type\": \"@id\"}}\n )\n input_dict, _ = loader.resolve_ref(jsonyaml_file, checklinks=False)\n basedir = os.path.dirname(jsonyaml_file)\n\n def fixpaths(d):\n \"\"\"Make sure all paths have a URI scheme.\"\"\"\n if isinstance(d, dict):\n if \"path\" in d:\n if \":\" not in d[\"path\"]:\n local_path = os.path.normpath(\n os.path.join(os.getcwd(), basedir, d[\"path\"])\n )\n d[\"location\"] = pathname2url(local_path)\n else:\n d[\"location\"] = d[\"path\"]\n del d[\"path\"]\n\n visit(input_dict, fixpaths)\n return json.dumps(input_dict)","def load_binarized(path, params):\n assert path.endswith('.pth')\n assert os.path.isfile(path), path\n logger.info(\"Loading dataOld from %s ...\" % path)\n data = torch.load(path)\n # dataOld = process_binarized(dataOld, params)\n return data","def resource_path(self,relative_path):\n try:\n # PyInstaller creates a temp folder and stores path in _MEIPASS\n base_path = sys._MEIPASS\n except Exception:\n base_path = os.path.abspath(\".\\\\Visual_Ressources\\\\\"+self.language+\"\\\\\") \n # \".\"\n # 'Content\\\\Back_End\\\\'\n return os.path.join(base_path, relative_path)","def _source_path_reader(self, src, encoding=\"utf-8\"):\n if src is None:\n return src\n if isinstance(src, dict) and \"content\" in src:\n with tempfile.NamedTemporaryFile(mode=\"w\", encoding=encoding, delete=False) as fp:\n fp.write(src[\"content\"])\n return fp.name\n elif isinstance(src, dict) and \"file\" in src:\n if os.path.exists(src[\"file\"]) is False:\n raise FileNotFound(src)\n return src[\"file\"]\n else:\n raise InvalidParameter(\"The parameter is invalid.\")","def assets():","def read_osu_file(path, convert=False, wav_name=\"wavfile.wav\", json_name=\"temp/temp_json_file.json\"):\n file_dir = os.path.dirname(os.path.abspath(path))\n\n # ask node.js to convert the .osu file to .json format\n result = run_command([\"node\", \"load_map.js\", \"jq\", path, json_name])\n if(len(result) > 1):\n print(result.decode(\"utf-8\"))\n raise Exception(\"Map Convert Failure\")\n\n with open(json_name, encoding=\"utf-8\") as map_json:\n map_dict = json.load(map_json)\n\n if convert:\n mp3_file = os.path.join(\n file_dir, map_dict[\"general\"][\"AudioFilename\"])\n # result = run_command([FFMPEG_PATH, \"-y\", \"-i\", mp3_file, wav_name]);\n # if(len(result) > 1):\n # print(result.decode(\"utf-8\"));\n # raise Exception(\"FFMPEG Failure\");\n\n # delete the temp json later\n # if json_name == \"temp/temp_json_file.json\":\n # os.remove(json_name);\n\n return map_dict, mp3_file","def load_json(self, unformatted_path: str):\n formatted_path = unformatted_path.format(\n experiment_folder=self.experiment_folder\n )\n if not os.path.isfile(formatted_path):\n self.dump_json(formatted_path, data={})\n with open(formatted_path, \"r\") as infile:\n json_data = json.load(infile)\n return json_data","def _resource(path): # pragma: NO COVER\n data = pkg_resources.resource_string(__name__, path)\n return data.decode(\"utf8\")","def load_variant(orig_url, orig_path, resolver, creator, **kwargs):\n output_url, output_path = resolver(orig_url, orig_path, **kwargs)\n create_path(orig_path, output_path, creator, **kwargs)\n return output_url, output_path","def test_import_system_asset(self):\n pass","def repository_pypi_json() -> Path:\n return Path()","def bundle_path(self, app):\n return (\n self.platform_path / self.output_format / safe_formal_name(app.formal_name)\n )","def resource_path(relative_path):\n try:\n # PyInstaller creates a temp folder and stores path in _MEIPASS\n base_path = sys._MEIPASS\n except Exception:\n base_path = os.path.dirname(os.path.realpath(__file__))\n \n return os.path.join(base_path, relative_path)","def verilog_to_json(verilog_filename, json_filename):\n system(f'yosys -p \"read_verilog {verilog_filename}\" '\n '-p \"write_json {json_filename}\"')","def post_asset_update(lock, course):\r\n upload_date = datetime(2013, 6, 1, 10, 30, tzinfo=UTC)\r\n asset_location = course.id.make_asset_key('asset', 'sample_static.txt')\r\n url = reverse_course_url('assets_handler', course.id, kwargs={'asset_key_string': unicode(asset_location)})\r\n\r\n resp = self.client.post(\r\n url,\r\n json.dumps(assets._get_asset_json(\"sample_static.txt\", upload_date, asset_location, None, lock)),\r\n \"application/json\"\r\n )\r\n self.assertEqual(resp.status_code, 201)\r\n return json.loads(resp.content)","def get_raw_path(self):\n\n return self.raw_img_path","def transcode(self, src: Path, dest: Path) -> None:\n pass","def resourcePath(relative, dirname=\"data\"):\n # first look in pyinstaller bundle\n if hasattr(sys, \"_MEIPASS\"):\n path = os.path.join(sys._MEIPASS, dirname)\n \n else:\n # then look in py2app bundle\n path = os.environ.get(\"RESOURCEPATH\", None)\n if path is None:\n # then look in source code directory\n path = os.path.join(RESOURCE_BASE, dirname)\n \n path = os.path.join(path, relative)\n \n return path","def _get_data_file(self, data_path):\n\n return json.load(open(data_path))","def resource_path(relative_path) :\n\n try :\n # PyInstaller creates a temp folder and stores path in _MEIPASS\n base_path = sys._MEIPASS\n except :\n base_path = os.path.abspath(\".\")\n return os.path.join(base_path, relative_path)","def parse_json_from_path(self, infile_path):\r\n with open(infile_path, 'r') as infile:\r\n return self.parse_from_json(infile)","def minify_json(self) -> None:\n print(f\"[FileManager: INFO] Minifing file {self.path}\")\n file_data = open(self.path, \"r\", encoding=\"utf-8\").read()\n json_data = json.loads(file_data) \n json_string = json.dumps(json_data, separators=(',', \":\")) \n path = str(self.path).replace(\".json\", \"\")\n new_path = \"{0}_min.json\".format(path)\n open(new_path, \"w+\", encoding=\"utf-8\").write(json_string)","def get_file_path(bin_name, dir_path=\"\"):\n with open('../../../.build_vars.json') as json_file:\n build_paths = json.load(json_file)\n basepath = os.path.normpath(build_paths['PREFIX'] + \"/../{0}\"\n .format(dir_path))\n\n file_path = list(Path(basepath).glob('**/{0}'.format(bin_name)))\n if not file_path:\n raise OSError(ENOENT, \"File {0} not found inside {1} Directory\"\n .format(bin_name, basepath))\n else:\n return file_path","def change_pic(link_to:str):\n with open(os.path.dirname(os.path.abspath(__file__))+'\\\\data.json', 'r') as test:\n f = json.load(test)\n f['button_pic'] = link_to\n with open(os.path.dirname(os.path.abspath(__file__))+'\\\\data.json', 'w') as test:\n json.dump(f, test, indent=2)","def load(src_path):\n satdat = rasterio.open(src_path)\n return satdat","def _make_code(self, name, code):\n path = os.path.join(self.meta, name)\n data = code.encode('utf8') if isinstance(code, unicode) else code\n with open(path, 'w') as f:\n f.write(data)\n return path","def convert_realia():\n local('cd import_scripts;../bin/python import_realia.py')","def resource_path(relative_path):\n try:\n # PyInstaller creates a temp folder and stores path in _MEIPASS\n base_path = sys._MEIPASS\n except Exception:\n base_path = os.path.abspath(\".\")\n\n return os.path.join(base_path, relative_path)\n\t# \"\"\" Get absolute path to resource, works for dev and for PyInstaller \"\"\"\n\t# base_path = getattr(sys, '_MEIPASS', os.path.dirname(os.path.abspath(__file__)))\n\t# return os.path.join(base_path, relative_path)","def resource_path(relative_path):\n try:\n # PyInstaller creates a temp folder and stores path in _MEIPASS\n base_path = sys._MEIPASS\n except Exception:\n base_path = os.path.abspath(\"\")\n\n return os.path.join(base_path, relative_path)","def load_json(path, name):\n if 'txt' not in name:\n name += '.json'\n with open(os.path.join(path, name), 'r') as json_file:\n return json.load(json_file)","def get_reference_binary():\n return \"./Binary/linux-x64/astcenc\"","def source(request, filepath_pseudos):\n filepath_pseudo = pathlib.Path(filepath_pseudos()) / 'Ar.upf'\n\n if request.param is str:\n return str(filepath_pseudo)\n\n if request.param is pathlib.Path:\n return filepath_pseudo\n\n return io.BytesIO(filepath_pseudo.read_bytes())","def mangle_path(path):\n # Remove assigns\n path = servers.get_file_server().manglepath( str(path) )\n # Remove parent special directories\n path = os.path.abspath( path )\n # Convert path to Nebula format (slashes instead of backslashes)\n path = servers.get_file_server().manglepath( str(path) )\n # Convert drive letter to lowercase\n if len(path) > 1:\n if path[1] == ':':\n path = path[:1].lower() + path[1:]\n return path","def mapillary_raw_sample():\n return \"tests/data/mapillary/sample/\"","def test_file_asset(self):\n proto = struct_pb2.Struct()\n \n # pylint: disable=no-member\n subproto = proto.get_or_create_struct(\"asset\")\n subproto[rpc._special_sig_key] = rpc._special_asset_sig\n subproto[\"path\"] = \"foo.txt\"\n deserialized = rpc.deserialize_resource_props(proto)\n self.assertIsInstance(deserialized[\"asset\"], FileAsset)\n self.assertEqual(\"foo.txt\", deserialized[\"asset\"].path)","def resource_path(relative_path):\n try:\n # PyInstaller creates a temp folder and stores path in _MEIPASS\n base_path = sys._MEIPASS # pylint: disable=no-member\n except Exception:\n base_path = os.path.abspath(\".\")\n\n return os.path.join(base_path, relative_path)","def resource_path(relative_path):\n try:\n # PyInstaller creates a temp folder and stores path in _MEIPASS\n base_path = sys._MEIPASS\n except Exception:\n base_path = os.path.abspath(\".\")\n\n return os.path.join(base_path, relative_path)","def resource_path(relative_path):\n try:\n # PyInstaller creates a temp folder and stores path in _MEIPASS\n base_path = sys._MEIPASS\n except Exception:\n base_path = os.path.abspath(\".\")\n\n return os.path.join(base_path, relative_path)","def resource_path(relative_path):\n try:\n # PyInstaller creates a temp folder and stores path in _MEIPASS\n base_path = sys._MEIPASS\n except Exception:\n base_path = os.path.abspath(\".\")\n\n return os.path.join(base_path, relative_path)","def resource_path(relative_path):\n try:\n # PyInstaller creates a temp folder and stores path in _MEIPASS\n base_path = sys._MEIPASS\n except Exception:\n base_path = os.path.abspath(\".\")\n\n return os.path.join(base_path, relative_path)","def resource_path(relative_path):\n try:\n # PyInstaller creates a temp folder and stores path in _MEIPASS\n base_path = sys._MEIPASS\n except Exception:\n base_path = os.path.abspath(\".\")\n\n return os.path.join(base_path, relative_path)","def resource_path(relative_path):\n try:\n # PyInstaller creates a temp folder and stores path in _MEIPASS\n base_path = sys._MEIPASS\n except Exception:\n base_path = os.path.abspath(\".\")\n\n return os.path.join(base_path, relative_path)"],"string":"[\n \"def processed_json_dir(path):\\n return os.path.dirname(path.replace(RAW_ASSETS_PATH, ASSETS_PATH))\",\n \"def processed_texture_path(path):\\n return path.replace(RAW_ASSETS_PATH, ASSETS_PATH).replace('png', 'webp')\",\n \"def copy_json():\\n sourcePath = 'contents/external/'\\n targetPath = 'build/external/'\\n for base,subdirs,files in os.walk(sourcePath):\\n for file in files:\\n orig = os.path.join(base, file)\\n if os.path.isfile(orig) and file[-5:] == '.json':\\n targetBase = os.path.join(targetPath, base[len(sourcePath):])\\n dest = os.path.join(targetBase, file)\\n puts(\\\"Checking diretory %s\\\" % targetBase)\\n if not os.path.exists(targetBase):\\n puts(yellow(\\\"Not found! Creating...\\\"))\\n os.makedirs(targetBase)\\n puts(\\\"Copying from %s to %s\\\" % (orig, dest))\\n copyfile(orig, dest)\",\n \"def bin_path(self) -> Path:\\n return self._root_path / \\\"stefan-on-software-api-client\\\" / \\\"bin\\\"\",\n \"def processed_to_raw_path(self, processed_path):\\n # Extract useful information from <path>\\n stage, hash_dir, cloud_id = \\\\\\n osp.splitext(processed_path)[0].split('/')[-3:]\\n\\n # Remove the tiling in the cloud_id, if any\\n base_cloud_id = self.id_to_base_id(cloud_id)\\n\\n # Read the raw cloud data\\n raw_ext = osp.splitext(self.raw_file_names_3d[0])[1]\\n raw_path = osp.join(self.raw_dir, base_cloud_id + raw_ext)\\n\\n return raw_path\",\n \"def Sourceify(path):\\n return path\",\n \"def get_source_file_name(json_name):\\n assert json_name.endswith(JSON_EXT)\\n (directory, base_name) = os.path.split(json_name)\\n new_directory = os.path.relpath(directory, start=CACHE)\\n new_directory = os.path.join(\\\"/\\\", new_directory)\\n new_base_name = base_name[:-len(JSON_EXT)]\\n i = new_base_name.rfind(\\\"-\\\")\\n if i != -1:\\n new_base_name = new_base_name[:i] + \\\".\\\" + new_base_name[i + 1:]\\n result = os.path.join(new_directory, new_base_name)\\n return result\",\n \"def resourcePath(relative):\\r\\n try:\\r\\n # PyInstaller creates a temp folder and stores path in _MEIPASS\\r\\n base_path = sys._MEIPASS\\r\\n except Exception:\\r\\n base_path = os.path.abspath(os.path.join(os.path.dirname(sys.modules[__name__].__file__), 'assets'))\\r\\n\\r\\n return os.path.join(base_path, relative)\",\n \"def Mapping2Bin(decode_cfg, jsonconfig, filename=\\\"\\\"):\\n if isinstance(decode_cfg, str):\\n decode_cfg = bytearray(decode_cfg)\\n\\n\\n # get binary header data to use the correct version template from device\\n _, version, _, setting = GetTemplateSetting(decode_cfg)\\n\\n # make empty binarray array\\n _buffer = bytearray()\\n # add data\\n _buffer.extend(decode_cfg)\\n\\n if setting is not None:\\n # iterate through restore data mapping\\n for name in jsonconfig:\\n # key must exist in both dict\\n if name in setting:\\n SetField(_buffer, name, setting[name], jsonconfig[name], addroffset=0, filename=filename)\\n else:\\n if name != 'header':\\n exit(ExitCode.RESTORE_DATA_ERROR, \\\"Restore file '{}' contains obsolete name '{}', skipped\\\".format(filename, name), type_=LogType.WARNING, doexit=not args.ignorewarning)\\n\\n if 'cfg_crc' in setting:\\n crc = GetSettingsCrc(_buffer)\\n struct.pack_into(setting['cfg_crc'][0], _buffer, setting['cfg_crc'][1], crc)\\n if 'cfg_crc32' in setting:\\n crc32 = GetSettingsCrc32(_buffer)\\n struct.pack_into(setting['cfg_crc32'][0], _buffer, setting['cfg_crc32'][1], crc32)\\n return _buffer\\n\\n else:\\n exit(ExitCode.UNSUPPORTED_VERSION,\\\"File '{}', Tasmota configuration version 0x{:x} not supported\\\".format(filename, version), type_=LogType.WARNING, doexit=not args.ignorewarning)\\n\\n return None\",\n \"def get_target_binary():\\n file_location = prompt_base(\\\"where is the file located?\\\")\\n file_location = os.path.abspath(file_location)\\n return file_location\",\n \"def convert_json_to_flatbuffer_binary(json, schema, out_dir):\\n command = [FLATC, '-o', out_dir, '-b', schema, json]\\n run_subprocess(command)\",\n \"def _path(name: str):\\n return os.path.join(ASSET_PATH, name)\",\n \"def _json_probe(srcfile):\\n return json.loads(__run(srcfile))\",\n \"def loadAsset(self, *args):\\n\\n asset = OL.loadAssemblyReference(self.name)\\n return asset\",\n \"def load_json(path):\\n with open(normpath(path), 'r', encoding='utf-8') as file:\\n return json.load(file)\",\n \"def _get_json(self, path):\\n cur_dir = path_stroke_fix(path)\\n path = f\\\"{cur_dir}config/config.json\\\"\\n return json.load(open(path, 'r'))\",\n \"def _localloadjson(path: str) -> JSONType:\\n with open(path, encoding=\\\"utf-8\\\") as fh:\\n return json.load(fh)\",\n \"def get_asset_path(test):\\n return DEVICE_ASSETS_PATH + os.path.basename(test)\",\n \"def normalizeNativePath(path: unicode) -> unicode:\\n ...\",\n \"def path(relative_path):\\n try:\\n # PyInstaller creates a temp folder and stores path in _MEIPASS\\n base_path = sys._MEIPASS\\n except Exception:\\n base_path = os.path.abspath(\\\"./\\\")\\n\\n print(\\\"[RESOURCE]\\\", relative_path)\\n rPath = os.path.join(base_path, relative_path)\\n return rPath\",\n \"def json_file():\\r\\n urlretrieve(URL, PATH)\\r\\n return PATH\",\n \"def json_file():\\r\\n urlretrieve(URL, PATH)\\r\\n return PATH\",\n \"def test_get_pathless_raw_file_name_json(self):\\n\\n this_pathless_file_name = probsevere_io._get_pathless_raw_file_name(\\n unix_time_sec=VALID_TIME_UNIX_SEC,\\n file_extension=probsevere_io.JSON_FILE_EXTENSION)\\n\\n self.assertTrue(this_pathless_file_name == PATHLESS_JSON_FILE_NAME)\",\n \"def parse_bundle_for_file(fhir_bundle_path):\\n\\n with open(fhir_bundle_path, 'r', encoding='UTF-8') as f:\\n bundle = bu.Bundle(json.load(f))\\n return bundle\",\n \"def get_asset_path(name):\\n return os.path.join(constants.ROOT_DIR, 'assets', name)\",\n \"def load_json_obj(path: str) -> RAW_CFG:\\n with fsspec.open(path) as json_file:\\n return json.load(json_file)\",\n \"def bin(self, resource, id):\\n self.cli.follow_redirects = False\\n response, content = self.cli.request(self.url + '/' + resource + '/' + str(id) + '.bin', 'GET')\\n self.cli.follow_redirects = True\\n return self.cli.request(response['location'], 'GET')[1]\",\n \"def setup_rawpath(job, raw_path):\\n\\n logging.info(f\\\"Destination is {raw_path}\\\")\\n if not os.path.exists(raw_path):\\n try:\\n os.makedirs(raw_path)\\n except OSError:\\n err = f\\\"Couldn't create the base file path: {raw_path}. Probably a permissions error\\\"\\n logging.error(err)\\n else:\\n logging.info(f\\\"{raw_path} exists. Adding timestamp.\\\")\\n raw_path = os.path.join(str(job.config.RAW_PATH), f\\\"{job.title}_{job.stage}\\\")\\n logging.info(f\\\"raw_path is {raw_path}\\\")\\n try:\\n os.makedirs(raw_path)\\n except OSError:\\n err = f\\\"Couldn't create the base file path: {raw_path}. Probably a permissions error\\\"\\n raise OSError(err) from OSError\\n return raw_path\",\n \"def fixup_bin(url):\\n f = open('build\\\\\\\\pop-nedry.bin', 'r+b')\\n f.seek(0x1dd)\\n f.write(url)\\n f.close()\",\n \"def dir_bin():\\n return abspath('bin')\",\n \"def process_json(path):\\n path = os.path.abspath(os.path.expanduser(path))\\n try:\\n with open(path) as f:\\n return json.load(f, object_hook=ascii_encode_dict)\\n except ValueError as e:\\n logging.error(\\\"File: %s\\\\nInvalid JSON:\\\\n%s\\\", str(path), str(e))\\n raise\\n except IOError as io:\\n logging.error(\\\"Provided json file path does not exist %s\\\", str(path))\\n raise\",\n \"def test_load_path(parser):\\n doc = parser.load(pathlib.Path('jsonexamples') / 'small' / 'demo.json')\\n doc.at_pointer('/Image/Width')\",\n \"def fullpath(data_folder, name):\\n return os.path.join(data_folder, f\\\"{alias(name)}.json\\\")\",\n \"def asset_path(bundle_key: str) -> str:\\n asset_base_path = current_app.config.get('ASSET_BASE_PATH', '')\\n asset_file = current_app.config.get('assets', {}).get(bundle_key)\\n if not asset_file:\\n raise LookupError(f\\\"Missing asset file for {bundle_key}.\\\")\\n return os.path.join(asset_base_path, asset_file)\",\n \"def from_path(cls, path: str):\\n with open(path) as f:\\n return json.load(f)\",\n \"def import_bin(self, bin_file):\\n self.__run_import_script(file=bin_file, is_bin=True)\",\n \"def resourcePath(self,relative_path):\\n base_path = getattr(sys, '_MEIPASS', os.path.dirname(os.path.dirname(os.path.abspath(__file__))))\\n output = base_path + relative_path\\n return output\",\n \"def get_test_binary():\\n return \\\"./Source/astcenc\\\"\",\n \"def calc_fullpath(data_folder, name):\\n return os.path.join(data_folder, f\\\"{alias(name)}.json\\\")\",\n \"def load_json(path: Path) -> Any:\\n with path.open() as f:\\n return json.load(f)\",\n \"def _get_json_file_content(file_dir_arg):\\n result = None\\n\\n try:\\n with open(file_dir_arg, 'r', encoding='UTF-8-SIG') as f:\\n result_tmp = f\\n result = load(result_tmp)\\n except Exception as e:\\n print(e.args)\\n\\n return result\",\n \"def setDataPath(_path_data_bundle, _path_bin_data, preload=True, verbose=True):\\n global path_bin_data\\n global path_data_bundle\\n path_data_bundle = _path_data_bundle\\n path_bin_data = _path_bin_data\\n if preload:\\n loadExistent(verbose)\",\n \"def from_json(path: str):\\n with open(path) as f:\\n return json.load(f)\",\n \"def _raw_read(self, filepath, dirpath=None):\\n self.json_dict = super().read(filepath)\\n return self.json_dict\",\n \"def test_find_raw_file_json(self):\\n\\n this_file_name = probsevere_io.find_raw_file(\\n top_directory_name=TOP_DIRECTORY_NAME,\\n unix_time_sec=VALID_TIME_UNIX_SEC,\\n file_extension=probsevere_io.JSON_FILE_EXTENSION,\\n raise_error_if_missing=False)\\n\\n self.assertTrue(this_file_name == JSON_FILE_NAME)\",\n \"def assetsPath(self, pattern, source):\\n return self.assets(pattern, AssetSource.create(source))\",\n \"def binpath(self):\\n return self.__bin\",\n \"def resource_path(relative_path):\\n try:\\n # PyInstaller creates a temp folder and stores path in _MEIPASS \\n base_path = sys._MEIPASS\\n _BINARY_DIST = True\\n #print sys._MEIPASS\\n except Exception:\\n base_path = os.path.abspath(\\\".\\\")\\n\\n return os.path.join(base_path, relative_path)\",\n \"def asset_forward(path):\\n asset = get(f'http://localhost:5001/assets/{path}', timeout=15)\\n return Response(\\n asset.content,\\n mimetype=asset.headers['Content-Type']\\n )\",\n \"def resource_path(relative_path) :\\n\\n try :\\n # PyInstaller creates a temp folder and stores path in _MEIPASS\\n base_path = sys._MEIPASS\\n except :\\n base_path = os.path.abspath(\\\".\\\")\\n return os.path.join(base_path, relative_path)\",\n \"def _remoteloadjson(path: str) -> JSONType:\\n return json.loads(request.urlopen(path).read())\",\n \"def get_full_path(_path: str):\\n if _path is None:\\n return None\\n\\n with open(r\\\"bot\\\\data\\\\image_config.json\\\") as f:\\n try:\\n image_config = json.load(f)\\n\\n except json.decoder.JSONDecodeError as e:\\n print(e)\\n return None\\n\\n base_url = image_config.get(\\\"base_url\\\")\\n poster_size = image_config.get(\\\"poster_sizes\\\")[-2]\\n return f\\\"{base_url}{poster_size}{_path}\\\"\",\n \"def get_script(blob):\\n return get_script_class(blob.path)(source=blob.abspath)\",\n \"def getAssetData(self, assetId):\\n return self.get_json('/asset/%s/raw' % assetId)\",\n \"def _json_txt_to_binary(self, json_text):\\n message = self.message_class()\\n try:\\n message = json_format.Parse(json_text, message)\\n binary = message.SerializeToString()\\n except json_format.ParseError:\\n # TODO log\\n return None\\n return binary\",\n \"def convert_service_banner_json(json):\\n\\n # open json file\\n with open(json, \\\"r\\\") as j:\\n json_string = j.read()\\n\\n return json_string\",\n \"def json_full_path(base_path, key):\\n if base_path is None or base_path == \\\"\\\":\\n return key\\n else:\\n return f'{base_path}.{key}'\",\n \"def transcode(filePath: str) -> str:\\n asset_uuid = uuid.uuid4()\\n outPath = os.path.join(\\\"/tmp\\\", str(asset_uuid)+'.mp4')\\n ffmpeg.input(filePath).output(outPath).run()\\n return outPath\",\n \"def modify_jsonyaml_paths(jsonyaml_file):\\n loader = schema_salad.ref_resolver.Loader(\\n {\\\"location\\\": {\\\"@type\\\": \\\"@id\\\"}, \\\"path\\\": {\\\"@type\\\": \\\"@id\\\"}}\\n )\\n input_dict, _ = loader.resolve_ref(jsonyaml_file, checklinks=False)\\n basedir = os.path.dirname(jsonyaml_file)\\n\\n def fixpaths(d):\\n \\\"\\\"\\\"Make sure all paths have a URI scheme.\\\"\\\"\\\"\\n if isinstance(d, dict):\\n if \\\"path\\\" in d:\\n if \\\":\\\" not in d[\\\"path\\\"]:\\n local_path = os.path.normpath(\\n os.path.join(os.getcwd(), basedir, d[\\\"path\\\"])\\n )\\n d[\\\"location\\\"] = pathname2url(local_path)\\n else:\\n d[\\\"location\\\"] = d[\\\"path\\\"]\\n del d[\\\"path\\\"]\\n\\n visit(input_dict, fixpaths)\\n return json.dumps(input_dict)\",\n \"def load_binarized(path, params):\\n assert path.endswith('.pth')\\n assert os.path.isfile(path), path\\n logger.info(\\\"Loading dataOld from %s ...\\\" % path)\\n data = torch.load(path)\\n # dataOld = process_binarized(dataOld, params)\\n return data\",\n \"def resource_path(self,relative_path):\\n try:\\n # PyInstaller creates a temp folder and stores path in _MEIPASS\\n base_path = sys._MEIPASS\\n except Exception:\\n base_path = os.path.abspath(\\\".\\\\\\\\Visual_Ressources\\\\\\\\\\\"+self.language+\\\"\\\\\\\\\\\") \\n # \\\".\\\"\\n # 'Content\\\\\\\\Back_End\\\\\\\\'\\n return os.path.join(base_path, relative_path)\",\n \"def _source_path_reader(self, src, encoding=\\\"utf-8\\\"):\\n if src is None:\\n return src\\n if isinstance(src, dict) and \\\"content\\\" in src:\\n with tempfile.NamedTemporaryFile(mode=\\\"w\\\", encoding=encoding, delete=False) as fp:\\n fp.write(src[\\\"content\\\"])\\n return fp.name\\n elif isinstance(src, dict) and \\\"file\\\" in src:\\n if os.path.exists(src[\\\"file\\\"]) is False:\\n raise FileNotFound(src)\\n return src[\\\"file\\\"]\\n else:\\n raise InvalidParameter(\\\"The parameter is invalid.\\\")\",\n \"def assets():\",\n \"def read_osu_file(path, convert=False, wav_name=\\\"wavfile.wav\\\", json_name=\\\"temp/temp_json_file.json\\\"):\\n file_dir = os.path.dirname(os.path.abspath(path))\\n\\n # ask node.js to convert the .osu file to .json format\\n result = run_command([\\\"node\\\", \\\"load_map.js\\\", \\\"jq\\\", path, json_name])\\n if(len(result) > 1):\\n print(result.decode(\\\"utf-8\\\"))\\n raise Exception(\\\"Map Convert Failure\\\")\\n\\n with open(json_name, encoding=\\\"utf-8\\\") as map_json:\\n map_dict = json.load(map_json)\\n\\n if convert:\\n mp3_file = os.path.join(\\n file_dir, map_dict[\\\"general\\\"][\\\"AudioFilename\\\"])\\n # result = run_command([FFMPEG_PATH, \\\"-y\\\", \\\"-i\\\", mp3_file, wav_name]);\\n # if(len(result) > 1):\\n # print(result.decode(\\\"utf-8\\\"));\\n # raise Exception(\\\"FFMPEG Failure\\\");\\n\\n # delete the temp json later\\n # if json_name == \\\"temp/temp_json_file.json\\\":\\n # os.remove(json_name);\\n\\n return map_dict, mp3_file\",\n \"def load_json(self, unformatted_path: str):\\n formatted_path = unformatted_path.format(\\n experiment_folder=self.experiment_folder\\n )\\n if not os.path.isfile(formatted_path):\\n self.dump_json(formatted_path, data={})\\n with open(formatted_path, \\\"r\\\") as infile:\\n json_data = json.load(infile)\\n return json_data\",\n \"def _resource(path): # pragma: NO COVER\\n data = pkg_resources.resource_string(__name__, path)\\n return data.decode(\\\"utf8\\\")\",\n \"def load_variant(orig_url, orig_path, resolver, creator, **kwargs):\\n output_url, output_path = resolver(orig_url, orig_path, **kwargs)\\n create_path(orig_path, output_path, creator, **kwargs)\\n return output_url, output_path\",\n \"def test_import_system_asset(self):\\n pass\",\n \"def repository_pypi_json() -> Path:\\n return Path()\",\n \"def bundle_path(self, app):\\n return (\\n self.platform_path / self.output_format / safe_formal_name(app.formal_name)\\n )\",\n \"def resource_path(relative_path):\\n try:\\n # PyInstaller creates a temp folder and stores path in _MEIPASS\\n base_path = sys._MEIPASS\\n except Exception:\\n base_path = os.path.dirname(os.path.realpath(__file__))\\n \\n return os.path.join(base_path, relative_path)\",\n \"def verilog_to_json(verilog_filename, json_filename):\\n system(f'yosys -p \\\"read_verilog {verilog_filename}\\\" '\\n '-p \\\"write_json {json_filename}\\\"')\",\n \"def post_asset_update(lock, course):\\r\\n upload_date = datetime(2013, 6, 1, 10, 30, tzinfo=UTC)\\r\\n asset_location = course.id.make_asset_key('asset', 'sample_static.txt')\\r\\n url = reverse_course_url('assets_handler', course.id, kwargs={'asset_key_string': unicode(asset_location)})\\r\\n\\r\\n resp = self.client.post(\\r\\n url,\\r\\n json.dumps(assets._get_asset_json(\\\"sample_static.txt\\\", upload_date, asset_location, None, lock)),\\r\\n \\\"application/json\\\"\\r\\n )\\r\\n self.assertEqual(resp.status_code, 201)\\r\\n return json.loads(resp.content)\",\n \"def get_raw_path(self):\\n\\n return self.raw_img_path\",\n \"def transcode(self, src: Path, dest: Path) -> None:\\n pass\",\n \"def resourcePath(relative, dirname=\\\"data\\\"):\\n # first look in pyinstaller bundle\\n if hasattr(sys, \\\"_MEIPASS\\\"):\\n path = os.path.join(sys._MEIPASS, dirname)\\n \\n else:\\n # then look in py2app bundle\\n path = os.environ.get(\\\"RESOURCEPATH\\\", None)\\n if path is None:\\n # then look in source code directory\\n path = os.path.join(RESOURCE_BASE, dirname)\\n \\n path = os.path.join(path, relative)\\n \\n return path\",\n \"def _get_data_file(self, data_path):\\n\\n return json.load(open(data_path))\",\n \"def resource_path(relative_path) :\\n\\n try :\\n # PyInstaller creates a temp folder and stores path in _MEIPASS\\n base_path = sys._MEIPASS\\n except :\\n base_path = os.path.abspath(\\\".\\\")\\n return os.path.join(base_path, relative_path)\",\n \"def parse_json_from_path(self, infile_path):\\r\\n with open(infile_path, 'r') as infile:\\r\\n return self.parse_from_json(infile)\",\n \"def minify_json(self) -> None:\\n print(f\\\"[FileManager: INFO] Minifing file {self.path}\\\")\\n file_data = open(self.path, \\\"r\\\", encoding=\\\"utf-8\\\").read()\\n json_data = json.loads(file_data) \\n json_string = json.dumps(json_data, separators=(',', \\\":\\\")) \\n path = str(self.path).replace(\\\".json\\\", \\\"\\\")\\n new_path = \\\"{0}_min.json\\\".format(path)\\n open(new_path, \\\"w+\\\", encoding=\\\"utf-8\\\").write(json_string)\",\n \"def get_file_path(bin_name, dir_path=\\\"\\\"):\\n with open('../../../.build_vars.json') as json_file:\\n build_paths = json.load(json_file)\\n basepath = os.path.normpath(build_paths['PREFIX'] + \\\"/../{0}\\\"\\n .format(dir_path))\\n\\n file_path = list(Path(basepath).glob('**/{0}'.format(bin_name)))\\n if not file_path:\\n raise OSError(ENOENT, \\\"File {0} not found inside {1} Directory\\\"\\n .format(bin_name, basepath))\\n else:\\n return file_path\",\n \"def change_pic(link_to:str):\\n with open(os.path.dirname(os.path.abspath(__file__))+'\\\\\\\\data.json', 'r') as test:\\n f = json.load(test)\\n f['button_pic'] = link_to\\n with open(os.path.dirname(os.path.abspath(__file__))+'\\\\\\\\data.json', 'w') as test:\\n json.dump(f, test, indent=2)\",\n \"def load(src_path):\\n satdat = rasterio.open(src_path)\\n return satdat\",\n \"def _make_code(self, name, code):\\n path = os.path.join(self.meta, name)\\n data = code.encode('utf8') if isinstance(code, unicode) else code\\n with open(path, 'w') as f:\\n f.write(data)\\n return path\",\n \"def convert_realia():\\n local('cd import_scripts;../bin/python import_realia.py')\",\n \"def resource_path(relative_path):\\n try:\\n # PyInstaller creates a temp folder and stores path in _MEIPASS\\n base_path = sys._MEIPASS\\n except Exception:\\n base_path = os.path.abspath(\\\".\\\")\\n\\n return os.path.join(base_path, relative_path)\\n\\t# \\\"\\\"\\\" Get absolute path to resource, works for dev and for PyInstaller \\\"\\\"\\\"\\n\\t# base_path = getattr(sys, '_MEIPASS', os.path.dirname(os.path.abspath(__file__)))\\n\\t# return os.path.join(base_path, relative_path)\",\n \"def resource_path(relative_path):\\n try:\\n # PyInstaller creates a temp folder and stores path in _MEIPASS\\n base_path = sys._MEIPASS\\n except Exception:\\n base_path = os.path.abspath(\\\"\\\")\\n\\n return os.path.join(base_path, relative_path)\",\n \"def load_json(path, name):\\n if 'txt' not in name:\\n name += '.json'\\n with open(os.path.join(path, name), 'r') as json_file:\\n return json.load(json_file)\",\n \"def get_reference_binary():\\n return \\\"./Binary/linux-x64/astcenc\\\"\",\n \"def source(request, filepath_pseudos):\\n filepath_pseudo = pathlib.Path(filepath_pseudos()) / 'Ar.upf'\\n\\n if request.param is str:\\n return str(filepath_pseudo)\\n\\n if request.param is pathlib.Path:\\n return filepath_pseudo\\n\\n return io.BytesIO(filepath_pseudo.read_bytes())\",\n \"def mangle_path(path):\\n # Remove assigns\\n path = servers.get_file_server().manglepath( str(path) )\\n # Remove parent special directories\\n path = os.path.abspath( path )\\n # Convert path to Nebula format (slashes instead of backslashes)\\n path = servers.get_file_server().manglepath( str(path) )\\n # Convert drive letter to lowercase\\n if len(path) > 1:\\n if path[1] == ':':\\n path = path[:1].lower() + path[1:]\\n return path\",\n \"def mapillary_raw_sample():\\n return \\\"tests/data/mapillary/sample/\\\"\",\n \"def test_file_asset(self):\\n proto = struct_pb2.Struct()\\n \\n # pylint: disable=no-member\\n subproto = proto.get_or_create_struct(\\\"asset\\\")\\n subproto[rpc._special_sig_key] = rpc._special_asset_sig\\n subproto[\\\"path\\\"] = \\\"foo.txt\\\"\\n deserialized = rpc.deserialize_resource_props(proto)\\n self.assertIsInstance(deserialized[\\\"asset\\\"], FileAsset)\\n self.assertEqual(\\\"foo.txt\\\", deserialized[\\\"asset\\\"].path)\",\n \"def resource_path(relative_path):\\n try:\\n # PyInstaller creates a temp folder and stores path in _MEIPASS\\n base_path = sys._MEIPASS # pylint: disable=no-member\\n except Exception:\\n base_path = os.path.abspath(\\\".\\\")\\n\\n return os.path.join(base_path, relative_path)\",\n \"def resource_path(relative_path):\\n try:\\n # PyInstaller creates a temp folder and stores path in _MEIPASS\\n base_path = sys._MEIPASS\\n except Exception:\\n base_path = os.path.abspath(\\\".\\\")\\n\\n return os.path.join(base_path, relative_path)\",\n \"def resource_path(relative_path):\\n try:\\n # PyInstaller creates a temp folder and stores path in _MEIPASS\\n base_path = sys._MEIPASS\\n except Exception:\\n base_path = os.path.abspath(\\\".\\\")\\n\\n return os.path.join(base_path, relative_path)\",\n \"def resource_path(relative_path):\\n try:\\n # PyInstaller creates a temp folder and stores path in _MEIPASS\\n base_path = sys._MEIPASS\\n except Exception:\\n base_path = os.path.abspath(\\\".\\\")\\n\\n return os.path.join(base_path, relative_path)\",\n \"def resource_path(relative_path):\\n try:\\n # PyInstaller creates a temp folder and stores path in _MEIPASS\\n base_path = sys._MEIPASS\\n except Exception:\\n base_path = os.path.abspath(\\\".\\\")\\n\\n return os.path.join(base_path, relative_path)\",\n \"def resource_path(relative_path):\\n try:\\n # PyInstaller creates a temp folder and stores path in _MEIPASS\\n base_path = sys._MEIPASS\\n except Exception:\\n base_path = os.path.abspath(\\\".\\\")\\n\\n return os.path.join(base_path, relative_path)\",\n \"def resource_path(relative_path):\\n try:\\n # PyInstaller creates a temp folder and stores path in _MEIPASS\\n base_path = sys._MEIPASS\\n except Exception:\\n base_path = os.path.abspath(\\\".\\\")\\n\\n return os.path.join(base_path, relative_path)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.61563104","0.57579714","0.54303104","0.5163897","0.5136838","0.5134525","0.50500184","0.50419426","0.50137776","0.50136286","0.50113374","0.49979314","0.49422348","0.49397087","0.493285","0.49240142","0.49194804","0.48922402","0.48879048","0.487408","0.48632804","0.48632804","0.48344612","0.48313734","0.4803892","0.47913563","0.47894514","0.47740072","0.4772803","0.47707918","0.47691622","0.4764435","0.47587693","0.47483763","0.47473058","0.4743131","0.4738475","0.4731922","0.4705163","0.4689068","0.46833527","0.4680517","0.4678912","0.46725714","0.4656964","0.4656872","0.46510035","0.46475452","0.46414265","0.4640034","0.46355852","0.4634631","0.46345106","0.4633271","0.46324533","0.4632325","0.46246463","0.46186978","0.46148664","0.46056688","0.4600435","0.45990598","0.45989445","0.45940152","0.45927244","0.4579774","0.4573036","0.45724478","0.45687184","0.45657608","0.45628503","0.4562554","0.4561105","0.4557945","0.4555935","0.45467323","0.4545846","0.45438054","0.45436773","0.45421872","0.4537277","0.45337397","0.4524949","0.45246607","0.45204955","0.45202178","0.45191157","0.4513487","0.4505469","0.44982168","0.44977468","0.44977292","0.4494693","0.44913363","0.44873232","0.44873232","0.44873232","0.44873232","0.44873232","0.44873232"],"string":"[\n \"0.61563104\",\n \"0.57579714\",\n \"0.54303104\",\n \"0.5163897\",\n \"0.5136838\",\n \"0.5134525\",\n \"0.50500184\",\n \"0.50419426\",\n \"0.50137776\",\n \"0.50136286\",\n \"0.50113374\",\n \"0.49979314\",\n \"0.49422348\",\n \"0.49397087\",\n \"0.493285\",\n \"0.49240142\",\n \"0.49194804\",\n \"0.48922402\",\n \"0.48879048\",\n \"0.487408\",\n \"0.48632804\",\n \"0.48632804\",\n \"0.48344612\",\n \"0.48313734\",\n \"0.4803892\",\n \"0.47913563\",\n \"0.47894514\",\n \"0.47740072\",\n \"0.4772803\",\n \"0.47707918\",\n \"0.47691622\",\n \"0.4764435\",\n \"0.47587693\",\n \"0.47483763\",\n \"0.47473058\",\n \"0.4743131\",\n \"0.4738475\",\n \"0.4731922\",\n \"0.4705163\",\n \"0.4689068\",\n \"0.46833527\",\n \"0.4680517\",\n \"0.4678912\",\n \"0.46725714\",\n \"0.4656964\",\n \"0.4656872\",\n \"0.46510035\",\n \"0.46475452\",\n \"0.46414265\",\n \"0.4640034\",\n \"0.46355852\",\n \"0.4634631\",\n \"0.46345106\",\n \"0.4633271\",\n \"0.46324533\",\n \"0.4632325\",\n \"0.46246463\",\n \"0.46186978\",\n \"0.46148664\",\n \"0.46056688\",\n \"0.4600435\",\n \"0.45990598\",\n \"0.45989445\",\n \"0.45940152\",\n \"0.45927244\",\n \"0.4579774\",\n \"0.4573036\",\n \"0.45724478\",\n \"0.45687184\",\n \"0.45657608\",\n \"0.45628503\",\n \"0.4562554\",\n \"0.4561105\",\n \"0.4557945\",\n \"0.4555935\",\n \"0.45467323\",\n \"0.4545846\",\n \"0.45438054\",\n \"0.45436773\",\n \"0.45421872\",\n \"0.4537277\",\n \"0.45337397\",\n \"0.4524949\",\n \"0.45246607\",\n \"0.45204955\",\n \"0.45202178\",\n \"0.45191157\",\n \"0.4513487\",\n \"0.4505469\",\n \"0.44982168\",\n \"0.44977468\",\n \"0.44977292\",\n \"0.4494693\",\n \"0.44913363\",\n \"0.44873232\",\n \"0.44873232\",\n \"0.44873232\",\n \"0.44873232\",\n \"0.44873232\",\n \"0.44873232\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.74835473"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":11296,"cells":{"query":{"kind":"string","value":"Run the flatbuffer compiler on the all of the flatbuffer json files."},"document":{"kind":"string","value":"def generate_flatbuffer_binaries():\n for element in FLATBUFFERS_CONVERSION_DATA:\n schema = element.schema\n output_path = element.output_path\n if not os.path.exists(output_path):\n os.makedirs(output_path)\n for json in element.input_files:\n target = processed_json_path(json)\n if needs_rebuild(json, target) or needs_rebuild(schema, target):\n convert_json_to_flatbuffer_binary(\n json, schema, output_path)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def main():\n os.makedirs(PATH)\n fetch_data()\n convert_to_json(model_list, 'models.json', is_model=True)\n convert_to_json(backend_list, 'backends.json')\n convert_to_json(type_list, 'types.json')\n convert_to_json(featurizer_list, 'featurizers.json')","def main(gtfs_file, input_json_file):\n\n with open(input_json_file) as jsonfile:\n input_json = json.load(jsonfile)\n\n gtfs_feed = mzgtfs.feed.Feed(filename=gtfs_file)\n\n for fare_id, rules_attributes in input_json.iteritems():\n add_fare_id(gtfs_feed, fare_id, rules_attributes)\n\n files = ['fare_attributes.txt', 'fare_rules.txt']\n gtfs_feed.write('fare_attributes.txt', gtfs_feed.fares())\n gtfs_feed.write('fare_rules.txt', gtfs_feed.fare_rules())\n\n gtfs_feed.make_zip('output.zip', files=files, clone=gtfs_file)\n shutil.move('output.zip', gtfs_file)\n\n util.delete_temp_files(files)","def clean_flatbuffer_binaries():\n for element in FLATBUFFERS_CONVERSION_DATA:\n for json in element.input_files:\n path = processed_json_path(json)\n if os.path.isfile(path):\n os.remove(path)","def main():\n for db_csv_export in current_dir.glob(\"template*.csv\"):\n data_projects = load_projects(db_csv_export)\n json_path = db_csv_export.with_suffix(\".json\")\n with open(json_path, \"w\") as fh:\n json.dump(data_projects, fh, indent=2)","def main(args=None):\n if args is None:\n args = sys.argv[1:]\n\n usage_ = \"\"\"%prog [OPTIONS] JsonFile [MoreJsonFiles...]\nFormat/Beautify one or more JSON file(s).\"\"\"\n parser = OptionParser(usage=usage_, version=VERSION)\n parser.add_option(\"-i\", \"--indent\", dest=\"indent_size\",\n default=DEFAULT_INDENT_SIZE, type=\"int\",\n help=\"Indent size to use (default: %default).\")\n parser.add_option(\"-c\", \"--compact\", dest=\"compact\",\n action=\"store_true\", default=False,\n help=\"Use compact format (default: %default).\")\n parser.add_option(\"-n\", \"--dry-run\", dest=\"dry_run\",\n action=\"store_true\", default=False,\n help=\"Check only if JSON is well-formed (default: %default).\")\n options, filenames = parser.parse_args(args) #< pylint: disable=W0612\n if not filenames:\n parser.error(\"OOPS, no filenames provided.\")\n if options.compact:\n options.indent_size = None\n\n # -- STEP: Init logging subsystem.\n format_ = \"json.format: %(message)s\"\n logging.basicConfig(level=logging.WARN, format=format_)\n console = logging.getLogger(\"console\")\n\n # -- DOS-SHELL SUPPORT: Perform filename globbing w/ wildcards.\n skipped = 0\n filenames2 = []\n for filename in filenames:\n if \"*\" in filenames:\n files = glob.glob(filename)\n filenames2.extend(files)\n elif os.path.isdir(filename):\n # -- CONVENIENCE-SHORTCUT: Use DIR as shortcut for JSON files.\n files = glob.glob(os.path.join(filename, \"*.json\"))\n filenames2.extend(files)\n if not files:\n console.info(\"SKIP %s, no JSON files found in dir.\", filename)\n skipped += 1\n elif not os.path.exists(filename):\n console.warning(\"SKIP %s, file not found.\", filename)\n skipped += 1\n continue\n else:\n assert os.path.exists(filename)\n filenames2.append(filename)\n filenames = filenames2\n\n # -- NORMAL PROCESSING:\n errors = json_formatall(filenames, options.indent_size,\n dry_run=options.dry_run)\n console.error(\"Processed %d files (%d with errors, skipped=%d).\",\n len(filenames), errors, skipped)\n if not filenames:\n errors += 1\n return errors","def convert_json_to_flatbuffer_binary(json, schema, out_dir):\n command = [FLATC, '-o', out_dir, '-b', schema, json]\n run_subprocess(command)","def run(self):\n if not os.path.exists(self.output_folder):\n os.makedirs(self.output_folder)\n for entry in glob.glob(os.path.join(self.data_folder, self.data_expression)):\n f = open(entry)\n text = json.loads(f.read())\n f.close()\n self.create_page_objects(text)","def parse(root_path, output_path, batch_file_paths):\n for file_path in batch_file_paths:\n d, f = split_path(file_path, root_path)\n print(time.ctime(), \"d =\", d, \"; f =\", f)\n if not os.path.exists(os.path.join(OUTPUT_PATH, d)):\n os.makedirs(os.path.join(OUTPUT_PATH, d))\n parsed_files = get_parsed_files(output_path, d)\n if f not in parsed_files:\n with open(file_path) as json_file:\n OUTPUT_FILE_PATH = os.path.join(OUTPUT_PATH, d, f)\n with open(OUTPUT_FILE_PATH, 'w') as writer:\n for num, line in enumerate(json_file):\n json_data = json.loads(line)\n title = json_data['title']\n if json_data['text']:\n definition = json_data['text'][0]['line']\n writer.write(f'{title}\\t{definition}\\n')\n else:\n writer.write(f'{title}\\tNone.\\n')","def main():\n\n obj_lookup = interfaces_dir / \"FrameLib-obj-jlookup.json\"\n\n worker = jParseAndBuild()\n\n refpages = [x for x in refpages_dir.rglob(\"fl.*.xml\")]\n\n for ref in refpages:\n worker.extract_from_refpage(ref)\n\n write_json(obj_lookup, worker.j_master_dict)","def pack():\n clean_local()\n build()\n copy_json()\n optimize()\n tarball()","def main():\n onlyfiles = [f for f in listdir(RAWDATA_PATH) if isfile(join(RAWDATA_PATH, f))]\n for file in onlyfiles:\n create_RCSB_fastas(file)","def main():\n\n # Command Line Interface\n parse = command_line()\n args = parse.parse_args()\n if not os.path.isdir(args.directory):\n raise IOError\n\n # Abstract File Tree\n filetree = tree_walk(args.directory, args.replace, args.depth)\n jsontree = json.dumps(\n filetree,\n indent=4,\n sort_keys=True,\n separators=(', ', ': '),\n )\n\n # Pipe vs Redirection\n if sys.stdout.isatty():\n try: jsontree = highlight(\n jsontree,\n JsonLexer(),\n Terminal256Formatter(style='autumn'))\n except:\n pass\n\n print(jsontree)","def fix_jsons_in(bids_dir: Path):\n\n print(\"Finalizing task json files.\")\n\n for path in bids_dir.rglob(\"func/*_task-*.json\"):\n append_to_json_file(key=\"TaskName\", value=task_name_of(path), path_to_json=path)\n\n print(\"Appending echo times to phase difference json files.\")\n\n for path in bids_dir.rglob(\"fmap/*_phasediff.json\"):\n magnitude1_path = the_path_that_matches(pattern=\"sub-*_magnitude1.json\", in_directory=path.parent)\n magnitude2_path = the_path_that_matches(pattern=\"sub-*_magnitude2.json\", in_directory=path.parent)\n echo_time1 = value_of_key_in_json_file(\"EchoTime\", magnitude1_path)\n echo_time2 = value_of_key_in_json_file(\"EchoTime\", magnitude2_path)\n append_to_json_file(key=\"EchoTime1\", value=echo_time1, path_to_json=path)\n append_to_json_file(key=\"EchoTime2\", value=echo_time2, path_to_json=path)\n\n print(\"Setting targets of fieldmap json files.\")\n\n for path in bids_dir.rglob(\"fmap/*.json\"):\n func_dir = path.parent.parent / \"func\"\n trimmed_func_paths = [\"func/\" + func_path.name for func_path in func_dir.glob(\"*.nii\")]\n append_to_json_file(key=\"IntendedFor\", value=trimmed_func_paths, path_to_json=path)","def analyze(self):\n for f in self.files:\n tokenizer = Tokenizer(f)\n self.write_tokens(tokenizer)\n compilation_engine = CompilationEngine(tokenizer, f)\n compilation_engine.compile()\n self.write_syntax_tree(compilation_engine)\n compilation_engine.VMwriter.create_file(f[:-5])","def process_files_json():\n # chdir into beep root\n pwd = os.getcwd()\n os.chdir(os.environ.get(\"BEEP_ROOT\", \"/\"))\n\n meta_list = list(filter(lambda x: '_Metadata.csv' in x, os.listdir(SRC_DIR)))\n file_list = list(filter(lambda x: '.csv' in x if x not in meta_list else None, os.listdir(SRC_DIR)))\n all_list = list(filter(lambda x: '.csv' in x, os.listdir(SRC_DIR)))\n\n all_list = sorted(all_list)\n dumpfn(all_list, \"all_files.json\")\n\n [file_id, mapdf] = init_map(PROJECT_NAME, DEST_DIR)\n\n new_file_index = file_id\n\n for filename in tqdm(sorted(file_list)):\n # If the file has already been renamed another entry should not be made\n if mapdf['filename'].str.contains(filename).sum() > 0:\n continue\n old_file = os.path.join(SRC_DIR, filename)\n new_path = os.path.join(DEST_DIR, PROJECT_NAME)\n shutil.copy(old_file, new_path) # copy main data file\n shutil.copy(old_file.replace(\".csv\", '_Metadata.csv'), new_path) # copy meta data file\n\n if PROJECT_NAME == 'FastCharge':\n [date, channel_no, strname, protocol] = get_parameters_fastcharge(filename, SRC_DIR)\n elif PROJECT_NAME == 'ClosedLoopOED':\n [date, channel_no, strname, protocol] = get_parameters_oed(filename, SRC_DIR)\n else:\n raise ValueError(\"Unsupported PROJECT_NAME: {}\".format(PROJECT_NAME))\n\n df_dup = mapdf.set_index(['protocol', 'date'])\n if (protocol, date) in df_dup.index:\n row = mapdf[(mapdf['protocol'] == protocol) & (mapdf['date'] == date)]\n file_id = row['fid'].iloc[0]\n protocol = row['protocol'].iloc[0]\n date = row['date'].iloc[0]\n strname = row['strname'].iloc[0]\n else:\n file_id = new_file_index\n new_file_index = new_file_index + 1\n\n new_name = \"{}_{}_{}\".format(PROJECT_NAME, f'{file_id:06}', channel_no)\n new_file = os.path.join(DEST_DIR, PROJECT_NAME, \"{}.csv\".format(new_name))\n\n new_row = pd.DataFrame([[file_id, protocol, channel_no, date, strname,\n os.path.abspath(old_file),\n os.path.abspath(new_file)]],\n columns=METADATA_COLUMN_NAMES)\n mapdf = mapdf.append(new_row)\n\n os.rename(os.path.join(DEST_DIR, PROJECT_NAME, filename), new_file)\n os.rename(os.path.join(DEST_DIR, PROJECT_NAME, filename).replace(\".csv\", \"_Metadata.csv\"),\n new_file.replace(\".csv\", \"_Metadata.csv\"))\n\n mapdf.to_csv(os.path.join(DEST_DIR, PROJECT_NAME, PROJECT_NAME + \"map.csv\"), index=False)\n mapdf = mapdf.reset_index(drop=True)\n os.chdir(pwd)\n return json.dumps(mapdf.to_dict(\"list\"))","def main(arguments):\n\n # The input file can be optionally encoded with gzip format:\n input_file = arguments.input_file[0]\n assert isinstance(input_file, str)\n if input_file.endswith(\".gz\"):\n _open = gzip.open\n else:\n _open = open\n with _open(input_file, \"rt\",\n encoding='utf-8') as fd:\n print(\"Loading JSON content into memory....\")\n raw = json.load(fd) # Parses all the input file.\n\n # Also the output file can be optionally encoded with gzip format:\n output_file = arguments.output_file[0]\n assert isinstance(output_file, str)\n uuid = 0\n if output_file.endswith(\".gz\"):\n _open = gzip.open\n else:\n _open = open\n with _open(output_file, \"wt\",\n encoding='utf-8') as fd:\n # for each element extracted from the input\n print(\"Generating distilled file\")\n for item in load_input(raw):\n uuid += 1 # generates incremental uuid from 1\n item['uuid'] = uuid\n fd.write(json.dumps(item,\n sort_keys=True))\n fd.write(\"\\n\") # one encoded document per line\n\n print(\"{} documents imported\".format(uuid))","def main():\n\n # Set up argument parser.\n parser = argparse.ArgumentParser(\n description='Removes duplicate key-value pairs from JSON files.')\n parser.add_argument('--suffix', default='',\n help='optional suffix for output files; '\n 'if empty, files will be changed in place')\n parser.add_argument('files', nargs='+', help='input files')\n args = parser.parse_args()\n\n # Iterate over files.\n for filename in args.files:\n # Read in json using Python libraries. This eliminates duplicates.\n print('Processing ' + filename + '...')\n try:\n with codecs.open(filename, 'r', 'utf-8') as infile:\n j = json.load(infile)\n except ValueError as e:\n print('Error reading ' + filename)\n raise InputError(filename, str(e))\n\n # Built up output strings as an array to make output of delimiters easier.\n output = []\n for key in j:\n if key != '@metadata':\n output.append('\\t\"' + key + '\": \"' +\n j[key].replace('\\n', '\\\\n') + '\"')\n\n # Output results.\n with codecs.open(filename + args.suffix, 'w', 'utf-8') as outfile:\n outfile.write('{\\n')\n outfile.write(',\\n'.join(output))\n outfile.write('\\n}\\n')","def file_loader(self):\n\n for folder in self.config[\"data_folders\"]:\n f = os.path.join(folder, self.data_file)\n yield jsonlist.load_file(f)","def process_data(*args, **kwargs):\n\n filepath = kwargs[\"filepath\"]\n func = kwargs[\"func\"]\n \n # get all files matching extension from directory\n all_files = []\n for root, dirs, files in os.walk(filepath):\n files = glob.glob(os.path.join(root,'*.json'))\n for f in files :\n all_files.append(os.path.abspath(f))\n\n # get total number of files found\n num_files = len(all_files)\n print('{} files found in {}'.format(num_files, filepath))\n\n # iterate over files and process\n for i, datafile in enumerate(all_files, 1):\n func(datafile)\n print('{}/{} files processed.'.format(i, num_files))","def process_dart(self):\n self.dartfiles = set()\n self.jsfiles = set()\n self.htmlfiles = set()\n self.cssfiles = set()\n self.otherfiles = set()\n for src in self.source:\n if isinstance(src,str):\n node = self.path.find_node(src)\n else:\n node = src\n if node.suffix() == '.dart':\n self.dartfiles.add(node)\n elif node.suffix() == '.js':\n self.jsfiles.add(node)\n elif node.suffix() == '.html':\n self.htmlfiles.add(node)\n elif node.suffix() == '.css':\n self.cssfiles.add(node)\n else:\n self.otherfiles.add(node)\n self.source = []\n self.outdir = self.path.make_node(self.target + '/').get_bld()\n self.outdir.mkdir()","def main():\n processSetOfCerFiles(sys.argv[1:])","def run_main():\n # Matching lines against a matcher function.\n matched_lines = match_file(file_names, matcher)\n\n # Will contain data sorted by file.\n binned_data = {}\n\n # Looking through the lines that were inserted into the metrics file via the metrics component.\n for key in matched_lines:\n\n # Grabbing matched lines by the file or orgination.\n buffer = matched_lines[key]\n\n # This will contain dictionaries converted from JSON.\n data = []\n\n # Loop through the collection, appending data converted from JSON entries.\n for line in buffer:\n data.append(extract_data(line))\n\n # Sort the data by file.\n binned_data[key] = sort_data(data)\n\n # Output the final results.\n generate_statistics(binned_data)\n return 0","def runall(sources='sources'):\n init()\n upload_sources(sources)\n compile()\n run()","def main():\n print(\n \"\"\"\n\n ##########################################################\n # #\n # #\n # Compiling Colocalized Cyano Datasets #\n # #\n # #\n ##########################################################\n\n \n \"\"\"\n )\n cyanoFiles = glob.glob(f\"{COLOCALIZED_DIR}*.csv\")\n makedir(COMPILED_DIR)\n dfCompiled = pd.DataFrame({})\n for cyanoFile in cyanoFiles:\n print(f\"Compiling {cyanoFile}\")\n data = unify(cyanoFile)\n if len(dfCompiled ) < 1:\n dfCompiled = data\n else:\n dfCompiled = pd.concat([dfCompiled, data], ignore_index=True) \n dfCompiled.to_csv(f\"{COMPILED_DIR}compiled.csv\", index=False)","def main():\n # There are no args, but parse them just so help works\n args = docopt(__doc__)\n print(process_files_json(), end=\"\")\n return None","def process_input_files(inputs):\n for ifile in inputs:\n with open(ifile) as fin:\n exec(compile(fin.read(), ifile, 'exec'))","def test_json():\n schemas = {\n 'schema-languages': 'bible/languages.json',\n 'schema-book-metadata': 'bible/book-metadata.json',\n 'schema-bible': 'bible/bible-*.json'\n }\n for schema_name, data_path_glob in schemas.items():\n schema_path = 'schemas/{}.json'.format(schema_name)\n with open(schema_path) as schema_file:\n schema = json.load(schema_file)\n data_paths = glob.iglob(data_path_glob)\n for data_path in data_paths:\n with open(data_path) as data_file:\n data = json.load(data_file)\n yield jsonschema.validate, data, schema","def main(filename):\n with open(filename) as json_file:\n data = json.load(json_file)\n\n course_dict = {}\n course_dict['course_id'] = str(os.path.split(filename.strip('/'))[-1])\n course_dict['blocks'] = build_course_map(data)\n\n filename = '%s' % course_dict['course_id']\n filepath = os.path.join('../input/', filename)\n\n with open(filepath, 'w') as outfile:\n json.dump(course_dict, outfile, indent=4)","def apply_dart(self):\n shutil.copyfile(self.env['DART_JS_BOOTSTRAP'], self.outdir.make_node('dart.js').abspath())\n for filetype in ['dartfiles','jsfiles','htmlfiles','cssfiles','otherfiles']:\n files = getattr(self, filetype)\n for f in files:\n if f.is_bld():\n outf = self.outdir.make_node(f.path_from(self.path.get_bld()))\n elif f.is_src():\n outf = self.outdir.make_node(f.path_from(self.path.get_src()))\n else:\n raise Exception(\"I don't know what I'm doing anymore.\")\n self.create_task('copytask',f,outf)","def process(filename, exclude_dirs=['unittest','test',]):\n print(\"Generating {}\".format(filename))\n nb = 0\n nb_err = 0\n _main_root = os.path.dirname(filename)\n _VFS = {}\n for _mydir in (\"libs\", \"Lib\"):\n for _root, _dir, _files in os.walk(os.path.join(_main_root, _mydir)):\n #if _root.endswith('lib_migration'):\n _flag=False\n for _exclude in exclude_dirs:\n if _exclude in _root: #_root.endswith(_exclude):\n _flag=True\n continue\n if _flag:\n continue # skip these modules\n if '__pycache__' in _root:\n continue\n nb += 1\n\n for _file in _files:\n _ext = os.path.splitext(_file)[1]\n if _ext not in ('.js', '.py'):\n continue\n nb += 1\n\n with open(os.path.join(_root, _file), \"r\") as file_with_data:\n _data = file_with_data.read()\n \n if len(_data) == 0:\n print('no data for %s' % _file)\n _data = unicode('')\n print(_data, type(_data))\n else:\n _data = _data.decode('utf-8')\n\n if _ext in '.js':\n if js_minify is not None:\n try:\n _data = js_minify(_data)\n except Exception as error:\n print(error)\n elif _ext == '.py' and len(_data) > 0:\n try:\n _data = pyminifier.remove_comments_and_docstrings(_data)\n _data = pyminifier.dedent(_data)\n except Exception as error:\n print(error)\n nb_err += 1\n\n _vfs_filename = os.path.join(_root, _file).replace(_main_root, '')\n _vfs_filename = _vfs_filename.replace(\"\\\\\", \"/\")\n\n if _vfs_filename.startswith('/libs/crypto_js/rollups/'):\n if _file not in ('md5.js', 'sha1.js', 'sha3.js',\n 'sha224.js', 'sha384.js', 'sha512.js'):\n continue\n\n mod_name = _vfs_filename[len(_mydir) + 2:].replace('/', '.')\n mod_name, ext = os.path.splitext(mod_name)\n is_package = mod_name.endswith('__init__')\n if is_package:\n mod_name = mod_name[:-9]\n _VFS[mod_name] = [ext, _data, 1]\n else:\n _VFS[mod_name] = [ext, _data]\n print((\"adding %s %s\" % (mod_name, _vfs_filename)))\n print('%s files, %s errors' % (nb, nb_err))\n with open(filename, \"w\") as file_to_write_VFS:\n file_to_write_VFS.write('__BRYTHON__.use_VFS = true;\\n')\n file_to_write_VFS.write('__BRYTHON__.VFS=%s;\\n\\n' % json.dumps(_VFS))","def main(args):\n if os.path.isfile(args.dev_ctm) and os.path.isfile(args.eval_ctm):\n ctm_files = [args.dev_ctm, args.eval_ctm]\n else:\n raise FileNotFoundError('Please ensure that both {} and {} are valid files'.format(args.dev_ctm, args.eval_ctm))\n\n utils.mkdir(args.output_dir)\n\n for ctm_file in ctm_files:\n segments, num_lines = get_segments(ctm_file)\n\n if segments[0].endswith('Line'):\n abbrev = True\n else:\n abbrev = False\n\n split_ctm(ctm_file, segments, num_lines, args.output_dir, abbrev)","def execute():\n # Write JSON files for each country. Ex: US.json lists ALL recently active users from the USA\n for country_code in config['countries']:\n json_data = get_users(country_code=country_code)\n write_json(json_data=json_data, country_code=country_code)","def main():\n def parse_arguments():\n \"\"\"Create a parser for the program and use it to parse arguments\n\n Returns:\n ArgumentParser: The argument parser of the program\n \"\"\"\n parser = ArgumentParser()\n parser.add_argument(\"-p\", \"--path\", required=True, nargs=1,\n dest=\"file_path\", type=str, action=\"store\",\n help=\"path to the root directory\")\n parser.add_argument(\"-q\", \"--quick\", action=\"store_true\",\n default=False, dest=\"quick\",\n help=\"Speed up the search or not\")\n return parser.parse_args()\n try:\n arguments = parse_arguments()\n # Get all files in the root directory, ignoring symlink\n file_path_names = scan_files(arguments.file_path[0])\n # Get groups of duplicated files\n duplicated_file_groups = find_duplicate_files(\n file_path_names, arguments.quick\n )\n # Print those groups in json format\n print(dumps(duplicated_file_groups, indent=4, separators=(\"\", \"\")))\n except (TypeError, ValueError):\n return","def maps():\n flatmap_list = []\n root_path = pathlib.Path(settings['FLATMAP_ROOT'])\n if root_path.is_dir():\n for flatmap_dir in root_path.iterdir():\n index = os.path.join(settings['FLATMAP_ROOT'], flatmap_dir, 'index.json')\n mbtiles = os.path.join(settings['FLATMAP_ROOT'], flatmap_dir, 'index.mbtiles')\n if os.path.isdir(flatmap_dir) and os.path.exists(index) and os.path.exists(mbtiles):\n with open(index) as fp:\n index = json.loads(fp.read())\n version = index.get('version', 1.0)\n reader = MBTilesReader(mbtiles)\n if version >= 1.3:\n metadata = read_metadata(reader, 'metadata')\n if (('id' not in metadata or flatmap_dir.name != metadata['id'])\n and ('uuid' not in metadata or flatmap_dir.name != metadata['uuid'].split(':')[-1])):\n app.logger.error(f'Flatmap id mismatch: {flatmap_dir}')\n continue\n flatmap = {\n 'id': metadata['id'],\n 'source': metadata['source'],\n 'version': version\n }\n if 'created' in metadata:\n flatmap['created'] = metadata['created']\n if 'taxon' in metadata:\n flatmap['taxon'] = normalise_identifier(metadata['taxon'])\n flatmap['describes'] = metadata['describes'] if 'describes' in metadata else flatmap['taxon']\n elif 'describes' in metadata:\n flatmap['taxon'] = normalise_identifier(metadata['describes'])\n flatmap['describes'] = flatmap['taxon']\n if 'biological-sex' in metadata:\n flatmap['biologicalSex'] = metadata['biological-sex']\n if 'uuid' in metadata:\n flatmap['uuid'] = metadata['uuid']\n if 'name' in metadata:\n flatmap['name'] = metadata['name']\n else:\n try:\n source_row = reader._query(\"SELECT value FROM metadata WHERE name='source'\").fetchone()\n except (InvalidFormatError, sqlite3.OperationalError):\n flask.abort(404, 'Cannot read tile database: {}'.format(mbtiles))\n if source_row is None:\n continue\n flatmap = {\n 'id': flatmap_dir.name,\n 'source': source_row[0]\n }\n created = reader._query(\"SELECT value FROM metadata WHERE name='created'\").fetchone()\n if created is not None:\n flatmap['created'] = created[0]\n describes = reader._query(\"SELECT value FROM metadata WHERE name='describes'\").fetchone()\n if describes is not None and describes[0]:\n flatmap['describes'] = normalise_identifier(describes[0])\n flatmap_list.append(flatmap)\n return flask.jsonify(flatmap_list)","def process_data(cur, conn, filepath, func):\n all_files = []\n for root, dirs, files in os.walk(filepath):\n files = glob.glob(os.path.join(root,'*.json'))\n for f in files :\n all_files.append(os.path.abspath(f))\n\n num_files = len(all_files)\n print('{} files found in {}'.format(num_files, filepath))\n\n for i, datafile in enumerate(all_files, 1):\n func(cur, datafile)\n conn.commit()\n print('{} files processed.'.format(num_files))","def main():\n # if there are no command line args, run as normal with the input from stdin\n if len(sys.argv) == 1:\n json_string = \"\".join(sys.stdin)\n hierarchical_jsons = [load_json(json_string)]\n # if there are command line args, run using the first argument as a file path to a file with\n # correctly formatted test json values\n else:\n with open(sys.argv[1]) as file:\n hierarchical_jsons = json.load(file)\n\n flattened_jsons = []\n\n # for all the jsons given, in the non-test case there will only be one\n for hierarchical_json in hierarchical_jsons:\n # flatten the json object\n flat_json = flatten_json(hierarchical_json)\n # print the prettied json to stdout\n json_string = json.dumps(flat_json, indent=4)\n flattened_jsons.append(json_string)\n print(json_string)\n\n return flattened_jsons","def main(infile: List[str], prefix: str, verbose: bool, no_origin_filter: bool):\n common.init_logging(verbosity=int(verbose) + 1)\n _LOGGER.info(\"Running with arguments: %s.\", locals())\n\n file_id = -1\n with fileinput.input(files=infile, openhook=fileinput.hook_compressed) \\\n as json_lines:\n results = (json.loads(line) for line in json_lines)\n\n for file_id, graph in enumerate(\n extract_graphs(results, use_origin=not no_origin_filter)\n ):\n path = Path(f\"{prefix}{file_id:04d}.json\")\n if not path.is_file():\n path.write_text(graph.to_json())\n else:\n _LOGGER.info(\"Refusing to overwrite: %s\", path)\n _LOGGER.info(\"Script complete. Extracted %d dependency graphs.\", file_id+1)","def local(ctx):\n _do_codegen_user(\"demo\")\n _do_codegen_user(\"errors\")\n _do_codegen_user(\"mpi\")\n _do_codegen_user(\"omp\")\n _do_codegen_user(\"python\")\n\n # Do codegen for libfake\n for so in LIB_FAKE_FILES:\n _do_codegen_file(so)\n\n # Run the WAMR codegen required by the tests\n codegen(ctx, \"demo\", \"echo\", wamr=True)\n codegen(ctx, \"demo\", \"chain\", wamr=True)\n\n # Run the SGX codegen required by the tests\n codegen(ctx, \"demo\", \"hello\", wamr=True, sgx=True)\n codegen(ctx, \"demo\", \"chain_named_a\", wamr=True, sgx=True)\n codegen(ctx, \"demo\", \"chain_named_b\", wamr=True, sgx=True)\n codegen(ctx, \"demo\", \"chain_named_c\", wamr=True, sgx=True)","def main(json_input, output_folder):\n with open(json_input, 'r') as f:\n data = json.loads(f.read())['data']\n\n with open(path.join(output_folder, 'qgeneration.context.txt'), 'w') as context_f, \\\n open(path.join(output_folder, 'qgeneration.context.nojson.txt'), 'w') as context_f_nojson, \\\n open(path.join(output_folder, 'qgeneration.gold.txt'), 'w') as question_f:\n for item in data:\n context_f.write(json.dumps(item['context']) + '\\n')\n context_f_nojson.write(item['context'][:1000] + '\\n')\n question_f.write(json.dumps(item['answer']) + '\\n')\n\n print('DONE')","def reconstitute():\n with open(TEXT_FPATH, 'w') as txt:\n for jfpath in json_fpaths():\n with open(jfpath) as f:\n jstruct = json.load(f)\n\n for recipe in jstruct.keys():\n _reconstitute_recipe(txt, jstruct[recipe])","def process_data(cur, conn, filepath, func):\n\n all_files = []\n for root, dirs, files in os.walk(filepath):\n files = glob.glob(os.path.join(root, '*.json'))\n for f in files:\n all_files.append(os.path.abspath(f))\n\n num_files = len(all_files)\n print('{} files found in {}'.format(num_files, filepath))\n\n for i, datafile in enumerate(all_files, 1):\n func(cur, datafile)\n conn.commit()\n print('{}/{} files processed.'.format(i, num_files))","def main():\n\t# import training data\n\tfiles = [INPATH + f for f in os.listdir(INPATH) if \".json\" in f]\n\n\t# import books\n\tprint(\"Loading training data...\")\n\tbookList = loadBooks(files)\n\tprint(\"Load complete.\")\n\n\t# loop through element types and store data structure\n\tfor key, value in ELEMENTS.items():\n\t\tprint(\"Generating: %s\" % key)\n\n\t\t# set file outpath\n\t\toutfile = \"%s.json\" % key\n\t\toutpath = OUTPATH % outfile\n\n\t\tgenerateTrain(bookList, key, value, outpath)","def main():\n\n # Load arguments\n args = get_args()\n \n assert os.path.exists(args.csv), ' [ERR] File' + os.path.exists(args.csv) +'does not exist'\n\n print(args)\n try:\n dir_name = os.path.dirname(args.json)\n os.mkdir(dir_name)\n print(' [INFO] Creating', dir_name, 'directory')\n except:\n print(' [INFO] Directory', dir_name, 'already exists. Data will be replaced')\n pass\n\n if args.config:\n assert os.path.exists(args.config), ' [ERR] File' + os.path.exists(args.config) +'does not exist'\n dic_types = read_config(args.config)\n else:\n dic_types = {}\n \n # Create json\n create_json_from_csv(args.csv, args.delimiter, args.cols_delimiter, args.keep, dic_types, args.infer_types, args.max_docs, args.json, args.per_line)\n\n return 0","def build():\n for root, dirs, files in os.walk(IN_PATH):\n for filename in files:\n if filename.endswith('.csv'):\n with open(os.path.join(IN_PATH, filename), encoding='utf-8') as f:\n reader = csv.reader(f)\n next(reader)\n data = nested_dict()\n web_data = nested_dict()\n for row in reader:\n if row[0].startswith('report.') or row[0].startswith('cardset.'):\n d = data\n elif row[0].startswith('web.'):\n d = web_data\n path = row[0].split('.')\n for i in range(len(path)):\n if i == len(path) - 1:\n d[path[i]] = row[1]\n else:\n d = d[path[i]]\n with open (os.path.join(OUT_PATH, filename.replace('.csv', '.json')), 'w', encoding='utf-8') as fout:\n json.dump({**data, **web_data}, fout)\n with open (os.path.join(WEB_PATH, filename.replace('.csv', '.js')), 'w', encoding='utf-8') as fout:\n fout.write('var STRINGS = {};'.format(json.dumps(web_data)))\n\n with open(os.path.join(IN_PATH, 'en_US.csv'), encoding='utf-8') as f:\n reader = csv.reader(f)\n next(reader)\n data = nested_dict()\n web_data = nested_dict()\n for row in reader:\n path = row[0].split('.')\n if row[0].startswith('report.') or row[0].startswith('cardset.'):\n d = data\n elif row[0].startswith('web.'):\n d = web_data\n\n for i in range(len(path)):\n if i == len(path) - 1:\n d[path[i]] = zz_string(row[1], row[0])\n else:\n d = d[path[i]]\n with open(os.path.join(OUT_PATH, 'zz_ZZ.json'), 'w', encoding='utf-8') as fout:\n json.dump({**data, **web_data}, fout)\n with open(os.path.join(WEB_PATH, 'zz_ZZ.js'), 'w', encoding='utf-8') as fout:\n fout.write('var STRINGS = {};'.format(json.dumps(web_data)))","def main():\n # Create / clean output dir\n if os.path.isdir(OUT_DIR):\n shutil.rmtree(OUT_DIR)\n os.mkdir(OUT_DIR)\n\n # Write all assets to the directory\n for fname, bb in create_assets().items():\n filename = os.path.join(OUT_DIR, fname)\n dirname = os.path.dirname(filename)\n if not os.path.isdir(dirname):\n os.makedirs(dirname)\n with open(filename, \"wb\") as f:\n f.write(bb)","def patch_files():\n args = parser.parse_args()\n doc = json.load(args.ORIGINAL)\n patch = json.load(args.PATCH)\n result = jsonpatch.apply_patch(doc, patch)\n print(json.dumps(result, indent=args.indent))","def flush_buffers(self):\n\n for file_dictionary in [self.infiles, self.outfiles]:\n for name, fi in file_dictionary.items():\n fi.flush_buffers()","def compile(root, folder, filepathList):\n # Init param\n fpath = os.path.join(folder, \"current\")\n\n # YUIDoc don't need any exlude: vendor folder and min content are disable by default\n params = [\n \"yuidoc\",\n \"--outdir\", fpath,\n\t \"--themedir\", tmpl,\n root\n ]\n\n # Run everything (see launcher.py)\n launch(params)","def process(self, args):\n for benchmark_file in args.benchmark_files:\n self.process_individual_file(benchmark_file)\n self.total_files += 1","def run_parsers_on_path(self, path):\n file_paths = glob.glob(path)\n file_paths.sort()\n path_max_len = max(len(path) for path in file_paths) + 2\n for i, path in enumerate(file_paths):\n sys.stdout.write(f\"{i}. Doing {path.ljust(path_max_len)}\")\n t1 = time.time()\n dirname, filename = os.path.split(path)\n filename_sans_ext = filename.split(\".\")[0]\n json_path = os.path.join(\n dirname, f\"{filename_sans_ext}_result.json\"\n )\n\n lasc = LASCSearch(session=None)\n with open(path, \"rb\") as f:\n data = json.load(f)\n clean_data = lasc._parse_case_data(data)\n\n if not os.path.isfile(json_path):\n # First time testing this docket\n bar = \"*\" * 50\n print(\n \"\\n\\n%s\\nJSON FILE DID NOT EXIST. CREATING IT AT:\"\n \"\\n\\n %s\\n\\n\"\n \"Please test the data in this file before assuming \"\n \"everything worked.\\n%s\\n\" % (bar, json_path, bar)\n )\n with open(json_path, \"w\") as f:\n json.dump(clean_data, f, indent=2, sort_keys=True)\n continue\n\n with open(json_path) as f:\n j = json.load(f)\n self.assertEqual(j, clean_data)\n\n t2 = time.time()\n duration = t2 - t1\n warn_or_crash_slow_parser(duration, max_duration=1)\n sys.stdout.write(f\"✓ - {t2 - t1:0.1f}s\\n\")","def main():\n currentpath = parsers.currentpath # -d directory to search\n outfile = parsers.currentfile # -f output json file\n\n try:\n if not os.path.exists(currentpath):\n raise MyError('The path (\"{0}\") does not exist. \\n'\\\n .format(currentpath))\n except MyError as err:\n sys.stdout.write(err.message)\n exit(0)\n\n #array of files in dict-format, quantity of dirs, files checked and qfi\n files, q_dirs, q_files, qfi_files = search_qcow.parse_dirs(currentpath)\n\n try:\n if q_files == 0:\n raise MyError(\"There are no any files in folder ('{0}').\\n\"\\\n .format(currentpath))\n except MyError as err: #if error catched\n sys.stdout.write(err.message)\n exit(0)\n else: #if there are no any exceptions\n with open(outfile, 'w') as outfile:\n #indent - friendly view in json, ensure-russian letters in path\n json.dump(files, outfile, indent=2, ensure_ascii=False)\n #folders, include current\n sys.stdout.write(\\\n '\\n\\nFoldes: {0}, files: {1}, Qcow-files: {2}.\\n'\\\n .format(q_dirs, q_files, qfi_files))","def main():\n parser = optparse.OptionParser()\n info = ('The input json-format file name. Such as: '\n '--dest_dir=/hangonman/android')\n parser.add_option('-d', '--dest_dir', action='store', dest='dest_dir',\n help=info)\n opts, _ = parser.parse_args()\n if not os.path.exists(opts.dest_dir):\n print 'Destination directory is not existed!'\n return 1\n latest_url = ('http://wrt-build.sh.intel.com/archive/'\n 'snapshots/dev-wrt-android-build/LATEST')\n file_name = 'xwalk_app_template.tar.gz'\n app_template_handler = GetXWalkAppTemplate(latest_url, file_name)\n try:\n app_template_handler.ExtractFile(opts.dest_dir)\n except tarfile.TarError:\n raise Exception('Error in the process of tar file.')\n return 0","def process_data(cur, conn, filepath, func):\n \n # get all files matching extension from directory\n all_files = []\n for root, dirs, files in os.walk(filepath):\n files = glob.glob(os.path.join(root,'*.json'))\n for f in files:\n all_files.append(os.path.abspath(f))\n \n # get total number of files found\n num_files = len(all_files)\n print('{} files found in {}'.format(num_files, filepath))\n \n # iterate over files and process\n for i, datafile in enumerate(all_files, 1):\n func(cur, datafile)\n conn.commit()\n print('{}/{} files processed.'.format(i, num_files))","def parse_files():\n pfuncs = [ # parse py files : add #\n parse_test_files,\n parse_model_files,\n parse_url_files,\n parse_route_files,\n parse_settings_files,\n parse_setup_files,\n ]\n\n while PY_FILES:\n for _ in range(len(pfuncs)):\n a_func = pfuncs.pop()\n a_func()\n break","def process_data(cur, conn, filepath, func):\n # get all files matching extension from directory\n all_files = []\n for root, dirs, files in os.walk(filepath):\n files = glob.glob(os.path.join(root,'*.json'))\n for f in files :\n all_files.append(os.path.abspath(f))\n\n num_files = len(all_files)\n print('{} files found in {}'.format(num_files, filepath))\n\n for i, datafile in enumerate(all_files, 1):\n func(cur, datafile)\n conn.commit()\n print('{}/{} files processed.'.format(i, num_files))","def start(self, **kwargs):\n super(Json, self).start(**kwargs)\n flat = self.get_arg_value(\"json_flat\")\n\n self._first_row = True\n self.open_fd()\n begin = \"\" if flat else \"[\"\n self._fd.write(begin)","def main():\n\n parser = argparse.ArgumentParser()\n\n parser.add_argument(\"api_base_url\", type=str,\n help=\"base url for all tests\")\n parser.add_argument(\"test_file_name\", type=str,\n help=\"name of file containing JSON array of tests\")\n parser.add_argument(\"-f\", \"--format\", default=\"json\", type=str,\n help=\"output format - must be either json or text\")\n\n args = parser.parse_args()\n\n try:\n\n run_tests_from_file(args.api_base_url, args.test_file_name, \n args.format)\n\n except KeyError as e:\n print(\"Required key '%s' not found. Check tests file.\" % str(e.args[0]))\n exit(1)\n\n except FileNotFoundError:\n print(\"Cannot open file '%s'. File not found.\" % args.test_file_name)\n exit(1)\n\n except ValueError:\n print(\"Cannot decode JSON from file '%s'.\" % args.test_file_name)\n exit(1)","def main(json_files, merged_filename=\"\", pretty_print_json=True):\n\n # Check to ensure xml_files is not empty.\n if not json_files:\n print(\"No JSON files were found.\")\n return \"\"\n\n # Remove potential duplicate files.\n json_files = list(set(json_files))\n\n try:\n # Generate the final XML file name if not provided.\n if not merged_filename:\n # Create the merged filename.\n now = time.localtime()\n timestamp = time.strftime(\"%Y%m%d_%H%M%S\", now)\n merged_filename = f\"masscan_merged_{timestamp}.json\"\n\n final_json_list = []\n\n for json_file in json_files:\n\n if os.path.getsize(json_file) == 0:\n print(f\"File is 0 bytes: {json_file}\")\n continue\n\n with open(json_file, \"r\") as fh_json_file:\n final_json_list.extend(json.load(fh_json_file))\n\n with open(merged_filename, \"w\") as fh:\n if pretty_print_json:\n json.dump(final_json_list, fh, indent=4)\n else:\n json.dump(final_json_list, fh)\n\n print(f\"Final merged file: {merged_filename}\")\n\n except Exception as e:\n print(f\"Exception: {e}\")\n merged_filename = \"\"\n\n return merged_filename","def main():\n\n handler = PlanetSourceHandler()\n\n parser = xml.sax.make_parser()\n parser.setFeature(xml.sax.handler.feature_namespaces, 1)\n parser.setContentHandler(handler)\n parser.parse(sys.stdin)\n\n print simplejson.JSONEncoder(indent=True).encode(handler.sources)","def main():\n for filename in sys.argv[1:]:\n test(filename)","def main(input_filepath, output_filepath):\n logger = logging.getLogger(__name__)\n logger.info('making final data set from raw data')\n\n conn = sqlite3.connect('../raw/td_V2.db')\n git_commits = pd.read_sql_query(\"SELECT * FROM GIT_COMMITS\",conn)\n szz_fault_inducing_commits = pd.read_sql_query(\"SELECT * FROM szz_fault_inducing_commits\",conn)\n refactoring_miner = pd.read_sql_query(\"SELECT * FROM refactoring_miner\",conn)\n refactoring_miner = refactoring_miner[refactoring_miner[\"COMMIT_HASH\"].isin(git_commits[\"COMMIT_HASH\"])]\n git_commits_changes = pd.read_sql_query(\"SELECT * FROM GIT_COMMITS_CHANGES\", conn)\n git_commits_changes = git_commits_changes[git_commits_changes[\"COMMIT_HASH\"].isin(refactoring_miner[\"COMMIT_HASH\"])]\n\n preprocess(git_commits, szz_fault_inducing_commits, refactoring_miner, git_commits_changes)","def main(argv):\n ret = 0\n parser = utils.parser.createParser()\n\n opt = parser.parse_args(argv)\n\n try:\n # Stop execution if help flag is on\n if opt.help:\n raise ShowHelpException()\n\n # Read materials we need for later processing\n cfg = auxiliary.readConfig(opt.config)\n rawJson = auxiliary.readJson(opt.input[0])\n\n # List we are going to manipulate :)\n commands = rawJson['commands']\n\n # Pump commands and instruction for filtering, sorting etc\n rawJson['commands'] = pump(commands, cfg)\n\n # Write final result\n auxiliary.writeJSONFile(rawJson, opt.output[0])\n\n except ShowHelpException:\n parser.print_help()\n ret = 0\n except FilterException, e:\n ret = 1\n print('ERROR')\n print(e)\n\n return ret","def main():\n f_name = sys.argv[1]\n file_contents = open(f_name).read()\n C = CAST([], \"python\")\n C2 = C.from_json_str(file_contents)\n\n V = CASTToAGraphVisitor(C2)\n last_slash_idx = f_name.rfind(\"/\")\n file_ending_idx = f_name.rfind(\".\")\n pdf_file_name = f\"{f_name[last_slash_idx + 1 : file_ending_idx]}.pdf\"\n V.to_pdf(pdf_file_name)","def process_all_files():\n src_files = get_doc_files()\n\n for src_pathname in src_files:\n if src_pathname.suffix in MARKDOWN_EXTENSIONS:\n process_file_markdown(src_pathname)\n elif src_pathname.suffix in STATIC_ASSET_EXTENSIONS:\n process_file_copytodest(src_pathname)","def main():\n\n json_str = sys.stdin.read()\n\n try:\n object_json = json.loads(json_str)\n\n except json.JSONDecodeError:\n print(\"Error with the JSON input. Please check your JSON file.\")\n sys.exit(1)\n\n final_object = flatten_json(object_json, prefix=\"\")\n object_json_output = json.dumps(final_object, indent=4)\n\n print(object_json_output)","def test_files(self, location):\n for filename in os.listdir(location):\n with open(location + '/' + filename) as json_file:\n data = json.load(json_file)\n self.test_data(data)","def batch(infolder, outfile): # type: (str, str) -> None\n\n if not os.path.isdir(infolder):\n return\n\n results = []\n\n for filename in os.listdir(infolder):\n print('Processing ' + filename)\n curresults = []\n if filename.endswith('.txt'):\n with open(os.path.join(infolder, filename), 'r') as curfile:\n curdata = curfile.read() + '\\n'\n curresults = processClauseText(curdata, 'text')\n elif filename.endswith('.pdf'):\n with open(os.path.join(infolder, filename), 'rb') as curfile:\n curdata = base64.b64encode(curfile.read()).decode()\n curresults = processClauseText(curdata, 'pdf')\n elif filename.endswith('.docx'):\n with open(os.path.join(infolder, filename), 'rb') as curfile:\n curdata = base64.b64encode(curfile.read()).decode()\n curresults = processClauseText(curdata, 'word')\n if len(curresults) > 0:\n for result in curresults:\n result['filename'] = filename\n results.extend(curresults)\n\n if outfile is not None:\n with open(outfile, 'w') as outfile:\n json.dump(results, outfile, indent=2)","def main(input_dir, bids_dir, exclude_fieldmaps):\n\n input_dir = Path(input_dir).absolute()\n bids_dir = Path(bids_dir).absolute()\n raw_dir = bids_dir / \"sourcedata\"\n\n copy_all_paths_to_sourcedata(input_dir, raw_dir)\n\n fix_acquisition_numbers_of_json_files_in(raw_dir)\n\n old_and_new_paths = create_dictionary_of_old_and_new_paths(raw_dir, bids_dir, exclude_fieldmaps)\n\n copy_files_to_their_new_homes(old_and_new_paths)\n\n fix_jsons_in(bids_dir)\n\n add_dataset_description_to(bids_dir)\n\n write_tsvs(raw_dir, bids_dir)\n\n print(\"Congratulations! You're BIDS-compliant, yay!\")\n print(\"To double-check, use this handy dandy BIDS validator: https://bids-standard.github.io/bids-validator/\")","def generate_code_files(code_list: List[str], base: str) -> None:\n for code in code_list:\n parts = code.split(' ')\n status = parts[0]\n name = \" \".join(parts[1:])\n path = os.path.join('codes', base, f'{status[0]}XX', f'{status}.json')\n data = {\n 'code': int(status),\n 'name': name,\n 'messages': []\n }\n with open(path, 'w') as jsonfile:\n json.dump(data, jsonfile, indent=4)","def main(args):\n write_files = args.no_write is False\n ffiles = _open(args)\n ffiles = update_bpms(ffiles)\n if write_files:\n _write_files(ffiles, args.prefix, args.clobber)\n\n return","def main():\n widget = ParseGrypeJSON()\n logging.debug(f'argv {\",\".join(sys.argv)}')\n\n if len(sys.argv) > 1:\n widget.filename(sys.argv[1])\n\n sys.exit(widget.report())","def main():\n LESSONS_PATH = os.path.join(LESSON_LOCATOR_DATA, LESSON_SETS[0])\n ORIGINAL_LESSONS_PATH = os.path.join(LESSONS_PATH, \"original\")\n ANNOTATED_LESSONS_PATH = os.path.join(LESSONS_PATH, \"annotated\")\n\n if not os.path.exists(ANNOTATED_LESSONS_PATH):\n os.mkdir(ANNOTATED_LESSONS_PATH)\n\n print(\"Scanning original lessons in %s...\" % ORIGINAL_LESSONS_PATH)\n\n for item in os.listdir(ORIGINAL_LESSONS_PATH):\n if item == \".DS_Store\": continue\n\n print(\" found: %s\" % item)\n\n item_path = os.path.join(ORIGINAL_LESSONS_PATH, item)\n\n lesson_number = None\n lesson_description = None\n mobj = re.search(r'^AY\\s+(\\d+)\\s*-\\s*(.+)\\.txt$', item)\n if mobj:\n lesson_number = mobj.group(1)\n lesson_description = mobj.group(2)\n\n print(\" number: %s\" % lesson_number)\n print(\" description: %s\" % lesson_description)\n\n lesson = dict()\n lesson['number'] = lesson_number\n lesson['description'] = lesson_description\n\n fh = open(item_path)\n lesson_raw_text = fh.read()\n fh.close()\n lesson_text = re.split(r'\\n', lesson_raw_text)\n# lesson_raw_text_reencoded = lesson_raw_text.decode('mac-roman').encode('utf-8')\n# lesson_text = re.split(r'\\n', lesson_raw_text_reencoded)\n\n lesson['text'] = lesson_text\n lesson['parsed'] = parseLesson(lesson_text)\n\n if lesson['parsed']['end_of_lesson'] is None:\n print(\" lesson has no 'end of lesson' marker\")\n\n lesson_json = json.dumps(lesson, indent=4)\n annotated_lesson_path = os.path.join(ANNOTATED_LESSONS_PATH, \"ay_%04d.json\" % int(lesson_number))\n fh = open(annotated_lesson_path, \"w\")\n fh.write(lesson_json)\n fh.close()\n\n else:\n print(\"ERROR: File name not understood: %s\" % item)\n\n return 0","def collect_files(self):\n self.files = []\n for bundle in self.bundles:\n bundle.init_build(self, self.builder)\n bundle_files = bundle.prepare()\n self.files.extend(bundle_files)\n return self","def generate(self):\n try:\n self._parse_groups()\n self._parse_types()\n self._parse_enums()\n self._parse_features()\n self._parse_extensions()\n self._add_extra_enums()\n self._parse_and_build_commands()\n self._build_all_enums()\n self._build_enum_groups()\n self._generate_files()\n except Exception as exception:\n print('Generate failed: {}'.format(str(exception)))\n raise","def execute(self):\n cwd = self.fm.thisdir\n marked_files = cwd.get_selection()\n\n if not marked_files:\n return\n\n def refresh(_):\n cwd = self.fm.get_directory(original_path)\n cwd.load_content()\n\n original_path = cwd.path\n parts = self.line.split()\n au_flags = parts[1:]\n\n descr = \"compressing files in: \" + os.path.basename(parts[1])\n obj = CommandLoader(args=['apack'] + au_flags + \\\n [os.path.relpath(f.path, cwd.path) for f in marked_files], descr=descr)\n\n obj.signal_bind('after', refresh)\n self.fm.loader.add(obj)","def process_data(cur, conn, filepath, func):\r\n\r\n # get all files matching extension from directory\r\n all_files = []\r\n for root, dirs, files in os.walk(filepath):\r\n files = glob.glob(os.path.join(root,'*.json'))\r\n for f in files :\r\n all_files.append(os.path.abspath(f))\r\n\r\n # get total number of files found\r\n num_files = len(all_files)\r\n print('{} files found in {}'.format(num_files, filepath))\r\n\r\n # iterate over files and process\r\n try:\r\n for i, datafile in enumerate(all_files, 1):\r\n func(cur, datafile)\r\n conn.commit()\r\n print('{}/{} files processed.'.format(i, num_files))\r\n except Exception as e:\r\n print(e)","def process_data(cur, conn, filepath, func):\n # get all files matching extension from directory\n all_files = []\n for root, dirs, files in os.walk(filepath):\n files = glob.glob(os.path.join(root, '*.json'))\n for f in files:\n all_files.append(os.path.abspath(f))\n\n # get total number of files found\n num_files = len(all_files)\n print('{} files found in {}'.format(num_files, filepath))\n\n # iterate over files and process\n for i, datafile in enumerate(all_files, 1):\n func(cur, datafile)\n conn.commit()\n print('{}/{} files processed.'.format(i, num_files))","def process_data(cur, conn, filepath, func):\n\n # get all files matching extension from directory\n all_files = []\n for root, dirs, files in os.walk(filepath):\n files = glob.glob(os.path.join(root, '*.json'))\n for f in files:\n all_files.append(os.path.abspath(f))\n\n # get total number of files found\n num_files = len(all_files)\n print('{} files found in {}'.format(num_files, filepath))\n\n # iterate over files and process\n for i, datafile in enumerate(all_files, 1):\n func(cur, datafile)\n conn.commit()\n print('{}/{} files processed.'.format(i, num_files))","def process_data(cur, conn, filepath, func):\n # get all files matching extension from directory\n all_files = []\n for root, dirs, files in os.walk(filepath):\n files = glob.glob(os.path.join(root,'*.json'))\n for f in files :\n all_files.append(os.path.abspath(f))\n\n # get total number of files found\n num_files = len(all_files)\n print('{} files found in {}'.format(num_files, filepath))\n\n # iterate over files and process\n for i, datafile in enumerate(all_files, 1):\n func(cur, datafile)\n conn.commit()\n print('{}/{} files processed.'.format(i, num_files))","def process_data(cur, conn, filepath, func):\n # get all files matching extension from directory\n all_files = []\n for root, dirs, files in os.walk(filepath):\n files = glob.glob(os.path.join(root,'*.json'))\n for f in files :\n all_files.append(os.path.abspath(f))\n\n # get total number of files found\n num_files = len(all_files)\n print('{} files found in {}'.format(num_files, filepath))\n\n # iterate over files and process\n for i, datafile in enumerate(all_files, 1):\n func(cur, datafile)\n conn.commit()\n print('{}/{} files processed.'.format(i, num_files))","def process_data(cursor, connection, filepath, function):\n # get all files matching extension from directory\n all_files = []\n for root, dirs, files in os.walk(filepath):\n files = glob.glob(os.path.join(root, '*.json'))\n for f in files:\n all_files.append(os.path.abspath(f))\n\n # get total number of files found\n num_files = len(all_files)\n print('{} files found in {}'.format(num_files, filepath))\n\n # iterate over files and process\n for i, datafile in enumerate(all_files, 1):\n function(cursor, datafile)\n connection.commit()\n print('{}/{} files processed.'.format(i, num_files))","def batch(f, fdir='.'):\n \n os.chdir(fdir)\n names = glob.iglob('*')\n # Absolute path rather than relative path allows changing of directories in fn_name.\n names = sorted([os.path.abspath(name) for name in names])\n for name in names:\n print os.path.basename(name)\n f(name)","def main():\n arg_parser = argparse.ArgumentParser(\n description='Runs jdeps (dependency analysis tool) on a given JAR and '\n 'writes the resulting dependency graph into a JSON file.')\n required_arg_group = arg_parser.add_argument_group('required arguments')\n required_arg_group.add_argument(\n '-t',\n '--target',\n required=True,\n help='Path to the JAR file to run jdeps on.')\n required_arg_group.add_argument(\n '-o',\n '--output',\n required=True,\n help='Path to the file to write JSON output to. Will be created '\n 'if it does not yet exist and overwrite existing '\n 'content if it does.')\n arg_parser.add_argument('-j',\n '--jdeps-path',\n default=JDEPS_PATH,\n help='Path to the jdeps executable.')\n arguments = arg_parser.parse_args()\n\n print('Running jdeps and parsing output...')\n raw_jdeps_output = run_jdeps(arguments.jdeps_path, arguments.target)\n jdeps_parser = JavaClassJdepsParser()\n jdeps_parser.parse_raw_jdeps_output(raw_jdeps_output)\n\n class_graph = jdeps_parser.graph\n print(f'Parsed class-level dependency graph, '\n f'got {class_graph.num_nodes} nodes '\n f'and {class_graph.num_edges} edges.')\n\n package_graph = package_dependency.JavaPackageDependencyGraph(class_graph)\n print(f'Created package-level dependency graph, '\n f'got {package_graph.num_nodes} nodes '\n f'and {package_graph.num_edges} edges.')\n\n print(f'Dumping JSON representation to {arguments.output}.')\n serialization.dump_class_and_package_graphs_to_file(\n class_graph, package_graph, arguments.output)","def run(self):\n self.load_template()\n self.load_data()\n self.load_files()\n self.render_content()\n self.process()\n # pprint(self.data)","def process_data(cur, conn, filepath, func):\n # get all files matching extension from directory\n all_files = []\n for root, dirs, files in os.walk(filepath):\n files = glob.glob(os.path.join(root, '*.json'))\n for file in files:\n all_files.append(os.path.abspath(file))\n\n # get total number of files found\n num_files = len(all_files)\n print('{} files found in {}'.format(num_files, filepath))\n\n # iterate over files and process\n for i, datafile in enumerate(all_files, 1):\n func(cur, datafile)\n conn.commit()\n print('{}/{} files processed.'.format(i, num_files))","def main(self, verbose=0):\n indepdict=self.scan_for_loop(self.indeploop)\n pegdict1 = self.scan_for_loop(self.pegloop1)\n pegdict2 = self.scan_for_loop(self.pegloop2)\n if len(indepdict.keys()) == 0 and len(pegdict1.keys()) == 0 and len(pegdict2.keys()) == 0:\n return dict()\n alldict = dict(indepdict)\n alldict.update(pegdict1)\n alldict.update(pegdict2)\n indepcomb=self.get_combo_list(indepdict, 0)\n pegcomb1=self.get_combo_list(pegdict1, 1)\n pegcomb2=self.get_combo_list(pegdict2, 1)\n allcombs = self.combine_three_combo_lists(indepcomb, pegcomb1, pegcomb2)\n datasets = self.prepare_looped_datasets(alldict, allcombs)\n createdfiles = self.create_input_files(datasets)\n if verbose == 1:\n self.print_list(indepcomb)\n self.print_list(pegcomb1)\n self.print_list(pegcomb2)\n self.print_list(allcombs)\n for datakey in datasets:\n self.print_list(datasets[datakey])\n return createdfiles","def process_datasets(self):\n\n with open(self.mappings, \"r+\") as json_file:\n emsl_to_jgi = json.load(json_file)\n emsl_to_jgi_copy = copy.deepcopy(emsl_to_jgi)\n\n contaminant_file_loc = emsl_to_jgi[\"contaminant_file_loc\"]\n # run for each dataset\n for dataset_id, values in emsl_to_jgi.items():\n if dataset_id not in [\n \"contaminant_file_loc\",\n \"analysis_activity_file_loc\",\n \"data_objects_file_loc\",\n \"STUDY\",\n \"tools_used\",\n ]:\n raw_file_loc = values[\"raw_file_loc\"]\n self.dataset_name = values[\"dataset_name\"]\n # dataset search against a fasta file\n for genome_directory, locations in values[\n \"genome_directory\"\n ].items():\n # clear object to prepare next job\n ANALYSIS_JOBS_OBJECT.clear()\n\n # create log_dir\n self.save_job_results = os.path.join(\n self.result_loc, dataset_id, genome_directory\n )\n self.log_collected_at = os.path.join(\n os.path.abspath(self.save_job_results), \"analysis_jobs_logs\"\n )\n if not os.path.exists(self.log_collected_at):\n os.makedirs(self.log_collected_at)\n\n files = [locations[\"faa_file_loc\"], contaminant_file_loc]\n contaminated_faa_file_loc = self.contaminate_fasta(files)\n\n self.register_job_in_emsl_to_jgi(\n dataset_id,\n genome_directory,\n \"contaminated_faa_file_loc\",\n contaminated_faa_file_loc,\n emsl_to_jgi_copy,\n )\n # convert .faa to .txt\n faa_txt_file = self.convert_faa2txt(\n dataset_id, contaminated_faa_file_loc\n )\n self.register_job_in_emsl_to_jgi(\n dataset_id,\n genome_directory,\n \"txt_faa_file_loc\",\n faa_txt_file,\n emsl_to_jgi_copy,\n )\n\n # log & run job\n self.run_n_log_job(\n dataset_id,\n genome_directory,\n contaminated_faa_file_loc,\n raw_file_loc,\n emsl_to_jgi_copy,\n )\n\n # merge analysis\n resultant_file = self.merge_analysis_jobs(\n dataset_id, genome_directory\n )\n self.register_job_in_emsl_to_jgi(\n dataset_id,\n genome_directory,\n \"resultant_file_loc\",\n resultant_file,\n emsl_to_jgi_copy,\n )\n\n # capture the job metadata object\n logger.info(\"Jobrun\", extra=LOGGED_ANALYSIS_JOB)\n\n # update emsl_to_jgi.json\n json_file.seek(0) # move back to BOF.\n json_file.truncate()\n json_file.write(json.dumps(emsl_to_jgi_copy, default=str, indent=4))\n pass","def process_data(db_cursor, filepath, func):\n\n # Get all files matching extension from directory\n all_files = []\n for root, _, files in os.walk(filepath):\n files = glob.glob(os.path.join(root, '*.json'))\n for f in files:\n all_files.append(os.path.abspath(f))\n\n num_files = len(all_files)\n print(f'{num_files} files found in {filepath}')\n\n # iterate over files and process\n for i, datafile in enumerate(all_files, 1):\n func(db_cursor, datafile)\n print(f'{i}/{num_files} files processed.')","def run():\n build_no_documentation()\n build_sphinx_build()\n #build_sphinx_pdf()\n build_graphviz_files()","def process(filename, exclude_dirs=['unittest','test','site-packages']):\n print(\"Generating {}\".format(filename))\n nb = 0\n nb_err = 0\n _main_root = os.path.dirname(filename)\n _VFS = {}\n for _mydir in (\"libs\", \"Lib\"):\n for _root, _dir, _files in os.walk(os.path.join(_main_root, _mydir)):\n #if _root.endswith('lib_migration'):\n _flag=False\n for _exclude in exclude_dirs:\n if _exclude in _root: #_root.endswith(_exclude):\n _flag=True\n continue\n if _flag:\n continue # skip these modules\n if '__pycache__' in _root:\n continue\n nb += 1\n\n for _file in _files:\n _ext = os.path.splitext(_file)[1]\n if _ext not in ('.js', '.py'):\n continue\n if re.match(r'^module\\d+\\..*$', _file):\n continue\n nb += 1\n\n file_name = os.path.join(_root, _file)\n _data = open(file_name, encoding='utf-8').read()\n \n if _ext == '.py':\n _data = python_minifier.minify(_data, preserve_lines=True)\n\n _vfs_filename = os.path.join(_root, _file).replace(_main_root, '')\n _vfs_filename = _vfs_filename.replace(\"\\\\\", \"/\")\n\n if _vfs_filename.startswith('/libs/crypto_js/rollups/'):\n if _file not in ('md5.js', 'sha1.js', 'sha3.js',\n 'sha224.js', 'sha384.js', 'sha512.js'):\n continue\n\n mod_name = _vfs_filename[len(_mydir) + 2:].replace('/', '.')\n mod_name, ext = os.path.splitext(mod_name)\n is_package = mod_name.endswith('__init__')\n if is_package:\n mod_name = mod_name[:-9]\n _VFS[mod_name] = [ext, _data, 1]\n else:\n _VFS[mod_name] = [ext, _data]\n print((\"adding %s %s\" % (mod_name, _vfs_filename)))\n print('%s files, %s errors' % (nb, nb_err))\n with open(filename, \"w\") as file_to_write_VFS:\n file_to_write_VFS.write('__BRYTHON__.use_VFS = true;\\n')\n file_to_write_VFS.write('__BRYTHON__.VFS=%s;\\n\\n' % json.dumps(_VFS))","def run(filts, jgen, argsj={}, verbose=False):\n if len(filts) == 0:\n return jgen\n\n filt0 = filts[0]\n cls = {\n \"commenter\": Commenter,\n \"setup\": SetupFilter,\n }[filt0]\n return run(filts[1:], cls(argsj).run(jgen), argsj=argsj, verbose=verbose)","def run() -> ():\n if len(sys.argv) > 1:\n show_help()\n errs = get_cargo_input()\n main_stack = make_proj_stack(errs)\n while len(main_stack) > 0:\n file_stack = make_file_stack(main_stack)\n overwrite(file_stack)\n\n # FIXME","def start_recoding(self, calc_stats=False):\n self.__reset_stats()\n for dir_name in self.__dirs:\n for ext in self.__exts:\n for file_name in self.__glob.iglob(dir_name + '\\\\**\\\\*.' + ext, recursive=True):\n encode = self.__define_encoding(file_name)\n if calc_stats:\n self.__collect_stats(encode, file_name)\n self.__correct_encoding(encode, file_name)","def assemble_files():\r\n path = os.path.expanduser(sys.argv[1])\r\n if os.path.isdir(path):\r\n file_root = path + \"/\"\r\n for file in os.listdir(path):\r\n filename = os.path.splitext(file)\r\n if filename[1] == \".asm\":\r\n hack_file_name = file_root + filename[0] + \".hack\"\r\n assemble_file(file_root + file, hack_file_name)\r\n else:\r\n filename = os.path.splitext(path)\r\n hack_file_name = filename[0] + \".hack\"\r\n assemble_file(path, hack_file_name)","def test_main():\n for template in templates:\n main([\"-g\", template])\n\n # One at a time\n for xyz_file in example_xyz_files:\n main([template, xyz_file])\n\n # All at once\n main([template] + list(example_xyz_files))\n\n # Allow use of template in the parent directory\n with cd(\"data\"):\n main([\"../pnictogen/repo/ADF.in\", \"water-dimer.xyz\"])","def batch_run_cfg2json():\n cfg_path = os.environ.get(\"CFG_FILE_PATH\")\n cfg_list = ['any_n1.cfg',\n 'ir_grism_n2.cfg',\n 'ir_grism_n4.cfg',\n 'ir_any_n2.cfg',\n 'ir_any_n4.cfg',\n 'uvis_any_n2.cfg',\n 'uvis_any_n4.cfg',\n 'uvis_any_n6.cfg',\n 'uvis_any_pre2012_n2.cfg',\n 'uvis_any_pre2012_n4.cfg',\n 'uvis_any_pre2012_n6.cfg',\n 'wfc_any_n2.cfg',\n 'wfc_any_n4.cfg',\n 'wfc_any_n6.cfg',\n 'sbc_blue_n2.cfg',\n 'sbc_blue_n6.cfg',\n 'sbc_any_n2.cfg',\n 'sbc_any_n6.cfg',\n 'hrc_any_n2.cfg',\n 'hrc_any_n4.cfg',\n 'hrc_any_n6.cfg']\n for cfgfile in cfg_list:\n cfgfile = os.path.join(cfg_path, cfgfile)\n cfg2json(cfgfile)\n\n cfg_path = os.path.realpath(__file__).replace(\"devutils/pars_utils.py\", \"pars/\")\n out_path = os.path.realpath(__file__).replace(\"devutils/pars_utils.py\", \"pars/hap_pars/any/\")\n cfg_list = [\"astrodrizzle_filter_hap.cfg\", \"astrodrizzle_single_hap.cfg\", \"astrodrizzle_total_hap.cfg\"]\n for cfgfile in cfg_list:\n cfgfile = os.path.join(cfg_path, cfgfile)\n cfg2json(cfgfile, outpath=out_path)","def Main(root_directory):\n filepaths = GetAllFilepaths(root_directory)\n for filepath in filepaths:\n parser = fileparser.CreateParser(filepath)\n if not parser:\n ReportWarning('cannot find a parser for file %s, skipping...' %\n filepath)\n continue\n old_file_contents = ReadFileIntoString(filepath)\n comment_blocks = parser.FindAllCommentBlocks(old_file_contents)\n if not comment_blocks:\n ReportWarning('cannot find any comment blocks in file %s' %\n filepath)\n old_copyright_block = parser.FindCopyrightBlock(comment_blocks)\n if not old_copyright_block:\n ReportWarning('cannot find copyright block in file %s' % filepath)\n (year, holder) = parser.GetCopyrightBlockAttributes(old_copyright_block)\n if holder and not ConfirmAllowedCopyrightHolder(holder):\n ReportWarning(\n 'unrecognized copyright holder \"%s\" in file %s, skipping...' % (\n holder, filepath))\n continue\n new_copyright_block = parser.CreateCopyrightBlock(year, holder)\n if old_copyright_block:\n new_file_contents = old_file_contents.replace(\n old_copyright_block, new_copyright_block, 1)\n else:\n new_file_contents = new_copyright_block + old_file_contents\n WriteStringToFile(new_file_contents, filepath)","def main():\n for tourney in tourneys:\n read_tourney(tourney)\n with open(\"obj/tournaments.pkl\", \"wb\") as f:\n pickle.dump(tournaments, f, pickle.HIGHEST_PROTOCOL)\n with open(\"obj/participants.pkl\", \"wb\") as f:\n pickle.dump(raw_participants, f, pickle.HIGHEST_PROTOCOL)\n with open(\"obj/matches.pkl\", \"wb\") as f:\n pickle.dump(all_matches, f, pickle.HIGHEST_PROTOCOL)","def process_data(cur, conn, filepath: str, func: Callable) -> None:\n # get all files matching extension from directory\n all_files = []\n for root, dirs, files in os.walk(filepath):\n files = glob.glob(os.path.join(root, \"*.json\"))\n for f in files:\n all_files.append(os.path.abspath(f))\n\n # get total number of files found\n num_files = len(all_files)\n print(\"{} files found in {}\".format(num_files, filepath))\n\n # iterate over files and process\n for i, datafile in enumerate(all_files, 1):\n func(cur, datafile)\n conn.commit()\n print(\"{}/{} files processed.\".format(i, num_files))","def _main(args):\n if args.files:\n _update_files()\n\n if args.templates:\n _update_template(args.template_definition)"],"string":"[\n \"def main():\\n os.makedirs(PATH)\\n fetch_data()\\n convert_to_json(model_list, 'models.json', is_model=True)\\n convert_to_json(backend_list, 'backends.json')\\n convert_to_json(type_list, 'types.json')\\n convert_to_json(featurizer_list, 'featurizers.json')\",\n \"def main(gtfs_file, input_json_file):\\n\\n with open(input_json_file) as jsonfile:\\n input_json = json.load(jsonfile)\\n\\n gtfs_feed = mzgtfs.feed.Feed(filename=gtfs_file)\\n\\n for fare_id, rules_attributes in input_json.iteritems():\\n add_fare_id(gtfs_feed, fare_id, rules_attributes)\\n\\n files = ['fare_attributes.txt', 'fare_rules.txt']\\n gtfs_feed.write('fare_attributes.txt', gtfs_feed.fares())\\n gtfs_feed.write('fare_rules.txt', gtfs_feed.fare_rules())\\n\\n gtfs_feed.make_zip('output.zip', files=files, clone=gtfs_file)\\n shutil.move('output.zip', gtfs_file)\\n\\n util.delete_temp_files(files)\",\n \"def clean_flatbuffer_binaries():\\n for element in FLATBUFFERS_CONVERSION_DATA:\\n for json in element.input_files:\\n path = processed_json_path(json)\\n if os.path.isfile(path):\\n os.remove(path)\",\n \"def main():\\n for db_csv_export in current_dir.glob(\\\"template*.csv\\\"):\\n data_projects = load_projects(db_csv_export)\\n json_path = db_csv_export.with_suffix(\\\".json\\\")\\n with open(json_path, \\\"w\\\") as fh:\\n json.dump(data_projects, fh, indent=2)\",\n \"def main(args=None):\\n if args is None:\\n args = sys.argv[1:]\\n\\n usage_ = \\\"\\\"\\\"%prog [OPTIONS] JsonFile [MoreJsonFiles...]\\nFormat/Beautify one or more JSON file(s).\\\"\\\"\\\"\\n parser = OptionParser(usage=usage_, version=VERSION)\\n parser.add_option(\\\"-i\\\", \\\"--indent\\\", dest=\\\"indent_size\\\",\\n default=DEFAULT_INDENT_SIZE, type=\\\"int\\\",\\n help=\\\"Indent size to use (default: %default).\\\")\\n parser.add_option(\\\"-c\\\", \\\"--compact\\\", dest=\\\"compact\\\",\\n action=\\\"store_true\\\", default=False,\\n help=\\\"Use compact format (default: %default).\\\")\\n parser.add_option(\\\"-n\\\", \\\"--dry-run\\\", dest=\\\"dry_run\\\",\\n action=\\\"store_true\\\", default=False,\\n help=\\\"Check only if JSON is well-formed (default: %default).\\\")\\n options, filenames = parser.parse_args(args) #< pylint: disable=W0612\\n if not filenames:\\n parser.error(\\\"OOPS, no filenames provided.\\\")\\n if options.compact:\\n options.indent_size = None\\n\\n # -- STEP: Init logging subsystem.\\n format_ = \\\"json.format: %(message)s\\\"\\n logging.basicConfig(level=logging.WARN, format=format_)\\n console = logging.getLogger(\\\"console\\\")\\n\\n # -- DOS-SHELL SUPPORT: Perform filename globbing w/ wildcards.\\n skipped = 0\\n filenames2 = []\\n for filename in filenames:\\n if \\\"*\\\" in filenames:\\n files = glob.glob(filename)\\n filenames2.extend(files)\\n elif os.path.isdir(filename):\\n # -- CONVENIENCE-SHORTCUT: Use DIR as shortcut for JSON files.\\n files = glob.glob(os.path.join(filename, \\\"*.json\\\"))\\n filenames2.extend(files)\\n if not files:\\n console.info(\\\"SKIP %s, no JSON files found in dir.\\\", filename)\\n skipped += 1\\n elif not os.path.exists(filename):\\n console.warning(\\\"SKIP %s, file not found.\\\", filename)\\n skipped += 1\\n continue\\n else:\\n assert os.path.exists(filename)\\n filenames2.append(filename)\\n filenames = filenames2\\n\\n # -- NORMAL PROCESSING:\\n errors = json_formatall(filenames, options.indent_size,\\n dry_run=options.dry_run)\\n console.error(\\\"Processed %d files (%d with errors, skipped=%d).\\\",\\n len(filenames), errors, skipped)\\n if not filenames:\\n errors += 1\\n return errors\",\n \"def convert_json_to_flatbuffer_binary(json, schema, out_dir):\\n command = [FLATC, '-o', out_dir, '-b', schema, json]\\n run_subprocess(command)\",\n \"def run(self):\\n if not os.path.exists(self.output_folder):\\n os.makedirs(self.output_folder)\\n for entry in glob.glob(os.path.join(self.data_folder, self.data_expression)):\\n f = open(entry)\\n text = json.loads(f.read())\\n f.close()\\n self.create_page_objects(text)\",\n \"def parse(root_path, output_path, batch_file_paths):\\n for file_path in batch_file_paths:\\n d, f = split_path(file_path, root_path)\\n print(time.ctime(), \\\"d =\\\", d, \\\"; f =\\\", f)\\n if not os.path.exists(os.path.join(OUTPUT_PATH, d)):\\n os.makedirs(os.path.join(OUTPUT_PATH, d))\\n parsed_files = get_parsed_files(output_path, d)\\n if f not in parsed_files:\\n with open(file_path) as json_file:\\n OUTPUT_FILE_PATH = os.path.join(OUTPUT_PATH, d, f)\\n with open(OUTPUT_FILE_PATH, 'w') as writer:\\n for num, line in enumerate(json_file):\\n json_data = json.loads(line)\\n title = json_data['title']\\n if json_data['text']:\\n definition = json_data['text'][0]['line']\\n writer.write(f'{title}\\\\t{definition}\\\\n')\\n else:\\n writer.write(f'{title}\\\\tNone.\\\\n')\",\n \"def main():\\n\\n obj_lookup = interfaces_dir / \\\"FrameLib-obj-jlookup.json\\\"\\n\\n worker = jParseAndBuild()\\n\\n refpages = [x for x in refpages_dir.rglob(\\\"fl.*.xml\\\")]\\n\\n for ref in refpages:\\n worker.extract_from_refpage(ref)\\n\\n write_json(obj_lookup, worker.j_master_dict)\",\n \"def pack():\\n clean_local()\\n build()\\n copy_json()\\n optimize()\\n tarball()\",\n \"def main():\\n onlyfiles = [f for f in listdir(RAWDATA_PATH) if isfile(join(RAWDATA_PATH, f))]\\n for file in onlyfiles:\\n create_RCSB_fastas(file)\",\n \"def main():\\n\\n # Command Line Interface\\n parse = command_line()\\n args = parse.parse_args()\\n if not os.path.isdir(args.directory):\\n raise IOError\\n\\n # Abstract File Tree\\n filetree = tree_walk(args.directory, args.replace, args.depth)\\n jsontree = json.dumps(\\n filetree,\\n indent=4,\\n sort_keys=True,\\n separators=(', ', ': '),\\n )\\n\\n # Pipe vs Redirection\\n if sys.stdout.isatty():\\n try: jsontree = highlight(\\n jsontree,\\n JsonLexer(),\\n Terminal256Formatter(style='autumn'))\\n except:\\n pass\\n\\n print(jsontree)\",\n \"def fix_jsons_in(bids_dir: Path):\\n\\n print(\\\"Finalizing task json files.\\\")\\n\\n for path in bids_dir.rglob(\\\"func/*_task-*.json\\\"):\\n append_to_json_file(key=\\\"TaskName\\\", value=task_name_of(path), path_to_json=path)\\n\\n print(\\\"Appending echo times to phase difference json files.\\\")\\n\\n for path in bids_dir.rglob(\\\"fmap/*_phasediff.json\\\"):\\n magnitude1_path = the_path_that_matches(pattern=\\\"sub-*_magnitude1.json\\\", in_directory=path.parent)\\n magnitude2_path = the_path_that_matches(pattern=\\\"sub-*_magnitude2.json\\\", in_directory=path.parent)\\n echo_time1 = value_of_key_in_json_file(\\\"EchoTime\\\", magnitude1_path)\\n echo_time2 = value_of_key_in_json_file(\\\"EchoTime\\\", magnitude2_path)\\n append_to_json_file(key=\\\"EchoTime1\\\", value=echo_time1, path_to_json=path)\\n append_to_json_file(key=\\\"EchoTime2\\\", value=echo_time2, path_to_json=path)\\n\\n print(\\\"Setting targets of fieldmap json files.\\\")\\n\\n for path in bids_dir.rglob(\\\"fmap/*.json\\\"):\\n func_dir = path.parent.parent / \\\"func\\\"\\n trimmed_func_paths = [\\\"func/\\\" + func_path.name for func_path in func_dir.glob(\\\"*.nii\\\")]\\n append_to_json_file(key=\\\"IntendedFor\\\", value=trimmed_func_paths, path_to_json=path)\",\n \"def analyze(self):\\n for f in self.files:\\n tokenizer = Tokenizer(f)\\n self.write_tokens(tokenizer)\\n compilation_engine = CompilationEngine(tokenizer, f)\\n compilation_engine.compile()\\n self.write_syntax_tree(compilation_engine)\\n compilation_engine.VMwriter.create_file(f[:-5])\",\n \"def process_files_json():\\n # chdir into beep root\\n pwd = os.getcwd()\\n os.chdir(os.environ.get(\\\"BEEP_ROOT\\\", \\\"/\\\"))\\n\\n meta_list = list(filter(lambda x: '_Metadata.csv' in x, os.listdir(SRC_DIR)))\\n file_list = list(filter(lambda x: '.csv' in x if x not in meta_list else None, os.listdir(SRC_DIR)))\\n all_list = list(filter(lambda x: '.csv' in x, os.listdir(SRC_DIR)))\\n\\n all_list = sorted(all_list)\\n dumpfn(all_list, \\\"all_files.json\\\")\\n\\n [file_id, mapdf] = init_map(PROJECT_NAME, DEST_DIR)\\n\\n new_file_index = file_id\\n\\n for filename in tqdm(sorted(file_list)):\\n # If the file has already been renamed another entry should not be made\\n if mapdf['filename'].str.contains(filename).sum() > 0:\\n continue\\n old_file = os.path.join(SRC_DIR, filename)\\n new_path = os.path.join(DEST_DIR, PROJECT_NAME)\\n shutil.copy(old_file, new_path) # copy main data file\\n shutil.copy(old_file.replace(\\\".csv\\\", '_Metadata.csv'), new_path) # copy meta data file\\n\\n if PROJECT_NAME == 'FastCharge':\\n [date, channel_no, strname, protocol] = get_parameters_fastcharge(filename, SRC_DIR)\\n elif PROJECT_NAME == 'ClosedLoopOED':\\n [date, channel_no, strname, protocol] = get_parameters_oed(filename, SRC_DIR)\\n else:\\n raise ValueError(\\\"Unsupported PROJECT_NAME: {}\\\".format(PROJECT_NAME))\\n\\n df_dup = mapdf.set_index(['protocol', 'date'])\\n if (protocol, date) in df_dup.index:\\n row = mapdf[(mapdf['protocol'] == protocol) & (mapdf['date'] == date)]\\n file_id = row['fid'].iloc[0]\\n protocol = row['protocol'].iloc[0]\\n date = row['date'].iloc[0]\\n strname = row['strname'].iloc[0]\\n else:\\n file_id = new_file_index\\n new_file_index = new_file_index + 1\\n\\n new_name = \\\"{}_{}_{}\\\".format(PROJECT_NAME, f'{file_id:06}', channel_no)\\n new_file = os.path.join(DEST_DIR, PROJECT_NAME, \\\"{}.csv\\\".format(new_name))\\n\\n new_row = pd.DataFrame([[file_id, protocol, channel_no, date, strname,\\n os.path.abspath(old_file),\\n os.path.abspath(new_file)]],\\n columns=METADATA_COLUMN_NAMES)\\n mapdf = mapdf.append(new_row)\\n\\n os.rename(os.path.join(DEST_DIR, PROJECT_NAME, filename), new_file)\\n os.rename(os.path.join(DEST_DIR, PROJECT_NAME, filename).replace(\\\".csv\\\", \\\"_Metadata.csv\\\"),\\n new_file.replace(\\\".csv\\\", \\\"_Metadata.csv\\\"))\\n\\n mapdf.to_csv(os.path.join(DEST_DIR, PROJECT_NAME, PROJECT_NAME + \\\"map.csv\\\"), index=False)\\n mapdf = mapdf.reset_index(drop=True)\\n os.chdir(pwd)\\n return json.dumps(mapdf.to_dict(\\\"list\\\"))\",\n \"def main(arguments):\\n\\n # The input file can be optionally encoded with gzip format:\\n input_file = arguments.input_file[0]\\n assert isinstance(input_file, str)\\n if input_file.endswith(\\\".gz\\\"):\\n _open = gzip.open\\n else:\\n _open = open\\n with _open(input_file, \\\"rt\\\",\\n encoding='utf-8') as fd:\\n print(\\\"Loading JSON content into memory....\\\")\\n raw = json.load(fd) # Parses all the input file.\\n\\n # Also the output file can be optionally encoded with gzip format:\\n output_file = arguments.output_file[0]\\n assert isinstance(output_file, str)\\n uuid = 0\\n if output_file.endswith(\\\".gz\\\"):\\n _open = gzip.open\\n else:\\n _open = open\\n with _open(output_file, \\\"wt\\\",\\n encoding='utf-8') as fd:\\n # for each element extracted from the input\\n print(\\\"Generating distilled file\\\")\\n for item in load_input(raw):\\n uuid += 1 # generates incremental uuid from 1\\n item['uuid'] = uuid\\n fd.write(json.dumps(item,\\n sort_keys=True))\\n fd.write(\\\"\\\\n\\\") # one encoded document per line\\n\\n print(\\\"{} documents imported\\\".format(uuid))\",\n \"def main():\\n\\n # Set up argument parser.\\n parser = argparse.ArgumentParser(\\n description='Removes duplicate key-value pairs from JSON files.')\\n parser.add_argument('--suffix', default='',\\n help='optional suffix for output files; '\\n 'if empty, files will be changed in place')\\n parser.add_argument('files', nargs='+', help='input files')\\n args = parser.parse_args()\\n\\n # Iterate over files.\\n for filename in args.files:\\n # Read in json using Python libraries. This eliminates duplicates.\\n print('Processing ' + filename + '...')\\n try:\\n with codecs.open(filename, 'r', 'utf-8') as infile:\\n j = json.load(infile)\\n except ValueError as e:\\n print('Error reading ' + filename)\\n raise InputError(filename, str(e))\\n\\n # Built up output strings as an array to make output of delimiters easier.\\n output = []\\n for key in j:\\n if key != '@metadata':\\n output.append('\\\\t\\\"' + key + '\\\": \\\"' +\\n j[key].replace('\\\\n', '\\\\\\\\n') + '\\\"')\\n\\n # Output results.\\n with codecs.open(filename + args.suffix, 'w', 'utf-8') as outfile:\\n outfile.write('{\\\\n')\\n outfile.write(',\\\\n'.join(output))\\n outfile.write('\\\\n}\\\\n')\",\n \"def file_loader(self):\\n\\n for folder in self.config[\\\"data_folders\\\"]:\\n f = os.path.join(folder, self.data_file)\\n yield jsonlist.load_file(f)\",\n \"def process_data(*args, **kwargs):\\n\\n filepath = kwargs[\\\"filepath\\\"]\\n func = kwargs[\\\"func\\\"]\\n \\n # get all files matching extension from directory\\n all_files = []\\n for root, dirs, files in os.walk(filepath):\\n files = glob.glob(os.path.join(root,'*.json'))\\n for f in files :\\n all_files.append(os.path.abspath(f))\\n\\n # get total number of files found\\n num_files = len(all_files)\\n print('{} files found in {}'.format(num_files, filepath))\\n\\n # iterate over files and process\\n for i, datafile in enumerate(all_files, 1):\\n func(datafile)\\n print('{}/{} files processed.'.format(i, num_files))\",\n \"def process_dart(self):\\n self.dartfiles = set()\\n self.jsfiles = set()\\n self.htmlfiles = set()\\n self.cssfiles = set()\\n self.otherfiles = set()\\n for src in self.source:\\n if isinstance(src,str):\\n node = self.path.find_node(src)\\n else:\\n node = src\\n if node.suffix() == '.dart':\\n self.dartfiles.add(node)\\n elif node.suffix() == '.js':\\n self.jsfiles.add(node)\\n elif node.suffix() == '.html':\\n self.htmlfiles.add(node)\\n elif node.suffix() == '.css':\\n self.cssfiles.add(node)\\n else:\\n self.otherfiles.add(node)\\n self.source = []\\n self.outdir = self.path.make_node(self.target + '/').get_bld()\\n self.outdir.mkdir()\",\n \"def main():\\n processSetOfCerFiles(sys.argv[1:])\",\n \"def run_main():\\n # Matching lines against a matcher function.\\n matched_lines = match_file(file_names, matcher)\\n\\n # Will contain data sorted by file.\\n binned_data = {}\\n\\n # Looking through the lines that were inserted into the metrics file via the metrics component.\\n for key in matched_lines:\\n\\n # Grabbing matched lines by the file or orgination.\\n buffer = matched_lines[key]\\n\\n # This will contain dictionaries converted from JSON.\\n data = []\\n\\n # Loop through the collection, appending data converted from JSON entries.\\n for line in buffer:\\n data.append(extract_data(line))\\n\\n # Sort the data by file.\\n binned_data[key] = sort_data(data)\\n\\n # Output the final results.\\n generate_statistics(binned_data)\\n return 0\",\n \"def runall(sources='sources'):\\n init()\\n upload_sources(sources)\\n compile()\\n run()\",\n \"def main():\\n print(\\n \\\"\\\"\\\"\\n\\n ##########################################################\\n # #\\n # #\\n # Compiling Colocalized Cyano Datasets #\\n # #\\n # #\\n ##########################################################\\n\\n \\n \\\"\\\"\\\"\\n )\\n cyanoFiles = glob.glob(f\\\"{COLOCALIZED_DIR}*.csv\\\")\\n makedir(COMPILED_DIR)\\n dfCompiled = pd.DataFrame({})\\n for cyanoFile in cyanoFiles:\\n print(f\\\"Compiling {cyanoFile}\\\")\\n data = unify(cyanoFile)\\n if len(dfCompiled ) < 1:\\n dfCompiled = data\\n else:\\n dfCompiled = pd.concat([dfCompiled, data], ignore_index=True) \\n dfCompiled.to_csv(f\\\"{COMPILED_DIR}compiled.csv\\\", index=False)\",\n \"def main():\\n # There are no args, but parse them just so help works\\n args = docopt(__doc__)\\n print(process_files_json(), end=\\\"\\\")\\n return None\",\n \"def process_input_files(inputs):\\n for ifile in inputs:\\n with open(ifile) as fin:\\n exec(compile(fin.read(), ifile, 'exec'))\",\n \"def test_json():\\n schemas = {\\n 'schema-languages': 'bible/languages.json',\\n 'schema-book-metadata': 'bible/book-metadata.json',\\n 'schema-bible': 'bible/bible-*.json'\\n }\\n for schema_name, data_path_glob in schemas.items():\\n schema_path = 'schemas/{}.json'.format(schema_name)\\n with open(schema_path) as schema_file:\\n schema = json.load(schema_file)\\n data_paths = glob.iglob(data_path_glob)\\n for data_path in data_paths:\\n with open(data_path) as data_file:\\n data = json.load(data_file)\\n yield jsonschema.validate, data, schema\",\n \"def main(filename):\\n with open(filename) as json_file:\\n data = json.load(json_file)\\n\\n course_dict = {}\\n course_dict['course_id'] = str(os.path.split(filename.strip('/'))[-1])\\n course_dict['blocks'] = build_course_map(data)\\n\\n filename = '%s' % course_dict['course_id']\\n filepath = os.path.join('../input/', filename)\\n\\n with open(filepath, 'w') as outfile:\\n json.dump(course_dict, outfile, indent=4)\",\n \"def apply_dart(self):\\n shutil.copyfile(self.env['DART_JS_BOOTSTRAP'], self.outdir.make_node('dart.js').abspath())\\n for filetype in ['dartfiles','jsfiles','htmlfiles','cssfiles','otherfiles']:\\n files = getattr(self, filetype)\\n for f in files:\\n if f.is_bld():\\n outf = self.outdir.make_node(f.path_from(self.path.get_bld()))\\n elif f.is_src():\\n outf = self.outdir.make_node(f.path_from(self.path.get_src()))\\n else:\\n raise Exception(\\\"I don't know what I'm doing anymore.\\\")\\n self.create_task('copytask',f,outf)\",\n \"def process(filename, exclude_dirs=['unittest','test',]):\\n print(\\\"Generating {}\\\".format(filename))\\n nb = 0\\n nb_err = 0\\n _main_root = os.path.dirname(filename)\\n _VFS = {}\\n for _mydir in (\\\"libs\\\", \\\"Lib\\\"):\\n for _root, _dir, _files in os.walk(os.path.join(_main_root, _mydir)):\\n #if _root.endswith('lib_migration'):\\n _flag=False\\n for _exclude in exclude_dirs:\\n if _exclude in _root: #_root.endswith(_exclude):\\n _flag=True\\n continue\\n if _flag:\\n continue # skip these modules\\n if '__pycache__' in _root:\\n continue\\n nb += 1\\n\\n for _file in _files:\\n _ext = os.path.splitext(_file)[1]\\n if _ext not in ('.js', '.py'):\\n continue\\n nb += 1\\n\\n with open(os.path.join(_root, _file), \\\"r\\\") as file_with_data:\\n _data = file_with_data.read()\\n \\n if len(_data) == 0:\\n print('no data for %s' % _file)\\n _data = unicode('')\\n print(_data, type(_data))\\n else:\\n _data = _data.decode('utf-8')\\n\\n if _ext in '.js':\\n if js_minify is not None:\\n try:\\n _data = js_minify(_data)\\n except Exception as error:\\n print(error)\\n elif _ext == '.py' and len(_data) > 0:\\n try:\\n _data = pyminifier.remove_comments_and_docstrings(_data)\\n _data = pyminifier.dedent(_data)\\n except Exception as error:\\n print(error)\\n nb_err += 1\\n\\n _vfs_filename = os.path.join(_root, _file).replace(_main_root, '')\\n _vfs_filename = _vfs_filename.replace(\\\"\\\\\\\\\\\", \\\"/\\\")\\n\\n if _vfs_filename.startswith('/libs/crypto_js/rollups/'):\\n if _file not in ('md5.js', 'sha1.js', 'sha3.js',\\n 'sha224.js', 'sha384.js', 'sha512.js'):\\n continue\\n\\n mod_name = _vfs_filename[len(_mydir) + 2:].replace('/', '.')\\n mod_name, ext = os.path.splitext(mod_name)\\n is_package = mod_name.endswith('__init__')\\n if is_package:\\n mod_name = mod_name[:-9]\\n _VFS[mod_name] = [ext, _data, 1]\\n else:\\n _VFS[mod_name] = [ext, _data]\\n print((\\\"adding %s %s\\\" % (mod_name, _vfs_filename)))\\n print('%s files, %s errors' % (nb, nb_err))\\n with open(filename, \\\"w\\\") as file_to_write_VFS:\\n file_to_write_VFS.write('__BRYTHON__.use_VFS = true;\\\\n')\\n file_to_write_VFS.write('__BRYTHON__.VFS=%s;\\\\n\\\\n' % json.dumps(_VFS))\",\n \"def main(args):\\n if os.path.isfile(args.dev_ctm) and os.path.isfile(args.eval_ctm):\\n ctm_files = [args.dev_ctm, args.eval_ctm]\\n else:\\n raise FileNotFoundError('Please ensure that both {} and {} are valid files'.format(args.dev_ctm, args.eval_ctm))\\n\\n utils.mkdir(args.output_dir)\\n\\n for ctm_file in ctm_files:\\n segments, num_lines = get_segments(ctm_file)\\n\\n if segments[0].endswith('Line'):\\n abbrev = True\\n else:\\n abbrev = False\\n\\n split_ctm(ctm_file, segments, num_lines, args.output_dir, abbrev)\",\n \"def execute():\\n # Write JSON files for each country. Ex: US.json lists ALL recently active users from the USA\\n for country_code in config['countries']:\\n json_data = get_users(country_code=country_code)\\n write_json(json_data=json_data, country_code=country_code)\",\n \"def main():\\n def parse_arguments():\\n \\\"\\\"\\\"Create a parser for the program and use it to parse arguments\\n\\n Returns:\\n ArgumentParser: The argument parser of the program\\n \\\"\\\"\\\"\\n parser = ArgumentParser()\\n parser.add_argument(\\\"-p\\\", \\\"--path\\\", required=True, nargs=1,\\n dest=\\\"file_path\\\", type=str, action=\\\"store\\\",\\n help=\\\"path to the root directory\\\")\\n parser.add_argument(\\\"-q\\\", \\\"--quick\\\", action=\\\"store_true\\\",\\n default=False, dest=\\\"quick\\\",\\n help=\\\"Speed up the search or not\\\")\\n return parser.parse_args()\\n try:\\n arguments = parse_arguments()\\n # Get all files in the root directory, ignoring symlink\\n file_path_names = scan_files(arguments.file_path[0])\\n # Get groups of duplicated files\\n duplicated_file_groups = find_duplicate_files(\\n file_path_names, arguments.quick\\n )\\n # Print those groups in json format\\n print(dumps(duplicated_file_groups, indent=4, separators=(\\\"\\\", \\\"\\\")))\\n except (TypeError, ValueError):\\n return\",\n \"def maps():\\n flatmap_list = []\\n root_path = pathlib.Path(settings['FLATMAP_ROOT'])\\n if root_path.is_dir():\\n for flatmap_dir in root_path.iterdir():\\n index = os.path.join(settings['FLATMAP_ROOT'], flatmap_dir, 'index.json')\\n mbtiles = os.path.join(settings['FLATMAP_ROOT'], flatmap_dir, 'index.mbtiles')\\n if os.path.isdir(flatmap_dir) and os.path.exists(index) and os.path.exists(mbtiles):\\n with open(index) as fp:\\n index = json.loads(fp.read())\\n version = index.get('version', 1.0)\\n reader = MBTilesReader(mbtiles)\\n if version >= 1.3:\\n metadata = read_metadata(reader, 'metadata')\\n if (('id' not in metadata or flatmap_dir.name != metadata['id'])\\n and ('uuid' not in metadata or flatmap_dir.name != metadata['uuid'].split(':')[-1])):\\n app.logger.error(f'Flatmap id mismatch: {flatmap_dir}')\\n continue\\n flatmap = {\\n 'id': metadata['id'],\\n 'source': metadata['source'],\\n 'version': version\\n }\\n if 'created' in metadata:\\n flatmap['created'] = metadata['created']\\n if 'taxon' in metadata:\\n flatmap['taxon'] = normalise_identifier(metadata['taxon'])\\n flatmap['describes'] = metadata['describes'] if 'describes' in metadata else flatmap['taxon']\\n elif 'describes' in metadata:\\n flatmap['taxon'] = normalise_identifier(metadata['describes'])\\n flatmap['describes'] = flatmap['taxon']\\n if 'biological-sex' in metadata:\\n flatmap['biologicalSex'] = metadata['biological-sex']\\n if 'uuid' in metadata:\\n flatmap['uuid'] = metadata['uuid']\\n if 'name' in metadata:\\n flatmap['name'] = metadata['name']\\n else:\\n try:\\n source_row = reader._query(\\\"SELECT value FROM metadata WHERE name='source'\\\").fetchone()\\n except (InvalidFormatError, sqlite3.OperationalError):\\n flask.abort(404, 'Cannot read tile database: {}'.format(mbtiles))\\n if source_row is None:\\n continue\\n flatmap = {\\n 'id': flatmap_dir.name,\\n 'source': source_row[0]\\n }\\n created = reader._query(\\\"SELECT value FROM metadata WHERE name='created'\\\").fetchone()\\n if created is not None:\\n flatmap['created'] = created[0]\\n describes = reader._query(\\\"SELECT value FROM metadata WHERE name='describes'\\\").fetchone()\\n if describes is not None and describes[0]:\\n flatmap['describes'] = normalise_identifier(describes[0])\\n flatmap_list.append(flatmap)\\n return flask.jsonify(flatmap_list)\",\n \"def process_data(cur, conn, filepath, func):\\n all_files = []\\n for root, dirs, files in os.walk(filepath):\\n files = glob.glob(os.path.join(root,'*.json'))\\n for f in files :\\n all_files.append(os.path.abspath(f))\\n\\n num_files = len(all_files)\\n print('{} files found in {}'.format(num_files, filepath))\\n\\n for i, datafile in enumerate(all_files, 1):\\n func(cur, datafile)\\n conn.commit()\\n print('{} files processed.'.format(num_files))\",\n \"def main():\\n # if there are no command line args, run as normal with the input from stdin\\n if len(sys.argv) == 1:\\n json_string = \\\"\\\".join(sys.stdin)\\n hierarchical_jsons = [load_json(json_string)]\\n # if there are command line args, run using the first argument as a file path to a file with\\n # correctly formatted test json values\\n else:\\n with open(sys.argv[1]) as file:\\n hierarchical_jsons = json.load(file)\\n\\n flattened_jsons = []\\n\\n # for all the jsons given, in the non-test case there will only be one\\n for hierarchical_json in hierarchical_jsons:\\n # flatten the json object\\n flat_json = flatten_json(hierarchical_json)\\n # print the prettied json to stdout\\n json_string = json.dumps(flat_json, indent=4)\\n flattened_jsons.append(json_string)\\n print(json_string)\\n\\n return flattened_jsons\",\n \"def main(infile: List[str], prefix: str, verbose: bool, no_origin_filter: bool):\\n common.init_logging(verbosity=int(verbose) + 1)\\n _LOGGER.info(\\\"Running with arguments: %s.\\\", locals())\\n\\n file_id = -1\\n with fileinput.input(files=infile, openhook=fileinput.hook_compressed) \\\\\\n as json_lines:\\n results = (json.loads(line) for line in json_lines)\\n\\n for file_id, graph in enumerate(\\n extract_graphs(results, use_origin=not no_origin_filter)\\n ):\\n path = Path(f\\\"{prefix}{file_id:04d}.json\\\")\\n if not path.is_file():\\n path.write_text(graph.to_json())\\n else:\\n _LOGGER.info(\\\"Refusing to overwrite: %s\\\", path)\\n _LOGGER.info(\\\"Script complete. Extracted %d dependency graphs.\\\", file_id+1)\",\n \"def local(ctx):\\n _do_codegen_user(\\\"demo\\\")\\n _do_codegen_user(\\\"errors\\\")\\n _do_codegen_user(\\\"mpi\\\")\\n _do_codegen_user(\\\"omp\\\")\\n _do_codegen_user(\\\"python\\\")\\n\\n # Do codegen for libfake\\n for so in LIB_FAKE_FILES:\\n _do_codegen_file(so)\\n\\n # Run the WAMR codegen required by the tests\\n codegen(ctx, \\\"demo\\\", \\\"echo\\\", wamr=True)\\n codegen(ctx, \\\"demo\\\", \\\"chain\\\", wamr=True)\\n\\n # Run the SGX codegen required by the tests\\n codegen(ctx, \\\"demo\\\", \\\"hello\\\", wamr=True, sgx=True)\\n codegen(ctx, \\\"demo\\\", \\\"chain_named_a\\\", wamr=True, sgx=True)\\n codegen(ctx, \\\"demo\\\", \\\"chain_named_b\\\", wamr=True, sgx=True)\\n codegen(ctx, \\\"demo\\\", \\\"chain_named_c\\\", wamr=True, sgx=True)\",\n \"def main(json_input, output_folder):\\n with open(json_input, 'r') as f:\\n data = json.loads(f.read())['data']\\n\\n with open(path.join(output_folder, 'qgeneration.context.txt'), 'w') as context_f, \\\\\\n open(path.join(output_folder, 'qgeneration.context.nojson.txt'), 'w') as context_f_nojson, \\\\\\n open(path.join(output_folder, 'qgeneration.gold.txt'), 'w') as question_f:\\n for item in data:\\n context_f.write(json.dumps(item['context']) + '\\\\n')\\n context_f_nojson.write(item['context'][:1000] + '\\\\n')\\n question_f.write(json.dumps(item['answer']) + '\\\\n')\\n\\n print('DONE')\",\n \"def reconstitute():\\n with open(TEXT_FPATH, 'w') as txt:\\n for jfpath in json_fpaths():\\n with open(jfpath) as f:\\n jstruct = json.load(f)\\n\\n for recipe in jstruct.keys():\\n _reconstitute_recipe(txt, jstruct[recipe])\",\n \"def process_data(cur, conn, filepath, func):\\n\\n all_files = []\\n for root, dirs, files in os.walk(filepath):\\n files = glob.glob(os.path.join(root, '*.json'))\\n for f in files:\\n all_files.append(os.path.abspath(f))\\n\\n num_files = len(all_files)\\n print('{} files found in {}'.format(num_files, filepath))\\n\\n for i, datafile in enumerate(all_files, 1):\\n func(cur, datafile)\\n conn.commit()\\n print('{}/{} files processed.'.format(i, num_files))\",\n \"def main():\\n\\t# import training data\\n\\tfiles = [INPATH + f for f in os.listdir(INPATH) if \\\".json\\\" in f]\\n\\n\\t# import books\\n\\tprint(\\\"Loading training data...\\\")\\n\\tbookList = loadBooks(files)\\n\\tprint(\\\"Load complete.\\\")\\n\\n\\t# loop through element types and store data structure\\n\\tfor key, value in ELEMENTS.items():\\n\\t\\tprint(\\\"Generating: %s\\\" % key)\\n\\n\\t\\t# set file outpath\\n\\t\\toutfile = \\\"%s.json\\\" % key\\n\\t\\toutpath = OUTPATH % outfile\\n\\n\\t\\tgenerateTrain(bookList, key, value, outpath)\",\n \"def main():\\n\\n # Load arguments\\n args = get_args()\\n \\n assert os.path.exists(args.csv), ' [ERR] File' + os.path.exists(args.csv) +'does not exist'\\n\\n print(args)\\n try:\\n dir_name = os.path.dirname(args.json)\\n os.mkdir(dir_name)\\n print(' [INFO] Creating', dir_name, 'directory')\\n except:\\n print(' [INFO] Directory', dir_name, 'already exists. Data will be replaced')\\n pass\\n\\n if args.config:\\n assert os.path.exists(args.config), ' [ERR] File' + os.path.exists(args.config) +'does not exist'\\n dic_types = read_config(args.config)\\n else:\\n dic_types = {}\\n \\n # Create json\\n create_json_from_csv(args.csv, args.delimiter, args.cols_delimiter, args.keep, dic_types, args.infer_types, args.max_docs, args.json, args.per_line)\\n\\n return 0\",\n \"def build():\\n for root, dirs, files in os.walk(IN_PATH):\\n for filename in files:\\n if filename.endswith('.csv'):\\n with open(os.path.join(IN_PATH, filename), encoding='utf-8') as f:\\n reader = csv.reader(f)\\n next(reader)\\n data = nested_dict()\\n web_data = nested_dict()\\n for row in reader:\\n if row[0].startswith('report.') or row[0].startswith('cardset.'):\\n d = data\\n elif row[0].startswith('web.'):\\n d = web_data\\n path = row[0].split('.')\\n for i in range(len(path)):\\n if i == len(path) - 1:\\n d[path[i]] = row[1]\\n else:\\n d = d[path[i]]\\n with open (os.path.join(OUT_PATH, filename.replace('.csv', '.json')), 'w', encoding='utf-8') as fout:\\n json.dump({**data, **web_data}, fout)\\n with open (os.path.join(WEB_PATH, filename.replace('.csv', '.js')), 'w', encoding='utf-8') as fout:\\n fout.write('var STRINGS = {};'.format(json.dumps(web_data)))\\n\\n with open(os.path.join(IN_PATH, 'en_US.csv'), encoding='utf-8') as f:\\n reader = csv.reader(f)\\n next(reader)\\n data = nested_dict()\\n web_data = nested_dict()\\n for row in reader:\\n path = row[0].split('.')\\n if row[0].startswith('report.') or row[0].startswith('cardset.'):\\n d = data\\n elif row[0].startswith('web.'):\\n d = web_data\\n\\n for i in range(len(path)):\\n if i == len(path) - 1:\\n d[path[i]] = zz_string(row[1], row[0])\\n else:\\n d = d[path[i]]\\n with open(os.path.join(OUT_PATH, 'zz_ZZ.json'), 'w', encoding='utf-8') as fout:\\n json.dump({**data, **web_data}, fout)\\n with open(os.path.join(WEB_PATH, 'zz_ZZ.js'), 'w', encoding='utf-8') as fout:\\n fout.write('var STRINGS = {};'.format(json.dumps(web_data)))\",\n \"def main():\\n # Create / clean output dir\\n if os.path.isdir(OUT_DIR):\\n shutil.rmtree(OUT_DIR)\\n os.mkdir(OUT_DIR)\\n\\n # Write all assets to the directory\\n for fname, bb in create_assets().items():\\n filename = os.path.join(OUT_DIR, fname)\\n dirname = os.path.dirname(filename)\\n if not os.path.isdir(dirname):\\n os.makedirs(dirname)\\n with open(filename, \\\"wb\\\") as f:\\n f.write(bb)\",\n \"def patch_files():\\n args = parser.parse_args()\\n doc = json.load(args.ORIGINAL)\\n patch = json.load(args.PATCH)\\n result = jsonpatch.apply_patch(doc, patch)\\n print(json.dumps(result, indent=args.indent))\",\n \"def flush_buffers(self):\\n\\n for file_dictionary in [self.infiles, self.outfiles]:\\n for name, fi in file_dictionary.items():\\n fi.flush_buffers()\",\n \"def compile(root, folder, filepathList):\\n # Init param\\n fpath = os.path.join(folder, \\\"current\\\")\\n\\n # YUIDoc don't need any exlude: vendor folder and min content are disable by default\\n params = [\\n \\\"yuidoc\\\",\\n \\\"--outdir\\\", fpath,\\n\\t \\\"--themedir\\\", tmpl,\\n root\\n ]\\n\\n # Run everything (see launcher.py)\\n launch(params)\",\n \"def process(self, args):\\n for benchmark_file in args.benchmark_files:\\n self.process_individual_file(benchmark_file)\\n self.total_files += 1\",\n \"def run_parsers_on_path(self, path):\\n file_paths = glob.glob(path)\\n file_paths.sort()\\n path_max_len = max(len(path) for path in file_paths) + 2\\n for i, path in enumerate(file_paths):\\n sys.stdout.write(f\\\"{i}. Doing {path.ljust(path_max_len)}\\\")\\n t1 = time.time()\\n dirname, filename = os.path.split(path)\\n filename_sans_ext = filename.split(\\\".\\\")[0]\\n json_path = os.path.join(\\n dirname, f\\\"{filename_sans_ext}_result.json\\\"\\n )\\n\\n lasc = LASCSearch(session=None)\\n with open(path, \\\"rb\\\") as f:\\n data = json.load(f)\\n clean_data = lasc._parse_case_data(data)\\n\\n if not os.path.isfile(json_path):\\n # First time testing this docket\\n bar = \\\"*\\\" * 50\\n print(\\n \\\"\\\\n\\\\n%s\\\\nJSON FILE DID NOT EXIST. CREATING IT AT:\\\"\\n \\\"\\\\n\\\\n %s\\\\n\\\\n\\\"\\n \\\"Please test the data in this file before assuming \\\"\\n \\\"everything worked.\\\\n%s\\\\n\\\" % (bar, json_path, bar)\\n )\\n with open(json_path, \\\"w\\\") as f:\\n json.dump(clean_data, f, indent=2, sort_keys=True)\\n continue\\n\\n with open(json_path) as f:\\n j = json.load(f)\\n self.assertEqual(j, clean_data)\\n\\n t2 = time.time()\\n duration = t2 - t1\\n warn_or_crash_slow_parser(duration, max_duration=1)\\n sys.stdout.write(f\\\"✓ - {t2 - t1:0.1f}s\\\\n\\\")\",\n \"def main():\\n currentpath = parsers.currentpath # -d directory to search\\n outfile = parsers.currentfile # -f output json file\\n\\n try:\\n if not os.path.exists(currentpath):\\n raise MyError('The path (\\\"{0}\\\") does not exist. \\\\n'\\\\\\n .format(currentpath))\\n except MyError as err:\\n sys.stdout.write(err.message)\\n exit(0)\\n\\n #array of files in dict-format, quantity of dirs, files checked and qfi\\n files, q_dirs, q_files, qfi_files = search_qcow.parse_dirs(currentpath)\\n\\n try:\\n if q_files == 0:\\n raise MyError(\\\"There are no any files in folder ('{0}').\\\\n\\\"\\\\\\n .format(currentpath))\\n except MyError as err: #if error catched\\n sys.stdout.write(err.message)\\n exit(0)\\n else: #if there are no any exceptions\\n with open(outfile, 'w') as outfile:\\n #indent - friendly view in json, ensure-russian letters in path\\n json.dump(files, outfile, indent=2, ensure_ascii=False)\\n #folders, include current\\n sys.stdout.write(\\\\\\n '\\\\n\\\\nFoldes: {0}, files: {1}, Qcow-files: {2}.\\\\n'\\\\\\n .format(q_dirs, q_files, qfi_files))\",\n \"def main():\\n parser = optparse.OptionParser()\\n info = ('The input json-format file name. Such as: '\\n '--dest_dir=/hangonman/android')\\n parser.add_option('-d', '--dest_dir', action='store', dest='dest_dir',\\n help=info)\\n opts, _ = parser.parse_args()\\n if not os.path.exists(opts.dest_dir):\\n print 'Destination directory is not existed!'\\n return 1\\n latest_url = ('http://wrt-build.sh.intel.com/archive/'\\n 'snapshots/dev-wrt-android-build/LATEST')\\n file_name = 'xwalk_app_template.tar.gz'\\n app_template_handler = GetXWalkAppTemplate(latest_url, file_name)\\n try:\\n app_template_handler.ExtractFile(opts.dest_dir)\\n except tarfile.TarError:\\n raise Exception('Error in the process of tar file.')\\n return 0\",\n \"def process_data(cur, conn, filepath, func):\\n \\n # get all files matching extension from directory\\n all_files = []\\n for root, dirs, files in os.walk(filepath):\\n files = glob.glob(os.path.join(root,'*.json'))\\n for f in files:\\n all_files.append(os.path.abspath(f))\\n \\n # get total number of files found\\n num_files = len(all_files)\\n print('{} files found in {}'.format(num_files, filepath))\\n \\n # iterate over files and process\\n for i, datafile in enumerate(all_files, 1):\\n func(cur, datafile)\\n conn.commit()\\n print('{}/{} files processed.'.format(i, num_files))\",\n \"def parse_files():\\n pfuncs = [ # parse py files : add #\\n parse_test_files,\\n parse_model_files,\\n parse_url_files,\\n parse_route_files,\\n parse_settings_files,\\n parse_setup_files,\\n ]\\n\\n while PY_FILES:\\n for _ in range(len(pfuncs)):\\n a_func = pfuncs.pop()\\n a_func()\\n break\",\n \"def process_data(cur, conn, filepath, func):\\n # get all files matching extension from directory\\n all_files = []\\n for root, dirs, files in os.walk(filepath):\\n files = glob.glob(os.path.join(root,'*.json'))\\n for f in files :\\n all_files.append(os.path.abspath(f))\\n\\n num_files = len(all_files)\\n print('{} files found in {}'.format(num_files, filepath))\\n\\n for i, datafile in enumerate(all_files, 1):\\n func(cur, datafile)\\n conn.commit()\\n print('{}/{} files processed.'.format(i, num_files))\",\n \"def start(self, **kwargs):\\n super(Json, self).start(**kwargs)\\n flat = self.get_arg_value(\\\"json_flat\\\")\\n\\n self._first_row = True\\n self.open_fd()\\n begin = \\\"\\\" if flat else \\\"[\\\"\\n self._fd.write(begin)\",\n \"def main():\\n\\n parser = argparse.ArgumentParser()\\n\\n parser.add_argument(\\\"api_base_url\\\", type=str,\\n help=\\\"base url for all tests\\\")\\n parser.add_argument(\\\"test_file_name\\\", type=str,\\n help=\\\"name of file containing JSON array of tests\\\")\\n parser.add_argument(\\\"-f\\\", \\\"--format\\\", default=\\\"json\\\", type=str,\\n help=\\\"output format - must be either json or text\\\")\\n\\n args = parser.parse_args()\\n\\n try:\\n\\n run_tests_from_file(args.api_base_url, args.test_file_name, \\n args.format)\\n\\n except KeyError as e:\\n print(\\\"Required key '%s' not found. Check tests file.\\\" % str(e.args[0]))\\n exit(1)\\n\\n except FileNotFoundError:\\n print(\\\"Cannot open file '%s'. File not found.\\\" % args.test_file_name)\\n exit(1)\\n\\n except ValueError:\\n print(\\\"Cannot decode JSON from file '%s'.\\\" % args.test_file_name)\\n exit(1)\",\n \"def main(json_files, merged_filename=\\\"\\\", pretty_print_json=True):\\n\\n # Check to ensure xml_files is not empty.\\n if not json_files:\\n print(\\\"No JSON files were found.\\\")\\n return \\\"\\\"\\n\\n # Remove potential duplicate files.\\n json_files = list(set(json_files))\\n\\n try:\\n # Generate the final XML file name if not provided.\\n if not merged_filename:\\n # Create the merged filename.\\n now = time.localtime()\\n timestamp = time.strftime(\\\"%Y%m%d_%H%M%S\\\", now)\\n merged_filename = f\\\"masscan_merged_{timestamp}.json\\\"\\n\\n final_json_list = []\\n\\n for json_file in json_files:\\n\\n if os.path.getsize(json_file) == 0:\\n print(f\\\"File is 0 bytes: {json_file}\\\")\\n continue\\n\\n with open(json_file, \\\"r\\\") as fh_json_file:\\n final_json_list.extend(json.load(fh_json_file))\\n\\n with open(merged_filename, \\\"w\\\") as fh:\\n if pretty_print_json:\\n json.dump(final_json_list, fh, indent=4)\\n else:\\n json.dump(final_json_list, fh)\\n\\n print(f\\\"Final merged file: {merged_filename}\\\")\\n\\n except Exception as e:\\n print(f\\\"Exception: {e}\\\")\\n merged_filename = \\\"\\\"\\n\\n return merged_filename\",\n \"def main():\\n\\n handler = PlanetSourceHandler()\\n\\n parser = xml.sax.make_parser()\\n parser.setFeature(xml.sax.handler.feature_namespaces, 1)\\n parser.setContentHandler(handler)\\n parser.parse(sys.stdin)\\n\\n print simplejson.JSONEncoder(indent=True).encode(handler.sources)\",\n \"def main():\\n for filename in sys.argv[1:]:\\n test(filename)\",\n \"def main(input_filepath, output_filepath):\\n logger = logging.getLogger(__name__)\\n logger.info('making final data set from raw data')\\n\\n conn = sqlite3.connect('../raw/td_V2.db')\\n git_commits = pd.read_sql_query(\\\"SELECT * FROM GIT_COMMITS\\\",conn)\\n szz_fault_inducing_commits = pd.read_sql_query(\\\"SELECT * FROM szz_fault_inducing_commits\\\",conn)\\n refactoring_miner = pd.read_sql_query(\\\"SELECT * FROM refactoring_miner\\\",conn)\\n refactoring_miner = refactoring_miner[refactoring_miner[\\\"COMMIT_HASH\\\"].isin(git_commits[\\\"COMMIT_HASH\\\"])]\\n git_commits_changes = pd.read_sql_query(\\\"SELECT * FROM GIT_COMMITS_CHANGES\\\", conn)\\n git_commits_changes = git_commits_changes[git_commits_changes[\\\"COMMIT_HASH\\\"].isin(refactoring_miner[\\\"COMMIT_HASH\\\"])]\\n\\n preprocess(git_commits, szz_fault_inducing_commits, refactoring_miner, git_commits_changes)\",\n \"def main(argv):\\n ret = 0\\n parser = utils.parser.createParser()\\n\\n opt = parser.parse_args(argv)\\n\\n try:\\n # Stop execution if help flag is on\\n if opt.help:\\n raise ShowHelpException()\\n\\n # Read materials we need for later processing\\n cfg = auxiliary.readConfig(opt.config)\\n rawJson = auxiliary.readJson(opt.input[0])\\n\\n # List we are going to manipulate :)\\n commands = rawJson['commands']\\n\\n # Pump commands and instruction for filtering, sorting etc\\n rawJson['commands'] = pump(commands, cfg)\\n\\n # Write final result\\n auxiliary.writeJSONFile(rawJson, opt.output[0])\\n\\n except ShowHelpException:\\n parser.print_help()\\n ret = 0\\n except FilterException, e:\\n ret = 1\\n print('ERROR')\\n print(e)\\n\\n return ret\",\n \"def main():\\n f_name = sys.argv[1]\\n file_contents = open(f_name).read()\\n C = CAST([], \\\"python\\\")\\n C2 = C.from_json_str(file_contents)\\n\\n V = CASTToAGraphVisitor(C2)\\n last_slash_idx = f_name.rfind(\\\"/\\\")\\n file_ending_idx = f_name.rfind(\\\".\\\")\\n pdf_file_name = f\\\"{f_name[last_slash_idx + 1 : file_ending_idx]}.pdf\\\"\\n V.to_pdf(pdf_file_name)\",\n \"def process_all_files():\\n src_files = get_doc_files()\\n\\n for src_pathname in src_files:\\n if src_pathname.suffix in MARKDOWN_EXTENSIONS:\\n process_file_markdown(src_pathname)\\n elif src_pathname.suffix in STATIC_ASSET_EXTENSIONS:\\n process_file_copytodest(src_pathname)\",\n \"def main():\\n\\n json_str = sys.stdin.read()\\n\\n try:\\n object_json = json.loads(json_str)\\n\\n except json.JSONDecodeError:\\n print(\\\"Error with the JSON input. Please check your JSON file.\\\")\\n sys.exit(1)\\n\\n final_object = flatten_json(object_json, prefix=\\\"\\\")\\n object_json_output = json.dumps(final_object, indent=4)\\n\\n print(object_json_output)\",\n \"def test_files(self, location):\\n for filename in os.listdir(location):\\n with open(location + '/' + filename) as json_file:\\n data = json.load(json_file)\\n self.test_data(data)\",\n \"def batch(infolder, outfile): # type: (str, str) -> None\\n\\n if not os.path.isdir(infolder):\\n return\\n\\n results = []\\n\\n for filename in os.listdir(infolder):\\n print('Processing ' + filename)\\n curresults = []\\n if filename.endswith('.txt'):\\n with open(os.path.join(infolder, filename), 'r') as curfile:\\n curdata = curfile.read() + '\\\\n'\\n curresults = processClauseText(curdata, 'text')\\n elif filename.endswith('.pdf'):\\n with open(os.path.join(infolder, filename), 'rb') as curfile:\\n curdata = base64.b64encode(curfile.read()).decode()\\n curresults = processClauseText(curdata, 'pdf')\\n elif filename.endswith('.docx'):\\n with open(os.path.join(infolder, filename), 'rb') as curfile:\\n curdata = base64.b64encode(curfile.read()).decode()\\n curresults = processClauseText(curdata, 'word')\\n if len(curresults) > 0:\\n for result in curresults:\\n result['filename'] = filename\\n results.extend(curresults)\\n\\n if outfile is not None:\\n with open(outfile, 'w') as outfile:\\n json.dump(results, outfile, indent=2)\",\n \"def main(input_dir, bids_dir, exclude_fieldmaps):\\n\\n input_dir = Path(input_dir).absolute()\\n bids_dir = Path(bids_dir).absolute()\\n raw_dir = bids_dir / \\\"sourcedata\\\"\\n\\n copy_all_paths_to_sourcedata(input_dir, raw_dir)\\n\\n fix_acquisition_numbers_of_json_files_in(raw_dir)\\n\\n old_and_new_paths = create_dictionary_of_old_and_new_paths(raw_dir, bids_dir, exclude_fieldmaps)\\n\\n copy_files_to_their_new_homes(old_and_new_paths)\\n\\n fix_jsons_in(bids_dir)\\n\\n add_dataset_description_to(bids_dir)\\n\\n write_tsvs(raw_dir, bids_dir)\\n\\n print(\\\"Congratulations! You're BIDS-compliant, yay!\\\")\\n print(\\\"To double-check, use this handy dandy BIDS validator: https://bids-standard.github.io/bids-validator/\\\")\",\n \"def generate_code_files(code_list: List[str], base: str) -> None:\\n for code in code_list:\\n parts = code.split(' ')\\n status = parts[0]\\n name = \\\" \\\".join(parts[1:])\\n path = os.path.join('codes', base, f'{status[0]}XX', f'{status}.json')\\n data = {\\n 'code': int(status),\\n 'name': name,\\n 'messages': []\\n }\\n with open(path, 'w') as jsonfile:\\n json.dump(data, jsonfile, indent=4)\",\n \"def main(args):\\n write_files = args.no_write is False\\n ffiles = _open(args)\\n ffiles = update_bpms(ffiles)\\n if write_files:\\n _write_files(ffiles, args.prefix, args.clobber)\\n\\n return\",\n \"def main():\\n widget = ParseGrypeJSON()\\n logging.debug(f'argv {\\\",\\\".join(sys.argv)}')\\n\\n if len(sys.argv) > 1:\\n widget.filename(sys.argv[1])\\n\\n sys.exit(widget.report())\",\n \"def main():\\n LESSONS_PATH = os.path.join(LESSON_LOCATOR_DATA, LESSON_SETS[0])\\n ORIGINAL_LESSONS_PATH = os.path.join(LESSONS_PATH, \\\"original\\\")\\n ANNOTATED_LESSONS_PATH = os.path.join(LESSONS_PATH, \\\"annotated\\\")\\n\\n if not os.path.exists(ANNOTATED_LESSONS_PATH):\\n os.mkdir(ANNOTATED_LESSONS_PATH)\\n\\n print(\\\"Scanning original lessons in %s...\\\" % ORIGINAL_LESSONS_PATH)\\n\\n for item in os.listdir(ORIGINAL_LESSONS_PATH):\\n if item == \\\".DS_Store\\\": continue\\n\\n print(\\\" found: %s\\\" % item)\\n\\n item_path = os.path.join(ORIGINAL_LESSONS_PATH, item)\\n\\n lesson_number = None\\n lesson_description = None\\n mobj = re.search(r'^AY\\\\s+(\\\\d+)\\\\s*-\\\\s*(.+)\\\\.txt$', item)\\n if mobj:\\n lesson_number = mobj.group(1)\\n lesson_description = mobj.group(2)\\n\\n print(\\\" number: %s\\\" % lesson_number)\\n print(\\\" description: %s\\\" % lesson_description)\\n\\n lesson = dict()\\n lesson['number'] = lesson_number\\n lesson['description'] = lesson_description\\n\\n fh = open(item_path)\\n lesson_raw_text = fh.read()\\n fh.close()\\n lesson_text = re.split(r'\\\\n', lesson_raw_text)\\n# lesson_raw_text_reencoded = lesson_raw_text.decode('mac-roman').encode('utf-8')\\n# lesson_text = re.split(r'\\\\n', lesson_raw_text_reencoded)\\n\\n lesson['text'] = lesson_text\\n lesson['parsed'] = parseLesson(lesson_text)\\n\\n if lesson['parsed']['end_of_lesson'] is None:\\n print(\\\" lesson has no 'end of lesson' marker\\\")\\n\\n lesson_json = json.dumps(lesson, indent=4)\\n annotated_lesson_path = os.path.join(ANNOTATED_LESSONS_PATH, \\\"ay_%04d.json\\\" % int(lesson_number))\\n fh = open(annotated_lesson_path, \\\"w\\\")\\n fh.write(lesson_json)\\n fh.close()\\n\\n else:\\n print(\\\"ERROR: File name not understood: %s\\\" % item)\\n\\n return 0\",\n \"def collect_files(self):\\n self.files = []\\n for bundle in self.bundles:\\n bundle.init_build(self, self.builder)\\n bundle_files = bundle.prepare()\\n self.files.extend(bundle_files)\\n return self\",\n \"def generate(self):\\n try:\\n self._parse_groups()\\n self._parse_types()\\n self._parse_enums()\\n self._parse_features()\\n self._parse_extensions()\\n self._add_extra_enums()\\n self._parse_and_build_commands()\\n self._build_all_enums()\\n self._build_enum_groups()\\n self._generate_files()\\n except Exception as exception:\\n print('Generate failed: {}'.format(str(exception)))\\n raise\",\n \"def execute(self):\\n cwd = self.fm.thisdir\\n marked_files = cwd.get_selection()\\n\\n if not marked_files:\\n return\\n\\n def refresh(_):\\n cwd = self.fm.get_directory(original_path)\\n cwd.load_content()\\n\\n original_path = cwd.path\\n parts = self.line.split()\\n au_flags = parts[1:]\\n\\n descr = \\\"compressing files in: \\\" + os.path.basename(parts[1])\\n obj = CommandLoader(args=['apack'] + au_flags + \\\\\\n [os.path.relpath(f.path, cwd.path) for f in marked_files], descr=descr)\\n\\n obj.signal_bind('after', refresh)\\n self.fm.loader.add(obj)\",\n \"def process_data(cur, conn, filepath, func):\\r\\n\\r\\n # get all files matching extension from directory\\r\\n all_files = []\\r\\n for root, dirs, files in os.walk(filepath):\\r\\n files = glob.glob(os.path.join(root,'*.json'))\\r\\n for f in files :\\r\\n all_files.append(os.path.abspath(f))\\r\\n\\r\\n # get total number of files found\\r\\n num_files = len(all_files)\\r\\n print('{} files found in {}'.format(num_files, filepath))\\r\\n\\r\\n # iterate over files and process\\r\\n try:\\r\\n for i, datafile in enumerate(all_files, 1):\\r\\n func(cur, datafile)\\r\\n conn.commit()\\r\\n print('{}/{} files processed.'.format(i, num_files))\\r\\n except Exception as e:\\r\\n print(e)\",\n \"def process_data(cur, conn, filepath, func):\\n # get all files matching extension from directory\\n all_files = []\\n for root, dirs, files in os.walk(filepath):\\n files = glob.glob(os.path.join(root, '*.json'))\\n for f in files:\\n all_files.append(os.path.abspath(f))\\n\\n # get total number of files found\\n num_files = len(all_files)\\n print('{} files found in {}'.format(num_files, filepath))\\n\\n # iterate over files and process\\n for i, datafile in enumerate(all_files, 1):\\n func(cur, datafile)\\n conn.commit()\\n print('{}/{} files processed.'.format(i, num_files))\",\n \"def process_data(cur, conn, filepath, func):\\n\\n # get all files matching extension from directory\\n all_files = []\\n for root, dirs, files in os.walk(filepath):\\n files = glob.glob(os.path.join(root, '*.json'))\\n for f in files:\\n all_files.append(os.path.abspath(f))\\n\\n # get total number of files found\\n num_files = len(all_files)\\n print('{} files found in {}'.format(num_files, filepath))\\n\\n # iterate over files and process\\n for i, datafile in enumerate(all_files, 1):\\n func(cur, datafile)\\n conn.commit()\\n print('{}/{} files processed.'.format(i, num_files))\",\n \"def process_data(cur, conn, filepath, func):\\n # get all files matching extension from directory\\n all_files = []\\n for root, dirs, files in os.walk(filepath):\\n files = glob.glob(os.path.join(root,'*.json'))\\n for f in files :\\n all_files.append(os.path.abspath(f))\\n\\n # get total number of files found\\n num_files = len(all_files)\\n print('{} files found in {}'.format(num_files, filepath))\\n\\n # iterate over files and process\\n for i, datafile in enumerate(all_files, 1):\\n func(cur, datafile)\\n conn.commit()\\n print('{}/{} files processed.'.format(i, num_files))\",\n \"def process_data(cur, conn, filepath, func):\\n # get all files matching extension from directory\\n all_files = []\\n for root, dirs, files in os.walk(filepath):\\n files = glob.glob(os.path.join(root,'*.json'))\\n for f in files :\\n all_files.append(os.path.abspath(f))\\n\\n # get total number of files found\\n num_files = len(all_files)\\n print('{} files found in {}'.format(num_files, filepath))\\n\\n # iterate over files and process\\n for i, datafile in enumerate(all_files, 1):\\n func(cur, datafile)\\n conn.commit()\\n print('{}/{} files processed.'.format(i, num_files))\",\n \"def process_data(cursor, connection, filepath, function):\\n # get all files matching extension from directory\\n all_files = []\\n for root, dirs, files in os.walk(filepath):\\n files = glob.glob(os.path.join(root, '*.json'))\\n for f in files:\\n all_files.append(os.path.abspath(f))\\n\\n # get total number of files found\\n num_files = len(all_files)\\n print('{} files found in {}'.format(num_files, filepath))\\n\\n # iterate over files and process\\n for i, datafile in enumerate(all_files, 1):\\n function(cursor, datafile)\\n connection.commit()\\n print('{}/{} files processed.'.format(i, num_files))\",\n \"def batch(f, fdir='.'):\\n \\n os.chdir(fdir)\\n names = glob.iglob('*')\\n # Absolute path rather than relative path allows changing of directories in fn_name.\\n names = sorted([os.path.abspath(name) for name in names])\\n for name in names:\\n print os.path.basename(name)\\n f(name)\",\n \"def main():\\n arg_parser = argparse.ArgumentParser(\\n description='Runs jdeps (dependency analysis tool) on a given JAR and '\\n 'writes the resulting dependency graph into a JSON file.')\\n required_arg_group = arg_parser.add_argument_group('required arguments')\\n required_arg_group.add_argument(\\n '-t',\\n '--target',\\n required=True,\\n help='Path to the JAR file to run jdeps on.')\\n required_arg_group.add_argument(\\n '-o',\\n '--output',\\n required=True,\\n help='Path to the file to write JSON output to. Will be created '\\n 'if it does not yet exist and overwrite existing '\\n 'content if it does.')\\n arg_parser.add_argument('-j',\\n '--jdeps-path',\\n default=JDEPS_PATH,\\n help='Path to the jdeps executable.')\\n arguments = arg_parser.parse_args()\\n\\n print('Running jdeps and parsing output...')\\n raw_jdeps_output = run_jdeps(arguments.jdeps_path, arguments.target)\\n jdeps_parser = JavaClassJdepsParser()\\n jdeps_parser.parse_raw_jdeps_output(raw_jdeps_output)\\n\\n class_graph = jdeps_parser.graph\\n print(f'Parsed class-level dependency graph, '\\n f'got {class_graph.num_nodes} nodes '\\n f'and {class_graph.num_edges} edges.')\\n\\n package_graph = package_dependency.JavaPackageDependencyGraph(class_graph)\\n print(f'Created package-level dependency graph, '\\n f'got {package_graph.num_nodes} nodes '\\n f'and {package_graph.num_edges} edges.')\\n\\n print(f'Dumping JSON representation to {arguments.output}.')\\n serialization.dump_class_and_package_graphs_to_file(\\n class_graph, package_graph, arguments.output)\",\n \"def run(self):\\n self.load_template()\\n self.load_data()\\n self.load_files()\\n self.render_content()\\n self.process()\\n # pprint(self.data)\",\n \"def process_data(cur, conn, filepath, func):\\n # get all files matching extension from directory\\n all_files = []\\n for root, dirs, files in os.walk(filepath):\\n files = glob.glob(os.path.join(root, '*.json'))\\n for file in files:\\n all_files.append(os.path.abspath(file))\\n\\n # get total number of files found\\n num_files = len(all_files)\\n print('{} files found in {}'.format(num_files, filepath))\\n\\n # iterate over files and process\\n for i, datafile in enumerate(all_files, 1):\\n func(cur, datafile)\\n conn.commit()\\n print('{}/{} files processed.'.format(i, num_files))\",\n \"def main(self, verbose=0):\\n indepdict=self.scan_for_loop(self.indeploop)\\n pegdict1 = self.scan_for_loop(self.pegloop1)\\n pegdict2 = self.scan_for_loop(self.pegloop2)\\n if len(indepdict.keys()) == 0 and len(pegdict1.keys()) == 0 and len(pegdict2.keys()) == 0:\\n return dict()\\n alldict = dict(indepdict)\\n alldict.update(pegdict1)\\n alldict.update(pegdict2)\\n indepcomb=self.get_combo_list(indepdict, 0)\\n pegcomb1=self.get_combo_list(pegdict1, 1)\\n pegcomb2=self.get_combo_list(pegdict2, 1)\\n allcombs = self.combine_three_combo_lists(indepcomb, pegcomb1, pegcomb2)\\n datasets = self.prepare_looped_datasets(alldict, allcombs)\\n createdfiles = self.create_input_files(datasets)\\n if verbose == 1:\\n self.print_list(indepcomb)\\n self.print_list(pegcomb1)\\n self.print_list(pegcomb2)\\n self.print_list(allcombs)\\n for datakey in datasets:\\n self.print_list(datasets[datakey])\\n return createdfiles\",\n \"def process_datasets(self):\\n\\n with open(self.mappings, \\\"r+\\\") as json_file:\\n emsl_to_jgi = json.load(json_file)\\n emsl_to_jgi_copy = copy.deepcopy(emsl_to_jgi)\\n\\n contaminant_file_loc = emsl_to_jgi[\\\"contaminant_file_loc\\\"]\\n # run for each dataset\\n for dataset_id, values in emsl_to_jgi.items():\\n if dataset_id not in [\\n \\\"contaminant_file_loc\\\",\\n \\\"analysis_activity_file_loc\\\",\\n \\\"data_objects_file_loc\\\",\\n \\\"STUDY\\\",\\n \\\"tools_used\\\",\\n ]:\\n raw_file_loc = values[\\\"raw_file_loc\\\"]\\n self.dataset_name = values[\\\"dataset_name\\\"]\\n # dataset search against a fasta file\\n for genome_directory, locations in values[\\n \\\"genome_directory\\\"\\n ].items():\\n # clear object to prepare next job\\n ANALYSIS_JOBS_OBJECT.clear()\\n\\n # create log_dir\\n self.save_job_results = os.path.join(\\n self.result_loc, dataset_id, genome_directory\\n )\\n self.log_collected_at = os.path.join(\\n os.path.abspath(self.save_job_results), \\\"analysis_jobs_logs\\\"\\n )\\n if not os.path.exists(self.log_collected_at):\\n os.makedirs(self.log_collected_at)\\n\\n files = [locations[\\\"faa_file_loc\\\"], contaminant_file_loc]\\n contaminated_faa_file_loc = self.contaminate_fasta(files)\\n\\n self.register_job_in_emsl_to_jgi(\\n dataset_id,\\n genome_directory,\\n \\\"contaminated_faa_file_loc\\\",\\n contaminated_faa_file_loc,\\n emsl_to_jgi_copy,\\n )\\n # convert .faa to .txt\\n faa_txt_file = self.convert_faa2txt(\\n dataset_id, contaminated_faa_file_loc\\n )\\n self.register_job_in_emsl_to_jgi(\\n dataset_id,\\n genome_directory,\\n \\\"txt_faa_file_loc\\\",\\n faa_txt_file,\\n emsl_to_jgi_copy,\\n )\\n\\n # log & run job\\n self.run_n_log_job(\\n dataset_id,\\n genome_directory,\\n contaminated_faa_file_loc,\\n raw_file_loc,\\n emsl_to_jgi_copy,\\n )\\n\\n # merge analysis\\n resultant_file = self.merge_analysis_jobs(\\n dataset_id, genome_directory\\n )\\n self.register_job_in_emsl_to_jgi(\\n dataset_id,\\n genome_directory,\\n \\\"resultant_file_loc\\\",\\n resultant_file,\\n emsl_to_jgi_copy,\\n )\\n\\n # capture the job metadata object\\n logger.info(\\\"Jobrun\\\", extra=LOGGED_ANALYSIS_JOB)\\n\\n # update emsl_to_jgi.json\\n json_file.seek(0) # move back to BOF.\\n json_file.truncate()\\n json_file.write(json.dumps(emsl_to_jgi_copy, default=str, indent=4))\\n pass\",\n \"def process_data(db_cursor, filepath, func):\\n\\n # Get all files matching extension from directory\\n all_files = []\\n for root, _, files in os.walk(filepath):\\n files = glob.glob(os.path.join(root, '*.json'))\\n for f in files:\\n all_files.append(os.path.abspath(f))\\n\\n num_files = len(all_files)\\n print(f'{num_files} files found in {filepath}')\\n\\n # iterate over files and process\\n for i, datafile in enumerate(all_files, 1):\\n func(db_cursor, datafile)\\n print(f'{i}/{num_files} files processed.')\",\n \"def run():\\n build_no_documentation()\\n build_sphinx_build()\\n #build_sphinx_pdf()\\n build_graphviz_files()\",\n \"def process(filename, exclude_dirs=['unittest','test','site-packages']):\\n print(\\\"Generating {}\\\".format(filename))\\n nb = 0\\n nb_err = 0\\n _main_root = os.path.dirname(filename)\\n _VFS = {}\\n for _mydir in (\\\"libs\\\", \\\"Lib\\\"):\\n for _root, _dir, _files in os.walk(os.path.join(_main_root, _mydir)):\\n #if _root.endswith('lib_migration'):\\n _flag=False\\n for _exclude in exclude_dirs:\\n if _exclude in _root: #_root.endswith(_exclude):\\n _flag=True\\n continue\\n if _flag:\\n continue # skip these modules\\n if '__pycache__' in _root:\\n continue\\n nb += 1\\n\\n for _file in _files:\\n _ext = os.path.splitext(_file)[1]\\n if _ext not in ('.js', '.py'):\\n continue\\n if re.match(r'^module\\\\d+\\\\..*$', _file):\\n continue\\n nb += 1\\n\\n file_name = os.path.join(_root, _file)\\n _data = open(file_name, encoding='utf-8').read()\\n \\n if _ext == '.py':\\n _data = python_minifier.minify(_data, preserve_lines=True)\\n\\n _vfs_filename = os.path.join(_root, _file).replace(_main_root, '')\\n _vfs_filename = _vfs_filename.replace(\\\"\\\\\\\\\\\", \\\"/\\\")\\n\\n if _vfs_filename.startswith('/libs/crypto_js/rollups/'):\\n if _file not in ('md5.js', 'sha1.js', 'sha3.js',\\n 'sha224.js', 'sha384.js', 'sha512.js'):\\n continue\\n\\n mod_name = _vfs_filename[len(_mydir) + 2:].replace('/', '.')\\n mod_name, ext = os.path.splitext(mod_name)\\n is_package = mod_name.endswith('__init__')\\n if is_package:\\n mod_name = mod_name[:-9]\\n _VFS[mod_name] = [ext, _data, 1]\\n else:\\n _VFS[mod_name] = [ext, _data]\\n print((\\\"adding %s %s\\\" % (mod_name, _vfs_filename)))\\n print('%s files, %s errors' % (nb, nb_err))\\n with open(filename, \\\"w\\\") as file_to_write_VFS:\\n file_to_write_VFS.write('__BRYTHON__.use_VFS = true;\\\\n')\\n file_to_write_VFS.write('__BRYTHON__.VFS=%s;\\\\n\\\\n' % json.dumps(_VFS))\",\n \"def run(filts, jgen, argsj={}, verbose=False):\\n if len(filts) == 0:\\n return jgen\\n\\n filt0 = filts[0]\\n cls = {\\n \\\"commenter\\\": Commenter,\\n \\\"setup\\\": SetupFilter,\\n }[filt0]\\n return run(filts[1:], cls(argsj).run(jgen), argsj=argsj, verbose=verbose)\",\n \"def run() -> ():\\n if len(sys.argv) > 1:\\n show_help()\\n errs = get_cargo_input()\\n main_stack = make_proj_stack(errs)\\n while len(main_stack) > 0:\\n file_stack = make_file_stack(main_stack)\\n overwrite(file_stack)\\n\\n # FIXME\",\n \"def start_recoding(self, calc_stats=False):\\n self.__reset_stats()\\n for dir_name in self.__dirs:\\n for ext in self.__exts:\\n for file_name in self.__glob.iglob(dir_name + '\\\\\\\\**\\\\\\\\*.' + ext, recursive=True):\\n encode = self.__define_encoding(file_name)\\n if calc_stats:\\n self.__collect_stats(encode, file_name)\\n self.__correct_encoding(encode, file_name)\",\n \"def assemble_files():\\r\\n path = os.path.expanduser(sys.argv[1])\\r\\n if os.path.isdir(path):\\r\\n file_root = path + \\\"/\\\"\\r\\n for file in os.listdir(path):\\r\\n filename = os.path.splitext(file)\\r\\n if filename[1] == \\\".asm\\\":\\r\\n hack_file_name = file_root + filename[0] + \\\".hack\\\"\\r\\n assemble_file(file_root + file, hack_file_name)\\r\\n else:\\r\\n filename = os.path.splitext(path)\\r\\n hack_file_name = filename[0] + \\\".hack\\\"\\r\\n assemble_file(path, hack_file_name)\",\n \"def test_main():\\n for template in templates:\\n main([\\\"-g\\\", template])\\n\\n # One at a time\\n for xyz_file in example_xyz_files:\\n main([template, xyz_file])\\n\\n # All at once\\n main([template] + list(example_xyz_files))\\n\\n # Allow use of template in the parent directory\\n with cd(\\\"data\\\"):\\n main([\\\"../pnictogen/repo/ADF.in\\\", \\\"water-dimer.xyz\\\"])\",\n \"def batch_run_cfg2json():\\n cfg_path = os.environ.get(\\\"CFG_FILE_PATH\\\")\\n cfg_list = ['any_n1.cfg',\\n 'ir_grism_n2.cfg',\\n 'ir_grism_n4.cfg',\\n 'ir_any_n2.cfg',\\n 'ir_any_n4.cfg',\\n 'uvis_any_n2.cfg',\\n 'uvis_any_n4.cfg',\\n 'uvis_any_n6.cfg',\\n 'uvis_any_pre2012_n2.cfg',\\n 'uvis_any_pre2012_n4.cfg',\\n 'uvis_any_pre2012_n6.cfg',\\n 'wfc_any_n2.cfg',\\n 'wfc_any_n4.cfg',\\n 'wfc_any_n6.cfg',\\n 'sbc_blue_n2.cfg',\\n 'sbc_blue_n6.cfg',\\n 'sbc_any_n2.cfg',\\n 'sbc_any_n6.cfg',\\n 'hrc_any_n2.cfg',\\n 'hrc_any_n4.cfg',\\n 'hrc_any_n6.cfg']\\n for cfgfile in cfg_list:\\n cfgfile = os.path.join(cfg_path, cfgfile)\\n cfg2json(cfgfile)\\n\\n cfg_path = os.path.realpath(__file__).replace(\\\"devutils/pars_utils.py\\\", \\\"pars/\\\")\\n out_path = os.path.realpath(__file__).replace(\\\"devutils/pars_utils.py\\\", \\\"pars/hap_pars/any/\\\")\\n cfg_list = [\\\"astrodrizzle_filter_hap.cfg\\\", \\\"astrodrizzle_single_hap.cfg\\\", \\\"astrodrizzle_total_hap.cfg\\\"]\\n for cfgfile in cfg_list:\\n cfgfile = os.path.join(cfg_path, cfgfile)\\n cfg2json(cfgfile, outpath=out_path)\",\n \"def Main(root_directory):\\n filepaths = GetAllFilepaths(root_directory)\\n for filepath in filepaths:\\n parser = fileparser.CreateParser(filepath)\\n if not parser:\\n ReportWarning('cannot find a parser for file %s, skipping...' %\\n filepath)\\n continue\\n old_file_contents = ReadFileIntoString(filepath)\\n comment_blocks = parser.FindAllCommentBlocks(old_file_contents)\\n if not comment_blocks:\\n ReportWarning('cannot find any comment blocks in file %s' %\\n filepath)\\n old_copyright_block = parser.FindCopyrightBlock(comment_blocks)\\n if not old_copyright_block:\\n ReportWarning('cannot find copyright block in file %s' % filepath)\\n (year, holder) = parser.GetCopyrightBlockAttributes(old_copyright_block)\\n if holder and not ConfirmAllowedCopyrightHolder(holder):\\n ReportWarning(\\n 'unrecognized copyright holder \\\"%s\\\" in file %s, skipping...' % (\\n holder, filepath))\\n continue\\n new_copyright_block = parser.CreateCopyrightBlock(year, holder)\\n if old_copyright_block:\\n new_file_contents = old_file_contents.replace(\\n old_copyright_block, new_copyright_block, 1)\\n else:\\n new_file_contents = new_copyright_block + old_file_contents\\n WriteStringToFile(new_file_contents, filepath)\",\n \"def main():\\n for tourney in tourneys:\\n read_tourney(tourney)\\n with open(\\\"obj/tournaments.pkl\\\", \\\"wb\\\") as f:\\n pickle.dump(tournaments, f, pickle.HIGHEST_PROTOCOL)\\n with open(\\\"obj/participants.pkl\\\", \\\"wb\\\") as f:\\n pickle.dump(raw_participants, f, pickle.HIGHEST_PROTOCOL)\\n with open(\\\"obj/matches.pkl\\\", \\\"wb\\\") as f:\\n pickle.dump(all_matches, f, pickle.HIGHEST_PROTOCOL)\",\n \"def process_data(cur, conn, filepath: str, func: Callable) -> None:\\n # get all files matching extension from directory\\n all_files = []\\n for root, dirs, files in os.walk(filepath):\\n files = glob.glob(os.path.join(root, \\\"*.json\\\"))\\n for f in files:\\n all_files.append(os.path.abspath(f))\\n\\n # get total number of files found\\n num_files = len(all_files)\\n print(\\\"{} files found in {}\\\".format(num_files, filepath))\\n\\n # iterate over files and process\\n for i, datafile in enumerate(all_files, 1):\\n func(cur, datafile)\\n conn.commit()\\n print(\\\"{}/{} files processed.\\\".format(i, num_files))\",\n \"def _main(args):\\n if args.files:\\n _update_files()\\n\\n if args.templates:\\n _update_template(args.template_definition)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.608436","0.6008992","0.59380484","0.5935062","0.5781285","0.5768037","0.5759169","0.57256293","0.57182896","0.5655328","0.5653805","0.56392854","0.5626525","0.55395234","0.5531216","0.5497599","0.5481104","0.54802907","0.5461012","0.5456868","0.5416904","0.5410798","0.5381749","0.5353524","0.53343827","0.5326129","0.53258646","0.5319313","0.53175837","0.5280997","0.5280924","0.5274578","0.527399","0.5269204","0.5253678","0.52512133","0.5245849","0.5245297","0.52397805","0.5235405","0.52337784","0.52251005","0.5224165","0.52152276","0.52091706","0.5206303","0.52047914","0.51956177","0.518473","0.5182694","0.51811016","0.5179675","0.5166386","0.51644105","0.51605624","0.515994","0.5159845","0.51587224","0.51445717","0.5127951","0.5110036","0.51098406","0.5105691","0.51023304","0.510141","0.5100684","0.50977147","0.509226","0.5091852","0.5089464","0.50849915","0.50746506","0.5073829","0.5073343","0.5073088","0.50727564","0.5069639","0.5068109","0.5068039","0.5068039","0.5066529","0.5066506","0.5065057","0.50554585","0.50548744","0.5054777","0.5048831","0.50474465","0.50446016","0.5041998","0.50413465","0.50412726","0.50401044","0.5036353","0.5035099","0.5023195","0.50212693","0.5019999","0.5016002","0.5013501"],"string":"[\n \"0.608436\",\n \"0.6008992\",\n \"0.59380484\",\n \"0.5935062\",\n \"0.5781285\",\n \"0.5768037\",\n \"0.5759169\",\n \"0.57256293\",\n \"0.57182896\",\n \"0.5655328\",\n \"0.5653805\",\n \"0.56392854\",\n \"0.5626525\",\n \"0.55395234\",\n \"0.5531216\",\n \"0.5497599\",\n \"0.5481104\",\n \"0.54802907\",\n \"0.5461012\",\n \"0.5456868\",\n \"0.5416904\",\n \"0.5410798\",\n \"0.5381749\",\n \"0.5353524\",\n \"0.53343827\",\n \"0.5326129\",\n \"0.53258646\",\n \"0.5319313\",\n \"0.53175837\",\n \"0.5280997\",\n \"0.5280924\",\n \"0.5274578\",\n \"0.527399\",\n \"0.5269204\",\n \"0.5253678\",\n \"0.52512133\",\n \"0.5245849\",\n \"0.5245297\",\n \"0.52397805\",\n \"0.5235405\",\n \"0.52337784\",\n \"0.52251005\",\n \"0.5224165\",\n \"0.52152276\",\n \"0.52091706\",\n \"0.5206303\",\n \"0.52047914\",\n \"0.51956177\",\n \"0.518473\",\n \"0.5182694\",\n \"0.51811016\",\n \"0.5179675\",\n \"0.5166386\",\n \"0.51644105\",\n \"0.51605624\",\n \"0.515994\",\n \"0.5159845\",\n \"0.51587224\",\n \"0.51445717\",\n \"0.5127951\",\n \"0.5110036\",\n \"0.51098406\",\n \"0.5105691\",\n \"0.51023304\",\n \"0.510141\",\n \"0.5100684\",\n \"0.50977147\",\n \"0.509226\",\n \"0.5091852\",\n \"0.5089464\",\n \"0.50849915\",\n \"0.50746506\",\n \"0.5073829\",\n \"0.5073343\",\n \"0.5073088\",\n \"0.50727564\",\n \"0.5069639\",\n \"0.5068109\",\n \"0.5068039\",\n \"0.5068039\",\n \"0.5066529\",\n \"0.5066506\",\n \"0.5065057\",\n \"0.50554585\",\n \"0.50548744\",\n \"0.5054777\",\n \"0.5048831\",\n \"0.50474465\",\n \"0.50446016\",\n \"0.5041998\",\n \"0.50413465\",\n \"0.50412726\",\n \"0.50401044\",\n \"0.5036353\",\n \"0.5035099\",\n \"0.5023195\",\n \"0.50212693\",\n \"0.5019999\",\n \"0.5016002\",\n \"0.5013501\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.73920244"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":11297,"cells":{"query":{"kind":"string","value":"Run the webp converter on off of the png files."},"document":{"kind":"string","value":"def generate_webp_textures():\n input_files = PNG_TEXTURES['input_files']\n output_files = PNG_TEXTURES['output_files']\n if not os.path.exists(TEXTURE_PATH):\n os.makedirs(TEXTURE_PATH)\n for png, out in zip(input_files, output_files):\n if needs_rebuild(png, out):\n convert_png_image_to_webp(png, out, WEBP_QUALITY)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def main():\n argvs = sys.argv\n argc = len(argvs)\n if argc == 1:\n print('usage: convert2png.py <path/to/*.ppm> ...')\n sys.exit(1)\n\n os.makedirs('result/convert2png', exist_ok=True)\n\n for i in range(1, argc):\n img = cv2.imread(argvs[i])\n\n # root, ext = os.path.splitext(argvs[i])\n # cv2.imwrite(root + '.png', img)\n\n root, ext = os.path.splitext(argvs[i])\n strImgName = root.split('/')[-1]\n cv2.imwrite('result/convert2png/' + strImgName + '.png', img)","def convert_png_image_to_webp(png, out, quality=80):\n command = [CWEBP, '-q', str(quality), png, '-o', out]\n run_subprocess(command)","def photo2web_process_hattenbach():\n\n os.chdir('/Volumes/SSD External/Hattenbach_v2')\n \n dir_base = os.getcwd()\n \n dir_p2w = '/Users/throop/photos/Trips/'\n \n dirs = sorted(glob.glob(os.path.join(dir_base, '*')))\n \n quality_out = '60'\n size_out = '2000x2000'\n \n for i,dir in enumerate(dirs):\n if os.path.isdir(dir):\n os.chdir(dir)\n dir_originals = os.path.join(dir, 'originals')\n dir_originals_fullres = os.path.join(dir, 'originals_fullres')\n\n# For HH files, copy the 'actual' originals into a 'fullres' folder, for safekeeping\n\n if not os.path.isdir(dir_originals_fullres):\n os.rename(dir_originals, dir_originals_fullres)\n os.mkdir(dir_originals)\n \n files = glob.glob(os.path.join(dir_originals_fullres, '*'))\n\n# Get a list of all the images\n\n# For each image, make a low-res, low-quality image. This is just because the scanned files\n# are huge and high-quality, and not useful for online. They are much larger than necessary. \n# So we use 'convert' to shrink them in size and quality, and put the output into 'originals' directory \n# for photo2web.\n\n for file in files:\n file_short = os.path.basename(file)\n file_in = os.path.join(dir_originals_fullres,file_short)\n file_out = os.path.join(dir_originals,file_short)\n if not os.path.isfile(file_out):\n cmd = (f'convert -resize {size_out} -quality {quality_out}' +\n f' {file_in}' +\n f' {file_out}')\n print(f'{cmd}')\n \n subprocess.run(['convert', '-resize', size_out, '-quality', quality_out,\n file_in,\n file_out])\n\n# Now, finally, go thru and do photo2web on all of them.\n \n print(f'\\nProcessing directory {i}/{len(dirs)} {dir}\\n')\n subprocess.run(['cp', '-r', os.path.join(dir_p2w, 'header.txt'), '.'])\n subprocess.run(['cp', '-r', os.path.join(dir_p2w, 'photos.css'), '.'])\n if not os.path.exists('captions.txt'):\n subprocess.run(['captions_photo2web']) \n subprocess.run(['photo2web_old'])\n subprocess.run(['photo2web'])","def convert_to_web(base_path, files):\n for i, f in enumerate(files):\n imagetype = get_imagetype_from_filename(f)\n cmd = ('rsync '\n '{base_path}/qc/phantom/{imagetype}/{f} '\n '{base_path}/website/assets/{output}'.format(\n base_path=base_path, imagetype=imagetype, \n f=f, output=f[9:]))\n os.system(cmd)","def convert_pdf_to_images(self, inputpath, outputpath, widget):\n tmp_jpeg_folder = t.tmp_folder(inputpath, hash=True, delete=True)\n tmp_folder = t.tmp_folder(outputpath, hash=True, delete=True)\n\n image_list = []\n\n poppler_path = self.get_poppler_path()\n widget.status_label.setText('EXTRACTING')\n if self.pdf_threads.isChecked():\n rv = self.decide_pages_per_cpu(inputpath)\n if rv:\n image_list = convert_files_to_jpeg(\n rv, inputpath, tmp_jpeg_folder, poppler_path)\n\n if not image_list:\n image_list = pdf_to_jpeg((inputpath, tmp_jpeg_folder, None, None, None, poppler_path,))\n\n if not image_list:\n return False\n\n jobs = []\n\n for count, jpeg_image_path in enumerate(image_list):\n filename = t.zero_prefiller(count, lenght=5)\n webp_save_path = f'{tmp_folder}/{filename}.webp'\n webp_save_path = os.path.abspath(os.path.expanduser(webp_save_path))\n\n jobs.append(\n (jpeg_image_path, webp_save_path, outputpath, self.webp_slider.value(), self.check_4k.isChecked(),)\n )\n\n widget.status_label.setText('CONVERTING')\n if not self.wepb_threads.isChecked():\n for i in jobs:\n convert_files_to_webp([i])\n else:\n convert_files_to_webp(jobs)\n\n widget.status_label.setText('RECOMPRESSING')\n rv = recompress_fucntion(outputpath, tmp_folder)\n\n return dict(status=rv, tmp_webp_folder=tmp_folder, tmp_jpeg_folder=tmp_jpeg_folder, outputpath=outputpath)","def clean_webp_textures():\n for webp in PNG_TEXTURES['output_files']:\n if os.path.isfile(webp):\n os.remove(webp)","def img2webp(path):\n file, ext = os.path.splitext(path)\n image = Image.open(path).convert(\"RGBA\")\n image = ImageOps.expand(image, 75)\n image.save(file + \".webp\", \"WEBP\")\n os.remove(path)","def image_webp():\n data = resource(\"images/wolf_1.webp\")\n return Response(data, headers={\"Content-Type\": \"image/webp\"})","def __main__() :\n try :\n poly = Polyhedre(sys.argv[1])\n \n name = sys.argv[2]\n \n createAllFiles(poly, name)\n\n createAllImageFiles(poly, name)\n \n except FileNotFoundError :\n print(\"Use an existing file\")","def main():\n try:\n pixid = sys.argv[1]\n except IndexError:\n print('Usage: python pixget.py [pixid] (save_path)')\n exit(1)\n\n # get the path\n if len(sys.argv) > 2:\n path = sys.argv[2]\n else:\n path = '.'\n\n imgInfoPool = []\n if get_image_url(pixid, imgInfoPool):\n exit(1)\n download_image(path, imgInfoPool)","def main():\n\n parser = argparse.ArgumentParser(description='codec_compare')\n parser.add_argument('path', metavar='DIR',\n help='path to images folder')\n args = parser.parse_args()\n classpath = args.path\n classname = classpath.split('/')[1]\n\n images = set(listdir_full_path(classpath))\n if len(images) <= 0:\n print \"\\033[91m[ERROR]\\033[0m\" + \" no source files in ./images.\"\n sys.exit(1)\n\n codeclist_full = set(['aom', 'deepcoder', 'deepcoder-lite', 'fuif', 'fvdo', 'hevc', 'kakadu', 'jpeg',\n 'pik', 'tat', 'xavs', 'xavs-fast', 'xavs-median', 'webp'])\n\n bpp_targets = set([0.06, 0.12, 0.25, 0.50, 0.75, 1.00, 1.50, 2.00])\n for image in images:\n width, height, depth = get_dimensions(image, classname)\n name, imgfmt = os.path.splitext(image)\n imgfmt = os.path.basename(image).split(\".\")[-1]\n derivative_images = []\n if classname[:6] == 'classB':\n derivative_images = create_derivatives(image, classname)\n else:\n derivative_images.append((image, imgfmt))\n\n for derivative_image, pix_fmt in derivative_images:\n json_dir = 'metrics'\n mkdir_p(json_dir)\n json_file = os.path.join(json_dir,\n os.path.splitext(os.path.basename(derivative_image))[0] + \".\" + pix_fmt + \".json\")\n # if os.path.isfile(json_file):\n # print \"\\033[92m[JSON OK]\\033[0m \" + json_file\n # continue\n main_dict = dict()\n derivative_image_metrics = dict()\n for codecname in codeclist_full:\n convertflag = 1\n caseflag = pix_fmt\n if (codecname == 'webp' or codecname == 'tat' or 'deepcoder' in codecname) and depth != '8':\n continue\n if 'xavs' in codecname and depth != '8' and depth != '10':\n continue\n if 'classE' in classname and ('tat' in codecname or 'xavs' in codecname or 'deepcoder' in codecname):\n continue\n if codecname == 'kakadu' and classname[:6] == 'classB':\n convertflag = 0\n caseflag = imgfmt\n bpp_target_metrics = dict()\n for bpp_target in bpp_targets:\n print(codecname)\n if codecname == 'aom' and classname[:6] == 'classB':\n # ('AERIAL2' in image or 'CATS' in image or 'XRAY' in image or 'GOLD' in image or 'TEXTURE1' in image):\n encoded_image_name = os.path.splitext(os.path.basename(derivative_image))[\n 0] + '_' + str(bpp_target) + '_' + imgfmt + '.' + 'av1'\n encoded_image = os.path.join('outputs', codecname, encoded_image_name)\n decoded_image = os.path.join('outputs', codecname, 'decoded', encoded_image_name + '.' + imgfmt)\n original_image = image\n elif codecname == 'kakadu' and classname[:6] == 'classB':\n encoded_image_name = os.path.splitext(os.path.basename(derivative_image))[\n 0] + '_' + str(bpp_target) + '_' + imgfmt + '.' + codecname\n encoded_image = os.path.join('outputs', codecname, encoded_image_name)\n decoded_image = os.path.join('outputs', codecname, 'decoded', encoded_image_name + '.' + imgfmt)\n original_image = image\n elif 'xavs' in codecname and classname[:6] == 'classB':\n encoded_image_name = os.path.splitext(os.path.basename(derivative_image))[\n 0] + '_' + str(bpp_target) + '_' + imgfmt + '.' + codecname\n encoded_image = os.path.join('outputs', codecname, encoded_image_name)\n decoded_image = os.path.join('outputs', codecname, 'decoded', encoded_image_name + '.' + imgfmt)\n original_image = image\n elif codecname == 'fvdo' and classname[:6] == 'classB':\n encoded_image_name = os.path.splitext(os.path.basename(derivative_image))[\n 0] + '_' + str(bpp_target) + '_pgm' + '.' + codecname\n encoded_image = os.path.join('outputs', codecname, encoded_image_name)\n decoded_image = os.path.join('outputs', codecname, 'decoded', encoded_image_name + '.pgm')\n original_image = image\n else:\n if codecname == 'fuif' and 'tif' in imgfmt:\n encoded_image_name = os.path.splitext(os.path.basename(derivative_image))[\n 0] + '.tif_' + str(bpp_target) + '_' + pix_fmt + '.' + codecname\n elif codecname == 'webp' or codecname == 'tat':\n encoded_image_name = os.path.splitext(os.path.basename(derivative_image))[\n 0] + '_' + str(bpp_target) + '_yuv420p.' + codecname\n else:\n encoded_image_name = os.path.splitext(os.path.basename(derivative_image))[\n 0] + '_' + str(bpp_target) + '_' + pix_fmt + '.' + codecname\n encoded_image = os.path.join('outputs', codecname, encoded_image_name)\n decoded_image_path = os.path.join('outputs', codecname, 'decoded')\n decoded_image = ''\n for decodedfile in os.listdir(decoded_image_path):\n encoderoot = '_'.join(os.path.splitext(os.path.basename(encoded_image_name))[0].split('_')[:-1])\n if encoderoot in decodedfile:\n if ('tat' in codecname or 'webp' in codecname) and os.path.splitext(os.path.basename(decodedfile))[1] == '.yuv':\n decoded_image = os.path.join('outputs', codecname, 'decoded', decodedfile)\n print(decoded_image)\n if ('tat' not in codecname or 'webp' not in codecname) and os.path.splitext(os.path.basename(decodedfile))[1] != '.yuv':\n decoded_image = os.path.join('outputs', codecname, 'decoded', decodedfile)\n if 'classE' not in classname and 'classB' not in classname and os.path.isfile(decoded_image):\n decoded_image = convert_decoded(decoded_image, width, height, depth, codecname)\n original_image = convert_decoded(derivative_image, width, height, depth, 'reference')\n else:\n original_image = derivative_image\n\n print('Reference:' + original_image)\n print('Encoded:' + encoded_image)\n print('Decoded:' + decoded_image)\n if (os.path.isfile(original_image) and os.path.isfile(decoded_image) and os.path.isfile(encoded_image)):\n if 'classE' in classname:\n metrics = compute_metrics_HDR(original_image, decoded_image, encoded_image, bpp_target,\n codecname, width, height, pix_fmt, depth)\n\n elif 'classB' in classname:\n metrics = compute_metrics(original_image, decoded_image, encoded_image, bpp_target, codecname,\n width, height, pix_fmt)\n else:\n metrics = compute_metrics_SDR(original_image, decoded_image, encoded_image, bpp_target,\n codecname, width,\n height, imgfmt, depth)\n measured_bpp = (os.path.getsize(encoded_image) * 1.024 * 8) / (float((int(width) * int(height))))\n bpp_target_metrics[measured_bpp] = metrics\n else:\n continue\n \n derivative_image_metrics[codecname] = bpp_target_metrics\n main_dict[derivative_image] = derivative_image_metrics\n\n mkdir_p(json_dir)\n with open(json_file, 'w') as f:\n f.write(json.dumps(main_dict, indent=2))","def main():\n folder = \"D:\\\\Noam10\\\\Documents\\\\Documents\\\\dither 2\"\n filename = \"kirigiri\"\n filetype = \".jpg\"\n input_file = folder + \"\\\\\" + filename + filetype\n for palette in paletteDict.keys():\n output_file = folder + \"\\\\\" + filename + \"(\" + palette + \").bmp\"\n Dither(input_file, output=output_file, palette=paletteDict[palette])\n print(output_file)","def TextureFiles():\n import shutil\n\n # first convert the .psd files to .png\n\n FbmDir = glo.outputFolder + '.fbm'\n\n for d1, d2, filenames in os.walk(FbmDir):\n for filename in filenames:\n \"\"\"filename: vitrin_diffuse.psd\n \"\"\"\n # print \"TextureFiles():\", filename\n if filename[-4:].upper() == '.PSD':\n #print \" -- FbmDir:\" , FbmDir\n #print \" -- in the if clause with filename:\" , filename\n #print \" -- glo.outputFolder\" , glo.outputFolder\n # FbmDir = '../fbx/simplelifeembedmedia.fbm'\n # filename = 'shelves_light.PSD'\n PsdToPngConverter(FbmDir, filename)\n\n # Move only the .png file to the ../png/ directory\n filename = filename[:-4] + '.png'\n src = os.path.join(FbmDir, filename)\n elif filename[0] != '.':\n src = os.path.join(FbmDir, filename)\n pass\n\n shutil.copy(src, glo.outputFolder)\n print os.path.join(glo.outputFolder, filename), \"\\n\"\n sys.stdout.flush()\n # for d1, d2, files in os.walk(glo.outputFolder):\n # if not filename in files:\n # #print \"moving: \", files, filename, not filename in files\n # shutil.copy(src, glo.outputFolder)\n # print os.path.join(glo.outputFolder, filename), \"\\n\"\n # else:\n # print \"%s/%s already exists. File not moved\" % (glo.outputFolder,filename)","def JPGtoPNGConverter(source, dest):\n files = os.listdir(f\"./{source}\")\n if not os.path.exists(f\"./{dest}\"):os.makedirs(f\"./{dest}\")\n\n for file in files:\n if os.path.splitext(file)[-1] == \".jpg\":\n img = Image.open(f\"./{source}/{file}\")\n clean_text = os.path.splitext(file)[0]\n img.save(f\"./{dest}/{clean_text}.png\",\"png\")\n else:\n print(f\"Your filename: {file} is not in .JPG format !!\")\n return \"All files converted successfully :) \"","def main():\n print(\"For each image, type the new name of the file.\" +\n \" No extension necessary!\", end=\"\\n\\n\")\n file_list = input_path.glob(f\"*.{args.ext}\")\n plt.ion()\n\n for pic in file_list:\n img = io.imread(str(pic))\n img = rescale(img, 0.25)\n img = rotate(img, 90, resize = True)\n plt.draw()\n plt.pause(0.001)\n if args.vinyl:\n new_name = get_vinyl_name()\n else:\n print(\"\\n\")\n new_name = input(\n \"Please enter a new filename. Press [enter] to skip: \")\n if new_name:\n if not new_name.endswith(args.ext):\n new_name += \".\" + args.ext\n # io.imsave(output_path / new_name, img)\n shutil.copyfile(pic, output_path / new_name)\n if args.replace:\n os.remove(pic)","def buildImages(files, targets, type):\n images = []\n for file in files:\n targets.append(file)\n with open(file, \"rb\") as f:\n if type == \"Byte\":\n images.append(bytePlot(list(f.read())))\n elif type == \"Markov\":\n images.append(markovPlot(list(f.read())))\n elif type == \"Hilbert\":\n images.append(hilbertPlot(list(f.read())))\n smp.imsave(\"{}.png\".format(file), images[-1])\n return images, targets","def main(args):\n parser = create_parser()\n\n if not args:\n parser.print_usage()\n sys.exit(1)\n\n parsed_args = parser.parse_args(args)\n\n img_urls = read_urls(parsed_args.logfile)\n\n if parsed_args.todir:\n download_images(img_urls, parsed_args.todir)\n else:\n print('\\n'.join(img_urls))","def main(args):\n parser = create_parser()\n\n if not args:\n parser.print_usage()\n sys.exit(1)\n\n parsed_args = parser.parse_args(args)\n\n img_urls = read_urls(parsed_args.logfile)\n\n if parsed_args.todir:\n download_images(img_urls, parsed_args.todir)\n else:\n print('\\n'.join(img_urls))","def convert_gif(ctx):\n ctx.run(\n 'ffmpeg '\n '-i resources/demo.mkv -filter_complex \"[0:v] palettegen\" '\n 'resources/palette.png',\n pty=True\n )\n ctx.run(\n 'ffmpeg -i resources/demo.mkv '\n '-i resources/palette.png '\n '-filter_complex \"[0:v][1:v] paletteuse\" '\n 'resources/demo.gif',\n pty=True\n )","def createAllImageFiles(poly, name) :\n \n for i in range(len(poly.getPaths())):\n fileName = name + \"_\" + str(i) + \".dot\"\n imgName = name + \"_\" + str(i) + \".jpg\"\n \n Command = \"neato -Tjpeg \" + fileName + \" -o \" + imgName\n run(Command, shell=True)","def main(args):\n parser = create_parser()\n\n if not args:\n parser.print_usage()\n sys.exit(1)\n\n parsed_args = parser.parse_args(args)\n\n img_urls = read_urls(parsed_args.logfile)\n if parsed_args.todir:\n download_images(img_urls, parsed_args.todir)\n else:\n print('\\n'.join(img_urls))","def exercise():\n\n #\n # Convert Lena Tiff image to raw format\n #\n for f in glob.glob('*.jpg'):\n os.remove(f)\n \n for f in glob.glob('*.dat'):\n os.remove(f)\n \n input_raw_file = convert_to_raw('Lena.tiff')\n\n for device in ['cpu', 'gpu']:\n for interp in ['nn', 'bl']:\n for (w,h) in ((256, 300), (486, 486),(2000, 1000),(1000, 2000),(8000, 4000)):\n (t, f) = interpolate(input_raw_file, device + '_' + interp + '_lena.dat', device, 0, interp, w, h)\n convert_to_jpg(f)\n\n \n for f in glob.glob('*.dat'):\n convert_to_jpg(f)\n os.remove(f)\n \n quit()","def pdftoimages(input_dir,output_dir): \n dirListing = os.listdir(input_dir)\n files = []\n imagespath = output_dir\n for item in dirListing:\n files.append(item)\n n = len(files)\n for num in range(n):\n doc = fitz.open(input_dir+\"/\"+files[num])\n for img in doc.getPageImageList(0):\n xref = img[0]\n pix = fitz.Pixmap(doc, xref)\n if pix.n < 5: # this is GRAY or RGB\n pix.writePNG(os.path.join(imagespath,\"p%s-%s.png\" % (num, xref)))\n else: # CMYK: convert to RGB first\n pix1 = fitz.Pixmap(fitz.csRGB, pix)\n pix1.writePNG(os.path.join(imagespath,\"p%s-%s.png\" % (num, xref)))\n pix1 = None \n pix=None\n break","def test_basic_run_png(self):\n self.expect_datatore_lookup('SomeBlobKey', True)\n self.expect_open_image('SomeBlobKey', (1600, 1200), mime_type='PNG')\n self.expect_resize(blob_image._DEFAULT_SERVING_SIZE)\n self.expect_encode_image('SomeImageInPng',\n images_service_pb.OutputSettings.PNG)\n self.run_request('image/png', 'SomeImageInPng')","def main():\n if not Path(EXPORT_DIR).exists():\n print(\"Kindle is not connected.\", file=sys.stderr)\n sys.exit(1)\n\n type_zip = ('zip file', '*.zip')\n type_pdf = ('pdf file', '*.pdf')\n\n files = filedialog.askopenfiles(filetypes=[type_zip, type_pdf], initialdir=os.path.expanduser('~'))\n for f in files:\n export_path = Path(EXPORT_DIR) / f\"{Path(f.name).stem}.pdf\"\n if f.name.endswith('.zip'):\n with open(export_path, 'wb') as pdf, zipfile.ZipFile(f.name, 'r') as _zip:\n pdf.write(img2pdf.convert([_zip.open(img) for img in _zip.infolist()]))\n else:\n shutil.copy(f.name, export_path)","def processImages(self):\n for file in os.listdir(self.config[\"tempPath\"]):\n self.logger.debug(\"Calling generateImages for the file: {0}\".format(file))\n self.generateText(file)","def process_files(self):\n for filename in self.temp_directory.iterdir():\n im = Image.open(str(filename))\n scaled = im.resize((640, 480))\n scaled.save(str(filename))","def run(self):\n generated_gif = self.generate()\n with open(self.out_filename, 'wb') as out_fd:\n out_fd.write(generated_gif)","def main():\n\n # Just grab all files - we'll use try/except to filter\n images = glob.glob(os.path.join(args.input_dir, '*.*'))\n if not os.path.exists(args.output_dir):\n os.makedirs(args.output_dir)\n for img_file in images:\n print(img_file)\n try:\n np_img = plt.imread(img_file)\n print(np_img.shape)\n img_name = img_file.split(os.sep)[-1]\n new_img_file = os.path.join(args.output_dir, img_name)\n pad_image(np_img, new_img_file)\n except Exception as e:\n print('Warning: {}. Skpping file.'.format(e))\n continue","def main():\n convert(\"env_100000.mp4\", TargetFormat.GIF)","def imageProcessing():\n\n # Parser initialization\n parser = argparse.ArgumentParser(description=colourers.toCyan('Image processor for reading/writing images into BMP/PNG formats and applying transformations on it.'))\n \n # Formats Parser\n group = parser.add_argument_group(colourers.toGreen('formats'))\n formatParser = group.add_mutually_exclusive_group(required=True)\n formatParser.add_argument('--bmp',\n type=str,\n metavar=colourers.toRed('<bmp file name>'), \n help=colourers.toMagenta('bmp file to parse'))\n formatParser.add_argument('--png',\n type=str,\n metavar=colourers.toRed('<png file name>'),\n help=colourers.toMagenta('png file to parse'))\n\n # Printers Parser\n group = parser.add_argument_group(colourers.toYellow('printers'))\n printers = group.add_mutually_exclusive_group()\n printers.add_argument('--header',\n help=colourers.toMagenta('print the file format header'),\n action='store_true')\n printers.add_argument('--print-color',\n '-pc',\n type=int,\n nargs=2,\n metavar=(colourers.toRed('<width>'), colourers.toRed('<height>')),\n help=colourers.toMagenta('pixel to print'))\n printers.add_argument('--histogram',\n action='store_true',\n help=colourers.toMagenta('print histogram associated'))\n printers.add_argument('--output',\n '-o',\n type=str,\n metavar=colourers.toRed('<output file>'),\n help=colourers.toMagenta('image output file'))\n\n # Transformers Parser\n transformers = parser.add_argument_group(colourers.toBlue('transformers'))\n transformers.add_argument('--half',\n action='store_true',\n help='applying the filter on one half of the image')\n transformers.add_argument('--rotate',\n '-r',\n type=int,\n choices=[90, 180, 270],\n metavar=colourers.toRed('<degree of rotation>'),\n help=colourers.toMagenta('rotate the image'))\n transformers.add_argument('--scale',\n '-s',\n type=int,\n nargs='+',\n action=required_length(1, 2),\n metavar=(colourers.toRed('<scaleRatio> | [<width>'), colourers.toRed('<height>')),\n help=colourers.toMagenta('scale/shrink the image'))\n transformers.add_argument('--contrast',\n '-c',\n type=float,\n metavar=colourers.toRed('<contrast factor>'),\n help=colourers.toMagenta('apply a factor contrast'))\n transformers.add_argument('--grayscale',\n '-gs',\n action='store_true',\n help=colourers.toMagenta('to grayscale image'))\n transformers.add_argument('--binary',\n '-b',\n action='store_true',\n help=colourers.toMagenta('to binary image'))\n transformers.add_argument('--invert',\n '-i',\n action='store_true',\n help=colourers.toMagenta('to inverted image, equivalent to --contrast -1'))\n transformers.add_argument('--channel',\n type=str,\n choices=['blue', 'green', 'red'],\n metavar=colourers.toRed('<channel>'),\n nargs='+',\n action=required_length(1, 2),\n help=colourers.toMagenta('to the specified channel'))\n \n # Filters Parser\n filters = parser.add_argument_group(colourers.toCyan('filters'))\n filters.add_argument('--edge-detection',\n '-ed',\n type=str,\n choices=['canny', 'sobel', 'prewitt', 'roberts', 'kirsch'],\n metavar=colourers.toRed('<filter name>'),\n help=colourers.toMagenta('perform an edge detection'))\n filters.add_argument('--retrieve-color',\n '-rv',\n action='store_true',\n help=colourers.toMagenta('retrieve the colors of a grayscale image'))\n filters.add_argument('--edge-enhancement',\n '-ee',\n action='store_true', \n help=colourers.toMagenta('applying increased edge enhancement filter'))\n filters.add_argument('--sharpen',\n action='store_true',\n help=colourers.toMagenta('sharpening the image'))\n filters.add_argument('--unsharp',\n action='store_true',\n help=colourers.toMagenta('unsharp the image')) \n filters.add_argument('--denoise',\n action='store_true',\n help=colourers.toMagenta('denoise the image'))\n filters.add_argument('--texture-detection',\n '-td',\n action='store_true',\n help=colourers.toMagenta('applying texture detection (Gabor Filter)'))\n filters.add_argument('--blur',\n type=str,\n choices=['simple', 'more', 'average', 'gaussian', 'motion'],\n metavar=colourers.toRed('<type of blur>'),\n help=colourers.toMagenta('perform the selected blur'))\n filters.add_argument('--blur-iteration',\n '-bi',\n type=int,\n default=1,\n metavar=colourers.toRed('<number of iteration>'),\n help=colourers.toMagenta('apply N times the blur function'))\n filters.add_argument('--emboss',\n action='store_true',\n help=colourers.toMagenta('perform an embossing filter'))\n filters.add_argument('--overlap',\n type=str,\n nargs='+',\n metavar=colourers.toRed('<image to overlap>'),\n help=colourers.toMagenta('overlap an image given on the selected image'))\n\n # Args parsing\n args = parser.parse_args()\n\n filename = \"\"\n # BMP Block\n if args.bmp:\n filename = args.bmp\n\n if not os.path.isfile(filename):\n colourers.error('\"{}\" does not exist !'.format(filename))\n sys.exit(-1)\n colourers.success('Success Opening {}...'.format(filename))\n\n bmp = BMP(filename)\n half = args.half\n\n if args.print_color:\n width, height = args.print_color\n colourers.info(f'Printing pixel color of ({width}, {height})')\n Printers.printPixel(bmp, width, height)\n sys.exit(0)\n \n elif args.header:\n colourers.info(f'Printing BMP header of {bmp.filename}')\n Printers.printHeader(bmp)\n sys.exit(0)\n \n elif args.histogram:\n colourers.info(f'Printing color histogram of {bmp.filename}')\n Printers.printHistogram(bmp)\n sys.exit(0)\n \n if (args.rotate or args.scale or args.contrast or args.grayscale or \n args.binary or args.channel or args.edge_detection or args.retrieve_color or\n args.edge_enhancement or args.blur or args.emboss or args.overlap or args.texture_detection or\n args.denoise or args.sharpen or args.unsharp):\n if not hp.atLeastOne(args.output, (\n args.rotate,\n args.scale,\n args.contrast,\n args.grayscale,\n args.binary,\n args.channel,\n args.edge_detection,\n args.retrieve_color,\n args.edge_enhancement,\n args.blur,\n args.emboss,\n args.overlap,\n args.texture_detection,\n args.denoise,\n args.sharpen,\n args.unsharp\n )):\n parser.error('--rotate/--scale/--contrast/--grayscale/--binary/--channel/--edge-detection/--retrieve-color/--edge-enhancement/--blur/--emboss/--overlap/--texture-detection/--denoise/--sharpen/--unsharp and --output must be given together')\n \n if args.rotate:\n degree = args.rotate\n colourers.info(f'Rotating image to {degree} degree')\n bmp.imageData = Transformers.rotate(bmp, degree)\n\n if args.scale:\n if len(args.scale) == 2:\n width, height = args.scale\n colourers.info(f'Scaling image to {width}x{height} pixels')\n bmp.imageData = Transformers.scale(bmp, height, width)\n else:\n scaleRatio = args.scale[0]\n\n colourers.info(f'Scaling image to {scaleRatio} scale ratio')\n\n height = int(hp.readLittleEndian(bmp.height))\n width = int(hp.readLittleEndian(bmp.width))\n\n bmp.imageData = Transformers.scale(bmp, height * scaleRatio, width * scaleRatio)\n \n if args.contrast:\n factor = args.contrast\n colourers.info(f'Applying a factor contrast of {factor}')\n bmp.imageData = Transformers.contrast(bmp, factor)\n \n if args.grayscale:\n colourers.info(f'Applying grayscale mask to the image')\n bmp.imageData = Transformers.grayscale(bmp, half)\n \n if args.binary:\n colourers.info(f'Applying binary mask to the image')\n bmp.imageData = Transformers.binary(bmp, half)\n \n if args.invert:\n colourers.info(f'Inverting image colours')\n bmp.imageData = Transformers.invert(bmp, half)\n \n if args.channel:\n if len(args.channel) == 2:\n c1, c2 = args.channel\n colourers.info(f'Keeping only {c1} and {c2} channels of the image')\n bmp.imageData = Transformers.toChannel(bmp, [c1, c2], half)\n else:\n channel = args.channel[0]\n colourers.info(f'Keeping only {channel} channel of the image')\n bmp.imageData = Transformers.toChannel(bmp, channel, half)\n \n if args.denoise:\n colourers.info(f'Denoising the image')\n bmp.imageData = Filters.wienerFilter(bmp.imageData, gaussianKernel(9, sigma=0.33), K=10)\n \n if args.texture_detection:\n colourers.info(f'Applying texture detection (Gabor Filter)')\n bmp.imageData = Filters.gaborFilter(bmp.imageData, gaborKernel(0))\n \n if args.edge_enhancement:\n colourers.info(f'Applying increased edge enhancement filter')\n bmp.imageData = Filters.iee(bmp.imageData)\n\n if args.edge_detection:\n filterName = args.edge_detection\n if filterName == 'canny':\n colourers.info(f'Performing Canny filter for edge detection')\n bmp.imageData = Filters.ced(bmp.imageData, sigma=0.33, kernelSize=9, weakPix=50)\n if filterName == 'sobel':\n colourers.info(f'Performing Sobel filter for edge detection')\n bmp.imageData = Filters.sed(bmp.imageData, sigma=0.33, kernelSize=9)\n if filterName == 'prewitt':\n colourers.info(f'Performing Prewitt filter for edge detection')\n bmp.imageData = Filters.ped(bmp.imageData, sigma=0.33, kernelSize=9)\n if filterName == 'roberts':\n colourers.info(f'Performing Roberts filter for edge detection')\n bmp.imageData = Filters.red(bmp.imageData, sigma=0.33, kernelSize=9)\n if filterName == 'kirsch':\n colourers.info(f'Performing Kirsch filter for edge detection')\n bmp.imageData = Filters.ked(bmp.imageData, sigma=0.33, kernelSize=9)\n\n if args.sharpen:\n colourers.info(f'Sharpening the image')\n bmp.imageData = Filters.sharpen(bmp.imageData)\n \n if args.unsharp:\n colourers.info(f'Unsharpening the image')\n bmp.imageData = Filters.unsharp(bmp.imageData)\n\n if args.retrieve_color:\n colourers.info(f'Retrieving color')\n bmp.imageData = Filters.retrieveColor(bmp.imageData)\n \n if args.blur:\n blurType = args.blur\n colourers.info(f'Performing a {blurType} blur')\n for _ in range(args.blur_iteration):\n blurFunc = Filters.blur.switcher.get(blurType)\n bmp.imageData = blurFunc(bmp.imageData)\n \n if args.emboss:\n colourers.info(f'Performing emboss filter')\n bmp.imageData = Filters.emboss(bmp.imageData)\n \n if args.overlap:\n overlappers = []\n for ov in args.overlap:\n overlappers.append(BMP(ov).imageData)\n colourers.info(f'Performing an overlapping between {bmp.filename} and {args.overlap}')\n bmp.imageData = Filters.overlap(bmp.imageData, overlappers)\n \n if args.output:\n outputFile = args.output\n hp.saveBMP(bmp, bmp.imageData, outputFile)\n colourers.success(f'Succesfully saved into {outputFile}')\n sys.exit(0)\n \n parser.error('Give at least one more argument')\n \n # PNG Block\n else:\n filename = args.png\n\n if not os.path.isfile(filename):\n print('\"{}\" does not exist'.format(filename), file=sys.stderr)\n sys.exit(-1)\n print('Success Opening {}...'.format(filename))\n \n png = PNG(filename)","def load_images():\n print(\"[+] UPDATE - Begin loading images\")\n\n colors = [\"w\", \"b\"]\n piece_types = [\"p\", \"R\", \"N\", \"B\", \"K\", \"Q\"]\n for color in colors:\n for type in piece_types:\n piece = color + type\n IMAGES[piece] = p.transform.scale(p.image.load(\"images/\" + piece + \".png\"), (SQ_SIZE, SQ_SIZE))\n\n print(\"[+] UPDATE - Images loaded\")","def main():\n\n wallpaper_url = argv[1]\n if '://getwallpapers.com/' not in wallpaper_url:\n print(\"Invalid URL\")\n exit(0)\n else:\n soup = parse_link(wallpaper_url)\n folder_name = get_folder(soup)\n download(soup, folder_name)","def run(self):\n\n # need to think about outpath\n\n # Make sure all files are here and okay...\n\n if not self.config.galfile_pixelized:\n raise ValueError(\"Code only runs with pixelized galfile.\")\n\n self.config.check_files(check_zredfile=True, check_bkgfile=True, check_bkgfile_components=True, check_parfile=True, check_zlambdafile=True)\n\n # Compute the border size\n\n self.config.border = self.config.compute_border()\n\n self.config.d.hpix = [self.pixel]\n self.config.d.nside = self.nside\n self.config.d.outbase = '%s_%d_%05d' % (self.config.outbase, self.nside, self.pixel)\n\n # Do the run\n self.config.start_file_logging()\n self.config.logger.info(\"Running redMaPPer on pixel %d\" % (self.pixel))\n\n firstpass = RunFirstPass(self.config)\n\n if not os.path.isfile(firstpass.filename):\n firstpass.run()\n firstpass.output(savemembers=False, withversion=False)\n else:\n self.config.logger.info(\"Firstpass file %s already present. Skipping...\" % (firstpass.filename))\n\n self.config.catfile = firstpass.filename\n\n # Clear out the firstpass memory\n del firstpass\n\n like = RunLikelihoods(self.config)\n\n if not os.path.isfile(like.filename):\n like.run()\n like.output(savemembers=False, withversion=False)\n else:\n self.config.logger.info(\"Likelihood file %s already present. Skipping...\" % (like.filename))\n\n self.config.catfile = like.filename\n\n # Clear out the likelihood memory\n del like\n\n perc = RunPercolation(self.config)\n\n if not os.path.isfile(perc.filename):\n perc.run()\n perc.output(savemembers=True, withversion=False)\n else:\n self.config.logger.info(\"Percolation file %s already present. Skipping...\" % (perc.filename))\n\n self.config.stop_file_logging()","def OnBuildPNGs(self, e):\n if (not defaults.use_tex):\n msg = \"LaTeX is disabled in the defaults.py file. To use this functionality, change the\"\n msg += \" use_tex option to True and restart the GUI.\"\n ShowMessage(msg, kind='warn')\n return\n question = \"Quantity code formulas are displayed using PNG images, which need to be generated.\"\n question += \"\\n\\n\\nImages should only be generated if they do not already exist or\"\n question += \" the quantity codes have changed, e.g., more custom outputs have been added.\"\n question += \"\\n\\n\\nThis can take ~60 sec, do you want to proceed?\"\n proceed = AskYesNo(question, title='Generate LaTeX Formula Images?')\n if (not proceed): return\n\n question = \"Choose a path where the images will be saved. The default value from defaults.py is shown.\"\n path = AskText(question, default=defaults.quantity_code_image_path, title=\"Where to store images?\")\n if (path is None): return\n defaults.quantity_code_image_path = path # user overrode this quantity, remember for later\n\n question = \"If image files already exist, do you want to overwrite them?\"\n overwrite = AskYesNo(question, title='Overwrite Existing Files?')\n\n # call render routine and display a progress bar\n Nq = len(self.mainparent.nmlpanel.output_quantities.quantities)\n offsets = list(self.mainparent.nmlpanel.output_quantities.offsets.keys())\n\n P = ProgressBar(Nq)\n P(0)\n for i,Q in enumerate(self.mainparent.nmlpanel.output_quantities.quantities):\n if (Q.name in offsets): continue\n render_tex(Q.code, Q.tex, defaults.quantity_code_image_path, overwrite=overwrite)\n\n P(i+1) # update progress bar","def main():\n test_image = load_image()\n\n pixelate_image(\n normalize_image(test_image)\n )\n pass","def __call__(self, images, targets):\n pass","def main():\n\n import codecs\n\n file_path = '../sample_texts/hi-Deva.txt'\n with codecs.open(file_path, 'r', encoding='UTF-8') as input_file:\n sample_text = input_file.read()\n create_png(sample_text.strip(), 'hindi.png',\n family='Noto Sans Devanagari', language='hi', rtl=False)\n\n file_path = '../sample_texts/ar-Arab.txt'\n with codecs.open(file_path, 'r', encoding='UTF-8') as input_file:\n sample_text = input_file.read()\n create_png(sample_text.strip(), 'arabic.png',\n family='Noto Naskh Arabic', language='ar', rtl=True)\n\n file_path = '../sample_texts/mn-Mong.txt'\n with codecs.open(file_path, 'r', encoding='UTF-8') as input_file:\n sample_text = input_file.read()\n create_png(sample_text.strip(), 'mong.png',\n family='Noto Sans Mongolian', language='mn', vertical=True, rtl=False)","def run_turtle_program(source):\n ast = parser.parse(source)\n\n t = turtle.Turtle()\n for stmt in ast.statement:\n do_statement(stmt, t)\n canvas = turtle.Screen().getcanvas()\n canvas.postscript(file='image.eps')\n img = Image.open('image.eps')\n img.save('image.png', 'png')\n turtle.Screen().bye()\n return 'image.png'","def testConvertHtmlWithPngDataUrlToPdf(self):\n self._testBase(\"data/test_with_png_dataurl.html\")","def main():\n\n imagepaths = (\"icons/facebook.png\", \"icons/github.png\", \"icons/linkedin.png\", \"icons/pinterest.png\", \"icons/twitter.png\", \"icons/youtube.png\")\n\n try:\n\n spritemaker.create_sprites(imagepaths, \"sprites.png\", \"spritestyles.css\")\n\n except IOError as e:\n\n print(e)","def process(directory):\n files = []\n\n options = [\"Load\", \"Create\"]\n choice = options[int(ui.prompt(options=options))]\n\n for item in os.listdir(directory):\n if os.path.isfile(os.path.join(directory, item)):\n filename = os.path.join(directory, item)\n if choice == \"Load\" and item.endswith(\".png\"):\n files.append(filename)\n elif choice == \"Create\" and item.endswith(\".file\"):\n files.append(filename)\n\n filenames, pageNames = imagePages(files, choice)\n \n targets = [name.split('/')[-1][:5] for name in filenames]\n return pageNames, targets, filenames","def latex2img(expression, filename):\n webp = False\n\n extension = \"png\"\n\n # Preparing text strings\n server = \"http://latex.codecogs.com/\" + extension + \".download?\"\n fullname = filename + \".\" + extension\n size = \"%5Cdpi%7B100%7D%20\"\n\n # Quote expression引用表达式\n expression = quote(expression)\n url = server + size + expression\n\n # Download file from url and save to output_file:\n with urlopen(url) as response, open(fullname, 'wb') as output_file:\n data = response.read() # Un objeto \"bytes\"\n output_file.write(data) # Se escribe en disco\n\n if webp:\n img2webp(fullname)\n extension = \"webp\"\n\n return filename + \".\" + extension","def main():\n p = argparse.ArgumentParser(description='Convert images into unicode')\n p.add_argument('image', metavar='<path>', type=str,\n help='path to the file, use - for stdin')\n p.add_argument('--no-x256', action='store_false', dest='x256', default=True,\n help='prints with x256 unicode coloring')\n p.add_argument('--char-set', metavar='<name>', default='default',\n help='prints with character set (e.g. windows)')\n args = p.parse_args()\n print_image_as_unicode(args.image, char_set=CHAR_SETS[args.char_set],\n x256=args.x256)","def bands_py_to_png(py_dir: str, png_dir: str):\n import glob\n\n py_pattern = \"bandsdiff_*.py\"\n print(f\"Searching {py_pattern} in {py_dir}, save png in {png_dir}\")\n\n globbed = glob.glob(f\"{py_dir}/{py_pattern}\")\n for filename in globbed:\n with open(filename, encoding=\"utf-8\") as handle:\n mplcode = \"\".join(handle.readlines())\n mplcode = mplcode.replace(\n \"fig = pl.figure()\", \"fig = pl.figure(figsize=(16,10))\"\n )\n png_filename = filename.removesuffix(\".py\") + \".png\"\n print(f\"{py_dir}/{filename} -> {png_dir}/{png_filename}\")\n mplcode = mplcode.replace(\n \"pl.show()\",\n f\"pl.savefig('{png_dir}/{png_filename}', bbox_inches='tight')\",\n )\n exec(mplcode) # pylint: disable=exec-used","def main(url):\n print(f\"Running main with URL = {url}...\")\n imagehits(downloaddata(url))","def handle_as_url(view: sublime.View, point: int, string: str, name: str):\n\n # Let's assume this url as input:\n # (https://upload.wikimedia.org/wikipedia/commons/8/84/Example.svg)\n\n # Download the image\n # FIXME: avoid nested try-except clauses\n try:\n try:\n f = urlopen(unquote(string)) # <==\n except Exception:\n try:\n url_path = quote(string).replace(\"%3A\", ':', 1)\n f = urlopen(url_path)\n except Exception:\n f = urlopen(string)\n # don't fill the console with stack-trace when there`s no connection !!\n except Exception as e:\n print(e)\n return\n\n # file needs conversion ?\n need_conversion = name.endswith(formats_to_convert) # => True\n basename, ext = osp.splitext(name) # => (\"Example\", \".svg\")\n # create a temporary file\n temp_img = osp.join(TEMP_DIR, \"tmp_image\" + ext) # => \"TEMP_DIR/tmp_image.svg\"\n\n # Save downloaded data in the temporary file\n content = f.read()\n with open(temp_img, \"wb\") as img:\n img.write(content)\n\n # if the file needs conversion, convert it then read data from the resulting png\n if need_conversion:\n ext = \".png\"\n # keep the image's temporary file and name for later use\n conv_file = temp_img # => \"TEMP_DIR/tmp_image.svg\"\n\n # => \"TEMP_DIR/tmp_image.png\"\n temp_png = osp.splitext(temp_img)[0] + \".png\"\n\n # use the magick command of Imagemagick to convert the image to png\n magick(temp_img, temp_png)\n\n # read data from the resulting png\n with open(temp_png, \"rb\") as png:\n content = png.read()\n\n # set temp_file and name to the png file\n temp_img = temp_png # => \"TEMP_DIR/tmp_image.png\"\n\n width, height, real_width, real_height, size = get_data(view, temp_img)\n encoded = str(base64.b64encode(content), \"utf-8\")\n\n def on_navigate(href):\n\n if href == \"save\":\n if need_conversion:\n save(conv_file, name, \"url\")\n else:\n save(temp_img, name, \"url\")\n elif href == \"save_as\":\n if need_conversion:\n convert(conv_file, \"url\", name)\n else:\n convert(temp_img, \"url\", name)\n else:\n sublime.active_window().open_file(temp_img)\n\n view.show_popup(\n TEMPLATE % (width, height, ext, encoded, real_width, real_height,\n str(size // 1024) + \"KB\" if size >= 1024 else str(size) + 'B'),\n sublime.HIDE_ON_MOUSE_MOVE_AWAY,\n point,\n *view.viewport_extent(),\n on_navigate=on_navigate\n )","def main():\n tmp_dir = xm.constants['dir_tmp']\n exr_f = join(tmp_dir, 'test.exr')\n exr = EXR(exr_f)\n exr.extract_normal(join(tmp_dir, 'test.png'), vis=True)","def process_files(input_path, output_path, las_srs,\n wms_url, wms_layer, wms_srs,\n wms_version, wms_format, wms_ppm,\n wms_max_image_size, divide=1):\n path = os.path.dirname(os.path.realpath(__file__))\n\n if os.path.isdir(input_path):\n for i, f in enumerate(os.listdir(input_path)):\n if f.endswith(\".las\") or f.endswith(\".laz\"):\n las = os.path.join(input_path, f).replace('\\\\', '/')\n\n if os.path.isdir(output_path):\n output_path = output_path + '/' if output_path[-1] != '/' else output_path\n basename, ext = os.path.splitext(f)\n out = '{}{}_color{}'.format(output_path, basename, ext)\n else:\n basename, ext = os.path.splitext(output_path)\n out = '{}_{}{}'.format(basename, i, ext)\n\n run_pdal(path, las, out, las_srs, wms_url, wms_layer, wms_srs,\n wms_version, wms_format, wms_ppm, wms_max_image_size,\n divide)\n else:\n run_pdal(path, input_path, output_path, las_srs, wms_url, wms_layer,\n wms_srs, wms_version, wms_format, wms_ppm, wms_max_image_size,\n divide)","def process(image):\n pass","def main_convert(args):\n try:\n file_path = args.file_name # os.path.join(static_path, args.file_name)\n if args.slides:\n config_path = os.path.join(static_path, \"config\", \"slides_config.py\")\n output = subprocess.check_output(\n [\n \"jupyter\",\n \"nbconvert\",\n file_path,\n \"--to\",\n \"slides\",\n \"--CSSHTMLHeaderPreprocessor.style=colorful\",\n \"--reveal-prefix\",\n args.reveal_prefix,\n \"--config\",\n config_path,\n ],\n stderr=subprocess.STDOUT,\n ).decode(\"utf-8\")\n else:\n config_path = os.path.join(static_path, \"config\", \"nb_config.py\")\n output = subprocess.check_output(\n [\n \"jupyter\",\n \"nbconvert\",\n file_path,\n \"--to\",\n \"html\",\n \"--config\",\n config_path,\n ],\n stderr=subprocess.STDOUT,\n ).decode(\"utf-8\")\n print(output.rstrip())\n _name = get_out_name(args)\n # _name = output.split(\" \")[-1].rstrip()\n if args.c:\n with open(_name, \"r\") as f:\n clean_file = clean_html(f.read())\n with open(_name, \"w\") as f:\n f.write(clean_file)\n if args.bib_name is not None:\n add_ref(_name, args.bib_name, keep_label=args.l, slides=args.slides)\n else:\n with open(_name, \"r\") as f:\n clean_file = clean_html_refs(clean_file)\n with open(_name, \"w\") as f:\n f.write(clean_file)\n except IndexError:\n print(\"Provide the name of the notebook.\")","def main():\n \n # for inserting other images, add tem to /input folder and list them here\n images = (\n 'image-0',\n 'image-1',\n 'image-2'\n )\n\n for image_name in images:\n print(image_name, \"image:\")\n\n image = open_image(image_name)\n display_image(image, \"Original input \" + image_name)\n\n grayscale_v = transform_colors(image)\n display_image(grayscale_v[:,:,0], \"Grayscale \" + image_name)\n save_image(image_name + \"-grayscale\", grayscale_v[:,:,0])\n\n contours_v, contours = get_contours(grayscale_v)\n display_image(contours_v, \"Contours \" + image_name)\n save_image(image_name + \"-contours\", contours_v)\n\n labeled_img, areas = get_measures(image, contours[1:])\n display_image(labeled_img, \"Labeled \" + image_name)\n save_image(image_name + \"-labeled\", labeled_img)\n\n areas_histogram(areas, image_name)","def main(args):\n input_image = make_input_image(args.input_format, args.input_images)\n output_pattern = f\"output/{args.prefix}{{}}.png\"\n \n # Exit early if input == output\n if args.input_format == args.output_format:\n print(\"Input format = output format. Copying files.\")\n input_image.write(output_pattern)\n return\n\n output_image = make_output_image(args.output_format, args.output_shape)\n\n input_is_sphere = args.input_format == \"sphere\"\n output_is_sphere = args.output_format == \"sphere\"\n\n try:\n if input_is_sphere:\n output_image.project_from(input_image)\n output_image.write(output_pattern)\n elif output_is_sphere:\n input_image.unproject_to(output_image)\n output_image.write(output_pattern)\n else:\n raise NotImplementedError\n except NotImplementedError:\n print(f\"Sorry, {args.input_format} -> {args.output_format} not supported.\")\n return","def main():\n\ttile_dimensions = (512, 512)\n\ttile_count = (5, 4) \n\timage = Image.new(\"RGB\", (tile_dimensions[0] * tile_count[0], tile_dimensions[1] * tile_count[1]), None)\n\n\ttry:\n\t\turl = sys.argv[1]\n\texcept:\n\t\tprint \"Please enter a Google Maps URL as a parameter.\"\n\t\tprint \"Usage: $ python get_pano.py https://www.google.com/maps/@34.1027387,-118.340471,3a,75y,32.1h,87.53t/data=!3m7!1e1!3m5!1soInaTCic7TsAAAQDMaZ31A!2e0!3e2!7i13312!8i6656\"\n\t\treturn\n\n\ttry:\n\t\tprint \"************************************\"\n\t\tprint \"Fetching images from Google Maps, this could take some time...\"\n\t\tregex = re.compile(r'panoid\\=([^&]*)', re.I)\n\t\tpano_id = regex.findall(urllib.unquote(url))[0]\n\t\tfor y in range(tile_count[1]):\n\t\t\tfor x in range(tile_count[0]):\n\t\t\t\timg_url = \"https://geo2.ggpht.com/cbk?cb_client=maps_sv.tactile&authuser=0&hl=en&panoid=\" + pano_id + \"&output=tile&x=\"+str(x)+\"&y=\"+str(y)+\"&zoom=3&nbt&fover=2\"\n\t\t\t\tresponse = urllib2.urlopen(img_url)\n\t\t\t\tfile_data = StringIO(response.read())\n\t\t\t\timage.paste(Image.open(file_data), (x * tile_dimensions[0], y * tile_dimensions[1]))\n\t\t\n\t\tfilename = \"pano-\" + pano_id + \".jpg\"\n\t\timage.save(filename)\n\t\tprint \"Success, image saved as \\033[96m\" + filename + \"\\033[00m\"\n\t\tprint \"************************************\"\n\n\n\texcept Exception as e:\n\t\tprint \"Sorry something broke.\"\n\t\tprint e","def download_images_png(self):\n self.show_as_waiting(True)\n self.download_images('PNG')\n self.show_as_waiting(False)","def change_img_pixel_format():\n\tbackground_module.bg = background_module.bg.convert()\n\tbackground_module.snow = background_module.snow.convert_alpha()\n\n\tforeground_module.ground = foreground_module.ground.convert_alpha()\n\t\n\tplayer_module.Player.imgs = [img.convert_alpha() for img in player_module.player.imgs]\n\tplayer_module.Propeller.propeller_imgs = [img.convert_alpha() for img in player_module.Propeller.propeller_imgs]\n\n\tcoins_module.Coin.resized_imgs = [img.convert_alpha() for img in coins_module.Coin.resized_imgs]\n\tcoins_module.coin_board = coins_module.coin_board.convert_alpha()\n\n\tobstacles_module.Tree.resized_imgs = [img.convert_alpha() for img in obstacles_module.Tree.imgs]\n\tobstacles_module.Rock_n_Bush.imgs = [img.convert_alpha() for img in obstacles_module.Rock_n_Bush.imgs]\t\n\n\teffects_module.Coin_spark_effects.imgs = [img.convert_alpha() for img in effects_module.Coin_spark_effects.imgs]\n\teffects_module.Hit_effects.imgs = [img.convert_alpha() for img in effects_module.Hit_effects.imgs]\n\n\tdisplay_module.heart = display_module.heart.convert_alpha()\n\tdisplay_module.line = display_module.line.convert_alpha()\n\tdisplay_module.start = display_module.start.convert_alpha()\n\tdisplay_module.finish = display_module.finish.convert_alpha()\n\tdisplay_module.fuel_bar.img_icon = display_module.fuel_bar.img_icon.convert_alpha()\n\n\tfor fuel in display_module.Fuel.fuel_list:\n\t\tfuel.img = fuel.img.convert_alpha()\n\tfor extra_life in display_module.Extra_life.extra_lives_list:\n\t\textra_life.img = extra_life.img.convert_alpha()\n\t\n\tdynamic_obstacle_giftbox.Gift.imgs_list = [img.convert_alpha() for img in dynamic_obstacle_giftbox.Gift.imgs_list]\n\tdynamic_obstacle_santa.Santa.imgs_list = [img.convert_alpha() for img in dynamic_obstacle_santa.Santa.imgs_list]\n\tdynamic_obstacle_olaf.Olaf.imgs_list = [img.convert_alpha() for img in dynamic_obstacle_olaf.Olaf.imgs_list]\n\tbird_module.Bird.list_of_lists = [[img.convert_alpha() for img in lst] for lst in bird_module.Bird.list_of_lists]","def process(self, image):","def convert (self, lossless=False):\n self._has_errors = False\n if self._progress:\n max_val = 0\n for root, dirs, files in os.walk(self._in_dir):\n max_val += len(files)\n self._bar = pb.ProgressBar(widgets=[pb.Percentage(), pb.Bar()],\n maxval=max_val).start()\n pool = multiprocessing.Pool()\n command = CONVERT_TO_JP2_LOSSY\n if lossless:\n command = CONVERT_TO_JP2_LOSSLESS\n for root, dirs, files in os.walk(self._in_dir):\n out_rel_path = os.path.relpath(root, self._in_dir)\n out_full_path = os.path.abspath(\n os.path.join(self._out_dir, out_rel_path))\n try:\n os.mkdir(out_full_path)\n except OSError:\n # It is not an error for the directory to already exist.\n pass\n for name in files:\n basename = os.path.splitext(name)[0]\n in_file = os.path.join(root, name)\n base_out_file = os.path.join(out_full_path, basename)\n tiff_file = '%s.tif' % base_out_file\n jp2_file = '%s.jp2' % base_out_file\n if self._force or not(os.path.isfile(jp2_file)):\n params = (in_file, tiff_file, jp2_file, command)\n pool.apply_async(self._convert, params,\n callback=self._result_callback)\n elif self._progress:\n self._bar.update(self._bar.currval + 1)\n pool.close()\n pool.join()\n if self._progress:\n self._bar.finish()\n return not(self._has_errors)","def _webp(self, tile: bytes) -> np.ndarray:\n decoded = np.rollaxis(imagecodecs.webp_decode(tile), 2, 0)\n return decoded","def convert_pptx_to_rgb(input_file, output_file):\n with ZipFile(input_file) as input_pptx_file:\n # theme is defined in the presentation rels\n rel = PresentationRel(input_pptx_file)\n theme_name = f'ppt/{rel.theme}'\n theme = read_color_map(zip_file=input_pptx_file, file_name=theme_name)\n color_map = theme[1]\n\n new_file_data = []\n\n file_list = input_pptx_file.namelist()\n\n for file in file_list:\n with input_pptx_file.open(name=file) as current_file:\n file_contents = current_file.read()\n # only convert XML files of slides\n if re.match(r'ppt/slides/slide(\\d+).xml', file):\n new_contents = convert(content=file_contents, color_map=color_map)\n else:\n new_contents = file_contents\n new_file_data.append(\n {\n 'file': file,\n 'data': new_contents\n }\n )\n\n # create/write the new ZIP\n with ZipFile(output_file, 'w') as output_pptx_file:\n for data in new_file_data:\n output_pptx_file.writestr(data['file'], data['data'], ZIP_DEFLATED)","def do_pyrclassdiagram(self, file_names):\r\n self.file_names = file_names\r\n pyreverse_command = 'pyreverse -ASmn -o png -p ' + file_names\r\n subprocess.call(pyreverse_command)\r\n print(file_names + ' are done')","def copy_support_files() -> None:\n # root folder files\n filelist = {\"favicon128.png\",\n \"favicon96.png\",\n \"favicon72.png\",\n \"favicon48.png\",\n \"favicon32.png\",\n \"favicon24.png\",\n \"favicon16.png\",\n \"favicon.ico\",\n \"apple-touch-icon.png\",\n \"apple-touch-icon-precomposed.png\",\n \"apple-touch-icon-72x72.png\",\n \"apple-touch-icon-72x72-precomposed.png\",\n \"apple-touch-icon-114x114.png\",\n \"apple-touch-icon-114x114-precomposed.png\",\n \"apple-touch-icon-144x144.png\",\n \"apple-touch-icon-144x144-precomposed.png\",\n \"uca_style.css\"}\n for filename in filelist:\n try:\n shutil.copy2(\"resources/\" + filename, WEBOUT_PATH)\n except FileNotFoundError:\n report_error(\"Missing file: resources/\" + filename)\n # image folder files\n filelist = {\"film.png\",\n \"stylifera75.png\",\n \"DOI_logo.svg\",\n \"size_hist.png\",\n \"size_ind.png\",\n \"size_mean.png\",\n \"size_range.png\",\n \"size_summary.png\",\n \"double_clawed.jpg\"}\n for filename in filelist:\n try:\n shutil.copy2(\"resources/images/\" + filename, WEBOUT_PATH + \"images/\")\n except FileNotFoundError:\n report_error(\"Missing file: resources/images/\" + filename)\n filelist = {\"specific_word_cloud.png\",\n \"binomial_word_cloud.png\"}\n for filename in filelist:\n try:\n shutil.copy2(TMP_PATH + filename, WEBOUT_PATH + \"images/\")\n except FileNotFoundError:\n report_error(\"Missing file: \" + TMP_PATH + filename)\n # font-awesome files\n filelist = {\"fontawesome.min.js\",\n \"brands.min.js\",\n \"regular.min.js\",\n \"solid.min.js\"}\n for filename in filelist:\n try:\n shutil.copy2(\"resources/font-awesome/js/\" + filename, WEBOUT_PATH + \"js/\")\n except FileNotFoundError:\n report_error(\"Missing file: resources/font-awesome/js/\" + TMP_PATH + filename)\n # flag-icon files\n filelist = {\"flag-icons.min.css\"}\n for filename in filelist:\n try:\n shutil.copy2(\"resources/flag-icon-css/css/\" + filename, WEBOUT_PATH + \"images/flag-icon-css/css/\")\n except FileNotFoundError:\n report_error(\"Missing file: images/flag-icon-css/css/\" + TMP_PATH + filename)\n filelist = {\"de.svg\", # Germany\n \"es.svg\", # Spain\n \"ru.svg\", # Russia\n \"fr.svg\", # France\n \"pt.svg\", # Portugal\n \"dk.svg\", # Denmark\n \"nl.svg\", # Netherlands\n \"jp.svg\", # Japan\n \"cn.svg\", # China\n \"us.svg\", # USA\n \"th.svg\", # Thailand\n \"va.svg\", # Vatican\n \"it.svg\", # Italy\n \"kr.svg\", # South Korea\n \"pl.svg\", # Poland\n \"mm.svg\", # Myanamar (Burma)\n \"sa.svg\", # Saudi Arabia (best option for Arabic of those available)\n \"id.svg\", # Indonesia\n \"za.svg\", # South Africa (best option for Afrikaans)\n \"my.svg\", # Malaysia (for Malay)\n \"mg.svg\", # Madagascar (for Malagasy)\n \"ir.svg\", # Iran (for Persian)\n \"vn.svg\"} # Vietnam\n for filename in filelist:\n try:\n shutil.copy2(\"resources/flag-icon-css/flags/4x3/\" + filename, WEBOUT_PATH +\n \"images/flag-icon-css/flags/4x3/\")\n except FileNotFoundError:\n report_error(\"Missing file: images/flag-icon-css/flags/4x3/\" + TMP_PATH + filename)","def save(images, output):\n for image, frame in images:\n image.save(output(frame))","def main():\n\n location = input(\"Enter the pathway to the directory containing the files\"\n \"to be converted:\\n\")\n os.chdir(location)\n gtiff(location)\n tiff(location)","def run(self):\n openShutter = True\n actuateXed = False\n image_type = \"PPUMP\"\n\n wl = float(self.eo_config.get(\"PPUMP_WL\", 550))\n meas_flux = self.measured_flux(wl)\n seqno = 0\n for tokens in self.instructions:\n exptime = float(tokens[1])\n nframes = int(tokens[2])\n shifts = int(tokens[3])\n for iframe in range(nframes):\n self.image_clears()\n self.bias_image(seqno)\n self.take_image(seqno, exptime, openShutter, actuateXed,\n image_type)\n seqno += 1","def process_data(output_folder):\n # select imgs\n img_folder = join(output_folder, 'img')\n select_img(output_folder, img_folder, 'HE-green')\n\n mask_folder = join(output_folder, 'mask')\n select_img(output_folder, mask_folder, '_EF5')","def main(base_dir: str, output_dir: str) -> None:\n base_path = pathlib.Path(base_dir)\n output_path = pathlib.Path(output_dir).expanduser()\n\n stage_copy_images(base_path, output_path)\n stage_extract_videos(base_path, output_path)","def test_encode_webp():\n width = 51\n height = 26\n channels = 3\n bmp_file = os.path.join(\n os.path.dirname(os.path.abspath(__file__)), \"test_image\", \"lena.bmp\")\n with open(bmp_file, 'rb') as f:\n bmp_contents = f.read()\n image_v = tf.image.decode_bmp(bmp_contents)\n assert image_v.shape == [height, width, channels]\n bmp_encoded = image_io.encode_bmp(image_v)\n image_e = tf.image.decode_bmp(bmp_encoded)\n assert np.all(image_v.numpy() == image_e.numpy())","def getimgs():","def pepsi(directory=None):\r\n \r\n if directory == None:\r\n directory = os.getcwd() # Use working directory if unspecified\r\n \r\n # Create a new directory 'modified'\r\n new_directory = os.path.join(directory, 'modified')\r\n try:\r\n os.mkdir(new_directory)\r\n except OSError:\r\n pass # if the directory already exists, proceed \r\n \r\n #load all the images\r\n image_list, file_list = get_images(directory) \r\n\r\n #go through the images and save modified versions\r\n red = PIL.Image.open(os.path.join(directory, 'red.png'))\r\n blue = PIL.Image.open(os.path.join(directory, 'blue.png'))\r\n template =PIL.Image.open(os.path.join(directory, 'template.png'))\r\n topp = PIL.Image.open(os.path.join(directory, '1.jpeg'))\r\n bottomm = PIL.Image.open(os.path.join(directory, '2.jpg'))\r\n \r\n # Round the corners with radius = 30% of short side\r\n\r\n top = redlogo(topp,red,template)\r\n bottom = bluelogo(bottomm,blue,template)\r\n new_image = template\r\n new_image.paste(bottom,(0,0), mask=bottom)\r\n new_image.paste(top,(0,0), mask=top)\r\n #save the altered image, suing PNG to retain transparency\r\n new_image_filename = os.path.join(new_directory, 'final' + '.png')\r\n new_image.save(new_image_filename) #9b: ","def animator_pdf_maker(rounds, pump_index):\n print(\"making pdf's and animations.\")\n space = ('wavelength', 'freequency', 'time')\n for sp in space:\n file_loc = 'output/output'+str(pump_index)+'/figures/'+sp+'/'\n strings_large = ['convert '+file_loc+'00.png ']\n for i in range(4):\n strings_large.append('convert ')\n for ro in range(rounds):\n for i in range(4):\n strings_large[i+1] += file_loc+str(ro)+str(i+1)+'.png '\n for w in range(1, 4):\n if i == 5:\n break\n strings_large[0] += file_loc+str(ro)+str(w)+'.png '\n for i in range(4):\n os.system(strings_large[i]+file_loc+str(i)+'.pdf')\n\n file_loca = file_loc+'portA/'\n file_locb = file_loc+'portB/'\n string_porta = 'convert '\n string_portb = 'convert '\n for i in range(rounds):\n string_porta += file_loca + str(i) + '.png '\n string_portb += file_locb + str(i) + '.png '\n\n string_porta += file_loca+'porta.pdf '\n string_portb += file_locb+'portb.pdf '\n os.system(string_porta)\n os.system(string_portb)\n\n for i in range(4):\n os.system(\n 'convert -delay 30 '+file_loc+str(i)+'.pdf '+file_loc+str(i)+'.mp4')\n os.system('convert -delay 30 ' + file_loca +\n 'porta.pdf ' + file_loca+'porta.mp4 ')\n os.system('convert -delay 30 ' + file_locb +\n 'portb.pdf ' + file_locb+'portb.mp4 ')\n\n for i in (file_loc, file_loca, file_locb):\n print('rm ' + i + '*.png')\n os.system('rm ' + i + '*.png')\n os.system('sleep 5')\n return None","def start_pupil():\n # runs Pupil Capture from source\n path = os.path.abspath(\"../../pupil/pupil_src/capture/main.py\")\n return subprocess.call('python ' + shellformat(path), shell=True)\n \n # if running Pupil Capture using the app, comment the above code and uncomment below:\n '''\n path = os.path.abspath(\"../pupil_capture_0.4.1_mac.app\")\n return subprocess.call('open ' + shellformat(path), shell=True)\n '''","def main(dataset, n, ms=False, out=sys.stdout):\n # build lists of paths previews files, tif, rpc and dzi files\n prv_paths = ' '.join([os.path.join(dataset, 'prv_%02d.jpg' % (i+1)) for i in xrange(n)])\n tif_paths = ' '.join([os.path.join(dataset, 'im_panchro_%02d.tif' % (i+1)) for i in xrange(n)])\n rpc_paths = ' '.join([os.path.join(dataset, 'rpc_%02d.xml' % (i+1)) for i in xrange(n)])\n dzi8_paths, dzi16_paths = None, None\n if ms:\n ms_paths = ' '.join([os.path.join(dataset, 'im_ms_%02d.tif' % (i+1)) for i in xrange(n)])\n if os.path.isfile(os.path.abspath(os.path.join(dataset,\n 'im_panchro_8BITS_01.dzi'))):\n dzi8_paths = ' '.join([os.path.join('input', dataset,\n 'im_panchro_8BITS_%02d.dzi' %\n (i+1)) for i in xrange(n)])\n if os.path.isfile(os.path.abspath(os.path.join(dataset,\n 'im_panchro_16BITS_01.dzi'))):\n dzi16_paths = ' '.join([os.path.join('input', dataset,\n 'im_panchro_16BITS_%02d.dzi' %\n (i+1)) for i in xrange(n)])\n\n # read infos in DIM*.XML file\n dim_xml_file = os.path.join(dataset, 'dim_01.xml')\n tif_file = os.path.join(dataset, 'im_panchro_01.tif')\n if os.path.isfile(dim_xml_file): # check if the link points to an existing file\n date = grep_xml(dim_xml_file, \"IMAGING_DATE\")\n satellite = grep_xml(dim_xml_file, \"INSTRUMENT_INDEX\")\n elif os.path.isfile(tif_file):\n date = extract_date_from_pleiades_filename(os.readlink(tif_file))\n satellite = extract_satellite_from_pleiades_filename(os.readlink(tif_file))\n else:\n date = 'DD-MM-YYYY'\n satellite = 'Pleiades 1X'\n\n # print to stdout\n if dzi8_paths or dzi16_paths:\n print('[%s]' % dataset, file=out)\n print('files = ', prv_paths, file=out)\n print('tif = ', tif_paths, file=out)\n print('rpc = ', rpc_paths, file=out)\n if ms:\n print('clr = ', ms_paths, file=out)\n if dzi8_paths:\n print('dzi8 = ', dzi8_paths, file=out)\n if dzi16_paths:\n print('dzi16 = ', dzi16_paths, file=out)\n s = dataset.split(os.path.sep)\n if len(s) == 3: # ie the path is of the kind 'pleiades/reunion/dataset_1'\n print('title = %s (%s)' % (s[1].capitalize(), s[2][-1]), file=out) # ie 'Reunion (1)'\n elif len(s) == 2: # ie the path is of the kind 'pleiades/reunion'\n print('title = %s' % s[1].capitalize(), file=out) # ie 'Reunion'\n else:\n print('path %s not expected by the author of the script: ' % dataset, s, file=sys.stderr)\n print('date = %s' % date, file=out)\n print('satellite = Pleiades %s' % satellite, file=out)\n print('nb_img = %d' % n, file=out)\n if ms:\n print('color = panchro_xs', file=out)\n else:\n print('color = panchro', file=out)","def main():\n mip = parametros()\n mir = Reporte(CURRENT_PATH, mip.debug, mip.overwrite)\n pdfs = mir.obtener()\n if pdfs:\n print(\"Obteniendo nuevos pdf:\")\n for pdf in pdfs:\n print(f\"* {pdf}\")\n\n for file in glob.glob(f\"{CURRENT_PATH}/resources/pdf/*.pdf\"):\n data = mir.parser(file)\n mir.escribir(data)","def preprocess(exam, data_folder, save_path, image_format):\n for v in ['L-CC', 'L-MLO', 'R-CC', 'R-MLO']:\n if len(exam[v]) == 0:\n continue\n else:\n for image in exam[v]:\n image_path = data_folder + '/' + image + '.' + image_format\n # Extract subdirectories\n subdirs = \"/\".join(image.split('/')[:-1])\n save_dirs = os.path.join(save_path, subdirs)\n # Extract image id\n image_id = image.split('/')[-1]\n # Create save directories\n os.makedirs(save_dirs, exist_ok=True)\n png_save_path = os.path.join(save_dirs, image_id + '.png')\n with Image(filename=image_path, format=image_format) as img:\n with img.clone() as i:\n i.auto_level()\n with i.convert('png') as png_image:\n png_image.transform(resize='896x1152!')\n png_image.save(filename=png_save_path)","def main():\n me = SimpleImage(\"images/me.JPG\")\n dinosaur = SimpleImage(\"images/dinosaur.jpg\")\n\n dinosaur.make_as_big_as(me)\n combine = magic(me, dinosaur)\n combine.show()","def process_images():\n create_dirs()\n for root, dirs, files in os.walk(IN):\n for name in files:\n if name[0] == '.':\n continue\n process_image(name)","def main():\n args = get_args()\n\n urls = get_urls(args)\n\n if len(urls) < 1:\n print >> sys.stderr, render_date_iso8601(), \"No valid URLs to process.\"\n exit(1)\n\n outfile = None\n\n if args.output:\n try:\n outfile = open(args.output, 'a' if args.append == True else 'w')\n except IOError as error:\n print >> sys.stderr, render_date_iso8601(), \"Error: opening file:\", args.output, \">>\", url\n\n\n for url in urls:\n if verify_url(url) == True:\n try:\n m3u8_obj = m3u8.load(url)\n if m3u8_obj.is_variant:\n print >> sys.stdout if outfile is None else outfile, render_csv(url, m3u8_obj.playlists)\n else:\n print >> sys.stderr, render_date_iso8601(), \"Error for url:\", url, \"Doesn't contain any stream playlists\"\n except IOError as error:\n print >> sys.stderr, render_date_iso8601(), \"Error for url:\", url, \">>\", error\n else:\n print >> sys.stderr, render_date_iso8601(), \"Error: Not a valid URL >>\", url\n\n\n return 0","def handle_as_url(view: View, point: int, string: str, name: str):\n\n # Let's assume this url as input:\n # (https://upload.wikimedia.org/wikipedia/commons/8/84/Example.svg)\n\n # Download the image\n # FIXME: avoid nested try-except clauses\n try:\n try:\n f = urlopen(unquote(string)) # <==\n except:\n try:\n url_path = quote(string).replace(\"%3A\", ':', 1)\n f = urlopen(url_path)\n except:\n f = urlopen(string)\n # don't fill the console with stack-trace when there`s no connection !!\n except Exception as e:\n print(e)\n return\n\n # file needs conversion ?\n need_conversion = name.endswith(FORMAT_TO_CONVERT) # => True\n basename, ext = osp.splitext(name) # => (\"Example\", \".svg\")\n # create a temporary file\n tmp_file = osp.join(TEMP_DIR,\n \"tmp_image\" + (ext if need_conversion else \".png\")\n ) # => \"TEMP_DIR/tmp_image.svg\"\n\n # Save downloaded data in the temporary file\n content = f.read()\n with open(tmp_file, \"wb\") as dst:\n dst.write(content)\n\n # if the file needs conversion, convert it then read data from the resulting png\n if need_conversion:\n # keep the image's temporary file and name for later use\n conv_file = tmp_file # => \"TEMP_DIR/tmp_image.svg\"\n conv_name = name # => \"Example.svg\"\n\n # => \"TEMP_DIR/tmp_image.png\"\n png = osp.splitext(tmp_file)[0] + \".png\"\n\n # use the magick command of Imagemagick to convert the image to png\n magick(tmp_file, png)\n\n # set temp_file and name to the png file\n tmp_file = png # => \"TEMP_DIR/tmp_image.png\"\n name = basename + \".png\" # => \"Example.png\"\n\n # read data from the resulting png\n with open(tmp_file, \"rb\") as dst:\n content = dst.read()\n\n real_width, real_height, size = get_image_size(tmp_file)\n width, height = get_dimensions(view, tmp_file)\n encoded = str(base64.b64encode(content), \"utf-8\")\n size = str(size // 1024) + \"KB\" if size >= 1024 else str(size) + 'B'\n\n def on_navigate(href):\n\n if href == \"save\":\n if need_conversion:\n save(conv_file, conv_name, \"url\")\n else:\n save(tmp_file, name, \"url\")\n elif href == \"save_as\":\n if need_conversion:\n convert(conv_file, \"url\", conv_name)\n else:\n convert(tmp_file, \"url\", name)\n else:\n sublime.active_window().open_file(tmp_file)\n\n view.show_popup(\n TEMPLATE % (width, height, \"png\", encoded, real_width, real_height, size),\n sublime.HIDE_ON_MOUSE_MOVE_AWAY,\n point,\n *view.viewport_extent(),\n on_navigate=on_navigate\n )","def main():\n parser = argparse.ArgumentParser(\n description=\"A program to generate geometric wallpaper.\")\n\n parser.add_argument(\"-s\", \"--shape\",\n help=\"the name of shapes to generate\",\n type=str,\n choices=(\"hexagon\",\n \"square\",\n \"diamond\",\n \"half-diamond\"),\n default=\"hexagon\")\n\n parser.add_argument(\"-n\", \"--num-across\",\n help=\"the number of shapes across the canvas to create\",\n type=int,\n default=10)\n\n parser.add_argument(\"--size\",\n help=\"the size of the created image\",\n type=int,\n nargs=2,\n default=instagram_size)\n\n parser.add_argument(\"-o\", \"--outfile\",\n help=\"name of the created file\",\n type=str)\n\n args = parser.parse_args()\n\n # Create the image.\n im = Image.new('RGB', args.size)\n draw = ImageDraw.Draw(im)\n\n for shape in gw.shapes.cover_in_shapes(args.shape, im.size,\n args.num_across):\n draw.polygon(shape, fill=gw.colors.palette_chooser())\n\n # Save the image.\n if args.outfile:\n im.save(args.outfile)\n else:\n im.save(\"{}_{}_{}x{}.png\".format(args.shape, args.num_across,\n args.size[0], args.size[1]),\n \"PNG\")","def bulk_process_images(inputpath, outputpath, extension):\n\n for dirpath, dirnames, filenames in os.walk(inputpath):\n structure = os.path.join(outputpath, dirpath[len(inputpath) + 1:])\n for file in filenames:\n if file.endswith(extension):\n src = os.path.join(dirpath, file)\n dest = os.path.join(structure, file)\n img = load_and_preprocess_image(src)\n cv2.imwrite(dest, img)","def compute_web_src(depc, gid_list, config=None):\n ibs = depc.controller\n\n gpath_list = ibs.get_image_paths(gid_list)\n orient_list = ibs.get_image_orientation(gid_list)\n args_list = list(zip(gpath_list, orient_list))\n\n genkw = {\n 'ordered': True,\n 'futures_threaded': True,\n 'force_serial': ibs.force_serial or config['force_serial'],\n }\n gen = ut.generate2(draw_web_src, args_list, nTasks=len(args_list), **genkw)\n for val in gen:\n yield (val,)","def main():\n\tparser = construct_parser()\n\targs = parser.parse_args()\n\ttiles = slice(args.image, args.num_tiles, save=False)\n\tsave_tiles(tiles, prefix=get_basename(args.image), directory=args.dir,\n\t\t format=args.format)","def process_file(pool, path):\n _, ext = os.path.splitext(path)\n if ext in ('.png',):\n update_png_crusher()\n pool.apply_async(run, (path, [PNG_CRUSHER, path]))\n elif ext in ('.jpg', '.jpeg'):\n pool.apply_async(run, (path, [JPG_CRUSHER, path]))","def main():\n base_dir = '/home/sjimenez/imagenes_prueba'\n out_dir = '/home/sjimenez/easy_analysis'\n for _, _, files in os.walk(base_dir, topdown=False):\n for f in files:\n print('--------- {} ---------'.format(f))\n act_dir = osp.join(base_dir, f)\n act_im = cv2.imread(act_dir)\n if act_im is not None:\n get_image_stats(act_im, out_dir, f)\n else:\n print('Not able to open the image')","def make_pbeam_images(metadata, in_dir, write_tag):\n filenames = metadata['FITSImageFilename']\n for i, in_file in enumerate(filenames):\n kat_target = katpoint.Target(metadata['KatpointTargets'][i])\n\n out_filebase = os.path.splitext(in_file)[0]\n out_filebase_pb = out_filebase + '_PB'\n log.info('Write primary beam corrected FITS output: %s',\n out_filebase_pb + FITS_EXT)\n\n in_path = os.path.join(in_dir + write_tag, in_file)\n pb_dir = _productdir(metadata, in_dir, i, '_PB', write_tag)\n\n os.mkdir(pb_dir)\n pbc_path = os.path.join(pb_dir, out_filebase_pb + FITS_EXT)\n raw_image = pbc.read_fits(in_path)\n beam_model = pbc.get_beam_model(raw_image.header)\n pbc_image = pbc.primary_beam_correction(beam_model, raw_image, px_cut=0.1)\n pbc.write_new_fits(pbc_image, in_path, outputFilename=pbc_path)\n\n log.info('Write primary beam corrected PNG output: %s',\n out_filebase_pb + PNG_EXT)\n _caption_pngs(pb_dir, out_filebase_pb,\n kat_target, 'PB Corrected', contrast=PB_CONTRAST)","def runAll(self):\n \n worker = worker()\n if self.FileFolder.text() == \"\":\n self.makeWarningPopup(\"Please Select a file or Files to run\") \n elif self.OutputFolder.text() == \"\":\n self.makeWarningPopup(\"Please select an output folder\")\n else:\n TheFiles = self.FileFolder.text()\n TheOutPutFolder = self.OutputFolder.text()\n \n runArt = worker.MakeUITeamConversion(self,TheFiles,TheOutPutFolder)","def main():\n\n parser = argparse.ArgumentParser(description=DESCRIPTION)\n parser.add_argument('-o', '--output', default='output.jpg',\n help=\"Filename to use for the output image\")\n parser.add_argument('INPUT_IMAGE', type=str,\n help=\"Filename for the input content image\")\n parser.add_argument('STYLE_IMAGE', type=str,\n help=\"Filename to for the input style image\")\n parser.add_argument('-s', '--style-weight', default=1e-2, type=float,\n help=\"Weight to apply for the loss associated with the style image. \"\n \"Making this bigger results in a final image more like the style image\")\n parser.add_argument('-c', '--content-weight', default=1e4, type=float,\n help=\"Weight to apply for the loss associated with the content image. \"\n \"Making this bigger results in a final image more like the content image\")\n parser.add_argument('-t', '--total-variation-loss', default=30, type=float,\n help=\"Weight for the total variation loss.\")\n\n args = parser.parse_args()\n\n print(args)","def process_image(self):\n pass","def convertHPURL(files):\n return urllib.parse.urlunsplit( ('http', HPpla, HPdoc + files, '', '') )","def main():\r\n original = SimpleImage(\"images/poppy.png\")\r\n original.show()\r\n # shrink function\r\n after_shrink = shrink('images/poppy.png')\r\n after_shrink.show()","def hover_preview_callback():\n\n global PREVIEW_ON_HOVER, MAX_CHARS, ALL_FORMATS,\\\n FORMAT_TO_CONVERT, SEARCH_MODE, RECURSIVE, IMAGE_FOLDER_NAME,\\\n IMAGE_URL_RE, IMAGE_FILE_RE, IMAGE_FILE_NAME_RE\n\n PREVIEW_ON_HOVER = settings.get(\"preview_on_hover\", True)\n MAX_CHARS = settings.get(\"max_chars\", 2028) // 2\n\n format_to_convert = settings.get(\"formats_to_convert\",\n [\"svg\", \"svgz\", \"ico\", \"webp\"])\n ALL_FORMATS = [\"png\", \"jpg\", \"jpeg\", \"bmp\", \"gif\"] + format_to_convert\n FORMAT_TO_CONVERT = tuple('.' + ext for ext in format_to_convert)\n\n IMAGE_FOLDER_NAME = settings.get(\"image_folder_name\", \"Hovered Images\")\n\n SEARCH_MODE = settings.get(\"search_mode\", \"project\")\n RECURSIVE = settings.get(\"recursive\", True)\n\n formats_ored = '|'.join(ALL_FORMATS)\n IMAGE_URL_RE = re.compile(r\"(?:(https?)://)?\" # http(s)://\n r\"(?:[^./\\\"'\\s]+\\.){1,3}[^/\\\"'.\\s]+/\" # host\n r\"(?:[^/\\\"'\\s]+/)*\" # path\n r\"([^\\\"'/\\s]+?\\.(?:%s))\" % formats_ored) # name\n\n IMAGE_FILE_RE = re.compile(r\"(?P<path>(\" # drive\n r\"\\w:(\\\\\\\\|/)|\" # Windows (e.g C:\\)\n r\"\\\\\\\\|\" # Linux (\\\\)\n r\"\\.{1,2}(\\\\\\\\|/)|\" # Mac OS and/or relative\n\t\t\t\t\t\t\t\t\tr\"\\w+(\\\\\\\\|/)\"\n r\")\"\n r\"([-\\.@\\w]+?(\\\\\\\\|/))*\" # body\n r\"[-\\.@\\w]+?\" # name\n r\"\\.(%s))\" % formats_ored # extension\n )\n IMAGE_FILE_NAME_RE = re.compile(r\"[-.@\\w]+\" # name\n r\"\\.(?:%s)\" % formats_ored # extension\n )","def download_images(img_urls, dest_dir, base_url=\"http://code.google.com\"):\n create_dir(dest_dir)\n img_tags = fetch_call(img_urls, dest_dir)\n create_html(dest_dir, img_tags)","def main():\n # the url for african daily and global daily\n african_dialy_url = \"https://data.chc.ucsb.edu/products/CHIRPS-2.0/africa_daily/tifs/p25/\"\n global_daily_url = \"https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/tifs/p25/\"\n\n\n each_year_list = GetRasterYears(url=african_dialy_url)\n new_path = makenewdir(each_year_list)\n years_new_list = fecthrasterurl(url=african_dialy_url)\n downloadwithwget(each_year_list, years_new_list, new_path)","def create_png_images(self):\n if self.subject is None:\n print Console.WARNING + 'You need to specify a subject first' + Console.ENDC\n return\n\n check_dir_of = self.locations.check_dir_of\n check_dir_of(self.locations.HISTO_PNG_U)\n check_dir_of(self.locations.HISTO_PNG)\n check_dir_of(self.locations.SOURCE_PNG)\n\n\n\n fmap_img = ImageUtils.load_nifti_image(self.locations.HIST_FMAP) #loading subject nifti files\n volumes = []\n try:\n for s in self.locations.SOURCES:\n volumes.append(ImageUtils.load_nifti_image(s))\n except IOError as e:\n print Console.FAIL + 'There are errors loading nifi files for subject %s'%self.subject + Console.ENDC\n return False\n \n\n num_slices = volumes[0].shape[2] #use first volume to check expected number of slices\n\n self.locations.create_empty_dir(self.locations.IMAGES_DIR)\n\n print 'Creating input PNGs for %s'%self.subject\n for k, vol in enumerate(volumes):\n for i in range(num_slices):\n imslice = ImageUtils.data_to_bytescale_rgb(vol[:, :, i])\n im = Image.fromarray(imslice)\n im.save(self.locations.SOURCE_PNG % (self.locations.LABELS[k],i))\n\n \n print 'Creating histology PNGs for %s'%self.subject\n for i in range(num_slices):\n\n im_unscaled = ImageUtils.data_to_unscaled_rgb(fmap_img[:, :, i]); #keeps the original values\n im_unscaled = Image.fromarray(im_unscaled)\n im_unscaled = im_unscaled.filter(ImageFilter.GaussianBlur(radius=2)) #Filter requested by Ali Khan\n im_unscaled.save(self.locations.HISTO_PNG_U % i)\n\n im_scaled = ImageUtils.data_to_bytescale_rgb(fmap_img[:,:,i]); # bytescaled histology\n im_scaled = Image.fromarray(im_scaled)\n im_scaled = im_scaled.filter(ImageFilter.GaussianBlur(radius=2)) #Filter requested by Ali Khan\n im_scaled.save(self.locations.HISTO_PNG % i)\n\n print\n return True","def make_gif():\n if MIGRATION:\n import imageio\n for n, JPG_DIR in enumerate(JPG_DIRS):\n images, image_file_names = [], []\n for file_name in os.listdir(JPG_DIR):\n if file_name.endswith('.jpg'):\n image_file_names.append(file_name) \n sorted_files = sorted(image_file_names, key=lambda y: int(y.split('_')[1]))\n for i in range(len(sorted_files)): \n file_path = os.path.join(JPG_DIR, sorted_files[i])\n images.append(imageio.imread(file_path))\n imageio.mimsave(FNAME.rsplit('.', 1)[0] + '_migration' + str(n) + '.gif', images, 'GIF', loop=1, fps=FPS)","def main(input):\n path = os.path.abspath(input)\n name = os.path.splitext(os.path.basename(path))[0]\n p = os.path.join(os.getcwd(),name)\n i = 1\n p1 = p\n while os.path.exists(p1):\n p1 = \"{p}-{i}\".format(p=p,i=i)\n i += 1\n p = p1\n os.mkdir(p1)\n os.mkdir(os.path.join(p1,\"media\"))\n with zipfile.ZipFile(path) as zf:\n for file in zf.namelist():\n # Path traversal defense copied from\n # http://hg.python.org/cpython/file/tip/Lib/http/server.py#l789\n words = file.split('/')\n dest = os.path.join(p1, \"media\")\n if words[0] == \"word\" and words[1] == \"media\":\n for word in words[2:]:\n while True:\n drive, word = os.path.splitdrive(word)\n head, word = os.path.split(word)\n if not drive:\n break\n if word in (os.curdir, os.pardir, ''):\n continue\n dest = os.path.join(dest, word)\n click.echo(\"{} -> {}\".format(file, dest))\n of = open(dest, 'wb')\n of.write(zf.read(file))\n of.close()\n\n newdoc = os.path.join(p1, os.path.basename(path))\n lyxfile = os.path.join(p1, name + \".lyx\")\n texfile = os.path.join(p1, name + \".tex\")\n shutil.copyfile(path, newdoc)\n os.system(\"pandoc -s -f docx -t latex -o '{of}' '{i}'\".format(of=texfile, i=newdoc))\n os.system(\"tex2lyx '{i}' '{o}'\".format(i=texfile, o=lyxfile))\n os.remove(texfile)\n os.system(\"convertwmf {dir}\".format(dir=os.path.join(p1, \"media\")))\n click.echo(lyxfile)","def _convert_batch(self, bucket, pdf_path, pages, jpeg_prefixes,\n webhook_url, webhook_data):\n # download PDF locally, use first JPEG prefix as its name\n pdf_key = s3.Key(bucket)\n pdf_key.key = pdf_path\n\n local_jpeg_prefix = jpeg_prefixes[0].replace('/', '-')\n local_pdf_path = '%s/%s.pdf' % (self.working_dir, local_jpeg_prefix)\n\n pdf_key.get_contents_to_filename(local_pdf_path)\n threads = []\n\n # convert each page in a separate thread using ImageMagick\n for page_number, jpeg_prefix in zip(pages, jpeg_prefixes):\n args = (local_pdf_path, page_number, jpeg_prefix, bucket, webhook_url,\n webhook_data)\n threads.append(threading.Thread(target=self._upload_page, args=args))\n\n [thread.start() for thread in threads]\n\n # wait until all threads have completed\n [thread.join() for thread in threads]","def decode(p):\n #assert p.endswith('.' + EXTENSION)\n p2 = os.path.basename(p).replace('baseline.png', '.png')\n p2p = os.path.join('/mnt/Volume0/test/clic2020-devkit/result/', p2) #add by me\n pp = os.path.join('/mnt/Volume0/test/clic2020-devkit/targets',p2)\n p2 = os.path.join('/mnt/Volume0/test/clic2020-devkit/inputs/', p2) #add by me\n p1 = pframe_dataset_shared.get_previous_frame_path(p2)\n #p1 = os.path.join('/mnt/Volume0/test/clic2020-devkit/test_data/inputs/', p1)\n #assert os.path.isfile(p1), (p2, p1, p, len(glob.glob('*.png')))\n b = Image.open(p).convert('L')\n f2_reconstructed = decoder(np.array(Image.open(p1)), b)\n Image.fromarray(f2_reconstructed).save(p2p)\n return f2_reconstructed, np.array(Image.open(pp))","def process_images():\n image_path = os.path.join(settings.BASE_DIR, 'themes/CMESH/assets/img/')\n static_images = os.path.join(settings.BASE_DIR, 'static/CMESH/img/')\n\n copy_files(image_path, static_images)"],"string":"[\n \"def main():\\n argvs = sys.argv\\n argc = len(argvs)\\n if argc == 1:\\n print('usage: convert2png.py <path/to/*.ppm> ...')\\n sys.exit(1)\\n\\n os.makedirs('result/convert2png', exist_ok=True)\\n\\n for i in range(1, argc):\\n img = cv2.imread(argvs[i])\\n\\n # root, ext = os.path.splitext(argvs[i])\\n # cv2.imwrite(root + '.png', img)\\n\\n root, ext = os.path.splitext(argvs[i])\\n strImgName = root.split('/')[-1]\\n cv2.imwrite('result/convert2png/' + strImgName + '.png', img)\",\n \"def convert_png_image_to_webp(png, out, quality=80):\\n command = [CWEBP, '-q', str(quality), png, '-o', out]\\n run_subprocess(command)\",\n \"def photo2web_process_hattenbach():\\n\\n os.chdir('/Volumes/SSD External/Hattenbach_v2')\\n \\n dir_base = os.getcwd()\\n \\n dir_p2w = '/Users/throop/photos/Trips/'\\n \\n dirs = sorted(glob.glob(os.path.join(dir_base, '*')))\\n \\n quality_out = '60'\\n size_out = '2000x2000'\\n \\n for i,dir in enumerate(dirs):\\n if os.path.isdir(dir):\\n os.chdir(dir)\\n dir_originals = os.path.join(dir, 'originals')\\n dir_originals_fullres = os.path.join(dir, 'originals_fullres')\\n\\n# For HH files, copy the 'actual' originals into a 'fullres' folder, for safekeeping\\n\\n if not os.path.isdir(dir_originals_fullres):\\n os.rename(dir_originals, dir_originals_fullres)\\n os.mkdir(dir_originals)\\n \\n files = glob.glob(os.path.join(dir_originals_fullres, '*'))\\n\\n# Get a list of all the images\\n\\n# For each image, make a low-res, low-quality image. This is just because the scanned files\\n# are huge and high-quality, and not useful for online. They are much larger than necessary. \\n# So we use 'convert' to shrink them in size and quality, and put the output into 'originals' directory \\n# for photo2web.\\n\\n for file in files:\\n file_short = os.path.basename(file)\\n file_in = os.path.join(dir_originals_fullres,file_short)\\n file_out = os.path.join(dir_originals,file_short)\\n if not os.path.isfile(file_out):\\n cmd = (f'convert -resize {size_out} -quality {quality_out}' +\\n f' {file_in}' +\\n f' {file_out}')\\n print(f'{cmd}')\\n \\n subprocess.run(['convert', '-resize', size_out, '-quality', quality_out,\\n file_in,\\n file_out])\\n\\n# Now, finally, go thru and do photo2web on all of them.\\n \\n print(f'\\\\nProcessing directory {i}/{len(dirs)} {dir}\\\\n')\\n subprocess.run(['cp', '-r', os.path.join(dir_p2w, 'header.txt'), '.'])\\n subprocess.run(['cp', '-r', os.path.join(dir_p2w, 'photos.css'), '.'])\\n if not os.path.exists('captions.txt'):\\n subprocess.run(['captions_photo2web']) \\n subprocess.run(['photo2web_old'])\\n subprocess.run(['photo2web'])\",\n \"def convert_to_web(base_path, files):\\n for i, f in enumerate(files):\\n imagetype = get_imagetype_from_filename(f)\\n cmd = ('rsync '\\n '{base_path}/qc/phantom/{imagetype}/{f} '\\n '{base_path}/website/assets/{output}'.format(\\n base_path=base_path, imagetype=imagetype, \\n f=f, output=f[9:]))\\n os.system(cmd)\",\n \"def convert_pdf_to_images(self, inputpath, outputpath, widget):\\n tmp_jpeg_folder = t.tmp_folder(inputpath, hash=True, delete=True)\\n tmp_folder = t.tmp_folder(outputpath, hash=True, delete=True)\\n\\n image_list = []\\n\\n poppler_path = self.get_poppler_path()\\n widget.status_label.setText('EXTRACTING')\\n if self.pdf_threads.isChecked():\\n rv = self.decide_pages_per_cpu(inputpath)\\n if rv:\\n image_list = convert_files_to_jpeg(\\n rv, inputpath, tmp_jpeg_folder, poppler_path)\\n\\n if not image_list:\\n image_list = pdf_to_jpeg((inputpath, tmp_jpeg_folder, None, None, None, poppler_path,))\\n\\n if not image_list:\\n return False\\n\\n jobs = []\\n\\n for count, jpeg_image_path in enumerate(image_list):\\n filename = t.zero_prefiller(count, lenght=5)\\n webp_save_path = f'{tmp_folder}/{filename}.webp'\\n webp_save_path = os.path.abspath(os.path.expanduser(webp_save_path))\\n\\n jobs.append(\\n (jpeg_image_path, webp_save_path, outputpath, self.webp_slider.value(), self.check_4k.isChecked(),)\\n )\\n\\n widget.status_label.setText('CONVERTING')\\n if not self.wepb_threads.isChecked():\\n for i in jobs:\\n convert_files_to_webp([i])\\n else:\\n convert_files_to_webp(jobs)\\n\\n widget.status_label.setText('RECOMPRESSING')\\n rv = recompress_fucntion(outputpath, tmp_folder)\\n\\n return dict(status=rv, tmp_webp_folder=tmp_folder, tmp_jpeg_folder=tmp_jpeg_folder, outputpath=outputpath)\",\n \"def clean_webp_textures():\\n for webp in PNG_TEXTURES['output_files']:\\n if os.path.isfile(webp):\\n os.remove(webp)\",\n \"def img2webp(path):\\n file, ext = os.path.splitext(path)\\n image = Image.open(path).convert(\\\"RGBA\\\")\\n image = ImageOps.expand(image, 75)\\n image.save(file + \\\".webp\\\", \\\"WEBP\\\")\\n os.remove(path)\",\n \"def image_webp():\\n data = resource(\\\"images/wolf_1.webp\\\")\\n return Response(data, headers={\\\"Content-Type\\\": \\\"image/webp\\\"})\",\n \"def __main__() :\\n try :\\n poly = Polyhedre(sys.argv[1])\\n \\n name = sys.argv[2]\\n \\n createAllFiles(poly, name)\\n\\n createAllImageFiles(poly, name)\\n \\n except FileNotFoundError :\\n print(\\\"Use an existing file\\\")\",\n \"def main():\\n try:\\n pixid = sys.argv[1]\\n except IndexError:\\n print('Usage: python pixget.py [pixid] (save_path)')\\n exit(1)\\n\\n # get the path\\n if len(sys.argv) > 2:\\n path = sys.argv[2]\\n else:\\n path = '.'\\n\\n imgInfoPool = []\\n if get_image_url(pixid, imgInfoPool):\\n exit(1)\\n download_image(path, imgInfoPool)\",\n \"def main():\\n\\n parser = argparse.ArgumentParser(description='codec_compare')\\n parser.add_argument('path', metavar='DIR',\\n help='path to images folder')\\n args = parser.parse_args()\\n classpath = args.path\\n classname = classpath.split('/')[1]\\n\\n images = set(listdir_full_path(classpath))\\n if len(images) <= 0:\\n print \\\"\\\\033[91m[ERROR]\\\\033[0m\\\" + \\\" no source files in ./images.\\\"\\n sys.exit(1)\\n\\n codeclist_full = set(['aom', 'deepcoder', 'deepcoder-lite', 'fuif', 'fvdo', 'hevc', 'kakadu', 'jpeg',\\n 'pik', 'tat', 'xavs', 'xavs-fast', 'xavs-median', 'webp'])\\n\\n bpp_targets = set([0.06, 0.12, 0.25, 0.50, 0.75, 1.00, 1.50, 2.00])\\n for image in images:\\n width, height, depth = get_dimensions(image, classname)\\n name, imgfmt = os.path.splitext(image)\\n imgfmt = os.path.basename(image).split(\\\".\\\")[-1]\\n derivative_images = []\\n if classname[:6] == 'classB':\\n derivative_images = create_derivatives(image, classname)\\n else:\\n derivative_images.append((image, imgfmt))\\n\\n for derivative_image, pix_fmt in derivative_images:\\n json_dir = 'metrics'\\n mkdir_p(json_dir)\\n json_file = os.path.join(json_dir,\\n os.path.splitext(os.path.basename(derivative_image))[0] + \\\".\\\" + pix_fmt + \\\".json\\\")\\n # if os.path.isfile(json_file):\\n # print \\\"\\\\033[92m[JSON OK]\\\\033[0m \\\" + json_file\\n # continue\\n main_dict = dict()\\n derivative_image_metrics = dict()\\n for codecname in codeclist_full:\\n convertflag = 1\\n caseflag = pix_fmt\\n if (codecname == 'webp' or codecname == 'tat' or 'deepcoder' in codecname) and depth != '8':\\n continue\\n if 'xavs' in codecname and depth != '8' and depth != '10':\\n continue\\n if 'classE' in classname and ('tat' in codecname or 'xavs' in codecname or 'deepcoder' in codecname):\\n continue\\n if codecname == 'kakadu' and classname[:6] == 'classB':\\n convertflag = 0\\n caseflag = imgfmt\\n bpp_target_metrics = dict()\\n for bpp_target in bpp_targets:\\n print(codecname)\\n if codecname == 'aom' and classname[:6] == 'classB':\\n # ('AERIAL2' in image or 'CATS' in image or 'XRAY' in image or 'GOLD' in image or 'TEXTURE1' in image):\\n encoded_image_name = os.path.splitext(os.path.basename(derivative_image))[\\n 0] + '_' + str(bpp_target) + '_' + imgfmt + '.' + 'av1'\\n encoded_image = os.path.join('outputs', codecname, encoded_image_name)\\n decoded_image = os.path.join('outputs', codecname, 'decoded', encoded_image_name + '.' + imgfmt)\\n original_image = image\\n elif codecname == 'kakadu' and classname[:6] == 'classB':\\n encoded_image_name = os.path.splitext(os.path.basename(derivative_image))[\\n 0] + '_' + str(bpp_target) + '_' + imgfmt + '.' + codecname\\n encoded_image = os.path.join('outputs', codecname, encoded_image_name)\\n decoded_image = os.path.join('outputs', codecname, 'decoded', encoded_image_name + '.' + imgfmt)\\n original_image = image\\n elif 'xavs' in codecname and classname[:6] == 'classB':\\n encoded_image_name = os.path.splitext(os.path.basename(derivative_image))[\\n 0] + '_' + str(bpp_target) + '_' + imgfmt + '.' + codecname\\n encoded_image = os.path.join('outputs', codecname, encoded_image_name)\\n decoded_image = os.path.join('outputs', codecname, 'decoded', encoded_image_name + '.' + imgfmt)\\n original_image = image\\n elif codecname == 'fvdo' and classname[:6] == 'classB':\\n encoded_image_name = os.path.splitext(os.path.basename(derivative_image))[\\n 0] + '_' + str(bpp_target) + '_pgm' + '.' + codecname\\n encoded_image = os.path.join('outputs', codecname, encoded_image_name)\\n decoded_image = os.path.join('outputs', codecname, 'decoded', encoded_image_name + '.pgm')\\n original_image = image\\n else:\\n if codecname == 'fuif' and 'tif' in imgfmt:\\n encoded_image_name = os.path.splitext(os.path.basename(derivative_image))[\\n 0] + '.tif_' + str(bpp_target) + '_' + pix_fmt + '.' + codecname\\n elif codecname == 'webp' or codecname == 'tat':\\n encoded_image_name = os.path.splitext(os.path.basename(derivative_image))[\\n 0] + '_' + str(bpp_target) + '_yuv420p.' + codecname\\n else:\\n encoded_image_name = os.path.splitext(os.path.basename(derivative_image))[\\n 0] + '_' + str(bpp_target) + '_' + pix_fmt + '.' + codecname\\n encoded_image = os.path.join('outputs', codecname, encoded_image_name)\\n decoded_image_path = os.path.join('outputs', codecname, 'decoded')\\n decoded_image = ''\\n for decodedfile in os.listdir(decoded_image_path):\\n encoderoot = '_'.join(os.path.splitext(os.path.basename(encoded_image_name))[0].split('_')[:-1])\\n if encoderoot in decodedfile:\\n if ('tat' in codecname or 'webp' in codecname) and os.path.splitext(os.path.basename(decodedfile))[1] == '.yuv':\\n decoded_image = os.path.join('outputs', codecname, 'decoded', decodedfile)\\n print(decoded_image)\\n if ('tat' not in codecname or 'webp' not in codecname) and os.path.splitext(os.path.basename(decodedfile))[1] != '.yuv':\\n decoded_image = os.path.join('outputs', codecname, 'decoded', decodedfile)\\n if 'classE' not in classname and 'classB' not in classname and os.path.isfile(decoded_image):\\n decoded_image = convert_decoded(decoded_image, width, height, depth, codecname)\\n original_image = convert_decoded(derivative_image, width, height, depth, 'reference')\\n else:\\n original_image = derivative_image\\n\\n print('Reference:' + original_image)\\n print('Encoded:' + encoded_image)\\n print('Decoded:' + decoded_image)\\n if (os.path.isfile(original_image) and os.path.isfile(decoded_image) and os.path.isfile(encoded_image)):\\n if 'classE' in classname:\\n metrics = compute_metrics_HDR(original_image, decoded_image, encoded_image, bpp_target,\\n codecname, width, height, pix_fmt, depth)\\n\\n elif 'classB' in classname:\\n metrics = compute_metrics(original_image, decoded_image, encoded_image, bpp_target, codecname,\\n width, height, pix_fmt)\\n else:\\n metrics = compute_metrics_SDR(original_image, decoded_image, encoded_image, bpp_target,\\n codecname, width,\\n height, imgfmt, depth)\\n measured_bpp = (os.path.getsize(encoded_image) * 1.024 * 8) / (float((int(width) * int(height))))\\n bpp_target_metrics[measured_bpp] = metrics\\n else:\\n continue\\n \\n derivative_image_metrics[codecname] = bpp_target_metrics\\n main_dict[derivative_image] = derivative_image_metrics\\n\\n mkdir_p(json_dir)\\n with open(json_file, 'w') as f:\\n f.write(json.dumps(main_dict, indent=2))\",\n \"def main():\\n folder = \\\"D:\\\\\\\\Noam10\\\\\\\\Documents\\\\\\\\Documents\\\\\\\\dither 2\\\"\\n filename = \\\"kirigiri\\\"\\n filetype = \\\".jpg\\\"\\n input_file = folder + \\\"\\\\\\\\\\\" + filename + filetype\\n for palette in paletteDict.keys():\\n output_file = folder + \\\"\\\\\\\\\\\" + filename + \\\"(\\\" + palette + \\\").bmp\\\"\\n Dither(input_file, output=output_file, palette=paletteDict[palette])\\n print(output_file)\",\n \"def TextureFiles():\\n import shutil\\n\\n # first convert the .psd files to .png\\n\\n FbmDir = glo.outputFolder + '.fbm'\\n\\n for d1, d2, filenames in os.walk(FbmDir):\\n for filename in filenames:\\n \\\"\\\"\\\"filename: vitrin_diffuse.psd\\n \\\"\\\"\\\"\\n # print \\\"TextureFiles():\\\", filename\\n if filename[-4:].upper() == '.PSD':\\n #print \\\" -- FbmDir:\\\" , FbmDir\\n #print \\\" -- in the if clause with filename:\\\" , filename\\n #print \\\" -- glo.outputFolder\\\" , glo.outputFolder\\n # FbmDir = '../fbx/simplelifeembedmedia.fbm'\\n # filename = 'shelves_light.PSD'\\n PsdToPngConverter(FbmDir, filename)\\n\\n # Move only the .png file to the ../png/ directory\\n filename = filename[:-4] + '.png'\\n src = os.path.join(FbmDir, filename)\\n elif filename[0] != '.':\\n src = os.path.join(FbmDir, filename)\\n pass\\n\\n shutil.copy(src, glo.outputFolder)\\n print os.path.join(glo.outputFolder, filename), \\\"\\\\n\\\"\\n sys.stdout.flush()\\n # for d1, d2, files in os.walk(glo.outputFolder):\\n # if not filename in files:\\n # #print \\\"moving: \\\", files, filename, not filename in files\\n # shutil.copy(src, glo.outputFolder)\\n # print os.path.join(glo.outputFolder, filename), \\\"\\\\n\\\"\\n # else:\\n # print \\\"%s/%s already exists. File not moved\\\" % (glo.outputFolder,filename)\",\n \"def JPGtoPNGConverter(source, dest):\\n files = os.listdir(f\\\"./{source}\\\")\\n if not os.path.exists(f\\\"./{dest}\\\"):os.makedirs(f\\\"./{dest}\\\")\\n\\n for file in files:\\n if os.path.splitext(file)[-1] == \\\".jpg\\\":\\n img = Image.open(f\\\"./{source}/{file}\\\")\\n clean_text = os.path.splitext(file)[0]\\n img.save(f\\\"./{dest}/{clean_text}.png\\\",\\\"png\\\")\\n else:\\n print(f\\\"Your filename: {file} is not in .JPG format !!\\\")\\n return \\\"All files converted successfully :) \\\"\",\n \"def main():\\n print(\\\"For each image, type the new name of the file.\\\" +\\n \\\" No extension necessary!\\\", end=\\\"\\\\n\\\\n\\\")\\n file_list = input_path.glob(f\\\"*.{args.ext}\\\")\\n plt.ion()\\n\\n for pic in file_list:\\n img = io.imread(str(pic))\\n img = rescale(img, 0.25)\\n img = rotate(img, 90, resize = True)\\n plt.draw()\\n plt.pause(0.001)\\n if args.vinyl:\\n new_name = get_vinyl_name()\\n else:\\n print(\\\"\\\\n\\\")\\n new_name = input(\\n \\\"Please enter a new filename. Press [enter] to skip: \\\")\\n if new_name:\\n if not new_name.endswith(args.ext):\\n new_name += \\\".\\\" + args.ext\\n # io.imsave(output_path / new_name, img)\\n shutil.copyfile(pic, output_path / new_name)\\n if args.replace:\\n os.remove(pic)\",\n \"def buildImages(files, targets, type):\\n images = []\\n for file in files:\\n targets.append(file)\\n with open(file, \\\"rb\\\") as f:\\n if type == \\\"Byte\\\":\\n images.append(bytePlot(list(f.read())))\\n elif type == \\\"Markov\\\":\\n images.append(markovPlot(list(f.read())))\\n elif type == \\\"Hilbert\\\":\\n images.append(hilbertPlot(list(f.read())))\\n smp.imsave(\\\"{}.png\\\".format(file), images[-1])\\n return images, targets\",\n \"def main(args):\\n parser = create_parser()\\n\\n if not args:\\n parser.print_usage()\\n sys.exit(1)\\n\\n parsed_args = parser.parse_args(args)\\n\\n img_urls = read_urls(parsed_args.logfile)\\n\\n if parsed_args.todir:\\n download_images(img_urls, parsed_args.todir)\\n else:\\n print('\\\\n'.join(img_urls))\",\n \"def main(args):\\n parser = create_parser()\\n\\n if not args:\\n parser.print_usage()\\n sys.exit(1)\\n\\n parsed_args = parser.parse_args(args)\\n\\n img_urls = read_urls(parsed_args.logfile)\\n\\n if parsed_args.todir:\\n download_images(img_urls, parsed_args.todir)\\n else:\\n print('\\\\n'.join(img_urls))\",\n \"def convert_gif(ctx):\\n ctx.run(\\n 'ffmpeg '\\n '-i resources/demo.mkv -filter_complex \\\"[0:v] palettegen\\\" '\\n 'resources/palette.png',\\n pty=True\\n )\\n ctx.run(\\n 'ffmpeg -i resources/demo.mkv '\\n '-i resources/palette.png '\\n '-filter_complex \\\"[0:v][1:v] paletteuse\\\" '\\n 'resources/demo.gif',\\n pty=True\\n )\",\n \"def createAllImageFiles(poly, name) :\\n \\n for i in range(len(poly.getPaths())):\\n fileName = name + \\\"_\\\" + str(i) + \\\".dot\\\"\\n imgName = name + \\\"_\\\" + str(i) + \\\".jpg\\\"\\n \\n Command = \\\"neato -Tjpeg \\\" + fileName + \\\" -o \\\" + imgName\\n run(Command, shell=True)\",\n \"def main(args):\\n parser = create_parser()\\n\\n if not args:\\n parser.print_usage()\\n sys.exit(1)\\n\\n parsed_args = parser.parse_args(args)\\n\\n img_urls = read_urls(parsed_args.logfile)\\n if parsed_args.todir:\\n download_images(img_urls, parsed_args.todir)\\n else:\\n print('\\\\n'.join(img_urls))\",\n \"def exercise():\\n\\n #\\n # Convert Lena Tiff image to raw format\\n #\\n for f in glob.glob('*.jpg'):\\n os.remove(f)\\n \\n for f in glob.glob('*.dat'):\\n os.remove(f)\\n \\n input_raw_file = convert_to_raw('Lena.tiff')\\n\\n for device in ['cpu', 'gpu']:\\n for interp in ['nn', 'bl']:\\n for (w,h) in ((256, 300), (486, 486),(2000, 1000),(1000, 2000),(8000, 4000)):\\n (t, f) = interpolate(input_raw_file, device + '_' + interp + '_lena.dat', device, 0, interp, w, h)\\n convert_to_jpg(f)\\n\\n \\n for f in glob.glob('*.dat'):\\n convert_to_jpg(f)\\n os.remove(f)\\n \\n quit()\",\n \"def pdftoimages(input_dir,output_dir): \\n dirListing = os.listdir(input_dir)\\n files = []\\n imagespath = output_dir\\n for item in dirListing:\\n files.append(item)\\n n = len(files)\\n for num in range(n):\\n doc = fitz.open(input_dir+\\\"/\\\"+files[num])\\n for img in doc.getPageImageList(0):\\n xref = img[0]\\n pix = fitz.Pixmap(doc, xref)\\n if pix.n < 5: # this is GRAY or RGB\\n pix.writePNG(os.path.join(imagespath,\\\"p%s-%s.png\\\" % (num, xref)))\\n else: # CMYK: convert to RGB first\\n pix1 = fitz.Pixmap(fitz.csRGB, pix)\\n pix1.writePNG(os.path.join(imagespath,\\\"p%s-%s.png\\\" % (num, xref)))\\n pix1 = None \\n pix=None\\n break\",\n \"def test_basic_run_png(self):\\n self.expect_datatore_lookup('SomeBlobKey', True)\\n self.expect_open_image('SomeBlobKey', (1600, 1200), mime_type='PNG')\\n self.expect_resize(blob_image._DEFAULT_SERVING_SIZE)\\n self.expect_encode_image('SomeImageInPng',\\n images_service_pb.OutputSettings.PNG)\\n self.run_request('image/png', 'SomeImageInPng')\",\n \"def main():\\n if not Path(EXPORT_DIR).exists():\\n print(\\\"Kindle is not connected.\\\", file=sys.stderr)\\n sys.exit(1)\\n\\n type_zip = ('zip file', '*.zip')\\n type_pdf = ('pdf file', '*.pdf')\\n\\n files = filedialog.askopenfiles(filetypes=[type_zip, type_pdf], initialdir=os.path.expanduser('~'))\\n for f in files:\\n export_path = Path(EXPORT_DIR) / f\\\"{Path(f.name).stem}.pdf\\\"\\n if f.name.endswith('.zip'):\\n with open(export_path, 'wb') as pdf, zipfile.ZipFile(f.name, 'r') as _zip:\\n pdf.write(img2pdf.convert([_zip.open(img) for img in _zip.infolist()]))\\n else:\\n shutil.copy(f.name, export_path)\",\n \"def processImages(self):\\n for file in os.listdir(self.config[\\\"tempPath\\\"]):\\n self.logger.debug(\\\"Calling generateImages for the file: {0}\\\".format(file))\\n self.generateText(file)\",\n \"def process_files(self):\\n for filename in self.temp_directory.iterdir():\\n im = Image.open(str(filename))\\n scaled = im.resize((640, 480))\\n scaled.save(str(filename))\",\n \"def run(self):\\n generated_gif = self.generate()\\n with open(self.out_filename, 'wb') as out_fd:\\n out_fd.write(generated_gif)\",\n \"def main():\\n\\n # Just grab all files - we'll use try/except to filter\\n images = glob.glob(os.path.join(args.input_dir, '*.*'))\\n if not os.path.exists(args.output_dir):\\n os.makedirs(args.output_dir)\\n for img_file in images:\\n print(img_file)\\n try:\\n np_img = plt.imread(img_file)\\n print(np_img.shape)\\n img_name = img_file.split(os.sep)[-1]\\n new_img_file = os.path.join(args.output_dir, img_name)\\n pad_image(np_img, new_img_file)\\n except Exception as e:\\n print('Warning: {}. Skpping file.'.format(e))\\n continue\",\n \"def main():\\n convert(\\\"env_100000.mp4\\\", TargetFormat.GIF)\",\n \"def imageProcessing():\\n\\n # Parser initialization\\n parser = argparse.ArgumentParser(description=colourers.toCyan('Image processor for reading/writing images into BMP/PNG formats and applying transformations on it.'))\\n \\n # Formats Parser\\n group = parser.add_argument_group(colourers.toGreen('formats'))\\n formatParser = group.add_mutually_exclusive_group(required=True)\\n formatParser.add_argument('--bmp',\\n type=str,\\n metavar=colourers.toRed('<bmp file name>'), \\n help=colourers.toMagenta('bmp file to parse'))\\n formatParser.add_argument('--png',\\n type=str,\\n metavar=colourers.toRed('<png file name>'),\\n help=colourers.toMagenta('png file to parse'))\\n\\n # Printers Parser\\n group = parser.add_argument_group(colourers.toYellow('printers'))\\n printers = group.add_mutually_exclusive_group()\\n printers.add_argument('--header',\\n help=colourers.toMagenta('print the file format header'),\\n action='store_true')\\n printers.add_argument('--print-color',\\n '-pc',\\n type=int,\\n nargs=2,\\n metavar=(colourers.toRed('<width>'), colourers.toRed('<height>')),\\n help=colourers.toMagenta('pixel to print'))\\n printers.add_argument('--histogram',\\n action='store_true',\\n help=colourers.toMagenta('print histogram associated'))\\n printers.add_argument('--output',\\n '-o',\\n type=str,\\n metavar=colourers.toRed('<output file>'),\\n help=colourers.toMagenta('image output file'))\\n\\n # Transformers Parser\\n transformers = parser.add_argument_group(colourers.toBlue('transformers'))\\n transformers.add_argument('--half',\\n action='store_true',\\n help='applying the filter on one half of the image')\\n transformers.add_argument('--rotate',\\n '-r',\\n type=int,\\n choices=[90, 180, 270],\\n metavar=colourers.toRed('<degree of rotation>'),\\n help=colourers.toMagenta('rotate the image'))\\n transformers.add_argument('--scale',\\n '-s',\\n type=int,\\n nargs='+',\\n action=required_length(1, 2),\\n metavar=(colourers.toRed('<scaleRatio> | [<width>'), colourers.toRed('<height>')),\\n help=colourers.toMagenta('scale/shrink the image'))\\n transformers.add_argument('--contrast',\\n '-c',\\n type=float,\\n metavar=colourers.toRed('<contrast factor>'),\\n help=colourers.toMagenta('apply a factor contrast'))\\n transformers.add_argument('--grayscale',\\n '-gs',\\n action='store_true',\\n help=colourers.toMagenta('to grayscale image'))\\n transformers.add_argument('--binary',\\n '-b',\\n action='store_true',\\n help=colourers.toMagenta('to binary image'))\\n transformers.add_argument('--invert',\\n '-i',\\n action='store_true',\\n help=colourers.toMagenta('to inverted image, equivalent to --contrast -1'))\\n transformers.add_argument('--channel',\\n type=str,\\n choices=['blue', 'green', 'red'],\\n metavar=colourers.toRed('<channel>'),\\n nargs='+',\\n action=required_length(1, 2),\\n help=colourers.toMagenta('to the specified channel'))\\n \\n # Filters Parser\\n filters = parser.add_argument_group(colourers.toCyan('filters'))\\n filters.add_argument('--edge-detection',\\n '-ed',\\n type=str,\\n choices=['canny', 'sobel', 'prewitt', 'roberts', 'kirsch'],\\n metavar=colourers.toRed('<filter name>'),\\n help=colourers.toMagenta('perform an edge detection'))\\n filters.add_argument('--retrieve-color',\\n '-rv',\\n action='store_true',\\n help=colourers.toMagenta('retrieve the colors of a grayscale image'))\\n filters.add_argument('--edge-enhancement',\\n '-ee',\\n action='store_true', \\n help=colourers.toMagenta('applying increased edge enhancement filter'))\\n filters.add_argument('--sharpen',\\n action='store_true',\\n help=colourers.toMagenta('sharpening the image'))\\n filters.add_argument('--unsharp',\\n action='store_true',\\n help=colourers.toMagenta('unsharp the image')) \\n filters.add_argument('--denoise',\\n action='store_true',\\n help=colourers.toMagenta('denoise the image'))\\n filters.add_argument('--texture-detection',\\n '-td',\\n action='store_true',\\n help=colourers.toMagenta('applying texture detection (Gabor Filter)'))\\n filters.add_argument('--blur',\\n type=str,\\n choices=['simple', 'more', 'average', 'gaussian', 'motion'],\\n metavar=colourers.toRed('<type of blur>'),\\n help=colourers.toMagenta('perform the selected blur'))\\n filters.add_argument('--blur-iteration',\\n '-bi',\\n type=int,\\n default=1,\\n metavar=colourers.toRed('<number of iteration>'),\\n help=colourers.toMagenta('apply N times the blur function'))\\n filters.add_argument('--emboss',\\n action='store_true',\\n help=colourers.toMagenta('perform an embossing filter'))\\n filters.add_argument('--overlap',\\n type=str,\\n nargs='+',\\n metavar=colourers.toRed('<image to overlap>'),\\n help=colourers.toMagenta('overlap an image given on the selected image'))\\n\\n # Args parsing\\n args = parser.parse_args()\\n\\n filename = \\\"\\\"\\n # BMP Block\\n if args.bmp:\\n filename = args.bmp\\n\\n if not os.path.isfile(filename):\\n colourers.error('\\\"{}\\\" does not exist !'.format(filename))\\n sys.exit(-1)\\n colourers.success('Success Opening {}...'.format(filename))\\n\\n bmp = BMP(filename)\\n half = args.half\\n\\n if args.print_color:\\n width, height = args.print_color\\n colourers.info(f'Printing pixel color of ({width}, {height})')\\n Printers.printPixel(bmp, width, height)\\n sys.exit(0)\\n \\n elif args.header:\\n colourers.info(f'Printing BMP header of {bmp.filename}')\\n Printers.printHeader(bmp)\\n sys.exit(0)\\n \\n elif args.histogram:\\n colourers.info(f'Printing color histogram of {bmp.filename}')\\n Printers.printHistogram(bmp)\\n sys.exit(0)\\n \\n if (args.rotate or args.scale or args.contrast or args.grayscale or \\n args.binary or args.channel or args.edge_detection or args.retrieve_color or\\n args.edge_enhancement or args.blur or args.emboss or args.overlap or args.texture_detection or\\n args.denoise or args.sharpen or args.unsharp):\\n if not hp.atLeastOne(args.output, (\\n args.rotate,\\n args.scale,\\n args.contrast,\\n args.grayscale,\\n args.binary,\\n args.channel,\\n args.edge_detection,\\n args.retrieve_color,\\n args.edge_enhancement,\\n args.blur,\\n args.emboss,\\n args.overlap,\\n args.texture_detection,\\n args.denoise,\\n args.sharpen,\\n args.unsharp\\n )):\\n parser.error('--rotate/--scale/--contrast/--grayscale/--binary/--channel/--edge-detection/--retrieve-color/--edge-enhancement/--blur/--emboss/--overlap/--texture-detection/--denoise/--sharpen/--unsharp and --output must be given together')\\n \\n if args.rotate:\\n degree = args.rotate\\n colourers.info(f'Rotating image to {degree} degree')\\n bmp.imageData = Transformers.rotate(bmp, degree)\\n\\n if args.scale:\\n if len(args.scale) == 2:\\n width, height = args.scale\\n colourers.info(f'Scaling image to {width}x{height} pixels')\\n bmp.imageData = Transformers.scale(bmp, height, width)\\n else:\\n scaleRatio = args.scale[0]\\n\\n colourers.info(f'Scaling image to {scaleRatio} scale ratio')\\n\\n height = int(hp.readLittleEndian(bmp.height))\\n width = int(hp.readLittleEndian(bmp.width))\\n\\n bmp.imageData = Transformers.scale(bmp, height * scaleRatio, width * scaleRatio)\\n \\n if args.contrast:\\n factor = args.contrast\\n colourers.info(f'Applying a factor contrast of {factor}')\\n bmp.imageData = Transformers.contrast(bmp, factor)\\n \\n if args.grayscale:\\n colourers.info(f'Applying grayscale mask to the image')\\n bmp.imageData = Transformers.grayscale(bmp, half)\\n \\n if args.binary:\\n colourers.info(f'Applying binary mask to the image')\\n bmp.imageData = Transformers.binary(bmp, half)\\n \\n if args.invert:\\n colourers.info(f'Inverting image colours')\\n bmp.imageData = Transformers.invert(bmp, half)\\n \\n if args.channel:\\n if len(args.channel) == 2:\\n c1, c2 = args.channel\\n colourers.info(f'Keeping only {c1} and {c2} channels of the image')\\n bmp.imageData = Transformers.toChannel(bmp, [c1, c2], half)\\n else:\\n channel = args.channel[0]\\n colourers.info(f'Keeping only {channel} channel of the image')\\n bmp.imageData = Transformers.toChannel(bmp, channel, half)\\n \\n if args.denoise:\\n colourers.info(f'Denoising the image')\\n bmp.imageData = Filters.wienerFilter(bmp.imageData, gaussianKernel(9, sigma=0.33), K=10)\\n \\n if args.texture_detection:\\n colourers.info(f'Applying texture detection (Gabor Filter)')\\n bmp.imageData = Filters.gaborFilter(bmp.imageData, gaborKernel(0))\\n \\n if args.edge_enhancement:\\n colourers.info(f'Applying increased edge enhancement filter')\\n bmp.imageData = Filters.iee(bmp.imageData)\\n\\n if args.edge_detection:\\n filterName = args.edge_detection\\n if filterName == 'canny':\\n colourers.info(f'Performing Canny filter for edge detection')\\n bmp.imageData = Filters.ced(bmp.imageData, sigma=0.33, kernelSize=9, weakPix=50)\\n if filterName == 'sobel':\\n colourers.info(f'Performing Sobel filter for edge detection')\\n bmp.imageData = Filters.sed(bmp.imageData, sigma=0.33, kernelSize=9)\\n if filterName == 'prewitt':\\n colourers.info(f'Performing Prewitt filter for edge detection')\\n bmp.imageData = Filters.ped(bmp.imageData, sigma=0.33, kernelSize=9)\\n if filterName == 'roberts':\\n colourers.info(f'Performing Roberts filter for edge detection')\\n bmp.imageData = Filters.red(bmp.imageData, sigma=0.33, kernelSize=9)\\n if filterName == 'kirsch':\\n colourers.info(f'Performing Kirsch filter for edge detection')\\n bmp.imageData = Filters.ked(bmp.imageData, sigma=0.33, kernelSize=9)\\n\\n if args.sharpen:\\n colourers.info(f'Sharpening the image')\\n bmp.imageData = Filters.sharpen(bmp.imageData)\\n \\n if args.unsharp:\\n colourers.info(f'Unsharpening the image')\\n bmp.imageData = Filters.unsharp(bmp.imageData)\\n\\n if args.retrieve_color:\\n colourers.info(f'Retrieving color')\\n bmp.imageData = Filters.retrieveColor(bmp.imageData)\\n \\n if args.blur:\\n blurType = args.blur\\n colourers.info(f'Performing a {blurType} blur')\\n for _ in range(args.blur_iteration):\\n blurFunc = Filters.blur.switcher.get(blurType)\\n bmp.imageData = blurFunc(bmp.imageData)\\n \\n if args.emboss:\\n colourers.info(f'Performing emboss filter')\\n bmp.imageData = Filters.emboss(bmp.imageData)\\n \\n if args.overlap:\\n overlappers = []\\n for ov in args.overlap:\\n overlappers.append(BMP(ov).imageData)\\n colourers.info(f'Performing an overlapping between {bmp.filename} and {args.overlap}')\\n bmp.imageData = Filters.overlap(bmp.imageData, overlappers)\\n \\n if args.output:\\n outputFile = args.output\\n hp.saveBMP(bmp, bmp.imageData, outputFile)\\n colourers.success(f'Succesfully saved into {outputFile}')\\n sys.exit(0)\\n \\n parser.error('Give at least one more argument')\\n \\n # PNG Block\\n else:\\n filename = args.png\\n\\n if not os.path.isfile(filename):\\n print('\\\"{}\\\" does not exist'.format(filename), file=sys.stderr)\\n sys.exit(-1)\\n print('Success Opening {}...'.format(filename))\\n \\n png = PNG(filename)\",\n \"def load_images():\\n print(\\\"[+] UPDATE - Begin loading images\\\")\\n\\n colors = [\\\"w\\\", \\\"b\\\"]\\n piece_types = [\\\"p\\\", \\\"R\\\", \\\"N\\\", \\\"B\\\", \\\"K\\\", \\\"Q\\\"]\\n for color in colors:\\n for type in piece_types:\\n piece = color + type\\n IMAGES[piece] = p.transform.scale(p.image.load(\\\"images/\\\" + piece + \\\".png\\\"), (SQ_SIZE, SQ_SIZE))\\n\\n print(\\\"[+] UPDATE - Images loaded\\\")\",\n \"def main():\\n\\n wallpaper_url = argv[1]\\n if '://getwallpapers.com/' not in wallpaper_url:\\n print(\\\"Invalid URL\\\")\\n exit(0)\\n else:\\n soup = parse_link(wallpaper_url)\\n folder_name = get_folder(soup)\\n download(soup, folder_name)\",\n \"def run(self):\\n\\n # need to think about outpath\\n\\n # Make sure all files are here and okay...\\n\\n if not self.config.galfile_pixelized:\\n raise ValueError(\\\"Code only runs with pixelized galfile.\\\")\\n\\n self.config.check_files(check_zredfile=True, check_bkgfile=True, check_bkgfile_components=True, check_parfile=True, check_zlambdafile=True)\\n\\n # Compute the border size\\n\\n self.config.border = self.config.compute_border()\\n\\n self.config.d.hpix = [self.pixel]\\n self.config.d.nside = self.nside\\n self.config.d.outbase = '%s_%d_%05d' % (self.config.outbase, self.nside, self.pixel)\\n\\n # Do the run\\n self.config.start_file_logging()\\n self.config.logger.info(\\\"Running redMaPPer on pixel %d\\\" % (self.pixel))\\n\\n firstpass = RunFirstPass(self.config)\\n\\n if not os.path.isfile(firstpass.filename):\\n firstpass.run()\\n firstpass.output(savemembers=False, withversion=False)\\n else:\\n self.config.logger.info(\\\"Firstpass file %s already present. Skipping...\\\" % (firstpass.filename))\\n\\n self.config.catfile = firstpass.filename\\n\\n # Clear out the firstpass memory\\n del firstpass\\n\\n like = RunLikelihoods(self.config)\\n\\n if not os.path.isfile(like.filename):\\n like.run()\\n like.output(savemembers=False, withversion=False)\\n else:\\n self.config.logger.info(\\\"Likelihood file %s already present. Skipping...\\\" % (like.filename))\\n\\n self.config.catfile = like.filename\\n\\n # Clear out the likelihood memory\\n del like\\n\\n perc = RunPercolation(self.config)\\n\\n if not os.path.isfile(perc.filename):\\n perc.run()\\n perc.output(savemembers=True, withversion=False)\\n else:\\n self.config.logger.info(\\\"Percolation file %s already present. Skipping...\\\" % (perc.filename))\\n\\n self.config.stop_file_logging()\",\n \"def OnBuildPNGs(self, e):\\n if (not defaults.use_tex):\\n msg = \\\"LaTeX is disabled in the defaults.py file. To use this functionality, change the\\\"\\n msg += \\\" use_tex option to True and restart the GUI.\\\"\\n ShowMessage(msg, kind='warn')\\n return\\n question = \\\"Quantity code formulas are displayed using PNG images, which need to be generated.\\\"\\n question += \\\"\\\\n\\\\n\\\\nImages should only be generated if they do not already exist or\\\"\\n question += \\\" the quantity codes have changed, e.g., more custom outputs have been added.\\\"\\n question += \\\"\\\\n\\\\n\\\\nThis can take ~60 sec, do you want to proceed?\\\"\\n proceed = AskYesNo(question, title='Generate LaTeX Formula Images?')\\n if (not proceed): return\\n\\n question = \\\"Choose a path where the images will be saved. The default value from defaults.py is shown.\\\"\\n path = AskText(question, default=defaults.quantity_code_image_path, title=\\\"Where to store images?\\\")\\n if (path is None): return\\n defaults.quantity_code_image_path = path # user overrode this quantity, remember for later\\n\\n question = \\\"If image files already exist, do you want to overwrite them?\\\"\\n overwrite = AskYesNo(question, title='Overwrite Existing Files?')\\n\\n # call render routine and display a progress bar\\n Nq = len(self.mainparent.nmlpanel.output_quantities.quantities)\\n offsets = list(self.mainparent.nmlpanel.output_quantities.offsets.keys())\\n\\n P = ProgressBar(Nq)\\n P(0)\\n for i,Q in enumerate(self.mainparent.nmlpanel.output_quantities.quantities):\\n if (Q.name in offsets): continue\\n render_tex(Q.code, Q.tex, defaults.quantity_code_image_path, overwrite=overwrite)\\n\\n P(i+1) # update progress bar\",\n \"def main():\\n test_image = load_image()\\n\\n pixelate_image(\\n normalize_image(test_image)\\n )\\n pass\",\n \"def __call__(self, images, targets):\\n pass\",\n \"def main():\\n\\n import codecs\\n\\n file_path = '../sample_texts/hi-Deva.txt'\\n with codecs.open(file_path, 'r', encoding='UTF-8') as input_file:\\n sample_text = input_file.read()\\n create_png(sample_text.strip(), 'hindi.png',\\n family='Noto Sans Devanagari', language='hi', rtl=False)\\n\\n file_path = '../sample_texts/ar-Arab.txt'\\n with codecs.open(file_path, 'r', encoding='UTF-8') as input_file:\\n sample_text = input_file.read()\\n create_png(sample_text.strip(), 'arabic.png',\\n family='Noto Naskh Arabic', language='ar', rtl=True)\\n\\n file_path = '../sample_texts/mn-Mong.txt'\\n with codecs.open(file_path, 'r', encoding='UTF-8') as input_file:\\n sample_text = input_file.read()\\n create_png(sample_text.strip(), 'mong.png',\\n family='Noto Sans Mongolian', language='mn', vertical=True, rtl=False)\",\n \"def run_turtle_program(source):\\n ast = parser.parse(source)\\n\\n t = turtle.Turtle()\\n for stmt in ast.statement:\\n do_statement(stmt, t)\\n canvas = turtle.Screen().getcanvas()\\n canvas.postscript(file='image.eps')\\n img = Image.open('image.eps')\\n img.save('image.png', 'png')\\n turtle.Screen().bye()\\n return 'image.png'\",\n \"def testConvertHtmlWithPngDataUrlToPdf(self):\\n self._testBase(\\\"data/test_with_png_dataurl.html\\\")\",\n \"def main():\\n\\n imagepaths = (\\\"icons/facebook.png\\\", \\\"icons/github.png\\\", \\\"icons/linkedin.png\\\", \\\"icons/pinterest.png\\\", \\\"icons/twitter.png\\\", \\\"icons/youtube.png\\\")\\n\\n try:\\n\\n spritemaker.create_sprites(imagepaths, \\\"sprites.png\\\", \\\"spritestyles.css\\\")\\n\\n except IOError as e:\\n\\n print(e)\",\n \"def process(directory):\\n files = []\\n\\n options = [\\\"Load\\\", \\\"Create\\\"]\\n choice = options[int(ui.prompt(options=options))]\\n\\n for item in os.listdir(directory):\\n if os.path.isfile(os.path.join(directory, item)):\\n filename = os.path.join(directory, item)\\n if choice == \\\"Load\\\" and item.endswith(\\\".png\\\"):\\n files.append(filename)\\n elif choice == \\\"Create\\\" and item.endswith(\\\".file\\\"):\\n files.append(filename)\\n\\n filenames, pageNames = imagePages(files, choice)\\n \\n targets = [name.split('/')[-1][:5] for name in filenames]\\n return pageNames, targets, filenames\",\n \"def latex2img(expression, filename):\\n webp = False\\n\\n extension = \\\"png\\\"\\n\\n # Preparing text strings\\n server = \\\"http://latex.codecogs.com/\\\" + extension + \\\".download?\\\"\\n fullname = filename + \\\".\\\" + extension\\n size = \\\"%5Cdpi%7B100%7D%20\\\"\\n\\n # Quote expression引用表达式\\n expression = quote(expression)\\n url = server + size + expression\\n\\n # Download file from url and save to output_file:\\n with urlopen(url) as response, open(fullname, 'wb') as output_file:\\n data = response.read() # Un objeto \\\"bytes\\\"\\n output_file.write(data) # Se escribe en disco\\n\\n if webp:\\n img2webp(fullname)\\n extension = \\\"webp\\\"\\n\\n return filename + \\\".\\\" + extension\",\n \"def main():\\n p = argparse.ArgumentParser(description='Convert images into unicode')\\n p.add_argument('image', metavar='<path>', type=str,\\n help='path to the file, use - for stdin')\\n p.add_argument('--no-x256', action='store_false', dest='x256', default=True,\\n help='prints with x256 unicode coloring')\\n p.add_argument('--char-set', metavar='<name>', default='default',\\n help='prints with character set (e.g. windows)')\\n args = p.parse_args()\\n print_image_as_unicode(args.image, char_set=CHAR_SETS[args.char_set],\\n x256=args.x256)\",\n \"def bands_py_to_png(py_dir: str, png_dir: str):\\n import glob\\n\\n py_pattern = \\\"bandsdiff_*.py\\\"\\n print(f\\\"Searching {py_pattern} in {py_dir}, save png in {png_dir}\\\")\\n\\n globbed = glob.glob(f\\\"{py_dir}/{py_pattern}\\\")\\n for filename in globbed:\\n with open(filename, encoding=\\\"utf-8\\\") as handle:\\n mplcode = \\\"\\\".join(handle.readlines())\\n mplcode = mplcode.replace(\\n \\\"fig = pl.figure()\\\", \\\"fig = pl.figure(figsize=(16,10))\\\"\\n )\\n png_filename = filename.removesuffix(\\\".py\\\") + \\\".png\\\"\\n print(f\\\"{py_dir}/{filename} -> {png_dir}/{png_filename}\\\")\\n mplcode = mplcode.replace(\\n \\\"pl.show()\\\",\\n f\\\"pl.savefig('{png_dir}/{png_filename}', bbox_inches='tight')\\\",\\n )\\n exec(mplcode) # pylint: disable=exec-used\",\n \"def main(url):\\n print(f\\\"Running main with URL = {url}...\\\")\\n imagehits(downloaddata(url))\",\n \"def handle_as_url(view: sublime.View, point: int, string: str, name: str):\\n\\n # Let's assume this url as input:\\n # (https://upload.wikimedia.org/wikipedia/commons/8/84/Example.svg)\\n\\n # Download the image\\n # FIXME: avoid nested try-except clauses\\n try:\\n try:\\n f = urlopen(unquote(string)) # <==\\n except Exception:\\n try:\\n url_path = quote(string).replace(\\\"%3A\\\", ':', 1)\\n f = urlopen(url_path)\\n except Exception:\\n f = urlopen(string)\\n # don't fill the console with stack-trace when there`s no connection !!\\n except Exception as e:\\n print(e)\\n return\\n\\n # file needs conversion ?\\n need_conversion = name.endswith(formats_to_convert) # => True\\n basename, ext = osp.splitext(name) # => (\\\"Example\\\", \\\".svg\\\")\\n # create a temporary file\\n temp_img = osp.join(TEMP_DIR, \\\"tmp_image\\\" + ext) # => \\\"TEMP_DIR/tmp_image.svg\\\"\\n\\n # Save downloaded data in the temporary file\\n content = f.read()\\n with open(temp_img, \\\"wb\\\") as img:\\n img.write(content)\\n\\n # if the file needs conversion, convert it then read data from the resulting png\\n if need_conversion:\\n ext = \\\".png\\\"\\n # keep the image's temporary file and name for later use\\n conv_file = temp_img # => \\\"TEMP_DIR/tmp_image.svg\\\"\\n\\n # => \\\"TEMP_DIR/tmp_image.png\\\"\\n temp_png = osp.splitext(temp_img)[0] + \\\".png\\\"\\n\\n # use the magick command of Imagemagick to convert the image to png\\n magick(temp_img, temp_png)\\n\\n # read data from the resulting png\\n with open(temp_png, \\\"rb\\\") as png:\\n content = png.read()\\n\\n # set temp_file and name to the png file\\n temp_img = temp_png # => \\\"TEMP_DIR/tmp_image.png\\\"\\n\\n width, height, real_width, real_height, size = get_data(view, temp_img)\\n encoded = str(base64.b64encode(content), \\\"utf-8\\\")\\n\\n def on_navigate(href):\\n\\n if href == \\\"save\\\":\\n if need_conversion:\\n save(conv_file, name, \\\"url\\\")\\n else:\\n save(temp_img, name, \\\"url\\\")\\n elif href == \\\"save_as\\\":\\n if need_conversion:\\n convert(conv_file, \\\"url\\\", name)\\n else:\\n convert(temp_img, \\\"url\\\", name)\\n else:\\n sublime.active_window().open_file(temp_img)\\n\\n view.show_popup(\\n TEMPLATE % (width, height, ext, encoded, real_width, real_height,\\n str(size // 1024) + \\\"KB\\\" if size >= 1024 else str(size) + 'B'),\\n sublime.HIDE_ON_MOUSE_MOVE_AWAY,\\n point,\\n *view.viewport_extent(),\\n on_navigate=on_navigate\\n )\",\n \"def main():\\n tmp_dir = xm.constants['dir_tmp']\\n exr_f = join(tmp_dir, 'test.exr')\\n exr = EXR(exr_f)\\n exr.extract_normal(join(tmp_dir, 'test.png'), vis=True)\",\n \"def process_files(input_path, output_path, las_srs,\\n wms_url, wms_layer, wms_srs,\\n wms_version, wms_format, wms_ppm,\\n wms_max_image_size, divide=1):\\n path = os.path.dirname(os.path.realpath(__file__))\\n\\n if os.path.isdir(input_path):\\n for i, f in enumerate(os.listdir(input_path)):\\n if f.endswith(\\\".las\\\") or f.endswith(\\\".laz\\\"):\\n las = os.path.join(input_path, f).replace('\\\\\\\\', '/')\\n\\n if os.path.isdir(output_path):\\n output_path = output_path + '/' if output_path[-1] != '/' else output_path\\n basename, ext = os.path.splitext(f)\\n out = '{}{}_color{}'.format(output_path, basename, ext)\\n else:\\n basename, ext = os.path.splitext(output_path)\\n out = '{}_{}{}'.format(basename, i, ext)\\n\\n run_pdal(path, las, out, las_srs, wms_url, wms_layer, wms_srs,\\n wms_version, wms_format, wms_ppm, wms_max_image_size,\\n divide)\\n else:\\n run_pdal(path, input_path, output_path, las_srs, wms_url, wms_layer,\\n wms_srs, wms_version, wms_format, wms_ppm, wms_max_image_size,\\n divide)\",\n \"def process(image):\\n pass\",\n \"def main_convert(args):\\n try:\\n file_path = args.file_name # os.path.join(static_path, args.file_name)\\n if args.slides:\\n config_path = os.path.join(static_path, \\\"config\\\", \\\"slides_config.py\\\")\\n output = subprocess.check_output(\\n [\\n \\\"jupyter\\\",\\n \\\"nbconvert\\\",\\n file_path,\\n \\\"--to\\\",\\n \\\"slides\\\",\\n \\\"--CSSHTMLHeaderPreprocessor.style=colorful\\\",\\n \\\"--reveal-prefix\\\",\\n args.reveal_prefix,\\n \\\"--config\\\",\\n config_path,\\n ],\\n stderr=subprocess.STDOUT,\\n ).decode(\\\"utf-8\\\")\\n else:\\n config_path = os.path.join(static_path, \\\"config\\\", \\\"nb_config.py\\\")\\n output = subprocess.check_output(\\n [\\n \\\"jupyter\\\",\\n \\\"nbconvert\\\",\\n file_path,\\n \\\"--to\\\",\\n \\\"html\\\",\\n \\\"--config\\\",\\n config_path,\\n ],\\n stderr=subprocess.STDOUT,\\n ).decode(\\\"utf-8\\\")\\n print(output.rstrip())\\n _name = get_out_name(args)\\n # _name = output.split(\\\" \\\")[-1].rstrip()\\n if args.c:\\n with open(_name, \\\"r\\\") as f:\\n clean_file = clean_html(f.read())\\n with open(_name, \\\"w\\\") as f:\\n f.write(clean_file)\\n if args.bib_name is not None:\\n add_ref(_name, args.bib_name, keep_label=args.l, slides=args.slides)\\n else:\\n with open(_name, \\\"r\\\") as f:\\n clean_file = clean_html_refs(clean_file)\\n with open(_name, \\\"w\\\") as f:\\n f.write(clean_file)\\n except IndexError:\\n print(\\\"Provide the name of the notebook.\\\")\",\n \"def main():\\n \\n # for inserting other images, add tem to /input folder and list them here\\n images = (\\n 'image-0',\\n 'image-1',\\n 'image-2'\\n )\\n\\n for image_name in images:\\n print(image_name, \\\"image:\\\")\\n\\n image = open_image(image_name)\\n display_image(image, \\\"Original input \\\" + image_name)\\n\\n grayscale_v = transform_colors(image)\\n display_image(grayscale_v[:,:,0], \\\"Grayscale \\\" + image_name)\\n save_image(image_name + \\\"-grayscale\\\", grayscale_v[:,:,0])\\n\\n contours_v, contours = get_contours(grayscale_v)\\n display_image(contours_v, \\\"Contours \\\" + image_name)\\n save_image(image_name + \\\"-contours\\\", contours_v)\\n\\n labeled_img, areas = get_measures(image, contours[1:])\\n display_image(labeled_img, \\\"Labeled \\\" + image_name)\\n save_image(image_name + \\\"-labeled\\\", labeled_img)\\n\\n areas_histogram(areas, image_name)\",\n \"def main(args):\\n input_image = make_input_image(args.input_format, args.input_images)\\n output_pattern = f\\\"output/{args.prefix}{{}}.png\\\"\\n \\n # Exit early if input == output\\n if args.input_format == args.output_format:\\n print(\\\"Input format = output format. Copying files.\\\")\\n input_image.write(output_pattern)\\n return\\n\\n output_image = make_output_image(args.output_format, args.output_shape)\\n\\n input_is_sphere = args.input_format == \\\"sphere\\\"\\n output_is_sphere = args.output_format == \\\"sphere\\\"\\n\\n try:\\n if input_is_sphere:\\n output_image.project_from(input_image)\\n output_image.write(output_pattern)\\n elif output_is_sphere:\\n input_image.unproject_to(output_image)\\n output_image.write(output_pattern)\\n else:\\n raise NotImplementedError\\n except NotImplementedError:\\n print(f\\\"Sorry, {args.input_format} -> {args.output_format} not supported.\\\")\\n return\",\n \"def main():\\n\\ttile_dimensions = (512, 512)\\n\\ttile_count = (5, 4) \\n\\timage = Image.new(\\\"RGB\\\", (tile_dimensions[0] * tile_count[0], tile_dimensions[1] * tile_count[1]), None)\\n\\n\\ttry:\\n\\t\\turl = sys.argv[1]\\n\\texcept:\\n\\t\\tprint \\\"Please enter a Google Maps URL as a parameter.\\\"\\n\\t\\tprint \\\"Usage: $ python get_pano.py https://www.google.com/maps/@34.1027387,-118.340471,3a,75y,32.1h,87.53t/data=!3m7!1e1!3m5!1soInaTCic7TsAAAQDMaZ31A!2e0!3e2!7i13312!8i6656\\\"\\n\\t\\treturn\\n\\n\\ttry:\\n\\t\\tprint \\\"************************************\\\"\\n\\t\\tprint \\\"Fetching images from Google Maps, this could take some time...\\\"\\n\\t\\tregex = re.compile(r'panoid\\\\=([^&]*)', re.I)\\n\\t\\tpano_id = regex.findall(urllib.unquote(url))[0]\\n\\t\\tfor y in range(tile_count[1]):\\n\\t\\t\\tfor x in range(tile_count[0]):\\n\\t\\t\\t\\timg_url = \\\"https://geo2.ggpht.com/cbk?cb_client=maps_sv.tactile&authuser=0&hl=en&panoid=\\\" + pano_id + \\\"&output=tile&x=\\\"+str(x)+\\\"&y=\\\"+str(y)+\\\"&zoom=3&nbt&fover=2\\\"\\n\\t\\t\\t\\tresponse = urllib2.urlopen(img_url)\\n\\t\\t\\t\\tfile_data = StringIO(response.read())\\n\\t\\t\\t\\timage.paste(Image.open(file_data), (x * tile_dimensions[0], y * tile_dimensions[1]))\\n\\t\\t\\n\\t\\tfilename = \\\"pano-\\\" + pano_id + \\\".jpg\\\"\\n\\t\\timage.save(filename)\\n\\t\\tprint \\\"Success, image saved as \\\\033[96m\\\" + filename + \\\"\\\\033[00m\\\"\\n\\t\\tprint \\\"************************************\\\"\\n\\n\\n\\texcept Exception as e:\\n\\t\\tprint \\\"Sorry something broke.\\\"\\n\\t\\tprint e\",\n \"def download_images_png(self):\\n self.show_as_waiting(True)\\n self.download_images('PNG')\\n self.show_as_waiting(False)\",\n \"def change_img_pixel_format():\\n\\tbackground_module.bg = background_module.bg.convert()\\n\\tbackground_module.snow = background_module.snow.convert_alpha()\\n\\n\\tforeground_module.ground = foreground_module.ground.convert_alpha()\\n\\t\\n\\tplayer_module.Player.imgs = [img.convert_alpha() for img in player_module.player.imgs]\\n\\tplayer_module.Propeller.propeller_imgs = [img.convert_alpha() for img in player_module.Propeller.propeller_imgs]\\n\\n\\tcoins_module.Coin.resized_imgs = [img.convert_alpha() for img in coins_module.Coin.resized_imgs]\\n\\tcoins_module.coin_board = coins_module.coin_board.convert_alpha()\\n\\n\\tobstacles_module.Tree.resized_imgs = [img.convert_alpha() for img in obstacles_module.Tree.imgs]\\n\\tobstacles_module.Rock_n_Bush.imgs = [img.convert_alpha() for img in obstacles_module.Rock_n_Bush.imgs]\\t\\n\\n\\teffects_module.Coin_spark_effects.imgs = [img.convert_alpha() for img in effects_module.Coin_spark_effects.imgs]\\n\\teffects_module.Hit_effects.imgs = [img.convert_alpha() for img in effects_module.Hit_effects.imgs]\\n\\n\\tdisplay_module.heart = display_module.heart.convert_alpha()\\n\\tdisplay_module.line = display_module.line.convert_alpha()\\n\\tdisplay_module.start = display_module.start.convert_alpha()\\n\\tdisplay_module.finish = display_module.finish.convert_alpha()\\n\\tdisplay_module.fuel_bar.img_icon = display_module.fuel_bar.img_icon.convert_alpha()\\n\\n\\tfor fuel in display_module.Fuel.fuel_list:\\n\\t\\tfuel.img = fuel.img.convert_alpha()\\n\\tfor extra_life in display_module.Extra_life.extra_lives_list:\\n\\t\\textra_life.img = extra_life.img.convert_alpha()\\n\\t\\n\\tdynamic_obstacle_giftbox.Gift.imgs_list = [img.convert_alpha() for img in dynamic_obstacle_giftbox.Gift.imgs_list]\\n\\tdynamic_obstacle_santa.Santa.imgs_list = [img.convert_alpha() for img in dynamic_obstacle_santa.Santa.imgs_list]\\n\\tdynamic_obstacle_olaf.Olaf.imgs_list = [img.convert_alpha() for img in dynamic_obstacle_olaf.Olaf.imgs_list]\\n\\tbird_module.Bird.list_of_lists = [[img.convert_alpha() for img in lst] for lst in bird_module.Bird.list_of_lists]\",\n \"def process(self, image):\",\n \"def convert (self, lossless=False):\\n self._has_errors = False\\n if self._progress:\\n max_val = 0\\n for root, dirs, files in os.walk(self._in_dir):\\n max_val += len(files)\\n self._bar = pb.ProgressBar(widgets=[pb.Percentage(), pb.Bar()],\\n maxval=max_val).start()\\n pool = multiprocessing.Pool()\\n command = CONVERT_TO_JP2_LOSSY\\n if lossless:\\n command = CONVERT_TO_JP2_LOSSLESS\\n for root, dirs, files in os.walk(self._in_dir):\\n out_rel_path = os.path.relpath(root, self._in_dir)\\n out_full_path = os.path.abspath(\\n os.path.join(self._out_dir, out_rel_path))\\n try:\\n os.mkdir(out_full_path)\\n except OSError:\\n # It is not an error for the directory to already exist.\\n pass\\n for name in files:\\n basename = os.path.splitext(name)[0]\\n in_file = os.path.join(root, name)\\n base_out_file = os.path.join(out_full_path, basename)\\n tiff_file = '%s.tif' % base_out_file\\n jp2_file = '%s.jp2' % base_out_file\\n if self._force or not(os.path.isfile(jp2_file)):\\n params = (in_file, tiff_file, jp2_file, command)\\n pool.apply_async(self._convert, params,\\n callback=self._result_callback)\\n elif self._progress:\\n self._bar.update(self._bar.currval + 1)\\n pool.close()\\n pool.join()\\n if self._progress:\\n self._bar.finish()\\n return not(self._has_errors)\",\n \"def _webp(self, tile: bytes) -> np.ndarray:\\n decoded = np.rollaxis(imagecodecs.webp_decode(tile), 2, 0)\\n return decoded\",\n \"def convert_pptx_to_rgb(input_file, output_file):\\n with ZipFile(input_file) as input_pptx_file:\\n # theme is defined in the presentation rels\\n rel = PresentationRel(input_pptx_file)\\n theme_name = f'ppt/{rel.theme}'\\n theme = read_color_map(zip_file=input_pptx_file, file_name=theme_name)\\n color_map = theme[1]\\n\\n new_file_data = []\\n\\n file_list = input_pptx_file.namelist()\\n\\n for file in file_list:\\n with input_pptx_file.open(name=file) as current_file:\\n file_contents = current_file.read()\\n # only convert XML files of slides\\n if re.match(r'ppt/slides/slide(\\\\d+).xml', file):\\n new_contents = convert(content=file_contents, color_map=color_map)\\n else:\\n new_contents = file_contents\\n new_file_data.append(\\n {\\n 'file': file,\\n 'data': new_contents\\n }\\n )\\n\\n # create/write the new ZIP\\n with ZipFile(output_file, 'w') as output_pptx_file:\\n for data in new_file_data:\\n output_pptx_file.writestr(data['file'], data['data'], ZIP_DEFLATED)\",\n \"def do_pyrclassdiagram(self, file_names):\\r\\n self.file_names = file_names\\r\\n pyreverse_command = 'pyreverse -ASmn -o png -p ' + file_names\\r\\n subprocess.call(pyreverse_command)\\r\\n print(file_names + ' are done')\",\n \"def copy_support_files() -> None:\\n # root folder files\\n filelist = {\\\"favicon128.png\\\",\\n \\\"favicon96.png\\\",\\n \\\"favicon72.png\\\",\\n \\\"favicon48.png\\\",\\n \\\"favicon32.png\\\",\\n \\\"favicon24.png\\\",\\n \\\"favicon16.png\\\",\\n \\\"favicon.ico\\\",\\n \\\"apple-touch-icon.png\\\",\\n \\\"apple-touch-icon-precomposed.png\\\",\\n \\\"apple-touch-icon-72x72.png\\\",\\n \\\"apple-touch-icon-72x72-precomposed.png\\\",\\n \\\"apple-touch-icon-114x114.png\\\",\\n \\\"apple-touch-icon-114x114-precomposed.png\\\",\\n \\\"apple-touch-icon-144x144.png\\\",\\n \\\"apple-touch-icon-144x144-precomposed.png\\\",\\n \\\"uca_style.css\\\"}\\n for filename in filelist:\\n try:\\n shutil.copy2(\\\"resources/\\\" + filename, WEBOUT_PATH)\\n except FileNotFoundError:\\n report_error(\\\"Missing file: resources/\\\" + filename)\\n # image folder files\\n filelist = {\\\"film.png\\\",\\n \\\"stylifera75.png\\\",\\n \\\"DOI_logo.svg\\\",\\n \\\"size_hist.png\\\",\\n \\\"size_ind.png\\\",\\n \\\"size_mean.png\\\",\\n \\\"size_range.png\\\",\\n \\\"size_summary.png\\\",\\n \\\"double_clawed.jpg\\\"}\\n for filename in filelist:\\n try:\\n shutil.copy2(\\\"resources/images/\\\" + filename, WEBOUT_PATH + \\\"images/\\\")\\n except FileNotFoundError:\\n report_error(\\\"Missing file: resources/images/\\\" + filename)\\n filelist = {\\\"specific_word_cloud.png\\\",\\n \\\"binomial_word_cloud.png\\\"}\\n for filename in filelist:\\n try:\\n shutil.copy2(TMP_PATH + filename, WEBOUT_PATH + \\\"images/\\\")\\n except FileNotFoundError:\\n report_error(\\\"Missing file: \\\" + TMP_PATH + filename)\\n # font-awesome files\\n filelist = {\\\"fontawesome.min.js\\\",\\n \\\"brands.min.js\\\",\\n \\\"regular.min.js\\\",\\n \\\"solid.min.js\\\"}\\n for filename in filelist:\\n try:\\n shutil.copy2(\\\"resources/font-awesome/js/\\\" + filename, WEBOUT_PATH + \\\"js/\\\")\\n except FileNotFoundError:\\n report_error(\\\"Missing file: resources/font-awesome/js/\\\" + TMP_PATH + filename)\\n # flag-icon files\\n filelist = {\\\"flag-icons.min.css\\\"}\\n for filename in filelist:\\n try:\\n shutil.copy2(\\\"resources/flag-icon-css/css/\\\" + filename, WEBOUT_PATH + \\\"images/flag-icon-css/css/\\\")\\n except FileNotFoundError:\\n report_error(\\\"Missing file: images/flag-icon-css/css/\\\" + TMP_PATH + filename)\\n filelist = {\\\"de.svg\\\", # Germany\\n \\\"es.svg\\\", # Spain\\n \\\"ru.svg\\\", # Russia\\n \\\"fr.svg\\\", # France\\n \\\"pt.svg\\\", # Portugal\\n \\\"dk.svg\\\", # Denmark\\n \\\"nl.svg\\\", # Netherlands\\n \\\"jp.svg\\\", # Japan\\n \\\"cn.svg\\\", # China\\n \\\"us.svg\\\", # USA\\n \\\"th.svg\\\", # Thailand\\n \\\"va.svg\\\", # Vatican\\n \\\"it.svg\\\", # Italy\\n \\\"kr.svg\\\", # South Korea\\n \\\"pl.svg\\\", # Poland\\n \\\"mm.svg\\\", # Myanamar (Burma)\\n \\\"sa.svg\\\", # Saudi Arabia (best option for Arabic of those available)\\n \\\"id.svg\\\", # Indonesia\\n \\\"za.svg\\\", # South Africa (best option for Afrikaans)\\n \\\"my.svg\\\", # Malaysia (for Malay)\\n \\\"mg.svg\\\", # Madagascar (for Malagasy)\\n \\\"ir.svg\\\", # Iran (for Persian)\\n \\\"vn.svg\\\"} # Vietnam\\n for filename in filelist:\\n try:\\n shutil.copy2(\\\"resources/flag-icon-css/flags/4x3/\\\" + filename, WEBOUT_PATH +\\n \\\"images/flag-icon-css/flags/4x3/\\\")\\n except FileNotFoundError:\\n report_error(\\\"Missing file: images/flag-icon-css/flags/4x3/\\\" + TMP_PATH + filename)\",\n \"def save(images, output):\\n for image, frame in images:\\n image.save(output(frame))\",\n \"def main():\\n\\n location = input(\\\"Enter the pathway to the directory containing the files\\\"\\n \\\"to be converted:\\\\n\\\")\\n os.chdir(location)\\n gtiff(location)\\n tiff(location)\",\n \"def run(self):\\n openShutter = True\\n actuateXed = False\\n image_type = \\\"PPUMP\\\"\\n\\n wl = float(self.eo_config.get(\\\"PPUMP_WL\\\", 550))\\n meas_flux = self.measured_flux(wl)\\n seqno = 0\\n for tokens in self.instructions:\\n exptime = float(tokens[1])\\n nframes = int(tokens[2])\\n shifts = int(tokens[3])\\n for iframe in range(nframes):\\n self.image_clears()\\n self.bias_image(seqno)\\n self.take_image(seqno, exptime, openShutter, actuateXed,\\n image_type)\\n seqno += 1\",\n \"def process_data(output_folder):\\n # select imgs\\n img_folder = join(output_folder, 'img')\\n select_img(output_folder, img_folder, 'HE-green')\\n\\n mask_folder = join(output_folder, 'mask')\\n select_img(output_folder, mask_folder, '_EF5')\",\n \"def main(base_dir: str, output_dir: str) -> None:\\n base_path = pathlib.Path(base_dir)\\n output_path = pathlib.Path(output_dir).expanduser()\\n\\n stage_copy_images(base_path, output_path)\\n stage_extract_videos(base_path, output_path)\",\n \"def test_encode_webp():\\n width = 51\\n height = 26\\n channels = 3\\n bmp_file = os.path.join(\\n os.path.dirname(os.path.abspath(__file__)), \\\"test_image\\\", \\\"lena.bmp\\\")\\n with open(bmp_file, 'rb') as f:\\n bmp_contents = f.read()\\n image_v = tf.image.decode_bmp(bmp_contents)\\n assert image_v.shape == [height, width, channels]\\n bmp_encoded = image_io.encode_bmp(image_v)\\n image_e = tf.image.decode_bmp(bmp_encoded)\\n assert np.all(image_v.numpy() == image_e.numpy())\",\n \"def getimgs():\",\n \"def pepsi(directory=None):\\r\\n \\r\\n if directory == None:\\r\\n directory = os.getcwd() # Use working directory if unspecified\\r\\n \\r\\n # Create a new directory 'modified'\\r\\n new_directory = os.path.join(directory, 'modified')\\r\\n try:\\r\\n os.mkdir(new_directory)\\r\\n except OSError:\\r\\n pass # if the directory already exists, proceed \\r\\n \\r\\n #load all the images\\r\\n image_list, file_list = get_images(directory) \\r\\n\\r\\n #go through the images and save modified versions\\r\\n red = PIL.Image.open(os.path.join(directory, 'red.png'))\\r\\n blue = PIL.Image.open(os.path.join(directory, 'blue.png'))\\r\\n template =PIL.Image.open(os.path.join(directory, 'template.png'))\\r\\n topp = PIL.Image.open(os.path.join(directory, '1.jpeg'))\\r\\n bottomm = PIL.Image.open(os.path.join(directory, '2.jpg'))\\r\\n \\r\\n # Round the corners with radius = 30% of short side\\r\\n\\r\\n top = redlogo(topp,red,template)\\r\\n bottom = bluelogo(bottomm,blue,template)\\r\\n new_image = template\\r\\n new_image.paste(bottom,(0,0), mask=bottom)\\r\\n new_image.paste(top,(0,0), mask=top)\\r\\n #save the altered image, suing PNG to retain transparency\\r\\n new_image_filename = os.path.join(new_directory, 'final' + '.png')\\r\\n new_image.save(new_image_filename) #9b: \",\n \"def animator_pdf_maker(rounds, pump_index):\\n print(\\\"making pdf's and animations.\\\")\\n space = ('wavelength', 'freequency', 'time')\\n for sp in space:\\n file_loc = 'output/output'+str(pump_index)+'/figures/'+sp+'/'\\n strings_large = ['convert '+file_loc+'00.png ']\\n for i in range(4):\\n strings_large.append('convert ')\\n for ro in range(rounds):\\n for i in range(4):\\n strings_large[i+1] += file_loc+str(ro)+str(i+1)+'.png '\\n for w in range(1, 4):\\n if i == 5:\\n break\\n strings_large[0] += file_loc+str(ro)+str(w)+'.png '\\n for i in range(4):\\n os.system(strings_large[i]+file_loc+str(i)+'.pdf')\\n\\n file_loca = file_loc+'portA/'\\n file_locb = file_loc+'portB/'\\n string_porta = 'convert '\\n string_portb = 'convert '\\n for i in range(rounds):\\n string_porta += file_loca + str(i) + '.png '\\n string_portb += file_locb + str(i) + '.png '\\n\\n string_porta += file_loca+'porta.pdf '\\n string_portb += file_locb+'portb.pdf '\\n os.system(string_porta)\\n os.system(string_portb)\\n\\n for i in range(4):\\n os.system(\\n 'convert -delay 30 '+file_loc+str(i)+'.pdf '+file_loc+str(i)+'.mp4')\\n os.system('convert -delay 30 ' + file_loca +\\n 'porta.pdf ' + file_loca+'porta.mp4 ')\\n os.system('convert -delay 30 ' + file_locb +\\n 'portb.pdf ' + file_locb+'portb.mp4 ')\\n\\n for i in (file_loc, file_loca, file_locb):\\n print('rm ' + i + '*.png')\\n os.system('rm ' + i + '*.png')\\n os.system('sleep 5')\\n return None\",\n \"def start_pupil():\\n # runs Pupil Capture from source\\n path = os.path.abspath(\\\"../../pupil/pupil_src/capture/main.py\\\")\\n return subprocess.call('python ' + shellformat(path), shell=True)\\n \\n # if running Pupil Capture using the app, comment the above code and uncomment below:\\n '''\\n path = os.path.abspath(\\\"../pupil_capture_0.4.1_mac.app\\\")\\n return subprocess.call('open ' + shellformat(path), shell=True)\\n '''\",\n \"def main(dataset, n, ms=False, out=sys.stdout):\\n # build lists of paths previews files, tif, rpc and dzi files\\n prv_paths = ' '.join([os.path.join(dataset, 'prv_%02d.jpg' % (i+1)) for i in xrange(n)])\\n tif_paths = ' '.join([os.path.join(dataset, 'im_panchro_%02d.tif' % (i+1)) for i in xrange(n)])\\n rpc_paths = ' '.join([os.path.join(dataset, 'rpc_%02d.xml' % (i+1)) for i in xrange(n)])\\n dzi8_paths, dzi16_paths = None, None\\n if ms:\\n ms_paths = ' '.join([os.path.join(dataset, 'im_ms_%02d.tif' % (i+1)) for i in xrange(n)])\\n if os.path.isfile(os.path.abspath(os.path.join(dataset,\\n 'im_panchro_8BITS_01.dzi'))):\\n dzi8_paths = ' '.join([os.path.join('input', dataset,\\n 'im_panchro_8BITS_%02d.dzi' %\\n (i+1)) for i in xrange(n)])\\n if os.path.isfile(os.path.abspath(os.path.join(dataset,\\n 'im_panchro_16BITS_01.dzi'))):\\n dzi16_paths = ' '.join([os.path.join('input', dataset,\\n 'im_panchro_16BITS_%02d.dzi' %\\n (i+1)) for i in xrange(n)])\\n\\n # read infos in DIM*.XML file\\n dim_xml_file = os.path.join(dataset, 'dim_01.xml')\\n tif_file = os.path.join(dataset, 'im_panchro_01.tif')\\n if os.path.isfile(dim_xml_file): # check if the link points to an existing file\\n date = grep_xml(dim_xml_file, \\\"IMAGING_DATE\\\")\\n satellite = grep_xml(dim_xml_file, \\\"INSTRUMENT_INDEX\\\")\\n elif os.path.isfile(tif_file):\\n date = extract_date_from_pleiades_filename(os.readlink(tif_file))\\n satellite = extract_satellite_from_pleiades_filename(os.readlink(tif_file))\\n else:\\n date = 'DD-MM-YYYY'\\n satellite = 'Pleiades 1X'\\n\\n # print to stdout\\n if dzi8_paths or dzi16_paths:\\n print('[%s]' % dataset, file=out)\\n print('files = ', prv_paths, file=out)\\n print('tif = ', tif_paths, file=out)\\n print('rpc = ', rpc_paths, file=out)\\n if ms:\\n print('clr = ', ms_paths, file=out)\\n if dzi8_paths:\\n print('dzi8 = ', dzi8_paths, file=out)\\n if dzi16_paths:\\n print('dzi16 = ', dzi16_paths, file=out)\\n s = dataset.split(os.path.sep)\\n if len(s) == 3: # ie the path is of the kind 'pleiades/reunion/dataset_1'\\n print('title = %s (%s)' % (s[1].capitalize(), s[2][-1]), file=out) # ie 'Reunion (1)'\\n elif len(s) == 2: # ie the path is of the kind 'pleiades/reunion'\\n print('title = %s' % s[1].capitalize(), file=out) # ie 'Reunion'\\n else:\\n print('path %s not expected by the author of the script: ' % dataset, s, file=sys.stderr)\\n print('date = %s' % date, file=out)\\n print('satellite = Pleiades %s' % satellite, file=out)\\n print('nb_img = %d' % n, file=out)\\n if ms:\\n print('color = panchro_xs', file=out)\\n else:\\n print('color = panchro', file=out)\",\n \"def main():\\n mip = parametros()\\n mir = Reporte(CURRENT_PATH, mip.debug, mip.overwrite)\\n pdfs = mir.obtener()\\n if pdfs:\\n print(\\\"Obteniendo nuevos pdf:\\\")\\n for pdf in pdfs:\\n print(f\\\"* {pdf}\\\")\\n\\n for file in glob.glob(f\\\"{CURRENT_PATH}/resources/pdf/*.pdf\\\"):\\n data = mir.parser(file)\\n mir.escribir(data)\",\n \"def preprocess(exam, data_folder, save_path, image_format):\\n for v in ['L-CC', 'L-MLO', 'R-CC', 'R-MLO']:\\n if len(exam[v]) == 0:\\n continue\\n else:\\n for image in exam[v]:\\n image_path = data_folder + '/' + image + '.' + image_format\\n # Extract subdirectories\\n subdirs = \\\"/\\\".join(image.split('/')[:-1])\\n save_dirs = os.path.join(save_path, subdirs)\\n # Extract image id\\n image_id = image.split('/')[-1]\\n # Create save directories\\n os.makedirs(save_dirs, exist_ok=True)\\n png_save_path = os.path.join(save_dirs, image_id + '.png')\\n with Image(filename=image_path, format=image_format) as img:\\n with img.clone() as i:\\n i.auto_level()\\n with i.convert('png') as png_image:\\n png_image.transform(resize='896x1152!')\\n png_image.save(filename=png_save_path)\",\n \"def main():\\n me = SimpleImage(\\\"images/me.JPG\\\")\\n dinosaur = SimpleImage(\\\"images/dinosaur.jpg\\\")\\n\\n dinosaur.make_as_big_as(me)\\n combine = magic(me, dinosaur)\\n combine.show()\",\n \"def process_images():\\n create_dirs()\\n for root, dirs, files in os.walk(IN):\\n for name in files:\\n if name[0] == '.':\\n continue\\n process_image(name)\",\n \"def main():\\n args = get_args()\\n\\n urls = get_urls(args)\\n\\n if len(urls) < 1:\\n print >> sys.stderr, render_date_iso8601(), \\\"No valid URLs to process.\\\"\\n exit(1)\\n\\n outfile = None\\n\\n if args.output:\\n try:\\n outfile = open(args.output, 'a' if args.append == True else 'w')\\n except IOError as error:\\n print >> sys.stderr, render_date_iso8601(), \\\"Error: opening file:\\\", args.output, \\\">>\\\", url\\n\\n\\n for url in urls:\\n if verify_url(url) == True:\\n try:\\n m3u8_obj = m3u8.load(url)\\n if m3u8_obj.is_variant:\\n print >> sys.stdout if outfile is None else outfile, render_csv(url, m3u8_obj.playlists)\\n else:\\n print >> sys.stderr, render_date_iso8601(), \\\"Error for url:\\\", url, \\\"Doesn't contain any stream playlists\\\"\\n except IOError as error:\\n print >> sys.stderr, render_date_iso8601(), \\\"Error for url:\\\", url, \\\">>\\\", error\\n else:\\n print >> sys.stderr, render_date_iso8601(), \\\"Error: Not a valid URL >>\\\", url\\n\\n\\n return 0\",\n \"def handle_as_url(view: View, point: int, string: str, name: str):\\n\\n # Let's assume this url as input:\\n # (https://upload.wikimedia.org/wikipedia/commons/8/84/Example.svg)\\n\\n # Download the image\\n # FIXME: avoid nested try-except clauses\\n try:\\n try:\\n f = urlopen(unquote(string)) # <==\\n except:\\n try:\\n url_path = quote(string).replace(\\\"%3A\\\", ':', 1)\\n f = urlopen(url_path)\\n except:\\n f = urlopen(string)\\n # don't fill the console with stack-trace when there`s no connection !!\\n except Exception as e:\\n print(e)\\n return\\n\\n # file needs conversion ?\\n need_conversion = name.endswith(FORMAT_TO_CONVERT) # => True\\n basename, ext = osp.splitext(name) # => (\\\"Example\\\", \\\".svg\\\")\\n # create a temporary file\\n tmp_file = osp.join(TEMP_DIR,\\n \\\"tmp_image\\\" + (ext if need_conversion else \\\".png\\\")\\n ) # => \\\"TEMP_DIR/tmp_image.svg\\\"\\n\\n # Save downloaded data in the temporary file\\n content = f.read()\\n with open(tmp_file, \\\"wb\\\") as dst:\\n dst.write(content)\\n\\n # if the file needs conversion, convert it then read data from the resulting png\\n if need_conversion:\\n # keep the image's temporary file and name for later use\\n conv_file = tmp_file # => \\\"TEMP_DIR/tmp_image.svg\\\"\\n conv_name = name # => \\\"Example.svg\\\"\\n\\n # => \\\"TEMP_DIR/tmp_image.png\\\"\\n png = osp.splitext(tmp_file)[0] + \\\".png\\\"\\n\\n # use the magick command of Imagemagick to convert the image to png\\n magick(tmp_file, png)\\n\\n # set temp_file and name to the png file\\n tmp_file = png # => \\\"TEMP_DIR/tmp_image.png\\\"\\n name = basename + \\\".png\\\" # => \\\"Example.png\\\"\\n\\n # read data from the resulting png\\n with open(tmp_file, \\\"rb\\\") as dst:\\n content = dst.read()\\n\\n real_width, real_height, size = get_image_size(tmp_file)\\n width, height = get_dimensions(view, tmp_file)\\n encoded = str(base64.b64encode(content), \\\"utf-8\\\")\\n size = str(size // 1024) + \\\"KB\\\" if size >= 1024 else str(size) + 'B'\\n\\n def on_navigate(href):\\n\\n if href == \\\"save\\\":\\n if need_conversion:\\n save(conv_file, conv_name, \\\"url\\\")\\n else:\\n save(tmp_file, name, \\\"url\\\")\\n elif href == \\\"save_as\\\":\\n if need_conversion:\\n convert(conv_file, \\\"url\\\", conv_name)\\n else:\\n convert(tmp_file, \\\"url\\\", name)\\n else:\\n sublime.active_window().open_file(tmp_file)\\n\\n view.show_popup(\\n TEMPLATE % (width, height, \\\"png\\\", encoded, real_width, real_height, size),\\n sublime.HIDE_ON_MOUSE_MOVE_AWAY,\\n point,\\n *view.viewport_extent(),\\n on_navigate=on_navigate\\n )\",\n \"def main():\\n parser = argparse.ArgumentParser(\\n description=\\\"A program to generate geometric wallpaper.\\\")\\n\\n parser.add_argument(\\\"-s\\\", \\\"--shape\\\",\\n help=\\\"the name of shapes to generate\\\",\\n type=str,\\n choices=(\\\"hexagon\\\",\\n \\\"square\\\",\\n \\\"diamond\\\",\\n \\\"half-diamond\\\"),\\n default=\\\"hexagon\\\")\\n\\n parser.add_argument(\\\"-n\\\", \\\"--num-across\\\",\\n help=\\\"the number of shapes across the canvas to create\\\",\\n type=int,\\n default=10)\\n\\n parser.add_argument(\\\"--size\\\",\\n help=\\\"the size of the created image\\\",\\n type=int,\\n nargs=2,\\n default=instagram_size)\\n\\n parser.add_argument(\\\"-o\\\", \\\"--outfile\\\",\\n help=\\\"name of the created file\\\",\\n type=str)\\n\\n args = parser.parse_args()\\n\\n # Create the image.\\n im = Image.new('RGB', args.size)\\n draw = ImageDraw.Draw(im)\\n\\n for shape in gw.shapes.cover_in_shapes(args.shape, im.size,\\n args.num_across):\\n draw.polygon(shape, fill=gw.colors.palette_chooser())\\n\\n # Save the image.\\n if args.outfile:\\n im.save(args.outfile)\\n else:\\n im.save(\\\"{}_{}_{}x{}.png\\\".format(args.shape, args.num_across,\\n args.size[0], args.size[1]),\\n \\\"PNG\\\")\",\n \"def bulk_process_images(inputpath, outputpath, extension):\\n\\n for dirpath, dirnames, filenames in os.walk(inputpath):\\n structure = os.path.join(outputpath, dirpath[len(inputpath) + 1:])\\n for file in filenames:\\n if file.endswith(extension):\\n src = os.path.join(dirpath, file)\\n dest = os.path.join(structure, file)\\n img = load_and_preprocess_image(src)\\n cv2.imwrite(dest, img)\",\n \"def compute_web_src(depc, gid_list, config=None):\\n ibs = depc.controller\\n\\n gpath_list = ibs.get_image_paths(gid_list)\\n orient_list = ibs.get_image_orientation(gid_list)\\n args_list = list(zip(gpath_list, orient_list))\\n\\n genkw = {\\n 'ordered': True,\\n 'futures_threaded': True,\\n 'force_serial': ibs.force_serial or config['force_serial'],\\n }\\n gen = ut.generate2(draw_web_src, args_list, nTasks=len(args_list), **genkw)\\n for val in gen:\\n yield (val,)\",\n \"def main():\\n\\tparser = construct_parser()\\n\\targs = parser.parse_args()\\n\\ttiles = slice(args.image, args.num_tiles, save=False)\\n\\tsave_tiles(tiles, prefix=get_basename(args.image), directory=args.dir,\\n\\t\\t format=args.format)\",\n \"def process_file(pool, path):\\n _, ext = os.path.splitext(path)\\n if ext in ('.png',):\\n update_png_crusher()\\n pool.apply_async(run, (path, [PNG_CRUSHER, path]))\\n elif ext in ('.jpg', '.jpeg'):\\n pool.apply_async(run, (path, [JPG_CRUSHER, path]))\",\n \"def main():\\n base_dir = '/home/sjimenez/imagenes_prueba'\\n out_dir = '/home/sjimenez/easy_analysis'\\n for _, _, files in os.walk(base_dir, topdown=False):\\n for f in files:\\n print('--------- {} ---------'.format(f))\\n act_dir = osp.join(base_dir, f)\\n act_im = cv2.imread(act_dir)\\n if act_im is not None:\\n get_image_stats(act_im, out_dir, f)\\n else:\\n print('Not able to open the image')\",\n \"def make_pbeam_images(metadata, in_dir, write_tag):\\n filenames = metadata['FITSImageFilename']\\n for i, in_file in enumerate(filenames):\\n kat_target = katpoint.Target(metadata['KatpointTargets'][i])\\n\\n out_filebase = os.path.splitext(in_file)[0]\\n out_filebase_pb = out_filebase + '_PB'\\n log.info('Write primary beam corrected FITS output: %s',\\n out_filebase_pb + FITS_EXT)\\n\\n in_path = os.path.join(in_dir + write_tag, in_file)\\n pb_dir = _productdir(metadata, in_dir, i, '_PB', write_tag)\\n\\n os.mkdir(pb_dir)\\n pbc_path = os.path.join(pb_dir, out_filebase_pb + FITS_EXT)\\n raw_image = pbc.read_fits(in_path)\\n beam_model = pbc.get_beam_model(raw_image.header)\\n pbc_image = pbc.primary_beam_correction(beam_model, raw_image, px_cut=0.1)\\n pbc.write_new_fits(pbc_image, in_path, outputFilename=pbc_path)\\n\\n log.info('Write primary beam corrected PNG output: %s',\\n out_filebase_pb + PNG_EXT)\\n _caption_pngs(pb_dir, out_filebase_pb,\\n kat_target, 'PB Corrected', contrast=PB_CONTRAST)\",\n \"def runAll(self):\\n \\n worker = worker()\\n if self.FileFolder.text() == \\\"\\\":\\n self.makeWarningPopup(\\\"Please Select a file or Files to run\\\") \\n elif self.OutputFolder.text() == \\\"\\\":\\n self.makeWarningPopup(\\\"Please select an output folder\\\")\\n else:\\n TheFiles = self.FileFolder.text()\\n TheOutPutFolder = self.OutputFolder.text()\\n \\n runArt = worker.MakeUITeamConversion(self,TheFiles,TheOutPutFolder)\",\n \"def main():\\n\\n parser = argparse.ArgumentParser(description=DESCRIPTION)\\n parser.add_argument('-o', '--output', default='output.jpg',\\n help=\\\"Filename to use for the output image\\\")\\n parser.add_argument('INPUT_IMAGE', type=str,\\n help=\\\"Filename for the input content image\\\")\\n parser.add_argument('STYLE_IMAGE', type=str,\\n help=\\\"Filename to for the input style image\\\")\\n parser.add_argument('-s', '--style-weight', default=1e-2, type=float,\\n help=\\\"Weight to apply for the loss associated with the style image. \\\"\\n \\\"Making this bigger results in a final image more like the style image\\\")\\n parser.add_argument('-c', '--content-weight', default=1e4, type=float,\\n help=\\\"Weight to apply for the loss associated with the content image. \\\"\\n \\\"Making this bigger results in a final image more like the content image\\\")\\n parser.add_argument('-t', '--total-variation-loss', default=30, type=float,\\n help=\\\"Weight for the total variation loss.\\\")\\n\\n args = parser.parse_args()\\n\\n print(args)\",\n \"def process_image(self):\\n pass\",\n \"def convertHPURL(files):\\n return urllib.parse.urlunsplit( ('http', HPpla, HPdoc + files, '', '') )\",\n \"def main():\\r\\n original = SimpleImage(\\\"images/poppy.png\\\")\\r\\n original.show()\\r\\n # shrink function\\r\\n after_shrink = shrink('images/poppy.png')\\r\\n after_shrink.show()\",\n \"def hover_preview_callback():\\n\\n global PREVIEW_ON_HOVER, MAX_CHARS, ALL_FORMATS,\\\\\\n FORMAT_TO_CONVERT, SEARCH_MODE, RECURSIVE, IMAGE_FOLDER_NAME,\\\\\\n IMAGE_URL_RE, IMAGE_FILE_RE, IMAGE_FILE_NAME_RE\\n\\n PREVIEW_ON_HOVER = settings.get(\\\"preview_on_hover\\\", True)\\n MAX_CHARS = settings.get(\\\"max_chars\\\", 2028) // 2\\n\\n format_to_convert = settings.get(\\\"formats_to_convert\\\",\\n [\\\"svg\\\", \\\"svgz\\\", \\\"ico\\\", \\\"webp\\\"])\\n ALL_FORMATS = [\\\"png\\\", \\\"jpg\\\", \\\"jpeg\\\", \\\"bmp\\\", \\\"gif\\\"] + format_to_convert\\n FORMAT_TO_CONVERT = tuple('.' + ext for ext in format_to_convert)\\n\\n IMAGE_FOLDER_NAME = settings.get(\\\"image_folder_name\\\", \\\"Hovered Images\\\")\\n\\n SEARCH_MODE = settings.get(\\\"search_mode\\\", \\\"project\\\")\\n RECURSIVE = settings.get(\\\"recursive\\\", True)\\n\\n formats_ored = '|'.join(ALL_FORMATS)\\n IMAGE_URL_RE = re.compile(r\\\"(?:(https?)://)?\\\" # http(s)://\\n r\\\"(?:[^./\\\\\\\"'\\\\s]+\\\\.){1,3}[^/\\\\\\\"'.\\\\s]+/\\\" # host\\n r\\\"(?:[^/\\\\\\\"'\\\\s]+/)*\\\" # path\\n r\\\"([^\\\\\\\"'/\\\\s]+?\\\\.(?:%s))\\\" % formats_ored) # name\\n\\n IMAGE_FILE_RE = re.compile(r\\\"(?P<path>(\\\" # drive\\n r\\\"\\\\w:(\\\\\\\\\\\\\\\\|/)|\\\" # Windows (e.g C:\\\\)\\n r\\\"\\\\\\\\\\\\\\\\|\\\" # Linux (\\\\\\\\)\\n r\\\"\\\\.{1,2}(\\\\\\\\\\\\\\\\|/)|\\\" # Mac OS and/or relative\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tr\\\"\\\\w+(\\\\\\\\\\\\\\\\|/)\\\"\\n r\\\")\\\"\\n r\\\"([-\\\\.@\\\\w]+?(\\\\\\\\\\\\\\\\|/))*\\\" # body\\n r\\\"[-\\\\.@\\\\w]+?\\\" # name\\n r\\\"\\\\.(%s))\\\" % formats_ored # extension\\n )\\n IMAGE_FILE_NAME_RE = re.compile(r\\\"[-.@\\\\w]+\\\" # name\\n r\\\"\\\\.(?:%s)\\\" % formats_ored # extension\\n )\",\n \"def download_images(img_urls, dest_dir, base_url=\\\"http://code.google.com\\\"):\\n create_dir(dest_dir)\\n img_tags = fetch_call(img_urls, dest_dir)\\n create_html(dest_dir, img_tags)\",\n \"def main():\\n # the url for african daily and global daily\\n african_dialy_url = \\\"https://data.chc.ucsb.edu/products/CHIRPS-2.0/africa_daily/tifs/p25/\\\"\\n global_daily_url = \\\"https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/tifs/p25/\\\"\\n\\n\\n each_year_list = GetRasterYears(url=african_dialy_url)\\n new_path = makenewdir(each_year_list)\\n years_new_list = fecthrasterurl(url=african_dialy_url)\\n downloadwithwget(each_year_list, years_new_list, new_path)\",\n \"def create_png_images(self):\\n if self.subject is None:\\n print Console.WARNING + 'You need to specify a subject first' + Console.ENDC\\n return\\n\\n check_dir_of = self.locations.check_dir_of\\n check_dir_of(self.locations.HISTO_PNG_U)\\n check_dir_of(self.locations.HISTO_PNG)\\n check_dir_of(self.locations.SOURCE_PNG)\\n\\n\\n\\n fmap_img = ImageUtils.load_nifti_image(self.locations.HIST_FMAP) #loading subject nifti files\\n volumes = []\\n try:\\n for s in self.locations.SOURCES:\\n volumes.append(ImageUtils.load_nifti_image(s))\\n except IOError as e:\\n print Console.FAIL + 'There are errors loading nifi files for subject %s'%self.subject + Console.ENDC\\n return False\\n \\n\\n num_slices = volumes[0].shape[2] #use first volume to check expected number of slices\\n\\n self.locations.create_empty_dir(self.locations.IMAGES_DIR)\\n\\n print 'Creating input PNGs for %s'%self.subject\\n for k, vol in enumerate(volumes):\\n for i in range(num_slices):\\n imslice = ImageUtils.data_to_bytescale_rgb(vol[:, :, i])\\n im = Image.fromarray(imslice)\\n im.save(self.locations.SOURCE_PNG % (self.locations.LABELS[k],i))\\n\\n \\n print 'Creating histology PNGs for %s'%self.subject\\n for i in range(num_slices):\\n\\n im_unscaled = ImageUtils.data_to_unscaled_rgb(fmap_img[:, :, i]); #keeps the original values\\n im_unscaled = Image.fromarray(im_unscaled)\\n im_unscaled = im_unscaled.filter(ImageFilter.GaussianBlur(radius=2)) #Filter requested by Ali Khan\\n im_unscaled.save(self.locations.HISTO_PNG_U % i)\\n\\n im_scaled = ImageUtils.data_to_bytescale_rgb(fmap_img[:,:,i]); # bytescaled histology\\n im_scaled = Image.fromarray(im_scaled)\\n im_scaled = im_scaled.filter(ImageFilter.GaussianBlur(radius=2)) #Filter requested by Ali Khan\\n im_scaled.save(self.locations.HISTO_PNG % i)\\n\\n print\\n return True\",\n \"def make_gif():\\n if MIGRATION:\\n import imageio\\n for n, JPG_DIR in enumerate(JPG_DIRS):\\n images, image_file_names = [], []\\n for file_name in os.listdir(JPG_DIR):\\n if file_name.endswith('.jpg'):\\n image_file_names.append(file_name) \\n sorted_files = sorted(image_file_names, key=lambda y: int(y.split('_')[1]))\\n for i in range(len(sorted_files)): \\n file_path = os.path.join(JPG_DIR, sorted_files[i])\\n images.append(imageio.imread(file_path))\\n imageio.mimsave(FNAME.rsplit('.', 1)[0] + '_migration' + str(n) + '.gif', images, 'GIF', loop=1, fps=FPS)\",\n \"def main(input):\\n path = os.path.abspath(input)\\n name = os.path.splitext(os.path.basename(path))[0]\\n p = os.path.join(os.getcwd(),name)\\n i = 1\\n p1 = p\\n while os.path.exists(p1):\\n p1 = \\\"{p}-{i}\\\".format(p=p,i=i)\\n i += 1\\n p = p1\\n os.mkdir(p1)\\n os.mkdir(os.path.join(p1,\\\"media\\\"))\\n with zipfile.ZipFile(path) as zf:\\n for file in zf.namelist():\\n # Path traversal defense copied from\\n # http://hg.python.org/cpython/file/tip/Lib/http/server.py#l789\\n words = file.split('/')\\n dest = os.path.join(p1, \\\"media\\\")\\n if words[0] == \\\"word\\\" and words[1] == \\\"media\\\":\\n for word in words[2:]:\\n while True:\\n drive, word = os.path.splitdrive(word)\\n head, word = os.path.split(word)\\n if not drive:\\n break\\n if word in (os.curdir, os.pardir, ''):\\n continue\\n dest = os.path.join(dest, word)\\n click.echo(\\\"{} -> {}\\\".format(file, dest))\\n of = open(dest, 'wb')\\n of.write(zf.read(file))\\n of.close()\\n\\n newdoc = os.path.join(p1, os.path.basename(path))\\n lyxfile = os.path.join(p1, name + \\\".lyx\\\")\\n texfile = os.path.join(p1, name + \\\".tex\\\")\\n shutil.copyfile(path, newdoc)\\n os.system(\\\"pandoc -s -f docx -t latex -o '{of}' '{i}'\\\".format(of=texfile, i=newdoc))\\n os.system(\\\"tex2lyx '{i}' '{o}'\\\".format(i=texfile, o=lyxfile))\\n os.remove(texfile)\\n os.system(\\\"convertwmf {dir}\\\".format(dir=os.path.join(p1, \\\"media\\\")))\\n click.echo(lyxfile)\",\n \"def _convert_batch(self, bucket, pdf_path, pages, jpeg_prefixes,\\n webhook_url, webhook_data):\\n # download PDF locally, use first JPEG prefix as its name\\n pdf_key = s3.Key(bucket)\\n pdf_key.key = pdf_path\\n\\n local_jpeg_prefix = jpeg_prefixes[0].replace('/', '-')\\n local_pdf_path = '%s/%s.pdf' % (self.working_dir, local_jpeg_prefix)\\n\\n pdf_key.get_contents_to_filename(local_pdf_path)\\n threads = []\\n\\n # convert each page in a separate thread using ImageMagick\\n for page_number, jpeg_prefix in zip(pages, jpeg_prefixes):\\n args = (local_pdf_path, page_number, jpeg_prefix, bucket, webhook_url,\\n webhook_data)\\n threads.append(threading.Thread(target=self._upload_page, args=args))\\n\\n [thread.start() for thread in threads]\\n\\n # wait until all threads have completed\\n [thread.join() for thread in threads]\",\n \"def decode(p):\\n #assert p.endswith('.' + EXTENSION)\\n p2 = os.path.basename(p).replace('baseline.png', '.png')\\n p2p = os.path.join('/mnt/Volume0/test/clic2020-devkit/result/', p2) #add by me\\n pp = os.path.join('/mnt/Volume0/test/clic2020-devkit/targets',p2)\\n p2 = os.path.join('/mnt/Volume0/test/clic2020-devkit/inputs/', p2) #add by me\\n p1 = pframe_dataset_shared.get_previous_frame_path(p2)\\n #p1 = os.path.join('/mnt/Volume0/test/clic2020-devkit/test_data/inputs/', p1)\\n #assert os.path.isfile(p1), (p2, p1, p, len(glob.glob('*.png')))\\n b = Image.open(p).convert('L')\\n f2_reconstructed = decoder(np.array(Image.open(p1)), b)\\n Image.fromarray(f2_reconstructed).save(p2p)\\n return f2_reconstructed, np.array(Image.open(pp))\",\n \"def process_images():\\n image_path = os.path.join(settings.BASE_DIR, 'themes/CMESH/assets/img/')\\n static_images = os.path.join(settings.BASE_DIR, 'static/CMESH/img/')\\n\\n copy_files(image_path, static_images)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6570944","0.64156437","0.61769783","0.5975264","0.5974723","0.5941165","0.59038806","0.5838966","0.58314997","0.5811597","0.5809414","0.5798533","0.5736212","0.56903666","0.56399405","0.56066835","0.557636","0.557636","0.5565635","0.5556461","0.55448854","0.55334276","0.5527023","0.5442887","0.544092","0.54306245","0.5419744","0.5414164","0.54075223","0.5405304","0.53649753","0.53594786","0.53570074","0.5354956","0.53527546","0.5339434","0.53251076","0.5322256","0.53121704","0.53055096","0.5301834","0.529864","0.529283","0.52921087","0.52833813","0.52779615","0.5273823","0.5265695","0.5265155","0.525938","0.5255034","0.5209934","0.5179127","0.5174478","0.5173421","0.5173086","0.51639706","0.5148193","0.51455635","0.51454765","0.5130791","0.5126231","0.51246566","0.51217353","0.51130956","0.5107963","0.5107609","0.5089641","0.5077812","0.5073268","0.50705713","0.50662833","0.5065469","0.5060721","0.5055751","0.5050469","0.50470185","0.504392","0.5036003","0.50350213","0.50325173","0.5028095","0.5022829","0.5022341","0.5012141","0.50059545","0.50058335","0.50030774","0.4991757","0.49802214","0.49789968","0.4977496","0.49751133","0.4974953","0.49741662","0.49677628","0.49580836","0.49577075","0.4956221","0.4952096"],"string":"[\n \"0.6570944\",\n \"0.64156437\",\n \"0.61769783\",\n \"0.5975264\",\n \"0.5974723\",\n \"0.5941165\",\n \"0.59038806\",\n \"0.5838966\",\n \"0.58314997\",\n \"0.5811597\",\n \"0.5809414\",\n \"0.5798533\",\n \"0.5736212\",\n \"0.56903666\",\n \"0.56399405\",\n \"0.56066835\",\n \"0.557636\",\n \"0.557636\",\n \"0.5565635\",\n \"0.5556461\",\n \"0.55448854\",\n \"0.55334276\",\n \"0.5527023\",\n \"0.5442887\",\n \"0.544092\",\n \"0.54306245\",\n \"0.5419744\",\n \"0.5414164\",\n \"0.54075223\",\n \"0.5405304\",\n \"0.53649753\",\n \"0.53594786\",\n \"0.53570074\",\n \"0.5354956\",\n \"0.53527546\",\n \"0.5339434\",\n \"0.53251076\",\n \"0.5322256\",\n \"0.53121704\",\n \"0.53055096\",\n \"0.5301834\",\n \"0.529864\",\n \"0.529283\",\n \"0.52921087\",\n \"0.52833813\",\n \"0.52779615\",\n \"0.5273823\",\n \"0.5265695\",\n \"0.5265155\",\n \"0.525938\",\n \"0.5255034\",\n \"0.5209934\",\n \"0.5179127\",\n \"0.5174478\",\n \"0.5173421\",\n \"0.5173086\",\n \"0.51639706\",\n \"0.5148193\",\n \"0.51455635\",\n \"0.51454765\",\n \"0.5130791\",\n \"0.5126231\",\n \"0.51246566\",\n \"0.51217353\",\n \"0.51130956\",\n \"0.5107963\",\n \"0.5107609\",\n \"0.5089641\",\n \"0.5077812\",\n \"0.5073268\",\n \"0.50705713\",\n \"0.50662833\",\n \"0.5065469\",\n \"0.5060721\",\n \"0.5055751\",\n \"0.5050469\",\n \"0.50470185\",\n \"0.504392\",\n \"0.5036003\",\n \"0.50350213\",\n \"0.50325173\",\n \"0.5028095\",\n \"0.5022829\",\n \"0.5022341\",\n \"0.5012141\",\n \"0.50059545\",\n \"0.50058335\",\n \"0.50030774\",\n \"0.4991757\",\n \"0.49802214\",\n \"0.49789968\",\n \"0.4977496\",\n \"0.49751133\",\n \"0.4974953\",\n \"0.49741662\",\n \"0.49677628\",\n \"0.49580836\",\n \"0.49577075\",\n \"0.4956221\",\n \"0.4952096\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.70537895"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":11298,"cells":{"query":{"kind":"string","value":"Delete all the processed webp textures."},"document":{"kind":"string","value":"def clean_webp_textures():\n for webp in PNG_TEXTURES['output_files']:\n if os.path.isfile(webp):\n os.remove(webp)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def clean():\n clean_flatbuffer_binaries()\n clean_webp_textures()","def delete(self):\n\t\tif self.hasUdim:\n\t\t\tfor a in self.udimPaths:\n\t\t\t\ta.delete()\n\t\telse:\n\t\t\tsuper( textureFile, self ).delete()","def destroy(self):\n\n self.cmapTexture.destroy()\n\n for tex in (self.modulateTexture,\n self.clipTexture,\n self.colourTexture):\n tex.deregister(self.name)\n glresources.delete(tex.getTextureName())\n\n self.removeListeners()\n self.deregisterAuxImage('modulate')\n self.deregisterAuxImage('clip')\n self.deregisterAuxImage('colour')\n\n self.modulateTexture = None\n self.clipTexture = None\n self.colourTexture = None\n self.modulateImage = None\n self.clipImage = None\n self.colourImage = None\n self.modulateOpts = None\n self.clipOpts = None\n self.colourOpts = None\n\n glimageobject.GLImageObject.destroy(self)","def delete_AllImgs(self):\n self.listImages.remove_all_imgs()","def cleanup(self):\r\n for f in [i for d in self.data.values() for i in d[\"filenames\"]]:\r\n try:\r\n os.unlink(f)\r\n except Exception: pass\r\n self.Destroy()","def clearAllPictures(self):\n shutil.rmtree(PNG_OUTPUT_PATH)\n os.makedirs(PNG_OUTPUT_PATH)","def destroyTempFrames(self):\n for frame in self.tempFrames:\n frame.destroy()\n self.tempFrames = []","def teardown():\n os.remove('green-dot.tif')\n os.remove('green-dot.jpg')\n os.remove('green-dot.png')","def cleanup(self):\n self.GP.cleanup()","def teardown():\n for filename in files_to_delete:\n delete_file(filename)","def __del__(self):\n for filename in self.files:\n unlink(filename)","def unloadallskills(self):\r\n for skill in skills.skills.copy():\r\n es.unload(\"%s/skills/%s\" % (info.basename, skill))\r\n skills.clearList()","def removeTextureToOcc(self):\n\t\tshas = self._getShapes()\n\t\tfor sha in shas:\n\t\t\tif sha.a.texture_Occ.exists:\n\t\t\t\tsha.a.texture_Occ.delete()","def clean(self):\n print(\"Cleaning outputs in %s\" % self.args.output)\n files = glob.glob(self.args.output + \"*.pkl\")\n for f in files:\n if os.path.exists(f):\n os.remove(f)","def clear_images(self):\r\n\r\n with translate_errors():\r\n self.audio.clear_pictures()\r\n self.audio.save()\r\n\r\n super().clear_images()","def _cleanup():\n if os.path.exists(WEBPROPERTIES_PATH):\n os.remove(WEBPROPERTIES_PATH)\n if os.path.exists(PROFILES_PATH):\n os.remove(PROFILES_PATH)","def remove_images(self):\n hardware_components.log_method(self, \"remove_images\")\n communication_object = self._get_control_software().connection\n communication_object.remove_all()","def clear(self):\n\n Console.info(\"Cleaning sprite files...\")\n Console.indent()\n \n for dirPath, dirNames, fileNames in os.walk(self.base):\n for fileName in fileNames:\n if fileName.startswith(\"jasysprite\"):\n filePath = os.path.join(dirPath, fileName)\n Console.debug(\"Removing file: %s\", filePath)\n os.remove(filePath)\n \n Console.outdent()","def cleanup(self):\n\t\tself.loader.cleanup()\n\t\tself.Loaded = False","def clean(self):\n if self.image:\n self.glance.images.delete(self.image['id'])\n\n if self.image_file:\n shutil.rmtree(self.download_path)","def cleanupResources():\n None","def tearDown(self):\n for fn in self.tempImages:\n os.remove(os.path.join(self.root, fn))\n os.rmdir(self.root)","def __del__(self):\r\n train_data_sources = list(self._train_data.values())\r\n test_data_sources = list(self._test_data.values())\r\n all_data_sources = train_data_sources + test_data_sources\r\n for data_source in all_data_sources:\r\n data_source.cleanup()\r\n self._tester.__del__()","def cleanup(self):\r\n for action in self._actions:\r\n action.cleanup()","def remove_all():\n storage = FileStorage()\n objects = storage.all()\n objects = list(objects.values())\n\n for element in objects:\n storage.delete(element)\n objects = storage.all()","def clear_cache(self):\n for fle in self.cache_location.glob(\"*.pickle\"):\n fle.unlink()","def unload(self):\n for obj in self.objects:\n self.scene.removeItem(obj)\n for plant in self.plants:\n self.scene.removeItem(plant)\n for tile in self.tiles:\n tile.unload()\n self.scene.removeItem(tile)\n if self.region_back:\n self.scene.removeItem(self.region_back)\n self.tiles = []\n self.objects = []\n self.plants = []\n self.region_back = None\n self.loaded = False","def __del__(self):\n try:\n self._frame._destroy()\n except:\n pass\n self._turtles = []\n self._pencils = []\n del self._frame","def clear(self):\n\n self.wads = []\n\n self.sprites = {}\n self.sprite_image_cache = {}\n self.palette = None\n\n self.sound_cache = {}","def erase_captured_urls(url_list):\n if gs.local:\n erase_captured_urls_local(url_list)\n else:\n erase_captured_urls_aws(url_list)","def cleanup_all(cls):\n for i in tuple(cls.instances):\n i.cleanup()","def generate_webp_textures():\n input_files = PNG_TEXTURES['input_files']\n output_files = PNG_TEXTURES['output_files']\n if not os.path.exists(TEXTURE_PATH):\n os.makedirs(TEXTURE_PATH)\n for png, out in zip(input_files, output_files):\n if needs_rebuild(png, out):\n convert_png_image_to_webp(png, out, WEBP_QUALITY)","def _cleanup():\n for itr_ref in ITERATORS_LIST:\n if context:\n device_type = context.get_context(\"device_target\")\n if device_type == \"GPU\":\n itr_ref.release()\n else:\n itr = itr_ref()\n if itr is not None:\n itr.release()\n else:\n itr = itr_ref()\n if itr is not None:\n itr.release()","def finalizer():\n for resource_type in pods, pvcs, storageclasses, secrets:\n for resource in resource_type:\n resource.delete()\n resource.ocp.wait_for_delete(resource.name)\n if pools:\n # Delete only the RBD pool\n pools[0].delete()\n if projects:\n for project in projects:\n project.delete(resource_name=project.namespace)\n project.wait_for_delete(project.namespace)","def __del__(self):\n for f in self._files:\n f.close()","def cleanup(self):\n self.all_wav_to_mp3()\n self.past_songs_db.close()\n self.move_tracks_to_music_folder( )\n self.delete_leftovers()\n print \"Cleanup finished\"","def delete_all_images(self, index):\n self.__accessed_image[:] = False\n self.__check_delete_images()","def __del__(self):\n for component_name, file in self._file_list.items():\n file.close()","def cleanUp(self):\r\n for endpoint in self._endpoints.keys():\r\n endpoint.destroy()\r\n\r\n assert len(self._endpoints) == 0","def cleanup(self):\n self.qemu.clean_run_files()\n for tmp in glob.glob(self.configfile + \"?*\"):\n os.unlink(tmp)","def deleteAll(self):\n for tlight in self.trafficLights:\n del tlight\n del self.trafficLights","def __del__(self):\n\t\tif self.pictures:\n\t\t\twith open(self.path(JSON_FILENAME), \"w\") as f:\n\t\t\t\tjson.dump(self.pictures, f)","def del_all_sounds(self) -> None:\n self._sou_mut.acquire()\n self.sounds = []\n self._sou_mut.release()","def cleanup():","def clean_up(self):\n cv2.destroyAllWindows()\n # self.vs.release()","def clean(self):\n # Delete vertices / faces / colors / normals :\n self._vert_buffer.delete()\n self._index_buffer.delete()\n self._normals_buffer.delete()\n self._xrange_buffer.delete()\n self._math_buffer.delete()","def __del__(self):\n for file in list(self.mFiles.values()):\n file.close()","def clear_images(self):\r\n\r\n # audio = self.MutagenType(self['filename'])\r\n self.audio.pop(\"metadata_block_picture\", None)\r\n self.audio.pop(\"coverart\", None)\r\n self.audio.pop(\"coverartmime\", None)\r\n self.audio.save()","def destroy(self):\n\n self.renderTexture .destroy()\n self.cmapTexture .destroy()\n self.negCmapTexture.destroy()\n self.lutTexture .destroy()\n\n self.removeListeners()\n self.deregisterLut()\n\n globject.GLObject.destroy(self)\n\n if self.flatShader is not None: self.flatShader.destroy()\n if self.dataShader is not None: self.dataShader.destroy()\n\n self.dataShader = None\n self.flatShader = None\n self.activeShader = None\n\n self.lut = None\n self.renderTexture = None\n self.cmapTexture = None\n self.negCmapTexture = None\n self.lutTexture = None","def cleanup(filePath):\n restoreRenderSettings()\n cleanupCamera()\n cleanupWorld()\n deleteOutputPath(filePath)","def clean_flatbuffer_binaries():\n for element in FLATBUFFERS_CONVERSION_DATA:\n for json in element.input_files:\n path = processed_json_path(json)\n if os.path.isfile(path):\n os.remove(path)","def destructor(self):\n cv2.destroyAllWindows()","def purge():\n all_hashes = read_all()\n used_hashes = read_used()\n\n for kind, hashes in used_hashes.items():\n to_remove = all_hashes[kind].difference(hashes)\n if kind == 'evs':\n delete_from_directory_by_hashes(EV_DIRECTORY, to_remove)\n elif kind == 'cache':\n delete_from_directory_by_hashes(CACHE_DIRECTORY, to_remove)\n elif kind == 'seeds':\n delete_from_directory_by_hashes(SEED_DIRECTORY, to_remove)\n\n reset_used()","def clean(self):\n if self.options.format != 'svg':\n for svgfile in self.svgouts.itervalues():\n os.remove(svgfile)\n os.rmdir(self.tmpdir)","def pibooth_cleanup(app):","def cleanup_resources(self, restart=False):","def tearDown(self):\n account_models.User.objects.all().delete()\n photo_models.PhotoFeed.objects.all().delete()\n photo_models.PhotoClassification.objects.filter(name__in=[\"Rural\", \"Abstract\", \"City\"]).delete()\n test_helpers.clear_directory('backend/media/', '*.jpg')","def clear_data():\n for i in range(_MAX_NUM_TESTS):\n rand, ref = filename(i)\n if os.path.exists(rand):\n os.remove(rand)\n if os.path.exists(ref):\n os.remove(ref)","def _cleanup(self):\n os.system(\"rm -r %s/*\" %(self._snippet_index_dir))\n os.system(\"rm %s/*\" %(self._para_dir))\n os.system(\"rm %s/*\" %(self._temp_dir))\n os.system(\"rm %s/*\" %(self._snippet_result_dir))","def delete_all_runtimes(self):\n self.compute_handler.delete_all_runtimes()","def cleanup(self):\n pygame.quit()","def __cleanup(self):\n \n self.logger.debug(\"Deleting assignment and definition of policy\")\n self.interactor.delete_policy_assignment(self.assignment_id)\n self.interactor.delete_policy_definition(self.policy_id)","def removeResourceSims(self):\n for sim in self.resourceSims:\n self.world.removeFromWorld(sim)\n self.resourceSims = []","def resource_cleanup(self):\n for lb in self.loadbalancers:\n self.octavia_client.load_balancer_delete(lb['id'], cascade=True)\n try:\n self.wait_for_lb_resource(\n self.octavia_client.load_balancer_show, lb['id'],\n provisioning_status='DELETED')\n except osc_lib.exceptions.NotFound:\n pass\n for fip in self.fips:\n self.neutron_client.delete_floatingip(fip)\n # we run the parent resource_cleanup last as it will remove instances\n # referenced as members in the above cleaned up load balancers\n super(LBAASv2Test, self).resource_cleanup()","def clear_all_triplex_loads(self):\n # Start by getting all the triplex_load objects.\n tl_list = self.get_objects_by_type(object_type='triplex_load')\n\n # If there aren't any triplex loads, warn and return.\n if tl_list is None:\n self.log.warning('clear_all_triplex_loads called, but there '\n 'are not any triplex_loads in the model!')\n return\n\n # Clear 'em out!\n for tl in tl_list:\n self.remove_properties_from_item(item_dict=tl,\n property_list=TRIPLEX_PARAMS)\n\n # All done.","def space_cleaning():\n for file in os.listdir(\".\"):\n if file.endswith(\".png\"):\n os.remove(file)","def clear_thumbnails(self):","def unlink_all(self):\n del self._links[:]","def PurgeAll(self):\n\t\tself.acad.ActiveDocument.PurgeAll()","def clean_old_data():\n logger.info('Cleaning standalone files on disk...')\n for absolute_path in glob.glob(MEDIA_URL + '*'):\n file_name = os.path.basename(absolute_path)\n try:\n relative_path = os.path.join(AUDIOS_URL, file_name)\n audio = Audio.objects.get(filename=relative_path)\n if audio.get_type() == 'episode':\n try:\n # If there are inactive audios on its being\n for e in audio.podcast.episode_set.exclude(pk=audio.podcast.active_episode.pk):\n if not e.is_active():\n logger.info('Inactive audio found in podcast set. Erasing files.')\n e.delete_files()\n except Exception, e:\n logger.exception(e.message)\n except ObjectDoesNotExist, e:\n logger.info('A file with no audio registered in database')\n if os.path.isfile(relative_path):\n logger.info('Erasing: %s' % relative_path)\n os.remove(relative_path)\n logger.info('... Done.')","def delete_all(self):\n if not self.created:\n return\n self.shotgun.batch([dict(\n request_type='delete',\n entity_type=type_,\n entity_id=id_,\n ) for type_, id_ in reversed(self.created)])\n self.created = []","def tearDown(self):\n self.image.delete()","def cleanup(self):","def cleanup(self):","def cleanup(self):","def clear_shots(context):\n\n for shot in context.scene.milkshake_shots:\n bpy.data.cameras.remove(shot.camera)\n context.scene.milkshake_shots.clear()","def destroy_guis(self):\n for filter_ in self._filters.values():\n if filter_._panel:\n filter_._panel.Destroy()","def cleanup(self):\n results = run_command(\"gppkg -q --all\")\n gppkgs = results.split('\\n')[self.start_output:self.end_output] #The first line is 'Starting gppkg with args', which we want to ignore.\n\n for gppkg in gppkgs:\n run_command(\"gppkg --remove \" + gppkg)","def __del__(self):\n for handle in self._filehandles:\n handle.close()","def tearDown(self):\n Image.objects.all().delete()","def unload():\r\n database.save() # Save the database\r\n\r\n \"\"\" Remove any popups \"\"\"\r\n deleted = []\r\n for popup in popuplib.gPopups:\r\n if popup.startswith('sourcerpg_'):\r\n deleted.append(popup)\r\n for popup in deleted:\r\n popuplib.delete(popup)\r\n\r\n \"\"\" Unload all skills \"\"\"\r\n for skill in skills:\r\n es.unload(\"sourcerpg/skills/\" + skill.name)\r\n\r\n \"\"\" Unload all addons \"\"\"\r\n for addon in addons:\r\n es.unload(\"sourcerpg/addons/\" + addon.name)\r\n\r\n \"\"\" Unregister the server commands \"\"\"\r\n cmdlib.unregisterServerCommand(\"srpg\")\r\n cmdlib.unregisterSayCommand(\"rpgmenu\")\r\n cmdlib.unregisterSayCommand(\"rpgupgrade\")\r\n cmdlib.unregisterSayCommand(\"rpgsell\")\r\n cmdlib.unregisterSayCommand(\"rpghelp\")\r\n cmdlib.unregisterSayCommand(\"rpgstats\")\r\n cmdlib.unregisterSayCommand(\"rpgrank\")\r\n cmdlib.unregisterSayCommand(\"rpgpopup\")\r\n cmdlib.unregisterSayCommand(\"rpgtop10\")\r\n \r\n gamethread.cancelDelayed('sourcerpg_databasesave')","def resetAssets(self):\n self.__assets = {}\n gc.collect()\n return True","def clean(self):\r\n\r\n for _, data in self.composition.items():\r\n index_file = Path(data['file'] + '.fxi')\r\n if index_file.exists():\r\n index_file.unlink()","def _cleanup(self):\n\n self.netIns = []\n self.netOuts = []\n self.Gradients = [None]*self.size","def destroyer(): # ;-)\n\n def find_files_to_remove(pyfile):\n for filename in (\"%sc\" % pyfile, \"%so\" % pyfile):\n if exists(filename):\n yield filename\n\n counter = 0\n try:\n while True:\n pyfile = (yield)\n for filename in find_files_to_remove(pyfile):\n try:\n log.debug('removing %s', filename)\n remove(filename)\n counter += 1\n except (IOError, OSError), e:\n log.error('cannot remove %s', filename)\n log.debug(e)\n except GeneratorExit:\n log.info(\"removed files: %s\", counter)","def bye(self):\n self._frame._destroy()\n self._turtles = []\n self._gpens = []\n del self._frame","def clean():\n try:\n os.unlink(options.coords + 'mirza_mrna_input' + '.fa')\n os.unlink(options.coords + 'mirza_mirna_input' + '.fa')\n os.unlink(options.coords + 'mirza_mirna_expressions' + '.fa')\n except:\n pass","def cleanUp(self):\n\n tapeList = sorted(glob.glob('TAPE?'))\n tapeList = ['TAPE%d' % num for num in [1, 2, 5, 6, 7, 10]]\n for tape in tapeList:\n if os.path.isfile(tape): os.remove(tape)\n # end TAPE loop","def release_handlers(self):\n for handler in self.handlers:\n handler.close()","def clean(self):\n for i in self.winfo_children():\n i.destroy()","def cleanup(self):\n logging.debug(\"cleanup called\")\n self.delete_networks()\n self.delete_machines()","def _final_cleanup(self):\n # Clean up and remove the temporary gisdbase\n self._cleanup()\n # Remove resource directories\n if \"error\" in self.run_state or \"terminated\" in self.run_state:\n self.storage_interface.remove_resources()","def cleanup(self): \n if os.path.exists(self.inpms):\n shutil.rmtree(self.inpms)","def clean_frames(self):\n for fn in os.listdir(self.frame_directory):\n if fn.endswith(\".png\") and fn in self.frame_fns:\n os.remove(fn)","def cleanup(self):\n\n pass","def clear_figures() -> None:\n \n for filename in os.listdir(FIGURE_DIR):\n filepath = os.path.join(FIGURE_DIR, filename)\n try:\n shutil.rmtree(filepath)\n except OSError:\n os.remove(filepath)","def clean(self):\n tags = self.get_tags()\n for tag in tags:\n image_name = self.build_image_name(tag)\n try:\n self.client.images.remove(image_name, force=True)\n except Exception as ex:\n print('Cannot remove {}: {}'.format(tag, str(ex)))","def tearDown(self):\n self.recipe.image.delete()","def destroy(self):\r\n for container in self._containers.copy():\r\n container.destroy()\r\n\r\n assert len(self._containers) == 0","def clean_up_artifacts():\n logger.info(\"Removing artifacts from Sandbox\")\n for item in Analyzer.uploaded:\n # Perform the delete\n response = Samples.delete_sample(ids=item)\n if response[\"status_code\"] > 201:\n # File was not removed, log the failure\n logger.warning(\"Failed to delete %s\", item)\n else:\n logger.debug(\"Deleted %s\", item)\n logger.info(\"Artifact cleanup complete\")"],"string":"[\n \"def clean():\\n clean_flatbuffer_binaries()\\n clean_webp_textures()\",\n \"def delete(self):\\n\\t\\tif self.hasUdim:\\n\\t\\t\\tfor a in self.udimPaths:\\n\\t\\t\\t\\ta.delete()\\n\\t\\telse:\\n\\t\\t\\tsuper( textureFile, self ).delete()\",\n \"def destroy(self):\\n\\n self.cmapTexture.destroy()\\n\\n for tex in (self.modulateTexture,\\n self.clipTexture,\\n self.colourTexture):\\n tex.deregister(self.name)\\n glresources.delete(tex.getTextureName())\\n\\n self.removeListeners()\\n self.deregisterAuxImage('modulate')\\n self.deregisterAuxImage('clip')\\n self.deregisterAuxImage('colour')\\n\\n self.modulateTexture = None\\n self.clipTexture = None\\n self.colourTexture = None\\n self.modulateImage = None\\n self.clipImage = None\\n self.colourImage = None\\n self.modulateOpts = None\\n self.clipOpts = None\\n self.colourOpts = None\\n\\n glimageobject.GLImageObject.destroy(self)\",\n \"def delete_AllImgs(self):\\n self.listImages.remove_all_imgs()\",\n \"def cleanup(self):\\r\\n for f in [i for d in self.data.values() for i in d[\\\"filenames\\\"]]:\\r\\n try:\\r\\n os.unlink(f)\\r\\n except Exception: pass\\r\\n self.Destroy()\",\n \"def clearAllPictures(self):\\n shutil.rmtree(PNG_OUTPUT_PATH)\\n os.makedirs(PNG_OUTPUT_PATH)\",\n \"def destroyTempFrames(self):\\n for frame in self.tempFrames:\\n frame.destroy()\\n self.tempFrames = []\",\n \"def teardown():\\n os.remove('green-dot.tif')\\n os.remove('green-dot.jpg')\\n os.remove('green-dot.png')\",\n \"def cleanup(self):\\n self.GP.cleanup()\",\n \"def teardown():\\n for filename in files_to_delete:\\n delete_file(filename)\",\n \"def __del__(self):\\n for filename in self.files:\\n unlink(filename)\",\n \"def unloadallskills(self):\\r\\n for skill in skills.skills.copy():\\r\\n es.unload(\\\"%s/skills/%s\\\" % (info.basename, skill))\\r\\n skills.clearList()\",\n \"def removeTextureToOcc(self):\\n\\t\\tshas = self._getShapes()\\n\\t\\tfor sha in shas:\\n\\t\\t\\tif sha.a.texture_Occ.exists:\\n\\t\\t\\t\\tsha.a.texture_Occ.delete()\",\n \"def clean(self):\\n print(\\\"Cleaning outputs in %s\\\" % self.args.output)\\n files = glob.glob(self.args.output + \\\"*.pkl\\\")\\n for f in files:\\n if os.path.exists(f):\\n os.remove(f)\",\n \"def clear_images(self):\\r\\n\\r\\n with translate_errors():\\r\\n self.audio.clear_pictures()\\r\\n self.audio.save()\\r\\n\\r\\n super().clear_images()\",\n \"def _cleanup():\\n if os.path.exists(WEBPROPERTIES_PATH):\\n os.remove(WEBPROPERTIES_PATH)\\n if os.path.exists(PROFILES_PATH):\\n os.remove(PROFILES_PATH)\",\n \"def remove_images(self):\\n hardware_components.log_method(self, \\\"remove_images\\\")\\n communication_object = self._get_control_software().connection\\n communication_object.remove_all()\",\n \"def clear(self):\\n\\n Console.info(\\\"Cleaning sprite files...\\\")\\n Console.indent()\\n \\n for dirPath, dirNames, fileNames in os.walk(self.base):\\n for fileName in fileNames:\\n if fileName.startswith(\\\"jasysprite\\\"):\\n filePath = os.path.join(dirPath, fileName)\\n Console.debug(\\\"Removing file: %s\\\", filePath)\\n os.remove(filePath)\\n \\n Console.outdent()\",\n \"def cleanup(self):\\n\\t\\tself.loader.cleanup()\\n\\t\\tself.Loaded = False\",\n \"def clean(self):\\n if self.image:\\n self.glance.images.delete(self.image['id'])\\n\\n if self.image_file:\\n shutil.rmtree(self.download_path)\",\n \"def cleanupResources():\\n None\",\n \"def tearDown(self):\\n for fn in self.tempImages:\\n os.remove(os.path.join(self.root, fn))\\n os.rmdir(self.root)\",\n \"def __del__(self):\\r\\n train_data_sources = list(self._train_data.values())\\r\\n test_data_sources = list(self._test_data.values())\\r\\n all_data_sources = train_data_sources + test_data_sources\\r\\n for data_source in all_data_sources:\\r\\n data_source.cleanup()\\r\\n self._tester.__del__()\",\n \"def cleanup(self):\\r\\n for action in self._actions:\\r\\n action.cleanup()\",\n \"def remove_all():\\n storage = FileStorage()\\n objects = storage.all()\\n objects = list(objects.values())\\n\\n for element in objects:\\n storage.delete(element)\\n objects = storage.all()\",\n \"def clear_cache(self):\\n for fle in self.cache_location.glob(\\\"*.pickle\\\"):\\n fle.unlink()\",\n \"def unload(self):\\n for obj in self.objects:\\n self.scene.removeItem(obj)\\n for plant in self.plants:\\n self.scene.removeItem(plant)\\n for tile in self.tiles:\\n tile.unload()\\n self.scene.removeItem(tile)\\n if self.region_back:\\n self.scene.removeItem(self.region_back)\\n self.tiles = []\\n self.objects = []\\n self.plants = []\\n self.region_back = None\\n self.loaded = False\",\n \"def __del__(self):\\n try:\\n self._frame._destroy()\\n except:\\n pass\\n self._turtles = []\\n self._pencils = []\\n del self._frame\",\n \"def clear(self):\\n\\n self.wads = []\\n\\n self.sprites = {}\\n self.sprite_image_cache = {}\\n self.palette = None\\n\\n self.sound_cache = {}\",\n \"def erase_captured_urls(url_list):\\n if gs.local:\\n erase_captured_urls_local(url_list)\\n else:\\n erase_captured_urls_aws(url_list)\",\n \"def cleanup_all(cls):\\n for i in tuple(cls.instances):\\n i.cleanup()\",\n \"def generate_webp_textures():\\n input_files = PNG_TEXTURES['input_files']\\n output_files = PNG_TEXTURES['output_files']\\n if not os.path.exists(TEXTURE_PATH):\\n os.makedirs(TEXTURE_PATH)\\n for png, out in zip(input_files, output_files):\\n if needs_rebuild(png, out):\\n convert_png_image_to_webp(png, out, WEBP_QUALITY)\",\n \"def _cleanup():\\n for itr_ref in ITERATORS_LIST:\\n if context:\\n device_type = context.get_context(\\\"device_target\\\")\\n if device_type == \\\"GPU\\\":\\n itr_ref.release()\\n else:\\n itr = itr_ref()\\n if itr is not None:\\n itr.release()\\n else:\\n itr = itr_ref()\\n if itr is not None:\\n itr.release()\",\n \"def finalizer():\\n for resource_type in pods, pvcs, storageclasses, secrets:\\n for resource in resource_type:\\n resource.delete()\\n resource.ocp.wait_for_delete(resource.name)\\n if pools:\\n # Delete only the RBD pool\\n pools[0].delete()\\n if projects:\\n for project in projects:\\n project.delete(resource_name=project.namespace)\\n project.wait_for_delete(project.namespace)\",\n \"def __del__(self):\\n for f in self._files:\\n f.close()\",\n \"def cleanup(self):\\n self.all_wav_to_mp3()\\n self.past_songs_db.close()\\n self.move_tracks_to_music_folder( )\\n self.delete_leftovers()\\n print \\\"Cleanup finished\\\"\",\n \"def delete_all_images(self, index):\\n self.__accessed_image[:] = False\\n self.__check_delete_images()\",\n \"def __del__(self):\\n for component_name, file in self._file_list.items():\\n file.close()\",\n \"def cleanUp(self):\\r\\n for endpoint in self._endpoints.keys():\\r\\n endpoint.destroy()\\r\\n\\r\\n assert len(self._endpoints) == 0\",\n \"def cleanup(self):\\n self.qemu.clean_run_files()\\n for tmp in glob.glob(self.configfile + \\\"?*\\\"):\\n os.unlink(tmp)\",\n \"def deleteAll(self):\\n for tlight in self.trafficLights:\\n del tlight\\n del self.trafficLights\",\n \"def __del__(self):\\n\\t\\tif self.pictures:\\n\\t\\t\\twith open(self.path(JSON_FILENAME), \\\"w\\\") as f:\\n\\t\\t\\t\\tjson.dump(self.pictures, f)\",\n \"def del_all_sounds(self) -> None:\\n self._sou_mut.acquire()\\n self.sounds = []\\n self._sou_mut.release()\",\n \"def cleanup():\",\n \"def clean_up(self):\\n cv2.destroyAllWindows()\\n # self.vs.release()\",\n \"def clean(self):\\n # Delete vertices / faces / colors / normals :\\n self._vert_buffer.delete()\\n self._index_buffer.delete()\\n self._normals_buffer.delete()\\n self._xrange_buffer.delete()\\n self._math_buffer.delete()\",\n \"def __del__(self):\\n for file in list(self.mFiles.values()):\\n file.close()\",\n \"def clear_images(self):\\r\\n\\r\\n # audio = self.MutagenType(self['filename'])\\r\\n self.audio.pop(\\\"metadata_block_picture\\\", None)\\r\\n self.audio.pop(\\\"coverart\\\", None)\\r\\n self.audio.pop(\\\"coverartmime\\\", None)\\r\\n self.audio.save()\",\n \"def destroy(self):\\n\\n self.renderTexture .destroy()\\n self.cmapTexture .destroy()\\n self.negCmapTexture.destroy()\\n self.lutTexture .destroy()\\n\\n self.removeListeners()\\n self.deregisterLut()\\n\\n globject.GLObject.destroy(self)\\n\\n if self.flatShader is not None: self.flatShader.destroy()\\n if self.dataShader is not None: self.dataShader.destroy()\\n\\n self.dataShader = None\\n self.flatShader = None\\n self.activeShader = None\\n\\n self.lut = None\\n self.renderTexture = None\\n self.cmapTexture = None\\n self.negCmapTexture = None\\n self.lutTexture = None\",\n \"def cleanup(filePath):\\n restoreRenderSettings()\\n cleanupCamera()\\n cleanupWorld()\\n deleteOutputPath(filePath)\",\n \"def clean_flatbuffer_binaries():\\n for element in FLATBUFFERS_CONVERSION_DATA:\\n for json in element.input_files:\\n path = processed_json_path(json)\\n if os.path.isfile(path):\\n os.remove(path)\",\n \"def destructor(self):\\n cv2.destroyAllWindows()\",\n \"def purge():\\n all_hashes = read_all()\\n used_hashes = read_used()\\n\\n for kind, hashes in used_hashes.items():\\n to_remove = all_hashes[kind].difference(hashes)\\n if kind == 'evs':\\n delete_from_directory_by_hashes(EV_DIRECTORY, to_remove)\\n elif kind == 'cache':\\n delete_from_directory_by_hashes(CACHE_DIRECTORY, to_remove)\\n elif kind == 'seeds':\\n delete_from_directory_by_hashes(SEED_DIRECTORY, to_remove)\\n\\n reset_used()\",\n \"def clean(self):\\n if self.options.format != 'svg':\\n for svgfile in self.svgouts.itervalues():\\n os.remove(svgfile)\\n os.rmdir(self.tmpdir)\",\n \"def pibooth_cleanup(app):\",\n \"def cleanup_resources(self, restart=False):\",\n \"def tearDown(self):\\n account_models.User.objects.all().delete()\\n photo_models.PhotoFeed.objects.all().delete()\\n photo_models.PhotoClassification.objects.filter(name__in=[\\\"Rural\\\", \\\"Abstract\\\", \\\"City\\\"]).delete()\\n test_helpers.clear_directory('backend/media/', '*.jpg')\",\n \"def clear_data():\\n for i in range(_MAX_NUM_TESTS):\\n rand, ref = filename(i)\\n if os.path.exists(rand):\\n os.remove(rand)\\n if os.path.exists(ref):\\n os.remove(ref)\",\n \"def _cleanup(self):\\n os.system(\\\"rm -r %s/*\\\" %(self._snippet_index_dir))\\n os.system(\\\"rm %s/*\\\" %(self._para_dir))\\n os.system(\\\"rm %s/*\\\" %(self._temp_dir))\\n os.system(\\\"rm %s/*\\\" %(self._snippet_result_dir))\",\n \"def delete_all_runtimes(self):\\n self.compute_handler.delete_all_runtimes()\",\n \"def cleanup(self):\\n pygame.quit()\",\n \"def __cleanup(self):\\n \\n self.logger.debug(\\\"Deleting assignment and definition of policy\\\")\\n self.interactor.delete_policy_assignment(self.assignment_id)\\n self.interactor.delete_policy_definition(self.policy_id)\",\n \"def removeResourceSims(self):\\n for sim in self.resourceSims:\\n self.world.removeFromWorld(sim)\\n self.resourceSims = []\",\n \"def resource_cleanup(self):\\n for lb in self.loadbalancers:\\n self.octavia_client.load_balancer_delete(lb['id'], cascade=True)\\n try:\\n self.wait_for_lb_resource(\\n self.octavia_client.load_balancer_show, lb['id'],\\n provisioning_status='DELETED')\\n except osc_lib.exceptions.NotFound:\\n pass\\n for fip in self.fips:\\n self.neutron_client.delete_floatingip(fip)\\n # we run the parent resource_cleanup last as it will remove instances\\n # referenced as members in the above cleaned up load balancers\\n super(LBAASv2Test, self).resource_cleanup()\",\n \"def clear_all_triplex_loads(self):\\n # Start by getting all the triplex_load objects.\\n tl_list = self.get_objects_by_type(object_type='triplex_load')\\n\\n # If there aren't any triplex loads, warn and return.\\n if tl_list is None:\\n self.log.warning('clear_all_triplex_loads called, but there '\\n 'are not any triplex_loads in the model!')\\n return\\n\\n # Clear 'em out!\\n for tl in tl_list:\\n self.remove_properties_from_item(item_dict=tl,\\n property_list=TRIPLEX_PARAMS)\\n\\n # All done.\",\n \"def space_cleaning():\\n for file in os.listdir(\\\".\\\"):\\n if file.endswith(\\\".png\\\"):\\n os.remove(file)\",\n \"def clear_thumbnails(self):\",\n \"def unlink_all(self):\\n del self._links[:]\",\n \"def PurgeAll(self):\\n\\t\\tself.acad.ActiveDocument.PurgeAll()\",\n \"def clean_old_data():\\n logger.info('Cleaning standalone files on disk...')\\n for absolute_path in glob.glob(MEDIA_URL + '*'):\\n file_name = os.path.basename(absolute_path)\\n try:\\n relative_path = os.path.join(AUDIOS_URL, file_name)\\n audio = Audio.objects.get(filename=relative_path)\\n if audio.get_type() == 'episode':\\n try:\\n # If there are inactive audios on its being\\n for e in audio.podcast.episode_set.exclude(pk=audio.podcast.active_episode.pk):\\n if not e.is_active():\\n logger.info('Inactive audio found in podcast set. Erasing files.')\\n e.delete_files()\\n except Exception, e:\\n logger.exception(e.message)\\n except ObjectDoesNotExist, e:\\n logger.info('A file with no audio registered in database')\\n if os.path.isfile(relative_path):\\n logger.info('Erasing: %s' % relative_path)\\n os.remove(relative_path)\\n logger.info('... Done.')\",\n \"def delete_all(self):\\n if not self.created:\\n return\\n self.shotgun.batch([dict(\\n request_type='delete',\\n entity_type=type_,\\n entity_id=id_,\\n ) for type_, id_ in reversed(self.created)])\\n self.created = []\",\n \"def tearDown(self):\\n self.image.delete()\",\n \"def cleanup(self):\",\n \"def cleanup(self):\",\n \"def cleanup(self):\",\n \"def clear_shots(context):\\n\\n for shot in context.scene.milkshake_shots:\\n bpy.data.cameras.remove(shot.camera)\\n context.scene.milkshake_shots.clear()\",\n \"def destroy_guis(self):\\n for filter_ in self._filters.values():\\n if filter_._panel:\\n filter_._panel.Destroy()\",\n \"def cleanup(self):\\n results = run_command(\\\"gppkg -q --all\\\")\\n gppkgs = results.split('\\\\n')[self.start_output:self.end_output] #The first line is 'Starting gppkg with args', which we want to ignore.\\n\\n for gppkg in gppkgs:\\n run_command(\\\"gppkg --remove \\\" + gppkg)\",\n \"def __del__(self):\\n for handle in self._filehandles:\\n handle.close()\",\n \"def tearDown(self):\\n Image.objects.all().delete()\",\n \"def unload():\\r\\n database.save() # Save the database\\r\\n\\r\\n \\\"\\\"\\\" Remove any popups \\\"\\\"\\\"\\r\\n deleted = []\\r\\n for popup in popuplib.gPopups:\\r\\n if popup.startswith('sourcerpg_'):\\r\\n deleted.append(popup)\\r\\n for popup in deleted:\\r\\n popuplib.delete(popup)\\r\\n\\r\\n \\\"\\\"\\\" Unload all skills \\\"\\\"\\\"\\r\\n for skill in skills:\\r\\n es.unload(\\\"sourcerpg/skills/\\\" + skill.name)\\r\\n\\r\\n \\\"\\\"\\\" Unload all addons \\\"\\\"\\\"\\r\\n for addon in addons:\\r\\n es.unload(\\\"sourcerpg/addons/\\\" + addon.name)\\r\\n\\r\\n \\\"\\\"\\\" Unregister the server commands \\\"\\\"\\\"\\r\\n cmdlib.unregisterServerCommand(\\\"srpg\\\")\\r\\n cmdlib.unregisterSayCommand(\\\"rpgmenu\\\")\\r\\n cmdlib.unregisterSayCommand(\\\"rpgupgrade\\\")\\r\\n cmdlib.unregisterSayCommand(\\\"rpgsell\\\")\\r\\n cmdlib.unregisterSayCommand(\\\"rpghelp\\\")\\r\\n cmdlib.unregisterSayCommand(\\\"rpgstats\\\")\\r\\n cmdlib.unregisterSayCommand(\\\"rpgrank\\\")\\r\\n cmdlib.unregisterSayCommand(\\\"rpgpopup\\\")\\r\\n cmdlib.unregisterSayCommand(\\\"rpgtop10\\\")\\r\\n \\r\\n gamethread.cancelDelayed('sourcerpg_databasesave')\",\n \"def resetAssets(self):\\n self.__assets = {}\\n gc.collect()\\n return True\",\n \"def clean(self):\\r\\n\\r\\n for _, data in self.composition.items():\\r\\n index_file = Path(data['file'] + '.fxi')\\r\\n if index_file.exists():\\r\\n index_file.unlink()\",\n \"def _cleanup(self):\\n\\n self.netIns = []\\n self.netOuts = []\\n self.Gradients = [None]*self.size\",\n \"def destroyer(): # ;-)\\n\\n def find_files_to_remove(pyfile):\\n for filename in (\\\"%sc\\\" % pyfile, \\\"%so\\\" % pyfile):\\n if exists(filename):\\n yield filename\\n\\n counter = 0\\n try:\\n while True:\\n pyfile = (yield)\\n for filename in find_files_to_remove(pyfile):\\n try:\\n log.debug('removing %s', filename)\\n remove(filename)\\n counter += 1\\n except (IOError, OSError), e:\\n log.error('cannot remove %s', filename)\\n log.debug(e)\\n except GeneratorExit:\\n log.info(\\\"removed files: %s\\\", counter)\",\n \"def bye(self):\\n self._frame._destroy()\\n self._turtles = []\\n self._gpens = []\\n del self._frame\",\n \"def clean():\\n try:\\n os.unlink(options.coords + 'mirza_mrna_input' + '.fa')\\n os.unlink(options.coords + 'mirza_mirna_input' + '.fa')\\n os.unlink(options.coords + 'mirza_mirna_expressions' + '.fa')\\n except:\\n pass\",\n \"def cleanUp(self):\\n\\n tapeList = sorted(glob.glob('TAPE?'))\\n tapeList = ['TAPE%d' % num for num in [1, 2, 5, 6, 7, 10]]\\n for tape in tapeList:\\n if os.path.isfile(tape): os.remove(tape)\\n # end TAPE loop\",\n \"def release_handlers(self):\\n for handler in self.handlers:\\n handler.close()\",\n \"def clean(self):\\n for i in self.winfo_children():\\n i.destroy()\",\n \"def cleanup(self):\\n logging.debug(\\\"cleanup called\\\")\\n self.delete_networks()\\n self.delete_machines()\",\n \"def _final_cleanup(self):\\n # Clean up and remove the temporary gisdbase\\n self._cleanup()\\n # Remove resource directories\\n if \\\"error\\\" in self.run_state or \\\"terminated\\\" in self.run_state:\\n self.storage_interface.remove_resources()\",\n \"def cleanup(self): \\n if os.path.exists(self.inpms):\\n shutil.rmtree(self.inpms)\",\n \"def clean_frames(self):\\n for fn in os.listdir(self.frame_directory):\\n if fn.endswith(\\\".png\\\") and fn in self.frame_fns:\\n os.remove(fn)\",\n \"def cleanup(self):\\n\\n pass\",\n \"def clear_figures() -> None:\\n \\n for filename in os.listdir(FIGURE_DIR):\\n filepath = os.path.join(FIGURE_DIR, filename)\\n try:\\n shutil.rmtree(filepath)\\n except OSError:\\n os.remove(filepath)\",\n \"def clean(self):\\n tags = self.get_tags()\\n for tag in tags:\\n image_name = self.build_image_name(tag)\\n try:\\n self.client.images.remove(image_name, force=True)\\n except Exception as ex:\\n print('Cannot remove {}: {}'.format(tag, str(ex)))\",\n \"def tearDown(self):\\n self.recipe.image.delete()\",\n \"def destroy(self):\\r\\n for container in self._containers.copy():\\r\\n container.destroy()\\r\\n\\r\\n assert len(self._containers) == 0\",\n \"def clean_up_artifacts():\\n logger.info(\\\"Removing artifacts from Sandbox\\\")\\n for item in Analyzer.uploaded:\\n # Perform the delete\\n response = Samples.delete_sample(ids=item)\\n if response[\\\"status_code\\\"] > 201:\\n # File was not removed, log the failure\\n logger.warning(\\\"Failed to delete %s\\\", item)\\n else:\\n logger.debug(\\\"Deleted %s\\\", item)\\n logger.info(\\\"Artifact cleanup complete\\\")\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.70913655","0.6556138","0.6525104","0.6477908","0.6368207","0.6253116","0.62028897","0.6191404","0.6172181","0.6160886","0.6124062","0.6107779","0.60488415","0.60330206","0.6033018","0.6028536","0.6020569","0.6011445","0.5983217","0.590569","0.5905648","0.58951694","0.58934015","0.5878972","0.5870694","0.5866326","0.5863111","0.5862527","0.5858258","0.58563614","0.58517635","0.58398324","0.5837288","0.58364326","0.58184654","0.58181053","0.5817142","0.5801048","0.57908547","0.5779055","0.57646686","0.57578945","0.57487345","0.57484454","0.57417214","0.574081","0.57263124","0.57145804","0.57099026","0.568453","0.56814635","0.5678126","0.567601","0.56657034","0.5659031","0.5622953","0.5614182","0.56134164","0.5571388","0.5568815","0.55624115","0.55457807","0.5543361","0.55433524","0.553815","0.55337965","0.55272603","0.55267525","0.55125844","0.55124867","0.5508746","0.55062526","0.5505415","0.5505415","0.5505415","0.55047226","0.54968977","0.5495961","0.54896873","0.5480714","0.54788625","0.54762495","0.54751384","0.54740113","0.5470612","0.5470175","0.54697824","0.5468881","0.54659855","0.54653555","0.54646873","0.5461519","0.5460556","0.5459839","0.5449862","0.54483724","0.54452616","0.5440162","0.5439166","0.5436235"],"string":"[\n \"0.70913655\",\n \"0.6556138\",\n \"0.6525104\",\n \"0.6477908\",\n \"0.6368207\",\n \"0.6253116\",\n \"0.62028897\",\n \"0.6191404\",\n \"0.6172181\",\n \"0.6160886\",\n \"0.6124062\",\n \"0.6107779\",\n \"0.60488415\",\n \"0.60330206\",\n \"0.6033018\",\n \"0.6028536\",\n \"0.6020569\",\n \"0.6011445\",\n \"0.5983217\",\n \"0.590569\",\n \"0.5905648\",\n \"0.58951694\",\n \"0.58934015\",\n \"0.5878972\",\n \"0.5870694\",\n \"0.5866326\",\n \"0.5863111\",\n \"0.5862527\",\n \"0.5858258\",\n \"0.58563614\",\n \"0.58517635\",\n \"0.58398324\",\n \"0.5837288\",\n \"0.58364326\",\n \"0.58184654\",\n \"0.58181053\",\n \"0.5817142\",\n \"0.5801048\",\n \"0.57908547\",\n \"0.5779055\",\n \"0.57646686\",\n \"0.57578945\",\n \"0.57487345\",\n \"0.57484454\",\n \"0.57417214\",\n \"0.574081\",\n \"0.57263124\",\n \"0.57145804\",\n \"0.57099026\",\n \"0.568453\",\n \"0.56814635\",\n \"0.5678126\",\n \"0.567601\",\n \"0.56657034\",\n \"0.5659031\",\n \"0.5622953\",\n \"0.5614182\",\n \"0.56134164\",\n \"0.5571388\",\n \"0.5568815\",\n \"0.55624115\",\n \"0.55457807\",\n \"0.5543361\",\n \"0.55433524\",\n \"0.553815\",\n \"0.55337965\",\n \"0.55272603\",\n \"0.55267525\",\n \"0.55125844\",\n \"0.55124867\",\n \"0.5508746\",\n \"0.55062526\",\n \"0.5505415\",\n \"0.5505415\",\n \"0.5505415\",\n \"0.55047226\",\n \"0.54968977\",\n \"0.5495961\",\n \"0.54896873\",\n \"0.5480714\",\n \"0.54788625\",\n \"0.54762495\",\n \"0.54751384\",\n \"0.54740113\",\n \"0.5470612\",\n \"0.5470175\",\n \"0.54697824\",\n \"0.5468881\",\n \"0.54659855\",\n \"0.54653555\",\n \"0.54646873\",\n \"0.5461519\",\n \"0.5460556\",\n \"0.5459839\",\n \"0.5449862\",\n \"0.54483724\",\n \"0.54452616\",\n \"0.5440162\",\n \"0.5439166\",\n \"0.5436235\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8128452"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":11299,"cells":{"query":{"kind":"string","value":"Delete all the processed flatbuffer binaries."},"document":{"kind":"string","value":"def clean_flatbuffer_binaries():\n for element in FLATBUFFERS_CONVERSION_DATA:\n for json in element.input_files:\n path = processed_json_path(json)\n if os.path.isfile(path):\n os.remove(path)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def clean():\n clean_flatbuffer_binaries()\n clean_webp_textures()","def clean(self):\n # Delete vertices / faces / colors / normals :\n self._vert_buffer.delete()\n self._index_buffer.delete()\n self._normals_buffer.delete()\n self._xrange_buffer.delete()\n self._math_buffer.delete()","def clear_all(self):\n self.clear_files_paths()\n self.clear_programs()","async def clear_all(self) -> None:","def clean_data_fragments(self) -> None:\n read_path: Path = Path(os.environ[\"DATA_PATH\"]) / \"fragments\"\n try:\n shutil.rmtree(read_path / \"__MACOSX\")\n except FileNotFoundError:\n print('Folder \"__MACOSX\" already removed.')\n\n # delete non-binarized images\n frag_paths: list = list((read_path / \"image-data\").iterdir())\n frags_binarized: list = [fp for fp in frag_paths if \"binarized\" in fp.name]\n frags_delete: set = set(frag_paths).difference(set(frags_binarized))\n for frag in frags_delete:\n frag.unlink()\n frag_paths = frags_binarized\n for frag_path in frag_paths:\n # Python 3.8 hack, seems to be supported without str() on 3.9\n shutil.move(str(frag_path.resolve()), str(read_path.resolve()))\n\n (read_path / \"image-data\").rmdir() # delete empty folder","def clean(self):\n print(\"Cleaning outputs in %s\" % self.args.output)\n files = glob.glob(self.args.output + \"*.pkl\")\n for f in files:\n if os.path.exists(f):\n os.remove(f)","def purge():\n all_hashes = read_all()\n used_hashes = read_used()\n\n for kind, hashes in used_hashes.items():\n to_remove = all_hashes[kind].difference(hashes)\n if kind == 'evs':\n delete_from_directory_by_hashes(EV_DIRECTORY, to_remove)\n elif kind == 'cache':\n delete_from_directory_by_hashes(CACHE_DIRECTORY, to_remove)\n elif kind == 'seeds':\n delete_from_directory_by_hashes(SEED_DIRECTORY, to_remove)\n\n reset_used()","def clean(cls, pdb_object):\n if not cls.computed(pdb_object):\n return\n for successor in cls.successors:\n successor.clean(pdb_object)\n pdb_object.uncomplete(cls.name)\n for file in cls.files(pdb_object):\n file.delete()","def _clean_bins():\n rmtree(LIBS_DIR)\n rmtree(BINS_DIR)\n rmtree(HEADERS_DIR)","def clean():\n clean_files()","def delete_all(self):\n with self.__lock:\n self.__data = dict()\n self.flush()","def clear_data():\n dir_list = [\"generated/*\", \"pub/static/*\", \"var/cache/*\", \"var/page_cache/*\", \"var/view_preprocessed/*\", \"var/tmp/*\"]\n\n for item in dir_list:\n print(\"[ - ] Removing\", item, \"\\n\")\n subprocess.run([\"rm\", \"-rf\", item])","def clean():\n os.system('killall -9 lnd')\n os.system('killall -9 btcd')\n \n shutil.rmtree(btcd_dir)\n os.remove(btcd_log)\n\n index = 0\n while True:\n node = Node.from_index(index)\n try:\n shutil.rmtree(node.path())\n os.remove(node.log())\n except:\n click.echo(f'removed {index} nodes.')\n break\n index += 1","def deleteAllFiles(self, flush=True): \n \n if flush: \n self.flush(False) \n \n for filePath in self.filePathDict.keys(): \n if self.outDir is None: \n fullPath = filePath \n else: \n fullPath = os.path.join(self.outDir,filePath) \n \n if os.path.exists(fullPath): \n os.remove(fullPath)","def delete_all():\n if os.path.exists(DATA_DIR):\n shutil.rmtree(DATA_DIR)","def clean(self):\n if self.options.format != 'svg':\n for svgfile in self.svgouts.itervalues():\n os.remove(svgfile)\n os.rmdir(self.tmpdir)","def delete_all(self, prog:progress=None): \n\t\tself.__output_status('Delete all files')\n\t\tif (self.__check_terminated()):\n\t\t\treturn;\t\n\t\tdelete_dir(self.root)\n\t\ttime.sleep(0.3)","def delete_all(self):\n # delete everything\n shutil.rmtree(self.location)","def cleanup(self):\r\n for f in [i for d in self.data.values() for i in d[\"filenames\"]]:\r\n try:\r\n os.unlink(f)\r\n except Exception: pass\r\n self.Destroy()","def clean(self):\n\n for metric in self.metricList:\n listf = glob.glob(\n '{}/*_{}_{}*'.format(self.outDir, metric.name, self.num))\n if len(listf) > 0:\n for val in listf:\n os.system('rm {}'.format(val))","def delete_b_files(intermediate_files: List[File]) -> None:\n for f in intermediate_files:\n f.remove()","def clear_client_outputs():\n directory = client_variables.output_zip_folder\n for name in glob.glob(directory + '\\\\*'):\n os.remove(name)","def _cleanup_files(self):\n\n for root, dirs, files in os.walk(self.build_directory):\n dirs_to_delete = [\n Path(root).joinpath(x) for x in dirs if x == '__pycache__'\n ]\n files_to_delete = [\n Path(root).joinpath(x) for x in files if Path(x).suffix == '.pyc'\n ]\n for d in dirs_to_delete:\n logger.info('Deleting: %s', d)\n shutil.rmtree(d)\n for f in files_to_delete:\n logger.info('Deleting: %s', f)\n f.unlink()","def _cleanUp(self):\r\n limit = datetime.now() - timedelta(seconds=self._timeout)\r\n\r\n toClean = [msg for msg in self._incompleteMsgs if msg.older(limit)]\r\n\r\n if toClean:\r\n for msg in toClean:\r\n self._incompleteMsgs.remove(msg)\r\n\r\n log.msg('{0} incomplete messages have been dropped '\r\n 'from assembler.'.format(len(toClean)))\r\n\r\n toClean = [uri for uri, (_, timestamp) in self._binaries.iteritems()\r\n if timestamp < limit]\r\n\r\n if toClean:\r\n for uri in toClean:\r\n del self._binaries[uri]\r\n\r\n log.msg('{0} unused binaries have been dropped '\r\n 'from assembler.'.format(len(toClean)))","def clean(self):\n\t\tself.archiver.closeFile()","def cleanup(self):\n self.qemu.clean_run_files()\n for tmp in glob.glob(self.configfile + \"?*\"):\n os.unlink(tmp)","def delete():\n\n # Check the pipe setup.\n check_pipe_setup(sequence=True, j=True)\n\n # The interatomic data.\n for interatom in interatomic_loop():\n # The data.\n if hasattr(interatom, 'j_coupling'):\n del interatom.j_coupling\n\n # The error.\n if hasattr(interatom, 'j_coupling_err'):\n del interatom.j_coupling_err","def clear_batch(self):\n self._batch_idx = 0\n self.variant_states = None\n self.object_specs = None\n self.object_attribute_values = None","def free(self):\n for device_buffer in self.device_buffers.values():\n device_buffer.free()","def PurgeAll(self):\n\t\tself.acad.ActiveDocument.PurgeAll()","def cleanup_intermediate_files(self):\n self.cmd(\"rm -f {local_temp_dir}/*rg_dict* \\\n {local_temp_dir}/*aln* \\\n {local_temp_dir}/snappy*\".\n format(\n local_temp_dir=self.local_temp_dir\n ),\n shell=True)","def delete_all_messages(self):\n\t\tassert len(self.data) > 0, \"Data file is already empty!\"\n\n\t\tfor element in self:\n\t\t\tself.data.remove(element)\n\n\t\tself.update_data()","def clear(self):\n\n Console.info(\"Cleaning sprite files...\")\n Console.indent()\n \n for dirPath, dirNames, fileNames in os.walk(self.base):\n for fileName in fileNames:\n if fileName.startswith(\"jasysprite\"):\n filePath = os.path.join(dirPath, fileName)\n Console.debug(\"Removing file: %s\", filePath)\n os.remove(filePath)\n \n Console.outdent()","def clear_cache(self):\n for fle in self.cache_location.glob(\"*.pickle\"):\n fle.unlink()","def erase_all(self):\n result = self._lib.NRFJPROG_erase_all()\n if result != NrfjprogdllErr.SUCCESS:\n raise APIError(result)","def clean(self):\r\n\r\n for _, data in self.composition.items():\r\n index_file = Path(data['file'] + '.fxi')\r\n if index_file.exists():\r\n index_file.unlink()","def cleanup_files(self):\n\n self.backup_files()\n self.delete_files()","def _clean_files(self):\n if self.delfiles & 1:\n ProcUtils.remove(self.okm)\n if self.delfiles & 2:\n ProcUtils.remove(self.hkm)\n if self.delfiles & 4:\n ProcUtils.remove(self.qkm)\n if self.delfiles & 8:\n ProcUtils.remove(self.obc)\n\n if self.log is False:\n ProcUtils.remove(self.pcf_file)\n base = os.path.basename(self.okm)\n ProcUtils.remove(os.path.join(self.dirs['run'],\n '.'.join(['LogReport', base])))\n ProcUtils.remove(os.path.join(self.dirs['run'],\n '.'.join(['LogStatus', base])))\n ProcUtils.remove(os.path.join(self.dirs['run'],\n '.'.join(['LogUser', base])))","def remove_all(self):\n self._options.clear()\n self._programs.clear()","def delete_all_runtimes(self):\n self.compute_handler.delete_all_runtimes()","def remove_all():\n storage = FileStorage()\n objects = storage.all()\n objects = list(objects.values())\n\n for element in objects:\n storage.delete(element)\n objects = storage.all()","def clean_outputs(self) -> None:\n\n def _delete_if_not_none(fn: Optional[str]) -> None:\n if fn is not None:\n Path(fn).unlink()\n\n _delete_if_not_none(self.config[\"LOG_FILE\"])\n\n for file_ in self.exporter.get_all_files():\n file_.unlink()","def clearQueueAll():","def cleanup(self):\n\n # uninstall sourcedata\n if self.conversion.install_dataset_path.exists():\n # without the ChangeWorkingDir the command does not operate inside\n # of dataset_path\n with utils.ChangeWorkingDir(self.dataset_path):\n datalad.uninstall(\n path=self.conversion.install_dataset_name,\n dataset=self.dataset_path,\n recursive=True\n )\n\n # remove bids conversion\n bids_dir = self._get_bids_dir()\n if bids_dir.exists():\n self.log.info(\"Remove %s\", bids_dir)\n shutil.rmtree(bids_dir)","def cleanup(self):\n\t\tself.pb.cleanup()\n\t\tsys.exit()","def clear_all(self):\n for key in list(self.keys):\n os.remove(self._get_path(key))\n self.keys.remove(key)","def clear_all(self):\n raise NotImplementedError","def clean(self) -> None:\n # remove all *.py and *.pyi files in the folder\n for wc in [\"*.py\", \"*.pyi\", \"modules.json\"]:\n for f in (self.package_path).rglob(wc):\n f.unlink()","def __del__(self):\n for f in self._files:\n f.close()","def clear_binstar(cli, owner):\n for channel in cli.list_channels(owner):\n cli.remove_channel(owner, channel)\n\n for package in cli.user_packages(owner):\n cli.remove_package(owner, package['name'])","def clear_all():\n bpy.ops.object.select_all(action='SELECT')\n bpy.ops.object.delete()","def clean():\n C.libs.clear()\n shutil.rmtree(C.cache_dir, ignore_errors=True)","def flush(self):\n self.genomes = []","def clear(self):\r\n shutil.rmtree(self._output_dir, ignore_errors=True)","def delete_backup(self):\n for pathname in file_io.get_matching_files(self.write_checkpoint_manager._prefix + '*'):\n _delete_file_or_dir(pathname)\n for pathname in file_io.get_matching_files(os.path.join(self.write_checkpoint_manager.directory, 'checkpoint')):\n _delete_file_or_dir(pathname)","def cleanup(self):\n files = self.nlst()\n latest = self.latest_filename\n for filename in files:\n if filename != latest:\n result = self.delete(filename)\n logger.info(f\"Deleted old export from FTP: {result}\")","def flush_buffers(self):\n\n for file_dictionary in [self.infiles, self.outfiles]:\n for name, fi in file_dictionary.items():\n fi.flush_buffers()","def flush(self):\n for f in self.files:\n f.flush()","def __del__(self):\n for filename in self.files:\n unlink(filename)","def cleanup(self):\n results = run_command(\"gppkg -q --all\")\n gppkgs = results.split('\\n')[self.start_output:self.end_output] #The first line is 'Starting gppkg with args', which we want to ignore.\n\n for gppkg in gppkgs:\n run_command(\"gppkg --remove \" + gppkg)","def cleanUp(self):\n print(\" cleaning up\",self.folderSave)\n for fname in glob.glob(self.folderSave+\"/*.*\"):\n if not fname.endswith(\".npy\") and not fname.endswith(\".csv\"):\n print(\" deleting\",os.path.basename(fname))\n os.remove(fname)","def remove_all_state_files():\n for state_file in Path(CONFIG_DIR).glob('**/state.json'):\n state_file.unlink()","def cleanup(self):\n\n print \"Cleaning up...\",\n sys.stdout.flush()\n\n builddir = os.path.join(self.build)\n\n comm = 'rm -rf '+builddir\n #+' '+libdir+' '+logdir\n (output, error, retz) = runShellCommand(comm)\n\n print \"done.\"","def clear_array():\n while len(lines) != 0:\n lines.pop()\n while len(assembly_lines) != 0:\n assembly_lines.pop()","def clean(self):\n files = ['CHG', 'CHGCAR', 'POSCAR', 'INCAR', 'CONTCAR',\n 'DOSCAR', 'EIGENVAL', 'IBZKPT', 'KPOINTS', 'OSZICAR',\n 'OUTCAR', 'PCDAT', 'POTCAR', 'vasprun.xml',\n 'WAVECAR', 'XDATCAR', 'PROCAR', 'ase-sort.dat',\n 'LOCPOT', 'AECCAR0', 'AECCAR1', 'AECCAR2',\n 'WAVECAR.GTO', 'vasp.out', 'vasp.err']\n for f in files:\n try:\n os.remove(f)\n except OSError:\n pass","def DeleteAllItems(self):\r\n\r\n self.DeleteRoot()","def clean(self):\n os.remove(self.apk_path)","def remove_all(self):\n if self._processed:\n res, messages = self._mailconn.search(None, 'ALL')\n if res == 'OK':\n for msg in messages[0].split():\n res, data = self._mailconn.store(msg.decode('utf-8'), '+FLAGS', '\\\\Deleted')\n print(res)","def cleanup(self): \n if os.path.exists(self.inpms):\n shutil.rmtree(self.inpms)","def deleteOutputFiles(self):\n self._deleteFiles(self._outputFileList)","def __del__(self):\n for handle in self._filehandles:\n handle.close()","def cleanup_intermediate_files():\n\n dirs = (DIR_PAGE, DIR_SRGB, DIR_VTI, DIR_TIFF, DIR_BACK, DIR_TEXT)\n map(lambda dir: shutil.rmtree(os.path.join(cwd, dir)) , dirs)","def clean(session):\n clean_dirs = (\n get_path(\".cache\"),\n get_path(\".coverage\"),\n get_path(\".pytest_cache\"),\n get_path(\"__pycache__\"),\n get_path(\"build\"),\n get_path(\"dist\"),\n get_path(\"docs\", \"__pycache__\"),\n get_path(\"docs\", \"build\"),\n get_path(\"scripts\", \"macos\", \"__pycache__\"),\n get_path(\"src\", \"python\", \"bezier.egg-info\"),\n get_path(\"src\", \"python\", \"bezier\", \"__pycache__\"),\n get_path(\"tests\", \"__pycache__\"),\n get_path(\"tests\", \"functional\", \"__pycache__\"),\n get_path(\"tests\", \"unit\", \"__pycache__\"),\n get_path(\"tests\", \"unit\", \"hazmat\", \"__pycache__\"),\n get_path(\"wheelhouse\"),\n )\n clean_globs = (\n get_path(\".coverage\"),\n get_path(\"*.mod\"),\n get_path(\"*.pyc\"),\n get_path(\"docs\", \"abi\", \"example\"),\n get_path(\"src\", \"python\", \"bezier\", \"*.pyc\"),\n get_path(\"src\", \"python\", \"bezier\", \"*.pyd\"),\n get_path(\"src\", \"python\", \"bezier\", \"*.so\"),\n get_path(\"src\", \"fortran\", \"*.o\"),\n get_path(\"tests\", \"*.pyc\"),\n get_path(\"tests\", \"functional\", \"*.pyc\"),\n get_path(\"tests\", \"unit\", \"*.pyc\"),\n )\n for dir_path in clean_dirs:\n session.run(shutil.rmtree, dir_path, ignore_errors=True)\n for glob_path in clean_globs:\n for filename in glob.glob(glob_path):\n session.run(os.remove, filename)","def clean_chunk_files(dirpath):\n workdir = os.getcwd()\n os.chdir(dirpath)\n for filename in glob.glob(\"[0-9]*_[0-9]*_[0-9]*.hdf5\"):\n os.remove(filename)\n os.chdir(workdir)","def finish(self):\r\n\r\n\t\t# Write any unempty buffers to file\r\n logger.info('Writing un-empty buffers...')\r\n self._writeBuffers()\r\n\r\n\t\t# Compact datasets\r\n logger.info('Compacting datasets...')\r\n for ds in self.datasets.keys():\r\n\r\n self._resizeDataset(self.datasets[ds], ds, finished=self.idxs[ds])\r\n\r\n\t\t# close the database\r\n self.db.close()","def KillAllAnts(cls):\n cls.antArray.clear()","def clean():\n try:\n os.unlink(options.coords + 'mirza_mrna_input' + '.fa')\n os.unlink(options.coords + 'mirza_mirna_input' + '.fa')\n os.unlink(options.coords + 'mirza_mirna_expressions' + '.fa')\n except:\n pass","def cleanup():\r\n compiledir = theano.config.compiledir\r\n for directory in os.listdir(compiledir):\r\n file = None\r\n try:\r\n try:\r\n filename = os.path.join(compiledir, directory, \"key.pkl\")\r\n file = open(filename, 'rb')\r\n #print file\r\n try:\r\n keydata = cPickle.load(file)\r\n for key in list(keydata.keys):\r\n have_npy_abi_version = False\r\n have_c_compiler = False\r\n for obj in flatten(key):\r\n if isinstance(obj, numpy.ndarray):\r\n #Reuse have_npy_abi_version to\r\n #force the removing of key\r\n have_npy_abi_version = False\r\n break\r\n elif isinstance(obj, basestring):\r\n if obj.startswith('NPY_ABI_VERSION=0x'):\r\n have_npy_abi_version = True\r\n elif obj.startswith('c_compiler_str='):\r\n have_c_compiler = True\r\n elif (isinstance(obj, (theano.gof.Op, theano.gof.Type)) and\r\n hasattr(obj, 'c_code_cache_version')):\r\n v = obj.c_code_cache_version()\r\n if v not in [(), None] and v not in key[0]:\r\n #Reuse have_npy_abi_version to\r\n #force the removing of key\r\n have_npy_abi_version = False\r\n break\r\n\r\n if not have_npy_abi_version or not have_c_compiler:\r\n try:\r\n #This can happen when we move the compiledir.\r\n if keydata.key_pkl != filename:\r\n keydata.key_pkl = filename\r\n keydata.remove_key(key)\r\n except IOError, e:\r\n _logger.error(\r\n \"Could not remove file '%s'. To complete \"\r\n \"the clean-up, please remove manually \"\r\n \"the directory containing it.\",\r\n filename)\r\n if len(keydata.keys) == 0:\r\n shutil.rmtree(os.path.join(compiledir, directory))\r\n\r\n except EOFError:\r\n _logger.error(\r\n \"Could not read key file '%s'. To complete \"\r\n \"the clean-up, please remove manually \"\r\n \"the directory containing it.\",\r\n filename)\r\n except IOError:\r\n _logger.error(\r\n \"Could not clean up this directory: '%s'. To complete \"\r\n \"the clean-up, please remove it manually.\",\r\n directory)\r\n finally:\r\n if file is not None:\r\n file.close()","def clear_local_output_directory():\n output_path = '../output/*'\n files = glob.glob(output_path)\n for single_file in files:\n os.remove(single_file)","def __do_binary_delete(item):\n\n file_path = DTF_BINARIES_DIR + item.install_name\n\n if utils.delete_file(file_path) != 0:\n log.e(TAG, \"Error removing binary file! Continuing.\")\n\n conn = sqlite3.connect(DTF_DB)\n cur = conn.cursor()\n\n # Remove the line first.\n sql = ('DELETE FROM binaries '\n \"WHERE name='%s'\" % item.name)\n\n cur.execute(sql)\n conn.commit()\n\n return 0","def destroy_all(self):\n self.log.info(\"Destroying the %s cluster\" % self.cluster_name)\n for n in self.all_nodes:\n n.destroy()\n remove(self.save_file)","def remove_all(self):\n # Post a delete all notice to the manager\n self._remove_all()","def remove_all(self):\n # Post a delete all notice to the manager\n self._remove_all()","def _cleanup(self):\n os.system(\"rm -r %s/*\" %(self._snippet_index_dir))\n os.system(\"rm %s/*\" %(self._para_dir))\n os.system(\"rm %s/*\" %(self._temp_dir))\n os.system(\"rm %s/*\" %(self._snippet_result_dir))","def cleanup():","def clean_up(self):\n dist.destroy_process_group()","def deleteAll(self):\n for tlight in self.trafficLights:\n del tlight\n del self.trafficLights","def clear_buffer(self):\n for i, value in enumerate(self.buffer):\n self.buffer[i] = 0","def cleanup(self):\n process_handler.terminate_root_and_child_processes(self._proc.pid)\n self._read_thread.join()\n if self._data_dir:\n shutil.rmtree(self._data_dir, ignore_errors=True)","def clean_files(self):\n self.filenames.clear()","def clean(obj):\n clean_up_generated_files(obj)","def clear_brain():\n\n if os.path.exists(os.path.abspath(\"papaya_data\")):\n shutil.rmtree(os.path.abspath(\"papaya_data\"))","def clear_checkpoint(checkpoint_dir):\n filelist = [f for f in os.listdir(checkpoint_dir) if f.endswith(\".pth.tar\")]\n for f in filelist:\n os.remove(os.path.join(checkpoint_dir, f))\n\n print(\"Checkpoint successfully removed\")","def delete_all(self):\n raise NotImplementedError()","def purge(self):\n pass","def remove_all_outputs(self):\n self._outs.clear()","def clean():\n for dirpath, dirnames, filenames in os.walk('.'):\n for filename in filenames:\n if filename.endswith('.pyc') or filename.endswith('.pyo'):\n full_pathname = os.path.join(dirpath, filename)\n click.echo('Removing {}'.format(full_pathname))\n os.remove(full_pathname)","def unlink_all(self):\n del self._links[:]","def _clean_workdir(self):\n\t\ttoremove = [self._get_config_filepath(), self._get_params_filepath(), self._get_conv_filepath(), self._get_psf_filepath()]\n\t\tfor filepath in toremove:\n\t\t\tif os.path.exists(filepath):\t\n\t\t\t\tlogger.debug(\"Removing existing file %s...\" % (filepath))\n\t\t\t\tos.remove(filepath)","def _clean_native_build():\n rmtree(BUILD_DIR)"],"string":"[\n \"def clean():\\n clean_flatbuffer_binaries()\\n clean_webp_textures()\",\n \"def clean(self):\\n # Delete vertices / faces / colors / normals :\\n self._vert_buffer.delete()\\n self._index_buffer.delete()\\n self._normals_buffer.delete()\\n self._xrange_buffer.delete()\\n self._math_buffer.delete()\",\n \"def clear_all(self):\\n self.clear_files_paths()\\n self.clear_programs()\",\n \"async def clear_all(self) -> None:\",\n \"def clean_data_fragments(self) -> None:\\n read_path: Path = Path(os.environ[\\\"DATA_PATH\\\"]) / \\\"fragments\\\"\\n try:\\n shutil.rmtree(read_path / \\\"__MACOSX\\\")\\n except FileNotFoundError:\\n print('Folder \\\"__MACOSX\\\" already removed.')\\n\\n # delete non-binarized images\\n frag_paths: list = list((read_path / \\\"image-data\\\").iterdir())\\n frags_binarized: list = [fp for fp in frag_paths if \\\"binarized\\\" in fp.name]\\n frags_delete: set = set(frag_paths).difference(set(frags_binarized))\\n for frag in frags_delete:\\n frag.unlink()\\n frag_paths = frags_binarized\\n for frag_path in frag_paths:\\n # Python 3.8 hack, seems to be supported without str() on 3.9\\n shutil.move(str(frag_path.resolve()), str(read_path.resolve()))\\n\\n (read_path / \\\"image-data\\\").rmdir() # delete empty folder\",\n \"def clean(self):\\n print(\\\"Cleaning outputs in %s\\\" % self.args.output)\\n files = glob.glob(self.args.output + \\\"*.pkl\\\")\\n for f in files:\\n if os.path.exists(f):\\n os.remove(f)\",\n \"def purge():\\n all_hashes = read_all()\\n used_hashes = read_used()\\n\\n for kind, hashes in used_hashes.items():\\n to_remove = all_hashes[kind].difference(hashes)\\n if kind == 'evs':\\n delete_from_directory_by_hashes(EV_DIRECTORY, to_remove)\\n elif kind == 'cache':\\n delete_from_directory_by_hashes(CACHE_DIRECTORY, to_remove)\\n elif kind == 'seeds':\\n delete_from_directory_by_hashes(SEED_DIRECTORY, to_remove)\\n\\n reset_used()\",\n \"def clean(cls, pdb_object):\\n if not cls.computed(pdb_object):\\n return\\n for successor in cls.successors:\\n successor.clean(pdb_object)\\n pdb_object.uncomplete(cls.name)\\n for file in cls.files(pdb_object):\\n file.delete()\",\n \"def _clean_bins():\\n rmtree(LIBS_DIR)\\n rmtree(BINS_DIR)\\n rmtree(HEADERS_DIR)\",\n \"def clean():\\n clean_files()\",\n \"def delete_all(self):\\n with self.__lock:\\n self.__data = dict()\\n self.flush()\",\n \"def clear_data():\\n dir_list = [\\\"generated/*\\\", \\\"pub/static/*\\\", \\\"var/cache/*\\\", \\\"var/page_cache/*\\\", \\\"var/view_preprocessed/*\\\", \\\"var/tmp/*\\\"]\\n\\n for item in dir_list:\\n print(\\\"[ - ] Removing\\\", item, \\\"\\\\n\\\")\\n subprocess.run([\\\"rm\\\", \\\"-rf\\\", item])\",\n \"def clean():\\n os.system('killall -9 lnd')\\n os.system('killall -9 btcd')\\n \\n shutil.rmtree(btcd_dir)\\n os.remove(btcd_log)\\n\\n index = 0\\n while True:\\n node = Node.from_index(index)\\n try:\\n shutil.rmtree(node.path())\\n os.remove(node.log())\\n except:\\n click.echo(f'removed {index} nodes.')\\n break\\n index += 1\",\n \"def deleteAllFiles(self, flush=True): \\n \\n if flush: \\n self.flush(False) \\n \\n for filePath in self.filePathDict.keys(): \\n if self.outDir is None: \\n fullPath = filePath \\n else: \\n fullPath = os.path.join(self.outDir,filePath) \\n \\n if os.path.exists(fullPath): \\n os.remove(fullPath)\",\n \"def delete_all():\\n if os.path.exists(DATA_DIR):\\n shutil.rmtree(DATA_DIR)\",\n \"def clean(self):\\n if self.options.format != 'svg':\\n for svgfile in self.svgouts.itervalues():\\n os.remove(svgfile)\\n os.rmdir(self.tmpdir)\",\n \"def delete_all(self, prog:progress=None): \\n\\t\\tself.__output_status('Delete all files')\\n\\t\\tif (self.__check_terminated()):\\n\\t\\t\\treturn;\\t\\n\\t\\tdelete_dir(self.root)\\n\\t\\ttime.sleep(0.3)\",\n \"def delete_all(self):\\n # delete everything\\n shutil.rmtree(self.location)\",\n \"def cleanup(self):\\r\\n for f in [i for d in self.data.values() for i in d[\\\"filenames\\\"]]:\\r\\n try:\\r\\n os.unlink(f)\\r\\n except Exception: pass\\r\\n self.Destroy()\",\n \"def clean(self):\\n\\n for metric in self.metricList:\\n listf = glob.glob(\\n '{}/*_{}_{}*'.format(self.outDir, metric.name, self.num))\\n if len(listf) > 0:\\n for val in listf:\\n os.system('rm {}'.format(val))\",\n \"def delete_b_files(intermediate_files: List[File]) -> None:\\n for f in intermediate_files:\\n f.remove()\",\n \"def clear_client_outputs():\\n directory = client_variables.output_zip_folder\\n for name in glob.glob(directory + '\\\\\\\\*'):\\n os.remove(name)\",\n \"def _cleanup_files(self):\\n\\n for root, dirs, files in os.walk(self.build_directory):\\n dirs_to_delete = [\\n Path(root).joinpath(x) for x in dirs if x == '__pycache__'\\n ]\\n files_to_delete = [\\n Path(root).joinpath(x) for x in files if Path(x).suffix == '.pyc'\\n ]\\n for d in dirs_to_delete:\\n logger.info('Deleting: %s', d)\\n shutil.rmtree(d)\\n for f in files_to_delete:\\n logger.info('Deleting: %s', f)\\n f.unlink()\",\n \"def _cleanUp(self):\\r\\n limit = datetime.now() - timedelta(seconds=self._timeout)\\r\\n\\r\\n toClean = [msg for msg in self._incompleteMsgs if msg.older(limit)]\\r\\n\\r\\n if toClean:\\r\\n for msg in toClean:\\r\\n self._incompleteMsgs.remove(msg)\\r\\n\\r\\n log.msg('{0} incomplete messages have been dropped '\\r\\n 'from assembler.'.format(len(toClean)))\\r\\n\\r\\n toClean = [uri for uri, (_, timestamp) in self._binaries.iteritems()\\r\\n if timestamp < limit]\\r\\n\\r\\n if toClean:\\r\\n for uri in toClean:\\r\\n del self._binaries[uri]\\r\\n\\r\\n log.msg('{0} unused binaries have been dropped '\\r\\n 'from assembler.'.format(len(toClean)))\",\n \"def clean(self):\\n\\t\\tself.archiver.closeFile()\",\n \"def cleanup(self):\\n self.qemu.clean_run_files()\\n for tmp in glob.glob(self.configfile + \\\"?*\\\"):\\n os.unlink(tmp)\",\n \"def delete():\\n\\n # Check the pipe setup.\\n check_pipe_setup(sequence=True, j=True)\\n\\n # The interatomic data.\\n for interatom in interatomic_loop():\\n # The data.\\n if hasattr(interatom, 'j_coupling'):\\n del interatom.j_coupling\\n\\n # The error.\\n if hasattr(interatom, 'j_coupling_err'):\\n del interatom.j_coupling_err\",\n \"def clear_batch(self):\\n self._batch_idx = 0\\n self.variant_states = None\\n self.object_specs = None\\n self.object_attribute_values = None\",\n \"def free(self):\\n for device_buffer in self.device_buffers.values():\\n device_buffer.free()\",\n \"def PurgeAll(self):\\n\\t\\tself.acad.ActiveDocument.PurgeAll()\",\n \"def cleanup_intermediate_files(self):\\n self.cmd(\\\"rm -f {local_temp_dir}/*rg_dict* \\\\\\n {local_temp_dir}/*aln* \\\\\\n {local_temp_dir}/snappy*\\\".\\n format(\\n local_temp_dir=self.local_temp_dir\\n ),\\n shell=True)\",\n \"def delete_all_messages(self):\\n\\t\\tassert len(self.data) > 0, \\\"Data file is already empty!\\\"\\n\\n\\t\\tfor element in self:\\n\\t\\t\\tself.data.remove(element)\\n\\n\\t\\tself.update_data()\",\n \"def clear(self):\\n\\n Console.info(\\\"Cleaning sprite files...\\\")\\n Console.indent()\\n \\n for dirPath, dirNames, fileNames in os.walk(self.base):\\n for fileName in fileNames:\\n if fileName.startswith(\\\"jasysprite\\\"):\\n filePath = os.path.join(dirPath, fileName)\\n Console.debug(\\\"Removing file: %s\\\", filePath)\\n os.remove(filePath)\\n \\n Console.outdent()\",\n \"def clear_cache(self):\\n for fle in self.cache_location.glob(\\\"*.pickle\\\"):\\n fle.unlink()\",\n \"def erase_all(self):\\n result = self._lib.NRFJPROG_erase_all()\\n if result != NrfjprogdllErr.SUCCESS:\\n raise APIError(result)\",\n \"def clean(self):\\r\\n\\r\\n for _, data in self.composition.items():\\r\\n index_file = Path(data['file'] + '.fxi')\\r\\n if index_file.exists():\\r\\n index_file.unlink()\",\n \"def cleanup_files(self):\\n\\n self.backup_files()\\n self.delete_files()\",\n \"def _clean_files(self):\\n if self.delfiles & 1:\\n ProcUtils.remove(self.okm)\\n if self.delfiles & 2:\\n ProcUtils.remove(self.hkm)\\n if self.delfiles & 4:\\n ProcUtils.remove(self.qkm)\\n if self.delfiles & 8:\\n ProcUtils.remove(self.obc)\\n\\n if self.log is False:\\n ProcUtils.remove(self.pcf_file)\\n base = os.path.basename(self.okm)\\n ProcUtils.remove(os.path.join(self.dirs['run'],\\n '.'.join(['LogReport', base])))\\n ProcUtils.remove(os.path.join(self.dirs['run'],\\n '.'.join(['LogStatus', base])))\\n ProcUtils.remove(os.path.join(self.dirs['run'],\\n '.'.join(['LogUser', base])))\",\n \"def remove_all(self):\\n self._options.clear()\\n self._programs.clear()\",\n \"def delete_all_runtimes(self):\\n self.compute_handler.delete_all_runtimes()\",\n \"def remove_all():\\n storage = FileStorage()\\n objects = storage.all()\\n objects = list(objects.values())\\n\\n for element in objects:\\n storage.delete(element)\\n objects = storage.all()\",\n \"def clean_outputs(self) -> None:\\n\\n def _delete_if_not_none(fn: Optional[str]) -> None:\\n if fn is not None:\\n Path(fn).unlink()\\n\\n _delete_if_not_none(self.config[\\\"LOG_FILE\\\"])\\n\\n for file_ in self.exporter.get_all_files():\\n file_.unlink()\",\n \"def clearQueueAll():\",\n \"def cleanup(self):\\n\\n # uninstall sourcedata\\n if self.conversion.install_dataset_path.exists():\\n # without the ChangeWorkingDir the command does not operate inside\\n # of dataset_path\\n with utils.ChangeWorkingDir(self.dataset_path):\\n datalad.uninstall(\\n path=self.conversion.install_dataset_name,\\n dataset=self.dataset_path,\\n recursive=True\\n )\\n\\n # remove bids conversion\\n bids_dir = self._get_bids_dir()\\n if bids_dir.exists():\\n self.log.info(\\\"Remove %s\\\", bids_dir)\\n shutil.rmtree(bids_dir)\",\n \"def cleanup(self):\\n\\t\\tself.pb.cleanup()\\n\\t\\tsys.exit()\",\n \"def clear_all(self):\\n for key in list(self.keys):\\n os.remove(self._get_path(key))\\n self.keys.remove(key)\",\n \"def clear_all(self):\\n raise NotImplementedError\",\n \"def clean(self) -> None:\\n # remove all *.py and *.pyi files in the folder\\n for wc in [\\\"*.py\\\", \\\"*.pyi\\\", \\\"modules.json\\\"]:\\n for f in (self.package_path).rglob(wc):\\n f.unlink()\",\n \"def __del__(self):\\n for f in self._files:\\n f.close()\",\n \"def clear_binstar(cli, owner):\\n for channel in cli.list_channels(owner):\\n cli.remove_channel(owner, channel)\\n\\n for package in cli.user_packages(owner):\\n cli.remove_package(owner, package['name'])\",\n \"def clear_all():\\n bpy.ops.object.select_all(action='SELECT')\\n bpy.ops.object.delete()\",\n \"def clean():\\n C.libs.clear()\\n shutil.rmtree(C.cache_dir, ignore_errors=True)\",\n \"def flush(self):\\n self.genomes = []\",\n \"def clear(self):\\r\\n shutil.rmtree(self._output_dir, ignore_errors=True)\",\n \"def delete_backup(self):\\n for pathname in file_io.get_matching_files(self.write_checkpoint_manager._prefix + '*'):\\n _delete_file_or_dir(pathname)\\n for pathname in file_io.get_matching_files(os.path.join(self.write_checkpoint_manager.directory, 'checkpoint')):\\n _delete_file_or_dir(pathname)\",\n \"def cleanup(self):\\n files = self.nlst()\\n latest = self.latest_filename\\n for filename in files:\\n if filename != latest:\\n result = self.delete(filename)\\n logger.info(f\\\"Deleted old export from FTP: {result}\\\")\",\n \"def flush_buffers(self):\\n\\n for file_dictionary in [self.infiles, self.outfiles]:\\n for name, fi in file_dictionary.items():\\n fi.flush_buffers()\",\n \"def flush(self):\\n for f in self.files:\\n f.flush()\",\n \"def __del__(self):\\n for filename in self.files:\\n unlink(filename)\",\n \"def cleanup(self):\\n results = run_command(\\\"gppkg -q --all\\\")\\n gppkgs = results.split('\\\\n')[self.start_output:self.end_output] #The first line is 'Starting gppkg with args', which we want to ignore.\\n\\n for gppkg in gppkgs:\\n run_command(\\\"gppkg --remove \\\" + gppkg)\",\n \"def cleanUp(self):\\n print(\\\" cleaning up\\\",self.folderSave)\\n for fname in glob.glob(self.folderSave+\\\"/*.*\\\"):\\n if not fname.endswith(\\\".npy\\\") and not fname.endswith(\\\".csv\\\"):\\n print(\\\" deleting\\\",os.path.basename(fname))\\n os.remove(fname)\",\n \"def remove_all_state_files():\\n for state_file in Path(CONFIG_DIR).glob('**/state.json'):\\n state_file.unlink()\",\n \"def cleanup(self):\\n\\n print \\\"Cleaning up...\\\",\\n sys.stdout.flush()\\n\\n builddir = os.path.join(self.build)\\n\\n comm = 'rm -rf '+builddir\\n #+' '+libdir+' '+logdir\\n (output, error, retz) = runShellCommand(comm)\\n\\n print \\\"done.\\\"\",\n \"def clear_array():\\n while len(lines) != 0:\\n lines.pop()\\n while len(assembly_lines) != 0:\\n assembly_lines.pop()\",\n \"def clean(self):\\n files = ['CHG', 'CHGCAR', 'POSCAR', 'INCAR', 'CONTCAR',\\n 'DOSCAR', 'EIGENVAL', 'IBZKPT', 'KPOINTS', 'OSZICAR',\\n 'OUTCAR', 'PCDAT', 'POTCAR', 'vasprun.xml',\\n 'WAVECAR', 'XDATCAR', 'PROCAR', 'ase-sort.dat',\\n 'LOCPOT', 'AECCAR0', 'AECCAR1', 'AECCAR2',\\n 'WAVECAR.GTO', 'vasp.out', 'vasp.err']\\n for f in files:\\n try:\\n os.remove(f)\\n except OSError:\\n pass\",\n \"def DeleteAllItems(self):\\r\\n\\r\\n self.DeleteRoot()\",\n \"def clean(self):\\n os.remove(self.apk_path)\",\n \"def remove_all(self):\\n if self._processed:\\n res, messages = self._mailconn.search(None, 'ALL')\\n if res == 'OK':\\n for msg in messages[0].split():\\n res, data = self._mailconn.store(msg.decode('utf-8'), '+FLAGS', '\\\\\\\\Deleted')\\n print(res)\",\n \"def cleanup(self): \\n if os.path.exists(self.inpms):\\n shutil.rmtree(self.inpms)\",\n \"def deleteOutputFiles(self):\\n self._deleteFiles(self._outputFileList)\",\n \"def __del__(self):\\n for handle in self._filehandles:\\n handle.close()\",\n \"def cleanup_intermediate_files():\\n\\n dirs = (DIR_PAGE, DIR_SRGB, DIR_VTI, DIR_TIFF, DIR_BACK, DIR_TEXT)\\n map(lambda dir: shutil.rmtree(os.path.join(cwd, dir)) , dirs)\",\n \"def clean(session):\\n clean_dirs = (\\n get_path(\\\".cache\\\"),\\n get_path(\\\".coverage\\\"),\\n get_path(\\\".pytest_cache\\\"),\\n get_path(\\\"__pycache__\\\"),\\n get_path(\\\"build\\\"),\\n get_path(\\\"dist\\\"),\\n get_path(\\\"docs\\\", \\\"__pycache__\\\"),\\n get_path(\\\"docs\\\", \\\"build\\\"),\\n get_path(\\\"scripts\\\", \\\"macos\\\", \\\"__pycache__\\\"),\\n get_path(\\\"src\\\", \\\"python\\\", \\\"bezier.egg-info\\\"),\\n get_path(\\\"src\\\", \\\"python\\\", \\\"bezier\\\", \\\"__pycache__\\\"),\\n get_path(\\\"tests\\\", \\\"__pycache__\\\"),\\n get_path(\\\"tests\\\", \\\"functional\\\", \\\"__pycache__\\\"),\\n get_path(\\\"tests\\\", \\\"unit\\\", \\\"__pycache__\\\"),\\n get_path(\\\"tests\\\", \\\"unit\\\", \\\"hazmat\\\", \\\"__pycache__\\\"),\\n get_path(\\\"wheelhouse\\\"),\\n )\\n clean_globs = (\\n get_path(\\\".coverage\\\"),\\n get_path(\\\"*.mod\\\"),\\n get_path(\\\"*.pyc\\\"),\\n get_path(\\\"docs\\\", \\\"abi\\\", \\\"example\\\"),\\n get_path(\\\"src\\\", \\\"python\\\", \\\"bezier\\\", \\\"*.pyc\\\"),\\n get_path(\\\"src\\\", \\\"python\\\", \\\"bezier\\\", \\\"*.pyd\\\"),\\n get_path(\\\"src\\\", \\\"python\\\", \\\"bezier\\\", \\\"*.so\\\"),\\n get_path(\\\"src\\\", \\\"fortran\\\", \\\"*.o\\\"),\\n get_path(\\\"tests\\\", \\\"*.pyc\\\"),\\n get_path(\\\"tests\\\", \\\"functional\\\", \\\"*.pyc\\\"),\\n get_path(\\\"tests\\\", \\\"unit\\\", \\\"*.pyc\\\"),\\n )\\n for dir_path in clean_dirs:\\n session.run(shutil.rmtree, dir_path, ignore_errors=True)\\n for glob_path in clean_globs:\\n for filename in glob.glob(glob_path):\\n session.run(os.remove, filename)\",\n \"def clean_chunk_files(dirpath):\\n workdir = os.getcwd()\\n os.chdir(dirpath)\\n for filename in glob.glob(\\\"[0-9]*_[0-9]*_[0-9]*.hdf5\\\"):\\n os.remove(filename)\\n os.chdir(workdir)\",\n \"def finish(self):\\r\\n\\r\\n\\t\\t# Write any unempty buffers to file\\r\\n logger.info('Writing un-empty buffers...')\\r\\n self._writeBuffers()\\r\\n\\r\\n\\t\\t# Compact datasets\\r\\n logger.info('Compacting datasets...')\\r\\n for ds in self.datasets.keys():\\r\\n\\r\\n self._resizeDataset(self.datasets[ds], ds, finished=self.idxs[ds])\\r\\n\\r\\n\\t\\t# close the database\\r\\n self.db.close()\",\n \"def KillAllAnts(cls):\\n cls.antArray.clear()\",\n \"def clean():\\n try:\\n os.unlink(options.coords + 'mirza_mrna_input' + '.fa')\\n os.unlink(options.coords + 'mirza_mirna_input' + '.fa')\\n os.unlink(options.coords + 'mirza_mirna_expressions' + '.fa')\\n except:\\n pass\",\n \"def cleanup():\\r\\n compiledir = theano.config.compiledir\\r\\n for directory in os.listdir(compiledir):\\r\\n file = None\\r\\n try:\\r\\n try:\\r\\n filename = os.path.join(compiledir, directory, \\\"key.pkl\\\")\\r\\n file = open(filename, 'rb')\\r\\n #print file\\r\\n try:\\r\\n keydata = cPickle.load(file)\\r\\n for key in list(keydata.keys):\\r\\n have_npy_abi_version = False\\r\\n have_c_compiler = False\\r\\n for obj in flatten(key):\\r\\n if isinstance(obj, numpy.ndarray):\\r\\n #Reuse have_npy_abi_version to\\r\\n #force the removing of key\\r\\n have_npy_abi_version = False\\r\\n break\\r\\n elif isinstance(obj, basestring):\\r\\n if obj.startswith('NPY_ABI_VERSION=0x'):\\r\\n have_npy_abi_version = True\\r\\n elif obj.startswith('c_compiler_str='):\\r\\n have_c_compiler = True\\r\\n elif (isinstance(obj, (theano.gof.Op, theano.gof.Type)) and\\r\\n hasattr(obj, 'c_code_cache_version')):\\r\\n v = obj.c_code_cache_version()\\r\\n if v not in [(), None] and v not in key[0]:\\r\\n #Reuse have_npy_abi_version to\\r\\n #force the removing of key\\r\\n have_npy_abi_version = False\\r\\n break\\r\\n\\r\\n if not have_npy_abi_version or not have_c_compiler:\\r\\n try:\\r\\n #This can happen when we move the compiledir.\\r\\n if keydata.key_pkl != filename:\\r\\n keydata.key_pkl = filename\\r\\n keydata.remove_key(key)\\r\\n except IOError, e:\\r\\n _logger.error(\\r\\n \\\"Could not remove file '%s'. To complete \\\"\\r\\n \\\"the clean-up, please remove manually \\\"\\r\\n \\\"the directory containing it.\\\",\\r\\n filename)\\r\\n if len(keydata.keys) == 0:\\r\\n shutil.rmtree(os.path.join(compiledir, directory))\\r\\n\\r\\n except EOFError:\\r\\n _logger.error(\\r\\n \\\"Could not read key file '%s'. To complete \\\"\\r\\n \\\"the clean-up, please remove manually \\\"\\r\\n \\\"the directory containing it.\\\",\\r\\n filename)\\r\\n except IOError:\\r\\n _logger.error(\\r\\n \\\"Could not clean up this directory: '%s'. To complete \\\"\\r\\n \\\"the clean-up, please remove it manually.\\\",\\r\\n directory)\\r\\n finally:\\r\\n if file is not None:\\r\\n file.close()\",\n \"def clear_local_output_directory():\\n output_path = '../output/*'\\n files = glob.glob(output_path)\\n for single_file in files:\\n os.remove(single_file)\",\n \"def __do_binary_delete(item):\\n\\n file_path = DTF_BINARIES_DIR + item.install_name\\n\\n if utils.delete_file(file_path) != 0:\\n log.e(TAG, \\\"Error removing binary file! Continuing.\\\")\\n\\n conn = sqlite3.connect(DTF_DB)\\n cur = conn.cursor()\\n\\n # Remove the line first.\\n sql = ('DELETE FROM binaries '\\n \\\"WHERE name='%s'\\\" % item.name)\\n\\n cur.execute(sql)\\n conn.commit()\\n\\n return 0\",\n \"def destroy_all(self):\\n self.log.info(\\\"Destroying the %s cluster\\\" % self.cluster_name)\\n for n in self.all_nodes:\\n n.destroy()\\n remove(self.save_file)\",\n \"def remove_all(self):\\n # Post a delete all notice to the manager\\n self._remove_all()\",\n \"def remove_all(self):\\n # Post a delete all notice to the manager\\n self._remove_all()\",\n \"def _cleanup(self):\\n os.system(\\\"rm -r %s/*\\\" %(self._snippet_index_dir))\\n os.system(\\\"rm %s/*\\\" %(self._para_dir))\\n os.system(\\\"rm %s/*\\\" %(self._temp_dir))\\n os.system(\\\"rm %s/*\\\" %(self._snippet_result_dir))\",\n \"def cleanup():\",\n \"def clean_up(self):\\n dist.destroy_process_group()\",\n \"def deleteAll(self):\\n for tlight in self.trafficLights:\\n del tlight\\n del self.trafficLights\",\n \"def clear_buffer(self):\\n for i, value in enumerate(self.buffer):\\n self.buffer[i] = 0\",\n \"def cleanup(self):\\n process_handler.terminate_root_and_child_processes(self._proc.pid)\\n self._read_thread.join()\\n if self._data_dir:\\n shutil.rmtree(self._data_dir, ignore_errors=True)\",\n \"def clean_files(self):\\n self.filenames.clear()\",\n \"def clean(obj):\\n clean_up_generated_files(obj)\",\n \"def clear_brain():\\n\\n if os.path.exists(os.path.abspath(\\\"papaya_data\\\")):\\n shutil.rmtree(os.path.abspath(\\\"papaya_data\\\"))\",\n \"def clear_checkpoint(checkpoint_dir):\\n filelist = [f for f in os.listdir(checkpoint_dir) if f.endswith(\\\".pth.tar\\\")]\\n for f in filelist:\\n os.remove(os.path.join(checkpoint_dir, f))\\n\\n print(\\\"Checkpoint successfully removed\\\")\",\n \"def delete_all(self):\\n raise NotImplementedError()\",\n \"def purge(self):\\n pass\",\n \"def remove_all_outputs(self):\\n self._outs.clear()\",\n \"def clean():\\n for dirpath, dirnames, filenames in os.walk('.'):\\n for filename in filenames:\\n if filename.endswith('.pyc') or filename.endswith('.pyo'):\\n full_pathname = os.path.join(dirpath, filename)\\n click.echo('Removing {}'.format(full_pathname))\\n os.remove(full_pathname)\",\n \"def unlink_all(self):\\n del self._links[:]\",\n \"def _clean_workdir(self):\\n\\t\\ttoremove = [self._get_config_filepath(), self._get_params_filepath(), self._get_conv_filepath(), self._get_psf_filepath()]\\n\\t\\tfor filepath in toremove:\\n\\t\\t\\tif os.path.exists(filepath):\\t\\n\\t\\t\\t\\tlogger.debug(\\\"Removing existing file %s...\\\" % (filepath))\\n\\t\\t\\t\\tos.remove(filepath)\",\n \"def _clean_native_build():\\n rmtree(BUILD_DIR)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7080938","0.6617103","0.64717567","0.6464323","0.6414247","0.6351224","0.6346113","0.6323048","0.6296938","0.62765425","0.6252906","0.6217102","0.61619747","0.61525744","0.6143297","0.6128095","0.6123065","0.6101668","0.60976034","0.6087722","0.6086085","0.6081509","0.6037965","0.60286456","0.60147667","0.60073555","0.59967035","0.59785587","0.5977556","0.5951491","0.5951453","0.59428537","0.5905471","0.59013414","0.58751553","0.5868863","0.58681923","0.5863693","0.5854113","0.5847265","0.5843536","0.5843095","0.5841818","0.5839819","0.58321786","0.58314085","0.5823872","0.58219343","0.5819485","0.58191776","0.5808978","0.5808529","0.5808346","0.57945985","0.5794049","0.578942","0.57858837","0.5779845","0.57767487","0.57762635","0.5767602","0.57639736","0.5760155","0.57571155","0.5753744","0.5751544","0.574811","0.5738529","0.5734028","0.5731879","0.5729243","0.57224876","0.5716469","0.57120883","0.57106924","0.5706923","0.5706409","0.57062405","0.5703026","0.5686053","0.5682818","0.5681307","0.5681307","0.5678239","0.5676669","0.56751883","0.5667274","0.5664859","0.56590086","0.56589884","0.5658925","0.56479836","0.5638447","0.56349474","0.56284505","0.561548","0.561245","0.56053954","0.55986357","0.559365"],"string":"[\n \"0.7080938\",\n \"0.6617103\",\n \"0.64717567\",\n \"0.6464323\",\n \"0.6414247\",\n \"0.6351224\",\n \"0.6346113\",\n \"0.6323048\",\n \"0.6296938\",\n \"0.62765425\",\n \"0.6252906\",\n \"0.6217102\",\n \"0.61619747\",\n \"0.61525744\",\n \"0.6143297\",\n \"0.6128095\",\n \"0.6123065\",\n \"0.6101668\",\n \"0.60976034\",\n \"0.6087722\",\n \"0.6086085\",\n \"0.6081509\",\n \"0.6037965\",\n \"0.60286456\",\n \"0.60147667\",\n \"0.60073555\",\n \"0.59967035\",\n \"0.59785587\",\n \"0.5977556\",\n \"0.5951491\",\n \"0.5951453\",\n \"0.59428537\",\n \"0.5905471\",\n \"0.59013414\",\n \"0.58751553\",\n \"0.5868863\",\n \"0.58681923\",\n \"0.5863693\",\n \"0.5854113\",\n \"0.5847265\",\n \"0.5843536\",\n \"0.5843095\",\n \"0.5841818\",\n \"0.5839819\",\n \"0.58321786\",\n \"0.58314085\",\n \"0.5823872\",\n \"0.58219343\",\n \"0.5819485\",\n \"0.58191776\",\n \"0.5808978\",\n \"0.5808529\",\n \"0.5808346\",\n \"0.57945985\",\n \"0.5794049\",\n \"0.578942\",\n \"0.57858837\",\n \"0.5779845\",\n \"0.57767487\",\n \"0.57762635\",\n \"0.5767602\",\n \"0.57639736\",\n \"0.5760155\",\n \"0.57571155\",\n \"0.5753744\",\n \"0.5751544\",\n \"0.574811\",\n \"0.5738529\",\n \"0.5734028\",\n \"0.5731879\",\n \"0.5729243\",\n \"0.57224876\",\n \"0.5716469\",\n \"0.57120883\",\n \"0.57106924\",\n \"0.5706923\",\n \"0.5706409\",\n \"0.57062405\",\n \"0.5703026\",\n \"0.5686053\",\n \"0.5682818\",\n \"0.5681307\",\n \"0.5681307\",\n \"0.5678239\",\n \"0.5676669\",\n \"0.56751883\",\n \"0.5667274\",\n \"0.5664859\",\n \"0.56590086\",\n \"0.56589884\",\n \"0.5658925\",\n \"0.56479836\",\n \"0.5638447\",\n \"0.56349474\",\n \"0.56284505\",\n \"0.561548\",\n \"0.561245\",\n \"0.56053954\",\n \"0.55986357\",\n \"0.559365\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7819535"},"document_rank":{"kind":"string","value":"0"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":112,"numItemsPerPage":100,"numTotalItems":95772,"offset":11200,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc4OTk5MDkzOCwianRpIjoiYjQ5N2UwNTgtZmFlOC00YTQ4LWI0M2QtM2IwMmEwNTI0ZDg3Iiwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3ODk5OTQ1MzgsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.36gEpqZBYqqpuT43r8tGGSOwvPquBEgPAsMybwZfU4Tcsvble4ayoGfnszwjURClZnyPzjyuGD_I-OOm6WpuBQ","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":95772,"numBytesParquet":1928375891}]},"discussionsStats":{"closed":1,"open":1,"total":2},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
query stringlengths 9 3.4k | document stringlengths 9 87.4k | metadata dict | negatives listlengths 4 101 | negative_scores listlengths 4 101 | document_score stringlengths 3 10 | document_rank stringclasses 102
values |
|---|---|---|---|---|---|---|
Information about Configuration page for app | def infoAboutConfiguration(request):
username = request.session.get('username', False)
profile = request.session.get('profile', False)
if(username):
context = {'username': username,'profile':profile}
return render(request, 'MedTAG_sket_dock_App/index.html', context)
else:
return... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def configuration():",
"def show_info(self):\n print 'Querying the station for the configuration...'\n config = self.station.getConfig()\n for key in sorted(config):\n print '%s: %s' % (key, config[key])",
"def config(self):\n pass",
"def config(self):\n pass",
... | [
"0.70983917",
"0.6656398",
"0.661873",
"0.661873",
"0.65734",
"0.65734",
"0.64934826",
"0.6467568",
"0.63883966",
"0.6383557",
"0.6382818",
"0.63664263",
"0.6353951",
"0.6350565",
"0.63502836",
"0.6347238",
"0.6346799",
"0.63267547",
"0.6300264",
"0.6297027",
"0.6291528",
"... | 0.0 | -1 |
Tutorial page for app | def tutorial(request):
username = request.session.get('username', False)
profile = request.session.get('profile', False)
if(username):
context = {'username': username,'profile':profile}
return render(request, 'MedTAG_sket_dock_App/index.html', context)
else:
return redirect('Med... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def tutorials(request):\n # Test Comment\n assert isinstance(request, HttpRequest)\n return render(\n request,\n 'tutorials.html',\n context_instance=RequestContext(request, {})\n )",
"def callForTutorials(request):\n # Test Comment\n assert isinstance(request, HttpRequest)... | [
"0.7307964",
"0.67953116",
"0.6754259",
"0.66745186",
"0.66534",
"0.6528225",
"0.64401436",
"0.64039856",
"0.63926584",
"0.63527006",
"0.6335105",
"0.62391394",
"0.6103462",
"0.60975313",
"0.6084304",
"0.6075097",
"0.6068865",
"0.603304",
"0.6026463",
"0.59866536",
"0.5966442... | 0.5979742 | 20 |
User's reports stats page for app | def my_stats(request):
username = request.session.get('username', False)
profile = request.session.get('profile', False)
if(username):
context = {'username': username,'profile':profile}
return render(request, 'MedTAG_sket_dock_App/index.html', context)
else:
return redirect('Med... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def index():\r\n\r\n title = \"Global Statistics\"\r\n\r\n n_auth = n_auth_users()\r\n\r\n n_anon = n_anon_users()\r\n\r\n n_total_users = n_anon + n_auth\r\n\r\n n_published_apps = cached_apps.n_published()\r\n n_draft_apps = cached_apps.n_draft()\r\n n_total_apps = n_published_apps + n_draft... | [
"0.75568897",
"0.7478348",
"0.7337845",
"0.7281632",
"0.72750634",
"0.726631",
"0.7097302",
"0.69867873",
"0.6986714",
"0.69798213",
"0.6931392",
"0.68784565",
"0.6758122",
"0.66787434",
"0.659769",
"0.6564091",
"0.65558517",
"0.6452271",
"0.64451945",
"0.64124894",
"0.638154... | 0.6499229 | 17 |
Reports' stats page for app | def reports_stats(request):
username = request.session.get('username', False)
profile = request.session.get('profile', False)
if(username):
context = {'username': username,'profile':profile}
return render(request, 'MedTAG_sket_dock_App/index.html', context)
else:
return redirect... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def index():\r\n\r\n title = \"Global Statistics\"\r\n\r\n n_auth = n_auth_users()\r\n\r\n n_anon = n_anon_users()\r\n\r\n n_total_users = n_anon + n_auth\r\n\r\n n_published_apps = cached_apps.n_published()\r\n n_draft_apps = cached_apps.n_draft()\r\n n_total_apps = n_published_apps + n_draft... | [
"0.76013976",
"0.75708693",
"0.73110414",
"0.70818317",
"0.70160705",
"0.7005195",
"0.6829563",
"0.66889244",
"0.662684",
"0.6610973",
"0.6600029",
"0.6582012",
"0.6565331",
"0.655495",
"0.65318143",
"0.6431495",
"0.6416689",
"0.63672954",
"0.6351216",
"0.63458294",
"0.631499... | 0.68279994 | 7 |
This view returns the current session parameters | def get_session_params(request):
json_resp = {}
usecase = request.session.get('usecase',None)
language = request.session.get('language',None)
institute = request.session.get('institute',None)
annotation = request.session.get('mode',None)
team_member = request.session.get('team_member',None)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def show_params(self):\n \n return self.params[self.profile]",
"def get_parameters(self):\n return self.context.params",
"def SessionInfo(self):\r\n\t\treturn self._get_attribute('sessionInfo')",
"def get(self):\n return self.params",
"def get(self):\n return self._params",
... | [
"0.6704671",
"0.64286435",
"0.624347",
"0.6236312",
"0.6195294",
"0.6193571",
"0.61749065",
"0.61749065",
"0.6170325",
"0.6170325",
"0.6170325",
"0.6147238",
"0.6137965",
"0.61196494",
"0.6095869",
"0.6027674",
"0.60054123",
"0.5997175",
"0.5986101",
"0.5984785",
"0.59830755"... | 0.6681705 | 1 |
Login page for app | def login(request):
print('login')
if request.method == 'POST':
md5_pwd = ''
admin = False
username = request.POST.get('username', False)
mode1 = 'Human'
mode = NameSpace.objects.get(ns_id=mode1)
password = request.POST.get('password', False)
if username... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def login():",
"def login():",
"def login():\n login_page = Login()\n login_page.login_main_page()",
"def login():\n return render_template('auth/login.html')",
"def login():\n return render_template('auth/login.html')",
"def login():\n\n return render_template('login.html')",
"def login... | [
"0.8198486",
"0.8198486",
"0.8095299",
"0.80503243",
"0.80503243",
"0.80076325",
"0.7953975",
"0.7920944",
"0.7919374",
"0.7890743",
"0.7798588",
"0.7792746",
"0.7684994",
"0.76642966",
"0.75997424",
"0.75369537",
"0.7479572",
"0.7455991",
"0.7453318",
"0.74508756",
"0.744456... | 0.0 | -1 |
This view handles the GET and POST requestes for LABELS ANNOTATION ACTION | def annotationlabel(request,action=None):
username = request.session['username']
mode1 = request.session['mode']
auto_required = request.GET.get('ns_id', None)
mode = NameSpace.objects.get(ns_id=mode1)
# print('mode',mode1)
usecase = request.session['usecase']
# language = request.GET.get(... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def label():\n\n if request.method == \"GET\":\n return render_template(\"/label.html\")\n\n else:\n # initialise the variables from the hidden html form input\n type = request.form.get(\"type\")\n url = request.form.get(\"url\")\n thumb = request.form.get(\"thumb\")\n\n ... | [
"0.66144353",
"0.63546795",
"0.63470924",
"0.60510325",
"0.5695163",
"0.5651894",
"0.5647814",
"0.5625091",
"0.5582534",
"0.5574167",
"0.5572838",
"0.55621165",
"0.5510184",
"0.5436197",
"0.54305923",
"0.5367623",
"0.5344053",
"0.532556",
"0.5323372",
"0.526705",
"0.5223831",... | 0.7058827 | 0 |
This view handles the GET and POST requests concerning the mentions identification action | def mention_insertion(request,action=None):
username = request.session['username']
mode1 = request.session['mode']
mode = NameSpace.objects.get(ns_id=mode1)
# language = request.GET.get('language',request.session['language'])
# print(language)
usecase = request.session['usecase']
type = 'm... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def mentor_list_view(request):\n # TODO: this view\n pass",
"def mentee_list_view(request):\n # TODO: this view\n pass",
"def insert_link(request,action=None):\n\n username = request.session['username']\n mode1 = request.session['mode']\n mode = NameSpace.objects.get(ns_id=mode1)\n # la... | [
"0.73995537",
"0.73620844",
"0.60392785",
"0.5866405",
"0.56873083",
"0.56122965",
"0.5517016",
"0.54710096",
"0.5438025",
"0.54133826",
"0.5351744",
"0.5338681",
"0.5333359",
"0.53294235",
"0.5317595",
"0.5270952",
"0.5252377",
"0.52442586",
"0.5146937",
"0.51071715",
"0.505... | 0.6561825 | 2 |
This view handles ENTITY LINKING action. | def insert_link(request,action=None):
username = request.session['username']
mode1 = request.session['mode']
mode = NameSpace.objects.get(ns_id=mode1)
# language = request.GET.get('language',request.session['language'])
usecase = request.session['usecase']
auto_required = request.GET.get('ns_id... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getLink(self):",
"async def link_to(self, *args):\n pass",
"def link(self):\n if self.resource is None:\n self.resource = self.client.get_resource(self.href)\n self.client.post_linked_resource(\n self.resource, RelationType.LINK_TO_TEMPLATE,\n EntityTyp... | [
"0.6529409",
"0.6261671",
"0.6061944",
"0.5991588",
"0.5865839",
"0.5847417",
"0.58065325",
"0.57768536",
"0.57447076",
"0.5732444",
"0.56826234",
"0.5680303",
"0.5644154",
"0.562023",
"0.56031066",
"0.55818963",
"0.5573664",
"0.5553531",
"0.5547808",
"0.55261755",
"0.5525279... | 0.53762984 | 35 |
This view handles the GET AND POST requests to insert, delete, get concepts. | def contains(request, action=None):
username = request.session.get('username', False)
mode1 = request.session.get('mode', False)
mode = NameSpace.objects.get(ns_id=mode1)
error_json = {"Error": "No user authenticated"}
if (username):
response_json = {}
if request.method == 'GET':
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def conc_view(request):\n\n usecase = request.session['usecase']\n mode = request.session['mode']\n auto_required = request.GET.get('ns_id',None)\n jsonDict = {}\n concepts = {}\n notEmpty = False\n jsonDict['concepts'] = []\n if mode == 'Human' or auto_required == 'Human':\n cursor ... | [
"0.61219996",
"0.59057087",
"0.56041396",
"0.5594849",
"0.55910647",
"0.5548109",
"0.5538304",
"0.55363566",
"0.551329",
"0.551329",
"0.5454264",
"0.5444275",
"0.5436636",
"0.5426927",
"0.54256684",
"0.5419429",
"0.5403076",
"0.53987503",
"0.53939396",
"0.5388849",
"0.5387255... | 0.60170645 | 1 |
This view allows to test the four actions a user can perform. | def test(request, table):
username = request.session.get('username', False)
error_json = {"Error": "No user authenticated"}
if (username):
context = {'username': username}
if table == "contains":
return render(request, 'MedTAG_sket_dock_App/test/test-contains.html', context)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_user_actions_post(self):\n pass",
"def test_view_displays_all(self):\n set_up_one_user(self, 1, 0)\n login = self.client.login(username='test', password='2HJ1vRV0Z&3iD')\n response = self.client.get(reverse('index'))\n self.assertEqual(response.status_code, 200)\n ... | [
"0.7148795",
"0.67888236",
"0.66133183",
"0.6519565",
"0.6417866",
"0.6320076",
"0.6318223",
"0.63096946",
"0.62790465",
"0.62453616",
"0.6233635",
"0.6223224",
"0.6203222",
"0.6198867",
"0.61912817",
"0.6169254",
"0.61673194",
"0.6163158",
"0.612668",
"0.61203367",
"0.611683... | 0.0 | -1 |
This view returns the list of reports associated to a single use_case, institute and language | def get_reports(request):
inst = request.GET.get('institute',None)
use = request.GET.get('usec',None)
print(use)
lang = request.GET.get('lang',None)
batch = request.GET.get('batch',None)
all = request.GET.get('all',None)
actual_report = request.GET.get('actual_report',None)
if all == 'a... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_data(request):\n\n json_resp = {}\n # reports = Report.objects.filter(name = UseCase.objects.get(name=request.session['usecase']),institute = request.session['institute'],language = request.session['language'])\n\n json_resp['reports'] = []\n institute = request.GET.get('institute',request.sess... | [
"0.61894846",
"0.60761696",
"0.5919785",
"0.58525854",
"0.5772856",
"0.575343",
"0.5743181",
"0.57404655",
"0.5713519",
"0.56065595",
"0.5573967",
"0.5552891",
"0.54978424",
"0.5465102",
"0.5448308",
"0.5387445",
"0.53673327",
"0.5361622",
"0.53593826",
"0.52845144",
"0.52631... | 0.6606941 | 0 |
This view returns the admin of MedTAG (if any) | def get_admin(request):
jsonResp = {}
jsonResp['admin'] = ''
if User.objects.filter(profile = 'Admin').exists():
mode = NameSpace.objects.get(ns_id='Human')
name = User.objects.get(profile = 'Admin',ns_id=mode)
admin = name.username
jsonResp['admin'] = admin
return Json... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getAdmin():",
"def admin(request):\n response = {\n 'articleform': ArticleForm(),\n 'categoryform': CategoryForm(),\n 'tagform': TagForm()\n }\n\n return render(request, 'knowledgebase/admin.html', response)",
"def admin_only():\n return 'Super-seekrit admin page.'",
"def... | [
"0.68099904",
"0.6166164",
"0.6129299",
"0.6103094",
"0.6095433",
"0.60935694",
"0.6068538",
"0.5937125",
"0.5867055",
"0.58188266",
"0.57358885",
"0.57048804",
"0.5703868",
"0.56482583",
"0.5623533",
"0.5601087",
"0.55903524",
"0.5528616",
"0.5527308",
"0.5515824",
"0.550808... | 0.5809595 | 10 |
This view checks whether the configuration files the user inserted are well formed and returns the response | def check_input_files(request):
reports = []
labels = []
pubmedfiles = []
concepts = []
type1 = request.POST.get('type',None)
username = request.POST.get('username',None)
# email = request.POST.get('email',None)
password = request.POST.get('password',None)
for filename, file in requ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_config_verify(self,\n raw_response: Any,\n *args,\n **kwargs) -> bool:\n pass",
"def edit_config_verify(self,\n raw_response: Any,\n *args,\n **k... | [
"0.59801537",
"0.5930928",
"0.59076905",
"0.58123827",
"0.5767755",
"0.5741729",
"0.5710974",
"0.5606",
"0.5606",
"0.5601504",
"0.5598409",
"0.5596937",
"0.5566562",
"0.55586517",
"0.5556762",
"0.5550772",
"0.55330944",
"0.55282456",
"0.5525261",
"0.5514157",
"0.5479723",
"... | 0.61117023 | 0 |
This view returns the list of groundtruths associated to a user and a specific configuration of institute, usecase and language. | def get_gt_list(request):
groundTruths = 0
json_resp = {}
username =request.GET.get('username',None)
ins = request.GET.get('inst',None)
lang = request.GET.get('lang',None)
use = request.GET.get('use',None)
action = request.GET.get('action',None)
token = request.GET.get('token',None)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_user_ground_truth(request):\n\n user = request.GET.get('user',None)\n action = request.GET.get('action',None)\n mode = request.GET.get('mode',None)\n report = request.GET.get('report',None)\n language = request.GET.get('language',request.session['language'])\n mode_obj = NameSpace.objects... | [
"0.5999815",
"0.56888205",
"0.55822563",
"0.50436574",
"0.5027748",
"0.5008548",
"0.49218768",
"0.49068975",
"0.48958996",
"0.48903573",
"0.48482397",
"0.4839935",
"0.48096266",
"0.48037243",
"0.4765853",
"0.47516873",
"0.4731364",
"0.47203898",
"0.469531",
"0.4694096",
"0.46... | 0.5156828 | 3 |
This view checks whether the files inserted by the user to update the configuration are well formed | def check_files_for_update(request):
reports = []
pubmedfiles = []
labels = []
concepts = []
type1 = request.POST.get('type',None)
for filename, file in request.FILES.items():
if filename.startswith('reports'):
reports.append(file)
if filename.startswith('pubmed'):
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def check_config(self):\n try:\n config_metadata = self.dbc.get_metadata(\"config.txt\")\n except rest.ErrorResponse:\n print str(datetime.datetime.now()) \\\n + \": No config.txt in Dropbox directory. Exiting.\"\n sys.exit()\n if config_metadata... | [
"0.5990421",
"0.5856733",
"0.58000755",
"0.5700677",
"0.5700643",
"0.5694012",
"0.56602377",
"0.5652492",
"0.5576875",
"0.5533964",
"0.55261713",
"0.5525095",
"0.54940057",
"0.5441861",
"0.54376626",
"0.54344094",
"0.5424616",
"0.5412756",
"0.54043454",
"0.53829175",
"0.53707... | 0.6431848 | 0 |
This view handles the update of the database once the files check went well | def update_db(request):
reports = []
pubmedfiles = []
labels = []
concepts = []
usecase = request.session['usecase']
type1 = request.POST.get('type', None)
for filename, file in request.FILES.items():
if filename.startswith('reports'):
reports.append(file)
if f... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def db_update_files():\n _populate_table_files(File)\n _populate_table_files(Software)\n return redirect(url_for('view_index'))",
"def check_files_for_update(request):\n\n reports = []\n pubmedfiles = []\n\n labels = []\n concepts = []\n type1 = request.POST.get('type',None)\n for file... | [
"0.78468007",
"0.6518412",
"0.6347776",
"0.6281645",
"0.6253969",
"0.6233721",
"0.6062642",
"0.5973955",
"0.59582907",
"0.589399",
"0.5749623",
"0.57444847",
"0.5734826",
"0.5730112",
"0.5681989",
"0.56654054",
"0.5635508",
"0.5635349",
"0.5631634",
"0.5626919",
"0.5592777",
... | 0.6331503 | 3 |
This view handles the initial configuration, the first the admin performs | def configure_db(request):
reports = []
pubmedfiles = []
areas = []
labels = []
concepts = []
type = request.POST.get('type',None)
load_concepts = request.POST.get('exa_concepts',None)
load_labels = request.POST.get('exa_labels',None)
for filename, file in request.FILES.items():
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def configure(self):\n inject(self.urls, self.names_for(\"urls\"))\n inject(self.models, self.names_for(\"models\"))\n self.load_admin()",
"def load_admin(self):\n self.config.load_admin()",
"def initialConfig(self):\r\r\n\r\r\n loggerCmw = logging.getLogger('initialConfig')\... | [
"0.67497563",
"0.6583361",
"0.6476776",
"0.64530045",
"0.6267945",
"0.62559855",
"0.62546116",
"0.6244819",
"0.6207696",
"0.6193051",
"0.6186991",
"0.6186991",
"0.6175873",
"0.6175873",
"0.6175873",
"0.6175873",
"0.6153204",
"0.61281705",
"0.6114283",
"0.6088599",
"0.6088599"... | 0.0 | -1 |
This view returns the list of all the distinct keys present in the json reports. This view is called during configuration | def get_keys(request):
keys=[]
reports = Report.objects.all().exclude(institute = 'PUBMED')
for report in reports:
json_rep = report.report_json
for el in json_rep.keys():
if el not in keys:
keys.append(el)
json_resp = {'keys':keys}
return JsonResponse(js... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def AllKeys(self) -> _n_0_t_1[str]:",
"def distinct(self, key):\n return self.database.command({'distinct': self.name,\n 'key': key})['values']",
"def keys(self) -> KeysView:\n return self._dict.keys()",
"def GET(self, key):\n header('Content-Type', '... | [
"0.6015251",
"0.5963718",
"0.5916041",
"0.58546275",
"0.5769133",
"0.57459795",
"0.5695538",
"0.5680063",
"0.5674234",
"0.56573683",
"0.56514174",
"0.56393325",
"0.5621896",
"0.55753326",
"0.55740577",
"0.5571665",
"0.55628973",
"0.55588096",
"0.55510443",
"0.55476856",
"0.55... | 0.79304016 | 0 |
This view returns the ground truths created by the user according to the configuration she required. | def download_ground_truths(request):
workpath = os.path.dirname(os.path.abspath(__file__)) # Returns the Path your .py file is in
path1 = os.path.join(workpath, './static/temp/temp.csv')
path2 = os.path.join(workpath, './static/BioC/temp_files/to_download.csv')
if os.path.exists(path1):
os.rem... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_user_ground_truth(request):\n\n user = request.GET.get('user',None)\n action = request.GET.get('action',None)\n mode = request.GET.get('mode',None)\n report = request.GET.get('report',None)\n language = request.GET.get('language',request.session['language'])\n mode_obj = NameSpace.objects... | [
"0.6326191",
"0.62316513",
"0.5909118",
"0.58245844",
"0.57520556",
"0.56995493",
"0.56758773",
"0.5547157",
"0.55310655",
"0.5481521",
"0.5378445",
"0.53661126",
"0.5318335",
"0.53141564",
"0.5298512",
"0.52543676",
"0.52284783",
"0.52028716",
"0.5168641",
"0.51581955",
"0.5... | 0.52988386 | 14 |
This view returns ALL the ground truths to be downloaded. This view can be called only by the admin and the ground truths returned are those of ALL the users in the platform | def download_all_ground_truths(request):
json_resp = {}
json_resp['ground_truth'] = []
cursor = connection.cursor()
mode = request.GET.get('gt_mode',None)
if mode is None:
human = NameSpace.objects.get(ns_id = 'Human')
robot = NameSpace.objects.get(ns_id = 'Robot')
gt_human ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def download_ground_truths(request):\n\n workpath = os.path.dirname(os.path.abspath(__file__)) # Returns the Path your .py file is in\n path1 = os.path.join(workpath, './static/temp/temp.csv')\n path2 = os.path.join(workpath, './static/BioC/temp_files/to_download.csv')\n if os.path.exists(path1):\n ... | [
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"0.5122737",
"0.5103302",
"0.51019853",
"0.50845915... | 0.71700454 | 0 |
This view returns the key files of BioC mentions and linking. | def download_key_files(request):
workpath = os.path.dirname(os.path.abspath(__file__)) # Returns the Path your .py file is in
path = os.path.join(workpath, './static/BioC/linking.key')
path1 = os.path.join(workpath, './static/BioC/mention.key')
ment = request.GET.get('type_key',None)
if ment == 'm... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def take_auth_data():\n home = str(Path.home())\n path_to_keys = '/Documents/twitter/keys/'\n\n files = [f for f in listdir(home+path_to_keys) if '.DS' not in f]\n\n tokens = []\n for f in files:\n with open(home+path_to_keys+f, 'r') as lines:\n ln = lines.readline().replace(\" \",... | [
"0.5546954",
"0.5437735",
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"0.4955711",
"0.49460372",
"0.4943244... | 0.7325465 | 0 |
This view returns an array of tuples, where each tuple contains the id of the annotated report and the associated insertion time | def get_reports_from_action(request):
username = request.session['username']
mode1 = request.session['mode']
mode = NameSpace.objects.get(ns_id=mode1)
language = request.session['language']
report_to_ret = []
action = request.GET.get('action',None)
user = User.objects.get(username = usernam... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_analysis_annotations():\n sample_id = demisto.getArg('id')\n r = req('GET', SUB_API + 'samples/' + sample_id + '/analysis/annotations')\n\n annotations = []\n context_path = 'ThreatGrid.AnalysisResults.Sample.Id.Annotations'\n ec = {context_path: []} # type: ignore\n ips = demisto.get(... | [
"0.5831102",
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"0.5073391",
"0.5015745",
"0.49979466",
"0.4995804",
"0.49945",
... | 0.5134248 | 12 |
This view returns the last ground truth created by the user for the session's parameters | def get_last_gt(request):
username = request.session['username']
mode1 = request.session['mode']
mode = NameSpace.objects.get(ns_id=mode1)
language = request.session['language']
usecase = request.session['usecase']
institute = request.session['institute']
batch = request.session['batch']
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_user_ground_truth(request):\n\n user = request.GET.get('user',None)\n action = request.GET.get('action',None)\n mode = request.GET.get('mode',None)\n report = request.GET.get('report',None)\n language = request.GET.get('language',request.session['language'])\n mode_obj = NameSpace.objects... | [
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"0.503414",
"0.5029794",
"0.5022567",
"0.50175035",
"0.49976113"... | 0.65990674 | 0 |
This view returns the concept_urls related to each semantic_area. The concepts can be those added by the admin (in the manual mode), those of EXAMODE added EXCLUSIVELY for automatic annotation (in the automatic mode) or those of EXAMODE added for automatic AND manual mode. | def conc_view(request):
usecase = request.session['usecase']
mode = request.session['mode']
auto_required = request.GET.get('ns_id',None)
jsonDict = {}
concepts = {}
notEmpty = False
jsonDict['concepts'] = []
if mode == 'Human' or auto_required == 'Human':
cursor = connection.cu... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def lookup_semantic_areas(semantic_areas, use_case_ontology):\r\n\t\t\r\n\t\tif type(semantic_areas) == list: # search for list of semantic areas\r\n\t\t\trows = use_case_ontology.loc[use_case_ontology['semantic_area_label'].isin(semantic_areas)][['iri', 'label', 'semantic_area_label']]\r\n\t\telse: # search for... | [
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"0.46203625",
"0.45999643",
"0.45799875",
"0.45757028",
"... | 0.5373202 | 2 |
This view returns the json report of the required report | def report(request,report_id,language):
json_resp = {}
error_json = {'error':'the report does not exist!'}
if Report.objects.filter(id_report = report_id, language = language).exists():
report = Report.objects.get(id_report = report_id, language = language)
json_resp['report_json'] = repor... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def json_report(request):\n\n close_old_connections()\n\n # Not allow unauthenticated users.\n if not request.user.is_authenticated:\n return HttpResponseForbidden(\"You're not authenticated.\")\n \n # Get orders.\n orders = get_orders(request)\n\n # Form the audit.\n audit = {\n ... | [
"0.7473013",
"0.71653223",
"0.71193326",
"0.711113",
"0.7037173",
"0.7030517",
"0.69930786",
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"0.6539282",
"0.6421162",
"0.6380125",
"0.6352547",
"0.62871504",
"0.624473... | 0.6822793 | 7 |
This view returns for each key of the json report required its text, the indexes of the start and stop chars in the json report string and the number of words that compose the fields to annotate. | def report_start_end(request):
report = request.GET.get('report_id')
lang = request.GET.get('language',None)
usecase = request.session['usecase']
data = get_fields_from_json()
json_keys_to_display = data['fields']
json_keys_to_display.extend(['journal','authors','year','volume'])
json_keys_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def annotation_all_stats(request):\n\n id_report = request.GET.get('report',None)\n language = request.GET.get('language',None)\n\n json_dict = get_annotations_count(id_report,language)\n\n # print('annotations',json_dict)\n return JsonResponse(json_dict)",
"def keyword_analysis(request): \n ... | [
"0.5901755",
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"0.53263783",
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"0.51046854",
"0.51033866",
"0.50946844",
"0.5073294",
"0.5071243",
"0.507050... | 0.5978528 | 0 |
This view returns the fields to display and annotate. If the annotation mode is automatic the fields to annotate are those the concepts and mentions have been extracted from. The fields are returned to give the user the chance to update the fields she wants to display/annotate in MANUAL CONFIGURATION. | def get_fields(request):
json_resp = {}
json_resp['fields'] = []
json_resp['fields_to_ann'] = []
all = request.GET.get('all',None)
workpath = os.path.dirname(os.path.abspath(__file__)) # Returns the Path your .py file is in
auto_request = request.GET.get('ns_id', None)
report = request.GET... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_auto_annotations(request): # post\n\n request_body_json = json.loads(request.body)\n usecase_list = request_body_json['usecase']\n fields_list = request_body_json['selected']\n report_key = request_body_json['report_type']\n batch = request_body_json['batch']\n\n # check existence of e... | [
"0.5912422",
"0.5710874",
"0.5643516",
"0.5519478",
"0.5514924",
"0.541801",
"0.5411552",
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"0.5080195",
"0.5075947",
"0.50657296",
"0.50644743",
"0.50619006",
"0.5053078... | 0.63069195 | 0 |
This view creates the HttpResponse object with the CSV examples files, these are the examples the user can download. | def download_examples(request):
file_required = request.GET.get('token',None)
path = ''
workpath = os.path.dirname(os.path.abspath(__file__)) # Returns the Path your .py file is in
if file_required == 'reports':
path = os.path.join(workpath, './static/examples/report.csv')
elif file_requi... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def csv_download_view(request):\n logging.info(\" CSV file download is working\")\n now = datetime.now()\n timestamp = now.strftime(\"%Y_%m_%d\")\n response = HttpResponse(content_type='text/csv')\n response['Content-Disposition'] = 'attachment; filename=\"results_' + \\\n GLOBAL_VARIABLE.get... | [
"0.66388816",
"0.6486266",
"0.6369911",
"0.6286953",
"0.62677735",
"0.6229694",
"0.6140815",
"0.60950977",
"0.60798424",
"0.6048974",
"0.588191",
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"0.58131593",
"0.5644796",
"0.5629658",
"0.5619012",
"0.5589778",
"0.55788064",
"0.5575425... | 0.8027104 | 0 |
This view creates the HttpResponse object with the appropriate CSV header, these are the templates the user can download. | def download_templates(request):
file_required = request.GET.get('token',None)
path = ''
workpath = os.path.dirname(os.path.abspath(__file__)) # Returns the Path your .py file is in
if file_required == 'reports':
path = os.path.join(workpath, './static/templates/report.csv')
elif file_re... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def csv_download_view(request):\n logging.info(\" CSV file download is working\")\n now = datetime.now()\n timestamp = now.strftime(\"%Y_%m_%d\")\n response = HttpResponse(content_type='text/csv')\n response['Content-Disposition'] = 'attachment; filename=\"results_' + \\\n GLOBAL_VARIABLE.get... | [
"0.77301335",
"0.73666567",
"0.7355991",
"0.7228909",
"0.7226983",
"0.7217648",
"0.7094669",
"0.70587486",
"0.7015085",
"0.7014507",
"0.7010828",
"0.70096016",
"0.6967033",
"0.69572055",
"0.69189924",
"0.6718752",
"0.66366225",
"0.6557932",
"0.64799553",
"0.6463194",
"0.64586... | 0.75247663 | 1 |
This view returns the list of all the keys found in report files (other than institute,usecase,id_report,language) and the list of usecases, this list contains the usecases where automatic annotation can be applied. | def get_keys_and_uses_from_csv(request):
labels = []
pubmed = []
reports = []
concepts = []
json_resp = {}
type_selected = ''
for filename, file in request.FILES.items():
if filename.startswith('reports'):
type_selected = 'reports'
reports.append(file)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_keys(request):\n\n keys=[]\n reports = Report.objects.all().exclude(institute = 'PUBMED')\n for report in reports:\n json_rep = report.report_json\n for el in json_rep.keys():\n if el not in keys:\n keys.append(el)\n json_resp = {'keys':keys}\n return ... | [
"0.64362574",
"0.5788912",
"0.56906444",
"0.5603826",
"0.55491775",
"0.54180676",
"0.5360192",
"0.53036547",
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"0.5022587",
"0.50090915",
"0.5000231",
"0.49811894",
"0.49524757",
"0.4935... | 0.5535878 | 5 |
This view returns the list of all the keys found in report files (other than institute,usecase,id_report,language) the admin has just inserted to update the database (only the keys that have never been detected before are returned). | def get_keys_from_csv_update(request):
reports = []
json_resp = {}
for filename, file in request.FILES.items():
if filename.startswith('reports'):
reports.append(file)
elif filename.startswith('pubmed'):
reports.append(file)
keys,uses = get_keys_csv_update(repor... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_keys(request):\n\n keys=[]\n reports = Report.objects.all().exclude(institute = 'PUBMED')\n for report in reports:\n json_rep = report.report_json\n for el in json_rep.keys():\n if el not in keys:\n keys.append(el)\n json_resp = {'keys':keys}\n return ... | [
"0.71772546",
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"0.54670197",
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"0.5140861",
"0.5136076",
"0.51293594",
"0.51133555",
"0.5111757... | 0.6515069 | 1 |
This view returns the list of usecases (colon,uterine cervix,lung) for which the examode concepts have not been added in the database. | def get_presence_examode_concepts(request):
json_resp = {}
json_resp['concepts'] = get_presence_exa_concepts()
json_resp['labels'] = get_presence_exa_labels()
# print(json_resp)
return JsonResponse(json_resp) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_uses_missing_exa(request):\n\n use_to_ret = {}\n use_to_ret['labels_present'] = []\n use_to_ret['concepts_present'] = []\n use_to_ret['labels_missing'] = []\n use_to_ret['concepts_missing'] = []\n uses = ['colon','uterine cervix','lung']\n for el in uses:\n usecase = UseCase.obj... | [
"0.6790827",
"0.6196254",
"0.5533098",
"0.5533098",
"0.5495612",
"0.54655147",
"0.53392214",
"0.5315234",
"0.5315234",
"0.5278193",
"0.5278193",
"0.51534927",
"0.51332116",
"0.51185906",
"0.5101442",
"0.5099715",
"0.50912744",
"0.5083736",
"0.5083736",
"0.5083736",
"0.5004337... | 0.48476055 | 33 |
This view returns for each usecase the statistics concerning the user's annotations | def get_stats_array_per_usecase(request):
mode = request.GET.get('mode',None)
usern = request.GET.get('member',request.session['username'])
username = User.objects.get(username=usern, ns_id=mode)
language = request.GET.get('language',request.session['language'])
institute = request.GET.get('institu... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def annotation_all_stats(request):\n\n id_report = request.GET.get('report',None)\n language = request.GET.get('language',None)\n\n json_dict = get_annotations_count(id_report,language)\n\n # print('annotations',json_dict)\n return JsonResponse(json_dict)",
"def overview(request):\n LOGGER.info... | [
"0.6520774",
"0.61430424",
"0.57960534",
"0.5767102",
"0.5748662",
"0.57239825",
"0.5620116",
"0.5618035",
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"0.5402873",
"0.53957576",
"0.53901374",
"0.5381438",
"0.53607213",
"0.53559... | 0.585126 | 2 |
This view returns the rows to be inserted in the reports' table | def get_data(request):
json_resp = {}
# reports = Report.objects.filter(name = UseCase.objects.get(name=request.session['usecase']),institute = request.session['institute'],language = request.session['language'])
json_resp['reports'] = []
institute = request.GET.get('institute',request.session['instit... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def rows(self):\n for investment_project in get_spi_report_queryset().iterator():\n yield self.get_row(investment_project)",
"def reports(self, request):\n serializer = ReportSerializer(data=request.data, context={'request': request})\n serializer.is_valid(raise_exception=True)\n ... | [
"0.5972984",
"0.5820921",
"0.57325846",
"0.5699228",
"0.5583971",
"0.55286187",
"0.5527511",
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"0.5472222",
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"0.5362646",
"0.5357459",
"0.53312767",
"0.53297734",
"0.5322626",
"0.53118074",
"0.53095... | 0.0 | -1 |
Check the number of reports annotated by the machine. It is needed by frontend to display how many reports have not been alg annotated yet if one or more reports have been inserted later. | def report_missing_auto(request):
usecases = UseCase.objects.all()
json_resp = {}
languages = ['english', 'English']
for el in usecases:
use = el.name
batches = []
batches.append('all')
if use.lower() in ['colon','lung','uterine cervix']:
report = Report.obje... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def GetNumberOfAnalysisReports(self):\n return self._analysis_report_stream_number - 1",
"async def test_nr_of_missing_metrics_without_correct_report(self):\n self.reports[\"reports\"] = []\n response = await self.collect(get_request_json_side_effect=[self.data_model, self.reports])\n sel... | [
"0.68508667",
"0.644907",
"0.6323937",
"0.6197335",
"0.61852753",
"0.58833945",
"0.58439714",
"0.5674733",
"0.56427646",
"0.56376135",
"0.5631575",
"0.5622997",
"0.5576571",
"0.55721563",
"0.5543924",
"0.55300975",
"0.5517729",
"0.55001414",
"0.54755",
"0.5464594",
"0.5449464... | 0.0 | -1 |
Check the number of reports annotated by the machine. It is needed by frontend to display how many reports have not been alg annotated yet if one or more reports have been inserted later. | def pubmed_missing_auto(request):
usecases = UseCase.objects.all()
json_resp = {}
json_resp['annotated'] = 0
json_resp['tot'] = 0
json_resp['usecase'] = []
languages = ['English','english']
for el in usecases:
use = el.name
json_resp[use] = {}
batches = []
ba... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def GetNumberOfAnalysisReports(self):\n return self._analysis_report_stream_number - 1",
"async def test_nr_of_missing_metrics_without_correct_report(self):\n self.reports[\"reports\"] = []\n response = await self.collect(get_request_json_side_effect=[self.data_model, self.reports])\n sel... | [
"0.6849571",
"0.64489937",
"0.6324095",
"0.6198976",
"0.61840296",
"0.58828866",
"0.584472",
"0.567382",
"0.56436783",
"0.5636645",
"0.563118",
"0.56241053",
"0.557589",
"0.55721855",
"0.5544101",
"0.5530149",
"0.5517418",
"0.5499138",
"0.54746366",
"0.5463975",
"0.5450596",
... | 0.0 | -1 |
This view handles the creation of automatic annotations | def create_auto_annotations(request): # post
request_body_json = json.loads(request.body)
usecase_list = request_body_json['usecase']
fields_list = request_body_json['selected']
report_key = request_body_json['report_type']
batch = request_body_json['batch']
# check existence of examode labels... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def annotate(self, request, pk=None):\n\n # Get the document of the detail view and deserialize data\n document = self.get_object()\n serializer = AnnotationSerializer(\n data=request.data,\n context={'request': request, 'document': document}\n )\n\n # Val... | [
"0.6415329",
"0.639421",
"0.639421",
"0.635931",
"0.63100845",
"0.6303881",
"0.6297316",
"0.6156496",
"0.6034312",
"0.60098106",
"0.60085005",
"0.5985925",
"0.5960007",
"0.593711",
"0.59337866",
"0.5889605",
"0.5885899",
"0.58781826",
"0.5856027",
"0.58548445",
"0.58407944",
... | 0.6387488 | 3 |
It returns the statistics of all the annotations, depending on Automatic or Manual annotation mode. | def annotation_all_stats(request):
id_report = request.GET.get('report',None)
language = request.GET.get('language',None)
json_dict = get_annotations_count(id_report,language)
# print('annotations',json_dict)
return JsonResponse(json_dict) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def showAnnotationsExamples(annotation=1.0, showTotalOnly=False):\n total = 0\n # get the right annotation\n if type(annotation) is float:\n annotation = str(annotation)\n # get the lists of annotations and sentences\n annotatedFolderPathList = [u'./002manuallyAnnotated/', u'./003negativeNaiv... | [
"0.6183956",
"0.60259265",
"0.60116893",
"0.59949404",
"0.5988531",
"0.5978838",
"0.57993627",
"0.57060766",
"0.57036275",
"0.568836",
"0.56590897",
"0.55572796",
"0.54674685",
"0.5456113",
"0.54523754",
"0.54500777",
"0.54468614",
"0.54467595",
"0.53747386",
"0.53728575",
"0... | 0.60161185 | 2 |
This view removes one or more entries from the reports table | def delete_reports(request):
request_body_json = json.loads(request.body)
report_list = request_body_json['report_list']
# print(type(report_list))
try:
with transaction.atomic():
json_resp = {'msg':'ok'}
for report in report_list:
# print(report)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def purge_entries_command():\n incident = demisto.args().get('id', get_investigation_id())\n cursor = COLLECTION.find({})\n deleted = 0\n # Iterate, collecting any name/value pairs associated with the incident\n for i in cursor:\n if incident in i:\n object_id = i.get('_id')\n ... | [
"0.6340911",
"0.6036938",
"0.5985408",
"0.5947363",
"0.5903566",
"0.5845712",
"0.5842439",
"0.58321166",
"0.5829431",
"0.5829399",
"0.5783813",
"0.57735264",
"0.57605934",
"0.5721502",
"0.57111275",
"0.5709054",
"0.5609433",
"0.5608257",
"0.55689377",
"0.5568891",
"0.55685073... | 0.595967 | 3 |
This view returns the number of ground truths for the required action. | def get_gt_action_based(request):
action = request.GET.get('action',None)
ns = request.GET.get('annotation_mode',None)
if ns == 'Manual':
ns = 'Human'
elif ns == 'Automatic':
ns = 'Robot'
gts = GroundTruthLogFile.objects.filter(gt_type=action)
if ns is not None:
ns_id ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_num_actions():\n return 6",
"def action_count(self):\n raise NotImplementedError",
"def get_number_of_actions(self):\n return self.__environment.action_space.n",
"def n_actions(self):\n return sum(op.action is not None for op in self)",
"def n_actions(self) -> int:\n retu... | [
"0.6762627",
"0.66855824",
"0.6623974",
"0.65036833",
"0.6470496",
"0.6316444",
"0.629149",
"0.60368526",
"0.6007",
"0.59856224",
"0.59587497",
"0.5945216",
"0.59185743",
"0.5913962",
"0.59077424",
"0.59077424",
"0.59077424",
"0.59077424",
"0.59077424",
"0.59077424",
"0.59077... | 0.60456795 | 7 |
This view returns the insertion time of the ground truth of a specific report and action | def get_insertion_time_record(request):
report = request.GET.get('rep',None)
language = request.GET.get('language',None)
user_obj = request.GET.get('username',request.session['username'])
ns_id_str = request.GET.get('ns_id',None)
action = request.GET.get('action',None)
report1 = Report.objects.... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def action_time(self):\n return self._action_time",
"def getSubmitTime():",
"def action(self):\n return self.rowTime.activity",
"def get_reports_from_action(request):\n\n username = request.session['username']\n mode1 = request.session['mode']\n mode = NameSpace.objects.get(ns_id=mode1... | [
"0.61869544",
"0.6098068",
"0.59390426",
"0.5899511",
"0.57239664",
"0.5698437",
"0.55129707",
"0.5507397",
"0.54787785",
"0.5462038",
"0.542069",
"0.5413636",
"0.5274124",
"0.5269431",
"0.52571",
"0.52457213",
"0.52457213",
"0.52457213",
"0.52457213",
"0.52457213",
"0.524572... | 0.5993164 | 2 |
This view returns the list of users | def get_users_list(request):
if request.method == 'POST':
request_body_json = json.loads(request.body)
lista = []
reports = request_body_json['reports']
action = request_body_json['action']
for rep in reports:
r = Report.objects.get(id_report=rep[0], language=rep... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def user_list():\n\n users = User.query.all()\n\n return render_template(\"user_list.html\", users=users)",
"def user_list():\n\n users = User.query.all()\n return render_template(\"/user_list.html\", users=users)",
"def user_list():\n\n users = User.query.all()\n return render_template(\"use... | [
"0.8763392",
"0.8762155",
"0.874486",
"0.874486",
"0.874486",
"0.874486",
"0.874486",
"0.874486",
"0.8736582",
"0.8494802",
"0.83495146",
"0.83075815",
"0.82329273",
"0.82251996",
"0.8126732",
"0.81214386",
"0.8088157",
"0.8074572",
"0.8040432",
"0.8009119",
"0.80086416",
"... | 0.0 | -1 |
This view returns the groundtruth associated to a specific user,action,report | def get_user_ground_truth(request):
user = request.GET.get('user',None)
action = request.GET.get('action',None)
mode = request.GET.get('mode',None)
report = request.GET.get('report',None)
language = request.GET.get('language',request.session['language'])
mode_obj = NameSpace.objects.get(ns_id=m... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_reports_from_action(request):\n\n username = request.session['username']\n mode1 = request.session['mode']\n mode = NameSpace.objects.get(ns_id=mode1)\n language = request.session['language']\n report_to_ret = []\n action = request.GET.get('action',None)\n user = User.objects.get(usern... | [
"0.6772657",
"0.6000003",
"0.59264755",
"0.5880304",
"0.57433313",
"0.5722247",
"0.56435704",
"0.5437227",
"0.5424971",
"0.54028565",
"0.5310623",
"0.5304719",
"0.5268187",
"0.5193207",
"0.51647246",
"0.51642805",
"0.51576793",
"0.515311",
"0.5151735",
"0.5143959",
"0.5133754... | 0.69742775 | 0 |
This view returns the number of reports automatically annotated by the algorithm, needed when a set has not been annotated yet | def check_auto_presence_for_configuration(request):
report_type = request.GET.get('report_type',None)
usecase = request.GET.get('usecase',None)
language = request.GET.get('language',None)
institute = request.GET.get('institute',None)
batch = request.GET.get('batch',None)
languages = ['English',... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def GetNumberOfAnalysisReports(self):\n return self._analysis_report_stream_number - 1",
"def get_annotation_count(self):\n return self._num_annos",
"def test_set_count(self) -> int:\n return pulumi.get(self, \"test_set_count\")",
"def count():",
"def count(self):\n\n raise NotImple... | [
"0.63487315",
"0.61534566",
"0.5975057",
"0.59637153",
"0.5936202",
"0.59227645",
"0.59115213",
"0.58910114",
"0.58910114",
"0.58910114",
"0.58910114",
"0.5875141",
"0.5845569",
"0.5814561",
"0.5792397",
"0.5784166",
"0.5683807",
"0.56606853",
"0.5635077",
"0.5620295",
"0.559... | 0.0 | -1 |
This view returns the list of batches associated to a use case | def get_batch_list(request):
json_resp = {}
json_resp['batch_list'] = []
usecase = request.GET.get('usecase',None)
# print(usecase)
if usecase is None:
batch = Report.objects.all().exclude(institute='PUBMED').values('batch')
else:
use_obj = UseCase.objects.get(name=usecase)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def view_batches(request):\n\n template = 'batch_list.html'\n\n context = {\n 'invalid_due_date': request.GET.get('invalid_due_date')\n }\n\n try:\n get_batches(request, context)\n except Exception as e:\n context['error'] = '{} {}'.format(e, traceback.format_exc())\n\n # TOD... | [
"0.65461797",
"0.6110669",
"0.60014474",
"0.5702793",
"0.56802934",
"0.5664144",
"0.56525695",
"0.5584406",
"0.5498826",
"0.5481742",
"0.5451509",
"0.54278344",
"0.53596485",
"0.53492475",
"0.53231066",
"0.52532625",
"0.52472556",
"0.5156872",
"0.5120976",
"0.51208967",
"0.51... | 0.6295193 | 1 |
This view returns the list of batches associated to a use case which have english language | def get_auto_anno_batch_list(request):
json_resp = {}
usecase = request.GET.get('usecase')
# print(usecase)
use_obj = UseCase.objects.get(name=usecase)
json_resp['batch_list'] = []
languages = ['English','english']
batch = Report.objects.filter(name=use_obj,language__in = languages).exclude... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_auto_anno_PUBMED_batch_list(request):\n\n json_resp = {}\n usecase = request.GET.get('usecase')\n # print(usecase)\n languages = ['English', 'english']\n use_obj = UseCase.objects.get(name=usecase)\n json_resp['batch_list'] = []\n batch = Report.objects.filter(name=use_obj,language__in... | [
"0.6487992",
"0.5999556",
"0.58158106",
"0.57203585",
"0.5676394",
"0.56275505",
"0.5532938",
"0.5520498",
"0.5510358",
"0.55094916",
"0.5498899",
"0.54472136",
"0.53090847",
"0.5303731",
"0.52797216",
"0.5278192",
"0.52427185",
"0.5226427",
"0.5222747",
"0.5220931",
"0.52123... | 0.6674985 | 0 |
This view returns the list of batches associated to a PUBMED use case | def get_PUBMED_batch_list(request):
json_resp = {}
usecase = request.GET.get('usecase')
# print(usecase)
use_obj = UseCase.objects.get(name=usecase)
json_resp['batch_list'] = []
batch = Report.objects.filter(name=use_obj,institute = 'PUBMED').values('batch')
for el in batch:
if el['... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def view_batches(request):\n\n template = 'batch_list.html'\n\n context = {\n 'invalid_due_date': request.GET.get('invalid_due_date')\n }\n\n try:\n get_batches(request, context)\n except Exception as e:\n context['error'] = '{} {}'.format(e, traceback.format_exc())\n\n # TOD... | [
"0.6342829",
"0.6229871",
"0.59056467",
"0.58580834",
"0.5704401",
"0.5654031",
"0.5634934",
"0.5585",
"0.5473519",
"0.53536516",
"0.53245217",
"0.53240734",
"0.52888346",
"0.5250839",
"0.52369195",
"0.5228739",
"0.52019703",
"0.5178603",
"0.5165569",
"0.5162922",
"0.51555914... | 0.6636962 | 0 |
This view returns the list of batches associated to a PUBMED use case in english language | def get_auto_anno_PUBMED_batch_list(request):
json_resp = {}
usecase = request.GET.get('usecase')
# print(usecase)
languages = ['English', 'english']
use_obj = UseCase.objects.get(name=usecase)
json_resp['batch_list'] = []
batch = Report.objects.filter(name=use_obj,language__in = languages,... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_PUBMED_batch_list(request):\n\n json_resp = {}\n usecase = request.GET.get('usecase')\n # print(usecase)\n use_obj = UseCase.objects.get(name=usecase)\n json_resp['batch_list'] = []\n batch = Report.objects.filter(name=use_obj,institute = 'PUBMED').values('batch')\n for el in batch:\n ... | [
"0.65641147",
"0.6093252",
"0.59606016",
"0.5839453",
"0.55629605",
"0.5310953",
"0.5296694",
"0.51423275",
"0.5020829",
"0.49711797",
"0.49675223",
"0.496288",
"0.49492076",
"0.48867327",
"0.48834974",
"0.48540273",
"0.48291108",
"0.4824442",
"0.48152086",
"0.48104975",
"0.4... | 0.6816767 | 0 |
This view returns whether the automatic annotations are available given an id,language,usecase | def get_presence_robot_user(request):
id_report = request.GET.get('id_report', None)
language = request.GET.get('language', None)
use = request.GET.get('usecase', None)
rep = request.GET.get('report_type', None)
json_resp = {'auto_annotation_count': 0}
cursor = connection.cursor()
reports_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def annotationlabel(request,action=None):\n\n username = request.session['username']\n mode1 = request.session['mode']\n auto_required = request.GET.get('ns_id', None)\n mode = NameSpace.objects.get(ns_id=mode1)\n\n # print('mode',mode1)\n usecase = request.session['usecase']\n # language = re... | [
"0.59271",
"0.57985866",
"0.56535566",
"0.54744357",
"0.5473918",
"0.545701",
"0.54296786",
"0.5419987",
"0.5314468",
"0.52762425",
"0.5261458",
"0.5237899",
"0.5226847",
"0.52122855",
"0.51996255",
"0.51626873",
"0.51587135",
"0.50677544",
"0.5053909",
"0.5049901",
"0.503424... | 0.47268173 | 45 |
This view returns true if for a usecase there are only examode labels and concepts | def check_presence_exa_conc_lab(request):
# reports = request.GET.get('reports',None)
rep = request.GET.get('id_report',None)
language = request.GET.get('language',None)
usecase = request.GET.get('usecase',None)
reports = None
if request.method == 'POST':
request_body_json = json.loads(... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def isOk(annots):\n if annots == []:\n return True\n for a in annots:\n for label in a.labels:\n if (label != 'hasOkCopies' and\n label != 'hasBadCopies' and\n not label.startswith('count_')):\n return False\n return True",
"def get_uses_missing_exa(request):\n\n use_to_... | [
"0.5940529",
"0.59398687",
"0.5865526",
"0.58527815",
"0.5814739",
"0.5643155",
"0.5637188",
"0.56277835",
"0.5610501",
"0.558338",
"0.5558472",
"0.5549566",
"0.550001",
"0.5440629",
"0.5440629",
"0.54309857",
"0.54284567",
"0.5419301",
"0.5419301",
"0.53711116",
"0.5362047",... | 0.5685875 | 5 |
This view handles the download of one or more reports' groundtruths (including the GT majority vote based. | def download_all_reports(request):
request_body_json = json.loads(request.body)
report_list = request_body_json['report_list']
mode = request_body_json['format']
action = request_body_json['action']
annot = request_body_json['annotation_mode']
if annot == 'Manual':
annot = 'Human'
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def download_all_ground_truths(request):\n\n json_resp = {}\n json_resp['ground_truth'] = []\n cursor = connection.cursor()\n mode = request.GET.get('gt_mode',None)\n if mode is None:\n human = NameSpace.objects.get(ns_id = 'Human')\n robot = NameSpace.objects.get(ns_id = 'Robot')\n ... | [
"0.6838261",
"0.65341705",
"0.62299836",
"0.6090916",
"0.59187865",
"0.5832907",
"0.5767083",
"0.5675658",
"0.56663036",
"0.5666215",
"0.5635911",
"0.5478633",
"0.5468885",
"0.54647183",
"0.54031974",
"0.5370372",
"0.53674585",
"0.5365349",
"0.5343065",
"0.53406143",
"0.53295... | 0.65963656 | 1 |
This view returns the number of pubmed articles in the db | def check_PUBMED_reports(request):
json_resp = {}
json_resp['count'] = 0
pubmed_arts = Report.objects.filter(institute = 'PUBMED')
for el in pubmed_arts:
if el.id_report.startswith('PUBMED'):
json_resp['count'] += 1
return JsonResponse(json_resp,safe=False) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_article_count(cls):\n return int(cls.db.get(\"article_count\"))",
"def num_articles(self):\n\t\treturn len(index)",
"def count_articles(all_articles):\n print(f\"There are {len(all_articles)} articles.\")",
"def n_published():\r\n sql = text('''\r\n WITH published_apps as\r... | [
"0.717347",
"0.7089709",
"0.67698693",
"0.67413735",
"0.66592926",
"0.66326725",
"0.6570169",
"0.65614486",
"0.6494197",
"0.6483129",
"0.6440703",
"0.62871206",
"0.6274295",
"0.6271812",
"0.6266821",
"0.6202164",
"0.6179825",
"0.61742187",
"0.6136083",
"0.6135398",
"0.6103563... | 0.6168546 | 18 |
This view returns the number of pubmed articles in the db | def check_medtag_reports(request):
json_resp = {}
json_resp['count'] = 0
medtag_arts = Report.objects.all().exclude(institute = 'PUBMED')
# for el in pubmed_arts:
# if el.id_report.startswith('PUBMED'):
json_resp['count'] = medtag_arts.count()
return JsonResponse(json_resp,safe=False) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_article_count(cls):\n return int(cls.db.get(\"article_count\"))",
"def num_articles(self):\n\t\treturn len(index)",
"def count_articles(all_articles):\n print(f\"There are {len(all_articles)} articles.\")",
"def n_published():\r\n sql = text('''\r\n WITH published_apps as\r... | [
"0.7172277",
"0.7088439",
"0.6768442",
"0.6739416",
"0.6658017",
"0.6632473",
"0.6568596",
"0.6559386",
"0.64938533",
"0.6480836",
"0.64391303",
"0.62844735",
"0.62724674",
"0.62704456",
"0.6265808",
"0.6203237",
"0.6178769",
"0.6171713",
"0.6168154",
"0.6135542",
"0.6135206"... | 0.5918936 | 36 |
This view returns the usecases which have not nor exa labels nor exa concepts | def get_uses_missing_exa(request):
use_to_ret = {}
use_to_ret['labels_present'] = []
use_to_ret['concepts_present'] = []
use_to_ret['labels_missing'] = []
use_to_ret['concepts_missing'] = []
uses = ['colon','uterine cervix','lung']
for el in uses:
usecase = UseCase.objects.get(name=... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def check_presence_exa_conc_lab(request):\n\n # reports = request.GET.get('reports',None)\n rep = request.GET.get('id_report',None)\n language = request.GET.get('language',None)\n usecase = request.GET.get('usecase',None)\n reports = None\n if request.method == 'POST':\n request_body_json ... | [
"0.59388274",
"0.58805186",
"0.58260775",
"0.53480375",
"0.53480375",
"0.53390783",
"0.5233926",
"0.5202363",
"0.5183914",
"0.5163577",
"0.5127641",
"0.5122592",
"0.5121663",
"0.5119788",
"0.51088226",
"0.5098412",
"0.5095758",
"0.5095758",
"0.50584406",
"0.50419766",
"0.5028... | 0.7342495 | 0 |
This view handles the transfer of the ground truths from a user to another. COPY WHEN DATABASE IS SHARED | def handle_copy_rows(request):
request_body_json = json.loads(request.body)
username = request_body_json['username']
print(username)
overwrite = request_body_json['overwrite']
if overwrite == 'true':
overwrite = True
elif overwrite == 'false':
overwrite = False
usecases = U... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def copy_from_teamusercopy(apps, schema_editor):\n TeamUser = apps.get_model('status', 'TeamUser')\n TeamUserCopy = apps.get_model('status', 'TeamUserCopy')\n\n for teamusercopy in TeamUserCopy.objects.all():\n if TeamUser.objects.filter(team_id=teamusercopy.team_id, user_id=teamusercopy.user_id).c... | [
"0.5468712",
"0.54597455",
"0.53516954",
"0.5244661",
"0.52367324",
"0.52106",
"0.5095629",
"0.5095629",
"0.50866884",
"0.50758433",
"0.49885663",
"0.49556786",
"0.4921544",
"0.4918357",
"0.4901763",
"0.48940924",
"0.4889287",
"0.48406535",
"0.48265398",
"0.48065147",
"0.4802... | 0.51210505 | 6 |
This view checks the files uploaded for the transfer of the groundtruths | def handle_check_upload_files(request):
files = []
for filename, file in request.FILES.items():
files.append(file)
json_message = check_uploaded_files(files)
return JsonResponse(json_message) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_file_can_be_uploaded_and_viewed_by_correct_users(self):\n \n project = self.testproject \n \n name1 = self.giverandomfilename(self.root)\n name2 = self.giverandomfilename(self.projectadmin)\n name3 = self.giverandomfilename(self.participant)\n name4 ... | [
"0.71557355",
"0.6669732",
"0.6654686",
"0.6649415",
"0.6637934",
"0.65880555",
"0.6528329",
"0.65050566",
"0.64086163",
"0.62588286",
"0.62116396",
"0.621024",
"0.61883146",
"0.61708736",
"0.6109043",
"0.6054689",
"0.60014397",
"0.59427094",
"0.5915164",
"0.5909347",
"0.5896... | 0.65448797 | 6 |
This view adds in the database the data included in the files uploaded for the transfer of the groundtruths | def handle_upload_files(request):
files = []
for filename, file in request.FILES.items():
files.append(file)
overwrite = request.POST.get('overwrite',None)
if overwrite == 'false':
overwrite = False
elif overwrite == 'true':
overwrite = True
json_message = upload_files... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def upload(request):\n gi = GalaxyInstance(url=request.session.get('server'), email=request.session.get('galaxyemail'), password=request.session.get(\"galaxypass\"))\n selected = request.POST.get('selected')\n selectedmeta = request.POST.get('meta')\n filetype = request.POST.get('filetype')\n dbkey ... | [
"0.69999224",
"0.62321997",
"0.6190771",
"0.61144507",
"0.6061765",
"0.59894365",
"0.59611374",
"0.5958438",
"0.59491634",
"0.59320164",
"0.59125924",
"0.5912157",
"0.58943003",
"0.5881315",
"0.58605087",
"0.58275455",
"0.5812308",
"0.5809562",
"0.5800417",
"0.57793623",
"0.5... | 0.0 | -1 |
This view returns the languages available for a report | def get_report_translations(request):
id_report = request.GET.get('id_report',None)
if id_report is not None:
languages = []
lang = Report.objects.filter(id_report = id_report)
for el in lang:
if el.language not in languages:
languages.append(el.language)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def languages(self):\r\n url = '{0}/{1}'.format(self.get_url(), 'languages')\r\n\r\n return http.Request('GET', url), parsers.parse_json",
"def wikiLanguages():\n return languages",
"def languages(self):\n\n return self._request('/languages')",
"def languages():\n r = requests.get(... | [
"0.7279651",
"0.72215253",
"0.7170446",
"0.6814636",
"0.6712208",
"0.66994166",
"0.66713786",
"0.6651076",
"0.65438974",
"0.6543734",
"0.6529538",
"0.65234035",
"0.6458752",
"0.64573747",
"0.6453006",
"0.6448716",
"0.64473933",
"0.64339",
"0.64140487",
"0.6352627",
"0.633507"... | 0.7563172 | 0 |
This view returns return the usecases of medtag reports | def medtag_reports(request):
json_resp = {}
json_resp['usecase'] = []
reps = Report.objects.all()
for r in reps:
if not r.id_report.startswith('PUBMED_') and not str(r.name_id) in json_resp['usecase']:
json_resp['usecase'].append(str(r.name_id))
return JsonResponse(json_resp) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def check_medtag_reports(request):\n\n json_resp = {}\n json_resp['count'] = 0\n medtag_arts = Report.objects.all().exclude(institute = 'PUBMED')\n # for el in pubmed_arts:\n # if el.id_report.startswith('PUBMED'):\n json_resp['count'] = medtag_arts.count()\n return JsonResponse(json_resp,... | [
"0.665116",
"0.59123224",
"0.55685633",
"0.55224574",
"0.5493389",
"0.5459506",
"0.54581726",
"0.5311828",
"0.530699",
"0.52737117",
"0.5272512",
"0.527097",
"0.52511895",
"0.5246451",
"0.5195243",
"0.5169995",
"0.51681095",
"0.5164607",
"0.51173073",
"0.50936943",
"0.5049706... | 0.7262409 | 0 |
This view returns return the usecases of pubmed reports | def pubmed_reports(request):
json_resp = {}
json_resp['usecase'] = []
reps = Report.objects.all()
for r in reps:
if r.id_report.startswith('PUBMED_') and not str(r.name_id) in json_resp['usecase']:
json_resp['usecase'].append(str(r.name_id))
return JsonResponse(json_resp) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def pubmed_missing_auto(request):\n\n usecases = UseCase.objects.all()\n json_resp = {}\n json_resp['annotated'] = 0\n json_resp['tot'] = 0\n json_resp['usecase'] = []\n languages = ['English','english']\n for el in usecases:\n use = el.name\n json_resp[use] = {}\n batches... | [
"0.6889685",
"0.67816097",
"0.6578456",
"0.6462005",
"0.6354941",
"0.60960084",
"0.6039205",
"0.5924308",
"0.59200716",
"0.58842385",
"0.58725315",
"0.58610183",
"0.58609855",
"0.5834699",
"0.5742797",
"0.5734736",
"0.572304",
"0.56792724",
"0.5666692",
"0.56550825",
"0.56486... | 0.7125116 | 0 |
This view checks if a user has an associated gt for a report | def check_gt_existence(request):
action = request.GET.get('action',None)
id_report = request.GET.get('id_report',None)
language = request.GET.get('language',None)
username = request.session['username']
mode = request.session['mode']
cursor = connection.cursor()
cursor.execute("SELECT COUNT... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def reported_by(self, user):\n return Report.objects.filter(recipe=self, chef=user).exists()",
"def has_ta(self, user, allow_superusers=True):\n return (user.is_superuser and allow_superusers) or len(self.tas.filter(id=user.id)) > 0",
"def _can_download_report(user):\r\n try:\r\n access... | [
"0.6260666",
"0.6116303",
"0.5899749",
"0.5702829",
"0.5593343",
"0.5565848",
"0.5547417",
"0.5547417",
"0.5541943",
"0.5512871",
"0.55068207",
"0.5493356",
"0.54900455",
"0.5473812",
"0.5417657",
"0.53915375",
"0.5383512",
"0.53717935",
"0.5370812",
"0.53642553",
"0.5324591"... | 0.5986029 | 2 |
Returns the fuzz target of |benchmark| | def get_fuzz_target(benchmark):
# Do this because of OSS-Fuzz-on-demand.
# TODO(metzman): Use classes to mock a benchmark config for
# OSS_FUZZ_ON_DEMAND.
return benchmark_config.get_config(benchmark).get(
'fuzz_target', environment.get('FUZZ_TARGET')) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_fuzzer_benchmark_key(fuzzer: str, benchmark: str):\n return fuzzer + ' ' + benchmark",
"def get_benchmark(self, benchmark):\n\t\tif not isinstance(benchmark, str) and not callable(benchmark): return benchmark\n\t\telif benchmark in self.classes:\treturn self.classes[benchmark]()\n\t\traise TypeError('... | [
"0.6541956",
"0.62079185",
"0.57205427",
"0.5588023",
"0.55567586",
"0.543854",
"0.54338294",
"0.53948295",
"0.5363798",
"0.532333",
"0.5268365",
"0.5229397",
"0.5206228",
"0.51935446",
"0.51308554",
"0.5129264",
"0.5088721",
"0.5079886",
"0.5032553",
"0.5032553",
"0.50242513... | 0.8522614 | 0 |
Returns the project of |benchmark| | def get_project(benchmark):
return benchmark_config.get_config(benchmark)['project'] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def benchmark_result(self):\n return self._benchmark_id",
"def benchmark_selection(self):\n return self._benchmark_selection",
"def get_benchmark(client):\n r = client.get(config.API_PATH() + '/benchmarks')\n benchmarks = json.loads(r.data)\n return benchmarks['benchmarks'][0]['id']",
... | [
"0.6512932",
"0.6404195",
"0.634007",
"0.62146837",
"0.6029168",
"0.59504235",
"0.5943799",
"0.5894067",
"0.581235",
"0.5640167",
"0.54331803",
"0.53937525",
"0.53754103",
"0.53711575",
"0.53711575",
"0.53711575",
"0.53711575",
"0.53711575",
"0.53711575",
"0.53711575",
"0.537... | 0.81019706 | 0 |
Returns the type of |benchmark| | def get_type(benchmark):
# TODO(metzman): Use classes to mock a benchmark config for
# OSS_FUZZ_ON_DEMAND.
default_value = os.getenv('EXPERIMENT_TYPE', BenchmarkType.CODE.value)
return benchmark_config.get_config(benchmark).get('type', default_value) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_benchmark(self, benchmark):\n\t\tif not isinstance(benchmark, str) and not callable(benchmark): return benchmark\n\t\telif benchmark in self.classes:\treturn self.classes[benchmark]()\n\t\traise TypeError('Passed benchmark is not defined!')",
"def validate_type(benchmark):\n benchmark_type = get_type(... | [
"0.7427512",
"0.6534945",
"0.6310375",
"0.63042027",
"0.6122598",
"0.6042788",
"0.58986205",
"0.58470327",
"0.58307236",
"0.58241946",
"0.5813828",
"0.5782032",
"0.5728832",
"0.570958",
"0.5697104",
"0.56663436",
"0.56439966",
"0.5572599",
"0.5552849",
"0.5550577",
"0.5532445... | 0.8139171 | 0 |
Get the URL of the docker runner image for fuzzing the benchmark with fuzzer. | def get_runner_image_url(experiment, benchmark, fuzzer, docker_registry):
tag = 'latest' if environment.get('LOCAL_EXPERIMENT') else experiment
return f'{docker_registry}/runners/{fuzzer}/{benchmark}:{tag}' | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_builder_image_url(benchmark, fuzzer, docker_registry):\n return f'{docker_registry}/builders/{fuzzer}/{benchmark}'",
"def _to_dockerfile_url(image):\n path = \"/\".join((image.platform, image.release, image.architecture, \"Dockerfile\"))\n return git.get_github_blob_url(path, ref=f\"v{image.vers... | [
"0.8196786",
"0.6458271",
"0.5928085",
"0.5869728",
"0.5578148",
"0.55031866",
"0.5449406",
"0.54289633",
"0.5422988",
"0.53856117",
"0.5382283",
"0.534175",
"0.5297771",
"0.5248548",
"0.5238496",
"0.5210609",
"0.5198708",
"0.519582",
"0.5189414",
"0.512294",
"0.51160717",
... | 0.87715006 | 0 |
Get the URL of the docker builder image for fuzzing the benchmark with fuzzer. | def get_builder_image_url(benchmark, fuzzer, docker_registry):
return f'{docker_registry}/builders/{fuzzer}/{benchmark}' | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_runner_image_url(experiment, benchmark, fuzzer, docker_registry):\n tag = 'latest' if environment.get('LOCAL_EXPERIMENT') else experiment\n return f'{docker_registry}/runners/{fuzzer}/{benchmark}:{tag}'",
"def _to_dockerfile_url(image):\n path = \"/\".join((image.platform, image.release, image.a... | [
"0.800936",
"0.68247736",
"0.62919927",
"0.6119331",
"0.60827637",
"0.5836558",
"0.5680232",
"0.5606805",
"0.5593281",
"0.55321765",
"0.5527971",
"0.55201703",
"0.5511697",
"0.5464959",
"0.5450358",
"0.5436414",
"0.5423743",
"0.5383551",
"0.53793126",
"0.5351387",
"0.53473455... | 0.8987414 | 0 |
Returns True if |benchmark| is a valid fuzzbench benchmark name. | def validate_name(benchmark):
if VALID_BENCHMARK_REGEX.match(benchmark) is None:
logs.error('%s does not conform to %s pattern.', benchmark,
VALID_BENCHMARK_REGEX.pattern)
return False
return True | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def validate(benchmark):\n if not validate_name(benchmark):\n return False\n\n if benchmark not in get_all_benchmarks():\n logs.error('%s must have a benchmark.yaml.', benchmark)\n return False\n\n # Validate config file can be parsed.\n try:\n get_fuzz_target(benchmark)\n ... | [
"0.75245595",
"0.70732296",
"0.5810576",
"0.5674972",
"0.5586452",
"0.55509335",
"0.55345166",
"0.5492754",
"0.54216146",
"0.5396599",
"0.533939",
"0.52890986",
"0.52809453",
"0.5276064",
"0.5262049",
"0.5236118",
"0.5226639",
"0.51745903",
"0.51519513",
"0.51472944",
"0.5053... | 0.816597 | 0 |
Returns True if |benchmark| has a valid type. | def validate_type(benchmark):
benchmark_type = get_type(benchmark)
if benchmark_type not in BENCHMARK_TYPE_STRS:
logs.error('%s has an invalid benchmark type %s, must be one of %s',
benchmark, benchmark_type, BENCHMARK_TYPE_STRS)
return False
return True | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def validate(benchmark):\n if not validate_name(benchmark):\n return False\n\n if benchmark not in get_all_benchmarks():\n logs.error('%s must have a benchmark.yaml.', benchmark)\n return False\n\n # Validate config file can be parsed.\n try:\n get_fuzz_target(benchmark)\n ... | [
"0.6986698",
"0.6440675",
"0.6303851",
"0.62154716",
"0.6090882",
"0.608178",
"0.6064316",
"0.6046675",
"0.5970051",
"0.5952641",
"0.58843154",
"0.58391774",
"0.5755268",
"0.57513607",
"0.5732901",
"0.57144207",
"0.56996477",
"0.56742626",
"0.55769104",
"0.5570763",
"0.556722... | 0.837678 | 0 |
Returns True if |benchmark| is a valid fuzzbench benchmark. | def validate(benchmark):
if not validate_name(benchmark):
return False
if benchmark not in get_all_benchmarks():
logs.error('%s must have a benchmark.yaml.', benchmark)
return False
# Validate config file can be parsed.
try:
get_fuzz_target(benchmark)
except yaml.pa... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def validate_type(benchmark):\n benchmark_type = get_type(benchmark)\n if benchmark_type not in BENCHMARK_TYPE_STRS:\n logs.error('%s has an invalid benchmark type %s, must be one of %s',\n benchmark, benchmark_type, BENCHMARK_TYPE_STRS)\n return False\n return True",
"de... | [
"0.69998956",
"0.67811495",
"0.5615518",
"0.5273485",
"0.5138776",
"0.5041119",
"0.48480907",
"0.48040372",
"0.47753853",
"0.47583094",
"0.4682528",
"0.4670917",
"0.466327",
"0.4620052",
"0.45891234",
"0.45834735",
"0.45678422",
"0.4562975",
"0.45465788",
"0.45328742",
"0.451... | 0.7685098 | 0 |
Returns the list of all benchmarks. | def get_all_benchmarks():
all_benchmarks = []
for benchmark in os.listdir(BENCHMARKS_DIR):
benchmark_path = os.path.join(BENCHMARKS_DIR, benchmark)
if os.path.isfile(os.path.join(benchmark_path, 'benchmark.yaml')):
all_benchmarks.append(benchmark)
return sorted(all_benchmarks) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def run_all(self):\n runs = []\n for run in self.benchmarks:\n run.start()\n run.wait()\n runs.append(run.metadata)\n return runs",
"def list(self, classes):\n\n def add(benchmarks, parts, flags, exclude):\n if (\n flags[\"lan... | [
"0.71444166",
"0.6889727",
"0.64351124",
"0.6397667",
"0.6397363",
"0.62924254",
"0.6077235",
"0.59082776",
"0.58880574",
"0.57301986",
"0.56475395",
"0.5644938",
"0.5587757",
"0.55655915",
"0.5550465",
"0.5548563",
"0.5522972",
"0.5509917",
"0.5496706",
"0.54954356",
"0.5495... | 0.79568917 | 0 |
Returns the list of all coverage benchmarks. | def get_coverage_benchmarks():
return [
benchmark for benchmark in get_all_benchmarks()
if get_type(benchmark) == BenchmarkType.CODE.value
] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_all_benchmarks():\n all_benchmarks = []\n for benchmark in os.listdir(BENCHMARKS_DIR):\n benchmark_path = os.path.join(BENCHMARKS_DIR, benchmark)\n if os.path.isfile(os.path.join(benchmark_path, 'benchmark.yaml')):\n all_benchmarks.append(benchmark)\n return sorted(all_ben... | [
"0.6990162",
"0.6294294",
"0.61898667",
"0.57358605",
"0.5668825",
"0.5625119",
"0.5591114",
"0.54327077",
"0.5404844",
"0.5391268",
"0.53692985",
"0.5357925",
"0.5353722",
"0.5342524",
"0.53213197",
"0.5292404",
"0.52296937",
"0.52178067",
"0.5196261",
"0.51935357",
"0.51894... | 0.80346346 | 0 |
Returns the list of standard bug benchmarks. | def get_bug_benchmarks():
return [
benchmark for benchmark in get_all_benchmarks()
if get_type(benchmark) == BenchmarkType.BUG.value
] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_all_benchmarks():\n all_benchmarks = []\n for benchmark in os.listdir(BENCHMARKS_DIR):\n benchmark_path = os.path.join(BENCHMARKS_DIR, benchmark)\n if os.path.isfile(os.path.join(benchmark_path, 'benchmark.yaml')):\n all_benchmarks.append(benchmark)\n return sorted(all_ben... | [
"0.6447797",
"0.58259064",
"0.5799858",
"0.5799062",
"0.5770896",
"0.5712318",
"0.5706508",
"0.5695553",
"0.5673421",
"0.5479634",
"0.54711425",
"0.5403768",
"0.5383399",
"0.5374102",
"0.5373461",
"0.5352589",
"0.5352316",
"0.5337508",
"0.5330979",
"0.53233963",
"0.5322599",
... | 0.8056906 | 0 |
Returns True if |benchmark| is written in C/C++. | def is_cpp(benchmark):
return get_language(benchmark) == 'c++' | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _c_optimizations_required():\n pure_env = os.environ.get('PURE_PYTHON')\n require_c = pure_env == \"0\"\n return require_c",
"def _should_attempt_c_optimizations():\n if PYPY:\n return False\n\n if _c_optimizations_required():\n return True\n return not _c_optimizations_ignore... | [
"0.6263037",
"0.6229608",
"0.6097612",
"0.6020831",
"0.5982895",
"0.58478355",
"0.57693976",
"0.57392836",
"0.5669367",
"0.55203027",
"0.5460529",
"0.53889805",
"0.5354336",
"0.5303164",
"0.5300068",
"0.5299565",
"0.5283687",
"0.5242149",
"0.52138",
"0.5201266",
"0.5121601",
... | 0.82368374 | 0 |
Returns |benchmarks| with only benchmarks written in C/C++. | def exclude_non_cpp(benchmarks):
return [benchmark for benchmark in benchmarks if is_cpp(benchmark)] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_coverage_benchmarks():\n return [\n benchmark for benchmark in get_all_benchmarks()\n if get_type(benchmark) == BenchmarkType.CODE.value\n ]",
"def get_bug_benchmarks():\n return [\n benchmark for benchmark in get_all_benchmarks()\n if get_type(benchmark) == Benchmark... | [
"0.69631344",
"0.64020723",
"0.61482394",
"0.56974643",
"0.5492792",
"0.54700804",
"0.5441572",
"0.5409977",
"0.5309843",
"0.52634937",
"0.5233209",
"0.52182776",
"0.52005297",
"0.5169763",
"0.5148801",
"0.51456755",
"0.51412374",
"0.51203877",
"0.5105856",
"0.5091856",
"0.50... | 0.801431 | 0 |
Returns the prorgamming language the benchmark was written in. | def get_language(benchmark):
config = benchmark_config.get_config(benchmark)
return config.get('language', 'c++') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def programming_language(self) -> str:\n return self.random.choice(PROGRAMMING_LANGS)",
"def generation_language(self) -> pulumi.Output[Optional[str]]:\n return pulumi.get(self, \"generation_language\")",
"def code(self):\n return self.language()",
"def language(self):\n return \"... | [
"0.72861236",
"0.65404236",
"0.6198768",
"0.61458135",
"0.6069835",
"0.6027784",
"0.59970075",
"0.5983806",
"0.5961204",
"0.5961204",
"0.5915103",
"0.5895517",
"0.5860599",
"0.58391285",
"0.58170784",
"0.57956773",
"0.5790107",
"0.57584816",
"0.57295835",
"0.572077",
"0.57125... | 0.76412416 | 0 |
Remove any nonwhite listed chars from a string | def _filter_string(cls, string, extra_chars=""):
char_white_list = ascii_letters + digits + extra_chars
return "".join([char for char in string if char in char_white_list]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _removeWhitespaces(self, s):\n return s.translate({ord(c): None for c in string.whitespace})",
"def filter(string):\n # remove all unwanted characters\n return regex2.sub(' ', string)",
"def stripword( s ) :\n return re.sub( '[\\W\\d]', '', s )",
"def string_cleanup(s, garbage=\":,-()&\")... | [
"0.7945294",
"0.7738483",
"0.7600506",
"0.75452673",
"0.74735063",
"0.7469556",
"0.7400172",
"0.73478335",
"0.73437715",
"0.73410106",
"0.7331665",
"0.7326543",
"0.7289203",
"0.72830564",
"0.728172",
"0.727238",
"0.7259143",
"0.7258027",
"0.72176147",
"0.7203688",
"0.72031343... | 0.7396111 | 7 |
Return the object whose id is _id | def by_id(cls, _id):
return dbsession.query(cls).filter_by(id=_id).first() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_object(id):",
"def get_object(self, id_):\n return self._objects.get(id_, None)",
"def at(cls, _id):\n return cls.where(cls.primarykey == _id)",
"def get(self, _id):",
"def get_object(self, id=None):\n if id is None and self.kwargs.get('field') == 'id':\n id = self.k... | [
"0.85694164",
"0.8270094",
"0.7589233",
"0.75782853",
"0.7455623",
"0.7441776",
"0.7432799",
"0.7382152",
"0.73811334",
"0.7301479",
"0.72642654",
"0.7239448",
"0.7217969",
"0.72114927",
"0.71807003",
"0.7132641",
"0.71304023",
"0.71304023",
"0.7111594",
"0.70885956",
"0.7020... | 0.7470613 | 6 |
Return the object whose uuid is _uuid | def by_uuid(cls, _uuid):
return dbsession.query(cls).filter_by(uuid=_uuid).first() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_by_uuid(self, uuid):\n return self.get(uuid=uuid)",
"def by_uuid(cls, uuid):\n return dbsession.query(cls).filter_by(uuid=uuid).first()",
"def find(cls, uuid):\n entries = cls.objects.filter(uuid=uuid)\n if not entries:\n return None\n else:\n re... | [
"0.82511705",
"0.7933739",
"0.7587574",
"0.74948627",
"0.74105513",
"0.705008",
"0.69275194",
"0.6883316",
"0.68186414",
"0.68186414",
"0.6762099",
"0.6762099",
"0.6734712",
"0.67203295",
"0.6711133",
"0.66830343",
"0.6674249",
"0.6659611",
"0.6656396",
"0.6648054",
"0.660445... | 0.7979588 | 2 |
Return the object whose name is _name | def by_name(cls, name):
return dbsession.query(cls).filter_by(_name=str(name)).first() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_real_object(self, name):\n name = name if isinstance(name, str) else name.name\n for obj in self._objects:\n if name == obj.name:\n return obj\n else:\n raise ValueError(\"Cannot retrieve object. Unknown name {}. \".format(name))",
"def getObject... | [
"0.81269974",
"0.80951434",
"0.7999025",
"0.79542536",
"0.79282635",
"0.7537779",
"0.73879355",
"0.73344094",
"0.7329022",
"0.7317708",
"0.7281263",
"0.7255151",
"0.7209064",
"0.7190735",
"0.71191245",
"0.7043156",
"0.7034967",
"0.703377",
"0.703377",
"0.69024086",
"0.683505"... | 0.72039217 | 15 |
Remove any nonwhite listed chars from a string | def _filter_string(cls, string, extra_chars=""):
char_white_list = ascii_letters + digits + extra_chars
return "".join([char for char in string if char in char_white_list]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _removeWhitespaces(self, s):\n return s.translate({ord(c): None for c in string.whitespace})",
"def filter(string):\n # remove all unwanted characters\n return regex2.sub(' ', string)",
"def stripword( s ) :\n return re.sub( '[\\W\\d]', '', s )",
"def string_cleanup(s, garbage=\":,-()&\")... | [
"0.7945294",
"0.7738483",
"0.7600506",
"0.75452673",
"0.74735063",
"0.7469556",
"0.7400172",
"0.73478335",
"0.73437715",
"0.73410106",
"0.7331665",
"0.7326543",
"0.7289203",
"0.72830564",
"0.728172",
"0.727238",
"0.7259143",
"0.7258027",
"0.72176147",
"0.7203688",
"0.72031343... | 0.7396111 | 8 |
Returns list of IDs of tags for specified model name by (code, name) pair | def get_tag_ids(self, cr, uid, model, code=None, name=None, context=None):
assert bool(code) or bool(name), "code or name must not be None! (code=%s;name=%s)" % (code, name)
tag_domain = [('model_id.model', '=', model)]
if code is not None:
tag_domain.append(('code', '=', code))
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _getTagIDs(self):\n paths = self._criteria.get('paths')\n if paths:\n store = getMainStore()\n return list(store.find(Tag.id, Tag.path.is_in(paths)))",
"def get_tag_ids(tag_names):\n\ttag_names = tuple(tag_names)\n\twith postgres, postgres.cursor() as cur:\n\t\tcur.execute... | [
"0.6331528",
"0.61098045",
"0.5848875",
"0.5817225",
"0.57898426",
"0.57898426",
"0.5781574",
"0.5777682",
"0.5743095",
"0.56093746",
"0.5590287",
"0.5589514",
"0.5496177",
"0.54333645",
"0.5430249",
"0.5416349",
"0.54159564",
"0.54110205",
"0.54062647",
"0.53955483",
"0.5384... | 0.8124625 | 0 |
Adds tag new tag to object. | def add_tag(self, cr, uid, ids, code=None, name=None, create=False, context=None):
tag_obj = self.pool.get('res.tag')
tag_ids = tag_obj.get_tag_ids(cr, uid, self._name, code=code, name=name, context=context)
if not tag_ids and create:
model_id = self.pool.get('res.tag.model').search(... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add(self, tag):\n self.tags[tag.name] = tag",
"def add_tag(self, tag):\n self.tags.append(tag)",
"def add_tag(self, obj, tag_name):\r\n tag_names = parse_tag_input(tag_name)\r\n if not len(tag_names):\r\n raise AttributeError(_('No tags were given: \"%s\".') % tag_nam... | [
"0.8477626",
"0.83942044",
"0.7990679",
"0.79390234",
"0.77830034",
"0.7674143",
"0.7644332",
"0.761934",
"0.72644126",
"0.7179257",
"0.7171568",
"0.71568304",
"0.70963436",
"0.70908105",
"0.6981176",
"0.69674325",
"0.69674325",
"0.69674325",
"0.69674325",
"0.68249136",
"0.68... | 0.0 | -1 |
Removes tags specified by code/name from specified cargoes | def remove_tag(self, cr, uid, ids, code=None, name=None, context=None):
tag_obj = self.pool.get('res.tag')
tag_ids = tag_obj.get_tag_ids(cr, uid, self._name, code=code, name=name, context=context)
if tag_ids:
self.write(cr, uid, ids, {'tag_ids': [(3, tid) for tid in tag_ids]}, conte... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def remove_tag(args):",
"def remove_tags(self, tags):\n\n tags = H.to_list(tags)\n # self._tags.difference_update(tags)\n self.tags.difference_update(tags)",
"def remove_tags(html, *tags):\n to_remove = []\n for t in tags:\n to_remove.extend(list(_make_tag_patterns(t)))\n r... | [
"0.7223684",
"0.65725476",
"0.6513158",
"0.64437586",
"0.64056754",
"0.6315268",
"0.6279433",
"0.6213618",
"0.6207464",
"0.6185791",
"0.6162285",
"0.60926586",
"0.6090244",
"0.6027659",
"0.5954956",
"0.5875241",
"0.58474445",
"0.58383733",
"0.5830504",
"0.5779694",
"0.5775148... | 0.56744355 | 26 |
Checks if all of supplied objects have tag with specified code and/or name Return True if all object ids has specified tags | def check_tag(self, cr, uid, ids, code=None, name=None, context=None):
assert bool(code is None) or bool(name is None), "code or name must not be None"
tag_domain = [('id', 'in', ids)]
if code is not None:
tag_domain.append(('tag_ids.code', '=', code))
if name is not None:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def check_for_tag(tags, tagged_events):\n found_tags = set()\n tags_set = set(tags)\n for tag in tags:\n for tag_event in tagged_events:\n if tag in tag_event[1][\"tag\"][\"labels\"]:\n found_tags.add(tag)\n not_found = tags_set - found_tags\n tag_status = {}\n fo... | [
"0.61141014",
"0.6103309",
"0.5986072",
"0.5873774",
"0.5779968",
"0.57688856",
"0.57678956",
"0.576175",
"0.5726771",
"0.57225233",
"0.57074004",
"0.563474",
"0.5622186",
"0.5621437",
"0.5620893",
"0.56001145",
"0.55933976",
"0.55933976",
"0.5574528",
"0.5566219",
"0.5503636... | 0.67218775 | 0 |
A simple slug generator. Slugs are pure ASCII lowercase strings that can be used in URLs an other places where a name has to be machinesafe. | def slugify(value: Any, sep: str = "-") -> Optional[str]:
text = stringify(value)
if text is None:
return None
text = text.replace(sep, WS)
# run this first because it'll give better results on special
# characters.
text = category_replace(text, SLUG_CATEGORIES)
text = latinize_text(... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def generate_slug(self):\n return slugify(self.name)",
"def generate_slug(name):\n # Slugify the title and check\n slug = secure_filename(name).lower().replace('_', '-')\n slug_n = slug\n n = 1\n\n # Append an increasing number to the slug until we find an available url\n while not slug_... | [
"0.7597692",
"0.7439659",
"0.7417883",
"0.7406391",
"0.73748773",
"0.7310329",
"0.72734207",
"0.72529685",
"0.717194",
"0.7162989",
"0.70815015",
"0.70509",
"0.70415765",
"0.7019472",
"0.69859445",
"0.6970225",
"0.694027",
"0.69378775",
"0.6929656",
"0.68890053",
"0.68677574"... | 0.0 | -1 |
(train / valid / test) are all using data from 20102017 | def split_dataset(valid_datetime):
print("all_frames: ", valid_datetime.shape[0])
print("valid_frames: ", np.sum(valid_datetime[:,1]))
print("no_valid_frames: ", np.sum(1-valid_datetime[:,1]))
valid_datetime_bool = (valid_datetime[:,1] == 1)
valid_datetime_idx = np.where(valid_datetime_bool)[0]
valid_datetime = ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_data(self):\n\n return self.__valid_data, self.__valid_labels",
"def test_training(self):\n\t\tpass",
"def prep_data_fn(self, st_train_dt, end_train_dt, st_val_dt, end_val_dt, st_test_dt, end_test_dt):\n df = self.get_prep_data()\n train = df[(df['ft_data_dt'] >= st_train_dt) & (d... | [
"0.70418376",
"0.6841699",
"0.68156826",
"0.67502785",
"0.6705622",
"0.66910946",
"0.66823065",
"0.6620052",
"0.66126984",
"0.6508516",
"0.64929533",
"0.6471718",
"0.644612",
"0.6441467",
"0.64393294",
"0.64252573",
"0.64106035",
"0.6407785",
"0.6401149",
"0.6393558",
"0.6393... | 0.6403365 | 18 |
read train/valid/test datetime set | def read_datetime_set(filename, seq_len):
date_set = []
with open(os.path.join(info_path, "squence_len_{}".format(seq_len), filename), 'r') as f:
for line in f:
new_line = line.rstrip('\n').split('\t')
date_set.append([int(new_line[0]), int(new_line[1])])
return np.array(date_set) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def split_dataset(valid_datetime):\n\tprint(\"all_frames: \", valid_datetime.shape[0])\n\tprint(\"valid_frames: \", np.sum(valid_datetime[:,1]))\n\tprint(\"no_valid_frames: \", np.sum(1-valid_datetime[:,1]))\n\n\tvalid_datetime_bool = (valid_datetime[:,1] == 1)\n\tvalid_datetime_idx = np.where(valid_datetime_bool)... | [
"0.66455895",
"0.6168198",
"0.6038736",
"0.6012535",
"0.59450936",
"0.5928109",
"0.5890639",
"0.5885514",
"0.58625025",
"0.58129436",
"0.57628024",
"0.57618076",
"0.5719605",
"0.5701487",
"0.5698947",
"0.569637",
"0.5668824",
"0.5663031",
"0.565223",
"0.5649751",
"0.5647297",... | 0.6003768 | 4 |
Take the path to a raw json asset and convert it to target directory. | def processed_json_dir(path):
return os.path.dirname(path.replace(RAW_ASSETS_PATH, ASSETS_PATH)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def processed_json_path(path):\n return path.replace(RAW_ASSETS_PATH, ASSETS_PATH).replace('.json', '.bin')",
"def copy_json():\n sourcePath = 'contents/external/'\n targetPath = 'build/external/'\n for base,subdirs,files in os.walk(sourcePath):\n for file in files:\n orig = os.path.j... | [
"0.7122769",
"0.6316052",
"0.5610777",
"0.54790395",
"0.52522516",
"0.522991",
"0.5213277",
"0.51795095",
"0.51795095",
"0.51695764",
"0.51168185",
"0.5074967",
"0.5063665",
"0.50393045",
"0.50152886",
"0.4990693",
"0.49906608",
"0.49886268",
"0.49885842",
"0.49729767",
"0.49... | 0.6894641 | 1 |
Initializes this object's schema, input_files and output_path. | def __init__(self, schema, input_files, output_path):
self.schema = schema
self.input_files = input_files
self.output_path = output_path | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self):\n\n self.read_input_file()\n self.read_simulation_files()",
"def __init__(self):\n\t\tself.parsed = False\n\t\tdir_path = os.path.dirname(os.path.realpath(__file__))\n\t\tself.xsdfilename = os.path.join(dir_path, 'xml', 'schema.xsd')\n\t\tself.schema = 'schema.xsd'\n\t\tself.pr... | [
"0.69186145",
"0.6781864",
"0.6756163",
"0.6735031",
"0.6637486",
"0.6575436",
"0.6545899",
"0.65405905",
"0.6535554",
"0.65054566",
"0.6497379",
"0.6446711",
"0.6439875",
"0.64162254",
"0.6388665",
"0.63697845",
"0.63621044",
"0.63425386",
"0.6337463",
"0.6333185",
"0.633097... | 0.8291974 | 0 |
Take the path to a raw png asset and convert it to target webp path. | def processed_texture_path(path):
return path.replace(RAW_ASSETS_PATH, ASSETS_PATH).replace('png', 'webp') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def img2webp(path):\n file, ext = os.path.splitext(path)\n image = Image.open(path).convert(\"RGBA\")\n image = ImageOps.expand(image, 75)\n image.save(file + \".webp\", \"WEBP\")\n os.remove(path)",
"def image_webp():\n data = resource(\"images/wolf_1.webp\")\n return Response(data, headers... | [
"0.66254675",
"0.6108834",
"0.59269124",
"0.57612014",
"0.54063636",
"0.5362226",
"0.5322664",
"0.53141046",
"0.5290474",
"0.5259612",
"0.520573",
"0.51810825",
"0.51549965",
"0.51190436",
"0.5110097",
"0.51075953",
"0.50395477",
"0.5022422",
"0.48821872",
"0.48676977",
"0.48... | 0.72864443 | 0 |
Searches for a file with named `name` in the given paths and returns it. | def find_executable(name, paths):
for path in paths:
full_path = os.path.join(path, name)
if os.path.isfile(full_path):
return full_path
# If not found, just assume it's in the PATH.
return name | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def find_in_paths(name, paths):\n for path in paths:\n file_ = osp.join(path, name)\n if osp.exists(file_) and not osp.isdir(file_):\n abs_file = osp.abspath(file_)\n if os.access(file_, os.X_OK):\n return abs_file",
"def search_file(file_name, search_path):\... | [
"0.80682737",
"0.72417116",
"0.706997",
"0.7050534",
"0.67950416",
"0.6720487",
"0.67091864",
"0.6678427",
"0.6671641",
"0.6649197",
"0.6628015",
"0.6592693",
"0.6471008",
"0.6440933",
"0.64034975",
"0.63482285",
"0.63181823",
"0.63181823",
"0.6291936",
"0.62775",
"0.62671673... | 0.66123325 | 11 |
Run the flatbuffer compiler on the given json file and schema. | def convert_json_to_flatbuffer_binary(json, schema, out_dir):
command = [FLATC, '-o', out_dir, '-b', schema, json]
run_subprocess(command) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def run(\n self,\n input_file=sys.stdin,\n output_file=sys.stdout,\n schema_map=None,\n ):\n schema_map, error_logs = self.deduce_schema(\n input_file, schema_map=schema_map\n )\n\n for error in error_logs:\n logging.info(\n f... | [
"0.6357114",
"0.634119",
"0.6240263",
"0.6036583",
"0.598429",
"0.59565043",
"0.57677084",
"0.56092983",
"0.5584701",
"0.55134827",
"0.54945713",
"0.5492932",
"0.5417545",
"0.5400936",
"0.5390043",
"0.53854465",
"0.5339671",
"0.53241897",
"0.5318094",
"0.5313744",
"0.5285585"... | 0.69766474 | 0 |
Run the webp converter on the given png file. | def convert_png_image_to_webp(png, out, quality=80):
command = [CWEBP, '-q', str(quality), png, '-o', out]
run_subprocess(command) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def img2webp(path):\n file, ext = os.path.splitext(path)\n image = Image.open(path).convert(\"RGBA\")\n image = ImageOps.expand(image, 75)\n image.save(file + \".webp\", \"WEBP\")\n os.remove(path)",
"def generate_webp_textures():\n input_files = PNG_TEXTURES['input_files']\n output_files = PNG_... | [
"0.59895134",
"0.5819123",
"0.5774753",
"0.5767522",
"0.57351494",
"0.57216007",
"0.56728303",
"0.5661198",
"0.5626706",
"0.55680203",
"0.5460881",
"0.5396583",
"0.53937674",
"0.5302584",
"0.5294534",
"0.5288155",
"0.52613574",
"0.523901",
"0.51677066",
"0.5115962",
"0.510950... | 0.67798376 | 0 |
Checks if the source file needs to be rebuilt. | def needs_rebuild(source, target):
return not os.path.isfile(target) or (
os.path.getmtime(source) > os.path.getmtime(target)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def has_source_file( self ):\n return self._source_file is not None",
"def source_changed(source, cache):\n return os.path.getmtime(source)>os.path.getmtime(cache)",
"def should_run(self):\n # from IPython.html.tasks.py\n\n css_targets = [pjoin(static, 'css', 'style.min.css')]\n ... | [
"0.6899336",
"0.66479534",
"0.6410455",
"0.63760024",
"0.63628346",
"0.63059103",
"0.62350976",
"0.619009",
"0.61705863",
"0.615026",
"0.6144818",
"0.6059382",
"0.6036777",
"0.5976113",
"0.5958864",
"0.5946087",
"0.5920723",
"0.591227",
"0.5905643",
"0.58806807",
"0.58798784"... | 0.78058094 | 0 |
Take the path to a raw json asset and convert it to target bin path. | def processed_json_path(path):
return path.replace(RAW_ASSETS_PATH, ASSETS_PATH).replace('.json', '.bin') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def processed_json_dir(path):\n return os.path.dirname(path.replace(RAW_ASSETS_PATH, ASSETS_PATH))",
"def processed_texture_path(path):\n return path.replace(RAW_ASSETS_PATH, ASSETS_PATH).replace('png', 'webp')",
"def copy_json():\n sourcePath = 'contents/external/'\n targetPath = 'build/external/'\n ... | [
"0.61563104",
"0.57579714",
"0.54303104",
"0.5163897",
"0.5136838",
"0.5134525",
"0.50500184",
"0.50419426",
"0.50137776",
"0.50136286",
"0.50113374",
"0.49979314",
"0.49422348",
"0.49397087",
"0.493285",
"0.49240142",
"0.49194804",
"0.48922402",
"0.48879048",
"0.487408",
"0.... | 0.74835473 | 0 |
Run the flatbuffer compiler on the all of the flatbuffer json files. | def generate_flatbuffer_binaries():
for element in FLATBUFFERS_CONVERSION_DATA:
schema = element.schema
output_path = element.output_path
if not os.path.exists(output_path):
os.makedirs(output_path)
for json in element.input_files:
target = processed_json_path(json)
if needs_rebuild(... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def main():\n os.makedirs(PATH)\n fetch_data()\n convert_to_json(model_list, 'models.json', is_model=True)\n convert_to_json(backend_list, 'backends.json')\n convert_to_json(type_list, 'types.json')\n convert_to_json(featurizer_list, 'featurizers.json')",
"def main(gtfs_file, input_json_file):\... | [
"0.608436",
"0.6008992",
"0.59380484",
"0.5935062",
"0.5781285",
"0.5768037",
"0.5759169",
"0.57256293",
"0.57182896",
"0.5655328",
"0.5653805",
"0.56392854",
"0.5626525",
"0.55395234",
"0.5531216",
"0.5497599",
"0.5481104",
"0.54802907",
"0.5461012",
"0.5456868",
"0.5416904"... | 0.73920244 | 0 |
Run the webp converter on off of the png files. | def generate_webp_textures():
input_files = PNG_TEXTURES['input_files']
output_files = PNG_TEXTURES['output_files']
if not os.path.exists(TEXTURE_PATH):
os.makedirs(TEXTURE_PATH)
for png, out in zip(input_files, output_files):
if needs_rebuild(png, out):
convert_png_image_to_webp(png, out, WEBP_QU... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def main():\n argvs = sys.argv\n argc = len(argvs)\n if argc == 1:\n print('usage: convert2png.py <path/to/*.ppm> ...')\n sys.exit(1)\n\n os.makedirs('result/convert2png', exist_ok=True)\n\n for i in range(1, argc):\n img = cv2.imread(argvs[i])\n\n # root, ext = os.path.s... | [
"0.6570944",
"0.64156437",
"0.61769783",
"0.5975264",
"0.5974723",
"0.5941165",
"0.59038806",
"0.5838966",
"0.58314997",
"0.5811597",
"0.5809414",
"0.5798533",
"0.5736212",
"0.56903666",
"0.56399405",
"0.56066835",
"0.557636",
"0.557636",
"0.5565635",
"0.5556461",
"0.55448854... | 0.70537895 | 0 |
Delete all the processed webp textures. | def clean_webp_textures():
for webp in PNG_TEXTURES['output_files']:
if os.path.isfile(webp):
os.remove(webp) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def clean():\n clean_flatbuffer_binaries()\n clean_webp_textures()",
"def delete(self):\n\t\tif self.hasUdim:\n\t\t\tfor a in self.udimPaths:\n\t\t\t\ta.delete()\n\t\telse:\n\t\t\tsuper( textureFile, self ).delete()",
"def destroy(self):\n\n self.cmapTexture.destroy()\n\n for tex in (self.modul... | [
"0.70913655",
"0.6556138",
"0.6525104",
"0.6477908",
"0.6368207",
"0.6253116",
"0.62028897",
"0.6191404",
"0.6172181",
"0.6160886",
"0.6124062",
"0.6107779",
"0.60488415",
"0.60330206",
"0.6033018",
"0.6028536",
"0.6020569",
"0.6011445",
"0.5983217",
"0.590569",
"0.5905648",
... | 0.8128452 | 0 |
Delete all the processed flatbuffer binaries. | def clean_flatbuffer_binaries():
for element in FLATBUFFERS_CONVERSION_DATA:
for json in element.input_files:
path = processed_json_path(json)
if os.path.isfile(path):
os.remove(path) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def clean():\n clean_flatbuffer_binaries()\n clean_webp_textures()",
"def clean(self):\n # Delete vertices / faces / colors / normals :\n self._vert_buffer.delete()\n self._index_buffer.delete()\n self._normals_buffer.delete()\n self._xrange_buffer.delete()\n self._mat... | [
"0.7080938",
"0.6617103",
"0.64717567",
"0.6464323",
"0.6414247",
"0.6351224",
"0.6346113",
"0.6323048",
"0.6296938",
"0.62765425",
"0.6252906",
"0.6217102",
"0.61619747",
"0.61525744",
"0.6143297",
"0.6128095",
"0.6123065",
"0.6101668",
"0.60976034",
"0.6087722",
"0.6086085"... | 0.7819535 | 0 |
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